From 8d6ef346a019c9ee8e6abdd5408447e07dc89c6f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adri=C3=A1n=20Diaz?= Date: Thu, 22 Jan 2026 10:49:28 +0100 Subject: [PATCH 01/21] Adding logs --- constava/calc/calculator.py | 19 ++++++++++++++++--- constava/calc/subsampling.py | 4 +++- 2 files changed, 19 insertions(+), 4 deletions(-) diff --git a/constava/calc/calculator.py b/constava/calc/calculator.py index bf7ded9..582edac 100644 --- a/constava/calc/calculator.py +++ b/constava/calc/calculator.py @@ -3,6 +3,7 @@ a protein ensemble """ +import logging from typing import List import tqdm from .subsampling import SubsamplingABC, SubsamplingMethodError @@ -10,6 +11,8 @@ from ..utils.ensembles import ProteinEnsemble from ..utils.results import ConstavaResults, ConstavaResultsEntry +# The logger for the wrapper +logger = logging.getLogger("Constava") def check_subsampling_methods(func): """Decorator for ConfStateCalculator that checks, if appropriate subsampling @@ -64,19 +67,29 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: A list of ConstavaResults, each representing the same ProteinEnsemble but calculated with one or more subsampling methods. """ + + logger.debug(f"Instantiating Constava results for each method ({len(self.methods)} methods)...") + results = [ ConstavaResults(method = method.getShortName(), protein = ensemble, state_labels = self.csmodels.get_labels()) for method in self.methods ] + logger.debug(f"Calculating the state propensities and variability for each residue ({ensemble.n_residues}) for each method ({len(self.methods)} methods)...") + for res in tqdm.tqdm(ensemble.get_residues(), total=ensemble.n_residues, unit='residues'): + # Memoisation candidate logpdf = self.csmodels.get_logpdf(res.phipsi) - + + # Parallelisation candidate per method for method, result in zip(self.methods, results): + # Memoisation candidate state_propensities, state_variability = method.calculate(logpdf) + result.add_entry(ConstavaResultsEntry( res, state_propensities, state_variability)) - + + logger.debug(f"Returning the state propensities and variability results ({len(results)} results)...") + return results - diff --git a/constava/calc/subsampling.py b/constava/calc/subsampling.py index a2009a2..68afab9 100644 --- a/constava/calc/subsampling.py +++ b/constava/calc/subsampling.py @@ -49,8 +49,10 @@ def calculate(self, state_logpdfs): """ subsampled_pdf = self._subsampling(state_logpdfs) + state_propensities = self.calculateStatePropensities(subsampled_pdf) state_variability = self.calculateStateVariability(subsampled_pdf) + return state_propensities, state_variability def calculateStatePropensities(self, state_likelihoods): @@ -367,4 +369,4 @@ def calculateStateVariability(self, state_likelihoods): mean_likelihoods = np.mean(state_likelihoods, axis=1) squard_dev = np.sum((state_likelihoods.T - mean_likelihoods) ** 2, axis=1) state_var = np.sqrt(squard_dev) - return state_var \ No newline at end of file + return state_var From 000fb9bd1f34f60bc15df7fca863cf88f23c21b3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adri=C3=A1n=20Diaz?= Date: Thu, 22 Jan 2026 14:16:25 +0100 Subject: [PATCH 02/21] add pools --- constava/calc/calculator.py | 33 +++++++++++++++++++++------------ constava/utils/ensembles.py | 4 ++-- 2 files changed, 23 insertions(+), 14 deletions(-) diff --git a/constava/calc/calculator.py b/constava/calc/calculator.py index 582edac..b9cd0f6 100644 --- a/constava/calc/calculator.py +++ b/constava/calc/calculator.py @@ -3,6 +3,7 @@ a protein ensemble """ +import concurrent import logging from typing import List import tqdm @@ -70,22 +71,23 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: logger.debug(f"Instantiating Constava results for each method ({len(self.methods)} methods)...") - results = [ - ConstavaResults(method = method.getShortName(), protein = ensemble, - state_labels = self.csmodels.get_labels()) - for method in self.methods - ] + results = [ConstavaResults(method = method.getShortName(), protein = ensemble, state_labels = self.csmodels.get_labels()) for method in self.methods] logger.debug(f"Calculating the state propensities and variability for each residue ({ensemble.n_residues}) for each method ({len(self.methods)} methods)...") - + + logpdf_workers = min(ensemble.n_residues, 8) or 1 + logger.debug(f"Calculating log-probability densities of ({ensemble.n_residues}) using {logpdf_workers} max parallel workers ...") + + with tqdm.tqdm(total=ensemble.n_residues, unit="residue") as pbar: + with concurrent.futures.ThreadPoolExecutor(max_workers=logpdf_workers) as executor: + futures = { executor.submit(self._compute_logpdf, res): res for res in ensemble.get_residues() } + + for _future in concurrent.futures.as_completed(futures): + pbar.update(1) + for res in tqdm.tqdm(ensemble.get_residues(), total=ensemble.n_residues, unit='residues'): - # Memoisation candidate - logpdf = self.csmodels.get_logpdf(res.phipsi) - - # Parallelisation candidate per method for method, result in zip(self.methods, results): - # Memoisation candidate - state_propensities, state_variability = method.calculate(logpdf) + state_propensities, state_variability = method.calculate(res.logpdf) result.add_entry(ConstavaResultsEntry( res, state_propensities, state_variability)) @@ -93,3 +95,10 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: logger.debug(f"Returning the state propensities and variability results ({len(results)} results)...") return results + + def _compute_logpdf(self, residue): + logpdf = self.csmodels.get_logpdf(residue.phipsi) + + residue.logpdf = logpdf + + return logpdf diff --git a/constava/utils/ensembles.py b/constava/utils/ensembles.py index bde9abc..8fddffd 100644 --- a/constava/utils/ensembles.py +++ b/constava/utils/ensembles.py @@ -26,13 +26,14 @@ class ResidueEnsemble: phipsi: array[N,2] Array of the phi/psi angles of the residue for all conformations in the ensemble proteinensemble: ProteinEnsemble - Refernece to the ProteinEnsemble-object, the + Reference to the ProteinEnsemble-object, the residue belongs to. """ restype: str = "" respos: int = None phipsi: np.ndarray = None protein = None + logpdf = None @property def restype1(self): @@ -131,4 +132,3 @@ def to_dict(self): "residues": [res.to_dict() for res in self.get_residues()] } return _data - From eadc7c86089d09ed590545effc2f5ebcdf7b2833 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adri=C3=A1n=20Diaz?= Date: Thu, 22 Jan 2026 14:24:29 +0100 Subject: [PATCH 03/21] Use ProcessPoolExecutor --- constava/calc/calculator.py | 69 ++++++++++++++++++++++++------------- 1 file changed, 46 insertions(+), 23 deletions(-) diff --git a/constava/calc/calculator.py b/constava/calc/calculator.py index b9cd0f6..4f3613b 100644 --- a/constava/calc/calculator.py +++ b/constava/calc/calculator.py @@ -3,8 +3,8 @@ a protein ensemble """ -import concurrent import logging +from concurrent.futures import ProcessPoolExecutor, as_completed from typing import List import tqdm from .subsampling import SubsamplingABC, SubsamplingMethodError @@ -15,6 +15,17 @@ # The logger for the wrapper logger = logging.getLogger("Constava") +_LOGPDF_WORKER_CSMODEL = None + +def _init_logpdf_worker(csmodel: ConfStateModelABC): + """Initializer to share the conformational state model with worker processes.""" + global _LOGPDF_WORKER_CSMODEL + _LOGPDF_WORKER_CSMODEL = csmodel + +def _compute_logpdf_worker(phipsi): + """Worker function executed in separate processes to compute logpdf.""" + return _LOGPDF_WORKER_CSMODEL.get_logpdf(phipsi) + def check_subsampling_methods(func): """Decorator for ConfStateCalculator that checks, if appropriate subsampling methods have been defined. @@ -70,35 +81,47 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: """ logger.debug(f"Instantiating Constava results for each method ({len(self.methods)} methods)...") - - results = [ConstavaResults(method = method.getShortName(), protein = ensemble, state_labels = self.csmodels.get_labels()) for method in self.methods] + results = [ + ConstavaResults( + method=method.getShortName(), + protein=ensemble, + state_labels=self.csmodels.get_labels() + ) + for method in self.methods + ] - logger.debug(f"Calculating the state propensities and variability for each residue ({ensemble.n_residues}) for each method ({len(self.methods)} methods)...") - - logpdf_workers = min(ensemble.n_residues, 8) or 1 - logger.debug(f"Calculating log-probability densities of ({ensemble.n_residues}) using {logpdf_workers} max parallel workers ...") + residues = list(ensemble.get_residues()) + logger.debug(f"Calculating the state propensities and variability for each residue ({len(residues)}) for each method ({len(self.methods)} methods)...") + + logpdf_workers = min(len(residues), 8) or 1 + logger.debug(f"Calculating log-probability densities of ({len(residues)}) residues using {logpdf_workers} process workers ...") + + logpdfs = [None] * len(residues) - with tqdm.tqdm(total=ensemble.n_residues, unit="residue") as pbar: - with concurrent.futures.ThreadPoolExecutor(max_workers=logpdf_workers) as executor: - futures = { executor.submit(self._compute_logpdf, res): res for res in ensemble.get_residues() } - - for _future in concurrent.futures.as_completed(futures): + with tqdm.tqdm(total=len(residues), unit="residue") as pbar: + with ProcessPoolExecutor( + max_workers=logpdf_workers, + initializer=_init_logpdf_worker, + initargs=(self.csmodels,) + ) as executor: + futures = { + executor.submit(_compute_logpdf_worker, residue.phipsi): idx + for idx, residue in enumerate(residues) + } + for future in as_completed(futures): + idx = futures[future] + logpdfs[idx] = future.result() pbar.update(1) - - for res in tqdm.tqdm(ensemble.get_residues(), total=ensemble.n_residues, unit='residues'): + + logger.debug("Finished computing log-probability densities, aggregating per-method results...") + + for res, logpdf in tqdm.tqdm(zip(residues, logpdfs), total=len(residues), unit='residues'): for method, result in zip(self.methods, results): - state_propensities, state_variability = method.calculate(res.logpdf) - + state_propensities, state_variability = method.calculate(logpdf) + result.add_entry(ConstavaResultsEntry( res, state_propensities, state_variability)) logger.debug(f"Returning the state propensities and variability results ({len(results)} results)...") return results - - def _compute_logpdf(self, residue): - logpdf = self.csmodels.get_logpdf(residue.phipsi) - - residue.logpdf = logpdf - - return logpdf From 646f5e08337c67a512041e342152c1daa7e7f0e6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adri=C3=A1n=20Diaz?= Date: Thu, 22 Jan 2026 14:39:38 +0100 Subject: [PATCH 04/21] Simplify code --- constava/calc/calculator.py | 19 +++++++++++-------- constava/utils/ensembles.py | 1 - 2 files changed, 11 insertions(+), 9 deletions(-) diff --git a/constava/calc/calculator.py b/constava/calc/calculator.py index 4f3613b..6178ffc 100644 --- a/constava/calc/calculator.py +++ b/constava/calc/calculator.py @@ -5,6 +5,7 @@ import logging from concurrent.futures import ProcessPoolExecutor, as_completed +import multiprocessing from typing import List import tqdm from .subsampling import SubsamplingABC, SubsamplingMethodError @@ -91,14 +92,15 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: ] residues = list(ensemble.get_residues()) - logger.debug(f"Calculating the state propensities and variability for each residue ({len(residues)}) for each method ({len(self.methods)} methods)...") + n_residues = ensemble.n_residues + logger.debug(f"Calculating the state propensities and variability for each residue ({n_residues}) for each method ({len(self.methods)} methods)...") - logpdf_workers = min(len(residues), 8) or 1 - logger.debug(f"Calculating log-probability densities of ({len(residues)}) residues using {logpdf_workers} process workers ...") + logpdf_workers = min(n_residues, multiprocessing.cpu_count()) + logger.debug(f"Calculating log-probability densities of ({n_residues}) residues using {logpdf_workers} process workers ...") - logpdfs = [None] * len(residues) + logpdfs = [None] * n_residues - with tqdm.tqdm(total=len(residues), unit="residue") as pbar: + with tqdm.tqdm(total=n_residues, unit="residue") as pbar: with ProcessPoolExecutor( max_workers=logpdf_workers, initializer=_init_logpdf_worker, @@ -115,12 +117,13 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: logger.debug("Finished computing log-probability densities, aggregating per-method results...") - for res, logpdf in tqdm.tqdm(zip(residues, logpdfs), total=len(residues), unit='residues'): + for res, logpdf in tqdm.tqdm(zip(residues, logpdfs), total=n_residues, unit='residues'): for method, result in zip(self.methods, results): state_propensities, state_variability = method.calculate(logpdf) - result.add_entry(ConstavaResultsEntry( - res, state_propensities, state_variability)) + result.add_entry( + ConstavaResultsEntry(res, state_propensities, state_variability) + ) logger.debug(f"Returning the state propensities and variability results ({len(results)} results)...") diff --git a/constava/utils/ensembles.py b/constava/utils/ensembles.py index 8fddffd..c661bbd 100644 --- a/constava/utils/ensembles.py +++ b/constava/utils/ensembles.py @@ -33,7 +33,6 @@ class ResidueEnsemble: respos: int = None phipsi: np.ndarray = None protein = None - logpdf = None @property def restype1(self): From c4fe091ff8945a2d5939467159309c328f05c9b7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adri=C3=A1n=20Diaz?= Date: Thu, 22 Jan 2026 15:00:57 +0100 Subject: [PATCH 05/21] Better logs --- constava/calc/calculator.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/constava/calc/calculator.py b/constava/calc/calculator.py index 6178ffc..1bfa375 100644 --- a/constava/calc/calculator.py +++ b/constava/calc/calculator.py @@ -93,10 +93,9 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: residues = list(ensemble.get_residues()) n_residues = ensemble.n_residues - logger.debug(f"Calculating the state propensities and variability for each residue ({n_residues}) for each method ({len(self.methods)} methods)...") logpdf_workers = min(n_residues, multiprocessing.cpu_count()) - logger.debug(f"Calculating log-probability densities of ({n_residues}) residues using {logpdf_workers} process workers ...") + logger.debug(f"Calculating log-probability densities of ({n_residues}) residues using {logpdf_workers} process workers...") logpdfs = [None] * n_residues @@ -110,14 +109,15 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: executor.submit(_compute_logpdf_worker, residue.phipsi): idx for idx, residue in enumerate(residues) } + for future in as_completed(futures): idx = futures[future] logpdfs[idx] = future.result() pbar.update(1) - logger.debug("Finished computing log-probability densities, aggregating per-method results...") + logger.debug(f"Calculating the state propensities and variability for each residue ({n_residues}) using the {len(self.methods)} available methods...") - for res, logpdf in tqdm.tqdm(zip(residues, logpdfs), total=n_residues, unit='residues'): + for res, logpdf in zip(residues, logpdfs): for method, result in zip(self.methods, results): state_propensities, state_variability = method.calculate(logpdf) From a76bde6aee55504e332404e9e9f3a86604cb61e6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adri=C3=A1n=20Diaz?= Date: Thu, 22 Jan 2026 16:16:33 +0100 Subject: [PATCH 06/21] execute methods in parallel too --- constava/calc/calculator.py | 67 ++++++++++++++++++++++-------------- constava/calc/subsampling.py | 7 ++++ 2 files changed, 48 insertions(+), 26 deletions(-) diff --git a/constava/calc/calculator.py b/constava/calc/calculator.py index 1bfa375..57c44af 100644 --- a/constava/calc/calculator.py +++ b/constava/calc/calculator.py @@ -1,4 +1,4 @@ -"""constava.calculator contains the calculator class that calculates the +"""constava.calculator contains the calculator class that calculates the conformational state propensities and conformational state variability from a protein ensemble """ @@ -18,14 +18,30 @@ _LOGPDF_WORKER_CSMODEL = None -def _init_logpdf_worker(csmodel: ConfStateModelABC): +def _init_logpdf_worker(csmodel: ConfStateModelABC, methods: List[SubsamplingABC]): """Initializer to share the conformational state model with worker processes.""" global _LOGPDF_WORKER_CSMODEL + global _LOGPDF_WORKER_METHODS + _LOGPDF_WORKER_CSMODEL = csmodel + _LOGPDF_WORKER_METHODS = methods def _compute_logpdf_worker(phipsi): """Worker function executed in separate processes to compute logpdf.""" - return _LOGPDF_WORKER_CSMODEL.get_logpdf(phipsi) + logpdf = _LOGPDF_WORKER_CSMODEL.get_logpdf(phipsi) + + result = { "logpdf": logpdf } + + for method in _LOGPDF_WORKER_METHODS: + state_propensities, state_variability = method.calculate(logpdf) + + result[method.getShortName()] = { + "state_propensities": state_propensities, + "state_variability": state_variability + } + + return result + def check_subsampling_methods(func): """Decorator for ConfStateCalculator that checks, if appropriate subsampling @@ -44,7 +60,7 @@ def __inner(self, *args, **kwargs): class ConfStateCalculator: def __init__(self, csmodels: ConfStateModelABC, methods: List[SubsamplingABC] = None): - """Initializes the calcualtor class with given conformational state + """Initializes the calcualtor class with given conformational state models (csmodels) and zero or more subsampling methods. Parameters: @@ -64,10 +80,10 @@ def add_method(self, new_method: SubsamplingABC): @check_subsampling_methods def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: - """Calculates conformational state propensities and conformational - state variabilites based on the given model and methods for the + """Calculates conformational state propensities and conformational + state variabilites based on the given model and methods for the ProteinEnsemble. - + Parameters: ----------- ensemble : ProteinEnsemble @@ -77,10 +93,10 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: Returns: -------- results : List[ConstavaResults] - A list of ConstavaResults, each representing the same + A list of ConstavaResults, each representing the same ProteinEnsemble but calculated with one or more subsampling methods. """ - + logger.debug(f"Instantiating Constava results for each method ({len(self.methods)} methods)...") results = [ ConstavaResults( @@ -93,38 +109,37 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: residues = list(ensemble.get_residues()) n_residues = ensemble.n_residues + logpdf_workers = min(4, multiprocessing.cpu_count()) + logpdf_results = {} - logpdf_workers = min(n_residues, multiprocessing.cpu_count()) - logger.debug(f"Calculating log-probability densities of ({n_residues}) residues using {logpdf_workers} process workers...") - - logpdfs = [None] * n_residues - with tqdm.tqdm(total=n_residues, unit="residue") as pbar: with ProcessPoolExecutor( max_workers=logpdf_workers, initializer=_init_logpdf_worker, - initargs=(self.csmodels,) + initargs=(self.csmodels, self.methods) ) as executor: futures = { executor.submit(_compute_logpdf_worker, residue.phipsi): idx for idx, residue in enumerate(residues) } - + for future in as_completed(futures): idx = futures[future] - logpdfs[idx] = future.result() + logpdf_results[idx] = future.result() pbar.update(1) - logger.debug(f"Calculating the state propensities and variability for each residue ({n_residues}) using the {len(self.methods)} available methods...") - - for res, logpdf in zip(residues, logpdfs): - for method, result in zip(self.methods, results): - state_propensities, state_variability = method.calculate(logpdf) + for idx, res in enumerate(residues): + res_logpdf_results = logpdf_results[idx] + for result in results: + current_method = result.method + result.add_entry( - ConstavaResultsEntry(res, state_propensities, state_variability) + ConstavaResultsEntry( + res, + res_logpdf_results[current_method]["state_propensities"], + res_logpdf_results[current_method]["state_variability"] + ) ) - - logger.debug(f"Returning the state propensities and variability results ({len(results)} results)...") - + return results diff --git a/constava/calc/subsampling.py b/constava/calc/subsampling.py index 68afab9..bbab6c1 100644 --- a/constava/calc/subsampling.py +++ b/constava/calc/subsampling.py @@ -168,6 +168,9 @@ def _subsampling(self, logpdf): pdf = np.exp(logpdf) pdf /= np.sum(pdf, axis=0) return pdf + + def __str__(self): + return "SubsamplingWindow" class SubsamplingBootstrap(SubsamplingABC): @@ -256,6 +259,10 @@ def _subsampling(self, logpdf): pdf = np.exp(logpdf) pdf /= np.sum(pdf, axis=0) return pdf + + def __str__(self): + return "SubsamplingBootstrap" + class SubsamplingWindowSeries(SubsamplingWindow): From 5b99df3fe6d8d79b6c475fde4025552c99fae3e0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adri=C3=A1n=20Diaz?= Date: Thu, 22 Jan 2026 16:37:37 +0100 Subject: [PATCH 07/21] improvements --- constava/calc/calculator.py | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/constava/calc/calculator.py b/constava/calc/calculator.py index 57c44af..6634754 100644 --- a/constava/calc/calculator.py +++ b/constava/calc/calculator.py @@ -3,9 +3,9 @@ a protein ensemble """ +from collections import defaultdict import logging from concurrent.futures import ProcessPoolExecutor, as_completed -import multiprocessing from typing import List import tqdm from .subsampling import SubsamplingABC, SubsamplingMethodError @@ -30,7 +30,7 @@ def _compute_logpdf_worker(phipsi): """Worker function executed in separate processes to compute logpdf.""" logpdf = _LOGPDF_WORKER_CSMODEL.get_logpdf(phipsi) - result = { "logpdf": logpdf } + result = dict() for method in _LOGPDF_WORKER_METHODS: state_propensities, state_variability = method.calculate(logpdf) @@ -109,14 +109,12 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: residues = list(ensemble.get_residues()) n_residues = ensemble.n_residues - logpdf_workers = min(4, multiprocessing.cpu_count()) - logpdf_results = {} + logpdf_results = [None] * n_residues + with tqdm.tqdm(total=n_residues, unit="residue") as pbar: with ProcessPoolExecutor( - max_workers=logpdf_workers, - initializer=_init_logpdf_worker, - initargs=(self.csmodels, self.methods) + initializer=_init_logpdf_worker, initargs=(self.csmodels, self.methods) ) as executor: futures = { executor.submit(_compute_logpdf_worker, residue.phipsi): idx @@ -126,9 +124,10 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: for future in as_completed(futures): idx = futures[future] logpdf_results[idx] = future.result() + pbar.update(1) - for idx, res in enumerate(residues): + for idx, residue in enumerate(residues): res_logpdf_results = logpdf_results[idx] for result in results: @@ -136,7 +135,7 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: result.add_entry( ConstavaResultsEntry( - res, + residue, res_logpdf_results[current_method]["state_propensities"], res_logpdf_results[current_method]["state_variability"] ) From bfe055e079f3184ea4f9caf7d5ab01717838c10c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adri=C3=A1n=20Diaz?= Date: Fri, 23 Jan 2026 14:18:45 +0100 Subject: [PATCH 08/21] Support 3.14, use 32-bit floats, better parallel calls --- constava/calc/calculator.py | 86 +++++++++++++++-------- constava/calc/subsampling.py | 131 ++++++++++++++++++----------------- constava/io/rstrategies.py | 19 +++-- constava/utils/logging.py | 4 +- constava/wrapper/params.py | 6 +- constava/wrapper/wrapper.py | 3 + 6 files changed, 150 insertions(+), 99 deletions(-) diff --git a/constava/calc/calculator.py b/constava/calc/calculator.py index 6634754..3ac045c 100644 --- a/constava/calc/calculator.py +++ b/constava/calc/calculator.py @@ -3,10 +3,13 @@ a protein ensemble """ -from collections import defaultdict +import os +import multiprocessing import logging -from concurrent.futures import ProcessPoolExecutor, as_completed +from concurrent.futures import ProcessPoolExecutor, wait, FIRST_COMPLETED from typing import List +import numpy as np + import tqdm from .subsampling import SubsamplingABC, SubsamplingMethodError from .csmodels import ConfStateModelABC @@ -16,23 +19,13 @@ # The logger for the wrapper logger = logging.getLogger("Constava") -_LOGPDF_WORKER_CSMODEL = None - -def _init_logpdf_worker(csmodel: ConfStateModelABC, methods: List[SubsamplingABC]): - """Initializer to share the conformational state model with worker processes.""" - global _LOGPDF_WORKER_CSMODEL - global _LOGPDF_WORKER_METHODS - - _LOGPDF_WORKER_CSMODEL = csmodel - _LOGPDF_WORKER_METHODS = methods - -def _compute_logpdf_worker(phipsi): +def _self_compute_logpdf_worker(phipsi: np.ndarray, csmodel: ConfStateModelABC, methods: List[SubsamplingABC]): """Worker function executed in separate processes to compute logpdf.""" - logpdf = _LOGPDF_WORKER_CSMODEL.get_logpdf(phipsi) + logpdf = csmodel.get_logpdf(phipsi) result = dict() - for method in _LOGPDF_WORKER_METHODS: + for method in methods: state_propensities, state_variability = method.calculate(logpdf) result[method.getShortName()] = { @@ -111,22 +104,59 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: n_residues = ensemble.n_residues logpdf_results = [None] * n_residues + # import ipdb; ipdb.set_trace() - with tqdm.tqdm(total=n_residues, unit="residue") as pbar: - with ProcessPoolExecutor( - initializer=_init_logpdf_worker, initargs=(self.csmodels, self.methods) - ) as executor: - futures = { - executor.submit(_compute_logpdf_worker, residue.phipsi): idx - for idx, residue in enumerate(residues) - } - - for future in as_completed(futures): - idx = futures[future] - logpdf_results[idx] = future.result() + try: + max_workers = os.process_cpu_count() + except AttributeError: + max_workers = multiprocessing.cpu_count() + + max_in_flight = max_workers * 4 # tweak: 1–4x workers is usually sane + + logger.debug(f"Starting the parallel inference of log-probability densities and calculations for propensities & variability...") + with ProcessPoolExecutor(max_workers=max_workers) as ex: + it = iter(enumerate(residues)) + in_flight = set() + + logger.debug(f"Process Pool Executor is ready to start: max_in_flight={max_in_flight}, max_workers={max_workers}") + + with tqdm.tqdm(total=n_residues, unit="residue") as progress_bar: + + # Prime the pipeline + for _ in range(min(max_in_flight, n_residues)): + idx, residue = next(it) + phipsi = np.ascontiguousarray(residue.phipsi, dtype=np.float32) - pbar.update(1) + fut = ex.submit(_self_compute_logpdf_worker, phipsi, self.csmodels, self.methods) + + fut.idx = idx # attach index (simple and cheap) + in_flight.add(fut) + completed = 0 + while in_flight: + done, in_flight = wait(in_flight, return_when=FIRST_COMPLETED) + + for fut in done: + idx = fut.idx + logpdf_results[idx] = fut.result() + + progress_bar.update(1) + completed += 1 + + # refill + try: + idx, residue = next(it) + except StopIteration: + continue + + phipsi = np.ascontiguousarray(residue.phipsi, dtype=np.float32) + + nfut = ex.submit(_self_compute_logpdf_worker, phipsi, self.csmodels, self.methods) + + nfut.idx = idx + in_flight.add(nfut) + + logger.debug(f"Building the results objects...") for idx, residue in enumerate(residues): res_logpdf_results = logpdf_results[idx] diff --git a/constava/calc/subsampling.py b/constava/calc/subsampling.py index bbab6c1..82b4c9d 100644 --- a/constava/calc/subsampling.py +++ b/constava/calc/subsampling.py @@ -1,5 +1,5 @@ """constava.methods contains classes representing subsampling schemes fro the -probability state propensity calculations. Multiple of these schemes can be +probability state propensity calculations. Multiple of these schemes can be dynamically defined on run-time.""" import abc @@ -11,10 +11,10 @@ class SubsamplingMethodError(ValueError): pass class SubsamplingABC(metaclass=abc.ABCMeta): - """Base class to subsample the logPDF values obtained from the probabilistic - conformational state models and calculate the conformational state + """Base class to subsample the logPDF values obtained from the probabilistic + conformational state models and calculate the conformational state propensities and conformational state variability. - + Methods: -------- calculate(state_logpdfs) @@ -27,7 +27,7 @@ class SubsamplingABC(metaclass=abc.ABCMeta): getShortName() Name of the method for reference in the output. _subsampling(state_logpdfs) - Subsamples from the distribution of original data points. + Sub-samples from the distribution of original data points. """ def calculate(self, state_logpdfs): """Calculates the conformational state likelihoods and conformational @@ -40,19 +40,19 @@ def calculate(self, state_logpdfs): Returns: -------- - state_propensities : Array[M] - Average likelihood for samples to fall in any + state_propensities : Array[M] + Average likelihood for samples to fall in any of the M states state_variability : float variability fo the state propensities throughout the sampling - + """ subsampled_pdf = self._subsampling(state_logpdfs) - + state_propensities = self.calculateStatePropensities(subsampled_pdf) state_variability = self.calculateStateVariability(subsampled_pdf) - + return state_propensities, state_variability def calculateStatePropensities(self, state_likelihoods): @@ -60,23 +60,24 @@ def calculateStatePropensities(self, state_likelihoods): return np.mean(state_likelihoods, axis=1) def calculateStateVariability(self, state_likelihoods): - """Calculation of the variability in the conformational states. This is - calculated as the RMSF (root mean square fluctuation) of the state + """Calculation of the variability in the conformational states. This is + calculated as the RMSF (root mean square fluctuation) of the state likelihoods across all samples. Parameters: ----------- state_likelihoods : Array[M,N] Likelihoods for each of the M states along N samples. - + Returns: -------- state_var : float Conformational state variability """ mean_likelihoods = np.mean(state_likelihoods, axis=1) - squard_dev = np.sum((state_likelihoods.T - mean_likelihoods) ** 2, axis=1) + squard_dev = np.sum(np.square(state_likelihoods.T - mean_likelihoods), axis=1) state_var = np.sqrt(np.mean(squard_dev)) + return state_var @abc.abstractmethod @@ -86,34 +87,34 @@ def getShortName(self) -> str: @abc.abstractmethod def _subsampling(self, logpdf): - """Method used to subsample from the distribution of logPDF values and - convert them into individual liklihoods for each conformational state + """Method used to subsample from the distribution of logPDF values and + convert them into individual likelihoods for each conformational state model. - + Parameters: ----------- logpdf : Array[M,X] log-probability densities for M states across X original data points. - + Retruns: -------- pdf : Array[M,N] - Likelihoods obtained by subsampling N times using the described method. + Likelihoods obtained by sub-sampling N times using the described method. """ pass class SubsamplingWindow(SubsamplingABC): - """Class to subsample the logPDF values obtained from the probabilistic - conformational state models and calculate the conformational state - propensities and conformational state variability. Subsampling is done + """Class to subsample the logPDF values obtained from the probabilistic + conformational state models and calculate the conformational state + propensities and conformational state variability. Sub-sampling is done using a sliding window. Attributes: ----------- window_size : int - Size of the sliding window used in subsampling. - + Size of the sliding window used in sub-sampling. + Methods: -------- calculate(state_logpdfs) @@ -126,11 +127,11 @@ class SubsamplingWindow(SubsamplingABC): getShortName() Name of the method for reference in the output. _subsampling(state_logpdfs) - Subsamples from the distribution of original data points. + Sub-samples from the distribution of original data points. """ def __init__(self, window_size: int): - """Inititialize class to subsample and calcualte conformational state - propensities and conformational state variability. Subsampling is done + """Inititialize class to subsample and calcualte conformational state + propensities and conformational state variability. Sub-sampling is done using a sliding window. Parameters: @@ -145,38 +146,40 @@ def getShortName(self) -> str: return f"window/{self.window_size:d}/" def _subsampling(self, logpdf): - """Subsampling from the distribution of logPDF using a sliding window of + """Sub-sampling from the distribution of logPDF using a sliding window of size `window_size`. With a `window_size == 1`, this effectively uses the - original data points. Finally, liklihoods for each conformational state + original data points. Finally, likelihoods for each conformational state model are calculated for each sample. - + Parameters: ----------- logpdf : Array[M,X] log-Probability densities for M states across X original data points. - - Retruns: + + Returns: -------- pdf : Array[M,N] - Likelihoods obtained by subsampling N times using the described method. + Likelihoods obtained by sub-sampling N times using the described method. """ # Subsampling using consecutive windows of window_size samples logpdf = np.stack([ - np.convolve(x, np.ones((self.window_size,)), mode="valid") - for x in logpdf]) - # Exponentiate and normalize to obtain likelihoods + np.convolve(x, np.ones((self.window_size,)), mode="valid") for x in logpdf + ]) + + # Exponentiation and normalize to obtain likelihoods pdf = np.exp(logpdf) pdf /= np.sum(pdf, axis=0) + return pdf - + def __str__(self): return "SubsamplingWindow" class SubsamplingBootstrap(SubsamplingABC): - """Class to subsample the logPDF values obtained from the probabilistic - conformational state models and calculate the conformational state - propensities and conformational state variability. Subsampling is done + """Class to subsample the logPDF values obtained from the probabilistic + conformational state models and calculate the conformational state + propensities and conformational state variability. Subsampling is done using bootstrapping. Attributes: @@ -187,7 +190,7 @@ class SubsamplingBootstrap(SubsamplingABC): Number of samples to bootstrap. seed: int Random seed used during bootstrapping - + Methods: -------- calculate(state_logpdfs) @@ -203,8 +206,8 @@ class SubsamplingBootstrap(SubsamplingABC): Subsamples from the distribution of original data points. """ def __init__(self, sample_size: int, n_samples = 500, seed: Optional[int] = None): - """Inititialize class to subsample and calcualte conformational state - propensities and conformational state variability. Subsampling is done + """Inititialize class to subsample and calcualte conformational state + propensities and conformational state variability. Subsampling is done using bootstrapping. Parameters: @@ -226,17 +229,17 @@ def getShortName(self) -> str: self.sample_size, self.n_samples, self.seed or "") def _subsampling(self, logpdf): - """Subsampling from the distribution of logPDF using bootstrapping. - `n_samples` are subsampled from the distribution, where each sample - contains `sample_size`randomly selected original data points. Finally, - liklihoods for each conformational state model are calculated for each + """Subsampling from the distribution of logPDF using bootstrapping. + `n_samples` are subsampled from the distribution, where each sample + contains `sample_size`randomly selected original data points. Finally, + liklihoods for each conformational state model are calculated for each sample. - + Parameters: ----------- logpdf : Array[M,X] log-Probability densities for M states across X original data points. - + Retruns: -------- pdf : Array[M,N] @@ -244,38 +247,42 @@ def _subsampling(self, logpdf): """ # Get dimensions of the input data n_states, n_measurements = logpdf.shape + # Randomly select the samples by bootstrapping, with each # sample containing exactly measurements from the original # distribution. -> Array[n_states, n_samples, sample_size] rng = np.random.default_rng(self.seed) - samples = rng.integers(n_measurements, size=self.sample_size*self.n_samples) + samples = rng.integers(n_measurements, size=self.sample_size * self.n_samples) logpdf = np.reshape( - logpdf[:,samples], (n_states, self.n_samples, self.sample_size), + logpdf[:,samples], (n_states, self.n_samples, self.sample_size), order="C") - # Accumulate logpdfs within a sample = logpdf for each of these samples + + # Accumulate logpdfs within a sample = logpdf for each of these samples # to be sampled from the same conformational state logpdf = np.sum(logpdf, axis=2) + # Exponentiate and normalize to obtain likelihoods pdf = np.exp(logpdf) pdf /= np.sum(pdf, axis=0) + return pdf - + def __str__(self): return "SubsamplingBootstrap" class SubsamplingWindowSeries(SubsamplingWindow): - """Class to subsample the logPDF values obtained from the probabilistic - conformational state models and calculate the conformational state - propensities and conformational state variability. Subsampling is done + """Class to subsample the logPDF values obtained from the probabilistic + conformational state models and calculate the conformational state + propensities and conformational state variability. Subsampling is done using a sliding window. For each window the results are returned. Attributes: ----------- window_size : int Size of the sliding window used in subsampling. - + Methods: -------- calculate(state_logpdfs) @@ -294,7 +301,7 @@ class SubsamplingWindowSeries(SubsamplingWindow): def getShortName(self) -> str: """Name of the method for reference in the output.""" return f"window_series/{self.window_size:d}/" - + def calculateStatePropensities(self, state_likelihoods): """Calculates the conformational state likelihoods for the given sample.""" return state_likelihoods @@ -320,10 +327,10 @@ def calculateStateVariability(self, state_likelihoods): class SubsamplingBootstrapSeries(SubsamplingBootstrap): - """Class to subsample the logPDF values obtained from the probabilistic - conformational state models and calculate the conformational state - propensities and conformational state variability. Subsampling is done - using bootstrapping. For each bootstrapped subsample the results are + """Class to subsample the logPDF values obtained from the probabilistic + conformational state models and calculate the conformational state + propensities and conformational state variability. Subsampling is done + using bootstrapping. For each bootstrapped subsample the results are returned. Attributes: diff --git a/constava/io/rstrategies.py b/constava/io/rstrategies.py index c27baea..c023559 100644 --- a/constava/io/rstrategies.py +++ b/constava/io/rstrategies.py @@ -95,9 +95,11 @@ def readFiles(self, *input_files) -> ProteinEnsemble: unique_residues = data[["ResIndex", "ResName"]].drop_duplicates(inplace=False) residue_list = [] for i, (resid, resname) in unique_residues.iterrows(): - phipsi = data.loc[(data["ResIndex"] == resid) & (data["ResName"] == resname), [phicol, psicol]].to_numpy() + phipsi = data.loc[(data["ResIndex"] == resid) & (data["ResName"] == resname), [phicol, psicol]].to_numpy(dtype=np.float32, copy=False) + if self.degrees: - phipsi = np.radians(phipsi) + phipsi = np.radians(phipsi, dtype=np.float32) + check_dihedral_range(phipsi) # Throws errors/warnings if data is not in radians residue_list.append( ResidueEnsemble(restype=resname, respos=resid, phipsi=phipsi)) @@ -169,10 +171,15 @@ def readFiles(self, *input_files) -> ProteinEnsemble: f"File does not match the structure of `gmx chi` outputs: {filename}")) restype = m.group(1) respos = int(m.group(2)) - phipsi = np.loadtxt(infile, comments=["#", "@"]) + phipsi = np.loadtxt(infile, comments=["#", "@"], dtype=np.float32, copy=False) + if self.degrees: - phipsi = np.radians(phipsi) + phipsi = np.radians(phipsi, dtype=np.float32) + check_dihedral_range(phipsi) # Throws errors/warnings if data is not in radians + residue_list.append( - ResidueEnsemble(restype=restype, respos=respos, phipsi=phipsi)) - return ProteinEnsemble(residue_list) \ No newline at end of file + ResidueEnsemble(restype=restype, respos=respos, phipsi=phipsi) + ) + + return ProteinEnsemble(residue_list) diff --git a/constava/utils/logging.py b/constava/utils/logging.py index cecbedc..a206738 100644 --- a/constava/utils/logging.py +++ b/constava/utils/logging.py @@ -5,9 +5,9 @@ "version" : 1, "formatters" : { "default": { - "format" : "[{asctime}] {message}", + "format" : "%(asctime)s.%(msecs)03d %(levelname)s %(message)s", "datefmt" : "%Y-%m-%d %H:%M:%S", - "style" : "{", + "style" : "%", "validate" : True }, }, diff --git a/constava/wrapper/params.py b/constava/wrapper/params.py index 09ca1a2..a1d5cfb 100644 --- a/constava/wrapper/params.py +++ b/constava/wrapper/params.py @@ -21,7 +21,11 @@ def _inner_(self, __attr, __value): def set_single_as_list(func): """Allows list-attributes (e.g., window) to be set with a single value""" def _inner_(self, __attr, __value): - dtype = typing.get_type_hints(self).get(__attr, None) + # ADIAZ: It works for <=3.13 + # dtype = typing.get_type_hints(self).get(__attr, None) + hints = typing.get_type_hints(type(self)) # or self.__class__ + dtype = hints.get(__attr, None) + if typing.get_origin(dtype) is list: if __value is None: __value = [] diff --git a/constava/wrapper/wrapper.py b/constava/wrapper/wrapper.py index cb3c7f6..24c0d95 100644 --- a/constava/wrapper/wrapper.py +++ b/constava/wrapper/wrapper.py @@ -332,9 +332,11 @@ def initialize_calculator(self, csmodel: ConfStateModelABC = None, # Quickly generate a csmodel if not done before if csmodel is None: csmodel = self.fit_csmodel() + # Initialize the calculator logger.info(f"Initializing calculator with {csmodel}...") calculator = ConfStateCalculator(csmodel) + # Add subsampling methods to calculator for window_size in (window or []): new_method = SubsamplingWindow(window_size) @@ -352,4 +354,5 @@ def initialize_calculator(self, csmodel: ConfStateModelABC = None, new_method = SubsamplingBootstrapSeries(sample_size, bootstrap_samples, seed=bootstrap_seed) logger.info(f"... adding subsampling method: {new_method.getShortName()}") calculator.add_method(new_method) + return calculator From 55764183092524e894d5c4cf9d0d50554fbfddf8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adri=C3=A1n=20Diaz?= Date: Fri, 23 Jan 2026 16:22:11 +0100 Subject: [PATCH 09/21] Add indent param and roll back float32 --- constava/__main__.py | 5 +- constava/calc/calculator.py | 165 ++++++++++++++++++++++-------------- constava/calc/csmodels.py | 33 +++++--- constava/io/rstrategies.py | 8 +- constava/io/writer.py | 7 +- constava/io/wstrategies.py | 10 ++- constava/wrapper/params.py | 3 + constava/wrapper/wrapper.py | 16 ++-- setup.py | 2 +- 9 files changed, 156 insertions(+), 93 deletions(-) diff --git a/constava/__main__.py b/constava/__main__.py index ba32392..ff8d1f2 100644 --- a/constava/__main__.py +++ b/constava/__main__.py @@ -200,6 +200,8 @@ def parse_parameters(cmdline_arguments): degrees.""")) anaMisc.add_argument("--precision", metavar="", type=int, default=4, help="Sets the number of decimals in the output files.") + anaMisc.add_argument("--indent_size", metavar="", type=int, default=0, + help="Sets the number of spaces used to indent the output document.") anaMisc.add_argument("--seed", metavar="", type=int, default=None, required=False, help="Set random seed for bootstrap sampling") anaMisc.add_argument("-v", "--verbose", action="count", default=0, help=tw.dedent( @@ -282,6 +284,7 @@ def run_analyze(args): params.bootstrap_samples = args.bootstrap_samples params.input_degrees = args.degrees params.precision = args.precision + params.indent_size = args.indent_size params.seed = args.seed # Initialze and run Constava cva = Constava(params) @@ -314,4 +317,4 @@ def main(): run_unittest() if __name__ == "__main__": - sys.exit(main()) \ No newline at end of file + sys.exit(main()) diff --git a/constava/calc/calculator.py b/constava/calc/calculator.py index 3ac045c..6242c42 100644 --- a/constava/calc/calculator.py +++ b/constava/calc/calculator.py @@ -3,6 +3,7 @@ a protein ensemble """ +from collections import defaultdict import os import multiprocessing import logging @@ -11,6 +12,7 @@ import numpy as np import tqdm +from tqdm.contrib.logging import logging_redirect_tqdm from .subsampling import SubsamplingABC, SubsamplingMethodError from .csmodels import ConfStateModelABC from ..utils.ensembles import ProteinEnsemble @@ -19,21 +21,31 @@ # The logger for the wrapper logger = logging.getLogger("Constava") -def _self_compute_logpdf_worker(phipsi: np.ndarray, csmodel: ConfStateModelABC, methods: List[SubsamplingABC]): - """Worker function executed in separate processes to compute logpdf.""" +_SELF_WORKER_CSMODEL = None +_SELF_WORKER_METHODS = None + +def _init_self_worker(csmodels, methods): + global _SELF_WORKER_CSMODEL, _SELF_WORKER_METHODS + _SELF_WORKER_CSMODEL = csmodels + _SELF_WORKER_METHODS = methods + +def _self_compute_logpdf_worker(phipsi: np.ndarray): + csmodel = _SELF_WORKER_CSMODEL + methods = _SELF_WORKER_METHODS + logpdf = csmodel.get_logpdf(phipsi) - - result = dict() - - for method in methods: - state_propensities, state_variability = method.calculate(logpdf) - result[method.getShortName()] = { - "state_propensities": state_propensities, - "state_variability": state_variability - } + n_methods = len(methods) + n_states = len(csmodel.get_labels()) + + matrix_results = np.empty((n_methods, n_states + 1)) + + for method_idx, method in enumerate(methods): + state_propensities, state_variability = method.calculate(logpdf) + matrix_results[method_idx, :n_states] = state_propensities + matrix_results[method_idx, n_states] = state_variability - return result + return matrix_results def check_subsampling_methods(func): @@ -100,75 +112,98 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: for method in self.methods ] - residues = list(ensemble.get_residues()) n_residues = ensemble.n_residues - logpdf_results = [None] * n_residues - # import ipdb; ipdb.set_trace() - + logpdf_results = np.empty( + (n_residues, len(self.methods), len(self.csmodels.get_labels()) + 1) + ) + try: max_workers = os.process_cpu_count() except AttributeError: max_workers = multiprocessing.cpu_count() - max_in_flight = max_workers * 4 # tweak: 1–4x workers is usually sane + max_in_flight = max_workers * 3 + future_to_idx = defaultdict() - logger.debug(f"Starting the parallel inference of log-probability densities and calculations for propensities & variability...") - with ProcessPoolExecutor(max_workers=max_workers) as ex: - it = iter(enumerate(residues)) + logger.debug(f"Starting inference of log-probability densities & propens/var") + + with ProcessPoolExecutor( + max_workers=max_workers, + initializer=_init_self_worker, + initargs=(self.csmodels, self.methods) + ) as process_pool_executor: + + it = iter(enumerate(ensemble.get_residues())) in_flight = set() - logger.debug(f"Process Pool Executor is ready to start: max_in_flight={max_in_flight}, max_workers={max_workers}") - - with tqdm.tqdm(total=n_residues, unit="residue") as progress_bar: - - # Prime the pipeline - for _ in range(min(max_in_flight, n_residues)): - idx, residue = next(it) - phipsi = np.ascontiguousarray(residue.phipsi, dtype=np.float32) - - fut = ex.submit(_self_compute_logpdf_worker, phipsi, self.csmodels, self.methods) - - fut.idx = idx # attach index (simple and cheap) - in_flight.add(fut) - - completed = 0 - while in_flight: - done, in_flight = wait(in_flight, return_when=FIRST_COMPLETED) - - for fut in done: - idx = fut.idx - logpdf_results[idx] = fut.result() - - progress_bar.update(1) - completed += 1 - - # refill - try: - idx, residue = next(it) - except StopIteration: - continue - - phipsi = np.ascontiguousarray(residue.phipsi, dtype=np.float32) - - nfut = ex.submit(_self_compute_logpdf_worker, phipsi, self.csmodels, self.methods) - - nfut.idx = idx - in_flight.add(nfut) - - logger.debug(f"Building the results objects...") - for idx, residue in enumerate(residues): + logger.debug(f"Max_in_flight={max_in_flight}, Max_workers={max_workers}") + + with tqdm.tqdm( + total=n_residues, + desc="Residues", + unit="residue", + bar_format="{l_bar}{bar} | {n_fmt}/{total_fmt} " + "[{rate_fmt}, elapsed: {elapsed}, remaining: {remaining}]", + ) as progress_bar: + with logging_redirect_tqdm(): + # Prime the pipeline + for _ in range(min(max_in_flight, n_residues)): + idx, residue = next(it) + phi_psi_angles = np.ascontiguousarray(residue.phipsi) + + fut = process_pool_executor.submit( + _self_compute_logpdf_worker, phi_psi_angles + ) + + future_to_idx[fut] = idx + + in_flight.add(fut) + + completed = 0 + while in_flight: + done, in_flight = wait(in_flight, return_when=FIRST_COMPLETED) + + for fut in done: + idx = future_to_idx.pop(fut) + logpdf_results[idx] = fut.result() + + progress_bar.update(1) + completed += 1 + + # refill + try: + idx, residue = next(it) + phi_psi_angles = np.ascontiguousarray(residue.phipsi) + except StopIteration: + continue + + nfut = process_pool_executor.submit( + _self_compute_logpdf_worker, phi_psi_angles + ) + + # logger.debug(f"Parallel task submitted ({idx}: {residue})") + + future_to_idx[nfut] = idx + + in_flight.add(nfut) + + logger.debug("Building the results objects...") + for idx, residue in enumerate(ensemble.get_residues()): res_logpdf_results = logpdf_results[idx] - for result in results: - current_method = result.method - + for result_idx, result in enumerate(results): + state_propensities = res_logpdf_results[result_idx][:-1] + state_variability = res_logpdf_results[result_idx][-1] + result.add_entry( ConstavaResultsEntry( residue, - res_logpdf_results[current_method]["state_propensities"], - res_logpdf_results[current_method]["state_variability"] + state_propensities, + state_variability ) ) + logger.debug("All the calculations have been done with success!") + return results diff --git a/constava/calc/csmodels.py b/constava/calc/csmodels.py index c6afb92..e8b0576 100644 --- a/constava/calc/csmodels.py +++ b/constava/calc/csmodels.py @@ -133,10 +133,16 @@ def __init__(self, state_labels: List[str], state_kdes: List[KernelDensity]): self.state_kdes = tuple(state_kdes) def get_logpdf(self, data: np.ndarray) -> np.ndarray: - result = np.stack([ - kde.score_samples(data) for kde in self.state_kdes - ]) - return result + X = np.ascontiguousarray(data, dtype=np.float64) + + n_states = len(self.state_kdes) + n_samples = X.shape[0] + + result = np.empty((n_states, n_samples), dtype=np.float64) + for i, kde in enumerate(self.state_kdes): + result[i, :] = kde.score_samples(X) + + return result.astype(np.float32, copy=False) @classmethod def from_fitting(cls, training_data_json: str, *, in_degrees=False, bandwidth=.13, **_): @@ -208,14 +214,21 @@ class ConfStateModelGrid(ConfStateModelABC): def __init__(self, state_labels: List[str], state_grids: np.ndarray, grid_crds: Tuple): self.state_labels = tuple(state_labels) - self.state_grids = state_grids + self.state_grids = [np.ascontiguousarray(g, dtype=np.float64) for g in state_grids] self.grid_crds = grid_crds def get_logpdf(self, data: np.ndarray) -> np.ndarray: - result = np.stack([ - interpn(self.grid_crds, grid, data) for grid in self.state_grids - ]) - return result + X = np.ascontiguousarray(data, dtype=np.float64) + + n_states = len(self.state_grids) + n_samples = X.shape[0] + + result = np.empty((n_states, n_samples), dtype=np.float64) + + for i, grid in enumerate(self.state_grids): + result[i, :] = interpn(self.grid_crds, grid, X) + + return result.astype(np.float32, copy=False) @classmethod def from_fitting(cls, training_data_json: str, *, in_degrees=False, bandwidth=.13, grid_points=10_000, **_): @@ -252,4 +265,4 @@ def from_fitting(cls, training_data_json: str, *, in_degrees=False, bandwidth=.1 kde_model = ConfStateModelKDE.from_fitting(training_data_json, bandwidth=.13, in_degrees=in_degrees) _labels = kde_model.get_labels() _grids = np.reshape(kde_model.get_logpdf(gridcrds), (-1,n,n), order="C") - return cls(state_labels=_labels, state_grids=_grids, grid_crds=(_phi, _psi)) \ No newline at end of file + return cls(state_labels=_labels, state_grids=_grids, grid_crds=(_phi, _psi)) diff --git a/constava/io/rstrategies.py b/constava/io/rstrategies.py index c023559..6b7c8f8 100644 --- a/constava/io/rstrategies.py +++ b/constava/io/rstrategies.py @@ -95,10 +95,10 @@ def readFiles(self, *input_files) -> ProteinEnsemble: unique_residues = data[["ResIndex", "ResName"]].drop_duplicates(inplace=False) residue_list = [] for i, (resid, resname) in unique_residues.iterrows(): - phipsi = data.loc[(data["ResIndex"] == resid) & (data["ResName"] == resname), [phicol, psicol]].to_numpy(dtype=np.float32, copy=False) + phipsi = data.loc[(data["ResIndex"] == resid) & (data["ResName"] == resname), [phicol, psicol]].to_numpy() if self.degrees: - phipsi = np.radians(phipsi, dtype=np.float32) + phipsi = np.radians(phipsi) check_dihedral_range(phipsi) # Throws errors/warnings if data is not in radians residue_list.append( @@ -171,10 +171,10 @@ def readFiles(self, *input_files) -> ProteinEnsemble: f"File does not match the structure of `gmx chi` outputs: {filename}")) restype = m.group(1) respos = int(m.group(2)) - phipsi = np.loadtxt(infile, comments=["#", "@"], dtype=np.float32, copy=False) + phipsi = np.loadtxt(infile, comments=["#", "@"]) if self.degrees: - phipsi = np.radians(phipsi, dtype=np.float32) + phipsi = np.radians(phipsi) check_dihedral_range(phipsi) # Throws errors/warnings if data is not in radians diff --git a/constava/io/writer.py b/constava/io/writer.py index ac05a41..80481a8 100644 --- a/constava/io/writer.py +++ b/constava/io/writer.py @@ -15,10 +15,11 @@ class ResultsWriter: "tsv": TsvWriter, } - def __init__(self, filename: str, format: str = "auto", float_precision: int = 4): + def __init__(self, filename: str, format: str = "auto", float_precision: int = 4, indent_size: int = 0): self.filename = filename self.format = format self.float_precision = float_precision + self.indent_size = indent_size self._strategy = self.get_strategy() def __setattr__(self, __name: str, __value: Any) -> None: @@ -33,10 +34,10 @@ def get_strategy(self): else: __fmt = self.format if __fmt in self.STRATEGIES: - return self.STRATEGIES[__fmt](self.float_precision) + return self.STRATEGIES[__fmt](self.float_precision, self.indent_size) else: raise ValueError(f"Unknown output file format: `{__fmt}`") def write_results(self, results: ConstavaResults): with open(self.filename, "w") as fhandle: - self._strategy.write_results(fhandle, results) \ No newline at end of file + self._strategy.write_results(fhandle, results) diff --git a/constava/io/wstrategies.py b/constava/io/wstrategies.py index a38ec4e..5c43685 100644 --- a/constava/io/wstrategies.py +++ b/constava/io/wstrategies.py @@ -10,8 +10,9 @@ class WriterABC(metaclass=abc.ABCMeta): """Abstract base class for file writers""" - def __init__(self, float_precision: int = 4): + def __init__(self, float_precision: int = 4, indent_size: int = 0): self.float_precision = float_precision + self.indent_size = indent_size @abc.abstractmethod def write_results(self, fhandle: TextIO, results: List[ConstavaResults]): @@ -40,7 +41,10 @@ def write_results(self, fhandle: TextIO, results: List[ConstavaResults]): } __dict["results"][result.method]["Variability"] = csvariability.tolist() - json.dump(__dict, fhandle) + if self.indent_size and self.indent_size > 0: + json.dump(__dict, fhandle, indent=self.indent_size) + else: + json.dump(__dict, fhandle) class CsvWriter(WriterABC): @@ -76,4 +80,4 @@ def _write_entry(self, entry: ConstavaResultsEntry, method: str) -> List: class TsvWriter(CsvWriter): """Writer strategy for writing TSV files (based on CsvWriter)""" - csvdialect = {"delimiter": "\t"} \ No newline at end of file + csvdialect = {"delimiter": "\t"} diff --git a/constava/wrapper/params.py b/constava/wrapper/params.py index a1d5cfb..85a7f9e 100644 --- a/constava/wrapper/params.py +++ b/constava/wrapper/params.py @@ -90,6 +90,8 @@ class ConstavaParameters: Set `True` if the data given under `model_data` to is given in degrees. precision : int Sets the number of decimals in the output files. By default, 4 decimal. + indent_size : int + Sets the number of spaces used to indent the output document. By default, 0. kde_bandwidth : float This controls the bandwidth of the Gaussian kernel density estimator. grid_points : int @@ -121,6 +123,7 @@ class ConstavaParameters: input_degrees : bool = False model_data_degrees : bool = False precision : int = 4 + indent_size : int = 0 kde_bandwidth : float = .13 grid_points : int = 10_000 seed : int = None diff --git a/constava/wrapper/wrapper.py b/constava/wrapper/wrapper.py index 24c0d95..955f574 100644 --- a/constava/wrapper/wrapper.py +++ b/constava/wrapper/wrapper.py @@ -66,9 +66,9 @@ class Constava: initialize_reader(format="auto", in_degrees=False): Initializes an EnsembleReader for reading input files, with the format specified by 'format' and whether the data is in degrees specified by 'in_degrees'. - initialize_writer(outfile, format="auto", float_precision=4): + initialize_writer(outfile, format="auto", float_precision=4, indent_size=0): Initializes a ResultsWriter for writing output results to a file specified by 'outfile', in the format - specified by 'format', and with float precision specified by 'float_precision'. + specified by 'format', and with float precision specified by 'float_precision' (and indent_size if needed) fit_csmodel() Fits a conformational state model to the provided data. Complex method, read its docstrings for details. load_csmodel(pickled_csmodel): @@ -151,7 +151,9 @@ def run(self) -> None: writer = self.initialize_writer( outfile=self.get_param("output_file"), format=self.get_param("output_format"), - float_precision=self.get_param("precision")) + float_precision=self.get_param("precision"), + indent_size=self.get_param("indent_size") + ) # Fit or load a conformational state model if os.path.isfile(self.get_param("model_load") or ""): @@ -208,7 +210,7 @@ def initialize_reader(self, format: str = "auto", in_degrees: bool = False) -> E reader = EnsembleReader(filetype_str=format, degrees2radians=in_degrees) return reader - def initialize_writer(self, outfile, format: str = "auto", float_precision: int = 4) -> ResultsWriter: + def initialize_writer(self, outfile, format: str = "auto", float_precision: int = 4, indent_size: int = 0) -> ResultsWriter: """Initializes a ResultsWriter. Parameters: @@ -220,6 +222,8 @@ def initialize_writer(self, outfile, format: str = "auto", float_precision: int Format in which the results should be written out. {'auto', 'csv', 'json'} float_precision: int Sets de number of decimals in the output files. By default, 4 decimal. + indent_size: int + Sets the spaces used to indent the JSON documents Returns: -------- @@ -229,8 +233,8 @@ def initialize_writer(self, outfile, format: str = "auto", float_precision: int if outfile is None: return None logger.info("Initializing writer for results...") - logger.debug(f"... setting writer parameters: {outfile=}, {format=}, {float_precision=}") - writer = ResultsWriter(outfile, format=format, float_precision=float_precision) + logger.debug(f"... setting writer parameters: {outfile=}, {format=}, {float_precision=} {indent_size=}") + writer = ResultsWriter(outfile, format=format, float_precision=float_precision, indent_size=indent_size) return writer @cache_csmodel diff --git a/setup.py b/setup.py index 5ecdbf7..25e45ad 100644 --- a/setup.py +++ b/setup.py @@ -8,7 +8,7 @@ setup( name="constava", - version="1.1.2", + version="1.2.0b1", author="Wim Vranken", author_email="wim.vranken@vub.be", description="This software is used to calculate conformational states probability & conformational state " From 89267fcb9805a89efa4a072954c53a4996920089 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adri=C3=A1n=20Diaz?= Date: Fri, 23 Jan 2026 16:34:03 +0100 Subject: [PATCH 10/21] Simplify code --- constava/calc/calculator.py | 48 ++++++++++++++++--------------------- 1 file changed, 21 insertions(+), 27 deletions(-) diff --git a/constava/calc/calculator.py b/constava/calc/calculator.py index 6242c42..55639da 100644 --- a/constava/calc/calculator.py +++ b/constava/calc/calculator.py @@ -102,19 +102,9 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: ProteinEnsemble but calculated with one or more subsampling methods. """ - logger.debug(f"Instantiating Constava results for each method ({len(self.methods)} methods)...") - results = [ - ConstavaResults( - method=method.getShortName(), - protein=ensemble, - state_labels=self.csmodels.get_labels() - ) - for method in self.methods - ] - n_residues = ensemble.n_residues - logpdf_results = np.empty( + logpdf_state_propens_variabs = np.empty( (n_residues, len(self.methods), len(self.csmodels.get_labels()) + 1) ) @@ -122,8 +112,11 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: max_workers = os.process_cpu_count() except AttributeError: max_workers = multiprocessing.cpu_count() + + if max_workers > 2: + max_workers = max_workers - 1 - max_in_flight = max_workers * 3 + max_in_flight = max_workers * 2 future_to_idx = defaultdict() logger.debug(f"Starting inference of log-probability densities & propens/var") @@ -133,7 +126,6 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: initializer=_init_self_worker, initargs=(self.csmodels, self.methods) ) as process_pool_executor: - it = iter(enumerate(ensemble.get_residues())) in_flight = set() @@ -166,7 +158,7 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: for fut in done: idx = future_to_idx.pop(fut) - logpdf_results[idx] = fut.result() + logpdf_state_propens_variabs[idx] = fut.result() progress_bar.update(1) completed += 1 @@ -182,28 +174,30 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: _self_compute_logpdf_worker, phi_psi_angles ) - # logger.debug(f"Parallel task submitted ({idx}: {residue})") - future_to_idx[nfut] = idx in_flight.add(nfut) - logger.debug("Building the results objects...") - for idx, residue in enumerate(ensemble.get_residues()): - res_logpdf_results = logpdf_results[idx] + logger.debug(f"Building results for the {len(self.methods)} methods...") + + results = [ + ConstavaResults( + method=method.getShortName(), + protein=ensemble, + state_labels=self.csmodels.get_labels() + ) + for method in self.methods + ] + for idx, residue in enumerate(ensemble.get_residues()): for result_idx, result in enumerate(results): - state_propensities = res_logpdf_results[result_idx][:-1] - state_variability = res_logpdf_results[result_idx][-1] + state_propensities = logpdf_state_propens_variabs[idx][result_idx][:-1] + state_variability = logpdf_state_propens_variabs[idx][result_idx][-1] result.add_entry( - ConstavaResultsEntry( - residue, - state_propensities, - state_variability - ) + ConstavaResultsEntry(residue, state_propensities, state_variability) ) - logger.debug("All the calculations have been done with success!") + logger.debug("All the results have been calculated with success!") return results From f71ad0549c350a4012cc4d800b2054cd6af12b6d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adri=C3=A1n=20Diaz?= Date: Fri, 23 Jan 2026 16:54:17 +0100 Subject: [PATCH 11/21] More optimisations --- constava/calc/calculator.py | 5 +++-- constava/utils/ensembles.py | 7 +++++-- constava/utils/results.py | 6 ++++-- 3 files changed, 12 insertions(+), 6 deletions(-) diff --git a/constava/calc/calculator.py b/constava/calc/calculator.py index 55639da..ff1d40e 100644 --- a/constava/calc/calculator.py +++ b/constava/calc/calculator.py @@ -189,13 +189,14 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: for method in self.methods ] - for idx, residue in enumerate(ensemble.get_residues()): + for idx, residue in enumerate(ensemble.get_residues(sorted_list=True)): for result_idx, result in enumerate(results): state_propensities = logpdf_state_propens_variabs[idx][result_idx][:-1] state_variability = logpdf_state_propens_variabs[idx][result_idx][-1] result.add_entry( - ConstavaResultsEntry(residue, state_propensities, state_variability) + ConstavaResultsEntry(residue, state_propensities, state_variability), + sorted_insertion=True ) logger.debug("All the results have been calculated with success!") diff --git a/constava/utils/ensembles.py b/constava/utils/ensembles.py index c661bbd..8f46dee 100644 --- a/constava/utils/ensembles.py +++ b/constava/utils/ensembles.py @@ -111,9 +111,12 @@ def _getPropertyFromResidues(self, resattr: str, *, fillvalue=np.nan, dtype=None result[i] = value return result - def get_residues(self) -> Generator[ResidueEnsemble, None, None]: + def get_residues(self, sorted_list: bool = False) -> Generator[ResidueEnsemble, None, None]: """ Returns a generator for all residues in the class """ - return (res for res in self._residues) + if sorted_list: + return (res for res in sorted(self._residues, key=lambda x: x.respos)) + else: + return (res for res in self._residues) def add_residues(self, *new_residues: ResidueEnsemble): """ Adds a new residue to the ensemble, and sorts the residue list diff --git a/constava/utils/results.py b/constava/utils/results.py index 62613b2..9c966a6 100644 --- a/constava/utils/results.py +++ b/constava/utils/results.py @@ -71,9 +71,11 @@ def __init__(self, method: str, protein: ProteinEnsemble, state_labels: List[str def __repr__(self): return f"Results(method={self.method}, {len(self.entries)} entries)" - def add_entry(self, new_entry: ConstavaResultsEntry): + def add_entry(self, new_entry: ConstavaResultsEntry, sorted_insertion: bool = False): """Adds an additional entry to the result class""" if new_entry.residue.protein is not self.protein: raise EnsembleMismatchError(f"Result for residue {new_entry.residue} does not belong to the given Protein") self.entries.append(new_entry) - self.entries.sort(key=lambda x: x.residue.respos) \ No newline at end of file + + if not sorted_insertion: + self.entries.sort(key=lambda x: x.residue.respos) From af4ce0280adc7760e4c18301f7ea29af07277bfb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adri=C3=A1n=20Diaz?= Date: Mon, 26 Jan 2026 14:18:38 +0100 Subject: [PATCH 12/21] Fix tests --- constava/calc/calculator.py | 75 ++++++++++++++---------- constava/calc/csmodels.py | 111 +++++++++++++++++++++--------------- constava/io/wstrategies.py | 49 +++++++++++----- 3 files changed, 147 insertions(+), 88 deletions(-) diff --git a/constava/calc/calculator.py b/constava/calc/calculator.py index ff1d40e..461d1bc 100644 --- a/constava/calc/calculator.py +++ b/constava/calc/calculator.py @@ -24,26 +24,28 @@ _SELF_WORKER_CSMODEL = None _SELF_WORKER_METHODS = None + def _init_self_worker(csmodels, methods): global _SELF_WORKER_CSMODEL, _SELF_WORKER_METHODS _SELF_WORKER_CSMODEL = csmodels _SELF_WORKER_METHODS = methods + def _self_compute_logpdf_worker(phipsi: np.ndarray): csmodel = _SELF_WORKER_CSMODEL methods = _SELF_WORKER_METHODS logpdf = csmodel.get_logpdf(phipsi) - n_methods = len(methods) - n_states = len(csmodel.get_labels()) - - matrix_results = np.empty((n_methods, n_states + 1)) + matrix_results = defaultdict() for method_idx, method in enumerate(methods): + matrix_results[method.getShortName()] = defaultdict() + state_propensities, state_variability = method.calculate(logpdf) - matrix_results[method_idx, :n_states] = state_propensities - matrix_results[method_idx, n_states] = state_variability + + matrix_results[method.getShortName()]["propensities"] = state_propensities + matrix_results[method.getShortName()]["variability"] = state_variability return matrix_results @@ -52,19 +54,25 @@ def check_subsampling_methods(func): """Decorator for ConfStateCalculator that checks, if appropriate subsampling methods have been defined. """ + def __inner(self, *args, **kwargs): check = [isinstance(m, SubsamplingABC) for m in self.methods] if len(check) < 1: raise SubsamplingMethodError("No subsampling methods specified.") elif not all(check): - raise SubsamplingMethodError("Incompatible subsampling method found. All subsampling methods should inherit from SubsamplingABC.") + raise SubsamplingMethodError( + "Incompatible subsampling method found. All subsampling methods should inherit from SubsamplingABC." + ) return func(self, *args, **kwargs) + return __inner class ConfStateCalculator: - def __init__(self, csmodels: ConfStateModelABC, methods: List[SubsamplingABC] = None): + def __init__( + self, csmodels: ConfStateModelABC, methods: List[SubsamplingABC] = None + ): """Initializes the calcualtor class with given conformational state models (csmodels) and zero or more subsampling methods. @@ -104,39 +112,39 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: n_residues = ensemble.n_residues - logpdf_state_propens_variabs = np.empty( - (n_residues, len(self.methods), len(self.csmodels.get_labels()) + 1) - ) + logpdf_state_propens_variabs = defaultdict() try: max_workers = os.process_cpu_count() except AttributeError: max_workers = multiprocessing.cpu_count() - + if max_workers > 2: max_workers = max_workers - 1 max_in_flight = max_workers * 2 future_to_idx = defaultdict() - - logger.debug(f"Starting inference of log-probability densities & propens/var") - + + logger.debug( + "Starting inference of log-probability densities & propensities/variability" + ) + with ProcessPoolExecutor( max_workers=max_workers, initializer=_init_self_worker, - initargs=(self.csmodels, self.methods) + initargs=(self.csmodels, self.methods), ) as process_pool_executor: it = iter(enumerate(ensemble.get_residues())) in_flight = set() - + logger.debug(f"Max_in_flight={max_in_flight}, Max_workers={max_workers}") with tqdm.tqdm( - total=n_residues, - desc="Residues", + total=n_residues, + desc="Residues", unit="residue", bar_format="{l_bar}{bar} | {n_fmt}/{total_fmt} " - "[{rate_fmt}, elapsed: {elapsed}, remaining: {remaining}]", + "[{rate_fmt}, elapsed: {elapsed}, remaining: {remaining}]", ) as progress_bar: with logging_redirect_tqdm(): # Prime the pipeline @@ -147,7 +155,7 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: fut = process_pool_executor.submit( _self_compute_logpdf_worker, phi_psi_angles ) - + future_to_idx[fut] = idx in_flight.add(fut) @@ -173,30 +181,37 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: nfut = process_pool_executor.submit( _self_compute_logpdf_worker, phi_psi_angles ) - + future_to_idx[nfut] = idx - + in_flight.add(nfut) logger.debug(f"Building results for the {len(self.methods)} methods...") - + results = [ ConstavaResults( method=method.getShortName(), protein=ensemble, - state_labels=self.csmodels.get_labels() + state_labels=self.csmodels.get_labels(), ) for method in self.methods ] for idx, residue in enumerate(ensemble.get_residues(sorted_list=True)): - for result_idx, result in enumerate(results): - state_propensities = logpdf_state_propens_variabs[idx][result_idx][:-1] - state_variability = logpdf_state_propens_variabs[idx][result_idx][-1] + for result in results: + + state_propensities = logpdf_state_propens_variabs[idx][result.method][ + "propensities" + ] + state_variability = logpdf_state_propens_variabs[idx][result.method][ + "variability" + ] result.add_entry( - ConstavaResultsEntry(residue, state_propensities, state_variability), - sorted_insertion=True + ConstavaResultsEntry( + residue, state_propensities, state_variability + ), + sorted_insertion=True, ) logger.debug("All the results have been calculated with success!") diff --git a/constava/calc/csmodels.py b/constava/calc/csmodels.py index e8b0576..999b60a 100644 --- a/constava/calc/csmodels.py +++ b/constava/calc/csmodels.py @@ -1,4 +1,4 @@ -""" constava.csmodels contains various definitions how the conformational +"""constava.csmodels contains various definitions how the conformational states can be represented as probabilistic models. """ @@ -18,11 +18,11 @@ class ConfStateModelLoadingError(ValueError): class ConfStateModelABC(metaclass=abc.ABCMeta): - """AbstractBaseClass for the probabilistic model describing the + """AbstractBaseClass for the probabilistic model describing the conformational states. """ - model_type : str = None + model_type: str = None def __init__(self, state_labels: List[str], **kwargs): """Initialize probabilistic model of conformational states. The first @@ -42,12 +42,12 @@ def get_labels(self): def get_logpdf(self, data: np.ndarray) -> np.ndarray: """Inference of log-probability densities based on the probabilistic model. - + Parameters: ----------- data : Array[N,2] N original observations of (phi, psi) angle pairs. - + Returns: -------- logpdf : Array[M,N] @@ -55,14 +55,14 @@ def get_logpdf(self, data: np.ndarray) -> np.ndarray: state models across the N original observations. """ pass - + @abc.abstractclassmethod def from_fitting(cls, training_data_json: str, **kwargs): pass def dump_pickle(self, output_file: str): """Save the probabilistic as a pickle. - + Parameters: ----------- output_file : str @@ -74,7 +74,7 @@ def dump_pickle(self, output_file: str): @classmethod def from_pickle(cls, pickled_file: str): """Load the probabilistic from a pickle. - + Parameters: ----------- pickled_file : str @@ -83,14 +83,17 @@ def from_pickle(cls, pickled_file: str): with open(pickled_file, "rb") as fhandle: csmodel = pickle.load(fhandle) if not isinstance(csmodel, cls): - raise ConfStateModelLoadingError(( - "Loaded conformational state models of wrong type: `{0}` " - "(expected: `{1}`)").format(csmodel.model_type, cls.model_type)) + raise ConfStateModelLoadingError( + ( + "Loaded conformational state models of wrong type: `{0}` " + "(expected: `{1}`)" + ).format(csmodel.model_type, cls.model_type) + ) return csmodel class ConfStateModelKDE(ConfStateModelABC): - """Probabilistic model of conformational states based on a Gaussian kernel + """Probabilistic model of conformational states based on a Gaussian kernel density estimator. Attributes: @@ -98,9 +101,9 @@ class ConfStateModelKDE(ConfStateModelABC): state_labels : Tuple List labels for the conformational state models state_kdes: Tuple - List of the Gaussian kernel density estimators representing the + List of the Gaussian kernel density estimators representing the probabilistic models for the conformational states - + Methods: -------- get_logpdf(data) @@ -115,18 +118,19 @@ class ConfStateModelKDE(ConfStateModelABC): from_fitting(json_file) Fit conformational state models to data provided in json file """ + model_type = "kde" def __init__(self, state_labels: List[str], state_kdes: List[KernelDensity]): - """Initialize probabilistic model of conformational states based on + """Initialize probabilistic model of conformational states based on Gaussian kernel density estimator. - + Parameters: ----------- state_labels : Tuple List labels for the conformational state models state_kdes: Tuple - List of the Gaussian kernel density estimators representing the + List of the Gaussian kernel density estimators representing the probabilistic models for the conformational states """ self.state_labels = tuple(state_labels) @@ -134,21 +138,23 @@ def __init__(self, state_labels: List[str], state_kdes: List[KernelDensity]): def get_logpdf(self, data: np.ndarray) -> np.ndarray: X = np.ascontiguousarray(data, dtype=np.float64) - + n_states = len(self.state_kdes) n_samples = X.shape[0] - + result = np.empty((n_states, n_samples), dtype=np.float64) for i, kde in enumerate(self.state_kdes): result[i, :] = kde.score_samples(X) - return result.astype(np.float32, copy=False) + return result @classmethod - def from_fitting(cls, training_data_json: str, *, in_degrees=False, bandwidth=.13, **_): + def from_fitting( + cls, training_data_json: str, *, in_degrees=False, bandwidth=0.13, **_ + ): """Generate the probabilistic models at runtime, by fitting the models to the provided training data. Training data must be a json. - + Parameters: ----------- training_data_json : str @@ -161,13 +167,13 @@ def from_fitting(cls, training_data_json: str, *, in_degrees=False, bandwidth=.1 Returns: -------- model : KdeStatePdf - Probabilistic model of conformational states based on a Gaussian + Probabilistic model of conformational states based on a Gaussian kernel density estimator """ with open(training_data_json, "r") as fhandle: training_data = json.load(fhandle) # Iterate over conformational states and train pdf estimator - kde_list, lbl_list = [], [] + kde_list, lbl_list = [], [] for label, data in training_data.items(): data = np.radians(data) if in_degrees else np.array(data) check_dihedral_range(data) @@ -180,9 +186,9 @@ def from_fitting(cls, training_data_json: str, *, in_degrees=False, bandwidth=.1 class ConfStateModelGrid(ConfStateModelABC): - """Probabilistic model of conformational states based on a Gaussian kernel - density estimator. The actual inference is done by linear interpolation - between fixed grid points. This significantly speeds up the inference, while + """Probabilistic model of conformational states based on a Gaussian kernel + density estimator. The actual inference is done by linear interpolation + between fixed grid points. This significantly speeds up the inference, while sacrificing slightly on the accuracy of the inference. Attributes: @@ -195,7 +201,7 @@ class ConfStateModelGrid(ConfStateModelABC): grid_crds: Tuple[Array[N], Array[N]] Tuple of two arrays that describe the (phi,psi) coordinates of the grid points. - + Methods: -------- get_logpdf(data) @@ -210,31 +216,44 @@ class ConfStateModelGrid(ConfStateModelABC): from_fitting(json_file) Fit conformational state models to data provided in json file """ + model_type = "grid" - def __init__(self, state_labels: List[str], state_grids: np.ndarray, grid_crds: Tuple): + def __init__( + self, state_labels: List[str], state_grids: np.ndarray, grid_crds: Tuple + ): self.state_labels = tuple(state_labels) - self.state_grids = [np.ascontiguousarray(g, dtype=np.float64) for g in state_grids] + self.state_grids = [ + np.ascontiguousarray(g, dtype=np.float64) for g in state_grids + ] self.grid_crds = grid_crds - + def get_logpdf(self, data: np.ndarray) -> np.ndarray: X = np.ascontiguousarray(data, dtype=np.float64) - + n_states = len(self.state_grids) n_samples = X.shape[0] - + result = np.empty((n_states, n_samples), dtype=np.float64) - + for i, grid in enumerate(self.state_grids): result[i, :] = interpn(self.grid_crds, grid, X) - return result.astype(np.float32, copy=False) - + return result + @classmethod - def from_fitting(cls, training_data_json: str, *, in_degrees=False, bandwidth=.13, grid_points=10_000, **_): + def from_fitting( + cls, + training_data_json: str, + *, + in_degrees=False, + bandwidth=0.13, + grid_points=10_000, + **_, + ): """Generate the probabilistic models at runtime, by fitting the models to the provided training data. Training data must be a json. - + Parameters: ----------- training_data_json : str @@ -245,8 +264,8 @@ def from_fitting(cls, training_data_json: str, *, in_degrees=False, bandwidth=.1 Bandwidth of the Gaussian kernel density estimator grid_points : int The number of gridpoints between which the PDF will be estimated - by interpolation. Note, that for generating the grid for both - axes sqrt(`grid_points`) are used. Thus, if `grid_points` is not + by interpolation. Note, that for generating the grid for both + axes sqrt(`grid_points`) are used. Thus, if `grid_points` is not a square number, the final grid_points may be less. Returns: @@ -258,11 +277,13 @@ def from_fitting(cls, training_data_json: str, *, in_degrees=False, bandwidth=.1 # Generate grid points in the (phi,psi)-space n = math.isqrt(grid_points) _phi, _psi = np.linspace(-np.pi, np.pi, n), np.linspace(-np.pi, np.pi, n) - gridcrds = np.stack([ - arr.flatten() for arr in np.meshgrid(_phi, _psi, indexing="ij")], - axis=1) + gridcrds = np.stack( + [arr.flatten() for arr in np.meshgrid(_phi, _psi, indexing="ij")], axis=1 + ) # Infer PDF grid from a KDE model - kde_model = ConfStateModelKDE.from_fitting(training_data_json, bandwidth=.13, in_degrees=in_degrees) + kde_model = ConfStateModelKDE.from_fitting( + training_data_json, bandwidth=0.13, in_degrees=in_degrees + ) _labels = kde_model.get_labels() - _grids = np.reshape(kde_model.get_logpdf(gridcrds), (-1,n,n), order="C") + _grids = np.reshape(kde_model.get_logpdf(gridcrds), (-1, n, n), order="C") return cls(state_labels=_labels, state_grids=_grids, grid_crds=(_phi, _psi)) diff --git a/constava/io/wstrategies.py b/constava/io/wstrategies.py index 5c43685..fdfce77 100644 --- a/constava/io/wstrategies.py +++ b/constava/io/wstrategies.py @@ -1,8 +1,10 @@ import abc import csv import json +import platform +import sys from datetime import datetime -from typing import List, TextIO +from typing import List, TextIO import numpy as np from ..utils.constants import CONSTAVA_NAME, CONSTAVA_VERSION from ..utils.results import ConstavaResults, ConstavaResultsEntry @@ -10,6 +12,7 @@ class WriterABC(metaclass=abc.ABCMeta): """Abstract base class for file writers""" + def __init__(self, float_precision: int = 4, indent_size: int = 0): self.float_precision = float_precision self.indent_size = indent_size @@ -27,16 +30,24 @@ def write_results(self, fhandle: TextIO, results: List[ConstavaResults]): "tool": CONSTAVA_NAME, "version": CONSTAVA_VERSION, "creation_date": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "python_version": sys.version, + "python_short_version": platform.python_version(), "results": { "ResIndex": [entry.residue.respos for entry in results[0].entries], "ResName": [entry.residue.restype for entry in results[0].entries], - } + }, } for result in results: - cspropensites = np.round(np.stack([entry.state_propensities for entry in result.entries]), decimals=self.float_precision) - csvariability = np.round(np.stack([entry.state_variability for entry in result.entries]), decimals=self.float_precision) + cspropensites = np.round( + np.stack([entry.state_propensities for entry in result.entries]), + decimals=self.float_precision, + ) + csvariability = np.round( + np.stack([entry.state_variability for entry in result.entries]), + decimals=self.float_precision, + ) __dict["results"][result.method] = { - state_label: cspropensites[:,i].tolist() + state_label: cspropensites[:, i].tolist() for i, state_label in enumerate(result.state_labels) } __dict["results"][result.method]["Variability"] = csvariability.tolist() @@ -45,20 +56,27 @@ def write_results(self, fhandle: TextIO, results: List[ConstavaResults]): json.dump(__dict, fhandle, indent=self.indent_size) else: json.dump(__dict, fhandle) - - + + class CsvWriter(WriterABC): """Writer strategy for writing CSV files""" csvdialect = {} - + def write_results(self, fhandle: TextIO, results: List[ConstavaResults]): # Initialize csv writer writer = csv.writer(fhandle, **self.csvdialect) # Write the header line - writer.writerow([ - "#Method", "SeriesIndex", "ResIndex", "ResName", - *results[0].state_labels, "Variability"]) + writer.writerow( + [ + "#Method", + "SeriesIndex", + "ResIndex", + "ResName", + *results[0].state_labels, + "Variability", + ] + ) # Iterate over results (methods) and entries (residues) for result in results: for entry in result.entries: @@ -69,15 +87,20 @@ def _write_entry(self, entry: ConstavaResultsEntry, method: str) -> List: respos, restype = entry.residue.respos, entry.residue.restype # If average state propensities are reported, one row is written if len(entry.state_propensities.shape) == 1: - values = np.concatenate([entry.state_propensities, [entry.state_variability]]) + values = np.concatenate( + [entry.state_propensities, [entry.state_variability]] + ) yield (method, None, respos, restype, *map(flt2str, values)) # If state propensities are reported as series, multiple rows are written else: - arr = np.concatenate([entry.state_propensities, [entry.state_variability]]).T + arr = np.concatenate( + [entry.state_propensities, [entry.state_variability]] + ).T for i, values in enumerate(arr): yield (method, i, respos, restype, *map(flt2str, values)) class TsvWriter(CsvWriter): """Writer strategy for writing TSV files (based on CsvWriter)""" + csvdialect = {"delimiter": "\t"} From b8c2170a5834f5fa38b3af453ad8dc16f10b824c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adri=C3=A1n=20Diaz?= Date: Mon, 26 Jan 2026 17:59:19 +0100 Subject: [PATCH 13/21] Apply optimisations (helped by AI) --- constava/calc/calculator.py | 211 ++++++++++++++++++++++++------------ 1 file changed, 141 insertions(+), 70 deletions(-) diff --git a/constava/calc/calculator.py b/constava/calc/calculator.py index 461d1bc..4880356 100644 --- a/constava/calc/calculator.py +++ b/constava/calc/calculator.py @@ -32,22 +32,29 @@ def _init_self_worker(csmodels, methods): def _self_compute_logpdf_worker(phipsi: np.ndarray): + """ + Worker function executed in a child process. + + Performance change: + - Returns two lists aligned to method order instead of a nested defaultdict, + greatly reducing pickle/serialization overhead. + """ csmodel = _SELF_WORKER_CSMODEL methods = _SELF_WORKER_METHODS logpdf = csmodel.get_logpdf(phipsi) - matrix_results = defaultdict() + # Return plain lists to minimize serialization costs. + # props_list[i] and var_list[i] correspond to methods[i]. + props_list = [None] * len(methods) + var_list = [None] * len(methods) for method_idx, method in enumerate(methods): - matrix_results[method.getShortName()] = defaultdict() - state_propensities, state_variability = method.calculate(logpdf) + props_list[method_idx] = state_propensities + var_list[method_idx] = state_variability - matrix_results[method.getShortName()]["propensities"] = state_propensities - matrix_results[method.getShortName()]["variability"] = state_variability - - return matrix_results + return props_list, var_list def check_subsampling_methods(func): @@ -87,10 +94,76 @@ def __init__( self.csmodels = csmodels self.methods = methods or [] + # Improvement: keep a reusable process pool to avoid paying startup cost + # on every calculate() call. + self._executor = None + self._executor_max_workers = None + self._mp_context = None + def add_method(self, new_method: SubsamplingABC): """Adds a new subsampling methods to the calculator.""" self.methods.append(new_method) + def close(self): + """Shut down the internal reusable pool, if any.""" + if self._executor is not None: + try: + self._executor.shutdown(wait=True, cancel_futures=False) + finally: + self._executor = None + self._executor_max_workers = None + self._mp_context = None + + def __del__(self): + # Best-effort cleanup (don't raise during GC) + try: + self.close() + except Exception: + pass + + def _get_mp_context(self): + """ + Try to choose a fast start method. + - 'fork' is typically fastest on Linux (low startup overhead). + - On platforms where 'fork' isn't available/appropriate, fall back to default. + """ + if self._mp_context is not None: + return self._mp_context + + try: + # "fork" is usually fastest on Linux. + ctx = multiprocessing.get_context("fork") + except (ValueError, RuntimeError, AttributeError): + # Fallback to default context ("spawn" on Windows, often "spawn" on macOS). + ctx = multiprocessing.get_context() + + self._mp_context = ctx + return ctx + + def _get_or_create_executor(self, max_workers): + """ + Reuse a cached executor if possible; otherwise create a new one. + + Note: Reusing requires that csmodels/methods are intended to stay the same. + If you mutate methods frequently, consider calling close() and rebuilding. + """ + if self._executor is not None and self._executor_max_workers == max_workers: + return self._executor + + # If something changed, rebuild the pool. + self.close() + + ctx = self._get_mp_context() + + self._executor = ProcessPoolExecutor( + max_workers=max_workers, + initializer=_init_self_worker, + initargs=(self.csmodels, self.methods), + mp_context=ctx, + ) + self._executor_max_workers = max_workers + return self._executor + @check_subsampling_methods def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: """Calculates conformational state propensities and conformational @@ -112,6 +185,8 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: n_residues = ensemble.n_residues + # Improvement: store per-residue results as compact lists aligned by method index. + # logpdf_state_propens_variabs[idx] = (props_list, var_list) logpdf_state_propens_variabs = defaultdict() try: @@ -129,83 +204,79 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: "Starting inference of log-probability densities & propensities/variability" ) - with ProcessPoolExecutor( - max_workers=max_workers, - initializer=_init_self_worker, - initargs=(self.csmodels, self.methods), - ) as process_pool_executor: - it = iter(enumerate(ensemble.get_residues())) - in_flight = set() - - logger.debug(f"Max_in_flight={max_in_flight}, Max_workers={max_workers}") - - with tqdm.tqdm( - total=n_residues, - desc="Residues", - unit="residue", - bar_format="{l_bar}{bar} | {n_fmt}/{total_fmt} " - "[{rate_fmt}, elapsed: {elapsed}, remaining: {remaining}]", - ) as progress_bar: - with logging_redirect_tqdm(): - # Prime the pipeline - for _ in range(min(max_in_flight, n_residues)): - idx, residue = next(it) - phi_psi_angles = np.ascontiguousarray(residue.phipsi) - - fut = process_pool_executor.submit( + # Improvement: reuse pool across calls to avoid process startup cost + process_pool_executor = self._get_or_create_executor(max_workers=max_workers) + + it = iter(enumerate(ensemble.get_residues())) + in_flight = set() + + logger.debug(f"Max_in_flight={max_in_flight}, Max_workers={max_workers}") + + with tqdm.tqdm( + total=n_residues, + desc="Residues", + unit="residue", + bar_format="{l_bar}{bar} | {n_fmt}/{total_fmt} " + "[{rate_fmt}, elapsed: {elapsed}, remaining: {remaining}]", + ) as progress_bar: + with logging_redirect_tqdm(): + # Prime the pipeline + for _ in range(min(max_in_flight, n_residues)): + idx, residue = next(it) + phi_psi_angles = np.ascontiguousarray(residue.phipsi) + + fut = process_pool_executor.submit( + _self_compute_logpdf_worker, phi_psi_angles + ) + + future_to_idx[fut] = idx + in_flight.add(fut) + + completed = 0 + while in_flight: + done, in_flight = wait(in_flight, return_when=FIRST_COMPLETED) + + for fut in done: + idx = future_to_idx.pop(fut) + logpdf_state_propens_variabs[idx] = fut.result() + + progress_bar.update(1) + completed += 1 + + # refill + try: + idx, residue = next(it) + phi_psi_angles = np.ascontiguousarray(residue.phipsi) + except StopIteration: + continue + + nfut = process_pool_executor.submit( _self_compute_logpdf_worker, phi_psi_angles ) - future_to_idx[fut] = idx - - in_flight.add(fut) - - completed = 0 - while in_flight: - done, in_flight = wait(in_flight, return_when=FIRST_COMPLETED) - - for fut in done: - idx = future_to_idx.pop(fut) - logpdf_state_propens_variabs[idx] = fut.result() - - progress_bar.update(1) - completed += 1 - - # refill - try: - idx, residue = next(it) - phi_psi_angles = np.ascontiguousarray(residue.phipsi) - except StopIteration: - continue - - nfut = process_pool_executor.submit( - _self_compute_logpdf_worker, phi_psi_angles - ) - - future_to_idx[nfut] = idx - - in_flight.add(nfut) + future_to_idx[nfut] = idx + in_flight.add(nfut) logger.debug(f"Building results for the {len(self.methods)} methods...") + # Improvement: compute method names once (no repeated calls inside workers) + method_names = [method.getShortName() for method in self.methods] + results = [ ConstavaResults( - method=method.getShortName(), + method=method_name, protein=ensemble, state_labels=self.csmodels.get_labels(), ) - for method in self.methods + for method_name in method_names ] for idx, residue in enumerate(ensemble.get_residues(sorted_list=True)): - for result in results: - - state_propensities = logpdf_state_propens_variabs[idx][result.method][ - "propensities" - ] - state_variability = logpdf_state_propens_variabs[idx][result.method][ - "variability" - ] + props_list, var_list = logpdf_state_propens_variabs[idx] + + for method_idx, result in enumerate(results): + state_propensities = props_list[method_idx] + state_variability = var_list[method_idx] result.add_entry( ConstavaResultsEntry( From 9cdc00cc4708ac29470e203048980087165af007 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adri=C3=A1n=20Diaz?= Date: Tue, 27 Jan 2026 08:59:09 +0100 Subject: [PATCH 14/21] Improve the git ignore file --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index 4ade178..a3b3d8b 100644 --- a/.gitignore +++ b/.gitignore @@ -61,3 +61,6 @@ build/ conformine_training_dataset/ constava/data/*.pkl datagen/ + +# Python +__pycache__ From 4720687682f435dbdf9a5916f5db16fec8fad1ac Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adri=C3=A1n=20Diaz?= Date: Wed, 28 Jan 2026 11:21:26 +0100 Subject: [PATCH 15/21] Add metadata --- CITATION.cff | 47 ++++++++++++++++++++++++++++++++++ MANIFEST.in | 16 ++++++++++-- Makefile | 10 +++++--- README.md | 15 ++++++++--- authors.md | 71 ++++++++++++++++++++++++++++++++++++++++++++++++++++ setup.py | 18 ++++++++----- 6 files changed, 161 insertions(+), 16 deletions(-) create mode 100644 CITATION.cff create mode 100644 authors.md diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000..3456df4 --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,47 @@ +cff-version: 1.2.0 +message: "If you use this software, please cite it as below." +authors: +- family-names: "Bickel" + given-names: "David" + orcid: "https://orcid.org/0000-0001-6431-3442" +- family-names: "Gavalda-Garcia" + given-names: "Jose" + orcid: "https://orcid.org/0000-0003-0332-8338" +- family-names: "Díaz" + given-names: "Adrián" + orcid: "https://orcid.org/0000-0003-0165-1318" +title: "Bio2Byte/constava" +version: 1.1.0 +doi: 10.5281/zenodo.12699038 +date-released: 2024-07-09 +url: "https://github.com/Bio2Byte/constava" +preferred-citation: + type: article + authors: + - family-names: "Gavalda-Garcia" + given-names: "Jose" + orcid: "https://orcid.org/0000-0001-6431-3442" + - family-names: "Bickel" + given-names: "David" + orcid: "https://orcid.org/0000-0003-0332-8338" + - family-names: "Roca-Martinez" + given-names: "Joel" + orcid: "https://orcid.org/0000-0002-4313-3845" + - family-names: "Raimondi" + given-names: "Daniele" + orcid: "https://orcid.org/0000-0003-1157-1899" + - family-names: "Orlando" + given-names: "Gabriele" + orcid: "https://orcid.org/0000-0002-5935-5258" + - family-names: "Vranken" + given-names: "Wim" + orcid: "https://orcid.org/0000-0001-7470-4324" + doi: "10.1093/nargab/lqae082" + journal: "NAR Genomics and Bioinformatics" + month: 7 + start: lqae082 + end: lqae082 + title: "Data-driven probabilistic definition of the low energy conformational states of protein residues" + issue: 3 + volume: 6 + year: 2024 diff --git a/MANIFEST.in b/MANIFEST.in index 5cf8a75..25f710f 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,2 +1,14 @@ -include constava/data/* -include requirements.txt \ No newline at end of file +recursive-include constava/data * +include requirements.txt +include LICENSE +include authors.md + +global-exclude .DS_Store +global-exclude *.pyc +global-exclude *.pyo +global-exclude __pycache__ +global-exclude *.log +global-exclude *.tmp +global-exclude *.swp +global-exclude *.bak +global-exclude *.orig diff --git a/Makefile b/Makefile index 6c7e37a..b1a75e4 100644 --- a/Makefile +++ b/Makefile @@ -3,13 +3,15 @@ PACKAGE_NAME = constava .PHONY: build install uninstall publish clean build: - python3 setup.py sdist bdist_wheel +# python3 setup.py sdist bdist_wheel + python3 -m build install: - pip install dist/$(PACKAGE_NAME)-*.tar.gz + python3 -m pip install dist/$(PACKAGE_NAME)-*.tar.gz +.PHONY: test test: - python -m $(PACKAGE_NAME) test + constava test uninstall: -pip uninstall -y $(PACKAGE_NAME) @@ -18,4 +20,4 @@ publish: twine upload dist/* clean: - rm -rf dist build $(PACKAGE_NAME).egg-info \ No newline at end of file + rm -rf dist build $(PACKAGE_NAME).egg-info diff --git a/README.md b/README.md index 59d79db..ec91b90 100644 --- a/README.md +++ b/README.md @@ -68,7 +68,7 @@ submodule, which supports a wide range of MD and structure formats. ## Installation ### Prerequisites -- Python 3.8 or higher +- Python 3.8 or higher (up to 3.14 inclusive) - pip [](#constava) @@ -95,7 +95,7 @@ If the package requires to be uninstalled, run `pip uninstall constava`. ### Installation through conda -To install constava through conda please follow the instructions below (both [Conda-Forge](https://conda-forge.org) and [Bioconda](https://bioconda.github.io) channels are required to install Constava dependencies). +To install Constava through conda please follow the instructions below (both [Conda-Forge](https://conda-forge.org) and [Bioconda](https://bioconda.github.io) channels are required to install Constava dependencies). ```sh # Create a conda environment (optional but recommended): @@ -116,7 +116,7 @@ If the package requires to be uninstalled, run `conda remove constava`. ### Installation from the source To download and install the latest version of the software from the source code -follow the instructions below. +follow the instructions below (you will have to install `wheel`, `build` & `setuptools`) ```sh # Clone the repository: @@ -127,6 +127,9 @@ cd constava python3 -m venv constava source constava/bin/activate +# Install the building dependencies: +python3 -m pip install wheel build setuptools + # Build and install the package from the packages root directory: # ... build package from source make build @@ -363,6 +366,8 @@ Miscellaneous options: --degrees Set this flag, if dihedrals in the input files are in degrees. --precision Sets the number of decimals in the output files. + --indent_size Sets the number of spaces used to indent + the output document (default: 0) --seed Set random seed for bootstrap sampling -v, --verbose Set verbosity level of screen output. Flag can be given multiple times (up to 2) to gradually increase output to @@ -375,7 +380,9 @@ An example: # Run constava with debug-level output constava analyze \ -i "2mkx_dihedrals.csv" \ - -o "2mkx_constava.json" --output-format json \ + -o "2mkx_constava.json" \ + --output-format json \ + --indent_size 4 \ --window 3 5 25 \ -vv ``` diff --git a/authors.md b/authors.md new file mode 100644 index 0000000..86d49d7 --- /dev/null +++ b/authors.md @@ -0,0 +1,71 @@ +title: Authors + +Primary Authors +=============== + +* __[Jose Gavalda-Garcia ♠](https://orcid.org/0000-0001-6431-3442)__ + + - Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium + - Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium + + **Author contributed equally to this work together David Bickel.** + +* __[David Bickel ♠](https://orcid.org/0000-0003-0332-8338)__ + + - Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium + - Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium + + **Author contributed equally to this work together Jose Gavalda-Garcia.** + +* __[Joel Roca-Martinez](https://orcid.org/0000-0002-4313-3845)__ + + - Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium + - Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium + +* __[Daniele Raimondi](https://orcid.org/0000-0003-1157-1899)__ + + - ESAT-STADIUS, KU Leuven, Leuven, Belgium + + +* __[Gabriele Orlando](https://orcid.org/0000-0002-5935-5258)__ + + - Switch Laboratory, KU Leuven Leuven, Belgium + +* __[Wim Vranken](https://orcid.org/0000-0001-7470-4324)__ + + - Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium + - Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium + + +Main Contributors +================== + +* __[Adrián Díaz](https://orcid.org/0000-0003-0165-1318)__ + + - Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium + - Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium + + He is the Scientific collaborator at the Bio2Byte group. He contributes to + develop and maintain software applications within the group. + + **Adrián continues to keep this project up-to-date and works on upgrades and fixes.** + +* __[Boris Depoortere](https://www.vibtrainingandconferences.be/trainer/boris-depoortere)__ + + - VIB Data Core, ELIXIR, Belgium + + Boris Depoortere is a bioinformatician who has been strengthening the + Compute Team of Data Core since November 2023. + + **Author contributed to the publication of this tool on Galaxy together Adrián Díaz and Iman Jouiad.** + +More Contributors +================== + +The incomplete list of individuals below have provided patches or otherwise +contributed to the project prior to the project being hosted on GitHub. See the +GitHub commit log for a list of recent contributors. We would like to thank +everyone who has contributed to the project in any way. + +* Iman Jouiad +* and many others who helped by reporting bugs... diff --git a/setup.py b/setup.py index 25e45ad..f1a92e6 100644 --- a/setup.py +++ b/setup.py @@ -12,12 +12,13 @@ author="Wim Vranken", author_email="wim.vranken@vub.be", description="This software is used to calculate conformational states probability & conformational state " - "variability from a protein structure ensemble.", - license="OSI Approved :: GNU General Public License v3 (GPLv3)", + "variability from a protein structure ensemble.", + license="GPL-3.0-only", + license_files=["LICEN[CS]E*", "authors.md"], long_description=long_description, long_description_content_type="text/markdown", - maintainer="Jose Gavalda-Garcia, David Bickel, Adrian Diaz, Wim Vranken", - maintainer_email="jose.gavalda.garcia@vub.be, david.bickel@vub.be, adrian.diaz@vub.be, wim.vranken@vub.be", + maintainer="Adrián Díaz, Wim Vranken", + maintainer_email="bio2byte@vub.be, adrian.diaz@vub.be, wim.vranken@vub.be", url="https://bitbucket.org/bio2byte/constava/", packages=find_packages(), include_package_data=True, @@ -28,6 +29,8 @@ "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", "Operating System :: OS Independent", "Topic :: Scientific/Engineering :: Bio-Informatics", @@ -35,13 +38,16 @@ "Topic :: Scientific/Engineering :: Physics", "Intended Audience :: Science/Research", "Intended Audience :: Education", - "Development Status :: 5 - Production/Stable" + "Development Status :: 5 - Production/Stable", ], - python_requires=">=3.8", + python_requires=">=3.8,<3.15", install_requires=requirements, entry_points={ "console_scripts": [ "constava = constava.__main__:main", ], }, + package_data={ + "constava": ["constava/data/*"], + }, ) From 16ade260145bee01816d0ab72d96ff8616659bce Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adri=C3=A1n=20Diaz?= Date: Wed, 28 Jan 2026 11:22:00 +0100 Subject: [PATCH 16/21] More changes --- constava/__main__.py | 465 +- constava/calc/calculator.py | 61 +- constava/calc/csmodels.py | 40 +- constava/calc/subsampling.py | 9 +- constava/data/constava_testdata.tgz | Bin 826628 -> 0 bytes .../data/constava_testdata/csv/dihedrals.csv | 18686 ++++++++ .../constava_testdata/csv/result_grid.csv | 37556 ++++++++++++++++ .../constava_testdata/csv/result_grid.json | 1 + .../constava_testdata/xvg/ramaPhiPsiALA18.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiALA19.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiALA26.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiARG29.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiASN15.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiASN27.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiASN3.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiASN32.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiASP12.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiASP37.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiGLN20.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiGLN43.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiGLY1.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiGLY11.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiGLY22.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiGLY33.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiGLY36.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiGLY42.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiGLY50.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiILE17.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiILE34.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiILE39.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiILE46.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiLEU14.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiLEU28.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiLEU38.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiLEU45.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiLYS16.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiLYS44.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiLYS48.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiLYS49.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiLYS9.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiPHE40.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiSER10.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiSER24.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiSER30.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiSER35.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiSER51.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiTHR13.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiTHR2.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiTHR4.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiTHR7.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiTRP31.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiTYR21.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiTYR5.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiTYR6.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiVAL23.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiVAL25.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiVAL41.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiVAL47.xvg | 27 + .../constava_testdata/xvg/ramaPhiPsiVAL8.xvg | 27 + .../data/constava_testdata/xvg/result_kde.csv | 2602 ++ .../constava_testdata/xvg/result_kde.json | 1 + constava/io/rstrategies.py | 5 +- constava/io/writer.py | 42 +- constava/io/wstrategies.py | 4 + constava/tests/tests.py | 263 +- constava/utils/dihedrals.py | 114 +- constava/utils/ensembles.py | 81 +- constava/utils/utils.py | 25 +- constava/wrapper/params.py | 8 +- constava/wrapper/wrapper.py | 492 +- 70 files changed, 61277 insertions(+), 555 deletions(-) delete mode 100644 constava/data/constava_testdata.tgz create mode 100644 constava/data/constava_testdata/csv/dihedrals.csv create mode 100644 constava/data/constava_testdata/csv/result_grid.csv create mode 100644 constava/data/constava_testdata/csv/result_grid.json create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiALA18.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiALA19.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiALA26.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiARG29.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiASN15.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiASN27.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiASN3.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiASN32.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiASP12.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiASP37.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiGLN20.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiGLN43.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiGLY1.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiGLY11.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiGLY22.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiGLY33.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiGLY36.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiGLY42.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiGLY50.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiILE17.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiILE34.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiILE39.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiILE46.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiLEU14.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiLEU28.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiLEU38.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiLEU45.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiLYS16.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiLYS44.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiLYS48.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiLYS49.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiLYS9.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiPHE40.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiSER10.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiSER24.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiSER30.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiSER35.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiSER51.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiTHR13.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiTHR2.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiTHR4.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiTHR7.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiTRP31.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiTYR21.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiTYR5.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiTYR6.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiVAL23.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiVAL25.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiVAL41.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiVAL47.xvg create mode 100644 constava/data/constava_testdata/xvg/ramaPhiPsiVAL8.xvg create mode 100644 constava/data/constava_testdata/xvg/result_kde.csv create mode 100644 constava/data/constava_testdata/xvg/result_kde.json diff --git a/constava/__main__.py b/constava/__main__.py index ff8d1f2..920c48f 100644 --- a/constava/__main__.py +++ b/constava/__main__.py @@ -1,5 +1,6 @@ -"""constava.__main__ is the executable for the command line functionality of +"""constava.__main__ is the executable for the command line functionality of the tool.""" + import os, sys import argparse import textwrap as tw @@ -9,10 +10,10 @@ def parse_parameters(cmdline_arguments): - """Parse command line arguments and return them as ConstavaParameters object - """ - parser = argparse.ArgumentParser(description=tw.dedent( - """\ + """Parse command line arguments and return them as ConstavaParameters object""" + parser = argparse.ArgumentParser( + description=tw.dedent( + """\ Constava analyzes conformational ensembles calculating conformational state propensities and conformational state variability. The conformational state propensities indicate the likelihood of a residue residing in a given @@ -42,22 +43,38 @@ def parse_parameters(cmdline_arguments): * Core Sheet - Exclusively beta-sheet, low backbone dynamics * Surrounding Sheet - Mostly extended conformation, high backbone dynamics * Turn - Mostly turn, high backbone dynamics - * Other - Mostly coil, high backbone dynamics"""), - formatter_class=argparse.RawTextHelpFormatter, add_help=False) - + * Other - Mostly coil, high backbone dynamics""" + ), + formatter_class=argparse.RawTextHelpFormatter, + add_help=False, + ) + # General flags for the main software genOpt = parser.add_argument_group("Generic options") - genOpt.add_argument("-h", "--help", action="help", help=tw.dedent( - """\ + genOpt.add_argument( + "-h", + "--help", + action="help", + help=tw.dedent( + """\ Show this help message and exit. For detailed information on the subcommands, run: - `%(prog)s SUBCOMMAND -h`""")) - genOpt.add_argument("--version", action="version", version=f"%(prog)s {__version__}", - help="Show the program's version number and exit") + `%(prog)s SUBCOMMAND -h`""" + ), + ) + genOpt.add_argument( + "--version", + action="version", + version=f"%(prog)s {__version__}", + help="Show the program's version number and exit", + ) - subparsers = parser.add_subparsers(title="Subcommands", - dest="subcommand", required=True, help=tw.dedent( - """\ + subparsers = parser.add_subparsers( + title="Subcommands", + dest="subcommand", + required=True, + help=tw.dedent( + """\ fit-model Fit a custom conformational state model analyze @@ -65,13 +82,17 @@ def parse_parameters(cmdline_arguments): dihedrals Obtain the phi/psi backbone dihedral angles from a MD simulation test - Run unit tests of the installed package""")) + Run unit tests of the installed package""" + ), + ) # ====================== # Subparser: fit-model # ====================== - parser_fit_model = subparsers.add_parser("fit-model", description=tw.dedent( - """\ + parser_fit_model = subparsers.add_parser( + "fit-model", + description=tw.dedent( + """\ The `constava fit-model` submodule is used to generate the probabilistic conformational state models used in the analysis. By default, when running `constava analyze` these models are generated on-the-fly. In selected cases @@ -83,56 +104,107 @@ def parse_parameters(cmdline_arguments): the continuous probability density function of the kde-Model by a fixed set of grid-points. The PDF for any sample is then estimated by linear interpolation between the nearest grid points. This is slightly less - accurate than the kde-Model but speeds up inference significantly."""), - formatter_class=argparse.RawTextHelpFormatter) - + accurate than the kde-Model but speeds up inference significantly.""" + ), + formatter_class=argparse.RawTextHelpFormatter, + ) + fitIO = parser_fit_model.add_argument_group("Input and output options") - fitIO.add_argument("-i", "--input", type=str, metavar="", help=tw.dedent( - """\ + fitIO.add_argument( + "-i", + "--input", + type=str, + metavar="", + help=tw.dedent( + """\ The data to which the new conformational state models will be fitted. It should be provided as a JSON file. The top-most key should indicate the names of the conformational states. On the level below, lists of phi-/ psi pairs for each stat should be provided. If not provided - the default data from the publication will be used.""")) - fitIO.add_argument("-o", "--output", type=str, metavar="", required=True, help=tw.dedent( - """\ + the default data from the publication will be used.""" + ), + ) + fitIO.add_argument( + "-o", + "--output", + type=str, + metavar="", + required=True, + help=tw.dedent( + """\ Write the generated model to a pickled file, that can be - loaded gain using `constava analyze --load-model`""")) - + loaded gain using `constava analyze --load-model`""" + ), + ) + fitMdl = parser_fit_model.add_argument_group("Conformational state model options") - fitMdl.add_argument("--model-type", choices=["kde", "grid"], default="kde", help=tw.dedent( - """\ + fitMdl.add_argument( + "--model-type", + choices=["kde", "grid"], + default="kde", + help=tw.dedent( + """\ The probabilistic conformational state model used. The default is `kde`. The alternative `grid` runs significantly faster while slightly sacrificing accuracy: {'kde', 'grid'} - (default: 'kde')""")) - fitMdl.add_argument("--kde-bandwidth", type=float, metavar="", default=.13, help=tw.dedent( - """\ + (default: 'kde')""" + ), + ) + fitMdl.add_argument( + "--kde-bandwidth", + type=float, + metavar="", + default=0.13, + help=tw.dedent( + """\ This flag controls the bandwidth of the Gaussian kernel - density estimator. (default: 0.13)""")) - fitMdl.add_argument("--grid-points", type=int, metavar="", default=10_000, help=tw.dedent( - """\ + density estimator. (default: 0.13)""" + ), + ) + fitMdl.add_argument( + "--grid-points", + type=int, + metavar="", + default=10_000, + help=tw.dedent( + """\ This flag controls how many grid points are used to describe the probability density function. Only applies if - `--model-type` is set to `grid`. (default: 10000)""")) - + `--model-type` is set to `grid`. (default: 10000)""" + ), + ) + fitMisc = parser_fit_model.add_argument_group("Miscellaneous options") - fitMisc.add_argument("--degrees", action="store_true", help=tw.dedent( - """\ + fitMisc.add_argument( + "--degrees", + action="store_true", + help=tw.dedent( + """\ Set this flag, if dihedrals in `model-data` are in degrees - instead of radians.""")) - fitMisc.add_argument("-v", "--verbose", action="count", default=0, help=tw.dedent( - """\ + instead of radians.""" + ), + ) + fitMisc.add_argument( + "-v", + "--verbose", + action="count", + default=0, + help=tw.dedent( + """\ Set verbosity level of screen output. Flag can be given multiple times (up to 2) to gradually increase output to - debugging mode.""")) - + debugging mode.""" + ), + ) + # ==================== # Subparser: analyze # ==================== - parser_analyze = subparsers.add_parser("analyze", description=tw.dedent( - """\ + parser_analyze = subparsers.add_parser( + "analyze", + description=tw.dedent( + """\ The `constava analyze` submodule analyzes the provided backbone dihedral angles and infers the propensities for each residue to reside in a given conformational state. @@ -146,128 +218,271 @@ def parse_parameters(cmdline_arguments): As input data the backbone dihedral angles extracted from the conformational ensemble need to be provided. Those can be generated using the `constava dihedrals` submodule (`--input-format csv`) or GROMACS' - `gmx chi` module (`--input-format xvg`)."""), - formatter_class=argparse.RawTextHelpFormatter) - + `gmx chi` module (`--input-format xvg`).""" + ), + formatter_class=argparse.RawTextHelpFormatter, + ) + anaIO = parser_analyze.add_argument_group("Input & output options") - anaIO.add_argument("-i", "--input", nargs="+", type=str, metavar="", - help="Input file(s) that contain the dihedral angles.") - anaIO.add_argument("--input-format", choices=["auto", "xvg", "csv"], default="auto", - help="Format of the input file: {'auto', 'csv', 'xvg'}") - anaIO.add_argument("-o", "--output", type=str, metavar="", - help="The file to write the results to.") - anaIO.add_argument("--output-format", choices=["auto", "csv", "json", "tsv"], default="auto", - help="Format of output file: {'csv', 'json', 'tsv'}. (default: 'auto')") + anaIO.add_argument( + "-i", + "--input", + nargs="+", + type=str, + metavar="", + help="Input file(s) that contain the dihedral angles.", + ) + anaIO.add_argument( + "--input-format", + choices=["auto", "xvg", "csv"], + default="auto", + help="Format of the input file: {'auto', 'csv', 'xvg'}", + ) + anaIO.add_argument( + "-o", + "--output", + type=str, + metavar="", + help="The file to write the results to.", + ) + anaIO.add_argument( + "--output-format", + choices=["auto", "csv", "json", "tsv"], + default="auto", + help="Format of output file: {'csv', 'json', 'tsv'}. (default: 'auto')", + ) anaMdl = parser_analyze.add_argument_group("Conformational state model options") - anaMdl.add_argument("-m", "--load-model", type=str, metavar="", help=tw.dedent( - """\ + anaMdl.add_argument( + "-m", + "--load-model", + type=str, + metavar="", + help=tw.dedent( + """\ Load a conformational state model from the given pickled - file. If not provided, the default model will be used.""")) - + file. If not provided, the default model will be used.""" + ), + ) + anaSmpl = parser_analyze.add_argument_group("Subsampling options") - anaSmpl.add_argument("--window", metavar="", type=int, nargs='+', help=tw.dedent( - """\ + anaSmpl.add_argument( + "--window", + metavar="", + type=int, + nargs="+", + help=tw.dedent( + """\ Do inference using a moving reading-frame. Each reading frame consists of consecutive samples. Multiple - values can be provided.""")) - anaSmpl.add_argument("--window-series", metavar="", type=int, nargs='+', help=tw.dedent( - """\ + values can be provided.""" + ), + ) + anaSmpl.add_argument( + "--window-series", + metavar="", + type=int, + nargs="+", + help=tw.dedent( + """\ Do inference using a moving reading-frame. Each reading frame consists of consecutive samples. Return the results for every window rather than the average. This can result in very large output files. Multiple values can be - provided.""")) - anaSmpl.add_argument("--bootstrap", metavar="", type=int, nargs='+', help=tw.dedent( - """\ + provided.""" + ), + ) + anaSmpl.add_argument( + "--bootstrap", + metavar="", + type=int, + nargs="+", + help=tw.dedent( + """\ Do inference using samples obtained through - bootstrapping. Multiple values can be provided.""")) - anaSmpl.add_argument("--bootstrap-series", metavar="", type=int, nargs='+', help=tw.dedent( - """\ + bootstrapping. Multiple values can be provided.""" + ), + ) + anaSmpl.add_argument( + "--bootstrap-series", + metavar="", + type=int, + nargs="+", + help=tw.dedent( + """\ Do inference using samples obtained through bootstrapping. Return the results for every subsample rather than the average. This can result in very - large output files. Multiple values can be provided.""")) - anaSmpl.add_argument("--bootstrap-samples", metavar="", type=int, default=500, help=tw.dedent( - """\ + large output files. Multiple values can be provided.""" + ), + ) + anaSmpl.add_argument( + "--bootstrap-samples", + metavar="", + type=int, + default=500, + help=tw.dedent( + """\ When bootstrapping, sample times from the input data. - (default: 500)""")) - + (default: 500)""" + ), + ) + anaMisc = parser_analyze.add_argument_group("Miscellaneous options") - anaMisc.add_argument("--degrees", action="store_true", help=tw.dedent( - """\ + anaMisc.add_argument( + "--degrees", + action="store_true", + help=tw.dedent( + """\ Set this flag, if dihedrals in the input files are in - degrees.""")) - anaMisc.add_argument("--precision", metavar="", type=int, default=4, - help="Sets the number of decimals in the output files.") - anaMisc.add_argument("--indent_size", metavar="", type=int, default=0, - help="Sets the number of spaces used to indent the output document.") - anaMisc.add_argument("--seed", metavar="", type=int, default=None, - required=False, help="Set random seed for bootstrap sampling") - anaMisc.add_argument("-v", "--verbose", action="count", default=0, help=tw.dedent( - """\ + degrees.""" + ), + ) + anaMisc.add_argument( + "--precision", + metavar="", + type=int, + default=4, + help="Sets the number of decimals in the output files.", + ) + anaMisc.add_argument( + "--indent_size", + metavar="", + type=int, + default=0, + help="Sets the number of spaces used to indent the output document.", + ) + anaMisc.add_argument( + "--seed", + metavar="", + type=int, + default=None, + required=False, + help="Set random seed for bootstrap sampling", + ) + anaMisc.add_argument( + "-v", + "--verbose", + action="count", + default=0, + help=tw.dedent( + """\ Set verbosity level of screen output. Flag can be given multiple times (up to 2) to gradually increase output to - debugging mode.""")) + debugging mode.""" + ), + ) # ===================== # Subparser: dihedrals # ===================== - parser_dihedrals = subparsers.add_parser("dihedrals", description=tw.dedent( - """\ + parser_dihedrals = subparsers.add_parser( + "dihedrals", + description=tw.dedent( + """\ The `constava dihedrals` submodule is used to extract the backbone dihedrals needed for the analysis from confromational ensembles. By default the results are written out in radians as this is the preferred format for `constava analyze`. Note: For the first and last residue in a protein only one backbone dihedral - can be extracted. Thus, those residues are omitted by default."""), - formatter_class=argparse.RawTextHelpFormatter) + can be extracted. Thus, those residues are omitted by default.""" + ), + formatter_class=argparse.RawTextHelpFormatter, + ) dihIO = parser_dihedrals.add_argument_group("Input & output options") - dihIO.add_argument("-s", "--structure", metavar="", - help="Structure file with atomic information: [pdb, gro, tpr]") - dihIO.add_argument("-f", "--trajectory", nargs="+", metavar="", - help="Trajectory file with coordinates: [pdb, gro, trr, xtc, crd, nc]") - dihIO.add_argument("-o", "--output", default=None, required=False, - help="CSV file to write dihedral information to. (default: dihedrals.csv)") - + dihIO.add_argument( + "-s", + "--structure", + metavar="", + help="Structure file with atomic information: [pdb, gro, tpr]", + ) + dihIO.add_argument( + "-f", + "--trajectory", + nargs="+", + metavar="", + help="Trajectory file with coordinates: [pdb, gro, trr, xtc, crd, nc]", + ) + dihIO.add_argument( + "-o", + "--output", + default=None, + required=False, + help="CSV file to write dihedral information to. (default: dihedrals.csv)", + ) + dihMisc = parser_dihedrals.add_argument_group("Input & output options") - dihMisc.add_argument("--selection", default="protein", - help="Selection for the dihedral calculation. (default: 'protein')") - dihMisc.add_argument("--precision", default=5, type=int, - help="Defines the number of decimals written for the dihedrals. (default: 5)") - dihMisc.add_argument("--degrees", action="store_true", - help="If set results are written in degrees instead of radians.") - dihMisc.add_argument("-O", "--overwrite", action="store_true", - help="If set any previously generated output will be overwritten.") + dihMisc.add_argument( + "--selection", + default="protein", + help="Selection for the dihedral calculation. (default: 'protein')", + ) + dihMisc.add_argument( + "--precision", + default=5, + type=int, + help="Defines the number of decimals written for the dihedrals. (default: 5)", + ) + dihMisc.add_argument( + "--degrees", + action="store_true", + help="If set results are written in degrees instead of radians.", + ) + dihMisc.add_argument( + "-O", + "--overwrite", + action="store_true", + help="If set any previously generated output will be overwritten.", + ) # ====================== # Subparser: test # ====================== - parser_test = subparsers.add_parser("test", description=tw.dedent( - """\ + parser_test = subparsers.add_parser( + "test", + description=tw.dedent( + """\ The `constava test` submodule runs a couple of test cases to check, if consistent results are achieved. This should be done once after - installation and takes about a minute."""), - formatter_class=argparse.RawTextHelpFormatter) + installation and takes about a minute.""" + ), + formatter_class=argparse.RawTextHelpFormatter, + ) + + parser_test.add_argument( + "-v", + "--verbose", + action="count", + default=0, + help=tw.dedent( + """\ + Set verbosity level of screen output. Flag can be given + multiple times (up to 2) to gradually increase output to + debugging mode.""" + ), + ) # Parse command line arguments return parser.parse_args(cmdline_arguments) + def run_fit_model(args): """Run fit-model subcommand when invoked from command line""" # Initialze and run Constava cva = Constava(ConstavaParameters(verbose=args.verbose)) csmodel = cva.fit_csmodel( - model_type = args.model_type, - model_data = args.input, - kde_bandwidth = args.kde_bandwidth, - grid_points = args.grid_points, - model_data_degrees = args.degrees) + model_type=args.model_type, + model_data=args.input, + kde_bandwidth=args.kde_bandwidth, + grid_points=args.grid_points, + model_data_degrees=args.degrees, + ) # Write the fitted model out as a pickle csmodel.dump_pickle(args.output) + def run_analyze(args): """Run analyze subcommand when invoked from command line.""" # Convert command line arguments to ConstavaParameters @@ -290,6 +505,7 @@ def run_analyze(args): cva = Constava(params) cva.run() + def run_dihedrals(args): """Run analyze subcommand when invoked from command line.""" # Set output to default value if needed @@ -297,12 +513,14 @@ def run_dihedrals(args): if not args.overwrite and os.path.exists(args.output): raise FileExistsError(f"Cannot overwrite existing file: {args.output}") # Calculate dihedrals - dihedrals = calculate_dihedrals(args.structure, args.trajectory, - args.selection, args.degrees) + dihedrals = calculate_dihedrals( + args.structure, args.trajectory, args.selection, args.degrees + ) # Write results - float2str = f"%.{args.precision}f" # Definition of float format in output + float2str = f"%.{args.precision}f" # Definition of float format in output dihedrals.to_csv(args.output, header=True, index=False, float_format=float2str) + def main(): """main function executed when running script in command line mode""" # Parse command line parameters @@ -314,7 +532,8 @@ def main(): elif args.subcommand == "dihedrals": run_dihedrals(args) elif args.subcommand == "test": - run_unittest() + run_unittest(args.verbose) + if __name__ == "__main__": sys.exit(main()) diff --git a/constava/calc/calculator.py b/constava/calc/calculator.py index 4880356..c523480 100644 --- a/constava/calc/calculator.py +++ b/constava/calc/calculator.py @@ -12,7 +12,6 @@ import numpy as np import tqdm -from tqdm.contrib.logging import logging_redirect_tqdm from .subsampling import SubsamplingABC, SubsamplingMethodError from .csmodels import ConfStateModelABC from ..utils.ensembles import ProteinEnsemble @@ -194,9 +193,6 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: except AttributeError: max_workers = multiprocessing.cpu_count() - if max_workers > 2: - max_workers = max_workers - 1 - max_in_flight = max_workers * 2 future_to_idx = defaultdict() @@ -219,43 +215,42 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: bar_format="{l_bar}{bar} | {n_fmt}/{total_fmt} " "[{rate_fmt}, elapsed: {elapsed}, remaining: {remaining}]", ) as progress_bar: - with logging_redirect_tqdm(): - # Prime the pipeline - for _ in range(min(max_in_flight, n_residues)): - idx, residue = next(it) - phi_psi_angles = np.ascontiguousarray(residue.phipsi) + # Prime the pipeline + for _ in range(min(max_in_flight, n_residues)): + idx, residue = next(it) + phi_psi_angles = np.ascontiguousarray(residue.phipsi) - fut = process_pool_executor.submit( - _self_compute_logpdf_worker, phi_psi_angles - ) + fut = process_pool_executor.submit( + _self_compute_logpdf_worker, phi_psi_angles + ) - future_to_idx[fut] = idx - in_flight.add(fut) + future_to_idx[fut] = idx + in_flight.add(fut) - completed = 0 - while in_flight: - done, in_flight = wait(in_flight, return_when=FIRST_COMPLETED) + completed = 0 + while in_flight: + done, in_flight = wait(in_flight, return_when=FIRST_COMPLETED) - for fut in done: - idx = future_to_idx.pop(fut) - logpdf_state_propens_variabs[idx] = fut.result() + for fut in done: + idx = future_to_idx.pop(fut) + logpdf_state_propens_variabs[idx] = fut.result() - progress_bar.update(1) - completed += 1 + progress_bar.update(1) + completed += 1 - # refill - try: - idx, residue = next(it) - phi_psi_angles = np.ascontiguousarray(residue.phipsi) - except StopIteration: - continue + # refill + try: + idx, residue = next(it) + phi_psi_angles = np.ascontiguousarray(residue.phipsi) + except StopIteration: + continue - nfut = process_pool_executor.submit( - _self_compute_logpdf_worker, phi_psi_angles - ) + nfut = process_pool_executor.submit( + _self_compute_logpdf_worker, phi_psi_angles + ) - future_to_idx[nfut] = idx - in_flight.add(nfut) + future_to_idx[nfut] = idx + in_flight.add(nfut) logger.debug(f"Building results for the {len(self.methods)} methods...") diff --git a/constava/calc/csmodels.py b/constava/calc/csmodels.py index 999b60a..3d447ea 100644 --- a/constava/calc/csmodels.py +++ b/constava/calc/csmodels.py @@ -56,7 +56,8 @@ def get_logpdf(self, data: np.ndarray) -> np.ndarray: """ pass - @abc.abstractclassmethod + @classmethod + @abc.abstractmethod def from_fitting(cls, training_data_json: str, **kwargs): pass @@ -82,6 +83,7 @@ def from_pickle(cls, pickled_file: str): """ with open(pickled_file, "rb") as fhandle: csmodel = pickle.load(fhandle) + if not isinstance(csmodel, cls): raise ConfStateModelLoadingError( ( @@ -137,15 +139,9 @@ def __init__(self, state_labels: List[str], state_kdes: List[KernelDensity]): self.state_kdes = tuple(state_kdes) def get_logpdf(self, data: np.ndarray) -> np.ndarray: - X = np.ascontiguousarray(data, dtype=np.float64) - - n_states = len(self.state_kdes) - n_samples = X.shape[0] - - result = np.empty((n_states, n_samples), dtype=np.float64) - for i, kde in enumerate(self.state_kdes): - result[i, :] = kde.score_samples(X) - + result = np.stack([ + kde.score_samples(data) for kde in self.state_kdes + ]) return result @classmethod @@ -170,13 +166,15 @@ def from_fitting( Probabilistic model of conformational states based on a Gaussian kernel density estimator """ - with open(training_data_json, "r") as fhandle: + with open(training_data_json, "r", encoding="utf-8") as fhandle: training_data = json.load(fhandle) + # Iterate over conformational states and train pdf estimator kde_list, lbl_list = [], [] for label, data in training_data.items(): data = np.radians(data) if in_degrees else np.array(data) check_dihedral_range(data) + # Fit Gaussian kernel density estimator kde = KernelDensity(bandwidth=bandwidth) kde.fit(data) @@ -223,22 +221,13 @@ def __init__( self, state_labels: List[str], state_grids: np.ndarray, grid_crds: Tuple ): self.state_labels = tuple(state_labels) - self.state_grids = [ - np.ascontiguousarray(g, dtype=np.float64) for g in state_grids - ] + self.state_grids = state_grids self.grid_crds = grid_crds def get_logpdf(self, data: np.ndarray) -> np.ndarray: - X = np.ascontiguousarray(data, dtype=np.float64) - - n_states = len(self.state_grids) - n_samples = X.shape[0] - - result = np.empty((n_states, n_samples), dtype=np.float64) - - for i, grid in enumerate(self.state_grids): - result[i, :] = interpn(self.grid_crds, grid, X) - + result = np.stack([ + interpn(self.grid_crds, grid, data) for grid in self.state_grids + ]) return result @classmethod @@ -282,8 +271,9 @@ def from_fitting( ) # Infer PDF grid from a KDE model kde_model = ConfStateModelKDE.from_fitting( - training_data_json, bandwidth=0.13, in_degrees=in_degrees + training_data_json, bandwidth=bandwidth, in_degrees=in_degrees ) _labels = kde_model.get_labels() _grids = np.reshape(kde_model.get_logpdf(gridcrds), (-1, n, n), order="C") + return cls(state_labels=_labels, state_grids=_grids, grid_crds=(_phi, _psi)) diff --git a/constava/calc/subsampling.py b/constava/calc/subsampling.py index 82b4c9d..9c12c8c 100644 --- a/constava/calc/subsampling.py +++ b/constava/calc/subsampling.py @@ -1,4 +1,4 @@ -"""constava.methods contains classes representing subsampling schemes fro the +"""constava.methods contains classes representing sub-sampling schemes fro the probability state propensity calculations. Multiple of these schemes can be dynamically defined on run-time.""" @@ -7,7 +7,7 @@ from typing import Optional class SubsamplingMethodError(ValueError): - """Raised when there are no subsampling methods passed to the calculator""" + """Raised when there are no sub-sampling methods passed to the calculator""" pass class SubsamplingABC(metaclass=abc.ABCMeta): @@ -253,9 +253,10 @@ def _subsampling(self, logpdf): # distribution. -> Array[n_states, n_samples, sample_size] rng = np.random.default_rng(self.seed) samples = rng.integers(n_measurements, size=self.sample_size * self.n_samples) + logpdf = np.reshape( - logpdf[:,samples], (n_states, self.n_samples, self.sample_size), - order="C") + logpdf[:, samples], (n_states, self.n_samples, self.sample_size),order="C" + ) # Accumulate logpdfs within a sample = logpdf for each of these samples # to be sampled from the same conformational state diff --git a/constava/data/constava_testdata.tgz b/constava/data/constava_testdata.tgz deleted file 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b/constava/data/constava_testdata/xvg/ramaPhiPsiALA18.xvg new file mode 100644 index 0000000..65220cf --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiALA18.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -69.977 -38.8943 + -58.6285 -41.5784 + -56.8258 -39.2284 + -64.0002 -41.0959 + -71.8404 -36.1627 + -66.3372 -36.8324 + -56.4627 -51.495 + -54.9343 -40.9863 + -57.4485 -42.6722 + -59.481 -38.3147 + -59.2806 -44.2375 + -59.5897 -44.4555 + -58.5681 -38.4243 + -55.1649 -48.8579 + -80.3551 -31.6699 + -59.4868 -31.8371 + -68.3125 -36.4705 + -68.4594 -39.3007 + -61.2507 -38.4114 + -62.0661 -30.2265 + -62.0436 -36.44 + -63.6976 -36.0248 + -56.9324 -40.2755 + -61.527 -32.6244 + -45.4653 -49.4835 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiALA19.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiALA19.xvg new file mode 100644 index 0000000..30f7dd3 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiALA19.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -75.4872 -17.6648 + -66.9022 -32.6871 + -67.1887 -31.8829 + -60.2405 -43.6356 + -69.6513 -26.6054 + -65.8675 -42.3495 + -68.136 -40.4671 + -70.4895 -33.6945 + -60.1302 -35.3151 + -74.4131 -23.6793 + -62.1179 -44.8482 + -55.4056 -43.0754 + -59.6432 -32.8489 + -63.9693 -36.4876 + -64.2119 -41.3707 + -64.752 -20.1831 + -62.8115 -40.8596 + -72.7087 -16.331 + -69.3938 -40.5049 + -80.2946 -17.4096 + -67.2834 -27.1436 + -78.9865 -16.9284 + -61.3557 -49.346 + -61.0762 -43.3649 + -69.9743 -39.0576 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiALA26.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiALA26.xvg new file mode 100644 index 0000000..33122ec --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiALA26.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -78.2176 -12.8407 + -63.6687 -37.0708 + -60.4393 -34.3637 + -58.293 -43.6674 + -61.0636 -49.6124 + -67.6552 -33.9583 + -64.3298 -38.1885 + -67.1706 -41.172 + -68.1174 -30.6837 + -66.9888 -38.874 + -61.0564 -44.8444 + -54.2585 -48.4123 + -58.8691 -49.8332 + -50.6003 -55.4731 + -71.6762 -36.1857 + -66.2534 -31.0372 + -59.0057 -41.1664 + -56.6521 -40.0902 + -62.4077 -39.9261 + -59.5843 -36.2625 + -53.9696 -40.5172 + -59.9662 -47.2276 + -67.7947 -26.8764 + -61.3336 -32.3155 + -56.9008 -44.9878 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiARG29.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiARG29.xvg new file mode 100644 index 0000000..132124e --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiARG29.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -65.6365 -31.4058 + -64.864 -47.4203 + -59.9178 -52.4209 + -59.8031 -38.5547 + -60.7739 -42.2233 + -67.4755 -31.0249 + -72.0532 -37.6716 + -82.1811 -16.7054 + -73.8088 -24.0615 + -62.9173 -39.1244 + -69.3825 -35.3613 + -64.4864 -31.2529 + -66.1503 -50.3092 + -60.8585 -40.2115 + -60.7357 -49.6778 + -62.7703 -40.2346 + -76.0201 -27.4844 + -67.8327 -44.5855 + -59.1269 -49.0375 + -57.9132 -50.4387 + -63.4934 -46.706 + -60.2384 -31.4484 + -73.6607 -39.0191 + -55.926 -38.9951 + -86.5127 -0.584101 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiASN15.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiASN15.xvg new file mode 100644 index 0000000..b59701b --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiASN15.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -59.8154 -33.7455 + -61.225 -46.2676 + -67.654 -41.4153 + -62.7125 -35.8312 + -55.2804 -39.7899 + -55.4242 -49.3875 + -63.8238 -43.8857 + -65.3185 -41.3174 + -64.7925 -43.4814 + -61.4725 -39.8809 + -64.2331 -38.57 + -61.5685 -44.6794 + -55.9821 -50.9809 + -60.2997 -44.3093 + -65.2003 -42.7896 + -55.5939 -45.2789 + -67.4947 -31.8467 + -67.9034 -38.6345 + -66.2946 -39.261 + -58.4745 -38.3955 + -62.9086 -33.7705 + -59.5431 -44.4827 + -60.4397 -35.2855 + -58.6715 -47.103 + -70.9334 -29.6351 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiASN27.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiASN27.xvg new file mode 100644 index 0000000..70e5c2b --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiASN27.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -76.0332 -37.7923 + -68.0223 -48.5336 + -56.8314 -45.4196 + -82.6338 -37.2975 + -54.3602 -35.9033 + -56.4268 -40.149 + -72.4157 -41.4313 + -58.8258 -42.4618 + -63.6445 -44.8335 + -66.7995 -35.2146 + -64.2225 -36.1593 + -62.1109 -44.157 + -58.3288 -39.7266 + -56.3314 -39.4012 + -62.1673 -45.9699 + -79.0584 -27.9079 + -62.8035 -44.9818 + -63.9313 -43.5565 + -64.0261 -38.6229 + -57.9733 -41.1211 + -62.8605 -44.5949 + -58.1554 -39.5405 + -72.5757 -34.8506 + -59.0564 -41.0338 + -58.0318 -42.5154 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiASN3.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiASN3.xvg new file mode 100644 index 0000000..a340184 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiASN3.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -68.5968 158.22 + -128.524 165.682 + -106.799 115.446 + -73.487 164.396 + -70.8714 152.654 + -84.6823 130.2 + -117.589 132.615 + -90.9702 142.699 + -58.1784 154.479 + -104.887 126.115 + -95.6711 136.26 + -115.736 135.533 + -116.289 129.49 + -93.6141 147.179 + -82.9878 134.439 + -112.306 140.307 + -95.761 138.527 + -95.1143 144.276 + -109.697 142.67 + -118.985 140.277 + -81.9833 130.258 + -88.0109 149.531 + -96.7936 112.825 + -58.7845 135.587 + -125.279 160.102 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiASN32.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiASN32.xvg new file mode 100644 index 0000000..973e481 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiASN32.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -86.3496 -49.5397 + -117.245 3.63639 + -117.508 4.67715 + -112.013 -21.9737 + -90.8556 -24.5084 + -67.8667 -43.8326 + -93.6696 0.728385 + -82.0973 -27.6236 + -76.7358 -23.9718 + -98.7762 0.55813 + -97.2108 4.95765 + -105.66 0.408476 + -77.7384 -25.598 + -101.615 0.453498 + -87.6595 -5.33222 + -82.5207 -14.3554 + -91.7632 -9.11051 + -87.0378 -14.8731 + -70.4841 -55.2938 + -83.0155 -20.5163 + -89.5593 -15.0827 + -83.8368 -11.1372 + -89.5427 -13.2401 + -106.662 9.93853 + -74.1924 -19.7083 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiASP12.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiASP12.xvg new file mode 100644 index 0000000..06bbeda --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiASP12.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -84.2966 -8.08449 + -159.105 -14.0983 + -137.889 -20.3528 + -134.014 -20.4816 + -152.782 -2.77705 + -134.624 -2.29327 + -151.701 1.06338 + -117.694 -21.164 + -157.746 -6.05811 + -127.462 1.32079 + -153.994 -7.49357 + -153.048 -25.459 + -113.153 -77.9532 + -157.853 -9.56973 + -133.981 -11.2973 + -126.465 -11.3882 + -141.851 0.3947 + -106.999 -11.1844 + -138.018 -20.2732 + -148.526 -5.46934 + -125.5 -7.84833 + -159.821 -7.41768 + -131.94 -14.3285 + -138.29 -15.2751 + -153.952 -13.1196 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiASP37.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiASP37.xvg new file mode 100644 index 0000000..21c5af5 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiASP37.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -94.8511 -7.74207 + -88.179 -18.2662 + -82.6577 -35.6931 + -76.8575 -18.7037 + -106.621 -4.90861 + -104.399 -29.2694 + -105.998 -24.5497 + -91.2896 -0.376097 + -111.236 -13.7529 + -107.955 -25.2282 + -87.3301 -19.4219 + -86.3458 -37.0459 + -104.932 -39.9683 + -93.3162 -24.3842 + -92.3178 -26.139 + -101.397 -6.24733 + -96.4379 -14.1164 + -93.8721 19.2451 + -109.246 -1.4636 + -100.265 -36.9337 + -88.7635 -28.5627 + -110.94 -12.4994 + -106.744 -26.4059 + -94.4393 -16.1028 + -102.825 -6.23328 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiGLN20.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiGLN20.xvg new file mode 100644 index 0000000..366612b --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiGLN20.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -65.7817 -37.8694 + -65.7021 -34.8871 + -66.69 -46.4891 + -64.3724 -33.9125 + -61.9784 -48.1193 + -61.8636 -41.6754 + -60.759 -45.6846 + -66.0927 -33.4013 + -64.7673 -44.8269 + -56.97 -43.159 + -56.725 -41.4043 + -82.4472 -13.8942 + -67.2993 -38.3385 + -57.8931 -37.1151 + -62.6161 -43.3311 + -79.7253 -29.6185 + -54.85 -45.8453 + -65.6718 -40.439 + -60.5641 -38.0672 + -68.1031 -38.2128 + -59.5361 -42.1575 + -65.5308 -43.743 + -55.9728 -41.1013 + -76.8292 -31.6565 + -53.6981 -50.8697 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiGLN43.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiGLN43.xvg new file mode 100644 index 0000000..a4dc092 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiGLN43.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -63.3991 149.789 + -70.042 154.195 + -87.8307 142.071 + -58.53 134.759 + -67.099 135.862 + -67.9631 140.355 + -63.1338 146.733 + -83.1827 148.994 + -108.952 134.195 + -63.1095 146.687 + -66.6234 137.241 + -65.6144 144.836 + -54.7697 141.035 + -61.0789 146.753 + -70.1762 143.001 + -55.628 141.159 + -75.3933 142.764 + -56.2397 144.359 + -59.6232 148.116 + -93.8119 158.987 + -64.7497 133.399 + -63.7466 162.9 + -77.0224 136.597 + -68.4267 148.584 + -80.3374 145.486 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiGLY1.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiGLY1.xvg new file mode 100644 index 0000000..f0ca227 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiGLY1.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + 115.553 -166.365 + -85.6893 -159.921 + -102.448 -122.227 + 121.432 145.57 + 119.953 -165.248 + 95.4345 -175.494 + 128.863 166.355 + -12.2407 151.959 + 7.01884 179.063 + 107.124 -172.261 + -115.232 -132.275 + 100.879 -104.916 + -129.648 -121.815 + -2.04525 -174.47 + 132.237 -168.644 + -116.359 178.057 + 118.794 -176.658 + 103.684 133.624 + 123.05 -174.427 + -20.2604 -129.645 + -136.065 -175.764 + 111.96 137.601 + -13.1379 -155.557 + -121.145 175.842 + -127.083 -176.301 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiGLY11.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiGLY11.xvg new file mode 100644 index 0000000..f09c673 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiGLY11.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + 82.4407 130.386 + 99.8417 177.984 + 141.693 150.238 + 149.87 155.104 + 108.994 -172.535 + 81.2472 158.043 + 73.9161 -170.801 + 115.234 169.416 + 70.1543 178.891 + 103.131 144.104 + 84.9164 -159.375 + 103.263 171.791 + 61.7901 179.749 + 71.7059 -166.104 + 64.635 -179.242 + 98.7111 176.612 + 93.2352 169.359 + 73.3979 147.829 + 93.1167 177.214 + 104.22 -173.504 + 84.1445 171.325 + 82.3377 -173.734 + 92.8793 164.969 + 96.4121 168.862 + 164.927 170.306 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiGLY22.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiGLY22.xvg new file mode 100644 index 0000000..f6e244f --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiGLY22.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + 53.8055 59.7266 + 53.6222 40.5077 + 80.2462 39.6485 + 73.7631 40.971 + 88.1713 21.8278 + 105.624 19.0884 + 99.7923 45.4826 + 73.7264 39.6537 + 77.1024 30.1348 + 65.244 42.7563 + 87.5379 11.4128 + 68.2238 31.6964 + 80.6102 8.81374 + 67.0376 30.0979 + 68.4343 40.6261 + 90.245 30.2499 + 54.4105 32.1567 + 84.6578 46.2306 + 77.3739 37.5356 + 52.9905 41.8111 + 79.6959 46.2329 + 69.6357 45.119 + 95.0253 25.5472 + 73.7673 43.9505 + 84.0498 38.45 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiGLY33.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiGLY33.xvg new file mode 100644 index 0000000..182f6fe --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiGLY33.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + 81.6976 63.5281 + 58.9777 43.4816 + 63.8162 31.2938 + 69.9945 33.9137 + 74.0253 27.754 + 104.962 25.3666 + 54.2303 35.0805 + 79.2808 42.3684 + 65.8475 33.921 + 66.123 48.0019 + 53.7329 35.9217 + 77.6868 22.2727 + 71.2075 21.3187 + 68.8467 26.6388 + 59.235 35.9392 + 74.1354 37.1098 + 83.3853 18.7945 + 84.0146 16.4932 + 124.594 16.1537 + 83.0138 33.4906 + 65.0311 36.267 + 62.2808 46.3476 + 65.0379 48.7252 + 62.7612 22.7655 + 79.0625 16.1465 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiGLY36.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiGLY36.xvg new file mode 100644 index 0000000..c62cc6e --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiGLY36.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + 80.0797 136.72 + -65.1734 147.766 + -56.437 140.947 + -61.9771 121.634 + 65.5893 179.025 + 92.6318 160.199 + 83.202 -179.104 + 86.4371 158.226 + 58.06 179.39 + 73.1089 -179.218 + 98.6128 149.589 + 92.7465 168.326 + 56.96 -179.116 + 99.7659 159.747 + 129.786 142.538 + 54.591 -174.165 + 65.5667 -172.904 + 76.964 -178.234 + 47.849 -136.515 + 54.5284 -158.338 + 95.8126 160.369 + 73.9145 -173.201 + 71.4746 177.954 + 68.8447 160.313 + 60.366 -160.121 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiGLY42.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiGLY42.xvg new file mode 100644 index 0000000..6493d2f --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiGLY42.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + 129.577 -2.83145 + 86.8891 5.31449 + 95.0913 -0.854466 + 121.686 -40.9867 + 96.8279 6.29641 + 75.2252 13.2918 + 118.208 -8.61314 + 82.2399 17.318 + 70.8032 29.3299 + 81.7692 8.05546 + 121.335 -15.299 + 107.888 -15.3551 + 100.971 -5.35302 + 111.783 -16.3603 + 114.214 -6.00062 + 87.3817 -8.056 + 110.652 -6.52283 + 127.7 -33.9407 + 102.136 -17.4581 + 108.039 2.25197 + 87.4675 6.53038 + 95.7425 -2.05629 + 117.341 -2.0799 + 117.105 -13.9612 + 99.5863 17.2906 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiGLY50.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiGLY50.xvg new file mode 100644 index 0000000..24ce891 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiGLY50.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + 37.4598 -140.299 + 70.882 174.069 + 64.9118 164.578 + 53.0611 -158.316 + -60.4306 -173.211 + 73.6654 155.79 + 62.1639 -146.48 + 58.1511 -160.008 + 82.7623 -137.676 + 68.5732 -161.097 + 79.0159 -174.424 + 57.093 177.451 + 52.4385 -170.687 + 65.8211 168.242 + 75.9184 150.477 + 80.4887 156.896 + 61.9464 -175.533 + 43.2774 -134.026 + 64.9216 -168.512 + 68.7267 -172.924 + 57.6631 -166.296 + 50.6991 -151.181 + 61.0746 -157.206 + 81.8324 175.698 + 75.2657 -170.73 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiILE17.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiILE17.xvg new file mode 100644 index 0000000..27e707b --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiILE17.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -94.5011 -9.36939 + -60.6415 -47.7531 + -66.0468 -39.2702 + -53.5538 -41.7525 + -68.0456 -34.0297 + -56.308 -43.937 + -65.4853 -43.4305 + -63.0039 -48.3292 + -51.6675 -53.7176 + -52.7177 -46.223 + -52.5622 -36.4445 + -65.0545 -44.5162 + -64.8718 -47.3552 + -70.0855 -38.1027 + -65.3136 -37.0222 + -60.6756 -51.498 + -80.875 -29.9579 + -58.8752 -39.1099 + -56.259 -44.3044 + -50.5925 -47.9112 + -68.9886 -43.6329 + -75.6925 -34.1242 + -53.0479 -56.2509 + -59.1693 -44.6797 + -68.9221 -50.7459 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiILE34.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiILE34.xvg new file mode 100644 index 0000000..10ad549 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiILE34.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -110.761 136.65 + -125.652 148.978 + -123.037 119.933 + -113.789 137.255 + -80.2517 134.567 + -63.7395 111.291 + -75.2243 115.649 + -59.0477 132.172 + -77.8275 134.102 + -96.5333 133.799 + -75.8114 127.927 + -76.4351 149.19 + -46.2998 118.692 + -74.5296 119.582 + -68.5511 108.443 + -90.0012 112.733 + -66.9948 149.08 + -65.2133 125.082 + -72.7111 120.766 + -82.4187 126.758 + -52.6106 111.955 + -67.7383 136.564 + -71.2341 140.996 + -66.1715 136.013 + -54.2779 137.427 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiILE39.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiILE39.xvg new file mode 100644 index 0000000..bb2cdd9 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiILE39.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -95.4353 126.179 + -113.266 137.066 + -123.131 146.467 + -131.442 130.668 + -112.612 121.387 + -82.6148 130.073 + -130.084 124.846 + -117.98 121.388 + -115.658 147.465 + -122.696 138.337 + -116.226 128.916 + -100.753 128.222 + -93.1446 128.443 + -120.052 108.776 + -102.633 137.727 + -118.054 142.734 + -128.519 142.195 + -157.004 150.708 + -130.254 131.893 + -112.94 136.887 + -102.455 112.793 + -107.132 118.631 + -124.836 147.429 + -97.1969 150.14 + -137.848 134.632 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiILE46.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiILE46.xvg new file mode 100644 index 0000000..8d2602f --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiILE46.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -87.8235 138.548 + -77.8293 129.062 + -73.2415 116.938 + -65.9706 122.818 + -118.638 137.052 + -125.713 128.111 + -123.427 117.37 + -123.287 129.179 + -118.168 128.446 + -111.426 133.031 + -123.241 122.473 + -91.9004 130.315 + -138.428 138.85 + -115.793 124.996 + -127.953 127.187 + -137.26 144.381 + -142.362 127.454 + -113.879 125.544 + -126.253 121.654 + -122.28 134.768 + -114.412 129.024 + -105.117 108.899 + -124.934 113.983 + -113.756 104.74 + -129 103.576 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiLEU14.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiLEU14.xvg new file mode 100644 index 0000000..b10949e --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiLEU14.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -47.3761 -49.4359 + -51.5956 -35.1069 + -51.9304 -42.3186 + -53.9678 -45.2263 + -55.2931 -51.6268 + -54.8887 -45.6071 + -58.3315 -47.5191 + -20.8784 -59.1733 + -41.5164 -41.8482 + -60.79 -44.1932 + -64.1218 -43.4068 + -58.8321 -41.5341 + -52.8632 -42.1493 + -51.7804 -49.564 + -62.4758 -43.7762 + -39.938 -59.67 + -55.6002 -41.4391 + -59.8931 -52.2402 + -51.0599 -42.7466 + -53.6025 -46.7576 + -59.0983 -41.826 + -55.3595 -53.2352 + -60.1841 -47.3011 + -59.5762 -40.8873 + -62.1555 -38.1528 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiLEU28.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiLEU28.xvg new file mode 100644 index 0000000..372e39c --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiLEU28.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -70.909 -29.1473 + -57.9639 -37.8211 + -71.1826 -37.6463 + -57.7776 -40.606 + -64.6404 -49.7101 + -66.0659 -49.8439 + -62.3888 -34.3761 + -65.1919 -43.5876 + -64.5229 -42.6324 + -62.9483 -51.3837 + -60.7814 -44.54 + -58.6 -48.5809 + -70.0821 -47.2278 + -58.9417 -47.5668 + -59.5722 -49.3951 + -64.6375 -41.0463 + -64.0405 -39.9224 + -63.5176 -44.2405 + -66.3189 -42.2467 + -78.632 -30.7447 + -65.1921 -45.1385 + -71.4131 -49.3914 + -68.7096 -42.2209 + -58.7026 -46.1205 + -70.1037 -46.627 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiLEU38.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiLEU38.xvg new file mode 100644 index 0000000..1f29988 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiLEU38.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -56.0027 110.021 + -78.1111 120.239 + -76.999 114.866 + -66.7907 130.196 + -104.746 109.521 + -75.5842 84.2362 + -85.7359 125.147 + -85.3783 123.113 + -98.3187 124.533 + -70.1357 119.069 + -83.9033 124.582 + -84.3319 103.755 + -69.421 108.821 + -86.059 118.377 + -95.7565 114.801 + -86.7699 125.245 + -86.7617 120.754 + -130.933 162.804 + -96.1329 132.391 + -59.1499 108.555 + -118.44 138.762 + -92.8107 116.717 + -84.1152 125.475 + -94.448 107.741 + -86.8828 156.965 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiLEU45.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiLEU45.xvg new file mode 100644 index 0000000..09aeb88 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiLEU45.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -108.986 120.66 + -121.989 121.556 + -115.029 109.018 + -90.0942 123.275 + -138.667 118.516 + -124.683 130.627 + -114.103 130.863 + -112.361 136.676 + -130.744 137.515 + -128.011 123.761 + -116.658 124.072 + -101.277 116.126 + -95.2211 157.073 + -112.592 139.36 + -109.866 136.374 + -127.267 142.821 + -127.033 137.506 + -119.506 118.109 + -109.611 135.665 + -114.696 145.387 + -111.097 132.234 + -90.7959 128.12 + -127.961 137.166 + -128.319 112.782 + -114.292 133.869 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiLYS16.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiLYS16.xvg new file mode 100644 index 0000000..8f548fc --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiLYS16.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -60.1661 -46.8856 + -68.0584 -37.304 + -64.9067 -39.7044 + -69.291 -40.3046 + -73.5581 -34.9943 + -68.6748 -40.6436 + -70.3395 -31.694 + -64.1123 -41.9303 + -61.7019 -49.0082 + -68.9662 -52.4751 + -75.3393 -51.9056 + -62.0058 -41.6732 + -54.8219 -42.2327 + -63.998 -38.8075 + -60.8468 -46.2306 + -77.5703 -27.3142 + -77.1268 -30.2281 + -60.4627 -48.3236 + -63.6055 -39.4032 + -71.6909 -45.2127 + -89.4512 -23.6356 + -59.66 -45.9243 + -72.9485 -39.5991 + -59.3056 -52.2867 + -75.3759 -35.2104 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiLYS44.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiLYS44.xvg new file mode 100644 index 0000000..6f716a3 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiLYS44.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -118.81 119.343 + -112.813 119.4 + -89.7025 122.967 + -110.995 105.711 + -90.896 142.815 + -109.617 132.83 + -107.757 131.365 + -111.387 130.015 + -82.0455 144.195 + -111.289 127.316 + -83.7951 138.468 + -111.64 126.885 + -102.667 108.411 + -116.259 132.651 + -99.6246 121.582 + -98.246 128.773 + -82.7521 137.985 + -104.436 131.911 + -94.7404 147.259 + -105.864 136.764 + -97.2112 102.909 + -116.38 140.353 + -83.8568 146.04 + -103.225 143.11 + -103.442 124.087 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiLYS48.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiLYS48.xvg new file mode 100644 index 0000000..9b76899 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiLYS48.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -148.751 154.412 + -110.495 131.328 + -113.488 161.219 + -82.0387 119.373 + -66.5833 95.9781 + -80.3068 123.314 + -70.9571 128.702 + -82.3456 135.284 + -77.8656 140.504 + -70.4073 130.797 + -107.077 137.459 + -109.939 135.614 + -92.613 109.069 + -140.115 167.714 + -86.0599 123.365 + -114.314 120.073 + -95.4855 115.089 + -94.7387 116.464 + -85.4934 128.109 + -106.255 131.093 + -78.3215 130.029 + -125.566 108.434 + -85.8833 121.404 + -117.664 142.543 + -88.0517 144.36 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiLYS49.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiLYS49.xvg new file mode 100644 index 0000000..b2785b9 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiLYS49.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -83.837 126.187 + -159.494 157.631 + -72.1389 154.352 + -74.9086 154.642 + -125.86 6.49518 + -118.127 115.312 + -114.824 147.115 + -111.275 158.45 + -129.981 -176.083 + -116.73 135.39 + -114.046 130.18 + -120.223 134.552 + -91.297 135.913 + -134.038 152.793 + -114.606 131.766 + -89.9557 127.402 + -83.506 123.558 + -74.2787 134.745 + -108.543 125.292 + -131.495 133.372 + -101.307 149.261 + -61.5482 132.46 + -82.3099 120.17 + -101.367 105.602 + -126.299 136.491 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiLYS9.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiLYS9.xvg new file mode 100644 index 0000000..2649600 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiLYS9.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -115.337 11.8357 + -113.867 -65.4089 + -116.499 -51.5195 + -99.8839 -11.3659 + -124.653 -12.1658 + -90.3879 -38.9612 + -105.524 -7.96882 + -106.866 -35.7338 + -113.423 10.9463 + -108.488 -35.5731 + -101.972 -26.5307 + -104.888 -25.1728 + -90.2655 -30.1858 + -109.351 -15.0201 + -127.059 -1.86428 + -128.844 -2.55573 + -111.97 -6.27519 + -137.159 -30.147 + -101.56 -16.5085 + -110.062 -8.68745 + -109.351 -13.2678 + -105.534 -12.8193 + -106.173 14.2146 + -113.047 10.8203 + -111.406 -30.2876 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiPHE40.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiPHE40.xvg new file mode 100644 index 0000000..aef1dd7 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiPHE40.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -97.2434 151.169 + -122.269 144.959 + -113.767 147.068 + -100.245 140.312 + -96.2344 149.157 + -99.0824 152.831 + -90.1408 150.249 + -85.294 155.688 + -112.303 156.018 + -89.0277 164.688 + -99.523 159.592 + -81.176 152.613 + -83.3828 134.729 + -59.2144 142.68 + -119.103 146.559 + -119.574 159.547 + -104.734 146.115 + -122.177 164.803 + -83.0799 157.634 + -98.122 156.011 + -70.4314 150.254 + -76.7619 146.777 + -121.283 150.5 + -104.522 151.313 + -123.532 140.951 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiSER10.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiSER10.xvg new file mode 100644 index 0000000..f06a57d --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiSER10.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -161.812 151.361 + -115.803 159.926 + -133.842 123.577 + -152.177 138.966 + -140.921 125.572 + -139.766 158.941 + -150.198 153.277 + -141.641 133.504 + -161.454 149.483 + -136.448 162.648 + -163.273 172.665 + -151.328 151.573 + -150.629 -147.097 + -167.103 158.698 + -156.963 161.267 + -157.313 140.216 + -155.988 154.318 + -157.356 178.939 + -147.206 146.629 + -175.562 155.562 + -162.581 158.096 + -152.988 163.531 + -167.797 165.554 + -172.831 146.885 + -134.993 129.542 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiSER24.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiSER24.xvg new file mode 100644 index 0000000..df22ce8 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiSER24.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -76.7345 143.809 + -53.6257 151.826 + -59.3852 128.333 + -72.7024 143.855 + -60.9565 116.614 + -80.1089 113.823 + -72.5534 117.287 + -62.7144 121.142 + -67.8576 127.142 + -53.3852 135.153 + -60.045 109.466 + -89.3587 129.797 + -70.5249 127.815 + -79.91 113.08 + -89.9439 133.49 + -71.7775 130.234 + -58.1746 118.688 + -77.171 119.858 + -94.0957 119.378 + -58.7686 122.583 + -55.9384 105.726 + -52.7812 116.99 + -52.074 112.118 + -67.4336 113.248 + -51.4468 113.427 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiSER30.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiSER30.xvg new file mode 100644 index 0000000..b88de48 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiSER30.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -73.0409 -42.6514 + -68.4458 -38.5685 + -63.8023 -41.8222 + -74.3909 -42.6106 + -59.4402 -44.6742 + -101.294 -82.569 + -94.274 -84.924 + -96.9784 -82.1061 + -80.345 -91.6774 + -92.8993 -75.4733 + -95.1973 -81.1949 + -91.4009 -60.6981 + -89.2329 -58.538 + -84.8483 -84.8071 + -79.4977 -89.2211 + -105.132 -77.2792 + -103.679 -64.4562 + -73.6603 -80.1813 + -73.8961 -78.5224 + -103.446 -63.1857 + -88.5515 -58.6309 + -92.8981 -94.0734 + -82.3549 -80.3726 + -95.862 -85.8726 + -107.211 -83.6372 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiSER35.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiSER35.xvg new file mode 100644 index 0000000..39927bf --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiSER35.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -145.551 135.298 + -127.534 -34.8936 + -87.1592 -33.0197 + -127.406 -14.5642 + -95.4029 136.378 + -90.7513 119.639 + -92.9084 118.044 + -128.682 108.915 + -121.908 129.902 + -129.45 128.908 + -130.412 133.423 + -143.188 151.161 + -72.5141 138.539 + -112.334 133.236 + -132.596 125.736 + -97.9951 134.309 + -124.728 139.205 + -78.2956 131.42 + -71.7961 138.513 + -90.8432 128.841 + -93.4031 135.37 + -125.426 134.264 + -105.706 126.611 + -143.636 145.408 + -106.401 158.995 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiSER51.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiSER51.xvg new file mode 100644 index 0000000..7d50c1b --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiSER51.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -117.363 -69.962 + -104.901 67.4457 + -104.438 122.546 + -101.953 112.546 + -93.9989 99.2587 + -70.6882 -67.6511 + -132.384 -77.2367 + -133.675 -61.5018 + -93.3073 113.867 + -95.1988 -70.8936 + -85.4859 147.103 + -84.1476 -77.5651 + -73.1107 123.323 + -76.3257 115.708 + -81.972 114.413 + -59.2131 -64.8112 + -77.6362 126.856 + -118.068 138.313 + -90.4893 118.075 + -94.4654 -63.3605 + -59.7634 140.565 + -79.3903 -77.0758 + -89.1506 105.043 + -125.012 -47.0952 + -87.9355 94.3875 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiTHR13.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiTHR13.xvg new file mode 100644 index 0000000..aa62255 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiTHR13.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -148.263 142.969 + -106.386 117.144 + -112.271 115.722 + -122.166 123.973 + -138.658 123.919 + -118.746 122.413 + -124.011 122.599 + -127.731 113.992 + -138.417 114.159 + -145.359 125.832 + -125.922 125.132 + -110.542 126.436 + -84.5897 145.774 + -131.889 130.784 + -114.064 132.847 + -131.067 124.862 + -126.974 114.191 + -122.613 125.136 + -109.408 107.16 + -142.061 115.56 + -122.262 117.674 + -126.449 132.492 + -120.575 127.717 + -118.074 115.397 + -137.604 122.813 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiTHR2.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiTHR2.xvg new file mode 100644 index 0000000..76d3a3a --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiTHR2.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -57.9875 132.721 + -73.1593 142.842 + -167.038 149.357 + -146.355 150.157 + -152.376 154.51 + -133.882 160.345 + -152.291 177.091 + -85.2951 139.37 + -59.4125 150.815 + -124.696 152.745 + -133.546 126.66 + -154.805 155.813 + -127.5 134.465 + -136.481 120.667 + -135.214 156.282 + -126.031 141.547 + -132.417 148.451 + -56.6932 141.485 + -138.47 159.17 + -159.178 160.747 + -123.786 127.017 + -80.1453 122.416 + -131.639 117.475 + -57.8964 148.531 + -144.441 157.308 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiTHR4.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiTHR4.xvg new file mode 100644 index 0000000..dc2e7d5 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiTHR4.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -73.5826 110.252 + -38.3872 124.961 + -92.8742 147.973 + -91.0033 125.416 + -104.274 132.104 + -118.398 143.36 + -120.366 134.228 + -131.704 134.342 + -123.766 134.796 + -116.768 129.992 + -130.138 141.18 + -112.432 146.836 + -115.972 139.735 + -135.813 140.333 + -115.472 120.56 + -121.835 124.418 + -124.495 121.298 + -119.51 111.06 + -126.077 126.595 + -122.83 125.966 + -122.325 112.46 + -147.835 130.932 + -101.635 118.623 + -120.774 128.34 + -133.793 118.986 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiTHR7.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiTHR7.xvg new file mode 100644 index 0000000..a9969f7 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiTHR7.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -94.034 115.736 + -126.594 144.281 + -129.788 117.859 + -86.8958 127.272 + -127.452 117.294 + -129.624 116.706 + -83.9407 131.344 + -94.8128 127.807 + -115.485 131.76 + -123.344 124.397 + -148.391 146.115 + -91.9678 118.279 + -124.982 127.145 + -62.909 137.398 + -83.3025 138.891 + -121.954 114.862 + -111.566 128.246 + -123.044 128.597 + -122.516 125.811 + -107.368 109.031 + -121.255 128.584 + -91.7674 132.308 + -122.921 129.983 + -128.813 142.215 + -110.503 123.486 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiTRP31.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiTRP31.xvg new file mode 100644 index 0000000..9c1d2a6 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiTRP31.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -83.9344 -8.57467 + -59.5227 -25.0572 + -68.8755 -20.295 + -69.7328 -20.7689 + -79.4829 -41.5103 + -65.3514 -42.7167 + -54.913 -38.0758 + -65.8897 -47.9606 + -57.0428 -45.9624 + -58.5609 -40.1272 + -67.6748 -23.9706 + -90.3397 -26.5051 + -65.6499 -50.0595 + -51.2892 -38.9604 + -59.802 -42.5657 + -60.3393 -38.3576 + -63.897 -43.5056 + -72.7705 -35.2639 + -72.6621 -42.4692 + -58.7891 -44.9394 + -69.3396 -45.0622 + -53.2774 -44.1286 + -64.9788 -42.5677 + -60.3918 -31.5358 + -63.0136 -45.1889 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiTYR21.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiTYR21.xvg new file mode 100644 index 0000000..86b3fb1 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiTYR21.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -96.8088 -5.64907 + -95.629 2.45734 + -63.3818 -42.5078 + -99.0407 -0.429276 + -69.8195 -30.6675 + -64.0889 -48.2056 + -69.7431 -38.5195 + -96.613 -9.24493 + -82.627 -19.1713 + -94.6612 -4.32551 + -87.4319 -20.3158 + -96.5712 -11.7088 + -87.8556 -9.13889 + -107.885 -0.497373 + -82.8218 -4.27528 + -83.3911 -31.6562 + -91.9802 2.05131 + -86.021 -22.7679 + -86.7078 -15.6285 + -96.4952 -0.77617 + -98.1482 -14.6364 + -103.464 -2.37127 + -67.4286 -35.3726 + -75.4085 -20.8137 + -71.9515 -25.5785 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiTYR5.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiTYR5.xvg new file mode 100644 index 0000000..7d4af05 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiTYR5.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -111.483 155.807 + -77.1143 145.902 + -154.197 150.707 + -142.426 160.82 + -114.026 127.145 + -115.646 139.769 + -111.519 128.681 + -117.249 129.725 + -110.217 144.085 + -111.656 127.754 + -129.041 129.769 + -116.47 150.47 + -117.045 146.126 + -117.493 130.363 + -105.351 136.738 + -81.7285 150.104 + -97.457 134.932 + -98.1409 135.058 + -108.315 125.699 + -102.455 130.546 + -101.798 123.341 + -111.453 132.896 + -90.4309 141.277 + -95.9905 131.7 + -92.545 130.396 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiTYR6.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiTYR6.xvg new file mode 100644 index 0000000..fce5358 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiTYR6.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -116.474 146.835 + -100.665 169.267 + -91.0302 153.345 + -118.284 126.153 + -136.935 154.097 + -139.664 144.717 + -135.045 124.493 + -129.366 111.806 + -128.194 150.464 + -128.233 131.494 + -137.471 166.419 + -135.889 110.729 + -146.35 142.117 + -114.753 101.979 + -128.309 131.391 + -130.971 143.79 + -141.236 145.926 + -128.465 154.499 + -124.893 159.241 + -125.135 133.033 + -121.256 146.828 + -124.582 105.813 + -141.102 152.904 + -134.884 148.935 + -119.624 143.615 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiVAL23.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiVAL23.xvg new file mode 100644 index 0000000..23d94fe --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiVAL23.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -125.73 147.472 + -120.706 125.682 + -118.939 156.871 + -130.617 162.222 + -106.691 155.124 + -125.308 166.501 + -139.565 161.28 + -119.996 154.011 + -101.747 160.491 + -120.463 150.532 + -101.002 165.732 + -97.409 160.245 + -112.795 168.18 + -104.357 160.573 + -132.44 175.323 + -107.675 164.567 + -111.222 155.797 + -133.762 136.259 + -119.795 -178.172 + -121.3 147.132 + -114.168 151.082 + -116.486 141.519 + -119.683 155.264 + -112.265 154.101 + -124.941 152.586 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiVAL25.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiVAL25.xvg new file mode 100644 index 0000000..cb22773 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiVAL25.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -62.1678 -46.3274 + -60.4785 -44.6612 + -72.5251 -41.2948 + -73.7545 -21.7405 + -62.9306 -30.59 + -56.2982 -39.5235 + -66.4924 -32.2111 + -69.5892 -26.3637 + -81.7926 -21.6609 + -83.2976 -24.4521 + -83.8391 -20.6286 + -68.807 -36.1741 + -71.4603 -27.3755 + -57.9051 -42.0355 + -61.2572 -34.884 + -63.4071 -46.7775 + -65.9965 -40.3268 + -64.0759 -25.6734 + -58.3649 -44.5867 + -79.0833 -29.1006 + -61.1558 -42.8528 + -69.7732 -35.655 + -75.128 -23.0716 + -61.2555 -37.915 + -79.9205 -15.6167 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiVAL41.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiVAL41.xvg new file mode 100644 index 0000000..ee35937 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiVAL41.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -71.0338 99.7705 + -53.6471 135.007 + -57.3565 137.775 + -67.5215 140.725 + -63.808 142.025 + -55.7879 149.789 + -55.2411 128.285 + -56.7608 134.055 + -47.8072 148.65 + -64.5089 135.205 + -64.546 122.688 + -62.275 122.637 + -55.7519 131.789 + -73.6044 128.43 + -70.3302 128.348 + -73.606 149.351 + -61.1233 126.287 + -65.9406 111.345 + -58.7703 132.424 + -66.0262 121.922 + -59.1546 131.752 + -59.5925 135.07 + -55.8344 123.604 + -58.8905 115.528 + -61.4395 133.352 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiVAL47.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiVAL47.xvg new file mode 100644 index 0000000..8158d21 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiVAL47.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -142.478 119.689 + -133.506 113.262 + -111.612 149.553 + -130.828 99.412 + -122.147 120.35 + -114.959 141.972 + -123.325 138.797 + -123.346 112.972 + -102.273 113.955 + -121.593 125.755 + -120.072 136.607 + -113.405 133.788 + -112.632 132.166 + -130.152 147.858 + -108.689 139.145 + -140.552 148.694 + -115.769 125.462 + -105.179 131.206 + -104.96 111.671 + -114.318 145.633 + -112.23 109.658 + -92.5924 140.587 + -109.391 118.003 + -98.3337 151.827 + -92.3109 129.718 diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiVAL8.xvg b/constava/data/constava_testdata/xvg/ramaPhiPsiVAL8.xvg new file mode 100644 index 0000000..525b468 --- /dev/null +++ b/constava/data/constava_testdata/xvg/ramaPhiPsiVAL8.xvg @@ -0,0 +1,27 @@ +# Comment - Should not be read +@ Comment - Should not be read + -84.6511 117.594 + -75.2674 127.793 + -57.135 130.025 + -80.3743 103.851 + -66.126 113.482 + -61.192 104.433 + -74.0587 100.538 + -73.5031 116.788 + -82.7031 109.24 + -76.4254 116.215 + -95.4223 110.057 + -76.2207 106.635 + -86.8286 110.841 + -74.592 113.916 + -69.6737 116.211 + -56.5077 130.898 + -69.4134 115.654 + -71.0762 132.794 + -70.4888 97.9028 + -50.9424 117.032 + -62.7552 120.427 + -90.9426 106.777 + -70.6268 98.7298 + -96.3465 114.408 + -59.3642 104.602 diff --git a/constava/data/constava_testdata/xvg/result_kde.csv b/constava/data/constava_testdata/xvg/result_kde.csv new file mode 100644 index 0000000..143de1c --- /dev/null +++ b/constava/data/constava_testdata/xvg/result_kde.csv @@ -0,0 +1,2602 @@ +#Method,SeriesIndex,ResIndex,ResName,coreHelix,surrHelix,coreSheet,surrSheet,Turn,Other,Variability +window/1/,,1,GLY,0.0000,0.0067,0.1442,0.1939,0.2439,0.4113,0.3645 +window/1/,,2,THR,0.0000,0.0011,0.4126,0.2898,0.0912,0.2052,0.3007 +window/1/,,3,ASN,0.0000,0.0014,0.3303,0.3289,0.1073,0.2321,0.2703 +window/1/,,4,THR,0.0000,0.0002,0.4785,0.3248,0.0586,0.1378,0.2013 +window/1/,,5,TYR,0.0000,0.0006,0.4638,0.3342,0.0615,0.1399,0.1618 +window/1/,,6,TYR,0.0000,0.0002,0.5184,0.3268,0.0461,0.1085,0.1325 +window/1/,,7,THR,0.0000,0.0007,0.4488,0.3364,0.0648,0.1493,0.1913 +window/1/,,8,VAL,0.0000,0.0022,0.1101,0.2912,0.1985,0.3981,0.1898 +window/1/,,9,LYS,0.0317,0.3548,0.0023,0.0273,0.3768,0.2072,0.1786 +window/1/,,10,SER,0.0000,0.0003,0.4694,0.3144,0.0588,0.1571,0.1554 +window/1/,,11,GLY,0.0000,0.0001,0.0302,0.2307,0.2680,0.4710,0.2002 +window/1/,,12,ASP,0.0049,0.1702,0.0016,0.0237,0.5246,0.2750,0.1851 +window/1/,,13,THR,0.0000,0.0001,0.5000,0.3452,0.0450,0.1097,0.0986 +window/1/,,14,LEU,0.6538,0.2752,0.0000,0.0036,0.0513,0.0160,0.1109 +window/1/,,15,ASN,0.6521,0.2840,0.0000,0.0030,0.0449,0.0160,0.0761 +window/1/,,16,LYS,0.5965,0.3122,0.0000,0.0049,0.0618,0.0245,0.1614 +window/1/,,17,ILE,0.6208,0.2912,0.0000,0.0043,0.0604,0.0233,0.1722 +window/1/,,18,ALA,0.6274,0.3021,0.0000,0.0032,0.0500,0.0172,0.0927 +window/1/,,19,ALA,0.5162,0.3719,0.0001,0.0036,0.0821,0.0262,0.2328 +window/1/,,20,GLN,0.6164,0.3027,0.0000,0.0035,0.0572,0.0200,0.1615 +window/1/,,21,TYR,0.1972,0.4310,0.0006,0.0074,0.2603,0.1036,0.2930 +window/1/,,22,GLY,0.0000,0.0066,0.0001,0.0050,0.6953,0.2930,0.0689 +window/1/,,23,VAL,0.0000,0.0004,0.4729,0.3460,0.0557,0.1250,0.1148 +window/1/,,24,SER,0.0000,0.0026,0.0800,0.2449,0.2249,0.4476,0.1905 +window/1/,,25,VAL,0.4978,0.3801,0.0001,0.0046,0.0886,0.0289,0.2284 +window/1/,,26,ALA,0.6132,0.3088,0.0000,0.0031,0.0560,0.0188,0.1541 +window/1/,,27,ASN,0.6284,0.2973,0.0000,0.0038,0.0515,0.0191,0.1183 +window/1/,,28,LEU,0.6498,0.2795,0.0000,0.0039,0.0476,0.0192,0.0956 +window/1/,,29,ARG,0.5710,0.3243,0.0001,0.0038,0.0748,0.0260,0.2138 +window/1/,,30,SER,0.1788,0.3644,0.0059,0.0466,0.2344,0.1698,0.2993 +window/1/,,31,TRP,0.5481,0.3468,0.0001,0.0041,0.0746,0.0263,0.2278 +window/1/,,32,ASN,0.1431,0.4323,0.0008,0.0098,0.2924,0.1216,0.2660 +window/1/,,33,GLY,0.0000,0.0081,0.0000,0.0055,0.6850,0.3013,0.0694 +window/1/,,34,ILE,0.0000,0.0016,0.1617,0.2817,0.1784,0.3765,0.2705 +window/1/,,35,SER,0.0101,0.0420,0.3759,0.2991,0.1034,0.1694,0.3086 +window/1/,,36,GLY,0.0000,0.0072,0.0111,0.1308,0.3047,0.5462,0.1367 +window/1/,,37,ASP,0.0815,0.4533,0.0010,0.0174,0.3037,0.1430,0.1722 +window/1/,,38,LEU,0.0000,0.0015,0.2370,0.3312,0.1434,0.2870,0.2346 +window/1/,,39,ILE,0.0000,0.0004,0.4994,0.3320,0.0500,0.1182,0.1161 +window/1/,,40,PHE,0.0000,0.0010,0.3297,0.3503,0.1030,0.2161,0.2359 +window/1/,,41,VAL,0.0000,0.0037,0.0290,0.1978,0.2575,0.5120,0.0640 +window/1/,,42,GLY,0.0000,0.0034,0.0000,0.0040,0.8115,0.1811,0.0725 +window/1/,,43,GLN,0.0000,0.0019,0.0746,0.2539,0.2178,0.4517,0.1855 +window/1/,,44,LYS,0.0000,0.0009,0.4117,0.3529,0.0743,0.1602,0.1629 +window/1/,,45,LEU,0.0000,0.0002,0.5050,0.3348,0.0474,0.1126,0.1025 +window/1/,,46,ILE,0.0000,0.0005,0.4481,0.3359,0.0646,0.1509,0.2130 +window/1/,,47,VAL,0.0000,0.0002,0.4873,0.3422,0.0522,0.1180,0.1090 +window/1/,,48,LYS,0.0000,0.0011,0.3238,0.3415,0.1052,0.2283,0.2362 +window/1/,,49,LYS,0.0000,0.0078,0.3604,0.3171,0.1083,0.2064,0.2779 +window/1/,,50,GLY,0.0000,0.0181,0.0156,0.0995,0.3390,0.5277,0.1389 +window/1/,,51,SER,0.0582,0.1574,0.1426,0.2225,0.1825,0.2368,0.3694 +window/3/,,1,GLY,0.0000,0.0000,0.0272,0.0555,0.1649,0.7525,0.3312 +window/3/,,2,THR,0.0000,0.0000,0.4865,0.3603,0.0107,0.1425,0.4543 +window/3/,,3,ASN,0.0000,0.0000,0.3062,0.5039,0.0170,0.1730,0.3449 +window/3/,,4,THR,0.0000,0.0000,0.7198,0.2174,0.0056,0.0571,0.3052 +window/3/,,5,TYR,0.0000,0.0000,0.6659,0.3031,0.0023,0.0286,0.2762 +window/3/,,6,TYR,0.0000,0.0000,0.7607,0.2278,0.0009,0.0106,0.1976 +window/3/,,7,THR,0.0000,0.0000,0.5961,0.3454,0.0044,0.0542,0.3032 +window/3/,,8,VAL,0.0000,0.0000,0.0175,0.2887,0.0772,0.6167,0.2905 +window/3/,,9,LYS,0.0003,0.4409,0.0000,0.0002,0.4793,0.0794,0.3205 +window/3/,,10,SER,0.0000,0.0000,0.6964,0.2569,0.0026,0.0440,0.2066 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0.4741, 0.5966, 0.5988, 0.5003, 0.3734, 0.4627, 0.3012], [0.1111, 0.1134, 0.1175, 0.1231, 0.1124, 0.1226, 0.0055, 0.1126, 0.0937, 0.0745, 0.0821, 0.088, 0.0969, 0.0145, 0.0405, 0.0487, 0.0019, 0.1722, 0.0873], [0.0282, 0.0366, 0.0122, 0.0083, 0.0176, 0.0374, 0.0314, 0.0339, 0.0182, 0.0194, 0.0067, 0.0079, 0.0214, 0.0159, 0.0204, 0.0172, 0.0246, 0.0203, 0.0367], [0.0075, 0.0026, 0.0014, 0.0026, 0.0033, 0.0031, 0.0007, 0.0064, 0.0064, 0.0043, 0.0012, 0.0003, 0.0024, 0.0033, 0.0035, 0.0014, 0.0001, 0.0021, 0.002], [0.0941, 0.0656, 0.0339, 0.0383, 0.0419, 0.0529, 0.1632, 0.1442, 0.0633, 0.0033, 0.0257, 0.0267, 0.0997, 0.0436, 0.0194, 0.0261, 0.0339, 0.0441, 0.0258], [0.0025, 0.0052, 0.0053, 0.0033, 0.0033, 0.0048, 0.0067, 0.0076, 0.0023, 0.003, 0.0027, 0.003, 0.0027, 0.0031, 0.003, 0.0032, 0.0037, 0.0037, 0.0033], [0.0005, 0.0015, 0.0013, 0.0014, 0.0017, 0.0021, 0.0022, 0.0022, 0.0023, 0.0024, 0.0014, 0.0012, 0.0004, 0.0009, 0.0039, 0.0032, 0.0064, 0.001, 0.0052], [0.0222, 0.0202, 0.0251, 0.0252, 0.0218, 0.0274, 0.0282, 0.0306, 0.0311, 0.0219, 0.0029, 0.0182, 0.0165, 0.0133, 0.129, 0.1294, 0.0398, 0.0116, 0.0177], [0.0809, 0.008, 0.0078, 0.0083, 0.0075, 0.0087, 0.0088, 0.0081, 0.007, 0.0072, 0.0023, 0.001, 0.0012, 0.0004, 0.0023, 0.0033, 0.0039, 0.007, 0.0071], [0.0077, 0.0085, 0.0086, 0.0086, 0.009, 0.0106, 0.0108, 0.0107, 0.0071, 0.0025, 0.0004, 0.0037, 0.0077, 0.0226, 0.0382, 0.0082, 0.0001, 0.0024, 0.0016], [0.2016, 0.3002, 0.3061, 0.2746, 0.2757, 0.3113, 0.3001, 0.2973, 0.3107, 0.2526, 0.3108, 0.1629, 0.1309, 0.3557, 0.6104, 1.0095, 0.7099, 0.6835, 0.0658], [0.0277, 0.0267, 0.0282, 0.0283, 0.0292, 0.0019, 0.0099, 0.0262, 0.0035, 0.0882, 0.0838, 0.0896, 0.0205, 0.0202, 0.0226, 0.0223, 0.0285, 0.0236, 0.0252], [0.0293, 0.0291, 0.0281, 0.0299, 0.0299, 0.0296, 0.0233, 0.0458, 0.0373, 0.0193, 0.0334, 0.0273, 0.0083, 0.0382, 0.0025, 0.0255, 0.0041, 0.0218, 0.0252], [0.002, 0.0001, 0.001, 0.0, 0.0007, 0.0004, 0.0011, 0.0009, 0.0005, 0.0004, 0.0008, 0.0007, 0.001, 0.0008, 0.0005, 0.0008, 0.0011, 0.0011, 0.0012], [0.2471, 0.2377, 0.1903, 0.204, 0.103, 0.0217, 0.0465, 0.2957, 0.3281, 0.3058, 0.4201, 0.1337, 0.1855, 0.0251, 0.0713, 0.1167, 0.1781, 0.1848, 0.1859], [0.0716, 0.0833, 0.0785, 0.0788, 0.0841, 0.0535, 0.0744, 0.062, 0.0501, 0.0865, 0.0694, 0.2793, 0.3086, 0.1514, 0.0251, 0.0817, 0.0852, 0.0828, 0.0851], [0.4202, 0.3391, 0.12, 0.6042, 0.7218, 0.656, 0.8172, 0.729, 0.5471, 0.1132, 0.3792, 0.4272, 0.4382, 0.434, 0.4349, 0.433, 0.34, 0.3342, 0.1021], [0.052, 0.004, 0.0007, 0.0022, 0.0025, 0.0022, 0.0055, 0.0063, 0.0051, 0.0053, 0.0059, 0.0052, 0.0043, 0.0017, 0.0003, 0.0041, 0.0017, 0.0033, 0.0012], [0.015, 0.0039, 0.0029, 0.0091, 0.0011, 0.0036, 0.0036, 0.0043, 0.0046, 0.0048, 0.0002, 0.0019, 0.0005, 0.0009, 0.0022, 0.0009, 0.0045, 0.0041, 0.004], [0.0062, 0.0004, 0.0003, 0.0016, 0.0019, 0.002, 0.0017, 0.0025, 0.0027, 0.0026, 0.0024, 0.0023, 0.002, 0.0007, 0.0012, 0.0034, 0.004, 0.0025, 0.0038], [0.0942, 0.0552, 0.082, 0.1311, 0.1945, 0.3719, 0.1789, 0.1046, 0.0837, 0.0974, 0.0879, 0.0925, 0.0978, 0.0984, 0.099, 0.095, 0.0914, 0.0984, 0.0279], [0.091, 0.0749, 0.0705, 0.0573, 0.0103, 0.0601, 0.0384, 0.0422, 0.0247, 0.0438, 0.0398, 0.0402, 0.0053, 0.0531, 0.035, 0.0354, 0.005, 0.0889, 0.2529], [1.145, 0.2476, 0.004, 0.0965, 0.1035, 0.0538, 0.0585, 0.0537, 0.0523, 0.0343, 0.0423, 0.0828, 0.1153, 0.1155, 0.1166, 0.1163, 0.1169, 0.1132, 0.1169], [0.0903, 0.1151, 0.0469, 0.06, 0.0572, 0.0252, 0.005, 0.021, 0.1158, 0.19, 0.0857, 0.0071, 0.059, 0.0564, 0.0637, 0.0649, 0.0642, 0.057, 0.0612], [0.0036, 0.0004, 0.0004, 0.0008, 0.0017, 0.0016, 0.0005, 0.0006, 0.0007, 0.0, 0.0016, 0.0023, 0.0021, 0.002, 0.0014, 0.001, 0.0008, 0.0007, 0.0005], [1.016, 0.9009, 0.5685, 0.4133, 0.0163, 0.0915, 0.177, 0.1896, 0.108, 0.0189, 0.2271, 0.2681, 0.2749, 0.1441, 0.2556, 0.2681, 0.2962, 0.2972, 0.2992], [0.8055, 0.8159, 0.5352, 0.4154, 0.5061, 0.6284, 0.4124, 0.5163, 0.5922, 0.5143, 0.5307, 0.1285, 0.5651, 0.3476, 0.4967, 0.4697, 0.3976, 0.3908, 0.1232], [0.0197, 0.0181, 0.0191, 0.0178, 0.0202, 0.0226, 0.0219, 0.0262, 0.0271, 0.0276, 0.0269, 0.0234, 0.0012, 0.0249, 0.0236, 0.0404, 0.0781, 0.0398, 0.065], [0.1073, 0.1024, 0.0652, 0.033, 0.0667, 0.0824, 0.085, 0.1009, 0.1155, 0.1167, 0.1177, 0.0807, 0.1147, 0.0836, 0.0425, 0.2939, 0.1767, 0.147, 0.016], [0.8366, 0.3211, 0.0895, 0.2299, 0.102, 0.0887, 0.0716, 0.0713, 0.0909, 0.0738, 0.1598, 0.2071, 0.1996, 0.1704, 0.2298, 0.2125, 0.1956, 0.2351, 0.1809], [0.0674, 0.0227, 0.0273, 0.0231, 0.0231, 0.0432, 0.0229, 0.0175, 0.0349, 0.0221, 0.0236, 0.0178, 0.0107, 0.0499, 0.0597, 0.0651, 0.065, 0.0289, 0.0207], [0.6476, 0.3379, 0.2147, 0.0581, 0.0801, 0.1138, 0.1173, 0.1552, 0.1062, 0.1051, 0.087, 0.0685, 0.0688, 0.025, 0.0453, 0.1019, 0.099, 0.1021, 0.0948], [0.0026, 0.0021, 0.0024, 0.0017, 0.0019, 0.002, 0.002, 0.0016, 0.0024, 0.0034, 0.0028, 0.0034, 0.0031, 0.005, 0.0018, 0.0007, 0.0014, 0.0021, 0.002], [0.0, 0.0, 0.0001, 0.0001, 0.0001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0267, 0.0011, 0.1672, 0.0139, 0.0284, 0.0255, 0.009, 0.0067, 0.02, 0.0361, 0.0342, 0.0343, 0.0344, 0.0234, 0.0226, 0.0253, 0.0048, 0.0177, 0.0276], [0.2294, 0.2631, 0.2072, 0.0355, 0.2607, 0.0566, 0.0555, 0.0021, 0.0622, 0.2984, 0.1313, 0.2474, 0.0239, 0.0972, 0.1229, 0.0216, 0.4051, 0.0033, 0.0201], [0.0557, 0.0445, 0.0269, 0.0061, 0.0399, 0.0449, 0.009, 0.0091, 0.0141, 0.0089, 0.0023, 0.008, 0.0018, 0.0471, 0.0458, 0.0141, 0.0121, 0.0294, 0.0085], [1.0914, 1.02, 0.6495, 0.0044, 0.2213, 0.1814, 0.1775, 0.1897, 0.1875, 0.1943, 0.1879, 0.1897, 0.2188, 0.2225, 0.2239, 0.21, 0.1841, 0.1421, 0.0317], [0.0833, 0.0896, 0.055, 0.0335, 0.0625, 0.069, 0.0669, 0.0678, 0.0775, 0.0888, 0.0881, 0.0819, 0.0733, 0.0732, 0.0497, 0.0068, 0.0549, 0.1685, 0.3071], [0.3679, 0.3431, 0.3648, 0.7939, 0.5789, 0.3247, 0.3508, 0.325, 0.2693, 0.2659, 0.7434, 0.6856, 0.3903, 0.5397, 0.1599, 0.1669, 0.2252, 0.1586, 0.2772], [0.5772, 0.5145, 0.523, 0.4661, 0.1647, 0.6814, 0.6247, 0.6545, 0.6884, 0.6709, 0.4855, 0.1582, 0.2079, 0.1327, 0.1672, 0.5614, 0.5728, 0.5517, 0.2202], [0.0018, 0.0081, 0.0141, 0.0833, 0.0471, 0.0054, 0.0023, 0.0411, 0.0546, 0.0612, 0.0634, 0.0569, 0.047, 0.0342, 0.0151, 0.0335, 0.09, 0.0825, 0.0215], [0.2273, 0.0811, 0.2822, 0.1856, 0.203, 0.5233, 0.1847, 0.2317, 0.1472, 0.1757, 0.1569, 0.1693, 0.6764, 0.3012, 0.1322, 0.1794, 0.1717, 0.2178, 0.2077]]}}} \ No newline at end of file diff --git a/constava/io/rstrategies.py b/constava/io/rstrategies.py index 6b7c8f8..3db6220 100644 --- a/constava/io/rstrategies.py +++ b/constava/io/rstrategies.py @@ -1,5 +1,6 @@ -import abc, os, re -from warnings import warn +import abc +import os +import re import numpy as np import pandas as pd diff --git a/constava/io/writer.py b/constava/io/writer.py index 80481a8..bc98f47 100644 --- a/constava/io/writer.py +++ b/constava/io/writer.py @@ -1,13 +1,16 @@ -"""constava.writer contains classes the write the output to different +"""constava.writer contains classes the write the output to different data formats.""" -import os +import os from typing import Any from .wstrategies import JsonWriter, CsvWriter, TsvWriter from ..utils.results import ConstavaResults class ResultsWriter: + """ + ResultsWriter writes the ConstavaResults to a file using different strategies. + """ STRATEGIES = { "json": JsonWriter, @@ -15,29 +18,48 @@ class ResultsWriter: "tsv": TsvWriter, } - def __init__(self, filename: str, format: str = "auto", float_precision: int = 4, indent_size: int = 0): + def __init__( + self, + filename: str, + file_format: str = "auto", + float_precision: int = 4, + indent_size: int = 0, + ): self.filename = filename - self.format = format - self.float_precision = float_precision + self.file_format = file_format + self.float_precision = float_precision self.indent_size = indent_size self._strategy = self.get_strategy() def __setattr__(self, __name: str, __value: Any) -> None: super().__setattr__(__name, __value) # Update the strategy - if __name in ("filename", "format", "float_precision") and hasattr(self, "_strategy"): + if __name in ("filename", "file_format", "float_precision", "indent_size") and hasattr( + self, "_strategy" + ): super().__setattr__(self._strategy, self.get_strategy()) - + def get_strategy(self): - if (self.format or "").lower() in ["auto", "guess", ""]: + """ + Getter of the Writer strategy based on the file_format attribute (file_format). + """ + + if (self.file_format or "").lower() in ["auto", "guess", ""]: __fmt = os.path.splitext(self.filename)[1].lstrip(".") else: - __fmt = self.format + __fmt = self.file_format if __fmt in self.STRATEGIES: return self.STRATEGIES[__fmt](self.float_precision, self.indent_size) else: raise ValueError(f"Unknown output file format: `{__fmt}`") def write_results(self, results: ConstavaResults): - with open(self.filename, "w") as fhandle: + """ + Write the results to the output file using the `filename` attribute as filename. + + :param results: The results to write to a file. + :type results: ConstavaResults + """ + + with open(self.filename, "w", encoding="utf-8") as fhandle: self._strategy.write_results(fhandle, results) diff --git a/constava/io/wstrategies.py b/constava/io/wstrategies.py index fdfce77..28c946f 100644 --- a/constava/io/wstrategies.py +++ b/constava/io/wstrategies.py @@ -66,6 +66,7 @@ class CsvWriter(WriterABC): def write_results(self, fhandle: TextIO, results: List[ConstavaResults]): # Initialize csv writer writer = csv.writer(fhandle, **self.csvdialect) + # Write the header line writer.writerow( [ @@ -85,17 +86,20 @@ def write_results(self, fhandle: TextIO, results: List[ConstavaResults]): def _write_entry(self, entry: ConstavaResultsEntry, method: str) -> List: flt2str = lambda x: "{0:.{1:d}f}".format(x, self.float_precision) respos, restype = entry.residue.respos, entry.residue.restype + # If average state propensities are reported, one row is written if len(entry.state_propensities.shape) == 1: values = np.concatenate( [entry.state_propensities, [entry.state_variability]] ) yield (method, None, respos, restype, *map(flt2str, values)) + # If state propensities are reported as series, multiple rows are written else: arr = np.concatenate( [entry.state_propensities, [entry.state_variability]] ).T + for i, values in enumerate(arr): yield (method, i, respos, restype, *map(flt2str, values)) diff --git a/constava/tests/tests.py b/constava/tests/tests.py index 535d895..234d703 100644 --- a/constava/tests/tests.py +++ b/constava/tests/tests.py @@ -1,14 +1,19 @@ """module for unit testing constava""" + +import json import os, glob, filecmp -import tarfile import tempfile import unittest from constava import Constava from constava.utils.constants import CONSTAVA_DATA_DIR -from constava.calc.csmodels import ConfStateModelLoadingError, ConfStateModelKDE, ConfStateModelGrid +from constava.calc.csmodels import ( + ConfStateModelKDE, + ConfStateModelGrid, +) from constava.utils.logging import logging logger = logging.getLogger("Constava") +VERBOSE_LEVEL = 0 class TestWrapper(unittest.TestCase): @@ -19,6 +24,7 @@ class TestWrapper(unittest.TestCase): TEST_TEMPDIR = None TEST_MODELDUMP0 = None TEST_MODELDUMP1 = None + CONSTAVA_TEST_DATA_DIR = None @classmethod def setUpClass(cls): @@ -26,16 +32,19 @@ def setUpClass(cls): that are used by all tests""" if cls.TEST_TEMPORARY_DIRECTORY is None: # Create the temporary directory and files for tests - cls.TEST_TEMPORARY_DIRECTORY = tempfile.TemporaryDirectory(prefix="ConstavaTest.") + cls.TEST_TEMPORARY_DIRECTORY = tempfile.TemporaryDirectory( + prefix="ConstavaTest." + ) cls.TEST_TEMPDIR = cls.TEST_TEMPORARY_DIRECTORY.name - _, cls.TEST_MODELDUMP0 = tempfile.mkstemp(prefix="model.", suffix=".pkl", dir=cls.TEST_TEMPDIR) - _, cls.TEST_MODELDUMP1 = tempfile.mkstemp(prefix="model.", suffix=".pkl", dir=cls.TEST_TEMPDIR) - logger.warning(f"TEST PREPARATION: Creating temporary directory: {cls.TEST_TEMPDIR}") - - # Extract the test files into the new temporary directory - logger.warning(f"TEST PREPARATION: Untarring test data in: {cls.TEST_TEMPDIR}") - with tarfile.open(cls.TEST_SOURCE, mode="r:gz") as tarchive: - tarchive.extractall(cls.TEST_TEMPDIR) + _, cls.TEST_MODELDUMP0 = tempfile.mkstemp( + prefix="model.", suffix=".pkl", dir=cls.TEST_TEMPDIR + ) + _, cls.TEST_MODELDUMP1 = tempfile.mkstemp( + prefix="model.", suffix=".pkl", dir=cls.TEST_TEMPDIR + ) + cls.CONSTAVA_TEST_DATA_DIR = os.path.join( + CONSTAVA_DATA_DIR, "constava_testdata" + ) @classmethod def get_test_count(cls): @@ -45,113 +54,253 @@ def get_test_count(cls): cls.TEST_COUNTER += 1 print() return cls.TEST_COUNTER - + def test_0a_KDEModelFitting(self): """Test fitting of a KDE model, dumps model for further tests""" logger.warning(f"TEST #{self.get_test_count()}: Fitting of 'kde' model...") - cva = Constava(verbose=0) - csmodel = cva.fit_csmodel(kde_bandwidth=.1291) - self.assertIsInstance(csmodel, ConfStateModelKDE, "Failed to fit conformational state model.") + cva = Constava(verbose=VERBOSE_LEVEL) + csmodel = cva.fit_csmodel(kde_bandwidth=0.1291) + self.assertIsInstance( + csmodel, ConfStateModelKDE, "Failed to fit conformational state model." + ) logger.warning(f"TEST #{self.get_test_count()}: Caching of 'kde' model...") - cva.fit_csmodel(kde_bandwidth=.1291) - self.assertIs(csmodel, cva._csmodel, "Failed to reuse preloaded conformational state model.") + cva.fit_csmodel(kde_bandwidth=0.1291) + self.assertIs( + csmodel, + cva._csmodel, + "Failed to reuse preloaded conformational state model.", + ) logger.warning(f"TEST #{self.get_test_count()}: Fitting a new 'kde' model...") - cva.fit_csmodel(kde_bandwidth=.1) - self.assertIsNot(csmodel, cva._csmodel, "Failed to update conformational state model after parameter change.") + cva.fit_csmodel(kde_bandwidth=0.1) + self.assertIsNot( + csmodel, + cva._csmodel, + "Failed to update conformational state model after parameter change.", + ) - logger.warning(f"TEST #{self.get_test_count()}: Storing of pickled 'kde' model...") + logger.warning( + f"TEST #{self.get_test_count()}: Storing of pickled 'kde' model..." + ) logger.warning(f"... Dumping model in: {self.TEST_MODELDUMP0}") cva._csmodel.dump_pickle(self.TEST_MODELDUMP0) - self.assertTrue(os.path.isfile(self.TEST_MODELDUMP0), "Failed to conformational state dump model.") + self.assertTrue( + os.path.isfile(self.TEST_MODELDUMP0), + "Failed to conformational state dump model.", + ) def test_1a_KDEModelLoading(self): """Test loading of pre-fitted KDE model""" - logger.warning(f"TEST #{self.get_test_count()}: Loading of pickled 'kde' models...") - cva = Constava(verbose=0, model_load=self.TEST_MODELDUMP0) + logger.warning( + f"TEST #{self.get_test_count()}: Loading of pickled 'kde' models..." + ) + cva = Constava(verbose=VERBOSE_LEVEL, model_load=self.TEST_MODELDUMP0) cva.load_csmodel(pickled_csmodel=self.TEST_MODELDUMP0) - self.assertIsInstance(cva._csmodel, ConfStateModelKDE, "Failed to fit conformational state model.") + self.assertIsInstance( + cva._csmodel, ConfStateModelKDE, "Failed to fit conformational state model." + ) def test_2a_KDEModelInference(self): """Test inference from pre-fitted KDE model""" - logger.warning(f"TEST #{self.get_test_count()}: Inference from 'kde' conformational state model...") - input_files = glob.glob(f"{self.TEST_TEMPDIR}/xvg/ramaPhiPsi*.xvg") - expected_result = f"{self.TEST_TEMPDIR}/xvg/result_kde.csv" - output_file = tempfile.NamedTemporaryFile(prefix="kde.", suffix=".csv", dir=self.TEST_TEMPDIR) + logger.warning( + f"TEST #{self.get_test_count()}: Inference from 'kde' conformational state model..." + ) + input_files = glob.glob(f"{self.CONSTAVA_TEST_DATA_DIR}/xvg/ramaPhiPsi*.xvg") + expected_result = f"{self.CONSTAVA_TEST_DATA_DIR}/xvg/result_kde.csv" + output_file = tempfile.NamedTemporaryFile( + prefix="kde.", suffix=".csv", dir=self.TEST_TEMPDIR + ) + cva = Constava( + input_files=input_files, + output_file=output_file.name, + model_type="kde", + model_load=self.TEST_MODELDUMP0, + window=[1, 3, 7, 23], + bootstrap=[3, 7, 23], + window_series=[1, 7], + seed=42, + verbose=VERBOSE_LEVEL, + input_degrees=True, + ) + cva.run() + self.assertTrue(filecmp.cmp(output_file.name, expected_result)) + + def test_3a_KDEModelInference(self): + """Test inference from pre-fitted KDE model""" + logger.warning(f"TEST #{self.get_test_count()}: Inference from 'kde' conformational state model in JSON format...") + input_files = glob.glob(f"{self.CONSTAVA_TEST_DATA_DIR}/xvg/ramaPhiPsi*.xvg") + expected_result = f"{self.CONSTAVA_TEST_DATA_DIR}/xvg/result_kde.json" + output_file = tempfile.NamedTemporaryFile(prefix="kde.", suffix=".json", dir=self.TEST_TEMPDIR) cva = Constava( input_files = input_files, output_file = output_file.name, model_type = "kde", model_load = self.TEST_MODELDUMP0, - window = [1,3,7,23], + window = [1,3,7,23], bootstrap = [3,7,23], window_series = [1,7], seed = 42, - verbose = 0, + verbose=VERBOSE_LEVEL, input_degrees=True) cva.run() - self.assertTrue(filecmp.cmp(output_file.name, expected_result)) + + with open(expected_result, "r", encoding="utf-8") as f_exp: + expected_obj = json.load(f_exp) + + with open(os.path.join(self.TEST_TEMPDIR, "expected_test_3a_KDEModelInference.json"), "w", encoding="utf-8") as f: + json.dump(expected_obj['results'], f) + + with open(output_file.name, "r", encoding="utf-8") as f_act: + actual_obj = json.load(f_act) + + with open(os.path.join(self.TEST_TEMPDIR, "actual_test_3a_KDEModelInference.json"), "w", encoding="utf-8") as f: + json.dump(actual_obj['results'], f) + + self.assertTrue(filecmp.cmp( + os.path.join(self.TEST_TEMPDIR, "expected_test_3a_KDEModelInference.json"), + os.path.join(self.TEST_TEMPDIR, "actual_test_3a_KDEModelInference.json") + )) def test_0b_GridModelFitting(self): """Test fitting of a grid-interpolation model, dumps model for further tests""" logger.warning(f"TEST #{self.get_test_count()}: Fitting of 'grid' models...") - cva = Constava(verbose=0) - csmodel = cva.fit_csmodel(model_type="grid", kde_bandwidth=.42, grid_points=145) - self.assertIsInstance(csmodel, ConfStateModelGrid, "Failed to fit conformational state model.") + cva = Constava(verbose=VERBOSE_LEVEL) + csmodel = cva.fit_csmodel( + model_type="grid", kde_bandwidth=0.42, grid_points=145 + ) + self.assertIsInstance( + csmodel, ConfStateModelGrid, "Failed to fit conformational state model." + ) logger.warning(f"TEST #{self.get_test_count()}: Caching of 'grid' models...") - cva.fit_csmodel(model_type="grid", kde_bandwidth=.42, grid_points=145) - self.assertIs(csmodel, cva._csmodel, "Failed to reuse preloaded conformational state model.") + cva.fit_csmodel(model_type="grid", kde_bandwidth=0.42, grid_points=145) + self.assertIs( + csmodel, + cva._csmodel, + "Failed to reuse preloaded conformational state model.", + ) logger.warning(f"TEST #{self.get_test_count()}: Fitting a new 'grid' model...") - cva.fit_csmodel(model_type="grid", kde_bandwidth=.1, grid_points=3601) - self.assertIsNot(csmodel, cva._csmodel, "Failed to update conformational state model after parameter change.") + cva.fit_csmodel(model_type="grid", kde_bandwidth=0.1, grid_points=3601) + self.assertIsNot( + csmodel, + cva._csmodel, + "Failed to update conformational state model after parameter change.", + ) - logger.warning(f"TEST #{self.get_test_count()}: Storing of pickled 'grid' models...") + logger.warning( + f"TEST #{self.get_test_count()}: Storing of pickled 'grid' models..." + ) logger.warning(f"... Dumping model in: {self.TEST_MODELDUMP1}") cva._csmodel.dump_pickle(self.TEST_MODELDUMP1) - self.assertTrue(os.path.isfile(self.TEST_MODELDUMP1), "Failed to conformational state dump model.") + self.assertTrue( + os.path.isfile(self.TEST_MODELDUMP1), + "Failed to conformational state dump model.", + ) def test_1b_GridModelLoading(self): """Test loading of pre-fitted grid-inference model""" - logger.warning(f"TEST #{self.get_test_count()}: Loading of pickled 'grid' models...") - cva = Constava(verbose=0, model_load=self.TEST_MODELDUMP1) + logger.warning( + f"TEST #{self.get_test_count()}: Loading of pickled 'grid' models..." + ) + cva = Constava(verbose=VERBOSE_LEVEL, model_load=self.TEST_MODELDUMP1) cva.load_csmodel(pickled_csmodel=self.TEST_MODELDUMP1) - self.assertIsInstance(cva._csmodel, ConfStateModelGrid, "Failed to fit conformational state model.") + self.assertIsInstance( + cva._csmodel, + ConfStateModelGrid, + "Failed to fit conformational state model.", + ) def test_2b_GridModelInference(self): """Test inference from pre-fitted grid-inference model""" - logger.warning(f"TEST #{self.get_test_count()}: Inference from 'grid' conformational state model...") - input_files = f"{self.TEST_TEMPDIR}/csv/dihedrals.csv" - expected_result = f"{self.TEST_TEMPDIR}/csv/result_grid.csv" - output_file = tempfile.NamedTemporaryFile(prefix="grid.", suffix=".csv", dir=self.TEST_TEMPDIR) + logger.warning( + f"TEST #{self.get_test_count()}: Inference from 'grid' conformational state model..." + ) + input_files = f"{self.CONSTAVA_TEST_DATA_DIR}/csv/dihedrals.csv" + expected_result = f"{self.CONSTAVA_TEST_DATA_DIR}/csv/result_grid.csv" + output_file = tempfile.NamedTemporaryFile( + prefix="grid.", suffix=".csv", dir=self.TEST_TEMPDIR + ) + c = Constava( + input_files=input_files, + output_file=output_file.name, + model_type="grid", + model_load=self.TEST_MODELDUMP1, + window=[1, 3, 7, 23], + bootstrap=[3, 7, 23], + window_series=[1, 7], + seed=42, + verbose=VERBOSE_LEVEL, + input_degrees=False, + ) + c.run() + import ipdb; ipdb.set_trace() + self.assertTrue(filecmp.cmp(output_file.name, expected_result)) + + def test_3b_GridModelInference(self): + """Test inference from pre-fitted grid-inference model""" + logger.warning(f"TEST #{self.get_test_count()}: Inference from 'grid' conformational state model in JSON format...") + input_files = f"{self.CONSTAVA_TEST_DATA_DIR}/csv/dihedrals.csv" + expected_result = f"{self.CONSTAVA_TEST_DATA_DIR}/csv/result_grid.json" + output_file = tempfile.NamedTemporaryFile(prefix="grid.", suffix=".json", dir=self.TEST_TEMPDIR) c = Constava( input_files = input_files, output_file = output_file.name, model_type = "grid", model_load = self.TEST_MODELDUMP1, - window = [1,3,7,23], + window = [1,3,7,23], bootstrap = [3,7,23], window_series = [1,7], seed = 42, - verbose = 0, + verbose = VERBOSE_LEVEL, input_degrees=False) c.run() - self.assertTrue(filecmp.cmp(output_file.name, expected_result)) -def run_unittest(): - """Run the unittests in this module""" - suite = unittest.TestSuite(( + with open(expected_result, "r", encoding="utf-8") as f_exp: + expected_obj = json.load(f_exp) + + with open(os.path.join(self.TEST_TEMPDIR, "expected_test_3b_GridModelInference.json"), "w", encoding="utf-8") as f: + json.dump(expected_obj['results'], f) + + with open(output_file.name, "r", encoding="utf-8") as f_act: + actual_obj = json.load(f_act) + + with open(os.path.join(self.TEST_TEMPDIR, "actual_test_3b_GridModelInference.json"), "w", encoding="utf-8") as f: + json.dump(actual_obj['results'], f) + + self.assertTrue(filecmp.cmp( + os.path.join(self.TEST_TEMPDIR, "expected_test_3b_GridModelInference.json"), + os.path.join(self.TEST_TEMPDIR, "actual_test_3b_GridModelInference.json") + )) + +def run_unittest(verbose: int = 0): + """Run the unittests in this module""" + + VERBOSE_LEVEL = verbose + logger.warning(f"Starting Test Suite with verbosity={VERBOSE_LEVEL}") + + if verbose == 0: + logger.setLevel(logging.WARNING) + elif verbose == 1: + logger.setLevel(logging.INFO) + else: + logger.setLevel(logging.DEBUG) + + suite = unittest.TestSuite( + ( TestWrapper("test_0a_KDEModelFitting"), TestWrapper("test_1a_KDEModelLoading"), TestWrapper("test_2a_KDEModelInference"), + TestWrapper("test_3a_KDEModelInference"), TestWrapper("test_0b_GridModelFitting"), TestWrapper("test_1b_GridModelLoading"), TestWrapper("test_2b_GridModelInference"), - )) - runner = unittest.TextTestRunner() - runner.run(suite) + ) + ) + runner = unittest.TextTestRunner(verbosity=verbose, failfast=True) + runner.run(suite) + if __name__ == "__main__": - run_unittest() \ No newline at end of file + run_unittest() diff --git a/constava/utils/dihedrals.py b/constava/utils/dihedrals.py index 72ac27e..ac26a24 100644 --- a/constava/utils/dihedrals.py +++ b/constava/utils/dihedrals.py @@ -1,5 +1,5 @@ """constava.dihedrals is a stand-alone executable script that extracts the -phi/psi backbone dihedral angles from a conformational ensmeble. """ +phi/psi backbone dihedral angles from a conformational ensmeble.""" import argparse, os, sys from typing import List, NamedTuple @@ -9,66 +9,97 @@ import numpy as np import pandas as pd + def parse_arguments(cmdline_arguments: List[str]) -> NamedTuple: """Parses the command line arguments and does some minor sanity checking. - + Parameters: ----------- cmdline_arguments : List[str] Parses the command line arguments provided as a list of strings, as returned by `sys.argv` - + Returns: -------- args : NameSpace - Object containing the parsed parameters. + Object containing the parsed parameters. """ parser = argparse.ArgumentParser() - parser.add_argument("-s", "--structure", - help="Structure file with atomic information: [pdb, gro, tpr]") - parser.add_argument("-f", "--trajectory", nargs="+", - help="Trajectory file with coordinates: [pdb, gro, trr, xtc, crd, nc]") - parser.add_argument("-o", "--output", default=None, required=False, - help="(Optional) CSV file to write dihedral information to. (default: dihedrals.csv)") - parser.add_argument("--selection", default="protein", - help="(Optional) Selection for the dihedral calculation. (default: 'protein')") - parser.add_argument("--precision", default=5, type=int, - help="(Optional) Defines the number of decimals written for the dihedrals. (default: 5)") - parser.add_argument("--degrees", action="store_true", - help="(Optional) Results are written in degrees instead of radians. (default: radians)") + parser.add_argument( + "-s", + "--structure", + help="Structure file with atomic information: [pdb, gro, tpr]", + ) + parser.add_argument( + "-f", + "--trajectory", + nargs="+", + help="Trajectory file with coordinates: [pdb, gro, trr, xtc, crd, nc]", + ) + parser.add_argument( + "-o", + "--output", + default=None, + required=False, + help="(Optional) CSV file to write dihedral information to. (default: dihedrals.csv)", + ) + parser.add_argument( + "--selection", + default="protein", + help="(Optional) Selection for the dihedral calculation. (default: 'protein')", + ) + parser.add_argument( + "--precision", + default=5, + type=int, + help="(Optional) Defines the number of decimals written for the dihedrals. (default: 5)", + ) + parser.add_argument( + "--degrees", + action="store_true", + help="(Optional) Results are written in degrees instead of radians. (default: radians)", + ) args = parser.parse_args(cmdline_arguments) if args.output is None: args.output = "dihedrals.csv" if os.path.exists(args.output): - raise FileExistsError(( - "Output file `{args.outfile}` exists already. Please, specify " - "--outfile explicitely to overwrite or choose a different file name." - )) + raise FileExistsError( + ( + "Output file `{args.outfile}` exists already. Please, specify " + "--outfile explicitely to overwrite or choose a different file name." + ) + ) return args -def calculate_dihedrals(structure: str, trajectory: List[str], selection: str = "protein", degree: bool = False): - """Calculates the backbone dihedral angles and returns the result as a + +def calculate_dihedrals( + structure: str, + trajectory: List[str], + selection: str = "protein", + degree: bool = False, +): + """Calculates the backbone dihedral angles and returns the result as a DataFrame. - + Parameters: ----------- structure : str Path to the structure File to use [*.gro, *.pdb, *.tpr, ...] trajectory : List[str] - List of paths to trajectory files corresponding to the structure + List of paths to trajectory files corresponding to the structure file [*.trr, *.xtc, ...] selection : str - Atom selection for which the dihedral angles should be calculated. + Atom selection for which the dihedral angles should be calculated. Corresponds to the MDAnalysis selection syntax. [default: `protein`] degrees : bool - If True dihedrals are reported in degrees, else in radians. + If True dihedrals are reported in degrees, else in radians. [default: False] - + Returns: -------- dihedrals : DataFrame - DataFrame containing the backbone dihedral angles calculated along + DataFrame containing the backbone dihedral angles calculated along the trajectory """ # Load trajectory files @@ -83,29 +114,36 @@ def calculate_dihedrals(structure: str, trajectory: List[str], selection: str = phicol, psicol = "Phi[rad]", "Psi[rad]" results = np.radians(rama.results.angles) # Convert resulting numpy array into a comprehensive DataFrame - dihedrals = pd.DataFrame(columns = ["#Frame", "ResIndex", "ResName", phicol, psicol]) + dihedrals = pd.DataFrame(columns=["#Frame", "ResIndex", "ResName", phicol, psicol]) # Iterate over selection (C-alphas) used to define the dihedrals for i, atom in enumerate(rama.ag3): # Select results for given - df = pd.DataFrame({ - "#Frame": rama.frames, - "ResIndex": np.full(rama.frames.shape, atom.resid, dtype=int), - "ResName": np.full(rama.frames.shape, atom.resname), - phicol: results[:,i,0], - psicol: results[:,i,1]}) + df = pd.DataFrame( + { + "#Frame": rama.frames, + "ResIndex": np.full(rama.frames.shape, atom.resid, dtype=int), + "ResName": np.full(rama.frames.shape, atom.resname), + phicol: results[:, i, 0], + psicol: results[:, i, 1], + } + ) dihedrals = pd.concat([dihedrals, df], ignore_index=True) return dihedrals + def main(cmdline_arguments: List[str]): """Main function executed when the script is run from the command line""" # Parse command line arguments args = parse_arguments(cmdline_arguments) + # Calculate dihedrals dihedrals = calculate_dihedrals( - args.structure, args.trajectory, args.selection, args.degrees) + args.structure, args.trajectory, args.selection, args.degrees + ) # Write results - float2str = f"%.{args.precision}f" # Definition of float format in output + float2str = f"%.{args.precision}f" # Definition of float format in output dihedrals.to_csv(args.output, header=True, index=False, float_format=float2str) + if __name__ == "__main__": - sys.exit(main(sys.argv[1:])) \ No newline at end of file + sys.exit(main(sys.argv[1:])) diff --git a/constava/utils/ensembles.py b/constava/utils/ensembles.py index 8f46dee..84e8a5c 100644 --- a/constava/utils/ensembles.py +++ b/constava/utils/ensembles.py @@ -1,4 +1,4 @@ -"""constava.ensembles contains classes describing the input data from the +"""constava.ensembles contains classes describing the input data from the conformational ensemble. """ @@ -11,12 +11,13 @@ class EnsembleMismatchError(ValueError): """Error raised when ResidueEnsembles from different ProteinEnsembles are mixed""" + pass @dataclass class ResidueEnsemble: - """ + """ Dataclass to hold information on a given residue in a conformational ensemble: Attributes: @@ -26,9 +27,10 @@ class ResidueEnsemble: phipsi: array[N,2] Array of the phi/psi angles of the residue for all conformations in the ensemble proteinensemble: ProteinEnsemble - Reference to the ProteinEnsemble-object, the + Reference to the ProteinEnsemble-object, the residue belongs to. """ + restype: str = "" respos: int = None phipsi: np.ndarray = None @@ -36,49 +38,54 @@ class ResidueEnsemble: @property def restype1(self): - """ Converts the restype attribute to one-letter code """ + """Converts the restype attribute to one-letter code""" return aminoacids3to1.get(self.restype, "X") def __repr__(self): - """ Short representation of the object """ + """Short representation of the object""" return f"<{self.restype}:{self.respos}>" - + def __lt__(self, other): - """ Implemented to allow for easy sorting of residues """ + """Implemented to allow for easy sorting of residues""" if not isinstance(other, self.__class__): - raise TypeError(f"Cannot compare {self.__class__.__name__} with {other.__class__.__name__}") + raise TypeError( + f"Cannot compare {self.__class__.__name__} with {other.__class__.__name__}" + ) return self.respos < other.respos - + def to_dict(self): - """ Returns the residue data as a dictionary (e.g., for later conversion to JSON) """ + """Returns the residue data as a dictionary (e.g., for later conversion to JSON)""" _data = { - "restype": self.restype, - "respos": self.respos, - "phipsi": (self.phipsi.tolist() if isinstance(self.phipsi, np.ndarray) else None), + "restype": self.restype, + "respos": self.respos, + "phipsi": ( + self.phipsi.tolist() if isinstance(self.phipsi, np.ndarray) else None + ), } return _data class ProteinEnsemble: - """ + """ Class to hold information on conformational ensemble. It contains multiple ResidueEnsemble objects which hold the actual data per residue """ + def __init__(self, residues: List[ResidueEnsemble] = None): - """ Constructor for the ProteinEnsemble class - + """Constructor for the ProteinEnsemble class + Parameters: ----------- - residues: List[ResidueEnsemble] A list of ResidueEnsemble objects - to be added to the ensemble. + residues: List[ResidueEnsemble] A list of ResidueEnsemble objects + to be added to the ensemble. """ self._residues = [] self.add_residues(*residues) def __repr__(self): - """ Short string representation of a class-object """ + """Short string representation of a class-object""" return f"" - + @property def n_residues(self): """Returns the number of residues in the ensemble""" @@ -92,35 +99,39 @@ def resrange(self): @property def sequence(self): - """ Returns the protein sequence as a string """ + """Returns the protein sequence as a string""" seq = self._getPropertyFromResidues("restype1", fillvalue="-") return "".join(seq) - - def _getPropertyFromResidues(self, resattr: str, *, fillvalue=np.nan, dtype=None): - """ Private helper function, to retrieve properties from the individual - residues across the whole protein. """ + + def _getPropertyFromResidues(self, resattr: str, *, fillvalue=np.nan): + """Private helper function, to retrieve properties from the individual + residues across the whole protein.""" offset = self._residues[0].respos result = None - for i, value in ((res.respos-offset, getattr(res, resattr)) for res in self.get_residues()): + for i, value in ( + (res.respos - offset, getattr(res, resattr)) for res in self.get_residues() + ): if value is None: continue elif result is None and isinstance(value, np.ndarray): - result = np.full((self.n_residues,)+value.shape, fillvalue) + result = np.full((self.n_residues,) + value.shape, fillvalue) elif result is None: result = np.full((self.n_residues,), fillvalue) result[i] = value return result - - def get_residues(self, sorted_list: bool = False) -> Generator[ResidueEnsemble, None, None]: - """ Returns a generator for all residues in the class """ + + def get_residues( + self, sorted_list: bool = False + ) -> Generator[ResidueEnsemble, None, None]: + """Returns a generator for all residues in the class""" if sorted_list: return (res for res in sorted(self._residues, key=lambda x: x.respos)) else: return (res for res in self._residues) - + def add_residues(self, *new_residues: ResidueEnsemble): - """ Adds a new residue to the ensemble, and sorts the residue list - according to their indices """ + """Adds a new residue to the ensemble, and sorts the residue list + according to their indices""" for res in new_residues: res.protein = self self._residues.append(res) @@ -130,7 +141,5 @@ def add_residues(self, *new_residues: ResidueEnsemble): # self.__dict__.pop('conformation', None) def to_dict(self): - _data = { - "residues": [res.to_dict() for res in self.get_residues()] - } + _data = {"residues": [res.to_dict() for res in self.get_residues()]} return _data diff --git a/constava/utils/utils.py b/constava/utils/utils.py index 7983176..a1ad359 100644 --- a/constava/utils/utils.py +++ b/constava/utils/utils.py @@ -3,18 +3,23 @@ from warnings import warn import numpy as np + class DihedralRangeError(ValueError): """Raised if any dihedral angles are not correctly in radians""" + pass + class DihedralRangeWarning(UserWarning): """Raised on the suspicion that dihedral angles are not correctly in radians""" + pass + def check_dihedral_range(arr: np.ndarray): - """Helper method that checks if the dihedral angles are correctly in + """Helper method that checks if the dihedral angles are correctly in radians. - + Parameters: ----------- arr : Array[N,2] @@ -26,13 +31,19 @@ def check_dihedral_range(arr: np.ndarray): If any dihedrals fall outside the range [-pi, pi] DihedralRangeWarning - If all dihedrals fall in the range of [-(pi*pi/180), (pi*pi/180)], + If all dihedrals fall in the range of [-(pi*pi/180), (pi*pi/180)], as this suggests that angles were converted to radians twice. """ vmin, vmax = np.min(arr), np.max(arr) if vmin < -np.pi or vmax > np.pi: - raise DihedralRangeError(f"Dihedrals outside the range [-pi, pi] detected: [{vmin:.3f}, {vmax:.3f}]") + raise DihedralRangeError( + f"Dihedrals outside the range [-pi, pi] detected: [{vmin:.3f}, {vmax:.3f}]" + ) elif vmin >= np.radians(np.pi) and vmax <= np.radians(np.pi): - warn(("Provided dihedrals a very small: [{vmin:.3f}, {vmax:.3f}]. " - "Please check that convertion to radians was only applied once."), - DihedralRangeWarning()) + warn( + ( + "Provided dihedrals a very small: [{vmin:.3f}, {vmax:.3f}]. " + "Please check that convertion to radians was only applied once." + ), + DihedralRangeWarning(), + ) diff --git a/constava/wrapper/params.py b/constava/wrapper/params.py index 85a7f9e..258aeef 100644 --- a/constava/wrapper/params.py +++ b/constava/wrapper/params.py @@ -21,11 +21,9 @@ def _inner_(self, __attr, __value): def set_single_as_list(func): """Allows list-attributes (e.g., window) to be set with a single value""" def _inner_(self, __attr, __value): - # ADIAZ: It works for <=3.13 - # dtype = typing.get_type_hints(self).get(__attr, None) - hints = typing.get_type_hints(type(self)) # or self.__class__ + hints = typing.get_type_hints(type(self)) dtype = hints.get(__attr, None) - + if typing.get_origin(dtype) is list: if __value is None: __value = [] @@ -38,7 +36,7 @@ def _inner_(self, __attr, __value): @dataclass class ConstavaParameters: """The parameters that govern the function of Constava - + Parameters: ----------- input_files : List[str] or str diff --git a/constava/wrapper/wrapper.py b/constava/wrapper/wrapper.py index 955f574..37f5a1a 100644 --- a/constava/wrapper/wrapper.py +++ b/constava/wrapper/wrapper.py @@ -6,8 +6,12 @@ from .params import ConstavaParameters from ..io import ResultsWriter, EnsembleReader from ..calc.calculator import ConfStateCalculator -from ..calc.subsampling import SubsamplingBootstrap, SubsamplingBootstrapSeries, SubsamplingWindow, \ - SubsamplingWindowSeries +from ..calc.subsampling import ( + SubsamplingBootstrap, + SubsamplingBootstrapSeries, + SubsamplingWindow, + SubsamplingWindowSeries, +) from ..calc.csmodels import ConfStateModelABC, ConfStateModelKDE, ConfStateModelGrid # The logger for the wrapper @@ -15,42 +19,56 @@ def cache_csmodel(func): - """Decorator for caching conformational state models""" - - def __inner(self: "Constava", *args, **kwargs): - if func.__name__ == "load_csmodel": - new_cshash = hash(( - args[0] if len(args) > 0 else kwargs.get("pickled_csmodel", None),)) - elif func.__name__ == "fit_csmodel": - new_cshash = hash(( - args[0] if len(args) > 0 else kwargs.get("model_type", "kde"), - args[1] if len(args) > 1 else kwargs.get("model_data", None), - args[2] if len(args) > 2 else kwargs.get("kde_bandwidth", .13), - args[3] if len(args) > 3 else kwargs.get("grid_points", 10_000), - args[4] if len(args) > 4 else kwargs.get("model_data_degrees", False),)) - else: - raise TypeError("Decorator `cache_csmodel` only works with `load_csmodel` and `fit_csmodel`") - - if self._cshash == new_cshash: - logger.info("No change to model parameters. Using preloaded model.") - else: - csmodel = func(self, *args, **kwargs) - self._csmodel, self._cshash = csmodel, new_cshash - return self._csmodel - - return __inner + """Decorator for caching conformational state models""" + + def __inner(self: "Constava", *args, **kwargs): + if func.__name__ == "load_csmodel": + new_cshash = hash( + (args[0] if len(args) > 0 else kwargs.get("pickled_csmodel", None),) + ) + elif func.__name__ == "fit_csmodel": + new_cshash = hash( + ( + args[0] if len(args) > 0 else kwargs.get("model_type", "kde"), + args[1] if len(args) > 1 else kwargs.get("model_data", None), + args[2] if len(args) > 2 else kwargs.get("kde_bandwidth", 0.13), + args[3] if len(args) > 3 else kwargs.get("grid_points", 10_000), + ( + args[4] + if len(args) > 4 + else kwargs.get("model_data_degrees", False) + ), + ) + ) + else: + raise TypeError( + "Decorator `cache_csmodel` only works with `load_csmodel` and `fit_csmodel`" + ) + + if self._cshash == new_cshash: + logger.info("No change to model parameters. Using preloaded model.") + else: + csmodel = func(self, *args, **kwargs) + self._csmodel, self._cshash = csmodel, new_cshash + return self._csmodel + + return __inner class Constava: - """Interface class for all functionalities of Constava. This tool allows the probabilistic description of protein - conformation from a set of density-defined conformational states. Six solution NMR-defined conformational states - are provided by default, but users can define their own probability density functions with the method fit_csmodel(). + """ + Interface class for all functionalities of Constava. This tool allows the + probabilistic description of protein conformation from a set of density-defined + conformational states. Six solution NMR-defined conformational states + are provided by default, but users can define their own probability density + functions with the method fit_csmodel(). Methods: -------- run() - Runs Constava with the current parameters. If the parameters have been provided with the initialisation - of the Constava() object, this is the only method necessary to run any analysis. + Runs Constava with the current parameters. If the parameters have been + provided with the initialisation of the Constava() object, this is the + only method necessary to run any analysis. # Methods to adjust parameters of the Constava toolkit. set_param(parameter, value) @@ -62,22 +80,22 @@ class Constava: show_params() -> str Returns the current set of parameters as a string. - # Methods to access sub-functionalities of the Constava toolkit. - initialize_reader(format="auto", in_degrees=False): - Initializes an EnsembleReader for reading input files, with the format specified by 'format' and whether + # Methods to access sub-functionalities of the Constava toolkit. + initialize_reader(file_format="auto", in_degrees=False): + Initializes an EnsembleReader for reading input files, with the format specified by 'file_format' and whether the data is in degrees specified by 'in_degrees'. - initialize_writer(outfile, format="auto", float_precision=4, indent_size=0): + initialize_writer(outfile, file_format="auto", float_precision=4, indent_size=0): Initializes a ResultsWriter for writing output results to a file specified by 'outfile', in the format - specified by 'format', and with float precision specified by 'float_precision' (and indent_size if needed) + specified by 'file_format', and with float precision specified by 'float_precision' (and indent_size if needed) fit_csmodel() Fits a conformational state model to the provided data. Complex method, read its docstrings for details. load_csmodel(pickled_csmodel): Loads a conformational state model from a pickled file specified by the 'pickled_csmodel' path. - initialize_calculator(csmodel=None, window=None, window_series=None, bootstrap=None, bootstrap_series=None, - bootstrap_samples=500, bootstrap_seed=None): - Initializes a ConfStateCalculator with the specified conformational state model 'csmodel', subsampling - parameters 'window', 'window_series', 'bootstrap', 'bootstrap_series', and bootstrap parameters - 'bootstrap_samples' and 'bootstrap_seed'. + initialize_calculator(csmodel=None, window=None, window_series=None, bootstrap=None, bootstrap_series=None, + bootstrap_samples=500, bootstrap_seed=None): + Initializes a ConfStateCalculator with the specified conformational state model 'csmodel', subsampling + parameters 'window', 'window_series', 'bootstrap', 'bootstrap_series', and bootstrap parameters + 'bootstrap_samples' and 'bootstrap_seed'. Parameters (Defined in ConstavaParameters class): ----------- @@ -92,173 +110,197 @@ class Constava: ``` """ - def __init__(self, parameters: ConstavaParameters = None, **kwargs): - """Initializes the python interface for Constava. Parameters can be + def __init__(self, parameters: ConstavaParameters = None, **kwargs): + """Initializes the python interface for Constava. Parameters can be provided as a ConstavaParameters class. For details see `help(Constava().parameters)` - + Parameters: ----------- parameters : ConstavaParameters ConstavaParameters object containing all parameters (if provided kwargs will be ignored) **kwargs : - To only set individual parameters, those parameters can be + To only set individual parameters, those parameters can be provided as keyword arguments. For all other parameters default values are used. For a full list of available settings and their defaults, check: `help(Constava().parameters)` """ - logger.info("Constava: Initializing python interface...") - if parameters is None: - parameters = ConstavaParameters(**kwargs) - parameters._constava = self - self.parameters = parameters - self.results = None - self._csmodel = None # Preloaded conformational state models - self._cshash = None # Hashed parameters of the models - - def get_param(self, parameter: str): - """Returns the current value of the given parameter""" - return getattr(self.parameters, parameter) - - def set_param(self, parameter: str, value): - """Sets a parameter to a given value - """ - - logger.info(f"Setting `{parameter} = {value}`") - setattr(self.parameters, parameter, value) - logger.debug(f"New parameters: {self.show_params()}") - - def unset_param(self, parameter: str): - """Sets a parameter to None""" - setattr(self.parameters, parameter, None) - - def show_params(self) -> str: - """Returns a string with all currently set parameters""" - return repr(self.parameters) - - def run(self) -> None: - """Calculate conformational state variabilities and conformational + logger.info("Constava: Initializing python interface...") + if parameters is None: + parameters = ConstavaParameters(**kwargs) + parameters._constava = self + self.parameters = parameters + self.results = None + self._csmodel = None # Preloaded conformational state models + self._cshash = None # Hashed parameters of the models + + def get_param(self, parameter: str): + """Returns the current value of the given parameter""" + return getattr(self.parameters, parameter) + + def set_param(self, parameter: str, value): + """Sets a parameter to a given value""" + + logger.info(f"Setting `{parameter} = {value}`") + setattr(self.parameters, parameter, value) + logger.debug(f"New parameters: {self.show_params()}") + + def unset_param(self, parameter: str): + """Sets a parameter to None""" + setattr(self.parameters, parameter, None) + + def show_params(self) -> str: + """Returns a string with all currently set parameters""" + return repr(self.parameters) + + def run(self) -> None: + """Calculate conformational state variabilities and conformational state variabilites with the given parameters.""" - # Reset results - self.results = None - - # Initialize a reader for input file(s) - reader = self.initialize_reader( - format=self.get_param("input_format"), - in_degrees=self.get_param("input_degrees")) - - # Initialize writer for results - writer = self.initialize_writer( - outfile=self.get_param("output_file"), - format=self.get_param("output_format"), - float_precision=self.get_param("precision"), - indent_size=self.get_param("indent_size") - ) - - # Fit or load a conformational state model - if os.path.isfile(self.get_param("model_load") or ""): - csmodel = self.load_csmodel(pickled_csmodel=self.get_param("model_load")) - else: - csmodel = self.fit_csmodel( - model_type=self.get_param("model_type"), - model_data=self.get_param("model_data"), - kde_bandwidth=self.get_param("kde_bandwidth"), - grid_points=self.get_param("grid_points"), - model_data_degrees=self.get_param("model_data_degrees")) - - # Initialize a calculator (logged inside function) - calculator = self.initialize_calculator( - csmodel=csmodel, - window=self.get_param("window"), - window_series=self.get_param("window_series"), - bootstrap=self.get_param("bootstrap"), - bootstrap_series=self.get_param("bootstrap_series"), - bootstrap_samples=self.get_param("bootstrap_samples"), - bootstrap_seed=self.get_param("seed")) - - # Read input files - input_files = self.get_param("input_files") - logger.info(f"Reading dihedrals from {len(input_files)} files...") - logger.debug("\n\t* ".join(["... input file list:", *input_files])) - ensemble = reader.readFiles(*input_files) - - # Do the inference - logger.info("Starting inference...") - self.results = calculator.calculate(ensemble) - - # Write results - logger.info(f"Writing results to file: {writer.filename}") - writer.write_results(self.results) - - def initialize_reader(self, format: str = "auto", in_degrees: bool = False) -> EnsembleReader: - """Initializes an EnsembleReader. - + # Reset results + self.results = None + + # Initialize a reader for input file(s) + reader = self.initialize_reader( + file_format=self.get_param("input_format"), + in_degrees=self.get_param("input_degrees"), + ) + + # Initialize writer for results + writer = self.initialize_writer( + outfile=self.get_param("output_file"), + file_format=self.get_param("output_format"), + float_precision=self.get_param("precision"), + indent_size=self.get_param("indent_size"), + ) + + # Fit or load a conformational state model + if os.path.isfile(self.get_param("model_load") or ""): + csmodel = self.load_csmodel(pickled_csmodel=self.get_param("model_load")) + else: + csmodel = self.fit_csmodel( + model_type=self.get_param("model_type"), + model_data=self.get_param("model_data"), + kde_bandwidth=self.get_param("kde_bandwidth"), + grid_points=self.get_param("grid_points"), + model_data_degrees=self.get_param("model_data_degrees"), + ) + + # Initialize a calculator (logged inside function) + calculator = self.initialize_calculator( + csmodel=csmodel, + window=self.get_param("window"), + window_series=self.get_param("window_series"), + bootstrap=self.get_param("bootstrap"), + bootstrap_series=self.get_param("bootstrap_series"), + bootstrap_samples=self.get_param("bootstrap_samples"), + bootstrap_seed=self.get_param("seed"), + ) + + # Read input files + input_files = self.get_param("input_files") + logger.info(f"Reading dihedrals from {len(input_files)} files...") + logger.debug("\n\t* ".join(["... input file list:", *input_files])) + ensemble = reader.readFiles(*input_files) + + # Do the inference + logger.info("Starting inference...") + self.results = calculator.calculate(ensemble) + + # Write results + logger.info(f"Writing results to file: {writer.filename}") + writer.write_results(self.results) + + def initialize_reader( + self, file_format: str = "auto", in_degrees: bool = False + ) -> EnsembleReader: + """Initializes an EnsembleReader. + Parameters: ----------- - format: str + file_format: str File format of the files to be read. {'auto', 'csv', 'xvg'} in_degrees: bool Set `True` if input files are in degrees, - + Returns: -------- reader An EnsembleReader object. """ - logger.info("Initializing reader for input file(s)...") - logger.debug(f"... setting reader parameters: {format=}, {in_degrees=}") - reader = EnsembleReader(filetype_str=format, degrees2radians=in_degrees) - return reader - - def initialize_writer(self, outfile, format: str = "auto", float_precision: int = 4, indent_size: int = 0) -> ResultsWriter: - """Initializes a ResultsWriter. + logger.info("Initializing reader for input file(s)...") + logger.debug(f"... setting reader parameters: {file_format=}, {in_degrees=}") + reader = EnsembleReader(filetype_str=file_format, degrees2radians=in_degrees) + return reader + + def initialize_writer( + self, + outfile: str, + file_format: str = "auto", + float_precision: int = 4, + indent_size: int = 0, + ) -> ResultsWriter: + """Initializes a ResultsWriter. + Parameters: ----------- outfile: str The file path to write results to. File extension is used to infer the output format, if not provided explicitly. - format: str + file_format: str Format in which the results should be written out. {'auto', 'csv', 'json'} float_precision: int Sets de number of decimals in the output files. By default, 4 decimal. - indent_size: int - Sets the spaces used to indent the JSON documents - + indent_size: int + Sets the spaces used to indent the JSON documents + Returns: -------- writer A ResultsWriter object. """ - if outfile is None: - return None - logger.info("Initializing writer for results...") - logger.debug(f"... setting writer parameters: {outfile=}, {format=}, {float_precision=} {indent_size=}") - writer = ResultsWriter(outfile, format=format, float_precision=float_precision, indent_size=indent_size) - return writer - - @cache_csmodel - def fit_csmodel(self, model_type: str = "kde", model_data: str = None, - kde_bandwidth: float = .13, grid_points: int = 10_000, - model_data_degrees: bool = False) -> ConfStateModelABC: - """Fits a conformational state model to the provided data. + if outfile is None: + return None + logger.info("Initializing writer for results...") + logger.debug( + f"... setting writer parameters: {outfile=}, {file_format=}, {float_precision=} {indent_size=}" + ) + writer = ResultsWriter( + outfile, + file_format=file_format, + float_precision=float_precision, + indent_size=indent_size, + ) + return writer + + @cache_csmodel + def fit_csmodel( + self, + model_type: str = "kde", + model_data: str = None, + kde_bandwidth: float = 0.13, + grid_points: int = 10_000, + model_data_degrees: bool = False, + ) -> ConfStateModelABC: + """Fits a conformational state model to the provided data. + Parameters: ----------- model_type : str The probabilistic model used: {'kde', 'grid'}. (default: 'kde') model_data : str - File in JSON format with the fitting data for the conformational - state models. If not provided, the default data from the + File in JSON format with the fitting data for the conformational + state models. If not provided, the default data from the publication is used. kde_bandwidth : float - This controls the bandwidth of the Gaussian kernel density + This controls the bandwidth of the Gaussian kernel density estimator. (default: 0.13) grid_points : int When `model_type` == 'grid', this controls how many grid points are used to describe the probability density function (default: 10000). model_data_degrees : bool - Set `True` if the data given under `model_data` to is given in + Set `True` if the data given under `model_data` to is given in degrees. (default: False) Returns: @@ -266,22 +308,27 @@ def fit_csmodel(self, model_type: str = "kde", model_data: str = None, csmodel : ConfStateModelABC Probabilistic model describing the conformational states """ - PdfModel = {"kde": ConfStateModelKDE, "grid": ConfStateModelGrid}[model_type] - model_data = (model_data or DEFAULT_TRAINING_DATA_PATH) - logger.info(f"Fitting model to data in: {model_data}") - csmodel = PdfModel.from_fitting( - model_data, - in_degrees=model_data_degrees, - bandwidth=kde_bandwidth, - grid_points=grid_points) - logger.info(f"... model fitted: {csmodel}") - return csmodel - - @cache_csmodel - def load_csmodel(self, pickled_csmodel: str) -> ConfStateModelABC: - """Load a previously fitted conformational state model from a pickled - file. + PdfModel = {"kde": ConfStateModelKDE, "grid": ConfStateModelGrid}[model_type] + model_data = model_data or DEFAULT_TRAINING_DATA_PATH + + logger.info(f"Fitting model to data in: {model_data}") + + csmodel = PdfModel.from_fitting( + model_data, + in_degrees=model_data_degrees, + bandwidth=kde_bandwidth, + grid_points=grid_points, + ) + logger.info(f"... model fitted: {csmodel}") + + return csmodel + + @cache_csmodel + def load_csmodel(self, pickled_csmodel: str) -> ConfStateModelABC: + """Load a previously fitted conformational state model from a pickled + file. + Parameters: ----------- pickled_csmodel : str @@ -292,36 +339,46 @@ def load_csmodel(self, pickled_csmodel: str) -> ConfStateModelABC: csmodel : ConfStateModelABC Probabilistic model describing the conformational states """ - logger.info(f"Loading conformational state models from file: {pickled_csmodel}") - csmodel = ConfStateModelABC.from_pickle(pickled_csmodel) - logger.info(f"... model loaded: {csmodel}") - return csmodel - - def initialize_calculator(self, csmodel: ConfStateModelABC = None, - window: List[int] = None, window_series: List[int] = None, - bootstrap: List[int] = None, bootstrap_series: List[int] = None, - bootstrap_samples: int = 500, bootstrap_seed: int = None) -> ConfStateCalculator: - """Initializes a ConfStateCalculator. + + logger.info(f"Loading conformational state models from file: {pickled_csmodel}") + + csmodel = ConfStateModelABC.from_pickle(pickled_csmodel) + + logger.info(f"... model loaded: {csmodel}") + + return csmodel + + def initialize_calculator( + self, + csmodel: ConfStateModelABC = None, + window: List[int] = None, + window_series: List[int] = None, + bootstrap: List[int] = None, + bootstrap_series: List[int] = None, + bootstrap_samples: int = 500, + bootstrap_seed: int = None, + ) -> ConfStateCalculator: + """Initializes a ConfStateCalculator. Parameters: ----------- csmodel : ConfStateModel - A conformational state model (as returned by the fit_csmodel + A conformational state model (as returned by the fit_csmodel method). If None, the standard model from the publication will be used. window : List[int] Subsampling using a moving reading-frame of size . Multiple values can be given as a list. window_series : List[int] - Subsampling using a moving reading-frame of size . Returns + Subsampling using a moving reading-frame of size . Returns the results for every window rather than the average. Multiple values can be given as a list. bootstrap : List[int] Subsampling using by bootstrapping datapoints. Multiple values can be given as a list. bootstrap_series : List[int] or int - Subsampling using by bootstrapping datapoints. Returns the - results for every subsample rather than the average. Multiple + Subsampling using by bootstrapping datapoints. Returns the + results for every subsample rather than the average. Multiple values can be given as a list. bootstrap_samples : int When bootstrapping, sample times from the input data. @@ -333,30 +390,35 @@ def initialize_calculator(self, csmodel: ConfStateModelABC = None, calculator: ConfStateCalculator A ConfStateCalculator object """ - # Quickly generate a csmodel if not done before - if csmodel is None: - csmodel = self.fit_csmodel() - - # Initialize the calculator - logger.info(f"Initializing calculator with {csmodel}...") - calculator = ConfStateCalculator(csmodel) - - # Add subsampling methods to calculator - for window_size in (window or []): - new_method = SubsamplingWindow(window_size) - logger.info(f"... adding subsampling method: {new_method.getShortName()}") - calculator.add_method(new_method) - for sample_size in (bootstrap or []): - new_method = SubsamplingBootstrap(sample_size, bootstrap_samples, seed=bootstrap_seed) - logger.info(f"... adding subsampling method: {new_method.getShortName()}") - calculator.add_method(new_method) - for window_size in (window_series or []): - new_method = SubsamplingWindowSeries(window_size) - logger.info(f"... adding subsampling method: {new_method.getShortName()}") - calculator.add_method(new_method) - for sample_size in (bootstrap_series or []): - new_method = SubsamplingBootstrapSeries(sample_size, bootstrap_samples, seed=bootstrap_seed) - logger.info(f"... adding subsampling method: {new_method.getShortName()}") - calculator.add_method(new_method) - - return calculator + # Quickly generate a csmodel if not done before + if csmodel is None: + csmodel = self.fit_csmodel() + + # Initialize the calculator + logger.info(f"Initializing calculator with {csmodel}...") + + calculator = ConfStateCalculator(csmodel) + + # Add subsampling methods to calculator + for window_size in window or []: + new_method = SubsamplingWindow(window_size) + logger.info(f"... adding subsampling method: {new_method.getShortName()}") + calculator.add_method(new_method) + for sample_size in bootstrap or []: + new_method = SubsamplingBootstrap( + sample_size, bootstrap_samples, seed=bootstrap_seed + ) + logger.info(f"... adding subsampling method: {new_method.getShortName()}") + calculator.add_method(new_method) + for window_size in window_series or []: + new_method = SubsamplingWindowSeries(window_size) + logger.info(f"... adding subsampling method: {new_method.getShortName()}") + calculator.add_method(new_method) + for sample_size in bootstrap_series or []: + new_method = SubsamplingBootstrapSeries( + sample_size, bootstrap_samples, seed=bootstrap_seed + ) + logger.info(f"... adding subsampling method: {new_method.getShortName()}") + calculator.add_method(new_method) + + return calculator From 8ff62147225806d654055cdd9a8ddaa9a51a543c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adri=C3=A1n=20Diaz?= Date: Wed, 28 Jan 2026 16:36:49 +0100 Subject: [PATCH 17/21] Fixing tests (solving an existent bug) --- constava/calc/calculator.py | 39 +- constava/calc/csmodels.py | 32 +- constava/calc/subsampling.py | 31 +- .../csv/result_grid.bandwidth.42.csv | 37556 ++++++++++++++++ .../constava_testdata/csv/result_grid.json | 2 +- constava/tests/tests.py | 179 +- 6 files changed, 37724 insertions(+), 115 deletions(-) create mode 100644 constava/data/constava_testdata/csv/result_grid.bandwidth.42.csv diff --git a/constava/calc/calculator.py b/constava/calc/calculator.py index c523480..b5a6cb9 100644 --- a/constava/calc/calculator.py +++ b/constava/calc/calculator.py @@ -20,8 +20,8 @@ # The logger for the wrapper logger = logging.getLogger("Constava") -_SELF_WORKER_CSMODEL = None -_SELF_WORKER_METHODS = None +_SELF_WORKER_CSMODEL: ConfStateModelABC = None +_SELF_WORKER_METHODS: List[SubsamplingABC] = None def _init_self_worker(csmodels, methods): @@ -50,6 +50,7 @@ def _self_compute_logpdf_worker(phipsi: np.ndarray): for method_idx, method in enumerate(methods): state_propensities, state_variability = method.calculate(logpdf) + props_list[method_idx] = state_propensities var_list[method_idx] = state_variability @@ -117,7 +118,7 @@ def __del__(self): # Best-effort cleanup (don't raise during GC) try: self.close() - except Exception: + except BaseException: pass def _get_mp_context(self): @@ -183,10 +184,8 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: """ n_residues = ensemble.n_residues - - # Improvement: store per-residue results as compact lists aligned by method index. - # logpdf_state_propens_variabs[idx] = (props_list, var_list) - logpdf_state_propens_variabs = defaultdict() + residues_propensities_variabilities = dict() + future_to_idx = dict() try: max_workers = os.process_cpu_count() @@ -194,7 +193,6 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: max_workers = multiprocessing.cpu_count() max_in_flight = max_workers * 2 - future_to_idx = defaultdict() logger.debug( "Starting inference of log-probability densities & propensities/variability" @@ -206,8 +204,6 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: it = iter(enumerate(ensemble.get_residues())) in_flight = set() - logger.debug(f"Max_in_flight={max_in_flight}, Max_workers={max_workers}") - with tqdm.tqdm( total=n_residues, desc="Residues", @@ -218,10 +214,9 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: # Prime the pipeline for _ in range(min(max_in_flight, n_residues)): idx, residue = next(it) - phi_psi_angles = np.ascontiguousarray(residue.phipsi) fut = process_pool_executor.submit( - _self_compute_logpdf_worker, phi_psi_angles + _self_compute_logpdf_worker, residue.phipsi ) future_to_idx[fut] = idx @@ -233,7 +228,7 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: for fut in done: idx = future_to_idx.pop(fut) - logpdf_state_propens_variabs[idx] = fut.result() + residues_propensities_variabilities[idx] = fut.result() progress_bar.update(1) completed += 1 @@ -241,12 +236,11 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: # refill try: idx, residue = next(it) - phi_psi_angles = np.ascontiguousarray(residue.phipsi) except StopIteration: continue nfut = process_pool_executor.submit( - _self_compute_logpdf_worker, phi_psi_angles + _self_compute_logpdf_worker, residue.phipsi ) future_to_idx[nfut] = idx @@ -254,29 +248,24 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: logger.debug(f"Building results for the {len(self.methods)} methods...") - # Improvement: compute method names once (no repeated calls inside workers) - method_names = [method.getShortName() for method in self.methods] - results = [ ConstavaResults( - method=method_name, + method=method.getShortName(), protein=ensemble, state_labels=self.csmodels.get_labels(), ) - for method_name in method_names + for method in self.methods ] - for idx, residue in enumerate(ensemble.get_residues(sorted_list=True)): - props_list, var_list = logpdf_state_propens_variabs[idx] + for idx, res in enumerate(ensemble.get_residues(sorted_list=True)): + props_list, var_list = residues_propensities_variabilities[idx] for method_idx, result in enumerate(results): state_propensities = props_list[method_idx] state_variability = var_list[method_idx] result.add_entry( - ConstavaResultsEntry( - residue, state_propensities, state_variability - ), + ConstavaResultsEntry(res, state_propensities, state_variability), sorted_insertion=True, ) diff --git a/constava/calc/csmodels.py b/constava/calc/csmodels.py index 3d447ea..d11100d 100644 --- a/constava/calc/csmodels.py +++ b/constava/calc/csmodels.py @@ -14,7 +14,9 @@ class ConfStateModelLoadingError(ValueError): - pass + """ + Specialized class for a sub-type of ValueError. + """ class ConfStateModelABC(metaclass=abc.ABCMeta): @@ -24,7 +26,7 @@ class ConfStateModelABC(metaclass=abc.ABCMeta): model_type: str = None - def __init__(self, state_labels: List[str], **kwargs): + def __init__(self, state_labels: List[str], **_kwargs): """Initialize probabilistic model of conformational states. The first parameter are the labels of the conformational states, while further parameters describe the probabilistic model. @@ -54,12 +56,15 @@ def get_logpdf(self, data: np.ndarray) -> np.ndarray: Log-Probability densities for all M probabilistic conformational state models across the N original observations. """ - pass @classmethod @abc.abstractmethod def from_fitting(cls, training_data_json: str, **kwargs): - pass + """ + Loads the probabilistic from a trainign data JSON + """ + + # pass def dump_pickle(self, output_file: str): """Save the probabilistic as a pickle. @@ -91,6 +96,7 @@ def from_pickle(cls, pickled_file: str): "(expected: `{1}`)" ).format(csmodel.model_type, cls.model_type) ) + return csmodel @@ -135,13 +141,13 @@ def __init__(self, state_labels: List[str], state_kdes: List[KernelDensity]): List of the Gaussian kernel density estimators representing the probabilistic models for the conformational states """ + super().__init__(state_labels) + self.state_labels = tuple(state_labels) self.state_kdes = tuple(state_kdes) def get_logpdf(self, data: np.ndarray) -> np.ndarray: - result = np.stack([ - kde.score_samples(data) for kde in self.state_kdes - ]) + result = np.stack([kde.score_samples(data) for kde in self.state_kdes]) return result @classmethod @@ -220,14 +226,16 @@ class ConfStateModelGrid(ConfStateModelABC): def __init__( self, state_labels: List[str], state_grids: np.ndarray, grid_crds: Tuple ): + super().__init__(state_labels) + self.state_labels = tuple(state_labels) self.state_grids = state_grids self.grid_crds = grid_crds def get_logpdf(self, data: np.ndarray) -> np.ndarray: - result = np.stack([ - interpn(self.grid_crds, grid, data) for grid in self.state_grids - ]) + result = np.stack( + [interpn(self.grid_crds, grid, data) for grid in self.state_grids] + ) return result @classmethod @@ -263,16 +271,20 @@ def from_fitting( Probabilistic model of conformational states using a grid from which the PDF is estimated by interpolation """ + # Generate grid points in the (phi,psi)-space n = math.isqrt(grid_points) _phi, _psi = np.linspace(-np.pi, np.pi, n), np.linspace(-np.pi, np.pi, n) + gridcrds = np.stack( [arr.flatten() for arr in np.meshgrid(_phi, _psi, indexing="ij")], axis=1 ) + # Infer PDF grid from a KDE model kde_model = ConfStateModelKDE.from_fitting( training_data_json, bandwidth=bandwidth, in_degrees=in_degrees ) + _labels = kde_model.get_labels() _grids = np.reshape(kde_model.get_logpdf(gridcrds), (-1, n, n), order="C") diff --git a/constava/calc/subsampling.py b/constava/calc/subsampling.py index 9c12c8c..8971034 100644 --- a/constava/calc/subsampling.py +++ b/constava/calc/subsampling.py @@ -6,9 +6,10 @@ import numpy as np from typing import Optional + class SubsamplingMethodError(ValueError): """Raised when there are no sub-sampling methods passed to the calculator""" - pass + class SubsamplingABC(metaclass=abc.ABCMeta): """Base class to subsample the logPDF values obtained from the probabilistic @@ -29,6 +30,7 @@ class SubsamplingABC(metaclass=abc.ABCMeta): _subsampling(state_logpdfs) Sub-samples from the distribution of original data points. """ + def calculate(self, state_logpdfs): """Calculates the conformational state likelihoods and conformational state variability from the sampled state logPDFs. @@ -51,7 +53,7 @@ def calculate(self, state_logpdfs): subsampled_pdf = self._subsampling(state_logpdfs) state_propensities = self.calculateStatePropensities(subsampled_pdf) - state_variability = self.calculateStateVariability(subsampled_pdf) + state_variability = self.calculateStateVariability(subsampled_pdf) return state_propensities, state_variability @@ -75,7 +77,7 @@ def calculateStateVariability(self, state_likelihoods): Conformational state variability """ mean_likelihoods = np.mean(state_likelihoods, axis=1) - squard_dev = np.sum(np.square(state_likelihoods.T - mean_likelihoods), axis=1) + squard_dev = np.sum((state_likelihoods.T - mean_likelihoods) ** 2, axis=1) state_var = np.sqrt(np.mean(squard_dev)) return state_var @@ -83,7 +85,6 @@ def calculateStateVariability(self, state_likelihoods): @abc.abstractmethod def getShortName(self) -> str: """Name of the method for reference in the output.""" - pass @abc.abstractmethod def _subsampling(self, logpdf): @@ -101,7 +102,6 @@ def _subsampling(self, logpdf): pdf : Array[M,N] Likelihoods obtained by sub-sampling N times using the described method. """ - pass class SubsamplingWindow(SubsamplingABC): @@ -129,6 +129,7 @@ class SubsamplingWindow(SubsamplingABC): _subsampling(state_logpdfs) Sub-samples from the distribution of original data points. """ + def __init__(self, window_size: int): """Inititialize class to subsample and calcualte conformational state propensities and conformational state variability. Sub-sampling is done @@ -162,9 +163,9 @@ def _subsampling(self, logpdf): Likelihoods obtained by sub-sampling N times using the described method. """ # Subsampling using consecutive windows of window_size samples - logpdf = np.stack([ - np.convolve(x, np.ones((self.window_size,)), mode="valid") for x in logpdf - ]) + logpdf = np.stack( + [np.convolve(x, np.ones((self.window_size,)), mode="valid") for x in logpdf] + ) # Exponentiation and normalize to obtain likelihoods pdf = np.exp(logpdf) @@ -205,7 +206,8 @@ class SubsamplingBootstrap(SubsamplingABC): _subsampling(state_logpdfs) Subsamples from the distribution of original data points. """ - def __init__(self, sample_size: int, n_samples = 500, seed: Optional[int] = None): + + def __init__(self, sample_size: int, n_samples=500, seed: Optional[int] = None): """Inititialize class to subsample and calcualte conformational state propensities and conformational state variability. Subsampling is done using bootstrapping. @@ -226,7 +228,8 @@ def __init__(self, sample_size: int, n_samples = 500, seed: Optional[int] = None def getShortName(self) -> str: """Name of the method for reference in the output.""" return "bootstrap/{0:d}/{1:d}/{2}/".format( - self.sample_size, self.n_samples, self.seed or "") + self.sample_size, self.n_samples, self.seed or "" + ) def _subsampling(self, logpdf): """Subsampling from the distribution of logPDF using bootstrapping. @@ -253,9 +256,9 @@ def _subsampling(self, logpdf): # distribution. -> Array[n_states, n_samples, sample_size] rng = np.random.default_rng(self.seed) samples = rng.integers(n_measurements, size=self.sample_size * self.n_samples) - + logpdf = np.reshape( - logpdf[:, samples], (n_states, self.n_samples, self.sample_size),order="C" + logpdf[:, samples], (n_states, self.n_samples, self.sample_size), order="C" ) # Accumulate logpdfs within a sample = logpdf for each of these samples @@ -272,7 +275,6 @@ def __str__(self): return "SubsamplingBootstrap" - class SubsamplingWindowSeries(SubsamplingWindow): """Class to subsample the logPDF values obtained from the probabilistic conformational state models and calculate the conformational state @@ -361,7 +363,8 @@ class SubsamplingBootstrapSeries(SubsamplingBootstrap): def getShortName(self) -> str: """Name of the method for reference in the output.""" return "bootstrap_series/{0:d}/{1:d}/{2}/".format( - self.sample_size, self.n_samples, self.seed or "") + self.sample_size, self.n_samples, self.seed or "" + ) def calculateStatePropensities(self, state_likelihoods): """Calculates the conformational state likelihoods for the given sample.""" diff --git a/constava/data/constava_testdata/csv/result_grid.bandwidth.42.csv b/constava/data/constava_testdata/csv/result_grid.bandwidth.42.csv new file mode 100644 index 0000000..2fe4624 --- /dev/null +++ b/constava/data/constava_testdata/csv/result_grid.bandwidth.42.csv @@ -0,0 +1,37556 @@ 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a/constava/tests/tests.py +++ b/constava/tests/tests.py @@ -1,7 +1,9 @@ """module for unit testing constava""" import json -import os, glob, filecmp +import os +import glob +import filecmp import tempfile import unittest from constava import Constava @@ -13,11 +15,14 @@ from constava.utils.logging import logging logger = logging.getLogger("Constava") -VERBOSE_LEVEL = 0 class TestWrapper(unittest.TestCase): - """Class that runs test runs on the wrapper as a whole""" + """Class that runs unit tests as a whole""" + + def __init__(self, testName, verbosity): + super(TestWrapper, self).__init__(testName) + self.verbosity = verbosity TEST_SOURCE = os.path.join(CONSTAVA_DATA_DIR, "constava_testdata.tgz") TEST_TEMPORARY_DIRECTORY = None @@ -58,7 +63,7 @@ def get_test_count(cls): def test_0a_KDEModelFitting(self): """Test fitting of a KDE model, dumps model for further tests""" logger.warning(f"TEST #{self.get_test_count()}: Fitting of 'kde' model...") - cva = Constava(verbose=VERBOSE_LEVEL) + cva = Constava(verbose=self.verbosity) csmodel = cva.fit_csmodel(kde_bandwidth=0.1291) self.assertIsInstance( csmodel, ConfStateModelKDE, "Failed to fit conformational state model." @@ -95,7 +100,7 @@ def test_1a_KDEModelLoading(self): logger.warning( f"TEST #{self.get_test_count()}: Loading of pickled 'kde' models..." ) - cva = Constava(verbose=VERBOSE_LEVEL, model_load=self.TEST_MODELDUMP0) + cva = Constava(verbose=self.verbosity, model_load=self.TEST_MODELDUMP0) cva.load_csmodel(pickled_csmodel=self.TEST_MODELDUMP0) self.assertIsInstance( cva._csmodel, ConfStateModelKDE, "Failed to fit conformational state model." @@ -120,52 +125,73 @@ def test_2a_KDEModelInference(self): bootstrap=[3, 7, 23], window_series=[1, 7], seed=42, - verbose=VERBOSE_LEVEL, + verbose=self.verbosity, input_degrees=True, ) cva.run() + filecmp.clear_cache() self.assertTrue(filecmp.cmp(output_file.name, expected_result)) - + def test_3a_KDEModelInference(self): """Test inference from pre-fitted KDE model""" - logger.warning(f"TEST #{self.get_test_count()}: Inference from 'kde' conformational state model in JSON format...") + logger.warning( + f"TEST #{self.get_test_count()}: Inference from 'kde' conformational state model in JSON format..." + ) input_files = glob.glob(f"{self.CONSTAVA_TEST_DATA_DIR}/xvg/ramaPhiPsi*.xvg") expected_result = f"{self.CONSTAVA_TEST_DATA_DIR}/xvg/result_kde.json" - output_file = tempfile.NamedTemporaryFile(prefix="kde.", suffix=".json", dir=self.TEST_TEMPDIR) + output_file = tempfile.NamedTemporaryFile( + prefix="kde.", suffix=".json", dir=self.TEST_TEMPDIR + ) cva = Constava( - input_files = input_files, - output_file = output_file.name, - model_type = "kde", - model_load = self.TEST_MODELDUMP0, - window = [1,3,7,23], - bootstrap = [3,7,23], - window_series = [1,7], - seed = 42, - verbose=VERBOSE_LEVEL, - input_degrees=True) + input_files=input_files, + output_file=output_file.name, + model_type="kde", + model_load=self.TEST_MODELDUMP0, + window=[1, 3, 7, 23], + bootstrap=[3, 7, 23], + window_series=[1, 7], + seed=42, + verbose=self.verbosity, + input_degrees=True, + ) cva.run() with open(expected_result, "r", encoding="utf-8") as f_exp: expected_obj = json.load(f_exp) - with open(os.path.join(self.TEST_TEMPDIR, "expected_test_3a_KDEModelInference.json"), "w", encoding="utf-8") as f: - json.dump(expected_obj['results'], f) + with open( + os.path.join(self.TEST_TEMPDIR, "expected_test_3a_KDEModelInference.json"), + "w", + encoding="utf-8", + ) as f: + json.dump(expected_obj["results"], f) with open(output_file.name, "r", encoding="utf-8") as f_act: actual_obj = json.load(f_act) - with open(os.path.join(self.TEST_TEMPDIR, "actual_test_3a_KDEModelInference.json"), "w", encoding="utf-8") as f: - json.dump(actual_obj['results'], f) + with open( + os.path.join(self.TEST_TEMPDIR, "actual_test_3a_KDEModelInference.json"), + "w", + encoding="utf-8", + ) as f: + json.dump(actual_obj["results"], f) - self.assertTrue(filecmp.cmp( - os.path.join(self.TEST_TEMPDIR, "expected_test_3a_KDEModelInference.json"), - os.path.join(self.TEST_TEMPDIR, "actual_test_3a_KDEModelInference.json") - )) + filecmp.clear_cache() + self.assertTrue( + filecmp.cmp( + os.path.join( + self.TEST_TEMPDIR, "expected_test_3a_KDEModelInference.json" + ), + os.path.join( + self.TEST_TEMPDIR, "actual_test_3a_KDEModelInference.json" + ), + ) + ) def test_0b_GridModelFitting(self): """Test fitting of a grid-interpolation model, dumps model for further tests""" logger.warning(f"TEST #{self.get_test_count()}: Fitting of 'grid' models...") - cva = Constava(verbose=VERBOSE_LEVEL) + cva = Constava(verbose=self.verbosity) csmodel = cva.fit_csmodel( model_type="grid", kde_bandwidth=0.42, grid_points=145 ) @@ -204,7 +230,7 @@ def test_1b_GridModelLoading(self): logger.warning( f"TEST #{self.get_test_count()}: Loading of pickled 'grid' models..." ) - cva = Constava(verbose=VERBOSE_LEVEL, model_load=self.TEST_MODELDUMP1) + cva = Constava(verbose=self.verbosity, model_load=self.TEST_MODELDUMP1) cva.load_csmodel(pickled_csmodel=self.TEST_MODELDUMP1) self.assertIsInstance( cva._csmodel, @@ -218,7 +244,9 @@ def test_2b_GridModelInference(self): f"TEST #{self.get_test_count()}: Inference from 'grid' conformational state model..." ) input_files = f"{self.CONSTAVA_TEST_DATA_DIR}/csv/dihedrals.csv" - expected_result = f"{self.CONSTAVA_TEST_DATA_DIR}/csv/result_grid.csv" + expected_result = ( + f"{self.CONSTAVA_TEST_DATA_DIR}/csv/result_grid.bandwidth.42.csv" + ) output_file = tempfile.NamedTemporaryFile( prefix="grid.", suffix=".csv", dir=self.TEST_TEMPDIR ) @@ -231,54 +259,74 @@ def test_2b_GridModelInference(self): bootstrap=[3, 7, 23], window_series=[1, 7], seed=42, - verbose=VERBOSE_LEVEL, + verbose=self.verbosity, input_degrees=False, ) c.run() - import ipdb; ipdb.set_trace() + + filecmp.clear_cache() self.assertTrue(filecmp.cmp(output_file.name, expected_result)) def test_3b_GridModelInference(self): """Test inference from pre-fitted grid-inference model""" - logger.warning(f"TEST #{self.get_test_count()}: Inference from 'grid' conformational state model in JSON format...") + logger.warning( + f"TEST #{self.get_test_count()}: Inference from 'grid' conformational state model in JSON format..." + ) input_files = f"{self.CONSTAVA_TEST_DATA_DIR}/csv/dihedrals.csv" expected_result = f"{self.CONSTAVA_TEST_DATA_DIR}/csv/result_grid.json" - output_file = tempfile.NamedTemporaryFile(prefix="grid.", suffix=".json", dir=self.TEST_TEMPDIR) + output_file = tempfile.NamedTemporaryFile( + prefix="grid.", suffix=".json", dir=self.TEST_TEMPDIR + ) + c = Constava( - input_files = input_files, - output_file = output_file.name, - model_type = "grid", - model_load = self.TEST_MODELDUMP1, - window = [1,3,7,23], - bootstrap = [3,7,23], - window_series = [1,7], - seed = 42, - verbose = VERBOSE_LEVEL, - input_degrees=False) + input_files=input_files, + output_file=output_file.name, + model_type="grid", + model_load=self.TEST_MODELDUMP1, + window=[1, 3, 7, 23], + bootstrap=[3, 7, 23], + window_series=[1, 7], + seed=42, + verbose=self.verbosity, + input_degrees=False, + ) c.run() with open(expected_result, "r", encoding="utf-8") as f_exp: expected_obj = json.load(f_exp) - with open(os.path.join(self.TEST_TEMPDIR, "expected_test_3b_GridModelInference.json"), "w", encoding="utf-8") as f: - json.dump(expected_obj['results'], f) + with open( + os.path.join(self.TEST_TEMPDIR, "expected_test_3b_GridModelInference.json"), + "w", + encoding="utf-8", + ) as f: + json.dump(expected_obj["results"], f) with open(output_file.name, "r", encoding="utf-8") as f_act: actual_obj = json.load(f_act) - with open(os.path.join(self.TEST_TEMPDIR, "actual_test_3b_GridModelInference.json"), "w", encoding="utf-8") as f: - json.dump(actual_obj['results'], f) + with open( + os.path.join(self.TEST_TEMPDIR, "actual_test_3b_GridModelInference.json"), + "w", + encoding="utf-8", + ) as f: + json.dump(actual_obj["results"], f) - self.assertTrue(filecmp.cmp( - os.path.join(self.TEST_TEMPDIR, "expected_test_3b_GridModelInference.json"), - os.path.join(self.TEST_TEMPDIR, "actual_test_3b_GridModelInference.json") - )) - -def run_unittest(verbose: int = 0): - """Run the unittests in this module""" + filecmp.clear_cache() + self.assertTrue( + filecmp.cmp( + os.path.join( + self.TEST_TEMPDIR, "expected_test_3b_GridModelInference.json" + ), + os.path.join( + self.TEST_TEMPDIR, "actual_test_3b_GridModelInference.json" + ), + ) + ) - VERBOSE_LEVEL = verbose - logger.warning(f"Starting Test Suite with verbosity={VERBOSE_LEVEL}") + +def run_unittest(verbose: int = 0): + """Run the unit tests in this module""" if verbose == 0: logger.setLevel(logging.WARNING) @@ -289,16 +337,17 @@ def run_unittest(verbose: int = 0): suite = unittest.TestSuite( ( - TestWrapper("test_0a_KDEModelFitting"), - TestWrapper("test_1a_KDEModelLoading"), - TestWrapper("test_2a_KDEModelInference"), - TestWrapper("test_3a_KDEModelInference"), - TestWrapper("test_0b_GridModelFitting"), - TestWrapper("test_1b_GridModelLoading"), - TestWrapper("test_2b_GridModelInference"), + TestWrapper("test_0a_KDEModelFitting", verbose), + TestWrapper("test_1a_KDEModelLoading", verbose), + TestWrapper("test_2a_KDEModelInference", verbose), + TestWrapper("test_3a_KDEModelInference", verbose), + TestWrapper("test_0b_GridModelFitting", verbose), + TestWrapper("test_1b_GridModelLoading", verbose), + TestWrapper("test_2b_GridModelInference", verbose), + TestWrapper("test_3b_GridModelInference", verbose), ) ) - runner = unittest.TextTestRunner(verbosity=verbose, failfast=True) + runner = unittest.TextTestRunner(verbosity=verbose) runner.run(suite) From 96f6c9defa274372bd9f917d8144af6642f4d855 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adri=C3=A1n=20Diaz?= Date: Wed, 28 Jan 2026 17:15:55 +0100 Subject: [PATCH 18/21] Update metadata --- CODE_OF_CONDUCT.md | 14 ++++++++ CONTRIBUTING.md | 80 ++++++++++++++++++++++++++++++++++++++++++++++ README.md | 60 ++++++++++++++++++++++------------ setup.py | 8 ++--- 4 files changed, 137 insertions(+), 25 deletions(-) create mode 100644 CODE_OF_CONDUCT.md create mode 100644 CONTRIBUTING.md diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..fb27032 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,14 @@ +# Code of Conduct + +**We pledge to make our Constava community welcoming, safe, and equitable for all.** + +## Expected standards + +We agree to behave mindfully toward each other and act in ways that center +our shared values, including: + +1. Respecting the **purpose of our community**, our activities, and our ways of gathering. +2. Engaging **kindly and honestly** with others. +3. Respecting **different viewpoints** and experiences. +4. **Taking responsibility** for our actions and contributions. +5. Gracefully giving and accepting **constructive feedback**. diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..04f54b8 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,80 @@ +# Contributing to Constava + +Contributions of all kinds are welcome: bug reports, documentation improvements, feature requests, and code changes. + +Please read the guidelines below to make the contribution process smooth for everyone. + +--- + +## How to Contribute + +### Reporting Bugs + +If you find a bug, please open an issue and include: +- A clear and descriptive title +- Steps to reproduce the issue +- Expected and actual behavior +- Your environment (OS, Python version, Constava version) + +If possible, include a minimal example or traceback. + +### Suggesting Enhancements + +Feature requests and improvements are welcome. +When opening an issue, try to explain: +- The problem you are trying to solve +- Why the current behavior is insufficient +- Any alternative solutions you have considered + +### Development Setup + +1. Fork the repository and clone your fork. +2. Create a virtual environment. +3. Install dependencies: + ```bash + pip install -r requirements.txt + pip install -e . + ``` + +4. Run the test suite to ensure everything works: + ```bash + constava test + ``` + +### Making Changes + +1. Create a new branch for your changes. +1. Keep commits focused and atomic. +1. Write or update tests when changing behavior. +1. Update documentation if your change affects public APIs or CLI usage. + + +### Code Style + +1. Follow standard Python conventions (PEP 8). +1. Keep code readable and well-documented. +1. Avoid unnecessary complexity; clarity beats cleverness. + +### Testing + +All changes should pass the test suite before submission: + +```bash +constava test +``` + +Pull requests that break tests will not be merged. + + +### Submitting a Pull Request + +1. Open a pull request against the default branch. +1. Clearly describe what the PR does and why it is needed. +1. Link related issues where applicable. +1. Be prepared to revise your PR based on review feedback. + + +## Code of Conduct + +This project follows a Code of Conduct. +By participating, you are expected to uphold it in all interactions. diff --git a/README.md b/README.md index ec91b90..5b966ac 100644 --- a/README.md +++ b/README.md @@ -14,6 +14,8 @@ # Constava +> *Conformational states probability & conformational state variability from a protein structure ensemble* + ## Table of content * [Table of content](#table-of-content) @@ -50,7 +52,7 @@ Constava analyzes conformational ensembles calculating conformational state propensities and conformational state variability. The conformational state propensities indicate the likelihood of a residue residing in a given conformational state, while the conformational state variability is a measure -of the residues ability to transiton between conformational states. +of the residues ability to transition between conformational states. Each conformational state is a statistical model of based on the backbone dihedrals (phi, psi). The default models were derived from an analysis of NMR @@ -75,7 +77,7 @@ submodule, which supports a wide range of MD and structure formats. ### Installation through PyPI -We recommend this installation for most users. +We *recommend* this installation for most users. ```sh # Create a virtual environment (optional but recommended): @@ -160,8 +162,10 @@ conda install libtiff To use constava's Docker image generated by the Biocontainers project based on the Bioconda package, follow the instructions below. You can find the container -tags on https://quay.io/repository/biocontainers/constava?tab=tags. In this -example, the latest tag is `1.1.0--pyhdfd78af_0`: +tags on +[the Biocontainers archive](https://quay.io/repository/biocontainers/constava?tab=tags). + +In this example, the latest tag is `1.1.0--pyhdfd78af_0`: ```sh # Pull the constava image from quay.io @@ -243,12 +247,12 @@ The `constava fit-model` can be used to train a custom probabilistic model of conformational states. The default models were derived from an analysis of NMR ensembles and chemical shifts; they cover six conformational states: -* Core Helix - Exclusively alpha-helical, low backbone dynamics -* Surrounding Helix - Mostly alpha-helical, high backbone dynamics -* Core Sheet - Exclusively beta-sheet, low backbone dynamics -* Surrounding Sheet - Mostly extended conformation, high backbone dynamics -* Turn - Mostly turn, high backbone dynamics -* Other - Mostly coil, high backbone dynamics +- *Core Helix* - Exclusively alpha-helical, low backbone dynamics +- *Surrounding Helix* - Mostly alpha-helical, high backbone dynamics +- *Core Sheet* - Exclusively beta-sheet, low backbone dynamics +- *Surrounding Sheet* - Mostly extended conformation, high backbone dynamics +- *Turn* - Mostly turn, high backbone dynamics +- *Other* - Mostly coil, high backbone dynamics [](#constava) @@ -258,7 +262,9 @@ To extract dihedral angles from a trajectory the `constava dihedrals` submodule is used. ``` -usage: constava dihedrals [-h] [-s ] [-f [ ...]] [-o OUTPUT] [--selection SELECTION] [--precision PRECISION] [--degrees] [-O] +usage: constava dihedrals [-h] [-s ] [-f [ ...]] + [-o OUTPUT] [--selection SELECTION] [--precision PRECISION] + [--degrees] [-O] The `constava dihedrals` submodule is used to extract the backbone dihedrals needed for the analysis from conformational ensembles. By default the results @@ -303,8 +309,16 @@ To analyze the backbone dihedral angles extracted from a conformational ensemble the `constava analyze` submodule is used. ``` -usage: constava analyze [-h] [-i [ ...]] [--input-format {auto,xvg,csv}] [-o ] [--output-format {auto,csv,json,tsv}] [-m ] [--window [ ...]] - [--window-series [ ...]] [--bootstrap [ ...]] [--bootstrap-series [ ...]] [--bootstrap-samples ] [--degrees] [--precision ] [--seed ] [-v] +usage: constava analyze [-h] [-i [ ...]] + [--input-format {auto,xvg,csv}] [-o ] + [--output-format {auto,csv,json,tsv}] [-m ] + [--window [ ...]] [--window-series [ ...]] + [--bootstrap [ ...]] + [--bootstrap-series [ ...]] + [--bootstrap-samples ] + [--degrees] [--precision ] + [--indent_size ] [--seed ] + [-v] The `constava analyze` submodule analyzes the provided backbone dihedral angles and infers the propensities for each residue to reside in a given @@ -339,7 +353,7 @@ Conformational state model options: Load a conformational state model from the given pickled file. If not provided, the default model will be used. -Subsampling options: +Sub-sampling options: --window [ ...] Do inference using a moving reading-frame. Each reading frame consists of consecutive samples. Multiple @@ -374,7 +388,7 @@ Miscellaneous options: debugging mode. ``` -An example: +An example with debug-level output: ```sh # Run constava with debug-level output @@ -395,7 +409,10 @@ To train a custom probabilistic model of conformational states, the `constava f submodule is used. ``` -usage: constava fit-model [-h] [-i ] -o [--model-type {kde,grid}] [--kde-bandwidth ] [--grid-points ] [--degrees] [-v] +usage: constava fit-model [-h] [-i ] -o + [--model-type {kde,grid}] [--kde-bandwidth ] + [--grid-points ] [--degrees] + [-v] The `constava fit-model` submodule is used to generate the probabilistic conformational state models used in the analysis. By default, when running @@ -446,7 +463,7 @@ Miscellaneous options: debugging mode. ``` -An example: +An example using the default dataset: ```sh # Generates a faster 'grid-interpolation model' using the default dataset @@ -510,6 +527,7 @@ c.set_param("window", [1,3,5]) # Run the calculation and write results c.run() ``` + This protein, with 48 residues and 100 frames per residue runs in about 1 minute. The original MD ensembles from the manuscript can be found in @@ -636,14 +654,14 @@ https://orcid.org/0000-0002-4313-3845) - ## Acknowledgments -We thank Adrian Diaz [![ORCID](https://orcid.org/sites/default/files/images/orcid_16x16.png)](https://orcid.org/0000-0003-0165-1318) for the invaluable help in the distribution of this software. +We thank Adrián Díaz [![ORCID](https://orcid.org/sites/default/files/images/orcid_16x16.png)](https://orcid.org/0000-0003-0165-1318) for the invaluable help in the distribution of this software. [](#constava) ## Contact -Wim Vranken - [wim.vranken@vub.be](mailto:wim.vranken@vub.be) - -Bio2Byte website: [https://bio2byte.be/](https://bio2byte.be/) +1. Maintainers - [bio2byte@vub.be](bio2byte@vub.be) +1. Wim Vranken - [wim.vranken@vub.be](mailto:wim.vranken@vub.be) +1. Bio2Byte website: [https://bio2byte.be/](https://bio2byte.be/) [](#constava) diff --git a/setup.py b/setup.py index f1a92e6..57dbefb 100644 --- a/setup.py +++ b/setup.py @@ -14,12 +14,12 @@ description="This software is used to calculate conformational states probability & conformational state " "variability from a protein structure ensemble.", license="GPL-3.0-only", - license_files=["LICEN[CS]E*", "authors.md"], + license_files=["LICENSE", "authors.md"], long_description=long_description, long_description_content_type="text/markdown", - maintainer="Adrián Díaz, Wim Vranken", - maintainer_email="bio2byte@vub.be, adrian.diaz@vub.be, wim.vranken@vub.be", - url="https://bitbucket.org/bio2byte/constava/", + maintainer="Adrián Díaz", + maintainer_email="bio2byte@vub.be, adrian.diaz@vub.be", + url="https://github.com/bio2byte/constava/", packages=find_packages(), include_package_data=True, classifiers=[ From 948d98147eb99dac660237bdc2876d5d206405ce Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adri=C3=A1n=20Diaz?= Date: Wed, 28 Jan 2026 17:39:02 +0100 Subject: [PATCH 19/21] Rename directories to avoid package warnings --- MANIFEST.in | 19 ++++++++++++++++++- .../csv-files}/dihedrals.csv | 0 .../csv-files}/result_grid.bandwidth.42.csv | 0 .../csv-files}/result_grid.csv | 0 .../csv-files}/result_grid.json | 0 .../xvg-files}/ramaPhiPsiALA18.xvg | 0 .../xvg-files}/ramaPhiPsiALA19.xvg | 0 .../xvg-files}/ramaPhiPsiALA26.xvg | 0 .../xvg-files}/ramaPhiPsiARG29.xvg | 0 .../xvg-files}/ramaPhiPsiASN15.xvg | 0 .../xvg-files}/ramaPhiPsiASN27.xvg | 0 .../xvg-files}/ramaPhiPsiASN3.xvg | 0 .../xvg-files}/ramaPhiPsiASN32.xvg | 0 .../xvg-files}/ramaPhiPsiASP12.xvg | 0 .../xvg-files}/ramaPhiPsiASP37.xvg | 0 .../xvg-files}/ramaPhiPsiGLN20.xvg | 0 .../xvg-files}/ramaPhiPsiGLN43.xvg | 0 .../xvg-files}/ramaPhiPsiGLY1.xvg | 0 .../xvg-files}/ramaPhiPsiGLY11.xvg | 0 .../xvg-files}/ramaPhiPsiGLY22.xvg | 0 .../xvg-files}/ramaPhiPsiGLY33.xvg | 0 .../xvg-files}/ramaPhiPsiGLY36.xvg | 0 .../xvg-files}/ramaPhiPsiGLY42.xvg | 0 .../xvg-files}/ramaPhiPsiGLY50.xvg | 0 .../xvg-files}/ramaPhiPsiILE17.xvg | 0 .../xvg-files}/ramaPhiPsiILE34.xvg | 0 .../xvg-files}/ramaPhiPsiILE39.xvg | 0 .../xvg-files}/ramaPhiPsiILE46.xvg | 0 .../xvg-files}/ramaPhiPsiLEU14.xvg | 0 .../xvg-files}/ramaPhiPsiLEU28.xvg | 0 .../xvg-files}/ramaPhiPsiLEU38.xvg | 0 .../xvg-files}/ramaPhiPsiLEU45.xvg | 0 .../xvg-files}/ramaPhiPsiLYS16.xvg | 0 .../xvg-files}/ramaPhiPsiLYS44.xvg | 0 .../xvg-files}/ramaPhiPsiLYS48.xvg | 0 .../xvg-files}/ramaPhiPsiLYS49.xvg | 0 .../xvg-files}/ramaPhiPsiLYS9.xvg | 0 .../xvg-files}/ramaPhiPsiPHE40.xvg | 0 .../xvg-files}/ramaPhiPsiSER10.xvg | 0 .../xvg-files}/ramaPhiPsiSER24.xvg | 0 .../xvg-files}/ramaPhiPsiSER30.xvg | 0 .../xvg-files}/ramaPhiPsiSER35.xvg | 0 .../xvg-files}/ramaPhiPsiSER51.xvg | 0 .../xvg-files}/ramaPhiPsiTHR13.xvg | 0 .../xvg-files}/ramaPhiPsiTHR2.xvg | 0 .../xvg-files}/ramaPhiPsiTHR4.xvg | 0 .../xvg-files}/ramaPhiPsiTHR7.xvg | 0 .../xvg-files}/ramaPhiPsiTRP31.xvg | 0 .../xvg-files}/ramaPhiPsiTYR21.xvg | 0 .../xvg-files}/ramaPhiPsiTYR5.xvg | 0 .../xvg-files}/ramaPhiPsiTYR6.xvg | 0 .../xvg-files}/ramaPhiPsiVAL23.xvg | 0 .../xvg-files}/ramaPhiPsiVAL25.xvg | 0 .../xvg-files}/ramaPhiPsiVAL41.xvg | 0 .../xvg-files}/ramaPhiPsiVAL47.xvg | 0 .../xvg-files}/ramaPhiPsiVAL8.xvg | 0 .../xvg-files}/result_kde.csv | 0 .../xvg-files}/result_kde.json | 0 .../constava_csdata.json | 0 constava/tests/tests.py | 19 +++++++++---------- constava/utils/constants.py | 2 +- setup.py | 6 ++++-- 62 files changed, 32 insertions(+), 14 deletions(-) rename constava/{data/constava_testdata/csv => internal-data/constava-testdata/csv-files}/dihedrals.csv (100%) rename constava/{data/constava_testdata/csv => internal-data/constava-testdata/csv-files}/result_grid.bandwidth.42.csv (100%) rename constava/{data/constava_testdata/csv => internal-data/constava-testdata/csv-files}/result_grid.csv (100%) rename constava/{data/constava_testdata/csv => internal-data/constava-testdata/csv-files}/result_grid.json (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiALA18.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiALA19.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiALA26.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiARG29.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiASN15.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiASN27.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiASN3.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiASN32.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiASP12.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiASP37.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiGLN20.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiGLN43.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiGLY1.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiGLY11.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiGLY22.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiGLY33.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiGLY36.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiGLY42.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiGLY50.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiILE17.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiILE34.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiILE39.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiILE46.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiLEU14.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiLEU28.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiLEU38.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiLEU45.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiLYS16.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiLYS44.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiLYS48.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiLYS49.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiLYS9.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiPHE40.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiSER10.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiSER24.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiSER30.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiSER35.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiSER51.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiTHR13.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiTHR2.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiTHR4.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiTHR7.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiTRP31.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiTYR21.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiTYR5.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiTYR6.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiVAL23.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiVAL25.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiVAL41.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiVAL47.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/ramaPhiPsiVAL8.xvg (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/result_kde.csv (100%) rename constava/{data/constava_testdata/xvg => internal-data/constava-testdata/xvg-files}/result_kde.json (100%) rename constava/{data => internal-data}/constava_csdata.json (100%) diff --git a/MANIFEST.in b/MANIFEST.in index 25f710f..a7c2fc8 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,7 +1,11 @@ -recursive-include constava/data * +recursive-include constava/internal-data * + include requirements.txt include LICENSE include authors.md +include CITATION.cff +include CONTRIBUTING.md +include CODE_OF_CONDUCT.md global-exclude .DS_Store global-exclude *.pyc @@ -12,3 +16,16 @@ global-exclude *.tmp global-exclude *.swp global-exclude *.bak global-exclude *.orig + +global-exclude *.egg-info +global-exclude .pytest_cache +global-exclude .coverage +global-exclude htmlcov +global-exclude .ruff_cache +global-exclude .mypy_cache +global-exclude .tox + +global-exclude dist +global-exclude build +global-exclude .idea +global-exclude .vscode diff --git a/constava/data/constava_testdata/csv/dihedrals.csv b/constava/internal-data/constava-testdata/csv-files/dihedrals.csv similarity index 100% rename from constava/data/constava_testdata/csv/dihedrals.csv rename to constava/internal-data/constava-testdata/csv-files/dihedrals.csv diff --git a/constava/data/constava_testdata/csv/result_grid.bandwidth.42.csv b/constava/internal-data/constava-testdata/csv-files/result_grid.bandwidth.42.csv similarity index 100% rename from constava/data/constava_testdata/csv/result_grid.bandwidth.42.csv rename to constava/internal-data/constava-testdata/csv-files/result_grid.bandwidth.42.csv diff --git a/constava/data/constava_testdata/csv/result_grid.csv b/constava/internal-data/constava-testdata/csv-files/result_grid.csv similarity index 100% rename from constava/data/constava_testdata/csv/result_grid.csv rename to constava/internal-data/constava-testdata/csv-files/result_grid.csv diff --git a/constava/data/constava_testdata/csv/result_grid.json b/constava/internal-data/constava-testdata/csv-files/result_grid.json similarity index 100% rename from constava/data/constava_testdata/csv/result_grid.json rename to constava/internal-data/constava-testdata/csv-files/result_grid.json diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiALA18.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiALA18.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiALA18.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiALA18.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiALA19.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiALA19.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiALA19.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiALA19.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiALA26.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiALA26.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiALA26.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiALA26.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiARG29.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiARG29.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiARG29.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiARG29.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiASN15.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiASN15.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiASN15.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiASN15.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiASN27.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiASN27.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiASN27.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiASN27.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiASN3.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiASN3.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiASN3.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiASN3.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiASN32.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiASN32.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiASN32.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiASN32.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiASP12.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiASP12.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiASP12.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiASP12.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiASP37.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiASP37.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiASP37.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiASP37.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiGLN20.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiGLN20.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiGLN20.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiGLN20.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiGLN43.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiGLN43.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiGLN43.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiGLN43.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiGLY1.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiGLY1.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiGLY1.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiGLY1.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiGLY11.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiGLY11.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiGLY11.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiGLY11.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiGLY22.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiGLY22.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiGLY22.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiGLY22.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiGLY33.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiGLY33.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiGLY33.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiGLY33.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiGLY36.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiGLY36.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiGLY36.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiGLY36.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiGLY42.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiGLY42.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiGLY42.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiGLY42.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiGLY50.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiGLY50.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiGLY50.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiGLY50.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiILE17.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiILE17.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiILE17.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiILE17.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiILE34.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiILE34.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiILE34.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiILE34.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiILE39.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiILE39.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiILE39.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiILE39.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiILE46.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiILE46.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiILE46.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiILE46.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiLEU14.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiLEU14.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiLEU14.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiLEU14.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiLEU28.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiLEU28.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiLEU28.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiLEU28.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiLEU38.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiLEU38.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiLEU38.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiLEU38.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiLEU45.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiLEU45.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiLEU45.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiLEU45.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiLYS16.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiLYS16.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiLYS16.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiLYS16.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiLYS44.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiLYS44.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiLYS44.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiLYS44.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiLYS48.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiLYS48.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiLYS48.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiLYS48.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiLYS49.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiLYS49.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiLYS49.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiLYS49.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiLYS9.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiLYS9.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiLYS9.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiLYS9.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiPHE40.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiPHE40.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiPHE40.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiPHE40.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiSER10.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiSER10.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiSER10.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiSER10.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiSER24.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiSER24.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiSER24.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiSER24.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiSER30.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiSER30.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiSER30.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiSER30.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiSER35.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiSER35.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiSER35.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiSER35.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiSER51.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiSER51.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiSER51.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiSER51.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiTHR13.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiTHR13.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiTHR13.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiTHR13.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiTHR2.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiTHR2.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiTHR2.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiTHR2.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiTHR4.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiTHR4.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiTHR4.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiTHR4.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiTHR7.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiTHR7.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiTHR7.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiTHR7.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiTRP31.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiTRP31.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiTRP31.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiTRP31.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiTYR21.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiTYR21.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiTYR21.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiTYR21.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiTYR5.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiTYR5.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiTYR5.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiTYR5.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiTYR6.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiTYR6.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiTYR6.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiTYR6.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiVAL23.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiVAL23.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiVAL23.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiVAL23.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiVAL25.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiVAL25.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiVAL25.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiVAL25.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiVAL41.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiVAL41.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiVAL41.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiVAL41.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiVAL47.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiVAL47.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiVAL47.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiVAL47.xvg diff --git a/constava/data/constava_testdata/xvg/ramaPhiPsiVAL8.xvg b/constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiVAL8.xvg similarity index 100% rename from constava/data/constava_testdata/xvg/ramaPhiPsiVAL8.xvg rename to constava/internal-data/constava-testdata/xvg-files/ramaPhiPsiVAL8.xvg diff --git a/constava/data/constava_testdata/xvg/result_kde.csv b/constava/internal-data/constava-testdata/xvg-files/result_kde.csv similarity index 100% rename from constava/data/constava_testdata/xvg/result_kde.csv rename to constava/internal-data/constava-testdata/xvg-files/result_kde.csv diff --git a/constava/data/constava_testdata/xvg/result_kde.json b/constava/internal-data/constava-testdata/xvg-files/result_kde.json similarity index 100% rename from constava/data/constava_testdata/xvg/result_kde.json rename to constava/internal-data/constava-testdata/xvg-files/result_kde.json diff --git a/constava/data/constava_csdata.json b/constava/internal-data/constava_csdata.json similarity index 100% rename from constava/data/constava_csdata.json rename to constava/internal-data/constava_csdata.json diff --git a/constava/tests/tests.py b/constava/tests/tests.py index d958ed4..58a52c5 100644 --- a/constava/tests/tests.py +++ b/constava/tests/tests.py @@ -24,7 +24,6 @@ def __init__(self, testName, verbosity): super(TestWrapper, self).__init__(testName) self.verbosity = verbosity - TEST_SOURCE = os.path.join(CONSTAVA_DATA_DIR, "constava_testdata.tgz") TEST_TEMPORARY_DIRECTORY = None TEST_TEMPDIR = None TEST_MODELDUMP0 = None @@ -48,7 +47,7 @@ def setUpClass(cls): prefix="model.", suffix=".pkl", dir=cls.TEST_TEMPDIR ) cls.CONSTAVA_TEST_DATA_DIR = os.path.join( - CONSTAVA_DATA_DIR, "constava_testdata" + CONSTAVA_DATA_DIR, "constava-testdata" ) @classmethod @@ -111,8 +110,8 @@ def test_2a_KDEModelInference(self): logger.warning( f"TEST #{self.get_test_count()}: Inference from 'kde' conformational state model..." ) - input_files = glob.glob(f"{self.CONSTAVA_TEST_DATA_DIR}/xvg/ramaPhiPsi*.xvg") - expected_result = f"{self.CONSTAVA_TEST_DATA_DIR}/xvg/result_kde.csv" + input_files = glob.glob(f"{self.CONSTAVA_TEST_DATA_DIR}/xvg-files/ramaPhiPsi*.xvg") + expected_result = f"{self.CONSTAVA_TEST_DATA_DIR}/xvg-files/result_kde.csv" output_file = tempfile.NamedTemporaryFile( prefix="kde.", suffix=".csv", dir=self.TEST_TEMPDIR ) @@ -137,8 +136,8 @@ def test_3a_KDEModelInference(self): logger.warning( f"TEST #{self.get_test_count()}: Inference from 'kde' conformational state model in JSON format..." ) - input_files = glob.glob(f"{self.CONSTAVA_TEST_DATA_DIR}/xvg/ramaPhiPsi*.xvg") - expected_result = f"{self.CONSTAVA_TEST_DATA_DIR}/xvg/result_kde.json" + input_files = glob.glob(f"{self.CONSTAVA_TEST_DATA_DIR}/xvg-files/ramaPhiPsi*.xvg") + expected_result = f"{self.CONSTAVA_TEST_DATA_DIR}/xvg-files/result_kde.json" output_file = tempfile.NamedTemporaryFile( prefix="kde.", suffix=".json", dir=self.TEST_TEMPDIR ) @@ -243,9 +242,9 @@ def test_2b_GridModelInference(self): logger.warning( f"TEST #{self.get_test_count()}: Inference from 'grid' conformational state model..." ) - input_files = f"{self.CONSTAVA_TEST_DATA_DIR}/csv/dihedrals.csv" + input_files = f"{self.CONSTAVA_TEST_DATA_DIR}/csv-files/dihedrals.csv" expected_result = ( - f"{self.CONSTAVA_TEST_DATA_DIR}/csv/result_grid.bandwidth.42.csv" + f"{self.CONSTAVA_TEST_DATA_DIR}/csv-files/result_grid.bandwidth.42.csv" ) output_file = tempfile.NamedTemporaryFile( prefix="grid.", suffix=".csv", dir=self.TEST_TEMPDIR @@ -272,8 +271,8 @@ def test_3b_GridModelInference(self): logger.warning( f"TEST #{self.get_test_count()}: Inference from 'grid' conformational state model in JSON format..." ) - input_files = f"{self.CONSTAVA_TEST_DATA_DIR}/csv/dihedrals.csv" - expected_result = f"{self.CONSTAVA_TEST_DATA_DIR}/csv/result_grid.json" + input_files = f"{self.CONSTAVA_TEST_DATA_DIR}/csv-files/dihedrals.csv" + expected_result = f"{self.CONSTAVA_TEST_DATA_DIR}/csv-files/result_grid.json" output_file = tempfile.NamedTemporaryFile( prefix="grid.", suffix=".json", dir=self.TEST_TEMPDIR ) diff --git a/constava/utils/constants.py b/constava/utils/constants.py index 2caffc3..3db577c 100644 --- a/constava/utils/constants.py +++ b/constava/utils/constants.py @@ -5,7 +5,7 @@ CONSTAVA_NAME = __name__.split(".")[0] CONSTAVA_VERSION = version(CONSTAVA_NAME) CONSTAVA_SOURCE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) -CONSTAVA_DATA_DIR = os.path.join(CONSTAVA_SOURCE_DIR, "data") +CONSTAVA_DATA_DIR = os.path.join(CONSTAVA_SOURCE_DIR, "internal-data") DEFAULT_TRAINING_DATA_PATH = os.path.join(CONSTAVA_DATA_DIR, "constava_csdata.json") aminoacids1to3 = dict( diff --git a/setup.py b/setup.py index 57dbefb..b87f1be 100644 --- a/setup.py +++ b/setup.py @@ -1,4 +1,4 @@ -from setuptools import setup, find_packages +from setuptools import setup, find_namespace_packages with open("README.md", "r", encoding="utf-8") as fh: long_description = fh.read() @@ -20,7 +20,9 @@ maintainer="Adrián Díaz", maintainer_email="bio2byte@vub.be, adrian.diaz@vub.be", url="https://github.com/bio2byte/constava/", - packages=find_packages(), + packages=find_namespace_packages( + include=["constava*"], + ), include_package_data=True, classifiers=[ "Natural Language :: English", From cf8e88d8ed2fbf821debd89af0c7e4d5440a593d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adri=C3=A1n=20Diaz?= Date: Wed, 28 Jan 2026 18:23:54 +0100 Subject: [PATCH 20/21] Polish code --- constava/__main__.py | 213 +++++++++++++++++++---------------- constava/calc/calculator.py | 19 ++-- constava/calc/csmodels.py | 11 +- constava/calc/subsampling.py | 52 +++++---- constava/io/reader.py | 14 ++- constava/io/rstrategies.py | 60 ++++++---- constava/utils/dihedrals.py | 9 +- constava/utils/results.py | 46 +++++--- constava/utils/utils.py | 4 - constava/wrapper/params.py | 4 +- constava/wrapper/wrapper.py | 38 ++++--- 11 files changed, 257 insertions(+), 213 deletions(-) diff --git a/constava/__main__.py b/constava/__main__.py index 920c48f..f13773d 100644 --- a/constava/__main__.py +++ b/constava/__main__.py @@ -1,7 +1,8 @@ """constava.__main__ is the executable for the command line functionality of the tool.""" -import os, sys +import os +import sys import argparse import textwrap as tw from constava import Constava, ConstavaParameters, __version__ @@ -14,29 +15,29 @@ def parse_parameters(cmdline_arguments): parser = argparse.ArgumentParser( description=tw.dedent( """\ - Constava analyzes conformational ensembles calculating conformational state - propensities and conformational state variability. The conformational state - propensities indicate the likelihood of a residue residing in a given - conformational state, while the conformational state variability is a measure - of the residues ability to transiton between conformational states. - - Each conformational state is a statistical model of based on the backbone + Constava analyzes conformational ensembles calculating conformational state + propensities and conformational state variability. The conformational state + propensities indicate the likelihood of a residue residing in a given + conformational state, while the conformational state variability is a measure + of the residues ability to transition between conformational states. + + Each conformational state is a statistical model of based on the backbone dihedrals (phi, psi). The default models were derived from an analysis of NMR - ensembles and chemical shifts. To analyze a conformational ensemble, the phi- - and psi-angles for each conformational state in the ensemble need to be - provided. - - The `constava dihedrals` submodule provides a simple way to extract backbone + ensembles and chemical shifts. To analyze a conformational ensemble, the phi- + and psi-angles for each conformational state in the ensemble need to be + provided. + + The `constava dihedrals` submodule provides a simple way to extract backbone dihedral angles from MD simulations or PDB ensembles. For more information run: `constava dihedrals -h`. Alternatively, the backbone dihedrals may be extracted with GROMACS' `gmx chi` module. The `constava analyze` submodule analyzes the provided backbone dihedral angles - and infers the propensities for each residue to reside in a given + and infers the propensities for each residue to reside in a given conformational state. For more information run: `constava analyze -h`. The `constava fit-model` can be used to train a custom probabilistic model of - confromational states. The default models were derived from an analysis of NMR + conformational states. The default models were derived from an analysis of NMR ensembles and chemical shifts; they cover six conformational states: * Core Helix - Exclusively alpha-helical, low backbone dynamics * Surrounding Helix - Mostly alpha-helical, high backbone dynamics @@ -50,19 +51,19 @@ def parse_parameters(cmdline_arguments): ) # General flags for the main software - genOpt = parser.add_argument_group("Generic options") - genOpt.add_argument( + gen_opt = parser.add_argument_group("Generic options") + gen_opt.add_argument( "-h", "--help", action="help", help=tw.dedent( """\ - Show this help message and exit. For detailed - information on the subcommands, run: + Show this help message and exit. For detailed + information on the subcommands, run: `%(prog)s SUBCOMMAND -h`""" ), ) - genOpt.add_argument( + gen_opt.add_argument( "--version", action="version", version=f"%(prog)s {__version__}", @@ -95,22 +96,22 @@ def parse_parameters(cmdline_arguments): """\ The `constava fit-model` submodule is used to generate the probabilistic conformational state models used in the analysis. By default, when running - `constava analyze` these models are generated on-the-fly. In selected cases + `constava analyze` these models are generated on-the-fly. In selected cases generating a model beforehand and loading it can be useful, though. We provide two model types. kde-Models are the default. They are fast to fit - but may be slow in the inference in large conformational ensembles (e.g., + but may be slow in the inference in large conformational ensembles (e.g., long-timescale MD simulations). The idea of grid-Models is, to replace the continuous probability density function of the kde-Model by a fixed set - of grid-points. The PDF for any sample is then estimated by linear + of grid-points. The PDF for any sample is then estimated by linear interpolation between the nearest grid points. This is slightly less accurate than the kde-Model but speeds up inference significantly.""" ), formatter_class=argparse.RawTextHelpFormatter, ) - fitIO = parser_fit_model.add_argument_group("Input and output options") - fitIO.add_argument( + fit_io = parser_fit_model.add_argument_group("Input and output options") + fit_io.add_argument( "-i", "--input", type=str, @@ -118,14 +119,14 @@ def parse_parameters(cmdline_arguments): help=tw.dedent( """\ The data to which the new conformational state models will - be fitted. It should be provided as a JSON file. The - top-most key should indicate the names of the + be fitted. It should be provided as a JSON file. The + top-most key should indicate the names of the conformational states. On the level below, lists of phi-/ - psi pairs for each stat should be provided. If not provided + psi pairs for each stat should be provided. If not provided the default data from the publication will be used.""" ), ) - fitIO.add_argument( + fit_io.add_argument( "-o", "--output", type=str, @@ -138,62 +139,62 @@ def parse_parameters(cmdline_arguments): ), ) - fitMdl = parser_fit_model.add_argument_group("Conformational state model options") - fitMdl.add_argument( + fit_mdl = parser_fit_model.add_argument_group("Conformational state model options") + fit_mdl.add_argument( "--model-type", choices=["kde", "grid"], default="kde", help=tw.dedent( """\ - The probabilistic conformational state model used. The + The probabilistic conformational state model used. The default is `kde`. The alternative `grid` runs significantly faster while slightly sacrificing accuracy: {'kde', 'grid'} (default: 'kde')""" ), ) - fitMdl.add_argument( + fit_mdl.add_argument( "--kde-bandwidth", type=float, metavar="", default=0.13, help=tw.dedent( """\ - This flag controls the bandwidth of the Gaussian kernel + This flag controls the bandwidth of the Gaussian kernel density estimator. (default: 0.13)""" ), ) - fitMdl.add_argument( + fit_mdl.add_argument( "--grid-points", type=int, metavar="", default=10_000, help=tw.dedent( """\ - This flag controls how many grid points are used to + This flag controls how many grid points are used to describe the probability density function. Only applies if `--model-type` is set to `grid`. (default: 10000)""" ), ) - fitMisc = parser_fit_model.add_argument_group("Miscellaneous options") - fitMisc.add_argument( + fit_misc = parser_fit_model.add_argument_group("Miscellaneous options") + fit_misc.add_argument( "--degrees", action="store_true", help=tw.dedent( """\ - Set this flag, if dihedrals in `model-data` are in degrees + Set this flag, if dihedrals in `model-data` are in degrees instead of radians.""" ), ) - fitMisc.add_argument( + fit_misc.add_argument( "-v", "--verbose", action="count", default=0, help=tw.dedent( """\ - Set verbosity level of screen output. Flag can be given - multiple times (up to 2) to gradually increase output to + Set verbosity level of screen output. Flag can be given + multiple times (up to 2) to gradually increase output to debugging mode.""" ), ) @@ -206,25 +207,25 @@ def parse_parameters(cmdline_arguments): description=tw.dedent( """\ The `constava analyze` submodule analyzes the provided backbone dihedral angles - and infers the propensities for each residue to reside in a given - conformational state. + and infers the propensities for each residue to reside in a given + conformational state. - Each conformational state is a statistical model of based on the backbone + Each conformational state is a statistical model of based on the backbone dihedrals (phi, psi). The default models were derived from an analysis of NMR - ensembles and chemical shifts. To analyze a conformational ensemble, the phi- - and psi-angles for each conformational state in the ensemble need to be - provided. - - As input data the backbone dihedral angles extracted from the conformational - ensemble need to be provided. Those can be generated using the + ensembles and chemical shifts. To analyze a conformational ensemble, the phi- + and psi-angles for each conformational state in the ensemble need to be + provided. + + As input data the backbone dihedral angles extracted from the conformational + ensemble need to be provided. Those can be generated using the `constava dihedrals` submodule (`--input-format csv`) or GROMACS' `gmx chi` module (`--input-format xvg`).""" ), formatter_class=argparse.RawTextHelpFormatter, ) - anaIO = parser_analyze.add_argument_group("Input & output options") - anaIO.add_argument( + ana_io = parser_analyze.add_argument_group("Input & output options") + ana_io.add_argument( "-i", "--input", nargs="+", @@ -232,91 +233,91 @@ def parse_parameters(cmdline_arguments): metavar="", help="Input file(s) that contain the dihedral angles.", ) - anaIO.add_argument( + ana_io.add_argument( "--input-format", choices=["auto", "xvg", "csv"], default="auto", help="Format of the input file: {'auto', 'csv', 'xvg'}", ) - anaIO.add_argument( + ana_io.add_argument( "-o", "--output", type=str, metavar="", help="The file to write the results to.", ) - anaIO.add_argument( + ana_io.add_argument( "--output-format", choices=["auto", "csv", "json", "tsv"], default="auto", help="Format of output file: {'csv', 'json', 'tsv'}. (default: 'auto')", ) - anaMdl = parser_analyze.add_argument_group("Conformational state model options") - anaMdl.add_argument( + ana_mdl = parser_analyze.add_argument_group("Conformational state model options") + ana_mdl.add_argument( "-m", "--load-model", type=str, metavar="", help=tw.dedent( """\ - Load a conformational state model from the given pickled + Load a conformational state model from the given pickled file. If not provided, the default model will be used.""" ), ) - anaSmpl = parser_analyze.add_argument_group("Subsampling options") - anaSmpl.add_argument( + ana_smpl = parser_analyze.add_argument_group("Sub-sampling options") + ana_smpl.add_argument( "--window", metavar="", type=int, nargs="+", help=tw.dedent( """\ - Do inference using a moving reading-frame. Each reading - frame consists of consecutive samples. Multiple + Do inference using a moving reading-frame. Each reading + frame consists of consecutive samples. Multiple values can be provided.""" ), ) - anaSmpl.add_argument( + ana_smpl.add_argument( "--window-series", metavar="", type=int, nargs="+", help=tw.dedent( """\ - Do inference using a moving reading-frame. Each reading - frame consists of consecutive samples. Return the + Do inference using a moving reading-frame. Each reading + frame consists of consecutive samples. Return the results for every window rather than the average. This can - result in very large output files. Multiple values can be + result in very large output files. Multiple values can be provided.""" ), ) - anaSmpl.add_argument( + ana_smpl.add_argument( "--bootstrap", metavar="", type=int, nargs="+", help=tw.dedent( """\ - Do inference using samples obtained through + Do inference using samples obtained through bootstrapping. Multiple values can be provided.""" ), ) - anaSmpl.add_argument( + ana_smpl.add_argument( "--bootstrap-series", metavar="", type=int, nargs="+", help=tw.dedent( """\ - Do inference using samples obtained through + Do inference using samples obtained through bootstrapping. Return the results for every subsample - rather than the average. This can result in very + rather than the average. This can result in very large output files. Multiple values can be provided.""" ), ) - anaSmpl.add_argument( + ana_smpl.add_argument( "--bootstrap-samples", metavar="", type=int, @@ -328,31 +329,31 @@ def parse_parameters(cmdline_arguments): ), ) - anaMisc = parser_analyze.add_argument_group("Miscellaneous options") - anaMisc.add_argument( + ana_misc = parser_analyze.add_argument_group("Miscellaneous options") + ana_misc.add_argument( "--degrees", action="store_true", help=tw.dedent( """\ - Set this flag, if dihedrals in the input files are in + Set this flag, if dihedrals in the input files are in degrees.""" ), ) - anaMisc.add_argument( + ana_misc.add_argument( "--precision", metavar="", type=int, default=4, help="Sets the number of decimals in the output files.", ) - anaMisc.add_argument( + ana_misc.add_argument( "--indent_size", metavar="", type=int, default=0, help="Sets the number of spaces used to indent the output document.", ) - anaMisc.add_argument( + ana_misc.add_argument( "--seed", metavar="", type=int, @@ -360,15 +361,15 @@ def parse_parameters(cmdline_arguments): required=False, help="Set random seed for bootstrap sampling", ) - anaMisc.add_argument( + ana_misc.add_argument( "-v", "--verbose", action="count", default=0, help=tw.dedent( """\ - Set verbosity level of screen output. Flag can be given - multiple times (up to 2) to gradually increase output to + Set verbosity level of screen output. Flag can be given + multiple times (up to 2) to gradually increase output to debugging mode.""" ), ) @@ -381,31 +382,31 @@ def parse_parameters(cmdline_arguments): description=tw.dedent( """\ The `constava dihedrals` submodule is used to extract the backbone dihedrals - needed for the analysis from confromational ensembles. By default the results - are written out in radians as this is the preferred format for + needed for the analysis from conformational ensembles. By default the results + are written out in radians as this is the preferred format for `constava analyze`. - + Note: For the first and last residue in a protein only one backbone dihedral can be extracted. Thus, those residues are omitted by default.""" ), formatter_class=argparse.RawTextHelpFormatter, ) - dihIO = parser_dihedrals.add_argument_group("Input & output options") - dihIO.add_argument( + dih_io = parser_dihedrals.add_argument_group("Input & output options") + dih_io.add_argument( "-s", "--structure", metavar="", help="Structure file with atomic information: [pdb, gro, tpr]", ) - dihIO.add_argument( + dih_io.add_argument( "-f", "--trajectory", nargs="+", metavar="", help="Trajectory file with coordinates: [pdb, gro, trr, xtc, crd, nc]", ) - dihIO.add_argument( + dih_io.add_argument( "-o", "--output", default=None, @@ -413,24 +414,24 @@ def parse_parameters(cmdline_arguments): help="CSV file to write dihedral information to. (default: dihedrals.csv)", ) - dihMisc = parser_dihedrals.add_argument_group("Input & output options") - dihMisc.add_argument( + dih_misc = parser_dihedrals.add_argument_group("Input & output options") + dih_misc.add_argument( "--selection", default="protein", help="Selection for the dihedral calculation. (default: 'protein')", ) - dihMisc.add_argument( + dih_misc.add_argument( "--precision", default=5, type=int, help="Defines the number of decimals written for the dihedrals. (default: 5)", ) - dihMisc.add_argument( + dih_misc.add_argument( "--degrees", action="store_true", help="If set results are written in degrees instead of radians.", ) - dihMisc.add_argument( + dih_misc.add_argument( "-O", "--overwrite", action="store_true", @@ -444,8 +445,8 @@ def parse_parameters(cmdline_arguments): "test", description=tw.dedent( """\ - The `constava test` submodule runs a couple of test cases to check, if - consistent results are achieved. This should be done once after + The `constava test` submodule runs a couple of test cases to check, if + consistent results are achieved. This should be done once after installation and takes about a minute.""" ), formatter_class=argparse.RawTextHelpFormatter, @@ -458,8 +459,8 @@ def parse_parameters(cmdline_arguments): default=0, help=tw.dedent( """\ - Set verbosity level of screen output. Flag can be given - multiple times (up to 2) to gradually increase output to + Set verbosity level of screen output. Flag can be given + multiple times (up to 2) to gradually increase output to debugging mode.""" ), ) @@ -470,8 +471,10 @@ def parse_parameters(cmdline_arguments): def run_fit_model(args): """Run fit-model subcommand when invoked from command line""" - # Initialze and run Constava + + # Initialize and run Constava cva = Constava(ConstavaParameters(verbose=args.verbose)) + csmodel = cva.fit_csmodel( model_type=args.model_type, model_data=args.input, @@ -479,43 +482,52 @@ def run_fit_model(args): grid_points=args.grid_points, model_data_degrees=args.degrees, ) + # Write the fitted model out as a pickle csmodel.dump_pickle(args.output) def run_analyze(args): """Run analyze subcommand when invoked from command line.""" + # Convert command line arguments to ConstavaParameters params = ConstavaParameters(verbose=args.verbose) params.input_files = args.input params.input_format = args.input_format params.output_file = args.output params.output_format = args.output_format + params.model_load = args.load_model params.window = args.window params.window_series = args.window_series + params.bootstrap = args.bootstrap params.bootstrap_series = args.bootstrap_series params.bootstrap_samples = args.bootstrap_samples + params.input_degrees = args.degrees params.precision = args.precision params.indent_size = args.indent_size params.seed = args.seed - # Initialze and run Constava + + # Initialize and run Constava cva = Constava(params) cva.run() def run_dihedrals(args): """Run analyze subcommand when invoked from command line.""" + # Set output to default value if needed args.output = args.output or "dihedrals.csv" if not args.overwrite and os.path.exists(args.output): raise FileExistsError(f"Cannot overwrite existing file: {args.output}") + # Calculate dihedrals dihedrals = calculate_dihedrals( args.structure, args.trajectory, args.selection, args.degrees ) + # Write results float2str = f"%.{args.precision}f" # Definition of float format in output dihedrals.to_csv(args.output, header=True, index=False, float_format=float2str) @@ -525,6 +537,7 @@ def main(): """main function executed when running script in command line mode""" # Parse command line parameters args = parse_parameters(sys.argv[1:]) + if args.subcommand == "fit-model": run_fit_model(args) elif args.subcommand == "analyze": diff --git a/constava/calc/calculator.py b/constava/calc/calculator.py index b5a6cb9..757a9b9 100644 --- a/constava/calc/calculator.py +++ b/constava/calc/calculator.py @@ -3,7 +3,6 @@ a protein ensemble """ -from collections import defaultdict import os import multiprocessing import logging @@ -80,8 +79,8 @@ class ConfStateCalculator: def __init__( self, csmodels: ConfStateModelABC, methods: List[SubsamplingABC] = None ): - """Initializes the calcualtor class with given conformational state - models (csmodels) and zero or more subsampling methods. + """Initializes the calculator class with given conformational state + models (csmodels) and zero or more sub-sampling methods. Parameters: ----------- @@ -89,7 +88,7 @@ def __init__( Probabilistic model of conformational states used for the calculation. methods : List[SubsamplingABC] = None - (Optional) A list of subsampling methods to use in the calculation. + (Optional) A list of sub-sampling methods to use in the calculation. """ self.csmodels = csmodels self.methods = methods or [] @@ -101,7 +100,7 @@ def __init__( self._mp_context = None def add_method(self, new_method: SubsamplingABC): - """Adds a new subsampling methods to the calculator.""" + """Adds a new sub-sampling methods to the calculator.""" self.methods.append(new_method) def close(self): @@ -167,20 +166,20 @@ def _get_or_create_executor(self, max_workers): @check_subsampling_methods def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: """Calculates conformational state propensities and conformational - state variabilites based on the given model and methods for the + state variabilities based on the given model and methods for the ProteinEnsemble. Parameters: ----------- ensemble : ProteinEnsemble A ProteinEnsemble object with the relevant backbone - dihedal information of the ensemble. + dihedral information of the ensemble. Returns: -------- results : List[ConstavaResults] A list of ConstavaResults, each representing the same - ProteinEnsemble but calculated with one or more subsampling methods. + ProteinEnsemble but calculated with one or more sub-sampling methods. """ n_residues = ensemble.n_residues @@ -246,8 +245,6 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: future_to_idx[nfut] = idx in_flight.add(nfut) - logger.debug(f"Building results for the {len(self.methods)} methods...") - results = [ ConstavaResults( method=method.getShortName(), @@ -269,6 +266,4 @@ def calculate(self, ensemble: ProteinEnsemble) -> List[ConstavaResults]: sorted_insertion=True, ) - logger.debug("All the results have been calculated with success!") - return results diff --git a/constava/calc/csmodels.py b/constava/calc/csmodels.py index d11100d..27d6b39 100644 --- a/constava/calc/csmodels.py +++ b/constava/calc/csmodels.py @@ -61,13 +61,12 @@ def get_logpdf(self, data: np.ndarray) -> np.ndarray: @abc.abstractmethod def from_fitting(cls, training_data_json: str, **kwargs): """ - Loads the probabilistic from a trainign data JSON + Loads the probabilistic from a training data JSON """ - # pass def dump_pickle(self, output_file: str): - """Save the probabilistic as a pickle. + """Save the probabilistic as a pickle file. Parameters: ----------- @@ -79,7 +78,7 @@ def dump_pickle(self, output_file: str): @classmethod def from_pickle(cls, pickled_file: str): - """Load the probabilistic from a pickle. + """Load the probabilistic from a pickle file. Parameters: ----------- @@ -201,7 +200,7 @@ class ConfStateModelGrid(ConfStateModelABC): List labels for the conformational state models state_grid : Array[M,N,N] Grid from which probability densities for conformational states are - inferred by interpolation between gridpoints. + inferred by interpolation between grid points. grid_crds: Tuple[Array[N], Array[N]] Tuple of two arrays that describe the (phi,psi) coordinates of the grid points. @@ -260,7 +259,7 @@ def from_fitting( bandwidth : float Bandwidth of the Gaussian kernel density estimator grid_points : int - The number of gridpoints between which the PDF will be estimated + The number of grid points between which the PDF will be estimated by interpolation. Note, that for generating the grid for both axes sqrt(`grid_points`) are used. Thus, if `grid_points` is not a square number, the final grid_points may be less. diff --git a/constava/calc/subsampling.py b/constava/calc/subsampling.py index 8971034..3a9c963 100644 --- a/constava/calc/subsampling.py +++ b/constava/calc/subsampling.py @@ -131,14 +131,14 @@ class SubsamplingWindow(SubsamplingABC): """ def __init__(self, window_size: int): - """Inititialize class to subsample and calcualte conformational state + """Initialize class to subsample and calculate conformational state propensities and conformational state variability. Sub-sampling is done using a sliding window. Parameters: ----------- window_size : int - Size of the sliding window used in subsampling. + Size of the sliding window used in sub-sampling. """ self.window_size = window_size @@ -162,13 +162,14 @@ def _subsampling(self, logpdf): pdf : Array[M,N] Likelihoods obtained by sub-sampling N times using the described method. """ - # Subsampling using consecutive windows of window_size samples - logpdf = np.stack( + + # Sub-sampling using consecutive windows of window_size samples + convolved_logpdf = np.stack( [np.convolve(x, np.ones((self.window_size,)), mode="valid") for x in logpdf] ) # Exponentiation and normalize to obtain likelihoods - pdf = np.exp(logpdf) + pdf = np.exp(convolved_logpdf) pdf /= np.sum(pdf, axis=0) return pdf @@ -180,7 +181,7 @@ def __str__(self): class SubsamplingBootstrap(SubsamplingABC): """Class to subsample the logPDF values obtained from the probabilistic conformational state models and calculate the conformational state - propensities and conformational state variability. Subsampling is done + propensities and conformational state variability. Sub-sampling is done using bootstrapping. Attributes: @@ -204,12 +205,12 @@ class SubsamplingBootstrap(SubsamplingABC): getShortName() Name of the method for reference in the output. _subsampling(state_logpdfs) - Subsamples from the distribution of original data points. + Sub-samples from the distribution of original data points. """ def __init__(self, sample_size: int, n_samples=500, seed: Optional[int] = None): - """Inititialize class to subsample and calcualte conformational state - propensities and conformational state variability. Subsampling is done + """Initialize class to subsample and calcualte conformational state + propensities and conformational state variability. Sub-sampling is done using bootstrapping. Parameters: @@ -232,21 +233,22 @@ def getShortName(self) -> str: ) def _subsampling(self, logpdf): - """Subsampling from the distribution of logPDF using bootstrapping. - `n_samples` are subsampled from the distribution, where each sample + """Sub-sampling from the distribution of logPDF using bootstrapping. + `n_samples` are sub-sampled from the distribution, where each sample contains `sample_size`randomly selected original data points. Finally, - liklihoods for each conformational state model are calculated for each + likelihoods for each conformational state model are calculated for each sample. Parameters: ----------- logpdf : Array[M,X] - log-Probability densities for M states across X original data points. + The log-Probability densities for M states across X + original data points. - Retruns: + Returns: -------- pdf : Array[M,N] - Likelihoods obtained by subsampling N times using the described method. + Likelihoods obtained by sub-sampling N times using the described method. """ # Get dimensions of the input data n_states, n_measurements = logpdf.shape @@ -257,16 +259,16 @@ def _subsampling(self, logpdf): rng = np.random.default_rng(self.seed) samples = rng.integers(n_measurements, size=self.sample_size * self.n_samples) - logpdf = np.reshape( + reshaped_logpdf = np.reshape( logpdf[:, samples], (n_states, self.n_samples, self.sample_size), order="C" ) # Accumulate logpdfs within a sample = logpdf for each of these samples # to be sampled from the same conformational state - logpdf = np.sum(logpdf, axis=2) + aggregated_logpdf = np.sum(reshaped_logpdf, axis=2) - # Exponentiate and normalize to obtain likelihoods - pdf = np.exp(logpdf) + # Exponential and normalize to obtain likelihoods + pdf = np.exp(aggregated_logpdf) pdf /= np.sum(pdf, axis=0) return pdf @@ -278,13 +280,13 @@ def __str__(self): class SubsamplingWindowSeries(SubsamplingWindow): """Class to subsample the logPDF values obtained from the probabilistic conformational state models and calculate the conformational state - propensities and conformational state variability. Subsampling is done + propensities and conformational state variability. Sub-sampling is done using a sliding window. For each window the results are returned. Attributes: ----------- window_size : int - Size of the sliding window used in subsampling. + Size of the sliding window used in sub-sampling. Methods: -------- @@ -298,7 +300,7 @@ class SubsamplingWindowSeries(SubsamplingWindow): getShortName() Name of the method for reference in the output. _subsampling(state_logpdfs) - Subsamples from the distribution of original data points. + Sub-samples from the distribution of original data points. """ def getShortName(self) -> str: @@ -332,7 +334,7 @@ def calculateStateVariability(self, state_likelihoods): class SubsamplingBootstrapSeries(SubsamplingBootstrap): """Class to subsample the logPDF values obtained from the probabilistic conformational state models and calculate the conformational state - propensities and conformational state variability. Subsampling is done + propensities and conformational state variability. Sub-sampling is done using bootstrapping. For each bootstrapped subsample the results are returned. @@ -357,7 +359,7 @@ class SubsamplingBootstrapSeries(SubsamplingBootstrap): getShortName() Name of the method for reference in the output. _subsampling(state_logpdfs) - Subsamples from the distribution of original data points. + Sub-samples from the distribution of original data points. """ def getShortName(self) -> str: @@ -385,6 +387,8 @@ def calculateStateVariability(self, state_likelihoods): Conformational state distances from the average """ mean_likelihoods = np.mean(state_likelihoods, axis=1) + squard_dev = np.sum((state_likelihoods.T - mean_likelihoods) ** 2, axis=1) state_var = np.sqrt(squard_dev) + return state_var diff --git a/constava/io/reader.py b/constava/io/reader.py index 1f7056b..3db93a6 100644 --- a/constava/io/reader.py +++ b/constava/io/reader.py @@ -61,7 +61,7 @@ def readFiles(self, *input_files: str) -> ProteinEnsemble: def get_strategy(self, filetype: str, input_files: List[str]) -> ReaderABC: """Method to return an appropriate reader strategy. This is primarily - based on the `filetype_str`. If `auto` was provieded the reader strategy + based on the `filetype_str`. If `auto` was provided the reader strategy is guessed based on file structures and filenames. Parameters: @@ -105,10 +105,12 @@ def guess_strategy(cls, input_files: List[str], degrees=False) -> ReaderABC: If no appropriate reader strategy was found. """ # Check for the internal CSV-format - for Reader in cls.STRATEGIES.values(): - reader = Reader(degrees=degrees) + for reader_cls in cls.STRATEGIES.values(): + reader = reader_cls(degrees=degrees) if reader.checkFileFormat(*input_files): return reader - else: - # IF all checks fail, raise an UnknownFileStructureError - raise UnknownFileStructureError("Dihedral input corresponds to no known input format.") \ No newline at end of file + + # IF all checks fail, raise an UnknownFileStructureError + raise UnknownFileStructureError( + "Dihedral input corresponds to no known input format." + ) diff --git a/constava/io/rstrategies.py b/constava/io/rstrategies.py index 3db6220..93a7e50 100644 --- a/constava/io/rstrategies.py +++ b/constava/io/rstrategies.py @@ -22,15 +22,13 @@ def __init__(self, degrees: bool = False): @abc.abstractmethod def checkFileFormat(self, *input_files: str) -> bool: """Checks if the files provided adhere to the given format.""" - pass @abc.abstractmethod def readFiles(self, *input_files: str) -> ProteinEnsemble: """Reads the dihedral angles from one or more input files, converts them - into radians (if degrees == True) and returns them as a + into radians (if degrees == True) and returns them as a ProteinEnsemble object """ - pass class DihedralCsvReader(ReaderABC): @@ -51,7 +49,7 @@ class DihedralCsvReader(ReaderABC): def checkFileFormat(self, *input_files: str) -> bool: """Checks if the files provided adhere to the given format. - + Parameters: ----------- *input_files : str @@ -65,19 +63,22 @@ def checkFileFormat(self, *input_files: str) -> bool: phicol = "Phi[{0}]".format("deg" if self.degrees else "rad") psicol = "Psi[{0}]".format("deg" if self.degrees else "rad") expected_columns = {"#Frame", "ResIndex", "ResName", phicol, psicol} + for infile in input_files: - with open(infile, "r") as f: + with open(infile, "r", encoding="utf-8") as f: first_line = f.readline() + columns = set(first_line.strip().split(",")) if expected_columns.intersection(columns) != expected_columns: return False + return True def readFiles(self, *input_files) -> ProteinEnsemble: """Reads the dihedral angles from one or more input files, converts them - into radians (if degrees == True) and returns them as a + into radians (if degrees == True) and returns them as a ProteinEnsemble object. - + Parameters: ----------- *input_files : str @@ -91,27 +92,35 @@ def readFiles(self, *input_files) -> ProteinEnsemble: phicol = "Phi[{0}]".format("deg" if self.degrees else "rad") psicol = "Psi[{0}]".format("deg" if self.degrees else "rad") data = pd.DataFrame() + for infile in input_files: data = pd.concat([data, pd.read_csv(infile)], axis=0) + unique_residues = data[["ResIndex", "ResName"]].drop_duplicates(inplace=False) residue_list = [] - for i, (resid, resname) in unique_residues.iterrows(): + + for _i, (resid, resname) in unique_residues.iterrows(): phipsi = data.loc[(data["ResIndex"] == resid) & (data["ResName"] == resname), [phicol, psicol]].to_numpy() - + if self.degrees: phipsi = np.radians(phipsi) - - check_dihedral_range(phipsi) # Throws errors/warnings if data is not in radians + + # Throws errors/warnings if data is not in radians + check_dihedral_range(phipsi) + residue_list.append( - ResidueEnsemble(restype=resname, respos=resid, phipsi=phipsi)) + ResidueEnsemble(restype=resname, respos=resid, phipsi=phipsi) + ) + return ProteinEnsemble(residue_list) class GmxChiReader(ReaderABC): - """A reader strategy designed to read the output of GROMACS' chi module + """A reader strategy designed to read the output of GROMACS' chi module command: `gmx chi -s -f -rama`. The files are - generally named `ramaPhiPsi[RESNAME][RESINDEX].xvg`. Both RESNAME and - RESINDEX are directly extracted from the filename. + generally named `ramaPhiPsi[RESNAME][RESINDEX].xvg`. + + Both RESNAME and RESINDEX are directly extracted from the filename. Attributes: ----------- @@ -119,7 +128,7 @@ class GmxChiReader(ReaderABC): Regular expression to extract RESNAME and RESINDEX from the filename. degrees : bool Sets if read data should be converted from degrees to radians. - + Methods: -------- checkFileFormat(*input_files) @@ -132,7 +141,7 @@ class GmxChiReader(ReaderABC): @classmethod def checkFileFormat(cls, *input_files: str) -> bool: """Checks if the files provided adhere to the given format. - + Parameters: ----------- *input_files : str @@ -147,12 +156,12 @@ def checkFileFormat(cls, *input_files: str) -> bool: if not cls.FILENAME_REGEX.match(filename): return False return True - + def readFiles(self, *input_files) -> ProteinEnsemble: """Reads the dihedral angles from one or more input files, converts them - into radians (if degrees == True) and returns them as a + into radians (if degrees == True) and returns them as a ProteinEnsemble object. - + Parameters: ----------- *input_files : str @@ -163,6 +172,7 @@ def readFiles(self, *input_files) -> ProteinEnsemble: prot : ProteinEnsemble Object that stores the dihedral angles for all the residues. """ + residue_list = [] for infile in input_files: filename = os.path.basename(infile) @@ -170,15 +180,17 @@ def readFiles(self, *input_files) -> ProteinEnsemble: if m is None: raise UnknownFileStructureError(( f"File does not match the structure of `gmx chi` outputs: {filename}")) + restype = m.group(1) respos = int(m.group(2)) phipsi = np.loadtxt(infile, comments=["#", "@"]) - + if self.degrees: phipsi = np.radians(phipsi) - - check_dihedral_range(phipsi) # Throws errors/warnings if data is not in radians - + + # Throws errors/warnings if data is not in radians + check_dihedral_range(phipsi) + residue_list.append( ResidueEnsemble(restype=restype, respos=respos, phipsi=phipsi) ) diff --git a/constava/utils/dihedrals.py b/constava/utils/dihedrals.py index ac26a24..d57b751 100644 --- a/constava/utils/dihedrals.py +++ b/constava/utils/dihedrals.py @@ -1,7 +1,9 @@ """constava.dihedrals is a stand-alone executable script that extracts the phi/psi backbone dihedral angles from a conformational ensmeble.""" -import argparse, os, sys +import argparse +import os +import sys from typing import List, NamedTuple import MDAnalysis as mda @@ -104,6 +106,7 @@ def calculate_dihedrals( """ # Load trajectory files u = mda.Universe(structure, trajectory) + # Calculate dihedral angles. rama = Ramachandran(u.select_atoms(selection)) rama.run() @@ -113,8 +116,10 @@ def calculate_dihedrals( else: phicol, psicol = "Phi[rad]", "Psi[rad]" results = np.radians(rama.results.angles) + # Convert resulting numpy array into a comprehensive DataFrame dihedrals = pd.DataFrame(columns=["#Frame", "ResIndex", "ResName", phicol, psicol]) + # Iterate over selection (C-alphas) used to define the dihedrals for i, atom in enumerate(rama.ag3): # Select results for given @@ -128,6 +133,7 @@ def calculate_dihedrals( } ) dihedrals = pd.concat([dihedrals, df], ignore_index=True) + return dihedrals @@ -140,6 +146,7 @@ def main(cmdline_arguments: List[str]): dihedrals = calculate_dihedrals( args.structure, args.trajectory, args.selection, args.degrees ) + # Write results float2str = f"%.{args.precision}f" # Definition of float format in output dihedrals.to_csv(args.output, header=True, index=False, float_format=float2str) diff --git a/constava/utils/results.py b/constava/utils/results.py index 9c966a6..59ab2b5 100644 --- a/constava/utils/results.py +++ b/constava/utils/results.py @@ -1,50 +1,50 @@ -""" constava.results contains classes that hold output information. +"""constava.results contains classes that hold output information. -ConstavaResultsEntry stores state_propensities and state_varaibility for +ConstavaResultsEntry stores state_propensities and state_variability for a single ResidueEnsemble. -ConstavaResults hold multiple ConstavaResultsEntry objects, thus describing +ConstavaResults hold multiple ConstavaResultsEntry objects, thus describing a while ProteinEnsemble. """ - from dataclasses import dataclass from typing import List import numpy as np from .ensembles import ProteinEnsemble, ResidueEnsemble, EnsembleMismatchError -from ..calc.subsampling import SubsamplingABC + @dataclass class ConstavaResultsEntry: """Results for a single ResidueEnsemble. - + Attributes: ----------- residue : ResidueEnsemble The residue ensemble for which the results were calculated state_propensities : Array[N] or Array[M,N] - The calculated conformational state propensities for N states. If + The calculated conformational state propensities for N states. If time series information is calculated, this is a 2-dimensional array of M samples * N conformational states. - + state_variability : float or Array[M] The calculated conformational state variability across the models. - If time series information is calculated, this is an array of + If time series information is calculated, this is an array of M samples. """ + residue: ResidueEnsemble = None state_propensities: np.ndarray = None state_variability: float or np.ndarray = None class ConstavaResults: - """ Results from ConfStateCalculator for a given ProteinEnsemble and Method + """Results from ConfStateCalculator for a given ProteinEnsemble and Method Attributes: ----------- method : str - A string indicating the subsampling method with which the results + A string indicating the sub-sampling method with which the results were calculated protein : ProteinEnsemble @@ -52,7 +52,7 @@ class ConstavaResults: state_labels : List[str] The labels of the conformational states, which should be human-readable. - + entries : List[ConstavaResultsEntry] A list of result entries, each of which contains the information for a single residue in the conformational ensemble. @@ -62,20 +62,32 @@ class ConstavaResults: add_entry(new_entry) Adds an additional entry to the result class """ - def __init__(self, method: str, protein: ProteinEnsemble, state_labels: List[str], entries: List[ConstavaResultsEntry] = None): + + def __init__( + self, + method: str, + protein: ProteinEnsemble, + state_labels: List[str], + entries: List[ConstavaResultsEntry] = None, + ): self.method = method self.protein = protein self.state_labels = state_labels self.entries = entries or [] - + def __repr__(self): return f"Results(method={self.method}, {len(self.entries)} entries)" - def add_entry(self, new_entry: ConstavaResultsEntry, sorted_insertion: bool = False): + def add_entry( + self, new_entry: ConstavaResultsEntry, sorted_insertion: bool = False + ): """Adds an additional entry to the result class""" + if new_entry.residue.protein is not self.protein: - raise EnsembleMismatchError(f"Result for residue {new_entry.residue} does not belong to the given Protein") + raise EnsembleMismatchError( + f"Result for residue {new_entry.residue} does not belong to the given Protein" + ) self.entries.append(new_entry) - + if not sorted_insertion: self.entries.sort(key=lambda x: x.residue.respos) diff --git a/constava/utils/utils.py b/constava/utils/utils.py index a1ad359..f7ff77c 100644 --- a/constava/utils/utils.py +++ b/constava/utils/utils.py @@ -7,14 +7,10 @@ class DihedralRangeError(ValueError): """Raised if any dihedral angles are not correctly in radians""" - pass - class DihedralRangeWarning(UserWarning): """Raised on the suspicion that dihedral angles are not correctly in radians""" - pass - def check_dihedral_range(arr: np.ndarray): """Helper method that checks if the dihedral angles are correctly in diff --git a/constava/wrapper/params.py b/constava/wrapper/params.py index 258aeef..be181a4 100644 --- a/constava/wrapper/params.py +++ b/constava/wrapper/params.py @@ -110,7 +110,7 @@ class ConstavaParameters: model_data : str = None model_dump : str = None - # Subsampling Options + # Sub-sampling Options window : typing.List[int] = field(default_factory=list) bootstrap : typing.List[int] = field(default_factory=list) window_series : typing.List[int] = field(default_factory=list) @@ -133,5 +133,5 @@ class ConstavaParameters: @set_logger_level @set_single_as_list def __setattr__(self, __attr, __value) -> None: - """Custom function to set attributes, to catch certain special behaviours""" + """Custom function to set attributes, to catch certain special behaviors""" super().__setattr__(__attr, __value) diff --git a/constava/wrapper/wrapper.py b/constava/wrapper/wrapper.py index 37f5a1a..3a41ef7 100644 --- a/constava/wrapper/wrapper.py +++ b/constava/wrapper/wrapper.py @@ -67,7 +67,7 @@ class Constava: -------- run() Runs Constava with the current parameters. If the parameters have been - provided with the initialisation of the Constava() object, this is the + provided with the initialization of the Constava() object, this is the only method necessary to run any analysis. # Methods to adjust parameters of the Constava toolkit. @@ -88,12 +88,12 @@ class Constava: Initializes a ResultsWriter for writing output results to a file specified by 'outfile', in the format specified by 'file_format', and with float precision specified by 'float_precision' (and indent_size if needed) fit_csmodel() - Fits a conformational state model to the provided data. Complex method, read its docstrings for details. + Fits a conformational state model to the provided data. Complex method, read its docstring for details. load_csmodel(pickled_csmodel): Loads a conformational state model from a pickled file specified by the 'pickled_csmodel' path. initialize_calculator(csmodel=None, window=None, window_series=None, bootstrap=None, bootstrap_series=None, bootstrap_samples=500, bootstrap_seed=None): - Initializes a ConfStateCalculator with the specified conformational state model 'csmodel', subsampling + Initializes a ConfStateCalculator with the specified conformational state model 'csmodel', sub-sampling parameters 'window', 'window_series', 'bootstrap', 'bootstrap_series', and bootstrap parameters 'bootstrap_samples' and 'bootstrap_seed'. @@ -155,7 +155,7 @@ def show_params(self) -> str: def run(self) -> None: """Calculate conformational state variabilities and conformational - state variabilites with the given parameters.""" + state variabilities with the given parameters.""" # Reset results self.results = None @@ -198,8 +198,10 @@ def run(self) -> None: # Read input files input_files = self.get_param("input_files") + logger.info(f"Reading dihedrals from {len(input_files)} files...") logger.debug("\n\t* ".join(["... input file list:", *input_files])) + ensemble = reader.readFiles(*input_files) # Do the inference @@ -208,6 +210,7 @@ def run(self) -> None: # Write results logger.info(f"Writing results to file: {writer.filename}") + writer.write_results(self.results) def initialize_reader( @@ -229,8 +232,9 @@ def initialize_reader( """ logger.info("Initializing reader for input file(s)...") logger.debug(f"... setting reader parameters: {file_format=}, {in_degrees=}") + reader = EnsembleReader(filetype_str=file_format, degrees2radians=in_degrees) - + return reader def initialize_writer( @@ -261,7 +265,7 @@ def initialize_writer( """ if outfile is None: return None - + logger.info("Initializing writer for results...") logger.debug( f"... setting writer parameters: {outfile=}, {file_format=}, {float_precision=} {indent_size=}" @@ -319,9 +323,9 @@ def fit_csmodel( bandwidth=kde_bandwidth, grid_points=grid_points, ) - + logger.info(f"... model fitted: {csmodel}") - + return csmodel @cache_csmodel @@ -339,13 +343,13 @@ def load_csmodel(self, pickled_csmodel: str) -> ConfStateModelABC: csmodel : ConfStateModelABC Probabilistic model describing the conformational states """ - + logger.info(f"Loading conformational state models from file: {pickled_csmodel}") - + csmodel = ConfStateModelABC.from_pickle(pickled_csmodel) - + logger.info(f"... model loaded: {csmodel}") - + return csmodel def initialize_calculator( @@ -367,17 +371,17 @@ def initialize_calculator( method). If None, the standard model from the publication will be used. window : List[int] - Subsampling using a moving reading-frame of size . Multiple + Sub-sampling using a moving reading-frame of size . Multiple values can be given as a list. window_series : List[int] - Subsampling using a moving reading-frame of size . Returns + Sub-sampling using a moving reading-frame of size . Returns the results for every window rather than the average. Multiple values can be given as a list. bootstrap : List[int] - Subsampling using by bootstrapping datapoints. Multiple + Sub-sampling using by bootstrapping datapoints. Multiple values can be given as a list. bootstrap_series : List[int] or int - Subsampling using by bootstrapping datapoints. Returns the + Sub-sampling using by bootstrapping datapoints. Returns the results for every subsample rather than the average. Multiple values can be given as a list. bootstrap_samples : int @@ -396,7 +400,7 @@ def initialize_calculator( # Initialize the calculator logger.info(f"Initializing calculator with {csmodel}...") - + calculator = ConfStateCalculator(csmodel) # Add subsampling methods to calculator From c3c01866c277b2a404b44d44578a0c2051e2dc5c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adri=C3=A1n=20Diaz?= Date: Wed, 28 Jan 2026 18:46:26 +0100 Subject: [PATCH 21/21] Polish code documentation and fix typos --- constava/__init__.py | 5 +++- constava/calc/__init__.py | 3 +++ constava/calc/calculator.py | 2 +- constava/calc/csmodels.py | 2 +- constava/calc/subsampling.py | 4 +-- constava/internal-data/README.md | 4 +++ constava/io/__init__.py | 6 ++++- constava/io/reader.py | 2 +- constava/io/rstrategies.py | 15 ++++++++---- constava/io/writer.py | 2 +- constava/io/wstrategies.py | 4 +++ constava/tests/__init__.py | 3 +++ constava/utils/__init__.py | 3 +++ constava/utils/constants.py | 6 ++--- constava/utils/dihedrals.py | 6 ++--- constava/utils/ensembles.py | 21 ++++++++++------ constava/utils/logging.py | 42 +++++++++++++++++--------------- constava/utils/results.py | 2 +- constava/utils/utils.py | 6 +++-- constava/wrapper/__init__.py | 6 ++++- constava/wrapper/params.py | 5 ++++ constava/wrapper/wrapper.py | 4 +++ setup.py | 8 ++++-- 23 files changed, 109 insertions(+), 52 deletions(-) create mode 100644 constava/internal-data/README.md diff --git a/constava/__init__.py b/constava/__init__.py index 949bb3a..1af89c0 100644 --- a/constava/__init__.py +++ b/constava/__init__.py @@ -1,2 +1,5 @@ +""" +Constava upper module. +""" from .wrapper import Constava, ConstavaParameters -from .utils.constants import CONSTAVA_VERSION as __version__ \ No newline at end of file +from .utils.constants import CONSTAVA_VERSION as __version__ diff --git a/constava/calc/__init__.py b/constava/calc/__init__.py index e69de29..53c0006 100644 --- a/constava/calc/__init__.py +++ b/constava/calc/__init__.py @@ -0,0 +1,3 @@ +""" +constava.calc module. +""" diff --git a/constava/calc/calculator.py b/constava/calc/calculator.py index 757a9b9..7e074d3 100644 --- a/constava/calc/calculator.py +++ b/constava/calc/calculator.py @@ -1,4 +1,4 @@ -"""constava.calculator contains the calculator class that calculates the +"""constava.calc.calculator contains the calculator class that calculates the conformational state propensities and conformational state variability from a protein ensemble """ diff --git a/constava/calc/csmodels.py b/constava/calc/csmodels.py index 27d6b39..5bfbb7f 100644 --- a/constava/calc/csmodels.py +++ b/constava/calc/csmodels.py @@ -1,4 +1,4 @@ -"""constava.csmodels contains various definitions how the conformational +"""constava.calc.csmodels contains various definitions how the conformational states can be represented as probabilistic models. """ diff --git a/constava/calc/subsampling.py b/constava/calc/subsampling.py index 3a9c963..57d5306 100644 --- a/constava/calc/subsampling.py +++ b/constava/calc/subsampling.py @@ -1,10 +1,10 @@ -"""constava.methods contains classes representing sub-sampling schemes fro the +"""constava.calc.methods contains classes representing sub-sampling schemes fro the probability state propensity calculations. Multiple of these schemes can be dynamically defined on run-time.""" import abc -import numpy as np from typing import Optional +import numpy as np class SubsamplingMethodError(ValueError): diff --git a/constava/internal-data/README.md b/constava/internal-data/README.md new file mode 100644 index 0000000..3ecae82 --- /dev/null +++ b/constava/internal-data/README.md @@ -0,0 +1,4 @@ +This directory contains: + +1. `constava-testdata`: required files to perform the unit test validation via `constava test` command. +1. `constava_csdata.json`: defined as `DEFAULT_TRAINING_DATA_PATH` in the `constants` module. diff --git a/constava/io/__init__.py b/constava/io/__init__.py index 51338d4..bcd51cb 100644 --- a/constava/io/__init__.py +++ b/constava/io/__init__.py @@ -1,2 +1,6 @@ +""" +constava.io module. +""" + from .reader import EnsembleReader -from .writer import ResultsWriter \ No newline at end of file +from .writer import ResultsWriter diff --git a/constava/io/reader.py b/constava/io/reader.py index 3db93a6..a3f688d 100644 --- a/constava/io/reader.py +++ b/constava/io/reader.py @@ -1,4 +1,4 @@ -"""constava.reader contains the reader interface to read input data""" +"""constava.io.reader contains the reader interface to read input data""" from typing import List from .rstrategies import ReaderABC, DihedralCsvReader, GmxChiReader, UnknownFileStructureError diff --git a/constava/io/rstrategies.py b/constava/io/rstrategies.py index 93a7e50..d173cb7 100644 --- a/constava/io/rstrategies.py +++ b/constava/io/rstrategies.py @@ -1,3 +1,7 @@ +""" +constava.io.rstrategies contains the reader classes to handle input files. +""" + import abc import os import re @@ -10,7 +14,6 @@ class UnknownFileStructureError(ValueError): """Raised if the file structure is not recognized""" - pass class ReaderABC(metaclass=abc.ABCMeta): @@ -118,8 +121,8 @@ def readFiles(self, *input_files) -> ProteinEnsemble: class GmxChiReader(ReaderABC): """A reader strategy designed to read the output of GROMACS' chi module command: `gmx chi -s -f -rama`. The files are - generally named `ramaPhiPsi[RESNAME][RESINDEX].xvg`. - + generally named `ramaPhiPsi[RESNAME][RESINDEX].xvg`. + Both RESNAME and RESINDEX are directly extracted from the filename. Attributes: @@ -152,6 +155,7 @@ def checkFileFormat(cls, *input_files: str) -> bool: check_ok : bool True if all provided files adhere to the file format, else False. """ + for filename in (os.path.basename(fp) for fp in input_files): if not cls.FILENAME_REGEX.match(filename): return False @@ -172,15 +176,16 @@ def readFiles(self, *input_files) -> ProteinEnsemble: prot : ProteinEnsemble Object that stores the dihedral angles for all the residues. """ - + residue_list = [] + for infile in input_files: filename = os.path.basename(infile) m = self.FILENAME_REGEX.match(filename) if m is None: raise UnknownFileStructureError(( f"File does not match the structure of `gmx chi` outputs: {filename}")) - + restype = m.group(1) respos = int(m.group(2)) phipsi = np.loadtxt(infile, comments=["#", "@"]) diff --git a/constava/io/writer.py b/constava/io/writer.py index bc98f47..f4cb391 100644 --- a/constava/io/writer.py +++ b/constava/io/writer.py @@ -1,4 +1,4 @@ -"""constava.writer contains classes the write the output to different +"""constava.io.writer contains classes the write the output to different data formats.""" import os diff --git a/constava/io/wstrategies.py b/constava/io/wstrategies.py index 28c946f..f152ded 100644 --- a/constava/io/wstrategies.py +++ b/constava/io/wstrategies.py @@ -1,3 +1,7 @@ +""" +constava.io.wstrategies contains the writer classes to handle output files. +""" + import abc import csv import json diff --git a/constava/tests/__init__.py b/constava/tests/__init__.py index e69de29..855a767 100644 --- a/constava/tests/__init__.py +++ b/constava/tests/__init__.py @@ -0,0 +1,3 @@ +""" +constava.tests module. +""" diff --git a/constava/utils/__init__.py b/constava/utils/__init__.py index e69de29..abc16f1 100644 --- a/constava/utils/__init__.py +++ b/constava/utils/__init__.py @@ -0,0 +1,3 @@ +""" +constava.utils module. +""" diff --git a/constava/utils/constants.py b/constava/utils/constants.py index 3db577c..246ef55 100644 --- a/constava/utils/constants.py +++ b/constava/utils/constants.py @@ -1,4 +1,4 @@ -"""constava.constants contains general information for all modules""" +"""constava.utils.constants contains general information for all modules""" from importlib.metadata import version import os @@ -8,11 +8,11 @@ CONSTAVA_DATA_DIR = os.path.join(CONSTAVA_SOURCE_DIR, "internal-data") DEFAULT_TRAINING_DATA_PATH = os.path.join(CONSTAVA_DATA_DIR, "constava_csdata.json") -aminoacids1to3 = dict( +AMINO_ACIDS_1_TO_3 = dict( A="ALA", C="CYS", D="ASP", E="GLU", F="PHE", G="GLY", H="HIS", I="ILE", K="LYS", L="LEU", M="MET", N="ASN", P="PRO", Q="GLN", R="ARG", S="SER", T="THR", V="VAL", W="TRP", Y="TYR", ) -aminoacids3to1 = {j: i for i, j in aminoacids1to3.items()} +AMINO_ACIDS_3_TO_1 = {j: i for i, j in AMINO_ACIDS_1_TO_3.items()} diff --git a/constava/utils/dihedrals.py b/constava/utils/dihedrals.py index d57b751..7f5d94f 100644 --- a/constava/utils/dihedrals.py +++ b/constava/utils/dihedrals.py @@ -1,5 +1,5 @@ -"""constava.dihedrals is a stand-alone executable script that extracts the -phi/psi backbone dihedral angles from a conformational ensmeble.""" +"""constava.utils.dihedrals is a stand-alone executable script that extracts the +phi/psi backbone dihedral angles from a conformational ensemble.""" import argparse import os @@ -69,7 +69,7 @@ def parse_arguments(cmdline_arguments: List[str]) -> NamedTuple: raise FileExistsError( ( "Output file `{args.outfile}` exists already. Please, specify " - "--outfile explicitely to overwrite or choose a different file name." + "--outfile explicitly to overwrite or choose a different file name." ) ) return args diff --git a/constava/utils/ensembles.py b/constava/utils/ensembles.py index 84e8a5c..6e06a08 100644 --- a/constava/utils/ensembles.py +++ b/constava/utils/ensembles.py @@ -1,4 +1,4 @@ -"""constava.ensembles contains classes describing the input data from the +"""constava.utils.ensembles contains classes describing the input data from the conformational ensemble. """ @@ -6,13 +6,12 @@ from typing import List, Generator import numpy as np -from .constants import aminoacids3to1 +from .constants import AMINO_ACIDS_3_TO_1 class EnsembleMismatchError(ValueError): """Error raised when ResidueEnsembles from different ProteinEnsembles are mixed""" - pass @dataclass @@ -26,7 +25,7 @@ class ResidueEnsemble: respos: int Index of the residue in the sequence phipsi: array[N,2] Array of the phi/psi angles of the residue for all conformations in the ensemble - proteinensemble: ProteinEnsemble + protein: ProteinEnsemble: Reference to the ProteinEnsemble-object, the residue belongs to. """ @@ -35,11 +34,12 @@ class ResidueEnsemble: respos: int = None phipsi: np.ndarray = None protein = None + amino_acids_3_to_1 = AMINO_ACIDS_3_TO_1.copy() @property def restype1(self): """Converts the restype attribute to one-letter code""" - return aminoacids3to1.get(self.restype, "X") + return self.amino_acids_3_to_1.get(self.restype, "X") def __repr__(self): """Short representation of the object""" @@ -108,6 +108,7 @@ def _getPropertyFromResidues(self, resattr: str, *, fillvalue=np.nan): residues across the whole protein.""" offset = self._residues[0].respos result = None + for i, value in ( (res.respos - offset, getattr(res, resattr)) for res in self.get_residues() ): @@ -118,12 +119,14 @@ def _getPropertyFromResidues(self, resattr: str, *, fillvalue=np.nan): elif result is None: result = np.full((self.n_residues,), fillvalue) result[i] = value + return result def get_residues( self, sorted_list: bool = False ) -> Generator[ResidueEnsemble, None, None]: """Returns a generator for all residues in the class""" + if sorted_list: return (res for res in sorted(self._residues, key=lambda x: x.respos)) else: @@ -135,11 +138,13 @@ def add_residues(self, *new_residues: ResidueEnsemble): for res in new_residues: res.protein = self self._residues.append(res) + self._residues.sort() - # self.__dict__.pop('sequence', None) - # self.__dict__.pop('dynamics', None) - # self.__dict__.pop('conformation', None) def to_dict(self): + """ + Return the residues data as a Python dictionary. + """ + _data = {"residues": [res.to_dict() for res in self.get_residues()]} return _data diff --git a/constava/utils/logging.py b/constava/utils/logging.py index a206738..906b86c 100644 --- a/constava/utils/logging.py +++ b/constava/utils/logging.py @@ -1,33 +1,37 @@ +""" +constava.utils.logging contains the standard logging configurations +""" + import logging import logging.config logger_configuration = { - "version" : 1, - "formatters" : { + "version": 1, + "formatters": { "default": { - "format" : "%(asctime)s.%(msecs)03d %(levelname)s %(message)s", - "datefmt" : "%Y-%m-%d %H:%M:%S", - "style" : "%", - "validate" : True + "format": "%(asctime)s.%(msecs)03d %(levelname)s %(message)s", + "datefmt": "%Y-%m-%d %H:%M:%S", + "style": "%", + "validate": True, }, }, - "handlers" : { - "console" : { - "class" : "logging.StreamHandler", - "formatter" : "default", + "handlers": { + "console": { + "class": "logging.StreamHandler", + "formatter": "default", "stream": "ext://sys.stdout", }, - "null" : { - "class" : "logging.NullHandler", - } + "null": { + "class": "logging.NullHandler", + }, }, - "loggers" : { - "Constava" : { - "handlers" : ["console"], - "level" : "WARNING", + "loggers": { + "Constava": { + "handlers": ["console"], + "level": "WARNING", }, - "Dummy" : { - "handlers" : ["null"], + "Dummy": { + "handlers": ["null"], }, }, } diff --git a/constava/utils/results.py b/constava/utils/results.py index 59ab2b5..6898eb6 100644 --- a/constava/utils/results.py +++ b/constava/utils/results.py @@ -1,4 +1,4 @@ -"""constava.results contains classes that hold output information. +"""constava.utils.results contains classes that hold output information. ConstavaResultsEntry stores state_propensities and state_variability for a single ResidueEnsemble. diff --git a/constava/utils/utils.py b/constava/utils/utils.py index f7ff77c..8fdcfba 100644 --- a/constava/utils/utils.py +++ b/constava/utils/utils.py @@ -1,4 +1,6 @@ -"""Stand-alone functions that are used in multiple submodules""" +"""constava.utils.utils contains stand-alone functions +that are used in multiple submodules +""" from warnings import warn import numpy as np @@ -39,7 +41,7 @@ def check_dihedral_range(arr: np.ndarray): warn( ( "Provided dihedrals a very small: [{vmin:.3f}, {vmax:.3f}]. " - "Please check that convertion to radians was only applied once." + "Please check that conversion to radians was only applied once." ), DihedralRangeWarning(), ) diff --git a/constava/wrapper/__init__.py b/constava/wrapper/__init__.py index 62b9c46..02c2d9b 100644 --- a/constava/wrapper/__init__.py +++ b/constava/wrapper/__init__.py @@ -1 +1,5 @@ -from .wrapper import Constava, ConstavaParameters \ No newline at end of file +""" +constava.wrapper module. +""" + +from .wrapper import Constava, ConstavaParameters diff --git a/constava/wrapper/params.py b/constava/wrapper/params.py index be181a4..4274acc 100644 --- a/constava/wrapper/params.py +++ b/constava/wrapper/params.py @@ -1,3 +1,6 @@ +""" +constava.wrapper.params contains the logic to handle user parameters +""" from dataclasses import dataclass, field import typing from ..utils.logging import logging @@ -16,6 +19,7 @@ def _inner_(self, __attr, __value): else: logger.setLevel(logging.DEBUG) return rvalue + return _inner_ def set_single_as_list(func): @@ -30,6 +34,7 @@ def _inner_(self, __attr, __value): elif not isinstance(__value, typing.Iterable) or isinstance(__value, str): __value = [__value] return func(self, __attr, __value) + return _inner_ diff --git a/constava/wrapper/wrapper.py b/constava/wrapper/wrapper.py index 3a41ef7..b6ae74c 100644 --- a/constava/wrapper/wrapper.py +++ b/constava/wrapper/wrapper.py @@ -1,3 +1,7 @@ +""" +constava.wrapper.wrapper contains the main wrapper for Constava. +""" + import os from typing import List diff --git a/setup.py b/setup.py index b87f1be..bf86496 100644 --- a/setup.py +++ b/setup.py @@ -1,3 +1,7 @@ +""" +Python package definition. +""" + from setuptools import setup, find_namespace_packages with open("README.md", "r", encoding="utf-8") as fh: @@ -8,7 +12,7 @@ setup( name="constava", - version="1.2.0b1", + version="1.2.0", author="Wim Vranken", author_email="wim.vranken@vub.be", description="This software is used to calculate conformational states probability & conformational state " @@ -50,6 +54,6 @@ ], }, package_data={ - "constava": ["constava/data/*"], + "constava": ["constava/internal-data/*"], }, )