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Merge pull request #1 from hejamu/feature
Added functionality and some fixes
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,118 @@ | ||
| import numpy as np | ||
| import pickle | ||
| import time | ||
| import os | ||
|
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| import alchemlyb.postprocessors.units as units | ||
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| class Pickle: | ||
| name = 'pickle' | ||
|
|
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| @classmethod | ||
| def ls(cls, estimators): | ||
| """ | ||
| Return a list of lambda values | ||
| :param estimators: Series | ||
| List of estimator plugins | ||
| :return: | ||
| The list of lambda values | ||
| """ | ||
| ls = [] | ||
| if estimators: | ||
| if estimators[0].needs_dhdls: | ||
| means = estimators[0].dhdls.mean(level=estimators[0].dhdls.index.names[1:]) | ||
| ls = np.array(means.reset_index()[means.index.names[:]]) | ||
| elif estimators[0].needs_u_nks: | ||
| means = estimators[0].u_nks.mean(level=estimators[0].u_nks.index.names[1:]) | ||
| ls = np.array(means.reset_index()[means.index.names[:]]) | ||
|
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| return ls | ||
|
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| @classmethod | ||
| def l_types(cls, estimators): | ||
| """ | ||
| Return a list of lambda types | ||
| :param estimators: Series | ||
| List of estimator plugins | ||
| :return: | ||
| The list of lambda types | ||
| """ | ||
| l_types = [] | ||
| if estimators: | ||
| if estimators[0].needs_dhdls: | ||
| l_types = estimators[0].dhdls.index.names[1:] | ||
| elif estimators[0].needs_u_nks: | ||
| l_types = estimators[0].u_nks.index.names[1:] | ||
|
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||
| return l_types | ||
|
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||
| @classmethod | ||
| def segments(cls, estimators): | ||
| """ | ||
| Collect and prepare values from different `estimators` into a series of values. | ||
| :param estimators: Series | ||
| List of estimator plugins | ||
| :return: | ||
| Segments of values to output | ||
| """ | ||
| segments = [] | ||
| l_types = cls.l_types(estimators) | ||
| ls = cls.ls(estimators) | ||
| if estimators: | ||
| segstart = 0 | ||
| ill = [0] * len(l_types) | ||
| nl = 0 | ||
| for i in range(len(ls)): | ||
| l = ls[i] | ||
| if (i < len(ls) - 1 and list(np.array(ls[i + 1], dtype=bool)).count(True) > nl) or i == len(ls) - 1: | ||
| if nl > 0: | ||
| inl = np.array(np.array(l, dtype=bool), dtype=int) | ||
| l_name = l_types[list(inl - ill).index(1)] | ||
| ill = inl | ||
| segments.append((segstart, i, l_name)) | ||
|
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||
| if i + 1 < len(ls): | ||
| nl = list(np.array(ls[i + 1], dtype=bool)).count(True) | ||
| segstart = i | ||
| return segments | ||
|
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| def output(self, estimators, args): | ||
|
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| P = args | ||
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| P.datafile_directory = os.getcwd() | ||
| P.when_analyzed = time.asctime() | ||
| P.dFs = {} | ||
| P.ddFs = {} | ||
| P.dF = {} | ||
|
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| segments = self.segments(estimators) | ||
|
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| for estimator in estimators: | ||
|
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| data = {} | ||
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| df = units.get_unit_converter(args.unit)(estimator.delta_f) | ||
| ddf = units.get_unit_converter(args.unit)(estimator.d_delta_f) | ||
|
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| for segstart, segend, l_name in reversed(segments): | ||
| data[l_name] = (df.values[segstart, segend], | ||
| ddf.values[segstart, segend]) | ||
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| data['total'] = (df.values[0, -1], ddf.values[0, -1]) | ||
|
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| P.dFs[estimator.name] = df | ||
| P.ddFs[estimator.name] = ddf | ||
|
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| P.dF[estimator.name] = data | ||
|
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| pickle.dump(P, open(args.resultfilename + '.pickle', 'wb')) | ||
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|
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| def get_plugin(): | ||
| """ | ||
| Get simple output plugin | ||
| :return: | ||
| simple output plugin | ||
| """ | ||
| return Pickle() |
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