{ "cells": [ { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.038736, "end_time": "2021-01-15T21:55:01.502335", "exception": false, "start_time": "2021-01-15T21:55:01.463599", "status": "completed" }, "tags": [] }, "source": [ "# Generating statistics for subset of Wikidata\n", "\n", "Example Dataset wikidata subset: https://drive.google.com/drive/u/1/folders/1KjNwV5M2G3JzCrPgqk_TSx8wTE49O2Sx \\\n", "Example Dataset statistics: https://drive.google.com/drive/u/0/folders/1_4Mxd0MAo0l9aR3aInv0YMTJrtneh7HW \n", "\n", "## Example Invocation command\n", "\n", " papermill Knowledge-Graph-Profiler.ipynb \\\n", " Knowledge-Graph-Profiler-output.ipynb \\\n", " -p wikidata_parts_folder '/Users/shashanksaurabh/Desktop/Data_isi/Chemical' \\\n", " -p cache_folder '/Users/shashanksaurabh/Desktop/Data_isi/Temp' \\\n", " -p output_folder '/Users/shashanksaurabh/Desktop/Data_isi/output' \\\n", " -p compute_graph_statistics false \\\n", " -p restart_global 'True' \\\n", " -p K \"10\"\n", " \n", "**wikidata_parts_folder:** Folder containing the input parts files for the Knowledge Graph \n", "**cache_folder:** Folder used by Kypher query language for its sql store. \n", "**output_folder:** Output folder \n", "**restart_global:** set this to \"false\" to force recreation of all output files \n", "**compute_graph_statistics:** compute graph statistics or not \n", "**K:** display top k classes and properties \n", " \n", "## The following files are expected in the input \"parts\" folder\n", " \"claims.external-id.tsv.gz\"\n", " \"claims.time.tsv.gz\"\n", " \"claims.wikibase-item.tsv.gz\"\n", " \"claims.quantity.tsv.gz\"\n", " \"claims.statistics.tsv.gz\"\n", " \"claims.wikibase-form.tsv.gz\"\n", " \"claims.monolingualtext.tsv.gz\"\n", " \"claims.math.tsv.gz\"\n", " \"claims.commonsMedia.tsv.gz\"\n", " \"claims.globe-coordinate.tsv.gz\"\n", " \"claims.musical-notation.tsv.gz\"\n", " \"claims.geo-shape.tsv.gz\"\n", " \"claims.url.tsv.gz\"\n", " \"claims.string.tsv.gz\"\n", " \"aliases.en.tsv.gz\"\n", " \"descriptions.en.tsv.gz\"\n", " \"labels.en.tsv.gz\"\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:01.573702Z", "iopub.status.busy": "2021-01-15T21:55:01.572864Z", "iopub.status.idle": "2021-01-15T21:55:01.576243Z", "shell.execute_reply": "2021-01-15T21:55:01.576670Z" }, "papermill": { "duration": 0.04035, "end_time": "2021-01-15T21:55:01.576904", "exception": false, "start_time": "2021-01-15T21:55:01.536554", "status": "completed" }, "tags": [ "parameters" ] }, "outputs": [], "source": [ "# path to folder which contains all files corresponding to the wikidata subset. \n", "#(For more information on wikidata subset please check Example 8)\n", "\n", "wikidata_parts_folder = \"/Users/amandeep/Documents/kypher/wikidata_os_v5/parts\"\n", "\n", "# The notebook creates a cache, which is present in the cache_folder. The cache can be deleted after the execution.\n", "cache_folder = \"/Users/amandeep/Documents/kypher/temp.wikidata_os_v5/temp\"\n", "\n", "# path to the folder where the output (here statistics) would be stored\n", "output_folder = \"/Users/amandeep/Documents/kypher/wikidata_os_v5/profiler\"\n", "\n", "# In each of statistics top K results are chosen.\n", "#In the following examples this has been implemented using the --limit attribute.\n", "K = \"5\"\n", "K = int(K)\n", "\n", "# Set it False if you want to generate all the files from scratch and don't use any previous files\n", "restart_global = True\n", "\n", "compute_graph_statistics = 'true'\n", "if compute_graph_statistics and compute_graph_statistics.lower() == 'true':\n", " compute_graph_statistics = True" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:01.636591Z", "iopub.status.busy": "2021-01-15T21:55:01.636025Z", "iopub.status.idle": "2021-01-15T21:55:01.637750Z", "shell.execute_reply": "2021-01-15T21:55:01.638088Z" }, "papermill": { "duration": 0.034989, "end_time": "2021-01-15T21:55:01.638238", "exception": false, "start_time": "2021-01-15T21:55:01.603249", "status": "completed" }, "tags": [ "injected-parameters" ] }, "outputs": [], "source": [ "# Parameters\n", "wikidata_parts_folder = \"/Users/amandeep/Documents/kypher/wikidata_os_v5/parts\"\n", "cache_folder = \"/Users/amandeep/Documents/kypher/temp.wikidata_os_v5/profiler_temp\"\n", "output_folder = \"/Users/amandeep/Documents/kypher/wikidata_os_v5/profiler\"\n", "compute_graph_statistics = \"true\"\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:01.696280Z", "iopub.status.busy": "2021-01-15T21:55:01.695287Z", "iopub.status.idle": "2021-01-15T21:55:01.698379Z", "shell.execute_reply": "2021-01-15T21:55:01.699000Z" }, "papermill": { "duration": 0.033884, "end_time": "2021-01-15T21:55:01.699190", "exception": false, "start_time": "2021-01-15T21:55:01.665306", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# This is there so that it can be passed via papermill parameters\n", "try:\n", " if \"restart_global\" in globals() and (restart_global == \"True\" or restart_global == True):\n", " restart_global = True\n", " elif \"restart_global\" in globals() and (restart_global == \"False\" or restart_global ==False):\n", " restart_global = False\n", " else:\n", " restart_global = True\n", "except Exception as e:\n", " restart_global = True" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:01.755733Z", "iopub.status.busy": "2021-01-15T21:55:01.755235Z", "iopub.status.idle": "2021-01-15T21:55:02.507997Z", "shell.execute_reply": "2021-01-15T21:55:02.508449Z" }, "papermill": { "duration": 0.78257, "end_time": "2021-01-15T21:55:02.508647", "exception": false, "start_time": "2021-01-15T21:55:01.726077", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import io\n", "import os\n", "import subprocess\n", "import sys\n", "import math\n", "import gc\n", "\n", "import numpy as np\n", "import pandas as pd\n", "\n", "from pathlib import Path\n", "\n", "\n", "from IPython.display import display, HTML, Markdown, Image\n", "\n", "from kgtk.kypher.sqlstore import *\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "import shutil\n", "\n", "# from qwikidata.sparql import return_sparql_query_results\n", "\n", "# import altair as alt\n", "# alt.renderers.enable('altair_viewer')\n", "\n", "# from IPython.display import display, HTML, Image\n", "# from pandas_profiling import ProfileReport" ] }, { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.02859, "end_time": "2021-01-15T21:55:02.568770", "exception": false, "start_time": "2021-01-15T21:55:02.540180", "status": "completed" }, "tags": [] }, "source": [ "### Set up environment variables and folders that we need" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:02.664417Z", "iopub.status.busy": "2021-01-15T21:55:02.663512Z", "iopub.status.idle": "2021-01-15T21:55:02.665701Z", "shell.execute_reply": "2021-01-15T21:55:02.666182Z" }, "papermill": { "duration": 0.07022, "end_time": "2021-01-15T21:55:02.666422", "exception": false, "start_time": "2021-01-15T21:55:02.596202", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "Path(cache_folder).mkdir(parents=True, exist_ok=True)\n", "Path(output_folder).mkdir(parents=True, exist_ok=True)\n", "\n", "# path to folder which contains all files corresponding to the wikidata subset. \n", "#(For more information on wikidata subset please check Example 8)\n", "os.environ['WIKIDATA_PARTS'] = wikidata_parts_folder\n", "\n", "# path to the folder where the output (here statistics) would be stored\n", "os.environ['OUTPUT_FOLDER'] = output_folder\n", "\n", "# The statistics are stored in two different folders, overview folder and class folder.\n", "# If the folders are not present then in the following command they are created\n", "\n", "overview_folder = f'{output_folder}/overview'\n", "class_overview_folder = f'{output_folder}/class_overview'\n", "property_overview_folder = f'{output_folder}/property_overview'\n", "raw_files_overview_folder = f'{output_folder}/raw_files/overview'\n", "raw_files_class_overview_folder = f'{output_folder}/raw_files/class_overview'\n", "raw_files_property_overview_folder = f'{output_folder}/raw_files/property_overview'\n", "\n", "Path(overview_folder).mkdir(parents=True, exist_ok=True)\n", "Path(class_overview_folder).mkdir(parents=True, exist_ok=True)\n", "Path(property_overview_folder).mkdir(parents=True, exist_ok=True)\n", "\n", "Path(raw_files_overview_folder).mkdir(parents=True, exist_ok=True)\n", "Path(raw_files_class_overview_folder).mkdir(parents=True, exist_ok=True)\n", "Path(raw_files_property_overview_folder).mkdir(parents=True, exist_ok=True)\n", "\n", "# Environmnet variable for the two sub folders where the statistics would be stored \n", "os.environ['OVERVIEW_FOLDER'] = overview_folder\n", "os.environ['CLASS_FOLDER'] = class_overview_folder\n", "os.environ['PROPERTY_OVERVIEW'] = property_overview_folder\n", "os.environ['RAW_FILES_OVERVIEW'] = raw_files_overview_folder\n", "os.environ['RAW_FILES_PROPERTY'] = raw_files_property_overview_folder\n", "os.environ['RAW_FILES_CLASS'] = raw_files_class_overview_folder\n", "\n", "# kgtk command to run\n", "os.environ['kgtk'] = \"kgtk\"\n", "os.environ['kgtk'] = \"time kgtk --debug\"\n", "\n", "# absolute path of the db\n", "os.environ['STORE'] = \"{}/wikidata.sqlite3.db\".format(cache_folder)\n", "os.environ['TEMP_FOLDER'] = cache_folder\n", "os.environ['label'] = \"labels.en.tsv.gz\"\n", "\n", "# file name corresponding to different part of the Wikidata subgraph.\n", "os.environ['external_id'] = \"claims.external-id.tsv.gz\"\n", "os.environ['time'] = \"claims.time.tsv.gz\"\n", "os.environ['wikibase_item'] = \"claims.wikibase-item.tsv.gz\"\n", "os.environ['quantity'] = \"claims.quantity.tsv.gz\"\n", "os.environ['statistics'] = \"claims.statistics.tsv.gz\"\n", "os.environ['wikibase_form'] = \"claims.wikibase-form.tsv.gz\"\n", "os.environ['monolingualtext'] = \"claims.monolingualtext.tsv.gz\"\n", "os.environ['math'] = \"claims.math.tsv.gz\"\n", "os.environ['commonsMedia'] = \"claims.commonsMedia.tsv.gz\"\n", "os.environ['globe_coordinate'] = \"claims.globe-coordinate.tsv.gz\"\n", "os.environ['musical_notation'] = \"claims.musical-notation.tsv.gz\"\n", "os.environ['geo_shape'] = \"claims.geo-shape.tsv.gz\"\n", "os.environ['url'] = \"claims.url.tsv.gz\"\n", "os.environ['string'] = \"claims.string.tsv.gz\"\n", "os.environ['alias'] = \"aliases.en.tsv.gz\"\n", "os.environ['description'] = \"descriptions.en.tsv.gz\"\n", "\n", "# file name corresponding to the qualifiers\n", "os.environ['external_id_qualifiers'] = \"qualifiers.external-id.tsv.gz\"\n", "os.environ['time_qualifiers'] = \"qualifiers.time.tsv.gz\"\n", "os.environ['wikibase_item_qualifiers'] = \"qualifiers.wikibase-item.tsv.gz\"\n", "os.environ['quantity_qualifiers'] = \"qualifiers.quantity.tsv.gz\"\n", "os.environ['statistics_qualifiers'] = \"qualifiers.statistics.tsv.gz\"\n", "os.environ['wikibase_form_qualifiers'] = \"qualifiers.wikibase-form.tsv.gz\"\n", "os.environ['monolingualtext_qualifiers'] = \"qualifiers.monolingualtext.tsv.gz\"\n", "os.environ['math_qualifiers'] = \"qualifiers.math.tsv.gz\"\n", "os.environ['commonsMedia_qualifiers'] = \"qualifiers.commonsMedia.tsv.gz\"\n", "os.environ['globe_coordinate_qualifiers'] = \"qualifiers.globe-coordinate.tsv.gz\"\n", "os.environ['musical_notation_qualifiers'] = \"qualifiers.musical-notation.tsv.gz\"\n", "os.environ['geo_shape_qualifiers'] = \"qualifiers.geo-shape.tsv.gz\"\n", "os.environ['url_qualifiers'] = \"qualifiers.url.tsv.gz\"\n", "os.environ['string_qualifiers'] = \"qualifiers.string.tsv.gz\"\n", "\n", "\n", "# Output file corresponding to the overview folder (contains overview part of the statistics)\n", "os.environ['class_summary'] = \"Overview.class.tsv\"\n", "os.environ['top_pagrank'] = \"top_pagrank.tsv\"\n", "os.environ['stats'] = \"Overview.nodes.tsv\"\n", "os.environ['all_degree'] = \"allDegree.tsv\"\n", "os.environ['degree'] = \"Overview.degree.tsv\"\n", "os.environ['temp'] = \"temp_tsv.tsv\"\n", "os.environ['magnitudes_destribution'] = \"magnitude_destribution_for_quantity.tsv\"\n", "os.environ['year_destribution_per_deacade'] = \"year_destribution_for_time_per_decade.tsv\"\n", "os.environ['year_destribution_per_50_years'] = \"year_destribution_for_time_per_50_years.tsv\"\n", "os.environ['globe_coordinate_destribution'] = \"coordinate_destribution_for_globe_coordinate.tsv\"\n", "os.environ['geo_shape_random_samples'] = \"Property_overview.geo_shape.random.tsv\"\n", "\n", "# Output files corresponding to the class summary folder (contains class summary part of the statistics)\n", "os.environ['property_summary_external_id'] = \"Overview.property.externalID.tsv\"\n", "os.environ['property_summary_time'] = \"Overview.property.time.tsv\"\n", "os.environ['property_summary_wikibase_item'] = \"Overview.property.wikibaseItem.tsv\"\n", "os.environ['property_summary_quantity'] = \"Overview.property.quantity.tsv\"\n", "os.environ['property_summary_wikibase_form'] = \"Overview.property.wikibaseForm.tsv\"\n", "os.environ['property_summary_monolingualtext'] = \"Overview.property.monolingualText.tsv\"\n", "os.environ['property_summary_math'] = \"Overview.property.math.tsv\"\n", "os.environ['property_summary_commonsMedia'] = \"Overview.property.commonsMedia.tsv\"\n", "os.environ['property_summary_globe_coordinate'] = \"Overview.property.globeCoordinate.tsv\"\n", "os.environ['property_summary_musical_notation'] = \"Overview.property.musicalNotation.tsv\"\n", "os.environ['property_summary_geo_shape'] = \"Overview.property.geoShape.tsv\"\n", "os.environ['property_summary_url'] = \"Overview.property.url.tsv\"\n", "os.environ['property_summary_string'] = \"Overview.property.strings.tsv\"" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:02.736206Z", "iopub.status.busy": "2021-01-15T21:55:02.735361Z", "iopub.status.idle": "2021-01-15T21:55:02.738101Z", "shell.execute_reply": "2021-01-15T21:55:02.738626Z" }, "papermill": { "duration": 0.035602, "end_time": "2021-01-15T21:55:02.738863", "exception": false, "start_time": "2021-01-15T21:55:02.703261", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#Variable storing all the datatypes of a properties\n", "types = [ \"time\",\n", " \"wikibase_item\",\n", " \"math\",\n", " \"wikibase_form\",\n", " \"quantity\",\n", " \"string\",\n", " \"external_id\",\n", " \"commonsMedia\",\n", " \"globe_coordinate\",\n", " \"monolingualtext\",\n", " \"musical_notation\",\n", " \"geo_shape\",\n", " \"url\"\n", " ]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:02.796047Z", "iopub.status.busy": "2021-01-15T21:55:02.795460Z", "iopub.status.idle": "2021-01-15T21:55:02.797534Z", "shell.execute_reply": "2021-01-15T21:55:02.797872Z" }, "papermill": { "duration": 0.0323, "end_time": "2021-01-15T21:55:02.798023", "exception": false, "start_time": "2021-01-15T21:55:02.765723", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "types_with_fileName = [\n", " (\"time\",\"Time\",\"property_summary_time\"),\n", " (\"wikibase_item\",\"Wikibase_item\",\"property_summary_wikibase_item\"),\n", " (\"math\",\"Math\",\"property_summary_math\"),\n", " (\"wikibase_form\",\"Wikibase-form\",\"property_summary_wikibase_form\"),\n", " (\"quantity\",\"Quantity\",\"property_summary_quantity\"),\n", " (\"string\",\"String\",\"property_summary_string\"),\n", " (\"external_id\",\"External-id\",\"property_summary_external_id\"),\n", " (\"commonsMedia\",\"CommonsMedia\",\"property_summary_commonsMedia\"),\n", " (\"globe_coordinate\",\"Globe-coordinate\",\"property_summary_globe_coordinate\"),\n", " (\"monolingualtext\",\"Monolingualtext\",\"property_summary_monolingualtext\"),\n", " (\"musical_notation\",\"Musical-notation\",\"property_summary_musical_notation\"),\n", " (\"geo_shape\",\"Geo-shape\",\"property_summary_geo_shape\"),\n", " (\"url\",\"Url\",\"property_summary_url\"),\n", " ]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:02.857033Z", "iopub.status.busy": "2021-01-15T21:55:02.856019Z", "iopub.status.idle": "2021-01-15T21:55:02.859150Z", "shell.execute_reply": "2021-01-15T21:55:02.859489Z" }, "papermill": { "duration": 0.035033, "end_time": "2021-01-15T21:55:02.859648", "exception": false, "start_time": "2021-01-15T21:55:02.824615", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# Checking if all the required files are present or not\n", "for file in types:\n", " if not os.path.exists(os.path.join(os.getenv(\"WIKIDATA_PARTS\"),os.getenv(file))):\n", " exception_string = file + \" file not present\"\n", " raise Exception(exception_string)\n", "if not os.path.exists(os.path.join(os.getenv(\"WIKIDATA_PARTS\"),os.getenv(\"label\"))):\n", " raise Exception(\"label file not present\")\n", "if not os.path.exists(os.path.join(os.getenv(\"WIKIDATA_PARTS\"),os.getenv(\"alias\"))):\n", " raise Exception(\"alias file not present\")\n", "if not os.path.exists(os.path.join(os.getenv(\"WIKIDATA_PARTS\"),os.getenv(\"description\"))):\n", " raise Exception(\"description file not present\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:02.918550Z", "iopub.status.busy": "2021-01-15T21:55:02.917967Z", "iopub.status.idle": "2021-01-15T21:55:02.919641Z", "shell.execute_reply": "2021-01-15T21:55:02.919972Z" }, "papermill": { "duration": 0.033375, "end_time": "2021-01-15T21:55:02.920120", "exception": false, "start_time": "2021-01-15T21:55:02.886745", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#Deleting any files in the output folder\n", "if restart_global == False:\n", " !rm -f $OVERVIEW_FOLDER/*\n", " !rm -f $CLASS_FOLDER/*\n", " !rm -f $PROPERTY_OVERVIEW/*" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:02.980585Z", "iopub.status.busy": "2021-01-15T21:55:02.979670Z", "iopub.status.idle": "2021-01-15T21:55:02.981987Z", "shell.execute_reply": "2021-01-15T21:55:02.982330Z" }, "papermill": { "duration": 0.035303, "end_time": "2021-01-15T21:55:02.982491", "exception": false, "start_time": "2021-01-15T21:55:02.947188", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "FNULL = open(os.devnull, 'w')\n", "def run_command(cmd, substitution_dictionary = {}):\n", " \"\"\"Run a templetized command.\"\"\"\n", " debug = False\n", " for k, v in substitution_dictionary.items():\n", " cmd = cmd.replace(k, v)\n", " \n", " if debug:\n", " print(cmd)\n", " output = subprocess.run([cmd], shell=True, universal_newlines=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n", " print(output.stdout)\n", " print(output.stderr)\n", " else:\n", " output = subprocess.run([cmd], shell=True, universal_newlines=True, stdout=FNULL, stderr=subprocess.PIPE)\n", " #print(output.returncode)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:03.045497Z", "iopub.status.busy": "2021-01-15T21:55:03.044364Z", "iopub.status.idle": "2021-01-15T21:55:03.047701Z", "shell.execute_reply": "2021-01-15T21:55:03.048067Z" }, "papermill": { "duration": 0.039515, "end_time": "2021-01-15T21:55:03.048350", "exception": false, "start_time": "2021-01-15T21:55:03.008835", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "FNULL = open(os.devnull, 'w')\n", "def run_command_return_df(cmd,folder,output_file, substitution_dictionary = {},save=True,restart=True):\n", " \"\"\"Run a templetized command.\"\"\"\n", " debug = False\n", " file_path = os.path.join(os.getenv(folder),output_file)\n", " # Restart here means if the command has already been run then just read the dataframe from file and return it.\n", " if restart and os.path.exists(file_path):\n", " try:\n", " df = pd.read_csv(file_path,delimiter='\\t')\n", " return df\n", " except:\n", " os.remove(file_path) \n", " \n", " #Replace the string with the substitution dictionary\n", " for k,v in substitution_dictionary.items():\n", " cmd = cmd.replace(k, v)\n", "\n", " #Based on debug print the statement or not\n", " if debug:\n", " print(cmd)\n", " output = subprocess.run([cmd], shell=True, universal_newlines=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n", " print(output.stdout)\n", " print(output.stderr)\n", " else:\n", " output = subprocess.run([cmd], shell=True, universal_newlines=True, stdout=FNULL, stderr=subprocess.PIPE)\n", " \n", " \n", " try:\n", " df = pd.read_csv(file_path,delimiter='\\t')\n", " except Exception as e:\n", " df = pd.DataFrame()\n", " \n", " #If save is set to False then delete the output file.\n", " if not save:\n", " if os.path.exists(file_path):\n", " os.remove(file_path)\n", " return df\n", "# print(output.returncode)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:03.107359Z", "iopub.status.busy": "2021-01-15T21:55:03.106532Z", "iopub.status.idle": "2021-01-15T21:55:03.109158Z", "shell.execute_reply": "2021-01-15T21:55:03.109591Z" }, "papermill": { "duration": 0.033767, "end_time": "2021-01-15T21:55:03.109815", "exception": false, "start_time": "2021-01-15T21:55:03.076048", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# This function is used to print the string in different font, colors and font-size.\n", "def printmd(string,color='black',size='25',fontWeight=\"bold\"):\n", " colorstr = \"{}\".format(fontWeight,str(size)+'px',color, string)\n", " display(HTML(colorstr))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:03.167929Z", "iopub.status.busy": "2021-01-15T21:55:03.167415Z", "iopub.status.idle": "2021-01-15T21:55:03.169129Z", "shell.execute_reply": "2021-01-15T21:55:03.169472Z" }, "papermill": { "duration": 0.032469, "end_time": "2021-01-15T21:55:03.169632", "exception": false, "start_time": "2021-01-15T21:55:03.137163", "status": "completed" }, "scrolled": true, "tags": [] }, "outputs": [], "source": [ "# Its a helper function which given a qnode or pnode returns the link corresponding to it.\n", "def generate_link(string):\n", " if string[0] == 'P':\n", " return \"https://www.wikidata.org/wiki/Property:\"+string \n", " elif string[0] == 'Q':\n", " return \"https://www.wikidata.org/wiki/\"+string\n", " return string" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:03.230694Z", "iopub.status.busy": "2021-01-15T21:55:03.229354Z", "iopub.status.idle": "2021-01-15T21:55:03.231884Z", "shell.execute_reply": "2021-01-15T21:55:03.232294Z" }, "papermill": { "duration": 0.035465, "end_time": "2021-01-15T21:55:03.232455", "exception": false, "start_time": "2021-01-15T21:55:03.196990", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# It returns the label of the qnode but uses wikidata sparql endpoint, Can only be used when KG is a subgraph from wikidata\n", "def find_label_wikidata(qnode):\n", " try:\n", " if(str(qnode)==\"nan\"):\n", " return \"Number\"\n", " query_string = \"\"\"\n", " PREFIX rdfs: \\\n", " PREFIX wd: \\\n", " SELECT * \\\n", " WHERE { \\\n", " wd:__qnode rdfs:label ?label . \\\n", " FILTER (langMatches( lang(?label), \"EN\" ) ) \\\n", " } \\\n", " LIMIT 1\n", " \"\"\"\n", " query_string = query_string.replace(\"__qnode\",str(qnode))\n", " res = return_sparql_query_results(query_string)\n", "# print(query_string)\n", "# print(res)\n", " if 'results' in res and \"bindings\" in res['results'] and len(res['results']['bindings'])>=1 and \"label\" in res['results']['bindings'][0] and \"value\" in res['results']['bindings'][0][\"label\"] :\n", " return res['results']['bindings'][0][\"label\"][\"value\"].capitalize()\n", " return qnode\n", " except Exception as e:\n", " return qnode" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:03.291905Z", "iopub.status.busy": "2021-01-15T21:55:03.291371Z", "iopub.status.idle": "2021-01-15T21:55:03.293097Z", "shell.execute_reply": "2021-01-15T21:55:03.293431Z" }, "papermill": { "duration": 0.03286, "end_time": "2021-01-15T21:55:03.293582", "exception": false, "start_time": "2021-01-15T21:55:03.260722", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def find_label(qnode):\n", " try:\n", " if(str(qnode)==\"nan\"):\n", " return \"Number\"\n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$label --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'label: (n1:__qnode)-[:label]->(n2)' \\\n", " --return 'kgtk_lqstring_text(n2) as Label' \\\n", " --where 'kgtk_lqstring_lang_suffix(n2) = \\\"en\\\"' \\\n", " --limit 1\"\n", " df_temp = run_command_return_df(cmd,\"RAW_FILES_OVERVIEW\",\"temp.tsv\",{\"__output_file\":\"temp.tsv\",\"__qnode\":qnode,\"__folder\":\"RAW_FILES_OVERVIEW\"},save=False,restart=False)\n", " label = df_temp.iloc[0]['Label']\n", " st=0\n", " en=len(label)\n", " if label[0] == '\"':\n", " st=1\n", " if label[-1] == '\"':\n", " en=-1\n", " label = label[st:en]\n", " return label.capitalize()\n", " except Exception as e:\n", " return qnode" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:03.351929Z", "iopub.status.busy": "2021-01-15T21:55:03.350853Z", "iopub.status.idle": "2021-01-15T21:55:03.353775Z", "shell.execute_reply": "2021-01-15T21:55:03.354234Z" }, "papermill": { "duration": 0.034418, "end_time": "2021-01-15T21:55:03.354400", "exception": false, "start_time": "2021-01-15T21:55:03.319982", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def find_node(label):\n", " try:\n", " label = str(label.lower())\n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$label --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'label: (n1)-[:label]->(n2)' \\\n", " --return 'n1 as qnode' \\\n", " --where 'kgtk_lqstring_lang_suffix(n2) = \\\"en\\\" AND kgtk_lqstring_text(n2) in [\\\"__label\\\"]' \\\n", " --limit 1\"\n", " df_temp = run_command_return_df(cmd,\"RAW_FILES_OVERVIEW\",\"temp.tsv\",{\"__output_file\":\"temp.tsv\",\"__label\":label,\"__folder\":\"RAW_FILES_OVERVIEW\"},save=False,restart=False)\n", " qnode = str(df_temp.iloc[0]['qnode'])\n", " return qnode\n", " except Exception as e:\n", " return label" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:03.411347Z", "iopub.status.busy": "2021-01-15T21:55:03.410747Z", "iopub.status.idle": "2021-01-15T21:55:03.413246Z", "shell.execute_reply": "2021-01-15T21:55:03.412806Z" }, "papermill": { "duration": 0.032642, "end_time": "2021-01-15T21:55:03.413420", "exception": false, "start_time": "2021-01-15T21:55:03.380778", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def Capitalize(String_item):\n", " try:\n", " if type(String_item)==\"str\":\n", " return String_item.capitalize()\n", " else:\n", " return String_item.to_string().capitalize()\n", " except Exception as e:\n", " return String_item" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:03.489431Z", "iopub.status.busy": "2021-01-15T21:55:03.488858Z", "iopub.status.idle": "2021-01-15T21:55:03.490455Z", "shell.execute_reply": "2021-01-15T21:55:03.490790Z" }, "papermill": { "duration": 0.047493, "end_time": "2021-01-15T21:55:03.490948", "exception": false, "start_time": "2021-01-15T21:55:03.443455", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#This function display the top units for the property of datatype:quantity\n", "#@argument\n", "#pnode: The pnode of the property for which the top units and destribution is to be found\n", "#prop_label: The label of the property for which the units and destribution is to be found\n", "#_restart: Here restart means if you allow to use any existing, keep it true if you re running it or you have imported the files.\n", "#_save: If it is set then it will save the file otherwise will delete any generated file.\n", "#folder: The folder in which the output files to be stored\n", "#raw_folder: This work in conjunction with _restart, in this folder all the raw files are stored\n", "def quantity_distribution(pnode,prop_label,_restart=True,_save=True,folder=\"PROPERTY_OVERVIEW\",raw_folder=\"RAW_FILES_PROPERTY\"):\n", " #Free all the garbage\n", " gc.collect()\n", " \n", " #This Kypher querry can be used to find the destribution of all the wikidata units for a property\n", " cmd1 = \"$kgtk query -i $WIKIDATA_PARTS/$quantity --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match '(n1)-[r{label:llab}]->(v)' \\\n", " --return 'distinct kgtk_quantity_wd_units(v) as Unit , count(n1) as Number_of_Statements ' \\\n", " --where 'kgtk_quantity(v) AND (llab in [\\\"__prop\\\"])' \\\n", " --order-by 'count(n1) desc' \"\n", "\n", " #This Kypher querry can be used to find the destribution of all the wikidata units(SI) for a property\n", " cmd2 = \"$kgtk query -i $WIKIDATA_PARTS/$quantity --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match '(n1)-[r{label:llab}]->(v)' \\\n", " --return 'distinct kgtk_quantity_si_units(v) as Unit , count(n1) as Number_of_Statements ' \\\n", " --where 'kgtk_quantity(v) AND (llab in [\\\"__prop\\\"])' \\\n", " --order-by 'count(n1) desc' \"\n", "\n", " #This Kypher querry can be used to find all the statements corresponding to a unit(Qnode) and a property \n", " cmd3 = \"$kgtk query -i $WIKIDATA_PARTS/$quantity --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match '(n1)-[r{label:llab}]->(v)' \\\n", " --return 'kgtk_quantity_number(v) as Magnitude ' \\\n", " --where 'kgtk_quantity(v) AND (kgtk_quantity_wd_units(v) in [\\\"__unit\\\"]) AND (llab in [\\\"__prop\\\"])' \"\n", "\n", " #This Kypher querry can be used to find all the statements corresponding to a unit(SI unit) and a property \n", " cmd4 = \"$kgtk query -i $WIKIDATA_PARTS/$quantity --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match '(n1)-[r{label:llab}]->(v)' \\\n", " --return 'kgtk_quantity_number(v) as Magnitude ' \\\n", " --where 'kgtk_quantity(v) AND (kgtk_quantity_si_units(v) in [\\\"__unit\\\"]) AND (llab in [\\\"__prop\\\"])' \"\n", " \n", " #This Kypher querry can be used to find the destribution number when there is no unit but number for a datatype\n", " cmd5 = \"$kgtk query -i $WIKIDATA_PARTS/$quantity --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match '(n1)-[r{label:llab}]->(v)' \\\n", " --return 'kgtk_quantity_number(v) as Magnitude ' \\\n", " --where 'kgtk_number(v) AND (llab in [\\\"__prop\\\"])' \"\n", "\n", "\n", " #Dynamically setting the name of the output file based on the name of property\n", " output_file_units_destribution = \"Property_overview.quantity.\"+prop_label+\".units_distribution.raw.tsv\"\n", "\n", " output_file_units_destribution_saving = \"Property_overview.quantity.\"+prop_label+\".units_distribution.tsv\"\n", "\n", " printmd(\"Below is the distribution of units for \" + prop_label + \" in the subgraph\",'blue')\n", "\n", " #Running command 1 and loading the dataframe\n", " pd_unit_destribution = run_command_return_df(cmd1,raw_folder,output_file_units_destribution,{\"__output_file\":output_file_units_destribution,\"__prop\":pnode,\"__folder\":raw_folder},save=_save,restart=_restart);\n", "\n", " #If there are no wikidata units then trying for SI units\n", " if(len(pd_unit_destribution)==0):\n", " output_file_units_destribution = \"Property_overview.quantity.\"+prop_label+\".units_distribution.si.raw.tsv\"\n", " pd_unit_destribution = run_command_return_df(cmd2,raw_folder,output_file_units_destribution,{\"__output_file\":output_file_units_destribution,\"__prop\":pnode,\"__folder\":raw_folder},save=_save,restart=_restart);\n", " if(len(pd_unit_destribution)==0):\n", " printmd('There are no units for this property');\n", " output_file_magnitude_destribution = \"Property_overview.quantity.\"+prop_label+ \".number.value_distibution.raw.tsv\"\n", " output_file_magnitude_destribution_saving = \"Property_overview.quantity.\"+prop_label+ \".number.value_distibution.tsv\"\n", " \n", " # Running Command 5 to find the destribution of numbers and loading the output in a dataframe apart from saving it.\n", " magnitude_distribution = run_command_return_df(cmd5,raw_folder,output_file_magnitude_destribution,{\"__output_file\":output_file_magnitude_destribution,\"__prop\":pnode,\"__folder\":raw_folder},save=_save,restart=_restart);\n", " if (len(magnitude_distribution)>0):\n", " # print(magnitude_distribution)\n", " # Binnig the magnitudes for displaying the histogram\n", " #This is done to remove the log tails\n", " start_histogram = magnitude_distribution.quantile(0.01)['Magnitude']\n", " end_histogram = magnitude_distribution.quantile(0.99)['Magnitude']\n", " step_size = (end_histogram-start_histogram)/1000\n", "\n", " #This is done to ensure the tail are included if they are small\n", " start_histogram = start_histogram -20*step_size\n", " end_histogram = end_histogram + 20*step_size\n", " bin_values = np.arange(start=start_histogram, stop=end_histogram, step=step_size)\n", " # bin_values = np.arange(start=min(magnitude_distribution['Magnitude']), stop=max(magnitude_distribution['Magnitude']), step=10)\n", "\n", "\n", " #Displaying the histogram\n", " printmd('Below is a histogram of distribution of Number for ' + str(prop_label).capitalize() + ' in the subgraph','blue')\n", " magnitude_distribution['Magnitude'].hist(bins=bin_values, figsize=[14,6])\n", " magnitude_distribution = magnitude_distribution['Magnitude'].value_counts(bins=bin_values, sort=False)\n", "\n", " plt.xlabel(\"Magnitude\")\n", " plt.ylabel(\"Number of values\")\n", " plt.show()\n", " plt.figure()\n", " else:\n", " printmd('There is some error with unit in the subgraph');\n", " return\n", " magnitude_distribution.to_csv(os.path.join(os.getenv(folder),output_file_magnitude_destribution_saving),sep='\\t')\n", " return\n", " else:\n", " cmd_for_value_distribution = cmd4\n", " pd_unit_destribution_display =pd_unit_destribution.copy()\n", " pd_unit_destribution_display[\"Unit\"] = pd_unit_destribution_display[\"Unit\"].map(Capitalize)\n", " else:\n", " cmd_for_value_distribution = cmd3\n", " pd_unit_destribution_display =pd_unit_destribution.copy()\n", " pd_unit_destribution_display[\"Unit\"] = pd_unit_destribution_display[\"Unit\"].map(find_label)\n", "\n", "\n", " #Saving the file after mapping qnode with label\n", " pd_unit_destribution_display.to_csv(os.path.join(os.getenv(folder),output_file_units_destribution_saving),sep='\\t')\n", "\n", " #Displaying the destribution of qunatities for a property\n", " display(HTML(pd_unit_destribution_display[:5].to_html(index=False)))\n", "\n", " #Finding the most prevalent unit\n", " if len(pd_unit_destribution)>=1:\n", "\n", " #Finding the most prevalent unit\n", "# print(pd_unit_destribution);\n", " most_prevalent_unit_qnode = str(pd_unit_destribution.iloc[0]['Unit']);\n", " most_prevalent_unit_label = re.sub(\"[^0-9a-zA-Z]+\", \"_\", str(pd_unit_destribution_display.iloc[0][\"Unit\"]));\n", " \n", " if most_prevalent_unit_qnode == \"nan\":\n", " most_prevalent_unit_qnode =\"\"\n", " most_prevalent_unit_label = \"Number\"\n", " \n", " # Dynamically setting the name of output file corresponding to the magnitude destrbution of most prevalent unit\n", " output_file_magnitude_destribution = \"Property_overview.quantity.\"+prop_label+ \".\"+most_prevalent_unit_label+ \".value_distibution.raw.tsv\"\n", " output_file_magnitude_destribution_saving = \"Property_overview.quantity.\"+prop_label+ \".\"+most_prevalent_unit_label+ \".value_distibution.tsv\"\n", "\n", " # Running Command 3/4/5 and loading the output in a dataframe apart from saving it.\n", " if most_prevalent_unit_label == \"Number\":\n", " magnitude_distribution = run_command_return_df(cmd5,raw_folder,output_file_magnitude_destribution,{\"__output_file\":output_file_magnitude_destribution,\"__prop\":pnode,\"__folder\":raw_folder},save=_save,restart=_restart);\n", " else:\n", " magnitude_distribution = run_command_return_df(cmd_for_value_distribution,raw_folder,output_file_magnitude_destribution,{\"__output_file\":output_file_magnitude_destribution,\"__prop\":pnode,\"__unit\":most_prevalent_unit_qnode,\"__folder\":raw_folder},save=_save,restart=_restart);\n", " if (len(magnitude_distribution)>0):\n", "# print(magnitude_distribution)\n", " # Binnig the magnitudes for displaying the histogram\n", " #This is done to remove the log tails\n", " start_histogram = magnitude_distribution.quantile(0.01)['Magnitude']\n", " end_histogram = magnitude_distribution.quantile(0.99)['Magnitude']\n", " step_size = (end_histogram-start_histogram)/1000\n", "\n", " #This is done to ensure the tail are included if they are small\n", " start_histogram = start_histogram -20*step_size\n", " end_histogram = end_histogram + 20*step_size\n", " bin_values = np.arange(start=start_histogram, stop=end_histogram, step=step_size)\n", " # bin_values = np.arange(start=min(magnitude_distribution['Magnitude']), stop=max(magnitude_distribution['Magnitude']), step=10)\n", "\n", "\n", " #Displaying the histogram\n", " printmd('Below is a histogram of distribution of values for unit: \"'+most_prevalent_unit_label +'\" for ' + prop_label + ' in the subgraph','blue')\n", " magnitude_distribution['Magnitude'].hist(bins=bin_values, figsize=[14,6])\n", " magnitude_distribution = magnitude_distribution['Magnitude'].value_counts(bins=bin_values, sort=False)\n", "\n", " plt.xlabel(most_prevalent_unit_label)\n", " plt.ylabel(\"Number of values\")\n", " plt.show()\n", " plt.figure()\n", " else:\n", " printmd('There is some error with unit in the subgraph');\n", "\n", " #Saving the file for the display using html\n", " magnitude_distribution.to_csv(os.path.join(os.getenv(folder),output_file_magnitude_destribution_saving),sep='\\t')\n", " return" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:03.564393Z", "iopub.status.busy": "2021-01-15T21:55:03.563831Z", "iopub.status.idle": "2021-01-15T21:55:03.565544Z", "shell.execute_reply": "2021-01-15T21:55:03.565889Z" }, "papermill": { "duration": 0.046967, "end_time": "2021-01-15T21:55:03.566046", "exception": false, "start_time": "2021-01-15T21:55:03.519079", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#This function displays the stacked histogram for the distribution of instances of the property (property must be of time data type)\n", "#@argument\n", "#pnode: The pnode corresponding to the property\n", "#prop_label: The label for the property\n", "#output_file_time_distribution_raw: It is the file name for saving the output file thus reducing re run time.\n", "#restart: Here restart means if you allow to use any existing, keep it true if you re running it or you have imported the files.\n", "#save: If it is set then it will save the file otherwise will delete any generated file.\n", "#min_year: It is the start year for the histogram\n", "#max_year: It is the end year for the histogram\n", "#folder: It is the environment variable corresponding to the path of the folder.\n", "#number_of_unique_classes: It sets the number of unique classes for the histogram\n", "#@return\n", "#It returns a dataframe corresponding to the histogram in the following format\n", "#Year Class1(Count of instances of Class1) Class2(Count of instances of Class2)\n", "def time_prop_distribution(pnode,prop_label,_save=True,_restart=True,output_file_time_distribution_raw=None,_min_year=1800,_max_year=2100,number_of_unique_classes=5,folder=\"PROPERTY_OVERVIEW\",raw_folder=\"RAW_FILES_PROPERTY\"):\n", " if _min_year > _max_year:\n", " printmd(\"_max_year should be greater than the _min_year\",'blue')\n", " return\n", " \n", " #Free all the garbage\n", " gc.collect()\n", " \n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$time -i $WIKIDATA_PARTS/$wikibase_item -i $WIKIDATA_PARTS/$label --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'time: (n1)-[r{label:llab}]->(v), item:(n1)-[l{label:llab2}]->(class), label:(class)-[:label]->(class_with_label)' \\\n", " --return 'kgtk_date_year(v) as Year, kgtk_lqstring_text(class_with_label) as Class' \\\n", " --where 'kgtk_date(v) AND llab in [\\\"__prop\\\"] AND llab2 in [\\\"P31\\\"] AND kgtk_lqstring_lang_suffix(class_with_label) = \\\"en\\\" AND kgtk_date_precision(v)>8 '\"\n", " \n", " cmd_without_class = \"$kgtk query -i $WIKIDATA_PARTS/$time -i $WIKIDATA_PARTS/$wikibase_item -i $WIKIDATA_PARTS/$label --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'time: (n1)-[r{label:llab}]->(v)' \\\n", " --return 'kgtk_date_year(v) as Year' \\\n", " --where 'kgtk_date(v) AND llab in [\\\"__prop\\\"] AND kgtk_date_precision(v)>8 '\"\n", " \n", " prop_label = re.sub(\"[^0-9a-zA-Z]+\", \"_\", prop_label) \n", "\n", " if output_file_time_distribution_raw == None:\n", " output_file_time_distribution_raw = \"Property_overview.time.\"+prop_label+\".year_distibution.raw.tsv\"\n", " \n", " #Running command 1\n", " time_distribution = run_command_return_df(cmd,raw_folder,output_file_time_distribution_raw,{\"__output_file\":output_file_time_distribution_raw,\"__prop\":pnode,\"__folder\":raw_folder},save=_save,restart=_restart);\n", " if(len(time_distribution)>0 and \"Class\" in time_distribution):\n", " \n", "#Part-1, This is for saving the statistics to file, we are saving everything irrespect of _min_year and _max_year\n", " \n", " #Finding the top five unique classes sorted based on number of statements\n", " unique_classes = time_distribution[\"Class\"].value_counts().keys()[:number_of_unique_classes]\n", "\n", " #Dataframe for stacked histogram\n", " time_distribution_stacked = pd.DataFrame();\n", "\n", " #Will be saving the result in different format\n", " time_distribution_stacked_saving = pd.DataFrame();\n", "\n", " #A local function to find the if a class belong to top five classes.\n", " def names_not_in_top_five(class_name):\n", " if(class_name not in unique_classes):\n", " return True\n", " return False\n", "\n", " #Creating the a boolean column to see if the class belongs to non of the top five class;\n", " time_distribution[\"Other\"] = time_distribution[\"Class\"].map(names_not_in_top_five)\n", " for index, name in enumerate(unique_classes): \n", "\n", " # Ditribution of year for a property and class are stacked in the dataframe time_distribution_stacked\n", " time_distribution_class = pd.DataFrame()\n", " time_distribution_class[\"Year\"] = time_distribution[time_distribution[\"Class\"] == name][\"Year\"]\n", " time_distribution_stacked[name.capitalize()] = list(time_distribution_class[\"Year\"]) + (len(time_distribution)-len(time_distribution_class))*[np.nan]\n", "\n", " # Adding count for all other classess\n", " time_distribution_class = pd.DataFrame()\n", " time_distribution_class[\"Year\"] = time_distribution[time_distribution[\"Other\"]][\"Year\"]\n", " time_distribution_stacked[\"Others\"] = list(time_distribution_class[\"Year\"]) + (len(time_distribution)-len(time_distribution_class))*[np.nan]\n", "\n", " #Applying value count to all the columns\n", " time_distribution_stacked_saving = time_distribution_stacked.apply(pd.Series.value_counts)\n", " time_distribution_stacked_saving.rename_axis(\"Year\",inplace=True)\n", " \n", " output_file_time_distribution = \"Property_overview.time.\"+prop_label+\".year_distibution.tsv\"\n", " time_distribution_stacked_saving.to_csv(os.path.join(os.getenv(folder),output_file_time_distribution),sep=\"\\t\")\n", " \n", "#Part-2, This is for showing the histogram, here we are doing everything with reference to the _min_year and _max_year\n", " time_distribution_zoomed = pd.DataFrame()\n", " time_distribution_stacked = pd.DataFrame()\n", " time_distribution_zoomed = (time_distribution[(time_distribution[\"Year\"]>=int(_min_year)) & (time_distribution[\"Year\"]<=int(_max_year))]).copy()\n", " #Finding the top five unique classes sorted based on number of statements\n", " unique_classes = time_distribution_zoomed[\"Class\"].value_counts().keys()[:number_of_unique_classes]\n", " printmd(\"Below is stacked histogram for \" + prop_label.capitalize() + \" binned per year from \"+str(_min_year).capitalize() +\" to \"+str(_max_year).capitalize(),'blue')\n", " #Dataframe for stacked histogram\n", " time_distribution_stacked = pd.DataFrame();\n", "\n", " #A local function to find the if a class belong to top five classes.\n", " def names_not_in_top_five_zoomed(class_name):\n", " if(class_name not in unique_classes):\n", " return True\n", " return False\n", "\n", " #Creating the a boolean column to see if the class belongs to non of the top five class;\n", " time_distribution_zoomed[\"Other\"] = time_distribution_zoomed[\"Class\"].map(names_not_in_top_five_zoomed)\n", " for index, name in enumerate(unique_classes): \n", "\n", " # Ditribution of year for a property and class are stacked in the dataframe time_distribution_stacked\n", " time_distribution_class = pd.DataFrame()\n", " time_distribution_class[\"Year\"] = time_distribution_zoomed[time_distribution[\"Class\"] == name][\"Year\"]\n", " time_distribution_stacked[name.capitalize()] = list(time_distribution_class[\"Year\"]) + (len(time_distribution_zoomed)-len(time_distribution_class))*[np.nan]\n", "\n", " # Adding count for all other classess\n", " time_distribution_class = pd.DataFrame()\n", " time_distribution_class[\"Year\"] = time_distribution_zoomed[time_distribution_zoomed[\"Other\"]][\"Year\"]\n", " time_distribution_stacked[\"Others\"] = list(time_distribution_class[\"Year\"]) + (len(time_distribution_zoomed)-len(time_distribution_class))*[np.nan]\n", " # time_distribution_class[\"Year\"].value_counts().to_csv(os.path.join(os.getenv('PROPERTY_OVERVIEW'),output_file_time_distribution_class),sep='\\t')\n", " # The smallest year in the dataframe\n", " min_year = max(_min_year,min(time_distribution[\"Year\"]))\n", "\n", " # The largest year in the dataframe\n", " max_year = min(_max_year,max(time_distribution[\"Year\"]))\n", "\n", "\n", " bin_values = np.arange(start=((min_year-20)//10)*10, stop=((max_year+20)//10)*10, step=1)\n", "\n", " time_distribution_stacked.plot.hist(bins=bin_values, stacked=True,figsize=[14,6])\n", "\n", " plt.show()\n", " \n", " time_distribution_stacked_saving = pd.DataFrame()\n", " #Applying value count to all the columns\n", " time_distribution_stacked_saving = time_distribution_stacked.apply(pd.Series.value_counts)\n", " time_distribution_stacked_saving.rename_axis(\"Year\",inplace=True)\n", " \n", " output_file_time_distribution = \"Property_overview.time.\"+prop_label+\".range.year_distibution.raw.tsv\"\n", " time_distribution_stacked_saving.to_csv(os.path.join(os.getenv(raw_folder),output_file_time_distribution),sep=\"\\t\")\n", "\n", " else:\n", " # Checking if the file is produced with the above \"command\" then deleting it\n", " file_path = os.path.join(os.getenv(folder),output_file_time_distribution_raw)\n", " if os.path.exists(file_path):\n", " try:\n", " time_distribution = pd.read_csv(file_path,delimiter=\"\\t\")\n", " if \"Class\" in time_distribution:\n", " os.remove(file_path)\n", " except Exception as e:\n", " os.remove(file_path)\n", " \n", " printmd(\"Below is histogram for \" + prop_label.capitalize() + \" binned per year\",'blue')\n", " #Running cmd_without_class \n", " time_distribution = run_command_return_df(cmd_without_class,folder,output_file_time_distribution_raw,{\"__output_file\":output_file_time_distribution_raw,\"__prop\":pnode,\"__folder\":folder},save=_save,restart=_restart);\n", " if(len(time_distribution)>0):\n", " # The smallest year in the dataframe\n", " min_year = _min_year\n", "\n", " # The largest year in the dataframe\n", " max_year = _max_year\n", "\n", "\n", " printmd(\"Below is a histogram for the distribution of '\" + prop_label + \"' binned per year from \"+ str(_min_year).capitalize() +\" to \"+str(_max_year).capitalize() ,'blue')\n", "\n", " bin_values = np.arange(start=((min_year-20)//10)*10, stop=((max_year+20)//10)*10, step=1)\n", "\n", "\n", " time_distribution['Year'].hist(bins=bin_values, figsize=[14,6])\n", "\n", " plt.show()\n", " time_distribution_stacked_saving = time_distribution[\"Year\"].value_counts()\n", " time_distribution_stacked_saving.rename_axis(\"Year\",inplace=True)\n", " output_file_time_distribution = \"Property_overview.time.\"+prop_label+\".year_distibution.tsv\"\n", " time_distribution_stacked_saving.to_csv(os.path.join(os.getenv(folder),output_file_time_distribution),sep=\"\\t\")\n", " else:\n", " printmd(\"There is some error with this property\")\n", " time_distribution_stacked_saving = pd.DataFrame(columns=[\"Year\",\"Count\"])\n", " return time_distribution_stacked_saving" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:03.631202Z", "iopub.status.busy": "2021-01-15T21:55:03.630663Z", "iopub.status.idle": "2021-01-15T21:55:03.632292Z", "shell.execute_reply": "2021-01-15T21:55:03.632625Z" }, "papermill": { "duration": 0.039507, "end_time": "2021-01-15T21:55:03.632785", "exception": false, "start_time": "2021-01-15T21:55:03.593278", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def property_distribution_with_class_items(pnode,prop_label,_save=True,_restart=True,incoming_classes=[],outgoing_classes=[],start=0,end=5,output_file=None,folder=\"PROPERTY_OVERVIEW\",raw_folder=\"RAW_FILES_PROPERTY\"):\n", " \n", " #Free all the garbage\n", " gc.collect()\n", " \n", " def display_histgram(property_class_distribution,start,end):\n", " if start>=len(property_class_distribution):\n", " printmd(\"The value of start is more than the number of classes\")\n", " return\n", " else:\n", " property_distribution_histogram = property_class_distribution[start:end]\n", " \n", " if(len(property_class_distribution)>end):\n", " other_classes = sum(property_class_distribution[\"Number_of_Instances\"][end:])\n", " property_distribution_histogram = property_distribution_histogram.append({\"Class\":\"Other Classes\",\"Number_of_Instances\":other_classes,\"Qnode\":\"NA\"},ignore_index=True)\n", " \n", " property_distribution_histogram[[\"Class\",\"Number_of_Instances\"]].plot.bar(x=\"Class\")\n", " plt.show()\n", " \n", " \n", " property_type = \"wikibase_item\"\n", " prop_label = re.sub(\"[^0-9a-zA-Z]+\", \"_\", prop_label)\n", " output_file_in_out_class_distribution = \"Property_overview.\"+property_type+\".\"+prop_label+\".in_out_class_distribution.raw.tsv\"\n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$wikibase_item -i $WIKIDATA_PARTS/$label --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'item: (n1)-[r{label:llab}]->(n2), item:(n1)-[l1{label:llab2}]->(class_n1),label:(class_n1)-[:label]->(class_with_label_n1), item:(n2)-[l2{label:llab3}]->(class_n2),label:(class_n2)-[:label]->(class_with_label_n2)' \\\n", " --return 'kgtk_lqstring_text(class_with_label_n1) as Class1, kgtk_lqstring_text(class_with_label_n2) as Class2' \\\n", " --where 'llab in [\\\"__prop\\\"] AND llab2 in [\\\"P31\\\"] AND llab3 in [\\\"P31\\\"] AND kgtk_lqstring_lang_suffix(class_with_label_n1) = \\\"en\\\" AND kgtk_lqstring_lang_suffix(class_with_label_n2) = \\\"en\\\" '\"\n", " property_in_out_class_distribution = run_command_return_df(cmd,raw_folder,output_file_in_out_class_distribution,{\"__output_file\":output_file_in_out_class_distribution,\"__prop\":pnode,\"__input_file\":property_type,\"__folder\":raw_folder},save=_save,restart=_restart);\n", " if len(incoming_classes) == 0 and len(outgoing_classes)==0:\n", " printmd(\"Below is ditribution of \" + prop_label.capitalize() + \" in different outgoing classes\",'blue')\n", " df_temp = property_in_out_class_distribution[\"Class1\"].value_counts().rename_axis('Class').reset_index(name='Number_of_Instances')\n", " display_histgram(df_temp,start,end)\n", " printmd(\"Below is ditribution of \" + prop_label.capitalize() + \" in different incoming classes\",'blue')\n", " df_temp = property_in_out_class_distribution[\"Class2\"].value_counts().rename_axis('Class').reset_index(name='Number_of_Instances')\n", " display_histgram(df_temp,start,end)\n", " if len(incoming_classes)>0:\n", " for incoming_class in incoming_classes:\n", " printmd(\"Below is ditribution of \" + prop_label.capitalize() + \" in different outgoing classes for \"+incoming_class.capitalize()+\" Class\",'blue')\n", " incoming_class = incoming_class.lower()\n", " df_temp = property_in_out_class_distribution[property_in_out_class_distribution[\"Class1\"]==incoming_class][\"Class2\"].value_counts().rename_axis('Class').reset_index(name='Number_of_Instances')\n", " print(df_temp)\n", " display_histgram(df_temp,start,end)\n", " if len(outgoing_classes)>0:\n", " for outgoing_class in outgoing_classes:\n", " printmd(\"Below is ditribution of \" + prop_label.capitalize() + \" in different incoming classes for \"+outgoing_class.capitalize()+\" Class\",'blue')\n", " outgoing_class = outgoing_class.lower()\n", " df_temp = property_in_out_class_distribution[property_in_out_class_distribution[\"Class2\"]==outgoing_class][\"Class1\"].value_counts().rename_axis('Class').reset_index(name='Number_of_Instances')\n", " display_histgram(df_temp,start,end)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:03.695657Z", "iopub.status.busy": "2021-01-15T21:55:03.695139Z", "iopub.status.idle": "2021-01-15T21:55:03.696825Z", "shell.execute_reply": "2021-01-15T21:55:03.697164Z" }, "papermill": { "duration": 0.03509, "end_time": "2021-01-15T21:55:03.697314", "exception": false, "start_time": "2021-01-15T21:55:03.662224", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#This function displays the histogram for the distribution of instances of the property (property must be of time data type)\n", "#@argument\n", "#pnode: The pnode corresponding to the property\n", "#prop_label: The label for the property\n", "#property_type: Datatype of the property\n", "#output_file_time_distribution_raw: It is the file name for saving the output file thus reducing re run time.\n", "#restart: Here restart means if you allow to use any existing, keep it true if you re running it or you have imported the files.\n", "#save: If it is set then it will save the file otherwise will delete any generated file.\n", "#folder: It is the environment variable corresponding to the path of the folder.\n", "def property_distribution(pnode,property_type,prop_label,_save=True,_restart=True,output_file_class_distribution=None,number_of_unique_classes=5,start=0,end=5,incoming_classes=[],outgoing_classes=[],folder=\"PROPERTY_OVERVIEW\",raw_folder=\"RAW_FILES_PROPERTY\"):\n", " \n", " #Free all the garbage\n", " gc.collect()\n", " \n", " cmd_distribution = \"$kgtk query -i $WIKIDATA_PARTS/$__input_file -i $WIKIDATA_PARTS/$wikibase_item -i $WIKIDATA_PARTS/$label --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'claims: (n1)-[r{label:llab}]->(v), item:(n1)-[l{label:llab2}]->(class), label:(class)-[:label]->(class_with_label)' \\\n", " --return 'distinct kgtk_lqstring_text(class_with_label) as Class, count(class_with_label) as Number_of_Instances, class as Qnode' \\\n", " --where 'llab in [\\\"__prop\\\"] AND llab2 in [\\\"P31\\\"] AND kgtk_lqstring_lang_suffix(class_with_label) = \\\"en\\\" ' \\\n", " --order-by 'count(class_with_label) desc' \"\n", "\n", " cmd_wikibase_item_distribution = \"$kgtk query -i $WIKIDATA_PARTS/$wikibase_item -i $WIKIDATA_PARTS/$label --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'item: (n1)-[r{label:llab}]->(v), item:(n1)-[l{label:llab2}]->(class), label:(class)-[:label]->(class_with_label)' \\\n", " --return 'distinct kgtk_lqstring_text(class_with_label) as Class, count(class_with_label) as Number_of_Instances, class as Qnode ' \\\n", " --where 'llab in [\\\"__prop\\\"] AND llab2 in [\\\"P31\\\"] AND kgtk_lqstring_lang_suffix(class_with_label) = \\\"en\\\" ' \\\n", " --order-by 'count(class_with_label) desc' \"\n", "\n", " prop_label = re.sub(\"[^0-9a-zA-Z]+\", \"_\", prop_label) \n", " property_type = re.sub(\"[^0-9a-zA-Z]+\", \"_\", property_type) \n", "\n", " if output_file_class_distribution == None:\n", " #Dynamically setting the name of the output file based on the name of property\n", " output_file_class_distribution= \"Property_overview.\"+property_type+\".\"+prop_label+\".class_distribution.tsv\"\n", "\n", " if (property_type.lower().find(\"wikibase\") != -1 and property_type.lower().find(\"item\") != -1 ):\n", " #Running command for wikibase item\n", "# property_class_distribution = run_command_return_df(cmd_wikibase_item_distribution,folder,output_file_class_distribution,{\"__output_file\":output_file_class_distribution,\"__prop\":pnode,\"__input_file\":property_type,\"__folder\":folder},save=_save,restart=_restart);\n", " property_distribution_with_class_items(pnode,prop_label)\n", " return\n", " else:\n", " #Running command for other data types\n", " property_class_distribution = run_command_return_df(cmd_distribution,folder,output_file_class_distribution,{\"__output_file\":output_file_class_distribution,\"__prop\":pnode,\"__input_file\":property_type,\"__folder\":folder},save=_save,restart=_restart);\n", " printmd(\"Below is ditribution of \" + prop_label.capitalize() + \" in different classes\",'blue')\n", " if len(property_class_distribution)>0:\n", "\n", " property_distribution_histogram = pd.DataFrame();\n", "\n", " property_distribution_histogram = property_class_distribution[:number_of_unique_classes]\n", "\n", " if(len(property_class_distribution)>number_of_unique_classes):\n", " other_classes = sum(property_class_distribution[\"Number_of_Instances\"][5:])\n", " property_distribution_histogram = property_distribution_histogram.append({\"Class\":\"Other Classes\",\"Number_of_Instances\":other_classes,\"Qnode\":\"NA\"},ignore_index=True)\n", "\n", " property_distribution_histogram[[\"Class\",\"Number_of_Instances\"]].plot.bar(x=\"Class\")\n", "\n", " plt.show()\n", " else:\n", " printmd(\"The subgraph does not have any infromation about the class of the instances of this property\",)\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:03.764344Z", "iopub.status.busy": "2021-01-15T21:55:03.763793Z", "iopub.status.idle": "2021-01-15T21:55:03.765568Z", "shell.execute_reply": "2021-01-15T21:55:03.765940Z" }, "papermill": { "duration": 0.041461, "end_time": "2021-01-15T21:55:03.766109", "exception": false, "start_time": "2021-01-15T21:55:03.724648", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#This function displays the overview of the property\n", "#@argument\n", "#Current_Pnode : The pnode of the property for which the overview is to be found\n", "#Property_label : The property label of the property\n", "#_save: If its true then it saves the generated file\n", "#_restart: If its true then it looks the for the saved the file, if it is present loads instead of running the query again\n", "#output_file_overview: It is the file if provided saves the output in that file otherwise saves output in Property_overview.[Property_label].overview.tsv file\n", "#folder: This corresponds to the environment variable corresponding to the folder in which the file would be stored\n", "def property_overview(Current_Pnode,Property_label,Property_type,_save=True,_restart=True,output_file_overview=None,_min_year=1800,_max_year=2100,number_of_unique_classes=5,folder=\"PROPERTY_OVERVIEW\",raw_folder=\"RAW_FILES_PROPERTY\"): \n", " \n", " #Free all the garbage\n", " gc.collect()\n", " \n", " Property_label = re.sub(\"[^0-9a-zA-Z]+\", \"_\", Property_label)\n", " printmd('Property: '+ Property_label.capitalize(),'blue')\n", " # Find the list of all the description in English language\n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$description --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'description: (n1:__pnode)-[:description]->(n2)' \\\n", " --return 'kgtk_lqstring_text(n2) as Description_ ' \\\n", " --where 'kgtk_lqstring_lang_suffix(n2) = \\\"en\\\"'\"\n", " \n", " Property_label = re.sub(\"[^0-9a-zA-Z]+\", \"_\", Property_label) \n", "\n", " output_file_for_discription_raw = \"Property_overview.description.\"+Property_label+\".raw.tsv\";\n", "\n", " df_temp = run_command_return_df(cmd,raw_folder,output_file_for_discription_raw,{\"__output_file\":output_file_for_discription_raw,\"__pnode\":Current_Pnode,\"__folder\":raw_folder},_save,_restart);\n", " description_array = df_temp[\"Description_\"].values.tolist();\n", " description_current = \"\".join(description_array[0] if len(description_array)>=1 else \"\")\n", "\n", "\n", " # Find the list of all the aliases in English language\n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$alias --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'alias: (n1:__qnode)-[:alias]->(n2)' \\\n", " --return 'kgtk_lqstring_text(n2) as Alias' \\\n", " --where 'kgtk_lqstring_lang_suffix(n2) = \\\"en\\\"' \"\n", "\n", " output_file_for_aliases_raw = \"Property_overview.aliases.\"+Property_label+\".raw.tsv\";\n", " df_temp = run_command_return_df(cmd,raw_folder,output_file_for_aliases_raw,{\"__output_file\":output_file_for_aliases_raw,\"__qnode\":Current_Pnode,\"__folder\":raw_folder},_save,_restart)\n", " aliases_current = \", \".join(df_temp[\"Alias\"].values.tolist())\n", " \n", " #Find the number of qualifiers for a property\n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$__input_file_prop -i $WIKIDATA_PARTS/$__input_file_qualifiers --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'claims: ()-[l{label:llab}]->(), qualifiers: (l)-[lq]->(n2)' \\\n", " --return 'count(n2) as Number_of_Qualifiers' \\\n", " --where 'llab in [\\\"__prop\\\"]' \"\n", " \n", " input_file = Property_type\n", " input_file_qualifier = Property_type+\"_qualifiers\"\n", " if os.path.exists(os.path.join(os.getenv(\"WIKIDATA_PARTS\"),os.getenv(input_file_qualifier))):\n", " output_file_for_qualifiers_raw = \"Property_overview.number_of_qualifiers.\"+Property_label+\".raw.tsv\";\n", " df_temp_qual = run_command_return_df(cmd,raw_folder,output_file_for_qualifiers_raw,{\"__output_file\":output_file_for_qualifiers_raw,\"__prop\":Current_Pnode,\"__folder\":raw_folder,\"__input_file_prop\":input_file,\"__input_file_qualifiers\":input_file_qualifier},_save,_restart)\n", " Number_of_qualifiers = df_temp_qual.loc[0][\"Number_of_Qualifiers\"]\n", " else:\n", " Number_of_qualifiers = 0\n", "\n", " #Find the number of Statements for a property\n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$__input_file --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'claims: (n1)-[l{label:llab}]->(n2)' \\\n", " --return 'count(llab) as Number_of_Statements' \\\n", " --where 'llab in [\\\"__prop\\\"]' \"\n", " \n", " output_file_for_statements_raw = \"Property_overview.number_of_statements.\"+Property_label+\".raw.tsv\";\n", " df_temp_qual = run_command_return_df(cmd,raw_folder,output_file_for_statements_raw,{\"__output_file\":output_file_for_statements_raw,\"__prop\":Current_Pnode,\"__folder\":raw_folder,\"__input_file\":Property_type},_save,_restart)\n", " Number_of_statements_of_current_property = df_temp_qual.loc[0][\"Number_of_Statements\"]\n", " \n", " # Print statments\n", " printmd('Description: '+ description_current.capitalize(),size=15, fontWeight='Light')\n", " printmd('Aliases: '+ aliases_current.capitalize(),size=15,fontWeight='Light')\n", " printmd(\"Number of Statements: \" +str(Number_of_statements_of_current_property),size=15,fontWeight='Light')\n", " printmd(\"Number of Qualifiers: \" +str(Number_of_qualifiers),size=15,fontWeight='Light')\n", " printmd(\"Property Type: \" +str(Property_type),size=15,fontWeight='Light')\n", "\n", " #Create a dataframe corresponding to the above generated statistics\n", " property_overview = pd.DataFrame([[\"Description\",description_current.capitalize()],[\"Aliases\",aliases_current.capitalize()],[\"Number of Statements: \",str(Number_of_statements_of_current_property)],[\"Number of Qualifiers: \",str(Number_of_qualifiers)],[\"Property Type: \",str(Property_type)]],columns=[\"Stat\",\"Value\"])\n", "\n", " if output_file_overview == None:\n", " #Dynamically create the name of the output file where the overview would be stored\n", " output_file_overview =\"Property_overview.\"+Property_label+\".overview.tsv\" \n", "\n", " # Save the overview to the output file\n", " property_overview.to_csv(os.path.join(os.getenv(folder),output_file_overview),sep='\\t')\n", " \n", " if Property_type.lower().find(\"time\") != -1:\n", " time_prop_distribution(Current_Pnode,Property_label,_save,_restart,_min_year=_min_year,_max_year=_max_year,number_of_unique_classes=number_of_unique_classes)\n", " else:\n", " property_distribution(Current_Pnode,Property_type,Property_label,_save,_restart,number_of_unique_classes=number_of_unique_classes)\n", " \n", " if Property_type.lower().find(\"quantity\") != -1:\n", " quantity_distribution(Current_Pnode,Property_label,_save=_save,_restart=_restart)\n", "\n", " printmd(\"------------------------------------------------------------------------------------------\")" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:03.825169Z", "iopub.status.busy": "2021-01-15T21:55:03.824548Z", "iopub.status.idle": "2021-01-15T21:55:03.826487Z", "shell.execute_reply": "2021-01-15T21:55:03.826833Z" }, "papermill": { "duration": 0.032437, "end_time": "2021-01-15T21:55:03.826985", "exception": false, "start_time": "2021-01-15T21:55:03.794548", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#This is a helper function which extract the qnode/pnode from the link\n", "def get_qnodes_from_link(link):\n", " return str(link.split(\"/\")[-1])" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:03.890691Z", "iopub.status.busy": "2021-01-15T21:55:03.890164Z", "iopub.status.idle": "2021-01-15T21:55:03.891991Z", "shell.execute_reply": "2021-01-15T21:55:03.892295Z" }, "papermill": { "duration": 0.037933, "end_time": "2021-01-15T21:55:03.892632", "exception": false, "start_time": "2021-01-15T21:55:03.854699", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# Find labels of all the properties of the class\n", "def find_label_property(output_file_labels_property = None, _restart = True,_save=True,folder=\"RAW_FILES_OVERVIEW\"):\n", " \n", " #Free all the garbage\n", " gc.collect()\n", " \n", " if output_file_labels_property == None:\n", " output_file_labels_property = \"Overview.properties.label.raw.tsv\"\n", " file_path = os.path.join(os.getenv(folder),output_file_labels_property)\n", " \n", " #Loading the file if it already exists\n", " if _restart and os.path.exists(file_path):\n", " try:\n", " df_label = pd.read_csv(file_path,delimiter='\\t')\n", " return df_label\n", " except:\n", " pass\n", " df_label = pd.DataFrame([],columns=['Property','Label'])\n", " \n", " \n", " #Variable storing all the datatypes of a properties\n", " types = [ \"time\",\n", " \"wikibase_item\",\n", " \"math\",\n", " \"wikibase_form\",\n", " \"quantity\",\n", " \"string\",\n", " \"external_id\",\n", " \"commonsMedia\",\n", " \"globe_coordinate\",\n", " \"monolingualtext\",\n", " \"musical_notation\",\n", " \"geo_shape\",\n", " \"url\"\n", " ]\n", " \n", " #Iterating over all the types\n", " for __type in types:\n", " #Find label of properties of a Datatype:__type\n", " __type_label = re.sub(\"[^0-9a-zA-Z]+\", \"_\", __type)\n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$__type -i $WIKIDATA_PARTS/$label --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'claims: (n1)-[l{label:llab}]->(n2), label: (llab)-[:label]->(n1label)' \\\n", " --return 'distinct llab as Property, n1label as `Label`'\"\n", " \n", " # Dynamically genrating the output files for each of the labels for a particular property type\n", " output_file_labels_property_type = \"Overview.properties.label.\"+__type_label+\".raw.tsv\"\n", " df_temp = run_command_return_df(cmd,folder,output_file_labels_property_type,{\"__output_file\":output_file_labels_property_type,\"__type\":__type,\"__folder\":folder},restart=_restart,save=_save)\n", " df_temp = df_temp.reset_index()\n", " del df_temp[\"index\"]\n", "\n", "\n", " # Dropping if there are many statements for same instances and same property\n", " # For example a node can have more than P31 statements\n", " df_temp = df_temp.drop_duplicates()\n", " df_label = pd.concat([df_label,df_temp])\n", " df_label.to_csv(os.path.join(os.getenv(folder),output_file_labels_property),sep=\"\\t\")\n", " return df_label" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:03.958724Z", "iopub.status.busy": "2021-01-15T21:55:03.957578Z", "iopub.status.idle": "2021-01-15T21:55:03.960392Z", "shell.execute_reply": "2021-01-15T21:55:03.960872Z" }, "papermill": { "duration": 0.040057, "end_time": "2021-01-15T21:55:03.961049", "exception": false, "start_time": "2021-01-15T21:55:03.920992", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# This is a helper function which finds the statistics on the outcoming properties on the instances of the class\n", "# returns a dataframe object with the statistics\n", "#@parameters\n", "#df_class_example: The dataframe object corresponding to the instances of the class\n", "def find_properties(class_label,number_of_instances,df_label,output_class_example_with_index,folder=\"CLASS_FOLDER\",_save=True,_restart=True,raw_folder=\"RAW_FILES_CLASS\"):\n", " #Free all the garbage\n", " gc.collect()\n", " \n", " #This is a helper function which finds the percentage corresponding to the passed value\n", " def find_percent(val):\n", " return round(val*100/number_of_instances,2)\n", " \n", " # This is a helper function which finds the label for the node\n", " def label_node(node):\n", " for ele in df_label[df_label['Property']==node]['Label']:\n", " if kgtk_lqstring_lang_suffix(ele) == \"en\":\n", " label = kgtk_lqstring_text(ele)\n", " if label[0] == '\"':\n", " return label[1:-1]\n", " return label\n", " return node\n", " \n", " # Creating the data frame for the output\n", " df_all_prop = pd.DataFrame([],columns=['id','node1','label','node2'])\n", " for type_ in types:\n", " \n", " cmd = \"$kgtk query -i $__folder/__input_file_1 -i $WIKIDATA_PARTS/$__type --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'example: (n1)-[l1]->(n2), claims:(n1)-[l{label:llab}]->(val)' \\\n", " --return 'n1 as node1, llab as `label` '\"\n", " type_label = re.sub(\"[^0-9a-zA-Z]+\", \"_\", type_) \n", " \n", " #Dynamically creating the output file for storing all the statements for all the instances of the class\n", " output_file_instances_claims_type = \"Class_overview.\"+class_label+\".\"+type_label+\".claims.raw.tsv\"\n", " df_temp = run_command_return_df(cmd,raw_folder,output_file_instances_claims_type,{\"__output_file\":output_file_instances_claims_type,\"__type\":type_,\"__input_file_1\":output_class_example_with_index,\"__folder\":raw_folder},_save,_restart)\n", " df_temp = df_temp.reset_index()\n", " del df_temp[\"index\"]\n", " \n", " \n", " # Dropping if there are many statements for same instances and same property\n", " # For example a node can have more than P31 statements\n", " df_temp = df_temp.drop_duplicates()\n", " df_all_prop = pd.concat([df_all_prop,df_temp])\n", " df_all_prop = df_all_prop.reset_index()\n", " \n", " # Finding the count for each of the properties\n", " df_all_prop = df_all_prop['label'].value_counts()\n", " df_all_prop = df_all_prop.reset_index()\n", " df_all_prop.columns = [\"Property Name\",\"Instances\"]\n", " \n", " # Finding the Percent\n", " df_all_prop[\"% Instances\"] = df_all_prop[\"Instances\"].map(find_percent)\n", " df_all_prop[\"Property Name\"] = df_all_prop[\"Property Name\"].map(label_node)\n", "\n", " return df_all_prop" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:04.025999Z", "iopub.status.busy": "2021-01-15T21:55:04.025462Z", "iopub.status.idle": "2021-01-15T21:55:04.027025Z", "shell.execute_reply": "2021-01-15T21:55:04.027420Z" }, "papermill": { "duration": 0.037718, "end_time": "2021-01-15T21:55:04.027584", "exception": false, "start_time": "2021-01-15T21:55:03.989866", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# This is a helper function which finds the statistics on the incoming properties on the instances of the class\n", "# returns a dataframe object with the statistics\n", "#@parameters\n", "#df_class_example: The dataframe object corresponding to the instances of the class\n", "def find_properties_incoming(class_label,df_label,output_class_example_with_index,_save=True,_restart=True,folder=\"CLASS_FOLDER\",raw_folder=\"RAW_FILES_CLASS\"):\n", " #Free all the garbage\n", " gc.collect()\n", " \n", " #Environment variable corresponding to the statistics of each of the property data type\n", " # Here wikibase_item is only relevant because it only has node2 as wikibase-item.\n", " types = [\n", " \"wikibase_item\"\n", " ]\n", " \n", " # This is a helper function which finds the label for the node\n", " def label_node(node):\n", " for ele in df_label[df_label['Property']==node]['Label']:\n", " if kgtk_lqstring_lang_suffix(ele) == \"en\":\n", " label = kgtk_lqstring_text(ele)\n", " if label[0] == '\"':\n", " return label[1:-1]\n", " return label\n", " return node\n", " \n", " # Creating the data frame for the output\n", " df_all_prop = pd.DataFrame([],columns=['id','node1','label','node2'])\n", " for type_ in types:\n", " \n", " #Command to find all the properties of the instances\n", " cmd = \"$kgtk query -i $__folder/__input_file_1 -i $WIKIDATA_PARTS/$__type --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'examples: (n1)-[l1]->(n2), claims:(val)-[l{label:llab}]->(n1)' \\\n", " --return 'n1 as node1, llab as `label` '\"\n", " \n", " type_label = re.sub(\"[^0-9a-zA-Z]+\", \"_\", type_) \n", " \n", " #Dynamically Generating output file for saving all the incoming properties to instances of a class\n", " output_file_incoming_claims_type = \"Class_overview.\"+class_label+\".\"+type_label+\".incoming_claims.raw.tsv\"\n", " df_temp = run_command_return_df(cmd,raw_folder,output_file_incoming_claims_type,{\"__output_file\":output_file_incoming_claims_type,\"__type\":type_,\"__input_file_1\":output_class_example_with_index,\"__folder\":raw_folder},_save,_restart)\n", " \n", " #removing the index from the dataframe\n", " df_temp = df_temp.reset_index()\n", " del df_temp[\"index\"]\n", " \n", " \n", " # Dropping if there are many statements for same instances and same property\n", " # For example a node can have more than P31 statements\n", " df_temp = df_temp.drop_duplicates()\n", " df_all_prop = pd.concat([df_all_prop,df_temp])\n", " \n", " df_all_prop = df_all_prop.reset_index()\n", " \n", " # Finding the count for each of the properties\n", " df_all_prop = df_all_prop['label'].value_counts()\n", " df_all_prop = df_all_prop.reset_index()\n", " df_all_prop.columns = [\"Property Name\",\"Instances\"]\n", " df_all_prop[\"Property Name\"] = df_all_prop[\"Property Name\"].map(label_node)\n", "\n", " return df_all_prop" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:04.104748Z", "iopub.status.busy": "2021-01-15T21:55:04.104128Z", "iopub.status.idle": "2021-01-15T21:55:04.106058Z", "shell.execute_reply": "2021-01-15T21:55:04.106426Z" }, "papermill": { "duration": 0.04678, "end_time": "2021-01-15T21:55:04.106589", "exception": false, "start_time": "2021-01-15T21:55:04.059809", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#This function gives the overview of a property \n", "#@parameter\n", "#Current_Qnode: The qnode corresponding to the class\n", "#class_label: The label corresponding ot the class\n", "#_save: This options saves any intermediate file and work in relation with _restrart\n", "#_restart: This option uses any intermediate file saved during the execution and thus reduce the time of run\n", "#folder: The folder in which the files are saved.\n", "def class_overview(Current_Qnode,class_label,_save=True,_restart=True,number_of_examples=3,number_of_incoming_properties=10,number_of_outgoing_properties=10,folder=\"CLASS_FOLDER\",raw_folder=\"RAW_FILES_CLASS\"):\n", " #Free all the garbage\n", " gc.collect()\n", " \n", " # Heading\n", " printmd('Class: '+class_label,'blue')\n", " \n", " class_label = re.sub(\"[^0-9a-zA-Z]+\", \"_\", class_label) \n", "\n", " # Find the list of all the description in English language\n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$description --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'descriptions: (n1:__qnode)-[:description]->(n2)' \\\n", " --return 'kgtk_lqstring_text(n2) as Description_ ' \\\n", " --where 'kgtk_lqstring_lang_suffix(n2) = \\\"en\\\"'\"\n", "\n", " output_file_class_description = \"Class_overview.description.\"+class_label+\".raw.tsv\"\n", " df_temp = run_command_return_df(cmd,raw_folder,output_file_class_description,{\"__output_file\":output_file_class_description,\"__qnode\":Current_Qnode,\"__folder\":raw_folder},_save,_restart);\n", " description_array = df_temp[\"Description_\"].values.tolist();\n", " description_current = description_array[0] if len(description_array)>=1 else \"\"\n", "\n", " # Find the list of all the aliases in English language\n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$alias --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'alias: (n1:__qnode)-[:alias]->(n2)' \\\n", " --return 'kgtk_lqstring_text(n2) as Alias' \\\n", " --where 'kgtk_lqstring_lang_suffix(n2) = \\\"en\\\"' \"\n", "\n", " output_file_class_aliases = \"Class_overview.aliases.\"+class_label+\".raw.tsv\"\n", " df_temp = run_command_return_df(cmd,raw_folder,output_file_class_aliases,{\"__output_file\":output_file_class_aliases,\"__qnode\":Current_Qnode,\"__folder\":raw_folder},_save,_restart)\n", " aliases_current = \", \".join(df_temp[\"Alias\"].values.tolist())\n", "\n", " # Find the list of all the super classes in English language\n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$wikibase_item -i $WIKIDATA_PARTS/$label --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'item: (n1:__qnode)-[l{label:llab}]->(n2), label:(n2)-[:label]->(n2_label)' \\\n", " --return 'distinct kgtk_lqstring_text(n2_label) as Superclass' \\\n", " --where 'kgtk_lqstring_lang_suffix(n2_label) = \\\"en\\\" AND llab in [\\\"P279\\\"]' \"\n", "\n", " output_file_class_super_class = \"Class_overview.super_class.\"+class_label+\".raw.tsv\"\n", " df_temp = run_command_return_df(cmd,raw_folder,output_file_class_super_class,{\"__output_file\":output_file_class_super_class,\"__qnode\":Current_Qnode,\"__folder\":raw_folder},_save,_restart)\n", "# print(df_temp[\"Superclass\"].values.tolist())\n", " direct_super_class_current = df_temp[\"Superclass\"].values.tolist()\n", "\n", " # Find the list of all the subclasses in English language\n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$wikibase_item -i $WIKIDATA_PARTS/$label --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'item: (n1)-[l{label:llab}]->(n2:__qnode), label:(n1)-[:label]->(n1_label)' \\\n", " --return 'distinct kgtk_lqstring_text(n1_label) as Subclass' \\\n", " --where 'kgtk_lqstring_lang_suffix(n1_label) = \\\"en\\\" AND llab in [\\\"P279\\\"]' \"\n", "\n", " output_file_class_super_class = \"Class_overview.subclass_class.\"+class_label+\".raw.tsv\"\n", " df_temp = run_command_return_df(cmd,raw_folder,output_file_class_super_class,{\"__output_file\":output_file_class_super_class,\"__qnode\":Current_Qnode,\"__folder\":raw_folder},_save,_restart)\n", "# print(df_temp[\"Subclass\"].values.tolist())\n", " subclass_current = df_temp[\"Subclass\"].values.tolist()\n", "\n", " #Find number of instances of the class\n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$wikibase_item --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'item: (n1)-[l{label:llab}]->(n2:__qnode)' \\\n", " --return 'count(distinct n1) as Number_of_Instances' \\\n", " --where 'llab in [\\\"P31\\\"]' \"\n", "\n", " output_file_class_number_of_instances = \"Class_overview.number_of_instances.\"+class_label+\".raw.tsv\"\n", " df_temp = run_command_return_df(cmd,raw_folder,output_file_class_number_of_instances,{\"__output_file\":output_file_class_number_of_instances,\"__qnode\":Current_Qnode,\"__folder\":raw_folder},_save,_restart)\n", " Number_of_instances_of_current_class = df_temp.loc[0][\"Number_of_Instances\"];\n", "\n", " # Print statments\n", " printmd('Description: '+ description_current.capitalize(),size=15, fontWeight='Light')\n", " printmd('Aliases: '+ aliases_current.capitalize(),size=15,fontWeight='Light')\n", " printmd('Direct Superclasses: '+ \", \".join(direct_super_class_current[:10]).capitalize(),size=15,fontWeight='Light')\n", " printmd('Direct Subclasses: '+ \", \".join(subclass_current[:10]).capitalize(),size=15,fontWeight='Light')\n", " printmd(\"Number of Instances: \" +str(Number_of_instances_of_current_class),size=15,fontWeight='Light')\n", " printmd(\"Number of Superclasses: \" +str(len(direct_super_class_current)),size=15,fontWeight='Light')\n", " printmd(\"Number of Subclasses: \" +str(len(subclass_current)),size=15,fontWeight='Light')\n", " printmd(\"Examples for \" +class_label +\" Class\",'black',size=18)\n", "\n", " #Create a dataframe corresponding to the above generated statistics\n", " class_overview = pd.DataFrame([[\"Description\",description_current.capitalize()],[\"Aliases\",aliases_current.capitalize()],[\"Subclass of\",\", \".join(subclass_current).capitalize()],[\"Number of Instances\",Number_of_instances_of_current_class],[\"Number of Subclass\",len(subclass_current)],[\"Number of Superclass\",len(direct_super_class_current)]],columns=[\"Stat\",\"Value\"])\n", "\n", " #Dynamically create the name of the output file where the overview would be stored\n", " # Save the overview to the output file\n", " output_file_overview = \"Class_overview.\"+class_label+\".overview.tsv\"\n", " class_overview.to_csv(os.path.join(os.getenv(folder),output_file_overview),sep='\\t')\n", "\n", " # Find the example instances of the class\n", " #query to find the example instances of a class\n", " #Dynamically create the name of the output file where the example instances would be stored\n", " output_file_examples_temp = \"Class_overview.\"+class_label+\".examples.temp.tsv\"\n", "\n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$wikibase_item --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'item: (n1)-[l{label:llab}]->(n2:__class)' \\\n", " --return ' distinct n1 as node1 ,n1 as `label`,n1 as node2 ' \\\n", " --where 'llab in [\\\"P31\\\"]'\"\n", " df_class_instances = run_command_return_df(cmd,raw_folder,output_file_examples_temp,{\"__output_file\":output_file_examples_temp,\"__class\":Current_Qnode,\"__folder\":raw_folder},_save,_restart)\n", " Number_of_instances = len(df_class_instances)\n", " \n", " #Remove the index from the dataframe\n", " df_class_instances = df_class_instances.reset_index()\n", " del df_class_instances[\"index\"]\n", " \n", " #This file is being saved for the GUI\n", " output_file = \"Class_overview.\"+class_label+\".examples.tsv\"\n", " #Save the file to csv.\n", " #This is the name of the temporary file which is created with the\n", " #column names changed to [node1,label,node2] so that we can run kgtk query on it.\n", " output_file_temp = \"temp.\"+output_file\n", " df_class_instances.to_csv(os.path.join(os.getenv(raw_folder),output_file_temp),sep='\\t',index=False)\n", " \n", " output_file_examples_with_label_temp = \"Class_overview.\"+class_label+\".examples.with_label.temp.tsv\"\n", " if os.path.exists(os.path.join(os.getenv(\"WIKIDATA_PARTS\"),os.getenv(\"statistics\"))):\n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$wikibase_item -i $WIKIDATA_PARTS/$label -i $WIKIDATA_PARTS/$statistics --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'item: (n1)-[l{label:llab}]->(n2:__class), label: (n1)-[:label]->(label), statistics:(n1)-[:vertex_pagerank]->(pagerank)' \\\n", " --return ' distinct n1 as Link, kgtk_lqstring_text(label) as `Label_`, pagerank as Pagerank' \\\n", " --where 'label.kgtk_lqstring_lang_suffix = \\\"en\\\" AND llab in [\\\"P31\\\"]' \\\n", " --order-by 'pagerank' \"\n", " \n", " df_class_example = run_command_return_df(cmd,raw_folder,output_file_examples_with_label_temp,{\"__output_file\":output_file_examples_with_label_temp,\"__class\":Current_Qnode,\"__folder\":raw_folder},_save,_restart)\n", " # This condition is to check if the we are using previously generated file\n", " if 'Pagerank' not in df_class_example:\n", " df_class_example = run_command_return_df(cmd,raw_folder,output_file_examples_with_label_temp,{\"__output_file\":output_file_examples_with_label_temp,\"__class\":Current_Qnode,\"__folder\":raw_folder},_save,restart=False)\n", " # Change the order of the Columns\n", " df_class_example = df_class_example[[\"Label_\",\"Pagerank\",\"Link\"]]\n", " else:\n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$wikibase_item -i $WIKIDATA_PARTS/$label --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'item: (n1)-[l{label:llab}]->(n2:__class), label: (n1)-[:label]->(label)' \\\n", " --return ' distinct n1 as Link, kgtk_lqstring_text(label) as `Label_`' \\\n", " --where 'label.kgtk_lqstring_lang_suffix = \\\"en\\\" AND llab in [\\\"P31\\\"]'\"\n", " \n", " df_class_example = run_command_return_df(cmd,raw_folder,output_file_examples_with_label_temp,{\"__output_file\":output_file_examples_with_label_temp,\"__class\":Current_Qnode,\"__folder\":raw_folder},_save,_restart)\n", " # Change the order of the Columns\n", " df_class_example = df_class_example[[\"Label_\",\"Link\"]]\n", " \n", " df_class_example['Link'] = df_class_example['Link'].apply(generate_link)\n", " \n", " df_class_example.to_csv(os.path.join(os.getenv(folder),output_file),sep='\\t')\n", "\n", " display(HTML(df_class_example[:number_of_examples].to_html(index=False)))\n", "\n", " #Add index to the instances of the class\n", " cmd =\"$kgtk add-id -i $__folder/__output_file --id-style node1-label-node2 -o $__folder/__output_class_example_with_index\"\n", " cmd = cmd.replace(\"__output_file\",output_file_temp);\n", " \n", " #This is the name of the temporary file which is created with the\n", " #index added in it.\n", " output_class_example_with_index = \"indexed.\" + output_file\n", " cmd = cmd.replace(\"__output_class_example_with_index\",output_class_example_with_index);\n", " cmd = cmd.replace(\"__folder\",raw_folder);\n", " run_command(cmd)\n", " \n", " #Find labels for all the properties\n", " df_label = find_label_property()\n", " \n", " #Dynamically create the name of the output file where the outgoing properties would be stored\n", " output_file_prop = \"Class_overview.\"+class_label+\".outgoing_properties.tsv\"\n", " df_prop_all = find_properties(class_label,Number_of_instances,df_label,output_class_example_with_index,_save=_save,_restart=_restart)\n", " df_prop_all.to_csv(os.path.join(os.getenv(folder),output_file_prop),sep='\\t')\n", "\n", " printmd(\"Distribution of outgoing Properties from instances of \" + class_label +\" Class\",'black',size=18)\n", " display(HTML(df_prop_all[:number_of_outgoing_properties].to_html(index=False)))\n", "\n", " #Dynamically create the name of the output file where the incoming properties would be stored\n", " output_file_prop_incoming = \"Class_overview.\"+class_label +\".incoming_properties.tsv\"\n", " df_prop_incoming = find_properties_incoming(class_label,df_label,output_class_example_with_index,_save=_save,_restart=_restart)\n", " df_prop_incoming.to_csv(os.path.join(os.getenv(folder),output_file_prop_incoming),sep='\\t')\n", "\n", " printmd(\"Distribution of incoming Properties to instances of \" + class_label +\" Class\",'black',size=18)\n", " display(HTML(df_prop_incoming[:number_of_incoming_properties].to_html(index=False)))\n", "\n", " printmd(\"------------------------------------------------------------------------------------------\")\n", "# if os.path.exists(os.path.join(os.getenv(raw_folder),output_file_temp)):\n", "# os.remove(os.path.join(os.getenv(raw_folder),output_file_temp))\n", "# if os.path.exists(os.path.join(os.getenv(raw_folder),output_class_example_with_index)):\n", "# os.remove(os.path.join(os.getenv(raw_folder),output_class_example_with_index))" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:04.200952Z", "iopub.status.busy": "2021-01-15T21:55:04.200116Z", "iopub.status.idle": "2021-01-15T21:55:04.295889Z", "shell.execute_reply": "2021-01-15T21:55:04.296427Z" }, "papermill": { "duration": 0.162238, "end_time": "2021-01-15T21:55:04.296725", "exception": false, "start_time": "2021-01-15T21:55:04.134487", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "Overview of the Graph" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Number of Nodes in the Graph: 16686" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Number of Edges in the Graph: 183595" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Number of Classes in the Graph: 2437" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Number of Properties in the Graph: 1614" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Types contain tuple of the environmnet variable corresponding to the input property file, \n", "#the name of the Datatype and environmnet variable corresponding to the output file.\n", "try:\n", " #Free all the garbage\n", " gc.collect()\n", " \n", " ## Make it False if you are facing some issue. Setting it False will lead to generating all files from scratch which might take some time.\n", " restart = restart_global\n", " number_of_node = 0\n", " number_of_edges = 0\n", " number_of_classes = 0\n", " number_of_properties = 0\n", " printmd(\"Overview of the Graph\",'blue')\n", "\n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$wikibase_item --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'item: (n1)-[l]->(n2)' \\\n", " --return 'count(distinct n1) as number_of_nodes' \"\n", "\n", " output_file_number_of_nodes = \"Overview.number_of_nodes.raw.tsv\"\n", " df_temp = run_command_return_df(cmd,\"RAW_FILES_OVERVIEW\",output_file_number_of_nodes,{\"__output_file\":output_file_number_of_nodes,\"__folder\":\"RAW_FILES_OVERVIEW\"},restart=restart)\n", " number_of_nodes = df_temp.iloc[0]['number_of_nodes']\n", "\n", " number_of_edges = 0\n", " # finding the edges for each of the property data type\n", " for type_,name,file in types_with_fileName:\n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$__type --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'claims: (n1)-[l{label:llab}]->(n2)' \\\n", " --return 'count(llab) as number_of_edges' \"\n", " type_label = re.sub(\"[^0-9a-zA-Z]+\", \"_\", type_)\n", " output_file_number_of_edges_type = \"Overview.number_of_edges.\"+type_label+\".raw.tsv\"\n", " df_temp = run_command_return_df(cmd,\"RAW_FILES_OVERVIEW\",output_file_number_of_edges_type,{\"__output_file\":output_file_number_of_edges_type,\"__type\":type_,\"__folder\":\"RAW_FILES_OVERVIEW\"},restart=restart)\n", " if len(df_temp)>0:\n", " number_of_edges += df_temp.iloc[0]['number_of_edges']\n", "\n", " #Finding the number of classes in the subgraph \n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$wikibase_item --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'item: (n1)-[l{label:llab}]->(n2)' \\\n", " --where 'llab in [\\\"P279\\\"]' \\\n", " --return 'count(distinct n2) as number_of_classes' \"\n", "\n", " output_file_number_of_classes = \"Overview.number_of_classes.raw.tsv\"\n", " df_temp = run_command_return_df(cmd,\"RAW_FILES_OVERVIEW\",output_file_number_of_classes,{\"__output_file\":output_file_number_of_classes,\"__folder\":\"RAW_FILES_OVERVIEW\"},restart=restart)\n", " number_of_classes = df_temp.iloc[0]['number_of_classes']\n", "\n", " number_of_properties = 0\n", " #Finding the number of properties in the sub graph\n", " for type_,name,file in types_with_fileName:\n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$__type --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'claims: (n1)-[l{label:llab}]->(n2)' \\\n", " --return 'count(distinct llab) as number_of_properties' \"\n", " type_label = re.sub(\"[^0-9a-zA-Z]+\", \"_\", type_)\n", " output_file_number_of_properties_type = \"Overview.number_of_properties.\"+type_label+\".raw.tsv\"\n", " df_temp = run_command_return_df(cmd,\"RAW_FILES_OVERVIEW\",output_file_number_of_properties_type,{\"__output_file\":output_file_number_of_properties_type,\"__type\":type_,\"__folder\":\"RAW_FILES_OVERVIEW\"},restart=restart)\n", " if len(df_temp)>0:\n", " number_of_properties += df_temp.iloc[0]['number_of_properties']\n", " del df_temp\n", "\n", " df_stats = pd.DataFrame([[\"Number of Nodes\",number_of_nodes],[\"Number of Edges\",number_of_edges],[\"Number of Classes\",number_of_classes],[\"Number of Properties\",number_of_properties]],columns=['Statistics', 'Value'])\n", " printmd(\"Number of Nodes in the Graph: \"+str(number_of_nodes),size=15,fontWeight='Light')\n", " printmd(\"Number of Edges in the Graph: \"+str(number_of_edges),size=15,fontWeight='Light')\n", " printmd(\"Number of Classes in the Graph: \"+str(number_of_classes),size=15,fontWeight='Light')\n", " printmd(\"Number of Properties in the Graph: \"+str(number_of_properties),size=15,fontWeight='Light')\n", " df_stats.to_csv(os.path.join(os.getenv('OVERVIEW_FOLDER'),os.getenv('stats')),sep='\\t')\n", " \n", " del df_stats\n", " \n", " #Free all the garbage\n", " gc.collect()\n", "except Exception as e:\n", " print(e)" ] }, { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.033156, "end_time": "2021-01-15T21:55:04.371005", "exception": false, "start_time": "2021-01-15T21:55:04.337849", "status": "completed" }, "tags": [] }, "source": [ "## Compute graph statistics" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:04.433894Z", "iopub.status.busy": "2021-01-15T21:55:04.433227Z", "iopub.status.idle": "2021-01-15T21:55:11.161616Z", "shell.execute_reply": "2021-01-15T21:55:11.162119Z" }, "papermill": { "duration": 6.762412, "end_time": "2021-01-15T21:55:11.162456", "exception": false, "start_time": "2021-01-15T21:55:04.400044", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/amandeep/anaconda3/envs/kgtk-env/lib/python3.7/site-packages/graph_tool/draw/cairo_draw.py:1494: RuntimeWarning: Error importing Gtk module: No module named 'gi'; GTK+ drawing will not work.\r\n", " warnings.warn(msg, RuntimeWarning)\r\n" ] } ], "source": [ "if compute_graph_statistics:\n", " !kgtk graph-statistics -i \"$WIKIDATA_PARTS/$wikibase_item\" \\\n", " --degrees --pagerank -o \"$WIKIDATA_PARTS/claims.statistics.tsv.gz\" \\\n", " --log \"$TEMP_FOLDER/graph_statistics_log.txt\" --statistics-only" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:11.288973Z", "iopub.status.busy": "2021-01-15T21:55:11.288121Z", "iopub.status.idle": "2021-01-15T21:55:11.420425Z", "shell.execute_reply": "2021-01-15T21:55:11.421099Z" }, "papermill": { "duration": 0.218154, "end_time": "2021-01-15T21:55:11.421323", "exception": false, "start_time": "2021-01-15T21:55:11.203169", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "Overview of the Graph" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Average in-degree of the Graph: 2.3543090015581924" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Minimum in-degree of the Graph: 0.0" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Maximum in-degree of the Graph: 90.0" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Average out-degree of the Graph: 5.776758959606856" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Minimum out-degree of the Graph: 1" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Maximum out-degree of the Graph: 99" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# /Volumes/BACKUP/Data_isi/wikidata_/parts/claims.wikibase-item.tsv.gz\n", "try:\n", " #Free all the garbage\n", " gc.collect()\n", " \n", " ## Make it False if you are facing some issue. Setting it False will lead to generating all files from scratch which might take some time.\n", " restart = restart_global\n", "# This query finds the statistics for all the nodes in wikibase_item and save it in a file\n", " file_path = os.path.join(os.getenv(\"WIKIDATA_PARTS\"),\"claims.statistics.tsv.gz\")\n", " if os.path.exists(file_path):\n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$wikibase_item -i $WIKIDATA_PARTS/claims.statistics.tsv.gz --graph-cache $STORE \\\n", " -o $__folder/$all_degree \\\n", " --match 'item: (n1)-[l]->(n2), statistics:(n1)-[stats_property{label: llab}]->(stats) ' \\\n", " --return 'distinct n1 as node1, llab as `label`, stats as node2' \"\n", " run_command_return_df(cmd,\"RAW_FILES_OVERVIEW\",os.getenv('all_degree'),{\"__folder\":\"RAW_FILES_OVERVIEW\"},restart=restart)\n", "\n", "\n", " cmd =\"$kgtk add-id -i $RAW_FILES_OVERVIEW/$all_degree --id-style node1-label-node2 -o $RAW_FILES_OVERVIEW/all_degree.temp.tsv\"\n", " run_command_return_df(cmd,\"RAW_FILES_OVERVIEW\",\"all_degree.temp.tsv\",{},True,True)\n", "\n", " cmd = \"kgtk --debug query -i $RAW_FILES_OVERVIEW/all_degree.temp.tsv --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'tsv: (n1)-[l{label:llab}]->(n2)' \\\n", " --where 'llab in [\\\"vertex_in_degree\\\"]' \\\n", " --return 'avg(n2) as indegree_avg, min(n2) as indegree_min, max(n2) as indegree_max' \"\n", "\n", " output_file_in_degree = \"Overview.in_degree.raw.tsv\"\n", " df_temp = run_command_return_df(cmd,\"RAW_FILES_OVERVIEW\",output_file_in_degree,{\"__output_file\":output_file_in_degree,\"__folder\":\"RAW_FILES_OVERVIEW\"},restart=restart)\n", " indegree_avg = df_temp.iloc[0]['indegree_avg']\n", " indegree_min = df_temp.iloc[0]['indegree_min']\n", " indegree_max = df_temp.iloc[0]['indegree_max']\n", "\n", " cmd = \"kgtk --debug query -i $RAW_FILES_OVERVIEW/all_degree.temp.tsv --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'tsv: (n1)-[l{label:llab}]->(n2)' \\\n", " --where 'llab in [\\\"vertex_out_degree\\\"]' \\\n", " --return 'avg(n2) as outdegree_avg, min(n2) as outdegree_min, max(n2) as outdegree_max' \"\n", "\n", "\n", " output_file_out_degree = \"Overview.out_degree.raw.tsv\"\n", " df_temp = run_command_return_df(cmd,\"RAW_FILES_OVERVIEW\",output_file_out_degree,{\"__output_file\":output_file_out_degree,\"__folder\":\"RAW_FILES_OVERVIEW\"},restart=restart)\n", " outdegree_avg = df_temp.iloc[0]['outdegree_avg']\n", " outdegree_min = df_temp.iloc[0]['outdegree_min']\n", " outdegree_max = df_temp.iloc[0]['outdegree_max']\n", " \n", " del df_temp\n", " printmd(\"Overview of the Graph\",'blue')\n", " if(indegree_avg == 0 or outdegree_avg == 0):\n", " printmd(\"There is no information about the degree for this subgraph\",size=18)\n", " else:\n", " df_degree = pd.DataFrame([[\"in-degree\",indegree_avg,indegree_min,indegree_max],[\"out-degree\",outdegree_avg,outdegree_min,outdegree_max]],columns=[\"Stat\",\"Average\",\"Min\",\"Max\"])\n", " df_degree.to_csv(os.path.join(os.getenv('OVERVIEW_FOLDER'),os.getenv('degree')),sep='\\t')\n", "\n", " printmd(\"Average in-degree of the Graph: \"+str(indegree_avg),size=15,fontWeight='Light')\n", " printmd(\"Minimum in-degree of the Graph: \"+str(indegree_min),size=15,fontWeight='Light')\n", " printmd(\"Maximum in-degree of the Graph: \"+str(indegree_max),size=15,fontWeight='Light')\n", " printmd(\"Average out-degree of the Graph: \"+str(outdegree_avg),size=15,fontWeight='Light')\n", " printmd(\"Minimum out-degree of the Graph: \"+str(int(outdegree_min)),size=15,fontWeight='Light')\n", " printmd(\"Maximum out-degree of the Graph: \"+str(int(outdegree_max)),size=15,fontWeight='Light')\n", " del df_degree\n", " else:\n", " printmd(\"There is no information about the degree for this subgraph\",size=18)\n", " #Free all the garbage\n", " gc.collect()\n", "# if os.path.exists(os.path.join(os.getenv('OVERVIEW_FOLDER'),os.getenv('all_degree'))):\n", "# os.remove(os.path.join(os.getenv('OVERVIEW_FOLDER'),os.getenv('all_degree')))\n", "# if os.path.exists(os.path.join(os.getenv('OVERVIEW_FOLDER'),\"all_degree.temp.tsv\")):\n", "# os.remove(os.path.join(os.getenv('OVERVIEW_FOLDER'),\"all_degree.temp.tsv\"))\n", "except Exception as e:\n", " #Free all the garbage\n", " gc.collect()\n", " print(e)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:11.514273Z", "iopub.status.busy": "2021-01-15T21:55:11.513724Z", "iopub.status.idle": "2021-01-15T21:55:11.554692Z", "shell.execute_reply": "2021-01-15T21:55:11.555288Z" }, "papermill": { "duration": 0.088032, "end_time": "2021-01-15T21:55:11.555514", "exception": false, "start_time": "2021-01-15T21:55:11.467482", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "Class Summary of the Subgraph" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below is the list of Top K Classes of the Subgraph ordered based on number of instances" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Class_LabelNumber of InstancesLink
wine2276https://www.wikidata.org/wiki/Q282
First Growth1078https://www.wikidata.org/wiki/Q10750129
white wine734https://www.wikidata.org/wiki/Q10210
Alsace wine722https://www.wikidata.org/wiki/Q80114014
beer brand683https://www.wikidata.org/wiki/Q15075508
Other Classes8305NA
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "try:\n", " ## Make it False if you are facing some issue. Setting it False will lead to generating all files from scratch which might take some time.\n", " restart = restart_global\n", " # This query finds all the classes based on number of Instances\n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$wikibase_item -i $WIKIDATA_PARTS/$label --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'item: (n1)-[l{label:llab}]->(n2), label: (n2)-[:label]->(label_n2)' \\\n", " --return 'distinct n2 as Link, kgtk_lqstring_text(label_n2) as `Class_Label`, count(distinct n1) as `Number of Instances`' \\\n", " --where 'label_n2.kgtk_lqstring_lang_suffix = \\\"en\\\" AND (llab IN [\\\"P31\\\"]) ' \\\n", " --order-by 'count(distinct n1) desc' \"\n", "\n", " # Find all the classes in the sub graph\n", " output_file_temp_class_summary = \"temp.\"+os.getenv('class_summary')\n", " df_class_summary = run_command_return_df(cmd,\"RAW_FILES_CLASS\",output_file_temp_class_summary,{\"__output_file\":output_file_temp_class_summary,\"__folder\":\"RAW_FILES_CLASS\"},restart=restart)\n", "\n", " #The statistics for top K properties are generated and sum of Number of instances for \n", " #the remaining properties are stored in other_instances\n", " other_instances = df_class_summary[K:][\"Number of Instances\"].sum()\n", "\n", " #Take top K properties and disregard rest of the properties\n", " df_class_summary = df_class_summary[:K]\n", "\n", " #Take top K properties and disregard rest of the properties\n", " df_class_summary = df_class_summary[[\"Class_Label\",\"Number of Instances\",\"Link\"]]\n", "\n", " df_class_summary = df_class_summary.append({\"Class_Label\":\"Other Classes\",\"Number of Instances\":other_instances,\"Link\":\"NA\"},ignore_index=True)\n", "\n", " #Generate hyperlinks from the qnode/pnode\n", " df_class_summary['Link'] = df_class_summary['Link'].apply(generate_link)\n", "\n", " #Save the dataframe to the output file\n", " df_class_summary.to_csv(os.path.join(os.getenv('OVERVIEW_FOLDER'),os.getenv('class_summary')),sep='\\t')\n", " printmd(\"Class Summary of the Subgraph\",'blue')\n", " if(len(df_class_summary)>0):\n", " printmd(\"Below is the list of Top K Classes of the Subgraph ordered based on number of instances\",size=\"15\",fontWeight=\"Light\")\n", " display(HTML(df_class_summary.to_html(index=False)))\n", " else:\n", " printmd(\"There is no information about Classes(identified by 'P31') in this graph\")\n", " del df_class_summary\n", " #Free all the garbage\n", " gc.collect()\n", "except Exception as e:\n", " #Free all the garbage\n", " gc.collect()\n", " print(e)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:11.666716Z", "iopub.status.busy": "2021-01-15T21:55:11.665987Z", "iopub.status.idle": "2021-01-15T21:55:12.162951Z", "shell.execute_reply": "2021-01-15T21:55:12.163509Z" }, "papermill": { "duration": 0.557106, "end_time": "2021-01-15T21:55:12.163807", "exception": false, "start_time": "2021-01-15T21:55:11.606701", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "Datatype: Time" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below are the top K properties of Datatype:Time ordered based on number of statements" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Property_LabelNumber_of_StatementsLink
inception1386https://www.wikidata.org/wiki/Property:P571
dissolved, abolished or demolished date107https://www.wikidata.org/wiki/Property:P576
date of birth70https://www.wikidata.org/wiki/Property:P569
date of death54https://www.wikidata.org/wiki/Property:P570
publication date54https://www.wikidata.org/wiki/Property:P577
Other Properties124NA
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "------------------------------------------------------------------------------------------" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Datatype: Wikibase_item" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below are the top K properties of Datatype:Wikibase_item ordered based on number of statements" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Property_LabelNumber_of_StatementsLink
contains administrative territorial entity14689https://www.wikidata.org/wiki/Property:P150
instance of13824https://www.wikidata.org/wiki/Property:P31
subclass of9940https://www.wikidata.org/wiki/Property:P279
language used5567https://www.wikidata.org/wiki/Property:P2936
diplomatic relation4889https://www.wikidata.org/wiki/Property:P530
Other Properties47482NA
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "------------------------------------------------------------------------------------------" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Datatype: Math" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "No Property is present for Datatype:Math" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "------------------------------------------------------------------------------------------" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Datatype: Wikibase-form" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "No Property is present for Datatype:Wikibase-form" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "------------------------------------------------------------------------------------------" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Datatype: Quantity" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below are the top K properties of Datatype:Quantity ordered based on number of statements" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Property_LabelNumber_of_StatementsLink
population7797https://www.wikidata.org/wiki/Property:P1082
nominal GDP4627https://www.wikidata.org/wiki/Property:P2131
nominal GDP per capita4583https://www.wikidata.org/wiki/Property:P2132
Human Development Index3546https://www.wikidata.org/wiki/Property:P1081
PPP GDP per capita2702https://www.wikidata.org/wiki/Property:P2299
Other Properties11183NA
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "------------------------------------------------------------------------------------------" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Datatype: String" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below are the top K properties of Datatype:String ordered based on number of statements" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Property_LabelNumber_of_StatementsLink
Commons category2042https://www.wikidata.org/wiki/Property:P373
postal code502https://www.wikidata.org/wiki/Property:P281
Commons gallery472https://www.wikidata.org/wiki/Property:P935
licence plate code281https://www.wikidata.org/wiki/Property:P395
IPA transcription221https://www.wikidata.org/wiki/Property:P898
Other Properties1355NA
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "------------------------------------------------------------------------------------------" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Datatype: External-id" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below are the top K properties of Datatype:External-id ordered based on number of statements" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Property_LabelNumber_of_StatementsLink
Freebase ID1677https://www.wikidata.org/wiki/Property:P646
VIAF ID750https://www.wikidata.org/wiki/Property:P214
Quora topic ID710https://www.wikidata.org/wiki/Property:P3417
TasteAtlas ID670https://www.wikidata.org/wiki/Property:P5456
GND ID657https://www.wikidata.org/wiki/Property:P227
Other Properties25637NA
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "------------------------------------------------------------------------------------------" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Datatype: CommonsMedia" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below are the top K properties of Datatype:CommonsMedia ordered based on number of statements" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Property_LabelNumber_of_StatementsLink
image2240https://www.wikidata.org/wiki/Property:P18
locator map image774https://www.wikidata.org/wiki/Property:P242
pronunciation audio747https://www.wikidata.org/wiki/Property:P443
flag image555https://www.wikidata.org/wiki/Property:P41
coat of arms image446https://www.wikidata.org/wiki/Property:P94
Other Properties888NA
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "------------------------------------------------------------------------------------------" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Datatype: Globe-coordinate" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below are the top K properties of Datatype:Globe-coordinate ordered based on number of statements" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Property_LabelNumber_of_StatementsLink
coordinate location855https://www.wikidata.org/wiki/Property:P625
coordinates of northernmost point210https://www.wikidata.org/wiki/Property:P1332
coordinates of westernmost point201https://www.wikidata.org/wiki/Property:P1335
coordinates of southernmost point198https://www.wikidata.org/wiki/Property:P1333
coordinates of easternmost point197https://www.wikidata.org/wiki/Property:P1334
Other Properties18NA
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "------------------------------------------------------------------------------------------" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Datatype: Monolingualtext" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below are the top K properties of Datatype:Monolingualtext ordered based on number of statements" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Property_LabelNumber_of_StatementsLink
demonym4114https://www.wikidata.org/wiki/Property:P1549
official name862https://www.wikidata.org/wiki/Property:P1448
short name432https://www.wikidata.org/wiki/Property:P1813
native label412https://www.wikidata.org/wiki/Property:P1705
Wikidata usage instructions361https://www.wikidata.org/wiki/Property:P2559
Other Properties774NA
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "------------------------------------------------------------------------------------------" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Datatype: Musical-notation" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "No Property is present for Datatype:Musical-notation" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "------------------------------------------------------------------------------------------" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Datatype: Geo-shape" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below are the top K properties of Datatype:Geo-shape ordered based on number of statements" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Property_LabelNumber_of_StatementsLink
geoshape244https://www.wikidata.org/wiki/Property:P3896
Other Properties0NA
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "------------------------------------------------------------------------------------------" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Datatype: Url" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below are the top K properties of Datatype:Url ordered based on number of statements" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Property_LabelNumber_of_StatementsLink
official website1177https://www.wikidata.org/wiki/Property:P856
described at URL94https://www.wikidata.org/wiki/Property:P973
exact match49https://www.wikidata.org/wiki/Property:P2888
equivalent class29https://www.wikidata.org/wiki/Property:P1709
external data available at18https://www.wikidata.org/wiki/Property:P1325
Other Properties102NA
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "------------------------------------------------------------------------------------------" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "try:\n", " ## Make it False if you are facing some issue. Setting it False will lead to generating all files from scratch which might take some time.\n", " restart = restart_global\n", " df_property_summary = []\n", " # This query finds all the properties ordered based on Number of Statements\n", " cmd = \"$kgtk query -i $WIKIDATA_PARTS/$TYPE_FILE -i $WIKIDATA_PARTS/$label --graph-cache $STORE \\\n", " -o $__folder/__output_file \\\n", " --match 'claims: (n1)-[l{label: llab}]->(n2), label: (llab)-[:label]->(label)' \\\n", " --return 'distinct llab as Link, kgtk_lqstring_text(label) as `Property_Label`, count(llab) as `Number_of_Statements`' \\\n", " --where 'label.kgtk_lqstring_lang_suffix = \\\"en\\\" ' \\\n", " --order-by 'count(llab) desc '\"\n", " \n", " # Do it for all the Datatypes\n", " for type,name, output_file in types_with_fileName:\n", " output_file_temp = \"temp.\"+os.getenv(output_file)\n", " temp = run_command_return_df(cmd,\"RAW_FILES_PROPERTY\",output_file_temp,{\"TYPE_FILE\": type,\"__output_file\":output_file_temp,\"__folder\":\"RAW_FILES_PROPERTY\"},restart=restart)\n", " \n", " #The statistics for top K properties are generated and sum of Number of statements for \n", " #the remaining properties are stored in other_instances\n", " other_instances = temp[K:][\"Number_of_Statements\"].sum()\n", " \n", " #Take top K properties and disregard rest of the properties\n", " temp = temp[:K]\n", " \n", " #Generated hyperlinks from the qnode/pnode\n", " temp['Link'] = temp['Link'].apply(generate_link)\n", " \n", " #Changing the order of the columns\n", " temp = temp[[\"Property_Label\",\"Number_of_Statements\",\"Link\"]]\n", " \n", " temp = temp.append({\"Property_Label\":\"Other Properties\",\"Number_of_Statements\":other_instances,\"Link\":\"NA\"},ignore_index=True)\n", " \n", " # Storing the temp to output file\n", " temp.to_csv(os.path.join(os.getenv('OVERVIEW_FOLDER'),os.getenv(output_file)),sep='\\t')\n", " \n", " # The output if there are no properties for a Datatype\n", " if len(temp) == 1:\n", " printmd(\"Datatype: \"+name, 'blue')\n", " printmd(\"No Property is present for Datatype:\" + name,size=15, fontWeight='Light')\n", " printmd(\"------------------------------------------------------------------------------------------\")\n", " continue\n", " printmd(\"Datatype: \"+name, 'blue')\n", " printmd(\"Below are the top K properties of Datatype:\" + name +\" ordered based on number of statements\",size =15,fontWeight='Light')\n", " df_property_summary.append(temp)\n", " if len(df_property_summary[-1])>0:\n", " display(HTML(df_property_summary[-1].to_html(index=False)))\n", " else:\n", " printmd(\"There are no properties for this datatype or there are no label for the properties of this datatype\")\n", " printmd(\"------------------------------------------------------------------------------------------\")\n", " #Free all the garbage\n", " gc.collect()\n", " del df_property_summary\n", " #Free all the garbage\n", " gc.collect()\n", "except Exception as e:\n", " #Free all the garbage\n", " gc.collect()\n", " print(e)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:12.299545Z", "iopub.status.busy": "2021-01-15T21:55:12.298497Z", "iopub.status.idle": "2021-01-15T21:55:12.301040Z", "shell.execute_reply": "2021-01-15T21:55:12.301426Z" }, "papermill": { "duration": 0.068077, "end_time": "2021-01-15T21:55:12.301598", "exception": false, "start_time": "2021-01-15T21:55:12.233521", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#This is a helper function which extract the qnode/pnode from the link\n", "def get_qnodes_from_link(link):\n", " return str(link.split(\"/\")[-1])" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:12.410130Z", "iopub.status.busy": "2021-01-15T21:55:12.409335Z", "iopub.status.idle": "2021-01-15T21:55:17.460351Z", "shell.execute_reply": "2021-01-15T21:55:17.461027Z" }, "papermill": { "duration": 5.105007, "end_time": "2021-01-15T21:55:17.461238", "exception": false, "start_time": "2021-01-15T21:55:12.356231", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "Class Overview" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Class: wine" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Description: Alcoholic drink typically made from grapes" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Aliases: Wine, 🍷, vino" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Direct Superclasses: Alcoholic beverage" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Direct Subclasses: White wine, dry wine (madeira), hungarian wine, straw wine, italian wine, albanian wine, de-alcoholised wine, vi ranci, dessert wine, muscat" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Number of Instances: 2276" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Number of Superclasses: 1" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Number of Subclasses: 123" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Examples for wine Class" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Label_PagerankLink
Etna bianco0.000011https://www.wikidata.org/wiki/Q3733811
Etna bianco superiore0.000011https://www.wikidata.org/wiki/Q3733815
Honoro Vera Garnacha0.000011https://www.wikidata.org/wiki/Q62059888
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Property NameInstances% Instances
instance of2276100.00
subclass of214494.20
country1848.08
product certification1596.99
inception743.25
TasteAtlas ID602.64
located in the administrative territorial entity502.20
image321.41
Freebase ID291.27
Commons category200.88
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Property NameInstances
product or material produced16
has part8
category\\\\\\\\'s main topic3
typically sells2
named after1
instance of1
replaced by1
Wikidata property example1
different from1
subclass of1
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Label_PagerankLink
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Property NameInstances% Instances
instance of1078100.0
subclass of272.5
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Property NameInstances
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "------------------------------------------------------------------------------------------" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Class: white_wine" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Description: Wine that is fermented without grape skin, with a yellowish color" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Aliases: " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Direct Superclasses: Wine" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Direct Subclasses: Cava, chablis wine, tokaji szamorodni, champagne, romagna albana, rosazzo, verdicchio dei castelli di jesi, verdea, roero arneis, txakoli" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Number of Instances: 734" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Number of Superclasses: 1" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Number of Subclasses: 31" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Examples for white_wine Class" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Label_PagerankLink
Champagne0.000372https://www.wikidata.org/wiki/Q134862
Pouilly-Fumé0.000011https://www.wikidata.org/wiki/Q2106740
Clairette du Languedoc0.000025https://www.wikidata.org/wiki/Q1094850
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Property NameInstances% Instances
instance of734100.00
subclass of425.72
country172.32
country of origin91.23
product certification91.23
image81.09
native label81.09
inception70.95
TasteAtlas ID70.95
Freebase ID70.95
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Property NameInstances
subclass of5
has part3
product or material produced2
category\\\\\\\\'s main topic2
material used2
different from1
use1
facet of1
named after1
main subject1
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Label_PagerankLink
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Property NameInstances% Instances
instance of722100.00
subclass of283.88
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Property NameInstances
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "------------------------------------------------------------------------------------------" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Class: beer_brand" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Description: " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Aliases: " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Direct Superclasses: Brand, beer" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Direct Subclasses: " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Number of Instances: 683" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Number of Superclasses: 2" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Number of Subclasses: 0" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Examples for beer_brand Class" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Label_PagerankLink
Kamil0.00001https://www.wikidata.org/wiki/Q2349766
Cruzcampo0.00001https://www.wikidata.org/wiki/Q1142160
Coral beer0.00001https://www.wikidata.org/wiki/Q3489357
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Property NameInstances% Instances
instance of683100.00
manufacturer12918.89
image12818.74
country of origin10114.79
Commons category9013.18
official website7711.27
inception7711.27
country6910.10
fabrication method618.93
Freebase ID324.69
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Property NameInstances
owner of16
product or material produced15
brand8
manufacturer6
different from5
sponsor5
named after3
category\\\\\\\\'s main topic2
advertises2
owned by2
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "------------------------------------------------------------------------------------------" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "try:\n", " ## Make it False if you are facing some issue. Setting it False will lead to generating all files from scratch which might take some time.\n", " restart = restart_global\n", " printmd('Class Overview','blue')\n", " # Load the list of classes generated in the 9th cell\n", " df = pd.read_csv(os.path.join(os.getenv('OVERVIEW_FOLDER'),os.getenv('class_summary')),delimiter='\\t')\n", "\n", " df_label = find_label_property()\n", "\n", " # Do for all the classes generated in the 9th cell\n", " for index, ele in df.iterrows():\n", " \n", " # Ignore the Other Classes part of the statistics\n", " if index==len(df)-1:\n", " continue;\n", " # Parse the Qnode from the Link\n", " Current_Qnode = ele[\"Link\"].split(\"/\")[-1]\n", "\n", " class_label = re.sub(\"[^0-9a-zA-Z]+\", \"_\", ele[\"Class_Label\"])\n", "\n", " try:\n", " #Free all the garbage\n", " gc.collect()\n", " Number_of_instances_of_current_class = ele[\"Number of Instances\"]\n", " class_overview(Current_Qnode,class_label,_restart=restart)\n", " except Exception as e:\n", " printmd('There is some error while finding the overview of the Class:'+class_label.capitalize())\n", " display(e)\n", "except Exception as e:\n", " #Free all the garbage\n", " gc.collect()\n", " display(e)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:17.615158Z", "iopub.status.busy": "2021-01-15T21:55:17.614571Z", "iopub.status.idle": "2021-01-15T21:55:17.697144Z", "shell.execute_reply": "2021-01-15T21:55:17.697662Z" }, "papermill": { "duration": 0.159459, "end_time": "2021-01-15T21:55:17.697894", "exception": false, "start_time": "2021-01-15T21:55:17.538435", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "Property Overview" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Top Ten Properties are " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Property_LabelNumber_of_StatementsLinkProperty Type
contains administrative territorial entity14689https://www.wikidata.org/wiki/Property:P150wikibase_item
instance of13824https://www.wikidata.org/wiki/Property:P31wikibase_item
subclass of9940https://www.wikidata.org/wiki/Property:P279wikibase_item
population7797https://www.wikidata.org/wiki/Property:P1082quantity
language used5567https://www.wikidata.org/wiki/Property:P2936wikibase_item
diplomatic relation4889https://www.wikidata.org/wiki/Property:P530wikibase_item
nominal GDP4627https://www.wikidata.org/wiki/Property:P2131quantity
nominal GDP per capita4583https://www.wikidata.org/wiki/Property:P2132quantity
demonym4114https://www.wikidata.org/wiki/Property:P1549monolingualtext
Human Development Index3546https://www.wikidata.org/wiki/Property:P1081quantity
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "try:\n", " ## Make it False if you are facing some issue. Setting it False will lead to generating all files from scratch which might take some time.\n", " restart = restart_global\n", " printmd('Property Overview','blue')\n", " # List of all the datatypes for the property and their tile.\n", " types_with_titles = [\n", " (\"property_summary_time\",\"time\"),\n", " (\"property_summary_wikibase_item\", \"wikibase_item\"),\n", " (\"property_summary_math\",\"math\"),\n", " (\"property_summary_wikibase_form\", \"wikibase_form\"),\n", " (\"property_summary_quantity\", \"quantity\"),\n", " (\"property_summary_string\", \"string\"),\n", " (\"property_summary_external_id\",\"external_id\"),\n", " (\"property_summary_commonsMedia\",\"commonsMedia\"),\n", " (\"property_summary_globe_coordinate\", \"globe_coordinate\"),\n", " (\"property_summary_monolingualtext\",\"monolingualtext\"),\n", " (\"property_summary_musical_notation\", \"musical_notation\"),\n", " (\"property_summary_geo_shape\", \"geo_shape\"),\n", " (\"property_summary_url\", \"url\")\n", " ]\n", " \n", " # Create a new Dataframe to store the result.\n", " df_all_prop = pd.DataFrame([],columns=[\"Property_Label\",\"Number_of_Statements\",\"Link\"])\n", " \n", " # For all types of the properties\n", " for type_, type_name in types_with_titles:\n", " \n", " # Read the file generated above while calculating the statistics\n", " df_temp = pd.read_csv(os.path.join(os.getenv('OVERVIEW_FOLDER'),os.getenv(type_)),delimiter='\\t')\n", " \n", " # Adding the column for the data type\n", " df_temp[\"Property Type\"] = type_name\n", " \n", " # Concatenating the data frame\n", " df_all_prop = pd.concat([df_all_prop,df_temp])\n", " df_all_prop = df_all_prop.sort_values(\"Number_of_Statements\",ascending=False).reset_index()\n", " del df_all_prop['index']\n", " del df_all_prop[\"Unnamed: 0\"]\n", " del df_temp\n", " \n", " # Removing the \"Other Properties row\" which was calculated earlier in the overview section\n", " df_all_prop = df_all_prop[df_all_prop[\"Property_Label\"]!=\"Other Properties\"]\n", " printmd(\"Top Ten Properties are \",'black',size=18)\n", " display(HTML(df_all_prop[:10].to_html(index=False)))\n", " df_all_prop.to_csv(os.path.join(os.getenv('PROPERTY_OVERVIEW'),\"Property_overview.top.tsv\"),sep='\\t')\n", " del df_all_prop\n", " #Free all the garbage\n", " gc.collect()\n", "except Exception as e:\n", " #Free all the garbage\n", " gc.collect()\n", " display(e)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:17.855304Z", "iopub.status.busy": "2021-01-15T21:55:17.854797Z", "iopub.status.idle": "2021-01-15T21:55:20.823826Z", "shell.execute_reply": "2021-01-15T21:55:20.824362Z" }, "papermill": { "duration": 3.044333, "end_time": "2021-01-15T21:55:20.824629", "exception": false, "start_time": "2021-01-15T21:55:17.780296", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "Property: Contains_administrative_territorial_entity" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Description: (list of) direct subdivisions of an administrative territorial entity" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Aliases: Has counties, has towns, has states, has local government areas, has cities, has regions, has villages, divides into, has shires, has municipalities, contains, has countries, has wards, subdivided into, has districts, has rural cities, has boroughs, has members, has arrondissements, divided into, has administrative divisions" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Number of Statements: 14689" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Number of Qualifiers: 4713" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Property Type: wikibase_item" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below is ditribution of Contains_administrative_territorial_entity in different outgoing classes" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", 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a subtype of" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Number of Statements: 9940" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Number of Qualifiers: 579" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Property Type: wikibase_item" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below is ditribution of Subclass_of in different outgoing classes" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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\n", 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{ "data": { "text/html": [ "Property Type: quantity" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below is ditribution of Population in different classes" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/html": [ "Below is the distribution of units for population in the subgraph" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
UnitNumber_of_Statements
Number171
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below is a histogram of distribution of values for unit: \"Number\" for population in the subgraph" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "try:\n", " ## Make it False if you are facing some issue. Setting it False will lead to generating all files from scratch which might take some time.\n", " restart = restart_global\n", " # Load the list of classes generated in the 9th cell\n", " df = pd.read_csv(os.path.join(os.getenv('PROPERTY_OVERVIEW'),\"Property_overview.top.tsv\"),delimiter='\\t')\n", "\n", " # Do for all the classes generated in the 9th cell\n", " for index, ele in df.iterrows():\n", "\n", " if index>=5:\n", " break;\n", "\n", " Current_Pnode = ele['Link'].split('/')[-1].split(\":\")[-1]\n", " Number_of_statements_of_current_property = ele[\"Number_of_Statements\"]\n", " Property_type = ele[\"Property Type\"]\n", " Property_label = re.sub(\"[^0-9a-zA-Z]+\", \"_\", ele[\"Property_Label\"])\n", "\n", " # Heading\n", " try:\n", " #Free all the garbage\n", " gc.collect()\n", " property_overview(Current_Pnode,Property_label,Property_type,_restart=restart)\n", " except Exception as e:\n", " printmd(\"There some problem finding the overview of the Property:\"+Property_label.capitalize())\n", "except Exception as e:\n", " display(e)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:21.040856Z", "iopub.status.busy": "2021-01-15T21:55:21.039980Z", "iopub.status.idle": "2021-01-15T21:55:33.758780Z", "shell.execute_reply": "2021-01-15T21:55:33.759211Z" }, "papermill": { "duration": 12.836849, "end_time": "2021-01-15T21:55:33.759381", "exception": false, "start_time": "2021-01-15T21:55:20.922532", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "Below is the distribution of units for population in the subgraph" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
UnitNumber_of_Statements
Number171
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below is a histogram of distribution of values for unit: \"Number\" for population in the subgraph" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/html": [ "Below is the distribution of units for nominal_GDP in the subgraph" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
UnitNumber_of_Statements
United states dollar4619
Euro7
Russian ruble1
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below is a histogram of distribution of values for unit: \"United_states_dollar\" for nominal_GDP in the subgraph" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/html": [ "Below is the distribution of units for nominal_GDP_per_capita in the subgraph" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
UnitNumber_of_Statements
United states dollar4576
Euro6
Russian ruble1
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below is a histogram of distribution of values for unit: \"United_states_dollar\" for nominal_GDP_per_capita in the subgraph" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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UnitNumber_of_Statements
Number1
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below is a histogram of distribution of values for unit: \"Number\" for Human_Development_Index in the subgraph" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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UnitNumber_of_Statements
Q5502072669
United states dollar33
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below is a histogram of distribution of values for unit: \"Q550207\" for PPP_GDP_per_capita in the subgraph" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "try:\n", " ## Make it False if you are facing some issue. Setting it False will lead to generating all files from scratch which might take some time.\n", " restart = restart_global\n", " #Initializing the dataframe pd_quantity_property with the list of top properties of datatype:quantity found above in the notebook \n", " pd_quantity_property = pd.read_csv(os.path.join(os.getenv('OVERVIEW_FOLDER'),os.getenv('property_summary_quantity')),delimiter='\\t')\n", "\n", " #Iterating over all the rows of dataframe\n", " for index,ele in pd_quantity_property.iterrows():\n", "\n", " #Ignoring the 'Other Instances' row\n", " if index>=K or ele['Property_Label'] ==\"Other Properties\":\n", " break;\n", "\n", " # Extracting the \"Pnode\" corresponding to the property\n", " pnode = ele['Link'].split('/')[-1].split(\":\")[-1]\n", "\n", " #Extracting the 'Label' corresponding to the property\n", " prop_label = re.sub(\"[^0-9a-zA-Z]+\", \"_\", ele['Property_Label']) \n", "\n", " quantity_distribution(pnode,prop_label,_restart=restart)\n", "except Exception as e:\n", " print(e)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:33.960881Z", "iopub.status.busy": "2021-01-15T21:55:33.960178Z", "iopub.status.idle": "2021-01-15T21:55:45.343511Z", "shell.execute_reply": "2021-01-15T21:55:45.343888Z" }, "papermill": { "duration": 11.488429, "end_time": "2021-01-15T21:55:45.344065", "exception": false, "start_time": "2021-01-15T21:55:33.855636", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "Below are statistics for top five properties of Datatype:Time" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below is stacked histogram for Inception binned per year from 1800 to 2100" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/amandeep/anaconda3/envs/kgtk-env/lib/python3.7/site-packages/ipykernel_launcher.py:104: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n" ] }, { "data": { "image/png": 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hhCtDCPNCCPPWrl2bxjArZ82aNTRr1owGDRoA0KxZMw455BAAZs6cSZcuXejYsSOXXnopW7du5dlnn+W8885Lbj979mwGDSp8sNn06dM5/vjj6dq1K+eddx4bN24EoE2bNtx444107dqVP//5z6Wu16dPH4oeXDthwgSOOuooevTowRVXXMG1114LwPDhw/mv//ovTjjhBA4//HCmTp26wzlt2rSJ008/nc6dO9OhQwdyc3P57W9/y0cffcQpp5zCKaecAsDVV19NTk4Oxx57LDfddBNAieuVFq8kSZJUW6QzIVoNrI4xFt1HNpXCBOnjEMLBAIl/Pylp4xjjgzHGnBhjTvPmzdMYZuX069ePDz74gKOOOoqRI0fy4osvArBlyxaGDx9Obm4uixYtIj8/n/vuu4/vfve7vPHGG2zatAmA3Nxchg4dyrp16xg3bhwzZsxgwYIF5OTkcOeddyaPc8ABB7BgwQK++93v7nQ9gI8++ohf/vKX/POf/+TVV19l+fLlxZavWbOGV155hWnTpjFmzI6lW88++yyHHHIICxcuZPHixQwYMID/+q//4pBDDuGFF17ghRdeAApvFZw3bx5vvvkmL774Im+++eYO65V1XpIkSVJtkLaEKMb4H+CDEMLRiVl9gaXAU8CwxLxhwN/SFUM6NWrUiPnz5/Pggw/SvHlzhgwZwsSJE3nrrbdo27YtRx11FADDhg3jpZdeol69egwYMIC///3v5Ofn849//IMzzzyTf/7znyxdupQTTzyR7OxsJk2axPvvv588zpAhQwDKXA9gzpw59O7dm/3335/69esX65ECOOuss9hjjz1o3749H3/88Q7n1LFjR55//nluvPFGXn75ZZo0aVLiuT/++ON07dqVLl26sGTJkhLrl8oTryRJklTT0llDBHAd8GgIYU/gXeASCpOwx0MIlwHvA+enOYa0ycrKok+fPvTp04eOHTsyadIkunTpUur6Q4cO5Xe/+x37778/OTk5NG7cmBgj3/ve95gyZUqJ2+yzzz4AZa5XHkW39xXt79uOOuooFixYwNNPP80vfvEL+vbty3//938XW+e9997j9ttvZ+7cuTRt2pThw4ezZcuWHfZVFfFKkiRJ6ZbWYbdjjHmJ2946xRjPijF+FmNcH2PsG2M8Msb43Rjjp+mMIV3eeustVq5cmXydl5dH69atOfroo1m1ahVvv/02AH/84x/p3bs3AL1792bBggX8/ve/Z+jQoQD07NmTV199Nbn+pk2bWLFixQ7HK8963bt358UXX+Szzz4jPz+fv/zlLxU6p48++oi9996biy66iNGjR7NgwQIAGjduzIYNGwD48ssv2WeffWjSpAkff/wxzzzzTHL71PXKe16SJElSTUp3D1GdtXHjRq677jo+//xz6tWrxxFHHMGDDz5Iw4YN+cMf/sB5551Hfn4+3bt3Z8SIEUBhj9KgQYOYOHEikyYVjjzevHlzJk6cyAUXXMDWrVsBGDduXPKWuyLlWe/QQw/lZz/7GT169GD//ffnmGOOKfW2t5IsWrSI0aNHs8cee1C/fn3uu+8+AK688koGDBiQrBHq0qULxxxzDK1ateLEE09Mbv/t9cpzXpIkSVJNCiXdOlXb5OTkxKJR1IosW7aMdu2qdqjtumDjxo00atSI/Px8Bg8ezKWXXsrgwYNrOizVIH9XJElV6Y4hg5LTo3Kn1WAkUnEhhPkxxpyKbpfWW+ZU/W6++Ways7Pp0KEDbdu25ayzzqrpkCRJkqRay1vm6pjbb7+9pkOQJEmSdhv2EEmSJEnKWCZEkiRJkjKWCZEkSZKkjGVCJEmSJCljmRBJtUBubi5vvPFGmev96U9/4t///nc1RCRJkpQZ6swoc/eOmFWl+7vm/lPLtd6TTz7J4MGDWbZsGcccc0yVxgCFw2g3atSIG264geHDhzNo0CDOPffcUtefOHEi/fr145BDDgHg8ssv5yc/+Qnt27ev0rjatGnDvHnz6NevH2+88Qb169dn3LhxHHHEEQwdOpTVq1dzxRVX8MwzzwDQr18/Jk+ezEEHHZQ8rzZt2jB8+PByH/P+++9n77335uKLLy51nXnz5jF58mR++9vf7tL5VaX//d//5Wc/+1mpy5999lk+/PBDXnjhBQ499FBatmzJqlWrGDRoEIsXL06uN2HCBDZs2MBhhx1WHWFLkiRlBHuIdtGUKVM46aSTmDJlSk2HAhQmRB999FHy9UMPPVTlyVCqE044gVdffRWAAQMG8OyzzwLQsmVL1q1bx+bNm4HChKhoWWXk5+czYsSInSZDADk5ObUqGYLChKgkMUa2bdvGgAED+MlPfsL9999Py5YtS93PZZddxvXXX5+mKCVJkjKTCdEu2LhxI6+88goTJkzgscceS86fPXs2J598MqeffjpHH300I0aMYNu2bQA0atSIH//4xxx77LH07duXtWvXAvDOO+8wYMAAunXrRq9evVi+fPlOj/0///M/dO/enQ4dOnDllVcSY2Tq1KnMmzePCy+8kOzsbDZv3kyfPn2YN28eUJi8dezYkQ4dOnDjjTcm99WoUSN+/vOf07lzZ3r27MnHH3+8w/HWr19Pv379OPbYY7n88suJMQJw2mmnJROdbt268a9//Su5rE+fPrz44os7rFd0zL322guAvLw8evbsSadOnRg8eDCfffZZcvvrr7+enJwc7r77bm6++ebkc5bmzp1Lp06dyM7OZvTo0XTo0CF57QcNKnyC9s0338yll15Knz59OPzww4slSo888gg9evQgOzubq666ioKCgh3Oee7cuZxwwgl07tyZHj16sGHDBrZs2cIll1xCx44d6dKlCy+88AJQmIieffbZDBgwgCOPPJKf/vSnAIwZM4bNmzeTnZ3NhRdeyKpVqzj66KO5+OKL6dChAx988EEy/o4dO5Kbm7tDHAUFBYwePZru3bvTqVMnHnjggeSy2267LTn/pptuKrGtSJIkqXQmRLvgb3/7GwMGDOCoo47igAMOYP78+cllc+bM4Z577mHp0qW88847/PWvfwVg06ZN5OTksGTJEnr37s0tt9wCwJVXXsk999zD/Pnzuf322xk5cuROj33ttdcyd+5cFi9ezObNm5k2bRrnnnsuOTk5PProo+Tl5SUTDoCPPvqIG2+8kVmzZpGXl8fcuXN58sknkzH17NmThQsXcvLJJ/P73/9+h+PdcsstnHTSSSxZsoTBgwcn61hOOeWUZFIQQqBTp07k5eUBhT1GRbfMHXvssSxfvjyZeNxwww0MGTIEgIsvvpj/+7//480336Rjx47JawLw9ddfM2/ePEaNGlUsnksuuYQHHniAvLw8srKySr1Oy5cv57nnnmPOnDnccsstfPPNNyxbtozc3FxeffXV5PaPPvpose2+/vprhgwZwt13383ChQuZMWMGe+21F/feey8hBBYtWsSUKVMYNmwYW7ZsAQoTu9zcXBYtWkRubi4ffPAB48ePZ6+99iIvLy95jJUrVzJy5EiWLFnCvHnzyMvLSx5j9OjRrFmzplgsEyZMoEmTJsydO5e5c+fy+9//nvfee4/p06ezcuVK5syZQ15eHvPnz+ell14q9VpIkiRpRyZEu2DKlCkMHToUgKFDhxa7ba5Hjx4cfvjhZGVlccEFF/DKK68AsMceeyQTgYsuuohXXnmFjRs38tprr3Heeecleyy+/aX421544QWOO+44OnbsyKxZs1iyZMlO1587dy59+vShefPm1KtXjwsvvDD55XnPPfdM9qp069aNVatW7bD9Sy+9xEUXXQTA6aefTtOmTQHYe++92W+//ZK36aX2BPXq1St53kXXZM6cOcX2+8UXX/D555/Tu3dvAIYNG1bsS33RtUr1+eefs2HDBo4//ngAfvCDH5R63qeffjoNGjSgWbNmHHjggXz88cfMnDmT+fPn0717d7Kzs5k5cybvvvtuse3eeustDj74YLp37w7AvvvuS7169XjllVeS1+GYY46hdevWrFixAoC+ffvSpEkTGjZsSPv27Xn//fdLjKl169b07NkTgFdeeYULLriArKwsWrRoQe/evZk7d26x9adPn87kyZPJzs7muOOOY/369axcuZLp06czffp0unTpQteuXVm+fDkrV64s9VpIkiRpR3VmUIXq9umnnzJr1iwWLVpECIGCggJCCNx2221AYW9Jqm+/Tp2/bds29ttvv2TPSlm2bNnCyJEjmTdvHq1ateLmm29O9lJURv369ZPxZWVlkZ+fX6Ht+/fvz7PPPsull15Kv379OO+88xg7dix77rknLVu25L333qNt27bJHqOiRKY89tlnnwrF8m0NGjRIThedW4yRYcOGceutt+7SvstzrJJU9JxijNxzzz3079+/2PznnnuOsWPHctVVV1U8WEmSJAH2EFXa1KlT+eEPf8j777/PqlWr+OCDD2jbti0vv/wyUHjL3Hvvvce2bdvIzc3lpJNOAmDbtm1MnToVKBxC+aSTTmLfffelbdu2/PnPfwYKvwAvXLiw1GMXJT/NmjVj48aNyf0BNG7cmA0bNuywTY8ePXjxxRdZt24dBQUFTJkyJdkrUx4nn3wyf/rTnwB45plnknU+ULxXqFmzZnzzzTd88cUXQPHb5r773e8yc+bMYvtt0qQJTZs2TV63P/7xj2XGtd9++9G4cePkMNWp9Vvl0bdvX6ZOnconn3wCFCa33+7NOfroo1mzZk2yt2bDhg3k5+fTq1ev5K1vK1as4N///jdHH330To9Xv359vvnmmxKX9erVi9zcXAoKCli7di0vvfQSPXr0KLZO//79ue+++5L7WLFiBZs2baJ///48/PDDbNy4EYAPP/wweU6SJEkqnzrTQ1TeYbKrypQpU4oNTABwzjnnMGXKFIYMGUL37t259tprefvttznllFMYPHgwUNg7MGfOHMaNG8eBBx6YLKJ/9NFHufrqqxk3bhzffPMNQ4cOpXPnziUee7/99uOKK66gQ4cOHHTQQcnbugCGDx/OiBEj2GuvvXj99deT8w8++GDGjx/PKaecQoyR008/nTPPPLPc53vTTTdxwQUXcOyxx3LCCScUG/q5Xbt2rFixgoKCArKysujbty8zZszgnHPOYcCAAfzoRz9i5MiR7LvvvjRo0IB169bRrFmz5PaTJk1ixIgRfPXVVxx++OH84Q9/KDOeCRMmcMUVV7DHHnvQu3dvmjRpUu5zad++PePGjaNfv35s27aN+vXrc++999K6devkOnvuuSe5ublcd911bN68mb322osZM2YwcuRIrr76ajp27Ei9evWYOHFisZ6hklx55ZV06tSJrl278qtf/arYssGDB/P666/TuXNnQgj8+te/5qCDDip22+Lll1/OqlWr6Nq1KzFGmjdvzpNPPkm/fv1YtmxZssetUaNGPPLIIxx44IHlvhaSJEmZLhSNCFab5eTkxKKR0oosW7aMdu3a1VBEOzd79mxuv/12pk2btsOyRo0aJf9Hvy4ZMWIEP/zhDznxxBP55z//yYQJE5KDM+Tk5PDqq6/SoEEDxo8fT6tWrbjwwgt36XgbN26kUaNGAIwfP541a9Zw99137/J51EW1+XdFkrT7uWPIoOT0qNwdv+tINSWEMD/GmFPR7epMD5Fq1v3335+c7tmzZ3LQAIDUZHbMmDFVcrx//OMf3HrrreTn59O6dWsmTpxYJfuVJElSZjEhSoM+ffrQp0+fEpfVxd6hmjBkyJASR6CTJEmSKsJBFSRJkiRlLBMiSZIkSRnLhEiSJElSxjIhkiRJkpSx6sygCqlDQFaF8gwjuXr1aq655hqWLl3Ktm3bGDRoELfddhtLly7lo48+YuDAgQDcfPPNNGrUiBtuuKFKY5QkSZK0a+whqqQYI2effTZnnXUWK1euZMWKFWzcuJGf//zn5OXl8fTTT1fZsQoKCqpsX5IkSZK2MyGqpFmzZtGwYUMuueQSALKysrjrrrt46KGH+OlPf0pubi7Z2dnk5uYCsHTpUvr06cPhhx/Ob3/72+R+HnnkEXr06EF2djZXXXVVMvlp1KgRo0aNonPnzrz++uuMGTOG9u3b06lTJ3uaJEmSpCpiQlRJS5YsoVu3bsXm7bvvvrRp04Zf/OIXDBkyhLy8vOSzcpYvX85zzz3HnDlzuOWWW/jmm29YtmwZubm5vPrqq+Tl5ZGVlcWjjz4KwKZNmzjuuONYuHAh7dq144knnmDJkiW8+eab/OIXv6j285UkSZLqojpTQ1TbnX766TRo0IAGDRpw4IEH8vHHHzNz5kzmz59P9+7dAdi8eTMHHnggUNjjdM455wDQpEkTGjZsyGWXXcagQYMYNKhq66UkSZKkTGVCVEnt27dn6tSpxeZ9+eWX/Pvf/6ZevR0va4MGDZLTWVlZ5OfnE2Nk2LBh3HrrrTus37BhQ7KysgCoV68ec+bMYebMmUydOpXf/e53zJo1q4rPSJIkSco83jJXSX379uWrr75i8uTJQOHAB6NGjWL48OG0aNGCDRs2lGsfU6dO5ZNPPgHg008/5f33399hvY0bN/LFF18wcOBA7rrrLhYuXFi1JyNJkiRlqDrTQ1SeYbKrUgiBJ554gpEjR/LLX/6Sbdu2MXDgQP73f/+XTZs2MX78eLKzsxk7dmyp+2jfvj3jxo2jX79+bNu2jfr163PvvffSunXrYutt2LCBM888ky1bthBj5M4770z36UmSJEkZoc4kRDWhVatW/P3vf99hfoMGDZg7d26p2y1evDg5PWTIkOTAC6k2btyYnD744IOZM2fOLkYrSZIk6du8ZU6SJElSxjIhkiRJkpSxduuEKMZY0yFItZq/I5IkSTuX1oQohLAqhLAohJAXQpiXmLd/COH5EMLKxL9NK7Pvhg0bsn79er/wSaWIMbJ+/XoaNmxY06FIkiTVWtUxqMIpMcZ1Ka/HADNjjONDCGMSr2+s6E5btmzJ6tWrWbt2bVXFKdU5DRs2pGXLljUdhiRJUq1VE6PMnQn0SUxPAmZTiYSofv36tG3btuqikiRJkpRx0l1DFIHpIYT5IYQrE/NaxBjXJKb/A7RIcwySJEmSVKJ09xCdFGP8MIRwIPB8CGF56sIYYwwhlFgElEigrgQ47LDD0hymJEmS0umOIYMAGJU7rYYjkYpLaw9RjPHDxL+fAE8APYCPQwgHAyT+/aSUbR+MMebEGHOaN2+ezjAlSZIkZai0JUQhhH1CCI2LpoF+wGLgKWBYYrVhwN/SFYMkSZIk7Uw6b5lrATwRQig6zp9ijM+GEOYCj4cQLgPeB85PYwySJEmSVKq0JUQxxneBziXMXw/0TddxJUnS9noNsGZDknYm3aPMSZIkSVKtZUIkSZIkKWOZEEmSJEnKWCZEkiRJkjKWCZEkSZKkjGVCJEmSJCljmRBJkiRJylgmRJIkSZIylgmRJEmSpIxlQiRJkiQpY5kQSZIkScpYJkSSJEmSMpYJkSRJkqSMZUIkSZIkKWOZEEmSJEnKWCZEkiRJkjKWCZEkSZKkjGVCJEmSJCljmRBJkiRJylgmRJIkSZIylgmRJEmSpIxlQiRJkiQpY5kQSZIkScpYJkSSJEmSMpYJkSRJkqSMZUIkSZIkKWOZEEmSJEnKWCZEkiRJkjKWCZEkSZKkjGVCJEmSJCljmRBJkiRJylgmRJIkSZIylgmRJEmSpIxVroQohNAx3YFIkiRJUnUrbw/R/wshzAkhjAwhNElrRJIkSZJUTcqVEMUYewEXAq2A+SGEP4UQvpfWyCRJkqRa6o4hg7hjyKCaDkNVoNw1RDHGlcAvgBuB3sBvQwjLQwhn72y7EEJWCOFfIYRpiddtQwhvhBDeDiHkhhD23JUTkCRJkqTKKm8NUacQwl3AMuBU4PsxxnaJ6bvK2PxHie2K/B9wV4zxCOAz4LIKRy1JkiRJVaC8PUT3AAuAzjHGa2KMCwBijB9R2GtUohBCS+B04KHE60BhEjU1scok4KxKRS5JkiRJu6heOdc7HdgcYywACCHsATSMMX4VY/zjTrb7DfBToHHi9QHA5zHG/MTr1cChFY5akiRJkqpAeROiGcB3gY2J13sD04ETStsghDAI+CTGOD+E0KeigYUQrgSuBDjssMMqurkkSZJqodSBCEblTqvBSKRC5b1lrmGMsSgZIjG9dxnbnAicEUJYBTxG4a1ydwP7hRCKErGWwIclbRxjfDDGmBNjzGnevHk5w5QkSZKk8itvQrQphNC16EUIoRuweWcbxBjHxhhbxhjbAEOBWTHGC4EXgHMTqw0D/lbhqCVJkiSpCpT3lrnrgT+HED4CAnAQMKSSx7wReCyEMA74FzChkvuRJEmSpF1SroQoxjg3hHAMcHRi1lsxxm/Ke5AY42xgdmL6XaBHxcKUJEmSpKpX3h4igO5Am8Q2XUMIxBgnpyUqSZIkSaoG5UqIQgh/BL4D5AEFidkRMCGSJEmStNsqbw9RDtA+xhjTGYwkSZIkVafyjjK3mMKBFCRJkiSpzihvD1EzYGkIYQ6wtWhmjPGMtEQlSZIkSdWgvAnRzekMQpIkSZJqQnmH3X4xhNAaODLGOCOEsDeQld7QJEmSJCm9ylVDFEK4ApgKPJCYdSjwZJpikiRJkqRqUd5BFa4BTgS+BIgxrgQOTFdQkiRJklQdypsQbY0xfl30IoRQj8LnEEmSJEnSbqu8CdGLIYSfAXuFEL4H/Bn4e/rCkiRJkqT0K29CNAZYCywCrgKeBn6RrqAkSZIkqTqUd5S5bcDvEz+SJEmSVCeUKyEKIbxHCTVDMcbDqzwiSZIkSaom5X0wa07KdEPgPGD/qg9HkiRJkqpPuWqIYozrU34+jDH+Bjg9vaFJkiRJUnqV95a5rikv96Cwx6i8vUuSJEmSVCuVN6m5I2U6H1gFnF/l0UiSJElSNSrvKHOnpDsQSZIkSapu5b1l7ic7Wx5jvLNqwpEkSZKk6lORUea6A08lXn8fmAOsTEdQkiRJklQdypsQtQS6xhg3AIQQbgb+EWO8KF2BSZIkSVK6lWvYbaAF8HXK668T8yRJkiRpt1XeHqLJwJwQwhOJ12cBk9ISkSRJkiRVk/KOMverEMIzQK/ErEtijP9KX1iSJEmSlH7lvWUOYG/gyxjj3cDqEELbNMUkSZIkSdWiXAlRCOEm4EZgbGJWfeCRdAUlSZIkSdWhvD1Eg4EzgE0AMcaPgMbpCkqSJEmSqkN5E6KvY4wRiAAhhH3SF5IkSZIkVY/yJkSPhxAeAPYLIVwBzAB+n76wJEmSJCn9yhxlLoQQgFzgGOBL4Gjgv2OMz6c5NkmSJElKqzITohhjDCE8HWPsCJgESZLqvDuGDAJgVO60Go5EkpRu5b1lbkEIoXtaI5EkSZKkalauB7MCxwEXhRBWUTjSXKCw86hTugKTJEmSpHTbaUIUQjgsxvhvoH81xSNJkiRJ1aasHqInga4xxvdDCH+JMZ5TDTFJkiRJUrUoq4YopEwfXpEdhxAahhDmhBAWhhCWhBBuScxvG0J4I4TwdgghN4SwZ0WDliRJkqSqUFZCFEuZLo+twKkxxs5ANjAghNAT+D/grhjjEcBnwGUV3K8kSZIkVYmyEqLOIYQvQwgbgE6J6S9DCBtCCF/ubMNYaGPiZf3ETwROBaYm5k8Czqp8+JIkSZJUeTutIYoxZu3KzkMIWcB84AjgXuAd4PMYY35ildXAobtyDEmSJEmqrPI+h6hSYowFMcZsoCXQAzimvNuGEK4MIcwLIcxbu3ZtukKUJEmSlMHSmhAViTF+DrwAHA/sF0Io6plqCXxYyjYPxhhzYow5zZs3r44wJUmSJGWYtCVEIYTmIYT9EtN7Ad8DllGYGJ2bWG0Y8Ld0xSBJkiRJO1PWc4h2xcHApEQd0R7A4zHGaSGEpcBjIYRxwL+ACWmMQZIkSZJKlbaEKMb4JtClhPnvUlhPJEmSpAx2x5BByelRudNqMJLiamtcSo9qqSGSJEmSpNrIhEiSJElSxjIhkiRJkpSxTIgkSZIkZax0jjInSZJqAQvEJal09hBJkiRJylgmRJIkSZIylgmRJEmSpIxlQiRJkiQpY5kQSZIkScpYJkSSJEmSMpYJkSRJkqSMZUIkSZIkKWOZEEmSJEnKWCZEkiRJkjKWCZEkSZKkjGVCJEmSJCljmRBJkiRJylgmRJIkSZIylgmRJEmSpIxlQiRJkiQpY5kQSZIkScpY9Wo6AEmSJO0+Gjb9SbnXvWPIoDRGIlUNe4gkSZIkZSwTIkmSJEkZy4RIkiRJUsayhkiSJEk7SK3/GZU7rQYjkdLLHiJJkiRJGcuESJIkSVLGMiGSJEmSlLFMiCRJkiRlLAdVkCSpDkp9eOaWz+6swUgKFRXoW5y/e0odYGHgwndSliyr/mCkKmYPkSRJkqSMZUIkSZIkKWOZEEmSJEnKWNYQSZIkaZf4EFftztLWQxRCaBVCeCGEsDSEsCSE8KPE/P1DCM+HEFYm/m2arhgkSZIkaWfSectcPjAqxtge6AlcE0JoD4wBZsYYjwRmJl5LkiRJUrVLW0IUY1wTY1yQmN5A4biMhwJnApMSq00CzkpXDJIkSZK0M9UyqEIIoQ3QBXgDaBFjXJNY9B+gRXXEIEmSJEnflvaEKITQCPgLcH2M8cvUZTHGCMRStrsyhDAvhDBv7dq16Q5TkiRJUgZKa0IUQqhPYTL0aIzxr4nZH4cQDk4sPxj4pKRtY4wPxhhzYow5zZs3T2eYkiRJkjJUOkeZC8AEYFmM8c6URU8BwxLTw4C/pSsGSZIkSdqZdD6H6ETgh8CiEEJeYt7PgPHA4yGEy4D3gfPTGIMkSZIklSptCVGM8RUglLK4b7qOK0mSaofUh3VKo9q9DMAdy3qVuLw2PNzVNpuZqmWUOUmSJEmqjUyIJEmSJGUsEyJJkiRJGcuESJIkSVLGSucoc5IkSVIxDZv+JDm95bM7d7Jm7RhoQXWfPUSSJEmSMpYJkSRJkqSMZUIkSZIkKWNZQyRJkjJKuupSMrHepTIPMr3moMHbt/+s5Ie0VuS4mXKtlT72EEmSJEnKWCZEkiRJkjKWCZEkSZKkjGVCJEmSJCljOaiCJEl1XOqDMNOlMsX11cHie0llsYdIkiRJUsYyIZIkSZKUsUyIJEmSJGUsa4gkqRzKU4dQtI51Cru/gQvfqekQ6ixrenauNlyfdNWDtdnypx3mXcd9aTlWbbiO2n3YQyRJkiQpY5kQSZIkScpYJkSSJEmSMpYJkSRJkqSM5aAKyjgWvkuqStX5mZJpheJ18XxLai/V+VDbunhNpV1lD5EkSZKkjGVCJEmSJCljmRBJkiRJyljWEEmSqpV1fFLVKOt3yXqh6uc13z3ZQyRJkiQpY5kQSZIkScpYJkSSJEmSMpYJkSRJkqSM5aAKkiQpY1VFEXx1Pli1LJWNpTadA1Qsnl0dqCVd5+4AC7sPe4gkSZIkZSwTIkmSJEkZy4RIkiRJUsayhkiSvsX7vpUJqqOdN2z6k+T0ls/uTMsxMkFtqO9JfS/PH/uj5PTwp3e+3cCF7ySn72lb5WEVUxuuk3ZPaeshCiE8HEL4JISwOGXe/iGE50MIKxP/Nk3X8SVJkiSpLOm8ZW4iMOBb88YAM2OMRwIzE68lSZIkqUakLSGKMb4EfPqt2WcCkxLTk4Cz0nV8SZIkSSpLdQ+q0CLGuCYx/R+gRTUfX5IkSZKSamxQhRhjDCHE0paHEK4ErgQ47LDDqi0u1X2pBZ5SEYtxlSmqsq3X5d+bks6trg2yUh3v3/ljU75qLtu1fdXl9qaaVd09RB+HEA4GSPz7SWkrxhgfjDHmxBhzmjdvXm0BSpIkScoc1Z0QPQUMS0wPA/5WzceXJEmSpKR0Drs9BXgdODqEsDqEcBkwHvheCGEl8N3Ea0mSJEmqEWmrIYoxXlDKor7pOqZqv93tgZdF8e4Osarush1Wj9TPp4FlLN/V92J3/SyEisV76uxrktNPd/5Olcak3cOi9/6dnG5TQzHsbr9vqn7VfcucJEmSJNUaJkSSJEmSMpYJkSRJkqSMZUIkSZIkKWPV2INZpbrK4s2qUxsGE6hNDwIsq21VR9uzfRcqqV2k69rUpjZYW+xu1zrd72F59l+ZGEa1ezk5veyx7Q81v//47V8fGzb9yU6PdU/bP1X4uOlUmb8rZV07f0d3f/YQSZIkScpYJkSSJEmSMpYJkSRJkqSMZQ2RpDLVhlqeIqXVDlQkxnSdT+r99jXNeqJdc/7Ywj+Pi2o4jopKfU9Kqu2oDsWPe02p68Hu97DbihyjInUlqddsy2d3Vjywat5vVdodYlTdZw+RJEmSpIxlQiRJkiQpY5kQSZIkScpYJkSSJEmSMpaDKqhWSdeDJ1O3G1jJ2LR7q44HLo5ql5ZDVKmSrkNVFodXVlUMllHWvnY1rnSpis+1ujCoRUnnUxUDQwxc+E7ZK9WwU2dvH3zi6c7fSU7vTg/8LO0cKiL1/b7moMEA3LGs1y7vq6TBGmrq2ta139u6wh4iSZIkSRnLhEiSJElSxjIhkiRJkpSxrCFSpezO98BW5IGLNXXveXVe37JqN2oqLlWdyt4rX2K9UcrDZ0u6t7+0Y+0OD93MNKXV51TkWpe0j9RakqLPW4DHby38PK1sfUlFlFU3mvrZXtl4Kvt7VVZdVG19UGnqe5kuRXVDqcr6zJGqgj1EkiRJkjKWCZEkSZKkjGVCJEmSJCljmRBJkiRJylgOqqAas+yYwqdYtlu+LDmvsoMYVKbgurYVaVfFQ+Iq8xDL6pauGFMLb3d1+9TC3crstzwDVVTmYai7eo7lOVZ1tp1ihe8pv/sV+V2oDQ+uLOtza1evb2n739WHX6YOfnDHkMrtK3Uf6XD/8T9KTm8Z0jo5nTrwwL0jZu3SMcoaYKEqBmAoSWq7mFhs8Ildf8DpzqReu9T3b1afe5PTRYMbtNnyp+S8xowpcX+72gZKGkjh28cuch33lXu/u/qg2PIM5nBP26sL43qv/HGpdrKHSJIkSVLGMiGSJEmSlLFMiCRJkiRlLGuIVCml3dNeYt1DBe6ZT33wW+qDU8t6SGhVKum+7vI8xLUqlXR9K1t/Utq6NXVNy6qDKWvdYvd1F1u3hHmlnHu6anGK7nlPvc+9ste0Ig9Arc66n9T7+ityP3+qovZd2n39JZ17ReoLK7JuaXVMEwfu+OexrP2mfn4NL+V9Lzpe6r7aDf1op+tOLOWBmMOf3nHd0pRUK1Is3pR9lfTg0NR5ZT30tLQHeBbNTz1W6nVIrWGpjNJqfUr6u1JajEUPj01V2nWqrKKamTs+K/9DRstqe5WtNSp2bd4ruZanIvsoUtnrVFLdUHkUtc/UB9mmxjXi9fI/7Lbo86cqPuu0+7CHSJIkSVLGMiGSJEmSlLFMiCRJkiRlLBMiSZIkSRnLQRWUFuUpWi8qeHw88YBWgEWlFBdX9oGtO1PV+yypWLqsdUsrhi+pOLu0a1rZB3gWPVBuVcMflLzfMh5OWloRd0nnlLp9UaHqqlLiKnb92pWyUgnKGtCgpP2mLr+nbUoxb9vtk/dsuXqHY11XRgF7qe9VJQdHKKvIt6w2UNpDBctS0nmkXo+yHqZa2vKJJRTXFy/u3nEfpQ24UtLvW2nrlqWk4vDU/ReP8d/J6Y5tD9th3h1sf4hoWcdKPd+SlHSs0pQ0IMK3jzfi9R33mxpv6gAM9464d4d5qYMfnD92+4NTKyM1rg3LNientyTay6J2ZcdY0r4qUtRf7PqO3fH6pi6/t+nd22NMKc4vqai/IkqLYdF7hb+DHQfu/H1PHQyirMEpbttv+3Vm2fjt0yl/C0oa3KC0dlg0P3Ve6mdHSZ+hqSo7kELR37BChec0mtT2v71tpj78tePxhXEOf7rk39Gy4inr3FJ/B0t76KxqH3uIJEmSJGUsEyJJkiRJGcuESJIkSVLGsoZIlVLWPfqp9+CuqsC+NiwrebvSHqJXkmQ8NzdJzhuVUosykcN22OeilHVhe73FhqL7q1OWF6utKaHmo9RrU8oxSrIh5b7uexL1LKn3Kq9KWbekWpHSrn+bMf/Y6XFTt3tm4Q3b57fd8Z7q1BqWsmqWKnKPeEntYVW5ty49ltR78Iv2m66H7ZV2vhU5XkXuYy9LSesWq0d6LyWuYu1057EUr4PZ8Rip1zy1pqOofd+TUqvVmDElHq/x2DHFttlZDN/eP5Reo3VHCbV5o1JqeYrqIYq3x+37bZNyvFXv/WCHuFe1216PkfqZkax7WJayg5TajZJqsYrt972Umr+U+prttp/Pvf95osTYS9rv6KZ7JadT64KKHtZZvK5le13E/ey85qak4zVMOVbqvtrsZBso/vtT0vteWq1Pai1J0fXd0Lbk97LY+1LC8lUpNSFt9kt5XxoWzr+XJyhJam1R0TmlXvPU963YsYtqLlPe99RanaL39fyx239/imrEvq2orZdW01PZWp7qVFb9TmrNzjWl1OYVtZPibSTV5h3mlFQHW5rUGq3bKvBdSDWrRnqIQggDQghvhRDeDiGMKXsLSZIkSap61Z4QhRCygHuB04D2wAUhhPbVHYckSZIk1UQPUQ/g7RjjuzHGr4HHgDNrIA5JkiRJGa4mEqJDgQ9SXq9OzJMkSZKkahVijNV7wBDOBQbEGC9PvP4hcFyM8dpvrXclcGXi5dHAW9UaaGZqBqyr6SBUq9lGVBbbiMpiG1F52E5UlpLaSOsYY/OK7qgmRpn7EGiV8rplYl4xMcYHgQerKyhBCGFejDGnpuNQ7WUbUVlsIyqLbUTlYTtRWaqyjdTELXNzgSNDCG1DCHsCQ4GnaiAOSZIkSRmu2nuIYoz5IYRrgeeALODhGOOS6o5DkiRJkmrkwawxxqeBp8tcUdXNWxRVFtuIymIbUVlsIyoP24nKUmVtpNoHVZAkSZKk2qImaogkSZIkqVYwIarjQggPhxA+CSEsTpmXHUL4ZwghL4QwL4TQIzE/hBB+G0J4O4TwZgiha8o2w0IIKxM/w2riXJQeFWwjfUIIXyTm54UQ/jtlmwEhhLcS7WdMTZyL0qOUNtI5hPB6CGFRCOHvIYR9U5aNTbSDt0II/VPm20bqqIq0kRBCmxDC5pTPkftTtumWWP/txN+jUBPno6oXQmgVQnghhLA0hLAkhPCjxPz9QwjPJ75fPB9CaJqY73eSDFOJNlJ130lijP7U4R/gZKArsDhl3nTgtMT0QGB2yvQzQAB6Am8k5u8PvJv4t2liumlNn5s/NdJG+gDTSthHFvAOcDiwJ7AQaF/T5+ZPWtvIXKB3YvpS4JeJ6faJ978B0DbRLrJsI3X7p4JtpE3qet/az5zE35+Q+Ht0Wk2fmz9V1kYOBromphsDKxKfF78GxiTmjwH+LzHtd5IM+6lEG6my7yT2ENVxMcaXgE+/PRso+t/cJsBHiekzgcmx0D+B/UIIBwP9gedjjJ/GGD8DngcGpD96VYcKtpHS9ADejjG+G2P8GniMwvakOqCUNnIU8FJi+nngnMT0mcBjMcatMcb3gLcpbB+2kTqsgm2kRIm/N/vGGP8ZC7/VTAbOquJQVUNijGtijAsS0xuAZcChFH4OTEqsNont77nfSTJMJdpIaSr898aEKDNdD9wWQvgAuB0Ym5h/KPBBynqrE/NKm6+663pKbiMAx4cQFoYQngkhHJuYZxvJPEvY/gfmPLY/cNvPERUprY0AtA0h/CuE8GIIoVdi3qEUtositpE6KoTQBugCvAG0iDGuSSz6D9AiMe1nSQYrZxuBKvpOYkKUma4GfhxjbAX8GJhQw/Go9imtjSwAWscYOwP3AE/WTHiqBS4FRoYQ5lN4a8PXNRyPap/S2sga4LAYYxfgJ8CfUmvQVLeFEBoBfwGujzF+mbos0TPo8McZrgJtpMq+k5gQZaZhwF8T03+msGsR4EOK/w9ey8S80uar7iqxjcQYv4wxbkxMPw3UDyE0wzaScWKMy2OM/WKM3YApFN6vDX6OKKG0NpK4nXJ9Ynp+Yv5RFLaHlim7sI3UMSGE+hR+0X00xlj0N+bjxK1wRbdNfpKY72dJBqpIG6nK7yQmRJnpI6B3YvpUYGVi+ing4sTILj2BLxJdlM8B/UIITRMje/RLzFPdVWIbCSEcVDTqUygceW4PYD2FxdNHhhDahhD2BIZS2J5UR4UQDkz8uwfwC6BopLCngKEhhAYhhLbAkRQWyttGMkxpbSSE0DyEkJWYPpzCNvJu4u/NlyGEnonPmYuBv9VI8Kpyifd0ArAsxnhnyqKnKPxPOBL//i1lvt9JMkhF20hVfiepV5UnotonhDCFwlE4moUQVgM3AVcAd4cQ6gFbgCsTqz9N4agubwNfAZcAxBg/DSH8ksIGBvA/McZvF89qN1XBNnIucHUIIR/YDAxNdF/nhxCupfCPUhbwcIxxSfWeidKllDbSKIRwTWKVvwJ/AIgxLgkhPA4sBfKBa2KMBYn92EbqqIq0EQpHpPufEMI3wDZgRMrflJHARGAvCkcYe6ZaTkDV4UTgh8CiEEJeYt7PgPHA4yGEy4D3gfMTy/xOknkq2kaq7DtJSAxPJ0mSJEkZx1vmJEmSJGUsEyJJkiRJGcuESJIkSVLGMiGSJEmSlLFMiCRJkiRlLBMiSZIkSRnLhEiSJElSxjIhkiRJkpSx/j/SMmrFMPGHjQAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/html": [ "Below is stacked histogram for Dissolved_abolished_or_demolished_date binned per year from 1800 to 2100" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/amandeep/anaconda3/envs/kgtk-env/lib/python3.7/site-packages/ipykernel_launcher.py:104: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n" ] }, { "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/html": [ "Below is stacked histogram for Publication_date binned per year from 1800 to 2100" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "try:\n", " ## Make it False if you are facing some issue. Setting it False will lead to generating all files from scratch which might take some time.\n", " restart = restart_global\n", " #Initializing the dataframe pd_time_property with the list of top properties of datatype:time found above in the notebook \n", " pd_time_property = pd.read_csv(os.path.join(os.getenv('OVERVIEW_FOLDER'),os.getenv('property_summary_time')),delimiter='\\t')\n", " printmd(\"Below are statistics for top five properties of Datatype:\" + \"time\".capitalize(),'blue')\n", " for index,ele in pd_time_property.iterrows():\n", "\n", " #Ignoring the 'Other Instances' row\n", " if index>=K or ele['Property_Label'] ==\"Other Properties\":\n", " break;\n", "\n", " # Extracting the \"Pnode\" corresponding to the property\n", " pnode = ele['Link'].split('/')[-1].split(\":\")[-1]\n", "\n", " #Extracting the 'Label' corresponding to the property\n", " prop_label = re.sub(\"[^0-9a-zA-Z]+\", \"_\", ele['Property_Label']) \n", " \n", " \n", " #Free all the garbage\n", " gc.collect()\n", " time_distribution_stacked_saving = time_prop_distribution(pnode,prop_label,_restart=restart)\n", "\n", " #Dynamically setting the name of the output file based on the name of property\n", " output_file_time_distribution = \"Property_overview.time.\"+prop_label+\".year_distibution.tsv\"\n", "\n", " #Saving the dataframe\n", " time_distribution_stacked_saving.to_csv(os.path.join(os.getenv('PROPERTY_OVERVIEW'),output_file_time_distribution),sep='\\t')\n", " del time_distribution_stacked_saving\n", "except Exception as e:\n", " #Free all the garbage\n", " gc.collect()\n", " print(e)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:45.566920Z", "iopub.status.busy": "2021-01-15T21:55:45.566360Z", "iopub.status.idle": "2021-01-15T21:55:50.927704Z", "shell.execute_reply": "2021-01-15T21:55:50.928071Z" }, "papermill": { "duration": 5.47198, "end_time": "2021-01-15T21:55:50.928239", "exception": false, "start_time": "2021-01-15T21:55:45.456259", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "Below are statistics for top properties of Datatype:Wikibase_item" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below is ditribution of Contains_administrative_territorial_entity in different outgoing classes" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", 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tgL7ADGAm0DeNHOpI1sl8S0QEcC9wVDp/DPCn9YnJzMzWX6M1A0m/BfYBtpC0GDgH2EfSYCCARcBXACLiSUk3Ak8Bq4CTI2J1ep1TgDuB9sCUiHgyvcWZwA2SzgP+AVxVroszM7O102gyiIhj6iku+YUdEecD59dTfjtwez3lC8lGG5mZWU7Wq8+gNes94bYWfb9FFxzSYu9VzddmZs3Ly1GYmZmTgZmZORmYmRlOBmZmhpOBmZnhZGBmZjgZmJkZTgZmZoaTgZmZ4WRgZmY4GZiZGU4GZmaGk4GZmeFkYGZmOBmYmRlOBmZmhpOBmZnhZGBmZjgZmJkZTgZmZoaTgZmZ4WRgZmY4GZiZGU4GZmaGk4GZmeFkYGZmOBmYmRlOBmZmhpOBmZnhZGBmZjgZmJkZTgZmZoaTgZmZsRbJQNIUSS9LeqKorKuk6ZLmp79dUrkkTZS0QNJjkoYUnTMmHT9f0pii8l0lPZ7OmShJ5b5IMzNr2NrUDK4BDqpTNgG4JyL6AvekxwAHA33TbTwwCbLkAZwD7AYMB84pJJB0zIlF59V9LzMza2aNJoOIuB94rU7xKODadP9a4PCi8qmReQTYXNLWwIHA9Ih4LSJeB6YDB6XnOkfEIxERwNSi1zIzsxayvn0GW0XE0nT/38BW6X4P4MWi4xansobKF9dTXi9J4yXNkjTrlVdeWc/QzcysriZ3IKdf9FGGWNbmvS6PiKERMbR79+4t8ZZmZm3C+iaDl1ITD+nvy6l8CdCr6Lieqayh8p71lJuZWQta32RwC1AYETQG+FNR+eg0qmgEsCw1J90JjJTUJXUcjwTuTM+9KWlEGkU0uui1zMyshXRo7ABJvwX2AbaQtJhsVNAFwI2Svgy8ABydDr8d+DSwAHgHGAcQEa9J+iEwMx33g4godEqfRDZiaUPgL+lmZmYtqNFkEBHHlHhq/3qODeDkEq8zBZhST/ksYEBjcZiZWfPxDGQzM3MyMDMzJwMzM8PJwMzMcDIwMzOcDMzMDCcDMzPDycDMzHAyMDMznAzMzAwnAzMzw8nAzMxwMjAzM5wMzMwMJwMzM8PJwMzMcDIwMzOcDMzMDCcDMzPDycDMzHAyMDMznAzMzAwnAzMzw8nAzMxwMjAzM5wMzMwMJwMzM8PJwMzMcDIwMzOcDMzMDCcDMzPDycDMzHAyMDMzmpgMJC2S9LikuZJmpbKukqZLmp/+dknlkjRR0gJJj0kaUvQ6Y9Lx8yWNadolmZnZuipHzWDfiBgcEUPT4wnAPRHRF7gnPQY4GOibbuOBSZAlD+AcYDdgOHBOIYGYmVnLaI5molHAten+tcDhReVTI/MIsLmkrYEDgekR8VpEvA5MBw5qhrjMzKyEpiaDAO6SNFvS+FS2VUQsTff/DWyV7vcAXiw6d3EqK1VuZmYtpEMTz/9URCyRtCUwXdIzxU9GREiKJr5HrZRwxgNss8025XpZM7M2r0k1g4hYkv6+DEwja/N/KTX/kP6+nA5fAvQqOr1nKitVXt/7XR4RQyNiaPfu3ZsSupmZFVnvZCBpY0mbFu4DI4EngFuAwoigMcCf0v1bgNFpVNEIYFlqTroTGCmpS+o4HpnKzMyshTSlmWgrYJqkwuv8JiLukDQTuFHSl4EXgKPT8bcDnwYWAO8A4wAi4jVJPwRmpuN+EBGvNSEuMzNbR+udDCJiITConvJXgf3rKQ/g5BKvNQWYsr6xmJlZ03gGspmZORmYmZmTgZmZ4WRgZmY4GZiZGU4GZmaGk4GZmeFkYGZmOBmYmRlOBmZmhpOBmZnR9P0MzFpM7wm3tej7LbrgkBZ9P7M8ORmYVQgnO8uTk4GZtQgnu8rmPgMzM3MyMDMzJwMzM8PJwMzMcDIwMzOcDMzMDCcDMzPDycDMzHAyMDMznAzMzAwnAzMzw8nAzMxwMjAzM5wMzMwMJwMzM8PJwMzMcDIwMzOcDMzMDCcDMzPDycDMzKigZCDpIEnPSlogaULe8ZiZtSUVkQwktQcuAw4G+gPHSOqfb1RmZm1HRSQDYDiwICIWRsT7wA3AqJxjMjNrMxQReceApKOAgyLihPT4OGC3iDilznHjgfHpYT/g2RYMcwvgPy34fi2pmq8NfH2tna+vvLaNiO51Czu0YABNFhGXA5fn8d6SZkXE0Dzeu7lV87WBr6+18/W1jEppJloC9Cp63DOVmZlZC6iUZDAT6CtpO0kdgS8At+Qck5lZm1ERzUQRsUrSKcCdQHtgSkQ8mXNYdeXSPNVCqvnawNfX2vn6WkBFdCCbmVm+KqWZyMzMcuRkYGZmTgZmZuZkUJKkjSR9X9IV6XFfSZ/JOy4zAEmfkjQu3e8uabu8YyoXSd3yjqE5SdpD0sbp/rGSLpK0bd5xORmUdjXwHrB7erwEOC+/cMpL0s2SDpFUlZ8BSUdI2qzo8eaSDs8xpLKRdA5wJvDdVFQD/Dq/iMruEUk3Sfq0JOUdTDOYBLwjaRBwBvAcMDXfkJwMGrJ9RPwUWAkQEe8A1fTB/CXwRWC+pAsk9cs7oDI7JyKWFR5ExBvAOfmFU1ZHAIcBbwNExL+ATXONqLx2IBtueRzZ5/NHknbIOaZyWhXZMM5RwKURcRkV8P/PyaC09yVtCASApO3JagpVISLujogvAUOARcDdkh6SNE5STb7RlUV9n+2KmFdTBu+nL5PCZ3PjnOMpq8hMj4hjgBOBMcAMSX+TtHsjp7cGyyV9lyzZ3ZZq57n/m3MyKO1c4A6gl6TrgXvIquZVI7XNjgVOAP4BXEyWHKbnGFa5zEptsdun20XA7LyDKpMbJf0K2FzSicDdwJU5x1Q2krpJ+rqkWcC3gFPJFnM7A/hNrsGVx+fJflgeHxH/Jlt+52f5huRJZw1KX5YjyJqHHomIqlk5UdI0spVfrwOuiYilRc9VxMJZTZF+LX8f+O9UNB04LyLezi+q8pF0ADCS7LN5Z0RUQwIHQNI/yT6XV0fE4jrPnRkRP8knsvJJHcZ9I+JuSRsB7SNiea4xORnUT9I9EbF/Y2WtUaqWfi8iqqZDvC2R9JOIOLOxstYobXT104g4I+9YmkuqzY0HukbE9pL6ApPz/m5xM1EdkjpJ6gpsIamLpK7p1hvokXN4ZRERHwBH5h1Hc5D0i/T3z5JuqXvLObxyOaCesoNbPIpmEBGrgU/mHUczOxnYA3gTICLmA1vmGhHV06FWTl8BTgc+RtbGXBhB9CZwaU4xNYd7JB0J3BzVVT28Lv29MNcomoGkrwEnAX0kPVb01KbA3/OJqlnMTYn7JtKIKYCIuDm/kMrqvYh4vzBqVlIH0mCAPLmZqARJp0bEJXnH0VwkLQc2BlYB75IlvYiIzrkGZiWleRNdgB8DE4qeWh4Rr+UTVflJurqe4oiI41s8mGYg6afAG8Boss7xk4CnIuKsXONyMihN0gCgP9CpUBYRuU8OsdIkPU79v7IKyW5gC4fUbCRtyZqfzf/LMZyykbRHRPy9sbLWKvXZfZmiAQDAlXnX0J0MSkizPPchSwa3k7XJPhgRR+UZV7lUawd5Y9P6I+KFloqluUg6FLiIrCnzZWBb4OmI2CnXwMpE0pyIGNJYWTVI/ZM9I+KxRg9uZu4zKO0oYBDwj4gYJ2krqmDKv6ROwEakDnI+7BPpTBV0kBd/2Uv6L2A4WU1hZhrTXQ3OIxvyfHdE7CJpX+DYnGNqsjSh7JNAd0nfLHqqM9mmV1VB0n1kM8g7kPVLvizpoYj4Rp5xeTRRaSvSqJtVkjqT/QLr1cg5rcFXyD6An0h/C7c/UUUd5JJOAGYAnyVL7I9Iqoo2Z2BlRLwKtJPULiLuBVr1vJCkI7AJ2ZfkpkW3N8n+H1aLzSLiTbLP5tSI2A3IvUbumkFpsyRtDlxB9mX5FvBwrhGVQURcDFxc7R3kwLeBXdKXZmEC4UPAlFyjKo83JG0C3A9cL+llikbdtFYR8Tfgb5KuqYbmvAZ0kLQ1cDSQa6dxMfcZrIU0x6BzJbTrlZOkTwK9KfpRUC0d5JIeAvaJiPfT447AfRHR6sewp9nVK8hq9l8CNgN+XS0jitKidN/io5/N/fKKqZwkfY5sdvyDEXGSpD7AzyIi17k/TgYlVGsHa4Gk64DtgbnA6lQcEXFabkGVQVFb82BgZ7Lmr8IKkY9FxNh8Iiufap6BDCBpHjCZrEZe+GwSEdWytlRFcjNRHdXewVpkKNA/7+FszaCwFPBz6VbwpxxiaS4H8NFFEw+up6y1WhURk/IOormkeQbnkdXu7gAGAt+IiFwHqDgZfFRbmYH8BPBfwNLGDmxNIuJ/8o6huRTNQN6+ymcg/1nSScA0ipaNr5ZmMGBkRHxH0hFky8d/lqz/J9dk4GaiEqq9g1XSvWRNKTNY8x/cYXnFVE7p+j7y4W7N7c5taAby8/UUR0T0afFgmoGkJyJigKQrgd9HxB2S5kXEoDzjcs2gtH9L2jQilks6m2yd//MiYk7egZXJuXkH0My+VXS/E9nCfKtyiqUs0s5ty9Ln8d8R8Z6kfYCBkqam3dxavYiomv2cS7hV0jNkzURfk9SdbEmYXLlmUIKkxyJioKRPkbXv/Qz4f2lMsLVCkmZExPC842gqSXPJ+nx6k82O/xOwU0R8OsewykbS6PrKq2WkG9TOPF4WEavTfgad854U6ZpBaYVRDIcAl0fEbZKqZv3/tFBd4ZdAR7Jt996uloXq0j+2gnbArmRDMKvBBxGxStJngUsi4hJJ/8g7qDIaVnS/E9mErDlUwKbxZfQx4L/TgJWCXK/PyaC0Jcq2FjwA+ImkDaiiGdsRUbsBt7K1dEeRLXFQLWaTJTuRNQ89T7Y4WDVYKekYslUvD01lue+hWy4RcWrx4zT584Z8oim/UuuekXMycDNRCanqdhDweETMTzMGd46Iu3IOrdlI+kdE7JJ3HNYwSf2BrwIPR8RvJW0HHF0N20HWR1IN8ERE9Ms7lnJIK+sW1j0bVFj3LCLq27SoxbhmUEJEvAPcXPR4KVU0DDM1MRS0I2uDzr0Tq1xS9fsk4FNkNYQHyLYWbPXXGBFPAacVPX4eqJpEIOnPfNiE2R7YEbgxv4jKbkVEfCCpotY9czJouw4tur+KbLzzqHxCaRZTgeVAYXjwF8l2QftcbhHZ2irepW4V8EJELM4rmGZQkeueuZnIqpKkpyKif2NlVplS00mhI3lGRLycZzzNpZLWPauaDlFbN5J6Spom6eV0+4OknnnHVUZzJNV2iEvaDZiVYzxlI2nnvGNoTpKOJpsM+TmylT0fldTql7CWNKTuDehKtopp7hv3uGZQQmpT/wmwJdmIlKraI1jSdOA3fLiB/LHAl/LuxCoXSU8D/YDCVpDbAM+SNTu06u0vJT0AbABcA1yfJqNVjbRQ3QGF2kCalHV33jN0myrNii8l8p4d72RQgqQFwKER8XTesTQHSXMjYnBjZa2Vqnz7S0l9gePJfj3PAK6OiOn5RlUekh6PiJ2LHrcD5hWXWfm5mai0l6o1ESSvSjpWUvt0OxZ4Ne+gyiV92fcC9kv33wbaRcQLrT0RAETEfOBsspVK9wYmSnqmziix1uoOSXdKGitpLHAb8JecY2qy9O/tuHrKj5P0xTxiWiMO1wzqJ+lislU9/8iaC7ndXOqc1iT9cr4E2J1sGN9DwKkR8WKugZVJmtgzFOgXETtI+hhwU0TskXNoTSZpIDCObHb8dOCqiJiTrvHhiGiwVtQapKT2qfTwgYiYlmc85SDpUWD/iHirTvnGwP0RsWs+kWU8tLS0zsA7wMiisqBo7kEr9wNgTES8DrXLN1xI1vRQDY4AdiFbxoCI+JekTRs+pdW4BLgS+F5ErCgUpms8O7+wyiNNoru98MNL0oaSekfEonwja7KauokAICLeThPrcuVkUEJEjMs7hmY2sJAIIFsrXlI1zT5+PyJCUkDtr69WT1J7YElEXFff86XKW5mbgOLtSVensmH1H95qbChp44hYY7/q9COlY04x1XIyKEHSxHqKlwGzIqIads1qJ6lLnZpBVXwe0lpLt6a1pTaXdCJZjeeKfCNrurTKZS9JHSPt71yFOhRfW0S8r2wP69buKuD3kr5a6LdK8wwuS8/lqir+8TeTTsAnyH6RQLYe/vPAIEn7RsTpeQVWJv8LPCypcH2fA87PMZ6ySTWCzwHfJNuhrh/Z8uNVMdqG7HP4d0m3kHWMAxARF+UXUlm9IumwiLgFQNIo4D85x9RkEXGhpLeA+yVtkorfAi6ohG0+3YFcgqRHgD0iYnV63IFsfZtPkS1e1+pnsqYFzwpjm/+a1rypCpKuBS6NiJl5x1JuqXP8I6JKtvyUtD1wPdkyzwCLgeMi4rnSZ7Uuhf6riFiedywFTgYlSHoWGF6Y0KNsy8EZEdHPq3tWPmU7SX0cKAwrBaA1TzZrawq/nuvrdLXyczNRaT8F5kq6j2z28V7Aj1JH5N15BmZr5cC8A2guaUbud4CdyJozgda9v3N9nARalmsGDUh7GBS2SZwZEf/KMx4zAEl3Ab8j2+f5q8AY4JWIODPXwKxRaTb1iIh4KO9Y6nIyqEPSJyLimVILR0XEnJaOyayYpNkRsavSPt2pbGZEtPahl21CpTYzu5noo74JjCcbbVNX8GGHq1leVqa/SyUdAvyLbPXLqiHpk0Bvir6jIqJa9kC+R9KRwM1RQb/GXTMwa2UkfYZsZFsvstnInYFzI+LPuQZWJpKuA7YH5pJNOINsxPBpJU9qRSQtBzYmu7YVVMiKyK4ZlKBsD+RvAttExPi0SmS/iLg159DMXk+j3JYB+wJIavVrLhUZCvSvpF/N5RQRFbksilctLe1q4H0+nBa/BDgvv3DMal2ylmWt1RNki0RWJWWOlfT99LiXpOGNndfcXDMobfuI+LykYwAi4p20zIFZLiTtTvbjpLukbxY91Zls4/hqsQXwlKQZrLli8GH5hVRWvwQ+IOt//CHZLOTLyHntJSeD0t6XtCFZp3FhVuR7DZ9i1qw6ApuQ/bstbmp4E2j120IWOTfvAJrZbhExRNI/ACLi9UpYe8nJoLRzgDuAXpKuB/YAxuYakbVpEfE34G+SrqmGDXpKSddZzVam1WcLPzS7k9UUcuVkUI80MaQL8FlgBFlv/9cjotUvlmWtl6RfpAUSLy0szV2sWppRJI0g6wPZkaw21B54O+/RNmU0EZgGbCnpfLJaXe77UHhoaQmSZkXE0LzjMCuQtGtEzJa0d33PV8svakmzgC+QrRg8FBgN7BAR3801sDKS9Algf7IfmvdUwha7TgYlSLqAbNnc37HmQmev5RaUWRtQ+CFWZ4Z1Rc7aXV+pmWgr1pxU93/5ReRmooZ8Pv09uagsgD45xGJWK80pOBfYluzfcGHSUrV8Nt9JHapzJf0UWEoVDYOXdCpZn+RLZBPPRPbdkuuKuq4ZmLUyaXnubwCz+XCGLhHxam5BlZGkbcm+KDuSXedmwC8jYkGugZWJpAVkI4oq6v+XawYleAayVbBlEfGXvINoLhHxQhrWvXW1bNhTx4tks8crimsGJUj6Hdkvr9ERMSAlh4ciYnC+kVlbl/qz2gM3s+akrKpYUVfSocCFQMeI2E7SYOAHrX20VNFEwZ3ItmK9jTX//+W6balrBqV5BrJVqt3S3+LRbtW0ou65ZPuI3AcQEXMlbZdnQGVSmCj4f+nWMd0gzTnIk5NBaZ6BbBUpIvbNO4ZmtjIiltX57ZX7l2VTFZq8JH0uIm4qfk7S5/KJ6kNOBqWdi2cgWwWS9P/qK4+IH7R0LM3kSUlfBNqnvrrTgIrbGawJvks2h6KxshblZFBCRNwlaTaegWyV5+2i+52AzwC5T1oqo1OBs8hq4r8F7iRb0K1Vk3Qw8Gmgh6SJRU91BlblE9WH3IFcgqQ/A78BbomItxs73iwvkjYA7oyIffKOxUqTNAjYBfgfoLh2txy4NyJezyWwxMmghDTl//PAIcBM4Abg1oh4N9fAzOqQ1AWYGREfzzuWcpC0A/AtPrrtZVV0kEvahOzaABZUyneKk0Ej0rTx/YATgYOqaLEsa6UkPc6HHartge5kQy8vzS+q8pE0D5jMRyfVzc4tqDKQ1AH4ETCObDSRyLYuvRo4KyJWNnB6s3OfQQPSaKJDyWoIQ4Br843IDMj6CApWAS9FRO5tzmW0KiIm5R1EM/gZ2fDSPhGxHEBSZ7I5FRcCX88xNtcMSpF0I9lY5zvIFqv7W0Tkvua4WbWTdC7wMtkyz8WTslr1IpGS5pOtvhp1ytsDz0RE33wiS3E4GdRP0oHA3RGxutGDzaxsJD1fT3GrX4hP0j8jYod1fa6luJmotL8CJ0vaKz3+GzA573Y9s2oXEdUw27g+T0kaHRFTiwslHQs8k1NMH8bhmkH9JF0J1PBhP8FxwOqIOCG/qMzaBkkDgP5k8ygAqPsl2tpI6kG2ntQKss5xyJYU2RA4IiKW5BUbOBmUJGleRAxqrMzMykvSOcA+ZMngduBg4MGIOCrPuMpF0n5ki9UBPBUR9+QZT4GbiUpbLWn7iHgOQFIfioa5mVmzOQoYBPwjIsZJ2gr4dc4xlU1E/JWsGbqiOBmU9m3gXkkLycYDb0s2PtjMmteKiPhA0qo09PJlsvH41oycDEqIiHsKG9qkomcjwquWmjW/WZI2B64ga1t/C3g414jaAPcZlJCWlL0jIpZLOpts0tl51bKBiFklSnuG9IyIF9Pj3kDniHgs18DagKrZZLoZfD8lgk8B+wNXAdU4K9KsYqQJWbcXPV7kRNAynAxKK3QWHwJcERG38eGuRGbWfOZIGpZ3EG2Nm4lKkHQrsAQ4gKyJaAUww0NLzZqXpGeAjwMvkO3dILJKw8BcA6tyTgYlSNoIOAh4PCLmS9oa2Dki7so5NLOqlfoM9iRLBGuIiI+UWfk4GZhZRZH0eETsnHccbY37DMys0rjPIAeuGZhZRUl9Bn2BRbjPoMU4GZhZRZG0bX3l7jNoXm4mMrOKkr70ewH7pfvv4O+qZueagZlVlLRq6VCgX0TsIOljwE0RsUfOoVU1Z1szqzRHAIeR9RcQEf8i2zvYmpGTgZlVmvfTshQBIGnjnONpE5wMzKzS3CjpV8Dmkk4E7gauzDmmquc+AzOrOJIOAEaSDSu9MyKm5xxS1XMyMLOKIuknEXFmY2VWXm4mMrNKc0A9ZQe3eBRtjHc6M7OKIOlrwElAH0nFexhsCvw9n6jaDjcTmVlFkLQZ0AX4MTCh6KnlEfFaPlG1HU4GZlYRJHWOiDclda3veSeE5uVkYGYVQdKtEfEZSc+TzTFQ0dMREX1yCq1NcDIwMzN3IJtZZZA0pKHnI2JOS8XSFrlmYGYVQdK96W4nsoXq5pE1FQ0EZkXE7nnF1hZ4noGZVYSI2Dci9gWWAkMiYmhE7ArsAizJN7rq52RgZpWmX0Q8XngQEU8AO+YYT5vgPgMzqzSPSboS+HV6/CXgsQaOtzJwn4GZVRRJnYCvAXulovuBSRHxbn5RVT8nAzOrOJI2BLaJiGfzjqWtcJ+BmVUUSYcBc4E70uPBkm7JNag2wMnAzCrNOcBw4A2AiJgLbJdjPG2Ck4GZVZqVEbGsTpnbs5uZRxOZWaV5UtIXgfaS+gKnAQ/lHFPVc83AzCrNqcBOwHvAb4E3gdPzDKgt8GgiMzNzM5GZVYbGRgxFxGEtFUtb5GRgZpVid+BFsqahR1lzPwNrZm4mMrOKIKk9cABwDNlKpbcBv42IJ3MNrI1wB7KZVYSIWB0Rd0TEGGAEsAC4T9IpOYfWJriZyMwqhqQNgEPIage9gYnAtDxjaivcTGRmFUHSVGAAcDtwQ1q62lqIk4GZVQRJHwBvp4fFX0wCIiI6t3xUbYeTgZmZuQPZzMycDMzMDCcDMzPDycCsUZL+S9INkp6TNFvS7ZJ2kOTRLlY1PM/ArAGSRDbO/dqI+EIqGwRslWtgZmXmmoFZw/Yl22xlcqEgIuaRraEDgKTekh6QNCfdPpnKt5Z0v6S5kp6QtKek9pKuSY8fl/SNlr8ks49yzcCsYQOA2Y0c8zJwQES8mzZj+S0wFPgicGdEnJ/W3dkIGAz0iIgBAJI2b67AzdaFk4FZ09UAl0oaDKwGdkjlM4EpkmqAP0bEXEkLgT6SLiFbiO2uPAI2q8vNRGYNexLYtZFjvgG8BAwiqxF0BIiI+4G9gCXANZJGR8Tr6bj7gK8CVzZP2GbrxsnArGF/BTaQNL5QIGkg0KvomM2ApRHxAXAc0D4dty3wUkRcQfalP0TSFkC7iPgDcDYwpGUuw6xhbiYya0BEhKQjgF9IOhN4F1jEmnvy/hL4g6TRwB18uL7OPsC3Ja0E3gJGAz2AqyUVfoh9t7mvwWxteG0iMzNzM5GZmTkZmJkZTgZmZoaTgZmZ4WRgZmY4GZiZGU4GZmYG/H8x5DIWJAxU1AAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/html": [ "Below are statistics for top properties of Datatype:Math" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below are statistics for top properties of Datatype:Wikibase-form" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below are statistics for top properties of Datatype:External-id" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below is ditribution of Freebase_id in different classes" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/html": [ "Below are statistics for top properties of Datatype:Commonsmedia" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below is ditribution of Image in different classes" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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u/0Ze08zMWtlqk35E3AO81qDtjoj4KG0+BPRe1WukhdS7RMRDERHARODAZkVsZmbNVos+/f8A/lK1vYWkxyT9VdKo1NYLmFd1zLzUZmZmbWi1C6OviqTTgI+Aq1LTAqBPRCySNBS4WdK2zXjdscBYgD59+rQkRDMzq9LsK31Jo4H9gCNSlw0R8X5ELEqPpwHPA1sB81mxC6h3amtURFwSEcMiYljPnj2bG6KZmTXQrKQvaW/gZGD/iHinqr2npI7pcT+yG7ZzImIB8KakkWnUzpHAH1scvZmZrZHVdu9IuhrYDeghaR5wOtlonXWASWnk5UNppM4uwI8lfQgsBb4REZWbwMeSjQRal+weQPV9ADMzawOrTfoRcXgjzZet5NgbgBtWsm8qMGCNojMzs5ryjFwzsxJx0jczKxEnfTOzEnHSNzMrESd9M7MScdI3MysRJ30zsxJx0jczKxEnfTOzEnHSNzMrESd9M7MScdI3MysRJ30zsxJx0jczKxEnfTOzEnHSNzMrESd9M7MSaVLSlzRB0suSZla1dZM0SdJz6XvX1C5J50uaLekJSUOqnnNUOv45SUfV/nTMzGxVmnqlfwWwd4O2U4DJEdEfmJy2AfYhWxC9PzAWGAfZHwmy9XV3AEYAp1f+UJiZWdtoUtKPiHuA1xo0HwBcmR5fCRxY1T4xMg8BG0vaFNgLmBQRr0XE68AkPv6HxMzMWlFL+vQ3iYgF6fE/gE3S417AS1XHzUttK2v/GEljJU2VNPWVV15pQYhmZlatJjdyIyKAqMVrpde7JCKGRcSwnj171uplzcxKryVJf2HqtiF9fzm1zwc2qzqud2pbWbuZmbWRliT9W4DKCJyjgD9WtR+ZRvGMBN5I3UC3A3tK6ppu4O6Z2szMrI2s1ZSDJF0N7Ab0kDSPbBTOz4A/SPoa8CJwaDr8VmBfYDbwDnA0QES8JuknwKPpuB9HRMObw2Zm1oqalPQj4vCV7NqjkWMDOG4lrzMBmNDk6MzMrKY8I9fMrESc9M3MSsRJ38ysRJz0zcxKxEnfzKxEnPTNzErESd/MrESc9M3MSsRJ38ysRJz0zcxKxEnfzKxEnPTNzErESd/MrESc9M3MSsRJ38ysRJz0zcxKpNlJX9LWkmZUfb0p6URJZ0iaX9W+b9VzTpU0W9KzkvaqzSmYmVlTNWnlrMZExLPAYABJHckWOb+JbHnEX0XEL6uPl7QNcBiwLfAvwJ2StoqIJc2NwczM1kytunf2AJ6PiBdXccwBwDUR8X5EvEC2hu6IGr2/mZk1Qa2S/mHA1VXb35L0hKQJkrqmtl7AS1XHzEttZmbWRlqc9CWtDewPXJeaxgFbknX9LADOacZrjpU0VdLUV155paUhmplZUosr/X2A6RGxECAiFkbEkohYClzK8i6c+cBmVc/rndo+JiIuiYhhETGsZ8+eNQjRzMygNkn/cKq6diRtWrXvIGBmenwLcJikdSRtAfQHHqnB+5uZWRM1e/QOgKT1gc8CX69q/m9Jg4EA5lb2RcRTkv4APA18BBznkTtmZm2rRUk/It4Gujdo++oqjj8LOKsl72lmZs3nGblmZiXipG9mViJO+mZmJeKkb2ZWIk76ZmYl4qRvZlYiTvpmZiXipG9mViJO+mZmJeKkb2ZWIk76ZmYl4qRvZlYiTvpmZiXipG9mViJO+mZmJeKkb2ZWIrVYGH2upCclzZA0NbV1kzRJ0nPpe9fULknnS5ot6QlJQ1r6/mZm1nS1utLfPSIGR8SwtH0KMDki+gOT0zZki6j3T19jgXE1en8zM2uC1ureOQC4Mj2+Ejiwqn1iZB4CNm6wkLqZmbWiWiT9AO6QNE3S2NS2SUQsSI//AWySHvcCXqp67rzUZmZmbaBFC6MnO0fEfEmfACZJmlW9MyJCUqzJC6Y/HmMB+vTpU4MQzcwManClHxHz0/eXgZuAEcDCSrdN+v5yOnw+sFnV03untoaveUlEDIuIYT179mxpiGZmlrQo6UtaX9KGlcfAnsBM4BbgqHTYUcAf0+NbgCPTKJ6RwBtV3UBmZtbKWtq9swlwk6TKa/0+Im6T9CjwB0lfA14EDk3H3wrsC8wG3gGObuH7m5nZGmhR0o+IOcCgRtoXAXs00h7AcS15TzMzaz7PyDUzKxEnfTOzEnHSNzMrESd9M7MScdI3MysRJ30zsxJx0jczKxEnfTOzEnHSNzMrESd9M7MScdI3MysRJ30zsxJx0jczKxEnfTOzEnHSNzMrESd9M7MSaXbSl7SZpLslPS3pKUknpPYzJM2XNCN97Vv1nFMlzZb0rKS9anECZmbWdC1ZOesj4LsRMT2tkztN0qS071cR8cvqgyVtAxwGbAv8C3CnpK0iYkkLYjAzszXQ7Cv9iFgQEdPT48XAM0CvVTzlAOCaiHg/Il4gWyd3RHPf38zM1lxN+vQl9QW2Bx5OTd+S9ISkCZK6prZewEtVT5vHqv9ImJlZjbU46UvaALgBODEi3gTGAVsCg4EFwDnNeM2xkqZKmvrKK6+0NEQzM0talPQldSJL+FdFxI0AEbEwIpZExFLgUpZ34cwHNqt6eu/U9jERcUlEDIuIYT179mxJiGZmVqUlo3cEXAY8ExHnVrVvWnXYQcDM9PgW4DBJ60jaAugPPNLc9zczszXXktE7OwFfBZ6UNCO1/QA4XNJgIIC5wNcBIuIpSX8AniYb+XOcR+6YmbWtZif9iLgPUCO7bl3Fc84Czmrue5qZWct4Rq6ZWYk46ZuZlYiTvplZiTjpm5mViJO+mVmJOOmbmZWIk76ZWYm0ZHJWu9b3lD+32XvN/dnn2uy9zMxawlf6ZmYl4qRvZlYiTvpmZiVS2D79ImvL+xXgexZmReKkb+2O/6iZtR4nfbM25j9qlicnfTOrKf9Ra998I9fMrESc9M3MSqTNk76kvSU9K2m2pFPa+v3NzMqsTZO+pI7ARcA+wDZk6+lu05YxmJmVWVtf6Y8AZkfEnIj4ALgGOKCNYzAzK622Tvq9gJeqtuelNjMzawOKiLZ7M+kQYO+IGJO2vwrsEBHfanDcWGBs2twaeLaNQuwBvNpG75UHn1998/nVr7Y+t80jomdjO9p6nP58YLOq7d6pbQURcQlwSVsFVSFpakQMa+v3bSs+v/rm86tf7enc2rp751Ggv6QtJK0NHAbc0sYxmJmVVpte6UfER5K+BdwOdAQmRMRTbRmDmVmZtXkZhoi4Fbi1rd+3idq8S6mN+fzqm8+vfrWbc2vTG7lmZpYvl2EwMysRJ32zdkzSupK2zjsOK45SJn1J3Vb1lXd8tSRpPUn/KenStN1f0n55x1UrkrrnHUNrkfR5YAZwW9oeLKnuR7uV4fdP0k6S1k+PvyLpXEmb5x0XlLRPX9ILQAAC+gCvp8cbA3+PiC3yi662JF0LTAOOjIgBktYDHoiIwflGVhuSniNLjJcDf4kC/UBLmgZ8BpgSEdunticjYrt8I2uZBr9/DUVE9GvjkGpO0hPAIGAgcAXwW+DQiNg1z7igpFf6EbFF+sG6E/h8RPSIiO7AfsAd+UZXc1tGxH8DHwJExDs0/stWr7YiGxnxVeA5SWdL2irnmGrlw4h4o0Fb3f9Rq/z+pe8Nv+o+4ScfpQuQA4ALI+IiYMOcYwK8ctbIiDimshERf5H033kG1Ao+kLQuKVlI2hJ4P9+Qaif9Yk0CJknaHfgdcKykx4FTIuLBXANsmackfRnoKKk/cDzwQM4xtZikf4uIWZKGNLY/Iqa3dUytYLGkU8kuRkZJ6gB0yjkmoKTdOxWSbgfuJUsUAEcAu0TEXvlFVVuSPgv8kKyU9R3ATsDoiJiSZ1y1kvr0v0L2y7UQuIxslvdg4Lp67qpLXXGnAXumptuBMyPivfyiajlJl0TEWEl3N7I7IuIzbR5UjUn6JPBl4NGIuFdSH2C3iJiYc2ilT/rdgNOBXVLTPcCPIuK1/KKqvZQYR5J16zwUEYUpaiXpb8D/AJdHxLwG+74fET/PJzIru3Tjtn9E3Jn+gHeMiMW5x1XmpF8GkkT2CaZfRPw4XXF8MiIeyTm0FkuL8vx3RHw371hag6RJwBcj4p9puytwTcE+iQ4g+xTaudLWHq6GW0rSMWSVgrtFxJape258ROyRc2jl7tNPN/z+H9CXqn+Lev94KWln4MGIWAJcDCwlGwXyY2AxcAMwPL8IayMilkjaMe84WlGPSsIHiIjXJX0ix3hqStLpwG5kSf9WshX17gPqPukDx5EtGvUwQEQ8117+70qd9IHrgPFkw6mW5BxLLS0FxpFdaewQEUMkPQbLEsfauUZXWzPS2PXrgLcrjRFxY34h1cxSSX0i4u+wrLugSB/NDyEb1vhYRBwtaROW31+rd+9HxAfZB22QtBbt5P+u7En/o4gYl3cQtRYRD0h6J21+mLpBKqN3epL9USiKzsAisk8yFQEUIemfBtwn6a9k92NGsXxxoSJ4NyKWSvpIUhfgZVZcb6Oe/VXSD4B102CKY4H/zTkmoOR9+pLOIPtBu4mqYYxFupEr6QjgS8AQ4Eqyq6sfRsR1uQZmTSKpB9lNeCjeTfiLgR+QravxXeAtYEZEHJ1rYDWQhmh+jWzklchGXv22PUweLHvSf6GR5kLMCKwm6d+APch++CZHxDM5h1Qz6b7MOGCTNON4ILB/RJyZc2g1IakXsDkr3nO6J7+IWoekvkCXiHgi71hqLY0S7N1ezq3USb/IVlfDpCifZlLXx/eA31SVKpgZEQPyjazlJP2c7FPaUyzvkouI2D+/qGpH0uSGo1kaa6tHkqYA+5P9sZ5G1qPwQER8J8+4wH36hR0yRvaDttL6JkBRPs2sFxGPVG6YJR/lFUyNHQhsHRGFmUENIKkzsB7QIw1DrfzndQF65RZYbW0UEW9KGgNMjIjTUz2e3JU66Rd5yFg9z0RdQ6+m0hKVG9WHAAvyDalm5pBN3S9U0ge+DpwI/AvZxUkl6b8JXJhTTLW2lqRNgUPJbsi3G6VO+hR7yNgykvZn+azjKRHxpzzjqbHjyAqu/Zuk+cALZJPRiuAdsiGpk1lxoMHx+YXUchFxHnCepOMj4vzqfZLWySmsWvsx2c3b+yLiUUn9gOdyjglw0i/skDFJe0bEHZJ+RjYR66q06wRJO0bED3IMr2YiYg7w76l2eYf2MM29hm5JX0U1uHoj/R/eQjbooK6l0XHXVW3PAQ7OL6Llyp70p0raGLiU7GPmW0A9V2UEQNKhZB+d7wD2BQZHxNK070rgMbKhcnVP0vPAQ2SF8+4lu+lZCBFxZd4xtLJ5ki6OiGNT3/6fyX4X616q1nsm8C7ZIjgDge9ERO49CaUdvZNq0vSOiJfSdl8KMmRM0giySprHpptHu1VG66RRPVMiYmCuQdZI6g7YgWzi0k7A1sATEXFQroHVQKrX8lM+PtCgKDfhK8mxCzAU+FlE3JBzSDUhaUZEDJZ0ENk6HScB90TEoJxDK++VfkSEpFuB7dL23Hwjqp00mmV+2jwbeCyVsRVZ3/4puQVXe0vIFohZQjas8eX0VQSXk1WB/RWwO3A0BVj4SNIXqjYfBv4TeAQISV8oSAmNSm79HFmJ7zcajDDLTWmTfjJd0vCIeDTvQGotIuanWYFLyWZ0VgqsfT8i/pFfZDX3JvAkcC5waUQsyjmeWlo3IiZLUkS8CJyhbAnF/8o7sBb6fIPtx8hGKX2e4pTQ+JOkWWTdO99M5U/axToIpe3eAUj/Kf8KvEhWrEtkHwIK0fUBIGlqRAzLO47WIukAYGeyioYfkK0sdU9ETM41sBqQ9ADZuV0P3AXMJ+sC2TrXwKxJUlfqG6ka7Hpk3ce5X3CVPek3ujp9uqoqhDR651XgWlasQlmIGbkVqdTEPmTjvz8REevmG1HLSRoOPANsDPyErO/7FxHxUJ5x1UoaVHBCg/UCzomI/8g1sBpprxM/S530AZSt07kz2cfK+6MY63MuU/T6QpJuIJtr8TzZymf3AQ9H/S8p2BH4eUT8v7xjaS2SHquUzlhVWz1a2cTPiDgkz7ig5H36kv4L+CLL+xAvl3RdUYp1JZ9qmADTNPii+CnZ5LoirYdQWSBm57zjaGUdJHWNiNdhWXdIUXJSu534WZR/4OY6AhhUSYqpK2QG2fjaoniArKzy6trq1dPAqWmxkbFpmOPWBZl1/FiBF4gBOAd4UNJ1ZPfTDgHOyjekmmm3Ez/LnvT/j6y/rXIlvA7ZzbK6J+mTZMWr1pW0PSsWtVovt8Bq73KyiXWVZRPnkyXJIiT9Ii8QQ0RMlDSV7PwC+EJEPJ1zWLXSbid+ljLpS7qA7IfsDeApZQtQB/BZsvHCRbAXMBroTTacsWIxBZmNm2wZEV+SdDhARLyj9jIgupkknR0RP0jdAp+NiEl5x9SKOrH8gqRTnoHUUkQcmx6Ol3Qb7WjiZylv5Eo6alX7izT9XdLBRZnl2Jg0rHEPspvwQ1LFzasjYkTOoTWbpOkRMaTh46KRdAJwDHADWeI/CLgkIi7INbAWSANDVqo9DBQpZdIvk1Sm4GCgLyuuvvTjvGKqJWXrj/6QbJTEHWSlGEZHxJQ842qJEiX9J4BPR8TbaXt94MF6nieTZr6vTETEZ1axv02UsnunZP5I1o01jYLVZU8zjrsCXyCbdSyycd/1vo7sJySdRHY+lcfLRMS5jT+t7oisfEbFEhpf9KduRMTuecewOk76xdc7IvbOO4jWkEZHnBwRfyCr0FgUlwIbNvK4aC4HHpZ0U9o+ELgsv3BaTtJXyHpQ/qdB+1eBJRHx+3wiq4qlrN07ZZj8AiDpEuCCiHgy71haQ1lmHBdV1eRIgHsj4rE842kpSQ8De0TEWw3a1ycrDzI0n8iqYilr0geQ9FBEjMw7jtYk6Wmy+kIvkHXvFKq+UJpx/LEf4qLMOC6iVF6iR0T8pUH7vsDCiJiWT2Qtt6p7MJKeaA+/d2Xv3in65BfIpn8X2TbAsSwvpXEvMD7XiGx1fk5WJrqhp8i6fHK/2dkC60pav3JzukLShsDaOcW0grqvzd1C1ZNfPp++9ss1ohpLxeM2Zvn5bVykgnLAlcCngPOBC8j+CNT1kNs0lBFJO+UdSyvZsLGfwdTWI4d4auky4PrqYo5pgaZraCf3K0rdvVMGVWOhK59e6n4sdDVJT0fENqtrqydVqy4VcrimpNkR8a9ruq9eSPoGcCqwQWp6i6wk9rj8olqu1Elf0lbAOGCTiBggaSCwf5EKrhVxLHQ1Sb8DLqyUG5a0A3BcRByZb2TNJ+lqYBjZOsfPV++iAPdjJI0n+4T9w0gJKM2i/hHwyYgYm2d8tZK6dIiIxXnHUq3sSf+vwPeA31TKuUqaGRED8o2sdiQ9CQyvKirXGXg0IrbLN7KWSecVZFP3twb+nrY3B2bV85U+LKuddDuwf8N99d49ly48fku28M2M1DwImAqMaTjyxWqr7Ddy14tsPdnqto/yCqaVFG4sdFKoey8NpRWWBklaG9gqNT8bER/mGFZNpE+dh0vqB2ybmp+KiDk5hlUaZU/6r6ZaLZWPmIcAC/INqbYi4lxJU1g+Fvroeh8LDfV/tdsUknYFJgJzybp2NpN0VETck2tgNZKSfOESfZopPjIiHsg7lsaUvXunH3AJWVne18nGsn8lIubmGVctSRpJdhW1OG13IVtY5eF8I7PVUbYI+pcj4tm0vRVZMbncJ/jYqrXnFcBKnfQrUh9jh/Z2w6UWJD0GDKm6YdYBmFrEUSFF09hknvYywcdWTdIvyern3xjtLMmWOumnJczOBv4lIvaRtA3ZSJci9HkDy4f/NWhz4qgDkiYAS1m+zN4RQMeo84XD07KIK1WEEhqSFgPrkxWRe5flI6+65BoYTvp/IbvReVpEDJK0FtmalnU9sqWapBuBKWRDUyGbvbp7RByYV0zWNKks9nFU1aYBLo6Iuq6WWlU6o7GKmuESGq2r7En/0YgYXt3/1tiVcT2T9Amy2aqVJekmAydGxMu5BmZWYGnewRHAFhHxE0mbAZtGRO4r85V99M7bkrqzfPTOSLLa84WRkvthecdh1hhJXYH+ZCVRACjI6KSLybrmPgP8hGxW7kXA8DyDAif9k4BbgC0l3Q/0BA7JNySzcpA0BjiBbB3nGWQL4TxIfRdcq9ghsuU7HwOIiNfTnIvclTrpR8T0NBZ6a7L+xUJMfrFikbReRLyTdxyt4ASyK9+HImJ3Sf9GNrCiCD5Ma3ZUehF6kl35567UVTZTSYLjyT5+/Qg4LrWZ5U7Sjmk9hFlpe5Cki3MOq5beqyoPsk5EzCK7ACuC84GbyJa7PAu4j3byB63sN3L/ACxm+ZC4L5OVHv5iflHVVhmGpRZVWoXpEOCWItaGSqVBjgZOJOvSeR3oFBH75hlXraRPLnuQ9SJMjohncg4JcNIvXFnehsowLLWoJD0cETs0GF32eEQMyju2WkvdrBsBt0XEB3nHUwupe2cTqrrRI+Lv+UWUKXWfPjBd0sgGZXmn5hxTrfWIiD9IOhUgIj6StCTvoKxJXpK0IxCSOpH1gbeLq8WWkNQlIt5sMEmrsobzBkARJmd9GzgdWEg2QUtk/fu5T4osZdJvUJb3AUkrlOXNM7ZWUPhhqQX2DeA8oBcwH7iDbLJWvfs9WZXUaSyfpFX9vQiTs04Ato6IRXkH0lApu3eqlzJrTJEqOEoaQraM4ABgJmlYakQ8kWtgZgUm6W7gsxHR7kq1lzLpl03qx/ew1DqThvkdA/RlxX7huq69UyHpIOCuiHgjbW8M7BYRN+cZV0tIOik93Jbsd+7PwLKyGRFxbh5xVXPSL4HUL9yXFRPHxNwCsiaR9ABZvZ1pZP3CAETEDbkFVUMrKQbYbksSN4Wk01exOyLix20WzEqUsk+/TCT9D7Al2YzHSuIIssU5rH1bLyK+n3cQraixeUJ1nZMi4kcAkr4YEddV75PULoaC+0q/4CQ9A2zT3mp62+pJOhN4ICJuzTuW1pBKR/+TrCYNZDepu0XE6LxiqhVJ0xuuWdFYWx6c9AtO0nXA8RFRqGUgy6CqJvv7wIe0o5rstZAWL/pP4N9T0yTgzLSGbl2StA+wL3AocG3Vri5kF18jcgmsSl1/lLIm6QE8LekRVryhtH9+IVlTRMSGecfQmlJyPyXvOGrs/8juweyfvlcsBr6TS0QN+Eq/4NJMx4+JiL+2dSy2ZiTt0lh7vZcelvTriDhR0v+S5o9UK8IFiaQNyAZPAMyu1BhqD5z0zdqplBQrOgMjgGkRUdelhyUNqapw+zH1fEGShkefTVZT6O9kXXKbsbwUSu7DpZ30C0rSfRGxc+oXrv5PLlS/cJmk1Zd+HREH5x1LS0iaHBF7SPp50UYnSfoVsCHwnYhYnNq6AL8E3o2IE/KMD5z0zepGWoLvqXovCJjKRY8BLiOrbLvCWrkRMT2PuGpB0nPAVg1Hy6Xia7Mion8+kS3nG7kF16CoVcXi9vAx01ZN0gUs/5TWARgM1G1CrPJfZKN2egPnsGLSD+p75axobHh0RCyR1C6usJ30i286WZ/i62S/XBsD/5C0EDgmIqat4rmWr+qKrx8BV0fE/XkFU0ML0toO/9UeZqjW2NOSjmw4413SV2gnxRzdvVNwki4Fro+I29P2nsDBZDeWzouIHfKMz8pH0rSIGNpeJivVkqRewI3AuywfsjkMWBc4KCLm5xVbhZN+wUl6suGCKZKeiIiBjdU+sfZD0n5kS3luTvapvBA34SU9BDwBHMCKE5gAiIjj2zyoGpP0GbKiawBPR8TkPOOp5u6d4lsg6fvANWn7S8DCdGOpXSzUbCv1a+ALwJMFK6OxH9ks3L1YcQJTYUTEXcBdecfRGF/pF5ykHmQr+Oycmu4nWwT+DaBPRMzOKzZbtVSTfY+IKOQfZ0mDIuLxvOMoGyd9s3ZK0nCy7p2/0s5qsrdUqlFzKlAZfvoU8POiFpdrT9y9U1BlmOpeAmcBb5HNxl0751hqRtIxwNeBk1k+QmkY8DNJvSPiktyCKwFf6ReUpKERMa2IU93LQtLMiBiQdxy1liZn7RwRrzVo7w7cFxGfyieycvCVfkGlhN8RGBsRR+QdjzXLrZL2jIg78g6kxtQw4QNExKJs0rG1psZWrrGCiIglwOaSCtM1UDLfBG6T9K6kNyUtlvRm3kHVwJuSBjVsTG2Lc4inVHylX3xzgPsl3QIsW5yiCDcDi67A9fS/C9wi6XJWnMB0FPCV3KIqCSf94ns+fXUgq/5ndUTSQD6+qP2NuQVUAxFxn6QRZMsjjk7NTwMjI+IfuQVWEr6RWxJpUQci4q28Y7GmSWvIDiQbzlgZqx8R8R/5RWX1zkm/4CQNAP4HqFTbfBU4MiKeyi8qawpJT9d7GWVrf3wjt/guAU6KiM0jYnOy/tRLc47JmuZBSU76VlPu0y++9SPi7spGREyRtH6eAVmTTSRL/P8gm5FbKbg2MN+wWoekzsDnI+K6vGMpMif94psj6T/JunggGx0xJ8d4rOkuA74KPElBi+OluSR7AYcDewL3Ak76rch9+gUnqStZgbWdycox3Av8KCJezzUwWy1JD0bEp/OOozWkmeJfBvYFHgF2AvpFxDu5BlYCTvpm7ZSki8lWOvtfViy4VtdDNiXNA/4OjANujojFkl6IiC1yDq0U3L1TUGniSwBvRMR38o7HmmVdsmS/Z1VbkK3MVM+uBw4kW9thiaQ/0khRQGsdvtIvqKpCax9ExIO5BmPWgLIiO7uR9eXvC2wEfA241XNJWpeTfslI2hD4LLBXRHw973hs5ST1Bi4g6++G7H7MCRExL7+oak9SJ2Bv4DCyn8seOYdUaE76JZAKWe1D1k2wFjAZuC0iHs41MFslSZOA37PiyKsjIuKz+UXVuiStGxHv5h1HkTnpF5SkQ8kS/WCyRahvA+6IiEV5xmVN19jC9UVYzF5Sf+A04DXgXLLJgqPIakR9LSKmruLp1kKekVtc/YELI2L7iDgqIq6uJHxlCjkUsGAWSfqKpI7p6ytAEf5oXw48APwf8DAwAegB/D/gohzjKgVf6ZeEpE+S9ZvuA2wFPBgRx+Ybla2KpM3J+vQ/TTa65QHg+Ij4e66BtVD1pxVJsyPiXxvbZ63DQzYLKs103Iksye8OvA7cDpweEbPyjM2aJiJeBIq4lnH17OKGi8IUcuZxe+Ir/YKSNA24n6wv/27fHKs/kq4kG63zz7TdFTin3ksrS3oHmE1WS2jL9Ji03S8iXBuqFflKv6AiYmjDtlTQajeycdE7RsSwto7L1sjASsIHiIjXJW2fYzy14oXPc+SkX3CStiJL8nuRTemfQjaj86T8orIm6iCpa6VOkqRuFOB3NnVbWU7q/gfIGidpHDCC7KPz7WRD4f4v36hsDZ1DVlq5UnXyi8BZOcZTE5IW03jZhUrp6C5tHFKpuE+/oCTtBDwUEUsa2bc9sHdE/LTtI7M1kRZR+UzavCsins4zHqt/TvolkG4A7knWzTMQmEE2I/faPOMys7bnpF9QkkaQDdf8d+BD4A7g9oh4LNfAzCxXTvoFJelS4C/AnRHRcCy0mZWUk35BSTqYVdQor/eFOMyseTx6p7j2S98/AewI3JW2dyebzu+kb1ZCTvoFFRFHA0i6A9gmIhak7U2BK3IMzcxy5CqbxbdZJeEnC4E+eQVjZvnylX7xTZZ0O3B12v4ScGeO8ZhZjnwjtwQkHQTskjbviYib8ozHzPLjpG9mViLu0zczKxEnfTOzEnHSNzMrEY/eKbhUbfMMYHOy/+9K+dp+ecZlZvnwjdyCkzQL+A4wDVhWZjkiFuUWlJnlxlf6xfdGRPwl7yDMrH3wlX7BSfoZ0JGs1s77lfaImJ5bUGaWGyf9gpN0dyPNERGfaaTdzArOSd/MrEQ8ZLPgJG0k6VxJU9PXOZI2yjsuM8uHk37xTQAWA4emrzeBy3ONyMxy4+6dgpM0IyIGr67NzMrBV/rF966knSsbabLWuznGY2Y58pV+wUkaDFwJbEQ2G/c1YHREPJ5nXGaWDyf9kpDUBSAi3sw7FjPLj5N+QUn6SkT8TtJJje2PiHPbOiYzy5/LMBTX+un7ho3s8196s5LylX7BSdopIu5fXZuZlYOTfsFJmh4RQ1bXZmbl4O6dgpL0aWBHoGeDfv0uZAXYzKyEnPSLa21gA7L/4+p+/TeBQ3KJyMxy5+6dgpO0eUS8mHccZtY++Eq/+N6R9AtgW6BzpdGllc3KyWUYiu8qYBawBfAjYC7waJ4BmVl+3L1TcJKmRcRQSU9ExMDU9mhEDM87NjNre+7eKb4P0/cFkj4H/B/QLcd4zCxHTvrFd2ZaNOW7wAVkQza/k29IZpYXd++YmZWIr/QLStIFrKLGTkQc34bhmFk74aRfXFPzDsDM2h9375iZlYiv9AtK0v+y6u6d/dswHDNrJ5z0i+uXeQdgZu2Pu3dKQNK6QJ+IeDbvWMwsXy7DUHCSPg/MAG5L24Ml3ZJrUGaWGyf94jsDGAH8EyAiZpDV4TGzEnLSL74PI+KNBm3u0zMrKd/ILb6nJH0Z6CipP3A88EDOMZlZTnylX3zfJqul/z5wNdnKWSfmGZCZ5cejd0pCUhcgImJx3rGYWX58pV9wkoZLehJ4AnhS0uOShuYdl5nlw1f6BSfpCeC4iLg3be8MXFxZUMXMysVX+sW3pJLwASLiPuCjHOMxsxz5Sr/gJP0aWJfsJm4AXwLeA34HEBHTcwvOzNqck37BSbp7FbsjIj7TZsGYWe6c9M3MSsR9+gUnqbuk8yVNlzRN0nmSuucdl5nlw0m/+K4BXgEOBg5Jj6/NNSIzy427dwpO0syIGNCg7cmI2C6vmMwsP77SL747JB0mqUP6OhS4Pe+gzCwfvtIvOEmLgfWBpampA/B2ehwR0SWXwMwsF076ZmYl4tLKJSCpK9Af6Fxpi4h78ovIzPLipF9wksYAJwC9yZZNHAk8CHhSllkJ+UZu8Z0ADAdejIjdge1JSyeaWfk46RffexHxHoCkdSJiFrB1zjGZWU7cvVN88yRtDNwMTJL0OvBirhGZWW48eqdEJO0KbATcFhEf5B2PmbU9J30zsxJxn76ZWYk46ZuZlYiTvlki6ZOSrpH0fCpDfaukrSTNzDs2s1rx6B0zQJKAm4ArI+Kw1DYI2CTXwMxqzFf6ZpndgQ8jYnylISIeB16qbEvqK+netCDNdEk7pvZNJd0jaYakmZJGSeoo6Yq0/aSk77T9KZl9nK/0zTIDgGmrOeZl4LMR8Z6k/mSLzQ8DvgzcHhFnSeoIrAcMBnpV1jJIcyXMcuekb9Z0nYALJQ0GlgBbpfZHgQmSOgE3R8QMSXOAfpIuAP4M3JFHwGYNuXvHLPMUMHQ1x3wHWAgMIrvCXxuWVSzdBZgPXCHpyIh4PR03BfgG8NvWCdtszTjpm2XuAtaRNLbSIGkgsFnVMRsBCyJiKfBVoGM6bnNgYURcSpbch0jqAXSIiBuAHwJD2uY0zFbN3TtmZEuISToI+LWk7wPvAXOBE6sOuxi4QdKRwG0sX4FsN+B7kj4E3gKOBHoBl0uqXFid2trnYNYULsNgZlYi7t4xMysRJ30zsxJx0jczKxEnfTOzEnHSNzMrESd9M7MScdI3MysRJ30zsxL5/2anNRWU7iLLAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/html": [ "Below are statistics for top properties of Datatype:Monolingualtext" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below is ditribution of Demonym in different classes" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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o6UfERODtNYoHA79P7/8eOLKo/MZITAK2lrQ9cBAwPiLejoj/AONZ+4PEzMw2oaa06W8XEQvS+/8GtkvvdwLeKDpvXlq2rnIzMyuTknTkRkQAUYrXApA0QtJkSZMXL15cqpc1M8u9piT9hWmzDenPRWn5fGCHovM6p2XrKl9LRIyJiP4R0b9jx45NCNHMzIo1JenfBxRG4JwM3FtUflI6imdPYEnaDPQX4EBJ7dIO3APTMjMzK5MWDTlJ0q3A/sA2kuaRjMK5BLhd0jeB14Fj09MfBA4FZgHvA8MAIuJtST8Dnk3P+2lErNk5bGZmm1CDkn5EHL+OQ4PqOTeAM9bxOmOBsQ2OzszMSsozcs3McsRJ38wsR5z0zcxyxEnfzCxHnPTNzHLESd/MLEec9M3McsRJ38wsR5z0zcxyxEnfzCxHnPTNzHLESd/MLEec9M3McsRJ38wsR5z0zcxyxEnfzCxHnPTNzHLESd/MLEec9M3McqTRSV9Sd0nTim7vSjpb0gWS5heVH1r0nB9KmiXpFUkHleYSzMysoRq0MXp9IuIVoA+ApObAfGAcMAz4dURcWny+pB7AccCuwKeBCZJ2iYiVjY3BzMw2TqmadwYBsyPi9fWcMxj4Y0R8GBGvAbOAgSV6fzMza4BSJf3jgFuLHn9b0nRJYyW1S8s6AW8UnTMvLTMzszJpctKX1Ao4ArgjLRoF7EzS9LMAuKwRrzlC0mRJkxcvXtzUEM3MLFWKmv4hwNSIWAgQEQsjYmVErAKu5ZMmnPnADkXP65yWrSUixkRE/4jo37FjxxKEaGZmUJqkfzxFTTuSti86NgSYkd6/DzhOUmtJOwHdgGdK8P5mZtZAjR69AyBpC+DLwGlFxb+U1AcIYG7hWES8KOl24CXgY+AMj9wxMyuvJiX9iFgGdFij7BvrOf8i4KKmvKeZmTWeZ+SameWIk76ZWY446ZuZ5YiTvplZjjjpm5nliJO+mVmOOOmbmeWIk76ZWY446ZuZ5YiTvplZjjjpm5nliJO+mVmOOOmbmeWIk76ZWY446ZuZ5YiTvplZjjjpm5nliJO+mVmOOOmbmeVIk5O+pLmSXpA0TdLktKy9pPGSXk1/tkvLJWmkpFmSpkvq19T3NzOzhitVTf+AiOgTEf3Tx+cBj0REN+CR9DHAIUC39DYCGFWi9zczswbYVM07g4Hfp/d/DxxZVH5jJCYBW0vafhPFYGZma2hRgtcI4GFJAfw2IsYA20XEgvT4v4Ht0vudgDeKnjsvLVtAiXU574FSv+Q6zb3ksLK9l5lZU5Qi6X8hIuZL2hYYL2lm8cGIiPQDocEkjSBp/mHHHXcsQYhmZgYlaN6JiPnpz0XAOGAgsLDQbJP+XJSePh/YoejpndOyNV9zTET0j4j+HTt2bGqIZmaWalJNX9IWQLOIWJrePxD4KXAfcDJwSfrz3vQp9wHflvRHYA9gSVEzkDVQOZuuwM1XZrWkqc072wHjJBVe65aIeEjSs8Dtkr4JvA4cm57/IHAoMAt4HxjWxPc3M7ON0KSkHxFzgN71lL8FDKqnPIAzmvKeZmbWeJ6Ra2aWI076ZmY54qRvZpYjTvpmZjnipG9mliNO+mZmOeKkb2aWI076ZmY54qRvZpYjTvpmZjnipG9mliNO+mZmOeKkb2aWI076ZmY54qRvZpYjTvpmZjnipG9mliNO+mZmOeKkb2aWI41O+pJ2kPSYpJckvSjpO2n5BZLmS5qW3g4tes4PJc2S9Iqkg0pxAWZm1nBN2Rj9Y+B7ETFV0qeAKZLGp8d+HRGXFp8sqQdwHLAr8GlggqRdImJlE2IwM7ON0OiafkQsiIip6f2lwMtAp/U8ZTDwx4j4MCJeA2YBAxv7/mZmtvFK0qYvqQvQF/h7WvRtSdMljZXULi3rBLxR9LR5rONDQtIISZMlTV68eHEpQjQzM0qQ9CVtCdwFnB0R7wKjgJ2BPsAC4LKNfc2IGBMR/SOif8eOHZsaopmZpZqU9CW1JEn4N0fE3QARsTAiVkbEKuBaPmnCmQ/sUPT0zmmZmZmVSVNG7wi4Dng5Ii4vKt++6LQhwIz0/n3AcZJaS9oJ6AY809j3NzOzjdeU0Tt7A98AXpA0LS37EXC8pD5AAHOB0wAi4kVJtwMvkYz8OcMjd8zMyqvRST8ingRUz6EH1/Oci4CLGvueZmbWNJ6Ra2aWI076ZmY54qRvZpYjTvpmZjnipG9mliNO+mZmOeKkb2aWI076ZmY50pQZuWabRJfzHijr+8295LCyvp9Zlpz0zcrMH2qWJSd9Myspf6hVNrfpm5nliJO+mVmOOOmbmeWIk76ZWY446ZuZ5YiTvplZjjjpm5nliJO+mVmOlD3pSzpY0iuSZkk6r9zvb2aWZ2VN+pKaA1cDhwA9gOMl9ShnDGZmeVbumv5AYFZEzImIj4A/AoPLHIOZWW4pIsr3ZtIxwMERMTx9/A1gj4j49hrnjQBGpA+7A6+UKcRtgDfL9F5Z8PVVN19f9Sr3tX0mIjrWd6AiF1yLiDHAmHK/r6TJEdG/3O9bLr6+6ubrq16VdG3lbt6ZD+xQ9LhzWmZmZmVQ7qT/LNBN0k6SWgHHAfeVOQYzs9wqa/NORHws6dvAX4DmwNiIeLGcMWxA2ZuUyszXV918fdWrYq6trB25ZmaWLc/INTPLESd9M7MccdI3qwKSmklqm3UcVv1ynfQlbS7pvyVdmz7uJunwrOMyA5B0i6S2krYAZgAvSfp+1nGVkqQvSBqW3u8oaaesYyoFSXunvzcknSjpckmfyTouyHnSB64HPgT2Sh/PBy7MLpzSk3S3pMMk1eTvWtIQSVsVPd5a0pEZhlRKPSLiXeBI4M/ATsA3Mo2ohCT9BPgB8MO0qCXwh+wiKqlRwPuSegPfA2YDN2YbUqImE8FG2DkifgmsAIiI9wFlG1LJXQN8HXhV0iWSumcdUIn9JCKWFB5ExDvAT7ILp6RaSmpJkvTvi4gVGcdTakOAI4BlABHxL+BTmUZUOh9HMjRyMHBVRFxNhVxb3pP+R5I2AwJA0s4kNf+aERETIuIEoB8wF5gg6SlJw9KEUu3q+z9ckcuLNMJvSX5nWwAT0+aBJet9RnX5KE2Mhb+/LTKOp5SWSvohyTezB9Jv2hXx95b3pH8B8BCwg6SbgUdIvm7WFEkdgKHAcOA54AqSD4HxGYZVKpPT9tKd09vlwJSsgyqRP0VEp4g4NE2O/wROyTqoErpd0m+BrSWdCkwAfpdxTKXyNZIK5CkR8W+SJWd+lW1IidxPzkoT4p4kzTqTIqKmVvmTNI5kpdKbgBsiYkHRsYpZBKqx0trhfwNfSovGAxdGxLLsoioNSVMjot8aZVMiYvesYio1SV8GDiT5+/tLRNRCRQSA9JtZt4iYIGlzoHlELM06rlr5Gtwokh6JiEHAA/WUVb30K+WUiBhS3/FqT/gAaXKvqR3YJH0W2BXYStJRRYfaAm2yiar0JP0iIn5A0TfOorKqln5zGQG0B3YGOgGjgcxzSy6TvqQ2wObANpLa8UnnbVuSX05NiIhVko6mxkYkAUj6TUScLelPpG3CxSLiiAzCKpXuwOHA1sBXisqXAqdmEdAm8mXWbk49pJ6yanQGyaZRfweIiFclbZttSIlcJn3gNOBs4NMk7b+FpP8ucFVGMW0qj6SJ/+6orba8m9Kfl2YaxSYQEfcC90raKyKezjqeUpP0f4FvAV0lTS869Cngb9lEVXIfRsRHUpJaJLWgnspJFnLdpi/pzIi4Mus4NiVJS0lGf3wMfEDyARcR4dmdFU5SR5KafReKKmgRUdWduem8inbAz1m9aW5pRLydTVSlJemXwDvAScCZJB9yL0XE+VnGBTlP+gCSepJs0l7XVhoRFTGJwtZN0gvUX3MqfKj1KnNIJSfpKeAJkm+jKwvlEXFXZkFtAmmzR/Hf3z8zDKck0v60b1LUSQ38rhK+bec66aczAvcnSfoPkrQnPhkRx2QZVynV1zFdC53VG5rSHhGvlyuWTUXStIjok3Ucm4qkrwCXkzSzLgI+A7wcEbtmGliJSWoPdI6I6Rs8uQzyPk7/GJLe9H9HxDCgN7DV+p9SHSS1Sf+zbSOpnaT26a0LNdBZHRGvF24k46F7A71I2lKrPuGn7pd0aNZBbEIXkgyX/kdE7ETytzgp25BKQ9Lj6bpJ7Um+qV0r6ddZxwVO+ssjYhXwcbqC4SJW38O3mp1G8p/ts+nPwu1eaqizWtJw4BngKJIP8UmSqrrNu8h3SBL/cknvSloq6d2sgyqhFRHxFtBMUrOIeAyo+mHEqa3SdZOOAm6MiD2ogOGakN/ROwWTJW0NXEuSEN8DamK0RERcAVyRg87q7wN90+RRmGz3FDA206hKICIqYq2WTegdSVsCE4GbJS0iXYenBrSQtD1wLJB5522xXLfpF0ubPdpWSrtbKUn6PGuPAKmJzuq0s3P/iPgofdwKeDwiPp9tZKWRziPpxuodnROzi6h00tnUy0laHE4gaVr9Qy2M4JH0VZKZ4k9GxLckdQV+FRFHZxxavpN+rXZyFpN0E8mMwGl8MgIkIuKszIIqAUnnpHf7ALuRNFsVVjWcHhFDs4msdNKmq++QrNsyjaT9++mI+GKWcZVKfbNva2VGbiXLZfNOXmbkpvqTrMtea5/uhaaP2emt4N4MYtlUvgMMIFkT6oB0eYaLM46plGp2Rm46Tv9Ckm8yD5EMMvhuRGS+X0Aukz75mpE7A/gvYMGGTqwmEfG/WcdQBh9ExAeSkNQ6ImbWwn4IRTNyd67hGbkHRsS5koaQLI99FEnfhZN+FnLUyQmwDck2e89QtFdAla9NU0fSY9S/9k4tNIHMSwca3AOMl/QfoBaGo95CshNYzc7I5ZPcehhwR0QsKSzJkLVcJv0i/5b0qYhYKunHJGvMXxgRU7MOrIQuyDqATez/Fd1vAxxNsuRE1StaHfWC9MNtK5JkWdXSnc6WpH9z/46IDyXtD/SSdGO6+1m1u1/STJLmnf+bLqnxQcYxAe7InR4RvSR9gaT97VfA/6Rjaq1KSXomIgZmHUdTSbopIr6xobJqJWkaSZ9TF5IZ8fcCu0ZETUxISydmLYmIlel6+m3TDVUylfeafmE0y2HAmIh4QFJNLUOcLrhW+GRvRbJl27JaWXAt/cMqaAbsTo3MqiZZU7+OpOYk11crVkXEx+meAVdGxJWSnss6qBL6NPCldOBIQeZDpfOe9Ocr2a7ty8AvJLWmxmYpF0/wUdKoOJhk6F+tmELyoSaSZp3XSBa6qlpK9lb9EbBZ0QxcAR8BYzILrPRWSDqeZCXKwr4BFbGPbFOta10vKiDp5715Z3PgYOCFdJOD7YHdIuLhjEPbpCQ9FxF9s47D1k/SzyPih1nHsalI6gGcTjL34FZJOwHHRsQvMg6tydJVYHsDz0VEb0nbkUw8+3LGoeU76eeBVt9urxlJG+p+EbFXRiGVVPrV+VvAF0hq/E8AoyOiIjrNGkPSZ9Phmf3qO15jAw1qUqFfSdIU4ACSXc9ejojPZhxa7pt38qB4u72PScYMD84mlE3iRpI/qMLQ26+T7Kr11cwiarpzSPZXvayeYwHUwnDUWlex63q5pm9VTdJLEdFjQ2VmWam0db1c069xkjqT1IL3ToueAL4TEfOyi6qkpkraMyImAUjaA5iccUwlUYtNV8Uk7RYRL2QdRymtq0mucKwSmuZyXdNP27t/AWxLMjqi5vaPlTSeZAZkYSPxE4ETKqFDqRQkvQx0Bwpb7O0IvELSlFXV2yZKup2k6aowdf/rwNYRUc1NV3UkPQG0Bm4Abk4nbVW1dBLdukQlzBTPe9KfBXwlIl7OOpZNpb4t9+orq1aq4W0T89B0JakbcApJH8wzwPURMT7bqGpbTY1Jb4SFtZzwU29JOlFS8/R2IvBW1kGVSprUdwC+mN5fBjQr2kqxmk2VVDenopaargoi4lXgxyQra+4HjJQ0c41RZ1Uj/Vtba8a0pG9I+noWMa0p7zX9K0hWoLyH1RcjuzurmEotrQlfCexF0i78FHBmRLyRaWAlkk6C6Q90j4hdJH2aZIGrvTfw1IqVjvEOkolKhaarINk4fGat1PQl9QKGkcyIHw9cFxFT09/h0xGx3m9xlUjS34FBEfHeGuVbABMjIvMZ1XnvyG0LvA8cWFQWQM0kfeCnwMkR8R+oW7bgUpKv1LVgCNAXmAoQEf+SVO3bDB6edQBlciXwO+BHEbG8UJj+Dn+cXVhN0nLNhA8QEcskVcRs41wn/YgYlnUMZdCrkPABIuJtSbU0G/ejiAhJAXU1qqq2ZrOUpG0p2i6xFqTrCM2PiJvqO76u8iqwmaQtImK1vX7TikirjGJaTa6TvqSR9RQvASZHRK3swNRMUrs1avo18XtP1xK6P10/aWtJp5J8g7k228hKQ9IRJBO0Pg0sImneeZk1FmKrRunKkztIahXp/sY14jrgTkmnFz6803H6V6fHMlcTf/xN0Ab4LHBH+vhokgW7eks6ICLOziqwEroMeFpS4Rq/ClyUYTwlk9bwv0oyg/Vdkvbv/6mh0R8/I1kcb0JE9JV0AMmQ21rxGvA3SfeRdMADEBGXZxdS00TEpZLeAyZK2jItfg+4JCJGZRhanbx35E4C9o6IlenjFiQTYL5AsghbrXSY9eCTqfuPRsRLWcZTSpJ+D1wVEc9mHUupSZocEf0lPQ/0jYhVkp6PiN5Zx1YKaSf8WqJGtsIs9C1FxNKsYymW95p+O2BLkiYdgC2A9ulXzw/X/bTqkib5mkn0a9gDOEFSYbgmANU8KavIO2ltcSJws6RFFF1jtauV5L4ulZbsC/Ke9H8JTJP0OMls3H2Bi9POwAlZBmYNdlDWAWxCg0m22PsucALJ5jA/zTSiElKyheC5JH0UdR3VlTBrtZblunkHIF1Dv7C13rMR8a8s4zHLC0kPA7eR7HN8OnAysDgifpBpYE0kqRmwZ0Q8lXUs9cll0vd65VYNan1tKElTImJ3pXtVp2XPRsSArGNrqkreqCivzTter9yqwS+p7bWhVqQ/F0g6DPgX0H4951eTRyQdDdwdFVazzmVN36waSPpbNS8nsSGSDicZLbcDyezctsAFEfGnTAMrAUlLSQaGrASWU0Hf0vJa0wfq9sg9B9gxIkakK/51j4j7Mw7NcqxosbHJkm6jdteG+k+6nPISki0FkVQTH3IRUbFLgeS6pp/+QU0BToqInumHwFO1suywVSdJ16/ncERETaybJGlqRPTbUFk1SmeLnwDsFBE/k7QDsH1EPJNxaPmu6QM7R8TXJB0PEBHvp78ss8zU+ppQkvYCPg90lHRO0aG2QPNsoiq5a4BVJP2DPyOZlXs1kHkndd6T/keSNiPpvEXSzhR9jTbLUjqO/VSgC0V/qzVQ029FMimyBVDcDPIucEwmEZXeHhHRT9JzABHxH0lecK0C/AR4CNhB0s0k+8gOzTQis0/cS9LROYGkQ7AmRMRfgb9KuqEGNrpZlxXpSqKFCmVHkpp/5nKb9NMJFO2Ao0gWtRLJhuFvZhqY2Sc2r/aJSvWR9Jt0McOrCktiF4uII8ofVcmNBMYB20q6iOQbTEXsEZD3jtzJEdE/6zjM6iPpQpKBBQ9mHUspSdo9IqZI2q++4+k3gaon6bPAIJIK5SOVMt8i70n/EuBNkqngxYt1vZ1ZUGaporHeH5JMZKqYsd62YWnzznas3h/zz+wiSuQ96b9WT3FERNeyB2OWM+mY/AtINodpwScfalX/9yfpTJI+w4Uk/TGFa8t89ddcJ32zSpSXtaEkzSRZQXQKRR3VEfFWZkGViKRZJCN4Ku5actuRC56RaxUrL2tDLYmIP2cdxCbyBp/s01FRcl3T94xcs+ykfWrNgbtZfZmJqv0mUzTZbFeS7TsfYPVry3wryFzX9PGMXLMs7ZH+LB5BV+3fZAqTzf6Z3lqlN0jH7Gct70nfM3LNMhIRB2QdQ6kVtoCU9NWIuKP4mKSvZhPV6vLevHMgcD7QA3iYdEZuRDyeZVyWb5L2joi/SWodETVbCZH0P/WVR0TVbwlZyYvJ5bqmHxEPS5qCZ+RaZRkJ7A48DWSeJDah4k3e2wCHAxUxgamxJB0CHAp0kjSy6FBb4ONsolpd3mv6fwJuAe6LiGUbOt+sHCRNAqaTbIx+25rHI+KssgdVBpJaA3+JiP2zjqWxJPUG+gL/CxR/k1kKPBYR/8kksCJ5T/r7AV8DDgOeBf4I3B8RH2QamOWapG2AL5Hsj7tWE0hE/L7sQZWBpHbAsxHxf7KOpakkbUmyOirArErKKblO+gXpdOkvkixje7CnuVslkNQ7Ip7POo5NRdILfDKipTnQEfhpRFyVXVRNI6kFcDEwjGT0jki2g7weOD8iVqzn6WWR6zZ9gHT0zldIavz9gJqsRVlVekvSOJIBBpAss/ydiJiXYUyldHjR/Y+BhRFREe3eTfArkmGbXSNiKYCktsCl6e07GcYG5LymL+l2YCDJmvq3AX+NiIpY89pM0niSPqeb0qITgRMi4svZRWXrI+lVYJdYI7GmrQkzI6JbNpEVxZLzpH8QMCEiamaDCqsdkp6PiN5rlE3zjPHKJekfEbHLxh4rp2ZZB5CxR4EzJN2Z3s6U1DLroMxSb0o6UVLz9HYiUHELeNlqXpJ00pqF6e9uZgbxrCXvNf3fAS35pB3/G8DKiBieXVRmCUmfAa4E9iLp8HwKOKsS1mS3+knqRLKW0HKSdb0gWWZiM2BIRMzPKraCvCf9+r4+r1VmZrYxJH2RZNE1gJci4pEs4ymW99E7KyXtHBGzASR1pYY2oDazbETEoyTNxxUn70n/+8BjkuaQjKf9DMn4WjOzmpTr5h2om/rdPX34Si0vcGVmluvRO+lSp60iYjpwBHDruraoMys3Sd+R1FaJ6yRNTVeGNWu0XCd94L8jYqmkLwCDgOuAURnHZFZwSkS8CxwItCMZXXZJtiFZtct70i902h4GXBsRD/DJLjdmWSvs4nYocFNEvFhUZtYoeU/68yX9lmTdnQfT9v28/5tY5Zgi6WGSpP8XSZ8CvEyINUmuO3LTjdAPBl6IiFclbQ/sFhEPZxyaGZKaAX2AORHxjqQOQKe0D8qsUXKd9M0qXTrD8zMUDa+OiInZRWTVLu/j9M0qlqRfkDQ9vsQn/U8BOOlbo7mmb1ahJL0C9PLcESsld1qaVa45JAsCmpWMm3fMKtf7wDRJjwB1tf1a3RjdysNJ36xy3ZfezErGbfpmFUxSK6Cw29IrlbCxtlU3J32zCiVpf5INfuaSzMTdATjZQzatKZz0zSqUpCnA1yPilfTxLsCtEbF7tpFZNfPoHbPK1bKQ8AEi4h94NI81kTtyzSrX5HQf5z+kj08AJmcYj9UAN++YVah0AcAzgC+kRU8A13iyljWFk76ZWY64eceswki6PSKOlfQCyVo7q4mIXhmEZTXCNX2zCiNp+4hYIOkz9R2PiNfLHZPVDid9M7MccfOOWYWRtJR6mnVIJmhFRLQtc0hWQ1zTNzPLEdf0zSqMpPbrOx4Rb5crFqs9rumbVRhJr5E076iewxERXcscktUQJ30zsxxx845ZhZH02YiYKalffccjYmq5Y7La4Zq+WYWRNCYiRkh6rJ7DERFfLHtQVjOc9M3McsTNO2YVSlJz4DCgC0V/qxFxeVYxWfVz0jerXH8CPgBeAFZlHIvVCCd9s8rV2YurWal55yyzyvVnSQdmHYTVFtf0zSrXJGCcpGbACrz2jpWAR++YVah0Zu5g4IXwH6qViJt3zCrXG8AMJ3wrJTfvmFWuOcDjkv4M1O2L6yGb1hRO+maV67X01iq9mTWZ2/TNzHLEbfpmZjnipG9mliNO+mZmOeKOXLMKJakjcCprL7h2SlYxWfVz0jerXPcCTwATgJUZx2I1wqN3zCqUpGkR0SfrOKy2uE3frHLdL+nQrIOw2uKavlmFkbQUCJIF1rYgmY3rBdesJJz0zcxyxM07ZhVK0iMNKTPbGB69Y1ZhJLUhadbZRlI7kmYdgLZAp8wCs5rgpG9WeU4DzgY+DUwtKn8XuCqLgKx2uE3frEJJOjMirsw6DqstTvpmFUbSFyPiUUlH1Xc8Iu4ud0xWO9y8Y1Z59gMeBb5Sz7EAnPSt0VzTNzPLEdf0zSqUpNnAJJL1d56IiBczDslqgGv6ZhVKUmtgD2AfYG+gOzA9IoZkGphVNU/OMqtcK0mWX1gJrAIWpTezRnNN36xCSXofeAG4HJgQEW9lHJLVACd9swolaTDwBWAg8BHwFDAxIrwUgzWak75ZhZP0WeAQklm620bEZtlGZNXMbfpmFUrSXZJmAVcAmwMnAe2yjcqqnWv6ZhVKUn/guYjwVolWMk76ZmY54uYdM7MccdI3M8sRJ32zCiXpbkmHSfLfqZWM/zOZVa5rgK8Dr0q6RFL3rAOy6ueOXLMKJ2kr4HjgfOAN4FrgDxGxItPArCq5pm9WwSR1AIYCw4HnSMbs9wPGZxiWVTHX9M0qlKRxJCtr3gTcEBELio5Njoj+mQVnVctJ36xCSTo0Ih5co6x1RHyYVUxW/dy8Y1a5Lqyn7OmyR2E1xTtnmVUYSf8FdAI2k9QXUHqoLckaPGaN5qRvVnkOIum87Uyyln7BUuBHWQRktcNt+mYVStLREXFX1nFYbXHSN6swkk6MiD9I+h6w1h9oRFxez9PMGsTNO2aVZ4v055aZRmE1yTV9M7Mc8ZBNswol6feSti563E7S2AxDshrgpG9WuXpFxDuFBxHxH6BvduFYLXDSN6tczSTV7YkrqT3uh7Mm8n8gs8p1GfC0pDvSx18FLsowHqsB7sg1q2CSegBfTB8+GhEvZRmPVT8nfTOzHHGbvplZjjjpm5nliJO+WUrSf0n6o6TZkqZIelDSLpJmZB2bWal49I4ZIEnAOOD3EXFcWtYb2C7TwMxKzDV9s8QBwIqIGF0oiIjnSTYiB0BSF0lPSJqa3j6flm8vaaKkaZJmSNpHUnNJN6SPX5D03fJfktnaXNM3S/QEpmzgnEXAlyPiA0ndgFuB/sDXgb9ExEWSmpNsdNIH6BQRPQGKl1Mwy5KTvlnDtQSuktQHWAnskpY/C4yV1BK4JyKmSZoDdJV0JfAA8HAWAZutyc07ZokXgd03cM53gYVAb5IafiuAiJgI7AvMB26QdFK6Tk5v4HHgdOB3myZss43jpG+WeBRoLWlEoUBSL2CHonO2AhZExCrgG0Dz9LzPAAsj4lqS5N5P0jZAs3Tnqx8D/cpzGWbr5+YdMyAiQtIQ4DeSfgB8AMwFzi467RrgLkknAQ8By9Ly/YHvS1oBvAecRLKx+fWSChWrH27qazBrCC/DYGaWI27eMTPLESd9M7MccdI3M8sRJ30zsxxx0jczyxEnfTOzHHHSNzPLESd9M7Mc+f8WiNtqUblegAAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/html": [ "Below are statistics for top properties of Datatype:Musical-notation" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below are statistics for top properties of Datatype:Geo-shape" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Below is ditribution of Geoshape in different classes" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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coordinatelatitudelongitudeNumber_of_points
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coordinatelatitudelongitudeNumber_of_points
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coordinatelatitudelongitudeNumber_of_points
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coordinatelatitudelongitudeNumber_of_points
@-21.366666666667/55.616666666667-21.36666755.6166671
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@21.72569656/33.5560226421.72569733.5560231
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coordinatelatitudelongitudeNumber_of_points
@-0.61746/14.5266-0.61746014.5266001
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@-18.23022/-57.4538-18.230220-57.4538001
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@-26.24965082/-53.63749757-26.249651-53.6374981
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coordinatelatitudelongitudeNumber_of_points
@59.37047831/28.2089477859.37047828.2089482
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coordinatelatitudelongitudeNumber_of_points
@41.7137522/9.408226641.7137529.4082271
@42.2821276/9.560363742.2821289.5603641
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coordinatelatitudelongitudeNumber_of_points
@36.8431519/14.580538736.84315214.5805391
@36.961707/14.649709836.96170714.6497101
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coordinatelatitudelongitudeNumber_of_points
@-21.1/55.783333333333-21.1000055.7833331
@22.05445965/36.8943214422.0544636.8943211
@23.49999976/11.9999981123.5000011.9999981
@31.65736/25.1490931.6573625.1490901
@32.51311/-0.9984232.51311-0.9984201
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "try:\n", " ## Make it False if you are facing some issue. Setting it False will lead to generating all files from scratch which might take some time.\n", " restart = restart_global\n", " ## Make it False if you are facing some issue. Setting it False will lead to generating all files from scratch which might take some time.\n", " restart = restart_global\n", " cmd_coord_distribution_class = \"$kgtk query -i $WIKIDATA_PARTS/$globe_coordinate -i $WIKIDATA_PARTS/$wikibase_item -i $WIKIDATA_PARTS/$label --graph-cache $STORE \\\n", " -o $PROPERTY_OVERVIEW/__output_file \\\n", " --match 'coordinate: (n1)-[l{label:llab}]->(n2), item: (n1)-[r{label:llab2}]->(class:__class) ' \\\n", " --return 'distinct n2 as coordinate, kgtk_geo_coords_lat(n2) as latitude, kgtk_geo_coords_long(n2) as longitude, count(n2) as Number_of_points '\\\n", " --where 'llab in [\\\"__prop\\\"] AND llab2 in [\\\"P31\\\"]' \\\n", " --order-by 'count(n2) desc'\"\n", "\n", " cmd_distribution = \"$kgtk query -i $WIKIDATA_PARTS/$globe_coordinate -i $WIKIDATA_PARTS/$wikibase_item -i $WIKIDATA_PARTS/$label --graph-cache $STORE \\\n", " -o $PROPERTY_OVERVIEW/__output_file \\\n", " --match 'coordinate: (n1)-[r{label:llab}]->(v), item:(n1)-[l{label:llab2}]->(class), label:(class)-[:label]->(class_with_label)' \\\n", " --return 'distinct kgtk_lqstring_text(class_with_label) as Class, count(kgtk_lqstring_text(class_with_label)) as Number_of_Instances, class as Qnode' \\\n", " --where 'llab in [\\\"__prop\\\"] AND llab2 in [\\\"P31\\\"] AND kgtk_lqstring_lang_suffix(class_with_label) = \\\"en\\\" ' \\\n", " --order-by 'Number_of_Instances desc' \"\n", "\n", " #Initializing the dataframe pd_time_property with the list of top properties of datatype:time found above in the notebook \n", " pd_coord_property = pd.read_csv(os.path.join(os.getenv('OVERVIEW_FOLDER'),os.getenv('property_summary_globe_coordinate')),delimiter='\\t')\n", " for index,ele in pd_coord_property.iterrows():\n", " #Ignoring the 'Other Instances' row\n", " if index>=K or ele['Property_Label'] ==\"Other Properties\":\n", " break;\n", "\n", " # Extracting the \"Pnode\" corresponding to the property\n", " pnode = ele['Link'].split('/')[-1].split(\":\")[-1]\n", "\n", " #Extracting the 'Label' corresponding to the property\n", " prop_label = re.sub(\"[^0-9a-zA-Z]+\", \"_\", ele['Property_Label'])\n", "\n", " printmd(\"Below is the distribution of coordinate for Property:\" + prop_label.capitalize(),'blue')\n", "\n", " output_file_class_distribution= \"Property_overview.globe_coordinate.\"+prop_label+\".class_distribution.tsv\"\n", " property_class_distribution = run_command_return_df(cmd_distribution,\"PROPERTY_OVERVIEW\",output_file_class_distribution,{\"__output_file\":output_file_class_distribution,\"__prop\":pnode},restart=restart);\n", " \n", " if(len(property_class_distribution)>0):\n", " for index2,ele2 in property_class_distribution.iterrows(): \n", " if index2>=K or ele['Property_Label'] ==\"Other Classes\":\n", " break;\n", "\n", " class_label = re.sub(\"[^0-9a-zA-Z]+\", \"_\", ele2['Class'])\n", "\n", " qnode = ele2[\"Qnode\"]\n", "\n", " #Dynamically setting the name of the output file based on the name of property\n", " output_file_coord_destribution = \"Property_overview.globe_coordinate.\"+prop_label+\".\"+class_label+\".distribution.tsv\";\n", " \n", " #Free all the garbage\n", " gc.collect()\n", " #Running command cmd_coord_distribution_class\n", " coordinate_destribution = run_command_return_df(cmd_coord_distribution_class,\"PROPERTY_OVERVIEW\",output_file_coord_destribution,{\"__output_file\":output_file_coord_destribution,\"__prop\":pnode,\"__class\":qnode},restart=restart);\n", " printmd(\"Below is the distribution of coordinate for Property:\" + prop_label.capitalize()+\" for Class:\"+class_label.capitalize(),'blue')\n", " if(len(coordinate_destribution)>0):\n", " display(HTML(coordinate_destribution[:K].to_html(index=False)))\n", " else:\n", " printmd(\"There is no information about the coordinates for this class\")\n", " del coordinate_destribution\n", " #Free all the garbage\n", " gc.collect()\n", " else:\n", " printmd(\"There is no information about the classes(identified by 'P31') in this subgraph\")\n", "except Exception as e:\n", " #Free all the garbage\n", " gc.collect()\n", " print(e)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:53.192555Z", "iopub.status.busy": "2021-01-15T21:55:53.192012Z", "iopub.status.idle": "2021-01-15T21:55:53.203960Z", "shell.execute_reply": "2021-01-15T21:55:53.204320Z" }, "papermill": { "duration": 0.155564, "end_time": "2021-01-15T21:55:53.204486", "exception": false, "start_time": "2021-01-15T21:55:53.048922", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "Below are random samples from Geo-shape data" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idnode1labelnode2node2;wikidatatype
150Q3148-P3896-5d2174-0cf6a5e5-0Q3148P3896Data:Ardèche.mapgeo-shape
175Q39-P3896-2cce92-829cdb9c-0Q39P3896Data:Switzerland.mapgeo-shape
41Q12717-P3896-cbaab4-25a6c631-0Q12717P3896Data:Bas-Rhin.mapgeo-shape
227Q822-P3896-2aaf42-92678e4f-0Q822P3896Data:Lebanon.mapgeo-shape
7Q1037393-P3896-54fb99-d5f9f36c-0Q1037393P3896Data:Japan/Hokkaido.mapgeo-shape
\n", "
" ], "text/plain": [ " id node1 label \\\n", "150 Q3148-P3896-5d2174-0cf6a5e5-0 Q3148 P3896 \n", "175 Q39-P3896-2cce92-829cdb9c-0 Q39 P3896 \n", "41 Q12717-P3896-cbaab4-25a6c631-0 Q12717 P3896 \n", "227 Q822-P3896-2aaf42-92678e4f-0 Q822 P3896 \n", "7 Q1037393-P3896-54fb99-d5f9f36c-0 Q1037393 P3896 \n", "\n", " node2 node2;wikidatatype \n", "150 Data:Ardèche.map geo-shape \n", "175 Data:Switzerland.map geo-shape \n", "41 Data:Bas-Rhin.map geo-shape \n", "227 Data:Lebanon.map geo-shape \n", "7 Data:Japan/Hokkaido.map geo-shape " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "try:\n", " df = pd.read_csv(os.path.join(os.getenv('WIKIDATA_PARTS'),os.getenv('geo_shape')),delimiter='\\t',index_col=False)\n", " try:\n", " num_rows = min(int(K[1:-1]),len(df))\n", " except Exception as e:\n", " num_rows = min(int(K),len(df))\n", " df = df.sample(n=num_rows)\n", " printmd(\"Below are random samples from Geo-shape data\" ,'blue')\n", " display(df)\n", " df.to_csv(os.path.join(os.getenv('PROPERTY_OVERVIEW'),os.getenv('geo_shape_random_samples')),sep='\\t',index=False)\n", "except Exception as e:\n", " print(e)" ] }, { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.147342, "end_time": "2021-01-15T21:55:53.494635", "exception": false, "start_time": "2021-01-15T21:55:53.347293", "status": "completed" }, "tags": [] }, "source": [ "## Demonstration of Class overview, Property Overview and other functions" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:53.780653Z", "iopub.status.busy": "2021-01-15T21:55:53.780015Z", "iopub.status.idle": "2021-01-15T21:55:53.781964Z", "shell.execute_reply": "2021-01-15T21:55:53.782290Z" }, "papermill": { "duration": 0.143131, "end_time": "2021-01-15T21:55:53.782437", "exception": false, "start_time": "2021-01-15T21:55:53.639306", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#class_overview(\"Q483501\",\"artist\")" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:54.081814Z", "iopub.status.busy": "2021-01-15T21:55:54.081262Z", "iopub.status.idle": "2021-01-15T21:55:54.083181Z", "shell.execute_reply": "2021-01-15T21:55:54.083517Z" }, "papermill": { "duration": 0.153488, "end_time": "2021-01-15T21:55:54.083669", "exception": false, "start_time": "2021-01-15T21:55:53.930181", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#class_overview(\"Q483501\",\"artist\",number_of_outgoing_properties=3)" ] }, { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.149925, "end_time": "2021-01-15T21:55:54.386329", "exception": false, "start_time": "2021-01-15T21:55:54.236404", "status": "completed" }, "tags": [] }, "source": [ "# Property types\n", "#### [ \"time\",\n", "#### \"wikibase_item\",\n", "#### \"math\",\n", "#### \"wikibase_form\",\n", "#### \"quantity\",\n", "#### \"string\",\n", "#### \"external_id\",\n", "#### \"commonsMedia\",\n", "#### \"globe_coordinate\",\n", "#### \"monolingualtext\",\n", "#### \"musical_notation\",\n", "#### \"geo_shape\",\n", "#### \"url\"\n", "#### ]" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:54.681969Z", "iopub.status.busy": "2021-01-15T21:55:54.681476Z", "iopub.status.idle": "2021-01-15T21:55:54.683183Z", "shell.execute_reply": "2021-01-15T21:55:54.683553Z" }, "papermill": { "duration": 0.146711, "end_time": "2021-01-15T21:55:54.683712", "exception": false, "start_time": "2021-01-15T21:55:54.537001", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#property_overview(\"P619\",\"space_craft_launch_time\",\"time\")" ] }, { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.142871, "end_time": "2021-01-15T21:55:54.974392", "exception": false, "start_time": "2021-01-15T21:55:54.831521", "status": "completed" }, "tags": [] }, "source": [ "## Can in zoom into between _min_year and _max_year \n", "## Can also increase the number of classes" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:55.269056Z", "iopub.status.busy": "2021-01-15T21:55:55.268204Z", "iopub.status.idle": "2021-01-15T21:55:55.270451Z", "shell.execute_reply": "2021-01-15T21:55:55.270787Z" }, "papermill": { "duration": 0.150428, "end_time": "2021-01-15T21:55:55.270942", "exception": false, "start_time": "2021-01-15T21:55:55.120514", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#property_overview(\"P619\",\"space_craft_launch_time\",\"time\",_restart=True,_min_year=1980,_max_year=2000,number_of_unique_classes=20)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:55.560944Z", "iopub.status.busy": "2021-01-15T21:55:55.560322Z", "iopub.status.idle": "2021-01-15T21:55:55.562088Z", "shell.execute_reply": "2021-01-15T21:55:55.562449Z" }, "papermill": { "duration": 0.147366, "end_time": "2021-01-15T21:55:55.562607", "exception": false, "start_time": "2021-01-15T21:55:55.415241", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#property_overview(\"P2207\",\"spotify_track\",\"external_id\")" ] }, { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.14307, "end_time": "2021-01-15T21:55:55.858200", "exception": false, "start_time": "2021-01-15T21:55:55.715130", "status": "completed" }, "tags": [] }, "source": [ "## Can set the number of unique classes also" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:56.153510Z", "iopub.status.busy": "2021-01-15T21:55:56.152778Z", "iopub.status.idle": "2021-01-15T21:55:56.155101Z", "shell.execute_reply": "2021-01-15T21:55:56.155460Z" }, "papermill": { "duration": 0.15246, "end_time": "2021-01-15T21:55:56.155607", "exception": false, "start_time": "2021-01-15T21:55:56.003147", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#property_overview(\"P2207\",\"spotify_track\",\"external_id\",number_of_unique_classes=20)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:56.447978Z", "iopub.status.busy": "2021-01-15T21:55:56.447350Z", "iopub.status.idle": "2021-01-15T21:55:56.449540Z", "shell.execute_reply": "2021-01-15T21:55:56.449874Z" }, "papermill": { "duration": 0.151314, "end_time": "2021-01-15T21:55:56.450024", "exception": false, "start_time": "2021-01-15T21:55:56.298710", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#property_overview(\"P585\",\"point_in_time\",\"time\")" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:56.737721Z", "iopub.status.busy": "2021-01-15T21:55:56.737036Z", "iopub.status.idle": "2021-01-15T21:55:56.738827Z", "shell.execute_reply": "2021-01-15T21:55:56.739173Z" }, "papermill": { "duration": 0.143741, "end_time": "2021-01-15T21:55:56.739320", "exception": false, "start_time": "2021-01-15T21:55:56.595579", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#property_overview(\"P585\",\"point_in_time\",\"time\",_min_year=1880,_max_year=1920,number_of_unique_classes=15)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:57.040087Z", "iopub.status.busy": "2021-01-15T21:55:57.039384Z", "iopub.status.idle": "2021-01-15T21:55:57.041300Z", "shell.execute_reply": "2021-01-15T21:55:57.041716Z" }, "papermill": { "duration": 0.15591, "end_time": "2021-01-15T21:55:57.041895", "exception": false, "start_time": "2021-01-15T21:55:56.885985", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#property_overview(\"P1090\",\"redshift\",\"quantity\")" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:57.336041Z", "iopub.status.busy": "2021-01-15T21:55:57.335437Z", "iopub.status.idle": "2021-01-15T21:55:57.337085Z", "shell.execute_reply": "2021-01-15T21:55:57.337466Z" }, "papermill": { "duration": 0.143328, "end_time": "2021-01-15T21:55:57.337612", "exception": false, "start_time": "2021-01-15T21:55:57.194284", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#property_overview(\"P1086\",\"atomic number\",\"quantity\")" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:57.636969Z", "iopub.status.busy": "2021-01-15T21:55:57.636263Z", "iopub.status.idle": "2021-01-15T21:55:57.638198Z", "shell.execute_reply": "2021-01-15T21:55:57.638661Z" }, "papermill": { "duration": 0.157701, "end_time": "2021-01-15T21:55:57.638857", "exception": false, "start_time": "2021-01-15T21:55:57.481156", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#property_overview(\"P1093\",\"gross tonnage\",\"quantity\")" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:57.934392Z", "iopub.status.busy": "2021-01-15T21:55:57.933460Z", "iopub.status.idle": "2021-01-15T21:55:57.935965Z", "shell.execute_reply": "2021-01-15T21:55:57.936402Z" }, "papermill": { "duration": 0.152025, "end_time": "2021-01-15T21:55:57.936549", "exception": false, "start_time": "2021-01-15T21:55:57.784524", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#class_overview(\"Q154\",\"Alcoholic_Beverages\")" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:58.227291Z", "iopub.status.busy": "2021-01-15T21:55:58.226800Z", "iopub.status.idle": "2021-01-15T21:55:58.228469Z", "shell.execute_reply": "2021-01-15T21:55:58.228811Z" }, "papermill": { "duration": 0.148135, "end_time": "2021-01-15T21:55:58.229023", "exception": false, "start_time": "2021-01-15T21:55:58.080888", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#find_label(\"Q1025792\")" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:58.532160Z", "iopub.status.busy": "2021-01-15T21:55:58.531598Z", "iopub.status.idle": "2021-01-15T21:55:58.533397Z", "shell.execute_reply": "2021-01-15T21:55:58.533734Z" }, "papermill": { "duration": 0.159007, "end_time": "2021-01-15T21:55:58.533885", "exception": false, "start_time": "2021-01-15T21:55:58.374878", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#class_overview(\"Q2976049\",\"drug class\")" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:58.838508Z", "iopub.status.busy": "2021-01-15T21:55:58.837800Z", "iopub.status.idle": "2021-01-15T21:55:58.839657Z", "shell.execute_reply": "2021-01-15T21:55:58.839984Z" }, "papermill": { "duration": 0.152467, "end_time": "2021-01-15T21:55:58.840129", "exception": false, "start_time": "2021-01-15T21:55:58.687662", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#class_overview(\"Q19644607\",\"pharmaceutical company\")" ] }, { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.14618, "end_time": "2021-01-15T21:55:59.160555", "exception": false, "start_time": "2021-01-15T21:55:59.014375", "status": "completed" }, "tags": [] }, "source": [ "## To find the qnode corresponding to a label in the subgraph" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:59.463182Z", "iopub.status.busy": "2021-01-15T21:55:59.462279Z", "iopub.status.idle": "2021-01-15T21:55:59.464387Z", "shell.execute_reply": "2021-01-15T21:55:59.464720Z" }, "papermill": { "duration": 0.154136, "end_time": "2021-01-15T21:55:59.464870", "exception": false, "start_time": "2021-01-15T21:55:59.310734", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#find_node(\"pharmaceutical company\")" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:55:59.762600Z", "iopub.status.busy": "2021-01-15T21:55:59.761974Z", "iopub.status.idle": "2021-01-15T21:55:59.763785Z", "shell.execute_reply": "2021-01-15T21:55:59.764133Z" }, "papermill": { "duration": 0.153217, "end_time": "2021-01-15T21:55:59.764283", "exception": false, "start_time": "2021-01-15T21:55:59.611066", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#property_overview(\"P176\",\"manufacturer\",\"wikibase_item\")" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:56:00.057439Z", "iopub.status.busy": "2021-01-15T21:56:00.056784Z", "iopub.status.idle": "2021-01-15T21:56:00.058346Z", "shell.execute_reply": "2021-01-15T21:56:00.058857Z" }, "papermill": { "duration": 0.147921, "end_time": "2021-01-15T21:56:00.059024", "exception": false, "start_time": "2021-01-15T21:55:59.911103", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#property_overview(\"P859\",\"sponsor\",\"wikibase_item\",_restart=False)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:56:00.360214Z", "iopub.status.busy": "2021-01-15T21:56:00.359661Z", "iopub.status.idle": "2021-01-15T21:56:00.361412Z", "shell.execute_reply": "2021-01-15T21:56:00.361783Z" }, "papermill": { "duration": 0.152845, "end_time": "2021-01-15T21:56:00.361948", "exception": false, "start_time": "2021-01-15T21:56:00.209103", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#property_overview(\"P859\",\"sponsor\",\"wikibase_item\")" ] }, { "cell_type": "markdown", "metadata": { "papermill": { "duration": 0.140323, "end_time": "2021-01-15T21:56:00.652869", "exception": false, "start_time": "2021-01-15T21:56:00.512546", "status": "completed" }, "tags": [] }, "source": [ "## The property goes from outgoing class to incoming class\n", "## When specifying the outgoing class we can find the distribution of incoming classes for Specified outgoing class\n", "## When specifying the incoming class we can find the distribution of outgoing classes for Specified incoming class" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:56:00.945128Z", "iopub.status.busy": "2021-01-15T21:56:00.944579Z", "iopub.status.idle": "2021-01-15T21:56:00.946228Z", "shell.execute_reply": "2021-01-15T21:56:00.946560Z" }, "papermill": { "duration": 0.146119, "end_time": "2021-01-15T21:56:00.946768", "exception": false, "start_time": "2021-01-15T21:56:00.800649", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#property_distribution_with_class_items(\"P176\",\"manufacturer\",outgoing_classes=[\"pharmaceutical company\"],start=0,end=16)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:56:01.243258Z", "iopub.status.busy": "2021-01-15T21:56:01.242772Z", "iopub.status.idle": "2021-01-15T21:56:01.244629Z", "shell.execute_reply": "2021-01-15T21:56:01.244963Z" }, "papermill": { "duration": 0.149815, "end_time": "2021-01-15T21:56:01.245111", "exception": false, "start_time": "2021-01-15T21:56:01.095296", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#property_distribution_with_class_items(\"P859\",\"sponsor\")" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:56:01.549873Z", "iopub.status.busy": "2021-01-15T21:56:01.549294Z", "iopub.status.idle": "2021-01-15T21:56:01.551133Z", "shell.execute_reply": "2021-01-15T21:56:01.551542Z" }, "papermill": { "duration": 0.155553, "end_time": "2021-01-15T21:56:01.551717", "exception": false, "start_time": "2021-01-15T21:56:01.396164", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#property_distribution_with_class_items(\"P859\",\"sponsor\",outgoing_classes=[\"pharmaceutical company\"],start=1,end=15)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:56:01.851797Z", "iopub.status.busy": "2021-01-15T21:56:01.851147Z", "iopub.status.idle": "2021-01-15T21:56:01.853082Z", "shell.execute_reply": "2021-01-15T21:56:01.853444Z" }, "papermill": { "duration": 0.15513, "end_time": "2021-01-15T21:56:01.853689", "exception": false, "start_time": "2021-01-15T21:56:01.698559", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#property_distribution_with_class_items(\"P2176\",\"drug used for treatment\")" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:56:02.149564Z", "iopub.status.busy": "2021-01-15T21:56:02.149021Z", "iopub.status.idle": "2021-01-15T21:56:02.150658Z", "shell.execute_reply": "2021-01-15T21:56:02.151029Z" }, "papermill": { "duration": 0.146173, "end_time": "2021-01-15T21:56:02.151252", "exception": false, "start_time": "2021-01-15T21:56:02.005079", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#property_distribution_with_class_items(\"P127\",\"owned by\")" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:56:02.451683Z", "iopub.status.busy": "2021-01-15T21:56:02.450981Z", "iopub.status.idle": "2021-01-15T21:56:02.453043Z", "shell.execute_reply": "2021-01-15T21:56:02.453438Z" }, "papermill": { "duration": 0.154428, "end_time": "2021-01-15T21:56:02.453588", "exception": false, "start_time": "2021-01-15T21:56:02.299160", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#property_distribution_with_class_items(\"P127\",\"owned by\",outgoing_classes=[\"pharmaceutical company\"],start=0,end=15)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "execution": { "iopub.execute_input": "2021-01-15T21:56:02.753632Z", "iopub.status.busy": "2021-01-15T21:56:02.753149Z", "iopub.status.idle": "2021-01-15T21:56:02.755088Z", "shell.execute_reply": "2021-01-15T21:56:02.755426Z" }, "papermill": { "duration": 0.148664, "end_time": "2021-01-15T21:56:02.755579", "exception": false, "start_time": "2021-01-15T21:56:02.606915", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#property_distribution_with_class_items(\"P924\",\"possible treatment\")" ] } ], "metadata": { "kernelspec": { "display_name": "kgtk-env", "language": "python", "name": "kgtk-env" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" }, "papermill": { "default_parameters": {}, "duration": 62.652132, "end_time": "2021-01-15T21:56:03.224789", "environment_variables": {}, "exception": null, "input_path": "/Users/amandeep/Github/kgtk/use-cases/Knowledge-Graph-Profiler.ipynb", "output_path": "/Users/amandeep/Documents/kypher/temp.wikidata_os_v5/Knowledge-Graph-Profiler.out.ipynb", "parameters": { "cache_folder": "/Users/amandeep/Documents/kypher/temp.wikidata_os_v5/profiler_temp", "compute_graph_statistics": "true", "output_folder": "/Users/amandeep/Documents/kypher/wikidata_os_v5/profiler", "wikidata_parts_folder": "/Users/amandeep/Documents/kypher/wikidata_os_v5/parts" }, "start_time": "2021-01-15T21:55:00.572657", "version": "2.2.2" } }, "nbformat": 4, "nbformat_minor": 4 }