def run_module():
"""Produce a combined cases and deaths signal using data from JHU and USA Facts."""
variants = [
tuple((metric, geo_res) + sensor_signal(metric, sensor, smoother))
for (metric, geo_res, sensor, smoother
) in product(METRICS, GEO_RESOLUTIONS, SENSORS, SMOOTH_TYPES)
]
params = configure(variants)
for metric, geo_res, sensor_name, signal in variants:
df = combine_usafacts_and_jhu(
signal, geo_res, extend_raw_date_range(params, sensor_name))
df["timestamp"] = pd.to_datetime(df["timestamp"])
start_date = pd.to_datetime(params['export_start_date'])
export_dir = params["export_dir"]
dates = pd.Series(df[df["timestamp"] >= start_date]
["timestamp"].unique()).sort_values()
signal_name = add_prefix([signal],
wip_signal=params["wip_signal"],
prefix="wip_")
for date_ in dates:
export_fn = f'{date_.strftime("%Y%m%d")}_{geo_res}_{signal_name[0]}.csv'
df[df["timestamp"] == date_][[
"geo_id",
"val",
"se",
"sample_size",
]].to_csv(f"{export_dir}/{export_fn}", index=False, na_rep="NA")
def update_sensor(
state_files: List[str],
mmwr_info: pd.DataFrame,
output_path: str,
start_date: datetime,
end_date: datetime) -> pd.DataFrame:
"""
Generate sensor values, and write to csv format.
Args:
state_files: List of JSON files representing COVID-NET hospitalization data for each state
mmwr_info: Mappings from MMWR week to actual dates, as a pd.DataFrame
output_path: Path to write the csvs to
start_date: First sensor date (datetime.datetime)
end_date: Last sensor date (datetime.datetime)
Returns:
The overall pd.DataFrame after all processing
"""
assert start_date < end_date, "start_date >= end_date"
# Combine and format hospitalizations dataframe
hosp_df = CovidNet.read_all_hosp_data(state_files)
hosp_df = hosp_df.merge(mmwr_info, how="left",
left_on=["mmwr-year", "mmwr-week"],
right_on=["year", "weeknumber"])
# Select relevant columns and standardize naming
hosp_df = hosp_df.loc[:, APIConfig.HOSP_RENAME_COLS.keys()]\
.rename(columns=APIConfig.HOSP_RENAME_COLS)
# Restrict to start and end date
hosp_df = hosp_df[
(hosp_df["date"] >= start_date) & (
hosp_df["date"] < end_date)
]
# Set state id to two-letter abbreviation
gmpr = GeoMapper()
hosp_df = gmpr.add_geocode(hosp_df,
from_col=APIConfig.STATE_COL,
from_code="state_name",
new_code="state_id",
dropna=False)
# To use the original column name, reassign original column and drop new one
hosp_df[APIConfig.STATE_COL] = hosp_df["state_id"]
hosp_df.drop("state_id", axis=1, inplace=True)
assert not hosp_df.duplicated(["date", "geo_id"]).any(), "Non-unique (date, geo_id) pairs"
hosp_df.set_index(["date", "geo_id"], inplace=True)
# Fill in remaining expected columns
hosp_df["se"] = np.nan
hosp_df["sample_size"] = np.nan
# Write results
signals = add_prefix(SIGNALS, wip_signal=read_params()["wip_signal"], prefix="wip_")
for signal in signals:
write_to_csv(hosp_df, signal, output_path)
return hosp_df
def run_module(params):
"""
Produce a combined cases and deaths signal using data from JHU and USA Facts.
Parameters
----------
params
Dictionary containing indicator configuration. Expected to have the following structure:
- "common":
- "export_dir": str, directory to write output.
- "log_exceptions" (optional): bool, whether to log exceptions to file.
- "log_filename" (optional): str, name of file to write logs
- "indicator":
- "export_start_date": list of ints, [year, month, day] format, first day to begin
data exports from.
- "date_range": str, YYYYMMDD-YYYYMMDD format, range of dates to generate data for.
- "source": str, name of combo indicator in metadata.
- "wip_signal": list of str or bool, to be passed to delphi_utils.add_prefix.
"""
start_time = time.time()
variants = [
tuple((metric, geo_res) + sensor_signal(metric, sensor, smoother))
for (metric, geo_res, sensor, smoother
) in product(METRICS, GEO_RESOLUTIONS, SENSORS, SMOOTH_TYPES)
]
params = configure(variants, params)
logger = get_structured_logger(
__name__,
filename=params["common"].get("log_filename"),
log_exceptions=params["common"].get("log_exceptions", True))
for metric, geo_res, sensor_name, signal in variants:
df = combine_usafacts_and_jhu(
signal, geo_res, extend_raw_date_range(params, sensor_name),
params['indicator']['issue_range'])
df["timestamp"] = pd.to_datetime(df["timestamp"])
start_date = pd.to_datetime(params['indicator']['export_start_date'])
export_dir = params["common"]["export_dir"]
dates = pd.Series(df[df["timestamp"] >= start_date]
["timestamp"].unique()).sort_values()
signal_name = add_prefix([signal],
wip_signal=params['indicator']["wip_signal"],
prefix="wip_")
for date_ in dates:
export_fn = f'{date_.strftime("%Y%m%d")}_{geo_res}_{signal_name[0]}.csv'
df[df["timestamp"] == date_][[
"geo_id",
"val",
"se",
"sample_size",
]].to_csv(f"{export_dir}/{export_fn}", index=False, na_rep="NA")
elapsed_time_in_seconds = round(time.time() - start_time, 2)
logger.info("Completed indicator run",
elapsed_time_in_seconds=elapsed_time_in_seconds)
def test_output_files(self, run_as_module):
"""Tests that the output files contain the correct results of the run."""
params = read_params()
# Test output exists
csv_files = listdir("receiving")
dates_for_covid_ag = [
"20200702", "20200703", "20200704", "20200705", "20200706",
"20200707", "20200708", "20200709"
]
dates_for_flu_ag = [
"20200623", "20200624", "20200625", "20200626", "20200627",
"20200628", "20200629", "20200630", "20200701", "20200702",
"20200703"
]
geos = GEO_RESOLUTIONS.copy()
sensors = add_prefix(list(SENSORS.keys()),
wip_signal=params["wip_signal"],
prefix="wip_")
expected_files = []
for geo in geos:
for sensor in sensors:
if "covid_ag" in sensor:
for date in dates_for_covid_ag:
expected_files += [
date + "_" + geo + "_" + sensor + ".csv"
]
else:
for date in dates_for_flu_ag:
expected_files += [
date + "_" + geo + "_" + sensor + ".csv"
]
assert set(expected_files).issubset(set(csv_files))
# Test output format
df = pd.read_csv(
join("./receiving",
"20200709_state_covid_ag_raw_pct_positive.csv"))
assert (df.columns.values == ["geo_id", "val", "se",
"sample_size"]).all()
# test_intermediate_file
flag = None
for fname in listdir("./cache"):
if ".csv" in fname:
flag = 1
assert flag is not None
def __init__(self,
startdate,
enddate,
dropdate,
geo,
parallel,
weekday,
numtype,
se):
"""Init Sensor Updator.
Args:
startdate: first sensor date (YYYY-mm-dd)
enddate: last sensor date (YYYY-mm-dd)
dropdate: data drop date (YYYY-mm-dd)
geo: geographic resolution, one of ["county", "state", "msa", "hrr", "hhs", "nation"]
parallel: boolean to run the sensor update in parallel
weekday: boolean to adjust for weekday effects
numtype: type of count data used, one of ["covid", "cli"]
se: boolean to write out standard errors, if true, use an obfuscated name
"""
self.startdate, self.enddate, self.dropdate = [
pd.to_datetime(t) for t in (startdate, enddate, dropdate)]
# handle dates
assert (self.startdate > (Config.FIRST_DATA_DATE + Config.BURN_IN_PERIOD)
), f"not enough data to produce estimates starting {self.startdate}"
assert self.startdate < self.enddate, "start date >= end date"
assert self.enddate <= self.dropdate, "end date > drop date"
self.geo, self.parallel, self.weekday, self.numtype, self.se = geo.lower(), parallel, \
weekday, numtype, se
# output file naming
if self.numtype == "covid":
signals = [SMOOTHED_ADJ if self.weekday else SMOOTHED]
elif self.numtype == "cli":
signals = [SMOOTHED_ADJ_CLI if self.weekday else SMOOTHED_CLI]
signal_names = add_prefix(
signals,
wip_signal=read_params()["wip_signal"])
self.updated_signal_names = signal_names
# initialize members set in shift_dates().
self.burnindate = None
self.fit_dates = None
self.burn_in_dates = None
self.sensor_dates = None
def test_output_files(self, run_as_module):
"""Tests that the proper files are output."""
# Test output exists
csv_files = listdir("receiving")
dates = [
"20200702", "20200703", "20200704", "20200705", "20200706",
"20200707", "20200708", "20200709"
]
geos = GEO_RESOLUTIONS.copy()
sensors = add_prefix(SENSORS,
wip_signal=read_params()["wip_signal"],
prefix="wip_")
expected_files = []
for date in dates:
for geo in geos:
for sensor in sensors:
expected_files += [
date + "_" + geo + "_" + sensor + ".csv"
]
assert set(expected_files).issubset(set(csv_files))
# Test output format
df = pd.read_csv(
join("./receiving",
"20200709_state_covid_ag_raw_pct_positive.csv"))
assert (df.columns.values == ["geo_id", "val", "se",
"sample_size"]).all()
# test_intermediate_file
flag = None
for fname in listdir("./cache"):
if ".csv" in fname:
flag = 1
assert flag is not None
def test_output_files(self, clean_receiving_dir):
"""Tests that the proper files are output."""
run_module(self.PARAMS)
csv_files = listdir("receiving")
dates = ["20200718", "20200719", "20200720"]
geos = GEO_RESOLUTIONS.copy()
sensors = add_prefix(SENSORS,
wip_signal=self.PARAMS["indicator"]["wip_signal"],
prefix="wip_")
expected_files = []
for date in dates:
for geo in geos:
for sensor in sensors:
expected_files += [
date + "_" + geo + "_" + sensor + ".csv"
]
assert set(expected_files).issubset(set(csv_files))
assert '20200721_state_covid_ag_raw_pct_positive.csv' not in csv_files
assert '20200722_state_covid_ag_raw_pct_positive.csv' not in csv_files
# Test output format
df = pd.read_csv(
join("./receiving",
"20200718_state_covid_ag_smoothed_pct_positive.csv"))
assert (df.columns.values == ["geo_id", "val", "se",
"sample_size"]).all()
# test_intermediate_file
flag = None
for fname in listdir("./cache"):
if ".csv" in fname:
flag = 1
assert flag is not None
def run_module(params):
"""
Main function run when calling the module.
Inputs parameters from the file 'params.json' and produces output data in
the directory defined by the `export_dir` (should be "receiving" expect for
testing purposes).
Parameters
----------
params
Dictionary containing indicator configuration. Expected to have the following structure:
- "common":
- "export_dir": str, directory to write output
- "log_exceptions" (optional): bool, whether to log exceptions to file
- "log_filename" (optional): str, name of file to write logs
- "indicator":
- "static_file_dir": str, path to DMA mapping files
- "data_dir": str, location of cached CSVs
- "start_date": str, YYYY-MM-DD format, first day to generate data for
- "end_date": str, YYYY-MM-DD format or empty string, last day to generate data for.
- "ght_key": str, GHT API key
- "wip_signal": list of str or bool, to be passed to delphi_utils.add_prefix
- "test_data_dir": str, path to test data
- "archive" (optional): if provided, output will be archived with S3
- "aws_credentials": Dict[str, str], AWS login credentials (see S3 documentation)
- "bucket_name: str, name of S3 bucket to read/write
- "cache_dir": str, directory of locally cached data
"""
start_time = time.time()
csv_export_count = 0
oldest_final_export_date = None
# read parameters
ght_key = params["indicator"]["ght_key"]
start_date = params["indicator"]["start_date"]
end_date = params["indicator"]["end_date"]
static_dir = params["indicator"]["static_file_dir"]
export_dir = params["common"]["export_dir"]
data_dir = params["indicator"]["data_dir"]
wip_signal = params["indicator"]["wip_signal"]
logger = get_structured_logger(
__name__,
filename=params["common"].get("log_filename"),
log_exceptions=params["common"].get("log_exceptions", True))
if "archive" in params:
arch_diff = S3ArchiveDiffer(params["archive"]["cache_dir"], export_dir,
params["archive"]["bucket_name"], "ght",
params["archive"]["aws_credentials"])
arch_diff.update_cache()
# if missing start_date, set to today (GMT) minus 5 days
if start_date == "":
now = datetime.datetime.now(datetime.timezone.utc)
start_date = (now - datetime.timedelta(days=4)).strftime("%Y-%m-%d")
# if missing end_date, set to today (GMT) minus 5 days
if end_date == "":
now = datetime.datetime.now(datetime.timezone.utc)
end_date = (now - datetime.timedelta(days=4)).strftime("%Y-%m-%d")
# Turn on basic logging messages (level INFO)
logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.INFO)
logging.info("Creating data from %s through %s.", start_date, end_date)
# Dictionary mapping geo resolution to the data corresponding to that resolution.
df_by_geo_res = {}
if not params["indicator"]["test_data_dir"]:
# setup class to handle API calls
ght = GoogleHealthTrends(ght_key=ght_key)
# read data frame version of the data
df_by_geo_res[STATE] = get_counts_states(ght,
PULL_START_DATE,
end_date,
static_dir=static_dir,
data_dir=data_dir)
df_by_geo_res[DMA] = get_counts_dma(ght,
PULL_START_DATE,
end_date,
static_dir=static_dir,
data_dir=data_dir)
else:
df_by_geo_res[STATE] = pd.read_csv(
params["indicator"]["test_data_dir"].format(geo_res="state"))
df_by_geo_res[DMA] = pd.read_csv(
params["indicator"]["test_data_dir"].format(geo_res="dma"))
df_by_geo_res[HRR], df_by_geo_res[MSA] = derived_counts_from_dma(
df_by_geo_res[DMA], static_dir=static_dir)
signal_names = add_prefix(SIGNALS, wip_signal, prefix="wip_")
for signal in signal_names:
is_smoothed = signal.endswith(SMOOTHED)
for geo_res, df in df_by_geo_res.items():
exported_csv_dates = create_export_csv(format_for_export(
df, is_smoothed),
geo_res=geo_res,
sensor=signal,
start_date=start_date,
export_dir=export_dir)
if not exported_csv_dates.empty:
csv_export_count += exported_csv_dates.size
if not oldest_final_export_date:
oldest_final_export_date = max(exported_csv_dates)
oldest_final_export_date = min(oldest_final_export_date,
max(exported_csv_dates))
if "archive" in params:
archive(arch_diff)
elapsed_time_in_seconds = round(time.time() - start_time, 2)
max_lag_in_days = None
formatted_oldest_final_export_date = None
if oldest_final_export_date:
max_lag_in_days = (datetime.datetime.now() -
oldest_final_export_date).days
formatted_oldest_final_export_date = oldest_final_export_date.strftime(
"%Y-%m-%d")
logger.info("Completed indicator run",
elapsed_time_in_seconds=elapsed_time_in_seconds,
csv_export_count=csv_export_count,
max_lag_in_days=max_lag_in_days,
oldest_final_export_date=formatted_oldest_final_export_date)
def run_module(params: Dict[str, Any]):
"""Run the quidel_covidtest indicator.
The `params` argument is expected to have the following structure:
- "common":
- "export_dir": str, directory to write output
- "log_exceptions" (optional): bool, whether to log exceptions to file
- "log_filename" (optional): str, name of file to write logs
- indicator":
- "static_file_dir": str, directory name with population information
- "input_cache_dir": str, directory in which to cache input data
- "export_start_date": str, YYYY-MM-DD format of earliest date to create output
- "export_end_date": str, YYYY-MM-DD format of latest date to create output or "" to create
through the present
- "pull_start_date": str, YYYY-MM-DD format of earliest date to pull input
- "pull_end_date": str, YYYY-MM-DD format of latest date to create output or "" to create
through the present
- "aws_credentials": Dict[str, str], authentication parameters for AWS S3; see S3
documentation
- "bucket_name": str, name of AWS bucket in which to find data
- "wip_signal": List[str], list of signal names that are works in progress
- "test_mode": bool, whether we are running in test mode
"""
start_time = time.time()
logger = get_structured_logger(
__name__,
filename=params["common"].get("log_filename"),
log_exceptions=params["common"].get("log_exceptions", True))
atexit.register(log_exit, start_time, logger)
cache_dir = params["indicator"]["input_cache_dir"]
export_dir = params["common"]["export_dir"]
export_start_date = params["indicator"]["export_start_date"]
export_end_date = params["indicator"]["export_end_date"]
# Pull data and update export date
df, _end_date = pull_quidel_covidtest(params["indicator"])
if _end_date is None:
print("The data is up-to-date. Currently, no new data to be ingested.")
return
export_end_date = check_export_end_date(export_end_date, _end_date,
END_FROM_TODAY_MINUS)
export_start_date = check_export_start_date(export_start_date,
export_end_date,
EXPORT_DAY_RANGE)
first_date, last_date = df["timestamp"].min(), df["timestamp"].max()
# State Level
data = df.copy()
state_groups = geo_map("state", data).groupby("state_id")
# Add prefix, if required
sensors = add_prefix(SENSORS,
wip_signal=params["indicator"]["wip_signal"],
prefix="wip_")
smoothers = SMOOTHERS.copy()
for sensor in sensors:
# For State Level
print("state", sensor)
if sensor.endswith(SMOOTHED_POSITIVE):
smoothers[sensor] = smoothers.pop(SMOOTHED_POSITIVE)
elif sensor.endswith(RAW_POSITIVE):
smoothers[sensor] = smoothers.pop(RAW_POSITIVE)
elif sensor.endswith(SMOOTHED_TEST_PER_DEVICE):
smoothers[sensor] = smoothers.pop(SMOOTHED_TEST_PER_DEVICE)
else:
smoothers[sensor] = smoothers.pop(RAW_TEST_PER_DEVICE)
state_df = generate_sensor_for_states(state_groups,
smooth=smoothers[sensor][1],
device=smoothers[sensor][0],
first_date=first_date,
last_date=last_date)
create_export_csv(state_df,
geo_res="state",
sensor=sensor,
export_dir=export_dir,
start_date=export_start_date,
end_date=export_end_date)
# County/HRR/MSA level
for geo_res in GEO_RESOLUTIONS:
geo_data, res_key = geo_map(geo_res, data)
for sensor in sensors:
print(geo_res, sensor)
res_df = generate_sensor_for_other_geores(
state_groups,
geo_data,
res_key,
smooth=smoothers[sensor][1],
device=smoothers[sensor][0],
first_date=first_date,
last_date=last_date)
create_export_csv(res_df,
geo_res=geo_res,
sensor=sensor,
export_dir=export_dir,
start_date=export_start_date,
end_date=export_end_date,
remove_null_samples=True)
# Export the cache file if the pipeline runs successfully.
# Otherwise, don't update the cache file
update_cache_file(df, _end_date, cache_dir)
def run_module():
"""Run the quidel_covidtest indicator."""
params = read_params()
cache_dir = params["cache_dir"]
export_dir = params["export_dir"]
export_start_date = params["export_start_date"]
export_end_date = params["export_end_date"]
# Pull data and update export date
df, _end_date = pull_quidel_covidtest(params)
if _end_date is None:
print("The data is up-to-date. Currently, no new data to be ingested.")
return
export_end_date = check_export_end_date(export_end_date, _end_date,
END_FROM_TODAY_MINUS)
export_start_date = check_export_start_date(export_start_date,
export_end_date,
EXPORT_DAY_RANGE)
first_date, last_date = df["timestamp"].min(), df["timestamp"].max()
# State Level
data = df.copy()
state_groups = geo_map("state", data).groupby("state_id")
# Add prefix, if required
sensors = add_prefix(SENSORS,
wip_signal=read_params()["wip_signal"],
prefix="wip_")
smoothers = SMOOTHERS.copy()
for sensor in sensors:
# For State Level
print("state", sensor)
if sensor.endswith(SMOOTHED_POSITIVE):
smoothers[sensor] = smoothers.pop(SMOOTHED_POSITIVE)
elif sensor.endswith(RAW_POSITIVE):
smoothers[sensor] = smoothers.pop(RAW_POSITIVE)
elif sensor.endswith(SMOOTHED_TEST_PER_DEVICE):
smoothers[sensor] = smoothers.pop(SMOOTHED_TEST_PER_DEVICE)
else:
smoothers[sensor] = smoothers.pop(RAW_TEST_PER_DEVICE)
state_df = generate_sensor_for_states(state_groups,
smooth=smoothers[sensor][1],
device=smoothers[sensor][0],
first_date=first_date,
last_date=last_date)
create_export_csv(state_df,
geo_res="state",
sensor=sensor,
export_dir=export_dir,
start_date=export_start_date,
end_date=export_end_date)
# County/HRR/MSA level
for geo_res in GEO_RESOLUTIONS:
geo_data, res_key = geo_map(geo_res, data)
for sensor in sensors:
print(geo_res, sensor)
res_df = generate_sensor_for_other_geores(
state_groups,
geo_data,
res_key,
smooth=smoothers[sensor][1],
device=smoothers[sensor][0],
first_date=first_date,
last_date=last_date)
create_export_csv(res_df,
geo_res=geo_res,
sensor=sensor,
export_dir=export_dir,
start_date=export_start_date,
end_date=export_end_date,
remove_null_samples=True)
# Export the cache file if the pipeline runs successfully.
# Otherwise, don't update the cache file
update_cache_file(df, _end_date, cache_dir)
def run_module():
"""Main function run when calling the module.
Inputs parameters from the file 'params.json' and produces output data in
the directory defined by the `export_dir` (should be "receiving" expect for
testing purposes).
"""
# read parameters
params = read_params()
ght_key = params["ght_key"]
start_date = params["start_date"]
end_date = params["end_date"]
static_dir = params["static_file_dir"]
export_dir = params["export_dir"]
data_dir = params["data_dir"]
wip_signal = params["wip_signal"]
cache_dir = params["cache_dir"]
arch_diff = S3ArchiveDiffer(cache_dir, export_dir, params["bucket_name"],
"ght", params["aws_credentials"])
arch_diff.update_cache()
print(arch_diff)
# if missing start_date, set to today (GMT) minus 5 days
if start_date == "":
now = datetime.datetime.now(datetime.timezone.utc)
start_date = (now - datetime.timedelta(days=4)).strftime("%Y-%m-%d")
# if missing start_date, set to today (GMT) minus 5 days
if start_date == "":
now = datetime.datetime.now(datetime.timezone.utc)
start_date = (now - datetime.timedelta(days=4)).strftime("%Y-%m-%d")
# if missing end_date, set to today (GMT) minus 5 days
if end_date == "":
now = datetime.datetime.now(datetime.timezone.utc)
end_date = (now - datetime.timedelta(days=4)).strftime("%Y-%m-%d")
# Turn on basic logging messages (level INFO)
logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.INFO)
logging.info("Creating data from %s through %s.", start_date, end_date)
# Dictionary mapping geo resolution to the data corresponding to that resolution.
df_by_geo_res = {}
if not params["test"]:
# setup class to handle API calls
ght = GoogleHealthTrends(ght_key=ght_key)
# read data frame version of the data
df_by_geo_res[STATE] = get_counts_states(ght,
PULL_START_DATE,
end_date,
static_dir=static_dir,
data_dir=data_dir)
df_by_geo_res[DMA] = get_counts_dma(ght,
PULL_START_DATE,
end_date,
static_dir=static_dir,
data_dir=data_dir)
else:
df_by_geo_res[STATE] = pd.read_csv(
params["test_data_dir"].format(geo_res="state"))
df_by_geo_res[DMA] = pd.read_csv(
params["test_data_dir"].format(geo_res="dma"))
df_by_geo_res[HRR], df_by_geo_res[MSA] = derived_counts_from_dma(
df_by_geo_res[DMA], static_dir=static_dir)
signal_names = add_prefix(SIGNALS, wip_signal, prefix="wip_")
for signal in signal_names:
is_smoothed = signal.endswith(SMOOTHED)
for geo_res, df in df_by_geo_res.items():
create_export_csv(format_for_export(df, is_smoothed),
geo_res=geo_res,
sensor=signal,
start_date=start_date,
export_dir=export_dir)
if not params["test"]:
# Diff exports, and make incremental versions
_, common_diffs, new_files = arch_diff.diff_exports()
# Archive changed and new files only
to_archive = [
f for f, diff in common_diffs.items() if diff is not None
]
to_archive += new_files
_, fails = arch_diff.archive_exports(to_archive)
# Filter existing exports to exclude those that failed to archive
succ_common_diffs = {
f: diff
for f, diff in common_diffs.items() if f not in fails
}
arch_diff.filter_exports(succ_common_diffs)
# Report failures: someone should probably look at them
for exported_file in fails:
print(f"Failed to archive '{exported_file}'")
def run_module(params: Dict[str, Any]):
"""Run Quidel flu test module.
The `params` argument is expected to have the following structure:
- "common":
- "export_dir": str, directory to write output
- "log_exceptions" (optional): bool, whether to log exceptions to file
- "log_filename" (optional): str, name of file to write logs
- indicator":
- "static_file_dir": str, directory name with population information
- "input_cache_dir": str, directory in which to cache input data
- "export_start_date": str, YYYY-MM-DD format of earliest date to create output
- "export_end_date": str, YYYY-MM-DD format of latest date to create output or "" to create
through the present
- "pull_start_date": str, YYYY-MM-DD format of earliest date to pull input
- "pull_end_date": str, YYYY-MM-DD format of latest date to create output or "" to create
through the present
- "aws_credentials": Dict[str, str], authentication parameters for AWS S3; see S3
documentation
- "bucket_name": str, name of AWS bucket in which to find data
- "wip_signal": List[str], list of signal names that are works in progress
- "test_mode": bool, whether we are running in test mode
"""
start_time = time.time()
logger = get_structured_logger(
__name__,
filename=params["common"].get("log_filename"),
log_exceptions=params["common"].get("log_exceptions", True))
cache_dir = params["indicator"]["input_cache_dir"]
export_dir = params["common"]["export_dir"]
static_file_dir = params["indicator"]["static_file_dir"]
export_start_dates = params["indicator"]["export_start_date"]
export_end_dates = params["indicator"]["export_end_date"]
map_df = pd.read_csv(join(static_file_dir, "fips_prop_pop.csv"),
dtype={"fips": int})
# Pull data and update export date
dfs, _end_date = pull_quidel_data(params["indicator"])
if _end_date is None:
print("The data is up-to-date. Currently, no new data to be ingested.")
return
export_end_dates = check_export_end_date(export_end_dates, _end_date,
END_FROM_TODAY_MINUS)
export_start_dates = check_export_start_date(export_start_dates,
export_end_dates,
EXPORT_DAY_RANGE)
# Add prefix, if required
sensors = add_prefix(list(SENSORS.keys()),
wip_signal=params["indicator"]["wip_signal"],
prefix="wip_")
for sensor in sensors:
# Check either covid_ag or flu_ag
test_type = "covid_ag" if "covid_ag" in sensor else "flu_ag"
print("state", sensor)
data = dfs[test_type].copy()
state_groups = geo_map("state", data, map_df).groupby("state_id")
first_date, last_date = data["timestamp"].min(), data["timestamp"].max(
)
# For State Level
state_df = generate_sensor_for_states(state_groups,
smooth=SENSORS[sensor][1],
device=SENSORS[sensor][0],
first_date=first_date,
last_date=last_date)
create_export_csv(state_df,
geo_res="state",
sensor=sensor,
export_dir=export_dir,
start_date=export_start_dates[test_type],
end_date=export_end_dates[test_type])
# County/HRR/MSA level
for geo_res in GEO_RESOLUTIONS:
print(geo_res, sensor)
data = dfs[test_type].copy()
data, res_key = geo_map(geo_res, data, map_df)
res_df = generate_sensor_for_other_geores(
state_groups,
data,
res_key,
smooth=SENSORS[sensor][1],
device=SENSORS[sensor][0],
first_date=first_date,
last_date=last_date)
create_export_csv(res_df,
geo_res=geo_res,
sensor=sensor,
export_dir=export_dir,
start_date=export_start_dates[test_type],
end_date=export_end_dates[test_type],
remove_null_samples=True)
# Export the cache file if the pipeline runs successfully.
# Otherwise, don't update the cache file
update_cache_file(dfs, _end_date, cache_dir)
elapsed_time_in_seconds = round(time.time() - start_time, 2)
logger.info("Completed indicator run",
elapsed_time_in_seconds=elapsed_time_in_seconds)