diff --git a/.github/workflows/python-ci.yml b/.github/workflows/python-ci.yml index 6e23c61d27..498a9555c9 100644 --- a/.github/workflows/python-ci.yml +++ b/.github/workflows/python-ci.yml @@ -39,6 +39,8 @@ jobs: dir: "delphi_quidel_covidtest" - package: "sir_complainsalot" dir: "delphi_sir_complainsalot" + - package: "nhsn" + dir: "delphi_nhsn" defaults: run: working-directory: ${{ matrix.package }} diff --git a/Jenkinsfile b/Jenkinsfile index 3fcdc904a8..35d6788aec 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -10,7 +10,7 @@ - TODO: #527 Get this list automatically from python-ci.yml at runtime. */ -def indicator_list = ['backfill_corrections', 'changehc', 'claims_hosp', 'google_symptoms', 'hhs_hosp', 'nchs_mortality', 'quidel_covidtest', 'sir_complainsalot', 'doctor_visits', 'nwss_wastewater', 'nssp'] +def indicator_list = ['backfill_corrections', 'changehc', 'claims_hosp', 'google_symptoms', 'hhs_hosp', 'nchs_mortality', 'quidel_covidtest', 'sir_complainsalot', 'doctor_visits', 'nwss_wastewater', 'nssp', 'nhsn'] def build_package_main = [:] def build_package_prod = [:] def deploy_staging = [:] diff --git a/ansible/templates/nhsn-params-prod.json.j2 b/ansible/templates/nhsn-params-prod.json.j2 new file mode 100644 index 0000000000..436017ba9d --- /dev/null +++ b/ansible/templates/nhsn-params-prod.json.j2 @@ -0,0 +1,30 @@ +{ + "common": { + "export_dir": "/common/covidcast/receiving/nhsn", + "backup_dir": "./raw_data_backups", + "log_filename": "/var/log/indicators/nhsn.log", + "log_exceptions": false + }, + "indicator": { + "wip_signal": true, + "static_file_dir": "./static", + "socrata_token": "{{ nhsn_token }}" + }, + "validation": { + "common": { + "data_source": "nhsn", + "api_credentials": "{{ validation_api_key }}", + "span_length": 15, + "min_expected_lag": {"all": "7"}, + "max_expected_lag": {"all": "13"}, + "dry_run": true, + "suppressed_errors": [] + }, + "static": { + "minimum_sample_size": 0, + "missing_se_allowed": true, + "missing_sample_size_allowed": true + }, + "dynamic": {} + } +} diff --git a/ansible/templates/sir_complainsalot-params-prod.json.j2 b/ansible/templates/sir_complainsalot-params-prod.json.j2 index 3247287677..7bb2d179a5 100644 --- a/ansible/templates/sir_complainsalot-params-prod.json.j2 +++ b/ansible/templates/sir_complainsalot-params-prod.json.j2 @@ -45,6 +45,10 @@ "nssp": { "max_age":19, "maintainers": [] + }, + "nhsn": { + "max_age":19, + "maintainers": [] } } } diff --git a/ansible/vars.yaml b/ansible/vars.yaml index ff9ba135ce..a50cda37b4 100644 --- a/ansible/vars.yaml +++ b/ansible/vars.yaml @@ -59,6 +59,9 @@ nwss_wastewater_token: "{{ vault_cdc_socrata_token }}" # nssp nssp_token: "{{ vault_cdc_socrata_token }}" +# nhsn +nhsn_token: "{{ vault_cdc_socrata_token }}" + # SirCAL sir_complainsalot_api_key: "{{ vault_sir_complainsalot_api_key }}" sir_complainsalot_slack_token: "{{ vault_sir_complainsalot_slack_token }}" diff --git a/nhsn/DETAILS.md b/nhsn/DETAILS.md new file mode 100644 index 0000000000..900f2f62e3 --- /dev/null +++ b/nhsn/DETAILS.md @@ -0,0 +1,30 @@ +# NHSN data + +We import the NHSN Weekly Hospital Respiratory Data + +There are 2 sources we grab data from for nhsn: +Note that they are from the same source, but with different cadence and one reporting preliminary data for the previous reporting week + +Primary source: https://data.cdc.gov/Public-Health-Surveillance/Weekly-Hospital-Respiratory-Data-HRD-Metrics-by-Ju/ua7e-t2fy/about_data +Secondary (preliminary source): https://data.cdc.gov/Public-Health-Surveillance/Weekly-Hospital-Respiratory-Data-HRD-Metrics-by-Ju/mpgq-jmmr/about_data + +## Geographical Levels +* `state`: reported using two-letter postal code +* `national`: just `us` for now +* `hhs`: reporting using Geomapper with state level + +## Metrics +* `confirmed_admissions_covid`: total number of confirmed admission for covid +* `confirmed_admissions_flu`: total number of confirmed admission for flu +* `prelim_confirmed_admissions_covid`: total number of confirmed admission for covid from preliminary source +* `prelim_confirmed_admissions_flu`: total number of confirmed admission for flu from preliminary source + +## Additional Notes +HHS dataset and NHSN dataset covers the equivalent data of hospital admission for covid and flu. +As a general trend, HHS and NHSN data matches pretty well. +However, there are differences between some of the states, notably for GA (untill 2023), LA, NV, PR (late 2020-early 2021), TN all have HHS substantially lower, HHS is substantially lower than NHSN. + +Some states have this spike in NHSN or hhs where the other source doesn't have a spike and spikes don't happen at the same time_values across states + +More details regarding the analysis is available in the [analysis.ipynb](notebook%2Fanalysis.ipynb) +(may require installing additional packages to work) \ No newline at end of file diff --git a/nhsn/Makefile b/nhsn/Makefile new file mode 100644 index 0000000000..390113eef7 --- /dev/null +++ b/nhsn/Makefile @@ -0,0 +1,32 @@ +.PHONY = venv, lint, test, clean + +dir = $(shell find ./delphi_* -name __init__.py | grep -o 'delphi_[_[:alnum:]]*' | head -1) +venv: + python3.8 -m venv env + +install: venv + . env/bin/activate; \ + pip install wheel ; \ + pip install -e ../_delphi_utils_python ;\ + pip install -e . + +install-ci: venv + . env/bin/activate; \ + pip install wheel ; \ + pip install ../_delphi_utils_python ;\ + pip install . + +lint: + . env/bin/activate; pylint $(dir) --rcfile=../pyproject.toml + . env/bin/activate; pydocstyle $(dir) + +format: + . env/bin/activate; darker $(dir) + +test: + . env/bin/activate ;\ + (cd tests && ../env/bin/pytest --cov=$(dir) --cov-report=term-missing) + +clean: + rm -rf env + rm -f params.json diff --git a/nhsn/delphi_nhsn/__init__.py b/nhsn/delphi_nhsn/__init__.py new file mode 100644 index 0000000000..52a5072595 --- /dev/null +++ b/nhsn/delphi_nhsn/__init__.py @@ -0,0 +1,13 @@ +# -*- coding: utf-8 -*- +"""Module to pull and clean indicators from the XXXXX source. + +This file defines the functions that are made public by the module. As the +module is intended to be executed though the main method, these are primarily +for testing. +""" + +from __future__ import absolute_import + +from . import run + +__version__ = "0.1.0" diff --git a/nhsn/delphi_nhsn/__main__.py b/nhsn/delphi_nhsn/__main__.py new file mode 100644 index 0000000000..67d95f968b --- /dev/null +++ b/nhsn/delphi_nhsn/__main__.py @@ -0,0 +1,13 @@ +# -*- coding: utf-8 -*- +"""Call the function run_module when executed. + +This file indicates that calling the module (`python -m MODULE_NAME`) will +call the function `run_module` found within the run.py file. There should be +no need to change this template. +""" + +from delphi_utils import read_params + +from .run import run_module # pragma: no cover + +run_module(read_params()) # pragma: no cover diff --git a/nhsn/delphi_nhsn/constants.py b/nhsn/delphi_nhsn/constants.py new file mode 100644 index 0000000000..e6e6e43597 --- /dev/null +++ b/nhsn/delphi_nhsn/constants.py @@ -0,0 +1,31 @@ +"""Registry for signal names.""" + +GEOS = ["state", "nation", "hhs"] + +# column name from socrata +TOTAL_ADMISSION_COVID_API = "totalconfc19newadm" +TOTAL_ADMISSION_FLU_API = "totalconfflunewadm" + +SIGNALS_MAP = { + "confirmed_admissions_covid_ew": TOTAL_ADMISSION_COVID_API, + "confirmed_admissions_flu_ew": TOTAL_ADMISSION_FLU_API, +} + +TYPE_DICT = { + "timestamp": "datetime64[ns]", + "geo_id": str, + "confirmed_admissions_covid_ew": float, + "confirmed_admissions_flu_ew": float, +} + +# signal mapping for secondary, preliminary source +PRELIM_SIGNALS_MAP = { + "confirmed_admissions_covid_ew_prelim": TOTAL_ADMISSION_COVID_API, + "confirmed_admissions_flu_ew_prelim": TOTAL_ADMISSION_FLU_API, +} +PRELIM_TYPE_DICT = { + "timestamp": "datetime64[ns]", + "geo_id": str, + "confirmed_admissions_covid_ew_prelim": float, + "confirmed_admissions_flu_ew_prelim": float, +} diff --git a/nhsn/delphi_nhsn/pull.py b/nhsn/delphi_nhsn/pull.py new file mode 100644 index 0000000000..7c200bf3aa --- /dev/null +++ b/nhsn/delphi_nhsn/pull.py @@ -0,0 +1,123 @@ +# -*- coding: utf-8 -*- +"""Functions for pulling NSSP ER data.""" +import logging +from typing import Optional + +import pandas as pd +from delphi_utils import create_backup_csv +from sodapy import Socrata + +from .constants import PRELIM_SIGNALS_MAP, PRELIM_TYPE_DICT, SIGNALS_MAP, TYPE_DICT + + +def pull_data(socrata_token: str, dataset_id: str): + """Pull data from Socrata API.""" + client = Socrata("data.cdc.gov", socrata_token) + results = [] + offset = 0 + limit = 50000 # maximum limit allowed by SODA 2.0 + while True: + page = client.get(dataset_id, limit=limit, offset=offset) + if not page: + break # exit the loop if no more results + results.extend(page) + offset += limit + + df = pd.DataFrame.from_records(results) + return df + + +def pull_nhsn_data(socrata_token: str, backup_dir: str, custom_run: bool, logger: Optional[logging.Logger] = None): + """Pull the latest NSSP ER visits data, and conforms it into a dataset. + + The output dataset has: + + - Each row corresponds to a single observation + - Each row additionally has columns for the signals in SIGNALS + + Parameters + ---------- + socrata_token: str + My App Token for pulling the NHSN 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 + logger: Optional[logging.Logger] + logger object + + Returns + ------- + pd.DataFrame + Dataframe as described above. + """ + # Pull data from Socrata API + df = pull_data(socrata_token, dataset_id="ua7e-t2fy") + + keep_columns = list(TYPE_DICT.keys()) + + if not df.empty: + create_backup_csv(df, backup_dir, custom_run, logger=logger) + + df = df.rename(columns={"weekendingdate": "timestamp", "jurisdiction": "geo_id"}) + + for signal, col_name in SIGNALS_MAP.items(): + df[signal] = df[col_name] + + df = df[keep_columns] + df["geo_id"] = df["geo_id"].str.lower() + df.loc[df["geo_id"] == "usa", "geo_id"] = "us" + df = df.astype(TYPE_DICT) + else: + df = pd.DataFrame(columns=keep_columns) + + return df + + +def pull_preliminary_nhsn_data( + socrata_token: str, backup_dir: str, custom_run: bool, logger: Optional[logging.Logger] = None +): + """Pull the latest NSSP ER visits data, and conforms it into a dataset. + + The output dataset has: + + - Each row corresponds to a single observation + - Each row additionally has columns for the signals in SIGNALS + + Parameters + ---------- + socrata_token: str + My App Token for pulling the NHSN 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 + logger: Optional[logging.Logger] + logger object + + Returns + ------- + pd.DataFrame + Dataframe as described above. + """ + # Pull data from Socrata API + df = pull_data(socrata_token, dataset_id="mpgq-jmmr") + + keep_columns = list(PRELIM_TYPE_DICT.keys()) + + if not df.empty: + create_backup_csv(df, backup_dir, custom_run, sensor="prelim", logger=logger) + + df = df.rename(columns={"weekendingdate": "timestamp", "jurisdiction": "geo_id"}) + + for signal, col_name in PRELIM_SIGNALS_MAP.items(): + df[signal] = df[col_name] + + df = df[keep_columns] + df = df.astype(PRELIM_TYPE_DICT) + df["geo_id"] = df["geo_id"].str.lower() + df.loc[df["geo_id"] == "usa", "geo_id"] = "us" + else: + df = pd.DataFrame(columns=keep_columns) + + return df diff --git a/nhsn/delphi_nhsn/run.py b/nhsn/delphi_nhsn/run.py new file mode 100644 index 0000000000..80f7cab472 --- /dev/null +++ b/nhsn/delphi_nhsn/run.py @@ -0,0 +1,102 @@ +# -*- coding: utf-8 -*- +"""Functions to call when running the function. + +This module should contain a function called `run_module`, that is executed +when the module is run with `python -m MODULE_NAME`. `run_module`'s lone argument should be a +nested dictionary of parameters loaded from the params.json file. We expect the `params` to have +the following structure: + - "common": + - "export_dir": str, directory to which the results are exported + - "log_filename": (optional) str, path to log file + - "indicator": (optional) + - "wip_signal": (optional) Any[str, bool], list of signals that are works in progress, or + True if all signals in the registry are works in progress, or False if only + unpublished signals are. See `delphi_utils.add_prefix()` + - Any other indicator-specific settings +""" +import time +from datetime import date, datetime, timedelta + +import numpy as np +from delphi_utils import GeoMapper, get_structured_logger +from delphi_utils.export import create_export_csv + +from .constants import GEOS, PRELIM_SIGNALS_MAP, SIGNALS_MAP +from .pull import pull_nhsn_data, pull_preliminary_nhsn_data + + +def run_module(params): + """ + Run the indicator. + + Arguments + -------- + params: Dict[str, Any] + Nested dictionary of parameters. + """ + start_time = time.time() + logger = get_structured_logger( + __name__, + filename=params["common"].get("log_filename"), + log_exceptions=params["common"].get("log_exceptions", True), + ) + export_dir = params["common"]["export_dir"] + backup_dir = params["common"]["backup_dir"] + custom_run = params["common"].get("custom_run", False) + socrata_token = params["indicator"]["socrata_token"] + export_start_date = params["indicator"]["export_start_date"] + run_stats = [] + + if export_start_date == "latest": # Find the previous Saturday + export_start_date = date.today() - timedelta(days=date.today().weekday() + 2) + export_start_date = export_start_date.strftime("%Y-%m-%d") + + nhsn_df = pull_nhsn_data(socrata_token, backup_dir, custom_run=custom_run, logger=logger) + preliminary_nhsn_df = pull_preliminary_nhsn_data(socrata_token, backup_dir, custom_run=custom_run, logger=logger) + + geo_mapper = GeoMapper() + signal_df_dict = {signal: nhsn_df for signal in SIGNALS_MAP} + signal_df_dict.update({signal: preliminary_nhsn_df for signal in PRELIM_SIGNALS_MAP}) + + for signal, df_pull in signal_df_dict.items(): + for geo in GEOS: + df = df_pull.copy() + df = df[["timestamp", "geo_id", signal]] + df.rename({signal: "val"}, axis=1, inplace=True) + if geo == "nation": + df = df[df["geo_id"] == "us"] + elif geo == "hhs": + df = df[df["geo_id"] != "us"] + df.rename(columns={"geo_id": "state_id"}, inplace=True) + df = geo_mapper.add_geocode(df, "state_id", "state_code", from_col="state_id") + df = geo_mapper.add_geocode(df, "state_code", "hhs", from_col="state_code", new_col="hhs") + df = geo_mapper.replace_geocode( + df, from_col="state_code", from_code="state_code", new_col="geo_id", new_code="hhs" + ) + else: + df = df[df_pull["geo_id"] != "us"] + df["se"] = np.nan + df["sample_size"] = np.nan + dates = create_export_csv( + df, + geo_res=geo, + export_dir=export_dir, + start_date=datetime.strptime(export_start_date, "%Y-%m-%d"), + sensor=signal, + weekly_dates=True, + ) + if len(dates) > 0: + run_stats.append((max(dates), len(dates))) + + elapsed_time_in_seconds = round(time.time() - start_time, 2) + min_max_date = run_stats and min(s[0] for s in run_stats) + csv_export_count = sum(s[-1] for s in run_stats) + max_lag_in_days = min_max_date and (datetime.now() - min_max_date).days + formatted_min_max_date = min_max_date and min_max_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_min_max_date, + ) diff --git a/nhsn/notebook/analysis.ipynb b/nhsn/notebook/analysis.ipynb new file mode 100644 index 0000000000..a679e6d788 --- /dev/null +++ b/nhsn/notebook/analysis.ipynb @@ -0,0 +1,7931 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "# Setup\n", + "install plotly\n", + "locally run nhsn indicator in order to generate the data\n", + "get api key for epidata and fill in the API_KEY \n" + ], + "id": "ac71b219bacb886e" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T21:05:46.881968Z", + "start_time": "2024-12-06T21:05:46.869306Z" + } + }, + "cell_type": "code", + "source": [ + "from pathlib import Path\n", + "import pandas as pd\n", + "import os\n", + "\n", + "from delphi_nhsn.constants import SIGNALS_MAP, PRELIM_SIGNALS_MAP\n", + "from delphi_epidata import Epidata\n", + "from epiweeks import Week\n", + "API_KEY = os.environ.get('DELPHI_API_KEY')\n", + "\n", + "CSV_PATH = Path(os.path.abspath('')).parent.joinpath('receiving')\n", + "SIGNALS = list(SIGNALS_MAP.keys()) + list(PRELIM_SIGNALS_MAP.keys())\n", + "Epidata.auth = (\"epidata\", API_KEY)\n" + ], + "id": "initial_id", + "outputs": [], + "execution_count": 106 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T19:45:52.690687Z", + "start_time": "2024-12-06T19:45:52.686556Z" + } + }, + "cell_type": "code", + "source": [ + "def get_nhsn_df(geo, pathogen, prelim=False):\n", + " lst = []\n", + " geo_filter = f'*{geo}'\n", + " pathogen_filter = f'confirmed_admissions_{pathogen}.csv'\n", + " prelim_filter = f'prelim_' if prelim else \"\"\n", + " filter = f\"{geo_filter}_{prelim_filter}{pathogen_filter}\"\n", + " for files in Path(CSV_PATH).glob(filter):\n", + " filename_parts = files.name.split('_')\n", + " signal = \"_\".join(filename_parts[3:]).replace('.csv', '')\n", + " time_value = filename_parts[1]\n", + " df = pd.read_csv(files)\n", + " df['signal'] = signal\n", + " df['time_value'] = time_value\n", + " df[\"geo_id\"] = df[\"geo_id\"].astype(str)\n", + " if geo == \"nation\":\n", + " df[\"geo_id\"] = \"US\"\n", + " elif geo == \"state\":\n", + " df[\"geo_id\"] = df[\"geo_id\"].str.upper()\n", + " df.time_value = df.time_value.astype(str)\n", + " lst.append(df)\n", + " df = pd.concat(lst)\n", + " df = df.drop(columns=['se','sample_size'], axis=1)\n", + " return df" + ], + "id": "fd6cc0b834450ccc", + "outputs": [], + "execution_count": 95 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T19:45:54.311661Z", + "start_time": "2024-12-06T19:45:54.307487Z" + } + }, + "cell_type": "code", + "source": [ + "def get_hhs_df(geo, pathogen):\n", + " response = Epidata.covidcast(\"hhs\", f\"confirmed_admissions_{pathogen}_1d\",\n", + " geo_type=geo, time_values=Epidata.range(\"20200808\", \"20240426\"),\n", + " geo_value=\"*\", as_of=None, time_type=\"day\")\n", + " df = pd.DataFrame.from_dict(response[\"epidata\"])\n", + " df.rename(columns={\"geo_value\": \"geo_id\", \"value\": \"val\", \"stderr\": \"se\"}, inplace=True)\n", + " df = df[[\"geo_id\", \"time_value\", \"val\", \"signal\"]]\n", + " df[\"time_value\"] = pd.to_datetime(df[\"time_value\"], format=\"%Y%m%d\")\n", + " # aggregating over 7 days matching epiweek for nhsn\n", + " df[\"week_time\"] = df[\"time_value\"].apply(lambda x: Week.fromdate(x))\n", + " df = df.groupby([\"geo_id\", \"signal\", \"week_time\"]).val.sum().reset_index()\n", + " df[\"geo_id\"] = df[\"geo_id\"].astype(str)\n", + " if geo != \"hhs\":\n", + " df[\"geo_id\"] = df[\"geo_id\"].str.upper()\n", + " df.rename({\"week_time\": \"time_value\"}, axis=1, inplace=True)\n", + " df.time_value = df.time_value.astype(str)\n", + " return df\n" + ], + "id": "c2b36cc1a6812be3", + "outputs": [], + "execution_count": 96 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T19:45:55.884628Z", + "start_time": "2024-12-06T19:45:55.881237Z" + } + }, + "cell_type": "code", + "source": [ + "def generate_spearman(nhsn_df, hhs_df, slice_col):\n", + " \n", + " joined = pd.merge(nhsn_df, hhs_df, on=[\"geo_id\", \"time_value\"], how=\"left\", suffixes=[\"_nhsn\", \"_hhs\"])\n", + " \n", + " spearmanr_df = joined.groupby(slice_col)[[\"val_nhsn\", \"val_hhs\"]].corr(\"spearman\").iloc[0::2,-1].reset_index()\n", + " spearmanr_df.rename({\"val_hhs\": \"correlation\"}, axis=1, inplace=True)\n", + " spearmanr_df = spearmanr_df[[slice_col, \"correlation\"]]\n", + " spearmanr_df[slice_col] = spearmanr_df[slice_col].str.upper()\n", + " return spearmanr_df\n", + " " + ], + "id": "a21bf17412d82522", + "outputs": [], + "execution_count": 97 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T19:45:57.349603Z", + "start_time": "2024-12-06T19:45:57.344672Z" + } + }, + "cell_type": "code", + "source": [ + "from plotly.offline import init_notebook_mode\n", + "\n", + "init_notebook_mode(connected=True)\n", + "\n", + "def plot_spearman_choropleth(nhsn_df, hhs_df, pathogen, prelim_flag, geo_val, download=False):\n", + " spearmanr = generate_spearman(nhsn_df, hhs_df, \"geo_id\")\n", + " title = f'Spearman Correlations between {geo_val} HHS {pathogen} admission and NHSN {pathogen} admission'\n", + " filename = f'spearman_choropleth_{pathogen}_{geo_val}'\n", + " \n", + " if prelim_flag:\n", + " title = f\"{title} (prelim data)\"\n", + " filename = f\"{filename}_prelim\"\n", + " \n", + " filename = filename + \".pdf\"\n", + " \n", + " fig = go.Figure(data=go.Choropleth(\n", + " locations=spearmanr['geo_id'], # Spatial coordinates\n", + " z = spearmanr['correlation'].astype(float), # Data to be color-coded\n", + " locationmode = 'USA-states', # set of locations match entries in `locations`\n", + " colorscale = 'Viridis',\n", + " colorbar_title = \"spearman rank\",\n", + " ))\n", + " \n", + " fig.update_layout(\n", + " title_text = title,\n", + " geo_scope='usa', # limite map scope to USA\n", + " )\n", + " \n", + " if download:\n", + " pio.write_image(fig, filename, format='pdf')\n", + " \n", + " else:\n", + " fig.show()\n" + ], + "id": "e7e54b9bd736a29d", + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 98 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T19:45:59.173894Z", + "start_time": "2024-12-06T19:45:59.168723Z" + } + }, + "cell_type": "code", + "source": [ + "from plotly.offline import init_notebook_mode\n", + "\n", + "init_notebook_mode(connected=True)\n", + "\n", + "def plot_timeplot(df, pathogen, prelim_flag, geo_val, y_col,download=False):\n", + " df.sort_values(\"time_value\", inplace=True)\n", + "\n", + " if y_col == \"correlation\":\n", + " title = f'Spearman Correlations between {pathogen} {geo_val} HHS and NHSN over time'\n", + " filename = f'spearman_timeplot_{pathogen}_{geo_val}'\n", + " elif y_col == \"count\":\n", + " title = f'{geo_val} NHSN count over time'\n", + " filename = f'{geo_val}_{pathogen}_nhsn_row_count_timeplot'\n", + " elif y_col == \"diff_percentage\":\n", + " title = f'{geo_val} proportion between nhsn vs hhs ((nhsn-hhs)/hhs)'\n", + " filename = f'{geo_val}_{pathogen}_nhsn_diff_percentage_timeplot'\n", + " df.to_csv(f\"{filename}.csv\", index=False)\n", + " elif y_col == \"val\":\n", + " title = f'{geo_val} NHSN value over time'\n", + " filename = f'{geo_val}_{pathogen}_nhsn_timeplot'\n", + " df.to_csv(f\"{filename}.csv\", index=False)\n", + " elif y_col == \"val_hhs\":\n", + " title = f'{geo_val} HHS value over time'\n", + " filename = f'{geo_val}_{pathogen}_hhs_timeplot'\n", + " y_col = \"val\"\n", + "\n", + " \n", + " if prelim_flag:\n", + " filename = f\"{filename}_prelim\"\n", + " title = f\"{title} (prelim data)\"\n", + " \n", + " filename = filename + \".pdf\"\n", + " \n", + " fig = go.Figure()\n", + " fig.add_trace(go.Scatter(x=df['time_value'], y=df[y_col], mode='lines', name='Time Series'))\n", + " fig.update_layout(title=title)\n", + "\n", + " if download:\n", + " pio.write_image(fig, filename, format='pdf')\n", + " else:\n", + " fig.show()" + ], + "id": "7c1338ae44f73d22", + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 99 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T19:46:01.622458Z", + "start_time": "2024-12-06T19:46:01.397580Z" + } + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "from plotly.offline import init_notebook_mode\n", + "\n", + "init_notebook_mode(connected=True)\n", + "\n", + "geo_type = \"state\"\n", + "pathogen = \"covid\"\n", + "nhsn_df = get_nhsn_df(geo_type, pathogen)\n", + "\n", + "def plot_choropleth_overtime(df, pathogen, prelim_flag, geo_val, z_col):\n", + " color_bar_title = \"\"\n", + " layout_title = \"\" \n", + " if z_col == \"val\" :\n", + " color_bar_title = f'{geo_val} Total Confirmed {pathogen} Cases'\n", + " layout_title = f'{geo_val} NHSN total confirmed {pathogen} cases' \n", + "\n", + " elif z_col == \"diff_percentage\":\n", + " color_bar_title = f\"{geo_val} difference in percentage ((nhsn-hhs)/hhs)\"\n", + " layout_title = f'Proportion between NHSN and HHS for {geo_val} confirmed {pathogen} over time'\n", + " \n", + " if prelim_flag:\n", + " layout_title = f'{layout_title} (prelim data)'\n", + " \n", + " df[\"time_value\"] = pd.to_datetime(df.time_value.map(lambda x: str(x)+'-0'), format=\"%Y%W-%w\")\n", + " \n", + " data_slider = []\n", + " \n", + " for time_value in df['time_value'].unique():\n", + " df_segmented = df[(df['time_value']== time_value)]\n", + " \n", + " data_each_yr = dict(\n", + " type='choropleth',\n", + " locations = df_segmented['geo_id'],\n", + " z=df_segmented[z_col].astype(float),\n", + " locationmode='USA-states',\n", + " colorscale = 'Viridis',\n", + " colorbar= {'title': color_bar_title},)\n", + " \n", + " data_slider.append(data_each_yr)\n", + " \n", + " steps = []\n", + " for idx, time_value in enumerate(sorted(df['time_value'].unique())):\n", + " step = {\n", + " \"method\":'restyle',\n", + " \"args\":[\n", + " {'visible': [False] * len(data_slider)}\n", + " ],\n", + " \"label\": f\"Epiweek {Week.fromdate(pd.to_datetime(time_value))}\"\n", + " }\n", + " step['args'][0][\"visible\"][idx] = True\n", + " steps.append(step)\n", + " \n", + " sliders = [dict(active=0, pad={\"t\": 1}, steps=steps)]\n", + " \n", + " layout = dict(title =layout_title, geo=dict(scope='usa',\n", + " projection={'type': 'albers usa'}),\n", + " sliders=sliders)\n", + " \n", + " fig = go.Figure(data=data_slider, layout=layout)\n", + " \n", + " fig.show()\n" + ], + "id": "7a077af8d4d1caed", + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 100 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T19:46:03.864513Z", + "start_time": "2024-12-06T19:46:03.857987Z" + } + }, + "cell_type": "code", + "source": [ + "from plotly.offline import init_notebook_mode\n", + "\n", + "init_notebook_mode(connected=True)\n", + "\n", + "def plot_compare_timeplot(nhsn_df, hhs_df, pathogen, prelim_flag=\"False\", geo_val=\"\", download=False, val_cutoff=100, percent_cutoff=20):\n", + " \"\"\"\n", + " \n", + " Parameters\n", + " ----------\n", + " nhsn_df\n", + " hhs_df\n", + " pathogen\n", + " geo_val\n", + " download\n", + " val_cutoff: min value to consider for suspicous\n", + " percent_cutoff: min percentage of difference between NHSN and HHS to consider for suspicous\n", + "\n", + " Returns\n", + " -------\n", + "\n", + " \"\"\"\n", + " title = f'{geo_val} NHSN vs HHS value over time with (diff > {percent_cutoff}% and val > {val_cutoff}) markers'\n", + " filename = f'nhsn_vs_hhs_timeplot_{geo_val}_{pathogen}'\n", + " \n", + " if prelim_flag:\n", + " title = f\"{title} (prelim data)\"\n", + " filename = f\"{filename}_prelim\"\n", + "\n", + " fig = go.Figure()\n", + " fig.add_trace(go.Scatter(x=nhsn_df['time_value'], y=nhsn_df[\"val\"], mode='lines', name='NHSN Time Series'))\n", + " fig.add_trace(go.Scatter(x=hhs_df['time_value'], y=hhs_df[\"val\"], mode='lines', name='HHS Time Series'))\n", + " \n", + " joined = pd.merge(nhsn_df, hhs_df, on=[\"geo_id\", \"time_value\"], how=\"left\", suffixes=[\"_nhsn\", \"_hhs\"])\n", + " joined = joined[[\"geo_id\", \"time_value\", \"val_nhsn\", \"val_hhs\"]]\n", + " joined[\"diff_percentage\"] = ((joined[\"val_nhsn\"] - joined[\"val_hhs\"]) / joined[\"val_hhs\"]) * 100\n", + " joined.sort_values(by=\"time_value\", inplace=True)\n", + " percent_filter = (abs(joined[\"diff_percentage\"]) > percent_cutoff)\n", + " num_filter = (joined['val_nhsn'] > val_cutoff)\n", + " sus_data = joined[(percent_filter & num_filter)]\n", + " sus_data.sort_values(by=\"time_value\", inplace=True)\n", + " if len(sus_data) > 0:\n", + " sus_data.to_csv(f\"{filename}.csv\", index=False)\n", + " \n", + " fig.add_trace(go.Scatter(x=sus_data['time_value'], y=sus_data[\"val_nhsn\"] , mode='markers', name=f'NHSN points', marker={\"color\":\"green\"}))\n", + " fig.add_trace(go.Scatter(x=sus_data['time_value'], y=sus_data[\"val_hhs\"] , mode='markers', name=f'HHS points', marker={\"color\":\"green\"}))\n", + "\n", + " fig.update_layout(title=title)\n", + " \n", + " if download:\n", + " pio.write_image(fig, f\"{filename}.pdf\", format='pdf')\n", + " else:\n", + " fig.show()\n" + ], + "id": "3bf257e9913f5742", + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 101 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T19:46:15.969120Z", + "start_time": "2024-12-06T19:46:15.964371Z" + } + }, + "cell_type": "code", + "source": [ + "import plotly.graph_objects as go\n", + "from plotly.offline import init_notebook_mode\n", + "import plotly.io as pio\n", + "\n", + "init_notebook_mode(connected=True)\n", + "\n", + "def plot_count_overtime(df, pathogen, prelim_flag, geo_val, y_col, download=False):\n", + "\n", + " title = f'{geo_val} NHSN row count over time'\n", + " filename = f'{geo_val}_{pathogen}_nhsn_rowcount_overtime'\n", + "\n", + " if prelim_flag:\n", + " filename = f\"{filename}_prelim\"\n", + " title = f\"{title} (prelim data)\"\n", + " \n", + " \n", + " df.sort_values(\"time_value\", inplace=True)\n", + " fig = go.Figure()\n", + " for geo_id in df[\"geo_id\"].unique():\n", + " partial_df = df[df[\"geo_id\"] == geo_id]\n", + " partial_df.sort_values(\"time_value\", inplace=True)\n", + " if any(partial_df[\"count\"] == 0):\n", + " partial_df.to_csv(f\"{geo_id}_{filename}_missing.csv\", index=False)\n", + " fig.add_trace(go.Histogram(x=partial_df['time_value'], y=partial_df[y_col], name=geo_id))\n", + " fig.update_layout(title=title, barmode='stack')\n", + "\n", + " \n", + " if download:\n", + " pio.write_image(fig, f\"{filename}.pdf\", format='pdf')\n", + " else:\n", + " fig.show()\n", + " " + ], + "id": "d3b6e9396c9b1723", + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 102 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "# all analysis download", + "id": "2e81b397bdd67170" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "", + "id": "1b935bb75e8e37cf" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T19:53:47.785286Z", + "start_time": "2024-12-06T19:53:19.199988Z" + } + }, + "cell_type": "code", + "source": [ + "from itertools import product\n", + "geos = [\"nation\", \"state\", \"hhs\"]\n", + "hhs_pathogen_mapping = {\"flu\": \"influenza\", \"covid\": \"covid\"}\n", + "geo_pathogen_flag_combo = list(product(geos, hhs_pathogen_mapping.keys(), [True, False]))\n", + "percent_cutoff = 30\n", + "val_cutoff = 100\n", + "\n", + "for geo, pathogen, prelim_flag in geo_pathogen_flag_combo:\n", + " nhsn_df = get_nhsn_df(geo, pathogen, prelim_flag)\n", + " hhs_df = get_hhs_df(geo, hhs_pathogen_mapping[pathogen])\n", + " spearmanr = generate_spearman(nhsn_df, hhs_df, \"time_value\")\n", + " joined = pd.merge(nhsn_df, hhs_df, on=[\"geo_id\", \"time_value\"], how=\"left\", suffixes=[\"_nhsn\", \"_hhs\"])\n", + " joined = joined[[\"geo_id\", \"time_value\", \"val_nhsn\", \"val_hhs\"]]\n", + " joined[\"diff_percentage\"] = ((joined[\"val_nhsn\"] - joined[\"val_hhs\"]) / joined[\"val_hhs\"]) * 100\n", + " row_count_df = nhsn_df[[\"geo_id\",\"time_value\"]].value_counts().reset_index(name=\"count\")\n", + " if geo == \"state\":\n", + " # plot_timeplot(spearmanr, pathogen, prelim_flag, geo, y_col=\"correlation\", download=True)\n", + " pass\n", + " else:\n", + " plot_timeplot(joined, pathogen, prelim_flag, geo, y_col=\"diff_percentage\", download=True)\n", + "\n", + " if geo != \"state\":\n", + " plot_timeplot(nhsn_df, pathogen, prelim_flag, geo, y_col=\"val\", download=True)\n", + " plot_timeplot(hhs_df, pathogen, prelim_flag, geo, y_col=\"val_hhs\", download=True)\n", + "\n", + " plot_count_overtime(row_count_df, pathogen, prelim_flag, geo, y_col=\"count\", download=True)\n", + " # \n", + " # if geo == \"state\":\n", + " # plot_spearman_choropleth(nhsn_df, hhs_df, pathogen, prelim_flag, geo, download=True)\n", + " # \n", + " if geo == \"hhs\":\n", + " geos = list(nhsn_df.geo_id.unique())\n", + " for geo_val in geos:\n", + " part_nhsn_df = nhsn_df[nhsn_df[\"geo_id\"] == geo_val].sort_values(by=\"time_value\")\n", + " part_hhs_df = hhs_df[hhs_df[\"geo_id\"] == geo_val].sort_values(by=\"time_value\")\n", + "\n", + " plot_compare_timeplot(part_nhsn_df, part_hhs_df, pathogen, prelim_flag, geo_val=geo_val, val_cutoff=val_cutoff, percent_cutoff=percent_cutoff, download=True)\n", + " elif geo == \"nation\":\n", + " plot_compare_timeplot(nhsn_df, hhs_df, pathogen, prelim_flag, geo_val=\"us\", val_cutoff=val_cutoff, percent_cutoff=percent_cutoff, download=True)" + ], + "id": "2efa75ca5d6496c5", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/3735297055.py:21: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/3735297055.py:21: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/3735297055.py:21: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/3735297055.py:21: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/3735297055.py:21: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: 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on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/3735297055.py:21: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be 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"/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/3735297055.py:21: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/3735297055.py:21: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/832067892.py:42: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n" + ] + } + ], + "execution_count": 105 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "# Spearman over time example", + "id": "f9979b019260fb75" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "pathogen_mapping = {\"flu\": \"influenza\", \"covid\": \"covid\"}\n", + "pathogen = \"covid\"\n", + "prelim_flag = False\n", + "geo = \"state\"\n", + "\n", + "nhsn_df = get_nhsn_df(geo, pathogen, prelim_flag)\n", + "hhs_df = get_hhs_df(geo, pathogen_mapping[pathogen])\n", + "spearmanr = generate_spearman(nhsn_df, hhs_df, \"time_value\")\n", + "plot_timeplot(spearmanr, pathogen, prelim_flag, geo, y_col=\"correlation\", download=True)\n" + ], + "id": "3c13ddde889337dd", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "# Spearman over geo", + "id": "389ff20f354256f0" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "pathogen_mapping = {\"flu\": \"influenza\", \"covid\": \"covid\"}\n", + "pathogen = \"covid\"\n", + "prelim_flag = False\n", + "geo = \"state\"\n", + "\n", + "nhsn_df = get_nhsn_df(geo, pathogen, prelim_flag)\n", + "hhs_df = get_hhs_df(geo, pathogen_mapping[pathogen])\n", + "plot_spearman_choropleth(nhsn_df, hhs_df, pathogen, prelim_flag, geo, download=True)\n" + ], + "id": "e134e9da693e3be1", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "# comparison over time across geo", + "id": "a5c34a5e6c662b36" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "pathogen_mapping = {\"flu\": \"influenza\", \"covid\": \"covid\"}\n", + "pathogen = \"covid\"\n", + "prelim_flag = False\n", + "geo = \"state\"\n", + "\n", + "nhsn_df = get_nhsn_df(geo, pathogen, prelim_flag)\n", + "hhs_df = get_hhs_df(geo, pathogen_mapping[pathogen])\n", + "joined = pd.merge(nhsn_df, hhs_df, on=[\"geo_id\", \"time_value\"], how=\"left\", suffixes=[\"_nhsn\", \"_hhs\"])\n", + "joined = joined[[\"geo_id\", \"time_value\", \"val_nhsn\", \"val_hhs\"]]\n", + "joined[\"diff_percentage\"] = ((joined[\"val_nhsn\"] - joined[\"val_hhs\"]) / joined[\"val_hhs\"]) * 100\n", + "plot_choropleth_overtime(joined, pathogen, prelim_flag, geo, z_col=\"diff_percentage\")" + ], + "id": "aa8d00e6d6c8e888", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "# compare count over time", + "id": "291a7b1da3535a4c" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T17:28:43.754542Z", + "start_time": "2024-12-06T17:28:42.709301Z" + } + }, + "cell_type": "code", + "source": [ + "pathogen_mapping = {\"flu\": \"influenza\", \"covid\": \"covid\"}\n", + "pathogen = \"covid\"\n", + "prelim_flag = False\n", + "geo = \"hhs\"\n", + "val_cutoff = 100\n", + "percent_cutoff = 30\n", + "\n", + "\n", + "nhsn_df = get_nhsn_df(geo, pathogen).sort_values(by=\"time_value\")\n", + "hhs_df = get_hhs_df(geo, pathogen_mapping[pathogen]).sort_values(by=\"time_value\")\n", + "\n", + "row_count_df = nhsn_df[[\"geo_id\",\"time_value\"]].value_counts().reset_index(name=\"count\")\n", + "plot_count_overtime(row_count_df, pathogen, prelim_flag, geo, y_col=\"count\", download=False)\n" + ], + "id": "d6a49cb0a01eeb93", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/s6/ytv8f9z11q32lhh5ksxrp_tm0000gn/T/ipykernel_91259/3450248032.py:22: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n" + ] + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "name": "1", + "x": [ + "202032", + "202033", + "202034", + "202035", + "202036", + "202037", + "202038", + "202039", + "202040", + "202041", + "202042", + "202043", + "202044", + "202045", + "202046", + "202047", + "202048", + "202049", + "202050", + "202051", + "202052", + "202053", + "202101", + "202102", + "202103", + "202104", + "202105", 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"data_source": "nhsn", + "span_length": 14, + "min_expected_lag": {"all": "1"}, + "max_expected_lag": {"all": "3"}, + "dry_run": true, + "suppressed_errors": [] + }, + "static": { + "minimum_sample_size": 0, + "missing_se_allowed": true, + "missing_sample_size_allowed": true + }, + "dynamic": {} + } +} diff --git a/nhsn/raw_data_backups/.gitignore b/nhsn/raw_data_backups/.gitignore new file mode 100644 index 0000000000..e69de29bb2 diff --git a/nhsn/receiving/.gitignore b/nhsn/receiving/.gitignore new file mode 100644 index 0000000000..e69de29bb2 diff --git a/nhsn/setup.py b/nhsn/setup.py new file mode 100644 index 0000000000..d7dabe2413 --- /dev/null +++ b/nhsn/setup.py @@ -0,0 +1,32 @@ +from setuptools import setup +from setuptools import find_packages + +required = [ + "numpy", + "pandas", + "pydocstyle", + "pytest", + "pytest-cov", + "pylint==2.8.3", + "delphi-utils", + "sodapy", + "epiweeks", + "freezegun", + "us", +] + +setup( + name="delphi_nhsn", + version="0.1.0", + description="Indicators NHSN Hospital Respiratory Data", + author="", + author_email="", + url="https://github.com/cmu-delphi/covidcast-indicators", + install_requires=required, + classifiers=[ + "Development Status :: 1 - Planning", + "Intended Audience :: Developers", + "Programming Language :: Python :: 3.8", + ], + packages=find_packages(), +) diff --git a/nhsn/static/.gitignore b/nhsn/static/.gitignore new file mode 100644 index 0000000000..e69de29bb2 diff --git a/nhsn/tests/backups/.gitignore b/nhsn/tests/backups/.gitignore new file mode 100644 index 0000000000..e69de29bb2 diff --git a/nhsn/tests/conftest.py b/nhsn/tests/conftest.py new file mode 100644 index 0000000000..525d8ae7ec --- /dev/null +++ b/nhsn/tests/conftest.py @@ -0,0 +1,73 @@ +import copy +import json +from unittest.mock import patch + +import pytest +from pathlib import Path + +from delphi_nhsn.run import run_module + +TEST_DIR = Path(__file__).parent + +# test data generated with following url with socrata: +# https://data.cdc.gov/resource/ua7e-t2fy.json?$where=weekendingdate%20between%20%272021-08-19T00:00:00.000%27%20and%20%272021-10-19T00:00:00.000%27%20and%20jurisdiction%20in(%27CO%27,%27USA%27) +# preliminary source +# https://data.cdc.gov/resource/mpgq-jmmr.json?$where=weekendingdate%20between%20%272021-08-19T00:00:00.000%27%20and%20%272021-10-19T00:00:00.000%27%20and%20jurisdiction%20in(%27CO%27,%27USA%27) +# queries the nhsn data with timestamp (2021-08-19, 2021-10-19) with CO and USA data + + +with open("test_data/page.json", "r") as f: + TEST_DATA = json.load(f) + +with open("test_data/prelim_page.json", "r") as f: + PRELIM_TEST_DATA = json.load(f) + +@pytest.fixture(scope="session") +def params(): + params = { + "common": { + "export_dir": f"{TEST_DIR}/receiving", + "log_filename": f"{TEST_DIR}/test.log", + "backup_dir": f"{TEST_DIR}/backups", + "custom_run": False + }, + "indicator": { + "wip_signal": True, + "export_start_date": "2020-08-01", + "static_file_dir": "./static", + "socrata_token": "test_token" + }, + "validation": { + "common": { + "span_length": 14, + "min_expected_lag": {"all": "3"}, + "max_expected_lag": {"all": "4"}, + } + } + } + return copy.deepcopy(params) + +@pytest.fixture +def params_w_patch(params): + params_copy = copy.deepcopy(params) + params_copy["patch"] = { + "start_issue": "2024-06-27", + "end_issue": "2024-06-29", + "patch_dir": "./patch_dir" + } + return params_copy + +@pytest.fixture(scope="function") +def run_as_module(params): + with patch('sodapy.Socrata.get') as mock_get: + def side_effect(*args, **kwargs): + if kwargs['offset'] == 0: + if "ua7e-t2fy" in args[0]: + return TEST_DATA + if "mpgq-jmmr" in args[0]: + return PRELIM_TEST_DATA + else: + return [] + mock_get.side_effect = side_effect + run_module(params) + diff --git a/nhsn/tests/receiving/.gitignore b/nhsn/tests/receiving/.gitignore new file mode 100644 index 0000000000..e69de29bb2 diff --git a/nhsn/tests/test_data/page.json b/nhsn/tests/test_data/page.json new 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"pctconfrsvinptbedsperchosprep": "0.0", + "pctconfc19icubedsperchosprep": "0.9477", + "pctconffluicubedsperchosprep": "0.7562", + "pctconfrsvicubedsperchosprep": "0.0", + "numinptbedsperchosprepabschg": "-0.0007", + "numinptbedsoccperchospre": "-0.0007", + "numicubedsperchosprepabschg": "-0.0007", + "numicubedsoccperchosprepabschg": "-0.0007", + "totalconfc19hosppatsperc_1": "-0.0002", + "totalconffluhosppatsperc_1": "-0.0123", + "totalconfrsvhosppatsperc_1": "0.0", + "totalconfc19icupatsperch": "-0.0002", + "totalconffluicupatsperch": "-0.0119", + "totalconfrsvicupatsperch": "0.0", + "totalconfc19newadmpedper_1": "-0.0004", + "totalconfc19newadmadultp_1": "-0.0004", + "totalconfc19newadmpercho": "-0.0004", + "totalconfflunewadmpedper_1": "0.0", + "totalconfflunewadmadultp_1": "0.0", + "totalconfflunewadmpercho": "-0.0126", + "totalconfrsvnewadmpedper_1": "0.0", + "totalconfrsvnewadmadultp_1": "0.0", + "totalconfrsvnewadmpercho": "0.0", + "pctinptbedsoccperchospre": "-0.0007", + "pcticubedsoccperchosprepabschg": "-0.0007", + "pctconfc19inptbedspercho": "-0.0002", + "pctconffluinptbedspercho": "-0.0123", + "pctconfrsvinptbedspercho": "0.0", + "pctconfc19icubedsperchos": "-0.0002", + "pctconffluicubedsperchos": "-0.0119", + "pctconfrsvicubedsperchos": "0.0" + } +] \ No newline at end of file diff --git a/nhsn/tests/test_pull.py b/nhsn/tests/test_pull.py new file mode 100644 index 0000000000..c09c838d75 --- /dev/null +++ b/nhsn/tests/test_pull.py @@ -0,0 +1,148 @@ +import glob +from unittest.mock import patch, MagicMock +import os +import pytest + +import pandas as pd + +from delphi_nhsn.pull import ( + pull_nhsn_data, + pull_data, + pull_preliminary_nhsn_data +) +from delphi_nhsn.constants import SIGNALS_MAP, PRELIM_SIGNALS_MAP + +from delphi_utils import get_structured_logger +from conftest import TEST_DATA, PRELIM_TEST_DATA + + +DATASETS = [{"id":"ua7e-t2fy", + "test_data": TEST_DATA}, + {"id":"mpgq-jmmr", + "test_data":PRELIM_TEST_DATA} + ] + + +class TestPullNHSNData: + @patch("delphi_nhsn.pull.Socrata") + @pytest.mark.parametrize('dataset', DATASETS, ids=["data", "prelim_data"]) + def test_socrata_call(self, mock_socrata, dataset, params): + test_token = params["indicator"]["socrata_token"] + + # Mock Socrata client and its get method + mock_client = MagicMock() + mock_socrata.return_value = mock_client + mock_client.get.side_effect = [[]] + + pull_data(test_token, dataset["id"]) + + # Check that Socrata client was initialized with correct arguments + mock_socrata.assert_called_once_with("data.cdc.gov", test_token) + + # Check that get method was called with correct arguments + mock_client.get.assert_any_call(dataset["id"], limit=50000, offset=0) + + def test_pull_nhsn_data_output(self, caplog, params): + with patch('sodapy.Socrata.get') as mock_get: + mock_get.side_effect = [TEST_DATA, []] + backup_dir = params["common"]["backup_dir"] + test_token = params["indicator"]["socrata_token"] + custom_run = True + + logger = get_structured_logger() + + result = pull_nhsn_data(test_token, backup_dir, custom_run, logger) + + # Check result + assert result["timestamp"].notnull().all(), "timestamp has rogue NaN" + assert result["geo_id"].notnull().all(), "geography has rogue NaN" + + # Check for each signal in SIGNALS + for signal in SIGNALS_MAP.keys(): + assert result[signal].notnull().all(), f"{signal} has rogue NaN" + def test_pull_nhsn_data_backup(self, caplog, params): + with patch('sodapy.Socrata.get') as mock_get: + mock_get.side_effect = [TEST_DATA, []] + + today = pd.Timestamp.today().strftime("%Y%m%d") + backup_dir = params["common"]["backup_dir"] + custom_run = params["common"]["custom_run"] + test_token = params["indicator"]["socrata_token"] + + # Load test data + expected_data = pd.DataFrame(TEST_DATA) + + logger = get_structured_logger() + # Call function with test token + pull_nhsn_data(test_token, backup_dir, custom_run, logger) + + # Check logger used: + assert "Backup file created" in caplog.text + + # Check that backup file was created + backup_files = glob.glob(f"{backup_dir}/{today}*") + assert len(backup_files) == 2, "Backup file was not created" + + for backup_file in backup_files: + if backup_file.endswith(".csv.gz"): + dtypes = expected_data.dtypes.to_dict() + actual_data = pd.read_csv(backup_file, dtype=dtypes) + else: + actual_data = pd.read_parquet(backup_file) + pd.testing.assert_frame_equal(expected_data, actual_data) + + # clean up + for file in backup_files: + os.remove(file) + def test_pull_prelim_nhsn_data_output(self, caplog, params): + with patch('sodapy.Socrata.get') as mock_get: + mock_get.side_effect = [PRELIM_TEST_DATA, []] + backup_dir = params["common"]["backup_dir"] + test_token = params["indicator"]["socrata_token"] + custom_run = True + + logger = get_structured_logger() + + result = pull_preliminary_nhsn_data(test_token, backup_dir, custom_run, logger) + + # Check result + assert result["timestamp"].notnull().all(), "timestamp has rogue NaN" + assert result["geo_id"].notnull().all(), "geography has rogue NaN" + + # Check for each signal in SIGNALS + for signal in PRELIM_SIGNALS_MAP.keys(): + assert result[signal].notnull().all(), f"{signal} has rogue NaN" + def test_pull_prelim_nhsn_data_backup(self, caplog, params): + with patch('sodapy.Socrata.get') as mock_get: + mock_get.side_effect = [PRELIM_TEST_DATA, []] + + today = pd.Timestamp.today().strftime("%Y%m%d") + backup_dir = params["common"]["backup_dir"] + custom_run = params["common"]["custom_run"] + test_token = params["indicator"]["socrata_token"] + + # Load test data + expected_data = pd.DataFrame(PRELIM_TEST_DATA) + + logger = get_structured_logger() + # Call function with test token + pull_preliminary_nhsn_data(test_token, backup_dir, custom_run, logger) + + # Check logger used: + assert "Backup file created" in caplog.text + + # Check that backup file was created + backup_files = glob.glob(f"{backup_dir}/{today}*") + assert len(backup_files) == 2, "Backup file was not created" + + for backup_file in backup_files: + if backup_file.endswith(".csv.gz"): + dtypes = expected_data.dtypes.to_dict() + actual_data = pd.read_csv(backup_file, dtype=dtypes) + else: + actual_data = pd.read_parquet(backup_file) + pd.testing.assert_frame_equal(expected_data, actual_data) + + # clean up + for file in backup_files: + os.remove(file) \ No newline at end of file diff --git a/nhsn/tests/test_run.py b/nhsn/tests/test_run.py new file mode 100644 index 0000000000..cfc47e50f0 --- /dev/null +++ b/nhsn/tests/test_run.py @@ -0,0 +1,49 @@ +import os +from pathlib import Path + +import pandas as pd +from epiweeks import Week + +from delphi_nhsn.constants import SIGNALS_MAP, PRELIM_SIGNALS_MAP + + +class TestRun: + def generate_week_file_prefix(self, dates): + + epiweeks_lst = [ Week.fromdate(pd.to_datetime(str(date))) for date in dates ] + date_prefix = [ + str(t.year) + str(t.week).zfill(2) + for t in epiweeks_lst + ] + return date_prefix + + def test_output_files_exist(self, params, run_as_module): + export_dir = params["common"]["export_dir"] + csv_files = [f.name for f in Path(export_dir).glob("*.csv")] + geos = ["nation", "state", "hhs"] + metrics = list(SIGNALS_MAP.keys()) + list(PRELIM_SIGNALS_MAP.keys()) + dates = [ + "2021-08-21", "2021-08-28", "2021-09-04", + "2021-09-11", "2021-09-18", "2021-09-25", + "2021-10-02", "2021-10-09", "2021-10-16" + ] + date_prefix = self.generate_week_file_prefix(dates) + + expected_files = [] + for geo in geos: + for d in date_prefix: + for metric in metrics: + expected_files += [f"weekly_{d}_{geo}_{metric}.csv"] + assert set(csv_files).issubset(set(expected_files)) + + for geo in geos: + df = pd.read_csv( + f"{export_dir}/weekly_{date_prefix[3]}_{geo}_{metrics[0]}.csv") + + expected_columns = [ + "geo_id", "val", "se", "sample_size", + ] + assert (df.columns.values == expected_columns).all() + + for file in Path(export_dir).glob("*.csv"): + os.remove(file)