def confirmed_by_testdate(context):
source_name = jobs["confirmed_by_testdate"]["source"]["name"]
source_url = jobs["confirmed_by_testdate"]["source"]["url"]
data = c19api.timeseries("confirmed")
curr_data = list(filter(lambda x: x["source"] == "fhi:git", data))[-1]
curr_total = curr_data.get("total")
last_data = file_open_json("confirmed_by_testdate")
last_total = last_data.get("total")
if curr_total - last_total > 0:
ret_str = "🦠 <b>Antall meldte smittetilfeller</b>"
ret_str += "\nAntall meldte COVID-19 tilfeller etter prøvetakingsdato."
ret_str += "\nDet er 1-2 dagers forsinkelse i tiden fra diagnose til registrering i MSIS."
ret_str += f"\n\nKilde: <a href='{source_url}'>{source_name}</a>"
file_write_json("confirmed_by_testdate", curr_data)
print(ret_str, "\n")
context.bot.send_photo(
bot["autopost"]["chatid"],
graphs.confirmed(),
parse_mode=ParseMode.HTML,
caption=ret_str,
)
else:
return None
def tested_lab(context):
source_name = jobs["tested_lab"]["source"]["name"]
source_url = jobs["tested_lab"]["source"]["url"]
curr_data = c19api.timeseries("tested_lab")[-1]
curr_total = curr_data.get("total")
last_data = file_open_json("tested_lab")
last_total = last_data.get("total")
if curr_total - last_total > 0:
ret_str = "🔬 <b>Antall testet (Laboratoriedata)</b>"
ret_str += "\nAntall personer testet og andelen positive blant disse i Norge siden epidemiens start."
ret_str += "\nEn ny test på en person defineres som en test utført minst 7 dager etter forrige test av samme person."
ret_str += f"\n\nKilde: <a href='{source_url}'>{source_name}</a>"
file_write_json("tested_lab", curr_data)
print(ret_str, "\n")
context.bot.send_photo(
bot["autopost"]["chatid"],
graphs.tested(),
parse_mode=ParseMode.HTML,
caption=ret_str,
)
else:
return None
def vaccine(context):
population = 5391369
source_name = jobs["vaccine"]["source"]["name"]
source_url = jobs["vaccine"]["source"]["url"]
data = c19api.timeseries("vaccine_doses")
curr_data = list(filter(lambda x: x["granularity_geo"] == "nation",
data))[-1]
curr_total_doses = curr_data.get("total_doses")
last_data = file_open_json("vaccine_doses")
last_total_doses = last_data.get("total_doses")
diff_total_doses = curr_total_doses - last_total_doses
if diff_total_doses > 0:
curr_total_dose_1 = curr_data.get("total_dose_1")
curr_total_dose_2 = curr_data.get("total_dose_2")
curr_total_dose_1_pct = curr_total_dose_1 / population
curr_total_dose_2_pct = curr_total_dose_2 / population
last_total_dose_1 = last_data.get("total_dose_1")
last_total_dose_2 = last_data.get("total_dose_2")
diff_total_dose_1 = curr_total_dose_1 - last_total_dose_1
diff_total_dose_2 = curr_total_dose_2 - last_total_dose_2
ret_str = "💉 <b>Antall vaksinerte</b>"
if diff_total_dose_1 != 0:
ret_str += (
f"\n<b>{diff_total_dose_1:,}</b> nye personer vaksinert med 1. dose"
)
if diff_total_dose_2 != 0:
ret_str += f"\n<b>{diff_total_dose_2:,}</b> nye personer fullvaksinert"
ret_str += f"\n\nTotalt <b>{curr_total_dose_1:,}</b> personer (<b>{curr_total_dose_1_pct:,.02%}</b> av befolkningen) har fått minst én vaksinedose"
ret_str += f"\nTotalt <b>{curr_total_dose_2:,}</b> personer (<b>{curr_total_dose_2_pct:,.02%}</b> av befolkningen) er fullvaksinert"
ret_str += f"\n\nKilde: <a href='{source_url}'>{source_name}</a>"
file_write_json("vaccine_doses", curr_data)
ret_str = ret_str.replace(",", " ")
print(ret_str, "\n")
context.bot.send_photo(
bot["autopost"]["chatid"],
graphs.vaccine_doses(),
parse_mode=ParseMode.HTML,
caption=ret_str,
)
else:
return None
def smittestopp(context):
source_name = jobs["smittestopp"]["source"]["name"]
source_url = jobs["smittestopp"]["source"]["url"]
curr_data = c19api.timeseries("smittestopp")[-1]
curr_total_downloads = int(curr_data.get("total_downloads"))
curr_total_reported = int(curr_data.get("total_reported"))
last_data = file_open_json("smittestopp")
last_total_downloads = int(last_data.get("total_downloads"))
last_total_reported = int(last_data.get("total_reported"))
if (curr_total_downloads != last_total_downloads
or curr_total_reported != last_total_reported):
new_downloads = int(curr_data.get("new_downloads"))
new_reported = int(curr_data.get("new_reported"))
if new_downloads == 1:
new_downloads_text = "ny nedlasting av appen Smittestopp"
else:
new_downloads_text = "nye nedlastinger av appen Smittestopp"
if new_reported == 1:
new_reported_text = "ny person meldt smittet gjennom appen Smittestopp"
else:
new_reported_text = "nye personer meldt smittet gjennom appen Smittestopp"
ret_str = "📱 <b>Smittestopp</b>"
if new_downloads != 0:
ret_str += f"\n<b>{new_downloads:,}</b> {new_downloads_text}"
if new_reported != 0:
ret_str += f"\n<b>{new_reported:,}</b> {new_reported_text}"
ret_str += f"\n\nTotalt antall nedlastinger: <b>{curr_total_downloads:,}</b>"
ret_str += f"\nTotalt <b>{curr_total_reported:,}</b> personer er meldt smittet gjennom appen"
ret_str += f"\n\nKilde: <a href='{source_url}'>{source_name}</a>"
file_write_json("smittestopp", curr_data)
ret_str = ret_str.replace(",", " ")
print(ret_str, "\n")
context.bot.send_photo(
bot["autopost"]["chatid"],
graphs.smittestopp(),
parse_mode=ParseMode.HTML,
caption=ret_str,
)
else:
return None
def hospitalized(context):
source_name = jobs["hospitalized"]["source"]["name"]
source_url = jobs["hospitalized"]["source"]["url"]
curr_data = c19api.timeseries("hospitalized")[-1]
curr_respiratory = int(curr_data.get("respiratory"))
curr_admissions = int(curr_data.get("admissions"))
last_data = file_open_json("hospitalized")
last_respiratory = int(last_data.get("respiratory"))
last_admissions = int(last_data.get("admissions"))
if curr_admissions != last_admissions or curr_respiratory != last_respiratory:
diff_admissions = curr_admissions - last_admissions
diff_respiratory = curr_respiratory - last_respiratory
respiratory_pct = curr_respiratory / curr_admissions
ret_str = "🏥 <b>Innlagte pasienter på sykehus</b>"
if diff_admissions != 0:
ret_str += f"\nEndring i antall innlagte: <b>{diff_admissions:+,}</b>"
if diff_respiratory != 0:
ret_str += f"\nEndring i antall på respirator: <b>{diff_respiratory:+,}</b>"
ret_str += f"\n\n<b>{curr_admissions:,}</b> personer er innlagt på sykehus"
ret_str += f"\n<b>{curr_respiratory:,}</b> personer er på respirator ({respiratory_pct:.01%})"
ret_str += f"\n\nKilde: <a href='{source_url}'>{source_name}</a>"
ret_str = ret_str.replace(",", " ")
print(ret_str, "\n")
file_write_json("hospitalized", curr_data)
context.bot.send_photo(
bot["autopost"]["chatid"],
graphs.hospitalized(),
parse_mode=ParseMode.HTML,
caption=ret_str,
)
else:
return None
def dead(context):
source_name = jobs["dead"]["source"]["name"]
source_url = jobs["dead"]["source"]["url"]
curr_data = c19api.timeseries("dead")[-1]
curr_total = curr_data.get("total")
last_data = file_open_json("dead")
last_total = last_data.get("total")
diff_dead = curr_total - last_total
if diff_dead > 0:
messagetext = get_messagetext("dead", diff_dead)
curr_new_today = curr_data.get("new")
ret_str = "❗ <b>COVID-19 assosierte dødsfall</b>"
ret_str += f"\n<b>{diff_dead}</b> {messagetext}"
ret_str += (
f"\nTotalt: <b>{curr_total:,}</b> (Nye i dag: <b>{curr_new_today:,}</b>)"
)
ret_str += f"\n\nKilde: <a href='{source_url}'>{source_name}</a>"
file_write_json("dead", curr_data)
ret_str = ret_str.replace(",", " ")
print(ret_str, "\n")
context.bot.send_photo(
bot["autopost"]["chatid"],
graphs.dead(),
parse_mode=ParseMode.HTML,
caption=ret_str,
)
else:
return None
def stats(update, context):
today = date.today().strftime("%d.%m.%Y")
# metadata
population = 5391369
tested = c19api.metadata("tested")
confirmed = c19api.metadata("confirmed")
dead = c19api.metadata("dead")
admissions = c19api.metadata("admissions")
respiratory = c19api.metadata("respiratory")
vaccine_doses = c19api.timeseries("vaccine_doses")
# totals
tested_total = tested.get("total")
confirmed_total = confirmed.get("total")
dead_total = dead.get("total")
admissions_total = admissions.get("total")
respiratory_total = respiratory.get("total")
# newToday
tested_newToday = tested.get("newToday", 0)
confirmed_newToday = confirmed.get("newToday", 0)
dead_newToday = dead.get("newToday", 0)
# newSince
confirmed_newSince_d7 = confirmed.get("newSince_d7", 0)
confirmed_newSince_d14 = confirmed.get("newSince_d14", 0)
# percentages
dead_pct = round(dead_total / confirmed_total * 100, 1)
respiratory_pct = round(respiratory_total / admissions_total * 100, 1)
# vaccine data
vaccine_data = list(
filter(lambda x: x["granularity_geo"] == "nation", vaccine_doses)
)[-1]
vacc_total_dose_1 = vaccine_data.get("total_dose_1")
vacc_total_dose_2 = vaccine_data.get("total_dose_2")
vacc_total_dose_1_pct = vacc_total_dose_1 / population
vacc_total_dose_2_pct = vacc_total_dose_2 / population
ret_str = f"🔢 <b>Nøkkeltall - {today}</b>"
ret_str += f"\n\n🦠 Smittetilfeller i dag: <b>{confirmed_newToday:,}</b>"
ret_str += f"\nSiste 7d: <b>{confirmed_newSince_d7:,}</b>"
ret_str += f"\nSiste 14d: <b>{confirmed_newSince_d14:,}</b>"
ret_str += f"\nTotalt: <b>{confirmed_total:,}</b>"
ret_str += f"\n\n❗ Dødsfall i dag: <b>{dead_newToday:,}</b>"
ret_str += f"\nTotalt: <b>{dead_total:,}</b> ({dead_pct}% av smittede)"
ret_str += f"\n\n🔬 Testede i dag: <b>{tested_newToday:,}</b>"
ret_str += f"\nTotalt: <b>{tested_total:,}</b>"
ret_str += f"\n\n🏥 Innlagt på sykehus: <b>{admissions_total:,}</b>"
ret_str += f"\n😷 Tilkoblet respirator: <b>{respiratory_total:,}</b> ({respiratory_pct}% av innlagte)"
ret_str += "\n\n💉 Andel av befolkningen vaksinert"
ret_str += f"\n<b>{vacc_total_dose_1_pct:,.02%}</b> har fått minst én dose (<b>{vacc_total_dose_1:,}</b> personer)"
ret_str += f"\n<b>{vacc_total_dose_2_pct:,.02%}</b> er fullvaksinert (<b>{vacc_total_dose_2:,}</b> personer)"
ret_str = ret_str.replace(",", " ")
context.bot.send_message(
chat_id=update.message.chat_id, text=ret_str, parse_mode=ParseMode.HTML
)
def tested():
filename = "./graphs/no_tested.png"
if os.path.exists(filename):
os.remove(filename)
data = c19api.timeseries("tested_lab")
df = pd.DataFrame(data)
mapping = {
"new_neg": "Nye (Negative)",
"new_pos": "Nye (Positive)",
"new_total": "Nye",
"pr100_pos": "Andel Positive",
"total": "Akkumulert",
}
df = df.rename(columns=mapping)
df["date"] = pd.to_datetime(df["date"])
df["Andel Negative"] = 100 - df["Andel Positive"]
df = df.melt(id_vars=["date", "Andel Positive"],
var_name="category",
value_name="value")
base = alt.Chart(
df,
title=
"Antall personer testet for covid-19 per dag og andel positive blant disse (Kilde: FHI)",
).encode(
alt.X("yearmonthdate(date):O",
axis=alt.Axis(title=None, labelAngle=-40)))
andel = base.mark_line(color="red", opacity=0.8).encode(y=alt.Y(
"Andel Positive:Q", title="% Positive", axis=alt.Axis(grid=True)))
bar = (base.transform_filter(
(alt.datum.category == "Nye (Negative)")
| (alt.datum.category == "Nye (Positive)")).mark_bar().encode(
y=alt.Y("value:Q",
title="Antall personer testet for covid-19 per dag"),
color=alt.Color(
"category:N",
scale=alt.Scale(
domain=["Nye (Positive)", "Nye (Negative)", "% Positive"],
range=["#FF9622", "#6DA9FF", "red"],
),
legend=alt.Legend(title=None),
),
))
chart = (alt.layer(bar, andel).resolve_scale(y="independent").properties(
width=1200, height=600).configure_legend(
strokeColor="gray",
fillColor="#FFFFFF",
labelFontSize=12,
symbolStrokeWidth=2,
symbolSize=160,
padding=6,
cornerRadius=5,
direction="horizontal",
orient="none",
legendX=480,
legendY=660,
))
save(chart, filename)
return open(filename, "rb")
def confirmed():
data = c19api.timeseries("confirmed")
filename = "./graphs/no_confirmed.png"
if os.path.exists(filename):
os.remove(filename)
df = pd.DataFrame(data)
df["date"] = pd.to_datetime(df["date"])
df = df.loc[df["source"] == "fhi:git"]
df["new_sma7"] = df.new.rolling(window=7).mean().shift()
df = df.melt(
id_vars=["date"],
value_vars=["new", "new_sma7", "total"],
var_name="category",
value_name="value",
).dropna()
rename = {"new": "Nye", "new_sma7": "Snitt 7 d.", "total": "Akkumulert"}
df["category"] = df["category"].replace(rename)
base = alt.Chart(
df,
title=
"Antall meldte COVID-19 tilfeller etter prøvetakingsdato (Kilde: FHI/MSIS)",
).encode(
alt.X("yearmonthdate(date):O",
axis=alt.Axis(title=None, labelAngle=-40)))
bar = (base.transform_filter(alt.datum.category == "Nye").mark_bar(
color="#FFD1D1").encode(
y=alt.Y("value:Q", axis=alt.Axis(title="Nye per dag", grid=True))))
line = (base.transform_filter(
alt.datum.category == "Akkumulert").mark_line(
color="#2E507B", strokeWidth=3).encode(
y=alt.Y("value:Q", axis=alt.Axis(title="Akkumulert")),
color=alt.Color(
"category:N",
scale=alt.Scale(
domain=["Nye", "Snitt 7 d.", "Akkumulert"],
range=["#FFD1D1", "red", "#2E507B"],
),
legend=alt.Legend(title=None),
),
))
ma7 = (base.transform_filter(alt.datum.category == "Snitt 7 d.").mark_line(
opacity=0.8).encode(y=alt.Y("value:Q"), color=alt.Color("category:N")))
chart = (alt.layer(bar + ma7,
line).resolve_scale(y="independent").properties(
width=1200, height=600).configure_legend(
strokeColor="gray",
fillColor="#FFFFFF",
labelFontSize=12,
symbolStrokeWidth=2,
symbolSize=160,
padding=6,
cornerRadius=5,
direction="horizontal",
orient="none",
legendX=480,
legendY=660,
))
save(chart, filename)
return open(filename, "rb")
def vaccine_doses():
data = c19api.timeseries("vaccine_doses")
filename = "./graphs/no_vaccine_doses.png"
if os.path.exists(filename):
os.remove(filename)
df = pd.DataFrame(data)
df["date"] = pd.to_datetime(df["date"])
df = df[df["granularity_geo"] == "nation"]
df["new_sma7"] = df.new_doses.rolling(window=7).mean().shift()
df = df.melt(
id_vars=["date"],
value_vars=["total_dose_1", "total_dose_2"],
var_name="category",
value_name="value",
).dropna()
rename = {
"total_dose_1": "Har fått minst én dose",
"total_dose_2": "Fullvaksinert",
}
df["category"] = df["category"].replace(rename)
chart = (alt.Chart(
df,
title=
"Antall personer vaksinert med 1. og 2. dose av vaksine mot COVID-19 i Norge (Kilde: FHI)",
).mark_area(line={}, opacity=0.3).encode(
x=alt.X("yearmonthdate(date):O",
axis=alt.Axis(title=None, labelAngle=-40)),
y=alt.Y(
"value:Q",
stack=None,
title="Antall",
),
color=alt.Color(
"category:N",
scale=alt.Scale(
domain=["Har fått minst én dose", "Fullvaksinert"],
range=["#5dade2", "#2ecc71"],
),
legend=alt.Legend(title=None),
),
).properties(width=1200, height=600).configure_legend(
strokeColor="gray",
fillColor="#FFFFFF",
labelFontSize=12,
symbolStrokeWidth=2,
symbolSize=160,
padding=6,
cornerRadius=5,
direction="horizontal",
orient="none",
legendX=480,
legendY=660,
))
save(chart, filename)
return open(filename, "rb")
def smittestopp():
data = c19api.timeseries("smittestopp")
filename = "./graphs/no_smittestopp.png"
if os.path.exists(filename):
os.remove(filename)
df = pd.DataFrame(data)
df["date"] = pd.to_datetime(df["date"])
df = df.melt(
id_vars=["date"],
value_vars=["new_reported", "total_downloads"],
var_name="category",
value_name="value",
).dropna()
rename = {
"new_reported": "Antall meldt smittet i Smittestopp",
"total_downloads": "Antall nedlastinger av Smittestopp",
}
df["category"] = df["category"].replace(rename)
base = alt.Chart(
df,
title=
"Antall nedlastinger av Smittestopp og antall som har meldt gjennom appen at de er smittet (Kilde: FHI)",
).encode(
alt.X("yearmonthdate(date):O",
axis=alt.Axis(title=None, labelAngle=-40)))
downloads = (base.transform_filter(
alt.datum.category == "Antall nedlastinger av Smittestopp").mark_area(
line={}, color="#5BC1FF", opacity=0.2).encode(y=alt.Y(
"value:Q",
axis=alt.Axis(title="Antall nedlastinger av Smittestopp",
grid=True),
)))
reported = (base.transform_filter(
alt.datum.category == "Antall meldt smittet i Smittestopp").mark_bar(
color="#FFA57E").encode(
y=alt.Y(
"value:Q",
axis=alt.Axis(title="Antall meldt smittet i Smittestopp")),
color=alt.Color(
"category:N",
scale=alt.Scale(
domain=[
"Antall nedlastinger av Smittestopp",
"Antall meldt smittet i Smittestopp",
],
range=["#5BC1FF", "#FFA57E"],
),
legend=alt.Legend(title=None),
),
))
chart = (alt.layer(reported,
downloads).resolve_scale(y="independent").properties(
width=1200, height=600).configure_legend(
strokeColor="gray",
fillColor="#FFFFFF",
labelFontSize=12,
symbolStrokeWidth=2,
symbolSize=160,
labelLimit=200,
padding=6,
cornerRadius=5,
direction="horizontal",
orient="none",
legendX=390,
legendY=660,
))
save(chart, filename)
return open(filename, "rb")
def hospitalized():
data = c19api.timeseries("hospitalized")
today = date.today()
filename = "./graphs/no_hospitalized.png"
if os.path.exists(filename):
os.remove(filename)
df = pd.DataFrame(data)
idx = pd.date_range("2020-03-08", today)
df.index = pd.DatetimeIndex(df["date"])
df = df.reindex(idx)
df["date"] = df.index
df = df.reset_index(drop=True)
df["admissions"] = df["admissions"].fillna(method="ffill").astype(int)
df["respiratory"] = df["respiratory"].fillna(method="ffill").astype(int)
df_melt = pd.melt(
df,
id_vars=["date"],
value_vars=["admissions", "respiratory"],
value_name="value",
).replace({
"admissions": "Innlagt",
"respiratory": "På respirator"
})
chart = (alt.Chart(
df_melt,
title="Innlagt på sykehus (Kilde: Helsedirektoratet)").mark_area(
line={}, opacity=0.3).encode(
x=alt.X("yearmonthdate(date):O",
axis=alt.Axis(title=None, labelAngle=-40)),
y=alt.Y(
"value:Q",
stack=None,
title="Antall innlagte med påvist COVID-19 i Norge",
),
color=alt.Color(
"variable:N",
scale=alt.Scale(domain=["Innlagt", "På respirator"],
range=["#5A9DFF", "#FF8B1B"]),
legend=alt.Legend(title=None),
),
).properties(width=1200, height=600).configure_legend(
strokeColor="gray",
fillColor="#FFFFFF",
labelFontSize=12,
symbolStrokeWidth=2,
symbolSize=160,
padding=6,
cornerRadius=5,
direction="horizontal",
orient="none",
legendX=480,
legendY=660,
))
save(chart, filename)
return open(filename, "rb")
def dead():
data = c19api.timeseries("dead")
today = date.today()
filename = "./graphs/no_dead.png"
if os.path.exists(filename):
os.remove(filename)
df = pd.DataFrame(data)
idx = pd.date_range("2020-03-07", df["date"].max())
df.index = pd.DatetimeIndex(df["date"])
df = df.reindex(idx)
df["date"] = df.index
df = df.reset_index(drop=True)
df = df[df.date <= str(today)]
df["new"] = df["new"].fillna(0).astype(int)
df["total"] = df["total"].fillna(method="bfill").astype(int)
df["new_sma7"] = df.new.rolling(window=7).mean()
df = df.melt(
id_vars=["date"],
value_vars=["new", "new_sma7", "total"],
var_name="category",
value_name="value",
).dropna()
rename = {"new": "Nye", "new_sma7": "Snitt 7 d.", "total": "Akkumulert"}
df["category"] = df["category"].replace(rename)
base = alt.Chart(df, title="COVID-19 dødsfall (Kilde: FHI)").encode(
alt.X("yearmonthdate(date):O",
axis=alt.Axis(title=None, labelAngle=-40)))
bar = (base.transform_filter(alt.datum.category == "Nye").mark_bar(
color="#FFD1D1").encode(
y=alt.Y("value:Q", axis=alt.Axis(title="Nye per dag", grid=True))))
line = (base.transform_filter(
alt.datum.category == "Akkumulert").mark_line(
color="#2E507B", strokeWidth=3).encode(
y=alt.Y("value:Q", axis=alt.Axis(title="Akkumulert")),
color=alt.Color(
"category:N",
scale=alt.Scale(
domain=["Nye", "Snitt 7 d.", "Akkumulert"],
range=["#FFD1D1", "red", "#2E507B"],
),
legend=alt.Legend(title=None),
),
))
ma7 = (base.transform_filter(alt.datum.category == "Snitt 7 d.").mark_line(
opacity=0.8).encode(y=alt.Y("value:Q"), color=alt.Color("category:N")))
chart = (alt.layer(bar + ma7,
line).resolve_scale(y="independent").properties(
width=1200, height=600).configure_legend(
strokeColor="gray",
fillColor="#FFFFFF",
labelFontSize=12,
symbolStrokeWidth=2,
symbolSize=160,
padding=6,
cornerRadius=5,
direction="horizontal",
orient="none",
legendX=480,
legendY=660,
))
save(chart, filename)
return open(filename, "rb")
