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0.6.3
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0992f0e746
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| 0992f0e746 |
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CHANGELOG.md
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CHANGELOG.md
@@ -2,97 +2,6 @@
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All notable changes to this project will be documented in this file. See [standard-version](https://github.com/conventional-changelog/standard-version) for commit guidelines.
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All notable changes to this project will be documented in this file. See [standard-version](https://github.com/conventional-changelog/standard-version) for commit guidelines.
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### [0.6.3](https://gitea.deepak.science:2222/physics/deepdog/compare/0.6.2...0.6.3) (2022-06-12)
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### Features
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* adds fast filter variant ([2c5c122](https://gitea.deepak.science:2222/physics/deepdog/commit/2c5c1228209e51d17253f07470e2f1e6dc6872d7))
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* adds tester for fast filter real spectrum ([0a1a277](https://gitea.deepak.science:2222/physics/deepdog/commit/0a1a27759b0d4ab01da214b76ab14bf2b1fe00e3))
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### [0.6.2](https://gitea.deepak.science:2222/physics/deepdog/compare/0.6.1...0.6.2) (2022-05-26)
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### Features
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* adds better import api for real data run ([d7e0f13](https://gitea.deepak.science:2222/physics/deepdog/commit/d7e0f13ca55197b24cb534c80f321ee76b9c4a40))
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### [0.6.1](https://gitea.deepak.science:2222/physics/deepdog/compare/0.6.0...0.6.1) (2022-05-22)
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### Features
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* adds new runner for real spectra ([bd56f24](https://gitea.deepak.science:2222/physics/deepdog/commit/bd56f247748babb2ee1f2a1182d25aa968bff5a5))
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## [0.6.0](https://gitea.deepak.science:2222/physics/deepdog/compare/0.5.0...0.6.0) (2022-05-22)
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### ⚠ BREAKING CHANGES
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* bayes run now handles multidipoles with changes to output file format etc.
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* logs multiple dipoles better maybe
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* switches over to pdme new stuff, uses models and scraps discretisations entirely
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* removes alt_bayes bayes distinction, which was superfluous when only alt worked
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### Features
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* adds pdme 0.7.0 for multiprocessing ([874d876](https://gitea.deepak.science:2222/physics/deepdog/commit/874d876c9d774433b034d47c4cc0cdac41e6f2c7))
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* bayes run now handles multidipoles with changes to output file format etc. ([5d0a7a4](https://gitea.deepak.science:2222/physics/deepdog/commit/5d0a7a4be09c58f8f8f859384f01d7912a98b8b9))
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* logs multiple dipoles better maybe ([ae8977b](https://gitea.deepak.science:2222/physics/deepdog/commit/ae8977bb1e4d6cd71e88ea0876da8f4318e030b6))
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* removes alt_bayes bayes distinction, which was superfluous when only alt worked ([101569d](https://gitea.deepak.science:2222/physics/deepdog/commit/101569d749e4f3f1842886aa2fd3321b8132278b))
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* switches over to pdme new stuff, uses models and scraps discretisations entirely ([6e29f7a](https://gitea.deepak.science:2222/physics/deepdog/commit/6e29f7a702b578c266a42bba23ac973d155ada10))
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* Uses multidipole for bayes run, with more verbose output ([df89776](https://gitea.deepak.science:2222/physics/deepdog/commit/df8977655de977fd3c4f7383dd9571e551eb1382))
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### Bug Fixes
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* another bug fix for csv generation ([b7da3d6](https://gitea.deepak.science:2222/physics/deepdog/commit/b7da3d61cc5c128cba1d2fcb3770b71b7f6fc4b8))
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* fixes crash when dipole count is smaller than expected max during file write ([b5e0ecb](https://gitea.deepak.science:2222/physics/deepdog/commit/b5e0ecb52886b32d9055302eacfabb69338026b4))
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* fixes format string in csv output for headers ([9afa209](https://gitea.deepak.science:2222/physics/deepdog/commit/9afa209864cdb9255988778e987fe05952848fd4))
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* fixes random issue ([eec926a](https://gitea.deepak.science:2222/physics/deepdog/commit/eec926aaac654f78942b4c6b612e4d1cdcbf81dc))
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* moves logging successes to after they've actually happened ([0caad05](https://gitea.deepak.science:2222/physics/deepdog/commit/0caad05e3cc6a9adba8bf937c3d2f944e1b096a3))
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* now doesn't double randomise frequency ([23b202b](https://gitea.deepak.science:2222/physics/deepdog/commit/23b202beb81cb89f7f20b691e83116fa53764902))
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* whoops deleted word multiprocessing ([31070b5](https://gitea.deepak.science:2222/physics/deepdog/commit/31070b5342c265d930b4c51402f42a3ee2415066))
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## [0.5.0](https://gitea.deepak.science:2222/physics/deepdog/compare/0.4.0...0.5.0) (2022-04-30)
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### ⚠ BREAKING CHANGES
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* simulpairs now uses different rng calculator
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### Features
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* adds simulpairs run ([e9277c3](https://gitea.deepak.science:2222/physics/deepdog/commit/e9277c3da777359feb352c0b19f3bb029248ba2f))
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* has better parallelisation ([edf0ba6](https://gitea.deepak.science:2222/physics/deepdog/commit/edf0ba6532c0588fce32341709cdb70e384b83f4))
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* simulpairs now uses different rng calculator ([50dbc48](https://gitea.deepak.science:2222/physics/deepdog/commit/50dbc4835e60bace9e9b4ba37415f073a3c9e479))
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### Bug Fixes
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* better parallelisation hopefully ([42829c0](https://gitea.deepak.science:2222/physics/deepdog/commit/42829c0327e080e18be2fb75e746f6ac0d7c2f6d))
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* Makes altbayessimulpairs available in package ([492a5e6](https://gitea.deepak.science:2222/physics/deepdog/commit/492a5e6681c85f95840e28cfd5d4ce4ca1d54eba))
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* stronger names ([0954429](https://gitea.deepak.science:2222/physics/deepdog/commit/0954429e2d015a105ff16dfbb9e7a352bf53e5e9))
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* Uses correct filename arg for passed in rng ([349341b](https://gitea.deepak.science:2222/physics/deepdog/commit/349341b405375a43b933f1fd7db4ee9fc501def3))
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* uses correct filename for pairs guy ([4c06b39](https://gitea.deepak.science:2222/physics/deepdog/commit/4c06b3912c811c93c310b1d9e4c153f2014c4f8b))
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## [0.4.0](https://gitea.deepak.science:2222/physics/deepdog/compare/0.3.5...0.4.0) (2022-04-10)
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### ⚠ BREAKING CHANGES
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* Adds pair calculations, with changing api format
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### Features
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* Adds dynamic cycle count increases to help reach minimum success count ([ec7b4ca](https://gitea.deepak.science:2222/physics/deepdog/commit/ec7b4cac393c15e94c513215c4f1ba32be2ae87a))
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* Adds pair calculations, with changing api format ([6463b13](https://gitea.deepak.science:2222/physics/deepdog/commit/6463b135ef2d212b565864b5ac1b655e014d2194))
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### Bug Fixes
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* uses bigfix from pdme for negatives ([c1c711f](https://gitea.deepak.science:2222/physics/deepdog/commit/c1c711f47b574d3a9b8a24dbcbdd7f50b9be8ea9))
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### [0.3.5](https://gitea.deepak.science:2222/physics/deepdog/compare/0.3.4...0.3.5) (2022-03-07)
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### [0.3.5](https://gitea.deepak.science:2222/physics/deepdog/compare/0.3.4...0.3.5) (2022-03-07)
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import logging
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import logging
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from deepdog.meta import __version__
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from deepdog.meta import __version__
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from deepdog.bayes_run import BayesRun
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from deepdog.bayes_run import BayesRun
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from deepdog.bayes_run_simulpairs import BayesRunSimulPairs
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from deepdog.alt_bayes_run import AltBayesRun
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from deepdog.real_spectrum_run import RealSpectrumRun
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from deepdog.diagnostic import Diagnostic
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def get_version():
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def get_version():
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return __version__
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return __version__
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__all__ = [
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__all__ = ["get_version", "BayesRun", "AltBayesRun", "Diagnostic"]
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"get_version",
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"BayesRun",
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"BayesRunSimulPairs",
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"RealSpectrumRun",
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]
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logging.getLogger(__name__).addHandler(logging.NullHandler())
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logging.getLogger(__name__).addHandler(logging.NullHandler())
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134
deepdog/alt_bayes_run.py
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134
deepdog/alt_bayes_run.py
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import pdme.model
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import pdme.measurement.oscillating_dipole
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import pdme.util.fast_v_calc
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from typing import Sequence, Tuple, List
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import datetime
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import csv
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import multiprocessing
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import logging
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import numpy
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# TODO: remove hardcode
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CHUNKSIZE = 50
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# TODO: It's garbage to have this here duplicated from pdme.
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DotInput = Tuple[numpy.typing.ArrayLike, float]
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_logger = logging.getLogger(__name__)
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def get_a_result(input) -> int:
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discretisation, dot_inputs, lows, highs, monte_carlo_count, max_frequency = input
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sample_dipoles = discretisation.get_model().get_n_single_dipoles(monte_carlo_count, max_frequency)
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vals = pdme.util.fast_v_calc.fast_vs_for_dipoles(dot_inputs, sample_dipoles)
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return numpy.count_nonzero(pdme.util.fast_v_calc.between(vals, lows, highs))
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class AltBayesRun():
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'''
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A single Bayes run for a given set of dots.
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Parameters
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----------
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dot_inputs : Sequence[DotInput]
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The dot inputs for this bayes run.
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discretisations_with_names : Sequence[Tuple(str, pdme.model.Model)]
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The models to evaluate.
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actual_model_discretisation : pdme.model.Discretisation
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The discretisation for the model which is actually correct.
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filename_slug : str
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The filename slug to include.
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run_count: int
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The number of runs to do.
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'''
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def __init__(self, dot_inputs: Sequence[DotInput], discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]], actual_model: pdme.model.Model, filename_slug: str, run_count: int, low_error: float = 0.9, high_error: float = 1.1, monte_carlo_count: int = 10000, monte_carlo_cycles: int = 10, max_frequency: float = 20, end_threshold: float = None, chunksize: int = CHUNKSIZE) -> None:
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self.dot_inputs = dot_inputs
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self.dot_inputs_array = pdme.measurement.oscillating_dipole.dot_inputs_to_array(dot_inputs)
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self.discretisations = [disc for (_, disc) in discretisations_with_names]
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self.model_names = [name for (name, _) in discretisations_with_names]
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self.actual_model = actual_model
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self.model_count = len(self.discretisations)
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self.monte_carlo_count = monte_carlo_count
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self.monte_carlo_cycles = monte_carlo_cycles
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self.run_count = run_count
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self.low_error = low_error
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self.high_error = high_error
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self.csv_fields = ["dipole_moment", "dipole_location", "dipole_frequency"]
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self.compensate_zeros = True
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self.chunksize = chunksize
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for name in self.model_names:
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self.csv_fields.extend([f"{name}_success", f"{name}_count", f"{name}_prob"])
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self.probabilities = [1 / self.model_count] * self.model_count
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timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
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self.filename = f"{timestamp}-{filename_slug}.altbayes.csv"
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self.max_frequency = max_frequency
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if end_threshold is not None:
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if 0 < end_threshold < 1:
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self.end_threshold: float = end_threshold
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self.use_end_threshold = True
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_logger.info(f"Will abort early, at {self.end_threshold}.")
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else:
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raise ValueError(f"end_threshold should be between 0 and 1, but is actually {end_threshold}")
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def go(self) -> None:
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with open(self.filename, "a", newline="") as outfile:
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writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
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writer.writeheader()
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for run in range(1, self.run_count + 1):
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rng = numpy.random.default_rng()
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frequency = rng.uniform(1, self.max_frequency)
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# Generate the actual dipoles
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actual_dipoles = self.actual_model.get_dipoles(frequency)
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dots = actual_dipoles.get_percent_range_dot_measurements(self.dot_inputs, self.low_error, self.high_error)
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lows, highs = pdme.measurement.oscillating_dipole.dot_range_measurements_low_high_arrays(dots)
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_logger.info(f"Going to work on dipole at {actual_dipoles.dipoles}")
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results = []
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_logger.debug("Going to iterate over discretisations now")
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for disc_count, discretisation in enumerate(self.discretisations):
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_logger.debug(f"Doing discretisation #{disc_count}")
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with multiprocessing.Pool(multiprocessing.cpu_count() - 1 or 1) as pool:
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results.append(sum(
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pool.imap_unordered(get_a_result, [(discretisation, self.dot_inputs_array, lows, highs, self.monte_carlo_count, self.max_frequency)] * self.monte_carlo_cycles, self.chunksize)
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))
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_logger.debug("Done, constructing output now")
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row = {
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"dipole_moment": actual_dipoles.dipoles[0].p,
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"dipole_location": actual_dipoles.dipoles[0].s,
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"dipole_frequency": actual_dipoles.dipoles[0].w
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}
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successes: List[float] = []
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counts: List[int] = []
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for model_index, (name, result) in enumerate(zip(self.model_names, results)):
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row[f"{name}_success"] = result
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row[f"{name}_count"] = self.monte_carlo_count * self.monte_carlo_cycles
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successes.append(max(result, 0.5))
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counts.append(self.monte_carlo_count * self.monte_carlo_cycles)
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success_weight = sum([(succ / count) * prob for succ, count, prob in zip(successes, counts, self.probabilities)])
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new_probabilities = [(succ / count) * old_prob / success_weight for succ, count, old_prob in zip(successes, counts, self.probabilities)]
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self.probabilities = new_probabilities
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for name, probability in zip(self.model_names, self.probabilities):
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row[f"{name}_prob"] = probability
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_logger.info(row)
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with open(self.filename, "a", newline="") as outfile:
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writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
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writer.writerow(row)
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if self.use_end_threshold:
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max_prob = max(self.probabilities)
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if max_prob > self.end_threshold:
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_logger.info(f"Aborting early, because {max_prob} is greater than {self.end_threshold}")
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break
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@@ -1,19 +1,17 @@
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import pdme.inputs
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import pdme.model
|
import pdme.model
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import pdme.measurement.input_types
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import pdme.measurement.oscillating_dipole
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import pdme.util.fast_v_calc
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import pdme.util.fast_nonlocal_spectrum
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from typing import Sequence, Tuple, List
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from typing import Sequence, Tuple, List
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import datetime
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import datetime
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import itertools
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import csv
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import csv
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import multiprocessing
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import logging
|
import logging
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import numpy
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import numpy
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import scipy.optimize
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import multiprocessing
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||||||
# TODO: remove hardcode
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# TODO: remove hardcode
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||||||
CHUNKSIZE = 50
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COST_THRESHOLD = 1e-10
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||||||
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||||||
# TODO: It's garbage to have this here duplicated from pdme.
|
# TODO: It's garbage to have this here duplicated from pdme.
|
||||||
DotInput = Tuple[numpy.typing.ArrayLike, float]
|
DotInput = Tuple[numpy.typing.ArrayLike, float]
|
||||||
@@ -22,126 +20,43 @@ DotInput = Tuple[numpy.typing.ArrayLike, float]
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_logger = logging.getLogger(__name__)
|
_logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
def get_a_result(input) -> int:
|
def get_a_result(discretisation, dots, index) -> Tuple[Tuple[int, ...], scipy.optimize.OptimizeResult]:
|
||||||
model, dot_inputs, lows, highs, monte_carlo_count, max_frequency, seed = input
|
return (index, discretisation.solve_for_index(dots, index))
|
||||||
|
|
||||||
rng = numpy.random.default_rng(seed)
|
|
||||||
sample_dipoles = model.get_monte_carlo_dipole_inputs(
|
|
||||||
monte_carlo_count, max_frequency, rng_to_use=rng
|
|
||||||
)
|
|
||||||
vals = pdme.util.fast_v_calc.fast_vs_for_dipoleses(dot_inputs, sample_dipoles)
|
|
||||||
return numpy.count_nonzero(pdme.util.fast_v_calc.between(vals, lows, highs))
|
|
||||||
|
|
||||||
|
|
||||||
def get_a_result_using_pairs(input) -> int:
|
class BayesRun():
|
||||||
(
|
'''
|
||||||
model,
|
|
||||||
dot_inputs,
|
|
||||||
pair_inputs,
|
|
||||||
local_lows,
|
|
||||||
local_highs,
|
|
||||||
nonlocal_lows,
|
|
||||||
nonlocal_highs,
|
|
||||||
monte_carlo_count,
|
|
||||||
max_frequency,
|
|
||||||
) = input
|
|
||||||
sample_dipoles = model.get_n_single_dipoles(monte_carlo_count, max_frequency)
|
|
||||||
local_vals = pdme.util.fast_v_calc.fast_vs_for_dipoles(dot_inputs, sample_dipoles)
|
|
||||||
local_matches = pdme.util.fast_v_calc.between(local_vals, local_lows, local_highs)
|
|
||||||
nonlocal_vals = pdme.util.fast_nonlocal_spectrum.fast_s_nonlocal(
|
|
||||||
pair_inputs, sample_dipoles
|
|
||||||
)
|
|
||||||
nonlocal_matches = pdme.util.fast_v_calc.between(
|
|
||||||
nonlocal_vals, nonlocal_lows, nonlocal_highs
|
|
||||||
)
|
|
||||||
combined_matches = numpy.logical_and(local_matches, nonlocal_matches)
|
|
||||||
return numpy.count_nonzero(combined_matches)
|
|
||||||
|
|
||||||
|
|
||||||
class BayesRun:
|
|
||||||
"""
|
|
||||||
A single Bayes run for a given set of dots.
|
A single Bayes run for a given set of dots.
|
||||||
|
|
||||||
Parameters
|
Parameters
|
||||||
----------
|
----------
|
||||||
dot_inputs : Sequence[DotInput]
|
dot_inputs : Sequence[DotInput]
|
||||||
The dot inputs for this bayes run.
|
The dot inputs for this bayes run.
|
||||||
|
discretisations_with_names : Sequence[Tuple(str, pdme.model.Model)]
|
||||||
models_with_names : Sequence[Tuple(str, pdme.model.DipoleModel)]
|
|
||||||
The models to evaluate.
|
The models to evaluate.
|
||||||
|
actual_model_discretisation : pdme.model.Discretisation
|
||||||
actual_model : pdme.model.DipoleModel
|
The discretisation for the model which is actually correct.
|
||||||
The model which is actually correct.
|
|
||||||
|
|
||||||
filename_slug : str
|
filename_slug : str
|
||||||
The filename slug to include.
|
The filename slug to include.
|
||||||
|
|
||||||
run_count: int
|
run_count: int
|
||||||
The number of runs to do.
|
The number of runs to do.
|
||||||
"""
|
'''
|
||||||
|
def __init__(self, dot_inputs: Sequence[DotInput], discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]], actual_model: pdme.model.Model, filename_slug: str, run_count: int, max_frequency: float = None, end_threshold: float = None) -> None:
|
||||||
def __init__(
|
self.dot_inputs = dot_inputs
|
||||||
self,
|
self.discretisations = [disc for (_, disc) in discretisations_with_names]
|
||||||
dot_positions: Sequence[numpy.typing.ArrayLike],
|
self.model_names = [name for (name, _) in discretisations_with_names]
|
||||||
frequency_range: Sequence[float],
|
|
||||||
models_with_names: Sequence[Tuple[str, pdme.model.DipoleModel]],
|
|
||||||
actual_model: pdme.model.DipoleModel,
|
|
||||||
filename_slug: str,
|
|
||||||
run_count: int = 100,
|
|
||||||
low_error: float = 0.9,
|
|
||||||
high_error: float = 1.1,
|
|
||||||
monte_carlo_count: int = 10000,
|
|
||||||
monte_carlo_cycles: int = 10,
|
|
||||||
target_success: int = 100,
|
|
||||||
max_monte_carlo_cycles_steps: int = 10,
|
|
||||||
max_frequency: float = 20,
|
|
||||||
end_threshold: float = None,
|
|
||||||
chunksize: int = CHUNKSIZE,
|
|
||||||
) -> None:
|
|
||||||
self.dot_inputs = pdme.inputs.inputs_with_frequency_range(
|
|
||||||
dot_positions, frequency_range
|
|
||||||
)
|
|
||||||
self.dot_inputs_array = pdme.measurement.input_types.dot_inputs_to_array(
|
|
||||||
self.dot_inputs
|
|
||||||
)
|
|
||||||
|
|
||||||
self.models = [model for (_, model) in models_with_names]
|
|
||||||
self.model_names = [name for (name, _) in models_with_names]
|
|
||||||
self.actual_model = actual_model
|
self.actual_model = actual_model
|
||||||
|
self.model_count = len(self.discretisations)
|
||||||
self.n: int
|
|
||||||
try:
|
|
||||||
self.n = self.actual_model.n # type: ignore
|
|
||||||
except AttributeError:
|
|
||||||
self.n = 1
|
|
||||||
|
|
||||||
self.model_count = len(self.models)
|
|
||||||
self.monte_carlo_count = monte_carlo_count
|
|
||||||
self.monte_carlo_cycles = monte_carlo_cycles
|
|
||||||
self.target_success = target_success
|
|
||||||
self.max_monte_carlo_cycles_steps = max_monte_carlo_cycles_steps
|
|
||||||
self.run_count = run_count
|
self.run_count = run_count
|
||||||
self.low_error = low_error
|
self.csv_fields = ["dipole_moment", "dipole_location", "dipole_frequency"]
|
||||||
self.high_error = high_error
|
|
||||||
|
|
||||||
self.csv_fields = []
|
|
||||||
for i in range(self.n):
|
|
||||||
self.csv_fields.extend(
|
|
||||||
[
|
|
||||||
f"dipole_moment_{i+1}",
|
|
||||||
f"dipole_location_{i+1}",
|
|
||||||
f"dipole_frequency_{i+1}",
|
|
||||||
]
|
|
||||||
)
|
|
||||||
self.compensate_zeros = True
|
self.compensate_zeros = True
|
||||||
self.chunksize = chunksize
|
|
||||||
for name in self.model_names:
|
for name in self.model_names:
|
||||||
self.csv_fields.extend([f"{name}_success", f"{name}_count", f"{name}_prob"])
|
self.csv_fields.extend([f"{name}_success", f"{name}_count", f"{name}_prob"])
|
||||||
|
|
||||||
self.probabilities = [1 / self.model_count] * self.model_count
|
self.probabilities = [1 / self.model_count] * self.model_count
|
||||||
|
|
||||||
timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
|
timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
|
||||||
self.filename = f"{timestamp}-{filename_slug}.bayesrun.csv"
|
self.filename = f"{timestamp}-{filename_slug}.csv"
|
||||||
self.max_frequency = max_frequency
|
self.max_frequency = max_frequency
|
||||||
|
|
||||||
if end_threshold is not None:
|
if end_threshold is not None:
|
||||||
@@ -150,9 +65,7 @@ class BayesRun:
|
|||||||
self.use_end_threshold = True
|
self.use_end_threshold = True
|
||||||
_logger.info(f"Will abort early, at {self.end_threshold}.")
|
_logger.info(f"Will abort early, at {self.end_threshold}.")
|
||||||
else:
|
else:
|
||||||
raise ValueError(
|
raise ValueError(f"end_threshold should be between 0 and 1, but is actually {end_threshold}")
|
||||||
f"end_threshold should be between 0 and 1, but is actually {end_threshold}"
|
|
||||||
)
|
|
||||||
|
|
||||||
def go(self) -> None:
|
def go(self) -> None:
|
||||||
with open(self.filename, "a", newline="") as outfile:
|
with open(self.filename, "a", newline="") as outfile:
|
||||||
@@ -160,122 +73,56 @@ class BayesRun:
|
|||||||
writer.writeheader()
|
writer.writeheader()
|
||||||
|
|
||||||
for run in range(1, self.run_count + 1):
|
for run in range(1, self.run_count + 1):
|
||||||
|
frequency: float = run
|
||||||
|
if self.max_frequency is not None and self.max_frequency > 1:
|
||||||
|
rng = numpy.random.default_rng()
|
||||||
|
frequency = rng.uniform(1, self.max_frequency)
|
||||||
|
dipoles = self.actual_model.get_dipoles(frequency)
|
||||||
|
|
||||||
# Generate the actual dipoles
|
dots = dipoles.get_dot_measurements(self.dot_inputs)
|
||||||
actual_dipoles = self.actual_model.get_dipoles(self.max_frequency)
|
_logger.info(f"Going to work on dipole at {dipoles.dipoles}")
|
||||||
|
|
||||||
dots = actual_dipoles.get_percent_range_dot_measurements(
|
|
||||||
self.dot_inputs, self.low_error, self.high_error
|
|
||||||
)
|
|
||||||
(
|
|
||||||
lows,
|
|
||||||
highs,
|
|
||||||
) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
|
|
||||||
dots
|
|
||||||
)
|
|
||||||
|
|
||||||
_logger.info(f"Going to work on dipole at {actual_dipoles.dipoles}")
|
|
||||||
|
|
||||||
# define a new seed sequence for each run
|
|
||||||
seed_sequence = numpy.random.SeedSequence(run)
|
|
||||||
|
|
||||||
results = []
|
results = []
|
||||||
_logger.debug("Going to iterate over models now")
|
_logger.debug("Going to iterate over discretisations now")
|
||||||
for model_count, model in enumerate(self.models):
|
for disc_count, discretisation in enumerate(self.discretisations):
|
||||||
_logger.debug(f"Doing model #{model_count}")
|
_logger.debug(f"Doing discretisation #{disc_count}")
|
||||||
core_count = multiprocessing.cpu_count() - 1 or 1
|
with multiprocessing.Pool(multiprocessing.cpu_count() - 1 or 1) as pool:
|
||||||
with multiprocessing.Pool(core_count) as pool:
|
results.append(pool.starmap(get_a_result, zip(itertools.repeat(discretisation), itertools.repeat(dots), discretisation.all_indices())))
|
||||||
cycle_count = 0
|
|
||||||
cycle_success = 0
|
|
||||||
cycles = 0
|
|
||||||
while (cycles < self.max_monte_carlo_cycles_steps) and (
|
|
||||||
cycle_success <= self.target_success
|
|
||||||
):
|
|
||||||
_logger.debug(f"Starting cycle {cycles}")
|
|
||||||
cycles += 1
|
|
||||||
current_success = 0
|
|
||||||
cycle_count += self.monte_carlo_count * self.monte_carlo_cycles
|
|
||||||
|
|
||||||
# generate a seed from the sequence for each core.
|
|
||||||
# note this needs to be inside the loop for monte carlo cycle steps!
|
|
||||||
# that way we get more stuff.
|
|
||||||
seeds = seed_sequence.spawn(self.monte_carlo_cycles)
|
|
||||||
|
|
||||||
current_success = sum(
|
|
||||||
pool.imap_unordered(
|
|
||||||
get_a_result,
|
|
||||||
[
|
|
||||||
(
|
|
||||||
model,
|
|
||||||
self.dot_inputs_array,
|
|
||||||
lows,
|
|
||||||
highs,
|
|
||||||
self.monte_carlo_count,
|
|
||||||
self.max_frequency,
|
|
||||||
seed,
|
|
||||||
)
|
|
||||||
for seed in seeds
|
|
||||||
],
|
|
||||||
self.chunksize,
|
|
||||||
)
|
|
||||||
)
|
|
||||||
|
|
||||||
cycle_success += current_success
|
|
||||||
_logger.debug(f"current running successes: {cycle_success}")
|
|
||||||
results.append((cycle_count, cycle_success))
|
|
||||||
|
|
||||||
_logger.debug("Done, constructing output now")
|
_logger.debug("Done, constructing output now")
|
||||||
row = {
|
row = {
|
||||||
"dipole_moment_1": actual_dipoles.dipoles[0].p,
|
"dipole_moment": dipoles.dipoles[0].p,
|
||||||
"dipole_location_1": actual_dipoles.dipoles[0].s,
|
"dipole_location": dipoles.dipoles[0].s,
|
||||||
"dipole_frequency_1": actual_dipoles.dipoles[0].w,
|
"dipole_frequency": dipoles.dipoles[0].w
|
||||||
}
|
}
|
||||||
for i in range(1, self.n):
|
|
||||||
try:
|
|
||||||
current_dipoles = actual_dipoles.dipoles[i]
|
|
||||||
row[f"dipole_moment_{i+1}"] = current_dipoles.p
|
|
||||||
row[f"dipole_location_{i+1}"] = current_dipoles.s
|
|
||||||
row[f"dipole_frequency_{i+1}"] = current_dipoles.w
|
|
||||||
except IndexError:
|
|
||||||
_logger.info(f"Not writing anymore, saw end after {i}")
|
|
||||||
break
|
|
||||||
|
|
||||||
successes: List[float] = []
|
successes: List[float] = []
|
||||||
counts: List[int] = []
|
counts: List[int] = []
|
||||||
for model_index, (name, (count, result)) in enumerate(
|
for model_index, (name, result) in enumerate(zip(self.model_names, results)):
|
||||||
zip(self.model_names, results)
|
count = 0
|
||||||
):
|
success = 0
|
||||||
|
for idx, val in result:
|
||||||
|
count += 1
|
||||||
|
if val.success and val.cost <= COST_THRESHOLD:
|
||||||
|
success += 1
|
||||||
|
|
||||||
row[f"{name}_success"] = result
|
row[f"{name}_success"] = success
|
||||||
row[f"{name}_count"] = count
|
row[f"{name}_count"] = count
|
||||||
successes.append(max(result, 0.5))
|
successes.append(max(success, 0.5))
|
||||||
counts.append(count)
|
counts.append(count)
|
||||||
|
|
||||||
success_weight = sum(
|
success_weight = sum([(succ / count) * prob for succ, count, prob in zip(successes, counts, self.probabilities)])
|
||||||
[
|
new_probabilities = [(succ / count) * old_prob / success_weight for succ, count, old_prob in zip(successes, counts, self.probabilities)]
|
||||||
(succ / count) * prob
|
|
||||||
for succ, count, prob in zip(successes, counts, self.probabilities)
|
|
||||||
]
|
|
||||||
)
|
|
||||||
new_probabilities = [
|
|
||||||
(succ / count) * old_prob / success_weight
|
|
||||||
for succ, count, old_prob in zip(successes, counts, self.probabilities)
|
|
||||||
]
|
|
||||||
self.probabilities = new_probabilities
|
self.probabilities = new_probabilities
|
||||||
for name, probability in zip(self.model_names, self.probabilities):
|
for name, probability in zip(self.model_names, self.probabilities):
|
||||||
row[f"{name}_prob"] = probability
|
row[f"{name}_prob"] = probability
|
||||||
_logger.info(row)
|
_logger.info(row)
|
||||||
|
|
||||||
with open(self.filename, "a", newline="") as outfile:
|
with open(self.filename, "a", newline="") as outfile:
|
||||||
writer = csv.DictWriter(
|
writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
|
||||||
outfile, fieldnames=self.csv_fields, dialect="unix"
|
|
||||||
)
|
|
||||||
writer.writerow(row)
|
writer.writerow(row)
|
||||||
|
|
||||||
if self.use_end_threshold:
|
if self.use_end_threshold:
|
||||||
max_prob = max(self.probabilities)
|
max_prob = max(self.probabilities)
|
||||||
if max_prob > self.end_threshold:
|
if max_prob > self.end_threshold:
|
||||||
_logger.info(
|
_logger.info(f"Aborting early, because {max_prob} is greater than {self.end_threshold}")
|
||||||
f"Aborting early, because {max_prob} is greater than {self.end_threshold}"
|
|
||||||
)
|
|
||||||
break
|
break
|
||||||
|
|||||||
@@ -1,382 +0,0 @@
|
|||||||
import pdme.inputs
|
|
||||||
import pdme.model
|
|
||||||
import pdme.measurement.input_types
|
|
||||||
import pdme.measurement.oscillating_dipole
|
|
||||||
import pdme.util.fast_v_calc
|
|
||||||
import pdme.util.fast_nonlocal_spectrum
|
|
||||||
from typing import Sequence, Tuple, List
|
|
||||||
import datetime
|
|
||||||
import csv
|
|
||||||
import multiprocessing
|
|
||||||
import logging
|
|
||||||
import numpy
|
|
||||||
import numpy.random
|
|
||||||
|
|
||||||
|
|
||||||
# TODO: remove hardcode
|
|
||||||
CHUNKSIZE = 50
|
|
||||||
|
|
||||||
# TODO: It's garbage to have this here duplicated from pdme.
|
|
||||||
DotInput = Tuple[numpy.typing.ArrayLike, float]
|
|
||||||
|
|
||||||
|
|
||||||
_logger = logging.getLogger(__name__)
|
|
||||||
|
|
||||||
|
|
||||||
def get_a_simul_result_using_pairs(input) -> numpy.ndarray:
|
|
||||||
(
|
|
||||||
model,
|
|
||||||
dot_inputs,
|
|
||||||
pair_inputs,
|
|
||||||
local_lows,
|
|
||||||
local_highs,
|
|
||||||
nonlocal_lows,
|
|
||||||
nonlocal_highs,
|
|
||||||
monte_carlo_count,
|
|
||||||
monte_carlo_cycles,
|
|
||||||
max_frequency,
|
|
||||||
seed,
|
|
||||||
) = input
|
|
||||||
|
|
||||||
rng = numpy.random.default_rng(seed)
|
|
||||||
local_total = 0
|
|
||||||
combined_total = 0
|
|
||||||
|
|
||||||
sample_dipoles = model.get_monte_carlo_dipole_inputs(
|
|
||||||
monte_carlo_count, max_frequency, rng_to_use=rng
|
|
||||||
)
|
|
||||||
local_vals = pdme.util.fast_v_calc.fast_vs_for_dipoleses(dot_inputs, sample_dipoles)
|
|
||||||
local_matches = pdme.util.fast_v_calc.between(local_vals, local_lows, local_highs)
|
|
||||||
nonlocal_vals = pdme.util.fast_nonlocal_spectrum.fast_s_nonlocal_dipoleses(
|
|
||||||
pair_inputs, sample_dipoles
|
|
||||||
)
|
|
||||||
nonlocal_matches = pdme.util.fast_v_calc.between(
|
|
||||||
nonlocal_vals, nonlocal_lows, nonlocal_highs
|
|
||||||
)
|
|
||||||
combined_matches = numpy.logical_and(local_matches, nonlocal_matches)
|
|
||||||
|
|
||||||
local_total += numpy.count_nonzero(local_matches)
|
|
||||||
combined_total += numpy.count_nonzero(combined_matches)
|
|
||||||
return numpy.array([local_total, combined_total])
|
|
||||||
|
|
||||||
|
|
||||||
class BayesRunSimulPairs:
|
|
||||||
"""
|
|
||||||
A dual pairs-nonpairs Bayes run for a given set of dots.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
dot_inputs : Sequence[DotInput]
|
|
||||||
The dot inputs for this bayes run.
|
|
||||||
|
|
||||||
models_with_names : Sequence[Tuple(str, pdme.model.DipoleModel)]
|
|
||||||
The models to evaluate.
|
|
||||||
|
|
||||||
actual_model : pdme.model.DipoleModel
|
|
||||||
The modoel for the model which is actually correct.
|
|
||||||
|
|
||||||
filename_slug : str
|
|
||||||
The filename slug to include.
|
|
||||||
|
|
||||||
run_count: int
|
|
||||||
The number of runs to do.
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(
|
|
||||||
self,
|
|
||||||
dot_positions: Sequence[numpy.typing.ArrayLike],
|
|
||||||
frequency_range: Sequence[float],
|
|
||||||
models_with_names: Sequence[Tuple[str, pdme.model.DipoleModel]],
|
|
||||||
actual_model: pdme.model.DipoleModel,
|
|
||||||
filename_slug: str,
|
|
||||||
run_count: int = 100,
|
|
||||||
low_error: float = 0.9,
|
|
||||||
high_error: float = 1.1,
|
|
||||||
pairs_high_error=None,
|
|
||||||
pairs_low_error=None,
|
|
||||||
monte_carlo_count: int = 10000,
|
|
||||||
monte_carlo_cycles: int = 10,
|
|
||||||
target_success: int = 100,
|
|
||||||
max_monte_carlo_cycles_steps: int = 10,
|
|
||||||
max_frequency: float = 20,
|
|
||||||
end_threshold: float = None,
|
|
||||||
chunksize: int = CHUNKSIZE,
|
|
||||||
) -> None:
|
|
||||||
self.dot_inputs = pdme.inputs.inputs_with_frequency_range(
|
|
||||||
dot_positions, frequency_range
|
|
||||||
)
|
|
||||||
self.dot_inputs_array = pdme.measurement.input_types.dot_inputs_to_array(
|
|
||||||
self.dot_inputs
|
|
||||||
)
|
|
||||||
|
|
||||||
self.dot_pair_inputs = pdme.inputs.input_pairs_with_frequency_range(
|
|
||||||
dot_positions, frequency_range
|
|
||||||
)
|
|
||||||
self.dot_pair_inputs_array = (
|
|
||||||
pdme.measurement.input_types.dot_pair_inputs_to_array(self.dot_pair_inputs)
|
|
||||||
)
|
|
||||||
|
|
||||||
self.models = [mod for (_, mod) in models_with_names]
|
|
||||||
self.model_names = [name for (name, _) in models_with_names]
|
|
||||||
self.actual_model = actual_model
|
|
||||||
|
|
||||||
self.n: int
|
|
||||||
try:
|
|
||||||
self.n = self.actual_model.n # type: ignore
|
|
||||||
except AttributeError:
|
|
||||||
self.n = 1
|
|
||||||
|
|
||||||
self.model_count = len(self.models)
|
|
||||||
self.monte_carlo_count = monte_carlo_count
|
|
||||||
self.monte_carlo_cycles = monte_carlo_cycles
|
|
||||||
self.target_success = target_success
|
|
||||||
self.max_monte_carlo_cycles_steps = max_monte_carlo_cycles_steps
|
|
||||||
self.run_count = run_count
|
|
||||||
self.low_error = low_error
|
|
||||||
self.high_error = high_error
|
|
||||||
if pairs_low_error is None:
|
|
||||||
self.pairs_low_error = self.low_error
|
|
||||||
else:
|
|
||||||
self.pairs_low_error = pairs_low_error
|
|
||||||
if pairs_high_error is None:
|
|
||||||
self.pairs_high_error = self.high_error
|
|
||||||
else:
|
|
||||||
self.pairs_high_error = pairs_high_error
|
|
||||||
|
|
||||||
self.csv_fields = []
|
|
||||||
for i in range(self.n):
|
|
||||||
self.csv_fields.extend(
|
|
||||||
[
|
|
||||||
f"dipole_moment_{i+1}",
|
|
||||||
f"dipole_location_{i+1}",
|
|
||||||
f"dipole_frequency_{i+1}",
|
|
||||||
]
|
|
||||||
)
|
|
||||||
self.compensate_zeros = True
|
|
||||||
self.chunksize = chunksize
|
|
||||||
for name in self.model_names:
|
|
||||||
self.csv_fields.extend([f"{name}_success", f"{name}_count", f"{name}_prob"])
|
|
||||||
|
|
||||||
self.probabilities_no_pairs = [1 / self.model_count] * self.model_count
|
|
||||||
self.probabilities_pairs = [1 / self.model_count] * self.model_count
|
|
||||||
|
|
||||||
timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
|
|
||||||
self.filename_pairs = f"{timestamp}-{filename_slug}.simulpairs.yespairs.csv"
|
|
||||||
self.filename_no_pairs = f"{timestamp}-{filename_slug}.simulpairs.noopairs.csv"
|
|
||||||
|
|
||||||
self.max_frequency = max_frequency
|
|
||||||
|
|
||||||
if end_threshold is not None:
|
|
||||||
if 0 < end_threshold < 1:
|
|
||||||
self.end_threshold: float = end_threshold
|
|
||||||
self.use_end_threshold = True
|
|
||||||
_logger.info(f"Will abort early, at {self.end_threshold}.")
|
|
||||||
else:
|
|
||||||
raise ValueError(
|
|
||||||
f"end_threshold should be between 0 and 1, but is actually {end_threshold}"
|
|
||||||
)
|
|
||||||
|
|
||||||
def go(self) -> None:
|
|
||||||
with open(self.filename_pairs, "a", newline="") as outfile:
|
|
||||||
writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
|
|
||||||
writer.writeheader()
|
|
||||||
with open(self.filename_no_pairs, "a", newline="") as outfile:
|
|
||||||
writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
|
|
||||||
writer.writeheader()
|
|
||||||
|
|
||||||
for run in range(1, self.run_count + 1):
|
|
||||||
|
|
||||||
# Generate the actual dipoles
|
|
||||||
actual_dipoles = self.actual_model.get_dipoles(self.max_frequency)
|
|
||||||
|
|
||||||
dots = actual_dipoles.get_percent_range_dot_measurements(
|
|
||||||
self.dot_inputs, self.low_error, self.high_error
|
|
||||||
)
|
|
||||||
(
|
|
||||||
lows,
|
|
||||||
highs,
|
|
||||||
) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
|
|
||||||
dots
|
|
||||||
)
|
|
||||||
|
|
||||||
pair_lows, pair_highs = (None, None)
|
|
||||||
pair_measurements = actual_dipoles.get_percent_range_dot_pair_measurements(
|
|
||||||
self.dot_pair_inputs, self.pairs_low_error, self.pairs_high_error
|
|
||||||
)
|
|
||||||
(
|
|
||||||
pair_lows,
|
|
||||||
pair_highs,
|
|
||||||
) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
|
|
||||||
pair_measurements
|
|
||||||
)
|
|
||||||
|
|
||||||
_logger.info(f"Going to work on dipole at {actual_dipoles.dipoles}")
|
|
||||||
|
|
||||||
# define a new seed sequence for each run
|
|
||||||
seed_sequence = numpy.random.SeedSequence(run)
|
|
||||||
|
|
||||||
results_pairs = []
|
|
||||||
results_no_pairs = []
|
|
||||||
_logger.debug("Going to iterate over models now")
|
|
||||||
for model_count, model in enumerate(self.models):
|
|
||||||
_logger.debug(f"Doing model #{model_count}")
|
|
||||||
|
|
||||||
core_count = multiprocessing.cpu_count() - 1 or 1
|
|
||||||
with multiprocessing.Pool(core_count) as pool:
|
|
||||||
cycle_count = 0
|
|
||||||
cycle_success_pairs = 0
|
|
||||||
cycle_success_no_pairs = 0
|
|
||||||
cycles = 0
|
|
||||||
while (cycles < self.max_monte_carlo_cycles_steps) and (
|
|
||||||
min(cycle_success_pairs, cycle_success_no_pairs)
|
|
||||||
<= self.target_success
|
|
||||||
):
|
|
||||||
_logger.debug(f"Starting cycle {cycles}")
|
|
||||||
|
|
||||||
cycles += 1
|
|
||||||
current_success_pairs = 0
|
|
||||||
current_success_no_pairs = 0
|
|
||||||
cycle_count += self.monte_carlo_count * self.monte_carlo_cycles
|
|
||||||
|
|
||||||
# generate a seed from the sequence for each core.
|
|
||||||
# note this needs to be inside the loop for monte carlo cycle steps!
|
|
||||||
# that way we get more stuff.
|
|
||||||
|
|
||||||
seeds = seed_sequence.spawn(self.monte_carlo_cycles)
|
|
||||||
_logger.debug(f"Creating {self.monte_carlo_cycles} seeds")
|
|
||||||
current_success_both = numpy.array(
|
|
||||||
sum(
|
|
||||||
pool.imap_unordered(
|
|
||||||
get_a_simul_result_using_pairs,
|
|
||||||
[
|
|
||||||
(
|
|
||||||
model,
|
|
||||||
self.dot_inputs_array,
|
|
||||||
self.dot_pair_inputs_array,
|
|
||||||
lows,
|
|
||||||
highs,
|
|
||||||
pair_lows,
|
|
||||||
pair_highs,
|
|
||||||
self.monte_carlo_count,
|
|
||||||
self.monte_carlo_cycles,
|
|
||||||
self.max_frequency,
|
|
||||||
seed,
|
|
||||||
)
|
|
||||||
for seed in seeds
|
|
||||||
],
|
|
||||||
self.chunksize,
|
|
||||||
)
|
|
||||||
)
|
|
||||||
)
|
|
||||||
current_success_no_pairs = current_success_both[0]
|
|
||||||
current_success_pairs = current_success_both[1]
|
|
||||||
|
|
||||||
cycle_success_no_pairs += current_success_no_pairs
|
|
||||||
cycle_success_pairs += current_success_pairs
|
|
||||||
_logger.debug(
|
|
||||||
f"(pair, no_pair) successes are {(cycle_success_pairs, cycle_success_no_pairs)}"
|
|
||||||
)
|
|
||||||
results_pairs.append((cycle_count, cycle_success_pairs))
|
|
||||||
results_no_pairs.append((cycle_count, cycle_success_no_pairs))
|
|
||||||
|
|
||||||
_logger.debug("Done, constructing output now")
|
|
||||||
row_pairs = {
|
|
||||||
"dipole_moment_1": actual_dipoles.dipoles[0].p,
|
|
||||||
"dipole_location_1": actual_dipoles.dipoles[0].s,
|
|
||||||
"dipole_frequency_1": actual_dipoles.dipoles[0].w,
|
|
||||||
}
|
|
||||||
row_no_pairs = {
|
|
||||||
"dipole_moment_1": actual_dipoles.dipoles[0].p,
|
|
||||||
"dipole_location_1": actual_dipoles.dipoles[0].s,
|
|
||||||
"dipole_frequency_1": actual_dipoles.dipoles[0].w,
|
|
||||||
}
|
|
||||||
for i in range(1, self.n):
|
|
||||||
try:
|
|
||||||
current_dipoles = actual_dipoles.dipoles[i]
|
|
||||||
row_pairs[f"dipole_moment_{i+1}"] = current_dipoles.p
|
|
||||||
row_pairs[f"dipole_location_{i+1}"] = current_dipoles.s
|
|
||||||
row_pairs[f"dipole_frequency_{i+1}"] = current_dipoles.w
|
|
||||||
row_no_pairs[f"dipole_moment_{i+1}"] = current_dipoles.p
|
|
||||||
row_no_pairs[f"dipole_location_{i+1}"] = current_dipoles.s
|
|
||||||
row_no_pairs[f"dipole_frequency_{i+1}"] = current_dipoles.w
|
|
||||||
except IndexError:
|
|
||||||
_logger.info(f"Not writing anymore, saw end after {i}")
|
|
||||||
break
|
|
||||||
|
|
||||||
successes_pairs: List[float] = []
|
|
||||||
successes_no_pairs: List[float] = []
|
|
||||||
counts: List[int] = []
|
|
||||||
for model_index, (
|
|
||||||
name,
|
|
||||||
(count_pair, result_pair),
|
|
||||||
(count_no_pair, result_no_pair),
|
|
||||||
) in enumerate(zip(self.model_names, results_pairs, results_no_pairs)):
|
|
||||||
|
|
||||||
row_pairs[f"{name}_success"] = result_pair
|
|
||||||
row_pairs[f"{name}_count"] = count_pair
|
|
||||||
successes_pairs.append(max(result_pair, 0.5))
|
|
||||||
|
|
||||||
row_no_pairs[f"{name}_success"] = result_no_pair
|
|
||||||
row_no_pairs[f"{name}_count"] = count_no_pair
|
|
||||||
successes_no_pairs.append(max(result_no_pair, 0.5))
|
|
||||||
|
|
||||||
counts.append(count_pair)
|
|
||||||
|
|
||||||
success_weight_pair = sum(
|
|
||||||
[
|
|
||||||
(succ / count) * prob
|
|
||||||
for succ, count, prob in zip(
|
|
||||||
successes_pairs, counts, self.probabilities_pairs
|
|
||||||
)
|
|
||||||
]
|
|
||||||
)
|
|
||||||
success_weight_no_pair = sum(
|
|
||||||
[
|
|
||||||
(succ / count) * prob
|
|
||||||
for succ, count, prob in zip(
|
|
||||||
successes_no_pairs, counts, self.probabilities_no_pairs
|
|
||||||
)
|
|
||||||
]
|
|
||||||
)
|
|
||||||
new_probabilities_pair = [
|
|
||||||
(succ / count) * old_prob / success_weight_pair
|
|
||||||
for succ, count, old_prob in zip(
|
|
||||||
successes_pairs, counts, self.probabilities_pairs
|
|
||||||
)
|
|
||||||
]
|
|
||||||
new_probabilities_no_pair = [
|
|
||||||
(succ / count) * old_prob / success_weight_no_pair
|
|
||||||
for succ, count, old_prob in zip(
|
|
||||||
successes_no_pairs, counts, self.probabilities_no_pairs
|
|
||||||
)
|
|
||||||
]
|
|
||||||
self.probabilities_pairs = new_probabilities_pair
|
|
||||||
self.probabilities_no_pairs = new_probabilities_no_pair
|
|
||||||
for name, probability_pair, probability_no_pair in zip(
|
|
||||||
self.model_names, self.probabilities_pairs, self.probabilities_no_pairs
|
|
||||||
):
|
|
||||||
row_pairs[f"{name}_prob"] = probability_pair
|
|
||||||
row_no_pairs[f"{name}_prob"] = probability_no_pair
|
|
||||||
_logger.debug(row_pairs)
|
|
||||||
_logger.debug(row_no_pairs)
|
|
||||||
|
|
||||||
with open(self.filename_pairs, "a", newline="") as outfile:
|
|
||||||
writer = csv.DictWriter(
|
|
||||||
outfile, fieldnames=self.csv_fields, dialect="unix"
|
|
||||||
)
|
|
||||||
writer.writerow(row_pairs)
|
|
||||||
with open(self.filename_no_pairs, "a", newline="") as outfile:
|
|
||||||
writer = csv.DictWriter(
|
|
||||||
outfile, fieldnames=self.csv_fields, dialect="unix"
|
|
||||||
)
|
|
||||||
writer.writerow(row_no_pairs)
|
|
||||||
|
|
||||||
if self.use_end_threshold:
|
|
||||||
max_prob = min(
|
|
||||||
max(self.probabilities_pairs), max(self.probabilities_no_pairs)
|
|
||||||
)
|
|
||||||
if max_prob > self.end_threshold:
|
|
||||||
_logger.info(
|
|
||||||
f"Aborting early, because {max_prob} is greater than {self.end_threshold}"
|
|
||||||
)
|
|
||||||
break
|
|
||||||
99
deepdog/diagnostic.py
Normal file
99
deepdog/diagnostic.py
Normal file
@@ -0,0 +1,99 @@
|
|||||||
|
from pdme.measurement import OscillatingDipole, OscillatingDipoleArrangement
|
||||||
|
import pdme
|
||||||
|
from deepdog.bayes_run import DotInput
|
||||||
|
import datetime
|
||||||
|
import numpy
|
||||||
|
from dataclasses import dataclass
|
||||||
|
import logging
|
||||||
|
from typing import Sequence, Tuple
|
||||||
|
import csv
|
||||||
|
import itertools
|
||||||
|
import multiprocessing
|
||||||
|
|
||||||
|
_logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
def get_a_result(discretisation, dots, index):
|
||||||
|
return (index, discretisation.solve_for_index(dots, index))
|
||||||
|
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class SingleDipoleDiagnostic():
|
||||||
|
model: str
|
||||||
|
index: Tuple
|
||||||
|
bounds: Tuple
|
||||||
|
actual_dipole: OscillatingDipole
|
||||||
|
result_dipole: OscillatingDipole
|
||||||
|
success: bool
|
||||||
|
|
||||||
|
def __post_init__(self) -> None:
|
||||||
|
self.p_actual_x = self.actual_dipole.p[0]
|
||||||
|
self.p_actual_y = self.actual_dipole.p[1]
|
||||||
|
self.p_actual_z = self.actual_dipole.p[2]
|
||||||
|
self.s_actual_x = self.actual_dipole.s[0]
|
||||||
|
self.s_actual_y = self.actual_dipole.s[1]
|
||||||
|
self.s_actual_z = self.actual_dipole.s[2]
|
||||||
|
self.p_result_x = self.result_dipole.p[0]
|
||||||
|
self.p_result_y = self.result_dipole.p[1]
|
||||||
|
self.p_result_z = self.result_dipole.p[2]
|
||||||
|
self.s_result_x = self.result_dipole.s[0]
|
||||||
|
self.s_result_y = self.result_dipole.s[1]
|
||||||
|
self.s_result_z = self.result_dipole.s[2]
|
||||||
|
self.w_actual = self.actual_dipole.w
|
||||||
|
self.w_result = self.result_dipole.w
|
||||||
|
|
||||||
|
|
||||||
|
class Diagnostic():
|
||||||
|
'''
|
||||||
|
Represents a diagnostic for a single dipole moment given a set of discretisations.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
dot_inputs : Sequence[DotInput]
|
||||||
|
The dot inputs for this diagnostic.
|
||||||
|
discretisations_with_names : Sequence[Tuple(str, pdme.model.Model)]
|
||||||
|
The models to evaluate.
|
||||||
|
actual_model_discretisation : pdme.model.Discretisation
|
||||||
|
The discretisation for the model which is actually correct.
|
||||||
|
filename_slug : str
|
||||||
|
The filename slug to include.
|
||||||
|
run_count: int
|
||||||
|
The number of runs to do.
|
||||||
|
'''
|
||||||
|
def __init__(self, actual_dipole_moment: numpy.ndarray, actual_dipole_position: numpy.ndarray, actual_dipole_frequency: float, dot_inputs: Sequence[DotInput], discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]], filename_slug: str) -> None:
|
||||||
|
self.dipoles = OscillatingDipoleArrangement([OscillatingDipole(actual_dipole_moment, actual_dipole_position, actual_dipole_frequency)])
|
||||||
|
self.dots = self.dipoles.get_dot_measurements(dot_inputs)
|
||||||
|
|
||||||
|
self.discretisations_with_names = discretisations_with_names
|
||||||
|
self.model_count = len(self.discretisations_with_names)
|
||||||
|
|
||||||
|
self.csv_fields = ["model", "index", "bounds", "p_actual_x", "p_actual_y", "p_actual_z", "s_actual_x", "s_actual_y", "s_actual_z", "w_actual", "success", "p_result_x", "p_result_y", "p_result_z", "s_result_x", "s_result_y", "s_result_z", "w_result"]
|
||||||
|
|
||||||
|
timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
|
||||||
|
self.filename = f"{timestamp}-{filename_slug}.diag.csv"
|
||||||
|
|
||||||
|
def go(self):
|
||||||
|
with open(self.filename, "a", newline="") as outfile:
|
||||||
|
# csv fields
|
||||||
|
writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect='unix')
|
||||||
|
writer.writeheader()
|
||||||
|
|
||||||
|
for (name, discretisation) in self.discretisations_with_names:
|
||||||
|
_logger.info(f"Working on discretisation {name}")
|
||||||
|
|
||||||
|
results = []
|
||||||
|
with multiprocessing.Pool(multiprocessing.cpu_count() - 1 or 1) as pool:
|
||||||
|
results = pool.starmap(get_a_result, zip(itertools.repeat(discretisation), itertools.repeat(self.dots), discretisation.all_indices()))
|
||||||
|
|
||||||
|
with open(self.filename, "a", newline='') as outfile:
|
||||||
|
writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect='unix', extrasaction="ignore")
|
||||||
|
|
||||||
|
for idx, result in results:
|
||||||
|
|
||||||
|
bounds = discretisation.bounds(idx)
|
||||||
|
|
||||||
|
actual_success = result.success and result.cost <= 1e-10
|
||||||
|
diag_row = SingleDipoleDiagnostic(name, idx, bounds, self.dipoles.dipoles[0], discretisation.model.solution_as_dipoles(result.normalised_x)[0], actual_success)
|
||||||
|
row = vars(diag_row)
|
||||||
|
_logger.debug(f"Writing result {row}")
|
||||||
|
writer.writerow(row)
|
||||||
@@ -1,3 +1,3 @@
|
|||||||
from importlib.metadata import version
|
from importlib.metadata import version
|
||||||
|
|
||||||
__version__ = version("deepdog")
|
__version__ = version('deepdog')
|
||||||
|
|||||||
@@ -1,220 +0,0 @@
|
|||||||
import pdme.inputs
|
|
||||||
import pdme.model
|
|
||||||
import pdme.measurement
|
|
||||||
import pdme.measurement.input_types
|
|
||||||
import pdme.measurement.oscillating_dipole
|
|
||||||
import pdme.util.fast_v_calc
|
|
||||||
import pdme.util.fast_nonlocal_spectrum
|
|
||||||
from typing import Sequence, Tuple, List, Dict, Union
|
|
||||||
import datetime
|
|
||||||
import csv
|
|
||||||
import multiprocessing
|
|
||||||
import logging
|
|
||||||
import numpy
|
|
||||||
|
|
||||||
|
|
||||||
# TODO: remove hardcode
|
|
||||||
CHUNKSIZE = 50
|
|
||||||
|
|
||||||
|
|
||||||
_logger = logging.getLogger(__name__)
|
|
||||||
|
|
||||||
|
|
||||||
def get_a_result(input) -> int:
|
|
||||||
model, dot_inputs, lows, highs, monte_carlo_count, seed = input
|
|
||||||
|
|
||||||
rng = numpy.random.default_rng(seed)
|
|
||||||
# TODO: A long term refactor is to pull the frequency stuff out from here. The None stands for max_frequency, which is unneeded in the actually useful models.
|
|
||||||
sample_dipoles = model.get_monte_carlo_dipole_inputs(
|
|
||||||
monte_carlo_count, None, rng_to_use=rng
|
|
||||||
)
|
|
||||||
vals = pdme.util.fast_v_calc.fast_vs_for_dipoleses(dot_inputs, sample_dipoles)
|
|
||||||
return numpy.count_nonzero(pdme.util.fast_v_calc.between(vals, lows, highs))
|
|
||||||
|
|
||||||
|
|
||||||
def get_a_result_fast_filter(input) -> int:
|
|
||||||
model, dot_inputs, lows, highs, monte_carlo_count, seed = input
|
|
||||||
|
|
||||||
rng = numpy.random.default_rng(seed)
|
|
||||||
# TODO: A long term refactor is to pull the frequency stuff out from here. The None stands for max_frequency, which is unneeded in the actually useful models.
|
|
||||||
sample_dipoles = model.get_monte_carlo_dipole_inputs(
|
|
||||||
monte_carlo_count, None, rng_to_use=rng
|
|
||||||
)
|
|
||||||
|
|
||||||
current_sample = sample_dipoles
|
|
||||||
for di, low, high in zip(dot_inputs, lows, highs):
|
|
||||||
|
|
||||||
if len(current_sample) < 1:
|
|
||||||
break
|
|
||||||
vals = pdme.util.fast_v_calc.fast_vs_for_dipoleses(
|
|
||||||
numpy.array([di]), current_sample
|
|
||||||
)
|
|
||||||
|
|
||||||
current_sample = current_sample[numpy.all((vals > low) & (vals < high), axis=1)]
|
|
||||||
return len(current_sample)
|
|
||||||
|
|
||||||
|
|
||||||
class RealSpectrumRun:
|
|
||||||
"""
|
|
||||||
A bayes run given some real data.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
measurements : Sequence[pdme.measurement.DotRangeMeasurement]
|
|
||||||
The dot inputs for this bayes run.
|
|
||||||
|
|
||||||
models_with_names : Sequence[Tuple(str, pdme.model.DipoleModel)]
|
|
||||||
The models to evaluate.
|
|
||||||
|
|
||||||
actual_model : pdme.model.DipoleModel
|
|
||||||
The model which is actually correct.
|
|
||||||
|
|
||||||
filename_slug : str
|
|
||||||
The filename slug to include.
|
|
||||||
|
|
||||||
run_count: int
|
|
||||||
The number of runs to do.
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(
|
|
||||||
self,
|
|
||||||
measurements: Sequence[pdme.measurement.DotRangeMeasurement],
|
|
||||||
models_with_names: Sequence[Tuple[str, pdme.model.DipoleModel]],
|
|
||||||
filename_slug: str,
|
|
||||||
monte_carlo_count: int = 10000,
|
|
||||||
monte_carlo_cycles: int = 10,
|
|
||||||
target_success: int = 100,
|
|
||||||
max_monte_carlo_cycles_steps: int = 10,
|
|
||||||
chunksize: int = CHUNKSIZE,
|
|
||||||
initial_seed: int = 12345,
|
|
||||||
use_fast_filter: bool = True,
|
|
||||||
) -> None:
|
|
||||||
self.measurements = measurements
|
|
||||||
self.dot_inputs = [(measure.r, measure.f) for measure in self.measurements]
|
|
||||||
|
|
||||||
self.dot_inputs_array = pdme.measurement.input_types.dot_inputs_to_array(
|
|
||||||
self.dot_inputs
|
|
||||||
)
|
|
||||||
|
|
||||||
self.models = [model for (_, model) in models_with_names]
|
|
||||||
self.model_names = [name for (name, _) in models_with_names]
|
|
||||||
self.model_count = len(self.models)
|
|
||||||
|
|
||||||
self.monte_carlo_count = monte_carlo_count
|
|
||||||
self.monte_carlo_cycles = monte_carlo_cycles
|
|
||||||
self.target_success = target_success
|
|
||||||
self.max_monte_carlo_cycles_steps = max_monte_carlo_cycles_steps
|
|
||||||
|
|
||||||
self.csv_fields = []
|
|
||||||
|
|
||||||
self.compensate_zeros = True
|
|
||||||
self.chunksize = chunksize
|
|
||||||
for name in self.model_names:
|
|
||||||
self.csv_fields.extend([f"{name}_success", f"{name}_count", f"{name}_prob"])
|
|
||||||
|
|
||||||
# for now initialise priors as uniform.
|
|
||||||
self.probabilities = [1 / self.model_count] * self.model_count
|
|
||||||
|
|
||||||
timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
|
|
||||||
self.use_fast_filter = use_fast_filter
|
|
||||||
ff_string = "no_fast_filter"
|
|
||||||
if self.use_fast_filter:
|
|
||||||
ff_string = "fast_filter"
|
|
||||||
self.filename = f"{timestamp}-{filename_slug}.realdata.{ff_string}.bayesrun.csv"
|
|
||||||
self.initial_seed = initial_seed
|
|
||||||
|
|
||||||
def go(self) -> None:
|
|
||||||
with open(self.filename, "a", newline="") as outfile:
|
|
||||||
writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
|
|
||||||
writer.writeheader()
|
|
||||||
|
|
||||||
(
|
|
||||||
lows,
|
|
||||||
highs,
|
|
||||||
) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
|
|
||||||
self.measurements
|
|
||||||
)
|
|
||||||
|
|
||||||
# define a new seed sequence for each run
|
|
||||||
seed_sequence = numpy.random.SeedSequence(self.initial_seed)
|
|
||||||
|
|
||||||
results = []
|
|
||||||
_logger.debug("Going to iterate over models now")
|
|
||||||
for model_count, model in enumerate(self.models):
|
|
||||||
_logger.debug(f"Doing model #{model_count}")
|
|
||||||
core_count = multiprocessing.cpu_count() - 1 or 1
|
|
||||||
with multiprocessing.Pool(core_count) as pool:
|
|
||||||
cycle_count = 0
|
|
||||||
cycle_success = 0
|
|
||||||
cycles = 0
|
|
||||||
while (cycles < self.max_monte_carlo_cycles_steps) and (
|
|
||||||
cycle_success <= self.target_success
|
|
||||||
):
|
|
||||||
_logger.debug(f"Starting cycle {cycles}")
|
|
||||||
cycles += 1
|
|
||||||
current_success = 0
|
|
||||||
cycle_count += self.monte_carlo_count * self.monte_carlo_cycles
|
|
||||||
|
|
||||||
# generate a seed from the sequence for each core.
|
|
||||||
# note this needs to be inside the loop for monte carlo cycle steps!
|
|
||||||
# that way we get more stuff.
|
|
||||||
seeds = seed_sequence.spawn(self.monte_carlo_cycles)
|
|
||||||
|
|
||||||
if self.use_fast_filter:
|
|
||||||
result_func = get_a_result_fast_filter
|
|
||||||
else:
|
|
||||||
result_func = get_a_result
|
|
||||||
current_success = sum(
|
|
||||||
pool.imap_unordered(
|
|
||||||
result_func,
|
|
||||||
[
|
|
||||||
(
|
|
||||||
model,
|
|
||||||
self.dot_inputs_array,
|
|
||||||
lows,
|
|
||||||
highs,
|
|
||||||
self.monte_carlo_count,
|
|
||||||
seed,
|
|
||||||
)
|
|
||||||
for seed in seeds
|
|
||||||
],
|
|
||||||
self.chunksize,
|
|
||||||
)
|
|
||||||
)
|
|
||||||
|
|
||||||
cycle_success += current_success
|
|
||||||
_logger.debug(f"current running successes: {cycle_success}")
|
|
||||||
results.append((cycle_count, cycle_success))
|
|
||||||
|
|
||||||
_logger.debug("Done, constructing output now")
|
|
||||||
row: Dict[str, Union[int, float, str]] = {}
|
|
||||||
|
|
||||||
successes: List[float] = []
|
|
||||||
counts: List[int] = []
|
|
||||||
for model_index, (name, (count, result)) in enumerate(
|
|
||||||
zip(self.model_names, results)
|
|
||||||
):
|
|
||||||
|
|
||||||
row[f"{name}_success"] = result
|
|
||||||
row[f"{name}_count"] = count
|
|
||||||
successes.append(max(result, 0.5))
|
|
||||||
counts.append(count)
|
|
||||||
|
|
||||||
success_weight = sum(
|
|
||||||
[
|
|
||||||
(succ / count) * prob
|
|
||||||
for succ, count, prob in zip(successes, counts, self.probabilities)
|
|
||||||
]
|
|
||||||
)
|
|
||||||
new_probabilities = [
|
|
||||||
(succ / count) * old_prob / success_weight
|
|
||||||
for succ, count, old_prob in zip(successes, counts, self.probabilities)
|
|
||||||
]
|
|
||||||
self.probabilities = new_probabilities
|
|
||||||
for name, probability in zip(self.model_names, self.probabilities):
|
|
||||||
row[f"{name}_prob"] = probability
|
|
||||||
_logger.info(row)
|
|
||||||
|
|
||||||
with open(self.filename, "a", newline="") as outfile:
|
|
||||||
writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
|
|
||||||
writer.writerow(row)
|
|
||||||
5
do.sh
5
do.sh
@@ -16,11 +16,6 @@ test() {
|
|||||||
poetry run pytest
|
poetry run pytest
|
||||||
}
|
}
|
||||||
|
|
||||||
fmt() {
|
|
||||||
poetry run black .
|
|
||||||
find . -type f -name "*.py" -exec sed -i -e 's/ /\t/g' {} \;
|
|
||||||
}
|
|
||||||
|
|
||||||
release() {
|
release() {
|
||||||
./scripts/release.sh
|
./scripts/release.sh
|
||||||
}
|
}
|
||||||
|
|||||||
321
poetry.lock
generated
321
poetry.lock
generated
@@ -20,28 +20,6 @@ docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"]
|
|||||||
tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "cloudpickle"]
|
tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "cloudpickle"]
|
||||||
tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "cloudpickle"]
|
tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "cloudpickle"]
|
||||||
|
|
||||||
[[package]]
|
|
||||||
name = "black"
|
|
||||||
version = "22.3.0"
|
|
||||||
description = "The uncompromising code formatter."
|
|
||||||
category = "dev"
|
|
||||||
optional = false
|
|
||||||
python-versions = ">=3.6.2"
|
|
||||||
|
|
||||||
[package.dependencies]
|
|
||||||
click = ">=8.0.0"
|
|
||||||
mypy-extensions = ">=0.4.3"
|
|
||||||
pathspec = ">=0.9.0"
|
|
||||||
platformdirs = ">=2"
|
|
||||||
tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
|
|
||||||
typing-extensions = {version = ">=3.10.0.0", markers = "python_version < \"3.10\""}
|
|
||||||
|
|
||||||
[package.extras]
|
|
||||||
colorama = ["colorama (>=0.4.3)"]
|
|
||||||
d = ["aiohttp (>=3.7.4)"]
|
|
||||||
jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
|
|
||||||
uvloop = ["uvloop (>=0.15.2)"]
|
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "bleach"
|
name = "bleach"
|
||||||
version = "4.1.0"
|
version = "4.1.0"
|
||||||
@@ -117,14 +95,14 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
|
|||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "coverage"
|
name = "coverage"
|
||||||
version = "6.4.1"
|
version = "6.3.2"
|
||||||
description = "Code coverage measurement for Python"
|
description = "Code coverage measurement for Python"
|
||||||
category = "dev"
|
category = "dev"
|
||||||
optional = false
|
optional = false
|
||||||
python-versions = ">=3.7"
|
python-versions = ">=3.7"
|
||||||
|
|
||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.11.0a6\" and extra == \"toml\""}
|
tomli = {version = "*", optional = true, markers = "extra == \"toml\""}
|
||||||
|
|
||||||
[package.extras]
|
[package.extras]
|
||||||
toml = ["tomli"]
|
toml = ["tomli"]
|
||||||
@@ -282,7 +260,7 @@ python-versions = "*"
|
|||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "mypy"
|
name = "mypy"
|
||||||
version = "0.961"
|
version = "0.940"
|
||||||
description = "Optional static typing for Python"
|
description = "Optional static typing for Python"
|
||||||
category = "dev"
|
category = "dev"
|
||||||
optional = false
|
optional = false
|
||||||
@@ -290,7 +268,7 @@ python-versions = ">=3.6"
|
|||||||
|
|
||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
mypy-extensions = ">=0.4.3"
|
mypy-extensions = ">=0.4.3"
|
||||||
tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
|
tomli = ">=1.1.0"
|
||||||
typing-extensions = ">=3.10"
|
typing-extensions = ">=3.10"
|
||||||
|
|
||||||
[package.extras]
|
[package.extras]
|
||||||
@@ -308,7 +286,7 @@ python-versions = "*"
|
|||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "numpy"
|
name = "numpy"
|
||||||
version = "1.22.3"
|
version = "1.22.1"
|
||||||
description = "NumPy is the fundamental package for array computing with Python."
|
description = "NumPy is the fundamental package for array computing with Python."
|
||||||
category = "main"
|
category = "main"
|
||||||
optional = false
|
optional = false
|
||||||
@@ -325,25 +303,17 @@ python-versions = ">=3.6"
|
|||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
pyparsing = ">=2.0.2,<3.0.5 || >3.0.5"
|
pyparsing = ">=2.0.2,<3.0.5 || >3.0.5"
|
||||||
|
|
||||||
[[package]]
|
|
||||||
name = "pathspec"
|
|
||||||
version = "0.9.0"
|
|
||||||
description = "Utility library for gitignore style pattern matching of file paths."
|
|
||||||
category = "dev"
|
|
||||||
optional = false
|
|
||||||
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
|
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "pdme"
|
name = "pdme"
|
||||||
version = "0.8.5"
|
version = "0.5.4"
|
||||||
description = "Python dipole model evaluator"
|
description = "Python dipole model evaluator"
|
||||||
category = "main"
|
category = "main"
|
||||||
optional = false
|
optional = false
|
||||||
python-versions = ">=3.8,<3.10"
|
python-versions = ">=3.8,<3.10"
|
||||||
|
|
||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
numpy = ">=1.22.3,<2.0.0"
|
numpy = ">=1.21.1,<2.0.0"
|
||||||
scipy = ">=1.8,<1.9"
|
scipy = ">=1.5,<1.6"
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "pkginfo"
|
name = "pkginfo"
|
||||||
@@ -356,18 +326,6 @@ python-versions = "*"
|
|||||||
[package.extras]
|
[package.extras]
|
||||||
testing = ["coverage", "nose"]
|
testing = ["coverage", "nose"]
|
||||||
|
|
||||||
[[package]]
|
|
||||||
name = "platformdirs"
|
|
||||||
version = "2.5.1"
|
|
||||||
description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
|
|
||||||
category = "dev"
|
|
||||||
optional = false
|
|
||||||
python-versions = ">=3.7"
|
|
||||||
|
|
||||||
[package.extras]
|
|
||||||
docs = ["Sphinx (>=4)", "furo (>=2021.7.5b38)", "proselint (>=0.10.2)", "sphinx-autodoc-typehints (>=1.12)"]
|
|
||||||
test = ["appdirs (==1.4.4)", "pytest (>=6)", "pytest-cov (>=2.7)", "pytest-mock (>=3.6)"]
|
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "pluggy"
|
name = "pluggy"
|
||||||
version = "1.0.0"
|
version = "1.0.0"
|
||||||
@@ -575,14 +533,14 @@ idna2008 = ["idna"]
|
|||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "scipy"
|
name = "scipy"
|
||||||
version = "1.8.0"
|
version = "1.5.4"
|
||||||
description = "SciPy: Scientific Library for Python"
|
description = "SciPy: Scientific Library for Python"
|
||||||
category = "main"
|
category = "main"
|
||||||
optional = false
|
optional = false
|
||||||
python-versions = ">=3.8,<3.11"
|
python-versions = ">=3.6"
|
||||||
|
|
||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
numpy = ">=1.17.3,<1.25.0"
|
numpy = ">=1.14.5"
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "secretstorage"
|
name = "secretstorage"
|
||||||
@@ -740,7 +698,7 @@ testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-
|
|||||||
[metadata]
|
[metadata]
|
||||||
lock-version = "1.1"
|
lock-version = "1.1"
|
||||||
python-versions = "^3.8,<3.10"
|
python-versions = "^3.8,<3.10"
|
||||||
content-hash = "0196604b0dfc7b3dd0bdb19f983358ef9934d47f3c209dbbc7d07712ac5f779d"
|
content-hash = "ac69ab9be2cde12f64be445f46af378b6943d3c19cbf9fd3e1b6b81371c7a5a6"
|
||||||
|
|
||||||
[metadata.files]
|
[metadata.files]
|
||||||
atomicwrites = [
|
atomicwrites = [
|
||||||
@@ -751,31 +709,6 @@ attrs = [
|
|||||||
{file = "attrs-21.4.0-py2.py3-none-any.whl", hash = "sha256:2d27e3784d7a565d36ab851fe94887c5eccd6a463168875832a1be79c82828b4"},
|
{file = "attrs-21.4.0-py2.py3-none-any.whl", hash = "sha256:2d27e3784d7a565d36ab851fe94887c5eccd6a463168875832a1be79c82828b4"},
|
||||||
{file = "attrs-21.4.0.tar.gz", hash = "sha256:626ba8234211db98e869df76230a137c4c40a12d72445c45d5f5b716f076e2fd"},
|
{file = "attrs-21.4.0.tar.gz", hash = "sha256:626ba8234211db98e869df76230a137c4c40a12d72445c45d5f5b716f076e2fd"},
|
||||||
]
|
]
|
||||||
black = [
|
|
||||||
{file = "black-22.3.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:2497f9c2386572e28921fa8bec7be3e51de6801f7459dffd6e62492531c47e09"},
|
|
||||||
{file = "black-22.3.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5795a0375eb87bfe902e80e0c8cfaedf8af4d49694d69161e5bd3206c18618bb"},
|
|
||||||
{file = "black-22.3.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e3556168e2e5c49629f7b0f377070240bd5511e45e25a4497bb0073d9dda776a"},
|
|
||||||
{file = "black-22.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:67c8301ec94e3bcc8906740fe071391bce40a862b7be0b86fb5382beefecd968"},
|
|
||||||
{file = "black-22.3.0-cp310-cp310-win_amd64.whl", hash = "sha256:fd57160949179ec517d32ac2ac898b5f20d68ed1a9c977346efbac9c2f1e779d"},
|
|
||||||
{file = "black-22.3.0-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:cc1e1de68c8e5444e8f94c3670bb48a2beef0e91dddfd4fcc29595ebd90bb9ce"},
|
|
||||||
{file = "black-22.3.0-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d2fc92002d44746d3e7db7cf9313cf4452f43e9ea77a2c939defce3b10b5c82"},
|
|
||||||
{file = "black-22.3.0-cp36-cp36m-win_amd64.whl", hash = "sha256:a6342964b43a99dbc72f72812bf88cad8f0217ae9acb47c0d4f141a6416d2d7b"},
|
|
||||||
{file = "black-22.3.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:328efc0cc70ccb23429d6be184a15ce613f676bdfc85e5fe8ea2a9354b4e9015"},
|
|
||||||
{file = "black-22.3.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:06f9d8846f2340dfac80ceb20200ea5d1b3f181dd0556b47af4e8e0b24fa0a6b"},
|
|
||||||
{file = "black-22.3.0-cp37-cp37m-win_amd64.whl", hash = "sha256:ad4efa5fad66b903b4a5f96d91461d90b9507a812b3c5de657d544215bb7877a"},
|
|
||||||
{file = "black-22.3.0-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:e8477ec6bbfe0312c128e74644ac8a02ca06bcdb8982d4ee06f209be28cdf163"},
|
|
||||||
{file = "black-22.3.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:637a4014c63fbf42a692d22b55d8ad6968a946b4a6ebc385c5505d9625b6a464"},
|
|
||||||
{file = "black-22.3.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:863714200ada56cbc366dc9ae5291ceb936573155f8bf8e9de92aef51f3ad0f0"},
|
|
||||||
{file = "black-22.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:10dbe6e6d2988049b4655b2b739f98785a884d4d6b85bc35133a8fb9a2233176"},
|
|
||||||
{file = "black-22.3.0-cp38-cp38-win_amd64.whl", hash = "sha256:cee3e11161dde1b2a33a904b850b0899e0424cc331b7295f2a9698e79f9a69a0"},
|
|
||||||
{file = "black-22.3.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:5891ef8abc06576985de8fa88e95ab70641de6c1fca97e2a15820a9b69e51b20"},
|
|
||||||
{file = "black-22.3.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:30d78ba6bf080eeaf0b7b875d924b15cd46fec5fd044ddfbad38c8ea9171043a"},
|
|
||||||
{file = "black-22.3.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:ee8f1f7228cce7dffc2b464f07ce769f478968bfb3dd1254a4c2eeed84928aad"},
|
|
||||||
{file = "black-22.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6ee227b696ca60dd1c507be80a6bc849a5a6ab57ac7352aad1ffec9e8b805f21"},
|
|
||||||
{file = "black-22.3.0-cp39-cp39-win_amd64.whl", hash = "sha256:9b542ced1ec0ceeff5b37d69838106a6348e60db7b8fdd245294dc1d26136265"},
|
|
||||||
{file = "black-22.3.0-py3-none-any.whl", hash = "sha256:bc58025940a896d7e5356952228b68f793cf5fcb342be703c3a2669a1488cb72"},
|
|
||||||
{file = "black-22.3.0.tar.gz", hash = "sha256:35020b8886c022ced9282b51b5a875b6d1ab0c387b31a065b84db7c33085ca79"},
|
|
||||||
]
|
|
||||||
bleach = [
|
bleach = [
|
||||||
{file = "bleach-4.1.0-py2.py3-none-any.whl", hash = "sha256:4d2651ab93271d1129ac9cbc679f524565cc8a1b791909c4a51eac4446a15994"},
|
{file = "bleach-4.1.0-py2.py3-none-any.whl", hash = "sha256:4d2651ab93271d1129ac9cbc679f524565cc8a1b791909c4a51eac4446a15994"},
|
||||||
{file = "bleach-4.1.0.tar.gz", hash = "sha256:0900d8b37eba61a802ee40ac0061f8c2b5dee29c1927dd1d233e075ebf5a71da"},
|
{file = "bleach-4.1.0.tar.gz", hash = "sha256:0900d8b37eba61a802ee40ac0061f8c2b5dee29c1927dd1d233e075ebf5a71da"},
|
||||||
@@ -853,47 +786,47 @@ colorama = [
|
|||||||
{file = "colorama-0.4.4.tar.gz", hash = "sha256:5941b2b48a20143d2267e95b1c2a7603ce057ee39fd88e7329b0c292aa16869b"},
|
{file = "colorama-0.4.4.tar.gz", hash = "sha256:5941b2b48a20143d2267e95b1c2a7603ce057ee39fd88e7329b0c292aa16869b"},
|
||||||
]
|
]
|
||||||
coverage = [
|
coverage = [
|
||||||
{file = "coverage-6.4.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:f1d5aa2703e1dab4ae6cf416eb0095304f49d004c39e9db1d86f57924f43006b"},
|
{file = "coverage-6.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9b27d894748475fa858f9597c0ee1d4829f44683f3813633aaf94b19cb5453cf"},
|
||||||
{file = "coverage-6.4.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:4ce1b258493cbf8aec43e9b50d89982346b98e9ffdfaae8ae5793bc112fb0068"},
|
{file = "coverage-6.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:37d1141ad6b2466a7b53a22e08fe76994c2d35a5b6b469590424a9953155afac"},
|
||||||
{file = "coverage-6.4.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:83c4e737f60c6936460c5be330d296dd5b48b3963f48634c53b3f7deb0f34ec4"},
|
{file = "coverage-6.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f9987b0354b06d4df0f4d3e0ec1ae76d7ce7cbca9a2f98c25041eb79eec766f1"},
|
||||||
{file = "coverage-6.4.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:84e65ef149028516c6d64461b95a8dbcfce95cfd5b9eb634320596173332ea84"},
|
{file = "coverage-6.3.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:26e2deacd414fc2f97dd9f7676ee3eaecd299ca751412d89f40bc01557a6b1b4"},
|
||||||
{file = "coverage-6.4.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f69718750eaae75efe506406c490d6fc5a6161d047206cc63ce25527e8a3adad"},
|
{file = "coverage-6.3.2-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4dd8bafa458b5c7d061540f1ee9f18025a68e2d8471b3e858a9dad47c8d41903"},
|
||||||
{file = "coverage-6.4.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:e57816f8ffe46b1df8f12e1b348f06d164fd5219beba7d9433ba79608ef011cc"},
|
{file = "coverage-6.3.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:46191097ebc381fbf89bdce207a6c107ac4ec0890d8d20f3360345ff5976155c"},
|
||||||
{file = "coverage-6.4.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:01c5615d13f3dd3aa8543afc069e5319cfa0c7d712f6e04b920431e5c564a749"},
|
{file = "coverage-6.3.2-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:6f89d05e028d274ce4fa1a86887b071ae1755082ef94a6740238cd7a8178804f"},
|
||||||
{file = "coverage-6.4.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:75ab269400706fab15981fd4bd5080c56bd5cc07c3bccb86aab5e1d5a88dc8f4"},
|
{file = "coverage-6.3.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:58303469e9a272b4abdb9e302a780072c0633cdcc0165db7eec0f9e32f901e05"},
|
||||||
{file = "coverage-6.4.1-cp310-cp310-win32.whl", hash = "sha256:a7f3049243783df2e6cc6deafc49ea123522b59f464831476d3d1448e30d72df"},
|
{file = "coverage-6.3.2-cp310-cp310-win32.whl", hash = "sha256:2fea046bfb455510e05be95e879f0e768d45c10c11509e20e06d8fcaa31d9e39"},
|
||||||
{file = "coverage-6.4.1-cp310-cp310-win_amd64.whl", hash = "sha256:ee2ddcac99b2d2aec413e36d7a429ae9ebcadf912946b13ffa88e7d4c9b712d6"},
|
{file = "coverage-6.3.2-cp310-cp310-win_amd64.whl", hash = "sha256:a2a8b8bcc399edb4347a5ca8b9b87e7524c0967b335fbb08a83c8421489ddee1"},
|
||||||
{file = "coverage-6.4.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:fb73e0011b8793c053bfa85e53129ba5f0250fdc0392c1591fd35d915ec75c46"},
|
{file = "coverage-6.3.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:f1555ea6d6da108e1999b2463ea1003fe03f29213e459145e70edbaf3e004aaa"},
|
||||||
{file = "coverage-6.4.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:106c16dfe494de3193ec55cac9640dd039b66e196e4641fa8ac396181578b982"},
|
{file = "coverage-6.3.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e5f4e1edcf57ce94e5475fe09e5afa3e3145081318e5fd1a43a6b4539a97e518"},
|
||||||
{file = "coverage-6.4.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:87f4f3df85aa39da00fd3ec4b5abeb7407e82b68c7c5ad181308b0e2526da5d4"},
|
{file = "coverage-6.3.2-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7a15dc0a14008f1da3d1ebd44bdda3e357dbabdf5a0b5034d38fcde0b5c234b7"},
|
||||||
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{file = "scipy-1.8.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c17a1878d00a5dd2797ccd73623ceca9d02375328f6218ee6d921e1325e61aff"},
|
{file = "scipy-1.5.4-cp36-cp36m-manylinux2014_aarch64.whl", hash = "sha256:4598cf03136067000855d6b44d7a1f4f46994164bcd450fb2c3d481afc25dd06"},
|
||||||
{file = "scipy-1.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:937d28722f13302febde29847bbe554b89073fbb924a30475e5ed7b028898b5f"},
|
{file = "scipy-1.5.4-cp36-cp36m-win32.whl", hash = "sha256:e98d49a5717369d8241d6cf33ecb0ca72deee392414118198a8e5b4c35c56340"},
|
||||||
{file = "scipy-1.8.0-cp310-cp310-win_amd64.whl", hash = "sha256:8f4d059a97b29c91afad46b1737274cb282357a305a80bdd9e8adf3b0ca6a3f0"},
|
{file = "scipy-1.5.4-cp36-cp36m-win_amd64.whl", hash = "sha256:65923bc3809524e46fb7eb4d6346552cbb6a1ffc41be748535aa502a2e3d3389"},
|
||||||
{file = "scipy-1.8.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:38aa39b6724cb65271e469013aeb6f2ce66fd44f093e241c28a9c6bc64fd79ed"},
|
{file = "scipy-1.5.4-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:9ad4fcddcbf5dc67619379782e6aeef41218a79e17979aaed01ed099876c0e62"},
|
||||||
{file = "scipy-1.8.0-cp38-cp38-macosx_12_0_arm64.whl", hash = "sha256:559a8a4c03a5ba9fe3232f39ed24f86457e4f3f6c0abbeae1fb945029f092720"},
|
{file = "scipy-1.5.4-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:f87b39f4d69cf7d7529d7b1098cb712033b17ea7714aed831b95628f483fd012"},
|
||||||
{file = "scipy-1.8.0-cp38-cp38-macosx_12_0_universal2.macosx_10_9_x86_64.whl", hash = "sha256:f4a6d3b9f9797eb2d43938ac2c5d96d02aed17ef170c8b38f11798717523ddba"},
|
{file = "scipy-1.5.4-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:25b241034215247481f53355e05f9e25462682b13bd9191359075682adcd9554"},
|
||||||
{file = "scipy-1.8.0-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:92b2c2af4183ed09afb595709a8ef5783b2baf7f41e26ece24e1329c109691a7"},
|
{file = "scipy-1.5.4-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:fa789583fc94a7689b45834453fec095245c7e69c58561dc159b5d5277057e4c"},
|
||||||
{file = "scipy-1.8.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a279e27c7f4566ef18bab1b1e2c37d168e365080974758d107e7d237d3f0f484"},
|
{file = "scipy-1.5.4-cp37-cp37m-win32.whl", hash = "sha256:d6d25c41a009e3c6b7e757338948d0076ee1dd1770d1c09ec131f11946883c54"},
|
||||||
{file = "scipy-1.8.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ad5be4039147c808e64f99c0e8a9641eb5d2fa079ff5894dcd8240e94e347af4"},
|
{file = "scipy-1.5.4-cp37-cp37m-win_amd64.whl", hash = "sha256:2c872de0c69ed20fb1a9b9cf6f77298b04a26f0b8720a5457be08be254366c6e"},
|
||||||
{file = "scipy-1.8.0-cp38-cp38-win32.whl", hash = "sha256:3d9dd6c8b93a22bf9a3a52d1327aca7e092b1299fb3afc4f89e8eba381be7b59"},
|
{file = "scipy-1.5.4-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:e360cb2299028d0b0d0f65a5c5e51fc16a335f1603aa2357c25766c8dab56938"},
|
||||||
{file = "scipy-1.8.0-cp38-cp38-win_amd64.whl", hash = "sha256:5e73343c5e0d413c1f937302b2e04fb07872f5843041bcfd50699aef6e95e399"},
|
{file = "scipy-1.5.4-cp38-cp38-manylinux1_i686.whl", hash = "sha256:3397c129b479846d7eaa18f999369a24322d008fac0782e7828fa567358c36ce"},
|
||||||
{file = "scipy-1.8.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:de2e80ee1d925984c2504812a310841c241791c5279352be4707cdcd7c255039"},
|
{file = "scipy-1.5.4-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:168c45c0c32e23f613db7c9e4e780bc61982d71dcd406ead746c7c7c2f2004ce"},
|
||||||
{file = "scipy-1.8.0-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:c2bae431d127bf0b1da81fc24e4bba0a84d058e3a96b9dd6475dfcb3c5e8761e"},
|
{file = "scipy-1.5.4-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:213bc59191da2f479984ad4ec39406bf949a99aba70e9237b916ce7547b6ef42"},
|
||||||
{file = "scipy-1.8.0-cp39-cp39-macosx_12_0_universal2.macosx_10_9_x86_64.whl", hash = "sha256:723b9f878095ed994756fa4ee3060c450e2db0139c5ba248ee3f9628bd64e735"},
|
{file = "scipy-1.5.4-cp38-cp38-win32.whl", hash = "sha256:634568a3018bc16a83cda28d4f7aed0d803dd5618facb36e977e53b2df868443"},
|
||||||
{file = "scipy-1.8.0-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:011d4386b53b933142f58a652aa0f149c9b9242abd4f900b9f4ea5fbafc86b89"},
|
{file = "scipy-1.5.4-cp38-cp38-win_amd64.whl", hash = "sha256:b03c4338d6d3d299e8ca494194c0ae4f611548da59e3c038813f1a43976cb437"},
|
||||||
{file = "scipy-1.8.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e6f0cd9c0bd374ef834ee1e0f0999678d49dcc400ea6209113d81528958f97c7"},
|
{file = "scipy-1.5.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:3d5db5d815370c28d938cf9b0809dade4acf7aba57eaf7ef733bfedc9b2474c4"},
|
||||||
{file = "scipy-1.8.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f3720d0124aced49f6f2198a6900304411dbbeed12f56951d7c66ebef05e3df6"},
|
{file = "scipy-1.5.4-cp39-cp39-manylinux1_i686.whl", hash = "sha256:6b0ceb23560f46dd236a8ad4378fc40bad1783e997604ba845e131d6c680963e"},
|
||||||
{file = "scipy-1.8.0-cp39-cp39-win32.whl", hash = "sha256:3d573228c10a3a8c32b9037be982e6440e411b443a6267b067cac72f690b8d56"},
|
{file = "scipy-1.5.4-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:ed572470af2438b526ea574ff8f05e7f39b44ac37f712105e57fc4d53a6fb660"},
|
||||||
{file = "scipy-1.8.0-cp39-cp39-win_amd64.whl", hash = "sha256:bb7088e89cd751acf66195d2f00cf009a1ea113f3019664032d9075b1e727b6c"},
|
{file = "scipy-1.5.4-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:8c8d6ca19c8497344b810b0b0344f8375af5f6bb9c98bd42e33f747417ab3f57"},
|
||||||
{file = "scipy-1.8.0.tar.gz", hash = "sha256:31d4f2d6b724bc9a98e527b5849b8a7e589bf1ea630c33aa563eda912c9ff0bd"},
|
{file = "scipy-1.5.4-cp39-cp39-win32.whl", hash = "sha256:d84cadd7d7998433334c99fa55bcba0d8b4aeff0edb123b2a1dfcface538e474"},
|
||||||
|
{file = "scipy-1.5.4-cp39-cp39-win_amd64.whl", hash = "sha256:cc1f78ebc982cd0602c9a7615d878396bec94908db67d4ecddca864d049112f2"},
|
||||||
|
{file = "scipy-1.5.4.tar.gz", hash = "sha256:4a453d5e5689de62e5d38edf40af3f17560bfd63c9c5bd228c18c1f99afa155b"},
|
||||||
]
|
]
|
||||||
secretstorage = [
|
secretstorage = [
|
||||||
{file = "SecretStorage-3.3.1-py3-none-any.whl", hash = "sha256:422d82c36172d88d6a0ed5afdec956514b189ddbfb72fefab0c8a1cee4eaf71f"},
|
{file = "SecretStorage-3.3.1-py3-none-any.whl", hash = "sha256:422d82c36172d88d6a0ed5afdec956514b189ddbfb72fefab0c8a1cee4eaf71f"},
|
||||||
|
|||||||
@@ -1,20 +1,19 @@
|
|||||||
[tool.poetry]
|
[tool.poetry]
|
||||||
name = "deepdog"
|
name = "deepdog"
|
||||||
version = "0.6.3"
|
version = "0.3.5"
|
||||||
description = ""
|
description = ""
|
||||||
authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]
|
authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]
|
||||||
|
|
||||||
[tool.poetry.dependencies]
|
[tool.poetry.dependencies]
|
||||||
python = "^3.8,<3.10"
|
python = "^3.8,<3.10"
|
||||||
pdme = "^0.8.5"
|
pdme = "^0.5.4"
|
||||||
|
|
||||||
[tool.poetry.dev-dependencies]
|
[tool.poetry.dev-dependencies]
|
||||||
pytest = ">=6"
|
pytest = ">=6"
|
||||||
flake8 = "^4.0.1"
|
flake8 = "^4.0.1"
|
||||||
pytest-cov = "^3.0.0"
|
pytest-cov = "^3.0.0"
|
||||||
mypy = "^0.961"
|
mypy = "^0.940"
|
||||||
python-semantic-release = "^7.24.0"
|
python-semantic-release = "^7.24.0"
|
||||||
black = "^22.3.0"
|
|
||||||
|
|
||||||
[build-system]
|
[build-system]
|
||||||
requires = ["poetry-core>=1.0.0"]
|
requires = ["poetry-core>=1.0.0"]
|
||||||
|
|||||||
Reference in New Issue
Block a user