def tweet_france_maps():
_, _, dates, _, _, _, _, df_incid, _ = data.import_data()
df_incid = df_incid[df_incid["cl_age90"] == 0]
lastday_df_incid = datetime.strptime(df_incid['jour'].max(), '%Y-%m-%d')
## TWEET2
df_incid_lastday = df_incid.loc[df_incid['jour'] ==
df_incid['jour'].max(), :]
nb_dep = len(df_incid_lastday.loc[
df_incid_lastday['incidence_color'] == 'Alerte', :]) + len(
df_incid_lastday.loc[df_incid_lastday['incidence_color'] ==
'Alerte Renforcée', :]
) + len(df_incid_lastday.loc[df_incid_lastday['incidence_color'] ==
'Alerte Maximale', :])
images_path2 = [
PATH + "images/charts/france/dep-map-incid-cat/latest.jpeg"
]
media_ids2 = []
for filename in images_path2:
res = api.media_upload(filename)
media_ids2.append(res.media_id)
tweet2 = "🔴 {} départements devraient être classés rouge, car ils dépassent le niveau d'alerte de 50 cas pour 100 000 habitants en 7 jours (données du {})\n➡️ Plus d'infos : covidtracker.fr/covidtracker-france".format(
nb_dep, lastday_df_incid.strftime('%d/%m'))
api.update_status(status=tweet2, media_ids=media_ids2)
def tweet_france():
#data.download_data()
_, _, dates, df_new, _, _, _, df_incid, _ = data.import_data()
df_new_france = df_new.groupby(["jour"]).sum().reset_index()
df_incid_france = df_incid.groupby(["jour"]).sum().reset_index()
lastday_df_new = datetime.strptime(df_new_france['jour'].max(), '%Y-%m-%d')
hosp = df_new_france[df_new_france['jour']==lastday_df_new.strftime('%Y-%m-%d')]['incid_hosp'].values[-1]
date_j7 = (lastday_df_new - timedelta(days=7)).strftime("%Y-%m-%d")
hosp_j7 = df_new_france[df_new_france['jour'] == date_j7]['incid_hosp'].values[-1]
deaths = df_new_france[df_new_france['jour']==lastday_df_new.strftime('%Y-%m-%d')]['incid_dc'].values[-1]
deaths_j7 = df_new_france[df_new_france['jour'] == date_j7]['incid_dc'].values[-1]
lastday_df_incid = datetime.strptime(df_incid_france['jour'].max(), '%Y-%m-%d')
tests = df_incid_france[df_incid_france['jour']==lastday_df_incid.strftime('%Y-%m-%d')]['P'].values[-1]
date_j7_incid = (lastday_df_incid - timedelta(days=7)).strftime("%Y-%m-%d")
tests_j7 = df_incid_france[df_incid_france['jour'] == date_j7_incid]['P'].values[-1]
date = datetime.strptime(dates[-1], '%Y-%m-%d').strftime('%d %B')
hosp_tendance, hosp_sign = "en hausse", "+"
if hosp_j7>hosp:
hosp_tendance, hosp_sign = "en baisse", ""
if hosp_j7==hosp:
hosp_tendance, hosp_sign = "stable", "+"
deaths_tendance, deaths_sign = "en hausse", "+"
if deaths_j7>deaths:
deaths_tendance, deaths_sign = "en baisse", ""
if deaths_j7==deaths:
deaths_tendance, deaths_sign = "stable", "+"
tests_tendance, tests_sign = "en hausse", "+"
if tests_j7>tests:
tests_tendance, tests_sign = "en baisse", ""
if tests_j7==tests:
tests_tendance, tests_sign = "stable", "+"
date_incid = datetime.strptime(sorted(list(dict.fromkeys(list(df_incid_france['jour'].values))))[-1], '%Y-%m-%d').strftime('%d %B')
tweet ="Chiffres #Covid19 France :\n• {} personnes décédées en milieu hospitalier ({}), {} sur 7 jours ({}{})\n• {} admissions à l'hôpital ({}), {} sur 7 jours ({}{})\n• {} cas positifs ({}), {} sur 7 jours ({}{})\n➡️ Plus d'infos : covidtracker.fr/covidtracker-france".format(deaths, lastday_df_new.strftime('%d/%m'), deaths_tendance, deaths_sign, deaths-deaths_j7, hosp, lastday_df_new.strftime('%d/%m'), hosp_tendance, hosp_sign, hosp-hosp_j7, tests, lastday_df_incid.strftime('%d/%m'), tests_tendance, tests_sign, tests-tests_j7) # toDo
images_path =["images/charts/france/var_journ_lines_recent.jpeg", "images/charts/france/entrees_sorties_hosp_rea_ROLLING_recent.jpeg", "images/charts/france/dc_new_bar.jpeg", "images/charts/france/reffectif.jpeg"]
media_ids = []
for filename in images_path:
res = api.media_upload(filename)
media_ids.append(res.media_id)
# to attach the media file
api.update_status(status=tweet, media_ids=media_ids)
import json
import locale
import france_data_management as data
import numpy as np
import plotly.figure_factory as ff
PATH="../../"
locale.setlocale(locale.LC_ALL, 'fr_FR.UTF-8')
now = datetime.now()
# In[3]:
df, df_confirmed, dates, _, _, _, _, df_incid, df_tests_viros = data.import_data()
# In[4]:
deps_tests = list(dict.fromkeys(list(df_tests_viros['dep'].values)))
deps_name = np.array(list(dict.fromkeys(list(df["departmentName"].values)))[:])
#df_tests_viros = df_tests_viros[df_tests_viros['cl_age90'] != 0]
for (name, data, title, scale_txt, data_example, digits) in [("cas", '', "Taux d'<br>incidence", " cas", " cas", 1)]:
for idx,dep in enumerate(deps_tests): #deps_tests.drop("975", "976", "977", "978")
locale.setlocale(locale.LC_ALL, 'fr_FR.UTF-8')
df_tests_viros_dep = df_tests_viros[df_tests_viros["dep"] == dep]
from tqdm import tqdm
import json
import plotly.express as px
from datetime import datetime
import imageio
import multiprocessing
import locale
import shutil
import os
locale.setlocale(locale.LC_ALL, 'fr_FR.UTF-8')
# In[3]:
# Import data from Santé publique France
_, _, _, _, _, _, _, df_incid, _ = data.import_data()
df_incid = df_incid[df_incid["cl_age90"] == 0]
with open('data/france/dep.geojson') as response:
depa = json.load(response)
# In[4]:
def build_map(
data_df,
img_folder,
date_val,
date_str="date",
dep_str="departement",
color_str='indic_synthese',
import json
import plotly.express as px
from datetime import datetime
import imageio
import multiprocessing
import locale
import shutil
import os
locale.setlocale(locale.LC_ALL, 'fr_FR.UTF-8')
# ## Data import
# In[3]:
# Import data from Santé publique France
df, df_confirmed, dates, _, _, df_deconf, df_sursaud, df_incid, _ = data.import_data(
)
df_incid = df_incid[df_incid["cl_age90"] == 0]
# In[4]:
#df_incid["incidence"] = df_incid["P"]/df_incid["pop"]*100
#df_incid.loc[:,"incidence_color"] = ["white"] * len(df_incid)
for dep in pd.unique(df_incid["dep"].values):
df_incid.loc[df_incid["dep"] == dep, "incidence"] = df_incid["P"].rolling(
window=7).sum() / df_incid["pop"] * 100000
df_incid.loc[:, "incidence_color"] = [
'Rouge (>50)' if x >= 50 else 'Orange (25-50)' if x >= 25 else 'Vert (<25)'
for x in df_incid['incidence']
]
# In[5]:
"""
# In[2]:
import pandas as pd
import plotly.graph_objects as go
import france_data_management as data
from datetime import datetime
from datetime import timedelta
import plotly
import math
import os
# In[3]:
df, df_confirmed, dates, df_new, df_tests, df_deconf, df_sursaud, df_incid, df_tests_viros = data.import_data(
)
# In[4]:
df_regions = df.groupby(["jour", "regionName"]).sum().reset_index()
df_incid_regions = df_incid[df_incid["cl_age90"] == 0].groupby(
["jour", "regionName"]).sum().reset_index()
regions = list(dict.fromkeys(list(df_regions['regionName'].values)))
dates_incid = list(dict.fromkeys(list(df_incid['jour'].values)))
last_day_plot = (datetime.strptime(max(dates), '%Y-%m-%d') +
timedelta(days=1)).strftime("%Y-%m-%d")
df_new_regions = df_new.groupby(["jour", "regionName"]).sum().reset_index()
# In[5]: