Merge dev into main for 0.4.2 release
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* loosely re-implement distributions using torch.distributions * further enhancement * rename * log_uniform as transformed dist * delete uniform and log_normal since they are already implemented * bring back LogNormal, change parameter sampler
Delta function distribution
…q's for batch/channels
…y frequency range in which to search for max energy tile
Q-transform
Generalize Q Transform Input Dimensionality
Fix type hints in qtransform
* in memory dataset inherits from torch iterable dataset * pre-commit issues * fix yielding logic * fix in memory dataset tests
* udpate poetry lock * increment version once more * re add gwpy dev dep
* remove gwpy dep for now * poetry lock file
EthanMarx
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May 14, 2024
* Adding funding statement * Add requirements to docs (#107) * add requirements to docs * remove myst_parser req * Update requirements.txt * Update conf.py * Update docs.yaml (#108) * Update docs.yaml * Update docs.yaml (#109) * simplify torch dependencies (#122) * simplify torch dependency * remove python constratints * fix pre-commit * Merge `dev` into `main` for 0.4.2 release (#123) * re-implement distributions using torch.distributions (#110) * loosely re-implement distributions using torch.distributions * further enhancement * rename * log_uniform as transformed dist * delete uniform and log_normal since they are already implemented * bring back LogNormal, change parameter sampler * add delta function distribution, minor fixes to other distributions * fix * Initial commit of q-transform * Added a MultiQTransform * Reparameterized to number of t and f bins * Re-factored SingleQTransform to allow for eventually using different q's for batch/channels * Changed interpolation to better match gwpy and added option to specify frequency range in which to search for max energy tile * Added QScan to __init__ * Changed from torch median to quantile to match numpy median * Added documentation to qtransform * Added gwpy to dev dependencies * Added tests and corrected get_freqs bug * Changed method of normalization and updated documentation * Updated more documentation and changed interpolation method * Changed how interpolation shape is parameterized * Reverted documentation to state expectation of 3D input * Fixed type hint for spectrogram_shape in qtransform * Switched tuple to Tuple * `InMemoryDataset` improvements (#119) * in memory dataset inherits from torch iterable dataset * pre-commit issues * fix yielding logic * fix in memory dataset tests * handle deprecated transpose ops in phenomd (#120) * Torch dependency fix into dev (#124) * udpate poetry lock * increment version once more * re add gwpy dev dep * update poetry lock * fix poetry conflic * fix poety conflict * Poetry fix (#126) * remove gwpy dep for now * poetry lock file --------- Co-authored-by: Deep Chatterjee <[email protected]> Co-authored-by: wbenoit26 <[email protected]> Co-authored-by: William Benoit <[email protected]> Co-authored-by: William Benoit <[email protected]> * increment version (#127) --------- Co-authored-by: wbenoit26 <[email protected]> Co-authored-by: Deep Chatterjee <[email protected]> Co-authored-by: William Benoit <[email protected]> Co-authored-by: William Benoit <[email protected]>
ravioli1369
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Jun 10, 2024
* re-implement distributions using torch.distributions (ML4GW#110) * loosely re-implement distributions using torch.distributions * further enhancement * rename * log_uniform as transformed dist * delete uniform and log_normal since they are already implemented * bring back LogNormal, change parameter sampler * add delta function distribution, minor fixes to other distributions * fix * Initial commit of q-transform * Added a MultiQTransform * Reparameterized to number of t and f bins * Re-factored SingleQTransform to allow for eventually using different q's for batch/channels * Changed interpolation to better match gwpy and added option to specify frequency range in which to search for max energy tile * Added QScan to __init__ * Changed from torch median to quantile to match numpy median * Added documentation to qtransform * Added gwpy to dev dependencies * Added tests and corrected get_freqs bug * Changed method of normalization and updated documentation * Updated more documentation and changed interpolation method * Changed how interpolation shape is parameterized * Reverted documentation to state expectation of 3D input * Fixed type hint for spectrogram_shape in qtransform * Switched tuple to Tuple * `InMemoryDataset` improvements (ML4GW#119) * in memory dataset inherits from torch iterable dataset * pre-commit issues * fix yielding logic * fix in memory dataset tests * handle deprecated transpose ops in phenomd (ML4GW#120) * Torch dependency fix into dev (ML4GW#124) * udpate poetry lock * increment version once more * re add gwpy dev dep * update poetry lock * fix poetry conflic * fix poety conflict * Poetry fix (ML4GW#126) * remove gwpy dep for now * poetry lock file --------- Co-authored-by: Deep Chatterjee <[email protected]> Co-authored-by: wbenoit26 <[email protected]> Co-authored-by: William Benoit <[email protected]> Co-authored-by: William Benoit <[email protected]>
EthanMarx
added a commit
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Jul 5, 2024
* Adding funding statement * Add requirements to docs (#107) * add requirements to docs * remove myst_parser req * Update requirements.txt * Update conf.py * Update docs.yaml (#108) * Update docs.yaml * Update docs.yaml (#109) * simplify torch dependencies (#122) * simplify torch dependency * remove python constratints * fix pre-commit * Merge `dev` into `main` for 0.4.2 release (#123) * re-implement distributions using torch.distributions (#110) * loosely re-implement distributions using torch.distributions * further enhancement * rename * log_uniform as transformed dist * delete uniform and log_normal since they are already implemented * bring back LogNormal, change parameter sampler * add delta function distribution, minor fixes to other distributions * fix * Initial commit of q-transform * Added a MultiQTransform * Reparameterized to number of t and f bins * Re-factored SingleQTransform to allow for eventually using different q's for batch/channels * Changed interpolation to better match gwpy and added option to specify frequency range in which to search for max energy tile * Added QScan to __init__ * Changed from torch median to quantile to match numpy median * Added documentation to qtransform * Added gwpy to dev dependencies * Added tests and corrected get_freqs bug * Changed method of normalization and updated documentation * Updated more documentation and changed interpolation method * Changed how interpolation shape is parameterized * Reverted documentation to state expectation of 3D input * Fixed type hint for spectrogram_shape in qtransform * Switched tuple to Tuple * `InMemoryDataset` improvements (#119) * in memory dataset inherits from torch iterable dataset * pre-commit issues * fix yielding logic * fix in memory dataset tests * handle deprecated transpose ops in phenomd (#120) * Torch dependency fix into dev (#124) * udpate poetry lock * increment version once more * re add gwpy dev dep * update poetry lock * fix poetry conflic * fix poety conflict * Poetry fix (#126) * remove gwpy dep for now * poetry lock file --------- Co-authored-by: Deep Chatterjee <[email protected]> Co-authored-by: wbenoit26 <[email protected]> Co-authored-by: William Benoit <[email protected]> Co-authored-by: William Benoit <[email protected]> * increment version (#127) --------- Co-authored-by: wbenoit26 <[email protected]> Co-authored-by: Deep Chatterjee <[email protected]> Co-authored-by: William Benoit <[email protected]> Co-authored-by: William Benoit <[email protected]>
EthanMarx
added a commit
that referenced
this pull request
Jul 15, 2024
* re-implement distributions using torch.distributions (#110) * loosely re-implement distributions using torch.distributions * further enhancement * rename * log_uniform as transformed dist * delete uniform and log_normal since they are already implemented * bring back LogNormal, change parameter sampler * add delta function distribution, minor fixes to other distributions * fix * Initial commit of q-transform * Added a MultiQTransform * Reparameterized to number of t and f bins * Re-factored SingleQTransform to allow for eventually using different q's for batch/channels * Changed interpolation to better match gwpy and added option to specify frequency range in which to search for max energy tile * Added QScan to __init__ * Changed from torch median to quantile to match numpy median * Added documentation to qtransform * Added gwpy to dev dependencies * Added tests and corrected get_freqs bug * Changed method of normalization and updated documentation * Updated more documentation and changed interpolation method * Changed how interpolation shape is parameterized * Reverted documentation to state expectation of 3D input * Fixed type hint for spectrogram_shape in qtransform * Switched tuple to Tuple * `InMemoryDataset` improvements (#119) * in memory dataset inherits from torch iterable dataset * pre-commit issues * fix yielding logic * fix in memory dataset tests * handle deprecated transpose ops in phenomd (#120) * Torch dependency fix into dev (#124) * udpate poetry lock * increment version once more * re add gwpy dev dep * update poetry lock * fix poetry conflic * fix poety conflict * Poetry fix (#126) * remove gwpy dep for now * poetry lock file * Merge `main` into `dev` (#128) * Adding funding statement * Add requirements to docs (#107) * add requirements to docs * remove myst_parser req * Update requirements.txt * Update conf.py * Update docs.yaml (#108) * Update docs.yaml * Update docs.yaml (#109) * simplify torch dependencies (#122) * simplify torch dependency * remove python constratints * fix pre-commit * Merge `dev` into `main` for 0.4.2 release (#123) * re-implement distributions using torch.distributions (#110) * loosely re-implement distributions using torch.distributions * further enhancement * rename * log_uniform as transformed dist * delete uniform and log_normal since they are already implemented * bring back LogNormal, change parameter sampler * add delta function distribution, minor fixes to other distributions * fix * Initial commit of q-transform * Added a MultiQTransform * Reparameterized to number of t and f bins * Re-factored SingleQTransform to allow for eventually using different q's for batch/channels * Changed interpolation to better match gwpy and added option to specify frequency range in which to search for max energy tile * Added QScan to __init__ * Changed from torch median to quantile to match numpy median * Added documentation to qtransform * Added gwpy to dev dependencies * Added tests and corrected get_freqs bug * Changed method of normalization and updated documentation * Updated more documentation and changed interpolation method * Changed how interpolation shape is parameterized * Reverted documentation to state expectation of 3D input * Fixed type hint for spectrogram_shape in qtransform * Switched tuple to Tuple * `InMemoryDataset` improvements (#119) * in memory dataset inherits from torch iterable dataset * pre-commit issues * fix yielding logic * fix in memory dataset tests * handle deprecated transpose ops in phenomd (#120) * Torch dependency fix into dev (#124) * udpate poetry lock * increment version once more * re add gwpy dev dep * update poetry lock * fix poetry conflic * fix poety conflict * Poetry fix (#126) * remove gwpy dep for now * poetry lock file --------- Co-authored-by: Deep Chatterjee <[email protected]> Co-authored-by: wbenoit26 <[email protected]> Co-authored-by: William Benoit <[email protected]> Co-authored-by: William Benoit <[email protected]> * increment version (#127) --------- Co-authored-by: wbenoit26 <[email protected]> Co-authored-by: Deep Chatterjee <[email protected]> Co-authored-by: William Benoit <[email protected]> Co-authored-by: William Benoit <[email protected]> * Fix Distributions module typehints (#129) * fix type hints in dists * fix type hints in dists * Added ringdown waveform function * fix prefactor four thirds * Ringdown Waveform updated * Updated Ringdown with added precision and equation changes * Added KAGRA geometry * Add test for K1 geometry * Fix pre-commit issue * Restrict numpy * Unrestrict numpy * Update tox settings * Added GroupNorm1D dimension check * fix thinko in variable names (#135) * Refactor TaylorF2 and IMRPhenomD (#137) * refactor taylorf2 and phenom_d into a torch module * subtract 3PN terms from phasing and Dphasing * update tolerance for phenomd tests * Added ringdown * fixed negative time issues * Add IMRPhenomPv2 waveform (#138) * add IMRPhenomPv2 torch implementation * perform phase correction term by taking finite difference derivative * add tests for phenomPv2 * added constants * use dtype of parameters for sg waveform * Fixed constants import * Allow arbitrary window in `SpectralDensity` (#140) * allow arbitrary window function * allow arbitrary window function * update error message * revert spectral transform filename * delete test_spectral * constrain numpy version to <2.0.0 (#145) * Use .mT instead of .T (#147) * update to use .mT instead of .T * update taylorf2 to use .mT * Docstring for phenom_d, phenom_p, tf2 (#148) * docstring for phenom_d, phenom_p, tf2 * update docs for consistency * add units to some params * forward/__call__ and cross/plus refactor (#149) * remove .forward and call directly * update sine gaussian to use forward instead of __call__ * update ringdown to use forward instead of __call__ * swap cross and plus polarisations in return. now returns (cross, plus) * Resolve main conflicts (#151) * Adding funding statement * Add requirements to docs (#107) * add requirements to docs * remove myst_parser req * Update requirements.txt * Update conf.py * Update docs.yaml (#108) * Update docs.yaml * Update docs.yaml (#109) * simplify torch dependencies (#122) * simplify torch dependency * remove python constratints * fix pre-commit * Merge `dev` into `main` for 0.4.2 release (#123) * re-implement distributions using torch.distributions (#110) * loosely re-implement distributions using torch.distributions * further enhancement * rename * log_uniform as transformed dist * delete uniform and log_normal since they are already implemented * bring back LogNormal, change parameter sampler * add delta function distribution, minor fixes to other distributions * fix * Initial commit of q-transform * Added a MultiQTransform * Reparameterized to number of t and f bins * Re-factored SingleQTransform to allow for eventually using different q's for batch/channels * Changed interpolation to better match gwpy and added option to specify frequency range in which to search for max energy tile * Added QScan to __init__ * Changed from torch median to quantile to match numpy median * Added documentation to qtransform * Added gwpy to dev dependencies * Added tests and corrected get_freqs bug * Changed method of normalization and updated documentation * Updated more documentation and changed interpolation method * Changed how interpolation shape is parameterized * Reverted documentation to state expectation of 3D input * Fixed type hint for spectrogram_shape in qtransform * Switched tuple to Tuple * `InMemoryDataset` improvements (#119) * in memory dataset inherits from torch iterable dataset * pre-commit issues * fix yielding logic * fix in memory dataset tests * handle deprecated transpose ops in phenomd (#120) * Torch dependency fix into dev (#124) * udpate poetry lock * increment version once more * re add gwpy dev dep * update poetry lock * fix poetry conflic * fix poety conflict * Poetry fix (#126) * remove gwpy dep for now * poetry lock file --------- Co-authored-by: Deep Chatterjee <[email protected]> Co-authored-by: wbenoit26 <[email protected]> Co-authored-by: William Benoit <[email protected]> Co-authored-by: William Benoit <[email protected]> * increment version (#127) --------- Co-authored-by: wbenoit26 <[email protected]> Co-authored-by: Deep Chatterjee <[email protected]> Co-authored-by: William Benoit <[email protected]> Co-authored-by: William Benoit <[email protected]> * resolve merge conflicts * resolve merge conflicts --------- Co-authored-by: Deep Chatterjee <[email protected]> Co-authored-by: wbenoit26 <[email protected]> Co-authored-by: William Benoit <[email protected]> Co-authored-by: William Benoit <[email protected]> Co-authored-by: Trevor William Blodgett <[email protected]> Co-authored-by: Ravi Kumar <[email protected]> Co-authored-by: Trevor William Blodgett <[email protected]> Co-authored-by: Ravi Kumar <[email protected]>
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