Merge pull request #1 from TeamNCMC/rename-to-cuisto

Rename package to cuisto

CHANGELOG.md

@@ -1,5 +1,8 @@
 This file might no be up to date nor complete. Please check the Releases page for more information on versions.
 
+## Version 2024.12.19
+- Renamed the package to cuisto (because histoquant is already the name of a commercial software).
+
 ## Version 2024.12.10
 - Fixed offset writing geojson file in segmentation module.
 - Added original_pixelsize parameters to rescale coordinates to match final image size. This allows the use of QuPath pixel classifier trained on resized image, eg. with a lower Resolution parameter (higher pixel size).

README.md

@@ -1,10 +1,10 @@
-# histoquant
+# cuisto
 
 Python package for histological quantification of objects in reference atlas regions.
 
-`histoquant` uses data exported from [QuPath](https://qupath.github.io) used with [ABBA](https://abba-documentation.readthedocs.io/en/latest/) to pool data and derive, average and display metrics.
+`cuisto` uses data exported from [QuPath](https://qupath.github.io) used with [ABBA](https://abba-documentation.readthedocs.io/en/latest/) to pool data and derive, average and display metrics.
 
-Check the full documentation : [https://teamncmc.github.io/histoquant](https://teamncmc.github.io/histoquant)
+Check the full documentation : [https://teamncmc.github.io/cuisto](https://teamncmc.github.io/cuisto)
 
 ## Install
 Steps 1-3 below need to be performed only once. If Anaconda or conda is already installed, skip steps 1-2 and use the Anaconda prompt instead.
@@ -18,16 +18,16 @@ Steps 1-3 below need to be performed only once. If Anaconda or conda is already
     ```
     conda activate hq
     ```
-5. Download the latest release from [here](https://github.com/TeamNCMC/histoquant/releases/latest) (choose "Source code (zip)) and unzip it on your computer.
-6. Browse to the repository from the terminal and install the `histoquant` package:
+5. Download the latest release from [here](https://github.com/TeamNCMC/cuisto/releases/latest) (choose "Source code (zip)) and unzip it on your computer.
+6. Browse to the repository from the terminal and install the `cuisto` package:
     ```
     cd /path/to/the/unzipped/folder
     pip install .
     ```
 
-The `histoquant` will be then available in Python from anywhere as long as the `hq` conda environment is activated. You can get started by looking and using the [Jupyter notebooks](#using-notebooks).
+The `cuisto` will be then available in Python from anywhere as long as the `hq` conda environment is activated. You can get started by looking and using the [Jupyter notebooks](#using-notebooks).
 
-For more complete installation instructions, see the [documentation](https://teamncmc.github.io/histoquant/main-getting-started.html#slow-start).
+For more complete installation instructions, see the [documentation](https://teamncmc.github.io/cuisto/main-getting-started.html#slow-start).
 
 ## Using notebooks
 Some Jupyter notebooks are available in the "docs/demo_notebooks" folder. You can open them in an IDE (such as [vscode](https://code.visualstudio.com/), select the "hq" environment as kernel in the top right) or in the Jupyter web interface (`jupyter notebook` in the terminal, with the "hq" environment activated).
@@ -51,4 +51,4 @@ Head to [http://localhost:8000/](http://localhost:8000/) from a web browser.
 The documentation is built with [MkDocs](https://www.mkdocs.org/) using the [Material theme](https://squidfunk.github.io/mkdocs-material/). [KaTeX](https://katex.org/) CSS and fonts are embedded instead of using a CDN, and are under a [MIT license](https://opensource.org/license/MIT).
 
 ## Credits
-`histoquant` has been primarly developed by [Guillaume Le Goc](https://legoc.fr) in [Julien Bouvier's lab](https://www.bouvier-lab.com/) at [NeuroPSI](https://neuropsi.cnrs.fr/).
+`cuisto` has been primarly developed by [Guillaume Le Goc](https://legoc.fr) in [Julien Bouvier's lab](https://www.bouvier-lab.com/) at [NeuroPSI](https://neuropsi.cnrs.fr/). The clever name was found by Aurélie Bodeau.

configs/config_template.toml

@@ -1,5 +1,5 @@
 ########################################################################################
-# Configuration file for histoquant package
+# Configuration file for cuisto package
 # -----------------------------------------
 # This is a TOML file. It maps a key to a value : `key = value`.
 # Each key must exist and be filled. The keys' names can't be modified, except:
@@ -11,7 +11,7 @@
 #   - the TOML specification : https://toml.io/en/
 #   - matplotlib colors : https://matplotlib.org/stable/gallery/color/color_demo.html
 #
-# Configuration file part of the python histoquant package.
+# Configuration file part of the python cuisto package.
 # version : 2.1
 ########################################################################################
 

histoquant/__init__.py → cuisto/__init__.py

@@ -1,4 +1,4 @@
-"""histoquant package.
+"""cuisto package.
 Perform quantification of objects in registered and segmented histological slices.
 """
 

histoquant/compute.py → cuisto/compute.py

@@ -1,4 +1,4 @@
-"""compute module, part of histoquant.
+"""compute module, part of cuisto.
 
 Contains actual computation functions.
 
@@ -7,7 +7,7 @@
 import numpy as np
 import pandas as pd
 
-from histoquant.utils import get_starter_cells, select_hemisphere_channel
+from cuisto.utils import get_starter_cells, select_hemisphere_channel
 
 
 def get_regions_metrics(

histoquant/config.py → cuisto/config.py

@@ -1,4 +1,4 @@
-"""config module, part of histoquant.
+"""config module, part of cuisto.
 
 Contains the Config class.
 
@@ -8,7 +8,7 @@
 import warnings
 
 from brainglobe_atlasapi import BrainGlobeAtlas
-from histoquant import utils
+from cuisto import utils
 
 
 class Config:
@@ -42,7 +42,7 @@ def __init__(self, config_file):
         self.get_leaves_list()
 
     def get_blacklist(self):
-        """Wraps histoquant.utils.get_blacklist."""
+        """Wraps cuisto.utils.get_blacklist."""
 
         self.atlas["blacklist"] = utils.get_blacklist(
             self.files["blacklist"], self.bg_atlas

histoquant/display.py → cuisto/display.py

@@ -1,4 +1,4 @@
-"""display module, part of histoquant.
+"""display module, part of cuisto.
 
 Contains display functions, essentially wrapping matplotlib and seaborn functions.
 
@@ -12,7 +12,7 @@
 import seaborn as sns
 from matplotlib import patches
 
-from histoquant import utils
+from cuisto import utils
 
 
 def add_injection_patch(X: list, ax: plt.Axes, **kwargs) -> plt.Axes:

histoquant/io.py → cuisto/io.py

@@ -1,4 +1,4 @@
-"""io module, part of histoquant.
+"""io module, part of cuisto.
 
 Contains loading and saving functions.
 
@@ -7,7 +7,7 @@
 import os
 
 from brainglobe_atlasapi import BrainGlobeAtlas
-from histoquant import utils
+from cuisto import utils
 import orjson
 import pandas as pd
 
@@ -282,7 +282,7 @@ def load_dfs(
         File(s) format.
     identifiers : list of str, optional
         List of identifiers to load from files. Defaults to the ones saved in
-        histoquant.process.process_animals().
+        cuisto.process.process_animals().
 
     Returns
     -------

histoquant/process.py → cuisto/process.py

@@ -1,4 +1,4 @@
-"""process module, part of histoquant.
+"""process module, part of cuisto.
 
 Wraps other functions for a click&play behaviour. Relies on the configuration file.
 
@@ -9,7 +9,7 @@
 import pandas as pd
 from tqdm import tqdm
 
-from histoquant import compute, io, utils
+from cuisto import compute, io, utils
 
 
 def process_animal(
@@ -31,7 +31,7 @@ def process_animal(
         Animal ID.
     df_annotations, df_detections : pd.DataFrame
         DataFrames of QuPath Annotations and Detections.
-    cfg : histoquant.Config
+    cfg : cuisto.Config
         The configuration loaded from TOML configuration file.
     compute_distributions : bool, optional
         If False, do not compute the 1D distributions and return an empty list.Default
@@ -185,7 +185,7 @@ def process_animals(
         Base working directory, containing `animals` folders.
     animals : list-like of str
         List of animals ID.
-    cfg: histoquant.Config
+    cfg: cuisto.Config
         Configuration object.
     out_fmt : {None, "h5", "csv", "tsv", "xslx", "pickle"}
         Output file(s) format, if None, nothing is saved (default).

histoquant/seg.py → cuisto/seg.py

@@ -1,4 +1,4 @@
-"""seg module, part of histoquant.
+"""seg module, part of cuisto.
 
 Functions for segmentating probability map stored as an image.
 

histoquant/utils.py → cuisto/utils.py

@@ -1,4 +1,4 @@
-"""utils module, part of histoquant.
+"""utils module, part of cuisto.
 
 Contains utilities functions.
 

docs/api-compute.md

@@ -1 +1 @@
-:::histoquant.compute
+:::cuisto.compute

docs/api-config-config.md

@@ -78,7 +78,7 @@
 `normalize_starter_cells` : normalize non-relative metrics by the number of starter cells
 
 <h4>metrics</h4>
-*Names of metrics. The keys are used internally in histoquant as is so should NOT be modified. The values will only chang etheir names in the ouput file*
+*Names of metrics. The keys are used internally in cuisto as is so should NOT be modified. The values will only chang etheir names in the ouput file*
 
 `"density µm^-2"` : relevant name  
 `"density mm^-2"` : relevant name  

docs/api-config.md

@@ -1 +1 @@
-:::histoquant.config
+:::cuisto.config

docs/api-display.md

@@ -1 +1 @@
-:::histoquant.display
+:::cuisto.display

docs/api-io.md

@@ -1 +1 @@
-:::histoquant.io
+:::cuisto.io

docs/api-process.md

@@ -1 +1 @@
-:::histoquant.process
+:::cuisto.process

docs/api-seg.md

@@ -1 +1 @@
-:::histoquant.seg
+:::cuisto.seg

docs/api-utils.md

@@ -1 +1 @@
-:::histoquant.utils
+:::cuisto.utils

docs/demo_notebooks/cells_distributions.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "This notebook shows how to load data exported from QuPath, compute metrics and display them, according to the configuration file. This is meant for a single-animal.\n",
     "\n",
-    "There are some conventions that need to be met in the QuPath project so that the measurements are usable with `histoquant`:\n",
+    "There are some conventions that need to be met in the QuPath project so that the measurements are usable with `cuisto`:\n",
     "+ Objects' classifications must be derived, eg. be in the form \"something: else\". The primary classification (\"something\") will be refered to \"object_type\" and the secondary classification (\"else\") to \"channel\" in the configuration file.\n",
     "+ Only one \"object_type\" can be processed at once, but supports any numbers of channels.\n",
     "+ Annotations (brain regions) must have properly formatted measurements. For punctual objects, it would be the count. Run the \"add_regions_count.groovy\" script to add them. The measurements names must be in the form \"something: else name\", for instance, \"something: else Count\". \"name\" is refered to \"base_measurement\" in the configuration file.\n",
@@ -24,7 +24,7 @@
    "source": [
     "import pandas as pd\n",
     "\n",
-    "import histoquant as hq"
+    "import cuisto"
    ]
   },
   {
@@ -59,7 +59,7 @@
    "outputs": [],
    "source": [
     "# get configuration\n",
-    "cfg = hq.config.Config(config_file)"
+    "cfg = cuisto.config.Config(config_file)"
    ]
   },
   {
@@ -812,7 +812,7 @@
    ],
    "source": [
     "# get distributions per regions, spatial distributions and coordinates\n",
-    "df_regions, dfs_distributions, df_coordinates = hq.process.process_animal(\n",
+    "df_regions, dfs_distributions, df_coordinates = cuisto.process.process_animal(\n",
     "    animal, df_annotations, df_detections, cfg, compute_distributions=True\n",
     ")\n",
     "\n",
@@ -849,9 +849,9 @@
    ],
    "source": [
     "# plot distributions per regions\n",
-    "figs_regions = hq.display.plot_regions(df_regions, cfg)\n",
+    "figs_regions = cuisto.display.plot_regions(df_regions, cfg)\n",
     "# specify which regions to plot\n",
-    "# figs_regions = hq.display.plot_regions(df_regions, cfg, names_list=[\"GRN\", \"IRN\", \"MDRNv\"])\n",
+    "# figs_regions = cuisto.display.plot_regions(df_regions, cfg, names_list=[\"GRN\", \"IRN\", \"MDRNv\"])\n",
     "\n",
     "# save as svg\n",
     "# figs_regions[0].savefig(r\"C:\\Users\\glegoc\\Downloads\\regions_count.svg\")\n",
@@ -876,7 +876,7 @@
    ],
    "source": [
     "# plot 1D distributions\n",
-    "fig_distrib = hq.display.plot_1D_distributions(\n",
+    "fig_distrib = cuisto.display.plot_1D_distributions(\n",
     "    dfs_distributions, cfg, df_coordinates=df_coordinates\n",
     ")"
    ]
@@ -906,7 +906,7 @@
    ],
    "source": [
     "# plot heatmap (all types of cells pooled)\n",
-    "fig_heatmap = hq.display.plot_2D_distributions(df_coordinates, cfg)"
+    "fig_heatmap = cuisto.display.plot_2D_distributions(df_coordinates, cfg)"
    ]
   }
  ],