def parse_daily_areas_pdf(date, country, local_pdf_file):
if country == "Northern Ireland":
pdf = pdfplumber.open(local_pdf_file)
for page in pdf.pages:
try:
table = page.extract_table()
if table[0][0] == "Local Government District":
output_rows = [[
"Date", "Country", "AreaCode", "Area", "TotalCases"
]]
for table_row in table[1:]:
if table_row[0].lower() == "total":
continue
area = normalize_whitespace(titlecase(table_row[0]))
area = area.replace("Ards and North Down",
"North Down and Ards")
area_code = lookup_local_government_district_code(area)
cases = normalize_int(table_row[1])
output_row = [date, country, area_code, area, cases]
output_rows.append(output_row)
return output_rows
except IndexError:
pass # no table on page
elif country == "Wales":
pdf = pdfplumber.open(local_pdf_file)
for page in pdf.pages:
try:
table = page.extract_table(
table_settings={
# use text alignment since the table doesn't have lines
"vertical_strategy": "text",
"horizontal_strategy": "text"
})
found_start = False
output_rows = [[
"Date", "Country", "AreaCode", "Area", "TotalCases"
]]
for table_row in table:
if table_row[0] is not None and table_row[0].startswith(
"Aneurin"):
found_start = True
if found_start:
area = (
normalize_whitespace(table_row[2]).replace(
"Anglesey", "Isle of Anglesey").replace(
"ff", "ff") # fix ligatures
.replace("fi", "fi"))
if area.startswith("Wales total"):
continue
area_code = lookup_local_authority_code(area)
cases = normalize_int(table_row[4])
output_row = [date, country, area_code, area, cases]
output_rows.append(output_row)
if table_row[2] is not None and normalize_whitespace(
table_row[2]) == 'Resident outside Wales':
break
return convert_wales_la_to_hb(date, country, output_rows)
except IndexError:
pass # no table on page
return None
def crawl_arcgis(date, country, check_only):
if country == "UK":
item_id = "bc8ee90225644ef7a6f4dd1b13ea1d67"
local_data_file = "data/raw/DailyIndicators-{}.xslx".format(date)
ret = download_arcgis_item(date, item_id, local_data_file, check_only)
if check_only:
return ret
df = pd.read_excel(local_data_file)
print(df)
d = df.to_dict("records")[0]
date = d["DateVal"].strftime("%Y-%m-%d")
with sqlite3.connect('data/covid-19-uk.db') as conn:
c = conn.cursor()
c.execute(
f"INSERT OR REPLACE INTO indicators VALUES ('{date}', 'UK', 'ConfirmedCases', {d['TotalUKCases']})"
)
c.execute(
f"INSERT OR REPLACE INTO indicators VALUES ('{date}', 'UK', 'Deaths', {d['TotalUKDeaths']})"
)
c.execute(
f"INSERT OR REPLACE INTO indicators VALUES ('{date}', 'England', 'ConfirmedCases', {d['EnglandCases']})"
)
c.execute(
f"INSERT OR REPLACE INTO indicators VALUES ('{date}', 'England', 'Deaths', {d['EnglandDeaths']})"
)
# c.execute(f"INSERT OR REPLACE INTO indicators VALUES ('{date}', 'Scotland', 'ConfirmedCases', {d['ScotlandCases']})")
# c.execute(f"INSERT OR REPLACE INTO indicators VALUES ('{date}', 'Scotland', 'Deaths', {d['ScotlandDeaths']})")
# c.execute(f"INSERT OR REPLACE INTO indicators VALUES ('{date}', 'Wales', 'ConfirmedCases', {d['WalesCases']})")
# c.execute(f"INSERT OR REPLACE INTO indicators VALUES ('{date}', 'Wales', 'Deaths', {d['WalesDeaths']})")
# c.execute(f"INSERT OR REPLACE INTO indicators VALUES ('{date}', 'Northern Ireland', 'ConfirmedCases', {d['NICases']})")
# c.execute(f"INSERT OR REPLACE INTO indicators VALUES ('{date}', 'Northern Ireland', 'Deaths', {d['NIDeaths']})")
elif country == "England":
item_id = "b684319181f94875a6879bbc833ca3a6"
local_data_file = "data/raw/CountyUAs_cases_table-{}.csv".format(date)
ret = download_arcgis_item(date, item_id, local_data_file, check_only)
if check_only:
return ret
df = pd.read_csv(local_data_file)
df["Date"] = date
df["Country"] = "England"
df = df.rename(columns={"GSS_CD": "AreaCode", "GSS_NM": "Area"})
df = df[["Date", "Country", "AreaCode", "Area", "TotalCases"]]
daily_areas = df.to_dict("split")["data"]
for row in daily_areas:
row[4] = normalize_int(normalize_whitespace(row[4]))
daily_areas = [["Date", "Country", "AreaCode", "Area", "TotalCases"]
] + daily_areas
#save_daily_areas(date, country, daily_areas)
save_daily_areas_to_sqlite(date, country, daily_areas)
def parse_tests(country, html):
def is_testing_table(table):
headers = [th.text for th in table.findAll("th")]
return "Tests" in headers
soup = BeautifulSoup(html, features="html.parser")
tables = soup.find_all("table")
testing_tables = [table for table in tables if is_testing_table(table)]
if len(testing_tables) == 0:
print("Testing table not found")
return None
elif len(testing_tables) > 1:
print("More than one testing table found")
return None
testing_table = testing_tables[0]
table_rows = testing_table.findAll("tr")
if len(table_rows) != 3:
print("Expecting 3 table rows")
return None
daily_row = [td.text for td in table_rows[1].findAll("td")]
total_row = [td.text for td in table_rows[2].findAll("td")]
text = get_text_from_html(html)
pattern_dict = {
"Date": (r"As of (?P<Time>\d+\s*(am|pm)?) (on )?(?P<Date>.+?),", date_value_parser_fn)
}
result = parse_totals_general(pattern_dict, country, text)
result["DailyTestsPerformed"] = normalize_int(daily_row[1])
result["DailyPeopleTested"] = normalize_int(daily_row[2])
result["DailyPositive"] = normalize_int(daily_row[3])
result["TotalTestsPerformed"] = normalize_int(total_row[1])
result["TotalPeopleTested"] = normalize_int(total_row[2])
result["TotalPositive"] = normalize_int(total_row[3])
return result
def parse_daily_areas_json(date, country, json_data):
if country == "England":
output_rows = [["Date", "Country", "AreaCode", "Area", "TotalCases"]]
for area_code, o in json_data["utlas"].items():
area = o["name"]["value"]
cases = normalize_int(o["totalCases"]["value"])
if area_code != lookup_local_authority_code(area):
print("Area code mismatch for {}, JSON file gave {}, but lookup was {}".format(area, area_code, lookup_local_authority_code(area)))
return None
output_row = [date, country, area_code, area, cases]
output_rows.append(output_row)
return output_rows
return None
def parse_totals_pdf(date, country, local_pdf_file):
if country == "Northern Ireland":
text = get_text_from_pdf(local_pdf_file)
pattern_dict = {
"Date": (r"Date generated: (?P<Date>[\d,]+/[\d,]+/[\d,]+)",
date_value_parser_fn),
"Tests":
(r"Number of Individuals tested( for COVID-19| for SARS-COV2 Virus)?:? (?P<Tests>[\d,]+)",
int_value_parser_fn),
"ConfirmedCases":
(r"(Number of Individuals (with confirmed|testing positive for) (COVID-19|SARS-COV2 Virus)|Cumulative number of laboratory confirmed COVID-19 cases):? (?P<ConfirmedCases>[\d,]+)",
int_value_parser_fn),
"Deaths":
(r"(Total|Cumulative) number of (Trust |reported )?deaths( associated with COVID-19)?: (?P<Deaths>[\d,]+)",
int_value_parser_fn),
}
result = parse_totals_general(pattern_dict, country, text)
return result
elif country == "Wales":
pdf = pdfplumber.open(local_pdf_file)
for page in pdf.pages:
try:
table = page.extract_table(
table_settings={
# use text alignment since the table doesn't have lines
"horizontal_strategy": "text"
})
result = {"Date": date, "Country": country}
for table_row in table:
if table_row[0] == "":
continue
label = table_row[0].replace("\t", " ")
value = normalize_int(table_row[1])
if label == "Cumulative individuals tested":
result["Tests"] = value
elif label == "Cumulative confirmed COVID-19 case total":
result["ConfirmedCases"] = value
elif label == "Cumulative number of suspected COVID-19 deaths* reported to PHW":
# Get deaths from XLSX after this date
if date < "2020-04-29":
result["Deaths"] = value
return result
except IndexError:
pass # no table on page
return None
def parse_daily_areas_pdf(date, country, local_pdf_file):
if country == "Northern Ireland":
pdf = pdfplumber.open(local_pdf_file)
for page in pdf.pages:
try:
table = page.extract_table()
if table[0][0] == "Local Government District":
output_rows = [["Date", "Country", "AreaCode", "Area", "TotalCases"]]
for table_row in table[1:]:
if table_row[0].lower() == "total":
continue
area = normalize_whitespace(titlecase(table_row[0]))
area_code = lookup_local_government_district_code(area)
cases = normalize_int(table_row[1])
output_row = [date, country, area_code, area, cases]
output_rows.append(output_row)
return output_rows
except IndexError:
pass # no table on page
return None
def int_value_parser_fn(value):
return normalize_int(value)
def test_normalize_int():
assert normalize_int("1") == 1
assert normalize_int("1,001") == 1001
assert normalize_int("seven") == 7
def parse_tests(country, html):
def is_testing_table(table):
headers = [th.text for th in table.findAll("th")]
return "Tests" in headers
soup = BeautifulSoup(html, features="html.parser")
tables = soup.find_all("table")
testing_tables = [table for table in tables if is_testing_table(table)]
if len(testing_tables) == 0:
print("Testing table not found")
return None
elif len(testing_tables) > 1:
print("More than one testing table found")
return None
testing_table = testing_tables[0]
table_rows = testing_table.findAll("tr")
if len(table_rows) != 3:
print("Expecting 3 table rows")
return None
daily_row = [td.text for td in table_rows[1].findAll("td")]
total_row = [td.text for td in table_rows[2].findAll("td")]
text = get_text_from_html(html)
pattern_dict = {
"Date": (r"As of (?P<Time>\d+\s*(am|pm)?) (on )?(?P<Date>.+?),",
date_value_parser_fn)
}
result = parse_totals_general(pattern_dict, country, text)
result["DailyTestsPerformed"] = normalize_int(daily_row[1])
result["DailyPeopleTested"] = normalize_int_with_unavailable(daily_row[2])
result["DailyPositive"] = normalize_int(daily_row[3])
result["TotalTestsPerformed"] = normalize_int(total_row[1])
result["TotalPeopleTested"] = normalize_int_with_unavailable(total_row[2])
result["TotalPositive"] = normalize_int(total_row[3])
def is_pillar_table(table):
headers = [th.text for th in table.findAll("th")]
return "Pillar 1" in headers
pillar_tables = [table for table in tables if is_pillar_table(table)]
if len(pillar_tables) == 0:
# no pillar tables
return result
elif len(pillar_tables) != 2:
print("Expecting two pillar tables (daily and cumulative)")
return None
for table_num, pillar_table in enumerate(pillar_tables):
daily_or_total = "Daily" if table_num == 0 else "Total"
table_rows = pillar_table.findAll("tr")
if len(table_rows) != 4:
print("Expecting 4 table rows")
return None
for i, col in enumerate(table_rows[0].findAll(re.compile("th|td"))):
if col.text.startswith("Pillar"):
pillar = remove_whitespace(col.text)
for row in table_rows[1:]:
test_stat = normalize_whitespace(row.findAll("td")[0].text)
if test_stat == "Tests":
test_stat = "TestsPerformed"
elif test_stat == "People tested":
test_stat = "PeopleTested"
indicator = "{}{}{}".format(daily_or_total, pillar,
test_stat)
str_val = row.findAll("td")[i].text
val = "" if str_val == "-" else normalize_int_with_unavailable(
str_val)
result[indicator] = val
def is_pillar2_breakdown_table(table):
headers = [th.text for th in table.findAll("th")]
return any([header.startswith("In-person") for header in headers])
pillar2_breakdown_tables = [
table for table in tables if is_pillar2_breakdown_table(table)
]
if len(pillar2_breakdown_tables) == 0:
# no pillar 2 breakdown table
return result
elif len(pillar2_breakdown_tables) > 1:
print("More than one pillar 2 breakdown table found")
return None
pillar2_breakdown_table = pillar2_breakdown_tables[0]
table_rows = pillar2_breakdown_table.findAll("tr")
if len(table_rows) not in (3, 4):
print("Expecting 3 (or 4) table rows in pillar 2 breakdown table")
return None
daily_row = [td.text for td in table_rows[1].findAll("td")]
total_row = [td.text for td in table_rows[2].findAll("td")]
result["DailyPillar2InPersonRoutes"] = normalize_int(daily_row[1])
result["DailyPillar2DeliveryRoutes"] = normalize_int(daily_row[2])
result["TotalPillar2InPersonRoutes"] = normalize_int(total_row[1])
result["TotalPillar2DeliveryRoutes"] = normalize_int(total_row[2])
return result
def normalize_int_with_unavailable(num):
if num.lower() == "unavailable":
return ""
return normalize_int(num)
soup = BeautifulSoup(html, features="html.parser")
text = soup.get_text()
text = text.replace(u"\xa0",
u" ") # replace non-breaking spaces with regular spaces
patterns = [uk_pattern, wales_pattern, scotland_pattern, ni_pattern]
for pattern in patterns:
m = re.search(pattern, text)
if m is not None:
groups = m.groupdict()
date = dateparser.parse(groups["date"]).strftime("%Y-%m-%d")
country = normalize_whitespace(groups.get("country")).replace(
"Scottish", "Scotland")
tests = normalize_int(groups.get("tests", float("nan")))
positive_tests = normalize_int(groups["positive_tests"])
negative_tests = normalize_int(
groups.get("negative_tests", float("nan")))
deaths = normalize_int(groups.get("deaths", float("nan")))
if not math.isnan(tests):
print("{},{},{},{}".format(date, country, "Tests", tests))
# with open(
# "data/daily/indicators/covid-19-{}-{}-tests.csv".format(
# date, format_country(country)
# ),
# "w",
# ) as f:
# f.write("{},{},{},{}\n".format(date, country, "Tests", tests))
if not math.isnan(positive_tests):
print("{},{},{},{}".format(date, country, "ConfirmedCases",
