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Andreaierardi
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"E:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3058: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n",
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" interactivity=interactivity, compiler=compiler, result=result)\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"from datetime import date\n",
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"\n",
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"## Real data: begins with \"R\"\n",
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"# Real Deaths: RD\n",
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"RD = pd.read_csv(\"https://raw.githubusercontent.com/reichlab/covid19-forecast-hub/master/data-truth/truth-Cumulative%20Deaths.csv\")\n",
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"# Real Cases: RC\n",
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"RC = pd.read_csv(\"https://raw.githubusercontent.com/reichlab/covid19-forecast-hub/master/data-truth/truth-Cumulative%20Cases.csv\")\n",
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"\n",
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"Rstates = RD.location_name.unique()\n",
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"\n",
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"# Real Series getter\n",
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"def getRS(type, state, aggregateOn = 5):\n",
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" \"\"\"Gets the real cases or deaths series by state\n",
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"\n",
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" Parameters\n",
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" ----------\n",
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" type : str\n",
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" 'C' for cumulative cases. \n",
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" 'D' for cumulative deaths.\n",
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" state : str\n",
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" The state where deaths were recorded\n",
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" aggregateOn : int or bool\n",
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" The weekday to aggregate the observations on.\n",
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" 0 is Monday, 6 is Sunday.\n",
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" Set to false to prevent aggregation.\n",
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"\n",
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" Returns\n",
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" -------\n",
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" pandas.Series : the series of real cases or deaths of the specified state. Indexes are of class pandas.DatetimeIndex.\n",
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" \"\"\"\n",
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" if(type == 'C'):\n",
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" out = pd.Series(RC[RC['location_name'] == state].iloc[:,3].values,\n",
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" index = pd.to_datetime(RC[RC['location_name'] == state].iloc[:,0].values, format=\"%Y-%m-%d\"),\n",
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" name = state + \": Cumulative cases\")\n",
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" elif(type == 'D'):\n",
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" out = pd.Series(RD[RD['location_name'] == state].iloc[:,3].values,\n",
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" index = pd.to_datetime(RD[RD['location_name'] == state].iloc[:,0].values, format=\"%Y-%m-%d\"),\n",
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" name = state + \": Cumulative deaths\"\n",
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" )\n",
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" if(aggregateOn is not False):\n",
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" out = out[out.index.weekday == aggregateOn]\n",
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"\n",
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" return(out)\n",
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"\n",
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"# Example: getRS('D',Rstates[1])\n",
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"\n",
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"# # shift series to first non-zero occurence\n",
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"# daily_s = daily_s[daily_s>0]\n",
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"# # switch aggregation range to weekly (every Saturday)\n",
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"# D = daily_s[daily_s.index.weekday == 5]\n",
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"\n",
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"\n",
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"## Forecast data: begins with \"F\"\n",
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"# Forecasted cases: FC\n",
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"FC = pd.read_csv(\"https://www.cdc.gov/coronavirus/2019-ncov/downloads/cases-updates/2020-10-19-all-forecasted-cases-model-data.csv\")\n",
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"# Forecasted deaths: FD\n",
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"FD = pd.read_csv('https://www.cdc.gov/coronavirus/2019-ncov/covid-data/files/2020-10-19-model-data.csv')\n",
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"\n",
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"Fmodels = FD.model.unique()\n",
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"Fstates = FD.location_name.unique()\n",
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"\n",
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"# Forecast Series getter\n",
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"def getFS(type, model, state, Fdate):\n",
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" \"\"\"Gets the forecasted deaths series by model, state and forecast date\n",
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"\n",
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" Parameters\n",
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" ----------\n",
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" type : str\n",
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" 'C' for cumulative cases. \n",
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" 'D' for cumulative deaths.\n",
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" model : str\n",
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" The model of the forecast\n",
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" state : str\n",
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" The target state of the forecast\n",
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" Fdate : str or datetime\n",
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" The date when the forecast was performed. If a string, provide the format '%Y-%m-%d'.\n",
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"\n",
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" Returns\n",
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" -------\n",
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" pandas.DataFrame\n",
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" a data frame containing 5 series:\n",
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" - point series\n",
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" - 2.5% quantile\n",
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" - 25% quantile\n",
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" - 75% quantile\n",
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" - 97.5% quantile\n",
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" Indexes are of class pandas.DatetimeIndex.\n",
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" \"\"\"\n",
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" if(type == 'C'):\n",
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" out = FC[(FC.model == model) & (FC.location_name == state) & (FC.forecast_date == Fdate)] \n",
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" elif(type == 'D'):\n",
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" out = FD[(FD.model == model) & (FD.location_name == state) & (FD.forecast_date == Fdate) & FD.target.apply(str.endswith, args=('cum death',0))]\n",
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" else:\n",
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" return None\n",
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" if( out.empty ):\n",
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" return None\n",
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" out = pd.DataFrame(out.iloc[:,-5:].values,\n",
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" columns = out.columns[-5:],\n",
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" index = pd.to_datetime(out.iloc[:,3], format=\"%Y-%m-%d\")\n",
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" )\n",
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" \n",
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" return out\n",
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" \n",
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"# Example: getFS('C', Fmodels[1], Fstates[1], FD.forecast_date[1])\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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"# prova1 = pd.ExcelFile('Matlab to python/data_models-Florida.xlsx')\n",
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"\n",
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"# prova1.sheet_names\n",
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"\n",
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"# prova2 = prova1.parse('Ensamble')\n",
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"# prova2\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array(['Alabama', 'Alaska', 'American Samoa', ..., 'Uinta County',\n",
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" 'Washakie County', 'Weston County'], dtype=object)"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"2020-01-25 0\n",
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"2020-02-01 0\n",
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"2020-02-08 0\n",
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"2020-02-15 0\n",
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"2020-02-22 0\n",
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"2020-02-29 0\n",
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"2020-03-07 0\n",
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"2020-03-14 0\n",
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"2020-03-21 0\n",
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"2020-03-28 2\n",
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"2020-04-04 5\n",
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"2020-04-11 8\n",
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"2020-04-18 9\n",
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"2020-04-25 9\n",
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"2020-05-02 9\n",
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"2020-05-09 10\n",
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"2020-05-16 10\n",
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"2020-05-23 10\n",
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"2020-05-30 10\n",
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"2020-06-06 10\n",
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"2020-06-13 12\n",
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"2020-06-20 12\n",
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"2020-06-27 14\n",
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"2020-07-04 16\n",
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"2020-07-11 17\n",
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"2020-07-18 18\n",
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"2020-07-25 20\n",
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"2020-08-01 24\n",
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"2020-08-08 26\n",
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"2020-08-15 28\n",
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"2020-08-22 31\n",
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"2020-08-29 37\n",
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"2020-09-05 42\n",
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"2020-09-12 44\n",
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"2020-09-19 45\n",
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"2020-09-26 52\n",
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"2020-10-03 58\n",
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"2020-10-10 60\n",
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"2020-10-17 67\n",
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"2020-10-24 68\n",
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"2020-10-31 82\n",
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"2020-11-07 84\n",
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"2020-11-14 98\n",
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"Name: Alaska: Cumulative deaths, dtype: int64"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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" getRS('D',Rstates[1])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"metadata": {},
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"outputs": [],
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"source": [
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"data= getFS('D', Fmodels[1], Fstates[1], FD.forecast_date[1])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[ 74., 67., 71., 78., 84.],\n",
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" [ 81., 67., 75., 89., 105.],\n",
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" [ 89., 67., 79., 101., 123.],\n",
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" [ 99., 67., 81., 111., 141.]])"
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]
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},
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"execution_count": 33,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data.values"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2020-10-24 00:00:00\n",
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"2020-10-31 00:00:00\n",
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"2020-11-07 00:00:00\n",
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"2020-11-14 00:00:00\n"
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]
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}
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],
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"source": [
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"for i in data.index.tolist():\n",
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" print(i)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"metadata": {},
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"outputs": [
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{
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"ename": "KeyError",
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"evalue": "'target_week_end_data'",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32mE:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 2896\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2898\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
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"\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
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"\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
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"\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
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"\u001b[1;31mKeyError\u001b[0m: 'target_week_end_data'",
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"\nDuring handling of the above exception, another exception occurred:\n",
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"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m<ipython-input-38-62575f9ff9bf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"target_week_end_data\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[1;32mE:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 2978\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2979\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2980\u001b[1;33m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2981\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2982\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
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"\u001b[1;32mE:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 2897\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2898\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2899\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2900\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2901\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
|
||||
"\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
|
||||
"\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
|
||||
"\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
|
||||
"\u001b[1;31mKeyError\u001b[0m: 'target_week_end_data'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data[\"target_week_end_data\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"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": "3.7.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@ -0,0 +1,609 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"E:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3058: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n",
|
||||
" interactivity=interactivity, compiler=compiler, result=result)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"from datetime import date\n",
|
||||
"\n",
|
||||
"## Real data: begins with \"R\"\n",
|
||||
"# Real Deaths: RD\n",
|
||||
"RD = pd.read_csv(\"https://raw.githubusercontent.com/reichlab/covid19-forecast-hub/master/data-truth/truth-Cumulative%20Deaths.csv\")\n",
|
||||
"# Real Cases: RC\n",
|
||||
"RC = pd.read_csv(\"https://raw.githubusercontent.com/reichlab/covid19-forecast-hub/master/data-truth/truth-Cumulative%20Cases.csv\")\n",
|
||||
"\n",
|
||||
"Rstates = RD.location_name.unique()\n",
|
||||
"\n",
|
||||
"# Real Series getter\n",
|
||||
"def getRS(type, state, aggregateOn = 5):\n",
|
||||
" \"\"\"Gets the real cases or deaths series by state\n",
|
||||
"\n",
|
||||
" Parameters\n",
|
||||
" ----------\n",
|
||||
" type : str\n",
|
||||
" 'C' for cumulative cases. \n",
|
||||
" 'D' for cumulative deaths.\n",
|
||||
" state : str\n",
|
||||
" The state where deaths were recorded\n",
|
||||
" aggregateOn : int or bool\n",
|
||||
" The weekday to aggregate the observations on.\n",
|
||||
" 0 is Monday, 6 is Sunday.\n",
|
||||
" Set to false to prevent aggregation.\n",
|
||||
"\n",
|
||||
" Returns\n",
|
||||
" -------\n",
|
||||
" pandas.Series : the series of real cases or deaths of the specified state. Indexes are of class pandas.DatetimeIndex.\n",
|
||||
" \"\"\"\n",
|
||||
" if(type == 'C'):\n",
|
||||
" out = pd.Series(RC[RC['location_name'] == state].iloc[:,3].values,\n",
|
||||
" index = pd.to_datetime(RC[RC['location_name'] == state].iloc[:,0].values, format=\"%Y-%m-%d\"),\n",
|
||||
" name = state + \": Cumulative cases\")\n",
|
||||
" elif(type == 'D'):\n",
|
||||
" out = pd.Series(RD[RD['location_name'] == state].iloc[:,3].values,\n",
|
||||
" index = pd.to_datetime(RD[RD['location_name'] == state].iloc[:,0].values, format=\"%Y-%m-%d\"),\n",
|
||||
" name = state + \": Cumulative deaths\"\n",
|
||||
" )\n",
|
||||
" if(aggregateOn is not False):\n",
|
||||
" out = out[out.index.weekday == aggregateOn]\n",
|
||||
"\n",
|
||||
" return(out)\n",
|
||||
"\n",
|
||||
"# Example: getRS('D',Rstates[1])\n",
|
||||
"\n",
|
||||
"# # shift series to first non-zero occurence\n",
|
||||
"# daily_s = daily_s[daily_s>0]\n",
|
||||
"# # switch aggregation range to weekly (every Saturday)\n",
|
||||
"# D = daily_s[daily_s.index.weekday == 5]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"## Forecast data: begins with \"F\"\n",
|
||||
"# Forecasted cases: FC\n",
|
||||
"FC = pd.read_csv(\"https://www.cdc.gov/coronavirus/2019-ncov/downloads/cases-updates/2020-10-19-all-forecasted-cases-model-data.csv\")\n",
|
||||
"# Forecasted deaths: FD\n",
|
||||
"FD = pd.read_csv('https://www.cdc.gov/coronavirus/2019-ncov/covid-data/files/2020-10-19-model-data.csv')\n",
|
||||
"\n",
|
||||
"Fmodels = FD.model.unique()\n",
|
||||
"Fstates = FD.location_name.unique()\n",
|
||||
"\n",
|
||||
"# Forecast Series getter\n",
|
||||
"def getFS(type, model, state, Fdate):\n",
|
||||
" \"\"\"Gets the forecasted deaths series by model, state and forecast date\n",
|
||||
"\n",
|
||||
" Parameters\n",
|
||||
" ----------\n",
|
||||
" type : str\n",
|
||||
" 'C' for cumulative cases. \n",
|
||||
" 'D' for cumulative deaths.\n",
|
||||
" model : str\n",
|
||||
" The model of the forecast\n",
|
||||
" state : str\n",
|
||||
" The target state of the forecast\n",
|
||||
" Fdate : str or datetime\n",
|
||||
" The date when the forecast was performed. If a string, provide the format '%Y-%m-%d'.\n",
|
||||
"\n",
|
||||
" Returns\n",
|
||||
" -------\n",
|
||||
" pandas.DataFrame\n",
|
||||
" a data frame containing 5 series:\n",
|
||||
" - point series\n",
|
||||
" - 2.5% quantile\n",
|
||||
" - 25% quantile\n",
|
||||
" - 75% quantile\n",
|
||||
" - 97.5% quantile\n",
|
||||
" Indexes are of class pandas.DatetimeIndex.\n",
|
||||
" \"\"\"\n",
|
||||
" if(type == 'C'):\n",
|
||||
" out = FC[(FC.model == model) & (FC.location_name == state) & (FC.forecast_date == Fdate)] \n",
|
||||
" elif(type == 'D'):\n",
|
||||
" out = FD[(FD.model == model) & (FD.location_name == state) & (FD.forecast_date == Fdate) & FD.target.apply(str.endswith, args=('cum death',0))]\n",
|
||||
" else:\n",
|
||||
" return None\n",
|
||||
" if( out.empty ):\n",
|
||||
" return None\n",
|
||||
" out = pd.DataFrame(out.iloc[:,-5:].values,\n",
|
||||
" columns = out.columns[-5:],\n",
|
||||
" index = pd.to_datetime(out.iloc[:,3], format=\"%Y-%m-%d\")\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" return out\n",
|
||||
" \n",
|
||||
"# Example: getFS('C', Fmodels[1], Fstates[1], FD.forecast_date[1])\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# prova1 = pd.ExcelFile('Matlab to python/data_models-Florida.xlsx')\n",
|
||||
"\n",
|
||||
"# prova1.sheet_names\n",
|
||||
"\n",
|
||||
"# prova2 = prova1.parse('Ensamble')\n",
|
||||
"# prova2\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array(['Alabama', 'Alaska', 'American Samoa', ..., 'Uinta County',\n",
|
||||
" 'Washakie County', 'Weston County'], dtype=object)"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"2020-01-25 0\n",
|
||||
"2020-02-01 0\n",
|
||||
"2020-02-08 0\n",
|
||||
"2020-02-15 0\n",
|
||||
"2020-02-22 0\n",
|
||||
"2020-02-29 0\n",
|
||||
"2020-03-07 0\n",
|
||||
"2020-03-14 0\n",
|
||||
"2020-03-21 0\n",
|
||||
"2020-03-28 2\n",
|
||||
"2020-04-04 5\n",
|
||||
"2020-04-11 8\n",
|
||||
"2020-04-18 9\n",
|
||||
"2020-04-25 9\n",
|
||||
"2020-05-02 9\n",
|
||||
"2020-05-09 10\n",
|
||||
"2020-05-16 10\n",
|
||||
"2020-05-23 10\n",
|
||||
"2020-05-30 10\n",
|
||||
"2020-06-06 10\n",
|
||||
"2020-06-13 12\n",
|
||||
"2020-06-20 12\n",
|
||||
"2020-06-27 14\n",
|
||||
"2020-07-04 16\n",
|
||||
"2020-07-11 17\n",
|
||||
"2020-07-18 18\n",
|
||||
"2020-07-25 20\n",
|
||||
"2020-08-01 24\n",
|
||||
"2020-08-08 26\n",
|
||||
"2020-08-15 28\n",
|
||||
"2020-08-22 31\n",
|
||||
"2020-08-29 37\n",
|
||||
"2020-09-05 42\n",
|
||||
"2020-09-12 44\n",
|
||||
"2020-09-19 45\n",
|
||||
"2020-09-26 52\n",
|
||||
"2020-10-03 58\n",
|
||||
"2020-10-10 60\n",
|
||||
"2020-10-17 67\n",
|
||||
"2020-10-24 68\n",
|
||||
"2020-10-31 82\n",
|
||||
"2020-11-07 84\n",
|
||||
"2020-11-14 98\n",
|
||||
"Name: Alaska: Cumulative deaths, dtype: int64"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
" getRS('D',Rstates[1])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"data= getFS('D', Fmodels[1], Fstates[1], FD.forecast_date[1])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 74., 67., 71., 78., 84.],\n",
|
||||
" [ 81., 67., 75., 89., 105.],\n",
|
||||
" [ 89., 67., 79., 101., 123.],\n",
|
||||
" [ 99., 67., 81., 111., 141.]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data.values"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2020-10-24 00:00:00\n",
|
||||
"2020-10-31 00:00:00\n",
|
||||
"2020-11-07 00:00:00\n",
|
||||
"2020-11-14 00:00:00\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for i in data.index.tolist():\n",
|
||||
" print(i)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 39,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>point</th>\n",
|
||||
" <th>quantile_0.025</th>\n",
|
||||
" <th>quantile_0.25</th>\n",
|
||||
" <th>quantile_0.75</th>\n",
|
||||
" <th>quantile_0.975</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>target_week_end_date</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <td>2020-10-24</td>\n",
|
||||
" <td>74.0</td>\n",
|
||||
" <td>67.0</td>\n",
|
||||
" <td>71.0</td>\n",
|
||||
" <td>78.0</td>\n",
|
||||
" <td>84.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <td>2020-10-31</td>\n",
|
||||
" <td>81.0</td>\n",
|
||||
" <td>67.0</td>\n",
|
||||
" <td>75.0</td>\n",
|
||||
" <td>89.0</td>\n",
|
||||
" <td>105.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <td>2020-11-07</td>\n",
|
||||
" <td>89.0</td>\n",
|
||||
" <td>67.0</td>\n",
|
||||
" <td>79.0</td>\n",
|
||||
" <td>101.0</td>\n",
|
||||
" <td>123.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <td>2020-11-14</td>\n",
|
||||
" <td>99.0</td>\n",
|
||||
" <td>67.0</td>\n",
|
||||
" <td>81.0</td>\n",
|
||||
" <td>111.0</td>\n",
|
||||
" <td>141.0</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" point quantile_0.025 quantile_0.25 quantile_0.75 \\\n",
|
||||
"target_week_end_date \n",
|
||||
"2020-10-24 74.0 67.0 71.0 78.0 \n",
|
||||
"2020-10-31 81.0 67.0 75.0 89.0 \n",
|
||||
"2020-11-07 89.0 67.0 79.0 101.0 \n",
|
||||
"2020-11-14 99.0 67.0 81.0 111.0 \n",
|
||||
"\n",
|
||||
" quantile_0.975 \n",
|
||||
"target_week_end_date \n",
|
||||
"2020-10-24 84.0 \n",
|
||||
"2020-10-31 105.0 \n",
|
||||
"2020-11-07 123.0 \n",
|
||||
"2020-11-14 141.0 "
|
||||
]
|
||||
},
|
||||
"execution_count": 39,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 42,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"pandas.core.frame.DataFrame"
|
||||
]
|
||||
},
|
||||
"execution_count": 42,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"type(data)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "AttributeError",
|
||||
"evalue": "'Timestamp' object has no attribute 'split'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-51-a8ece29f578b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
|
||||
"\u001b[1;32m<ipython-input-51-a8ece29f578b>\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
|
||||
"\u001b[1;31mAttributeError\u001b[0m: 'Timestamp' object has no attribute 'split'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"[i.split() for i in data.index.tolist()]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 52,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>point</th>\n",
|
||||
" <th>quantile_0.025</th>\n",
|
||||
" <th>quantile_0.25</th>\n",
|
||||
" <th>quantile_0.75</th>\n",
|
||||
" <th>quantile_0.975</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>target_week_end_date</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <td>2020-10-24</td>\n",
|
||||
" <td>74.0</td>\n",
|
||||
" <td>67.0</td>\n",
|
||||
" <td>71.0</td>\n",
|
||||
" <td>78.0</td>\n",
|
||||
" <td>84.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <td>2020-10-31</td>\n",
|
||||
" <td>81.0</td>\n",
|
||||
" <td>67.0</td>\n",
|
||||
" <td>75.0</td>\n",
|
||||
" <td>89.0</td>\n",
|
||||
" <td>105.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <td>2020-11-07</td>\n",
|
||||
" <td>89.0</td>\n",
|
||||
" <td>67.0</td>\n",
|
||||
" <td>79.0</td>\n",
|
||||
" <td>101.0</td>\n",
|
||||
" <td>123.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <td>2020-11-14</td>\n",
|
||||
" <td>99.0</td>\n",
|
||||
" <td>67.0</td>\n",
|
||||
" <td>81.0</td>\n",
|
||||
" <td>111.0</td>\n",
|
||||
" <td>141.0</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" point quantile_0.025 quantile_0.25 quantile_0.75 \\\n",
|
||||
"target_week_end_date \n",
|
||||
"2020-10-24 74.0 67.0 71.0 78.0 \n",
|
||||
"2020-10-31 81.0 67.0 75.0 89.0 \n",
|
||||
"2020-11-07 89.0 67.0 79.0 101.0 \n",
|
||||
"2020-11-14 99.0 67.0 81.0 111.0 \n",
|
||||
"\n",
|
||||
" quantile_0.975 \n",
|
||||
"target_week_end_date \n",
|
||||
"2020-10-24 84.0 \n",
|
||||
"2020-10-31 105.0 \n",
|
||||
"2020-11-07 123.0 \n",
|
||||
"2020-11-14 141.0 "
|
||||
]
|
||||
},
|
||||
"execution_count": 52,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 53,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"DatetimeIndex(['2020-10-24', '2020-10-31', '2020-11-07', '2020-11-14'], dtype='datetime64[ns]', name='target_week_end_date', freq=None)"
|
||||
]
|
||||
},
|
||||
"execution_count": 53,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data.index"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 56,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'2020-10-31 00:00:00'"
|
||||
]
|
||||
},
|
||||
"execution_count": 56,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"str(data.index[1])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 63,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2020-10-24 00:00:00\n",
|
||||
"2020-10-31 00:00:00\n",
|
||||
"2020-11-07 00:00:00\n",
|
||||
"2020-11-14 00:00:00\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
" for i in pd.Series(data.index.tolist()):\n",
|
||||
" print(i)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"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": "3.7.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user