diff --git a/source/introduction_jupyter.ipynb b/source/introduction_jupyter.ipynb
index 58860bc90d95100dad586d896c4f5f4ec5ab1779..d0f0c9ff75cc2e6b82e254c7e69568144fbdc081 100644
--- a/source/introduction_jupyter.ipynb
+++ b/source/introduction_jupyter.ipynb
@@ -11,7 +11,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -22,12 +22,12 @@
"import ipywidgets as widgets\n",
"from IPython.display import display, clear_output, display_html\n",
"\n",
- "from source.settings import *\n",
+ "from settings import *\n",
"\n",
- "from source.dataset_preanalysis import PreVis\n",
- "from source.dataset_preanalysis import PreVis\n",
- "from source.dataset_preanalysis import PreMis\n",
- "from source.dataset_datasplit import DataSplit\n",
+ "from dataset_preanalysis import PreVis\n",
+ "from dataset_preanalysis import PreVis\n",
+ "from dataset_preanalysis import PreMis\n",
+ "from dataset_datasplit import DataSplit\n",
"\n",
"from mapping_data import Data\n",
"from mapping_linear_regression import LinearRegression\n",
@@ -49,482 +49,11 @@
},
{
"cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>id</th>\n",
- " <th>country</th>\n",
- " <th>htap_region</th>\n",
- " <th>climatic_zone</th>\n",
- " <th>lon</th>\n",
- " <th>lat</th>\n",
- " <th>alt</th>\n",
- " <th>relative_alt</th>\n",
- " <th>type</th>\n",
- " <th>type_of_area</th>\n",
- " <th>water_25km</th>\n",
- " <th>evergreen_needleleaf_forest_25km</th>\n",
- " <th>evergreen_broadleaf_forest_25km</th>\n",
- " <th>deciduous_needleleaf_forest_25km</th>\n",
- " <th>deciduous_broadleaf_forest_25km</th>\n",
- " <th>mixed_forest_25km</th>\n",
- " <th>closed_shrublands_25km</th>\n",
- " <th>open_shrublands_25km</th>\n",
- " <th>woody_savannas_25km</th>\n",
- " <th>savannas_25km</th>\n",
- " <th>grasslands_25km</th>\n",
- " <th>permanent_wetlands_25km</th>\n",
- " <th>croplands_25km</th>\n",
- " <th>urban_and_built-up_25km</th>\n",
- " <th>cropland-natural_vegetation_mosaic_25km</th>\n",
- " <th>snow_and_ice_25km</th>\n",
- " <th>barren_or_sparsely_vegetated_25km</th>\n",
- " <th>wheat_production</th>\n",
- " <th>rice_production</th>\n",
- " <th>nox_emissions</th>\n",
- " <th>no2_column</th>\n",
- " <th>population_density</th>\n",
- " <th>max_population_density_5km</th>\n",
- " <th>max_population_density_25km</th>\n",
- " <th>nightlight_1km</th>\n",
- " <th>nightlight_5km</th>\n",
- " <th>max_nightlight_25km</th>\n",
- " <th>o3_average_values</th>\n",
- " <th>o3_daytime_avg</th>\n",
- " <th>o3_nighttime_avg</th>\n",
- " <th>o3_median</th>\n",
- " <th>o3_perc25</th>\n",
- " <th>o3_perc75</th>\n",
- " <th>o3_perc90</th>\n",
- " <th>o3_perc98</th>\n",
- " <th>o3_dma8eu</th>\n",
- " <th>o3_avgdma8epax</th>\n",
- " <th>o3_drmdmax1h</th>\n",
- " <th>o3_w90</th>\n",
- " <th>o3_aot40</th>\n",
- " <th>o3_nvgt070</th>\n",
- " <th>o3_nvgt100</th>\n",
- " <th>dataset</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>3336</td>\n",
- " <td>Germany</td>\n",
- " <td>EUR</td>\n",
- " <td>cool_moist</td>\n",
- " <td>8.308210</td>\n",
- " <td>54.924970</td>\n",
- " <td>12.0</td>\n",
- " <td>3</td>\n",
- " <td>background</td>\n",
- " <td>rural</td>\n",
- " <td>86.1</td>\n",
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- " <td>4.8</td>\n",
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- " <td>4.6</td>\n",
- " <td>0.0</td>\n",
- " <td>1.1</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.000</td>\n",
- " <td>0.0</td>\n",
- " <td>0.672115</td>\n",
- " <td>2.27</td>\n",
- " <td>953</td>\n",
- " <td>953</td>\n",
- " <td>1017</td>\n",
- " <td>46</td>\n",
- " <td>20.73</td>\n",
- " <td>56</td>\n",
- " <td>33.4050</td>\n",
- " <td>34.7121</td>\n",
- " <td>32.1032</td>\n",
- " <td>35.3825</td>\n",
- " <td>25.9166</td>\n",
- " <td>41.2871</td>\n",
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- " <td>10197.4742</td>\n",
- " <td>2.0</td>\n",
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- " <td>test</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>3338</td>\n",
- " <td>Germany</td>\n",
- " <td>EUR</td>\n",
- " <td>cool_moist</td>\n",
- " <td>12.725280</td>\n",
- " <td>54.436670</td>\n",
- " <td>1.0</td>\n",
- " <td>1</td>\n",
- " <td>background</td>\n",
- " <td>rural</td>\n",
- " <td>55.7</td>\n",
- " <td>1.2</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>8.7</td>\n",
- " <td>0.0</td>\n",
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- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>2.2</td>\n",
- " <td>2.3</td>\n",
- " <td>20.5</td>\n",
- " <td>0.0</td>\n",
- " <td>8.3</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>1.380</td>\n",
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- " <td>0.093612</td>\n",
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- " <td>51.3996</td>\n",
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- " <td>1.0</td>\n",
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- " <td>train</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>3339</td>\n",
- " <td>Germany</td>\n",
- " <td>EUR</td>\n",
- " <td>cool_moist</td>\n",
- " <td>6.093923</td>\n",
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- " <td>7.06</td>\n",
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- " <th>3</th>\n",
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- " <tr>\n",
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- " <td>Germany</td>\n",
- " <td>EUR</td>\n",
- " <td>cool_moist</td>\n",
- " <td>6.874554</td>\n",
- " <td>51.862000</td>\n",
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- " <td>0.0</td>\n",
- " <td>2.209770</td>\n",
- " <td>7.99</td>\n",
- " <td>3566</td>\n",
- " <td>3569</td>\n",
- " <td>12635</td>\n",
- " <td>48</td>\n",
- " <td>24.14</td>\n",
- " <td>58</td>\n",
- " <td>21.4072</td>\n",
- " <td>26.1913</td>\n",
- " <td>16.1860</td>\n",
- " <td>20.5503</td>\n",
- " <td>9.2702</td>\n",
- " <td>30.4239</td>\n",
- " <td>40.6623</td>\n",
- " <td>58.4082</td>\n",
- " <td>53.9564</td>\n",
- " <td>31.0051</td>\n",
- " <td>50.6809</td>\n",
- " <td>203.4584</td>\n",
- " <td>9045.4745</td>\n",
- " <td>6.8</td>\n",
- " <td>1.2</td>\n",
- " <td>train</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " id country htap_region climatic_zone lon lat alt \\\n",
- "0 3336 Germany EUR cool_moist 8.308210 54.924970 12.0 \n",
- "1 3338 Germany EUR cool_moist 12.725280 54.436670 1.0 \n",
- "2 3339 Germany EUR cool_moist 6.093923 50.754704 205.0 \n",
- "3 3340 Germany EUR cool_moist 8.548389 52.023169 102.0 \n",
- "4 3341 Germany EUR cool_moist 6.874554 51.862000 45.0 \n",
- "\n",
- " relative_alt type type_of_area water_25km \\\n",
- "0 3 background rural 86.1 \n",
- "1 1 background rural 55.7 \n",
- "2 66 background urban 0.0 \n",
- "3 29 background urban 0.0 \n",
- "4 8 background rural 0.0 \n",
- "\n",
- " evergreen_needleleaf_forest_25km evergreen_broadleaf_forest_25km \\\n",
- "0 0.0 0.0 \n",
- "1 1.2 0.0 \n",
- "2 1.9 0.0 \n",
- "3 0.0 0.0 \n",
- "4 0.0 0.0 \n",
- "\n",
- " deciduous_needleleaf_forest_25km deciduous_broadleaf_forest_25km \\\n",
- "0 0.0 0.0 \n",
- "1 0.0 0.0 \n",
- "2 0.0 0.0 \n",
- "3 0.0 0.0 \n",
- "4 0.0 0.0 \n",
- "\n",
- " mixed_forest_25km closed_shrublands_25km open_shrublands_25km \\\n",
- "0 0.0 0.0 0.0 \n",
- "1 8.7 0.0 0.0 \n",
- "2 32.8 0.0 0.0 \n",
- "3 11.4 0.0 0.0 \n",
- "4 18.5 0.0 0.0 \n",
- "\n",
- " woody_savannas_25km savannas_25km grasslands_25km \\\n",
- "0 0.0 0.0 4.8 \n",
- "1 0.0 0.0 2.2 \n",
- "2 0.0 0.0 3.5 \n",
- "3 0.0 0.0 0.0 \n",
- "4 0.0 0.0 0.0 \n",
- "\n",
- " permanent_wetlands_25km croplands_25km urban_and_built-up_25km \\\n",
- "0 1.8 4.6 0.0 \n",
- "1 2.3 20.5 0.0 \n",
- "2 0.0 15.9 16.5 \n",
- "3 0.0 38.6 16.4 \n",
- "4 0.0 15.2 9.8 \n",
- "\n",
- " cropland-natural_vegetation_mosaic_25km snow_and_ice_25km \\\n",
- "0 1.1 0.0 \n",
- "1 8.3 0.0 \n",
- "2 29.2 0.0 \n",
- "3 32.5 0.0 \n",
- "4 55.4 0.0 \n",
- "\n",
- " barren_or_sparsely_vegetated_25km wheat_production rice_production \\\n",
- "0 0.0 0.000 0.0 \n",
- "1 0.0 1.380 0.0 \n",
- "2 0.0 0.959 0.0 \n",
- "3 0.0 2.332 0.0 \n",
- "4 0.0 2.954 0.0 \n",
- "\n",
- " nox_emissions no2_column population_density max_population_density_5km \\\n",
- "0 0.672115 2.27 953 953 \n",
- "1 0.093612 2.31 349 386 \n",
- "2 4.941520 7.06 14514 20125 \n",
- "3 14.142200 5.23 16176 16191 \n",
- "4 2.209770 7.99 3566 3569 \n",
- "\n",
- " max_population_density_25km nightlight_1km nightlight_5km \\\n",
- "0 1017 46 20.73 \n",
- "1 6619 9 6.38 \n",
- "2 26839 48 46.87 \n",
- "3 16191 60 54.30 \n",
- "4 12635 48 24.14 \n",
- "\n",
- " max_nightlight_25km o3_average_values o3_daytime_avg o3_nighttime_avg \\\n",
- "0 56 33.4050 34.7121 32.1032 \n",
- "1 60 29.8555 32.2933 27.3245 \n",
- "2 62 23.8597 28.0062 19.3949 \n",
- "3 62 20.3253 23.9555 16.3471 \n",
- "4 58 21.4072 26.1913 16.1860 \n",
- "\n",
- " o3_median o3_perc25 o3_perc75 o3_perc90 o3_perc98 o3_dma8eu \\\n",
- "0 35.3825 25.9166 41.2871 46.4399 54.8468 53.5738 \n",
- "1 30.2799 21.9242 37.6381 44.0575 53.7778 51.3996 \n",
- "2 23.8515 13.9652 32.0123 41.1803 58.4009 54.9030 \n",
- "3 19.8611 8.9930 29.0232 38.1730 53.5109 50.1112 \n",
- "4 20.5503 9.2702 30.4239 40.6623 58.4082 53.9564 \n",
- "\n",
- " o3_avgdma8epax o3_drmdmax1h o3_w90 o3_aot40 o3_nvgt070 o3_nvgt100 \\\n",
- "0 38.8078 50.7704 86.1266 10197.4742 2.0 0.0 \n",
- "1 35.8313 48.3935 69.0987 7573.2222 1.0 0.0 \n",
- "2 32.6169 49.8276 154.1263 8655.4730 5.4 1.0 \n",
- "3 28.6179 46.2694 120.2575 6036.5851 2.6 0.0 \n",
- "4 31.0051 50.6809 203.4584 9045.4745 6.8 1.2 \n",
- "\n",
- " dataset \n",
- "0 test \n",
- "1 train \n",
- "2 train \n",
- "3 test \n",
- "4 train "
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
"source": [
"dataset = pd.read_csv(resources_dir + AQbench_dataset_file)\n",
"dataset.head()"
@@ -539,800 +68,11 @@
},
{
"cell_type": "code",
- "execution_count": 3,
- "metadata": {},
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- " <th>column_name</th>\n",
- " <th>description</th>\n",
- " <th>categorical_continuous</th>\n",
- " <th>input_target</th>\n",
- " <th>unit</th>\n",
- " <th>data_type</th>\n",
- " <th>fill_value</th>\n",
- " <th>preparation</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>id</td>\n",
- " <td>Station ID</td>\n",
- " <td>categorical</td>\n",
- " <td>index</td>\n",
- " <td>-</td>\n",
- " <td>int</td>\n",
- " <td>NaN</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>country</td>\n",
- " <td>Country</td>\n",
- " <td>categorical</td>\n",
- " <td>input</td>\n",
- " <td>-</td>\n",
- " <td>str</td>\n",
- " <td>NaN</td>\n",
- " <td>one-hot</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>htap_region</td>\n",
- " <td>HTAP region</td>\n",
- " <td>categorical</td>\n",
- " <td>input</td>\n",
- " <td>-</td>\n",
- " <td>str</td>\n",
- " <td>NaN</td>\n",
- " <td>one-hot</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>climatic_zone</td>\n",
- " <td>Climatic zone</td>\n",
- " <td>categorical</td>\n",
- " <td>input</td>\n",
- " <td>-</td>\n",
- " <td>str</td>\n",
- " <td>NaN</td>\n",
- " <td>one-hot</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>lon</td>\n",
- " <td>Longitude</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>deg</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>circular</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>lat</td>\n",
- " <td>Latitude</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>deg</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>alt</td>\n",
- " <td>Altitude</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>m</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>relative_alt</td>\n",
- " <td>Relative altitude</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>m</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>type</td>\n",
- " <td>Type</td>\n",
- " <td>categorical</td>\n",
- " <td>input</td>\n",
- " <td>-</td>\n",
- " <td>str</td>\n",
- " <td>NaN</td>\n",
- " <td>one-hot</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>type_of_area</td>\n",
- " <td>Type of area</td>\n",
- " <td>categorical</td>\n",
- " <td>input</td>\n",
- " <td>-</td>\n",
- " <td>str</td>\n",
- " <td>NaN</td>\n",
- " <td>one-hot</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>water_25km</td>\n",
- " <td>Water in 25km area</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>%</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>evergreen_needleleaf_forest_25km</td>\n",
- " <td>Evergreen needleleaf forest in 25km area</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>%</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>evergreen_broadleaf_forest_25km</td>\n",
- " <td>Evergreen broadleaf forest in 25km area</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>%</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13</th>\n",
- " <td>deciduous_needleleaf_forest_25km</td>\n",
- " <td>Deciduous needleleaf forest in 25km area</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>%</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>14</th>\n",
- " <td>deciduous_broadleaf_forest_25km</td>\n",
- " <td>Deciduous broadleaf forest in 25km area</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>%</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>15</th>\n",
- " <td>mixed_forest_25km</td>\n",
- " <td>Mixed forest in 25km area</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>%</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16</th>\n",
- " <td>closed_shrublands_25km</td>\n",
- " <td>Closed shrublands in 25km area</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>%</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>17</th>\n",
- " <td>open_shrublands_25km</td>\n",
- " <td>Open shrublands in 25km area</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>%</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>18</th>\n",
- " <td>woody_savannas_25km</td>\n",
- " <td>Woody savannas in 25km area</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>%</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>19</th>\n",
- " <td>savannas_25km</td>\n",
- " <td>Savannas in 25km area</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>%</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>20</th>\n",
- " <td>grasslands_25km</td>\n",
- " <td>Grasslands in 25km area</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>%</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>21</th>\n",
- " <td>permanent_wetlands_25km</td>\n",
- " <td>Permanent wetlands in 25km area</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>%</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>22</th>\n",
- " <td>croplands_25km</td>\n",
- " <td>Croplands in 25km area</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>%</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>23</th>\n",
- " <td>urban_and_built-up_25km</td>\n",
- " <td>Urban And Built-Up in 25km area</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>%</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>24</th>\n",
- " <td>cropland-natural_vegetation_mosaic_25km</td>\n",
- " <td>Cropland / Natural vegetation mosaic in 25km area</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>%</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25</th>\n",
- " <td>snow_and_ice_25km</td>\n",
- " <td>Snow and ice in 25km area</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>%</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>26</th>\n",
- " <td>barren_or_sparsely_vegetated_25km</td>\n",
- " <td>Barren or sparsely vegetated in 25km area</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>%</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>27</th>\n",
- " <td>wheat_production</td>\n",
- " <td>Wheat production</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>1000 tons</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>28</th>\n",
- " <td>rice_production</td>\n",
- " <td>Rice production</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>1000 tons</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29</th>\n",
- " <td>nox_emissions</td>\n",
- " <td>NOx emissions</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>g m-2 y-1</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>30</th>\n",
- " <td>no2_column</td>\n",
- " <td>NO2 column</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>10^5 molec cm-2</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>31</th>\n",
- " <td>population_density</td>\n",
- " <td>Population density</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>person km-2</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>32</th>\n",
- " <td>max_population_density_5km</td>\n",
- " <td>Max population density 5km</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>person km-2</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>33</th>\n",
- " <td>max_population_density_25km</td>\n",
- " <td>Max population density 25km</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>person km-2</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>34</th>\n",
- " <td>nightlight_1km</td>\n",
- " <td>Nightlight 1km</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>-</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>35</th>\n",
- " <td>nightlight_5km</td>\n",
- " <td>Nightlight 5km</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>-</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>36</th>\n",
- " <td>max_nightlight_25km</td>\n",
- " <td>Max nightlight 25km</td>\n",
- " <td>continuous</td>\n",
- " <td>input</td>\n",
- " <td>-</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>37</th>\n",
- " <td>o3_average_values</td>\n",
- " <td>Ozone average values</td>\n",
- " <td>continuous</td>\n",
- " <td>target</td>\n",
- " <td>ppb</td>\n",
- " <td>float</td>\n",
- " <td>NaN</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>38</th>\n",
- " <td>o3_daytime_avg</td>\n",
- " <td>Ozone daytime avg</td>\n",
- " <td>continuous</td>\n",
- " <td>target</td>\n",
- " <td>ppb</td>\n",
- " <td>float</td>\n",
- " <td>-999.0</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>39</th>\n",
- " <td>o3_nighttime_avg</td>\n",
- " <td>Ozone nighttime avg</td>\n",
- " <td>continuous</td>\n",
- " <td>target</td>\n",
- " <td>ppb</td>\n",
- " <td>float</td>\n",
- " <td>-999.0</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>40</th>\n",
- " <td>o3_median</td>\n",
- " <td>Ozone mediam</td>\n",
- " <td>continuous</td>\n",
- " <td>target</td>\n",
- " <td>ppb</td>\n",
- " <td>float</td>\n",
- " <td>-999.0</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>41</th>\n",
- " <td>o3_perc25</td>\n",
- " <td>Ozone 25% percentile</td>\n",
- " <td>continuous</td>\n",
- " <td>target</td>\n",
- " <td>ppb</td>\n",
- " <td>float</td>\n",
- " <td>-999.0</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>42</th>\n",
- " <td>o3_perc75</td>\n",
- " <td>Ozone 75% percentile</td>\n",
- " <td>continuous</td>\n",
- " <td>target</td>\n",
- " <td>ppb</td>\n",
- " <td>float</td>\n",
- " <td>-999.0</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>43</th>\n",
- " <td>o3_perc90</td>\n",
- " <td>Ozone 90% percentile</td>\n",
- " <td>continuous</td>\n",
- " <td>target</td>\n",
- " <td>ppb</td>\n",
- " <td>float</td>\n",
- " <td>-999.0</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>44</th>\n",
- " <td>o3_perc98</td>\n",
- " <td>Ozone 98% percentile</td>\n",
- " <td>continuous</td>\n",
- " <td>target</td>\n",
- " <td>ppb</td>\n",
- " <td>float</td>\n",
- " <td>-999.0</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>45</th>\n",
- " <td>o3_dma8eu</td>\n",
- " <td>Ozone dma8eu</td>\n",
- " <td>continuous</td>\n",
- " <td>target</td>\n",
- " <td>-</td>\n",
- " <td>float</td>\n",
- " <td>-999.0</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>46</th>\n",
- " <td>o3_avgdma8epax</td>\n",
- " <td>Ozone avgdma8epax</td>\n",
- " <td>continuous</td>\n",
- " <td>target</td>\n",
- " <td>-</td>\n",
- " <td>float</td>\n",
- " <td>-999.0</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>47</th>\n",
- " <td>o3_drmdmax1h</td>\n",
- " <td>Ozone drmdmax1h</td>\n",
- " <td>continuous</td>\n",
- " <td>target</td>\n",
- " <td>-</td>\n",
- " <td>float</td>\n",
- " <td>-999.0</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>48</th>\n",
- " <td>o3_w90</td>\n",
- " <td>Ozone w90</td>\n",
- " <td>continuous</td>\n",
- " <td>target</td>\n",
- " <td>-</td>\n",
- " <td>float</td>\n",
- " <td>-999.0</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49</th>\n",
- " <td>o3_aot40</td>\n",
- " <td>Ozone aot40</td>\n",
- " <td>continuous</td>\n",
- " <td>target</td>\n",
- " <td>-</td>\n",
- " <td>float</td>\n",
- " <td>-999.0</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>50</th>\n",
- " <td>o3_nvgt070</td>\n",
- " <td>Ozone nvgt070</td>\n",
- " <td>continuous</td>\n",
- " <td>target</td>\n",
- " <td>-</td>\n",
- " <td>float</td>\n",
- " <td>-999.0</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>51</th>\n",
- " <td>o3_nvgt100</td>\n",
- " <td>Ozone nvgt100</td>\n",
- " <td>continuous</td>\n",
- " <td>target</td>\n",
- " <td>-</td>\n",
- " <td>float</td>\n",
- " <td>-999.0</td>\n",
- " <td>scale</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>52</th>\n",
- " <td>dataset</td>\n",
- " <td>Data Set (train, val, test)</td>\n",
- " <td>categorical</td>\n",
- " <td>dataset</td>\n",
- " <td>-</td>\n",
- " <td>str</td>\n",
- " <td>NaN</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " column_name \\\n",
- "0 id \n",
- "1 country \n",
- "2 htap_region \n",
- "3 climatic_zone \n",
- "4 lon \n",
- "5 lat \n",
- "6 alt \n",
- "7 relative_alt \n",
- "8 type \n",
- "9 type_of_area \n",
- "10 water_25km \n",
- "11 evergreen_needleleaf_forest_25km \n",
- "12 evergreen_broadleaf_forest_25km \n",
- "13 deciduous_needleleaf_forest_25km \n",
- "14 deciduous_broadleaf_forest_25km \n",
- "15 mixed_forest_25km \n",
- "16 closed_shrublands_25km \n",
- "17 open_shrublands_25km \n",
- "18 woody_savannas_25km \n",
- "19 savannas_25km \n",
- "20 grasslands_25km \n",
- "21 permanent_wetlands_25km \n",
- "22 croplands_25km \n",
- "23 urban_and_built-up_25km \n",
- "24 cropland-natural_vegetation_mosaic_25km \n",
- "25 snow_and_ice_25km \n",
- "26 barren_or_sparsely_vegetated_25km \n",
- "27 wheat_production \n",
- "28 rice_production \n",
- "29 nox_emissions \n",
- "30 no2_column \n",
- "31 population_density \n",
- "32 max_population_density_5km \n",
- "33 max_population_density_25km \n",
- "34 nightlight_1km \n",
- "35 nightlight_5km \n",
- "36 max_nightlight_25km \n",
- "37 o3_average_values \n",
- "38 o3_daytime_avg \n",
- "39 o3_nighttime_avg \n",
- "40 o3_median \n",
- "41 o3_perc25 \n",
- "42 o3_perc75 \n",
- "43 o3_perc90 \n",
- "44 o3_perc98 \n",
- "45 o3_dma8eu \n",
- "46 o3_avgdma8epax \n",
- "47 o3_drmdmax1h \n",
- "48 o3_w90 \n",
- "49 o3_aot40 \n",
- "50 o3_nvgt070 \n",
- "51 o3_nvgt100 \n",
- "52 dataset \n",
- "\n",
- " description categorical_continuous \\\n",
- "0 Station ID categorical \n",
- "1 Country categorical \n",
- "2 HTAP region categorical \n",
- "3 Climatic zone categorical \n",
- "4 Longitude continuous \n",
- "5 Latitude continuous \n",
- "6 Altitude continuous \n",
- "7 Relative altitude continuous \n",
- "8 Type categorical \n",
- "9 Type of area categorical \n",
- "10 Water in 25km area continuous \n",
- "11 Evergreen needleleaf forest in 25km area continuous \n",
- "12 Evergreen broadleaf forest in 25km area continuous \n",
- "13 Deciduous needleleaf forest in 25km area continuous \n",
- "14 Deciduous broadleaf forest in 25km area continuous \n",
- "15 Mixed forest in 25km area continuous \n",
- "16 Closed shrublands in 25km area continuous \n",
- "17 Open shrublands in 25km area continuous \n",
- "18 Woody savannas in 25km area continuous \n",
- "19 Savannas in 25km area continuous \n",
- "20 Grasslands in 25km area continuous \n",
- "21 Permanent wetlands in 25km area continuous \n",
- "22 Croplands in 25km area continuous \n",
- "23 Urban And Built-Up in 25km area continuous \n",
- "24 Cropland / Natural vegetation mosaic in 25km area continuous \n",
- "25 Snow and ice in 25km area continuous \n",
- "26 Barren or sparsely vegetated in 25km area continuous \n",
- "27 Wheat production continuous \n",
- "28 Rice production continuous \n",
- "29 NOx emissions continuous \n",
- "30 NO2 column continuous \n",
- "31 Population density continuous \n",
- "32 Max population density 5km continuous \n",
- "33 Max population density 25km continuous \n",
- "34 Nightlight 1km continuous \n",
- "35 Nightlight 5km continuous \n",
- "36 Max nightlight 25km continuous \n",
- "37 Ozone average values continuous \n",
- "38 Ozone daytime avg continuous \n",
- "39 Ozone nighttime avg continuous \n",
- "40 Ozone mediam continuous \n",
- "41 Ozone 25% percentile continuous \n",
- "42 Ozone 75% percentile continuous \n",
- "43 Ozone 90% percentile continuous \n",
- "44 Ozone 98% percentile continuous \n",
- "45 Ozone dma8eu continuous \n",
- "46 Ozone avgdma8epax continuous \n",
- "47 Ozone drmdmax1h continuous \n",
- "48 Ozone w90 continuous \n",
- "49 Ozone aot40 continuous \n",
- "50 Ozone nvgt070 continuous \n",
- "51 Ozone nvgt100 continuous \n",
- "52 Data Set (train, val, test) categorical \n",
- "\n",
- " input_target unit data_type fill_value preparation \n",
- "0 index - int NaN None \n",
- "1 input - str NaN one-hot \n",
- "2 input - str NaN one-hot \n",
- "3 input - str NaN one-hot \n",
- "4 input deg float NaN circular \n",
- "5 input deg float NaN scale \n",
- "6 input m float NaN scale \n",
- "7 input m float NaN scale \n",
- "8 input - str NaN one-hot \n",
- "9 input - str NaN one-hot \n",
- "10 input % float NaN scale \n",
- "11 input % float NaN scale \n",
- "12 input % float NaN scale \n",
- "13 input % float NaN scale \n",
- "14 input % float NaN scale \n",
- "15 input % float NaN scale \n",
- "16 input % float NaN scale \n",
- "17 input % float NaN scale \n",
- "18 input % float NaN scale \n",
- "19 input % float NaN scale \n",
- "20 input % float NaN scale \n",
- "21 input % float NaN scale \n",
- "22 input % float NaN scale \n",
- "23 input % float NaN scale \n",
- "24 input % float NaN scale \n",
- "25 input % float NaN scale \n",
- "26 input % float NaN scale \n",
- "27 input 1000 tons float NaN scale \n",
- "28 input 1000 tons float NaN scale \n",
- "29 input g m-2 y-1 float NaN scale \n",
- "30 input 10^5 molec cm-2 float NaN scale \n",
- "31 input person km-2 float NaN scale \n",
- "32 input person km-2 float NaN scale \n",
- "33 input person km-2 float NaN scale \n",
- "34 input - float NaN scale \n",
- "35 input - float NaN scale \n",
- "36 input - float NaN scale \n",
- "37 target ppb float NaN scale \n",
- "38 target ppb float -999.0 scale \n",
- "39 target ppb float -999.0 scale \n",
- "40 target ppb float -999.0 scale \n",
- "41 target ppb float -999.0 scale \n",
- "42 target ppb float -999.0 scale \n",
- "43 target ppb float -999.0 scale \n",
- "44 target ppb float -999.0 scale \n",
- "45 target - float -999.0 scale \n",
- "46 target - float -999.0 scale \n",
- "47 target - float -999.0 scale \n",
- "48 target - float -999.0 scale \n",
- "49 target - float -999.0 scale \n",
- "50 target - float -999.0 scale \n",
- "51 target - float -999.0 scale \n",
- "52 dataset - str NaN None "
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
"source": [
"information = pd.read_csv(resources_dir + AQbench_variables_file)\n",
"information"
@@ -1347,34 +87,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "60313258c93f48f89de0505c4ec51cf1",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "interactive(children=(Dropdown(description='variable:', options=('country', 'htap_region', 'climatic_zone', 'l…"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "<function __main__.plot_previs(column_name)>"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
@@ -1398,34 +113,9 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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- "text/plain": [
- "<Figure size 1800x720 with 2 Axes>"
- ]
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\n",
- "text/plain": [
- "<Figure size 1440x864 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
@@ -1447,34 +137,9 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "699dbbe4bb324091b075e9a29517f43b",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "interactive(children=(Dropdown(description='target', options=('o3_average_values', 'o3_daytime_avg', 'o3_night…"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "<function __main__.print_data(target, scaling, scale_target)>"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"def print_data(target, scaling, scale_target):\n",
" data = Data(target=target, scaling=scaling, scale_target=scale_target)\n",
@@ -1499,1918 +164,9 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "application/javascript": [
- "/* Put everything inside the global mpl namespace */\n",
- "/* global mpl */\n",
- "window.mpl = {};\n",
- "\n",
- "mpl.get_websocket_type = function () {\n",
- " if (typeof WebSocket !== 'undefined') {\n",
- " return WebSocket;\n",
- " } else if (typeof MozWebSocket !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert(\n",
- " 'Your browser does not have WebSocket support. ' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.'\n",
- " );\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
- " this.id = figure_id;\n",
- "\n",
- " this.ws = websocket;\n",
- "\n",
- " this.supports_binary = this.ws.binaryType !== undefined;\n",
- "\n",
- " if (!this.supports_binary) {\n",
- " var warnings = document.getElementById('mpl-warnings');\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent =\n",
- " 'This browser does not support binary websocket messages. ' +\n",
- " 'Performance may be slow.';\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
- " this.canvas = undefined;\n",
- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = document.createElement('div');\n",
- " this.root.setAttribute('style', 'display: inline-block');\n",
- " this._root_extra_style(this.root);\n",
- "\n",
- " parent_element.appendChild(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message('supports_binary', { value: fig.supports_binary });\n",
- " fig.send_message('send_image_mode', {});\n",
- " if (fig.ratio !== 1) {\n",
- " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
- " }\n",
- " fig.send_message('refresh', {});\n",
- " };\n",
- "\n",
- " this.imageObj.onload = function () {\n",
- " if (fig.image_mode === 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
- "\n",
- " this.imageObj.onunload = function () {\n",
- " fig.ws.close();\n",
- " };\n",
- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
- " this.ondownload = ondownload;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._init_header = function () {\n",
- " var titlebar = document.createElement('div');\n",
- " titlebar.classList =\n",
- " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
- " var titletext = document.createElement('div');\n",
- " titletext.classList = 'ui-dialog-title';\n",
- " titletext.setAttribute(\n",
- " 'style',\n",
- " 'width: 100%; text-align: center; padding: 3px;'\n",
- " );\n",
- " titlebar.appendChild(titletext);\n",
- " this.root.appendChild(titlebar);\n",
- " this.header = titletext;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function () {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = (this.canvas_div = document.createElement('div'));\n",
- " canvas_div.setAttribute(\n",
- " 'style',\n",
- " 'border: 1px solid #ddd;' +\n",
- " 'box-sizing: content-box;' +\n",
- " 'clear: both;' +\n",
- " 'min-height: 1px;' +\n",
- " 'min-width: 1px;' +\n",
- " 'outline: 0;' +\n",
- " 'overflow: hidden;' +\n",
- " 'position: relative;' +\n",
- " 'resize: both;'\n",
- " );\n",
- "\n",
- " function on_keyboard_event_closure(name) {\n",
- " return function (event) {\n",
- " return fig.key_event(event, name);\n",
- " };\n",
- " }\n",
- "\n",
- " canvas_div.addEventListener(\n",
- " 'keydown',\n",
- " on_keyboard_event_closure('key_press')\n",
- " );\n",
- " canvas_div.addEventListener(\n",
- " 'keyup',\n",
- " on_keyboard_event_closure('key_release')\n",
- " );\n",
- "\n",
- " this._canvas_extra_style(canvas_div);\n",
- " this.root.appendChild(canvas_div);\n",
- "\n",
- " var canvas = (this.canvas = document.createElement('canvas'));\n",
- " canvas.classList.add('mpl-canvas');\n",
- " canvas.setAttribute('style', 'box-sizing: content-box;');\n",
- "\n",
- " this.context = canvas.getContext('2d');\n",
- "\n",
- " var backingStore =\n",
- " this.context.backingStorePixelRatio ||\n",
- " this.context.webkitBackingStorePixelRatio ||\n",
- " this.context.mozBackingStorePixelRatio ||\n",
- " this.context.msBackingStorePixelRatio ||\n",
- " this.context.oBackingStorePixelRatio ||\n",
- " this.context.backingStorePixelRatio ||\n",
- " 1;\n",
- "\n",
- " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
- " if (this.ratio !== 1) {\n",
- " fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
- " }\n",
- "\n",
- " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
- " 'canvas'\n",
- " ));\n",
- " rubberband_canvas.setAttribute(\n",
- " 'style',\n",
- " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
- " );\n",
- "\n",
- " var resizeObserver = new ResizeObserver(function (entries) {\n",
- " var nentries = entries.length;\n",
- " for (var i = 0; i < nentries; i++) {\n",
- " var entry = entries[i];\n",
- " var width, height;\n",
- " if (entry.contentBoxSize) {\n",
- " if (entry.contentBoxSize instanceof Array) {\n",
- " // Chrome 84 implements new version of spec.\n",
- " width = entry.contentBoxSize[0].inlineSize;\n",
- " height = entry.contentBoxSize[0].blockSize;\n",
- " } else {\n",
- " // Firefox implements old version of spec.\n",
- " width = entry.contentBoxSize.inlineSize;\n",
- " height = entry.contentBoxSize.blockSize;\n",
- " }\n",
- " } else {\n",
- " // Chrome <84 implements even older version of spec.\n",
- " width = entry.contentRect.width;\n",
- " height = entry.contentRect.height;\n",
- " }\n",
- "\n",
- " // Keep the size of the canvas and rubber band canvas in sync with\n",
- " // the canvas container.\n",
- " if (entry.devicePixelContentBoxSize) {\n",
- " // Chrome 84 implements new version of spec.\n",
- " canvas.setAttribute(\n",
- " 'width',\n",
- " entry.devicePixelContentBoxSize[0].inlineSize\n",
- " );\n",
- " canvas.setAttribute(\n",
- " 'height',\n",
- " entry.devicePixelContentBoxSize[0].blockSize\n",
- " );\n",
- " } else {\n",
- " canvas.setAttribute('width', width * fig.ratio);\n",
- " canvas.setAttribute('height', height * fig.ratio);\n",
- " }\n",
- " canvas.setAttribute(\n",
- " 'style',\n",
- " 'width: ' + width + 'px; height: ' + height + 'px;'\n",
- " );\n",
- "\n",
- " rubberband_canvas.setAttribute('width', width);\n",
- " rubberband_canvas.setAttribute('height', height);\n",
- "\n",
- " // And update the size in Python. We ignore the initial 0/0 size\n",
- " // that occurs as the element is placed into the DOM, which should\n",
- " // otherwise not happen due to the minimum size styling.\n",
- " if (width != 0 && height != 0) {\n",
- " fig.request_resize(width, height);\n",
- " }\n",
- " }\n",
- " });\n",
- " resizeObserver.observe(canvas_div);\n",
- "\n",
- " function on_mouse_event_closure(name) {\n",
- " return function (event) {\n",
- " return fig.mouse_event(event, name);\n",
- " };\n",
- " }\n",
- "\n",
- " rubberband_canvas.addEventListener(\n",
- " 'mousedown',\n",
- " on_mouse_event_closure('button_press')\n",
- " );\n",
- " rubberband_canvas.addEventListener(\n",
- " 'mouseup',\n",
- " on_mouse_event_closure('button_release')\n",
- " );\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband_canvas.addEventListener(\n",
- " 'mousemove',\n",
- " on_mouse_event_closure('motion_notify')\n",
- " );\n",
- "\n",
- " rubberband_canvas.addEventListener(\n",
- " 'mouseenter',\n",
- " on_mouse_event_closure('figure_enter')\n",
- " );\n",
- " rubberband_canvas.addEventListener(\n",
- " 'mouseleave',\n",
- " on_mouse_event_closure('figure_leave')\n",
- " );\n",
- "\n",
- " canvas_div.addEventListener('wheel', function (event) {\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " on_mouse_event_closure('scroll')(event);\n",
- " });\n",
- "\n",
- " canvas_div.appendChild(canvas);\n",
- " canvas_div.appendChild(rubberband_canvas);\n",
- "\n",
- " this.rubberband_context = rubberband_canvas.getContext('2d');\n",
- " this.rubberband_context.strokeStyle = '#000000';\n",
- "\n",
- " this._resize_canvas = function (width, height, forward) {\n",
- " if (forward) {\n",
- " canvas_div.style.width = width + 'px';\n",
- " canvas_div.style.height = height + 'px';\n",
- " }\n",
- " };\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
- " event.preventDefault();\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus() {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function () {\n",
- " var fig = this;\n",
- "\n",
- " var toolbar = document.createElement('div');\n",
- " toolbar.classList = 'mpl-toolbar';\n",
- " this.root.appendChild(toolbar);\n",
- "\n",
- " function on_click_closure(name) {\n",
- " return function (_event) {\n",
- " return fig.toolbar_button_onclick(name);\n",
- " };\n",
- " }\n",
- "\n",
- " function on_mouseover_closure(tooltip) {\n",
- " return function (event) {\n",
- " if (!event.currentTarget.disabled) {\n",
- " return fig.toolbar_button_onmouseover(tooltip);\n",
- " }\n",
- " };\n",
- " }\n",
- "\n",
- " fig.buttons = {};\n",
- " var buttonGroup = document.createElement('div');\n",
- " buttonGroup.classList = 'mpl-button-group';\n",
- " for (var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " /* Instead of a spacer, we start a new button group. */\n",
- " if (buttonGroup.hasChildNodes()) {\n",
- " toolbar.appendChild(buttonGroup);\n",
- " }\n",
- " buttonGroup = document.createElement('div');\n",
- " buttonGroup.classList = 'mpl-button-group';\n",
- " continue;\n",
- " }\n",
- "\n",
- " var button = (fig.buttons[name] = document.createElement('button'));\n",
- " button.classList = 'mpl-widget';\n",
- " button.setAttribute('role', 'button');\n",
- " button.setAttribute('aria-disabled', 'false');\n",
- " button.addEventListener('click', on_click_closure(method_name));\n",
- " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
- "\n",
- " var icon_img = document.createElement('img');\n",
- " icon_img.src = '_images/' + image + '.png';\n",
- " icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
- " icon_img.alt = tooltip;\n",
- " button.appendChild(icon_img);\n",
- "\n",
- " buttonGroup.appendChild(button);\n",
- " }\n",
- "\n",
- " if (buttonGroup.hasChildNodes()) {\n",
- " toolbar.appendChild(buttonGroup);\n",
- " }\n",
- "\n",
- " var fmt_picker = document.createElement('select');\n",
- " fmt_picker.classList = 'mpl-widget';\n",
- " toolbar.appendChild(fmt_picker);\n",
- " this.format_dropdown = fmt_picker;\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = document.createElement('option');\n",
- " option.selected = fmt === mpl.default_extension;\n",
- " option.innerHTML = fmt;\n",
- " fmt_picker.appendChild(option);\n",
- " }\n",
- "\n",
- " var status_bar = document.createElement('span');\n",
- " status_bar.classList = 'mpl-message';\n",
- " toolbar.appendChild(status_bar);\n",
- " this.message = status_bar;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.send_message = function (type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function () {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1], msg['forward']);\n",
- " fig.send_message('refresh', {});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
- " var x0 = msg['x0'] / fig.ratio;\n",
- " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
- " var x1 = msg['x1'] / fig.ratio;\n",
- " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0,\n",
- " 0,\n",
- " fig.canvas.width / fig.ratio,\n",
- " fig.canvas.height / fig.ratio\n",
- " );\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch (cursor) {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
- " for (var key in msg) {\n",
- " if (!(key in fig.buttons)) {\n",
- " continue;\n",
- " }\n",
- " fig.buttons[key].disabled = !msg[key];\n",
- " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
- " if (msg['mode'] === 'PAN') {\n",
- " fig.buttons['Pan'].classList.add('active');\n",
- " fig.buttons['Zoom'].classList.remove('active');\n",
- " } else if (msg['mode'] === 'ZOOM') {\n",
- " fig.buttons['Pan'].classList.remove('active');\n",
- " fig.buttons['Zoom'].classList.add('active');\n",
- " } else {\n",
- " fig.buttons['Pan'].classList.remove('active');\n",
- " fig.buttons['Zoom'].classList.remove('active');\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function () {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message('ack', {});\n",
- "};\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = 'image/png';\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src\n",
- " );\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data\n",
- " );\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " } else if (\n",
- " typeof evt.data === 'string' &&\n",
- " evt.data.slice(0, 21) === 'data:image/png;base64'\n",
- " ) {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig['handle_' + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\n",
- " \"No handler for the '\" + msg_type + \"' message type: \",\n",
- " msg\n",
- " );\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\n",
- " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
- " e,\n",
- " e.stack,\n",
- " msg\n",
- " );\n",
- " }\n",
- " }\n",
- " };\n",
- "};\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function (e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e) {\n",
- " e = window.event;\n",
- " }\n",
- " if (e.target) {\n",
- " targ = e.target;\n",
- " } else if (e.srcElement) {\n",
- " targ = e.srcElement;\n",
- " }\n",
- " if (targ.nodeType === 3) {\n",
- " // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- " }\n",
- "\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " var boundingRect = targ.getBoundingClientRect();\n",
- " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
- " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
- "\n",
- " return { x: x, y: y };\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys(original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object') {\n",
- " obj[key] = original[key];\n",
- " }\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function (event, name) {\n",
- " var canvas_pos = mpl.findpos(event);\n",
- "\n",
- " if (name === 'button_press') {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x * this.ratio;\n",
- " var y = canvas_pos.y * this.ratio;\n",
- "\n",
- " this.send_message(name, {\n",
- " x: x,\n",
- " y: y,\n",
- " button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event),\n",
- " });\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.key_event = function (event, name) {\n",
- " // Prevent repeat events\n",
- " if (name === 'key_press') {\n",
- " if (event.which === this._key) {\n",
- " return;\n",
- " } else {\n",
- " this._key = event.which;\n",
- " }\n",
- " }\n",
- " if (name === 'key_release') {\n",
- " this._key = null;\n",
- " }\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which !== 17) {\n",
- " value += 'ctrl+';\n",
- " }\n",
- " if (event.altKey && event.which !== 18) {\n",
- " value += 'alt+';\n",
- " }\n",
- " if (event.shiftKey && event.which !== 16) {\n",
- " value += 'shift+';\n",
- " }\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
- " return false;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
- " if (name === 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message('toolbar_button', { name: name });\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";/* global mpl */\n",
- "\n",
- "var comm_websocket_adapter = function (comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function () {\n",
- " comm.close();\n",
- " };\n",
- " ws.send = function (m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function (msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data']);\n",
- " });\n",
- " return ws;\n",
- "};\n",
- "\n",
- "mpl.mpl_figure_comm = function (comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = document.getElementById(id);\n",
- " var ws_proxy = comm_websocket_adapter(comm);\n",
- "\n",
- " function ondownload(figure, _format) {\n",
- " window.open(figure.canvas.toDataURL());\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element;\n",
- " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
- " if (!fig.cell_info) {\n",
- " console.error('Failed to find cell for figure', id, fig);\n",
- " return;\n",
- " }\n",
- " fig.cell_info[0].output_area.element.one(\n",
- " 'cleared',\n",
- " { fig: fig },\n",
- " fig._remove_fig_handler\n",
- " );\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
- " var width = fig.canvas.width / fig.ratio;\n",
- " fig.cell_info[0].output_area.element.off(\n",
- " 'cleared',\n",
- " fig._remove_fig_handler\n",
- " );\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable();\n",
- " fig.parent_element.innerHTML =\n",
- " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
- " fig.close_ws(fig, msg);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var width = this.canvas.width / this.ratio;\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] =\n",
- " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function () {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message('ack', {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () {\n",
- " fig.push_to_output();\n",
- " }, 1000);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function () {\n",
- " var fig = this;\n",
- "\n",
- " var toolbar = document.createElement('div');\n",
- " toolbar.classList = 'btn-toolbar';\n",
- " this.root.appendChild(toolbar);\n",
- "\n",
- " function on_click_closure(name) {\n",
- " return function (_event) {\n",
- " return fig.toolbar_button_onclick(name);\n",
- " };\n",
- " }\n",
- "\n",
- " function on_mouseover_closure(tooltip) {\n",
- " return function (event) {\n",
- " if (!event.currentTarget.disabled) {\n",
- " return fig.toolbar_button_onmouseover(tooltip);\n",
- " }\n",
- " };\n",
- " }\n",
- "\n",
- " fig.buttons = {};\n",
- " var buttonGroup = document.createElement('div');\n",
- " buttonGroup.classList = 'btn-group';\n",
- " var button;\n",
- " for (var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " /* Instead of a spacer, we start a new button group. */\n",
- " if (buttonGroup.hasChildNodes()) {\n",
- " toolbar.appendChild(buttonGroup);\n",
- " }\n",
- " buttonGroup = document.createElement('div');\n",
- " buttonGroup.classList = 'btn-group';\n",
- " continue;\n",
- " }\n",
- "\n",
- " button = fig.buttons[name] = document.createElement('button');\n",
- " button.classList = 'btn btn-default';\n",
- " button.href = '#';\n",
- " button.title = name;\n",
- " button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
- " button.addEventListener('click', on_click_closure(method_name));\n",
- " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
- " buttonGroup.appendChild(button);\n",
- " }\n",
- "\n",
- " if (buttonGroup.hasChildNodes()) {\n",
- " toolbar.appendChild(buttonGroup);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = document.createElement('span');\n",
- " status_bar.classList = 'mpl-message pull-right';\n",
- " toolbar.appendChild(status_bar);\n",
- " this.message = status_bar;\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = document.createElement('div');\n",
- " buttongrp.classList = 'btn-group inline pull-right';\n",
- " button = document.createElement('button');\n",
- " button.classList = 'btn btn-mini btn-primary';\n",
- " button.href = '#';\n",
- " button.title = 'Stop Interaction';\n",
- " button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
- " button.addEventListener('click', function (_evt) {\n",
- " fig.handle_close(fig, {});\n",
- " });\n",
- " button.addEventListener(\n",
- " 'mouseover',\n",
- " on_mouseover_closure('Stop Interaction')\n",
- " );\n",
- " buttongrp.appendChild(button);\n",
- " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
- " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
- " var fig = event.data.fig;\n",
- " fig.close_ws(fig, {});\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function (el) {\n",
- " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
- " // this is important to make the div 'focusable\n",
- " el.setAttribute('tabindex', 0);\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " } else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager) {\n",
- " manager = IPython.keyboard_manager;\n",
- " }\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which === 13) {\n",
- " this.canvas_div.blur();\n",
- " // select the cell after this one\n",
- " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
- " IPython.notebook.select(index + 1);\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
- " fig.ondownload(fig, null);\n",
- "};\n",
- "\n",
- "mpl.find_output_cell = function (html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i = 0; i < ncells; i++) {\n",
- " var cell = cells[i];\n",
- " if (cell.cell_type === 'code') {\n",
- " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
- " var data = cell.output_area.outputs[j];\n",
- " if (data.data) {\n",
- " // IPython >= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] === html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "};\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel !== null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target(\n",
- " 'matplotlib',\n",
- " mpl.mpl_figure_comm\n",
- " );\n",
- "}\n"
- ],
- "text/plain": [
- "<IPython.core.display.Javascript object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAbAAAAEgCAYAAADVKCZpAAAB+UlEQVR4nO3BMQEAAADCoPVP7WkJoAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA4AaZiQAB1KMUfAAAAABJRU5ErkJggg==\" width=\"432\">"
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- ]
- },
- "metadata": {},
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- },
- {
- "data": {
- "application/javascript": [
- "/* Put everything inside the global mpl namespace */\n",
- "/* global mpl */\n",
- "window.mpl = {};\n",
- "\n",
- "mpl.get_websocket_type = function () {\n",
- " if (typeof WebSocket !== 'undefined') {\n",
- " return WebSocket;\n",
- " } else if (typeof MozWebSocket !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert(\n",
- " 'Your browser does not have WebSocket support. ' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.'\n",
- " );\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
- " this.id = figure_id;\n",
- "\n",
- " this.ws = websocket;\n",
- "\n",
- " this.supports_binary = this.ws.binaryType !== undefined;\n",
- "\n",
- " if (!this.supports_binary) {\n",
- " var warnings = document.getElementById('mpl-warnings');\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent =\n",
- " 'This browser does not support binary websocket messages. ' +\n",
- " 'Performance may be slow.';\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
- " this.canvas = undefined;\n",
- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = document.createElement('div');\n",
- " this.root.setAttribute('style', 'display: inline-block');\n",
- " this._root_extra_style(this.root);\n",
- "\n",
- " parent_element.appendChild(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message('supports_binary', { value: fig.supports_binary });\n",
- " fig.send_message('send_image_mode', {});\n",
- " if (fig.ratio !== 1) {\n",
- " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
- " }\n",
- " fig.send_message('refresh', {});\n",
- " };\n",
- "\n",
- " this.imageObj.onload = function () {\n",
- " if (fig.image_mode === 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
- "\n",
- " this.imageObj.onunload = function () {\n",
- " fig.ws.close();\n",
- " };\n",
- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
- " this.ondownload = ondownload;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._init_header = function () {\n",
- " var titlebar = document.createElement('div');\n",
- " titlebar.classList =\n",
- " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
- " var titletext = document.createElement('div');\n",
- " titletext.classList = 'ui-dialog-title';\n",
- " titletext.setAttribute(\n",
- " 'style',\n",
- " 'width: 100%; text-align: center; padding: 3px;'\n",
- " );\n",
- " titlebar.appendChild(titletext);\n",
- " this.root.appendChild(titlebar);\n",
- " this.header = titletext;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function () {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = (this.canvas_div = document.createElement('div'));\n",
- " canvas_div.setAttribute(\n",
- " 'style',\n",
- " 'border: 1px solid #ddd;' +\n",
- " 'box-sizing: content-box;' +\n",
- " 'clear: both;' +\n",
- " 'min-height: 1px;' +\n",
- " 'min-width: 1px;' +\n",
- " 'outline: 0;' +\n",
- " 'overflow: hidden;' +\n",
- " 'position: relative;' +\n",
- " 'resize: both;'\n",
- " );\n",
- "\n",
- " function on_keyboard_event_closure(name) {\n",
- " return function (event) {\n",
- " return fig.key_event(event, name);\n",
- " };\n",
- " }\n",
- "\n",
- " canvas_div.addEventListener(\n",
- " 'keydown',\n",
- " on_keyboard_event_closure('key_press')\n",
- " );\n",
- " canvas_div.addEventListener(\n",
- " 'keyup',\n",
- " on_keyboard_event_closure('key_release')\n",
- " );\n",
- "\n",
- " this._canvas_extra_style(canvas_div);\n",
- " this.root.appendChild(canvas_div);\n",
- "\n",
- " var canvas = (this.canvas = document.createElement('canvas'));\n",
- " canvas.classList.add('mpl-canvas');\n",
- " canvas.setAttribute('style', 'box-sizing: content-box;');\n",
- "\n",
- " this.context = canvas.getContext('2d');\n",
- "\n",
- " var backingStore =\n",
- " this.context.backingStorePixelRatio ||\n",
- " this.context.webkitBackingStorePixelRatio ||\n",
- " this.context.mozBackingStorePixelRatio ||\n",
- " this.context.msBackingStorePixelRatio ||\n",
- " this.context.oBackingStorePixelRatio ||\n",
- " this.context.backingStorePixelRatio ||\n",
- " 1;\n",
- "\n",
- " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
- " if (this.ratio !== 1) {\n",
- " fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
- " }\n",
- "\n",
- " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
- " 'canvas'\n",
- " ));\n",
- " rubberband_canvas.setAttribute(\n",
- " 'style',\n",
- " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
- " );\n",
- "\n",
- " var resizeObserver = new ResizeObserver(function (entries) {\n",
- " var nentries = entries.length;\n",
- " for (var i = 0; i < nentries; i++) {\n",
- " var entry = entries[i];\n",
- " var width, height;\n",
- " if (entry.contentBoxSize) {\n",
- " if (entry.contentBoxSize instanceof Array) {\n",
- " // Chrome 84 implements new version of spec.\n",
- " width = entry.contentBoxSize[0].inlineSize;\n",
- " height = entry.contentBoxSize[0].blockSize;\n",
- " } else {\n",
- " // Firefox implements old version of spec.\n",
- " width = entry.contentBoxSize.inlineSize;\n",
- " height = entry.contentBoxSize.blockSize;\n",
- " }\n",
- " } else {\n",
- " // Chrome <84 implements even older version of spec.\n",
- " width = entry.contentRect.width;\n",
- " height = entry.contentRect.height;\n",
- " }\n",
- "\n",
- " // Keep the size of the canvas and rubber band canvas in sync with\n",
- " // the canvas container.\n",
- " if (entry.devicePixelContentBoxSize) {\n",
- " // Chrome 84 implements new version of spec.\n",
- " canvas.setAttribute(\n",
- " 'width',\n",
- " entry.devicePixelContentBoxSize[0].inlineSize\n",
- " );\n",
- " canvas.setAttribute(\n",
- " 'height',\n",
- " entry.devicePixelContentBoxSize[0].blockSize\n",
- " );\n",
- " } else {\n",
- " canvas.setAttribute('width', width * fig.ratio);\n",
- " canvas.setAttribute('height', height * fig.ratio);\n",
- " }\n",
- " canvas.setAttribute(\n",
- " 'style',\n",
- " 'width: ' + width + 'px; height: ' + height + 'px;'\n",
- " );\n",
- "\n",
- " rubberband_canvas.setAttribute('width', width);\n",
- " rubberband_canvas.setAttribute('height', height);\n",
- "\n",
- " // And update the size in Python. We ignore the initial 0/0 size\n",
- " // that occurs as the element is placed into the DOM, which should\n",
- " // otherwise not happen due to the minimum size styling.\n",
- " if (width != 0 && height != 0) {\n",
- " fig.request_resize(width, height);\n",
- " }\n",
- " }\n",
- " });\n",
- " resizeObserver.observe(canvas_div);\n",
- "\n",
- " function on_mouse_event_closure(name) {\n",
- " return function (event) {\n",
- " return fig.mouse_event(event, name);\n",
- " };\n",
- " }\n",
- "\n",
- " rubberband_canvas.addEventListener(\n",
- " 'mousedown',\n",
- " on_mouse_event_closure('button_press')\n",
- " );\n",
- " rubberband_canvas.addEventListener(\n",
- " 'mouseup',\n",
- " on_mouse_event_closure('button_release')\n",
- " );\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband_canvas.addEventListener(\n",
- " 'mousemove',\n",
- " on_mouse_event_closure('motion_notify')\n",
- " );\n",
- "\n",
- " rubberband_canvas.addEventListener(\n",
- " 'mouseenter',\n",
- " on_mouse_event_closure('figure_enter')\n",
- " );\n",
- " rubberband_canvas.addEventListener(\n",
- " 'mouseleave',\n",
- " on_mouse_event_closure('figure_leave')\n",
- " );\n",
- "\n",
- " canvas_div.addEventListener('wheel', function (event) {\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " on_mouse_event_closure('scroll')(event);\n",
- " });\n",
- "\n",
- " canvas_div.appendChild(canvas);\n",
- " canvas_div.appendChild(rubberband_canvas);\n",
- "\n",
- " this.rubberband_context = rubberband_canvas.getContext('2d');\n",
- " this.rubberband_context.strokeStyle = '#000000';\n",
- "\n",
- " this._resize_canvas = function (width, height, forward) {\n",
- " if (forward) {\n",
- " canvas_div.style.width = width + 'px';\n",
- " canvas_div.style.height = height + 'px';\n",
- " }\n",
- " };\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
- " event.preventDefault();\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus() {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function () {\n",
- " var fig = this;\n",
- "\n",
- " var toolbar = document.createElement('div');\n",
- " toolbar.classList = 'mpl-toolbar';\n",
- " this.root.appendChild(toolbar);\n",
- "\n",
- " function on_click_closure(name) {\n",
- " return function (_event) {\n",
- " return fig.toolbar_button_onclick(name);\n",
- " };\n",
- " }\n",
- "\n",
- " function on_mouseover_closure(tooltip) {\n",
- " return function (event) {\n",
- " if (!event.currentTarget.disabled) {\n",
- " return fig.toolbar_button_onmouseover(tooltip);\n",
- " }\n",
- " };\n",
- " }\n",
- "\n",
- " fig.buttons = {};\n",
- " var buttonGroup = document.createElement('div');\n",
- " buttonGroup.classList = 'mpl-button-group';\n",
- " for (var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " /* Instead of a spacer, we start a new button group. */\n",
- " if (buttonGroup.hasChildNodes()) {\n",
- " toolbar.appendChild(buttonGroup);\n",
- " }\n",
- " buttonGroup = document.createElement('div');\n",
- " buttonGroup.classList = 'mpl-button-group';\n",
- " continue;\n",
- " }\n",
- "\n",
- " var button = (fig.buttons[name] = document.createElement('button'));\n",
- " button.classList = 'mpl-widget';\n",
- " button.setAttribute('role', 'button');\n",
- " button.setAttribute('aria-disabled', 'false');\n",
- " button.addEventListener('click', on_click_closure(method_name));\n",
- " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
- "\n",
- " var icon_img = document.createElement('img');\n",
- " icon_img.src = '_images/' + image + '.png';\n",
- " icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
- " icon_img.alt = tooltip;\n",
- " button.appendChild(icon_img);\n",
- "\n",
- " buttonGroup.appendChild(button);\n",
- " }\n",
- "\n",
- " if (buttonGroup.hasChildNodes()) {\n",
- " toolbar.appendChild(buttonGroup);\n",
- " }\n",
- "\n",
- " var fmt_picker = document.createElement('select');\n",
- " fmt_picker.classList = 'mpl-widget';\n",
- " toolbar.appendChild(fmt_picker);\n",
- " this.format_dropdown = fmt_picker;\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = document.createElement('option');\n",
- " option.selected = fmt === mpl.default_extension;\n",
- " option.innerHTML = fmt;\n",
- " fmt_picker.appendChild(option);\n",
- " }\n",
- "\n",
- " var status_bar = document.createElement('span');\n",
- " status_bar.classList = 'mpl-message';\n",
- " toolbar.appendChild(status_bar);\n",
- " this.message = status_bar;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.send_message = function (type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function () {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1], msg['forward']);\n",
- " fig.send_message('refresh', {});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
- " var x0 = msg['x0'] / fig.ratio;\n",
- " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
- " var x1 = msg['x1'] / fig.ratio;\n",
- " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0,\n",
- " 0,\n",
- " fig.canvas.width / fig.ratio,\n",
- " fig.canvas.height / fig.ratio\n",
- " );\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch (cursor) {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
- " for (var key in msg) {\n",
- " if (!(key in fig.buttons)) {\n",
- " continue;\n",
- " }\n",
- " fig.buttons[key].disabled = !msg[key];\n",
- " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
- " if (msg['mode'] === 'PAN') {\n",
- " fig.buttons['Pan'].classList.add('active');\n",
- " fig.buttons['Zoom'].classList.remove('active');\n",
- " } else if (msg['mode'] === 'ZOOM') {\n",
- " fig.buttons['Pan'].classList.remove('active');\n",
- " fig.buttons['Zoom'].classList.add('active');\n",
- " } else {\n",
- " fig.buttons['Pan'].classList.remove('active');\n",
- " fig.buttons['Zoom'].classList.remove('active');\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function () {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message('ack', {});\n",
- "};\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = 'image/png';\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src\n",
- " );\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data\n",
- " );\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " } else if (\n",
- " typeof evt.data === 'string' &&\n",
- " evt.data.slice(0, 21) === 'data:image/png;base64'\n",
- " ) {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig['handle_' + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\n",
- " \"No handler for the '\" + msg_type + \"' message type: \",\n",
- " msg\n",
- " );\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\n",
- " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
- " e,\n",
- " e.stack,\n",
- " msg\n",
- " );\n",
- " }\n",
- " }\n",
- " };\n",
- "};\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function (e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e) {\n",
- " e = window.event;\n",
- " }\n",
- " if (e.target) {\n",
- " targ = e.target;\n",
- " } else if (e.srcElement) {\n",
- " targ = e.srcElement;\n",
- " }\n",
- " if (targ.nodeType === 3) {\n",
- " // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- " }\n",
- "\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " var boundingRect = targ.getBoundingClientRect();\n",
- " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
- " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
- "\n",
- " return { x: x, y: y };\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys(original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object') {\n",
- " obj[key] = original[key];\n",
- " }\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function (event, name) {\n",
- " var canvas_pos = mpl.findpos(event);\n",
- "\n",
- " if (name === 'button_press') {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x * this.ratio;\n",
- " var y = canvas_pos.y * this.ratio;\n",
- "\n",
- " this.send_message(name, {\n",
- " x: x,\n",
- " y: y,\n",
- " button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event),\n",
- " });\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.key_event = function (event, name) {\n",
- " // Prevent repeat events\n",
- " if (name === 'key_press') {\n",
- " if (event.which === this._key) {\n",
- " return;\n",
- " } else {\n",
- " this._key = event.which;\n",
- " }\n",
- " }\n",
- " if (name === 'key_release') {\n",
- " this._key = null;\n",
- " }\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which !== 17) {\n",
- " value += 'ctrl+';\n",
- " }\n",
- " if (event.altKey && event.which !== 18) {\n",
- " value += 'alt+';\n",
- " }\n",
- " if (event.shiftKey && event.which !== 16) {\n",
- " value += 'shift+';\n",
- " }\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
- " return false;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
- " if (name === 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message('toolbar_button', { name: name });\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";/* global mpl */\n",
- "\n",
- "var comm_websocket_adapter = function (comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function () {\n",
- " comm.close();\n",
- " };\n",
- " ws.send = function (m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function (msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data']);\n",
- " });\n",
- " return ws;\n",
- "};\n",
- "\n",
- "mpl.mpl_figure_comm = function (comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = document.getElementById(id);\n",
- " var ws_proxy = comm_websocket_adapter(comm);\n",
- "\n",
- " function ondownload(figure, _format) {\n",
- " window.open(figure.canvas.toDataURL());\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element;\n",
- " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
- " if (!fig.cell_info) {\n",
- " console.error('Failed to find cell for figure', id, fig);\n",
- " return;\n",
- " }\n",
- " fig.cell_info[0].output_area.element.one(\n",
- " 'cleared',\n",
- " { fig: fig },\n",
- " fig._remove_fig_handler\n",
- " );\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
- " var width = fig.canvas.width / fig.ratio;\n",
- " fig.cell_info[0].output_area.element.off(\n",
- " 'cleared',\n",
- " fig._remove_fig_handler\n",
- " );\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable();\n",
- " fig.parent_element.innerHTML =\n",
- " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
- " fig.close_ws(fig, msg);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var width = this.canvas.width / this.ratio;\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] =\n",
- " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function () {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message('ack', {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () {\n",
- " fig.push_to_output();\n",
- " }, 1000);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function () {\n",
- " var fig = this;\n",
- "\n",
- " var toolbar = document.createElement('div');\n",
- " toolbar.classList = 'btn-toolbar';\n",
- " this.root.appendChild(toolbar);\n",
- "\n",
- " function on_click_closure(name) {\n",
- " return function (_event) {\n",
- " return fig.toolbar_button_onclick(name);\n",
- " };\n",
- " }\n",
- "\n",
- " function on_mouseover_closure(tooltip) {\n",
- " return function (event) {\n",
- " if (!event.currentTarget.disabled) {\n",
- " return fig.toolbar_button_onmouseover(tooltip);\n",
- " }\n",
- " };\n",
- " }\n",
- "\n",
- " fig.buttons = {};\n",
- " var buttonGroup = document.createElement('div');\n",
- " buttonGroup.classList = 'btn-group';\n",
- " var button;\n",
- " for (var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " /* Instead of a spacer, we start a new button group. */\n",
- " if (buttonGroup.hasChildNodes()) {\n",
- " toolbar.appendChild(buttonGroup);\n",
- " }\n",
- " buttonGroup = document.createElement('div');\n",
- " buttonGroup.classList = 'btn-group';\n",
- " continue;\n",
- " }\n",
- "\n",
- " button = fig.buttons[name] = document.createElement('button');\n",
- " button.classList = 'btn btn-default';\n",
- " button.href = '#';\n",
- " button.title = name;\n",
- " button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
- " button.addEventListener('click', on_click_closure(method_name));\n",
- " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
- " buttonGroup.appendChild(button);\n",
- " }\n",
- "\n",
- " if (buttonGroup.hasChildNodes()) {\n",
- " toolbar.appendChild(buttonGroup);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = document.createElement('span');\n",
- " status_bar.classList = 'mpl-message pull-right';\n",
- " toolbar.appendChild(status_bar);\n",
- " this.message = status_bar;\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = document.createElement('div');\n",
- " buttongrp.classList = 'btn-group inline pull-right';\n",
- " button = document.createElement('button');\n",
- " button.classList = 'btn btn-mini btn-primary';\n",
- " button.href = '#';\n",
- " button.title = 'Stop Interaction';\n",
- " button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
- " button.addEventListener('click', function (_evt) {\n",
- " fig.handle_close(fig, {});\n",
- " });\n",
- " button.addEventListener(\n",
- " 'mouseover',\n",
- " on_mouseover_closure('Stop Interaction')\n",
- " );\n",
- " buttongrp.appendChild(button);\n",
- " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
- " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
- " var fig = event.data.fig;\n",
- " fig.close_ws(fig, {});\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function (el) {\n",
- " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
- " // this is important to make the div 'focusable\n",
- " el.setAttribute('tabindex', 0);\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " } else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager) {\n",
- " manager = IPython.keyboard_manager;\n",
- " }\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which === 13) {\n",
- " this.canvas_div.blur();\n",
- " // select the cell after this one\n",
- " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
- " IPython.notebook.select(index + 1);\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
- " fig.ondownload(fig, null);\n",
- "};\n",
- "\n",
- "mpl.find_output_cell = function (html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i = 0; i < ncells; i++) {\n",
- " var cell = cells[i];\n",
- " if (cell.cell_type === 'code') {\n",
- " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
- " var data = cell.output_area.outputs[j];\n",
- " if (data.data) {\n",
- " // IPython >= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] === html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "};\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel !== null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target(\n",
- " 'matplotlib',\n",
- " mpl.mpl_figure_comm\n",
- " );\n",
- "}\n"
- ],
- "text/plain": [
- "<IPython.core.display.Javascript object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAbAAAAEgCAYAAADVKCZpAAAB+UlEQVR4nO3BMQEAAADCoPVP7WkJoAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA4AaZiQAB1KMUfAAAAABJRU5ErkJggg==\" width=\"432\">"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"%matplotlib notebook\n",
"datasplit = DataSplit()\n",
@@ -3427,34 +183,9 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "833f7907fbf74f54be191f398fa051be",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "interactive(children=(Dropdown(description='variable:', options=('country', 'htap_region', 'climatic_zone', 'l…"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "<function __main__.plot_previs_sets(column_name, set_)>"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
@@ -3495,40 +226,11 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {
"scrolled": false
},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "8dac8f6c7ceb4df9ba1b4aed1bf61ae4",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Tab(children=(VBox(children=(Dropdown(description='target', layout=Layout(height='auto', width='99%'), options…"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "5f6d11eb1d6846b49c537ba4e697b11f",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Output()"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"%run mapping_jupyter.ipynb"
]