EOS/requirements.txt

numpy==2.1.3
matplotlib==3.9.2
fastapi[standard]==0.115.0
uvicorn==0.31.1
pydantic==2.9.2
scikit-learn==1.5.2
timezonefinder==6.5.4
deap==1.4.1
requests==2.32.3
pandas==2.2.3
pydantic==2.9.2
Bump numpy from 2.1.2 to 2.1.3 (#189) Bumps [numpy](https://github.com/numpy/numpy) from 2.1.2 to 2.1.3. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v2.1.2...v2.1.3) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> 2024-11-10 23:34:10 +01:00			`numpy==2.1.3`
fixup and freeze requirements.txt 2024-09-10 17:01:53 +02:00			`matplotlib==3.9.2`
Migrate from Flask to FastAPI (#163) * Migrate from Flask to FastAPI * FastAPI migration: - Use pydantic model classes as input parameters to the data/calculation classes. - Interface field names changed to constructor parameter names (for simplicity only during transition, should be updated in a followup PR). - Add basic interface requirements (e.g. some values > 0, etc.). * Update tests for new data format. * Python requirement down to 3.9 (TypeGuard no longer needed) * Makefile: Add helpful targets (e.g. development server with reload) * Move API doc from README to pydantic model classes (swagger) * Link to swagger.io with own openapi.yml. * Commit openapi.json and check with pytest for changes so the documentation is always up-to-date. * Streamline docker * FastAPI: Run startup action on dev server * Fix config for /strompreis, endpoint still broken however. * test_openapi: Compare against docs/.../openapi.json * Move fastapi to server/ submodule * See #187 for new repository structure. 2024-11-15 22:27:25 +01:00			`fastapi[standard]==0.115.0`
			`uvicorn==0.31.1`
			`pydantic==2.9.2`
Bump scikit-learn from 1.5.1 to 1.5.2 Bumps [scikit-learn](https://github.com/scikit-learn/scikit-learn) from 1.5.1 to 1.5.2. - [Release notes](https://github.com/scikit-learn/scikit-learn/releases) - [Commits](https://github.com/scikit-learn/scikit-learn/compare/1.5.1...1.5.2) --- updated-dependencies: - dependency-name: scikit-learn dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> 2024-09-23 23:21:59 +00:00			`scikit-learn==1.5.2`
Add test to PVForecast (#174) * Add documentation to class_pv_forecast.py. Added documentation. Beware mostly generated by ChatGPT. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> * Add CacheFileStore, datetime and logger utilities. The `CacheFileStore` class is a singleton-based, thread-safe key-value store for managing temporary file objects, allowing the creation, retrieval, and management of cache files. The utility modules offer a flexible logging setup (`get_logger`) and utilities to handle different date-time formats (`to_datetime`, `to_timestamp`) and timezone detection (`to_timezone). - Cache files are automatically valid for the the current date unless specified otherwise. This is to mimic the current behaviour used in several classes. - The logger supports rotating log files to prevent excessive log file size. - The `to_datetime` and `to_timestamp`functions support a wide variety of input types and formats. They provide the time conversion that is e.g. used in PVForecast. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> * Improve testability of PVForecast Improvements for testing of PVForecast - Use common utility functions to allow for general testing at one spot. - to_datetime - CacheFileStore - Use logging instead of print to easily capture in testing. - Add validation of the json schema for Akkudoktor PV forecast data. - Allow to create an empty PVForecast instance as base instance for testing. - Make process_data() complete for filling a PVForecast instance for testing. - Normalize forecast datetime to timezone of system given in loaded data. - Do not print report but provide report for test checks. - Get rid of cache file path using the CachFileStore to automate cache file usage. - Improved module documentation. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> * Add test for PVForecast and newly extracted utility modules. - Add test for PVForecast - Add test for CacheFileStore in the new cachefilestore module - Add test for to_datetime, to_timestamp, to_timezone in the new datetimeutil module - Add test for get_logger in the new logutil module Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> --------- Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Normann <github@koldrack.com> 2024-11-10 23:49:10 +01:00			`timezonefinder==6.5.4`
Update requirements.txt 2024-10-02 21:10:29 +02:00			`deap==1.4.1`
			`requests==2.32.3`
Add docker multi-arch build with github package push and cleaned up Dockerfile for smaller builds 2024-10-03 18:40:38 +02:00			`pandas==2.2.3`
Add test to PVForecast (#174) * Add documentation to class_pv_forecast.py. Added documentation. Beware mostly generated by ChatGPT. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> * Add CacheFileStore, datetime and logger utilities. The `CacheFileStore` class is a singleton-based, thread-safe key-value store for managing temporary file objects, allowing the creation, retrieval, and management of cache files. The utility modules offer a flexible logging setup (`get_logger`) and utilities to handle different date-time formats (`to_datetime`, `to_timestamp`) and timezone detection (`to_timezone). - Cache files are automatically valid for the the current date unless specified otherwise. This is to mimic the current behaviour used in several classes. - The logger supports rotating log files to prevent excessive log file size. - The `to_datetime` and `to_timestamp`functions support a wide variety of input types and formats. They provide the time conversion that is e.g. used in PVForecast. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> * Improve testability of PVForecast Improvements for testing of PVForecast - Use common utility functions to allow for general testing at one spot. - to_datetime - CacheFileStore - Use logging instead of print to easily capture in testing. - Add validation of the json schema for Akkudoktor PV forecast data. - Allow to create an empty PVForecast instance as base instance for testing. - Make process_data() complete for filling a PVForecast instance for testing. - Normalize forecast datetime to timezone of system given in loaded data. - Do not print report but provide report for test checks. - Get rid of cache file path using the CachFileStore to automate cache file usage. - Improved module documentation. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> * Add test for PVForecast and newly extracted utility modules. - Add test for PVForecast - Add test for CacheFileStore in the new cachefilestore module - Add test for to_datetime, to_timestamp, to_timezone in the new datetimeutil module - Add test for get_logger in the new logutil module Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> --------- Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Normann <github@koldrack.com> 2024-11-10 23:49:10 +01:00			`pydantic==2.9.2`