different colormaps for rows/columns by default in decorated clusterm…

…ap; add tests for decorated clustermap, related to #10

Makefile

@@ -1,6 +1,6 @@
-.DEFAULT_GOAL := install
+.DEFAULT_GOAL := all
 
-all: install clean
+all: install clean test
 
 move_models_out:
 	mv _models ../
@@ -31,6 +31,9 @@ install:
 	${MAKE} clean
 	${MAKE} move_models_in
 
+test:
+	python -m pytest imc/
+
 run:
 	python imcpipeline/runner.py \
 		--divvy slurm \

imc/__init__.py

@@ -14,7 +14,7 @@
 
 
 def setup_logger(level=logging.INFO):
-    logger = logging.getLogger("imcpipeline")
+    logger = logging.getLogger("imc")
     logger.setLevel(level)
 
     handler = logging.StreamHandler(sys.stdout)
@@ -28,7 +28,7 @@ def setup_logger(level=logging.INFO):
 LOGGER = setup_logger()
 
 # Setup joblib memory
-JOBLIB_CACHE_DIR = os.path.join(os.path.expanduser("~"), ".imcpipeline")
+JOBLIB_CACHE_DIR = os.path.join(os.path.expanduser("~"), ".imc")
 MEMORY = Memory(location=JOBLIB_CACHE_DIR, verbose=0)
 
 # Decorate seaborn clustermap

imc/data_models/project.py

@@ -349,9 +349,7 @@ def channel_summary(
                     res,
                     cbar_kws=dict(label=red_func.capitalize() + cbar_label),
                     row_colors=channel_mean,
-                    row_colors_cmap="Greens",
                     col_colors=cell_density,
-                    col_colors_cmap="BuPu",
                     metric="correlation",
                     xticklabels=True,
                     yticklabels=True,
@@ -441,9 +439,8 @@ def quantify_cells(
             quantification = self.quantify_cell_intensity(samples=samples, rois=rois)
         if not set_attribute:
             return quantification
-        else:
-            self.quantification = quantification
-            return None
+        self.quantification = quantification
+        return None
 
     def quantify_cell_intensity(
         self,
@@ -948,10 +945,11 @@ def measure_adjacency(
         )
         sns.clustermap(mean_f, cmap="RdBu_r", center=0, robust=True)
 
-        __v = np.percentile(melted["value"].abs(), 95)
+        v = np.percentile(melted["value"].abs(), 95)
         n, m = get_grid_dims(len(freqs))
         fig, axes = plt.subplots(n, m, figsize=(m * 5, n * 5), sharex=True, sharey=True)
         axes = axes.flatten()
+        i = -1
         for i, (dfs, roi) in enumerate(zip(freqs, rois)):
             axes[i].set_title(roi.name)
             sns.heatmap(
@@ -963,8 +961,8 @@ def measure_adjacency(
                 square=True,
                 xticklabels=True,
                 yticklabels=True,
-                vmin=-__v,
-                vmax=__v,
+                vmin=-v,
+                vmax=v,
             )
         for axs in axes[i + 1 :]:
             axs.axis("off")

imc/graphics.py

@@ -18,12 +18,41 @@
 
 
 SEQUENCIAL_CMAPS = [
-    "Purples", "Greens", "Oranges", "Greys", "Reds", "Blues",
-    "YlOrBr", "YlOrRd", "OrRd", "PuRd", "RdPu", "BuPu",
-    "GnBu", "PuBu", "YlGnBu", "PuBuGn", "BuGn", "YlGn",
-    "binary", "gist_yarg", "gist_gray", "gray", "bone", "pink",
-    "spring", "summer", "autumn", "winter", "cool", "Wistia",
-    "hot", "afmhot", "gist_heat", "copper"]
+    "Purples",
+    "Greens",
+    "Oranges",
+    "Greys",
+    "Reds",
+    "Blues",
+    "YlOrBr",
+    "YlOrRd",
+    "OrRd",
+    "PuRd",
+    "RdPu",
+    "BuPu",
+    "GnBu",
+    "PuBu",
+    "YlGnBu",
+    "PuBuGn",
+    "BuGn",
+    "YlGn",
+    "binary",
+    "gist_yarg",
+    "gist_gray",
+    "gray",
+    "bone",
+    "pink",
+    "spring",
+    "summer",
+    "autumn",
+    "winter",
+    "cool",
+    "Wistia",
+    "hot",
+    "afmhot",
+    "gist_heat",
+    "copper",
+]
 
 
 def to_color_series(x: pd.Series, cmap: Optional[str] = "Greens") -> pd.Series:
@@ -32,13 +61,16 @@ def to_color_series(x: pd.Series, cmap: Optional[str] = "Greens") -> pd.Series:
 
 
 def to_color_dataframe(
-    x: Union[pd.Series, pd.DataFrame], cmaps: Optional[Union[str, List[str]]] = None
+    x: Union[pd.Series, pd.DataFrame],
+    cmaps: Optional[Union[str, List[str]]] = None,
+    offset: int = 0,
 ) -> pd.DataFrame:
     """Map a numeric pandas DataFrame to RGB values."""
     if isinstance(x, pd.Series):
         x = x.to_frame()
     if cmaps is None:
-        cmaps = [plt.get_cmap(cmap) for cmap in SEQUENCIAL_CMAPS[: x.shape[1]]]
+        # the offset is in order to get different colors for rows and columns by default
+        cmaps = [plt.get_cmap(cmap) for cmap in SEQUENCIAL_CMAPS[offset:]]
     if isinstance(cmaps, str):
         cmaps = [cmaps]
     return pd.concat([to_color_series(x[col], cmap) for col, cmap in zip(x, cmaps)], axis=1)
@@ -129,7 +161,7 @@ def clustermap(*args, **kwargs):
                 if isinstance(kwargs[arg + "_colors"], (pd.DataFrame, pd.Series)):
                     _kwargs[arg + "s"] = kwargs[arg + "_colors"]
                     kwargs[arg + "_colors"] = to_color_dataframe(
-                        kwargs[arg + "_colors"], cmaps[arg]
+                        x=kwargs[arg + "_colors"], cmaps=cmaps[arg], offset=1 if arg == "row" else 0
                     )
         grid = f(*args, **kwargs)
         _add_colorbars(grid, **_kwargs, row_cmaps=cmaps["row"], col_cmaps=cmaps["col"])
@@ -239,8 +271,8 @@ def get_transparent_cmaps(n: int = 3, from_palette: Optional[str] = "colorblind"
 def cell_labels_to_mask(mask: Array, labels: Union[Series, Dict]) -> Array:
     """Replaces integers in `mask` with values from the mapping in `labels`."""
     res = np.zeros(mask.shape, dtype=int)
-    for __k, __v in labels.items():
-        res[mask == __k] = __v
+    for k, v in labels.items():
+        res[mask == k] = v
     return res
 
 

imc/operations.py

@@ -4,8 +4,7 @@
 Functions for high order operations.
 """
 
-from __future__ import annotations
-# ^^ this will fix the type annotatiton of not yet undefined classes
+from __future__ import annotations  # fix the type annotatiton of not yet undefined classes
 from collections import Counter
 import os
 import re
@@ -42,12 +41,7 @@
 from imc.exceptions import cast
 from imc.types import DataFrame, Series, Array, Path, MultiIndexSeries
 from imc.utils import read_image_from_file, estimate_noise, double_z_score
-from imc.graphics import colorbar_decorator, get_grid_dims, rasterize_scanpy, add_legend
-
-# import leidenalg as la
-
-
-sns.clustermap = colorbar_decorator(sns.clustermap)
+from imc.graphics import get_grid_dims, rasterize_scanpy, add_legend
 
 
 matplotlib.rcParams["svg.fonttype"] = "none"
@@ -582,9 +576,7 @@ def single_cell_analysis(
 
     kwargs = dict(
         row_colors=row_means,
-        row_colors_cmaps=["Greens"],
         col_colors=col_counts,
-        col_colors_cmaps=["Purples"],
         metric="correlation",
         robust=True,
         xticklabels=True,
@@ -742,8 +734,8 @@ def derive_reference_cell_type_labels(
         robust=True,
         xticklabels=True,
         yticklabels=True,
-        row_colors=cmeans,  #  row_colors_cmaps=['Greens'],
-        # col_colors=fractions, col_colors_cmaps=["Purples"]
+        row_colors=cmeans,
+        # col_colors=fractions,
     )
     opts = [
         (mean_expr, "original", dict()),
@@ -775,9 +767,7 @@ def derive_reference_cell_type_labels(
         cmap="RdBu_r",
         cbar_kws=dict(label="Mean expression (Z-score)"),
         row_colors=cmeans,
-        row_colors_cmaps=["Greens"],
         col_colors=fractions,
-        col_colors_cmaps=["Purples"],
     )
     opts = [
         (mean_expr_z_l, "labeled.both_z", dict()),
@@ -798,9 +788,7 @@ def derive_reference_cell_type_labels(
         xticklabels=True,
         yticklabels=True,
         row_colors=fractions_l,
-        row_colors_cmaps=["Purples"],
         col_colors=fractions_l,
-        col_colors_cmaps=["Purples"],
     )
     grid.savefig(
         output_prefix + "mean_expression_per_cluster.labeled.both_z.correlation.svg", **FIG_KWS
@@ -1030,6 +1018,7 @@ def measure_cell_type_adjacency(
     kws2 = dict(vmin=-v, vmax=v, cbar_kws=dict(label="Log odds interaction"))
     sns.heatmap(norm_freqs, ax=axes[1], **kws, **kws2)
     fig.savefig(output_prefix + "cluster_adjacency_graph.norm_over_random.heatmap.svg", **FIG_KWS)
+    del kws["square"]
     grid = sns.clustermap(norm_freqs, **kws, **kws2)
     grid.savefig(
         output_prefix + "cluster_adjacency_graph.norm_over_random.clustermap.svg", **FIG_KWS

imc/tests/test_graphics.py

@@ -0,0 +1,13 @@
+#!/usr/bin/env python
+
+import numpy as np
+import pandas as pd
+import seaborn as sns
+import pytest
+
+
+class Test_colorbar_decorator:
+    def test_random_plot(self):
+        x = pd.DataFrame(np.random.random((10, 5))).rename_axis(index="rows", columns="columns")
+        g = sns.clustermap(x, row_colors=x.mean(1), col_colors=x.mean(0))
+        assert len(g.fig.get_axes()) == 8

imc/types.py

@@ -1,20 +1,36 @@
+#!/usr/bin/env python
+
+"""
+Types used in the library as defined here.
+
+See https://docs.python.org/3/library/typing.html
+for more information.
+"""
+
 from __future__ import annotations
 import os
 from typing import Union, TypeVar
 import pathlib
 
-import matplotlib
-import pandas
-import numpy
+import matplotlib  # type: ignore
+import pandas  # type: ignore
+import numpy  # type: ignore
 
 
 class Path(pathlib.Path):
     """
-    A pathlib.Path child that allows concatenation with strings using the
-    addition operator
+    A pathlib.Path child class that allows concatenation with strings using the
+    addition operator.
+
+    In addition, it implements the ``startswith`` and ``endswith`` methods
+    just like in the base :obj:`str` type.
     """
 
-    _flavour = pathlib._windows_flavour if os.name == "nt" else pathlib._posix_flavour
+    _flavour = (
+        pathlib._windows_flavour  # pylint: disable=W0212
+        if os.name == "nt"
+        else pathlib._posix_flavour  # pylint: disable=W0212
+    )
 
     def __add__(self, string: str) -> "Path":
         return Path(str(self) + string)
@@ -32,11 +48,12 @@ def replace_(self, patt: str, repl: str) -> Path:
 GenericType = TypeVar("GenericType")
 
 # type aliasing (done with Union to distinguish from other declared variables)
-Axis = Union[matplotlib.axis.Axis]
-Figure = Union[matplotlib.figure.Figure]
-Patch = Union[matplotlib.patches.Patch]
 Array = Union[numpy.ndarray]
-DataFrame = Union[pandas.DataFrame]
 Series = Union[pandas.Series]
 MultiIndexSeries = Union[pandas.Series]
+DataFrame = Union[pandas.DataFrame]
+
+Figure = Union[matplotlib.figure.Figure]
+Axis = Union[matplotlib.axis.Axis]
+Patch = Union[matplotlib.patches.Patch]
 ColorMap = Union[matplotlib.colors.LinearSegmentedColormap]

setup.py

@@ -63,6 +63,7 @@ def parse_requirements(req_file):
     classifiers=[
         "Programming Language :: Python :: 3 :: Only",
         "Programming Language :: Python :: 3.7",
+        "Programming Language :: Python :: 3.8",
         "Development Status :: 3 - Alpha",
         "Typing :: Typed",
         "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",