Ensured Datasets Are Bundled Properly

[AFg6K7h4fhy2]

These datasets include

• location_data.csv
• example_forecast.nc (NETCDF)
• nhsn_fit_COVID.csv
• nhsn_fit_flu.csv
• example_flusight_submission.csv

[AFg6K7h4fhy2]

As forecasttools stands now, the following data objects are contained:

• example_flu_forecast_wo_dates.nc
• example_flu_forecast_w_dates.nc
• example_flusight_submission.parquet
• location_table.parquet
• nhsn_hosp_COVID.parquet
• nhsn_hosp_flu.parquet

When an individual wants to use forecasttools via

pip3 install git+https://github.com/CDCgov/forecasttools-py@main

then the individual should expect to have accessible these data objects.

The code in pyproject.toml ensures the data objects are bundled in source distribution (sdist) and wheel (.whl) formats:

packages = [{include = "forecasttools"}]
include = [
    { path = "forecasttools/location_table.parquet", format = "sdist" },
    { path = "forecasttools/location_table.parquet", format = "wheel" },
    { path = "forecasttools/example_flusight_submission.parquet", format = "sdist" },
    { path = "forecasttools/example_flusight_submission.parquet", format = "wheel" },
    { path = "forecasttools/example_flu_forecast_wo_dates.nc", format = "sdist" },
    { path = "forecasttools/example_flu_forecast_wo_dates.nc", format = "wheel" },
    { path = "forecasttools/example_flu_forecast_w_dates.nc", format = "sdist" },
    { path = "forecasttools/example_flu_forecast_w_dates.nc", format = "wheel" },
    { path = "forecasttools/nhsn_hosp_COVID.parquet", format = "sdist" },
    { path = "forecasttools/nhsn_hosp_COVID.parquet", format = "wheel" },
    { path = "forecasttools/nhsn_hosp_flu.parquet", format = "sdist" },
    { path = "forecasttools/nhsn_hosp_flu.parquet", format = "wheel" },
]

The following can be used to test whether the data objects are properly packaged:

1. Create an empty virtual environment, then activate it.

python3 -m venv data_bundling_test_env
source data_bundling_test_env/bin/activate

2. Install forecasttools in the virtual environment.

pip3 install git+https://github.com/CDCgov/forecasttools-py@main

The following was outputted:

Collecting git+https://github.com/CDCgov/forecasttools-py@main
  Cloning https://github.com/CDCgov/forecasttools-py (to revision main) to /private/var/folders/cm/dlbpxkls2djcc1psjg2fzstc0000gn/T/pip-req-build-c0sxofrs
  Running command git clone --filter=blob:none --quiet https://github.com/CDCgov/forecasttools-py /private/var/folders/cm/dlbpxkls2djcc1psjg2fzstc0000gn/T/pip-req-build-c0sxofrs
  Resolved https://github.com/CDCgov/forecasttools-py to commit 4de2b4139c7200466094c5cfe4c9375a1a0fe104
  Installing build dependencies ... done
  Getting requirements to build wheel ... done
  Preparing metadata (pyproject.toml) ... done
Collecting arviz<0.21.0,>=0.20.0 (from forecasttools==0.0.1)
  Downloading arviz-0.20.0-py3-none-any.whl.metadata (8.8 kB)
Collecting epiweeks<3.0.0,>=2.3.0 (from forecasttools==0.0.1)
  Using cached epiweeks-2.3.0-py3-none-any.whl.metadata (2.6 kB)
Collecting matplotlib<4.0.0,>=3.9.2 (from forecasttools==0.0.1)
  Using cached matplotlib-3.9.2-cp312-cp312-macosx_11_0_arm64.whl.metadata (11 kB)
Collecting polars<2.0.0,>=1.8.2 (from forecasttools==0.0.1)
  Downloading polars-1.12.0-cp39-abi3-macosx_11_0_arm64.whl.metadata (14 kB)
Collecting xarray<2025.0.0,>=2024.9.0 (from forecasttools==0.0.1)
  Downloading xarray-2024.10.0-py3-none-any.whl.metadata (11 kB)
Collecting setuptools>=60.0.0 (from arviz<0.21.0,>=0.20.0->forecasttools==0.0.1)
  Using cached setuptools-75.3.0-py3-none-any.whl.metadata (6.9 kB)
Collecting numpy>=1.23.0 (from arviz<0.21.0,>=0.20.0->forecasttools==0.0.1)
  Downloading numpy-2.1.3-cp312-cp312-macosx_11_0_arm64.whl.metadata (62 kB)
Collecting scipy>=1.9.0 (from arviz<0.21.0,>=0.20.0->forecasttools==0.0.1)
  Using cached scipy-1.14.1-cp312-cp312-macosx_12_0_arm64.whl.metadata (60 kB)
Requirement already satisfied: packaging in ./data_bundling_test_env/lib/python3.12/site-packages (from arviz<0.21.0,>=0.20.0->forecasttools==0.0.1) (24.1)
Collecting pandas>=1.5.0 (from arviz<0.21.0,>=0.20.0->forecasttools==0.0.1)
  Downloading pandas-2.2.3-cp312-cp312-macosx_11_0_arm64.whl.metadata (89 kB)
Collecting h5netcdf>=1.0.2 (from arviz<0.21.0,>=0.20.0->forecasttools==0.0.1)
  Downloading h5netcdf-1.4.0-py3-none-any.whl.metadata (13 kB)
Collecting typing-extensions>=4.1.0 (from arviz<0.21.0,>=0.20.0->forecasttools==0.0.1)
  Using cached typing_extensions-4.12.2-py3-none-any.whl.metadata (3.0 kB)
Collecting xarray-einstats>=0.3 (from arviz<0.21.0,>=0.20.0->forecasttools==0.0.1)
  Downloading xarray_einstats-0.8.0-py3-none-any.whl.metadata (5.8 kB)
Collecting contourpy>=1.0.1 (from matplotlib<4.0.0,>=3.9.2->forecasttools==0.0.1)
  Downloading contourpy-1.3.0-cp312-cp312-macosx_11_0_arm64.whl.metadata (5.4 kB)
Collecting cycler>=0.10 (from matplotlib<4.0.0,>=3.9.2->forecasttools==0.0.1)
  Using cached cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)
Collecting fonttools>=4.22.0 (from matplotlib<4.0.0,>=3.9.2->forecasttools==0.0.1)
  Downloading fonttools-4.54.1-cp312-cp312-macosx_11_0_arm64.whl.metadata (163 kB)
Collecting kiwisolver>=1.3.1 (from matplotlib<4.0.0,>=3.9.2->forecasttools==0.0.1)
  Downloading kiwisolver-1.4.7-cp312-cp312-macosx_11_0_arm64.whl.metadata (6.3 kB)
Collecting pillow>=8 (from matplotlib<4.0.0,>=3.9.2->forecasttools==0.0.1)
  Downloading pillow-11.0.0-cp312-cp312-macosx_11_0_arm64.whl.metadata (9.1 kB)
Collecting pyparsing>=2.3.1 (from matplotlib<4.0.0,>=3.9.2->forecasttools==0.0.1)
  Downloading pyparsing-3.2.0-py3-none-any.whl.metadata (5.0 kB)
Collecting python-dateutil>=2.7 (from matplotlib<4.0.0,>=3.9.2->forecasttools==0.0.1)
  Using cached python_dateutil-2.9.0.post0-py2.py3-none-any.whl.metadata (8.4 kB)
Collecting h5py (from h5netcdf>=1.0.2->arviz<0.21.0,>=0.20.0->forecasttools==0.0.1)
  Downloading h5py-3.12.1-cp312-cp312-macosx_11_0_arm64.whl.metadata (2.5 kB)
Collecting pytz>=2020.1 (from pandas>=1.5.0->arviz<0.21.0,>=0.20.0->forecasttools==0.0.1)
  Downloading pytz-2024.2-py2.py3-none-any.whl.metadata (22 kB)
Collecting tzdata>=2022.7 (from pandas>=1.5.0->arviz<0.21.0,>=0.20.0->forecasttools==0.0.1)
  Downloading tzdata-2024.2-py2.py3-none-any.whl.metadata (1.4 kB)
Collecting six>=1.5 (from python-dateutil>=2.7->matplotlib<4.0.0,>=3.9.2->forecasttools==0.0.1)
  Using cached six-1.16.0-py2.py3-none-any.whl.metadata (1.8 kB)
Downloading arviz-0.20.0-py3-none-any.whl (1.7 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.7/1.7 MB 4.6 MB/s eta 0:00:00
Using cached epiweeks-2.3.0-py3-none-any.whl (5.7 kB)
Using cached matplotlib-3.9.2-cp312-cp312-macosx_11_0_arm64.whl (7.8 MB)
Downloading polars-1.12.0-cp39-abi3-macosx_11_0_arm64.whl (28.9 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 28.9/28.9 MB 16.9 MB/s eta 0:00:00
Downloading xarray-2024.10.0-py3-none-any.whl (1.2 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.2/1.2 MB 20.0 MB/s eta 0:00:00
Downloading contourpy-1.3.0-cp312-cp312-macosx_11_0_arm64.whl (251 kB)
Using cached cycler-0.12.1-py3-none-any.whl (8.3 kB)
Downloading fonttools-4.54.1-cp312-cp312-macosx_11_0_arm64.whl (2.3 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.3/2.3 MB 28.2 MB/s eta 0:00:00
Downloading h5netcdf-1.4.0-py3-none-any.whl (50 kB)
Downloading kiwisolver-1.4.7-cp312-cp312-macosx_11_0_arm64.whl (63 kB)
Downloading numpy-2.1.3-cp312-cp312-macosx_11_0_arm64.whl (13.5 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 13.5/13.5 MB 26.0 MB/s eta 0:00:00
Downloading pandas-2.2.3-cp312-cp312-macosx_11_0_arm64.whl (11.4 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 11.4/11.4 MB 26.2 MB/s eta 0:00:00
Downloading pillow-11.0.0-cp312-cp312-macosx_11_0_arm64.whl (3.0 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 3.0/3.0 MB 1.6 MB/s eta 0:00:00
Downloading pyparsing-3.2.0-py3-none-any.whl (106 kB)
Using cached python_dateutil-2.9.0.post0-py2.py3-none-any.whl (229 kB)
Using cached scipy-1.14.1-cp312-cp312-macosx_12_0_arm64.whl (29.9 MB)
Using cached setuptools-75.3.0-py3-none-any.whl (1.3 MB)
Using cached typing_extensions-4.12.2-py3-none-any.whl (37 kB)
Downloading xarray_einstats-0.8.0-py3-none-any.whl (32 kB)
Downloading pytz-2024.2-py2.py3-none-any.whl (508 kB)
Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
Downloading tzdata-2024.2-py2.py3-none-any.whl (346 kB)
Downloading h5py-3.12.1-cp312-cp312-macosx_11_0_arm64.whl (2.9 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.9/2.9 MB 82.1 MB/s eta 0:00:00
Building wheels for collected packages: forecasttools
  Building wheel for forecasttools (pyproject.toml) ... done
  Created wheel for forecasttools: filename=forecasttools-0.0.1-py3-none-any.whl size=3869507 sha256=68c5e07f045a855de942150fbd220155f98686d1d738a07592bb5dad960b358e
  Stored in directory: /private/var/folders/cm/dlbpxkls2djcc1psjg2fzstc0000gn/T/pip-ephem-wheel-cache-faq93ygh/wheels/0e/26/44/7657c96db44897dd4745e06a9bacc4e4e01b0229bb5236a838
Successfully built forecasttools
Installing collected packages: pytz, tzdata, typing-extensions, six, setuptools, pyparsing, polars, pillow, numpy, kiwisolver, fonttools, epiweeks, cycler, scipy, python-dateutil, h5py, contourpy, pandas, matplotlib, h5netcdf, xarray, xarray-einstats, arviz, forecasttools
Successfully installed arviz-0.20.0 contourpy-1.3.0 cycler-0.12.1 epiweeks-2.3.0 fonttools-4.54.1 forecasttools-0.0.1 h5netcdf-1.4.0 h5py-3.12.1 kiwisolver-1.4.7 matplotlib-3.9.2 numpy-2.1.3 pandas-2.2.3 pillow-11.0.0 polars-1.12.0 pyparsing-3.2.0 python-dateutil-2.9.0.post0 pytz-2024.2 scipy-1.14.1 setuptools-75.3.0 six-1.16.0 typing-extensions-4.12.2 tzdata-2024.2 xarray-2024.10.0 xarray-einstats-0.8.0

[notice] A new release of pip is available: 24.2 -> 24.3.1
[notice] To update, run: pip install --upgrade pip

3. Create and run a Python file with the following:

import forecasttools


print(forecasttools.location_table)
print(forecasttools.nhsn_hosp_flu)
print(forecasttools.nhsn_hosp_COVID)
print(forecasttools.nhsn_flu_forecast_wo_dates)
print(forecasttools.nhsn_flu_forecast_w_dates)
print(forecasttools.example_flusight_submission)

4. Deactivate the virtual environment:

deactivate