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def create(cls, name, ncpus=None):
"""Create a Moap instance based on the predictor name. Parameters name : str ncpus : int, optional Number of threads. Default ... |
try:
return cls._predictors[name.lower()](ncpus=ncpus)
except KeyError:
raise Exception("Unknown class") |
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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()
return subclass
return decorator |
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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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def _activate(self):
"""Activates the stream.""" |
if six.callable(self.streamer):
# If it's a function, create the stream.
self.stream_ = self.streamer(*(self.args), **(self.kwargs))
else:
# If it's iterable, use it directly.
self.stream_ = iter(self.streamer) |
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| def iterate(self, max_iter=None):
'''Instantiate an iterator.
Parameters
----------
max_iter : None or int > 0
Maximum number of iterations to yield.
If ``None``, exhaust the stream.
Yields
------
obj : Objects yielded by the streamer pro... |
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| def cycle(self, max_iter=None):
'''Iterate from the streamer infinitely.
This function will force an infinite stream, restarting
the streamer even if a StopIteration is raised.
Parameters
----------
max_iter : None or int > 0
Maximum number of iterations to ... |
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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(
[stats[m][bg][metric] for bg in background]) for m in motif_ids]
ranked_metric_stats = rankdata(mean_metric_stats)
... |
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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())[0].values())[0].keys())
f.write("{}\t{}\n".format("Motif", "\t".join(stat_keys)))
... |
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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)[-1] for x in fg_result.values()]
#
# bg_result = motif.pwm_scan_score(Fasta(bg_file), c... |
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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_id, m in motifs.items():
k = "{}_{}".format(str(m), bg)
jobs[k] = pool.ap... |
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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():
if str(motif) not in stats:
logger.error("####")
logger.error("{} not found".format(str(motif)))
... |
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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) |
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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)
# Define the tree
t, ts = _get_motif_tree(tree, data, circle, vmin, vmax)
# Save image
t.render(outfile, tree_st... |
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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)):
if line.startswith("#") or line.startswith("track") or line.startswith("browser"):
# comment or BED specific stuff
pass
e... |
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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 ["bed", "narrowpeak"]:
genome = params["genome"]
valid_bg = BED_VALID_BGS
if "genomic" in background or "g... |
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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... |
# Initialize scanner
s = Scanner(ncpus=ncpus)
s.set_motifs(motifs)
s.set_threshold(threshold=0.0)
if genome:
s.set_genome(genome)
if isinstance(motifs, six.string_types):
motifs = read_motifs(motifs)
logger.debug("scanning %s...", fname)
result = dict([(m.id, []) f... |
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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 speci... |
length = int(length)
if genome and fname:
raise ValueError("Need either genome or filename for background.")
if fname:
if not os.path.exists(fname):
raise IOError("Background file {} does not exist!".format(fname))
self.background = Fasta(f... |
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def set_threshold(self, fpr=None, threshold=None):
"""Set motif scanning threshold based on background sequences. Parameters fpr : float, optional Desired FPR, b... |
if threshold and fpr:
raise ValueError("Need either fpr or threshold.")
if fpr:
fpr = float(fpr)
if not (0.0 < fpr < 1.0):
raise ValueError("Parameter fpr should be between 0 and 1")
if not self.motifs:
raise ValueErro... |
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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 conta... |
self.set_threshold(threshold=0.0)
if normalize and len(self.meanstd) == 0:
self.set_meanstd()
means = np.array([self.meanstd[m][0] for m in self.motif_ids])
stds = np.array([self.meanstd[m][1] for m in self.motif_ids])
for matches in self.scan(seqs, 1, scan_... |
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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 args.ids:
ids = args.ids.split(",")
else:
ids = [m.id for m in motifs]
... |
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def seqcor(m1, m2, seq=None):
"""Calculates motif similarity based on Pearson correlation of scores. Based on Kielbasa (2015) and Grau (2015). Scores are calcula... |
l1 = len(m1)
l2 = len(m2)
l = max(l1, l2)
if seq is None:
seq = RCDB
L = len(seq)
# Scan RC de Bruijn sequence
result1 = pfmscan(seq, m1.pwm, m1.pwm_min_score(), len(seq), False, True)
result2 = pfmscan(seq, m2.pwm, m2.pwm_min_score(), len(seq), False, True)
# ... |
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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... |
if metric == "seqcor":
return seqcor(m1, m2)
elif match == "partial":
if pval:
return self.pvalue(m1, m2, "total", metric, combine, self.max_partial(m1.pwm, m2.pwm, metric, combine))
elif metric in ["pcc", "ed", "distance", "wic", "chisq", "ssd"]:
... |
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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 c... |
# trim motifs first, if specified
if trim:
for m in motifs:
m.trim(trim)
for m in dbmotifs:
m.trim(trim)
# hash of result scores
scores = {}
if parallel:
# Divide the job into big chunks, t... |
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def get_closest_match(self, motifs, dbmotifs=None, match="partial", metric="wic",combine="mean", parallel=True, ncpus=None):
"""Return best match in database for... |
if dbmotifs is None:
pwm = self.config.get_default_params()["motif_db"]
pwmdir = self.config.get_motif_dir()
dbmotifs = os.path.join(pwmdir, pwm)
motifs = parse_motifs(motifs)
dbmotifs = parse_motifs(dbmotifs)
dbmotif_lookup = dict([(m.id, m... |
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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,
} |
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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]
ports = '\n'.join(ports)
zones = '\n'.join(b.availability_zones)
t.add_row([b.name, b.dns_name, port... |
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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.state, i.monitored, i.image_id, name, i.instance_type, i.key_name, i.dns_name])
return t |
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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:
t.add_row([i.id, i.state, i.name, i.ownerId, i.root_device_type, i.is_public, i.description])
return t |
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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_FILE')
fab.env.user = settings.get('SSH', 'USER', getpass.getuser())
fab.env.parall... |
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| 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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def tuples(stream, *keys):
"""Reformat data as tuples. Parameters stream : iterable Stream of data objects. *keys : strings Keys to use for ordering data. Yields... |
if not keys:
raise PescadorError('Unable to generate tuples from '
'an empty item set')
for data in stream:
try:
yield tuple(data[key] for key in keys)
except TypeError:
raise DataError("Malformed data stream: {}".format(data)) |
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def keras_tuples(stream, inputs=None, outputs=None):
"""Reformat data objects as keras-compatible tuples. For more detail: https://keras.io/models/model/#fit Par... |
flatten_inputs, flatten_outputs = False, False
if inputs and isinstance(inputs, six.string_types):
inputs = [inputs]
flatten_inputs = True
if outputs and isinstance(outputs, six.string_types):
outputs = [outputs]
flatten_outputs = True
inputs, outputs = (inputs or []), ... |
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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(f.items()[0][1])
f = None
jobs = []
motifs = pwmfile_to_motifs(pwmfile)
ids = [motif.id for motif in motifs]
if args.ids:
ids = args.i... |
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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(p, os.X_OK) and not os.path.isdir(p):
return... |
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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, shell=True, stdout=sp.PIPE, stderr=sp.PIPE)
stdout, _stderr = p.communicate... |
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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)
with open(fname) as f:
motifs = read_motifs(f)
return motifs |
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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 : li... |
width = len(align[0])
nucs = {"A":0,"C":1,"G":2,"T":3}
pfm = [[0 for _ in range(4)] for _ in range(width)]
for row in align:
for i in range(len(row)):
pfm[i][nucs[row[i]]] += 1
m = Motif(pfm)
m.align = align[:]
return m |
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def motif_from_consensus(cons, n=12):
"""Convert consensus sequence to motif. Converts a consensus sequences using the nucleotide IUPAC alphabet to a motif. Para... |
width = len(cons)
nucs = {"A":0,"C":1,"G":2,"T":3}
pfm = [[0 for _ in range(4)] for _ in range(width)]
m = Motif()
for i,char in enumerate(cons):
for nuc in m.iupac[char.upper()]:
pfm[i][nucs[nuc]] = n / len(m.iupac[char.upper()])
m = Motif(pfm)
m.id = cons
return m |
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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 o... |
if isinstance(motifs, six.string_types):
with open(motifs) as f:
if motifs.endswith("pwm") or motifs.endswith("pfm"):
motifs = read_motifs(f, fmt="pwm")
elif motifs.endswith("transfac"):
motifs = read_motifs(f, fmt="transfac")
else:
... |
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def read_motifs(infile=None, fmt="pwm", as_dict=False):
""" Read motifs from a file or stream or file-like object. Parameters infile : string or file-like object... |
if infile is None or isinstance(infile, six.string_types):
infile = pwmfile_location(infile)
with open(infile) as f:
motifs = _read_motifs_from_filehandle(f, fmt)
else:
motifs = _read_motifs_from_filehandle(infile, fmt)
if as_dict:
motifs = {m.id:m for m in mot... |
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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 row[x] > 0])
return ic |
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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)
self.min_score = score
return self.min_score |
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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)
self.max_score = score
return self.max_score |
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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 ... |
if len(kmer) != len(self.pwm):
raise Exception("incorrect k-mer length")
score = 0.0
d = {"A":0, "C":1, "G":2, "T":3}
for nuc, row in zip(kmer.upper(), self.pwm):
score += log(row[d[nuc]] / 0.25 + 0.01)
return score |
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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... |
return [[(x + pseudo)/(float(np.sum(row)) + pseudo * 4) for x in row] for row in pfm] |
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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):
if a > 0:
score += a * log(a / b) / log(2)
return score |
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def pcc_pos(self, row1, row2):
"""Calculate the Pearson correlation coefficient of one position compared to another position. Returns ------- score : float Pears... |
mean1 = np.mean(row1)
mean2 = np.mean(row2)
a = 0
x = 0
y = 0
for n1, n2 in zip(row1, row2):
a += (n1 - mean1) * (n2 - mean2)
x += (n1 - mean1) ** 2
y += (n2 - mean2) ** 2
if a == 0:
return 0
else:... |
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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 row in self.pwm[::-1]]
m.id = self.id + "_revcomp"
return m |
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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 b... |
pwm = self.pwm[:]
while len(pwm) > 0 and self.ic_pos(pwm[0]) < edge_ic_cutoff:
pwm = pwm[1:]
self.pwm = self.pwm[1:]
self.pfm = self.pfm[1:]
while len(pwm) > 0 and self.ic_pos(pwm[-1]) < edge_ic_cutoff:
pwm = pwm[:-1]
self.pwm = self.p... |
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def consensus_scan(self, fa):
"""Scan FASTA with the motif as a consensus sequence. Parameters fa : Fasta object Fasta object to scan Returns ------- matches : d... |
regexp = "".join(["[" + "".join(self.iupac[x.upper()]) + "]" for x in self.to_consensusv2()])
p = re.compile(regexp)
matches = {}
for name,seq in fa.items():
matches[name] = []
for match in p.finditer(seq):
middle = (match.span()[1] + match.span(... |
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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 sequen... |
if append:
out = open(gfffile, "a")
else:
out = open(gfffile, "w")
c = self.pwm_min_score() + (self.pwm_max_score() - self.pwm_min_score()) * cutoff
pwm = self.pwm
strandmap = {-1:"-","-1":"-","-":"-","1":"+",1:"+","+":"+"}
gff_line ... |
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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 av... |
# xxCATGYT
# GGCTTGYx
# pos = -2
pfm1 = self.pfm[:]
pfm2 = other.pfm[:]
if orientation < 0:
pfm2 = [row[::-1] for row in pfm2[::-1]]
pfm1_count = float(np.sum(pfm1[0]))
pfm2_count = float(np.sum(pfm2[0]))
if include_... |
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def _pwm_to_str(self, precision=4):
"""Return string representation of pwm. Parameters precision : int, optional, default 4 Floating-point precision. Returns ---... |
if not self.pwm:
return ""
fmt = "{{:.{:d}f}}".format(precision)
return "\n".join(
["\t".join([fmt.format(p) for p in row])
for row in self.pwm]
) |
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def to_pwm(self, precision=4, extra_str=""):
"""Return pwm as string. Parameters precision : int, optional, default 4 Floating-point precision. extra_str |: str,... |
motif_id = self.id
if extra_str:
motif_id += "_%s" % extra_str
if not self.pwm:
self.pwm = [self.iupac_pwm[char]for char in self.consensus.upper()]
return ">%s\n%s" % (
motif_id,
self._pwm_to_str(precision)
... |
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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. Va... |
if not seqlogo:
seqlogo = self.seqlogo
if not seqlogo:
raise ValueError("seqlogo not specified or configured")
#TODO: split to_align function
VALID_FORMATS = ["EPS", "GIF", "PDF", "PNG"]
N = 1000
fmt = fmt.upper()
if not ... |
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def randomize(self):
"""Create a new motif with shuffled positions. Shuffle the positions of this motif and return a new Motif instance. Returns ------- m : Moti... |
random_pfm = [[c for c in row] for row in self.pfm]
random.shuffle(random_pfm)
m = Motif(pfm=random_pfm)
m.id = "random"
return m |
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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))
if methods:
methods = [x.strip() for x in met... |
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def zmq_recv_data(socket, flags=0, copy=True, track=False):
"""Receive data over a socket.""" |
data = dict()
msg = socket.recv_multipart(flags=flags, copy=copy, track=track)
headers = json.loads(msg[0].decode('ascii'))
if len(headers) == 0:
raise StopIteration
for header, payload in zip(headers, msg[1:]):
data[header['key']] = np.frombuffer(buffer(payload),
... |
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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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def get_random(self, n, l=None):
""" Return n random sequences from this Fasta object """ |
random_f = Fasta()
if l:
ids = self.ids[:]
random.shuffle(ids)
i = 0
while (i < n) and (len(ids) > 0):
seq_id = ids.pop()
if (len(self[seq_id]) >= l):
start = random.randint(0, len(self[seq_id]) - l)
... |
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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() |
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| 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
The number of samples in this b... |
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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_counts_ = np.zeros(self.k, dtype=int)
# Array of pointers in... |
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def iterate(self, max_iter=None):
"""Yields items from the mux, and handles stream exhaustion and replacement. """ |
if max_iter is None:
max_iter = np.inf
# Calls Streamer's __enter__, which calls activate()
with self as active_mux:
# Main sampling loop
n = 0
while n < max_iter and active_mux._streamers_available():
# Pick a stream from the ac... |
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def _next_sample_index(self):
"""StochasticMux chooses its next sample stream randomly""" |
return self.rng.choice(self.n_active,
p=(self.stream_weights_ /
self.weight_norm_)) |
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def _activate(self):
"""ShuffledMux's activate is similar to StochasticMux, but there is no 'n_active', since all the streams are always available. """ |
self.streams_ = [None] * self.n_streams
# Weights of the active streams.
# Once a stream is exhausted, it is set to 0.
# Upon activation, this is just a copy of self.weights.
self.stream_weights_ = np.array(self.weights, dtype=float)
# How many samples have been drawn f... |
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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_)) |
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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.streams_):
self.active_index_ = 0
# Continue to increment if this streamer is exhausted (N... |
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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. F... |
# Get the stream index from the candidate pool
stream_index = self.stream_idxs_[idx]
# Activate the Streamer, and get the weights
self.streams_[idx] = self.streamers[stream_index].iterate()
# Reset the sample count to zero
self.stream_counts_[idx] = 0 |
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| 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,
will raise a StopIteration. If `mode == cycle`, it
will instead restart itera... |
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def npz_generator(npz_path):
"""Generate data from an npz file.""" |
npz_data = np.load(npz_path)
X = npz_data['X']
# Y is a binary maxtrix with shape=(n, k), each y will have shape=(k,)
y = npz_data['Y']
n = X.shape[0]
while True:
i = np.random.randint(0, n)
yield {'X': X[i], 'Y': y[i]} |
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def phyper(k, good, bad, N):
""" Current hypergeometric implementation in scipy is broken, so here's the correct version """ |
pvalues = [phyper_single(x, good, bad, N) for x in range(k + 1, N + 1)]
return np.sum(pvalues) |
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def calc_motif_enrichment(sample, background, mtc=None, len_sample=None, len_back=None):
"""Calculate enrichment based on hypergeometric distribution""" |
INF = "Inf"
if mtc not in [None, "Bonferroni", "Benjamini-Hochberg", "None"]:
raise RuntimeError("Unknown correction: %s" % mtc)
sig = {}
p_value = {}
n_sample = {}
n_back = {}
if not(len_sample):
len_sample = sample.seqn()
if not(len_back):
len_bac... |
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def parse_cutoff(motifs, cutoff, default=0.9):
""" Provide either a file with one cutoff per motif or a single cutoff returns a hash with motif id as key and cut... |
cutoffs = {}
if os.path.isfile(str(cutoff)):
for i,line in enumerate(open(cutoff)):
if line != "Motif\tScore\tCutoff\n":
try:
motif,_,c = line.strip().split("\t")
c = float(c)
cutoffs[motif] = c
... |
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def determine_file_type(fname):
""" Detect file type. The following file types are supported: BED, narrowPeak, FASTA, list of chr:start-end regions If the extens... |
if not (isinstance(fname, str) or isinstance(fname, unicode)):
raise ValueError("{} is not a file name!", fname)
if not os.path.isfile(fname):
raise ValueError("{} is not a file!", fname)
ext = os.path.splitext(fname)[1].lower()
if ext in ["bed"]:
return "bed"
elif ext in ... |
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def file_checksum(fname):
"""Return md5 checksum of file. Note: only works for files < 4GB. Parameters filename : str File used to calculate checksum. Returns --... |
size = os.path.getsize(fname)
with open(fname, "r+") as f:
checksum = hashlib.md5(mmap.mmap(f.fileno(), size)).hexdigest()
return checksum |
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def download_annotation(genomebuild, gene_file):
""" Download gene annotation from UCSC based on genomebuild. Will check UCSC, Ensembl and RefSeq annotation. Par... |
pred_bin = "genePredToBed"
pred = find_executable(pred_bin)
if not pred:
sys.stderr.write("{} not found in path!\n".format(pred_bin))
sys.exit(1)
tmp = NamedTemporaryFile(delete=False, suffix=".gz")
anno = []
f = urlopen(UCSC_GENE_URL.format(genomebuild))
p = re.compile(r'... |
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def _make_index(self, fasta, index):
""" Index a single, one-sequence fasta-file""" |
out = open(index, "wb")
f = open(fasta)
# Skip first line of fasta-file
line = f.readline()
offset = f.tell()
line = f.readline()
while line:
out.write(pack(self.pack_char, offset))
offset = f.tell()
line = f.readline()
... |
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def _read_index_file(self):
"""read the param_file, index_dir should already be set """ |
param_file = os.path.join(self.index_dir, self.param_file)
with open(param_file) as f:
for line in f.readlines():
(name, fasta_file, index_file, line_size, total_size) = line.strip().split("\t")
self.size[name] = int(total_size)
self.fasta_fil... |
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def _read_seq_from_fasta(self, fasta, offset, nr_lines):
""" retrieve a number of lines from a fasta file-object, starting at offset""" |
fasta.seek(offset)
lines = [fasta.readline().strip() for _ in range(nr_lines)]
return "".join(lines) |
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def get_sequence(self, chrom, start, end, strand=None):
""" Retrieve a sequence """ |
# Check if we have an index_dir
if not self.index_dir:
print("Index dir is not defined!")
sys.exit()
# retrieve all information for this specific sequence
fasta_file = self.fasta_file[chrom]
index_file = self.index_file[chrom]
line_size = sel... |
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def get_size(self, chrom=None):
""" Return the sizes of all sequences in the index, or the size of chrom if specified as an optional argument """ |
if len(self.size) == 0:
raise LookupError("no chromosomes in index, is the index correct?")
if chrom:
if chrom in self.size:
return self.size[chrom]
else:
raise KeyError("chromosome {} not in index".format(chrom))
total = 0
... |
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def get_tool(name):
""" Returns an instance of a specific tool. Parameters name : str Name of the tool (case-insensitive). Returns ------- tool : MotifProgram in... |
tool = name.lower()
if tool not in __tools__:
raise ValueError("Tool {0} not found!\n".format(name))
t = __tools__[tool]()
if not t.is_installed():
sys.stderr.write("Tool {0} not installed!\n".format(tool))
if not t.is_configured():
sys.stderr.write("Tool {0} not configur... |
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def locate_tool(name, verbose=True):
""" Returns the binary of a tool. Parameters name : str Name of the tool (case-insensitive). Returns ------- tool_bin : str ... |
m = get_tool(name)
tool_bin = which(m.cmd)
if tool_bin:
if verbose:
print("Found {} in {}".format(m.name, tool_bin))
return tool_bin
else:
print("Couldn't find {}".format(m.name)) |
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def bin(self):
""" Get the command used to run the tool. Returns ------- command : str The tool system command. """ |
if self.local_bin:
return self.local_bin
else:
return self.config.bin(self.name) |
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def run(self, fastafile, params=None, tmp=None):
""" Run the tool and predict motifs from a FASTA file. Parameters fastafile : str Name of the FASTA input file. ... |
if not self.is_configured():
raise ValueError("%s is not configured" % self.name)
if not self.is_installed():
raise ValueError("%s is not installed or not correctly configured" % self.name)
self.tmpdir = mkdtemp(prefix="{0}.".format(self.name), dir=tmp)
... |
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def _run_program(self, bin, fastafile, params=None):
""" Run XXmotif and predict motifs from a FASTA file. Parameters bin : str Command used to run the tool. fas... |
params = self._parse_params(params)
outfile = os.path.join(
self.tmpdir,
os.path.basename(fastafile.replace(".fa", ".pwm")))
stdout = ""
stderr = ""
cmd = "%s %s %s --localization --batch %s %s" % (
bin,
... |
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def _run_program(self, bin, fastafile, params=None):
""" Run Homer and predict motifs from a FASTA file. Parameters bin : str Command used to run the tool. fasta... |
params = self._parse_params(params)
outfile = NamedTemporaryFile(
mode="w",
dir=self.tmpdir,
prefix= "homer_w{}.".format(params["width"])
).name
cmd = "%s denovo -i %s -b %s -len %s -S %s %s -o %s -p 8" % (
... |
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def _run_program(self, bin, fastafile, params=None):
""" Run HMS and predict motifs from a FASTA file. Parameters bin : str Command used to run the tool. fastafi... |
params = self._parse_params(params)
default_params = {"width":10}
if params is not None:
default_params.update(params)
fgfile, summitfile, outfile = self._prepare_files(fastafile)
current_path = os.getcwd()
os.chdir(self.tm... |
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def _run_program(self, bin, fastafile, params=None):
""" Run AMD and predict motifs from a FASTA file. Parameters bin : str Command used to run the tool. fastafi... |
params = self._parse_params(params)
fgfile = os.path.join(self.tmpdir, "AMD.in.fa")
outfile = fgfile + ".Matrix"
shutil.copy(fastafile, fgfile)
current_path = os.getcwd()
os.chdir(self.tmpdir)
stdout = ""
stderr = ""
cm... |
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def _run_program(self, bin, fastafile, params=None):
""" Run Trawler and predict motifs from a FASTA file. Parameters bin : str Command used to run the tool. fas... |
params = self._parse_params(params)
tmp = NamedTemporaryFile(mode="w", dir=self.tmpdir, delete=False)
shutil.copy(fastafile, tmp.name)
fastafile = tmp.name
current_path = os.getcwd()
os.chdir(self.dir())
motifs = []
stdout = ""
stde... |
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Description:
def _run_program(self, bin,fastafile, params=None):
""" Run Weeder and predict motifs from a FASTA file. Parameters bin : str Command used to run the tool. fasta... |
params = self._parse_params(params)
organism = params["organism"]
weeder_organisms = {
"hg18":"HS",
"hg19":"HS",
"hg38":"HS",
"mm9":"MM",
"mm10":"MM",
"dm3":"DM",
"dm5":"DM",
"dm6":"DM",
... |
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def _run_program(self, bin, fastafile, params=None):
""" Run MotifSampler and predict motifs from a FASTA file. Parameters bin : str Command used to run the tool... |
params = self._parse_params(params)
# TODO: test organism
#cmd = "%s -f %s -b %s -m %s -w %s -n %s -o %s -s %s > /dev/null 2>&1" % (
cmd = "%s -f %s -b %s -m %s -w %s -n %s -o %s -s %s" % (
bin,
fastafile,
params["background_model"],
... |
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def _run_program(self, bin, fastafile, params=None):
""" Run MDmodule and predict motifs from a FASTA file. Parameters bin : str Command used to run the tool. fa... |
default_params = {"width":10, "number":10}
if params is not None:
default_params.update(params)
new_file = os.path.join(self.tmpdir, "mdmodule_in.fa")
shutil.copy(fastafile, new_file)
fastafile = new_file
pwmfile = fastafile + ".out"
... |
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def _run_program(self, bin, fastafile, params=None):
""" Run ChIPMunk and predict motifs from a FASTA file. Parameters bin : str Command used to run the tool. fa... |
params = self._parse_params(params)
basename = "munk_in.fa"
new_file = os.path.join(self.tmpdir, basename)
out = open(new_file, "w")
f = Fasta(fastafile)
for seq in f.seqs:
header = len(seq) // 2
out.write(">%s\n" % header)
out.write(... |
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def _run_program(self, bin, fastafile, params=None):
""" Run Posmo and predict motifs from a FASTA file. Parameters bin : str Command used to run the tool. fasta... |
default_params = {}
if params is not None:
default_params.update(params)
width = params.get("width", 8)
basename = "posmo_in.fa"
new_file = os.path.join(self.tmpdir, basename)
shutil.copy(fastafile, new_file)
fastafile = new_file
... |
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def _run_program(self, bin, fastafile, params=None):
""" Get enriched JASPAR motifs in a FASTA file. Parameters bin : str Command used to run the tool. fastafile... |
fname = os.path.join(self.config.get_motif_dir(), "JASPAR2010_vertebrate.pwm")
motifs = read_motifs(fname, fmt="pwm")
for motif in motifs:
motif.id = "JASPAR_%s" % motif.id
return motifs, "", "" |
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def _run_program(self, bin, fastafile, params=None):
""" Run MEME and predict motifs from a FASTA file. Parameters bin : str Command used to run the tool. fastaf... |
default_params = {"width":10, "single":False, "number":10}
if params is not None:
default_params.update(params)
tmp = NamedTemporaryFile(dir=self.tmpdir)
tmpname = tmp.name
strand = "-revcomp"
width = default_params["width"]
number = de... |
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def scan_to_table(input_table, genome, scoring, pwmfile=None, ncpus=None):
"""Scan regions in input table with motifs. Parameters input_table : str Filename of i... |
config = MotifConfig()
if pwmfile is None:
pwmfile = config.get_default_params().get("motif_db", None)
if pwmfile is not None:
pwmfile = os.path.join(config.get_motif_dir(), pwmfile)
if pwmfile is None:
raise ValueError("no pwmfile given and no default database spe... |
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def get_args(parser):
""" Converts arguments extracted from a parser to a dict, and will dismiss arguments which default to NOT_SET. :param parser: an ``argparse... |
args = vars(parser.parse_args()).items()
return {key: val for key, val in args if not isinstance(val, NotSet)} |
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