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ahartikainen/Guessing_colourmaps-small.ipynb

Created April 29, 2019 18:37
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Guessing_colourmaps-small.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from scipy import signal"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nx, ny = 100, 100\n",
"rng = np.random.RandomState(seed=24)\n",
"z = rng.rand(nx, ny)\n",
"\n",
"sizex, sizey = 30, 30\n",
"x, y = np.mgrid[-sizex:sizex+1, -sizey:sizey+1]\n",
"g = np.exp(-0.333*(x**2/float(sizex)+y**2/float(sizey)))\n",
"f = g/g.sum()\n",
"\n",
"z = signal.convolve(z, f, mode='valid')\n",
"z = (z - z.min())/(z.max() - z.min())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cmap = 'viridis' # Perceptual\n",
"cmap = 'spectral' # Classic rainbow\n",
"cmap = 'seismic' # Classic diverging\n",
"cmap = 'Accent' # Needs coolinearity constraint\n",
"cmap = 'Dark2' # Needs coolinearity constraint\n",
"cmap = 'Paired' # Needs coolinearity constraint, ultimate test!\n",
"cmap = 'gist_ncar' # Works with new cool-point start location\n",
"cmap = 'Pastel1' # Amazing that it works for start point\n",
"cmap = 'Set2' # Difficult\n",
"\n",
"#cmap = 'Dark2'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x199e52e3be0>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(frameon=False)\n",
"fig.set_size_inches(5,5)\n",
"\n",
"ax = plt.Axes(fig, [0., 0., 1., 1.])\n",
"ax.set_axis_off()\n",
"fig.add_axes(ax)\n",
"\n",
"# Note: interpolation introduces new colours.\n",
"plt.imshow(z, cmap=cmap, aspect='auto')\n",
"#plt.imshow(z, cmap=cmap, interpolation='none')\n",
"fig.savefig('test.png')\n",
"plt.colorbar()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(40, 40)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"z.shape"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cmaps = [('Perceptually Uniform Sequential',\n",
" ['viridis', 'inferno', 'plasma', 'magma']),\n",
" ('Sequential', ['Blues', 'BuGn', 'BuPu',\n",
" 'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd',\n",
" 'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu',\n",
" 'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']),\n",
" ('Sequential (2)', ['afmhot', 'autumn', 'bone', 'cool',\n",
" 'copper', 'gist_heat', 'gray', 'hot',\n",
" 'pink', 'spring', 'summer', 'winter']),\n",
" ('Diverging', ['BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'PuOr',\n",
" 'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'Spectral',\n",
" 'seismic']),\n",
" ('Qualitative', ['Accent', 'Dark2', 'Paired', 'Pastel1',\n",
" 'Pastel2', 'Set1', 'Set2', 'Set3']),\n",
" ('Miscellaneous', ['gist_earth', 'terrain', 'ocean', 'gist_stern',\n",
" 'brg', 'CMRmap', 'cubehelix',\n",
" 'gnuplot', 'gnuplot2', 'gist_ncar',\n",
" 'nipy_spectral', 'jet', 'rainbow',\n",
" 'gist_rainbow', 'hsv', 'flag', 'prism'])]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read an image"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x199e63a2a90>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = plt.imread('test.png')\n",
"plt.imshow(img)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"img_ = img[..., :-1]"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_colors = set()\n",
"for c in img_.reshape(-1, 3):\n",
" unique_colors.add(tuple(c))\n",
"unique_colors = {c : i for i, c in enumerate(unique_colors)}\n",
"unique_colors_reverse = {val : key for key, val in unique_colors.items()}\n",
"len(unique_colors)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"from collections import defaultdict"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"differences = {\"top\" : defaultdict(lambda: defaultdict(int)),\n",
" \"bottom\" : defaultdict(lambda: defaultdict(int)),\n",
" \"left\" : defaultdict(lambda: defaultdict(int)),\n",
" \"right\" : defaultdict(lambda: defaultdict(int)),\n",
" }\n",
"rows, cols = img_.shape[:2]\n",
"for row in range(rows):\n",
" for col in range(cols):\n",
" cval = unique_colors[tuple(img_[row, col])]\n",
" if (row != 0):\n",
" val = unique_colors[tuple(img_[row-1, col])]\n",
" differences[\"top\"][f\"from_{cval}\"][f\"to_{val}\"] += 1\n",
" if (row != rows-1):\n",
" val = unique_colors[tuple(img_[row+1, col])]\n",
" differences[\"bottom\"][f\"from_{cval}\"][f\"to_{val}\"] += 1\n",
" if (col != 0):\n",
" val = unique_colors[tuple(img_[row, col-1])]\n",
" differences[\"left\"][f\"from_{cval}\"][f\"to_{val}\"] += 1\n",
" if (col != cols-1):\n",
" val = unique_colors[tuple(img_[row, col+1])]\n",
" differences[\"right\"][f\"from_{cval}\"][f\"to_{val}\"] += 1"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"MM = sum([pd.DataFrame(differences[key]).fillna(0).astype(int) for key in differences]).sort_index().sort_index(axis=1).T\n",
"for i in range(len(MM)):\n",
" MM.iloc[i, i] = 0"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
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" } #T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row5_col1 {\n",
" background-color: #eef5fc;\n",
" } #T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row5_col2 {\n",
" background-color: #eef5fc;\n",
" } #T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row5_col3 {\n",
" background-color: #5ba3d0;\n",
" } #T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row5_col4 {\n",
" background-color: #eef5fc;\n",
" } #T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row5_col5 {\n",
" background-color: #eef5fc;\n",
" } #T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row5_col6 {\n",
" background-color: #eef5fc;\n",
" } #T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row5_col7 {\n",
" background-color: #eef5fc;\n",
" } #T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row6_col0 {\n",
" background-color: #b3d3e8;\n",
" } #T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row6_col1 {\n",
" background-color: #eef5fc;\n",
" } #T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row6_col2 {\n",
" background-color: #eef5fc;\n",
" } #T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row6_col3 {\n",
" background-color: #eef5fc;\n",
" } #T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row6_col4 {\n",
" background-color: #eef5fc;\n",
" } #T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row6_col5 {\n",
" background-color: #eef5fc;\n",
" } #T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row6_col6 {\n",
" background-color: #eef5fc;\n",
" } #T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row6_col7 {\n",
" background-color: #5ba3d0;\n",
" } #T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row7_col0 {\n",
" background-color: #eef5fc;\n",
" } #T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row7_col1 {\n",
" background-color: #eef5fc;\n",
" } #T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row7_col2 {\n",
" background-color: #eef5fc;\n",
" } #T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row7_col3 {\n",
" background-color: #eef5fc;\n",
" } #T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row7_col4 {\n",
" background-color: #eef5fc;\n",
" } #T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row7_col5 {\n",
" background-color: #eef5fc;\n",
" } #T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row7_col6 {\n",
" background-color: #89bedc;\n",
" } #T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row7_col7 {\n",
" background-color: #eef5fc;\n",
" }</style> \n",
"<table id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93\" > \n",
"<thead> <tr> \n",
" <th class=\"blank level0\" ></th> \n",
" <th class=\"col_heading level0 col0\" >to_0</th> \n",
" <th class=\"col_heading level0 col1\" >to_1</th> \n",
" <th class=\"col_heading level0 col2\" >to_2</th> \n",
" <th class=\"col_heading level0 col3\" >to_3</th> \n",
" <th class=\"col_heading level0 col4\" >to_4</th> \n",
" <th class=\"col_heading level0 col5\" >to_5</th> \n",
" <th class=\"col_heading level0 col6\" >to_6</th> \n",
" <th class=\"col_heading level0 col7\" >to_7</th> \n",
" </tr></thead> \n",
"<tbody> <tr> \n",
" <th id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93level0_row0\" class=\"row_heading level0 row0\" >from_0</th> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row0_col0\" class=\"data row0 col0\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row0_col1\" class=\"data row0 col1\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row0_col2\" class=\"data row0 col2\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row0_col3\" class=\"data row0 col3\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row0_col4\" class=\"data row0 col4\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row0_col5\" class=\"data row0 col5\" >981</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row0_col6\" class=\"data row0 col6\" >513</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row0_col7\" class=\"data row0 col7\" >0</td> \n",
" </tr> <tr> \n",
" <th id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93level0_row1\" class=\"row_heading level0 row1\" >from_1</th> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row1_col0\" class=\"data row1 col0\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row1_col1\" class=\"data row1 col1\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row1_col2\" class=\"data row1 col2\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row1_col3\" class=\"data row1 col3\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row1_col4\" class=\"data row1 col4\" >135</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row1_col5\" class=\"data row1 col5\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row1_col6\" class=\"data row1 col6\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row1_col7\" class=\"data row1 col7\" >0</td> \n",
" </tr> <tr> \n",
" <th id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93level0_row2\" class=\"row_heading level0 row2\" >from_2</th> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row2_col0\" class=\"data row2 col0\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row2_col1\" class=\"data row2 col1\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row2_col2\" class=\"data row2 col2\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row2_col3\" class=\"data row2 col3\" >1071</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row2_col4\" class=\"data row2 col4\" >369</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row2_col5\" class=\"data row2 col5\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row2_col6\" class=\"data row2 col6\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row2_col7\" class=\"data row2 col7\" >0</td> \n",
" </tr> <tr> \n",
" <th id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93level0_row3\" class=\"row_heading level0 row3\" >from_3</th> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row3_col0\" class=\"data row3 col0\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row3_col1\" class=\"data row3 col1\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row3_col2\" class=\"data row3 col2\" >1071</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row3_col3\" class=\"data row3 col3\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row3_col4\" class=\"data row3 col4\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row3_col5\" class=\"data row3 col5\" >1206</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row3_col6\" class=\"data row3 col6\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row3_col7\" class=\"data row3 col7\" >0</td> \n",
" </tr> <tr> \n",
" <th id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93level0_row4\" class=\"row_heading level0 row4\" >from_4</th> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row4_col0\" class=\"data row4 col0\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row4_col1\" class=\"data row4 col1\" >135</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row4_col2\" class=\"data row4 col2\" >369</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row4_col3\" class=\"data row4 col3\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row4_col4\" class=\"data row4 col4\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row4_col5\" class=\"data row4 col5\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row4_col6\" class=\"data row4 col6\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row4_col7\" class=\"data row4 col7\" >0</td> \n",
" </tr> <tr> \n",
" <th id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93level0_row5\" class=\"row_heading level0 row5\" >from_5</th> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row5_col0\" class=\"data row5 col0\" >981</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row5_col1\" class=\"data row5 col1\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row5_col2\" class=\"data row5 col2\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row5_col3\" class=\"data row5 col3\" >1206</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row5_col4\" class=\"data row5 col4\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row5_col5\" class=\"data row5 col5\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row5_col6\" class=\"data row5 col6\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row5_col7\" class=\"data row5 col7\" >0</td> \n",
" </tr> <tr> \n",
" <th id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93level0_row6\" class=\"row_heading level0 row6\" >from_6</th> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row6_col0\" class=\"data row6 col0\" >513</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row6_col1\" class=\"data row6 col1\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row6_col2\" class=\"data row6 col2\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row6_col3\" class=\"data row6 col3\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row6_col4\" class=\"data row6 col4\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row6_col5\" class=\"data row6 col5\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row6_col6\" class=\"data row6 col6\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row6_col7\" class=\"data row6 col7\" >387</td> \n",
" </tr> <tr> \n",
" <th id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93level0_row7\" class=\"row_heading level0 row7\" >from_7</th> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row7_col0\" class=\"data row7 col0\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row7_col1\" class=\"data row7 col1\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row7_col2\" class=\"data row7 col2\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row7_col3\" class=\"data row7 col3\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row7_col4\" class=\"data row7 col4\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row7_col5\" class=\"data row7 col5\" >0</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row7_col6\" class=\"data row7 col6\" >387</td> \n",
" <td id=\"T_0f68025c_6aaa_11e9_ac35_48ba4e91ae93row7_col7\" class=\"data row7 col7\" >0</td> \n",
" </tr></tbody> \n",
"</table> "
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x199e7929a20>"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MM.style.background_gradient(cmap=\"Blues\", low=0.1, high=0.9)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"1, 4, 2, 3, 5, 0, 6, 7"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"MM_ = MM.copy()\n",
"cfrom = MM_.astype(bool).sum(1).idxmin()\n",
"order = [cfrom]\n",
"last_sum = 0\n",
"while MM_.sum().sum() and (last_sum != MM_.sum().sum()):\n",
" last_sum = MM_.sum().sum()\n",
" \n",
" cto = MM_.loc[cfrom, :].idxmax()\n",
" MM_.loc[cfrom, cto] = 0\n",
" cfrom, cto = f'from_{cto.split(\"_\")[-1]}', f'to_{cfrom.split(\"_\")[-1]}'\n",
" order.append(cfrom)\n",
" MM_.loc[cfrom, cto] = 0"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"my_custom_cmap = [unique_colors_reverse[int(key.split(\"_\")[-1])] for key in order]"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
"my_cmap = plt.matplotlib.colors.LinearSegmentedColormap.from_list(\"my_cmap\", my_custom_cmap, N=len(my_custom_cmap))"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x199e8194438>"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 648x108 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"a = np.array([[0,1]])\n",
"plt.figure(figsize=(9, 1.5))\n",
"img = plt.imshow(a, cmap=my_cmap)\n",
"plt.gca().set_visible(False)\n",
"cax = plt.axes([0.1, 0.2, 0.8, 0.6])\n",
"plt.colorbar(orientation=\"horizontal\", cax=cax)"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x199e820b898>"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 648x108 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"a = np.array([[0,1]])\n",
"plt.figure(figsize=(9, 1.5))\n",
"img = plt.imshow(a, cmap=cmap)\n",
"plt.gca().set_visible(False)\n",
"cax = plt.axes([0.1, 0.2, 0.8, 0.6])\n",
"plt.colorbar(orientation=\"horizontal\", cax=cax)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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