Standardize logging

combo_cases_and_deaths/delphi_combo_cases_and_deaths/run.py

@@ -33,14 +33,6 @@
 covidcast.covidcast._ASYNC_CALL = True  # pylint: disable=protected-access
 
 
-def check_none_data_frame(data_frame, label, date_range):
-    """Log and return True when a data frame is None."""
-    if data_frame is None:
-        print(f"{label} completely unavailable in range {date_range}")
-        return True
-    return False
-
-
 def maybe_append(usa_facts, jhu):
     """
     Append dataframes if available, otherwise return USAFacts.
@@ -133,7 +125,7 @@ def get_updated_dates(signal, geo, date_range, issue_range=None, fetcher=covidca
         issues=issue_range
     )
 
-    if check_none_data_frame(usafacts_df, "USA-FACTS", date_range):
+    if usafacts_df is None:
         return None
 
     merged_df = merge_dfs_by_geos(usafacts_df, jhu_df, geo)
@@ -142,7 +134,8 @@ def get_updated_dates(signal, geo, date_range, issue_range=None, fetcher=covidca
     return unique_dates
 
 
-def combine_usafacts_and_jhu(signal, geo, date_range, issue_range=None, fetcher=covidcast.signal):
+def combine_usafacts_and_jhu(signal, geo, date_range, logger,
+                             issue_range=None, fetcher=covidcast.signal):
     """Add rows for PR from JHU signals to USA-FACTS signals.
 
     For hhs and nation, fetch the county `num` data so we can compute the proportions correctly
@@ -158,6 +151,7 @@ def combine_usafacts_and_jhu(signal, geo, date_range, issue_range=None, fetcher=
 
     # This occurs if the usafacts ~and the jhu query were empty
     if unique_dates is None:
+        logger.info("USA-FACTS completely unavailable for dates", date_range=date_range)
         return EMPTY_FRAME
 
     # Query only the represented window so that every geo is represented; a single window call is
@@ -329,9 +323,15 @@ def run_module(params):
         log_exceptions=params["common"].get("log_exceptions", True))
 
     for metric, geo_res, sensor_name, signal in variants:
+        logger.info("Generating signal and exporting to CSV",
+                    geo_res = geo_res,
+                    metric = metric,
+                    sensor = sensor_name,
+                    signal = signal)
         df = combine_usafacts_and_jhu(signal,
                                       geo_res,
                                       extend_raw_date_range(params, sensor_name),
+                                      logger,
                                       params['indicator']['issue_range'])
         df["timestamp"] = pd.to_datetime(df["timestamp"])
         start_date = pd.to_datetime(params['indicator']['export_start_date'])

combo_cases_and_deaths/tests/test_run.py

@@ -1,4 +1,5 @@
 """Tests for running combo cases and deaths indicator."""
+import logging
 from datetime import date
 from itertools import product
 import os
@@ -17,6 +18,7 @@
     COLUMN_MAPPING)
 from delphi_combo_cases_and_deaths.constants import METRICS, SMOOTH_TYPES, SENSORS
 
+TEST_LOGGER = logging.getLogger()
 
 def test_issue_dates():
     """The smoothed value for a particular date is computed from the raw
@@ -98,7 +100,7 @@ def make_mock(geo):
                 ("1 1", 4, 1 if geo in ["nation", "hhs"] else 2),
                 ("0 0", 2, 0)
         ]:
-            df = combine_usafacts_and_jhu("", geo, date_range, fetcher=mock_covidcast_signal)
+            df = combine_usafacts_and_jhu("", geo, date_range, TEST_LOGGER, fetcher=mock_covidcast_signal)
             assert df.size == expected_size * len(COLUMN_MAPPING), f"""
 Wrong number of rows in combined data frame for the number of available signals.
 
@@ -126,7 +128,7 @@ def test_multiple_issues(mock_covidcast_signal):
         }),
         None
     ] * 2
-    result = combine_usafacts_and_jhu("confirmed_incidence_num", "county", date_range=(0, 1), fetcher=mock_covidcast_signal)
+    result = combine_usafacts_and_jhu("confirmed_incidence_num", "county", date_range=(0, 1), logger=TEST_LOGGER, fetcher=mock_covidcast_signal)
     pd.testing.assert_frame_equal(
         result,
         pd.DataFrame(
@@ -186,23 +188,23 @@ def test_combine_usafacts_and_jhu_special_geos(mock_covidcast_signal):
     ] * 6 # each call to combine_usafacts_and_jhu makes (2 + 2 * len(unique_timestamps)) = 12 calls to the fetcher
 
     pd.testing.assert_frame_equal(
-        combine_usafacts_and_jhu("confirmed_incidence_num", "nation", date_range=(0, 1), fetcher=mock_covidcast_signal),
+        combine_usafacts_and_jhu("confirmed_incidence_num", "nation", date_range=(0, 1), logger=TEST_LOGGER, fetcher=mock_covidcast_signal),
         pd.DataFrame({"timestamp": [20200101],
                       "geo_id": ["us"],
                       "val": [50 + 100 + 200],
                       "se": [None],
                       "sample_size": [None]})
     )
     pd.testing.assert_frame_equal(
-        combine_usafacts_and_jhu("confirmed_incidence_prop", "nation", date_range=(0, 1), fetcher=mock_covidcast_signal),
+        combine_usafacts_and_jhu("confirmed_incidence_prop", "nation", date_range=(0, 1), logger=TEST_LOGGER, fetcher=mock_covidcast_signal),
         pd.DataFrame({"timestamp": [20200101],
                       "geo_id": ["us"],
                       "val": [(50 + 100 + 200) / (4903185 + 3723066) * 100000],
                       "se": [None],
                       "sample_size": [None]})
     )
     pd.testing.assert_frame_equal(
-        combine_usafacts_and_jhu("confirmed_incidence_num", "county", date_range=(0, 1), fetcher=mock_covidcast_signal),
+        combine_usafacts_and_jhu("confirmed_incidence_num", "county", date_range=(0, 1), logger=TEST_LOGGER, fetcher=mock_covidcast_signal),
         pd.DataFrame({"geo_id": ["01000", "01001", "72001"],
                       "val": [50, 100, 200],
                       "timestamp": [20200101, 20200101, 20200101]},
@@ -229,7 +231,7 @@ def test_no_nation_jhu(mock_covidcast_signal):
                       "value": [1],
                       "timestamp": [20200101]})
     ]
-    result = combine_usafacts_and_jhu("_num", "nation", date_range=(0, 1), fetcher=mock_covidcast_signal)
+    result = combine_usafacts_and_jhu("_num", "nation", date_range=(0, 1), logger=TEST_LOGGER, fetcher=mock_covidcast_signal)
 
     assert mock_covidcast_signal.call_args_list[-1] == call(
         "jhu-csse",

covid_act_now/delphi_covid_act_now/run.py

@@ -45,7 +45,7 @@ def run_module(params):
     parquet_url = params["indicator"]["parquet_url"]
 
     # Load CAN county-level testing data
-    print("Pulling CAN data")
+    logger.info("Pulling CAN data")
     df_pq = load_data(parquet_url)
     df_county_testing = extract_testing_metrics(df_pq)
 
@@ -54,7 +54,8 @@ def run_module(params):
     max_dates_exported = []
     # Perform geo aggregations and export to receiving
     for geo_res in GEO_RESOLUTIONS:
-        print(f"Processing {geo_res}")
+        logger.info("Generating signal and exporting to CSV",
+                    geo_res = geo_res)
         df = geo_map(df_county_testing, geo_res)
 
         # Export 'pcr_specimen_positivity_rate'
@@ -79,7 +80,7 @@ def run_module(params):
         max_dates_exported.append(latest)
         # x2 to count both positivity and tests signals
         num_exported_files += exported_csv_dates.size * 2
-        print(f"Exported dates: {earliest} to {latest}")
+        logger.info("Exported for dates between", earliest=earliest, latest=latest)
 
     elapsed_time_in_seconds = round(time.time() - start_time, 2)
     max_lag_in_days = (datetime.now() - min(max_dates_exported)).days

hhs_facilities/delphi_hhs_facilities/run.py

@@ -36,6 +36,9 @@ def run_module(params) -> None:
     filled_fips_df = fill_missing_fips(raw_df, gmpr)
     stats = []
     for geo, (sig_name, sig_cols, sig_func, sig_offset) in product(GEO_RESOLUTIONS, SIGNALS):
+        logger.info("Generating signal and exporting to CSV",
+                    geo_res = geo,
+                    signal_name = sig_name)
         mapped_df = convert_geo(filled_fips_df, geo, gmpr)
         output_df = generate_signal(mapped_df, sig_cols, sig_func, sig_offset)
         dates = create_export_csv(output_df, params["common"]["export_dir"], geo, sig_name)

hhs_hosp/delphi_hhs/run.py

@@ -105,6 +105,10 @@ def run_module(params):
     geo_mapper = GeoMapper()
     stats = []
     for sensor, smoother, geo in product(SIGNALS, SMOOTHERS, GEOS):
+        logger.info("Generating signal and exporting to CSV",
+                    geo_res = geo,
+                    sensor = sensor,
+                    smoother = smoother)
         df = geo_mapper.add_geocode(make_signal(all_columns, sensor),
                                     "state_id",
                                     "state_code",

nchs_mortality/delphi_nchs_mortality/archive_diffs.py

@@ -8,7 +8,7 @@
 
 from delphi_utils import S3ArchiveDiffer
 
-def arch_diffs(params, daily_arch_diff):
+def arch_diffs(params, daily_arch_diff, logger):
     """
     Archive differences between new updates and existing data.
 
@@ -23,6 +23,8 @@ def arch_diffs(params, daily_arch_diff):
         Read from params.json
     daily_arch_diff: S3ArchiveDiffer
         Used to store and update cache
+    logger: logging.Logger
+        The structured logger.
     """
     weekly_export_dir = params["common"]["weekly_export_dir"]
     daily_export_dir = params["common"]["daily_export_dir"]
@@ -59,7 +61,7 @@ def arch_diffs(params, daily_arch_diff):
 
         # Report failures: someone should probably look at them
         for exported_file in fails:
-            print(f"Failed to archive (weekly) '{exported_file}'")
+            logger.info("Failed to archive (weekly)", filename={exported_file})
 
     # Daily run of archiving utility
     # - Uploads changed files to S3
@@ -83,4 +85,4 @@ def arch_diffs(params, daily_arch_diff):
 
     # Report failures: someone should probably look at them
     for exported_file in fails:
-        print(f"Failed to archive (daily) '{exported_file}'")
+        logger.info("Failed to archive (daily)", filename={exported_file})

nchs_mortality/delphi_nchs_mortality/run.py

@@ -62,7 +62,8 @@ def run_module(params: Dict[str, Any]):
     df_pull = pull_nchs_mortality_data(token, test_file)
     for metric in METRICS:
         if metric == 'percent_of_expected_deaths':
-            print(metric)
+            logger.info("Generating signal and exporting to CSV",
+                        metric = metric)
             df = df_pull.copy()
             df["val"] = df[metric]
             df["se"] = np.nan
@@ -80,7 +81,9 @@ def run_module(params: Dict[str, Any]):
                 stats.append((max(dates), len(dates)))
         else:
             for sensor in SENSORS:
-                print(metric, sensor)
+                logger.info("Generating signal and exporting to CSV",
+                            metric = metric,
+                            sensor = sensor)
                 df = df_pull.copy()
                 if sensor == "num":
                     df["val"] = df[metric]
@@ -107,7 +110,7 @@ def run_module(params: Dict[str, Any]):
 #     - Uploads changed files to S3
 #     - Does not export any issues into receiving
     if "archive" in params:
-        arch_diffs(params, daily_arch_diff)
+        arch_diffs(params, daily_arch_diff, logger)
 
     elapsed_time_in_seconds = round(time.time() - start_time, 2)
     min_max_date = stats and min(s[0] for s in stats)

quidel/delphi_quidel/data_tools.py

@@ -86,8 +86,6 @@ def _geographical_pooling(tpooled_tests, tpooled_ptests, min_obs, max_borrow_obs
             Same length as tests; proportion of parent observations to borrow.
     """
     if (np.any(np.isnan(tpooled_tests)) or np.any(np.isnan(tpooled_ptests))):
-        print(tpooled_tests)
-        print(tpooled_ptests)
         raise ValueError('[parent] tests should be non-negative '
                          'with no np.nan')
     if max_borrow_obs > min_obs:
@@ -153,7 +151,6 @@ def raw_positive_prop(positives, tests, min_obs):
     positives = positives.astype(float)
     tests = tests.astype(float)
     if np.any(np.isnan(positives)) or np.any(np.isnan(tests)):
-        print(positives, tests)
         raise ValueError('positives and tests should be non-negative '
                          'with no np.nan')
     if np.any(positives > tests):
@@ -290,11 +287,10 @@ def raw_tests_per_device(devices, tests, min_obs):
     """
     devices = devices.astype(float)
     tests = tests.astype(float)
-    if (np.any(np.isnan(devices)) or np.any(np.isnan(tests))):
-        print(devices)
-        print(tests)
-        raise ValueError('devices and tests should be non-negative '
-                         'with no np.nan')
+    if np.any(np.isnan(devices)) or np.any(devices < 0):
+        raise ValueError("devices should be non-negative with no np.nan")
+    if np.any(np.isnan(tests)) or np.any(tests < 0):
+        raise ValueError("tests should be non-negative with no np.nan")
     if min_obs <= 0:
         raise ValueError('min_obs should be positive')
     tests[tests < min_obs] = np.nan

quidel/delphi_quidel/pull.py

@@ -82,7 +82,7 @@ def regulate_column_names(df, test_type):
     return df
 
 def get_from_email(column_names, start_dates, end_dates, mail_server,
-                   account, sender, password):
+                   account, sender, password, logger):
     """
     Get raw data from email account.
 
@@ -98,6 +98,8 @@ def get_from_email(column_names, start_dates, end_dates, mail_server,
             email account of the sender
         password: str
             password of the datadrop email
+        logger: logging.Logger
+            The structured logger.
 
     Returns:
         df: pd.DataFrame
@@ -131,7 +133,7 @@ def get_from_email(column_names, start_dates, end_dates, mail_server,
                     if not whether_in_range:
                         continue
 
-                    print(f"Pulling {test} data received on %s"%search_date.date())
+                    logger.info("Pulling data", test=test, date=search_date.date())
                     toread = io.BytesIO()
                     toread.write(att.payload)
                     toread.seek(0)  # reset the pointer
@@ -153,10 +155,9 @@ def fix_zipcode(df):
             zipcode = int(float(zipcode))
             zipcode5.append(zipcode)
     df['zip'] = zipcode5
-    # print('Fixing %.2f %% of the data' % (fixnum * 100 / len(zipcode5)))
     return df
 
-def fix_date(df):
+def fix_date(df, logger):
     """
     Remove invalid dates and select correct test date to use.
 
@@ -175,16 +176,16 @@ def fix_date(df):
     df.insert(2, "timestamp", df["TestDate"])
 
     mask = df["TestDate"] <= df["StorageDate"]
-    print("Removing %.2f%% of unusual data" % ((len(df) - np.sum(mask)) * 100 / len(df)))
+    logger.info(f"Removing {((len(df) - np.sum(mask)) * 100 / len(df)):.2f}% of unusual data")
     df = df[mask]
 
     mask = df["StorageDate"] - df["TestDate"] > pd.Timedelta(days=90)
-    print("Fixing %.2f%% of outdated data" % (np.sum(mask) * 100 / len(df)))
+    logger.info(f"Fixing {(np.sum(mask) * 100 / len(df)):.2f}% of outdated data")
     df["timestamp"].values[mask] = df["StorageDate"].values[mask]
     return df
 
 def preprocess_new_data(start_dates, end_dates, mail_server, account,
-                        sender, password, test_mode):
+                        sender, password, test_mode, logger):
     """
     Pull and pre-process Quidel Antigen Test data from datadrop email.
 
@@ -206,6 +207,8 @@ def preprocess_new_data(start_dates, end_dates, mail_server, account,
             password of the datadrop email
         test_mode: bool
             pull raw data from email or not
+        logger: logging.Logger
+            The structured logger.
     Returns:
         df: pd.DataFrame
         time_flag: datetime.date:
@@ -220,21 +223,20 @@ def preprocess_new_data(start_dates, end_dates, mail_server, account,
     else:
         # Get new data from email
         dfs, time_flag = get_from_email(COLUMN_NAMES, start_dates, end_dates,
-                                       mail_server, account, sender, password)
+                                       mail_server, account, sender, password, logger)
 
     # No new data can be pulled
     if time_flag is None:
         return dfs, time_flag
 
     df_finals = {}
     for test_type in TEST_TYPES:
-        print(f"For {test_type}:")
+        logger.info(f"For {test_type}:")
         df = dfs[test_type]
         # Fix some of the fipcodes that are 9 digit instead of 5 digit
         df = fix_zipcode(df)
         # Create a column CanonicalDate according to StarageDate and TestDate
-        df = fix_date(df)
-
+        df = fix_date(df, logger)
         # Compute numUniqueDevices
         numUniqueDevices = df.groupby(
             by=["timestamp", "zip"],
@@ -309,17 +311,15 @@ def check_intermediate_file(cache_dir, pull_start_dates):
                         sep=",", parse_dates=["timestamp"])
     return previous_dfs, pull_start_dates
 
-def pull_quidel_data(params):
+def pull_quidel_data(params, logger):
     """
     Pull new quidel test data and decide whether to combine it with historical records in ./cache.
 
     Parameters:
         params: dict
             including all the information read from params.json
-        END_FROM_TODAY_MINUS: int
-            report data until - X days
-        EXPORT_DAY_RANGE: int
-            number of dates to report
+        logger: logging.Logger
+            The structured logger.
 
     Returns:
         DataFrame:
@@ -355,7 +355,7 @@ def pull_quidel_data(params):
     # Use _end_date to check the most recent date that we received data
     dfs, _end_date = preprocess_new_data(
             pull_start_dates, pull_end_dates, mail_server,
-            account, sender, password, test_mode)
+            account, sender, password, test_mode, logger)
 
     # Utilize previously stored data
     for test_type in TEST_TYPES: