Made plots interactive
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Roger Labbe committed
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8 files changed
+72765
-721
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@@ -50,7 +50,7 @@
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" font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
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"}\n",
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" div.cell{\n",
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" width: 850px;\n",
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" //width: 950px;\n",
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" margin-left: 0% !important;\n",
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" margin-right: auto;\n",
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" }\n",
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@@ -64,7 +64,7 @@
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" //font-family: 'Chivo',verdana,arial,sans-serif;\n",
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" //font-family: 'Cardo',verdana,arial,sans-serif;\n",
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" //font-family: 'Arvo',verdana,arial,sans-serif;\n",
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" //font-family: 'Poppins',verdana,arial,sans-serif; \n",
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" //font-family: 'Poppins',verdana,arial,sans-serif;\n",
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" //font-family: 'Ubuntu',verdana,arial,sans-serif;\n",
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" //font-family: 'Fontin',verdana,arial,sans-serif;\n",
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" //font-family: 'Raleway',verdana,arial,sans-serif;\n",
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@@ -88,10 +88,10 @@
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" h1 {\n",
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" font-family: 'Open sans',verdana,arial,sans-serif;\n",
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"\t}\n",
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"\t\n",
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"\n",
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" div.input_area {\n",
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" background: #F6F6F9;\n",
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" border: 1px solid #586e75; \n",
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" border: 1px solid #586e75;\n",
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" }\n",
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"\n",
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" .text_cell_render h1 {\n",
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@@ -104,7 +104,7 @@
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" display: block;\n",
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" white-space: wrap;\n",
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" text-align: left;\n",
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" } \n",
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" }\n",
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" h2 {\n",
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" font-family: 'Open sans',verdana,arial,sans-serif;\n",
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" text-align: left;\n",
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@@ -120,7 +120,7 @@
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" display: block;\n",
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" white-space: wrap;\n",
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" text-align: left;\n",
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" } \n",
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" }\n",
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" h3 {\n",
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" font-family: 'Open sans',verdana,arial,sans-serif;\n",
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" }\n",
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@@ -187,27 +187,27 @@
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" margin: 2em;\n",
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" }\n",
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" ul li{\n",
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" padding-left: 0.5em; \n",
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" margin-bottom: 0.5em; \n",
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" margin-top: 0.5em; \n",
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" padding-left: 0.5em;\n",
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" margin-bottom: 0.5em;\n",
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" margin-top: 0.5em;\n",
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" }\n",
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" ul li li{\n",
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" padding-left: 0.2em; \n",
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" margin-bottom: 0.2em; \n",
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" margin-top: 0.2em; \n",
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" padding-left: 0.2em;\n",
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" margin-bottom: 0.2em;\n",
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" margin-top: 0.2em;\n",
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" }\n",
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" ol{\n",
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" margin: 2em;\n",
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" }\n",
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" ol li{\n",
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" padding-left: 0.5em; \n",
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" margin-bottom: 0.5em; \n",
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" margin-top: 0.5em; \n",
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" padding-left: 0.5em;\n",
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" margin-bottom: 0.5em;\n",
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" margin-top: 0.5em;\n",
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" }\n",
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" ul li{\n",
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" padding-left: 0.5em; \n",
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" margin-bottom: 0.5em; \n",
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" margin-top: 0.2em; \n",
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" padding-left: 0.5em;\n",
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" margin-bottom: 0.5em;\n",
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" margin-top: 0.2em;\n",
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" }\n",
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" a:link{\n",
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" color:#447adb;\n",
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@@ -226,10 +226,10 @@
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" color:#447adb;\n",
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" }\n",
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" .rendered_html :link {\n",
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" text-decoration: underline; \n",
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" text-decoration: underline;\n",
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" }\n",
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" .rendered_html :hover {\n",
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" text-decoration: none; \n",
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" text-decoration: none;\n",
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" }\n",
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" .rendered_html :visited {\n",
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" text-decoration: none;\n",
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@@ -242,7 +242,7 @@
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" }\n",
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" .warning{\n",
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" color: rgb( 240, 20, 20 )\n",
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" } \n",
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" }\n",
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" hr {\n",
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" color: #f3f3f3;\n",
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" background-color: #f3f3f3;\n",
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@@ -325,9 +325,9 @@
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"outputs": [
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{
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"data": {
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"image/png": 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truncated
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAERCAYAAACO3iZiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl0VPX9//FXkiEsQwIBE5EtLBIhqPzEBLGUFND0BKwo\nghqBRK1tFYpS4GiEL5iirSm2/fn9tSda/KYugAouKHTTrykoirQshVQiiyiSsCcEyMKShJnfHzQD\ngUAmyb0z9859Ps7xyGTuTN68c8m85nPfc2+Y1+v1CgAAwKbCg10AAABASxBmAACArRFmAACArRFm\nAACArRFmAACArRFmAACArZkeZgoKCpSRkVHvazk5OVq2bNklH3PkyBGNGDFCu3fvNrs8AABgcy4z\nnzwvL08rVqyQ2+2WJJWVlSkrK0t79uxRnz59GnxMbW2tsrOz1aZNGzNLAwAAIcLUlZn4+Hjl5ub6\nbp84cUKPPvqoxo4de8nHLFiwQPfdd5/i4uLMLA0AAIQIU8NMamqqIiIifLe7d++u66+//pLbL1++\nXJ07d9awYcPEiYkBAIA/LDUAvHz5cq1du1YZGRnavn27srKydOTIkWCXBQAALMzUmZk6/q6yLFmy\nxPfnjIwMPf300+rcubNfzx8WFtbs+gAACHUP5+TreFW13nxmdMi9ZgYkzDTWtKysLM2YMUNdunTx\n+zEXPn9JSUWz60N9sbFR9NMg9NJY9NNY9NM4Vu/liVO12l9apQHxMSotrQx2OY2KjY1q0vZhoXLV\nbCvvRHZj9X+UdkIvjUU/jUU/jWP1Xm7bc1S/fnOzRt/UU3ePvDrY5TSqqWHGUjMzAADAeHsOng1a\n8V2aFhLsgjADAECI+/ZguSSpF2EGAADY0bcHK9SutUuxHdsGuxRTEGYAAAhhJ07V6vDRk4rvEhVy\nn2KqQ5gBACCE7Tl0dl4mVA8xSYQZAABCWqgP/0qEGQAAQlqoD/9KhBkAAEJaqA//SoQZAABClhOG\nfyXCDAAAIcsJw78SYQYAgJDlhOFfiTADAEDIcsLwr0SYAQAgZDlh+FcizAAAEJKcMvwrEWYAAAhJ\nThn+lQgzAACEJKcM/0qEGQAAQpJThn8lwgwAACHJKcO/EmEGAICQ46ThX4kwAwBAyHHS8K8UgDBT\nUFCgjIyMel/LycnRsmXLLtq2trZWTzzxhCZNmqR77rlHq1atMrs8AABCjpOGfyXJZeaT5+XlacWK\nFXK73ZKksrIyZWVlac+ePerTp89F269cuVIxMTF67rnndPz4cd15550aNWqUmSUCABBynDT8K5m8\nMhMfH6/c3Fzf7RMnTujRRx/V2LFjG9x+9OjRmj59uiTJ4/HI5TI1awEAEJKcNPwrmRxmUlNTFRER\n4bvdvXt3XX/99Zfcvm3btmrXrp0qKys1ffp0zZgxw8zyAAAIOU4b/pVMPszUHAcOHNC0adM0efJk\njRkzxu/HxcY6YyktUOinceilseinseincazSy3/vKpEkJfbpbJmazBaQMOP1ev3arrS0VA899JCe\neuopDR06tEnfo6SkojmloQGxsVH00yD00lj001j00zhW6mXB9sOSpNjo1papqamaGsIC8tHsxpa5\nnnzySR08eFALFy5UeXm5XnjhBWVkZCgzM1PV1dWBKBEAgJDgtOFfSQrz+rtsYnF2TZ9WZKV3GHZH\nL41FP41FP41jpV4+uXCdKk/U6Pc/G27bmRlLrswAAADzOXH4VyLMAAAQMpx25t86hBkAAEKE0878\nW4cwAwBAiHDi8K9EmAEAIGQ47cy/dQgzAACEAKcO/0qEGQAAQoJTh38lwgwAACHBqcO/EmEGAICQ\n4NThX4kwAwBASHDq8K9EmAEAwPacPPwrEWYAALA9Jw//SoQZAABsz8nDvxJhBgAA23Py8K9EmAEA\nwPacPPwrEWYAALA1pw//SoQZAABszenDvxJhBgAAW3P68K9EmAEAwNacPvwrEWYAALA1pw//SgEI\nMwUFBcrIyKj3tZycHC1btuyibb1er7Kzs5Wenq7MzEwVFxebXR4AALbF8O9ZpoaZvLw8zZ07VzU1\nNZKksrIy/fjHP9bq1asb3D4/P1/V1dVaunSpZs2apZycHDPLAwDA1hj+PcvUMBMfH6/c3Fzf7RMn\nTujRRx/V2LFjG9x+06ZNGj58uCRp0KBB2rp1q5nlAQBgawz/nmVqmElNTVVERITvdvfu3XX99ddf\ncvvKykpFRZ37gbhcLnk8HjNLBADAthj+PcsV7ALO1759e1VVVfluezwehYf7l7diY539gzQa/TQO\nvTQW/TQW/TROMHpZXFIld9tWSuwX5+iZmYCEGa/X69d2gwcP1urVq5WWlqYtW7YoISHB7+9RUlLR\n3PJwgdjYKPppEHppLPppLPppnGD08sSpWh0ordKA+BiVllYG9HubranBMCAfzW4sLWZlZengwYNK\nTU1VZGSk0tPT9atf/UqzZ88ORHkAANgOw7/nhHn9XTaxON5dGId3a8ahl8ain8ain8YJRi8/+GeR\n3lq9S4/cMVBDBlwZ0O9tNkuuzAAAAGMx/HsOYQYAABvizL/nEGYAALAZzvxbH2EGAACbYfi3PsIM\nAAA2w5l/6yPMAABgMwz/1keYAQDAZhj+rY8wAwCAjTD8ezHCDAAANsLw78UIMwAA2AjDvxcjzAAA\nYCMM/16MMAMAgI0w/HsxwgwAADbB8G/DCDMAANgEw78NI8wAAGATDP82jDADAIBNMPzbMMIMAAA2\nwfBvwwgzAADYAMO/l0aYAQDABhj+vTTCDAAANsDw76WZHmYKCgqUkZEhSSoqKtLEiRM1efJkzZ8/\n/6Jta2trNWvWLKWnp2vy5MnavXu32eUBAGALDP9emqlhJi8vT3PnzlVNTY0kKScnRzNnztSSJUvk\n8XiUn59fb/tPPvlEHo9HS5cu1dSpU/X888+bWR4AALbB8O+lmRpm4uPjlZub67tdWFiopKQkSVJK\nSorWrVtXb/tevXrpzJkz8nq9qqioUKtWrcwsDwAAW2D49/JcZj55amqq9u3b57vt9Xp9f3a73aqo\nqKi3vdvt1t69e5WWlqZjx45p4cKFZpYHAIAtMPx7eaaGmQuFh59bCKqqqlJ0dHS9+1999VUNHz5c\nM2bM0KFDh5SZmak//elPioyMbPS5Y2P5ARuJfhqHXhqLfhqLfhrHzF5+uvWQJOm6hDh+Zg0IaJhJ\nTEzUhg0blJycrDVr1mjo0KH17u/QoYNcrrMlRUVFqba2Vh6Px6/nLimpaHwj+CU2Nop+GoReGot+\nGot+GsfsXhZ+XSJJ6tTO5YifWVMDW0A/mp2VlaXf/e53Sk9PV21trdLS0nxfP3jwoO6//34VFhZq\n0qRJevDBBzVr1iy1adMmkCUCAGA5DP9eXpj3/EEWG3NCUg0U3q0Zh14ai34ai34ax8xenjhVq2n/\nvUYD4mP0+H03mPI9rMbSKzMAAKBpGP5tHGEGAAAL48y/jSPMAABgYZz5t3GEGQAALIzh38YRZgAA\nsCjO/OsfwgwAABbF8K9/CDMAAFgUw7/+8fsMwF999ZWOHz9e7/pKycnJphQFAAAY/vWXX2Fm/vz5\nWr16tXr06OH7WlhYmBYtWmRaYQAAOB3Dv/7xK8ysXbtWH3zwAZcWAAAgQOqGfwfExzD82wi/ZmZ6\n9OihELnqAQAAtsDwr//8Wpnp0KGDbrvtNt1www2KjIz0fT0nJ8e0wgAAcDKGf/3nV5gZPny4hg8f\nbnYtAADgPxj+9Z9fh5nGjRungQMHqqqqSsePH1f//v01btw4s2sDAMCxGP71n19h5v3339fUqVO1\nd+9e7d+/X9OmTdM777xjdm0AADgSZ/5tGr8OM73yyit6++23FRMTI0l65JFHlJmZqQkTJphaHAAA\nTsTwb9P4tTLj8Xh8QUaSOnXqRFIEAMAkDP82jV8rM9dcc41++ctf+lZi3nnnHfXv39/UwgAAcCqG\nf5vGr5WZX/ziF2rVqpXmzJmj2bNny+VyKTs72+zaAABwJIZ/m8avlZk2bdroiSeeMLsWAAAcjzP/\nNt1lV2bqPn7dv39/DRgwwPdf3W1/FBQUKCMjQ5JUVFSkiRMnavLkyZo/f36D27/00ktKT0/X+PHj\n9e677zbl7wIAgO0x/Nt0l12Zee+99yRJ27dvv+i+6urqRp88Ly9PK1askNvtlnT2jMEzZ85UUlKS\nsrOzlZ+fr1tvvdW3/fr167V582YtXbpUJ06c0Msvv9ykvwwAAHbH8G/T+TUzc++999a77fF4NH78\n+EYfFx8fr9zcXN/twsJCJSUlSZJSUlK0bt26ett/9tlnSkhI0NSpUzVlyhSNHDnSn/IAAAgZDP82\n3WVXZjIzM7V+/XpJqvfpJZfLpVGjRjX65Kmpqdq3b5/v9vkXq3S73aqoqKi3/dGjR7V//34tXLhQ\nxcXFmjJlij744AP//iYAAIQAhn+b7rJhZtGiRZLOfppp7ty5Lf5m4eHnFoKqqqoUHR1d7/6OHTuq\nb9++crlc6t27t1q3bq2ysjJ16tSp0eeOjSXBGol+GodeGot+Got+GseIXladrNHhoyc1qN8ViouL\nbvwBkNRImFm9erVGjhypgQMH6v3337/o/jvvvLNJ3ywxMVEbNmxQcnKy1qxZo6FDh9a7/8Ybb9Ti\nxYv1wAMP6NChQzp16lS9k/VdTklJReMbwS+xsVH00yD00lj001j00zhG9XLbnqOSpK6d2jn6Z9PU\nYHjZMPPFF19o5MiRvkNNF2pqmMnKytK8efNUU1Ojvn37Ki0tzff1GTNmaMSIEdq4caMmTJggr9er\n7OxsPpYGAHAMhn+bJ8x7/iCLHyorK3XgwAH169fPrJqaxckJ1mi8WzMOvTQW/TQW/Line truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x73e8cf8>"
|
||||
"<matplotlib.figure.Figure at 0x2d9deb0e278>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -374,9 +374,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x73e8978>"
|
||||
"<matplotlib.figure.Figure at 0x2d9db2becc0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -406,9 +406,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x80088d0>"
|
||||
"<matplotlib.figure.Figure at 0x2d9e045fb70>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -537,9 +537,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8098a90>"
|
||||
"<matplotlib.figure.Figure at 0x2d9e04d54e0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -612,9 +612,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x803d550>"
|
||||
"<matplotlib.figure.Figure at 0x2d9e045d438>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -641,9 +641,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8025d68>"
|
||||
"<matplotlib.figure.Figure at 0x2d9e05e2390>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -680,9 +680,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8931f98>"
|
||||
"<matplotlib.figure.Figure at 0x2d9e03f3cf8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -719,9 +719,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAt4AAACeCAYAAAAFUw0aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMskRrzcgCcQAAAAASUVORK5CYII=\n",
|
||||
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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8a3ea20>"
|
||||
"<matplotlib.figure.Figure at 0x2d9e04714e0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -768,9 +768,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
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truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAiIAAAECCAYAAADD8LyBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8b113c8>"
|
||||
"<matplotlib.figure.Figure at 0x2d9e068f7f0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
+21591
-144
File diff suppressed because it is too large.
Load diff
@@ -40,8 +40,9 @@ def plot_track_and_residuals(t, xs, z_xs, res):
|
||||
plt.title('residuals')
|
||||
plt.show()
|
||||
|
||||
|
||||
def plot_markov_chain():
|
||||
plt.figure(figsize=(4,4), facecolor='w')
|
||||
fig = plt.figure(figsize=(4,4), facecolor='w')
|
||||
ax = plt.axes((0, 0, 1, 1),
|
||||
xticks=[], yticks=[], frameon=False)
|
||||
#ax.set_xlim(0, 10)
|
||||
@@ -98,6 +99,7 @@ def plot_markov_chain():
|
||||
|
||||
plt.axis('equal')
|
||||
plt.show()
|
||||
bp.end_interactive(fig)
|
||||
|
||||
|
||||
def turning_target(N=600, turn_start=400):
|
||||
|
||||
+85
-82
@@ -309,28 +309,31 @@ def test_pf2():
|
||||
plt.show()
|
||||
|
||||
|
||||
from book_plots import figsize, interactive_plot
|
||||
def plot_cumsum(a):
|
||||
|
||||
N = len(a)
|
||||
with figsize(y=2):
|
||||
with interactive_plot():
|
||||
N = len(a)
|
||||
|
||||
cmap = mpl.colors.ListedColormap([[0., .4, 1.],
|
||||
[0., .8, 1.],
|
||||
[1., .8, 0.],
|
||||
[1., .4, 0.]]*(int(N/4) + 1))
|
||||
cumsum = np.cumsum(np.asarray(a) / np.sum(a))
|
||||
cumsum = np.insert(cumsum, 0, 0)
|
||||
cmap = mpl.colors.ListedColormap([[0., .4, 1.],
|
||||
[0., .8, 1.],
|
||||
[1., .8, 0.],
|
||||
[1., .4, 0.]]*(int(N/4) + 1))
|
||||
cumsum = np.cumsum(np.asarray(a) / np.sum(a))
|
||||
cumsum = np.insert(cumsum, 0, 0)
|
||||
|
||||
fig = plt.figure(figsize=(6,3))
|
||||
ax = fig.add_axes([0.05, 0.475, 0.9, 0.15])
|
||||
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
|
||||
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
|
||||
norm=norm,
|
||||
drawedges=False,
|
||||
spacing='proportional',
|
||||
orientation='horizontal')
|
||||
if N > 10:
|
||||
bar.set_ticks([])
|
||||
plt.show()
|
||||
#fig = plt.figure(figsize=(6,3))
|
||||
fig=plt.gcf()
|
||||
ax = fig.add_axes([0.05, 0.475, 0.9, 0.15])
|
||||
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
|
||||
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
|
||||
norm=norm,
|
||||
drawedges=False,
|
||||
spacing='proportional',
|
||||
orientation='horizontal')
|
||||
if N > 10:
|
||||
bar.set_ticks([])
|
||||
|
||||
|
||||
def plot_stratified_resample(a):
|
||||
@@ -343,23 +346,23 @@ def plot_stratified_resample(a):
|
||||
cumsum = np.cumsum(np.asarray(a) / np.sum(a))
|
||||
cumsum = np.insert(cumsum, 0, 0)
|
||||
|
||||
fig = plt.figure(figsize=(6,3))
|
||||
ax = fig.add_axes([0.05, 0.475, 0.9, 0.15])
|
||||
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
|
||||
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
|
||||
norm=norm,
|
||||
drawedges=False,
|
||||
spacing='proportional',
|
||||
orientation='horizontal')
|
||||
xs = np.linspace(0., 1.-1./N, N)
|
||||
ax.vlines(xs, 0, 1, lw=2)
|
||||
with figsize(y=2):
|
||||
with interactive_plot():
|
||||
ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
|
||||
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
|
||||
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
|
||||
norm=norm,
|
||||
drawedges=False,
|
||||
spacing='proportional',
|
||||
orientation='horizontal')
|
||||
xs = np.linspace(0., 1.-1./N, N)
|
||||
ax.vlines(xs, 0, 1, lw=2)
|
||||
|
||||
# make N subdivisions, and chose a random position within each one
|
||||
b = (random(N) + range(N)) / N
|
||||
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
|
||||
bar.set_ticks([])
|
||||
plt.title('stratified resampling')
|
||||
plt.show()
|
||||
# make N subdivisions, and chose a random position within each one
|
||||
b = (random(N) + range(N)) / N
|
||||
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
|
||||
bar.set_ticks([])
|
||||
plt.title('stratified resampling')
|
||||
|
||||
|
||||
def plot_systematic_resample(a):
|
||||
@@ -372,23 +375,23 @@ def plot_systematic_resample(a):
|
||||
cumsum = np.cumsum(np.asarray(a) / np.sum(a))
|
||||
cumsum = np.insert(cumsum, 0, 0)
|
||||
|
||||
fig = plt.figure(figsize=(6,3))
|
||||
ax = fig.add_axes([0.05, 0.475, 0.9, 0.15])
|
||||
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
|
||||
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
|
||||
norm=norm,
|
||||
drawedges=False,
|
||||
spacing='proportional',
|
||||
orientation='horizontal')
|
||||
xs = np.linspace(0., 1.-1./N, N)
|
||||
ax.vlines(xs, 0, 1, lw=2)
|
||||
with figsize(y=2):
|
||||
with interactive_plot():
|
||||
ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
|
||||
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
|
||||
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
|
||||
norm=norm,
|
||||
drawedges=False,
|
||||
spacing='proportional',
|
||||
orientation='horizontal')
|
||||
xs = np.linspace(0., 1.-1./N, N)
|
||||
ax.vlines(xs, 0, 1, lw=2)
|
||||
|
||||
# make N subdivisions, and chose a random position within each one
|
||||
b = (random() + np.array(range(N))) / N
|
||||
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
|
||||
bar.set_ticks([])
|
||||
plt.title('systematic resampling')
|
||||
plt.show()
|
||||
# make N subdivisions, and chose a random position within each one
|
||||
b = (random() + np.array(range(N))) / N
|
||||
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
|
||||
bar.set_ticks([])
|
||||
plt.title('systematic resampling')
|
||||
|
||||
|
||||
def plot_multinomial_resample(a):
|
||||
@@ -401,21 +404,21 @@ def plot_multinomial_resample(a):
|
||||
cumsum = np.cumsum(np.asarray(a) / np.sum(a))
|
||||
cumsum = np.insert(cumsum, 0, 0)
|
||||
|
||||
fig = plt.figure(figsize=(6,3))
|
||||
ax = fig.add_axes([0.05, 0.475, 0.9, 0.15])
|
||||
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
|
||||
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
|
||||
norm=norm,
|
||||
drawedges=False,
|
||||
spacing='proportional',
|
||||
orientation='horizontal')
|
||||
with figsize(y=2):
|
||||
with interactive_plot():
|
||||
ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
|
||||
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
|
||||
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
|
||||
norm=norm,
|
||||
drawedges=False,
|
||||
spacing='proportional',
|
||||
orientation='horizontal')
|
||||
|
||||
# make N subdivisions, and chose a random position within each one
|
||||
b = random(N)
|
||||
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
|
||||
bar.set_ticks([])
|
||||
plt.title('multinomial resampling')
|
||||
plt.show()
|
||||
# make N subdivisions, and chose a random position within each one
|
||||
b = random(N)
|
||||
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
|
||||
bar.set_ticks([])
|
||||
plt.title('multinomial resampling')
|
||||
|
||||
|
||||
def plot_residual_resample(a):
|
||||
@@ -430,25 +433,25 @@ def plot_residual_resample(a):
|
||||
[1., .8, 0.],
|
||||
[1., .4, 0.]]*(int(N/4) + 1))
|
||||
|
||||
fig = plt.figure(figsize=(6,3))
|
||||
ax = fig.add_axes([0.05, 0.475, 0.9, 0.15])
|
||||
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
|
||||
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
|
||||
norm=norm,
|
||||
drawedges=False,
|
||||
spacing='proportional',
|
||||
orientation='horizontal')
|
||||
with figsize(y=2):
|
||||
with interactive_plot():
|
||||
ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
|
||||
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
|
||||
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
|
||||
norm=norm,
|
||||
drawedges=False,
|
||||
spacing='proportional',
|
||||
orientation='horizontal')
|
||||
|
||||
indexes = residual_resample(a_norm)
|
||||
bins = np.bincount(indexes)
|
||||
for i in range(1, N):
|
||||
n = bins[i-1] # number particles in this sample
|
||||
if n > 0:
|
||||
b = np.linspace(cumsum[i-1], cumsum[i], n+2)[1:-1]
|
||||
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
|
||||
bar.set_ticks([])
|
||||
plt.title('residual resampling')
|
||||
plt.show()
|
||||
indexes = residual_resample(a_norm)
|
||||
bins = np.bincount(indexes)
|
||||
for i in range(1, N):
|
||||
n = bins[i-1] # number particles in this sample
|
||||
if n > 0:
|
||||
b = np.linspace(cumsum[i-1], cumsum[i], n+2)[1:-1]
|
||||
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
|
||||
bar.set_ticks([])
|
||||
plt.title('residual resampling')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
@@ -93,7 +93,7 @@ def show_four_gps():
|
||||
|
||||
|
||||
def show_sigma_transform(with_text=False):
|
||||
fig = plt.figure()
|
||||
fig = plt.gcf()
|
||||
ax=fig.gca()
|
||||
|
||||
x = np.array([0, 5])
|
||||
|
||||
Reference in new issue
Block a user