{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "9de5907f-18f5-4cb1-903e-26028ff1fa03", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "pd.set_option('display.max_rows', 100)\n", "pd.set_option('display.max_columns', None)" ] }, { "cell_type": "code", "execution_count": null, "id": "1dbf9321", "metadata": {}, "outputs": [], "source": [ "from bs4 import BeautifulSoup\n", "import requests\n", "import re\n", "from dateutil.parser import parse\n", "\n", "def needsUpdate():\n", " lastUpdated = _getLastUpdated()\n", " print(' lastUpdated:', lastUpdated)\n", "\n", " lastUpdatedDataSource = _getLastUpdatedDataSource()\n", " print('lastUpdatedDataSource:', lastUpdatedDataSource)\n", "\n", " return lastUpdated < lastUpdatedDataSource\n", " \n", "def _getLastUpdated():\n", " return __getLastUpdated(\n", " url = \"https://knollfrank.github.io/HowBadIsMyBatch/batchCodeTable.html\",\n", " getDateStr = lambda soup: soup.find(id = \"last_updated\").text)\n", "\n", "def _getLastUpdatedDataSource():\n", " def getDateStr(soup):\n", " lastUpdated = soup.find(string = re.compile(\"Last updated\"))\n", " return re.search('Last updated: (.+).', lastUpdated).group(1)\n", "\n", " return __getLastUpdated(url = \"https://vaers.hhs.gov/data/datasets.html\", getDateStr = getDateStr)\n", "\n", "def __getLastUpdated(url, getDateStr):\n", " htmlContent = requests.get(url).text\n", " soup = BeautifulSoup(htmlContent, \"lxml\")\n", " dateStr = getDateStr(soup)\n", " return parse(dateStr).date()\n", "\n", "print('needsUpdate:', needsUpdate())" ] }, { "cell_type": "code", "execution_count": null, "id": "a271254b", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "class VaersDescrReader:\n", " \n", " def __init__(self, dataDir):\n", " self.dataDir = dataDir\n", "\n", " def readVaersDescrsForYears(self, years):\n", " return [self.readVaersDescrForYear(year) for year in years]\n", "\n", " def readVaersDescrForYear(self, year):\n", " return {\n", " 'VAERSDATA': self._readVAERSDATA('{dataDir}/{year}VAERSDATA.csv'.format(dataDir = self.dataDir, year = year)),\n", " 'VAERSVAX': self._readVAERSVAX('{dataDir}/{year}VAERSVAX.csv'.format(dataDir = self.dataDir, year = year))\n", " }\n", "\n", " def readNonDomesticVaersDescr(self):\n", " return {\n", " 'VAERSDATA': self._readVAERSDATA(self.dataDir + \"/NonDomesticVAERSDATA.csv\"),\n", " 'VAERSVAX': self._readVAERSVAX(self.dataDir + \"/NonDomesticVAERSVAX.csv\")\n", " }\n", "\n", " def _readVAERSDATA(self, file):\n", " return self._read_csv(\n", " file = file,\n", " usecols = ['VAERS_ID', 'RECVDATE', 'DIED', 'L_THREAT', 'DISABLE', 'HOSPITAL', 'ER_VISIT', 'SPLTTYPE'],\n", " parse_dates = ['RECVDATE'],\n", " date_parser = lambda dateStr: pd.to_datetime(dateStr, format = \"%m/%d/%Y\"))\n", "\n", " def _readVAERSVAX(self, file):\n", " return self._read_csv(\n", " file = file,\n", " usecols = ['VAERS_ID', 'VAX_DOSE_SERIES', 'VAX_TYPE', 'VAX_MANU', 'VAX_LOT'],\n", " dtype = {\"VAX_DOSE_SERIES\": \"string\"})\n", "\n", " def _read_csv(self, file, **kwargs):\n", " return pd.read_csv(\n", " file,\n", " index_col = 'VAERS_ID',\n", " encoding = 'latin1',\n", " low_memory = False,\n", " **kwargs)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "7b5d6df0", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "class VaersDescr2DataFrameConverter:\n", "\n", " @staticmethod\n", " def createDataFrameFromDescr(vaersDescr):\n", " return pd.merge(\n", " vaersDescr['VAERSDATA'],\n", " vaersDescr['VAERSVAX'],\n", " how = 'left',\n", " left_index = True,\n", " right_index = True,\n", " validate = 'one_to_many')\n", "\n", " @staticmethod\n", " def createDataFrameFromDescrs(vaersDescrs):\n", " dataFrames = [VaersDescr2DataFrameConverter.createDataFrameFromDescr(vaersDescr) for vaersDescr in vaersDescrs]\n", " return pd.concat(dataFrames)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "6b639196", "metadata": {}, "outputs": [], "source": [ "class DataFrameNormalizer:\n", " \n", " @staticmethod\n", " def normalize(dataFrame):\n", " DataFrameNormalizer.removeUnknownBatchCodes(dataFrame)\n", " DataFrameNormalizer.convertVAX_LOTColumnToUpperCase(dataFrame)\n", " DataFrameNormalizer._convertColumnsOfDataFrame_Y_to_1_else_0(\n", " dataFrame,\n", " ['DIED', 'L_THREAT', 'DISABLE', 'HOSPITAL', 'ER_VISIT'])\n", "\n", " @staticmethod\n", " def convertVAX_LOTColumnToUpperCase(dataFrame):\n", " dataFrame['VAX_LOT'] = dataFrame['VAX_LOT'].str.upper()\n", "\n", " @staticmethod\n", " def removeUnknownBatchCodes(dataFrame):\n", " dataFrame.drop(DataFrameNormalizer._isUnknownBatchCode(dataFrame).index, inplace = True)\n", "\n", " @staticmethod\n", " def _isUnknownBatchCode(dataFrame):\n", " return dataFrame[dataFrame['VAX_LOT'].str.contains(pat = 'UNKNOWN', regex = False, case = False, na = False)]\n", "\n", " @staticmethod\n", " def _convertColumnsOfDataFrame_Y_to_1_else_0(dataFrame, columns):\n", " for column in columns:\n", " DataFrameNormalizer._convertColumnOfDataFrame_Y_to_1_else_0(dataFrame, column)\n", "\n", " @staticmethod\n", " def _convertColumnOfDataFrame_Y_to_1_else_0(dataFrame, column):\n", " dataFrame[column] = DataFrameNormalizer._where(\n", " condition = dataFrame[column] == 'Y',\n", " trueValue = 1,\n", " falseValue = 0)\n", "\n", " @staticmethod\n", " def _where(condition, trueValue, falseValue):\n", " return np.where(condition, trueValue, falseValue) \n", " " ] }, { "cell_type": "code", "execution_count": null, "id": "3ebcba86", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "class DataFrameFilter:\n", " \n", " def filterByCovid19(self, dataFrame):\n", " return dataFrame[self._isCovid19(dataFrame)]\n", "\n", " def _isCovid19(self, dataFrame):\n", " return dataFrame[\"VAX_TYPE\"] == \"COVID19\"\n" ] }, { "cell_type": "code", "execution_count": null, "id": "c62cfaff", "metadata": {}, "outputs": [], "source": [ "class SummationTableFactory:\n", "\n", " @staticmethod\n", " def createSummationTable(dataFrame):\n", " summationTable = dataFrame.agg(\n", " **{\n", " 'Deaths': pd.NamedAgg(column = 'DIED', aggfunc = 'sum'),\n", " 'Adverse Reaction Reports': pd.NamedAgg(column = 'DIED', aggfunc = 'size'),\n", " 'Life Threatening Illnesses': pd.NamedAgg(column = 'L_THREAT', aggfunc = 'sum'), \n", " 'Disabilities': pd.NamedAgg(column = 'DISABLE', aggfunc = 'sum'),\n", " 'Severities': pd.NamedAgg(column = 'SEVERE', aggfunc = 'sum'),\n", " 'Countries': pd.NamedAgg(column = 'COUNTRY', aggfunc = SummationTableFactory.countries2str)\n", " })\n", " summationTable['Severe reports'] = summationTable['Severities'] / summationTable['Adverse Reaction Reports'] * 100\n", " summationTable['Lethality'] = summationTable['Deaths'] / summationTable['Adverse Reaction Reports'] * 100\n", " return summationTable[\n", " [\n", " 'Adverse Reaction Reports',\n", " 'Deaths',\n", " 'Disabilities',\n", " 'Life Threatening Illnesses',\n", " 'Severe reports',\n", " 'Lethality',\n", " 'Countries'\n", " ]]\n", "\n", " @staticmethod\n", " def countries2str(countries):\n", " return ', '.join(sorted(set(countries)))" ] }, { "cell_type": "code", "execution_count": null, "id": "c40bd0f0", "metadata": {}, "outputs": [], "source": [ "import pycountry\n", "\n", "class CountryColumnAdder:\n", " \n", " @staticmethod\n", " def addCountryColumn(dataFrame):\n", " dataFrame['COUNTRY'] = CountryColumnAdder.getCountryColumn(dataFrame)\n", " return dataFrame.astype({'COUNTRY': \"string\"})\n", "\n", " @staticmethod\n", " def getCountryColumn(dataFrame):\n", " return dataFrame.apply(\n", " lambda row:\n", " CountryColumnAdder._getCountryNameOfSplttypeOrDefault(\n", " splttype = row['SPLTTYPE'],\n", " default = 'Unknown Country'),\n", " axis = 'columns')\n", "\n", " @staticmethod\n", " def _getCountryNameOfSplttypeOrDefault(splttype, default):\n", " if not isinstance(splttype, str):\n", " return default\n", " \n", " country = pycountry.countries.get(alpha_2 = splttype[:2])\n", " return country.name if country is not None else default" ] }, { "cell_type": "code", "execution_count": null, "id": "3abe3384", "metadata": {}, "outputs": [], "source": [ "class SevereColumnAdder:\n", " \n", " @staticmethod\n", " def addSevereColumn(dataFrame):\n", " dataFrame['SEVERE'] = (dataFrame['DIED'] + dataFrame['L_THREAT'] + dataFrame['DISABLE']) > 0\n", " dataFrame['SEVERE'].replace({True: 1, False: 0}, inplace = True)\n", " return dataFrame\n" ] }, { "cell_type": "code", "execution_count": null, "id": "2dad09e5", "metadata": {}, "outputs": [], "source": [ "class CompanyColumnAdder:\n", " \n", " def __init__(self, dataFrame_VAX_LOT_VAX_MANU):\n", " self.dataFrame_VAX_LOT_VAX_MANU = dataFrame_VAX_LOT_VAX_MANU\n", "\n", " def addCompanyColumn(self, batchCodeTable):\n", " return pd.merge(\n", " batchCodeTable,\n", " self._createCompanyByBatchCodeTable(),\n", " how = 'left',\n", " left_index = True,\n", " right_index = True,\n", " validate = 'one_to_one')\n", "\n", " def _createCompanyByBatchCodeTable(self):\n", " manufacturerByBatchCodeTable = self.dataFrame_VAX_LOT_VAX_MANU[['VAX_LOT', 'VAX_MANU']]\n", " manufacturerByBatchCodeTable = manufacturerByBatchCodeTable.drop_duplicates(subset = ['VAX_LOT'])\n", " manufacturerByBatchCodeTable = manufacturerByBatchCodeTable.set_index('VAX_LOT')\n", " return manufacturerByBatchCodeTable.rename(columns = {\"VAX_MANU\": \"Company\"})" ] }, { "cell_type": "code", "execution_count": null, "id": "71456a79", "metadata": {}, "outputs": [], "source": [ "class BatchCodeTableFactory:\n", "\n", " def __init__(self, dataFrame: pd.DataFrame):\n", " self.dataFrame = dataFrame\n", " self.companyColumnAdder = CompanyColumnAdder(dataFrame)\n", " self.countryBatchCodeTable = SummationTableFactory.createSummationTable(\n", " dataFrame.groupby(\n", " [\n", " dataFrame['COUNTRY'],\n", " dataFrame['VAX_LOT']\n", " ]))\n", "\n", " def createGlobalBatchCodeTable(self):\n", " return self._postProcess(SummationTableFactory.createSummationTable(self.dataFrame.groupby('VAX_LOT')))\n", "\n", " def createBatchCodeTableByCountry(self, country):\n", " return self._postProcess(self._getBatchCodeTableByCountry(country))\n", "\n", " def _postProcess(self, batchCodeTable):\n", " batchCodeTable = self.companyColumnAdder.addCompanyColumn(batchCodeTable)\n", " batchCodeTable = batchCodeTable[\n", " [\n", " 'Adverse Reaction Reports',\n", " 'Deaths',\n", " 'Disabilities',\n", " 'Life Threatening Illnesses',\n", " 'Company',\n", " 'Countries',\n", " 'Severe reports',\n", " 'Lethality'\n", " ]]\n", " return batchCodeTable.sort_values(by = 'Severe reports', ascending = False)\n", "\n", " def _getBatchCodeTableByCountry(self, country):\n", " if country in self.countryBatchCodeTable.index:\n", " return self.countryBatchCodeTable.loc[country]\n", " else:\n", " return self._getEmptyBatchCodeTable()\n", "\n", " def _getEmptyBatchCodeTable(self):\n", " return self.countryBatchCodeTable[0:0].droplevel(0)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "6aa28541", "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "class IOUtils:\n", "\n", " @staticmethod\n", " def saveDataFrame(dataFrame, file):\n", " # IOUtils.saveDataFrameAsExcelFile(dataFrame, file)\n", " # IOUtils.saveDataFrameAsHtml(dataFrame, file)\n", " IOUtils.saveDataFrameAsJson(dataFrame, file)\n", "\n", " @staticmethod\n", " def saveDataFrameAsExcelFile(dataFrame, file):\n", " IOUtils.ensurePath(file)\n", " dataFrame.to_excel(file + '.xlsx')\n", "\n", " @staticmethod\n", " def saveDataFrameAsHtml(dataFrame, file):\n", " IOUtils.ensurePath(file)\n", " dataFrame.reset_index().to_html(\n", " file + '.html',\n", " index = False,\n", " table_id = 'batchCodeTable',\n", " classes = 'display',\n", " justify = 'unset',\n", " border = 0)\n", "\n", " @staticmethod\n", " def saveDataFrameAsJson(dataFrame, file):\n", " IOUtils.ensurePath(file)\n", " dataFrame.reset_index().to_json(\n", " file + '.json',\n", " orient = \"split\",\n", " index = False)\n", "\n", " @staticmethod\n", " def ensurePath(file):\n", " directory = os.path.dirname(file)\n", " if not os.path.exists(directory):\n", " os.makedirs(directory)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "3dacedfd", "metadata": {}, "outputs": [], "source": [ "import unittest" ] }, { "cell_type": "code", "execution_count": null, "id": "fcc855dd", "metadata": {}, "outputs": [], "source": [ "class TestHelper:\n", "\n", " @staticmethod\n", " def createDataFrame(index, columns, data, dtypes = {}):\n", " return pd.DataFrame(index = index, columns = columns, data = data).astype(dtypes)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "ccb9838d", "metadata": {}, "outputs": [], "source": [ "from pandas.testing import assert_frame_equal\n", "\n", "class DataFrameNormalizerTest(unittest.TestCase):\n", "\n", " def test_convertVAX_LOTColumnToUpperCase(self):\n", " # Given\n", " dataFrame = TestHelper.createDataFrame(\n", " columns = ['VAX_LOT'],\n", " data = [ ['037K20A'],\n", " ['025l20A'],\n", " ['025L20A']],\n", " index = [\n", " \"0916600\",\n", " \"0916601\",\n", " \"1996874\"])\n", " \n", " # When\n", " DataFrameNormalizer.convertVAX_LOTColumnToUpperCase(dataFrame)\n", " \n", " # Then\n", " dataFrameExpected = TestHelper.createDataFrame(\n", " columns = ['VAX_LOT'],\n", " data = [ ['037K20A'],\n", " ['025L20A'],\n", " ['025L20A']],\n", " index = [\n", " \"0916600\",\n", " \"0916601\",\n", " \"1996874\"])\n", " assert_frame_equal(dataFrame, dataFrameExpected, check_dtype = False)\n", "\n", " def test_removeUnknownBatchCodes(self):\n", " # Given\n", " dataFrame = TestHelper.createDataFrame(\n", " columns = ['VAX_LOT'],\n", " data = [ ['UNKNOWN'],\n", " ['N/A Unknown'],\n", " [np.nan],\n", " ['UNKNOWN TO ME'],\n", " ['030L20B']],\n", " index = [\n", " \"1048786\",\n", " \"1048786\",\n", " \"123\",\n", " \"4711\",\n", " \"0815\"])\n", " \n", " # When\n", " DataFrameNormalizer.removeUnknownBatchCodes(dataFrame)\n", " \n", " # Then\n", " dataFrameExpected = TestHelper.createDataFrame(\n", " columns = ['VAX_LOT'],\n", " data = [ [np.nan],\n", " ['030L20B']],\n", " index = [\n", " \"123\",\n", " \"0815\"])\n", " assert_frame_equal(dataFrame, dataFrameExpected, check_dtype = False)" ] }, { "cell_type": "code", "execution_count": null, "id": "e59a1825", "metadata": {}, "outputs": [], "source": [ "from pandas.testing import assert_frame_equal\n", "\n", "class DataFrameFilterTest(unittest.TestCase):\n", "\n", " def test_filterByCovid19(self):\n", " # Given\n", " dataFrame = VaersDescr2DataFrameConverter.createDataFrameFromDescrs(\n", " [\n", " {\n", " 'VAERSDATA': TestHelper.createDataFrame(\n", " columns = ['DIED', 'L_THREAT', 'DISABLE'],\n", " data = [ [1, 0, 0],\n", " [0, 0, 1]],\n", " index = [\n", " \"0916600\",\n", " \"0916601\"]),\n", " 'VAERSVAX': TestHelper.createDataFrame(\n", " columns = ['VAX_TYPE', 'VAX_MANU', 'VAX_LOT', 'VAX_DOSE_SERIES'],\n", " data = [ ['COVID19', 'MODERNA', '037K20A', '1'],\n", " ['COVID19', 'MODERNA', '025L20A', '1']],\n", " index = [\n", " \"0916600\",\n", " \"0916601\"],\n", " dtypes = {'VAX_DOSE_SERIES': \"string\"})\n", " },\n", " {\n", " 'VAERSDATA': TestHelper.createDataFrame(\n", " columns = ['DIED', 'L_THREAT', 'DISABLE'],\n", " data = [ [0, 0, 0],\n", " [0, 0, 1]],\n", " index = [\n", " \"1996873\",\n", " \"1996874\"]),\n", " 'VAERSVAX': TestHelper.createDataFrame(\n", " columns = ['VAX_TYPE', 'VAX_MANU', 'VAX_LOT', 'VAX_DOSE_SERIES'],\n", " data = [ ['HPV9', 'MERCK & CO. INC.', 'R017624', 'UNK'],\n", " ['COVID19', 'MODERNA', '025L20A', '1']],\n", " index = [\n", " \"1996873\",\n", " \"1996874\"],\n", " dtypes = {'VAX_DOSE_SERIES': \"string\"})\n", " }\n", " ])\n", " dataFrameFilter = DataFrameFilter()\n", " \n", " # When\n", " dataFrame = dataFrameFilter.filterByCovid19(dataFrame)\n", " \n", " # Then\n", " dataFrameExpected = TestHelper.createDataFrame(\n", " columns = ['DIED', 'L_THREAT', 'DISABLE', 'VAX_TYPE', 'VAX_MANU', 'VAX_LOT', 'VAX_DOSE_SERIES'],\n", " data = [ [1, 0, 0, 'COVID19', 'MODERNA', '037K20A', '1'],\n", " [0, 0, 1, 'COVID19', 'MODERNA', '025L20A', '1'],\n", " [0, 0, 1, 'COVID19', 'MODERNA', '025L20A', '1']],\n", " index = [\n", " \"0916600\",\n", " \"0916601\",\n", " \"1996874\"],\n", " dtypes = {'VAX_DOSE_SERIES': \"string\"})\n", " assert_frame_equal(dataFrame, dataFrameExpected, check_dtype = False)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "c784bfef", "metadata": {}, "outputs": [], "source": [ "from pandas.testing import assert_frame_equal\n", "\n", "class BatchCodeTableFactoryTest(unittest.TestCase):\n", "\n", " def test_createBatchCodeTableByCountry(self):\n", " # Given\n", " dataFrame = TestHelper.createDataFrame(\n", " columns = ['DIED', 'L_THREAT', 'DISABLE', 'VAX_TYPE', 'VAX_MANU', 'VAX_LOT', 'VAX_DOSE_SERIES', 'SPLTTYPE', 'HOSPITAL', 'ER_VISIT', 'COUNTRY'],\n", " data = [ [1, 0, 0, 'COVID19', 'PFIZER\\BIONTECH', '016M20A', '2', 'GBPFIZER INC2020486806', 0, 0, 'United Kingdom'],\n", " [0, 0, 0, 'COVID19', 'MODERNA', '030L20A', '1', 'FRMODERNATX, INC.MOD20224', 0, 0, 'France'],\n", " [1, 1, 1, 'COVID19', 'MODERNA', '030L20B', '1', 'FRMODERNATX, INC.MOD20224', 0, 0, 'France'],\n", " [0, 1, 1, 'COVID19', 'MODERNA', '030L20B', '1', 'FRMODERNATX, INC.MOD20224', 0, 0, 'France']],\n", " index = [\n", " \"1048786\",\n", " \"1048786\",\n", " \"4711\",\n", " \"0815\"])\n", " dataFrame = SevereColumnAdder.addSevereColumn(dataFrame)\n", " batchCodeTableFactory = BatchCodeTableFactory(dataFrame)\n", " \n", " # When\n", " batchCodeTable = batchCodeTableFactory.createBatchCodeTableByCountry('France')\n", "\n", " # Then\n", " assert_frame_equal(\n", " batchCodeTable,\n", " TestHelper.createDataFrame(\n", " columns = ['Adverse Reaction Reports', 'Deaths', 'Disabilities', 'Life Threatening Illnesses', 'Company', 'Countries', 'Severe reports', 'Lethality'],\n", " data = [ [2, 1, 2, 2, 'MODERNA', 'France', 2/2 * 100, 1/2 * 100],\n", " [1, 0, 0, 0, 'MODERNA', 'France', 0/1 * 100, 0/1 * 100]],\n", " index = pd.Index(\n", " [\n", " '030L20B',\n", " '030L20A'\n", " ],\n", " name = 'VAX_LOT')),\n", " check_dtype = False)\n", "\n", " def test_createGlobalBatchCodeTable(self):\n", " # Given\n", " dataFrame = TestHelper.createDataFrame(\n", " columns = ['DIED', 'L_THREAT', 'DISABLE', 'VAX_TYPE', 'VAX_MANU', 'VAX_LOT', 'VAX_DOSE_SERIES', 'SPLTTYPE', 'HOSPITAL', 'ER_VISIT', 'COUNTRY'],\n", " data = [ [1, 0, 0, 'COVID19', 'PFIZER\\BIONTECH', '016M20A', '2', 'GBPFIZER INC2020486806', 0, 0, 'United Kingdom'],\n", " [0, 0, 0, 'COVID19', 'MODERNA', '030L20A', '1', 'FRMODERNATX, INC.MOD20224', 0, 0, 'France'],\n", " [1, 1, 1, 'COVID19', 'MODERNA', '030L20B', '1', 'FRMODERNATX, INC.MOD20224', 0, 0, 'France'],\n", " [0, 1, 1, 'COVID19', 'MODERNA', '030L20B', '1', 'FRMODERNATX, INC.MOD20224', 0, 0, 'United Kingdom']],\n", " index = [\n", " \"1048786\",\n", " \"1048786\",\n", " \"4711\",\n", " \"0815\"])\n", " dataFrame = SevereColumnAdder.addSevereColumn(dataFrame)\n", " batchCodeTableFactory = BatchCodeTableFactory(dataFrame)\n", " \n", " # When\n", " batchCodeTable = batchCodeTableFactory.createGlobalBatchCodeTable()\n", "\n", " # Then\n", " assert_frame_equal(\n", " batchCodeTable,\n", " TestHelper.createDataFrame(\n", " columns = ['Adverse Reaction Reports', 'Deaths', 'Disabilities', 'Life Threatening Illnesses', 'Company', 'Countries', 'Severe reports', 'Lethality'],\n", " data = [ [1, 1, 0, 0, 'PFIZER\\BIONTECH', 'United Kingdom', 1/1 * 100, 1/1 * 100],\n", " [2, 1, 2, 2, 'MODERNA', 'France, United Kingdom', 2/2 * 100, 1/2 * 100],\n", " [1, 0, 0, 0, 'MODERNA', 'France', 0/1 * 100, 0/1 * 100]],\n", " index = pd.Index(\n", " [\n", " '016M20A',\n", " '030L20B',\n", " '030L20A'\n", " ],\n", " name = 'VAX_LOT')),\n", " check_dtype = False)\n", "\n", " def test_createBatchCodeTableByNonExistingCountry(self):\n", " # Given\n", " dataFrame = TestHelper.createDataFrame(\n", " columns = ['DIED', 'L_THREAT', 'DISABLE', 'VAX_TYPE', 'VAX_MANU', 'VAX_LOT', 'VAX_DOSE_SERIES', 'SPLTTYPE', 'HOSPITAL', 'ER_VISIT', 'COUNTRY'],\n", " data = [ [1, 0, 0, 'COVID19', 'PFIZER\\BIONTECH', '016M20A', '2', 'GBPFIZER INC2020486806', 0, 0, 'United Kingdom'],\n", " [0, 0, 0, 'COVID19', 'MODERNA', '030L20A', '1', 'FRMODERNATX, INC.MOD20224', 0, 0, 'France'],\n", " [1, 1, 1, 'COVID19', 'MODERNA', '030L20B', '1', 'FRMODERNATX, INC.MOD20224', 0, 0, 'France'],\n", " [0, 1, 1, 'COVID19', 'MODERNA', '030L20B', '1', 'FRMODERNATX, INC.MOD20224', 0, 0, 'France']],\n", " index = [\n", " \"1048786\",\n", " \"1048786\",\n", " \"4711\",\n", " \"0815\"])\n", " dataFrame = SevereColumnAdder.addSevereColumn(dataFrame)\n", " batchCodeTableFactory = BatchCodeTableFactory(dataFrame)\n", " \n", " # When\n", " batchCodeTable = batchCodeTableFactory.createBatchCodeTableByCountry('non existing country')\n", "\n", " # Then\n", " assert_frame_equal(\n", " batchCodeTable,\n", " TestHelper.createDataFrame(\n", " columns = ['Adverse Reaction Reports', 'Deaths', 'Disabilities', 'Life Threatening Illnesses', 'Company', 'Countries', 'Severe reports', 'Lethality'],\n", " data = [ ],\n", " index = pd.Index([], name = 'VAX_LOT')),\n", " check_dtype = False)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "5a8bff1b", "metadata": {}, "outputs": [], "source": [ "unittest.main(argv = [''], verbosity = 2, exit = False)" ] }, { "cell_type": "code", "execution_count": null, "id": "86e0e4f2", "metadata": {}, "outputs": [], "source": [ "def getVaersForYears(years):\n", " def addCountryColumn(dataFrame):\n", " dataFrame['COUNTRY'] = 'United States'\n", " return dataFrame\n", "\n", " return _getVaers(\n", " _getVaersDescrReader().readVaersDescrsForYears(years),\n", " addCountryColumn)\n", "\n", "def getNonDomesticVaers():\n", " return _getVaers(\n", " [_getVaersDescrReader().readNonDomesticVaersDescr()],\n", " CountryColumnAdder.addCountryColumn)\n", "\n", "def _getVaersDescrReader():\n", " return VaersDescrReader(dataDir = \"VAERS\")\n", "\n", "def _getVaers(vaersDescrs, addCountryColumn):\n", " dataFrame = VaersDescr2DataFrameConverter.createDataFrameFromDescrs(vaersDescrs)\n", " dataFrame = addCountryColumn(dataFrame)\n", " DataFrameNormalizer.normalize(dataFrame)\n", " dataFrame = SevereColumnAdder.addSevereColumn(dataFrame)\n", " return dataFrame" ] }, { "cell_type": "code", "execution_count": null, "id": "781ac80e", "metadata": {}, "outputs": [], "source": [ "internationalVaers = pd.concat([getVaersForYears([2020, 2021, 2022]), getNonDomesticVaers()])\n", "internationalVaersCovid19 = DataFrameFilter().filterByCovid19(internationalVaers)\n", "internationalVaersCovid19" ] }, { "cell_type": "code", "execution_count": null, "id": "ff259a35", "metadata": {}, "outputs": [], "source": [ "def createAndSaveBatchCodeTableForCountry(createBatchCodeTableForCountry, country, minADRsForLethality = None):\n", " batchCodeTable = createBatchCodeTableForCountry(country)\n", " batchCodeTable.index.set_names(\"Batch\", inplace = True)\n", " if minADRsForLethality is not None:\n", " batchCodeTable.loc[batchCodeTable['Adverse Reaction Reports'] < minADRsForLethality, ['Severe reports', 'Lethality']] = [np.nan, np.nan]\n", " IOUtils.saveDataFrame(batchCodeTable, '../docs/data/batchCodeTables/' + country)\n", " # display(country + \":\", batchCodeTable)\n", " display(country)\n", "\n", "def createAndSaveBatchCodeTablesForCountries(createBatchCodeTableForCountry, countries, minADRsForLethality = None):\n", " for country in countries:\n", " createAndSaveBatchCodeTableForCountry(createBatchCodeTableForCountry, country, minADRsForLethality)" ] }, { "cell_type": "code", "execution_count": null, "id": "cc1ef82a", "metadata": {}, "outputs": [], "source": [ "# FK-TODO: analog zu Intensivstationen.ipynb einen KreisOptionsSetter einsetzen zum automatischen Speichern der Options in der html-Datei\n", "def printCountryOptions(countries):\n", " for country in countries:\n", " printCountryOption(country)\n", "\n", "def printCountryOption(country):\n", " print(''.format(country = country))" ] }, { "cell_type": "code", "execution_count": null, "id": "0c4d04fb", "metadata": {}, "outputs": [], "source": [ "countries = sorted(internationalVaersCovid19['COUNTRY'].unique())\n", "printCountryOptions(countries)" ] }, { "cell_type": "code", "execution_count": null, "id": "7e7e01a5", "metadata": {}, "outputs": [], "source": [ "minADRsForLethality = 100\n", "batchCodeTableFactory = BatchCodeTableFactory(internationalVaersCovid19)\n", "\n", "createAndSaveBatchCodeTablesForCountries(\n", " createBatchCodeTableForCountry = lambda country: batchCodeTableFactory.createBatchCodeTableByCountry(country),\n", " countries = countries,\n", " minADRsForLethality = minADRsForLethality)\n", "\n", "createAndSaveBatchCodeTableForCountry(\n", " createBatchCodeTableForCountry = lambda country: batchCodeTableFactory.createGlobalBatchCodeTable(),\n", " country = 'Global',\n", " minADRsForLethality = minADRsForLethality)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 5 }