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+ "
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+ " "
+ ],
+ "text/plain": [
+ "\n",
+ " \n",
+ " 
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+ " \n",
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+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " Perceptual Models Around the World v2.0\n",
+ " \n",
+ "
description
Web Map by raraki8159_SDSUGeo\n",
+ "
Last Modified: March 20, 2023\n", + "
0 comments, 0 views\n", + "
\n",
+ " description
Web Map by raraki8159_SDSUGeo\n",
+ " Last Modified: March 20, 2023\n", + "
0 comments, 0 views\n", + "
{model_type} of {watershed_name}
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"text\": \"Text model:{textmodel_snipped}
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"mediaInfos\": [\n", + " {\n", + " \"altText\": \"Text model\",\n", + " \"caption\": \"Caption: {figure_caption}; From Figure {figure_num} in {citation}; {attribution}\",\n", + " \"refreshInterval\": 0,\n", + " \"title\": \"\",\n", + " \"type\": \"image\",\n", + " \"value\": {\n", + " \"linkURL\": \"{url}\",\n", + " \"sourceURL\": \"{figure_url}\"\n", + " }\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"media\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Process information in the model
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"num_store\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Number of stores\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"store_list\",\n", + " \"isEditable\": true,\n", + " \"label\": \"List of stores\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"store_id_list\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Hashtags of stores\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"num_flux\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Number of fluxes\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"flux_list\",\n", + " \"isEditable\": true,\n", + " \"label\": \"List of fluxes\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"flux_id_list\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Hashtags of fluxes\",\n", + " \"visible\": true\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Spatio-temporal heterogeneity in the model
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"num_spatial_zones\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Number of spatial zones\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"spatial_property\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Spatial property\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"num_temporal_zones\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Number of temporal zones\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"temporal_property\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Temporal property\",\n", + " \"visible\": true\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Ancillary information in the model
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"vegetation_info\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Vegetation\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"soil_info\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Soil\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"geol_info\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Geology\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"topo_info\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Topography\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"uncertainty_info\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Uncertainty range\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"three_d_info\",\n", + " \"isEditable\": true,\n", + " \"label\": \"3-dimensional description\",\n", + " \"visible\": true\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Watershed information
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"area_km2\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Watershed area (km2)\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"huc_watershed_id\",\n", + " \"isEditable\": true,\n", + " \"label\": \"US HUC8 watershed ID\",\n", + " \"visible\": true\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " }\n", + " ],\n", + " \"showAttachments\": true,\n", + " \"title\": \"\"\n", + " }\n", + " }\n", + " ],\n", + " \"tables\": []\n", + "}\n" + ] + } + ], + "source": [ + "feature_layer_id = '0f495168b855499dbf628920f0d74da4'\n", + "feature_layer_data = get_map(feature_layer_id)\n", + "print(json.dumps(feature_layer_data, indent=4, sort_keys=True))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8031f31f", + "metadata": {}, + "outputs": [], + "source": [ + "# Update the basemaps\n", + "\n", + "# US HUC8 layer \n", + "HUC8_dict = {\n", + " \"id\": \"183343475b2-layer-50\",\n", + " \"itemId\": \"988c850f4c944d53bd29bd934dd6ac22\",\n", + " \"layerType\": \"ArcGISMapServiceLayer\",\n", + " \"opacity\": 0.25,\n", + " \"title\": \"Watershed Boundary Dataset (WBD) from NHDPlus Version 2.1 (Simplified/Nested), EPA OW\",\n", + " \"url\": \"https://watersgeo.epa.gov/arcgis/rest/services/NHDPlus_NP21/WBD_NP21_Simplified/MapServer\"\n", + " }\n", + "\n", + "# World waterbody maps\n", + "river_dict = {\n", + " \"id\": \"1830ae1b2b9-layer-53\",\n", + " \"itemId\": \"9f86716d941c4410b0b406d911754b2c\",\n", + " \"layerType\": \"ArcGISTiledMapServiceLayer\",\n", + " \"title\": \"Esri Hydro Reference Overlay\",\n", + " \"url\": \"https://tiles.arcgis.com/tiles/P3ePLMYs2RVChkJx/arcgis/rest/services/Esri_Hydro_Reference_Overlay/MapServer\",\n", + " \"visibility\": True\n", + " }\n", + "\n", + "webmap_data['baseMap']['baseMapLayers'].append(HUC8_dict)\n", + "webmap_data['baseMap']['baseMapLayers'].append(river_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "66219435", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The dictionary is in correct order\n", + "id\n", + "Model ID\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "model_type\n", + "Model type\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "citation\n", + "Citation\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "url\n", + "URL\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "attribution\n", + "Attribution\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "attribution_url\n", + "Attribution link\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "figure_num\n", + "Figure number\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "figure_caption\n", + "Figure caption\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "figure_url\n", + "Figure URL\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "textmodel_snipped\n", + "Text model snipped\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "textmodel_section_number\n", + "Text section number\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "textmodel_section_name\n", + "Text section name\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "textmodel_page_number\n", + "Text page number\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "watershed_name\n", + "Watershed name\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "lat\n", + "latitude\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "lon\n", + "longitude\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "area_km2\n", + "longitude\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "num_spatial_zones\n", + "Number of spatial zones\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "spatial_property\n", + "Spatial property\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "num_temporal_zones\n", + "Number of temporal zones\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "temporal_property\n", + "Temporal property\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "vegetation_info\n", + "Vegetation\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "soil_info\n", + "Soil\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "geol_info\n", + "Geology\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "topo_info\n", + "Topography\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "three_d_info\n", + "3-dimensional description\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "uncertainty_info\n", + "Uncertainty range\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "other_info\n", + "Information: other\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "num_store\n", + "Number of stores\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "store_list\n", + "List of stores\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "store_id_list\n", + "Hashtags of stores\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "num_flux\n", + "Number of fluxes\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "flux_list\n", + "List of fluxes\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "flux_id_list\n", + "Hashtags of fluxes\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "dummy_column\n", + "dummy_column\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "huc_watershed_id\n", + "US HUC8\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "ObjectId\n", + "ObjectID\n", + "True\n", + "False\n" + ] + } + ], + "source": [ + "# Make some changes\n", + "\n", + "# Change the display names \n", + "mylabel_dict = {0:{\"fieldName\":\"id\",\"label\":\"Model ID\",\"visible\":False},\n", + " 1:{\"fieldName\":\"model_type\",\"label\":\"Model type\",\"visible\":False},\n", + " 2:{\"fieldName\":\"citation\",\"label\":\"Citation\",\"visible\":False},\n", + " 3:{\"fieldName\":\"url\",\"label\":\"URL\",\"visible\":False},\n", + " 4:{\"fieldName\":\"attribution\",\"label\":\"Attribution\",\"visible\":False},\n", + " 5:{\"fieldName\":\"attribution_url\",\"label\":\"Attribution link\",\"visible\":False},\n", + " 6:{\"fieldName\":\"figure_num\",\"label\":\"Figure number\",\"visible\":False},\n", + " 7:{\"fieldName\":\"figure_caption\",\"label\":\"Figure caption\",\"visible\":False},\n", + " 8:{\"fieldName\":\"figure_url\",\"label\":\"Figure URL\",\"visible\":False},\n", + " 9:{\"fieldName\":\"textmodel_snipped\",\"label\":\"Text model snipped\",\"visible\":True},\n", + " 10:{\"fieldName\":\"textmodel_section_number\",\"label\":\"Text section number\",\"visible\":False},\n", + " 11:{\"fieldName\":\"textmodel_section_name\",\"label\":\"Text section name\",\"visible\":False},\n", + " 12:{\"fieldName\":\"textmodel_page_number\",\"label\":\"Text page number\",\"visible\":False},\n", + " 13:{\"fieldName\":\"watershed_name\",\"label\":\"Watershed name\",\"visible\":True},\n", + " 14:{\"fieldName\":\"lat\",\"label\":\"latitude\",\"visible\":False},\n", + " 15:{\"fieldName\":\"lon\",\"label\":\"longitude\",\"visible\":False},\n", + " 16:{\"fieldName\":\"area_km2\",\"label\":\"longitude\",\"visible\":False},\n", + " 17:{\"fieldName\":\"num_spatial_zones\",\"label\":\"Number of spatial zones\",\"visible\":True},\n", + " 18:{\"fieldName\":\"spatial_property\",\"label\":\"Spatial property\",\"visible\":True},\n", + " 19:{\"fieldName\":\"num_temporal_zones\",\"label\":\"Number of temporal zones\",\"visible\":True},\n", + " 20:{\"fieldName\":\"temporal_property\",\"label\":\"Temporal property\",\"visible\":True},\n", + " 21:{\"fieldName\":\"vegetation_info\",\"label\":\"Vegetation\",\"visible\":True},\n", + " 22:{\"fieldName\":\"soil_info\",\"label\":\"Soil\",\"visible\":True},\n", + " 23:{\"fieldName\":\"geol_info\",\"label\":\"Geology\",\"visible\":True},\n", + " 24:{\"fieldName\":\"topo_info\",\"label\":\"Topography\",\"visible\":True},\n", + " 25:{\"fieldName\":\"three_d_info\",\"label\":\"3-dimensional description\",\"visible\":True},\n", + " 26:{\"fieldName\":\"uncertainty_info\",\"label\":\"Uncertainty range\",\"visible\":True},\n", + " 27:{\"fieldName\":\"other_info\",\"label\":\"Information: other\",\"visible\":True},\n", + " 28:{\"fieldName\":\"num_store\",\"label\":\"Number of stores\",\"visible\":True},\n", + " 29:{\"fieldName\":\"store_list\",\"label\":\"List of stores\",\"visible\":True},\n", + " 30:{\"fieldName\":\"store_id_list\",\"label\":\"Hashtags of stores\",\"visible\":True},\n", + " 31:{\"fieldName\":\"num_flux\",\"label\":\"Number of fluxes\",\"visible\":True},\n", + " 32:{\"fieldName\":\"flux_list\",\"label\":\"List of fluxes\",\"visible\":True},\n", + " 33:{\"fieldName\":\"flux_id_list\",\"label\":\"Hashtags of fluxes\",\"visible\":True},\n", + " 34:{\"fieldName\":\"dummy_column\",\"label\":\"dummy_column\",\"visible\":False},\n", + " 35:{\"fieldName\":\"huc_watershed_id\",\"label\":\"US HUC8\",\"visible\":False},\n", + " 36:{\"fieldName\":\"ObjectId\",\"label\":\"ObjectID\",\"visible\":False}\n", + " }\n", + "\n", + "for i in range(len(mylabel_dict)): \n", + " if webmap_data['operationalLayers'][0]['popupInfo']['fieldInfos'][i]['fieldName'] == mylabel_dict[i]['fieldName']:\n", + " print('The dictionary is in correct order')\n", + " \n", + " print(webmap_data['operationalLayers'][0]['popupInfo']['fieldInfos'][i]['label'])\n", + " webmap_data['operationalLayers'][0]['popupInfo']['fieldInfos'][i]['label'] = mylabel_dict[i]['label']\n", + " print(webmap_data['operationalLayers'][0]['popupInfo']['fieldInfos'][i]['label'])\n", + "\n", + " print(webmap_data['operationalLayers'][0]['popupInfo']['fieldInfos'][i]['visible'])\n", + " webmap_data['operationalLayers'][0]['popupInfo']['fieldInfos'][i]['visible'] = mylabel_dict[i]['visible']\n", + " print(webmap_data['operationalLayers'][0]['popupInfo']['fieldInfos'][i]['visible'])\n", + " else:\n", + " print('The dictionary is in wrong order')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2168e832", + "metadata": {}, + "outputs": [], + "source": [ + "# Turn off the layer name\n", + "webmap_data['operationalLayers'][0]['popupInfo']['title'] = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8bb3bf8b", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Add 'hand drawn figures' later \n", + "render = {'type': 'uniqueValue',\n", + " 'visualVariables': [{'type': 'sizeInfo',\n", + " 'valueExpression': '$view.scale',\n", + " 'stops': [{'size': 46.875, 'value': 1155581.108577},\n", + " {'size': 37.5, 'value': 9244648.868618},\n", + " {'size': 18.75, 'value': 73957190.948944},\n", + " {'size': 9.375, 'value': 591657527.591555}]}],\n", + " 'field1': 'model_type',\n", + " 'uniqueValueGroups': [{'classes': [{'label': 'Text model',\n", + " 'symbol': {'type': 'esriPMS',\n", + " 'angle': 0,\n", + " 'xoffset': 0,\n", + " 'yoffset': 0,\n", + " 'contentType': 'image/png',\n", + " 'imageData': 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+ " 'url': 'https://static.arcgis.com/images/Symbols/Government/Warrant.png',\n", + " 'height': 15,\n", + " 'width': 15},\n", + " 'values': [['Text model']]},\n", + " {'label': 'Figure model',\n", + " 'symbol': {'type': 'esriPMS',\n", + " 'angle': 0,\n", + " 'xoffset': 0,\n", + " 'yoffset': 0,\n", + " 'contentType': 'image/png',\n", + " 'imageData': 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+ " 'url': 'https://static.arcgis.com/images/Symbols/Government/Water-Teatment-Plant-Permit.png',\n", + " 'height': 18.75,\n", + " 'width': 18.75},\n", + " 'values': [['Figure model']]}]}],\n", + " 'uniqueValueInfos': [{'label': 'Text model',\n", + " 'symbol': {'type': 'esriPMS',\n", + " 'angle': 0,\n", + " 'xoffset': 0,\n", + " 'yoffset': 0,\n", + " 'contentType': 'image/png',\n", + " 'imageData': 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+ " 'url': 'https://static.arcgis.com/images/Symbols/Government/Warrant.png',\n", + " 'height': 15,\n", + " 'width': 15},\n", + " 'value': 'Text model'},\n", + " {'label': 'Figure model',\n", + " 'symbol': {'type': 'esriPMS',\n", + " 'angle': 0,\n", + " 'xoffset': 0,\n", + " 'yoffset': 0,\n", + " 'contentType': 'image/png',\n", + " 'imageData': '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',\n", + " 'url': 'https://static.arcgis.com/images/Symbols/Government/Water-Teatment-Plant-Permit.png',\n", + " 'height': 18.75,\n", + " 'width': 18.75},\n", + " 'value': 'Figure model'}]}\n", + "\n", + "webmap_data['operationalLayers'][0]['layerDefinition']['drawingInfo']['renderer'] = render\n", + "# feature_layer_data['layers'][0]['layerDefinition']['drawingInfo']['renderer']['uniqueValueInfos']\n", + "# feature_layer_data['layers'][0]['layerDefinition']['drawingInfo']['renderer']['field1'] == 'model_type' #['uniqueValueGroups'] #['uniqueValueGroups'] #['drawingInfo'] #['renderer']['symbol']['imageData']\n", + "\n", + "# Update the icon to prettier one\n", + "# webmap_data['operationalLayers'][0]['layerDefinition']['drawingInfo']['renderer']['symbol']['url'] = \"https://static.arcgis.com/images/Symbols/Government/Water-Teatment-Plant-Permit.png\"\n", + "# webmap_data['operationalLayers'][0]['layerDefinition']['drawingInfo']['renderer']['symbol']['imageData'] = \"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\"" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "eed843dc", + "metadata": {}, + "outputs": [], + "source": [ + "# Change the pop-up\n", + "webmap_data['operationalLayers'][0]['popupInfo']['popupElements'] = [\n", + " {\n", + " \"text\": \"Perceptual model of {watershed_name}
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"mediaInfos\": [\n", + " {\n", + " \"altText\": \"\",\n", + " \"caption\": \"{citation}; {attribution}; From Figure {figure_num}; Caption: {figure_caption}; Section {textmodel_section_number} “{textmodel_section_name}” of pp.{textmodel_page_number}\",\n", + " \"refreshInterval\": 0,\n", + " \"title\": \"\",\n", + " \"type\": \"image\",\n", + " \"value\": {\n", + " \"linkURL\": \"{url}\",\n", + " \"sourceURL\": \"{figure_url}\"\n", + " }\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"media\"\n", + " },\n", + " {\n", + " \"text\": \"Text model{textmodel_snipped}
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Process information in the model
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"num_store\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Number of stores\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"store_list\",\n", + " \"isEditable\": True,\n", + " \"label\": \"List of stores\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"store_id_list\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Hashtags of stores\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"num_flux\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Number of fluxes\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"flux_list\",\n", + " \"isEditable\": True,\n", + " \"label\": \"List of fluxes\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"flux_id_list\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Hashtags of fluxes\",\n", + " \"visible\": True\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Spatio-temporal heterogeneity in the model
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"num_spatial_zones\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Number of spatial zones\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"spatial_property\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Spatial property\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"num_temporal_zones\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Number of temporal zones\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"temporal_property\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Temporal property\",\n", + " \"visible\": True\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Ancillary information in the model
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"vegetation_info\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Vegetation\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"soil_info\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Soil\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"geol_info\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Geology\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"topo_info\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Topography\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"uncertainty_info\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Uncertainty range\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"three_d_info\",\n", + " \"isEditable\": True,\n", + " \"label\": \"3-dimensional description\",\n", + " \"visible\": True\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Watershed information
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"area_km2\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Watershed area (km2)\",\n", + " \"visible\": True\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " }\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "01251618", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# # Update the map\n", + "# feature_layer_update = update_map(feature_layer_id, feature_layer_data)\n", + "# feature_layer_update\n", + "\n", + "# Update the map\n", + "webmap_update = update_map(webmap_item_id, webmap_data)\n", + "webmap_update" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "4f8a2737", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Verify changes updated\n", + "webbrowser.open('https://sdsugeo.maps.arcgis.com/apps/mapviewer/index.html?webmap={}'.format(webmap_item_id), new=2)\n", + "\n", + "# https://sdsugeo.maps.arcgis.com/apps/mapviewer/index.html?webmap=e535103892fd41cd915d60ca4d1d9d44" + ] + }, + { + "cell_type": "markdown", + "id": "71111d63", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "arcgis", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + }, + "vscode": { + "interpreter": { + "hash": "3140ae756112501f18b23ec79a6bbbb54e715b030c96600dfefb60d12a8f240f" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/init_create_webmap_public.ipynb b/src/init_create_webmap_public.ipynb new file mode 100644 index 0000000..6091712 --- /dev/null +++ b/src/init_create_webmap_public.ipynb @@ -0,0 +1,2349 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "17ab89f6", + "metadata": {}, + "source": [ + "# Create ArcGIS webmap on perceptual-model database\n", + "\n", + "I referred to Jessica's instruction to create this notebook https://github.com/jlembury/GEOG594-Embury/blob/master/DEMO_ArcGIS_API_Python.ipynb" + ] + }, + { + "cell_type": "markdown", + "id": "0109e593", + "metadata": {}, + "source": [ + "# Config" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d15ac2b", + "metadata": {}, + "outputs": [], + "source": [ + "data_dir = f\"G:\\Shared drives\\Perceptual model review\\ForRyoko\\data\"" + ] + }, + { + "cell_type": "markdown", + "id": "4d8e4271", + "metadata": {}, + "source": [ + "## Import modules" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "22ba798c-c407-427b-9f94-de4752a43cd2", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import webbrowser\n", + "import pandas as pd\n", + "pd.options.mode.chained_assignment = None # default='warn'\n", + "import json\n", + "from arcgis.gis import GIS" + ] + }, + { + "cell_type": "markdown", + "id": "463eae66", + "metadata": {}, + "source": [ + "## Connect with ArcGIS\n", + "Reference for authentication schemes: https://developers.arcgis.com/python/guide/working-with-different-authentication-schemes/" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9f19dd88", + "metadata": {}, + "source": [ + "I ended up in using User autentification with OAuth 2.0 in Reference for authentication schemes: https://developers.arcgis.com/python/guide/working-with-different-authentication-schemes/\n", + "\n", + "Note: log-in expires after an hour or so.\n", + "When commands return \"Exception: A general error occurred: 'Response' object is not subscriptable\", log-in again. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5328c70c", + "metadata": {}, + "outputs": [], + "source": [ + "with open('./auth.json', 'r') as infile:\n", + " my_credentials = json.load(infile)\n", + "\n", + "gis = GIS(\"https://sdsugeo.maps.arcgis.com/\", client_id=my_credentials['client_id'])\n", + "print(\"Successfully logged in as: \" + gis.properties.user.username)\n", + "\n", + "# Clear the output for security \n", + "from IPython.display import clear_output\n", + "clear_output(wait=False)" + ] + }, + { + "cell_type": "markdown", + "id": "bfa15a0b", + "metadata": {}, + "source": [ + "open" + ] + }, + { + "cell_type": "markdown", + "id": "0108b7af", + "metadata": {}, + "source": [ + "If you have ArcGIS pro, this is the most straightforward and easy connection" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da85ed62", + "metadata": {}, + "outputs": [], + "source": [ + "# gis = GIS('Pro')\n", + "# print(\"Logged in as \" + str(gis.properties.user.username))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f70e7222", + "metadata": {}, + "source": [ + "## Newly create a webmap (skip the rest of the blocks if you are updating)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "84b0fa5d", + "metadata": {}, + "source": [ + "### Publish the CSV data to ArcGIS online" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aee09ff8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n",
+ "
\n",
+ " "
+ ],
+ "text/plain": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " Perceptual Models Around the World v2.0\n",
+ " \n",
+ "
descriptionWeb Map by raraki8159_SDSUGeo\n",
+ "
Last Modified: March 04, 2023\n", + "
0 comments, 0 views\n", + "
\n",
+ " description
Web Map by raraki8159_SDSUGeo\n",
+ " Last Modified: March 04, 2023\n", + "
0 comments, 0 views\n", + "
Perceptual model of {watershed_name}
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"mediaInfos\": [\n", + " {\n", + " \"altText\": \"\",\n", + " \"caption\": \"{citation}; {attribution}; From Figure {figure_num}; Caption: {figure_caption}; Section {textmodel_section_number} “{textmodel_section_name}” of pp.{textmodel_page_number}\",\n", + " \"refreshInterval\": 0,\n", + " \"title\": \"\",\n", + " \"type\": \"image\",\n", + " \"value\": {\n", + " \"linkURL\": \"{url}\",\n", + " \"sourceURL\": \"{figure_url}\"\n", + " }\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"media\"\n", + " },\n", + " {\n", + " \"text\": \"Text model;{textmodel_snipped}
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Process information in the model
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"num_store\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Number of stores\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"store_list\",\n", + " \"isEditable\": true,\n", + " \"label\": \"List of stores\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"store_id_list\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Hashtags of stores\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"num_flux\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Number of fluxes\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"flux_list\",\n", + " \"isEditable\": true,\n", + " \"label\": \"List of fluxes\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"flux_id_list\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Hashtags of fluxes\",\n", + " \"visible\": true\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Spatio-temporal heterogeneity in the model
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"num_spatial_zones\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Number of spatial zones\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"spatial_property\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Spatial property\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"num_temporal_zones\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Number of temporal zones\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"temporal_property\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Temporal property\",\n", + " \"visible\": true\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Ancillary information in the model
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"vegetation_info\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Vegetation\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"soil_info\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Soil\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"geol_info\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Geology\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"topo_info\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Topography\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"uncertainty_info\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Uncertainty range\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"three_d_info\",\n", + " \"isEditable\": true,\n", + " \"label\": \"3-dimensional description\",\n", + " \"visible\": true\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Watershed information
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"area_km2\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Watershed area (km2)\",\n", + " \"visible\": true\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " }\n", + " ],\n", + " \"showAttachments\": true,\n", + " \"title\": \"\"\n", + " },\n", + " \"title\": \"alltype_models_v1\",\n", + " \"url\": \"https://services1.arcgis.com/SIYkiqjmENweC50g/arcgis/rest/services/alltype_models_v1/FeatureServer/0\",\n", + " \"visibility\": true\n", + " }\n", + " ],\n", + " \"spatialReference\": {\n", + " \"latestWkid\": 3857,\n", + " \"wkid\": 102100\n", + " },\n", + " \"version\": \"2.10\"\n", + "}\n" + ] + } + ], + "source": [ + "webmap_item_id = '94bdbb06d8aa435598182f21c9ba067c'\n", + "webmap_data = get_map(webmap_item_id)\n", + "print(json.dumps(webmap_data, indent=4, sort_keys=True))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "5755f340", + "metadata": {}, + "source": [ + "### Feature layer update" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb1633fa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{model_type} of {watershed_name}
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"text\": \"Text model:{textmodel_snipped}
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"mediaInfos\": [\n", + " {\n", + " \"altText\": \"Text model\",\n", + " \"caption\": \"Caption: {figure_caption}; From Figure {figure_num} in {citation}; {attribution}\",\n", + " \"refreshInterval\": 0,\n", + " \"title\": \"\",\n", + " \"type\": \"image\",\n", + " \"value\": {\n", + " \"linkURL\": \"{url}\",\n", + " \"sourceURL\": \"{figure_url}\"\n", + " }\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"media\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Process information in the model
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"num_store\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Number of stores\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"store_list\",\n", + " \"isEditable\": true,\n", + " \"label\": \"List of stores\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"store_id_list\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Hashtags of stores\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"num_flux\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Number of fluxes\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"flux_list\",\n", + " \"isEditable\": true,\n", + " \"label\": \"List of fluxes\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"flux_id_list\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Hashtags of fluxes\",\n", + " \"visible\": true\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Spatio-temporal heterogeneity in the model
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"num_spatial_zones\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Number of spatial zones\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"spatial_property\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Spatial property\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"num_temporal_zones\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Number of temporal zones\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"temporal_property\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Temporal property\",\n", + " \"visible\": true\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Ancillary information in the model
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"vegetation_info\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Vegetation\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"soil_info\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Soil\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"geol_info\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Geology\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"topo_info\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Topography\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"uncertainty_info\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Uncertainty range\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"three_d_info\",\n", + " \"isEditable\": true,\n", + " \"label\": \"3-dimensional description\",\n", + " \"visible\": true\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Watershed information
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"area_km2\",\n", + " \"isEditable\": true,\n", + " \"label\": \"Watershed area (km2)\",\n", + " \"visible\": true\n", + " },\n", + " {\n", + " \"fieldName\": \"huc_watershed_id\",\n", + " \"isEditable\": true,\n", + " \"label\": \"US HUC8 watershed ID\",\n", + " \"visible\": true\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " }\n", + " ],\n", + " \"showAttachments\": true,\n", + " \"title\": \"\"\n", + " }\n", + " }\n", + " ],\n", + " \"tables\": []\n", + "}\n" + ] + } + ], + "source": [ + "feature_layer_id = '91f146af61004ce799233fc30f52a783'\n", + "feature_layer_data = get_map(feature_layer_id)\n", + "print(json.dumps(feature_layer_data, indent=4, sort_keys=True))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8031f31f", + "metadata": {}, + "outputs": [], + "source": [ + "# Update the basemaps\n", + "\n", + "# US HUC8 layer \n", + "HUC8_dict = {\n", + " \"id\": \"183343475b2-layer-50\",\n", + " \"itemId\": \"988c850f4c944d53bd29bd934dd6ac22\",\n", + " \"layerType\": \"ArcGISMapServiceLayer\",\n", + " \"opacity\": 0.25,\n", + " \"title\": \"Watershed Boundary Dataset (WBD) from NHDPlus Version 2.1 (Simplified/Nested), EPA OW\",\n", + " \"url\": \"https://watersgeo.epa.gov/arcgis/rest/services/NHDPlus_NP21/WBD_NP21_Simplified/MapServer\"\n", + " }\n", + "\n", + "# World waterbody maps\n", + "river_dict = {\n", + " \"id\": \"1830ae1b2b9-layer-53\",\n", + " \"itemId\": \"9f86716d941c4410b0b406d911754b2c\",\n", + " \"layerType\": \"ArcGISTiledMapServiceLayer\",\n", + " \"title\": \"Esri Hydro Reference Overlay\",\n", + " \"url\": \"https://tiles.arcgis.com/tiles/P3ePLMYs2RVChkJx/arcgis/rest/services/Esri_Hydro_Reference_Overlay/MapServer\",\n", + " \"visibility\": True\n", + " }\n", + "\n", + "webmap_data['baseMap']['baseMapLayers'].append(HUC8_dict)\n", + "webmap_data['baseMap']['baseMapLayers'].append(river_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66219435", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The dictionary is in correct order\n", + "id\n", + "Model ID\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "model_type\n", + "Model type\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "citation\n", + "Citation\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "url\n", + "URL\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "attribution\n", + "Attribution\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "attribution_url\n", + "Attribution link\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "figure_num\n", + "Figure number\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "figure_caption\n", + "Figure caption\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "figure_url\n", + "Figure URL\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "textmodel_snipped\n", + "Text model snipped\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "textmodel_section_number\n", + "Text section number\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "textmodel_section_name\n", + "Text section name\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "textmodel_page_number\n", + "Text page number\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "watershed_name\n", + "Watershed name\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "lat\n", + "latitude\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "lon\n", + "longitude\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "area_km2\n", + "longitude\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "num_spatial_zones\n", + "Number of spatial zones\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "spatial_property\n", + "Spatial property\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "num_temporal_zones\n", + "Number of temporal zones\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "temporal_property\n", + "Temporal property\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "vegetation_info\n", + "Vegetation\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "soil_info\n", + "Soil\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "geol_info\n", + "Geology\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "topo_info\n", + "Topography\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "three_d_info\n", + "3-dimensional description\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "uncertainty_info\n", + "Uncertainty range\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "other_info\n", + "Information: other\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "num_store\n", + "Number of stores\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "store_list\n", + "List of stores\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "store_id_list\n", + "Hashtags of stores\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "num_flux\n", + "Number of fluxes\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "flux_list\n", + "List of fluxes\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "flux_id_list\n", + "Hashtags of fluxes\n", + "True\n", + "True\n", + "The dictionary is in correct order\n", + "dummy_column\n", + "dummy_column\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "huc_watershed_id\n", + "US HUC8\n", + "True\n", + "False\n", + "The dictionary is in correct order\n", + "ObjectId\n", + "ObjectID\n", + "True\n", + "False\n" + ] + } + ], + "source": [ + "# Make some changes\n", + "\n", + "# Change the display names \n", + "mylabel_dict = {0:{\"fieldName\":\"id\",\"label\":\"Model ID\",\"visible\":False},\n", + " 1:{\"fieldName\":\"model_type\",\"label\":\"Model type\",\"visible\":False},\n", + " 2:{\"fieldName\":\"citation\",\"label\":\"Citation\",\"visible\":False},\n", + " 3:{\"fieldName\":\"url\",\"label\":\"URL\",\"visible\":False},\n", + " 4:{\"fieldName\":\"attribution\",\"label\":\"Attribution\",\"visible\":False},\n", + " 5:{\"fieldName\":\"attribution_url\",\"label\":\"Attribution link\",\"visible\":False},\n", + " 6:{\"fieldName\":\"figure_num\",\"label\":\"Figure number\",\"visible\":False},\n", + " 7:{\"fieldName\":\"figure_caption\",\"label\":\"Figure caption\",\"visible\":False},\n", + " 8:{\"fieldName\":\"figure_url\",\"label\":\"Figure URL\",\"visible\":False},\n", + " 9:{\"fieldName\":\"textmodel_snipped\",\"label\":\"Text model snipped\",\"visible\":True},\n", + " 10:{\"fieldName\":\"textmodel_section_number\",\"label\":\"Text section number\",\"visible\":False},\n", + " 11:{\"fieldName\":\"textmodel_section_name\",\"label\":\"Text section name\",\"visible\":False},\n", + " 12:{\"fieldName\":\"textmodel_page_number\",\"label\":\"Text page number\",\"visible\":False},\n", + " 13:{\"fieldName\":\"watershed_name\",\"label\":\"Watershed name\",\"visible\":True},\n", + " 14:{\"fieldName\":\"lat\",\"label\":\"latitude\",\"visible\":False},\n", + " 15:{\"fieldName\":\"lon\",\"label\":\"longitude\",\"visible\":False},\n", + " 16:{\"fieldName\":\"area_km2\",\"label\":\"longitude\",\"visible\":False},\n", + " 17:{\"fieldName\":\"num_spatial_zones\",\"label\":\"Number of spatial zones\",\"visible\":True},\n", + " 18:{\"fieldName\":\"spatial_property\",\"label\":\"Spatial property\",\"visible\":True},\n", + " 19:{\"fieldName\":\"num_temporal_zones\",\"label\":\"Number of temporal zones\",\"visible\":True},\n", + " 20:{\"fieldName\":\"temporal_property\",\"label\":\"Temporal property\",\"visible\":True},\n", + " 21:{\"fieldName\":\"vegetation_info\",\"label\":\"Vegetation\",\"visible\":True},\n", + " 22:{\"fieldName\":\"soil_info\",\"label\":\"Soil\",\"visible\":True},\n", + " 23:{\"fieldName\":\"geol_info\",\"label\":\"Geology\",\"visible\":True},\n", + " 24:{\"fieldName\":\"topo_info\",\"label\":\"Topography\",\"visible\":True},\n", + " 25:{\"fieldName\":\"three_d_info\",\"label\":\"3-dimensional description\",\"visible\":True},\n", + " 26:{\"fieldName\":\"uncertainty_info\",\"label\":\"Uncertainty range\",\"visible\":True},\n", + " 27:{\"fieldName\":\"other_info\",\"label\":\"Information: other\",\"visible\":True},\n", + " 28:{\"fieldName\":\"num_store\",\"label\":\"Number of stores\",\"visible\":True},\n", + " 29:{\"fieldName\":\"store_list\",\"label\":\"List of stores\",\"visible\":True},\n", + " 30:{\"fieldName\":\"store_id_list\",\"label\":\"Hashtags of stores\",\"visible\":True},\n", + " 31:{\"fieldName\":\"num_flux\",\"label\":\"Number of fluxes\",\"visible\":True},\n", + " 32:{\"fieldName\":\"flux_list\",\"label\":\"List of fluxes\",\"visible\":True},\n", + " 33:{\"fieldName\":\"flux_id_list\",\"label\":\"Hashtags of fluxes\",\"visible\":True},\n", + " 34:{\"fieldName\":\"dummy_column\",\"label\":\"dummy_column\",\"visible\":False},\n", + " 35:{\"fieldName\":\"huc_watershed_id\",\"label\":\"US HUC8\",\"visible\":False},\n", + " 36:{\"fieldName\":\"ObjectId\",\"label\":\"ObjectID\",\"visible\":False}\n", + " }\n", + "\n", + "for i in range(len(mylabel_dict)): \n", + " if webmap_data['operationalLayers'][0]['popupInfo']['fieldInfos'][i]['fieldName'] == mylabel_dict[i]['fieldName']:\n", + " print('The dictionary is in correct order')\n", + " \n", + " print(webmap_data['operationalLayers'][0]['popupInfo']['fieldInfos'][i]['label'])\n", + " webmap_data['operationalLayers'][0]['popupInfo']['fieldInfos'][i]['label'] = mylabel_dict[i]['label']\n", + " print(webmap_data['operationalLayers'][0]['popupInfo']['fieldInfos'][i]['label'])\n", + "\n", + " print(webmap_data['operationalLayers'][0]['popupInfo']['fieldInfos'][i]['visible'])\n", + " webmap_data['operationalLayers'][0]['popupInfo']['fieldInfos'][i]['visible'] = mylabel_dict[i]['visible']\n", + " print(webmap_data['operationalLayers'][0]['popupInfo']['fieldInfos'][i]['visible'])\n", + " else:\n", + " print('The dictionary is in wrong order')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2168e832", + "metadata": {}, + "outputs": [], + "source": [ + "# Turn off the layer name\n", + "webmap_data['operationalLayers'][0]['popupInfo']['title'] = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8bb3bf8b", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Add 'hand drawn figures' later \n", + "render = {'type': 'uniqueValue',\n", + " 'visualVariables': [{'type': 'sizeInfo',\n", + " 'valueExpression': '$view.scale',\n", + " 'stops': [{'size': 46.875, 'value': 1155581.108577},\n", + " {'size': 37.5, 'value': 9244648.868618},\n", + " {'size': 18.75, 'value': 73957190.948944},\n", + " {'size': 9.375, 'value': 591657527.591555}]}],\n", + " 'field1': 'model_type',\n", + " 'uniqueValueGroups': [{'classes': [{'label': 'Text model',\n", + " 'symbol': {'type': 'esriPMS',\n", + " 'angle': 0,\n", + " 'xoffset': 0,\n", + " 'yoffset': 0,\n", + " 'contentType': 'image/png',\n", + " 'imageData': 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+ " 'url': 'https://static.arcgis.com/images/Symbols/Government/Warrant.png',\n", + " 'height': 15,\n", + " 'width': 15},\n", + " 'values': [['Text model']]},\n", + " {'label': 'Figure model',\n", + " 'symbol': {'type': 'esriPMS',\n", + " 'angle': 0,\n", + " 'xoffset': 0,\n", + " 'yoffset': 0,\n", + " 'contentType': 'image/png',\n", + " 'imageData': 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+ " 'url': 'https://static.arcgis.com/images/Symbols/Government/Warrant.png',\n", + " 'height': 15,\n", + " 'width': 15},\n", + " 'value': 'Text model'},\n", + " {'label': 'Figure model',\n", + " 'symbol': {'type': 'esriPMS',\n", + " 'angle': 0,\n", + " 'xoffset': 0,\n", + " 'yoffset': 0,\n", + " 'contentType': 'image/png',\n", + " 'imageData': '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',\n", + " 'url': 'https://static.arcgis.com/images/Symbols/Government/Water-Teatment-Plant-Permit.png',\n", + " 'height': 18.75,\n", + " 'width': 18.75},\n", + " 'value': 'Figure model'}]}\n", + "\n", + "webmap_data['operationalLayers'][0]['layerDefinition']['drawingInfo']['renderer'] = render\n", + "# feature_layer_data['layers'][0]['layerDefinition']['drawingInfo']['renderer']['uniqueValueInfos']\n", + "# feature_layer_data['layers'][0]['layerDefinition']['drawingInfo']['renderer']['field1'] == 'model_type' #['uniqueValueGroups'] #['uniqueValueGroups'] #['drawingInfo'] #['renderer']['symbol']['imageData']\n", + "\n", + "# Update the icon to prettier one\n", + "# webmap_data['operationalLayers'][0]['layerDefinition']['drawingInfo']['renderer']['symbol']['url'] = \"https://static.arcgis.com/images/Symbols/Government/Water-Teatment-Plant-Permit.png\"\n", + "# webmap_data['operationalLayers'][0]['layerDefinition']['drawingInfo']['renderer']['symbol']['imageData'] = \"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\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eed843dc", + "metadata": {}, + "outputs": [], + "source": [ + "# Change the pop-up\n", + "webmap_data['operationalLayers'][0]['popupInfo']['popupElements'] = [\n", + " {\n", + " \"text\": \"Perceptual model of {watershed_name}
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"mediaInfos\": [\n", + " {\n", + " \"altText\": \"\",\n", + " \"caption\": \"{citation}; {attribution}; From Figure {figure_num}; Caption: {figure_caption}; Section {textmodel_section_number} “{textmodel_section_name}” of pp.{textmodel_page_number}\",\n", + " \"refreshInterval\": 0,\n", + " \"title\": \"\",\n", + " \"type\": \"image\",\n", + " \"value\": {\n", + " \"linkURL\": \"{url}\",\n", + " \"sourceURL\": \"{figure_url}\"\n", + " }\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"media\"\n", + " },\n", + " {\n", + " \"text\": \"Text model{textmodel_snipped}
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Process information in the model
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"num_store\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Number of stores\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"store_list\",\n", + " \"isEditable\": True,\n", + " \"label\": \"List of stores\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"store_id_list\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Hashtags of stores\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"num_flux\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Number of fluxes\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"flux_list\",\n", + " \"isEditable\": True,\n", + " \"label\": \"List of fluxes\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"flux_id_list\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Hashtags of fluxes\",\n", + " \"visible\": True\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Spatio-temporal heterogeneity in the model
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"num_spatial_zones\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Number of spatial zones\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"spatial_property\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Spatial property\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"num_temporal_zones\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Number of temporal zones\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"temporal_property\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Temporal property\",\n", + " \"visible\": True\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Ancillary information in the model
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"vegetation_info\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Vegetation\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"soil_info\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Soil\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"geol_info\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Geology\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"topo_info\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Topography\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"uncertainty_info\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Uncertainty range\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"three_d_info\",\n", + " \"isEditable\": True,\n", + " \"label\": \"3-dimensional description\",\n", + " \"visible\": True\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Watershed information
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"area_km2\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Watershed area (km2)\",\n", + " \"visible\": True\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " }\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "01251618", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 180, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# # Update the map\n", + "# feature_layer_update = update_map(feature_layer_id, feature_layer_data)\n", + "# feature_layer_update\n", + "\n", + "# Update the map\n", + "webmap_update = update_map(webmap_item_id, webmap_data)\n", + "webmap_update" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f8a2737", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 181, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Verify changes updated\n", + "webbrowser.open('https://sdsugeo.maps.arcgis.com/apps/mapviewer/index.html?webmap={}'.format(webmap_item_id), new=2)\n", + "\n", + "# https://sdsugeo.maps.arcgis.com/apps/mapviewer/index.html?webmap=e535103892fd41cd915d60ca4d1d9d44" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68607e8b", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "71111d63", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "814a091a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d173d565", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "arcgis", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + }, + "vscode": { + "interpreter": { + "hash": "3140ae756112501f18b23ec79a6bbbb54e715b030c96600dfefb60d12a8f240f" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/old_codes/building_database_onlyFigModels.py b/src/old_codes/building_database_onlyFigModels.py new file mode 100644 index 0000000..0230a51 --- /dev/null +++ b/src/old_codes/building_database_onlyFigModels.py @@ -0,0 +1,548 @@ + +####################################################### +## Script to join all Excel files and build a database +####################################################### + +import os +print(os.environ['PATH']) +import pandas as pd +import numpy as np +import configparser +from sqlalchemy import create_engine +from sqlalchemy import text + +################################### +## 0. Read data +################################### +os.chdir(r'G:\Shared drives\Perceptual model review\ForRyoko\perceptual-model-arcgis\dev\scripts') + +df_loc = pd.read_excel('../data/Location_formatted.xlsx') # The lat/lon should be pre-formatted in decimal units +# df_model = pd.read_csv('../data/ModelAnalysis.csv', encoding='utf-8') +df_model = pd.read_excel('../data/ModelAnalysis.xlsx') +df_taxonomy = pd.read_excel('../data/ProcessHierarchyNetwork.xlsx') +df_FunctionType = pd.read_excel('../data/FunctionType.xlsx') + +## Parse dataframe into table + +# Location table +# Sanity check if data can be joined +# df.set_index('key').join(other.set_index('key')) +df_loc["id"] = df_loc.index + 1 +df_loc = df_loc.drop(columns='Unnamed: 0') +df_loc["huc_watershed_id"] = np.nan + +# FunctionType table +df_FunctionType["id"] = df_FunctionType.index + 1 + +# Citation table +df_model["attribution_url"].fillna(df_model["url"], inplace=True) +df_citation = df_model[["citation", "url"]].copy() +df_citation["attribution"] = df_model["attribution"].copy() +df_citation["attribution_url"] = df_model["attribution_url"].copy() +df_citation["id"] = df_citation.index + 1 + +# Spatial and temporal zone tables +df_spatialZoneType = df_model["spatial_property"].copy().drop_duplicates() +df_spatialZoneType = df_spatialZoneType.to_frame() +df_spatialZoneType.reset_index(inplace=True) +df_spatialZoneType = df_spatialZoneType.drop(columns='index') +df_spatialZoneType['id'] = df_spatialZoneType.index + 1 +df_temporalZoneType = df_model["temporal_property"].copy().drop_duplicates() +df_temporalZoneType = df_temporalZoneType.to_frame() +df_temporalZoneType.reset_index(inplace=True) +df_temporalZoneType = df_temporalZoneType.drop(columns='index') +df_temporalZoneType['id'] = df_temporalZoneType.index + 1 + +# Alternative name table +df_altNames0 = df_taxonomy.set_index(['process', 'function', 'identifier', 'process_level']).apply( + lambda x: x.str.split(',').explode()).reset_index() +df_altNames = df_altNames0[['alternative_names', 'process']].copy() +df_altNames['alternative_names'] = df_altNames['alternative_names'].str.strip() +df_altNames['alternative_names'] = df_altNames['alternative_names'].str.capitalize() +df_altNames.dropna(axis=0, inplace=True) +df_altNames["id"] = df_altNames.index + 1 + +# Model table +df_model["id"] = df_model.index + 1 +df_modelmain = df_model[['id', 'citation', 'watershed_name', + 'figure_num', 'figure_url', 'figure_caption', + 'spatial_property', 'num_spatial_zones', 'temporal_property', + 'num_temporal_zones', 'vegetation_info', 'soil_info', 'geol_info', + 'topo_info', 'three_d_info', 'uncertainty_info', 'other_info' + ]].copy() + +# corgi_fig = "https://i.ibb.co/stdkk6P/corgi-with-notes.png" +corgi_fig = "https://sdsugeo.maps.arcgis.com/sharing/rest/content/items/df499e37a2ef40d7885966952c627e01/data" +df_modelmain['figure_url'] = df_modelmain['figure_url'].fillna(corgi_fig) +df_modelmain.rename(columns={"figure_num": "model_ref"}) + +# LinkProcessPerceptual table +# Get all the process original text and taxonomy name from model +frames = [df_model[['id', 'flux1', 'flux1_taxonomy']].copy().rename( + columns={"id": "entry_id", "flux1": "original_text", "flux1_taxonomy": "process"}), + df_model[['id', 'flux2', 'flux2_taxonomy']].copy().rename( + columns={"id": "entry_id", "flux2": "original_text", "flux2_taxonomy": "process"}), + df_model[['id', 'flux3', 'flux3_taxonomy']].copy().rename( + columns={"id": "entry_id", "flux3": "original_text", "flux3_taxonomy": "process"}), + df_model[['id', 'flux4', 'flux4_taxonomy']].copy().rename( + columns={"id": "entry_id", "flux4": "original_text", "flux4_taxonomy": "process"}), + df_model[['id', 'flux5', 'flux5_taxonomy']].copy().rename( + columns={"id": "entry_id", "flux5": "original_text", "flux5_taxonomy": "process"}), + df_model[['id', 'flux6', 'flux6_taxonomy']].copy().rename( + columns={"id": "entry_id", "flux6": "original_text", "flux6_taxonomy": "process"}), + df_model[['id', 'flux7', 'flux7_taxonomy']].copy().rename( + columns={"id": "entry_id", "flux7": "original_text", "flux7_taxonomy": "process"}), + df_model[['id', 'flux8', 'flux8_taxonomy']].copy().rename( + columns={"id": "entry_id", "flux8": "original_text", "flux8_taxonomy": "process"}), + df_model[['id', 'flux9', 'flux9_taxonomy']].copy().rename( + columns={"id": "entry_id", "flux9": "original_text", "flux9_taxonomy": "process"}), + df_model[['id', 'store1', 'store1_taxonomy']].copy().rename( + columns={"id": "entry_id", "store1": "original_text", "store1_taxonomy": "process"}), + df_model[['id', 'store2', 'store2_taxonomy']].copy().rename( + columns={"id": "entry_id", "store2": "original_text", "store2_taxonomy": "process"}), + df_model[['id', 'store3', 'store3_taxonomy']].copy().rename( + columns={"id": "entry_id", "store3": "original_text", "store3_taxonomy": "process"}), + df_model[['id', 'store4', 'store4_taxonomy']].copy().rename( + columns={"id": "entry_id", "store4": "original_text", "store4_taxonomy": "process"}), + df_model[['id', 'store5', 'store5_taxonomy']].copy().rename( + columns={"id": "entry_id", "store5": "original_text", "store5_taxonomy": "process"}), + df_model[['id', 'store6', 'store6_taxonomy']].copy().rename( + columns={"id": "entry_id", "store6": "original_text", "store6_taxonomy": "process"}), + df_model[['id', 'store7', 'store7_taxonomy']].copy().rename( + columns={"id": "entry_id", "store7": "original_text", "store7_taxonomy": "process"}), + df_model[['id', 'store8', 'store8_taxonomy']].copy().rename( + columns={"id": "entry_id", "store8": "original_text", "store8_taxonomy": "process"}), + ] + +df_linkProcessPerceptual0 = pd.concat(frames, axis=0, ignore_index=True) +df_linkProcessPerceptual0["id"] = df_linkProcessPerceptual0.index + 1 + +# Create taxonomy table +df_process0 = df_taxonomy.drop(columns='alternative_names') + +# join process taxonomy and model table +df_linkProcessPerceptual0["process_lower"] = df_linkProcessPerceptual0['process'].str.lower() +df_linkProcessPerceptual0["process_lower"] = df_linkProcessPerceptual0['process_lower'].str.strip() +df_process0["process_lower"] = df_process0['process'].str.lower() +df_process0["process_lower"] = df_process0['process_lower'].str.strip() + +# find and add some new process from model table to taxonomy table +df_linkProcessPerceptual1 = df_linkProcessPerceptual0.merge(df_process0, on='process_lower', how='left') +new_process = df_linkProcessPerceptual1.loc[(df_linkProcessPerceptual1['process_x'].isnull() == False) & ( + df_linkProcessPerceptual1['process_y'].isnull() == True)] +new_process.drop_duplicates(subset='process_lower', inplace=True) + +add_new_process = pd.DataFrame( + {'process': new_process['process_x'], 'process_lower': new_process['process_x'].str.lower(), + 'identifier': ['NewProcess'] * len(new_process['process_x'])}) +df_process1 = pd.concat([df_process0, add_new_process]) +df_process1["id"] = df_process1.reset_index().index + 1 + + +# re-join process taxonomy and model table with new process +df_linkProcessPerceptual2 = df_linkProcessPerceptual0.merge(df_process1, on='process_lower', how='left') +df_linkProcessPerceptual2.rename(columns={"id_y": "process_id"}, inplace=True) +df_linkProcessPerceptual = df_linkProcessPerceptual2.drop( + columns={'process_x', 'id_x', 'process_lower', 'process_y', 'function', 'identifier', 'process_level'}) +df_linkProcessPerceptual.dropna(subset=['original_text'], axis=0, inplace=True) +df_linkProcessPerceptual["id"] = df_linkProcessPerceptual.reset_index().index + 1 +df_linkProcessPerceptual["process_id"] = df_linkProcessPerceptual["process_id"].astype('int') +# df_linkProcessPerceptual[df_linkProcessPerceptual['process_id'].isnull()] +df_process = df_process1.drop(columns='process_lower') + +################################### +## 1. Connect to SQL database +################################### + +# Read config +config = configparser.ConfigParser() +config.read('config.ini') + +DB_NAME = config['postgresql']['DB_NAME'] +HOST = config['postgresql']['HOST'] +PORT = config['postgresql']['PORT'] +USER_NAME = config['postgresql']['USER_NAME'] +PASSWD = config['postgresql']['PASSWD'] + +# Connect to database +conn_string = f'postgresql://{USER_NAME}:{PASSWD}@{HOST}:{PORT}/{DB_NAME}' +db = create_engine(conn_string, client_encoding='utf8') +try: + conn = db.connect() + print("connection to '%s'@'%s' success!" % (DB_NAME, HOST)) +except Exception as e: + print("connection to '%s'@'%s' failed." % (DB_NAME, HOST)) + print(e) + +################################### +## 2. Create database +################################### + +tables = ['locations', 'citations', 'spatial_zone_type', 'temporal_zone_type', 'process_alt_names', + 'function_type', 'perceptual_model', 'link_process_perceptual', 'process_taxonomy'] +conn.execute(text("set search_path to public, perceptual_model")) +conn.execute(text("SET CLIENT_ENCODING TO 'UTF8';")) +for table in tables: + try: + conn.execute(text(f"DROP TABLE {table} CASCADE;")) + print('drop old tables') + except Exception as e: + print(e) + +schema = 'perceptual_model' +connarg = {'con': conn, 'schema': 'perceptual_model', 'if_exists': 'replace', 'index': False} + +# set table names in lower case https://github.com/pandas-dev/pandas/issues/13206 +df_loc.to_sql('locations', **connarg) +df_citation.to_sql('citations', **connarg) +df_spatialZoneType.to_sql('spatial_zone_type', **connarg) +df_temporalZoneType.to_sql('temporal_zone_type', **connarg) +df_altNames.to_sql('process_alt_names', **connarg) +df_FunctionType.to_sql('function_type', **connarg) +df_modelmain.to_sql('perceptual_model', **connarg) +df_linkProcessPerceptual.to_sql('link_process_perceptual', **connarg) +df_process.to_sql('process_taxonomy', **connarg) + +# Relate the location table to HUC8 watershed table +query = "ALTER TABLE perceptual_model.locations \ +ADD COLUMN pt geometry(point, 4326); \ +WITH pt_geom AS ( \ + SELECT id, ST_SetSRID(ST_MakePoint(lon, lat), 4326) AS pt \ + from perceptual_model.locations \ + ) \ +UPDATE perceptual_model.locations \ +SET pt = pt_geom.pt \ +FROM pt_geom \ +WHERE pt_geom.id = locations.id; \ +ALTER TABLE locations \ +ALTER COLUMN huc_watershed_id TYPE INTEGER USING huc_watershed_id::integer; \ +WITH joined_huc8_pt AS ( \ +SELECT locations.id AS pt_id, huc_watersheds.id as huc8_id, huc_watersheds.huc8 AS huc8_code \ +FROM locations \ +JOIN huc_watersheds \ +ON ST_contains(ST_Transform(huc_watersheds.geom, 4326), locations.pt) \ +) \ +UPDATE locations \ +SET huc_watershed_id = joined_huc8_pt.huc8_id \ +FROM joined_huc8_pt \ +WHERE locations.id = joined_huc8_pt.pt_id;" + +try: + conn.execute(text(query)) + print("success") +except Exception as e: + print(e) + +# add primary keys +for table in tables: + try: + conn.execute(text(f"ALTER TABLE {table} ADD PRIMARY KEY (id);")) + print(f"successfully created table {table}") + except Exception as e: + print(e) + +# add foreign keys +# model & location +query = "ALTER TABLE perceptual_model ADD COLUMN location_id int; \ +UPDATE perceptual_model \ +SET location_id = locations.id \ +FROM locations \ +WHERE perceptual_model.watershed_name = locations.name; \ +ALTER TABLE perceptual_model \ +ADD FOREIGN KEY (location_id) REFERENCES locations(id); \ +ALTER TABLE perceptual_model DROP COLUMN watershed_name;" + +try: + conn.execute(text(query)) + print("success") +except Exception as e: + print(e) + +# model & citation +query = "ALTER TABLE perceptual_model ADD COLUMN citation_id int; \ +UPDATE perceptual_model \ +SET citation_id = citations.id \ +FROM citations \ +WHERE perceptual_model.citation = citations.citation; \ +ALTER TABLE perceptual_model \ +ADD FOREIGN KEY (citation_id) REFERENCES citations(id); \ +ALTER TABLE perceptual_model DROP COLUMN citation;" + +try: + conn.execute(text(query)) + print("success") +except Exception as e: + print(e) + +# model & spatial zone +query = "ALTER TABLE perceptual_model ADD COLUMN spatialzone_id int; \ +UPDATE perceptual_model \ +SET spatialzone_id = spatial_zone_type.id \ +FROM spatial_zone_type \ +WHERE perceptual_model.spatial_property = spatial_zone_type.spatial_property; \ +ALTER TABLE perceptual_model \ +ADD FOREIGN KEY (spatialzone_id) REFERENCES spatial_zone_type(id); \ +ALTER TABLE perceptual_model DROP COLUMN spatial_property;" + +try: + conn.execute(text(query)) + print("success") +except Exception as e: + print(e) + +# model & temporal zone +query = "ALTER TABLE perceptual_model ADD COLUMN temporalzone_id int; \ +UPDATE perceptual_model \ +SET temporalzone_id = temporal_zone_type.id \ +FROM temporal_zone_type \ +WHERE perceptual_model.temporal_property = temporal_zone_type.temporal_property; \ +ALTER TABLE perceptual_model \ +ADD FOREIGN KEY (temporalzone_id) REFERENCES temporal_zone_type(id); \ +ALTER TABLE perceptual_model DROP COLUMN temporal_property;" + +try: + conn.execute(text(query)) + print("success") +except Exception as e: + print(e) + +# model & linktable +query = "ALTER TABLE link_process_perceptual \ +ADD FOREIGN KEY (entry_id) REFERENCES perceptual_model(id);" + +try: + conn.execute(text(query)) + print("success") +except Exception as e: + print(e) + +# process taxonomy & alternative names +query = "ALTER TABLE process_alt_names ADD COLUMN process_id int; \ +UPDATE process_alt_names \ +SET process_id = process_taxonomy.id \ +FROM process_taxonomy \ +WHERE process_alt_names.process = process_taxonomy.process; \ +ALTER TABLE process_alt_names \ +ADD FOREIGN KEY (process_id) REFERENCES process_taxonomy(id); \ +ALTER TABLE process_alt_names DROP COLUMN process;" + +try: + conn.execute(text(query)) + print("success") +except Exception as e: + print(e) + +# process taxonomy & function type +query = "ALTER TABLE process_taxonomy ADD COLUMN function_id int; \ +UPDATE process_taxonomy \ +SET function_id = function_type.id \ +FROM function_type \ +WHERE process_taxonomy.function = function_type.name; \ +ALTER TABLE process_taxonomy \ +ADD FOREIGN KEY (function_id) REFERENCES function_type(id); \ +ALTER TABLE process_taxonomy DROP COLUMN function;" + +try: + conn.execute(text(query)) + print("success") +except Exception as e: + print(e) + +# huc_watersheds & locations +query = "ALTER TABLE locations \ +ADD FOREIGN KEY (huc_watershed_id) \ +REFERENCES huc_watersheds(id);" + +try: + conn.execute(text(query)) + print("success") +except Exception as e: + print(e) + + +# linktable & process taxonomy + +# somehow this does not work ... +""" +# try joining on pandas ... +query = "ALTER TABLE link_process_perceptual ADD COLUMN process_id int; \ +UPDATE link_process_perceptual \ +SET process_id = process_taxonomy.id \ +FROM process_taxonomy \ +WHERE link_process_perceptual.process::text ILIKE process_taxonomy.process::text; \ +ALTER TABLE link_process_perceptual \ +ADD FOREIGN KEY (process_id) REFERENCES process_taxonomy(id);" # \ +# ALTER TABLE link_process_perceptual DROP COLUMN process_name;" +""" + +# try joining on pandas ... +query = "ALTER TABLE link_process_perceptual \ +ADD FOREIGN KEY (process_id) REFERENCES process_taxonomy(id);" +# \ +# ALTER TABLE link_process_perceptual DROP COLUMN process_name;" + +try: + conn.execute(text(query)) + print("success") +except Exception as e: + print(e) + +drop_tables = "DROP TABLE giant_table, giant_table_flux, giant_table_store, giant_table_update;" +try: + conn.execute(text(drop_tables)) + print("success") +except Exception as e: + print(e) + +## Join tables and dump everything into csv file +# https://hevodata.com/learn/postgres-export-to-csv/ +join_desired_tables = "\ +CREATE TEMP TABLE giant_table AS (\ +SELECT perceptual_model.id, \ + citations.citation, \ + citations.url, \ + citations.attribution, \ + citations.attribution_url, \ + perceptual_model.figure_num, \ + perceptual_model.figure_caption, \ + perceptual_model.figure_url, \ + locations.name AS watershed_name, \ + locations.lat, \ + locations.lon, \ + locations.area_km2, \ + huc_watersheds.huc8::text AS huc8_id, \ + process_taxonomy.process, \ + process_taxonomy.identifier,\ + function_type.name AS function_name,\ + perceptual_model.num_spatial_zones,\ + spatial_zone_type.spatial_property,\ + perceptual_model.num_temporal_zones,\ + temporal_zone_type.temporal_property,\ + perceptual_model.vegetation_info,\ + perceptual_model.soil_info,\ + perceptual_model.geol_info,\ + perceptual_model.topo_info,\ + perceptual_model.three_d_info,\ + perceptual_model.uncertainty_info,\ + perceptual_model.other_info\ + FROM perceptual_model \ + INNER JOIN citations \ + ON citations.id = perceptual_model.citation_id \ + INNER JOIN locations \ + ON locations.id = perceptual_model.location_id \ + INNER JOIN spatial_zone_type \ + ON spatial_zone_type.id = perceptual_model.spatialzone_id \ + INNER JOIN temporal_zone_type \ + ON temporal_zone_type.id = perceptual_model.temporalzone_id \ + INNER JOIN link_process_perceptual \ + ON perceptual_model.id = link_process_perceptual.entry_id \ + INNER JOIN process_taxonomy \ + ON link_process_perceptual.process_id = process_taxonomy.id \ + INNER JOIN function_type \ + ON process_taxonomy.function_id = function_type.id \ + LEFT JOIN huc_watersheds \ + ON locations.huc_watershed_id = huc_watersheds.id \ +ORDER BY perceptual_model.id \ +);" + +join_desired_tables2 = "\ +CREATE TEMP TABLE giant_table_base AS ( \ + SELECT DISTINCT \ + id, \ + citation, \ + url, \ + attribution, \ + attribution_url, \ + figure_num, \ + figure_caption, \ + figure_url, \ + watershed_name, \ + lat, \ + lon, \ + area_km2, \ + huc8_id AS huc_watershed_id, \ + num_spatial_zones, \ + spatial_property, \ + num_temporal_zones, \ + temporal_property, \ + vegetation_info, \ + soil_info, \ + geol_info, \ + topo_info, \ + three_d_info, \ + uncertainty_info, \ + other_info \ + FROM giant_table \ +); \ +CREATE TEMP TABLE giant_table_flux AS ( \ +SELECT DISTINCT \ + id AS temp_id1, \ + COUNT(process) OVER(PARTITION BY id) AS num_flux, \ + STRING_AGG(process, ', ') OVER(PARTITION BY id) AS flux_list, \ + STRING_AGG(identifier, ', ') OVER(PARTITION BY id) AS flux_id_list \ +FROM giant_table \ + WHERE function_name ILIKE 'Filling of store' \ + OR function_name ILIKE 'Release from store' \ + OR function_name ILIKE 'In-catchment flux' \ + OR function_name ILIKE 'In-store flux' \ + OR function_name ILIKE 'Release' \ + ); \ +CREATE TEMP TABLE giant_table_store AS ( \ +SELECT DISTINCT id AS temp_id2, \ +COUNT(process) OVER(PARTITION BY id) AS num_store, \ + STRING_AGG(process, ', ') OVER(PARTITION BY id) AS store_list, \ + STRING_AGG(identifier, ', ') OVER(PARTITION BY id) AS store_id_list \ +FROM giant_table \ + WHERE function_name ILIKE 'Store' \ + OR function_name ILIKE 'Store, temporary' \ + OR function_name ILIKE 'Store characteristics, temporary' \ + OR function_name ILIKE 'Store characteristics, permanent' \ + ); \ +CREATE TEMP TABLE giant_table_update AS ( \ +SELECT * FROM giant_table_base \ +LEFT JOIN giant_table_store \ +ON giant_table_base.id = giant_table_store.temp_id2 \ +LEFT JOIN giant_table_flux \ +ON giant_table_base.id = giant_table_flux.temp_id1 \ +ORDER BY id \ + ); \ +ALTER TABLE giant_table_update \ +DROP COLUMN temp_id1, \ +DROP COLUMN temp_id2, \ +ADD COLUMN dummy_column NUMERIC DEFAULT 1; \ +" +try: + conn.execute(text(join_desired_tables)) + print("success") +except Exception as e: + print(e) + +try: + conn.execute(text(join_desired_tables2)) + print("success") +except Exception as e: + print(e) + + +# save_table = "\copy giant_table_update TO 'G://Shared drives/Perceptual model review/ForRyoko/perceptual-model-arcgis/db_dev/data/giant_table_update.csv' WITH DELIMITER ',' CSV HEADER;" +# conn.execute(save_table) + +query = "SELECT * FROM giant_table_update" +try: + df_results = pd.read_sql(text(query), conn) + # results = conn.execute(query).fetchall() + print("success") + print(df_results.columns) +except Exception as e: + print(e) + +df_results['huc_watershed_id'].fillna('N/A', inplace=True) +df_results['num_store'].fillna(0, inplace=True) +df_results['num_flux'].fillna(0, inplace=True) +df_results['area_km2'].fillna(-9999, inplace=True) +df_results.fillna('N/A', inplace=True) +df_results.to_csv('../data/giant_table_v4.csv', sep=',', header=True, index=False, encoding='utf-8') + +conn.close() +db.dispose() +print('closed connections') diff --git a/src/old_codes/building_database_onlyFigModels_wo_hucwatersheds.py b/src/old_codes/building_database_onlyFigModels_wo_hucwatersheds.py new file mode 100644 index 0000000..7d93b81 --- /dev/null +++ b/src/old_codes/building_database_onlyFigModels_wo_hucwatersheds.py @@ -0,0 +1,534 @@ + +####################################################### +## Script to join all Excel files and build a database +####################################################### + +import os +import numpy as np +import pandas as pd +import configparser +from sqlalchemy import create_engine +from sqlalchemy import text + +################################### +## 0. Read data +################################### +os.chdir(r'G:\Shared drives\Perceptual model review\ForRyoko\perceptual-model-arcgis\dev\scripts') + +df_loc = pd.read_excel('../data/Location_formatted.xlsx') # The lat/lon should be pre-formatted in decimal units +# df_model = pd.read_csv('../data/ModelAnalysis.csv', encoding='utf-8') +df_model = pd.read_excel('../data/ModelAnalysis.xlsx') +df_taxonomy = pd.read_excel('../data/ProcessHierarchyNetwork.xlsx') +df_FunctionType = pd.read_excel('../data/FunctionType.xlsx') + +## Parse dataframe into table + +# Location table +# Sanity check if data can be joined +# df.set_index('key').join(other.set_index('key')) +df_loc["id"] = df_loc.index + 1 +df_loc = df_loc.drop(columns='Unnamed: 0') +df_loc["huc_watershed_id"] = np.nan + +# FunctionType table +df_FunctionType["id"] = df_FunctionType.index + 1 + +# Citation table +df_model["attribution_url"].fillna(df_model["url"], inplace=True) +df_citation = df_model[["citation", "url"]].copy() +df_citation["attribution"] = df_model["attribution"].copy() +df_citation["attribution_url"] = df_model["attribution_url"].copy() +df_citation["id"] = df_citation.index + 1 + +# Spatial and temporal zone tables +df_spatialZoneType = df_model["spatial_property"].copy().drop_duplicates() +df_spatialZoneType = df_spatialZoneType.to_frame() +df_spatialZoneType.reset_index(inplace=True) +df_spatialZoneType = df_spatialZoneType.drop(columns='index') +df_spatialZoneType['id'] = df_spatialZoneType.index + 1 +df_temporalZoneType = df_model["temporal_property"].copy().drop_duplicates() +df_temporalZoneType = df_temporalZoneType.to_frame() +df_temporalZoneType.reset_index(inplace=True) +df_temporalZoneType = df_temporalZoneType.drop(columns='index') +df_temporalZoneType['id'] = df_temporalZoneType.index + 1 + +# Alternative name table +df_altNames0 = df_taxonomy.set_index(['process', 'function', 'identifier', 'process_level']).apply( + lambda x: x.str.split(',').explode()).reset_index() +df_altNames = df_altNames0[['alternative_names', 'process']].copy() +df_altNames['alternative_names'] = df_altNames['alternative_names'].str.strip() +df_altNames['alternative_names'] = df_altNames['alternative_names'].str.capitalize() +df_altNames.dropna(axis=0, inplace=True) +df_altNames["id"] = df_altNames.index + 1 + +# Model table +df_model["id"] = df_model.index + 1 +df_modelmain = df_model[['id', 'citation', 'watershed_name', + 'figure_num', 'figure_url', 'figure_caption', + 'spatial_property', 'num_spatial_zones', 'temporal_property', + 'num_temporal_zones', 'vegetation_info', 'soil_info', 'geol_info', + 'topo_info', 'three_d_info', 'uncertainty_info', 'other_info' + ]].copy() + +# corgi_fig = "https://i.ibb.co/stdkk6P/corgi-with-notes.png" +corgi_fig = "https://sdsugeo.maps.arcgis.com/sharing/rest/content/items/df499e37a2ef40d7885966952c627e01/data" +df_modelmain['figure_url'] = df_modelmain['figure_url'].fillna(corgi_fig) +df_modelmain['figure_url'][df_citation['attribution']=="Not open-access"] = corgi_fig +df_citation['YN'] = df_citation['attribution']=="Not open-access" +df_modelmain.rename(columns={"figure_num": "model_ref"}) + +# LinkProcessPerceptual table +# Get all the process original text and taxonomy name from model +frames = [df_model[['id', 'flux1', 'flux1_taxonomy']].copy().rename( + columns={"id": "entry_id", "flux1": "original_text", "flux1_taxonomy": "process"}), + df_model[['id', 'flux2', 'flux2_taxonomy']].copy().rename(columns={"id": "entry_id", "flux2": "original_text", "flux2_taxonomy": "process"}), + df_model[['id', 'flux3', 'flux3_taxonomy']].copy().rename(columns={"id": "entry_id", "flux3": "original_text", "flux3_taxonomy": "process"}), + df_model[['id', 'flux4', 'flux4_taxonomy']].copy().rename(columns={"id": "entry_id", "flux4": "original_text", "flux4_taxonomy": "process"}), + df_model[['id', 'flux5', 'flux5_taxonomy']].copy().rename(columns={"id": "entry_id", "flux5": "original_text", "flux5_taxonomy": "process"}), + df_model[['id', 'flux6', 'flux6_taxonomy']].copy().rename(columns={"id": "entry_id", "flux6": "original_text", "flux6_taxonomy": "process"}), + df_model[['id', 'flux7', 'flux7_taxonomy']].copy().rename(columns={"id": "entry_id", "flux7": "original_text", "flux7_taxonomy": "process"}), + df_model[['id', 'flux8', 'flux8_taxonomy']].copy().rename(columns={"id": "entry_id", "flux8": "original_text", "flux8_taxonomy": "process"}), + df_model[['id', 'flux9', 'flux9_taxonomy']].copy().rename(columns={"id": "entry_id", "flux9": "original_text", "flux9_taxonomy": "process"}), + df_model[['id', 'flux10', 'flux10_taxonomy']].copy().rename(columns={"id": "entry_id", "flux10": "original_text", "flux10_taxonomy": "process"}), + df_model[['id', 'flux11', 'flux11_taxonomy']].copy().rename(columns={"id": "entry_id", "flux11": "original_text", "flux11_taxonomy": "process"}), + df_model[['id', 'flux12', 'flux12_taxonomy']].copy().rename(columns={"id": "entry_id", "flux12": "original_text", "flux12_taxonomy": "process"}), + df_model[['id', 'store1', 'store1_taxonomy']].copy().rename(columns={"id": "entry_id", "store1": "original_text", "store1_taxonomy": "process"}), + df_model[['id', 'store2', 'store2_taxonomy']].copy().rename(columns={"id": "entry_id", "store2": "original_text", "store2_taxonomy": "process"}), + df_model[['id', 'store3', 'store3_taxonomy']].copy().rename(columns={"id": "entry_id", "store3": "original_text", "store3_taxonomy": "process"}), + df_model[['id', 'store4', 'store4_taxonomy']].copy().rename( columns={"id": "entry_id", "store4": "original_text", "store4_taxonomy": "process"}), + df_model[['id', 'store5', 'store5_taxonomy']].copy().rename(columns={"id": "entry_id", "store5": "original_text", "store5_taxonomy": "process"}), + df_model[['id', 'store6', 'store6_taxonomy']].copy().rename(columns={"id": "entry_id", "store6": "original_text", "store6_taxonomy": "process"}), + df_model[['id', 'store7', 'store7_taxonomy']].copy().rename(columns={"id": "entry_id", "store7": "original_text", "store7_taxonomy": "process"}), + df_model[['id', 'store8', 'store8_taxonomy']].copy().rename(columns={"id": "entry_id", "store8": "original_text", "store8_taxonomy": "process"}), + ] + +df_linkProcessPerceptual0 = pd.concat(frames, axis=0, ignore_index=True) +df_linkProcessPerceptual0["id"] = df_linkProcessPerceptual0.index + 1 + +# Create taxonomy table +df_process0 = df_taxonomy.drop(columns='alternative_names') + +# join process taxonomy and model table +df_linkProcessPerceptual0["process_lower"] = df_linkProcessPerceptual0['process'].str.lower() +df_linkProcessPerceptual0["process_lower"] = df_linkProcessPerceptual0['process_lower'].str.strip() +df_process0["process_lower"] = df_process0['process'].str.lower() +df_process0["process_lower"] = df_process0['process_lower'].str.strip() + +# find and add some new process from model table to taxonomy table (# Check here if you want to check process miscategorization) +df_linkProcessPerceptual1 = df_linkProcessPerceptual0.merge(df_process0, on='process_lower', how='left') +new_process = df_linkProcessPerceptual1.loc[(df_linkProcessPerceptual1['process_x'].isnull() == False) & ( + df_linkProcessPerceptual1['process_y'].isnull() == True)] +new_process.drop_duplicates(subset='process_lower', inplace=True) + +add_new_process = pd.DataFrame( + {'process': new_process['process_x'], 'process_lower': new_process['process_x'].str.lower(), + 'identifier': ['NewProcess'] * len(new_process['process_x'])}) +df_process1 = pd.concat([df_process0, add_new_process]) +df_process1["id"] = df_process1.reset_index().index + 1 + + +# re-join process taxonomy and model table with new process +df_linkProcessPerceptual2 = df_linkProcessPerceptual0.merge(df_process1, on='process_lower', how='left') +df_linkProcessPerceptual2.rename(columns={"id_y": "process_id"}, inplace=True) +df_linkProcessPerceptual = df_linkProcessPerceptual2.drop( + columns={'process_x', 'id_x', 'process_lower', 'process_y', 'function', 'identifier', 'process_level'}) +df_linkProcessPerceptual.dropna(subset=['original_text'], axis=0, inplace=True) +df_linkProcessPerceptual["id"] = df_linkProcessPerceptual.reset_index().index + 1 +df_linkProcessPerceptual["process_id"] = df_linkProcessPerceptual["process_id"].astype('int') +# df_linkProcessPerceptual[df_linkProcessPerceptual['process_id'].isnull()] +df_process = df_process1.drop(columns='process_lower') + +################################### +## 1. Connect to SQL database +################################### + +# Read config +config = configparser.ConfigParser() +config.read('config.ini') + +DB_NAME = config['postgresql']['DB_NAME'] +HOST = config['postgresql']['HOST'] +PORT = config['postgresql']['PORT'] +USER_NAME = config['postgresql']['USER_NAME'] +PASSWD = config['postgresql']['PASSWD'] + +# Connect to database +conn_string = f'postgresql://{USER_NAME}:{PASSWD}@{HOST}:{PORT}/{DB_NAME}' +db = create_engine(conn_string, client_encoding='utf8') +try: + conn = db.connect() + print("connection to '%s'@'%s' success!" % (DB_NAME, HOST)) +except Exception as e: + print("connection to '%s'@'%s' failed." % (DB_NAME, HOST)) + print(e) + +################################### +## 2. Create database +################################### + +tables = ['locations', 'citations', 'spatial_zone_type', 'temporal_zone_type', 'process_alt_names', + 'function_type', 'perceptual_model', 'link_process_perceptual', 'process_taxonomy'] +conn.execute(text("set search_path to public, perceptual_model")) +conn.execute(text("SET CLIENT_ENCODING TO 'UTF8';")) +for table in tables: + try: + conn.execute(text(f"DROP TABLE {table} CASCADE;")) + print('drop old tables') + except Exception as e: + print(e) + +schema = 'perceptual_model' +connarg = {'con': conn, 'schema': 'perceptual_model', 'if_exists': 'replace', 'index': False} + +# set table names in lower case https://github.com/pandas-dev/pandas/issues/13206 +df_loc.to_sql('locations', **connarg) +df_citation.to_sql('citations', **connarg) +df_spatialZoneType.to_sql('spatial_zone_type', **connarg) +df_temporalZoneType.to_sql('temporal_zone_type', **connarg) +df_altNames.to_sql('process_alt_names', **connarg) +df_FunctionType.to_sql('function_type', **connarg) +df_modelmain.to_sql('perceptual_model', **connarg) +df_linkProcessPerceptual.to_sql('link_process_perceptual', **connarg) +df_process.to_sql('process_taxonomy', **connarg) + +# Relate the location table to HUC8 watershed table +query = "ALTER TABLE perceptual_model.locations \ +ADD COLUMN pt geometry(point, 4326); \ +WITH pt_geom AS ( \ + SELECT id, ST_SetSRID(ST_MakePoint(lon, lat), 4326) AS pt \ + from perceptual_model.locations \ + ) \ +UPDATE perceptual_model.locations \ +SET pt = pt_geom.pt \ +FROM pt_geom \ +WHERE pt_geom.id = locations.id; \ + " +# ALTER TABLE locations \ +# ALTER COLUMN huc_watershed_id TYPE INTEGER USING huc_watershed_id::integer; \ +# WITH joined_huc8_pt AS ( \ +# SELECT locations.id AS pt_id, huc_watersheds.id as huc8_id, huc_watersheds.huc8 AS huc8_code \ +# FROM locations \ +# JOIN huc_watersheds \ +# ON ST_contains(ST_Transform(huc_watersheds.geom, 4326), locations.pt) \ +# ) \ +# UPDATE locations \ +# SET huc_watershed_id = joined_huc8_pt.huc8_id \ +# FROM joined_huc8_pt \ +# WHERE locations.id = joined_huc8_pt.pt_id;" + +try: + conn.execute(text(query)) + print("success") +except Exception as e: + print(e) + +# add primary keys +for table in tables: + try: + conn.execute(text(f"ALTER TABLE {table} ADD PRIMARY KEY (id);")) + print(f"successfully created table {table}") + except Exception as e: + print(e) + +# add foreign keys +# model & location +query = "ALTER TABLE perceptual_model ADD COLUMN location_id int; \ +UPDATE perceptual_model \ +SET location_id = locations.id \ +FROM locations \ +WHERE perceptual_model.watershed_name = locations.name; \ +ALTER TABLE perceptual_model \ +ADD FOREIGN KEY (location_id) REFERENCES locations(id); \ +ALTER TABLE perceptual_model DROP COLUMN watershed_name;" + +try: + conn.execute(text(query)) + print("success") +except Exception as e: + print(e) + +# model & citation +query = "ALTER TABLE perceptual_model ADD COLUMN citation_id int; \ +UPDATE perceptual_model \ +SET citation_id = citations.id \ +FROM citations \ +WHERE perceptual_model.citation = citations.citation; \ +ALTER TABLE perceptual_model \ +ADD FOREIGN KEY (citation_id) REFERENCES citations(id); \ +ALTER TABLE perceptual_model DROP COLUMN citation;" + +try: + conn.execute(text(query)) + print("success") +except Exception as e: + print(e) + +# model & spatial zone +query = "ALTER TABLE perceptual_model ADD COLUMN spatialzone_id int; \ +UPDATE perceptual_model \ +SET spatialzone_id = spatial_zone_type.id \ +FROM spatial_zone_type \ +WHERE perceptual_model.spatial_property = spatial_zone_type.spatial_property; \ +ALTER TABLE perceptual_model \ +ADD FOREIGN KEY (spatialzone_id) REFERENCES spatial_zone_type(id); \ +ALTER TABLE perceptual_model DROP COLUMN spatial_property;" + +try: + conn.execute(text(query)) + print("success") +except Exception as e: + print(e) + +# model & temporal zone +query = "ALTER TABLE perceptual_model ADD COLUMN temporalzone_id int; \ +UPDATE perceptual_model \ +SET temporalzone_id = temporal_zone_type.id \ +FROM temporal_zone_type \ +WHERE perceptual_model.temporal_property = temporal_zone_type.temporal_property; \ +ALTER TABLE perceptual_model \ +ADD FOREIGN KEY (temporalzone_id) REFERENCES temporal_zone_type(id); \ +ALTER TABLE perceptual_model DROP COLUMN temporal_property;" + +try: + conn.execute(text(query)) + print("success") +except Exception as e: + print(e) + +# model & linktable +query = "ALTER TABLE link_process_perceptual \ +ADD FOREIGN KEY (entry_id) REFERENCES perceptual_model(id);" + +try: + conn.execute(text(query)) + print("success") +except Exception as e: + print(e) + +# process taxonomy & alternative names +query = "ALTER TABLE process_alt_names ADD COLUMN process_id int; \ +UPDATE process_alt_names \ +SET process_id = process_taxonomy.id \ +FROM process_taxonomy \ +WHERE process_alt_names.process = process_taxonomy.process; \ +ALTER TABLE process_alt_names \ +ADD FOREIGN KEY (process_id) REFERENCES process_taxonomy(id); \ +ALTER TABLE process_alt_names DROP COLUMN process;" + +try: + conn.execute(text(query)) + print("success") +except Exception as e: + print(e) + +# process taxonomy & function type +query = "ALTER TABLE process_taxonomy ADD COLUMN function_id int; \ +UPDATE process_taxonomy \ +SET function_id = function_type.id \ +FROM function_type \ +WHERE process_taxonomy.function = function_type.name; \ +ALTER TABLE process_taxonomy \ +ADD FOREIGN KEY (function_id) REFERENCES function_type(id); \ +ALTER TABLE process_taxonomy DROP COLUMN function;" + +try: + conn.execute(text(query)) + print("success") +except Exception as e: + print(e) + +# # huc_watersheds & locations +# query = "ALTER TABLE locations \ +# ADD FOREIGN KEY (huc_watershed_id) \ +# REFERENCES huc_watersheds(id);" + +# try: +# conn.execute(text(query)) +# print("success") +# except Exception as e: +# print(e) + + +# linktable & process taxonomy + +# somehow this does not work ... +""" +# try joining on pandas ... +query = "ALTER TABLE link_process_perceptual ADD COLUMN process_id int; \ +UPDATE link_process_perceptual \ +SET process_id = process_taxonomy.id \ +FROM process_taxonomy \ +WHERE link_process_perceptual.process::text ILIKE process_taxonomy.process::text; \ +ALTER TABLE link_process_perceptual \ +ADD FOREIGN KEY (process_id) REFERENCES process_taxonomy(id);" # \ +# ALTER TABLE link_process_perceptual DROP COLUMN process_name;" +""" + +# try joining on pandas ... +query = "ALTER TABLE link_process_perceptual \ +ADD FOREIGN KEY (process_id) REFERENCES process_taxonomy(id);" +# \ +# ALTER TABLE link_process_perceptual DROP COLUMN process_name;" + +try: + conn.execute(text(query)) + print("Link taxonomy & perceptual model -- success") +except Exception as e: + print(e) + +# drop_tables = "DROP TABLE giant_table, giant_table_flux, giant_table_store, giant_table_update;" +# try: +# conn.execute(text(drop_tables)) +# print("Drop old temp tables -- success") +# except Exception as e: +# print(e) + +## Join tables and dump everything into csv file +# https://hevodata.com/learn/postgres-export-to-csv/ +join_desired_tables = "\ +CREATE TEMP TABLE giant_table AS (\ +SELECT perceptual_model.id, \ + citations.citation, \ + citations.url, \ + citations.attribution, \ + citations.attribution_url, \ + perceptual_model.figure_num, \ + perceptual_model.figure_caption, \ + perceptual_model.figure_url, \ + locations.name AS watershed_name, \ + locations.lat, \ + locations.lon, \ + locations.area_km2, \ + process_taxonomy.process, \ + process_taxonomy.identifier,\ + function_type.name AS function_name,\ + perceptual_model.num_spatial_zones,\ + spatial_zone_type.spatial_property,\ + perceptual_model.num_temporal_zones,\ + temporal_zone_type.temporal_property,\ + perceptual_model.vegetation_info,\ + perceptual_model.soil_info,\ + perceptual_model.geol_info,\ + perceptual_model.topo_info,\ + perceptual_model.three_d_info,\ + perceptual_model.uncertainty_info,\ + perceptual_model.other_info\ + FROM perceptual_model \ + INNER JOIN citations \ + ON citations.id = perceptual_model.citation_id \ + INNER JOIN locations \ + ON locations.id = perceptual_model.location_id \ + INNER JOIN spatial_zone_type \ + ON spatial_zone_type.id = perceptual_model.spatialzone_id \ + INNER JOIN temporal_zone_type \ + ON temporal_zone_type.id = perceptual_model.temporalzone_id \ + INNER JOIN link_process_perceptual \ + ON perceptual_model.id = link_process_perceptual.entry_id \ + INNER JOIN process_taxonomy \ + ON link_process_perceptual.process_id = process_taxonomy.id \ + INNER JOIN function_type \ + ON process_taxonomy.function_id = function_type.id \ +ORDER BY perceptual_model.id \ +);" + +join_desired_tables2 = "\ +CREATE TEMP TABLE giant_table_base AS ( \ +SELECT DISTINCT \ + id, \ + citation, \ + url, \ + attribution, \ + attribution_url, \ + figure_num, \ + figure_caption, \ + figure_url, \ + watershed_name, \ + lat, \ + lon, \ + area_km2, \ + num_spatial_zones, \ + spatial_property, \ + num_temporal_zones, \ + temporal_property, \ + vegetation_info, \ + soil_info, \ + geol_info, \ + topo_info, \ + three_d_info, \ + uncertainty_info, \ + other_info \ +FROM giant_table \ +); \ +CREATE TEMP TABLE giant_table_flux AS ( \ +SELECT DISTINCT \ +id AS temp_id1, \ +COUNT(process) OVER(PARTITION BY id) AS num_flux, \ +STRING_AGG(process, ', ') OVER(PARTITION BY id) AS flux_list, \ +STRING_AGG(identifier, ', ') OVER(PARTITION BY id) AS flux_id_list \ +FROM giant_table \ + WHERE function_name ILIKE 'Filling of store' \ + OR function_name ILIKE 'Release from store' \ + OR function_name ILIKE 'In-catchment flux' \ + OR function_name ILIKE 'In-store flux' \ + OR function_name ILIKE 'Release' \ + ); \ +CREATE TEMP TABLE giant_table_store AS ( \ +SELECT DISTINCT id AS temp_id2, \ +COUNT(process) OVER(PARTITION BY id) AS num_store, \ +STRING_AGG(process, ', ') OVER(PARTITION BY id) AS store_list, \ +STRING_AGG(identifier, ', ') OVER(PARTITION BY id) AS store_id_list \ +FROM giant_table \ + WHERE function_name ILIKE 'Store' \ + OR function_name ILIKE 'Store, temporary' \ + OR function_name ILIKE 'Store characteristics, temporary' \ + OR function_name ILIKE 'Store characteristics, permanent' \ + ); \ +CREATE TEMP TABLE giant_table_update AS ( \ +SELECT * FROM giant_table_base \ +LEFT JOIN giant_table_store \ +ON giant_table_base.id = giant_table_store.temp_id2 \ +LEFT JOIN giant_table_flux \ +ON giant_table_base.id = giant_table_flux.temp_id1 \ +ORDER BY id \ + ); \ +ALTER TABLE giant_table_update \ +DROP COLUMN temp_id1, \ +DROP COLUMN temp_id2, \ +ADD COLUMN dummy_column NUMERIC DEFAULT 1; \ +" + +try: + conn.execute(text(join_desired_tables)) + print("success") +except Exception as e: + print(e) + +try: + conn.execute(text(join_desired_tables2)) + print("success") +except Exception as e: + print(e) + + +# save_table = "\copy giant_table_update TO 'G://Shared drives/Perceptual model review/ForRyoko/perceptual-model-arcgis/db_dev/data/giant_table_update.csv' WITH DELIMITER ',' CSV HEADER;" +# conn.execute(save_table) + +query = "SELECT * FROM giant_table_update" +try: + df_results = pd.read_sql(text(query), conn) + # results = conn.execute(query).fetchall() + print("success") + print(df_results.columns) +except Exception as e: + print(e) + +df_results['huc_watershed_id'] = 'N/A' +df_results['num_store'].fillna(0, inplace=True) +df_results['num_flux'].fillna(0, inplace=True) +df_results['area_km2'].fillna(-9999, inplace=True) +df_results.fillna('N/A', inplace=True) +df_results.to_csv('../data/giant_table_v4.csv', sep=',', header=True, index=False, encoding='utf-8') + +conn.close() +db.dispose() +print('closed connections') diff --git a/src/old_codes/building_database_read_huc8.sql b/src/old_codes/building_database_read_huc8.sql new file mode 100644 index 0000000..3661f37 --- /dev/null +++ b/src/old_codes/building_database_read_huc8.sql @@ -0,0 +1,114 @@ +-- This script reads HUC8 watershed shapefile and relate it with perceptual model locations using PostGIS +-- Ryoko Araki + +-- 0. Build database using "building_database.py" + +-- 1. Read HUC8 shapefiles into PostgreSQL +-- Run the following command in the command prompt. +-- Only need to read once when creating a new database +-- "C:\OSGeo4W64\bin\ogr2ogr.exe" -f "PostgreSQL" PG:"host=[HOST_NAME] port=[PORT_NUM] dbname=[DB_NAME] user=[USER_NAME] password=[PASSWROD]" -nln "perceptual_model.huc_watersheds" -nlt POLYGON -lco GEOMETRY_NAME=geom -lco FID=id -lco PRECISION=NO [PATH_TO_THE_SHAPEFILE] + +-- c.f. +-- https://gdal.org/programs/ogr2ogr.html +-- "-f": output format name +-- "-nln\n",
+ "
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+ ],
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+ " Perceptual Models Around the World\n",
+ " \n",
+ "
descriptionWeb Map by raraki8159_SDSUGeo\n",
+ "
Last Modified: September 12, 2022\n", + "
0 comments, 0 views\n", + "
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+ " description
Web Map by raraki8159_SDSUGeo\n",
+ " Last Modified: September 12, 2022\n", + "
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+ " figure_models_v4\n",
+ " \n",
+ "
Feature Layer Collection by raraki8159_SDSUGeo\n",
+ "
Last Modified: February 24, 2023\n", + "
0 comments, 326 views\n", + "
\n",
+ " Feature Layer Collection by raraki8159_SDSUGeo\n",
+ " Last Modified: February 24, 2023\n", + "
0 comments, 326 views\n", + "
A perceptual model of {watershed_name}
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"mediaInfos\": [\n", + " {\n", + " \"altText\": \"\",\n", + " \"caption\": \"Caption: {figure_caption}; From Figure {figure_num} in {citation}; {attribution}\",\n", + " \"refreshInterval\": 0,\n", + " \"title\": \"\",\n", + " \"type\": \"image\",\n", + " \"value\": {\n", + " \"linkURL\": \"{url}\",\n", + " \"sourceURL\": \"{figure_url}\"\n", + " }\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"media\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Process information in the model
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"num_store\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Number of stores\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"store_list\",\n", + " \"isEditable\": True,\n", + " \"label\": \"List of stores\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"store_id_list\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Hashtags of stores\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"num_flux\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Number of fluxes\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"flux_list\",\n", + " \"isEditable\": True,\n", + " \"label\": \"List of fluxes\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"flux_id_list\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Hashtags of fluxes\",\n", + " \"visible\": True\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Spatio-temporal heterogeneity in the model
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"num_spatial_zones\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Number of spatial zones\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"spatial_property\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Spatial property\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"num_temporal_zones\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Number of temporal zones\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"temporal_property\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Temporal property\",\n", + " \"visible\": True\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Ancillary information in the model
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"vegetation_info\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Vegetation\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"soil_info\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Soil\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"geol_info\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Geology\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"topo_info\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Topography\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"uncertainty_info\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Uncertainty range\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"three_d_info\",\n", + " \"isEditable\": True,\n", + " \"label\": \"3-dimensional description\",\n", + " \"visible\": True\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " },\n", + " {\n", + " \"text\": \"\\u25bc Watershed information
\",\n", + " \"type\": \"text\"\n", + " },\n", + " {\n", + " \"description\": \"\",\n", + " \"fieldInfos\": [\n", + " {\n", + " \"fieldName\": \"area_km2\",\n", + " \"isEditable\": True,\n", + " \"label\": \"Watershed area (km2)\",\n", + " \"visible\": True\n", + " },\n", + " {\n", + " \"fieldName\": \"huc_watershed_id\",\n", + " \"isEditable\": True,\n", + " \"label\": \"US HUC8 watershed ID\",\n", + " \"visible\": True\n", + " }\n", + " ],\n", + " \"title\": \"\",\n", + " \"type\": \"fields\"\n", + " }\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "8f454aaa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Update the map\n", + "webmap_update = update_map(webmap_item_id, webmap_data)\n", + "webmap_update" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "4f8a2737", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Verify changes updated\n", + "webbrowser.open('https://sdsugeo.maps.arcgis.com/apps/mapviewer/index.html?webmap={}'.format(webmap_item_id), new=2)\n", + "\n", + "# https://sdsugeo.maps.arcgis.com/apps/mapviewer/index.html?webmap=e535103892fd41cd915d60ca4d1d9d44" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68607e8b", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "814a091a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d173d565", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "arcgis", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + }, + "vscode": { + "interpreter": { + "hash": "3140ae756112501f18b23ec79a6bbbb54e715b030c96600dfefb60d12a8f240f" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/utils/parse_location.py b/src/utils/parse_location.py new file mode 100644 index 0000000..56fc2d8 --- /dev/null +++ b/src/utils/parse_location.py @@ -0,0 +1,66 @@ +from hashlib import new +import pandas as pd +import numpy as np +import os +import folium + +df = pd.read_excel('Location.xlsx', sheet_name='WatershedLocations') + +strValue = 'N' +replacementStr = '-' + +column_name = df.columns +for col in column_name[1:7]: + """ + # Drop direction char and add sign + if 'lat' in col: + directions = ['S', 'N'] + coord = 'lat' + elif 'lon' in col: + directions = ['W', 'E'] + coord = 'lon' + """ + + df2 = df[col].copy() + """ + for d in range(len(directions)): + if directions[d] == 'S' or directions[d] == 'W': + idx_negative = df2.str.endswith(directions[d], na=False) + df2[idx_negative] = df2[idx_negative].str[:-1] + elif directions[d] == 'N' or directions[d] == 'E': + idx_positive = df2.str.endswith(directions[d], na=False) + df2[idx_positive] = df2[idx_positive].str[:-1] + """ + df_degree = df2.str.split('d', expand=True) + df_second = df_degree[1].str.split('s', expand=True) + df_miute = df_second[0].str.split('m', expand=True) + + df3 = pd.concat([df_degree[0].str.strip(), df_miute[0].str.strip(), df_miute[1].str.strip()], axis=1, ignore_index=True) + df3 = df3.replace(r'^\s*$', np.nan, regex=True) + df3.fillna(value=np.nan, inplace=True) + df3.fillna(value=0, inplace=True) + new_value = df3[0].astype(float) + df3[1].astype(float)/60 + df3[2].astype(float)/3600 + + if col == 'lat': + lat = new_value.to_numpy() + if col == 'lon': + lon = new_value.to_numpy() + if col == 'max_lat': + max_lat = new_value.to_numpy() + if col == 'min_lat': + min_lat = new_value.to_numpy() + if col == 'max_lon': + max_lon = new_value.to_numpy() + if col == 'min_lon': + min_lon = new_value.to_numpy() + +avg_lat = (max_lat + min_lat) / 2 +avg_lon = (max_lon + min_lon) / 2 +lat[lat==0] = avg_lat[lat==0] +lon[lon==0]= avg_lon[lon==0] + +list_of_tuples = list(zip(lat, lon)) +df_calc_coord = pd.DataFrame(list_of_tuples, columns=['lat','lon']) +df_calc_coord = pd.concat([df.Name, df_calc_coord],axis=1) +df_calc_coord.to_json('./location.json') +df_calc_coord.to_excel('./Location_formatted.xlsx', sheet_name='Formatted_coordinates')