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jay-johnson/network-pipeline | network_pipeline/scripts/icmp_send_msg.py | dump_stats | def dump_stats(myStats):
"""
Show stats when pings are done
"""
print("\n----%s PYTHON PING Statistics----" % (myStats.thisIP))
if myStats.pktsSent > 0:
myStats.fracLoss = (myStats.pktsSent - myStats.pktsRcvd) \
/ myStats.pktsSent
print(("%d packets transmitted, %d pack... | python | def dump_stats(myStats):
"""
Show stats when pings are done
"""
print("\n----%s PYTHON PING Statistics----" % (myStats.thisIP))
if myStats.pktsSent > 0:
myStats.fracLoss = (myStats.pktsSent - myStats.pktsRcvd) \
/ myStats.pktsSent
print(("%d packets transmitted, %d pack... | [
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mkouhei/bootstrap-py | bootstrap_py/update.py | Update.updatable | def updatable(self):
"""bootstrap-py package updatable?."""
if self.latest_version > self.current_version:
updatable_version = self.latest_version
else:
updatable_version = False
return updatable_version | python | def updatable(self):
"""bootstrap-py package updatable?."""
if self.latest_version > self.current_version:
updatable_version = self.latest_version
else:
updatable_version = False
return updatable_version | [
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mkouhei/bootstrap-py | bootstrap_py/update.py | Update.show_message | def show_message(self):
"""Show message updatable."""
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current_version=self.current_version,
latest_version=self.latest_version)) | python | def show_message(self):
"""Show message updatable."""
print(
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current_version=self.current_version,
latest_version=self.latest_version)) | [
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smdabdoub/phylotoast | bin/pick_otus_condense.py | condense_otus | def condense_otus(otuF, nuniqueF):
"""
Traverse the input otu-sequence file, collect the non-unique OTU IDs and
file the sequences associated with then under the unique OTU ID as defined
by the input matrix.
:@type otuF: file
:@param otuF: The output file from QIIME's pick_otus.py
:@type nu... | python | def condense_otus(otuF, nuniqueF):
"""
Traverse the input otu-sequence file, collect the non-unique OTU IDs and
file the sequences associated with then under the unique OTU ID as defined
by the input matrix.
:@type otuF: file
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christophertbrown/bioscripts | ctbBio/rRNA_copies.py | rna_bases | def rna_bases(rna_cov, scaffold, bases, line):
"""
determine if read overlaps with rna, if so count bases
"""
start = int(line[3])
stop = start + bases - 1
if scaffold not in rna_cov:
return rna_cov
for pos in rna_cov[scaffold][2]:
ol = get_overlap([start, stop], pos)
... | python | def rna_bases(rna_cov, scaffold, bases, line):
"""
determine if read overlaps with rna, if so count bases
"""
start = int(line[3])
stop = start + bases - 1
if scaffold not in rna_cov:
return rna_cov
for pos in rna_cov[scaffold][2]:
ol = get_overlap([start, stop], pos)
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christophertbrown/bioscripts | ctbBio/rRNA_copies.py | parse_s2bins | def parse_s2bins(s2bins):
"""
parse ggKbase scaffold-to-bin mapping
- scaffolds-to-bins and bins-to-scaffolds
"""
s2b = {}
b2s = {}
for line in s2bins:
line = line.strip().split()
s, b = line[0], line[1]
if 'UNK' in b:
continue
if len(line) > 2... | python | def parse_s2bins(s2bins):
"""
parse ggKbase scaffold-to-bin mapping
- scaffolds-to-bins and bins-to-scaffolds
"""
s2b = {}
b2s = {}
for line in s2bins:
line = line.strip().split()
s, b = line[0], line[1]
if 'UNK' in b:
continue
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christophertbrown/bioscripts | ctbBio/rRNA_copies.py | filter_missing_rna | def filter_missing_rna(s2bins, bins2s, rna_cov):
"""
remove any bins that don't have 16S
"""
for bin, scaffolds in list(bins2s.items()):
c = 0
for s in scaffolds:
if s in rna_cov:
c += 1
if c == 0:
del bins2s[bin]
for scaffold, bin in l... | python | def filter_missing_rna(s2bins, bins2s, rna_cov):
"""
remove any bins that don't have 16S
"""
for bin, scaffolds in list(bins2s.items()):
c = 0
for s in scaffolds:
if s in rna_cov:
c += 1
if c == 0:
del bins2s[bin]
for scaffold, bin in l... | [
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christophertbrown/bioscripts | ctbBio/rRNA_copies.py | calc_bin_cov | def calc_bin_cov(scaffolds, cov):
"""
calculate bin coverage
"""
bases = sum([cov[i][0] for i in scaffolds if i in cov])
length = sum([cov[i][1] for i in scaffolds if i in cov])
if length == 0:
return 0
return float(float(bases)/float(length)) | python | def calc_bin_cov(scaffolds, cov):
"""
calculate bin coverage
"""
bases = sum([cov[i][0] for i in scaffolds if i in cov])
length = sum([cov[i][1] for i in scaffolds if i in cov])
if length == 0:
return 0
return float(float(bases)/float(length)) | [
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dokterbob/django-multilingual-model | multilingual_model/forms.py | TranslationFormSet.clean | def clean(self):
"""
Make sure there is at least a translation has been filled in. If a
default language has been specified, make sure that it exists amongst
translations.
"""
# First make sure the super's clean method is called upon.
super(TranslationFormSet, se... | python | def clean(self):
"""
Make sure there is at least a translation has been filled in. If a
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"""
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dokterbob/django-multilingual-model | multilingual_model/forms.py | TranslationFormSet._get_default_language | def _get_default_language(self):
"""
If a default language has been set, and is still available in
`self.available_languages`, return it and remove it from the list.
If not, simply pop the first available language.
"""
assert hasattr(self, 'available_languages'), \
... | python | def _get_default_language(self):
"""
If a default language has been set, and is still available in
`self.available_languages`, return it and remove it from the list.
If not, simply pop the first available language.
"""
assert hasattr(self, 'available_languages'), \
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dokterbob/django-multilingual-model | multilingual_model/forms.py | TranslationFormSet._construct_form | def _construct_form(self, i, **kwargs):
"""
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"""
if not settings.HIDE_LANGUAGE:
self._construct_available_languages()
form = super(TranslationFormSet, self)._construct_form(i, **kwargs)
if ... | python | def _construct_form(self, i, **kwargs):
"""
Construct the form, overriding the initial value for `language_code`.
"""
if not settings.HIDE_LANGUAGE:
self._construct_available_languages()
form = super(TranslationFormSet, self)._construct_form(i, **kwargs)
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christophertbrown/bioscripts | ctbBio/fastq_merge.py | fq_merge | def fq_merge(R1, R2):
"""
merge separate fastq files
"""
c = itertools.cycle([1, 2, 3, 4])
for r1, r2 in zip(R1, R2):
n = next(c)
if n == 1:
pair = [[], []]
pair[0].append(r1.strip())
pair[1].append(r2.strip())
if n == 4:
yield pair | python | def fq_merge(R1, R2):
"""
merge separate fastq files
"""
c = itertools.cycle([1, 2, 3, 4])
for r1, r2 in zip(R1, R2):
n = next(c)
if n == 1:
pair = [[], []]
pair[0].append(r1.strip())
pair[1].append(r2.strip())
if n == 4:
yield pair | [
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disqus/nydus | nydus/contrib/ketama.py | Ketama._build_circle | def _build_circle(self):
"""
Creates hash ring.
"""
total_weight = 0
for node in self._nodes:
total_weight += self._weights.get(node, 1)
for node in self._nodes:
weight = self._weights.get(node, 1)
ks = math.floor((40 * len(self._... | python | def _build_circle(self):
"""
Creates hash ring.
"""
total_weight = 0
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total_weight += self._weights.get(node, 1)
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weight = self._weights.get(node, 1)
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disqus/nydus | nydus/contrib/ketama.py | Ketama._gen_key | def _gen_key(self, key):
"""
Return long integer for a given key, that represent it place on
the hash ring.
"""
b_key = self._md5_digest(key)
return self._hashi(b_key, lambda x: x) | python | def _gen_key(self, key):
"""
Return long integer for a given key, that represent it place on
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"""
b_key = self._md5_digest(key)
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scottrice/pysteam | pysteam/grid.py | has_custom_image | def has_custom_image(user_context, app_id):
"""Returns True if there exists a custom image for app_id."""
possible_paths = _valid_custom_image_paths(user_context, app_id)
return any(map(os.path.exists, possible_paths)) | python | def has_custom_image(user_context, app_id):
"""Returns True if there exists a custom image for app_id."""
possible_paths = _valid_custom_image_paths(user_context, app_id)
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scottrice/pysteam | pysteam/grid.py | get_custom_image | def get_custom_image(user_context, app_id):
"""Returns the custom image associated with a given app. If there are
multiple candidate images on disk, one is chosen arbitrarily."""
possible_paths = _valid_custom_image_paths(user_context, app_id)
existing_images = filter(os.path.exists, possible_paths)
if len(ex... | python | def get_custom_image(user_context, app_id):
"""Returns the custom image associated with a given app. If there are
multiple candidate images on disk, one is chosen arbitrarily."""
possible_paths = _valid_custom_image_paths(user_context, app_id)
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scottrice/pysteam | pysteam/grid.py | set_custom_image | def set_custom_image(user_context, app_id, image_path):
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cldf/segments | src/segments/profile.py | Profile.from_file | def from_file(cls, fname, form=None):
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"""
try:
tg = TableGroup.from_file(fname)
opfname = None
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"""
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mapping
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A Profile instance.
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Create a Profile instance from the Unicode graphemes found in `text`.
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A Profile instance.
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christophertbrown/bioscripts | ctbBio/name2fasta.py | split_fasta | def split_fasta(f, id2f):
"""
split fasta file into separate fasta files based on list of scaffolds
that belong to each separate file
"""
opened = {}
for seq in parse_fasta(f):
id = seq[0].split('>')[1].split()[0]
if id not in id2f:
continue
fasta = id2f[id]
... | python | def split_fasta(f, id2f):
"""
split fasta file into separate fasta files based on list of scaffolds
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"""
opened = {}
for seq in parse_fasta(f):
id = seq[0].split('>')[1].split()[0]
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scottrice/pysteam | pysteam/legacy/steam.py | Steam._is_user_directory | def _is_user_directory(self, pathname):
"""Check whether `pathname` is a valid user data directory
This method is meant to be called on the contents of the userdata dir.
As such, it will return True when `pathname` refers to a directory name
that can be interpreted as a users' userID.
"""... | python | def _is_user_directory(self, pathname):
"""Check whether `pathname` is a valid user data directory
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scottrice/pysteam | pysteam/legacy/steam.py | Steam.local_users | def local_users(self):
"""Returns an array of user ids for users on the filesystem"""
# Any users on the machine will have an entry inside of the userdata
# folder. As such, the easiest way to find a list of all users on the
# machine is to just list the folders inside userdata
u... | python | def local_users(self):
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opengridcc/opengrid | opengrid/library/weather.py | _calculate_degree_days | def _calculate_degree_days(temperature_equivalent, base_temperature, cooling=False):
"""
Calculates degree days, starting with a series of temperature equivalent values
Parameters
----------
temperature_equivalent : Pandas Series
base_temperature : float
cooling : bool
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Calculates degree days, starting with a series of temperature equivalent values
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temperature_equivalent : Pandas Series
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mkouhei/bootstrap-py | bootstrap_py/classifiers.py | Classifiers.status | def status(self):
"""Development status."""
return {self._acronym_status(l): l for l in self.resp_text.split('\n')
if l.startswith(self.prefix_status)} | python | def status(self):
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mkouhei/bootstrap-py | bootstrap_py/classifiers.py | Classifiers.licenses | def licenses(self):
"""OSI Approved license."""
return {self._acronym_lic(l): l for l in self.resp_text.split('\n')
if l.startswith(self.prefix_lic)} | python | def licenses(self):
"""OSI Approved license."""
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mkouhei/bootstrap-py | bootstrap_py/classifiers.py | Classifiers.licenses_desc | def licenses_desc(self):
"""Remove prefix."""
return {self._acronym_lic(l): l.split(self.prefix_lic)[1]
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if l.startswith(self.prefix_lic)} | python | def licenses_desc(self):
"""Remove prefix."""
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mkouhei/bootstrap-py | bootstrap_py/classifiers.py | Classifiers._acronym_lic | def _acronym_lic(self, license_statement):
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pat = re.compile(r'\(([\w+\W?\s?]+)\)')
if pat.search(license_statement):
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acronym_licence = lic[:4]
... | python | def _acronym_lic(self, license_statement):
"""Convert license acronym."""
pat = re.compile(r'\(([\w+\W?\s?]+)\)')
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lic = pat.search(license_statement).group(1)
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christophertbrown/bioscripts | ctbBio/ncbi_download.py | calcMD5 | def calcMD5(path):
"""
calc MD5 based on path
"""
# check that file exists
if os.path.exists(path) is False:
yield False
else:
command = ['md5sum', path]
p = Popen(command, stdout = PIPE)
for line in p.communicate()[0].splitlines():
yield line.decode('... | python | def calcMD5(path):
"""
calc MD5 based on path
"""
# check that file exists
if os.path.exists(path) is False:
yield False
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command = ['md5sum', path]
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christophertbrown/bioscripts | ctbBio/ncbi_download.py | wget | def wget(ftp, f = False, exclude = False, name = False, md5 = False, tries = 10):
"""
download files with wget
"""
# file name
if f is False:
f = ftp.rsplit('/', 1)[-1]
# downloaded file if it does not already exist
# check md5s on server (optional)
t = 0
while md5check(f, ft... | python | def wget(ftp, f = False, exclude = False, name = False, md5 = False, tries = 10):
"""
download files with wget
"""
# file name
if f is False:
f = ftp.rsplit('/', 1)[-1]
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christophertbrown/bioscripts | ctbBio/ncbi_download.py | check | def check(line, queries):
"""
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line = line.strip()
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matches = set(spLine).intersection(queries)
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"""
check that at least one of
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christophertbrown/bioscripts | ctbBio/ncbi_download.py | entrez | def entrez(db, acc):
"""
search entrez using specified database
and accession
"""
c1 = ['esearch', '-db', db, '-query', acc]
c2 = ['efetch', '-db', 'BioSample', '-format', 'docsum']
p1 = Popen(c1, stdout = PIPE, stderr = PIPE)
p2 = Popen(c2, stdin = p1.stdout, stdout = PIPE, stderr = PIP... | python | def entrez(db, acc):
"""
search entrez using specified database
and accession
"""
c1 = ['esearch', '-db', db, '-query', acc]
c2 = ['efetch', '-db', 'BioSample', '-format', 'docsum']
p1 = Popen(c1, stdout = PIPE, stderr = PIPE)
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christophertbrown/bioscripts | ctbBio/ncbi_download.py | searchAccession | def searchAccession(acc):
"""
attempt to use NCBI Entrez to get
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"""
# try genbank file
# genome database
out, error = entrez('genome', acc)
for line in out.splitlines():
line = line.decode('ascii').strip()
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"""
attempt to use NCBI Entrez to get
BioSample ID
"""
# try genbank file
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christophertbrown/bioscripts | ctbBio/ncbi_download.py | getFTPs | def getFTPs(accessions, ftp, search, exclude, convert = False, threads = 1, attempt = 1,
max_attempts = 2):
"""
download genome info from NCBI
"""
info = wget(ftp)[0]
allMatches = []
for genome in open(info, encoding = 'utf8'):
genome = str(genome)
matches, genomeInfo... | python | def getFTPs(accessions, ftp, search, exclude, convert = False, threads = 1, attempt = 1,
max_attempts = 2):
"""
download genome info from NCBI
"""
info = wget(ftp)[0]
allMatches = []
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genome = str(genome)
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christophertbrown/bioscripts | ctbBio/ncbi_download.py | download | def download(args):
"""
download genomes from NCBI
"""
accessions, infoFTP = set(args['g']), args['i']
search, exclude = args['s'], args['e']
FTPs = getFTPs(accessions, infoFTP, search, exclude, threads = args['t'],
convert = args['convert'])
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for ... | python | def download(args):
"""
download genomes from NCBI
"""
accessions, infoFTP = set(args['g']), args['i']
search, exclude = args['s'], args['e']
FTPs = getFTPs(accessions, infoFTP, search, exclude, threads = args['t'],
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christophertbrown/bioscripts | ctbBio/fix_fasta.py | fix_fasta | def fix_fasta(fasta):
"""
remove pesky characters from fasta file header
"""
for seq in parse_fasta(fasta):
seq[0] = remove_char(seq[0])
if len(seq[1]) > 0:
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"""
remove pesky characters from fasta file header
"""
for seq in parse_fasta(fasta):
seq[0] = remove_char(seq[0])
if len(seq[1]) > 0:
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"""
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timings = []
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timings.append(
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christophertbrown/bioscripts | ctbBio/unmapped.py | unmapped | def unmapped(sam, mates):
"""
get unmapped reads
"""
for read in sam:
if read.startswith('@') is True:
continue
read = read.strip().split()
if read[2] == '*' and read[6] == '*':
yield read
elif mates is True:
if read[2] == '*' or re... | python | def unmapped(sam, mates):
"""
get unmapped reads
"""
for read in sam:
if read.startswith('@') is True:
continue
read = read.strip().split()
if read[2] == '*' and read[6] == '*':
yield read
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christophertbrown/bioscripts | ctbBio/parallel.py | parallel | def parallel(processes, threads):
"""
execute jobs in processes using N threads
"""
pool = multithread(threads)
pool.map(run_process, processes)
pool.close()
pool.join() | python | def parallel(processes, threads):
"""
execute jobs in processes using N threads
"""
pool = multithread(threads)
pool.map(run_process, processes)
pool.close()
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deep-compute/basescript | basescript/log.py | define_log_renderer | def define_log_renderer(fmt, fpath, quiet):
"""
the final log processor that structlog requires to render.
"""
# it must accept a logger, method_name and event_dict (just like processors)
# but must return the rendered string, not a dictionary.
# TODO tty logic
if fmt:
return struct... | python | def define_log_renderer(fmt, fpath, quiet):
"""
the final log processor that structlog requires to render.
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# it must accept a logger, method_name and event_dict (just like processors)
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deep-compute/basescript | basescript/log.py | _structlog_default_keys_processor | def _structlog_default_keys_processor(logger_class, log_method, event):
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global HOSTNAME
if 'id' not in event:
event['id'] = '%s_%s' % (
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uuid.uuid1().hex
)
if 'type' not in even... | python | def _structlog_default_keys_processor(logger_class, log_method, event):
''' Add unique id, type and hostname '''
global HOSTNAME
if 'id' not in event:
event['id'] = '%s_%s' % (
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deep-compute/basescript | basescript/log.py | define_log_processors | def define_log_processors():
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log processors that structlog executes before final rendering
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# these processors should accept logger, method_name and event_dict
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"""
log processors that structlog executes before final rendering
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deep-compute/basescript | basescript/log.py | _configure_logger | def _configure_logger(fmt, quiet, level, fpath,
pre_hooks, post_hooks, metric_grouping_interval):
"""
configures a logger when required write to stderr or a file
"""
# NOTE not thread safe. Multiple BaseScripts cannot be instantiated concurrently.
level = getattr(logging, level.upper())
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configures a logger when required write to stderr or a file
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deep-compute/basescript | basescript/log.py | BoundLevelLogger._add_base_info | def _add_base_info(self, event_dict):
"""
Instead of using a processor, adding basic information like caller, filename etc
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"""
f = sys._getframe()
level_method_frame = f.f_back
caller_frame = level_method_frame.f_back
return event_dict | python | def _add_base_info(self, event_dict):
"""
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f = sys._getframe()
level_method_frame = f.f_back
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deep-compute/basescript | basescript/log.py | BoundLevelLogger._proxy_to_logger | def _proxy_to_logger(self, method_name, event, *event_args,
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"""
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smdabdoub/phylotoast | bin/core_overlap_plot.py | translate | def translate(rect, x, y, width=1):
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:type rect: list of tuples
:param rect: Four points describing a rectangle.
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christophertbrown/bioscripts | ctbBio/rax.py | remove_bad | def remove_bad(string):
"""
remove problem characters from string
"""
remove = [':', ',', '(', ')', ' ', '|', ';', '\'']
for c in remove:
string = string.replace(c, '_')
return string | python | def remove_bad(string):
"""
remove problem characters from string
"""
remove = [':', ',', '(', ')', ' ', '|', ';', '\'']
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christophertbrown/bioscripts | ctbBio/rax.py | get_ids | def get_ids(a):
"""
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"""
make copy of sequences with short identifier
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christophertbrown/bioscripts | ctbBio/rax.py | convert2phylip | def convert2phylip(convert):
"""
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if check(out) is False:
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"""
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out = '%s.phy' % (convert.rsplit('.', 1)[0])
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convert = open(convert, 'rU')
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christophertbrown/bioscripts | ctbBio/rax.py | run_iqtree | def run_iqtree(phy, model, threads, cluster, node):
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run IQ-Tree
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ppn = 24
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ppn = threads
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"""
run IQ-Tree
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christophertbrown/bioscripts | ctbBio/rax.py | fix_tree | def fix_tree(tree, a_id_lookup, out):
"""
get the names for sequences in the raxml tree
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tree = open(tree).read()
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get the names for sequences in the raxml tree
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disqus/nydus | nydus/db/__init__.py | create_cluster | def create_cluster(settings):
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>>> '... | python | def create_cluster(settings):
"""
Creates a new Nydus cluster from the given settings.
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>>> redis = create_cluster({
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"""
Wrapper to allow for easy unicode representation of an object by
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right translation of the specified property, it will return the
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christophertbrown/bioscripts | ctbBio/strip_align_inserts.py | strip_inserts | def strip_inserts(fasta):
"""
remove insertion columns from aligned fasta file
"""
for seq in parse_fasta(fasta):
seq[1] = ''.join([b for b in seq[1] if b == '-' or b.isupper()])
yield seq | python | def strip_inserts(fasta):
"""
remove insertion columns from aligned fasta file
"""
for seq in parse_fasta(fasta):
seq[1] = ''.join([b for b in seq[1] if b == '-' or b.isupper()])
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cldf/segments | src/segments/tokenizer.py | Tokenizer.transform | def transform(self, word, column=Profile.GRAPHEME_COL, error=errors.replace):
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Transform a string's graphemes into the mappings given in a different column
in the orthography profile.
Parameters
----------
word : str
The input string to be tokenized.
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Transform a string's graphemes into the mappings given in a different column
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cldf/segments | src/segments/tokenizer.py | Tokenizer.combine_modifiers | def combine_modifiers(self, graphemes):
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----------
string : str
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christophertbrown/bioscripts | ctbBio/rRNA_insertions_gff.py | parse_catalytic | def parse_catalytic(insertion, gff):
"""
parse catalytic RNAs to gff format
"""
offset = insertion['offset']
GeneStrand = insertion['strand']
if type(insertion['intron']) is not str:
return gff
for intron in parse_fasta(insertion['intron'].split('|')):
ID, annot, strand, pos ... | python | def parse_catalytic(insertion, gff):
"""
parse catalytic RNAs to gff format
"""
offset = insertion['offset']
GeneStrand = insertion['strand']
if type(insertion['intron']) is not str:
return gff
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christophertbrown/bioscripts | ctbBio/rRNA_insertions_gff.py | parse_orf | def parse_orf(insertion, gff):
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parse ORF to gff format
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"""
parse ORF to gff format
"""
offset = insertion['offset']
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return gff
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christophertbrown/bioscripts | ctbBio/rRNA_insertions_gff.py | parse_insertion | def parse_insertion(insertion, gff):
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parse insertion to gff format
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"""
parse insertion to gff format
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offset = insertion['offset']
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christophertbrown/bioscripts | ctbBio/rRNA_insertions_gff.py | parse_rRNA | def parse_rRNA(insertion, seq, gff):
"""
parse rRNA to gff format
"""
offset = insertion['offset']
strand = insertion['strand']
for rRNA in parse_masked(seq, 0)[0]:
rRNA = ''.join(rRNA)
Start = seq[1].find(rRNA) + 1
End = Start + len(rRNA) - 1
if strand == '-':
... | python | def parse_rRNA(insertion, seq, gff):
"""
parse rRNA to gff format
"""
offset = insertion['offset']
strand = insertion['strand']
for rRNA in parse_masked(seq, 0)[0]:
rRNA = ''.join(rRNA)
Start = seq[1].find(rRNA) + 1
End = Start + len(rRNA) - 1
if strand == '-':
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christophertbrown/bioscripts | ctbBio/rRNA_insertions_gff.py | iTable2GFF | def iTable2GFF(iTable, fa, contig = False):
"""
convert iTable to gff file
"""
columns = ['#seqname', 'source', 'feature', 'start', 'end', 'score', 'strand', 'frame', 'attribute']
gff = {c:[] for c in columns}
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insertion = insertion[1]
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"""
convert iTable to gff file
"""
columns = ['#seqname', 'source', 'feature', 'start', 'end', 'score', 'strand', 'frame', 'attribute']
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smdabdoub/phylotoast | bin/biom_phyla_summary.py | summarize_taxa | def summarize_taxa(biom):
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"""
tamtcounts = defaultdict(int)
tot_seqs = 0.0
for row, col, amt in biom['data']:
tot_seqs += amt
rtax = biom['rows'][row]['metadata']['taxonomy']
for i, t in enumera... | python | def summarize_taxa(biom):
"""
Given an abundance table, group the counts by every
taxonomic level.
"""
tamtcounts = defaultdict(int)
tot_seqs = 0.0
for row, col, amt in biom['data']:
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scottrice/pysteam | pysteam/legacy/game.py | Game.custom_image | def custom_image(self, user):
"""Returns the path to the custom image set for this game, or None if
no image is set"""
for ext in self.valid_custom_image_extensions():
image_location = self._custom_image_path(user, ext)
if os.path.isfile(image_location):
r... | python | def custom_image(self, user):
"""Returns the path to the custom image set for this game, or None if
no image is set"""
for ext in self.valid_custom_image_extensions():
image_location = self._custom_image_path(user, ext)
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"""Sets a custom image for the game. `image_path` should refer to
an image file on disk"""
_, ext = os.path.splitext(image_path)
shutil.copy(image_path, self._custom_image_path(user, ext)) | python | def set_image(self, user, image_path):
"""Sets a custom image for the game. `image_path` should refer to
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christophertbrown/bioscripts | ctbBio/filter_fastq_sam.py | sam_list | def sam_list(sam):
"""
get a list of mapped reads
"""
list = []
for file in sam:
for line in file:
if line.startswith('@') is False:
line = line.strip().split()
id, map = line[0], int(line[1])
if map != 4 and map != 8:
list.append(id)
return set(list) | python | def sam_list(sam):
"""
get a list of mapped reads
"""
list = []
for file in sam:
for line in file:
if line.startswith('@') is False:
line = line.strip().split()
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christophertbrown/bioscripts | ctbBio/filter_fastq_sam.py | sam_list_paired | def sam_list_paired(sam):
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get a list of mapped reads
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christophertbrown/bioscripts | ctbBio/filter_fastq_sam.py | filter_paired | def filter_paired(list):
"""
require that both pairs are mapped in the sam file in order to remove the reads
"""
pairs = {}
filtered = []
for id in list:
read = id.rsplit('/')[0]
if read not in pairs:
pairs[read] = []
pairs[read].append(id)
for read in pairs:
ids = pairs[read]
if len(ids) == 2:
f... | python | def filter_paired(list):
"""
require that both pairs are mapped in the sam file in order to remove the reads
"""
pairs = {}
filtered = []
for id in list:
read = id.rsplit('/')[0]
if read not in pairs:
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pairs[read].append(id)
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ids = pairs[read]
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christophertbrown/bioscripts | ctbBio/mapped.py | sam2fastq | def sam2fastq(line):
"""
print fastq from sam
"""
fastq = []
fastq.append('@%s' % line[0])
fastq.append(line[9])
fastq.append('+%s' % line[0])
fastq.append(line[10])
return fastq | python | def sam2fastq(line):
"""
print fastq from sam
"""
fastq = []
fastq.append('@%s' % line[0])
fastq.append(line[9])
fastq.append('+%s' % line[0])
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christophertbrown/bioscripts | ctbBio/mapped.py | check_mismatches | def check_mismatches(read, pair, mismatches, mm_option, req_map):
"""
- check to see if the read maps with <= threshold number of mismatches
- mm_option = 'one' or 'both' depending on whether or not one or both reads
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"""
- check to see if the read maps with <= threshold number of mismatches
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christophertbrown/bioscripts | ctbBio/mapped.py | check_region | def check_region(read, pair, region):
"""
determine whether or not reads map to specific region of scaffold
"""
if region is False:
return True
for mapping in read, pair:
if mapping is False:
continue
start, length = int(mapping[3]), len(mapping[9])
r = [s... | python | def check_region(read, pair, region):
"""
determine whether or not reads map to specific region of scaffold
"""
if region is False:
return True
for mapping in read, pair:
if mapping is False:
continue
start, length = int(mapping[3]), len(mapping[9])
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scottrice/pysteam | pysteam/steam.py | get_steam | def get_steam():
"""
Returns a Steam object representing the current Steam installation on the
users computer. If the user doesn't have Steam installed, returns None.
"""
# Helper function which checks if the potential userdata directory exists
# and returns a new Steam instance with that userdata directory... | python | def get_steam():
"""
Returns a Steam object representing the current Steam installation on the
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christophertbrown/bioscripts | ctbBio/transform.py | zero_to_one | def zero_to_one(table, option):
"""
normalize from zero to one for row or table
"""
if option == 'table':
m = min(min(table))
ma = max(max(table))
t = []
for row in table:
t_row = []
if option != 'table':
m, ma = min(row), max(row)
for i in row... | python | def zero_to_one(table, option):
"""
normalize from zero to one for row or table
"""
if option == 'table':
m = min(min(table))
ma = max(max(table))
t = []
for row in table:
t_row = []
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christophertbrown/bioscripts | ctbBio/transform.py | pertotal | def pertotal(table, option):
"""
calculate percent of total
"""
if option == 'table':
total = sum([i for line in table for i in line])
t = []
for row in table:
t_row = []
if option != 'table':
total = sum(row)
for i in row:
if total == 0:
... | python | def pertotal(table, option):
"""
calculate percent of total
"""
if option == 'table':
total = sum([i for line in table for i in line])
t = []
for row in table:
t_row = []
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christophertbrown/bioscripts | ctbBio/transform.py | scale | def scale(table):
"""
scale table based on the column with the largest sum
"""
t = []
columns = [[] for i in table[0]]
for row in table:
for i, v in enumerate(row):
columns[i].append(v)
sums = [float(sum(i)) for i in columns]
scale_to = float(max(sums))
scale_fact... | python | def scale(table):
"""
scale table based on the column with the largest sum
"""
t = []
columns = [[] for i in table[0]]
for row in table:
for i, v in enumerate(row):
columns[i].append(v)
sums = [float(sum(i)) for i in columns]
scale_to = float(max(sums))
scale_fact... | [
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christophertbrown/bioscripts | ctbBio/transform.py | norm | def norm(table):
"""
fit to normal distribution
"""
print('# norm dist is broken', file=sys.stderr)
exit()
from matplotlib.pyplot import hist as hist
t = []
for i in table:
t.append(np.ndarray.tolist(hist(i, bins = len(i), normed = True)[0]))
return t | python | def norm(table):
"""
fit to normal distribution
"""
print('# norm dist is broken', file=sys.stderr)
exit()
from matplotlib.pyplot import hist as hist
t = []
for i in table:
t.append(np.ndarray.tolist(hist(i, bins = len(i), normed = True)[0]))
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christophertbrown/bioscripts | ctbBio/transform.py | log_trans | def log_trans(table):
"""
log transform each value in table
"""
t = []
all = [item for sublist in table for item in sublist]
if min(all) == 0:
scale = min([i for i in all if i != 0]) * 10e-10
else:
scale = 0
for i in table:
t.append(np.ndarray.tolist(np.log10([j +... | python | def log_trans(table):
"""
log transform each value in table
"""
t = []
all = [item for sublist in table for item in sublist]
if min(all) == 0:
scale = min([i for i in all if i != 0]) * 10e-10
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scale = 0
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t.append(np.ndarray.tolist(np.log10([j +... | [
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christophertbrown/bioscripts | ctbBio/transform.py | box_cox | def box_cox(table):
"""
box-cox transform table
"""
from scipy.stats import boxcox as bc
t = []
for i in table:
if min(i) == 0:
scale = min([j for j in i if j != 0]) * 10e-10
else:
scale = 0
t.append(np.ndarray.tolist(bc(np.array([j + scale for j i... | python | def box_cox(table):
"""
box-cox transform table
"""
from scipy.stats import boxcox as bc
t = []
for i in table:
if min(i) == 0:
scale = min([j for j in i if j != 0]) * 10e-10
else:
scale = 0
t.append(np.ndarray.tolist(bc(np.array([j + scale for j i... | [
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christophertbrown/bioscripts | ctbBio/transform.py | inh | def inh(table):
"""
inverse hyperbolic sine transformation
"""
t = []
for i in table:
t.append(np.ndarray.tolist(np.arcsinh(i)))
return t | python | def inh(table):
"""
inverse hyperbolic sine transformation
"""
t = []
for i in table:
t.append(np.ndarray.tolist(np.arcsinh(i)))
return t | [
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christophertbrown/bioscripts | ctbBio/transform.py | diri | def diri(table):
"""
from SparCC - "randomly draw from the corresponding posterior
Dirichlet distribution with a uniform prior"
"""
t = []
for i in table:
a = [j + 1 for j in i]
t.append(np.ndarray.tolist(np.random.mtrand.dirichlet(a)))
return t | python | def diri(table):
"""
from SparCC - "randomly draw from the corresponding posterior
Dirichlet distribution with a uniform prior"
"""
t = []
for i in table:
a = [j + 1 for j in i]
t.append(np.ndarray.tolist(np.random.mtrand.dirichlet(a)))
return t | [
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smdabdoub/phylotoast | bin/sanger_qiimify.py | generate_barcodes | def generate_barcodes(nIds, codeLen=12):
"""
Given a list of sample IDs generate unique n-base barcodes for each.
Note that only 4^n unique barcodes are possible.
"""
def next_code(b, c, i):
return c[:i] + b + (c[i+1:] if i < -1 else '')
def rand_base():
return random.choice(['A... | python | def generate_barcodes(nIds, codeLen=12):
"""
Given a list of sample IDs generate unique n-base barcodes for each.
Note that only 4^n unique barcodes are possible.
"""
def next_code(b, c, i):
return c[:i] + b + (c[i+1:] if i < -1 else '')
def rand_base():
return random.choice(['A... | [
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smdabdoub/phylotoast | bin/sanger_qiimify.py | scrobble_data_dir | def scrobble_data_dir(dataDir, sampleMap, outF, qualF=None, idopt=None,
utf16=False):
"""
Given a sample ID and a mapping, modify a Sanger FASTA file
to include the barcode and 'primer' in the sequence data
and change the description line as needed.
"""
seqcount = 0
out... | python | def scrobble_data_dir(dataDir, sampleMap, outF, qualF=None, idopt=None,
utf16=False):
"""
Given a sample ID and a mapping, modify a Sanger FASTA file
to include the barcode and 'primer' in the sequence data
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seqcount = 0
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smdabdoub/phylotoast | bin/sanger_qiimify.py | handle_program_options | def handle_program_options():
"""
Uses the built-in argparse module to handle command-line options for the
program.
:return: The gathered command-line options specified by the user
:rtype: argparse.ArgumentParser
"""
parser = argparse.ArgumentParser(description="Convert Sanger-sequencing \
... | python | def handle_program_options():
"""
Uses the built-in argparse module to handle command-line options for the
program.
:return: The gathered command-line options specified by the user
:rtype: argparse.ArgumentParser
"""
parser = argparse.ArgumentParser(description="Convert Sanger-sequencing \
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smdabdoub/phylotoast | bin/transform_biom.py | arcsin_sqrt | def arcsin_sqrt(biom_tbl):
"""
Applies the arcsine square root transform to the
given BIOM-format table
"""
arcsint = lambda data, id_, md: np.arcsin(np.sqrt(data))
tbl_relabd = relative_abd(biom_tbl)
tbl_asin = tbl_relabd.transform(arcsint, inplace=False)
return tbl_asin | python | def arcsin_sqrt(biom_tbl):
"""
Applies the arcsine square root transform to the
given BIOM-format table
"""
arcsint = lambda data, id_, md: np.arcsin(np.sqrt(data))
tbl_relabd = relative_abd(biom_tbl)
tbl_asin = tbl_relabd.transform(arcsint, inplace=False)
return tbl_asin | [
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christophertbrown/bioscripts | ctbBio/genome_variation.py | parse_sam | def parse_sam(sam, qual):
"""
parse sam file and check mapping quality
"""
for line in sam:
if line.startswith('@'):
continue
line = line.strip().split()
if int(line[4]) == 0 or int(line[4]) < qual:
continue
yield line | python | def parse_sam(sam, qual):
"""
parse sam file and check mapping quality
"""
for line in sam:
if line.startswith('@'):
continue
line = line.strip().split()
if int(line[4]) == 0 or int(line[4]) < qual:
continue
yield line | [
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christophertbrown/bioscripts | ctbBio/genome_variation.py | rc_stats | def rc_stats(stats):
"""
reverse completement stats
"""
rc_nucs = {'A':'T', 'T':'A', 'G':'C', 'C':'G', 'N':'N'}
rcs = []
for pos in reversed(stats):
rc = {}
rc['reference frequencey'] = pos['reference frequency']
rc['consensus frequencey'] = pos['consensus frequency']
... | python | def rc_stats(stats):
"""
reverse completement stats
"""
rc_nucs = {'A':'T', 'T':'A', 'G':'C', 'C':'G', 'N':'N'}
rcs = []
for pos in reversed(stats):
rc = {}
rc['reference frequencey'] = pos['reference frequency']
rc['consensus frequencey'] = pos['consensus frequency']
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christophertbrown/bioscripts | ctbBio/genome_variation.py | parse_codons | def parse_codons(ref, start, end, strand):
"""
parse codon nucleotide positions in range start -> end, wrt strand
"""
codon = []
c = cycle([1, 2, 3])
ref = ref[start - 1:end]
if strand == -1:
ref = rc_stats(ref)
for pos in ref:
n = next(c)
codon.append(pos)
... | python | def parse_codons(ref, start, end, strand):
"""
parse codon nucleotide positions in range start -> end, wrt strand
"""
codon = []
c = cycle([1, 2, 3])
ref = ref[start - 1:end]
if strand == -1:
ref = rc_stats(ref)
for pos in ref:
n = next(c)
codon.append(pos)
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christophertbrown/bioscripts | ctbBio/genome_variation.py | calc_coverage | def calc_coverage(ref, start, end, length, nucs):
"""
calculate coverage for positions in range start -> end
"""
ref = ref[start - 1:end]
bases = 0
for pos in ref:
for base, count in list(pos.items()):
if base in nucs:
bases += count
return float(bases)/fl... | python | def calc_coverage(ref, start, end, length, nucs):
"""
calculate coverage for positions in range start -> end
"""
ref = ref[start - 1:end]
bases = 0
for pos in ref:
for base, count in list(pos.items()):
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bases += count
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christophertbrown/bioscripts | ctbBio/genome_variation.py | parse_gbk | def parse_gbk(gbks):
"""
parse gbk file
"""
for gbk in gbks:
for record in SeqIO.parse(open(gbk), 'genbank'):
for feature in record.features:
if feature.type == 'gene':
try:
locus = feature.qualifiers['locus_tag'][0]
... | python | def parse_gbk(gbks):
"""
parse gbk file
"""
for gbk in gbks:
for record in SeqIO.parse(open(gbk), 'genbank'):
for feature in record.features:
if feature.type == 'gene':
try:
locus = feature.qualifiers['locus_tag'][0]
... | [
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christophertbrown/bioscripts | ctbBio/genome_variation.py | parse_fasta_annotations | def parse_fasta_annotations(fastas, annot_tables, trans_table):
"""
parse gene call information from Prodigal fasta output
"""
if annot_tables is not False:
annots = {}
for table in annot_tables:
for cds in open(table):
ID, start, end, strand = cds.strip().spl... | python | def parse_fasta_annotations(fastas, annot_tables, trans_table):
"""
parse gene call information from Prodigal fasta output
"""
if annot_tables is not False:
annots = {}
for table in annot_tables:
for cds in open(table):
ID, start, end, strand = cds.strip().spl... | [
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christophertbrown/bioscripts | ctbBio/genome_variation.py | parse_annotations | def parse_annotations(annots, fmt, annot_tables, trans_table):
"""
parse annotations in either gbk or Prodigal fasta format
"""
annotations = {} # annotations[contig] = [features]
# gbk format
if fmt is False:
for contig, feature in parse_gbk(annots):
if contig not in annotat... | python | def parse_annotations(annots, fmt, annot_tables, trans_table):
"""
parse annotations in either gbk or Prodigal fasta format
"""
annotations = {} # annotations[contig] = [features]
# gbk format
if fmt is False:
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christophertbrown/bioscripts | ctbBio/genome_variation.py | codon2aa | def codon2aa(codon, trans_table):
"""
convert codon to amino acid
"""
return Seq(''.join(codon), IUPAC.ambiguous_dna).translate(table = trans_table)[0] | python | def codon2aa(codon, trans_table):
"""
convert codon to amino acid
"""
return Seq(''.join(codon), IUPAC.ambiguous_dna).translate(table = trans_table)[0] | [
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christophertbrown/bioscripts | ctbBio/genome_variation.py | find_consensus | def find_consensus(bases):
"""
find consensus base based on nucleotide
frequencies
"""
nucs = ['A', 'T', 'G', 'C', 'N']
total = sum([bases[nuc] for nuc in nucs if nuc in bases])
# save most common base as consensus (random nuc if there is a tie)
try:
top = max([bases[nuc] for nuc... | python | def find_consensus(bases):
"""
find consensus base based on nucleotide
frequencies
"""
nucs = ['A', 'T', 'G', 'C', 'N']
total = sum([bases[nuc] for nuc in nucs if nuc in bases])
# save most common base as consensus (random nuc if there is a tie)
try:
top = max([bases[nuc] for nuc... | [
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christophertbrown/bioscripts | ctbBio/genome_variation.py | print_consensus | def print_consensus(genomes):
"""
print consensensus sequences for each genome and sample
"""
# generate consensus sequences
cons = {} # cons[genome][sample][contig] = consensus
for genome, contigs in list(genomes.items()):
cons[genome] = {}
for contig, samples in list(contigs.it... | python | def print_consensus(genomes):
"""
print consensensus sequences for each genome and sample
"""
# generate consensus sequences
cons = {} # cons[genome][sample][contig] = consensus
for genome, contigs in list(genomes.items()):
cons[genome] = {}
for contig, samples in list(contigs.it... | [
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christophertbrown/bioscripts | ctbBio/genome_coverage.py | parse_cov | def parse_cov(cov_table, scaffold2genome):
"""
calculate genome coverage from scaffold coverage table
"""
size = {} # size[genome] = genome size
mapped = {} # mapped[genome][sample] = mapped bases
# parse coverage files
for line in open(cov_table):
line = line.strip().split('\t')
... | python | def parse_cov(cov_table, scaffold2genome):
"""
calculate genome coverage from scaffold coverage table
"""
size = {} # size[genome] = genome size
mapped = {} # mapped[genome][sample] = mapped bases
# parse coverage files
for line in open(cov_table):
line = line.strip().split('\t')
... | [
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christophertbrown/bioscripts | ctbBio/genome_coverage.py | genome_coverage | def genome_coverage(covs, s2b):
"""
calculate genome coverage from scaffold coverage
"""
COV = []
for cov in covs:
COV.append(parse_cov(cov, s2b))
return pd.concat(COV) | python | def genome_coverage(covs, s2b):
"""
calculate genome coverage from scaffold coverage
"""
COV = []
for cov in covs:
COV.append(parse_cov(cov, s2b))
return pd.concat(COV) | [
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christophertbrown/bioscripts | ctbBio/genome_coverage.py | parse_s2bs | def parse_s2bs(s2bs):
"""
convert s2b files to dictionary
"""
s2b = {}
for s in s2bs:
for line in open(s):
line = line.strip().split('\t')
s, b = line[0], line[1]
s2b[s] = b
return s2b | python | def parse_s2bs(s2bs):
"""
convert s2b files to dictionary
"""
s2b = {}
for s in s2bs:
for line in open(s):
line = line.strip().split('\t')
s, b = line[0], line[1]
s2b[s] = b
return s2b | [
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christophertbrown/bioscripts | ctbBio/genome_coverage.py | fa2s2b | def fa2s2b(fastas):
"""
convert fastas to s2b dictionary
"""
s2b = {}
for fa in fastas:
for seq in parse_fasta(fa):
s = seq[0].split('>', 1)[1].split()[0]
s2b[s] = fa.rsplit('/', 1)[-1].rsplit('.', 1)[0]
return s2b | python | def fa2s2b(fastas):
"""
convert fastas to s2b dictionary
"""
s2b = {}
for fa in fastas:
for seq in parse_fasta(fa):
s = seq[0].split('>', 1)[1].split()[0]
s2b[s] = fa.rsplit('/', 1)[-1].rsplit('.', 1)[0]
return s2b | [
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smdabdoub/phylotoast | bin/filter_ambiguity.py | filter_ambiguity | def filter_ambiguity(records, percent=0.5): # , repeats=6)
"""
Filters out sequences with too much ambiguity as defined by the method
parameters.
:type records: list
:param records: A list of sequences
:type repeats: int
:param repeats: Defines the number of repeated N that trigger truncat... | python | def filter_ambiguity(records, percent=0.5): # , repeats=6)
"""
Filters out sequences with too much ambiguity as defined by the method
parameters.
:type records: list
:param records: A list of sequences
:type repeats: int
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mkouhei/bootstrap-py | bootstrap_py/pypi.py | package_existent | def package_existent(name):
"""Search package.
* :class:`bootstrap_py.exceptions.Conflict` exception occurs
when user specified name has already existed.
* :class:`bootstrap_py.exceptions.BackendFailure` exception occurs
when PyPI service is down.
:param str name: package name
"""
... | python | def package_existent(name):
"""Search package.
* :class:`bootstrap_py.exceptions.Conflict` exception occurs
when user specified name has already existed.
* :class:`bootstrap_py.exceptions.BackendFailure` exception occurs
when PyPI service is down.
:param str name: package name
"""
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