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vanheeringen-lab/gimmemotifs | gimmemotifs/denovo.py | rename_motifs | def rename_motifs(motifs, stats=None):
"""Rename motifs to GimmeMotifs_1..GimmeMotifs_N.
If stats object is passed, stats will be copied."""
final_motifs = []
for i, motif in enumerate(motifs):
old = str(motif)
motif.id = "GimmeMotifs_{}".format(i + 1)
final_motifs.append(mo... | python | def rename_motifs(motifs, stats=None):
"""Rename motifs to GimmeMotifs_1..GimmeMotifs_N.
If stats object is passed, stats will be copied."""
final_motifs = []
for i, motif in enumerate(motifs):
old = str(motif)
motif.id = "GimmeMotifs_{}".format(i + 1)
final_motifs.append(mo... | [
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vanheeringen-lab/gimmemotifs | gimmemotifs/denovo.py | gimme_motifs | def gimme_motifs(inputfile, outdir, params=None, filter_significant=True, cluster=True, create_report=True):
"""De novo motif prediction based on an ensemble of different tools.
Parameters
----------
inputfile : str
Filename of input. Can be either BED, narrowPeak or FASTA.
outdir : str
... | python | def gimme_motifs(inputfile, outdir, params=None, filter_significant=True, cluster=True, create_report=True):
"""De novo motif prediction based on an ensemble of different tools.
Parameters
----------
inputfile : str
Filename of input. Can be either BED, narrowPeak or FASTA.
outdir : str
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vanheeringen-lab/gimmemotifs | gimmemotifs/db/__init__.py | MotifDb.register_db | def register_db(cls, dbname):
"""Register method to keep list of dbs."""
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"""Register method to keep list of dbs."""
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vanheeringen-lab/gimmemotifs | gimmemotifs/moap.py | moap | def moap(inputfile, method="hypergeom", scoring=None, outfile=None, motiffile=None, pwmfile=None, genome=None, fpr=0.01, ncpus=None,
subsample=None):
"""Run a single motif activity prediction algorithm.
Parameters
----------
inputfile : str
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subsample=None):
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vanheeringen-lab/gimmemotifs | gimmemotifs/moap.py | Moap.create | def create(cls, name, ncpus=None):
"""Create a Moap instance based on the predictor name.
Parameters
----------
name : str
Name of the predictor (eg. Xgboost, BayesianRidge, ...)
ncpus : int, optional
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Name of the predictor (eg. Xgboost, BayesianRidge, ...)
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vanheeringen-lab/gimmemotifs | gimmemotifs/moap.py | Moap.register_predictor | def register_predictor(cls, name):
"""Register method to keep list of predictors."""
def decorator(subclass):
"""Register as decorator function."""
cls._predictors[name.lower()] = subclass
subclass.name = name.lower()
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vanheeringen-lab/gimmemotifs | gimmemotifs/moap.py | Moap.list_classification_predictors | def list_classification_predictors(self):
"""List available classification predictors."""
preds = [self.create(x) for x in self._predictors.keys()]
return [x.name for x in preds if x.ptype == "classification"] | python | def list_classification_predictors(self):
"""List available classification predictors."""
preds = [self.create(x) for x in self._predictors.keys()]
return [x.name for x in preds if x.ptype == "classification"] | [
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pescadores/pescador | pescador/core.py | Streamer._activate | def _activate(self):
"""Activates the stream."""
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# If it's a function, create the stream.
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pescadores/pescador | pescador/core.py | Streamer.iterate | def iterate(self, max_iter=None):
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Parameters
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Maximum number of iterations to yield.
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Yields
------
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max_iter : None or int > 0
Maximum number of iterations to yield.
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vanheeringen-lab/gimmemotifs | gimmemotifs/stats.py | calc_stats_iterator | def calc_stats_iterator(motifs, fg_file, bg_file, genome=None, stats=None, ncpus=None):
"""Calculate motif enrichment metrics.
Parameters
----------
motifs : str, list or Motif instance
A file with motifs in pwm format, a list of Motif instances or a
single Motif instance.
fg_file... | python | def calc_stats_iterator(motifs, fg_file, bg_file, genome=None, stats=None, ncpus=None):
"""Calculate motif enrichment metrics.
Parameters
----------
motifs : str, list or Motif instance
A file with motifs in pwm format, a list of Motif instances or a
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vanheeringen-lab/gimmemotifs | gimmemotifs/stats.py | calc_stats | def calc_stats(motifs, fg_file, bg_file, genome=None, stats=None, ncpus=None):
"""Calculate motif enrichment metrics.
Parameters
----------
motifs : str, list or Motif instance
A file with motifs in pwm format, a list of Motif instances or a
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fg_file : str
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"""Calculate motif enrichment metrics.
Parameters
----------
motifs : str, list or Motif instance
A file with motifs in pwm format, a list of Motif instances or a
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vanheeringen-lab/gimmemotifs | gimmemotifs/stats.py | rank_motifs | def rank_motifs(stats, metrics=("roc_auc", "recall_at_fdr")):
"""Determine mean rank of motifs based on metrics."""
rank = {}
combined_metrics = []
motif_ids = stats.keys()
background = list(stats.values())[0].keys()
for metric in metrics:
mean_metric_stats = [np.mean(
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"""Determine mean rank of motifs based on metrics."""
rank = {}
combined_metrics = []
motif_ids = stats.keys()
background = list(stats.values())[0].keys()
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vanheeringen-lab/gimmemotifs | gimmemotifs/stats.py | write_stats | def write_stats(stats, fname, header=None):
"""write motif statistics to text file."""
# Write stats output to file
for bg in list(stats.values())[0].keys():
f = open(fname.format(bg), "w")
if header:
f.write(header)
stat_keys = sorted(list(list(stats.values())[... | python | def write_stats(stats, fname, header=None):
"""write motif statistics to text file."""
# Write stats output to file
for bg in list(stats.values())[0].keys():
f = open(fname.format(bg), "w")
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vanheeringen-lab/gimmemotifs | gimmemotifs/report.py | get_roc_values | def get_roc_values(motif, fg_file, bg_file):
"""Calculate ROC AUC values for ROC plots."""
#print(calc_stats(motif, fg_file, bg_file, stats=["roc_values"], ncpus=1))
#["roc_values"])
try:
# fg_result = motif.pwm_scan_score(Fasta(fg_file), cutoff=0.0, nreport=1)
# fg_vals = [sorted(x)[... | python | def get_roc_values(motif, fg_file, bg_file):
"""Calculate ROC AUC values for ROC plots."""
#print(calc_stats(motif, fg_file, bg_file, stats=["roc_values"], ncpus=1))
#["roc_values"])
try:
# fg_result = motif.pwm_scan_score(Fasta(fg_file), cutoff=0.0, nreport=1)
# fg_vals = [sorted(x)[... | [
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vanheeringen-lab/gimmemotifs | gimmemotifs/report.py | create_roc_plots | def create_roc_plots(pwmfile, fgfa, background, outdir):
"""Make ROC plots for all motifs."""
motifs = read_motifs(pwmfile, fmt="pwm", as_dict=True)
ncpus = int(MotifConfig().get_default_params()['ncpus'])
pool = Pool(processes=ncpus)
jobs = {}
for bg,fname in background.items():
for m_i... | python | def create_roc_plots(pwmfile, fgfa, background, outdir):
"""Make ROC plots for all motifs."""
motifs = read_motifs(pwmfile, fmt="pwm", as_dict=True)
ncpus = int(MotifConfig().get_default_params()['ncpus'])
pool = Pool(processes=ncpus)
jobs = {}
for bg,fname in background.items():
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vanheeringen-lab/gimmemotifs | gimmemotifs/report.py | _create_text_report | def _create_text_report(inputfile, motifs, closest_match, stats, outdir):
"""Create text report of motifs with statistics and database match."""
my_stats = {}
for motif in motifs:
match = closest_match[motif.id]
my_stats[str(motif)] = {}
for bg in list(stats.values())[0].keys():
... | python | def _create_text_report(inputfile, motifs, closest_match, stats, outdir):
"""Create text report of motifs with statistics and database match."""
my_stats = {}
for motif in motifs:
match = closest_match[motif.id]
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vanheeringen-lab/gimmemotifs | gimmemotifs/report.py | _create_graphical_report | def _create_graphical_report(inputfile, pwm, background, closest_match, outdir, stats, best_id=None):
"""Create main gimme_motifs output html report."""
if best_id is None:
best_id = {}
logger.debug("Creating graphical report")
class ReportMotif(object):
"""Placeholder for motif st... | python | def _create_graphical_report(inputfile, pwm, background, closest_match, outdir, stats, best_id=None):
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if best_id is None:
best_id = {}
logger.debug("Creating graphical report")
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vanheeringen-lab/gimmemotifs | gimmemotifs/report.py | create_denovo_motif_report | def create_denovo_motif_report(inputfile, pwmfile, fgfa, background, locfa, outdir, params, stats=None):
"""Create text and graphical (.html) motif reports."""
logger.info("creating reports")
motifs = read_motifs(pwmfile, fmt="pwm")
# ROC plots
create_roc_plots(pwmfile, fgfa, background, outdi... | python | def create_denovo_motif_report(inputfile, pwmfile, fgfa, background, locfa, outdir, params, stats=None):
"""Create text and graphical (.html) motif reports."""
logger.info("creating reports")
motifs = read_motifs(pwmfile, fmt="pwm")
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vanheeringen-lab/gimmemotifs | gimmemotifs/plot.py | axes_off | def axes_off(ax):
"""Get rid of all axis ticks, lines, etc.
"""
ax.set_frame_on(False)
ax.axes.get_yaxis().set_visible(False)
ax.axes.get_xaxis().set_visible(False) | python | def axes_off(ax):
"""Get rid of all axis ticks, lines, etc.
"""
ax.set_frame_on(False)
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vanheeringen-lab/gimmemotifs | gimmemotifs/plot.py | match_plot | def match_plot(plotdata, outfile):
"""Plot list of motifs with database match and p-value
"param plotdata: list of (motif, dbmotif, pval)
"""
fig_h = 2
fig_w = 7
nrows = len(plotdata)
ncols = 2
fig = plt.figure(figsize=(fig_w, nrows * fig_h))
for i, (motif, dbmotif, pval) in e... | python | def match_plot(plotdata, outfile):
"""Plot list of motifs with database match and p-value
"param plotdata: list of (motif, dbmotif, pval)
"""
fig_h = 2
fig_w = 7
nrows = len(plotdata)
ncols = 2
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vanheeringen-lab/gimmemotifs | gimmemotifs/plot.py | motif_tree_plot | def motif_tree_plot(outfile, tree, data, circle=True, vmin=None, vmax=None, dpi=300):
"""
Plot a "phylogenetic" tree
"""
try:
from ete3 import Tree, faces, AttrFace, TreeStyle, NodeStyle
except ImportError:
print("Please install ete3 to use this functionality")
sys.exit(1)
... | python | def motif_tree_plot(outfile, tree, data, circle=True, vmin=None, vmax=None, dpi=300):
"""
Plot a "phylogenetic" tree
"""
try:
from ete3 import Tree, faces, AttrFace, TreeStyle, NodeStyle
except ImportError:
print("Please install ete3 to use this functionality")
sys.exit(1)
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vanheeringen-lab/gimmemotifs | gimmemotifs/validation.py | check_bed_file | def check_bed_file(fname):
""" Check if the inputfile is a valid bed-file """
if not os.path.exists(fname):
logger.error("Inputfile %s does not exist!", fname)
sys.exit(1)
for i, line in enumerate(open(fname)):
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""" Check if the inputfile is a valid bed-file """
if not os.path.exists(fname):
logger.error("Inputfile %s does not exist!", fname)
sys.exit(1)
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vanheeringen-lab/gimmemotifs | gimmemotifs/validation.py | check_denovo_input | def check_denovo_input(inputfile, params):
"""
Check if an input file is valid, which means BED, narrowPeak or FASTA
"""
background = params["background"]
input_type = determine_file_type(inputfile)
if input_type == "fasta":
valid_bg = FA_VALID_BGS
elif input_type in ["... | python | def check_denovo_input(inputfile, params):
"""
Check if an input file is valid, which means BED, narrowPeak or FASTA
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background = params["background"]
input_type = determine_file_type(inputfile)
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vanheeringen-lab/gimmemotifs | gimmemotifs/scanner.py | scan_to_best_match | def scan_to_best_match(fname, motifs, ncpus=None, genome=None, score=False):
"""Scan a FASTA file with motifs.
Scan a FASTA file and return a dictionary with the best match per motif.
Parameters
----------
fname : str
Filename of a sequence file in FASTA format.
motifs : list
... | python | def scan_to_best_match(fname, motifs, ncpus=None, genome=None, score=False):
"""Scan a FASTA file with motifs.
Scan a FASTA file and return a dictionary with the best match per motif.
Parameters
----------
fname : str
Filename of a sequence file in FASTA format.
motifs : list
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vanheeringen-lab/gimmemotifs | gimmemotifs/scanner.py | Scanner.set_background | def set_background(self, fname=None, genome=None, length=200, nseq=10000):
"""Set the background to use for FPR and z-score calculations.
Background can be specified either as a genome name or as the
name of a FASTA file.
Parameters
----------
fname : str, opti... | python | def set_background(self, fname=None, genome=None, length=200, nseq=10000):
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vanheeringen-lab/gimmemotifs | gimmemotifs/scanner.py | Scanner.set_threshold | def set_threshold(self, fpr=None, threshold=None):
"""Set motif scanning threshold based on background sequences.
Parameters
----------
fpr : float, optional
Desired FPR, between 0.0 and 1.0.
threshold : float or str, optional
Desired motif threshold, ex... | python | def set_threshold(self, fpr=None, threshold=None):
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fpr : float, optional
Desired FPR, between 0.0 and 1.0.
threshold : float or str, optional
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vanheeringen-lab/gimmemotifs | gimmemotifs/scanner.py | Scanner.count | def count(self, seqs, nreport=100, scan_rc=True):
"""
count the number of matches above the cutoff
returns an iterator of lists containing integer counts
"""
for matches in self.scan(seqs, nreport, scan_rc):
counts = [len(m) for m in matches]
yield counts | python | def count(self, seqs, nreport=100, scan_rc=True):
"""
count the number of matches above the cutoff
returns an iterator of lists containing integer counts
"""
for matches in self.scan(seqs, nreport, scan_rc):
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vanheeringen-lab/gimmemotifs | gimmemotifs/scanner.py | Scanner.total_count | def total_count(self, seqs, nreport=100, scan_rc=True):
"""
count the number of matches above the cutoff
returns an iterator of lists containing integer counts
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"""
count the number of matches above the cutoff
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vanheeringen-lab/gimmemotifs | gimmemotifs/scanner.py | Scanner.best_score | def best_score(self, seqs, scan_rc=True, normalize=False):
"""
give the score of the best match of each motif in each sequence
returns an iterator of lists containing floats
"""
self.set_threshold(threshold=0.0)
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self.se... | python | def best_score(self, seqs, scan_rc=True, normalize=False):
"""
give the score of the best match of each motif in each sequence
returns an iterator of lists containing floats
"""
self.set_threshold(threshold=0.0)
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vanheeringen-lab/gimmemotifs | gimmemotifs/scanner.py | Scanner.best_match | def best_match(self, seqs, scan_rc=True):
"""
give the best match of each motif in each sequence
returns an iterator of nested lists containing tuples:
(score, position, strand)
"""
self.set_threshold(threshold=0.0)
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... | python | def best_match(self, seqs, scan_rc=True):
"""
give the best match of each motif in each sequence
returns an iterator of nested lists containing tuples:
(score, position, strand)
"""
self.set_threshold(threshold=0.0)
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vanheeringen-lab/gimmemotifs | gimmemotifs/scanner.py | Scanner.scan | def scan(self, seqs, nreport=100, scan_rc=True, normalize=False):
"""
scan a set of regions / sequences
"""
if not self.threshold:
sys.stderr.write(
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... | python | def scan(self, seqs, nreport=100, scan_rc=True, normalize=False):
"""
scan a set of regions / sequences
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vanheeringen-lab/gimmemotifs | gimmemotifs/commands/roc.py | roc | def roc(args):
""" Calculate ROC_AUC and other metrics and optionally plot ROC curve."""
outputfile = args.outfile
# Default extension for image
if outputfile and not outputfile.endswith(".png"):
outputfile += ".png"
motifs = read_motifs(args.pwmfile, fmt="pwm")
ids = []
if arg... | python | def roc(args):
""" Calculate ROC_AUC and other metrics and optionally plot ROC curve."""
outputfile = args.outfile
# Default extension for image
if outputfile and not outputfile.endswith(".png"):
outputfile += ".png"
motifs = read_motifs(args.pwmfile, fmt="pwm")
ids = []
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vanheeringen-lab/gimmemotifs | gimmemotifs/comparison.py | ssd | def ssd(p1, p2):
"""Calculates motif position similarity based on sum of squared distances.
Parameters
----------
p1 : list
Motif position 1.
p2 : list
Motif position 2.
Returns
-------
score : float
"""
return 2 - np.sum([(a-b)**2 for a,b in zip(p1... | python | def ssd(p1, p2):
"""Calculates motif position similarity based on sum of squared distances.
Parameters
----------
p1 : list
Motif position 1.
p2 : list
Motif position 2.
Returns
-------
score : float
"""
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vanheeringen-lab/gimmemotifs | gimmemotifs/comparison.py | seqcor | def seqcor(m1, m2, seq=None):
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Based on Kielbasa (2015) and Grau (2015).
Scores are calculated based on scanning a de Bruijn sequence of 7-mers.
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"""Calculates motif similarity based on Pearson correlation of scores.
Based on Kielbasa (2015) and Grau (2015).
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vanheeringen-lab/gimmemotifs | gimmemotifs/comparison.py | MotifComparer.compare_motifs | def compare_motifs(self, m1, m2, match="total", metric="wic", combine="mean", pval=False):
"""Compare two motifs.
The similarity metric can be any of seqcor, pcc, ed, distance, wic,
chisq, akl or ssd. If match is 'total' the similarity score is
calculated for the whole match, ... | python | def compare_motifs(self, m1, m2, match="total", metric="wic", combine="mean", pval=False):
"""Compare two motifs.
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vanheeringen-lab/gimmemotifs | gimmemotifs/comparison.py | MotifComparer.get_all_scores | def get_all_scores(self, motifs, dbmotifs, match, metric, combine,
pval=False, parallel=True, trim=None, ncpus=None):
"""Pairwise comparison of a set of motifs compared to reference motifs.
Parameters
----------
motifs : list
List of Motif instan... | python | def get_all_scores(self, motifs, dbmotifs, match, metric, combine,
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vanheeringen-lab/gimmemotifs | gimmemotifs/comparison.py | MotifComparer.get_closest_match | def get_closest_match(self, motifs, dbmotifs=None, match="partial", metric="wic",combine="mean", parallel=True, ncpus=None):
"""Return best match in database for motifs.
Parameters
----------
motifs : list or str
Filename of motifs or list of motifs.
dbmotifs : list... | python | def get_closest_match(self, motifs, dbmotifs=None, match="partial", metric="wic",combine="mean", parallel=True, ncpus=None):
"""Return best match in database for motifs.
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motifs : list or str
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eofs/aws | aws/main.py | list_regions | def list_regions(service):
"""
List regions for the service
"""
for region in service.regions():
print '%(name)s: %(endpoint)s' % {
'name': region.name,
'endpoint': region.endpoint,
} | python | def list_regions(service):
"""
List regions for the service
"""
for region in service.regions():
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eofs/aws | aws/main.py | elb_table | def elb_table(balancers):
"""
Print nice looking table of information from list of load balancers
"""
t = prettytable.PrettyTable(['Name', 'DNS', 'Ports', 'Zones', 'Created'])
t.align = 'l'
for b in balancers:
ports = ['%s: %s -> %s' % (l[2], l[0], l[1]) for l in b.listeners]
por... | python | def elb_table(balancers):
"""
Print nice looking table of information from list of load balancers
"""
t = prettytable.PrettyTable(['Name', 'DNS', 'Ports', 'Zones', 'Created'])
t.align = 'l'
for b in balancers:
ports = ['%s: %s -> %s' % (l[2], l[0], l[1]) for l in b.listeners]
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eofs/aws | aws/main.py | ec2_table | def ec2_table(instances):
"""
Print nice looking table of information from list of instances
"""
t = prettytable.PrettyTable(['ID', 'State', 'Monitored', 'Image', 'Name', 'Type', 'SSH key', 'DNS'])
t.align = 'l'
for i in instances:
name = i.tags.get('Name', '')
t.add_row([i.id, i... | python | def ec2_table(instances):
"""
Print nice looking table of information from list of instances
"""
t = prettytable.PrettyTable(['ID', 'State', 'Monitored', 'Image', 'Name', 'Type', 'SSH key', 'DNS'])
t.align = 'l'
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name = i.tags.get('Name', '')
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eofs/aws | aws/main.py | ec2_image_table | def ec2_image_table(images):
"""
Print nice looking table of information from images
"""
t = prettytable.PrettyTable(['ID', 'State', 'Name', 'Owner', 'Root device', 'Is public', 'Description'])
t.align = 'l'
for i in images:
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"""
Print nice looking table of information from images
"""
t = prettytable.PrettyTable(['ID', 'State', 'Name', 'Owner', 'Root device', 'Is public', 'Description'])
t.align = 'l'
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eofs/aws | aws/main.py | ec2_fab | def ec2_fab(service, args):
"""
Run Fabric commands against EC2 instances
"""
instance_ids = args.instances
instances = service.list(elb=args.elb, instance_ids=instance_ids)
hosts = service.resolve_hosts(instances)
fab.env.hosts = hosts
fab.env.key_filename = settings.get('SSH', 'KEY_FI... | python | def ec2_fab(service, args):
"""
Run Fabric commands against EC2 instances
"""
instance_ids = args.instances
instances = service.list(elb=args.elb, instance_ids=instance_ids)
hosts = service.resolve_hosts(instances)
fab.env.hosts = hosts
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eofs/aws | aws/main.py | main | def main():
"""
AWS support script's main method
"""
p = argparse.ArgumentParser(description='Manage Amazon AWS services',
prog='aws',
version=__version__)
subparsers = p.add_subparsers(help='Select Amazon AWS service to use')
# Au... | python | def main():
"""
AWS support script's main method
"""
p = argparse.ArgumentParser(description='Manage Amazon AWS services',
prog='aws',
version=__version__)
subparsers = p.add_subparsers(help='Select Amazon AWS service to use')
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pescadores/pescador | pescador/maps.py | buffer_stream | def buffer_stream(stream, buffer_size, partial=False, axis=None):
'''Buffer "data" from an stream into one data object.
Parameters
----------
stream : stream
The stream to buffer
buffer_size : int > 0
The number of examples to retain per batch.
partial : bool, default=False
... | python | def buffer_stream(stream, buffer_size, partial=False, axis=None):
'''Buffer "data" from an stream into one data object.
Parameters
----------
stream : stream
The stream to buffer
buffer_size : int > 0
The number of examples to retain per batch.
partial : bool, default=False
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The number of examples to retain per batch.
partial : bool, default=False
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pescadores/pescador | pescador/maps.py | tuples | def tuples(stream, *keys):
"""Reformat data as tuples.
Parameters
----------
stream : iterable
Stream of data objects.
*keys : strings
Keys to use for ordering data.
Yields
------
items : tuple of np.ndarrays
Data object reformated as a tuple.
Raises
-... | python | def tuples(stream, *keys):
"""Reformat data as tuples.
Parameters
----------
stream : iterable
Stream of data objects.
*keys : strings
Keys to use for ordering data.
Yields
------
items : tuple of np.ndarrays
Data object reformated as a tuple.
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pescadores/pescador | pescador/maps.py | keras_tuples | def keras_tuples(stream, inputs=None, outputs=None):
"""Reformat data objects as keras-compatible tuples.
For more detail: https://keras.io/models/model/#fit
Parameters
----------
stream : iterable
Stream of data objects.
inputs : string or iterable of strings, None
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"""Reformat data objects as keras-compatible tuples.
For more detail: https://keras.io/models/model/#fit
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stream : iterable
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inputs : string or iterable of strings, None
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vanheeringen-lab/gimmemotifs | gimmemotifs/commands/location.py | location | def location(args):
"""
Creates histrogram of motif location.
Parameters
----------
args : argparse object
Command line arguments.
"""
fastafile = args.fastafile
pwmfile = args.pwmfile
lwidth = args.width
if not lwidth:
f = Fasta(fastafile)
lwidth = len(... | python | def location(args):
"""
Creates histrogram of motif location.
Parameters
----------
args : argparse object
Command line arguments.
"""
fastafile = args.fastafile
pwmfile = args.pwmfile
lwidth = args.width
if not lwidth:
f = Fasta(fastafile)
lwidth = len(... | [
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vanheeringen-lab/gimmemotifs | gimmemotifs/shutils.py | which | def which(fname):
"""Find location of executable."""
if "PATH" not in os.environ or not os.environ["PATH"]:
path = os.defpath
else:
path = os.environ["PATH"]
for p in [fname] + [os.path.join(x, fname) for x in path.split(os.pathsep)]:
p = os.path.abspath(p)
if os.access(... | python | def which(fname):
"""Find location of executable."""
if "PATH" not in os.environ or not os.environ["PATH"]:
path = os.defpath
else:
path = os.environ["PATH"]
for p in [fname] + [os.path.join(x, fname) for x in path.split(os.pathsep)]:
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vanheeringen-lab/gimmemotifs | gimmemotifs/shutils.py | find_by_ext | def find_by_ext(dirname, ext):
"""Find all files in a directory by extension."""
# Get all fasta-files
try:
files = os.listdir(dirname)
except OSError:
if os.path.exists(dirname):
cmd = "find {0} -maxdepth 1 -name \"*\"".format(dirname)
p = sp.Popen(cmd, shel... | python | def find_by_ext(dirname, ext):
"""Find all files in a directory by extension."""
# Get all fasta-files
try:
files = os.listdir(dirname)
except OSError:
if os.path.exists(dirname):
cmd = "find {0} -maxdepth 1 -name \"*\"".format(dirname)
p = sp.Popen(cmd, shel... | [
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | default_motifs | def default_motifs():
"""Return list of Motif instances from default motif database."""
config = MotifConfig()
d = config.get_motif_dir()
m = config.get_default_params()['motif_db']
if not d or not m:
raise ValueError("default motif database not configured")
fname = os.path.join(d, m)
... | python | def default_motifs():
"""Return list of Motif instances from default motif database."""
config = MotifConfig()
d = config.get_motif_dir()
m = config.get_default_params()['motif_db']
if not d or not m:
raise ValueError("default motif database not configured")
fname = os.path.join(d, m)
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | motif_from_align | def motif_from_align(align):
"""Convert alignment to motif.
Converts a list with sequences to a motif. Sequences should be the same
length.
Parameters
----------
align : list
List with sequences (A,C,G,T).
Returns
-------
m : Motif instance
Motif created from ... | python | def motif_from_align(align):
"""Convert alignment to motif.
Converts a list with sequences to a motif. Sequences should be the same
length.
Parameters
----------
align : list
List with sequences (A,C,G,T).
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-------
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | motif_from_consensus | def motif_from_consensus(cons, n=12):
"""Convert consensus sequence to motif.
Converts a consensus sequences using the nucleotide IUPAC alphabet to a
motif.
Parameters
----------
cons : str
Consensus sequence using the IUPAC alphabet.
n : int , optional
Count used to conv... | python | def motif_from_consensus(cons, n=12):
"""Convert consensus sequence to motif.
Converts a consensus sequences using the nucleotide IUPAC alphabet to a
motif.
Parameters
----------
cons : str
Consensus sequence using the IUPAC alphabet.
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | parse_motifs | def parse_motifs(motifs):
"""Parse motifs in a variety of formats to return a list of motifs.
Parameters
----------
motifs : list or str
Filename of motif, list of motifs or single Motif instance.
Returns
-------
motifs : list
List of Motif instances.
"""
if isin... | python | def parse_motifs(motifs):
"""Parse motifs in a variety of formats to return a list of motifs.
Parameters
----------
motifs : list or str
Filename of motif, list of motifs or single Motif instance.
Returns
-------
motifs : list
List of Motif instances.
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | _read_motifs_from_filehandle | def _read_motifs_from_filehandle(handle, fmt):
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handle : file-like object
Motifs.
fmt : string, optional
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motifs... | python | def _read_motifs_from_filehandle(handle, fmt):
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Read motifs from a file-like object.
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handle : file-like object
Motifs.
fmt : string, optional
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | read_motifs | def read_motifs(infile=None, fmt="pwm", as_dict=False):
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infile : string or file-like object, optional
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | Motif.information_content | def information_content(self):
"""Return the total information content of the motif.
Return
------
ic : float
Motif information content.
"""
ic = 0
for row in self.pwm:
ic += 2.0 + np.sum([row[x] * log(row[x])/log(2) for x in range(4) if r... | python | def information_content(self):
"""Return the total information content of the motif.
Return
------
ic : float
Motif information content.
"""
ic = 0
for row in self.pwm:
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | Motif.pwm_min_score | def pwm_min_score(self):
"""Return the minimum PWM score.
Returns
-------
score : float
Minimum PWM score.
"""
if self.min_score is None:
score = 0
for row in self.pwm:
score += log(min(row) / 0.25 + 0.01)
s... | python | def pwm_min_score(self):
"""Return the minimum PWM score.
Returns
-------
score : float
Minimum PWM score.
"""
if self.min_score is None:
score = 0
for row in self.pwm:
score += log(min(row) / 0.25 + 0.01)
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | Motif.pwm_max_score | def pwm_max_score(self):
"""Return the maximum PWM score.
Returns
-------
score : float
Maximum PWM score.
"""
if self.max_score is None:
score = 0
for row in self.pwm:
score += log(max(row) / 0.25 + 0.01)
s... | python | def pwm_max_score(self):
"""Return the maximum PWM score.
Returns
-------
score : float
Maximum PWM score.
"""
if self.max_score is None:
score = 0
for row in self.pwm:
score += log(max(row) / 0.25 + 0.01)
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | Motif.score_kmer | def score_kmer(self, kmer):
"""Calculate the log-odds score for a specific k-mer.
Parameters
----------
kmer : str
String representing a kmer. Should be the same length as the motif.
Returns
-------
score : float
Log-odd score.
... | python | def score_kmer(self, kmer):
"""Calculate the log-odds score for a specific k-mer.
Parameters
----------
kmer : str
String representing a kmer. Should be the same length as the motif.
Returns
-------
score : float
Log-odd score.
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | Motif.pfm_to_pwm | def pfm_to_pwm(self, pfm, pseudo=0.001):
"""Convert PFM with counts to a PFM with fractions.
Parameters
----------
pfm : list
2-dimensional list with counts.
pseudo : float
Pseudocount used in conversion.
Returns
-------
p... | python | def pfm_to_pwm(self, pfm, pseudo=0.001):
"""Convert PFM with counts to a PFM with fractions.
Parameters
----------
pfm : list
2-dimensional list with counts.
pseudo : float
Pseudocount used in conversion.
Returns
-------
p... | [
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pfm : list
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Pseudocount used in conversion.
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | Motif.to_motevo | def to_motevo(self):
"""Return motif formatted in MotEvo (TRANSFAC-like) format
Returns
-------
m : str
String of motif in MotEvo format.
"""
m = "//\n"
m += "NA {}\n".format(self.id)
m += "P0\tA\tC\tG\tT\n"
for i, row in enume... | python | def to_motevo(self):
"""Return motif formatted in MotEvo (TRANSFAC-like) format
Returns
-------
m : str
String of motif in MotEvo format.
"""
m = "//\n"
m += "NA {}\n".format(self.id)
m += "P0\tA\tC\tG\tT\n"
for i, row in enume... | [
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | Motif.to_transfac | def to_transfac(self):
"""Return motif formatted in TRANSFAC format
Returns
-------
m : str
String of motif in TRANSFAC format.
"""
m = "%s\t%s\t%s\n" % ("DE", self.id, "unknown")
for i, (row, cons) in enumerate(zip(self.pfm, self.to_consensus... | python | def to_transfac(self):
"""Return motif formatted in TRANSFAC format
Returns
-------
m : str
String of motif in TRANSFAC format.
"""
m = "%s\t%s\t%s\n" % ("DE", self.id, "unknown")
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | Motif.to_meme | def to_meme(self):
"""Return motif formatted in MEME format
Returns
-------
m : str
String of motif in MEME format.
"""
motif_id = self.id.replace(" ", "_")
m = "MOTIF %s\n" % motif_id
m += "BL MOTIF %s width=0 seqs=0\n"% motif_id
... | python | def to_meme(self):
"""Return motif formatted in MEME format
Returns
-------
m : str
String of motif in MEME format.
"""
motif_id = self.id.replace(" ", "_")
m = "MOTIF %s\n" % motif_id
m += "BL MOTIF %s width=0 seqs=0\n"% motif_id
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | Motif.ic_pos | def ic_pos(self, row1, row2=None):
"""Calculate the information content of one position.
Returns
-------
score : float
Information content.
"""
if row2 is None:
row2 = [0.25,0.25,0.25,0.25]
score = 0
for a,b in zip(row1, row2):
... | python | def ic_pos(self, row1, row2=None):
"""Calculate the information content of one position.
Returns
-------
score : float
Information content.
"""
if row2 is None:
row2 = [0.25,0.25,0.25,0.25]
score = 0
for a,b in zip(row1, row2):
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | Motif.pcc_pos | def pcc_pos(self, row1, row2):
"""Calculate the Pearson correlation coefficient of one position
compared to another position.
Returns
-------
score : float
Pearson correlation coefficient.
"""
mean1 = np.mean(row1)
mean2 = np.mean(row2)
... | python | def pcc_pos(self, row1, row2):
"""Calculate the Pearson correlation coefficient of one position
compared to another position.
Returns
-------
score : float
Pearson correlation coefficient.
"""
mean1 = np.mean(row1)
mean2 = np.mean(row2)
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | Motif.rc | def rc(self):
"""Return the reverse complemented motif.
Returns
-------
m : Motif instance
New Motif instance with the reverse complement of the input motif.
"""
m = Motif()
m.pfm = [row[::-1] for row in self.pfm[::-1]]
m.pwm = [row[::-1] for ... | python | def rc(self):
"""Return the reverse complemented motif.
Returns
-------
m : Motif instance
New Motif instance with the reverse complement of the input motif.
"""
m = Motif()
m.pfm = [row[::-1] for row in self.pfm[::-1]]
m.pwm = [row[::-1] for ... | [
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | Motif.trim | def trim(self, edge_ic_cutoff=0.4):
"""Trim positions with an information content lower than the threshold.
The default threshold is set to 0.4. The Motif will be changed in-place.
Parameters
----------
edge_ic_cutoff : float, optional
Information content threshold.... | python | def trim(self, edge_ic_cutoff=0.4):
"""Trim positions with an information content lower than the threshold.
The default threshold is set to 0.4. The Motif will be changed in-place.
Parameters
----------
edge_ic_cutoff : float, optional
Information content threshold.... | [
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | Motif.consensus_scan | def consensus_scan(self, fa):
"""Scan FASTA with the motif as a consensus sequence.
Parameters
----------
fa : Fasta object
Fasta object to scan
Returns
-------
matches : dict
Dictionaru with matches.
"""
regexp = ... | python | def consensus_scan(self, fa):
"""Scan FASTA with the motif as a consensus sequence.
Parameters
----------
fa : Fasta object
Fasta object to scan
Returns
-------
matches : dict
Dictionaru with matches.
"""
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | Motif.pwm_scan | def pwm_scan(self, fa, cutoff=0.9, nreport=50, scan_rc=True):
"""Scan sequences with this motif.
Scan sequences from a FASTA object with this motif. Less efficient
than using a Scanner object. By setting the cutoff to 0.0 and
nreport to 1, the best match for every sequence will be ret... | python | def pwm_scan(self, fa, cutoff=0.9, nreport=50, scan_rc=True):
"""Scan sequences with this motif.
Scan sequences from a FASTA object with this motif. Less efficient
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | Motif.pwm_scan_all | def pwm_scan_all(self, fa, cutoff=0.9, nreport=50, scan_rc=True):
"""Scan sequences with this motif.
Scan sequences from a FASTA object with this motif. Less efficient
than using a Scanner object. By setting the cutoff to 0.0 and
nreport to 1, the best match for every sequence will be... | python | def pwm_scan_all(self, fa, cutoff=0.9, nreport=50, scan_rc=True):
"""Scan sequences with this motif.
Scan sequences from a FASTA object with this motif. Less efficient
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | Motif.pwm_scan_to_gff | def pwm_scan_to_gff(self, fa, gfffile, cutoff=0.9, nreport=50, scan_rc=True, append=False):
"""Scan sequences with this motif and save to a GFF file.
Scan sequences from a FASTA object with this motif. Less efficient
than using a Scanner object. By setting the cutoff to 0.0 and
nrepor... | python | def pwm_scan_to_gff(self, fa, gfffile, cutoff=0.9, nreport=50, scan_rc=True, append=False):
"""Scan sequences with this motif and save to a GFF file.
Scan sequences from a FASTA object with this motif. Less efficient
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nrepor... | [
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | Motif.average_motifs | def average_motifs(self, other, pos, orientation, include_bg=False):
"""Return the average of two motifs.
Combine this motif with another motif and return the average as a new
Motif object. The position and orientatien need to be supplied. The pos
parameter is the position of the second... | python | def average_motifs(self, other, pos, orientation, include_bg=False):
"""Return the average of two motifs.
Combine this motif with another motif and return the average as a new
Motif object. The position and orientatien need to be supplied. The pos
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | Motif._pwm_to_str | def _pwm_to_str(self, precision=4):
"""Return string representation of pwm.
Parameters
----------
precision : int, optional, default 4
Floating-point precision.
Returns
-------
pwm_string : str
"""
if not self.pwm:
return ... | python | def _pwm_to_str(self, precision=4):
"""Return string representation of pwm.
Parameters
----------
precision : int, optional, default 4
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | Motif.to_pwm | def to_pwm(self, precision=4, extra_str=""):
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Floating-point precision.
extra_str |: str, optional
Extra text to include with motif id line.
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | Motif.to_img | def to_img(self, fname, fmt="PNG", add_left=0, seqlogo=None, height=6):
"""Create a sequence logo using seqlogo.
Create a sequence logo and save it to a file. Valid formats are: PNG,
EPS, GIF and PDF.
Parameters
----------
fname : str
Output filename.
... | python | def to_img(self, fname, fmt="PNG", add_left=0, seqlogo=None, height=6):
"""Create a sequence logo using seqlogo.
Create a sequence logo and save it to a file. Valid formats are: PNG,
EPS, GIF and PDF.
Parameters
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fname : str
Output filename.
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vanheeringen-lab/gimmemotifs | gimmemotifs/motif.py | Motif.randomize | def randomize(self):
"""Create a new motif with shuffled positions.
Shuffle the positions of this motif and return a new Motif instance.
Returns
-------
m : Motif instance
Motif instance with shuffled positions.
"""
random_pfm = [[c for c in row] for... | python | def randomize(self):
"""Create a new motif with shuffled positions.
Shuffle the positions of this motif and return a new Motif instance.
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m : Motif instance
Motif instance with shuffled positions.
"""
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vanheeringen-lab/gimmemotifs | gimmemotifs/commands/maelstrom.py | maelstrom | def maelstrom(args):
"""Run the maelstrom method."""
infile = args.inputfile
genome = args.genome
outdir = args.outdir
pwmfile = args.pwmfile
methods = args.methods
ncpus = args.ncpus
if not os.path.exists(infile):
raise ValueError("file {} does not exist".format(infile))
... | python | def maelstrom(args):
"""Run the maelstrom method."""
infile = args.inputfile
genome = args.genome
outdir = args.outdir
pwmfile = args.pwmfile
methods = args.methods
ncpus = args.ncpus
if not os.path.exists(infile):
raise ValueError("file {} does not exist".format(infile))
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pescadores/pescador | pescador/zmq_stream.py | zmq_send_data | def zmq_send_data(socket, data, flags=0, copy=True, track=False):
"""Send data, e.g. {key: np.ndarray}, with metadata"""
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pescadores/pescador | pescador/zmq_stream.py | zmq_recv_data | def zmq_recv_data(socket, flags=0, copy=True, track=False):
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data = dict()
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data = dict()
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headers = json.loads(msg[0].decode('ascii'))
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pescadores/pescador | pescador/zmq_stream.py | ZMQStreamer.iterate | def iterate(self, max_iter=None):
"""
Note: A ZMQStreamer does not activate its stream,
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Yields
------
data : dict
Data drawn from `streamer(max_iter)`.
"""
context = zmq.Context()
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vanheeringen-lab/gimmemotifs | gimmemotifs/fasta.py | Fasta.hardmask | def hardmask(self):
""" Mask all lowercase nucleotides with N's """
p = re.compile("a|c|g|t|n")
for seq_id in self.fasta_dict.keys():
self.fasta_dict[seq_id] = p.sub("N", self.fasta_dict[seq_id])
return self | python | def hardmask(self):
""" Mask all lowercase nucleotides with N's """
p = re.compile("a|c|g|t|n")
for seq_id in self.fasta_dict.keys():
self.fasta_dict[seq_id] = p.sub("N", self.fasta_dict[seq_id])
return self | [
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vanheeringen-lab/gimmemotifs | gimmemotifs/fasta.py | Fasta.get_random | def get_random(self, n, l=None):
""" Return n random sequences from this Fasta object """
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i = 0
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""" Return n random sequences from this Fasta object """
random_f = Fasta()
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ids = self.ids[:]
random.shuffle(ids)
i = 0
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vanheeringen-lab/gimmemotifs | gimmemotifs/fasta.py | Fasta.writefasta | def writefasta(self, fname):
""" Write sequences to FASTA formatted file"""
f = open(fname, "w")
fa_str = "\n".join([">%s\n%s" % (id, self._format_seq(seq)) for id, seq in self.items()])
f.write(fa_str)
f.close() | python | def writefasta(self, fname):
""" Write sequences to FASTA formatted file"""
f = open(fname, "w")
fa_str = "\n".join([">%s\n%s" % (id, self._format_seq(seq)) for id, seq in self.items()])
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vanheeringen-lab/gimmemotifs | gimmemotifs/cluster.py | cluster_motifs | def cluster_motifs(motifs, match="total", metric="wic", combine="mean", pval=True, threshold=0.95, trim_edges=False, edge_ic_cutoff=0.2, include_bg=True, progress=True, ncpus=None):
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pescadores/pescador | pescador/util.py | batch_length | def batch_length(batch):
'''Determine the number of samples in a batch.
Parameters
----------
batch : dict
A batch dictionary. Each value must implement `len`.
All values must have the same `len`.
Returns
-------
n : int >= 0 or None
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'''Determine the number of samples in a batch.
Parameters
----------
batch : dict
A batch dictionary. Each value must implement `len`.
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pescadores/pescador | pescador/mux.py | Mux._activate | def _activate(self):
"""Activates a number of streams"""
self.distribution_ = 1. / self.n_streams * np.ones(self.n_streams)
self.valid_streams_ = np.ones(self.n_streams, dtype=bool)
self.streams_ = [None] * self.k
self.stream_weights_ = np.zeros(self.k)
self.stream_coun... | python | def _activate(self):
"""Activates a number of streams"""
self.distribution_ = 1. / self.n_streams * np.ones(self.n_streams)
self.valid_streams_ = np.ones(self.n_streams, dtype=bool)
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pescadores/pescador | pescador/mux.py | Mux._new_stream | def _new_stream(self, idx):
'''Randomly select and create a stream.
Parameters
----------
idx : int, [0:n_streams - 1]
The stream index to replace
'''
# instantiate
if self.rate is not None:
n_stream = 1 + self.rng.poisson(lam=self.rate)
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----------
idx : int, [0:n_streams - 1]
The stream index to replace
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# instantiate
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pescadores/pescador | pescador/mux.py | BaseMux.iterate | def iterate(self, max_iter=None):
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"""
if max_iter is None:
max_iter = np.inf
# Calls Streamer's __enter__, which calls activate()
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# Main sampling... | python | def iterate(self, max_iter=None):
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"""
if max_iter is None:
max_iter = np.inf
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pescadores/pescador | pescador/mux.py | StochasticMux._next_sample_index | def _next_sample_index(self):
"""StochasticMux chooses its next sample stream randomly"""
return self.rng.choice(self.n_active,
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"""StochasticMux chooses its next sample stream randomly"""
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pescadores/pescador | pescador/mux.py | StochasticMux._activate_stream | def _activate_stream(self, idx):
'''Randomly select and create a stream.
StochasticMux adds mode handling to _activate_stream, making it so that
if we're not sampling "with_replacement", the distribution for this
chosen streamer is set to 0, causing the streamer not to be available
... | python | def _activate_stream(self, idx):
'''Randomly select and create a stream.
StochasticMux adds mode handling to _activate_stream, making it so that
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pescadores/pescador | pescador/mux.py | StochasticMux._new_stream | def _new_stream(self, idx):
'''Randomly select and create a new stream.
Parameters
----------
idx : int, [0:n_streams - 1]
The stream index to replace
'''
# Choose the stream index from the candidate pool
self.stream_idxs_[idx] = self.rng.choice(
... | python | def _new_stream(self, idx):
'''Randomly select and create a new stream.
Parameters
----------
idx : int, [0:n_streams - 1]
The stream index to replace
'''
# Choose the stream index from the candidate pool
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pescadores/pescador | pescador/mux.py | ShuffledMux._activate | def _activate(self):
"""ShuffledMux's activate is similar to StochasticMux,
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"""
self.streams_ = [None] * self.n_streams
# Weights of the active streams.
# Once a stream is exhausted, it is set to 0.
... | python | def _activate(self):
"""ShuffledMux's activate is similar to StochasticMux,
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"""
self.streams_ = [None] * self.n_streams
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pescadores/pescador | pescador/mux.py | ShuffledMux._next_sample_index | def _next_sample_index(self):
"""ShuffledMux chooses its next sample stream randomly,
conditioned on the stream weights.
"""
return self.rng.choice(self.n_streams,
p=(self.stream_weights_ /
self.weight_norm_)) | python | def _next_sample_index(self):
"""ShuffledMux chooses its next sample stream randomly,
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"""
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pescadores/pescador | pescador/mux.py | ShuffledMux._new_stream | def _new_stream(self, idx):
'''Randomly select and create a new stream.
Parameters
----------
idx : int, [0:n_streams - 1]
The stream index to replace
'''
# Don't activate the stream if the weight is 0 or None
if self.stream_weights_[idx]:
... | python | def _new_stream(self, idx):
'''Randomly select and create a new stream.
Parameters
----------
idx : int, [0:n_streams - 1]
The stream index to replace
'''
# Don't activate the stream if the weight is 0 or None
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pescadores/pescador | pescador/mux.py | RoundRobinMux._next_sample_index | def _next_sample_index(self):
"""Rotates through each active sampler by incrementing the index"""
# Return the next streamer index where the streamer is not None,
# wrapping around.
idx = self.active_index_
self.active_index_ += 1
if self.active_index_ >= len(self.stream... | python | def _next_sample_index(self):
"""Rotates through each active sampler by incrementing the index"""
# Return the next streamer index where the streamer is not None,
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idx = self.active_index_
self.active_index_ += 1
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pescadores/pescador | pescador/mux.py | RoundRobinMux._new_stream | def _new_stream(self, idx):
"""Activate a new stream, given the index into the stream pool.
BaseMux's _new_stream simply chooses a new stream and activates it.
For special behavior (ie Weighted streams), you must override this
in a child class.
Parameters
----------
... | python | def _new_stream(self, idx):
"""Activate a new stream, given the index into the stream pool.
BaseMux's _new_stream simply chooses a new stream and activates it.
For special behavior (ie Weighted streams), you must override this
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Parameters
----------
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pescadores/pescador | pescador/mux.py | RoundRobinMux._replace_stream | def _replace_stream(self, idx=None):
"""Called by `BaseMux`'s iterate() when a stream is exhausted.
Set the stream to None so it is ignored once exhausted.
Parameters
----------
idx : int or None
Raises
------
StopIteration
If all streams are... | python | def _replace_stream(self, idx=None):
"""Called by `BaseMux`'s iterate() when a stream is exhausted.
Set the stream to None so it is ignored once exhausted.
Parameters
----------
idx : int or None
Raises
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StopIteration
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pescadores/pescador | pescador/mux.py | ChainMux._new_stream | def _new_stream(self):
'''Grab the next stream from the input streamers, and start it.
Raises
------
StopIteration
When the input list or generator of streamers is complete,
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'''Grab the next stream from the input streamers, and start it.
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------
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pescadores/pescador | examples/mux/mux_files_example.py | split_and_save_datasets | def split_and_save_datasets(X, Y, paths):
"""Shuffle X and Y into n / len(paths) datasets, and save them
to disk at the locations provided in paths.
"""
shuffled_idxs = np.random.permutation(np.arange(len(X)))
for i in range(len(paths)):
# Take every len(paths) item, starting at i.
... | python | def split_and_save_datasets(X, Y, paths):
"""Shuffle X and Y into n / len(paths) datasets, and save them
to disk at the locations provided in paths.
"""
shuffled_idxs = np.random.permutation(np.arange(len(X)))
for i in range(len(paths)):
# Take every len(paths) item, starting at i.
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