def donnesDepistage(days=40, floatingMean=15):
"""réduit les données de dépistage en une valeur par département"""
# chargement des données de dépistage
df = loadFromUrl(urlDepistQuot)
df = df[df.cl_age90 == 0]
today = dtt.date.today()
firsDay = dtt.timedelta(days=days)
firsDay = today - firsDay
df = timeFrame(df, firsDay.isoformat(), today.isoformat())
# régression linéaire sur le timeframe pour les taux de contamination
depDict = {}
deps = list(pd.unique(df.dep))
for dep in deps:
subData = df[df.dep == dep]
nb_jours = range(subData.count().jour)
taux = subData.P / pd.to_numeric(list(subData.T))
taux = taux.rolling(window=floatingMean, min_periods=1).mean()
slope, intercept, rvalue, pvalue, stderr = stats.linregress(
nb_jours, taux)
depDict[depFromCode(dep)] = slope
# transformation du dictionnaire en DataFrame
df = pd.DataFrame(depDict.items())
return df
def plotFrance(dep: str,
days: int = 40,
floatingMean=15,
params=None,
d1=None,
d2=None):
set_plt()
if params == None:
return False
else:
# print(params)
if d2 == None:
d2 = dtt.date.today()
if d1 == None:
d1 = dtt.timedelta(days=days)
d1 = d2 - d1
df = loadFromUrl(urlDepistQuot)
df = setAgeClass(df, "0")
df = timeFrame(df, d1, d2)
# print(df.shape)
df = df.groupby("jour", as_index=False).sum()
# print(df.shape)
# print(df)
nb_jours = range(df.count().jour)
if params["taux"]:
a = df.P
b = pd.to_numeric(list(df["T"]))
# print(b)
# print(a/b)
taux = a / b
else:
taux = df.P
if params["moy"]:
taux = taux.rolling(window=floatingMean, min_periods=1).mean()
if params["log"]:
taux = np.log(taux)
slope, intercept, rvalue, pvalue, stderr = stats.linregress(
nb_jours, taux)
fig, ax = plt.subplots()
ax.plot(df.jour, taux, label="Tests journaliers totaux en France")
if rvalue >= 0.9:
ax.plot(
nb_jours,
nb_jours * slope + intercept,
label=
f"Pente : {round(slope, 5)} /jour\nrvalue : {round(rvalue, 2)}"
)
ax.legend()
ax.xaxis.set_major_locator(plt.MaxNLocator(5))
ax.yaxis.set_major_locator(plt.MaxNLocator(10))
plt.title(f"Evolution des dépistages positifs en France")
plt.savefig("fig.jpg")
return "France", d1, d2
def plotFromArgs(args):
"""Creates a plot in jpg format with the given arguments formated by parseArgs()"""
df2 = anl.loadData()
if args["dep"] == "FR":
df2 = anl.france(df2)
else:
df2 = anl.parDep(df2, args["dep"])
df2 = anl.timeFrame(df2, args["d1"], args["d2"])
anl.plotThat(df2, args)
# generate some infos to be printed somewhere
finalInfos = getInfos(args)
return finalInfos
def plotGivenDep(dep: str,
days: int = 40,
floatingMean=15,
params=None,
d1=None,
d2=None):
set_plt()
if params == None:
return False
else:
if d2 == None:
d2 = dtt.date.today()
if d1 == None:
d1 = dtt.timedelta(days=days)
d1 = d2 - d1
df = loadFromUrl(urlDepistQuot)
df = setAgeClass(df, "0")
df = timeFrame(df, d1, d2)
df = df[df.dep == str(dep)]
nb_jours = range(df.count().jour)
taux = df.P / pd.to_numeric(list(df.T))
taux = taux.rolling(window=floatingMean, min_periods=1).mean()
if params["log"]:
taux = np.log(taux)
slope, intercept, rvalue, pvalue, stderr = stats.linregress(
nb_jours, taux)
fig, ax = plt.subplots()
ax.plot(df.jour, taux, label="Taux de tests positifs")
if rvalue >= 0.8:
ax.plot(
nb_jours,
nb_jours * slope + intercept,
label=
f"Pente : {round(slope, 5)} /jour\nrvalue : {round(rvalue, 2)}"
)
ax.legend()
ax.xaxis.set_major_locator(plt.MaxNLocator(5))
ax.yaxis.set_major_locator(plt.MaxNLocator(10))
plt.title(
f"Evolution des taux de dépistages positifs : {depFromCode(dep)}")
plt.savefig("fig.jpg")
return depFromCode(dep), d1, d2
def donnesHosp(days=40, floatingMean=15, type="hosp"):
df = loadData()
today = dtt.date.today()
firsDay = dtt.timedelta(days=days)
firsDay = today - firsDay
df = timeFrame(df, firsDay.isoformat(), today.isoformat())
# régression linéaire sur le timeframe pour les données hospitalières
depDict = {}
deps = list(pd.unique(df.dep))
for dep in deps:
subData = df[df.dep == dep]
nb_jours = range(subData.count().jour)
donnees = subData.dc
slope, intercept, rvalue, pvalue, stderr = stats.linregress(
nb_jours, donnees)
depDict[depFromCode(dep)] = slope
# transformation du dictionnaire en DataFrame
df = pd.DataFrame(depDict.items())
return df