Merge pull request #69 from histogrammar/cpp_code_cleanup

NEW: remove ROOT, cpp, cuda code; no longer maintained and supported

Author: mbaak

.github/workflows/test.yml

@@ -40,6 +40,11 @@ jobs:
             pip install ".[test,pandas,spark]"          
           fi
 
+      - name: Lint with pre-commit
+        run: |
+          pip install pre-commit
+          pre-commit run --all-files --show-diff-on-failure
+
       - name: Test with pytest
         run: |
           pytest tests

.pre-commit-config.yaml

@@ -0,0 +1,9 @@
+repos:
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  rev: v0.1.6
+  hooks:
+    - id: ruff
+      args: [--fix]
+      exclude: notebooks/
+    - id: ruff-format
+      exclude: notebooks/

CHANGES.rst

@@ -2,6 +2,10 @@
 Release notes
 =============
 
+Version 1.1.0, Dec 2024
+-----------------------
+* Removed all ROOT, cpp and cuda code, it was no longer supported.
+
 Version 1.0.34, Dec 2024
 ------------------------
 * Fix typo in build pipeline Python versions config list.

README.rst

@@ -11,18 +11,24 @@ At its core histogrammar is a suite of data aggregation primitives designed for
 In the simplest case, you can use this to compute histograms, but the generality of the primitives
 allows much more.
 
-Several common histogram types can be plotted in Matplotlib, Bokeh and PyROOT with a single method call.
+Several common histogram types can be plotted in Matplotlib and Bokeh with a single method call.
 If Numpy or Pandas is available, histograms and other aggregators can be filled from arrays ten to a hundred times
-more quickly via Numpy commands, rather than Python for loops. If PyROOT is available, histograms and other
-aggregators can be filled from ROOT TTrees hundreds of times more quickly by JIT-compiling a specialized C++ filler.
-Histograms and other aggregators may also be converted into CUDA code for inclusion in a GPU workflow. And if
-PyCUDA is available, they can also be filled from Numpy arrays by JIT-compiling the CUDA code.
+more quickly via Numpy commands, rather than Python for loops.
 
 This Python implementation of histogrammar been tested to guarantee compatibility with its Scala implementation.
 
-Latest Python release: v1.0.34 (Dec 2024).
+Latest Python release: v1.1.0 (Dec 2024).
 Latest update: Dec 2024.
 
+References
+==========
+
+Histogrammar is a core component of `popmon <https://github.com/ing-bank/popmon>`_, a package by ING bank
+that allows one to check the stability of a dataset. popmon works with both pandas and spark datasets,
+largely thanks to Histogrammar.
+
+
+
 Announcements
 =============