def update_percentage(date, accum_measurement, percentage_measurement):
accum = influx.get_stat_accumulated_until_day(accum_measurement, date)
data = {}
allowed_regions = list(CCAA_POPULATION.keys())
allowed_regions.append(SPAIN)
for region in filter(lambda x: x in allowed_regions, accum.keys()):
percentage = 100 * accum[region] / (CCAA_POPULATION[region]
if region in CCAA_POPULATION else
sum(CCAA_POPULATION.values()))
data[region] = percentage
influx.insert_stats(percentage_measurement, date, data)
def get_report_by_ccaa(date_in_header, ccaas_today, ccaas_yesterday, ccaas_accumulated_today):
tweets = []
for ccaa in filter(lambda x: x in CCAA_POPULATION.keys(), sorted(ccaas_today.keys())):
tweets.append(get_territorial_unit_report(ccaa, date_in_header, ccaas_today[ccaa],
ccaas_yesterday[ccaa], ccaas_accumulated_today[ccaa]))
return tweets
def get_completed_vaccination_sentence(territorial_unit, stat, accumulated, today_total):
population = CCAA_POPULATION[territorial_unit] if territorial_unit in CCAA_POPULATION \
else sum(CCAA_POPULATION.values())
percentage_population = accumulated / population * 100
return "- {0}: {1} ({2}%) 🔺{3}".format(stat, _format_number(accumulated),
_format_number(percentage_population),
_format_number(today_total))
def test_given_no_data_frame_acceded_when_access_then_tabula_used(
self, tabula_mock):
date = datetime(2020, 5, 5)
page = "1"
area = MagicMock()
report = SpainCovid19MinistryReport(date, page, area)
report._get_url = MagicMock()
valid_data = {
"Col1*": ["value1", "value2*", "value3", "value4*"] +
list(CCAA_POPULATION.keys())
}
d1 = DataFrame(data={"Col1": [1, 2, 3]})
d2 = DataFrame(data=valid_data)
tabula_mock.read_pdf.return_value = [d1, d2]
returned_data_frame = report.data_frame
tabula_mock.read_pdf.assert_called_once_with(
report._get_url.return_value,
pages=str(page),
area=area,
pandas_options={'dtype': str})
report._get_url.assert_called_once_with()
self.assertEqual(valid_data["Col1*"],
list(returned_data_frame['Col1']))
def test_given_dataframe_when_get_column_data_then_map_returned(self):
with patch.object(SpainCovid19MinistryReport, 'data_frame'):
report = SpainCovid19MinistryReport(None, None)
headers = ["heade1", "header2", "header3"]
ccaas = list(map(lambda x: x + "*", CCAA_POPULATION.keys()))
data1 = list(map(lambda x: str(x), range(1, 20)))
data2 = list(map(lambda x: str(x) + ".000", range(21, 40)))
report.data_frame = DataFrame(
data={
"Unnamed: 0": headers + ccaas,
"Column1": headers + data1,
"Column2": headers + data2
})
result = report.get_column_data(2)
self.assertEqual(list(CCAA_POPULATION.keys()), list(result.keys()))
self.assertEqual(list(range(21000, 40000, 1000)),
list(result.values()))
def calculate_global_incidence(dict_to_unpack, measurement):
population_to_compare = {
Measurement.ACCUMULATED_INCIDENCE.value: CCAA_POPULATION,
Measurement.PERCENTAGE_ADMITTED.value: CCAA_ADMITTED_BEDS,
Measurement.PERCENTAGE_ICU.value: CCAA_ICU_BEDS
}[measurement.value]
total_cases = 0
population = 0
ccaas = filter(lambda x: x in CCAA_POPULATION.keys(), dict_to_unpack.keys())
for ccaa in ccaas:
total_cases += dict_to_unpack[ccaa][measurement] * population_to_compare[ccaa] / 100000
population += population_to_compare[ccaa]
return total_cases / population * 100000 if population else 0
def get_global_data(dict_to_unpack):
keys = set([key for ccaa in dict_to_unpack for key in dict_to_unpack[ccaa].keys()])
ia_exists = Measurement.ACCUMULATED_INCIDENCE in keys
keys.discard(Measurement.ACCUMULATED_INCIDENCE)
result = defaultdict(lambda: 0)
for key in keys:
for ccaa in filter(lambda x: x in CCAA_POPULATION.keys(), dict_to_unpack.keys()):
result[key] += dict_to_unpack[ccaa][key] if dict_to_unpack[ccaa][key] else 0
if ia_exists:
result[Measurement.ACCUMULATED_INCIDENCE] = calculate_global_incidence(dict_to_unpack,
Measurement.ACCUMULATED_INCIDENCE)
result[Measurement.PERCENTAGE_ADMITTED] = calculate_global_incidence(dict_to_unpack,
Measurement.PERCENTAGE_ADMITTED)
result[Measurement.PERCENTAGE_ICU] = calculate_global_incidence(dict_to_unpack,
Measurement.PERCENTAGE_ICU)
return result
def publish_report(today):
today_vaccinations = influx.get_stat_group_by_day(Measurement.VACCINATIONS,
today)
today_completed_vaccinations = influx.get_stat_group_by_day(
Measurement.COMPLETED_VACCINATIONS, today)
today_first_doses = influx.get_stat_group_by_day(
Measurement.FIRST_DOSE_VACCINATIONS, today)
accumulated_vaccinations = influx.get_stat_accumulated_until_day(
Measurement.VACCINATIONS, today)
accumulated_completed_vaccinations = influx.get_stat_accumulated_until_day(
Measurement.COMPLETED_VACCINATIONS, today)
accumulated_first_doses = influx.get_stat_accumulated_until_day(
Measurement.FIRST_DOSE_VACCINATIONS, today)
today_str = today.strftime("%d/%m/%Y")
spain_tweet = get_vaccination_report(SPAIN, accumulated_vaccinations,
today_vaccinations,
accumulated_completed_vaccinations,
today_completed_vaccinations,
accumulated_first_doses,
today_first_doses)
interactive_graph_sentence = "➡️ Gráfico Interactivo: https://home.aitormagan.es/d/TeEplNgRk/covid-vacunas-espana?orgId=1"
spain_tweet = f"🇪🇸 España - Vacunación a {today_str}:\n\n{spain_tweet}\n\n{interactive_graph_sentence}"
graph_url = get_graph_url(datetime(2021, 1, 1),
today,
graph_path=VACCINE_IMAGE_PATH)
last_tweet = twitter.publish_tweet_with_media(spain_tweet, graph_url)
ccaa_tweets = []
for ccaa in filter(lambda x: x in CCAA_POPULATION.keys(),
sorted(accumulated_vaccinations.keys())):
ccaa_tweet = get_vaccination_report(
ccaa, accumulated_vaccinations, today_vaccinations,
accumulated_completed_vaccinations, today_completed_vaccinations,
accumulated_first_doses, today_first_doses)
ccaa_tweets.append(
f"{ccaa} - Vacunación a {today_str}:\n\n{ccaa_tweet}")
twitter.publish_tweets(ccaa_tweets, last_tweet)