def weeks_in_range(week_range: Tuple[int, int]) -> int:
start = week_value_to_week(week_range[0])
end = week_value_to_week(week_range[1])
acc = end.week - start.week
# accumulate the number of weeks in the years between
for y in range(start.year, end.year):
year = Year(y)
acc += year.totalweeks()
return acc + 1 # same week should lead to 1 week that will be queried
def fromEpiWeek2ModelWeek(self):
""" Convert epiweek to model week, defined as the number of epidemic weeks from 197001
"""
week = self.epiWeek
numWeeks = 0
w = self.refew
while True:
if w.year < week.year:
numWeeks += Year(w.year).totalweeks
w = Week(w.year + 1, 1)
else:
break
while w < week:
numWeeks += 1
w += 1
self.modelweek = numWeeks
def fromEpiWeek2ModelWeek(self, week):
from epiweeks import Week, Year
if type(week) is Week:
pass
elif type(week) is str:
week = Week(int(week[:4]), int(week[4:]))
elif type(week) is int:
week = str(week)
week = Week(int(week[:4]), int(week[4:]))
numWeeks = 0
w = Week(1970, 1)
while True:
if w.year < week.year:
numWeeks += Year(w.year).totalweeks
w = Week(w.year + 1, 1)
else:
break
while w < week:
numWeeks += 1
w += 1
return numWeeks
def date_string_to_epi_week(date_string):
"""
parse a date string in YYYY-MM-DD format and return
cumulative epi week which is cumulative total epidemiological
weeks since 2019-12-22. Week beginning 2019-12-22 is week 0
"""
try:
date = datetime.strptime(date_string, '%Y-%m-%d').date()
except:
return ""
# this is epi-week:
week = Week.fromdate(date)
if week.year < 2019 or (week.year == 2019 and week.week < 52):
return ""
elif week.year == 2019:
return ("0")
else:
cum_epi_week = week.week + len(
list(
chain(*[[x for x in Year(y).iterweeks()]
for y in range(2020, week.year)])))
return str(cum_epi_week)
import numpy as np
import geopandas as gpd
import pandas as pd
import seaborn as sns
from epiweeks import Week, Year
file_mask = 'D:/Utilisateurs/guillaume/Documents/GitHub/InSIGHT-PHAC/Mask/'
pr_in = 'K:/DATA/REANALYSES/ERA5/PR/'
data = pr_in + 'ERA5_PR_*.nc4'
ds = xr.open_mfdataset(data)
ds = ds * 1000 # convert from meter to mm
year_to_study = 2016
# compute epiweek for 2015 and 2016
df_epi = []
for week in Year(year_to_study).iterweeks():
day = week.week
day_ini = week.startdate().strftime("%Y-%m-%d")
day_end = week.enddate().strftime("%Y-%m-%d")
data = {'Week': [day], 'datei': [day_ini], 'datef': [day_end]}
df = pd.DataFrame(data)
df_epi.append(pd.DataFrame(data).set_index('Week'))
df_epi = pd.concat(df_epi, axis=0).sort_index()
lat_bnd = [62, -70]
lon_bnd = [230, 340]
Imp_Lats = ds.sel(
longitude=slice(*lon_bnd),
latitude=slice(*lat_bnd),
def make_epiweeks():
weeks = list(Year(2020).iterweeks())
return weeks
def get_weeks(self, year):
year = Year(year)
return year.totalweeks()
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 20 12:12:33 2019
@author: guillaume
"""
import pandas as pd
from epiweeks import Week, Year
df_epi = []
for week in Year(2015).iterweeks():
day = week.week
day_ini = week.startdate().strftime("%Y-%m-%d")
day_end = week.enddate().strftime("%Y-%m-%d")
data = {'Week': [day], 'datei': [day_ini], 'datef': [day_end]}
df = pd.DataFrame(data)
df_epi.append(pd.DataFrame(data).set_index('Week'))
df_epi = pd.concat(df_epi, axis=0).sort_index()
def make_epiweeks():
weeks = list(Year(2019).iterweeks())
weeks.extend(list(Year(2020).iterweeks()))
return weeks