From d3315d914becef13f511bd87bbd9d7ca1689c379 Mon Sep 17 00:00:00 2001
From: Filipe <ocefpaf@gmail.com>
Date: Fri, 4 Oct 2024 15:59:22 +0200
Subject: [PATCH] Fix glider names (#90)

* update

* relock

* skip conflicting ruff format rules

* fix glider names

* lint
---
 .pre-commit-config.yaml                      |     5 +-
 README.md                                    |     8 +-
 conda-lock.yml                               |  9760 ++++-----
 ioos_metrics/ioos_metrics.py                 |     7 +-
 notebooks/GTS_Totals_weather_act.ipynb       | 12209 ++++++------
 notebooks/IOOS_BTN.ipynb                     | 17567 +++++++++--------
 notebooks/glider_metrics.ipynb               |     8 +-
 notebooks/mbon_citation_visualizations.ipynb |    92 +-
 ruff.toml                                    |     2 +
 tests/test_metrics.py                        |     2 +-
 10 files changed, 18730 insertions(+), 20930 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 3c694ec..9cabb30 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -36,17 +36,16 @@ repos:
     - id: add-trailing-comma
 
 - repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: v0.4.6
+  rev: v0.6.8
   hooks:
     - id: ruff
       args: ["--fix", "--show-fixes"]
     - id: ruff-format
 
 - repo: https://github.com/nbQA-dev/nbQA
-  rev: 1.8.5
+  rev: 1.8.7
   hooks:
     - id: nbqa-check-ast
-    - id: nbqa-black
     - id: nbqa-ruff
       args: [
         --fix,
diff --git a/README.md b/README.md
index abaafbf..df38e9f 100644
--- a/README.md
+++ b/README.md
@@ -1,12 +1,12 @@
 # ioos-metrics
 
-A library to compute and compile metrics about the U.S. Integrated Ocean Observing System (U.S. IOOS®). U.S. IOOS® is a vital tool for tracking, predicting, managing, and adapting to changes in our ocean, coastal and Great Lakes environment.  
+A library to compute and compile metrics about the U.S. Integrated Ocean Observing System (U.S. IOOS®). U.S. IOOS® is a vital tool for tracking, predicting, managing, and adapting to changes in our ocean, coastal and Great Lakes environment.
 
 Part of the intent of this repository is to create metrics for the [IOOS by the numbers](https://ioos.noaa.gov/about/ioos-by-the-numbers/).
 
-"IOOS by the Numbers" is was developed as a graphical representation of IOOS  -- a system of includes our partnerships, research and observing components, and data management capabilities. 
-This figure is an annually updated collection of numbers that show the breadth of the IOOS Program as it is growing and evolving.  
-These data are obtained from multiple sources. The source for each value is captured in the functions defined at https://github.com/ioos/ioos_metrics/tree/main/ioos_metrics. 
+"IOOS by the Numbers" is was developed as a graphical representation of IOOS  -- a system of includes our partnerships, research and observing components, and data management capabilities.
+This figure is an annually updated collection of numbers that show the breadth of the IOOS Program as it is growing and evolving.
+These data are obtained from multiple sources. The source for each value is captured in the functions defined at https://github.com/ioos/ioos_metrics/tree/main/ioos_metrics.
 
 ## Installation instructions:
 
diff --git a/conda-lock.yml b/conda-lock.yml
index 0dcef1b..b2ccca8 100644
--- a/conda-lock.yml
+++ b/conda-lock.yml
@@ -13,10 +13,10 @@
 version: 1
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-    win-64: fefdaad07c9f1c3d355a7e6208b6a789d04ed8570f591eb559dd59355e682cbf
+    linux-64: 55828c681b90873f2b25d2843e79ca94ec90db5960d27acec46e05a9bf42b71f
+    osx-arm64: 838ec43cc41250b9861c9a6e5cf5a2d25020bb807b62bbb245d32a11ce9e21d0
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   channels:
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     used_env_vars: []
@@ -53,1171 +53,1151 @@ package:
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+  version: 4.6.0
   manager: conda
   platform: linux-64
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     idna: '>=2.8'
-    python: '>=3.8'
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     typing_extensions: '>=4.1'
-  url: https://conda.anaconda.org/conda-forge/noarch/anyio-4.3.0-pyhd8ed1ab_0.conda
+  url: https://conda.anaconda.org/conda-forge/noarch/anyio-4.6.0-pyhd8ed1ab_1.conda
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     sniffio: '>=1.1'
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     idna: '>=2.8'
     exceptiongroup: '>=1.0.2'
-  url: https://conda.anaconda.org/conda-forge/noarch/anyio-4.3.0-pyhd8ed1ab_0.conda
+  url: https://conda.anaconda.org/conda-forge/noarch/anyio-4.6.0-pyhd8ed1ab_1.conda
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   category: main
   optional: false
 - name: anyio
-  version: 4.3.0
+  version: 4.6.0
   manager: conda
   platform: osx-arm64
   dependencies:
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+    python: '>=3.9'
     sniffio: '>=1.1'
     typing_extensions: '>=4.1'
     idna: '>=2.8'
     exceptiongroup: '>=1.0.2'
-  url: https://conda.anaconda.org/conda-forge/noarch/anyio-4.3.0-pyhd8ed1ab_0.conda
+  url: https://conda.anaconda.org/conda-forge/noarch/anyio-4.6.0-pyhd8ed1ab_1.conda
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 - name: anyio
-  version: 4.3.0
+  version: 4.6.0
   manager: conda
   platform: win-64
   dependencies:
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+    python: '>=3.9'
     sniffio: '>=1.1'
     typing_extensions: '>=4.1'
     idna: '>=2.8'
     exceptiongroup: '>=1.0.2'
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   optional: false
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-  url: https://conda.anaconda.org/conda-forge/win-64/aws-c-cal-0.6.11-ha21e00f_0.conda
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+    python: '>=3.7'
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-  url: https://conda.anaconda.org/conda-forge/win-64/cfitsio-4.4.0-h9b0cee5_1.conda
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+    python: '>=3.6'
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     libjpeg-turbo: '>=3.0.0,<4.0a0'
-    libtiff: '>=4.6.0,<4.7.0a0'
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   url: https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.16-hb7c19ff_0.conda
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-    libtiff: '>=4.6.0,<4.7.0a0'
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-    libtiff: '>=4.6.0,<4.7.0a0'
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   url: https://conda.anaconda.org/conda-forge/osx-arm64/lcms2-2.16-ha0e7c42_0.conda
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@@ -5149,7 +4028,7 @@ package:
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@@ -5160,14 +4039,15 @@ package:
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   optional: false
 - name: ld_impl_linux-64
-  version: '2.40'
+  version: '2.43'
   manager: conda
   platform: linux-64
-  dependencies: {}
-  url: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-h55db66e_0.conda
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   dependencies:
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-  manager: conda
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-- name: libarrow-flight
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-  manager: conda
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-  dependencies:
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-- name: libarrow-flight
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-  manager: conda
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-  dependencies:
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-- name: libarrow-flight
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-  dependencies:
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-- name: libarrow-flight-sql
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-- name: libarrow-flight-sql
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+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.6.3-hf9b8971_0.conda
   hash:
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   category: main
   optional: false
 - name: libexpat
-  version: 2.6.2
+  version: 2.6.3
   manager: conda
   platform: win-64
-  dependencies: {}
-  url: https://conda.anaconda.org/conda-forge/win-64/libexpat-2.6.2-h63175ca_0.conda
+  dependencies:
+    ucrt: '>=10.0.20348.0'
+    vc: '>=14.2,<15'
+    vc14_runtime: '>=14.29.30139'
+  url: https://conda.anaconda.org/conda-forge/win-64/libexpat-2.6.3-he0c23c2_0.conda
   hash:
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-    sha256: 79f612f75108f3e16bbdc127d4885bb74729cf66a8702fca0373dad89d40c4b7
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+    sha256: 9543965d155b8da96fc67dd81705fe5c2571c7c00becc8de5534c850393d4e3c
   category: main
   optional: false
 - name: libffi
@@ -6635,226 +5247,202 @@ package:
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   category: main
   optional: false
-- name: libgcc-ng
-  version: 13.2.0
+- name: libgcc
+  version: 14.1.0
   manager: conda
   platform: linux-64
   dependencies:
     _libgcc_mutex: '0.1'
     _openmp_mutex: '>=4.5'
-  url: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-hc881cc4_6.conda
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgcc-14.1.0-h77fa898_1.conda
+  hash:
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+    sha256: 10fa74b69266a2be7b96db881e18fa62cfa03082b65231e8d652e897c4b335a3
+  category: main
+  optional: false
+- name: libgcc-ng
+  version: 14.1.0
+  manager: conda
+  platform: linux-64
+  dependencies:
+    libgcc: 14.1.0
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-14.1.0-h69a702a_1.conda
   hash:
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-    sha256: 836a0057525f1414de43642d357d0ab21ac7f85e24800b010dbc17d132e6efec
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+    sha256: b91f7021e14c3d5c840fbf0dc75370d6e1f7c7ff4482220940eaafb9c64613b7
   category: main
   optional: false
-- name: libgdal
-  version: 3.8.5
+- name: libgdal-core
+  version: 3.9.2
   manager: conda
   platform: linux-64
   dependencies:
     __glibc: '>=2.17,<3.0.a0'
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-    json-c: '>=0.17,<0.18.0a0'
-    kealib: '>=1.5.3,<1.6.0a0'
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-    libdeflate: '>=1.20,<1.21.0a0'
-    libexpat: '>=2.6.2,<3.0a0'
-    libgcc-ng: '>=12'
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+    libdeflate: '>=1.21,<1.22.0a0'
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     libiconv: '>=1.17,<2.0a0'
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     libkml: '>=1.3.0,<1.4.0a0'
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-    libpng: '>=1.6.43,<1.7.0a0'
-    libpq: '>=16.2,<17.0a0'
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     libspatialite: '>=5.1.0,<5.2.0a0'
-    libsqlite: '>=3.45.3,<4.0a0'
-    libstdcxx-ng: '>=12'
-    libtiff: '>=4.6.0,<4.7.0a0'
+    libsqlite: '>=3.46.1,<4.0a0'
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     libuuid: '>=2.38.1,<3.0a0'
     libwebp-base: '>=1.4.0,<2.0a0'
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-    libzlib: '>=1.2.13,<1.3.0a0'
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     lz4-c: '>=1.9.3,<1.10.0a0'
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-    postgresql: ''
-    proj: '>=9.4.0,<9.4.1.0a0'
-    tiledb: '>=2.22.0,<2.23.0a0'
+    openssl: '>=3.3.2,<4.0a0'
+    pcre2: '>=10.44,<10.45.0a0'
+    proj: '>=9.5.0,<9.6.0a0'
     xerces-c: '>=3.2.5,<3.3.0a0'
     xz: '>=5.2.6,<6.0a0'
-    zstd: '>=1.5.5,<1.6.0a0'
-  url: https://conda.anaconda.org/conda-forge/linux-64/libgdal-3.8.5-hf9625ee_2.conda
+    zstd: '>=1.5.6,<1.6.0a0'
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgdal-core-3.9.2-hceee5e3_6.conda
   hash:
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-    sha256: 06bab7e8c49e9c94ba5ac6dc8de9bc13468dccedc3d88e3153d576a30b36ee22
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+    sha256: dffd3c987894d36164c8e0ecf9ee8af0b50d7af2969c9287d296e4af296c3af5
   category: main
   optional: false
-- name: libgdal
-  version: 3.8.5
+- name: libgdal-core
+  version: 3.9.2
   manager: conda
   platform: osx-64
   dependencies:
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-    blosc: '>=1.21.5,<2.0a0'
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-    geos: '>=3.12.1,<3.12.2.0a0'
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-    json-c: '>=0.17,<0.18.0a0'
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-    libcxx: '>=16'
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-  url: https://conda.anaconda.org/conda-forge/osx-64/libgdal-3.8.5-h7db9259_2.conda
+    zstd: '>=1.5.6,<1.6.0a0'
+  url: https://conda.anaconda.org/conda-forge/osx-64/libgdal-core-3.9.2-ha8eff54_6.conda
   hash:
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   category: main
   optional: false
-- name: libgdal
-  version: 3.8.5
+- name: libgdal-core
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   manager: conda
   platform: osx-arm64
   dependencies:
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-    proj: '>=9.4.0,<9.4.1.0a0'
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-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-3.8.5-h2f7ae65_2.conda
+    zstd: '>=1.5.6,<1.6.0a0'
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-core-3.9.2-h3535123_6.conda
   hash:
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-    sha256: a4dd336fe93f1daabab7371878db45041d6ca543eb821057c9f2475c0848a0b3
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+    sha256: c727a9b605fdd5e968eb0aa42f23e173299aee7423ad987b9ae3d3f7cba8e654
   category: main
   optional: false
-- name: libgdal
-  version: 3.8.5
+- name: libgdal-core
+  version: 3.9.2
   manager: conda
   platform: win-64
   dependencies:
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-    postgresql: ''
-    proj: '>=9.4.0,<9.4.1.0a0'
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+    openssl: '>=3.3.2,<4.0a0'
+    pcre2: '>=10.44,<10.45.0a0'
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     vc: '>=14.2,<15'
     vc14_runtime: '>=14.29.30139'
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-    zstd: '>=1.5.5,<1.6.0a0'
-  url: https://conda.anaconda.org/conda-forge/win-64/libgdal-3.8.5-hfb9f81c_2.conda
+    zstd: '>=1.5.6,<1.6.0a0'
+  url: https://conda.anaconda.org/conda-forge/win-64/libgdal-core-3.9.2-hf78aeaf_6.conda
+  hash:
+    md5: 3b4af04e7623f1232287398a04a7e7a0
+    sha256: b17ca50a2f874efd2603dfc880b3722820b021df830b3b594a308709e26cbf74
+  category: main
+  optional: false
+- name: libgfortran
+  version: 14.1.0
+  manager: conda
+  platform: linux-64
+  dependencies:
+    libgfortran5: 14.1.0
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-14.1.0-h69a702a_1.conda
   hash:
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-    sha256: 9de3c0f28ea5b08ad974847c11e507ee648084bc1484480b327b6dfee0ff971a
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   category: main
   optional: false
 - name: libgfortran
@@ -6882,27 +5470,27 @@ package:
   category: main
   optional: false
 - name: libgfortran-ng
-  version: 13.2.0
+  version: 14.1.0
   manager: conda
   platform: linux-64
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-  url: https://conda.anaconda.org/conda-forge/win-64/libnetcdf-4.9.2-nompi_h07c049d_113.conda
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-  url: https://conda.anaconda.org/conda-forge/win-64/mkl-2024.1.0-h66d3029_692.conda
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-    libcurl: '>=8.5.0,<9.0a0'
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+    libstdcxx: '>=13'
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+    icu: '>=75.1,<76.0a0'
+    libcxx: '>=17'
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+    libcxx: '>=17'
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-  url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.8.9-h8ee46fc_0.conda
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-  url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.11-hd590300_0.conda
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+    libgcc: '>=13'
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-  url: https://conda.anaconda.org/conda-forge/osx-arm64/xorg-libxau-1.0.11-hb547adb_0.conda
+  dependencies:
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-  url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.3-h7f98852_0.tar.bz2
+    __glibc: '>=2.17,<3.0.a0'
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-  url: https://conda.anaconda.org/conda-forge/osx-64/xorg-libxdmcp-1.1.3-h35c211d_0.tar.bz2
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-  dependencies: {}
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/xorg-libxdmcp-1.1.3-h27ca646_0.tar.bz2
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   category: main
   optional: false
 - name: zlib
-  version: 1.2.13
+  version: 1.3.1
+  manager: conda
+  platform: win-64
+  dependencies:
+    libzlib: 1.3.1
+    ucrt: '>=10.0.20348.0'
+    vc: '>=14.2,<15'
+    vc14_runtime: '>=14.29.30139'
+  url: https://conda.anaconda.org/conda-forge/win-64/zlib-1.3.1-h2466b09_2.conda
+  hash:
+    md5: be60c4e8efa55fddc17b4131aa47acbd
+    sha256: 8c688797ba23b9ab50cef404eca4d004a948941b6ee533ead0ff3bf52012528c
+  category: main
+  optional: false
+- name: zstandard
+  version: 0.23.0
+  manager: conda
+  platform: linux-64
+  dependencies:
+    __glibc: '>=2.17,<3.0.a0'
+    cffi: '>=1.11'
+    libgcc: '>=13'
+    python: '>=3.12,<3.13.0a0'
+    python_abi: 3.12.*
+    zstd: '>=1.5.6,<1.6.0a0'
+  url: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py312hef9b889_1.conda
+  hash:
+    md5: 8b7069e9792ee4e5b4919a7a306d2e67
+    sha256: b97015e146437283f2213ff0e95abdc8e2480150634d81fbae6b96ee09f5e50b
+  category: main
+  optional: false
+- name: zstandard
+  version: 0.23.0
+  manager: conda
+  platform: osx-64
+  dependencies:
+    __osx: '>=10.13'
+    cffi: '>=1.11'
+    python: '>=3.12,<3.13.0a0'
+    python_abi: 3.12.*
+    zstd: '>=1.5.6,<1.6.0a0'
+  url: https://conda.anaconda.org/conda-forge/osx-64/zstandard-0.23.0-py312h7122b0e_1.conda
+  hash:
+    md5: bd132ba98f3fc0a6067f355f8efe4cb6
+    sha256: 2685dde42478fae0780fba5d1f8a06896a676ae105f215d32c9f9e76f3c6d8fd
+  category: main
+  optional: false
+- name: zstandard
+  version: 0.23.0
+  manager: conda
+  platform: osx-arm64
+  dependencies:
+    __osx: '>=11.0'
+    cffi: '>=1.11'
+    python: '>=3.12,<3.13.0a0'
+    python_abi: 3.12.*
+    zstd: '>=1.5.6,<1.6.0a0'
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.23.0-py312h15fbf35_1.conda
+  hash:
+    md5: a4cde595509a7ad9c13b1a3809bcfe51
+    sha256: d00ca25c1e28fd31199b26a94f8c96574475704a825d244d7a6351ad3745eeeb
+  category: main
+  optional: false
+- name: zstandard
+  version: 0.23.0
   manager: conda
   platform: win-64
   dependencies:
-    libzlib: 1.2.13
+    cffi: '>=1.11'
+    python: '>=3.12,<3.13.0a0'
+    python_abi: 3.12.*
     ucrt: '>=10.0.20348.0'
     vc: '>=14.2,<15'
     vc14_runtime: '>=14.29.30139'
-  url: https://conda.anaconda.org/conda-forge/win-64/zlib-1.2.13-hcfcfb64_5.conda
+    zstd: '>=1.5.6,<1.6.0a0'
+  url: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.23.0-py312h7606c53_1.conda
   hash:
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-    sha256: 0f91b719c7558046bcd37fdc7ae4b9eb2b7a8e335beb8b59ae7ccb285a46aa46
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   category: main
   optional: false
 - name: zstd
-  version: 1.5.5
+  version: 1.5.6
   manager: conda
   platform: linux-64
   dependencies:
     libgcc-ng: '>=12'
     libstdcxx-ng: '>=12'
-    libzlib: '>=1.2.13,<1.3.0a0'
-  url: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.5-hfc55251_0.conda
+    libzlib: '>=1.2.13,<2.0.0a0'
+  url: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda
   hash:
-    md5: 04b88013080254850d6c01ed54810589
-    sha256: 607cbeb1a533be98ba96cf5cdf0ddbb101c78019f1fda063261871dad6248609
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   category: main
   optional: false
 - name: zstd
-  version: 1.5.5
+  version: 1.5.6
   manager: conda
   platform: osx-64
   dependencies:
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-  url: https://conda.anaconda.org/conda-forge/osx-64/zstd-1.5.5-h829000d_0.conda
+    __osx: '>=10.9'
+    libzlib: '>=1.2.13,<2.0.0a0'
+  url: https://conda.anaconda.org/conda-forge/osx-64/zstd-1.5.6-h915ae27_0.conda
   hash:
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-    sha256: d54e31d3d8de5e254c0804abd984807b8ae5cd3708d758a8bf1adff1f5df166c
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   category: main
   optional: false
 - name: zstd
-  version: 1.5.5
+  version: 1.5.6
   manager: conda
   platform: osx-arm64
   dependencies:
-    libzlib: '>=1.2.13,<1.3.0a0'
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.5-h4f39d0f_0.conda
+    __osx: '>=11.0'
+    libzlib: '>=1.2.13,<2.0.0a0'
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.6-hb46c0d2_0.conda
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   category: main
   optional: false
 - name: zstd
-  version: 1.5.5
+  version: 1.5.6
   manager: conda
   platform: win-64
   dependencies:
-    libzlib: '>=1.2.13,<1.3.0a0'
+    libzlib: '>=1.2.13,<2.0.0a0'
     ucrt: '>=10.0.20348.0'
     vc: '>=14.2,<15'
     vc14_runtime: '>=14.29.30139'
-  url: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.5-h12be248_0.conda
+  url: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.6-h0ea2cb4_0.conda
   hash:
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-    sha256: d540dd56c5ec772b60e4ce7d45f67f01c6614942225885911964ea1e70bb99e3
+    md5: 9a17230f95733c04dc40a2b1e5491d74
+    sha256: 768e30dc513568491818fb068ee867c57c514b553915536da09e5d10b4ebf3c3
   category: main
   optional: false
diff --git a/ioos_metrics/ioos_metrics.py b/ioos_metrics/ioos_metrics.py
index b84b23d..4c90b90 100644
--- a/ioos_metrics/ioos_metrics.py
+++ b/ioos_metrics/ioos_metrics.py
@@ -169,6 +169,11 @@ def _extra_info(info_df, attribute_name) -> str:
             att = "unknown"
         return att
 
+    def _fix_glider_call_names(fname: str) -> str:
+        fname = fname.split("-")
+        fname.pop()
+        return "-".join(fname).strip()
+
     def _metadata(info_df) -> dict:
         """Build the metadata a specific dataset_id."""
         return {
@@ -181,7 +186,7 @@ def _metadata(info_df) -> dict:
                 info_df,
                 attribute_name="time_coverage_end",
             ),
-            "glider": dataset_id.split("-")[0],
+            "glider": _fix_glider_call_names(dataset_id),
             "geospatial_lat_min": _extra_info(
                 info_df,
                 attribute_name="geospatial_lat_min",
diff --git a/notebooks/GTS_Totals_weather_act.ipynb b/notebooks/GTS_Totals_weather_act.ipynb
index fe979a6..7ac9d06 100644
--- a/notebooks/GTS_Totals_weather_act.ipynb
+++ b/notebooks/GTS_Totals_weather_act.ipynb
@@ -1,6232 +1,6225 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/ioos/ioos_metrics/blob/main/GTS_Totals_weather_act.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/ioos/ioos_metrics/blob/main/GTS_Totals_weather_act.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Po0C-dJCrtWM"
+   },
+   "source": [
+    "# IOOS GTS Statistics\n",
+    "\n",
+    "Created: 2020-10-10\n",
+    "\n",
+    "Updated: 2023-06-23\n",
+    "\n",
+    "The Global Telecommunication System (GTS) is a coordinated effort for rapid distribution of observations.\n",
+    "The GTS monthly reports show the number of messages released to GTS for each station.\n",
+    "The reports contain the following fields:\n",
+    "\n",
+    "- location ID: Identifier that station messages are released under to the GTS;\n",
+    "- region: Designated IOOS Regional Association (only for IOOS regional report);\n",
+    "- sponsor: Organization that owns and maintains the station;\n",
+    "- Met: Total number of met messages released to the GTS\n",
+    "- Wave: Total number of wave messages released to the GTS\n",
+    "\n",
+    "In this notebook we will explore all of the statistics of the messages IOOS is releasing to GTS.\n",
+    "\n",
+    "NDBC has data going back to 2018-01-01, this notebook uses all of those data for plotting and statistics.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wgtBvHNmrtWU"
+   },
+   "source": [
+    "IOOS has added the csv files to https://erddap.ioos.us/erddap/search/index.html?page=1&itemsPerPage=1000&searchFor=GTS\n",
+    "\n",
+    "Source data can be found at NDBC ioosstats server that [hosts the CSV files](https://www.ndbc.noaa.gov/ioosstats/) with the ingest data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
     },
+    "id": "2c7dvH32tYt2",
+    "outputId": "038eb22a-90e6-43cd-a792-6d89abd0fa9f"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Po0C-dJCrtWM"
+     "data": {
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "summary": "{\n  \"name\": \"df_out\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2018,\n        \"max\": 2023,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2019,\n          2021,\n          2022\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 11,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          9,\n          1,\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time (UTC)\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-03-01 00:00:00+00:00\",\n        \"max\": \"2023-10-01 00:00:00+00:00\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"2023-07-01 00:00:00+00:00\",\n          \"2021-09-01 00:00:00+00:00\",\n          \"2018-03-01 00:00:00+00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"locationID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"ITKA2\",\n          \"MZXC1\",\n          \"LJPC1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"AOOS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sponsor\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"NOAA NOS PHYSICAL OCEANOGRAPHIC RT SYSTEM PROGRAM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"met\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6324,\n        \"min\": 0,\n        \"max\": 14670,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          14670\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wave\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1012,\n        \"min\": 0,\n        \"max\": 2844,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"source\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"NDBC\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+       "type": "dataframe"
       },
-      "source": [
-        "# IOOS GTS Statistics\n",
-        "\n",
-        "Created: 2020-10-10\n",
-        "\n",
-        "Updated: 2023-06-23\n",
-        "\n",
-        "The Global Telecommunication System (GTS) is a coordinated effort for rapid distribution of observations.\n",
-        "The GTS monthly reports show the number of messages released to GTS for each station.\n",
-        "The reports contain the following fields:\n",
-        "\n",
-        "- location ID: Identifier that station messages are released under to the GTS;\n",
-        "- region: Designated IOOS Regional Association (only for IOOS regional report);\n",
-        "- sponsor: Organization that owns and maintains the station;\n",
-        "- Met: Total number of met messages released to the GTS\n",
-        "- Wave: Total number of wave messages released to the GTS\n",
-        "\n",
-        "In this notebook we will explore all of the statistics of the messages IOOS is releasing to GTS.\n",
-        "\n",
-        "NDBC has data going back to 2018-01-01, this notebook uses all of those data for plotting and statistics.\n"
+      "text/html": [
+       "\n",
+       "  <div id=\"df-ef7fff1c-12f9-4c10-9bc2-221148d519ce\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Month</th>\n",
+       "      <th>time (UTC)</th>\n",
+       "      <th>locationID</th>\n",
+       "      <th>region</th>\n",
+       "      <th>sponsor</th>\n",
+       "      <th>met</th>\n",
+       "      <th>wave</th>\n",
+       "      <th>source</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3256</th>\n",
+       "      <td>2019</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2019-11-01 00:00:00+00:00</td>\n",
+       "      <td>42012</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NATIONAL WEATHER SERVICE</td>\n",
+       "      <td>8614</td>\n",
+       "      <td>1436</td>\n",
+       "      <td>NDBC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23127</th>\n",
+       "      <td>2021</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2021-09-01 00:00:00+00:00</td>\n",
+       "      <td>MZXC1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NOAA NOS PHYSICAL OCEANOGRAPHIC RT SYSTEM PROGRAM</td>\n",
+       "      <td>14372</td>\n",
+       "      <td>0</td>\n",
+       "      <td>non-NDBC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12162</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2023-10-01 00:00:00+00:00</td>\n",
+       "      <td>WCXA2</td>\n",
+       "      <td>AOOS</td>\n",
+       "      <td>MARINE EXCHANGE OF ALASKA</td>\n",
+       "      <td>8654</td>\n",
+       "      <td>0</td>\n",
+       "      <td>IOOS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13713</th>\n",
+       "      <td>2020</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2020-04-01 00:00:00+00:00</td>\n",
+       "      <td>46248</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SCRIPPS WAVERIDER COASTAL DATA INFORMATION PRO...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2844</td>\n",
+       "      <td>non-NDBC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1584</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2018-11-01 00:00:00+00:00</td>\n",
+       "      <td>46041</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NATIONAL WEATHER SERVICE</td>\n",
+       "      <td>1432</td>\n",
+       "      <td>1432</td>\n",
+       "      <td>NDBC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1066</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2018-03-01 00:00:00+00:00</td>\n",
+       "      <td>LJPC1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SCRIPPS WAVERIDER COASTAL DATA INFORMATION PRO...</td>\n",
+       "      <td>1484</td>\n",
+       "      <td>1480</td>\n",
+       "      <td>non-NDBC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24930</th>\n",
+       "      <td>2022</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2022-01-01 00:00:00+00:00</td>\n",
+       "      <td>NSTP6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NATIONAL OCEAN SERVICE</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>non-NDBC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20170</th>\n",
+       "      <td>2021</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2021-04-01 00:00:00+00:00</td>\n",
+       "      <td>LDTM4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NATIONAL OCEAN SERVICE</td>\n",
+       "      <td>13764</td>\n",
+       "      <td>0</td>\n",
+       "      <td>non-NDBC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34674</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2023-07-01 00:00:00+00:00</td>\n",
+       "      <td>ITKA2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NATIONAL OCEAN SERVICE</td>\n",
+       "      <td>14670</td>\n",
+       "      <td>0</td>\n",
+       "      <td>non-NDBC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25526</th>\n",
+       "      <td>2022</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2022-02-01 00:00:00+00:00</td>\n",
+       "      <td>MYPF1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NATIONAL OCEAN SERVICE</td>\n",
+       "      <td>13354</td>\n",
+       "      <td>0</td>\n",
+       "      <td>non-NDBC</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ef7fff1c-12f9-4c10-9bc2-221148d519ce')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-ef7fff1c-12f9-4c10-9bc2-221148d519ce button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-ef7fff1c-12f9-4c10-9bc2-221148d519ce');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-a159c3e8-8cac-44e4-aaa9-274788885edf\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a159c3e8-8cac-44e4-aaa9-274788885edf')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-a159c3e8-8cac-44e4-aaa9-274788885edf button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
+      ],
+      "text/plain": [
+       "       Year  Month                time (UTC) locationID region  \\\n",
+       "3256   2019     11 2019-11-01 00:00:00+00:00      42012    NaN   \n",
+       "23127  2021      9 2021-09-01 00:00:00+00:00      MZXC1    NaN   \n",
+       "12162  2023     10 2023-10-01 00:00:00+00:00      WCXA2   AOOS   \n",
+       "13713  2020      4 2020-04-01 00:00:00+00:00      46248    NaN   \n",
+       "1584   2018     11 2018-11-01 00:00:00+00:00      46041    NaN   \n",
+       "1066   2018      3 2018-03-01 00:00:00+00:00      LJPC1    NaN   \n",
+       "24930  2022      1 2022-01-01 00:00:00+00:00      NSTP6    NaN   \n",
+       "20170  2021      4 2021-04-01 00:00:00+00:00      LDTM4    NaN   \n",
+       "34674  2023      7 2023-07-01 00:00:00+00:00      ITKA2    NaN   \n",
+       "25526  2022      2 2022-02-01 00:00:00+00:00      MYPF1    NaN   \n",
+       "\n",
+       "                                                 sponsor    met  wave  \\\n",
+       "3256                            NATIONAL WEATHER SERVICE   8614  1436   \n",
+       "23127  NOAA NOS PHYSICAL OCEANOGRAPHIC RT SYSTEM PROGRAM  14372     0   \n",
+       "12162                          MARINE EXCHANGE OF ALASKA   8654     0   \n",
+       "13713  SCRIPPS WAVERIDER COASTAL DATA INFORMATION PRO...      0  2844   \n",
+       "1584                            NATIONAL WEATHER SERVICE   1432  1432   \n",
+       "1066   SCRIPPS WAVERIDER COASTAL DATA INFORMATION PRO...   1484  1480   \n",
+       "24930                             NATIONAL OCEAN SERVICE      0     0   \n",
+       "20170                             NATIONAL OCEAN SERVICE  13764     0   \n",
+       "34674                             NATIONAL OCEAN SERVICE  14670     0   \n",
+       "25526                             NATIONAL OCEAN SERVICE  13354     0   \n",
+       "\n",
+       "         source  \n",
+       "3256       NDBC  \n",
+       "23127  non-NDBC  \n",
+       "12162      IOOS  \n",
+       "13713  non-NDBC  \n",
+       "1584       NDBC  \n",
+       "1066   non-NDBC  \n",
+       "24930  non-NDBC  \n",
+       "20170  non-NDBC  \n",
+       "34674  non-NDBC  \n",
+       "25526  non-NDBC  "
       ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "url_ioos = \"https://erddap.ioos.us/erddap/tabledap/gts_regional_statistics.csvp\"\n",
+    "df_ioos = pd.read_csv(url_ioos,parse_dates=[2])\n",
+    "df_ioos[\"source\"] = \"IOOS\"\n",
+    "\n",
+    "url_ndbc = \"https://erddap.ioos.us/erddap/tabledap/gts_ndbc_statistics.csvp\"\n",
+    "df_ndbc = pd.read_csv(url_ndbc,parse_dates=[2])\n",
+    "df_ndbc[\"source\"] = \"NDBC\"\n",
+    "\n",
+    "url_nonndbc = \"https://erddap.ioos.us/erddap/tabledap/gts_non_ndbc_statistics.csvp\"\n",
+    "df_nonndbc = pd.read_csv(url_nonndbc,parse_dates=[2])\n",
+    "df_nonndbc[\"source\"] = \"non-NDBC\"\n",
+    "\n",
+    "df_out = pd.concat([df_ioos,df_ndbc,df_nonndbc])\n",
+    "\n",
+    "df_out.sample(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "h9KD4cjOg5kS"
+   },
+   "source": [
+    "# What's non-NDBC composed of?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
     },
+    "id": "MT2AdHB7chjy",
+    "outputId": "a9093ea0-de5d-42cc-df08-879f6457c37a"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "wgtBvHNmrtWU"
+     "data": {
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "summary": "{\n  \"name\": \"grp_out\",\n  \"rows\": 45,\n  \"fields\": [\n    {\n      \"column\": \"sponsor\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 45,\n        \"samples\": [\n          \"UNIVERSITY OF NEW HAMPSHIRE\",\n          \"NATIONAL WEATHER SERVICE, CENTRAL REGION\",\n          \"NATIONAL WEATHER SERVICE, EASTERN REGION\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"met\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33450686,\n        \"min\": 0,\n        \"max\": 222046882,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          62092,\n          25318,\n          106182\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wave\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1317179,\n        \"min\": 0,\n        \"max\": 8626560,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          31416,\n          53956,\n          58522\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33413659,\n        \"min\": 0,\n        \"max\": 222046882,\n        \"num_unique_values\": 44,\n        \"samples\": [\n          57188,\n          13786322,\n          5412862\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pcnt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10154757006806879,\n        \"min\": 0.0,\n        \"max\": 0.6748234563502864,\n        \"num_unique_values\": 44,\n        \"samples\": [\n          0.00017380025098375477,\n          0.0418980594485357,\n          0.016450247851654696\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+       "type": "dataframe",
+       "variable_name": "grp_out"
       },
-      "source": [
-        "IOOS has added the csv files to https://erddap.ioos.us/erddap/search/index.html?page=1&itemsPerPage=1000&searchFor=GTS\n",
-        "\n",
-        "Source data can be found at NDBC ioosstats server that [hosts the CSV files](https://www.ndbc.noaa.gov/ioosstats/) with the ingest data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import pandas as pd\n",
-        "\n",
-        "\n",
-        "url_ioos = 'https://erddap.ioos.us/erddap/tabledap/gts_regional_statistics.csvp'\n",
-        "df_ioos = pd.read_csv(url_ioos,parse_dates=[2])\n",
-        "df_ioos['source'] = 'IOOS'\n",
-        "\n",
-        "url_ndbc = 'https://erddap.ioos.us/erddap/tabledap/gts_ndbc_statistics.csvp'\n",
-        "df_ndbc = pd.read_csv(url_ndbc,parse_dates=[2])\n",
-        "df_ndbc['source'] = 'NDBC'\n",
-        "\n",
-        "url_nonndbc = 'https://erddap.ioos.us/erddap/tabledap/gts_non_ndbc_statistics.csvp'\n",
-        "df_nonndbc = pd.read_csv(url_nonndbc,parse_dates=[2])\n",
-        "df_nonndbc['source'] = 'non-NDBC'\n",
-        "\n",
-        "df_out = pd.concat([df_ioos,df_ndbc,df_nonndbc])\n",
-        "\n",
-        "df_out.sample(10)"
+      "text/html": [
+       "\n",
+       "  <div id=\"df-46f3a08d-fd8c-4f3e-adb4-e087fd51515d\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>met</th>\n",
+       "      <th>wave</th>\n",
+       "      <th>total</th>\n",
+       "      <th>pcnt</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sponsor</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ALASKA OCEAN OBSERVING SYSTEM</th>\n",
+       "      <td>0</td>\n",
+       "      <td>24310</td>\n",
+       "      <td>24310</td>\n",
+       "      <td>0.000074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BP INC.</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CARIBBEAN INTEGRATED COASTAL OCEAN OBSERVING SYSTEM</th>\n",
+       "      <td>0</td>\n",
+       "      <td>37884</td>\n",
+       "      <td>37884</td>\n",
+       "      <td>0.000115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CHESAPEAKE BAY INTERPRETIVE BUOY SYSTEM</th>\n",
+       "      <td>4207934</td>\n",
+       "      <td>1929292</td>\n",
+       "      <td>6137226</td>\n",
+       "      <td>0.018652</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CLEVELAND WATER ALLIANCE</th>\n",
+       "      <td>439812</td>\n",
+       "      <td>399030</td>\n",
+       "      <td>838842</td>\n",
+       "      <td>0.002549</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COASTAL DATA INFORMATION PROGRAM/PMEL</th>\n",
+       "      <td>0</td>\n",
+       "      <td>32434</td>\n",
+       "      <td>32434</td>\n",
+       "      <td>0.000099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COASTAL OCEAN RESEARCH AND MONITORING PROGRAM</th>\n",
+       "      <td>521224</td>\n",
+       "      <td>18696</td>\n",
+       "      <td>539920</td>\n",
+       "      <td>0.001641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EPA &amp; MEXICAN GOVERNMENT COOPERATIVE PROGRAM</th>\n",
+       "      <td>2736</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2736</td>\n",
+       "      <td>0.000008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EVERGLADES NATIONAL PARK</th>\n",
+       "      <td>98420</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98420</td>\n",
+       "      <td>0.000299</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GREAT LAKES RESEARCH LABORATORY</th>\n",
+       "      <td>4986012</td>\n",
+       "      <td>330292</td>\n",
+       "      <td>5316304</td>\n",
+       "      <td>0.016157</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GREAT LAKES WATER AUTHORITY</th>\n",
+       "      <td>58908</td>\n",
+       "      <td>58522</td>\n",
+       "      <td>117430</td>\n",
+       "      <td>0.000357</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ILLINOIS-INDIANA SEA GRANT</th>\n",
+       "      <td>29760</td>\n",
+       "      <td>31416</td>\n",
+       "      <td>61176</td>\n",
+       "      <td>0.000186</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>INTEGRATED CORAL OBSERVING NETWORK</th>\n",
+       "      <td>260612</td>\n",
+       "      <td>0</td>\n",
+       "      <td>260612</td>\n",
+       "      <td>0.000792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LIMNOTECH</th>\n",
+       "      <td>716696</td>\n",
+       "      <td>554956</td>\n",
+       "      <td>1271652</td>\n",
+       "      <td>0.003865</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LOUISIANA OFFSHORE OIL PORT</th>\n",
+       "      <td>88930</td>\n",
+       "      <td>78998</td>\n",
+       "      <td>167928</td>\n",
+       "      <td>0.000510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MARINE EXCHANGE OF ALASKA</th>\n",
+       "      <td>1668736</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1668736</td>\n",
+       "      <td>0.005071</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MICHIGAN TECHNICAL UNIVERSITY</th>\n",
+       "      <td>0</td>\n",
+       "      <td>23870</td>\n",
+       "      <td>23870</td>\n",
+       "      <td>0.000073</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MOSS LANDING MARINE LABORATORIES</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MURPHY OIL CORP.</th>\n",
+       "      <td>25318</td>\n",
+       "      <td>22938</td>\n",
+       "      <td>48256</td>\n",
+       "      <td>0.000147</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NATIONAL ESTUARINE RESEARCH RESERVE SYSTEM</th>\n",
+       "      <td>11803798</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11803798</td>\n",
+       "      <td>0.035873</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NATIONAL OCEAN SERVICE</th>\n",
+       "      <td>222046882</td>\n",
+       "      <td>0</td>\n",
+       "      <td>222046882</td>\n",
+       "      <td>0.674823</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NATIONAL PARK SERVICE - LAKE MEAD NATIONAL REC AREA</th>\n",
+       "      <td>1331640</td>\n",
+       "      <td>1403834</td>\n",
+       "      <td>2735474</td>\n",
+       "      <td>0.008313</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NATIONAL PARK SERVICES - SLEEPING BEAR DUNES</th>\n",
+       "      <td>65238</td>\n",
+       "      <td>47654</td>\n",
+       "      <td>112892</td>\n",
+       "      <td>0.000343</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NATIONAL RENEWABLE ENERGY LABORATORY</th>\n",
+       "      <td>0</td>\n",
+       "      <td>119534</td>\n",
+       "      <td>119534</td>\n",
+       "      <td>0.000363</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NATIONAL WEATHER SERVICE, ALASKA REGION</th>\n",
+       "      <td>1122278</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1122278</td>\n",
+       "      <td>0.003411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NATIONAL WEATHER SERVICE, CENTRAL REGION</th>\n",
+       "      <td>13786322</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13786322</td>\n",
+       "      <td>0.041898</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NATIONAL WEATHER SERVICE, EASTERN REGION</th>\n",
+       "      <td>5412862</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5412862</td>\n",
+       "      <td>0.016450</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NOAA NOS PHYSICAL OCEANOGRAPHIC RT SYSTEM PROGRAM</th>\n",
+       "      <td>41221206</td>\n",
+       "      <td>0</td>\n",
+       "      <td>41221206</td>\n",
+       "      <td>0.125276</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>OCEAN OBSERVATORIES INITIATIVE</th>\n",
+       "      <td>2876742</td>\n",
+       "      <td>402564</td>\n",
+       "      <td>3279306</td>\n",
+       "      <td>0.009966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PETROBRAS</th>\n",
+       "      <td>24906</td>\n",
+       "      <td>20274</td>\n",
+       "      <td>45180</td>\n",
+       "      <td>0.000137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SCRIPPS WAVERIDER COASTAL DATA INFORMATION PROGRAM</th>\n",
+       "      <td>106182</td>\n",
+       "      <td>8626560</td>\n",
+       "      <td>8732742</td>\n",
+       "      <td>0.026540</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SHELL OIL</th>\n",
+       "      <td>231056</td>\n",
+       "      <td>141144</td>\n",
+       "      <td>372200</td>\n",
+       "      <td>0.001131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STONY BROOK UNIVERSITY</th>\n",
+       "      <td>62092</td>\n",
+       "      <td>0</td>\n",
+       "      <td>62092</td>\n",
+       "      <td>0.000189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SUNY PLATTSBURGH CEES / LAKE CHAMPLAIN RESEARCH INST.</th>\n",
+       "      <td>99024</td>\n",
+       "      <td>0</td>\n",
+       "      <td>99024</td>\n",
+       "      <td>0.000301</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TEXAS COASTAL OCEAN OBSERVATION NETWORK</th>\n",
+       "      <td>129812</td>\n",
+       "      <td>0</td>\n",
+       "      <td>129812</td>\n",
+       "      <td>0.000395</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>U. S. COAST GUARD</th>\n",
+       "      <td>48486</td>\n",
+       "      <td>16200</td>\n",
+       "      <td>64686</td>\n",
+       "      <td>0.000197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>U.S. ARMY CORPS OF ENGINEERS</th>\n",
+       "      <td>0</td>\n",
+       "      <td>811626</td>\n",
+       "      <td>811626</td>\n",
+       "      <td>0.002467</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>U.S. NAVY</th>\n",
+       "      <td>0</td>\n",
+       "      <td>79348</td>\n",
+       "      <td>79348</td>\n",
+       "      <td>0.000241</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAGNE</th>\n",
+       "      <td>20956</td>\n",
+       "      <td>36232</td>\n",
+       "      <td>57188</td>\n",
+       "      <td>0.000174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UNIVERSITY OF NEW HAMPSHIRE</th>\n",
+       "      <td>78518</td>\n",
+       "      <td>0</td>\n",
+       "      <td>78518</td>\n",
+       "      <td>0.000239</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UNIVERSITY OF NORTH CAROLINA COASTAL STUDIES</th>\n",
+       "      <td>0</td>\n",
+       "      <td>53956</td>\n",
+       "      <td>53956</td>\n",
+       "      <td>0.000164</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UNIVERSITY OF WISCONSIN AT MILWAUKEE</th>\n",
+       "      <td>1112</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1112</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>USF COMPS MARINE NETWORK</th>\n",
+       "      <td>87656</td>\n",
+       "      <td>0</td>\n",
+       "      <td>87656</td>\n",
+       "      <td>0.000266</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VERMONT EPSCOR</th>\n",
+       "      <td>36322</td>\n",
+       "      <td>36322</td>\n",
+       "      <td>72644</td>\n",
+       "      <td>0.000221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>WOODS HOLE OCEANOGRAPHIC INSTITUTION</th>\n",
+       "      <td>0</td>\n",
+       "      <td>8330</td>\n",
+       "      <td>8330</td>\n",
+       "      <td>0.000025</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-46f3a08d-fd8c-4f3e-adb4-e087fd51515d')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-46f3a08d-fd8c-4f3e-adb4-e087fd51515d button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-46f3a08d-fd8c-4f3e-adb4-e087fd51515d');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-cadde741-f7d9-4649-be4f-aa6240e2c7ba\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cadde741-f7d9-4649-be4f-aa6240e2c7ba')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-cadde741-f7d9-4649-be4f-aa6240e2c7ba button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 363
-        },
-        "id": "2c7dvH32tYt2",
-        "outputId": "038eb22a-90e6-43cd-a792-6d89abd0fa9f"
-      },
-      "execution_count": 1,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "       Year  Month                time (UTC) locationID region  \\\n",
-              "3256   2019     11 2019-11-01 00:00:00+00:00      42012    NaN   \n",
-              "23127  2021      9 2021-09-01 00:00:00+00:00      MZXC1    NaN   \n",
-              "12162  2023     10 2023-10-01 00:00:00+00:00      WCXA2   AOOS   \n",
-              "13713  2020      4 2020-04-01 00:00:00+00:00      46248    NaN   \n",
-              "1584   2018     11 2018-11-01 00:00:00+00:00      46041    NaN   \n",
-              "1066   2018      3 2018-03-01 00:00:00+00:00      LJPC1    NaN   \n",
-              "24930  2022      1 2022-01-01 00:00:00+00:00      NSTP6    NaN   \n",
-              "20170  2021      4 2021-04-01 00:00:00+00:00      LDTM4    NaN   \n",
-              "34674  2023      7 2023-07-01 00:00:00+00:00      ITKA2    NaN   \n",
-              "25526  2022      2 2022-02-01 00:00:00+00:00      MYPF1    NaN   \n",
-              "\n",
-              "                                                 sponsor    met  wave  \\\n",
-              "3256                            NATIONAL WEATHER SERVICE   8614  1436   \n",
-              "23127  NOAA NOS PHYSICAL OCEANOGRAPHIC RT SYSTEM PROGRAM  14372     0   \n",
-              "12162                          MARINE EXCHANGE OF ALASKA   8654     0   \n",
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-              "24930                             NATIONAL OCEAN SERVICE      0     0   \n",
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-              "25526                             NATIONAL OCEAN SERVICE  13354     0   \n",
-              "\n",
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+      "text/plain": [
+       "                                                          met     wave  \\\n",
+       "sponsor                                                                  \n",
+       "ALASKA OCEAN OBSERVING SYSTEM                               0    24310   \n",
+       "BP INC.                                                     0        0   \n",
+       "CARIBBEAN INTEGRATED COASTAL OCEAN OBSERVING SY...          0    37884   \n",
+       "CHESAPEAKE BAY INTERPRETIVE BUOY SYSTEM               4207934  1929292   \n",
+       "CLEVELAND WATER ALLIANCE                               439812   399030   \n",
+       "COASTAL DATA INFORMATION PROGRAM/PMEL                       0    32434   \n",
+       "COASTAL OCEAN RESEARCH AND MONITORING PROGRAM          521224    18696   \n",
+       "EPA & MEXICAN GOVERNMENT COOPERATIVE PROGRAM             2736        0   \n",
+       "EVERGLADES NATIONAL PARK                                98420        0   \n",
+       "GREAT LAKES RESEARCH LABORATORY                       4986012   330292   \n",
+       "GREAT LAKES WATER AUTHORITY                             58908    58522   \n",
+       "ILLINOIS-INDIANA SEA GRANT                              29760    31416   \n",
+       "INTEGRATED CORAL OBSERVING NETWORK                     260612        0   \n",
+       "LIMNOTECH                                              716696   554956   \n",
+       "LOUISIANA OFFSHORE OIL PORT                             88930    78998   \n",
+       "MARINE EXCHANGE OF ALASKA                             1668736        0   \n",
+       "MICHIGAN TECHNICAL UNIVERSITY                               0    23870   \n",
+       "MOSS LANDING MARINE LABORATORIES                            0        0   \n",
+       "MURPHY OIL CORP.                                        25318    22938   \n",
+       "NATIONAL ESTUARINE RESEARCH RESERVE SYSTEM           11803798        0   \n",
+       "NATIONAL OCEAN SERVICE                              222046882        0   \n",
+       "NATIONAL PARK SERVICE - LAKE MEAD NATIONAL REC ...    1331640  1403834   \n",
+       "NATIONAL PARK SERVICES - SLEEPING BEAR DUNES            65238    47654   \n",
+       "NATIONAL RENEWABLE ENERGY LABORATORY                        0   119534   \n",
+       "NATIONAL WEATHER SERVICE, ALASKA REGION               1122278        0   \n",
+       "NATIONAL WEATHER SERVICE, CENTRAL REGION             13786322        0   \n",
+       "NATIONAL WEATHER SERVICE, EASTERN REGION              5412862        0   \n",
+       "NOAA NOS PHYSICAL OCEANOGRAPHIC RT SYSTEM PROGRAM    41221206        0   \n",
+       "OCEAN OBSERVATORIES INITIATIVE                        2876742   402564   \n",
+       "PETROBRAS                                               24906    20274   \n",
+       "SCRIPPS WAVERIDER COASTAL DATA INFORMATION PROGRAM     106182  8626560   \n",
+       "SHELL OIL                                              231056   141144   \n",
+       "STONY BROOK UNIVERSITY                                  62092        0   \n",
+       "SUNY PLATTSBURGH CEES / LAKE CHAMPLAIN RESEARCH...      99024        0   \n",
+       "TEXAS COASTAL OCEAN OBSERVATION NETWORK                129812        0   \n",
+       "U. S. COAST GUARD                                       48486    16200   \n",
+       "U.S. ARMY CORPS OF ENGINEERS                                0   811626   \n",
+       "U.S. NAVY                                                   0    79348   \n",
+       "UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAGNE              20956    36232   \n",
+       "UNIVERSITY OF NEW HAMPSHIRE                             78518        0   \n",
+       "UNIVERSITY OF NORTH CAROLINA COASTAL STUDIES                0    53956   \n",
+       "UNIVERSITY OF WISCONSIN AT MILWAUKEE                     1112        0   \n",
+       "USF COMPS MARINE NETWORK                                87656        0   \n",
+       "VERMONT EPSCOR                                          36322    36322   \n",
+       "WOODS HOLE OCEANOGRAPHIC INSTITUTION                        0     8330   \n",
+       "\n",
+       "                                                        total      pcnt  \n",
+       "sponsor                                                                  \n",
+       "ALASKA OCEAN OBSERVING SYSTEM                           24310  0.000074  \n",
+       "BP INC.                                                     0  0.000000  \n",
+       "CARIBBEAN INTEGRATED COASTAL OCEAN OBSERVING SY...      37884  0.000115  \n",
+       "CHESAPEAKE BAY INTERPRETIVE BUOY SYSTEM               6137226  0.018652  \n",
+       "CLEVELAND WATER ALLIANCE                               838842  0.002549  \n",
+       "COASTAL DATA INFORMATION PROGRAM/PMEL                   32434  0.000099  \n",
+       "COASTAL OCEAN RESEARCH AND MONITORING PROGRAM          539920  0.001641  \n",
+       "EPA & MEXICAN GOVERNMENT COOPERATIVE PROGRAM             2736  0.000008  \n",
+       "EVERGLADES NATIONAL PARK                                98420  0.000299  \n",
+       "GREAT LAKES RESEARCH LABORATORY                       5316304  0.016157  \n",
+       "GREAT LAKES WATER AUTHORITY                            117430  0.000357  \n",
+       "ILLINOIS-INDIANA SEA GRANT                              61176  0.000186  \n",
+       "INTEGRATED CORAL OBSERVING NETWORK                     260612  0.000792  \n",
+       "LIMNOTECH                                             1271652  0.003865  \n",
+       "LOUISIANA OFFSHORE OIL PORT                            167928  0.000510  \n",
+       "MARINE EXCHANGE OF ALASKA                             1668736  0.005071  \n",
+       "MICHIGAN TECHNICAL UNIVERSITY                           23870  0.000073  \n",
+       "MOSS LANDING MARINE LABORATORIES                            0  0.000000  \n",
+       "MURPHY OIL CORP.                                        48256  0.000147  \n",
+       "NATIONAL ESTUARINE RESEARCH RESERVE SYSTEM           11803798  0.035873  \n",
+       "NATIONAL OCEAN SERVICE                              222046882  0.674823  \n",
+       "NATIONAL PARK SERVICE - LAKE MEAD NATIONAL REC ...    2735474  0.008313  \n",
+       "NATIONAL PARK SERVICES - SLEEPING BEAR DUNES           112892  0.000343  \n",
+       "NATIONAL RENEWABLE ENERGY LABORATORY                   119534  0.000363  \n",
+       "NATIONAL WEATHER SERVICE, ALASKA REGION               1122278  0.003411  \n",
+       "NATIONAL WEATHER SERVICE, CENTRAL REGION             13786322  0.041898  \n",
+       "NATIONAL WEATHER SERVICE, EASTERN REGION              5412862  0.016450  \n",
+       "NOAA NOS PHYSICAL OCEANOGRAPHIC RT SYSTEM PROGRAM    41221206  0.125276  \n",
+       "OCEAN OBSERVATORIES INITIATIVE                        3279306  0.009966  \n",
+       "PETROBRAS                                               45180  0.000137  \n",
+       "SCRIPPS WAVERIDER COASTAL DATA INFORMATION PROGRAM    8732742  0.026540  \n",
+       "SHELL OIL                                              372200  0.001131  \n",
+       "STONY BROOK UNIVERSITY                                  62092  0.000189  \n",
+       "SUNY PLATTSBURGH CEES / LAKE CHAMPLAIN RESEARCH...      99024  0.000301  \n",
+       "TEXAS COASTAL OCEAN OBSERVATION NETWORK                129812  0.000395  \n",
+       "U. S. COAST GUARD                                       64686  0.000197  \n",
+       "U.S. ARMY CORPS OF ENGINEERS                           811626  0.002467  \n",
+       "U.S. NAVY                                               79348  0.000241  \n",
+       "UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAGNE              57188  0.000174  \n",
+       "UNIVERSITY OF NEW HAMPSHIRE                             78518  0.000239  \n",
+       "UNIVERSITY OF NORTH CAROLINA COASTAL STUDIES            53956  0.000164  \n",
+       "UNIVERSITY OF WISCONSIN AT MILWAUKEE                     1112  0.000003  \n",
+       "USF COMPS MARINE NETWORK                                87656  0.000266  \n",
+       "VERMONT EPSCOR                                          72644  0.000221  \n",
+       "WOODS HOLE OCEANOGRAPHIC INSTITUTION                     8330  0.000025  "
       ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "df_nonndbc[\"total\"] = df_nonndbc[\"met\"] + df_nonndbc[\"wave\"]\n",
+    "df_nonndbc[\"time (UTC)\"] = df_nonndbc[\"time (UTC)\"].dt.tz_localize(None)\n",
+    "\n",
+    "nonndbc_group = df_nonndbc.groupby(by=[\"sponsor\"])\n",
+    "\n",
+    "grp = nonndbc_group[[\"met\",\"wave\",\"total\"]].sum()\n",
+    "\n",
+    "grp_out = grp.assign(pcnt = grp[\"total\"] / grp[\"total\"].sum())\n",
+    "\n",
+    "grp_out"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "96Hn3bnpl_j5"
+   },
+   "source": [
+    "## Make pie chart using matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 424
     },
+    "id": "9y1Gbul3l-k0",
+    "outputId": "120557ba-8b7b-4e03-ac8d-37814fec6435"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": [
-        "# What's non-NDBC composed of?"
-      ],
-      "metadata": {
-        "id": "h9KD4cjOg5kS"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "import numpy as np\n",
-        "\n",
-        "\n",
-        "df_nonndbc['total'] = df_nonndbc['met'] + df_nonndbc['wave']\n",
-        "df_nonndbc[\"time (UTC)\"] = df_nonndbc[\"time (UTC)\"].dt.tz_localize(None)\n",
-        "\n",
-        "nonndbc_group = df_nonndbc.groupby(by=['sponsor'])\n",
-        "\n",
-        "grp = nonndbc_group[['met','wave','total']].sum()\n",
-        "\n",
-        "grp_out = grp.assign(pcnt = grp['total'] / grp['total'].sum())\n",
-        "\n",
-        "grp_out"
-      ],
-      "metadata": {
-        "id": "MT2AdHB7chjy",
-        "outputId": "a9093ea0-de5d-42cc-df08-879f6457c37a",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        }
-      },
-      "execution_count": 2,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "                                                          met     wave  \\\n",
-              "sponsor                                                                  \n",
-              "ALASKA OCEAN OBSERVING SYSTEM                               0    24310   \n",
-              "BP INC.                                                     0        0   \n",
-              "CARIBBEAN INTEGRATED COASTAL OCEAN OBSERVING SY...          0    37884   \n",
-              "CHESAPEAKE BAY INTERPRETIVE BUOY SYSTEM               4207934  1929292   \n",
-              "CLEVELAND WATER ALLIANCE                               439812   399030   \n",
-              "COASTAL DATA INFORMATION PROGRAM/PMEL                       0    32434   \n",
-              "COASTAL OCEAN RESEARCH AND MONITORING PROGRAM          521224    18696   \n",
-              "EPA & MEXICAN GOVERNMENT COOPERATIVE PROGRAM             2736        0   \n",
-              "EVERGLADES NATIONAL PARK                                98420        0   \n",
-              "GREAT LAKES RESEARCH LABORATORY                       4986012   330292   \n",
-              "GREAT LAKES WATER AUTHORITY                             58908    58522   \n",
-              "ILLINOIS-INDIANA SEA GRANT                              29760    31416   \n",
-              "INTEGRATED CORAL OBSERVING NETWORK                     260612        0   \n",
-              "LIMNOTECH                                              716696   554956   \n",
-              "LOUISIANA OFFSHORE OIL PORT                             88930    78998   \n",
-              "MARINE EXCHANGE OF ALASKA                             1668736        0   \n",
-              "MICHIGAN TECHNICAL UNIVERSITY                               0    23870   \n",
-              "MOSS LANDING MARINE LABORATORIES                            0        0   \n",
-              "MURPHY OIL CORP.                                        25318    22938   \n",
-              "NATIONAL ESTUARINE RESEARCH RESERVE SYSTEM           11803798        0   \n",
-              "NATIONAL OCEAN SERVICE                              222046882        0   \n",
-              "NATIONAL PARK SERVICE - LAKE MEAD NATIONAL REC ...    1331640  1403834   \n",
-              "NATIONAL PARK SERVICES - SLEEPING BEAR DUNES            65238    47654   \n",
-              "NATIONAL RENEWABLE ENERGY LABORATORY                        0   119534   \n",
-              "NATIONAL WEATHER SERVICE, ALASKA REGION               1122278        0   \n",
-              "NATIONAL WEATHER SERVICE, CENTRAL REGION             13786322        0   \n",
-              "NATIONAL WEATHER SERVICE, EASTERN REGION              5412862        0   \n",
-              "NOAA NOS PHYSICAL OCEANOGRAPHIC RT SYSTEM PROGRAM    41221206        0   \n",
-              "OCEAN OBSERVATORIES INITIATIVE                        2876742   402564   \n",
-              "PETROBRAS                                               24906    20274   \n",
-              "SCRIPPS WAVERIDER COASTAL DATA INFORMATION PROGRAM     106182  8626560   \n",
-              "SHELL OIL                                              231056   141144   \n",
-              "STONY BROOK UNIVERSITY                                  62092        0   \n",
-              "SUNY PLATTSBURGH CEES / LAKE CHAMPLAIN RESEARCH...      99024        0   \n",
-              "TEXAS COASTAL OCEAN OBSERVATION NETWORK                129812        0   \n",
-              "U. S. COAST GUARD                                       48486    16200   \n",
-              "U.S. ARMY CORPS OF ENGINEERS                                0   811626   \n",
-              "U.S. NAVY                                                   0    79348   \n",
-              "UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAGNE              20956    36232   \n",
-              "UNIVERSITY OF NEW HAMPSHIRE                             78518        0   \n",
-              "UNIVERSITY OF NORTH CAROLINA COASTAL STUDIES                0    53956   \n",
-              "UNIVERSITY OF WISCONSIN AT MILWAUKEE                     1112        0   \n",
-              "USF COMPS MARINE NETWORK                                87656        0   \n",
-              "VERMONT EPSCOR                                          36322    36322   \n",
-              "WOODS HOLE OCEANOGRAPHIC INSTITUTION                        0     8330   \n",
-              "\n",
-              "                                                        total      pcnt  \n",
-              "sponsor                                                                  \n",
-              "ALASKA OCEAN OBSERVING SYSTEM                           24310  0.000074  \n",
-              "BP INC.                                                     0  0.000000  \n",
-              "CARIBBEAN INTEGRATED COASTAL OCEAN OBSERVING SY...      37884  0.000115  \n",
-              "CHESAPEAKE BAY INTERPRETIVE BUOY SYSTEM               6137226  0.018652  \n",
-              "CLEVELAND WATER ALLIANCE                               838842  0.002549  \n",
-              "COASTAL DATA INFORMATION PROGRAM/PMEL                   32434  0.000099  \n",
-              "COASTAL OCEAN RESEARCH AND MONITORING PROGRAM          539920  0.001641  \n",
-              "EPA & MEXICAN GOVERNMENT COOPERATIVE PROGRAM             2736  0.000008  \n",
-              "EVERGLADES NATIONAL PARK                                98420  0.000299  \n",
-              "GREAT LAKES RESEARCH LABORATORY                       5316304  0.016157  \n",
-              "GREAT LAKES WATER AUTHORITY                            117430  0.000357  \n",
-              "ILLINOIS-INDIANA SEA GRANT                              61176  0.000186  \n",
-              "INTEGRATED CORAL OBSERVING NETWORK                     260612  0.000792  \n",
-              "LIMNOTECH                                             1271652  0.003865  \n",
-              "LOUISIANA OFFSHORE OIL PORT                            167928  0.000510  \n",
-              "MARINE EXCHANGE OF ALASKA                             1668736  0.005071  \n",
-              "MICHIGAN TECHNICAL UNIVERSITY                           23870  0.000073  \n",
-              "MOSS LANDING MARINE LABORATORIES                            0  0.000000  \n",
-              "MURPHY OIL CORP.                                        48256  0.000147  \n",
-              "NATIONAL ESTUARINE RESEARCH RESERVE SYSTEM           11803798  0.035873  \n",
-              "NATIONAL OCEAN SERVICE                              222046882  0.674823  \n",
-              "NATIONAL PARK SERVICE - LAKE MEAD NATIONAL REC ...    2735474  0.008313  \n",
-              "NATIONAL PARK SERVICES - SLEEPING BEAR DUNES           112892  0.000343  \n",
-              "NATIONAL RENEWABLE ENERGY LABORATORY                   119534  0.000363  \n",
-              "NATIONAL WEATHER SERVICE, ALASKA REGION               1122278  0.003411  \n",
-              "NATIONAL WEATHER SERVICE, CENTRAL REGION             13786322  0.041898  \n",
-              "NATIONAL WEATHER SERVICE, EASTERN REGION              5412862  0.016450  \n",
-              "NOAA NOS PHYSICAL OCEANOGRAPHIC RT SYSTEM PROGRAM    41221206  0.125276  \n",
-              "OCEAN OBSERVATORIES INITIATIVE                        3279306  0.009966  \n",
-              "PETROBRAS                                               45180  0.000137  \n",
-              "SCRIPPS WAVERIDER COASTAL DATA INFORMATION PROGRAM    8732742  0.026540  \n",
-              "SHELL OIL                                              372200  0.001131  \n",
-              "STONY BROOK UNIVERSITY                                  62092  0.000189  \n",
-              "SUNY PLATTSBURGH CEES / LAKE CHAMPLAIN RESEARCH...      99024  0.000301  \n",
-              "TEXAS COASTAL OCEAN OBSERVATION NETWORK                129812  0.000395  \n",
-              "U. S. COAST GUARD                                       64686  0.000197  \n",
-              "U.S. ARMY CORPS OF ENGINEERS                           811626  0.002467  \n",
-              "U.S. NAVY                                               79348  0.000241  \n",
-              "UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAGNE              57188  0.000174  \n",
-              "UNIVERSITY OF NEW HAMPSHIRE                             78518  0.000239  \n",
-              "UNIVERSITY OF NORTH CAROLINA COASTAL STUDIES            53956  0.000164  \n",
-              "UNIVERSITY OF WISCONSIN AT MILWAUKEE                     1112  0.000003  \n",
-              "USF COMPS MARINE NETWORK                                87656  0.000266  \n",
-              "VERMONT EPSCOR                                          72644  0.000221  \n",
-              "WOODS HOLE OCEANOGRAPHIC INSTITUTION                     8330  0.000025  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-46f3a08d-fd8c-4f3e-adb4-e087fd51515d\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
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-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>met</th>\n",
-              "      <th>wave</th>\n",
-              "      <th>total</th>\n",
-              "      <th>pcnt</th>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>sponsor</th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>ALASKA OCEAN OBSERVING SYSTEM</th>\n",
-              "      <td>0</td>\n",
-              "      <td>24310</td>\n",
-              "      <td>24310</td>\n",
-              "      <td>0.000074</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>BP INC.</th>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>CARIBBEAN INTEGRATED COASTAL OCEAN OBSERVING SYSTEM</th>\n",
-              "      <td>0</td>\n",
-              "      <td>37884</td>\n",
-              "      <td>37884</td>\n",
-              "      <td>0.000115</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>CHESAPEAKE BAY INTERPRETIVE BUOY SYSTEM</th>\n",
-              "      <td>4207934</td>\n",
-              "      <td>1929292</td>\n",
-              "      <td>6137226</td>\n",
-              "      <td>0.018652</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>CLEVELAND WATER ALLIANCE</th>\n",
-              "      <td>439812</td>\n",
-              "      <td>399030</td>\n",
-              "      <td>838842</td>\n",
-              "      <td>0.002549</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>COASTAL DATA INFORMATION PROGRAM/PMEL</th>\n",
-              "      <td>0</td>\n",
-              "      <td>32434</td>\n",
-              "      <td>32434</td>\n",
-              "      <td>0.000099</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>COASTAL OCEAN RESEARCH AND MONITORING PROGRAM</th>\n",
-              "      <td>521224</td>\n",
-              "      <td>18696</td>\n",
-              "      <td>539920</td>\n",
-              "      <td>0.001641</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>EPA &amp; MEXICAN GOVERNMENT COOPERATIVE PROGRAM</th>\n",
-              "      <td>2736</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2736</td>\n",
-              "      <td>0.000008</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>EVERGLADES NATIONAL PARK</th>\n",
-              "      <td>98420</td>\n",
-              "      <td>0</td>\n",
-              "      <td>98420</td>\n",
-              "      <td>0.000299</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>GREAT LAKES RESEARCH LABORATORY</th>\n",
-              "      <td>4986012</td>\n",
-              "      <td>330292</td>\n",
-              "      <td>5316304</td>\n",
-              "      <td>0.016157</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>GREAT LAKES WATER AUTHORITY</th>\n",
-              "      <td>58908</td>\n",
-              "      <td>58522</td>\n",
-              "      <td>117430</td>\n",
-              "      <td>0.000357</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>ILLINOIS-INDIANA SEA GRANT</th>\n",
-              "      <td>29760</td>\n",
-              "      <td>31416</td>\n",
-              "      <td>61176</td>\n",
-              "      <td>0.000186</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>INTEGRATED CORAL OBSERVING NETWORK</th>\n",
-              "      <td>260612</td>\n",
-              "      <td>0</td>\n",
-              "      <td>260612</td>\n",
-              "      <td>0.000792</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>LIMNOTECH</th>\n",
-              "      <td>716696</td>\n",
-              "      <td>554956</td>\n",
-              "      <td>1271652</td>\n",
-              "      <td>0.003865</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>LOUISIANA OFFSHORE OIL PORT</th>\n",
-              "      <td>88930</td>\n",
-              "      <td>78998</td>\n",
-              "      <td>167928</td>\n",
-              "      <td>0.000510</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>MARINE EXCHANGE OF ALASKA</th>\n",
-              "      <td>1668736</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1668736</td>\n",
-              "      <td>0.005071</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>MICHIGAN TECHNICAL UNIVERSITY</th>\n",
-              "      <td>0</td>\n",
-              "      <td>23870</td>\n",
-              "      <td>23870</td>\n",
-              "      <td>0.000073</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>MOSS LANDING MARINE LABORATORIES</th>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>MURPHY OIL CORP.</th>\n",
-              "      <td>25318</td>\n",
-              "      <td>22938</td>\n",
-              "      <td>48256</td>\n",
-              "      <td>0.000147</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>NATIONAL ESTUARINE RESEARCH RESERVE SYSTEM</th>\n",
-              "      <td>11803798</td>\n",
-              "      <td>0</td>\n",
-              "      <td>11803798</td>\n",
-              "      <td>0.035873</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>NATIONAL OCEAN SERVICE</th>\n",
-              "      <td>222046882</td>\n",
-              "      <td>0</td>\n",
-              "      <td>222046882</td>\n",
-              "      <td>0.674823</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>NATIONAL PARK SERVICE - LAKE MEAD NATIONAL REC AREA</th>\n",
-              "      <td>1331640</td>\n",
-              "      <td>1403834</td>\n",
-              "      <td>2735474</td>\n",
-              "      <td>0.008313</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>NATIONAL PARK SERVICES - SLEEPING BEAR DUNES</th>\n",
-              "      <td>65238</td>\n",
-              "      <td>47654</td>\n",
-              "      <td>112892</td>\n",
-              "      <td>0.000343</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>NATIONAL RENEWABLE ENERGY LABORATORY</th>\n",
-              "      <td>0</td>\n",
-              "      <td>119534</td>\n",
-              "      <td>119534</td>\n",
-              "      <td>0.000363</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>NATIONAL WEATHER SERVICE, ALASKA REGION</th>\n",
-              "      <td>1122278</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1122278</td>\n",
-              "      <td>0.003411</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>NATIONAL WEATHER SERVICE, CENTRAL REGION</th>\n",
-              "      <td>13786322</td>\n",
-              "      <td>0</td>\n",
-              "      <td>13786322</td>\n",
-              "      <td>0.041898</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>NATIONAL WEATHER SERVICE, EASTERN REGION</th>\n",
-              "      <td>5412862</td>\n",
-              "      <td>0</td>\n",
-              "      <td>5412862</td>\n",
-              "      <td>0.016450</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>NOAA NOS PHYSICAL OCEANOGRAPHIC RT SYSTEM PROGRAM</th>\n",
-              "      <td>41221206</td>\n",
-              "      <td>0</td>\n",
-              "      <td>41221206</td>\n",
-              "      <td>0.125276</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>OCEAN OBSERVATORIES INITIATIVE</th>\n",
-              "      <td>2876742</td>\n",
-              "      <td>402564</td>\n",
-              "      <td>3279306</td>\n",
-              "      <td>0.009966</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>PETROBRAS</th>\n",
-              "      <td>24906</td>\n",
-              "      <td>20274</td>\n",
-              "      <td>45180</td>\n",
-              "      <td>0.000137</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>SCRIPPS WAVERIDER COASTAL DATA INFORMATION PROGRAM</th>\n",
-              "      <td>106182</td>\n",
-              "      <td>8626560</td>\n",
-              "      <td>8732742</td>\n",
-              "      <td>0.026540</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>SHELL OIL</th>\n",
-              "      <td>231056</td>\n",
-              "      <td>141144</td>\n",
-              "      <td>372200</td>\n",
-              "      <td>0.001131</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>STONY BROOK UNIVERSITY</th>\n",
-              "      <td>62092</td>\n",
-              "      <td>0</td>\n",
-              "      <td>62092</td>\n",
-              "      <td>0.000189</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>SUNY PLATTSBURGH CEES / LAKE CHAMPLAIN RESEARCH INST.</th>\n",
-              "      <td>99024</td>\n",
-              "      <td>0</td>\n",
-              "      <td>99024</td>\n",
-              "      <td>0.000301</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>TEXAS COASTAL OCEAN OBSERVATION NETWORK</th>\n",
-              "      <td>129812</td>\n",
-              "      <td>0</td>\n",
-              "      <td>129812</td>\n",
-              "      <td>0.000395</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>U. S. COAST GUARD</th>\n",
-              "      <td>48486</td>\n",
-              "      <td>16200</td>\n",
-              "      <td>64686</td>\n",
-              "      <td>0.000197</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>U.S. ARMY CORPS OF ENGINEERS</th>\n",
-              "      <td>0</td>\n",
-              "      <td>811626</td>\n",
-              "      <td>811626</td>\n",
-              "      <td>0.002467</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>U.S. NAVY</th>\n",
-              "      <td>0</td>\n",
-              "      <td>79348</td>\n",
-              "      <td>79348</td>\n",
-              "      <td>0.000241</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAGNE</th>\n",
-              "      <td>20956</td>\n",
-              "      <td>36232</td>\n",
-              "      <td>57188</td>\n",
-              "      <td>0.000174</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>UNIVERSITY OF NEW HAMPSHIRE</th>\n",
-              "      <td>78518</td>\n",
-              "      <td>0</td>\n",
-              "      <td>78518</td>\n",
-              "      <td>0.000239</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>UNIVERSITY OF NORTH CAROLINA COASTAL STUDIES</th>\n",
-              "      <td>0</td>\n",
-              "      <td>53956</td>\n",
-              "      <td>53956</td>\n",
-              "      <td>0.000164</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>UNIVERSITY OF WISCONSIN AT MILWAUKEE</th>\n",
-              "      <td>1112</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1112</td>\n",
-              "      <td>0.000003</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>USF COMPS MARINE NETWORK</th>\n",
-              "      <td>87656</td>\n",
-              "      <td>0</td>\n",
-              "      <td>87656</td>\n",
-              "      <td>0.000266</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>VERMONT EPSCOR</th>\n",
-              "      <td>36322</td>\n",
-              "      <td>36322</td>\n",
-              "      <td>72644</td>\n",
-              "      <td>0.000221</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>WOODS HOLE OCEANOGRAPHIC INSTITUTION</th>\n",
-              "      <td>0</td>\n",
-              "      <td>8330</td>\n",
-              "      <td>8330</td>\n",
-              "      <td>0.000025</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-46f3a08d-fd8c-4f3e-adb4-e087fd51515d')\"\n",
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-              "  </svg>\n",
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-              "      width: 32px;\n",
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-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
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-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-46f3a08d-fd8c-4f3e-adb4-e087fd51515d button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-46f3a08d-fd8c-4f3e-adb4-e087fd51515d');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
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-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
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-              "\n",
-              "\n",
-              "<div id=\"df-cadde741-f7d9-4649-be4f-aa6240e2c7ba\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cadde741-f7d9-4649-be4f-aa6240e2c7ba')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
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-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
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-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-cadde741-f7d9-4649-be4f-aa6240e2c7ba button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "grp_out",
-              "summary": "{\n  \"name\": \"grp_out\",\n  \"rows\": 45,\n  \"fields\": [\n    {\n      \"column\": \"sponsor\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 45,\n        \"samples\": [\n          \"UNIVERSITY OF NEW HAMPSHIRE\",\n          \"NATIONAL WEATHER SERVICE, CENTRAL REGION\",\n          \"NATIONAL WEATHER SERVICE, EASTERN REGION\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"met\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33450686,\n        \"min\": 0,\n        \"max\": 222046882,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          62092,\n          25318,\n          106182\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wave\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1317179,\n        \"min\": 0,\n        \"max\": 8626560,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          31416,\n          53956,\n          58522\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33413659,\n        \"min\": 0,\n        \"max\": 222046882,\n        \"num_unique_values\": 44,\n        \"samples\": [\n          57188,\n          13786322,\n          5412862\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pcnt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10154757006806879,\n        \"min\": 0.0,\n        \"max\": 0.6748234563502864,\n        \"num_unique_values\": 44,\n        \"samples\": [\n          0.00017380025098375477,\n          0.0418980594485357,\n          0.016450247851654696\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 2
-        }
+     "data": {
+      "text/plain": [
+       "<matplotlib.patches.Circle at 0x7e5e5cb0be50>"
       ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "## Make pie chart using matplotlib"
-      ],
-      "metadata": {
-        "id": "96Hn3bnpl_j5"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "explode = 0.05*np.ones(len(grp_out))\n",
-        "\n",
-        "grp_out['total'].plot.pie(rotatelabels=False,\n",
-        "                          autopct='%1.1f%%',\n",
-        "                          ylabel='',\n",
-        "                          textprops={'fontsize':10},\n",
-        "                          #radius=2,\n",
-        "                          pctdistance=0.85,\n",
-        "                          explode=explode)\n",
-        "# draw circle\n",
-        "centre_circle = plt.Circle((0, 0), 0.7, fc='white')\n",
-        "fig = plt.gcf()\n",
-        "\n",
-        "# Adding Circle in Pie chart\n",
-        "fig.gca().add_artist(centre_circle)"
-      ],
-      "metadata": {
-        "id": "9y1Gbul3l-k0",
-        "outputId": "120557ba-8b7b-4e03-ac8d-37814fec6435",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 424
-        }
-      },
-      "execution_count": 3,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<matplotlib.patches.Circle at 0x7e5e5cb0be50>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 3
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "explode = 0.05*np.ones(len(grp_out))\n",
+    "\n",
+    "grp_out[\"total\"].plot.pie(rotatelabels=False,\n",
+    "                          autopct=\"%1.1f%%\",\n",
+    "                          ylabel=\"\",\n",
+    "                          textprops={\"fontsize\":10},\n",
+    "                          #radius=2,\n",
+    "                          pctdistance=0.85,\n",
+    "                          explode=explode)\n",
+    "# draw circle\n",
+    "centre_circle = plt.Circle((0, 0), 0.7, fc=\"white\")\n",
+    "fig = plt.gcf()\n",
+    "\n",
+    "# Adding Circle in Pie chart\n",
+    "fig.gca().add_artist(centre_circle)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IPiHXNr1ygRO"
+   },
+   "source": [
+    "# Above chart is busy, let's group entries <2% into 'OTHER' category"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 269
     },
+    "id": "UzBFlJFIt3Mi",
+    "outputId": "684129fa-f521-4d4c-c38b-f3a338b60d73"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": [
-        "# Above chart is busy, let's group entries <2% into 'OTHER' category"
-      ],
-      "metadata": {
-        "id": "IPiHXNr1ygRO"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "filtered_grp = grp_out.loc[grp_out['pcnt']>0.02]\n",
-        "\n",
-        "filtered_grp.reset_index(inplace=True)\n",
-        "\n",
-        "df = pd.DataFrame({'sponsor': 'OTHER',\n",
-        "              'total': [grp_out.loc[grp_out['pcnt']<0.02,'total'].sum()]\n",
-        "              })\n",
-        "\n",
-        "df_pie = pd.concat([filtered_grp, df])\n",
-        "\n",
-        "df_pie.set_index('sponsor',inplace=True)\n",
-        "\n",
-        "df_pie"
-      ],
-      "metadata": {
-        "id": "UzBFlJFIt3Mi",
-        "outputId": "684129fa-f521-4d4c-c38b-f3a338b60d73",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 269
-        }
+     "data": {
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "summary": "{\n  \"name\": \"df_pie\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"sponsor\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"NATIONAL ESTUARINE RESEARCH RESERVE SYSTEM\",\n          \"NATIONAL OCEAN SERVICE\",\n          \"OTHER\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"met\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 93050155.14298129,\n        \"min\": 106182.0,\n        \"max\": 222046882.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          222046882.0,\n          106182.0,\n          13786322.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wave\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3857914.914396117,\n        \"min\": 0.0,\n        \"max\": 8626560.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          8626560.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 82890218,\n        \"min\": 8732742,\n        \"max\": 222046882,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          11803798,\n          222046882\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pcnt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2789426211838672,\n        \"min\": 0.026539706780729814,\n        \"max\": 0.6748234563502864,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.6748234563502864,\n          0.026539706780729814\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+       "type": "dataframe",
+       "variable_name": "df_pie"
       },
-      "execution_count": 4,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
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-              "                                                            met       wave  \\\n",
-              "sponsor                                                                      \n",
-              "NATIONAL ESTUARINE RESEARCH RESERVE SYSTEM           11803798.0        0.0   \n",
-              "NATIONAL OCEAN SERVICE                              222046882.0        0.0   \n",
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-              "SCRIPPS WAVERIDER COASTAL DATA INFORMATION PROGRAM     106182.0  8626560.0   \n",
-              "OTHER                                                       NaN        NaN   \n",
-              "\n",
-              "                                                        total      pcnt  \n",
-              "sponsor                                                                  \n",
-              "NATIONAL ESTUARINE RESEARCH RESERVE SYSTEM           11803798  0.035873  \n",
-              "NATIONAL OCEAN SERVICE                              222046882  0.674823  \n",
-              "NATIONAL WEATHER SERVICE, CENTRAL REGION             13786322  0.041898  \n",
-              "NOAA NOS PHYSICAL OCEANOGRAPHIC RT SYSTEM PROGRAM    41221206  0.125276  \n",
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-              "OTHER                                                31453454       NaN  "
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-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
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-              "\n",
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-              "\n",
-              "  .colab-df-quickchart {\n",
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-              "    border-radius: 50%;\n",
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-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
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-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
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-              "      border-top-color: var(--fill-color);\n",
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-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
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-              "      border-bottom-color: var(--fill-color);\n",
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-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-4fffe472-d618-4f29-8c71-7be6aa08c733 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df_pie",
-              "summary": "{\n  \"name\": \"df_pie\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"sponsor\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"NATIONAL ESTUARINE RESEARCH RESERVE SYSTEM\",\n          \"NATIONAL OCEAN SERVICE\",\n          \"OTHER\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"met\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 93050155.14298129,\n        \"min\": 106182.0,\n        \"max\": 222046882.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          222046882.0,\n          106182.0,\n          13786322.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wave\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3857914.914396117,\n        \"min\": 0.0,\n        \"max\": 8626560.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          8626560.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 82890218,\n        \"min\": 8732742,\n        \"max\": 222046882,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          11803798,\n          222046882\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pcnt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2789426211838672,\n        \"min\": 0.026539706780729814,\n        \"max\": 0.6748234563502864,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.6748234563502864,\n          0.026539706780729814\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 4
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Make reduced pie using matplotlib"
+      "text/html": [
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+       "    <tr>\n",
+       "      <th>sponsor</th>\n",
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+       "      <th></th>\n",
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+       "      <th>NATIONAL ESTUARINE RESEARCH RESERVE SYSTEM</th>\n",
+       "      <td>11803798.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>0.035873</td>\n",
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+       "      <th>NATIONAL OCEAN SERVICE</th>\n",
+       "      <td>222046882.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>0.674823</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NATIONAL WEATHER SERVICE, CENTRAL REGION</th>\n",
+       "      <td>13786322.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13786322</td>\n",
+       "      <td>0.041898</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NOAA NOS PHYSICAL OCEANOGRAPHIC RT SYSTEM PROGRAM</th>\n",
+       "      <td>41221206.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>41221206</td>\n",
+       "      <td>0.125276</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SCRIPPS WAVERIDER COASTAL DATA INFORMATION PROGRAM</th>\n",
+       "      <td>106182.0</td>\n",
+       "      <td>8626560.0</td>\n",
+       "      <td>8732742</td>\n",
+       "      <td>0.026540</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>OTHER</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>31453454</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3799394e-67df-45fe-baf8-59ffc0ffa1fe')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-3799394e-67df-45fe-baf8-59ffc0ffa1fe button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-3799394e-67df-45fe-baf8-59ffc0ffa1fe');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-4fffe472-d618-4f29-8c71-7be6aa08c733\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4fffe472-d618-4f29-8c71-7be6aa08c733')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-4fffe472-d618-4f29-8c71-7be6aa08c733 button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "metadata": {
-        "id": "2ajyqDFgnf36"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "explode = 0.05*np.ones(len(df_pie))\n",
-        "\n",
-        "df_pie['total'].plot.pie(rotatelabels=False,\n",
-        "                          autopct='%1.1f%%',\n",
-        "                          ylabel='',\n",
-        "                          textprops={'fontsize':10},\n",
-        "                          #radius=2,\n",
-        "                          pctdistance=0.85,\n",
-        "                          explode=explode)\n",
-        "# draw circle\n",
-        "centre_circle = plt.Circle((0, 0), 0.7, fc='white')\n",
-        "fig = plt.gcf()\n",
-        "\n",
-        "# Adding Circle in Pie chart\n",
-        "fig.gca().add_artist(centre_circle)"
-      ],
-      "metadata": {
-        "id": "xE74j2jmne3L",
-        "outputId": "3f521881-25ff-470c-ae24-dd51b9262661",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 424
-        }
-      },
-      "execution_count": 5,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<matplotlib.patches.Circle at 0x7e5e5c823940>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 5
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
+      "text/plain": [
+       "                                                            met       wave  \\\n",
+       "sponsor                                                                      \n",
+       "NATIONAL ESTUARINE RESEARCH RESERVE SYSTEM           11803798.0        0.0   \n",
+       "NATIONAL OCEAN SERVICE                              222046882.0        0.0   \n",
+       "NATIONAL WEATHER SERVICE, CENTRAL REGION             13786322.0        0.0   \n",
+       "NOAA NOS PHYSICAL OCEANOGRAPHIC RT SYSTEM PROGRAM    41221206.0        0.0   \n",
+       "SCRIPPS WAVERIDER COASTAL DATA INFORMATION PROGRAM     106182.0  8626560.0   \n",
+       "OTHER                                                       NaN        NaN   \n",
+       "\n",
+       "                                                        total      pcnt  \n",
+       "sponsor                                                                  \n",
+       "NATIONAL ESTUARINE RESEARCH RESERVE SYSTEM           11803798  0.035873  \n",
+       "NATIONAL OCEAN SERVICE                              222046882  0.674823  \n",
+       "NATIONAL WEATHER SERVICE, CENTRAL REGION             13786322  0.041898  \n",
+       "NOAA NOS PHYSICAL OCEANOGRAPHIC RT SYSTEM PROGRAM    41221206  0.125276  \n",
+       "SCRIPPS WAVERIDER COASTAL DATA INFORMATION PROGRAM    8732742  0.026540  \n",
+       "OTHER                                                31453454       NaN  "
       ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_grp = grp_out.loc[grp_out[\"pcnt\"]>0.02]\n",
+    "\n",
+    "filtered_grp.reset_index(inplace=True)\n",
+    "\n",
+    "df = pd.DataFrame({\"sponsor\": \"OTHER\",\n",
+    "              \"total\": [grp_out.loc[grp_out[\"pcnt\"]<0.02,\"total\"].sum()]\n",
+    "              })\n",
+    "\n",
+    "df_pie = pd.concat([filtered_grp, df])\n",
+    "\n",
+    "df_pie.set_index(\"sponsor\",inplace=True)\n",
+    "\n",
+    "df_pie"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2ajyqDFgnf36"
+   },
+   "source": [
+    "## Make reduced pie using matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 424
     },
+    "id": "xE74j2jmne3L",
+    "outputId": "3f521881-25ff-470c-ae24-dd51b9262661"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": [
-        "## Make reduced pie chart using plotly"
-      ],
-      "metadata": {
-        "id": "RkyOj_PDmIel"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import plotly.express as px\n",
-        "fig = px.pie(df_pie,\n",
-        "             values='total',\n",
-        "             names=df_pie.index,\n",
-        "             #title='Distribution of NDBC messages',\n",
-        "             hole=0.6,\n",
-        "             #labels={'lifeExp':'life expectancy'},\n",
-        "             )\n",
-        "fig.update_traces(textposition='outside', textinfo='percent+label')\n",
-        "fig.update(layout_showlegend=False)\n",
-        "fig.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 542
-        },
-        "id": "NJ0eCSzFGK2v",
-        "outputId": "69ec382f-73d0-4ad2-b676-e49984fc6ae9"
-      },
-      "execution_count": 6,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
-              "<html>\n",
-              "<head><meta charset=\"utf-8\" /></head>\n",
-              "<body>\n",
-              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
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+       "<matplotlib.patches.Circle at 0x7e5e5c823940>"
       ]
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     {
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-      "source": [
-        "What is 'OTHER'?"
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-            "text/plain": [
-              "['ALASKA OCEAN OBSERVING SYSTEM',\n",
-              " 'BP INC.',\n",
-              " 'CARIBBEAN INTEGRATED COASTAL OCEAN OBSERVING SYSTEM',\n",
-              " 'CHESAPEAKE BAY INTERPRETIVE BUOY SYSTEM',\n",
-              " 'CLEVELAND WATER ALLIANCE',\n",
-              " 'COASTAL DATA INFORMATION PROGRAM/PMEL',\n",
-              " 'COASTAL OCEAN RESEARCH AND MONITORING PROGRAM',\n",
-              " 'EPA & MEXICAN GOVERNMENT COOPERATIVE PROGRAM',\n",
-              " 'EVERGLADES NATIONAL PARK',\n",
-              " 'GREAT LAKES RESEARCH LABORATORY',\n",
-              " 'GREAT LAKES WATER AUTHORITY',\n",
-              " 'ILLINOIS-INDIANA SEA GRANT',\n",
-              " 'INTEGRATED CORAL OBSERVING NETWORK',\n",
-              " 'LIMNOTECH',\n",
-              " 'LOUISIANA OFFSHORE OIL PORT',\n",
-              " 'MARINE EXCHANGE OF ALASKA',\n",
-              " 'MICHIGAN TECHNICAL UNIVERSITY',\n",
-              " 'MOSS LANDING MARINE LABORATORIES',\n",
-              " 'MURPHY OIL CORP.',\n",
-              " 'NATIONAL PARK SERVICE - LAKE MEAD NATIONAL REC AREA',\n",
-              " 'NATIONAL PARK SERVICES - SLEEPING BEAR DUNES',\n",
-              " 'NATIONAL RENEWABLE ENERGY LABORATORY',\n",
-              " 'NATIONAL WEATHER SERVICE, ALASKA REGION',\n",
-              " 'NATIONAL WEATHER SERVICE, EASTERN REGION',\n",
-              " 'OCEAN OBSERVATORIES INITIATIVE',\n",
-              " 'PETROBRAS',\n",
-              " 'SHELL OIL',\n",
-              " 'STONY BROOK UNIVERSITY',\n",
-              " 'SUNY PLATTSBURGH CEES / LAKE CHAMPLAIN RESEARCH INST.',\n",
-              " 'TEXAS COASTAL OCEAN OBSERVATION NETWORK',\n",
-              " 'U. S. COAST GUARD',\n",
-              " 'U.S. ARMY CORPS OF ENGINEERS',\n",
-              " 'U.S. NAVY',\n",
-              " 'UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAGNE',\n",
-              " 'UNIVERSITY OF NEW HAMPSHIRE',\n",
-              " 'UNIVERSITY OF NORTH CAROLINA COASTAL STUDIES',\n",
-              " 'UNIVERSITY OF WISCONSIN AT MILWAUKEE',\n",
-              " 'USF COMPS MARINE NETWORK',\n",
-              " 'VERMONT EPSCOR',\n",
-              " 'WOODS HOLE OCEANOGRAPHIC INSTITUTION']"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 7
-        }
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "explode = 0.05*np.ones(len(df_pie))\n",
+    "\n",
+    "df_pie[\"total\"].plot.pie(rotatelabels=False,\n",
+    "                          autopct=\"%1.1f%%\",\n",
+    "                          ylabel=\"\",\n",
+    "                          textprops={\"fontsize\":10},\n",
+    "                          #radius=2,\n",
+    "                          pctdistance=0.85,\n",
+    "                          explode=explode)\n",
+    "# draw circle\n",
+    "centre_circle = plt.Circle((0, 0), 0.7, fc=\"white\")\n",
+    "fig = plt.gcf()\n",
+    "\n",
+    "# Adding Circle in Pie chart\n",
+    "fig.gca().add_artist(centre_circle)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RkyOj_PDmIel"
+   },
+   "source": [
+    "## Make reduced pie chart using plotly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 542
     },
+    "id": "NJ0eCSzFGK2v",
+    "outputId": "69ec382f-73d0-4ad2-b676-e49984fc6ae9"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": [
-        "# What's NDBC composed of?"
-      ],
-      "metadata": {
-        "id": "RTfbgzjSg-Ma"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df_ndbc['total'] = df_ndbc['met'] + df_ndbc['wave']\n",
-        "df_ndbc[\"time (UTC)\"] = df_ndbc[\"time (UTC)\"].dt.tz_localize(None)\n",
-        "\n",
-        "ndbc_group = df_ndbc.groupby(by=['sponsor'])\n",
-        "\n",
-        "grp = ndbc_group[['met','wave','total']].sum()\n",
-        "\n",
-        "grp_out = grp.assign(pcnt = grp['total'] / grp['total'].sum())\n",
-        "\n",
-        "grp_out.sort_values(by='pcnt', ascending=False)"
-      ],
-      "metadata": {
-        "id": "TtOJLuRkhB0w",
-        "outputId": "d3aebdf1-9583-4a99-ab52-63416dad3bf8",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 363
-        }
-      },
-      "execution_count": 8,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "                                      met      wave     total      pcnt\n",
-              "sponsor                                                                \n",
-              "NATIONAL WEATHER SERVICE         47114634  10070708  57185342  0.961360\n",
-              "NATIONAL HURRICANE CENTER          915250    173158   1088408  0.018298\n",
-              "U. S. COAST GUARD                  613520    284920    898440  0.015104\n",
-              "CORPS OF ENGINEERS                 196298     62876    259174  0.004357\n",
-              "NDBC ENGINEERING                    14050      6698     20748  0.000349\n",
-              "SAILDRONE                            7702      7684     15386  0.000259\n",
-              "NATIONAL ACADEMY OF SCIENCES         7918      1306      9224  0.000155\n",
-              "GREAT LAKES RESEARCH LABORATORY      2400      2324      4724  0.000079\n",
-              "NATIONAL DATA BUOY CENTER            1180      1180      2360  0.000040"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-304b2b54-52cc-4748-9cb4-8c137ed64f58\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>met</th>\n",
-              "      <th>wave</th>\n",
-              "      <th>total</th>\n",
-              "      <th>pcnt</th>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>sponsor</th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>NATIONAL WEATHER SERVICE</th>\n",
-              "      <td>47114634</td>\n",
-              "      <td>10070708</td>\n",
-              "      <td>57185342</td>\n",
-              "      <td>0.961360</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>NATIONAL HURRICANE CENTER</th>\n",
-              "      <td>915250</td>\n",
-              "      <td>173158</td>\n",
-              "      <td>1088408</td>\n",
-              "      <td>0.018298</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>U. S. COAST GUARD</th>\n",
-              "      <td>613520</td>\n",
-              "      <td>284920</td>\n",
-              "      <td>898440</td>\n",
-              "      <td>0.015104</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>CORPS OF ENGINEERS</th>\n",
-              "      <td>196298</td>\n",
-              "      <td>62876</td>\n",
-              "      <td>259174</td>\n",
-              "      <td>0.004357</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>NDBC ENGINEERING</th>\n",
-              "      <td>14050</td>\n",
-              "      <td>6698</td>\n",
-              "      <td>20748</td>\n",
-              "      <td>0.000349</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>SAILDRONE</th>\n",
-              "      <td>7702</td>\n",
-              "      <td>7684</td>\n",
-              "      <td>15386</td>\n",
-              "      <td>0.000259</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>NATIONAL ACADEMY OF SCIENCES</th>\n",
-              "      <td>7918</td>\n",
-              "      <td>1306</td>\n",
-              "      <td>9224</td>\n",
-              "      <td>0.000155</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>GREAT LAKES RESEARCH LABORATORY</th>\n",
-              "      <td>2400</td>\n",
-              "      <td>2324</td>\n",
-              "      <td>4724</td>\n",
-              "      <td>0.000079</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>NATIONAL DATA BUOY CENTER</th>\n",
-              "      <td>1180</td>\n",
-              "      <td>1180</td>\n",
-              "      <td>2360</td>\n",
-              "      <td>0.000040</td>\n",
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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('08f5e467-7b2c-4494-aea9-ee115130a17e');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                            </script>        </div>\n",
+       "</body>\n",
+       "</html>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "\n",
+    "fig = px.pie(df_pie,\n",
+    "             values=\"total\",\n",
+    "             names=df_pie.index,\n",
+    "             #title='Distribution of NDBC messages',\n",
+    "             hole=0.6,\n",
+    "             #labels={'lifeExp':'life expectancy'},\n",
+    "             )\n",
+    "fig.update_traces(textposition=\"outside\", textinfo=\"percent+label\")\n",
+    "fig.update(layout_showlegend=False)\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "SgCbqTldzbG_"
+   },
+   "source": [
+    "What is 'OTHER'?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "FdZgBg9XzZYi",
+    "outputId": "1195e031-156e-4b59-820c-f516680ba8a7"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": [
-        "## Make pie chart using matplotlib"
-      ],
-      "metadata": {
-        "id": "L6YntjTjmSdM"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "explode = 0.05*np.ones(len(grp_out))\n",
-        "\n",
-        "grp_out['total'].plot.pie(rotatelabels=False,\n",
-        "                          autopct='%1.1f%%',\n",
-        "                          ylabel='',\n",
-        "                          textprops={'fontsize':10},\n",
-        "                          #radius=2,\n",
-        "                          pctdistance=0.85,\n",
-        "                          explode=explode)\n",
-        "\n",
-        "#draw circle\n",
-        "centre_circle = plt.Circle((0, 0), 0.7, fc='white')\n",
-        "fig = plt.gcf()\n",
-        "\n",
-        "# Adding Circle in Pie chart\n",
-        "fig.gca().add_artist(centre_circle)"
-      ],
-      "metadata": {
-        "id": "-JPhk6jNmRQv",
-        "outputId": "7c83949c-29ec-4e86-e52e-96db6df5fb94",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 424
-        }
-      },
-      "execution_count": 9,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<matplotlib.patches.Circle at 0x7e5e5c6d2560>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 9
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
+     "data": {
+      "text/plain": [
+       "['ALASKA OCEAN OBSERVING SYSTEM',\n",
+       " 'BP INC.',\n",
+       " 'CARIBBEAN INTEGRATED COASTAL OCEAN OBSERVING SYSTEM',\n",
+       " 'CHESAPEAKE BAY INTERPRETIVE BUOY SYSTEM',\n",
+       " 'CLEVELAND WATER ALLIANCE',\n",
+       " 'COASTAL DATA INFORMATION PROGRAM/PMEL',\n",
+       " 'COASTAL OCEAN RESEARCH AND MONITORING PROGRAM',\n",
+       " 'EPA & MEXICAN GOVERNMENT COOPERATIVE PROGRAM',\n",
+       " 'EVERGLADES NATIONAL PARK',\n",
+       " 'GREAT LAKES RESEARCH LABORATORY',\n",
+       " 'GREAT LAKES WATER AUTHORITY',\n",
+       " 'ILLINOIS-INDIANA SEA GRANT',\n",
+       " 'INTEGRATED CORAL OBSERVING NETWORK',\n",
+       " 'LIMNOTECH',\n",
+       " 'LOUISIANA OFFSHORE OIL PORT',\n",
+       " 'MARINE EXCHANGE OF ALASKA',\n",
+       " 'MICHIGAN TECHNICAL UNIVERSITY',\n",
+       " 'MOSS LANDING MARINE LABORATORIES',\n",
+       " 'MURPHY OIL CORP.',\n",
+       " 'NATIONAL PARK SERVICE - LAKE MEAD NATIONAL REC AREA',\n",
+       " 'NATIONAL PARK SERVICES - SLEEPING BEAR DUNES',\n",
+       " 'NATIONAL RENEWABLE ENERGY LABORATORY',\n",
+       " 'NATIONAL WEATHER SERVICE, ALASKA REGION',\n",
+       " 'NATIONAL WEATHER SERVICE, EASTERN REGION',\n",
+       " 'OCEAN OBSERVATORIES INITIATIVE',\n",
+       " 'PETROBRAS',\n",
+       " 'SHELL OIL',\n",
+       " 'STONY BROOK UNIVERSITY',\n",
+       " 'SUNY PLATTSBURGH CEES / LAKE CHAMPLAIN RESEARCH INST.',\n",
+       " 'TEXAS COASTAL OCEAN OBSERVATION NETWORK',\n",
+       " 'U. S. COAST GUARD',\n",
+       " 'U.S. ARMY CORPS OF ENGINEERS',\n",
+       " 'U.S. NAVY',\n",
+       " 'UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAGNE',\n",
+       " 'UNIVERSITY OF NEW HAMPSHIRE',\n",
+       " 'UNIVERSITY OF NORTH CAROLINA COASTAL STUDIES',\n",
+       " 'UNIVERSITY OF WISCONSIN AT MILWAUKEE',\n",
+       " 'USF COMPS MARINE NETWORK',\n",
+       " 'VERMONT EPSCOR',\n",
+       " 'WOODS HOLE OCEANOGRAPHIC INSTITUTION']"
       ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grp_out.loc[grp_out[\"pcnt\"]<0.02].index.tolist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RTfbgzjSg-Ma"
+   },
+   "source": [
+    "# What's NDBC composed of?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
     },
+    "id": "TtOJLuRkhB0w",
+    "outputId": "d3aebdf1-9583-4a99-ab52-63416dad3bf8"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": [
-        "## Make pie chart using plotly"
-      ],
-      "metadata": {
-        "id": "D9gUL6xmmVrO"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import plotly.express as px\n",
-        "fig = px.pie(grp_out,\n",
-        "             values='total',\n",
-        "             names=grp_out.index,\n",
-        "             #title='Distribution of NDBC messages',\n",
-        "             hole=0.6,\n",
-        "             #labels={'lifeExp':'life expectancy'},\n",
-        "             #rotation=90,\n",
-        "             )\n",
-        "fig.update_traces(textposition='outside', textinfo='percent+label')\n",
-        "fig.update(layout_showlegend=False)\n",
-        "fig.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 542
-        },
-        "id": "ioNrlM5ZCw-w",
-        "outputId": "2b4e0118-3f2a-41c2-d3ad-ad8ae50d683d"
+     "data": {
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "summary": "{\n  \"name\": \"grp_out\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"sponsor\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"GREAT LAKES RESEARCH LABORATORY\",\n          \"NATIONAL HURRICANE CENTER\",\n          \"SAILDRONE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"met\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15635078,\n        \"min\": 1180,\n        \"max\": 47114634,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          2400,\n          915250,\n          7702\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wave\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3335872,\n        \"min\": 1180,\n        \"max\": 10070708,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          2324,\n          173158,\n          7684\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18970622,\n        \"min\": 2360,\n        \"max\": 57185342,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          4724,\n          1088408,\n          15386\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pcnt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.31892078907780913,\n        \"min\": 3.967466372276179e-05,\n        \"max\": 0.961359836322511,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          7.941657263827402e-05,\n          0.018297551437781234,\n          0.00025865863391458173\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+       "type": "dataframe"
       },
-      "execution_count": 10,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
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-              "<head><meta charset=\"utf-8\" /></head>\n",
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-              "                            \n",
-              "var gd = document.getElementById('0baf765b-1d6f-4cb7-a58e-0d87107ddd7a');\n",
-              "var x = new MutationObserver(function (mutations, observer) {{\n",
-              "        var display = window.getComputedStyle(gd).display;\n",
-              "        if (!display || display === 'none') {{\n",
-              "            console.log([gd, 'removed!']);\n",
-              "            Plotly.purge(gd);\n",
-              "            observer.disconnect();\n",
-              "        }}\n",
-              "}});\n",
-              "\n",
-              "// Listen for the removal of the full notebook cells\n",
-              "var notebookContainer = gd.closest('#notebook-container');\n",
-              "if (notebookContainer) {{\n",
-              "    x.observe(notebookContainer, {childList: true});\n",
-              "}}\n",
-              "\n",
-              "// Listen for the clearing of the current output cell\n",
-              "var outputEl = gd.closest('.output');\n",
-              "if (outputEl) {{\n",
-              "    x.observe(outputEl, {childList: true});\n",
-              "}}\n",
-              "\n",
-              "                        })                };                            </script>        </div>\n",
-              "</body>\n",
-              "</html>"
-            ]
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# What's IOOS composed of?"
+      "text/html": [
+       "\n",
+       "  <div id=\"df-304b2b54-52cc-4748-9cb4-8c137ed64f58\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>met</th>\n",
+       "      <th>wave</th>\n",
+       "      <th>total</th>\n",
+       "      <th>pcnt</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sponsor</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>NATIONAL WEATHER SERVICE</th>\n",
+       "      <td>47114634</td>\n",
+       "      <td>10070708</td>\n",
+       "      <td>57185342</td>\n",
+       "      <td>0.961360</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NATIONAL HURRICANE CENTER</th>\n",
+       "      <td>915250</td>\n",
+       "      <td>173158</td>\n",
+       "      <td>1088408</td>\n",
+       "      <td>0.018298</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>U. S. COAST GUARD</th>\n",
+       "      <td>613520</td>\n",
+       "      <td>284920</td>\n",
+       "      <td>898440</td>\n",
+       "      <td>0.015104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CORPS OF ENGINEERS</th>\n",
+       "      <td>196298</td>\n",
+       "      <td>62876</td>\n",
+       "      <td>259174</td>\n",
+       "      <td>0.004357</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NDBC ENGINEERING</th>\n",
+       "      <td>14050</td>\n",
+       "      <td>6698</td>\n",
+       "      <td>20748</td>\n",
+       "      <td>0.000349</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SAILDRONE</th>\n",
+       "      <td>7702</td>\n",
+       "      <td>7684</td>\n",
+       "      <td>15386</td>\n",
+       "      <td>0.000259</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NATIONAL ACADEMY OF SCIENCES</th>\n",
+       "      <td>7918</td>\n",
+       "      <td>1306</td>\n",
+       "      <td>9224</td>\n",
+       "      <td>0.000155</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GREAT LAKES RESEARCH LABORATORY</th>\n",
+       "      <td>2400</td>\n",
+       "      <td>2324</td>\n",
+       "      <td>4724</td>\n",
+       "      <td>0.000079</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NATIONAL DATA BUOY CENTER</th>\n",
+       "      <td>1180</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>2360</td>\n",
+       "      <td>0.000040</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-304b2b54-52cc-4748-9cb4-8c137ed64f58')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-304b2b54-52cc-4748-9cb4-8c137ed64f58 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-304b2b54-52cc-4748-9cb4-8c137ed64f58');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-373b9fb3-b762-4dfc-b128-97524161e330\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-373b9fb3-b762-4dfc-b128-97524161e330')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-373b9fb3-b762-4dfc-b128-97524161e330 button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "metadata": {
-        "id": "rvSa7UfUhM2y"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df_ioos['total'] = df_ioos['met'] + df_ioos['wave']\n",
-        "df_ioos[\"time (UTC)\"] = df_ioos[\"time (UTC)\"].dt.tz_localize(None)\n",
-        "\n",
-        "ioos_group = df_ioos.groupby(by=['sponsor'])\n",
-        "\n",
-        "grp = ioos_group[['met','wave','total']].sum()\n",
-        "\n",
-        "grp_out = grp.assign(pcnt = grp['total'] / grp['total'].sum())\n",
-        "\n",
-        "grp_out.sort_values(by='pcnt', ascending=False)"
-      ],
-      "metadata": {
-        "id": "sfjOQs9lhSJO",
-        "outputId": "6a0b4d54-a700-4035-d1fb-0932982c6b17",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        }
-      },
-      "execution_count": 11,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "                                                         met     wave  \\\n",
-              "sponsor                                                                 \n",
-              "TEXAS COASTAL OCEAN OBSERVATION NETWORK             25840950        0   \n",
-              "MARINE EXCHANGE OF ALASKA                           20867546        0   \n",
-              "USF COMPS MARINE NETWORK                             4780472        0   \n",
-              "CARIBBEAN INTEGRATED COASTAL OCEAN OBSERVING SY...   2957646   585526   \n",
-              "SCRIPPS WAVERIDER COASTAL DATA INFORMATION PROGRAM         0  3207372   \n",
-              "LIMNOTECH                                            1462626  1141056   \n",
-              "MICHIGAN TECHNICAL UNIVERSITY                        1170326  1112668   \n",
-              "COASTAL OCEAN RESEARCH AND MONITORING PROGRAM        1205692   379864   \n",
-              "CALIFORNIA POLYTECHNIC STATE UNIVERSITY              1232956        0   \n",
-              "NORTHEASTERN REGIONAL ASSN OF COASTAL OCEAN OBS...    589124   582972   \n",
-              "MOSS LANDING MARINE LABORATORIES                      905020        0   \n",
-              "UNIVERSITY OF MICHIGAN CILER                          457866   414754   \n",
-              "ILLINOIS-INDIANA SEA GRANT/PURDUE CIVIL ENGINEE...    417300   425606   \n",
-              "CENTRAL AND NORTHERN CALIFORNIA OCEAN OBSERVING...    833244        0   \n",
-              "UNIVERSITY OF WASHINGTON                              809612        0   \n",
-              "UNIV OF CONNECTICUT MARINE MONITORING NETWORK (...    654548   135252   \n",
-              "UNIVERSITY OF MINNESOTA AT DULUTH                     412104   282614   \n",
-              "NORTHERN MICHIGAN UNIVERSITY                          569194   118064   \n",
-              "UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAGNE            311492   356274   \n",
-              "DAUPHIN ISLAND SEA LAB                                639214        0   \n",
-              "PUERTO RICO SEISMIC NETWORK                           583788        0   \n",
-              "UNIVERSITY OF WISCONSIN AT MILWAUKEE                  155734   125486   \n",
-              "ILLINOIS-INDIANA SEA GRANT                            138044   138042   \n",
-              "FLORIDA INSTITUTE OF TECHNOLOGY                       272582        0   \n",
-              "GREATER TAMPA BAY MARINE ADVISORY COUNCIL PORTS            0   203318   \n",
-              "CHICAGO PARK DISTRICT                                 161532     6572   \n",
-              "ALASKA OCEAN OBSERVING SYSTEM                              0   167592   \n",
-              "UNIVERSITY OF NORTH CAROLINA COASTAL STUDIES               0   163692   \n",
-              "TEXAS A & M                                           159936        0   \n",
-              "NATIONAL OCEAN SERVICE                                143696        0   \n",
-              "UNIVERSITY OF NEW HAMPSHIRE                            17838   103440   \n",
-              "NW ASSOCIATION OF NETWORKED OCEAN OBSERVING SYS...    111818        0   \n",
-              "MONTEREY BAY AQUARIUM RESEARCH INSTITUTE              100430        0   \n",
-              "REGIONAL SCIENCE CONSORTIUM                            59898    37462   \n",
-              "COASTAL DATA INFORMATION PROGRAM/PMEL                      0    90554   \n",
-              "COASTAL STUDIES INSTITUTE, LOUISIANA STATE UNIV.       66338        0   \n",
-              "U.S. ARMY CORPS OF ENGINEERS                               0    54202   \n",
-              "SALMON UNLIMITED WISCONSIN                             24578    24356   \n",
-              "STONY BROOK UNIVERSITY                                 38310        0   \n",
-              "UNIVERSITY OF NORTH CAROLINA, CHAPEL HILL              30736        0   \n",
-              "NW MICHIGAN COLLEGE GREAT LAKES WATER STUDIES I...     14948    14948   \n",
-              "UNIVERSITY OF SOUTHERN MISSISSIPPI                     11638    11630   \n",
-              "CENTER FOR COASTAL MARGIN OBSERVATION AND PREDI...     22524        0   \n",
-              "COLUMBIA RIVER INTER-TRIBAL FISH COMMISSION            14364        0   \n",
-              "UNIVERSITY OF CALIFORNIA AT DAVIS                          0        0   \n",
-              "SUPERIOR WATERSHED PARTNERSHIP                             0        0   \n",
-              "\n",
-              "                                                       total      pcnt  \n",
-              "sponsor                                                                 \n",
-              "TEXAS COASTAL OCEAN OBSERVATION NETWORK             25840950  0.330747  \n",
-              "MARINE EXCHANGE OF ALASKA                           20867546  0.267091  \n",
-              "USF COMPS MARINE NETWORK                             4780472  0.061187  \n",
-              "CARIBBEAN INTEGRATED COASTAL OCEAN OBSERVING SY...   3543172  0.045350  \n",
-              "SCRIPPS WAVERIDER COASTAL DATA INFORMATION PROGRAM   3207372  0.041052  \n",
-              "LIMNOTECH                                            2603682  0.033325  \n",
-              "MICHIGAN TECHNICAL UNIVERSITY                        2282994  0.029221  \n",
-              "COASTAL OCEAN RESEARCH AND MONITORING PROGRAM        1585556  0.020294  \n",
-              "CALIFORNIA POLYTECHNIC STATE UNIVERSITY              1232956  0.015781  \n",
-              "NORTHEASTERN REGIONAL ASSN OF COASTAL OCEAN OBS...   1172096  0.015002  \n",
-              "MOSS LANDING MARINE LABORATORIES                      905020  0.011584  \n",
-              "UNIVERSITY OF MICHIGAN CILER                          872620  0.011169  \n",
-              "ILLINOIS-INDIANA SEA GRANT/PURDUE CIVIL ENGINEE...    842906  0.010789  \n",
-              "CENTRAL AND NORTHERN CALIFORNIA OCEAN OBSERVING...    833244  0.010665  \n",
-              "UNIVERSITY OF WASHINGTON                              809612  0.010363  \n",
-              "UNIV OF CONNECTICUT MARINE MONITORING NETWORK (...    789800  0.010109  \n",
-              "UNIVERSITY OF MINNESOTA AT DULUTH                     694718  0.008892  \n",
-              "NORTHERN MICHIGAN UNIVERSITY                          687258  0.008796  \n",
-              "UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAGNE            667766  0.008547  \n",
-              "DAUPHIN ISLAND SEA LAB                                639214  0.008182  \n",
-              "PUERTO RICO SEISMIC NETWORK                           583788  0.007472  \n",
-              "UNIVERSITY OF WISCONSIN AT MILWAUKEE                  281220  0.003599  \n",
-              "ILLINOIS-INDIANA SEA GRANT                            276086  0.003534  \n",
-              "FLORIDA INSTITUTE OF TECHNOLOGY                       272582  0.003489  \n",
-              "GREATER TAMPA BAY MARINE ADVISORY COUNCIL PORTS       203318  0.002602  \n",
-              "CHICAGO PARK DISTRICT                                 168104  0.002152  \n",
-              "ALASKA OCEAN OBSERVING SYSTEM                         167592  0.002145  \n",
-              "UNIVERSITY OF NORTH CAROLINA COASTAL STUDIES          163692  0.002095  \n",
-              "TEXAS A & M                                           159936  0.002047  \n",
-              "NATIONAL OCEAN SERVICE                                143696  0.001839  \n",
-              "UNIVERSITY OF NEW HAMPSHIRE                           121278  0.001552  \n",
-              "NW ASSOCIATION OF NETWORKED OCEAN OBSERVING SYS...    111818  0.001431  \n",
-              "MONTEREY BAY AQUARIUM RESEARCH INSTITUTE              100430  0.001285  \n",
-              "REGIONAL SCIENCE CONSORTIUM                            97360  0.001246  \n",
-              "COASTAL DATA INFORMATION PROGRAM/PMEL                  90554  0.001159  \n",
-              "COASTAL STUDIES INSTITUTE, LOUISIANA STATE UNIV.       66338  0.000849  \n",
-              "U.S. ARMY CORPS OF ENGINEERS                           54202  0.000694  \n",
-              "SALMON UNLIMITED WISCONSIN                             48934  0.000626  \n",
-              "STONY BROOK UNIVERSITY                                 38310  0.000490  \n",
-              "UNIVERSITY OF NORTH CAROLINA, CHAPEL HILL              30736  0.000393  \n",
-              "NW MICHIGAN COLLEGE GREAT LAKES WATER STUDIES I...     29896  0.000383  \n",
-              "UNIVERSITY OF SOUTHERN MISSISSIPPI                     23268  0.000298  \n",
-              "CENTER FOR COASTAL MARGIN OBSERVATION AND PREDI...     22524  0.000288  \n",
-              "COLUMBIA RIVER INTER-TRIBAL FISH COMMISSION            14364  0.000184  \n",
-              "UNIVERSITY OF CALIFORNIA AT DAVIS                          0  0.000000  \n",
-              "SUPERIOR WATERSHED PARTNERSHIP                             0  0.000000  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-bf78e2d1-3b09-4b39-850a-5806992a8d19\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>met</th>\n",
-              "      <th>wave</th>\n",
-              "      <th>total</th>\n",
-              "      <th>pcnt</th>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>sponsor</th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>TEXAS COASTAL OCEAN OBSERVATION NETWORK</th>\n",
-              "      <td>25840950</td>\n",
-              "      <td>0</td>\n",
-              "      <td>25840950</td>\n",
-              "      <td>0.330747</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>MARINE EXCHANGE OF ALASKA</th>\n",
-              "      <td>20867546</td>\n",
-              "      <td>0</td>\n",
-              "      <td>20867546</td>\n",
-              "      <td>0.267091</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>USF COMPS MARINE NETWORK</th>\n",
-              "      <td>4780472</td>\n",
-              "      <td>0</td>\n",
-              "      <td>4780472</td>\n",
-              "      <td>0.061187</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>CARIBBEAN INTEGRATED COASTAL OCEAN OBSERVING SYSTEM</th>\n",
-              "      <td>2957646</td>\n",
-              "      <td>585526</td>\n",
-              "      <td>3543172</td>\n",
-              "      <td>0.045350</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>SCRIPPS WAVERIDER COASTAL DATA INFORMATION PROGRAM</th>\n",
-              "      <td>0</td>\n",
-              "      <td>3207372</td>\n",
-              "      <td>3207372</td>\n",
-              "      <td>0.041052</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>LIMNOTECH</th>\n",
-              "      <td>1462626</td>\n",
-              "      <td>1141056</td>\n",
-              "      <td>2603682</td>\n",
-              "      <td>0.033325</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>MICHIGAN TECHNICAL UNIVERSITY</th>\n",
-              "      <td>1170326</td>\n",
-              "      <td>1112668</td>\n",
-              "      <td>2282994</td>\n",
-              "      <td>0.029221</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>COASTAL OCEAN RESEARCH AND MONITORING PROGRAM</th>\n",
-              "      <td>1205692</td>\n",
-              "      <td>379864</td>\n",
-              "      <td>1585556</td>\n",
-              "      <td>0.020294</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>CALIFORNIA POLYTECHNIC STATE UNIVERSITY</th>\n",
-              "      <td>1232956</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1232956</td>\n",
-              "      <td>0.015781</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>NORTHEASTERN REGIONAL ASSN OF COASTAL OCEAN OBS SYSTEMS</th>\n",
-              "      <td>589124</td>\n",
-              "      <td>582972</td>\n",
-              "      <td>1172096</td>\n",
-              "      <td>0.015002</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>MOSS LANDING MARINE LABORATORIES</th>\n",
-              "      <td>905020</td>\n",
-              "      <td>0</td>\n",
-              "      <td>905020</td>\n",
-              "      <td>0.011584</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>UNIVERSITY OF MICHIGAN CILER</th>\n",
-              "      <td>457866</td>\n",
-              "      <td>414754</td>\n",
-              "      <td>872620</td>\n",
-              "      <td>0.011169</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>ILLINOIS-INDIANA SEA GRANT/PURDUE CIVIL ENGINEERING</th>\n",
-              "      <td>417300</td>\n",
-              "      <td>425606</td>\n",
-              "      <td>842906</td>\n",
-              "      <td>0.010789</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>CENTRAL AND NORTHERN CALIFORNIA OCEAN OBSERVING SYSTEM</th>\n",
-              "      <td>833244</td>\n",
-              "      <td>0</td>\n",
-              "      <td>833244</td>\n",
-              "      <td>0.010665</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>UNIVERSITY OF WASHINGTON</th>\n",
-              "      <td>809612</td>\n",
-              "      <td>0</td>\n",
-              "      <td>809612</td>\n",
-              "      <td>0.010363</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>UNIV OF CONNECTICUT MARINE MONITORING NETWORK (MYSOUND)</th>\n",
-              "      <td>654548</td>\n",
-              "      <td>135252</td>\n",
-              "      <td>789800</td>\n",
-              "      <td>0.010109</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>UNIVERSITY OF MINNESOTA AT DULUTH</th>\n",
-              "      <td>412104</td>\n",
-              "      <td>282614</td>\n",
-              "      <td>694718</td>\n",
-              "      <td>0.008892</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>NORTHERN MICHIGAN UNIVERSITY</th>\n",
-              "      <td>569194</td>\n",
-              "      <td>118064</td>\n",
-              "      <td>687258</td>\n",
-              "      <td>0.008796</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAGNE</th>\n",
-              "      <td>311492</td>\n",
-              "      <td>356274</td>\n",
-              "      <td>667766</td>\n",
-              "      <td>0.008547</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>DAUPHIN ISLAND SEA LAB</th>\n",
-              "      <td>639214</td>\n",
-              "      <td>0</td>\n",
-              "      <td>639214</td>\n",
-              "      <td>0.008182</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>PUERTO RICO SEISMIC NETWORK</th>\n",
-              "      <td>583788</td>\n",
-              "      <td>0</td>\n",
-              "      <td>583788</td>\n",
-              "      <td>0.007472</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>UNIVERSITY OF WISCONSIN AT MILWAUKEE</th>\n",
-              "      <td>155734</td>\n",
-              "      <td>125486</td>\n",
-              "      <td>281220</td>\n",
-              "      <td>0.003599</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>ILLINOIS-INDIANA SEA GRANT</th>\n",
-              "      <td>138044</td>\n",
-              "      <td>138042</td>\n",
-              "      <td>276086</td>\n",
-              "      <td>0.003534</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>FLORIDA INSTITUTE OF TECHNOLOGY</th>\n",
-              "      <td>272582</td>\n",
-              "      <td>0</td>\n",
-              "      <td>272582</td>\n",
-              "      <td>0.003489</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>GREATER TAMPA BAY MARINE ADVISORY COUNCIL PORTS</th>\n",
-              "      <td>0</td>\n",
-              "      <td>203318</td>\n",
-              "      <td>203318</td>\n",
-              "      <td>0.002602</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>CHICAGO PARK DISTRICT</th>\n",
-              "      <td>161532</td>\n",
-              "      <td>6572</td>\n",
-              "      <td>168104</td>\n",
-              "      <td>0.002152</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>ALASKA OCEAN OBSERVING SYSTEM</th>\n",
-              "      <td>0</td>\n",
-              "      <td>167592</td>\n",
-              "      <td>167592</td>\n",
-              "      <td>0.002145</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>UNIVERSITY OF NORTH CAROLINA COASTAL STUDIES</th>\n",
-              "      <td>0</td>\n",
-              "      <td>163692</td>\n",
-              "      <td>163692</td>\n",
-              "      <td>0.002095</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>TEXAS A &amp; M</th>\n",
-              "      <td>159936</td>\n",
-              "      <td>0</td>\n",
-              "      <td>159936</td>\n",
-              "      <td>0.002047</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>NATIONAL OCEAN SERVICE</th>\n",
-              "      <td>143696</td>\n",
-              "      <td>0</td>\n",
-              "      <td>143696</td>\n",
-              "      <td>0.001839</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>UNIVERSITY OF NEW HAMPSHIRE</th>\n",
-              "      <td>17838</td>\n",
-              "      <td>103440</td>\n",
-              "      <td>121278</td>\n",
-              "      <td>0.001552</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>NW ASSOCIATION OF NETWORKED OCEAN OBSERVING SYSTEMS</th>\n",
-              "      <td>111818</td>\n",
-              "      <td>0</td>\n",
-              "      <td>111818</td>\n",
-              "      <td>0.001431</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>MONTEREY BAY AQUARIUM RESEARCH INSTITUTE</th>\n",
-              "      <td>100430</td>\n",
-              "      <td>0</td>\n",
-              "      <td>100430</td>\n",
-              "      <td>0.001285</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>REGIONAL SCIENCE CONSORTIUM</th>\n",
-              "      <td>59898</td>\n",
-              "      <td>37462</td>\n",
-              "      <td>97360</td>\n",
-              "      <td>0.001246</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>COASTAL DATA INFORMATION PROGRAM/PMEL</th>\n",
-              "      <td>0</td>\n",
-              "      <td>90554</td>\n",
-              "      <td>90554</td>\n",
-              "      <td>0.001159</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>COASTAL STUDIES INSTITUTE, LOUISIANA STATE UNIV.</th>\n",
-              "      <td>66338</td>\n",
-              "      <td>0</td>\n",
-              "      <td>66338</td>\n",
-              "      <td>0.000849</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>U.S. ARMY CORPS OF ENGINEERS</th>\n",
-              "      <td>0</td>\n",
-              "      <td>54202</td>\n",
-              "      <td>54202</td>\n",
-              "      <td>0.000694</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>SALMON UNLIMITED WISCONSIN</th>\n",
-              "      <td>24578</td>\n",
-              "      <td>24356</td>\n",
-              "      <td>48934</td>\n",
-              "      <td>0.000626</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>STONY BROOK UNIVERSITY</th>\n",
-              "      <td>38310</td>\n",
-              "      <td>0</td>\n",
-              "      <td>38310</td>\n",
-              "      <td>0.000490</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>UNIVERSITY OF NORTH CAROLINA, CHAPEL HILL</th>\n",
-              "      <td>30736</td>\n",
-              "      <td>0</td>\n",
-              "      <td>30736</td>\n",
-              "      <td>0.000393</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>NW MICHIGAN COLLEGE GREAT LAKES WATER STUDIES INSTITUTE</th>\n",
-              "      <td>14948</td>\n",
-              "      <td>14948</td>\n",
-              "      <td>29896</td>\n",
-              "      <td>0.000383</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>UNIVERSITY OF SOUTHERN MISSISSIPPI</th>\n",
-              "      <td>11638</td>\n",
-              "      <td>11630</td>\n",
-              "      <td>23268</td>\n",
-              "      <td>0.000298</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>CENTER FOR COASTAL MARGIN OBSERVATION AND PREDICTION</th>\n",
-              "      <td>22524</td>\n",
-              "      <td>0</td>\n",
-              "      <td>22524</td>\n",
-              "      <td>0.000288</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>COLUMBIA RIVER INTER-TRIBAL FISH COMMISSION</th>\n",
-              "      <td>14364</td>\n",
-              "      <td>0</td>\n",
-              "      <td>14364</td>\n",
-              "      <td>0.000184</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>UNIVERSITY OF CALIFORNIA AT DAVIS</th>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>SUPERIOR WATERSHED PARTNERSHIP</th>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
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-              "      width: 32px;\n",
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-              "\n",
-              "    .colab-df-convert:hover {\n",
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-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
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-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
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-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
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-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-bf78e2d1-3b09-4b39-850a-5806992a8d19');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
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-              "        element.appendChild(docLink);\n",
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-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
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-              "     width=\"24px\">\n",
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-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
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-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
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-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
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-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
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-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
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-              "    90% {\n",
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-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-ff9b046f-6016-4caa-aee2-3e3df96daba2 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "summary": "{\n  \"name\": \"grp_out\",\n  \"rows\": 46,\n  \"fields\": [\n    {\n      \"column\": \"sponsor\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 46,\n        \"samples\": [\n          \"UNIVERSITY OF NORTH CAROLINA, CHAPEL HILL\",\n          \"CHICAGO PARK DISTRICT\",\n          \"ALASKA OCEAN OBSERVING SYSTEM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"met\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4818797,\n        \"min\": 0,\n        \"max\": 25840950,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          38310,\n          11638,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wave\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 522350,\n        \"min\": 0,\n        \"max\": 3207372,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          425606,\n          6572,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4808530,\n        \"min\": 0,\n        \"max\": 25840950,\n        \"num_unique_values\": 45,\n        \"samples\": [\n          30736,\n          168104,\n          167592\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pcnt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06154605270158947,\n        \"min\": 0.0,\n        \"max\": 0.3307473104090185,\n        \"num_unique_values\": 45,\n        \"samples\": [\n          0.0003934007585917543,\n          0.002151621587789832,\n          0.0021450683216394225\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 11
-        }
+      "text/plain": [
+       "                                      met      wave     total      pcnt\n",
+       "sponsor                                                                \n",
+       "NATIONAL WEATHER SERVICE         47114634  10070708  57185342  0.961360\n",
+       "NATIONAL HURRICANE CENTER          915250    173158   1088408  0.018298\n",
+       "U. S. COAST GUARD                  613520    284920    898440  0.015104\n",
+       "CORPS OF ENGINEERS                 196298     62876    259174  0.004357\n",
+       "NDBC ENGINEERING                    14050      6698     20748  0.000349\n",
+       "SAILDRONE                            7702      7684     15386  0.000259\n",
+       "NATIONAL ACADEMY OF SCIENCES         7918      1306      9224  0.000155\n",
+       "GREAT LAKES RESEARCH LABORATORY      2400      2324      4724  0.000079\n",
+       "NATIONAL DATA BUOY CENTER            1180      1180      2360  0.000040"
       ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_ndbc[\"total\"] = df_ndbc[\"met\"] + df_ndbc[\"wave\"]\n",
+    "df_ndbc[\"time (UTC)\"] = df_ndbc[\"time (UTC)\"].dt.tz_localize(None)\n",
+    "\n",
+    "ndbc_group = df_ndbc.groupby(by=[\"sponsor\"])\n",
+    "\n",
+    "grp = ndbc_group[[\"met\",\"wave\",\"total\"]].sum()\n",
+    "\n",
+    "grp_out = grp.assign(pcnt = grp[\"total\"] / grp[\"total\"].sum())\n",
+    "\n",
+    "grp_out.sort_values(by=\"pcnt\", ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "L6YntjTjmSdM"
+   },
+   "source": [
+    "## Make pie chart using matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 424
     },
+    "id": "-JPhk6jNmRQv",
+    "outputId": "7c83949c-29ec-4e86-e52e-96db6df5fb94"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": [
-        "## Make pie chart using matplotlib"
-      ],
-      "metadata": {
-        "id": "q44kMvB_meVF"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "explode = 0.05*np.ones(len(grp_out))\n",
-        "\n",
-        "grp_out['total'].plot.pie(rotatelabels=False,\n",
-        "                          autopct='%1.1f%%',\n",
-        "                          ylabel='',\n",
-        "                          textprops={'fontsize':10},\n",
-        "                          #radius=2,\n",
-        "                          pctdistance=0.85,\n",
-        "                          explode=explode)\n",
-        "# draw circle\n",
-        "centre_circle = plt.Circle((0, 0), 0.7, fc='white')\n",
-        "fig = plt.gcf()\n",
-        "\n",
-        "# Adding Circle in Pie chart\n",
-        "fig.gca().add_artist(centre_circle)"
-      ],
-      "metadata": {
-        "id": "wto4GI3vmbEU",
-        "outputId": "f93af446-b8cb-43f9-fa9d-610689fd878c",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 424
-        }
-      },
-      "execution_count": 12,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<matplotlib.patches.Circle at 0x7e5e5384fd90>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 12
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
+     "data": {
+      "text/plain": [
+       "<matplotlib.patches.Circle at 0x7e5e5c6d2560>"
       ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "## Make pie chart using plotly"
-      ],
-      "metadata": {
-        "id": "NyMScohImhQz"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import plotly.express as px\n",
-        "fig = px.pie(grp_out,\n",
-        "             values='total',\n",
-        "             names=grp_out.index,\n",
-        "             #title='Distribution of NDBC messages',\n",
-        "             hole=0.6,\n",
-        "             #labels={'lifeExp':'life expectancy'},\n",
-        "             )\n",
-        "fig.update_traces(textposition='outside', textinfo='percent+label')\n",
-        "fig.update(layout_showlegend=False)\n",
-        "fig.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 542
-        },
-        "id": "1e8EPzCZHthY",
-        "outputId": "88a9d0d0-1453-4985-d970-714726391edc"
-      },
-      "execution_count": 13,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
-              "<html>\n",
-              "<head><meta charset=\"utf-8\" /></head>\n",
-              "<body>\n",
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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "explode = 0.05*np.ones(len(grp_out))\n",
+    "\n",
+    "grp_out[\"total\"].plot.pie(rotatelabels=False,\n",
+    "                          autopct=\"%1.1f%%\",\n",
+    "                          ylabel=\"\",\n",
+    "                          textprops={\"fontsize\":10},\n",
+    "                          #radius=2,\n",
+    "                          pctdistance=0.85,\n",
+    "                          explode=explode)\n",
+    "\n",
+    "#draw circle\n",
+    "centre_circle = plt.Circle((0, 0), 0.7, fc=\"white\")\n",
+    "fig = plt.gcf()\n",
+    "\n",
+    "# Adding Circle in Pie chart\n",
+    "fig.gca().add_artist(centre_circle)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "D9gUL6xmmVrO"
+   },
+   "source": [
+    "## Make pie chart using plotly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 542
     },
+    "id": "ioNrlM5ZCw-w",
+    "outputId": "2b4e0118-3f2a-41c2-d3ad-ad8ae50d683d"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": [
-        "# Chart above is busy, let's make 'OTHER' category"
-      ],
-      "metadata": {
-        "id": "O2u92GcRy40C"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "filtered_grp = grp_out.loc[grp_out['pcnt']>0.02]\n",
-        "\n",
-        "filtered_grp.reset_index(inplace=True)\n",
-        "\n",
-        "df = pd.DataFrame({'sponsor': 'OTHER',\n",
-        "              'total': [grp_out.loc[grp_out['pcnt']<0.02,'total'].sum()]\n",
-        "              })\n",
-        "\n",
-        "df_pie = pd.concat([filtered_grp, df])\n",
-        "\n",
-        "df_pie"
-      ],
-      "metadata": {
-        "id": "KxzDJ3JTy-CF",
-        "outputId": "3cc71443-2fd3-43bf-f9ad-1a24068dff89",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 332
-        }
-      },
-      "execution_count": 14,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "                                             sponsor         met       wave  \\\n",
-              "0  CARIBBEAN INTEGRATED COASTAL OCEAN OBSERVING S...   2957646.0   585526.0   \n",
-              "1      COASTAL OCEAN RESEARCH AND MONITORING PROGRAM   1205692.0   379864.0   \n",
-              "2                                          LIMNOTECH   1462626.0  1141056.0   \n",
-              "3                          MARINE EXCHANGE OF ALASKA  20867546.0        0.0   \n",
-              "4                      MICHIGAN TECHNICAL UNIVERSITY   1170326.0  1112668.0   \n",
-              "5  SCRIPPS WAVERIDER COASTAL DATA INFORMATION PRO...         0.0  3207372.0   \n",
-              "6            TEXAS COASTAL OCEAN OBSERVATION NETWORK  25840950.0        0.0   \n",
-              "7                           USF COMPS MARINE NETWORK   4780472.0        0.0   \n",
-              "0                                              OTHER         NaN        NaN   \n",
-              "\n",
-              "      total      pcnt  \n",
-              "0   3543172  0.045350  \n",
-              "1   1585556  0.020294  \n",
-              "2   2603682  0.033325  \n",
-              "3  20867546  0.267091  \n",
-              "4   2282994  0.029221  \n",
-              "5   3207372  0.041052  \n",
-              "6  25840950  0.330747  \n",
-              "7   4780472  0.061187  \n",
-              "0  13417236       NaN  "
-            ],
-            "text/html": [
-              "\n",
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-              "      <th>total</th>\n",
-              "      <th>pcnt</th>\n",
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-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>CARIBBEAN INTEGRATED COASTAL OCEAN OBSERVING S...</td>\n",
-              "      <td>2957646.0</td>\n",
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-              "      <td>0.045350</td>\n",
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-              "      <th>1</th>\n",
-              "      <td>COASTAL OCEAN RESEARCH AND MONITORING PROGRAM</td>\n",
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-              "      <td>1585556</td>\n",
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-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>LIMNOTECH</td>\n",
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-              "      <td>0.0</td>\n",
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-              "      <td>1112668.0</td>\n",
-              "      <td>2282994</td>\n",
-              "      <td>0.029221</td>\n",
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-              "      <th>5</th>\n",
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-              "      <th>6</th>\n",
-              "      <td>TEXAS COASTAL OCEAN OBSERVATION NETWORK</td>\n",
-              "      <td>25840950.0</td>\n",
-              "      <td>0.0</td>\n",
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-              "      <td>0.330747</td>\n",
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-              "      <td>4780472.0</td>\n",
-              "      <td>0.0</td>\n",
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-              "      <td>0.061187</td>\n",
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-              "    async function quickchart(key) {\n",
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-          "metadata": {},
-          "execution_count": 14
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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('0baf765b-1d6f-4cb7-a58e-0d87107ddd7a');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                            </script>        </div>\n",
+       "</body>\n",
+       "</html>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "\n",
+    "fig = px.pie(grp_out,\n",
+    "             values=\"total\",\n",
+    "             names=grp_out.index,\n",
+    "             #title='Distribution of NDBC messages',\n",
+    "             hole=0.6,\n",
+    "             #labels={'lifeExp':'life expectancy'},\n",
+    "             #rotation=90,\n",
+    "             )\n",
+    "fig.update_traces(textposition=\"outside\", textinfo=\"percent+label\")\n",
+    "fig.update(layout_showlegend=False)\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rvSa7UfUhM2y"
+   },
+   "source": [
+    "# What's IOOS composed of?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
     },
+    "id": "sfjOQs9lhSJO",
+    "outputId": "6a0b4d54-a700-4035-d1fb-0932982c6b17"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": [
-        "## Make reduced pie chart using matplotlib"
-      ],
-      "metadata": {
-        "id": "RWLjqI05mq0s"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "explode = 0.05*np.ones(len(df_pie))\n",
-        "\n",
-        "df_pie.set_index('sponsor',inplace=True)\n",
-        "\n",
-        "df_pie['total'].plot.pie(rotatelabels=False,\n",
-        "                          autopct='%1.1f%%',\n",
-        "                          ylabel='',\n",
-        "                          textprops={'fontsize':10},\n",
-        "                          #radius=2,\n",
-        "                          pctdistance=0.85,\n",
-        "                          explode=explode)\n",
-        "# draw circle\n",
-        "centre_circle = plt.Circle((0, 0), 0.7, fc='white')\n",
-        "fig = plt.gcf()\n",
-        "\n",
-        "# Adding Circle in Pie chart\n",
-        "fig.gca().add_artist(centre_circle)"
-      ],
-      "metadata": {
-        "id": "tK1OvHykmp-A",
-        "outputId": "9eec95c3-89c4-4c5e-eaf5-e940e0d98c37",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 424
-        }
+     "data": {
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "summary": "{\n  \"name\": \"grp_out\",\n  \"rows\": 46,\n  \"fields\": [\n    {\n      \"column\": \"sponsor\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 46,\n        \"samples\": [\n          \"UNIVERSITY OF NORTH CAROLINA, CHAPEL HILL\",\n          \"CHICAGO PARK DISTRICT\",\n          \"ALASKA OCEAN OBSERVING SYSTEM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"met\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4818797,\n        \"min\": 0,\n        \"max\": 25840950,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          38310,\n          11638,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wave\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 522350,\n        \"min\": 0,\n        \"max\": 3207372,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          425606,\n          6572,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4808530,\n        \"min\": 0,\n        \"max\": 25840950,\n        \"num_unique_values\": 45,\n        \"samples\": [\n          30736,\n          168104,\n          167592\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pcnt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06154605270158947,\n        \"min\": 0.0,\n        \"max\": 0.3307473104090185,\n        \"num_unique_values\": 45,\n        \"samples\": [\n          0.0003934007585917543,\n          0.002151621587789832,\n          0.0021450683216394225\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+       "type": "dataframe"
       },
-      "execution_count": 15,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<matplotlib.patches.Circle at 0x7e5e537cead0>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 15
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Make reduced pie chart using plotly"
+      "text/html": [
+       "\n",
+       "  <div id=\"df-bf78e2d1-3b09-4b39-850a-5806992a8d19\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>met</th>\n",
+       "      <th>wave</th>\n",
+       "      <th>total</th>\n",
+       "      <th>pcnt</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sponsor</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>TEXAS COASTAL OCEAN OBSERVATION NETWORK</th>\n",
+       "      <td>25840950</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25840950</td>\n",
+       "      <td>0.330747</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MARINE EXCHANGE OF ALASKA</th>\n",
+       "      <td>20867546</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20867546</td>\n",
+       "      <td>0.267091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>USF COMPS MARINE NETWORK</th>\n",
+       "      <td>4780472</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4780472</td>\n",
+       "      <td>0.061187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CARIBBEAN INTEGRATED COASTAL OCEAN OBSERVING SYSTEM</th>\n",
+       "      <td>2957646</td>\n",
+       "      <td>585526</td>\n",
+       "      <td>3543172</td>\n",
+       "      <td>0.045350</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SCRIPPS WAVERIDER COASTAL DATA INFORMATION PROGRAM</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3207372</td>\n",
+       "      <td>3207372</td>\n",
+       "      <td>0.041052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LIMNOTECH</th>\n",
+       "      <td>1462626</td>\n",
+       "      <td>1141056</td>\n",
+       "      <td>2603682</td>\n",
+       "      <td>0.033325</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MICHIGAN TECHNICAL UNIVERSITY</th>\n",
+       "      <td>1170326</td>\n",
+       "      <td>1112668</td>\n",
+       "      <td>2282994</td>\n",
+       "      <td>0.029221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COASTAL OCEAN RESEARCH AND MONITORING PROGRAM</th>\n",
+       "      <td>1205692</td>\n",
+       "      <td>379864</td>\n",
+       "      <td>1585556</td>\n",
+       "      <td>0.020294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CALIFORNIA POLYTECHNIC STATE UNIVERSITY</th>\n",
+       "      <td>1232956</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1232956</td>\n",
+       "      <td>0.015781</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NORTHEASTERN REGIONAL ASSN OF COASTAL OCEAN OBS SYSTEMS</th>\n",
+       "      <td>589124</td>\n",
+       "      <td>582972</td>\n",
+       "      <td>1172096</td>\n",
+       "      <td>0.015002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MOSS LANDING MARINE LABORATORIES</th>\n",
+       "      <td>905020</td>\n",
+       "      <td>0</td>\n",
+       "      <td>905020</td>\n",
+       "      <td>0.011584</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UNIVERSITY OF MICHIGAN CILER</th>\n",
+       "      <td>457866</td>\n",
+       "      <td>414754</td>\n",
+       "      <td>872620</td>\n",
+       "      <td>0.011169</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ILLINOIS-INDIANA SEA GRANT/PURDUE CIVIL ENGINEERING</th>\n",
+       "      <td>417300</td>\n",
+       "      <td>425606</td>\n",
+       "      <td>842906</td>\n",
+       "      <td>0.010789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CENTRAL AND NORTHERN CALIFORNIA OCEAN OBSERVING SYSTEM</th>\n",
+       "      <td>833244</td>\n",
+       "      <td>0</td>\n",
+       "      <td>833244</td>\n",
+       "      <td>0.010665</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UNIVERSITY OF WASHINGTON</th>\n",
+       "      <td>809612</td>\n",
+       "      <td>0</td>\n",
+       "      <td>809612</td>\n",
+       "      <td>0.010363</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UNIV OF CONNECTICUT MARINE MONITORING NETWORK (MYSOUND)</th>\n",
+       "      <td>654548</td>\n",
+       "      <td>135252</td>\n",
+       "      <td>789800</td>\n",
+       "      <td>0.010109</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UNIVERSITY OF MINNESOTA AT DULUTH</th>\n",
+       "      <td>412104</td>\n",
+       "      <td>282614</td>\n",
+       "      <td>694718</td>\n",
+       "      <td>0.008892</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NORTHERN MICHIGAN UNIVERSITY</th>\n",
+       "      <td>569194</td>\n",
+       "      <td>118064</td>\n",
+       "      <td>687258</td>\n",
+       "      <td>0.008796</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAGNE</th>\n",
+       "      <td>311492</td>\n",
+       "      <td>356274</td>\n",
+       "      <td>667766</td>\n",
+       "      <td>0.008547</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DAUPHIN ISLAND SEA LAB</th>\n",
+       "      <td>639214</td>\n",
+       "      <td>0</td>\n",
+       "      <td>639214</td>\n",
+       "      <td>0.008182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PUERTO RICO SEISMIC NETWORK</th>\n",
+       "      <td>583788</td>\n",
+       "      <td>0</td>\n",
+       "      <td>583788</td>\n",
+       "      <td>0.007472</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UNIVERSITY OF WISCONSIN AT MILWAUKEE</th>\n",
+       "      <td>155734</td>\n",
+       "      <td>125486</td>\n",
+       "      <td>281220</td>\n",
+       "      <td>0.003599</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ILLINOIS-INDIANA SEA GRANT</th>\n",
+       "      <td>138044</td>\n",
+       "      <td>138042</td>\n",
+       "      <td>276086</td>\n",
+       "      <td>0.003534</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FLORIDA INSTITUTE OF TECHNOLOGY</th>\n",
+       "      <td>272582</td>\n",
+       "      <td>0</td>\n",
+       "      <td>272582</td>\n",
+       "      <td>0.003489</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GREATER TAMPA BAY MARINE ADVISORY COUNCIL PORTS</th>\n",
+       "      <td>0</td>\n",
+       "      <td>203318</td>\n",
+       "      <td>203318</td>\n",
+       "      <td>0.002602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CHICAGO PARK DISTRICT</th>\n",
+       "      <td>161532</td>\n",
+       "      <td>6572</td>\n",
+       "      <td>168104</td>\n",
+       "      <td>0.002152</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ALASKA OCEAN OBSERVING SYSTEM</th>\n",
+       "      <td>0</td>\n",
+       "      <td>167592</td>\n",
+       "      <td>167592</td>\n",
+       "      <td>0.002145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UNIVERSITY OF NORTH CAROLINA COASTAL STUDIES</th>\n",
+       "      <td>0</td>\n",
+       "      <td>163692</td>\n",
+       "      <td>163692</td>\n",
+       "      <td>0.002095</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TEXAS A &amp; M</th>\n",
+       "      <td>159936</td>\n",
+       "      <td>0</td>\n",
+       "      <td>159936</td>\n",
+       "      <td>0.002047</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NATIONAL OCEAN SERVICE</th>\n",
+       "      <td>143696</td>\n",
+       "      <td>0</td>\n",
+       "      <td>143696</td>\n",
+       "      <td>0.001839</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UNIVERSITY OF NEW HAMPSHIRE</th>\n",
+       "      <td>17838</td>\n",
+       "      <td>103440</td>\n",
+       "      <td>121278</td>\n",
+       "      <td>0.001552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NW ASSOCIATION OF NETWORKED OCEAN OBSERVING SYSTEMS</th>\n",
+       "      <td>111818</td>\n",
+       "      <td>0</td>\n",
+       "      <td>111818</td>\n",
+       "      <td>0.001431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MONTEREY BAY AQUARIUM RESEARCH INSTITUTE</th>\n",
+       "      <td>100430</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100430</td>\n",
+       "      <td>0.001285</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>REGIONAL SCIENCE CONSORTIUM</th>\n",
+       "      <td>59898</td>\n",
+       "      <td>37462</td>\n",
+       "      <td>97360</td>\n",
+       "      <td>0.001246</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COASTAL DATA INFORMATION PROGRAM/PMEL</th>\n",
+       "      <td>0</td>\n",
+       "      <td>90554</td>\n",
+       "      <td>90554</td>\n",
+       "      <td>0.001159</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COASTAL STUDIES INSTITUTE, LOUISIANA STATE UNIV.</th>\n",
+       "      <td>66338</td>\n",
+       "      <td>0</td>\n",
+       "      <td>66338</td>\n",
+       "      <td>0.000849</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>U.S. ARMY CORPS OF ENGINEERS</th>\n",
+       "      <td>0</td>\n",
+       "      <td>54202</td>\n",
+       "      <td>54202</td>\n",
+       "      <td>0.000694</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SALMON UNLIMITED WISCONSIN</th>\n",
+       "      <td>24578</td>\n",
+       "      <td>24356</td>\n",
+       "      <td>48934</td>\n",
+       "      <td>0.000626</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STONY BROOK UNIVERSITY</th>\n",
+       "      <td>38310</td>\n",
+       "      <td>0</td>\n",
+       "      <td>38310</td>\n",
+       "      <td>0.000490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UNIVERSITY OF NORTH CAROLINA, CHAPEL HILL</th>\n",
+       "      <td>30736</td>\n",
+       "      <td>0</td>\n",
+       "      <td>30736</td>\n",
+       "      <td>0.000393</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NW MICHIGAN COLLEGE GREAT LAKES WATER STUDIES INSTITUTE</th>\n",
+       "      <td>14948</td>\n",
+       "      <td>14948</td>\n",
+       "      <td>29896</td>\n",
+       "      <td>0.000383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UNIVERSITY OF SOUTHERN MISSISSIPPI</th>\n",
+       "      <td>11638</td>\n",
+       "      <td>11630</td>\n",
+       "      <td>23268</td>\n",
+       "      <td>0.000298</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CENTER FOR COASTAL MARGIN OBSERVATION AND PREDICTION</th>\n",
+       "      <td>22524</td>\n",
+       "      <td>0</td>\n",
+       "      <td>22524</td>\n",
+       "      <td>0.000288</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COLUMBIA RIVER INTER-TRIBAL FISH COMMISSION</th>\n",
+       "      <td>14364</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14364</td>\n",
+       "      <td>0.000184</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UNIVERSITY OF CALIFORNIA AT DAVIS</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SUPERIOR WATERSHED PARTNERSHIP</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bf78e2d1-3b09-4b39-850a-5806992a8d19')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-bf78e2d1-3b09-4b39-850a-5806992a8d19 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-bf78e2d1-3b09-4b39-850a-5806992a8d19');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-ff9b046f-6016-4caa-aee2-3e3df96daba2\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ff9b046f-6016-4caa-aee2-3e3df96daba2')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-ff9b046f-6016-4caa-aee2-3e3df96daba2 button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "metadata": {
-        "id": "kVj5ty7qmxWs"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import plotly.express as px\n",
-        "fig = px.pie(df_pie,\n",
-        "             values='total',\n",
-        "             names=df_pie.index,\n",
-        "             #title='Distribution of NDBC messages',\n",
-        "             hole=0.6,\n",
-        "             #labels={'lifeExp':'life expectancy'},\n",
-        "             )\n",
-        "fig.update_traces(textposition='outside', textinfo='percent+label')\n",
-        "fig.update(layout_showlegend=False)\n",
-        "fig.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 542
-        },
-        "id": "XHRGJYruH0V0",
-        "outputId": "2474d73a-8945-44dd-f0d5-678431b7b716"
-      },
-      "execution_count": 16,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
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-              "<head><meta charset=\"utf-8\" /></head>\n",
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+      "text/plain": [
+       "                                                         met     wave  \\\n",
+       "sponsor                                                                 \n",
+       "TEXAS COASTAL OCEAN OBSERVATION NETWORK             25840950        0   \n",
+       "MARINE EXCHANGE OF ALASKA                           20867546        0   \n",
+       "USF COMPS MARINE NETWORK                             4780472        0   \n",
+       "CARIBBEAN INTEGRATED COASTAL OCEAN OBSERVING SY...   2957646   585526   \n",
+       "SCRIPPS WAVERIDER COASTAL DATA INFORMATION PROGRAM         0  3207372   \n",
+       "LIMNOTECH                                            1462626  1141056   \n",
+       "MICHIGAN TECHNICAL UNIVERSITY                        1170326  1112668   \n",
+       "COASTAL OCEAN RESEARCH AND MONITORING PROGRAM        1205692   379864   \n",
+       "CALIFORNIA POLYTECHNIC STATE UNIVERSITY              1232956        0   \n",
+       "NORTHEASTERN REGIONAL ASSN OF COASTAL OCEAN OBS...    589124   582972   \n",
+       "MOSS LANDING MARINE LABORATORIES                      905020        0   \n",
+       "UNIVERSITY OF MICHIGAN CILER                          457866   414754   \n",
+       "ILLINOIS-INDIANA SEA GRANT/PURDUE CIVIL ENGINEE...    417300   425606   \n",
+       "CENTRAL AND NORTHERN CALIFORNIA OCEAN OBSERVING...    833244        0   \n",
+       "UNIVERSITY OF WASHINGTON                              809612        0   \n",
+       "UNIV OF CONNECTICUT MARINE MONITORING NETWORK (...    654548   135252   \n",
+       "UNIVERSITY OF MINNESOTA AT DULUTH                     412104   282614   \n",
+       "NORTHERN MICHIGAN UNIVERSITY                          569194   118064   \n",
+       "UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAGNE            311492   356274   \n",
+       "DAUPHIN ISLAND SEA LAB                                639214        0   \n",
+       "PUERTO RICO SEISMIC NETWORK                           583788        0   \n",
+       "UNIVERSITY OF WISCONSIN AT MILWAUKEE                  155734   125486   \n",
+       "ILLINOIS-INDIANA SEA GRANT                            138044   138042   \n",
+       "FLORIDA INSTITUTE OF TECHNOLOGY                       272582        0   \n",
+       "GREATER TAMPA BAY MARINE ADVISORY COUNCIL PORTS            0   203318   \n",
+       "CHICAGO PARK DISTRICT                                 161532     6572   \n",
+       "ALASKA OCEAN OBSERVING SYSTEM                              0   167592   \n",
+       "UNIVERSITY OF NORTH CAROLINA COASTAL STUDIES               0   163692   \n",
+       "TEXAS A & M                                           159936        0   \n",
+       "NATIONAL OCEAN SERVICE                                143696        0   \n",
+       "UNIVERSITY OF NEW HAMPSHIRE                            17838   103440   \n",
+       "NW ASSOCIATION OF NETWORKED OCEAN OBSERVING SYS...    111818        0   \n",
+       "MONTEREY BAY AQUARIUM RESEARCH INSTITUTE              100430        0   \n",
+       "REGIONAL SCIENCE CONSORTIUM                            59898    37462   \n",
+       "COASTAL DATA INFORMATION PROGRAM/PMEL                      0    90554   \n",
+       "COASTAL STUDIES INSTITUTE, LOUISIANA STATE UNIV.       66338        0   \n",
+       "U.S. ARMY CORPS OF ENGINEERS                               0    54202   \n",
+       "SALMON UNLIMITED WISCONSIN                             24578    24356   \n",
+       "STONY BROOK UNIVERSITY                                 38310        0   \n",
+       "UNIVERSITY OF NORTH CAROLINA, CHAPEL HILL              30736        0   \n",
+       "NW MICHIGAN COLLEGE GREAT LAKES WATER STUDIES I...     14948    14948   \n",
+       "UNIVERSITY OF SOUTHERN MISSISSIPPI                     11638    11630   \n",
+       "CENTER FOR COASTAL MARGIN OBSERVATION AND PREDI...     22524        0   \n",
+       "COLUMBIA RIVER INTER-TRIBAL FISH COMMISSION            14364        0   \n",
+       "UNIVERSITY OF CALIFORNIA AT DAVIS                          0        0   \n",
+       "SUPERIOR WATERSHED PARTNERSHIP                             0        0   \n",
+       "\n",
+       "                                                       total      pcnt  \n",
+       "sponsor                                                                 \n",
+       "TEXAS COASTAL OCEAN OBSERVATION NETWORK             25840950  0.330747  \n",
+       "MARINE EXCHANGE OF ALASKA                           20867546  0.267091  \n",
+       "USF COMPS MARINE NETWORK                             4780472  0.061187  \n",
+       "CARIBBEAN INTEGRATED COASTAL OCEAN OBSERVING SY...   3543172  0.045350  \n",
+       "SCRIPPS WAVERIDER COASTAL DATA INFORMATION PROGRAM   3207372  0.041052  \n",
+       "LIMNOTECH                                            2603682  0.033325  \n",
+       "MICHIGAN TECHNICAL UNIVERSITY                        2282994  0.029221  \n",
+       "COASTAL OCEAN RESEARCH AND MONITORING PROGRAM        1585556  0.020294  \n",
+       "CALIFORNIA POLYTECHNIC STATE UNIVERSITY              1232956  0.015781  \n",
+       "NORTHEASTERN REGIONAL ASSN OF COASTAL OCEAN OBS...   1172096  0.015002  \n",
+       "MOSS LANDING MARINE LABORATORIES                      905020  0.011584  \n",
+       "UNIVERSITY OF MICHIGAN CILER                          872620  0.011169  \n",
+       "ILLINOIS-INDIANA SEA GRANT/PURDUE CIVIL ENGINEE...    842906  0.010789  \n",
+       "CENTRAL AND NORTHERN CALIFORNIA OCEAN OBSERVING...    833244  0.010665  \n",
+       "UNIVERSITY OF WASHINGTON                              809612  0.010363  \n",
+       "UNIV OF CONNECTICUT MARINE MONITORING NETWORK (...    789800  0.010109  \n",
+       "UNIVERSITY OF MINNESOTA AT DULUTH                     694718  0.008892  \n",
+       "NORTHERN MICHIGAN UNIVERSITY                          687258  0.008796  \n",
+       "UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAGNE            667766  0.008547  \n",
+       "DAUPHIN ISLAND SEA LAB                                639214  0.008182  \n",
+       "PUERTO RICO SEISMIC NETWORK                           583788  0.007472  \n",
+       "UNIVERSITY OF WISCONSIN AT MILWAUKEE                  281220  0.003599  \n",
+       "ILLINOIS-INDIANA SEA GRANT                            276086  0.003534  \n",
+       "FLORIDA INSTITUTE OF TECHNOLOGY                       272582  0.003489  \n",
+       "GREATER TAMPA BAY MARINE ADVISORY COUNCIL PORTS       203318  0.002602  \n",
+       "CHICAGO PARK DISTRICT                                 168104  0.002152  \n",
+       "ALASKA OCEAN OBSERVING SYSTEM                         167592  0.002145  \n",
+       "UNIVERSITY OF NORTH CAROLINA COASTAL STUDIES          163692  0.002095  \n",
+       "TEXAS A & M                                           159936  0.002047  \n",
+       "NATIONAL OCEAN SERVICE                                143696  0.001839  \n",
+       "UNIVERSITY OF NEW HAMPSHIRE                           121278  0.001552  \n",
+       "NW ASSOCIATION OF NETWORKED OCEAN OBSERVING SYS...    111818  0.001431  \n",
+       "MONTEREY BAY AQUARIUM RESEARCH INSTITUTE              100430  0.001285  \n",
+       "REGIONAL SCIENCE CONSORTIUM                            97360  0.001246  \n",
+       "COASTAL DATA INFORMATION PROGRAM/PMEL                  90554  0.001159  \n",
+       "COASTAL STUDIES INSTITUTE, LOUISIANA STATE UNIV.       66338  0.000849  \n",
+       "U.S. ARMY CORPS OF ENGINEERS                           54202  0.000694  \n",
+       "SALMON UNLIMITED WISCONSIN                             48934  0.000626  \n",
+       "STONY BROOK UNIVERSITY                                 38310  0.000490  \n",
+       "UNIVERSITY OF NORTH CAROLINA, CHAPEL HILL              30736  0.000393  \n",
+       "NW MICHIGAN COLLEGE GREAT LAKES WATER STUDIES I...     29896  0.000383  \n",
+       "UNIVERSITY OF SOUTHERN MISSISSIPPI                     23268  0.000298  \n",
+       "CENTER FOR COASTAL MARGIN OBSERVATION AND PREDI...     22524  0.000288  \n",
+       "COLUMBIA RIVER INTER-TRIBAL FISH COMMISSION            14364  0.000184  \n",
+       "UNIVERSITY OF CALIFORNIA AT DAVIS                          0  0.000000  \n",
+       "SUPERIOR WATERSHED PARTNERSHIP                             0  0.000000  "
       ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_ioos[\"total\"] = df_ioos[\"met\"] + df_ioos[\"wave\"]\n",
+    "df_ioos[\"time (UTC)\"] = df_ioos[\"time (UTC)\"].dt.tz_localize(None)\n",
+    "\n",
+    "ioos_group = df_ioos.groupby(by=[\"sponsor\"])\n",
+    "\n",
+    "grp = ioos_group[[\"met\",\"wave\",\"total\"]].sum()\n",
+    "\n",
+    "grp_out = grp.assign(pcnt = grp[\"total\"] / grp[\"total\"].sum())\n",
+    "\n",
+    "grp_out.sort_values(by=\"pcnt\", ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "q44kMvB_meVF"
+   },
+   "source": [
+    "## Make pie chart using matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 424
     },
+    "id": "wto4GI3vmbEU",
+    "outputId": "f93af446-b8cb-43f9-fa9d-610689fd878c"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": [
-        "What is 'OTHER'?"
-      ],
-      "metadata": {
-        "id": "h6ibugaOzFsr"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "grp_out.loc[grp_out['pcnt']<0.02].index.tolist()"
-      ],
-      "metadata": {
-        "id": "n1G31sHxzDh3",
-        "outputId": "06256287-a9cc-43db-eee9-2fee1fd64d56",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "execution_count": 17,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "['ALASKA OCEAN OBSERVING SYSTEM',\n",
-              " 'CALIFORNIA POLYTECHNIC STATE UNIVERSITY',\n",
-              " 'CENTER FOR COASTAL MARGIN OBSERVATION AND PREDICTION',\n",
-              " 'CENTRAL AND NORTHERN CALIFORNIA OCEAN OBSERVING SYSTEM',\n",
-              " 'CHICAGO PARK DISTRICT',\n",
-              " 'COASTAL DATA INFORMATION PROGRAM/PMEL',\n",
-              " 'COASTAL STUDIES INSTITUTE, LOUISIANA STATE UNIV.',\n",
-              " 'COLUMBIA RIVER INTER-TRIBAL FISH COMMISSION',\n",
-              " 'DAUPHIN ISLAND SEA LAB',\n",
-              " 'FLORIDA INSTITUTE OF TECHNOLOGY',\n",
-              " 'GREATER TAMPA BAY MARINE ADVISORY COUNCIL PORTS',\n",
-              " 'ILLINOIS-INDIANA SEA GRANT',\n",
-              " 'ILLINOIS-INDIANA SEA GRANT/PURDUE CIVIL ENGINEERING',\n",
-              " 'MONTEREY BAY AQUARIUM RESEARCH INSTITUTE',\n",
-              " 'MOSS LANDING MARINE LABORATORIES',\n",
-              " 'NATIONAL OCEAN SERVICE',\n",
-              " 'NORTHEASTERN REGIONAL ASSN OF COASTAL OCEAN OBS SYSTEMS',\n",
-              " 'NORTHERN MICHIGAN UNIVERSITY',\n",
-              " 'NW ASSOCIATION OF NETWORKED OCEAN OBSERVING SYSTEMS',\n",
-              " 'NW MICHIGAN COLLEGE GREAT LAKES WATER STUDIES INSTITUTE',\n",
-              " 'PUERTO RICO SEISMIC NETWORK',\n",
-              " 'REGIONAL SCIENCE CONSORTIUM',\n",
-              " 'SALMON UNLIMITED WISCONSIN',\n",
-              " 'STONY BROOK UNIVERSITY',\n",
-              " 'SUPERIOR WATERSHED PARTNERSHIP',\n",
-              " 'TEXAS A & M',\n",
-              " 'U.S. ARMY CORPS OF ENGINEERS',\n",
-              " 'UNIV OF CONNECTICUT MARINE MONITORING NETWORK (MYSOUND)',\n",
-              " 'UNIVERSITY OF CALIFORNIA AT DAVIS',\n",
-              " 'UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAGNE',\n",
-              " 'UNIVERSITY OF MICHIGAN CILER',\n",
-              " 'UNIVERSITY OF MINNESOTA AT DULUTH',\n",
-              " 'UNIVERSITY OF NEW HAMPSHIRE',\n",
-              " 'UNIVERSITY OF NORTH CAROLINA COASTAL STUDIES',\n",
-              " 'UNIVERSITY OF NORTH CAROLINA, CHAPEL HILL',\n",
-              " 'UNIVERSITY OF SOUTHERN MISSISSIPPI',\n",
-              " 'UNIVERSITY OF WASHINGTON',\n",
-              " 'UNIVERSITY OF WISCONSIN AT MILWAUKEE']"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 17
-        }
+     "data": {
+      "text/plain": [
+       "<matplotlib.patches.Circle at 0x7e5e5384fd90>"
       ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5oP7aFrErtWX"
-      },
-      "source": [
-        "Let us check the monthly sum of data released both for individual met and wave and the totals."
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "explode = 0.05*np.ones(len(grp_out))\n",
+    "\n",
+    "grp_out[\"total\"].plot.pie(rotatelabels=False,\n",
+    "                          autopct=\"%1.1f%%\",\n",
+    "                          ylabel=\"\",\n",
+    "                          textprops={\"fontsize\":10},\n",
+    "                          #radius=2,\n",
+    "                          pctdistance=0.85,\n",
+    "                          explode=explode)\n",
+    "# draw circle\n",
+    "centre_circle = plt.Circle((0, 0), 0.7, fc=\"white\")\n",
+    "fig = plt.gcf()\n",
+    "\n",
+    "# Adding Circle in Pie chart\n",
+    "fig.gca().add_artist(centre_circle)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "NyMScohImhQz"
+   },
+   "source": [
+    "## Make pie chart using plotly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 542
     },
+    "id": "1e8EPzCZHthY",
+    "outputId": "88a9d0d0-1453-4985-d970-714726391edc"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 18,
-      "metadata": {
-        "id": "keIBv28trtWX"
-      },
-      "outputs": [],
-      "source": [
-        "import pandas as pd\n",
-        "\n",
-        "df_out[\"time (UTC)\"] = df_out[\"time (UTC)\"].dt.tz_localize(None)\n",
-        "\n",
-        "groups = df_out[['time (UTC)','met','wave']].groupby(pd.Grouper(key=\"time (UTC)\", freq=\"M\"))"
+     "data": {
+      "text/html": [
+       "<html>\n",
+       "<head><meta charset=\"utf-8\" /></head>\n",
+       "<body>\n",
+       "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
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+       "                            \n",
+       "var gd = document.getElementById('6dd33e19-0432-4c8a-9b09-0a963b6c9532');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                            </script>        </div>\n",
+       "</body>\n",
+       "</html>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "\n",
+    "fig = px.pie(grp_out,\n",
+    "             values=\"total\",\n",
+    "             names=grp_out.index,\n",
+    "             #title='Distribution of NDBC messages',\n",
+    "             hole=0.6,\n",
+    "             #labels={'lifeExp':'life expectancy'},\n",
+    "             )\n",
+    "fig.update_traces(textposition=\"outside\", textinfo=\"percent+label\")\n",
+    "fig.update(layout_showlegend=False)\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "O2u92GcRy40C"
+   },
+   "source": [
+    "# Chart above is busy, let's make 'OTHER' category"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 332
     },
+    "id": "KxzDJ3JTy-CF",
+    "outputId": "3cc71443-2fd3-43bf-f9ad-1a24068dff89"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [
-        "groups.sum().plot(kind='bar',figsize=(15,6))"
-      ],
-      "metadata": {
-        "id": "Vj1RG1XsvrwL",
-        "outputId": "814919f1-7450-4d87-bcdb-c773db759327",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 707
-        }
+     "data": {
+      "application/vnd.google.colaboratory.intrinsic+json": {
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+       "type": "dataframe",
+       "variable_name": "df_pie"
       },
-      "execution_count": 19,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<Axes: xlabel='time (UTC)'>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 19
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1500x600 with 1 Axes>"
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-          "metadata": {}
-        }
+      "text/html": [
+       "\n",
+       "  <div id=\"df-52c7f528-ca3b-464f-a62f-88c4a0e1611c\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sponsor</th>\n",
+       "      <th>met</th>\n",
+       "      <th>wave</th>\n",
+       "      <th>total</th>\n",
+       "      <th>pcnt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CARIBBEAN INTEGRATED COASTAL OCEAN OBSERVING S...</td>\n",
+       "      <td>2957646.0</td>\n",
+       "      <td>585526.0</td>\n",
+       "      <td>3543172</td>\n",
+       "      <td>0.045350</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>COASTAL OCEAN RESEARCH AND MONITORING PROGRAM</td>\n",
+       "      <td>1205692.0</td>\n",
+       "      <td>379864.0</td>\n",
+       "      <td>1585556</td>\n",
+       "      <td>0.020294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>LIMNOTECH</td>\n",
+       "      <td>1462626.0</td>\n",
+       "      <td>1141056.0</td>\n",
+       "      <td>2603682</td>\n",
+       "      <td>0.033325</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>MARINE EXCHANGE OF ALASKA</td>\n",
+       "      <td>20867546.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>20867546</td>\n",
+       "      <td>0.267091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>MICHIGAN TECHNICAL UNIVERSITY</td>\n",
+       "      <td>1170326.0</td>\n",
+       "      <td>1112668.0</td>\n",
+       "      <td>2282994</td>\n",
+       "      <td>0.029221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>SCRIPPS WAVERIDER COASTAL DATA INFORMATION PRO...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3207372.0</td>\n",
+       "      <td>3207372</td>\n",
+       "      <td>0.041052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>TEXAS COASTAL OCEAN OBSERVATION NETWORK</td>\n",
+       "      <td>25840950.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25840950</td>\n",
+       "      <td>0.330747</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>USF COMPS MARINE NETWORK</td>\n",
+       "      <td>4780472.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4780472</td>\n",
+       "      <td>0.061187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>OTHER</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13417236</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-52c7f528-ca3b-464f-a62f-88c4a0e1611c')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-52c7f528-ca3b-464f-a62f-88c4a0e1611c button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-52c7f528-ca3b-464f-a62f-88c4a0e1611c');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-f22b25ee-540d-4afb-bb0e-ce5b73ee8839\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f22b25ee-540d-4afb-bb0e-ce5b73ee8839')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-f22b25ee-540d-4afb-bb0e-ce5b73ee8839 button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
+      ],
+      "text/plain": [
+       "                                             sponsor         met       wave  \\\n",
+       "0  CARIBBEAN INTEGRATED COASTAL OCEAN OBSERVING S...   2957646.0   585526.0   \n",
+       "1      COASTAL OCEAN RESEARCH AND MONITORING PROGRAM   1205692.0   379864.0   \n",
+       "2                                          LIMNOTECH   1462626.0  1141056.0   \n",
+       "3                          MARINE EXCHANGE OF ALASKA  20867546.0        0.0   \n",
+       "4                      MICHIGAN TECHNICAL UNIVERSITY   1170326.0  1112668.0   \n",
+       "5  SCRIPPS WAVERIDER COASTAL DATA INFORMATION PRO...         0.0  3207372.0   \n",
+       "6            TEXAS COASTAL OCEAN OBSERVATION NETWORK  25840950.0        0.0   \n",
+       "7                           USF COMPS MARINE NETWORK   4780472.0        0.0   \n",
+       "0                                              OTHER         NaN        NaN   \n",
+       "\n",
+       "      total      pcnt  \n",
+       "0   3543172  0.045350  \n",
+       "1   1585556  0.020294  \n",
+       "2   2603682  0.033325  \n",
+       "3  20867546  0.267091  \n",
+       "4   2282994  0.029221  \n",
+       "5   3207372  0.041052  \n",
+       "6  25840950  0.330747  \n",
+       "7   4780472  0.061187  \n",
+       "0  13417236       NaN  "
       ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_grp = grp_out.loc[grp_out[\"pcnt\"]>0.02]\n",
+    "\n",
+    "filtered_grp.reset_index(inplace=True)\n",
+    "\n",
+    "df = pd.DataFrame({\"sponsor\": \"OTHER\",\n",
+    "              \"total\": [grp_out.loc[grp_out[\"pcnt\"]<0.02,\"total\"].sum()]\n",
+    "              })\n",
+    "\n",
+    "df_pie = pd.concat([filtered_grp, df])\n",
+    "\n",
+    "df_pie"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RWLjqI05mq0s"
+   },
+   "source": [
+    "## Make reduced pie chart using matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 424
     },
+    "id": "tK1OvHykmp-A",
+    "outputId": "9eec95c3-89c4-4c5e-eaf5-e940e0d98c37"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "9_iEHmiTrtWY"
-      },
-      "source": [
-        "We can create a table of observations per month,"
+     "data": {
+      "text/plain": [
+       "<matplotlib.patches.Circle at 0x7e5e537cead0>"
       ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "code",
-      "execution_count": 20,
-      "metadata": {
-        "id": "JxcIjc05rtWY",
-        "outputId": "393620b7-73be-4099-e7c1-05747e92ecd5",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Monthly totals:\n",
-            "                met    wave    total\n",
-            "time (UTC)                          \n",
-            "2018-01     5416106  368948  5785054\n",
-            "2018-02     4906308  337780  5244088\n",
-            "2018-03     5454492  382206  5836698\n",
-            "2018-04     5388108  379066  5767174\n",
-            "2018-05     5696122  490110  6186232\n",
-            "...             ...     ...      ...\n",
-            "2023-10     6672156  767336  7439492\n",
-            "2023-11     6341764  602932  6944696\n",
-            "2023-12     6195258  536102  6731360\n",
-            "2024-01     6320006  516370  6836376\n",
-            "2024-02     5864056  488424  6352480\n",
-            "\n",
-            "[74 rows x 3 columns]\n",
-            "\n",
-            "Sum for time period 2018-01 to 2024-02: 466961664\n"
-          ]
-        }
-      ],
-      "source": [
-        "s = groups[\n",
-        "    [ \"met\", \"wave\"]\n",
-        "].sum()  # reducing the columns so the summary is digestable\n",
-        "totals = s.assign(total=s[\"met\"] + s[\"wave\"])\n",
-        "totals.index = totals.index.to_period(\"M\")\n",
-        "\n",
-        "print(f\"Monthly totals:\\n{totals}\\n\")\n",
-        "\n",
-        "print(\n",
-        "    f\"Sum for time period {totals.index.min()} to {totals.index.max()}: {totals['total'].sum()}\"\n",
-        ")"
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "explode = 0.05*np.ones(len(df_pie))\n",
+    "\n",
+    "df_pie.set_index(\"sponsor\",inplace=True)\n",
+    "\n",
+    "df_pie[\"total\"].plot.pie(rotatelabels=False,\n",
+    "                          autopct=\"%1.1f%%\",\n",
+    "                          ylabel=\"\",\n",
+    "                          textprops={\"fontsize\":10},\n",
+    "                          #radius=2,\n",
+    "                          pctdistance=0.85,\n",
+    "                          explode=explode)\n",
+    "# draw circle\n",
+    "centre_circle = plt.Circle((0, 0), 0.7, fc=\"white\")\n",
+    "fig = plt.gcf()\n",
+    "\n",
+    "# Adding Circle in Pie chart\n",
+    "fig.gca().add_artist(centre_circle)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kVj5ty7qmxWs"
+   },
+   "source": [
+    "## Make reduced pie chart using plotly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 542
     },
+    "id": "XHRGJYruH0V0",
+    "outputId": "2474d73a-8945-44dd-f0d5-678431b7b716"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "mkEpqlRLrtWZ"
-      },
-      "source": [
-        "and visualize it in a bar chart."
+     "data": {
+      "text/html": [
+       "<html>\n",
+       "<head><meta charset=\"utf-8\" /></head>\n",
+       "<body>\n",
+       "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
+       "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.24.1.min.js\"></script>                <div id=\"f2d9fa30-6884-4a75-9446-a39351a5559a\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"f2d9fa30-6884-4a75-9446-a39351a5559a\")) {                    Plotly.newPlot(                        \"f2d9fa30-6884-4a75-9446-a39351a5559a\",                        [{\"domain\":{\"x\":[0.0,1.0],\"y\":[0.0,1.0]},\"hole\":0.6,\"hovertemplate\":\"sponsor=%{label}\\u003cbr\\u003etotal=%{value}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"labels\":[\"CARIBBEAN INTEGRATED COASTAL OCEAN OBSERVING SYSTEM\",\"COASTAL OCEAN RESEARCH AND MONITORING PROGRAM\",\"LIMNOTECH\",\"MARINE EXCHANGE OF ALASKA\",\"MICHIGAN TECHNICAL UNIVERSITY\",\"SCRIPPS WAVERIDER COASTAL DATA 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+       "                            \n",
+       "var gd = document.getElementById('f2d9fa30-6884-4a75-9446-a39351a5559a');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
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+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
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+       "}}\n",
+       "\n",
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+       "</body>\n",
+       "</html>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "\n",
+    "fig = px.pie(df_pie,\n",
+    "             values=\"total\",\n",
+    "             names=df_pie.index,\n",
+    "             #title='Distribution of NDBC messages',\n",
+    "             hole=0.6,\n",
+    "             #labels={'lifeExp':'life expectancy'},\n",
+    "             )\n",
+    "fig.update_traces(textposition=\"outside\", textinfo=\"percent+label\")\n",
+    "fig.update(layout_showlegend=False)\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "h6ibugaOzFsr"
+   },
+   "source": [
+    "What is 'OTHER'?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "n1G31sHxzDh3",
+    "outputId": "06256287-a9cc-43db-eee9-2fee1fd64d56"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 21,
-      "metadata": {
-        "id": "WEz1GN6zrtWZ",
-        "outputId": "cf60c824-9f15-432b-a69b-27814ad7d41d",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 429
-        }
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1100x375 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ],
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "fig, ax = plt.subplots(figsize=(11, 3.75))\n",
-        "\n",
-        "totals['total'].plot(ax=ax, kind=\"bar\",stacked=True)\n",
-        "ax.set_xticklabels(\n",
-        "    labels=s.index.to_series().dt.strftime(\"%Y-%b\"),\n",
-        "    rotation=70,\n",
-        "    rotation_mode=\"anchor\",\n",
-        "    ha=\"right\",\n",
-        ")\n",
-        "ax.yaxis.get_major_formatter().set_scientific(False)\n",
-        "ax.set_ylabel(\"# observations\")\n",
-        "ax.grid(axis='y')"
+     "data": {
+      "text/plain": [
+       "['ALASKA OCEAN OBSERVING SYSTEM',\n",
+       " 'CALIFORNIA POLYTECHNIC STATE UNIVERSITY',\n",
+       " 'CENTER FOR COASTAL MARGIN OBSERVATION AND PREDICTION',\n",
+       " 'CENTRAL AND NORTHERN CALIFORNIA OCEAN OBSERVING SYSTEM',\n",
+       " 'CHICAGO PARK DISTRICT',\n",
+       " 'COASTAL DATA INFORMATION PROGRAM/PMEL',\n",
+       " 'COASTAL STUDIES INSTITUTE, LOUISIANA STATE UNIV.',\n",
+       " 'COLUMBIA RIVER INTER-TRIBAL FISH COMMISSION',\n",
+       " 'DAUPHIN ISLAND SEA LAB',\n",
+       " 'FLORIDA INSTITUTE OF TECHNOLOGY',\n",
+       " 'GREATER TAMPA BAY MARINE ADVISORY COUNCIL PORTS',\n",
+       " 'ILLINOIS-INDIANA SEA GRANT',\n",
+       " 'ILLINOIS-INDIANA SEA GRANT/PURDUE CIVIL ENGINEERING',\n",
+       " 'MONTEREY BAY AQUARIUM RESEARCH INSTITUTE',\n",
+       " 'MOSS LANDING MARINE LABORATORIES',\n",
+       " 'NATIONAL OCEAN SERVICE',\n",
+       " 'NORTHEASTERN REGIONAL ASSN OF COASTAL OCEAN OBS SYSTEMS',\n",
+       " 'NORTHERN MICHIGAN UNIVERSITY',\n",
+       " 'NW ASSOCIATION OF NETWORKED OCEAN OBSERVING SYSTEMS',\n",
+       " 'NW MICHIGAN COLLEGE GREAT LAKES WATER STUDIES INSTITUTE',\n",
+       " 'PUERTO RICO SEISMIC NETWORK',\n",
+       " 'REGIONAL SCIENCE CONSORTIUM',\n",
+       " 'SALMON UNLIMITED WISCONSIN',\n",
+       " 'STONY BROOK UNIVERSITY',\n",
+       " 'SUPERIOR WATERSHED PARTNERSHIP',\n",
+       " 'TEXAS A & M',\n",
+       " 'U.S. ARMY CORPS OF ENGINEERS',\n",
+       " 'UNIV OF CONNECTICUT MARINE MONITORING NETWORK (MYSOUND)',\n",
+       " 'UNIVERSITY OF CALIFORNIA AT DAVIS',\n",
+       " 'UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAGNE',\n",
+       " 'UNIVERSITY OF MICHIGAN CILER',\n",
+       " 'UNIVERSITY OF MINNESOTA AT DULUTH',\n",
+       " 'UNIVERSITY OF NEW HAMPSHIRE',\n",
+       " 'UNIVERSITY OF NORTH CAROLINA COASTAL STUDIES',\n",
+       " 'UNIVERSITY OF NORTH CAROLINA, CHAPEL HILL',\n",
+       " 'UNIVERSITY OF SOUTHERN MISSISSIPPI',\n",
+       " 'UNIVERSITY OF WASHINGTON',\n",
+       " 'UNIVERSITY OF WISCONSIN AT MILWAUKEE']"
       ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grp_out.loc[grp_out[\"pcnt\"]<0.02].index.tolist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5oP7aFrErtWX"
+   },
+   "source": [
+    "Let us check the monthly sum of data released both for individual met and wave and the totals."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "id": "keIBv28trtWX"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df_out[\"time (UTC)\"] = df_out[\"time (UTC)\"].dt.tz_localize(None)\n",
+    "\n",
+    "groups = df_out[[\"time (UTC)\",\"met\",\"wave\"]].groupby(pd.Grouper(key=\"time (UTC)\", freq=\"M\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 707
     },
+    "id": "Vj1RG1XsvrwL",
+    "outputId": "814919f1-7450-4d87-bcdb-c773db759327"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": [
-        "Group by source, then by month"
-      ],
-      "metadata": {
-        "id": "kdp3jyaFxcbL"
-      }
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='time (UTC)'>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "code",
-      "source": [
-        "group = df_out.groupby(by=['source', pd.Grouper(key=\"time (UTC)\", freq=\"M\")])\n",
-        "\n",
-        "\n",
-        "s = group[\n",
-        "    [\"met\", \"wave\"]\n",
-        "].sum()  # reducing the columns so the summary is digestable\n",
-        "\n",
-        "totals = s.assign(total=s[\"met\"] + s[\"wave\"])\n",
-        "\n",
-        "totals.reset_index(['source'], inplace=True)\n",
-        "\n",
-        "totals.index = totals.index.to_period(\"M\").strftime('%Y-%b')\n",
-        "\n",
-        "totals"
-      ],
-      "metadata": {
-        "id": "3YcdQVCtxSv4",
-        "outputId": "cc5f344a-6af1-44d6-999f-9c5e03cae3c1",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 455
-        }
-      },
-      "execution_count": 22,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "              source      met    wave    total\n",
-              "time (UTC)                                    \n",
-              "2018-Jan        IOOS   701780   63624   765404\n",
-              "2018-Feb        IOOS   676688   61886   738574\n",
-              "2018-Mar        IOOS   759916   69014   828930\n",
-              "2018-Apr        IOOS   773482   75758   849240\n",
-              "2018-May        IOOS   890444  155768  1046212\n",
-              "...              ...      ...     ...      ...\n",
-              "2023-Oct    non-NDBC  4699234  331094  5030328\n",
-              "2023-Nov    non-NDBC  4514266  249716  4763982\n",
-              "2023-Dec    non-NDBC  4457820  224472  4682292\n",
-              "2024-Jan    non-NDBC  4584106  218032  4802138\n",
-              "2024-Feb    non-NDBC  4271722  214216  4485938\n",
-              "\n",
-              "[222 rows x 4 columns]"
-            ],
-            "text/html": [
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-              "  <tbody>\n",
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-              "      <th>2018-Jan</th>\n",
-              "      <td>IOOS</td>\n",
-              "      <td>701780</td>\n",
-              "      <td>63624</td>\n",
-              "      <td>765404</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2018-Feb</th>\n",
-              "      <td>IOOS</td>\n",
-              "      <td>676688</td>\n",
-              "      <td>61886</td>\n",
-              "      <td>738574</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2018-Mar</th>\n",
-              "      <td>IOOS</td>\n",
-              "      <td>759916</td>\n",
-              "      <td>69014</td>\n",
-              "      <td>828930</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2018-Apr</th>\n",
-              "      <td>IOOS</td>\n",
-              "      <td>773482</td>\n",
-              "      <td>75758</td>\n",
-              "      <td>849240</td>\n",
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-              "    <tr>\n",
-              "      <th>2018-May</th>\n",
-              "      <td>IOOS</td>\n",
-              "      <td>890444</td>\n",
-              "      <td>155768</td>\n",
-              "      <td>1046212</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2023-Oct</th>\n",
-              "      <td>non-NDBC</td>\n",
-              "      <td>4699234</td>\n",
-              "      <td>331094</td>\n",
-              "      <td>5030328</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2023-Nov</th>\n",
-              "      <td>non-NDBC</td>\n",
-              "      <td>4514266</td>\n",
-              "      <td>249716</td>\n",
-              "      <td>4763982</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2023-Dec</th>\n",
-              "      <td>non-NDBC</td>\n",
-              "      <td>4457820</td>\n",
-              "      <td>224472</td>\n",
-              "      <td>4682292</td>\n",
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-              "    <tr>\n",
-              "      <th>2024-Jan</th>\n",
-              "      <td>non-NDBC</td>\n",
-              "      <td>4584106</td>\n",
-              "      <td>218032</td>\n",
-              "      <td>4802138</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2024-Feb</th>\n",
-              "      <td>non-NDBC</td>\n",
-              "      <td>4271722</td>\n",
-              "      <td>214216</td>\n",
-              "      <td>4485938</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>222 rows × 4 columns</p>\n",
-              "</div>\n",
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-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
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-              "\n",
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-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
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-              "      width: 32px;\n",
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-              "\n",
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-              "\n",
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-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
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-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
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-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
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-              "        const element = document.querySelector('#df-48bcdc62-7912-4c3d-ba5d-b39acf57c1fd');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
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-              "        await google.colab.output.renderOutput(dataTable, element);\n",
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-              "\n",
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-              "\n",
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-              "      --bg-color: #E8F0FE;\n",
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-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
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-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
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-              "    background-color: var(--disabled-bg-color);\n",
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-              "  }\n",
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-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
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-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
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-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
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-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-e82d6f1b-6311-4611-9036-d06ace3fb249 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "totals",
-              "summary": "{\n  \"name\": \"totals\",\n  \"rows\": 222,\n  \"fields\": [\n    {\n      \"column\": \"time (UTC)\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 74,\n        \"samples\": [\n          \"2018-May\",\n          \"2023-Apr\",\n          \"2019-Jul\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"source\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"IOOS\",\n          \"NDBC\",\n          \"non-NDBC\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"met\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1646132,\n        \"min\": 370334,\n        \"max\": 4704766,\n        \"num_unique_values\": 222,\n        \"samples\": [\n          4366426,\n          4704766,\n          843884\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wave\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 63174,\n        \"min\": 44916,\n        \"max\": 393144,\n        \"num_unique_values\": 222,\n        \"samples\": [\n          214430,\n          393144,\n          81494\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1683006,\n        \"min\": 476930,\n        \"max\": 5097910,\n        \"num_unique_values\": 222,\n        \"samples\": [\n          4580856,\n          5097910,\n          925378\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 22
-        }
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+      "text/plain": [
+       "<Figure size 1500x600 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "groups.sum().plot(kind=\"bar\",figsize=(15,6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9_iEHmiTrtWY"
+   },
+   "source": [
+    "We can create a table of observations per month,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "JxcIjc05rtWY",
+    "outputId": "393620b7-73be-4099-e7c1-05747e92ecd5"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": [
-        "# Create stacked bar chart\n",
-        "\n",
-        "IOOS + non-NDBC + NDBC"
-      ],
-      "metadata": {
-        "id": "Qp5OoPgLRURB"
-      }
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Monthly totals:\n",
+      "                met    wave    total\n",
+      "time (UTC)                          \n",
+      "2018-01     5416106  368948  5785054\n",
+      "2018-02     4906308  337780  5244088\n",
+      "2018-03     5454492  382206  5836698\n",
+      "2018-04     5388108  379066  5767174\n",
+      "2018-05     5696122  490110  6186232\n",
+      "...             ...     ...      ...\n",
+      "2023-10     6672156  767336  7439492\n",
+      "2023-11     6341764  602932  6944696\n",
+      "2023-12     6195258  536102  6731360\n",
+      "2024-01     6320006  516370  6836376\n",
+      "2024-02     5864056  488424  6352480\n",
+      "\n",
+      "[74 rows x 3 columns]\n",
+      "\n",
+      "Sum for time period 2018-01 to 2024-02: 466961664\n"
+     ]
+    }
+   ],
+   "source": [
+    "s = groups[\n",
+    "    [ \"met\", \"wave\"]\n",
+    "].sum()  # reducing the columns so the summary is digestable\n",
+    "totals = s.assign(total=s[\"met\"] + s[\"wave\"])\n",
+    "totals.index = totals.index.to_period(\"M\")\n",
+    "\n",
+    "print(f\"Monthly totals:\\n{totals}\\n\")\n",
+    "\n",
+    "print(\n",
+    "    f\"Sum for time period {totals.index.min()} to {totals.index.max()}: {totals['total'].sum()}\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mkEpqlRLrtWZ"
+   },
+   "source": [
+    "and visualize it in a bar chart."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 429
     },
+    "id": "WEz1GN6zrtWZ",
+    "outputId": "cf60c824-9f15-432b-a69b-27814ad7d41d"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "from matplotlib.ticker import (MultipleLocator,\n",
-        "                               FormatStrFormatter,\n",
-        "                               AutoMinorLocator,\n",
-        "                               FuncFormatter)\n",
-        "\n",
-        "fig, axs = plt.subplots(nrows=2,ncols=1,figsize=(16,8))\n",
-        "\n",
-        "df_met = pd.DataFrame({'IOOS': totals.loc[totals['source']=='IOOS','met'],\n",
-        "                         'non-NDBC': totals.loc[totals['source']=='non-NDBC','met'],\n",
-        "                         'NDBC': totals.loc[totals['source']=='NDBC','met'],\n",
-        "                         },\n",
-        "                         index= totals.index.unique())\n",
-        "\n",
-        "df_met.plot.bar(stacked=True, xlabel='', ax=axs[0], rot=90, title='met')\n",
-        "\n",
-        "axs[0].get_legend().remove()\n",
-        "\n",
-        "axs[0].grid(axis='y')\n",
-        "\n",
-        "axs[0].yaxis.set_major_formatter(FuncFormatter(lambda x, p: format(int(x), ',')))\n",
-        "axs[0].axes.get_xaxis().set_visible(False)\n",
-        "\n",
-        "df_wave = pd.DataFrame({'IOOS': totals.loc[totals['source']=='IOOS','wave'],\n",
-        "                         'non-NDBC': totals.loc[totals['source']=='non-NDBC','wave'],\n",
-        "                         'NDBC': totals.loc[totals['source']=='NDBC','wave'],\n",
-        "                         },\n",
-        "                         index= totals.index.unique())\n",
-        "\n",
-        "df_wave.plot.bar(stacked=True, xlabel='', ax=axs[1], title='wave')\n",
-        "\n",
-        "axs[1].legend(loc='center',bbox_to_anchor=(0.5,-0.35,0,0),ncol=3)\n",
-        "\n",
-        "axs[1].grid(axis='y')\n",
-        "\n",
-        "axs[1].yaxis.set_major_formatter(FuncFormatter(lambda x, p: format(int(x), ',')))"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 785
-        },
-        "id": "0iN6qHYtAhgU",
-        "outputId": "14de8082-f192-4b8d-9647-7d3c514dd99c"
-      },
-      "execution_count": 23,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1600x800 with 2 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 1100x375 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(11, 3.75))\n",
+    "\n",
+    "totals[\"total\"].plot(ax=ax, kind=\"bar\",stacked=True)\n",
+    "ax.set_xticklabels(\n",
+    "    labels=s.index.to_series().dt.strftime(\"%Y-%b\"),\n",
+    "    rotation=70,\n",
+    "    rotation_mode=\"anchor\",\n",
+    "    ha=\"right\",\n",
+    ")\n",
+    "ax.yaxis.get_major_formatter().set_scientific(False)\n",
+    "ax.set_ylabel(\"# observations\")\n",
+    "ax.grid(axis=\"y\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kdp3jyaFxcbL"
+   },
+   "source": [
+    "Group by source, then by month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 455
     },
+    "id": "3YcdQVCtxSv4",
+    "outputId": "cc5f344a-6af1-44d6-999f-9c5e03cae3c1"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [
-        "df_total = df_met + df_wave\n",
-        "df_total"
-      ],
-      "metadata": {
-        "id": "FinAC94_cNt5",
-        "outputId": "99320f7f-49d5-4839-e5c3-e5954cce2565",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 455
-        }
+     "data": {
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "summary": "{\n  \"name\": \"totals\",\n  \"rows\": 222,\n  \"fields\": [\n    {\n      \"column\": \"time (UTC)\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 74,\n        \"samples\": [\n          \"2018-May\",\n          \"2023-Apr\",\n          \"2019-Jul\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"source\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"IOOS\",\n          \"NDBC\",\n          \"non-NDBC\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"met\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1646132,\n        \"min\": 370334,\n        \"max\": 4704766,\n        \"num_unique_values\": 222,\n        \"samples\": [\n          4366426,\n          4704766,\n          843884\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wave\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 63174,\n        \"min\": 44916,\n        \"max\": 393144,\n        \"num_unique_values\": 222,\n        \"samples\": [\n          214430,\n          393144,\n          81494\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1683006,\n        \"min\": 476930,\n        \"max\": 5097910,\n        \"num_unique_values\": 222,\n        \"samples\": [\n          4580856,\n          5097910,\n          925378\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+       "type": "dataframe",
+       "variable_name": "totals"
       },
-      "execution_count": 24,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "               IOOS  non-NDBC     NDBC\n",
-              "time (UTC)                            \n",
-              "2018-Jan     765404   4488860   530790\n",
-              "2018-Feb     738574   4028584   476930\n",
-              "2018-Mar     828930   4423008   584760\n",
-              "2018-Apr     849240   4333812   584122\n",
-              "2018-May    1046212   4502094   637926\n",
-              "...             ...       ...      ...\n",
-              "2023-Oct    1305808   5030328  1103356\n",
-              "2023-Nov    1129076   4763982  1051638\n",
-              "2023-Dec    1073466   4682292   975602\n",
-              "2024-Jan    1099192   4802138   935046\n",
-              "2024-Feb    1025814   4485938   840728\n",
-              "\n",
-              "[74 rows x 3 columns]"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-ad7d67ce-2276-41fe-b39c-09e5b34e803b\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>IOOS</th>\n",
-              "      <th>non-NDBC</th>\n",
-              "      <th>NDBC</th>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>time (UTC)</th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>2018-Jan</th>\n",
-              "      <td>765404</td>\n",
-              "      <td>4488860</td>\n",
-              "      <td>530790</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2018-Feb</th>\n",
-              "      <td>738574</td>\n",
-              "      <td>4028584</td>\n",
-              "      <td>476930</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2018-Mar</th>\n",
-              "      <td>828930</td>\n",
-              "      <td>4423008</td>\n",
-              "      <td>584760</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2018-Apr</th>\n",
-              "      <td>849240</td>\n",
-              "      <td>4333812</td>\n",
-              "      <td>584122</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2018-May</th>\n",
-              "      <td>1046212</td>\n",
-              "      <td>4502094</td>\n",
-              "      <td>637926</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2023-Oct</th>\n",
-              "      <td>1305808</td>\n",
-              "      <td>5030328</td>\n",
-              "      <td>1103356</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2023-Nov</th>\n",
-              "      <td>1129076</td>\n",
-              "      <td>4763982</td>\n",
-              "      <td>1051638</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2023-Dec</th>\n",
-              "      <td>1073466</td>\n",
-              "      <td>4682292</td>\n",
-              "      <td>975602</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2024-Jan</th>\n",
-              "      <td>1099192</td>\n",
-              "      <td>4802138</td>\n",
-              "      <td>935046</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2024-Feb</th>\n",
-              "      <td>1025814</td>\n",
-              "      <td>4485938</td>\n",
-              "      <td>840728</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>74 rows × 3 columns</p>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ad7d67ce-2276-41fe-b39c-09e5b34e803b')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-ad7d67ce-2276-41fe-b39c-09e5b34e803b button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-ad7d67ce-2276-41fe-b39c-09e5b34e803b');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-7a317ba2-1d16-4604-bf2f-977efe334858\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7a317ba2-1d16-4604-bf2f-977efe334858')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-7a317ba2-1d16-4604-bf2f-977efe334858 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df_total",
-              "summary": "{\n  \"name\": \"df_total\",\n  \"rows\": 74,\n  \"fields\": [\n    {\n      \"column\": \"time (UTC)\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 74,\n        \"samples\": [\n          \"2018-May\",\n          \"2023-Apr\",\n          \"2019-Jul\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IOOS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 172276,\n        \"min\": 575436,\n        \"max\": 1394504,\n        \"num_unique_values\": 74,\n        \"samples\": [\n          1046212,\n          1107356,\n          1261294\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"non-NDBC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 330148,\n        \"min\": 3046400,\n        \"max\": 5097910,\n        \"num_unique_values\": 74,\n        \"samples\": [\n          4502094,\n          4613168,\n          4413372\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NDBC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 170537,\n        \"min\": 476930,\n        \"max\": 1153548,\n        \"num_unique_values\": 74,\n        \"samples\": [\n          637926,\n          936966,\n          763014\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 24
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# Create stacked chart\n",
-        "\n",
-        "IOOS + non-IOOS (non-NDBC + NDBC)"
-      ],
-      "metadata": {
-        "id": "KkFXlY_O5Rdk"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "from matplotlib.ticker import (MultipleLocator,\n",
-        "                               FormatStrFormatter,\n",
-        "                               AutoMinorLocator,\n",
-        "                               FuncFormatter)\n",
-        "\n",
-        "color = {'IOOS':'C0',\n",
-        "         'non-IOOS': 'C1'}\n",
-        "\n",
-        "fig, axs = plt.subplots(nrows=2,ncols=1,figsize=(16,8))\n",
-        "\n",
-        "df_met = pd.DataFrame({'IOOS': totals.loc[totals['source']=='IOOS','met'],\n",
-        "                       'non-IOOS': totals.loc[totals['source']=='NDBC','met']+totals.loc[totals['source']=='non-NDBC','met'],\n",
-        "                         },\n",
-        "                         index= totals.index.unique())\n",
-        "\n",
-        "df_met.plot.bar(stacked=True,\n",
-        "                  xlabel='',\n",
-        "                  ax=axs[0],\n",
-        "                  rot=90,\n",
-        "                  title='met',\n",
-        "                  color=color)\n",
-        "\n",
-        "axs[0].get_legend().remove()\n",
-        "\n",
-        "axs[0].grid(axis='y')\n",
-        "\n",
-        "axs[0].yaxis.set_major_formatter(FuncFormatter(lambda x, p: format(int(x), ',')))\n",
-        "axs[0].axes.get_xaxis().set_visible(False)\n",
-        "\n",
-        "df_wave = pd.DataFrame({'IOOS': totals.loc[totals['source']=='IOOS','wave'],\n",
-        "                         'non-IOOS': totals.loc[totals['source']=='NDBC','wave']+totals.loc[totals['source']=='non-NDBC','wave'],\n",
-        "                         },\n",
-        "                         index= totals.index.unique())\n",
-        "\n",
-        "df_wave.plot.bar(\n",
-        "    stacked=True,\n",
-        "    xlabel='',\n",
-        "    ax=axs[1],\n",
-        "    title='wave',\n",
-        "    color=color)\n",
-        "\n",
-        "axs[1].legend(loc='center',\n",
-        "              bbox_to_anchor=(0.5,-0.35,0,0),\n",
-        "              ncol=3)\n",
-        "\n",
-        "axs[1].grid(axis='y')\n",
-        "\n",
-        "axs[1].yaxis.set_major_formatter(\n",
-        "    FuncFormatter(lambda x, p: format(int(x), ','))\n",
-        "    )\n"
+      "text/html": [
+       "\n",
+       "  <div id=\"df-48bcdc62-7912-4c3d-ba5d-b39acf57c1fd\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>source</th>\n",
+       "      <th>met</th>\n",
+       "      <th>wave</th>\n",
+       "      <th>total</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time (UTC)</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-Jan</th>\n",
+       "      <td>IOOS</td>\n",
+       "      <td>701780</td>\n",
+       "      <td>63624</td>\n",
+       "      <td>765404</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-Feb</th>\n",
+       "      <td>IOOS</td>\n",
+       "      <td>676688</td>\n",
+       "      <td>61886</td>\n",
+       "      <td>738574</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-Mar</th>\n",
+       "      <td>IOOS</td>\n",
+       "      <td>759916</td>\n",
+       "      <td>69014</td>\n",
+       "      <td>828930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-Apr</th>\n",
+       "      <td>IOOS</td>\n",
+       "      <td>773482</td>\n",
+       "      <td>75758</td>\n",
+       "      <td>849240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-May</th>\n",
+       "      <td>IOOS</td>\n",
+       "      <td>890444</td>\n",
+       "      <td>155768</td>\n",
+       "      <td>1046212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-Oct</th>\n",
+       "      <td>non-NDBC</td>\n",
+       "      <td>4699234</td>\n",
+       "      <td>331094</td>\n",
+       "      <td>5030328</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-Nov</th>\n",
+       "      <td>non-NDBC</td>\n",
+       "      <td>4514266</td>\n",
+       "      <td>249716</td>\n",
+       "      <td>4763982</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-Dec</th>\n",
+       "      <td>non-NDBC</td>\n",
+       "      <td>4457820</td>\n",
+       "      <td>224472</td>\n",
+       "      <td>4682292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-Jan</th>\n",
+       "      <td>non-NDBC</td>\n",
+       "      <td>4584106</td>\n",
+       "      <td>218032</td>\n",
+       "      <td>4802138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-Feb</th>\n",
+       "      <td>non-NDBC</td>\n",
+       "      <td>4271722</td>\n",
+       "      <td>214216</td>\n",
+       "      <td>4485938</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>222 rows × 4 columns</p>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-48bcdc62-7912-4c3d-ba5d-b39acf57c1fd')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-48bcdc62-7912-4c3d-ba5d-b39acf57c1fd button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-48bcdc62-7912-4c3d-ba5d-b39acf57c1fd');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-e82d6f1b-6311-4611-9036-d06ace3fb249\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e82d6f1b-6311-4611-9036-d06ace3fb249')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-e82d6f1b-6311-4611-9036-d06ace3fb249 button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "metadata": {
-        "id": "nZFZPUtm2vGs",
-        "outputId": "c9e3312d-26a8-4de4-f8b2-fa5e87cb551a",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 785
-        }
-      },
-      "execution_count": 25,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1600x800 with 2 Axes>"
-            ],
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DVl26dMGyZcswaNAg0/vezp07MWDAADRu3BiTJk2y+dw//fQTAKBx48aYNWuWxftCXFwc1q9fj19//RXHjh1z+DpiY2Oxdu1azJ8/HytWrEBwcDDeeOMNtGnTRvMPNtmzZ8dPP/1keq9IsHfvXjRs2NDhrHtX92liS5cuRcuWLVGmTBmLfmTXrl1YsGABmjZt6rCMRYsWoV+/fujVq5fVjGj5oWjYsGE4fvw4pk6dCl9fX4ePT+rGjRuYM2cOZs2ahUOHDuHVV19F586d0bhxY/j7+2sqw9V+efPmzZrrWbVqVYePqV69Ovbu3Yu4uDhTP3D8+HH4+PigUKFCOHbsGLy8vLBt2zYULlzYahktWrTAX3/9hXHjxlnkpEePHihRogQWLFhgdbuEL8i9evXC4MGDERISYrov4VTTs2fP4q+//tL0eg8dOoS5c+diwYIFuHjxIl577TW0adMGjRs3RnBwsMPtIyIisGHDBrz88ssWtx88eBA1a9bUtHyC3r7EU/oRQH9f4in9COB6X+Jp/QjAjCTGjJgxI2ZGyIhVQqkiODhYjhw5oquM7Nmzy7hx45LdPn78eMmWLZumMiZNmiRZsmSRPn36yLx582TFihUWf46UKFFCQkJCJCAgQAoUKCAlS5a0+HPWlStX5Ouvv5aXX35Z/Pz8pEGDBrJ8+XKJi4uzu11wcLCcOHHC6ec7e/as5j8tPv74Y+nSpYtFfePi4uS9996Tfv36SXx8vLz99ttSqVIlq9sHBgbK3r17k92+e/duCQoKsvvcvXr1kl69eom3t7e88847pv/36tVL3n//fSlXrpxUrFhR0+tI6sKFCzJixAgpVKiQ+Pj4aNomODhYDhw4kOz2AwcOOHwtCdt7QkZE1OaEGWFGEqRLl062bdvmdF0TK1q0qPzvf/+T2NhY022xsbHy9ttvS9GiRTWV4eXlZfrz9vY2/SX8X4v33ntPQkNDpXTp0vL2229b7JtevXppfj3Pnj2TpUuXSoMGDcTPz0+KFCkio0aNkmvXrjncNiQkRP766y/Nz+Uu8+bNk2rVqsnJkydNt504cUJeffVVWbBggVy4cEEqVaokTZs2tbp9aGiorFmzJtntq1evltDQULvPnXg/Jt6vXl5e4u/vLwUKFJCVK1c6/ZoePHggP/zwg9StW1f8/f0lT548mrYLCAiw+r514sQJCQgIcLi9in2aJ08e+fzzz5Pd/sUXX2h+HUnbMnEba81I48aNJW3atJI1a1apVauWvP766xZ/ztizZ4+89957Eh4eLuHh4dK9e3fZt2+fw+1U9MsqjBkzRpo0aSJ379413Xbnzh1p1qyZjB07Vh48eCCNGjWSWrVq2SwjODhYtm7dmuz2LVu2SHBwsM3tcuXKJbly5RIvLy/JkSOH6f+5cuWSAgUKSK1ateT333936XVt27ZNunbtKhEREZI2bVpN2wQGBsrRo0eT3X7kyBEJDAzUVIbevsTT+hER1/sST+lHRFzvSzytHxFhRqxhRpiRxIyQEWuc/2mYlChcuDBu3Lihq4w7d+6gTp06yW6vVatWstNZbEk4fXL06NHJ7tMyU6tx48aankerzJkzo3Llyjh+/DiOHz+OgwcPokOHDkifPj1mzJiBatWqWd2uXLlyOHnyJPLly+fU8+XMmVNBrc2mTZuG7du3w9vbvByut7c3unfvjooVK2LYsGF477338Morr1jdPkeOHHj27Fmy2+Pi4pAtWza7z50wi0BEcPDgQYuZGv7+/ihevDg++OADp1/Ts2fPsHv3bvzxxx84e/YsMmfOrGm76OhoTJkyBePGjbO4ffLkycl+ObLGUzICqM0JM8KMJEifPr2uUzsA4OTJk1iyZInFKS8+Pj7o3bs3Zs+erakMFWsB/v333yhVqhSA5zO0EtN62hLwfH3EJk2aoF69epg4cSL69euHDz74AJ988gmaN2+Or776ClmzZrW6bY4cOWzOYNbK0ZqvkZGRDsv47LPPsHTpUuTNm9d0W758+fD111+jadOmOH36NEaMGGFzRmFAQIDVmcW5c+d2OIMv4bSj3LlzY9euXU6fUmtLcHAwateujdu3b+PcuXOa157Mly8f1q5di/fee8/i9oQZ7o6o2KeXL19G+/btk93etm1bjBw5UlMZKtZlDAsL0zSLVItSpUohS5YsCA8Px/DhwzF9+nRMnDgRFSpUwOTJk1GkSBGr26nolx2tEa1l9sbIkSPx66+/WsxkSpcuHQYMGIBatWqhR48e+OKLL1CrVi2bZYSHh1s9oyFdunSmWT7WJOzL6tWrY9myZXYf66w0adIgKCgI/v7+uH//vqZtXn75ZSxcuBBffPGFxe0LFiywOas1Kb19iaf1I4DrfYmn9COA632Jp/UjADNiDTPCjCRmhIxY5fIwKemyfv16qVChgmzcuFFu3Lghd+/etfjTolWrVjJixIhkt48cOVJatGihuspudeXKFRk5cqQULlxYAgMDpWXLlvLrr7+KiMi///4rH330kURGRtrcftmyZVK4cGGZMWOG7N69W/bv32/xp8WsWbPs/mkRFhZmdbbfihUrJCwsTEREjh8/bvp3UsuXL5fo6GjZtWuX6bZdu3ZJ+fLl5ccff9RUhzfffFPzMWTPhg0b5K233pL06dNLunTppGPHjvLbb79JfHy8pu23bdsmgYGB8sorr8iAAQNkwIAB8sorr0hgYKBs2bLF4fbMiCVm5DlmxGzOnDnSrFkzefDggcuvoWLFilbb7ccff5Ry5cq5XG5q2bVrl3Tp0kXSp08vL730knz66ady+vRp2bJli9SoUUPKli1rc9tffvlFatWqJWfOnHH5+ZPOuEj6p0VQUJDF8Z3gzz//NM34PXPmjKRJk8bq9gMHDpRWrVrJ48ePTbc9fvxY2rRpIwMGDHDhVbkuYZZBTEyM+Pv7S968eeWzzz7TPDtw2rRpEhQUJF988YVs2rRJNm3aJJ9//rkEBwfLlClTHG6vYp/GxMTI9OnTk90+ffp0uzMHjejp06eyePFiiYmJEV9fXylfvrx8//338u+//8qZM2ekTZs2EhUVZXN7Ff2yrVmuzmQkTZo0snHjxmS3b9y4UUJCQkRE5NSpU3ZnR3733XdSs2ZNuXz5sum2y5cvS61atWTy5Mma6qHC6dOnZciQIVK4cGHx8fGRV199VaZOnSp37tzRtP1PP/0kvr6+0r59e5k5c6bMnDlT2rVrJ76+vpr7RL19iaf1IyKu9yWe0o+IGKcvSe1+RIQZsYYZYUYSM2pGONiZSqx9wNMyFf2bb74x/Q0ePFjSpUsndevWlcGDB8vgwYOlXr16EhYWJoMHD07BV/P8DW/27Nkye/Zs2b17t1Pb1q9f3zT9fcyYMXLz5s1kj7l69ap4eXnZLEPFKWJhYWEWf2nSpBEvLy8JCAiQ9OnTayqje/fukjFjRhk9erRs3bpVtm7dKqNHj5aMGTPK+++/LyIi33//vc1TdMPCwsTf31+8vb3F39/f4t/p06e3+LPlzp07Vtvw5s2bmr+MZMuWTQIDA6Vx48ayePFiizdxZ/z111/SqlUrKVy4sJQuXVo6duwox48f17Stp2VExPWcMCOWdWBGnitRooSkTZtWQkJCpGjRopqXR0g8yL1gwQKJjIyUkSNHmvbHyJEjJVeuXLJgwQKnXsuDBw/kyJEjLg2kJ3b+/Hk5f/68U9uMGjVKihYtKn5+ftKoUSNZuXJlsqUdLly4YHeJgcTHVkhIiObjKbF9+/ZZ/O3atUumTJkihQoVkqVLl2oqo27dulKqVCmL5Rr27t0rpUuXlnr16onI8wEOW6f1JJzunDFjRqlRo4bUqFFDMmbMKKGhoZpPfe7evbt88803yW4fN26c9OjRQ9PraNGihaRJk0YiIiKkW7dusmPHDk3bJTVx4kTJnj276X0rd+7cTv244so+TbxUyaRJk0yvYc6cOTJnzhzp1q2bZMqUSSZNmuTUazl06JD8/PPPLi2HkuDq1auyZcsW2bJli1y9elXzdgmnrWfIkEF69OghBw8eTPaYy5cva+pHnO2XE7tz547F3/Xr12XdunVSrlw5+e233zSV0bp1a8mdO7csW7ZMLly4IBcuXJBly5ZJnjx5pG3btiIiMn/+fCldurTNMhKWl/Hz85O8efNK3rx5xc/PT0JCQjS9lzZp0kSGDx+e7PavvvpKmjVrpul1lCtXTry9vaVEiRIycuRIuXjxoqbtklq1apVUrFhRgoODJTw8XKpXry6bNm3SvL0rfYkn9iMi+vsST+lHRPT3JZ7Sj4gwI4kxI2bMiJmRMpIYL1CUShwt1m5rgfbcuXNrKt/Ly0vzlXgfPHiAzZs34/z583j69KnFfUmvypWUiisjd+7cGW+99ZZpYV9rRATnz5+3eUrtuXPn7D6Hq6finjhxAl26dMGHH36I2rVrO3x8XFwchg8fjvHjx5sWhc+cOTO6d++Ojz/+GD4+Pjh//jy8vb2tto2KxZf1XuEZAL7//nu88cYbpn2aGjwlI4D+nDAjZsyI2cCBA+3e379/f6u3O7roVAKtyzRcv34dHTt2xM8//2z1fi1l6L2iZf78+dGpUye8+eabNk9Tf/r0KebPn2/zuHB0bOm5SNnq1asxcuRIbNq0yeFjr1y5gnbt2mH9+vWm1x0bG4saNWpgzpw5yJw5MzZu3Ihnz55ZPU23Y8eOmus1Y8YMq7erWKy+TZs2aNOmjUtXBrXm+vXrCAoKsrggjCOu7tPEy2zYozUjp0+fxuuvv46DBw9aZC/htEEtZdy7dw/dunVz+erhNWrUwFtvvYUmTZogICDA6mNiY2Oxfft2m/2rq/2yFps3b0bv3r2xZ88eh4/9999/0atXL8yePRuxsbEAnp9S2aFDB4wZMwZp0qTBvn37ADy/OJ81jt4/E7P2XqriwkCffvop2rRpo/l0c3dxpS/xxH4E0N+XeEo/AujvSzylHwGYkcSYETNmxMwIGbHK5WFS8gh79+6VLFmySGhoqPj4+EhERIR4eXlJmjRpJHfu3A63r127tpQrV85icfSjR49KhQoVpHbt2u6seorZtWuXFCxY0OntnDmtS6X06dPL4cOHk91+5MgRyZAhg9PlJcyacMXJkyfl008/lVatWplmoKxZs0b+/vtvl8pLDXozIuL5OWFGXqyMqL7oVOvWraVSpUqya9cuSZMmjaxbt07mzJkjBQsWlFWrVmkq491335VMmTLJ5MmTTb/yTp48WbJkySLvvvuunpdrCCdOnLB70RNrjhw5Ypr5Z+0CJO6kYrF6VZ49eya//vqrTJ48We7duyciIv/884/cv38/ReuhR/369aVRo0Zy/fp1CQkJkcOHD8vWrVslOjpa05IVIiLNmzeX/Pnzy9q1a03vnWvXrpWCBQu+cMuyWHPkyBG7pwtac//+fdP7RUofDyouDJRYfHy85iVQkrp9+7Z8//330q9fP9NZC3v27HF5pqgW7EdSHvsR16VGP8KMpDxmxHWekBFrOLMzlT18+NDqbLFixYppLuPp06c4c+YM8ubNC19f5645Va1aNRQoUACTJ09GunTpsH//fvj5+aFt27bo0aMHmjRpYnf7oKAg7NixAyVLlrS4fc+ePXjllVfw8OFDTfXQO3PO0cK11i4uoNW+fftQpUoV3Lt3z+UynHHq1CnMmDEDp06dwjfffINMmTLh559/RmRkpM2LBiSWJk0a/P7771ZnG5QrV07TPomPj8eQIUMwatQo0y9/adOmRZ8+ffDpp59qmvWyefNmxMTEoFKlStiyZQuOHDmCPHnyYPjw4di9ezeWLFnisAzgxc8IoCYnzIgZM2IsWbNmxYoVKxAdHY3Q0FDs3r0bBQoUwE8//YQRI0Zg27ZtDstIly4dFixYgJiYGIvb16xZg1atWuHu3bua6qLn/ULFgvdJMyAiuHz5MgYMGICjR4+aZpu5W2xsLDZt2oRTp06hdevWSJs2LS5duoTQ0FBNv9YXLVoU7777brLF6seNG4dJkybh8OHDmuqxefNmfP3116ZF8gsXLowPP/zQ5gXIkjp37hzq1KmD8+fP48mTJzh+/Djy5MmDHj164MmTJ5g8ebKmcgDg8ePHyY6LxBe40VpGYGCgU9sAQMaMGbFhwwYUK1YM6dKlw59//omCBQtiw4YN6NOnj+niafakSZMGv/zyCypXrmxx+9atW1GnTh08ePBAU10OHz5sNSMNGzZ0uK2KiwsdOHDA4v8JGRk+fDhiY2M1vV+ocufOHSxZsgSnTp3Chx9+iAwZMmDv3r3InDkzsmfPbnfb6Oho1K9fP9mFgQYMGICVK1dqmqEKPO+bR44ciRMnTgAAChQogA8//BDt2rXTtP2BAwdQs2ZNpEuXDmfPnsWxY8eQJ08efPbZZzh//ry+CzukICP1I4DrfYkn9SOAvr7EE/uR1MSMmDEjlpgRO5QMy5LTrl27JvXq1dO1sO6DBw+kU6dO4uPjIz4+PnLq1CkReb4u05dffqmpjHTp0pl+xUiXLp1pttPvv/+uaaZW/vz55Y8//kh2+x9//CF58+bVVAcVM+dUrCWYdA2t5cuXy6RJk6RIkSJSp04dTWXkypVLcufObfPPkU2bNklQUJDUrFlT/P39Tfv0yy+/lKZNm2qqQ7Vq1eS9995LdnvXrl2lcuXKmsro27evREREyMSJE02//E2YMEEiIiLkk08+0VRG+fLlZdSoUSIiEhISYnotf/zxh2TPnt3h9p6SERH9OWFGzJgRs9jYWBk5cqSULVtWMmfO7NK6RyIis2fPlooVK0rWrFlNv56OGTNGli9frmn7tGnTmhaaj4yMlG3btonI8wtvJCzy7khERITV2baHDx+WjBkzOtz+2rVrUrduXV3vFyoWvLe1nmFkZKTmdZQ6duxo98+Rs2fPSqFChSQ4ONjife/999+Xd955R1MdVC1W7+vrK82bNzeto9y8eXPx8/OTuXPnaiqjUaNG0rZtW3ny5IlFRjZu3Cj58uVzuP2///4r3bp1k4iICJf3aWxsrAwaNEiyZctm0Z6fffaZTJ06VVMZYWFhcvr0aRERyZMnj2zYsEFEns/s1pqRHDlyyIEDB5Ldvn//fk3vF6dOnZJixYpZrNec+HjVQsXFhZI+f8JfhQoVNF9MoVq1alK9enWbf1rs379fIiIiJF++fOLr62var59++qm0a9fO4fYqLgw0atQoCQ4Olo8++sjUr3744YcSHBwso0eP1lRGjRo15MMPPxQRy35k+/btkjNnTk1lqOhLPKEfEdHfl3hKPyKivy/xlH5EhBlJjBkxY0bMjJARazjYmUpUTEV///33pXTp0rJ161ZJkyaN6cBcvny5lChRQlMZGTNmNF0MI+H0KJHnU7q1TANXcWXkqlWryv/+9z+Ji4szBez8+fNSpUoVzYsMW3P8+HGpUaOG6TU5Yu0DfObMmaVVq1Zy6dIlTWWMHTvW4m/kyJHSunVryZAhg6bBNb2DHyL6r/AsIpI1a1arF01Yvny5ZMuWTVMZadKkMX25S/xazpw5o2lqvqdkJOH59OSEGTFjRsw+//xzyZo1q3z99dcSGBgogwcPls6dO0t4eLjVBc+tmThxomTMmFGGDBkiQUFBpjrMmDFDqlWrpqmMMmXKmI6hBg0aSLt27eTixYvy0UcfSZ48eTSVofeKlireL1QseJ/wYTXhb8uWLXLkyBF59uyZpu1Fni94n/ivXr16kjNnTkmXLp3diwolUPGhVUT/YvWFChWyOmAzatQoKVSokKYyMmTIYPqxKWlGtHy569q1q0RFRcmSJUskKChIpk+fLoMHD5aXXnpJfvjhB011GDhwoOTJk0d++OEHi4wsWLBAypcvr6mMypUrm97rW7VqJXXq1JFt27ZJ+/btpUiRIprK0Hv1cBWn0qu4uFDSU9POnz8vjx490rRtgp49e1r8devWTSpVqiTp0qUzXejOERWDhHovDJQrVy6rmZo5c6bkypVLUxmhoaFy8uRJEbF8HWfPntV8GqTevsRT+hER/X2Jp/QjImr6Ek/oR0SYkcSYETNmxMwIGbGGg52pJEuWLKaZXmnTppVjx46JyPOZU7auQpxUZGSk7Ny5U0QsD8wTJ05I2rRpNZXx2muvmUb933rrLYmOjpYffvhBateuLdHR0Q63V3FlZBUz52xxdS1B1caPHy9vvvmmw8fpHfxIoOcKzyLP1xBJOCYTO3r0qOa1qLJnzy7bt28XEcvXknDFVEc8JSMi+nPCjJgxI2Z58uQxfbAMCQkxfeH95ptvpFWrVprqEBUVZRqESVyHgwcPSnh4uKYy5syZIzNmzBARkd27d0vGjBnF29tbAgMDNV9BUe8VLVW8X9iyatUqqVq1qq4y9IqLi5O3335bvvrqK4ePVfGhNbFr1665tGaTv7+/7rWowsLC5NChQyJi+Vq2bt0qmTJlcrh9jhw5ZOPGjSLy/LhIqM/s2bMlJiZGUx3y5s1rGshLXIcjR45IWFiYpjLWrl1r+hJ34sQJKViwoHh5eUnGjBll/fr1msrQe/Xw8PBw0xV7Q0NDTcfI+vXrNf/4Z8umTZukVKlSuspQoX///tKnTx9Nj1UxSKiXrfXajh8/rrkOERERpqsJJ34d69atk5deeklTGXr7Ek/pR0Tc15e8aP2IiNq+5EXuR0SYkcSYETNmxMwIGbHGucXrSJkHDx4gU6ZMAID06dPj+vXrKFCgAF5++WXs3btXUxnXr183lZG07IQrfDoybNgw3L9/HwAwdOhQtG/fHl26dEH+/Pkxffp0h9uPHTtW0/PY4+fnZ1rfLlOmTDh//jyioqKQLl06XLhwQVfZvr6+uHTpku466hUTE4N+/frZvPJtgrCwMFy+fDnZFcX/+usvh+tHJVaiRAnMmzfPpboCQPHixTF+/Hh8++23FrePHz8exYsX11RGy5Yt8fHHH2Px4sXw8vJCfHw8tm/fjg8++EDT+pCekhFAf06YETNmxOzKlSumdUdDQkJMay3Vr18fn3/+uaY6nDlzJtlasgAQEBCgeQ3Atm3bmv5dunRpnDt3DkePHkVkZCQyZsyoqYywsDA0bdrU4rYcOXJo2hZQ835hS8GCBbFr1y67j/npp58cluPr64ssWbKgaNGi8Pf3d6oO3t7e6N27N6pVq4aPPvrI7mPj4+OtXrXy4sWLSJs2rebnPHDgAI4fPw7geRskXePWkRw5cmD9+vXIly+fxe2//fab5n1bq1YtjB07FlOmTAHw/Iqc//77L/r374+6des63P7WrVvIkycPgOfrc966dQsAULlyZXTp0kVTHf75559krwF43s7Pnj3TVEbt2rVN/86XLx+OHj2KW7duIX369Jr7osaNG2t6nC1xcXGm/Z8xY0ZcunQJBQsWRM6cOXHs2DFdZWfOnNlhGUnfK61JyEjlypWt9t2OtG3bFtHR0fj6668dPjYgIMDqOtPHjx9HRESEw+0fPXqEX3/91SIjNWvWRFBQkOb65suXD4sWLcInn3xicfvChQuRP39+TWU0bNgQgwYNwqJFiwA8z8j58+fx8ccfJ3tPtUVvX+Ip/Qjgvr7kRetHADV9iSf0IwAzkhgzYsaMmBkhI9ZwsDOVFCxYEMeOHUOuXLlQvHhxfPfdd8iVKxcmT56MrFmzaiqjTJkyWL16Nbp37w4Apg/MU6dORYUKFTSXkSBTpkxYu3atU6+jQ4cONu+7desWMmTI4LCMkiVLYteuXcifPz+qVq2KL774Ajdu3MCcOXNQtGhRTfVI+uYn/79Q8fjx41GpUiW72/bu3dth+QlvnDVq1NA8kJHYkiVLNLWF3sEP4PkXs6VLl1q8cTZt2hTZsmXTXN8RI0agXr16+O2330zH0s6dO3HhwgWsWbNGUxnDhg1Dt27dkCNHDsTFxaFw4cKIi4tD69at8dlnnznc3lMyAujPCTNixoyYvfTSS7h8+TIiIyORN29erFu3DqVKlcKuXbsQEBCgqQ65c+fGvn37kDNnTovb165di6ioKE1lJBUcHIxSpUo5tY2jQW5HVLxf2Fvw3tHAgzMDUVmyZMHChQs1Lxyf4NSpU4iNjXX4OL0fWv/880907twZhw8fhvz/NSy9vLxQpEgRTJs2DWXLltVU3z59+uD999/Hvn37ULFiRQDA9u3bMXPmTHzzzTeayhg1ahRq166NwoUL4/Hjx2jdujVOnDiBjBkzYv78+Q63z5MnD86cOYPIyEgUKlQIixYtQnR0NFauXImwsDBNdShcuDC2bt2aLCNLliyx+sFcKy3vd4n179/f5n1xcXHw8fGxu33RokWxf/9+5M6dG+XKlcOIESPg7++PKVOmmAaEHbF3caESJUrY3XbMmDEOy4+Pj8fNmzcRHx+PH374QdMFABPbuXOn5gtI6Rkk/Omnn/DWW2/hxo0bFrdnzJgR06ZNQ4MGDTTVYeDAgWjRogW2bNli6oe3b9+O9evXm+rlyKhRo9CsWTNkypQJjx49QtWqVXHlyhVUqFABQ4cO1VSG3r7EU/oRQH9f4in9CKCvL/GkfgRgRhJjRsyYETMjZgQAeDX2VPLDDz8gNjYWb775Jvbs2YM6derg1q1b8Pf3x8yZM9GiRQuHZWzbtg0xMTFo27YtZs6ciXfeeQeHDx/Gjh07sHnzZpQuXToFXkly69atw9SpU7Fy5Uo8evTI4eN3796N+/fvo3r16rh27Rrat2+PHTt2mGbOaRk4SXrlYy8vL0RERODVV1/FqFGj7L75Vq9e3WH58fHxuHbtGo4fP45x48aha9euVh9XsmRJi1kaIoIrV67g+vXrmDhxIt5++227z/P06VN069YNM2fORFxcHHx9fU2DHzNnznT4ZWbixIno3bs3nj59arrK7L179+Dv74/Ro0fbrLc1ly5dwoQJE3D06FEAQFRUFLp27erUgBDw/Kp7f//9N/7991+ULFlS82wFT84I4FxOmBEzZsSsb9++CA0NxSeffIKFCxeibdu2yJUrF86fP49evXph+PDhDsuYOnUqBgwYgFGjRqFz586YOnUqTp06hS+//BJTp05Fy5YtbW47aNAgTfVMerVire7du4e5c+di2rRp2L17t93Hqni/8Pb2TjbLTkSQI0cOLFiwQPMPJLaICK5evYohQ4Zgx44dNmdAJP1xIeGLwOrVq9GhQweMHz/e7vNcvHgRtWvXhojgxIkTKFOmjOlD65YtW+zOmDt8+DDKlSuHqKgo9OrVy/QB8/DhwxgzZgyOHTuG33//HYULF9b0mn/88UeMGjXKdIXQqKgofPjhh2jUqJGm7YHnVzpdsGABDhw4gH///RelSpVCmzZtNM2gGzNmDHx8fPD+++/jt99+Q4MGDSAiePbsGUaPHo0ePXo4LGPFihXo0KED+vXrh0GDBmHgwIE4duwYZs+ejVWrVuG1116zuW2nTp00vUatZwkkdfz4cUybNg2zZ8/G5cuX7T72l19+wYMHD9CkSROcPHkS9evXx/HjxxEeHo6FCxfi1Vdfdfh8CRlJ+tWhfPnymD59OgoVKuTS60gsPj4ew4cPx5w5c0zHTVJJB0ETMrJ79258/vnndgeGE9y9exfNmjUz9a/ZsmUzDRKuWbMGadKksbrdjh07UK1aNTRs2BB9+vSxyMioUaOwatUqbN68GeXLl9f0evfs2YMxY8ZYZKRPnz5OD6Rv27bNIiM1a9bUvK3evsRT+hFAf1/iKf0I4Hpf4mn9CMCMJMaMmDEjZqmZEXs42GkQDx8+dHoqOvD814fhw4dj//79pgPz448/djgFWsuHWgDYsGGDpsedO3cO06dPx6xZs3D79m3ExMSgadOmeOONNzRt/6KYNWsWBg0ahFOnTlm9f+DAgRb/9/b2RkREBKpVq+bUl4ALFy7g4MGDTg1+rF69Go0aNULPnj3Rp08f0+DV5cuXMXLkSIwbNw4rVqzQPB3daF70jAD/jZwwI6ln586d2LlzJ/Lnz695ZhEAzJ07FwMGDDDts2zZsmHgwIHo3Lmz3e28vb2RLVs2ZMqUKdngRwIvLy+nT2vauHEjpk+fjmXLliFdunR4/fXXMWHCBKfKcOX9YvPmzRb/Tzg28+XLB19fdSfCnD17FoUKFcLjx4+t3p/0x4WEerz66qvo1KmTprrExsZi4cKFFu97Wj60Nm/eHLGxsVi6dKnVLyNNmjSBn5+f5llnRnPu3Dns2bMH+fLlQ7FixTRvt3XrVgwaNMiiPb/44gvUqlXL7nbe3t7ImTMnSpYsaTMjwPMvKlo9fPgQCxcuxPTp07Fz506UKVMGTZs2xYcffqi5jATOnkp/7tw5i/8nHJtaZ1Nq9c8//6BEiRK4fv261fs7duxotR6vvvqqw32S1Pbt2y32q6NBwrp16yJHjhz47rvvrN7/zjvvODXD34hc6Us8sR8BnO9LPKkfAVzrSzy9HwGYkcSYEWbEmpTMiD0c7HwBbdiwAVWqVNH1hpDwAbxevXrw8/Oz+Th7px09ffoUy5Ytw9SpU7F9+3bUrFkTP//8M/766y+n15t4UVy/fh116tTBnj17nN5W62n9icXGxuLx48cICQlx+Nhq1aqhcuXKGDJkiNX7P/vsM2zbtg2bNm2yWcb58+c11SsyMtLu/Q8ePMBXX32FZcuW4ezZs/Dy8kLu3LnRrFkzfPDBBwgODtb0PK4ySkaA/15OmJHnjJ6R+Pj4ZLN9Hz58iH///VfzWnn16tXDhg0bULt2bXTq1An169dPVqZW//zzD2bOnIkZM2bgzp07uH37NubNm4fmzZtrHogxmu3bt6NMmTJWT9+5e/cu0qVLlwq1si8iIgI///yzxfIdie3atQt169a1OQil2oYNGywykidPHjRt2hRVqlRx+3OfPn0auXPn1nX8devWDfPnz0fOnDnRsWNHtG3b1un3uAS///47pk6disWLFyMyMhJHjhzBxo0bnT5Fz0jmz5+Phg0b2pxF6Qotp/TrkSFDBmzevNlm/33gwAFUrVoVt2/fdlsdEsTHx2PmzJlW+5F27dq5/b2T/Yj7sR/RLzX7EWbE/ZgR/V70jNjDwc4UpmLtOx8fH1y+fNl0AJQvXx5Lly516uIcI0eOxIwZM3Dz5k20adMGnTp10rz2HwB0794d8+fPR/78+dG2bVu0bNkS4eHh8PPzw/79+zVNu1Yxc85dawkOHz4c7777ruY1vezRcrryypUrcfPmTbz55pum24YOHYrBgwcjNjYWr776KhYuXIj06dPbfJ7Q0FDs2rULBQsWtHr/sWPHULZsWasL8iewdmoB8PxXpoTbvby87K5l8vTpU1SsWBF///03YmJiUKhQIYgIjhw5grVr16JUqVLYsmWLzQFET8kIoD8nzIgZM2KmYoH2pBn58MMP0a9fP6cHYi5duoRZs2Zh5syZuHfvHtq3b49OnTrZbOOkli5dimnTpmHLli2mJSdiYmKQJk0aTRlRcXqXuxa8Dw0Nxb59+zSvh2iPltPMjh8/jjt37iA6Otp02/r16zFkyBA8ePAAjRs3TnYhlKQCAwNx4sQJm4vaX7hwAfnz57c5UwKApgFCLy8vm7O/E7z77ruYMmUK0qdPjwIFCphOE7tz5w66du2KcePG2dxWxcVwkmakRYsW+Pbbb5E5c2aHZSf25MkTLFu2DNOnT8eOHTtQr149dO7cGbVq1dL05XLUqFGYPn067t69i1atWqFt27YoXry45n5Exan07rq4kMqMaD2lf+fOnbh58ybq169vum327Nno37+/KSfjxo2zucZYUFAQjh49mmxdsQTnzp1DoUKF7C5PY6sfScxRPyIiaNCgAdasWYPixYtb9CMHDx5Ew4YNsXz5crvPofe9z1P6EUB/X+Ip/Qigvy/xlH4EYEYSY0bMmBEzo2TEFg52pjAVa995e3vjypUrpoMibdq02L9/v0sh37lzJ6ZPn45FixahYMGC6NSpE1q3bm1ay84WX19ffPzxx+jbt6/F1cacGexUMXNO5VqCiel943T2dOXq1aujWbNm6NatG4Dna0K98sorGDRoEKKiovDpp58iJiYGo0ePtvmcadKkwcGDB23W+fTp03j55ZftXtFs//79Vm8XESxYsADffvstQkJCcO3aNZtlfPPNN/jyyy+xefPmZJ3w0aNHUa1aNXz66aemiwYl5SkZAfTnhBkxY0bMnPlF39YC7UkzouLD4pYtWzBjxgwsXboUL7/8Mn777TeHp02ryIje07tUtKc1et53Ejhzmtnrr7+Ol19+2fSF5MyZMyhSpAheeeUVFCpUCNOnT8fgwYPRs2dPm89XsGBBDBs2zOYFWpYsWYJPP/3U7pW37S2If/bsWXz33Xd48uSJ1SuYJvjxxx/RsmVLfPfdd+jQoYPpA33CTLYuXbpg8eLFaNiwodXtc+fObbPsBI4uhqOyH0lw7tw5zJw5E7Nnz0ZsbCwOHTrkcFZ6QkYGDRpkMWPR2X5Ez6n0KtrTGr1t6sop/TExMahWrRo+/vhjAMDBgwdRqlQpvPnmm4iKisLIkSPxzjvvYMCAAVa3L1asGHr16pXsVPoE06dPx9ixY5NdzCmxFStW2Lxv586d+PbbbxEfH2/3i+6MGTPQo0cPrFixIlk/v2HDBjRu3Bjjx4+3e+E+ve99ntKPJLwWPX2Jp/QjgP6+xFP6EYAZSYwZMWNGzIyYEQtChjVz5kzJkydPstu9vLzk6tWrpv+HhITIqVOndD3XgwcPZObMmVK2bFlJkyaN3L171+7j582bJzVr1pQ0adJI8+bNZeXKlRIbGyu+vr5y6NAhTc85YsQIiYqKkkyZMkmvXr3k4MGDul6DI7ba0xpX2vTJkycyf/58qVGjhgQGBkr9+vXFx8dHDhw44HDbiIgI2bt3r+n/vXr1ktq1a5v+v3r1asmXL5/dMsqWLSujR4+2ef+oUaOkbNmyGl6JpV9//VVKly4tadOmlf79+8u9e/fsPr5KlSoyfvx4m/d/++23UqVKFafrYY2RMyKiPyfMiBkz4pz4+Hi5fPmydOvWTUqWLJnsfndk5OHDhzJr1iyJjo6WoKAgTRl5++23JV26dFKxYkWZNGmS3Lp1S0REc0bq1q0rgYGB0qhRI1mxYoXExcXpeg22OGpPa1xt04sXL8qQIUMkb968Eh4eLt7e3rJgwQKJj4+3u91LL70kO3bsMP1/8ODBUrx4cdP/p06davF/a7744guJjIy0+l5z4MAByZkzp3z++edOvR4RkZs3b0rPnj0lICBAqlSpIjt37rT7+AYNGkjfvn1t3v/RRx9Jw4YNna5HUnFxcTJ06FApVKhQsvvckZHz58/LwIEDJXfu3JI9e3a5f/++w22GDRsm+fPnlxw5cshHH31k2jdaM9K1a1dJnz69lChRQr755hu5efOmrtdgj732tMbVNt25c6d07txZQkNDpWjRouLj4yNbtmzRtG2WLFlk165dpv9/8sknUqlSJdP/Fy1aJFFRUTa3Hz16tGTIkEFWr16d7L5Vq1ZJeHi4jBo1yolX89zRo0elcePG4uPjI+3bt5ezZ8/affxrr70mX375pc37hw4dKrVq1XK6HknZe+/zlH5EJGX6khehHxHR35f81/oRZkQdZoQZUZGRxDjYaWDXrl2TUqVKJbvd29tbrl27Zvp/2rRp5fTp07qea+vWrdKxY0cJCQmRcuXKycOHDzVtd/r0aVNgM2bMKN7e3rJ48WKnnnvHjh3y1ltvSWhoqJQtW1YmTZqk6Y3bWbba0xpng/bee+9JeHi4lC9fXsaPHy83btwQEe2dSGBgoJw7d870/7Jly8qIESNM/z979qwEBwfbLWPmzJkSFBQkEyZMkGfPnpluf/bsmYwfP16CgoJkxowZml/Tnj17pGbNmhIQECDdunWzeCOyJ2PGjPL333/bvP/gwYOSMWNGzfWw50XIiIj+nDAjzIirzpw5IwEBAcluV/nhIvHxWaZMGZkwYYLcvn1b8/YPHz6UmTNnSpUqVSQgIEAaNmwoPj4+mgf3//nnHxk2bJgUKFBAsmTJIh999JEcPXrUpdfiiK32tGbu3Lny77//ai57yZIlEhMTI2nSpJFmzZrJ8uXL5cmTJ05l5Pz586b/v/rqq/LZZ5+Z/n/y5ElJly6d3TIePXokFStWFB8fH6lTp4706tVLevbsKbVr1xYfHx+pUKGCPHr0SPNrevjwoQwZMkTCwsKkePHiVgeIrMmePbv88ccfNu///fffJXv27JrrYc/Fixet5i1pPxISEuJSP/L48WPTD1+BgYHSrFkzWb16tdNfFDdt2iTt27eX4OBgKVasmPj4+Mi2bducrkNwcLC88cYbsnbtWk1f6pxlqz2t2bp1qzx+/Fhz2V9//bUULlxYsmfPLh988IHs27dPRJz7wh4QEGCRk0qVKsmQIUNM/z9z5oyEhITY3D4uLk6aNWsmXl5eUqhQIXn99delcePGUrBgQfH29pYmTZo4tW//+ecfeeutt8TPz0/q16+v+X0vc+bM8tdff9m8f+/evZI5c2bN9XDE2nufJ/UjIinXlxi5HxHR35f8F/sREWZEJWaEGVGFg50G8OWXXzr1ZuXl5SUvv/yylCxZUkqWLCk+Pj5SpEgR0/8T/hz5559/ZOjQoZI/f37JnDmz9OnTR3PIk4qPj5e1a9fKG2+8IQEBAZI9e3bp3r27U2W4MnPOGmfb05rz58879WHVx8dHPvnkk2QzurS+cebNm1fWrl0rIiL3798Xf39/iy8xe/bs0fTloU+fPuLl5SWhoaFSsmRJKVGihISGhoq3t7f07NlT02s5efKkNG/eXHx8fKRVq1ZOv+H4+vrK5cuXbd5/6dIl8fPzc6pMT8iIiP6cGC0jsbGxmh/PjJi5IyPbtm2zOWhw586dZLd5eXnJO++8I7169ZJevXqJv7+/dOrUyfT/hD97vvrqK4mKipKIiAjp2bOn7N+/36k6W3P8+HHp27evZMuWTUJDQ6VVq1aydOlSzdtv3rxZ3nzzTUmbNq1UrFjRqR8lEnO2PZN6/PixU4M4Ivozki1bNtOH1ri4OAkNDZVVq1aZ7j98+LCEhoY6LOfJkycyfPhwKV68uAQFBUlQUJAUL15cvvzyS82vKTY2ViZNmiRZsmSRXLlyyezZs50aWAsICJB//vnH5v0XL16UwMBAzeWJPJ9p78wXIi8vL6lbt668/vrr8vrrr4uvr6/UqlXL9P+EP3u6dOki6dOnl2LFisnYsWPl+vXrTtXZmnv37snkyZMlOjra9KXImZmEZ8+elQEDBkiePHkkMjJS0+xSa5xtTxUSMpK073HmS2pkZKRs3rxZRJ4f60FBQfLbb7+Z7j9w4ICkT5/eYTkLFiyQRo0aSVRUlERFRUmjRo1k/vz5ml/LnTt35KOPPpKgoCCpUKGC5pmpCfz8/OTSpUs27//nn3/E39/fqTJFnHvv89R+RERNX6K3H3GF3n5ERE1f4qn9iAgzkoAZYUZsSemM2MM1Ow3A2bUJBg4cqOlx/fv3t3lf3bp1sXHjRtSqVQudOnVCvXr1dF25OrFbt25h9uzZmDFjhs317azZtm0bpk+fjsWLF6NIkSLYuHGjwzVIrHF1rYekV/D8888/ER8fj5IlS9pcqD7B/PnzTWtG1atXD+3atUNMTAwCAwM1rYXSr18/LF++HJ988gnWrFmDHTt24PTp06b6TJkyBbNnz8a2bdscvo7ff/8d8+fPx4kTJwAABQoUQMuWLVG+fHmH23bt2hXTpk1D9erVMXz4cJQoUcLhNkn5+PjgypUriIiIsHr/1atXkS1bNrtriCTlaRkBXMtJamcEeH418suXL8Pb2xt58uRBeHi4pu2YETMjZKRatWqaFja3d/Erb29vREZGon79+nYXkre3jqot8fHxWL16NaZNm4aff/4ZT5480bTdo0ePsHjxYkyYMAEHDx7ElStXNK2vm5QrGfn1118xZswY7Ny503SRq9DQUFSoUAG9e/dGzZo17W7/zjvvYOHChShSpAjatWuHFi1aIH369JrX1GrTpg3u3buHiRMnYvHixejfvz+uXLliutL10qVLMWjQIKf6ZVcsWrQIn332Ge7cuYNPP/0UXbp00XyhgQTe3t64evVqqmbE1pqMSc2YMcPmfQkZKVmypN28LVu2TNNzJXXw4EFMmzYN8+bNs7tOcGIXLlzAjBkzMHPmTDx9+hRHjx51uG6oNarX1Tpy5Ajq1auH06dP23zMl19+iRkzZuDx48do1aoV2rVrh6JFizq17lyXLl2wf/9+fPXVV1i+fDlmzZqFS5cumY7RuXPnYuzYsdi1a5eS12XNiBEj8NVXXyFLliwYNmwYGjVq5HQZ7uhHAOf2q6f2I4CavsSVjOzfvx8rV65EhgwZ0Lx5c2TMmNF0371799CzZ0+7FxPT248AxuhLjNqPAMxIgtTKyNSpU7F161ZUq1YNHTt2xMKFCzFgwAA8efIE7dq1c/gdkBkx85SM2N2Wg52pT8XCus7y9vZG1qxZkSlTJrsHmL2LOiTlytWZL126hJkzZ5quLte2bVt06tRJ0xuNLc6257lz59C0aVPs27cPr732GhYuXIimTZti/fr1AJ4vzP/zzz+jQIECDss6c+aM6fU8fPgQt27dwsKFC9GsWTO72z169AjvvPMOVq5ciSxZsmDKlCkWi/dWr14dderUMS2o7y7e3t4IDAxEoUKF7D7O0cU+ihYtanNgMOGiDM68cXpKRgDnc2KEjADAxIkT8dVXX+HixYsWt1eoUAHffPMNSpcurakcZsRzMuKuDyjbt29HmTJlTD8yXbt2zeHVnRNfSKxAgQLo2LEjWrdu7VR/lJiz7Tlr1iy89dZbaNasGWrXrm26YvfVq1exbt06LFmyBNOmTUO7du3slvPo0SMsWrQI06dPxx9//IHatWtj9erV2LdvH4oWLWp327Nnz+K1117DqVOn4OPjg2+//RZdunQx3d+4cWPkzp3b7gXNrEm6Pxzx9vZGUFAQWrVqZfeLj70vZt7e3nj77bcRHBxs9f6HDx/i+++/N3xG3nzzTU1XXLc3YGrN/Pnz0bBhQ9MXq2fPntm9iF3iK8Jv27YN9evXR8eOHVGnTh2nLi6QmOr23L9/P0qVKqVpn27evBnTp0/HkiVLkC9fPhw6dAibN29GpUqVHG5748YNNGnSBNu2bUNISAhmzZqF119/3XR/jRo1UL58eQwdOlRz3ZPuD0cSMlKzZk2LH9mTsjcI7u3tjZiYGJu5fPLkCdauXev0l9SUzomR+hFAbV/ibFuuW7cODRo0QP78+XH//n08ePAAixcvNl2ASuvAg55+BHBPX+Ip/QjAjKRmRsaOHYvPPvsMtWvXxs6dO9GtWzeMGTMGvXr1QlxcHEaNGoWRI0fi7bfftlsOM2IuwxMyYg8HOw1AxQHh7ACKiplvSTn764y7Zs45257NmjXDjRs38MEHH2DOnDn4559/4Ofnhx9++AHe3t7o2LEjgoKC7F6pNCkRwbp16zBt2jT89NNPyJgxI5o0aYJvv/3W1ZflktQ4LtxxbHlKRgDncmKUjHz99dcYM2YM+vXrh8DAQIwePRqtWrVC2bJlMW/ePCxduhSbN29GmTJlNNeBGdFXRlIqMuLsBy13cSYjI0aMwMyZM3Hjxg20adMGHTt2RLFixXTXwdn2LFCgAHr06IFu3bpZvX/ixIkYM2aMaTaxFidOnMD06dMxe/Zs/Pvvv6hXrx6aNWtm9yrXCQPlERERyJYtm8V9+/fvx0svvaR5NnaC1Jg1rKUM4PkVVLVSkRFnB7XcxZl90rVrVyxYsAA5cuRAp06d0KZNG4vZYq5ytj179+5t9/7r169j3rx5Tn2pun//PubNm4fp06djz549iI6ORrNmzRw+FwDcvXsXISEhyQYbb926hZCQEKdmyDibERWD4CpmHlujNycvYj8CuKcvcbYtK1asiOrVq2Po0KEQEYwcORKDBw/G4sWLUadOHZdmWbnSjwDq+xJP6UcAZiQ1MxIVFYXPP/8crVu3xl9//YXo6GhMnjwZnTt3BgBMmzYNkyZNwu7duzXXgRnx7IxwsNMALly4gOzZs7v86zqg/nQiVzh7YLtr5pyz7ZkpUyasW7cOJUqUwN27d5E+fXps2bIFlStXNj1/3bp1ceXKFafqkcDV0/pdmSmblBGOCxUuXLiAbNmy2Z0B4YhR2sKZnLgzI860Z+7cuTFx4kTExMQAAI4fP46KFSviypUr8PX1RY8ePXDkyBGsW7fOqXokYEb0mzdvHho1aqRrEEZFW6j4gOJsRtxxepez7ZmwHEPBggWt3n/s2DGUKFECjx49cqoegL7TzFJ6fxjZtm3bULZsWV1toSIjKgZMXcmI6lPpnW1PHx8flChRwuYMlH///Rd79+51egZJAldO6U+gd594SkYA/X3Ji9iPAO7pS5xty3Tp0mHv3r3ImzevRRlvv/02FixYgLJly7p0Simgrx8B9O8TZsSMGTFzti2Dg4Nx9OhRREZGAnj+2WvPnj0oUqQIAODkyZMoW7Ysbt++rbkOCZgRdYyQkQTqFqAjpyReHzJHjhxOrQ9pjYoxaxUDB85wdkacPXra8/Hjx0iXLh2A5280Pj4+SJs2ren+0NBQPHz40Ok6JbRnhgwZ0LNnT/Ts2dOp7YcNG4bmzZvr2h9GOS5cLSPx+pDBwcFOz0hKzCht4QyVGQFcb89r164hKirK9P/8+fPj7t27uH79OrJmzYpOnTqZfhxwBjOipownT56gadOmun8BVdEWMTExKTp4XKVKFXh5eeHQoUM2H6PlV+vEXGnPIkWKYNq0aRgxYoTV+6dPn+7S0hMJH54bNGiABg0aOD2Ik9L7wxYVX8z0luHKe1RSKjLyzjvvoFy5cim2T9q3b+90BrRwtj3z5cuHXr16oW3btlbv37dvn+blUBJLGKh8+eWXMXbsWIwcOdLpMlJ6n1ijYhBcRRmtW7d2eVvgxexHAPf0Jc62ZUBAAO7cuZOsDG9vb7Ro0QKjRo1yqrwEevsRwBh9iRH6EYAZSc2MBAcH48GDB6b/R0REJFtzOjY21qkyAWZEdRmtW7fGpk2bUK5cOZeuLaF0LqbLlzYil5w9e1ZKly4tPj4+UqdOHbl7967UrFlTvLy8xMvLS/LkySPHjh1zutyQkBCnrwicVNq0aXWV4ewVzFVQ0Z7ly5eXzz77TEREpk+fLpkzZ5a+ffua7h80aJCULl3a6brpbU8V+9QIx4UrZUyYMEEiIyPF29vb4q9SpUqye/dul+pglLZw9irmKuhtzxIlSsiUKVNM/1+/fr0EBwebrvh39OhRSZs2rdP1YkZcL2PdunUSExMjYWFhpv0ZFhYmMTEx8uuvv7pUB6O059y5c1P8Ks9623Pjxo2SJk0aefnll6VXr14yfPhwGT58uPTq1UuKFSsmISEhpitAO8MIGVGxP1IjI/YcPnxYcufO7fR2RsnI1q1bNV+tNSVoac/WrVtLz549bd6/b98+8fLycvq5VRwXeveJiv2RWhnZt2+fDB48WCZMmCDXr1+3uO/u3bvSsWNHp8ozSkZSox/R25avvfaajBw50up98+bNEz8/P/H29na6XkbIyIvcj3z//ffSvn17mT59uoiILFiwQAoVKiS5c+eWL774wuk6/JczorctK1WqJAsWLLB5/8qVK6Vo0aJO14sZUVuGiIifn58cPnzYpW1VHN8JONiZwpo2bSpVq1aVlStXSvPmzaVSpUpSrVo1uXjxoly6dElq164tjRs3drpcFQONrhxYSQdt/vjjD9m5c6fLH/q+/PJLuX37tubHq2jPtWvXSmBgoPj7+0tgYKBs3rxZChQoINHR0VK+fHnx8fGRhQsXOv1a9AZVRdBT67jQU8bIkSMlW7ZsMm7cOPn+++8lKipKBg0aJD///LO0a9dOgoODZdeuXU7XQcUgo562OHfunPz+++/y559/yo0bN1yug7MZUdGeCxcuFD8/P2nevLm0b99eQkJCLH4QmDx5slSoUMHp12KUjKTmceFKGTNnzhRfX19p2bKlzJgxQ9asWSNr1qyRGTNmSKtWrcTPz09mz57tdB1UfNDS2xaPHz/WPWiwbds2p8pQ1Z5nzpyRjz76SKpUqSIFChSQAgUKSJUqVeTjjz+WM2fOuPRaUjsjKvaHinqoKiPBvn37XBo0UDGopfJ1uGrevHlKv+Rqac/Lly/L2bNnlT1nAqMdW6lZB2fL+OWXX8Tf31+KFCkikZGREh4eLhs2bDDdf+XKFadzYoR+RBVn+hIVbbls2TK7PwjMnTtXqlWrpq3yiTAjrpcxZswYSZMmjTRp0kSyZs0qQ4YMkfDwcBkyZIgMHDhQQkND5bvvvnOqDv/VjKhoy23btslff/1l8/4JEybIuHHjnHkJIsKM6CmjZMmSVv+8vLwkKirK9H9nqByI52BnCouIiDCF9M6dO+Ll5SVbt2413b9nzx7JnDmzprJUDzQ6c3C7a4aqs78mqGrPM2fOyJIlS0xfSK9cuSKff/659OnTx+LDijP0vuG4OlCZmseFijJy5cola9asMf3/2LFjEh4eLs+ePRMRkffff19ee+01zc+tapBRxLW2UD1L1dmMqGrPNWvWSOvWraVp06YWszxFRG7cuOFS26rIiKsDlal9XOgpI3/+/DJ+/Hib90+YMEHy5cvn1POrGtRy5QOK6lmqzmbEHe2pSmrMFPCEWcO9evWy+9e2bVuXBjtVUD0r05VZqs5mxMjtabSZsi/SrOEKFSrIJ598IiIi8fHx8tVXX0lISIj8/PPPIuLaYKcKrrxvqZ6hKuJcTozaliKpMwvQHfsjNTJSqFAhmTt3roiI7N27V3x9fWXq1Kmm+6dOnerS2X96uZIR1TNURZzLiFHbUiR1MuKO/ZEaGfH19ZU6derIgAEDTH/9+/cXb29v6dq1q+k2Z23cuFEePnzo9HZJcbAzhaVNm1ZOnz4tIiJxcXHi6+sr+/btM91/4sQJh6eDumug0ZnBNXfNUHU2YCra011cGazUM1BphONCRRnBwcEWs6Di4+PF19dXLl26JCLPP0CFhIQ4LMcdp8I7O7jmjlmqzmZEVXu6g6uDlXoGKo1wXOgtIyAgQI4ePWrz/qNHj0pgYKDDclQParkyYOqOWarOZkRVe7qDnl+3jbI/RNT8Su9MGd7e3lKqVCmpVq2a1b8yZcroHnhwdVBLNVdmqTqbkZRoT1cZ7ZT+1Jw17GwZoaGhcvLkSYvb5s6dK2nSpJGVK1dqGqBzx6CWs9wxQ1XEuZyoaEt3SenTld21P1K6HxERCQoKknPnzpn+HxAQIH///bfp/ydOnJCwsDC7ZbhjUMtZ7pihKuJcRlS0pbukdEbctT9SIyPbtm2TvHnzyhdffGHxXd/X11cOHTrkcj30nAafGAc7U5iK9SFVDjS6OrimcoZqYs5+AHfXepuJPXv2zOLN2R5X21PFQKURjgsVZahYH1L1IKOrg2uqZ6mKOJ8Rd623mZgzGRFxvT31DlQa5bjQW0apUqXkww8/tHn/Rx99JKVKlbJbhqpBLb0Dpu6YVelsRlS0p8jzutaoUUPeeOMN+e233yzuu379utMDY64MVhpxf6iYNexsGQUKFJA5c+bYvP+vv/7SPfDg6qBWYloGTN0xq9LZjKhqT9UZcQdH+8TIs1ydFRERYbXvnD9/vgQHB8ukSZPsvhZVg1p6B0zdNavSmZzobcsERsmInn1i5FmuzgoPD7cYcHnppZcsluM4ceKE3QkDqga19A6YumtWpTMZ0duWCYySET37xMizXF1x584dadmypZQrV870o4/WwU53nAafGAc7U5iK9SFVDDTqHVxz14xKZ2cRumu9zcS0fKHR254qBiqNcFyoKEPF+pCqBhn1Dq65Y1als7MI3bXeZmJav/TraU8VA5VGOS70lqHiYjgqBrVUDJi6Y1als79Kq2jPb775RoKDg6Vbt27Stm1b8ff3l2HDhpnu1/rlTs9gpZH2h4pZw3rKUHExnJQY1NLy3umOWZXOzgBU0Z6qMmKPitm2jvbJizJrWEsZei+Io2JQS8WAqbtmVTrTl6i4uJCqjOgdPNa7T1TtDxWzhvWWofeCOCoGtVQMmLprVqUzGVFxcSFVGdE7eKx3n6jaHypmDauceTx9+nTJkiWLfPfdd+Ln56dpsNNdp8En4GBnKtC7PqSKgUa9g2sqZ1TqnUXojvU2E9PyZURve6oYqDTCcaGqDL3rQ6oYZFQxuKZyVqWeWYTuWG8zMS0Z0dueKgYqjXJcqChD78VwVAxqqRgwVTWrMoGrswj1tmfhwoVNX2hERLZv3y4RERHy+eefi4i2D+B6ByuNsj9UDLrqLUPFxXBUDGqpGDBNiVmqjqhoTxUZcURLX6R3n7wos4a1lKH3gjgqBrVUDJiqmlWph4qLC6nIiIrBY737RMX+UPE6VJSh94I4Kga1VAyYqppVqYeKiwupyIiKwWO9+0TF/lDxOtxxOv3x48elbNmy4uXlpWmw012nwSfgYOcLSMVAo97BNRUzKt21xqSzbE2fTvgrVKiQpg9aetpTxUClEY4LVWXopWKQUcXgmopZle5YY9JZKjKitz1VDFQa5bhwx/IGzlIxqKViwFTFrEp3XFDHWUFBQckGRQ8ePGh6H9byAVzvYKVR9oeKQVcjXDRKxaCWigFTFbMq7UmptUdVZETF4LHefWKUWcNGOJ1exaCWigFTFbMqRVJ//VEVGVExeKx3n6jYHypehxFOp1cxqKViwFTFrEqR1F9/VEVGVAwe690nKvaHitfhrtPp4+Li5M6dO6bvV47oOQ3eEQ52GoyWte9UDDSqGFzTO6PSXRc5SkxLewYEBEiHDh0spk8n/nvnnXccvnHqbU8VA5VGOS7cfdEoLftUxSCjqlPQ9cyqdMcFjpJKqYzobU8VA5VGOS6McNEoFYNaqmZl6plV6a4L6jgrR44csmXLlmS3Hzp0SDJnzizt27d3mBG9g5VG2B8iagZdjXDRKBWDWioGTFXMqrRHxSxCLVRkRMXgsd59YpRZwylxOr0jKga1VAyYqphV6a6L6jhDRUZUDB7r3Scq9oeK12GEi0apGNRSMWCqYlaluy6q4wwVGVExeKx3n6jYHypeh9EuGuXKafCOcLDTYLR+6NQ70JgSF/ZxJCVmAGppz9KlS8vEiRNt3q/ly4je9lS19qgRjgt3H1taM6L31O2UuLCPIykxAzClMqK3PVWtPWqE40JFGSoWaNc7qKViwFQvVTMA9bZnq1atbH65+/vvvyUiIsJhRvQOVhphf4ioGXRVUYbefapiUMvdszK1UDUD0AgZUTF4bIR9ouJ1GOGiUSoGtVTNytRLxSxAI2RExeCxEfaJitdhhItGqRjUUjUrUy8VswCNkBEVg8dG2CcqXocRLxrl7GnwjnCw02BS6hd2d1/YR8tsMXfPABTR1p7vv/++9OjRw+b9J0+edPhhTUV7unvtUS1UvA53H1splZGUuLCPo5ykxAzAlMqIivZ099qjWqh4HXrLSIkLfWild8BULxUzAFW05/79+02ndVlz8OBBhwusqxisTO39oep16C3DKBlx96xMLVTMADRKRlQMVBphn6h4HS/KRaMcUTFgqoLeWYBGyYiKgUoj7BMVr8NIF43SQ8WAqQp6ZwEaJSMqBiqNsE9UvA4jXTQqMWdPg7eHg50pTMXad45oGWgUce/gmpYBFBUzAFOiPbUywmClPSl5XOgpw0gZSe0L+6iYAWikjBhhsNKelDwu9JSREhf6SEl6fhFWMQPQSO1phMFKI8wa1luGkfapCnr2iYoZgEZpTyMMVIoYY9bwi3LRqJSid5/onQVolLY0wkCliP79oeJ1GOWiUUahd5/onQVolLY0wkBlwvPo2R8qXodRLhrlTl4iIqAUExgYiJYtWyJ37txW7798+TK+//57xMXFufwc+/fvR6lSpXSVoZeWOvzyyy9o3Lgx4uPj4e3tjV9++QX/+9//EBYWBm9vb+zatQvz5s1D8+bNbZaREu1pBLGxsbh06RIiIyNdLsMIx4UW/5WMaKnHokWL0LZtW7z++usIDAzEsmXL8N577+HLL78EAHz33XeYNWsWduzYYfM5mBHtjHJcOBIcHIzDhw8jV65cptv+/vtv1KxZEx07dkTPnj2RLVs2h69j4sSJWLZsGTJkyIB33nkHNWrUMN1348YNREdH4/Tp0+56GQCAb7/9Fv369UPHjh1x9+5dLFq0CAMGDEC/fv0AAFevXrX7WjZt2oT69esjT548qFmzJjJnzmzabv369Th9+jRWr16NKlWq2KyDqvYEgD///BM7d+7ElStXAABZsmRBhQoVEB0drbVJUpXe/WEUzIhZmzZtkClTJowZM8bq/fv370fJkiURHx9vsw7MiJmnZARQs189ISMAUKtWLdSqVQsffPBBsvvmz5+PDh06IC4uzmYZzIgZM2LJUzJSuXJldO/eHS1atLB6/6pVq9CvXz8cPHjQ6v3MiBkzYsmtGUm1Ydb/KBVr3zmi4jRfRzOcVM0W0zuLUGV7/vHHHzJ27Fjp27ev9O3bV8aOHSt//PGHpm0d0TpjzBYV+zQljgsVZXhKRkTU5ETvLEJmJGXLSImMqFigXdVpJ3p/mVbxi7DeWYQq2vPq1atSqVIl8fLykpw5c0p0dLRER0dLzpw5xcvLSypXrixXr151WBe9jLA/jIAZMVMxA9CTMiKib594SkZE9O9XT8mIiP5ZgMyIGTNi5kkZ0TsLkBkxY0bM3L1UBAc7U5iKte9S4rRUR1/6VVydWQUV7Xn16lWpXLmyW9849Q6iaNneCMeFijI8JSMixsgJM2JmlONCbxkqFmhX8UFLxQeUoKCgZAOSBw8eNC1rkhIf+FS0Z9OmTaVChQpW1w89evSoVKxYUZo1a+awLno+PBtpf6g4FV5PGcyIWp6SERH9+8RTMiKif78yI2bMiBkzYsaMmDEjZsyImbsHfjnY+QJKiQEUR1+2U2L2nYoZUlqoeuO0x1F7qhiEMcJxkVJlOGKUtnB3TpiR/2ZGVCzQruKDlooPKCp+6ddLRXuGhITI3r17bd6/e/duhxcT0/vh2Sj7Q8UXM71lMCNqeUpGRPTvE0/JiIj+/cqMmDEjZsyIGTNixoyYMSNm7h6I52DnC0jFAIregQMVs8UcSamrbqt449TbnioGYYxwXKgqQy+jtIW7c8KMMCOuUvFBS8UHFBW/9Kv4ZVuv8PBw2bRpk837N27cKOHh4XbL0Pvh2Sj7Q8UXMyOc4sWMqGWEjIjo3yfMiJknZUQk9XPCjKh9HcyIGTNixoyoLUMvdw/E+7q20ifppWdh3UqVKuHYsWM270+bNq3dizEAwOHDhx1etOT48eM2t//mm2/slp83b15s3LjR7mNU0tOeAQEBuHfvns3779+/j4CAALtl6G3PokWLoly5cujSpYvV+/ft24fvv//ebh2McFyoKgN48TMCGCsnzIhxjgsjZKRy5cpYtmwZXnnlFYvbCxcujPXr16N69eoOy8iYMSMuXLhgsSh50aJFsWHDBrz66qu4dOmSwzL69u2LPXv2WL2vSJEi2LBhA5YuXWpz+6SLvNetW9dikfe4uDicO3fOYT0Afe3ZokULdOjQAWPGjEGNGjUQGhoKALh37x7Wr1+P3r17o1WrVnbLOHPmDCpWrGj6f8WKFbFhwwbUrFkTz549Q8+ePe1ub4T9oeJ1qCoDYEYAZiQpvfvE0zICuL5fPSUjgLqcMCPMSGLMSHLMCDOSmIqM2KVnJJaclxJr32mREqehO6JidpOK9uzatavkzJlTli1bJnfv3jXdfvfuXVm2bJnkypVL3nvvPbtl6G3PlJgpq4WK40JvGcyIGTNixoyYqVigXcXpRKpmCuih4ldpFe35+PFjeffdd8Xf31+8vb0lMDBQAgMDxdvbW/z9/aVLly7y+PFju2Xo/XXbCPtDRM2v9HrLYEbMmBFLRtgnRsiIiP796ikZEdGfE2ZELWbEzAj7Q4QZScwI+4QZ0YYzO1NY165dERcXhyNHjqBgwYIW9x07dgydOnVCt27dsHjxYrfWQ8UMJ0DfrzMqZjepaM/Ro0cjPj4eLVu2RGxsLPz9/QEAT58+ha+vLzp37oyvv/7abj30tqdRZgCqOC70luFpGQFczwkzYsaMmHXt2hXx8fG69mmxYsVQrFgxm/cXLVoURYsWtfMq1PwyncDVjKj4VVpFewYEBGDSpEn46quvsGfPHovXUbp0adPsA3v0/rpthP2h4nWoKIMZMWNGLKnaJy96RgD9+9VTMgLozwkzkhwzwowkxowkx4yoyYhdLg+TkktUrH2X4I8//pCxY8dK3759pW/fvjJ27Fj5448/VFXVLhWzxVTMkFLZnnfv3pUNGzbIvHnzZN68ebJhwwaLWWwvitQ8LlTwlIyI6M8JM+IeqX1c6OVpGdHzi7CKX6VVtqce7v51WwujzIjUWwYzYsaMqOUpGRFRt19f9IyI6M8JM2LGjCTHjDAjiTEjybkrI5zZmcJUrH137do1NG3aFNu3b0dkZCQyZ84MALh69Sp69eqFSpUqYenSpciUKZPSuiemYraYihlSKtozQWhoqP51IXTS8wuPEY4LFTwlI4D+nDAjyTEj6jLSpEkT7NixQ3db6Nknen8RVvGrtKqMPHr0CHv27EGGDBlQuHBhi/seP36MRYsWoX379ja3V/XrdmruD1WvQ28ZzIgZM2Kdq/vEUzIC6N+vnpIRQH9OmBEzZsSMGTFjRsyYETOVGbFK93ApOUXF2ndNmzaVChUqyNGjR5Pdd/ToUalYsaI0a9ZMU31cHUU3yq8zKtpTROThw4eydetWOXToULL7Hj16JLNmzdJUH1fbU8VMWSMcFyrK8JSMiBgjJ8yImVGOC71lGCUjKn6Z1psRFb9Kq2jPY8eOmV63t7e3VKlSRf755x/T/c5c0VJPRlJ7f6h4HSrKYEbMmBFLeveJp2RERP9+9ZSMiOjPCTNixoyYMSNmzIgZM2Km8ruZNRzsTGEqFtZVERC9Awfh4eGyadMmm/dv3LhRwsPD7dZBBRXtqeKNU297qgi6EY4LFWV4SkZEjJETZsTMKMeFp2RExT7xlIw0btxY6tWrJ9evX5cTJ05IvXr1JHfu3HLu3DkRSZmLwBhlf6j4Yqa3DGZELU/JiIj+feIpGRHRv1+ZETNmxIwZMWNGzJgRM2bEzN0TgzjYmUr0rH2nIiB6Q6pqtpiIml8k9LSnijdOve2pIuhGOC5UlSHy4mdERF1OmBFmxJrUzoiKfeIpGcmUKZMcOHDA9P/4+Hh59913JTIyUk6dOvXCZMQoMyKZETNmxMwIP3p5WkYS6u7KfvW0jIjozwkzwowkxowkx4wwI4m5eyCeg50vIBUB0RtSFb/OqJghpYKKN0697aki6EY4LlSVoZdR2kJvTpgRM2ZELRVtoWKfqMiI3l+lVUibNq0cPnw42e3dunWTl156SbZs2fJCZMQoMyKZETNmxMwIP3oxI2aekhERY+SEGVH7OpgRM2bEjBlRW4ZeKgfireFgZyrQu/adioCoGkXX8+uMql8T9LanijdOve2pIuhGOS5UlOFJGRFxPSfMiBkzYskIGVH5AeVFz0jZsmVl9uzZVu/r1q2bhIWFvRAZSbxNas6IZEaSY0aM86NXwuNf9IyI6NuvnpIRETU5YUYsMSPMSFLMiCVmRE1G7OFgZwpTubCunoC4exRdCxW/JqhoTxVvnHrbU2XQU/u40FsGM2LGjJgxI2ZGyYi7P6BoYZSMDBs2TGJiYmze36VLF/Hy8rJbhpEyogczYmaEfcKMWDLCPjFCRkTU5eRFz4iI/pwwI2oxI2ZG2B8izEhiRtgnzIg2HOxMYSrWvlNBRUj1/jqj4tcEFe2p4o1T1Zueu4KulYrXobcMT8qIiL6cMCPJMSPGyUgCvfvEEzKiglEyYoQZkcyIJWbkOaP86OUJGRExzn4VSf33Lb05MUpbMiNqyzDKfhVhRlRhRtSWYZT9agsHO1OYirXvRPQHJIGrIVUxiq/i1wRV7alKag/EpPZxoaIMT8mIiP6cMCPqGeG40FuG0TKiBzOSXGpmxCgzIvWWwYyYMSNqeUpGRNTsV0/IiIj+nDAjZsyIJWbkOWbEjBmx5M6McLAzhalY+05lQFylYhRfxa8JKtrTKPQG3QjHhQqekhER/TlhRiwxI88ZKSN69wkzolZq7w+jYEbMmJHk9OwTT8mIiP796ikZEdGfE2bEjBkxY0bMmBEzZsTM3d/NONiZwlSsfacqIHpCqvLXGT2/JqhoT1X0tKeKoBvhuFBRhqdkRERdTpgRZiQxo2RExT5hRiyldkaMNCOSGXmOGbGU2j96eUpGRPTvV0/LiIjrOWFGzJgRM2bEjBkxY0bM3D3wy8HOFKZi7TsVAdEbUqP8OqOiPVXQ254qgm6E40JFGZ6SERFj5IQZMTPKceEpGVGxT5gRMyNkxCgzIpkRM2bEzAg/enlKRkT071dmxIwZMWNGzJgRM2bEjBkxc/fyBhzsfAGpCIjekKr6dcYI65iooLc9VQTdCMeFqjL0MkpbqMgJM/IcM6KWirZQsU+YETMjZMQoMyKZETNmxMwIP3oxI2aelBERz8gJM6K2DL2YEeNhRtSWoZe7B+I52PkCUhEQvSFV8euMp6yfJ6K/PVUE3QjHhaoy9DJKW+jNCTNixoyopaItVOwTZsTMCBkxyoxIZsSMGTEzwo9ezIiZp2RExHNywoyoLUMvZsR4mBG1Zejl7uUNONj5AlIRECNMZzfCrwmq6G1PFUE3ynFhhGPLKG2hFzNixoyopaItjLD+EjNiZoT9IcKMJGaEfcKMWDLCPmFGzIywP0Q8JyfMiNoy9GJGjIcZUVuGXu5e3oCDnf9RRgipEX5NUEVvexplHRMVx4URji0VjPA6mBEzZsR4jLBPmBEzI+wPEWYkMSPsE2bEkhH2CTNiZoT9IeI5OWFG1JZhBEbYHyLMSGJG2CfMiDYc7PyPMkJIjfBrgipGaE8VVLwOtoU6zIjxMCPGwowYDzNiLMyI8TAjxuMpOfGU44IZMR5mxFiYEW28RERAlAqio6PRvXt3tGvXLtl97733HubOnYt79+4hLi4uFWpHlPqYESL7mBEi+5gRIseYEyL7mBF6EXmndgXov+v111/H/Pnzrd43fvx4tGrVChyLp/8yZoTIPmaEyD5mhMgx5oTIPmaEXkSc2UlEREREREREREQegTM7iYiIiIiIiIiIyCNwsJOIiIiIiIiIiIg8Agc7iYiIiIiIiIiIyCP4pnYFiIiIiDxZfHw8nj59mtrVIPIYfn5+8PHxSe1qEBERkUFxsJOIiIjITZ4+fYozZ84gPj4+tatC5FHCwsKQJUsWeHl5pXZViIiIyGA42ElERETkBiKCy5cvw8fHBzly5IC3N1cPItJLRPDw4UNcu3YNAJA1a9ZUrhEREREZDQc7iYiIiNwgNjYWDx8+RLZs2RAcHJza1SHyGEFBQQCAa9euIVOmTDylnYiIiCxwigERERGRG8TFxQEA/P39U7kmRJ4n4QeEZ8+epXJNiIiIyGg42ElERETkRlxTkEg95oqIiIhs4WAnEREREREREREReQQOdhIREREREREREZFH4AWKiIiIiFJQrr6rU/T5zg6v59Tj33zzTdy5cwfLly8HAFy4cAH9+/fH2rVrcePGDWTNmhWNGzfGF198gfDwcIttDx06hIEDB2Ljxo24d+8ecubMiZYtW6Jv374WF2nav38/Pv/8c/z++++4d+8esmTJgnLlymHcuHHIlCmT7tfstAHpUvj57qbs87moWrVqKFGiBMaOHWu6Tes+BoAdO3ZgyJAh2LlzJx49eoT8+fOjY8eO6NGjh8VFhTZv3oyBAwdi3759ePz4MbJnz46KFSvi+++/55q3RERE5DTO7CQiIiIiq06fPo0yZcrgxIkTmD9/Pk6ePInJkydj/fr1qFChAm7dumV67O+//45y5crh6dOnWL16NY4fP46hQ4di5syZeO211/D06VMAwPXr11GjRg1kyJABv/zyC44cOYIZM2YgW7ZsePDgQWq9VNJA6z4GgB9//BFVq1bFSy+9hI0bN+Lo0aPo0aMHhgwZgpYtW0JEAACHDx9GnTp1UKZMGWzZsgUHDx7EuHHj4O/vb7rIFxEREZEzOLOTiIiIiKzq1q0b/P39sW7dOgQFBQEAIiMjUbJkSeTNmxeffvopJk2aBBFB586dERUVhWXLlsHb+/nv6Tlz5kSBAgVQsmRJjBkzBh9//DG2b9+Ou3fvYurUqfD1ff5RNHfu3KhevXqqvU6jq1atGooVK4bAwEBMnToV/v7+ePfddzFgwAAAwPnz59G9e3esX78e3t7eqFOnDsaNG4fMmTMDAAYMGIDly5ejT58++Pzzz3H79m3ExMTg+++/R9q0aTXVwZl9/ODBA/zvf/9Dw4YNMWXKFFMZb731FjJnzoyGDRti0aJFaNGiBdatW4csWbJgxIgRpsflzZsXderUUdR6RERE9F/DmZ1ERERElMytW7fwyy+/oGvXrqaBzgRZsmRBmzZtsHDhQogI9u3bh8OHD6N3796mQbAExYsXR82aNTF//nzTtrGxsfjxxx9Ns/vIsVmzZiFNmjT4448/MGLECAwaNAi//vor4uPj0ahRI9y6dQubN2/Gr7/+itOnT6NFixYW2586dQrLly/HqlWrsGrVKmzevBnDhw/X/PzO7ON169bh5s2b+OCDD5KV06BBAxQoUMDieLh8+TK2bNnibJMQERERWcWZnURERESUzIkTJyAiiIqKsnp/VFQUbt++jevXr+P48eOm22w9dtu2bQCA8uXL45NPPkHr1q3x7rvvIjo6Gq+++irat29vmolIyRUrVgz9+/cHAOTPnx/jx4/H+vXrAQAHDx7EmTNnkCNHDgDA7NmzUaRIEezatQtly5YFAMTHx2PmzJmmmZzt2rXD+vXrMXToUE3P78w+dvTYQoUKmR7zxhtv4JdffkHVqlWRJUsWlC9fHjVq1ED79u0RGhqqqW5EREREiXFmJxERERHZ5MzsS62PHTp0KK5cuYLJkyejSJEimDx5MgoVKoSDBw+6Wk2PV6xYMYv/Z82aFdeuXcORI0eQI0cO00AnABQuXBhhYWE4cuSI6bZcuXJZnLKesD0AzJ07FyEhIaa/rVu32qyH6uPBx8cHM2bMwMWLFzFixAhkz54dw4YNQ5EiRXD58mXNz0VERESUgIOdRERERJRMvnz54OXlZTFgltiRI0eQPn16REREoECBAqbbbD024TEJwsPD8cYbb+Drr7/GkSNHkC1bNnz99ddqX4QH8fPzs/i/l5cX4uPjlWzfsGFD7Nu3z/RXpkyZZNs7s49dOR6yZ8+Odu3aYfz48Th06BAeP36MyZMna359RERERAk42ElEREREyYSHh+O1117DxIkT8ejRI4v7rly5grlz56JFixbw8vJCiRIlUKhQIYwZMybZANz+/fvx22+/oVWrVjafy9/fH3nz5uXV2F0QFRWFCxcu4MKFC6bbDh8+jDt37qBw4cKaykibNi3y5ctn+ku6RisAp/ZxrVq1kCFDBowaNSpZOT/99BNOnDhh93hInz49smbNyuOBiIiIXMLBTiIiIiKyavz48Xjy5Alq166NLVu24MKFC1i7di1ee+01ZM+e3bTeo5eXF6ZNm4bDhw+jadOm+PPPP3H+/HksXrwYDRo0QIUKFdCzZ08AwKpVq9C2bVusWrUKx48fx7Fjx/D1119jzZo1aNSoUSq+2hdTzZo18fLLL6NNmzbYu3cv/vzzT7Rv3x5Vq1a1OkPTVc7s4zRp0uC7777DihUr8Pbbb+PAgQM4e/Yspk2bhjfffBPNmjVD8+bNAQDfffcdunTpgnXr1uHUqVM4dOgQPv74Yxw6dAgNGjRQVn8iIiL67+AFioiIiIhS0Nnh9VK7Cprlz58fu3fvRv/+/dG8eXPcunULWbJkQePGjdG/f39kyJDB9NiKFSvi999/x8CBAxETE4P79+8jMjISHTp0QL9+/RAQEADg+XqSwcHB6NOnDy5cuICAgADkz58fU6dORbt27VLnhQ64mzrPq4CXlxdWrFiB7t27o0qVKvD29kadOnUwbtw45c+ldR8DQLNmzbBx40YMHToUr7zyCh4/foz8+fPj008/Rc+ePeHl5QUAiI6OxrZt2/Duu+/i0qVLCAkJQZEiRbB8+XJUrVpV+WsgIiIiz+clzqwyTkRERESaPH78GGfOnEHu3LkRGBiY2tUh8ijMFxEREdnC09iJiIiIiIiIiIjII3Cwk4iIiIiIiIiIiDwCBzuJiIiIiIiIiIjII3Cwk4iIiIiIiIiIiDwCBzuJiIiI3IjXgiRSj7kiIiIiWzjYSUREROQGPj4+AICnT5+mck2IPM/Dhw8BAH5+fqlcEyIiIjIa39SuABEREZEn8vX1RXBwMK5fvw4/Pz94e/M3ZiK9RAQPHz7EtWvXEBYWZvpRgYiIiCiBl/AcECIiIiK3ePr0Kc6cOYP4+PjUrgqRRwkLC0OWLFng5eWV2lUhIiIig+FgJxEREZEbxcfH81R2IoX8/Pw4o5OIiIhs4mAnEREREREREREReQQuHkVEREREREREREQegYOdRERERERERERE5BE42ElEREREREREREQegYOdRERERERERERE5BE42ElEREREREREREQegYOdRERERERERERE5BE42ElEREREREREREQe4f8AznLIFYTwCQcAAAAASUVORK5CYII=\n"
-          },
-          "metadata": {}
-        }
+      "text/plain": [
+       "              source      met    wave    total\n",
+       "time (UTC)                                    \n",
+       "2018-Jan        IOOS   701780   63624   765404\n",
+       "2018-Feb        IOOS   676688   61886   738574\n",
+       "2018-Mar        IOOS   759916   69014   828930\n",
+       "2018-Apr        IOOS   773482   75758   849240\n",
+       "2018-May        IOOS   890444  155768  1046212\n",
+       "...              ...      ...     ...      ...\n",
+       "2023-Oct    non-NDBC  4699234  331094  5030328\n",
+       "2023-Nov    non-NDBC  4514266  249716  4763982\n",
+       "2023-Dec    non-NDBC  4457820  224472  4682292\n",
+       "2024-Jan    non-NDBC  4584106  218032  4802138\n",
+       "2024-Feb    non-NDBC  4271722  214216  4485938\n",
+       "\n",
+       "[222 rows x 4 columns]"
       ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "group = df_out.groupby(by=[\"source\", pd.Grouper(key=\"time (UTC)\", freq=\"M\")])\n",
+    "\n",
+    "\n",
+    "s = group[\n",
+    "    [\"met\", \"wave\"]\n",
+    "].sum()  # reducing the columns so the summary is digestable\n",
+    "\n",
+    "totals = s.assign(total=s[\"met\"] + s[\"wave\"])\n",
+    "\n",
+    "totals.reset_index([\"source\"], inplace=True)\n",
+    "\n",
+    "totals.index = totals.index.to_period(\"M\").strftime(\"%Y-%b\")\n",
+    "\n",
+    "totals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Qp5OoPgLRURB"
+   },
+   "source": [
+    "# Create stacked bar chart\n",
+    "\n",
+    "IOOS + non-NDBC + NDBC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 785
     },
+    "id": "0iN6qHYtAhgU",
+    "outputId": "14de8082-f192-4b8d-9647-7d3c514dd99c"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": [
-        "co-ops = non-ndbc['NATIONAL OCEAN SERVICE'] + non-ndbc['NOAA NOS PHYSICAL OCEANOGRAPHIC RT SYSTEM PROGRAM']\n",
-        "\n",
-        "ioos-regional = ioos_regional['met'] +ioos_regional['wave']\n",
-        "\n",
-        "nerrs = non-ndbc['NATIONAL ESTUARINE RESEARCH RESERVE SYSTEM']\n",
-        "\n",
-        "other = ndbc['total'] + non-ndbc NWS-regional + CBIBS + National Park Service + USACE (not including CDIP stations owned by RAs) + all other NDBC partners (including nonfederal partners outside of IOOS).\n",
-        " anything not Ocean Service\n",
-        "\n",
-        "\n",
-        "Ocean Service contributions compared to the total.\n",
-        "\n",
-        "split into met and wave"
-      ],
-      "metadata": {
-        "id": "8ERRtPTWyqN7"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# NOS & non-NOS"
-      ],
-      "metadata": {
-        "id": "wgLsdS0COFK2"
-      }
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1600x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.ticker import FuncFormatter\n",
+    "\n",
+    "fig, axs = plt.subplots(nrows=2,ncols=1,figsize=(16,8))\n",
+    "\n",
+    "df_met = pd.DataFrame({\"IOOS\": totals.loc[totals[\"source\"]==\"IOOS\",\"met\"],\n",
+    "                         \"non-NDBC\": totals.loc[totals[\"source\"]==\"non-NDBC\",\"met\"],\n",
+    "                         \"NDBC\": totals.loc[totals[\"source\"]==\"NDBC\",\"met\"],\n",
+    "                         },\n",
+    "                         index= totals.index.unique())\n",
+    "\n",
+    "df_met.plot.bar(stacked=True, xlabel=\"\", ax=axs[0], rot=90, title=\"met\")\n",
+    "\n",
+    "axs[0].get_legend().remove()\n",
+    "\n",
+    "axs[0].grid(axis=\"y\")\n",
+    "\n",
+    "axs[0].yaxis.set_major_formatter(FuncFormatter(lambda x, p: format(int(x), \",\")))\n",
+    "axs[0].axes.get_xaxis().set_visible(False)\n",
+    "\n",
+    "df_wave = pd.DataFrame({\"IOOS\": totals.loc[totals[\"source\"]==\"IOOS\",\"wave\"],\n",
+    "                         \"non-NDBC\": totals.loc[totals[\"source\"]==\"non-NDBC\",\"wave\"],\n",
+    "                         \"NDBC\": totals.loc[totals[\"source\"]==\"NDBC\",\"wave\"],\n",
+    "                         },\n",
+    "                         index= totals.index.unique())\n",
+    "\n",
+    "df_wave.plot.bar(stacked=True, xlabel=\"\", ax=axs[1], title=\"wave\")\n",
+    "\n",
+    "axs[1].legend(loc=\"center\",bbox_to_anchor=(0.5,-0.35,0,0),ncol=3)\n",
+    "\n",
+    "axs[1].grid(axis=\"y\")\n",
+    "\n",
+    "axs[1].yaxis.set_major_formatter(FuncFormatter(lambda x, p: format(int(x), \",\")))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 455
     },
+    "id": "FinAC94_cNt5",
+    "outputId": "99320f7f-49d5-4839-e5c3-e5954cce2565"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [
-        "df_out['LO']=pd.Series(dtype=str)\n",
-        "df_out.loc[df_out['sponsor']=='NATIONAL OCEAN SERVICE','LO'] = 'NOS'\n",
-        "df_out.loc[df_out['sponsor']=='NOAA NOS PHYSICAL OCEANOGRAPHIC RT SYSTEM PROGRAM','LO'] = 'NOS'\n",
-        "df_out.loc[df_out['source']=='IOOS','LO'] = 'NOS'\n",
-        "\n",
-        "df_out.loc[df_out['LO'].isna(),'LO'] = 'non-NOS'"
-      ],
-      "metadata": {
-        "id": "IQDWt3MUQins"
+     "data": {
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "summary": "{\n  \"name\": \"df_total\",\n  \"rows\": 74,\n  \"fields\": [\n    {\n      \"column\": \"time (UTC)\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 74,\n        \"samples\": [\n          \"2018-May\",\n          \"2023-Apr\",\n          \"2019-Jul\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IOOS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 172276,\n        \"min\": 575436,\n        \"max\": 1394504,\n        \"num_unique_values\": 74,\n        \"samples\": [\n          1046212,\n          1107356,\n          1261294\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"non-NDBC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 330148,\n        \"min\": 3046400,\n        \"max\": 5097910,\n        \"num_unique_values\": 74,\n        \"samples\": [\n          4502094,\n          4613168,\n          4413372\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NDBC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 170537,\n        \"min\": 476930,\n        \"max\": 1153548,\n        \"num_unique_values\": 74,\n        \"samples\": [\n          637926,\n          936966,\n          763014\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+       "type": "dataframe",
+       "variable_name": "df_total"
       },
-      "execution_count": 26,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "What are we classifying as non-NOS?"
-      ],
-      "metadata": {
-        "id": "sKrmbxACZipd"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df_out.loc[df_out['LO']=='non-NOS','sponsor'].unique().tolist()"
+      "text/html": [
+       "\n",
+       "  <div id=\"df-ad7d67ce-2276-41fe-b39c-09e5b34e803b\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>IOOS</th>\n",
+       "      <th>non-NDBC</th>\n",
+       "      <th>NDBC</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time (UTC)</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-Jan</th>\n",
+       "      <td>765404</td>\n",
+       "      <td>4488860</td>\n",
+       "      <td>530790</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-Feb</th>\n",
+       "      <td>738574</td>\n",
+       "      <td>4028584</td>\n",
+       "      <td>476930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-Mar</th>\n",
+       "      <td>828930</td>\n",
+       "      <td>4423008</td>\n",
+       "      <td>584760</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-Apr</th>\n",
+       "      <td>849240</td>\n",
+       "      <td>4333812</td>\n",
+       "      <td>584122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-May</th>\n",
+       "      <td>1046212</td>\n",
+       "      <td>4502094</td>\n",
+       "      <td>637926</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-Oct</th>\n",
+       "      <td>1305808</td>\n",
+       "      <td>5030328</td>\n",
+       "      <td>1103356</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-Nov</th>\n",
+       "      <td>1129076</td>\n",
+       "      <td>4763982</td>\n",
+       "      <td>1051638</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-Dec</th>\n",
+       "      <td>1073466</td>\n",
+       "      <td>4682292</td>\n",
+       "      <td>975602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-Jan</th>\n",
+       "      <td>1099192</td>\n",
+       "      <td>4802138</td>\n",
+       "      <td>935046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-Feb</th>\n",
+       "      <td>1025814</td>\n",
+       "      <td>4485938</td>\n",
+       "      <td>840728</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>74 rows × 3 columns</p>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ad7d67ce-2276-41fe-b39c-09e5b34e803b')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-ad7d67ce-2276-41fe-b39c-09e5b34e803b button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-ad7d67ce-2276-41fe-b39c-09e5b34e803b');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-7a317ba2-1d16-4604-bf2f-977efe334858\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7a317ba2-1d16-4604-bf2f-977efe334858')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-7a317ba2-1d16-4604-bf2f-977efe334858 button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "metadata": {
-        "id": "XOybMWBqZl3_",
-        "outputId": "718b5b3e-a69a-4c8f-b167-ae25290a29ff",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "execution_count": 27,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "['CORPS OF ENGINEERS',\n",
-              " 'NATIONAL HURRICANE CENTER',\n",
-              " 'NATIONAL WEATHER SERVICE',\n",
-              " 'U. S. COAST GUARD',\n",
-              " nan,\n",
-              " 'NDBC ENGINEERING',\n",
-              " 'NATIONAL DATA BUOY CENTER',\n",
-              " 'GREAT LAKES RESEARCH LABORATORY',\n",
-              " 'NATIONAL ACADEMY OF SCIENCES',\n",
-              " 'SAILDRONE',\n",
-              " 'BP INC.',\n",
-              " 'EPA & MEXICAN GOVERNMENT COOPERATIVE PROGRAM',\n",
-              " 'CHESAPEAKE BAY INTERPRETIVE BUOY SYSTEM',\n",
-              " 'SCRIPPS WAVERIDER COASTAL DATA INFORMATION PROGRAM',\n",
-              " 'EVERGLADES NATIONAL PARK',\n",
-              " 'INTEGRATED CORAL OBSERVING NETWORK',\n",
-              " 'LOUISIANA OFFSHORE OIL PORT',\n",
-              " 'MOSS LANDING MARINE LABORATORIES',\n",
-              " 'NATIONAL ESTUARINE RESEARCH RESERVE SYSTEM',\n",
-              " 'NATIONAL PARK SERVICE - LAKE MEAD NATIONAL REC AREA',\n",
-              " 'NATIONAL RENEWABLE ENERGY LABORATORY',\n",
-              " 'NATIONAL WEATHER SERVICE, ALASKA REGION',\n",
-              " 'NATIONAL WEATHER SERVICE, CENTRAL REGION',\n",
-              " 'NATIONAL WEATHER SERVICE, EASTERN REGION',\n",
-              " 'OCEAN OBSERVATORIES INITIATIVE',\n",
-              " 'PETROBRAS',\n",
-              " 'SHELL OIL',\n",
-              " 'U.S. ARMY CORPS OF ENGINEERS',\n",
-              " 'WOODS HOLE OCEANOGRAPHIC INSTITUTION',\n",
-              " 'VERMONT EPSCOR',\n",
-              " 'NATIONAL PARK SERVICES - SLEEPING BEAR DUNES',\n",
-              " 'ALASKA OCEAN OBSERVING SYSTEM',\n",
-              " 'SUNY PLATTSBURGH CEES / LAKE CHAMPLAIN RESEARCH INST.',\n",
-              " 'UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAGNE',\n",
-              " 'MARINE EXCHANGE OF ALASKA',\n",
-              " 'TEXAS COASTAL OCEAN OBSERVATION NETWORK',\n",
-              " 'LIMNOTECH',\n",
-              " 'USF COMPS MARINE NETWORK',\n",
-              " 'MURPHY OIL CORP.',\n",
-              " 'UNIVERSITY OF NEW HAMPSHIRE',\n",
-              " 'STONY BROOK UNIVERSITY',\n",
-              " 'COASTAL OCEAN RESEARCH AND MONITORING PROGRAM',\n",
-              " 'U.S. NAVY',\n",
-              " 'UNIVERSITY OF NORTH CAROLINA COASTAL STUDIES',\n",
-              " 'CARIBBEAN INTEGRATED COASTAL OCEAN OBSERVING SYSTEM',\n",
-              " 'UNIVERSITY OF WISCONSIN AT MILWAUKEE',\n",
-              " 'ILLINOIS-INDIANA SEA GRANT',\n",
-              " 'COASTAL DATA INFORMATION PROGRAM/PMEL',\n",
-              " 'GREAT LAKES WATER AUTHORITY',\n",
-              " 'CLEVELAND WATER ALLIANCE',\n",
-              " 'MICHIGAN TECHNICAL UNIVERSITY']"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 27
-        }
+      "text/plain": [
+       "               IOOS  non-NDBC     NDBC\n",
+       "time (UTC)                            \n",
+       "2018-Jan     765404   4488860   530790\n",
+       "2018-Feb     738574   4028584   476930\n",
+       "2018-Mar     828930   4423008   584760\n",
+       "2018-Apr     849240   4333812   584122\n",
+       "2018-May    1046212   4502094   637926\n",
+       "...             ...       ...      ...\n",
+       "2023-Oct    1305808   5030328  1103356\n",
+       "2023-Nov    1129076   4763982  1051638\n",
+       "2023-Dec    1073466   4682292   975602\n",
+       "2024-Jan    1099192   4802138   935046\n",
+       "2024-Feb    1025814   4485938   840728\n",
+       "\n",
+       "[74 rows x 3 columns]"
       ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_total = df_met + df_wave\n",
+    "df_total"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KkFXlY_O5Rdk"
+   },
+   "source": [
+    "# Create stacked chart\n",
+    "\n",
+    "IOOS + non-IOOS (non-NDBC + NDBC)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 785
     },
+    "id": "nZFZPUtm2vGs",
+    "outputId": "c9e3312d-26a8-4de4-f8b2-fa5e87cb551a"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": [
-        "## Group by the new LO column"
-      ],
-      "metadata": {
-        "id": "G7nxU22BaF3D"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "group = df_out.groupby(by=['LO', pd.Grouper(key=\"time (UTC)\", freq=\"M\")])\n",
-        "\n",
-        "s = group[\n",
-        "    [\"met\", \"wave\"]\n",
-        "].sum()  # reducing the columns so the summary is digestable\n",
-        "\n",
-        "totals = s.assign(total=s[\"met\"] + s[\"wave\"])\n",
-        "\n",
-        "totals.reset_index(['LO'], inplace=True)\n",
-        "\n",
-        "totals.index = totals.index.to_period(\"M\").strftime('%Y-%b')\n",
-        "\n",
-        "totals"
-      ],
-      "metadata": {
-        "id": "QFzkP-lTOhRO",
-        "outputId": "35b65707-f7de-44f2-c48c-f99de8a414d4",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 455
-        }
-      },
-      "execution_count": 28,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "                 LO      met    wave    total\n",
-              "time (UTC)                                   \n",
-              "2018-Jan        NOS  4350064   63624  4413688\n",
-              "2018-Feb        NOS  3947286   61886  4009172\n",
-              "2018-Mar        NOS  4360558   69014  4429572\n",
-              "2018-Apr        NOS  4290340   75758  4366098\n",
-              "2018-May        NOS  4530964  155768  4686732\n",
-              "...             ...      ...     ...      ...\n",
-              "2023-Oct    non-NOS  1758336  579780  2338116\n",
-              "2023-Nov    non-NOS  1613288  486862  2100150\n",
-              "2023-Dec    non-NOS  1532968  441676  1974644\n",
-              "2024-Jan    non-NOS  1463756  424248  1888004\n",
-              "2024-Feb    non-NOS  1333212  400760  1733972\n",
-              "\n",
-              "[148 rows x 4 columns]"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-fcaf099c-dc56-455c-b482-a543a694c6a2\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>LO</th>\n",
-              "      <th>met</th>\n",
-              "      <th>wave</th>\n",
-              "      <th>total</th>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>time (UTC)</th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>2018-Jan</th>\n",
-              "      <td>NOS</td>\n",
-              "      <td>4350064</td>\n",
-              "      <td>63624</td>\n",
-              "      <td>4413688</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2018-Feb</th>\n",
-              "      <td>NOS</td>\n",
-              "      <td>3947286</td>\n",
-              "      <td>61886</td>\n",
-              "      <td>4009172</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2018-Mar</th>\n",
-              "      <td>NOS</td>\n",
-              "      <td>4360558</td>\n",
-              "      <td>69014</td>\n",
-              "      <td>4429572</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2018-Apr</th>\n",
-              "      <td>NOS</td>\n",
-              "      <td>4290340</td>\n",
-              "      <td>75758</td>\n",
-              "      <td>4366098</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2018-May</th>\n",
-              "      <td>NOS</td>\n",
-              "      <td>4530964</td>\n",
-              "      <td>155768</td>\n",
-              "      <td>4686732</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2023-Oct</th>\n",
-              "      <td>non-NOS</td>\n",
-              "      <td>1758336</td>\n",
-              "      <td>579780</td>\n",
-              "      <td>2338116</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2023-Nov</th>\n",
-              "      <td>non-NOS</td>\n",
-              "      <td>1613288</td>\n",
-              "      <td>486862</td>\n",
-              "      <td>2100150</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2023-Dec</th>\n",
-              "      <td>non-NOS</td>\n",
-              "      <td>1532968</td>\n",
-              "      <td>441676</td>\n",
-              "      <td>1974644</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2024-Jan</th>\n",
-              "      <td>non-NOS</td>\n",
-              "      <td>1463756</td>\n",
-              "      <td>424248</td>\n",
-              "      <td>1888004</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2024-Feb</th>\n",
-              "      <td>non-NOS</td>\n",
-              "      <td>1333212</td>\n",
-              "      <td>400760</td>\n",
-              "      <td>1733972</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>148 rows × 4 columns</p>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fcaf099c-dc56-455c-b482-a543a694c6a2')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-fcaf099c-dc56-455c-b482-a543a694c6a2 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-fcaf099c-dc56-455c-b482-a543a694c6a2');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-ff647842-b9e4-4ecd-8265-56bfcbb78c27\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ff647842-b9e4-4ecd-8265-56bfcbb78c27')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-ff647842-b9e4-4ecd-8265-56bfcbb78c27 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "totals",
-              "summary": "{\n  \"name\": \"totals\",\n  \"rows\": 148,\n  \"fields\": [\n    {\n      \"column\": \"time (UTC)\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 74,\n        \"samples\": [\n          \"2018-May\",\n          \"2023-Apr\",\n          \"2019-Jul\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LO\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"non-NOS\",\n          \"NOS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"met\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1596000,\n        \"min\": 896988,\n        \"max\": 4955340,\n        \"num_unique_values\": 148,\n        \"samples\": [\n          1405670,\n          4411270\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wave\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 135475,\n        \"min\": 44916,\n        \"max\": 657898,\n        \"num_unique_values\": 148,\n        \"samples\": [\n          273798,\n          78488\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1498870,\n        \"min\": 1078936,\n        \"max\": 5179068,\n        \"num_unique_values\": 148,\n        \"samples\": [\n          1679468,\n          4489758\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 28
-        }
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 1600x800 with 2 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.ticker import FuncFormatter\n",
+    "\n",
+    "color = {\"IOOS\":\"C0\",\n",
+    "         \"non-IOOS\": \"C1\"}\n",
+    "\n",
+    "fig, axs = plt.subplots(nrows=2,ncols=1,figsize=(16,8))\n",
+    "\n",
+    "df_met = pd.DataFrame({\"IOOS\": totals.loc[totals[\"source\"]==\"IOOS\",\"met\"],\n",
+    "                       \"non-IOOS\": totals.loc[totals[\"source\"]==\"NDBC\",\"met\"]+totals.loc[totals[\"source\"]==\"non-NDBC\",\"met\"],\n",
+    "                         },\n",
+    "                         index= totals.index.unique())\n",
+    "\n",
+    "df_met.plot.bar(stacked=True,\n",
+    "                  xlabel=\"\",\n",
+    "                  ax=axs[0],\n",
+    "                  rot=90,\n",
+    "                  title=\"met\",\n",
+    "                  color=color)\n",
+    "\n",
+    "axs[0].get_legend().remove()\n",
+    "\n",
+    "axs[0].grid(axis=\"y\")\n",
+    "\n",
+    "axs[0].yaxis.set_major_formatter(FuncFormatter(lambda x, p: format(int(x), \",\")))\n",
+    "axs[0].axes.get_xaxis().set_visible(False)\n",
+    "\n",
+    "df_wave = pd.DataFrame({\"IOOS\": totals.loc[totals[\"source\"]==\"IOOS\",\"wave\"],\n",
+    "                         \"non-IOOS\": totals.loc[totals[\"source\"]==\"NDBC\",\"wave\"]+totals.loc[totals[\"source\"]==\"non-NDBC\",\"wave\"],\n",
+    "                         },\n",
+    "                         index= totals.index.unique())\n",
+    "\n",
+    "df_wave.plot.bar(\n",
+    "    stacked=True,\n",
+    "    xlabel=\"\",\n",
+    "    ax=axs[1],\n",
+    "    title=\"wave\",\n",
+    "    color=color)\n",
+    "\n",
+    "axs[1].legend(loc=\"center\",\n",
+    "              bbox_to_anchor=(0.5,-0.35,0,0),\n",
+    "              ncol=3)\n",
+    "\n",
+    "axs[1].grid(axis=\"y\")\n",
+    "\n",
+    "axs[1].yaxis.set_major_formatter(\n",
+    "    FuncFormatter(lambda x, p: format(int(x), \",\"))\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8ERRtPTWyqN7"
+   },
+   "source": [
+    "co-ops = non-ndbc['NATIONAL OCEAN SERVICE'] + non-ndbc['NOAA NOS PHYSICAL OCEANOGRAPHIC RT SYSTEM PROGRAM']\n",
+    "\n",
+    "ioos-regional = ioos_regional['met'] +ioos_regional['wave']\n",
+    "\n",
+    "nerrs = non-ndbc['NATIONAL ESTUARINE RESEARCH RESERVE SYSTEM']\n",
+    "\n",
+    "other = ndbc['total'] + non-ndbc NWS-regional + CBIBS + National Park Service + USACE (not including CDIP stations owned by RAs) + all other NDBC partners (including nonfederal partners outside of IOOS).\n",
+    " anything not Ocean Service\n",
+    "\n",
+    "\n",
+    "Ocean Service contributions compared to the total.\n",
+    "\n",
+    "split into met and wave"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wgLsdS0COFK2"
+   },
+   "source": [
+    "# NOS & non-NOS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "id": "IQDWt3MUQins"
+   },
+   "outputs": [],
+   "source": [
+    "df_out[\"LO\"]=pd.Series(dtype=str)\n",
+    "df_out.loc[df_out[\"sponsor\"]==\"NATIONAL OCEAN SERVICE\",\"LO\"] = \"NOS\"\n",
+    "df_out.loc[df_out[\"sponsor\"]==\"NOAA NOS PHYSICAL OCEANOGRAPHIC RT SYSTEM PROGRAM\",\"LO\"] = \"NOS\"\n",
+    "df_out.loc[df_out[\"source\"]==\"IOOS\",\"LO\"] = \"NOS\"\n",
+    "\n",
+    "df_out.loc[df_out[\"LO\"].isna(),\"LO\"] = \"non-NOS\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "sKrmbxACZipd"
+   },
+   "source": [
+    "What are we classifying as non-NOS?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "XOybMWBqZl3_",
+    "outputId": "718b5b3e-a69a-4c8f-b167-ae25290a29ff"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "from matplotlib.ticker import (MultipleLocator,\n",
-        "                               FormatStrFormatter,\n",
-        "                               AutoMinorLocator,\n",
-        "                               FuncFormatter)\n",
-        "\n",
-        "\n",
-        "color = {'NOS':'C0',\n",
-        "         'non-NOS': 'C1'}\n",
-        "\n",
-        "fig, axs = plt.subplots(nrows=2,ncols=1,figsize=(16,8))\n",
-        "\n",
-        "\n",
-        "# first chart\n",
-        "df_met = pd.DataFrame({'NOS': totals.loc[totals['LO']=='NOS','met'],\n",
-        "                         'non-NOS': totals.loc[totals['LO']=='non-NOS','met'],\n",
-        "                         'nos-percent':totals.loc[totals['LO']=='NOS','met'] / (totals.loc[totals['LO']=='NOS','met'] + totals.loc[totals['LO']=='non-NOS','met'])\n",
-        "                         },\n",
-        "                         index= totals.index.unique())\n",
-        "\n",
-        "bar = df_met[['NOS','non-NOS']].plot.bar(stacked=True, xlabel='', ax=axs[0], rot=90, title='met', color=color)\n",
-        "\n",
-        "axs[0].get_legend().remove()\n",
-        "\n",
-        "axs[0].grid(axis='y')\n",
-        "\n",
-        "axs[0].yaxis.set_major_formatter(FuncFormatter(lambda x, p: format(int(x), ',')))\n",
-        "axs[0].axes.get_xaxis().set_visible(False)\n",
-        "#axs[0].bar_label(bar,df_wave['percent'])\n",
-        "\n",
-        "\n",
-        "# second chart\n",
-        "df_wave = pd.DataFrame({'NOS': totals.loc[totals['LO']=='NOS','wave'],\n",
-        "                         'non-NOS': totals.loc[totals['LO']=='non-NOS','wave'],\n",
-        "                         'nos-percent':totals.loc[totals['LO']=='NOS','wave'] / (totals.loc[totals['LO']=='NOS','wave'] + totals.loc[totals['LO']=='non-NOS','wave'])\n",
-        "                         },\n",
-        "                         index= totals.index.unique())\n",
-        "\n",
-        "df_wave[['NOS','non-NOS']].plot.bar(stacked=True, xlabel='', ax=axs[1], title='wave', color=color)\n",
-        "\n",
-        "axs[1].legend(loc='center',bbox_to_anchor=(0.5,-0.35,0,0),ncol=3)\n",
-        "\n",
-        "axs[1].grid(axis='y')\n",
-        "\n",
-        "axs[1].yaxis.set_major_formatter(FuncFormatter(lambda x, p: format(int(x), ',')))"
-      ],
-      "metadata": {
-        "id": "olwiqb2gGGle",
-        "outputId": "da793288-b8fa-4ae6-f9e0-b4e6eb9e77d0",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 785
-        }
-      },
-      "execution_count": 29,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1600x800 with 2 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
+     "data": {
+      "text/plain": [
+       "['CORPS OF ENGINEERS',\n",
+       " 'NATIONAL HURRICANE CENTER',\n",
+       " 'NATIONAL WEATHER SERVICE',\n",
+       " 'U. S. COAST GUARD',\n",
+       " nan,\n",
+       " 'NDBC ENGINEERING',\n",
+       " 'NATIONAL DATA BUOY CENTER',\n",
+       " 'GREAT LAKES RESEARCH LABORATORY',\n",
+       " 'NATIONAL ACADEMY OF SCIENCES',\n",
+       " 'SAILDRONE',\n",
+       " 'BP INC.',\n",
+       " 'EPA & MEXICAN GOVERNMENT COOPERATIVE PROGRAM',\n",
+       " 'CHESAPEAKE BAY INTERPRETIVE BUOY SYSTEM',\n",
+       " 'SCRIPPS WAVERIDER COASTAL DATA INFORMATION PROGRAM',\n",
+       " 'EVERGLADES NATIONAL PARK',\n",
+       " 'INTEGRATED CORAL OBSERVING NETWORK',\n",
+       " 'LOUISIANA OFFSHORE OIL PORT',\n",
+       " 'MOSS LANDING MARINE LABORATORIES',\n",
+       " 'NATIONAL ESTUARINE RESEARCH RESERVE SYSTEM',\n",
+       " 'NATIONAL PARK SERVICE - LAKE MEAD NATIONAL REC AREA',\n",
+       " 'NATIONAL RENEWABLE ENERGY LABORATORY',\n",
+       " 'NATIONAL WEATHER SERVICE, ALASKA REGION',\n",
+       " 'NATIONAL WEATHER SERVICE, CENTRAL REGION',\n",
+       " 'NATIONAL WEATHER SERVICE, EASTERN REGION',\n",
+       " 'OCEAN OBSERVATORIES INITIATIVE',\n",
+       " 'PETROBRAS',\n",
+       " 'SHELL OIL',\n",
+       " 'U.S. ARMY CORPS OF ENGINEERS',\n",
+       " 'WOODS HOLE OCEANOGRAPHIC INSTITUTION',\n",
+       " 'VERMONT EPSCOR',\n",
+       " 'NATIONAL PARK SERVICES - SLEEPING BEAR DUNES',\n",
+       " 'ALASKA OCEAN OBSERVING SYSTEM',\n",
+       " 'SUNY PLATTSBURGH CEES / LAKE CHAMPLAIN RESEARCH INST.',\n",
+       " 'UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAGNE',\n",
+       " 'MARINE EXCHANGE OF ALASKA',\n",
+       " 'TEXAS COASTAL OCEAN OBSERVATION NETWORK',\n",
+       " 'LIMNOTECH',\n",
+       " 'USF COMPS MARINE NETWORK',\n",
+       " 'MURPHY OIL CORP.',\n",
+       " 'UNIVERSITY OF NEW HAMPSHIRE',\n",
+       " 'STONY BROOK UNIVERSITY',\n",
+       " 'COASTAL OCEAN RESEARCH AND MONITORING PROGRAM',\n",
+       " 'U.S. NAVY',\n",
+       " 'UNIVERSITY OF NORTH CAROLINA COASTAL STUDIES',\n",
+       " 'CARIBBEAN INTEGRATED COASTAL OCEAN OBSERVING SYSTEM',\n",
+       " 'UNIVERSITY OF WISCONSIN AT MILWAUKEE',\n",
+       " 'ILLINOIS-INDIANA SEA GRANT',\n",
+       " 'COASTAL DATA INFORMATION PROGRAM/PMEL',\n",
+       " 'GREAT LAKES WATER AUTHORITY',\n",
+       " 'CLEVELAND WATER ALLIANCE',\n",
+       " 'MICHIGAN TECHNICAL UNIVERSITY']"
       ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_out.loc[df_out[\"LO\"]==\"non-NOS\",\"sponsor\"].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "G7nxU22BaF3D"
+   },
+   "source": [
+    "## Group by the new LO column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 455
     },
+    "id": "QFzkP-lTOhRO",
+    "outputId": "35b65707-f7de-44f2-c48c-f99de8a414d4"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [
-        "df_all = df_met + df_wave\n",
-        "\n",
-        "df_all['nos-percent'] = df_all['NOS'] / (df_all['NOS'] + df_all['non-NOS'])\n",
-        "\n",
-        "df_all"
-      ],
-      "metadata": {
-        "id": "iBxxd3tCO4Cm",
-        "outputId": "0c03e4de-a4f4-48aa-e8ae-f63bfa16da89",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 455
-        }
+     "data": {
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "summary": "{\n  \"name\": \"totals\",\n  \"rows\": 148,\n  \"fields\": [\n    {\n      \"column\": \"time (UTC)\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 74,\n        \"samples\": [\n          \"2018-May\",\n          \"2023-Apr\",\n          \"2019-Jul\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LO\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"non-NOS\",\n          \"NOS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"met\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1596000,\n        \"min\": 896988,\n        \"max\": 4955340,\n        \"num_unique_values\": 148,\n        \"samples\": [\n          1405670,\n          4411270\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wave\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 135475,\n        \"min\": 44916,\n        \"max\": 657898,\n        \"num_unique_values\": 148,\n        \"samples\": [\n          273798,\n          78488\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1498870,\n        \"min\": 1078936,\n        \"max\": 5179068,\n        \"num_unique_values\": 148,\n        \"samples\": [\n          1679468,\n          4489758\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+       "type": "dataframe",
+       "variable_name": "totals"
       },
-      "execution_count": 30,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "                NOS  non-NOS  nos-percent\n",
-              "time (UTC)                               \n",
-              "2018-Jan    4413688  1371366     0.762947\n",
-              "2018-Feb    4009172  1234916     0.764513\n",
-              "2018-Mar    4429572  1407126     0.758917\n",
-              "2018-Apr    4366098  1401076     0.757060\n",
-              "2018-May    4686732  1499500     0.757607\n",
-              "...             ...      ...          ...\n",
-              "2023-Oct    5101376  2338116     0.685716\n",
-              "2023-Nov    4844546  2100150     0.697589\n",
-              "2023-Dec    4756716  1974644     0.706650\n",
-              "2024-Jan    4948372  1888004     0.723830\n",
-              "2024-Feb    4618508  1733972     0.727040\n",
-              "\n",
-              "[74 rows x 3 columns]"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-8b7db6be-2d65-4c68-8e2f-1e95cc4bb734\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
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-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>NOS</th>\n",
-              "      <th>non-NOS</th>\n",
-              "      <th>nos-percent</th>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>time (UTC)</th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>2018-Jan</th>\n",
-              "      <td>4413688</td>\n",
-              "      <td>1371366</td>\n",
-              "      <td>0.762947</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2018-Feb</th>\n",
-              "      <td>4009172</td>\n",
-              "      <td>1234916</td>\n",
-              "      <td>0.764513</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2018-Mar</th>\n",
-              "      <td>4429572</td>\n",
-              "      <td>1407126</td>\n",
-              "      <td>0.758917</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2018-Apr</th>\n",
-              "      <td>4366098</td>\n",
-              "      <td>1401076</td>\n",
-              "      <td>0.757060</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2018-May</th>\n",
-              "      <td>4686732</td>\n",
-              "      <td>1499500</td>\n",
-              "      <td>0.757607</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2023-Oct</th>\n",
-              "      <td>5101376</td>\n",
-              "      <td>2338116</td>\n",
-              "      <td>0.685716</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2023-Nov</th>\n",
-              "      <td>4844546</td>\n",
-              "      <td>2100150</td>\n",
-              "      <td>0.697589</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2023-Dec</th>\n",
-              "      <td>4756716</td>\n",
-              "      <td>1974644</td>\n",
-              "      <td>0.706650</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2024-Jan</th>\n",
-              "      <td>4948372</td>\n",
-              "      <td>1888004</td>\n",
-              "      <td>0.723830</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2024-Feb</th>\n",
-              "      <td>4618508</td>\n",
-              "      <td>1733972</td>\n",
-              "      <td>0.727040</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>74 rows × 3 columns</p>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8b7db6be-2d65-4c68-8e2f-1e95cc4bb734')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-8b7db6be-2d65-4c68-8e2f-1e95cc4bb734 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-8b7db6be-2d65-4c68-8e2f-1e95cc4bb734');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-60a9bdc9-23ac-43b0-82f6-c3764b48ba35\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-60a9bdc9-23ac-43b0-82f6-c3764b48ba35')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-60a9bdc9-23ac-43b0-82f6-c3764b48ba35 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df_all",
-              "summary": "{\n  \"name\": \"df_all\",\n  \"rows\": 74,\n  \"fields\": [\n    {\n      \"column\": \"time (UTC)\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 74,\n        \"samples\": [\n          \"2018-May\",\n          \"2023-Apr\",\n          \"2019-Jul\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NOS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 351806,\n        \"min\": 3060446,\n        \"max\": 5179068,\n        \"num_unique_values\": 74,\n        \"samples\": [\n          4686732,\n          4747106,\n          4871350\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"non-NOS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 297542,\n        \"min\": 1078936,\n        \"max\": 2456208,\n        \"num_unique_values\": 74,\n        \"samples\": [\n          1499500,\n          1910384,\n          1566330\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nos-percent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02533331682923217,\n        \"min\": 0.6783078961389215,\n        \"max\": 0.7820300641759647,\n        \"num_unique_values\": 74,\n        \"samples\": [\n          0.757606892208375,\n          0.713047409759534,\n          0.7566934050776056\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 30
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# Calculate the percent contributions per year"
+      "text/html": [
+       "\n",
+       "  <div id=\"df-fcaf099c-dc56-455c-b482-a543a694c6a2\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>LO</th>\n",
+       "      <th>met</th>\n",
+       "      <th>wave</th>\n",
+       "      <th>total</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time (UTC)</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-Jan</th>\n",
+       "      <td>NOS</td>\n",
+       "      <td>4350064</td>\n",
+       "      <td>63624</td>\n",
+       "      <td>4413688</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-Feb</th>\n",
+       "      <td>NOS</td>\n",
+       "      <td>3947286</td>\n",
+       "      <td>61886</td>\n",
+       "      <td>4009172</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-Mar</th>\n",
+       "      <td>NOS</td>\n",
+       "      <td>4360558</td>\n",
+       "      <td>69014</td>\n",
+       "      <td>4429572</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-Apr</th>\n",
+       "      <td>NOS</td>\n",
+       "      <td>4290340</td>\n",
+       "      <td>75758</td>\n",
+       "      <td>4366098</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-May</th>\n",
+       "      <td>NOS</td>\n",
+       "      <td>4530964</td>\n",
+       "      <td>155768</td>\n",
+       "      <td>4686732</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-Oct</th>\n",
+       "      <td>non-NOS</td>\n",
+       "      <td>1758336</td>\n",
+       "      <td>579780</td>\n",
+       "      <td>2338116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-Nov</th>\n",
+       "      <td>non-NOS</td>\n",
+       "      <td>1613288</td>\n",
+       "      <td>486862</td>\n",
+       "      <td>2100150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-Dec</th>\n",
+       "      <td>non-NOS</td>\n",
+       "      <td>1532968</td>\n",
+       "      <td>441676</td>\n",
+       "      <td>1974644</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-Jan</th>\n",
+       "      <td>non-NOS</td>\n",
+       "      <td>1463756</td>\n",
+       "      <td>424248</td>\n",
+       "      <td>1888004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-Feb</th>\n",
+       "      <td>non-NOS</td>\n",
+       "      <td>1333212</td>\n",
+       "      <td>400760</td>\n",
+       "      <td>1733972</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>148 rows × 4 columns</p>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fcaf099c-dc56-455c-b482-a543a694c6a2')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-fcaf099c-dc56-455c-b482-a543a694c6a2 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-fcaf099c-dc56-455c-b482-a543a694c6a2');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-ff647842-b9e4-4ecd-8265-56bfcbb78c27\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ff647842-b9e4-4ecd-8265-56bfcbb78c27')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-ff647842-b9e4-4ecd-8265-56bfcbb78c27 button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "metadata": {
-        "id": "l0knZirUYlwR"
-      }
+      "text/plain": [
+       "                 LO      met    wave    total\n",
+       "time (UTC)                                   \n",
+       "2018-Jan        NOS  4350064   63624  4413688\n",
+       "2018-Feb        NOS  3947286   61886  4009172\n",
+       "2018-Mar        NOS  4360558   69014  4429572\n",
+       "2018-Apr        NOS  4290340   75758  4366098\n",
+       "2018-May        NOS  4530964  155768  4686732\n",
+       "...             ...      ...     ...      ...\n",
+       "2023-Oct    non-NOS  1758336  579780  2338116\n",
+       "2023-Nov    non-NOS  1613288  486862  2100150\n",
+       "2023-Dec    non-NOS  1532968  441676  1974644\n",
+       "2024-Jan    non-NOS  1463756  424248  1888004\n",
+       "2024-Feb    non-NOS  1333212  400760  1733972\n",
+       "\n",
+       "[148 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "group = df_out.groupby(by=[\"LO\", pd.Grouper(key=\"time (UTC)\", freq=\"M\")])\n",
+    "\n",
+    "s = group[\n",
+    "    [\"met\", \"wave\"]\n",
+    "].sum()  # reducing the columns so the summary is digestable\n",
+    "\n",
+    "totals = s.assign(total=s[\"met\"] + s[\"wave\"])\n",
+    "\n",
+    "totals.reset_index([\"LO\"], inplace=True)\n",
+    "\n",
+    "totals.index = totals.index.to_period(\"M\").strftime(\"%Y-%b\")\n",
+    "\n",
+    "totals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 785
     },
+    "id": "olwiqb2gGGle",
+    "outputId": "da793288-b8fa-4ae6-f9e0-b4e6eb9e77d0"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [
-        "all = df_all.reset_index()\n",
-        "all['time (UTC)'] = pd.to_datetime(all['time (UTC)'])\n",
-        "year_group = all.groupby(by=pd.Grouper(key=\"time (UTC)\", freq=\"Y\"))\n",
-        "s = year_group[['NOS','non-NOS']].sum()\n",
-        "\n",
-        "year_totals = s.assign(nos_percent=s[\"NOS\"] / ( s[\"NOS\"]+ s[\"non-NOS\"]))\n",
-        "\n",
-        "year_totals.index = year_totals.index.to_period(\"M\").strftime('%Y')\n",
-        "\n",
-        "year_totals"
-      ],
-      "metadata": {
-        "id": "VW71fx9aUfqL",
-        "outputId": "6070539b-bf7a-4dc6-ceb0-a0424ccb7755",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 300
-        }
-      },
-      "execution_count": 31,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "                 NOS   non-NOS  nos_percent\n",
-              "time (UTC)                                 \n",
-              "2018        54063850  17153684     0.759137\n",
-              "2019        55582460  17399994     0.761587\n",
-              "2020        53539838  19468350     0.733340\n",
-              "2021        53032882  20170004     0.724464\n",
-              "2022        56699818  22449876     0.716362\n",
-              "2023        58911340  25300712     0.699559\n",
-              "2024         9566880   3621976     0.725376"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-5a532b18-8321-4633-b26f-da6081605af2\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
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-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
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-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>NOS</th>\n",
-              "      <th>non-NOS</th>\n",
-              "      <th>nos_percent</th>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>time (UTC)</th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>2018</th>\n",
-              "      <td>54063850</td>\n",
-              "      <td>17153684</td>\n",
-              "      <td>0.759137</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2019</th>\n",
-              "      <td>55582460</td>\n",
-              "      <td>17399994</td>\n",
-              "      <td>0.761587</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2020</th>\n",
-              "      <td>53539838</td>\n",
-              "      <td>19468350</td>\n",
-              "      <td>0.733340</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2021</th>\n",
-              "      <td>53032882</td>\n",
-              "      <td>20170004</td>\n",
-              "      <td>0.724464</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2022</th>\n",
-              "      <td>56699818</td>\n",
-              "      <td>22449876</td>\n",
-              "      <td>0.716362</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2023</th>\n",
-              "      <td>58911340</td>\n",
-              "      <td>25300712</td>\n",
-              "      <td>0.699559</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2024</th>\n",
-              "      <td>9566880</td>\n",
-              "      <td>3621976</td>\n",
-              "      <td>0.725376</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
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-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-5a532b18-8321-4633-b26f-da6081605af2 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-5a532b18-8321-4633-b26f-da6081605af2');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-cfda53df-19fa-416e-a8d1-686507b1e371\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cfda53df-19fa-416e-a8d1-686507b1e371')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-cfda53df-19fa-416e-a8d1-686507b1e371 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "year_totals",
-              "summary": "{\n  \"name\": \"year_totals\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"time (UTC)\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"2018\",\n          \"2019\",\n          \"2023\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NOS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17406758,\n        \"min\": 9566880,\n        \"max\": 58911340,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          54063850,\n          55582460,\n          58911340\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"non-NOS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6925117,\n        \"min\": 3621976,\n        \"max\": 25300712,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          17153684,\n          17399994,\n          25300712\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nos_percent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.022393012118187044,\n        \"min\": 0.699559488230972,\n        \"max\": 0.7615866136811459,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.7591367878590124,\n          0.7615866136811459,\n          0.699559488230972\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 31
-        }
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 1600x800 with 2 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.ticker import FuncFormatter\n",
+    "\n",
+    "color = {\"NOS\":\"C0\",\n",
+    "         \"non-NOS\": \"C1\"}\n",
+    "\n",
+    "fig, axs = plt.subplots(nrows=2,ncols=1,figsize=(16,8))\n",
+    "\n",
+    "\n",
+    "# first chart\n",
+    "df_met = pd.DataFrame({\"NOS\": totals.loc[totals[\"LO\"]==\"NOS\",\"met\"],\n",
+    "                         \"non-NOS\": totals.loc[totals[\"LO\"]==\"non-NOS\",\"met\"],\n",
+    "                         \"nos-percent\":totals.loc[totals[\"LO\"]==\"NOS\",\"met\"] / (totals.loc[totals[\"LO\"]==\"NOS\",\"met\"] + totals.loc[totals[\"LO\"]==\"non-NOS\",\"met\"])\n",
+    "                         },\n",
+    "                         index= totals.index.unique())\n",
+    "\n",
+    "bar = df_met[[\"NOS\",\"non-NOS\"]].plot.bar(stacked=True, xlabel=\"\", ax=axs[0], rot=90, title=\"met\", color=color)\n",
+    "\n",
+    "axs[0].get_legend().remove()\n",
+    "\n",
+    "axs[0].grid(axis=\"y\")\n",
+    "\n",
+    "axs[0].yaxis.set_major_formatter(FuncFormatter(lambda x, p: format(int(x), \",\")))\n",
+    "axs[0].axes.get_xaxis().set_visible(False)\n",
+    "#axs[0].bar_label(bar,df_wave['percent'])\n",
+    "\n",
+    "\n",
+    "# second chart\n",
+    "df_wave = pd.DataFrame({\"NOS\": totals.loc[totals[\"LO\"]==\"NOS\",\"wave\"],\n",
+    "                         \"non-NOS\": totals.loc[totals[\"LO\"]==\"non-NOS\",\"wave\"],\n",
+    "                         \"nos-percent\":totals.loc[totals[\"LO\"]==\"NOS\",\"wave\"] / (totals.loc[totals[\"LO\"]==\"NOS\",\"wave\"] + totals.loc[totals[\"LO\"]==\"non-NOS\",\"wave\"])\n",
+    "                         },\n",
+    "                         index= totals.index.unique())\n",
+    "\n",
+    "df_wave[[\"NOS\",\"non-NOS\"]].plot.bar(stacked=True, xlabel=\"\", ax=axs[1], title=\"wave\", color=color)\n",
+    "\n",
+    "axs[1].legend(loc=\"center\",bbox_to_anchor=(0.5,-0.35,0,0),ncol=3)\n",
+    "\n",
+    "axs[1].grid(axis=\"y\")\n",
+    "\n",
+    "axs[1].yaxis.set_major_formatter(FuncFormatter(lambda x, p: format(int(x), \",\")))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 455
     },
+    "id": "iBxxd3tCO4Cm",
+    "outputId": "0c03e4de-a4f4-48aa-e8ae-f63bfa16da89"
+   },
+   "outputs": [
     {
-      "source": [
-        "# @title nos_percent\n",
-        "\n",
-        "from matplotlib import pyplot as plt\n",
-        "year_totals['nos_percent'].plot(kind='line', figsize=(8, 4), title='Percent of NOAA observations sent to GTS that are NOS')\n",
-        "plt.gca().spines[['top', 'right']].set_visible(False)"
-      ],
-      "cell_type": "code",
-      "execution_count": 33,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 800x400 with 1 Axes>"
-            ],
-            "image/png": 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UCtG2bVsxbdo0sXfvXq0JEqpcvIqftAQGBsLOzg4uLi4YNGgQTE1NsX37djg7O2Pbtm1Qq9UYOHAgUlJSNDdHR0c0btwYBw8e1NqXUqnEqFGjtNq2bt0KW1vbIk8wL1iGJzQ0FBYWFnj11Ve1HsfX1xempqaFHsfDwwMdO3bU/NvOzg5NmzbFrVu3yjUG8fHxOHfuHEaOHAlra2tNe8uWLfHqq6/i999/L9d+n2VhYYGPPvoIO3fuxNmzZ4vs8+jRIwCAmZlZsfspuC8jI0PTduHCBVy8eFHrnOHBgwcjJSUFe/fuLbSPP/74Aw8ePCjU//z588Ve3VqgPGM1btw4rX8XPBcK+hac77Vjxw6o1eoiHzc0NBTNmjWDu7u71nOka9euAFDoOdK5c2d4eHhotXXt2hW2trbYtGmTpi01NRX79+9HUFCQpk2hUMDAwADA03PoHj58iPz8fLRu3RpnzpwpfnBeoOBYJ0+erNX+8ccfA4DmyvECpXmOW1pa4tKlS7h+/XqZailpu4cPH+LPP//EwIED8ejRI81YP3jwAD169MD169dx7949rW3effddrWW1OnbsCJVKhTt37pSptpK87Gs1IyOjyJUzZsyYATs7O81tyJAhZa5tzpw52LBhA3x8fLB3717MmDEDvr6+aNWqValW1SjJ+++/r/Xvjh074sGDB1rvBUZGRpr/T09PR0pKCjp37oxbt24hPT293I/97H5TU1ORnp6Ojh07Fvl6eP61J4TA1q1b8dZbb0EIofX67dGjB9LT08v0uvrss8+Qn5+Pr7/+usj7S/M++uz9BeP36quv4tixY+jVqxfOnz+Pb775Bj169ICzszN27txZ6vqo/BhQScvSpUuxf/9+HDx4EJcvX8atW7fQo0cPAMD169chhEDjxo213rzt7Oxw5cqVQieOOzs7a36xF7h58yaaNm36wgtGrl+/jvT0dNjb2xd6nMzMzEKPU69evUL7sLKyQmpqarnGoOCXaNOmTQvd16xZM6SkpLxweafSmjRpEiwtLTF79uwi7y94wyx4gy1KUW++69atg4mJCRo0aIAbN27gxo0bMDQ0hJubG9avX19oH+vWrUP9+vWhVCo1/Rs2bAhjY+Mi+z+rPGPVuHFjrX83bNgQcrlcswRYUFAQ2rdvjzFjxsDBwQGDBg3C5s2btcLq9evXcenSpULPjyZNmgAofBFD/fr1C9Wnp6eHfv36YceOHcjJyQHw9ErfvLw8rYAKAGvWrEHLli1haGgIGxsb2NnZ4bfffiv3L/k7d+5ALpdrXRUOAI6OjrC0tCwU5ErzHJ87dy7S0tLQpEkTeHp6YurUqbhw4UKJtZS03Y0bNyCEwP/93/8VGu9Zs2YBKDzez9drZWUFAOV+TRbnZV+rZmZmyMzMLNT+4YcfYv/+/di/f3+RK2SU1uDBg3HkyBGkpqZi3759GDJkCM6ePYu33noLT548Kfd+gdKN8dGjRxEYGAgTExNYWlrCzs4On376KQC8VEDdvXs32rRpA0NDQ1hbW8POzg7Lli0rcp/Pv/aSk5ORlpaGn376qdDzqWBCoywXITVo0ADDhg3DTz/9hPj4+EL3l+Z99Nn7n30v9fPzw7Zt25CamorIyEhMnz4djx49Qv/+/XH58uVS10jlw8tKSYu/v7/mKv7nqdVqyGQy/PHHH1AoFIXuf34m4tm/sstCrVbD3t6+2HD07BWYAIqsBXj6l7ouK5hFnT17dpGzqM2aNQPwdEa0uKVmCoJEwQyFEAIbN25EVlZWoRlD4Okbf2ZmpuZnlZGRgV27duHJkyeFgiMAbNiwAV9++WWlLjL//L6NjIzw119/4eDBg/jtt9+wZ88ebNq0CV27dsW+ffugUCigVqvh6emJBQsWFLlPFxeXQvssyqBBg7BixQr88ccf6N27NzZv3gx3d3d4eXlp+qxbtw4jR45E7969MXXqVNjb20OhUGDevHm4efNmhR57cUrzHO/UqRNu3ryJHTt2YN++ffjf//6H77//HsuXL3/higwlbVfwh8GUKVM0f6w+7/mgXV1ek+7u7jh37hzu3bsHZ2dnTXuTJk00f+xUxBXn5ubmePXVV/Hqq69CX18fa9aswYkTJ9C5c+dy77OkMb558ya6desGd3d3LFiwAC4uLjAwMMDvv/+O77//vthPJ0py5MgR9OrVC506dcKPP/6IOnXqQF9fH6tWrcKGDRsK9X/+tVfwuEOHDsWIESOKfIxnV0sojRkzZmDt2rWYP39+ofdKCwsL1KlTp8Q/1i5cuABnZ2eYm5sXus/AwAB+fn7w8/NDkyZNMGrUKISGhmr+QKPKwYBKpdawYUMIIVC/fn3Nm3d59nHixAnk5eVBX1+/2D4HDhxA+/btyx1yn1eWgFWw0H50dHSh+65evQpbW1uYmJhUSF0fffQRFi5ciDlz5hRayqRDhw6wtLTEhg0bMGPGjCJ/If3yyy8AgDfffBMAcPjwYdy9exdz587VBNwCqampePfdd/Hrr79qlsXZtm0bnjx5gmXLlsHW1larf3R0ND777DMcPXoUHTp0KLL+8ozV9evXtWZVbty4AbVarbU+rVwuR7du3dCtWzcsWLAAX331FWbMmIGDBw8iMDAQDRs2xPnz59GtW7eXCs+dOnVCnTp1sGnTJnTo0AF//vknZsyYodVny5YtaNCgAbZt26b1WM//cirrc0ytVuP69etaP6fExESkpaUV+2UPJbG2tsaoUaMwatQoZGZmolOnTpg9e3aJS4a9aLuCZXf09fURGBhYrrqKUpbxKq7vy75W33zzTYSEhGD9+vWYNm1aqet5Ga1bt8aaNWuKnO171sv+Ubhr1y7k5ORg586dWrOtz5/+UtbH2rp1KwwNDbF3714olUpN+6pVq0q1vZ2dHczMzKBSqSrs+dSwYUMMHToUK1asQEBAQKH733zzTaxcuRLh4eFFvpcdOXIEMTExeO+990p8rIIJnJJ+fvTy+BE/lVrfvn2hUCgwZ86cQjMhQgg8ePCgxH3069cPKSkpWLJkSaH7CvY5cOBAqFQqfP7554X65Ofnl+tbjgp+SZVm2zp16sDb2xtr1qzR6h8VFYV9+/bhjTfeKPPjF6dgFnXHjh2FviLV2NgYU6ZMQXR0dKHQBDw9T3H16tXo0aMH2rRpA+Cfj/enTp2K/v37a93Gjh2Lxo0ba81Mr1u3Dg0aNMD7779fqP+UKVNgamr6wo/5yzNWS5cu1fp3wbeyvP766wCenvP4PG9vbwDQfBQ/cOBA3Lt3DytXrizUNzs7u9SnYMjlcvTv3x+7du3C2rVrkZ+fX+jj/YI/DJ59zp84cQLHjh3T6lewxm9pnmMF47Jw4UKt9oIZ4Z49e5aq/mc9//ozNTVFo0aNNGNW3u3s7e3RpUsXrFixoshfysnJyWWuFXj6mizta7m4sX3Z1+rAgQPh4eGBzz//HMePHy+yT3lmfR8/flzo+VGgYM3iok5LeFZZxqcoRT1v09PTiwySZXkshUIBmUwGlUqlaYuJiSn1t14pFAr069cPW7duRVRUVKH7y/t8+uyzz5CXl4dvvvmm0H1Tp06FkZER3nvvvULP94cPH+L999+HsbExpk6dqmk/ePBgkT/7gvOaS/r50cvjDCqVWsOGDfHFF19g+vTpiImJQe/evWFmZobbt29j+/btePfddzFlypQX7mP48OH45ZdfMHnyZERGRqJjx47IysrCgQMH8OGHH+Ltt99G586d8d5772HevHk4d+4cunfvDn19fVy/fh2hoaFYtGgR+vfvX6bavb29oVAoMH/+fKSnp0OpVKJr166wt7cvsv+3336L119/HW3btsXo0aORnZ2NxYsXw8LCothzRstr0qRJ+P7773H+/PlCsz2ffPIJzp49i/nz5+PYsWPo168fjIyMEB4ejnXr1qFZs2ZYs2YNgKfhbevWrXj11VeL/ViyV69eWLRoEZKSkpCfn4+DBw9i4sSJRfZVKpXo0aMHQkND8cMPPxQ7413Wsbp9+zZ69eqF1157DceOHcO6deswZMgQzcfqc+fOxV9//YWePXvC1dUVSUlJ+PHHH1G3bl3N7MewYcOwefNmvP/++zh48CDat28PlUqFq1evYvPmzdi7d2+xp6o8LygoCIsXL8asWbPg6elZaOb5zTffxLZt29CnTx/07NkTt2/fxvLly+Hh4aF1/qKRkRE8PDywadMmNGnSBNbW1mjRogVatGhR6DG9vLwwYsQI/PTTT0hLS0Pnzp0RGRmJNWvWoHfv3njllVdKVfuzPDw80KVLF/j6+sLa2hqnTp3Cli1bMH78+JfebunSpejQoQM8PT0xduxYNGjQAImJiTh27Bju3r2L8+fPl7leX19fLFu2DF988QUaNWoEe3t7zUVuz3vR2L7Ma1VfXx/bt29Hjx490KFDB/Tt2xcdO3aEiYkJ7t27h507dyI2NrbMfzA8fvwY7dq1Q5s2bfDaa6/BxcUFaWlp+PXXX3HkyBH07t0bPj4+JY7PgQMHsGDBAjg5OaF+/fpFzg4Wp3v37jAwMMBbb72F9957D5mZmVi5ciXs7e0L/aFRlp9Fz549sWDBArz22msYMmQIkpKSsHTpUjRq1KhU5zwDwNdff42DBw8iICAAY8eOhYeHBx4+fIgzZ87gwIEDRf6RWpKCWdSC98NnNW7cGGvWrME777wDT0/PQt8klZKSgo0bN6Jhw4aabSZMmIDHjx+jT58+cHd3R25uLiIiIrBp0ya4ubkVugCYKkGVrxtAOqm4dVCLsnXrVtGhQwdhYmIiTExMhLu7uxg3bpyIjo7W9ClYqL8ojx8/FjNmzBD169cX+vr6wtHRUfTv31/cvHlTq99PP/0kfH19hZGRkTAzMxOenp5i2rRpmuVEhNBebuRZnTt3LrRsysqVK0WDBg2EQqEo1ZJTBw4cEO3btxdGRkbC3NxcvPXWW4UW/y7vMlPPK1g65tllpgqoVCqxatUq0b59e2Fubi4MDQ1F8+bNxZw5c0RmZqam39atWwX+/iKA4hw6dEgAEIsWLRL//e9/BQARFhZWbP/Vq1cLAIUWZH9eacaq4BgvX74s+vfvL8zMzISVlZUYP3681kL9YWFh4u233xZOTk7CwMBAODk5icGDB4tr165p7S83N1fMnz9fNG/eXCiVSmFlZSV8fX3FnDlztJYFwt+LhRdHrVYLFxcXAUB88cUXRd7/1VdfCVdXV6FUKoWPj4/YvXu3GDFihHB1ddXqGxERIXx9fYWBgYHWEkTFLdQ/Z84czevAxcXlhQv1P+/55/gXX3wh/P39haWlpTAyMhLu7u7iyy+/LHFZnNJud/PmTTF8+HDh6Ogo9PX1hbOzs3jzzTfFli1bNH2Kex8peO4/+5pLSEgQPXv2FGZmZqVaHL64sRWidM+/F0lLSxNz584VPj4+wtTUVBgYGAgXFxfRv3//QsuQFXjRMlN5eXli5cqVonfv3prnjbGxsfDx8RHffvttoWXLinL16lXRqVMnYWRkVORC/c8vw1Yw9s+u/bpz507RsmVLYWhoKNzc3MT8+fM1S/0926+sP4vg4GDNF624u7uLVatWFfkcf9FrLzExUYwbN064uLhofg9069ZN/PTTTyWOTXGvievXr2ve34t6n71w4YIYPHiwqFOnjuYxBw8eXORap3/88Yf417/+Jdzd3TXPiUaNGokJEyaIxMTEEmuklycTQsfOWiciIiKiWo3noBIRERGRTmFAJSIiIiKdwoBKRERERDqFAZWIiIiIdAoDKhERERHpFAZUIiIiItIpNSKgCiGQkZGhc9/zTERERERlVyMC6qNHj2BhYYFHjx5JXQoRERERvaQaEVCJiIiIqOZgQCUiIiIincKASkREREQ6hQGViIiIiHQKAyoRERER6RQGVCIiIiLSKQyoRERERKRTGFCJiIiISKcwoBIRERGRTmFAJSIiIiKdwoBKRERERDpFT+oCiEoihMDDrFzcT3uCB1k5CKhvAyMDhdRlERERUSVhQCXJPclTISH9Ce6nZePe37f7adm4n/ZPW06+WtO/UxM7rB7pB7lcJmHVREREVFkYUKlSPTv7+U/w/CeE3kt7gpTMnFLty95MibTHefjrWjJ+OnIL73duWMnVExERkRQYUOmlPD/7+eysZ1Gzn8Ux1JfD2dIITpZGmv8W/L+zpREcLJRQ6imwMTIW07ddxHd7o+Ff3xqt6llVwVESERFRVZIJIYTURbysjIwMWFhYID09Hebm5lKXU2MUN/t5P/3pzOe91OwyzX7+Ez4NCwVQS2N9yGQlf2QvhMCEjWex+0I86loZ4beJHWFhpP+yh0pEREQ6pFwzqEuXLsW3336LhIQEeHl5YfHixfD39y+yb5cuXXD48OFC7W+88QZ+++03zb+vXLmC//znPzh8+DDy8/Ph4eGBrVu3ol69euUpkUohJ1+F+LTCs59PA+jTMPokr/yzn06WhnC2NIKjhSGUehVzUZNMJsNXfT1x4W46Yh8+xidbL+DHd1qVKtwSERFR9VDmgLpp0yZMnjwZy5cvR0BAABYuXIgePXogOjoa9vb2hfpv27YNubm5mn8/ePAAXl5eGDBggKbt5s2b6NChA0aPHo05c+bA3Nwcly5dgqGhYTkPi4QQSH2ch/tp2bibWnj2835aNpIflW72085MqZnpfH7208nSCFalnP2sKOaG+lg82Af9lkXgj6gErD8Ri6FtXKvs8YmIiKhylfkj/oCAAPj5+WHJkiUAALVaDRcXF0yYMAGffPJJidsvXLgQM2fORHx8PExMTAAAgwYNgr6+PtauXVuOQ6idH/Hn5D899/NeWjbupb7c7OezH7VX5uxnRVv51y18+fsVGOjJsWNcezSrUzt+9kRERDVdmQJqbm4ujI2NsWXLFvTu3VvTPmLECKSlpWHHjh0l7sPT0xNt27bFTz/9BOBpwLWwsMC0adMQHh6Os2fPon79+pg+fbrWYzwrJycHOTn/zP5lZGTAxcWlxgTUZ2c/NRcbpZZ/9vNpADWEk4URnK2knf2sSGq1wOg1J3EwOhkN7Uywa0IHGBvwuj8iIqLqrky/zVNSUqBSqeDg4KDV7uDggKtXr5a4fWRkJKKiohAcHKxpS0pKQmZmJr7++mt88cUXmD9/Pvbs2YO+ffvi4MGD6Ny5c6H9zJs3D3PmzClL6Trl2dlPzVXvqS83++lk8XfwtKoes58VQS6X4bsBXnjjhyO4mZyFWTsu4dsBXlKXRURERC+pSqebgoOD4enpqXVBlVr9NIi9/fbb+Pe//w0A8Pb2RkREBJYvX15kQJ0+fTomT56s+XfBDKouKGr2858F6F9u9rPg4/e6VtV/9rOi2JgqsTDIB+/87zhCT99F+0a26O3jLHVZRERE9BLKFFBtbW2hUCiQmJio1Z6YmAhHR8cXbpuVlYWQkBDMnTu30D719PTg4eGh1d6sWTOEh4cXuS+lUgmlUlmW0ivUg8wcRCc++ue8z+e+Aam8s58FM59OlkaoY1mzZz8rUtuGNpjQtTEWhV3HjO0X4eViifq2JlKXRUREROVUpoBqYGAAX19fhIWFac4PVavVCAsLw/jx41+4bWhoKHJycjB06NBC+/Tz80N0dLRW+7Vr1+DqqptXZu88fx9zdl1+YR9bUyWcrQrPfhZcCW9tYlDrZz8r0oSujXDs1gNE3n6ICRvPYOsH7RjwiYiIqqkyf8Q/efJkjBgxAq1bt4a/vz8WLlyIrKwsjBo1CgAwfPhwODs7Y968eVrbBQcHo3fv3rCxsSm0z6lTpyIoKAidOnXCK6+8gj179mDXrl04dOhQ+Y6qkrnaGKOBrclz637+M/vpaGEIQ32Go6qkp5Djh0E+eH3RX4i6l4Gv/7iKWW81l7osIiIiKocyB9SgoCAkJydj5syZSEhIgLe3N/bs2aO5cCo2NhZyuVxrm+joaISHh2Pfvn1F7rNPnz5Yvnw55s2bh4kTJ6Jp06bYunUrOnToUI5Dqnxd3R3Q1d2h5I5UpRwtDPHfgV741+pTWHU0Bu0a2uJVD/6ciIiIqht+1SnVOJ/vvozg8NuwNNbH7xM7wsnSSOqSiIiIqAzkJXchql7+85o7PJ0tkPY4D5NCziJfVfJFa0RERKQ7GFCpxjHQk2PJEB+YKvVwMiYVi8KuS10SERERlQEDKtVIrjYm+KqvJwBgycEbiLiRInFFREREVFoMqFRj9fJywiA/FwgBTNp0DimZpfuCBCIiIpIWAyrVaLPeao7G9qZIfpSDyZvPQ62u9tcEEhER1XgMqFSjGRkosGRIKyj15PjrWjJWHrkldUlERERUAgZUqvGaOpppFu3/dm80zsSmSlwRERERvQgDKtUKg/1d0LNlHeSrBSZuPIv07DypSyIiIqJiMKBSrSCTyTCvrydcrI1wNzUb07ddQA34jgoiIqIaiQGVag1zQ30sHtwKenIZfr+YgPUnYqUuiYiIiIrAgEq1ireLJf7zmjsAYO7uy7gSnyFxRURERPQ8BlSqdUZ3qI9XmtohN1+N8RvO4HFuvtQlERER0TMYUKnWkctl+G6AFxzMlbiZnIVZOy5JXRIRERE9gwGVaiUbUyUWBvlAJgNCT9/Fr2fvSV0SERER/Y0BlWqttg1tMKFrYwDAjO0XcTslS+KKiIiICGBApVpuYtdG8K9vjaxcFSZsPIOcfJXUJREREdV6DKhUq+kp5Fg0yBtWxvqIupeBr/+4KnVJREREtR4DKtV6dSyM8N0ALwDAqqMx2H85UeKKiIiIajcGVCIA3Zo5YHSH+gCAqVvO435atsQVERER1V4MqER/m/ZaU3g6WyDtcR4mhZxFvkotdUlERES1EgMq0d+UegosGeIDU6UeTsak4oew61KXREREVCsxoBI9w9XGBF/19QQALD54AxE3UiSuiIiIqPZhQCV6Ti8vJwS1doEQwKRN55CSmSN1SURERLUKAypREWb3ao5G9qZIfpSDjzefh1otpC6JiIio1mBAJSqCkYECS4e0glJPjsPXkrHyyC2pSyIiIqo1GFCJitHU0Qyz3moOAPh2bzTOxqZKXBEREVHtwIBK9AKD/V3Qs2Ud5KsFJmw8i/TsPKlLIiIiqvEYUIleQCaTYV5fT7hYG+Fuajamb7sAIXg+KhERUWViQCUqgbmhPhYPbgU9uQy/X0zAhshYqUsiIiKq0RhQiUrB28US015rCgCYu+syriZkSFwRERFRzcWASlRKYzo0QJemdsjJV2P8hrN4nJsvdUlEREQ1EgMqUSnJ5TL8d4AX7M2UuJGUidk7L0ldEhERUY3EgEpUBjamSiwc5A2ZDNh86i52nLsndUlEREQ1DgMqURm1a2iLCV0bAwA+3XYRMSlZEldERERUs5QroC5duhRubm4wNDREQEAAIiMji+3bpUsXyGSyQreePXtq+owcObLQ/a+99lp5SiOqEhO7NoJ/fWtk5aowfuMZ5OSrpC6JiIioxihzQN20aRMmT56MWbNm4cyZM/Dy8kKPHj2QlJRUZP9t27YhPj5ec4uKioJCocCAAQO0+r322mta/TZu3Fi+IyKqAnoKORYN8oaVsT6i7mVg/h/RUpdERERUY5Q5oC5YsABjx47FqFGj4OHhgeXLl8PY2Bg///xzkf2tra3h6Oioue3fvx/GxsaFAqpSqdTqZ2VlVb4jIqoidSyM8N0ALwDAz0dv48DlRIkrIiIiqhnKFFBzc3Nx+vRpBAYG/rMDuRyBgYE4duxYqfYRHByMQYMGwcTERKv90KFDsLe3R9OmTfHBBx/gwYMHxe4jJycHGRkZWjciKXRr5oB/ta8PAJiy5Tzup2VLXBEREVH1V6aAmpKSApVKBQcHB612BwcHJCQklLh9ZGQkoqKiMGbMGK321157Db/88gvCwsIwf/58HD58GK+//jpUqqLP65s3bx4sLCw0NxcXl7IcBlGF+s/rTeHpbIG0x3n4KOQc8lVqqUsiIiKq1qr0Kv7g4GB4enrC399fq33QoEHo1asXPD090bt3b+zevRsnT57EoUOHitzP9OnTkZ6errnFxcVVQfVERVPqKbB4sA9MlXqIjHmIH8KuS10SERFRtVamgGprawuFQoHERO1z7RITE+Ho6PjCbbOyshASEoLRo0eX+DgNGjSAra0tbty4UeT9SqUS5ubmWjciKbnZmuDLPi0AAIsP3kDEzRSJKyIiIqq+yhRQDQwM4Ovri7CwME2bWq1GWFgY2rZt+8JtQ0NDkZOTg6FDh5b4OHfv3sWDBw9Qp06dspRHJKm3vZ0R1NoFQgAfhZxDSmaO1CURERFVS2X+iH/y5MlYuXIl1qxZgytXruCDDz5AVlYWRo0aBQAYPnw4pk+fXmi74OBg9O7dGzY2NlrtmZmZmDp1Ko4fP46YmBiEhYXh7bffRqNGjdCjR49yHhaRNGb18kAje1MkPcrBx5vPQ60WUpdERERU7eiVdYOgoCAkJydj5syZSEhIgLe3N/bs2aO5cCo2NhZyuXbujY6ORnh4OPbt21dofwqFAhcuXMCaNWuQlpYGJycndO/eHZ9//jmUSmU5D4tIGsYGelg6pBV6LQnH4WvJ+F/4LbzbqaHUZREREVUrMiFEtZ/iycjIgIWFBdLT03k+KumEDSdi8en2i9CTyxD6flv41OO6vkRERKVVpVfxE9UWg/1d0NOzDvLVAhM2nkV6dp7UJREREVUbDKhElUAmk2FeP0/UtTLC3dRsfLrtImrAhxVERERVggGVqJKYG+pjyZBW0JPL8NvFeGyIjJW6JCIiomqBAZWoEnm7WGLaa00BAHN3XcbVBH4tLxERUUkYUIkq2ZgODdClqR1y8tUYv+EsHufmS10SERGRTmNAJapkcrkM/x3gBXszJW4kZWLOzstSl0RERKTTGFCJqoCNqRILB3lDJgM2nYrDjnP3pC6JiIhIZzGgElWRdg1tMeGVRgCAT7ddRExKlsQVERER6SYGVKIqNLFbY/i7WSMrV4XxG88gJ18ldUlEREQ6hwGVqArpKeRYNNgblsb6iLqXgfl/REtdEhERkc5hQCWqYnUsjPBdfy8AwM9Hb+PA5USJKyIiItItDKhEEgj0cMC/2tcHAEzZch7x6dkSV0RERKQ7GFCJJPKf15vC09kCaY/zMGnjOeSr1FKXREREpBMYUIkkotRTYPFgH5gq9RAZ8xA//HlD6pKIiIh0AgMqkYTcbE3wZZ8WAIDFf15HxM0UiSsiIiKSHgMqkcTe9nbGwNZ1IQTwUcg5pGTmSF0SERGRpBhQiXTA7F7N0cjeFEmPcjAl9DzUaiF1SURERJJhQCXSAcYGelgyxAdKPTkORSfjf+G3pC6JiIhIMgyoRDrC3dEcM9/yAAB8syca5+LSpC2IiIhIIgyoRDpkiH899PSsg3y1wISNZ5DxJE/qkoiIiKocAyqRDpHJZPiqryfqWhkh7mE2pm+9CCF4PioREdUuDKhEOsbCSB9LhrSCnlyG3y7GY2NknNQlERERVSkGVCId5O1iiWmvNQUAzNl1CVcTMiSuiIiIqOowoBLpqDEdGqBLUzvk5KsxfsNZPM7Nl7okIiKiKsGASqSj5HIZvhvgBXszJW4kZWLOzstSl0RERFQlGFCJdJitqRILg7whkwGbTsVhx7l7UpdERERU6RhQiXRcu0a2mPBKIwDAjO1RiEnJkrgiIiKiysWASlQNTOzWGP5u1sjMyceEjWeRk6+SuiQiIqJKw4BKVA3oKeRYNNgblsb6uHgvHd/siZa6JCIiokrDgEpUTdSxMMJ3/b0AAMHhtxF2JVHiioiIiCoHAypRNRLo4YBR7d0AAB+Hnkd8era0BREREVUCBlSiauaT193RwtkcaY/zMGnjOeSr1FKXREREVKEYUImqGaWeAksGt4KJgQKRMQ/xw583pC6JiIioQjGgElVDbrYm+KqvJwBg8Z/XEXEzReKKiIiIKk65AurSpUvh5uYGQ0NDBAQEIDIysti+Xbp0gUwmK3Tr2bNnkf3ff/99yGQyLFy4sDylEdUab3s7Y2DruhAC+CjkHB5k5khdEhERUYUoc0DdtGkTJk+ejFmzZuHMmTPw8vJCjx49kJSUVGT/bdu2IT4+XnOLioqCQqHAgAEDCvXdvn07jh8/Dicnp7IfCVEtNLtXczSyN0XSoxx8HHoearWQuiQiIqKXVuaAumDBAowdOxajRo2Ch4cHli9fDmNjY/z8889F9re2toajo6Pmtn//fhgbGxcKqPfu3cOECROwfv166Ovrl+9oiGoZYwM9LBniA6WeHIeikxEcflvqkoiIiF5amQJqbm4uTp8+jcDAwH92IJcjMDAQx44dK9U+goODMWjQIJiYmGja1Go1hg0bhqlTp6J58+Yl7iMnJwcZGRlaN6Layt3RHDPf8gAAzN9zFefi0qQtiIiI6CWVKaCmpKRApVLBwcFBq93BwQEJCQklbh8ZGYmoqCiMGTNGq33+/PnQ09PDxIkTS1XHvHnzYGFhobm5uLiU/iCIaqAh/vXwhqcj8tUCEzaeQcaTPKlLIiIiKrcqvYo/ODgYnp6e8Pf317SdPn0aixYtwurVqyGTyUq1n+nTpyM9PV1zi4uLq6ySiaoFmUyGeX1boq6VEeIeZmP6tosQguejEhFR9VSmgGprawuFQoHERO2vWExMTISjo+MLt83KykJISAhGjx6t1X7kyBEkJSWhXr160NPTg56eHu7cuYOPP/4Ybm5uRe5LqVTC3Nxc60ZU21kY6WPxYB/oyWX47UI8NkbyDzciIqqeyhRQDQwM4Ovri7CwME2bWq1GWFgY2rZt+8JtQ0NDkZOTg6FDh2q1Dxs2DBcuXMC5c+c0NycnJ0ydOhV79+4tS3lEtZ5PPStM7dEUADBn1yVEJzySuCIiIqKy0yvrBpMnT8aIESPQunVr+Pv7Y+HChcjKysKoUaMAAMOHD4ezszPmzZuntV1wcDB69+4NGxsbrXYbG5tCbfr6+nB0dETTpk3LWh5RrTe2YwNE3HyAw9eSMX7DGewc3wFGBgqpyyIiIiq1MgfUoKAgJCcnY+bMmUhISIC3tzf27NmjuXAqNjYWcrn2xGx0dDTCw8Oxb9++iqmaiIoll8vw34FeeGPREVxPysScXZfwdb+WUpdFRERUajJRA66kyMjIgIWFBdLT03k+KtHfIm6k4J3gExAC+GGwD3p58QswiIioeqjSq/iJqOq0a2SLCa80AgB8uu0iYlKyJK6IiIiodBhQiWqwid0aw9/NGpk5+Ziw8Sxy89VSl0RERFQiBlSiGkxPIcfCQd6wNNbHxXvpmL/nqtQlERERlYgBlaiGc7I0wrf9vQAAweG3EXYlsYQtiIiIpMWASlQLvOrhgFHt3QAAU0LPIz49W9qCiIiIXoABlaiW+OR1d7RwNkfq4zxMCjmHfBXPRyUiIt3EgEpUSyj1FFg8uBVMDBSIvP0Qi/+8IXVJRERERWJAJapF6tua4Ku+ngCAxX9ex7GbDySuiIiIqDAGVKJa5m1vZwzwrQu1AD7adBYPMnOkLomIiEgLAypRLTTn7eZoZG+KxIwcTAk9D7W62n+hHBER1SAMqES1kLGBHpYM8YGBnhwHo5MRHH5b6pKIiIg0GFCJail3R3PMfNMDADB/z1Wcj0uTtiAiIqK/MaAS1WLvBNTDG56OyFcLjN94BhlP8qQuiYiIiAGVqDaTyWSY17cl6loZIe5hNqZvuwgheD4qERFJiwGVqJazMNLH4sE+0JPL8NuFeIScjJO6JCIiquUYUIkIPvWsMLVHUwDA7J2XEJ3wSOKKiIioNmNAJSIAwNiODdCpiR1y8tUYv+EMsnNVUpdERES1FAMqEQEA5HIZFgz0gr2ZEteTMjFn1yWpSyIiolqKAZWINGxNlVgY5A2ZDAg5GYed5+9LXRIREdVCDKhEpKVdI1uMf6URAODTbRdx50GWxBUREVFtw4BKRIVM6tYYfm5WyMzJx4SNZ5Gbr5a6JCIiqkUYUImoED2FHIsG+cDSWB8X7qbjmz1XpS6JiIhqEQZUIiqSk6URvu3vBQD4X/ht/HdfNJIePZG4KiIiqg1kogZ8bUxGRgYsLCyQnp4Oc3NzqcshqlHm7rqMn4/eBgDoK2To6VkHI9q5waeelcSVERFRTcWASkQvpFYL7L4Yj9VHb+NMbJqm3cvFEiPbueINzzpQ6imkK5CIiGocBlQiKrULd9OwOiIGu8/HI1f19MIpW1MDDPGvh3fauMLB3FDiComIqCZgQCWiMkvJzEFIZCzWHY9FQsbT81L15DK81sIRo9q7oVU9K8hkMomrJCKi6ooBlYjKLU+lxr5LiVgTEYPImIea9hbO5hjR1g1veTnBUJ8f/xMRUdkwoBJRhbh0Px1rImKw49x95Py9bqq1iQEG+blgaBtXOFkaSVwhERFVFwyoRFShUrNyEXIyDmuPxeB++tOP/xVyGXo0d8CItm7wr2/Nj/+JiOiFGFCJqFLkq9Q4cCURqyNicPzWPx//N6tjjhFtXfG2tzOMDPjxPxERFcaASkSV7mpCBtZE3MH2s3fxJO/px/+WxvoI8nPBsDauqGtlLHGFRESkSxhQiajKpD3OxeZTcfjl2B3cTc0GAMhlQGAzB4xs54a2DW348T8RETGgElHVU6kF/ryahDURMQi/kaJpb+JgihHt3NDHxxnGBnoSVkhERFKSl2ejpUuXws3NDYaGhggICEBkZGSxfbt06QKZTFbo1rNnT02f2bNnw93dHSYmJrCyskJgYCBOnDhRntKIqBpQyGV41cMB68YEYP+/O2Fom3owNlDgWmImZmyPQpuvwvDF7suIffBY6lKJiEgCZZ5B3bRpE4YPH47ly5cjICAACxcuRGhoKKKjo2Fvb1+o/8OHD5Gbm6v594MHD+Dl5YX//e9/GDlyJABgw4YNsLe3R4MGDZCdnY3vv/8eoaGhuHHjBuzs7EqsiTOoRNVfenYetpy+i1+OxeDO38FUJgO6NrXHiHZu6NjYlh//ExHVEmUOqAEBAfDz88OSJUsAAGq1Gi4uLpgwYQI++eSTErdfuHAhZs6cifj4eJiYmBTZpyBwHjhwAN26dStxnwyoRDWHWi1w6FoSVkfcwV/XkjXtDe1MMKKdG/q2qgtTJT/+JyKqycr0Lp+bm4vTp09j+vTpmja5XI7AwEAcO3asVPsIDg7GoEGDig2nubm5+Omnn2BhYQEvL68i++Tk5CAnJ0fz74yMjDIcBRHpMrlchq7uDujq7oCbyZlYe+wOtpy+i5vJWZi54xK+3RONfr51MaKdG+rbFv0+QkRE1VuZzkFNSUmBSqWCg4ODVruDgwMSEhJK3D4yMhJRUVEYM2ZMoft2794NU1NTGBoa4vvvv8f+/ftha2tb5H7mzZsHCwsLzc3FxaUsh0FE1URDO1PM7tUcx6Z3xZxezdHAzgSPcvKxOiIGr3x3CCNXReJgdBLU6mp/rScRET2jTB/x379/H87OzoiIiEDbtm017dOmTcPhw4dLvLDpvffew7Fjx3DhwoVC92VlZSE+Ph4pKSlYuXIl/vzzT5w4caLI81qLmkF1cXHhR/xENZxaLXDkRgrWRMTgYHQSCt696tuaYFgbV/RvXRfmhvrSFklERC+tTDOotra2UCgUSExM1GpPTEyEo6PjC7fNyspCSEgIRo8eXeT9JiYmaNSoEdq0aYPg4GDo6ekhODi4yL5KpRLm5uZaNyKq+eRyGTo3scPPI/1w8OMuGN2hPsyUeridkoW5uy+j7Vdh+L9fo3Aj6ZHUpRIR0UsoU0A1MDCAr68vwsLCNG1qtRphYWFaM6pFCQ0NRU5ODoYOHVqqx1Kr1VqzpEREz3KzNcH/vemB4592w+e9W6CRvSmyclVYe/wOAhf8hWHBJ3DgciJU/PifiKjaKdcyUyNGjMCKFSvg7++PhQsXYvPmzbh69SocHBwwfPhwODs7Y968eVrbdezYEc7OzggJCdFqz8rKwpdffolevXqhTp06SElJwdKlS7FhwwacPn0azZs3L7EmXsVPREIIRNx8gNURMThwJVHz8X89a2MMb+uKAa1dYGHEj/+JiKqDMq/VEhQUhOTkZMycORMJCQnw9vbGnj17NBdOxcbGQi7XnpiNjo5GeHg49u3bV2h/CoUCV69exZo1a5CSkgIbGxv4+fnhyJEjpQqnREQAIJPJ0L6RLdo3skXcw8dYe/wONp2MQ+zDx/jityv4775r6NPKGSPbuaGJg5nU5RIR0Qvwq06JqMbKzlXh13P3sCYiBlcT/jkvtW0DG4xo54ZXPRygkHPxfyIiXcOASkQ1nhACJ24/xOqjMdh3OQEFp6U6WxphWFtXBLV2gZWJgbRFEhGRBgMqEdUq99Kyse74HYRExiL1cR4AQKknR29vZ4xo5wYPJ76HEBFJjQGViGqlJ3kq7Dx3H6sjYnA5/p9vo/Ovb42R7dzQ3cMBeooyLXRCREQVhAGViGo1IQRO3UnF6ogY7IlK0CxLVcfCEEPbuGKQnwtsTJUSV0lEVLswoBIR/S0h/QnWn7iDDSdi8SArFwBgoCdHLy8njGznhhbOFhJXSERUOzCgEhE950meCr9diMeaYzG4cDdd0+7raoUR7dzwegtH6PPjfyKiSsOASkRUDCEEzsalYU1EDH67EI/8vz/+tzdTYmgbVwz2rwc7M378T0RU0RhQiYhKISnjCdafiMX6E7FIyXz6NcwGCjl6tqyDEe3c4O1iKW2BREQ1CAMqEVEZ5Oar8UdUPFZHxOBsbJqm3cvFEiPbueINzzpQ6imkK5CIqAZgQCUiKqfzf3/8v/tCPHJVagCArakSQwLq4Z2AenAwN5S4QiKi6okBlYjoJaVk5mDjiVisO3EHiRlPP/7Xk8vwumcdjGznilb1rCCT8StViYhKiwGViKiC5KnU2BOVgDURMTh1J1XT7ulsgRHt3PBmyzow1OfH/0REJWFAJSKqBFH30rEmIgY7zt9Hbv7Tj/+tTQww2N8FQ9u4oo6FkcQVElFtp1YLpGXnwdrEQOpSCmFAJSKqRA+zcrExMhbrjt9BfPoTAIBCLkOP5g4Y2a4+/Nz48T8RSeP7/dcQcjIWK4a11rmVSBhQiYiqQL5Kjf2XE7E6IgYnbj/UtDerY46R7VzxtrczP/4noiqz91IC3lt7GgDw3QAv9PetK3FF2hhQiYiq2JX4DPxyLAbbz97Dk7ynH/9bGusjyM8Fw9q4oq6VscQVElFNdi3xEfosPYqsXBVGtXfDrLeaS11SIQyoREQSSXuci00n4/DLsTu4l5YNAJDLgFc9HDCinRvaNrDhx/9EVKHSH+fh7aXhiHnwGG0b2OCX0f46+dXNDKhERBJTqQXCriRizbEYHL3xQNPe1MEMg/1d0NvHGZbGuncRAxFVLyq1wL9Wn8Tha8lwtjTCrgkddPICKYABlYhIp1xLfIQ1ETHYduYesvNUAAADPTleb+GIID8XtKlvA7mcs6pEVHZf/3EVyw/fhKG+HFs/aIfmThZSl1QsBlQiIh2Unp2H7WfuIuRkHK4mPNK017M2RpCfC/r71uU3VRFRqe06fx8TNp4FACwe7IO3vJwkrujFGFCJiHSYEAIX76Uj5GQcdp67j8ycfABPz1V9pak9gvxc8Iq7vU6eQ0ZEuuHy/Qz0XXYUT/LUeL9zQ3zyurvUJZWIAZWIqJp4nJuP3y8mYNPJWJyM+eebquzMlOjXqi6C/FxQ39ZEwgqJSNc8zMrFW4vDcS8tG52a2GHVSD8oqsFpQgyoRETV0I2kTISeisOW03fxICtX0x5Q3xpBfi54vUUdGBlwXVWi2ixfpcaw4Egcu/UAbjbG2DGuAyyM9aUuq1QYUImIqrHcfDX+vJqITSfjcPhaMtR/v6ObGeqht7czgvxc0MJZdy+EIKLKM3fXZfx89DZMDBTYPq49mjiYSV1SqTGgEhHVEPfTsrHl9F1sPhWHu6nZmvbmTuYY5OeCXt7OsDCqHrMnRPRytp6+i49DzwMAVgzzRY/mjhJXVDYMqERENYxaLRBx8wFCTsZi36VE5KqefluVUk+ONzzrIMjPBQH1rfklAEQ11Pm4NAxYcQy5+WpM6tYY/361idQllRkDKhFRDZaalYvtZ+9h08k4RCf+s1xVfVsTDGhdF/1b1YU9l6siqjGSH+XgrcXhSMh4gsBmDvhpmG+1XDuZAZWIqBYQQuBcXBo2n3q6XFVW7tMvAVDIZejqbo+g1i7o0tQOelyuiqjays1XY8jK4zh1JxWN7E2x/cN2MDOsnqf1MKASEdUyWTn5+O1iPDadjMPpO/8sV2VvpsSA1nUxsLULXG24XBVRdTNj+0WsPxELM0M97BjXHg3sTKUuqdwYUImIarHriY+w6WQctp29h4fPLFfVtoENBvm7oEdzRxjqc7kqIl234UQsPt1+ETIZ8PMIP7zibi91SS+FAZWIiJCbr8aBK4kIORmHI9eTUfCbwdxQD318nBHkVw8eTnx/JdJFp2IeYvDK48hTCUzt0RTjXmkkdUkvjQGViIi03EvLRuipOISeuot7af8sV+XpbIEgPxf08naCeTU9r42opklIf4I3F4cjJTMHPT3rYMkQnxqxQgcDKhERFUmlFjh6IwWbTsZh3+UE5Kme/row1H+6XNUgv3rwc7OqEb8MiaqjJ3kqBK04hvN30+HuaIZtH7aDsYGe1GVVCAZUIiIq0YPMHM1yVdeTMjXtDWxNMNDPBf1a1YWdmVLCColqFyEEpoRewNYzd2FprI9d4zvAxdpY6rIqDAMqERGVmhACZ2LTsPlkHHZduI/Hfy9XpSeXoVszewT5uaBTYy5XRVTZVh+9jdm7LkMuA9aODkD7RrZSl1ShyvUOsnTpUri5ucHQ0BABAQGIjIwstm+XLl0gk8kK3Xr27AkAyMvLw3/+8x94enrCxMQETk5OGD58OO7fv1++IyIiokojk8ng62qF+f1bInJGIL7u6wmfepbIVwvsvZSIf60+hQ7zD+K/+6IR9/Cx1OUS1UgRN1Pw+W9XAACfvtGsxoVToBwzqJs2bcLw4cOxfPlyBAQEYOHChQgNDUV0dDTs7QsvafDw4UPk5v6zdMmDBw/g5eWF//3vfxg5ciTS09PRv39/jB07Fl5eXkhNTcWkSZOgUqlw6tSpUtXEGVQiImlFJzxdrmr72btIfZynaW/fyAZBfvXQ3cOBy1URVYC4h4/Ra0k4Uh/noY+PMxYM9KqR54GXOaAGBATAz88PS5YsAQCo1Wq4uLhgwoQJ+OSTT0rcfuHChZg5cybi4+NhYlL0QtAnT56Ev78/7ty5g3r16hW6PycnBzk5OZp/Z2RkwMXFhQGViEhiOfkq7L+ciE0n4xB+I0WzXJWFkT76+DhjkL8L3B35Pk1UHtm5KvRbFoHL8RnwdLZA6Ptta+wffmX6iD83NxenT59GYGDgPzuQyxEYGIhjx46Vah/BwcEYNGhQseEUANLT0yGTyWBpaVnk/fPmzYOFhYXm5uLiUpbDICKiSqLUU+DNlk5YOzoAf019BRO7NUYdC0OkZ+dhdUQMXlt4BG8vPYoNJ2Lx6EleyTskIgBPz/+etvUCLsdnwNbUACuG+dbYcAqUcQb1/v37cHZ2RkREBNq2batpnzZtGg4fPowTJ068cPvIyEgEBATgxIkT8Pf3L7LPkydP0L59e7i7u2P9+vVF9uEMKhFR9aFSCxy5noxNJ+Ow/3Ii8tVPf+0Y6SvwZss6CPJzga8rl6siepEVh29i3h9XoSeXYcPYNvCvby11SZWqShfLCg4OhqenZ7HhNC8vDwMHDoQQAsuWLSt2P0qlEkollzMhIqoOFHIZujS1R5em9kjJzMH2M/cQcjIWN5OzEHr6LkJP30VDOxMM8quHPq2cYWvK93eiZx2+loz5e64CAGb1al7jwylQxo/4bW1toVAokJiYqNWemJgIR0fHF26blZWFkJAQjB49usj7C8LpnTt3sH//fs6EEhHVQLamSozt1AAHJnfGlvfbYoBvXRjpK3AzOQtf/n4Fbb4KwwfrTuNQdBJU6mq/CiLRS4tJycKEDWegFsAgPxcMDSh8bU5NVKaAamBgAF9fX4SFhWna1Go1wsLCtD7yL0poaChycnIwdOjQQvcVhNPr16/jwIEDsLGxKUtZRERUzchkMrR2s8a3A7wQOaMbvurjCS+Xp8tV/RGVgJGrTqLj/D+xYP81LldFtVZmTj7eXXsKGU/y0aqeJea83bzWnApTrmWmRowYgRUrVsDf3x8LFy7E5s2bcfXqVTg4OGD48OFwdnbGvHnztLbr2LEjnJ2dERISotWel5eH/v3748yZM9i9ezccHBw091lbW8PAwKDEmrjMFBFRzXAlPgObTsbh13P3kPb3clUyGdChkS2C/FzwqocDlHo198IQogJqtcAH609j76VE2JspsXtCB9ibG0pdVpUp8zmoQUFBSE5OxsyZM5GQkABvb2/s2bNHEyxjY2Mhl2tPzEZHRyM8PBz79u0rtL979+5h586dAABvb2+t+w4ePIguXbqUtUQiIqqmmtUxx+xezfHJ6+7YdzkRm07G4uiNBzhyPQVHrqfAylgffVvVRZCfC5o4mEldLlGlWXLwBvZeSoSBQo4Vw3xrVTgF+FWnRESk42IfPEbo6TiEnrqLhIwnmnafepYIau2CN72cYKqs0mt+iSrV/suJGPvL0y8r+qZ/SwxsXfuW02RAJSKiakGlFvjrWjJCTsYi7EqSZrkqYwMF3mrphCB/F/i4WNaac/SoZrqRlIneS48iMycfI9q6Ys7bLaQuSRIMqEREVO0kPXqCbWfuYfPJONxKydK0N7Y3RZCfC/q2qgtrk5KvYSDSJenZeeiz9ChupWQhoL411o0JgL6iTNez1xgMqEREVG0JIXAyJhUhJ2Px+8V4PMlTAwD0FTJ093BEkJ8LOjSyhVzOWVXSbSq1wJg1J3EwOhnOlkbYOb49bGrxmsAMqEREVCNkPMnDznP3selkHC7eS9e0O1saYUDruhjQ2gXOlkYSVkhUvG/3XsXSgzeh1JNj6wft0MLZQuqSJMWASkRENc6l++nYfDIO28/eQ8aTfABPl6vq1NgOQX4uCGzmAAO92vnRKeme3y7EY9yGMwCARYO88ba3s8QVSY8BlYiIaqwneSrsvZSAkMg4HLv1QNNuY2KAvq2cEeTngkb2XK6KpHMlPgN9f4xAdp4K73ZqgE/faCZ1STqBAZWIiGqFOw+ysPnU0+Wqkh7laNp9Xa0Q5OeCnp51YMLlqqgKpWblotfScMQ9zEbHxrZYNdIPerX0oqjnMaASEVGtkq9S41B0MjadisOfV5Og+nu5KhMDBXp5O2GQXz14uVhKWyTVePkqNUauOonwGymoZ22MnePbw9KYK08UYEAlIqJaKynjCbacuYvNJ+MQ8+Cxpv2dgHqY9VZznqdKlebL3y5j5ZHbMDZQYNuH7eDuyPzyLAZUIiKq9YQQOHH7IUIiY7Hj/H0IAfi5WeHHd3xhZ1Z7l/qhyvHr2Xv4aNM5AMCyd1rhdc860hakgxhQiYiInvHn1URM2ngOj3LyUcfCECuG+aJlXUupy6Ia4uLddPRfHoGcfDUmdG2Ej7s3lbokncTPLoiIiJ7R1d0Bv45vjwZ2JohPf4IBy49h+9m7UpdFNUBKZg7eW3sKOflqdHO3x78Dm0hdks5iQCUiInpOQztT/DquPbq52yMnX41/bzqPL3ZfRr5KLXVpVE3lqdT4cP0Z3E9/ggZ2Jvh+kDe/4ewFGFCJiIiKYG6oj5XDW2P8K40AAP8Lv42Rq04iNStX4sqoOvp892VE3n4IM6UeVg5vDXNDfalL0mkMqERERMWQy2WY0qMpfnynFYwNFAi/kYJeS8NxNSFD6tKoGtl0Mha/HLsDmQxYOMgbDe1MpS5J5zGgEhERleANzzrY9mE7uFgbIe5hNvr+GIE/LsZLXRZVA2diU/F/v14CAEwObIJuzRwkrqh6YEAlIiIqBXdHc+wc1wHtG9ngca4KH6w/g+/2RkOtrvaL4VAlScx4gvfXnkauSo3XWzhifNdGUpdUbTCgEhERlZKViQHWjPLHmA71AQBLDt7A2F9OIeNJnsSVka7JyVfh/XWnkfQoB00dzPDdAC/IZLwoqrQYUImIiMpATyHHZ296YMFALxjoyRF2NQm9lx7FzeRMqUsjHSGEwMxfL+FsbBosjPTx03BfmCj1pC6rWmFAJSIiKoe+repiy/ttUcfCELeSs9B7yVH8eTVR6rJIB6w7fgebTsVBLgMWD/aBq42J1CVVOwyoRERE5dSyriV2ju8APzcrPMrJx+g1p7D04A3UgC9ppHI6cesB5uy6DAD45HV3dGpiJ3FF1RMDKhER0UuwM1Ni/Zg2eCegHoQAvt0bjXEbziArJ1/q0qiK3UvLxofrzyBfLdDLywljOzaQuqRqiwGViIjoJRnoyfFlH0981ccT+goZfr+YgH7LIhD38LHUpVEVeZKnwntrT+FBVi6aO5ljfr+WvCjqJTCgEhERVZAhAfWwcWwb2JoqcTXhEd5aEo6jN1KkLosqmRACn2y9gKh7GbA2McCKYb4wMlBIXVa1xoBKRERUgVq7WWPXhPbwqmuBtMd5GBZ8Av87covnpdZgweG38eu5+1DIZfjxnVaoa2UsdUnVHgMqERFRBatjYYRN77VFv1Z1oRbAF79dwcebz+NJnkrq0qiChV9PwVe/XwEAzHzTA20a2EhcUc3AgEpERFQJDPUV+G5AS8x80wMKuQzbzt7DwBXHcD8tW+rSqILEPniM8RvPQC2AAb51Mbytq9Ql1RgMqERERJVEJpPhXx3qY+2//GFlrI8Ld9PRa0k4TsY8lLo0eklZOfl4d+0ppD3Og7eLJT7v3YIXRVUgBlQiIqJK1q6RLXaO7wB3RzOkZOZiyMrjWH/ijtRlUTkJITB1y3lcTXgEOzMllg/1haE+L4qqSAyoREREVcDF2hjbPmyHni3rIE8lMGN7FKZvu4jcfLXUpVEZ/XjoJn6/mAB9hQzLh7aCo4Wh1CXVOAyoREREVcTYQA9LBvtg2mtNIZMBGyNjMWTlcSQ9eiJ1aVRKf15NxHf7ogEAn7/dAr6u1hJXVDMxoBIREVUhmUyGD7s0ws8j/GBmqIdTd1LRa/FRnI9Lk7o0KsHN5ExM2ngOQgBD29TDIP96UpdUYzGgEhERSeAVd3vsGNceDe1MkJDxBANWHMPW03elLouK8ehJHt795RQe5eTDz80KM99sLnVJNRoDKhERkUQa2Jni13HtEdjMHrn5anwceh5zd11GvornpeoStVrg35vO4WZyFupYGOLHd3xhoMcIVZnKNbpLly6Fm5sbDA0NERAQgMjIyGL7dunSBTKZrNCtZ8+emj7btm1D9+7dYWNjA5lMhnPnzpWnLCIiomrHzFAfPw1rjYldGwEAfj56G8N/jkRqVq7ElVGBhWHXceBKEgz05FgxzBd2ZkqpS6rxyhxQN23ahMmTJ2PWrFk4c+YMvLy80KNHDyQlJRXZf9u2bYiPj9fcoqKioFAoMGDAAE2frKwsdOjQAfPnzy//kRAREVVTcrkMk7s3xfKhrWBsoEDEzQd4a0k4rsRnSF1arbcnKh4/hF0HAHzd1xMt61pKW1AtIRNl/HLggIAA+Pn5YcmSJQAAtVoNFxcXTJgwAZ988kmJ2y9cuBAzZ85EfHw8TExMtO6LiYlB/fr1cfbsWXh7e5e6poyMDFhYWCA9PR3m5uZlORwiIiKdEp3wCGN/OYXYh49hpK/AdwO80LNlHanLqpWiEx6hz49H8ThXhdEd6uP/3vSQuqRao0wzqLm5uTh9+jQCAwP/2YFcjsDAQBw7dqxU+wgODsagQYMKhdOyyMnJQUZGhtaNiIioJmjqaIad49ujY2NbZOepMG7DGXy79ypU6jLNJ9FLSn+ch3fXnsLjXBXaNbTB9NfdpS6pVilTQE1JSYFKpYKDg4NWu4ODAxISEkrcPjIyElFRURgzZkzZqnzOvHnzYGFhobm5uLi81P6IiIh0iaWxAVaN9MPYjvUBAEsP3sTYX04h40mexJXVDiq1wPiNZ3DnwWPUtTLCkiGtoKfgRVFVqUpHOzg4GJ6envD393+p/UyfPh3p6emaW1xcXAVVSEREpBv0FHLM6OmBhUHeUOrJ8efVJPRechQ3kjKlLq3G+2bvVRy5ngIjfQV+GtYa1iYGUpdU65QpoNra2kKhUCAxMVGrPTExEY6Oji/cNisrCyEhIRg9enTZq3yOUqmEubm51o2IiKgm6u3jjC3vt4OThSFupWShz9KjCLuSWPKGVC47zt3DisO3AADfDmgJDydmDCmUKaAaGBjA19cXYWFhmja1Wo2wsDC0bdv2hduGhoYiJycHQ4cOLV+lREREtZRnXQvsnNAB/vWt8SgnH2N+OYXFYdeh5nmpFSrqXjr+s/UCAODDLg3xZksniSuqvcr8Ef/kyZOxcuVKrFmzBleuXMEHH3yArKwsjBo1CgAwfPhwTJ8+vdB2wcHB6N27N2xsbArd9/DhQ5w7dw6XL18GAERHR+PcuXOlOq+ViIioNrA1VWL9mAAMb+sKIYD/7r+GD9efQVZOvtSl1QgPMnPw3trTeJKnRpemdvi4e1OpS6rV9Mq6QVBQEJKTkzFz5kwkJCTA29sbe/bs0Vw4FRsbC7lcO/dGR0cjPDwc+/btK3KfO3fu1ARcABg0aBAAYNasWZg9e3ZZSyQiIqqR9BVyzH27BTzqmOP/dkRhz6UE3P4xCz8N94WrTflXx6nt8lRqjNtwBvfSslHf1gSLBvlAIZdJXVatVuZ1UHUR10ElIqLa5vSdVLy/7jSSH+XAwkgfS4b4oGNjO6nLqpZm77yE1RExMFXq4ddx7dDI3kzqkmo9rplARERUDfm6WmHX+A7wcrFEenYeRvwciZV/3UINmHeqUqGn4rA6IgYAsGCgF8OpjmBAJSIiqqYcLQyx6d02GOBbF2oBfPn7FUzefB5P8lRSl1YtnItLw4xfowAAHwU2RvfmL16RiKoOAyoREVE1ZqivwDf9W2L2Wx5QyGXYfvYe+i+PwL20bKlL02lJj57gvbWnkJuvRncPB0zs2ljqkugZDKhERETVnEwmw8j29bF2tD+sjPURdS8DvRaHI/L2Q6lL00m5+Wp8sO4MEjNy0NjeFAuCvCHnRVE6hQGViIiohmjX0BY7x3eARx1zPMjKxZCVx7H2+B2el/qcWTsv4fSdVJgb6uGn4a1hqizzokZUyRhQiYiIahAXa2Ns/aAd3mxZB/lqgf/7NQqfbr+InHyelwoA60/cwcbIWMhkwA+DfVDflstz6SIGVCIiohrGyECBxYN98J/X3CGTARsj4zBk5QkkZTyRujRJnYx5iNk7LwEApvVwR5em9hJXRMVhQCUiIqqBZDIZPujSEKtG+sHMUA+n76TirSXhOBeXJnVpkohPz8YH684gTyXwZss6eL9zA6lLohdgQCUiIqrBujS1x87xHdDI3hSJGTkYuOIYtpy+K3VZVepJngrvrT2NlMwcNKtjjm/6t4RMxouidBkDKhERUQ1X39YE2z9sh8BmDsjNV2NK6HnM3nkJeSq11KVVOiEEZmyPwoW76bAy1sdPw3xhbMCLonQdAyoREVEtYGb4NJxN6vZ0vc/VETEYHhyJh1m5EldWuVYdjcHWM3ehkMuwdEgruFgbS10SlQIDKhERUS0hl8vw71ebYPlQX5gYKHDs1gP0WhKOy/czpC6tUkTcSMGXv18BAMx4oxnaNbKVuCIqLQZUIiKiWua1Fo7YPq49XG2McTc1G32XHcWu8/elLqtCxT18jHEbzkClFujXqi5GtXeTuiQqAwZUIiKiWqiJgxl2juuATk3s8CRPjQkbz2L+nqtQqav/ov6Pc/Px7trTSH2cB6+6FviyTwteFFXNMKASERHVUhbG+lg10g/v/b3k0rJDNzF6zUmkZ+dJXFn5CSEwbcsFXInPgK2pAZYP84WhvkLqsqiMGFCJiIhqMYVchumvN8OiQd4w1JfjUHQyei89iuuJj6QurVyWH76F3Rfioa+QYdlQX9SxMJK6JCoHBlQiIiLC297O2PJ+OzhbGuF2Shb6/BiBfZcSpC6rTA5FJ+GbvVcBALN7NYefm7XEFVF5MaASERERAKCFswV2jm+PgPrWyMx5eh7nogPXoa4G56XeTsnCxI1nIQQw2L8e3glwlbokegkMqERERKRhY6rEujEBGNH2acD7/sA1fLD+NDJz8iWurHiZOfl495dTyHiSD19XK8zp1VzqkuglMaASERGRFn2FHHPeboFv+rWEgUKOvZcS0ffHo4hJyZK6tELUaoHJm87helImHMyVWDa0FQz0GG+qO/4EiYiIqEgD/VwQ8l4b2JspcS0xE72WhOOva8lSl6Vl8Z83sO9yIgwUcqwY1hr2ZoZSl0QVgAGViIiIitWqnhV2TegAn3qWyHiSj5GrIvHTXzchhPTnpe67lIDvD1wDAHzZpwW8XSylLYgqDAMqERERvZCDuSFC3m2Dga3rQi2Ar36/io82nUN2rkqymq4nPsK/N50DAIxs54YBrV0kq4UqHgMqERERlUipp8D8fi0x9+3m0JPLsOPcffRfHoF7adlVXkt6dh7eXXsaWbkqtGlgjRk9m1V5DVS5GFCJiIioVGQyGYa3dcO6MQGwNjHApfsZ6LU4HCduPaiyGlRqgUkhZ3E7JQvOlkZYOqQV9BWMMzUNf6JERERUJm0a2GDn+PZo7mSOB1m5eOd/J/DLsZgqOS/1v/uicSg6GYb6cqwY5gsbU2WlPyZVPQZUIiIiKrO6VsbY8n479PJyQr5aYOaOS/hk60Xk5Ffeeam7L9zHj4duAgDm92uJFs4WlfZYJC0GVCIiIioXIwMFFg3yxvTX3SGXAZtOxWHwT8eRlPGkwh/r8v0MTA29AAB4r3MDvO3tXOGPQbqDAZWIiIjKTSaT4b3ODbFqlD/MDfVwJjYNby4Ox9nY1Ap7jNSsXLy79hSy81To2NgW03q4V9i+STcxoBIREdFL69zEDjvHd0Bje1MkPcpB0Irj2Hwq7qX3m69SY/zGM7ibmg1XG2MsGdwKCrmsAiomXcaASkRERBXCzdYE28e1R3cPB+Sq1Ji25QJm77yEPJW63Puc98dVHL3xAMYGCqwc3hoWxvoVWDHpKgZUIiIiqjCmSj0sH+qLfwc2AQCsjojBsOATeJCZU+Z9bTtzF8HhtwEACwZ6oYmDWYXWSrqLAZWIiIgqlFwuw6TAxvhpmC9MDBQ4fushei05ikv300u9jwt30/DJtosAgIldG+G1FnUqq1zSQQyoREREVCm6N3fEr+Pao76tCe6lZaPfsgjsPH+/xO2SH+XgvbWnkZuvRmAze3z092ws1R7lCqhLly6Fm5sbDA0NERAQgMjIyGL7dunSBTKZrNCtZ8+emj5CCMycORN16tSBkZERAgMDcf369fKURkRERDqksYMZfh3XHp2b2OFJnhoTN57FvD+uQKUuelH/3Hw1Plx/GvHpT9DQzgTfB3lDzouiap0yB9RNmzZh8uTJmDVrFs6cOQMvLy/06NEDSUlJRfbftm0b4uPjNbeoqCgoFAoMGDBA0+ebb77BDz/8gOXLl+PEiRMwMTFBjx498ORJxa+jRkRERFXLwkgfP4/0w/udGwIAVhy+hVGrTyL9cV6hvnN3X8LJmFSYKfXw0/DWMDPkRVG1kUyU8XvJAgIC4OfnhyVLlgAA1Go1XFxcMGHCBHzyySclbr9w4ULMnDkT8fHxMDExgRACTk5O+PjjjzFlyhQAQHp6OhwcHLB69WoMGjSoxH1mZGTAwsIC6enpMDc3L8vhEBERURXadf4+pm45jyd5arjZGOOn4a01Fz9tjIzF9G0XIZMBwSNao6u7g8TVklTKNIOam5uL06dPIzAw8J8dyOUIDAzEsWPHSrWP4OBgDBo0CCYmJgCA27dvIyEhQWufFhYWCAgIKHafOTk5yMjI0LoRERGR7nvLywlbP2gHZ0sjxDx4jD5Lj2LvpQScvvMQM3dEAQCmdG/KcFrLlSmgpqSkQKVSwcFB+0nj4OCAhISEErePjIxEVFQUxowZo2kr2K4s+5w3bx4sLCw0NxcXl7IcBhEREUmouZMFdo5vjzYNrJGVq8J7a09j5M8nkacSeMPTER92aSh1iSSxKr2KPzg4GJ6envD393+p/UyfPh3p6emaW1zcy39TBREREVUdG1Ml1o4OwMh2bgCARzn5cHc0w7f9vSCT8aKo2k6vLJ1tbW2hUCiQmJio1Z6YmAhHR8cXbpuVlYWQkBDMnTtXq71gu8TERNSp888aZ4mJifD29i5yX0qlEkqlsiylExERkY7RV8gxu1dz+NSzRNiVJEzt0RQmyjJFE6qhyjSDamBgAF9fX4SFhWna1Go1wsLC0LZt2xduGxoaipycHAwdOlSrvX79+nB0dNTaZ0ZGBk6cOFHiPomIiKj6e9vbGT8M9oGLtbHUpZCOKPOfKZMnT8aIESPQunVr+Pv7Y+HChcjKysKoUaMAAMOHD4ezszPmzZuntV1wcDB69+4NGxsbrXaZTIaPPvoIX3zxBRo3boz69evj//7v/+Dk5ITevXuX/8iIiIiIqFoqc0ANCgpCcnIyZs6ciYSEBHh7e2PPnj2ai5xiY2Mhl2tPzEZHRyM8PBz79u0rcp/Tpk1DVlYW3n33XaSlpaFDhw7Ys2cPDA0Ny3FIRERERFSdlXkdVF3EdVCJiIiIao4qvYqfiIiIiKgkDKhEREREpFMYUImIiIhIpzCgEhEREZFOYUAlIiIiIp3CgEpEREREOoUBlYiIiIh0CgMqEREREemUMn+TlC4q+K6BjIwMiSshIiIiohcxMzODTCZ7YZ8aEVAfPXoEAHBxcZG4EiIiIiJ6kdJ882eN+KpTtVqN+/fvlyqRV5SMjAy4uLggLi6OX69ahTju0uC4Vz2OuTQ47tLguEtDqnGvNTOocrkcdevWleSxzc3N+WKSAMddGhz3qscxlwbHXRocd2no4rjzIikiIiIi0ikMqERERESkUxhQy0mpVGLWrFlQKpVSl1KrcNylwXGvehxzaXDcpcFxl4Yuj3uNuEiKiIiIiGoOzqASERERkU5hQCUiIiIincKASkREREQ6hQGViIiIiHRKrQ2o8+bNg5+fH8zMzGBvb4/evXsjOjpaq8+TJ08wbtw42NjYwNTUFP369UNiYqJWn4kTJ8LX1xdKpRLe3t5FPtbevXvRpk0bmJmZwc7ODv369UNMTEwlHZluq8px37x5M7y9vWFsbAxXV1d8++23lXVYOq8ixv38+fMYPHgwXFxcYGRkhGbNmmHRokWFHuvQoUNo1aoVlEolGjVqhNWrV1f24emsqhr3+Ph4DBkyBE2aNIFcLsdHH31UFYens6pq3Ldt24ZXX30VdnZ2MDc3R9u2bbF3794qOUZdVFXjHh4ejvbt28PGxgZGRkZwd3fH999/XyXHqIuq8v29wNGjR6Gnp1fs798KIWqpHj16iFWrVomoqChx7tw58cYbb4h69eqJzMxMTZ/3339fuLi4iLCwMHHq1CnRpk0b0a5dO639TJgwQSxZskQMGzZMeHl5FXqcW7duCaVSKaZPny5u3LghTp8+LTp16iR8fHwq+xB1UlWN+++//y709PTEsmXLxM2bN8Xu3btFnTp1xOLFiyv7EHVSRYx7cHCwmDhxojh06JC4efOmWLt2rTAyMtIa01u3bgljY2MxefJkcfnyZbF48WKhUCjEnj17qvR4dUVVjfvt27fFxIkTxZo1a4S3t7eYNGlSVR6mzqmqcZ80aZKYP3++iIyMFNeuXRPTp08X+vr64syZM1V6vLqiqsb9zJkzYsOGDSIqKkrcvn1brF27VhgbG4sVK1ZU6fHqiqoa9wKpqamiQYMGonv37kX+/q0otTagPi8pKUkAEIcPHxZCCJGWlib09fVFaGiops+VK1cEAHHs2LFC28+aNavIH1RoaKjQ09MTKpVK07Zz504hk8lEbm5uxR9INVNZ4z548GDRv39/rbYffvhB1K1bV6jV6oo9iGroZce9wIcffiheeeUVzb+nTZsmmjdvrtUnKChI9OjRo4KPoHqqrHF/VufOnWt9QH1eVYx7AQ8PDzFnzpyKKbyaq8px79Onjxg6dGjFFF7NVfa4BwUFic8++6zY378VpdZ+xP+89PR0AIC1tTUA4PTp08jLy0NgYKCmj7u7O+rVq4djx46Ver++vr6Qy+VYtWoVVCoV0tPTsXbtWgQGBkJfX79iD6Iaqqxxz8nJgaGhoVabkZER7t69izt37lRA5dVbRY17enq6Zh8AcOzYMa19AECPHj3K9LOrySpr3OnFqmrc1Wo1Hj16xJ/N36pq3M+ePYuIiAh07ty5giqv3ipz3FetWoVbt25h1qxZlVC5NgZUPH1T+eijj9C+fXu0aNECAJCQkAADAwNYWlpq9XVwcEBCQkKp912/fn3s27cPn376KZRKJSwtLXH37l1s3ry5Ig+hWqrMce/Rowe2bduGsLAwqNVqXLt2Df/9738BPD1frzarqHGPiIjApk2b8O6772raEhIS4ODgUGgfGRkZyM7OrtgDqWYqc9ypeFU57t999x0yMzMxcODACqu/uqqKca9bty6USiVat26NcePGYcyYMRV+HNVNZY779evX8cknn2DdunXQ09OrtGMoUPmPUA2MGzcOUVFRCA8Pr/B9JyQkYOzYsRgxYgQGDx6MR48eYebMmejfvz/2798PmUxW4Y9ZXVTmuI8dOxY3b97Em2++iby8PJibm2PSpEmYPXs25PLa/XdZRYx7VFQU3n77bcyaNQvdu3evwOpqLo67NKpq3Dds2IA5c+Zgx44dsLe3L/dj1RRVMe5HjhxBZmYmjh8/jk8++QSNGjXC4MGDX6bsaq+yxl2lUmHIkCGYM2cOmjRpUlHlvlilnTxQTYwbN07UrVtX3Lp1S6s9LCxMABCpqala7fXq1RMLFiwotJ/izsX47LPPROvWrbXa4uLiSjz3o6ar7HEvkJ+fL+7evStycnLE77//LgCIpKSkijiEaqkixv3SpUvC3t5efPrpp4X237Fjx0LnP/7888/C3Ny8Quqvrip73J/Fc1D/UVXjvnHjRmFkZCR2795dYbVXZ1X5fC/w+eefiyZNmrxU3dVdZY57amqqACAUCoXmJpPJNG1hYWEVfjy1NqCq1Woxbtw44eTkJK5du1bo/oKTirds2aJpu3r1apkv1pk8ebLw9/fXart//74AII4ePfryB1LNVNW4F2XYsGGibdu25a69OquocY+KihL29vZi6tSpRT7OtGnTRIsWLbTaBg8eXGsvkqqqcX8WA2rVjvuGDRuEoaGh+PXXXyv2IKohKZ7vBebMmSNcXV1fqv7qqirGXaVSiYsXL2rdPvjgA9G0aVNx8eJFrRUDKkqtDagffPCBsLCwEIcOHRLx8fGa2+PHjzV93n//fVGvXj3x559/ilOnTom2bdsWCjjXr18XZ8+eFe+9955o0qSJOHv2rDh79qzIyckRQjz9y0Umk4k5c+aIa9euidOnT4sePXoIV1dXrceqLapq3JOTk8WyZcvElStXxNmzZ8XEiROFoaGhOHHiRJUer66oiHG/ePGisLOzE0OHDtXax7Mz0gXLTE2dOlVcuXJFLF26tFYvM1VV4y6E0LwGfH19xZAhQ8TZs2fFpUuXquxYdUlVjfv69euFnp6eWLp0qVaftLS0Kj1eXVFV475kyRKxc+dOce3aNXHt2jXxv//9T5iZmYkZM2ZU6fHqiqp8n3lWZV/FX2sDKoAib6tWrdL0yc7OFh9++KGwsrISxsbGok+fPiI+Pl5rP507dy5yP7dv39b02bhxo/Dx8REmJibCzs5O9OrVS1y5cqWKjlS3VNW4JycnizZt2ggTExNhbGwsunXrJo4fP16FR6pbKmLcZ82aVeQ+np+1OHjwoPD29hYGBgaiQYMGWo9R21TluJemT21RVeNe3PvQiBEjqu5gdUhVjfsPP/wgmjdvLoyNjYW5ubnw8fERP/74o9ZyjrVJVb7PPKuyA6pMCCGKOz+ViIiIiKiq1e7LmYmIiIhI5zCgEhEREZFOYUAlIiIiIp3CgEpEREREOoUBlYiIiIh0CgMqEREREekUBlQiIiIi0ikMqERERESkUxhQiYhe4NChQ5DJZEhLS5Pk8cPCwtCsWTOoVCpJHr9NmzbYunWrJI9NRLUXAyoR0d+6dOmCjz76SKutXbt2iI+Ph4WFhSQ1TZs2DZ999hkUCgUAYPbs2fD29i7ULyYmBjKZDOfOncPIkSMhk8mKvbm5uQEAMjIyMGPGDLi7u8PQ0BCOjo4IDAzEtm3bUPAlg5999hk++eQTqNXqqjpkIiIGVCKiFzEwMICjoyNkMlmVP3Z4eDhu3ryJfv36lWm7RYsWIT4+XnMDgFWrVmn+ffLkSaSlpaFdu3b45ZdfMH36dJw5cwZ//fUXgoKCMG3aNKSnpwMAXn/9dTx69Ah//PFHhR8fEVFxGFCJiACMHDkShw8fxqJFizQzjTExMYU+4l+9ejUsLS2xe/duNG3aFMbGxujfvz8eP36MNWvWwM3NDVZWVpg4caLWx/I5OTmYMmUKnJ2dYWJigoCAABw6dOiFNYWEhODVV1+FoaFhmY7FwsICjo6OmhsAWFpaav5tZ2eHTz/9FDExMThx4gRGjBgBDw8PNGnSBGPHjsW5c+dgamoKAFAoFHjjjTcQEhJSphqIiF4GAyoREZ7OOrZt2xZjx47VzDS6uLgU2ffx48f44YcfEBISgj179uDQoUPo06cPfv/9d/z+++9Yu3YtVqxYgS1btmi2GT9+PI4dO4aQkBBcuHABAwYMwGuvvYbr168XW9ORI0fQunXrCj9WtVqNkJAQvPPOO3Bycip0v6mpKfT09DT/9vf3x5EjRyq8DiKi4uiV3IWIqOazsLCAgYEBjI2NNbOOxcnLy8OyZcvQsGFDAED//v2xdu1aJCYmwtTUFB4eHnjllVdw8OBBBAUFITY2FqtWrUJsbKwmEE6ZMgV79uzBqlWr8NVXXxX5OHfu3CkyQL6slJQUpKamwt3dvVT9nZycEBcXB7VaDbmc8xpEVPkYUImIysjY2FgTTgHAwcEBbm5umo/FC9qSkpIAABcvXoRKpUKTJk209pOTkwMbG5tiHyc7O7vMH++XRsEFUKVlZGQEtVqNnJwcGBkZVXg9RETPY0AlIiojfX19rX/LZLIi2wqufM/MzIRCocDp06c1V+MXeDbUPs/W1hapqalabebm5poLmJ5VcI5saVYbsLOzg6WlJa5evVpiXwB4+PAhTExMGE6JqMrwsxoior8ZGBhUynqjPj4+UKlUSEpKQqNGjbRuLzqdwMfHB5cvX9Zqa9q0Ke7evYvExESt9jNnzsDQ0BD16tUrsR65XI5BgwZh/fr1uH//fqH7MzMzkZ+fr/l3VFQUfHx8StwvEVFFYUAlIvqbm5sbTpw4gZiYGKSkpFTY2p9NmjTBO++8g+HDh2Pbtm24ffs2IiMjMW/ePPz222/FbtejRw+Eh4cXamvatCkGDx6MiIgI3Lp1C1u2bMFnn32GSZMmFZqhLc6XX34JFxcXBAQE4JdffsHly5dx/fp1/Pzzz/Dx8UFmZqam75EjR9C9e/fyHTwRUTkwoBIR/W3KlClQKBTw8PCAnZ0dYmNjK2zfq1atwvDhw/Hxxx+jadOm6N27N06ePPnCGc933nkHly5dQnR0tKZNT08P+/btQ7169TB48GC0aNECs2bNwqRJk/D555+Xuh5ra2scP34cQ4cOxRdffAEfHx907NgRGzduxLfffqs5VeDevXuIiIjAqFGjyn/wRERlJBNlPVueiIiqzNSpU5GRkYEVK1ZI8vj/+c9/kJqaip9++kmSxyei2okzqEREOmzGjBlwdXWV7KtG7e3tyzQzS0RUETiDSkREREQ6hTOoRERERKRTGFCJiIiISKcwoBIRERGRTmFAJSIiIiKdwoBKRERERDqFAZWIiIiIdAoDKhERERHpFAZUIiIiItIpDKhEREREpFP+HwQCPv9Is0WlAAAAAElFTkSuQmCC\n"
-          },
-          "metadata": {}
-        }
+     "data": {
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "summary": "{\n  \"name\": \"df_all\",\n  \"rows\": 74,\n  \"fields\": [\n    {\n      \"column\": \"time (UTC)\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 74,\n        \"samples\": [\n          \"2018-May\",\n          \"2023-Apr\",\n          \"2019-Jul\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NOS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 351806,\n        \"min\": 3060446,\n        \"max\": 5179068,\n        \"num_unique_values\": 74,\n        \"samples\": [\n          4686732,\n          4747106,\n          4871350\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"non-NOS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 297542,\n        \"min\": 1078936,\n        \"max\": 2456208,\n        \"num_unique_values\": 74,\n        \"samples\": [\n          1499500,\n          1910384,\n          1566330\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nos-percent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02533331682923217,\n        \"min\": 0.6783078961389215,\n        \"max\": 0.7820300641759647,\n        \"num_unique_values\": 74,\n        \"samples\": [\n          0.757606892208375,\n          0.713047409759534,\n          0.7566934050776056\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+       "type": "dataframe",
+       "variable_name": "df_all"
+      },
+      "text/html": [
+       "\n",
+       "  <div id=\"df-8b7db6be-2d65-4c68-8e2f-1e95cc4bb734\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>NOS</th>\n",
+       "      <th>non-NOS</th>\n",
+       "      <th>nos-percent</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time (UTC)</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-Jan</th>\n",
+       "      <td>4413688</td>\n",
+       "      <td>1371366</td>\n",
+       "      <td>0.762947</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-Feb</th>\n",
+       "      <td>4009172</td>\n",
+       "      <td>1234916</td>\n",
+       "      <td>0.764513</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-Mar</th>\n",
+       "      <td>4429572</td>\n",
+       "      <td>1407126</td>\n",
+       "      <td>0.758917</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-Apr</th>\n",
+       "      <td>4366098</td>\n",
+       "      <td>1401076</td>\n",
+       "      <td>0.757060</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-May</th>\n",
+       "      <td>4686732</td>\n",
+       "      <td>1499500</td>\n",
+       "      <td>0.757607</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-Oct</th>\n",
+       "      <td>5101376</td>\n",
+       "      <td>2338116</td>\n",
+       "      <td>0.685716</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-Nov</th>\n",
+       "      <td>4844546</td>\n",
+       "      <td>2100150</td>\n",
+       "      <td>0.697589</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-Dec</th>\n",
+       "      <td>4756716</td>\n",
+       "      <td>1974644</td>\n",
+       "      <td>0.706650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-Jan</th>\n",
+       "      <td>4948372</td>\n",
+       "      <td>1888004</td>\n",
+       "      <td>0.723830</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-Feb</th>\n",
+       "      <td>4618508</td>\n",
+       "      <td>1733972</td>\n",
+       "      <td>0.727040</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>74 rows × 3 columns</p>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8b7db6be-2d65-4c68-8e2f-1e95cc4bb734')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-8b7db6be-2d65-4c68-8e2f-1e95cc4bb734 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-8b7db6be-2d65-4c68-8e2f-1e95cc4bb734');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-60a9bdc9-23ac-43b0-82f6-c3764b48ba35\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-60a9bdc9-23ac-43b0-82f6-c3764b48ba35')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-60a9bdc9-23ac-43b0-82f6-c3764b48ba35 button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 410
-        },
-        "id": "RtWChIjimDdm",
-        "outputId": "82e916f7-0a33-47dc-ccf5-0669cc646d0d"
-      }
+      "text/plain": [
+       "                NOS  non-NOS  nos-percent\n",
+       "time (UTC)                               \n",
+       "2018-Jan    4413688  1371366     0.762947\n",
+       "2018-Feb    4009172  1234916     0.764513\n",
+       "2018-Mar    4429572  1407126     0.758917\n",
+       "2018-Apr    4366098  1401076     0.757060\n",
+       "2018-May    4686732  1499500     0.757607\n",
+       "...             ...      ...          ...\n",
+       "2023-Oct    5101376  2338116     0.685716\n",
+       "2023-Nov    4844546  2100150     0.697589\n",
+       "2023-Dec    4756716  1974644     0.706650\n",
+       "2024-Jan    4948372  1888004     0.723830\n",
+       "2024-Feb    4618508  1733972     0.727040\n",
+       "\n",
+       "[74 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_all = df_met + df_wave\n",
+    "\n",
+    "df_all[\"nos-percent\"] = df_all[\"NOS\"] / (df_all[\"NOS\"] + df_all[\"non-NOS\"])\n",
+    "\n",
+    "df_all"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "l0knZirUYlwR"
+   },
+   "source": [
+    "# Calculate the percent contributions per year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 300
     },
+    "id": "VW71fx9aUfqL",
+    "outputId": "6070539b-bf7a-4dc6-ceb0-a0424ccb7755"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "enzRDTBfWtYe"
+     "data": {
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "summary": "{\n  \"name\": \"year_totals\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"time (UTC)\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"2018\",\n          \"2019\",\n          \"2023\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NOS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17406758,\n        \"min\": 9566880,\n        \"max\": 58911340,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          54063850,\n          55582460,\n          58911340\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"non-NOS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6925117,\n        \"min\": 3621976,\n        \"max\": 25300712,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          17153684,\n          17399994,\n          25300712\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nos_percent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.022393012118187044,\n        \"min\": 0.699559488230972,\n        \"max\": 0.7615866136811459,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.7591367878590124,\n          0.7615866136811459,\n          0.699559488230972\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+       "type": "dataframe",
+       "variable_name": "year_totals"
       },
-      "execution_count": 31,
-      "outputs": []
+      "text/html": [
+       "\n",
+       "  <div id=\"df-5a532b18-8321-4633-b26f-da6081605af2\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>NOS</th>\n",
+       "      <th>non-NOS</th>\n",
+       "      <th>nos_percent</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time (UTC)</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018</th>\n",
+       "      <td>54063850</td>\n",
+       "      <td>17153684</td>\n",
+       "      <td>0.759137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019</th>\n",
+       "      <td>55582460</td>\n",
+       "      <td>17399994</td>\n",
+       "      <td>0.761587</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020</th>\n",
+       "      <td>53539838</td>\n",
+       "      <td>19468350</td>\n",
+       "      <td>0.733340</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021</th>\n",
+       "      <td>53032882</td>\n",
+       "      <td>20170004</td>\n",
+       "      <td>0.724464</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022</th>\n",
+       "      <td>56699818</td>\n",
+       "      <td>22449876</td>\n",
+       "      <td>0.716362</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023</th>\n",
+       "      <td>58911340</td>\n",
+       "      <td>25300712</td>\n",
+       "      <td>0.699559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024</th>\n",
+       "      <td>9566880</td>\n",
+       "      <td>3621976</td>\n",
+       "      <td>0.725376</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5a532b18-8321-4633-b26f-da6081605af2')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-5a532b18-8321-4633-b26f-da6081605af2 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-5a532b18-8321-4633-b26f-da6081605af2');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-cfda53df-19fa-416e-a8d1-686507b1e371\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cfda53df-19fa-416e-a8d1-686507b1e371')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-cfda53df-19fa-416e-a8d1-686507b1e371 button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
+      ],
+      "text/plain": [
+       "                 NOS   non-NOS  nos_percent\n",
+       "time (UTC)                                 \n",
+       "2018        54063850  17153684     0.759137\n",
+       "2019        55582460  17399994     0.761587\n",
+       "2020        53539838  19468350     0.733340\n",
+       "2021        53032882  20170004     0.724464\n",
+       "2022        56699818  22449876     0.716362\n",
+       "2023        58911340  25300712     0.699559\n",
+       "2024         9566880   3621976     0.725376"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
     }
-  ],
-  "metadata": {
+   ],
+   "source": [
+    "all = df_all.reset_index()\n",
+    "all[\"time (UTC)\"] = pd.to_datetime(all[\"time (UTC)\"])\n",
+    "year_group = all.groupby(by=pd.Grouper(key=\"time (UTC)\", freq=\"Y\"))\n",
+    "s = year_group[[\"NOS\",\"non-NOS\"]].sum()\n",
+    "\n",
+    "year_totals = s.assign(nos_percent=s[\"NOS\"] / ( s[\"NOS\"]+ s[\"non-NOS\"]))\n",
+    "\n",
+    "year_totals.index = year_totals.index.to_period(\"M\").strftime(\"%Y\")\n",
+    "\n",
+    "year_totals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
     "colab": {
-      "provenance": [],
-      "include_colab_link": true
+     "base_uri": "https://localhost:8080/",
+     "height": 410
     },
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.11.0"
+    "id": "RtWChIjimDdm",
+    "outputId": "82e916f7-0a33-47dc-ccf5-0669cc646d0d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
+   ],
+   "source": [
+    "# @title nos_percent\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "year_totals[\"nos_percent\"].plot(kind=\"line\", figsize=(8, 4), title=\"Percent of NOAA observations sent to GTS that are NOS\")\n",
+    "plt.gca().spines[[\"top\", \"right\"]].set_visible(False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "id": "enzRDTBfWtYe"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "include_colab_link": true,
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/notebooks/IOOS_BTN.ipynb b/notebooks/IOOS_BTN.ipynb
index 8fc44dd..d0bb294 100644
--- a/notebooks/IOOS_BTN.ipynb
+++ b/notebooks/IOOS_BTN.ipynb
@@ -1,8911 +1,8912 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/ioos/ioos_metrics/blob/main/IOOS_BTN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "98GUIWOu35mJ"
-      },
-      "source": [
-        "# Creating the IOOS By The Numbers\n",
-        "\n",
-        "[Website](https://ioos.noaa.gov/about/ioos-by-the-numbers/)\n",
-        "\n",
-        "[Spreadsheet](https://docs.google.com/spreadsheets/d/1AUfXmc3OwxpVdeMNjZyTGWjyR4ku3kRD5eexNrMORnI/edit#gid=516871794)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 29,
-      "metadata": {
-        "id": "LdpKgY6ZsVy4"
-      },
-      "outputs": [],
-      "source": [
-        "import pandas as pd\n",
-        "\n",
-        "# read previous metrics\n",
-        "ioos_btn_df = pd.read_csv('https://github.com/ioos/ioos_metrics/raw/main/ioos_btn_metrics.csv')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 30,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 394
-        },
-        "id": "-afzN9SfS5tk",
-        "outputId": "713a1608-65ab-49e0-afeb-f7648dee77b8"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "<ipython-input-30-b7f5493e0ed0>:9: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
-            "  ioos_btn_df = ioos_btn_df.append({'date_UTC': today}, ignore_index=True)\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
-              "0  2018-02-01              17.0                   11.0              150.0   \n",
-              "1  2022-04-22              17.0                   11.0              165.0   \n",
-              "2  2022-07-08              17.0                   11.0              165.0   \n",
-              "3  2022-10-05              17.0                   11.0              165.0   \n",
-              "4  2023-01-05              17.0                   11.0              165.0   \n",
-              "5  2024-01-26               NaN                    NaN                NaN   \n",
-              "\n",
-              "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
-              "0            52027.0               737.0               335.0              NaN   \n",
-              "1            53672.0               763.0               517.0           4444.0   \n",
-              "2            55448.0               764.0               517.0           4444.0   \n",
-              "3            59088.0               390.0               635.0           4444.0   \n",
-              "4            62042.0               768.0               635.0           4444.0   \n",
-              "5                NaN                 NaN                 NaN              NaN   \n",
-              "\n",
-              "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
-              "0            NaN           NaN                 NaN            13.0   \n",
-              "1            6.0           8.0                 9.0            13.0   \n",
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-              "4            6.0           8.0                 9.0            13.0   \n",
-              "5            NaN           NaN                 NaN             NaN   \n",
-              "\n",
-              "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
-              "0                 34.0            8600.0   1.0            NaN  \n",
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-            ]
-          },
-          "metadata": {},
-          "execution_count": 30
-        }
-      ],
-      "source": [
-        "import pandas as pd\n",
-        "#today = pd.to_datetime\n",
-        "#ioos_btn_df = pd.DataFrame()#columns=['category','value','date'])\n",
-        "\n",
-        "today = pd.Timestamp.strftime(pd.Timestamp.today(tz='UTC'), '%Y-%m-%d')\n",
-        "\n",
-        "# only update numbers if it's a new day\n",
-        "if today not in ioos_btn_df['date_UTC'].to_list():\n",
-        "    ioos_btn_df = ioos_btn_df.append({'date_UTC': today}, ignore_index=True)\n",
-        "\n",
-        "ioos_btn_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "rMkHuVRJEGKq"
-      },
-      "source": [
-        "---\n",
-        "## Federal Partners\n",
-        "\n",
-        "ICOOS Act/COORA\n",
-        "\n",
-        "Typically 17, from https://ioos.noaa.gov/community/national#federal."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 31,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 689
-        },
-        "id": "jXMhi3tRQvMd",
-        "outputId": "f00a134b-84af-4bda-8414-db6f0d52c0e0"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "0                                               National Oceanic and Atmospheric Administration (NOAA)\n",
-            "1                                                 National Aeronautics and Space Administration (NASA)\n",
-            "2                        Bureau of Ocean Energy Management, Regulation and Enforcement (BOEM and BSEE)\n",
-            "3                                                                       Office of Naval Research (ONR)\n",
-            "4                                                                 U.S. Army Corps of Engineers (USACE)\n",
-            "5                                                                        U.S. Geological Survey (USGS)\n",
-            "6                                                                           Department of Energy (DOE)\n",
-            "7                                                                   Department of Transportation (DOT)\n",
-            "8                                                              U.S. Arctic Research Commission (USARC)\n",
-            "9                                                                    National Science Foundation (NSF)\n",
-            "10                                                               Environmental Protection Agency (EPA)\n",
-            "11                                                                      Marine Mammal Commission (MMC)\n",
-            "12                             Oceanographer of the Navy, representing the Joint Chiefs of Staff (JCS)\n",
-            "13                                                                             U.S. Coast Guard (USCG)\n",
-            "14    Department of Agriculture, Cooperative State Research, Education, and Extension Service (CSREES)\n",
-            "15                                                                           Department of State (DOS)\n",
-            "16                                                                  Food and Drug Administration (FDA)\n",
-            "\n",
-            "Federal Partners: 17\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
-              "0  2018-02-01              17.0                   11.0              150.0   \n",
-              "1  2022-04-22              17.0                   11.0              165.0   \n",
-              "2  2022-07-08              17.0                   11.0              165.0   \n",
-              "3  2022-10-05              17.0                   11.0              165.0   \n",
-              "4  2023-01-05              17.0                   11.0              165.0   \n",
-              "5  2024-01-26              17.0                    NaN                NaN   \n",
-              "\n",
-              "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
-              "0            52027.0               737.0               335.0              NaN   \n",
-              "1            53672.0               763.0               517.0           4444.0   \n",
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-              "4            62042.0               768.0               635.0           4444.0   \n",
-              "5                NaN                 NaN                 NaN              NaN   \n",
-              "\n",
-              "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
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-              "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
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-              "      <th></th>\n",
-              "      <th>date_UTC</th>\n",
-              "      <th>Federal Partners</th>\n",
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-              "      <th>HF Radar Stations</th>\n",
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-              "      const quickchartButtonEl =\n",
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-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 31
-        }
-      ],
-      "source": [
-        "import pandas as pd\n",
-        "import requests\n",
-        "from bs4 import BeautifulSoup\n",
-        "\n",
-        "url = 'https://ioos.noaa.gov/community/national#federal'\n",
-        "\n",
-        "headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36'}\n",
-        "\n",
-        "html = requests.get(url, headers=headers).text\n",
-        "\n",
-        "soup = BeautifulSoup(html, 'html.parser')\n",
-        "\n",
-        "df = pd.read_html(str(soup))\n",
-        "\n",
-        "df_clean = df[1].drop(columns=[0,2])\n",
-        "\n",
-        "df_fed_partners = pd.concat([df_clean[1] , df_clean[3]]).dropna().reset_index()\n",
-        "\n",
-        "fed_partners = df_fed_partners.shape[0]\n",
-        "\n",
-        "ioos_btn_df.loc[ioos_btn_df['date_UTC']==today, ['Federal Partners']] = [fed_partners]\n",
-        "\n",
-        "pd.set_option('max_colwidth', None)\n",
-        "\n",
-        "print(df_fed_partners[0].to_string())\n",
-        "\n",
-        "print(\"\\nFederal Partners:\", fed_partners)\n",
-        "\n",
-        "ioos_btn_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "elYWAAZSEGKq"
-      },
-      "source": [
-        "---\n",
-        "## Regional Associations"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 32,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 585
-        },
-        "id": "vnEk8a4Qd6L7",
-        "outputId": "21ce4dcb-5ff2-4162-ac33-4b600f8ea8d7"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Alaska Ocean Observing System (AOOS)\n",
-            "Caribbean Coastal Ocean Observing System (CARICOOS)\n",
-            "Central and Northern California Ocean Observing System (CeNCOOS)\n",
-            "Gulf of Mexico Coastal Ocean Observing System (GCOOS)\n",
-            "Great Lakes Observing System (GLOS)\n",
-            "Mid-Atlantic Coastal Ocean Observing System (MARACOOS)\n",
-            "Northwest Association of Networked Ocean Observing Systems (NANOOS)\n",
-            "Northeastern Regional Association of Coastal Ocean Observing Systems (NERACOOS)\n",
-            "Pacific Islands Ocean Observing System (PacIOOS)\n",
-            "Southern California Coastal Ocean Observing System (SCCOOS)\n",
-            "Southeast Coastal Ocean Observing Regional Association (SECOORA)\n",
-            "\n",
-            "Number of Regional Associations: 11\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
-              "0  2018-02-01              17.0                   11.0              150.0   \n",
-              "1  2022-04-22              17.0                   11.0              165.0   \n",
-              "2  2022-07-08              17.0                   11.0              165.0   \n",
-              "3  2022-10-05              17.0                   11.0              165.0   \n",
-              "4  2023-01-05              17.0                   11.0              165.0   \n",
-              "5  2024-01-26              17.0                   11.0                NaN   \n",
-              "\n",
-              "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
-              "0            52027.0               737.0               335.0              NaN   \n",
-              "1            53672.0               763.0               517.0           4444.0   \n",
-              "2            55448.0               764.0               517.0           4444.0   \n",
-              "3            59088.0               390.0               635.0           4444.0   \n",
-              "4            62042.0               768.0               635.0           4444.0   \n",
-              "5                NaN                 NaN                 NaN              NaN   \n",
-              "\n",
-              "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
-              "0            NaN           NaN                 NaN            13.0   \n",
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-              "      <th></th>\n",
-              "      <th>date_UTC</th>\n",
-              "      <th>Federal Partners</th>\n",
-              "      <th>Regional Associations</th>\n",
-              "      <th>HF Radar Stations</th>\n",
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-              "  <tbody>\n",
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-              "      <td>17.0</td>\n",
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-              "      <th>2</th>\n",
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-              "      <td>5.0</td>\n",
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-              "      <th>5</th>\n",
-              "      <td>2024-01-26</td>\n",
-              "      <td>17.0</td>\n",
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-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
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-              "\n",
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-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
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-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 32
-        }
-      ],
-      "source": [
-        "import requests\n",
-        "from bs4 import BeautifulSoup\n",
-        "import re\n",
-        "\n",
-        "regional_associations = 0\n",
-        "\n",
-        "url = 'https://ioos.noaa.gov/regions/regions-at-a-glance/'\n",
-        "\n",
-        "headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36'}\n",
-        "\n",
-        "html = requests.get(url, headers=headers).text\n",
-        "\n",
-        "soup = BeautifulSoup(html, 'html.parser')\n",
-        "\n",
-        "for tag in soup.find_all('a'):\n",
-        "\n",
-        "  if tag.find(\"strong\") is not None:\n",
-        "\n",
-        "     print(tag.find(\"strong\").text)\n",
-        "\n",
-        "     regional_associations+=1\n",
-        "\n",
-        "print(\"\\nNumber of Regional Associations:\", regional_associations)\n",
-        "\n",
-        "ioos_btn_df.loc[ioos_btn_df['date_UTC']==today, ['Regional Associations']] = [regional_associations]\n",
-        "\n",
-        "ioos_btn_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "HsPUEyNJEGKq"
-      },
-      "source": [
-        "---\n",
-        "## Coastal & Ocean Modeling Testbed\n",
-        "\n",
-        "The COMT serves as a conduit between the federal operational and research communities and allows sharing of numerical models, observations and software tools. The COMT supports integration, comparison, scientific analyses and archiving of data and model output needed to elucidate, prioritize, and resolve federal and regional operational coastal ocean issues associated with a range of existing and emerging coastal oceanic, hydrologic, and ecological models. The Testbed has enabled significant community building (within the modeling community as well as enhancing academic and federal operational relations) which has dramatically improved model development.\n",
-        "\n",
-        "Number of Active Projects via personal communication from COMT program manager."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 33,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 376
-        },
-        "id": "1edu4Ga1k5WW",
-        "outputId": "614f5cab-1c9e-456e-e105-e1439b17779c"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "COMT Projects: 5\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
-              "0  2018-02-01              17.0                   11.0              150.0   \n",
-              "1  2022-04-22              17.0                   11.0              165.0   \n",
-              "2  2022-07-08              17.0                   11.0              165.0   \n",
-              "3  2022-10-05              17.0                   11.0              165.0   \n",
-              "4  2023-01-05              17.0                   11.0              165.0   \n",
-              "5  2024-01-26              17.0                   11.0                NaN   \n",
-              "\n",
-              "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
-              "0            52027.0               737.0               335.0              NaN   \n",
-              "1            53672.0               763.0               517.0           4444.0   \n",
-              "2            55448.0               764.0               517.0           4444.0   \n",
-              "3            59088.0               390.0               635.0           4444.0   \n",
-              "4            62042.0               768.0               635.0           4444.0   \n",
-              "5                NaN                 NaN                 NaN              NaN   \n",
-              "\n",
-              "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
-              "0            NaN           NaN                 NaN            13.0   \n",
-              "1            6.0           8.0                 9.0            13.0   \n",
-              "2            6.0           8.0                 9.0            13.0   \n",
-              "3            6.0           8.0                 9.0            13.0   \n",
-              "4            6.0           8.0                 9.0            13.0   \n",
-              "5            NaN           NaN                 NaN             NaN   \n",
-              "\n",
-              "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
-              "0                 34.0            8600.0   1.0            NaN  \n",
-              "1                 34.0            7213.0   1.0            5.0  \n",
-              "2                 34.0            6217.0   1.0            5.0  \n",
-              "3                 34.0           24499.0   1.0            5.0  \n",
-              "4                 34.0           11840.0   1.0            5.0  \n",
-              "5                  NaN               NaN   NaN            5.0  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-fcf5be17-1d5a-40d9-8c43-c4ec46e7f1bd\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>date_UTC</th>\n",
-              "      <th>Federal Partners</th>\n",
-              "      <th>Regional Associations</th>\n",
-              "      <th>HF Radar Stations</th>\n",
-              "      <th>NGDAC Glider Days</th>\n",
-              "      <th>National Platforms</th>\n",
-              "      <th>Regional Platforms</th>\n",
-              "      <th>ATN Deployments</th>\n",
-              "      <th>MBON Projects</th>\n",
-              "      <th>OTT Projects</th>\n",
-              "      <th>HAB Pilot Projects</th>\n",
-              "      <th>QARTOD Manuals</th>\n",
-              "      <th>IOOS Core Variables</th>\n",
-              "      <th>Metadata Records</th>\n",
-              "      <th>IOOS</th>\n",
-              "      <th>COMT Projects</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>2018-02-01</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>150.0</td>\n",
-              "      <td>52027.0</td>\n",
-              "      <td>737.0</td>\n",
-              "      <td>335.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>8600.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>2022-04-22</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>53672.0</td>\n",
-              "      <td>763.0</td>\n",
-              "      <td>517.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>7213.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>2022-07-08</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>55448.0</td>\n",
-              "      <td>764.0</td>\n",
-              "      <td>517.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>6217.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>2022-10-05</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>59088.0</td>\n",
-              "      <td>390.0</td>\n",
-              "      <td>635.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>24499.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>2023-01-05</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>62042.0</td>\n",
-              "      <td>768.0</td>\n",
-              "      <td>635.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>11840.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>2024-01-26</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fcf5be17-1d5a-40d9-8c43-c4ec46e7f1bd')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-fcf5be17-1d5a-40d9-8c43-c4ec46e7f1bd button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-fcf5be17-1d5a-40d9-8c43-c4ec46e7f1bd');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-9b7a1253-2d8a-434a-b76f-8c8211ba1e73\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9b7a1253-2d8a-434a-b76f-8c8211ba1e73')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-9b7a1253-2d8a-434a-b76f-8c8211ba1e73 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 33
-        }
-      ],
-      "source": [
-        "## COMT website needs to be updated. Once updated this should work\n",
-        "\n",
-        "import requests\n",
-        "from bs4 import BeautifulSoup\n",
-        "import re\n",
-        "\n",
-        "regional_associations = 0\n",
-        "\n",
-        "url = 'https://ioos.noaa.gov/project/comt/'\n",
-        "\n",
-        "headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36'}\n",
-        "\n",
-        "html = requests.get(url, headers=headers).text\n",
-        "\n",
-        "soup = BeautifulSoup(html, 'html.parser')\n",
-        "\n",
-        "for tag in soup.find_all('h2'):\n",
-        "  if tag.text == 'Current Projects':\n",
-        "    comt = len(tag.next_sibling.find_all('li'))\n",
-        "\n",
-        "print('COMT Projects:',comt)\n",
-        "\n",
-        "ioos_btn_df.loc[ioos_btn_df['date_UTC']==today, ['COMT Projects']] = comt\n",
-        "\n",
-        "ioos_btn_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "08BPt9_HqPzx"
-      },
-      "source": [
-        "---\n",
-        "## HF Radar Installations\n",
-        "\n",
-        "The previous number of 181 included all locations where a HFR station had ever been sighted as part of the IOOS National Network, but doesn't appear to me to have accounted for temporary installations, HFRs unfunded by IOOS operated by international partners, or instances where an HFR being relocated from one site to another caused it to be double-counted.  Even the number 165 represents a \"high water mark\" for simultaneously operating HFRs, since HFRs routinely are taken offline for periods of time, for both planned preventative maintenance and in response to other exigent issues.\n",
-        "\n",
-        "From http://hfrnet.ucsd.edu/sitediag/stationList.php"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 34,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 359
-        },
-        "id": "Jh0A94gGTCy_",
-        "outputId": "c5d92d78-c7b7-48b9-ca0d-05e07a901736"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
-              "0  2018-02-01              17.0                   11.0              150.0   \n",
-              "1  2022-04-22              17.0                   11.0              165.0   \n",
-              "2  2022-07-08              17.0                   11.0              165.0   \n",
-              "3  2022-10-05              17.0                   11.0              165.0   \n",
-              "4  2023-01-05              17.0                   11.0              165.0   \n",
-              "5  2024-01-26              17.0                   11.0              165.0   \n",
-              "\n",
-              "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
-              "0            52027.0               737.0               335.0              NaN   \n",
-              "1            53672.0               763.0               517.0           4444.0   \n",
-              "2            55448.0               764.0               517.0           4444.0   \n",
-              "3            59088.0               390.0               635.0           4444.0   \n",
-              "4            62042.0               768.0               635.0           4444.0   \n",
-              "5                NaN                 NaN                 NaN              NaN   \n",
-              "\n",
-              "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
-              "0            NaN           NaN                 NaN            13.0   \n",
-              "1            6.0           8.0                 9.0            13.0   \n",
-              "2            6.0           8.0                 9.0            13.0   \n",
-              "3            6.0           8.0                 9.0            13.0   \n",
-              "4            6.0           8.0                 9.0            13.0   \n",
-              "5            NaN           NaN                 NaN             NaN   \n",
-              "\n",
-              "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
-              "0                 34.0            8600.0   1.0            NaN  \n",
-              "1                 34.0            7213.0   1.0            5.0  \n",
-              "2                 34.0            6217.0   1.0            5.0  \n",
-              "3                 34.0           24499.0   1.0            5.0  \n",
-              "4                 34.0           11840.0   1.0            5.0  \n",
-              "5                  NaN               NaN   NaN            5.0  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-592de482-5fd7-48ae-9682-c748116e35ae\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>date_UTC</th>\n",
-              "      <th>Federal Partners</th>\n",
-              "      <th>Regional Associations</th>\n",
-              "      <th>HF Radar Stations</th>\n",
-              "      <th>NGDAC Glider Days</th>\n",
-              "      <th>National Platforms</th>\n",
-              "      <th>Regional Platforms</th>\n",
-              "      <th>ATN Deployments</th>\n",
-              "      <th>MBON Projects</th>\n",
-              "      <th>OTT Projects</th>\n",
-              "      <th>HAB Pilot Projects</th>\n",
-              "      <th>QARTOD Manuals</th>\n",
-              "      <th>IOOS Core Variables</th>\n",
-              "      <th>Metadata Records</th>\n",
-              "      <th>IOOS</th>\n",
-              "      <th>COMT Projects</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>2018-02-01</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>150.0</td>\n",
-              "      <td>52027.0</td>\n",
-              "      <td>737.0</td>\n",
-              "      <td>335.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>8600.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>2022-04-22</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>53672.0</td>\n",
-              "      <td>763.0</td>\n",
-              "      <td>517.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>7213.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>2022-07-08</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>55448.0</td>\n",
-              "      <td>764.0</td>\n",
-              "      <td>517.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>6217.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>2022-10-05</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>59088.0</td>\n",
-              "      <td>390.0</td>\n",
-              "      <td>635.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>24499.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>2023-01-05</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>62042.0</td>\n",
-              "      <td>768.0</td>\n",
-              "      <td>635.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>11840.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>2024-01-26</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
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-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
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-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
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-              "                                                    [key], {});\n",
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-              "\n",
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-              "          + ' to learn more about interactive tables.';\n",
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-              "<style>\n",
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-              "      --disabled-fill-color: #AAA;\n",
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-              "  [theme=dark] .colab-df-quickchart {\n",
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-              "    border-color: transparent;\n",
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-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
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-              "      border-bottom-color: var(--fill-color);\n",
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-              "    80% {\n",
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-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
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-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 34
-        }
-      ],
-      "source": [
-        "# url = 'http://hfrnet.ucsd.edu/sitediag/stationList.php?output=CSV'\n",
-        "\n",
-        "# df_hfr = pd.read_csv(url)\n",
-        "\n",
-        "# hfr_installations = df_hfr['Station'].unique().size\n",
-        "\n",
-        "# print('HF Radar Installations:',hfr_installations)\n",
-        "\n",
-        "hfr_installations = 165\n",
-        "\n",
-        "ioos_btn_df.loc[ioos_btn_df['date_UTC']==today, ['HF Radar Stations']] = hfr_installations\n",
-        "\n",
-        "ioos_btn_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "V-s6dYEZqvlt"
-      },
-      "source": [
-        "---\n",
-        "## NGDAC Glider Days\n",
-        "Gliders monitor water currents, temperature, and conditions that reveal effects from storms, impacts on fisheries, and the quality of our water. This information creates a more complete picture of what is happening in the ocean, as well as trends scientists might be able to detect. U.S. IOOS began counting “Glider days” in 2008 with the intent to better coordinate across U.S. glider operations and to increase the data sharing and data management of this technology. One \"Glider Day\" is defined as 1 glider in the water collecting data for 1 day.\n",
-        "\n",
-        "From https://gliders.ioos.us/erddap/info/index.html?page=1&itemsPerPage=1000\n",
-        "\n",
-        "Cumulative from 2008 - present\n",
-        "\n",
-        "### Conditions on our calculations:\n",
-        "* drops all datasets with `datasetID` containing `delayed`.\n",
-        "* duration is calculated based on the metadata ERDDAP generates.\n",
-        "  * If a datum of `NaN` is in the `time` variable, the min/max will not be computed.\n",
-        "\n",
-        "### Checks\n",
-        "\n",
-        "[gist 1](https://nbviewer.org/urls/gist.githubusercontent.com/ocefpaf/e1e6f341c4149ff1d7b6541635d599fb/raw/f79b40741ed96672c025eaff9cbf87d7287b06f3/compute_gliders_days.ipynb)\n",
-        "\n",
-        "This one fetches individual info dataset_ids URLs and matches what you are doing with allDatasets. It is still much bigger than `gdutils`, not sure what else it is filtering.\n",
-        "\n",
-        "This is too slow (~20 mins), it only serves to double check this computation and to figure out how many gliders we are throwing away due to bad metadata, in this case, 1 `Nemesis-20170512T0000`.\n",
-        "\n",
-        "[gist 2](https://nbviewer.org/urls/gist.githubusercontent.com/ocefpaf/97b943abaa1490701cd386741fabeb56/raw/9628c27b678fbfd678c80195869112e64e32c5ad/compute_gliders_days-time-var.ipynb)\n",
-        "\n",
-        "This one is a similar approach but with the time variable instead. We drop the NaN in the time var, so we do include most of `Nemesis-20170512T0000`. Note that it is larger, but not by much, than just the inclusion of `Nemesis-20170512T0000` and that is probably because the metadata is wrong in a few places.\n",
-        "\n",
-        "In theory the last one is more accurate but the difference is small.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 35,
-      "metadata": {
-        "id": "4YBCBGGKTGG3"
-      },
-      "outputs": [],
-      "source": [
-        "# import pandas as pd\n",
-        "# df_glider = pd.read_csv('https://gliders.ioos.us/erddap/tabledap/allDatasets.csvp?minTime%2CmaxTime%2CdatasetID')\n",
-        "# df_glider.dropna(\n",
-        "#     axis=0,\n",
-        "#     inplace=True,\n",
-        "#     )\n",
-        "\n",
-        "# # drop delayed datasets\n",
-        "# df_glider = df_glider[df_glider[\"datasetID\"].str.contains(\"delayed\")==False]\n",
-        "\n",
-        "# df_glider[['minTime (UTC)','maxTime (UTC)']] = df_glider[\n",
-        "#                                                          ['minTime (UTC)','maxTime (UTC)']\n",
-        "#                                                          ].apply(pd.to_datetime)\n",
-        "\n",
-        "# df_glider['glider_days'] = (df_glider['maxTime (UTC)'] - df_glider['minTime (UTC)']).dt.days\n",
-        "\n",
-        "# glider_days = df_glider['glider_days'].sum()\n",
-        "\n",
-        "# print('Cumulative glider days:', glider_days)\n",
-        "\n",
-        "# ioos_btn_df.loc[ioos_btn_df['date_UTC']==today, ['NGDAC Glider Days']] = glider_days\n",
-        "\n",
-        "# ioos_btn_df"
-      ]
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/ioos/ioos_metrics/blob/main/IOOS_BTN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "98GUIWOu35mJ"
+   },
+   "source": [
+    "# Creating the IOOS By The Numbers\n",
+    "\n",
+    "[Website](https://ioos.noaa.gov/about/ioos-by-the-numbers/)\n",
+    "\n",
+    "[Spreadsheet](https://docs.google.com/spreadsheets/d/1AUfXmc3OwxpVdeMNjZyTGWjyR4ku3kRD5eexNrMORnI/edit#gid=516871794)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "id": "LdpKgY6ZsVy4"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# read previous metrics\n",
+    "ioos_btn_df = pd.read_csv(\"https://github.com/ioos/ioos_metrics/raw/main/ioos_btn_metrics.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 394
     },
+    "id": "-afzN9SfS5tk",
+    "outputId": "713a1608-65ab-49e0-afeb-f7648dee77b8"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "henQROnZk6G5"
-      },
-      "source": [
-        "Gliders picking a time period."
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-30-b7f5493e0ed0>:9: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  ioos_btn_df = ioos_btn_df.append({'date_UTC': today}, ignore_index=True)\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 36,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 535
-        },
-        "id": "fC0q963WEGKu",
-        "outputId": "cead366c-5a16-47c0-d6ff-e1d4a51a130c"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Glider days between 2000-01-01 and 2023-12-31: 73204\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "<ipython-input-36-29f3e59280d0>:50: SettingWithCopyWarning: \n",
-            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-            "Try using .loc[row_indexer,col_indexer] = value instead\n",
-            "\n",
-            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-            "  glider_day_upper.loc[:,'maxTime (UTC)'] = pd.to_datetime(end_date, utc=True)\n",
-            "<ipython-input-36-29f3e59280d0>:50: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
-            "  glider_day_upper.loc[:,'maxTime (UTC)'] = pd.to_datetime(end_date, utc=True)\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
-              "0  2018-02-01              17.0                   11.0              150.0   \n",
-              "1  2022-04-22              17.0                   11.0              165.0   \n",
-              "2  2022-07-08              17.0                   11.0              165.0   \n",
-              "3  2022-10-05              17.0                   11.0              165.0   \n",
-              "4  2023-01-05              17.0                   11.0              165.0   \n",
-              "5  2024-01-26              17.0                   11.0              165.0   \n",
-              "\n",
-              "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
-              "0            52027.0               737.0               335.0              NaN   \n",
-              "1            53672.0               763.0               517.0           4444.0   \n",
-              "2            55448.0               764.0               517.0           4444.0   \n",
-              "3            59088.0               390.0               635.0           4444.0   \n",
-              "4            62042.0               768.0               635.0           4444.0   \n",
-              "5            73204.0                 NaN                 NaN              NaN   \n",
-              "\n",
-              "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
-              "0            NaN           NaN                 NaN            13.0   \n",
-              "1            6.0           8.0                 9.0            13.0   \n",
-              "2            6.0           8.0                 9.0            13.0   \n",
-              "3            6.0           8.0                 9.0            13.0   \n",
-              "4            6.0           8.0                 9.0            13.0   \n",
-              "5            NaN           NaN                 NaN             NaN   \n",
-              "\n",
-              "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
-              "0                 34.0            8600.0   1.0            NaN  \n",
-              "1                 34.0            7213.0   1.0            5.0  \n",
-              "2                 34.0            6217.0   1.0            5.0  \n",
-              "3                 34.0           24499.0   1.0            5.0  \n",
-              "4                 34.0           11840.0   1.0            5.0  \n",
-              "5                  NaN               NaN   NaN            5.0  "
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-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>2022-07-08</td>\n",
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-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>59088.0</td>\n",
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-              "      <td>165.0</td>\n",
-              "      <td>73204.0</td>\n",
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-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-112f00eb-5815-4e91-b09f-85ebfa09fc00');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
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-              "        await google.colab.output.renderOutput(dataTable, element);\n",
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-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
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-              "\n",
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-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
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-              "      border-bottom-color: var(--fill-color);\n",
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-          "metadata": {},
-          "execution_count": 36
-        }
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+       "  <thead>\n",
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+       "      <th></th>\n",
+       "      <th>date_UTC</th>\n",
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+       "      <th>HF Radar Stations</th>\n",
+       "      <th>NGDAC Glider Days</th>\n",
+       "      <th>National Platforms</th>\n",
+       "      <th>Regional Platforms</th>\n",
+       "      <th>ATN Deployments</th>\n",
+       "      <th>MBON Projects</th>\n",
+       "      <th>OTT Projects</th>\n",
+       "      <th>HAB Pilot Projects</th>\n",
+       "      <th>QARTOD Manuals</th>\n",
+       "      <th>IOOS Core Variables</th>\n",
+       "      <th>Metadata Records</th>\n",
+       "      <th>IOOS</th>\n",
+       "      <th>COMT Projects</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2018-02-01</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>52027.0</td>\n",
+       "      <td>737.0</td>\n",
+       "      <td>335.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>8600.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2022-04-22</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>53672.0</td>\n",
+       "      <td>763.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>7213.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2022-07-08</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>55448.0</td>\n",
+       "      <td>764.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>6217.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2022-10-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>59088.0</td>\n",
+       "      <td>390.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>24499.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-01-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>62042.0</td>\n",
+       "      <td>768.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>11840.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2024-01-26</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-78022e70-837c-4a76-9fca-89adb4b1a804')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-78022e70-837c-4a76-9fca-89adb4b1a804 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-78022e70-837c-4a76-9fca-89adb4b1a804');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-67fd8d7a-7f11-4116-91a3-64c2be2a5cba\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-67fd8d7a-7f11-4116-91a3-64c2be2a5cba')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-67fd8d7a-7f11-4116-91a3-64c2be2a5cba button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "source": [
-        "import pandas as pd\n",
-        "df_glider = pd.read_csv('https://gliders.ioos.us/erddap/tabledap/allDatasets.csvp?minTime%2CmaxTime%2CdatasetID')\n",
-        "df_glider.dropna(\n",
-        "    axis=0,\n",
-        "    inplace=True,\n",
-        "    )\n",
-        "\n",
-        "# drop delayed datasets\n",
-        "df_glider = df_glider[df_glider[\"datasetID\"].str.contains(\"delayed\")==False]\n",
-        "\n",
-        "df_glider[['minTime (UTC)','maxTime (UTC)']] = df_glider[\n",
-        "                                                         ['minTime (UTC)','maxTime (UTC)']\n",
-        "                                                         ].apply(pd.to_datetime)\n",
-        "\n",
-        "start_date = '2000-01-01'\n",
-        "end_date = '2023-12-31'\n",
-        "\n",
-        "# find glider deployments between 10/01 and 12/31\n",
-        "glider_day_within = df_glider.loc[\n",
-        "    (df_glider['minTime (UTC)'] > pd.to_datetime(start_date,utc=True)) &\n",
-        "    (df_glider['maxTime (UTC)'] < pd.to_datetime(end_date,utc=True))\n",
-        "]\n",
-        "\n",
-        "# gliders that start before 10/01 and end after 12/31\n",
-        "glider_day_outside = df_glider.loc[\n",
-        "    (df_glider['minTime (UTC)'] < pd.to_datetime(start_date,utc=True)) &\n",
-        "    (df_glider['maxTime (UTC)'] > pd.to_datetime(end_date,utc=True))\n",
-        "]\n",
-        "\n",
-        "glider_day_outside.loc[:, 'maxTime (UTC)'] = pd.to_datetime(end_date, utc=True)\n",
-        "glider_day_outside.loc[:, 'minTime (UTC)'] = pd.to_datetime(start_date, utc=True)\n",
-        "\n",
-        "# drop the ones from above as they will be duplicates in the next round of filtering\n",
-        "df_glider.drop(axis=0, index=glider_day_outside.index, inplace=True)\n",
-        "\n",
-        "# Find gliders that start before 10/01 and end after 10/01\n",
-        "glider_day_lower = df_glider.loc[\n",
-        "    (df_glider['minTime (UTC)'] < pd.to_datetime(start_date,utc=True)) &\n",
-        "    (df_glider['maxTime (UTC)'] > pd.to_datetime(start_date,utc=True))\n",
-        "]\n",
-        "\n",
-        "glider_day_lower.loc[:,'minTime (UTC)'] = pd.to_datetime(start_date, utc=True)\n",
-        "\n",
-        "# Find gliders that start before 12/31 and end after 12/31.\n",
-        "glider_day_upper = df_glider.loc[\n",
-        "    (df_glider['minTime (UTC)']<pd.to_datetime(end_date,utc=True)) &\n",
-        "    (df_glider['maxTime (UTC)']>pd.to_datetime(end_date,utc=True))\n",
-        "]\n",
-        "\n",
-        "glider_day_upper.loc[:,'maxTime (UTC)'] = pd.to_datetime(end_date, utc=True)\n",
-        "\n",
-        "# Combine it all together into one DF.\n",
-        "glider_subset = pd.concat([glider_day_lower,\n",
-        "                           glider_day_within,\n",
-        "                           glider_day_upper,\n",
-        "                           glider_day_outside],\n",
-        "                          verify_integrity=True)\n",
-        "\n",
-        "# Calculate the days between min time and max time.\n",
-        "glider_subset['glider_days'] = (glider_subset['maxTime (UTC)'] - glider_subset['minTime (UTC)']).dt.days\n",
-        "\n",
-        "# Calculate total glider days.\n",
-        "glider_days = glider_subset['glider_days'].sum()\n",
-        "\n",
-        "print(\"Glider days between %s and %s: %s\" % (start_date,end_date,glider_days))\n",
-        "\n",
-        "ioos_btn_df.loc[ioos_btn_df['date_UTC']==today, ['NGDAC Glider Days']] = glider_days\n",
-        "\n",
-        "ioos_btn_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "WyiivPFtk_K9"
-      },
-      "source": [
-        "Kathy's glider code"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 37,
-      "metadata": {
-        "id": "4EvojPUElDDk"
-      },
-      "outputs": [],
-      "source": [
-        "# import ipydatetime\n",
-        "# from datetime import datetime\n",
-        "# from ipywidgets import VBox, HBox, Label, BoundedFloatText\n",
-        "\n",
-        "\n",
-        "# dt0 = ipydatetime.NaiveDatetimePicker(value=datetime(2000, 1, 1, 0, 0, 0), description='start time:')\n",
-        "# dt1 = ipydatetime.NaiveDatetimePicker(value=datetime(2022, 12, 31, 23, 59, 59), description='end time:')\n",
-        "\n",
-        "# south = BoundedFloatText(\n",
-        "#     value=-90,\n",
-        "#     min=-90,\n",
-        "#     max=90,\n",
-        "#     description='min lat:',\n",
-        "# )\n",
-        "\n",
-        "# north = BoundedFloatText(\n",
-        "#     value=90,\n",
-        "#     min=-90,\n",
-        "#     max=90,\n",
-        "#     description='max lat:',\n",
-        "# )\n",
-        "\n",
-        "\n",
-        "# west = BoundedFloatText(\n",
-        "#     value=-180,\n",
-        "#     min=-180,\n",
-        "#     max=180,\n",
-        "#     description='min lon:',\n",
-        "# )\n",
-        "\n",
-        "# east = BoundedFloatText(\n",
-        "#     value=180,\n",
-        "#     min=-180,\n",
-        "#     max=180,\n",
-        "#     description='max lon:',\n",
-        "# )\n",
-        "\n",
-        "# from ipywidgets import Layout, Button, Box\n",
-        "\n",
-        "\n",
-        "# box_layout = Layout(\n",
-        "#     display=\"flex\",\n",
-        "#     flex_flow=\"column\",\n",
-        "#     align_items=\"center\"\n",
-        "# )\n",
-        "\n",
-        "# items = [\n",
-        "#     VBox([dt0, dt1]),\n",
-        "#     north,\n",
-        "#     HBox([west, east]),\n",
-        "#     south,\n",
-        "# ]\n",
-        "\n",
-        "# box = Box(children=items, layout=box_layout)\n",
-        "# box\n",
-        "\n",
-        "# dt0 = dt0.value\n",
-        "# dt1 = dt1.value\n",
-        "# south = south.value\n",
-        "# north = north.value\n",
-        "# west = west.value\n",
-        "# east = east.value\n",
-        "\n",
-        "# params = {\n",
-        "#     \"min_time\": dt0,\n",
-        "#     \"max_time\": dt1,\n",
-        "#     \"min_lat\": south,\n",
-        "#     \"max_lat\": north,\n",
-        "#     \"min_lon\": west,\n",
-        "#     \"max_lon\": east,\n",
-        "# }\n",
-        "\n",
-        "# from gdutils import GdacClient\n",
-        "\n",
-        "\n",
-        "# client = GdacClient()\n",
-        "\n",
-        "# client.search_datasets(params=params, include_delayed_mode=False)\n",
-        "# client.datasets\n",
-        "\n",
-        "# # Count the total number of deployments within the dt0:dt1 time window\n",
-        "# num_deployments = client.datasets.shape[0]\n",
-        "\n",
-        "# # Count the number of glider days within the dt0:dt1 time window\n",
-        "# glider_days = client.glider_days_per_yyyymmdd.loc[dt0:dt1].sum()\n",
-        "\n",
-        "# # count the number of profiles per dataset\n",
-        "# profile_count = client.profiles_per_yyyymmdd.loc[dt0:dt1].sum()\n",
-        "\n",
-        "# datasets = client.datasets.copy()\n",
-        "\n",
-        "# import warnings\n",
-        "\n",
-        "\n",
-        "# sea_names = []\n",
-        "# funding_sources = []\n",
-        "# for dataset_id, row in datasets.iterrows():\n",
-        "\n",
-        "#     # Fetch the dataset description from ERDDAP\n",
-        "#     info = client.get_dataset_metadata(dataset_id)\n",
-        "\n",
-        "#     if info.empty:\n",
-        "#         continue\n",
-        "\n",
-        "#     # Find all global NetCDF attributes\n",
-        "#     nc_globals = info.loc[info[\"Variable Name\"] == \"NC_GLOBAL\"]\n",
-        "\n",
-        "#     # Find the sea_name global attribute\n",
-        "#     sea_name_attr = nc_globals.loc[nc_globals[\"Attribute Name\"] == \"sea_name\"]\n",
-        "#     sea_name = \"unknown\"\n",
-        "#     if not sea_name_attr.empty:\n",
-        "#         sea_name = sea_name_attr.Value.iloc[0]\n",
-        "#         sea_name = sea_name or \"unknown\"\n",
-        "#     else:\n",
-        "#         warnings.warn(f\"{dataset_id}: sea_name NC_GLOBAL not found\")\n",
-        "\n",
-        "#     # Find all global attributes that begin with \"acknowledg\" as this attribute typically contains the funding sources\n",
-        "#     funding_attr = nc_globals.loc[nc_globals[\"Attribute Name\"].str.startswith(\"acknowledg\")]\n",
-        "#     funding = \"unknown\"\n",
-        "#     if not funding_attr.empty:\n",
-        "#         funding = funding_attr.Value.iloc[0]\n",
-        "#         funding = funding or \"unknown\"\n",
-        "#     else:\n",
-        "#         warnings.warn(f\"{dataset_id}: acknowledgment NC_GLOBAL not found\")\n",
-        "\n",
-        "#     sea_names.append(sea_name)\n",
-        "#     funding_sources.append(funding)\n",
-        "\n",
-        "#     # Count only the days and profiles that are dt0:dt1 inclusive\n",
-        "#     days = client.datasets_days[dataset_id].loc[dt0:dt1].sum()\n",
-        "#     profiles = client.datasets_profiles[dataset_id].loc[dt0:dt1].sum()\n",
-        "#     datasets.loc[dataset_id, (\"days\", \"num_profiles\")] = [days, profiles]\n",
-        "\n",
-        "\n",
-        "# print(f\"time ranged from {dt0} to {dt1}\")\n",
-        "# print(f\"\"\"\n",
-        "# {num_deployments = }\n",
-        "# {glider_days = }\n",
-        "# {profile_count = }\n",
-        "# \"\"\"\n",
-        "# )"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "iYk1km3P861f"
-      },
-      "source": [
-        "---\n",
-        "## National Platforms\n",
-        "\n",
-        "The National backbone of IOOS includes buoys, water level gauges,as well as coastal and estuary stations run by our federal partners. Platforms calculated within this total are assets within the EEZ. For buoys this includes platforms managed by NOAA's National Data Buoy Center, the NOAA NMFS Chesapeake Bay Interpretive Buoy System (CBIBS), and the U.S. Army Corps of Engineers’ Coastal Data Information Program (CDIP), Ocean Acidification Program , Ecosystems and Fishery Oceanography Coordinated Investigations (EcoFOCI). For guages, this includes National Water Level Observation Network guages operated by the Center for Operational Oceanographic Products and Services (CO-OPS). The coastal and estuary stations are maintained through NOAA's National Estuarine Research Reserves (NERR) System-Wide Management Program (SWMP)."
+      "text/plain": [
+       "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
+       "0  2018-02-01              17.0                   11.0              150.0   \n",
+       "1  2022-04-22              17.0                   11.0              165.0   \n",
+       "2  2022-07-08              17.0                   11.0              165.0   \n",
+       "3  2022-10-05              17.0                   11.0              165.0   \n",
+       "4  2023-01-05              17.0                   11.0              165.0   \n",
+       "5  2024-01-26               NaN                    NaN                NaN   \n",
+       "\n",
+       "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
+       "0            52027.0               737.0               335.0              NaN   \n",
+       "1            53672.0               763.0               517.0           4444.0   \n",
+       "2            55448.0               764.0               517.0           4444.0   \n",
+       "3            59088.0               390.0               635.0           4444.0   \n",
+       "4            62042.0               768.0               635.0           4444.0   \n",
+       "5                NaN                 NaN                 NaN              NaN   \n",
+       "\n",
+       "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
+       "0            NaN           NaN                 NaN            13.0   \n",
+       "1            6.0           8.0                 9.0            13.0   \n",
+       "2            6.0           8.0                 9.0            13.0   \n",
+       "3            6.0           8.0                 9.0            13.0   \n",
+       "4            6.0           8.0                 9.0            13.0   \n",
+       "5            NaN           NaN                 NaN             NaN   \n",
+       "\n",
+       "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
+       "0                 34.0            8600.0   1.0            NaN  \n",
+       "1                 34.0            7213.0   1.0            5.0  \n",
+       "2                 34.0            6217.0   1.0            5.0  \n",
+       "3                 34.0           24499.0   1.0            5.0  \n",
+       "4                 34.0           11840.0   1.0            5.0  \n",
+       "5                  NaN               NaN   NaN            NaN  "
       ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "#today = pd.to_datetime\n",
+    "#ioos_btn_df = pd.DataFrame()#columns=['category','value','date'])\n",
+    "\n",
+    "today = pd.Timestamp.strftime(pd.Timestamp.today(tz=\"UTC\"), \"%Y-%m-%d\")\n",
+    "\n",
+    "# only update numbers if it's a new day\n",
+    "if today not in ioos_btn_df[\"date_UTC\"].to_list():\n",
+    "    ioos_btn_df = ioos_btn_df.append({\"date_UTC\": today}, ignore_index=True)\n",
+    "\n",
+    "ioos_btn_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rMkHuVRJEGKq"
+   },
+   "source": [
+    "---\n",
+    "## Federal Partners\n",
+    "\n",
+    "ICOOS Act/COORA\n",
+    "\n",
+    "Typically 17, from https://ioos.noaa.gov/community/national#federal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 689
     },
+    "id": "jXMhi3tRQvMd",
+    "outputId": "f00a134b-84af-4bda-8414-db6f0d52c0e0"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "xGHalY-14iGB"
-      },
-      "source": [
-        "### CO-OPS\n",
-        "* https://opendap.co-ops.nos.noaa.gov/stations/index.jsp\n",
-        "  * as xml: https://opendap.co-ops.nos.noaa.gov/stations/stationsXML.jsp\n",
-        "* https://tidesandcurrents.noaa.gov/cdata/StationList?type=Current+Data&filter=active"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0                                               National Oceanic and Atmospheric Administration (NOAA)\n",
+      "1                                                 National Aeronautics and Space Administration (NASA)\n",
+      "2                        Bureau of Ocean Energy Management, Regulation and Enforcement (BOEM and BSEE)\n",
+      "3                                                                       Office of Naval Research (ONR)\n",
+      "4                                                                 U.S. Army Corps of Engineers (USACE)\n",
+      "5                                                                        U.S. Geological Survey (USGS)\n",
+      "6                                                                           Department of Energy (DOE)\n",
+      "7                                                                   Department of Transportation (DOT)\n",
+      "8                                                              U.S. Arctic Research Commission (USARC)\n",
+      "9                                                                    National Science Foundation (NSF)\n",
+      "10                                                               Environmental Protection Agency (EPA)\n",
+      "11                                                                      Marine Mammal Commission (MMC)\n",
+      "12                             Oceanographer of the Navy, representing the Joint Chiefs of Staff (JCS)\n",
+      "13                                                                             U.S. Coast Guard (USCG)\n",
+      "14    Department of Agriculture, Cooperative State Research, Education, and Extension Service (CSREES)\n",
+      "15                                                                           Department of State (DOS)\n",
+      "16                                                                  Food and Drug Administration (FDA)\n",
+      "\n",
+      "Federal Partners: 17\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 38,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "wZKANnz8sC5D",
-        "outputId": "460399f8-8202-4a39-ba09-411a786de49d"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "All stations: 380\n",
-            "Ports: 75\n"
-          ]
-        }
+     "data": {
+      "text/html": [
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+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date_UTC</th>\n",
+       "      <th>Federal Partners</th>\n",
+       "      <th>Regional Associations</th>\n",
+       "      <th>HF Radar Stations</th>\n",
+       "      <th>NGDAC Glider Days</th>\n",
+       "      <th>National Platforms</th>\n",
+       "      <th>Regional Platforms</th>\n",
+       "      <th>ATN Deployments</th>\n",
+       "      <th>MBON Projects</th>\n",
+       "      <th>OTT Projects</th>\n",
+       "      <th>HAB Pilot Projects</th>\n",
+       "      <th>QARTOD Manuals</th>\n",
+       "      <th>IOOS Core Variables</th>\n",
+       "      <th>Metadata Records</th>\n",
+       "      <th>IOOS</th>\n",
+       "      <th>COMT Projects</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2018-02-01</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>52027.0</td>\n",
+       "      <td>737.0</td>\n",
+       "      <td>335.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>8600.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2022-04-22</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>53672.0</td>\n",
+       "      <td>763.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>7213.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2022-07-08</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>55448.0</td>\n",
+       "      <td>764.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>6217.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2022-10-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>59088.0</td>\n",
+       "      <td>390.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>24499.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-01-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>62042.0</td>\n",
+       "      <td>768.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>11840.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2024-01-26</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-30ee72e1-2e67-4207-92fd-e4539a856acd button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "source": [
-        "#from lxml import etree\n",
-        "import requests\n",
-        "import pandas as pd\n",
-        "\n",
-        "xml = requests.get('https://opendap.co-ops.nos.noaa.gov/stations/stationsXML.jsp').text\n",
-        "import re\n",
-        "COOPS = sum(1 for _ in re.finditer(r'\\b%s\\b' % re.escape(\"station name\"), xml))\n",
-        "print(\"All stations:\",COOPS)\n",
-        "\n",
-        "url = 'https://tidesandcurrents.noaa.gov/cdata/StationListFormat?type=Current+Data&filter=active&format=csv'\n",
-        "\n",
-        "df_coops = pd.read_csv(url)\n",
-        "#print(df_coops[' Project'].unique())\n",
-        "ports = df_coops[df_coops[' Project'].astype(str).str.contains('PORTS')].shape[0]\n",
-        "print(\"Ports:\", ports)"
+      "text/plain": [
+       "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
+       "0  2018-02-01              17.0                   11.0              150.0   \n",
+       "1  2022-04-22              17.0                   11.0              165.0   \n",
+       "2  2022-07-08              17.0                   11.0              165.0   \n",
+       "3  2022-10-05              17.0                   11.0              165.0   \n",
+       "4  2023-01-05              17.0                   11.0              165.0   \n",
+       "5  2024-01-26              17.0                    NaN                NaN   \n",
+       "\n",
+       "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
+       "0            52027.0               737.0               335.0              NaN   \n",
+       "1            53672.0               763.0               517.0           4444.0   \n",
+       "2            55448.0               764.0               517.0           4444.0   \n",
+       "3            59088.0               390.0               635.0           4444.0   \n",
+       "4            62042.0               768.0               635.0           4444.0   \n",
+       "5                NaN                 NaN                 NaN              NaN   \n",
+       "\n",
+       "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
+       "0            NaN           NaN                 NaN            13.0   \n",
+       "1            6.0           8.0                 9.0            13.0   \n",
+       "2            6.0           8.0                 9.0            13.0   \n",
+       "3            6.0           8.0                 9.0            13.0   \n",
+       "4            6.0           8.0                 9.0            13.0   \n",
+       "5            NaN           NaN                 NaN             NaN   \n",
+       "\n",
+       "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
+       "0                 34.0            8600.0   1.0            NaN  \n",
+       "1                 34.0            7213.0   1.0            5.0  \n",
+       "2                 34.0            6217.0   1.0            5.0  \n",
+       "3                 34.0           24499.0   1.0            5.0  \n",
+       "4                 34.0           11840.0   1.0            5.0  \n",
+       "5                  NaN               NaN   NaN            NaN  "
       ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "\n",
+    "url = \"https://ioos.noaa.gov/community/national#federal\"\n",
+    "\n",
+    "headers = {\"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36\"}\n",
+    "\n",
+    "html = requests.get(url, headers=headers).text\n",
+    "\n",
+    "soup = BeautifulSoup(html, \"html.parser\")\n",
+    "\n",
+    "df = pd.read_html(str(soup))\n",
+    "\n",
+    "df_clean = df[1].drop(columns=[0,2])\n",
+    "\n",
+    "df_fed_partners = pd.concat([df_clean[1] , df_clean[3]]).dropna().reset_index()\n",
+    "\n",
+    "fed_partners = df_fed_partners.shape[0]\n",
+    "\n",
+    "ioos_btn_df.loc[ioos_btn_df[\"date_UTC\"]==today, [\"Federal Partners\"]] = [fed_partners]\n",
+    "\n",
+    "pd.set_option(\"max_colwidth\", None)\n",
+    "\n",
+    "print(df_fed_partners[0].to_string())\n",
+    "\n",
+    "print(\"\\nFederal Partners:\", fed_partners)\n",
+    "\n",
+    "ioos_btn_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "elYWAAZSEGKq"
+   },
+   "source": [
+    "---\n",
+    "## Regional Associations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 585
     },
+    "id": "vnEk8a4Qd6L7",
+    "outputId": "21ce4dcb-5ff2-4162-ac33-4b600f8ea8d7"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TmDAxLm24q7g"
-      },
-      "source": [
-        "### NDBC\n",
-        "https://www.ndbc.noaa.gov/wstat.shtml\tBuoys: 106 (103 base-funded); CMAN: 45"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Alaska Ocean Observing System (AOOS)\n",
+      "Caribbean Coastal Ocean Observing System (CARICOOS)\n",
+      "Central and Northern California Ocean Observing System (CeNCOOS)\n",
+      "Gulf of Mexico Coastal Ocean Observing System (GCOOS)\n",
+      "Great Lakes Observing System (GLOS)\n",
+      "Mid-Atlantic Coastal Ocean Observing System (MARACOOS)\n",
+      "Northwest Association of Networked Ocean Observing Systems (NANOOS)\n",
+      "Northeastern Regional Association of Coastal Ocean Observing Systems (NERACOOS)\n",
+      "Pacific Islands Ocean Observing System (PacIOOS)\n",
+      "Southern California Coastal Ocean Observing System (SCCOOS)\n",
+      "Southeast Coastal Ocean Observing Regional Association (SECOORA)\n",
+      "\n",
+      "Number of Regional Associations: 11\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 39,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "OWcvMhvR4xWt",
-        "outputId": "14de096a-7a17-43c4-94e6-510924c59db9"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "NDBC: 146\n"
-          ]
-        }
+     "data": {
+      "text/html": [
+       "\n",
+       "  <div id=\"df-4517e8bf-3d60-4187-b59e-3e47b9d61e1c\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date_UTC</th>\n",
+       "      <th>Federal Partners</th>\n",
+       "      <th>Regional Associations</th>\n",
+       "      <th>HF Radar Stations</th>\n",
+       "      <th>NGDAC Glider Days</th>\n",
+       "      <th>National Platforms</th>\n",
+       "      <th>Regional Platforms</th>\n",
+       "      <th>ATN Deployments</th>\n",
+       "      <th>MBON Projects</th>\n",
+       "      <th>OTT Projects</th>\n",
+       "      <th>HAB Pilot Projects</th>\n",
+       "      <th>QARTOD Manuals</th>\n",
+       "      <th>IOOS Core Variables</th>\n",
+       "      <th>Metadata Records</th>\n",
+       "      <th>IOOS</th>\n",
+       "      <th>COMT Projects</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2018-02-01</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>52027.0</td>\n",
+       "      <td>737.0</td>\n",
+       "      <td>335.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>8600.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2022-04-22</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>53672.0</td>\n",
+       "      <td>763.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>7213.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2022-07-08</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>55448.0</td>\n",
+       "      <td>764.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>6217.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2022-10-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>59088.0</td>\n",
+       "      <td>390.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>24499.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-01-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>62042.0</td>\n",
+       "      <td>768.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>11840.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2024-01-26</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4517e8bf-3d60-4187-b59e-3e47b9d61e1c')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-4517e8bf-3d60-4187-b59e-3e47b9d61e1c button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-4517e8bf-3d60-4187-b59e-3e47b9d61e1c');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-8df3d328-6558-467f-a584-3a9328d9e825\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8df3d328-6558-467f-a584-3a9328d9e825')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-8df3d328-6558-467f-a584-3a9328d9e825 button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "source": [
-        "import requests\n",
-        "from bs4 import BeautifulSoup\n",
-        "import re\n",
-        "import pprint\n",
-        "\n",
-        "url = 'https://www.ndbc.noaa.gov/wstat.shtml'\n",
-        "\n",
-        "headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36'}\n",
-        "\n",
-        "html = requests.get(url, headers=headers).text\n",
-        "\n",
-        "soup = BeautifulSoup(html, 'html.parser')\n",
-        "\n",
-        "string_to_find = ['Total Base Funded Buoys:','Total Other Buoys:',\n",
-        "                  'Total Moored Buoys:','Total Base Funded Stations:',\n",
-        "                  'Total Stations:']\n",
-        "\n",
-        "ndbc = dict()\n",
-        "for string in string_to_find:\n",
-        "    for tag in soup.find_all(\"td\", string=string):\n",
-        "        ndbc[string] = int(tag.next_sibling.string)\n",
-        "\n",
-        "#pprint.pprint(ndbc)\n",
-        "\n",
-        "NDBC = ndbc['Total Moored Buoys:'] + ndbc['Total Base Funded Stations:']\n",
-        "print('NDBC:',NDBC)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "PvdJyWvN40Wk"
-      },
-      "source": [
-        "### NERRS\n",
-        "https://nosc.noaa.gov/OSC/OSN/index.php\tNERRS SWMP; Across 29 NERRS; Source = internal access only - NOAA Observing System Council.\n",
-        "\n",
-        "http://cdmo.baruch.sc.edu/webservices.cfm <- need IP address approval\n",
-        "\n",
-        "Need number of stations (120 last time)"
+      "text/plain": [
+       "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
+       "0  2018-02-01              17.0                   11.0              150.0   \n",
+       "1  2022-04-22              17.0                   11.0              165.0   \n",
+       "2  2022-07-08              17.0                   11.0              165.0   \n",
+       "3  2022-10-05              17.0                   11.0              165.0   \n",
+       "4  2023-01-05              17.0                   11.0              165.0   \n",
+       "5  2024-01-26              17.0                   11.0                NaN   \n",
+       "\n",
+       "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
+       "0            52027.0               737.0               335.0              NaN   \n",
+       "1            53672.0               763.0               517.0           4444.0   \n",
+       "2            55448.0               764.0               517.0           4444.0   \n",
+       "3            59088.0               390.0               635.0           4444.0   \n",
+       "4            62042.0               768.0               635.0           4444.0   \n",
+       "5                NaN                 NaN                 NaN              NaN   \n",
+       "\n",
+       "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
+       "0            NaN           NaN                 NaN            13.0   \n",
+       "1            6.0           8.0                 9.0            13.0   \n",
+       "2            6.0           8.0                 9.0            13.0   \n",
+       "3            6.0           8.0                 9.0            13.0   \n",
+       "4            6.0           8.0                 9.0            13.0   \n",
+       "5            NaN           NaN                 NaN             NaN   \n",
+       "\n",
+       "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
+       "0                 34.0            8600.0   1.0            NaN  \n",
+       "1                 34.0            7213.0   1.0            5.0  \n",
+       "2                 34.0            6217.0   1.0            5.0  \n",
+       "3                 34.0           24499.0   1.0            5.0  \n",
+       "4                 34.0           11840.0   1.0            5.0  \n",
+       "5                  NaN               NaN   NaN            NaN  "
       ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import re\n",
+    "\n",
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "\n",
+    "regional_associations = 0\n",
+    "\n",
+    "url = \"https://ioos.noaa.gov/regions/regions-at-a-glance/\"\n",
+    "\n",
+    "headers = {\"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36\"}\n",
+    "\n",
+    "html = requests.get(url, headers=headers).text\n",
+    "\n",
+    "soup = BeautifulSoup(html, \"html.parser\")\n",
+    "\n",
+    "for tag in soup.find_all(\"a\"):\n",
+    "\n",
+    "  if tag.find(\"strong\") is not None:\n",
+    "\n",
+    "     print(tag.find(\"strong\").text)\n",
+    "\n",
+    "     regional_associations+=1\n",
+    "\n",
+    "print(\"\\nNumber of Regional Associations:\", regional_associations)\n",
+    "\n",
+    "ioos_btn_df.loc[ioos_btn_df[\"date_UTC\"]==today, [\"Regional Associations\"]] = [regional_associations]\n",
+    "\n",
+    "ioos_btn_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HsPUEyNJEGKq"
+   },
+   "source": [
+    "---\n",
+    "## Coastal & Ocean Modeling Testbed\n",
+    "\n",
+    "The COMT serves as a conduit between the federal operational and research communities and allows sharing of numerical models, observations and software tools. The COMT supports integration, comparison, scientific analyses and archiving of data and model output needed to elucidate, prioritize, and resolve federal and regional operational coastal ocean issues associated with a range of existing and emerging coastal oceanic, hydrologic, and ecological models. The Testbed has enabled significant community building (within the modeling community as well as enhancing academic and federal operational relations) which has dramatically improved model development.\n",
+    "\n",
+    "Number of Active Projects via personal communication from COMT program manager."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 376
     },
+    "id": "1edu4Ga1k5WW",
+    "outputId": "614f5cab-1c9e-456e-e105-e1439b17779c"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 40,
-      "metadata": {
-        "id": "3pQsSjcrLGQt"
-      },
-      "outputs": [],
-      "source": [
-        "# !pip install config\n",
-        "# !pip install suds\n",
-        "# import config\n",
-        "\n",
-        "# from suds.client import Client\n",
-        "\n",
-        "# wsdlURL = \"https://cdmo.baruch.sc.edu/webservices2/requests.cfc?wsdl\"\n",
-        "\n",
-        "# user = config.CDMO_NAME\n",
-        "\n",
-        "# pwd = config.CDMO_KEY\n",
-        "\n",
-        "# soapClient = Client(wsdlURL,\n",
-        "#                     timeout=90,\n",
-        "#                     retxml=True,\n",
-        "#                     username=user,\n",
-        "#                     password=pwd,\n",
-        "#                     prettyxml=True)\n",
-        "\n",
-        "# wq_station_name = \"niwolwq\"\n",
-        "\n",
-        "# response = soapClient.service.exportAllParamsDateRangeXMLNew(wq_station_name, \"2023-03-23\", \"2023-03-23\")\n",
-        "\n",
-        "# # soapClient = Client(wsdlURL,\n",
-        "# #                     timeout=90,\n",
-        "# #                     retxml=True,\n",
-        "# #                     username=user,\n",
-        "# #                     password=pwd,\n",
-        "# #                    )\n",
-        "\n",
-        "# # #Get the station codes SOAP request example.\n",
-        "# # response = soapClient.service.exportStationCodesXMLNew()\n",
-        "# print(response)\n",
-        "# print(pd.read_xml(response,xpath=\".//returnData\"))"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COMT Projects: 5\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 41,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "vB1MEtDb_Map",
-        "outputId": "3ee7b4c7-52ed-4cae-f53a-35d55ac2ed13"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "NERRS stations: 93\n"
-          ]
-        }
+     "data": {
+      "text/html": [
+       "\n",
+       "  <div id=\"df-fcf5be17-1d5a-40d9-8c43-c4ec46e7f1bd\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date_UTC</th>\n",
+       "      <th>Federal Partners</th>\n",
+       "      <th>Regional Associations</th>\n",
+       "      <th>HF Radar Stations</th>\n",
+       "      <th>NGDAC Glider Days</th>\n",
+       "      <th>National Platforms</th>\n",
+       "      <th>Regional Platforms</th>\n",
+       "      <th>ATN Deployments</th>\n",
+       "      <th>MBON Projects</th>\n",
+       "      <th>OTT Projects</th>\n",
+       "      <th>HAB Pilot Projects</th>\n",
+       "      <th>QARTOD Manuals</th>\n",
+       "      <th>IOOS Core Variables</th>\n",
+       "      <th>Metadata Records</th>\n",
+       "      <th>IOOS</th>\n",
+       "      <th>COMT Projects</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2018-02-01</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>52027.0</td>\n",
+       "      <td>737.0</td>\n",
+       "      <td>335.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>8600.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2022-04-22</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>53672.0</td>\n",
+       "      <td>763.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>7213.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2022-07-08</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>55448.0</td>\n",
+       "      <td>764.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>6217.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2022-10-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>59088.0</td>\n",
+       "      <td>390.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>24499.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-01-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>62042.0</td>\n",
+       "      <td>768.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>11840.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2024-01-26</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fcf5be17-1d5a-40d9-8c43-c4ec46e7f1bd')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-fcf5be17-1d5a-40d9-8c43-c4ec46e7f1bd button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-fcf5be17-1d5a-40d9-8c43-c4ec46e7f1bd');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-9b7a1253-2d8a-434a-b76f-8c8211ba1e73\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9b7a1253-2d8a-434a-b76f-8c8211ba1e73')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-9b7a1253-2d8a-434a-b76f-8c8211ba1e73 button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "source": [
-        "\n",
-        "import requests\n",
-        "from bs4 import BeautifulSoup\n",
-        "#import re\n",
-        "import pandas as pd\n",
-        "\n",
-        "\n",
-        "url = 'https://cdmo.baruch.sc.edu//webservices/station_timing.cfm'\n",
-        "\n",
-        "headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36'}\n",
-        "\n",
-        "html = requests.get(url,headers=headers).text\n",
-        "\n",
-        "soup = BeautifulSoup(html, 'html.parser')\n",
-        "\n",
-        "df = pd.read_html(str(soup.find(attrs={'class':'row text-center'})),\n",
-        "                  header=0,\n",
-        "                  attrs = {'class': 'table'},)\n",
-        "\n",
-        "df_final = pd.concat([df[0],df[1]])\n",
-        "\n",
-        "NERRS = df_final.shape[0]\n",
-        "\n",
-        "# Should be around NERRS = 140\n",
-        "\n",
-        "print(\"NERRS stations:\",NERRS)\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_uH7fBzJ43vL"
-      },
-      "source": [
-        "### CBIBS\n",
-        "https://buoybay.noaa.gov/locations\n",
-        "\n",
-        "[API docs](https://buoybay.noaa.gov/node/174)\n",
-        "\n",
-        "Base URL: https://mw.buoybay.noaa.gov/api/v1\n",
-        "\n",
-        "Testing Key: f159959c117f473477edbdf3245cc2a4831ac61f\n",
-        "\n",
-        "Latest measurements:\n",
-        "https://mw.buoybay.noaa.gov/api/v1/json/station?key=f159959c117f473477edbdf3245cc2a4831ac61f"
+      "text/plain": [
+       "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
+       "0  2018-02-01              17.0                   11.0              150.0   \n",
+       "1  2022-04-22              17.0                   11.0              165.0   \n",
+       "2  2022-07-08              17.0                   11.0              165.0   \n",
+       "3  2022-10-05              17.0                   11.0              165.0   \n",
+       "4  2023-01-05              17.0                   11.0              165.0   \n",
+       "5  2024-01-26              17.0                   11.0                NaN   \n",
+       "\n",
+       "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
+       "0            52027.0               737.0               335.0              NaN   \n",
+       "1            53672.0               763.0               517.0           4444.0   \n",
+       "2            55448.0               764.0               517.0           4444.0   \n",
+       "3            59088.0               390.0               635.0           4444.0   \n",
+       "4            62042.0               768.0               635.0           4444.0   \n",
+       "5                NaN                 NaN                 NaN              NaN   \n",
+       "\n",
+       "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
+       "0            NaN           NaN                 NaN            13.0   \n",
+       "1            6.0           8.0                 9.0            13.0   \n",
+       "2            6.0           8.0                 9.0            13.0   \n",
+       "3            6.0           8.0                 9.0            13.0   \n",
+       "4            6.0           8.0                 9.0            13.0   \n",
+       "5            NaN           NaN                 NaN             NaN   \n",
+       "\n",
+       "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
+       "0                 34.0            8600.0   1.0            NaN  \n",
+       "1                 34.0            7213.0   1.0            5.0  \n",
+       "2                 34.0            6217.0   1.0            5.0  \n",
+       "3                 34.0           24499.0   1.0            5.0  \n",
+       "4                 34.0           11840.0   1.0            5.0  \n",
+       "5                  NaN               NaN   NaN            5.0  "
       ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## COMT website needs to be updated. Once updated this should work\n",
+    "\n",
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "\n",
+    "regional_associations = 0\n",
+    "\n",
+    "url = \"https://ioos.noaa.gov/project/comt/\"\n",
+    "\n",
+    "headers = {\"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36\"}\n",
+    "\n",
+    "html = requests.get(url, headers=headers).text\n",
+    "\n",
+    "soup = BeautifulSoup(html, \"html.parser\")\n",
+    "\n",
+    "for tag in soup.find_all(\"h2\"):\n",
+    "  if tag.text == \"Current Projects\":\n",
+    "    comt = len(tag.next_sibling.find_all(\"li\"))\n",
+    "\n",
+    "print(\"COMT Projects:\",comt)\n",
+    "\n",
+    "ioos_btn_df.loc[ioos_btn_df[\"date_UTC\"]==today, [\"COMT Projects\"]] = comt\n",
+    "\n",
+    "ioos_btn_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "08BPt9_HqPzx"
+   },
+   "source": [
+    "---\n",
+    "## HF Radar Installations\n",
+    "\n",
+    "The previous number of 181 included all locations where a HFR station had ever been sighted as part of the IOOS National Network, but doesn't appear to me to have accounted for temporary installations, HFRs unfunded by IOOS operated by international partners, or instances where an HFR being relocated from one site to another caused it to be double-counted.  Even the number 165 represents a \"high water mark\" for simultaneously operating HFRs, since HFRs routinely are taken offline for periods of time, for both planned preventative maintenance and in response to other exigent issues.\n",
+    "\n",
+    "From http://hfrnet.ucsd.edu/sitediag/stationList.php"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 359
     },
+    "id": "Jh0A94gGTCy_",
+    "outputId": "c5d92d78-c7b7-48b9-ca0d-05e07a901736"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 42,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "nIIbzvJO49Vb",
-        "outputId": "70d1c3f8-870b-4ea3-84d6-4a0d47ede4fb"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "CBIBS Stations: 11\n"
-          ]
-        }
+     "data": {
+      "text/html": [
+       "\n",
+       "  <div id=\"df-592de482-5fd7-48ae-9682-c748116e35ae\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date_UTC</th>\n",
+       "      <th>Federal Partners</th>\n",
+       "      <th>Regional Associations</th>\n",
+       "      <th>HF Radar Stations</th>\n",
+       "      <th>NGDAC Glider Days</th>\n",
+       "      <th>National Platforms</th>\n",
+       "      <th>Regional Platforms</th>\n",
+       "      <th>ATN Deployments</th>\n",
+       "      <th>MBON Projects</th>\n",
+       "      <th>OTT Projects</th>\n",
+       "      <th>HAB Pilot Projects</th>\n",
+       "      <th>QARTOD Manuals</th>\n",
+       "      <th>IOOS Core Variables</th>\n",
+       "      <th>Metadata Records</th>\n",
+       "      <th>IOOS</th>\n",
+       "      <th>COMT Projects</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2018-02-01</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>52027.0</td>\n",
+       "      <td>737.0</td>\n",
+       "      <td>335.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>8600.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2022-04-22</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>53672.0</td>\n",
+       "      <td>763.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>7213.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2022-07-08</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>55448.0</td>\n",
+       "      <td>764.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>6217.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2022-10-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>59088.0</td>\n",
+       "      <td>390.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>24499.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-01-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>62042.0</td>\n",
+       "      <td>768.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>11840.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2024-01-26</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
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+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
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+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
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+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
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+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
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+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
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+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-c03b428b-0528-4a7a-8d2d-157640c15e14 button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "source": [
-        "import json\n",
-        "\n",
-        "base_url = 'https://mw.buoybay.noaa.gov/api/v1'\n",
-        "apikey = 'f159959c117f473477edbdf3245cc2a4831ac61f'\n",
-        "start = '2021-12-08T01:00:00z'\n",
-        "end = '2021-12-09T23:59:59z'\n",
-        "var = 'Position'\n",
-        "\n",
-        "query_url = '{}/json/query?key={}&sd={}&ed={}&var={}'.format(base_url,apikey,start,end,var)\n",
-        "#query_url = '{}/json/station?key={}'.format(base_url, apikey)\n",
-        "\n",
-        "json = json.loads(requests.get(query_url).text)\n",
-        "\n",
-        "CBIBS = len(json['stations'])\n",
-        "\n",
-        "print(\"CBIBS Stations:\",CBIBS)"
+      "text/plain": [
+       "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
+       "0  2018-02-01              17.0                   11.0              150.0   \n",
+       "1  2022-04-22              17.0                   11.0              165.0   \n",
+       "2  2022-07-08              17.0                   11.0              165.0   \n",
+       "3  2022-10-05              17.0                   11.0              165.0   \n",
+       "4  2023-01-05              17.0                   11.0              165.0   \n",
+       "5  2024-01-26              17.0                   11.0              165.0   \n",
+       "\n",
+       "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
+       "0            52027.0               737.0               335.0              NaN   \n",
+       "1            53672.0               763.0               517.0           4444.0   \n",
+       "2            55448.0               764.0               517.0           4444.0   \n",
+       "3            59088.0               390.0               635.0           4444.0   \n",
+       "4            62042.0               768.0               635.0           4444.0   \n",
+       "5                NaN                 NaN                 NaN              NaN   \n",
+       "\n",
+       "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
+       "0            NaN           NaN                 NaN            13.0   \n",
+       "1            6.0           8.0                 9.0            13.0   \n",
+       "2            6.0           8.0                 9.0            13.0   \n",
+       "3            6.0           8.0                 9.0            13.0   \n",
+       "4            6.0           8.0                 9.0            13.0   \n",
+       "5            NaN           NaN                 NaN             NaN   \n",
+       "\n",
+       "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
+       "0                 34.0            8600.0   1.0            NaN  \n",
+       "1                 34.0            7213.0   1.0            5.0  \n",
+       "2                 34.0            6217.0   1.0            5.0  \n",
+       "3                 34.0           24499.0   1.0            5.0  \n",
+       "4                 34.0           11840.0   1.0            5.0  \n",
+       "5                  NaN               NaN   NaN            5.0  "
       ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# url = 'http://hfrnet.ucsd.edu/sitediag/stationList.php?output=CSV'\n",
+    "\n",
+    "# df_hfr = pd.read_csv(url)\n",
+    "\n",
+    "# hfr_installations = df_hfr['Station'].unique().size\n",
+    "\n",
+    "# print('HF Radar Installations:',hfr_installations)\n",
+    "\n",
+    "hfr_installations = 165\n",
+    "\n",
+    "ioos_btn_df.loc[ioos_btn_df[\"date_UTC\"]==today, [\"HF Radar Stations\"]] = hfr_installations\n",
+    "\n",
+    "ioos_btn_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "V-s6dYEZqvlt"
+   },
+   "source": [
+    "---\n",
+    "## NGDAC Glider Days\n",
+    "Gliders monitor water currents, temperature, and conditions that reveal effects from storms, impacts on fisheries, and the quality of our water. This information creates a more complete picture of what is happening in the ocean, as well as trends scientists might be able to detect. U.S. IOOS began counting “Glider days” in 2008 with the intent to better coordinate across U.S. glider operations and to increase the data sharing and data management of this technology. One \"Glider Day\" is defined as 1 glider in the water collecting data for 1 day.\n",
+    "\n",
+    "From https://gliders.ioos.us/erddap/info/index.html?page=1&itemsPerPage=1000\n",
+    "\n",
+    "Cumulative from 2008 - present\n",
+    "\n",
+    "### Conditions on our calculations:\n",
+    "* drops all datasets with `datasetID` containing `delayed`.\n",
+    "* duration is calculated based on the metadata ERDDAP generates.\n",
+    "  * If a datum of `NaN` is in the `time` variable, the min/max will not be computed.\n",
+    "\n",
+    "### Checks\n",
+    "\n",
+    "[gist 1](https://nbviewer.org/urls/gist.githubusercontent.com/ocefpaf/e1e6f341c4149ff1d7b6541635d599fb/raw/f79b40741ed96672c025eaff9cbf87d7287b06f3/compute_gliders_days.ipynb)\n",
+    "\n",
+    "This one fetches individual info dataset_ids URLs and matches what you are doing with allDatasets. It is still much bigger than `gdutils`, not sure what else it is filtering.\n",
+    "\n",
+    "This is too slow (~20 mins), it only serves to double check this computation and to figure out how many gliders we are throwing away due to bad metadata, in this case, 1 `Nemesis-20170512T0000`.\n",
+    "\n",
+    "[gist 2](https://nbviewer.org/urls/gist.githubusercontent.com/ocefpaf/97b943abaa1490701cd386741fabeb56/raw/9628c27b678fbfd678c80195869112e64e32c5ad/compute_gliders_days-time-var.ipynb)\n",
+    "\n",
+    "This one is a similar approach but with the time variable instead. We drop the NaN in the time var, so we do include most of `Nemesis-20170512T0000`. Note that it is larger, but not by much, than just the inclusion of `Nemesis-20170512T0000` and that is probably because the metadata is wrong in a few places.\n",
+    "\n",
+    "In theory the last one is more accurate but the difference is small.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "id": "4YBCBGGKTGG3"
+   },
+   "outputs": [],
+   "source": [
+    "# import pandas as pd\n",
+    "# df_glider = pd.read_csv('https://gliders.ioos.us/erddap/tabledap/allDatasets.csvp?minTime%2CmaxTime%2CdatasetID')\n",
+    "# df_glider.dropna(\n",
+    "#     axis=0,\n",
+    "#     inplace=True,\n",
+    "#     )\n",
+    "\n",
+    "# # drop delayed datasets\n",
+    "# df_glider = df_glider[df_glider[\"datasetID\"].str.contains(\"delayed\")==False]\n",
+    "\n",
+    "# df_glider[['minTime (UTC)','maxTime (UTC)']] = df_glider[\n",
+    "#                                                          ['minTime (UTC)','maxTime (UTC)']\n",
+    "#                                                          ].apply(pd.to_datetime)\n",
+    "\n",
+    "# df_glider['glider_days'] = (df_glider['maxTime (UTC)'] - df_glider['minTime (UTC)']).dt.days\n",
+    "\n",
+    "# glider_days = df_glider['glider_days'].sum()\n",
+    "\n",
+    "# print('Cumulative glider days:', glider_days)\n",
+    "\n",
+    "# ioos_btn_df.loc[ioos_btn_df['date_UTC']==today, ['NGDAC Glider Days']] = glider_days\n",
+    "\n",
+    "# ioos_btn_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "henQROnZk6G5"
+   },
+   "source": [
+    "Gliders picking a time period."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 535
     },
+    "id": "fC0q963WEGKu",
+    "outputId": "cead366c-5a16-47c0-d6ff-e1d4a51a130c"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "z66nOWtO5KKE"
-      },
-      "source": [
-        "### OAP\n",
-        "https://cdip.ucsd.edu/m/stn_table/\tIncludes overlap with the RAs and other programs\n",
-        "\n",
-        "19\n",
-        "\n",
-        "See buoys and moorings at https://oceanacidification.noaa.gov/WhatWeDo/Data.aspx\n",
-        "\n",
-        "pull kml from pmel"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Glider days between 2000-01-01 and 2023-12-31: 73204\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 43,
-      "metadata": {
-        "id": "lJnDfvb05PzD"
-      },
-      "outputs": [],
-      "source": [
-        "# import geopandas as gpd\n",
-        "# import fiona\n",
-        "\n",
-        "# gpd.io.file.fiona.drvsupport.supported_drivers['KML'] = 'rw'\n",
-        "\n",
-        "# kml = 'https://www.pmel.noaa.gov/co2/files/basekml.kml'\n",
-        "\n",
-        "# df = gpd.read_file(kml, driver='KML')\n",
-        "\n",
-        "# df['Name'].unique().size"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-36-29f3e59280d0>:50: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  glider_day_upper.loc[:,'maxTime (UTC)'] = pd.to_datetime(end_date, utc=True)\n",
+      "<ipython-input-36-29f3e59280d0>:50: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
+      "  glider_day_upper.loc[:,'maxTime (UTC)'] = pd.to_datetime(end_date, utc=True)\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 44,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "rxgwEEzbEGKx",
-        "outputId": "4d63eb07-5edd-4ca2-8776-e78253c0841e"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "OAP Stations: 19\n"
-          ]
-        }
+     "data": {
+      "text/html": [
+       "\n",
+       "  <div id=\"df-112f00eb-5815-4e91-b09f-85ebfa09fc00\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date_UTC</th>\n",
+       "      <th>Federal Partners</th>\n",
+       "      <th>Regional Associations</th>\n",
+       "      <th>HF Radar Stations</th>\n",
+       "      <th>NGDAC Glider Days</th>\n",
+       "      <th>National Platforms</th>\n",
+       "      <th>Regional Platforms</th>\n",
+       "      <th>ATN Deployments</th>\n",
+       "      <th>MBON Projects</th>\n",
+       "      <th>OTT Projects</th>\n",
+       "      <th>HAB Pilot Projects</th>\n",
+       "      <th>QARTOD Manuals</th>\n",
+       "      <th>IOOS Core Variables</th>\n",
+       "      <th>Metadata Records</th>\n",
+       "      <th>IOOS</th>\n",
+       "      <th>COMT Projects</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2018-02-01</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>52027.0</td>\n",
+       "      <td>737.0</td>\n",
+       "      <td>335.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>8600.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2022-04-22</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>53672.0</td>\n",
+       "      <td>763.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>7213.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2022-07-08</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>55448.0</td>\n",
+       "      <td>764.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>6217.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2022-10-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>59088.0</td>\n",
+       "      <td>390.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>24499.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-01-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>62042.0</td>\n",
+       "      <td>768.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>11840.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2024-01-26</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>73204.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-112f00eb-5815-4e91-b09f-85ebfa09fc00')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-112f00eb-5815-4e91-b09f-85ebfa09fc00 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-112f00eb-5815-4e91-b09f-85ebfa09fc00');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-36c85038-352c-4d66-82f9-6a1558ea82c3\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-36c85038-352c-4d66-82f9-6a1558ea82c3')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-36c85038-352c-4d66-82f9-6a1558ea82c3 button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "source": [
-        "import requests\n",
-        "from bs4 import BeautifulSoup\n",
-        "import re\n",
-        "\n",
-        "url = 'https://oceanacidification.noaa.gov/WhatWeDo/Data.aspx'\n",
-        "\n",
-        "#url = 'https://www.arcgis.com/apps/Embed/index.html?webmap=9512aae84cae409786339479e31b6c8a&amp;extent=-152.4023,-57.7072,146.7773,74.4006&amp;zoom=true&amp;scale=true&amp;disable_scroll=true&amp;theme=light'\n",
-        "\n",
-        "html = requests.get(url).text\n",
-        "\n",
-        "soup = BeautifulSoup(html, 'html.parser')\n",
-        "\n",
-        "#text = soup.find_all(attrs={'data-id':\"4fa1cacd\"})[0].find_all(attrs={'class':'lead'})[0].text #id=\"mapDiv\")\n",
-        "text = soup.find_all(attrs={'data-id':\"4fa1cacd\"})[0].find_all('h6')[0].text\n",
-        "\n",
-        "res = [int(i) for i in text.split() if i.isdigit()] # extract number\n",
-        "    #print(tag['content'])\n",
-        "OAP = int(res[0])\n",
-        "\n",
-        "print(\"OAP Stations:\",OAP)"
+      "text/plain": [
+       "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
+       "0  2018-02-01              17.0                   11.0              150.0   \n",
+       "1  2022-04-22              17.0                   11.0              165.0   \n",
+       "2  2022-07-08              17.0                   11.0              165.0   \n",
+       "3  2022-10-05              17.0                   11.0              165.0   \n",
+       "4  2023-01-05              17.0                   11.0              165.0   \n",
+       "5  2024-01-26              17.0                   11.0              165.0   \n",
+       "\n",
+       "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
+       "0            52027.0               737.0               335.0              NaN   \n",
+       "1            53672.0               763.0               517.0           4444.0   \n",
+       "2            55448.0               764.0               517.0           4444.0   \n",
+       "3            59088.0               390.0               635.0           4444.0   \n",
+       "4            62042.0               768.0               635.0           4444.0   \n",
+       "5            73204.0                 NaN                 NaN              NaN   \n",
+       "\n",
+       "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
+       "0            NaN           NaN                 NaN            13.0   \n",
+       "1            6.0           8.0                 9.0            13.0   \n",
+       "2            6.0           8.0                 9.0            13.0   \n",
+       "3            6.0           8.0                 9.0            13.0   \n",
+       "4            6.0           8.0                 9.0            13.0   \n",
+       "5            NaN           NaN                 NaN             NaN   \n",
+       "\n",
+       "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
+       "0                 34.0            8600.0   1.0            NaN  \n",
+       "1                 34.0            7213.0   1.0            5.0  \n",
+       "2                 34.0            6217.0   1.0            5.0  \n",
+       "3                 34.0           24499.0   1.0            5.0  \n",
+       "4                 34.0           11840.0   1.0            5.0  \n",
+       "5                  NaN               NaN   NaN            5.0  "
       ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df_glider = pd.read_csv(\"https://gliders.ioos.us/erddap/tabledap/allDatasets.csvp?minTime%2CmaxTime%2CdatasetID\")\n",
+    "df_glider.dropna(\n",
+    "    axis=0,\n",
+    "    inplace=True,\n",
+    "    )\n",
+    "\n",
+    "# drop delayed datasets\n",
+    "df_glider = df_glider[df_glider[\"datasetID\"].str.contains(\"delayed\")==False]\n",
+    "\n",
+    "df_glider[[\"minTime (UTC)\",\"maxTime (UTC)\"]] = df_glider[\n",
+    "                                                         [\"minTime (UTC)\",\"maxTime (UTC)\"]\n",
+    "                                                         ].apply(pd.to_datetime)\n",
+    "\n",
+    "start_date = \"2000-01-01\"\n",
+    "end_date = \"2023-12-31\"\n",
+    "\n",
+    "# find glider deployments between 10/01 and 12/31\n",
+    "glider_day_within = df_glider.loc[\n",
+    "    (df_glider[\"minTime (UTC)\"] > pd.to_datetime(start_date,utc=True)) &\n",
+    "    (df_glider[\"maxTime (UTC)\"] < pd.to_datetime(end_date,utc=True))\n",
+    "]\n",
+    "\n",
+    "# gliders that start before 10/01 and end after 12/31\n",
+    "glider_day_outside = df_glider.loc[\n",
+    "    (df_glider[\"minTime (UTC)\"] < pd.to_datetime(start_date,utc=True)) &\n",
+    "    (df_glider[\"maxTime (UTC)\"] > pd.to_datetime(end_date,utc=True))\n",
+    "]\n",
+    "\n",
+    "glider_day_outside.loc[:, \"maxTime (UTC)\"] = pd.to_datetime(end_date, utc=True)\n",
+    "glider_day_outside.loc[:, \"minTime (UTC)\"] = pd.to_datetime(start_date, utc=True)\n",
+    "\n",
+    "# drop the ones from above as they will be duplicates in the next round of filtering\n",
+    "df_glider.drop(axis=0, index=glider_day_outside.index, inplace=True)\n",
+    "\n",
+    "# Find gliders that start before 10/01 and end after 10/01\n",
+    "glider_day_lower = df_glider.loc[\n",
+    "    (df_glider[\"minTime (UTC)\"] < pd.to_datetime(start_date,utc=True)) &\n",
+    "    (df_glider[\"maxTime (UTC)\"] > pd.to_datetime(start_date,utc=True))\n",
+    "]\n",
+    "\n",
+    "glider_day_lower.loc[:,\"minTime (UTC)\"] = pd.to_datetime(start_date, utc=True)\n",
+    "\n",
+    "# Find gliders that start before 12/31 and end after 12/31.\n",
+    "glider_day_upper = df_glider.loc[\n",
+    "    (df_glider[\"minTime (UTC)\"]<pd.to_datetime(end_date,utc=True)) &\n",
+    "    (df_glider[\"maxTime (UTC)\"]>pd.to_datetime(end_date,utc=True))\n",
+    "]\n",
+    "\n",
+    "glider_day_upper.loc[:,\"maxTime (UTC)\"] = pd.to_datetime(end_date, utc=True)\n",
+    "\n",
+    "# Combine it all together into one DF.\n",
+    "glider_subset = pd.concat([glider_day_lower,\n",
+    "                           glider_day_within,\n",
+    "                           glider_day_upper,\n",
+    "                           glider_day_outside],\n",
+    "                          verify_integrity=True)\n",
+    "\n",
+    "# Calculate the days between min time and max time.\n",
+    "glider_subset[\"glider_days\"] = (glider_subset[\"maxTime (UTC)\"] - glider_subset[\"minTime (UTC)\"]).dt.days\n",
+    "\n",
+    "# Calculate total glider days.\n",
+    "glider_days = glider_subset[\"glider_days\"].sum()\n",
+    "\n",
+    "print(\"Glider days between %s and %s: %s\" % (start_date,end_date,glider_days))\n",
+    "\n",
+    "ioos_btn_df.loc[ioos_btn_df[\"date_UTC\"]==today, [\"NGDAC Glider Days\"]] = glider_days\n",
+    "\n",
+    "ioos_btn_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "WyiivPFtk_K9"
+   },
+   "source": [
+    "Kathy's glider code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "id": "4EvojPUElDDk"
+   },
+   "outputs": [],
+   "source": [
+    "# import ipydatetime\n",
+    "# from datetime import datetime\n",
+    "# from ipywidgets import VBox, HBox, Label, BoundedFloatText\n",
+    "\n",
+    "\n",
+    "# dt0 = ipydatetime.NaiveDatetimePicker(value=datetime(2000, 1, 1, 0, 0, 0), description='start time:')\n",
+    "# dt1 = ipydatetime.NaiveDatetimePicker(value=datetime(2022, 12, 31, 23, 59, 59), description='end time:')\n",
+    "\n",
+    "# south = BoundedFloatText(\n",
+    "#     value=-90,\n",
+    "#     min=-90,\n",
+    "#     max=90,\n",
+    "#     description='min lat:',\n",
+    "# )\n",
+    "\n",
+    "# north = BoundedFloatText(\n",
+    "#     value=90,\n",
+    "#     min=-90,\n",
+    "#     max=90,\n",
+    "#     description='max lat:',\n",
+    "# )\n",
+    "\n",
+    "\n",
+    "# west = BoundedFloatText(\n",
+    "#     value=-180,\n",
+    "#     min=-180,\n",
+    "#     max=180,\n",
+    "#     description='min lon:',\n",
+    "# )\n",
+    "\n",
+    "# east = BoundedFloatText(\n",
+    "#     value=180,\n",
+    "#     min=-180,\n",
+    "#     max=180,\n",
+    "#     description='max lon:',\n",
+    "# )\n",
+    "\n",
+    "# from ipywidgets import Layout, Button, Box\n",
+    "\n",
+    "\n",
+    "# box_layout = Layout(\n",
+    "#     display=\"flex\",\n",
+    "#     flex_flow=\"column\",\n",
+    "#     align_items=\"center\"\n",
+    "# )\n",
+    "\n",
+    "# items = [\n",
+    "#     VBox([dt0, dt1]),\n",
+    "#     north,\n",
+    "#     HBox([west, east]),\n",
+    "#     south,\n",
+    "# ]\n",
+    "\n",
+    "# box = Box(children=items, layout=box_layout)\n",
+    "# box\n",
+    "\n",
+    "# dt0 = dt0.value\n",
+    "# dt1 = dt1.value\n",
+    "# south = south.value\n",
+    "# north = north.value\n",
+    "# west = west.value\n",
+    "# east = east.value\n",
+    "\n",
+    "# params = {\n",
+    "#     \"min_time\": dt0,\n",
+    "#     \"max_time\": dt1,\n",
+    "#     \"min_lat\": south,\n",
+    "#     \"max_lat\": north,\n",
+    "#     \"min_lon\": west,\n",
+    "#     \"max_lon\": east,\n",
+    "# }\n",
+    "\n",
+    "# from gdutils import GdacClient\n",
+    "\n",
+    "\n",
+    "# client = GdacClient()\n",
+    "\n",
+    "# client.search_datasets(params=params, include_delayed_mode=False)\n",
+    "# client.datasets\n",
+    "\n",
+    "# # Count the total number of deployments within the dt0:dt1 time window\n",
+    "# num_deployments = client.datasets.shape[0]\n",
+    "\n",
+    "# # Count the number of glider days within the dt0:dt1 time window\n",
+    "# glider_days = client.glider_days_per_yyyymmdd.loc[dt0:dt1].sum()\n",
+    "\n",
+    "# # count the number of profiles per dataset\n",
+    "# profile_count = client.profiles_per_yyyymmdd.loc[dt0:dt1].sum()\n",
+    "\n",
+    "# datasets = client.datasets.copy()\n",
+    "\n",
+    "# import warnings\n",
+    "\n",
+    "\n",
+    "# sea_names = []\n",
+    "# funding_sources = []\n",
+    "# for dataset_id, row in datasets.iterrows():\n",
+    "\n",
+    "#     # Fetch the dataset description from ERDDAP\n",
+    "#     info = client.get_dataset_metadata(dataset_id)\n",
+    "\n",
+    "#     if info.empty:\n",
+    "#         continue\n",
+    "\n",
+    "#     # Find all global NetCDF attributes\n",
+    "#     nc_globals = info.loc[info[\"Variable Name\"] == \"NC_GLOBAL\"]\n",
+    "\n",
+    "#     # Find the sea_name global attribute\n",
+    "#     sea_name_attr = nc_globals.loc[nc_globals[\"Attribute Name\"] == \"sea_name\"]\n",
+    "#     sea_name = \"unknown\"\n",
+    "#     if not sea_name_attr.empty:\n",
+    "#         sea_name = sea_name_attr.Value.iloc[0]\n",
+    "#         sea_name = sea_name or \"unknown\"\n",
+    "#     else:\n",
+    "#         warnings.warn(f\"{dataset_id}: sea_name NC_GLOBAL not found\")\n",
+    "\n",
+    "#     # Find all global attributes that begin with \"acknowledg\" as this attribute typically contains the funding sources\n",
+    "#     funding_attr = nc_globals.loc[nc_globals[\"Attribute Name\"].str.startswith(\"acknowledg\")]\n",
+    "#     funding = \"unknown\"\n",
+    "#     if not funding_attr.empty:\n",
+    "#         funding = funding_attr.Value.iloc[0]\n",
+    "#         funding = funding or \"unknown\"\n",
+    "#     else:\n",
+    "#         warnings.warn(f\"{dataset_id}: acknowledgment NC_GLOBAL not found\")\n",
+    "\n",
+    "#     sea_names.append(sea_name)\n",
+    "#     funding_sources.append(funding)\n",
+    "\n",
+    "#     # Count only the days and profiles that are dt0:dt1 inclusive\n",
+    "#     days = client.datasets_days[dataset_id].loc[dt0:dt1].sum()\n",
+    "#     profiles = client.datasets_profiles[dataset_id].loc[dt0:dt1].sum()\n",
+    "#     datasets.loc[dataset_id, (\"days\", \"num_profiles\")] = [days, profiles]\n",
+    "\n",
+    "\n",
+    "# print(f\"time ranged from {dt0} to {dt1}\")\n",
+    "# print(f\"\"\"\n",
+    "# {num_deployments = }\n",
+    "# {glider_days = }\n",
+    "# {profile_count = }\n",
+    "# \"\"\"\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "iYk1km3P861f"
+   },
+   "source": [
+    "---\n",
+    "## National Platforms\n",
+    "\n",
+    "The National backbone of IOOS includes buoys, water level gauges,as well as coastal and estuary stations run by our federal partners. Platforms calculated within this total are assets within the EEZ. For buoys this includes platforms managed by NOAA's National Data Buoy Center, the NOAA NMFS Chesapeake Bay Interpretive Buoy System (CBIBS), and the U.S. Army Corps of Engineers’ Coastal Data Information Program (CDIP), Ocean Acidification Program , Ecosystems and Fishery Oceanography Coordinated Investigations (EcoFOCI). For guages, this includes National Water Level Observation Network guages operated by the Center for Operational Oceanographic Products and Services (CO-OPS). The coastal and estuary stations are maintained through NOAA's National Estuarine Research Reserves (NERR) System-Wide Management Program (SWMP)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "xGHalY-14iGB"
+   },
+   "source": [
+    "### CO-OPS\n",
+    "* https://opendap.co-ops.nos.noaa.gov/stations/index.jsp\n",
+    "  * as xml: https://opendap.co-ops.nos.noaa.gov/stations/stationsXML.jsp\n",
+    "* https://tidesandcurrents.noaa.gov/cdata/StationList?type=Current+Data&filter=active"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "wZKANnz8sC5D",
+    "outputId": "460399f8-8202-4a39-ba09-411a786de49d"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "wt9iwonW5Now"
-      },
-      "source": [
-        "### CDIP\n",
-        "https://cdip.ucsd.edu/m/stn_table/\tIncludes overlap with the RAs\n",
-        "\n",
-        "67\n",
-        "\n",
-        "https://cdip.ucsd.edu/themes/?d2=p1:m:mobile&regions=all&units=standard&zoom=auto&pub_set=public&tz=UTC&ll_fmt=dm&numcolorbands=10&palette=cdip_classic&high=6.096"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All stations: 380\n",
+      "Ports: 75\n"
+     ]
+    }
+   ],
+   "source": [
+    "#from lxml import etree\n",
+    "import pandas as pd\n",
+    "import requests\n",
+    "\n",
+    "xml = requests.get(\"https://opendap.co-ops.nos.noaa.gov/stations/stationsXML.jsp\").text\n",
+    "COOPS = sum(1 for _ in re.finditer(r\"\\b%s\\b\" % re.escape(\"station name\"), xml))\n",
+    "print(\"All stations:\",COOPS)\n",
+    "\n",
+    "url = \"https://tidesandcurrents.noaa.gov/cdata/StationListFormat?type=Current+Data&filter=active&format=csv\"\n",
+    "\n",
+    "df_coops = pd.read_csv(url)\n",
+    "#print(df_coops[' Project'].unique())\n",
+    "ports = df_coops[df_coops[\" Project\"].astype(str).str.contains(\"PORTS\")].shape[0]\n",
+    "print(\"Ports:\", ports)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "TmDAxLm24q7g"
+   },
+   "source": [
+    "### NDBC\n",
+    "https://www.ndbc.noaa.gov/wstat.shtml\tBuoys: 106 (103 base-funded); CMAN: 45"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "OWcvMhvR4xWt",
+    "outputId": "14de096a-7a17-43c4-94e6-510924c59db9"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 45,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "r7qEAAlV5NWj",
-        "outputId": "dbc04902-e88b-4734-d9a7-cfbf38fc819a"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "CDIP Stations: 72\n"
-          ]
-        }
-      ],
-      "source": [
-        "import lxml\n",
-        "import pandas as pd\n",
-        "\n",
-        "url = 'https://cdip.ucsd.edu/themes/?d2=p1:m:mobile&regions=all&units=standard&zoom=auto&pub_set=public&tz=UTC&ll_fmt=dm&numcolorbands=10&palette=cdip_classic&high=6.096'\n",
-        "#url = 'https://cdip.ucsd.edu/m/stn_table/'\n",
-        "table_list = pd.read_html(url, match='Stn')\n",
-        "\n",
-        "df = table_list[0]\n",
-        "\n",
-        "CDIP = df['Stn'].unique().size\n",
-        "\n",
-        "print(\"CDIP Stations:\",CDIP)"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NDBC: 146\n"
+     ]
+    }
+   ],
+   "source": [
+    "import re\n",
+    "\n",
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "\n",
+    "url = \"https://www.ndbc.noaa.gov/wstat.shtml\"\n",
+    "\n",
+    "headers = {\"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36\"}\n",
+    "\n",
+    "html = requests.get(url, headers=headers).text\n",
+    "\n",
+    "soup = BeautifulSoup(html, \"html.parser\")\n",
+    "\n",
+    "string_to_find = [\"Total Base Funded Buoys:\",\"Total Other Buoys:\",\n",
+    "                  \"Total Moored Buoys:\",\"Total Base Funded Stations:\",\n",
+    "                  \"Total Stations:\"]\n",
+    "\n",
+    "ndbc = dict()\n",
+    "for string in string_to_find:\n",
+    "    for tag in soup.find_all(\"td\", string=string):\n",
+    "        ndbc[string] = int(tag.next_sibling.string)\n",
+    "\n",
+    "#pprint.pprint(ndbc)\n",
+    "\n",
+    "NDBC = ndbc[\"Total Moored Buoys:\"] + ndbc[\"Total Base Funded Stations:\"]\n",
+    "print(\"NDBC:\",NDBC)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "PvdJyWvN40Wk"
+   },
+   "source": [
+    "### NERRS\n",
+    "https://nosc.noaa.gov/OSC/OSN/index.php\tNERRS SWMP; Across 29 NERRS; Source = internal access only - NOAA Observing System Council.\n",
+    "\n",
+    "http://cdmo.baruch.sc.edu/webservices.cfm <- need IP address approval\n",
+    "\n",
+    "Need number of stations (120 last time)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "id": "3pQsSjcrLGQt"
+   },
+   "outputs": [],
+   "source": [
+    "# !pip install config\n",
+    "# !pip install suds\n",
+    "# import config\n",
+    "\n",
+    "# from suds.client import Client\n",
+    "\n",
+    "# wsdlURL = \"https://cdmo.baruch.sc.edu/webservices2/requests.cfc?wsdl\"\n",
+    "\n",
+    "# user = config.CDMO_NAME\n",
+    "\n",
+    "# pwd = config.CDMO_KEY\n",
+    "\n",
+    "# soapClient = Client(wsdlURL,\n",
+    "#                     timeout=90,\n",
+    "#                     retxml=True,\n",
+    "#                     username=user,\n",
+    "#                     password=pwd,\n",
+    "#                     prettyxml=True)\n",
+    "\n",
+    "# wq_station_name = \"niwolwq\"\n",
+    "\n",
+    "# response = soapClient.service.exportAllParamsDateRangeXMLNew(wq_station_name, \"2023-03-23\", \"2023-03-23\")\n",
+    "\n",
+    "# # soapClient = Client(wsdlURL,\n",
+    "# #                     timeout=90,\n",
+    "# #                     retxml=True,\n",
+    "# #                     username=user,\n",
+    "# #                     password=pwd,\n",
+    "# #                    )\n",
+    "\n",
+    "# # #Get the station codes SOAP request example.\n",
+    "# # response = soapClient.service.exportStationCodesXMLNew()\n",
+    "# print(response)\n",
+    "# print(pd.read_xml(response,xpath=\".//returnData\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "vB1MEtDb_Map",
+    "outputId": "3ee7b4c7-52ed-4cae-f53a-35d55ac2ed13"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ScmxkStTNdf2"
-      },
-      "source": [
-        "### Calculating National Platforms\n",
-        "\n",
-        "from 2018: CO-OPS + NDBC + NERRS + CBIBS + OAP + CDIP = 747"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NERRS stations: 93\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "#import re\n",
+    "import pandas as pd\n",
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "\n",
+    "url = \"https://cdmo.baruch.sc.edu//webservices/station_timing.cfm\"\n",
+    "\n",
+    "headers = {\"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36\"}\n",
+    "\n",
+    "html = requests.get(url,headers=headers).text\n",
+    "\n",
+    "soup = BeautifulSoup(html, \"html.parser\")\n",
+    "\n",
+    "df = pd.read_html(str(soup.find(attrs={\"class\":\"row text-center\"})),\n",
+    "                  header=0,\n",
+    "                  attrs = {\"class\": \"table\"})\n",
+    "\n",
+    "df_final = pd.concat([df[0],df[1]])\n",
+    "\n",
+    "NERRS = df_final.shape[0]\n",
+    "\n",
+    "# Should be around NERRS = 140\n",
+    "\n",
+    "print(\"NERRS stations:\",NERRS)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_uH7fBzJ43vL"
+   },
+   "source": [
+    "### CBIBS\n",
+    "https://buoybay.noaa.gov/locations\n",
+    "\n",
+    "[API docs](https://buoybay.noaa.gov/node/174)\n",
+    "\n",
+    "Base URL: https://mw.buoybay.noaa.gov/api/v1\n",
+    "\n",
+    "Testing Key: f159959c117f473477edbdf3245cc2a4831ac61f\n",
+    "\n",
+    "Latest measurements:\n",
+    "https://mw.buoybay.noaa.gov/api/v1/json/station?key=f159959c117f473477edbdf3245cc2a4831ac61f"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "nIIbzvJO49Vb",
+    "outputId": "70d1c3f8-870b-4ea3-84d6-4a0d47ede4fb"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 46,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 376
-        },
-        "id": "IpX8NZl9Njwi",
-        "outputId": "ba2c66b3-d1d5-4787-ff0f-67ab273151ff"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "National Platforms: 721\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
-              "0  2018-02-01              17.0                   11.0              150.0   \n",
-              "1  2022-04-22              17.0                   11.0              165.0   \n",
-              "2  2022-07-08              17.0                   11.0              165.0   \n",
-              "3  2022-10-05              17.0                   11.0              165.0   \n",
-              "4  2023-01-05              17.0                   11.0              165.0   \n",
-              "5  2024-01-26              17.0                   11.0              165.0   \n",
-              "\n",
-              "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
-              "0            52027.0               737.0               335.0              NaN   \n",
-              "1            53672.0               763.0               517.0           4444.0   \n",
-              "2            55448.0               764.0               517.0           4444.0   \n",
-              "3            59088.0               390.0               635.0           4444.0   \n",
-              "4            62042.0               768.0               635.0           4444.0   \n",
-              "5            73204.0               721.0                 NaN              NaN   \n",
-              "\n",
-              "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
-              "0            NaN           NaN                 NaN            13.0   \n",
-              "1            6.0           8.0                 9.0            13.0   \n",
-              "2            6.0           8.0                 9.0            13.0   \n",
-              "3            6.0           8.0                 9.0            13.0   \n",
-              "4            6.0           8.0                 9.0            13.0   \n",
-              "5            NaN           NaN                 NaN             NaN   \n",
-              "\n",
-              "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
-              "0                 34.0            8600.0   1.0            NaN  \n",
-              "1                 34.0            7213.0   1.0            5.0  \n",
-              "2                 34.0            6217.0   1.0            5.0  \n",
-              "3                 34.0           24499.0   1.0            5.0  \n",
-              "4                 34.0           11840.0   1.0            5.0  \n",
-              "5                  NaN               NaN   NaN            5.0  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-0e730d48-e537-4cb7-a590-ba1fa32d2cbc\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>date_UTC</th>\n",
-              "      <th>Federal Partners</th>\n",
-              "      <th>Regional Associations</th>\n",
-              "      <th>HF Radar Stations</th>\n",
-              "      <th>NGDAC Glider Days</th>\n",
-              "      <th>National Platforms</th>\n",
-              "      <th>Regional Platforms</th>\n",
-              "      <th>ATN Deployments</th>\n",
-              "      <th>MBON Projects</th>\n",
-              "      <th>OTT Projects</th>\n",
-              "      <th>HAB Pilot Projects</th>\n",
-              "      <th>QARTOD Manuals</th>\n",
-              "      <th>IOOS Core Variables</th>\n",
-              "      <th>Metadata Records</th>\n",
-              "      <th>IOOS</th>\n",
-              "      <th>COMT Projects</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>2018-02-01</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>150.0</td>\n",
-              "      <td>52027.0</td>\n",
-              "      <td>737.0</td>\n",
-              "      <td>335.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>8600.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>2022-04-22</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>53672.0</td>\n",
-              "      <td>763.0</td>\n",
-              "      <td>517.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>7213.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>2022-07-08</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>55448.0</td>\n",
-              "      <td>764.0</td>\n",
-              "      <td>517.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>6217.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>2022-10-05</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>59088.0</td>\n",
-              "      <td>390.0</td>\n",
-              "      <td>635.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>24499.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>2023-01-05</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>62042.0</td>\n",
-              "      <td>768.0</td>\n",
-              "      <td>635.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>11840.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>2024-01-26</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>73204.0</td>\n",
-              "      <td>721.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e730d48-e537-4cb7-a590-ba1fa32d2cbc')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-0e730d48-e537-4cb7-a590-ba1fa32d2cbc button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-0e730d48-e537-4cb7-a590-ba1fa32d2cbc');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-317db02c-0115-4784-873d-e55b37c70bcb\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-317db02c-0115-4784-873d-e55b37c70bcb')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-317db02c-0115-4784-873d-e55b37c70bcb button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 46
-        }
-      ],
-      "source": [
-        "national_platforms = COOPS + NDBC + NERRS + CBIBS + OAP + CDIP\n",
-        "print(\"National Platforms:\",national_platforms)\n",
-        "\n",
-        "ioos_btn_df.loc[ioos_btn_df['date_UTC']==today, ['National Platforms']] = national_platforms\n",
-        "\n",
-        "ioos_btn_df"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CBIBS Stations: 11\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "\n",
+    "base_url = \"https://mw.buoybay.noaa.gov/api/v1\"\n",
+    "apikey = \"f159959c117f473477edbdf3245cc2a4831ac61f\"\n",
+    "start = \"2021-12-08T01:00:00z\"\n",
+    "end = \"2021-12-09T23:59:59z\"\n",
+    "var = \"Position\"\n",
+    "\n",
+    "query_url = f\"{base_url}/json/query?key={apikey}&sd={start}&ed={end}&var={var}\"\n",
+    "#query_url = '{}/json/station?key={}'.format(base_url, apikey)\n",
+    "\n",
+    "json = json.loads(requests.get(query_url).text)\n",
+    "\n",
+    "CBIBS = len(json[\"stations\"])\n",
+    "\n",
+    "print(\"CBIBS Stations:\",CBIBS)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "z66nOWtO5KKE"
+   },
+   "source": [
+    "### OAP\n",
+    "https://cdip.ucsd.edu/m/stn_table/\tIncludes overlap with the RAs and other programs\n",
+    "\n",
+    "19\n",
+    "\n",
+    "See buoys and moorings at https://oceanacidification.noaa.gov/WhatWeDo/Data.aspx\n",
+    "\n",
+    "pull kml from pmel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "id": "lJnDfvb05PzD"
+   },
+   "outputs": [],
+   "source": [
+    "# import geopandas as gpd\n",
+    "# import fiona\n",
+    "\n",
+    "# gpd.io.file.fiona.drvsupport.supported_drivers['KML'] = 'rw'\n",
+    "\n",
+    "# kml = 'https://www.pmel.noaa.gov/co2/files/basekml.kml'\n",
+    "\n",
+    "# df = gpd.read_file(kml, driver='KML')\n",
+    "\n",
+    "# df['Name'].unique().size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "rxgwEEzbEGKx",
+    "outputId": "4d63eb07-5edd-4ca2-8776-e78253c0841e"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_vmKPAdZ9SBU"
-      },
-      "source": [
-        "---\n",
-        "## Regional Platforms\n",
-        "\n",
-        "Regional platforms are calculated from the annual IOOS asset inventory submitted by each Regional Association. More information about the IOOS asset inventory can be found at https://github.com/ioos/ioos-asset-inventory\n",
-        "\n",
-        "The data from 2020 can be found [here](https://github.com/ioos/ioos-asset-inventory/tree/main/2020) and is available on [ERDDAP](http://erddap.ioos.us/erddap/tabledap/processed_asset_inventory.html)."
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "OAP Stations: 19\n"
+     ]
+    }
+   ],
+   "source": [
+    "import re\n",
+    "\n",
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "\n",
+    "url = \"https://oceanacidification.noaa.gov/WhatWeDo/Data.aspx\"\n",
+    "\n",
+    "#url = 'https://www.arcgis.com/apps/Embed/index.html?webmap=9512aae84cae409786339479e31b6c8a&amp;extent=-152.4023,-57.7072,146.7773,74.4006&amp;zoom=true&amp;scale=true&amp;disable_scroll=true&amp;theme=light'\n",
+    "\n",
+    "html = requests.get(url).text\n",
+    "\n",
+    "soup = BeautifulSoup(html, \"html.parser\")\n",
+    "\n",
+    "#text = soup.find_all(attrs={'data-id':\"4fa1cacd\"})[0].find_all(attrs={'class':'lead'})[0].text #id=\"mapDiv\")\n",
+    "text = soup.find_all(attrs={\"data-id\":\"4fa1cacd\"})[0].find_all(\"h6\")[0].text\n",
+    "\n",
+    "res = [int(i) for i in text.split() if i.isdigit()] # extract number\n",
+    "    #print(tag['content'])\n",
+    "OAP = int(res[0])\n",
+    "\n",
+    "print(\"OAP Stations:\",OAP)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wt9iwonW5Now"
+   },
+   "source": [
+    "### CDIP\n",
+    "https://cdip.ucsd.edu/m/stn_table/\tIncludes overlap with the RAs\n",
+    "\n",
+    "67\n",
+    "\n",
+    "https://cdip.ucsd.edu/themes/?d2=p1:m:mobile&regions=all&units=standard&zoom=auto&pub_set=public&tz=UTC&ll_fmt=dm&numcolorbands=10&palette=cdip_classic&high=6.096"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "r7qEAAlV5NWj",
+    "outputId": "dbc04902-e88b-4734-d9a7-cfbf38fc819a"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 47,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 376
-        },
-        "id": "WTQ0WgP09Vxc",
-        "outputId": "b25fe6d7-fae1-4365-d9bb-0920b0ea6852"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Regional platforms: 886\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
-              "0  2018-02-01              17.0                   11.0              150.0   \n",
-              "1  2022-04-22              17.0                   11.0              165.0   \n",
-              "2  2022-07-08              17.0                   11.0              165.0   \n",
-              "3  2022-10-05              17.0                   11.0              165.0   \n",
-              "4  2023-01-05              17.0                   11.0              165.0   \n",
-              "5  2024-01-26              17.0                   11.0              165.0   \n",
-              "\n",
-              "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
-              "0            52027.0               737.0               335.0              NaN   \n",
-              "1            53672.0               763.0               517.0           4444.0   \n",
-              "2            55448.0               764.0               517.0           4444.0   \n",
-              "3            59088.0               390.0               635.0           4444.0   \n",
-              "4            62042.0               768.0               635.0           4444.0   \n",
-              "5            73204.0               721.0               886.0              NaN   \n",
-              "\n",
-              "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
-              "0            NaN           NaN                 NaN            13.0   \n",
-              "1            6.0           8.0                 9.0            13.0   \n",
-              "2            6.0           8.0                 9.0            13.0   \n",
-              "3            6.0           8.0                 9.0            13.0   \n",
-              "4            6.0           8.0                 9.0            13.0   \n",
-              "5            NaN           NaN                 NaN             NaN   \n",
-              "\n",
-              "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
-              "0                 34.0            8600.0   1.0            NaN  \n",
-              "1                 34.0            7213.0   1.0            5.0  \n",
-              "2                 34.0            6217.0   1.0            5.0  \n",
-              "3                 34.0           24499.0   1.0            5.0  \n",
-              "4                 34.0           11840.0   1.0            5.0  \n",
-              "5                  NaN               NaN   NaN            5.0  "
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-              "      <th></th>\n",
-              "      <th>date_UTC</th>\n",
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-              "      <th>HF Radar Stations</th>\n",
-              "      <th>NGDAC Glider Days</th>\n",
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-              "      <th>QARTOD Manuals</th>\n",
-              "      <th>IOOS Core Variables</th>\n",
-              "      <th>Metadata Records</th>\n",
-              "      <th>IOOS</th>\n",
-              "      <th>COMT Projects</th>\n",
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-              "      <th>0</th>\n",
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-              "      <td>13.0</td>\n",
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-              "      <td>8600.0</td>\n",
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-              "      <td>2022-04-22</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
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-              "      <td>4444.0</td>\n",
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-              "      <td>9.0</td>\n",
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-              "      <td>34.0</td>\n",
-              "      <td>7213.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
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-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>2022-07-08</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>55448.0</td>\n",
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-              "      <td>34.0</td>\n",
-              "      <td>6217.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>2022-10-05</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>59088.0</td>\n",
-              "      <td>390.0</td>\n",
-              "      <td>635.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>24499.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>2023-01-05</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>62042.0</td>\n",
-              "      <td>768.0</td>\n",
-              "      <td>635.0</td>\n",
-              "      <td>4444.0</td>\n",
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-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>11840.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>2024-01-26</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>73204.0</td>\n",
-              "      <td>721.0</td>\n",
-              "      <td>886.0</td>\n",
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-              "      <td>NaN</td>\n",
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-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
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-              "      const quickchartButtonEl =\n",
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-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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-            ]
-          },
-          "metadata": {},
-          "execution_count": 47
-        }
-      ],
-      "source": [
-        "import pandas as pd\n",
-        "\n",
-        "url = 'https://erddap.ioos.us/erddap/tabledap/processed_asset_inventory.json?station_long_name&distinct()'\n",
-        "\n",
-        "df_regional_platforms = pd.read_json(url)\n",
-        "\n",
-        "regional_platforms = len(df_regional_platforms.loc['rows'][0])\n",
-        "\n",
-        "print('Regional platforms:',regional_platforms)\n",
-        "\n",
-        "ioos_btn_df.loc[ioos_btn_df['date_UTC']==today, ['Regional Platforms']] = regional_platforms\n",
-        "\n",
-        "ioos_btn_df"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CDIP Stations: 72\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "url = \"https://cdip.ucsd.edu/themes/?d2=p1:m:mobile&regions=all&units=standard&zoom=auto&pub_set=public&tz=UTC&ll_fmt=dm&numcolorbands=10&palette=cdip_classic&high=6.096\"\n",
+    "#url = 'https://cdip.ucsd.edu/m/stn_table/'\n",
+    "table_list = pd.read_html(url, match=\"Stn\")\n",
+    "\n",
+    "df = table_list[0]\n",
+    "\n",
+    "CDIP = df[\"Stn\"].unique().size\n",
+    "\n",
+    "print(\"CDIP Stations:\",CDIP)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ScmxkStTNdf2"
+   },
+   "source": [
+    "### Calculating National Platforms\n",
+    "\n",
+    "from 2018: CO-OPS + NDBC + NERRS + CBIBS + OAP + CDIP = 747"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 376
     },
+    "id": "IpX8NZl9Njwi",
+    "outputId": "ba2c66b3-d1d5-4787-ff0f-67ab273151ff"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "X5BeiqhIXme-"
-      },
-      "source": [
-        "---\n",
-        "## ATN Deployments\n",
-        "\n",
-        "See Deployments at https://portal.atn.ioos.us/#\n",
-        "Not sure if there is a way to scrape that page or get those values from somewhere\n",
-        "\n",
-        "4242"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "National Platforms: 721\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "source": [
-        "import requests\n",
-        "\n",
-        "headers = {\n",
-        "    \"Accept\": \"application/json\"\n",
-        "}\n",
-        "\n",
-        "raw_payload = requests.get(\"https://search.axds.co/v2/search?portalId=99\", headers=headers)\n",
-        "json_payload = raw_payload.json()\n",
-        "for plt in json_payload[\"types\"]:\n",
-        "    if plt[\"id\"] == \"platform2\":\n",
-        "        print(plt[\"count\"])\n",
-        "        atn_deployments = plt[\"count\"]\n",
-        "        break\n",
-        "\n",
-        "print(\"ATN Deployments:\",atn_deployments)\n",
-        "\n",
-        "ioos_btn_df.loc[ioos_btn_df['date_UTC']==today, ['ATN Deployments']] = atn_deployments\n",
-        "\n",
-        "ioos_btn_df"
+     "data": {
+      "text/html": [
+       "\n",
+       "  <div id=\"df-0e730d48-e537-4cb7-a590-ba1fa32d2cbc\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date_UTC</th>\n",
+       "      <th>Federal Partners</th>\n",
+       "      <th>Regional Associations</th>\n",
+       "      <th>HF Radar Stations</th>\n",
+       "      <th>NGDAC Glider Days</th>\n",
+       "      <th>National Platforms</th>\n",
+       "      <th>Regional Platforms</th>\n",
+       "      <th>ATN Deployments</th>\n",
+       "      <th>MBON Projects</th>\n",
+       "      <th>OTT Projects</th>\n",
+       "      <th>HAB Pilot Projects</th>\n",
+       "      <th>QARTOD Manuals</th>\n",
+       "      <th>IOOS Core Variables</th>\n",
+       "      <th>Metadata Records</th>\n",
+       "      <th>IOOS</th>\n",
+       "      <th>COMT Projects</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2018-02-01</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>52027.0</td>\n",
+       "      <td>737.0</td>\n",
+       "      <td>335.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>8600.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2022-04-22</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>53672.0</td>\n",
+       "      <td>763.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>7213.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2022-07-08</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>55448.0</td>\n",
+       "      <td>764.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>6217.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2022-10-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>59088.0</td>\n",
+       "      <td>390.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>24499.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-01-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>62042.0</td>\n",
+       "      <td>768.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>11840.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2024-01-26</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>73204.0</td>\n",
+       "      <td>721.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e730d48-e537-4cb7-a590-ba1fa32d2cbc')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-0e730d48-e537-4cb7-a590-ba1fa32d2cbc button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-0e730d48-e537-4cb7-a590-ba1fa32d2cbc');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-317db02c-0115-4784-873d-e55b37c70bcb\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-317db02c-0115-4784-873d-e55b37c70bcb')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-317db02c-0115-4784-873d-e55b37c70bcb button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 394
-        },
-        "id": "tiZLM5KqdGPI",
-        "outputId": "e83386d3-287f-4ff8-8b56-356f4c1f6786"
-      },
-      "execution_count": 48,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "5190\n",
-            "ATN Deployments: 5190\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
-              "0  2018-02-01              17.0                   11.0              150.0   \n",
-              "1  2022-04-22              17.0                   11.0              165.0   \n",
-              "2  2022-07-08              17.0                   11.0              165.0   \n",
-              "3  2022-10-05              17.0                   11.0              165.0   \n",
-              "4  2023-01-05              17.0                   11.0              165.0   \n",
-              "5  2024-01-26              17.0                   11.0              165.0   \n",
-              "\n",
-              "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
-              "0            52027.0               737.0               335.0              NaN   \n",
-              "1            53672.0               763.0               517.0           4444.0   \n",
-              "2            55448.0               764.0               517.0           4444.0   \n",
-              "3            59088.0               390.0               635.0           4444.0   \n",
-              "4            62042.0               768.0               635.0           4444.0   \n",
-              "5            73204.0               721.0               886.0           5190.0   \n",
-              "\n",
-              "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
-              "0            NaN           NaN                 NaN            13.0   \n",
-              "1            6.0           8.0                 9.0            13.0   \n",
-              "2            6.0           8.0                 9.0            13.0   \n",
-              "3            6.0           8.0                 9.0            13.0   \n",
-              "4            6.0           8.0                 9.0            13.0   \n",
-              "5            NaN           NaN                 NaN             NaN   \n",
-              "\n",
-              "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
-              "0                 34.0            8600.0   1.0            NaN  \n",
-              "1                 34.0            7213.0   1.0            5.0  \n",
-              "2                 34.0            6217.0   1.0            5.0  \n",
-              "3                 34.0           24499.0   1.0            5.0  \n",
-              "4                 34.0           11840.0   1.0            5.0  \n",
-              "5                  NaN               NaN   NaN            5.0  "
-            ],
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-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>date_UTC</th>\n",
-              "      <th>Federal Partners</th>\n",
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-              "      <th>HF Radar Stations</th>\n",
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-              "  </thead>\n",
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-              "      <th>0</th>\n",
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-              "      <td>17.0</td>\n",
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-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>8600.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>2022-04-22</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>53672.0</td>\n",
-              "      <td>763.0</td>\n",
-              "      <td>517.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>7213.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>2022-07-08</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>55448.0</td>\n",
-              "      <td>764.0</td>\n",
-              "      <td>517.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>6217.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>2022-10-05</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>59088.0</td>\n",
-              "      <td>390.0</td>\n",
-              "      <td>635.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>24499.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>2023-01-05</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>62042.0</td>\n",
-              "      <td>768.0</td>\n",
-              "      <td>635.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>11840.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>2024-01-26</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>73204.0</td>\n",
-              "      <td>721.0</td>\n",
-              "      <td>886.0</td>\n",
-              "      <td>5190.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c9fa7e46-1695-4dc8-9c3c-8b597e55d59d')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-c9fa7e46-1695-4dc8-9c3c-8b597e55d59d button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-c9fa7e46-1695-4dc8-9c3c-8b597e55d59d');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-69ed917b-5102-4ae2-9f21-4bf48cdc08a3\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-69ed917b-5102-4ae2-9f21-4bf48cdc08a3')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-69ed917b-5102-4ae2-9f21-4bf48cdc08a3 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 48
-        }
+      "text/plain": [
+       "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
+       "0  2018-02-01              17.0                   11.0              150.0   \n",
+       "1  2022-04-22              17.0                   11.0              165.0   \n",
+       "2  2022-07-08              17.0                   11.0              165.0   \n",
+       "3  2022-10-05              17.0                   11.0              165.0   \n",
+       "4  2023-01-05              17.0                   11.0              165.0   \n",
+       "5  2024-01-26              17.0                   11.0              165.0   \n",
+       "\n",
+       "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
+       "0            52027.0               737.0               335.0              NaN   \n",
+       "1            53672.0               763.0               517.0           4444.0   \n",
+       "2            55448.0               764.0               517.0           4444.0   \n",
+       "3            59088.0               390.0               635.0           4444.0   \n",
+       "4            62042.0               768.0               635.0           4444.0   \n",
+       "5            73204.0               721.0                 NaN              NaN   \n",
+       "\n",
+       "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
+       "0            NaN           NaN                 NaN            13.0   \n",
+       "1            6.0           8.0                 9.0            13.0   \n",
+       "2            6.0           8.0                 9.0            13.0   \n",
+       "3            6.0           8.0                 9.0            13.0   \n",
+       "4            6.0           8.0                 9.0            13.0   \n",
+       "5            NaN           NaN                 NaN             NaN   \n",
+       "\n",
+       "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
+       "0                 34.0            8600.0   1.0            NaN  \n",
+       "1                 34.0            7213.0   1.0            5.0  \n",
+       "2                 34.0            6217.0   1.0            5.0  \n",
+       "3                 34.0           24499.0   1.0            5.0  \n",
+       "4                 34.0           11840.0   1.0            5.0  \n",
+       "5                  NaN               NaN   NaN            5.0  "
       ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "national_platforms = COOPS + NDBC + NERRS + CBIBS + OAP + CDIP\n",
+    "print(\"National Platforms:\",national_platforms)\n",
+    "\n",
+    "ioos_btn_df.loc[ioos_btn_df[\"date_UTC\"]==today, [\"National Platforms\"]] = national_platforms\n",
+    "\n",
+    "ioos_btn_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_vmKPAdZ9SBU"
+   },
+   "source": [
+    "---\n",
+    "## Regional Platforms\n",
+    "\n",
+    "Regional platforms are calculated from the annual IOOS asset inventory submitted by each Regional Association. More information about the IOOS asset inventory can be found at https://github.com/ioos/ioos-asset-inventory\n",
+    "\n",
+    "The data from 2020 can be found [here](https://github.com/ioos/ioos-asset-inventory/tree/main/2020) and is available on [ERDDAP](http://erddap.ioos.us/erddap/tabledap/processed_asset_inventory.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 376
     },
+    "id": "WTQ0WgP09Vxc",
+    "outputId": "b25fe6d7-fae1-4365-d9bb-0920b0ea6852"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "IaoMWowM3cw6"
-      },
-      "source": [
-        "---\n",
-        "## MBON Projects\n",
-        "Living marine resources are essential to the health and recreational needs of billions of people, yet marine biodiversity and ecosystem processes remain major frontiers in ocean observing. IOOS has a critical role in implementing operational, sustained programs to observe biology and catalogue biodiversity to ensure these data are available for science, management, and the public. IOOS is leading development of the Marine Biodiversity Observation Network, with core funding from NOAA, NASA and BOEM. MBON connects regional networks of scientists, resource managers, and users and integrates data from existing long-term programs to understand human- and climate-induced change and its impacts on marine life. MBON partners are pioneering application of new remote sensing methods, imaging, molecular approaches (eDNA and ‘omics), and other technologies and integrating these with traditional research methods and coordinated experiments to understand changing patterns of biodiversity.\n",
-        "\n",
-        "These are the currently funded MBON projects. At this time, we are manually checking https://marinebon.org/ and counting the number of U.S. projects.\n",
-        "\n",
-        "We hope to be able to use the resources [here](https://github.com/marinebon/www_marinebon2/tree/master/content/project) to automatically harvest these metrics in the future.\n",
-        "\n",
-        "For 2022 the currently funded projects = 6"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Regional platforms: 886\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 49,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 463
-        },
-        "id": "P158yQSuwE_K",
-        "outputId": "d574fc61-4dc4-4084-a830-1bb9c247fc9a"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "PI:\t <h3>Francisco Chavez, Monterey Bay Aquarium Research Institute</h3>\n",
-            "PI:\t <h3>Nathan Furey, University of New Hampshire</h3>\n",
-            "PI:\t <h3>Cassandra Glaspie, Louisiana State University</h3>\n",
-            "PI:\t <h3>Katrin Iken, University of Alaska Fairbanks</h3>\n",
-            "PI:\t <h3>Frank Muller-Karger, University of South Florida</h3>\n",
-            "MBON Projects: 5\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
-              "0  2018-02-01              17.0                   11.0              150.0   \n",
-              "1  2022-04-22              17.0                   11.0              165.0   \n",
-              "2  2022-07-08              17.0                   11.0              165.0   \n",
-              "3  2022-10-05              17.0                   11.0              165.0   \n",
-              "4  2023-01-05              17.0                   11.0              165.0   \n",
-              "5  2024-01-26              17.0                   11.0              165.0   \n",
-              "\n",
-              "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
-              "0            52027.0               737.0               335.0              NaN   \n",
-              "1            53672.0               763.0               517.0           4444.0   \n",
-              "2            55448.0               764.0               517.0           4444.0   \n",
-              "3            59088.0               390.0               635.0           4444.0   \n",
-              "4            62042.0               768.0               635.0           4444.0   \n",
-              "5            73204.0               721.0               886.0           5190.0   \n",
-              "\n",
-              "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
-              "0            NaN           NaN                 NaN            13.0   \n",
-              "1            6.0           8.0                 9.0            13.0   \n",
-              "2            6.0           8.0                 9.0            13.0   \n",
-              "3            6.0           8.0                 9.0            13.0   \n",
-              "4            6.0           8.0                 9.0            13.0   \n",
-              "5            5.0           NaN                 NaN             NaN   \n",
-              "\n",
-              "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
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-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
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-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
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-          "metadata": {},
-          "execution_count": 49
-        }
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+       "      <td>24499.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-01-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>62042.0</td>\n",
+       "      <td>768.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>11840.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2024-01-26</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>73204.0</td>\n",
+       "      <td>721.0</td>\n",
+       "      <td>886.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ecef0173-185c-4489-968e-f190e076e5ef')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-ecef0173-185c-4489-968e-f190e076e5ef button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-ecef0173-185c-4489-968e-f190e076e5ef');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-96e316d5-f6ad-4651-a096-76c60164f8b4\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-96e316d5-f6ad-4651-a096-76c60164f8b4')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-96e316d5-f6ad-4651-a096-76c60164f8b4 button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "source": [
-        "## BROKEN\n",
-        "\n",
-        "import requests\n",
-        "from bs4 import BeautifulSoup\n",
-        "import re\n",
-        "\n",
-        "regional_associations = 0\n",
-        "\n",
-        "url = 'https://ioos.noaa.gov/project/mbon/'\n",
-        "\n",
-        "headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36'}\n",
-        "\n",
-        "html = requests.get(url, headers=headers).text\n",
-        "\n",
-        "soup = BeautifulSoup(html, 'html.parser')\n",
-        "\n",
-        "mbon_projects = 0\n",
-        "\n",
-        "for tag in soup.find_all('h3'):\n",
-        "  if 'class' in tag.attrs.keys():\n",
-        "    continue # skipping other headers\n",
-        "\n",
-        "  print('PI:\\t',tag)\n",
-        "  mbon_projects+=1\n",
-        "#   mbon_projects = len(tag.parent.find_all('li'))\n",
-        "\n",
-        "#   for project in tag.parent.find_all('li'):\n",
-        "\n",
-        "#     print(project.text)\n",
-        "\n",
-        "\n",
-        "print(\"MBON Projects:\",mbon_projects)\n",
-        "\n",
-        "ioos_btn_df.loc[ioos_btn_df['date_UTC']==today, ['MBON Projects']] = mbon_projects\n",
-        "\n",
-        "ioos_btn_df"
+      "text/plain": [
+       "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
+       "0  2018-02-01              17.0                   11.0              150.0   \n",
+       "1  2022-04-22              17.0                   11.0              165.0   \n",
+       "2  2022-07-08              17.0                   11.0              165.0   \n",
+       "3  2022-10-05              17.0                   11.0              165.0   \n",
+       "4  2023-01-05              17.0                   11.0              165.0   \n",
+       "5  2024-01-26              17.0                   11.0              165.0   \n",
+       "\n",
+       "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
+       "0            52027.0               737.0               335.0              NaN   \n",
+       "1            53672.0               763.0               517.0           4444.0   \n",
+       "2            55448.0               764.0               517.0           4444.0   \n",
+       "3            59088.0               390.0               635.0           4444.0   \n",
+       "4            62042.0               768.0               635.0           4444.0   \n",
+       "5            73204.0               721.0               886.0              NaN   \n",
+       "\n",
+       "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
+       "0            NaN           NaN                 NaN            13.0   \n",
+       "1            6.0           8.0                 9.0            13.0   \n",
+       "2            6.0           8.0                 9.0            13.0   \n",
+       "3            6.0           8.0                 9.0            13.0   \n",
+       "4            6.0           8.0                 9.0            13.0   \n",
+       "5            NaN           NaN                 NaN             NaN   \n",
+       "\n",
+       "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
+       "0                 34.0            8600.0   1.0            NaN  \n",
+       "1                 34.0            7213.0   1.0            5.0  \n",
+       "2                 34.0            6217.0   1.0            5.0  \n",
+       "3                 34.0           24499.0   1.0            5.0  \n",
+       "4                 34.0           11840.0   1.0            5.0  \n",
+       "5                  NaN               NaN   NaN            5.0  "
       ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "url = \"https://erddap.ioos.us/erddap/tabledap/processed_asset_inventory.json?station_long_name&distinct()\"\n",
+    "\n",
+    "df_regional_platforms = pd.read_json(url)\n",
+    "\n",
+    "regional_platforms = len(df_regional_platforms.loc[\"rows\"][0])\n",
+    "\n",
+    "print(\"Regional platforms:\",regional_platforms)\n",
+    "\n",
+    "ioos_btn_df.loc[ioos_btn_df[\"date_UTC\"]==today, [\"Regional Platforms\"]] = regional_platforms\n",
+    "\n",
+    "ioos_btn_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "X5BeiqhIXme-"
+   },
+   "source": [
+    "---\n",
+    "## ATN Deployments\n",
+    "\n",
+    "See Deployments at https://portal.atn.ioos.us/#\n",
+    "Not sure if there is a way to scrape that page or get those values from somewhere\n",
+    "\n",
+    "4242"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 394
     },
+    "id": "tiZLM5KqdGPI",
+    "outputId": "e83386d3-287f-4ff8-8b56-356f4c1f6786"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "DxsnHFRJEGKz"
-      },
-      "source": [
-        "## OTT Projects\n",
-        "\n",
-        "The IOOS Ocean Technology Transition project sponsors the transition of emerging marine observing technologies, for which there is an existing operational requirement and a demonstrated commitment to integration and use by the ocean observing community, to operational mode. Each year IOOS supports 2-4 projects. The number here reflects the total number projects supported by this effort.\n",
-        "\n",
-        "These are the current active OTT projects which was provided by the OTT Program Manager. Hopefully, we can find a good place to harvest these numbers from.\n",
-        "\n",
-        "For now, we have the [website](https://ioos.noaa.gov/project/ocean-technology-transition/) and personal communication that there are 8 live projects."
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5190\n",
+      "ATN Deployments: 5190\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 50,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 411
-        },
-        "id": "FqxRZ0ZnEGKz",
-        "outputId": "fc8c93cd-0787-4a00-fb4f-562eb7258b4b"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "7\n",
-            "7\n",
-            "OTT Projects: 14\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
-              "0  2018-02-01              17.0                   11.0              150.0   \n",
-              "1  2022-04-22              17.0                   11.0              165.0   \n",
-              "2  2022-07-08              17.0                   11.0              165.0   \n",
-              "3  2022-10-05              17.0                   11.0              165.0   \n",
-              "4  2023-01-05              17.0                   11.0              165.0   \n",
-              "5  2024-01-26              17.0                   11.0              165.0   \n",
-              "\n",
-              "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
-              "0            52027.0               737.0               335.0              NaN   \n",
-              "1            53672.0               763.0               517.0           4444.0   \n",
-              "2            55448.0               764.0               517.0           4444.0   \n",
-              "3            59088.0               390.0               635.0           4444.0   \n",
-              "4            62042.0               768.0               635.0           4444.0   \n",
-              "5            73204.0               721.0               886.0           5190.0   \n",
-              "\n",
-              "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
-              "0            NaN           NaN                 NaN            13.0   \n",
-              "1            6.0           8.0                 9.0            13.0   \n",
-              "2            6.0           8.0                 9.0            13.0   \n",
-              "3            6.0           8.0                 9.0            13.0   \n",
-              "4            6.0           8.0                 9.0            13.0   \n",
-              "5            5.0          14.0                 NaN             NaN   \n",
-              "\n",
-              "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
-              "0                 34.0            8600.0   1.0            NaN  \n",
-              "1                 34.0            7213.0   1.0            5.0  \n",
-              "2                 34.0            6217.0   1.0            5.0  \n",
-              "3                 34.0           24499.0   1.0            5.0  \n",
-              "4                 34.0           11840.0   1.0            5.0  \n",
-              "5                  NaN               NaN   NaN            5.0  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-d6b9be52-be04-463d-b711-031116075208\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>date_UTC</th>\n",
-              "      <th>Federal Partners</th>\n",
-              "      <th>Regional Associations</th>\n",
-              "      <th>HF Radar Stations</th>\n",
-              "      <th>NGDAC Glider Days</th>\n",
-              "      <th>National Platforms</th>\n",
-              "      <th>Regional Platforms</th>\n",
-              "      <th>ATN Deployments</th>\n",
-              "      <th>MBON Projects</th>\n",
-              "      <th>OTT Projects</th>\n",
-              "      <th>HAB Pilot Projects</th>\n",
-              "      <th>QARTOD Manuals</th>\n",
-              "      <th>IOOS Core Variables</th>\n",
-              "      <th>Metadata Records</th>\n",
-              "      <th>IOOS</th>\n",
-              "      <th>COMT Projects</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>2018-02-01</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>150.0</td>\n",
-              "      <td>52027.0</td>\n",
-              "      <td>737.0</td>\n",
-              "      <td>335.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>8600.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>2022-04-22</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>53672.0</td>\n",
-              "      <td>763.0</td>\n",
-              "      <td>517.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>7213.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>2022-07-08</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>55448.0</td>\n",
-              "      <td>764.0</td>\n",
-              "      <td>517.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>6217.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>2022-10-05</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>59088.0</td>\n",
-              "      <td>390.0</td>\n",
-              "      <td>635.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>24499.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>2023-01-05</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>62042.0</td>\n",
-              "      <td>768.0</td>\n",
-              "      <td>635.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>11840.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>2024-01-26</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>73204.0</td>\n",
-              "      <td>721.0</td>\n",
-              "      <td>886.0</td>\n",
-              "      <td>5190.0</td>\n",
-              "      <td>5.0</td>\n",
-              "      <td>14.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d6b9be52-be04-463d-b711-031116075208')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-d6b9be52-be04-463d-b711-031116075208 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-d6b9be52-be04-463d-b711-031116075208');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-243b082f-393e-4353-a7ce-bd6330cd7c2d\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-243b082f-393e-4353-a7ce-bd6330cd7c2d')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-243b082f-393e-4353-a7ce-bd6330cd7c2d button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 50
-        }
+     "data": {
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+       "\n",
+       "  <div id=\"df-c9fa7e46-1695-4dc8-9c3c-8b597e55d59d\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date_UTC</th>\n",
+       "      <th>Federal Partners</th>\n",
+       "      <th>Regional Associations</th>\n",
+       "      <th>HF Radar Stations</th>\n",
+       "      <th>NGDAC Glider Days</th>\n",
+       "      <th>National Platforms</th>\n",
+       "      <th>Regional Platforms</th>\n",
+       "      <th>ATN Deployments</th>\n",
+       "      <th>MBON Projects</th>\n",
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+       "      <th>HAB Pilot Projects</th>\n",
+       "      <th>QARTOD Manuals</th>\n",
+       "      <th>IOOS Core Variables</th>\n",
+       "      <th>Metadata Records</th>\n",
+       "      <th>IOOS</th>\n",
+       "      <th>COMT Projects</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2018-02-01</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>150.0</td>\n",
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+       "      <td>737.0</td>\n",
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+       "      <td>34.0</td>\n",
+       "      <td>8600.0</td>\n",
+       "      <td>1.0</td>\n",
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+       "      <th>1</th>\n",
+       "      <td>2022-04-22</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>53672.0</td>\n",
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+       "      <td>7213.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2022-07-08</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>55448.0</td>\n",
+       "      <td>764.0</td>\n",
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+       "      <td>34.0</td>\n",
+       "      <td>6217.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2022-10-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>59088.0</td>\n",
+       "      <td>390.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
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+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>24499.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-01-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>62042.0</td>\n",
+       "      <td>768.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
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+       "      <td>34.0</td>\n",
+       "      <td>11840.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2024-01-26</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>73204.0</td>\n",
+       "      <td>721.0</td>\n",
+       "      <td>886.0</td>\n",
+       "      <td>5190.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c9fa7e46-1695-4dc8-9c3c-8b597e55d59d')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-c9fa7e46-1695-4dc8-9c3c-8b597e55d59d button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-c9fa7e46-1695-4dc8-9c3c-8b597e55d59d');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-69ed917b-5102-4ae2-9f21-4bf48cdc08a3\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-69ed917b-5102-4ae2-9f21-4bf48cdc08a3')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-69ed917b-5102-4ae2-9f21-4bf48cdc08a3 button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "source": [
-        "from bs4 import BeautifulSoup\n",
-        "import requests\n",
-        "import pandas as pd\n",
-        "\n",
-        "headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36'}\n",
-        "\n",
-        "url = 'https://ioos.noaa.gov/project/ocean-technology-transition/'\n",
-        "\n",
-        "soup = BeautifulSoup(requests.get(url, headers=headers).text, 'html.parser')\n",
-        "\n",
-        "df = pd.read_html(str(soup.find(attrs={'class':'fg-text-dark ffb-one-desc-2-2'})),\n",
-        "                  header=0,\n",
-        "                  #attrs = {'class': 'table'},\n",
-        "                  )\n",
-        "\n",
-        "ott_projects = 0\n",
-        "\n",
-        "for entry in df[0]:\n",
-        "  print(df[0][entry][0].count('new in'))\n",
-        "  ott_projects += df[0][entry][0].count('new in')\n",
-        "\n",
-        "print('OTT Projects:',ott_projects)\n",
-        "\n",
-        "ioos_btn_df.loc[ioos_btn_df['date_UTC']==today, ['OTT Projects']] = ott_projects\n",
-        "\n",
-        "ioos_btn_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "wwXNR2m2EGKz"
-      },
-      "source": [
-        "## NHABON Pilot Projects\n",
-        "\n",
-        "These are the National Harmful Algal Bloom Observing Network Pilot Project awards. Currently these were calculated from the [award announcement pdf](https://cdn.ioos.noaa.gov/media/2021/10/NHABON-Funding-Awards-FY21_v2.pdf) which states that there are 9 total.\n",
-        "\n",
-        "Might be able to parse the pdf and calculate this on the fly."
+      "text/plain": [
+       "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
+       "0  2018-02-01              17.0                   11.0              150.0   \n",
+       "1  2022-04-22              17.0                   11.0              165.0   \n",
+       "2  2022-07-08              17.0                   11.0              165.0   \n",
+       "3  2022-10-05              17.0                   11.0              165.0   \n",
+       "4  2023-01-05              17.0                   11.0              165.0   \n",
+       "5  2024-01-26              17.0                   11.0              165.0   \n",
+       "\n",
+       "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
+       "0            52027.0               737.0               335.0              NaN   \n",
+       "1            53672.0               763.0               517.0           4444.0   \n",
+       "2            55448.0               764.0               517.0           4444.0   \n",
+       "3            59088.0               390.0               635.0           4444.0   \n",
+       "4            62042.0               768.0               635.0           4444.0   \n",
+       "5            73204.0               721.0               886.0           5190.0   \n",
+       "\n",
+       "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
+       "0            NaN           NaN                 NaN            13.0   \n",
+       "1            6.0           8.0                 9.0            13.0   \n",
+       "2            6.0           8.0                 9.0            13.0   \n",
+       "3            6.0           8.0                 9.0            13.0   \n",
+       "4            6.0           8.0                 9.0            13.0   \n",
+       "5            NaN           NaN                 NaN             NaN   \n",
+       "\n",
+       "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
+       "0                 34.0            8600.0   1.0            NaN  \n",
+       "1                 34.0            7213.0   1.0            5.0  \n",
+       "2                 34.0            6217.0   1.0            5.0  \n",
+       "3                 34.0           24499.0   1.0            5.0  \n",
+       "4                 34.0           11840.0   1.0            5.0  \n",
+       "5                  NaN               NaN   NaN            5.0  "
       ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "\n",
+    "headers = {\n",
+    "    \"Accept\": \"application/json\"\n",
+    "}\n",
+    "\n",
+    "raw_payload = requests.get(\"https://search.axds.co/v2/search?portalId=99\", headers=headers)\n",
+    "json_payload = raw_payload.json()\n",
+    "for plt in json_payload[\"types\"]:\n",
+    "    if plt[\"id\"] == \"platform2\":\n",
+    "        print(plt[\"count\"])\n",
+    "        atn_deployments = plt[\"count\"]\n",
+    "        break\n",
+    "\n",
+    "print(\"ATN Deployments:\",atn_deployments)\n",
+    "\n",
+    "ioos_btn_df.loc[ioos_btn_df[\"date_UTC\"]==today, [\"ATN Deployments\"]] = atn_deployments\n",
+    "\n",
+    "ioos_btn_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IaoMWowM3cw6"
+   },
+   "source": [
+    "---\n",
+    "## MBON Projects\n",
+    "Living marine resources are essential to the health and recreational needs of billions of people, yet marine biodiversity and ecosystem processes remain major frontiers in ocean observing. IOOS has a critical role in implementing operational, sustained programs to observe biology and catalogue biodiversity to ensure these data are available for science, management, and the public. IOOS is leading development of the Marine Biodiversity Observation Network, with core funding from NOAA, NASA and BOEM. MBON connects regional networks of scientists, resource managers, and users and integrates data from existing long-term programs to understand human- and climate-induced change and its impacts on marine life. MBON partners are pioneering application of new remote sensing methods, imaging, molecular approaches (eDNA and ‘omics), and other technologies and integrating these with traditional research methods and coordinated experiments to understand changing patterns of biodiversity.\n",
+    "\n",
+    "These are the currently funded MBON projects. At this time, we are manually checking https://marinebon.org/ and counting the number of U.S. projects.\n",
+    "\n",
+    "We hope to be able to use the resources [here](https://github.com/marinebon/www_marinebon2/tree/master/content/project) to automatically harvest these metrics in the future.\n",
+    "\n",
+    "For 2022 the currently funded projects = 6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 463
     },
+    "id": "P158yQSuwE_K",
+    "outputId": "d574fc61-4dc4-4084-a830-1bb9c247fc9a"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 51,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "B6Z7tPnspnjx",
-        "outputId": "497f5549-a5ae-4d49-e41f-5a1da21c4683"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Collecting pdfminer.six\n",
-            "  Downloading pdfminer.six-20231228-py3-none-any.whl (5.6 MB)\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.6/5.6 MB\u001b[0m \u001b[31m51.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hRequirement already satisfied: charset-normalizer>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from pdfminer.six) (3.3.2)\n",
-            "Requirement already satisfied: cryptography>=36.0.0 in /usr/local/lib/python3.10/dist-packages (from pdfminer.six) (42.0.0)\n",
-            "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.10/dist-packages (from cryptography>=36.0.0->pdfminer.six) (1.16.0)\n",
-            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.12->cryptography>=36.0.0->pdfminer.six) (2.21)\n",
-            "Installing collected packages: pdfminer.six\n",
-            "Successfully installed pdfminer.six-20231228\n"
-          ]
-        }
-      ],
-      "source": [
-        "!pip install pdfminer.six"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PI:\t <h3>Francisco Chavez, Monterey Bay Aquarium Research Institute</h3>\n",
+      "PI:\t <h3>Nathan Furey, University of New Hampshire</h3>\n",
+      "PI:\t <h3>Cassandra Glaspie, Louisiana State University</h3>\n",
+      "PI:\t <h3>Katrin Iken, University of Alaska Fairbanks</h3>\n",
+      "PI:\t <h3>Frank Muller-Karger, University of South Florida</h3>\n",
+      "MBON Projects: 5\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 52,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 359
-        },
-        "id": "0QvdLybSEGKz",
-        "outputId": "924f4073-0081-4772-d626-d8f78e4ace32"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
-              "0  2018-02-01              17.0                   11.0              150.0   \n",
-              "1  2022-04-22              17.0                   11.0              165.0   \n",
-              "2  2022-07-08              17.0                   11.0              165.0   \n",
-              "3  2022-10-05              17.0                   11.0              165.0   \n",
-              "4  2023-01-05              17.0                   11.0              165.0   \n",
-              "5  2024-01-26              17.0                   11.0              165.0   \n",
-              "\n",
-              "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
-              "0            52027.0               737.0               335.0              NaN   \n",
-              "1            53672.0               763.0               517.0           4444.0   \n",
-              "2            55448.0               764.0               517.0           4444.0   \n",
-              "3            59088.0               390.0               635.0           4444.0   \n",
-              "4            62042.0               768.0               635.0           4444.0   \n",
-              "5            73204.0               721.0               886.0           5190.0   \n",
-              "\n",
-              "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
-              "0            NaN           NaN                 NaN            13.0   \n",
-              "1            6.0           8.0                 9.0            13.0   \n",
-              "2            6.0           8.0                 9.0            13.0   \n",
-              "3            6.0           8.0                 9.0            13.0   \n",
-              "4            6.0           8.0                 9.0            13.0   \n",
-              "5            5.0          14.0                11.0             NaN   \n",
-              "\n",
-              "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
-              "0                 34.0            8600.0   1.0            NaN  \n",
-              "1                 34.0            7213.0   1.0            5.0  \n",
-              "2                 34.0            6217.0   1.0            5.0  \n",
-              "3                 34.0           24499.0   1.0            5.0  \n",
-              "4                 34.0           11840.0   1.0            5.0  \n",
-              "5                  NaN               NaN   NaN            5.0  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-790863c7-afab-4db3-8a41-7a640acd5b13\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>date_UTC</th>\n",
-              "      <th>Federal Partners</th>\n",
-              "      <th>Regional Associations</th>\n",
-              "      <th>HF Radar Stations</th>\n",
-              "      <th>NGDAC Glider Days</th>\n",
-              "      <th>National Platforms</th>\n",
-              "      <th>Regional Platforms</th>\n",
-              "      <th>ATN Deployments</th>\n",
-              "      <th>MBON Projects</th>\n",
-              "      <th>OTT Projects</th>\n",
-              "      <th>HAB Pilot Projects</th>\n",
-              "      <th>QARTOD Manuals</th>\n",
-              "      <th>IOOS Core Variables</th>\n",
-              "      <th>Metadata Records</th>\n",
-              "      <th>IOOS</th>\n",
-              "      <th>COMT Projects</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>2018-02-01</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>150.0</td>\n",
-              "      <td>52027.0</td>\n",
-              "      <td>737.0</td>\n",
-              "      <td>335.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>8600.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>2022-04-22</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>53672.0</td>\n",
-              "      <td>763.0</td>\n",
-              "      <td>517.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>7213.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>2022-07-08</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>55448.0</td>\n",
-              "      <td>764.0</td>\n",
-              "      <td>517.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>6217.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>2022-10-05</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>59088.0</td>\n",
-              "      <td>390.0</td>\n",
-              "      <td>635.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>24499.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>2023-01-05</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>62042.0</td>\n",
-              "      <td>768.0</td>\n",
-              "      <td>635.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>11840.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>2024-01-26</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>73204.0</td>\n",
-              "      <td>721.0</td>\n",
-              "      <td>886.0</td>\n",
-              "      <td>5190.0</td>\n",
-              "      <td>5.0</td>\n",
-              "      <td>14.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-790863c7-afab-4db3-8a41-7a640acd5b13')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-790863c7-afab-4db3-8a41-7a640acd5b13 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-790863c7-afab-4db3-8a41-7a640acd5b13');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-003047b7-177d-43ef-a8b2-d85c5a53c056\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-003047b7-177d-43ef-a8b2-d85c5a53c056')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-003047b7-177d-43ef-a8b2-d85c5a53c056 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 52
-        }
+     "data": {
+      "text/html": [
+       "\n",
+       "  <div id=\"df-102d1ce1-707a-4152-b150-aac5ce0d51c3\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date_UTC</th>\n",
+       "      <th>Federal Partners</th>\n",
+       "      <th>Regional Associations</th>\n",
+       "      <th>HF Radar Stations</th>\n",
+       "      <th>NGDAC Glider Days</th>\n",
+       "      <th>National Platforms</th>\n",
+       "      <th>Regional Platforms</th>\n",
+       "      <th>ATN Deployments</th>\n",
+       "      <th>MBON Projects</th>\n",
+       "      <th>OTT Projects</th>\n",
+       "      <th>HAB Pilot Projects</th>\n",
+       "      <th>QARTOD Manuals</th>\n",
+       "      <th>IOOS Core Variables</th>\n",
+       "      <th>Metadata Records</th>\n",
+       "      <th>IOOS</th>\n",
+       "      <th>COMT Projects</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2018-02-01</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>52027.0</td>\n",
+       "      <td>737.0</td>\n",
+       "      <td>335.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>8600.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2022-04-22</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>53672.0</td>\n",
+       "      <td>763.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>7213.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2022-07-08</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>55448.0</td>\n",
+       "      <td>764.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>6217.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2022-10-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>59088.0</td>\n",
+       "      <td>390.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>24499.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-01-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>62042.0</td>\n",
+       "      <td>768.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>11840.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2024-01-26</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>73204.0</td>\n",
+       "      <td>721.0</td>\n",
+       "      <td>886.0</td>\n",
+       "      <td>5190.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-102d1ce1-707a-4152-b150-aac5ce0d51c3')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-102d1ce1-707a-4152-b150-aac5ce0d51c3 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-102d1ce1-707a-4152-b150-aac5ce0d51c3');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-f1988e45-2f93-4b84-a922-983b1331ba52\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f1988e45-2f93-4b84-a922-983b1331ba52')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-f1988e45-2f93-4b84-a922-983b1331ba52 button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "source": [
-        "from pdfminer.high_level import extract_text\n",
-        "\n",
-        "import io\n",
-        "\n",
-        "import requests\n",
-        "\n",
-        "url = 'https://cdn.ioos.noaa.gov/media/2022/10/NHABON-Funding-Awards-FY22.pdf'\n",
-        "\n",
-        "data = requests.get(url)\n",
-        "\n",
-        "with io.BytesIO(data.content) as f:\n",
-        "  pdf = extract_text(f)\n",
-        "\n",
-        "content = pdf.split('\\n')\n",
-        "\n",
-        "nhabon_projects = sum(\"Funded amount\" in s for s in content)\n",
-        "\n",
-        "nhabon_projects = nhabon_projects + 1 # Gulf of Mexico project\n",
-        "\n",
-        "ioos_btn_df.loc[ioos_btn_df['date_UTC']==today, ['HAB Pilot Projects']] = nhabon_projects\n",
-        "\n",
-        "ioos_btn_df"
+      "text/plain": [
+       "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
+       "0  2018-02-01              17.0                   11.0              150.0   \n",
+       "1  2022-04-22              17.0                   11.0              165.0   \n",
+       "2  2022-07-08              17.0                   11.0              165.0   \n",
+       "3  2022-10-05              17.0                   11.0              165.0   \n",
+       "4  2023-01-05              17.0                   11.0              165.0   \n",
+       "5  2024-01-26              17.0                   11.0              165.0   \n",
+       "\n",
+       "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
+       "0            52027.0               737.0               335.0              NaN   \n",
+       "1            53672.0               763.0               517.0           4444.0   \n",
+       "2            55448.0               764.0               517.0           4444.0   \n",
+       "3            59088.0               390.0               635.0           4444.0   \n",
+       "4            62042.0               768.0               635.0           4444.0   \n",
+       "5            73204.0               721.0               886.0           5190.0   \n",
+       "\n",
+       "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
+       "0            NaN           NaN                 NaN            13.0   \n",
+       "1            6.0           8.0                 9.0            13.0   \n",
+       "2            6.0           8.0                 9.0            13.0   \n",
+       "3            6.0           8.0                 9.0            13.0   \n",
+       "4            6.0           8.0                 9.0            13.0   \n",
+       "5            5.0           NaN                 NaN             NaN   \n",
+       "\n",
+       "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
+       "0                 34.0            8600.0   1.0            NaN  \n",
+       "1                 34.0            7213.0   1.0            5.0  \n",
+       "2                 34.0            6217.0   1.0            5.0  \n",
+       "3                 34.0           24499.0   1.0            5.0  \n",
+       "4                 34.0           11840.0   1.0            5.0  \n",
+       "5                  NaN               NaN   NaN            5.0  "
       ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## BROKEN\n",
+    "\n",
+    "import re\n",
+    "\n",
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "\n",
+    "regional_associations = 0\n",
+    "\n",
+    "url = \"https://ioos.noaa.gov/project/mbon/\"\n",
+    "\n",
+    "headers = {\"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36\"}\n",
+    "\n",
+    "html = requests.get(url, headers=headers).text\n",
+    "\n",
+    "soup = BeautifulSoup(html, \"html.parser\")\n",
+    "\n",
+    "mbon_projects = 0\n",
+    "\n",
+    "for tag in soup.find_all(\"h3\"):\n",
+    "  if \"class\" in tag.attrs.keys():\n",
+    "    continue # skipping other headers\n",
+    "\n",
+    "  print(\"PI:\\t\",tag)\n",
+    "  mbon_projects+=1\n",
+    "#   mbon_projects = len(tag.parent.find_all('li'))\n",
+    "\n",
+    "#   for project in tag.parent.find_all('li'):\n",
+    "\n",
+    "#     print(project.text)\n",
+    "\n",
+    "\n",
+    "print(\"MBON Projects:\",mbon_projects)\n",
+    "\n",
+    "ioos_btn_df.loc[ioos_btn_df[\"date_UTC\"]==today, [\"MBON Projects\"]] = mbon_projects\n",
+    "\n",
+    "ioos_btn_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DxsnHFRJEGKz"
+   },
+   "source": [
+    "## OTT Projects\n",
+    "\n",
+    "The IOOS Ocean Technology Transition project sponsors the transition of emerging marine observing technologies, for which there is an existing operational requirement and a demonstrated commitment to integration and use by the ocean observing community, to operational mode. Each year IOOS supports 2-4 projects. The number here reflects the total number projects supported by this effort.\n",
+    "\n",
+    "These are the current active OTT projects which was provided by the OTT Program Manager. Hopefully, we can find a good place to harvest these numbers from.\n",
+    "\n",
+    "For now, we have the [website](https://ioos.noaa.gov/project/ocean-technology-transition/) and personal communication that there are 8 live projects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 411
     },
+    "id": "FqxRZ0ZnEGKz",
+    "outputId": "fc8c93cd-0787-4a00-fb4f-562eb7258b4b"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "915oRsuKEGK0"
-      },
-      "source": [
-        "## QARTOD Manuals\n",
-        "\n",
-        "As of the last update there are twelve QARTOD manuals in-place for IOOS. These manuals establish authoritative QA/QC procedures for oceanographic data.\n",
-        "\n",
-        "The five year plan lists 16 manuals/papers. There's 13 QC manuals plus the Flags document, the QA paper and the Glider DAC paper. The Glider DAC paper is an implementation plan of the TS QC manual, and it's posted under the Implementation tab on the QARTOD home page, at https://cdn.ioos.noaa.gov/media/2017/12/Manual-for-QC-of-Glider-Data_05_09_16.pdf.\n",
-        "\n",
-        "\n",
-        "https://ioos.noaa.gov/project/qartod/"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7\n",
+      "7\n",
+      "OTT Projects: 14\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 53,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 602
-        },
-        "id": "7NabPJxcEGK0",
-        "outputId": "f0627b86-830c-4735-8c19-04871e7b7407"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Real-Time Quality Control of pH Data Observations\n",
-            "Real-Time Quality Control of Stream Flow Observations\n",
-            "Real-Time Quality Control of Passive Acoustics Data\n",
-            "Real-Time Quality Control of Phytoplankton Data\n",
-            "Real-Time Quality Control of HF Radar Observations\n",
-            "Real-Time Quality Control of Dissolved Nutrients Observations\n",
-            "Real-Time Quality Control of Wind Data\n",
-            "Real-Time Quality Control of Water Level Data\n",
-            "Real-Time Quality Control of In-Situ Surface Wave Data\n",
-            "Real-Time Quality Control of Ocean Optics Data\n",
-            "Real-Time Quality Control of In-Situ Temperature and Salinity Data\n",
-            "Real-Time Quality Control of Dissolved Oxygen Observations in Coastal Oceans\n",
-            "Real-Time Quality Control of In-Situ Current Observations\n",
-            "QARTOD Manuals: 13\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
-              "0  2018-02-01              17.0                   11.0              150.0   \n",
-              "1  2022-04-22              17.0                   11.0              165.0   \n",
-              "2  2022-07-08              17.0                   11.0              165.0   \n",
-              "3  2022-10-05              17.0                   11.0              165.0   \n",
-              "4  2023-01-05              17.0                   11.0              165.0   \n",
-              "5  2024-01-26              17.0                   11.0              165.0   \n",
-              "\n",
-              "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
-              "0            52027.0               737.0               335.0              NaN   \n",
-              "1            53672.0               763.0               517.0           4444.0   \n",
-              "2            55448.0               764.0               517.0           4444.0   \n",
-              "3            59088.0               390.0               635.0           4444.0   \n",
-              "4            62042.0               768.0               635.0           4444.0   \n",
-              "5            73204.0               721.0               886.0           5190.0   \n",
-              "\n",
-              "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
-              "0            NaN           NaN                 NaN            13.0   \n",
-              "1            6.0           8.0                 9.0            13.0   \n",
-              "2            6.0           8.0                 9.0            13.0   \n",
-              "3            6.0           8.0                 9.0            13.0   \n",
-              "4            6.0           8.0                 9.0            13.0   \n",
-              "5            5.0          14.0                11.0            13.0   \n",
-              "\n",
-              "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
-              "0                 34.0            8600.0   1.0            NaN  \n",
-              "1                 34.0            7213.0   1.0            5.0  \n",
-              "2                 34.0            6217.0   1.0            5.0  \n",
-              "3                 34.0           24499.0   1.0            5.0  \n",
-              "4                 34.0           11840.0   1.0            5.0  \n",
-              "5                  NaN               NaN   NaN            5.0  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-66ade109-2da0-4c6a-9f84-b02508eeaaae\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>date_UTC</th>\n",
-              "      <th>Federal Partners</th>\n",
-              "      <th>Regional Associations</th>\n",
-              "      <th>HF Radar Stations</th>\n",
-              "      <th>NGDAC Glider Days</th>\n",
-              "      <th>National Platforms</th>\n",
-              "      <th>Regional Platforms</th>\n",
-              "      <th>ATN Deployments</th>\n",
-              "      <th>MBON Projects</th>\n",
-              "      <th>OTT Projects</th>\n",
-              "      <th>HAB Pilot Projects</th>\n",
-              "      <th>QARTOD Manuals</th>\n",
-              "      <th>IOOS Core Variables</th>\n",
-              "      <th>Metadata Records</th>\n",
-              "      <th>IOOS</th>\n",
-              "      <th>COMT Projects</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>2018-02-01</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>150.0</td>\n",
-              "      <td>52027.0</td>\n",
-              "      <td>737.0</td>\n",
-              "      <td>335.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>8600.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>2022-04-22</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>53672.0</td>\n",
-              "      <td>763.0</td>\n",
-              "      <td>517.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>7213.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>2022-07-08</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>55448.0</td>\n",
-              "      <td>764.0</td>\n",
-              "      <td>517.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>6217.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>2022-10-05</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>59088.0</td>\n",
-              "      <td>390.0</td>\n",
-              "      <td>635.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>24499.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>2023-01-05</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>62042.0</td>\n",
-              "      <td>768.0</td>\n",
-              "      <td>635.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>11840.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>2024-01-26</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>73204.0</td>\n",
-              "      <td>721.0</td>\n",
-              "      <td>886.0</td>\n",
-              "      <td>5190.0</td>\n",
-              "      <td>5.0</td>\n",
-              "      <td>14.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-66ade109-2da0-4c6a-9f84-b02508eeaaae')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-66ade109-2da0-4c6a-9f84-b02508eeaaae button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-66ade109-2da0-4c6a-9f84-b02508eeaaae');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-b1c1f865-5a0b-4a7a-8ab5-76647e181ad1\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b1c1f865-5a0b-4a7a-8ab5-76647e181ad1')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-b1c1f865-5a0b-4a7a-8ab5-76647e181ad1 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 53
-        }
+     "data": {
+      "text/html": [
+       "\n",
+       "  <div id=\"df-d6b9be52-be04-463d-b711-031116075208\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date_UTC</th>\n",
+       "      <th>Federal Partners</th>\n",
+       "      <th>Regional Associations</th>\n",
+       "      <th>HF Radar Stations</th>\n",
+       "      <th>NGDAC Glider Days</th>\n",
+       "      <th>National Platforms</th>\n",
+       "      <th>Regional Platforms</th>\n",
+       "      <th>ATN Deployments</th>\n",
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+       "      <th>OTT Projects</th>\n",
+       "      <th>HAB Pilot Projects</th>\n",
+       "      <th>QARTOD Manuals</th>\n",
+       "      <th>IOOS Core Variables</th>\n",
+       "      <th>Metadata Records</th>\n",
+       "      <th>IOOS</th>\n",
+       "      <th>COMT Projects</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2018-02-01</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>52027.0</td>\n",
+       "      <td>737.0</td>\n",
+       "      <td>335.0</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>8600.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2022-04-22</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>53672.0</td>\n",
+       "      <td>763.0</td>\n",
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+       "      <td>34.0</td>\n",
+       "      <td>7213.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2022-07-08</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>55448.0</td>\n",
+       "      <td>764.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
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+       "      <td>34.0</td>\n",
+       "      <td>6217.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2022-10-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>59088.0</td>\n",
+       "      <td>390.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>24499.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-01-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>62042.0</td>\n",
+       "      <td>768.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
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+       "      <td>34.0</td>\n",
+       "      <td>11840.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2024-01-26</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>73204.0</td>\n",
+       "      <td>721.0</td>\n",
+       "      <td>886.0</td>\n",
+       "      <td>5190.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d6b9be52-be04-463d-b711-031116075208')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-d6b9be52-be04-463d-b711-031116075208 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-d6b9be52-be04-463d-b711-031116075208');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-243b082f-393e-4353-a7ce-bd6330cd7c2d\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-243b082f-393e-4353-a7ce-bd6330cd7c2d')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-243b082f-393e-4353-a7ce-bd6330cd7c2d button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "source": [
-        "from bs4 import BeautifulSoup\n",
-        "import requests\n",
-        "\n",
-        "headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36'}\n",
-        "\n",
-        "url = 'https://ioos.noaa.gov/project/qartod/'\n",
-        "\n",
-        "soup = BeautifulSoup(requests.get(url, headers=headers).text, 'html.parser')\n",
-        "\n",
-        "qartod=0\n",
-        "\n",
-        "for tag in soup.find_all('li'):\n",
-        "\n",
-        "  if \"Real-Time Quality Control of\" in tag.text:\n",
-        "\n",
-        "    print(tag.text)\n",
-        "\n",
-        "    qartod+=1\n",
-        "\n",
-        "print('QARTOD Manuals:',qartod)\n",
-        "\n",
-        "ioos_btn_df.loc[ioos_btn_df['date_UTC']==today, ['QARTOD Manuals']] = qartod\n",
-        "\n",
-        "ioos_btn_df"
+      "text/plain": [
+       "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
+       "0  2018-02-01              17.0                   11.0              150.0   \n",
+       "1  2022-04-22              17.0                   11.0              165.0   \n",
+       "2  2022-07-08              17.0                   11.0              165.0   \n",
+       "3  2022-10-05              17.0                   11.0              165.0   \n",
+       "4  2023-01-05              17.0                   11.0              165.0   \n",
+       "5  2024-01-26              17.0                   11.0              165.0   \n",
+       "\n",
+       "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
+       "0            52027.0               737.0               335.0              NaN   \n",
+       "1            53672.0               763.0               517.0           4444.0   \n",
+       "2            55448.0               764.0               517.0           4444.0   \n",
+       "3            59088.0               390.0               635.0           4444.0   \n",
+       "4            62042.0               768.0               635.0           4444.0   \n",
+       "5            73204.0               721.0               886.0           5190.0   \n",
+       "\n",
+       "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
+       "0            NaN           NaN                 NaN            13.0   \n",
+       "1            6.0           8.0                 9.0            13.0   \n",
+       "2            6.0           8.0                 9.0            13.0   \n",
+       "3            6.0           8.0                 9.0            13.0   \n",
+       "4            6.0           8.0                 9.0            13.0   \n",
+       "5            5.0          14.0                 NaN             NaN   \n",
+       "\n",
+       "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
+       "0                 34.0            8600.0   1.0            NaN  \n",
+       "1                 34.0            7213.0   1.0            5.0  \n",
+       "2                 34.0            6217.0   1.0            5.0  \n",
+       "3                 34.0           24499.0   1.0            5.0  \n",
+       "4                 34.0           11840.0   1.0            5.0  \n",
+       "5                  NaN               NaN   NaN            5.0  "
       ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "\n",
+    "headers = {\"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36\"}\n",
+    "\n",
+    "url = \"https://ioos.noaa.gov/project/ocean-technology-transition/\"\n",
+    "\n",
+    "soup = BeautifulSoup(requests.get(url, headers=headers).text, \"html.parser\")\n",
+    "\n",
+    "df = pd.read_html(str(soup.find(attrs={\"class\":\"fg-text-dark ffb-one-desc-2-2\"})),\n",
+    "                  header=0,\n",
+    "                  #attrs = {'class': 'table'},\n",
+    "                  )\n",
+    "\n",
+    "ott_projects = 0\n",
+    "\n",
+    "for entry in df[0]:\n",
+    "  print(df[0][entry][0].count(\"new in\"))\n",
+    "  ott_projects += df[0][entry][0].count(\"new in\")\n",
+    "\n",
+    "print(\"OTT Projects:\",ott_projects)\n",
+    "\n",
+    "ioos_btn_df.loc[ioos_btn_df[\"date_UTC\"]==today, [\"OTT Projects\"]] = ott_projects\n",
+    "\n",
+    "ioos_btn_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wwXNR2m2EGKz"
+   },
+   "source": [
+    "## NHABON Pilot Projects\n",
+    "\n",
+    "These are the National Harmful Algal Bloom Observing Network Pilot Project awards. Currently these were calculated from the [award announcement pdf](https://cdn.ioos.noaa.gov/media/2021/10/NHABON-Funding-Awards-FY21_v2.pdf) which states that there are 9 total.\n",
+    "\n",
+    "Might be able to parse the pdf and calculate this on the fly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "B6Z7tPnspnjx",
+    "outputId": "497f5549-a5ae-4d49-e41f-5a1da21c4683"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "vZ2zMhziEGK0"
-      },
-      "source": [
-        "## IOOS Core Variables\n",
-        "\n",
-        "The IOOS Core Variables are presented on [this website](https://www.iooc.us/task-teams/core-ioos-variables/). For now, this is a hard coded value, but it should be easy to parse that page and count up the variables.\n",
-        "\n",
-        "Also available on mmi at https://mmisw.org/ont/ioos/core_variable."
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting pdfminer.six\n",
+      "  Downloading pdfminer.six-20231228-py3-none-any.whl (5.6 MB)\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.6/5.6 MB\u001b[0m \u001b[31m51.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hRequirement already satisfied: charset-normalizer>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from pdfminer.six) (3.3.2)\n",
+      "Requirement already satisfied: cryptography>=36.0.0 in /usr/local/lib/python3.10/dist-packages (from pdfminer.six) (42.0.0)\n",
+      "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.10/dist-packages (from cryptography>=36.0.0->pdfminer.six) (1.16.0)\n",
+      "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.12->cryptography>=36.0.0->pdfminer.six) (2.21)\n",
+      "Installing collected packages: pdfminer.six\n",
+      "Successfully installed pdfminer.six-20231228\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install pdfminer.six"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 359
     },
+    "id": "0QvdLybSEGKz",
+    "outputId": "924f4073-0081-4772-d626-d8f78e4ace32"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [
-        "import pandas as pd\n",
-        "\n",
-        "url = 'https://mmisw.org/ont/api/v0/ont?format=rj&iri=http://mmisw.org/ont/ioos/core_variable'\n",
-        "\n",
-        "df = pd.read_json(url,orient='index')\n",
-        "\n",
-        "# drop the rows where 'name' doesn't exist.\n",
-        "df.dropna(axis='index', how='any',subset='http://mmisw.org/ont/ioos/core_variable/name', inplace=True)\n",
-        "\n",
-        "core_vars = len(df.index.tolist())\n",
-        "\n",
-        "print('IOOS Core Variables:',core_vars)\n",
-        "\n",
-        "ioos_btn_df.loc[ioos_btn_df['date_UTC']==today, ['IOOS Core Variables']] = core_vars\n",
-        "\n",
-        "ioos_btn_df"
+     "data": {
+      "text/html": [
+       "\n",
+       "  <div id=\"df-790863c7-afab-4db3-8a41-7a640acd5b13\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date_UTC</th>\n",
+       "      <th>Federal Partners</th>\n",
+       "      <th>Regional Associations</th>\n",
+       "      <th>HF Radar Stations</th>\n",
+       "      <th>NGDAC Glider Days</th>\n",
+       "      <th>National Platforms</th>\n",
+       "      <th>Regional Platforms</th>\n",
+       "      <th>ATN Deployments</th>\n",
+       "      <th>MBON Projects</th>\n",
+       "      <th>OTT Projects</th>\n",
+       "      <th>HAB Pilot Projects</th>\n",
+       "      <th>QARTOD Manuals</th>\n",
+       "      <th>IOOS Core Variables</th>\n",
+       "      <th>Metadata Records</th>\n",
+       "      <th>IOOS</th>\n",
+       "      <th>COMT Projects</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2018-02-01</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>52027.0</td>\n",
+       "      <td>737.0</td>\n",
+       "      <td>335.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>8600.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2022-04-22</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>53672.0</td>\n",
+       "      <td>763.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>7213.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2022-07-08</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>55448.0</td>\n",
+       "      <td>764.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>6217.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2022-10-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>59088.0</td>\n",
+       "      <td>390.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>24499.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-01-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>62042.0</td>\n",
+       "      <td>768.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>11840.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2024-01-26</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>73204.0</td>\n",
+       "      <td>721.0</td>\n",
+       "      <td>886.0</td>\n",
+       "      <td>5190.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
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+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-790863c7-afab-4db3-8a41-7a640acd5b13')\"\n",
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+       "      background-color: #E8F0FE;\n",
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+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-790863c7-afab-4db3-8a41-7a640acd5b13 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-790863c7-afab-4db3-8a41-7a640acd5b13');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-003047b7-177d-43ef-a8b2-d85c5a53c056\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-003047b7-177d-43ef-a8b2-d85c5a53c056')\"\n",
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+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
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+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
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+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
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+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
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+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
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+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-003047b7-177d-43ef-a8b2-d85c5a53c056 button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "metadata": {
-        "id": "iqpVv31c4X6O",
-        "outputId": "132f7c85-5cb8-4300-edf1-2f0d06afdc99",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 376
-        }
-      },
-      "execution_count": 54,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "IOOS Core Variables: 34\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
-              "0  2018-02-01              17.0                   11.0              150.0   \n",
-              "1  2022-04-22              17.0                   11.0              165.0   \n",
-              "2  2022-07-08              17.0                   11.0              165.0   \n",
-              "3  2022-10-05              17.0                   11.0              165.0   \n",
-              "4  2023-01-05              17.0                   11.0              165.0   \n",
-              "5  2024-01-26              17.0                   11.0              165.0   \n",
-              "\n",
-              "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
-              "0            52027.0               737.0               335.0              NaN   \n",
-              "1            53672.0               763.0               517.0           4444.0   \n",
-              "2            55448.0               764.0               517.0           4444.0   \n",
-              "3            59088.0               390.0               635.0           4444.0   \n",
-              "4            62042.0               768.0               635.0           4444.0   \n",
-              "5            73204.0               721.0               886.0           5190.0   \n",
-              "\n",
-              "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
-              "0            NaN           NaN                 NaN            13.0   \n",
-              "1            6.0           8.0                 9.0            13.0   \n",
-              "2            6.0           8.0                 9.0            13.0   \n",
-              "3            6.0           8.0                 9.0            13.0   \n",
-              "4            6.0           8.0                 9.0            13.0   \n",
-              "5            5.0          14.0                11.0            13.0   \n",
-              "\n",
-              "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
-              "0                 34.0            8600.0   1.0            NaN  \n",
-              "1                 34.0            7213.0   1.0            5.0  \n",
-              "2                 34.0            6217.0   1.0            5.0  \n",
-              "3                 34.0           24499.0   1.0            5.0  \n",
-              "4                 34.0           11840.0   1.0            5.0  \n",
-              "5                 34.0               NaN   NaN            5.0  "
-            ],
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-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>date_UTC</th>\n",
-              "      <th>Federal Partners</th>\n",
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-              "      <th>HF Radar Stations</th>\n",
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-              "      <th>HAB Pilot Projects</th>\n",
-              "      <th>QARTOD Manuals</th>\n",
-              "      <th>IOOS Core Variables</th>\n",
-              "      <th>Metadata Records</th>\n",
-              "      <th>IOOS</th>\n",
-              "      <th>COMT Projects</th>\n",
-              "    </tr>\n",
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-              "      <th>0</th>\n",
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-              "      <td>8600.0</td>\n",
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-              "      <td>2022-04-22</td>\n",
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-              "      <td>11.0</td>\n",
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-              "      <td>7213.0</td>\n",
-              "      <td>1.0</td>\n",
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-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>2022-07-08</td>\n",
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-              "      <td>2022-10-05</td>\n",
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-              "      <td>165.0</td>\n",
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-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
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-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>2023-01-05</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>62042.0</td>\n",
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-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>2024-01-26</td>\n",
-              "      <td>17.0</td>\n",
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-              "      <td>165.0</td>\n",
-              "      <td>73204.0</td>\n",
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-              "      <td>5.0</td>\n",
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-              "    [theme=dark] .colab-df-convert {\n",
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-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
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-              "                                                    [key], {});\n",
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-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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-              "          + ' to learn more about interactive tables.';\n",
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-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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-            ]
-          },
-          "metadata": {},
-          "execution_count": 54
-        }
+      "text/plain": [
+       "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
+       "0  2018-02-01              17.0                   11.0              150.0   \n",
+       "1  2022-04-22              17.0                   11.0              165.0   \n",
+       "2  2022-07-08              17.0                   11.0              165.0   \n",
+       "3  2022-10-05              17.0                   11.0              165.0   \n",
+       "4  2023-01-05              17.0                   11.0              165.0   \n",
+       "5  2024-01-26              17.0                   11.0              165.0   \n",
+       "\n",
+       "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
+       "0            52027.0               737.0               335.0              NaN   \n",
+       "1            53672.0               763.0               517.0           4444.0   \n",
+       "2            55448.0               764.0               517.0           4444.0   \n",
+       "3            59088.0               390.0               635.0           4444.0   \n",
+       "4            62042.0               768.0               635.0           4444.0   \n",
+       "5            73204.0               721.0               886.0           5190.0   \n",
+       "\n",
+       "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
+       "0            NaN           NaN                 NaN            13.0   \n",
+       "1            6.0           8.0                 9.0            13.0   \n",
+       "2            6.0           8.0                 9.0            13.0   \n",
+       "3            6.0           8.0                 9.0            13.0   \n",
+       "4            6.0           8.0                 9.0            13.0   \n",
+       "5            5.0          14.0                11.0             NaN   \n",
+       "\n",
+       "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
+       "0                 34.0            8600.0   1.0            NaN  \n",
+       "1                 34.0            7213.0   1.0            5.0  \n",
+       "2                 34.0            6217.0   1.0            5.0  \n",
+       "3                 34.0           24499.0   1.0            5.0  \n",
+       "4                 34.0           11840.0   1.0            5.0  \n",
+       "5                  NaN               NaN   NaN            5.0  "
       ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import io\n",
+    "\n",
+    "import requests\n",
+    "from pdfminer.high_level import extract_text\n",
+    "\n",
+    "url = \"https://cdn.ioos.noaa.gov/media/2022/10/NHABON-Funding-Awards-FY22.pdf\"\n",
+    "\n",
+    "data = requests.get(url)\n",
+    "\n",
+    "with io.BytesIO(data.content) as f:\n",
+    "  pdf = extract_text(f)\n",
+    "\n",
+    "content = pdf.split(\"\\n\")\n",
+    "\n",
+    "nhabon_projects = sum(\"Funded amount\" in s for s in content)\n",
+    "\n",
+    "nhabon_projects = nhabon_projects + 1 # Gulf of Mexico project\n",
+    "\n",
+    "ioos_btn_df.loc[ioos_btn_df[\"date_UTC\"]==today, [\"HAB Pilot Projects\"]] = nhabon_projects\n",
+    "\n",
+    "ioos_btn_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "915oRsuKEGK0"
+   },
+   "source": [
+    "## QARTOD Manuals\n",
+    "\n",
+    "As of the last update there are twelve QARTOD manuals in-place for IOOS. These manuals establish authoritative QA/QC procedures for oceanographic data.\n",
+    "\n",
+    "The five year plan lists 16 manuals/papers. There's 13 QC manuals plus the Flags document, the QA paper and the Glider DAC paper. The Glider DAC paper is an implementation plan of the TS QC manual, and it's posted under the Implementation tab on the QARTOD home page, at https://cdn.ioos.noaa.gov/media/2017/12/Manual-for-QC-of-Glider-Data_05_09_16.pdf.\n",
+    "\n",
+    "\n",
+    "https://ioos.noaa.gov/project/qartod/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 602
     },
+    "id": "7NabPJxcEGK0",
+    "outputId": "f0627b86-830c-4735-8c19-04871e7b7407"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "LjDpDI_nEGK0"
-      },
-      "source": [
-        "---\n",
-        "## Metadata Records\n",
-        "These are the number of metadata records currently available through the [IOOS Catalog](https://data.ioos.us). Previously the number of records was on the order of 8,600. Below are three different mechanisms to calculate this metric, however they do differ and the reason for that difference is unclear.\n",
-        "\n",
-        "https://data.ioos.us/"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Real-Time Quality Control of pH Data Observations\n",
+      "Real-Time Quality Control of Stream Flow Observations\n",
+      "Real-Time Quality Control of Passive Acoustics Data\n",
+      "Real-Time Quality Control of Phytoplankton Data\n",
+      "Real-Time Quality Control of HF Radar Observations\n",
+      "Real-Time Quality Control of Dissolved Nutrients Observations\n",
+      "Real-Time Quality Control of Wind Data\n",
+      "Real-Time Quality Control of Water Level Data\n",
+      "Real-Time Quality Control of In-Situ Surface Wave Data\n",
+      "Real-Time Quality Control of Ocean Optics Data\n",
+      "Real-Time Quality Control of In-Situ Temperature and Salinity Data\n",
+      "Real-Time Quality Control of Dissolved Oxygen Observations in Coastal Oceans\n",
+      "Real-Time Quality Control of In-Situ Current Observations\n",
+      "QARTOD Manuals: 13\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 55,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 446
-        },
-        "id": "mm8KlKrUwy7P",
-        "outputId": "1e4815fc-b84c-4204-89e4-f891934ac410"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for ckanapi (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for docopt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "Found 42599 records from https://data.ioos.us.\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
-              "0  2018-02-01              17.0                   11.0              150.0   \n",
-              "1  2022-04-22              17.0                   11.0              165.0   \n",
-              "2  2022-07-08              17.0                   11.0              165.0   \n",
-              "3  2022-10-05              17.0                   11.0              165.0   \n",
-              "4  2023-01-05              17.0                   11.0              165.0   \n",
-              "5  2024-01-26              17.0                   11.0              165.0   \n",
-              "\n",
-              "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
-              "0            52027.0               737.0               335.0              NaN   \n",
-              "1            53672.0               763.0               517.0           4444.0   \n",
-              "2            55448.0               764.0               517.0           4444.0   \n",
-              "3            59088.0               390.0               635.0           4444.0   \n",
-              "4            62042.0               768.0               635.0           4444.0   \n",
-              "5            73204.0               721.0               886.0           5190.0   \n",
-              "\n",
-              "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
-              "0            NaN           NaN                 NaN            13.0   \n",
-              "1            6.0           8.0                 9.0            13.0   \n",
-              "2            6.0           8.0                 9.0            13.0   \n",
-              "3            6.0           8.0                 9.0            13.0   \n",
-              "4            6.0           8.0                 9.0            13.0   \n",
-              "5            5.0          14.0                11.0            13.0   \n",
-              "\n",
-              "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
-              "0                 34.0            8600.0   1.0            NaN  \n",
-              "1                 34.0            7213.0   1.0            5.0  \n",
-              "2                 34.0            6217.0   1.0            5.0  \n",
-              "3                 34.0           24499.0   1.0            5.0  \n",
-              "4                 34.0           11840.0   1.0            5.0  \n",
-              "5                 34.0           42599.0   NaN            5.0  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-5929764b-f372-4319-981b-98a03454fb8e\" class=\"colab-df-container\">\n",
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-              "<style scoped>\n",
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-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>date_UTC</th>\n",
-              "      <th>Federal Partners</th>\n",
-              "      <th>Regional Associations</th>\n",
-              "      <th>HF Radar Stations</th>\n",
-              "      <th>NGDAC Glider Days</th>\n",
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-              "      <th>Regional Platforms</th>\n",
-              "      <th>ATN Deployments</th>\n",
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-              "      <th>OTT Projects</th>\n",
-              "      <th>HAB Pilot Projects</th>\n",
-              "      <th>QARTOD Manuals</th>\n",
-              "      <th>IOOS Core Variables</th>\n",
-              "      <th>Metadata Records</th>\n",
-              "      <th>IOOS</th>\n",
-              "      <th>COMT Projects</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
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-              "      <td>165.0</td>\n",
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-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>7213.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>2022-07-08</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>55448.0</td>\n",
-              "      <td>764.0</td>\n",
-              "      <td>517.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>6217.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>2022-10-05</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>59088.0</td>\n",
-              "      <td>390.0</td>\n",
-              "      <td>635.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>24499.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>2023-01-05</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>62042.0</td>\n",
-              "      <td>768.0</td>\n",
-              "      <td>635.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>11840.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>2024-01-26</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>73204.0</td>\n",
-              "      <td>721.0</td>\n",
-              "      <td>886.0</td>\n",
-              "      <td>5190.0</td>\n",
-              "      <td>5.0</td>\n",
-              "      <td>14.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>42599.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5929764b-f372-4319-981b-98a03454fb8e')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-5929764b-f372-4319-981b-98a03454fb8e button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-5929764b-f372-4319-981b-98a03454fb8e');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-0e277c93-0c92-4119-853a-f063d0aab9b1\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0e277c93-0c92-4119-853a-f063d0aab9b1')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-0e277c93-0c92-4119-853a-f063d0aab9b1 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 55
-        }
+     "data": {
+      "text/html": [
+       "\n",
+       "  <div id=\"df-66ade109-2da0-4c6a-9f84-b02508eeaaae\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date_UTC</th>\n",
+       "      <th>Federal Partners</th>\n",
+       "      <th>Regional Associations</th>\n",
+       "      <th>HF Radar Stations</th>\n",
+       "      <th>NGDAC Glider Days</th>\n",
+       "      <th>National Platforms</th>\n",
+       "      <th>Regional Platforms</th>\n",
+       "      <th>ATN Deployments</th>\n",
+       "      <th>MBON Projects</th>\n",
+       "      <th>OTT Projects</th>\n",
+       "      <th>HAB Pilot Projects</th>\n",
+       "      <th>QARTOD Manuals</th>\n",
+       "      <th>IOOS Core Variables</th>\n",
+       "      <th>Metadata Records</th>\n",
+       "      <th>IOOS</th>\n",
+       "      <th>COMT Projects</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2018-02-01</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>52027.0</td>\n",
+       "      <td>737.0</td>\n",
+       "      <td>335.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>34.0</td>\n",
+       "      <td>8600.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2022-04-22</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>53672.0</td>\n",
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+       "      <td>4444.0</td>\n",
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+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>7213.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2022-07-08</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>55448.0</td>\n",
+       "      <td>764.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>6217.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2022-10-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>59088.0</td>\n",
+       "      <td>390.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>24499.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-01-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>62042.0</td>\n",
+       "      <td>768.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>11840.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2024-01-26</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>73204.0</td>\n",
+       "      <td>721.0</td>\n",
+       "      <td>886.0</td>\n",
+       "      <td>5190.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-66ade109-2da0-4c6a-9f84-b02508eeaaae')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-66ade109-2da0-4c6a-9f84-b02508eeaaae button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-66ade109-2da0-4c6a-9f84-b02508eeaaae');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-b1c1f865-5a0b-4a7a-8ab5-76647e181ad1\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b1c1f865-5a0b-4a7a-8ab5-76647e181ad1')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-b1c1f865-5a0b-4a7a-8ab5-76647e181ad1 button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "source": [
-        "try:\n",
-        "  import ckanapi\n",
-        "except:\n",
-        "  %pip install -q ckanapi\n",
-        "\n",
-        "from ckanapi import RemoteCKAN\n",
-        "ua = 'ckanapiioos/1.0 (+https://ioos.us/)'\n",
-        "\n",
-        "url = 'https://data.ioos.us'\n",
-        "\n",
-        "#ioos_catalog = RemoteCKAN('https://data.ioos.us', user_agent=ua, get_only=True)\n",
-        "ioos_catalog = RemoteCKAN(url, user_agent=ua)\n",
-        "datasets = ioos_catalog.action.package_search()\n",
-        "metadata_records = datasets['count']\n",
-        "print(\"Found {} records from {}.\".format(metadata_records,url))\n",
-        "\n",
-        "ioos_btn_df.loc[ioos_btn_df['date_UTC']==today, ['Metadata Records']] = metadata_records\n",
-        "\n",
-        "ioos_btn_df"
+      "text/plain": [
+       "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
+       "0  2018-02-01              17.0                   11.0              150.0   \n",
+       "1  2022-04-22              17.0                   11.0              165.0   \n",
+       "2  2022-07-08              17.0                   11.0              165.0   \n",
+       "3  2022-10-05              17.0                   11.0              165.0   \n",
+       "4  2023-01-05              17.0                   11.0              165.0   \n",
+       "5  2024-01-26              17.0                   11.0              165.0   \n",
+       "\n",
+       "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
+       "0            52027.0               737.0               335.0              NaN   \n",
+       "1            53672.0               763.0               517.0           4444.0   \n",
+       "2            55448.0               764.0               517.0           4444.0   \n",
+       "3            59088.0               390.0               635.0           4444.0   \n",
+       "4            62042.0               768.0               635.0           4444.0   \n",
+       "5            73204.0               721.0               886.0           5190.0   \n",
+       "\n",
+       "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
+       "0            NaN           NaN                 NaN            13.0   \n",
+       "1            6.0           8.0                 9.0            13.0   \n",
+       "2            6.0           8.0                 9.0            13.0   \n",
+       "3            6.0           8.0                 9.0            13.0   \n",
+       "4            6.0           8.0                 9.0            13.0   \n",
+       "5            5.0          14.0                11.0            13.0   \n",
+       "\n",
+       "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
+       "0                 34.0            8600.0   1.0            NaN  \n",
+       "1                 34.0            7213.0   1.0            5.0  \n",
+       "2                 34.0            6217.0   1.0            5.0  \n",
+       "3                 34.0           24499.0   1.0            5.0  \n",
+       "4                 34.0           11840.0   1.0            5.0  \n",
+       "5                  NaN               NaN   NaN            5.0  "
       ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "\n",
+    "headers = {\"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36\"}\n",
+    "\n",
+    "url = \"https://ioos.noaa.gov/project/qartod/\"\n",
+    "\n",
+    "soup = BeautifulSoup(requests.get(url, headers=headers).text, \"html.parser\")\n",
+    "\n",
+    "qartod=0\n",
+    "\n",
+    "for tag in soup.find_all(\"li\"):\n",
+    "\n",
+    "  if \"Real-Time Quality Control of\" in tag.text:\n",
+    "\n",
+    "    print(tag.text)\n",
+    "\n",
+    "    qartod+=1\n",
+    "\n",
+    "print(\"QARTOD Manuals:\",qartod)\n",
+    "\n",
+    "ioos_btn_df.loc[ioos_btn_df[\"date_UTC\"]==today, [\"QARTOD Manuals\"]] = qartod\n",
+    "\n",
+    "ioos_btn_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vZ2zMhziEGK0"
+   },
+   "source": [
+    "## IOOS Core Variables\n",
+    "\n",
+    "The IOOS Core Variables are presented on [this website](https://www.iooc.us/task-teams/core-ioos-variables/). For now, this is a hard coded value, but it should be easy to parse that page and count up the variables.\n",
+    "\n",
+    "Also available on mmi at https://mmisw.org/ont/ioos/core_variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 376
     },
+    "id": "iqpVv31c4X6O",
+    "outputId": "132f7c85-5cb8-4300-edf1-2f0d06afdc99"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "kN5S0DfoEGK1"
-      },
-      "source": [
-        "---\n",
-        "## IOOS\n",
-        "This represents the one IOOS Office."
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IOOS Core Variables: 34\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 56,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 376
-        },
-        "id": "cMZESRfTEGK1",
-        "outputId": "d3564143-ef80-4601-ca34-3e38d9abffb9"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "IOOS: 1\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
-              "0  2018-02-01              17.0                   11.0              150.0   \n",
-              "1  2022-04-22              17.0                   11.0              165.0   \n",
-              "2  2022-07-08              17.0                   11.0              165.0   \n",
-              "3  2022-10-05              17.0                   11.0              165.0   \n",
-              "4  2023-01-05              17.0                   11.0              165.0   \n",
-              "5  2024-01-26              17.0                   11.0              165.0   \n",
-              "\n",
-              "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
-              "0            52027.0               737.0               335.0              NaN   \n",
-              "1            53672.0               763.0               517.0           4444.0   \n",
-              "2            55448.0               764.0               517.0           4444.0   \n",
-              "3            59088.0               390.0               635.0           4444.0   \n",
-              "4            62042.0               768.0               635.0           4444.0   \n",
-              "5            73204.0               721.0               886.0           5190.0   \n",
-              "\n",
-              "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
-              "0            NaN           NaN                 NaN            13.0   \n",
-              "1            6.0           8.0                 9.0            13.0   \n",
-              "2            6.0           8.0                 9.0            13.0   \n",
-              "3            6.0           8.0                 9.0            13.0   \n",
-              "4            6.0           8.0                 9.0            13.0   \n",
-              "5            5.0          14.0                11.0            13.0   \n",
-              "\n",
-              "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
-              "0                 34.0            8600.0   1.0            NaN  \n",
-              "1                 34.0            7213.0   1.0            5.0  \n",
-              "2                 34.0            6217.0   1.0            5.0  \n",
-              "3                 34.0           24499.0   1.0            5.0  \n",
-              "4                 34.0           11840.0   1.0            5.0  \n",
-              "5                 34.0           42599.0   1.0            5.0  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-cae7c21a-ffa6-45ac-9377-a534c61c2729\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>date_UTC</th>\n",
-              "      <th>Federal Partners</th>\n",
-              "      <th>Regional Associations</th>\n",
-              "      <th>HF Radar Stations</th>\n",
-              "      <th>NGDAC Glider Days</th>\n",
-              "      <th>National Platforms</th>\n",
-              "      <th>Regional Platforms</th>\n",
-              "      <th>ATN Deployments</th>\n",
-              "      <th>MBON Projects</th>\n",
-              "      <th>OTT Projects</th>\n",
-              "      <th>HAB Pilot Projects</th>\n",
-              "      <th>QARTOD Manuals</th>\n",
-              "      <th>IOOS Core Variables</th>\n",
-              "      <th>Metadata Records</th>\n",
-              "      <th>IOOS</th>\n",
-              "      <th>COMT Projects</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>2018-02-01</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>150.0</td>\n",
-              "      <td>52027.0</td>\n",
-              "      <td>737.0</td>\n",
-              "      <td>335.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>8600.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>2022-04-22</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>53672.0</td>\n",
-              "      <td>763.0</td>\n",
-              "      <td>517.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>7213.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>2022-07-08</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>55448.0</td>\n",
-              "      <td>764.0</td>\n",
-              "      <td>517.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>6217.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>2022-10-05</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>59088.0</td>\n",
-              "      <td>390.0</td>\n",
-              "      <td>635.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>24499.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>2023-01-05</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>62042.0</td>\n",
-              "      <td>768.0</td>\n",
-              "      <td>635.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>11840.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>2024-01-26</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>73204.0</td>\n",
-              "      <td>721.0</td>\n",
-              "      <td>886.0</td>\n",
-              "      <td>5190.0</td>\n",
-              "      <td>5.0</td>\n",
-              "      <td>14.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>42599.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cae7c21a-ffa6-45ac-9377-a534c61c2729')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-cae7c21a-ffa6-45ac-9377-a534c61c2729 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-cae7c21a-ffa6-45ac-9377-a534c61c2729');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-c6a9b4df-9480-4c36-995d-65f316a28ead\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c6a9b4df-9480-4c36-995d-65f316a28ead')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-c6a9b4df-9480-4c36-995d-65f316a28ead button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 56
-        }
+     "data": {
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date_UTC</th>\n",
+       "      <th>Federal Partners</th>\n",
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+       "      <th>HF Radar Stations</th>\n",
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+       "      <th>Metadata Records</th>\n",
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+       "      <th>COMT Projects</th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2018-02-01</td>\n",
+       "      <td>17.0</td>\n",
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+       "      <td>150.0</td>\n",
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+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2022-07-08</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
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+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2022-10-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>59088.0</td>\n",
+       "      <td>390.0</td>\n",
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+       "      <td>4444.0</td>\n",
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+       "      <td>34.0</td>\n",
+       "      <td>24499.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-01-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>62042.0</td>\n",
+       "      <td>768.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
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+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2024-01-26</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
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+       "      <td>11.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7573b3f5-4b9e-4c42-8163-07c8d92b1056')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-7573b3f5-4b9e-4c42-8163-07c8d92b1056 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-7573b3f5-4b9e-4c42-8163-07c8d92b1056');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-10341b75-a6eb-4f59-9f48-99016f002ca8\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-10341b75-a6eb-4f59-9f48-99016f002ca8')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
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+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-10341b75-a6eb-4f59-9f48-99016f002ca8 button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "source": [
-        "ioos = 1\n",
-        "\n",
-        "print(\"IOOS:\",ioos)\n",
-        "\n",
-        "ioos_btn_df.loc[ioos_btn_df['date_UTC']==today, ['IOOS']] = ioos\n",
-        "\n",
-        "ioos_btn_df"
+      "text/plain": [
+       "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
+       "0  2018-02-01              17.0                   11.0              150.0   \n",
+       "1  2022-04-22              17.0                   11.0              165.0   \n",
+       "2  2022-07-08              17.0                   11.0              165.0   \n",
+       "3  2022-10-05              17.0                   11.0              165.0   \n",
+       "4  2023-01-05              17.0                   11.0              165.0   \n",
+       "5  2024-01-26              17.0                   11.0              165.0   \n",
+       "\n",
+       "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
+       "0            52027.0               737.0               335.0              NaN   \n",
+       "1            53672.0               763.0               517.0           4444.0   \n",
+       "2            55448.0               764.0               517.0           4444.0   \n",
+       "3            59088.0               390.0               635.0           4444.0   \n",
+       "4            62042.0               768.0               635.0           4444.0   \n",
+       "5            73204.0               721.0               886.0           5190.0   \n",
+       "\n",
+       "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
+       "0            NaN           NaN                 NaN            13.0   \n",
+       "1            6.0           8.0                 9.0            13.0   \n",
+       "2            6.0           8.0                 9.0            13.0   \n",
+       "3            6.0           8.0                 9.0            13.0   \n",
+       "4            6.0           8.0                 9.0            13.0   \n",
+       "5            5.0          14.0                11.0            13.0   \n",
+       "\n",
+       "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
+       "0                 34.0            8600.0   1.0            NaN  \n",
+       "1                 34.0            7213.0   1.0            5.0  \n",
+       "2                 34.0            6217.0   1.0            5.0  \n",
+       "3                 34.0           24499.0   1.0            5.0  \n",
+       "4                 34.0           11840.0   1.0            5.0  \n",
+       "5                 34.0               NaN   NaN            5.0  "
       ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "url = \"https://mmisw.org/ont/api/v0/ont?format=rj&iri=http://mmisw.org/ont/ioos/core_variable\"\n",
+    "\n",
+    "df = pd.read_json(url,orient=\"index\")\n",
+    "\n",
+    "# drop the rows where 'name' doesn't exist.\n",
+    "df.dropna(axis=\"index\", how=\"any\",subset=\"http://mmisw.org/ont/ioos/core_variable/name\", inplace=True)\n",
+    "\n",
+    "core_vars = len(df.index.tolist())\n",
+    "\n",
+    "print(\"IOOS Core Variables:\",core_vars)\n",
+    "\n",
+    "ioos_btn_df.loc[ioos_btn_df[\"date_UTC\"]==today, [\"IOOS Core Variables\"]] = core_vars\n",
+    "\n",
+    "ioos_btn_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LjDpDI_nEGK0"
+   },
+   "source": [
+    "---\n",
+    "## Metadata Records\n",
+    "These are the number of metadata records currently available through the [IOOS Catalog](https://data.ioos.us). Previously the number of records was on the order of 8,600. Below are three different mechanisms to calculate this metric, however they do differ and the reason for that difference is unclear.\n",
+    "\n",
+    "https://data.ioos.us/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 446
     },
+    "id": "mm8KlKrUwy7P",
+    "outputId": "1e4815fc-b84c-4204-89e4-f891934ac410"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "PpEovbEJQLBF"
-      },
-      "source": [
-        "---\n",
-        "## Final IOOS by the Numbers table"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+      "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+      "  Building wheel for ckanapi (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+      "  Building wheel for docopt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+      "Found 42599 records from https://data.ioos.us.\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 57,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 390
-        },
-        "id": "ZzLlve4OEGK1",
-        "outputId": "375be3d6-c1ad-4975-dc62-d691abcd4908"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "            Federal Partners  Regional Associations  HF Radar Stations  \\\n",
-              "date_UTC                                                                 \n",
-              "2018-02-01              17.0                   11.0              150.0   \n",
-              "2022-04-22              17.0                   11.0              165.0   \n",
-              "2022-07-08              17.0                   11.0              165.0   \n",
-              "2022-10-05              17.0                   11.0              165.0   \n",
-              "2023-01-05              17.0                   11.0              165.0   \n",
-              "2024-01-26              17.0                   11.0              165.0   \n",
-              "\n",
-              "            NGDAC Glider Days  National Platforms  Regional Platforms  \\\n",
-              "date_UTC                                                                \n",
-              "2018-02-01            52027.0               737.0               335.0   \n",
-              "2022-04-22            53672.0               763.0               517.0   \n",
-              "2022-07-08            55448.0               764.0               517.0   \n",
-              "2022-10-05            59088.0               390.0               635.0   \n",
-              "2023-01-05            62042.0               768.0               635.0   \n",
-              "2024-01-26            73204.0               721.0               886.0   \n",
-              "\n",
-              "            ATN Deployments  MBON Projects  OTT Projects  HAB Pilot Projects  \\\n",
-              "date_UTC                                                                       \n",
-              "2018-02-01              NaN            NaN           NaN                 NaN   \n",
-              "2022-04-22           4444.0            6.0           8.0                 9.0   \n",
-              "2022-07-08           4444.0            6.0           8.0                 9.0   \n",
-              "2022-10-05           4444.0            6.0           8.0                 9.0   \n",
-              "2023-01-05           4444.0            6.0           8.0                 9.0   \n",
-              "2024-01-26           5190.0            5.0          14.0                11.0   \n",
-              "\n",
-              "            QARTOD Manuals  IOOS Core Variables  Metadata Records  IOOS  \\\n",
-              "date_UTC                                                                  \n",
-              "2018-02-01            13.0                 34.0            8600.0   1.0   \n",
-              "2022-04-22            13.0                 34.0            7213.0   1.0   \n",
-              "2022-07-08            13.0                 34.0            6217.0   1.0   \n",
-              "2022-10-05            13.0                 34.0           24499.0   1.0   \n",
-              "2023-01-05            13.0                 34.0           11840.0   1.0   \n",
-              "2024-01-26            13.0                 34.0           42599.0   1.0   \n",
-              "\n",
-              "            COMT Projects  \n",
-              "date_UTC                   \n",
-              "2018-02-01            NaN  \n",
-              "2022-04-22            5.0  \n",
-              "2022-07-08            5.0  \n",
-              "2022-10-05            5.0  \n",
-              "2023-01-05            5.0  \n",
-              "2024-01-26            5.0  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-3a1badb0-21c3-470e-94fc-d6fb3634f1d8\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
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-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
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-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Federal Partners</th>\n",
-              "      <th>Regional Associations</th>\n",
-              "      <th>HF Radar Stations</th>\n",
-              "      <th>NGDAC Glider Days</th>\n",
-              "      <th>National Platforms</th>\n",
-              "      <th>Regional Platforms</th>\n",
-              "      <th>ATN Deployments</th>\n",
-              "      <th>MBON Projects</th>\n",
-              "      <th>OTT Projects</th>\n",
-              "      <th>HAB Pilot Projects</th>\n",
-              "      <th>QARTOD Manuals</th>\n",
-              "      <th>IOOS Core Variables</th>\n",
-              "      <th>Metadata Records</th>\n",
-              "      <th>IOOS</th>\n",
-              "      <th>COMT Projects</th>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>date_UTC</th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>2018-02-01</th>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>150.0</td>\n",
-              "      <td>52027.0</td>\n",
-              "      <td>737.0</td>\n",
-              "      <td>335.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>8600.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2022-04-22</th>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>53672.0</td>\n",
-              "      <td>763.0</td>\n",
-              "      <td>517.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>7213.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2022-07-08</th>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>55448.0</td>\n",
-              "      <td>764.0</td>\n",
-              "      <td>517.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>6217.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2022-10-05</th>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>59088.0</td>\n",
-              "      <td>390.0</td>\n",
-              "      <td>635.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>24499.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2023-01-05</th>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>62042.0</td>\n",
-              "      <td>768.0</td>\n",
-              "      <td>635.0</td>\n",
-              "      <td>4444.0</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>8.0</td>\n",
-              "      <td>9.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>11840.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2024-01-26</th>\n",
-              "      <td>17.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>73204.0</td>\n",
-              "      <td>721.0</td>\n",
-              "      <td>886.0</td>\n",
-              "      <td>5190.0</td>\n",
-              "      <td>5.0</td>\n",
-              "      <td>14.0</td>\n",
-              "      <td>11.0</td>\n",
-              "      <td>13.0</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>42599.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>5.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3a1badb0-21c3-470e-94fc-d6fb3634f1d8')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-3a1badb0-21c3-470e-94fc-d6fb3634f1d8 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-3a1badb0-21c3-470e-94fc-d6fb3634f1d8');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-8cf05b24-2c1f-42bb-a76b-9e02244b4342\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8cf05b24-2c1f-42bb-a76b-9e02244b4342')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-8cf05b24-2c1f-42bb-a76b-9e02244b4342 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 57
-        }
+     "data": {
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date_UTC</th>\n",
+       "      <th>Federal Partners</th>\n",
+       "      <th>Regional Associations</th>\n",
+       "      <th>HF Radar Stations</th>\n",
+       "      <th>NGDAC Glider Days</th>\n",
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+       "      <th>Metadata Records</th>\n",
+       "      <th>IOOS</th>\n",
+       "      <th>COMT Projects</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2018-02-01</td>\n",
+       "      <td>17.0</td>\n",
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+       "      <td>150.0</td>\n",
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+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2022-07-08</td>\n",
+       "      <td>17.0</td>\n",
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+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2022-10-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>59088.0</td>\n",
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+       "      <td>34.0</td>\n",
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+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-01-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>62042.0</td>\n",
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+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2024-01-26</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>73204.0</td>\n",
+       "      <td>721.0</td>\n",
+       "      <td>886.0</td>\n",
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+       "      <td>11.0</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5929764b-f372-4319-981b-98a03454fb8e')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-5929764b-f372-4319-981b-98a03454fb8e button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-5929764b-f372-4319-981b-98a03454fb8e');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-0e277c93-0c92-4119-853a-f063d0aab9b1\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0e277c93-0c92-4119-853a-f063d0aab9b1')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-0e277c93-0c92-4119-853a-f063d0aab9b1 button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "source": [
-        "ioos_btn_df['date_UTC']=pd.to_datetime(ioos_btn_df['date_UTC'])\n",
-        "ioos_btn_df = ioos_btn_df.set_index('date_UTC')\n",
-        "\n",
-        "ioos_btn_df"
+      "text/plain": [
+       "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
+       "0  2018-02-01              17.0                   11.0              150.0   \n",
+       "1  2022-04-22              17.0                   11.0              165.0   \n",
+       "2  2022-07-08              17.0                   11.0              165.0   \n",
+       "3  2022-10-05              17.0                   11.0              165.0   \n",
+       "4  2023-01-05              17.0                   11.0              165.0   \n",
+       "5  2024-01-26              17.0                   11.0              165.0   \n",
+       "\n",
+       "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
+       "0            52027.0               737.0               335.0              NaN   \n",
+       "1            53672.0               763.0               517.0           4444.0   \n",
+       "2            55448.0               764.0               517.0           4444.0   \n",
+       "3            59088.0               390.0               635.0           4444.0   \n",
+       "4            62042.0               768.0               635.0           4444.0   \n",
+       "5            73204.0               721.0               886.0           5190.0   \n",
+       "\n",
+       "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
+       "0            NaN           NaN                 NaN            13.0   \n",
+       "1            6.0           8.0                 9.0            13.0   \n",
+       "2            6.0           8.0                 9.0            13.0   \n",
+       "3            6.0           8.0                 9.0            13.0   \n",
+       "4            6.0           8.0                 9.0            13.0   \n",
+       "5            5.0          14.0                11.0            13.0   \n",
+       "\n",
+       "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
+       "0                 34.0            8600.0   1.0            NaN  \n",
+       "1                 34.0            7213.0   1.0            5.0  \n",
+       "2                 34.0            6217.0   1.0            5.0  \n",
+       "3                 34.0           24499.0   1.0            5.0  \n",
+       "4                 34.0           11840.0   1.0            5.0  \n",
+       "5                 34.0           42599.0   NaN            5.0  "
       ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "try:\n",
+    "  pass\n",
+    "except:\n",
+    "  %pip install -q ckanapi\n",
+    "\n",
+    "from ckanapi import RemoteCKAN\n",
+    "\n",
+    "ua = \"ckanapiioos/1.0 (+https://ioos.us/)\"\n",
+    "\n",
+    "url = \"https://data.ioos.us\"\n",
+    "\n",
+    "#ioos_catalog = RemoteCKAN('https://data.ioos.us', user_agent=ua, get_only=True)\n",
+    "ioos_catalog = RemoteCKAN(url, user_agent=ua)\n",
+    "datasets = ioos_catalog.action.package_search()\n",
+    "metadata_records = datasets[\"count\"]\n",
+    "print(f\"Found {metadata_records} records from {url}.\")\n",
+    "\n",
+    "ioos_btn_df.loc[ioos_btn_df[\"date_UTC\"]==today, [\"Metadata Records\"]] = metadata_records\n",
+    "\n",
+    "ioos_btn_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kN5S0DfoEGK1"
+   },
+   "source": [
+    "---\n",
+    "## IOOS\n",
+    "This represents the one IOOS Office."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 376
     },
+    "id": "cMZESRfTEGK1",
+    "outputId": "d3564143-ef80-4601-ca34-3e38d9abffb9"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "nVt9c6L9RDKU"
-      },
-      "source": [
-        "# Values to be reviewed by Office"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IOOS: 1\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 58,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "2ECsNOHlRHT6",
-        "outputId": "41968ef8-0426-4779-998e-be279fbbc034"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "Federal Partners            17.0\n",
-              "Regional Associations       11.0\n",
-              "HF Radar Stations          165.0\n",
-              "NGDAC Glider Days        73204.0\n",
-              "National Platforms         721.0\n",
-              "Regional Platforms         886.0\n",
-              "ATN Deployments           5190.0\n",
-              "MBON Projects                5.0\n",
-              "OTT Projects                14.0\n",
-              "HAB Pilot Projects          11.0\n",
-              "QARTOD Manuals              13.0\n",
-              "IOOS Core Variables         34.0\n",
-              "Metadata Records         42599.0\n",
-              "IOOS                         1.0\n",
-              "COMT Projects                5.0\n",
-              "Name: 2024-01-26 00:00:00, dtype: float64"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 58
-        }
+     "data": {
+      "text/html": [
+       "\n",
+       "  <div id=\"df-cae7c21a-ffa6-45ac-9377-a534c61c2729\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date_UTC</th>\n",
+       "      <th>Federal Partners</th>\n",
+       "      <th>Regional Associations</th>\n",
+       "      <th>HF Radar Stations</th>\n",
+       "      <th>NGDAC Glider Days</th>\n",
+       "      <th>National Platforms</th>\n",
+       "      <th>Regional Platforms</th>\n",
+       "      <th>ATN Deployments</th>\n",
+       "      <th>MBON Projects</th>\n",
+       "      <th>OTT Projects</th>\n",
+       "      <th>HAB Pilot Projects</th>\n",
+       "      <th>QARTOD Manuals</th>\n",
+       "      <th>IOOS Core Variables</th>\n",
+       "      <th>Metadata Records</th>\n",
+       "      <th>IOOS</th>\n",
+       "      <th>COMT Projects</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2018-02-01</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>52027.0</td>\n",
+       "      <td>737.0</td>\n",
+       "      <td>335.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>8600.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2022-04-22</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>53672.0</td>\n",
+       "      <td>763.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>7213.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2022-07-08</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>55448.0</td>\n",
+       "      <td>764.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>6217.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2022-10-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>59088.0</td>\n",
+       "      <td>390.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>24499.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-01-05</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>62042.0</td>\n",
+       "      <td>768.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>11840.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2024-01-26</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>73204.0</td>\n",
+       "      <td>721.0</td>\n",
+       "      <td>886.0</td>\n",
+       "      <td>5190.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>42599.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cae7c21a-ffa6-45ac-9377-a534c61c2729')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-cae7c21a-ffa6-45ac-9377-a534c61c2729 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-cae7c21a-ffa6-45ac-9377-a534c61c2729');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-c6a9b4df-9480-4c36-995d-65f316a28ead\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c6a9b4df-9480-4c36-995d-65f316a28ead')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-c6a9b4df-9480-4c36-995d-65f316a28ead button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "source": [
-        "ioos_btn_df.loc[today]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ytCvqJAaftqL"
-      },
-      "source": [
-        "---\n",
-        "## Save the calculated metrics\n",
-        "\n",
-        "Overwrite existing csv with previous and new data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 59,
-      "metadata": {
-        "id": "Kkip7muU2R2u"
-      },
-      "outputs": [],
-      "source": [
-        "#ioos_btn_df.to_csv('ioos_btn_metrics.csv', index=False)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "JzZpA6nrhBCk"
-      },
-      "source": [
-        "---\n",
-        "## Analysis\n",
-        "Now we have the opportunity to do some analysis on the metrics we've captured.\n",
-        "\n",
-        "Below is an attempt to draw some comparisons between the metrics in the previous iteration and subsequent runs."
+      "text/plain": [
+       "     date_UTC  Federal Partners  Regional Associations  HF Radar Stations  \\\n",
+       "0  2018-02-01              17.0                   11.0              150.0   \n",
+       "1  2022-04-22              17.0                   11.0              165.0   \n",
+       "2  2022-07-08              17.0                   11.0              165.0   \n",
+       "3  2022-10-05              17.0                   11.0              165.0   \n",
+       "4  2023-01-05              17.0                   11.0              165.0   \n",
+       "5  2024-01-26              17.0                   11.0              165.0   \n",
+       "\n",
+       "   NGDAC Glider Days  National Platforms  Regional Platforms  ATN Deployments  \\\n",
+       "0            52027.0               737.0               335.0              NaN   \n",
+       "1            53672.0               763.0               517.0           4444.0   \n",
+       "2            55448.0               764.0               517.0           4444.0   \n",
+       "3            59088.0               390.0               635.0           4444.0   \n",
+       "4            62042.0               768.0               635.0           4444.0   \n",
+       "5            73204.0               721.0               886.0           5190.0   \n",
+       "\n",
+       "   MBON Projects  OTT Projects  HAB Pilot Projects  QARTOD Manuals  \\\n",
+       "0            NaN           NaN                 NaN            13.0   \n",
+       "1            6.0           8.0                 9.0            13.0   \n",
+       "2            6.0           8.0                 9.0            13.0   \n",
+       "3            6.0           8.0                 9.0            13.0   \n",
+       "4            6.0           8.0                 9.0            13.0   \n",
+       "5            5.0          14.0                11.0            13.0   \n",
+       "\n",
+       "   IOOS Core Variables  Metadata Records  IOOS  COMT Projects  \n",
+       "0                 34.0            8600.0   1.0            NaN  \n",
+       "1                 34.0            7213.0   1.0            5.0  \n",
+       "2                 34.0            6217.0   1.0            5.0  \n",
+       "3                 34.0           24499.0   1.0            5.0  \n",
+       "4                 34.0           11840.0   1.0            5.0  \n",
+       "5                 34.0           42599.0   1.0            5.0  "
       ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ioos = 1\n",
+    "\n",
+    "print(\"IOOS:\",ioos)\n",
+    "\n",
+    "ioos_btn_df.loc[ioos_btn_df[\"date_UTC\"]==today, [\"IOOS\"]] = ioos\n",
+    "\n",
+    "ioos_btn_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "PpEovbEJQLBF"
+   },
+   "source": [
+    "---\n",
+    "## Final IOOS by the Numbers table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 390
     },
+    "id": "ZzLlve4OEGK1",
+    "outputId": "375be3d6-c1ad-4975-dc62-d691abcd4908"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 60,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 487
-        },
-        "id": "FmUaiMUl2pJ2",
-        "outputId": "f2a3dde2-2142-4eda-8ebe-5ae92be2faec"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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-          },
-          "metadata": {}
-        }
+     "data": {
+      "text/html": [
+       "\n",
+       "  <div id=\"df-3a1badb0-21c3-470e-94fc-d6fb3634f1d8\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
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+       "        vertical-align: top;\n",
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+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Federal Partners</th>\n",
+       "      <th>Regional Associations</th>\n",
+       "      <th>HF Radar Stations</th>\n",
+       "      <th>NGDAC Glider Days</th>\n",
+       "      <th>National Platforms</th>\n",
+       "      <th>Regional Platforms</th>\n",
+       "      <th>ATN Deployments</th>\n",
+       "      <th>MBON Projects</th>\n",
+       "      <th>OTT Projects</th>\n",
+       "      <th>HAB Pilot Projects</th>\n",
+       "      <th>QARTOD Manuals</th>\n",
+       "      <th>IOOS Core Variables</th>\n",
+       "      <th>Metadata Records</th>\n",
+       "      <th>IOOS</th>\n",
+       "      <th>COMT Projects</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>date_UTC</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-02-01</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>52027.0</td>\n",
+       "      <td>737.0</td>\n",
+       "      <td>335.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>8600.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-04-22</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>53672.0</td>\n",
+       "      <td>763.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>7213.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-07-08</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>55448.0</td>\n",
+       "      <td>764.0</td>\n",
+       "      <td>517.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>6217.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-05</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>59088.0</td>\n",
+       "      <td>390.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>24499.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-05</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>62042.0</td>\n",
+       "      <td>768.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>4444.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>11840.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-01-26</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>73204.0</td>\n",
+       "      <td>721.0</td>\n",
+       "      <td>886.0</td>\n",
+       "      <td>5190.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>42599.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3a1badb0-21c3-470e-94fc-d6fb3634f1d8')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-3a1badb0-21c3-470e-94fc-d6fb3634f1d8 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-3a1badb0-21c3-470e-94fc-d6fb3634f1d8');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-8cf05b24-2c1f-42bb-a76b-9e02244b4342\">\n",
+       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8cf05b24-2c1f-42bb-a76b-9e02244b4342')\"\n",
+       "            title=\"Suggest charts\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+       "     width=\"24px\">\n",
+       "    <g>\n",
+       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+       "    </g>\n",
+       "</svg>\n",
+       "  </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-8cf05b24-2c1f-42bb-a76b-9e02244b4342 button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
       ],
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "import numpy as np\n",
-        "\n",
-        "ioos_btn_df = ioos_btn_df.reset_index()\n",
-        "\n",
-        "labels = ioos_btn_df.drop(columns=['date_UTC']).columns.tolist()\n",
-        "\n",
-        "x = np.arange(len(labels))  # the label locations\n",
-        "width = 0.35  # the width of the bars\n",
-        "\n",
-        "fig, ax = plt.subplots()\n",
-        "\n",
-        "for index, row in ioos_btn_df.iterrows():\n",
-        "  means = row.drop(index=['date_UTC']).values\n",
-        "  rects = ax.bar(x - width/2, means, width, label=row['date_UTC'].strftime('%Y-%m-%d'))\n",
-        "  width = width*-1\n",
-        "\n",
-        "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
-        "ax.set_ylabel('Count')\n",
-        "ax.set_title('Metric by year and type')\n",
-        "ax.set_xticks(x, labels)\n",
-        "ax.legend()\n",
-        "\n",
-        "ax.xaxis.set_ticklabels(labels, rotation=90)\n",
-        "\n",
-        "fig.tight_layout()\n",
-        "\n",
-        "plt.show()"
+      "text/plain": [
+       "            Federal Partners  Regional Associations  HF Radar Stations  \\\n",
+       "date_UTC                                                                 \n",
+       "2018-02-01              17.0                   11.0              150.0   \n",
+       "2022-04-22              17.0                   11.0              165.0   \n",
+       "2022-07-08              17.0                   11.0              165.0   \n",
+       "2022-10-05              17.0                   11.0              165.0   \n",
+       "2023-01-05              17.0                   11.0              165.0   \n",
+       "2024-01-26              17.0                   11.0              165.0   \n",
+       "\n",
+       "            NGDAC Glider Days  National Platforms  Regional Platforms  \\\n",
+       "date_UTC                                                                \n",
+       "2018-02-01            52027.0               737.0               335.0   \n",
+       "2022-04-22            53672.0               763.0               517.0   \n",
+       "2022-07-08            55448.0               764.0               517.0   \n",
+       "2022-10-05            59088.0               390.0               635.0   \n",
+       "2023-01-05            62042.0               768.0               635.0   \n",
+       "2024-01-26            73204.0               721.0               886.0   \n",
+       "\n",
+       "            ATN Deployments  MBON Projects  OTT Projects  HAB Pilot Projects  \\\n",
+       "date_UTC                                                                       \n",
+       "2018-02-01              NaN            NaN           NaN                 NaN   \n",
+       "2022-04-22           4444.0            6.0           8.0                 9.0   \n",
+       "2022-07-08           4444.0            6.0           8.0                 9.0   \n",
+       "2022-10-05           4444.0            6.0           8.0                 9.0   \n",
+       "2023-01-05           4444.0            6.0           8.0                 9.0   \n",
+       "2024-01-26           5190.0            5.0          14.0                11.0   \n",
+       "\n",
+       "            QARTOD Manuals  IOOS Core Variables  Metadata Records  IOOS  \\\n",
+       "date_UTC                                                                  \n",
+       "2018-02-01            13.0                 34.0            8600.0   1.0   \n",
+       "2022-04-22            13.0                 34.0            7213.0   1.0   \n",
+       "2022-07-08            13.0                 34.0            6217.0   1.0   \n",
+       "2022-10-05            13.0                 34.0           24499.0   1.0   \n",
+       "2023-01-05            13.0                 34.0           11840.0   1.0   \n",
+       "2024-01-26            13.0                 34.0           42599.0   1.0   \n",
+       "\n",
+       "            COMT Projects  \n",
+       "date_UTC                   \n",
+       "2018-02-01            NaN  \n",
+       "2022-04-22            5.0  \n",
+       "2022-07-08            5.0  \n",
+       "2022-10-05            5.0  \n",
+       "2023-01-05            5.0  \n",
+       "2024-01-26            5.0  "
       ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ioos_btn_df[\"date_UTC\"]=pd.to_datetime(ioos_btn_df[\"date_UTC\"])\n",
+    "ioos_btn_df = ioos_btn_df.set_index(\"date_UTC\")\n",
+    "\n",
+    "ioos_btn_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "nVt9c6L9RDKU"
+   },
+   "source": [
+    "# Values to be reviewed by Office"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "2ECsNOHlRHT6",
+    "outputId": "41968ef8-0426-4779-998e-be279fbbc034"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "z7GtocvbmyBQ"
-      },
-      "source": [
-        "Plot percent increase."
+     "data": {
+      "text/plain": [
+       "Federal Partners            17.0\n",
+       "Regional Associations       11.0\n",
+       "HF Radar Stations          165.0\n",
+       "NGDAC Glider Days        73204.0\n",
+       "National Platforms         721.0\n",
+       "Regional Platforms         886.0\n",
+       "ATN Deployments           5190.0\n",
+       "MBON Projects                5.0\n",
+       "OTT Projects                14.0\n",
+       "HAB Pilot Projects          11.0\n",
+       "QARTOD Manuals              13.0\n",
+       "IOOS Core Variables         34.0\n",
+       "Metadata Records         42599.0\n",
+       "IOOS                         1.0\n",
+       "COMT Projects                5.0\n",
+       "Name: 2024-01-26 00:00:00, dtype: float64"
       ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ioos_btn_df.loc[today]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ytCvqJAaftqL"
+   },
+   "source": [
+    "---\n",
+    "## Save the calculated metrics\n",
+    "\n",
+    "Overwrite existing csv with previous and new data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "id": "Kkip7muU2R2u"
+   },
+   "outputs": [],
+   "source": [
+    "#ioos_btn_df.to_csv('ioos_btn_metrics.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JzZpA6nrhBCk"
+   },
+   "source": [
+    "---\n",
+    "## Analysis\n",
+    "Now we have the opportunity to do some analysis on the metrics we've captured.\n",
+    "\n",
+    "Below is an attempt to draw some comparisons between the metrics in the previous iteration and subsequent runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 487
     },
+    "id": "FmUaiMUl2pJ2",
+    "outputId": "f2a3dde2-2142-4eda-8ebe-5ae92be2faec"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 61,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 835
-        },
-        "id": "5FkUAY3tMrs3",
-        "outputId": "23e19d18-6334-452b-9327-d142bc1beefb"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1200x800 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ],
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "import numpy as np\n",
-        "\n",
-        "inc_df = ioos_btn_df.drop(columns=['date_UTC'])\n",
-        "\n",
-        "increase = inc_df.iloc[-1] - inc_df.iloc[-2]\n",
-        "\n",
-        "pcnt_increase = (increase / inc_df.iloc[-2]) * 100\n",
-        "\n",
-        "fig, ax = plt.subplots(figsize=(12,8))\n",
-        "width = 0.35  # the width of the bars\n",
-        "pcnt_increase.plot(kind='bar', ax=ax, ylabel='% change')\n",
-        "\n",
-        "plt.grid(visible=True, linestyle=':')\n",
-        "ax.set_ylim(-100,100)\n",
-        "plt.hlines(0,xmin=0,xmax=len(pcnt_increase),linestyles='solid')\n",
-        "\n",
-        "ax.set_title('% change of IOOS BTN metrics between {} and {}'.format(ioos_btn_df['date_UTC'].iloc[-2],ioos_btn_df['date_UTC'].iloc[-1]))\n",
-        "\n",
-        "plt.show()"
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "ioos_btn_df = ioos_btn_df.reset_index()\n",
+    "\n",
+    "labels = ioos_btn_df.drop(columns=[\"date_UTC\"]).columns.tolist()\n",
+    "\n",
+    "x = np.arange(len(labels))  # the label locations\n",
+    "width = 0.35  # the width of the bars\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "for index, row in ioos_btn_df.iterrows():\n",
+    "  means = row.drop(index=[\"date_UTC\"]).values\n",
+    "  rects = ax.bar(x - width/2, means, width, label=row[\"date_UTC\"].strftime(\"%Y-%m-%d\"))\n",
+    "  width = width*-1\n",
+    "\n",
+    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
+    "ax.set_ylabel(\"Count\")\n",
+    "ax.set_title(\"Metric by year and type\")\n",
+    "ax.set_xticks(x, labels)\n",
+    "ax.legend()\n",
+    "\n",
+    "ax.xaxis.set_ticklabels(labels, rotation=90)\n",
+    "\n",
+    "fig.tight_layout()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "z7GtocvbmyBQ"
+   },
+   "source": [
+    "Plot percent increase."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 835
     },
+    "id": "5FkUAY3tMrs3",
+    "outputId": "23e19d18-6334-452b-9327-d142bc1beefb"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 62,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "ukvS7ykhsaqq",
-        "outputId": "3c294253-fb28-4e4a-bff0-1237e813f350"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "Federal Partners           0.000000\n",
-              "Regional Associations      0.000000\n",
-              "HF Radar Stations          0.000000\n",
-              "NGDAC Glider Days         17.991038\n",
-              "National Platforms        -6.119792\n",
-              "Regional Platforms        39.527559\n",
-              "ATN Deployments           16.786679\n",
-              "MBON Projects            -16.666667\n",
-              "OTT Projects              75.000000\n",
-              "HAB Pilot Projects        22.222222\n",
-              "QARTOD Manuals             0.000000\n",
-              "IOOS Core Variables        0.000000\n",
-              "Metadata Records         259.788851\n",
-              "IOOS                       0.000000\n",
-              "COMT Projects              0.000000\n",
-              "dtype: float64"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 62
-        }
-      ],
-      "source": [
-        "pcnt_increase"
+     "data": {
+      "image/png": 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zsXbtWhQXFzf5d7p27QrbtiPuX1327t2LXbt2YefOnfj222/xu9/9Dvv27cNll112SPd78803Izs7+5De7/7WW2/h+eefx4033ojbb78dn376KS644ALce++9mDdvHn7/+9/j6quvxgcffIA77rgj5HfDGSvXXHMNTj311OD+DdV71qxZ+NOf/oSrr74ajz/+OFq1atVgX8OZZ9deey1mzpyJc889F88//zzuuOMOpKenY82aNWH9PS644AJUVVVh6tSpOP300/HMM8/g6quvDtnnj3/8Iy6//HL06tULTzzxBG655RYsWrQIo0ePDr5P9p577sGgQYPQpk2boPNTTz0FwzBwwgknIC8vL8QrcP2P/d9PvnTpUgwePBgtWrQAAHz77bc47rjjsGbNGkyaNAmPP/44mjdvjgkTJgTnFwBUVFTgxBNPxJtvvonLL78czzzzDE444QRMnjwZt912Wz3nt99+G48++iiuueYaPPzww9iwYQPOOecc+P3+sP5mddm+fTvatGkT/NlxHKxatQo/+9nP6u07bNgwrFu3DuXl5Qe9z2bNmqFZs2b1bisrK8OuXbuwdu1a3H333SgsLMTJJ5980H4WFBQAQL1+dezYEUcccUTw9mjQlMcaO3YsWrZsiWbNmuGss85CUVHRQR9ny5Yt2LFjR6N/6/0fp6CgAM2bN0e/fv3q7bd/nwHg/fffR79+/ULGWSROBQUFGDJkCEwz9GXksGHDUFFRge+//z64bfLkyejXrx+2bNkScV8j8W+Mp59+GoMHD8ZDDz2ERx55BF6vF+effz7mzp1bb9/PPvsM1113HS666CLMmDEDVVVVOPfcc7F79+7gPv/+978xbtw47NixAw888ACuvPJK3H///SF/y0gJXJth/3l2qON537592LdvX8h9Bnj33XeRn5+PGTNmRNzXSGp/OGjIa/Xq1di6dSuOOeYYXH311WjevDmaN2+OY445BkuWLDnofTb2tz722GNhmmbw9kif+y6//PJ6Y7ygoAC9e/dGy5Yt6/0+AKxcuRJA7TizLKveY/l8PgwaNKhe/fv16xfyeiiSvobrT1xGvI8yEHKoLFq0SLKzs4Nn12655RYREfn8888lPT1dNmzYEPF9jh49WjIyMuodFXUcJ9gOHLX/9a9/HbLPL3/5S2ndunXItrrvsxMRGT9+vPTo0SNkW9euXQWA5OXlBbft2LFDUlNT5fbbbw9uu/HGG8UwDCkoKAhu2717t7Rq1SrkTH55eblkZWXJb3/725DH2b59u2RmZtbbXpdbbrml3lmV8vJy6d69u3Tr1i3kSD4aeJ99Y9Tdd9CgQZKZmXnA33niiScEgPzzn/9s8u9s375d2rZtKwCkb9++cu2118rbb78dcjbrQDSWWkhNTZXXX3+90d8L90y+iMiDDz4YcjY/0jP5bdu2DfGZPHmyAKgX+bz44ovF5/MFz7pEMlYai+sH+tCyZUvZsWNHg7ft/zcIZ55lZmaGPa72JzA/zzrrrJDt1113nQCQb775RkRENmzYIB6PR/74xz+G7BeIsu6/vbG4/qOPPioejyd4VvqZZ56Rrl27yrBhw+T3v/+9iNSeacrKygo543/yySfL0UcfHXLmy3EcOf7446VXr17BbVOmTJHmzZvXe7/rpEmTxOPxBM/8BP7GrVu3DolXzpkzR7DfNSMiIS8vTwzDkPvuuy+4befOnY2maAJnaNeuXdvofRYVFUlaWpr86le/avD28ePHB+dW4Hob4bx3/dFHHw05E7Y/Q4cOleOOO67B32vKmfxIHuuvf/2rTJw4Ud544w15//335d5775VmzZpJmzZtGvz9/Qk8d/z5z3+ud9udd94pAILj54wzzqj3P0Wk9i0PAGTSpEnBbYHnsv3nYyROzZs3r/e/T+R/ZwTnzZsX3BZI/+yfMAu3r5H4N0bd/7+Bt3qddNJJIdsD4624uDi47ZtvvhEA8qc//Sm4bcKECZKWlhbyvLV69WrxeDxNPpN/1VVXicfjCZnjTR3PAaZMmSIAZNGiRSHbKyoqpEuXLsG4dqRn8iOp/f5E60x+Q17vvfde8LmvV69eMmvWLJk1a5b06tVLfD5f8Pm+Ma6//nrxeDwN3ta2bVu56KKLRCTy575AAmx/+vfvX2/sifwvuRF4u2ngeWn/14MBzj//fOnQoUPINgAhf99I+hquP3EXPJNPXM9JJ52ETZs24csvv8SmTZvw5JNPwnEc3HTTTbj99tvRtWtXzJw5E3379kWfPn3wwgsvHPD+du7ciby8PPz6179Gly5dQm5r6ONxrr322pCfR40ahd27d6OsrCy4LT09PdgOnAU+8cQT8Z///KfeFf+POuoojBo1Kvhz27Zt0adPn5Croc6bNw8jRozAoEGDgttatWqFSy+9NOS+Pv74Y5SWluLiiy/Grl27gl8ejwfDhw8/6BHu3NxcDBs2DCNHjgxua9GiBa6++mps2LABq1evPuDvh0t5eTkyMjIOuE/g9sDftSm/0759e3zzzTe49tprsWfPHrzwwgu45JJL0K5dO0yZMgUiElZ/n3vuOXz88cf4+OOP8eabb2Ls2LH4zW9+g/feey+s3z8QgbP5ddMg4XL++ecjMzMz+PPw4cMBAJdddhm8Xm/I9pqamuDZtUMdK/tz7rnnom3btgfcJ9x5lpWVhWXLlmHr1q1hP/7+XH/99SE/33jjjQBqxzZQe6Vrx3FwwQUXhHh36NABvXr1Cst71KhRsG0b+fn5AGrP2I8aNQqjRo3C0qVLAQCFhYUoLS0Nzu2SkhIsXrw4mAQKPO7u3bsxfvx4FBUVBWvz7rvvYtSoUcjOzg7p4ymnnALbtkNSBABw4YUXIjs7O6R/ACK6+jMA7NixA5dccgm6d++Ou+66K7i9srISAJCamlrvd9LS0kL2qUtFRQXOP/98pKenY9q0aQ3uM23aNCxYsACvvvoqjjvuONTU1DR4dfe6HKxfjfWpKUTyWBdccAFmzZqFyy+/HBMmTMCUKVMwf/587N69G3/84x8P6XH236eysjLsmkycOBEiEvyEj0idInms119/HSKCbt26Rfz7hzLWAuz//3fPnj3Yu3cvRo0a1eDV4E855RT07Nkz+PMxxxyDli1bBueObduYP38+JkyYEPK81a9fP4wfP/6A/WiMt99+G6+++ipuv/129OrVK7j9UMZzXl4eHnzwQVxwwQU46aSTQm6bNm0a/H4/7r777ib1N5LaR5vGvAJXmy8vL8eiRYswceJETJw4EQsXLoSIHDSxUFlZCZ/P1+Bt+/+tIx2Pn3zySb3XFdEa+3X/ziIS8gkhkT53hONP3IX34LsQkvi0aNEiuJgBauPC27dvx6RJk7Bw4ULceeedePPNN2EYBi655BL06dMHY8eObfC+Av/MBwwYENZj112gBF5c79mzJxjH+vzzz3H//ffjiy++QEVFRcj+e/fuDVmU1b2/wH3u2bMn+PPGjRsxYsSIevsdeeSRIT8H4qB1/8kHqBsXq8vGjRtD/q4BAvGzjRs3hv13OhAZGRkH/TzzQKwssHBvyu8AQE5ODmbOnInnn38eRUVFmD9/PqZPn44//OEPyMnJwW9+85uD9nfYsGEhsbaLL74YgwcPxg033IAzzzyz0X+W4ZCZmYlbbrkF999/PwoKCkIWa+FQd/wExlbnzp0b3B4YV4c6Vvane/fuB90n3Hk2Y8YMXHHFFejcuTOOPfZYnH766bj88svRo0ePsPqy/4tmAOjZsydM0wy+/aCoqAgiUm+/ACkpKQd9jCFDhqBZs2ZYunQpxo8fj6VLl+LBBx9Ehw4d8Kc//QlVVVXBxX7ggFlxcTFEBPfddx/uu+++Bu93x44d6NSpE4qKirBq1apGD5zs2LEj5OcDPSeFy08//YQzzzwT5eXl+Oyzz4JvMQD+t2iqrq6u93tVVVUh++yPbdu46KKLsHr1anz00Ufo2LFjg4+9/8HLyy67DEOGDMHEiRODH/FVUlKCmpqakP5kZmYetF8N9elgHK7HGjlyJIYPH97gRwnuTyR/6/T09IhrEslj7f/70XiscJ0O1KdwHuvDDz/Eww8/jJUrV4bcT0MH7Q/2/3fnzp2orKxs8PmiT58+wYOH4bJ06VJcddVVGD9+fL0DPk0dY2vXrsUvf/lLDBgwAK+88krIbRs2bMCjjz6K5557LmRO16WmpgYlJSUh29q2bQuPx3PItY/kscL1CjzmCSecEPK/rkuXLhg5cmTwAGxjpKenh8zz/dn/bx2N8RitsX+o87nuY4XjT9wFF/kk6SgrK8M999yDxx57DM2bN8c777yD8847DxMmTAAAnHfeeXjrrbcaXeRHSt1/RAECR2/XrVuHk08+GX379sUTTzyBzp07w+fzITc3N5g6iOT+IiFw37Nnz0aHDh3q3b7/md140q9fP6xcuRKbNm1q8EUWUPs+Z6A26dDU39kfwzDQu3dv9O7dG2eccQZ69eqFt956K6xFfl1M08TYsWPx9NNPo6ioCP3794/4Pvbn5ptvxpNPPokHH3ww4s9lb2z8HGxcRXOsRPMFwQUXXIBRo0bh/fffx4IFC/Doo49i+vTpeO+99/Dzn/884vur+8LecRwYhoGPPvqowb/RgV4IB0hJScHw4cORl5eH4uJibN++HaNGjUL79u3h9/uxbNkyLF26FH379g0u1AN/7zvuuKPRM4CBg3aO4+DUU08NOZu+P7179w75+VCfQ2pqanDOOedg1apVmD9/fr0DMa1atUJqamqDn1Ud2NbQAv63v/0tPvzwQ7z11luNHkyqi8/nw1lnnYVp06ahsrIS6enpOOecc/Dpp58G97niiivw+uuvIycnJ9iHuge1tm3bFnzPayQczsfq3LkzvvvuuwPus//j1GXbtm3BWgT2XbJkCUQkZJwfqCaNPdbBnHJyciKuf93HCqevkfg3xNKlS3HWWWdh9OjReP7555GTk4OUlBTMmjULb7/9dr39o/n/92B88803OOusszBgwAD8/e9/r/cc25QxtnnzZowbNw6ZmZnIzc2tl3b7wx/+gE6dOmHMmDHBA52B6wHs3LkTGzZsQJcuXZCfn1/vNdL69evRrVu3Q659XQ70WOF6BR6zffv29e6/Xbt2B31PeU5ODmzbxo4dO9CuXbvg9pqaGuzevTt4/0197qv7WPtfn6Kx3z/Y2D/Y40TS13D9ibtIjFf4hESRhx56CN27dw9G17du3YrBgwcHb+/YsWPwwiYNEThDWFhYGJX+fPDBB6iursY///nPkMVoJPHnunTt2jV4Mbn9qbstEDts164dTjnllCY9TkMvQteuXRu8PRqceeaZeOedd/DnP/8Z9957b73by8rKMGfOHPTt2ze48GnK7zRGjx49kJ2d3eA/w3AJxIkDscFDIXA2/4EHHsAVV1xxyPcXDpGMlYbOgEVKJPMsJycH1113Ha677jrs2LEDQ4YMwR//+MewFvlFRUUhyYLi4mI4jhN8AdmzZ0+ICLp3715vsVyXA3mPGjUK06dPx8KFC9GmTRv07dsXhmGgf//+WLp0KZYuXYozzzwzuH/APyUl5aB/7549e2Lfvn1NmsOR4jgOLr/8cixatAh/+9vfcOKJJ9bbxzRNHH300fjqq6/q3bZs2TL06NGj3ovwO++8E7NmzcJTTz2Fiy++OKI+VVZWQkRQXl6O9PR0PP744yGphMAL0EAC4KuvvgpZAG3duhU//PBDvQsuhsPhfKz//Oc/B31bS6dOndC2bdsG/9bLly8PST0MGjQIr7zyCtasWRNyYHPZsmUhfW6MSJwGDRqEpUuXwnGckAuwLVu2DM2aNTvoXAq3r5H4N8T//d//IS0tDfPnzw85GDBr1qwD/l5jtG3bFunp6Q1eNPFgB2z2Z926dTjttNPQrl075ObmNngwMdIxtnv3bowbNw7V1dVYtGhRcJG4P5s2bUJxcXGDSajrrrsOQG3iZ+DAgfj4449Dbg8c/D3U2tflQI8VrtfRRx+NlJSUBhfPW7duPeg82/9vffrppwe3f/XVV3AcJ3h7U577GnqsJUuWoKysLCQhV3fsDxgwAF6vF1999RUuuOCC4H41NTVYuXJlyLaGiKSv4foTlxHzqwAQchj57rvvJDU1NeRzUq+44gr55S9/Gfz57LPPlokTJx7wfiK58F7dz4AOXNAocJGhZ555RgCEXACwtLRUcnJy6l2MKPARenU58cQTQy6ocsMNN4R14b29e/dKy5Yt5cQTTwz5+KIAdS+OVpfAhffy8/OD2/bt2yc9evSI6oX3qqur5aijjpLmzZvX+3g627blkksuEQDyzjvvHNLvfPnll7Jv3756/Ql8bm3di7TVpbGP0KupqQle4Kfux+KIRHbhvQCBjwAcNGhQRBfeq7tfYxdVqusSyVj5/e9/LwBCPh7uQH3Y/7ZILrxnWVaDF0UcOnSo/OxnP2vgr/A/DnbhvZUrV4qISHFxsXg8HrnkkktC5negH7t27Qr+fOGFF0pWVlaDjxf43O4+ffrIhAkTgtt/97vfSe/evQUNfAzRmDFjpFWrVrJ169Z697f/3/uBBx5o9IJWe/bsCV5Q8UB/fwBy//33N9j3/Qn8fQ72+ePTpk2rNxfWrl0rHo8neLHBADNmzBAAIR+N2BA//vhjvW179uyRzp07S+fOnQ/adxGRvn37ysCBA0M+pu7ee+8VwzBk9erVDf5OUy68F8ljNfQ8G7hI2U033XTQx7n22mslPT095AJsCxcuFAAyc+bM4LbNmzc3+tnznTp1CulnQx+hF4nTX/7yl3rPKzt37pSsrCy58MILQ+6zoY/Qi6Sv4fo3xG233SbNmjUL+Ti19evXS7NmzepdEK2x/2Fdu3aVK664IvjzoV54b9u2bdKjRw/p2LFjvY+7rUu49di3b58MGzZMMjIy5Kuvvmr0/pYuXSrvv/9+yFfgQnZ33XWXvP/++w0+/+9PJLXfn6ZceC9cL5Ha13YejyfkI+sCdbnuuusO+LsVFRXSqlUrOfPMM0O2X3bZZdKsWbOQj5uN5LmvoY/Q+/LLL+s9T1dVVcmRRx5Z76NCTzvtNMnJyQn5qNFXXnlFANT7aMeGPkIv3L5G4k/cAxf5JKk4/fTT631W+QcffCCmacrkyZNl8uTJYpqm5ObmHvB+Vq5cKS1atJDWrVvL5MmT5aWXXpK7775bBg4cGNwn3EX+2rVrxefzydFHHy3PPvusTJs2TXr27CkDBw5s8iJ/06ZNkpWVJW3atJEHH3xQHnvsMenbt29wQbj/AYW33npLTNOUAQMGyMMPPywvvvii3HPPPTJo0KCDLsq3b98u7du3l8zMTLnvvvvkySeflEGDBolhGPLee++F7Hsoi3yR2n/GOTk5kpqaKtdee6288sor8thjj8mQIUMEQMinCzT1d66//nrJysqSK6+8Up599ll56aWX5NZbb5XMzExJS0uTL7/88oD9DtT2oYcektmzZ8vs2bPl8ccfl2OPPbbe1av3pymLfJH/jbFYLPJFwh8rf/vb3wSA/OpXv5I333wzeCAl0kX+webZnj17pHnz5nLFFVfIE088IS+99JJccMEFAkAef/zxA/49An+7o48+Wn7xi1/Ic889J5dddpkAkEsuuSRk36lTpwoAOf7442XGjBkyc+ZMueuuu6RXr14hLoHF6q233ipvv/128FMbRGpfjHq93np9e+edd4I1rPtJH99++61kZ2dL69atZdKkSfLSSy/JlClT5PTTT5djjjkmuN9PP/0kQ4YMEa/XK7/5zW9k5syZ8thjj8kVV1whzZs3Dz4HHeoi/8knnxQAMmLEiOD43v9r/wNkZWVl0rNnT2nXrp3MmDFDnnzySencubN07NgxZFEbuOp1r169GrzP7du3B/cdMmSInHXWWfLHP/5RXn75ZbnvvvvkiCOOENM0w77y9wcffCCGYchJJ50kL730ktx0001immaDnyQyZcoUmTJlilx00UUC1H5SSmBbNB/ryCOPlPPPP1+mT58uL7zwglx99dXi9Xqlc+fOIf6NsWnTJmndurX07NlTnnnmGXnkkUckOzu73icziPzvivNXX321vPzyy3LGGWcIAHnrrbdC9mvo6vqROFmWJccdd5y0aNFCHnzwQXnuueekf//+kpGRUe+TFRq6un4kfY3Evy6LFi0SADJq1CiZOXOmPPjgg9KuXTs55phjmrzI/+abbyQtLU26dOki06ZNk4cffljat2/f4H02ROB//1133VVvPixYsCBk33DrcfbZZwfHcN37fP/99w/Yn0ivrh9J7T/99NPgnGrXrp1069Yt+POnn3560MeKxOvbb7+VFi1aSE5OjkydOlWmTp0qOTk50rZtW/nhhx8O+liBK86fd9558vLLL8vll18uAOp98kq4z30iDV9dX6T26vher1fuvPNOefHFF+X4448Xr9db72/y9ddfS2pqqgwePFhmzpwp99xzj6Slpcm4cePq3SfqXF0/0r6G60/cAxf5JGmYO3eutGjRosGzYlOnTpWOHTtKTk6OTJ8+Paz7KywslF/+8peSlZUlaWlp0qdPn5CPkQp3kS8i8s9//lOOOeYYSUtLk27dusn06dPltddea/IiX0SkoKBARo0aJampqXLEEUfI1KlTg6mBui8clyxZIuPHjw8uZnv27CkTJ0486JFxEZF169bJeeedF/w7DBs2TD788MN6+x3qIl+k9ozXbbfdJkceeaSkpqZKVlaWnHLKKSGLqUP5nVWrVsmdd94pQ4YMkVatWonX65WcnBw5//zz5V//+tdB+93QR+ilpaXJoEGDZObMmfXOBAdo6iJ/z549kpmZGbNFfmD/g40Vy7LkxhtvlLZt24phGMEXMZEu8kUOPM+qq6vlzjvvlIEDB0pGRoY0b95cBg4cKM8///wB/xYi/5ufq1evlvPOO08yMjIkOztbbrjhhgY/ju3//u//ZOTIkdK8eXNp3ry59O3bV66//nr57rvvgvvs27dPLrnkEsnKyhIA9T5Ob+jQoQIgJEn0ww8/CIBGz0SvW7dOLr/8cunQoYOkpKRIp06d5Mwzz5S///3vIfuVl5fL5MmT5cgjjxSfzydt2rSR448/Xh577LHgmbdDXeQHFmONfdVdpG3evFnOO+88admypbRo0ULOPPNMKSoqCtln/wNVDX3tf/b82WeflZEjR0qbNm3E6/VK27Zt5Re/+EWDHyF1IN5//30ZNGhQ8Lnx3nvvbfDs5IH6Fc3HChwoy8zMlJSUFOnSpYv87ne/C2uBH6CwsFDGjRsnzZo1k6ysLLn00ksb/H3btuWRRx6Rrl27is/nk/79+8ubb75Zb7/GFvnhOomIlJSUyFVXXSWtW7eWZs2ayYknnljv+USk8UV+uH2NxL8hXn31VenVq5ekpqZK3759ZdasWcFxuT/hLvJFahevxx57rPh8PunRo4e88MILDd5nQxxo3DV0pjucegQ+frehr4Y+9nN/Il3ki4Rf+wPN/3CSRZF6ff3113LKKadI8+bNJSMjQ84+++x6Hz16IF566SXp06eP+Hw+6dmzpzz55JMN/l8P57lPpPFFfmVlpdxxxx3SoUMHSU1NlaFDhzb60YNLly6V448/XtLS0qRt27Zy/fXXh5zZD9DY+Am3r5H4E3dgiByGq4kQQuLCLbfcghdffBH79u1r9AJChBBCCCGEkOTFPPguhJBEpO7nlu7evRuzZ8/GyJEjucAnhBBCCCFEKby6PiEuZcSIERgzZgz69euHH3/8Ea+++irKysoa/cxtQgghhBBCSPLDRT4hLuX000/H3//+d7z00kswDANDhgzBq6++itGjR8e7a4QQQgghhJA44aq4fl5eHn7xi1+gY8eOMAwD//jHP0JuFxH84Q9/QE5ODtLT03HKKafU+yzTkpISXHrppWjZsiWysrJw1VVXReVzrQmJNY888gi+//57VFRU4KeffsLSpUtj8jnahBBCCCGEkMTFVYv8n376CQMHDsRzzz3X4O0zZszAM888gxdeeAHLli1D8+bNMX78eFRVVQX3ufTSS/Htt9/i448/xocffoi8vDxcffXVsVIghBBCCCGEEEIOG669ur5hGHj//fcxYcIEALVn8Tt27Ijbb78dd9xxBwBg7969aN++PV5//XVcdNFFWLNmDY466iisWLECP/vZzwAA8+bNw+mnn44ffvgBHTt2jJcOIYQQQgghhBByyCTNe/LXr1+P7du3h8SVMzMzMXz4cHzxxRe46KKL8MUXXyArKyu4wAeAU045BaZpYtmyZfjlL39Z736rq6tRXV0d/NlxHJSUlKB169YwDOPwShFCCCGEEEIIUY+IoLy8HB07doRpHjiQnzSL/O3btwMA2rdvH7K9ffv2wdu2b9+Odu3ahdzu9XrRqlWr4D51mTp1Kh588MHD0GNCCCGEEEIIISR8Nm/ejCOOOOKA+yTNIv9wMXnyZNx2223Bn/fu3YsuXbpgw4YNyM7Ohm3bAACPxxPStiwLhmEE26ZpwjTNRtt+vx8ejyfY9nq9MAwj2AYAv98Py7KQlpYG27aRkpICEYFlWUhJSYHjOMHtjuPAcRx4vd5G27ZtQ0SC7YY8DreTZVkh7cacLMuCz+cL9jcZnA5Wp4B3SkoKACSFUzh1EhH4/X6kpKSEeLjZKZw6GYaBmpoaeL3eYN/d7hROnUzTRE1NDTweT7DmbncKp04ejwdVVVXwer3B5za3O4VTJ6/Xi4qKCvh8vgP6uckpnDp5PJ4Q72RwCqdOpmnip59+QlpaGrxeb1I4hVMnwzCwb98+NGvWrJ6rW53CqVPAu3nz5iF+bnEa/OA82A7gwIDPFFiBtkfgtwEJaQM+D1BjAyYErdOAXVW1Cd8UD1BjGzAh8JpAjVPb9piA3zFgGgKPUdv2GALTAAruP811z3u2baOiogItWrSAiKh4Lg88j1VVVaF58+ZwHCemThUVFejSpQsyMjJwMJJmkd+hQwcAwI8//oicnJzg9h9//BGDBg0K7rNjx46Q37MsCyUlJcHfr0tqaipSU1Prbc/OzkbLli2j1PvwCAzIQLG1QG96a4De+rybN2+u0rtZs2b0VgK96e0m7JTmAGqvSm79d1ugbXgBo4G26QUAwT4TMFIBwNhv+3/3Cdx/A235bzvWa4poICJo0aKFa+vdVOL5uiVw4i+cx3XV1fUPRPfu3dGhQwcsWrQouK2srAzLli3DiBEjAAAjRoxAaWkpvv766+A+ixcvhuM4GD58eMz7HCmWZSE3NxeWZR185ySC3vTWAL3prQF601sD9NblneoBZgyzkeqJd09ii9Z6u8XbVVfX37dvH4qLiwEAgwcPxhNPPIGxY8eiVatW6NKlC6ZPn45p06bhjTfeQPfu3XHfffdh1apVWL16NdLS0gAAP//5z/Hjjz/ihRdegN/vx5VXXomf/exnePvtt8PqQ1lZGTIzM7F3716eyY8R9Ka3BuhNbw3Qm94aoLc7vbtNmtvE3xSkeoBqG6g9xx85G6ad0cTHjh9ur3dTiad3JOtQV8X1v/rqK4wdOzb4c+C98ldccQVef/113HXXXfjpp59w9dVXo7S0FCNHjsS8efOCC3wAeOutt3DDDTfg5JNPhmmaOPfcc/HMM8/E3KWp7P++Ek3QWxf01gW9dUFvXdBbFxq9DQBp/31/vmvOnEYJjfUG3OHtqrj+mDFjICL1vl5//XUAte9PeOihh7B9+3ZUVVVh4cKF6N27d8h9tGrVCm+//TbKy8uxd+9evPbaa2jRokUcbCLHsiwsWLAg4eMh0Ybe9NYAvemtAXrTWwP01uXt8wAPHWvDpzCur7HebvF2VVw/EYhnXJ8QQgghhBASfZoe1z903BjXJ7EnknWoq87ka0dEUFZWBm3HZehNbw3Qm94aoDe9NUBvXd4GBB3SBYaysL7WervFm4t8F2FZFpYuXZrw8ZBoQ296a4De9NYAvemtAXrr8vZ5gFsH6Izra6y3W7wZ148QxvUJIYQQQghJLhjXJ4kO4/pJiuM4KCkpgeM48e5KTKE3vTVAb3prgN701gC9dXmbEHRrITCVxfW11tst3lzkuwjbtrFixQrYth3vrsQUetNbA/SmtwboTW8N0FuXd4oJXNnbRoqyVZXWervFm3H9CGFcnxBCCCGEkOSCcX2S6DCun6Q4joMdO3YkfDwk2tCb3hqgN701QG96a4DeurxNCPpmOirj+hrr7RZvLvJdhOM4KCwsTPhBFW3oTW8N0JveGqA3vTVAb13eXhOY0M2BV9mqSmu93eLNuH6EMK5PCCGEEEJIcsG4Pkl0GNdPUhzHwZYtWxL+yFG0oTe9NUBvemuA3vTWAL11eZuGYFArB6ah67yp1nq7xZuLfBfhOA7WrVuX8IMq2tCb3hqgN701QG96a4Deury9BjCmowOvEe+exBat9XaLN+P6EcK4PiGEEEIIIckF4/ok0WFcP0lxHAcbN25M+CNH0Ybe9NYAvemtAXrTWwP01uXtMQTHtXPgURjX11hvt3hzke8i3PIekGhDb3prgN701gC96a0Beuvy9hjAoNYCj8K4vsZ6u8Wbcf0IYVyfEEIIIYSQ5IJxfZLoMK6fpNi2jeLiYti2He+uxBR601sD9Ka3BuhNbw3QW5e3xxCMydEX19dab7d4c5HvIkQEe/bsgbbwBb3prQF601sD9Ka3Buity9s0gG4ZAlNZXF9rvd3izbh+hDCuTwghhBBCSHLBuD5JdBjXT1Js28batWsTPh4SbehNbw3Qm94aoDe9NUBvXd4eQ3DaEbbKuL7GervFm4t8l1FZWRnvLsQFeuuC3rqgty7orQt660Kjt2kAWT6oi+sDOusNuMObcf0IYVyfEEIIIYSQ5IJxfZLoMK6fpNi2jcLCwoSPh0QbetNbA/SmtwboTW8N0FuXt9cQTOhqw6swrq+x3m7x5iKfEEIIIYQQQghJEhjXjxDG9QkhhBBCCEkuGNcniQ7j+kmKbdsoKChI+HhItKE3vTVAb3prgN701gC9dXmnmIKLethIMXWdN9Vab7d4c5HvMtLT0+PdhbhAb13QWxf01gW9dUFvXWj0dgQoran9rg2N9Qbc4c24foQwrk8IIYQQQkhywbg+SXQY109SLMvCihUrYFlWvLsSU+hNbw3Qm94aoDe9NUBvXd4ppmBib31xfa31dos3F/kuwjAMZGdnwzCMeHclptCb3hqgN701QG96a4DeurwdATaUG+ri+lrr7RZvxvUjhHF9QgghhBBCkgvG9Umiw7h+kmJZFvLz8xM+HhJt6E1vDdCb3hqgN701QG9d3j5TcG0/Gz6FcX2N9XaLNxf5LsI0TXTq1Ammqats9Ka3BuhNbw3Qm94aoLcub1uAlbsN2LrW+Grr7RZvxvUjhHF9QgghhBBCkgvG9Umiw7h+kmJZFvLy8hI+HhJt6E1vDdCb3hqgN701QG9d3j5TcMsAS2VcX2O93eLNRb6LME0TPXv2TPh4SLShN701QG96a4De9NYAvXV5WwJ8stWEpWuNr7bebvFmXD9CGNcnhBBCCCEkuWBcnyQ6jOsnKZZlYfHixQkfD4k29Ka3BuhNbw3Qm94aoLcub58pmDRQZ1xfY73d4s1FvoswTRMDBgxI+HhItKE3vTVAb3prgN701gC9dXlbDvCPDSYsJ949iS1a6+0Wb8b1I4RxfUIIIYQQQpILxvVJosO4fpLi9/sxf/58+P3+eHclptCb3hqgN701QG96a4DeurxTTcGDQyykKovra623W7x5Jj9C4nkm33EclJaWIisrK+EjItGE3vTWAL3prQF601sD9Hand1PP5JsQdGkBbNoHODCadB9uPJPv9no3lXh6R7IO5SI/QhjXJ4QQQgghJLlgXJ8kOozrJyl+vx9z585N+HhItKE3vTVAb3prgN701gC9dXmnegTTh1pI9eg6b6q13m7x5pn8CInnmXwRQXl5OTIyMmAYTYsDuRF601sD9Ka3BuhNbw3Q253eTT2Tb0DQPh34sRIQRXF9t9e7qcTTO5J1qDdGfSJRwDAMlW8RoLcu6K0LeuuC3rqgty60egsMbK+Mdy9ij9Z6u8WbcX0X4ff7MWfOnISPh0QbetNbA/SmtwboTW8N0FuXd6pH8PQInXF9jfV2izfj+hES77h+VVUV0tLS1MVi6E3vZIfe9NYAvemtAXq70/tQ4votfUBZjb64vpvr3VTi6c0L7yUxXq/Od1jQWxf01gW9dUFvXdBbFxq9BUCVXftdGxrrDbjDm4t8F2FZFnJzc2FZVry7ElPoTW8N0JveGqA3vTVAb13eqR5gxjAbqZ549yS2aK23W7wZ14+QeMf1LcuC1+tVF4uhN72THXrTWwP0prcG6O1O76bG9QFBqgeotgEoi+u7ud5NJZ7ejOsnMYl+1OhwQW9d0FsX9NYFvXVBb11o9DYApHmaurx3NxrrDbjDm4t8F2FZFhYsWOCKgRVN6E1vDdCb3hqgN701QG9d3j4P8NCxNnwK4/oa6+0Wb8b1IySecX1CCCGEEEJI9Gl6XP/QcWNcn8QexvWTFBFBWVkZtB2XoTe9NUBvemuA3vTWAL11eRsQdEgXGMqur6+13m7x5iLfRViWhaVLlyZ8PCTa0JveGqA3vTVAb3prgN66vH0e4NYBOuP6GuvtFm/G9SOEcX1CCCGEEEKSC8b1SaLDuH6S4jgOSkpK4DhOvLsSU+hNbw3Qm94aoDe9NUBvXd4mBN1aCExlcX2t9XaLNxf5LsK2baxYsQK2bce7KzGF3vTWAL3prQF601sD9NblnWICV/a2kaJsVaW13m7xZlw/QhjXJ4QQQgghJLlgXJ8kOozrJymO42DHjh0JHw+JNvSmtwboTW8N0JveGqC3Lm8Tgr6Zjsq4vsZ6u8Wbi3wX4TgOCgsLE35QRRt601sD9Ka3BuhNbw3QW5e31wQmdHPgVbaq0lpvt3gzrh8hjOsTQgghhBCSXDCuTxIdxvWTFMdxsGXLloQ/chRt6E1vDdCb3hqgN701QG9d3qYhGNTKgWnoOm+qtd5u8eYi30U4joN169Yl/KCKNvSmtwboTW8N0JveGqC3Lm+vAYzp6MBrxLsnsUVrvd3izbh+hDCuTwghhBBCSHLBuD5JdNTG9bt16wbDMOp9XX/99QCAMWPG1Lvt2muvjXOvw8dxHGzcuDHhjxxFG3rTWwP0prcG6E1vDdBbl7fHEBzXzoFHYVxfY73d4p1Ui/wVK1Zg27Ztwa+PP/4YAHD++ecH9/ntb38bss+MGTPi1d2Icct7QKINvemtAXrTWwP0prcG6K3L22MAg1oLPArj+hrr7RbvpI7r33LLLfjwww9RVFQEwzAwZswYDBo0CE899VST75NxfUIIIYQQQpILxvVJoqM2rr8/NTU1ePPNN/HrX/8ahvG/Q2tvvfUW2rRpgwEDBmDy5MmoqKg44P1UV1ejrKws5AsAbNsOfm+obVlWSDtwtKextt/vD2kHjr0E2iKCqqoqFBUVwbIs+P1+AICIBNuO44S0Lcs6YNu27ZB2PJzqthtyqq6uRnFxMfx+f9I4hVOngHdNTU3SOIVTp5qamqB3sjiFUye/34/i4mJUV1cnjVM4dQp4V1VVJY1TOHWyLAtFRUWoqqpKGqdw6mTbNoqKilBdXZ00TuHUybZtfP/996ipqUkap3DqVNc7GZzCqVPAO/C4yeAUTp1s28Z3330XfCy3OflMgYnaeqTu3/YIjHptQaqn9rvXcHBKRxseo/a22u2ACUGq+b+2L9A2/tf27Nd22/Oe3+/Hd999B9u24z72ouUUztirqanB999/H/I8F0uncEnaRf4//vEPlJaWYuLEicFtl1xyCd58800sWbIEkydPxuzZs3HZZZcd8H6mTp2KzMzM4Ffnzp0BAIWFhQCANWvWYM2aNQCAVatWoaioCABQUFCA9evXAwCWL1+OzZs3AwDy8/Oxbds2AEBeXh527doFAFi8eDFKS0sBAAsWLEB5eTkAIDc3F1VVVbAsC/Pnz0dJSQkqKyuRm5sLACgvL8eCBQsAAKWlpVi8eDEAYNeuXcjLywMAbNu2Dfn5+QCAzZs3Y/ny5QCA9evXo6CgAABQVFSEVatWxdwpNzcXlmWhqqqqUadPPvkEe/bsSSqncOr01VdfYc+ePdiwYUPSOIVTp7Vr12LPnj0oLCxMGqdw67Rnzx58+eWXSeUUTp327NmDRYsWJZXTwerk9/uxe/duzJ8/P2mcwqmTiGD79u347LPPXOk04qG5eO+fueg2aS5OeiQXb79f2z59+ly88u5cdJs0F+c8NhfP/qW2fclTc/HYW7noe99H+PirNZj25kfoNmkurp85F/e8WrvP7S/VfnWbVLvt+pm17Yf/nIurnq29/8feynXl856IYOPGjSguLo772IuWUzhjT0RQXFyM7du3J41TOHUSEXz33XeudbrtaBu9M2sXUXcPstGlBQAADw2x0T69tj1jmI2WPiDVU9tO9QBZqcAvugpMA2ifXrs/AHRpUXs/ANA7U3Db0bXtY7IF1x1V2x7aVvDrPk5M6wREb+wVFxdDROI+9qLpdLCxt2HDBqxfvx4iEhencEnauP748ePh8/nwwQcfNLrP4sWLcfLJJ6O4uBg9e/ZscJ/q6urgGQegNibRuXNnlJSUIDs7O3hkxuPxhLQty4JhGMG2aZowTbPRtt/vh8fjCba9Xi8Mwwi2gdqjPPu3U1JSICLBduAoaqDtOA68Xm+jbdu2ISLBdkMedKITnehEJzrRqdaj+6QP4fMA1bYBE4IUE6h2atteE6hxDJiGwGvUtj1G7ft0A23TAPx12t7/XqzLEgMppsARwK7T9pmCtQ+fzjrRiU6H0an33R/CcgAHBlJNgT/Q9ghqbEBC2rUL/WobMIDg84IBadJzxPePnMk60emgThUVFcjKygorrp+Ui/yNGzeiR48eeO+993D22Wc3ut9PP/2EFi1aYN68eRg/fnxY9x3P9+Tbdm3MsVevXvB4PDF97HhCb3prgN701oDbvZv6nl2PITi1k4OPt5iwpWlX53Lje3bdXu+mQm93enN+R4bb691U4umt/j35s2bNQrt27XDGGQeeMCtXrgQA5OTkxKBX0aGysjLeXYgL9NYFvXVBb11o9DYNIMtX+10bGusN0FsTnN/6cIN30p3JdxwH3bt3x8UXX4xp06YFt69btw5vv/02Tj/9dLRu3RqrVq3CrbfeiiOOOAKffvpp2PfPq+sTQgghOuHVtwlJXji/SaKj+kz+woULsWnTJvz6178O2e7z+bBw4UKMGzcOffv2xe23345zzz33gO/ZTzRs20ZhYWHwfR5aoDe9NUBvemtAq7fXEEzoagfff68FrfWmty5vzm9d9XaLtzfeHYg248aNa/DjBTp37hzRGXtCCCGEEEIIIcRtJF1c/3DDuD4hhBCiE8Z5CUleOL9JoqM6rp/M2LaNgoKChI+HRBt601sD9Ka3BrR6p5iCi3rYSDF1nVfRWm966/Lm/NZVb7d4c5HvMtLT0+PdhbhAb13QWxf01oVGb0eA0pra79rQWG+A3prg/NaHG7wZ148QxvUJIYQQnTDOS0jywvlNEh3G9ZMUy7KwYsUKWJYV767EFHrTWwP0prcGtHqnmIKJvfXFebXWm966vDm/ddXbLd5c5LsIwzCQnZ0NwzDi3ZWYQm96a4De9NaAVm9HgA3lhro4r9Z601uXN+e3rnq7xZtx/QhhXJ8QQgjRCeO8hCQvnN8k0WFcP0mxLAv5+fkJHw+JNvSmtwboTW8NaPX2mYJr+9nwKYzzaqw3vXV5c37rqrdbvLnIdxGmaaJTp04wTV1loze9NUBvemtAq7ctwMrdBmxdawC19aa3Lm/Ob131dos34/oRwrg+IYQQohPGeQlJXji/SaLDuH6SYlkW8vLyEj4eEm3oTW8N0JveGtDq7TMFtwywVMZ5Ndab3rq8Ob911dst3lzkuwjTNNGzZ8+Ej4dEG3rTWwP0prcGtHpbAnyy1YSlaw2gtt701uXN+a2r3m7xZlw/QhjXJ4QQQnTCOC8hyQvnN0l0GNdPUizLwuLFixM+HhJt6E1vDdCb3hrQ6u0zBZMG6ozzaqw3vXV5c37rqrdbvLnIdxGmaWLAgAEJHw+JNvSmtwboTW8NaPW2HOAfG0xYTrx7Elu01pveurw5v3XV2y3e3nh3gISPaZpo165dvLsRc+itC3rrgt660OrtwMDavUa8uxFztNab3rrg/NaFW7wT+xAECcHv92P+/Pnw+/3x7kpMoTe9NUBvemtAq3eqKXhwiIVUZXFerfWmty5vzm9d9XaLNxf5LsLj8WDo0KHweDzx7kpMoTe9NUBvemtAq7ffAWZ974FfWZxXa73prcub81tXvd3izbi+izBNE61atYp3N2IOvXVBb13QWxdavR0Y2LAv3r2IPVrrTW9dcH7rwi3ePJPvIvx+P+bOnZvw8ZBoQ296a4De9NaAVu9Uj2D6UAupHn1xXo31prcub85vXfV2i7chIrpG5CESyecTRhsRQXl5OTIyMmAYei7wQW96a4De9NaA272b+jnaBgTt04EfKwFB07zd+Dnabq93U6G3O705vyPD7fVuKvH0jmQdyri+izAMI+YHFhIBeuuC3rqgty60egsMbK+Mdy9ij9Z601sXnN+6cIs34/ouwu/3Y86cOQkfD4k29Ka3BuhNbw1o9U71CJ4eoTPOq7He9Nblzfmtq95u8WZcP0LiHdevqqpCWlqaulgMvemd7NCb3hpwu/ehxHlb+oCyGn1xXjfXu6nQ253enN+R4fZ6N5V4ekeyDuWZfJfh9ep8hwW9dUFvXdBbFxq9BUCVXftdGxrrDdBbE5zf+nCDNxf5LsKyLOTm5sKyrHh3JabQm94aoDe9NaDVO9UDzBhmIzWxP1Y56mitN711eXN+66q3W7wZ14+QeMf1LcuC1+tVF4uhN72THXrTWwNu925qnBcQpHqAahuAsjivm+vdVOjtTm/O78hwe72bSjy9GddPYhL9qNHhgt66oLcu6K0Ljd4GgDRPU1/+uxuN9QborQnOb324wZuLfBdhWRYWLFjgioEVTehNbw3Qm94a0Ort8wAPHWvDpzDOq7He9Nblzfmtq95u8WZcP0LiGdcnhBBCSPxoepz30HFjnJcQN8H5TRIdxvWTFBFBWVkZtB2XoTe9NUBvemtAq7cBQYd0gaHs+tta601vXd6c3/RORLjIdxGWZWHp0qUJHw+JNvSmtwboTW8NaPX2eYBbB+iM82qsN711eXN+66q3W7wZ148QxvUJIYQQnTDOS0jywvlNEh3G9ZMUx3FQUlICx3Hi3ZWYQm96a4De9NaAVm8Tgm4tBKayOK/WetNblzfnt656u8Wbi3wXYds2VqxYAdu2492VmEJvemuA3vTWgFbvFBO4sreNFGWvurTWm966vDm/ddXbLd6M60cI4/qEEEKIThjnJSR54fwmiQ7j+kmK4zjYsWNHwsdDog296a0BetNbA1q9TQj6Zjoq47wa601vXd6c37rq7RZvLvJdhOM4KCwsTPhBFW3oTW8N0JveGtDq7TWBCd0ceJW96tJab3rr8ub81lVvt3gzrh8hjOsTQgghOmGcl5DkhfObJDqM6ycpjuNgy5YtCX/kKNrQm94aoDe9NaDV2zQEg1o5MA1d51W01pveurw5v3XV2y3eXOS7CMdxsG7duoQfVNGG3vTWAL3prQGt3l4DGNPRgdeId09ii9Z601uXN+e3rnq7xZtx/QhhXJ8QQgjRCeO8hCQvnN8k0WFcP0lxHAcbN25M+CNH0Ybe9NYAvemtAa3eHkNwXDsHHoVxXo31prcub85vXfV2izcX+S7CLe8BiTb0prcG6E1vDWj19hjAoNYCj8I4r8Z601uXN+e3rnq7xZtx/QhhXJ8QQgjRCeO8hCQvnN8k0WFcP0mxbRvFxcWwbTveXYkp9Ka3BuhNbw1o9fYYgjE5+uK8WutNb13enN+66u0Wby7yXYSIYM+ePdAWvqA3vTVAb3prQKu3aQDdMgSmsjiv1nrTW5c357euervFm3H9CGFcnxBCCNEJ47yEJC+c3yTRYVw/SbFtG2vXrk34eEi0oTe9NUBvemtAq7fHEJx2hK0yzqux3vTW5c35ravebvHmIt9lVFZWxrsLcYHeuqC3LuitC43epgFk+aAuzgvorDdAb01wfuvDDd6M60cI4/qEEEKIThjnJSR54fwmiQ7j+kmKbdsoLCxM+HhItKE3vTVAb3prQKu31xBM6GrDqzDOq7He9Nblzfmtq95u8eYinxBCCCGEEEIISRIY148QxvUJIYQQnTDOS0jywvlNEh3G9ZMU27ZRUFCQ8PGQaENvemuA3vTWgFbvFFNwUQ8bKaau8ypa601vXd6c37rq7RZvLvJdRnp6ery7EBforQt664LeutDo7QhQWlP7XRsa6w3QWxOc3/pwgzfj+hHCuD4hhBCiE8Z5CUleOL9JosO4fpJiWRZWrFgBy7Li3ZWYQm96a4De9NaAVu8UUzCxt744r9Z601uXN+e3rnq7xZuLfBdhGAays7NhGEa8uxJT6E1vDdCb3hrQ6u0IsKHcUBfn1Vpveuvy5vzWVW+3eDOuHyGM6xNCCCE6YZyXkOSF85skOozrJymWZSE/Pz/h4yHRht701gC96a0Brd4+U3BtPxs+hXFejfWmty5vzm9d9XaLNxf5LsI0TXTq1Ammqats9Ka3BuhNbw1o9bYFWLnbgK1rDaC23vTW5c35ravebvFmXD9CGNcnhBBCdMI4LyHJC+c3SXQY109SLMtCXl5ewsdDog296a0BetNbA1q9fabglgGWyjivxnrTW5c357euervFm4t8F2GaJnr27Jnw8ZBoQ296a4De9NaAVm9LgE+2mrB0rQHU1pveurw5v3XV2y3ejOtHCOP6hBBCiE4Y5yUkeeH8JomO2rj+Aw88AMMwQr769u0bvL2qqgrXX389WrdujRYtWuDcc8/Fjz/+GMceR4ZlWVi8eHHCx0OiDb3prQF601sDWr19pmDSQJ1xXo31prcub85vXfV2i3dSLfIBoH///ti2bVvw67PPPgveduutt+KDDz7Au+++i08//RRbt27FOeecE8feRoZpmhgwYEDCx0OiDb3prQF601sDWr0tB/jHBhOWE++exBat9aa3Lm/Ob131dou3N94diDZerxcdOnSot33v3r149dVX8fbbb+Okk04CAMyaNQv9+vXDl19+ieOOOy7WXY0Y0zTRrl27eHcj5tBbF/TWBb11odXbgYG1e414dyPmaK03vXXB+a0Lt3gn9iGIJlBUVISOHTuiR48euPTSS7Fp0yYAwNdffw2/349TTjkluG/fvn3RpUsXfPHFF43eX3V1NcrKykK+AMC27eD3htqWZYW0Hcc5YNvv94e0A5dKCLRFBBUVFZg3bx5qamrg9/sBACISbDuOE9IOxEgaa9u2HdKOh1PddkNOlZWVmD9/Pqqrq5PGKZw6BbyrqqqSximcOlVVVQW9k8UpnDpVV1dj/vz5qKysTBqncOoU8K6oqEgap3DqVFNTg3nz5qGioiJpnMKpk9/vx7x581BZWelKJwOCVE/tviYEqeb/2oGormn8r+35bzvVFDw4xEILrxPcnvLffbyGwGvUtlNMgaeBts8UVz7vBepdVVUV0zrFez4FvKurq5PGKZw6Bbxrampc6eQzBSZq65G6f9sjMOq1A88FgjTTwYNDLKSa0uTniFjWaf/aHEqdqqurMW/evOD9uum5/FDGXlVVVdA7Hk7hklSL/OHDh+P111/HvHnzMHPmTKxfvx6jRo1CeXk5tm/fDp/Ph6ysrJDfad++PbZv397ofU6dOhWZmZnBr86dOwMACgsLAQBr1qzBmjVrAACrVq1CUVERAKCgoADr168HACxfvhybN28GAOTn52Pbtm0AgLy8POzatQsAsHjxYpSWlgIAFixYgPLycgBAbm5ucJH38ccfY8iQIfD7/cjNzQUAlJeXY8GCBQCA0tJSLF68GACwa9cu5OXlAQC2bduG/Px8AMDmzZuxfPlyAMD69etRUFAAoPbgyKpVq2LulJubC8uyUFVV1ahTXl4ehg4dij179iSNUzh1KigowNChQ4PtZHAKp05FRUUYOnRosJ0MTuHUaevWrRg6dChWrFiRNE7h1Km8vBxDhw7FkiVLksYpnDqJCAYOHIiPP/44aZzCqZPH40GvXr2CHm5zap8OPDSk9sValxbA3YNq270zBbcdXds+Jltw3VG17aFtBb/u48DvAN+UGDi/R+0Lt1M7OTi3W237zC4OzuxS2z63m4NTO9W2Lz3SwagOtS/oft3HceXznsfjQVZWVrA2Gp7LN2/eDI/HA5/Ph507dyaNUzh18ng8sCwreBDPbU63HW2jd2btnLt7kI0uLQCgds63T69tzxhmo6UPSPXUtlM9QLoXyEoF/E7TnyNiWScgOmNvz5498Hg88Hg8cR970XIKZ+xt3rwZGRkZ8Hg8cXEKl6S+un5paSm6du2KJ554Aunp6bjyyiuDR1UDDBs2DGPHjsX06dMbvI/q6uqQ3ykrK0Pnzp1RUlKC7Ozs4JEZj8cT0rYsC4ZhBNumacI0zUbbfr8fHo8n2PZ6vTAMI9gGao/y7N9OSUmBiATbjuPAtu1g23EceL3eRtu2bUNEgu2GPOhEJzrRiU50olOtR/dJH8LnAaptAyYEKSZQ7dS2vSZQ4xgwDYHXqG17DIFnv7ZpAP467cBZfEsMpJgCRwC7TttnCtY+fDrrRCc6HUan3nd/CMupjd+nmgJ/oO0R1NiAhLRrF/rVNmAAwecFA9Kk54jvHzmTdaLTQZ0qKiqQlZUV1tX1k3qRDwBDhw7FKaecglNPPRUnn3wy9uzZE3I2v2vXrrjllltw6623hnV/8fwIPb/fjwULFmDcuHFISUmJ6WPHE3rTWwP0prcG3O7d1I/YSvUIHhpi4w//8qDabtp7d934EVtur3dTobc7vTm/I8Pt9W4q8fRW+xF6ddm3bx/WrVuHnJwcHHvssUhJScGiRYuCt3/33XfYtGkTRowYEcdeho/X68WoUaOCR6O0QG96a4De9NaAVu8aG3iy0IMaO949iS1a601vXd6c37rq7RbvxO5dhNxxxx34xS9+ga5du2Lr1q24//774fF4cPHFFyMzMxNXXXUVbrvtNrRq1QotW7bEjTfeiBEjRrjiyvoAYBhGzNMDiQC9dUFvXdBbF1q9BQa2V8a7F7FHa73prQvOb124xTupzuT/8MMPuPjii9GnTx9ccMEFaN26Nb788ku0bdsWAPDkk0/izDPPxLnnnovRo0ejQ4cOeO+99+Lc6/Dx+/2YM2dO8AqQWqA3vTVAb3prQKt3qkfw9AgreNVtLWitN711eXN+66q3W7yT/j350Sae78kXEVRVVSEtLQ2GoefzOOlNbw3Qm94acLt3U9+za0DQ0geU1dSe9WsKbnzPrtvr3VTo7U5vzu/IcHu9m0o8vfme/CQm0d//cbigty7orQt660KjtwCo+u8VubWhsd4AvTXB+a0PN3hzke8i9v+sR03Qm94aoDe9NaDVe//P1NaE1nrTW5c357euervFm3H9CIl3XD/wWY/aYjH0pneyQ296a8Dt3k2N8wIS/ExtKIvzurneTYXe7vTm/I4Mt9e7qcTTm3H9JCbRjxodLuitC3rrgt660OhtAEjzNPXlv7vRWG+A3prg/NaHG7y5yHcRlmVhwYIFrhhY0YTe9NYAvemtAa3ePg/w0LE2fArjvBrrTW9d3pzfuurtFm/G9SMknnF9QgghhMSPpsd5Dx03xnkJcROc3yTRYVw/SRERlJWVQdtxGXrTWwP0prcGtHobEHRIFxjKrr+ttd701uXN+U3vRISLfBdhWRaWLl2a8PGQaENvemuA3vTWgFZvnwe4dYDOOK/GetNblzfnt656u8Wbcf0IYVyfEEII0QnjvIQkL5zfJNFhXD9JcRwHJSUlcBwn3l2JKfSmtwboTW8NaPU2IejWQmAqi/NqrTe9dXlzfuuqt1u8uch3EbZtY8WKFbBtO95diSn0prcG6E1vDWj1TjGBK3vbSFH2qktrvemty5vzW1e93eLNuH6EMK5PCCGE6IRxXkKSF85vkugwrp+kOI6DHTt2JHw8JNrQm94aoDe9NaDV24Sgb6ajMs6rsd701uXN+a2r3m7x5iLfRTiOg8LCwoQfVNGG3vTWAL3prQGt3l4TmNDNgVfZqy6t9aa3Lm/Ob131dos34/oRwrg+IYQQohPGeQlJXji/SaLDuH6S4jgOtmzZkvBHjqINvemtAXrTWwNavU1DMKiVA9PQdV5Fa73prcub81tXvd3izUW+i3AcB+vWrUv4QRVt6E1vDdCb3hrQ6u01gDEdHXiNePcktmitN711eXN+66q3W7wZ148QxvUJIYQQnTDOS0jywvlNEh3G9ZMUx3GwcePGhD9yFG3oTW8N0JveGtDq7TEEx7Vz4FEY59VYb3rr8ub81lVvt3hzke8i3PIekGhDb3prgN701oBWb48BDGot8CiM82qsN711eXN+66q3W7wZ148QxvUJIYQQnTDOS0jywvlNEh3G9ZMU27ZRXFwM27bj3ZWYQm96a4De9NaAVm+PIRiToy/Oq7Xe9Nblzfmtq95u8eYi30WICPbs2QNt4Qt601sD9Ka3BrR6mwbQLUNgKovzaq03vXV5c37rqrdbvBnXjxDG9QkhhBCdMM5LSPLC+U0SHcb1kxTbtrF27dqEj4dEG3rTWwP0prcGtHp7DMFpR9gq47wa601vXd6c37rq7RZvb7w7QCKjsrIy3l2IC/TWBb3dR1PPgKSYgnO7Ofi/PxfD7zQt6+jWMyBurvehoNHbNIAsX+13W9c6QGW9AXprgvNbH27wZlw/QhjXJ4SQ+jDmSDTAcU5I8sL5TRIdxvWTFNu2UVhYmPDxkGhDb3prQKu31xBM6GrDqzDmqLHeWr05znXVm966vDm/ddXbLd5c5BNCCCGEEEIIIUkC4/oRwrg+IYTUhzFHogGOc0KSF85vkugwrp+k2LaNgoKChI+HRBt601sDWr1TTMFFPWykmLqON2utt1ZvjnNd9aa3Lm/Ob131dos3F/kuIz09Pd5diAv01gW99eAIUFpT+10bGusN6PTmONcHvfXA+a0PN3gzrh8hjOsTQkh9GHMkGuA4JyR54fwmiQ7j+kmKZVlYsWIFLMuKd1diCr3prQGt3immYGJvfTFHrfXW6s1xrqve9Nblzfmtq95u8eYi30UYhoHs7GwYhhHvrsQUetNbA1q9HQE2lBvqYo5a663Vm+NcV73prcub81tXvd3i7Y13B0j4eDweHHnkkfHuRsyhty7orQtbDHyyLbH/UR4OtNZbqzfHuS7orQvOb124xZtn8l2EZVnIz89P+HhItKE3vTWg1dtnCq7tZ8OnMOaosd5avTnOddWb3rq8Ob911dst3lzkuwjTNNGpUyeYpq6y0ZveGtDqbQuwcrcBW9drI7X11urNca6r3vTW5c35ravebvFmXN9FmKaJrl27xrsbMYfeuqC3Lmwx8OUOfTFHrfXW6s1xrgt664LzWxdu8U7sQxAkBMuykJeXl/DxkGhDb3prQKu3zxTcMsBSGXPUWG+t3hznuupNb13enN+66u0Wby7yXYRpmujZs2fCx0OiDb3prQGt3pYAn2w1Yel6baS23lq9Oc511Zveurw5v3XV2y3ejOu7iMB7QLRBb13QWxeOGFhZojPmqLHeWr05znVBb11wfuvCLd6JfQiChGBZFhYvXpzw8ZBoQ296a0Crt88UTBqoM+aosd5avTnOddWb3rq8Ob911dst3lzkuwjTNDFgwICEj4dEG3rTWwNavS0H+McGE5YT757EFq311urNca6r3vTW5c35ravebvFmXN9FmKaJdu3axbsbMYfeuqC3LhwYWLtXZ8xRY721enOc64LeuuD81oVbvBP7EAQJwe/3Y/78+fD7/fHuSkyhN701oNU71RQ8OMRCqrKYo9Z6a/XmONdVb3rr8ub81lVvt3hzke8iPB4Phg4dCo/HE++uxBR601sDWr39DjDrew/8ymKOWuut1ZvjXFe96a3Lm/NbV73d4s24voswTROtWrWKdzdiDr11QW9dODCwYV+8exF7tNZbqzfHuS7orQvOb124xZtn8l2E3+/H3LlzEz4eEm3oTW8NaPVO9QimD7WQ6tEXc9RYb63eHOe66k1vXd6c37rq7RZvQ0R0jchDpKysDJmZmdi7dy9atmwZ08cWEZSXlyMjIwOGoecCH/Smtwbc7t1t0twm/Z4BQft04MdKQNA07w3TzmjS78UTt9e7qbjdm+M8Mtxe76ZCb3d6c35Hhtvr3VTi6R3JOpRxfRdhGEbMDywkAvTWBb11ITCwvTLevYg9Wuut1ZvjXBf01gXnty7c4s24vovw+/2YM2dOwsdDog296a0Brd6pHsHTI3TGHDXWW6s3x7muetNblzfnt656u8Wbcf0IiXdcv6qqCmlpaepiMfSmd7Ljdu9DiTm29AFlNfpijm6ud1NxuzfHeWS4vd5Nhd7u9Ob8jgy317upxNM7knUoz+S7DK9X5zss6K0LeutBAFTZtd+1obHegE5vjnN90FsPnN/6cIM3F/kuwrIs5ObmwrKseHclptCb3hrQ6p3qAWYMs5Ga2B83G3W01lurN8e5rnrTW5c357euervFm3H9CIl3XN+yLHi9XnWxGHrTO9lxu3dTY46AINUDVNsAlMUc3VzvpuJ2b47zyHB7vZsKvd3pzfkdGW6vd1OJpzfj+klMoh81OlzQWxf01oMBIM3T1JdF7kZjvQGd3hzn+qC3Hji/9eEGby7yXYRlWViwYIErBlY0oTe9NaDV2+cBHjrWhk9hzFFjvbV6c5zrqje9dXlzfuuqt1u8GdePkHjG9QkhJFFpeszx0HFjzJG4E45zQpIXzm+S6DCun6SICMrKyqDtuAy96a0Brd4GBB3SBYay6xJrrbdWb45zemtAqzfnN70TES7yXYRlWVi6dGnCx0OiDb3prQGt3j4PcOsAnTFHjfXW6s1xrqve9Nblzfmtq95u8WZcP0IY1yeEkPow5kg0wHFOSPLC+U0SHcb1kxTHcVBSUgLHceLdlZhCb3prQKu3CUG3FgJTWcxRa721enOc66o3vXV5c37rqrdbvLnIdxG2bWPFihWwbTveXYkp9Ka3BrR6p5jAlb1tpCj7b6S13lq9Oc511Zveurw5v3XV2y3ejOtHCOP6hBBSH8YciQY4zglJXji/SaLDuH6S4jgOduzYkfDxkGhDb3prQKu3CUHfTEdlzFFjvbV6c5zrqje9dXlzfuuqt1u8k2qRP3XqVAwdOhQZGRlo164dJkyYgO+++y5knzFjxsAwjJCva6+9Nk49jgzHcVBYWJjwgyra0JveGtDq7TWBCd0ceJPqv9HB0Vpvrd4c57rqTW9d3pzfuurtFu+kiuufdtppuOiiizB06FBYloW7774bhYWFWL16NZo3bw6gdpHfu3dvPPTQQ8Hfa9asWdjRe8b1CSGkPow5Eg1wnBOSvHB+k0RHbVx/3rx5mDhxIvr374+BAwfi9ddfx6ZNm/D111+H7NesWTN06NAh+OWWxbrjONiyZUvCHzmKNvSmtwa0epuGYFArB6aRNMebw0JrvbV6c5zrqje9dXlzfuuqt1u8k2qRX5e9e/cCAFq1ahWy/a233kKbNm0wYMAATJ48GRUVFY3eR3V1NcrKykK+AASvqGjbdoNty7JC2oGB0Fjb7/eHtAMBi0BbRFBdXY3i4mLYtg2/3w8AEJFg23GckLZlWQds27Yd0o6HU912Q041NTVYt24dLMtKGqdw6hTw9vv9SeMUTp38fn/QO1mcwqmTZVlYt24dampqXOuUYgo8/32R46vTNvdv//d9i6mmwGcIxnR00NwrMALbPYG2INVT+90IthHSNiExrVO0xp5t2yguLkZ1dXXM6xTP+eQ4DoqLi1FTU+NKp7pjL9X8X9sXaBv/a3v+2/YawJiODtI9/9ue8t99vIbA+9/5sf8cqjuf3Pi8F6h34D41PJc7jhP0DtxPMjiFU6eAd+B33eZU9/+TWe9/UsP/n1IMwdiODrxG058jYlmn/WtzKHWyLAvFxcXB2rvpufxQxp7f7w96x8MpXJJ2ke84Dm655RaccMIJGDBgQHD7JZdcgjfffBNLlizB5MmTMXv2bFx22WWN3s/UqVORmZkZ/OrcuTMAoLCwEACwZs0arFmzBgCwatUqFBUVAQAKCgqwfv16AMDy5cuxefNmAEB+fj62bdsGAMjLy8OuXbsAAIsXL0ZpaSkAYMGCBSgvLwcA5ObmoqqqCpZlYcGCBTj++ONhWRZyc3MBAOXl5ViwYAEAoLS0FIsXLwYA7Nq1C3l5eQCAbdu2IT8/HwCwefNmLF++HACwfv16FBQUAACKioqwatWqmDvl5ubCsixUVVU16vTpp59i9OjRKC0tTRqncOr0r3/9C6NHj8bmzZuTximcOhUVFWH06NFYvXp10jiFU6dt27Zh9OjRwbYbnS490sGoDrX/gH7dx8HQtrXt646ycUx2bfu2o230zqxt3z3IRodmBp4q9OLeQQ7apwMAMGOYjZY+INVT2071AC19tW0AaJ8OPDSktt2lBVz5vAcAw4YNC9ZGw3P54sWL4fV60a9fv6CH25zqjr27B9W2e2cKbju6tn1MtuC6o2rbQ9sKft3HQY1jYOVuE+f3qB37p3ZycG632hdxZ3ZxcGaX2va53Ryc2qm2XXc+ufF5z+v1omXLlsHaaHgu37x5M7xeLwzDwM6dO5PGKZw6eb1e7Nu3D5WVla50qvv/qUsLAKid8wf6/5TmNdAtA6hxjCY/R8SyTkB0xl5paSls24bX64372IuWUzhjb/PmzWjWrBm8Xm9cnMIlqd6Tvz+/+93v8NFHH+Gzzz7DEUcc0eh+ixcvxsknn4zi4mL07Nmz3u3V1dXBMy1A7XshOnfujJKSEmRnZwePzHg8npC2ZVkwDCPYNk0Tpmk22vb7/fB4PMF24B9EoA0ANTU12LZtGzp37gzHcZCSkgIRgWVZSElJCR5RCrQdx4HX6220bds2RCTYbsjjcDtZlhXSbsjJsixs27YNnTp1AoCkcAqnTgHvjh07wjCMpHAKp06O42Dr1q3o2LEjTNNMCqdw6gQAW7ZsQU5ODrxer+ucet4zDymmwBHAFgM+U2Dv17YEcAJtB3BgINUUOCI4ti2wqgSotAwIDKR6BDU2IKh9IVVtAwYAnweotg0YkGDbhOC7KeNd97xnmiY2b96MnJwcpKamqngut20bHo8HmzZtQseOHeHz+Vzn1H3ShyFjL8UEqp3attesfZFvGrVn7mscAx5D4DEAW4DhbR38a7eBKtuExxCYBuB3jOBZfEuMkDlUdz6tffh01z3vGYaBjRs34ogjjkBKSoqK53LTrD1/tmHDBnTp0gVerzcpnMKpU8C7a9eu8Hg8rnPqffeHIf+f/IF28H9Sw/+fvIbguHaCL3aYcARNeo74/pEzE/Z5r7E6WZaFTZs2oVu3bgDgqufyQxl7fr8fP/zwA7p27Ro8sx4rp4qKCmRlZYX1nnzvAW91KTfccAM+/PBD5OXlHXCBDwDDhw8HgEYX+ampqUhNTa23PfBkFvhetx0YTOG2U1JSDtr2eDzYunVr8J8lUPsPNNAODI5w2431PZZODbUbctqyZQs6deoUvP9kcAqnXdc7GZwO5iEiQe/AbW53aqhdt++WZQW9A/fpNie/YwTbNWG0qx0DPhMY1NrBv3aZENTeVm3vt0/t/0lISNsIth1E5pcozxGWZQWfzwE9z+V1vd3mVHfsVTv/a9cE2mKg5r+nT2wxYEttDPiY1sBXu4yQ7UDt4j7A/nOo7nxq6Hkh0Z8jAgerAylIDc/lAe/t27ejS5cuSeN0oHagX3W93eZU9/9TsG031q79bhrAgFaC5Ttr53ZTniMOl9PhHHsAgvUOnJyou4/bnMJpG4YRfF7bvy+xcDKM/42/g5FUZ/JFBDfeeCPef/99fPLJJ+jVq9dBf+fzzz/HyJEj8c033+CYY4456P68uj4hhNSHVyUmGuA4JyR54fwmiY7aq+tff/31ePPNN/H2228jIyMD27dvx/bt24PvDVq3bh2mTJmCr7/+Ghs2bMA///lPXH755Rg9enRYC/x4Y9t2yAVNtEBvemtAq7fHEIzJcYIXGNOC1npr9eY411Vveuvy5vzWVW+3eCfVIn/mzJnYu3cvxowZg5ycnODXX//6VwCAz+fDwoULMW7cOPTt2xe33347zj33XHzwwQdx7nl4iAj27NkT0ZUVkwF601sDWr1NA+iWUfv+ZE1orbdWb45zXfWmty5vzm9d9XaLd1LF9WMB4/qEEFIfxhyJBjjOCUleOL9JoqM2rp/s2LaNtWvXJnw8JNrQm94a0OrtMQSnHWGrjDlqrLdWb45zXfWmty5vzm9d9XaLd1JeXT+ZCVxfQBv01gW99WAaQJav9rut6/WRynoDOr3dPs6beoYzxRSc283B//25OOTTAiLBrWc4NY5zQKe32+f3oaCx3oA7vBnXjxDG9QkhpD6MORINaB3nWr2JLjjOSaLDuH6SYts2CgsLEz4eEm3oTW8NaPX2GoIJXW14FcYcNdZbq7fWca7VW+s41+rNca6r3m7x5iKfEEIIIYQQQghJEviefBfh8XgwYMCAeHcj5tBbF/TWhSUG/rHRE+9uxByt9dbqrXWca/XWOs61enOc68It3jyT7yJs20ZBQUHCx0OiDb3prQGt3imm4KIeNlJMfTFHjfXW6q11nGv11jrOtXpznOuqt1u8uch3Genp6fHuQlygty7orQdHgNKa2u/a0FhvQKe31nGu1RvQOc4Bnd4c5/pwgzfj+i7C4/Ggb9++8e5GzKG3LuitC1sMzPtBZ8xRY721emsd51q9tY5zrd4c57pwizfP5LsIy7KwYsUKWJYV767EFHrTWwNavVNMwcTe+mKOWuut1VvrONfqrXWca/XmONdVb7d4c5HvIgzDQHZ2NgzDiHdXYgq96a0Brd6OABvKDXUxR6311uqtdZxr9dY6zrV6c5zrqrdbvBnXdxEejwdHHnlkvLsRc+itC3rrwhYDn2xL7H+UhwOt9dbqrXWca/XWOs61enOc68It3jyT7yIsy0J+fn7Cx0OiDb3prQGt3j5TcG0/Gz6FMUeN9dbqrXWca/XWOs61enOc66q3W7y5yHcRpmmiU6dOME1dZaM3vTWg1dsWYOVuA7au10Zq663VW+s41+qtdZxr9eY411Vvt3gzru8iTNNE165d492NmENvd9Jt0txDvIfCJv/mhmlnHOJjxx6317up2GLgyx36Yo5a663VW+s41+qtdZxr9eY414VbvBP7EAQJwbIs5OXlJXw8JNrQW5e3zxTcMsBSGXtjvfWgtd5avbWOc63eWse5Vm+Oc131dos3F/kuwjRN9OzZM+HjIdGG3rq8LQE+2WrC0vW/kvVmvVWg1VvrONfqrXWca/XmONdVb7d4M67vIgLvAdEGvXXhiIGVJTpjb6y3HrTWW6u31nGu1VvrONfqzXGuC7d4c5HvIgLxkNGjR8Pr1VM6euvy9pmC24628cS/Pahx9PzTZL3dWe+mXnsiGt5uvPYEx7k7x3lT0eqtdZxr9eY411Vvt3gnds6AhGCaJgYMGJDw8ZBoQ29d3pYD/GODCcuJd09iC+sd757EFq3eHOfx7kls0eqtdZxr9eY411Vvt3gn7uEHUg/TNNGuXbt4dyPm0FsXDgys3avnSHgA1lsXWr05znWh1VvrONfqzXGuC7d4J/YhCBKC3+/H/Pnz4ff7492VmEJvXd6ppuDBIRZSlV2llvXWVW+t3hznuuqt1VvrONfqzXGuq95u8eYi30V4PB4MHToUHo8n3l2JKfTW5e13gFnfe+BXFntjvePdk9ii1ZvjPN49iS1avbWOc63eHOe66u0Wb8b1XYRpmmjVqlW8uxFz6K0LBwY27It3L2IP660Lrd4c57rQ6q11nGv15jjXhVu8eSbfRfj9fsydOzfh4yHRht66vFM9gulDLaR69MXeWG89aPXmONdVb63eWse5Vm+Oc131dos3F/kuwuv1YtSoUQn9cQ2HA3rr8q6xgScLPaix492T2MJ6x7snsUWrN8d5vHsSW7R6ax3nWr05znXV2y3eid07EoJhGGjZsmW8uxFz6K0LgYHtlfHuRexhvXWh1ZvjXBdavbWOc63eHOe6cIs3z+S7CL/fjzlz5iR8PCTa0FuXd6pH8PQInbE31lsPWr05znXVW6u31nGu1ZvjXFe93eLNRb6L8Hq9GDduXMLHQ6INvXV519jAH77WGXtjvfWg1ZvjPN49iS1avbWOc63eHOe66u0Wby7yXUaiD6jDBb31IACq7Nrv2mC99aDVG+A414RWb0DnOAd0enOc68MN3lzkuwjLspCbmwvLsuLdlZhCb13eqR5gxjAbqYn98aNRh/WOd09ii1ZvjvN49yS2aPXWOs61enOc66q3W7y5yHcRXq8Xp59+uiuOHkUTeuvyrraBu5Z7UK0w9sZ660GrN8d5vHsSW7R6ax3nWr05znXV2y3eXOS7jEQ/anS4oLceDABpntrv2mC99aDVG+A414RWb0DnOAd0enOc68MN3lzkuwjLsrBgwQJXDKxoQm9d3j4P8NCxNnwKY2+stx60enOcx7snsUWrt9ZxrtWb41xXvd3ibYiIxutENJmysjJkZmZi7969rviMRELiRbdJc+P22BumnRG3x9aK1npr9daK1npr9Sa64DgniU4k61CeyXcRIoKysjJoOy5Db13eBgQd0gWGsuvUst701gDHOb01oHWca/XmOKd3IsJFvouwLAtLly5N+HhItKG3Lm+fB7h1gM7YG+utB63eHOfx7kls0eqtdZxr9eY411Vvt3gzrh8hjOsTEh6MvelCa721emtFa721ehNdcJyTROewx/Vnz56NE044AR07dsTGjRsBAE899RTmzJnTlLsjYeI4DkpKSuA4Try7ElPorcvbhKBbC4GpLPbGeuuqt1ZvjnNd9dbqrXWca/XmONdVb7d4R7zInzlzJm677TacfvrpKC0thW3XfihkVlYWnnrqqWj3j+yHbdtYsWJF8G+uBXrr8k4xgSt720hR9mYi1jvePYktWr05zuPdk9ii1VvrONfqzXGuq95u8Y44rn/UUUfhkUcewYQJE5CRkYFvvvkGPXr0QGFhIcaMGYNdu3Ydrr4mBIzrExIejL3pQmu9tXprRWu9tXoTXXCck0TnsMb1169fj8GDB9fbnpqaip9++inSuyMR4DgOduzYkfDxkGhDb13eJgR9Mx2VsTfWWw9avTnOddVbq7fWca7Vm+NcV73d4h3xIr979+5YuXJlve3z5s1Dv379otEn0giO46CwsDDhB1W0obcub68JTOjmwKss9sZ6x7snsUWrN8d5vHsSW7R6ax3nWr05znXV2y3e3kh/4bbbbsP111+PqqoqiAiWL1+Od955B1OnTsUrr7xyOPpI/ovX68VJJ50U727EHHrrosYxMO2biJ+aXA/rrQut3hznutDqrXWca/XmONeFW7wjPub0m9/8BtOnT8e9996LiooKXHLJJZg5cyaefvppXHTRRYejj+S/OI6DLVu2JPyRo2hDb13epiEY1MqBaeiLvbHeetDqzXGuq95avbWOc63eHOe66u0W7yYFSy699FIUFRVh37592L59O3744QdcddVV0e4bqYPjOFi3bl3CD6poQ29d3l4DGNPRgdeId09iC+sd757EFq3eHOfx7kls0eqtdZxr9eY411Vvt3gfUrakWbNmaNasWbT6Qg6C1+vF6NGj492NmENvXdQ4Bp4q1Bl7Y731oNWb41wXWr21jnOt3hznunCLd8Rn8gcPHowhQ4bU+zr22GNxwgkn4IorrsCSJUsOR1/V4zgONm7cmPBHjqINvXV5ewzBce0ceBTG3lhvPWj15jjXVW+t3lrHuVZvjnNd9XaLd8SL/NNOOw3/+c9/0Lx5c4wdOxZjx45FixYtsG7dOgwdOhTbtm3DKaecgjlz5hyO/qrGLe8BiTb01uXtMYBBrQUehbE31lsPWr05zuPdk9ii1VvrONfqzXGuq95u8Y44W7Jr1y7cfvvtuO+++0K2P/zww9i4cSMWLFiA+++/H1OmTMHZZ58dtY6S2njI8ccfH+9uxBx666LGMfDCGk+8uxFzWG9daPXmONeFVm+t41yrN8e5LtziHfGZ/L/97W+4+OKL622/6KKL8Le//Q0AcPHFF+O777479N6REGzbRnFxMWzbjndXYgq9dXl7DMGYHH2xN9ZbV721enOc66q3Vm+t41yrN8e5rnq7xTviRX5aWhry8/Prbc/Pz0daWhqA2hhDoE2ih4hgz549ENH1JEJvXd6mAXTLEJjKYm+sd7x7Elu0enOcx7snsUWrt9ZxrtWb41xXvd3iHXFc/8Ybb8S1116Lr7/+GkOHDgUArFixAq+88gruvvtuAMD8+fMxaNCgqHaU1MZDAn9zTdBbF37HwOvf64y9sd560OrNca4Lrd5ax7lWb45zXbjFO+Iz+ffeey9efvllLF++HDfddBNuuukmLF++HC+//DLuueceAMC1116LDz74IOqd1Y5t21i7dm3Cx0OiDb11eXsMwWlH2Cpjb6y3HrR6c5zrqrdWb63jXKs3x7muervFu0kf6njppZfi0ksvbfT29PT0JneIHJjKysp4dyEu0FsPpgFk+Wq/27r+X7Leiuqt1RvgONdUb63egM5xDuj05jjXhxu8m7TIB4Camhrs2LGj3scHdOnS5ZA7RRrG4/Fg8ODB8e5GzKG3LvyOgb/8R1/sjfXWhVZvjnNdaPXWOs61enOc68It3hHH9YuKijBq1Cikp6eja9eu6N69O7p3745u3bqhe/fuh6OP5L/Yto3CwsKEj4dEG3rr8vYaggldbXgVxt5Ybz1o9eY411Vvrd5ax7lWb45zXfV2i3fEZ/InTpwIr9eLDz/8EDk5OTAMZZeSJIQQQgghhBBCEpSIF/krV67E119/jb59+x6O/pAD4PF4MGDAgHh3I+bQWxeWGPjHRp2xN9ZbD1q9Oc51odVb6zjX6s1xrgu3eEcc1z/qqKOwa9euw9EXchBs20ZBQUHCx0OiDb11eaeYgot62Egx9cXeWG89aPXmONdVb63eWse5Vm+Oc131dot3xIv86dOn46677sInn3yC3bt3o6ysLOSLHF60fnIBvfXgCFBaU/tdG6y3HrR6AxznmtDqDegc54BOb45zfbjBO+K4/imnnAIAOPnkk0O2iwgMw0j4oxpuxuPxqHybBL11YYuBeT/ojL2x3nrQ6s1xrgut3lrHuVZvjnNduMU74jP5S5YswZIlS7B48eKQr8A2cviwLAsrVqyAZVnx7kpMobcu7xRTMLG3vtgb662r3lq9Oc511Vurt9ZxrtWb41xXvd3iHfGZ/BNPPPFw9IOEgWEYyM7OVveJBvTW5e0IsKHcUBd7Y73j3ZPYotWb4zzePYktWr21jnOt3hznuurtFu+IF/kBKioqsGnTJtTU1IRsP+aYYw65U6RhPB4PjjzyyHh3I+bQWxe2GPhkW2I/cR4OWG9daPXmONeFVm+t41yrN8e5LtziHXFcf+fOnTjzzDORkZGB/v37Y/DgwSFf5PBhWRby8/MTPh4Sbeity9tnCq7tZ8OnMPbGeutBqzfHua56a/XWOs61enOc66q3W7wjXuTfcsstKC0txbJly5Ceno558+bhjTfeQK9evfDPf/7zcPTxsPDcc8+hW7duSEtLw/Dhw7F8+fJ4d+mgmKaJTp06wTQjLpurobcub1uAlbsN2Lr+V7Leyuqt1ZvjPN49iS1avbWOc63eHOe66u0W74jj+osXL8acOXPws5/9DKZpomvXrjj11FPRsmVLTJ06FWecccbh6GdU+etf/4rbbrsNL7zwAoYPH46nnnoK48ePx3fffYd27drFu3uNEvh7a4PeurDFwJc79MXeWG9daPXmONeFVm+t41yrN8e5LtziHfEi/6effgouhLOzs7Fz50707t0bRx99NP71r39FvYOHgyeeeAK//e1vceWVVwIAXnjhBcydOxevvfYaJk2aFOfeNY5lWViS9zmGHzccXm+TL6fgOizLwrIvl9FbCSmG4Op+Nl5a44FfmvZPs6ImsSNUDcF666q3Vm+Oc1311uqtdZxr9eY411XveHpHMk4MEYkoXDJ06FA8/PDDGD9+PM466yxkZWVh6tSpeOaZZ/D3v/8d69ati7jDsaSmpgbNmjXD3//+d0yYMCG4/YorrkBpaSnmzJkTsn91dTWqq6uDP5eVlaFz584oKSlBdnY2bNsGUHsRhv3blmXBMIxg2zRNmKbZaNvv98Pj8QTbXq8XhmEE24G+97l/4WH+CxFCCCGEEEIISSSc6gpsfuoC7N27Fy1btjzgvhG/meDmm2/Gtm3bAAD3338/PvroI3Tp0gXPPPMMHnnkkab1OIbs2rULtm2jffv2Idvbt2+P7du319t/6tSpyMzMDH517twZAFBYWAgAWLNmDdasWQMAWLVqFYqKigAABQUFWL9+PQBg+fLl2Lx5MwAgPz8/+PfLy8vDrl27ANS+DaK0tBQAsGDBApSXlwMAcnNzUVVVBcuyMG/evKj9HQghhBBCCCGEJB8Rn8mvS0VFBdauXYsuXbqgTZs20erXYWPr1q3o1KkT8vPzMWLEiOD2u+66C59++imWLVsWsn8incmvqqrCkqWf44QTTgAApKSkQERgWRZSUlLgOA5s2w62HceB1+tttG3bNkQk2G7I43A7WZYV0m7IqabGj2VffokRxx8P0zSSwimcOgW8jxsxAh6PmRRO4dTJsmx8+cUXOG7ECHi9Htc5HfPQIpgQmAZgiQEPap9ibRjwGgJHAKdOO8UQeAzBdUc5eGGNiUrbgPx3uyWAwIDPEPgDbVNQ4wAIaQPf3HdyzOoUrbEnAnyRn49hw4cjLS01rmMvlvMJAD7//HMMH34c0tPTXOfU//4FIWPPZ6JO2wAgwbYBQYoBCIAb+9t4cY2Jn2wTBgReA/CLARMCz37twByqO59W3n+q6573AOCzzz7DiBHHIzXVp+K5HABEJMQ7GZzCqZPjOPjss89w/PEnwOdLSQqncOoU8D7hhBOQkpKSFE7h1MmyLHz++ecYOXJk8P7d7hROnWpq/MjPr/UOjHu3O4VTp+rqGnzxRT5GjhwJwzBi6lRRUYGO7dqEdSb/kBf5biPSuH5dysrKkJmZGdYfN9o4joNdu3ahTZs2MM3EvqJjNKE3vd1Et0lzm/R7JgS9MwXf7zXgoGnv6dswLfEvfFoXt9e7qbjdm+M8Mtxe76ZCb3prgN70jhWRrEMjvlqAbdt4/fXXsWjRIuzYsSN4hDrA4sWLI73LmOLz+XDsscdi0aJFwUW+4zhYtGgRbrjhhvh27iCYppnQV/8/XNBbF1q9HRhYu1fn1Xk11lurN8e5LuitC3rrgt6JTZPek3/zzTfDtm0MGDAAAwcODPlyA7fddhtefvllvPHGG1izZg1+97vf4aeffgpebT9R8fv9mD9/Pvx+f7y7ElPoTW8NpJqCB4dYSDVVhavU1lurN8e5rnrTm94aoDe9E5GIz+T/5S9/wd/+9jecfvrph6M/MeHCCy/Ezp078Yc//AHbt2/HoEGDMG/evHoX40s0PB4Phg4dCo/HE++uxBR601sDfgeY9b0Hfufg+yYTWuut1ZvjXFe96U1vDdCb3olIxO/J79ixIz755BP07t37cPUpoYnne/IJIYlPU9+rHA3c+F5l4k44zgkhhJDYEsk6NOK4/u23346nn34ayq7XlxD4/X7MnTs34eMh0Ybe9NZAqkcwfaiFVI+u51at9dbqzXGuq970prcG6E3vRCSsM/nnnHNOyM+LFy9Gq1at0L9/f6SkpITc9t5770W3hwlGPM/kiwjKy8uRkZER/MgGDdCb3m6iqWc4DQjapwM/VtZ+TF5TcOMZTrfXu6m43ZvjPDLcXu+mQm96a4De9I4VUb+6fmZmZsjPv/zlL5veO9JkDMNQ+RYBeutCq7fAwPbKePci9mitt1ZvjnNd0FsX9NYFvRObsOL6s2bNCvuLHD78fj/mzJmT8PGQaENvemsg1SN4eoTOGLPGemv15jjXVW9601sD9KZ3IhLxhffWr18Py7LQq1evkO1FRUVISUlBt27dotm/hCPecf2qqiqkpaWpi8XQm95u4VBizC19QFmNvhizm+vdVNzuzXEeGW6vd1OhN701QG96x4rDeuG9iRMnIj8/v972ZcuWYeLEiZHeHYkQrzfiTz1MCuitC43eAqDKrv2uDY31BnR6c5zrg966oLcu6J24RLzILygowAknnFBv+3HHHYeVK1dGo0+kESzLQm5uLizLindXYgq96a2BVA8wY5iN1MT+2NWoo7XeWr05znXVm9701gC96Z2IRBzXz8zMxCeffILBgweHbP/6668xZswYlJeXR7WDiUa84/qWZcHr9aqLxdCb3m6h6Z8fLkj1ANU2AGUxZjfXu6m43ZvjPDLcXu+mQm96a4De9I4VhzWuP3r0aEydOhW2bQe32baNqVOnYuTIkZH3lkREoh81OlzQWxcavQ0AaZ6mLnvcjcZ6Azq9Oc71QW9d0FsX9E5cIl7kT58+HYsXL0afPn1w5ZVX4sorr0SfPn2Ql5eHRx999HD0kfwXy7KwYMECVwysaEJvemvA5wEeOtaGT2GMWWO9tXpznOuqN73prQF60zsRiTiuDwBbt27Fs88+i2+++Qbp6ek45phjcMMNN6BVq1aHo48JRTzj+oSQxKfpMeZDx40xZuJOOM4JIYSQ2BLJOrRJlwbs2LEjHnnkkSZ1jjQdEUF5eTkyMjLUvfeF3vROdgwI2qcDP1Y2/aPF3IjWemv15jjXVW9601sD9KZ3IpL41/8nQSzLwtKlSzFu3DikpKTEuzsxg970dhNNPcvo9/uxYMEC13o3FbfXu6lo9fZ5gFsH2PjDvzz/vfieDrTWm9701gC96Z2INCmurxnG9QkhhGiHcX1CCCEkthzWq+uT+OE4DkpKSuA4Try7ElPoTW8N0JveGjAh6NZCYELX+QWt9aY3vTVAb3onIlzkuwjbtrFixYqQjy/UAL3prQF601sDKSZwZW8bKcpefWitN73prQF60zsROaS4/q5du7Bs2TLYto2hQ4ciJycnmn1LSBjXJ4QQoh3G9QkhhJDYEpO4/v/93//hyCOPxIMPPoj7778fPXv2xKxZs5p6dyQMHMfBjh07Ej4eEm3oTW8N0JveGjAh6JvpqIzra6w3vemtAXrTOxEJe5G/b9++kJ8ffPBBLF++HMuXL0dBQQHeffdd3HPPPVHvIPkfjuOgsLAw4QdVtKE3vTVAb3prwGsCE7o58CqL62utN73prQF60zsRCTuu36dPH8yYMQNnn302AGDIkCF45plnMHLkSADAa6+9hoceeggbNmw4bJ1NBBjXJ4QQoh3G9QkhhJDYclji+vPnz8dLL72EX/7yl9i6dSuefvppXHjhhejQoQPatGmDSZMm4fnnnz/kzpPGcRwHW7ZsSfgjR9GG3vTWAL3prQHTEAxq5cA09MX1Ndab3vTWAL3pnYiEvcjv1q0b5s6diwsuuAAnnngiVq5cieLiYnz88cdYuHAhNm3ahNNPP/1w9lU9juNg3bp1CT+oog296a0BetNbA14DGNPRgdeId09ii9Z605veGqA3vRORJl1dv7S0FHfccQf+/e9/46WXXsLAgQMPR98SEsb1CSGEaIdxfUIIISS2HLar6+fm5uLxxx/HV199hVdeeQUzZszApZdeijvvvBOVlZWH1GlycBzHwcaNGxP+yFG0oTe9NUBvemvAYwiOa+fAozCur7He9Ka3BuhN70Qk7EX+7bffjiuvvBIrVqzANddcgylTpuDEE0/Ev/71L6SlpWHw4MH46KOPDmdf1eOW94BEG3rTWwP0prcGPAYwqLXAozCur7He9Ka3BuhN70Qk7Lh+69atsWDBAhx77LEoKSnBcccdh++//z54++rVq3HNNddg6dKlh62ziQDj+oQQQrTDuD4hhBASWw5LXL958+ZYv349AGDz5s1IS0sLuf2oo45K+gV+vLFtG8XFxbBtO95diSn0prcG6E1vDXgMwZgcfXF9rfWmN701QG96JyJhL/KnTp2Kyy+/HB07dsSJJ56IKVOmHM5+kQYQEezZswdNuFaiq6E3vTVAb3prwDSAbhkCU1lcX2u96U1vDdCb3olIRFfX3717N/7zn/+gV69eyMrKOozdSlwY1yeEEKIdxvUJIYSQ2HLYrq7funVrDB06VO0CP97Yto21a9cmfDwk2tCb3hqgN7014DEEpx1hq4zra6w3vemtAXrTOxGJaJFP4o/Wjyqkty7orQt668E0gCwf1MX1AZ31BuitDXrrgt6JS0RxfcK4PiGEEMK4PiGEEBJbDltcn8QX27ZRWFiY8PGQaENvemuA3vTWgNcQTOhqw6swrq+x3vSmtwboTe9EhIt8QgghhBBCCCEkSWBcP0IY1yeEEKIdxvUJIYSQ2MK4fpJi2zYKCgoSPh4SbehNbw3Qm94aSDEFF/WwkWLqOr+gtd70prcG6E3vRISLfJeRnp4e7y7EBXrrgt66oLceHAFKa2q/a0NjvQF6a4PeuqB34sK4foQwrk8IIUQ7jOsTQgghsYVx/STFsiysWLEClmXFuysxhd701gC96a2BFFMwsbe+uL7WetOb3hqgN70TES7yXYRhGMjOzoZhGPHuSkyhN701QG96a8ARYEO5oS6ur7Xe9Ka3BuhN70SEcf0IYVyfEEKIdhjXJ4QQQmIL4/pJimVZyM/PT/h4SLShN701QG96a8BnCq7tZ8OnMK6vsd70prcG6E3vRISLfBdhmiY6deoE09RVNnrTWwP0prcGbAFW7jZg61rjq603vemtAXrTOxFhXD9CGNcnhBCiHcb1CSGEkNgSyTrUG6M+kSgQiIccf/zx8Hr1lI7e9NYAventJpq60Ha7d1OhN701QG96a8At3omdMyAhmKaJnj17Jnw8JNrQm94aoDe9NUBvemuA3vTWAL0T25tx/QhhXJ8QQgghhBBCSCzh1fWTFMuysHjx4oS/mmO0oTe9NUBvemuA3vTWAL3prQF6J7Y3F/kuwjRNDBgwIOHjIdGG3vTWAL3prQF601sD9Ka3Buid2N6M60cI4/qEEEIIIYQQQmIJ4/pJit/vx/z58+H3++PdlZhCb3prgN701gC96a0BetNbA/RObG+eyY+QeJ7JdxwHpaWlyMrKSviISDShN701QG96a4De9NYAvemtAXrH3juSdSgX+RHCuD4hhBBCCCGEkFjCuH6S4vf7MXfu3ISPh0QbetNbA/SmtwboTW8N0JveGqB3YnvzTH6ExPNMvoigvLwcGRkZMAwjpo8dT+hNbw3Qm94aoDe9NUBvemuA3rH3Zlz/MMK4PiGEEEIIIYSQWMK4fpLi9/sxZ86chI+HRBt601sD9Ka3BuhNbw3Qm94aoHdie/NMfoTEO65fVVWFtLQ0dbEYetM72aE3vTVAb3prgN701gC9Y+/NM/lJjNfrjXcX4gK9dUFvXdBbF/TWBb11QW9d0Dtx4SLfRViWhdzcXFiWFe+uxBR601sD9Ka3BuhNbw3Qm94aoHdiezOuHyHxjutblgWv16suFkNveic79Ka3BuhNbw3Qm94aoHfsvRnXT2IS/ajR4YLeuqC3LuitC3rrgt66oLcu6J24cJHvIizLwoIFC1wxsKIJvemtAXrTWwP0prcG6E1vDdA7sb0Z14+QeMb1CSGEEEIIIYTog3H9JEVEUFZWBm3HZehNbw3Qm94aoDe9NUBvemuA3ontzUW+i7AsC0uXLk34eEi0oTe9NUBvemuA3vTWAL3prQF6J7Z30sT1N2zYgClTpmDx4sXYvn07OnbsiMsuuwz33HMPfD5fcJ/u3bvX+90vvvgCxx13XFiPw7g+IYQQQgghhJBYojKuv3btWjiOgxdffBHffvstnnzySbzwwgu4++676+27cOFCbNu2Lfh17LHHxqHHkeM4DkpKSuA4Try7ElPoTW8N0JveGqA3vTVAb3prgN6J7Z00i/zTTjsNs2bNwrhx49CjRw+cddZZuOOOO/Dee+/V27d169bo0KFD8CslJSUOPY4c27axYsUK2LYd767EFHrTWwP0prcG6E1vDdCb3hqgd2J7J01cvyHuvfdezJs3D1999RWA/8X1O3fujKqqKvTu3Rt33XUXzjrrrEbvo7q6GtXV1cGfy8rK0LlzZ5SUlCA7OztYYI/HE9K2LAuGYQTbpmnCNM1G236/Hx6PJ9j2er0wDCPYBmrfA7J/OyUlBSISbDuOA9u2g23HceD1ehtt27YNEQm2G/KgE53oRCc60YlOdKITnehEJzrF16miogJZWVm64vp1KS4uxp/+9Cdcc801wW0tWrTA448/jnfffRdz587FyJEjMWHCBPzzn/9s9H6mTp2KzMzM4Ffnzp0BAIWFhQCANWvWYM2aNQCAVatWoaioCABQUFCA9evXAwCWL1+OzZs3AwDy8/Oxbds2AEBeXh527doFAFi8eDFKS0sBAAsWLEB5eTkAIDc3F1VVVbAsC7m5udi2bRsqKiqQm5sLACgvL8eCBQsAAKWlpVi8eDEAYNeuXcjLywMAbNu2Dfn5+QCAzZs3Y/ny5QCA9evXo6CgAABQVFSEVatWxcXJsixUVVUd0GnHjh3YsWNHUjmFU6cdO3bgP//5T1I5HaxOq1evxo4dO5LKKZw6bdy4ETt27Egqp3DqVFJSgh07diSVUzh1qqmpwQ8//JBUTuHUyXEcFBcX49NPP00ap3Dq5DgO/v3vf+Nf//pX0jiFUyfHcbB8+XJ8//33SeMUTp0cx8Gnn36KrVu3Jo1TOHVyHAfz589HWVlZ0jiFU6fA63LHcZLGKZw67dixI/i8nixO4dTpP//5D7744gs4jhMXp7CRBOf3v/+9ADjg15o1a0J+54cffpCePXvKVVddddD7/9WvfiUjR45s9PaqqirZu3dv8Gvz5s0CQEpKSkRExLIssSyrXtvv94e0bds+YLumpiak7ThOSNtxHKmoqJCFCxdKTU2N1NTUiIiI4zjBtm3bIW2/33/AtmVZIe2GPA63U912Q06VlZWyaNEiqa6uThqncOoU8K6qqkoap3DqVFVVFfROFqdw6lRdXS2LFi2SysrKpHEKp04B74qKiqRxCqdONTU1snDhQqmoqEgap3Dq5Pf7ZeHChVJZWZk0TuHUKeBdVVWVNE7h1KmudzI4hVOngHd1dXXSOIVTp4B3oG/J4BROnQLP536/P2mcwqlTdXV10DtZnMKpU1VVVdA71k6lpaUCQPbu3SsHI+Hj+jt37sTu3bsPuE+PHj2CV9DfunUrxowZg+OOOw6vv/46TPPAYYXnnnsODz/8cPBIysHg1fUJIYQQQgghhMSSpLq6ftu2bdG3b98DfgUW+Fu2bMGYMWNw7LHHYtasWQdd4APAypUrkZOTc7g1ooLjONiyZQscJ7Gv5hht6E1vDdCb3hqgN701QG96a4Deie2d8Iv8cAks8Lt06YLHHnsMO3fuxPbt27F9+/bgPm+88QbeeecdrF27FmvXrsUjjzyC1157DTfeeGMcex4+juNg3bp1CT+oog296a0BetNbA/SmtwboTW8N0DuxvRM+rh8ur7/+Oq688soGbwsovvHGG5g+fTo2btwIr9eLvn374s4778R5550X9uMwrk8IIYQQQgghJJYkVVw/XCZOnAgRafArwBVXXIHVq1fjp59+wt69e7Fs2bKIFvjxxnEcbNy4MeGPHEUbetNbA/SmtwboTW8N0JveGqB3YnsnzSJfA255D0i0oTe9NUBvemuA3vTWAL3prQF6J7Z30sT1YwXj+oQQQgghhBBCYonKuL4GbNtGcXExbNuOd1diCr3prQF601sD9Ka3BuhNbw3QO7G9uch3ESKCPXv2QFv4gt701gC96a0BetNbA/SmtwbondjejOtHCOP6hBBCCCGEEEJiCeP6SYpt21i7dm3Cx0OiDb3prQF601sD9Ka3BuhNbw3QO7G9uch3GZWVlfHuQlygty7orQt664LeuqC3LuitC3onLozrRwjj+oQQQgghhBBCYgnj+kmKbdsoLCxM+HhItKE3vTVAb3prgN701gC96a0Beie2Nxf5hBBCCCGEEEJIksC4foQwrk8IIYQQQgghJJYwrp+k2LaNgoKChI+HRBt601sD9Ka3BuhNbw3Qm94aoHdie3OR7zLS09Pj3YW4QG9d0FsX9NYFvXVBb13QWxf0TlwY148QxvUJIYQQQgghhMQSxvWTFMuysGLFCliWFe+uxBR601sD9Ka3BuhNbw3Qm94aoHdie3OR7yIMw0B2djYMw4h3V2IKvemtAXrTWwP0prcG6E1vDdA7sb0Z148QxvUJIYQQQgghhMQSxvWTFMuykJ+fn/DxkGhDb3prgN701gC96a0BetNbA/RObG8u8l2EaZro1KkTTFNX2ehNbw3Qm94aoDe9NUBvemuA3ontzbh+hDCuTwghhBBCCCEkljCun6RYloW8vLyEj4dEG3rTWwP0prcG6E1vDdCb3hqgd2J7c5HvIkzTRM+ePRM+HhJt6E1vDdCb3hqgN701QG96a4Deie3NuH6EMK5PCCGEEEIIISSWMK6fpFiWhcWLFyd8PCTa0JveGqA3vTVAb3prgN701gC9E9ubi3wXYZomBgwYkPDxkGhDb3prgN701gC96a0BetNbA/RObG/G9SOEcX1CCCGEEEIIIbGEcf0kxe/3Y/78+fD7/fHuSkyhN701QG96a4De9NYAvemtAXontjfP5EdIPM/kO46D0tJSZGVlJXxEJJrQm94aoDe9NUBvemuA3vTWAL1j7x3JOpSL/AhhXJ8QQgghhBBCSCxhXD9J8fv9mDt3bsLHQ6INvemtAXrTWwP0prcG6E1vDdA7sb15Jj9C4nkmX0RQXl6OjIwMGIYR08eOJ/SmtwboTW8N0JveGqA3vTVA79h7M65/GGFcnxBCCCGEEEJILGFcP0nx+/2YM2dOwsdDog296a0BetNbA/SmtwboTW8N0DuxvXkmP0LiHdevqqpCWlqaulgMvemd7NCb3hqgN701QG96a4Desffmmfwkxuv1xrsLcYHeuqC3LuitC3rrgt66oLcu6J24cJHvIizLQm5uLizLindXYgq96a0BetNbA/SmtwboTW8N0DuxvRnXj5B4x/Uty4LX61UXi6E3vZMdetNbA/SmtwboTW8N0Dv23ozrJzGJftTocEFvXdBbF/TWBb11QW9d0FsX9E5cuMh3EZZlYcGCBa4YWNGE3vTWAL3prQF601sD9Ka3Buid2N6M60dIPOP6hBBCCCGEEEL0wbh+kiIiKCsrg7bjMvSmtwboTW8N0JveGqA3vTVA78T25iLfRViWhaVLlyZ8PCTa0JveGqA3vTVAb3prgN701gC9E9ubcf0IYVyfEEIIIYQQQkgsYVw/SXEcByUlJXAcJ95diSn0prcG6E1vDdCb3hqgN701QO/E9uYi30XYto0VK1bAtu14dyWm0JveGqA3vTVAb3prgN701gC9E9ubcf0IYVyfEEIIIYQQQkgsYVw/SXEcBzt27Ej4eEi0oTe9NUBvemuA3vTWAL3prQF6J7Y3F/kuwnEcFBYWJvygijb0prcG6E1vDdCb3hqgN701QO/E9mZcP0IY1yeEEEIIIYQQEksY109SHMfBli1bEv7IUbShN701QG96a4De9NYAvemtAXontjcX+S7CcRysW7cu4QdVtKE3vTVAb3prgN701gC96a0Beie2N+P6EcK4PiGEEEIIIYSQWMK4fpLiOA42btyY8EeOog296a0BetNbA/SmtwboTW8N0DuxvbnIdxFueQ9ItKE3vTVAb3prgN701gC96a0Beie2N+P6EcK4PiGEEEIIIYSQWMK4fpJi2zaKi4th23a8uxJT6E1vDdCb3hqgN701QG96a4Deie3NRb6LEBHs2bMH2sIX9Ka3BuhNbw3Qm94aoDe9NUDvxPZmXD9CGNcnhBBCCCGEEBJLGNdPUmzbxtq1axM+HhJt6E1vDdCb3hqgN701QG96a4Deie3NRb7LqKysjHcX4gK9dUFvXdBbF/TWBb11QW9d0DtxYVw/QhjXJ4QQQgghhBASSxjXT1Js20ZhYWHCx0OiDb3prQF601sD9Ka3BuhNbw3QO7G9ucgnhBBCCCGEEEKSBMb1I4RxfUIIIYQQQgghsYRx/STFtm0UFBQkfDwk2tCb3hqgN701QG96a4De9NYAvRPbm4t8l5Genh7vLsQFeuuC3rqgty7orQt664LeuqB34pJUcf1u3bph48aNIdumTp2KSZMmBX9etWoVrr/+eqxYsQJt27bFjTfeiLvuuivsx2BcnxBCCCGEEEJILFEd13/ooYewbdu24NeNN94YvK2srAzjxo1D165d8fXXX+PRRx/FAw88gJdeeimOPQ4fy7KwYsUKWJYV767EFHrTWwP0prcG6E1vDdCb3hqgd2J7e+PdgWiTkZGBDh06NHjbW2+9hZqaGrz22mvw+Xzo378/Vq5ciSeeeAJXX311jHsaOYZhIDs7G4ZhxLsrMYXe9NYAvemtAXrTWwP0prcG6J3Y3kl3Jn/atGlo3bo1Bg8ejEcffTTkKMsXX3yB0aNHw+fzBbeNHz8e3333Hfbs2dPg/VVXV6OsrCzkC0DwYgu2bTfYtiwrpO04zgHbfr8/pB14F0WgLSJwHAc9e/aEaZrw+/0AABEJth3HCWkH3Btr27Yd0o6HU912Q06O4+DII4+EYRhJ4xROnUQERx55ZLAPyeAUTp0ABL2TxSmcOhmGgSOPPDI415PBKZw6BbwDYz4ZnMKpk2ma6NmzZ3DfZHAKp04ejwc9evQI3kcyOIVTJ4/Hg+7duwcfJxmcwqlTwDtAMjiFUyePx4Nu3boFFwHJ4BROnTweD7p27QrTNJPGKZw6maaJrl27wuPxJI1TOHUyDAPdunWDx+NJGqdw6gQg6B0Pp3BJqkX+TTfdhL/85S9YsmQJrrnmGjzyyCMh77ffvn072rdvH/I7gZ+3b9/e4H1OnToVmZmZwa/OnTsDAAoLCwEAa9aswZo1awDUvt+/qKgIAFBQUID169cDAJYvX47NmzcDAPLz87Ft2zYAQF5eHnbt2gUAWLx4MUpLSwEACxYsQHl5OQAgNzcXVVVVsCwLubm5+Pzzz7Fv3z7k5uYCAMrLy7FgwQIAQGlpKRYvXgwA2LVrF/Ly8gAA27ZtQ35+PgBg8+bNWL58OQBg/fr1KCgoAAAUFRVh1apVcXGyLAtVVVUHdMrPz8ePP/6YVE4Hq9OyZcuQn5+PdevWJY1TOHX69ttvkZ+fj2+++SZpnMKp04YNG5Cfn4/PP/88aZzCqdPu3buRn5+fVE7h1KmqqgqfffZZUjmFUyfLspCXl4dPP/00aZzCqZNlWViyZAm+/vrrpHEKp06WZWHhwoX47rvvksYpnDpZloX58+djy5YtSeMUTp0sy8JHH30U9EsGp3DqFHhdbllW0jiFU6cff/wR8+bNg2VZSeMUTp3WrVuHjz/+GJZlxcUpXBL+wnuTJk3C9OnTD7jPmjVr0Ldv33rbX3vtNVxzzTXYt28fUlNTMW7cOHTv3h0vvvhicJ/Vq1ejf//+WL16Nfr161fvPqqrq1FdXR38uaysDJ07d0ZJSQmys7ODR2YCR3MCbcuqPTMXaJumCdM0G20HznAE2l6vF4ZhBNsAUFNTg23btqFz585wHAcpKSkQEViWhZSUFDiOA9u2g23HceD1ehtt27YNEQm2G/I43E6WZYW0G3IKPHl06tQJAJLCKZw6Bbw7duwIwzCSwimcOjmOg61bt6Jjx44wTTMpnMKpEwBs2bIFOTk58Hq9SeEUTp0Mw8CWLVvQoUMH+Hy+pHAKp06maWLz5s3IyclBampqUjiFUyePx4NNmzahY8eO8Pl8SeEUTp1M08TGjRvRqVMn+Hy+pHAKp06GYWDjxo044ogjkJKSkhRO4dQJADZs2IAuXbrA6/UmhVM4dQp4B85qJ4NTOHVyHAcbN24MpjeSwSmcOlmWhU2bNqFbt24AkBRO4dTJ7/fjhx9+QNeuXYNn1mPlVFFRgaysrLAuvJfwi/ydO3di9+7dB9ynR48eIRH8AN9++y0GDBiAtWvXok+fPrj88stRVlaGf/zjH8F9lixZgpNOOim4aD8YvLo+IYQQQgghhJBYklRX12/bti369u17wK+GFvgAsHLlSpimiXbt2gEA/p+9+46K6lrbAP7MIFVAEOxSRUHEgr3FWMESu7EgFiyxFyzX3msSFXuJooAmltiNvVdQLIAIKCCCGsSCqBSBmdnfHyzmcwQV752ZM5z9/tZyXTgzyX2ezAizz9ln7yZNmuDKlSvK+y4A4OzZs3B2di7SAF9oMlneNMf8q368oN7UmwfUm3rzgHpTbx5Qb+rNA+qt2711fpBfVMHBwVi9ejXCw8Px+PFj/Pnnn/D19YW3t7dyAO/l5QUDAwMMHToUDx48wN69e7FmzRpMmjRJ4PRFk79QU/5UMF5Qb+rNA+pNvXlAvak3D6g39eYB9dbt3jo/Xb+o7t69i9GjRyMmJgbZ2dlwcHDAgAEDMGnSJBgaGiqfFxERgTFjxiA0NBTW1tYYN24cpk2bVuT/H5quTwghhBBCCCFEm0Q1Xb+o6tati5CQEKSlpSErKwtRUVGYMWOGygAfAGrVqoWrV6/i48ePePbs2XcN8IUmk8mUqxPzhHpTbx5Qb+rNA+pNvXlAvak3D6i3bvcWzSCfB1KpFG5ubjo/PUTdqDf15gH1pt48oN7UmwfUm3rzgHrrdm/RTNfXFpquTwghhBBCCCFEm7icrs+D3NxcnD59WmV3AB5Qb+rNA+pNvXlAvak3D6g39eYB9dbt3nQl/zsJeSVfoVAgLS0NFhYWOj9FRJ2oN/XmAfWm3jyg3tSbB9SbevOAemu/9/eMQ2mQ/51ouj4hhBBCCCGEEG2i6foilZubi+PHj+v89BB1o97UmwfUm3rzgHpTbx5Qb+rNA+qt273pSv53EvJKPmMMHz58gJmZGSQSiVb/v4VEvak3D6g39eYB9abePKDe1JsH1Fv7vWm6vgbRdH1CCCGEEEIIIdpE0/VFKjc3F0eOHNH56SHqRr2pNw+oN/XmAfWm3jyg3tSbB9Rbt3vTlfzvJPR0/Y8fP8LIyIi7aTHUm3qLHfWm3jyg3tSbB9SbevOAemu/N13JF7ESJUoIHUEQ1Jsv1Jsv1Jsv1Jsv1Jsv1Jsv1Ft30SC/GJHJZDhx4gRkMpnQUbSKelNvHlBv6s0D6k29eUC9qTcPqLdu96bp+t9J6On6MpkMJUqU4G5aDPWm3mJHvak3D6g39eYB9abePKDe2u9N0/VFTNfPGmkK9eYL9eYL9eYL9eYL9eYL9eYL9dZdNMgvRmQyGc6cOVMs3ljqRL2pNw+oN/XmAfWm3jyg3tSbB9Rbt3vTdP3vJOR0fUIIIYQQQggh/KHp+iLFGMP79+/B23kZ6k29eUC9qTcPqDf15gH1pt48oN663ZsG+cWITCbD1atXdX56iLpRb+rNA+pNvXlAvak3D6g39eYB9dbt3jRd/zvRdH1CCCGEEEIIIdpE0/VFSqFQIDU1FQqFQugoWkW9qTcPqDf15gH1pt48oN7UmwfUW7d70yC/GJHL5QgNDYVcLhc6ilZRb+rNA+pNvXlAvak3D6g39eYB9dbt3jRd/zvRdH1CCCGEEEIIIdpE0/VFSqFQ4OXLlzo/PUTdqDf15gH1pt48oN7UmwfUm3rzgHrrdm8a5BcjCoUCkZGROv+mUjfqTb15QL2pNw+oN/XmAfWm3jyg3rrdm6brfyeark8IIYQQQgghRJtour5IKRQKPH/+XOfPHKkb9abePKDe1JsH1Jt684B6U28eUG/d7k2D/GJEoVAgPj5e599U6ka9qTcPqDf15gH1pt48oN7UmwfUW7d703T970TT9QkhhBBCCCGEaBNN1xcphUKBxMREnT9zpG7Um3rzgHpTbx5Qb+rNA+pNvXlAvXW7Nw3yi5Hicg+IulFv6s0D6k29eUC9qTcPqDf15gH11u3eNF3/O9F0fUIIIYQQQggh2kTT9UVKLpcjLi4Ocrlc6ChaRb2pNw+oN/XmAfWm3jyg3tSbB9Rbt3vTIL8YYYzh7du34G3yBfWm3jyg3tSbB9SbevOAelNvHlBv3e5N0/W/E03XJ4QQQgghhBCiTTRdX6TkcjliYmJ0fnqIulFv6s0D6k29eUC9qTcPqDf15gH11u3eNMgvZrKysoSOIAjqzRfqzRfqzRfqzRfqzRfqzRfqrbtouv53oun6hBBCCCGEEEK0iabri5RcLkdkZKTOTw9RN+pNvXlAvak3D6g39eYB9abePKDeut2bBvmEEEIIIYQQQohI0HT970TT9QkhhBBCCCGEaBNN1xcpuVyOe/fu6fz0EHWj3tSbB9SbevOAelNvHlBv6s0D6q3bvWmQX8wYGxsLHUEQ1Jsv1Jsv1Jsv1Jsv1Jsv1Jsv1Ft30XT970TT9QkhhBBCCCGEaBNN1xcpmUyG0NBQyGQyoaNoFfWm3jyg3tSbB9SbevOAelNvHlBv3e5Ng/xiRCKRwNLSEhKJROgoWkW9qTcPqDf15gH1pt48oN7UmwfUW7d703T970TT9QkhhBBCCCGEaBNN1xcpmUyGGzdu6Pz0EHWj3tSbB9SbevOAelNvHlBv6s0D6q3bvWmQX4xIpVJUqlQJUilfLxv1pt48oN7UmwfUm3rzgHpTbx5Qb93uTdP1vxNN1yeEEEIIIYQQok00XV+kZDIZrly5ovPTQ9SNelNvHlBv6s0D6k29eUC9qTcPqLdu96ZBfjEilUpRpUoVnZ8eom7Um3rzgHpTbx5Qb+rNA+pNvXlAvXW7N03X/040XZ8QQgghhBBCiDbRdH2RkslkuHDhgs5PD1E36k29eUC9qTcPqDf15gH1pt48oN663ZsG+cWIVCqFm5ubzk8PUTfqTb15QL2pNw+oN/XmAfWm3jyg3rrdm6brfyeark8IIYQQQgghRJtour5I5ebm4vTp08jNzRU6ilZRb+rNA+pNvXlAvak3D6g39eYB9dbt3nQl/zsJeSVfoVAgLS0NFhYWOj9FRJ2oN/XmAfWm3jyg3tSbB9SbevOAemu/9/eMQ2mQ/51ouj4hhBBCCCGEEG2i6foilZubi+PHj+v89BB1o97UmwfUm3rzgHpTbx5Qb+rNA+qt273pSv53EvJKPmMMHz58gJmZGSQSiVb/v4VEvak3D6g39eYB9abePKDe1JsH1Fv7vWm6vgbRdH1CCCGEEEIIIdpE0/VFKjc3F0eOHNH56SHqRr2pNw+oN/XmAfWm3jyg3tSbB9Rbt3vTlfzvJPR0/Y8fP8LIyIi7aTHUm3qLHfWm3jyg3tSbB9SbevOAemu/N13JF7ESJUoIHUEQ1Jsv1Jsv1Jsv1Jsv1Jsv1Jsv1Ft30SC/GJHJZDhx4gRkMpnQUbSKelNvHlBv6s0D6k29eUC9qTcPqLdu96bp+t9J6On6MpkMJUqU4G5aDPWm3mJHvak3D6g39eYB9abePKDe2u/N5XT9S5cuQSKRFPonNDQUAPDkyZNCHw8JCRE4fdHp+lkjTaHefKHefKHefKHefKHefKHefKHeuks0g/ymTZsiOTlZ5c+wYcPg4OCA+vXrqzz33LlzKs+rV6+eQKm/j0wmw5kzZ4rFG0udqDf15gH1pt48oN7UmwfUm3rzgHrrdm/RTtfPzc1FpUqVMG7cOMyZMwdA3pV8BwcH3Lt3D3Xq1Pmv/r1CTtcnhBBCCCGEEMIfLqfrf+7o0aN48+YNfHx8CjzWpUsXlC1bFs2bN8fRo0e/+u/Jzs7G+/fvVf4AgFwuV/5vYV/LZDKVrxUKxVe/zs3NVfk6/9xL/teMMeTk5ODdu3dQKBTKvRkZY8qvPz2uUCiUZ5i+9LVcLlf5WohOn3/9pU7v379XySuGTt96nfJ7y2Qy0XQqyuskk8mUvcXSqSivk1wux/v371WyF/dORXmd8nvn5OSIplNRXieFQoF3794hJydHNJ2K8joxxpCWlvbNfsWpU1Fep897i6FTUV4nxhjevn2rfI4YOhXldcrvXVjX4tqpKK8TYwypqakF+hXnTkV5nRQKBVJTUwv0K86divI6yeVyZW+xdCrK6ySTyfD27VswxgTpVFSiHeT7+/vD09MTlStXVh4zNTXFypUr8ffff+P48eNo3rw5unXr9tWB/rJly1CqVCnlHxsbGwBAZGQkACA6OhrR0dEAgIiICMTGxgIA7t27h4SEBADArVu38PTpUwDAjRs3kJycDAC4cuUKXr9+DQC4cOEC0tLSAABnzpzBhw8fAAAnTpzAx48fIZPJcPLkSVy9ehXp6ek4ceIEAODDhw84c+YMACAtLQ0XLlwAALx+/RpXrlwBACQnJ+PGjRsAgKdPn+LWrVsAgISEBNy7dw8AEBsbi4iICK13yl+d8uPHj1/tdPXqVaSkpIiqU1Fep6tXryI+Pl5Unb71Oj148ABXr15FeHi4aDoV5XVKTEzE1atXRdWpKK/TmzdvcPXqVZw9e1Y0nYryOn38+BFXr17FyZMnRdOpKK+TTCbD1atXRdWpKK9Tfu+7d++KplNRXqf83g8fPhRNp6K8TjKZDFeuXMHz589F06kor1P+653fTwydivI6paen4+rVq5DJZKLpVJTXKSUlRdlbLJ2K8jrFx8crewvRqah0frr+9OnT8euvv371OdHR0XBxcVF+/+zZM9jZ2WHfvn3o2bPnV//ZgQMHIiEhAVevXi308ezsbGRnZyu/f//+PWxsbJCamgpLS0vlmRk9PT2Vr2UyGSQSifJrqVQKqVT6xa9zc3Ohp6en/Dp/xcb8r4G8szyffq2vr69c4VFfXx8KhQJyuVz5tUKhQIkSJb74df5Z9vyvC+tBnagTdaJO1Ik6USfqRJ2oE3WiTtRJ2E6ZmZmwsLAo0nR9nR/kv3r1Cm/evPnqcxwdHWFgYKD8ftGiRVi3bh2eP38OfX39r/6zGzZswOLFi5VnUr5FyHvyFQoF0tLSYGFhAalUtJMwCqDe1JsH1Jt684B6U28eUG/qzQPqrf3eoronv0yZMnBxcfnqn08H+Iwx7NixAwMHDvzmAB8AwsLCUKFCBU1WUBu5XI7Q0FDl2SFeUG/qzQPqTb15QL2pNw+oN/XmAfXW7d46fyX/e50/fx5t27YtMIUfAAIDA2FgYAB3d3cAwMGDBzFnzhxs27at0AX6CkOr6xNCCCGEEEII0SZRXcn/Xv7+/mjatGmBAX6+RYsWoV69emjUqBGOHDmCvXv3FnmALzSFQoGXL19CoVAIHUWrqDf15gH1pt48oN7UmwfUm3rzgHrrdm/RDfL/+usvXL9+vdDHBg0ahKioKGRkZODdu3e4efMmevXqpeWE/z2FQoHIyEidf1OpG/Wm3jyg3tSbB9SbevOAelNvHlBv3e4tuun6mkbT9QkhhBBCCCGEaBPX0/XFTKFQ4Pnz5zp/5kjdqDf15gH1pt48oN7UmwfUm3rzgHrrdm8a5BcjCoUC8fHxOv+mUjfqTb15QL2pNw+oN/XmAfWm3jyg3rrdm6brfyeark8IIYQQQgghRJtour5IKRQKJCYm6vyZI3Wj3tSbB9SbevOAelNvHlBv6s0D6q3bvWmQX4wUl3tA1I16U28eUG/qzQPqTb15QL2pNw+ot273pun634mm6xNCCCGEEEII0Saari9ScrkccXFxkMvlQkfRKupNvXlAvak3D6g39eYB9abePKDeut2bBvnFCGMMb9++BW+TL6g39eYB9abePKDe1JsH1Jt684B663Zvmq7/nWi6PiGEEEIIIYQQbaLp+iIll8sRExOj89ND1I16U28eUG/qzQPqTb15QL2pNw+ot273pkF+MZOVlSV0BEFQb75Qb75Qb75Qb75Qb75Qb75Qb91F0/W/E03XJ4QQQgghhBCiTTRdX6TkcjkiIyN1fnqIulFv6s0D6k29eUC9qTcPqDf15gH11u3eNMgnhBBCCCGEEEJEgqbrfyeark8IIYQQQgghRJtour5IyeVy3Lt3T+enh6gb9abePKDe1JsH1Jt684B6U28eUG/d7k2D/GLG2NhY6AiCoN58od58od58od58od58od58od66i6brfyeark8IIYQQQgghRJtour5IyWQyhIaGQiaTCR1Fq6g39eYB9abePKDe1JsH1Jt684B663ZvGuQXIxKJBJaWlpBIJEJH0SrqTb15QL2pNw+oN/XmAfWm3jyg3rrdm6brfyeark8IIYQQQgghRJtour5IyWQy3LhxQ+enh6gb9abePKDe1JsH1Jt684B6U28eUG/d7k2D/GJEKpWiUqVKkEr5etmoN/XmAfWm3jyg3tSbB9SbevOAeut2b5qu/51ouj4hhBBCCCGEEG2i6foiJZPJcOXKFZ2fHqJu1Jt684B6U28eUG/qzQPqTb15QL11uzcN8osRqVSKKlWq6Pz0EHWj3tSbB9SbevOAelNvHlBv6s0D6q3bvWm6/nei6fqEEEIIIYQQQrSJpuuLlEwmw4ULF3R+eoi6UW/qzQPqTb15QL2pNw+oN/XmAfXW7d40yC9GpFIp3NzcdH56iLpRb+rNA+pNvXlAvak3D6g39eYB9dbt3jRd/zvRdH1CCCGEEEIIIdpE0/VFKjc3F6dPn0Zubq7QUbSKelNvHlBv6s0D6k29eUC9qTcPqLdu96Yr+d9JyCv5CoUCaWlpsLCw0PkpIupEvak3D6g39eYB9abePKDe1JsH1Fv7vb9nHEqD/O9E0/UJIYQQQgghhGgTTdcXqdzcXBw/flznp4eoG/Wm3jyg3tSbB9SbevOAelNvHlBv3e5NV/K/k5BX8hlj+PDhA8zMzCCRSLT6/y0k6k29eUC9qTcPqDf15gH1pt48oN7a703T9TWIpusTQgghhBBCCNEmmq4vUrm5uThy5IjOTw9RN+pNvXlAvak3D6g39eYB9abePKDeut2bruR/J6Gn63/8+BFGRkbcTYuh3tRb7Kg39eYB9abePKDe1JsH1Fv7velKvoiVKFFC6AiCoN58od58od58od58od58od58od66iwb5xYhMJsOJEycgk8mEjqJV1Jt684B6U28eUG/qzQPqTb15QL11uzdN1/9OQk/Xl8lkKFGiBHfTYqg39RY76k29eUC9qTcPqDf15gH11n5vmq4vYrp+1khTqDdfqDdfqDdfqDdfqDdfqDdfqLfuokF+MSKTyXDmzJli8cZSJ+pNvXlAvak3D6g39eYB9abePKDeut2bput/JyGn6xNCCCGEEEII4Q9N1xcpxhjev38P3s7LUG/qzQPqTb15QL2pNw+oN/XmAfXW7d40yC9GZDIZrl69qvPTQ9SNelNvHlBv6s0D6k29eUC9qTcPqLdu96bp+t+JpusTQgghhBBCCNEmmq4vUgqFAqmpqVAoFEJH0SrqTb15QL2pNw+oN/XmAfWm3jyg3rrdmwb5xYhcLkdoaCjkcrnQUbSKelNvHlBv6s0D6k29eUC9qTcPqLdu96bp+t+JpusTQgghhBBCCNEmmq4vUgqFAi9fvtT56SHqRr2pNw+oN/XmAfWm3jyg3tSbB9Rbt3vTIL8YUSgUiIyM1Pk3lbpRb+rNA+pNvXlAvak3D6g39eYB9dbt3jRd/zvRdH1CCCGEEEIIIdpE0/VFSqFQ4Pnz5zp/5kjdqDf15gH1pt48oN7UmwfUm3rzgHrrdm8a5BcjCoUC8fHxOv+mUjfqTb15QL2pNw+oN/XmAfWm3jyg3rrdm6brfyeark8IIYQQQgghRJtour5IKRQKJCYm6vyZI3Wj3tSbB9SbevOAelNvHlBv6s0D6q3bvWmQX4wUl3tA1I16U28eUG/qzQPqTb15QL2pNw+ot273pun634mm6xNCCCGEEEII0Saari9ScrkccXFxkMvlQkfRKupNvXlAvak3D6g39eYB9abePKDeut2bBvnFCGMMb9++BW+TL6g39eYB9abePKDe1JsH1Jt684B663Zvmq7/nWi6PiGEEEIIIYQQbaLp+iIll8sRExOj89ND1I16U28eUG/qzQPqTb15QL2pNw+ot273pkF+MZOVlSV0BEFQb75Qb75Qb75Qb75Qb75Qb75Qb91F0/W/E03XJ4QQQgghhBCiTaKcrr9kyRI0bdoUJiYmsLCwKPQ5SUlJ6NSpE0xMTFC2bFlMnToVMplM5TmXLl1C3bp1YWhoCCcnJwQEBGg+vJrI5XJERkbq/PQQdaPe1JsH1Jt684B6U28eUG/qzQPqrdu9i80gPycnBz///DNGjRpV6ONyuRydOnVCTk4Obty4gcDAQAQEBGDu3LnK5yQkJKBTp05o1aoVwsLCMHHiRAwbNgynT5/WVg1CCCGEEEIIIURjit10/YCAAEycOBFpaWkqx0+ePImffvoJ//77L8qVKwcA2Lx5M6ZNm4ZXr17BwMAA06ZNw/HjxxEZGan85/r27Yu0tDScOnWqSP//NF2fEEIIIYQQQog2fc84tISWMmlccHAwatasqRzgA4CnpydGjRqFBw8ewN3dHcHBwWjbtq3KP+fp6YmJEyd+8d+bnZ2N7Oxs5ffv3r0DALx9+xYAlFM19PT0VL6WyWSQSCTKr6VSKaRS6Re/zs3NhZ6envLrEiVKQCKRKL/OzxITEwM3NzcwxqCvrw/GGGQyGfT19aFQKCCXy5VfKxQKlChR4otfy+VyMMaUXxfWQ9OdZDKZyteFdcrNzUVMTAxcXV0hkUhE0akor1N+7+rVq0MqlYqiU1FeJ7lcjujoaFSvXh16enqi6FSU14kxhqioKLi4uEBfX18UnYryOgFAVFQUnJ2dYWhoKIpORXmdJBIJIiMj4eLiAiMjI1F0KsrrJJVKcf/+fVSvXh2Ghoai6FSU10kikeD+/ftwdXWFgYGBKDoV5XUCgIiICNSoUQMGBgai6FSU14kxhoiICLi5uUFfX18UnYryOuX3rlmzJkqUKCGKTkV5neRyOe7fv49atWop/z3FvVNRXqfc3FxERkaiVq1akEgkouhUlNcpJycHDx48QK1atZQ/57TVKTMzEwBQlGv0ohnkv3jxQmWAD0D5/YsXL776nPfv3yMrKwvGxsYF/r3Lli3DggULChy3t7dXU3JCCCGEEEIIIeTbPnz4gFKlSn31OYIO8qdPn45ff/31q8+Jjo6Gi4uLlhIVNGPGDEyaNEn5vUKhQGpqKqysrCCRSLSa5f3797CxscHTp0+5ulWAelNvHlBv6s0D6k29eUC9qTcPqLf2ezPG8OHDB1SsWPGbzxV0kD958mQMHjz4q89xdHQs0r+rfPnyuHXrlsqxlJQU5WP5/5t/7NPnmJubF3oVHwAMDQ1haGiocuxLq/tri7m5OVd/mfJRb75Qb75Qb75Qb75Qb75Qb75Qb+361hX8fIIO8suUKYMyZcqo5d/VpEkTLFmyBC9fvkTZsmUBAGfPnoW5uTlcXV2Vzzlx4oTKP3f27Fk0adJELRkIIYQQQgghhBAhFZst9JKSkhAWFoakpCTI5XKEhYUhLCwM6enpAAAPDw+4urpiwIABCA8Px+nTpzF79myMGTNGeSV+5MiRePz4Mf7zn/8gJiYGGzduxL59++Dr6ytkNUIIIYQQQgghRC2KzcJ7c+fORWBgoPJ7d3d3AMDFixfRsmVL6Onp4Z9//sGoUaPQpEkTlCxZEoMGDcLChQuV/4yDgwOOHz8OX19frFmzBpUrV8a2bdvg6emp9T7/DUNDQ8ybN6/A7QNiR72pNw+oN/XmAfWm3jyg3tSbB9Rbt3tLWFHW4CeEEEIIIYQQQojOKzbT9QkhhBBCCCGEEPJ1NMgnhBBCCCGEEEJEggb5hBBCCCGEEEKISNAgnxBCCCGEEEIIEQka5BOio96/f4/Dhw8jOjpa6Chal5aWJnQEoiF3797F/fv3ld8fOXIE3bp1w8yZM5GTkyNgMu3j8X2evwXu27dvhY6iVbz25un32NOnT/Hs2TPl97du3cLEiRPxxx9/CJhKs7KyspCZman8PjExEatXr8aZM2cETCUMHn+e86K4fm6hQb4OO3XqFK5du6b8fsOGDahTpw68vLxE/UGB1969e/fG+vXrAeT94qxfvz569+6NWrVq4cCBAwKn05xff/0Ve/fuVX7fu3dvWFlZoVKlSggPDxcwmWbx+j4fMWIEHj16BAB4/Pgx+vbtCxMTE/z999/4z3/+I3A6zeH1fT5x4kT4+/sDyBvo/vjjj6hbty5sbGxw6dIlYcNpEK+9ef09BgBeXl64ePEiAODFixdo164dbt26hVmzZqls5ywmXbt2RVBQEIC8QW6jRo2wcuVKdO3aFZs2bRI4nebw+vOcV8X2cwsjOsvNzY0dP36cMcZYREQEMzQ0ZDNmzGCNGzdmgwcPFjid5vDau1y5ciwsLIwxxtiff/7JnJycWEZGBtu4cSOrU6eOwOk0x97enl2/fp0xxtiZM2eYhYUFO336NBs6dChr166dwOk0h9f3ubm5OYuLi2OMMbZ8+XLm4eHBGGPs2rVrrHLlykJG0yhe3+eVKlVioaGhjDHGDh06xCpWrMgePnzIZs+ezZo2bSpwOs3htTevv8cYY8zCwoLFxMQwxhhbs2aN8nU+ffo0c3BwEDKaxlhZWbHIyEjGGGNbt25ltWrVYnK5nO3bt4+5uLgInE5zeP15zqvi+rmlhNAnGciXJSQkwNXVFQBw4MAB/PTTT1i6dCnu3r2Ljh07CpxOc3jt/e7dO5QuXRpA3lXenj17wsTEBJ06dcLUqVMFTqc5L168gI2NDQDgn3/+Qe/eveHh4QF7e3s0atRI4HSaw+v7nDEGhUIBADh37hx++uknAICNjQ1ev34tZDSN4vV9/vr1a5QvXx4AcOLECfz888+oVq0ahgwZgjVr1gicTnN47c3r7zEAyM3NhaGhIYC8n21dunQBALi4uCA5OVnIaBqTmZkJMzMzAMCZM2fQo0cPSKVSNG7cGImJiQKn0xyef55nZGTAzs5OeezBgwdYsWIFMjIy0K1bN3h5eQmYUDOK6+cWmq6vwwwMDJT3Op07dw4eHh4AgNKlS+P9+/dCRtMoXnvb2NggODgYGRkZOHXqlLL327dvYWRkJHA6zbG0tMTTp08B5H0obNu2LYC8H6pyuVzIaBrF6/u8fv36WLx4MXbu3InLly+jU6dOAPJOepQrV07gdJrD6/u8XLlyiIqKglwux6lTp9CuXTsAeYMDPT09gdNpDq+9ef09BgA1atTA5s2bcfXqVZw9exbt27cHAPz777+wsrISOJ1mODk54fDhw3j69ClOnz6tfL1fvnwJc3NzgdNpDq8/z8eNG4e1a9cqv3/58iV++OEHhIaGIjs7G4MHD8bOnTsFTKgZxfVzC13J12HNmjXDpEmT0KxZM9y6dUt5/8+jR49QuXJlgdNpTvPmzbnsPXHiRPTv3x+mpqaws7NDy5YtAQBXrlxBzZo1hQ2nQT169ICXlxeqVq2KN2/eoEOHDgCAe/fuwcnJSeB0msPr+3z16tXo378/Dh8+jFmzZilf4/3796Np06YCp9McXt/nPj4+6N27NypUqACJRKL8MHzz5k24uLgInE5zeO3N6+8xIO8+7e7du+P333/HoEGDULt2bQDA0aNH0bBhQ4HTacbcuXPh5eUFX19ftG7dGk2aNAGQd1Xf3d1d4HSaw+vP85CQEAQEBCi/DwoKQunSpREWFoYSJUpgxYoV2LBhAwYMGCBcSA3w8/ODt7d3sfvcImGMMaFDkMIlJSVhzJgxSEpKwvjx4zF06FAAgK+vL+RyucrZNDFJSkrC6NGj8fTpU656A8Dt27fx9OlTtGvXDqampgCA48ePw8LCAs2aNRM4nWbk5uZizZo1ePr0KQYPHqz8YODn5wczMzMMGzZM4ISawfP7vDAfP36Enp4e9PX1hY6iEby+z4G8D0JPnz7Fzz//rDyBFRgYCAsLC3Tt2lXgdJrDa+87d+4gKSmpwO8xS0tLnf5ArA5yuRzv37+HpaWl8tiTJ09gYmKCsmXLCphMc168eIHk5GTUrl0bUmneBOFbt27B3NxctCe0eP15bmxsjJiYGOV0/Y4dO8LNzQ2//fYbgLyLFE2aNMGbN2+EjKk1Hz9+RIkSJVCihG5eM6dBvo6SyWT466+/4OHhobyvjxAxysjIQMmSJYWOQQSQnp6uvM8tn1ineF65cgVNmzYt8GFAJpPhxo0baNGihUDJNCsoKAh9+vRR3qucLycnB3v27MHAgQMFSqZZvPZeuHAhpkyZAhMTE5XjWVlZ+P333zF37lyBkhFNiouLQ3x8PFq0aAFjY2MwxiCRSISORdSsXLlyOHPmjHKWirW1NbZs2YKePXsCAGJjY+Hu7o709HQhY6qdo6MjQkNDC9x2k5aWhrp16+Lx48cCJfs6GuTrMBMTE0RHR6sscMELhUKBuLg4vHz5ssAgQKwfhuVyOQICAnD+/PlCe1+4cEGgZJplamqK3r17Y8iQIWjevLnQcbTu5cuXhb7etWrVEiiRZiUkJGDs2LG4dOkSPn78qDye/6FQrPcz6unpITk5ucDVvDdv3qBs2bLUW2SoNx+93d3dizyYvXv3robTaN+bN2/Qu3dvXLx4ERKJBLGxsXB0dMSQIUNgaWmJlStXCh1RbY4ePVrk5+Yvuig2Xbt2hbW1NbZu3YqDBw+if//+ePHihXLmyvHjxzFlyhRER0cLnFS9pFIpXrx4UeDnWkpKCmxsbJCTkyNQsq/TzfkFBADQsGFD3Lt3j7tBfkhICLy8vJCYmIjPz0GJeRAwYcIEBAQEoFOnTnBzc+PmLPiuXbsQEBCA1q1bw97eHkOGDMHAgQNRsWJFoaNp1J07dzBo0CBER0cr3+cSiUT0g11vb28wxrB9+3aUK1eOm/f5l65svXnzRtQzWb7U+9mzZyhVqpQAibSDeqsKDw9XrrovJt26dRM6gqB8fX2hr6+PpKQkVK9eXXm8T58+mDRpkqgG+Z+/1vm/rz/9Pp9Yf38vWrQIbdq0wa5duyCTyTBz5kyVW1P27NmDH3/8UcCE6vXpiZ3Tp0+r/OyWy+U4f/48HBwchIhWJDTI12GjR4/G5MmT8ezZM9SrV6/AB0GxXukbOXIk6tevj+PHjysXLeLBnj17sG/fPlFvn1aYbt26oVu3bnj16hV27tyJgIAAzJkzB56enhgyZAi6dOmis/c7/S+GDBmCatWqwd/fn6vBbnh4OO7cuQNnZ2eho2hFjx49AOR9ABw8eLDK9G25XI6IiAhR3qecf4VTIpGgTZs2Kn+H5XI5EhISlKuPiwmvvS0tLZW9q1WrVmDAk56ejpEjRwqYUDPmzZsndARBnTlzBqdPny6wWGzVqlVFt4Xep7Ptzp07h2nTpmHp0qXKxQaDg4Mxe/ZsLF26VKiIGlerVi1ER0fj+vXrKF++fIHtAvv27avcGlgM8k/sSCQSDBo0SOUxfX192Nvb6/SJLPF9chaRvn37AgDGjx+vPMbDlb7Y2Fjs379f1CuUFsbAwIC7zp8qU6YMJk2ahEmTJmHdunWYOnUqTpw4AWtra4wcORLTp08vcJ9ncfb48WMcOHCAu9e8QYMGePr0KTeD/Pwz/4wxmJmZwdjYWPmYgYEBGjdujOHDhwsVT2PyPxyFhYXB09NTuQAbkNfb3t5eeR+nmPDae/Xq1WCMYciQIViwYIHKFa/83vmDISIeGRkZhf5eTk1NLbAehZhMnDgRmzdvVrnF0NPTEyYmJvjll19EN139U9bW1l9cODR/azmxyD+x4+DggNDQUFhbWwuc6PvQIF+HJSQkCB1BEI0aNUJcXBx3g5/JkydjzZo1WL9+PTdXdT+VkpKCwMBABAQEIDExEb169cLQoUPx7Nkz/PrrrwgJCcGZM2eEjqk2bdq0QXh4OHfv823btmHkyJF4/vw53NzcCqymL7YZSjt27AAA2NvbY8qUKaKemv+p/Cuc9vb26Nu3r6g/8H+K1975V7kcHBzQrFkzUc6++ha5XA4/Pz/s27cPSUlJBe7TTU1NFSiZ5vzwww8ICgrCokWLAORdiFIoFPjtt9/QqlUrgdNpTnx8PCwsLAocL1WqFJ48eaL1PNokk8ng5+eH3bt349GjRwCAatWqwcvLCxMmTBDlDjnFdTxGC+8RnXPo0CHMnj0bU6dORc2aNUU/CMjXvXt3XLx4EaVLl0aNGjUK9D548KBAyTTr4MGD2LFjB06fPg1XV1cMGzYM3t7eKr9A4+PjUb16dZ1d3OS/8fr1awwaNAgNGzYsdLAr1oV78tfc+PSDEA8zlHgVGhoKhUJRYFrnzZs3oaenh/r16wuUTLN47X3ixAno6enB09NT5fjp06ehUCiU+4mL0dy5c7Ft2zZMnjwZs2fPxqxZs/DkyRMcPnwYc+fOVZmVKRaRkZFo06YN6tatiwsXLqBLly548OABUlNTcf36dVSpUkXoiBrRokULGBkZYefOnShXrhyAvAsVAwcOxMePH3H58mWBE2pGVlYW2rVrh+DgYLRt21a5DkN0dDTOnTuHZs2a4cyZMzAyMhI4qXqNHz8eTk5OBf4Or1+/HnFxcVi9erUwwb6FEZ0WFBTEmjZtyipUqMCePHnCGGPMz8+PHT58WOBkmiORSAr8kUqlyv8Vq8GDB3/1j1iZm5uzX375hd26deuLz8nMzGTz58/XYirNO3r0KCtVqtQX3+9iVb16ddajRw8WEhLCEhIS2JMnT1T+iNWLFy+Yt7c3q1ChAtPT02NSqVTlj1g1aNCA/f333wWOHzhwgDVs2FCARNrBa++aNWuy48ePFzh+8uRJVqtWLQESaY+joyP7559/GGOMmZqasri4OMYYY2vWrGH9+vUTMppGpaWlscWLF7Off/6ZdejQgc2aNYv9+++/QsfSqEePHjE3NzdmYGDAqlSpwqpUqcIMDAxYjRo1WGxsrNDxNGbu3LnM1taWhYeHF3gsLCyM2drasnnz5mk/mIZVrFiR3b59u8DxO3fusEqVKgmQqGjoSr4O27RpE+bOnYuJEydiyZIliIyMhKOjIwICAhAYGIiLFy8KHVEjvrVYC2+7DYhdZmamqO61Lyp7e3v89NNPmDNnjvJKAA9KlizJ5W0KHTp0QFJSEsaOHVvogqJfusexuDM1NUVERAQcHR1VjickJKBWrVr48OGDQMk0i9fexsbGiI6Ohr29vcrxJ0+eoEaNGsjIyBAmmBaULFkS0dHRsLW1RYUKFXD8+HHlHtru7u549+6d0BGJGjHGcPbsWcTExAAAqlevjrZt24r6dktnZ2csXbr0i+uK/P3335g1a5ZyGr9YGBkZITIyssDnlri4OLi5ualsB6xL+LtpqhhZt24dtm7dim7dumH58uXK4/Xr18eUKVMETKZZvA/iX716hYcPHwLI+4FapkwZgRNp1qcD/I8fPxaYkm9ubq7tSFrx5s0b+Pr6cjXAB4DWrVtzOci/du0arl69ijp16ggdRasMDQ2RkpJSYLCbnJws6vu2ee1dqlQpPH78uMAgPy4uTvTrUVSuXBnJycmwtbVFlSpVcObMGdStWxehoaGiWpshIiKiyM8V4+2Vubm5MDY2RlhYGDw8PODh4SF0JK1JTExEw4YNv/h448aNkZSUpMVE2uHk5IRTp05h7NixKsdPnjxZ4Ge8LhHvbxoRSEhIgLu7e4HjhoaGoj4bDuTdg7169WrlCqWurq6YMGGCaO/vAvJWqR03bhyCgoKUK3rq6elh4MCBWLdunWivdmdkZGDatGnYt28f3rx5U+Bxsd6j3aNHD1y8eFHU7+nCdO7cGb6+vrh//36ha26IdS0CGxsb8DhxzsPDAzNmzMCRI0eUK66npaVh5syZaNeuncDpNIfX3l27dsXEiRNx6NAh5c+2uLg4TJ48WbR/t/N1794d58+fR6NGjTBu3Dh4e3vD398fSUlJ8PX1FTqe2tSpU6fAHvGFEesaK/r6+rC1tRVlt28xNzfHy5cvYWNjU+jjL168gJmZmZZTad6kSZMwduxYvHr1Cq1btwYAnD9/HitXrtTd+/FBC+/pNFdXVyxbtgxdu3aFmZkZwsPD4ejoiHXr1mHHjh24e/eu0BE14vTp0+jSpQvq1KmDZs2aAQCuX7+O8PBwHDt2TLQfkEaMGIFz585h/fr1yt7Xrl3D+PHj0a5dO2zatEnghJoxZswYXLx4EYsWLcKAAQOwYcMGPH/+HFu2bMHy5cvRv39/oSNqxJIlS7B69Wp06tSp0MGuGBdpAgCpVPrFx8T6oRDI20965cqV2LJlS4GrnGL2/PlztGjRAm/evFGetA4LC0O5cuVw9uzZL35YLO547f3u3Tu0b98et2/fVu6d/uzZM/zwww84ePBgoSuSi1VwcDCCg4NRtWpVdO7cWeg4avOtWyo/JdaZmf7+/jh48CB27tyJ0qVLCx1Ha/r06QOZTIYDBw4U+njPnj2hp6eHffv2aTmZ5m3atAlLlizBv//+CyDvlsv58+dj4MCBAif7Mhrk67Bt27Zh/vz5WLlyJYYOHYpt27YhPj4ey5Ytw7Zt29C3b1+hI2qEu7s7PD09VW5RAIDp06fjzJkzoj25YW1tjf3796Nly5Yqxy9evIjevXvj1atXwgTTMFtbWwQFBaFly5YwNzfH3bt34eTkhJ07d2L37t04ceKE0BE1wsHB4YuPSSQSPH78WItpiKZZWloiMzMTMpkMJiYmBU7qiHF7rXwZGRn4888/ER4eDmNjY9SqVQv9+vUT5VZLn+K1d/69yp/2btGihdCxCFEbd3d3xMXFITc3F3Z2dgVuRRHr59SoqCg0atQINWrUwKRJk+Di4gLGGKKjo+Hn54eoqCiEhISgRo0aQkfVmFevXsHY2BimpqZCR/kmGuTruD///BPz589HfHw8AKBixYpYsGABhg4dKnAyzTEyMsL9+/dRtWpVleOPHj1CrVq1dHaBi/+ViYkJ7ty5o9ySJN+DBw/QsGFD0d6iYWpqiqioKNja2qJy5co4ePAgGjZsiISEBNSsWRPp6elCR1Q7xhiSkpJQtmxZGBsbCx1Haz69l9HNzU3oOFoVGBj41cfz9xknRCw+fvwIQ0NDUS9E9qmgoKCvPq7LV/z+Fw8fPsS6deuUt1dWr14d48aNg7Ozs8DJNGfBggVffXzevHlaSqJ9ISEhGDp0KKKjo5V/txljcHFxgb+/P5o0aSJwQs2QyWS4dOkS4uPj4eXlBTMzM/z7778wNzfX2QE/DfKLiczMTKSnp6Ns2bJCR9E4GxsbrFq1Cj///LPK8X379mHKlCmiXNQDANq0aQMrKysEBQUp9xjNysrCoEGDkJqainPnzgmcUDNq1aqFdevW4ccff0Tbtm1Rp04drFixAmvXrsVvv/2GZ8+eCR1R7RQKBYyMjPDgwYMCJ7PEztHREYcOHULt2rWFjkK0ZOfOndiyZQseP36M4OBg2NnZwc/PD46OjqLdVQDgs7dCocCSJUuwefNmpKSk4NGjR3B0dMScOXNgb28v6gsUlpaWKt/n5uYiMzMTBgYGMDExEeVsnQMHDqBv376oX7++cnAXEhKC0NBQ7Nmz54ursJPi7969e4iNjQUAVKtWTdSLyiYmJqJ9+/ZISkpCdna28ufahAkTkJ2djc2bNwsdsXBa37SPkG9YsGABs7CwYMuXL2dXrlxhV65cYcuWLWMWFhZs4cKFQsfTmPv377OKFSsyKysr1rp1a9a6dWtmZWXFKlWqxCIjI4WOpzGrVq1ia9asYYwxdvbsWWZkZMQMDQ2ZVCplq1evFjid5ri6urLg4GChY2jdtm3bWMeOHdmbN2+EjqJ1cXFxbNasWaxv374sJSWFMcbYiRMnRP33e+PGjcza2potXryYGRkZsfj4eMYYYzt27GAtW7YUOJ3m8Np7wYIFzNHRke3atYsZGxsre+/Zs4c1btxY4HTa9+jRI9amTRt26tQpoaNohKOjI5szZ06B43PnzmWOjo4CJNKu27dvs507d7KdO3eyu3fvCh1H6169esVevXoldAyN69q1K/P29mbZ2dnM1NRU+XPt4sWLzMnJSeB0X0aDfB324sUL5u3tzSpUqMD09PSYVCpV+SNWCoWCrVq1ilWqVIlJJBImkUhYpUqV2OrVq5lCoRA6nkZlZGSwP/74g02aNIlNmjSJbd26lWVmZgodS6uePHnCDhw4wMLDw4WOolFHjx5lzZs3Z/fv3xc6ilbVqVOHmZqaMkNDQ1atWjXm7u6u8kesLl26xIyNjVnbtm2ZgYGB8kPCsmXLWM+ePQVOpznVq1dnhw4dYowxlQ9H9+/fZ1ZWVgIm0yxee1epUoWdO3eOMabaOzo6mllYWAgZTTChoaHM2dlZ6BgaYWxszGJjYwscf/ToETM2NhYgkXakpKSwVq1aMYlEwiwtLZmlpSWTSCSsdevW7OXLl0LH06i3b9+y0aNHMysrK+V4xMrKio0ZM4a9fftW6HgaUbp0aRYTE8MYU/25lpCQoNPvc9pCT4cNHjwYSUlJmDNnDipUqMDNfW0SiQS+vr7w9fXFhw8fAECUW3IUxsTEBMOHDxc6hqDs7OxEuyLvpwYOHIjMzEzUrl0bBgYGBe7NF+PUTgDo1q2b0BEEMX36dCxevBiTJk1S+XnWunVrrF+/XsBkmsXrVrC89n7+/DmcnJwKHFcoFMjNzRUgkfBKlCihXJFbbFq2bImrV68WeM2vXbuGH374QaBUmjdu3Dh8+PABDx48UK6jFBUVhUGDBmH8+PHYvXu3wAk1IzU1FU2aNMHz58/Rv39/le4BAQE4f/48bty4UeDWleJOoVAUuvPPs2fPdHp8QoN8HXbt2jVcvXpV1Pe5fIsu/+VRh6NHj6JDhw7Q19fH0aNHv/pcMe4xrFAoEBAQgIMHD+LJkyeQSCRwcHBAr169MGDAAFGf2NLlvVU1ScwLEn3N/fv38ddffxU4XrZsWbx+/VqARNrh4OCAsLCwAifuTp06VWCRUTHhtberqyuuXr1aoPf+/fsLPekhJp//DmeMITk5WWVbXDH4tGeXLl0wbdo03LlzB40bNwaQd0/+33///c3F6YqzU6dO4dy5cyp/l11dXbFhwwZ4eHgImEyzFi5cCAMDA8THx6NcuXIFHvPw8MDChQvh5+cnUELN8PDwwOrVq/HHH38AyLsYmZ6ejnnz5qFjx44Cp/syGuTrMBsbGzBO1kWsW7cuzp8/D0tLS7i7u391cCemrUm6deuGFy9eoGzZsl+9winG/cMZY+jSpQtOnDiB2rVro2bNmsqtWAYPHoyDBw/i8OHDQsfUGN5XU79z545yNeYaNWqIfgBgYWGB5OTkAlsn3rt3D5UqVRIoleZNmjQJY8aMwcePH8EYw61bt7B7927lVrBixWvvuXPnYtCgQXj+/DkUCgUOHjyIhw8fIigoCP/884/Q8TTq89/hEokEZcqUQevWrbFy5UphQmlAYZ9VNm7ciI0bN6ocGzNmDEaOHKmlVNqlUCgK3QpTX18fCoVCgETacfjwYWzZsqXAAB8Aypcvj99++w0jR44U3SB/5cqV8PT0hKurKz5+/AgvLy/ExsbC2tpap2dt0CBfh61evRrTp0/Hli1bYG9vL3QcjeratSsMDQ2VX4v5Cu6nPv1lIOZfDIUJCAjAlStXcP78ebRq1UrlsQsXLqBbt24ICgoS7bZDACCXy3H48GGVwW6XLl2gp6cncDLNefnyJfr27YtLly7BwsICAJCWloZWrVphz549KFOmjLABNaRv376YNm0a/v77b0gkEigUCly/fh1TpkwR9Xt82LBhMDY2xuzZs5GZmQkvLy9UrFgRa9asQd++fYWOpzG89u7atSuOHTuGhQsXomTJkpg7dy7q1q2LY8eOoV27dkLH0yhefofz0vNrWrdujQkTJmD37t2oWLEigLxbVXx9fdGmTRuB02lOcnIyatSo8cXH3dzc8OLFCy0m0o7KlSsjPDwce/bsQUREBNLT0zF06FD0799fp7dBpi30dJilpSUyMzMhk8lgYmJS4KyhWO/Z5VVQUBD69OmjPNmRLycnB3v27BHdQMDDwwOtW7fG9OnTC3186dKluHz5Mk6fPq3lZNoRFxeHjh074vnz58r9hB8+fAgbGxscP34cVapUETihZvTp0wePHz9GUFBQgXsZnZycdPqs+P8iJycHY8aMQUBAAORyOUqUKAG5XA4vLy8EBASI+sROPp62gv0Ur70JEaunT5+iS5cuePDgAWxsbJTH3NzccPToUVSuXFnghJpRqVIl7N27F82bNy/08atXr6JPnz6iXYOiuKFBvg4LCAj46hVtsU73dXR0RGhoKKysrFSOp6WloW7dunj8+LFAyTRLT08PycnJBT4IvnnzBmXLlhXddP3y5cvj1KlTX1xz4t69e+jQoYMozwoDQMeOHcEYw59//onSpUsDyHutvb29IZVKcfz4cYETakapUqVw7tw5NGjQQOX4rVu34OHhgbS0NGGCaUlSUhIiIyORnp4Od3d3VK1aVehIhBA1kMvlysXHXr58WeCK94ULFwRKplkZGRm4fPkykpKSkJOTo/LY+PHjBUqleYwxnDt3DjExMQCA6tWro23btgKn0qwhQ4YgPj4eZ8+ehYGBgcpj2dnZ8PT0hKOjI7Zv3y5QQvX5njWzTE1N4eLiopzVoStokE90jlQqVd6n/qmUlBTY2NgU+CUiFlKpFCkpKQWmK4eHh6NVq1aim7lhYGCAxMREVKhQodDH//33Xzg4OCA7O1vLybSjZMmSCAkJQc2aNVWOh4eHo1mzZkhPTxcomWaZmZkVuqDovXv38OOPP+L9+/fCBCNq8z1rrJiamqJGjRqYOXOm8opYccVr79KlS+PRo0ewtraGpaVlkXr/+uuvqFWrlhZTat7YsWMREBCATp06FbojktjuUwbyfm537NgRmZmZyMjIQOnSpfH69WuYmJigbNmyor0ow6tnz56hfv36MDQ0xJgxY+Di4qJcS2njxo3Izs7G7du3i/3PNEB1LCKVSr/5fD09Pfz222/w9fXVQrqioXvydRhvV3Y/PVN2+vRplCpVSvm9XC7H+fPnCyxaJQb5HwYlEgnatGmDEiX+/6+lXC5HQkIC2rdvL2BCzcifsvwlenp6kMlkWkykXYaGhsotIj+Vnp5e4Ay5mPB6LyNjDPv378fFixcLvcp38OBBgZKp36drrHxry8Ts7GycP38e3t7euHz5shbSaQ6vvf38/JQ74Xxr15Ds7GycOHECPj4+uHPnjhbSac+ePXuwb98+nV5tW918fX3RuXNnbN68GaVKlUJISAj09fXh7e2NCRMmCB1PY8aPHw8nJ6cCMxXWr1+PuLg40e6eU7lyZdy4cQNjxozBjBkzlIuDSyQStGvXDuvXrxfFAB/4vjWzcnJy8Ndff2HGjBk6NcinK/k67EtXtP/9919UqVIFWVlZAiXTjPwzZRKJpMCuAvr6+rC3t8fKlSvx008/CRFPY/K3mVmwYAEmT54MU1NT5WMGBgawt7dHz549RTfwk0ql6NChQ4E1CPJlZ2fj1KlTojuZlW/gwIG4e/cu/P390bBhQwDAzZs3MXz4cNSrVw8BAQHCBtQQXu9lnDBhArZs2YJWrVqhXLlyBa7y7dixQ6BkwouPj0eNGjXw8eNHoaNoFa+9nz59inr16uHly5dCR1GrihUr4tKlS6hWrZrQUbTGwsICN2/ehLOzMywsLBAcHIzq1avj5s2bGDRokHIqu9hUqlQJR48eRb169VSO3717F126dMGzZ88ESqY9b9++RWxsLADAyclJedshrz58+IB58+Zh1apVQkdRokG+Dlq7di2AvDOkixYtUhn0yeVyXLlyBU+ePMG9e/eEiqhRDg4OCA0NhbW1tdBRtCowMBB9+vSBkZGR0FG0wsfHp0jPE+vgJy0tDYMGDcKxY8eUi2rKZDJ06dIFAQEBKjNZxIbHexlLly6NXbt2cXWV71OvXr3Cw4cPAQDOzs4Fbkt69+6dKN/zvPb+dItMV1dX1K1bV+BEmrdy5Uo8fvwY69ev52aHoDJlyuDGjRuoWrUqqlWrhnXr1sHT0xMxMTGoV68eMjIyhI6oEUZGRoiMjISTk5PK8bi4OLi5uYn2xF2PHj2K9DwxzUzLFx8fj9WrV6v8XJswYYJOL5JM0/V1UP59W4wxbN68WWXV5fwru5s3bxYqnsYlJCQIHUEQYl1I8UvEOnj/mvfv38Pc3BxA3hWQI0eOIC4uTvlLo3r16gU+NIjBp/fsDhkyBGvWrEG7du1Ev6XWp0qVKgVHR0ehY2hdRkYGxo0bh507dypn5ejp6WHgwIFYt24dTExMAEB0A11ee/O6RSYAXLt2DRcvXsTJkydRo0aNAjsiiXHg4+7ujtDQUFStWhU//vgj5s6di9evX2Pnzp1wc3MTOp7GODk54dSpUxg7dqzK8ZMnT4r657zYfl4V1enTp9GlSxfUqVMHzZo1AwBcv34dNWrU0OntQelKvg5r1aoVDh06pPxFyRMeV2uVy+Xw8/PDvn37Cu0ttoX3ePTpOhutW7fGwYMHufj7bWpqioiICDg6OkJPTw8vXrwQ9Yf9wgQGBuLUqVPYvn27Tu+rq24jRozAuXPnsH79euWHo2vXrmH8+PFo164dNm3aJHBCzeC1N69bZALfnp0mxhPbt2/fxocPH9CqVSu8fPkSAwcOVF7Z3759O2rXri10RI3Yvn07xo4di6lTp6J169YAgPPnz2PlypVYvXo1hg8fLnBCok7u7u7w9PTE8uXLVY5Pnz4dZ86cwd27dwVK9nU0yNdRubm5cHFxwT///KP8RckLXldrnTt3LrZt24bJkydj9uzZmDVrFp48eYLDhw9j7ty5oj25wZP8hYmqV6/+xd0UxKhdu3ZISUlBvXr1lLelfGmgK4atdwqTlZWF7t274/r167C3ty9wlU9XPyT8r6ytrbF//360bNlS5fjFixfRu3dvvHr1SphgGsZrb963yCT82LRpE5YsWaLcE97e3h7z58/HwIEDBU5G1M3IyAj3798vsOXto0ePUKtWLZ29PYOm6+sofX19nX3TaBqvq7X++eef2Lp1Kzp16oT58+ejX79+qFKlCmrVqoWQkBAa5ItA27Zt0apVK+WJu+7du39xQUUx7am8a9cu+Pn5IT4+HkDefci8/XwbNGgQ7ty5A29v70IX3hOrzMxMlCtXrsDxsmXLIjMzU4BE2sFrb4VCUeAEFpD3meZbK1QTUpyMGjUKo0aNwqtXr2BsbKyyfhYRlzJlyiAsLKzAID8sLKzA4ui6hK7k67ClS5fi0aNH2LZt21e3GhMbXldrLVmyJKKjo2Fra4sKFSrg+PHjqFu3Lh4/fgx3d3e8e/dO6Ijkf5SVlYXAwEDEx8dj5cqVGD58uPLe3M+JcU9lIG9hzdu3b8PKykroKFpVsmRJnD59Gs2bNxc6ila1adMGVlZWCAoKUi4qmpWVhUGDBiE1NRXnzp0TOKFm8Nq7a9euSEtLK7BFZv/+/WFpaYlDhw4JnFCz9u/f/8Vb7sQyW6du3bo4f/48LC0tlVsAf4lYOhdGJpPh0qVLiI+Ph5eXF8zMzPDvv//C3NycBvwis3DhQvj5+WH69Olo2rQpgLx78n/99VdMmjQJc+bMEThh4fgZORZDoaGhOH/+PM6cOYOaNWuiZMmSKo+LcREXIO+Mf/52emXLlkVSUhKqV6+OUqVK4enTpwKn05zKlSsjOTkZtra2qFKlCs6cOYO6desiNDT0i9vMFXe5ubkYMWIE5syZAwcHB6HjaJyxsTFGjhwJIO9exl9//ZWLe/I/tWDBgkI/AOXk5GDPnj2inepoY2OjXHSRJ6tXr0b79u1RuXJl5f254eHhMDIywunTpwVOpzm89l6/fj26dOkCe3v7Altk7tq1S+B0mrV27VrMmjULgwcPxpEjR+Dj44P4+HiEhoZizJgxQsdTm65duyo/k3Tr1k3YMAJJTExE+/btkZSUhOzsbLRr1w5mZmb49ddfkZ2dLerFsXk0Z84cmJmZYeXKlZgxYwaAvC0z58+fr9OzbOlKvg7jcREXAPDw8MDgwYPh5eWF4cOHIyIiAuPHj8fOnTvx9u1b3Lx5U+iIGjF9+nSYm5tj5syZ2Lt3L7y9vWFvb4+kpCT4+voWWPBDLEqVKoWwsDAuBvmfWrhwIaZMmVLgSn5WVhZ+//13zJ07V6BkmvXp4oOfevPmDcqWLatciVxsjh8/jnXr1mHz5s2wt7cXOo5WZWZm4s8//1TZMrF///6iX4CQ1948bpEJAC4uLpg3bx769esHMzMzhIeHw9HREXPnzkVqairWr18vdES1ksvluH79OmrVqsXdyepu3brBzMwM/v7+sLKyUr7Wly5dwvDhw5X7x5PiTyaT4a+//oKnpyfKlSuHDx8+AADMzMwETvZtNMgnOofX1Vo/FxISouzduXNnoeNozKBBg1CnTh34+voKHUWreB3sfmnBwfDwcLRq1Uq0u0hYWloiMzMTMpkMJiYmBe5bFmNvXheQ5bm3sbExwsLCRL192peYmJggOjoadnZ2KFu2LM6ePYvatWsjNjYWjRs3xps3b4SOqHZGRkaIjo7m7iS9lZUVbty4AWdnZ5UTOk+ePIGrq6uo193g0ad/t4sTmq5PdE79+vWVX5ctWxanTp0SMI32XLlyBU2bNlWuv9C4cWM0btwYMpkMV65cQYsWLQROqBlVq1bFwoULcf36ddSrV6/AbSm6PBXqf8EYK/RexvDwcJQuXVqARJqVf++mRCJBmzZtVNYZkcvlSEhIQPv27QVMqFl+fn7cLLaXj9cFZHnubWtrK9oTlN9Svnx5pKamws7ODra2tggJCUHt2rWRkJAAsV5Pc3Nzw+PHj7kb5CsUikLf58+ePSsWV3jJ92nYsCHu3btX7Ab5dCVfx/GwiAvJw+uV3a99OJBIJKLbMtHS0hISiQTv3r2Dubm5ysBPLpcjPT0dI0eOxIYNGwRMqX4LFixQ/u/kyZNV7ss3MDCAvb09evbs+cXdBkjxxOsCsrz29vf3x8GDB7Fz505Rnqz8mmHDhsHGxgbz5s3Dhg0bMHXqVDRr1gy3b99Gjx494O/vL3REtTt16hRmzJiBRYsWFXqSXqzrkPTp0welSpXCH3/8ATMzM0RERKBMmTLo2rUrbG1tRXs7La/27duHGTNmwNfXt9D3ea1atQRK9nU0yNdhny7i8scffxRYxGXJkiVCR1QbWq31y9OYHz16hPr16+P9+/cCJSPqFBgYCMYYhgwZgtWrV6NUqVLKx/IHu02aNBEwoWYFBgaiT58+yhXHefHjjz9i6NCh+Pnnn0V/T/anunfvjvPnz8PU1JSrBWR57e3u7o64uDjk5ubCzs6uQG+x/v4G8q7uKhQK5UmdPXv2KG+5GzFihChPYOYvkgxA5XNb/kw1sV6cePbsGTw9PcEYQ2xsLOrXr4/Y2FhYW1vjypUrOr2tGvl+n77P80kkEp1/n/NzerkY2rhxI/744w/069cPAQEB+M9//qOyiIuY8Lxaa48ePQDk/cAYPHiwykr6crkcERERyi07xCwnJwcJCQmoUqWKqK98DRo0CEDeDIamTZsWuqe0mOX35427uzumTJmCcePGoXfv3hg6dCgaN24sdCyNs7CwQM+ePYWOoXW89u7atSt3t6Xkk0qlKoOBvn37om/fvgIm0ryLFy8KHUEQlStXRnh4OPbu3Yvw8HCkp6dj6NChXCysyaOEhAShI/xX6Eq+DuNxERce5e+iEBgYiN69e6v8gsi/sjt8+HBYW1sLFVGjMjMzMW7cOAQGBgLIm7ng6OiIcePGoVKlSpg+fbrACTXv48ePBW7HEes0R7lcDj8/vy/ehiS2E5ifkslkOHr0KAIDA3Hy5Ek4OTlhyJAhGDBgAMqVKyd0PELIfyEpKalIz7O1tdVwEiK05ORkLFmyRHQ7KfDs/fv3uHnzJnJyctCwYcMCs211GiM6y8HBgd29e5cxxli9evXY5s2bGWOMnT59mllaWgoZTaNu3brFQkJCChwPCQlhoaGhAiTSjvnz57P09HShY2jd+PHjWb169djVq1dZyZIlWXx8PGOMscOHD7M6deoInE5zMjIy2JgxY1iZMmWYVCot8Ees5syZwypUqMBWrFjBjIyM2KJFi9jQoUOZlZUVW7NmjdDxtCYlJYUtWrSIGRkZMX19fda1a1d2/vx5oWOpjVwuZ8uXL2dNmzZl9evXZ9OmTWOZmZlCx9I4Xnunp6ezkSNHsooVKzJra2vWp08f9vLlS6FjacWnP7clEgmTSCQFjon5Zzpjeb/PoqOjWXh4uMofMYqMjGTr1q1jW7ZsYW/fvmWMMfbq1Ss2ceJEZmRkxFxdXYUNSNTm3r17rEKFCsq/x+bm5uzUqVNCxyoyGuTrsKFDh7L58+czxhhbv349MzY2Zm3btmUWFhZsyJAhAqfTnAYNGrC///67wPEDBw6whg0bCpBIGJcuXWLHjx9nqampQkfRKFtbWxYcHMwYY8zU1FQ5yI+NjWVmZmZCRtOo0aNHs+rVq7P9+/czY2Njtn37drZo0SJWuXJltmvXLqHjaYyjoyP7559/GGN5r3dcXBxjjLE1a9awfv36CRlNa27evMlGjhzJLCwsmK2tLZs7dy4bOnQoMzY2ZpMnTxY6nlosXLiQSaVS5uHhwbp27cqMjIyYj4+P0LE0jtfevr6+rGTJkuyXX35h48ePZ2XKlGHdunUTOpZW6OnpMTs7OzZv3jx2+/ZtFhYWVugfMXr58iXr1KlToSeqxXhi48iRI0xfX195MqdKlSrswoULzNramnl6erKTJ08KHZGokYeHB2vatCm7ceMGu3v3LuvevTtzcnISOlaR0SBfh8nlcpabm6v8fvfu3WzcuHFs7dq1LDs7W8BkmvXp1dxPPX78mJmamgqQSLOWL1/OZs+erfxeoVAwT09P5S+RcuXKscjISAETapaxsbHy9f50kB8WFsbMzc2FjKZRNjY27OLFi4wxxszMzFhsbCxjjLGgoCDWoUMHAZNplomJCUtMTGSMMVa+fHl2584dxhhj8fHxon69U1JS2IoVK1iNGjWYgYEB69mzJzt58iRTKBTK5+TPZhEDJycn5ewzxhg7e/YsMzAwYHK5XMBUmsdrb3t7e7Zv3z7l97dv32YlSpRQ+QwjVsnJyWz58uXM2dmZlStXjk2ePJlFRUUJHUsrvLy8WLNmzVhoaCgrWbIkO3PmDNu5cydzdnZWnswVkwYNGrCJEyeyDx8+MD8/PyaRSJibmxu7deuW0NGIBlhZWSk/ozDG2Nu3b5lEImHv3r0TMFXR0SBfRwUHB7OZM2eyKVOmcHdmsHTp0uzGjRsFjl+/fp1ZWFgIkEiz3N3d2Z49e5Tf79u3jxkbG7Nr166xN2/esE6dOrGff/5ZwISa9cMPP7C1a9cyxvIG+Y8fP2aMMTZ27Fjm6ekpZDSNKlmypHKwW6lSJXbz5k3GWN7JLLEM9ApTrVo15e04zZo1Y8uWLWOMMbZnzx5WpkwZIaNplL6+PnNxcWG//fbbF6cxv3v3jrVs2VLLyTTDwMCAJSUlqRwzNDRkT58+FSiRdvDau0SJEuz58+cqx4yNjZU/43hx9epVNmTIEGZmZsYaNWrE/vjjD1Gf4Clfvrzyd5eZmRl7+PAhYyzvinezZs2EjKYR5ubmyhPyMpmM6enpsbNnzwqcimiKRCJhKSkpKsc+/Zyq68S7hHUxtn//fvTp0wfGxsbQ19fHqlWr8Ouvv2LKlClCR9MKDw8PzJgxA0eOHFFuL5aWloaZM2eiXbt2AqdTv4SEBJU9Nk+cOIFevXqhWbNmAIDZs2fj559/Fiqexi1duhQdOnRAVFQUZDIZ1qxZg6ioKNy4cQOXL18WOp7GODo6IiEhAba2tnBxccG+ffvQsGFDHDt2DBYWFkLH05j8rcUaNWqEcePGwdvbG/7+/khKSoKvr6/Q8TTm/Pnz+OGHH776HHNzc9GsVi2TyQpsk6ivr4/c3FyBEmkHr70VCkWBnUJKlCihs1tLaUrz5s3RvHlzLF26FP369cPIkSPRs2dPlC5dWuhoGpGRkaHcLs7S0hKvXr1CtWrVULNmTVFul/jhwwflorh6enowNjaGo6OjwKmIJkVFReHFixfK7xljiI6OxocPH5THPv0Mr0todX0dVK9ePTRo0AAbNmyAnp4eli1bht9//13Uq05/6vnz52jRogXevHkDd3d3AEBYWBjKlSuHs2fPwsbGRuCE6mVmZobw8HDlLwoXFxdMnDgRI0eOBJC3cq+zszOysrKEjKlR8fHxWL58uXIrmrp162LatGmoWbOm0NE0xs/PD3p6ehg/fjzOnTuHzp07gzGG3NxcrFq1ChMmTBA6olYEBwcjODgYVatWRefOnYWOo3GvXr3Cw4cPAQDOzs7Fa6Xe7yCVStGhQweVLUGPHTuG1q1bq+ydLrb94nnu7ebmprL9aUREBFxcXFT2hxfjwO9TN27cwPbt2/H333/D2dkZQ4YMwS+//FLoPtti0KBBAyxevBienp7o0qULLCwssGzZMqxduxb79+9HfHy80BHVSiqVIjAwUHkBql+/fli9enWB3VG6dOkiRDyiZlKpFBKJBIUNlfOPSyQSnT2ZSYN8HWRqaoqwsDA4OTkByNs/vGTJknj+/LnyjKnYZWRk4M8//0R4eDiMjY1Rq1Yt9OvXT5R7itepUwcTJ07E4MGDkZSUBHt7e0RGRsLV1RVA3oeG3r1749mzZwInJZqUmJiIO3fuwMnJSWfPCpP/XmZmJsaOHYudO3cqPxDo6elh4MCBWLduHUxMTAROqF75W4N+y44dOzScRLt47b1gwYIiPW/evHkaTqJ9ycnJCAoKwo4dO/D27Vv0798fQ4YMgZubm9DRNG7Xrl2QyWQYPHgw7ty5g/bt2yM1NRUGBgYICAhAnz59hI6oVkU5WaPLgz7yfRITE4v0PDs7Ow0n+e/QIF8HSaVSvHjxQmVA//nVXiIeW7duha+vL/r06YOQkBBYWFjg+vXryscXL16Mmzdv4tixYwKmVK/3798X+bli3S+eJ0ePHi3yc8V6BWTEiBE4d+4c1q9fr7wV59q1axg/fjzatWuHTZs2CZyQEPLf0NfXR6VKlTBo0CB06dLlixcjxHTytlevXhg2bBg8PT0hkUiUxzMzMxETEwNbW1tYW1sLmJAQQoN8HSSVSrF48WKYmpoqj02bNg1Tp05V+aE5fvx4IeJpRXx8PFavXo3o6GgAgKurKyZMmIAqVaoInEwztm/fjmPHjqF8+fKYN28eypcvr3xs9OjRaNeuHbp37y5gQvXKnwJVFGI6I7527doiP1dMf7+LOlVVzFdArK2tsX//frRs2VLl+MWLF9G7d2+8evVKmGCEkP/Jpz/f8n+vff7RWmw/29q0aYNLly6hYsWK8PHxweDBg+kiFCE6hgb5Osje3v6bAyCJRILHjx9rKZF2nT59Gl26dEGdOnWUV7yuX7+O8PBwHDt2TJSL7/Hm0wX1njx5gunTp2Pw4MFo0qQJgLz7tAMDA7Fs2TIMGjRIqJhq5+DgUKTnifnvN69MTExw584dVK9eXeX4gwcP0LBhQ2RkZAiUjBDyvyjuU3r/W4mJidixYweCgoKQmJiIH3/8EcOGDUPPnj1V1qQghAiDBvlE57i7u8PT0xPLly9XOT59+nScOXNG9Av38KZNmzYYNmwY+vXrp3L8r7/+wh9//IFLly4JE4yoHWMMcXFxyMnJgbOzs8oiXWLXpk0bWFlZISgoSLn6elZWFgYNGoTU1FScO3dO4ISEEPLfuXDhArZv345Dhw7B0NAQ/fr1w5AhQ1CvXj2hoxHCLRrkE51jZGSE+/fvo2rVqirHHz16hFq1auHjx48CJSOaYGJigvDw8EJf7zp16iAzM1OgZJrz/v173Lx5E7m5uWjQoIFoV1j/VEJCArp06YKoqCgAQKVKlXDgwAE0aNBA4GTaERkZCU9PT2RnZ6N27doAgPDwcBgZGeH06dOoUaOGwAkJIeR/8+HDB/z111+YOXMm3r17B5lMJnQkQv5rSUlJsLGxKfLtpbpGnHt6kGKtTJkyCAsLK3A8LCyMm90FeGJjY4OtW7cWOL5t2zbRbZcI5L2PXVxc4OnpiZ9++glOTk44ffq00LE0burUqZDJZNi1axf2798PGxsb5TaRPHBzc0NsbCyWLVuGOnXqoE6dOli+fDliY2NpgC8iQ4YMUdk/mTdBQUHIzs4ucDwnJwdBQUECJCLakpCQgBUrVmDp0qV49+4d2rZtK3QkQv4nDg4OxXq9HLqST3TOwoUL4efnh+nTp6Np06YA8u7J//XXXzFp0iTMmTNH4IREnU6cOIGePXvCyckJjRo1AgDcunULsbGxOHDgADp27ChwQvXy9PREeno6VqxYASMjIyxatAj3799HbGys0NE0qnz58ti/fz+aN28OIG/bqcqVK+P9+/cq+4cTcbhy5UqRnteiRQsNJ9EuPT09JCcnc3tC+kv937x5g7Jly4pq8TkCfPz4Efv378f27dtx5coV2NjYwMfHBz4+PqI8SU/4UthuZ8UJDfKJzmGMYfXq1Vi5ciX+/fdfAEDFihUxdepUjB8/vthOm/ma3NxcGBsbIywsjIu9dT/39OlTbNq0CTExMQCA6tWrY+TIkaL8kGBtbY0zZ86gbt26AIC0tDSULl0aaWlpot4uUCqVIjk5GeXKlVMeMzU1xf3794u8IGFxw/PWgV/bUSH/Z7hEIhHddN7i/qHwfyWVSpGSklLgFqTw8HC0atUKqampAiUj6nTr1i1s374de/fuxcePH9G9e3cMGTIEbdq0EeVntM/J5XL4+flh3759SEpKQk5Ojsrj9D4Xhy/9PCsu+Fn1iBQbEokEvr6+8PX1VU57NDMzEziVZunr68PW1pbbqxw2NjZYunSp0DG0IjU1FZUrV1Z+b2FhgZIlS+LNmzeiHuRLJBKkp6fD2NhYeUwqleLDhw94//698piY/ht069atSM8T2/ZaAPD27dtCj2dmZmLNmjVYu3ataLfc+vDhg3JxxS8R0/scyFswVyKRQCKRoE2bNiqLasrlciQkJKB9+/YCJtSO169f48mTJ5BIJLC3t4eVlZXQkTSicePGqF27NhYtWoT+/fvD0tJS6EhatWDBAmzbtg2TJ0/G7NmzMWvWLDx58gSHDx/G3LlzhY5H1GjOnDkwMTH56nNWrVqlpTTfhwb5OubTD7vfIrYPCfmysrLAGIOJiQnMzMyQmJgIf39/uLq6wsPDQ+h4GjNr1izMnDkTO3fuROnSpYWOo1ERERFwc3ODVCpFRETEV59bq1YtLaXSnqioKLx48UL5PWMM0dHRKvfyiq03YwzVqlUrcMzd3V35tdgGuwqFQugIgilVqpTK9wqFAtu3b8eCBQsglUqxYcMGUW2P+anP3+efEuP7HPj/E1phYWHw9PSEqamp8jEDAwPY29ujZ8+eAqXTvAcPHmDUqFG4fv26yvEff/wRmzZtgrOzs0DJNOP27dvK2Wg8+vPPP7F161Z06tQJ8+fPR79+/VClShXUqlULISEhGD9+vNARiZrcv38fBgYGX3xcl2eu0HR9HSOVSr/5hhHrh4R8Hh4e6NGjB0aOHIm0tDQ4OzvDwMAAr1+/xqpVqzBq1CihI2qEu7s74uLikJubCzs7uwL3KYtp68BPp7Tmv+cL+1Ekxvf5t/qK9e/35cuXi/S8H3/8UcNJiLYdPHgQM2fOxKtXrzBjxgyMGzdOtPtoS6VSHDhw4JsnasX6Pg8MDESfPn2+OZNBTF68eAE3NzeUKVMGI0eOhIuLCxhjiIqKwtatW/HmzRtERkZyewuHGJUsWRLR0dGwtbVFhQoVcPz4cdStWxePHz+Gu7s73r17J3REogbF/fYrupKvYy5evCh0BMHdvXsXfn5+AID9+/ejfPnyuHfvHg4cOIC5c+eKdpBf1Km9YpCQkKC8xykhIUHgNNrFW998Yh3UfI/z58/Dz88P0dHRAPLWnpg4caJoV6G+fPkypk2bhvv372PChAmYNm1agSv8YtSsWbNi+6Hwf5U/O+POnTvK93mNGjWUM3bEyM/PD3Z2drh+/brKyY327dtj1KhRaN68Ofz8/LBs2TIBUxJ1qly5MpKTk2Fra4sqVaoo19kJDQ0V7QlMHunyVfqioEG+jqEPwnn3bObfg3/mzBn06NEDUqkUjRs3RmJiosDpNGfevHlCR9AaOzu7Qr/mAW99SZ6NGzdiwoQJ6NWrFyZMmAAACAkJQceOHeHn54cxY8YInFC9OnbsiHPnzmHIkCE4fPgwypcvL3QkogUvX75E3759cenSJVhYWADIW1y0VatW2LNnT7FdwOprzp49i+nTpxc6e8HY2BhTp07Fb7/9RoN8EenevTvOnz+PRo0aYdy4cfD29oa/vz+SkpLg6+srdDyiJsV9sjtN1y8GMjMzC129U2z37OarVasWhg0bhu7du8PNzQ2nTp1CkyZNcOfOHXTq1EnlXmZSPPG86jjhU+XKlTF9+nSMHTtW5fiGDRuwdOlSPH/+XKBkmiGVSlGiRAmULFnyq1dDxLYKtYODA65du4ZKlSoJHUUQffr0wePHjxEUFITq1asDyFuDZNCgQXBycsLu3bsFTqh+FhYWuH37NpycnAp9PC4uDvXr10daWpp2gxGtCQkJwY0bN1C1alV07txZ6DhETQIDA9G3b99iOzuDBvk67NWrV/Dx8cHJkycLfVxs9+zm279/P7y8vCCXy9GmTRucOXMGALBs2TJcuXLli/89ijuetmT52vZanxLjvemET6ampggLCyswEIiNjYW7uzvS09MFSqYZgYGBRXqe2Bbf+9I+8bwoVaoUzp07hwYNGqgcv3XrFjw8PEQ50P3Wa56SkoJKlSqJbrvIfDKZDJcuXUJ8fDy8vLxgZmaGf//9F+bm5ioLMIrJlStX0LRpU5VdJIC8/xY3btxAixYtBEpG1OnKlStFep6uvt40XV+HTZw4EWlpabh58yZatmyJQ4cOISUlBYsXL8bKlSuFjqcxvXr1QvPmzZGcnIzatWsrj7dp0wbdu3cXMJlm8bQlC8+rjhM+denSBYcOHcLUqVNVjh85cgQ//fSTQKk0R2yD96Li/bqJQqGAvr5+geP6+vqi/rn/tW0T379/L9r3RWJiItq3b4+kpCRkZ2ejXbt2MDMzw6+//ors7Gxs3rxZ6Iga0apVq0JP7Lx79w6tWrWiixMi0bJlS+VMtC/9Hdbli1F0JV+HVahQAUeOHEHDhg1hbm6O27dvo1q1ajh69Ch+++03XLt2TeiIWvH+/XtcuHABzs7Oyul/YlSlShWsXbsWnTp1gpmZGcLCwpTHQkJC8NdffwkdkRDyX1q8eDFWrFiBZs2aoUmTJgDypnhev34dkydPVtkSVUzbL2VlZeHs2bN49OgRAMDZ2Rlt27aFsbGxwMk0QyqVIiUlRZT3nhdF165dkZaWht27d6NixYoAgOfPnyv3Uj906JDACdXvW7siiXXHFCBvwWAzMzP4+/vDysoK4eHhcHR0xKVLlzB8+HDExsYKHVEjvvT3/NGjR6hfv/53bYdNdJeVlRXMzMwwePBgDBgwANbW1oU+T1cXlKVBvg4zNzdHREQE7O3tYWdnh7/++gvNmjVDQkICatSogczMTKEjakTv3r3RokULjB07FllZWahduzaePHkCxhj27Nkj2r12eduSJSsrC+fPn1dexZwxYways7OVj+vp6WHRokVcbcUkVj169Cjycw8ePKjBJMJxcHAo0vMkEgkeP36s4TTacfToUQwbNgyvX79WOW5tbQ1/f39R3rsqlUrh5uZWYBrv58S0Jeqnnj59ii5duuDBgwewsbFRHnNzc8PRo0dRuXJlgROqH8/bg1pZWeHGjRtwdnaGmZmZcpD/5MkTuLq6iu5zav7vsiNHjqB9+/Yq92rL5XJERETA2dkZp06dEioiUaOcnBwcOnQI27dvx9WrV9GxY0cMHToU7du3LxYr79N0fR3m7OyMhw8fwt7eHrVr18aWLVtgb2+PzZs3o0KFCkLH05grV65g1qxZAIBDhw6BMYa0tDQEBgZi8eLFoh3k87YlS2BgII4fP64c5K9fvx41atRQXuGLiYlBxYoVRbVSrbu7e5F/MYhpEKCrZ7m1ibetE2/cuIFevXqhS5cumDx5ssoibCtXrkSvXr1w+fJlNG7cWOCk6ufp6Snae5G/xcbGBnfv3sW5c+cQExMDIG+rSLFuEwmIc/BeVAqFotAZCs+ePVPukiQm+b/LGGMwMzNTmZFkYGCAxo0bY/jw4ULFI2pmYGCAPn36oE+fPkhKSkJAQADGjh2L7OxsDBo02jVh6AAANrVJREFUCAsWLPjmCV0h0ZV8HbZr1y7IZDIMHjwYd+7cQfv27ZGamgoDAwMEBASgT58+QkfUCGNjYzx69Ag2NjYYOHAgKlasiOXLlyMpKQmurq6iW6Aq3/Tp02Fubo6ZM2di79698Pb2hr29vXJLluXLlwsdUa1++OEH/Oc//1Fezfv0KgCQ9/7fsGEDgoODhYypVgsWLCjyc3naUpEnOTk5SEhIQJUqVXT6w8H/qmPHjrCxscGWLVsKfXzEiBF4+vQpTpw4oeVkmiWVSvHixQtuF97j2fPnz3HgwAGVW1N69Ogh6p0W+vTpg1KlSuGPP/6AmZkZIiIiUKZMGXTt2hW2trbYsWOH0BE1YsGCBZgyZQpKliwpdBSiZQkJCRg6dCguX76MV69eoXTp0kJH+iIa5BcjmZmZiImJga2t7RfvCxGDatWqYfHixejUqRMcHBywZ88etG7dGuHh4WjTpk2BqZ9iFRwcjODgYNFuyVKhQgUEBwfD3t4eAFCmTBmEhoYqv3/06BEaNGggutsUCJ8yMzMxbtw45arzjx49gqOjI8aNG4dKlSph+vTpAidUr9KlS+Py5cuoWbNmoY9HRETgxx9/xNu3b7WcTLN4XF1/7dq1RX6umNab+NTGjRsxadIk5OTkKNfXeP/+PQwMDLBq1SqMHj1a4ISa8fTpU7Rv3x6MMcTGxqJ+/fqIjY2FtbU1rly5wtXfAyJe2dnZOHDgALZv347g4GB06tQJQ4YMQfv27YWO9lXivYxQzOXm5sLFxQX//POPcpqjiYkJ6tatK3AyzZs4cSL69+8PU1NT2NnZoWXLlgDypvF/6QOjGDVp0kS5QJcYpaWlqdyD/+rVK5XHFQqFyuNEPPbv3//FrSLFdJvCp2bMmIHw8HBcunRJ5YNB27ZtMX/+fNEN8rOyslQWE/xcqVKl8PHjRy0m0g4er5v4+fkV6XkSiUSUg/zjx49j/PjxmDhxIiZPnqy8nTI5ORm///47JkyYAHt7e3Ts2FHgpOpnY2OD8PBw7N27F+Hh4UhPT8fQoUPRv39/0S6umY/H32O8uXXrFnbs2IE9e/bA3t4ePj4+2Ldvn05fvf8UDfJ1lL6+vig/ABXF6NGj0ahRIyQlJaFdu3bKPdUdHR2xZMkSgdOp19GjR4v83C5dumgwifZVrlwZkZGRcHZ2LvTxiIgIUS7SlE8ul8PPz++LHxJSU1MFSqZZa9euxaxZszB48GAcOXIEPj4+iI+PR2hoKMaMGSN0PI05fPgw9u7di8aNG6usy1CjRg3Ex8cLmEwzqlatigsXLsDHx6fQx8+fP4+qVatqOZXmJSQkqMy0y595JubZd7ytN/G533//HdOnT8fixYtVjleoUAGrVq2CiYkJfvvtN9EN8j+9GNW/f3/0799f6Ehaw+vvMd40btwYtra2GD9+POrVqwcAhe5sprOfzxnRWUuWLGGDBg1iubm5QkfRCVFRUWzy5MlCx1AriUSi8kcqlRZ6TCqVCh1V7caPH89cXV1ZVlZWgccyMzOZq6srGz9+vADJtGPOnDmsQoUKbMWKFczIyIgtWrSIDR06lFlZWbE1a9YIHU9jnJ2d2V9//cUYY8zU1JTFx8czxvL+e4wZM0bIaBplbGys7Ppp77CwMGZubi5kNI1YtWoVK126NDt+/HiBx/755x9mZWXFVq5cKUAyzXv79i0bPXo0s7KyUv78trKyYmPGjGFv374VOh5RMzMzMxYTE/PFx2NiYpiZmZkWE2lPxYoVWVRUlNAxtI7X32O8+fzzeGF/dPnzOd2Tr8O6d++O8+fPw9TUFDVr1iywwIdYt5r6VEZGBvbs2QN/f3+EhITA1dUVkZGRQsfSiHPnzmHatGlYunSpcpp+cHAwZs+ejaVLl6Jdu3YCJ1SvlJQU1KlTBwYGBhg7diyqVasGAHj48CHWr18PmUyGe/fuoVy5cgIn1YwqVapg7dq16NSpE8zMzBAWFqY8FhISgr/++kvoiBphYmKC6Oho2NnZoWzZsjh79ixq166N2NhYNG7cGG/evBE6oka0aNECP//8M8aNG6dcoMrBwQHjxo1DbGys6LZcUigU6NOnDw4cOABnZ2dUr14djDFER0cjNjYW3bp1w99//62cqSUWqampaNKkiXJv+E93Ffjrr79gY2ODGzduwNLSUuCk6jNp0iQsWrQIJUuWxKRJk7763FWrVmkplfaULFkS9+/fVy4a+7nHjx+jZs2ayMjI0HIyzVu6dCkePXqEbdu2iXoh0c/x+nuMFC/8/I0shiwsLES7Xdy3XL9+Hf7+/ti3bx+ysrLg6+uL7du3w8XFRehoGjNx4kRs3rwZzZs3Vx7z9PSEiYkJfvnlF0RHRwuYTv3KlSuHGzduYNSoUZg+fbryXlaJRIJ27dph48aNoh3gA8CLFy+Ua0yYmpoqFxj86aefMGfOHCGjaVT58uWRmpoKOzs72NraIiQkBLVr10ZCQoKo72deunQpOnTogKioKMhkMqxZswZRUVG4ceNGkffZLk6kUin+/vtv7N27F7t371Zup+bi4oL58+ejb9++AifUjIULF8LAwADx8fEFfn4tXLgQHh4eWLhwYZHvYy8O7t27h9zcXOXXX1Ic9pX+b9SoUQNHjhz54navhw8fRo0aNbScSjtCQ0Nx/vx5nDlzhquLUbz+HiPFCw3ydZhYtx75kpcvXyIgIADbt2/Hu3fv0K9fP1y6dAlNmjTBkCFDRD3AB4D4+HhYWFgUOF6qVCk8efJE63m0wcHBAadOnUJqairi4uIAAE5OTsVmUZP/ReXKlZGcnAxbW1tUqVIFZ86cQd26dREaGgpDQ0Oh42lM69atcfToUbi7u8PHxwe+vr7Yv38/bt++jR49eggdT2OaN2+OsLAwLF++HDVr1lS+3sHBwaJeUDR/j2FeHD58GFu2bCn0BGX58uXx22+/YeTIkaIa5F+8eLHQr3kxZswYjBo1CoaGhvjll1+UV7RlMhm2bNmC2bNnY+PGjQKn1AxeL0bx+nuMV3///Td2796t3B6zWrVq8PLyQq9evQRO9nU0XV/HyWQyXLp0CfHx8fDy8oKZmRn+/fdfmJubw9TUVOh4amVsbIxevXrB29tbZcE9fX19hIeHw9XVVeCEmtWiRQsYGRlh586dyg+IKSkpGDhwID5+/CjKq308mz59OszNzTFz5kzs3bsX3t7esLe3R1JSEnx9fbF8+XKhI2qEQqGAQqFQfhDes2cPbty4gapVq2LEiBEwMDAQOCFRpzdv3sDKygpA3nZbW7duRVZWFjp37owWLVoInE79DA0NER8f/8VFQ589ewYnJydRLqy7d+9eHD16FDk5OWjTpg1GjhwpdCStmTJlClatWgUzMzNUqVIFjDE8fvwY6enpGD9+vKhO6hD6PcYLhUKBfv364e+//0a1atWUFxujo6MRFxeHn3/+Gbt379bZWUo0yNdhiYmJaN++PZKSkpCdna3cV3nChAnIzs7G5s2bhY6oVi4uLsjOzoaXlxcGDBig/MvEyyA/Li4O3bt3x6NHj2BjYwMg70Nx1apVcfjwYTg5OQmckGhScHAwgoODUbVqVXTu3FnoOETN3r17h7Nnz+LJkyeQSCRwdHREmzZtvrrNXHF2//59dO7cWfkzbM+ePWjfvj0yMjIglUqRkZGB/fv3o1u3bkJHVatKlSph7969Krddferq1avo06cP/v33Xy0n06xNmzZhzJgxqFq1KoyNjXH//n1MmjQJv//+u9DRtCYkJAS7d+9GbGwsgLyrfX379kXjxo0FTqZ5r169wsOHDwEAzs7OKFOmjMCJCPnf+fn5YfHixQgMDMRPP/2k8tjRo0fh4+ODOXPmYOLEicIE/AYa5Ouwbt26wczMDP7+/rCyskJ4eDgcHR1x6dIlDB8+XPmLREzy78XPP2vm7e2N//znP4iIiFAuYCRmjDGcPXtWef9q9erV0bZtW509S0jIfyMtLQ23bt3Cy5cvoVAoVB4bOHCgQKk0Z9euXRg7dizev3+vcrxUqVLYvHmzKKezd+jQASVKlMD06dOxc+dO/PPPP/D09MTWrVsBAOPGjcOdO3cQEhIicFL1GjJkCOLj43H27NkCV/Oys7Ph6ekJR0dHbN++XaCEmlGjRg307t0b8+bNA5D3nh8xYoQoF5v73MKFCzFlyhSYmJgIHUXrMjIyMG7cOAQFBSl/luvp6WHgwIFYt26dqP6bREREFPm5tWrV0mASoi21atXCxIkTMWTIkEIf9/f3x5o1a77rvaFNNMjXYVZWVrhx4wacnZ1hZmamHOQ/efIErq6uyMzMFDqixqSnp2P37t3YsWMHQkJC8OOPP8LLywvdunWjM8RENGJjY3Hx4sVCB7tz584VKJVmHTt2DP3790d6ejrMzc1VTmBJJBKkpqYKmE797t69i0aNGqF///7w9fWFi4sLGGOIiorC6tWrsWfPHoSGhqJ27dpCR1Ura2trXLhwAbVq1VK+1qGhocq9hmNiYtC4cWOkpaUJG1TNnj17hvr168PQ0BBjxoxRvt7R0dHYuHEjsrOzcfv2beVsLbEwNjZGdHQ07O3tAeRNczU2NsaTJ09QoUIFYcNpmJ6eHpKTk1G2bFmho2jdiBEjcO7cOaxfvx7NmjUDkLeP+Pjx49GuXTts2rRJ4ITqI5VKIZFIwBj75oUXuVyupVREk4yNjfHw4UPY2toW+nhiYiJcXFyQlZWl5WRFQ4N8HWZpaYnr16/D1dVVZZB/7do19OzZEykpKUJH1Iro6Gj4+/tj586dSE1NVa7iK0YZGRm4fPkykpKSkJOTo/LY+PHjBUpFNGHr1q0YNWoUrK2tUb58+QKD3bt37wqYTnOqVauGjh07YunSpaK6yvMlPj4+SE9Px99//13o47169YK5ubnoruxKpVK8ePFCOfD59HcYkLfeSMWKFUX5YTghIQGjR4/GmTNnCuwasn79elHeeiWVSpGSkqJyEv7z11ysPn+v88Ta2hr79+9Hy5YtVY5fvHgRvXv3xqtXr4QJpgGJiYnKr+/du4cpU6Zg6tSpKlser1y5Er/99pvobkPiVenSpXHp0qUvzsy4f/8+WrRogbdv32o5WdHQIF+H9enTB6VKlcIff/yh3Fe5TJky6Nq1K2xtbblbfV8mk+Ho0aOiXbn03r176NixIzIzM5GRkYHSpUvj9evXMDExQdmyZfH48WOhI6rVnTt3MGXKFBw5cqTAfcnv3r1Dt27dsHr1atFd4cxnZ2eH0aNHY9q0aUJH0apv7SktNtWqVcPGjRvRtm3bQh8/d+4cRo8erVy1Vyw+H/Tl/w5zcHAAIO5Bfr63b98qb6sT+64hUqkUv/zyi8qJuw0bNsDb2xulSpVSHlu1apUQ8TSqsBMcvDAxMcGdO3cK3E754MEDNGzYULS3azRs2BDz589Hx44dVY6fOHECc+bMwZ07dwRKRtSpU6dOsLW1/eKMlJEjRyIpKQknTpzQcrKioUG+Dnv27Bk8PT3BGENsbCzq16+P2NhYWFtb48qVK1yeNRazli1bolq1ati8eTNKlSqF8PBw6Ovrw9vbGxMmTBDdyQ0vLy9Ur179i3vCL126FFFRUdi1a5eWk2mHubk5wsLCuBns5uvRowf69u2L3r17Cx1FK0xNTREVFfXF6X5JSUmoXr266D4MS6VSdOjQQbkd5LFjx9C6dWvlPtrZ2dk4deqUqAf5PGnZsuU3pzBLJBJcuHBBS4m0RyqVolSpUt/sL7ZbkQCgTZs2sLKyQlBQEIyMjAAAWVlZGDRoEFJTU3Hu3DmBE2qGsbEx7t69W+DkRnR0NOrWrauz07fJ97lx4wZatmyJbt26YcqUKSq3X61cuRJHjhzBxYsXlbeq6Boa5Os4mUyGPXv2ICIiAunp6ahbty769+8PY2NjoaMRNbOwsMDNmzfh7OwMCwsLBAcHo3r16rh58yYGDRqkXIxPLKpUqYJDhw59dRpU165dRTeDId/QoUPRoEEDrraZAvIWqlm4cCF8fHxQs2ZN6OvrqzzepUsXgZJpxrem8or1iraPj0+RnsfbjDQiPlKpFKtXr1aZsVCYQYMGaSmR9kRGRsLT0xPZ2dnKWXfh4eEwMjLC6dOnUaNGDYETakbdunXh5uaGbdu2KRfYzMnJwbBhwxAZGSna2+14dOjQIfzyyy8FTtJZWlpiy5Yt6Nmzp0DJvo0G+YToiDJlyij3Wa1WrRrWrVsHT09PxMTEoF69eqK70mdkZITo6Gjl9N3PJSQkwNXVVbRnxJctW4ZVq1ahU6dOhQ52xboGg1Qq/eJjEolEdINdqVSKwMDALw4A0tLS4OPjI7rehPCC53vyASAzMxN//vmnyq5AYr8YdevWLXTu3BmMMeWFioiICEgkEhw7dgwNGzYUOCFRp8zMTJw+fVple0wPDw+dX1eIBvk65ujRo0V+rtiuePHOw8MDgwcPhpeXF4YPH46IiAiMHz8eO3fuxNu3b3Hz5k2hI6qVjY0Ntm7divbt2xf6+MmTJ/HLL7/g6dOnWk6mHV86uQHkDXbFOoOBN187qZFPjCc3COEFz6vr8ywjI6PAyQ0vLy/lLUmECI0G+Trm8w+E+dt1fH4MoC06xOb27dv48OEDWrVqhZcvX2LgwIHKK/v+/v6oU6eO0BHVysfHB3Fxcbh69WqBxxhj+OGHH1C1alWazktIMfSlfYU/J7ZdBQh/inIlf//+/ejVq5cWU2kW7wvnEj5cuHABY8eORUhISKHv86ZNm2Lz5s344YcfBEr4dTTI12Hnzp3DtGnTsHTpUpUtOmbPno2lS5eiXbt2AidUn+9ZVO7gwYMaTEK0JT4+HvXq1YOzszMmT54MZ2dnAHn7Z69cuRKPHj3C7du3Rbnd1Oc+3WaLB5cvX8aKFSsQHR0NAHB1dcXUqVN19hcl+X5SqRR2dnZwd3cvcKL6U4cOHdJiKkI0QyaTISYmBgYGBqhWrZry+JEjRzB37lzExMQgOztbwITqxfvCufmioqIK3fKYZtqKQ5cuXdCqVSv4+voW+vjatWtx8eJFnf09RoN8Hebm5obNmzejefPmKsevXr2KX375RfkBWQyKukgTwN9CTXfv3sXcuXPxzz//CB1F7W7fvo3BgwcjKipKOcBljMHV1RU7duxAgwYNBE6oWUFBQfj9999V7vOaOnUqBgwYIHAyzdm1axd8fHzQo0cP5Yq0169fx6FDhxAQEAAvLy+BExJ1GDNmDHbv3g07Ozv4+PjA29tb1FvIEX49ePAAnTp1Ut5a1rVrV2zatAm9e/dGZGQkhg8fjrFjx6Jy5coCJ1Uf3hfOffz4Mbp374779++rzLilmbbiYmdnh1OnThXYRSFfTEwMPDw8kJSUpOVkRUODfB1mbGyM0NBQuLm5qRyPiIhAo0aNRLsgGY9Onz6Ns2fPwsDAAMOGDYOjoyNiYmIwffp0HDt2DJ6enjq7D6c6hIWFITY2FowxVKtWTXS3JhRm1apVmDNnDsaOHasc7F67dg0bNmzA4sWLv3jmuLirXr06fvnllwL9Vq1aha1bt4rq5CXvsrOzcfDgQWzfvh03btxAp06dMHToUHh4eHAza4VnGRkZ2Lt3L7KysuDh4YGqVasKHUkjOnXqhOzsbEycOBG7d+/G7t274ezsjKFDh2LMmDGiXICO94VzO3fuDD09PWzbtg0ODg64desW3rx5g8mTJ2PFihU0K00kjIyMEBkZ+cUZpXFxcahZs6buvs8Z0Vk//PADa9euHXvx4oXy2IsXL5iHhwdr0aKFgMmIOm3bto1JJBJmZWXFpFIpK1OmDNu5cyezsLBgI0aMYFFRUUJH1Lp3796xjRs3snr16gkdRWPs7e1ZYGBggeMBAQHM3t5egETaYWBgwGJjYwscj42NZYaGhgIkItrw5MkTNn/+fObo6MhsbW3Zhw8fhI5E1CgxMZG1aNGCmZqasrZt27LExERWrVo1JpFImEQiYSYmJuzy5ctCx9SIMmXKsHv37jHGGEtLS2MSiYQFBQUJG0rDKleuzE6ePPnFx0+cOMEqV66sxUTaZWVlxcLDwxljjJmbm7OYmBjGGGPnz59nderUETIaUSNHR0d26NChLz5+4MAB5uDgoL1A3+nby/4SwWzfvh3JycmwtbWFk5MTnJycYGtri+fPn8Pf31/oeBq1f/9+9O7dG40bN0bdunVV/ojNmjVr8Ouvv+L169fYt28fXr9+jY0bN+L+/fvYvHnzF6cJidHFixcxYMAAVKhQAYsWLUKjRo2EjqQxycnJaNq0aYHjTZs2RXJysgCJtMPGxgbnz58vcPzcuXOwsbERIBHRBqlUqpzWSlNZxWfKlCnIycnB5s2bYWJiAk9PT1StWhXJyclISUlBhw4dMH/+fKFjasTr169RsWJFAECpUqVQsmRJNG7cWOBUmtW2bVssWbKk0McYY1iyZAnatm2r5VTaI5fLYWZmBgCwtrbGv//+CyBvevfDhw+FjEbUqGPHjpgzZw4+fvxY4LGsrCzMmzcPP/30kwDJiqaE0AHIlzk5OSEiIgJnz55V2aKjbdu2op7quHbtWsyaNQuDBw/GkSNH4OPjg/j4eISGhmLMmDFCx1O7+Ph4/PzzzwDyFiAsUaIEfv/9d1Hdv/c1z58/R0BAAHbs2IG0tDS8ffsWf/31F3r37i3q97mTkxP27duHmTNnqhzfu3evaKe1AsDkyZMxfvx4hIWFKU9yXL9+HQEBAVizZo3A6Yg6fTpd/9q1a/jpp5+wfv16tG/fvkhbC5Li48qVKzh69CgaNmyIDh06wNraGtu3b0e5cuUAAHPmzEGbNm0ETqkZEokEHz58gJGRERhjkEgkyMrKwvv371We9/nq3MXZ7NmzUa9ePTRq1OiLC+cGBAQIG1KD3NzcEB4eDgcHBzRq1Ai//fYbDAwM8Mcff8DR0VHoeERNZs+ejYMHD6JatWoYO3asyvt8w4YNkMvlmDVrlsApv4zuyS8mPn78CENDQ1EPevK5uLhg3rx56NevH8zMzBAeHg5HR0fMnTsXqampWL9+vdAR1erz7Xc+7SxmBw4cgL+/P65cuYIOHTrA29sbHTp0QMmSJREeHg5XV1ehI2rUgQMH0KdPH7Rt21ZlAbrz589j37596N69u8AJNefQoUNYuXKl8v776tWrY+rUqejatavAydTPwcHhmz+3JRIJ4uPjtZRIO0aPHo09e/bAxsYGQ4YMQf/+/WFtbS10LKIhUqkUycnJykG9qakpIiIilL/HUlJSULFiRVHO4sifpZIvf6D/+fdi687zwrmnT59GRkYGevTogbi4OPz000949OgRrKyssGfPHtGe0OJRYmIiRo0ahdOnT6sssOjp6YkNGzZ8cV0KXUCDfB2mUCiwZMkSbN68GSkpKXj06BEcHR0xZ84c2NvbY+jQoUJH1AgTExNER0fDzs4OZcuWxdmzZ1G7dm3ExsaicePGePPmjdAR1UoqlWLx4sUwNTUFAEybNg1Tp04t8IF4/PjxQsTTmBIlSmDatGmYPn26ctobAOjr63MxyAfy9hr28/NTGexOnjwZ7u7uAicj6vK12QlPnjzBli1bkJ2dLboBgFQqha2tLdzd3b96koO2RBWHb52sFvMg//Lly0V63o8//qjhJMLgceHcwqSmpsLS0pKLi3E8evv2LeLi4sAYQ9WqVWFpaSl0pG+i6fo6bPHixQgMDMRvv/2G4cOHK4+7ublh9erVoh3kly9fHqmpqbCzs4OtrS1CQkJQu3ZtJCQkfHW/5eLK1tYWW7duVX5fvnx57Ny5U+U5EolEdIP8oUOHYsOGDbh06RIGDBiAPn36FIsfmupUr1490e8jzLsJEyYUOJaamopFixZh06ZNaNSoEX799VcBkmnWwIED6cMuZ+bOnQsTExMAQE5ODpYsWYJSpUoBADIzM4WMplFFGbynpqZqIYkw6tSpw93AfsiQIVizZo3KBYrSpUsjIyMD48aNw/bt2wVMRzTB0tKy2M1OoSv5OszJyQlbtmxBmzZtVM6Kx8TEoEmTJnj79q3QETVi2LBhsLGxwbx587BhwwZMnToVzZo1w+3bt9GjRw/RLzrIk6ysLOzbtw/bt2/HzZs34enpiePHjyMsLKzA1pFi8P79e+V9mZ/fr/k5Md2/Wbp0aTx69AjW1tbfvNIh5g/DWVlZWLVqFVasWAE7OzssXboUHTt2FDoWIf+zli1bFumkzsWLF7WQRnecOXMG27Ztw7Fjx3R3my3y3fT09JCcnKycuZLv9evXKF++PGQymUDJCPl/dCVfhz1//rzQvRkVCgVyc3MFSKQdf/zxBxQKBQBgzJgxsLKywo0bN9ClSxeMGDFC4HREnYyNjTFo0CAMGjQIsbGx2LFjB27fvo1mzZqhU6dO6NWrF3r06CF0TLWxtLRUfjCwsLAo9EOxGO/f9PPzU17xWL16tbBhBCCXy7F161YsWLAARkZGWLt2Lby9velKNxGNS5cuCR1BZyQmJmL79u0IDAzE27dv0aFDBwQFBQkdi6jB+/fvwRgDY0y52GI+uVyOEydOFBj4EyIUupKvw+rVqwdfX194e3urXMlfuHAhzp49i6tXrwodkRC1UygUOH78OPz9/XHy5ElkZ2cLHUltLl++jGbNmqFEiRLfvI9TrPdv8mbfvn2YPXs20tLSMGvWLIwaNQoGBgZCxyJEq6Kjo+Hv748VK1YIHUUjcnJycPDgQWzbtg3Xr19H27ZtcfLkSdy7dw81a9YUOh5Rk88XWfycRCLBggULdHrFdcIPGuTrsCNHjmDQoEGYMWMGFi5ciAULFuDhw4cICgrCP//8g3bt2gkdUWPS0tJw69YtvHz5UnlVP9/AgQMFSkW07eXLl6I9K56UlAQbG5sCHxgYY3j69ClsbW0FSqZZX7pNQSKRwNDQUHQDYKlUCmNjY/Tr1++rt2CsWrVKi6kI0byMjAzs2bMH/v7+CAkJgaurKyIjI4WOpXbjxo3D7t27UbVqVXh7e6Nv376wsrLiahFZXly+fBmMMbRu3RoHDhxA6dKllY8ZGBjAzs4OFStWFDAhIf+PBvk67urVq1i4cCHCw8ORnp6OunXrYu7cufDw8BA6msYcO3YM/fv3R3p6OszNzVUGQRKJRNT37PImIyMDv/76Kw4ePIgnT55AIpHAwcEBvXr1wpQpU5SLOInRl+7pe/PmDcqWLSuq6fqf+taVkMqVK2Pw4MGYN2+eKPZSL8q9yhKJBBcuXNBSIkI06/r16/D398e+ffuQlZUFX19fDBs2DC4uLkJH0wged4p5/fo1MjIyYGdnpzz24MEDrFixAhkZGejWrRu8vLwETKhZiYmJsLW1pVuuiE6jQb4Oevz4cZH2VharatWqoWPHjli6dKmoB3m8y8nJQdOmTREZGYkOHTrAxcUFjDFER0fj1KlTqFu3Lq5cuQJ9fX2ho2qEVCpFSkoKypQpo3I8MTERrq6uyMjIECiZZgUFBWHWrFkYPHgwGjZsCAC4desWAgMDMXv2bLx69QorVqzA1KlTMXPmTIHTEkKK4uXLlwgICMD27dvx7t079OvXD15eXmjSpIloB7r5du/eje3btyM4OBidOnXCgAED0KFDBxgZGYm2e79+/VCxYkWsXLkSQN7r7+LigooVK6JKlSo4efIk/P39MWDAAIGTas7Vq1exZcsWPH78GH///TcqVaqEnTt3wsHBAc2bNxc6HiG08J4uqlq1qsoVvj59+mDt2rUoV66cwMm04/nz5xg/fjw3A/y5c+di+vTpyr5v377lYiu5TZs24dmzZwgPD4ezs7PKYzExMWjZsiU2b96McePGCZRQMyZNmgQg7+rtnDlzVN7ncrkcN2/eFPV2RIGBgVi5ciV69+6tPNa5c2fUrFkTW7Zswfnz52Fra4slS5aIYpDv6OiI0NBQWFlZCR2FEI2xs7NDr169sGbNGrRr104Us3CKql+/fujXrx8SEhIQEBCAMWPGIDMzEwqFAlFRUaIc5IeEhCAgIED5fVBQEEqXLo2wsDCUKFECK1aswIYNG0Q7yD9w4AAGDBiA/v374+7du8q1g969e4elS5fixIkTAickBODnp3Ax8vnkihMnToj2ql5hPD09cfv2baFjaM2SJUuQnp6u/N7Ozg6PHz8WMJF2HDx4EHPmzCkwwAcAFxcXzJo1C/v37xcgmWbdu3cP9+7dA2MM9+/fV35/7949xMTEoHbt2iofnsTmxo0bcHd3L3Dc3d0dwcHBAIDmzZsjKSlJ29E04smTJ6K99YKQfHZ2drh27RquXLmCR48eCR1HEA4ODliwYAGePHmCXbt2oWfPnvD29kblypUxfvx4oeOp1YsXL2Bvb6/8/sKFC+jRowdKlMi7dtilSxfExsYKlE7zFi9ejM2bN2Pr1q0qsw2bNWuGu3fvCpiMkP9HV/KJzunUqROmTp2KqKgo1KxZs8B07S5dugiUTDM+P6nDyx00UVFRaNmy5Rcfb9WqFRYuXKi9QFqSv0+0j48P1qxZ89XF2MTIxsYG/v7+WL58ucpxf39/2NjYAMhbl4CH2SyEiEVMTIzyXvwGDRqgWrVq8Pb2BgDubj2USCTw9PSEp6cnUlNTERQUhB07dggdS63Mzc2RlpamvCf/1q1bGDp0qPJxiUQiqp1xPvfw4UO0aNGiwPFSpUohLS1N+4EIKQQN8nWQRCIp8EuRp1+Sw4cPB4BCB3hi2z+cZ2lpaV+dwmxlZYV3795pMZF2ie1DX1GtWLECP//8M06ePIkGDRoAAG7fvo2YmBjlzI3Q0FD06dNHyJhqdfr0aZQqVeqrzxHbyUvCn2bNmqFZs2ZYu3Ytdu/ejR07dkAul2P06NHw8vJCt27dCqxBIkavX7+GgYEBzM3NUbp0aUycOBETJ04UOpZaNW7cGGvXrsXWrVtx8OBBfPjwAa1bt1Y+/ujRI+VJWzEqX7484uLiVGYzAMC1a9fg6OgoTChCPkML7+kgqVSKDh06wNDQEEDeavOtW7dGyZIlVZ538OBBIeIRNdPT08OjR49QpkwZMMZgY2ODa9euFfjlIbYrvnp6enjx4sUXP/SlpKSgYsWKoj6pc/v2bezbtw9JSUnIyclReUzMf78TEhKwZcsW5bReZ2dnjBgxosB7XgyKcm8ynbwkYhUdHQ1/f3/s3LkTqampyM3NFTqSRqSlpWHWrFnYu3cv3r59CwAoU6YMfHx8Cqy9IgYRERFo06YN3r9/D5lMhpkzZ2LRokXKxwcMGICSJUti8+bNAqbUnGXLlmHXrl3Yvn072rVrhxMnTiAxMRG+vr6YM2eO6NYSIsUTDfJ1kI+PT5Gex+uVQLH5fEsxxlih34ttECCVSuHm5qa8h+9zMpkMDx48EF3vfHv27MHAgQPh6emJM2fOwMPDA48ePUJKSgq6d+9Of79FQiqV4sWLFwW2SiSEJ7m5uTh27Bh69OghdBS1S01NRZMmTfD8+XP0798f1atXB5B3S9pff/0FFxcXXLt2DREREQgJCRHN/fmvX7/G9evXUb58eTRq1EjlsePHj8PV1RUODg4CpdMsxhiWLl2KZcuWITMzEwBgaGiIKVOmqJzsIERINMgnOuny5ctYsWIFoqOjAQCurq6YOnUqfvjhB4GTqd/ly5eL9Lwff/xRw0m0a8GCBUV63rx58zScRBi1atXCiBEjMGbMGJiZmSE8PBwODg4YMWIEKlSoUOT/PsURT1sP6enpqeyWQogYvX//vkjPE9uMNACYOHEizp8/j3PnzhXYBenFixfw8PCAs7Mzzpw5g7Vr12LQoEECJSXqlpOTg7i4OKSnp8PV1RWmpqZCRyJEiQb5ROfs2rULPj4+6NGjB5o1awYAuH79Og4dOoSAgAB4eXkJnJCQ/13JkiXx4MED2Nvbw8rKCpcuXULNmjURHR2N1q1bIzk5WeiIGvHp1kM7d+5EVFQUHB0dsX79epw4cUJ0Ww8V5Up+ZGQk3NzctJiKEPX6fEba58Q6Iw0A7O3tsWXLFnh6ehb6+KlTp9CxY0fMmzdPVCetZTIZ/Pz8sHv3buWtV9WqVYOXlxcmTJhQYNFkQoh20SCf6Jzq1avjl19+ga+vr8rxVatWYevWrcqr+2LB8xUQnlWuXBknT55EzZo1UatWLcyYMQP9+vVDcHAw2rdvL9pFB93d3eHr64uBAwcqZzA4Ojri3r176NChA168eCF0RLXy8fHB2rVrYWZmpnL8w4cP2L17N7Zt24Y7d+6IcvBD+PHpjDTGGDp27Iht27ahUqVKKs8T24w0IG+adnx8PCpXrlzo48+ePYO9vT1kMpmWk2lOVlYW2rVrh+DgYLRt21Z5i0J0dDTOnTuHZs2a4cyZMzAyMhI4qXoNGTKkSM/bvn27hpMQ8m20uj7ROY8fP0bnzp0LHO/SpQtmzpwpQCLNsrCw4PIKSHx8PJYsWaL8ZWhra4v09HTl43p6erh27RqcnZ2FiqhRLVq0wNmzZ1GzZk38/PPPmDBhAi5cuICzZ8+iTZs2QsfTGN62Hvp8bYUrV67A398fBw4cQMWKFdGjRw9s2LBBoHSEqMfng3c9PT00btyYi5XGra2t8eTJky8O8hMSEkR3u87y5cvx9OlT3Lt3D7Vq1VJ5LDw8HF26dMHy5csxf/58YQJqSEBAAOzs7ODu7s7Ndsek+KJBPtE5NjY2OH/+PJycnFSOnzt3TpRbsuTvmw58/QqI2Kxbt07l/sW3b99i7ty5yg9De/fuhZ+fn2hX512/fj0+fvwIAJg1axb09fVx48YN9OzZE7NnzxY4nebwuPXQixcvEBAQAH9/f7x//x69e/dGdnY2Dh8+DFdXV6HjEUL+B56enpg1axbOnj0LAwMDlceys7MxZ84ctG/fXqB0mrFnzx6sWrWqwAAfAGrXro0VK1Zg1qxZohvkjxo1Crt370ZCQgJ8fHzg7e2N0qVLCx2LkELRdH2iczZt2oSJEydiyJAhaNq0KYC8e/IDAgKwZs0ajBgxQuCEmvXpFGYxq1mzJvz9/dGwYUMABXtfvnwZw4YNQ2xsrJAxiZrxtvVQ586dceXKFXTq1An9+/dH+/btoaenB319fYSHh9Mgn4gSL7/HgLzp+PXr14ehoSHGjBkDFxcXMMYQHR2NjRs3Ijs7G6GhobC1tRU6qtoYGRkhNjb2ixdenj59iqpVqypPZItJdnY2Dh48iO3bt+PGjRvo1KkThg4dCg8Pj6/OyiRE2+hKPtE5o0aNQvny5bFy5Urs27cPQN59+nv37kXXrl0FTkfU5cmTJ6hYsaLy+2HDhqFUqVLK7+3t7fHs2TMhohENmj59OhQKBdq0aYPMzEy0aNFCufWQ2Ab4AHDy5EmMHz8eo0aNQtWqVYWOQ4jW8DLgqVy5MoKDgzF69GjMmDFDOY1bIpGgXbt2WL9+vagG+EDeGkEvX7784iD/xYsXBdYhEQtDQ0P069cP/fr1Q2JiIgICAjB69Gjltr+0wj7RFTTIJzqpe/fu6N69u9AxiAZJpVL8+++/yvsY/fz8VB5PSUkR5eq831qFGsj7cCimRZo+JZFIMGvWLEydOrXA1kNZWVkwNjYWOqJaXbt2Df7+/qhXrx6qV6+OAQMGoG/fvkLHIkStevToofL9x48fMXLkSJQsWVLl+MGDB7UZS2scHBxw8uRJvH37Vjn7zMnJSbRTuVu1aoWlS5fiwIEDhT6+fPlytGrVSsuptC//9zljTHTrJpHijwb5hOggHq6A1KhRA+fOnVNO1//c6dOnRbmt2KFDh774WHBwMNauXQuFQqHFRMIwMDBQTlXPzs7GqlWr8Ntvv4ludf3GjRujcePGWL16Nfbu3Yvt27dj0qRJUCgUOHv2LGxsbER7xYvw49NZWADg7e0tUBJhWVpafvF3mpjMmzcPjRo1QuPGjTFp0iSVWxT8/PwQFRWFkJAQoWNqxKfT9a9du4affvoJ69evR/v27SGVSoWOR4gS3ZNPdELp0qXx6NEjWFtbw9LS8quD3NTUVC0m07zPr4AcO3YMrVu3Fv0VkK1bt2LixInYt28fOnXqpPLYsWPH0LdvX6xevRrDhw8XKKH2PHz4ENOnT8exY8fQv39/LFy4EHZ2dkLHUqvs7GzMnz9fuTjVf/7zH3Tr1g07duzArFmzoKenh7Fjx2LatGlCR9W4hw8fwt/fHzt37kRaWhratWuHo0ePCh2LEEKKLCQkBEOHDkV0dLTyMxtjDC4uLvD390eTJk0ETqh+o0ePxp49e2BjY4MhQ4agf//+sLa2FjoWIYWiQT7RCYGBgejbty8MDQ0RGBj41ecOGjRIS6m0w8fHp0jP+3wrLjHo168f9u7dCxcXF+VWeQ8fPsTDhw/Rs2dP5ZoMYvXvv/9i3rx5CAwMhKenJ/6vvfsJiepf4zj+Ga05DBn9oUhQwxm0P4amYEFRbrSsjYvA/mDZcSFaESEIlhaBlGFIBUkwCyOTwrKiCCwKV4WYCaaIWWrTwkSw0iJMQ/O3iCu3n91uv3vLc2bm/drNObP47JzH7/d5nlOnTgXk7QVJKioqktfrVVpamhobGzU4OKicnBw1NTWpuLhYmZmZCg0NtTrmjJqYmNDdu3d18eJFinwAfqm1tXWqRWHZsmVKTEy0NtAfFBISoqVLlyopKemnh1GBdigD/0SRD8BStbW1qq2t1cuXLyVJsbGx2rVrV0D3LX/48EFlZWU6f/68EhMTVV5ero0bN1od64/yeDw6d+6cMjIy1NHRoYSEBJmmqaqqqqBoTwGAQPX27VtJCvhTbdM0f+nvVSAeysD/UOTDdj5+/PjD5w6HQ4ZhTNtDC/iT06dPq7y8XOHh4SorKwuajRFOp1M+n08RERGSJJfLpebmZsXHx1ucDADwTw0PD6ukpETXrl3T0NCQpG8zCXbu3KkTJ05o/vz51gYEghxFPmznv00fj4yMlGmaOn78OENO4HdCQkLkcrmUlpb20+vpgXbdLzQ0VAMDA1q8eLGkb3u029vb5Xa7LU4GAPgn3r9/r3Xr1unNmzfKysrSypUrJUmdnZ26evWqoqKi1NjYqAULFlicFAheTNeH7Vy6dEklJSUyTXNqSm1zc7Oqq6t19OhRDQ4OqqKiQoZhqLi42OK0+F8F6yq57OzsoLyePjk5KdM0ZRiGpOBbsQUAgaK0tFROp1O9vb1asmTJtHebN29WaWnptNW4AGYOJ/mwndTUVOXl5Wn79u3fPb9+/bq8Xq8aGhpUU1OjkydPqqury6KU+H/duXPnP77791Vyo6OjM5gKf0owD5gEgEASHR0tr9er9PT0H76/f/++8vPz9fr165kNBmAKRT5sx+Vyqb29XbGxsd897+7u1urVqzUyMiKfz6dVq1ZpZGTEopT4E4JhlRwAAP7MMAz19vYqMjLyh+/7+voUExPDP+kBC9HQDNuJiopSVVXVtOdVVVWKioqSJL17945erwDS39+v3NxcxcfHa3x8XM+ePVN1dTUFPgAANrNo0aKfntL7fD4tXLhw5gIBmIaefNhORUWFMjMzde/ePa1Zs0aS1NLSoq6uLt24cUOS9PTpU+3YscPKmPgN/r5KrqGhIeBXyQEA4M/S09NVUlKihw8fTtt4NDY2pmPHjmnLli0WpQMgcV0fNuXz+eT1eqd2py9fvlx5eXmKjo62Nhh+m2BdJQcAgD/r6+tTcnKyDMPQgQMHtGLFCk1OTur58+e6cOGCxsbG1NLSMnX7EsDMo8gHYIlgXSUHAIC/8/l82r9/vx48eKB/lRIOh0ObNm1SZWWlYmJiLE4IBDeKfNjSo0eP5PV69erVK9XV1SkiIkI1NTVyu93asGGD1fHwG5im+Uur5Ji2DgCAPQ0NDam7u1uSFBMTQy8+YBMU+bCdmzdvas+ePcrKylJNTY06Ozvl8XhUWVmp+vp61dfXWx0RAAAAAGyJIh+2k5SUpIKCAmVnZ2vu3Llqa2uTx+NRa2urtm7dqoGBAasjAgAABKVt27b90vdotwOsw3R92M6LFy+UkpIy7fm8efM0PDw884EAAAAg6dvvMQD2RpEP2wkPD1dPT8+0SfqPHz+Wx+OxJhQAAACYlQP4gRCrAwB/l5ubq0OHDunJkydyOBzq7+/XlStXVFhYqH379lkdDwAAAABsi5N82M7hw4f19etXpaamamRkRCkpKTIMQ4WFhTp48KDV8QAAAADAthi8B9v68uWLenp69OnTJ8XFxSksLEyfP3+Wy+WyOhoAAAAA2BLX9WFbTqdTcXFxWrt2rWbPnq0zZ87I7XZbHQsAAAAAbIsiH7YxNjamI0eOKDk5WevXr9ft27clfRvw4na7dfbsWRUUFFgbEgAAAABsjOv6sI2ioiJ5vV6lpaWpsbFRg4ODysnJUVNTk4qLi5WZmanQ0FCrYwIAAACAbTF4D7ZRV1eny5cvKyMjQx0dHUpISND4+Lja2trkcDisjgcAAAAAtsdJPmzD6XTK5/MpIiJCkuRyudTc3Kz4+HiLkwEAAACAf6AnH7YxMTEhp9M59XnWrFkKCwuzMBEAAAAA+Beu68M2JicnZZqmDMOQJI2Ojio/P19z5sz57nu3bt2yIh4AAAAA2B5FPmxj7969333evXu3RUkAAAAAwD/Rkw8AAAAAQICgJx8AAAAAgABBkQ8AAAAAQICgyAcAAAAAIEBQ5AMAAAAAECAo8gEAAAAACBAU+QAAAAAABAiKfAAAAAAAAgRFPgAAAAAAAeIvKTPc3xvpbhgAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
-  ],
-  "metadata": {
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "inc_df = ioos_btn_df.drop(columns=[\"date_UTC\"])\n",
+    "\n",
+    "increase = inc_df.iloc[-1] - inc_df.iloc[-2]\n",
+    "\n",
+    "pcnt_increase = (increase / inc_df.iloc[-2]) * 100\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(12,8))\n",
+    "width = 0.35  # the width of the bars\n",
+    "pcnt_increase.plot(kind=\"bar\", ax=ax, ylabel=\"% change\")\n",
+    "\n",
+    "plt.grid(visible=True, linestyle=\":\")\n",
+    "ax.set_ylim(-100,100)\n",
+    "plt.hlines(0,xmin=0,xmax=len(pcnt_increase),linestyles=\"solid\")\n",
+    "\n",
+    "ax.set_title(\"% change of IOOS BTN metrics between {} and {}\".format(ioos_btn_df[\"date_UTC\"].iloc[-2],ioos_btn_df[\"date_UTC\"].iloc[-1]))\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
     "colab": {
-      "name": "IOOS_BTN.ipynb",
-      "provenance": [],
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
+     "base_uri": "https://localhost:8080/"
     },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.11.2"
+    "id": "ukvS7ykhsaqq",
+    "outputId": "3c294253-fb28-4e4a-bff0-1237e813f350"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Federal Partners           0.000000\n",
+       "Regional Associations      0.000000\n",
+       "HF Radar Stations          0.000000\n",
+       "NGDAC Glider Days         17.991038\n",
+       "National Platforms        -6.119792\n",
+       "Regional Platforms        39.527559\n",
+       "ATN Deployments           16.786679\n",
+       "MBON Projects            -16.666667\n",
+       "OTT Projects              75.000000\n",
+       "HAB Pilot Projects        22.222222\n",
+       "QARTOD Manuals             0.000000\n",
+       "IOOS Core Variables        0.000000\n",
+       "Metadata Records         259.788851\n",
+       "IOOS                       0.000000\n",
+       "COMT Projects              0.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
     }
+   ],
+   "source": [
+    "pcnt_increase"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "include_colab_link": true,
+   "name": "IOOS_BTN.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/notebooks/glider_metrics.ipynb b/notebooks/glider_metrics.ipynb
index 8da7508..d520437 100644
--- a/notebooks/glider_metrics.ipynb
+++ b/notebooks/glider_metrics.ipynb
@@ -208,9 +208,7 @@
     "    \"northwest atlantic ocean\": \"northwest atlantic\",\n",
     "}\n",
     "\n",
-    "metadata_metrics[\"sea_name\"] = (\n",
-    "    metadata_metrics[\"sea_name\"].str.lower().str.strip().replace(fix_names)\n",
-    ")\n",
+    "metadata_metrics[\"sea_name\"] = metadata_metrics[\"sea_name\"].str.lower().str.strip().replace(fix_names)\n",
     "metadata_metrics[\"sea_name\"].value_counts().plot.barh();"
    ]
   },
@@ -245,9 +243,7 @@
     "    \"woods hole oceanographic institution\": \"WHOI\",\n",
     "}\n",
     "\n",
-    "metadata_metrics[\"institution\"] = (\n",
-    "    metadata_metrics[\"institution\"].str.lower().str.strip().replace(short_names)\n",
-    ")\n",
+    "metadata_metrics[\"institution\"] = metadata_metrics[\"institution\"].str.lower().str.strip().replace(short_names)\n",
     "metadata_metrics[\"institution\"].value_counts().plot.barh();"
    ]
   },
diff --git a/notebooks/mbon_citation_visualizations.ipynb b/notebooks/mbon_citation_visualizations.ipynb
index 86ff1f9..7608c66 100644
--- a/notebooks/mbon_citation_visualizations.ipynb
+++ b/notebooks/mbon_citation_visualizations.ipynb
@@ -7,7 +7,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import ioos_metrics.ioos_metrics as ioos_metrics"
+    "from ioos_metrics import ioos_metrics"
    ]
   },
   {
@@ -31,28 +31,28 @@
     "#| code-summary: define streamgraph function\n",
     "def make_streamgraph(og_df, y_colname, plt_title):\n",
     "  # subset the df to save RAM\n",
-    "  df_subset = og_df[[y_colname, 'literature_published']]\n",
+    "  df_subset = og_df[[y_colname, \"literature_published\"]]\n",
     "  # split any pipe-delimited rows in y_colname\n",
-    "  df = df_subset.assign(**{y_colname: df_subset[y_colname].str.split('|')}).explode(y_colname)\n",
+    "  df = df_subset.assign(**{y_colname: df_subset[y_colname].str.split(\"|\")}).explode(y_colname)\n",
     "  # Convert the 'literature_published' column to datetime\n",
-    "  df['literature_published'] = pd.to_datetime(df['literature_published'])\n",
-    "  \n",
+    "  df[\"literature_published\"] = pd.to_datetime(df[\"literature_published\"])\n",
+    "\n",
     "  # Extract month and year for aggregation\n",
-    "  df['year_month'] = df['literature_published'].dt.to_period('M')\n",
-    "  \n",
+    "  df[\"year_month\"] = df[\"literature_published\"].dt.to_period(\"M\")\n",
+    "\n",
     "  # Group by year_month and title, then get the cumulative count\n",
-    "  df['count'] = df.groupby(y_colname).cumcount() + 1\n",
-    "  monthly_counts = df.groupby(['year_month', y_colname]).agg({'count': 'max'}).reset_index()\n",
-    "  \n",
+    "  df[\"count\"] = df.groupby(y_colname).cumcount() + 1\n",
+    "  monthly_counts = df.groupby([\"year_month\", y_colname]).agg({\"count\": \"max\"}).reset_index()\n",
+    "\n",
     "  # Pivot the DataFrame to have year_month as rows and titles as columns\n",
-    "  pivot_df = monthly_counts.pivot(index='year_month', columns=y_colname, values='count').fillna(0).cumsum()\n",
-    "  \n",
+    "  pivot_df = monthly_counts.pivot(index=\"year_month\", columns=y_colname, values=\"count\").fillna(0).cumsum()\n",
+    "\n",
     "  # Plotting the streamgraph\n",
     "  plt.figure(figsize=(8, 6))\n",
     "  plt.stackplot(pivot_df.index.to_timestamp(), pivot_df.T, labels=pivot_df.columns)\n",
     "  plt.title(plt_title)\n",
-    "  plt.xlabel('Date')\n",
-    "  plt.ylabel('Cumulative Count')\n",
+    "  plt.xlabel(\"Date\")\n",
+    "  plt.ylabel(\"Cumulative Count\")\n",
     "  plt.xticks(rotation=45)"
    ]
   },
@@ -83,11 +83,10 @@
    ],
    "source": [
     "#| code-summary: streamgraph of citations per dataset\n",
-    "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
-    "import streamz\n",
+    "import pandas as pd\n",
     "\n",
-    "make_streamgraph(stats_df, \"obis_title\", 'Citation Count by MBON Dataset')\n",
+    "make_streamgraph(stats_df, \"obis_title\", \"Citation Count by MBON Dataset\")\n",
     "plt.show()"
    ]
   },
@@ -121,7 +120,7 @@
     "# split up column with pipe-delimited multi-values\n",
     "\n",
     "make_streamgraph(stats_df, \"literature_topics\", \"MBON Dataset Contributions to Topic Citations\")\n",
-    "plt.legend(loc='upper left',prop={'size': 12})\n",
+    "plt.legend(loc=\"upper left\",prop={\"size\": 12})\n",
     "plt.show()"
    ]
   },
@@ -1318,27 +1317,27 @@
     }
    ],
    "source": [
-    "#| code-summary: create chlopleth map \n",
+    "#| code-summary: create chlopleth map\n",
     "import plotly.express as px\n",
     "\n",
     "loc_colname = \"literature_countries_of_researcher\"\n",
     "og_df = stats_df[[loc_colname]]\n",
     "\n",
     "# split any pipe-delimited rows in y_colname\n",
-    "df = og_df.assign(**{loc_colname: og_df[loc_colname].str.split('|')}).explode(loc_colname)\n",
+    "df = og_df.assign(**{loc_colname: og_df[loc_colname].str.split(\"|\")}).explode(loc_colname)\n",
     "\n",
     "# Count the occurrences of each country\n",
     "country_counts = df[loc_colname].value_counts().reset_index()\n",
-    "country_counts.columns = ['Country', 'Count']\n",
+    "country_counts.columns = [\"Country\", \"Count\"]\n",
     "\n",
     "# Create the choropleth map with a monochrome color scale and gray for missing data\n",
     "fig = px.choropleth(country_counts,\n",
     "                    locations=\"Country\",\n",
-    "                    locationmode='country names',\n",
+    "                    locationmode=\"country names\",\n",
     "                    color=\"Count\",\n",
     "                    hover_name=\"Country\",\n",
     "                    color_continuous_scale=px.colors.sequential.Blues,\n",
-    "                    title='Choropleth Heatmap of # Citations in each Country'\n",
+    "                    title=\"Choropleth Heatmap of # Citations in each Country\"\n",
     "  )\n",
     "\n",
     "# Update layout to set color for missing data\n",
@@ -1361,21 +1360,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from wordcloud import WordCloud\n",
     "import matplotlib.pyplot as plt\n",
+    "from wordcloud import WordCloud\n",
+    "\n",
     "\n",
     "def make_wordcloud(df, colname):\n",
     "    # Combine all abstracts into a single string\n",
     "    text = \" \".join(abstract for abstract in df[colname])\n",
     "\n",
     "    # Generate the word cloud\n",
-    "    wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)\n",
+    "    wordcloud = WordCloud(width=800, height=400, background_color=\"white\").generate(text)\n",
     "\n",
     "    # Display the word cloud using matplotlib\n",
     "    plt.figure(figsize=(10, 5))\n",
-    "    plt.imshow(wordcloud, interpolation='bilinear')\n",
-    "    plt.axis('off')  # Turn off axis\n",
+    "    plt.imshow(wordcloud, interpolation=\"bilinear\")\n",
+    "    plt.axis(\"off\")  # Turn off axis\n",
     "    plt.show()\n"
    ]
   },
@@ -1418,15 +1417,12 @@
     }
    ],
    "source": [
-    "import pandas as pd\n",
-    "from wordcloud import WordCloud\n",
     "import matplotlib.pyplot as plt\n",
-    "import json\n",
     "\n",
     "df = stats_df\n",
     "# Extract givenname and surname from JSON\n",
     "names = []\n",
-    "for contacts in df['obis_contacts']:\n",
+    "for contacts in df[\"obis_contacts\"]:\n",
     "    contacts_list = contacts\n",
     "    for contact in contacts_list:\n",
     "        names.append(f\"{contact['givenname']}{contact['surname']}\".replace(\" \", \"\").replace(\"-\", \"\") or \"\")\n",
@@ -1437,12 +1433,12 @@
     "# print(sorted(set(names)))\n",
     "\n",
     "# Generate the word cloud\n",
-    "wordcloud = WordCloud(width=800, height=400, collocation_threshold=1e4, background_color='white').generate(text)\n",
+    "wordcloud = WordCloud(width=800, height=400, collocation_threshold=1e4, background_color=\"white\").generate(text)\n",
     "\n",
     "# Display the word cloud using matplotlib\n",
     "plt.figure(figsize=(10, 5))\n",
-    "plt.imshow(wordcloud, interpolation='bilinear')\n",
-    "plt.axis('off')  # Turn off axis\n",
+    "plt.imshow(wordcloud, interpolation=\"bilinear\")\n",
+    "plt.axis(\"off\")  # Turn off axis\n",
     "plt.show()\n"
    ]
   },
@@ -1463,7 +1459,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "downloads = stats_df.drop_duplicates(subset='obis_id',keep='first')[['obis_downloads','gbif_downloads']]"
+    "downloads = stats_df.drop_duplicates(subset=\"obis_id\",keep=\"first\")[[\"obis_downloads\",\"gbif_downloads\"]]"
    ]
   },
   {
@@ -1522,15 +1518,15 @@
    ],
    "source": [
     "df_obis = pd.DataFrame()\n",
-    "for index, row in downloads[['obis_downloads']].iterrows():\n",
+    "for index, row in downloads[[\"obis_downloads\"]].iterrows():\n",
     "    df_obis = pd.concat([df_obis, pd.DataFrame(row.iloc[0])])\n",
     "\n",
-    "df_obis.drop(columns='records',inplace=True)\n",
-    "df_obis_yearly_total = df_obis.groupby(by='year').sum()\n",
+    "df_obis.drop(columns=\"records\",inplace=True)\n",
+    "df_obis_yearly_total = df_obis.groupby(by=\"year\").sum()\n",
     "print(df_obis_yearly_total)\n",
     "print(f\"\\nOBIS: {df_obis_yearly_total['downloads'].sum()} downloads\")\n",
     "\n",
-    "df_obis_yearly_total['downloads'].plot(kind='bar')"
+    "df_obis_yearly_total[\"downloads\"].plot(kind=\"bar\")"
    ]
   },
   {
@@ -1574,12 +1570,12 @@
     "\n",
     "df_gbif = pd.DataFrame()\n",
     "\n",
-    "for index, row in downloads[['gbif_downloads']].iterrows():\n",
+    "for index, row in downloads[[\"gbif_downloads\"]].iterrows():\n",
     "    df_gbif = pd.concat([df_gbif, pd.DataFrame(ast.literal_eval(row.iloc[0]))])\n",
     "\n",
-    "df_gbif.index.name = 'year'\n",
+    "df_gbif.index.name = \"year\"\n",
     "\n",
-    "df_gbif_yearly_total = df_gbif.groupby(by='year').sum()\n",
+    "df_gbif_yearly_total = df_gbif.groupby(by=\"year\").sum()\n",
     "\n",
     "print(df_gbif_yearly_total)\n",
     "print(f\"\\nGBIF: {df_gbif_yearly_total['number_downloads'].sum()} downloads\")"
@@ -1621,15 +1617,15 @@
     }
    ],
    "source": [
-    "df_join = df_obis_yearly_total.join(df_gbif_yearly_total,how='outer')\n",
+    "df_join = df_obis_yearly_total.join(df_gbif_yearly_total,how=\"outer\")\n",
     "\n",
-    "df_join.rename(columns={'number_downloads':'GBIF',\n",
-    "                       'downloads':'OBIS'},\n",
+    "df_join.rename(columns={\"number_downloads\":\"GBIF\",\n",
+    "                       \"downloads\":\"OBIS\"},\n",
     "               inplace=True)\n",
     "\n",
     "ax = df_join.plot.bar(stacked=True, width=.8, figsize=(8, 6))\n",
-    "ax.legend(title='Source')\n",
-    "ax.set_ylabel('Number of Downloads')"
+    "ax.legend(title=\"Source\")\n",
+    "ax.set_ylabel(\"Number of Downloads\")"
    ]
   },
   {
diff --git a/ruff.toml b/ruff.toml
index 5b04dd3..2e007fd 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -18,6 +18,8 @@ lint.ignore = [
     "D401",  # First line of docstring should be in imperative mood
     "G004",  # logging-f-string
     "PD901",  # Avoid using the generic variable name `df` for DataFrames
+    "ISC001",  # single-line-implicit-string-concatenation
+    "COM812",  # missing-trailing-comma
     ]
 
 [lint.extend-per-file-ignores]
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index 6cea585..51ae804 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -26,7 +26,7 @@
 )
 
 
-@pytest.fixture()
+@pytest.fixture
 def df_previous_metrics():
     return ioos_metrics.previous_metrics()