Merge pull request #1 from mpes-kit/release

Release

Author: rettigl

.cspell/custom-dictionary.txt

@@ -0,0 +1,9 @@
+accum
+clahe
+coeff
+inds
+keepdims
+mclahe
+nbins
+ndims
+sess

.github/workflows/release.yml

@@ -0,0 +1,50 @@
+name: Publish to PyPI
+
+# Workflow runs a release job on every published tag.
+on:
+  release:
+    types: [published]
+
+# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued.
+# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete.
+concurrency:
+  group: "release"
+  cancel-in-progress: false
+
+env:
+  UV_SYSTEM_PYTHON: true
+
+jobs:
+  release:
+    name: Upload release to PyPI
+    runs-on: ubuntu-latest
+    environment:
+      name: pypi
+      url: https://pypi.org/p/mclahe
+    permissions:
+      id-token: write
+
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.x"
+
+      - name: Install dependencies
+        run: |
+          curl -LsSf https://astral.sh/uv/install.sh | sh
+          uv pip install build
+
+      - name: Build package
+        run: |
+          git reset --hard HEAD
+          python -m build
+
+      - name: Publish package distributions to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          verbose: true

.gitignore

@@ -0,0 +1,49 @@
+__pycache__/
+
+# C extensions
+*.so
+
+# pycharm
+.idea/
+.idea
+
+# Packages
+*.egg
+*.egg-info
+.pypirc
+build
+eggs
+parts
+bin
+var
+sdist
+develop-eggs
+.installed.cfg
+lib
+lib64
+dist/
+
+# Installer logs
+pip-log.txt
+
+# Unit test / coverage reports
+.coverage
+.tox
+nosetests.xml
+
+# Complexity
+output/*.html
+output/*/index.html
+
+# Sphinx
+docs/_build
+
+# Cookiecutter
+output/
+# data
+data/
+*.zip
+*.h5
+**/plots/
+
+.python-version

.pre-commit-config.yaml

@@ -0,0 +1,48 @@
+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.3.0
+    hooks:
+    -   id: trailing-whitespace
+    -   id: end-of-file-fixer
+    -   id: check-yaml
+    -   id: check-added-large-files
+    -   id: check-ast
+    -   id: check-docstring-first
+
+-   repo: https://github.com/astral-sh/ruff-pre-commit
+    # Ruff version.
+    rev: v0.2.2
+    hooks:
+      # Run the formatter.
+      - id: ruff-format
+      # Run the linter.
+      - id: ruff
+-   repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v1.7.1
+    hooks:
+      - id: mypy
+-   repo: https://github.com/asottile/reorder_python_imports
+    rev: v3.8.2
+    hooks:
+    -   id: reorder-python-imports
+        args: [--application-directories, '.:src', --py39-plus]
+-   repo: https://github.com/asottile/pyupgrade
+    rev: v3.16.0
+    hooks:
+    -   id: pyupgrade
+        args: [--py39-plus]
+-   repo: https://github.com/asottile/add-trailing-comma
+    rev: v2.2.3
+    hooks:
+    -   id: add-trailing-comma
+        args: [--py36-plus]
+-   repo: https://github.com/kynan/nbstripout
+    rev: 0.6.0
+    hooks:
+    -   id: nbstripout
+-   repo: https://github.com/streetsidesoftware/cspell-cli
+    rev: v6.31.1
+    hooks:
+      - id: cspell

MANIFEST.in

@@ -1,2 +1,4 @@
-include README.md
-include requirements.txt
+prune *
+exclude *
+recursive-include mclahe *.py
+include pyproject.toml README.md

cspell.json

@@ -0,0 +1,23 @@
+{
+    "version": "0.2",
+    "ignorePaths": [
+        "./tests/data/*",
+        "*.toml",
+        "Makefile",
+        "*.bat",
+        "*.egg-info",
+    ],
+    "dictionaryDefinitions": [
+        {
+            "name": "custom-dictionary",
+            "path": "./.cspell/custom-dictionary.txt",
+            "addWords": true
+        }
+    ],
+    "dictionaries": [  "custom-dictionary"
+    ],
+    "words": [],
+    "ignoreWords": [],
+    "import": [],
+    "language": "en-GB, en-US"
+}

mclahe/core.py

@@ -1,11 +1,11 @@
 #! /usr/bin/env python
-# -*- coding: utf-8 -*-
+from itertools import product
 
 import numpy as np
 import tensorflow as tf
+
 from .utils import *
 
-from itertools import product
 
 def mclahe(x, kernel_size=None, n_bins=128, clip_limit=0.01, adaptive_hist_range=False, use_gpu=True):
     """
@@ -37,10 +37,10 @@ def mclahe(x, kernel_size=None, n_bins=128, clip_limit=0.01, adaptive_hist_range
     padding_x_length = kernel_size - 1 - ((x_shape - 1) % kernel_size)
     padding_x = np.column_stack(((padding_x_length + 1) // 2, padding_x_length // 2))
     padding_hist = np.column_stack((kernel_size // 2, (kernel_size + 1) // 2)) + padding_x
-    x_hist_padded = np.pad(x, padding_hist, 'symmetric')
+    x_hist_padded = np.pad(x, padding_hist, "symmetric")
 
     # Set up tf graph
-    with tf.variable_scope("clahe") as scope:
+    with tf.variable_scope("clahe"):
         tf_x_hist_padded_init = tf.placeholder(tf.float32, shape=x_hist_padded.shape)
         tf_x_hist_padded = tf.Variable(tf_x_hist_padded_init)
         tf_x_padded = tf.slice(tf_x_hist_padded, kernel_size // 2, x_shape + padding_x_length)
@@ -67,15 +67,22 @@ def mclahe(x, kernel_size=None, n_bins=128, clip_limit=0.01, adaptive_hist_range
             tf_x_hist_max = tf.reduce_max(tf_x_hist, np.arange(-dim, 0))
             tf_x_hist_norm = tf.Variable(tf_x_hist_ex_init, dtype=tf.float32)
             tf_get_hist_min = tf.assign(tf_x_hist_min, tf.reduce_min(tf_x_hist, np.arange(-dim, 0)))
-            tf_get_hist_norm = tf.assign(tf_x_hist_norm, tf.where(tf.equal(tf_x_hist_min, tf_x_hist_max),
-                                                                  tf.ones_like(tf_x_hist_min),
-                                                                  tf_x_hist_max - tf_x_hist_min))
-
-            tf_x_hist_scaled = (tf_x_hist - tf.reshape(tf_x_hist_min, hist_ex_shape))\
-                               / tf.reshape(tf_x_hist_norm, hist_ex_shape)
+            tf_get_hist_norm = tf.assign(
+                tf_x_hist_norm,
+                tf.where(
+                    tf.equal(tf_x_hist_min, tf_x_hist_max),
+                    tf.ones_like(tf_x_hist_min),
+                    tf_x_hist_max - tf_x_hist_min,
+                ),
+            )
+
+            tf_x_hist_scaled = (tf_x_hist - tf.reshape(tf_x_hist_min, hist_ex_shape)) / tf.reshape(
+                tf_x_hist_norm,
+                hist_ex_shape,
+            )
         else:
             tf_x_hist_scaled = tf_x_hist
-        tf_hist = tf.cast(tf_batch_histogram(tf_x_hist_scaled, [0., 1.], dim, nbins=n_bins), tf.float32)
+        tf_hist = tf.cast(tf_batch_histogram(tf_x_hist_scaled, [0.0, 1.0], dim, nbins=n_bins), tf.float32)
         # Clip histogram
         tf_n_to_high = tf.reduce_sum(tf.nn.relu(tf_hist - np.prod(kernel_size) * clip_limit), -1, keepdims=True)
         tf_hist_clipped = tf.minimum(tf_hist, np.prod(kernel_size) * clip_limit) + tf_n_to_high / n_bins
@@ -105,13 +112,15 @@ def mclahe(x, kernel_size=None, n_bins=128, clip_limit=0.01, adaptive_hist_range
             tf_hist_norm_slice_shape = np.concatenate((n_blocks, [1] * dim))
             tf_x_hist_min_sub = tf.slice(tf_x_hist_min, tf_slice_begin, n_blocks)
             tf_x_hist_norm_sub = tf.slice(tf_x_hist_norm, tf_slice_begin, n_blocks)
-            tf_x_block_scaled = (tf_x_block - tf.reshape(tf_x_hist_min_sub, tf_hist_norm_slice_shape))\
-                                / tf.reshape(tf_x_hist_norm_sub, tf_hist_norm_slice_shape)
-            tf_bin = tf.histogram_fixed_width_bins(tf_x_block_scaled, [0., 1.], nbins=n_bins)
+            tf_x_block_scaled = (tf_x_block - tf.reshape(tf_x_hist_min_sub, tf_hist_norm_slice_shape)) / tf.reshape(
+                tf_x_hist_norm_sub,
+                tf_hist_norm_slice_shape,
+            )
+            tf_bin = tf.histogram_fixed_width_bins(tf_x_block_scaled, [0.0, 1.0], nbins=n_bins)
         else:
             # Global bins
             tf_bin = tf.Variable(tf.cast(tf_x_block_init, tf.int32), dtype=tf.int32)
-            tf_get_bin = tf.assign(tf_bin, tf.histogram_fixed_width_bins(tf_x_block, [0., 1.], nbins=n_bins))
+            tf_get_bin = tf.assign(tf_bin, tf.histogram_fixed_width_bins(tf_x_block, [0.0, 1.0], nbins=n_bins))
         # Apply map
         tf_mapped_sub = tf_batch_gather(tf_map_slice, tf_bin, dim)
         # Apply coefficients
@@ -132,32 +141,49 @@ def mclahe(x, kernel_size=None, n_bins=128, clip_limit=0.01, adaptive_hist_range
         new_shape = tuple((axis, axis + dim) for axis in range(dim))
         new_shape = tuple(j for i in new_shape for j in i)
         tf_res_transposed = tf.transpose(tf_res, new_shape)
-        tf_res_reshaped = tf.reshape(tf_res_transposed, tuple(n_blocks[axis] * kernel_size[axis] for axis in range(dim)))
+        tf_res_reshaped = tf.reshape(
+            tf_res_transposed,
+            tuple(n_blocks[axis] * kernel_size[axis] for axis in range(dim)),
+        )
 
         # Recover original size
         tf_res_cropped = tf.slice(tf_res_reshaped, padding_x[:, 0], x.shape)
 
         # Setting up tf session
         if use_gpu:
-            config = None
+            config = tf.ConfigProto()
+            config.gpu_options.allow_growth = True
         else:
-            config = tf.ConfigProto(device_count={'GPU': 0})
+            config = tf.ConfigProto(device_count={"GPU": 0})
 
         with tf.Session(config=config) as sess:
             map_init = np.zeros(map_shape, dtype=np.float32)
             x_block_init = np.zeros(shape_x_block, dtype=np.float32)
             # Initialize vars for local hist range if needed
             if adaptive_hist_range:
                 x_hist_ex_init = np.zeros(n_blocks_hist, dtype=np.float32)
-                tf_var_init = tf.initializers.variables([tf_x_hist_padded, tf_map, tf_res, tf_res_sub,
-                                                         tf_x_hist_min, tf_x_hist_norm])
-                sess.run(tf_var_init, feed_dict={tf_x_hist_padded_init: x_hist_padded,
-                                                 tf_map_init: map_init, tf_x_block_init: x_block_init,
-                                                 tf_x_hist_ex_init: x_hist_ex_init})
+                tf_var_init = tf.initializers.variables(
+                    [tf_x_hist_padded, tf_map, tf_res, tf_res_sub, tf_x_hist_min, tf_x_hist_norm],
+                )
+                sess.run(
+                    tf_var_init,
+                    feed_dict={
+                        tf_x_hist_padded_init: x_hist_padded,
+                        tf_map_init: map_init,
+                        tf_x_block_init: x_block_init,
+                        tf_x_hist_ex_init: x_hist_ex_init,
+                    },
+                )
             else:
                 tf_var_init = tf.initializers.variables([tf_x_hist_padded, tf_map, tf_bin, tf_res, tf_res_sub])
-                sess.run(tf_var_init, feed_dict={tf_x_hist_padded_init: x_hist_padded, tf_map_init: map_init,
-                                                 tf_x_block_init: x_block_init})
+                sess.run(
+                    tf_var_init,
+                    feed_dict={
+                        tf_x_hist_padded_init: x_hist_padded,
+                        tf_map_init: map_init,
+                        tf_x_block_init: x_block_init,
+                    },
+                )
 
             # Run calculations
             # Normalize histogram data if needed
@@ -178,7 +204,7 @@ def mclahe(x, kernel_size=None, n_bins=128, clip_limit=0.01, adaptive_hist_range
                     if kernel_size[axis] % 2 == 0:
                         coeff = 0.5 / kernel_size[axis] + coeff
                     if ind_map[axis] == 0:
-                        coeff = 1. - coeff
+                        coeff = 1.0 - coeff
                     new_shape = [1] * (dim + axis) + [kernel_size[axis]] + [1] * (dim - 1 - axis)
                     coeff = np.reshape(coeff, new_shape)
                     sess.run(tf_apply_coeff, feed_dict={tf_coeff: coeff})