main() - Code Metrics - andycasey/AnniesLasso - Measure and Improve Code Quality continuously with Scrutinizer

main() A
last analyzed 2018-05-24 08:09 UTC

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Complexity

Conditions

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Importance

Changes	6
Bugs	1	Features	0

Metric	Value
cc	3
c	6
b	1
f	0
dl	0
loc	74
rs	9.0335

How to fix Long Method

#!/usr/bin/env python
# -*- coding: utf-8 -*-

""" A command line utility for The Cannon. """

import argparse
import logging
import os
from collections import OrderedDict
from numpy import ceil, loadtxt, zeros, nan, diag, ones
from subprocess import check_output
from six.moves import cPickle as pickle
from tempfile import mkstemp
from time import sleep


def fit(model_filename, spectrum_filenames, threads, clobber, from_filename,
    **kwargs):
    """
    Fit a series of spectra.
    """

    import AnniesLasso as tc

    model = tc.load_model(model_filename, threads=threads)
    logger = logging.getLogger("AnniesLasso")
    assert model.is_trained

    chunk_size = kwargs.pop("parallel_chunks", 1000) if threads > 1 else 1
    fluxes = []
    ivars = []
    output_filenames = []
    failures = 0

    fit_velocity = kwargs.pop("fit_velocity", False)

    # MAGIC HACK
    delete_meta_keys = ("fjac", ) # To save space...
    initial_labels = loadtxt("initial_labels.txt")

    if from_filename:
        with open(spectrum_filenames[0], "r") as fp:
            _ = list(map(str.strip, fp.readlines()))
        spectrum_filenames = _

    output_suffix = kwargs.get("output_suffix", None)
    output_suffix = "result" if output_suffix is None else str(output_suffix)
    N = len(spectrum_filenames)
    for i, filename in enumerate(spectrum_filenames):
        logger.info("At spectrum {0}/{1}: {2}".format(i + 1, N, filename))

        basename, _ = os.path.splitext(filename)
        output_filename = "-".join([basename, output_suffix]) + ".pkl"
        
        if os.path.exists(output_filename) and not clobber:
            logger.info("Output filename {} already exists and not clobbering."\
                .format(output_filename))
            continue

        try:
            with open(filename, "rb") as fp:
                flux, ivar = pickle.load(fp)
                fluxes.append(flux)
                ivars.append(ivar)

            output_filenames.append(output_filename)

        except:
            logger.exception("Error occurred loading {}".format(filename))
            failures += 1

        else:
            if len(output_filenames) >= chunk_size:

                
                results, covs, metas = model.fit(fluxes, ivars,
                    initial_labels=initial_labels, model_redshift=fit_velocity,
                    full_output=True)

                for result, cov, meta, output_filename \
                in zip(results, covs, metas, output_filenames):

                    for key in delete_meta_keys:
                        if key in meta:
                            del meta[key]

                    with open(output_filename, "wb") as fp:
                        pickle.dump((result, cov, meta), fp, 2) # For legacy.
                    logger.info("Saved output to {}".format(output_filename))
                
                del output_filenames[0:], fluxes[0:], ivars[0:]


    if len(output_filenames) > 0:

        
        results, covs, metas = model.fit(fluxes, ivars, 
            initial_labels=initial_labels, model_redshift=fit_velocity,
            full_output=True)

        for result, cov, meta, output_filename \
        in zip(results, covs, metas, output_filenames):

            for key in delete_meta_keys:
                if key in meta:
                    del meta[key]

            with open(output_filename, "wb") as fp:
                pickle.dump((result, cov, meta), fp, 2) # For legacy.
            logger.info("Saved output to {}".format(output_filename))
        
        del output_filenames[0:], fluxes[0:], ivars[0:]


    logger.info("Number of failures: {}".format(failures))
    logger.info("Number of successes: {}".format(N - failures))

    return None




def join_results(output_filename, result_filenames, model_filename=None, 
    from_filename=False, clobber=False, errors=False, cov=False, **kwargs):
    """
    Join the test results from multiple files into a single table file.
    """

    import AnniesLasso as tc
    from astropy.table import Table, TableColumns

    meta_keys = kwargs.pop("meta_keys", {})
    meta_keys.update({
        "chi_sq": nan,
        "r_chi_sq": nan,
        "snr": nan,
    #    "redshift": nan,
    })

    logger = logging.getLogger("AnniesLasso")

    # Does the output filename already exist?
    if os.path.exists(output_filename) and not clobber:
        logger.info("Output filename {} already exists and not clobbering."\
            .format(output_filename))
        return None

    if from_filename:
        with open(result_filenames[0], "r") as fp:
            _ = list(map(str.strip, fp.readlines()))
        result_filenames = _

    # We might need the label names from the model.
    if model_filename is not None:
        model = tc.load_model(model_filename)
        assert model.is_trained
        label_names = model.vectorizer.label_names
        logger.warn(
            "Results produced from newer models do not need a model_filename "\
            "to be specified when joining results.")

    else:
        with open(result_filenames[0], "rb") as fp:
            contents = pickle.load(fp)
            if "label_names" not in contents[-1]:
                raise ValueError(
                    "cannot find label names; please provide the model used "\
                    "to produce these results")
            label_names = contents[-1]["label_names"]


    # Load results from each file.
    failed = []
    N = len(result_filenames)

    # Create an ordered dictionary of lists for all the data.
    data_dict = OrderedDict([("FILENAME", [])])
    for label_name in label_names:
        data_dict[label_name] = []
        
    if errors:
        for label_name in label_names:
            data_dict["E_{}".format(label_name)] = []
    
    if cov:
        data_dict["COV"] = []

    for key in meta_keys:
        data_dict[key] = []
    
    # Iterate over all the result filenames
    for i, filename in enumerate(result_filenames):
        logger.info("{}/{}: {}".format(i + 1, N, filename))

        if not os.path.exists(filename):
            logger.warn("Path {} does not exist. Continuing..".format(filename))
            failed.append(filename)
            continue

        with open(filename, "rb") as fp:
            contents = pickle.load(fp)

        assert len(contents) == 3, "You are using some old school version!"
        
        labels, Sigma, meta = contents

        if Sigma is None:
            Sigma = nan * ones((labels.size, labels.size))

        result = [filename] + list(labels) 
        if errors:
            result.extend(diag(Sigma)**0.5) 
        if cov:
            result.append(Sigma)
        result += [meta.get(k, v) for k, v in meta_keys.items()]

        for key, value in zip(data_dict.keys(), result):
            data_dict[key].append(value)

    # Warn of any failures.
    if failed:
        logger.warn(
            "The following {} result file(s) could not be found: \n{}".format(
                len(failed), "\n".join(failed)))

    # Construct the table.
    table = Table(TableColumns(data_dict))
    table.write(output_filename, overwrite=clobber)
    logger.info("Written to {}".format(output_filename))
    



def main():
    """
    The main command line interpreter. This is the console script entry point.
    """

    # Create the main parser.
    parser = argparse.ArgumentParser(
        description="The Cannon", epilog="http://TheCannon.io")

    # Create parent parser.
    parent_parser = argparse.ArgumentParser(add_help=False)
    parent_parser.add_argument("-v", "--verbose",
        dest="verbose", action="store_true", default=False, 
        help="Verbose logging mode.")
    parent_parser.add_argument("-t", "--threads",
        dest="threads", type=int, default=1,
        help="The number of threads to use.")
    
    # Allow for multiple actions.
    subparsers = parser.add_subparsers(title="action", dest="action",
        description="Specify the action to perform.")

    # Fitting parser.
    fit_parser = subparsers.add_parser("fit", parents=[parent_parser],
        help="Fit stacked spectra using a trained model.")
    fit_parser.add_argument("model_filename", type=str,
        help="The path of a trained Cannon model.")
    fit_parser.add_argument("spectrum_filenames", nargs="+", type=str,
        help="Paths of spectra to fit.")
    fit_parser.add_argument("--parallel-chunks", dest="parallel_chunks",
        type=int, default=1000, help="The number of spectra to fit in a chunk.")
    fit_parser.add_argument("--clobber", dest="clobber", default=False,
        action="store_true", help="Overwrite existing output files.")
    fit_parser.add_argument(
        "--output-suffix", dest="output_suffix", type=str,
        help="A string suffix that will be added to the spectrum filenames "\
             "when creating the result filename")
    fit_parser.add_argument("--from-filename", dest="from_filename",
        action="store_true", default=False, help="Read spectrum filenames from file")
    fit_parser.set_defaults(func=fit)


    # Join results parser.
    join_parser = subparsers.add_parser("join", parents=[parent_parser],
        help="Join results from individual stars into a single table.")
    join_parser.add_argument("output_filename", type=str,
        help="The path to write the output filename.")
    join_parser.add_argument("result_filenames", nargs="+", type=str,
        help="Paths of result files to include.")
    join_parser.add_argument("--from-filename", 
        dest="from_filename", action="store_true", default=False,
        help="Read result filenames from a file.")
    join_parser.add_argument(
        "--errors", dest="errors", default=False, action="store_true", 
        help="Include formal errors in destination table.")
    join_parser.add_argument(
        "--cov", dest="cov", default=False, action="store_true", 
        help="Include covariance matrix in destination table.")
    join_parser.add_argument(
        "--clobber", dest="clobber", default=False, action="store_true", 
        help="Ovewrite an existing table file.")

    join_parser.set_defaults(func=join_results)

    # Parse the arguments and take care of any top-level arguments.
    args = parser.parse_args()
    if args.action is None: return

    logger = logging.getLogger("AnniesLasso")
    if args.verbose:
        logger.setLevel(logging.DEBUG)

    # Do things.
    return args.func(**args.__dict__)


if __name__ == "__main__":

    """
    Usage examples:
    # tc train model.pickle --condor --chunks 100
    # tc train model.pickle --threads 8
    # tc join model.pickle --from-filename files

    """
    _ = main()


1		#!/usr/bin/env python
2		# -- coding: utf-8 --
3
4		""" A command line utility for The Cannon. """
5
6		import argparse
7		import logging
8		import os
9		from collections import OrderedDict
10		from numpy import ceil, loadtxt, zeros, nan, diag, ones
11		from subprocess import check_output
12		from six.moves import cPickle as pickle
13		from tempfile import mkstemp
14		from time import sleep
15
16
17		def fit(model_filename, spectrum_filenames, threads, clobber, from_filename,
18		**kwargs):
19		"""
20		Fit a series of spectra.
21		"""
22
23		import AnniesLasso as tc
24
25		model = tc.load_model(model_filename, threads=threads)
26		logger = logging.getLogger("AnniesLasso")
27		assert model.is_trained
28
29		chunk_size = kwargs.pop("parallel_chunks", 1000) if threads > 1 else 1
30		fluxes = []
31		ivars = []
32		output_filenames = []
33		failures = 0
34
35		fit_velocity = kwargs.pop("fit_velocity", False)
36
37		# MAGIC HACK
38		delete_meta_keys = ("fjac", ) # To save space...
39		initial_labels = loadtxt("initial_labels.txt")
40
41		if from_filename:
42		with open(spectrum_filenames[0], "r") as fp:
43		_ = list(map(str.strip, fp.readlines()))
44		spectrum_filenames = _
45
46		output_suffix = kwargs.get("output_suffix", None)
47		output_suffix = "result" if output_suffix is None else str(output_suffix)
48		N = len(spectrum_filenames)
49		for i, filename in enumerate(spectrum_filenames):
50		logger.info("At spectrum {0}/{1}: {2}".format(i + 1, N, filename))
51
52		basename, _ = os.path.splitext(filename)
53		output_filename = "-".join([basename, output_suffix]) + ".pkl"
54
55		if os.path.exists(output_filename) and not clobber:
56		logger.info("Output filename {} already exists and not clobbering."\
57		.format(output_filename))
58		continue
59
60		try:
61		with open(filename, "rb") as fp:
62		flux, ivar = pickle.load(fp)
63		fluxes.append(flux)
64		ivars.append(ivar)
65
66		output_filenames.append(output_filename)
67
68		except:
69		logger.exception("Error occurred loading {}".format(filename))
70		failures += 1
71
72		else:
73	View Code Duplication	if len(output_filenames) >= chunk_size:
		0 ignored issues – show Duplication introduced 2017-02-20 15:56 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
74
75		results, covs, metas = model.fit(fluxes, ivars,
76		initial_labels=initial_labels, model_redshift=fit_velocity,
77		full_output=True)
78
79		for result, cov, meta, output_filename \
80		in zip(results, covs, metas, output_filenames):
81
82		for key in delete_meta_keys:
83		if key in meta:
84		del meta[key]
85
86		with open(output_filename, "wb") as fp:
87		pickle.dump((result, cov, meta), fp, 2) # For legacy.
88		logger.info("Saved output to {}".format(output_filename))
89
90		del output_filenames[0:], fluxes[0:], ivars[0:]
91
92
93	View Code Duplication	if len(output_filenames) > 0:
		0 ignored issues – show Duplication introduced 2017-02-20 15:56 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
94
95		results, covs, metas = model.fit(fluxes, ivars,
96		initial_labels=initial_labels, model_redshift=fit_velocity,
97		full_output=True)
98
99		for result, cov, meta, output_filename \
100		in zip(results, covs, metas, output_filenames):
101
102		for key in delete_meta_keys:
103		if key in meta:
104		del meta[key]
105
106		with open(output_filename, "wb") as fp:
107		pickle.dump((result, cov, meta), fp, 2) # For legacy.
108		logger.info("Saved output to {}".format(output_filename))
109
110		del output_filenames[0:], fluxes[0:], ivars[0:]
111
112
113		logger.info("Number of failures: {}".format(failures))
114		logger.info("Number of successes: {}".format(N - failures))
115
116		return None
117
118
119
120
121		def join_results(output_filename, result_filenames, model_filename=None,
122		from_filename=False, clobber=False, errors=False, cov=False, **kwargs):
123		"""
124		Join the test results from multiple files into a single table file.
125		"""
126
127		import AnniesLasso as tc
128		from astropy.table import Table, TableColumns
129
130		meta_keys = kwargs.pop("meta_keys", {})
131		meta_keys.update({
132		"chi_sq": nan,
133		"r_chi_sq": nan,
134		"snr": nan,
135		# "redshift": nan,
136		})
137
138		logger = logging.getLogger("AnniesLasso")
139
140		# Does the output filename already exist?
141		if os.path.exists(output_filename) and not clobber:
142		logger.info("Output filename {} already exists and not clobbering."\
143		.format(output_filename))
144		return None
145
146		if from_filename:
147		with open(result_filenames[0], "r") as fp:
148		_ = list(map(str.strip, fp.readlines()))
149		result_filenames = _
150
151		# We might need the label names from the model.
152		if model_filename is not None:
153		model = tc.load_model(model_filename)
154		assert model.is_trained
155		label_names = model.vectorizer.label_names
156		logger.warn(
157		"Results produced from newer models do not need a model_filename "\
158		"to be specified when joining results.")
159
160		else:
161		with open(result_filenames[0], "rb") as fp:
162		contents = pickle.load(fp)
163		if "label_names" not in contents[-1]:
164		raise ValueError(
165		"cannot find label names; please provide the model used "\
166		"to produce these results")
167		label_names = contents[-1]["label_names"]
168
169
170		# Load results from each file.
171		failed = []
172		N = len(result_filenames)
173
174		# Create an ordered dictionary of lists for all the data.
175		data_dict = OrderedDict([("FILENAME", [])])
176		for label_name in label_names:
177		data_dict[label_name] = []
178
179		if errors:
180		for label_name in label_names:
181		data_dict["E_{}".format(label_name)] = []
182
183		if cov:
184		data_dict["COV"] = []
185
186		for key in meta_keys:
187		data_dict[key] = []
188
189		# Iterate over all the result filenames
190		for i, filename in enumerate(result_filenames):
191		logger.info("{}/{}: {}".format(i + 1, N, filename))
192
193		if not os.path.exists(filename):
194		logger.warn("Path {} does not exist. Continuing..".format(filename))
195		failed.append(filename)
196		continue
197
198		with open(filename, "rb") as fp:
199		contents = pickle.load(fp)
200
201		assert len(contents) == 3, "You are using some old school version!"
202
203		labels, Sigma, meta = contents
204
205		if Sigma is None:
206		Sigma = nan * ones((labels.size, labels.size))
207
208		result = [filename] + list(labels)
209		if errors:
210		result.extend(diag(Sigma)**0.5)
211		if cov:
212		result.append(Sigma)
213		result += [meta.get(k, v) for k, v in meta_keys.items()]
214
215		for key, value in zip(data_dict.keys(), result):
216		data_dict[key].append(value)
217
218		# Warn of any failures.
219		if failed:
220		logger.warn(
221		"The following {} result file(s) could not be found: \n{}".format(
222		len(failed), "\n".join(failed)))
223
224		# Construct the table.
225		table = Table(TableColumns(data_dict))
226		table.write(output_filename, overwrite=clobber)
227		logger.info("Written to {}".format(output_filename))
228
229
230
231
232		def main():
233		"""
234		The main command line interpreter. This is the console script entry point.
235		"""
236
237		# Create the main parser.
238		parser = argparse.ArgumentParser(
239		description="The Cannon", epilog="http://TheCannon.io")
240
241		# Create parent parser.
242		parent_parser = argparse.ArgumentParser(add_help=False)
243		parent_parser.add_argument("-v", "--verbose",
244		dest="verbose", action="store_true", default=False,
245		help="Verbose logging mode.")
246		parent_parser.add_argument("-t", "--threads",
247		dest="threads", type=int, default=1,
248		help="The number of threads to use.")
249
250		# Allow for multiple actions.
251		subparsers = parser.add_subparsers(title="action", dest="action",
252		description="Specify the action to perform.")
253
254		# Fitting parser.
255		fit_parser = subparsers.add_parser("fit", parents=[parent_parser],
256		help="Fit stacked spectra using a trained model.")
257		fit_parser.add_argument("model_filename", type=str,
258		help="The path of a trained Cannon model.")
259		fit_parser.add_argument("spectrum_filenames", nargs="+", type=str,
260		help="Paths of spectra to fit.")
261		fit_parser.add_argument("--parallel-chunks", dest="parallel_chunks",
262		type=int, default=1000, help="The number of spectra to fit in a chunk.")
263		fit_parser.add_argument("--clobber", dest="clobber", default=False,
264		action="store_true", help="Overwrite existing output files.")
265		fit_parser.add_argument(
266		"--output-suffix", dest="output_suffix", type=str,
267		help="A string suffix that will be added to the spectrum filenames "\
268		"when creating the result filename")
269		fit_parser.add_argument("--from-filename", dest="from_filename",
270		action="store_true", default=False, help="Read spectrum filenames from file")
271		fit_parser.set_defaults(func=fit)
272
273
274		# Join results parser.
275		join_parser = subparsers.add_parser("join", parents=[parent_parser],
276		help="Join results from individual stars into a single table.")
277		join_parser.add_argument("output_filename", type=str,
278		help="The path to write the output filename.")
279		join_parser.add_argument("result_filenames", nargs="+", type=str,
280		help="Paths of result files to include.")
281		join_parser.add_argument("--from-filename",
282		dest="from_filename", action="store_true", default=False,
283		help="Read result filenames from a file.")
284		join_parser.add_argument(
285		"--errors", dest="errors", default=False, action="store_true",
286		help="Include formal errors in destination table.")
287		join_parser.add_argument(
288		"--cov", dest="cov", default=False, action="store_true",
289		help="Include covariance matrix in destination table.")
290		join_parser.add_argument(
291		"--clobber", dest="clobber", default=False, action="store_true",
292		help="Ovewrite an existing table file.")
293
294		join_parser.set_defaults(func=join_results)
295
296		# Parse the arguments and take care of any top-level arguments.
297		args = parser.parse_args()
298		if args.action is None: return
299
300		logger = logging.getLogger("AnniesLasso")
301		if args.verbose:
302		logger.setLevel(logging.DEBUG)
303
304		# Do things.
305		return args.func(**args.__dict__)
306
307
308		if __name__ == "__main__":
309
310		"""
311		Usage examples:
312		# tc train model.pickle --condor --chunks 100
313		# tc train model.pickle --threads 8
314		# tc join model.pickle --from-filename files
315
316		"""
317		_ = main()
318

andycasey / AnniesLasso

main() A last analyzed 2018-05-24 08:09 UTC

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main() A
last analyzed 2018-05-24 08:09 UTC