Merge pull request #285 from rsagroup/colormap-consensus

Change default colormap

Author: JasperVanDenBosch

demos/demo_bootstrap.ipynb

@@ -72,27 +72,27 @@
      "output_type": "stream",
      "text": [
       "Predicting with theta = [1,0], should return the first rdm, which is:\n",
-      "[[0.20529161 0.18219921 0.14234779 0.15184744 0.21434174 0.1876438\n",
-      "  0.23245372 0.16816806 0.11574728 0.21842971]]\n",
+      "[[0.17948311 0.13203485 0.13632945 0.18298594 0.15166647 0.16234362\n",
+      "  0.14637612 0.12058838 0.15784657 0.12497874]]\n",
       "The output of the model is:\n",
-      "[0.20529161 0.18219921 0.14234779 0.15184744 0.21434174 0.1876438\n",
-      " 0.23245372 0.16816806 0.11574728 0.21842971]\n",
+      "[0.17948311 0.13203485 0.13632945 0.18298594 0.15166647 0.16234362\n",
+      " 0.14637612 0.12058838 0.15784657 0.12497874]\n",
       "Which is indeed identical\n",
       "\n",
       "Predicting with theta = [0,1], should return the second rdm, which is:\n",
-      "[[0.21021824 0.09930457 0.16356345 0.20092431 0.18402425 0.19685312\n",
-      "  0.13494642 0.13652705 0.1714637  0.12152749]]\n",
+      "[[0.14265908 0.17005546 0.18177815 0.1564901  0.13836761 0.17725487\n",
+      "  0.09220979 0.24478309 0.15514355 0.20351734]]\n",
       "The output of the model is:\n",
-      "[0.21021824 0.09930457 0.16356345 0.20092431 0.18402425 0.19685312\n",
-      " 0.13494642 0.13652705 0.1714637  0.12152749]\n",
+      "[0.14265908 0.17005546 0.18177815 0.1564901  0.13836761 0.17725487\n",
+      " 0.09220979 0.24478309 0.15514355 0.20351734]\n",
       "Which is indeed identical\n",
       "\n",
       "Predicting with theta = [1,1], should return the sum of the first two rdms, which is:\n",
-      "[[0.41550985 0.28150378 0.30591124 0.35277176 0.39836599 0.38449692\n",
-      "  0.36740013 0.3046951  0.28721099 0.3399572 ]]\n",
+      "[[0.32214218 0.30209031 0.3181076  0.33947604 0.29003408 0.33959849\n",
+      "  0.23858592 0.36537148 0.31299012 0.32849607]]\n",
       "The output of the model is:\n",
-      "[0.41550985 0.28150378 0.30591124 0.35277176 0.39836599 0.38449692\n",
-      " 0.36740013 0.3046951  0.28721099 0.3399572 ]\n",
+      "[0.32214218 0.30209031 0.3181076  0.33947604 0.29003408 0.33959849\n",
+      " 0.23858592 0.36537148 0.31299012 0.32849607]\n",
       "Which is indeed identical\n"
      ]
     }
@@ -136,11 +136,11 @@
       "squared euclidean\n",
       "\n",
       "dissimilarities[0] = \n",
-      "[[0.         0.20529161 0.18219921 0.14234779 0.15184744]\n",
-      " [0.20529161 0.         0.21434174 0.1876438  0.23245372]\n",
-      " [0.18219921 0.21434174 0.         0.16816806 0.11574728]\n",
-      " [0.14234779 0.1876438  0.16816806 0.         0.21842971]\n",
-      " [0.15184744 0.23245372 0.11574728 0.21842971 0.        ]]\n",
+      "[[0.         0.17948311 0.13203485 0.13632945 0.18298594]\n",
+      " [0.17948311 0.         0.15166647 0.16234362 0.14637612]\n",
+      " [0.13203485 0.15166647 0.         0.12058838 0.15784657]\n",
+      " [0.13632945 0.16234362 0.12058838 0.         0.12497874]\n",
+      " [0.18298594 0.14637612 0.15784657 0.12497874 0.        ]]\n",
       "\n",
       "descriptors: \n",
       "\n",
@@ -161,11 +161,11 @@
       "squared euclidean\n",
       "\n",
       "dissimilarities[0] = \n",
-      "[[0.         0.20529161 0.18219921 0.14234779 0.15184744]\n",
-      " [0.20529161 0.         0.21434174 0.1876438  0.23245372]\n",
-      " [0.18219921 0.21434174 0.         0.16816806 0.11574728]\n",
-      " [0.14234779 0.1876438  0.16816806 0.         0.21842971]\n",
-      " [0.15184744 0.23245372 0.11574728 0.21842971 0.        ]]\n",
+      "[[0.         0.17948311 0.13203485 0.13632945 0.18298594]\n",
+      " [0.17948311 0.         0.15166647 0.16234362 0.14637612]\n",
+      " [0.13203485 0.15166647 0.         0.12058838 0.15784657]\n",
+      " [0.13632945 0.16234362 0.12058838 0.         0.12497874]\n",
+      " [0.18298594 0.14637612 0.15784657 0.12497874 0.        ]]\n",
       "\n",
       "descriptors: \n",
       "\n",
@@ -209,9 +209,9 @@
      "output_type": "stream",
      "text": [
       "Theta based on optimization:\n",
-      "[0.54167376 0.8405888 ]\n",
+      "[0.94571213 0.32500549]\n",
       "Theta based on fit_regress:\n",
-      "[0.54166958 0.8405915 ]\n"
+      "[0.94571213 0.32500548]\n"
      ]
     }
    ],
@@ -245,10 +245,10 @@
     {
      "data": {
       "text/plain": [
-       "(<Figure size 144x144 with 1 Axes>,\n",
-       " array([[<AxesSubplot:>]], dtype=object),\n",
+       "(<Figure size 200x200 with 1 Axes>,\n",
+       " array([[<AxesSubplot: >]], dtype=object),\n",
        " defaultdict(dict,\n",
-       "             {<AxesSubplot:>: {'image': <matplotlib.image.AxesImage at 0x7fba190f22e0>}}))"
+       "             {<AxesSubplot: >: {'image': <matplotlib.image.AxesImage at 0x17f6279d0>}}))"
       ]
      },
      "execution_count": 6,
@@ -257,19 +257,19 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 144x144 with 1 Axes>"
+       "<Figure size 200x200 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 144x144 with 1 Axes>"
+       "<Figure size 200x200 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -301,9 +301,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.54171328 0.84056334]\n",
+      "[0.94571213 0.32500549]\n",
       "the used fitting function was:\n",
-      "<function fit_optimize at 0x7fba18bdb9d0>\n"
+      "<function fit_optimize at 0x153191000>\n"
      ]
     }
    ],
@@ -338,9 +338,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-0.04123096  0.99914964]\n",
-      "[-0.04123086  0.99914965]\n",
-      "[-0.04122998  0.99914968]\n"
+      "[ 0.996767   -0.08034645]\n",
+      "[ 0.996767   -0.08034645]\n",
+      "[ 0.996767   -0.08034647]\n"
      ]
     }
    ],
@@ -378,13 +378,13 @@
      "output_type": "stream",
      "text": [
       "The average correlation for the correlation parameters is:\n",
-      "0.20919570220266936\n",
+      "0.0890392385172882\n",
       "The average correlation for the cosine similarity parameters is:\n",
-      "0.1648685520549055\n",
+      "0.06085602619789239\n",
       "The average cosine similarity for the correlation parameters is:\n",
-      "0.9609090694876308\n",
+      "0.9649654254976477\n",
       "The average cosine similarity for the cosine similarity parameters is:\n",
-      "0.9712386973494105\n"
+      "0.9721299166013238\n"
      ]
     }
    ],
@@ -432,7 +432,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -446,7 +446,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.10.4"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "af6f0c1be22da210ce14b764d3d407b4e31df46360687c396ac7d1fbf0a9a76f"
+   }
   }
  },
  "nbformat": 4,

demos/demo_dissimilarities.ipynb

@@ -422,7 +422,8 @@ class Weighted_MDS(BaseEstimator):
 
     def __init__(self, n_components=2, *, metric=True, n_init=4,
                  max_iter=300, verbose=0, eps=1e-3, n_jobs=None,
-                 random_state=None, dissimilarity="euclidean"):
+                 random_state=None, dissimilarity="euclidean",
+                 normalized_stress='auto'):
         self.n_components = n_components
         self.dissimilarity = dissimilarity
         self.metric = metric
@@ -436,6 +437,8 @@ def __init__(self, n_components=2, *, metric=True, n_init=4,
         self.embedding_ = None
         self.stress_ = None
         self.n_iter_ = None
+        # not in use, declared for consistency with sklearn:
+        self.normalized_stress = normalized_stress
 
     @property
     def _pairwise(self):

demos/demo_flexible_models.ipynb

@@ -1,19 +1,17 @@
-#!/usr/bin/python
-# -*- coding: UTF-8 -*-
 """
-Definition of rsatoolbox's colors
+Classic colormap ported from matlab rsatoolbox
 
 @author: iancharest
 """
-
+from __future__ import annotations
 import numpy as np
 from skimage.color import rgb2hsv, hsv2rgb
 import matplotlib.pyplot as plt
 from matplotlib.colors import ListedColormap
 from scipy.interpolate import interp1d
 
 
-def color_scale(n_cols, anchor_cols=None, monitor=False):
+def color_scale(n_cols: int, anchor_cols=None, monitor=False):
     """ linearly interpolates between a set of given
     anchor colours to give n_cols and displays them
     if monitor is set
@@ -55,7 +53,7 @@ def color_scale(n_cols, anchor_cols=None, monitor=False):
     return cols
 
 
-def rdm_colormap(n_cols=256, monitor=None):
+def rdm_colormap_classic(n_cols: int = 256, monitor: bool = False):
     """this function provides a convenient colormap for visualizing
     dissimilarity matrices. it goes from blue to yellow and has grey for
     intermediate values.
@@ -73,8 +71,8 @@ def rdm_colormap(n_cols=256, monitor=None):
 
             import numpy as np
             import matplotlib.pyplot as plt
-            from rsatoolbox.vis.colors import rdm_colormap
-            plt.imshow(np.random.rand(10,10),cmap=rdm_colormap())
+            from rsatoolbox.vis.colors import rdm_colormap_classic
+            plt.imshow(np.random.rand(10,10),cmap=rdm_colormap_classic())
             plt.colorbar()
             plt.show()
 

demos/demo_rdm_comparison_scatterplot.ipynb

@@ -6,7 +6,7 @@
 
 import os
 import matplotlib.pyplot as plt
-from matplotlib import cm
+import matplotlib
 from matplotlib.offsetbox import OffsetImage, AnnotationBbox, DrawingArea
 import numpy as np
 import PIL
@@ -15,6 +15,10 @@
 from PIL import UnidentifiedImageError
 from rsatoolbox.rdm import RDMs
 from rsatoolbox.util.pooling import pool_rdm
+if hasattr(matplotlib.colormaps, 'get_cmap'):
+    mpl_get_cmap = matplotlib.colormaps.get_cmap
+else:
+    mpl_get_cmap = matplotlib.cm.get_cmap  # drop:py37
 
 
 class Icon:
@@ -243,7 +247,7 @@ def recompute_final_image(self):
             else:
                 im = self._image
             if self.cmap is not None:
-                im = cm.get_cmap(self.cmap)(im)
+                im = mpl_get_cmap(self.cmap)(im)
             im = PIL.Image.fromarray((im * 255).astype(np.uint8))
         else:  # we hope it is a PIL image or equivalent
             im = self._image

demos/demo_rdm_visualisation_92images.ipynb

@@ -6,26 +6,27 @@
 
 from __future__ import annotations
 import collections
-from typing import TYPE_CHECKING, Union, Tuple
+from typing import TYPE_CHECKING, Union, Tuple, Optional
 import pkg_resources
 import numpy as np
 import matplotlib
 import matplotlib.pyplot as plt
 import rsatoolbox.rdm
 from rsatoolbox import vis
-from rsatoolbox.vis.colors import rdm_colormap
+from rsatoolbox.vis.colors import rdm_colormap_classic
 if TYPE_CHECKING:
     import numpy.typing as npt
     import pathlib
     from matplotlib.axes._axes import Axes
+    from matplotlib.colors import Colormap
 
 RDM_STYLE = pkg_resources.resource_filename('rsatoolbox.vis', 'rdm.mplstyle')
 
 
 def show_rdm(
     rdm: rsatoolbox.rdm.RDMs,
     pattern_descriptor: str = None,
-    cmap: Union[str, matplotlib.colors.Colormap] = None,
+    cmap: Union[str, Colormap] = 'bone',
     rdm_descriptor: str = None,
     n_column: int = None,
     n_row: int = None,
@@ -48,8 +49,10 @@ def show_rdm(
         rdm (rsatoolbox.rdm.RDMs): RDMs object to be plotted.
         pattern_descriptor (str): Key into rdm.pattern_descriptors to use for axis
             labels.
-        cmap (Union[str, matplotlib.colors.Colormap]): colormap to be used (by
-            plt.imshow internally). By default we use rdm_colormap.
+        cmap (str or Colormap): Colormap to be used.
+            Either the name of a Matplotlib built-in colormap, a Matplotlib
+            Colormap compatible object, or 'classic' for the matlab toolbox
+            colormap. Defaults to 'bone'.
         rdm_descriptor (str): Key for rdm_descriptor to use as panel title, or
             str for direct labeling.
         n_column (int): Number of columns in subplot arrangement.
@@ -255,8 +258,8 @@ def _rdm_colorbar(
 
 def show_rdm_panel(
     rdm: rsatoolbox.rdm.RDMs,
-    ax: Axes = None,
-    cmap: Union[str, matplotlib.colors.Colormap] = None,
+    ax: Optional[Axes] = None,
+    cmap: Union[str, Colormap] = 'bone',
     nanmask: npt.ArrayLike = None,
     rdm_descriptor: str = None,
     gridlines: npt.ArrayLike = None,
@@ -268,8 +271,10 @@ def show_rdm_panel(
     Args:
         rdm (rsatoolbox.rdm.RDMs): RDMs object to be plotted (n_rdm must be 1).
         ax (matplotlib.axes._axes.Axes): Matplotlib axis handle. plt.gca() by default.
-        cmap (Union[str, matplotlib.colors.Colormap]): colormap to be used (by
-            plt.imshow internally). By default we use rdm_colormap.
+        cmap (str or Colormap): Colormap to be used.
+            Either the name of a Matplotlib built-in colormap, a Matplotlib
+            Colormap compatible object, or 'classic' for the matlab toolbox
+            colormap. Defaults to 'bone'.
         nanmask (npt.ArrayLike): boolean mask defining RDM elements to suppress
             (by default, the diagonals).
         rdm_descriptor (str): Key for rdm_descriptor to use as panel title, or
@@ -287,8 +292,8 @@ def show_rdm_panel(
         raise ValueError("expected single rdm - use show_rdm for multi-panel figures")
     if ax is None:
         ax = plt.gca()
-    if cmap is None:
-        cmap = rdm_colormap()
+    if cmap == 'classic':
+        cmap = rdm_colormap_classic()
     if nanmask is None:
         nanmask = np.eye(rdm.n_cond, dtype=bool)
     if not np.any(gridlines):

demos/demo_temporal.ipynb

@@ -102,7 +102,12 @@ def show_2d(
     """
     if method == 'MDS':
         MDS = sklearn.manifold.MDS if weights is None else Weighted_MDS
-        embedding = MDS(n_components=2, random_state=seed, dissimilarity='precomputed')
+        embedding = MDS(
+            n_components=2,
+            random_state=seed,
+            dissimilarity='precomputed',
+            # normalized_stress='auto' # drop:py37
+        )
     elif method == 't-SNE':
         embedding = sklearn.manifold.TSNE(n_components=2)
     elif method == 'Isomap':