def run_download(_):
run_date = pd.Timestamp.now().strftime("%d-%m-%Y")
print(f"Starting download of API files on {run_date}")
# set up
root = Path("/tmp")
data = mkdir(root/"data")
# download aggregated CSVs as well
download_data(data, "states.csv")
download_data(data, "districts.csv")
print("Uploading time series to storage bucket.")
bucket = storage.Client().bucket(bucket_name)
bucket.blob("pipeline/raw/districts.csv")\
.upload_from_filename(str(data/"districts.csv"), content_type = "text/csv")
bucket.blob("pipeline/raw/states.csv")\
.upload_from_filename(str(data/"states.csv"), content_type = "text/csv")
return 'OK!'
plt.show()
return coef
def notch_filter(ts):
"Implement notch filter with notches at {1/7, 2/7}"
fs, f0, Q = 1, 1/7, 1
b1, a1 = iirnotch(f0, Q, fs)
b2, a2 = iirnotch(2*f0, 2*Q, fs)
b = convolve(b1, b2)
a = convolve(a1, a2)
notched = pd.Series(filtfilt(b, a, ts))
notched.index = ts.index
return notched
root = cwd()
data = mkdir(root/"data")
figs = mkdir(root/"figs")
###########################################################
# download latest case data
# download_data(data, 'state_wise_daily.csv')
# df = load_statewise_data(data/"state_wise_daily.csv")
# ts = get_time_series(df, "state")
###########################################################
# load delay data
api_diff = pd.read_csv(data/"daily_diff.csv", parse_dates=["status_change_date", "report_date"], dayfirst=True)
delay = api_diff[(api_diff.current_status == "Hospitalized") & (api_diff.report_date > "2020-08-02")].copy()
delay = delay.drop(columns = [col for col in delay.columns if col.startswith("Unnamed")] + ["rowhash"])
delay["newhash"] = delay[["patient_number", "date_announced", "detected_district", "detected_state","current_status", "status_change_date", "num_cases"]].apply(lambda x: hash(tuple(x)), axis = 1)
delay = delay.drop_duplicates(subset=["newhash"], keep="first")
import pandas as pd
from epimargin.utils import setup, mkdir
data, _ = setup()
peru = mkdir(data/"peru")
schema = dict(zip(
"FECHA_CORTE;UUID;FECHA_FALLECIMIENTO;EDAD_DECLARADA;SEXO;FECHA_NAC;DEPARTAMENTO;PROVINCIA;DISTRITO".split(";"),
"CUT_DATE;UUID;DEATH_DATE;DECLARED_AGE;SEX;BIRTH_DATE;DEPARTMENT;PROVINCE;DISTRICT".lower().split(";")
))
#source: https://www.datosabiertos.gob.pe/dataset/fallecidos-por-covid-19-ministerio-de-salud-minsa
df = pd.read_csv(peru/"fallecidos_covid.csv", delimiter = ";", encoding = "ISO-8859-1")\
.rename(columns = schema)
df[df.district == "IQUITOS"][["death_date", "declared_age", "sex", "district"]]\
.assign(
death_date = lambda _: pd.to_datetime(_["death_date"].astype(str).str.lstrip("0"), format = "%Y%m%d"),
sex = lambda _: _["sex"].str[0]
)\
.to_csv(peru/"iquitos.csv")
from studies.age_structure.TN_CMIE.commons import *
from tqdm.std import tqdm
import epimargin.plots as plt
sns.set(style = "whitegrid")
num_sims = 1000
simulation_range = 1 * years
phi_points = [_ * percent * annually for _ in (25, 50, 100, 200)]
simulation_initial_conditions = pd.read_csv(data/"simulation_initial_conditions.csv")\
.drop(columns = ["Unnamed: 0"])\
.set_index("district")
districts_to_run = simulation_initial_conditions
num_age_bins = 7
seed = 0
dst = mkdir(data/f"sim_metrics{num_sims}")
def save_metrics(policy, tag):
np.savez(dst/f"{tag}.npz",
dT = policy.dT_total,
dD = policy.dD_total,
pi = policy.pi,
q0 = policy.q0,
q1 = policy.q1,
Dj = policy.D
)
def prioritize(num_doses, S, prioritization):
Sp = S[:, prioritization]
dV = np.where(Sp.cumsum(axis = 1) <= num_doses, Sp, 0)
dV[np.arange(len(dV)), (Sp.cumsum(axis = 1) > dV.cumsum(axis = 1)).argmax(axis = 1)] = num_doses - dV.sum(axis = 1)
infectious_period = 1/gamma
smooth = notched_smoothing(window)
# simulation parameters
simulation_start = pd.Timestamp("April 15, 2021")
num_sims = 1000
focus_states = ["Tamil Nadu", "Punjab", "Maharashtra", "Bihar", "West Bengal"]
# states to evaluate at state level (no district level data)
coalesce_states = ["Delhi", "Manipur", "Dadra And Nagar Haveli And Daman And Diu", "Andaman And Nicobar Islands"]
# experiment_tag = "OD_IFR_Rtdownscale_fullstate"
experiment_tag = "unitvaxhazard_TN_IFR"
# epi_dst = tev_src = mkdir(ext/f"{experiment_tag}_epi_{num_sims}_{simulation_start.strftime('%b%d')}")
# epi_dst = tev_src = mkdir(Path("/Volumes/dedomeno/covid/vax-nature/OD_IFR_Rtdownscale_fullstate_epi_1000_Apr15"))
epi_dst = tev_src = mkdir(Path("/Volumes/dedomeno/covid/vax-nature/all_india_coalesced_epi_1000_Apr15"))
tev_dst = fig_src = mkdir(ext/f"{experiment_tag}_tev_{num_sims}_{simulation_start.strftime('%b%d')}")
# misc
survey_date = "October 23, 2020"
# palette
TN_color = "firebrick"
IN_color = "#292f36"
no_vax_color = "black"
contactrate_vax_color = "darkorange"
random_vax_color = "royalblue"
mortality_vax_color = "forestgreen"
agebin_colors = [ "#05668d", "#427aa1", "#679436", "#a5be00", "#ffcb77", "#d0393b", "#7a306c"]