diff --git a/DEA_products/DEA_Water_Observations.ipynb b/DEA_products/DEA_Water_Observations.ipynb index ce36c2266..30c5a2465 100644 --- a/DEA_products/DEA_Water_Observations.ipynb +++ b/DEA_products/DEA_Water_Observations.ipynb @@ -4,12 +4,80 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Introduction to DEA Water Observations \n", + "\n", + "
\n", "\n", - "* **[Sign up to the DEA Sandbox](https://app.sandbox.dea.ga.gov.au/)** to run this notebook interactively from a browser\n", - "* **Compatibility:** Notebook currently compatible with both the `NCI` and `DEA Sandbox` environments\n", - "* **Products used:** \n", - "[ga_ls_wo_3](https://explorer.dea.ga.gov.au/ga_ls_wo_3)\n" + "\"DEA\n", + "\n", + "

Introduction to DEA Water Observations

\n", + "\n", + "

\n", + "๐Ÿ”Ž Visit DEA's Knowledge Hub for detailed technical information about the datasets and concepts used in this notebook.\n", + "

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\n", + "๐ŸŸก Intermediate\n", + "โšก Time to complete: ~10 mins\n", + "๐Ÿ—“๏ธ Updated: 2025-12\n", + "

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\n", + "๐Ÿ“ฆ Products used
\n", + "ga_ls_wo_3 \n", + "
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\n", + "๐Ÿงพ Prerequisites
\n", + "Optional list of prerequisite notebooks or background knowledge, e.g. Run the XYZ notebook before running this. #FIXME check if these are necessary\n", + "
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\n", + "๐Ÿ“ Citation
\n", + "Please cite:\n", + "Mueller, N., Lewis, A., Roberts, D., Ring, S., Melrose, R., Sixsmith, J., Lymburner, L., McIntyre, A., Tan, P., Curnow, S., & Ip, A. (2016)\n", + "https://doi.org/10.1016/j.rse.2015.11.003 \n", + "Remote Sensing of Environment, 174, 341โ€“352. when using the results of this notebook. \n", + "
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\n", + "โš ๏ธ Important
\n", + "This notebook is not to be used for safety of life decisions.\n", + "
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\n", + "๐Ÿš€ How you can run this notebook
\n", + "\n", + "- DEA Sandbox: Eligible users can sign up for the DEA Sandbox that provides free compute and a managed Python environment.\n", + "\n", + "- Personal computer: This notebook loads DEA data using odc-stac. Data streams via the internet, so can be run locally after installing the required DEA Tools Python package, e.g. pip install dea-tools[notebooks].\n", + "\n", + "- Run this notebook online: Click on the badge below to launch a GitHub Codespaces instance to run the notebook from your browser. See this wiki page for more information.\n", + "\n", + " \n", + " \"Open\n", + " \n", + "
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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\"A" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*A single Water Observation observed by the Landsat satellite constellation and classified using the Water Observations from Space algorithm (Mueller et al 2016), taken on November the 6th 2025. The image shows different classifications for where water was observed, where there were clouds, and where there was terrain, as in the description of the data further down this notebook.The area of the image is a strip selected across the Landsat scene, capturing an area of Australia on the eastern coast and including Lake George/Weereewa in the state of New South Wales, in which water was observed at this time*" ] }, { @@ -17,36 +85,45 @@ "metadata": {}, "source": [ "## Background\n", - "It is important to know where water is normally present in a landscape, where water is rarely observed, and where inundation has occasionally occurred.\n", - "\n", - "These observations tell us where flooding has occurred in the past, and allows us to understand wetlands, water connectivity and surface-groundwater relationships. This can lead to more effective emergency management and risk assessment.\n", + "This notebook demonstrates **individual Water Observations (WOs)** \n", + "This product shows where surface water was observed by the Landsat satellites on any **particular day where there was a satellite image** since mid 1986. These **single-observation** data layers are termed Water Observations (WOs).\n", "\n", - "This is the principal [Digital Earth Australia (DEA) Water Observations product](https://knowledge.dea.ga.gov.au/data/product/dea-water-observations-landsat) (previously known as Water Observations from Space), providing the individual water observations per satellite image that are subsequently used in the following DEA Water Observations suite and related water bodies products. \n", "\n", - "This product shows where surface water was observed by the Landsat satellites on any particular day since mid 1986. These daily data layers are termed Water Observations (WOs).\n", + "It is important to know where water is normally present in a landscape, where water is rarely observed, and where inundation has occasionally occurred.\n", + "These observations tell us where flooding has occurred in the past, and allows us to understand wetlands, water connectivity and surface-groundwater relationships. This can lead to more effective emergency management and risk assessment.\n", "\n", + "This is the **principal** [Digital Earth Australia (DEA) Water Observations product](https://knowledge.dea.ga.gov.au/data/product/dea-water-observations-landsat) **providing the individual water observations per satellite image** that are subsequently **used in the following DEA water products**: \n", + "* Water Observations Summary statistics products: [DEA Water Observations Statistics (Landsat)](https://knowledge.dea.ga.gov.au/data/product/dea-water-observations-statistics-landsat/)\n", + "* [DEA Waterbodies (Landsat)](https://knowledge.dea.ga.gov.au/data/product/dea-waterbodies-landsat/)\n", + "* [DEA Wetlands Insight Tool](https://knowledge.dea.ga.gov.au/data/category/dea-wetlands-insight-tool/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "### What this product offers\n", "DEA Water Observations provides surface water observations derived from Landsat satellite imagery for all of Australia from 1986 to present.\n", - "\n", "The WOs show the extent of water in a corresponding Landsat scene, along with the degree to which the scene was obscured by clouds, shadows or where sensor problems cause parts of a scene to not be observable. \n", "\n", "### Applications\n", - "The DEA Water Observations (WOs) are used to determine the area of surface water present in the corresponding satellite scene, and can be used for several water monitoring applications. Uses of the individual WOs include:\n", + "The DEA Water Observations (WOs) are used to indicate whether surface water was observed in the corresponding satellite scene, and can be used for several water monitoring applications. Uses of the individual WOs include:\n", "\n", - "* flood extent\n", - "* amount of water in water bodies, major rivers and the coastal zone.\n", + "* seeing if water was detected in a particular location at that time\n", + "* comparing two different times to see if water had similar detected extents\n", "\n", - "As the WOs are separated from the derived statistics of the associated DEA Water Observations statistical products, the WOs are most useful for performing analyses requiring the investigation of surface water extent for particular times rather than over long term time series.\n", + "These single WOs are most useful for performing analyses requiring the investigation of surface water extent for particular times rather than over long term time series. Other statistical products can be used to summarise the behaviour of surface water over the long term e.g. [DEA Water Observations Statistics (Landsat)](https://knowledge.dea.ga.gov.au/data/product/dea-water-observations-statistics-landsat/) which is made out of the single WOs.\n", "\n", "### Publications\n", - "* Mueller, N., Lewis, A., Roberts, D., Ring, S., Melrose, R., Sixsmith, J., Lymburner, L., McIntyre, A., Tan, P., Curnow, S., & Ip, A. (2016). [Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia](https://doi.org/10.1016/j.rse.2015.11.003). Remote Sensing of Environment, 174, 341โ€“352. \n" + "* For more information check out the DEA Knowledge Hub information on this product: [DEA Water Observations Statistics (Landsat)](https://knowledge.dea.ga.gov.au/data/product/dea-water-observations-landsat/)\n", + "* Mueller, N., Lewis, A., Roberts, D., Ring, S., Melrose, R., Sixsmith, J., Lymburner, L., McIntyre, A., Tan, P., Curnow, S., & Ip, A. (2016). [Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia](https://doi.org/10.1016/j.rse.2015.11.003). Remote Sensing of Environment, 174, 341โ€“352. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Description\n", + "## Description ##FIXME update to stac\n", "This notebook will demonstrate how to load and analyse DEA Water Observations using Digital Earth Australia, including:\n", "\n", "1. Inspecting the products and measurements available in the datacube\n", @@ -67,15 +144,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Getting started\n", + "## Setup\n", "\n", - "To run this analysis, run all the cells in the notebook, starting with the \"Load packages\" cell. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ "### Load packages\n", "Import Python packages that are used for the analysis." ] @@ -86,23 +156,28 @@ "metadata": {}, "outputs": [], "source": [ - "import datacube\n", - "from datacube.utils import masking\n", + "import odc.stac\n", + "import pystac_client\n", + "import xarray as xr\n", + "\n", "import matplotlib.pyplot as plt\n", + "from odc.geo import BoundingBox\n", + "from dea_tools import masking ##FIXME\n", "\n", - "import sys\n", - "sys.path.insert(1, '../Tools/')\n", - "from dea_tools.datahandling import wofs_fuser\n", - "from dea_tools.plotting import plot_wo" + "from dea_tools.datahandling import dea_stac_cfg, stac_assets, stac_collections, wofs_fuser #not sure if wofs_fuser is working yet ##FIXME\n", + "from dea_tools.plotting import plot_wo\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Connect to the datacube\n", + "### Connect to DEA's STAC API\n", "\n", - "Connect to the datacube so we can access DEA data." + "This allows us to search for data using DEA's STAC API (refer to the [SpatioTemporal Asset Catalogue guide](https://knowledge.dea.ga.gov.au/guides/setup/gis/stac/) or [Loading data with STAC notebook](../Beginners_guide/04a_Loading_data_STAC.ipynb) for details).\n", + "\n", + "To load data, we must also configure access to DEA's Amazon S3 buckets using `odc.stac.configure_s3_access`.\n", + "If launching a Dask client (e.g. `dea_tools.dask.create_local_dask_cluster()`), this is done automatically." ] }, { @@ -111,18 +186,33 @@ "metadata": {}, "outputs": [], "source": [ - "dc = datacube.Datacube(app='DEA_Water_Observations')" + "# Configure data access\n", + "odc.stac.configure_s3_access(cloud_defaults=True, aws_unsigned=True)\n", + "\n", + "# Connect to STAC API\n", + "catalog = pystac_client.Client.open(\"https://explorer.dea.ga.gov.au/stac\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Available products and measurements\n", + "## Analysis\n", + "\n", + "### Analysis Parameters\n", "\n", - "### List products available in Digital Earth Australia\n", - "We can use datacube's `list_products` functionality to inspect DEA Water Observations products that are available in Digital Earth Australia.\n", - "The table below shows the product name that we will use to load data, and a brief description of the product." + "First we need to define the location, time period and DEA product we want to load.\n", + "\n", + "In this example, we will load a ##FIXME" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Available products\n", + "\n", + "We can use the `stac_collections` function to inspect the DEA Landsat Surface Reflectance products that are available in DEA's STAC catalog:" ] }, { @@ -151,14 +241,14 @@ " \n", " \n", " \n", - " name\n", " description\n", + " bbox\n", + " start_date\n", + " end_date\n", " license\n", - " default_crs\n", - " default_resolution\n", " \n", " \n", - " name\n", + " product\n", " \n", " \n", " \n", @@ -169,24 +259,24 @@ " \n", " \n", " ga_ls_wo_3\n", - " ga_ls_wo_3\n", " Geoscience Australia Landsat Water Observation...\n", + " 95.62, -45.67, 169.59, -7.63\n", + " 1986-08-15\n", + " 2025-11-20\n", " CC-BY-4.0\n", - " None\n", - " None\n", " \n", " \n", "\n", "" ], "text/plain": [ - " name description \\\n", - "name \n", - "ga_ls_wo_3 ga_ls_wo_3 Geoscience Australia Landsat Water Observation... \n", + " description \\\n", + "product \n", + "ga_ls_wo_3 Geoscience Australia Landsat Water Observation... \n", "\n", - " license default_crs default_resolution \n", - "name \n", - "ga_ls_wo_3 CC-BY-4.0 None None " + " bbox start_date end_date license \n", + "product \n", + "ga_ls_wo_3 95.62, -45.67, 169.59, -7.63 1986-08-15 2025-11-20 CC-BY-4.0 " ] }, "execution_count": 3, @@ -195,23 +285,296 @@ } ], "source": [ - "# List DEA Water Observations products available in DEA\n", - "dc_products = dc.list_products()\n", - "dc_products.loc[['ga_ls_wo_3']]" + "products = [\"ga_ls_wo_3\"]\n", + "stac_collections(catalog, products)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### List measurements\n", - "We can inspect the contents of the DEA Water Observations product using datacube's `list_measurements` functionality. The table also provides information about the measurement data types, units, nodata value and other technical information about each measurement." + "## Loading data ##FIXME\n", + "Now that we know what products and measurements are available for the products, we can load WOs data from Digital Earth Australia for an example location.\n", + "\n", + "As WOs are created scene-by-scene and some scenes overlap, it is important when loading data to `group_by` solar day, and ensure that the data between scenes is combined correctly by using the WOs `fuse_func`.\n", + "This will merge observations taken on the same day, and ensure that important data isn not lost when overlapping datasets are combined.\n", + "\n", + "##FIXME - is wofs fuser working correctly? This may depend on the datatype??\n", + "##FIXME - is the config coming in, do I need to get a config in so the nodata values come in correctly?" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, + "outputs": [], + "source": [ + "# # Set up a region to load data\n", + "# query = {\n", + "# 'y': (-31.18, -31.12),\n", + "# 'x': (116.84, 116.90),\n", + "# 'time': ('1996-09-01', '1996-12-30'),\n", + "# }\n", + "\n", + "# # Load DEA Water Observations data from the datacube\n", + "# wo = dc.load(product='ga_ls_wo_3',\n", + "# output_crs='EPSG:3577',\n", + "# resolution=(-30, 30),\n", + "# group_by='solar_day',\n", + "# fuse_func=wofs_fuser,\n", + "# **query)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "##FIXME: using the loading data example until I can check whether we actually can get wos loaded and masked\n", + "\n", + "# Set a bounding box based on a point location and a buffer region\n", + "central_lat, central_lon = -35.3, 149.125\n", + "buffer = 0.05\n", + "\n", + "# Set a loading date - Landsat geomedians are annual products\n", + "start_date = \"2017-04-01\"\n", + "end_date = \"2017-04-30\"\n", + "\n", + "# Set product ID as the STAC \"collection\"\n", + "collections = [\"ga_ls_wo_3\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create and plot the bounding box region-of-interest" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Compute the bounding box object for the study area\n", + "bbox = BoundingBox(\n", + " left=central_lon - buffer,\n", + " bottom=central_lat - buffer,\n", + " right=central_lon + buffer,\n", + " top=central_lat + buffer,\n", + " crs=\"EPSG:4326\",\n", + ")\n", + "\n", + "bbox.explore()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Searching for STAC data using `pystac_client`\n", + "\n", + "We can use the `pystac_client` Python package to search for STAC items that match our query:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found: 14 STAC items\n" + ] + } + ], + "source": [ + "# Search the STAC catalog for all items matching the query\n", + "query = catalog.search(\n", + " bbox=bbox,\n", + " collections=collections,\n", + " datetime=f\"{start_date}/{end_date}\",\n", + ")\n", + "\n", + "# Convert to a list\n", + "items = list(query.items())\n", + "print(f\"Found: {len(items):d} STAC items\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# get the extra metadata attributes for the product\n", + "wo_cfg = dea_stac_cfg(products=\"ga_ls_wo_3\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Available bands (assets)\n", + "We can now use the `stac_assets` function to inspect the data available for a DEA Landsat Surface Reflectance product\n", + "\n", + "The table below lists each of the bands (also known as STAC \"assets\") available for DEA Water Observations (`ga_ls_wo_3`). \n", + "These bands have prefixes (e.g. `water`) that represent \"sub-products\"." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, "outputs": [ { "data": { @@ -235,18 +598,14 @@ " \n", " \n", " \n", - " name\n", - " dtype\n", - " units\n", + " roles\n", + " band_names\n", " nodata\n", - " aliases\n", - " flags_definition\n", + " dtype\n", " \n", " \n", " product\n", - " measurement\n", - " \n", - " \n", + " asset\n", " \n", " \n", " \n", @@ -257,570 +616,86 @@ " \n", " ga_ls_wo_3\n", " water\n", + " data\n", " water\n", - " uint8\n", - " 1\n", - " 1\n", - " NaN\n", - " {'dry': {'bits': [7, 6, 5, 4, 3, 2, 1, 0], 'va...\n", + " \n", + " \n", " \n", " \n", "\n", "" ], "text/plain": [ - " name dtype units nodata aliases \\\n", - "product measurement \n", - "ga_ls_wo_3 water water uint8 1 1 NaN \n", - "\n", - " flags_definition \n", - "product measurement \n", - "ga_ls_wo_3 water {'dry': {'bits': [7, 6, 5, 4, 3, 2, 1, 0], 'va... " + " roles band_names nodata dtype\n", + "product asset \n", + "ga_ls_wo_3 water data water " ] }, - "execution_count": 4, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dc_measurements = dc.list_measurements()\n", - "dc_measurements.loc[['ga_ls_wo_3']]" + "stac_assets(catalog, products=\"ga_ls_wo_3\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Loading data\n", - "Now that we know what products and measurements are available for the products, we can load WOs data from Digital Earth Australia for an example location.\n", - "\n", - "As WOs are created scene-by-scene and some scenes overlap, it is important when loading data to `group_by` solar day, and ensure that the data between scenes is combined correctly by using the WOs `fuse_func`.\n", - "This will merge observations taken on the same day, and ensure that important data isn not lost when overlapping datasets are combined." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Set up a region to load data\n", - "query = {\n", - " 'y': (-31.18, -31.12),\n", - " 'x': (116.84, 116.90),\n", - " 'time': ('1996-09-01', '1996-12-30'),\n", - "}\n", - "\n", - "# Load DEA Water Observations data from the datacube\n", - "wo = dc.load(product='ga_ls_wo_3',\n", - " output_crs='EPSG:3577',\n", - " resolution=(-30, 30),\n", - " group_by='solar_day',\n", - " fuse_func=wofs_fuser,\n", - " **query)" + "Here's the maps config, which isn't quite correct - some of the combined defs are probably not quite right and the colour ramp doesn't perfectly line up with what is actually possible. Which I think we are now about to fix.... \n", + "https://github.com/GeoscienceAustralia/dea-config/blob/master/dev/services/wms/ows_refactored/inland_water/wofs/individual/ows_s2_wo_provisional_3.py#L4 \n", + "need to use some of this stuff too: https://github.com/opendatacube/odc-algo/blob/main/odc/algo/_masking.py \n", + "- this is what I was working on with Benji https://github.com/GeoscienceAustralia/dea-knowledge-hub/pull/397\n", + "- this is the conflux flags https://github.com/GeoscienceAustralia/dea-conflux/blob/main/examples/ga_ls_wo_3_flags.conflux.py\n", + "- this is the deafrica bitflag notebook that is useful https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/blob/f6f4688d04fe7d5ddfe2e5003a2abb201ec7839e/Frequently_used_code/Applying_WOfS_bitmasking.ipynb\n", + "- deafrica wit notebook that is useful https://github.com/digitalearthafrica/deafrica-direct-access/blob/main/Real_world_examples/Wetland_inundation.ipynb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can now view the data that we loaded.\n", - "The measurements listed under `Data variables` should match the measurements displayed in the previous [List measurements](#List-measurements) step." + "## Loading data\n" ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:      (time: 8, y: 245, x: 215)\n",
-       "Coordinates:\n",
-       "  * time         (time) datetime64[ns] 1996-09-05T01:22:13.540962 ... 1996-12...\n",
-       "  * y            (y) float64 -3.473e+06 -3.473e+06 ... -3.48e+06 -3.48e+06\n",
-       "  * x            (x) float64 -1.428e+06 -1.428e+06 ... -1.422e+06 -1.422e+06\n",
-       "    spatial_ref  int32 3577\n",
-       "Data variables:\n",
-       "    water        (time, y, x) uint8 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0\n",
-       "Attributes:\n",
-       "    crs:           EPSG:3577\n",
-       "    grid_mapping:  spatial_ref
" - ], - "text/plain": [ - "\n", - "Dimensions: (time: 8, y: 245, x: 215)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 1996-09-05T01:22:13.540962 ... 1996-12...\n", - " * y (y) float64 -3.473e+06 -3.473e+06 ... -3.48e+06 -3.48e+06\n", - " * x (x) float64 -1.428e+06 -1.428e+06 ... -1.422e+06 -1.422e+06\n", - " spatial_ref int32 3577\n", - "Data variables:\n", - " water (time, y, x) uint8 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0\n", - "Attributes:\n", - " crs: EPSG:3577\n", - " grid_mapping: spatial_ref" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wo" - ] - }, - { - "cell_type": "markdown", + "execution_count": 10, "metadata": {}, + "outputs": [], "source": [ - "### Plotting data\n", - "We can plot DEA Water Observations using the `plot_wo` function. We can see that our study area includes one large and several small waterbodies which are changing in size over time. We can also see that some observations (e.g. first and fourth panels) contain clouds and cloud shadow.\n" + "wo = odc.stac.load(\n", + " items,\n", + " bands=[\"water\"],\n", + " stac_cfg=wo_cfg,\n", + " crs=\"EPSG:3577\", # geomedians are stored Albers Equal Area\n", + " resolution=30, # spatial resolution in metres\n", + " bbox=bbox,\n", + " groupby=\"solar_day\",\n", + " fuse_func=wofs_fuser\n", + ")" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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" ] @@ -830,7 +705,54 @@ } ], "source": [ - "plot_wo(wo.water, col='time', size=4, col_wrap=4)" + "plot_wo(wo.water, col='time', size=4, col_wrap=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# # this code from deafrica - issue with boolean and or array types\n", + "# def process_wofs_mask(ds_wofs, tcw, mask, verbose=False):\n", + "# \"\"\"\n", + "# Converts WOfS water observations to a monthly boolean wet mask,\n", + "# aligned to a reference dataset's time and spatial mask.\n", + "\n", + "# # Parameters\n", + "# ----------\n", + "# ds_wofs : xarray.Dataset\n", + "# Dataset containing the 'water' band from WOfS.\n", + "# tcw : xarray.DataArray or Dataset\n", + "# Reference dataset used to match the time dimension.\n", + "# mask : xarray.DataArray\n", + "# Spatial mask with True where data should be retained.\n", + "# verbose : bool\n", + "# If True, prints progress info.\n", + "\n", + "# Returns\n", + "# -------\n", + "# xarray.DataArray\n", + "# Boolean wet mask, monthly resampled, aligned to tcw and spatially masked.\n", + "# \"\"\"\n", + "\n", + "# # Create wet/dry boolean mask from WOfS water band\n", + "# wofs_mask = masking.make_mask(ds_wofs.water, wet=True)\n", + "# # # Attach the WOfS flag definitions manually\n", + "# # wo.water.attrs[\"flags_definition\"] = {\n", + "# # \"wet\": {\"bits\": [7, 6, 5, 4, 3, 2, 1, 0],\"values\": {\"128\": True},\"description\": \"Clear and wet\"},\n", + "# # \"dry\": {\"bits\": [7, 6, 5, 4, 3, 2, 1, 0],\"values\": {\"0\": True},\"description\": \"No water detected\"},\n", + "# # \"cloud\": {\"bits\": 6,\"values\": {\"1\": True},\"description\": \"Cloudy\"},\n", + "# # \"nodata\": {\"bits\": 0,\"values\": {\"1\": True},\"description\": \"No data\"},\n", + "# # \"water_observed\": {\"bits\": 7,\"values\": {\"1\": True},\"description\": \"Classified as water by the decision tree\"}}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##FIXME : add wet and dry definitions from Africa to the bottom of the understanding WOs bit flags notebook" ] }, { @@ -862,7 +784,80 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import yaml\n", + "import urllib.request\n", + "product_yaml_url = \"https://explorer.dea.ga.gov.au/products/ga_ls_wo_3.odc-product.yaml\"\n", + "with urllib.request.urlopen(product_yaml_url) as response:\n", + " product_yaml = yaml.safe_load(response)\n", + "wo['water'].attrs['flags_definition'] = product_yaml['measurements'][0]['flags_definition']\n", + "wo['water'].attrs['nodata'] = product_yaml['measurements'][0]['nodata']" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'nodata': 1,\n", + " 'flags_definition': {'dry': {'bits': [7, 6, 5, 4, 3, 2, 1, 0],\n", + " 'values': {'0': True},\n", + " 'description': 'No water detected'},\n", + " 'wet': {'bits': [7, 6, 5, 4, 3, 2, 1, 0],\n", + " 'values': {'128': True},\n", + " 'description': 'Clear and Wet'},\n", + " 'cloud': {'bits': 6,\n", + " 'values': {'0': False, '1': True},\n", + " 'description': 'Cloudy'},\n", + " 'nodata': {'bits': 0,\n", + " 'values': {'0': False, '1': True},\n", + " 'description': 'No data'},\n", + " 'high_slope': {'bits': 4,\n", + " 'values': {'0': False, '1': True},\n", + " 'description': 'High slope'},\n", + " 'cloud_shadow': {'bits': 5,\n", + " 'values': {'0': False, '1': True},\n", + " 'description': 'Cloud shadow'},\n", + " 'noncontiguous': {'bits': 1,\n", + " 'values': {'0': False, '1': True},\n", + " 'description': 'At least one EO band is missing or saturated'},\n", + " 'terrain_shadow': {'bits': 3,\n", + " 'values': {'0': False, '1': True},\n", + " 'description': 'Terrain shadow'},\n", + " 'water_observed': {'bits': 7,\n", + " 'values': {'0': False, '1': True},\n", + " 'description': 'Classified as water by the decision tree'},\n", + " 'low_solar_angle': {'bits': 2,\n", + " 'values': {'0': False, '1': True},\n", + " 'description': 'Low solar incidence angle'}}}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wo.water.attrs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting data\n", + "We can plot DEA Water Observations using the `plot_wo` function. We can see that our study area includes one large and several small waterbodies which are changing in size over time. We can also see that some observations (e.g. first and fourth panels) contain clouds and cloud shadow.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -982,7 +977,7 @@ "wet Clear and Wet " ] }, - "execution_count": 8, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -994,6 +989,27 @@ "flags.sort_values(by='bits')" ] }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show areas flagged as water only (with no other flags set)\n", + "(wo.water.isel(time=3) == 128).plot.imshow();" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1007,24 +1023,45 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 17, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" + "name": "stderr", + "output_type": "stream", + "text": [ + "/env/lib/python3.10/site-packages/matplotlib/axes/_axes.py:7132: RuntimeWarning: Converting input from bool to for compatibility.\n", + " m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)\n" + ] + }, + { + "ename": "TypeError", + "evalue": "numpy boolean subtract, the `-` operator, is not supported, use the bitwise_xor, the `^` operator, or the logical_xor function instead.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m/env/lib/python3.10/site-packages/numpy/lib/_histograms_impl.py:350\u001b[0m, in \u001b[0;36m_unsigned_subtract\u001b[0;34m(a, b)\u001b[0m\n\u001b[1;32m 349\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 350\u001b[0m unsigned_dt \u001b[38;5;241m=\u001b[39m \u001b[43msigned_to_unsigned\u001b[49m\u001b[43m[\u001b[49m\u001b[43mdt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtype\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 351\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: ", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[17], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Create a mask based on all 'wet' pixels\u001b[39;00m\n\u001b[1;32m 2\u001b[0m wetwofl \u001b[38;5;241m=\u001b[39m masking\u001b[38;5;241m.\u001b[39mmake_mask(wo, wet\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m----> 3\u001b[0m \u001b[43mwetwofl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwater\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m;\n", + "File \u001b[0;32m/env/lib/python3.10/site-packages/xarray/plot/accessor.py:48\u001b[0m, in \u001b[0;36mDataArrayPlotAccessor.__call__\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(dataarray_plot\u001b[38;5;241m.\u001b[39mplot, assigned\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__doc__\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__annotations__\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdataarray_plot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_da\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/env/lib/python3.10/site-packages/xarray/plot/dataarray_plot.py:310\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(darray, row, col, col_wrap, ax, hue, subplot_kws, **kwargs)\u001b[0m\n\u001b[1;32m 306\u001b[0m plotfunc \u001b[38;5;241m=\u001b[39m hist\n\u001b[1;32m 308\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124max\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m ax\n\u001b[0;32m--> 310\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mplotfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdarray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/env/lib/python3.10/site-packages/xarray/plot/dataarray_plot.py:705\u001b[0m, in \u001b[0;36mhist\u001b[0;34m(darray, figsize, size, aspect, ax, xincrease, yincrease, xscale, yscale, xticks, yticks, xlim, ylim, *args, **kwargs)\u001b[0m\n\u001b[1;32m 700\u001b[0m no_nan_arr \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mravel(darray\u001b[38;5;241m.\u001b[39mto_numpy())\n\u001b[1;32m 701\u001b[0m no_nan \u001b[38;5;241m=\u001b[39m no_nan_arr[pd\u001b[38;5;241m.\u001b[39mnotnull(no_nan_arr)]\n\u001b[1;32m 703\u001b[0m n, bins, patches \u001b[38;5;241m=\u001b[39m cast(\n\u001b[1;32m 704\u001b[0m \u001b[38;5;28mtuple\u001b[39m[np\u001b[38;5;241m.\u001b[39mndarray, np\u001b[38;5;241m.\u001b[39mndarray, Union[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBarContainer\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPolygon\u001b[39m\u001b[38;5;124m\"\u001b[39m]],\n\u001b[0;32m--> 705\u001b[0m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhist\u001b[49m\u001b[43m(\u001b[49m\u001b[43mno_nan\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 706\u001b[0m )\n\u001b[1;32m 708\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_title(darray\u001b[38;5;241m.\u001b[39m_title_for_slice())\n\u001b[1;32m 709\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_xlabel(label_from_attrs(darray))\n", + "File \u001b[0;32m/env/lib/python3.10/site-packages/matplotlib/_api/deprecation.py:453\u001b[0m, in \u001b[0;36mmake_keyword_only..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m name_idx:\n\u001b[1;32m 448\u001b[0m warn_deprecated(\n\u001b[1;32m 449\u001b[0m since, message\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPassing the \u001b[39m\u001b[38;5;132;01m%(name)s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m%(obj_type)s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 450\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpositionally is deprecated since Matplotlib \u001b[39m\u001b[38;5;132;01m%(since)s\u001b[39;00m\u001b[38;5;124m; the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 451\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter will become keyword-only in \u001b[39m\u001b[38;5;132;01m%(removal)s\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 452\u001b[0m name\u001b[38;5;241m=\u001b[39mname, obj_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m()\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 453\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/env/lib/python3.10/site-packages/matplotlib/__init__.py:1524\u001b[0m, in \u001b[0;36m_preprocess_data..inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1521\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1523\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1524\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1525\u001b[0m \u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1526\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcbook\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1527\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mcbook\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1529\u001b[0m bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1530\u001b[0m auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m 1531\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n", + "File \u001b[0;32m/env/lib/python3.10/site-packages/matplotlib/axes/_axes.py:7132\u001b[0m, in \u001b[0;36mAxes.hist\u001b[0;34m(self, x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)\u001b[0m\n\u001b[1;32m 7128\u001b[0m \u001b[38;5;66;03m# Loop through datasets\u001b[39;00m\n\u001b[1;32m 7129\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(nx):\n\u001b[1;32m 7130\u001b[0m \u001b[38;5;66;03m# this will automatically overwrite bins,\u001b[39;00m\n\u001b[1;32m 7131\u001b[0m \u001b[38;5;66;03m# so that each histogram uses the same bins\u001b[39;00m\n\u001b[0;32m-> 7132\u001b[0m m, bins \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhistogram\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mw\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mhist_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7133\u001b[0m tops\u001b[38;5;241m.\u001b[39mappend(m)\n\u001b[1;32m 7134\u001b[0m tops \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(tops, \u001b[38;5;28mfloat\u001b[39m) \u001b[38;5;66;03m# causes problems later if it's an int\u001b[39;00m\n", + "File \u001b[0;32m/env/lib/python3.10/site-packages/numpy/lib/_histograms_impl.py:827\u001b[0m, in \u001b[0;36mhistogram\u001b[0;34m(a, bins, range, density, weights)\u001b[0m\n\u001b[1;32m 825\u001b[0m \u001b[38;5;66;03m# Pre-compute histogram scaling factor\u001b[39;00m\n\u001b[1;32m 826\u001b[0m norm_numerator \u001b[38;5;241m=\u001b[39m n_equal_bins\n\u001b[0;32m--> 827\u001b[0m norm_denom \u001b[38;5;241m=\u001b[39m \u001b[43m_unsigned_subtract\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlast_edge\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfirst_edge\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 829\u001b[0m \u001b[38;5;66;03m# We iterate over blocks here for two reasons: the first is that for\u001b[39;00m\n\u001b[1;32m 830\u001b[0m \u001b[38;5;66;03m# large arrays, it is actually faster (for example for a 10^8 array it\u001b[39;00m\n\u001b[1;32m 831\u001b[0m \u001b[38;5;66;03m# is 2x as fast) and it results in a memory footprint 3x lower in the\u001b[39;00m\n\u001b[1;32m 832\u001b[0m \u001b[38;5;66;03m# limit of large arrays.\u001b[39;00m\n\u001b[1;32m 833\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m _range(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mlen\u001b[39m(a), BLOCK):\n", + "File \u001b[0;32m/env/lib/python3.10/site-packages/numpy/lib/_histograms_impl.py:352\u001b[0m, in \u001b[0;36m_unsigned_subtract\u001b[0;34m(a, b)\u001b[0m\n\u001b[1;32m 350\u001b[0m unsigned_dt \u001b[38;5;241m=\u001b[39m signed_to_unsigned[dt\u001b[38;5;241m.\u001b[39mtype]\n\u001b[1;32m 351\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[0;32m--> 352\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubtract\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 353\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 354\u001b[0m \u001b[38;5;66;03m# we know the inputs are integers, and we are deliberately casting\u001b[39;00m\n\u001b[1;32m 355\u001b[0m \u001b[38;5;66;03m# signed to unsigned. The input may be negative python integers so\u001b[39;00m\n\u001b[1;32m 356\u001b[0m \u001b[38;5;66;03m# ensure we pass in arrays with the initial dtype (related to NEP 50).\u001b[39;00m\n\u001b[1;32m 357\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39msubtract(np\u001b[38;5;241m.\u001b[39masarray(a, dtype\u001b[38;5;241m=\u001b[39mdt), np\u001b[38;5;241m.\u001b[39masarray(b, dtype\u001b[38;5;241m=\u001b[39mdt),\n\u001b[1;32m 358\u001b[0m casting\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munsafe\u001b[39m\u001b[38;5;124m'\u001b[39m, dtype\u001b[38;5;241m=\u001b[39munsigned_dt)\n", + "\u001b[0;31mTypeError\u001b[0m: numpy boolean subtract, the `-` operator, is not supported, use the bitwise_xor, the `^` operator, or the logical_xor function instead." + ] }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -1032,9 +1069,83 @@ } ], "source": [ - "# Keeping only dry, non-cloudy pixels\n", - "wo_wet = masking.make_mask(wo, wet=True)\n", + "# Create a mask based on all 'wet' pixels\n", + "wetwofl = masking.make_mask(wo, wet=True)\n", + "wetwofl.water.plot();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#testing the dea utils masking functions to see if I can create the binary flags\n", + "from collections.abc import Iterable\n", + "\n", + "import pandas\n", + "import xarray\n", + "from pandas.core.frame import DataFrame\n", + "from xarray import DataArray, Dataset\n", + "\n", + "from datacube.utils.math import valid_mask\n", + "\n", + "FLAGS_ATTR_NAME = \"flags_definition\"\n", + "\n", + "def make_mask(variable: Dataset | DataArray, **flags):\n", + " \"\"\"\n", + " Returns a mask array, based on provided flags\n", + "\n", + " When multiple flags are provided, they will be combined in a logical AND fashion.\n", + "\n", + " For example:\n", + "\n", + " >>> make_mask(\n", + " ... pqa, cloud_acca=False, cloud_fmask=False, land_obs=True\n", + " ... ) # doctest: +SKIP\n", + "\n", + " OR\n", + "\n", + " >>> make_mask(pqa, **GOOD_PIXEL_FLAGS) # doctest: +SKIP\n", + "\n", + " where `GOOD_PIXEL_FLAGS` is a dict of flag_name to True/False\n", "\n", + " :param variable:\n", + " :param flags: list of boolean flags\n", + " :return: boolean xarray.DataArray or xarray.Dataset\n", + " \"\"\"\n", + " flags_def = get_flags_def(variable)\n", + "\n", + " mask, mask_value = create_mask_value(flags_def, **flags)\n", + "\n", + " return variable & mask == mask_value" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "wo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Keeping only dry, non-cloudy pixels\n", + "wo_wet = masking.make_mask(wo, wet=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "# Plot output mask\n", "wo_wet.water.plot(col='time', size=4, col_wrap=4)" ] @@ -1068,30 +1179,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Identify clear pixels that were either dry or clear\n", "wo_dry = masking.make_mask(wo, dry=True)\n", @@ -1111,30 +1201,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Identify clear pixels that were not cloud, shadow or nodata\n", "wo_clear = masking.make_mask(wo, cloud_shadow=False, cloud=False, nodata=False)\n", @@ -1152,30 +1221,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Apply clear data as a mask to remove unclear pixels\n", "wo_masked = wo_wet.where(wo_clear).water\n", @@ -1197,30 +1245,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Inundation frequency (% wet observations)')" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Calculate mean wetness through time\n", "wo_freq = wo_masked.mean(dim=['time'])\n", @@ -1242,30 +1269,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Surface water over time (%)')" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Calculate percent surface water over time\n", "wo_time = wo_masked.mean(dim=['x', 'y'])\n", @@ -1287,30 +1293,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Calculate the percent of nodata pixels in each observation\n", "percent_nodata = wo_masked.isnull().mean(dim=['x', 'y'])\n", @@ -1343,17 +1328,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.8.13\n" - ] - } - ], + "outputs": [], "source": [ "print(datacube.__version__)" ] @@ -1392,7 +1369,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.18" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/Supplementary_data/DEA_Water_Observations/WO_screenshot_06112025_lakeGeorge.PNG b/Supplementary_data/DEA_Water_Observations/WO_screenshot_06112025_lakeGeorge.PNG new file mode 100644 index 000000000..10bf85dcf Binary files /dev/null and b/Supplementary_data/DEA_Water_Observations/WO_screenshot_06112025_lakeGeorge.PNG differ diff --git a/Tools/dea_tools/masking.py b/Tools/dea_tools/masking.py new file mode 100644 index 000000000..f72b0c64e --- /dev/null +++ b/Tools/dea_tools/masking.py @@ -0,0 +1,308 @@ +# This file is part of the Open Data Cube, see https://opendatacube.org for more information +# This file based off opendatacube.utils.masking +# Copyright (c) 2015-2025 ODC Contributors +# SPDX-License-Identifier: Apache-2.0 +""" +Tools for masking data based on a bit-mask variable with attached definition. + +The main functions are `make_mask(variable)` `describe_flags(variable)` +""" + +from collections.abc import Iterable + +import pandas +import xarray +from pandas.core.frame import DataFrame +from xarray import DataArray, Dataset + +from datacube.utils.math import valid_mask + +FLAGS_ATTR_NAME = "flags_definition" + + +def list_flag_names(variable): + """ + Returns the available masking flags for the variable + + :param variable: Masking xarray.Dataset or xarray.DataArray + :return: list + """ + flags_def = get_flags_def(variable) + return sorted(flags_def.keys()) + + + +def describe_variable_flags(variable, with_pandas: bool = True) -> DataFrame | str: + """ + Returns either a Pandas Dataframe (with_pandas=True - default) or a string + (with_pandas=False) describing the available flags for a masking variable + + Interprets the `flags_definition` attribute on the provided variable and + returns a Pandas Dataframe or string like:: + + Bits are listed from the MSB (bit 13) to the LSB (bit 0) + Bit Value Flag Name Description + 13 0 cloud_shadow_fmask Cloud Shadow (Fmask) + 12 0 cloud_shadow_acca Cloud Shadow (ACCA) + 11 0 cloud_fmask Cloud (Fmask) + 10 0 cloud_acca Cloud (ACCA) + + :param variable: Masking xarray.Dataset or xarray.DataArray + :return: Pandas Dataframe or str + """ + flags_def = get_flags_def(variable) + + if not with_pandas: + return describe_flags_def(flags_def) + + return pandas.DataFrame.from_dict(flags_def, orient="index") + + + + +def describe_flags_def(flags_def) -> str: + return "\n".join(generate_table(list(_table_contents(flags_def)))) + + +def _table_contents(flags_def): + yield "Flag name", "Description", "Bit. No", "Value", "Meaning" + for name, defn in sorted(flags_def.items(), key=_order_bitdefs_by_bits): + name, desc = name, defn["description"] + for value, meaning in defn["values"].items(): + yield name, desc, str(defn["bits"]), str(value), str(meaning) + name, desc = "", "" + + +def _order_bitdefs_by_bits(bitdef): + _, defn = bitdef + try: + return min(defn["bits"]) + except TypeError: + return defn["bits"] + + + +def make_mask(variable: Dataset | DataArray, **flags): + """ + Returns a mask array, based on provided flags + + When multiple flags are provided, they will be combined in a logical AND fashion. + + For example: + + >>> make_mask( + ... pqa, cloud_acca=False, cloud_fmask=False, land_obs=True + ... ) # doctest: +SKIP + + OR + + >>> make_mask(pqa, **GOOD_PIXEL_FLAGS) # doctest: +SKIP + + where `GOOD_PIXEL_FLAGS` is a dict of flag_name to True/False + + :param variable: + :param flags: list of boolean flags + :return: boolean xarray.DataArray or xarray.Dataset + """ + flags_def = get_flags_def(variable) + + mask, mask_value = create_mask_value(flags_def, **flags) + + return variable & mask == mask_value + + + + +def valid_data_mask(data): + """ + Returns bool arrays where the data is not `nodata` + + :param Dataset or DataArray data: + :return: Dataset or DataArray + """ + if isinstance(data, Dataset): + return data.map(valid_data_mask) + + if not isinstance(data, DataArray): + raise TypeError(f"valid_data_mask not supported for type {type(data)}") + + nodata = data.attrs.get("nodata", None) + + return xarray.apply_ufunc( + valid_mask, data, nodata, dask="parallelized", output_dtypes=[bool] + ) + + + +def mask_invalid_data(data, keep_attrs: bool = True): + """ + Sets all `nodata` values to ``nan``. + + This will convert numeric data to type `float`. + + :param Dataset or DataArray data: + :param keep_attrs: If the attributes of the data should be included in the returned . + :return: Dataset or DataArray + """ + if isinstance(data, Dataset): + # Pass keep_attrs as a positional arg to the DataArray func + return data.map(mask_invalid_data, keep_attrs=keep_attrs, args=(keep_attrs,)) + + if isinstance(data, DataArray): + if "nodata" not in data.attrs: + return data + out_data_array = data.where(data != data.nodata) + if keep_attrs: + out_data_array.attrs = { + key: value for key, value in data.attrs.items() if key != "nodata" + } + return out_data_array + + raise TypeError(f"mask_invalid_data not supported for type {type(data)}") + + + + +def create_mask_value(bits_def, **flags) -> tuple[int, int]: + mask = 0 + value = 0 + + for flag_name, flag_ref in flags.items(): + defn = bits_def.get(flag_name, None) + if defn is None: + raise ValueError(f'Unknown flag: "{flag_name}"') + + try: + [flag_value] = ( + bit_val + for bit_val, val_ref in defn["values"].items() + if val_ref == flag_ref + ) + flag_value = int(flag_value) # Might be string if coming from DB + except ValueError: + raise ValueError( + f"Unknown value {flag_ref} specified for flag {flag_name}" + ) from None + + if isinstance(defn["bits"], Iterable): # Multi-bit flag + # Set mask + for bit in defn["bits"]: + mask = set_value_at_index(mask, bit, True) + + shift = min(defn["bits"]) + real_val = flag_value << shift + + value |= real_val + + else: + bit = defn["bits"] + mask = set_value_at_index(mask, bit, True) + value = set_value_at_index(value, bit, bool(flag_value)) + + return mask, value + + +def mask_to_dict(bits_def: dict, mask_value: int) -> dict: + """ + Describes which flags are set for a mask value + + :param bits_def: + :param mask_value: + :return: Mapping of flag_name -> set_value + """ + return_dict = {} + for flag_name, flag_defn in bits_def.items(): + # Make bits a list, even if there is only one + flag_bits = flag_defn["bits"] + if not isinstance(flag_defn["bits"], list): + flag_bits = [flag_bits] + + # The amount to shift flag_value to line up with mask_value + flag_shift = min(flag_bits) + + # Mask our mask_value, we are only interested in the bits for this flag + flag_mask = 0 + for i in flag_bits: + flag_mask |= 1 << i + masked_mask_value = mask_value & flag_mask + + for flag_value, value in flag_defn["values"].items(): + shifted_value = int(flag_value) << flag_shift + if shifted_value == masked_mask_value: + assert flag_name not in return_dict + return_dict[flag_name] = value + return return_dict + + +def get_flags_def(variable): + flags = getattr(variable, FLAGS_ATTR_NAME, None) + if flags is not None: + return flags + + data_vars = getattr(variable, "data_vars", None) + + if data_vars is not None: + # Maybe we have a DataSet, not a DataArray + for var in data_vars.values(): + flags = getattr(var, FLAGS_ATTR_NAME, None) + if flags is not None: + return flags + + raise ValueError("No masking variable found") + + +def set_value_at_index(bitmask: int, index: int, value: bool) -> int: + """ + Set a bit value onto an integer bitmask + + eg. set bits 2 and 4 to True + >>> mask = 0 + >>> mask = set_value_at_index(mask, 2, True) + >>> mask = set_value_at_index(mask, 4, True) + >>> print(bin(mask)) + 0b10100 + >>> mask = set_value_at_index(mask, 2, False) + >>> print(bin(mask)) + 0b10000 + + :param bitmask: existing int bitmask to alter + """ + bit_val = 2**index + if value: + bitmask |= bit_val + else: + bitmask &= ~bit_val + return bitmask + + +def generate_table(rows): + """ + Yield strings to print a table using the data in `rows`. + + TODO: Maybe replace with Pandas + + :param rows: A sequence of sequences with the 0th element being the table + header + """ + # - figure out column widths + widths = [len(max(columns, key=len)) for columns in zip(*rows)] + + # - print the header + header, data = rows[0], rows[1:] + yield ( + " | ".join(format(title, f"{width}s") for width, title in zip(widths, header)) + ) + + # Print the separator + first_col = "" + # - print the data + for row in data: + if first_col == "" and row[0] != "": + # - print the separator + yield "-+-".join("-" * width for width in widths) + first_col = row[0] + + yield ( + " | ".join(format(cdata, f"{width}s") for width, cdata in zip(widths, row)) + ) +