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JasperVanDenBoschJasperVanDenBosch
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Merge pull request #285 from rsagroup/colormap-consensus
Change default colormap
2 parents 08feaf4 + 990860f commit f85bfd4

14 files changed

+373
-376
lines changed

‎demos/demo_bootstrap.ipynb‎

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‎demos/demo_dissimilarities.ipynb‎

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‎demos/demo_flexible_models.ipynb‎

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Original file line numberDiff line numberDiff line change
@@ -72,27 +72,27 @@
7272
"output_type": "stream",
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"text": [
7474
"Predicting with theta = [1,0], should return the first rdm, which is:\n",
75-
"[[0.20529161 0.18219921 0.14234779 0.15184744 0.21434174 0.1876438\n",
76-
" 0.23245372 0.16816806 0.11574728 0.21842971]]\n",
75+
"[[0.17948311 0.13203485 0.13632945 0.18298594 0.15166647 0.16234362\n",
76+
" 0.14637612 0.12058838 0.15784657 0.12497874]]\n",
7777
"The output of the model is:\n",
78-
"[0.20529161 0.18219921 0.14234779 0.15184744 0.21434174 0.1876438\n",
79-
" 0.23245372 0.16816806 0.11574728 0.21842971]\n",
78+
"[0.17948311 0.13203485 0.13632945 0.18298594 0.15166647 0.16234362\n",
79+
" 0.14637612 0.12058838 0.15784657 0.12497874]\n",
8080
"Which is indeed identical\n",
8181
"\n",
8282
"Predicting with theta = [0,1], should return the second rdm, which is:\n",
83-
"[[0.21021824 0.09930457 0.16356345 0.20092431 0.18402425 0.19685312\n",
84-
" 0.13494642 0.13652705 0.1714637 0.12152749]]\n",
83+
"[[0.14265908 0.17005546 0.18177815 0.1564901 0.13836761 0.17725487\n",
84+
" 0.09220979 0.24478309 0.15514355 0.20351734]]\n",
8585
"The output of the model is:\n",
86-
"[0.21021824 0.09930457 0.16356345 0.20092431 0.18402425 0.19685312\n",
87-
" 0.13494642 0.13652705 0.1714637 0.12152749]\n",
86+
"[0.14265908 0.17005546 0.18177815 0.1564901 0.13836761 0.17725487\n",
87+
" 0.09220979 0.24478309 0.15514355 0.20351734]\n",
8888
"Which is indeed identical\n",
8989
"\n",
9090
"Predicting with theta = [1,1], should return the sum of the first two rdms, which is:\n",
91-
"[[0.41550985 0.28150378 0.30591124 0.35277176 0.39836599 0.38449692\n",
92-
" 0.36740013 0.3046951 0.28721099 0.3399572 ]]\n",
91+
"[[0.32214218 0.30209031 0.3181076 0.33947604 0.29003408 0.33959849\n",
92+
" 0.23858592 0.36537148 0.31299012 0.32849607]]\n",
9393
"The output of the model is:\n",
94-
"[0.41550985 0.28150378 0.30591124 0.35277176 0.39836599 0.38449692\n",
95-
" 0.36740013 0.3046951 0.28721099 0.3399572 ]\n",
94+
"[0.32214218 0.30209031 0.3181076 0.33947604 0.29003408 0.33959849\n",
95+
" 0.23858592 0.36537148 0.31299012 0.32849607]\n",
9696
"Which is indeed identical\n"
9797
]
9898
}
@@ -136,11 +136,11 @@
136136
"squared euclidean\n",
137137
"\n",
138138
"dissimilarities[0] = \n",
139-
"[[0. 0.20529161 0.18219921 0.14234779 0.15184744]\n",
140-
" [0.20529161 0. 0.21434174 0.1876438 0.23245372]\n",
141-
" [0.18219921 0.21434174 0. 0.16816806 0.11574728]\n",
142-
" [0.14234779 0.1876438 0.16816806 0. 0.21842971]\n",
143-
" [0.15184744 0.23245372 0.11574728 0.21842971 0. ]]\n",
139+
"[[0. 0.17948311 0.13203485 0.13632945 0.18298594]\n",
140+
" [0.17948311 0. 0.15166647 0.16234362 0.14637612]\n",
141+
" [0.13203485 0.15166647 0. 0.12058838 0.15784657]\n",
142+
" [0.13632945 0.16234362 0.12058838 0. 0.12497874]\n",
143+
" [0.18298594 0.14637612 0.15784657 0.12497874 0. ]]\n",
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"\n",
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"descriptors: \n",
146146
"\n",
@@ -161,11 +161,11 @@
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"squared euclidean\n",
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"\n",
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"dissimilarities[0] = \n",
164-
"[[0. 0.20529161 0.18219921 0.14234779 0.15184744]\n",
165-
" [0.20529161 0. 0.21434174 0.1876438 0.23245372]\n",
166-
" [0.18219921 0.21434174 0. 0.16816806 0.11574728]\n",
167-
" [0.14234779 0.1876438 0.16816806 0. 0.21842971]\n",
168-
" [0.15184744 0.23245372 0.11574728 0.21842971 0. ]]\n",
164+
"[[0. 0.17948311 0.13203485 0.13632945 0.18298594]\n",
165+
" [0.17948311 0. 0.15166647 0.16234362 0.14637612]\n",
166+
" [0.13203485 0.15166647 0. 0.12058838 0.15784657]\n",
167+
" [0.13632945 0.16234362 0.12058838 0. 0.12497874]\n",
168+
" [0.18298594 0.14637612 0.15784657 0.12497874 0. ]]\n",
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"\n",
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"descriptors: \n",
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"\n",
@@ -209,9 +209,9 @@
209209
"output_type": "stream",
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"text": [
211211
"Theta based on optimization:\n",
212-
"[0.54167376 0.8405888 ]\n",
212+
"[0.94571213 0.32500549]\n",
213213
"Theta based on fit_regress:\n",
214-
"[0.54166958 0.8405915 ]\n"
214+
"[0.94571213 0.32500548]\n"
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]
216216
}
217217
],
@@ -245,10 +245,10 @@
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{
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"data": {
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"text/plain": [
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"(<Figure size 144x144 with 1 Axes>,\n",
249-
" array([[<AxesSubplot:>]], dtype=object),\n",
248+
"(<Figure size 200x200 with 1 Axes>,\n",
249+
" array([[<AxesSubplot: >]], dtype=object),\n",
250250
" defaultdict(dict,\n",
251-
" {<AxesSubplot:>: {'image': <matplotlib.image.AxesImage at 0x7fba190f22e0>}}))"
251+
" {<AxesSubplot: >: {'image': <matplotlib.image.AxesImage at 0x17f6279d0>}}))"
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]
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},
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"execution_count": 6,
@@ -257,19 +257,19 @@
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},
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{
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"data": {
260-
"image/png": "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\n",
260+
"image/png": "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",
261261
"text/plain": [
262-
"<Figure size 144x144 with 1 Axes>"
262+
"<Figure size 200x200 with 1 Axes>"
263263
]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
270-
"image/png": "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\n",
270+
"image/png": "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",
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"text/plain": [
272-
"<Figure size 144x144 with 1 Axes>"
272+
"<Figure size 200x200 with 1 Axes>"
273273
]
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},
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"metadata": {},
@@ -301,9 +301,9 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0.54171328 0.84056334]\n",
304+
"[0.94571213 0.32500549]\n",
305305
"the used fitting function was:\n",
306-
"<function fit_optimize at 0x7fba18bdb9d0>\n"
306+
"<function fit_optimize at 0x153191000>\n"
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]
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}
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],
@@ -338,9 +338,9 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
341-
"[-0.04123096 0.99914964]\n",
342-
"[-0.04123086 0.99914965]\n",
343-
"[-0.04122998 0.99914968]\n"
341+
"[ 0.996767 -0.08034645]\n",
342+
"[ 0.996767 -0.08034645]\n",
343+
"[ 0.996767 -0.08034647]\n"
344344
]
345345
}
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],
@@ -378,13 +378,13 @@
378378
"output_type": "stream",
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"text": [
380380
"The average correlation for the correlation parameters is:\n",
381-
"0.20919570220266936\n",
381+
"0.0890392385172882\n",
382382
"The average correlation for the cosine similarity parameters is:\n",
383-
"0.1648685520549055\n",
383+
"0.06085602619789239\n",
384384
"The average cosine similarity for the correlation parameters is:\n",
385-
"0.9609090694876308\n",
385+
"0.9649654254976477\n",
386386
"The average cosine similarity for the cosine similarity parameters is:\n",
387-
"0.9712386973494105\n"
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"0.9721299166013238\n"
388388
]
389389
}
390390
],
@@ -432,7 +432,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "env",
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"language": "python",
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"name": "python3"
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},
@@ -446,7 +446,12 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8"
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"version": "3.10.4"
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},
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"vscode": {
452+
"interpreter": {
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"hash": "af6f0c1be22da210ce14b764d3d407b4e31df46360687c396ac7d1fbf0a9a76f"
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}
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}
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},
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"nbformat": 4,

‎demos/demo_rdm_comparison_scatterplot.ipynb‎

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‎demos/demo_rdm_visualisation_92images.ipynb‎

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‎demos/demo_temporal.ipynb‎

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‎demos/temp_rdm.png‎

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‎src/rsatoolbox/util/vis_utils.py‎

Lines changed: 4 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -422,7 +422,8 @@ class Weighted_MDS(BaseEstimator):
422422

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def __init__(self, n_components=2, *, metric=True, n_init=4,
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max_iter=300, verbose=0, eps=1e-3, n_jobs=None,
425-
random_state=None, dissimilarity="euclidean"):
425+
random_state=None, dissimilarity="euclidean",
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normalized_stress='auto'):
426427
self.n_components = n_components
427428
self.dissimilarity = dissimilarity
428429
self.metric = metric
@@ -436,6 +437,8 @@ def __init__(self, n_components=2, *, metric=True, n_init=4,
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self.embedding_ = None
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self.stress_ = None
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self.n_iter_ = None
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# not in use, declared for consistency with sklearn:
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self.normalized_stress = normalized_stress
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@property
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def _pairwise(self):

‎src/rsatoolbox/vis/colors.py‎

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Original file line numberDiff line numberDiff line change
@@ -1,19 +1,17 @@
1-
#!/usr/bin/python
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# -*- coding: UTF-8 -*-
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"""
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Definition of rsatoolbox's colors
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Classic colormap ported from matlab rsatoolbox
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@author: iancharest
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"""
8-
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from __future__ import annotations
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import numpy as np
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from skimage.color import rgb2hsv, hsv2rgb
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import matplotlib.pyplot as plt
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from matplotlib.colors import ListedColormap
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from scipy.interpolate import interp1d
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1513

16-
def color_scale(n_cols, anchor_cols=None, monitor=False):
14+
def color_scale(n_cols: int, anchor_cols=None, monitor=False):
1715
""" linearly interpolates between a set of given
1816
anchor colours to give n_cols and displays them
1917
if monitor is set
@@ -55,7 +53,7 @@ def color_scale(n_cols, anchor_cols=None, monitor=False):
5553
return cols
5654

5755

58-
def rdm_colormap(n_cols=256, monitor=None):
56+
def rdm_colormap_classic(n_cols: int = 256, monitor: bool = False):
5957
"""this function provides a convenient colormap for visualizing
6058
dissimilarity matrices. it goes from blue to yellow and has grey for
6159
intermediate values.
@@ -73,8 +71,8 @@ def rdm_colormap(n_cols=256, monitor=None):
7371
7472
import numpy as np
7573
import matplotlib.pyplot as plt
76-
from rsatoolbox.vis.colors import rdm_colormap
77-
plt.imshow(np.random.rand(10,10),cmap=rdm_colormap())
74+
from rsatoolbox.vis.colors import rdm_colormap_classic
75+
plt.imshow(np.random.rand(10,10),cmap=rdm_colormap_classic())
7876
plt.colorbar()
7977
plt.show()
8078

‎src/rsatoolbox/vis/icon.py‎

Lines changed: 6 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -6,7 +6,7 @@
66

77
import os
88
import matplotlib.pyplot as plt
9-
from matplotlib import cm
9+
import matplotlib
1010
from matplotlib.offsetbox import OffsetImage, AnnotationBbox, DrawingArea
1111
import numpy as np
1212
import PIL
@@ -15,6 +15,10 @@
1515
from PIL import UnidentifiedImageError
1616
from rsatoolbox.rdm import RDMs
1717
from rsatoolbox.util.pooling import pool_rdm
18+
if hasattr(matplotlib.colormaps, 'get_cmap'):
19+
mpl_get_cmap = matplotlib.colormaps.get_cmap
20+
else:
21+
mpl_get_cmap = matplotlib.cm.get_cmap # drop:py37
1822

1923

2024
class Icon:
@@ -243,7 +247,7 @@ def recompute_final_image(self):
243247
else:
244248
im = self._image
245249
if self.cmap is not None:
246-
im = cm.get_cmap(self.cmap)(im)
250+
im = mpl_get_cmap(self.cmap)(im)
247251
im = PIL.Image.fromarray((im * 255).astype(np.uint8))
248252
else: # we hope it is a PIL image or equivalent
249253
im = self._image

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