Add nchs-mortality raw data backups and backup export utility

_delphi_utils_python/delphi_utils/__init__.py

@@ -4,15 +4,14 @@
 from __future__ import absolute_import
 
 from .archive import ArchiveDiffer, GitArchiveDiffer, S3ArchiveDiffer
-from .export import create_export_csv
-from .utils import read_params
-
-from .slack_notifier import SlackNotifier
-from .logger import get_structured_logger
+from .export import create_backup_csv, create_export_csv
 from .geomap import GeoMapper
-from .smooth import Smoother
-from .signal import add_prefix
+from .logger import get_structured_logger
 from .nancodes import Nans
+from .signal import add_prefix
+from .slack_notifier import SlackNotifier
+from .smooth import Smoother
+from .utils import read_params
 from .weekday import Weekday
 
 __version__ = "0.3.25"

_delphi_utils_python/delphi_utils/export.py

@@ -1,16 +1,18 @@
 """Export data in the format expected by the Delphi API."""
 # -*- coding: utf-8 -*-
+import gzip
+import logging
 from datetime import datetime
-from os.path import join
+from os.path import getsize, join
 from typing import Optional
-import logging
 
-from epiweeks import Week
 import numpy as np
 import pandas as pd
+from epiweeks import Week
 
 from .nancodes import Nans
 
+
 def filter_contradicting_missing_codes(df, sensor, metric, date, logger=None):
     """Find values with contradictory missingness codes, filter them, and log."""
     columns = ["val", "se", "sample_size"]
@@ -132,3 +134,70 @@ def create_export_csv(
             export_df = export_df.sort_values(by="geo_id")
         export_df.to_csv(export_file, index=False, na_rep="NA")
     return dates
+
+
+def create_backup_csv(
+    df: pd.DataFrame,
+    backup_dir: str,
+    custom_run: bool,
+    issue: Optional[str] = None,
+    geo_res: Optional[str] = None,
+    sensor: Optional[str] = None,
+    metric: Optional[str] = None,
+    logger: Optional[logging.Logger] = None,
+):
+    """Save data for use as a backup.
+
+    This function is meant to save raw data fetched from data sources.
+    Therefore, it avoids manipulating the data as much as possible to
+    preserve the input.
+
+    When only required arguments are passed, data will be saved to a file of
+    the format `<export_dir>/<today's date as YYYYMMDD>.csv`. Optional arguments
+    should be passed if the source data is fetched from different tables or
+    in batches by signal, geo, etc.
+
+    Parameters
+    ----------
+    df: pd.DataFrame
+        Columns: geo_id, timestamp, val, se, sample_size
+    backup_dir: str
+        Backup directory
+    custom_run: bool
+        Flag indicating if the current run is a patch, or other run where
+        backups aren't needed. If so, don't save any data to disk
+    issue: Optional[str]
+        The date the data was fetched, in YYYYMMDD format. Defaults to "today"
+        if not provided
+    geo_res: Optional[str]
+        Geographic resolution of the data
+    sensor: Optional[str]
+        Sensor that has been calculated (cumulative_counts vs new_counts)
+    metric: Optional[str]
+        Metric we are considering, if any.
+    logger: Optional[logging.Logger]
+        Pass a logger object here to log information about name and size of the backup file.
+
+    Returns
+    ---------
+    dates: pd.Series[datetime]
+        Series of dates for which CSV files were exported.
+    """
+    if not custom_run:
+        # Label the file with today's date (the date the data was fetched).
+        if not issue:
+            issue = datetime.today().strftime("%Y%m%d")
+
+        backup_filename = [issue, geo_res, metric, sensor]
+        backup_filename = "_".join(filter(None, backup_filename)) + ".csv.gz"
+        backup_file = join(backup_dir, backup_filename)
+
+        with gzip.open(backup_file, "wt", newline="") as f:
+            df.to_csv(f, index=False, na_rep="NA")
+
+        if logger:
+            logger.info(
+                "Backup file created",
+                backup_file=backup_file,
+                backup_size=getsize(backup_file),
+            )

ansible/templates/nchs_mortality-params-prod.json.j2

@@ -1,6 +1,7 @@
 {
   "common": {
     "daily_export_dir": "./daily_receiving",
+    "backup_dir": "./raw_data_backups",
     "log_filename": "/var/log/indicators/nchs_mortality.log",
     "weekly_export_dir": "/common/covidcast/receiving/nchs-mortality"
   },

nchs_mortality/delphi_nchs_mortality/pull.py

@@ -1,15 +1,17 @@
 # -*- coding: utf-8 -*-
 """Functions for pulling NCHS mortality data API."""
 
+import logging
 from typing import Optional
 
 import numpy as np
 import pandas as pd
+from delphi_utils import create_backup_csv
+from delphi_utils.geomap import GeoMapper
 from sodapy import Socrata
 
-from delphi_utils.geomap import GeoMapper
+from .constants import METRICS, NEWLINE, RENAME
 
-from .constants import METRICS, RENAME, NEWLINE
 
 def standardize_columns(df):
     """Rename columns to comply with a standard set.
@@ -22,7 +24,13 @@ def standardize_columns(df):
     return df.rename(columns=dict(rename_pairs))
 
 
-def pull_nchs_mortality_data(socrata_token: str, test_file: Optional[str] = None):
+def pull_nchs_mortality_data(
+    socrata_token: str,
+    backup_dir: str,
+    custom_run: bool,
+    logger: Optional[logging.Logger] = None,
+    test_file: Optional[str] = None,
+):
     """Pull the latest NCHS Mortality data, and conforms it into a dataset.
 
     The output dataset has:
@@ -40,6 +48,10 @@ def pull_nchs_mortality_data(socrata_token: str, test_file: Optional[str] = None
     ----------
     socrata_token: str
         My App Token for pulling the NCHS mortality data
+    backup_dir: str
+        Directory to which to save raw backup data
+    custom_run: bool
+        Flag indicating if the current run is a patch. If so, don't save any data to disk
     test_file: Optional[str]
         When not null, name of file from which to read test data
 
@@ -60,6 +72,10 @@ def pull_nchs_mortality_data(socrata_token: str, test_file: Optional[str] = None
         client = Socrata("data.cdc.gov", socrata_token)
         results = client.get("r8kw-7aab", limit=10**10)
         df = pd.DataFrame.from_records(results)
+
+    create_backup_csv(df, backup_dir, custom_run=custom_run, logger=logger)
+
+    if not test_file:
         # drop "By Total" rows
         df = df[df["group"].transform(str.lower) == "by week"]
 

nchs_mortality/delphi_nchs_mortality/run.py

@@ -59,6 +59,8 @@ def run_module(params: Dict[str, Any]):
                 days=date.today().weekday() + 2)
         export_start_date = export_start_date.strftime('%Y-%m-%d')
     daily_export_dir = params["common"]["daily_export_dir"]
+    backup_dir = params["common"]["backup_dir"]
+    custom_run = params["common"].get("custom_run", False)
     socrata_token = params["indicator"]["socrata_token"]
     test_file = params["indicator"].get("test_file", None)
 
@@ -70,7 +72,9 @@ def run_module(params: Dict[str, Any]):
         daily_arch_diff.update_cache()
 
     stats = []
-    df_pull = pull_nchs_mortality_data(socrata_token, test_file)
+    df_pull = pull_nchs_mortality_data(
+        socrata_token, backup_dir, custom_run=custom_run, test_file=test_file, logger=logger
+    )
     for metric in METRICS:
         for geo in ["state", "nation"]:
             if metric == 'percent_of_expected_deaths':

nchs_mortality/params.json.template

@@ -2,7 +2,8 @@
   "common": {
     "daily_export_dir": "./daily_receiving",
     "weekly_export_dir": "./receiving",
-    "log_filename": "/var/log/indicators/nchs_mortality.log",
+    "backup_dir": "./raw_data_backups",
+    "log_filename": "./nchs_mortality.log",
     "log_exceptions": false
   },
   "indicator": {

nchs_mortality/raw_data_backups/.gitignore

@@ -0,0 +1,120 @@
+# You should hard commit a prototype for this file, but we
+# want to avoid accidental adding of API tokens and other
+# private data parameters
+params.json
+
+# Do not commit output files
+receiving/*.csv
+
+# Remove macOS files
+.DS_Store
+
+# virtual environment
+dview/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+coverage.xml
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+.static_storage/
+.media/
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/

nchs_mortality/tests/conftest.py

@@ -14,8 +14,10 @@
 
 PARAMS = {
   "common": {
+    "custom_run": True,
     "daily_export_dir": "./daily_receiving",
-    "weekly_export_dir": "./receiving"
+    "weekly_export_dir": "./receiving",
+    "backup_dir": "./raw_data_backups"
   },
   "indicator": {
     "export_start_date": "2020-04-11",

nchs_mortality/tests/raw_data_backups/.gitignore

@@ -0,0 +1,2 @@
+*.csv
+*.gz

nchs_mortality/tests/test_pull.py

@@ -1,3 +1,4 @@
+import os
 import pytest
 
 import pandas as pd
@@ -34,7 +35,7 @@ def test_standardize_columns(self):
         pd.testing.assert_frame_equal(expected, df)
 
     def test_good_file(self):
-        df = pull_nchs_mortality_data(SOCRATA_TOKEN, "test_data.csv")
+        df = pull_nchs_mortality_data(SOCRATA_TOKEN, backup_dir = "", custom_run = True, test_file = "test_data.csv")
 
         # Test columns
         assert (
@@ -90,9 +91,20 @@ def test_good_file(self):
     def test_bad_file_with_inconsistent_time_col(self):
         with pytest.raises(ValueError):
             pull_nchs_mortality_data(
-                SOCRATA_TOKEN, "bad_data_with_inconsistent_time_col.csv"
+                SOCRATA_TOKEN, backup_dir = "", custom_run = True, test_file = "bad_data_with_inconsistent_time_col.csv"
             )
 
     def test_bad_file_with_missing_cols(self):
         with pytest.raises(ValueError):
-            pull_nchs_mortality_data(SOCRATA_TOKEN, "bad_data_with_missing_cols.csv")
+            pull_nchs_mortality_data(SOCRATA_TOKEN, backup_dir = "", custom_run = True, test_file = "bad_data_with_missing_cols.csv")
+
+    def test_backup_today_data(self):
+        today = pd.Timestamp.today().strftime("%Y%m%d")
+        backup_dir = "./raw_data_backups"
+        pull_nchs_mortality_data(SOCRATA_TOKEN, backup_dir = backup_dir, custom_run = False, test_file = "test_data.csv")
+        backup_file = f"{backup_dir}/{today}.csv.gz"
+        backup_df = pd.read_csv(backup_file)
+        source_df = pd.read_csv("test_data/test_data.csv")
+        pd.testing.assert_frame_equal(source_df, backup_df)
+        if os.path.exists(backup_file):
+            os.remove(backup_file)