From 914dd262ef54803cd55fa6f8c53adec44dfd7e41 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Wed, 1 May 2024 12:07:04 -0600 Subject: [PATCH 01/12] tutorials are now build and cc to the website --- .github/workflows/website.yaml | 4 +- docs/build/doctrees/bugs.doctree | Bin 12588 -> 0 bytes docs/build/doctrees/contribute.doctree | Bin 7469 -> 0 bytes docs/build/doctrees/ctoc.doctree | Bin 2950 -> 0 bytes docs/build/doctrees/environment.pickle | Bin 1004010 -> 0 bytes docs/build/doctrees/faq.doctree | Bin 3158 -> 0 bytes docs/build/doctrees/general/bugs.doctree | Bin 12604 -> 0 bytes .../build/doctrees/general/contribute.doctree | Bin 7485 -> 0 bytes docs/build/doctrees/general/ctoc.doctree | Bin 3571 -> 0 bytes docs/build/doctrees/general/faq.doctree | Bin 3174 -> 0 bytes docs/build/doctrees/general/glossary.doctree | Bin 3893 -> 0 bytes docs/build/doctrees/general/index.doctree | Bin 10983 -> 0 bytes docs/build/doctrees/general/license.doctree | Bin 6437 -> 0 bytes docs/build/doctrees/general/notices.doctree | Bin 14974 -> 0 bytes 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When it is not found, a full rebuild will be done. -config: 901af503b84dff1464beaf13282c3a02 # pragma: allowlist secret -tags: 645f666f9bcd5a90fca523b33c5a78b7 # pragma: allowlist secret diff --git a/docs/build/html/.nojekyll b/docs/build/html/.nojekyll deleted file mode 100644 index e69de29b..00000000 diff --git a/docs/build/html/_sources/bugs.rst.txt b/docs/build/html/_sources/bugs.rst.txt deleted file mode 100644 index 67e7f4b9..00000000 --- a/docs/build/html/_sources/bugs.rst.txt +++ /dev/null @@ -1,52 +0,0 @@ -Reporting Issues -================ - -To report issues and work on bugs, please head to ``. Please adopt the following template in making issues. This template should be used as an outline. It may not be necessary to fill out every section. Delete this block of text and fill in anything in brackets. - -Make sure you follow the project's standards specified in `this adr doc ` (private link) - -Goal ----- - -[1-3 sentence summary of the issue or feature request. E.g. "We want to be able to ..."] - -Context -------- - -[Short paragraph describing how the issue arose and constraints imposed by the existing code architecture] - -Required features ------------------ - -- [Describe each thing you need the code to do to achieve the goal] -- [Example 1: Use a config to set input and output paths] -- [Example 2: Read in some-dataset and output some-transformed-dataset] -- etc... - -Specifications --------------- - -[A checklist to keep track of details for each feature. At least one specification per feature is recommended. Edit the example below:] - -- [ ] EX2: A function that reads data from the `some-api` API and returns the dataset -- [ ] EX2: Another function that inputs the dataset, performs $x$ transform, and outputs $y$ -- [ ] EX1: A script that runs the workflow from a config -- [ ] All functions should follow PEP8 package conventions with blah, blah, blah -- [ ] The workflow should run in the VAP from `directory` -- [ ] All functions should have associated unit tests -- [ ] etc. etc. - -Out of scope ------------- - -- [Things out of scope from this issue/PR] - -Related documents ------------------ - -- [Link to related scripts, functions, issues, PRs, conversations, datasets, etc.] - - -Note: [this page is a work in progress; the above text was taken from `https://github.com/CDCgov/multisignal-epi-inference `_ on 2024/03/25] - -.. todo:: Get baseline, accurate version of the Reporting Issues page. diff --git a/docs/build/html/_sources/contribute.rst.txt b/docs/build/html/_sources/contribute.rst.txt deleted file mode 100644 index 96f2ec1d..00000000 --- a/docs/build/html/_sources/contribute.rst.txt +++ /dev/null @@ -1,10 +0,0 @@ -Contributing To MSEI -==================== - -Anyone is encouraged to contribute to the repository by `forking `_ and submitting a pull request. (If you are new to GitHub, you might start with a `basic tutorial `_.) By contributing to this project, you grant a world-wide, royalty-free, perpetual, irrevocable, non-exclusive, transferable license to all users under the terms of the `Apache Software License v2 `_) or later. - -All comments, messages, pull requests, and other submissions received through CDC including this GitHub page may be subject to applicable federal law, including but not limited to the Federal Records Act, and may be archived. Learn more at `http://www.cdc.gov/other/privacy.html `_. - -Note: [this page is a work in progress; the above text was taken from `https://github.com/CDCgov/multisignal-epi-inference `_ on 2024/03/25] - -.. todo:: Get baseline, accurate version of the CONTRIBUTING page. diff --git a/docs/build/html/_sources/ctoc.rst.txt b/docs/build/html/_sources/ctoc.rst.txt deleted file mode 100644 index 9f4ed5f1..00000000 --- a/docs/build/html/_sources/ctoc.rst.txt +++ /dev/null @@ -1,12 +0,0 @@ -Complete Table Of Contents -========================== - -.. toctree:: - :maxdepth: 4 - - msei_reference/index - test_reference/index - genindex - modindex - search - glossary diff --git a/docs/build/html/_sources/faq.rst.txt b/docs/build/html/_sources/faq.rst.txt deleted file mode 100644 index 61030d9d..00000000 --- a/docs/build/html/_sources/faq.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -FAQ -=== - -Note: [this page is a work in progress; this page is a placeholder] - -.. todo:: Get baseline, accurate version of the Frequently Asked Questions (FAQ) page. diff --git a/docs/build/html/_sources/general/bugs.rst.txt b/docs/build/html/_sources/general/bugs.rst.txt deleted file mode 100644 index 67e7f4b9..00000000 --- a/docs/build/html/_sources/general/bugs.rst.txt +++ /dev/null @@ -1,52 +0,0 @@ -Reporting Issues -================ - -To report issues and work on bugs, please head to ``. Please adopt the following template in making issues. This template should be used as an outline. It may not be necessary to fill out every section. Delete this block of text and fill in anything in brackets. - -Make sure you follow the project's standards specified in `this adr doc ` (private link) - -Goal ----- - -[1-3 sentence summary of the issue or feature request. E.g. "We want to be able to ..."] - -Context -------- - -[Short paragraph describing how the issue arose and constraints imposed by the existing code architecture] - -Required features ------------------ - -- [Describe each thing you need the code to do to achieve the goal] -- [Example 1: Use a config to set input and output paths] -- [Example 2: Read in some-dataset and output some-transformed-dataset] -- etc... - -Specifications --------------- - -[A checklist to keep track of details for each feature. At least one specification per feature is recommended. Edit the example below:] - -- [ ] EX2: A function that reads data from the `some-api` API and returns the dataset -- [ ] EX2: Another function that inputs the dataset, performs $x$ transform, and outputs $y$ -- [ ] EX1: A script that runs the workflow from a config -- [ ] All functions should follow PEP8 package conventions with blah, blah, blah -- [ ] The workflow should run in the VAP from `directory` -- [ ] All functions should have associated unit tests -- [ ] etc. etc. - -Out of scope ------------- - -- [Things out of scope from this issue/PR] - -Related documents ------------------ - -- [Link to related scripts, functions, issues, PRs, conversations, datasets, etc.] - - -Note: [this page is a work in progress; the above text was taken from `https://github.com/CDCgov/multisignal-epi-inference `_ on 2024/03/25] - -.. todo:: Get baseline, accurate version of the Reporting Issues page. diff --git a/docs/build/html/_sources/general/contribute.rst.txt b/docs/build/html/_sources/general/contribute.rst.txt deleted file mode 100644 index 96f2ec1d..00000000 --- a/docs/build/html/_sources/general/contribute.rst.txt +++ /dev/null @@ -1,10 +0,0 @@ -Contributing To MSEI -==================== - -Anyone is encouraged to contribute to the repository by `forking `_ and submitting a pull request. (If you are new to GitHub, you might start with a `basic tutorial `_.) By contributing to this project, you grant a world-wide, royalty-free, perpetual, irrevocable, non-exclusive, transferable license to all users under the terms of the `Apache Software License v2 `_) or later. - -All comments, messages, pull requests, and other submissions received through CDC including this GitHub page may be subject to applicable federal law, including but not limited to the Federal Records Act, and may be archived. Learn more at `http://www.cdc.gov/other/privacy.html `_. - -Note: [this page is a work in progress; the above text was taken from `https://github.com/CDCgov/multisignal-epi-inference `_ on 2024/03/25] - -.. todo:: Get baseline, accurate version of the CONTRIBUTING page. diff --git a/docs/build/html/_sources/general/ctoc.rst.txt b/docs/build/html/_sources/general/ctoc.rst.txt deleted file mode 100644 index 41df0b16..00000000 --- a/docs/build/html/_sources/general/ctoc.rst.txt +++ /dev/null @@ -1,21 +0,0 @@ -Complete Table Of Contents -========================== - -.. toctree:: - :maxdepth: 4 - - ../msei_reference/index - ../test_reference/index - ../genindex - ../modindex - ../search - faq - glossary - license - notices - state - usage - contribute - bugs - -.. todo:: Change order and naming of these toctree entities diff --git a/docs/build/html/_sources/general/faq.rst.txt b/docs/build/html/_sources/general/faq.rst.txt deleted file mode 100644 index 61030d9d..00000000 --- a/docs/build/html/_sources/general/faq.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -FAQ -=== - -Note: [this page is a work in progress; this page is a placeholder] - -.. todo:: Get baseline, accurate version of the Frequently Asked Questions (FAQ) page. diff --git a/docs/build/html/_sources/general/glossary.rst.txt b/docs/build/html/_sources/general/glossary.rst.txt deleted file mode 100644 index 2ed37067..00000000 --- a/docs/build/html/_sources/general/glossary.rst.txt +++ /dev/null @@ -1,12 +0,0 @@ -Glossary -======== - -.. glossary:: - - Term 1 - Definition for term 1 etc... - - Term 2 - Definition for term 2 etc... - -..todo:: Populate the glossary with terms (e.g., from model_definition.md) diff --git a/docs/build/html/_sources/general/license.rst.txt b/docs/build/html/_sources/general/license.rst.txt deleted file mode 100644 index 32a2b660..00000000 --- a/docs/build/html/_sources/general/license.rst.txt +++ /dev/null @@ -1,17 +0,0 @@ -License -======= - -This repository is licensed under ASL v2 or later. - -This source code in this repository is free: you can redistribute it and/or modify it under the terms of the Apache Software License version 2, or (at your option) any later version. - -This source code in this repository is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the Apache Software License for more details. - -You should have received a copy of the Apache Software License along with this program. If not, see `http://www.apache.org/licenses/LICENSE-2.0.html `_ - -The source code forked from other open source projects will inherit its license. - - -Note: [this page is a work in progress; the above text was taken from `https://github.com/CDCgov/multisignal-epi-inference `_ on 2024/03/25] - -.. todo:: Get baseline, accurate version of the License page. diff --git a/docs/build/html/_sources/general/notices.rst.txt b/docs/build/html/_sources/general/notices.rst.txt deleted file mode 100644 index 7972d6db..00000000 --- a/docs/build/html/_sources/general/notices.rst.txt +++ /dev/null @@ -1,37 +0,0 @@ -Other Notices -============= - -General Disclaimer ------------------- - -This repository was created for use by CDC programs to collaborate on public health related projects in support of the `CDC mission `_. GitHub is not hosted by the CDC, but is a third party website used by CDC and its partners to share information and collaborate on software. CDC use of GitHub does not imply an endorsement of any one particular service, product, or enterprise. - -Public Domain Standard Notice ------------------------------ - -This repository constitutes a work of the United States Government and is not subject to domestic copyright protection under 17 USC § 105. This repository is in the public domain within the United States, and copyright and related rights in the work worldwide are waived through the `CC0 1.0 Universal public domain dedication `_. All contributions to this repository will be released under the CC0 dedication. By submitting a pull request you are agreeing to comply with this waiver of copyright interest. - -Privacy Standard Notice ------------------------ - -This repository contains only non-sensitive, publicly available data and information. All material and community participation is covered by the `Disclaimer `_` and `Code of Conduct `_. For more information about CDC's privacy policy, please visit `http://www.cdc.gov/other/privacy.html `_. - -Records Management Standard Notice ----------------------------------- - -This repository is not a source of government records but is a copy to increase collaboration and collaborative potential. All government records will be published through the `CDC web site `. - -Additional Standard Notices ---------------------------- - -Please refer to `CDC's Template Repository `_ for more information about `contributing to this repository `_, `public domain notices and disclaimers `_, and `code of conduct `_. - - -Final Notes ------------ - -Please refer, under Meta Information, the License and Reporting Issues pages for other standard notices. - -Note: [this page is a work in progress; the above text was taken from `https://github.com/CDCgov/multisignal-epi-inference `_ on 2024/03/25] - -.. todo:: Get baseline, accurate version of the Notices page. diff --git a/docs/build/html/_sources/general/state.rst.txt b/docs/build/html/_sources/general/state.rst.txt deleted file mode 100644 index e5932b77..00000000 --- a/docs/build/html/_sources/general/state.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -State Of Development -==================== - -Note: [this page is a work in progress; this page is a placeholder] - -.. todo:: Get baseline, accurate version of the State Of Development page. diff --git a/docs/build/html/_sources/general/usage.rst.txt b/docs/build/html/_sources/general/usage.rst.txt deleted file mode 100644 index 6fabafd6..00000000 --- a/docs/build/html/_sources/general/usage.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -Setup & Usage -============= - -Note: [this page is a work in progress; this page is a placeholder] - -.. todo:: Get baseline, accurate version of the Setup & Usage page. diff --git a/docs/build/html/_sources/genindex.rst.txt b/docs/build/html/_sources/genindex.rst.txt deleted file mode 100644 index a04b8d29..00000000 --- a/docs/build/html/_sources/genindex.rst.txt +++ /dev/null @@ -1,4 +0,0 @@ -General Index -============= - -:ref:`genindex` diff --git a/docs/build/html/_sources/glossary.rst.txt b/docs/build/html/_sources/glossary.rst.txt deleted file mode 100644 index 2ed37067..00000000 --- a/docs/build/html/_sources/glossary.rst.txt +++ /dev/null @@ -1,12 +0,0 @@ -Glossary -======== - -.. glossary:: - - Term 1 - Definition for term 1 etc... - - Term 2 - Definition for term 2 etc... - -..todo:: Populate the glossary with terms (e.g., from model_definition.md) diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt deleted file mode 100644 index ce3e3920..00000000 --- a/docs/build/html/_sources/index.rst.txt +++ /dev/null @@ -1,91 +0,0 @@ -.. CFA Multisignal Renewal documentation master file, created by - sphinx-quickstart on Sun Mar 17 15:31:47 2024. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -CFA Multisignal Renewal Documentation -===================================== - -Welcome to the CFA's `Multisignal Epidemiological Inference `_ (MSEI) project (*a.k.a.* signal fusion project), which consists of an internal forecasting model that leverages multiple data sources for enhancing epidemiological modeling of infectious disease outbreaks. - -This repository is composed of two parts: - -1. **Model development** (`model folder `_). -2. **Analysis pipeline** (`pipeline folder `_). - -Overview of the project follows: - -.. mermaid:: - - flowchart TD - %% Main diagram - io((P1: I/O\nDefinition)) --> |Dependency of| model((P2: Model\nPackage)) - io --> |Is used by| etl[[P3: ETL]] - model --> |Is used in| run - io -.-> |Possible\ndependency of|ww((Wastewater\nPackage)) - - %% Definition of the pipe - subgraph pipeline["Pipeline\n(Azure + GHA)"] - etl --> |Feeds| run[["P4: Run the\nmodel"]] - end - run --> |Feeds| Outputs - - %% Definition of the outputs - subgraph Outputs - direction TB - postp[[P5: Post\nProduction]] - retro[[P6: Retrospective\nTesting]] - bench[[P7: Benchmarking\n&A/B testing]] - end - - %% Connections to the outputs - io --> |Is used by| Outputs - postp --> manual[[Manual review]] - manual --> share[[Share publicly]] - - - %% Tagging sub-projects - classDef tealNode fill:teal,color:white,stroke:white; - class io,model,etl,run,postp,retro,bench,project,process tealNode; - -Documentation Components -======================== - -.. toctree:: - :maxdepth: 1 - - msei_reference/index - test_reference/index - general/state - general/faq - general/usage - - -Indices And Tables -================== - -.. toctree:: - :maxdepth: 1 - - genindex - modindex - search - general/glossary - general/ctoc - -Meta Information -================ - -.. toctree:: - :maxdepth: 1 - - general/contribute - general/bugs - general/license - general/notices - -.. todo:: Have docs folder exist in main, deploy using GHA. - -.. todo:: Add [State, Usage, Help, Tutorials/index, FAQ, HOWTOs/index] pages in Documentation Components. - -.. todo:: Add [About, History, Download, Copyright] pages in Meta Information. diff --git a/docs/build/html/_sources/license.rst.txt b/docs/build/html/_sources/license.rst.txt deleted file mode 100644 index 32a2b660..00000000 --- a/docs/build/html/_sources/license.rst.txt +++ /dev/null @@ -1,17 +0,0 @@ -License -======= - -This repository is licensed under ASL v2 or later. - -This source code in this repository is free: you can redistribute it and/or modify it under the terms of the Apache Software License version 2, or (at your option) any later version. - -This source code in this repository is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the Apache Software License for more details. - -You should have received a copy of the Apache Software License along with this program. If not, see `http://www.apache.org/licenses/LICENSE-2.0.html `_ - -The source code forked from other open source projects will inherit its license. - - -Note: [this page is a work in progress; the above text was taken from `https://github.com/CDCgov/multisignal-epi-inference `_ on 2024/03/25] - -.. todo:: Get baseline, accurate version of the License page. diff --git a/docs/build/html/_sources/modindex.rst.txt b/docs/build/html/_sources/modindex.rst.txt deleted file mode 100644 index c2e9e67d..00000000 --- a/docs/build/html/_sources/modindex.rst.txt +++ /dev/null @@ -1,4 +0,0 @@ -Module Index -============ - -:ref:`modindex` diff --git a/docs/build/html/_sources/msei_reference/basic.rst.txt b/docs/build/html/_sources/msei_reference/basic.rst.txt deleted file mode 100644 index 1ba9d4c0..00000000 --- a/docs/build/html/_sources/msei_reference/basic.rst.txt +++ /dev/null @@ -1,9 +0,0 @@ -Basic Renewal Model Module -========================== - -.. automodule:: pyrenew.basic - :members: - :undoc-members: - :show-inheritance: - -.. todo:: Describe the `basic` module in greater detail. diff --git a/docs/build/html/_sources/msei_reference/convolve.rst.txt b/docs/build/html/_sources/msei_reference/convolve.rst.txt deleted file mode 100644 index b6b19acc..00000000 --- a/docs/build/html/_sources/msei_reference/convolve.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -Convolution Utility Module -========================== - -.. automodule:: pyrenew.convolve - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/msei_reference/distutil.rst.txt b/docs/build/html/_sources/msei_reference/distutil.rst.txt deleted file mode 100644 index 16ae2d15..00000000 --- a/docs/build/html/_sources/msei_reference/distutil.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -Distribution Utility Module -=========================== - -.. automodule:: pyrenew.distutil - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/msei_reference/index.rst.txt b/docs/build/html/_sources/msei_reference/index.rst.txt deleted file mode 100644 index a50d944f..00000000 --- a/docs/build/html/_sources/msei_reference/index.rst.txt +++ /dev/null @@ -1,15 +0,0 @@ -MSEI Reference -============== - -.. toctree:: - :maxdepth: 3 - - observation - process - latent - model - msei - -.. todolist:: - -.. todo:: Determine order and naming of the above toctree entities. diff --git a/docs/build/html/_sources/msei_reference/infection.rst.txt b/docs/build/html/_sources/msei_reference/infection.rst.txt deleted file mode 100644 index 8ba4f3f5..00000000 --- a/docs/build/html/_sources/msei_reference/infection.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -Infection Module -================ - -.. automodule:: pyrenew.infection - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/msei_reference/latent.rst.txt b/docs/build/html/_sources/msei_reference/latent.rst.txt deleted file mode 100644 index 6d11dc1b..00000000 --- a/docs/build/html/_sources/msei_reference/latent.rst.txt +++ /dev/null @@ -1,28 +0,0 @@ -Latent MSEI Components -====================== - -Hospital Admissions -------------------- - -.. automodule:: pyrenew.latent.hospitaladmissions - :members: - :undoc-members: - :show-inheritance: - -Infections ----------- - -.. automodule:: pyrenew.latent.infections - :members: - :undoc-members: - :show-inheritance: - -Infection Functions -------------------- - -.. automodule:: pyrenew.latent.infection_functions - :members: - :undoc-members: - :show-inheritance: - -.. todo:: Determine and naming order of these modules. diff --git a/docs/build/html/_sources/msei_reference/math.rst.txt b/docs/build/html/_sources/msei_reference/math.rst.txt deleted file mode 100644 index 58e64c3a..00000000 --- a/docs/build/html/_sources/msei_reference/math.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -Math Utility Module -=================== - -.. automodule:: pyrenew.math - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/msei_reference/model.rst.txt b/docs/build/html/_sources/msei_reference/model.rst.txt deleted file mode 100644 index d430154c..00000000 --- a/docs/build/html/_sources/msei_reference/model.rst.txt +++ /dev/null @@ -1,20 +0,0 @@ -MSEI Model -========== - -Hospitalizations ----------------- - -.. automodule:: pyrenew.model.hospitalizations - :members: - :undoc-members: - :show-inheritance: - -Reproduction Number Renewal Infections --------------------------------------- - -.. automodule:: pyrenew.model.rtinfectionsrenewal - :members: - :undoc-members: - :show-inheritance: - -.. todo:: Determine and naming order of these modules. diff --git a/docs/build/html/_sources/msei_reference/msei.rst.txt b/docs/build/html/_sources/msei_reference/msei.rst.txt deleted file mode 100644 index 8322fed0..00000000 --- a/docs/build/html/_sources/msei_reference/msei.rst.txt +++ /dev/null @@ -1,61 +0,0 @@ -General MSEI Support -==================== - -Metaclass Module -================ - -.. automodule:: pyrenew.metaclass - :members: - :undoc-members: - :show-inheritance: - -Convolution Utility Module --------------------------- - -.. automodule:: pyrenew.convolve - :members: - :undoc-members: - :show-inheritance: - -Mathematics Utilities Module ----------------------------- - -.. automodule:: pyrenew.math - :members: - :undoc-members: - :show-inheritance: - -Transform Utility Module ------------------------- - -.. automodule:: pyrenew.transform - :members: - :undoc-members: - :show-inheritance: - -Regression Module ------------------ - -.. automodule:: pyrenew.regression - :members: - :undoc-members: - :show-inheritance: - - -MCMC Utilities Module ---------------------- - -.. automodule:: pyrenew.mcmcutils - :members: - :undoc-members: - :show-inheritance: - -Distributions Utility Module ----------------------------- - -.. automodule:: pyrenew.distutil - :members: - :undoc-members: - :show-inheritance: - -.. todo:: Determine and naming order of these modules. diff --git a/docs/build/html/_sources/msei_reference/observation.rst.txt b/docs/build/html/_sources/msei_reference/observation.rst.txt deleted file mode 100644 index 7ea62d46..00000000 --- a/docs/build/html/_sources/msei_reference/observation.rst.txt +++ /dev/null @@ -1,20 +0,0 @@ -Observation Processes -===================== - -Negative Binomial ------------------ - -.. automodule:: pyrenew.observation.negativebinomial - :members: - :undoc-members: - :show-inheritance: - -Poisson -------- - -.. automodule:: pyrenew.observation.poisson - :members: - :undoc-members: - :show-inheritance: - -.. todo:: Determine order of these modules. diff --git a/docs/build/html/_sources/msei_reference/process.rst.txt b/docs/build/html/_sources/msei_reference/process.rst.txt deleted file mode 100644 index 15b9dd20..00000000 --- a/docs/build/html/_sources/msei_reference/process.rst.txt +++ /dev/null @@ -1,36 +0,0 @@ -Process MSEI -============ - -AR Processes ------------- - -.. automodule:: pyrenew.process.ar - :members: - :undoc-members: - :show-inheritance: - -First Difference (AR) ---------------------- - -.. automodule:: pyrenew.process.firstdifferencear - :members: - :undoc-members: - :show-inheritance: - -Reproduction Number Random Walk -------------------------------- - -.. automodule:: pyrenew.process.rtrandomwalk - :members: - :undoc-members: - :show-inheritance: - -Simple Random Walk ------------------- - -.. automodule:: pyrenew.process.simplerandomwalk - :members: - :undoc-members: - :show-inheritance: - -.. todo:: Determine order and naming of these modules. diff --git a/docs/build/html/_sources/msei_reference/regression.rst.txt b/docs/build/html/_sources/msei_reference/regression.rst.txt deleted file mode 100644 index 379c0ce6..00000000 --- a/docs/build/html/_sources/msei_reference/regression.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -Regression Module -================== - -.. automodule:: pyrenew.regression - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/msei_reference/transform.rst.txt b/docs/build/html/_sources/msei_reference/transform.rst.txt deleted file mode 100644 index 14ea482c..00000000 --- a/docs/build/html/_sources/msei_reference/transform.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -Transform Utility Module -======================== - -.. automodule:: pyrenew.transform - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/notices.rst.txt b/docs/build/html/_sources/notices.rst.txt deleted file mode 100644 index 7972d6db..00000000 --- a/docs/build/html/_sources/notices.rst.txt +++ /dev/null @@ -1,37 +0,0 @@ -Other Notices -============= - -General Disclaimer ------------------- - -This repository was created for use by CDC programs to collaborate on public health related projects in support of the `CDC mission `_. GitHub is not hosted by the CDC, but is a third party website used by CDC and its partners to share information and collaborate on software. CDC use of GitHub does not imply an endorsement of any one particular service, product, or enterprise. - -Public Domain Standard Notice ------------------------------ - -This repository constitutes a work of the United States Government and is not subject to domestic copyright protection under 17 USC § 105. This repository is in the public domain within the United States, and copyright and related rights in the work worldwide are waived through the `CC0 1.0 Universal public domain dedication `_. All contributions to this repository will be released under the CC0 dedication. By submitting a pull request you are agreeing to comply with this waiver of copyright interest. - -Privacy Standard Notice ------------------------ - -This repository contains only non-sensitive, publicly available data and information. All material and community participation is covered by the `Disclaimer `_` and `Code of Conduct `_. For more information about CDC's privacy policy, please visit `http://www.cdc.gov/other/privacy.html `_. - -Records Management Standard Notice ----------------------------------- - -This repository is not a source of government records but is a copy to increase collaboration and collaborative potential. All government records will be published through the `CDC web site `. - -Additional Standard Notices ---------------------------- - -Please refer to `CDC's Template Repository `_ for more information about `contributing to this repository `_, `public domain notices and disclaimers `_, and `code of conduct `_. - - -Final Notes ------------ - -Please refer, under Meta Information, the License and Reporting Issues pages for other standard notices. - -Note: [this page is a work in progress; the above text was taken from `https://github.com/CDCgov/multisignal-epi-inference `_ on 2024/03/25] - -.. todo:: Get baseline, accurate version of the Notices page. diff --git a/docs/build/html/_sources/observation.rst.txt b/docs/build/html/_sources/observation.rst.txt deleted file mode 100644 index 5be0e912..00000000 --- a/docs/build/html/_sources/observation.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -Observation Module -================== - -.. automodule:: pyrenew.observation - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/reference.rst.txt b/docs/build/html/_sources/reference.rst.txt deleted file mode 100644 index 4065f555..00000000 --- a/docs/build/html/_sources/reference.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -API Reference -============== - -.. toctree:: - :maxdepth: 4 - - observation diff --git a/docs/build/html/_sources/reference/index.rst.txt b/docs/build/html/_sources/reference/index.rst.txt deleted file mode 100644 index 4065f555..00000000 --- a/docs/build/html/_sources/reference/index.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -API Reference -============== - -.. toctree:: - :maxdepth: 4 - - observation diff --git a/docs/build/html/_sources/reference/observation.rst.txt b/docs/build/html/_sources/reference/observation.rst.txt deleted file mode 100644 index 5be0e912..00000000 --- a/docs/build/html/_sources/reference/observation.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -Observation Module -================== - -.. automodule:: pyrenew.observation - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/search.rst.txt b/docs/build/html/_sources/search.rst.txt deleted file mode 100644 index 24d8a887..00000000 --- a/docs/build/html/_sources/search.rst.txt +++ /dev/null @@ -1,4 +0,0 @@ -Search Page -=========== - -:ref:`search` diff --git a/docs/build/html/_sources/state.rst.txt b/docs/build/html/_sources/state.rst.txt deleted file mode 100644 index e5932b77..00000000 --- a/docs/build/html/_sources/state.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -State Of Development -==================== - -Note: [this page is a work in progress; this page is a placeholder] - -.. todo:: Get baseline, accurate version of the State Of Development page. diff --git a/docs/build/html/_sources/test_reference/index.rst.txt b/docs/build/html/_sources/test_reference/index.rst.txt deleted file mode 100644 index 79924955..00000000 --- a/docs/build/html/_sources/test_reference/index.rst.txt +++ /dev/null @@ -1,14 +0,0 @@ -Test Reference -============== - -.. toctree:: - :maxdepth: 3 - - test_ar_process - test_first_difference_ar - test_logistic_susceptibility_adjustment - test_random_walk - test_process_asymptotics - test_transforms - test_regression - test_leslie_matrix diff --git a/docs/build/html/_sources/test_reference/test_ar_process.rst.txt b/docs/build/html/_sources/test_reference/test_ar_process.rst.txt deleted file mode 100644 index 8cd482f4..00000000 --- a/docs/build/html/_sources/test_reference/test_ar_process.rst.txt +++ /dev/null @@ -1,4 +0,0 @@ -AR Process Testing Functions -============================ - -.. autofunction:: test.test_ar_process.test_ar_can_be_sampled diff --git a/docs/build/html/_sources/test_reference/test_first_difference_ar.rst.txt b/docs/build/html/_sources/test_reference/test_first_difference_ar.rst.txt deleted file mode 100644 index 2213dada..00000000 --- a/docs/build/html/_sources/test_reference/test_first_difference_ar.rst.txt +++ /dev/null @@ -1,4 +0,0 @@ -AR First Difference Testing Functions -===================================== - -.. autofunction:: test.test_first_difference_ar.test_fd_ar_can_be_sampled diff --git a/docs/build/html/_sources/test_reference/test_leslie_matrix.rst.txt b/docs/build/html/_sources/test_reference/test_leslie_matrix.rst.txt deleted file mode 100644 index 84a0421e..00000000 --- a/docs/build/html/_sources/test_reference/test_leslie_matrix.rst.txt +++ /dev/null @@ -1,4 +0,0 @@ -Leslie Matrix Testing Functions -=============================== - -.. autofunction:: test.test_leslie_matrix.test_get_leslie diff --git a/docs/build/html/_sources/test_reference/test_logistic_susceptibility_adjustment.rst.txt b/docs/build/html/_sources/test_reference/test_logistic_susceptibility_adjustment.rst.txt deleted file mode 100644 index 970b16b0..00000000 --- a/docs/build/html/_sources/test_reference/test_logistic_susceptibility_adjustment.rst.txt +++ /dev/null @@ -1,4 +0,0 @@ -Logistic Susceptibility Testing Functions -========================================= - -.. autofunction:: test.test_logistic_susceptibility_adjustment.test_logistic_susceptibility_adjustment diff --git a/docs/build/html/_sources/test_reference/test_process_asymptotics.rst.txt b/docs/build/html/_sources/test_reference/test_process_asymptotics.rst.txt deleted file mode 100644 index 7595f5b8..00000000 --- a/docs/build/html/_sources/test_reference/test_process_asymptotics.rst.txt +++ /dev/null @@ -1,4 +0,0 @@ -Processed Asymptomatic Testing Functions -======================================== - -.. autofunction:: test.test_process_asymptotics.test_asymptotic_properties diff --git a/docs/build/html/_sources/test_reference/test_random_walk.rst.txt b/docs/build/html/_sources/test_reference/test_random_walk.rst.txt deleted file mode 100644 index 022aec1c..00000000 --- a/docs/build/html/_sources/test_reference/test_random_walk.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -Random Walk Testing Functions -============================= - -.. autofunction:: test.test_random_walk.test_rw_can_be_sampled - - -.. autofunction:: test.test_random_walk.test_rw_samples_correctly_distributed diff --git a/docs/build/html/_sources/test_reference/test_regression.rst.txt b/docs/build/html/_sources/test_reference/test_regression.rst.txt deleted file mode 100644 index 07910bc7..00000000 --- a/docs/build/html/_sources/test_reference/test_regression.rst.txt +++ /dev/null @@ -1,4 +0,0 @@ -Regression Testing Functions -============================ - -.. autofunction:: test.test_regression.test_glm_prediction diff --git a/docs/build/html/_sources/test_reference/test_transforms.rst.txt b/docs/build/html/_sources/test_reference/test_transforms.rst.txt deleted file mode 100644 index b19a5752..00000000 --- a/docs/build/html/_sources/test_reference/test_transforms.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -Transforms Testing Functions -============================ - -.. autofunction:: test.test_transforms.generic_inversion_test - -.. autofunction:: test.test_transforms.test_invert_dists diff --git a/docs/build/html/_sources/usage.rst.txt b/docs/build/html/_sources/usage.rst.txt deleted file mode 100644 index 6fabafd6..00000000 --- a/docs/build/html/_sources/usage.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -Setup & Usage -============= - -Note: [this page is a work in progress; this page is a placeholder] - -.. todo:: Get baseline, accurate version of the Setup & Usage page. diff --git a/docs/build/html/_static/alabaster.css b/docs/build/html/_static/alabaster.css deleted file mode 100644 index 4c276908..00000000 --- a/docs/build/html/_static/alabaster.css +++ /dev/null @@ -1,708 +0,0 @@ -@import url("basic.css"); - -/* -- page layout ----------------------------------------------------------- */ - -body { - font-family: Georgia, serif; - font-size: 17px; - background-color: #fff; - color: #000; - margin: 0; - padding: 0; -} - - -div.document { - width: 940px; - margin: 30px auto 0 auto; -} - -div.documentwrapper { - float: left; - width: 100%; -} - -div.bodywrapper { - margin: 0 0 0 220px; -} - -div.sphinxsidebar { - width: 220px; - font-size: 14px; - line-height: 1.5; -} - -hr { - border: 1px solid #B1B4B6; -} - -div.body { - background-color: #fff; - color: #3E4349; - padding: 0 30px 0 30px; -} - -div.body > .section { - text-align: left; -} - -div.footer { - width: 940px; - margin: 20px auto 30px auto; - font-size: 14px; - color: #888; - text-align: right; -} - -div.footer a { - color: #888; -} - -p.caption { - font-family: inherit; - font-size: inherit; -} - - -div.relations { - display: none; -} - - -div.sphinxsidebar { - max-height: 100%; - overflow-y: auto; -} - -div.sphinxsidebar a { - color: #444; - text-decoration: none; - border-bottom: 1px dotted #999; -} - -div.sphinxsidebar a:hover { - border-bottom: 1px solid #999; -} - -div.sphinxsidebarwrapper { - padding: 18px 10px; -} - -div.sphinxsidebarwrapper p.logo { - padding: 0; - margin: -10px 0 0 0px; - text-align: center; -} - -div.sphinxsidebarwrapper h1.logo { - margin-top: -10px; - text-align: center; - margin-bottom: 5px; - text-align: left; -} - -div.sphinxsidebarwrapper h1.logo-name { - margin-top: 0px; -} - -div.sphinxsidebarwrapper p.blurb { - margin-top: 0; - font-style: normal; -} - -div.sphinxsidebar h3, -div.sphinxsidebar h4 { - font-family: Georgia, serif; - color: #444; - font-size: 24px; 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- float: right; - clear: right; - overflow-x: auto; -} - -p.sidebar-title { - font-weight: bold; -} - -nav.contents, -aside.topic, -div.admonition, div.topic, blockquote { - clear: left; -} - -/* -- topics ---------------------------------------------------------------- */ - -nav.contents, -aside.topic, -div.topic { - border: 1px solid #ccc; - padding: 7px; - margin: 10px 0 10px 0; -} - -p.topic-title { - font-size: 1.1em; - font-weight: bold; - margin-top: 10px; -} - -/* -- admonitions ----------------------------------------------------------- */ - -div.admonition { - margin-top: 10px; - margin-bottom: 10px; - padding: 7px; -} - -div.admonition dt { - font-weight: bold; -} - -p.admonition-title { - margin: 0px 10px 5px 0px; - font-weight: bold; -} - -div.body p.centered { - text-align: center; - margin-top: 25px; -} - -/* -- content of sidebars/topics/admonitions -------------------------------- */ - -div.sidebar > :last-child, -aside.sidebar > :last-child, -nav.contents > :last-child, -aside.topic > :last-child, -div.topic > :last-child, -div.admonition > :last-child { - margin-bottom: 0; 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-} - -th > :last-child, -td > :last-child { - margin-bottom: 0px; -} - -/* -- figures --------------------------------------------------------------- */ - -div.figure, figure { - margin: 0.5em; - padding: 0.5em; -} - -div.figure p.caption, figcaption { - padding: 0.3em; -} - -div.figure p.caption span.caption-number, -figcaption span.caption-number { - font-style: italic; -} - -div.figure p.caption span.caption-text, -figcaption span.caption-text { -} - -/* -- field list styles ----------------------------------------------------- */ - -table.field-list td, table.field-list th { - border: 0 !important; -} - -.field-list ul { - margin: 0; - padding-left: 1em; -} - -.field-list p { - margin: 0; -} - -.field-name { - -moz-hyphens: manual; - -ms-hyphens: manual; - -webkit-hyphens: manual; - hyphens: manual; -} - -/* -- hlist styles ---------------------------------------------------------- */ - -table.hlist { - margin: 1em 0; -} - -table.hlist td { - vertical-align: top; -} - -/* -- object description styles --------------------------------------------- */ - -.sig { - font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; 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-} - -:not(li) > ol > li:last-child > :last-child, -:not(li) > ul > li:last-child > :last-child { - margin-bottom: 0px; -} - -ol.simple ol p, -ol.simple ul p, -ul.simple ol p, -ul.simple ul p { - margin-top: 0; -} - -ol.simple > li:not(:first-child) > p, -ul.simple > li:not(:first-child) > p { - margin-top: 0; -} - -ol.simple p, -ul.simple p { - margin-bottom: 0; -} - -aside.footnote > span, -div.citation > span { - float: left; -} -aside.footnote > span:last-of-type, -div.citation > span:last-of-type { - padding-right: 0.5em; -} -aside.footnote > p { - margin-left: 2em; -} -div.citation > p { - margin-left: 4em; -} -aside.footnote > p:last-of-type, -div.citation > p:last-of-type { - margin-bottom: 0em; -} -aside.footnote > p:last-of-type:after, -div.citation > p:last-of-type:after { - content: ""; - clear: both; -} - -dl.field-list { - display: grid; - grid-template-columns: fit-content(30%) auto; -} - -dl.field-list > dt { - font-weight: bold; - word-break: break-word; - padding-left: 0.5em; - padding-right: 5px; -} - -dl.field-list > dd { - padding-left: 0.5em; - margin-top: 0em; - margin-left: 0em; - margin-bottom: 0em; -} - -dl { - margin-bottom: 15px; -} - -dd > :first-child { - margin-top: 0px; -} - -dd ul, dd table { - margin-bottom: 10px; -} - -dd { - margin-top: 3px; - margin-bottom: 10px; - margin-left: 30px; -} - -.sig dd { - margin-top: 0px; - margin-bottom: 0px; -} - -.sig dl { - margin-top: 0px; - margin-bottom: 0px; -} - -dl > dd:last-child, -dl > dd:last-child > :last-child { - margin-bottom: 0; -} - -dt:target, span.highlighted { - background-color: #fbe54e; -} - -rect.highlighted { - fill: #fbe54e; -} - -dl.glossary dt { - font-weight: bold; - font-size: 1.1em; -} - -.versionmodified { - font-style: italic; -} - -.system-message { - background-color: #fda; - padding: 5px; - border: 3px solid red; -} - -.footnote:target { - background-color: #ffa; -} - -.line-block { - display: block; - margin-top: 1em; - margin-bottom: 1em; -} - -.line-block .line-block { - margin-top: 0; - margin-bottom: 0; - margin-left: 1.5em; -} - -.guilabel, .menuselection { - font-family: sans-serif; -} - -.accelerator { - text-decoration: underline; -} - -.classifier { - font-style: oblique; -} - -.classifier:before { - font-style: normal; - margin: 0 0.5em; - content: ":"; - display: inline-block; -} - -abbr, acronym { - border-bottom: dotted 1px; - cursor: help; -} - -.translated { - background-color: rgba(207, 255, 207, 0.2) -} - -.untranslated { - background-color: rgba(255, 207, 207, 0.2) -} - -/* -- code displays --------------------------------------------------------- */ - -pre { - overflow: auto; - overflow-y: hidden; /* fixes display issues on Chrome browsers */ -} - -pre, div[class*="highlight-"] { - clear: both; -} - -span.pre { - -moz-hyphens: none; - -ms-hyphens: none; - -webkit-hyphens: none; - hyphens: none; - white-space: nowrap; -} - -div[class*="highlight-"] { - margin: 1em 0; -} - -td.linenos pre { - border: 0; - background-color: transparent; - color: #aaa; -} - -table.highlighttable { - display: block; -} - -table.highlighttable tbody { - display: block; -} - -table.highlighttable tr { - display: flex; -} - -table.highlighttable td { - margin: 0; - padding: 0; -} - -table.highlighttable td.linenos { - padding-right: 0.5em; -} - -table.highlighttable td.code { - flex: 1; - overflow: hidden; -} - -.highlight .hll { - display: block; -} - -div.highlight pre, -table.highlighttable pre { - margin: 0; -} - -div.code-block-caption + div { - margin-top: 0; -} - -div.code-block-caption { - margin-top: 1em; - padding: 2px 5px; - font-size: small; -} - -div.code-block-caption code { - background-color: transparent; -} - -table.highlighttable td.linenos, -span.linenos, -div.highlight span.gp { /* gp: Generic.Prompt */ - user-select: none; - -webkit-user-select: text; /* Safari fallback only */ - -webkit-user-select: none; /* Chrome/Safari */ - -moz-user-select: none; /* Firefox */ - -ms-user-select: none; /* IE10+ */ -} - -div.code-block-caption span.caption-number { - padding: 0.1em 0.3em; - font-style: italic; -} - -div.code-block-caption span.caption-text { -} - -div.literal-block-wrapper { - margin: 1em 0; -} - -code.xref, a code { - background-color: transparent; - font-weight: bold; -} - -h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { - background-color: transparent; -} - -.viewcode-link { - float: right; -} - -.viewcode-back { - float: right; - font-family: sans-serif; -} - -div.viewcode-block:target { - margin: -1px -10px; - padding: 0 10px; -} - -/* -- math display ---------------------------------------------------------- */ - -img.math { - vertical-align: middle; -} - -div.body div.math p { - text-align: center; -} - -span.eqno { - float: right; -} - -span.eqno a.headerlink { - position: absolute; - z-index: 1; -} - -div.math:hover a.headerlink { - visibility: visible; -} - -/* -- printout stylesheet --------------------------------------------------- */ - -@media print { - div.document, - div.documentwrapper, - div.bodywrapper { - margin: 0 !important; - width: 100%; - } - - div.sphinxsidebar, - div.related, - div.footer, - #top-link { - display: none; - } -} diff --git a/docs/build/html/_static/classic.css b/docs/build/html/_static/classic.css deleted file mode 100644 index 292e7c72..00000000 --- a/docs/build/html/_static/classic.css +++ /dev/null @@ -1,269 +0,0 @@ -/* - * classic.css_t - * ~~~~~~~~~~~~~ - * - * Sphinx stylesheet -- classic theme. - * - * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -@import url("basic.css"); - -/* -- page layout ----------------------------------------------------------- */ - -html { - /* CSS hack for macOS's scrollbar (see #1125) */ - background-color: #FFFFFF; -} - -body { - font-family: sans-serif; - font-size: 100%; - background-color: #11303d; - color: #000; - margin: 0; - padding: 0; -} - -div.document { - display: flex; - background-color: #1c4e63; -} - -div.documentwrapper { - float: left; - width: 100%; -} - -div.bodywrapper { - margin: 0 0 0 230px; -} - -div.body { - background-color: #ffffff; - color: #000000; - padding: 0 20px 30px 20px; -} - -div.footer { - color: #ffffff; - width: 100%; - padding: 9px 0 9px 0; - text-align: center; - font-size: 75%; -} - -div.footer a { - color: #ffffff; - text-decoration: underline; -} - -div.related { - background-color: #133f52; - line-height: 30px; - color: #ffffff; -} - -div.related a { - color: #ffffff; -} - -div.sphinxsidebar { -} - -div.sphinxsidebar h3 { - font-family: 'Trebuchet MS', sans-serif; - color: #ffffff; - font-size: 1.4em; - font-weight: normal; - margin: 0; - padding: 0; -} - -div.sphinxsidebar h3 a { - color: #ffffff; -} - -div.sphinxsidebar h4 { - font-family: 'Trebuchet MS', sans-serif; - color: #ffffff; - font-size: 1.3em; - font-weight: normal; - margin: 5px 0 0 0; - padding: 0; -} - -div.sphinxsidebar p { - color: #ffffff; -} - -div.sphinxsidebar p.topless { - margin: 5px 10px 10px 10px; -} - -div.sphinxsidebar ul { - margin: 10px; - padding: 0; - color: #ffffff; -} - -div.sphinxsidebar a { - color: #98dbcc; -} - -div.sphinxsidebar input { - border: 1px solid #98dbcc; - font-family: sans-serif; - font-size: 1em; -} - - - -/* -- hyperlink styles ------------------------------------------------------ */ - -a { - color: #355f7c; - text-decoration: none; -} - -a:visited { - color: #551a8b; - text-decoration: none; -} - -a:hover { - text-decoration: underline; -} - - - -/* -- body styles ----------------------------------------------------------- */ - -div.body h1, -div.body h2, -div.body h3, -div.body h4, -div.body h5, -div.body h6 { - font-family: 'Trebuchet MS', sans-serif; - background-color: #f2f2f2; - font-weight: normal; - color: #20435c; - border-bottom: 1px solid #ccc; - margin: 20px -20px 10px -20px; - padding: 3px 0 3px 10px; -} - -div.body h1 { margin-top: 0; font-size: 200%; } -div.body h2 { font-size: 160%; } -div.body h3 { font-size: 140%; } -div.body h4 { font-size: 120%; } -div.body h5 { font-size: 110%; } -div.body h6 { font-size: 100%; } - -a.headerlink { - color: #c60f0f; - font-size: 0.8em; - padding: 0 4px 0 4px; - text-decoration: none; -} - -a.headerlink:hover { - background-color: #c60f0f; - color: white; -} - -div.body p, div.body dd, div.body li, div.body blockquote { - text-align: justify; - line-height: 130%; -} - -div.admonition p.admonition-title + p { - display: inline; -} - -div.admonition p { - margin-bottom: 5px; -} - -div.admonition pre { - margin-bottom: 5px; -} - -div.admonition ul, div.admonition ol { - margin-bottom: 5px; -} - -div.note { - background-color: #eee; - border: 1px solid #ccc; -} - -div.seealso { - background-color: #ffc; - border: 1px solid #ff6; -} - -nav.contents, -aside.topic, -div.topic { - background-color: #eee; -} - -div.warning { - background-color: #ffe4e4; - border: 1px solid #f66; -} - -p.admonition-title { - display: inline; -} - -p.admonition-title:after { - content: ":"; -} - -pre { - padding: 5px; - background-color: unset; - color: unset; - line-height: 120%; - border: 1px solid #ac9; - border-left: none; - border-right: none; -} - -code { - background-color: #ecf0f3; - padding: 0 1px 0 1px; - font-size: 0.95em; -} - -th, dl.field-list > dt { - background-color: #ede; -} - -.warning code { - background: #efc2c2; -} - -.note code { - background: #d6d6d6; -} - -.viewcode-back { - font-family: sans-serif; -} - -div.viewcode-block:target { - background-color: #f4debf; - border-top: 1px solid #ac9; - border-bottom: 1px solid #ac9; -} - -div.code-block-caption { - color: #efefef; - background-color: #1c4e63; -} diff --git a/docs/build/html/_static/custom.css b/docs/build/html/_static/custom.css deleted file mode 100644 index 2a924f1d..00000000 --- a/docs/build/html/_static/custom.css +++ /dev/null @@ -1 +0,0 @@ -/* This file intentionally left blank. */ diff --git a/docs/build/html/_static/doctools.js b/docs/build/html/_static/doctools.js deleted file mode 100644 index d06a71d7..00000000 --- a/docs/build/html/_static/doctools.js +++ /dev/null @@ -1,156 +0,0 @@ -/* - * doctools.js - * ~~~~~~~~~~~ - * - * Base JavaScript utilities for all Sphinx HTML documentation. - * - * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ -"use strict"; - -const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ - "TEXTAREA", - "INPUT", - "SELECT", - "BUTTON", -]); - -const _ready = (callback) => { - if (document.readyState !== "loading") { - callback(); - } else { - document.addEventListener("DOMContentLoaded", callback); - } -}; - -/** - * Small JavaScript module for the documentation. - */ -const Documentation = { - init: () => { - Documentation.initDomainIndexTable(); - Documentation.initOnKeyListeners(); - }, - - /** - * i18n support - */ - TRANSLATIONS: {}, - PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), - LOCALE: "unknown", - - // gettext and ngettext don't access this so that the functions - // can safely bound to a different name (_ = Documentation.gettext) - gettext: (string) => { - const translated = Documentation.TRANSLATIONS[string]; - switch (typeof translated) { - case "undefined": - return string; // no translation - case "string": - return translated; // translation exists - default: - return translated[0]; // (singular, plural) translation tuple exists - } - }, - - ngettext: (singular, plural, n) => { - const translated = Documentation.TRANSLATIONS[singular]; - if (typeof translated !== "undefined") - return translated[Documentation.PLURAL_EXPR(n)]; - return n === 1 ? singular : plural; - }, - - addTranslations: (catalog) => { - Object.assign(Documentation.TRANSLATIONS, catalog.messages); - Documentation.PLURAL_EXPR = new Function( - "n", - `return (${catalog.plural_expr})` - ); - Documentation.LOCALE = catalog.locale; - }, - - /** - * helper function to focus on search bar - */ - focusSearchBar: () => { - document.querySelectorAll("input[name=q]")[0]?.focus(); - }, - - /** - * Initialise the domain index toggle buttons - */ - initDomainIndexTable: () => { - const toggler = (el) => { - const idNumber = el.id.substr(7); - const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); - if (el.src.substr(-9) === "minus.png") { - el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; - toggledRows.forEach((el) => (el.style.display = "none")); - } else { - el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; - toggledRows.forEach((el) => (el.style.display = "")); - } - }; - - const togglerElements = document.querySelectorAll("img.toggler"); - togglerElements.forEach((el) => - el.addEventListener("click", (event) => toggler(event.currentTarget)) - ); - togglerElements.forEach((el) => (el.style.display = "")); - if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); - }, - - initOnKeyListeners: () => { - // only install a listener if it is really needed - if ( - !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && - !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS - ) - return; - - document.addEventListener("keydown", (event) => { - // bail for input elements - if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; - // bail with special keys - if (event.altKey || event.ctrlKey || event.metaKey) return; - - if (!event.shiftKey) { - switch (event.key) { - case "ArrowLeft": - if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; - - const prevLink = document.querySelector('link[rel="prev"]'); - if (prevLink && prevLink.href) { - window.location.href = prevLink.href; - event.preventDefault(); - } - break; - case "ArrowRight": - if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; - - const nextLink = document.querySelector('link[rel="next"]'); - if (nextLink && nextLink.href) { - window.location.href = nextLink.href; - event.preventDefault(); - } - break; - } - } - - // some keyboard layouts may need Shift to get / - switch (event.key) { - case "/": - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; - Documentation.focusSearchBar(); - event.preventDefault(); - } - }); - }, -}; - -// quick alias for translations -const _ = Documentation.gettext; - -_ready(Documentation.init); diff --git a/docs/build/html/_static/documentation_options.js b/docs/build/html/_static/documentation_options.js deleted file mode 100644 index 7e0cecce..00000000 --- a/docs/build/html/_static/documentation_options.js +++ /dev/null @@ -1,13 +0,0 @@ -const DOCUMENTATION_OPTIONS = { - VERSION: '0.0.1', - LANGUAGE: 'en', - COLLAPSE_INDEX: false, - BUILDER: 'html', - FILE_SUFFIX: '.html', - LINK_SUFFIX: '.html', - HAS_SOURCE: true, - SOURCELINK_SUFFIX: '.txt', - NAVIGATION_WITH_KEYS: false, - SHOW_SEARCH_SUMMARY: true, - ENABLE_SEARCH_SHORTCUTS: true, -}; diff --git a/docs/build/html/_static/graphviz.css b/docs/build/html/_static/graphviz.css deleted file mode 100644 index 8d81c02e..00000000 --- a/docs/build/html/_static/graphviz.css +++ /dev/null @@ -1,19 +0,0 @@ -/* - * graphviz.css - * ~~~~~~~~~~~~ - * - * Sphinx stylesheet -- graphviz extension. - * - * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -img.graphviz { - border: 0; - max-width: 100%; -} - -object.graphviz { - max-width: 100%; -} diff --git a/docs/build/html/_static/language_data.js b/docs/build/html/_static/language_data.js deleted file mode 100644 index 68b9e83a..00000000 --- a/docs/build/html/_static/language_data.js +++ /dev/null @@ -1,198 +0,0 @@ -/* - * language_data.js - * ~~~~~~~~~~~~~~~~ - * - * This script contains the language-specific data used by searchtools.js, - * namely the list of stopwords, stemmer, scorer and splitter. - * - * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; - - -/* Non-minified version is copied as a separate JS file, is available */ - -/** - * Porter Stemmer - */ -var Stemmer = function() { - - var step2list = { - ational: 'ate', - tional: 'tion', - enci: 'ence', - anci: 'ance', - izer: 'ize', - bli: 'ble', - alli: 'al', - entli: 'ent', - eli: 'e', - ousli: 'ous', - ization: 'ize', - ation: 'ate', - ator: 'ate', - alism: 'al', - iveness: 'ive', - fulness: 'ful', - ousness: 'ous', - aliti: 'al', - iviti: 'ive', - biliti: 'ble', - logi: 'log' - }; - - var step3list = { - icate: 'ic', - ative: '', - alize: 'al', - iciti: 'ic', - ical: 'ic', - ful: '', - ness: '' - }; - - var c = "[^aeiou]"; // consonant - var v = "[aeiouy]"; // vowel - var C = c + "[^aeiouy]*"; // consonant sequence - var V = v + "[aeiou]*"; // vowel sequence - - var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 - var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 - var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 - var s_v = "^(" + C + ")?" + v; // vowel in stem - - this.stemWord = function (w) { - var stem; - var suffix; - var firstch; - var origword = w; - - if (w.length < 3) - return w; - - var re; - var re2; - var re3; - var re4; - - firstch = w.substr(0,1); - if (firstch == "y") - w = firstch.toUpperCase() + w.substr(1); - - // Step 1a - re = /^(.+?)(ss|i)es$/; - re2 = /^(.+?)([^s])s$/; - - if (re.test(w)) - w = w.replace(re,"$1$2"); - else if (re2.test(w)) - w = w.replace(re2,"$1$2"); - - // Step 1b - re = /^(.+?)eed$/; - re2 = /^(.+?)(ed|ing)$/; - if (re.test(w)) { - var fp = re.exec(w); - re = new RegExp(mgr0); - if (re.test(fp[1])) { - re = /.$/; - w = w.replace(re,""); - } - } - else if (re2.test(w)) { - var fp = re2.exec(w); - stem = fp[1]; - re2 = new RegExp(s_v); - if (re2.test(stem)) { - w = stem; - re2 = /(at|bl|iz)$/; - re3 = new RegExp("([^aeiouylsz])\\1$"); - re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); - if (re2.test(w)) - w = w + "e"; - else if (re3.test(w)) { - re = /.$/; - w = w.replace(re,""); - } - else if (re4.test(w)) - w = w + "e"; - } - } - - // Step 1c - re = /^(.+?)y$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(s_v); - if (re.test(stem)) - w = stem + "i"; - } - - // Step 2 - re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - suffix = fp[2]; - re = new RegExp(mgr0); - if (re.test(stem)) - w = stem + step2list[suffix]; - } - - // Step 3 - re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - suffix = fp[2]; - re = new RegExp(mgr0); - if (re.test(stem)) - w = stem + step3list[suffix]; - } - - // Step 4 - re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; - re2 = /^(.+?)(s|t)(ion)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(mgr1); - if (re.test(stem)) - w = stem; - } - else if (re2.test(w)) { - var fp = re2.exec(w); - stem = fp[1] + fp[2]; - re2 = new RegExp(mgr1); - if (re2.test(stem)) - w = stem; - } - - // Step 5 - re = /^(.+?)e$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(mgr1); - re2 = new RegExp(meq1); - re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); - if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) - w = stem; - } - re = /ll$/; - re2 = new RegExp(mgr1); - if (re.test(w) && re2.test(w)) { - re = /.$/; - w = w.replace(re,""); - } - - // and turn initial Y back to y - if (firstch == "y") - w = firstch.toLowerCase() + w.substr(1); - return w; - } -} diff --git a/docs/build/html/_static/msei.css b/docs/build/html/_static/msei.css deleted file mode 100644 index 92a7888e..00000000 --- a/docs/build/html/_static/msei.css +++ /dev/null @@ -1,5 +0,0 @@ -@import url('https://your-font-hosting-url.com/path/to/iosevka.css'); - -body { - font-family: 'Iosevka', monospace; -} diff --git a/docs/build/html/_static/pygments.css b/docs/build/html/_static/pygments.css deleted file mode 100644 index ebe2a901..00000000 --- a/docs/build/html/_static/pygments.css +++ /dev/null @@ -1,75 +0,0 @@ -pre { line-height: 125%; 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- -/** - * Simple result scoring code. - */ -if (typeof Scorer === "undefined") { - var Scorer = { - // Implement the following function to further tweak the score for each result - // The function takes a result array [docname, title, anchor, descr, score, filename] - // and returns the new score. - /* - score: result => { - const [docname, title, anchor, descr, score, filename] = result - return score - }, - */ - - // query matches the full name of an object - objNameMatch: 11, - // or matches in the last dotted part of the object name - objPartialMatch: 6, - // Additive scores depending on the priority of the object - objPrio: { - 0: 15, // used to be importantResults - 1: 5, // used to be objectResults - 2: -5, // used to be unimportantResults - }, - // Used when the priority is not in the mapping. - objPrioDefault: 0, - - // query found in title - title: 15, - partialTitle: 7, - // query found in terms - term: 5, - partialTerm: 2, - }; -} - -const _removeChildren = (element) => { - while (element && element.lastChild) element.removeChild(element.lastChild); -}; - -/** - * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping - */ -const _escapeRegExp = (string) => - string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string - -const _displayItem = (item, searchTerms, highlightTerms) => { - const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; - const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; - const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; - const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; - const contentRoot = document.documentElement.dataset.content_root; - - const [docName, title, anchor, descr, score, _filename] = item; - - let listItem = document.createElement("li"); - let requestUrl; - let linkUrl; - if (docBuilder === "dirhtml") { - // dirhtml builder - let dirname = docName + "/"; - if (dirname.match(/\/index\/$/)) - dirname = dirname.substring(0, dirname.length - 6); - else if (dirname === "index/") dirname = ""; - requestUrl = contentRoot + dirname; - linkUrl = requestUrl; - } else { - // normal html builders - requestUrl = contentRoot + docName + docFileSuffix; - linkUrl = docName + docLinkSuffix; - } - let linkEl = listItem.appendChild(document.createElement("a")); - linkEl.href = linkUrl + anchor; - linkEl.dataset.score = score; - linkEl.innerHTML = title; - if (descr) { - listItem.appendChild(document.createElement("span")).innerHTML = - " (" + descr + ")"; - // highlight search terms in the description - if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js - highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); - } - else if (showSearchSummary) - fetch(requestUrl) - .then((responseData) => responseData.text()) - .then((data) => { - if (data) - listItem.appendChild( - Search.makeSearchSummary(data, searchTerms) - ); - // highlight search terms in the summary - if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js - highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); - }); - Search.output.appendChild(listItem); -}; -const _finishSearch = (resultCount) => { - Search.stopPulse(); - Search.title.innerText = _("Search Results"); - if (!resultCount) - Search.status.innerText = Documentation.gettext( - "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." - ); - else - Search.status.innerText = _( - `Search finished, found ${resultCount} page(s) matching the search query.` - ); -}; -const _displayNextItem = ( - results, - resultCount, - searchTerms, - highlightTerms, -) => { - // results left, load the summary and display it - // this is intended to be dynamic (don't sub resultsCount) - if (results.length) { - _displayItem(results.pop(), searchTerms, highlightTerms); - setTimeout( - () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), - 5 - ); - } - // search finished, update title and status message - else _finishSearch(resultCount); -}; - -/** - * Default splitQuery function. Can be overridden in ``sphinx.search`` with a - * custom function per language. - * - * The regular expression works by splitting the string on consecutive characters - * that are not Unicode letters, numbers, underscores, or emoji characters. - * This is the same as ``\W+`` in Python, preserving the surrogate pair area. - */ -if (typeof splitQuery === "undefined") { - var splitQuery = (query) => query - .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) - .filter(term => term) // remove remaining empty strings -} - -/** - * Search Module - */ -const Search = { - _index: null, - _queued_query: null, - _pulse_status: -1, - - htmlToText: (htmlString) => { - const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); - htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); - const docContent = htmlElement.querySelector('[role="main"]'); - if (docContent !== undefined) return docContent.textContent; - console.warn( - "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." - ); - return ""; - }, - - init: () => { - const query = new URLSearchParams(window.location.search).get("q"); - document - .querySelectorAll('input[name="q"]') - .forEach((el) => (el.value = query)); - if (query) Search.performSearch(query); - }, - - loadIndex: (url) => - (document.body.appendChild(document.createElement("script")).src = url), - - setIndex: (index) => { - Search._index = index; - if (Search._queued_query !== null) { - const query = Search._queued_query; - Search._queued_query = null; - Search.query(query); - } - }, - - hasIndex: () => Search._index !== null, - - deferQuery: (query) => (Search._queued_query = query), - - stopPulse: () => (Search._pulse_status = -1), - - startPulse: () => { - if (Search._pulse_status >= 0) return; - - const pulse = () => { - Search._pulse_status = (Search._pulse_status + 1) % 4; - Search.dots.innerText = ".".repeat(Search._pulse_status); - if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); - }; - pulse(); - }, - - /** - * perform a search for something (or wait until index is loaded) - */ - performSearch: (query) => { - // create the required interface elements - const searchText = document.createElement("h2"); - searchText.textContent = _("Searching"); - const searchSummary = document.createElement("p"); - searchSummary.classList.add("search-summary"); - searchSummary.innerText = ""; - const searchList = document.createElement("ul"); - searchList.classList.add("search"); - - const out = document.getElementById("search-results"); - Search.title = out.appendChild(searchText); - Search.dots = Search.title.appendChild(document.createElement("span")); - Search.status = out.appendChild(searchSummary); - Search.output = out.appendChild(searchList); - - const searchProgress = document.getElementById("search-progress"); - // Some themes don't use the search progress node - if (searchProgress) { - searchProgress.innerText = _("Preparing search..."); - } - Search.startPulse(); - - // index already loaded, the browser was quick! - if (Search.hasIndex()) Search.query(query); - else Search.deferQuery(query); - }, - - /** - * execute search (requires search index to be loaded) - */ - query: (query) => { - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const titles = Search._index.titles; - const allTitles = Search._index.alltitles; - const indexEntries = Search._index.indexentries; - - // stem the search terms and add them to the correct list - const stemmer = new Stemmer(); - const searchTerms = new Set(); - const excludedTerms = new Set(); - const highlightTerms = new Set(); - const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); - splitQuery(query.trim()).forEach((queryTerm) => { - const queryTermLower = queryTerm.toLowerCase(); - - // maybe skip this "word" - // stopwords array is from language_data.js - if ( - stopwords.indexOf(queryTermLower) !== -1 || - queryTerm.match(/^\d+$/) - ) - return; - - // stem the word - let word = stemmer.stemWord(queryTermLower); - // select the correct list - if (word[0] === "-") excludedTerms.add(word.substr(1)); - else { - searchTerms.add(word); - highlightTerms.add(queryTermLower); - } - }); - - if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js - localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) - } - - // console.debug("SEARCH: searching for:"); - // console.info("required: ", [...searchTerms]); - // console.info("excluded: ", [...excludedTerms]); - - // array of [docname, title, anchor, descr, score, filename] - let results = []; - _removeChildren(document.getElementById("search-progress")); - - const queryLower = query.toLowerCase(); - for (const [title, foundTitles] of Object.entries(allTitles)) { - if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { - for (const [file, id] of foundTitles) { - let score = Math.round(100 * queryLower.length / title.length) - results.push([ - docNames[file], - titles[file] !== title ? `${titles[file]} > ${title}` : title, - id !== null ? "#" + id : "", - null, - score, - filenames[file], - ]); - } - } - } - - // search for explicit entries in index directives - for (const [entry, foundEntries] of Object.entries(indexEntries)) { - if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { - for (const [file, id] of foundEntries) { - let score = Math.round(100 * queryLower.length / entry.length) - results.push([ - docNames[file], - titles[file], - id ? "#" + id : "", - null, - score, - filenames[file], - ]); - } - } - } - - // lookup as object - objectTerms.forEach((term) => - results.push(...Search.performObjectSearch(term, objectTerms)) - ); - - // lookup as search terms in fulltext - results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); - - // let the scorer override scores with a custom scoring function - if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); - - // now sort the results by score (in opposite order of appearance, since the - // display function below uses pop() to retrieve items) and then - // alphabetically - results.sort((a, b) => { - const leftScore = a[4]; - const rightScore = b[4]; - if (leftScore === rightScore) { - // same score: sort alphabetically - const leftTitle = a[1].toLowerCase(); - const rightTitle = b[1].toLowerCase(); - if (leftTitle === rightTitle) return 0; - return leftTitle > rightTitle ? -1 : 1; // inverted is intentional - } - return leftScore > rightScore ? 1 : -1; - }); - - // remove duplicate search results - // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept - let seen = new Set(); - results = results.reverse().reduce((acc, result) => { - let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); - if (!seen.has(resultStr)) { - acc.push(result); - seen.add(resultStr); - } - return acc; - }, []); - - results = results.reverse(); - - // for debugging - //Search.lastresults = results.slice(); // a copy - // console.info("search results:", Search.lastresults); - - // print the results - _displayNextItem(results, results.length, searchTerms, highlightTerms); - }, - - /** - * search for object names - */ - performObjectSearch: (object, objectTerms) => { - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const objects = Search._index.objects; - const objNames = Search._index.objnames; - const titles = Search._index.titles; - - const results = []; - - const objectSearchCallback = (prefix, match) => { - const name = match[4] - const fullname = (prefix ? prefix + "." : "") + name; - const fullnameLower = fullname.toLowerCase(); - if (fullnameLower.indexOf(object) < 0) return; - - let score = 0; - const parts = fullnameLower.split("."); - - // check for different match types: exact matches of full name or - // "last name" (i.e. last dotted part) - if (fullnameLower === object || parts.slice(-1)[0] === object) - score += Scorer.objNameMatch; - else if (parts.slice(-1)[0].indexOf(object) > -1) - score += Scorer.objPartialMatch; // matches in last name - - const objName = objNames[match[1]][2]; - const title = titles[match[0]]; - - // If more than one term searched for, we require other words to be - // found in the name/title/description - const otherTerms = new Set(objectTerms); - otherTerms.delete(object); - if (otherTerms.size > 0) { - const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); - if ( - [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) - ) - return; - } - - let anchor = match[3]; - if (anchor === "") anchor = fullname; - else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; - - const descr = objName + _(", in ") + title; - - // add custom score for some objects according to scorer - if (Scorer.objPrio.hasOwnProperty(match[2])) - score += Scorer.objPrio[match[2]]; - else score += Scorer.objPrioDefault; - - results.push([ - docNames[match[0]], - fullname, - "#" + anchor, - descr, - score, - filenames[match[0]], - ]); - }; - Object.keys(objects).forEach((prefix) => - objects[prefix].forEach((array) => - objectSearchCallback(prefix, array) - ) - ); - return results; - }, - - /** - * search for full-text terms in the index - */ - performTermsSearch: (searchTerms, excludedTerms) => { - // prepare search - const terms = Search._index.terms; - const titleTerms = Search._index.titleterms; - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const titles = Search._index.titles; - - const scoreMap = new Map(); - const fileMap = new Map(); - - // perform the search on the required terms - searchTerms.forEach((word) => { - const files = []; - const arr = [ - { files: terms[word], score: Scorer.term }, - { files: titleTerms[word], score: Scorer.title }, - ]; - // add support for partial matches - if (word.length > 2) { - const escapedWord = _escapeRegExp(word); - Object.keys(terms).forEach((term) => { - if (term.match(escapedWord) && !terms[word]) - arr.push({ files: terms[term], score: Scorer.partialTerm }); - }); - Object.keys(titleTerms).forEach((term) => { - if (term.match(escapedWord) && !titleTerms[word]) - arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); - }); - } - - // no match but word was a required one - if (arr.every((record) => record.files === undefined)) return; - - // found search word in contents - arr.forEach((record) => { - if (record.files === undefined) return; - - let recordFiles = record.files; - if (recordFiles.length === undefined) recordFiles = [recordFiles]; - files.push(...recordFiles); - - // set score for the word in each file - recordFiles.forEach((file) => { - if (!scoreMap.has(file)) scoreMap.set(file, {}); - scoreMap.get(file)[word] = record.score; - }); - }); - - // create the mapping - files.forEach((file) => { - if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) - fileMap.get(file).push(word); - else fileMap.set(file, [word]); - }); - }); - - // now check if the files don't contain excluded terms - const results = []; - for (const [file, wordList] of fileMap) { - // check if all requirements are matched - - // as search terms with length < 3 are discarded - const filteredTermCount = [...searchTerms].filter( - (term) => term.length > 2 - ).length; - if ( - wordList.length !== searchTerms.size && - wordList.length !== filteredTermCount - ) - continue; - - // ensure that none of the excluded terms is in the search result - if ( - [...excludedTerms].some( - (term) => - terms[term] === file || - titleTerms[term] === file || - (terms[term] || []).includes(file) || - (titleTerms[term] || []).includes(file) - ) - ) - break; - - // select one (max) score for the file. - const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); - // add result to the result list - results.push([ - docNames[file], - titles[file], - "", - null, - score, - filenames[file], - ]); - } - return results; - }, - - /** - * helper function to return a node containing the - * search summary for a given text. keywords is a list - * of stemmed words. - */ - makeSearchSummary: (htmlText, keywords) => { - const text = Search.htmlToText(htmlText); - if (text === "") return null; - - const textLower = text.toLowerCase(); - const actualStartPosition = [...keywords] - .map((k) => textLower.indexOf(k.toLowerCase())) - .filter((i) => i > -1) - .slice(-1)[0]; - const startWithContext = Math.max(actualStartPosition - 120, 0); - - const top = startWithContext === 0 ? "" : "..."; - const tail = startWithContext + 240 < text.length ? "..." : ""; - - let summary = document.createElement("p"); - summary.classList.add("context"); - summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; - - return summary; - }, -}; - -_ready(Search.init); diff --git a/docs/build/html/_static/sidebar.js b/docs/build/html/_static/sidebar.js deleted file mode 100644 index 1e24d7b1..00000000 --- a/docs/build/html/_static/sidebar.js +++ /dev/null @@ -1,70 +0,0 @@ -/* - * sidebar.js - * ~~~~~~~~~~ - * - * This script makes the Sphinx sidebar collapsible. - * - * .sphinxsidebar contains .sphinxsidebarwrapper. This script adds - * in .sphixsidebar, after .sphinxsidebarwrapper, the #sidebarbutton - * used to collapse and expand the sidebar. - * - * When the sidebar is collapsed the .sphinxsidebarwrapper is hidden - * and the width of the sidebar and the margin-left of the document - * are decreased. When the sidebar is expanded the opposite happens. - * This script saves a per-browser/per-session cookie used to - * remember the position of the sidebar among the pages. - * Once the browser is closed the cookie is deleted and the position - * reset to the default (expanded). - * - * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -const initialiseSidebar = () => { - - - - - // global elements used by the functions. - const bodyWrapper = document.getElementsByClassName("bodywrapper")[0] - const sidebar = document.getElementsByClassName("sphinxsidebar")[0] - const sidebarWrapper = document.getElementsByClassName('sphinxsidebarwrapper')[0] - const sidebarButton = document.getElementById("sidebarbutton") - const sidebarArrow = sidebarButton.querySelector('span') - - // for some reason, the document has no sidebar; do not run into errors - if (typeof sidebar === "undefined") return; - - const flipArrow = element => element.innerText = (element.innerText === "»") ? "«" : "»" - - const collapse_sidebar = () => { - bodyWrapper.style.marginLeft = ".8em"; - sidebar.style.width = ".8em" - sidebarWrapper.style.display = "none" - flipArrow(sidebarArrow) - sidebarButton.title = _('Expand sidebar') - window.localStorage.setItem("sidebar", "collapsed") - } - - const expand_sidebar = () => { - bodyWrapper.style.marginLeft = "" - sidebar.style.removeProperty("width") - sidebarWrapper.style.display = "" - flipArrow(sidebarArrow) - sidebarButton.title = _('Collapse sidebar') - window.localStorage.setItem("sidebar", "expanded") - } - - sidebarButton.addEventListener("click", () => { - (sidebarWrapper.style.display === "none") ? expand_sidebar() : collapse_sidebar() - }) - - if (!window.localStorage.getItem("sidebar")) return - const value = window.localStorage.getItem("sidebar") - if (value === "collapsed") collapse_sidebar(); - else if (value === "expanded") expand_sidebar(); -} - -if (document.readyState !== "loading") initialiseSidebar() -else document.addEventListener("DOMContentLoaded", initialiseSidebar) diff --git a/docs/build/html/_static/sphinx_highlight.js b/docs/build/html/_static/sphinx_highlight.js deleted file mode 100644 index 8a96c69a..00000000 --- a/docs/build/html/_static/sphinx_highlight.js +++ /dev/null @@ -1,154 +0,0 @@ -/* Highlighting utilities for Sphinx HTML documentation. */ -"use strict"; - -const SPHINX_HIGHLIGHT_ENABLED = true - -/** - * highlight a given string on a node by wrapping it in - * span elements with the given class name. - */ -const _highlight = (node, addItems, text, className) => { - if (node.nodeType === Node.TEXT_NODE) { - const val = node.nodeValue; - const parent = node.parentNode; - const pos = val.toLowerCase().indexOf(text); - if ( - pos >= 0 && - !parent.classList.contains(className) && - !parent.classList.contains("nohighlight") - ) { - let span; - - const closestNode = parent.closest("body, svg, foreignObject"); - const isInSVG = closestNode && closestNode.matches("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.classList.add(className); - } - - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - const rest = document.createTextNode(val.substr(pos + text.length)); - parent.insertBefore( - span, - parent.insertBefore( - rest, - node.nextSibling - ) - ); - node.nodeValue = val.substr(0, pos); - /* There may be more occurrences of search term in this node. 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The CFA wastewater team created this dataset, which contains +simulated data. + +Model definition +---------------- + +In this section, we provide the formal definition of the model. The +hospitalization model is a semi-mechanistic model that describes the +number of observed hospital admissions as a function of a set of latent +variables. Mainly, the observed number of hospital admissions is +discretely distributed with location at the number of latent hospital +admissions: + +.. math:: + + + h(t) \sim \text{HospDist}\left(H(t)\right) + +Where :math:`h(t)` is the observed number of hospital admissions at time +:math:`t`, and :math:`H(t)` is the number of latent hospital admissions +at time :math:`t`. The distribution :math:`\text{HospDist}` is discrete. +For this example, we will use a negative binomial distribution: + +.. math:: + + + \begin{align*} + h(t) & \sim \text{NegativeNinomial}\left(\text{concentration} = 1, \text{mean} = H(t)\right) \\ + H(t) & = \omega(t) p_\mathrm{hosp}(t) \sum_{\tau = 0}^{T_d} d(\tau) I(t-\tau) + \end{align*} + +Were :math:`d(\tau)` is the infection to hospitalization interval, +:math:`I(t)` is the number of latent infections at time :math:`t`, +:math:`p_\mathrm{hosp}(t)` is the infection to hospitalization rate, and +:math:`\omega(t)` is the weekday effect at time :math:`t`; the last +section provides an example building a weekly effect ``RandomVariable``. + +The number of latent hospital admissions at time :math:`t` is a function +of the number of latent infections at time :math:`t` and the infection +to hospitalization rate. The latent infections are modeled as a renewal +process: + +.. math:: + + + \begin{align*} + I(t) &= R(t) \times \sum_{\tau < t} I(\tau) g(t - \tau) \\ + I(0) &\sim \text{LogNormal}(\mu = \log(80/0.05), \sigma = 1.5) + \end{align*} + +The reproductive number :math:`R(t)` is modeled as a random walk +process: + +.. math:: + + + \begin{align*} + R(t) & = R(t-1) + \epsilon\\ + \log{\epsilon} & \sim \text{Normal}(\mu=0, \sigma=0.1) \\ + R(0) &\sim \text{TruncatedNormal}(\text{loc}=1.2, \text{scale}=0.2, \text{min}=0) + \end{align*} + +Data processing +--------------- + +We start by loading the data and inspecting the first five rows. + +.. code:: python + + import polars as pl + from pyrenew import datasets + + dat = datasets.load_wastewater() + dat.head(5) + +.. container:: + + .. raw:: html + + + + shape: (5, 14) + + +---+------+---+----+---+---+------+---------+---+----+-------+---+---+----+ + | t | la | l | da | l | b | d | daily | p | f | h | s | w | in | + | | b_ww | o | te | o | e | aily | _hosp_a | o | or | osp_c | i | w | f_ | + | | tp_u | g | | d | l | _hos | dmits_f | p | ec | alibr | t | _ | pe | + | | niqu | _ | | _ | o | p_ad | or_eval | | as | ation | e | p | r_ | + | | e_id | c | | s | w | mits | | | t_ | _time | | o | ca | + | | | o | | e | _ | | | | da | | | p | pi | + | | | n | | w | L | | | | te | | | | ta | + | | | c | | a | O | | | | | | | | | + | | | | | g | D | | | | | | | | | + | | | | | e | | | | | | | | | | + +===+======+===+====+===+===+======+=========+===+====+=======+===+===+====+ + | i | i64 | f | s | f | i | i64 | i64 | f | s | i64 | i | f | f | + | 6 | | 6 | tr | 6 | 6 | | | 6 | tr | | 6 | 6 | 64 | + | 4 | | 4 | | 4 | 4 | | | 4 | | | 4 | 4 | | + +---+------+---+----+---+---+------+---------+---+----+-------+---+---+----+ + | 1 | 1 | n | “2 | n | n | 6 | 6 | 1 | “2 | 90 | 1 | 4 | 0. | + | | | u | 02 | u | u | | | e | 02 | | | 0 | 00 | + | | | l | 3- | l | l | | | 6 | 4- | | | 0 | 06 | + | | | l | 10 | l | l | | | | 02 | | | 0 | 63 | + | | | | -3 | | | | | | -0 | | | 0 | | + | | | | 0” | | | | | | 5” | | | 0 | | + | | | | | | | | | | | | | . | | + | | | | | | | | | | | | | 0 | | + +---+------+---+----+---+---+------+---------+---+----+-------+---+---+----+ + | 1 | 2 | n | “2 | n | n | 6 | 6 | 1 | “2 | 90 | 1 | 4 | 0. | + | | | u | 02 | u | u | | | e | 02 | | | 0 | 00 | + | | | l | 3- | l | l | | | 6 | 4- | | | 0 | 06 | + | | | l | 10 | l | l | | | | 02 | | | 0 | 63 | + | | | | -3 | | | | | | -0 | | | 0 | | + | | | | 0” | | | | | | 5” | | | 0 | | + | | | | | | | | | | | | | . | | + | | | | | | | | | | | | | 0 | | + +---+------+---+----+---+---+------+---------+---+----+-------+---+---+----+ + | 1 | 3 | n | “2 | n | n | 6 | 6 | 1 | “2 | 90 | 2 | 2 | 0. | + | | | u | 02 | u | u | | | e | 02 | | | 0 | 00 | + | | | l | 3- | l | l | | | 6 | 4- | | | 0 | 06 | + | | | l | 10 | l | l | | | | 02 | | | 0 | 63 | + | | | | -3 | | | | | | -0 | | | 0 | | + | | | | 0” | | | | | | 5” | | | 0 | | + | | | | | | | | | | | | | . | | + | | | | | | | | | | | | | 0 | | + +---+------+---+----+---+---+------+---------+---+----+-------+---+---+----+ + | 1 | 4 | n | “2 | n | n | 6 | 6 | 1 | “2 | 90 | 3 | 1 | 0. | + | | | u | 02 | u | u | | | e | 02 | | | 0 | 00 | + | | | l | 3- | l | l | | | 6 | 4- | | | 0 | 06 | + | | | l | 10 | l | l | | | | 02 | | | 0 | 63 | + | | | | -3 | | | | | | -0 | | | 0 | | + | | | | 0” | | | | | | 5” | | | 0 | | + | | | | | | | | | | | | | . | | + | | | | | | | | | | | | | 0 | | + +---+------+---+----+---+---+------+---------+---+----+-------+---+---+----+ + | 1 | 5 | n | “2 | n | n | 6 | 6 | 1 | “2 | 90 | 4 | 5 | 0. | + | | | u | 02 | u | u | | | e | 02 | | | 0 | 00 | + | | | l | 3- | l | l | | | 6 | 4- | | | 0 | 06 | + | | | l | 10 | l | l | | | | 02 | | | 0 | 63 | + | | | | -3 | | | | | | -0 | | | 0 | | + | | | | 0” | | | | | | 5” | | | . | | + | | | | | | | | | | | | | 0 | | + +---+------+---+----+---+---+------+---------+---+----+-------+---+---+----+ + +The data shows one entry per site, but the way it was simulated, the +number of admissions is the same across sites. Thus, we will only keep +the first observation per day. + +.. code:: python + + # Keeping the first observation of each date + dat = dat.group_by("date").first().select(["date", "daily_hosp_admits"]) + + # Now, sorting by date + dat = dat.sort("date") + + # Keeping the first 90 days + dat = dat.head(90) + + dat.head(5) + +.. container:: + + .. raw:: html + + + + shape: (5, 2) + + ============ ================= + date daily_hosp_admits + ============ ================= + str i64 + “2023-10-30” 6 + “2023-10-31” 8 + “2023-11-01” 4 + “2023-11-02” 8 + “2023-11-03” 4 + ============ ================= + +Let’s take a look at the daily prevalence of hospital admissions. + +.. code:: python + + import matplotlib.pyplot as plt + + # Rotating the x-axis labels, and only showing ~10 labels + ax = plt.gca() + ax.xaxis.set_major_locator(plt.MaxNLocator(nbins=10)) + ax.xaxis.set_tick_params(rotation=45) + plt.plot(dat["date"].to_numpy(), dat["daily_hosp_admits"].to_numpy()) + plt.xlabel("Date") + plt.ylabel("Admissions") + plt.show() + +Building the model +------------------ + +First, we will extract two datasets we will use as deterministic +quantities: the generation interval and the infection to hospitalization +interval. + +.. code:: python + + gen_int = datasets.load_generation_interval() + inf_hosp_int = datasets.load_infection_admission_interval() + + # We only need the probability_mass column of each dataset + gen_int = gen_int["probability_mass"].to_numpy() + inf_hosp_int = inf_hosp_int["probability_mass"].to_numpy() + + # Taking a pick at the first 5 elements of each + gen_int[:5], inf_hosp_int[:5] + + # Visualizing both quantities side by side + fig, axs = plt.subplots(1, 2) + + axs[0].plot(gen_int) + axs[0].set_title("Generation interval") + axs[1].plot(inf_hosp_int) + axs[1].set_title("Infection to hospitalization interval") + +:: + + Text(0.5, 1.0, 'Infection to hospitalization interval') + +With these two in hand, we can start building the model. First, we will +define the latent hospital admissions: + +.. code:: python + + from pyrenew import latent, deterministic + import jax.numpy as jnp + import numpyro.distributions as dist + + inf_hosp_int = deterministic.DeterministicPMF(inf_hosp_int) + + hosp_rate = latent.InfectHospRate( + dist=dist.LogNormal(jnp.log(0.05), 0.1) + ) + + latent_hosp = latent.HospitalAdmissions( + infection_to_admission_interval=inf_hosp_int, + infect_hosp_rate_dist=hosp_rate, + ) + +:: + + /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html + from .autonotebook import tqdm as notebook_tqdm + An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu. + +The ``inf_hosp_int`` is a ``DeterministicPMF`` object that takes the +infection to hospitalization interval as input. The ``hosp_rate`` is an +``InfectHospRate`` object that takes the infection to hospitalization +rate as input. The ``HospitalAdmissions`` class is a ``RandomVariable`` +that takes two distributions as inputs: the infection to admission +interval and the infection to hospitalization rate. Now, we can define +the rest of the other components: + +.. code:: python + + from pyrenew import model, process, observation + + # Infection process + latent_inf = latent.Infections() + I0 = latent.Infections0(I0_dist=dist.LogNormal(loc=jnp.log(80/.05), scale=1.5)) + + # Generation interval and Rt + gen_int = deterministic.DeterministicPMF(gen_int) + rtproc = process.RtRandomWalkProcess( + Rt_rw_dist=dist.Normal(0, 0.1) + ) + + # The observation model + obs = observation.NegativeBinomialObservation(concentration_prior=1.0) + +Notice all the components are ``RandomVariable`` instances. We can now +build the model: + +.. code:: python + + hosp_model = model.HospitalizationsModel( + latent_infections=latent_inf, + latent_hospitalizations=latent_hosp, + I0=I0, + gen_int=gen_int, + Rt_process=rtproc, + observation_process=obs, + ) + +Let’s simulate to check if the model is working: + +.. code:: python + + import numpyro as npro + import numpy as np + + timeframe = 120 + + np.random.seed(223) + with npro.handlers.seed(rng_seed = np.random.randint(1, timeframe)): + sim_data = hosp_model.sample(n_timepoints=timeframe) + +.. code:: python + + import matplotlib.pyplot as plt + + fig, axs = plt.subplots(1, 2) + + # Rt plot + axs[0].plot(range(0, timeframe + 1), sim_data.Rt) + axs[0].set_ylabel('Rt') + + # Infections plot + axs[1].plot(range(0, timeframe + 1), sim_data.sampled_admissions) + axs[1].set_ylabel('Infections') + axs[1].set_yscale('log') + + fig.suptitle('Basic renewal model') + fig.supxlabel('Time') + plt.tight_layout() + plt.show() + +.. figure:: example-with-datasets_files/figure-commonmark/basic-fig-output-1.png + :alt: Rt and Infections + + Rt and Infections + +Fitting the model +----------------- + +We can fit the model to the data. We will use the ``run`` method of the +model object. The two inputs this model requires are ``n_timepoints`` +and ``observed_hospitalizations`` + +.. code:: python + + import jax + + hosp_model.run( + num_samples=2000, + num_warmup=2000, + n_timepoints=dat.shape[0] - 1, + observed_hospitalizations=dat["daily_hosp_admits"].to_numpy(), + rng_key=jax.random.PRNGKey(54), + mcmc_args=dict(progress_bar=False), + ) + +We can use the ``plot_posterior`` method to visualize the results [1]_: + +.. code:: python + + out = hosp_model.plot_posterior( + var="predicted_hospitalizations", + ylab="Hospital Admissions", + obs_signal=dat["daily_hosp_admits"].to_numpy(), + ) + +.. figure:: example-with-datasets_files/figure-commonmark/output-hospital-admissions-output-1.png + :alt: Hospital Admissions posterior distribution + + Hospital Admissions posterior distribution + +The first half of the model is not looking good. The reason is that the +infection to hospitalization interval PMF makes it unlikely to observe +admissions from the beginning. The following section shows how to fix +this. + +Padding the model +----------------- + +We can use the padding argument to solve the overestimation of +hospitalizations in the first half of the model. By setting +``padding > 0``, the model then assumes that the first ``padding`` +observations are missing; thus, only observations after ``padding`` will +count towards the likelihood of the model. In practice, the model will +extend the estimated Rt and latent infections by ``padding`` days, given +time to adjust to the observed data. The following code will add 21 days +of missing data at the beginning of the model and re-estimate it with +``padding = 21``: + +.. code:: python + + days_to_impute = 21 + + dat2 = dat["daily_hosp_admits"].to_numpy() + + # Add 21 Nas to the beginning of dat2 + dat2 = np.hstack((np.repeat(np.nan, days_to_impute), dat2)) + + hosp_model.run( + num_samples=2000, + num_warmup=2000, + n_timepoints=dat2.shape[0] - 1, + observed_hospitalizations=dat2, + rng_key=jax.random.PRNGKey(54), + mcmc_args=dict(progress_bar=False), + padding=days_to_impute, # Padding the model + ) + +And plotting the results: + +.. code:: python + + out = hosp_model.plot_posterior( + var="predicted_hospitalizations", + ylab="Hospital Admissions", + obs_signal=dat2, + ) + +.. figure:: example-with-datasets_files/figure-commonmark/output-admissions-with-padding-output-1.png + :alt: Hospital Admissions posterior distribution + + Hospital Admissions posterior distribution + +We can also take a look at the latent infections: + +.. code:: python + + out2 = hosp_model.plot_posterior( + var="latent_infections", + ylab="Latent Infections" + ) + +.. figure:: example-with-datasets_files/figure-commonmark/output-infections-with-padding-output-1.png + :alt: Hospital Admissions posterior distribution + + Hospital Admissions posterior distribution + +Round 2: Incorporating weekday effects +-------------------------------------- + +We will re-use the infection to admission interval and infection to +hospitalization rate from the previous model. But we will also add a +weekday effect distribution. To do this, we will create a new instance +of ``RandomVariable`` to model the weekday effect. The weekday effect +will be a truncated normal distribution with a mean of 1.0 and a +standard deviation of 0.5. The distribution will be truncated between +0.1 and 10.0. The weekday effect will be repeated for the number of +weeks in the dataset. + +.. code:: python + + from pyrenew import metaclass + import numpyro as npro + + class WeekdayEffect(metaclass.RandomVariable): + """Weekday effect distribution""" + def __init__(self, len: int): + """ Initialize the weekday effect distribution + Parameters + ---------- + len : int + The number of observations + """ + self.nweeks = jnp.ceil(len/7).astype(int) + self.len = len + + @staticmethod + def validate(): + return None + + def sample(self, **kwargs): + ans = npro.sample( + name="weekday_effect", + fn=npro.distributions.TruncatedNormal( + loc=1.0, scale=.5, low=0.1, high=10.0 + ), + sample_shape=(7,) + ) + + return jnp.tile(ans, self.nweeks)[:self.len] + + # Initializing the weekday effect + weekday_effect = WeekdayEffect(dat.shape[0]) + +Notice that the instance’s ``nweeks`` and ``len`` members are passed +during construction. Trying to compute the number of weeks and the +length of the dataset in the ``validate`` method will raise a ``jit`` +error in ``jax`` as the shape and size of elements are not known during +the validation step, which happens before the model is run. With the new +weekday effect, we can rebuild the latent hospitalization model: + +.. code:: python + + latent_hosp_wday_effect = latent.HospitalAdmissions( + infection_to_admission_interval=inf_hosp_int, + infect_hosp_rate_dist=hosp_rate, + weekday_effect_dist=weekday_effect, + ) + + hosp_model_weekday = model.HospitalizationsModel( + latent_infections=latent_inf, + latent_hospitalizations=latent_hosp_wday_effect, + I0=I0, + gen_int=gen_int, + Rt_process=rtproc, + observation_process=obs, + ) + +Running the model (with the same padding as before): + +.. code:: python + + hosp_model_weekday.run( + num_samples=2000, + num_warmup=2000, + n_timepoints=dat2.shape[0] - 1, + observed_hospitalizations=dat2, + rng_key=jax.random.PRNGKey(54), + mcmc_args=dict(progress_bar=False), + padding=days_to_impute, + ) + +And plotting the results: + +.. code:: python + + out = hosp_model_weekday.plot_posterior( + var="predicted_hospitalizations", + ylab="Hospital Admissions", + obs_signal=dat2, + ) + +.. figure:: example-with-datasets_files/figure-commonmark/output-admissions-padding-and-weekday-output-1.png + :alt: Hospital Admissions posterior distribution + + Hospital Admissions posterior distribution + +.. [1] + The output is captured to avoid ``quarto`` from displaying the output + twice. diff --git a/docs/source/tutorials/getting-started.rst b/docs/source/tutorials/getting-started.rst new file mode 100644 index 00000000..bf2cb602 --- /dev/null +++ b/docs/source/tutorials/getting-started.rst @@ -0,0 +1,282 @@ +Getting started with pyrenew +============================ + +``pyrenew`` is a flexible tool for simulating and making statistical +inference of epidemiological models, emphasizing renewal models. Built +on ``numpyro``, ``pyrenew`` provides core components for model building +and pre-defined models for processing various observational processes. +This document illustrates how ``pyrenew`` can be used to build a basic +renewal model. + +The fundamentals +---------------- + +``pyrenew``\ ’s core components are the metaclasses ``RandomVariable`` +and ``Model``. From the package’s perspective, a ``RandomVariable`` is a +quantity models can sample and estimate, **including deterministic +quantities**. Mainly, sampling from a ``RandomVariable`` involves +calling the ``sample()`` method. The benefit of this design is the +definition of the sample function can be arbitrary, allowing the user to +either sample from a distribution using ``numpyro.sample()``, compute +fixed quantities (like a mechanistic equation), or return a fixed value +(like a pre-computed PMF.) For instance, we may be interested in +estimating a PMF, in which case a ``RandomVariable`` sampling function +may roughly be defined as: + +.. code:: python + + class MyRandVar(RandomVariable): + def sample(...) -> ArrayLike: + return numpyro.sample(...) + +Whereas, in some other cases, we may instead use a fixed quantity for +that variable (like a pre-computed PMF), where the +``RandomVariable``\ ’s sample function could be defined like: + +.. code:: python + + class MyRandVar(RandomVariable): + def sample(...) -> ArrayLike: + return jax.numpy.array([0.2, 0.7, 0.1]) + +This way, when a ``Model`` samples from ``MyRandVar``, it could be +either adding random variables to be estimated (first case) or just +retrieving some quantity needed for other calculations (second case.) + +The ``Model`` metaclass provides basic functionality for estimating and +simulation. Like ``RandomVariable``, the ``Model`` metaclass has a +``sample()`` method that defines the model structure. Ultimately, models +can be nested (or inherited), providing a straightforward way to add +layers of complexity. + +‘Hello world’ model +------------------- + +This section will show the steps to build a simple renewal model +featuring a latent infection process, a random walk Rt process, and an +observation process for the reported infections. We start by loading the +needed components to build a basic renewal model: + +.. code:: python + + import jax.numpy as jnp + import numpy as np + import numpyro as npro + import numpyro.distributions as dist + from pyrenew.process import RtRandomWalkProcess + from pyrenew.latent import Infections, Infections0 + from pyrenew.observation import PoissonObservation + from pyrenew.deterministic import DeterministicPMF + from pyrenew.model import RtInfectionsRenewalModel + +The basic renewal model defines five components: generation interval, +initial infections, Rt, latent infections, and observed infections. In +this example, the generation interval is not estimated but passed as a +deterministic instance of ``RandomVariable``. Here is the code to +initialize the five components: + +.. code:: python + + # (1) The generation interval (deterministic) + gen_int = DeterministicPMF(jnp.array([0.25, 0.25, 0.25, 0.25])) + + # (2) Initial infections (inferred with a prior) + I0 = Infections0(I0_dist=dist.LogNormal(0, 1)) + + # (3) The random process for Rt + rt_proc = RtRandomWalkProcess() + + # (4) Latent infection process (which will use 1 and 2) + latent_infections = Infections() + + # (5) The observed infections process (with mean at the latent infections) + observation_process = PoissonObservation() + +With these five pieces, we can build the basic renewal model: + +.. code:: python + + model1 = RtInfectionsRenewalModel( + gen_int = gen_int, + I0 = I0, + Rt_process = rt_proc, + latent_infections = latent_infections, + observation_process = observation_process, + ) + +The following diagram summarizes how the modules interact via +composition; notably, ``gen_int``, ``I0``, ``rt_proc``, +``latent_infections``, and ``observed_infections`` are instances of +``RandomVariable``, which means these can be easily replaced to generate +a different version of ``RtInfectionsRenewalModel``: + +.. code:: mermaid + + flowchart TB + genint["(1) gen_int\n(DetermnisticPMF)"] + i0["(2) I0\n(Infections0)"] + rt["(3) rt_proc\n(RtRandomWalkProcess)"] + inf["(4) latent_infections\n(Infections)"] + obs["(5) observation_process\n(PoissonObservation)"] + + model1["model1\n(RtInfectionsRenewalModel)"] + + i0-->|Composes|model1 + genint-->|Composes|model1 + rt-->|Composes|model1 + obs-->|Composes|model1 + inf-->|Composes|model1 + +Using ``numpyro``, we can simulate data using the ``sample()`` member +function of ``RtInfectionsRenewalModel``: + +.. code:: python + + np.random.seed(223) + with npro.handlers.seed(rng_seed = np.random.randint(1, 60)): + sim_data = model1.sample(n_timepoints=30) + + sim_data + +:: + + RtInfectionsRenewalSample(Rt=[1.2022278 1.2111099 1.2325984 1.2104921 1.2023039 1.1970979 1.2384264 + 1.2423582 1.245498 1.241344 1.2081108 1.1938375 1.271196 1.3189521 + 1.3054799 1.3165426 1.291952 1.3026639 1.2619467 1.2852622 1.3121517 + 1.2888998 1.2641873 1.2580931 1.2545817 1.3092988 1.2488269 1.2397509 + 1.2071848 1.2334517 1.21868 ], latent_infections=[ 2.3215084 3.0415602 4.0327816 5.180868 4.381411 4.978916 + 5.750626 6.3024273 6.66758 7.354823 7.8755097 8.416656 + 9.63394 10.973988 12.043082 13.516833 14.911659 16.75407 + 18.053928 20.318869 22.975292 25.166464 27.34265 30.13236 + 33.126217 37.89362 40.11695 43.784634 46.754696 51.974545 + 55.642136 ], sampled_infections=[ 1 2 3 5 4 4 7 4 8 4 7 3 8 12 13 18 14 20 17 18 28 27 36 37 + 26 31 40 27 48 54 60]) + +The ``sample()`` method of the ``RtInfectionsRenewalModel`` returns a +list composed of the ``Rt`` and ``infections`` sequences. + +.. code:: python + + import matplotlib.pyplot as plt + + fig, axs = plt.subplots(1, 2) + + # Rt plot + axs[0].plot(range(0, 31), sim_data.Rt) + axs[0].set_ylabel('Rt') + + # Infections plot + axs[1].plot(range(0, 31), sim_data.sampled_infections) + axs[1].set_ylabel('Infections') + + fig.suptitle('Basic renewal model') + fig.supxlabel('Time') + plt.tight_layout() + plt.show() + +.. figure:: getting-started_files/figure-commonmark/basic-fig-output-1.png + :alt: Rt and Infections + + Rt and Infections + +To fit the model, we can use the ``run()`` method of the model +``RtInfectionsRenewalModel``; an inherited method from the metaclass +``Model``: + +.. code:: python + + import jax + + model1.run( + num_warmup=2000, + num_samples=1000, + observed_infections=sim_data.sampled_infections, + n_timepoints = len(sim_data[1])-1, + rng_key=jax.random.PRNGKey(54), + mcmc_args=dict(progress_bar=False), + ) + +Now, let’s investigate the output, particularly the posterior +distribution of the Rt estimates: + +.. code:: python + + import polars as pl + samps = model1.spread_draws([('Rt', 'time')]) + + fig, ax = plt.subplots(figsize=[4, 5]) + + ax.plot(sim_data.Rt) + samp_ids = np.random.randint(size=25, low=0, high=999) + for samp_id in samp_ids: + sub_samps = samps.filter(pl.col("draw") == samp_id).sort(pl.col('time')) + ax.plot(sub_samps.select("time").to_numpy(), + sub_samps.select("Rt").to_numpy(), color="darkblue", alpha=0.1) + ax.set_ylim([0.4, 1/.4]) + ax.set_yticks([0.5, 1, 2]) + ax.set_yscale("log") + +.. figure:: getting-started_files/figure-commonmark/output-rt-output-1.png + :alt: Rt posterior distribution + + Rt posterior distribution + +Architecture of pyrenew +----------------------- + +``pyrenew`` leverages ``numpyro``\ ’s flexibility to build models via +composition. As a principle, most objects in ``pyrenew`` can be treated +as random variables we can sample. At the top-level ``pyrenew`` has two +metaclass from which most objects inherit: ``RandomVariable`` and +``Model``. From them, the following four sub-modules arise: + +- The ``process`` sub-module, +- The ``deterministic`` sub-module, +- The ``observation`` sub-module, +- The ``latent`` sub-module, and +- The ``models`` sub-module + +The first four are collections of instances of ``RandomVariable``, and +the last is a collection of instances of ``Model``. The following +diagram shows a detailed view of how meta classes, modules, and classes +interact to create the ``RtInfectionsRenewalModel`` instantiated in the +previous section: + +.. code:: mermaid + + flowchart LR + rand((RandomVariable\nmetaclass)) + models((Model\nmetaclass)) + + subgraph observations[Observations module] + obs["observation_process\n(PoissonObservation)"] + end + + subgraph latent[Latent module] + inf["latent_infections\n(Infections)"] + i0["I0\n(Infections0)"] + end + + subgraph process[Process module] + rt["rt_proc\n(RtRandomWalkProcess)"] + end + + subgraph deterministic[Deterministic module] + detpmf["gen_int\n(DeterministicPMF)"] + end + + subgraph model[Model module] + model1["model1\n(RtInfectionsRenewalModel)"] + end + + rand-->|Inherited by|observations + rand-->|Inherited by|process + rand-->|Inherited by|latent + rand-->|Inherited by|deterministic + models-->|Inherited by|model + + detpmf-->|Composes|model1 + i0-->|Composes|model1 + rt-->|Composes|model1 + obs-->|Composes|model1 + inf-->|Composes|model1 diff --git a/docs/source/tutorials/index.rst b/docs/source/tutorials/index.rst new file mode 100644 index 00000000..50151d10 --- /dev/null +++ b/docs/source/tutorials/index.rst @@ -0,0 +1,9 @@ +Tutorials Reference +============== + +.. toctree:: + :maxdepth: 3 + + getting-started + example-with-datasets + pyrenew_demo diff --git a/docs/source/tutorials/pyrenew_demo.rst b/docs/source/tutorials/pyrenew_demo.rst new file mode 100644 index 00000000..58b9a9a2 --- /dev/null +++ b/docs/source/tutorials/pyrenew_demo.rst @@ -0,0 +1,311 @@ +Pyrenew demo +============ + +This demo simulates some basic renewal process data and then fits to it +using ``pyrenew``. + +Assuming you’ve already installed Python and pip, you’ll need to first +install ``pyrenew``: + +.. code:: python + + pip install pyrenew + +You’ll also need working installations of ``matplotlib``, ``numpy``, +``jax``, ``numpyro``, and ``polars``: + +.. code:: python + + pip install matplotlib numpy jax numpyro polars + +To begin, run the following import section to call external modules and +functions necessary to run the ``pyrenew`` demo. The ``import`` +statement imports the module and the ``as`` statement renames the module +for use within this script. The ``from`` statement imports a specific +function from a module (named after the ``.``) within a package (named +before the ``.``). + +.. code:: python + + import matplotlib as mpl + import matplotlib.pyplot as plt + import jax + import jax.numpy as jnp + import numpy as np + from numpyro.handlers import seed + import numpyro.distributions as dist + +.. code:: python + + from pyrenew.process import SimpleRandomWalkProcess + +:: + + An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu. + +To understand the simple random walk process underlying the sampling +within the renewal process model, we first examine a single random walk +path. Using the ``sample`` method from an instance of the +``SimpleRandomWalkProcess`` class, we first create an instance of the +``SimpleRandomWalkProcess`` class with a normal distribution of mean = 0 +and standard deviation = 0.0001 as its input. Next, the ``with`` +statement sets the seed for the random number generator for the duration +of the block that follows. Inside the ``with`` block, the +``q_samp = q.sample(duration=100)`` generates the sample instance over a +duration of 100 time units. Finally, this single random walk process is +visualized using ``matplot.pyplot`` to plot the exponential of the +sample instance. + +.. code:: python + + np.random.seed(3312) + q = SimpleRandomWalkProcess(dist.Normal(0, 0.001)) + with seed(rng_seed=np.random.randint(0,1000)): + q_samp = q.sample(duration=100) + + plt.plot(np.exp(q_samp[0])) + +|image0| + +Next, import several additional functions from the ``latent`` module of +the ``pyrenew`` package to model infections, hospital admissions, +initial infections, and hospitalization rate due to infection. + +.. code:: python + + from pyrenew.latent import ( + Infections, HospitalAdmissions, Infections0, InfectHospRate, + ) + +Additionally, import several classes from Pyrenew, including a Poisson +observation process, determininstic PMF and variable classes, the +Pyrenew hospitalization model, and a renewal modle (Rt) random walk +process: + +.. code:: python + + from pyrenew.observation import PoissonObservation + from pyrenew.deterministic import DeterministicPMF, DeterministicVariable + from pyrenew.model import HospitalizationsModel + from pyrenew.process import RtRandomWalkProcess + +To initialize the model, we first define initial conditions, including: + +1) deterministic generation time, defined as an instance of the + ``DeterministicPMF`` class, which gives the probability of each + possible outcome for a discrete random variable given as a JAX NumPy + array of four possible outcomes + +2) initial infections at the start of simulation as a log-normal + distribution with mean = 0 and standard deviation = 1 + +3) latent infections as an instance of the ``Infections`` class with + default settings + +4) latent hospitalization process, modeled by first defining the time + interval from infections to hospitalizations as a + ``DeterministicPMF`` input with 18 possible outcomes and + corresponding probabilities given by the values in the array. The + ``HospitalAdmissions`` function then takes in this defined time + interval, as well as defining the rate at which infections are + admitted to the hospital due to infection, modeled as a log-normal + distribution with mean = ``jnp.log(0.05)`` and standard deviation = + 0.05. + +5) hospitalization observation process, modeled with a Poisson + distribution + +6) an Rt random walk process with default settings + +.. code:: python + + # Initializing model components: + + # 1) A deterministic generation time + gen_int = DeterministicPMF(jnp.array([0.25, 0.25, 0.25, 0.25])) + + # 2) Initial infections + I0 = Infections0(I0_dist=dist.LogNormal(0, 1)) + + # 3) The latent infections process + latent_infections = Infections() + + # 4) The latent hospitalization process: + + # First, define a deterministic infection to hosp pmf + inf_hosp_int = DeterministicPMF( + jnp.array([0, 0, 0,0,0,0,0,0,0,0,0,0,0, 0.25, 0.5, 0.1, 0.1, 0.05]), + ) + + latent_hospitalizations = HospitalAdmissions( + infection_to_admission_interval=inf_hosp_int, + infect_hosp_rate_dist = InfectHospRate( + dist=dist.LogNormal(jnp.log(0.05), 0.05), + ), + ) + + # 5) An observation process for the hospitalizations + obs_process = PoissonObservation() + + # 6) A random walk process (it could be deterministic using + # pyrenew.process.DeterministicProcess()) + Rt_process = RtRandomWalkProcess() + +The ``HospitalizationsModel`` is then initialized using the initial +conditions just defined: + +.. code:: python + + # Initializing the model + hospmodel = HospitalizationsModel( + gen_int=gen_int, + I0=I0, + latent_hospitalizations=latent_hospitalizations, + observation_process=obs_process, + latent_infections=latent_infections, + Rt_process=Rt_process + ) + +Next, we sample from the ``hospmodel`` for 30 time steps and view the +output of a single run: + +.. code:: python + + with seed(rng_seed=np.random.randint(1, 60)): + x = hospmodel.sample(n_timepoints=30) + x + +:: + + HospModelSample(Rt=[1.1791104 1.1995267 1.1772177 1.1913829 1.2075942 1.1444623 1.1514508 + 1.1976782 1.2292639 1.1719677 1.204649 1.2323451 1.2466507 1.2800207 + 1.2749145 1.2619376 1.2189837 1.2192641 1.2290158 1.2128737 1.1908046 + 1.2174997 1.1941082 1.2084603 1.1965215 1.2248698 1.2308019 1.2426206 + 1.2131014 1.207159 1.1837622], latent_infections=[0.05214045 0.06867922 0.08761451 0.11476436 0.09757317 0.10547114 + 0.1167062 0.13010225 0.13824694 0.14372033 0.15924728 0.17601486 + 0.19236736 0.21483542 0.23664482 0.25865382 0.27503362 0.30029488 + 0.3289544 0.35262382 0.37418258 0.41274938 0.43839005 0.47672123 + 0.50913286 0.5625195 0.6113282 0.67092246 0.7138808 0.77217466 + 0.819254 ], IHR=0.04929916560649872, latent_admissions=[0. 0. 0. 0. 0. 0. + 0. 0. 0. 0. 0. 0. + 0. 0.00064262 0.0021317 0.00302979 0.00416974 0.0049305 + 0.00487205 0.00530097 0.00576412 0.00624666 0.00665578 0.00711595 + 0.0078055 0.00854396 0.00939666 0.01042083 0.0114624 0.01246538 + 0.01345188], sampled_admissions=[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0]) + +Visualizations of the single model output show (top) infections over the +30 time steps, (middle) hospitalizations over the 30 time steps, and +(bottom) + +.. code:: python + + fig, ax = plt.subplots(nrows=3, sharex=True) + ax[0].plot(x.latent_infections) + ax[0].set_ylim([1/5, 5]) + ax[1].plot(x.latent_admissions) + ax[2].plot(x.sampled_admissions, 'o') + for axis in ax[:-1]: + axis.set_yscale("log") + +|image1| + +To fit the ``hospmodel`` to the simulated data, we call +``hospmodel.run()``, an MCMC algorithm, with the arguments generated in +``hospmodel`` object, using 1000 warmup stepts and 1000 samples to draw +from the posterior distribution of the model parameters. The model is +run for ``len(x.sampled_admissions)-1`` time steps with the seed set by +``jax.random.PRNGKey()`` + +.. code:: python + + # from numpyro.infer import MCMC, NUTS + hospmodel.run( + num_warmup=1000, + num_samples=1000, + observed_hospitalizations=x.sampled_admissions, + n_timepoints = len(x.sampled_admissions)-1, + rng_key=jax.random.PRNGKey(54), + mcmc_args=dict(progress_bar=False), + ) + +Print a summary of the model: + +.. code:: python + + hospmodel.print_summary() + +:: + + mean std median 5.0% 95.0% n_eff r_hat + I0 1.27 1.10 0.97 0.10 2.42 1132.34 1.00 + IHR 0.05 0.00 0.05 0.05 0.05 2306.45 1.00 + Rt0 1.23 0.17 1.23 0.93 1.48 1327.22 1.00 + Rt_transformed_rw_diffs[0] -0.00 0.02 -0.00 -0.04 0.04 1404.95 1.00 + Rt_transformed_rw_diffs[1] 0.00 0.03 0.00 -0.04 0.04 2280.86 1.00 + Rt_transformed_rw_diffs[2] -0.00 0.02 -0.00 -0.04 0.04 2119.83 1.00 + Rt_transformed_rw_diffs[3] 0.00 0.02 -0.00 -0.04 0.04 2196.86 1.00 + Rt_transformed_rw_diffs[4] 0.00 0.02 -0.00 -0.03 0.04 2391.45 1.00 + Rt_transformed_rw_diffs[5] 0.00 0.03 0.00 -0.04 0.04 2043.02 1.00 + Rt_transformed_rw_diffs[6] 0.00 0.02 0.00 -0.04 0.04 1514.40 1.00 + Rt_transformed_rw_diffs[7] -0.00 0.02 -0.00 -0.04 0.04 2619.69 1.00 + Rt_transformed_rw_diffs[8] 0.00 0.03 0.00 -0.04 0.04 1883.84 1.00 + Rt_transformed_rw_diffs[9] 0.00 0.03 0.00 -0.04 0.04 2015.66 1.00 + Rt_transformed_rw_diffs[10] 0.00 0.02 0.00 -0.04 0.04 2045.47 1.00 + Rt_transformed_rw_diffs[11] -0.00 0.03 0.00 -0.04 0.04 1615.10 1.00 + Rt_transformed_rw_diffs[12] 0.00 0.02 0.00 -0.04 0.04 2206.32 1.00 + Rt_transformed_rw_diffs[13] 0.00 0.03 0.00 -0.04 0.04 1175.93 1.00 + Rt_transformed_rw_diffs[14] -0.00 0.03 -0.00 -0.04 0.04 1606.26 1.00 + Rt_transformed_rw_diffs[15] -0.00 0.03 -0.00 -0.04 0.04 2344.62 1.00 + Rt_transformed_rw_diffs[16] -0.00 0.02 0.00 -0.04 0.04 1522.33 1.00 + Rt_transformed_rw_diffs[17] 0.00 0.03 0.00 -0.04 0.04 2157.17 1.00 + Rt_transformed_rw_diffs[18] -0.00 0.02 -0.00 -0.04 0.04 1594.95 1.00 + Rt_transformed_rw_diffs[19] 0.00 0.03 -0.00 -0.04 0.04 1698.70 1.00 + Rt_transformed_rw_diffs[20] 0.00 0.02 0.00 -0.04 0.04 1726.18 1.00 + Rt_transformed_rw_diffs[21] 0.00 0.02 -0.00 -0.04 0.04 2386.35 1.00 + Rt_transformed_rw_diffs[22] 0.00 0.03 0.00 -0.04 0.04 2028.63 1.00 + Rt_transformed_rw_diffs[23] 0.00 0.02 0.00 -0.04 0.03 1669.71 1.00 + Rt_transformed_rw_diffs[24] 0.00 0.02 0.00 -0.04 0.04 2126.33 1.00 + Rt_transformed_rw_diffs[25] -0.00 0.02 -0.00 -0.04 0.04 2119.74 1.00 + Rt_transformed_rw_diffs[26] 0.00 0.03 0.00 -0.04 0.04 2657.91 1.00 + Rt_transformed_rw_diffs[27] -0.00 0.03 0.00 -0.04 0.04 1939.30 1.00 + Rt_transformed_rw_diffs[28] -0.00 0.02 -0.00 -0.04 0.04 1737.84 1.00 + Rt_transformed_rw_diffs[29] -0.00 0.03 -0.00 -0.04 0.04 2105.55 1.00 + + Number of divergences: 0 + +Next, we will use the ``spread_draws`` function from the +``pyrenew.mcmcutils`` module to process the MCMC samples. The +``spread_draws`` function reformats the samples drawn from the +``mcmc.get_samples()`` from the ``hospmodel``. The samples are simulated +Rt values over time. + +.. code:: python + + from pyrenew.mcmcutils import spread_draws + samps = spread_draws(hospmodel.mcmc.get_samples(), [("Rt", "time")]) + +We visualize these samples below, with individual possible Rt estimates +over time shown in light blue, and the overall mean estimate Rt shown in +dark blue. + +.. code:: python + + import numpy as np + import polars as pl + fig, ax = plt.subplots(figsize=[4, 5]) + + ax.plot(x[0]) + samp_ids = np.random.randint(size=25, low=0, high=999) + for samp_id in samp_ids: + sub_samps = samps.filter(pl.col("draw") == samp_id).sort(pl.col('time')) + ax.plot(sub_samps.select("time").to_numpy(), + sub_samps.select("Rt").to_numpy(), color="darkblue", alpha=0.1) + ax.set_ylim([0.4, 1/.4]) + ax.set_yticks([0.5, 1, 2]) + ax.set_yscale("log") + +|image2| + +.. |image0| image:: pyrenew_demo_files/figure-commonmark/fig-randwalk-output-1.png +.. |image1| image:: pyrenew_demo_files/figure-commonmark/fig-hosp-output-1.png +.. |image2| image:: pyrenew_demo_files/figure-commonmark/fig-sampled-rt-output-1.png diff --git a/model/Makefile b/model/Makefile index 89dd88b6..6f9515d8 100644 --- a/model/Makefile +++ b/model/Makefile @@ -27,6 +27,16 @@ docs/notebooks: quarto convert docs/example-with-datasets.qmd --output \ docs/example-with-datasets.ipynb +sphinx-tutorials: docs + for i in docs/*.md; do \ + if [ "$$i" = "docs/README.md" ]; then \ + continue; \ + fi; \ + pandoc -s $$i -o ../docs/source/tutorials/$$(basename $$i .md).rst;\ + cp -r docs/$$(basename $$i .md)_files ../docs/source/tutorials/; \ + done + + test_images: echo "Generating reference images for tests" poetry run pytest --mpl-generate-path=src/test/baseline From e619179b562b5514877a5ba86d3a9f69419f6c61 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Wed, 1 May 2024 13:31:18 -0600 Subject: [PATCH 02/12] Same action for building docs and website --- .github/workflows/docs.yaml | 38 -- .github/workflows/website.yaml | 50 +- README.md | 1 - .../tutorials/example-with-datasets.rst | 550 ------------------ docs/source/tutorials/getting-started.rst | 282 --------- docs/source/tutorials/index.rst | 9 - docs/source/tutorials/pyrenew_demo.rst | 311 ---------- model/Makefile | 3 +- .../figure-commonmark/fig-hosp-output-1.png | Bin 14325 -> 14325 bytes .../fig-randwalk-output-1.png | Bin 29905 -> 29905 bytes .../fig-sampled-rt-output-1.png | Bin 38249 -> 38249 bytes 11 files changed, 49 insertions(+), 1195 deletions(-) delete mode 100644 .github/workflows/docs.yaml delete mode 100644 docs/source/tutorials/example-with-datasets.rst delete mode 100644 docs/source/tutorials/getting-started.rst delete mode 100644 docs/source/tutorials/index.rst delete mode 100644 docs/source/tutorials/pyrenew_demo.rst diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml deleted file mode 100644 index 87946a92..00000000 --- a/.github/workflows/docs.yaml +++ /dev/null @@ -1,38 +0,0 @@ -name: Quarto documents - -on: - pull_request: - branches: [main] - push: - branches: [main] - -jobs: - render-docs: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - - uses: quarto-dev/quarto-actions/setup@v2 - - - uses: actions/setup-python@v5 - with: - python-version: "3.12" - - - name: cache poetry - uses: actions/cache@v4 - with: - path: ~/.local - key: ${{ runner.os }}-poetry - - - name: install poetry - run: pip install poetry - - - name: install package - run: poetry install --with dev -C model - - - name: Render documents - run: | - cd model - for file in $(find docs -name '*.qmd'); do - poetry run quarto render $file - done diff --git a/.github/workflows/website.yaml b/.github/workflows/website.yaml index 2be17b78..5edc6fd4 100644 --- a/.github/workflows/website.yaml +++ b/.github/workflows/website.yaml @@ -15,7 +15,46 @@ permissions: id-token: write jobs: + render-docs: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - uses: quarto-dev/quarto-actions/setup@v2 + + - uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - name: cache poetry + uses: actions/cache@v4 + with: + path: ~/.local + key: ${{ runner.os }}-poetry + + - name: install poetry + run: pip install poetry + + - name: install package + run: poetry install --with dev -C model + + - name: Render documents + run: | + cd model + mkdir _compiled + for file in $(find docs -name '*.qmd'); do + poetry run quarto render --to rst $file --output-dir _compiled + done + + # Upload + - name: Upload artifacts + uses: actions/upload-pages-artifact@v3 + with: + name: github-pages-tutorials + path: model/_compiled/ + build: + needs: render-docs runs-on: ubuntu-latest permissions: contents: write @@ -26,6 +65,13 @@ jobs: - name: Setup Pages uses: actions/configure-pages@v4 + # Load the artifact github-pages-tutorials + - name: Download artifacts + uses: actions/download-artifact@v3 + with: + name: github-pages-tutorials + path: docs/source/tutorials/ + - name: Set up Python uses: actions/setup-python@v5 with: @@ -39,9 +85,7 @@ jobs: # Building - name: Build HTML using Poetry - run: | - poetry run -C model make sphinx-tutorials - poetry run -C docs make html + run: poetry run -C docs sphinx-build -M html docs/source/ docs/build/ # Upload - name: Upload artifacts diff --git a/README.md b/README.md index 06ad7253..fd1e4875 100755 --- a/README.md +++ b/README.md @@ -7,7 +7,6 @@ [![installation and testing pipeline](https://github.com/CDCgov/multisignal-epi-inference/actions/workflows/pipeline.yaml/badge.svg)](https://github.com/CDCgov/multisignal-epi-inference/actions/workflows/pipeline.yaml) [![Docs: model](https://github.com/CDCgov/multisignal-epi-inference/actions/workflows/website.yaml/badge.svg)](https://github.com/CDCgov/multisignal-epi-inference/actions/workflows/website.yaml) [![codecov (model)](https://codecov.io/gh/CDCgov/multisignal-epi-inference/graph/badge.svg?token=7Z06HOMYR1)](https://codecov.io/gh/CDCgov/multisignal-epi-inference) -[![Quarto documents](https://github.com/CDCgov/multisignal-epi-inference/actions/workflows/docs.yaml/badge.svg)](https://github.com/CDCgov/multisignal-epi-inference/actions/workflows/docs.yaml) This repo hosts the multisignal (*a.k.a.* signal fusion) renewal project: an internal forecasting model that leverages multiple data sources for enhancing epidemiological modeling of infectious disease outbreaks. diff --git a/docs/source/tutorials/example-with-datasets.rst b/docs/source/tutorials/example-with-datasets.rst deleted file mode 100644 index 5d8ae551..00000000 --- a/docs/source/tutorials/example-with-datasets.rst +++ /dev/null @@ -1,550 +0,0 @@ -Fitting a Hospital Admissions-only Model -======================================== - -This document illustrates how a hospital admissions-only model can be -fitted using data from the Pyrenew package, particularly the wastewater -dataset. The CFA wastewater team created this dataset, which contains -simulated data. - -Model definition ----------------- - -In this section, we provide the formal definition of the model. The -hospitalization model is a semi-mechanistic model that describes the -number of observed hospital admissions as a function of a set of latent -variables. Mainly, the observed number of hospital admissions is -discretely distributed with location at the number of latent hospital -admissions: - -.. math:: - - - h(t) \sim \text{HospDist}\left(H(t)\right) - -Where :math:`h(t)` is the observed number of hospital admissions at time -:math:`t`, and :math:`H(t)` is the number of latent hospital admissions -at time :math:`t`. The distribution :math:`\text{HospDist}` is discrete. -For this example, we will use a negative binomial distribution: - -.. math:: - - - \begin{align*} - h(t) & \sim \text{NegativeNinomial}\left(\text{concentration} = 1, \text{mean} = H(t)\right) \\ - H(t) & = \omega(t) p_\mathrm{hosp}(t) \sum_{\tau = 0}^{T_d} d(\tau) I(t-\tau) - \end{align*} - -Were :math:`d(\tau)` is the infection to hospitalization interval, -:math:`I(t)` is the number of latent infections at time :math:`t`, -:math:`p_\mathrm{hosp}(t)` is the infection to hospitalization rate, and -:math:`\omega(t)` is the weekday effect at time :math:`t`; the last -section provides an example building a weekly effect ``RandomVariable``. - -The number of latent hospital admissions at time :math:`t` is a function -of the number of latent infections at time :math:`t` and the infection -to hospitalization rate. The latent infections are modeled as a renewal -process: - -.. math:: - - - \begin{align*} - I(t) &= R(t) \times \sum_{\tau < t} I(\tau) g(t - \tau) \\ - I(0) &\sim \text{LogNormal}(\mu = \log(80/0.05), \sigma = 1.5) - \end{align*} - -The reproductive number :math:`R(t)` is modeled as a random walk -process: - -.. math:: - - - \begin{align*} - R(t) & = R(t-1) + \epsilon\\ - \log{\epsilon} & \sim \text{Normal}(\mu=0, \sigma=0.1) \\ - R(0) &\sim \text{TruncatedNormal}(\text{loc}=1.2, \text{scale}=0.2, \text{min}=0) - \end{align*} - -Data processing ---------------- - -We start by loading the data and inspecting the first five rows. - -.. code:: python - - import polars as pl - from pyrenew import datasets - - dat = datasets.load_wastewater() - dat.head(5) - -.. container:: - - .. raw:: html - - - - shape: (5, 14) - - +---+------+---+----+---+---+------+---------+---+----+-------+---+---+----+ - | t | la | l | da | l | b | d | daily | p | f | h | s | w | in | - | | b_ww | o | te | o | e | aily | _hosp_a | o | or | osp_c | i | w | f_ | - | | tp_u | g | | d | l | _hos | dmits_f | p | ec | alibr | t | _ | pe | - | | niqu | _ | | _ | o | p_ad | or_eval | | as | ation | e | p | r_ | - | | e_id | c | | s | w | mits | | | t_ | _time | | o | ca | - | | | o | | e | _ | | | | da | | | p | pi | - | | | n | | w | L | | | | te | | | | ta | - | | | c | | a | O | | | | | | | | | - | | | | | g | D | | | | | | | | | - | | | | | e | | | | | | | | | | - +===+======+===+====+===+===+======+=========+===+====+=======+===+===+====+ - | i | i64 | f | s | f | i | i64 | i64 | f | s | i64 | i | f | f | - | 6 | | 6 | tr | 6 | 6 | | | 6 | tr | | 6 | 6 | 64 | - | 4 | | 4 | | 4 | 4 | | | 4 | | | 4 | 4 | | - +---+------+---+----+---+---+------+---------+---+----+-------+---+---+----+ - | 1 | 1 | n | “2 | n | n | 6 | 6 | 1 | “2 | 90 | 1 | 4 | 0. | - | | | u | 02 | u | u | | | e | 02 | | | 0 | 00 | - | | | l | 3- | l | l | | | 6 | 4- | | | 0 | 06 | - | | | l | 10 | l | l | | | | 02 | | | 0 | 63 | - | | | | -3 | | | | | | -0 | | | 0 | | - | | | | 0” | | | | | | 5” | | | 0 | | - | | | | | | | | | | | | | . | | - | | | | | | | | | | | | | 0 | | - +---+------+---+----+---+---+------+---------+---+----+-------+---+---+----+ - | 1 | 2 | n | “2 | n | n | 6 | 6 | 1 | “2 | 90 | 1 | 4 | 0. | - | | | u | 02 | u | u | | | e | 02 | | | 0 | 00 | - | | | l | 3- | l | l | | | 6 | 4- | | | 0 | 06 | - | | | l | 10 | l | l | | | | 02 | | | 0 | 63 | - | | | | -3 | | | | | | -0 | | | 0 | | - | | | | 0” | | | | | | 5” | | | 0 | | - | | | | | | | | | | | | | . | | - | | | | | | | | | | | | | 0 | | - +---+------+---+----+---+---+------+---------+---+----+-------+---+---+----+ - | 1 | 3 | n | “2 | n | n | 6 | 6 | 1 | “2 | 90 | 2 | 2 | 0. | - | | | u | 02 | u | u | | | e | 02 | | | 0 | 00 | - | | | l | 3- | l | l | | | 6 | 4- | | | 0 | 06 | - | | | l | 10 | l | l | | | | 02 | | | 0 | 63 | - | | | | -3 | | | | | | -0 | | | 0 | | - | | | | 0” | | | | | | 5” | | | 0 | | - | | | | | | | | | | | | | . | | - | | | | | | | | | | | | | 0 | | - +---+------+---+----+---+---+------+---------+---+----+-------+---+---+----+ - | 1 | 4 | n | “2 | n | n | 6 | 6 | 1 | “2 | 90 | 3 | 1 | 0. | - | | | u | 02 | u | u | | | e | 02 | | | 0 | 00 | - | | | l | 3- | l | l | | | 6 | 4- | | | 0 | 06 | - | | | l | 10 | l | l | | | | 02 | | | 0 | 63 | - | | | | -3 | | | | | | -0 | | | 0 | | - | | | | 0” | | | | | | 5” | | | 0 | | - | | | | | | | | | | | | | . | | - | | | | | | | | | | | | | 0 | | - +---+------+---+----+---+---+------+---------+---+----+-------+---+---+----+ - | 1 | 5 | n | “2 | n | n | 6 | 6 | 1 | “2 | 90 | 4 | 5 | 0. | - | | | u | 02 | u | u | | | e | 02 | | | 0 | 00 | - | | | l | 3- | l | l | | | 6 | 4- | | | 0 | 06 | - | | | l | 10 | l | l | | | | 02 | | | 0 | 63 | - | | | | -3 | | | | | | -0 | | | 0 | | - | | | | 0” | | | | | | 5” | | | . | | - | | | | | | | | | | | | | 0 | | - +---+------+---+----+---+---+------+---------+---+----+-------+---+---+----+ - -The data shows one entry per site, but the way it was simulated, the -number of admissions is the same across sites. Thus, we will only keep -the first observation per day. - -.. code:: python - - # Keeping the first observation of each date - dat = dat.group_by("date").first().select(["date", "daily_hosp_admits"]) - - # Now, sorting by date - dat = dat.sort("date") - - # Keeping the first 90 days - dat = dat.head(90) - - dat.head(5) - -.. container:: - - .. raw:: html - - - - shape: (5, 2) - - ============ ================= - date daily_hosp_admits - ============ ================= - str i64 - “2023-10-30” 6 - “2023-10-31” 8 - “2023-11-01” 4 - “2023-11-02” 8 - “2023-11-03” 4 - ============ ================= - -Let’s take a look at the daily prevalence of hospital admissions. - -.. code:: python - - import matplotlib.pyplot as plt - - # Rotating the x-axis labels, and only showing ~10 labels - ax = plt.gca() - ax.xaxis.set_major_locator(plt.MaxNLocator(nbins=10)) - ax.xaxis.set_tick_params(rotation=45) - plt.plot(dat["date"].to_numpy(), dat["daily_hosp_admits"].to_numpy()) - plt.xlabel("Date") - plt.ylabel("Admissions") - plt.show() - -Building the model ------------------- - -First, we will extract two datasets we will use as deterministic -quantities: the generation interval and the infection to hospitalization -interval. - -.. code:: python - - gen_int = datasets.load_generation_interval() - inf_hosp_int = datasets.load_infection_admission_interval() - - # We only need the probability_mass column of each dataset - gen_int = gen_int["probability_mass"].to_numpy() - inf_hosp_int = inf_hosp_int["probability_mass"].to_numpy() - - # Taking a pick at the first 5 elements of each - gen_int[:5], inf_hosp_int[:5] - - # Visualizing both quantities side by side - fig, axs = plt.subplots(1, 2) - - axs[0].plot(gen_int) - axs[0].set_title("Generation interval") - axs[1].plot(inf_hosp_int) - axs[1].set_title("Infection to hospitalization interval") - -:: - - Text(0.5, 1.0, 'Infection to hospitalization interval') - -With these two in hand, we can start building the model. First, we will -define the latent hospital admissions: - -.. code:: python - - from pyrenew import latent, deterministic - import jax.numpy as jnp - import numpyro.distributions as dist - - inf_hosp_int = deterministic.DeterministicPMF(inf_hosp_int) - - hosp_rate = latent.InfectHospRate( - dist=dist.LogNormal(jnp.log(0.05), 0.1) - ) - - latent_hosp = latent.HospitalAdmissions( - infection_to_admission_interval=inf_hosp_int, - infect_hosp_rate_dist=hosp_rate, - ) - -:: - - /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html - from .autonotebook import tqdm as notebook_tqdm - An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu. - -The ``inf_hosp_int`` is a ``DeterministicPMF`` object that takes the -infection to hospitalization interval as input. The ``hosp_rate`` is an -``InfectHospRate`` object that takes the infection to hospitalization -rate as input. The ``HospitalAdmissions`` class is a ``RandomVariable`` -that takes two distributions as inputs: the infection to admission -interval and the infection to hospitalization rate. Now, we can define -the rest of the other components: - -.. code:: python - - from pyrenew import model, process, observation - - # Infection process - latent_inf = latent.Infections() - I0 = latent.Infections0(I0_dist=dist.LogNormal(loc=jnp.log(80/.05), scale=1.5)) - - # Generation interval and Rt - gen_int = deterministic.DeterministicPMF(gen_int) - rtproc = process.RtRandomWalkProcess( - Rt_rw_dist=dist.Normal(0, 0.1) - ) - - # The observation model - obs = observation.NegativeBinomialObservation(concentration_prior=1.0) - -Notice all the components are ``RandomVariable`` instances. We can now -build the model: - -.. code:: python - - hosp_model = model.HospitalizationsModel( - latent_infections=latent_inf, - latent_hospitalizations=latent_hosp, - I0=I0, - gen_int=gen_int, - Rt_process=rtproc, - observation_process=obs, - ) - -Let’s simulate to check if the model is working: - -.. code:: python - - import numpyro as npro - import numpy as np - - timeframe = 120 - - np.random.seed(223) - with npro.handlers.seed(rng_seed = np.random.randint(1, timeframe)): - sim_data = hosp_model.sample(n_timepoints=timeframe) - -.. code:: python - - import matplotlib.pyplot as plt - - fig, axs = plt.subplots(1, 2) - - # Rt plot - axs[0].plot(range(0, timeframe + 1), sim_data.Rt) - axs[0].set_ylabel('Rt') - - # Infections plot - axs[1].plot(range(0, timeframe + 1), sim_data.sampled_admissions) - axs[1].set_ylabel('Infections') - axs[1].set_yscale('log') - - fig.suptitle('Basic renewal model') - fig.supxlabel('Time') - plt.tight_layout() - plt.show() - -.. figure:: example-with-datasets_files/figure-commonmark/basic-fig-output-1.png - :alt: Rt and Infections - - Rt and Infections - -Fitting the model ------------------ - -We can fit the model to the data. We will use the ``run`` method of the -model object. The two inputs this model requires are ``n_timepoints`` -and ``observed_hospitalizations`` - -.. code:: python - - import jax - - hosp_model.run( - num_samples=2000, - num_warmup=2000, - n_timepoints=dat.shape[0] - 1, - observed_hospitalizations=dat["daily_hosp_admits"].to_numpy(), - rng_key=jax.random.PRNGKey(54), - mcmc_args=dict(progress_bar=False), - ) - -We can use the ``plot_posterior`` method to visualize the results [1]_: - -.. code:: python - - out = hosp_model.plot_posterior( - var="predicted_hospitalizations", - ylab="Hospital Admissions", - obs_signal=dat["daily_hosp_admits"].to_numpy(), - ) - -.. figure:: example-with-datasets_files/figure-commonmark/output-hospital-admissions-output-1.png - :alt: Hospital Admissions posterior distribution - - Hospital Admissions posterior distribution - -The first half of the model is not looking good. The reason is that the -infection to hospitalization interval PMF makes it unlikely to observe -admissions from the beginning. The following section shows how to fix -this. - -Padding the model ------------------ - -We can use the padding argument to solve the overestimation of -hospitalizations in the first half of the model. By setting -``padding > 0``, the model then assumes that the first ``padding`` -observations are missing; thus, only observations after ``padding`` will -count towards the likelihood of the model. In practice, the model will -extend the estimated Rt and latent infections by ``padding`` days, given -time to adjust to the observed data. The following code will add 21 days -of missing data at the beginning of the model and re-estimate it with -``padding = 21``: - -.. code:: python - - days_to_impute = 21 - - dat2 = dat["daily_hosp_admits"].to_numpy() - - # Add 21 Nas to the beginning of dat2 - dat2 = np.hstack((np.repeat(np.nan, days_to_impute), dat2)) - - hosp_model.run( - num_samples=2000, - num_warmup=2000, - n_timepoints=dat2.shape[0] - 1, - observed_hospitalizations=dat2, - rng_key=jax.random.PRNGKey(54), - mcmc_args=dict(progress_bar=False), - padding=days_to_impute, # Padding the model - ) - -And plotting the results: - -.. code:: python - - out = hosp_model.plot_posterior( - var="predicted_hospitalizations", - ylab="Hospital Admissions", - obs_signal=dat2, - ) - -.. figure:: example-with-datasets_files/figure-commonmark/output-admissions-with-padding-output-1.png - :alt: Hospital Admissions posterior distribution - - Hospital Admissions posterior distribution - -We can also take a look at the latent infections: - -.. code:: python - - out2 = hosp_model.plot_posterior( - var="latent_infections", - ylab="Latent Infections" - ) - -.. figure:: example-with-datasets_files/figure-commonmark/output-infections-with-padding-output-1.png - :alt: Hospital Admissions posterior distribution - - Hospital Admissions posterior distribution - -Round 2: Incorporating weekday effects --------------------------------------- - -We will re-use the infection to admission interval and infection to -hospitalization rate from the previous model. But we will also add a -weekday effect distribution. To do this, we will create a new instance -of ``RandomVariable`` to model the weekday effect. The weekday effect -will be a truncated normal distribution with a mean of 1.0 and a -standard deviation of 0.5. The distribution will be truncated between -0.1 and 10.0. The weekday effect will be repeated for the number of -weeks in the dataset. - -.. code:: python - - from pyrenew import metaclass - import numpyro as npro - - class WeekdayEffect(metaclass.RandomVariable): - """Weekday effect distribution""" - def __init__(self, len: int): - """ Initialize the weekday effect distribution - Parameters - ---------- - len : int - The number of observations - """ - self.nweeks = jnp.ceil(len/7).astype(int) - self.len = len - - @staticmethod - def validate(): - return None - - def sample(self, **kwargs): - ans = npro.sample( - name="weekday_effect", - fn=npro.distributions.TruncatedNormal( - loc=1.0, scale=.5, low=0.1, high=10.0 - ), - sample_shape=(7,) - ) - - return jnp.tile(ans, self.nweeks)[:self.len] - - # Initializing the weekday effect - weekday_effect = WeekdayEffect(dat.shape[0]) - -Notice that the instance’s ``nweeks`` and ``len`` members are passed -during construction. Trying to compute the number of weeks and the -length of the dataset in the ``validate`` method will raise a ``jit`` -error in ``jax`` as the shape and size of elements are not known during -the validation step, which happens before the model is run. With the new -weekday effect, we can rebuild the latent hospitalization model: - -.. code:: python - - latent_hosp_wday_effect = latent.HospitalAdmissions( - infection_to_admission_interval=inf_hosp_int, - infect_hosp_rate_dist=hosp_rate, - weekday_effect_dist=weekday_effect, - ) - - hosp_model_weekday = model.HospitalizationsModel( - latent_infections=latent_inf, - latent_hospitalizations=latent_hosp_wday_effect, - I0=I0, - gen_int=gen_int, - Rt_process=rtproc, - observation_process=obs, - ) - -Running the model (with the same padding as before): - -.. code:: python - - hosp_model_weekday.run( - num_samples=2000, - num_warmup=2000, - n_timepoints=dat2.shape[0] - 1, - observed_hospitalizations=dat2, - rng_key=jax.random.PRNGKey(54), - mcmc_args=dict(progress_bar=False), - padding=days_to_impute, - ) - -And plotting the results: - -.. code:: python - - out = hosp_model_weekday.plot_posterior( - var="predicted_hospitalizations", - ylab="Hospital Admissions", - obs_signal=dat2, - ) - -.. figure:: example-with-datasets_files/figure-commonmark/output-admissions-padding-and-weekday-output-1.png - :alt: Hospital Admissions posterior distribution - - Hospital Admissions posterior distribution - -.. [1] - The output is captured to avoid ``quarto`` from displaying the output - twice. diff --git a/docs/source/tutorials/getting-started.rst b/docs/source/tutorials/getting-started.rst deleted file mode 100644 index bf2cb602..00000000 --- a/docs/source/tutorials/getting-started.rst +++ /dev/null @@ -1,282 +0,0 @@ -Getting started with pyrenew -============================ - -``pyrenew`` is a flexible tool for simulating and making statistical -inference of epidemiological models, emphasizing renewal models. Built -on ``numpyro``, ``pyrenew`` provides core components for model building -and pre-defined models for processing various observational processes. -This document illustrates how ``pyrenew`` can be used to build a basic -renewal model. - -The fundamentals ----------------- - -``pyrenew``\ ’s core components are the metaclasses ``RandomVariable`` -and ``Model``. From the package’s perspective, a ``RandomVariable`` is a -quantity models can sample and estimate, **including deterministic -quantities**. Mainly, sampling from a ``RandomVariable`` involves -calling the ``sample()`` method. The benefit of this design is the -definition of the sample function can be arbitrary, allowing the user to -either sample from a distribution using ``numpyro.sample()``, compute -fixed quantities (like a mechanistic equation), or return a fixed value -(like a pre-computed PMF.) For instance, we may be interested in -estimating a PMF, in which case a ``RandomVariable`` sampling function -may roughly be defined as: - -.. code:: python - - class MyRandVar(RandomVariable): - def sample(...) -> ArrayLike: - return numpyro.sample(...) - -Whereas, in some other cases, we may instead use a fixed quantity for -that variable (like a pre-computed PMF), where the -``RandomVariable``\ ’s sample function could be defined like: - -.. code:: python - - class MyRandVar(RandomVariable): - def sample(...) -> ArrayLike: - return jax.numpy.array([0.2, 0.7, 0.1]) - -This way, when a ``Model`` samples from ``MyRandVar``, it could be -either adding random variables to be estimated (first case) or just -retrieving some quantity needed for other calculations (second case.) - -The ``Model`` metaclass provides basic functionality for estimating and -simulation. Like ``RandomVariable``, the ``Model`` metaclass has a -``sample()`` method that defines the model structure. Ultimately, models -can be nested (or inherited), providing a straightforward way to add -layers of complexity. - -‘Hello world’ model -------------------- - -This section will show the steps to build a simple renewal model -featuring a latent infection process, a random walk Rt process, and an -observation process for the reported infections. We start by loading the -needed components to build a basic renewal model: - -.. code:: python - - import jax.numpy as jnp - import numpy as np - import numpyro as npro - import numpyro.distributions as dist - from pyrenew.process import RtRandomWalkProcess - from pyrenew.latent import Infections, Infections0 - from pyrenew.observation import PoissonObservation - from pyrenew.deterministic import DeterministicPMF - from pyrenew.model import RtInfectionsRenewalModel - -The basic renewal model defines five components: generation interval, -initial infections, Rt, latent infections, and observed infections. In -this example, the generation interval is not estimated but passed as a -deterministic instance of ``RandomVariable``. Here is the code to -initialize the five components: - -.. code:: python - - # (1) The generation interval (deterministic) - gen_int = DeterministicPMF(jnp.array([0.25, 0.25, 0.25, 0.25])) - - # (2) Initial infections (inferred with a prior) - I0 = Infections0(I0_dist=dist.LogNormal(0, 1)) - - # (3) The random process for Rt - rt_proc = RtRandomWalkProcess() - - # (4) Latent infection process (which will use 1 and 2) - latent_infections = Infections() - - # (5) The observed infections process (with mean at the latent infections) - observation_process = PoissonObservation() - -With these five pieces, we can build the basic renewal model: - -.. code:: python - - model1 = RtInfectionsRenewalModel( - gen_int = gen_int, - I0 = I0, - Rt_process = rt_proc, - latent_infections = latent_infections, - observation_process = observation_process, - ) - -The following diagram summarizes how the modules interact via -composition; notably, ``gen_int``, ``I0``, ``rt_proc``, -``latent_infections``, and ``observed_infections`` are instances of -``RandomVariable``, which means these can be easily replaced to generate -a different version of ``RtInfectionsRenewalModel``: - -.. code:: mermaid - - flowchart TB - genint["(1) gen_int\n(DetermnisticPMF)"] - i0["(2) I0\n(Infections0)"] - rt["(3) rt_proc\n(RtRandomWalkProcess)"] - inf["(4) latent_infections\n(Infections)"] - obs["(5) observation_process\n(PoissonObservation)"] - - model1["model1\n(RtInfectionsRenewalModel)"] - - i0-->|Composes|model1 - genint-->|Composes|model1 - rt-->|Composes|model1 - obs-->|Composes|model1 - inf-->|Composes|model1 - -Using ``numpyro``, we can simulate data using the ``sample()`` member -function of ``RtInfectionsRenewalModel``: - -.. code:: python - - np.random.seed(223) - with npro.handlers.seed(rng_seed = np.random.randint(1, 60)): - sim_data = model1.sample(n_timepoints=30) - - sim_data - -:: - - RtInfectionsRenewalSample(Rt=[1.2022278 1.2111099 1.2325984 1.2104921 1.2023039 1.1970979 1.2384264 - 1.2423582 1.245498 1.241344 1.2081108 1.1938375 1.271196 1.3189521 - 1.3054799 1.3165426 1.291952 1.3026639 1.2619467 1.2852622 1.3121517 - 1.2888998 1.2641873 1.2580931 1.2545817 1.3092988 1.2488269 1.2397509 - 1.2071848 1.2334517 1.21868 ], latent_infections=[ 2.3215084 3.0415602 4.0327816 5.180868 4.381411 4.978916 - 5.750626 6.3024273 6.66758 7.354823 7.8755097 8.416656 - 9.63394 10.973988 12.043082 13.516833 14.911659 16.75407 - 18.053928 20.318869 22.975292 25.166464 27.34265 30.13236 - 33.126217 37.89362 40.11695 43.784634 46.754696 51.974545 - 55.642136 ], sampled_infections=[ 1 2 3 5 4 4 7 4 8 4 7 3 8 12 13 18 14 20 17 18 28 27 36 37 - 26 31 40 27 48 54 60]) - -The ``sample()`` method of the ``RtInfectionsRenewalModel`` returns a -list composed of the ``Rt`` and ``infections`` sequences. - -.. code:: python - - import matplotlib.pyplot as plt - - fig, axs = plt.subplots(1, 2) - - # Rt plot - axs[0].plot(range(0, 31), sim_data.Rt) - axs[0].set_ylabel('Rt') - - # Infections plot - axs[1].plot(range(0, 31), sim_data.sampled_infections) - axs[1].set_ylabel('Infections') - - fig.suptitle('Basic renewal model') - fig.supxlabel('Time') - plt.tight_layout() - plt.show() - -.. figure:: getting-started_files/figure-commonmark/basic-fig-output-1.png - :alt: Rt and Infections - - Rt and Infections - -To fit the model, we can use the ``run()`` method of the model -``RtInfectionsRenewalModel``; an inherited method from the metaclass -``Model``: - -.. code:: python - - import jax - - model1.run( - num_warmup=2000, - num_samples=1000, - observed_infections=sim_data.sampled_infections, - n_timepoints = len(sim_data[1])-1, - rng_key=jax.random.PRNGKey(54), - mcmc_args=dict(progress_bar=False), - ) - -Now, let’s investigate the output, particularly the posterior -distribution of the Rt estimates: - -.. code:: python - - import polars as pl - samps = model1.spread_draws([('Rt', 'time')]) - - fig, ax = plt.subplots(figsize=[4, 5]) - - ax.plot(sim_data.Rt) - samp_ids = np.random.randint(size=25, low=0, high=999) - for samp_id in samp_ids: - sub_samps = samps.filter(pl.col("draw") == samp_id).sort(pl.col('time')) - ax.plot(sub_samps.select("time").to_numpy(), - sub_samps.select("Rt").to_numpy(), color="darkblue", alpha=0.1) - ax.set_ylim([0.4, 1/.4]) - ax.set_yticks([0.5, 1, 2]) - ax.set_yscale("log") - -.. figure:: getting-started_files/figure-commonmark/output-rt-output-1.png - :alt: Rt posterior distribution - - Rt posterior distribution - -Architecture of pyrenew ------------------------ - -``pyrenew`` leverages ``numpyro``\ ’s flexibility to build models via -composition. As a principle, most objects in ``pyrenew`` can be treated -as random variables we can sample. At the top-level ``pyrenew`` has two -metaclass from which most objects inherit: ``RandomVariable`` and -``Model``. From them, the following four sub-modules arise: - -- The ``process`` sub-module, -- The ``deterministic`` sub-module, -- The ``observation`` sub-module, -- The ``latent`` sub-module, and -- The ``models`` sub-module - -The first four are collections of instances of ``RandomVariable``, and -the last is a collection of instances of ``Model``. The following -diagram shows a detailed view of how meta classes, modules, and classes -interact to create the ``RtInfectionsRenewalModel`` instantiated in the -previous section: - -.. code:: mermaid - - flowchart LR - rand((RandomVariable\nmetaclass)) - models((Model\nmetaclass)) - - subgraph observations[Observations module] - obs["observation_process\n(PoissonObservation)"] - end - - subgraph latent[Latent module] - inf["latent_infections\n(Infections)"] - i0["I0\n(Infections0)"] - end - - subgraph process[Process module] - rt["rt_proc\n(RtRandomWalkProcess)"] - end - - subgraph deterministic[Deterministic module] - detpmf["gen_int\n(DeterministicPMF)"] - end - - subgraph model[Model module] - model1["model1\n(RtInfectionsRenewalModel)"] - end - - rand-->|Inherited by|observations - rand-->|Inherited by|process - rand-->|Inherited by|latent - rand-->|Inherited by|deterministic - models-->|Inherited by|model - - detpmf-->|Composes|model1 - i0-->|Composes|model1 - rt-->|Composes|model1 - obs-->|Composes|model1 - inf-->|Composes|model1 diff --git a/docs/source/tutorials/index.rst b/docs/source/tutorials/index.rst deleted file mode 100644 index 50151d10..00000000 --- a/docs/source/tutorials/index.rst +++ /dev/null @@ -1,9 +0,0 @@ -Tutorials Reference -============== - -.. toctree:: - :maxdepth: 3 - - getting-started - example-with-datasets - pyrenew_demo diff --git a/docs/source/tutorials/pyrenew_demo.rst b/docs/source/tutorials/pyrenew_demo.rst deleted file mode 100644 index 58b9a9a2..00000000 --- a/docs/source/tutorials/pyrenew_demo.rst +++ /dev/null @@ -1,311 +0,0 @@ -Pyrenew demo -============ - -This demo simulates some basic renewal process data and then fits to it -using ``pyrenew``. - -Assuming you’ve already installed Python and pip, you’ll need to first -install ``pyrenew``: - -.. code:: python - - pip install pyrenew - -You’ll also need working installations of ``matplotlib``, ``numpy``, -``jax``, ``numpyro``, and ``polars``: - -.. code:: python - - pip install matplotlib numpy jax numpyro polars - -To begin, run the following import section to call external modules and -functions necessary to run the ``pyrenew`` demo. The ``import`` -statement imports the module and the ``as`` statement renames the module -for use within this script. The ``from`` statement imports a specific -function from a module (named after the ``.``) within a package (named -before the ``.``). - -.. code:: python - - import matplotlib as mpl - import matplotlib.pyplot as plt - import jax - import jax.numpy as jnp - import numpy as np - from numpyro.handlers import seed - import numpyro.distributions as dist - -.. code:: python - - from pyrenew.process import SimpleRandomWalkProcess - -:: - - An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu. - -To understand the simple random walk process underlying the sampling -within the renewal process model, we first examine a single random walk -path. Using the ``sample`` method from an instance of the -``SimpleRandomWalkProcess`` class, we first create an instance of the -``SimpleRandomWalkProcess`` class with a normal distribution of mean = 0 -and standard deviation = 0.0001 as its input. Next, the ``with`` -statement sets the seed for the random number generator for the duration -of the block that follows. Inside the ``with`` block, the -``q_samp = q.sample(duration=100)`` generates the sample instance over a -duration of 100 time units. Finally, this single random walk process is -visualized using ``matplot.pyplot`` to plot the exponential of the -sample instance. - -.. code:: python - - np.random.seed(3312) - q = SimpleRandomWalkProcess(dist.Normal(0, 0.001)) - with seed(rng_seed=np.random.randint(0,1000)): - q_samp = q.sample(duration=100) - - plt.plot(np.exp(q_samp[0])) - -|image0| - -Next, import several additional functions from the ``latent`` module of -the ``pyrenew`` package to model infections, hospital admissions, -initial infections, and hospitalization rate due to infection. - -.. code:: python - - from pyrenew.latent import ( - Infections, HospitalAdmissions, Infections0, InfectHospRate, - ) - -Additionally, import several classes from Pyrenew, including a Poisson -observation process, determininstic PMF and variable classes, the -Pyrenew hospitalization model, and a renewal modle (Rt) random walk -process: - -.. code:: python - - from pyrenew.observation import PoissonObservation - from pyrenew.deterministic import DeterministicPMF, DeterministicVariable - from pyrenew.model import HospitalizationsModel - from pyrenew.process import RtRandomWalkProcess - -To initialize the model, we first define initial conditions, including: - -1) deterministic generation time, defined as an instance of the - ``DeterministicPMF`` class, which gives the probability of each - possible outcome for a discrete random variable given as a JAX NumPy - array of four possible outcomes - -2) initial infections at the start of simulation as a log-normal - distribution with mean = 0 and standard deviation = 1 - -3) latent infections as an instance of the ``Infections`` class with - default settings - -4) latent hospitalization process, modeled by first defining the time - interval from infections to hospitalizations as a - ``DeterministicPMF`` input with 18 possible outcomes and - corresponding probabilities given by the values in the array. The - ``HospitalAdmissions`` function then takes in this defined time - interval, as well as defining the rate at which infections are - admitted to the hospital due to infection, modeled as a log-normal - distribution with mean = ``jnp.log(0.05)`` and standard deviation = - 0.05. - -5) hospitalization observation process, modeled with a Poisson - distribution - -6) an Rt random walk process with default settings - -.. code:: python - - # Initializing model components: - - # 1) A deterministic generation time - gen_int = DeterministicPMF(jnp.array([0.25, 0.25, 0.25, 0.25])) - - # 2) Initial infections - I0 = Infections0(I0_dist=dist.LogNormal(0, 1)) - - # 3) The latent infections process - latent_infections = Infections() - - # 4) The latent hospitalization process: - - # First, define a deterministic infection to hosp pmf - inf_hosp_int = DeterministicPMF( - jnp.array([0, 0, 0,0,0,0,0,0,0,0,0,0,0, 0.25, 0.5, 0.1, 0.1, 0.05]), - ) - - latent_hospitalizations = HospitalAdmissions( - infection_to_admission_interval=inf_hosp_int, - infect_hosp_rate_dist = InfectHospRate( - dist=dist.LogNormal(jnp.log(0.05), 0.05), - ), - ) - - # 5) An observation process for the hospitalizations - obs_process = PoissonObservation() - - # 6) A random walk process (it could be deterministic using - # pyrenew.process.DeterministicProcess()) - Rt_process = RtRandomWalkProcess() - -The ``HospitalizationsModel`` is then initialized using the initial -conditions just defined: - -.. code:: python - - # Initializing the model - hospmodel = HospitalizationsModel( - gen_int=gen_int, - I0=I0, - latent_hospitalizations=latent_hospitalizations, - observation_process=obs_process, - latent_infections=latent_infections, - Rt_process=Rt_process - ) - -Next, we sample from the ``hospmodel`` for 30 time steps and view the -output of a single run: - -.. code:: python - - with seed(rng_seed=np.random.randint(1, 60)): - x = hospmodel.sample(n_timepoints=30) - x - -:: - - HospModelSample(Rt=[1.1791104 1.1995267 1.1772177 1.1913829 1.2075942 1.1444623 1.1514508 - 1.1976782 1.2292639 1.1719677 1.204649 1.2323451 1.2466507 1.2800207 - 1.2749145 1.2619376 1.2189837 1.2192641 1.2290158 1.2128737 1.1908046 - 1.2174997 1.1941082 1.2084603 1.1965215 1.2248698 1.2308019 1.2426206 - 1.2131014 1.207159 1.1837622], latent_infections=[0.05214045 0.06867922 0.08761451 0.11476436 0.09757317 0.10547114 - 0.1167062 0.13010225 0.13824694 0.14372033 0.15924728 0.17601486 - 0.19236736 0.21483542 0.23664482 0.25865382 0.27503362 0.30029488 - 0.3289544 0.35262382 0.37418258 0.41274938 0.43839005 0.47672123 - 0.50913286 0.5625195 0.6113282 0.67092246 0.7138808 0.77217466 - 0.819254 ], IHR=0.04929916560649872, latent_admissions=[0. 0. 0. 0. 0. 0. - 0. 0. 0. 0. 0. 0. - 0. 0.00064262 0.0021317 0.00302979 0.00416974 0.0049305 - 0.00487205 0.00530097 0.00576412 0.00624666 0.00665578 0.00711595 - 0.0078055 0.00854396 0.00939666 0.01042083 0.0114624 0.01246538 - 0.01345188], sampled_admissions=[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0]) - -Visualizations of the single model output show (top) infections over the -30 time steps, (middle) hospitalizations over the 30 time steps, and -(bottom) - -.. code:: python - - fig, ax = plt.subplots(nrows=3, sharex=True) - ax[0].plot(x.latent_infections) - ax[0].set_ylim([1/5, 5]) - ax[1].plot(x.latent_admissions) - ax[2].plot(x.sampled_admissions, 'o') - for axis in ax[:-1]: - axis.set_yscale("log") - -|image1| - -To fit the ``hospmodel`` to the simulated data, we call -``hospmodel.run()``, an MCMC algorithm, with the arguments generated in -``hospmodel`` object, using 1000 warmup stepts and 1000 samples to draw -from the posterior distribution of the model parameters. The model is -run for ``len(x.sampled_admissions)-1`` time steps with the seed set by -``jax.random.PRNGKey()`` - -.. code:: python - - # from numpyro.infer import MCMC, NUTS - hospmodel.run( - num_warmup=1000, - num_samples=1000, - observed_hospitalizations=x.sampled_admissions, - n_timepoints = len(x.sampled_admissions)-1, - rng_key=jax.random.PRNGKey(54), - mcmc_args=dict(progress_bar=False), - ) - -Print a summary of the model: - -.. code:: python - - hospmodel.print_summary() - -:: - - mean std median 5.0% 95.0% n_eff r_hat - I0 1.27 1.10 0.97 0.10 2.42 1132.34 1.00 - IHR 0.05 0.00 0.05 0.05 0.05 2306.45 1.00 - Rt0 1.23 0.17 1.23 0.93 1.48 1327.22 1.00 - Rt_transformed_rw_diffs[0] -0.00 0.02 -0.00 -0.04 0.04 1404.95 1.00 - Rt_transformed_rw_diffs[1] 0.00 0.03 0.00 -0.04 0.04 2280.86 1.00 - Rt_transformed_rw_diffs[2] -0.00 0.02 -0.00 -0.04 0.04 2119.83 1.00 - Rt_transformed_rw_diffs[3] 0.00 0.02 -0.00 -0.04 0.04 2196.86 1.00 - Rt_transformed_rw_diffs[4] 0.00 0.02 -0.00 -0.03 0.04 2391.45 1.00 - Rt_transformed_rw_diffs[5] 0.00 0.03 0.00 -0.04 0.04 2043.02 1.00 - Rt_transformed_rw_diffs[6] 0.00 0.02 0.00 -0.04 0.04 1514.40 1.00 - Rt_transformed_rw_diffs[7] -0.00 0.02 -0.00 -0.04 0.04 2619.69 1.00 - Rt_transformed_rw_diffs[8] 0.00 0.03 0.00 -0.04 0.04 1883.84 1.00 - Rt_transformed_rw_diffs[9] 0.00 0.03 0.00 -0.04 0.04 2015.66 1.00 - Rt_transformed_rw_diffs[10] 0.00 0.02 0.00 -0.04 0.04 2045.47 1.00 - Rt_transformed_rw_diffs[11] -0.00 0.03 0.00 -0.04 0.04 1615.10 1.00 - Rt_transformed_rw_diffs[12] 0.00 0.02 0.00 -0.04 0.04 2206.32 1.00 - Rt_transformed_rw_diffs[13] 0.00 0.03 0.00 -0.04 0.04 1175.93 1.00 - Rt_transformed_rw_diffs[14] -0.00 0.03 -0.00 -0.04 0.04 1606.26 1.00 - Rt_transformed_rw_diffs[15] -0.00 0.03 -0.00 -0.04 0.04 2344.62 1.00 - Rt_transformed_rw_diffs[16] -0.00 0.02 0.00 -0.04 0.04 1522.33 1.00 - Rt_transformed_rw_diffs[17] 0.00 0.03 0.00 -0.04 0.04 2157.17 1.00 - Rt_transformed_rw_diffs[18] -0.00 0.02 -0.00 -0.04 0.04 1594.95 1.00 - Rt_transformed_rw_diffs[19] 0.00 0.03 -0.00 -0.04 0.04 1698.70 1.00 - Rt_transformed_rw_diffs[20] 0.00 0.02 0.00 -0.04 0.04 1726.18 1.00 - Rt_transformed_rw_diffs[21] 0.00 0.02 -0.00 -0.04 0.04 2386.35 1.00 - Rt_transformed_rw_diffs[22] 0.00 0.03 0.00 -0.04 0.04 2028.63 1.00 - Rt_transformed_rw_diffs[23] 0.00 0.02 0.00 -0.04 0.03 1669.71 1.00 - Rt_transformed_rw_diffs[24] 0.00 0.02 0.00 -0.04 0.04 2126.33 1.00 - Rt_transformed_rw_diffs[25] -0.00 0.02 -0.00 -0.04 0.04 2119.74 1.00 - Rt_transformed_rw_diffs[26] 0.00 0.03 0.00 -0.04 0.04 2657.91 1.00 - Rt_transformed_rw_diffs[27] -0.00 0.03 0.00 -0.04 0.04 1939.30 1.00 - Rt_transformed_rw_diffs[28] -0.00 0.02 -0.00 -0.04 0.04 1737.84 1.00 - Rt_transformed_rw_diffs[29] -0.00 0.03 -0.00 -0.04 0.04 2105.55 1.00 - - Number of divergences: 0 - -Next, we will use the ``spread_draws`` function from the -``pyrenew.mcmcutils`` module to process the MCMC samples. The -``spread_draws`` function reformats the samples drawn from the -``mcmc.get_samples()`` from the ``hospmodel``. The samples are simulated -Rt values over time. - -.. code:: python - - from pyrenew.mcmcutils import spread_draws - samps = spread_draws(hospmodel.mcmc.get_samples(), [("Rt", "time")]) - -We visualize these samples below, with individual possible Rt estimates -over time shown in light blue, and the overall mean estimate Rt shown in -dark blue. - -.. code:: python - - import numpy as np - import polars as pl - fig, ax = plt.subplots(figsize=[4, 5]) - - ax.plot(x[0]) - samp_ids = np.random.randint(size=25, low=0, high=999) - for samp_id in samp_ids: - sub_samps = samps.filter(pl.col("draw") == samp_id).sort(pl.col('time')) - ax.plot(sub_samps.select("time").to_numpy(), - sub_samps.select("Rt").to_numpy(), color="darkblue", alpha=0.1) - ax.set_ylim([0.4, 1/.4]) - ax.set_yticks([0.5, 1, 2]) - ax.set_yscale("log") - -|image2| - -.. |image0| image:: pyrenew_demo_files/figure-commonmark/fig-randwalk-output-1.png -.. |image1| image:: pyrenew_demo_files/figure-commonmark/fig-hosp-output-1.png -.. |image2| image:: pyrenew_demo_files/figure-commonmark/fig-sampled-rt-output-1.png diff --git a/model/Makefile b/model/Makefile index 6f9515d8..ca7c4e53 100644 --- a/model/Makefile +++ b/model/Makefile @@ -49,4 +49,5 @@ clean: rm -f docs/example-with-datasets.$$i; \ done -.PHONY: install test docs clean docs/notebooks docs/py test_images +.PHONY: install test docs clean docs/notebooks docs/py test_images \ + sphinx-tutorials diff --git a/model/docs/pyrenew_demo_files/figure-commonmark/fig-hosp-output-1.png b/model/docs/pyrenew_demo_files/figure-commonmark/fig-hosp-output-1.png index 73cb803fc8ea1b9b1f3d5ea5312ebcc9cbf3ecb1..d46590a6028bf8e6e94fe2e871e8278bbeaefeed 100644 GIT binary patch delta 41 wcmeyG|22Pthn$IyLPkkRL9vy-er{q(K~8>2PG*u`eo?x delta 41 wcmeyG|22Pthn%sFLPkkRL9vy-er{q(K~8>2PG*u`eo?yq@n;di8`JvD0cLd(8vpH6}j%B36AvP%GZ+z@8~ delta 43 zcmccklJVk8#t9yB#yScaB_##LR{Hw6i6sR&`6W4-NqYH3>H5c?MFek5%Ps)`h1C(_ diff --git a/model/docs/pyrenew_demo_files/figure-commonmark/fig-sampled-rt-output-1.png b/model/docs/pyrenew_demo_files/figure-commonmark/fig-sampled-rt-output-1.png index 7f94e989d16ceb16a1199be57b7159c24151992c..eea16418f256196e5a332cd3466c290648fa8a1f 100644 GIT binary patch delta 43 zcmaF4is|JlrU@Q$COQfkB_##LR{Hw6i6sR&`6W4-NqYH3>H6}j%B36An5O~&ZdDJ| delta 43 zcmaF4is|JlrU@Q$#yScaB_##LR{Hw6i6sR&`6W4-NqYH3>H5c?MFek5W1b2Cc}@{e From 2e8ab1d01b00297439e20fd7d3ab87ee39f2b9f8 Mon Sep 17 00:00:00 2001 From: "George G. 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The CFA wastewater team created this dataset, which contains -simulated data. - -## Model definition - -In this section, we provide the formal definition of the model. The -hospitalization model is a semi-mechanistic model that describes the -number of observed hospital admissions as a function of a set of latent -variables. Mainly, the observed number of hospital admissions is -discretely distributed with location at the number of latent hospital -admissions: - -$$ -h(t) \sim \text{HospDist}\left(H(t)\right) -$$ - -Where $h(t)$ is the observed number of hospital admissions at time $t$, -and $H(t)$ is the number of latent hospital admissions at time $t$. The -distribution $\text{HospDist}$ is discrete. For this example, we will -use a negative binomial distribution: - -$$ -\begin{align*} -h(t) & \sim \text{NegativeNinomial}\left(\text{concentration} = 1, \text{mean} = H(t)\right) \\ -H(t) & = \omega(t) p_\mathrm{hosp}(t) \sum_{\tau = 0}^{T_d} d(\tau) I(t-\tau) -\end{align*} -$$ - -Were $d(\tau)$ is the infection to hospitalization interval, $I(t)$ is -the number of latent infections at time $t$, $p_\mathrm{hosp}(t)$ is the -infection to hospitalization rate, and $\omega(t)$ is the weekday effect -at time $t$; the last section provides an example building a weekly -effect `RandomVariable`. - -The number of latent hospital admissions at time $t$ is a function of -the number of latent infections at time $t$ and the infection to -hospitalization rate. The latent infections are modeled as a renewal -process: - -$$ -\begin{align*} -I(t) &= R(t) \times \sum_{\tau < t} I(\tau) g(t - \tau) \\ -I(0) &\sim \text{LogNormal}(\mu = \log(80/0.05), \sigma = 1.5) -\end{align*} -$$ - -The reproductive number $R(t)$ is modeled as a random walk process: - -$$ -\begin{align*} -R(t) & = R(t-1) + \epsilon\\ -\log{\epsilon} & \sim \text{Normal}(\mu=0, \sigma=0.1) \\ -R(0) &\sim \text{TruncatedNormal}(\text{loc}=1.2, \text{scale}=0.2, \text{min}=0) -\end{align*} -$$ - -## Data processing - -We start by loading the data and inspecting the first five rows. - -``` python -import polars as pl -from pyrenew import datasets - -dat = datasets.load_wastewater() -dat.head(5) -``` - -
    -shape: (5, 14) - -| t | lab_wwtp_unique_id | log_conc | date | lod_sewage | below_LOD | daily_hosp_admits | daily_hosp_admits_for_eval | pop | forecast_date | hosp_calibration_time | site | ww_pop | inf_per_capita | -|-----|--------------------|----------|--------------|------------|-----------|-------------------|----------------------------|-----|---------------|-----------------------|------|----------|----------------| -| i64 | i64 | f64 | str | f64 | i64 | i64 | i64 | f64 | str | i64 | i64 | f64 | f64 | -| 1 | 1 | null | "2023-10-30" | null | null | 6 | 6 | 1e6 | "2024-02-05" | 90 | 1 | 400000.0 | 0.000663 | -| 1 | 2 | null | "2023-10-30" | null | null | 6 | 6 | 1e6 | "2024-02-05" | 90 | 1 | 400000.0 | 0.000663 | -| 1 | 3 | null | "2023-10-30" | null | null | 6 | 6 | 1e6 | "2024-02-05" | 90 | 2 | 200000.0 | 0.000663 | -| 1 | 4 | null | "2023-10-30" | null | null | 6 | 6 | 1e6 | "2024-02-05" | 90 | 3 | 100000.0 | 0.000663 | -| 1 | 5 | null | "2023-10-30" | null | null | 6 | 6 | 1e6 | "2024-02-05" | 90 | 4 | 50000.0 | 0.000663 | - -
    - -The data shows one entry per site, but the way it was simulated, the -number of admissions is the same across sites. Thus, we will only keep -the first observation per day. - -``` python -# Keeping the first observation of each date -dat = dat.group_by("date").first().select(["date", "daily_hosp_admits"]) - -# Now, sorting by date -dat = dat.sort("date") - -# Keeping the first 90 days -dat = dat.head(90) - -dat.head(5) -``` - -
    -shape: (5, 2) - -| date | daily_hosp_admits | -|--------------|-------------------| -| str | i64 | -| "2023-10-30" | 6 | -| "2023-10-31" | 8 | -| "2023-11-01" | 4 | -| "2023-11-02" | 8 | -| "2023-11-03" | 4 | - -
    - -Let’s take a look at the daily prevalence of hospital admissions. - -``` python -import matplotlib.pyplot as plt - -# Rotating the x-axis labels, and only showing ~10 labels -ax = plt.gca() -ax.xaxis.set_major_locator(plt.MaxNLocator(nbins=10)) -ax.xaxis.set_tick_params(rotation=45) -plt.plot(dat["date"].to_numpy(), dat["daily_hosp_admits"].to_numpy()) -plt.xlabel("Date") -plt.ylabel("Admissions") -plt.show() -``` - - - -## Building the model - -First, we will extract two datasets we will use as deterministic -quantities: the generation interval and the infection to hospitalization -interval. - -``` python -gen_int = datasets.load_generation_interval() -inf_hosp_int = datasets.load_infection_admission_interval() - -# We only need the probability_mass column of each dataset -gen_int = gen_int["probability_mass"].to_numpy() -inf_hosp_int = inf_hosp_int["probability_mass"].to_numpy() - -# Taking a pick at the first 5 elements of each -gen_int[:5], inf_hosp_int[:5] - -# Visualizing both quantities side by side -fig, axs = plt.subplots(1, 2) - -axs[0].plot(gen_int) -axs[0].set_title("Generation interval") -axs[1].plot(inf_hosp_int) -axs[1].set_title("Infection to hospitalization interval") -``` - - Text(0.5, 1.0, 'Infection to hospitalization interval') - - - -With these two in hand, we can start building the model. First, we will -define the latent hospital admissions: - -``` python -from pyrenew import latent, deterministic -import jax.numpy as jnp -import numpyro.distributions as dist - -inf_hosp_int = deterministic.DeterministicPMF(inf_hosp_int) - -hosp_rate = latent.InfectHospRate( - dist=dist.LogNormal(jnp.log(0.05), 0.1) -) - -latent_hosp = latent.HospitalAdmissions( - infection_to_admission_interval=inf_hosp_int, - infect_hosp_rate_dist=hosp_rate, - ) -``` - - /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html - from .autonotebook import tqdm as notebook_tqdm - An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu. - -The `inf_hosp_int` is a `DeterministicPMF` object that takes the -infection to hospitalization interval as input. The `hosp_rate` is an -`InfectHospRate` object that takes the infection to hospitalization rate -as input. The `HospitalAdmissions` class is a `RandomVariable` that -takes two distributions as inputs: the infection to admission interval -and the infection to hospitalization rate. Now, we can define the rest -of the other components: - -``` python -from pyrenew import model, process, observation - -# Infection process -latent_inf = latent.Infections() -I0 = latent.Infections0(I0_dist=dist.LogNormal(loc=jnp.log(80/.05), scale=1.5)) - -# Generation interval and Rt -gen_int = deterministic.DeterministicPMF(gen_int) -rtproc = process.RtRandomWalkProcess( - Rt_rw_dist=dist.Normal(0, 0.1) -) - -# The observation model -obs = observation.NegativeBinomialObservation(concentration_prior=1.0) -``` - -Notice all the components are `RandomVariable` instances. We can now -build the model: - -``` python -hosp_model = model.HospitalizationsModel( - latent_infections=latent_inf, - latent_hospitalizations=latent_hosp, - I0=I0, - gen_int=gen_int, - Rt_process=rtproc, - observation_process=obs, -) -``` - -Let’s simulate to check if the model is working: - -``` python -import numpyro as npro -import numpy as np - -timeframe = 120 - -np.random.seed(223) -with npro.handlers.seed(rng_seed = np.random.randint(1, timeframe)): - sim_data = hosp_model.sample(n_timepoints=timeframe) -``` - -``` python -import matplotlib.pyplot as plt - -fig, axs = plt.subplots(1, 2) - -# Rt plot -axs[0].plot(range(0, timeframe + 1), sim_data.Rt) -axs[0].set_ylabel('Rt') - -# Infections plot -axs[1].plot(range(0, timeframe + 1), sim_data.sampled_admissions) -axs[1].set_ylabel('Infections') -axs[1].set_yscale('log') - -fig.suptitle('Basic renewal model') -fig.supxlabel('Time') -plt.tight_layout() -plt.show() -``` - -![Rt and -Infections](example-with-datasets_files/figure-commonmark/basic-fig-output-1.png) - -## Fitting the model - -We can fit the model to the data. We will use the `run` method of the -model object. The two inputs this model requires are `n_timepoints` and -`observed_hospitalizations` - -``` python -import jax - -hosp_model.run( - num_samples=2000, - num_warmup=2000, - n_timepoints=dat.shape[0] - 1, - observed_hospitalizations=dat["daily_hosp_admits"].to_numpy(), - rng_key=jax.random.PRNGKey(54), - mcmc_args=dict(progress_bar=False), -) -``` - -We can use the `plot_posterior` method to visualize the results[^1]: - -``` python -out = hosp_model.plot_posterior( - var="predicted_hospitalizations", - ylab="Hospital Admissions", - obs_signal=dat["daily_hosp_admits"].to_numpy(), -) -``` - -![Hospital Admissions posterior -distribution](example-with-datasets_files/figure-commonmark/output-hospital-admissions-output-1.png) - -The first half of the model is not looking good. The reason is that the -infection to hospitalization interval PMF makes it unlikely to observe -admissions from the beginning. The following section shows how to fix -this. - -## Padding the model - -We can use the padding argument to solve the overestimation of -hospitalizations in the first half of the model. By setting -`padding > 0`, the model then assumes that the first `padding` -observations are missing; thus, only observations after `padding` will -count towards the likelihood of the model. In practice, the model will -extend the estimated Rt and latent infections by `padding` days, given -time to adjust to the observed data. The following code will add 21 days -of missing data at the beginning of the model and re-estimate it with -`padding = 21`: - -``` python -days_to_impute = 21 - -dat2 = dat["daily_hosp_admits"].to_numpy() - -# Add 21 Nas to the beginning of dat2 -dat2 = np.hstack((np.repeat(np.nan, days_to_impute), dat2)) - -hosp_model.run( - num_samples=2000, - num_warmup=2000, - n_timepoints=dat2.shape[0] - 1, - observed_hospitalizations=dat2, - rng_key=jax.random.PRNGKey(54), - mcmc_args=dict(progress_bar=False), - padding=days_to_impute, # Padding the model -) -``` - -And plotting the results: - -``` python -out = hosp_model.plot_posterior( - var="predicted_hospitalizations", - ylab="Hospital Admissions", - obs_signal=dat2, -) -``` - -![Hospital Admissions posterior -distribution](example-with-datasets_files/figure-commonmark/output-admissions-with-padding-output-1.png) - -We can also take a look at the latent infections: - -``` python -out2 = hosp_model.plot_posterior( - var="latent_infections", - ylab="Latent Infections" -) -``` - -![Hospital Admissions posterior -distribution](example-with-datasets_files/figure-commonmark/output-infections-with-padding-output-1.png) - -## Round 2: Incorporating weekday effects - -We will re-use the infection to admission interval and infection to -hospitalization rate from the previous model. But we will also add a -weekday effect distribution. To do this, we will create a new instance -of `RandomVariable` to model the weekday effect. The weekday effect will -be a truncated normal distribution with a mean of 1.0 and a standard -deviation of 0.5. The distribution will be truncated between 0.1 and -10.0. The weekday effect will be repeated for the number of weeks in the -dataset. - -``` python -from pyrenew import metaclass -import numpyro as npro - -class WeekdayEffect(metaclass.RandomVariable): - """Weekday effect distribution""" - def __init__(self, len: int): - """ Initialize the weekday effect distribution - Parameters - ---------- - len : int - The number of observations - """ - self.nweeks = jnp.ceil(len/7).astype(int) - self.len = len - - @staticmethod - def validate(): - return None - - def sample(self, **kwargs): - ans = npro.sample( - name="weekday_effect", - fn=npro.distributions.TruncatedNormal( - loc=1.0, scale=.5, low=0.1, high=10.0 - ), - sample_shape=(7,) - ) - - return jnp.tile(ans, self.nweeks)[:self.len] - -# Initializing the weekday effect -weekday_effect = WeekdayEffect(dat.shape[0]) -``` - -Notice that the instance’s `nweeks` and `len` members are passed during -construction. Trying to compute the number of weeks and the length of -the dataset in the `validate` method will raise a `jit` error in `jax` -as the shape and size of elements are not known during the validation -step, which happens before the model is run. With the new weekday -effect, we can rebuild the latent hospitalization model: - -``` python -latent_hosp_wday_effect = latent.HospitalAdmissions( - infection_to_admission_interval=inf_hosp_int, - infect_hosp_rate_dist=hosp_rate, - weekday_effect_dist=weekday_effect, - ) - -hosp_model_weekday = model.HospitalizationsModel( - latent_infections=latent_inf, - latent_hospitalizations=latent_hosp_wday_effect, - I0=I0, - gen_int=gen_int, - Rt_process=rtproc, - observation_process=obs, -) -``` - -Running the model (with the same padding as before): - -``` python -hosp_model_weekday.run( - num_samples=2000, - num_warmup=2000, - n_timepoints=dat2.shape[0] - 1, - observed_hospitalizations=dat2, - rng_key=jax.random.PRNGKey(54), - mcmc_args=dict(progress_bar=False), - padding=days_to_impute, -) -``` - -And plotting the results: - -``` python -out = hosp_model_weekday.plot_posterior( - var="predicted_hospitalizations", - ylab="Hospital Admissions", - obs_signal=dat2, -) -``` - -![Hospital Admissions posterior -distribution](example-with-datasets_files/figure-commonmark/output-admissions-padding-and-weekday-output-1.png) - -[^1]: The output is captured to avoid `quarto` from displaying the - output twice. diff --git a/model/docs/example-with-datasets_files/figure-commonmark/basic-fig-output-1.png b/model/docs/example-with-datasets_files/figure-commonmark/basic-fig-output-1.png deleted file mode 100644 index 5208cbd1e97fd940ccacd1f283f2bf736d16fab5..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 46416 zcmcHhcT`i|7X^w2K}hHjnn)9YP?RDF0VxtX(gg%W5D+PXbm>J1(m^TGOF*QHNU_md z6qMcsL_+AjHz{|=?|1K?_ue<&dt>M@gyfvF_g-brxz;jDPe+Z4oRu5`flz6vD;q!{ z7cdYA(JYh{yhHtGpAGyY?WtnodDrE!r?-{6EkxVO)78nv)5*b_>zS>)hl9&gabcve z_%$wjPfu4584(fZ|LX(7F79?B*m;w?;36Fy;ps$TVH#sRs%?C~c!kKtM)Sp* zSftWfZd%X0VyWJ0y&6g@i=3rZd1-@OkUwtx8We08SXOqNv7VFDIKShU$-h6o%0`P9|M&VT4L#<+ca^8E&}shn?kxNN_iyUY%Wk#9r;$3x z24%x6;*TlceO;Gfll5^mN%%D~B6+RS^4&GdMz)_ne}-GAYG!0+Rs|ew$L4x(G#rHD 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Built -on `numpyro`, `pyrenew` provides core components for model building and -pre-defined models for processing various observational processes. This -document illustrates how `pyrenew` can be used to build a basic renewal -model. - -## The fundamentals - -`pyrenew`’s core components are the metaclasses `RandomVariable` and -`Model`. From the package’s perspective, a `RandomVariable` is a -quantity models can sample and estimate, **including deterministic -quantities**. Mainly, sampling from a `RandomVariable` involves calling -the `sample()` method. The benefit of this design is the definition of -the sample function can be arbitrary, allowing the user to either sample -from a distribution using `numpyro.sample()`, compute fixed quantities -(like a mechanistic equation), or return a fixed value (like a -pre-computed PMF.) For instance, we may be interested in estimating a -PMF, in which case a `RandomVariable` sampling function may roughly be -defined as: - -``` python -class MyRandVar(RandomVariable): - def sample(...) -> ArrayLike: - return numpyro.sample(...) -``` - -Whereas, in some other cases, we may instead use a fixed quantity for -that variable (like a pre-computed PMF), where the `RandomVariable`’s -sample function could be defined like: - -``` python -class MyRandVar(RandomVariable): - def sample(...) -> ArrayLike: - return jax.numpy.array([0.2, 0.7, 0.1]) -``` - -This way, when a `Model` samples from `MyRandVar`, it could be either -adding random variables to be estimated (first case) or just retrieving -some quantity needed for other calculations (second case.) - -The `Model` metaclass provides basic functionality for estimating and -simulation. Like `RandomVariable`, the `Model` metaclass has a -`sample()` method that defines the model structure. Ultimately, models -can be nested (or inherited), providing a straightforward way to add -layers of complexity. - -## ‘Hello world’ model - -This section will show the steps to build a simple renewal model -featuring a latent infection process, a random walk Rt process, and an -observation process for the reported infections. We start by loading the -needed components to build a basic renewal model: - -``` python -import jax.numpy as jnp -import numpy as np -import numpyro as npro -import numpyro.distributions as dist -from pyrenew.process import RtRandomWalkProcess -from pyrenew.latent import Infections, Infections0 -from pyrenew.observation import PoissonObservation -from pyrenew.deterministic import DeterministicPMF -from pyrenew.model import RtInfectionsRenewalModel -``` - -The basic renewal model defines five components: generation interval, -initial infections, Rt, latent infections, and observed infections. In -this example, the generation interval is not estimated but passed as a -deterministic instance of `RandomVariable`. Here is the code to -initialize the five components: - -``` python -# (1) The generation interval (deterministic) -gen_int = DeterministicPMF(jnp.array([0.25, 0.25, 0.25, 0.25])) - -# (2) Initial infections (inferred with a prior) -I0 = Infections0(I0_dist=dist.LogNormal(0, 1)) - -# (3) The random process for Rt -rt_proc = RtRandomWalkProcess() - -# (4) Latent infection process (which will use 1 and 2) -latent_infections = Infections() - -# (5) The observed infections process (with mean at the latent infections) -observation_process = PoissonObservation() -``` - -With these five pieces, we can build the basic renewal model: - -``` python -model1 = RtInfectionsRenewalModel( - gen_int = gen_int, - I0 = I0, - Rt_process = rt_proc, - latent_infections = latent_infections, - observation_process = observation_process, - ) -``` - -The following diagram summarizes how the modules interact via -composition; notably, `gen_int`, `I0`, `rt_proc`, `latent_infections`, -and `observed_infections` are instances of `RandomVariable`, which means -these can be easily replaced to generate a different version of -`RtInfectionsRenewalModel`: - -``` mermaid -flowchart TB - genint["(1) gen_int\n(DetermnisticPMF)"] - i0["(2) I0\n(Infections0)"] - rt["(3) rt_proc\n(RtRandomWalkProcess)"] - inf["(4) latent_infections\n(Infections)"] - obs["(5) observation_process\n(PoissonObservation)"] - - model1["model1\n(RtInfectionsRenewalModel)"] - - i0-->|Composes|model1 - genint-->|Composes|model1 - rt-->|Composes|model1 - obs-->|Composes|model1 - inf-->|Composes|model1 -``` - -Using `numpyro`, we can simulate data using the `sample()` member -function of `RtInfectionsRenewalModel`: - -``` python -np.random.seed(223) -with npro.handlers.seed(rng_seed = np.random.randint(1, 60)): - sim_data = model1.sample(n_timepoints=30) - -sim_data -``` - - RtInfectionsRenewalSample(Rt=[1.2022278 1.2111099 1.2325984 1.2104921 1.2023039 1.1970979 1.2384264 - 1.2423582 1.245498 1.241344 1.2081108 1.1938375 1.271196 1.3189521 - 1.3054799 1.3165426 1.291952 1.3026639 1.2619467 1.2852622 1.3121517 - 1.2888998 1.2641873 1.2580931 1.2545817 1.3092988 1.2488269 1.2397509 - 1.2071848 1.2334517 1.21868 ], latent_infections=[ 2.3215084 3.0415602 4.0327816 5.180868 4.381411 4.978916 - 5.750626 6.3024273 6.66758 7.354823 7.8755097 8.416656 - 9.63394 10.973988 12.043082 13.516833 14.911659 16.75407 - 18.053928 20.318869 22.975292 25.166464 27.34265 30.13236 - 33.126217 37.89362 40.11695 43.784634 46.754696 51.974545 - 55.642136 ], sampled_infections=[ 1 2 3 5 4 4 7 4 8 4 7 3 8 12 13 18 14 20 17 18 28 27 36 37 - 26 31 40 27 48 54 60]) - -The `sample()` method of the `RtInfectionsRenewalModel` returns a list -composed of the `Rt` and `infections` sequences. - -``` python -import matplotlib.pyplot as plt - -fig, axs = plt.subplots(1, 2) - -# Rt plot -axs[0].plot(range(0, 31), sim_data.Rt) -axs[0].set_ylabel('Rt') - -# Infections plot -axs[1].plot(range(0, 31), sim_data.sampled_infections) -axs[1].set_ylabel('Infections') - -fig.suptitle('Basic renewal model') -fig.supxlabel('Time') -plt.tight_layout() -plt.show() -``` - -![Rt and -Infections](getting-started_files/figure-commonmark/basic-fig-output-1.png) - -To fit the model, we can use the `run()` method of the model -`RtInfectionsRenewalModel`; an inherited method from the metaclass -`Model`: - -``` python -import jax - -model1.run( - num_warmup=2000, - num_samples=1000, - observed_infections=sim_data.sampled_infections, - n_timepoints = len(sim_data[1])-1, - rng_key=jax.random.PRNGKey(54), - mcmc_args=dict(progress_bar=False), - ) -``` - -Now, let’s investigate the output, particularly the posterior -distribution of the Rt estimates: - -``` python -import polars as pl -samps = model1.spread_draws([('Rt', 'time')]) - -fig, ax = plt.subplots(figsize=[4, 5]) - -ax.plot(sim_data.Rt) -samp_ids = np.random.randint(size=25, low=0, high=999) -for samp_id in samp_ids: - sub_samps = samps.filter(pl.col("draw") == samp_id).sort(pl.col('time')) - ax.plot(sub_samps.select("time").to_numpy(), - sub_samps.select("Rt").to_numpy(), color="darkblue", alpha=0.1) -ax.set_ylim([0.4, 1/.4]) -ax.set_yticks([0.5, 1, 2]) -ax.set_yscale("log") -``` - -![Rt posterior -distribution](getting-started_files/figure-commonmark/output-rt-output-1.png) - -## Architecture of pyrenew - -`pyrenew` leverages `numpyro`’s flexibility to build models via -composition. As a principle, most objects in `pyrenew` can be treated as -random variables we can sample. At the top-level `pyrenew` has two -metaclass from which most objects inherit: `RandomVariable` and `Model`. -From them, the following four sub-modules arise: - -- The `process` sub-module, -- The `deterministic` sub-module, -- The `observation` sub-module, -- The `latent` sub-module, and -- The `models` sub-module - -The first four are collections of instances of `RandomVariable`, and the -last is a collection of instances of `Model`. The following diagram -shows a detailed view of how meta classes, modules, and classes interact -to create the `RtInfectionsRenewalModel` instantiated in the previous -section: - -``` mermaid -flowchart LR - rand((RandomVariable\nmetaclass)) - models((Model\nmetaclass)) - - subgraph observations[Observations module] - obs["observation_process\n(PoissonObservation)"] - end - - subgraph latent[Latent module] - inf["latent_infections\n(Infections)"] - i0["I0\n(Infections0)"] - end - - subgraph process[Process module] - rt["rt_proc\n(RtRandomWalkProcess)"] - end - - subgraph deterministic[Deterministic module] - detpmf["gen_int\n(DeterministicPMF)"] - end - - subgraph model[Model module] - model1["model1\n(RtInfectionsRenewalModel)"] - end - - rand-->|Inherited by|observations - rand-->|Inherited by|process - rand-->|Inherited by|latent - rand-->|Inherited by|deterministic - models-->|Inherited by|model - - detpmf-->|Composes|model1 - i0-->|Composes|model1 - rt-->|Composes|model1 - obs-->|Composes|model1 - inf-->|Composes|model1 -``` diff --git a/model/docs/getting-started_files/figure-commonmark/basic-fig-output-1.png b/model/docs/getting-started_files/figure-commonmark/basic-fig-output-1.png deleted file mode 100644 index 6aec59a4d98292ee6e55e9ef6ae4f882ff4cdbc2..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 39115 zcmd3OWmHvLyzeGN=@5`k5s)q2t%QVvpuz?uq)WORX{2KVA`Jo}4T5w?ZbCwkZjest zcypa|?;ZDz_uiNL=`juikFxh#YtH}t$1ir+3zet%I8-<=7z|%gK~^0GLr213Xv=r7 zz~2P!+3JEHB2ID$r4*}z%a;ayoZ(#Nvk zYgF}%IhS83_#(l;P-X&O>X`rYpV^o-9)B(Gyl3z&{AGzpCr+WM?=9f6JxMmUChoRp zfS{97OH546s;w2CMEy~-x992Y?S0ZTcd&wDyz~A5mnl0rJM0)el#TcLYS%ZXpx_Q? z67I$Ec6&G(huwh4+Wf_C^HWpP&)I{{3qki5KX5fkKYr|=9jz=YTOR4pq867d?#?S8 zMS~4Xmh=|I!NuK=&$z_Q@Vyopkh<}b%ixfbqCY!33zz)-W5wYyshl~#*BKL2xBxup zE(?3k&>fhaFwKF9=gCgTpwBTM9=#-uXc_?v3(M)RQlFsDpFjIu*i2O1k~-^AjY&=A zn6p`E3p)5alh~HweXR8?O_U7$_Qb?QvVax2+RZfOq>Q6**Na-*&&zl*_i32L)+_XmR3zx@hd=07PNH@ZCC 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The `import` statement -imports the module and the `as` statement renames the module for use -within this script. The `from` statement imports a specific function -from a module (named after the `.`) within a package (named before the -`.`). - -``` python -import matplotlib as mpl -import matplotlib.pyplot as plt -import jax -import jax.numpy as jnp -import numpy as np -from numpyro.handlers import seed -import numpyro.distributions as dist -``` - -``` python -from pyrenew.process import SimpleRandomWalkProcess -``` - - An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu. - -To understand the simple random walk process underlying the sampling -within the renewal process model, we first examine a single random walk -path. Using the `sample` method from an instance of the -`SimpleRandomWalkProcess` class, we first create an instance of the -`SimpleRandomWalkProcess` class with a normal distribution of mean = 0 -and standard deviation = 0.0001 as its input. Next, the `with` statement -sets the seed for the random number generator for the duration of the -block that follows. Inside the `with` block, the -`q_samp = q.sample(duration=100)` generates the sample instance over a -duration of 100 time units. Finally, this single random walk process is -visualized using `matplot.pyplot` to plot the exponential of the sample -instance. - -``` python -np.random.seed(3312) -q = SimpleRandomWalkProcess(dist.Normal(0, 0.001)) -with seed(rng_seed=np.random.randint(0,1000)): - q_samp = q.sample(duration=100) - -plt.plot(np.exp(q_samp[0])) -``` - -![](pyrenew_demo_files/figure-commonmark/fig-randwalk-output-1.png) - -Next, import several additional functions from the `latent` module of -the `pyrenew` package to model infections, hospital admissions, initial -infections, and hospitalization rate due to infection. - -``` python -from pyrenew.latent import ( - Infections, HospitalAdmissions, Infections0, InfectHospRate, -) -``` - -Additionally, import several classes from Pyrenew, including a Poisson -observation process, determininstic PMF and variable classes, the -Pyrenew hospitalization model, and a renewal modle (Rt) random walk -process: - -``` python -from pyrenew.observation import PoissonObservation -from pyrenew.deterministic import DeterministicPMF, DeterministicVariable -from pyrenew.model import HospitalizationsModel -from pyrenew.process import RtRandomWalkProcess -``` - -To initialize the model, we first define initial conditions, including: - -1) deterministic generation time, defined as an instance of the - `DeterministicPMF` class, which gives the probability of each - possible outcome for a discrete random variable given as a JAX NumPy - array of four possible outcomes - -2) initial infections at the start of simulation as a log-normal - distribution with mean = 0 and standard deviation = 1 - -3) latent infections as an instance of the `Infections` class with - default settings - -4) latent hospitalization process, modeled by first defining the time - interval from infections to hospitalizations as a `DeterministicPMF` - input with 18 possible outcomes and corresponding probabilities - given by the values in the array. The `HospitalAdmissions` function - then takes in this defined time interval, as well as defining the - rate at which infections are admitted to the hospital due to - infection, modeled as a log-normal distribution with mean = - `jnp.log(0.05)` and standard deviation = 0.05. - -5) hospitalization observation process, modeled with a Poisson - distribution - -6) an Rt random walk process with default settings - -``` python -# Initializing model components: - -# 1) A deterministic generation time -gen_int = DeterministicPMF(jnp.array([0.25, 0.25, 0.25, 0.25])) - -# 2) Initial infections -I0 = Infections0(I0_dist=dist.LogNormal(0, 1)) - -# 3) The latent infections process -latent_infections = Infections() - -# 4) The latent hospitalization process: - -# First, define a deterministic infection to hosp pmf -inf_hosp_int = DeterministicPMF( - jnp.array([0, 0, 0,0,0,0,0,0,0,0,0,0,0, 0.25, 0.5, 0.1, 0.1, 0.05]), - ) - -latent_hospitalizations = HospitalAdmissions( - infection_to_admission_interval=inf_hosp_int, - infect_hosp_rate_dist = InfectHospRate( - dist=dist.LogNormal(jnp.log(0.05), 0.05), - ), - ) - -# 5) An observation process for the hospitalizations -obs_process = PoissonObservation() - -# 6) A random walk process (it could be deterministic using -# pyrenew.process.DeterministicProcess()) -Rt_process = RtRandomWalkProcess() -``` - -The `HospitalizationsModel` is then initialized using the initial -conditions just defined: - -``` python -# Initializing the model -hospmodel = HospitalizationsModel( - gen_int=gen_int, - I0=I0, - latent_hospitalizations=latent_hospitalizations, - observation_process=obs_process, - latent_infections=latent_infections, - Rt_process=Rt_process - ) -``` - -Next, we sample from the `hospmodel` for 30 time steps and view the -output of a single run: - -``` python -with seed(rng_seed=np.random.randint(1, 60)): - x = hospmodel.sample(n_timepoints=30) -x -``` - - HospModelSample(Rt=[1.1791104 1.1995267 1.1772177 1.1913829 1.2075942 1.1444623 1.1514508 - 1.1976782 1.2292639 1.1719677 1.204649 1.2323451 1.2466507 1.2800207 - 1.2749145 1.2619376 1.2189837 1.2192641 1.2290158 1.2128737 1.1908046 - 1.2174997 1.1941082 1.2084603 1.1965215 1.2248698 1.2308019 1.2426206 - 1.2131014 1.207159 1.1837622], latent_infections=[0.05214045 0.06867922 0.08761451 0.11476436 0.09757317 0.10547114 - 0.1167062 0.13010225 0.13824694 0.14372033 0.15924728 0.17601486 - 0.19236736 0.21483542 0.23664482 0.25865382 0.27503362 0.30029488 - 0.3289544 0.35262382 0.37418258 0.41274938 0.43839005 0.47672123 - 0.50913286 0.5625195 0.6113282 0.67092246 0.7138808 0.77217466 - 0.819254 ], IHR=0.04929916560649872, latent_admissions=[0. 0. 0. 0. 0. 0. - 0. 0. 0. 0. 0. 0. - 0. 0.00064262 0.0021317 0.00302979 0.00416974 0.0049305 - 0.00487205 0.00530097 0.00576412 0.00624666 0.00665578 0.00711595 - 0.0078055 0.00854396 0.00939666 0.01042083 0.0114624 0.01246538 - 0.01345188], sampled_admissions=[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0]) - -Visualizations of the single model output show (top) infections over the -30 time steps, (middle) hospitalizations over the 30 time steps, and -(bottom) - -``` python -fig, ax = plt.subplots(nrows=3, sharex=True) -ax[0].plot(x.latent_infections) -ax[0].set_ylim([1/5, 5]) -ax[1].plot(x.latent_admissions) -ax[2].plot(x.sampled_admissions, 'o') -for axis in ax[:-1]: - axis.set_yscale("log") -``` - -![](pyrenew_demo_files/figure-commonmark/fig-hosp-output-1.png) - -To fit the `hospmodel` to the simulated data, we call `hospmodel.run()`, -an MCMC algorithm, with the arguments generated in `hospmodel` object, -using 1000 warmup stepts and 1000 samples to draw from the posterior -distribution of the model parameters. The model is run for -`len(x.sampled_admissions)-1` time steps with the seed set by -`jax.random.PRNGKey()` - -``` python -# from numpyro.infer import MCMC, NUTS -hospmodel.run( - num_warmup=1000, - num_samples=1000, - observed_hospitalizations=x.sampled_admissions, - n_timepoints = len(x.sampled_admissions)-1, - rng_key=jax.random.PRNGKey(54), - mcmc_args=dict(progress_bar=False), - ) -``` - -Print a summary of the model: - -``` python -hospmodel.print_summary() -``` - - - mean std median 5.0% 95.0% n_eff r_hat - I0 1.27 1.10 0.97 0.10 2.42 1132.34 1.00 - IHR 0.05 0.00 0.05 0.05 0.05 2306.45 1.00 - Rt0 1.23 0.17 1.23 0.93 1.48 1327.22 1.00 - Rt_transformed_rw_diffs[0] -0.00 0.02 -0.00 -0.04 0.04 1404.95 1.00 - Rt_transformed_rw_diffs[1] 0.00 0.03 0.00 -0.04 0.04 2280.86 1.00 - Rt_transformed_rw_diffs[2] -0.00 0.02 -0.00 -0.04 0.04 2119.83 1.00 - Rt_transformed_rw_diffs[3] 0.00 0.02 -0.00 -0.04 0.04 2196.86 1.00 - Rt_transformed_rw_diffs[4] 0.00 0.02 -0.00 -0.03 0.04 2391.45 1.00 - Rt_transformed_rw_diffs[5] 0.00 0.03 0.00 -0.04 0.04 2043.02 1.00 - Rt_transformed_rw_diffs[6] 0.00 0.02 0.00 -0.04 0.04 1514.40 1.00 - Rt_transformed_rw_diffs[7] -0.00 0.02 -0.00 -0.04 0.04 2619.69 1.00 - Rt_transformed_rw_diffs[8] 0.00 0.03 0.00 -0.04 0.04 1883.84 1.00 - Rt_transformed_rw_diffs[9] 0.00 0.03 0.00 -0.04 0.04 2015.66 1.00 - Rt_transformed_rw_diffs[10] 0.00 0.02 0.00 -0.04 0.04 2045.47 1.00 - Rt_transformed_rw_diffs[11] -0.00 0.03 0.00 -0.04 0.04 1615.10 1.00 - Rt_transformed_rw_diffs[12] 0.00 0.02 0.00 -0.04 0.04 2206.32 1.00 - Rt_transformed_rw_diffs[13] 0.00 0.03 0.00 -0.04 0.04 1175.93 1.00 - Rt_transformed_rw_diffs[14] -0.00 0.03 -0.00 -0.04 0.04 1606.26 1.00 - Rt_transformed_rw_diffs[15] -0.00 0.03 -0.00 -0.04 0.04 2344.62 1.00 - Rt_transformed_rw_diffs[16] -0.00 0.02 0.00 -0.04 0.04 1522.33 1.00 - Rt_transformed_rw_diffs[17] 0.00 0.03 0.00 -0.04 0.04 2157.17 1.00 - Rt_transformed_rw_diffs[18] -0.00 0.02 -0.00 -0.04 0.04 1594.95 1.00 - Rt_transformed_rw_diffs[19] 0.00 0.03 -0.00 -0.04 0.04 1698.70 1.00 - Rt_transformed_rw_diffs[20] 0.00 0.02 0.00 -0.04 0.04 1726.18 1.00 - Rt_transformed_rw_diffs[21] 0.00 0.02 -0.00 -0.04 0.04 2386.35 1.00 - Rt_transformed_rw_diffs[22] 0.00 0.03 0.00 -0.04 0.04 2028.63 1.00 - Rt_transformed_rw_diffs[23] 0.00 0.02 0.00 -0.04 0.03 1669.71 1.00 - Rt_transformed_rw_diffs[24] 0.00 0.02 0.00 -0.04 0.04 2126.33 1.00 - Rt_transformed_rw_diffs[25] -0.00 0.02 -0.00 -0.04 0.04 2119.74 1.00 - Rt_transformed_rw_diffs[26] 0.00 0.03 0.00 -0.04 0.04 2657.91 1.00 - Rt_transformed_rw_diffs[27] -0.00 0.03 0.00 -0.04 0.04 1939.30 1.00 - Rt_transformed_rw_diffs[28] -0.00 0.02 -0.00 -0.04 0.04 1737.84 1.00 - Rt_transformed_rw_diffs[29] -0.00 0.03 -0.00 -0.04 0.04 2105.55 1.00 - - Number of divergences: 0 - -Next, we will use the `spread_draws` function from the -`pyrenew.mcmcutils` module to process the MCMC samples. The -`spread_draws` function reformats the samples drawn from the -`mcmc.get_samples()` from the `hospmodel`. The samples are simulated Rt -values over time. - -``` python -from pyrenew.mcmcutils import spread_draws -samps = spread_draws(hospmodel.mcmc.get_samples(), [("Rt", "time")]) -``` - -We visualize these samples below, with individual possible Rt estimates -over time shown in light blue, and the overall mean estimate Rt shown in -dark blue. - -``` python -import numpy as np -import polars as pl -fig, ax = plt.subplots(figsize=[4, 5]) - -ax.plot(x[0]) -samp_ids = np.random.randint(size=25, low=0, high=999) -for samp_id in samp_ids: - sub_samps = samps.filter(pl.col("draw") == samp_id).sort(pl.col('time')) - ax.plot(sub_samps.select("time").to_numpy(), - sub_samps.select("Rt").to_numpy(), color="darkblue", alpha=0.1) -ax.set_ylim([0.4, 1/.4]) -ax.set_yticks([0.5, 1, 2]) -ax.set_yscale("log") -``` - -![](pyrenew_demo_files/figure-commonmark/fig-sampled-rt-output-1.png) diff --git a/model/docs/pyrenew_demo_files/figure-commonmark/fig-hosp-output-1.png 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zli#W=sX1Dg*2hk%@h|I^7GWz+Cp;TToOJs=PrZra!ej5EPPUuW)Pb9z3n8>knn+c|Cgt3Lb|K9N%BD_gwma0M*&z;Q#;t From 935d3321edb597e90b54576fa2a3eea95e070cd9 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Wed, 1 May 2024 13:46:18 -0600 Subject: [PATCH 04/12] Correct use of artifacts upload/download --- .github/workflows/website.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/website.yaml b/.github/workflows/website.yaml index 089d00dc..a34ce3f1 100644 --- a/.github/workflows/website.yaml +++ b/.github/workflows/website.yaml @@ -48,9 +48,9 @@ jobs: # Upload - name: Upload artifacts - uses: actions/upload-pages-artifact@v3 + uses: actions/upload-artifact@v3 with: - name: github-pages-tutorials + name: tutorials path: model/_compiled/ build: @@ -69,7 +69,7 @@ jobs: - name: Download artifacts uses: actions/download-artifact@v3 with: - name: github-pages-tutorials + name: tutorials path: docs/source/tutorials/ - name: Set up Python From 3fe4747500957db7811c3f5694893d31742c31bb Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Wed, 1 May 2024 13:55:35 -0600 Subject: [PATCH 05/12] Adding back index for tutorials --- docs/source/tutorials/index.rst | 9 +++++++++ 1 file changed, 9 insertions(+) create mode 100644 docs/source/tutorials/index.rst diff --git a/docs/source/tutorials/index.rst b/docs/source/tutorials/index.rst new file mode 100644 index 00000000..47c16476 --- /dev/null +++ b/docs/source/tutorials/index.rst @@ -0,0 +1,9 @@ +Tutorials +============== + +.. toctree:: + :maxdepth: 3 + + getting-started + example-with-datasets + pyrenew_demo From 979f75285664189a6d4d91ceff5a86f3e0a7e04c Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Wed, 1 May 2024 13:59:31 -0600 Subject: [PATCH 06/12] The sphinx-tutorial formula is not required anymore [skip ci] --- model/Makefile | 13 +------------ 1 file changed, 1 insertion(+), 12 deletions(-) diff --git a/model/Makefile b/model/Makefile index ca7c4e53..89dd88b6 100644 --- a/model/Makefile +++ b/model/Makefile @@ -27,16 +27,6 @@ docs/notebooks: quarto convert docs/example-with-datasets.qmd --output \ docs/example-with-datasets.ipynb -sphinx-tutorials: docs - for i in docs/*.md; do \ - if [ "$$i" = "docs/README.md" ]; then \ - continue; \ - fi; \ - pandoc -s $$i -o ../docs/source/tutorials/$$(basename $$i .md).rst;\ - cp -r docs/$$(basename $$i .md)_files ../docs/source/tutorials/; \ - done - - test_images: echo "Generating reference images for tests" poetry run pytest --mpl-generate-path=src/test/baseline @@ -49,5 +39,4 @@ clean: rm -f docs/example-with-datasets.$$i; \ done -.PHONY: install test docs clean docs/notebooks docs/py test_images \ - sphinx-tutorials +.PHONY: install test docs clean docs/notebooks docs/py test_images From ae805d027319175d16001eacd28f3a4e8c7bdd3e Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Wed, 1 May 2024 15:04:18 -0600 Subject: [PATCH 07/12] Chrome present in GHA allows rendering mermaid --- .github/workflows/website.yaml | 2 +- model/docs/getting-started.qmd | 2 ++ 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/workflows/website.yaml b/.github/workflows/website.yaml index a34ce3f1..493bd6f8 100644 --- a/.github/workflows/website.yaml +++ b/.github/workflows/website.yaml @@ -113,4 +113,4 @@ jobs: uses: actions/deploy-pages@v4 with: artifact_name: github-pages - preview: false # Not yet available to the public. + preview: true # Not yet available to the public. diff --git a/model/docs/getting-started.qmd b/model/docs/getting-started.qmd index 8ca96a11..2b36986b 100644 --- a/model/docs/getting-started.qmd +++ b/model/docs/getting-started.qmd @@ -85,6 +85,7 @@ The following diagram summarizes how the modules interact via composition; notab ```{mermaid} %%| label: overview-of-RtInfectionsRenewalModel +%%| include: true flowchart TB genint["(1) gen_int\n(DetermnisticPMF)"] i0["(2) I0\n(Infections0)"] @@ -188,6 +189,7 @@ The first four are collections of instances of `RandomVariable`, and the last is ```{mermaid} %%| label: overview-of-RtInfectionsRenewalModel +%%| include: true flowchart LR rand((RandomVariable\nmetaclass)) models((Model\nmetaclass)) From a17eecc18f2ce9f4d1cef8d4fd56fbc808fd11b2 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Wed, 1 May 2024 15:36:13 -0600 Subject: [PATCH 08/12] Fixed figures --- model/docs/example-with-datasets.qmd | 20 +++++++------------- model/docs/getting-started.qmd | 6 ++---- 2 files changed, 9 insertions(+), 17 deletions(-) diff --git a/model/docs/example-with-datasets.qmd b/model/docs/example-with-datasets.qmd index 6c58a08c..41f33859 100644 --- a/model/docs/example-with-datasets.qmd +++ b/model/docs/example-with-datasets.qmd @@ -77,7 +77,7 @@ dat.head(5) Let's take a look at the daily prevalence of hospital admissions. ```{python} -#| label: plot-hospital admissions +#| label: fig-plot-hospital-admissions import matplotlib.pyplot as plt # Rotating the x-axis labels, and only showing ~10 labels @@ -95,7 +95,7 @@ plt.show() First, we will extract two datasets we will use as deterministic quantities: the generation interval and the infection to hospitalization interval. ```{python} -#| label: data-extract +#| label: fig-data-extract gen_int = datasets.load_generation_interval() inf_hosp_int = datasets.load_infection_admission_interval() @@ -184,9 +184,8 @@ with npro.handlers.seed(rng_seed = np.random.randint(1, timeframe)): ``` ```{python} -#| label: basic-fig +#| label: fig-basic #| fig-cap: Rt and Infections -#| fig-cap-location: bottom import matplotlib.pyplot as plt fig, axs = plt.subplots(1, 2) @@ -230,9 +229,8 @@ We can use the `plot_posterior` method to visualize the results[^capture]: [^capture]: The output is captured to avoid `quarto` from displaying the output twice. ```{python} -#| label: output-hospital admissions +#| label: fig-output-hospital-admissions #| fig-cap: Hospital Admissions posterior distribution -#| fig-cap-location: bottom out = hosp_model.plot_posterior( var="predicted_hospitalizations", ylab="Hospital Admissions", @@ -269,9 +267,8 @@ hosp_model.run( And plotting the results: ```{python} -#| label: output-admissions-with-padding +#| label: fig-output-admissions-with-padding #| fig-cap: Hospital Admissions posterior distribution -#| fig-cap-location: bottom out = hosp_model.plot_posterior( var="predicted_hospitalizations", ylab="Hospital Admissions", @@ -282,9 +279,8 @@ out = hosp_model.plot_posterior( We can also take a look at the latent infections: ```{python} -#| label: output-infections-with-padding +#| label: fig-output-infections-with-padding #| fig-cap: Hospital Admissions posterior distribution -#| fig-cap-location: bottom out2 = hosp_model.plot_posterior( var="latent_infections", ylab="Latent Infections" @@ -369,10 +365,8 @@ hosp_model_weekday.run( And plotting the results: ```{python} -#| label: output-admissions-padding-and-weekday +#| label: fig-output-admissions-padding-and-weekday #| fig-cap: Hospital Admissions posterior distribution -#| fig-cap-location: bottom -#| out = hosp_model_weekday.plot_posterior( var="predicted_hospitalizations", ylab="Hospital Admissions", diff --git a/model/docs/getting-started.qmd b/model/docs/getting-started.qmd index 2b36986b..848d1c1b 100644 --- a/model/docs/getting-started.qmd +++ b/model/docs/getting-started.qmd @@ -116,9 +116,8 @@ sim_data The `sample()` method of the `RtInfectionsRenewalModel` returns a list composed of the `Rt` and `infections` sequences. ```{python} -#| label: basic-fig +#| label: fig-basic #| fig-cap: Rt and Infections -#| fig-cap-location: bottom import matplotlib.pyplot as plt fig, axs = plt.subplots(1, 2) @@ -156,9 +155,8 @@ model1.run( Now, let's investigate the output, particularly the posterior distribution of the Rt estimates: ```{python} -#| label: output-rt +#| label: fig-output-rt #| fig-cap: Rt posterior distribution -#| fig-cap-location: bottom import polars as pl samps = model1.spread_draws([('Rt', 'time')]) From 31cbbad730ec0f22de4c53a1d753c99e4a17273a Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Thu, 2 May 2024 12:30:19 -0600 Subject: [PATCH 09/12] Improving readability --- docs/pyproject.toml | 2 ++ docs/source/conf.py | 14 +++++++++++++- docs/source/general/ctoc.rst | 2 +- 3 files changed, 16 insertions(+), 2 deletions(-) diff --git a/docs/pyproject.toml b/docs/pyproject.toml index bdcdbfd2..7f2885cd 100644 --- a/docs/pyproject.toml +++ b/docs/pyproject.toml @@ -14,6 +14,8 @@ jaxlib = "^0.4.25" numpyro = "^0.14.0" sphinxcontrib-mermaid = "^0.9.2" polars = "^0.20.16" +matplotlib = "^3.8.3" +sphinx-autodoc-typehints = "^2.1.0" [build-system] diff --git a/docs/source/conf.py b/docs/source/conf.py index 5805d6bc..c9c6133a 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -32,12 +32,24 @@ "sphinx.ext.viewcode", "sphinx.ext.mathjax", "sphinxcontrib.mermaid", + "sphinx_autodoc_typehints", ] +# Simplifies printing of type hints +set_type_checking_flag = True +typehints_fully_qualified = False + +# Avoid appending the full module name to the class name +add_module_names = False + templates_path = ["_templates"] exclude_patterns = [] -todo_include_todos = True +# Default deph for documentation +toc_deph = 2 + +# We don't want that explicitly +todo_include_todos = False # -- Options for HTML output ------------------------------------------------- diff --git a/docs/source/general/ctoc.rst b/docs/source/general/ctoc.rst index 8aa01545..02dd1693 100644 --- a/docs/source/general/ctoc.rst +++ b/docs/source/general/ctoc.rst @@ -2,7 +2,7 @@ Complete Table Of Contents ========================== .. toctree:: - :maxdepth: 4 + :maxdepth: 3 ../msei_reference/index ../test_reference/index From 934e857f90ca461986b54e083fd401ddeeb6ad75 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Thu, 2 May 2024 13:12:59 -0600 Subject: [PATCH 10/12] Updating lock file --- docs/poetry.lock | 508 ++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 461 insertions(+), 47 deletions(-) diff --git a/docs/poetry.lock b/docs/poetry.lock index 6cdf3d2c..dd9828d5 100644 --- a/docs/poetry.lock +++ b/docs/poetry.lock @@ -1,10 +1,9 @@ -# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 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"HTTP library with thread-safe connection pooling, file post, and more." -category = "main" optional = false python-versions = ">=3.8" files = [ @@ -830,4 +1244,4 @@ zstd = ["zstandard (>=0.18.0)"] [metadata] lock-version = "2.0" python-versions = "^3.11" -content-hash = "25331ede4706a442aaa2bdbe2d021ab55b224dd2b15dc5266cfa4a7fc6f74167" +content-hash = "13a69925b7d4754d7abdeb92ed748fe3ea9224a2dfa3dcc319469eb315259f85" From f996d398e44125290b40ffcf4b6c78fbb6ff968b Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Thu, 2 May 2024 13:35:35 -0600 Subject: [PATCH 11/12] Update model/src/pyrenew/latent/hospitaladmissions.py Co-authored-by: Dylan H. Morris --- model/src/pyrenew/latent/hospitaladmissions.py | 1 - 1 file changed, 1 deletion(-) diff --git a/model/src/pyrenew/latent/hospitaladmissions.py b/model/src/pyrenew/latent/hospitaladmissions.py index 91d0fa81..12587529 100644 --- a/model/src/pyrenew/latent/hospitaladmissions.py +++ b/model/src/pyrenew/latent/hospitaladmissions.py @@ -14,7 +14,6 @@ class HospAdmissionsSample(NamedTuple): """ A container for holding the output from HospAdmissionsSample.sample. - >>>>>>> origin/main Parameters ---------- From 29858ae876ca4f0d67e0370930fc0a1e9a136372 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Thu, 2 May 2024 14:14:44 -0600 Subject: [PATCH 12/12] Fixing pre-commit --- model/src/pyrenew/latent/hospitaladmissions.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/model/src/pyrenew/latent/hospitaladmissions.py b/model/src/pyrenew/latent/hospitaladmissions.py index 12587529..bdd87322 100644 --- a/model/src/pyrenew/latent/hospitaladmissions.py +++ b/model/src/pyrenew/latent/hospitaladmissions.py @@ -13,14 +13,14 @@ class HospAdmissionsSample(NamedTuple): """ - A container for holding the output from HospAdmissionsSample.sample. + A container for holding the output from HospAdmissionsSample.sample. - Parameters - ---------- - infection_hosp_rate : float, optional - The infection-to-hospitalization rate. Defaults to None. - predicted : ArrayLike or None - The predicted number of hospital admissions. Defaults to None. + Parameters + ---------- + infection_hosp_rate : float, optional + The infection-to-hospitalization rate. Defaults to None. + predicted : ArrayLike or None + The predicted number of hospital admissions. Defaults to None. """ infection_hosp_rate: float | None = None