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get_data.py
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#%%
import pandas as pd
from pandas import DataFrame
from datetime import datetime, timedelta
import numpy as np
import pygsheets
import fbprophet
# names of vacccines
NAME_PFIZER = "pfizer"
NAME_PFIZER_EXTRA = f"{NAME_PFIZER}_extra"
NAME_MODERNA = "moderna"
NAME_MODERNA_EXTRA = f"{NAME_MODERNA}_extra"
NAME_JJ = "johnson_johnson"
NAME_CUREVAC = "curevac"
NAME_SANOFI = "sanofi"
NAME_ASTRA = "astra"
# different vaccinations strategies,
# scenario_a = current STIKO vaccination strategy, scenario_b =
VACCINATIONS_STRATEGIES = {"scenario_a": {NAME_PFIZER: 6,
NAME_MODERNA: 6,
NAME_SANOFI: 6,
NAME_CUREVAC: 6,
NAME_ASTRA: 12,
NAME_JJ: -1},
"scenario_b": {NAME_PFIZER: 12,
NAME_MODERNA: 12,
NAME_SANOFI: 12,
NAME_CUREVAC: 12,
NAME_ASTRA: 12,
NAME_JJ: -1}
}
# relevant urls RKI
MAPPING_NAME_RKI = {"comirnaty": NAME_PFIZER, "astra": NAME_ASTRA, "moderna": NAME_MODERNA}
URL_RKI_DELIVERIES_LATEST = "https://impfdashboard.de/static/data/germany_deliveries_timeseries_v2.tsv"
URL_RKI_IMPFREIHE_LATEST = "https://impfdashboard.de/static/data/germany_vaccinations_timeseries_v2.tsv"
# destatis data
# source https://de.statista.com/statistik/daten/studie/1200499/umfrage/prognose-zu-lieferungen-von-corona-impfstoffen/#professional
q4_2020 = {"quarter": [4], "year": [2020], "contract_name": [NAME_PFIZER], "amount": [1.3], "amount_usable": [1.3]}
q1_2021 = {"quarter": 1, "year": 2021,
"contract_name": [NAME_PFIZER, NAME_MODERNA, NAME_ASTRA],
"amount": [10.9, 1.8, 5.6],
"amount_usable": [10.9, 1.8, 2.9]}
# due to the decision to only vaccinate citiziens 60+, all persons vaccinated with AstraZeneca now have to receive a
# another mRNA vaccine, the usable doses are reduced by equal amounts
AMOUNT_ASTRA_FOR_SECOND_VACC = 2.6
q2_2021 = {"quarter": 2, "year": 2021,
"contract_name": [NAME_MODERNA, NAME_CUREVAC, NAME_PFIZER_EXTRA, NAME_JJ, NAME_PFIZER, NAME_ASTRA],
"amount": [6.42, 3.5, 8.7 + 10.10415, 10.1, 31.5, 12.4],
"amount_usable": [6.42-AMOUNT_ASTRA_FOR_SECOND_VACC/5,
3.5-AMOUNT_ASTRA_FOR_SECOND_VACC/5,
8.7 + 10.10415 - AMOUNT_ASTRA_FOR_SECOND_VACC/5,
10.1-AMOUNT_ASTRA_FOR_SECOND_VACC/5,
31.5 - AMOUNT_ASTRA_FOR_SECOND_VACC/5,
12.4]}
q3_2021 = {"quarter": 3, "year": 2021,
"contract_name": [NAME_MODERNA, NAME_MODERNA_EXTRA, NAME_CUREVAC, NAME_PFIZER_EXTRA, NAME_JJ, NAME_PFIZER, NAME_ASTRA],
"amount": [17.6, 9.1, 9.4, 17.1, 22, 17.6, 33.8],
"amount_usable": [17.6, 9.1, 9.4, 17.1, 22, 17.6, 33.8]}
q4_2021 = {"quarter": 4, "year": 2021,
"contract_name": [NAME_SANOFI, NAME_MODERNA, NAME_MODERNA_EXTRA, NAME_CUREVAC, NAME_JJ, NAME_MODERNA, NAME_PFIZER_EXTRA],
"amount": [27.5, 24.6, 18.3, 11.7, 10.8, 4.6, 2.7],
"amount_usable": [27.5, 24.6, 18.3, 11.7, 10.8, 4.6, 2.7]}
QUARTERLY_DELIVERIES_STATISTA = [q4_2020, q1_2021, q2_2021, q3_2021, q4_2021]
PATH_CUSTOM_DISTRIBUTION_QUARTERLY_DELIVERIES = "data/custom_time_distribution_vacc.xlsx"
SHEET_SCENARIO_JJ = "custom_jj"
SHEET_BGM_DATA = "bgm_data"
# helper type of vaccinations necessary for tableau plotting
TYPE_VACCS_HELPER = {"dosen_erst_minus_zweit_kum": "helper_1st",
"dosen_zweit_kum": "helper_2nd",
"dosen_reserve_kum": "helper_reserve"}
def prep_rki_deliveries(path_rki: str = URL_RKI_DELIVERIES_LATEST) -> DataFrame:
df_rki = pd.read_csv(path_rki,
sep="\t",
names=["date", "impfstoff", "region", "amount"],
header=0,
parse_dates=[0]
)
if set(df_rki["impfstoff"]) != set(MAPPING_NAME_RKI.keys()):
err_msg = "Some RKI Impfstoff names are not considered. Please change parameter `MAPPING_NAME_RKI`."
raise ValueError(err_msg)
df_rki["type_vacc"] = df_rki["impfstoff"].map(MAPPING_NAME_RKI)
#df_rki["state_code"] = df_rki["region"].str.split("-", expand=True)[1]
df_rki["first_day_of_week"] = df_rki['date'].apply(lambda x: (x - timedelta(days=x.dayofweek)))
df_rki_weekly = (df_rki.groupby(["first_day_of_week", "type_vacc"])
.agg(**{"amount_type_vacc_de_rki_weekly": ("amount", "sum")})
.reset_index(drop=False)
)
df_rki_weekly["year"] = df_rki_weekly["first_day_of_week"].dt.year
df_rki_weekly["week_of_year"] = df_rki_weekly["first_day_of_week"].dt.isocalendar().week
df_rki_weekly["quarter"] = df_rki_weekly["first_day_of_week"].dt.quarter
df_rki_weekly["future"] = False
df_rki_weekly["source"] = "impfdashboard"
return df_rki_weekly
def prep_rki_vaccs(path: str = URL_RKI_IMPFREIHE_LATEST,
cols_impfreihe: list = None,
agg_week: bool = True
) -> DataFrame:
df_ir_de = pd.read_csv(path, sep="\t", parse_dates=[0])
df_ir_de["first_day_of_week"] = df_ir_de['date'].apply(lambda x: (x - timedelta(days=x.dayofweek)))
if cols_impfreihe is None:
cols_impfreihe = df_ir_de.columns
cols_numeric = df_ir_de.select_dtypes(include=[np.number]).columns
cols_non_numeric = df_ir_de.columns.difference(cols_numeric)
cols_sum = {col: "sum" for col in cols_numeric}
cols_max = {col: "max" for col in cols_numeric if ("kumulativ" in col or "indikation" in col)}
cols_avg = {col: "mean" for col in cols_numeric if "quote" in col}
cols_first = {col: "first" for col in cols_non_numeric}
agg = {**cols_sum, **cols_max, **cols_first, **cols_avg}
if agg_week:
return df_ir_de.groupby(["first_day_of_week"]).agg(agg).reset_index(drop=True)
else:
return df_ir_de
def read_custom_vacc_distribution_time(path: str = PATH_CUSTOM_DISTRIBUTION_QUARTERLY_DELIVERIES,
sheet_name: str = SHEET_SCENARIO_JJ
)-> DataFrame:
df_distr_vacc_melted = pd.melt(pd.read_excel(path, sheet_name=sheet_name),
id_vars=["year", "quarter", "week_of_year", "first_day_of_week"],
var_name="type_vacc",
value_name="distr_vacc_custom")
# sanity check
df_sanity_check = df_distr_vacc_melted.dropna(subset=["distr_vacc_custom"]).groupby(["year", "quarter", "type_vacc"])[["distr_vacc_custom"]].sum()
assert np.allclose(df_sanity_check["distr_vacc_custom"], 1.0)
return df_distr_vacc_melted
def disaggregate_destatis_weekly(df_destatis: DataFrame,
path_custom_distr_deliv: str = PATH_CUSTOM_DISTRIBUTION_QUARTERLY_DELIVERIES,
scenario_distribution: str = SHEET_BGM_DATA
)-> DataFrame:
weekstarts = (pd.Series(pd.date_range(start="2020-12-21", end="2021-12-31"))
.apply(lambda x: (x - timedelta(days=x.dayofweek))).unique())
df_destatis_weekly = pd.DataFrame({"first_day_of_week": weekstarts})
df_destatis_weekly["year"] = df_destatis_weekly["first_day_of_week"].dt.year
df_destatis_weekly["week_of_year"] = df_destatis_weekly["first_day_of_week"].dt.isocalendar().week
df_destatis_weekly["quarter"] = df_destatis_weekly["first_day_of_week"].dt.quarter
df_destatis_weekly["distr_factor_equal"] = (df_destatis_weekly
.groupby(["year", "quarter"])
["week_of_year"]
.transform(lambda x: 1/len(x))
)
df_destatis_weekly = df_destatis_weekly.merge((df_destatis
.set_index(["year", "quarter"])
[["amount_type_vacc", "type_vacc", "amount_type_vacc_usable"]]
.rename(columns={"amount_type_vacc": "amount_type_vacc_de_quarter",
"amount_type_vacc_usable": "amount_type_vacc_de_quarter_usable"})
),
left_on=["year", "quarter"], right_index=True,
how="left")
if path_custom_distr_deliv is None:
df_destatis_weekly["distr_factor"] = df_destatis_weekly["distr_factor_equal"]
else:
df_distr_factors_weekly = read_custom_vacc_distribution_time(path_custom_distr_deliv, sheet_name=scenario_distribution)
df_destatis_weekly = df_destatis_weekly.merge(df_distr_factors_weekly.set_index(["year", "quarter", "week_of_year", "type_vacc"])[["distr_vacc_custom"]],
how="left",
validate="m:1",
left_on=["year", "quarter", "week_of_year", "type_vacc"],
right_index=True)
df_destatis_weekly["distr_factor"] = np.where(df_destatis_weekly["distr_vacc_custom"].isna(),
df_destatis_weekly["distr_factor_equal"],
df_destatis_weekly["distr_vacc_custom"])
df_destatis_weekly["amount_type_vacc_de_ds_weekly"] = df_destatis_weekly["amount_type_vacc_de_quarter"] * df_destatis_weekly["distr_factor"]
df_destatis_weekly["amount_type_vacc_de_ds_weekly_usable"] = df_destatis_weekly["amount_type_vacc_de_quarter_usable"] * df_destatis_weekly["distr_factor"]
df_destatis_weekly["source"] = "destatis"
df_destatis_weekly["scenario_distribution"] = scenario_distribution
return df_destatis_weekly
def prep_destatis(data: list = QUARTERLY_DELIVERIES_STATISTA) -> DataFrame:
df_destatis = pd.concat([pd.DataFrame(q) for q in data], ignore_index=True)
df_destatis["amount_contract"] = df_destatis["amount"] * 10**6
df_destatis["amount_usable"] = df_destatis["amount_usable"] * 10**6
df_destatis["type_vacc"] = df_destatis["contract_name"].str.replace("_extra", "")
df_destatis["amount_type_vacc"] = (df_destatis.groupby(["year", "quarter", "type_vacc"])
["amount_contract"]
.transform(lambda x: x.sum())
)
df_destatis["amount_type_vacc_usable"] = (df_destatis.groupby(["year", "quarter", "type_vacc"])
["amount_usable"]
.transform(lambda x: x.sum())
)
df_destatis = (df_destatis
.groupby(["year", "quarter", "type_vacc"], as_index=False)
.agg(**{**{col: (col, "first") for col in df_destatis},
**{"amount_contract": ("amount_contract", "sum")}})
.reset_index(drop=False)
)
df_destatis_weekly = disaggregate_destatis_weekly(df_destatis=df_destatis)
return df_destatis_weekly
def combine_datasources_deliveries(df_rki_deliv_weekly: DataFrame = None ,
df_destatis_weekly: DataFrame = None
) -> DataFrame:
if df_rki_deliv_weekly is None:
df_rki_weekly_type_vacc = prep_rki_deliveries()
if df_destatis_weekly is None:
df_destatis_weekly = prep_destatis()
cols_merge_rki = ["first_day_of_week", "type_vacc", "amount_type_vacc_de_rki_weekly"]
cols_merge_destatis = ["amount_type_vacc_de_ds_weekly", "amount_type_vacc_de_ds_weekly_usable"]
mask_latest_common_date = df_destatis_weekly["first_day_of_week"] <= df_rki_weekly_type_vacc.first_day_of_week.max()
df_de_weekly_type_vacc = pd.merge(df_rki_weekly_type_vacc[cols_merge_rki],
df_destatis_weekly[mask_latest_common_date].set_index(["first_day_of_week", "type_vacc"])[cols_merge_destatis],
how="outer",
left_on=["first_day_of_week", "type_vacc"],
right_index=True)
cols_merge_destatis = ["first_day_of_week", "type_vacc",
"amount_type_vacc_de_ds_weekly",
"amount_type_vacc_de_ds_weekly_usable"]
df_de_weekly_type_vacc = pd.concat([df_de_weekly_type_vacc,
df_destatis_weekly[~mask_latest_common_date][cols_merge_destatis]],
axis=0, ignore_index=True)
df_de_weekly_type_vacc["year"] = df_de_weekly_type_vacc["first_day_of_week"].dt.year
df_de_weekly_type_vacc["week_of_year"] = df_de_weekly_type_vacc["first_day_of_week"].dt.isocalendar().week
df_de_weekly_type_vacc["quarter"] = df_de_weekly_type_vacc["first_day_of_week"].dt.quarter
mask_is_nan = df_de_weekly_type_vacc["amount_type_vacc_de_rki_weekly"].isna()
df_de_weekly_type_vacc["amount_type_vacc_de_weekly"] = np.where(~mask_is_nan,
df_de_weekly_type_vacc["amount_type_vacc_de_rki_weekly"],
df_de_weekly_type_vacc["amount_type_vacc_de_ds_weekly"])
df_de_weekly_type_vacc["amount_type_vacc_de_weekly_usable"] = np.where(~mask_is_nan,
df_de_weekly_type_vacc["amount_type_vacc_de_rki_weekly"],
df_de_weekly_type_vacc["amount_type_vacc_de_ds_weekly_usable"])
mask_latest_common_date = df_destatis_weekly["first_day_of_week"] <= df_rki_weekly_type_vacc.first_day_of_week.max()
df_de_weekly_type_vacc["future"] = np.where(mask_latest_common_date, False, True)
return df_de_weekly_type_vacc
def combine_rki_datasources(df_de_weekly_type_vacc: DataFrame) -> DataFrame:
df_weekly_deliv_type_vacc = prep_rki_deliveries()
df_weekly_vac = prep_rki_vaccs().set_index("first_day_of_week")
df_weekly_deliv = (df_weekly_deliv_type_vacc
.groupby("first_day_of_week")
.agg(**{"amount_vacc_week_distributed": ("amount_type_vacc_de_rki_weekly", "sum")})
)
df_reindex = df_de_weekly_type_vacc.groupby("first_day_of_week")[["type_vacc"]].first()
df_rki_weekly = (df_weekly_vac.reindex(index=df_reindex.index)
.merge(df_weekly_deliv, on="first_day_of_week", validate="1:1", how="left")
)
return df_rki_weekly.reset_index(drop=False)
def _compute_number_of_vaccinations_by_type(df_de_type_vacc: DataFrame,
type_vacc: str,
scenario: dict,
suffix_scenario: str,
col_amount_vacc: str ="amount_type_vacc_de_weekly_usable"
) -> DataFrame:
period = scenario[type_vacc]
df_de_type_vacc.sort_values(by="first_day_of_week", inplace=True)
df_de_type_vacc.reset_index(drop=True, inplace=True)
col_amount_for_first_vacc = f"amount_available_for_first_vacc_{suffix_scenario}"
col_n_first_vacc = f"n_first_vacc_{suffix_scenario}"
col_n_second_vacc = f"n_second_vacc_{suffix_scenario}"
col_n_fully_immune_cum = f"n_fully_immune_{suffix_scenario}"
if period == -1:
df_de_type_vacc[col_amount_for_first_vacc] = df_de_type_vacc[col_amount_vacc].to_numpy()
df_de_type_vacc[col_n_first_vacc] = df_de_type_vacc[col_amount_vacc]
df_de_type_vacc[col_n_second_vacc] = 0
df_de_type_vacc[col_n_fully_immune_cum] = df_de_type_vacc[col_n_first_vacc].cumsum()
else:
init_amount = df_de_type_vacc[col_amount_vacc].to_numpy()
n_first_vacc = np.minimum(init_amount[0:period],
init_amount[0:period]/2 + init_amount[period: period*2]/2)
amount_available_for_first_vac = []
for i, amount in enumerate(df_de_type_vacc[col_amount_vacc]):
if i < period:
amount_available_for_first_vac += [n_first_vacc[i]]
else:
amount_available_for_first_vac += [max(0, init_amount[i] - amount_available_for_first_vac[i-period])]
df_de_type_vacc[col_amount_for_first_vacc] = amount_available_for_first_vac
df_de_type_vacc[col_n_first_vacc] = np.minimum(df_de_type_vacc[col_amount_for_first_vacc],
df_de_type_vacc[col_amount_vacc].shift(-period).fillna(0.0)/2 + df_de_type_vacc[col_amount_for_first_vacc]/2)
df_de_type_vacc[col_n_first_vacc].iloc[0:period] = df_de_type_vacc[col_amount_for_first_vacc].iloc[0:period]
df_de_type_vacc[col_n_second_vacc] = df_de_type_vacc[col_n_first_vacc].shift(period).fillna(0.0)
df_de_type_vacc[col_n_fully_immune_cum] = df_de_type_vacc[col_n_second_vacc].cumsum()
return df_de_type_vacc
def compute_number_of_vaccinations(df_de_weekly: DataFrame,
scenario: dict,
suffix_scenario: str
) -> DataFrame:
list_df_type_vacs = []
for type_vacc, df_de_type_vacc in df_de_weekly.groupby("type_vacc"):
list_df_type_vacs += [_compute_number_of_vaccinations_by_type(df_de_type_vacc,
type_vacc=type_vacc,
scenario=scenario,
suffix_scenario=suffix_scenario
)]
return pd.concat(list_df_type_vacs, ignore_index=True)
def predict_vaccinations(prediction_horizon_weeks: int = 4) -> DataFrame:
df_rki_daily = prep_rki_vaccs(agg_week=False)
df_1st = (df_rki_daily
[["date", "dosen_erst_differenz_zum_vortag"]]
.rename(columns={"date":"ds", "dosen_erst_differenz_zum_vortag": "y"})
.copy()
)
df_2nd = (df_rki_daily
[["date", "dosen_zweit_differenz_zum_vortag"]]
.rename(columns={"date":"ds", "dosen_zweit_differenz_zum_vortag": "y"})
.copy()
)
model_1st = fbprophet.Prophet(yearly_seasonality=False, daily_seasonality=True)
model_1st.fit(df_1st)
model_2nd = fbprophet.Prophet(yearly_seasonality=False, daily_seasonality=True)
model_2nd.fit(df_2nd)
date_first_prediction = df_rki_daily["date"].max() + pd.Timedelta(days=1)
df_future = pd.DataFrame([pd.to_datetime(date_first_prediction) + pd.Timedelta(days=i) for i in range(prediction_horizon_weeks*7)],
columns=["ds"])
df_future["ds"] = df_future["ds"].dt.date
df_predict_1st = model_1st.predict(df_future).set_index("ds")
df_predict_2nd = model_2nd.predict(df_future).set_index("ds")
df_1st = (pd.concat([df_1st.set_index("ds")["y"], df_predict_1st["yhat"]])
.reset_index(drop=False)
.rename(columns={0: "dosen_erst_mit_projektion"})
)
df_2nd = (pd.concat([df_2nd.set_index("ds")["y"], df_predict_2nd["yhat"]])
.reset_index(drop=False)
.rename(columns={0: "dosen_zweit_mit_projektion"})
.drop(columns=["ds"])
)
df_vacc_with_predict = pd.concat([df_1st, df_2nd], axis=1)
df_vacc_with_predict["first_day_of_week"] = df_vacc_with_predict['ds'].apply(lambda x: (x - timedelta(days=x.dayofweek)))
return df_vacc_with_predict
def cumsum_vaccinations(df_rki_weekly: DataFrame,
df_vacc_predicted: DataFrame,
last_rki_vacc_update: datetime.date,
last_rki_deliv_update: datetime.date
)-> DataFrame:
mask_stop_cumsum = df_rki_weekly["first_day_of_week"] > last_rki_deliv_update
doses_reserve =(df_rki_weekly["amount_vacc_week_distributed"]
- df_rki_weekly["dosen_erst_differenz_zum_vortag"]
- df_rki_weekly["dosen_zweit_differenz_zum_vortag"])
df_rki_weekly["dosen_reserve"] = np.where(mask_stop_cumsum, np.nan, doses_reserve)
agg = {"dosen_erst_mit_projektion": ("dosen_erst_mit_projektion", "sum"),
"n_days_in_week": ("dosen_erst_mit_projektion", len),
"dosen_zweit_mit_projektion": ("dosen_zweit_mit_projektion", "sum")}
df_vacc_pred_agg = df_vacc_predicted.groupby("first_day_of_week").agg(**agg)
df_rki_weekly = df_rki_weekly.merge(df_vacc_pred_agg, how="left", validate="1:1", on="first_day_of_week")
mask_stop_cumsum = (df_rki_weekly["first_day_of_week" ] > last_rki_vacc_update) & (df_rki_weekly["n_days_in_week"] != 7.0)
df_rki_weekly["dosen_erst_kum"] = np.where(mask_stop_cumsum, np.nan, df_rki_weekly["dosen_erst_mit_projektion"].cumsum())
df_rki_weekly["dosen_zweit_kum"] = np.where(mask_stop_cumsum, np.nan, df_rki_weekly["dosen_zweit_mit_projektion"].cumsum())
df_rki_weekly["dosen_erst_minus_zweit_kum"] = np.maximum(0, df_rki_weekly["dosen_erst_kum"] - df_rki_weekly["dosen_zweit_kum"])
df_rki_weekly["dosen_reserve_kum"] = - df_rki_weekly["dosen_reserve"].cumsum()
return df_rki_weekly
def prep_data_for_tableau(df_de_weekly_type_vacc: DataFrame,
df_rki_weekly_vacc: DataFrame,
last_rki_vacc_update: datetime.date,
type_vaccs_helper: dict = TYPE_VACCS_HELPER,
cols_relevant: list = None,
):
cols_melt = type_vaccs_helper.keys()
df_type_vacc_helper = (pd.melt(df_rki_weekly_vacc.set_index("first_day_of_week")[cols_melt],
value_name="amount_actual", var_name="col_rki_ir", ignore_index=False)
.reset_index(drop=False)
)
df_type_vacc_helper["type_vacc"] = df_type_vacc_helper["col_rki_ir"].map(type_vaccs_helper)
df_type_vacc_helper["year"] = df_type_vacc_helper["first_day_of_week"].dt.year
df_type_vacc_helper["week_of_year"] = df_type_vacc_helper["first_day_of_week"].dt.isocalendar().week
df_type_vacc_helper["quarter"] = df_type_vacc_helper["first_day_of_week"].dt.quarter
#df_type_vacc_helper["country"] = "de"
df_type_vacc_helper["future"] = df_type_vacc_helper["first_day_of_week"] > last_rki_vacc_update
if cols_relevant is None: cols_relevant = df_de_weekly_type_vacc.columns
df_de_weekly = pd.concat([df_de_weekly_type_vacc[cols_relevant],
df_type_vacc_helper.drop(columns=["col_rki_ir"])],
axis=0, ignore_index=True)
df_de_weekly.drop(columns=["amount_type_vacc_de_ds_weekly",
"amount_type_vacc_de_ds_weekly_usable",
"amount_type_vacc_de_rki_weekly"], inplace=True)
return df_de_weekly.sort_values(by="first_day_of_week")
def compute_reference_line(df_rki_weekly_vacc: DataFrame,
df_de_weekly_type_vacc: DataFrame,
date_start: str = "2021-04-01",
date_end: str = "2021-07-31",
num_people_fully_immune = 52_500_000):
df_rl = pd.DataFrame({"date": pd.date_range(start=date_start, end=date_end, freq="d")})
df_rl["first_day_of_week"] = df_rl['date'].apply(lambda x: (x - timedelta(days=x.dayofweek)))
num_people_fully_immune_start = (df_rki_weekly_vacc
.set_index("first_day_of_week")
.sort_index()
.truncate(before=date_start)
["personen_voll_kumulativ"]
.iloc[0])
n_second_per_day = (num_people_fully_immune - num_people_fully_immune_start)/df_rl.shape[0]
df_rl["amount_2nd_reference_line"] = n_second_per_day
df_rl["amount_2nd_reference_line"] = df_rl["amount_2nd_reference_line"].cumsum() + num_people_fully_immune_start
df_rl_agg = df_rl.groupby("first_day_of_week")[["amount_2nd_reference_line"]].max()
df_de_weekly = df_de_weekly_type_vacc.merge(df_rl_agg,
how="left", right_index=True, left_on="first_day_of_week",
validate="m:1")
return df_de_weekly
def save_to_googlesheets(df_save: DataFrame,
path_sf: str = "config/impf-dashboard-064b3ec57a70.json",
fname: str = "impftracker"
) -> None:
#authorization
google_client = pygsheets.authorize(service_file=path_sf)
sh = google_client.open(fname)
#select the first sheet
wks = sh[0]
wks.clear()
wks.set_dataframe(df_save.sort_values(by="first_day_of_week"), (1,1))
def save_locally(df_save: DataFrame,
dir_save: str = "data/results_history/"
) -> None:
dt = datetime.now().strftime(r"%y%m%d_%H%M")
fname = f"impftracker_{dt}.xlsx"
df_save.sort_values(by="first_day_of_week").to_excel(dir_save + fname, sheet_name="de_weekly")
def main():
# combine distributed vaccinations, number of vaccinations, and statista projection into single dataFrame
df_de_weekly_type_vacc = combine_datasources_deliveries()
df_rki_weekly_vacc = combine_rki_datasources(df_de_weekly_type_vacc=df_de_weekly_type_vacc)
last_rki_vacc_update = df_rki_weekly_vacc.dropna(subset=["dosen_kumulativ"])["first_day_of_week"].max()
last_rki_deliv_update = df_rki_weekly_vacc.dropna(subset=["amount_vacc_week_distributed"])["first_day_of_week"].max()
for suffix_scenario, scenario in VACCINATIONS_STRATEGIES.items():
df_de_weekly_type_vacc = compute_number_of_vaccinations(df_de_weekly_type_vacc,
scenario=scenario,
suffix_scenario=suffix_scenario)
# predictions of future vaccinations
df_vacc_predicted = predict_vaccinations()
df_rki_weekly_vacc = cumsum_vaccinations(df_rki_weekly_vacc,
df_vacc_predicted=df_vacc_predicted,
last_rki_deliv_update=last_rki_deliv_update,
last_rki_vacc_update=last_rki_vacc_update)
#return df_rki_weekly_vacc
df_de_weekly_type_vacc = compute_reference_line(df_rki_weekly_vacc=df_rki_weekly_vacc,
df_de_weekly_type_vacc=df_de_weekly_type_vacc)
# prep data for Tableau
df_tableau = prep_data_for_tableau(df_de_weekly_type_vacc=df_de_weekly_type_vacc,
df_rki_weekly_vacc=df_rki_weekly_vacc,
last_rki_vacc_update=last_rki_deliv_update)
save_to_googlesheets(df_tableau)
save_locally(df_tableau)
return df_tableau
#%%
if __name__ == "__main__":
main()
# %%