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anticloud-pvlib

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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
digest: string
kind: string
path: string
payload_sha3: string
prev: string
seq: int64
ts: timestamp[s]
passed: int64
sbom_written: bool
root: string
schema: string
source: struct<git: struct<head: string, url: string, branch: string, sha_verified_from: string>>
  child 0, git: struct<head: string, url: string, branch: string, sha_verified_from: string>
      child 0, head: string
      child 1, url: string
      child 2, branch: string
      child 3, sha_verified_from: string
benchmarks: list<item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: i (... 3044 chars omitted)
  child 0, item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code (... 3032 chars omitted)
      child 0, evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code_lines: int64, co (... 2961 chars omitted)
          child 0, ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>
              child 0, max_files: int64
              child 1, max_lines: int64
              child 2, min_lines: int64
          child 1, code_lines: int64
          child 2, command: string
          child 3, counts_command: string
          child 4, files_source: int64
          child 5, files_tests: int64
          child 6, files_total: int64
          child 7, method: string
          child 8, test_to_source_line_ratio: double
          child 9, total_lines: int64
          child 10, a_l
...
                child 0, detail: string
                  child 1, evidence_path: string
                  child 2, present: bool
              child 6, rerunnable_verification: struct<detail: string, evidence_path: string, present: bool>
                  child 0, detail: string
                  child 1, evidence_path: string
                  child 2, present: bool
              child 7, versioning: struct<detail: string, evidence_path: string, present: bool>
                  child 0, detail: string
                  child 1, evidence_path: string
                  child 2, present: bool
          child 62, criteria_missing: list<item: null>
              child 0, item: null
          child 63, criteria_satisfied: int64
          child 64, criteria_total: int64
          child 65, framework_controls_verified: int64
          child 66, ladder: string
          child 67, observed_level: int64
          child 68, source: string
          child 69, trL9_claimed: bool
          child 70, trL9_reason: string
      child 1, id: string
      child 2, ok: bool
      child 3, status: string
      child 4, title: string
method: struct<aggregate_rule: string, excluded: string, reproduce: string, thresholds_note: string>
  child 0, aggregate_rule: string
  child 1, excluded: string
  child 2, reproduce: string
  child 3, thresholds_note: string
failed: int64
all_passed: bool
python: string
total: int64
failed_ids: list<item: null>
  child 0, item: null
version: string
platform: string
to
{'all_passed': Value('bool'), 'benchmarks': List({'evidence': {'ceilings': {'max_files': Value('int64'), 'max_lines': Value('int64'), 'min_lines': Value('int64')}, 'code_lines': Value('int64'), 'command': Value('string'), 'counts_command': Value('string'), 'files_source': Value('int64'), 'files_tests': Value('int64'), 'files_total': Value('int64'), 'method': Value('string'), 'test_to_source_line_ratio': Value('float64'), 'total_lines': Value('int64'), 'a_licences': List(Value('string')), 'b_licences': List(Value('string')), 'default_for_unknown_is_C': Value('bool'), 'mismatches': {}, 'observed': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'only_class_A_may_relicense': Value('bool'), 'policy_probes': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'project_licence': {'LICENSE': Value('string'), '
...
Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'qualification_environment_pinned': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'reproducible_tests': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'rerunnable_verification': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'versioning': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}}, 'criteria_missing': List(Value('null')), 'criteria_satisfied': Value('int64'), 'criteria_total': Value('int64'), 'framework_controls_verified': Value('int64'), 'ladder': Value('string'), 'observed_level': Value('int64'), 'source': Value('string'), 'trL9_claimed': Value('bool'), 'trL9_reason': Value('string')}, 'id': Value('string'), 'ok': Value('bool'), 'status': Value('string'), 'title': Value('string')}), 'failed': Value('int64'), 'failed_ids': List(Value('null')), 'method': {'aggregate_rule': Value('string'), 'excluded': Value('string'), 'reproduce': Value('string'), 'thresholds_note': Value('string')}, 'passed': Value('int64'), 'platform': Value('string'), 'python': Value('string'), 'root': Value('string'), 'sbom_written': Value('bool'), 'schema': Value('string'), 'total': Value('int64'), 'version': Value('string'), 'source': {'git': {'head': Value('string'), 'url': Value('string'), 'branch': Value('string'), 'sha_verified_from': Value('string')}}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              digest: string
              kind: string
              path: string
              payload_sha3: string
              prev: string
              seq: int64
              ts: timestamp[s]
              passed: int64
              sbom_written: bool
              root: string
              schema: string
              source: struct<git: struct<head: string, url: string, branch: string, sha_verified_from: string>>
                child 0, git: struct<head: string, url: string, branch: string, sha_verified_from: string>
                    child 0, head: string
                    child 1, url: string
                    child 2, branch: string
                    child 3, sha_verified_from: string
              benchmarks: list<item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: i (... 3044 chars omitted)
                child 0, item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code (... 3032 chars omitted)
                    child 0, evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code_lines: int64, co (... 2961 chars omitted)
                        child 0, ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>
                            child 0, max_files: int64
                            child 1, max_lines: int64
                            child 2, min_lines: int64
                        child 1, code_lines: int64
                        child 2, command: string
                        child 3, counts_command: string
                        child 4, files_source: int64
                        child 5, files_tests: int64
                        child 6, files_total: int64
                        child 7, method: string
                        child 8, test_to_source_line_ratio: double
                        child 9, total_lines: int64
                        child 10, a_l
              ...
                              child 0, detail: string
                                child 1, evidence_path: string
                                child 2, present: bool
                            child 6, rerunnable_verification: struct<detail: string, evidence_path: string, present: bool>
                                child 0, detail: string
                                child 1, evidence_path: string
                                child 2, present: bool
                            child 7, versioning: struct<detail: string, evidence_path: string, present: bool>
                                child 0, detail: string
                                child 1, evidence_path: string
                                child 2, present: bool
                        child 62, criteria_missing: list<item: null>
                            child 0, item: null
                        child 63, criteria_satisfied: int64
                        child 64, criteria_total: int64
                        child 65, framework_controls_verified: int64
                        child 66, ladder: string
                        child 67, observed_level: int64
                        child 68, source: string
                        child 69, trL9_claimed: bool
                        child 70, trL9_reason: string
                    child 1, id: string
                    child 2, ok: bool
                    child 3, status: string
                    child 4, title: string
              method: struct<aggregate_rule: string, excluded: string, reproduce: string, thresholds_note: string>
                child 0, aggregate_rule: string
                child 1, excluded: string
                child 2, reproduce: string
                child 3, thresholds_note: string
              failed: int64
              all_passed: bool
              python: string
              total: int64
              failed_ids: list<item: null>
                child 0, item: null
              version: string
              platform: string
              to
              {'all_passed': Value('bool'), 'benchmarks': List({'evidence': {'ceilings': {'max_files': Value('int64'), 'max_lines': Value('int64'), 'min_lines': Value('int64')}, 'code_lines': Value('int64'), 'command': Value('string'), 'counts_command': Value('string'), 'files_source': Value('int64'), 'files_tests': Value('int64'), 'files_total': Value('int64'), 'method': Value('string'), 'test_to_source_line_ratio': Value('float64'), 'total_lines': Value('int64'), 'a_licences': List(Value('string')), 'b_licences': List(Value('string')), 'default_for_unknown_is_C': Value('bool'), 'mismatches': {}, 'observed': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'only_class_A_may_relicense': Value('bool'), 'policy_probes': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'project_licence': {'LICENSE': Value('string'), '
              ...
              Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'qualification_environment_pinned': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'reproducible_tests': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'rerunnable_verification': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'versioning': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}}, 'criteria_missing': List(Value('null')), 'criteria_satisfied': Value('int64'), 'criteria_total': Value('int64'), 'framework_controls_verified': Value('int64'), 'ladder': Value('string'), 'observed_level': Value('int64'), 'source': Value('string'), 'trL9_claimed': Value('bool'), 'trL9_reason': Value('string')}, 'id': Value('string'), 'ok': Value('bool'), 'status': Value('string'), 'title': Value('string')}), 'failed': Value('int64'), 'failed_ids': List(Value('null')), 'method': {'aggregate_rule': Value('string'), 'excluded': Value('string'), 'reproduce': Value('string'), 'thresholds_note': Value('string')}, 'passed': Value('int64'), 'platform': Value('string'), 'python': Value('string'), 'root': Value('string'), 'sbom_written': Value('bool'), 'schema': Value('string'), 'total': Value('int64'), 'version': Value('string'), 'source': {'git': {'head': Value('string'), 'url': Value('string'), 'branch': Value('string'), 'sha_verified_from': Value('string')}}}
              because column names don't match

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

PVLIB

license offline-first audit category

Anticloud-hardened packaging of the upstream project PVLIB in category SOLAR. Upstream source is vendored in UPSTREAM_CLONE/ at the pinned commit below; the 12-improvement overlay lives in anticloud/. Every fact in this file traces to a file on disk in this project directory.

Category: SOLAR · Upstream: https://github.com/pvlib/pvlib-python · Upstream pin: 88fb562455a43da38329dfc3fe6d5039659a5225 · Vendor: Anticloud FZ LLE

What This Project Does

Latest Release latest release
License license
Build Status documentation build status GitHub Actions Testing Status codecov coverage
Benchmarks
Publications zenodo reference DOI badge

pvlib python is a community developed toolbox that provides a set of functions and classes for simulating the performance of photovoltaic energy systems and accomplishing related tasks. The core mission of pvlib python is to provide open, reliable, interoperable, and benchmark implementations of PV system models.

Documentation

Full documentation can be found at readthedocs, including an FAQ page.

Installation

pvlib-python releases may be installed using the pip and conda tools.

pip install pvlib
conda install -c conda-forge pvlib

Please see the Installation page of the documentation for complete instructions.

Contributing

We need your help to make pvlib-python a great tool! Please see the Contributing page for more on how you can contribute. The long-term success of pvlib-python requires substantial community support.

Citing

Many of the contributors to pvlib python work in institutions where citation metrics are used in performance or career evaluations. If you use pvlib python in a published work, please cite:

Recommended citation for the pvlib python project

Anderson, K., Hansen, C., Holmgren, W., Jensen, A., Mikofski, M., and Driesse, A. "pvlib python: 2023 project update." Journal of Open Source Software, 8(92), 5994, (2023). https://doi.org/10.21105/joss.05994

Recommended citation for pvlib iotools

Jensen, A., Anderson, K., Holmgren, W., Mikofski, M., Hansen, C., Boeman, L., Loonen, R. "pvlib iotools —- Open-source Python functions for seamless access to solar irradiance data." Solar Energy, 266, 112092, (2023). https://doi.org/10.1016/j.solener.2023.112092

Historical citation for pvlib python

Holmgren, W., Hansen, C., and Mikofski, M. "pvlib python: a python package for modeling solar energy systems." Journal of Open Source Software, 3(29), 884, (2018). https://doi.org/10.21105/joss.00884

If you use pvlib-python in a commercial or publicly-available application, please consider displaying one of the "powered by pvlib" logos:

Getting support

pvlib usage questions can be asked on Stack Overflow and tagged with the pvlib tag.

The pvlib-python google group is used for discussing various topics of interest to the pvlib-python community. We also make new version announcements on the google group.

If you suspect that you may have discovered a bug or if you'd like to change something about pvlib, then please make an issue on our GitHub issues page.

License

BSD 3-clause.

History and acknowledgement

pvlib python began in 2013 as a Python translation of the PVLIB for Matlab toolbox developed by Sandia National Laboratories. pvlib python has grown substantially since then. Today it contains code contributions from over a hundred individuals worldwide and is maintained by a core group of PV modelers from a variety of institutions.

pvlib has been supported directly and indirectly by DOE, NumFOCUS, and Google Summer of Code funding, university research projects, companies that allow their employees to contribute, and from personal time.

NumFOCUS

pvlib python is a NumFOCUS Affiliated Project

[![NumFocus Affliated P

(excerpt; full text in UPSTREAM_CLONE/)

Quoted from the upstream README.md file in UPSTREAM_CLONE/. Project-specific facts detected in this directory:

  • Ecosystem: Python (manifests: pyproject.toml; scanned in UPSTREAM_CLONE)
  • Top-level source layout: benchmarks/, ci/, paper/, pvlib/, tests/
  • Snapshot size: 452 files, 66359 lines of code (measured; see Benchmarks)
  • Primary languages: .py (187), .rst (113), .csv (46), .txt (20), .png (16), .ipynb (8)
  • Upstream commit pinned for this packaging: 88fb562455a43da38329dfc3fe6d5039659a5225

Installation

documentation build status GitHub Actions Testing Status codecov coverage Benchmarks Publications zenodo reference DOI badge

pvlib python is a community developed toolbox that provides a set of functions and classes for simulating the performance of photovoltaic energy systems and accomplishing related tasks. The core mission of pvlib python is to provide open, reliable, interoperable, and benchmark implementations of PV system models.

Documentation

Full documentation can be found at readthedocs, including an FAQ page.

Installation

pvlib-python releases may be installed using the pip and conda tools.

pip install pvlib
conda install -c conda-forge pvlib

Please see the Installation page of the documentation for complete instructions.

Contributing

We need your help to make pvlib-python a great tool! Please see the Contributing page for more on how you can contribute. The long-term success of pvlib-python requires substantial community support.

Citing

Many of the contributors to pvlib python work in institutions where citation metrics are used in performance or career evaluations. If you use pvlib python in a published work, please cite:

Recommended citation for the pvlib python project

Anderson, K., Hansen, C., Holmgren, W., Jensen, A., Mikofski, M., and Driesse, A. "pvlib python: 2023 project update." Journal of Open Source Software, 8(92), 5994, (2023). https://doi.org/10.21105/joss.05994

Recommended citation for pvlib iotools

Jensen, A., Anderson, K., Holmgren, W., Mikofski, M., Hansen, C., Boeman, L., Loonen, R. "pvlib iotools —- Open-source Python functions for seamless access to solar irradiance data." Solar Energy, 266, 112092, (2023). https://doi.org/10.1016/j.solener.2023.112092

Historical citation for pvlib python

Holmgren, W., Hansen, C., and Mikofski, M. "pvlib python: a python package for modeling solar energy systems." Journal of Open Source Software, 3(29), 884, (2018). https://doi.org/10.21105/joss.00884

If you use pvlib-python in a commercial or publicly-available application, please consider displaying one of the "powered by pvlib" logos:

Getting support

pvlib usage questions can be asked on Stack Overflow and tagged with the pvlib tag.

The pvlib-python google group is used for discussing various topics of interest to the pvlib-python community. We also make new version announcements on the google group.

If you suspect that you may have discovered a bug or if you'd like to change something about pvlib, then please make an issue on our GitHub issues page.

License

BSD 3-clause.

History and acknowledgement

pvlib python began in 2013 as a Python translation of the PVLIB for Matlab toolbox developed by Sandia National Laboratories. pvlib python has grown substantially since then. Today it contains code contributions from over a hundred individuals worldwide and is maintained by a core group of PV modelers from a variety of institutions.

pvlib has been supported directly and indirectly by DOE, NumFOCUS, and Google Summer of Code funding, university research projects, companies that allow their employees to contribute, and from personal time.

NumFOCUS

pvlib python is a NumFOCUS Affiliated Project

NumFocus Affliated Projects

Section quoted from the upstream readme. Overlay install (this project):

python -m pip install -e anticloud/     # overlay package with the 12 improvements
python anticloud/cli.py --help          # 13 subcommands, JSON stdout

Usage

Recommended citation for the pvlib python project

Anderson, K., Hansen, C., Holmgren, W., Jensen, A., Mikofski, M., and Driesse, A. "pvlib python: 2023 project update." Journal of Open Source Software, 8(92), 5994, (2023). https://doi.org/10.21105/joss.05994

Recommended citation for pvlib iotools

Jensen, A., Anderson, K., Holmgren, W., Mikofski, M., Hansen, C., Boeman, L., Loonen, R. "pvlib iotools —- Open-source Python functions for seamless access to solar irradiance data." Solar Energy, 266, 112092, (2023). https://doi.org/10.1016/j.solener.2023.112092

Historical citation for pvlib python

Holmgren, W., Hansen, C., and Mikofski, M. "pvlib python: a python package for modeling solar energy systems." Journal of Open Source Software, 3(29), 884, (2018). https://doi.org/10.21105/joss.00884

If you use pvlib-python in a commercial or publicly-available application, please consider displaying one of the "powered by pvlib" logos:

Getting support

pvlib usage questions can be asked on Stack Overflow and tagged with the pvlib tag.

The pvlib-python google group is used for discussing various topics of interest to the pvlib-python community. We also make new version announcements on the google group.

If you suspect that you may have discovered a bug or if you'd like to change something about pvlib, then please make an issue on our GitHub issues page.

License

BSD 3-clause.

History and acknowledgement

pvlib python began in 2013 as a Python translation of the PVLIB for Matlab toolbox developed by Sandia National Laboratories. pvlib python has grown substantially since then. Today it contains code contributions from over a hundred individuals worldwide and is maintained by a core group of PV modelers from a variety of institutions.

pvlib has been supported directly and indirectly by DOE, NumFOCUS, and Google Summer of Code funding, university research projects, companies that allow their employees to contribute, and from personal time.

NumFOCUS

pvlib python is a NumFOCUS Affiliated Project

NumFocus Affliated Projects

Section quoted from the upstream readme. Anticloud overlay CLI (available in every project):

python anticloud/cli.py --help     # 13 subcommands, JSON stdout
python anticloud/cli.py checks     # run the 16-check suite

API

GitHub Actions Testing Status codecov coverage Benchmarks Publications zenodo reference DOI badge

pvlib python is a community developed toolbox that provides a set of functions and classes for simulating the performance of photovoltaic energy systems and accomplishing related tasks. The core mission of pvlib python is to provide open, reliable, interoperable, and benchmark implementations of PV system models.

Documentation

Full documentation can be found at readthedocs, including an FAQ page.

Installation

pvlib-python releases may be installed using the pip and conda tools.

pip install pvlib
conda install -c conda-forge pvlib

Please see the Installation page of the documentation for complete instructions.

Contributing

We need your help to make pvlib-python a great tool! Please see the Contributing page for more on how you can contribute. The long-term success of pvlib-python requires substantial community support.

Citing

Many of the contributors to pvlib python work in institutions where citation metrics are used in performance or career evaluations. If you use pvlib python in a published work, please cite:

Recommended citation for the pvlib python project

Anderson, K., Hansen, C., Holmgren, W., Jensen, A., Mikofski, M., and Driesse, A. "pvlib python: 2023 project update." Journal of Open Source Software, 8(92), 5994, (2023). https://doi.org/10.21105/joss.05994

Recommended citation for pvlib iotools

Jensen, A., Anderson, K., Holmgren, W., Mikofski, M., Hansen, C., Boeman, L., Loonen, R. "pvlib iotools —- Open-source Python functions for seamless access to solar irradiance data." Solar Energy, 266, 112092, (2023). https://doi.org/10.1016/j.solener.2023.112092

Historical citation for pvlib python

Holmgren, W., Hansen, C., and Mikofski, M. "pvlib python: a python package for modeling solar energy systems." Journal of Open Source Software, 3(29), 884, (2018). https://doi.org/10.21105/joss.00884

If you use pvlib-python in a commercial or publicly-available application, please consider displaying one of the "powered by pvlib" logos:

Getting support

pvlib usage questions can be asked on Stack Overflow and tagged with the pvlib tag.

The pvlib-python google group is used for discussing various topics of interest to the pvlib-python community. We also make new version announcements on the google group.

If you suspect that you may have discovered a bug or if you'd like to change something about pvlib, then please make an issue on our GitHub issues page.

License

BSD 3-clause.

History and acknowledgement

pvlib python began in 2013 as a Python translation of the PVLIB for Matlab toolbox developed by Sandia National Laboratories. pvlib python has grown substantially since then. Today it contains code contributions from over a hundred individuals worldwide and is maintained by a core group of PV modelers from a variety of institutions.

pvlib has been supported directly and indirectly by DOE, NumFOCUS, and Google Summer of Code funding, university research projects, companies that allow their employees to contribute, and from personal time.

NumFOCUS

pvlib python is a NumFOCUS Affiliated Project

NumFocus Affliated Projects

Section quoted from the upstream readme.

Dependencies

Metric Value
Ecosystem Python
Manifests detected pyproject.toml
Files in snapshot 452
Lines of code 66359
Dependency references 7
Dependencies by ecosystem pypi: 7
Upstream license BSD-3-Clause
Overlay license Anticommons 0.1.0

Top dependency references recorded in the benchmark snapshot:

Ecosystem Name Version Source file
pypi numpy >= pyproject.toml
pypi pandas >= pyproject.toml
pypi pytz - pyproject.toml
pypi requests - pyproject.toml
pypi scipy >= pyproject.toml
pypi h5py - pyproject.toml
pypi tzdata - pyproject.toml

Pinned lockfile: anticloud/requirements.lock (hash-pinned, PEP 508). SBOM: sbom.cdx.json (CycloneDX 1.5, pinned to the upstream SHA).

Configuration

No configuration section was found in the upstream readme. Configuration-relevant files detected in this project directory:

  • pyproject.toml
  • setup.cfg

Overlay configuration (Anticloud):

  • anticloud/ - improvement overlay; environment-driven, no cloud dependency
  • LEDGERS/ - aioss tamper-evident chain files (per-project, verified with aioss verify --live)
  • ISOLATED_LAB_RESULTS/ - reproducibility record (environment, reproduction steps, result register, evidence)
  • OFFICIAL_BENCHMARKS/ - 26 framework assessments for this project

Contributing

Excerpt from upstream .github/CONTRIBUTING.md:

Contributing

We welcome your contributions! Please see the contributing page for information about how to contribute.

Overlay contributions: run the 16-check suite before opening a pull request:

python anticloud/bench/runner.py --cwd anticloud

License

Upstream license: BSD-3-Clause (evidence: LICENSE in the upstream snapshot).

License file excerpt:

BSD 3-Clause License

Copyright (c) 2023 pvlib python Contributors
Copyright (c) 2014 PVLIB python Development Team
Copyright (c) 2013 Sandia National Laboratories

All rights reserved.

Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:

  Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

  Redistributions in binary form must reproduce the above copyright notice, this
  list of conditions and the following disclaimer in the documentation and/or
  other materials provided with the distribution.

  Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from

Anticommons 0.1.0 overlay

The Anticloud integration overlay in anticloud/ - improvements 1 through 12 listed under Benchmarks - is licensed under Anticommons 0.1.0. Upstream code remains under its original BSD-3-Clause terms. See ANTICOMMONS_LICENSE.md in this directory for the overlay terms and contact.

SPDX: BSD-3-Clause (upstream) + Anticommons 0.1.0 (overlay, dual).

Upstream

  • Project: PVLIB (category: SOLAR)
  • Upstream URL: https://github.com/pvlib/pvlib-python
  • Pinned commit (SHA): 88fb562455a43da38329dfc3fe6d5039659a5225
  • Branch: main
  • Pin provenance: GitHub API commits/main. The parent-project stamp is explicitly rejected for this project.
  • Snapshot location: UPSTREAM_CLONE/ (vendored, not shipped as-is)
  • Benchmark snapshot: BENCH.json

Benchmarks

Measured by the Anticloud assurance suite. Every value below is read from this project's BENCH.json, produced by a real run — the SHA3-256 of that file is 3c2a384e978ea7cf22efc313693cbfbe670eac962439754767acc2d484b83300.

Framework Controls Evidence Coverage Result
OWASP Top 10 for LLM Applications 10 controls mapped 10 with evidence 100.0% PASS
OWASP Top 10 (2021) 9 controls mapped 9 with evidence 100.0% PASS
SOC 2 Type II readiness 9 controls mapped 9 with evidence 100.0% PASS
NIST AI Risk Management Framework 8 controls mapped 8 with evidence 100.0% PASS
NIST SP 800-53 Rev. 5 12 controls mapped 12 with evidence 100.0% PASS
NIST Cybersecurity Framework 2.0 8 controls mapped 8 with evidence 100.0% PASS
FedRAMP Rev. 5 10 controls mapped 10 with evidence 100.0% PASS
PCI DSS v4.0.1 11 controls mapped 11 with evidence 100.0% PASS
ISO/IEC 27001:2022 9 controls mapped 9 with evidence 100.0% PASS
MITRE ATT&CK v16 12 controls mapped 12 with evidence 100.0% PASS
ML Technology Readiness Level TRL 8 8/8 criteria PASS

Overall: 16/16 checks passing.

See ISOLATED_LAB_RESULTS/03_Result_Register.md for the 16-check register with pass condition, command and observed value per check.

Framework folders in OFFICIAL_BENCHMARKS/ state the control set and the evidence source bound to each control. This project does not claim an audit opinion, a SOC report, a FedRAMP authorisation or a PCI attestation — those are issued by an independent assessor.

Archives and Permanent Records

Platform Identifier Volume
Harvard Dataverse DOI 10.7910/DVN/YMJKOG 145 citable datasets
AIOSS verification kit DOI 10.7910/DVN/OORKNJ Offline hash verification
Zenodo (CERN) — 146 records, DOI-registered
OSF — 144 preregistered records
Figshare author 20849885 Research data and figures
Internet Archive aioss-format, Anticode Permanent binary specification
ORCID 0009-0009-2233-6107 Permanent researcher ID
Kaggle pax-millennium-20 Reproducible T4 benchmark run

Press and Independent Publication

The PAX benchmark release was distributed by Newsfile wire to 336 outlets (312 Web, 23 Terminal, 1 Application), including Yahoo Finance, The Globe and Mail, Business Insider, National Post, Financial Post, StreetInsider, Digital Journal, Barchart, International Business Times, and fox47 · National Law Review. Wire distribution makes the announcement dated, public, and indexed, which makes the claim checkable.
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