{"id":18989758,"url":"https://github.com/cdcgov/forecasttools-py","last_synced_at":"2025-10-08T04:07:59.209Z","repository":{"id":234225651,"uuid":"788469112","full_name":"CDCgov/forecasttools-py","owner":"CDCgov","description":"A Python package for common pre- and post-processing operations done by CFA Predict for short term forecasting, nowcasting, and scenario modeling.","archived":false,"fork":false,"pushed_at":"2025-03-24T16:52:57.000Z","size":3486,"stargazers_count":7,"open_issues_count":22,"forks_count":2,"subscribers_count":11,"default_branch":"main","last_synced_at":"2025-03-24T17:50:13.808Z","etag":null,"topics":["abstraction","automation","forecasting","infectious-disease-modeling","infrastructure"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/CDCgov.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-04-18T13:28:56.000Z","updated_at":"2025-02-24T16:37:47.000Z","dependencies_parsed_at":"2024-04-18T15:18:42.355Z","dependency_job_id":"111409db-a783-4852-b125-6ac8d1ff4d0d","html_url":"https://github.com/CDCgov/forecasttools-py","commit_stats":null,"previous_names":["cdcgov/forecasttools-py"],"tags_count":0,"template":false,"template_full_name":"CDCgov/template","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CDCgov%2Fforecasttools-py","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CDCgov%2Fforecasttools-py/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CDCgov%2Fforecasttools-py/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CDCgov%2Fforecasttools-py/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/CDCgov","download_url":"https://codeload.github.com/CDCgov/forecasttools-py/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":249260464,"owners_count":21239592,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["abstraction","automation","forecasting","infectious-disease-modeling","infrastructure"],"created_at":"2024-11-08T17:07:48.840Z","updated_at":"2025-10-08T04:07:59.203Z","avatar_url":"https://github.com/CDCgov.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# CFA Forecast Tools (Python)\n\n\n\u003c!-- To learn more about using Quarto for\nrender a GitHub README, see\n\u003chttps://quarto.org/docs/output-formats/gfm.html\u003e\n--\u003e\n\n\u003c!-- ```{python}\n#| echo: false\nimport polars as pl\n\u0026#10;# format polars dataframe correctly in the\n# background\npl.Config.set_tbl_hide_dataframe_shape(False)\npl.Config.set_tbl_formatting(\"ASCII_MARKDOWN\")\npl.Config.set_tbl_hide_column_data_types(False)\n``` --\u003e\n\nSummary of `forecasttools-py`:\n\n- A Python package.\n- Primarily supports the Short Term Forecast’s team.\n- Intended to support wider Real Time Monitoring branch operations.\n- Has tools for pre- and post-processing.\n  - Conversion of `az.InferenceData` forecast to Hubverse format.\n  - Addition of time and or dates to `az.InferenceData`.\n\nNotes:\n\n- This repository is a WORK IN PROGRESS.\n- For the R version of this toolkit, see\n  [forecasttools](https://github.com/CDCgov/forecasttools).\n- For CDC project expected to use `forecasttools-py`, see\n  [pyrenew-hew](https://github.com/CDCgov/pyrenew-hew).\n\n\u003cdetails\u003e\n\n\u003csummary\u003e\n\nA Tentative Utilities Diagram\n\u003c/summary\u003e\n\n``` mermaid\n%%{init: {\"theme\": \"neutral\", \"themeVariables\": { \"fontFamily\": \"Iosevka\", \"fontSize\": \"25px\", \"lineColor\": \"#808b96\", \"arrowheadColor\": \"#808b96\", \"edgeStrokeWidth\": \"10px\", \"arrowheadLength\": \"20px\"}}}%%\nflowchart TD\n    A1[COVID-19 Data _from forecasttools_] --\u003e A4[NumPyro Model]\n    A2[Influenza Data _from forecasttools_] --\u003e A4[NumPyro Model]\n    A3[External Dataset] --\u003e A4[NumPyro Model]\n    A4[NumPyro Model] --\u003e|_arviz.from_numpyro_| A5[Forecast As InferenceData Object wo/ Dates]\n    A5[Forecast As InferenceData Object wo/ Dates] --\u003e|_Add Dates To InferenceData_ - done| A6[InferenceData Object w/ Dates]\n    A6[InferenceData Object w/ Dates] --\u003e|_Convert To Tidy-Like Dataframe_ - done| A7[Polars Forecast Dataframe w/ Draws]\n    A7[Polars Forecast Dataframe w/ Draws] --\u003e|_Convert To Hubverse Formatted Dataframe_ - done| A8[FluSight Submission Dataframe]\n    A7[Polars Forecast Dataframe w/ Draws] --\u003e|_Convert To ScoringUtils Formatted Dataframe_ - in progress| A9[ScoringUtils DataFrame]\n    A7[Polars Forecast Dataframe w/ Draws] --\u003e|_Save_| A10[Parquet File]\n    A8[FluSight Submission Dataframe] --\u003e|_Save_| A11[Parquet File]\n    A9[ScoringUtils DataFrame] --\u003e|_Save_| A12[Parquet File]\n    A8[FluSight Submission Dataframe] --\u003e|_Convert To ScoringUtils Formatted Dataframe_ - in progress| A9[ScoringUtils DataFrame]\n    A12[Parquet File] --\u003e|_Get scores in R_| A13[Forecast Scores]\n    A11[Parquet File] --\u003e|_Model Forecast Hypothesis Testing_| A14[Model Comparison Report]\n\n    B1[Pulled Parquet Hubverse Submissions] --\u003e|_Model Forecast Hypothesis Testing_| A14[Model Comparison Report]\n\n    linkStyle default stroke: #808b96\n    linkStyle default stroke-width: 2.0px\n```\n\n\u003c/details\u003e\n\n# Installation\n\nInstall `forecasttools-py` via:\n\n    pip3 install git+https://github.com/CDCgov/forecasttools-py@main\n\n# Vignettes\n\n- [Format Arviz Forecast Output For FluSight\n  Submission](https://github.com/CDCgov/forecasttools-py/blob/main/notebooks/flusight_from_idata.qmd)\n- [Community Meeting Utilities Demonstration\n  (2024-11-19)](https://github.com/CDCgov/forecasttools-py/blob/main/notebooks/forecasttools_community_demo_2024-11-19.qmd)\n- [Creating InferenceData Objects and Using Forecasttools\n  Datasets](https://github.com/CDCgov/forecasttools-py/blob/main/notebooks/datasets_and_idata_creation.qmd)\n\n*Coming soon as webpages, once [Issue\n26](https://github.com/CDCgov/forecasttools-py/issues/26) is completed*.\n\n# Datasets\n\nWithin `forecasttools-py`, one finds several packaged datasets. These\ndatasets can aid with experimentation; some are directly necessary to\nother utilities provided by `forecasttools-py`.\n\n``` python\nimport forecasttools\n```\n\nSummary of datasets:\n\n- `forecasttools.location_table`\n  - A Polars dataframe of location abbreviations, codes, and names for\n    Hubverse formatted forecast submissions.\n- `forecasttools.example_flusight_submission`\n  - An example Hubverse formatted influenza forecast submission (as a\n    Polars dataframe) submitted to the FluSight Hub.\n- `forecasttools.nhsn_hosp_COVID`\n  - A Polars dataframe of NHSN COVID hospital admissions data.\n- `forecasttools.nhsn_hosp_flu`\n  - A Polars dataframe of NHSN influenza hospital admissions data.\n- `forecasttools.nhsn_flu_forecast_wo_dates`\n  - An `az.InferenceData` object containing a forecast made using NSHN\n    influenza data for Texas.\n- `forecasttools.nhsn_flu_forecast_w_dates`\n  - An modified (with dates as coordinates) `az.InferenceData` object\n    containing a forecast made using NSHN influenza data for Texas.\n\nSee below for more information on the datasets.\n\n## Location Table\n\nThe location table contains abbreviations, codes, extended names, and\npopulations for the jurisdictions of the United States that the FluSight\nand COVID forecasting hubs require users to generate forecasts. The US\npopulation value is the sum of all available states and territories\n(some territories have `null` population values).\n\nThe location table is stored in `forecasttools-py` as a `polars`\ndataframe and is accessed via:\n\n``` python\nloc_table = forecasttools.location_table\nprint(loc_table)\n```\n\n    shape: (58, 5)\n    ┌───────────────┬────────────┬─────────────────────────────┬────────────┬──────────┐\n    │ location_code ┆ short_name ┆ long_name                   ┆ population ┆ is_state │\n    │ ---           ┆ ---        ┆ ---                         ┆ ---        ┆ ---      │\n    │ str           ┆ str        ┆ str                         ┆ i64        ┆ bool     │\n    ╞═══════════════╪════════════╪═════════════════════════════╪════════════╪══════════╡\n    │ US            ┆ US         ┆ United States               ┆ 334735155  ┆ false    │\n    │ 01            ┆ AL         ┆ Alabama                     ┆ 5024279    ┆ true     │\n    │ 02            ┆ AK         ┆ Alaska                      ┆ 733391     ┆ true     │\n    │ 04            ┆ AZ         ┆ Arizona                     ┆ 7151502    ┆ true     │\n    │ 05            ┆ AR         ┆ Arkansas                    ┆ 3011524    ┆ true     │\n    │ …             ┆ …          ┆ …                           ┆ …          ┆ …        │\n    │ 66            ┆ GU         ┆ Guam                        ┆ null       ┆ false    │\n    │ 69            ┆ MP         ┆ Northern Mariana Islands    ┆ null       ┆ false    │\n    │ 72            ┆ PR         ┆ Puerto Rico                 ┆ 3285874    ┆ false    │\n    │ 74            ┆ UM         ┆ U.S. Minor Outlying Islands ┆ null       ┆ false    │\n    │ 78            ┆ VI         ┆ U.S. Virgin Islands         ┆ null       ┆ false    │\n    └───────────────┴────────────┴─────────────────────────────┴────────────┴──────────┘\n\nUsing `./forecasttools/data.py`, the location table was created by\nrunning the following:\n\n``` python\nmake_census_dataset(\n    file_save_path=os.path.join(\n        os.getcwd(),\n        \"location_table.csv\"\n    ),\n)\n```\n\n## United States\n\nCalling `forecasttools.united_states` simply returns a Python list that\ncontains the 50 United States (`United States` itself is not included).\nWhile quite simple, it’s to have this capability available in fewer\nsteps than through calling and selecting values from `location_table`.\n\n``` python\nunited_states = forecasttools.united_states\nprint(united_states)\n```\n\n    ['Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California', 'Colorado', 'Connecticut', 'Delaware', 'Florida', 'Georgia', 'Hawaii', 'Idaho', 'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky', 'Louisiana', 'Maine', 'Maryland', 'Massachusetts', 'Michigan', 'Minnesota', 'Mississippi', 'Missouri', 'Montana', 'Nebraska', 'Nevada', 'New Hampshire', 'New Jersey', 'New Mexico', 'New York', 'North Carolina', 'North Dakota', 'Ohio', 'Oklahoma', 'Oregon', 'Pennsylvania', 'Rhode Island', 'South Carolina', 'South Dakota', 'Tennessee', 'Texas', 'Utah', 'Vermont', 'Virginia', 'Washington', 'West Virginia', 'Wisconsin', 'Wyoming']\n\n## Example FluSight Hub Submission\n\nThe example FluSight submission comes from the [following 2023-24\nsubmission](https://raw.githubusercontent.com/cdcepi/FluSight-forecast-hub/main/model-output/cfa-flumech/2023-10-14-cfa-flumech.csv).\n\nThe example FluSight submission is stored in `forecasttools-py` as a\n`polars` dataframe and is accessed via:\n\n``` python\nsubmission = forecasttools.example_flusight_submission\nprint(submission)\n```\n\n    shape: (4_876, 8)\n    ┌────────────┬────────────┬─────────┬────────────┬──────────┬────────────┬────────────┬────────────┐\n    │ reference_ ┆ target     ┆ horizon ┆ target_end ┆ location ┆ output_typ ┆ output_typ ┆ value      │\n    │ date       ┆ ---        ┆ ---     ┆ _date      ┆ ---      ┆ e          ┆ e_id       ┆ ---        │\n    │ ---        ┆ str        ┆ i64     ┆ ---        ┆ str      ┆ ---        ┆ ---        ┆ f64        │\n    │ str        ┆            ┆         ┆ str        ┆          ┆ str        ┆ f64        ┆            │\n    ╞════════════╪════════════╪═════════╪════════════╪══════════╪════════════╪════════════╪════════════╡\n    │ 2023-10-14 ┆ wk inc flu ┆ -1      ┆ 2023-10-07 ┆ 01       ┆ quantile   ┆ 0.01       ┆ 7.670286   │\n    │            ┆ hosp       ┆         ┆            ┆          ┆            ┆            ┆            │\n    │ 2023-10-14 ┆ wk inc flu ┆ -1      ┆ 2023-10-07 ┆ 01       ┆ quantile   ┆ 0.025      ┆ 9.968043   │\n    │            ┆ hosp       ┆         ┆            ┆          ┆            ┆            ┆            │\n    │ 2023-10-14 ┆ wk inc flu ┆ -1      ┆ 2023-10-07 ┆ 01       ┆ quantile   ┆ 0.05       ┆ 12.022354  │\n    │            ┆ hosp       ┆         ┆            ┆          ┆            ┆            ┆            │\n    │ 2023-10-14 ┆ wk inc flu ┆ -1      ┆ 2023-10-07 ┆ 01       ┆ quantile   ┆ 0.1        ┆ 14.497646  │\n    │            ┆ hosp       ┆         ┆            ┆          ┆            ┆            ┆            │\n    │ 2023-10-14 ┆ wk inc flu ┆ -1      ┆ 2023-10-07 ┆ 01       ┆ quantile   ┆ 0.15       ┆ 16.119813  │\n    │            ┆ hosp       ┆         ┆            ┆          ┆            ┆            ┆            │\n    │ …          ┆ …          ┆ …       ┆ …          ┆ …        ┆ …          ┆ …          ┆ …          │\n    │ 2023-10-14 ┆ wk inc flu ┆ 2       ┆ 2023-10-28 ┆ US       ┆ quantile   ┆ 0.85       ┆ 2451.87489 │\n    │            ┆ hosp       ┆         ┆            ┆          ┆            ┆            ┆ 9          │\n    │ 2023-10-14 ┆ wk inc flu ┆ 2       ┆ 2023-10-28 ┆ US       ┆ quantile   ┆ 0.9        ┆ 2806.92858 │\n    │            ┆ hosp       ┆         ┆            ┆          ┆            ┆            ┆ 8          │\n    │ 2023-10-14 ┆ wk inc flu ┆ 2       ┆ 2023-10-28 ┆ US       ┆ quantile   ┆ 0.95       ┆ 3383.74799 │\n    │            ┆ hosp       ┆         ┆            ┆          ┆            ┆            ┆            │\n    │ 2023-10-14 ┆ wk inc flu ┆ 2       ┆ 2023-10-28 ┆ US       ┆ quantile   ┆ 0.975      ┆ 3940.39253 │\n    │            ┆ hosp       ┆         ┆            ┆          ┆            ┆            ┆ 6          │\n    │ 2023-10-14 ┆ wk inc flu ┆ 2       ┆ 2023-10-28 ┆ US       ┆ quantile   ┆ 0.99       ┆ 4761.75738 │\n    │            ┆ hosp       ┆         ┆            ┆          ┆            ┆            ┆ 5          │\n    └────────────┴────────────┴─────────┴────────────┴──────────┴────────────┴────────────┴────────────┘\n\nUsing `data.py`, the example FluSight submission was created by running\nthe following:\n\n``` python\nget_and_save_flusight_submission(\n    file_save_path=os.path.join(\n        os.getcwd(),\n        \"example_flusight_submission.csv\"\n    ),\n)\n```\n\n## NHSN COVID And Flu Hospital Admissions\n\nNHSN hospital admissions fitting data for COVID and Flu is included in\n`forecasttools-py` as well, for user experimentation.\n\nThis data:\n\n- Is current as of `2024-04-27`\n- Comes from the website [HealthData.gov COVID-19 Reported Patient\n  Impact and Hospital Capacity by State\n  Timeseries](https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/g62h-syeh).\n\nFor influenza, the `previous_day_admission_influenza_confirmed` column\nis retained and for COVID the\n`previous_day_admission_adult_covid_confirmed` column is retained. As\ncan be seen in the example below, some early dates for each jurisdiction\ndo not have data.\n\nThe fitting data is stored in `forecasttools-py` as a `polars` dataframe\nand is accessed via:\n\n``` python\n# access COVID data\ncovid_nhsn_data = forecasttools.nhsn_hosp_COVID\n\n# access flu data\nflu_nhsn_data = forecasttools.nhsn_hosp_flu\n\n# display flu data\nprint(flu_nhsn_data)\n```\n\n    shape: (81_713, 3)\n    ┌───────┬────────────┬──────┐\n    │ state ┆ date       ┆ hosp │\n    │ ---   ┆ ---        ┆ ---  │\n    │ str   ┆ str        ┆ str  │\n    ╞═══════╪════════════╪══════╡\n    │ AK    ┆ 2020-03-23 ┆ null │\n    │ AK    ┆ 2020-03-24 ┆ null │\n    │ AK    ┆ 2020-03-25 ┆ null │\n    │ AK    ┆ 2020-03-26 ┆ null │\n    │ AK    ┆ 2020-03-27 ┆ null │\n    │ …     ┆ …          ┆ …    │\n    │ WY    ┆ 2024-04-23 ┆ 1    │\n    │ WY    ┆ 2024-04-24 ┆ 1    │\n    │ WY    ┆ 2024-04-25 ┆ 0    │\n    │ WY    ┆ 2024-04-26 ┆ 0    │\n    │ WY    ┆ 2024-04-27 ┆ 0    │\n    └───────┴────────────┴──────┘\n\nThe data was created by placing a csv file called\n`NHSN_RAW_20240926.csv` (the full NHSN dataset) into `./forecasttools/`\nand running, in `data.py`, the following:\n\n``` python\n# generate COVID dataset\nmake_nshn_fitting_dataset(\n    dataset=\"COVID\",\n    nhsn_dataset_path=\"NHSN_RAW_20240926.csv\",\n    file_save_path=os.path.join(\n        os.getcwd(),\n        \"nhsn_hosp_COVID.csv\"\n    )\n)\n\n# generate flu dataset\nmake_nshn_fitting_dataset(\n    dataset=\"flu\",\n    nhsn_dataset_path=\"NHSN_RAW_20240926.csv\",\n    file_save_path=os.path.join(\n        os.getcwd(),\n        \"nhsn_hosp_flu.csv\"\n    )\n)\n```\n\n## Influenza Hospitalizations Forecast(s)\n\nTwo example forecasts stored in Arviz `InferenceData` objects are\nincluded for vignettes and user experimentation. Both are 28 day\ninfluenza hospital admissions forecasts for Texas made using a spline\nregression model fitted to NHSN data between 2022-08-08 and 2022-12-08.\nThe only difference between the forecasts is that\n`example_flu_forecast_w_dates.nc` has had dates added as its coordinates\n(this is not a native Arviz feature).\n\nThe forecast `idata`s are accessed via:\n\n``` python\n# idata with dates as coordinates\nidata_w_dates = forecasttools.nhsn_flu_forecast_w_dates\nprint(idata_w_dates)\n```\n\n    Inference data with groups:\n        \u003e posterior\n        \u003e posterior_predictive\n        \u003e log_likelihood\n        \u003e sample_stats\n        \u003e prior\n        \u003e prior_predictive\n        \u003e observed_data\n\n``` python\n# show dates\nprint(idata_w_dates[\"observed_data\"][\"obs\"][\"obs_dim_0\"][:15])\n```\n\n    \u003cxarray.DataArray 'obs_dim_0' (obs_dim_0: 15)\u003e Size: 120B\n    array(['2022-08-08T00:00:00.000000000', '2022-08-09T00:00:00.000000000',\n           '2022-08-10T00:00:00.000000000', '2022-08-11T00:00:00.000000000',\n           '2022-08-12T00:00:00.000000000', '2022-08-13T00:00:00.000000000',\n           '2022-08-14T00:00:00.000000000', '2022-08-15T00:00:00.000000000',\n           '2022-08-16T00:00:00.000000000', '2022-08-17T00:00:00.000000000',\n           '2022-08-18T00:00:00.000000000', '2022-08-19T00:00:00.000000000',\n           '2022-08-20T00:00:00.000000000', '2022-08-21T00:00:00.000000000',\n           '2022-08-22T00:00:00.000000000'], dtype='datetime64[ns]')\n    Coordinates:\n      * obs_dim_0  (obs_dim_0) datetime64[ns] 120B 2022-08-08 ... 2022-08-22\n\n``` python\n# idata without dates as coordinates\nidata_wo_dates = forecasttools.nhsn_flu_forecast_wo_dates\nprint(idata_wo_dates[\"observed_data\"][\"obs\"][\"obs_dim_0\"][:20])\n```\n\n    \u003cxarray.DataArray 'obs_dim_0' (obs_dim_0: 20)\u003e Size: 160B\n    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n           18, 19])\n    Coordinates:\n      * obs_dim_0  (obs_dim_0) int64 160B 0 1 2 3 4 5 6 7 ... 13 14 15 16 17 18 19\n\n# CDC Open Source Considerations\n\n**General disclaimer** This repository was created for use by CDC\nprograms to collaborate on public health related projects in support of\nthe [CDC mission](https://www.cdc.gov/about/organization/mission.htm).\nGitHub is not hosted by the CDC, but is a third party website used by\nCDC and its partners to share information and collaborate on software.\nCDC use of GitHub does not imply an endorsement of any one particular\nservice, product, or enterprise.\n\n\u003cdetails\u003e\n\n\u003csummary\u003e\n\nRules, Policy, And Collaboration\n\u003c/summary\u003e\n\n- [Open Practices](./rules-and-policies/open_practices.md)\n- [Rules of Behavior](./rules-and-policies/rules_of_behavior.md)\n- [Thanks and Acknowledgements](./rules-and-policies/thanks.md)\n- [Disclaimer](DISCLAIMER.md)\n- [Contribution Notice](CONTRIBUTING.md)\n- [Code of Conduct](./rules-and-policies/code-of-conduct.md)\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\n\u003csummary\u003e\n\nPublic Domain Standard Notice\n\u003c/summary\u003e\n\nThis repository constitutes a work of the United States Government and\nis not subject to domestic copyright protection under 17 USC § 105. 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