def handle_ma(res, mapping):
tagged = {}
files = ['DeathsReported.csv', 'Testing2.csv',
'Hospitalization from Hospitals.csv', 'Cases.csv']
with MaContextManager(res) as zipdir:
for filename in files:
with open(os.path.join(zipdir, filename), 'r') as csvfile:
reader = csv.DictReader(csvfile, dialect='unix')
rows = list(reader)
last_row = rows[-1]
partial = map_attributes(last_row, mapping, 'MA')
tagged.update(partial)
hosp_key = ""
for k, v in mapping.items():
if v == Fields.HOSP.name:
hosp_key = k
hospfile = csv.DictReader(open(os.path.join(zipdir, "RaceEthnicity.csv"), 'r'))
hosprows = list(hospfile)
last_row = hosprows[-1]
hosprows = [x for x in hosprows if x['Date'] == last_row['Date']]
summed = csv_sum(hosprows, [hosp_key])
tagged[Fields.HOSP.name] = summed[hosp_key]
return tagged
def handle_va(res, mapping):
'''Getting multiple CVS files from the state and parsing each for
the specific data it contains
'''
tagged = {}
# Res:
# 0 -- cases & death, probable & confirmed
# 1 -- testing info
# 2 -- hospital/icu/vent
cases = res[0]
testing = res[1]
hospital = res[2]
date_format = "%m/%d/%Y"
# Cases
# expecting 2 rows in the following format
# Report Date,Case Status,Number of Cases,Number of Hospitalizations,Number of Deaths
# 5/14/2020,Probable,1344,24,28
# 5/14/2020,Confirmed,26469,3568,927
PROB = 'Probable'
CONF = 'Confirmed'
for row in cases:
if (row['Case Status'] == CONF):
for k, v in row.items():
if (k in mapping):
tagged[mapping[k]] = atoi(v)
elif (row['Case Status'] == PROB):
tagged[Fields.PROBABLE.name] = atoi(row['Number of Cases'])
tagged[Fields.DEATH_PROBABLE.name] = atoi(row['Number of Deaths'])
tagged[Fields.POSITIVE.
name] = tagged[Fields.CONFIRMED.name] + tagged[Fields.PROBABLE.name]
# sum everything
testing_cols = [
'Number of PCR Testing Encounters', 'Number of Positive PCR Tests',
'Total Number of Testing Encounters', 'Total Number of Positive Tests'
]
summed_testing = csv_sum(testing, testing_cols)
tagged[Fields.SPECIMENS.name] = summed_testing[testing_cols[0]]
tagged[Fields.SPECIMENS_POS.name] = summed_testing[testing_cols[1]]
tagged[Fields.ANTIBODY_TOTAL.name] = summed_testing[
testing_cols[2]] - summed_testing[testing_cols[0]]
tagged[Fields.ANTIBODY_POS.name] = summed_testing[
testing_cols[3]] - summed_testing[testing_cols[1]]
# Hospitalizations
hospital = sorted(hospital,
key=lambda x: datetime.strptime(x['Date'], date_format),
reverse=True)
mapped_hosp = map_attributes(hospital[0], mapping, 'VA')
tagged.update(mapped_hosp)
return tagged
def handle_nd(res, mapping):
soup = res[0]
tagged = {}
# Serology testing
table = soup.find('table')
rows = table.find_all('tr')
titles = rows[0]
data = rows[1].find_all('td')
for i, title in enumerate(titles.find_all("td")):
title = title.get_text(strip=True)
if title in mapping:
value = atoi(data[i].get_text(strip=True))
tagged[mapping[title]] = value
# confirmed+probable death
h2_death = soup.find("h2", string=re.compile("Deaths"))
death_table = h2_death.find_next("table")
for tr in death_table.find_all("tr"):
cols = tr.find_all("td")
if len(cols) < 2:
continue
strong = cols[0].find("strong")
if not strong or len(strong.get_text()) < 10:
continue
name = strong.get_text(strip=True)
value = atoi(cols[1].get_text(strip=True))
if len(cols) > 2:
value += atoi(cols[2].get_text(strip=True))
if name in mapping:
tagged[mapping[name]] = atoi(value)
# by county testing snapshot: for negatives
county_testing = res[1]
columns = [
k for k, v in mapping.items() if v in [
Fields.CONFIRMED.name, Fields.NEGATIVE.name,
Fields.DEATH_CONFIRMED.name
]
]
values = csv_sum(county_testing, columns=columns)
tagged.update(map_attributes(values, mapping))
# PCR encounters and other metrics
pcr = res[2]
partial = map_attributes(pcr.sum(), mapping)
tagged.update(partial)
# active hosp/icu should not be summed
hosp = pcr.groupby('Date').sum().filter(like='Active').iloc[-1]
tagged.update(map_attributes(hosp, mapping))
return tagged
def handle_ok(res, mapping):
# need to sum all values
res = res[0]
# sum all fields
# TODO: functools probably has something nice
cols = ['Cases', 'Deaths', 'Recovered']
summed = csv_sum(res, cols)
mapped = map_attributes(summed, mapping, 'OK')
mapped[Fields.DATE.name] = res[0].get('ReportDate')
return mapped
def handle_ok(res, mapping):
mapped = {}
for result in res[:-1]:
partial = map_attributes(result, mapping, 'OK')
mapped.update(partial)
# need to sum all values
res = res[1]
# sum all fields
# TODO: functools probably has something nice
cols = ['Cases', 'Deaths', 'Recovered']
summed = csv_sum(res, cols)
mapped.update(map_attributes(summed, mapping, 'OK'))
mapped[Fields.DATE.name] = res[0].get('ReportDate')
return mapped
def handle_ma(res, mapping):
soup = res[0]
link = soup.find('a', string=re.compile("COVID-19 Raw Data"))
link_part = link['href']
url = "https://www.mass.gov{}".format(link_part)
tagged = {}
# download zip
req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0'})
with urllib.request.urlopen(req) as response, \
NamedTemporaryFile(delete=True) as tmpfile , TemporaryDirectory() as tmpdir:
shutil.copyfileobj(response, tmpfile)
tmpfile.flush()
shutil.unpack_archive(tmpfile.name, tmpdir, format="zip")
# Now we can read the files
files = [
'DeathsReported.csv', 'Testing2.csv',
'Hospitalization from Hospitals.csv', 'Cases.csv'
]
for filename in files:
with open("{}/{}".format(tmpdir, filename), 'r') as csvfile:
reader = csv.DictReader(csvfile, dialect='unix')
rows = list(reader)
last_row = rows[-1]
partial = map_attributes(last_row, mapping, 'MA')
tagged.update(partial)
hosp_key = ""
for k, v in mapping.items():
if v == Fields.HOSP.name:
hosp_key = k
hospfile = csv.DictReader(open(tmpdir + "/RaceEthnicity.csv", 'r'))
hosprows = list(hospfile)
last_row = hosprows[-1]
hosprows = [x for x in hosprows if x['Date'] == last_row['Date']]
summed = csv_sum(hosprows, [hosp_key])
tagged[Fields.HOSP.name] = summed[hosp_key]
return tagged
