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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 packets received, " "%0.1f%% packet loss") % ( myStats.pktsSent, myStats.pktsRcvd, 100.0 * myStats.fracLoss )) if myStats.pktsRcvd > 0: print("round-trip (ms) min/avg/max = %d/%0.1f/%d" % ( myStats.minTime, myStats.totTime / myStats.pktsRcvd, myStats.maxTime )) print("") return
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 packets received, " "%0.1f%% packet loss") % ( myStats.pktsSent, myStats.pktsRcvd, 100.0 * myStats.fracLoss )) if myStats.pktsRcvd > 0: print("round-trip (ms) min/avg/max = %d/%0.1f/%d" % ( myStats.minTime, myStats.totTime / myStats.pktsRcvd, myStats.maxTime )) print("") return
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Show stats when pings are done
[ "Show", "stats", "when", "pings", "are", "done" ]
4e53ae13fe12085e0cf2e5e1aff947368f4f1ffa
https://github.com/jay-johnson/network-pipeline/blob/4e53ae13fe12085e0cf2e5e1aff947368f4f1ffa/network_pipeline/scripts/icmp_send_msg.py#L470-L495
train
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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bootstrap-py package updatable?.
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95d56ed98ef409fd9f019dc352fd1c3711533275
https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/update.py#L29-L35
train
mkouhei/bootstrap-py
bootstrap_py/update.py
Update.show_message
def show_message(self): """Show message updatable.""" print( 'current version: {current_version}\n' 'latest version : {latest_version}'.format( current_version=self.current_version, latest_version=self.latest_version))
python
def show_message(self): """Show message updatable.""" print( 'current version: {current_version}\n' 'latest version : {latest_version}'.format( current_version=self.current_version, latest_version=self.latest_version))
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Show message updatable.
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95d56ed98ef409fd9f019dc352fd1c3711533275
https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/update.py#L37-L43
train
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 nuniqueF: file :@param nuniqueF: The matrix of unique OTU IDs associated to the list of non-unique OTU IDs they replaced. :@rtype: dict :@return: The new condensed table of unique OTU IDs and the sequence IDs associated with them. """ uniqueOTUs = set() nuOTUs = {} # parse non-unique otu matrix for line in nuniqueF: line = line.split() uOTU = line[0] for nuOTU in line[1:]: nuOTUs[nuOTU] = uOTU uniqueOTUs.add(uOTU) otuFilter = defaultdict(list) # parse otu sequence file for line in otuF: line = line.split() otuID, seqIDs = line[0], line[1:] if otuID in uniqueOTUs: otuFilter[otuID].extend(seqIDs) elif otuID in nuOTUs: otuFilter[nuOTUs[otuID]].extend(seqIDs) return otuFilter
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 :@param otuF: The output file from QIIME's pick_otus.py :@type nuniqueF: file :@param nuniqueF: The matrix of unique OTU IDs associated to the list of non-unique OTU IDs they replaced. :@rtype: dict :@return: The new condensed table of unique OTU IDs and the sequence IDs associated with them. """ uniqueOTUs = set() nuOTUs = {} # parse non-unique otu matrix for line in nuniqueF: line = line.split() uOTU = line[0] for nuOTU in line[1:]: nuOTUs[nuOTU] = uOTU uniqueOTUs.add(uOTU) otuFilter = defaultdict(list) # parse otu sequence file for line in otuF: line = line.split() otuID, seqIDs = line[0], line[1:] if otuID in uniqueOTUs: otuFilter[otuID].extend(seqIDs) elif otuID in nuOTUs: otuFilter[nuOTUs[otuID]].extend(seqIDs) return otuFilter
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0b74ef171e6a84761710548501dfac71285a58a3
https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/pick_otus_condense.py#L14-L51
train
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) rna_cov[scaffold][0] += ol return rna_cov
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) rna_cov[scaffold][0] += ol return rna_cov
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determine if read overlaps with rna, if so count bases
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/rRNA_copies.py#L18-L29
train
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: g = ' '.join(line[2:]) else: g = 'n/a' b = '%s\t%s' % (b, g) s2b[s] = b if b not in b2s: b2s[b] = [] b2s[b].append(s) return s2b, b2s
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 if len(line) > 2: g = ' '.join(line[2:]) else: g = 'n/a' b = '%s\t%s' % (b, g) s2b[s] = b if b not in b2s: b2s[b] = [] b2s[b].append(s) return s2b, b2s
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parse ggKbase scaffold-to-bin mapping - scaffolds-to-bins and bins-to-scaffolds
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/rRNA_copies.py#L31-L52
train
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 list(s2bins.items()): if bin not in bins2s: del s2bins[scaffold] return s2bins, bins2s
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 list(s2bins.items()): if bin not in bins2s: del s2bins[scaffold] return s2bins, bins2s
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/rRNA_copies.py#L76-L90
train
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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calculate bin coverage
[ "calculate", "bin", "coverage" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/rRNA_copies.py#L92-L100
train
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, self).clean() if settings.HIDE_LANGUAGE: return if len(self.forms) > 0: # If a default language has been provided, make sure a translation # is available if settings.DEFAULT_LANGUAGE and not any(self.errors): # Don't bother validating the formset unless each form is # valid on its own. Reference: # http://docs.djangoproject.com/en/dev/topics/forms/formsets/#custom-formset-validation for form in self.forms: language_code = form.cleaned_data.get( 'language_code', None ) if language_code == settings.DEFAULT_LANGUAGE: # All is good, don't bother checking any further return raise forms.ValidationError(_( 'No translation provided for default language \'%s\'.' ) % settings.DEFAULT_LANGUAGE) else: raise forms.ValidationError( _('At least one translation should be provided.') )
python
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, self).clean() if settings.HIDE_LANGUAGE: return if len(self.forms) > 0: # If a default language has been provided, make sure a translation # is available if settings.DEFAULT_LANGUAGE and not any(self.errors): # Don't bother validating the formset unless each form is # valid on its own. Reference: # http://docs.djangoproject.com/en/dev/topics/forms/formsets/#custom-formset-validation for form in self.forms: language_code = form.cleaned_data.get( 'language_code', None ) if language_code == settings.DEFAULT_LANGUAGE: # All is good, don't bother checking any further return raise forms.ValidationError(_( 'No translation provided for default language \'%s\'.' ) % settings.DEFAULT_LANGUAGE) else: raise forms.ValidationError( _('At least one translation should be provided.') )
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2479b2c3d6f7b697e95aa1e082c8bc8699f1f638
https://github.com/dokterbob/django-multilingual-model/blob/2479b2c3d6f7b697e95aa1e082c8bc8699f1f638/multilingual_model/forms.py#L19-L58
train
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'), \ 'No available languages have been generated.' assert len(self.available_languages) > 0, \ 'No available languages to select from.' if ( settings.DEFAULT_LANGUAGE and settings.DEFAULT_LANGUAGE in self.available_languages ) or ( 'language_code' not in self.form.base_fields ): # Default language still available self.available_languages.remove(settings.DEFAULT_LANGUAGE) return settings.DEFAULT_LANGUAGE else: # Select the first item and return it return self.available_languages.pop(0)
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'), \ 'No available languages have been generated.' assert len(self.available_languages) > 0, \ 'No available languages to select from.' if ( settings.DEFAULT_LANGUAGE and settings.DEFAULT_LANGUAGE in self.available_languages ) or ( 'language_code' not in self.form.base_fields ): # Default language still available self.available_languages.remove(settings.DEFAULT_LANGUAGE) return settings.DEFAULT_LANGUAGE else: # Select the first item and return it return self.available_languages.pop(0)
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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.
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2479b2c3d6f7b697e95aa1e082c8bc8699f1f638
https://github.com/dokterbob/django-multilingual-model/blob/2479b2c3d6f7b697e95aa1e082c8bc8699f1f638/multilingual_model/forms.py#L68-L94
train
dokterbob/django-multilingual-model
multilingual_model/forms.py
TranslationFormSet._construct_form
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) if settings.HIDE_LANGUAGE: form.instance.language_code = settings.DEFAULT_LANGUAGE else: language_code = form.instance.language_code if language_code: logger.debug( u'Removing translation choice %s for instance %s' u' in form %d', language_code, form.instance, i ) self.available_languages.remove(language_code) else: initial_language_code = self._get_default_language() logger.debug( u'Preselecting language code %s for form %d', initial_language_code, i ) form.initial['language_code'] = initial_language_code return form
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) if settings.HIDE_LANGUAGE: form.instance.language_code = settings.DEFAULT_LANGUAGE else: language_code = form.instance.language_code if language_code: logger.debug( u'Removing translation choice %s for instance %s' u' in form %d', language_code, form.instance, i ) self.available_languages.remove(language_code) else: initial_language_code = self._get_default_language() logger.debug( u'Preselecting language code %s for form %d', initial_language_code, i ) form.initial['language_code'] = initial_language_code return form
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2479b2c3d6f7b697e95aa1e082c8bc8699f1f638
https://github.com/dokterbob/django-multilingual-model/blob/2479b2c3d6f7b697e95aa1e082c8bc8699f1f638/multilingual_model/forms.py#L96-L128
train
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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merge separate fastq files
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/fastq_merge.py#L13-L25
train
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._nodes) * weight) / total_weight) for i in xrange(0, int(ks)): b_key = self._md5_digest('%s-%s-salt' % (node, i)) for l in xrange(0, 4): key = ((b_key[3 + l * 4] << 24) | (b_key[2 + l * 4] << 16) | (b_key[1 + l * 4] << 8) | b_key[l * 4]) self._hashring[key] = node self._sorted_keys.append(key) self._sorted_keys.sort()
python
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._nodes) * weight) / total_weight) for i in xrange(0, int(ks)): b_key = self._md5_digest('%s-%s-salt' % (node, i)) for l in xrange(0, 4): key = ((b_key[3 + l * 4] << 24) | (b_key[2 + l * 4] << 16) | (b_key[1 + l * 4] << 8) | b_key[l * 4]) self._hashring[key] = node self._sorted_keys.append(key) self._sorted_keys.sort()
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Creates hash ring.
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9b505840da47a34f758a830c3992fa5dcb7bb7ad
https://github.com/disqus/nydus/blob/9b505840da47a34f758a830c3992fa5dcb7bb7ad/nydus/contrib/ketama.py#L35-L60
train
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 the hash ring. """ b_key = self._md5_digest(key) return self._hashi(b_key, lambda x: x)
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9b505840da47a34f758a830c3992fa5dcb7bb7ad
https://github.com/disqus/nydus/blob/9b505840da47a34f758a830c3992fa5dcb7bb7ad/nydus/contrib/ketama.py#L78-L84
train
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) return any(map(os.path.exists, possible_paths))
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Returns True if there exists a custom image for app_id.
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1eb2254b5235a053a953e596fa7602d0b110245d
https://github.com/scottrice/pysteam/blob/1eb2254b5235a053a953e596fa7602d0b110245d/pysteam/grid.py#L32-L35
train
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(existing_images) > 0: return existing_images[0]
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) existing_images = filter(os.path.exists, possible_paths) if len(existing_images) > 0: return existing_images[0]
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Returns the custom image associated with a given app. If there are multiple candidate images on disk, one is chosen arbitrarily.
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1eb2254b5235a053a953e596fa7602d0b110245d
https://github.com/scottrice/pysteam/blob/1eb2254b5235a053a953e596fa7602d0b110245d/pysteam/grid.py#L37-L43
train
scottrice/pysteam
pysteam/grid.py
set_custom_image
def set_custom_image(user_context, app_id, image_path): """Sets the custom image for `app_id` to be the image located at `image_path`. If there already exists a custom image for `app_id` it will be deleted. Returns True is setting the image was successful.""" if image_path is None: return False if not os.path.exists(image_path): return False (root, ext) = os.path.splitext(image_path) if not is_valid_extension(ext): # TODO: Maybe log that this happened? return False # If we don't remove the old image then theres no guarantee that Steam will # show our new image when it launches. if has_custom_image(user_context, app_id): img = get_custom_image(user_context, app_id) assert(img is not None) os.remove(img) # Set the new image parent_dir = paths.custom_images_directory(user_context) new_path = os.path.join(parent_dir, app_id + ext) shutil.copyfile(image_path, new_path) return True
python
def set_custom_image(user_context, app_id, image_path): """Sets the custom image for `app_id` to be the image located at `image_path`. If there already exists a custom image for `app_id` it will be deleted. Returns True is setting the image was successful.""" if image_path is None: return False if not os.path.exists(image_path): return False (root, ext) = os.path.splitext(image_path) if not is_valid_extension(ext): # TODO: Maybe log that this happened? return False # If we don't remove the old image then theres no guarantee that Steam will # show our new image when it launches. if has_custom_image(user_context, app_id): img = get_custom_image(user_context, app_id) assert(img is not None) os.remove(img) # Set the new image parent_dir = paths.custom_images_directory(user_context) new_path = os.path.join(parent_dir, app_id + ext) shutil.copyfile(image_path, new_path) return True
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1eb2254b5235a053a953e596fa7602d0b110245d
https://github.com/scottrice/pysteam/blob/1eb2254b5235a053a953e596fa7602d0b110245d/pysteam/grid.py#L45-L70
train
cldf/segments
src/segments/profile.py
Profile.from_file
def from_file(cls, fname, form=None): """ Read an orthography profile from a metadata file or a default tab-separated profile file. """ try: tg = TableGroup.from_file(fname) opfname = None except JSONDecodeError: tg = TableGroup.fromvalue(cls.MD) opfname = fname if len(tg.tables) != 1: raise ValueError('profile description must contain exactly one table') metadata = tg.common_props metadata.update(fname=Path(fname), form=form) return cls( *[{k: None if (k != cls.GRAPHEME_COL and v == cls.NULL) else v for k, v in d.items()} for d in tg.tables[0].iterdicts(fname=opfname)], **metadata)
python
def from_file(cls, fname, form=None): """ Read an orthography profile from a metadata file or a default tab-separated profile file. """ try: tg = TableGroup.from_file(fname) opfname = None except JSONDecodeError: tg = TableGroup.fromvalue(cls.MD) opfname = fname if len(tg.tables) != 1: raise ValueError('profile description must contain exactly one table') metadata = tg.common_props metadata.update(fname=Path(fname), form=form) return cls( *[{k: None if (k != cls.GRAPHEME_COL and v == cls.NULL) else v for k, v in d.items()} for d in tg.tables[0].iterdicts(fname=opfname)], **metadata)
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Read an orthography profile from a metadata file or a default tab-separated profile file.
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9136a4ec89555bf9b574399ffbb07f3cc9a9f45f
https://github.com/cldf/segments/blob/9136a4ec89555bf9b574399ffbb07f3cc9a9f45f/src/segments/profile.py#L100-L117
train
cldf/segments
src/segments/profile.py
Profile.from_text
def from_text(cls, text, mapping='mapping'): """ Create a Profile instance from the Unicode graphemes found in `text`. Parameters ---------- text mapping Returns ------- A Profile instance. """ graphemes = Counter(grapheme_pattern.findall(text)) specs = [ OrderedDict([ (cls.GRAPHEME_COL, grapheme), ('frequency', frequency), (mapping, grapheme)]) for grapheme, frequency in graphemes.most_common()] return cls(*specs)
python
def from_text(cls, text, mapping='mapping'): """ Create a Profile instance from the Unicode graphemes found in `text`. Parameters ---------- text mapping Returns ------- A Profile instance. """ graphemes = Counter(grapheme_pattern.findall(text)) specs = [ OrderedDict([ (cls.GRAPHEME_COL, grapheme), ('frequency', frequency), (mapping, grapheme)]) for grapheme, frequency in graphemes.most_common()] return cls(*specs)
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Create a Profile instance from the Unicode graphemes found in `text`. Parameters ---------- text mapping Returns ------- A Profile instance.
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9136a4ec89555bf9b574399ffbb07f3cc9a9f45f
https://github.com/cldf/segments/blob/9136a4ec89555bf9b574399ffbb07f3cc9a9f45f/src/segments/profile.py#L120-L141
train
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] if fasta not in opened: opened[fasta] = '%s.fa' % fasta seq[1] += '\n' with open(opened[fasta], 'a+') as f_out: f_out.write('\n'.join(seq))
python
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] if fasta not in opened: opened[fasta] = '%s.fa' % fasta seq[1] += '\n' with open(opened[fasta], 'a+') as f_out: f_out.write('\n'.join(seq))
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split fasta file into separate fasta files based on list of scaffolds that belong to each separate file
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/name2fasta.py#L7-L22
train
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. """ fullpath = os.path.join(self.userdata_location(), pathname) # SteamOS puts a directory named 'anonymous' in the userdata directory # by default. Since we assume that pathname is a userID, ignore any name # that can't be converted to a number return os.path.isdir(fullpath) and pathname.isdigit()
python
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. """ fullpath = os.path.join(self.userdata_location(), pathname) # SteamOS puts a directory named 'anonymous' in the userdata directory # by default. Since we assume that pathname is a userID, ignore any name # that can't be converted to a number return os.path.isdir(fullpath) and pathname.isdigit()
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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.
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1eb2254b5235a053a953e596fa7602d0b110245d
https://github.com/scottrice/pysteam/blob/1eb2254b5235a053a953e596fa7602d0b110245d/pysteam/legacy/steam.py#L47-L58
train
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 userdirs = filter(self._is_user_directory, os.listdir(self.userdata_location())) # Exploits the fact that the directory is named the same as the user id return map(lambda userdir: user.User(self, int(userdir)), userdirs)
python
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 userdirs = filter(self._is_user_directory, os.listdir(self.userdata_location())) # Exploits the fact that the directory is named the same as the user id return map(lambda userdir: user.User(self, int(userdir)), userdirs)
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Returns an array of user ids for users on the filesystem
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1eb2254b5235a053a953e596fa7602d0b110245d
https://github.com/scottrice/pysteam/blob/1eb2254b5235a053a953e596fa7602d0b110245d/pysteam/legacy/steam.py#L80-L87
train
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 Set True if you want cooling degree days instead of heating degree days Returns ------- Pandas Series called HDD_base_temperature for heating degree days or CDD_base_temperature for cooling degree days. """ if cooling: ret = temperature_equivalent - base_temperature else: ret = base_temperature - temperature_equivalent # degree days cannot be negative ret[ret < 0] = 0 prefix = 'CDD' if cooling else 'HDD' ret.name = '{}_{}'.format(prefix, base_temperature) return ret
python
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 Set True if you want cooling degree days instead of heating degree days Returns ------- Pandas Series called HDD_base_temperature for heating degree days or CDD_base_temperature for cooling degree days. """ if cooling: ret = temperature_equivalent - base_temperature else: ret = base_temperature - temperature_equivalent # degree days cannot be negative ret[ret < 0] = 0 prefix = 'CDD' if cooling else 'HDD' ret.name = '{}_{}'.format(prefix, base_temperature) return ret
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Calculates degree days, starting with a series of temperature equivalent values Parameters ---------- temperature_equivalent : Pandas Series base_temperature : float cooling : bool Set True if you want cooling degree days instead of heating degree days Returns ------- Pandas Series called HDD_base_temperature for heating degree days or CDD_base_temperature for cooling degree days.
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69b8da3c8fcea9300226c45ef0628cd6d4307651
https://github.com/opengridcc/opengrid/blob/69b8da3c8fcea9300226c45ef0628cd6d4307651/opengrid/library/weather.py#L31-L59
train
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): """Development status.""" return {self._acronym_status(l): l for l in self.resp_text.split('\n') if l.startswith(self.prefix_status)}
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Development status.
[ "Development", "status", "." ]
95d56ed98ef409fd9f019dc352fd1c3711533275
https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/classifiers.py#L33-L36
train
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.""" return {self._acronym_lic(l): l for l in self.resp_text.split('\n') if l.startswith(self.prefix_lic)}
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OSI Approved license.
[ "OSI", "Approved", "license", "." ]
95d56ed98ef409fd9f019dc352fd1c3711533275
https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/classifiers.py#L43-L46
train
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] for l in self.resp_text.split('\n') if l.startswith(self.prefix_lic)}
python
def licenses_desc(self): """Remove prefix.""" return {self._acronym_lic(l): l.split(self.prefix_lic)[1] for l in self.resp_text.split('\n') if l.startswith(self.prefix_lic)}
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Remove prefix.
[ "Remove", "prefix", "." ]
95d56ed98ef409fd9f019dc352fd1c3711533275
https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/classifiers.py#L48-L52
train
mkouhei/bootstrap-py
bootstrap_py/classifiers.py
Classifiers._acronym_lic
def _acronym_lic(self, license_statement): """Convert license acronym.""" pat = re.compile(r'\(([\w+\W?\s?]+)\)') if pat.search(license_statement): lic = pat.search(license_statement).group(1) if lic.startswith('CNRI'): acronym_licence = lic[:4] else: acronym_licence = lic.replace(' ', '') else: acronym_licence = ''.join( [w[0] for w in license_statement.split(self.prefix_lic)[1].split()]) return acronym_licence
python
def _acronym_lic(self, license_statement): """Convert license acronym.""" pat = re.compile(r'\(([\w+\W?\s?]+)\)') if pat.search(license_statement): lic = pat.search(license_statement).group(1) if lic.startswith('CNRI'): acronym_licence = lic[:4] else: acronym_licence = lic.replace(' ', '') else: acronym_licence = ''.join( [w[0] for w in license_statement.split(self.prefix_lic)[1].split()]) return acronym_licence
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Convert license acronym.
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95d56ed98ef409fd9f019dc352fd1c3711533275
https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/classifiers.py#L54-L67
train
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('ascii').strip().split()[0] p.wait() yield False
python
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('ascii').strip().split()[0] p.wait() yield False
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calc MD5 based on path
[ "calc", "MD5", "based", "on", "path" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/ncbi_download.py#L18-L31
train
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, ftp, md5, exclude) is not True: t += 1 if name is not False: print('# downloading:', name, f) if exclude is False: command = 'wget -q --random-wait %s' % (ftp) else: command = 'wget -q --random-wait -R %s %s' % (exclude, ftp) p = Popen(command, shell = True) p.communicate() if t >= tries: print('not downloaded:', name, f) return [f, False] return [f, True]
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] # downloaded file if it does not already exist # check md5s on server (optional) t = 0 while md5check(f, ftp, md5, exclude) is not True: t += 1 if name is not False: print('# downloading:', name, f) if exclude is False: command = 'wget -q --random-wait %s' % (ftp) else: command = 'wget -q --random-wait -R %s %s' % (exclude, ftp) p = Popen(command, shell = True) p.communicate() if t >= tries: print('not downloaded:', name, f) return [f, False] return [f, True]
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download files with wget
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/ncbi_download.py#L74-L97
train
christophertbrown/bioscripts
ctbBio/ncbi_download.py
check
def check(line, queries): """ check that at least one of queries is in list, l """ line = line.strip() spLine = line.replace('.', ' ').split() matches = set(spLine).intersection(queries) if len(matches) > 0: return matches, line.split('\t') return matches, False
python
def check(line, queries): """ check that at least one of queries is in list, l """ line = line.strip() spLine = line.replace('.', ' ').split() matches = set(spLine).intersection(queries) if len(matches) > 0: return matches, line.split('\t') return matches, False
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check that at least one of queries is in list, l
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/ncbi_download.py#L99-L109
train
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 = PIPE) return p2.communicate()
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) p2 = Popen(c2, stdin = p1.stdout, stdout = PIPE, stderr = PIPE) return p2.communicate()
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search entrez using specified database and accession
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/ncbi_download.py#L111-L120
train
christophertbrown/bioscripts
ctbBio/ncbi_download.py
searchAccession
def searchAccession(acc): """ attempt to use NCBI Entrez to get BioSample ID """ # try genbank file # genome database out, error = entrez('genome', acc) for line in out.splitlines(): line = line.decode('ascii').strip() if 'Assembly_Accession' in line or 'BioSample' in line: newAcc = line.split('>')[1].split('<')[0].split('.')[0].split(',')[0] if len(newAcc) > 0: return (True, acc, newAcc) # nucleotide database out, error = entrez('nucleotide', acc) for line in out.splitlines(): line = line.decode('ascii').strip() if 'Assembly_Accession' in line or 'BioSample' in line: newAcc = line.split('>')[1].split('<')[0].split('.')[0].split(',')[0] if len(newAcc) > 0: return (True, acc, newAcc) # assembly database out, error = entrez('assembly', acc) for line in out.splitlines(): line = line.decode('ascii').strip() if 'Assembly_Accession' in line or 'BioSample' in line: newAcc = line.split('>')[1].split('<')[0].split('.')[0].split(',')[0] if len(newAcc) > 0: return (True, acc, newAcc) for error in error.splitlines(): error = error.decode('ascii').strip() if '500 Can' in error: return (False, acc, 'no network') return (False, acc, 'efetch failed')
python
def searchAccession(acc): """ attempt to use NCBI Entrez to get BioSample ID """ # try genbank file # genome database out, error = entrez('genome', acc) for line in out.splitlines(): line = line.decode('ascii').strip() if 'Assembly_Accession' in line or 'BioSample' in line: newAcc = line.split('>')[1].split('<')[0].split('.')[0].split(',')[0] if len(newAcc) > 0: return (True, acc, newAcc) # nucleotide database out, error = entrez('nucleotide', acc) for line in out.splitlines(): line = line.decode('ascii').strip() if 'Assembly_Accession' in line or 'BioSample' in line: newAcc = line.split('>')[1].split('<')[0].split('.')[0].split(',')[0] if len(newAcc) > 0: return (True, acc, newAcc) # assembly database out, error = entrez('assembly', acc) for line in out.splitlines(): line = line.decode('ascii').strip() if 'Assembly_Accession' in line or 'BioSample' in line: newAcc = line.split('>')[1].split('<')[0].split('.')[0].split(',')[0] if len(newAcc) > 0: return (True, acc, newAcc) for error in error.splitlines(): error = error.decode('ascii').strip() if '500 Can' in error: return (False, acc, 'no network') return (False, acc, 'efetch failed')
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attempt to use NCBI Entrez to get BioSample ID
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/ncbi_download.py#L122-L156
train
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 = check(genome, accessions) if genomeInfo is not False: f = genomeInfo[0] + search Gftp = genomeInfo[19] Gftp = Gftp + '/' + search allMatches.extend(matches) yield (Gftp, f, exclude, matches) # print accessions that could not be matched # and whether or not they could be converted (optional) newAccs = [] missing = accessions.difference(set(allMatches)) if convert is True: pool = Pool(threads) pool = pool.imap_unordered(searchAccession, missing) for newAcc in tqdm(pool, total = len(missing)): status, accession, newAcc = newAcc if status is True: newAccs.append(newAcc) print('not found:', accession, '->', newAcc) else: for accession in missing: print('not found:', accession) # re-try after converting accessions (optional) if len(newAccs) > 0 and attempt <= max_attempts: print('convert accession attempt', attempt) attempt += 1 for hit in getFTPs(set(newAccs), ftp, search, exclude, convert, threads = 1, attempt = attempt): yield hit
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 = [] for genome in open(info, encoding = 'utf8'): genome = str(genome) matches, genomeInfo = check(genome, accessions) if genomeInfo is not False: f = genomeInfo[0] + search Gftp = genomeInfo[19] Gftp = Gftp + '/' + search allMatches.extend(matches) yield (Gftp, f, exclude, matches) # print accessions that could not be matched # and whether or not they could be converted (optional) newAccs = [] missing = accessions.difference(set(allMatches)) if convert is True: pool = Pool(threads) pool = pool.imap_unordered(searchAccession, missing) for newAcc in tqdm(pool, total = len(missing)): status, accession, newAcc = newAcc if status is True: newAccs.append(newAcc) print('not found:', accession, '->', newAcc) else: for accession in missing: print('not found:', accession) # re-try after converting accessions (optional) if len(newAccs) > 0 and attempt <= max_attempts: print('convert accession attempt', attempt) attempt += 1 for hit in getFTPs(set(newAccs), ftp, search, exclude, convert, threads = 1, attempt = attempt): yield hit
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download genome info from NCBI
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/ncbi_download.py#L158-L195
train
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']) if args['test'] is True: for genome in FTPs: print('found:', ';'.join(genome[-1]), genome[0]) return FTPs pool = Pool(args['t']) pool = pool.imap_unordered(wgetGenome, FTPs) files = [] for f in tqdm(pool, total = len(accessions)): files.append(f) return files
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'], convert = args['convert']) if args['test'] is True: for genome in FTPs: print('found:', ';'.join(genome[-1]), genome[0]) return FTPs pool = Pool(args['t']) pool = pool.imap_unordered(wgetGenome, FTPs) files = [] for f in tqdm(pool, total = len(accessions)): files.append(f) return files
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download genomes from NCBI
[ "download", "genomes", "from", "NCBI" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/ncbi_download.py#L204-L221
train
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: yield seq
python
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: yield seq
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remove pesky characters from fasta file header
[ "remove", "pesky", "characters", "from", "fasta", "file", "header" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/fix_fasta.py#L18-L25
train
ssanderson/pstats-view
pstatsviewer/viewer.py
_calc_frames
def _calc_frames(stats): """ Compute a DataFrame summary of a Stats object. """ timings = [] callers = [] for key, values in iteritems(stats.stats): timings.append( pd.Series( key + values[:-1], index=timing_colnames, ) ) for caller_key, caller_values in iteritems(values[-1]): callers.append( pd.Series( key + caller_key + caller_values, index=caller_columns, ) ) timings_df = pd.DataFrame(timings) callers_df = pd.DataFrame(callers) timings_df['filename:funcname'] = \ (timings_df['filename'] + ':' + timings_df['funcname']) timings_df = timings_df.groupby('filename:funcname').sum() return timings_df, callers_df
python
def _calc_frames(stats): """ Compute a DataFrame summary of a Stats object. """ timings = [] callers = [] for key, values in iteritems(stats.stats): timings.append( pd.Series( key + values[:-1], index=timing_colnames, ) ) for caller_key, caller_values in iteritems(values[-1]): callers.append( pd.Series( key + caller_key + caller_values, index=caller_columns, ) ) timings_df = pd.DataFrame(timings) callers_df = pd.DataFrame(callers) timings_df['filename:funcname'] = \ (timings_df['filename'] + ':' + timings_df['funcname']) timings_df = timings_df.groupby('filename:funcname').sum() return timings_df, callers_df
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Compute a DataFrame summary of a Stats object.
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62148d4e01765806bc5e6bb40628cdb186482c05
https://github.com/ssanderson/pstats-view/blob/62148d4e01765806bc5e6bb40628cdb186482c05/pstatsviewer/viewer.py#L40-L66
train
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 read[6] == '*': yield read for i in read: if i == 'YT:Z:UP': yield read
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 elif mates is True: if read[2] == '*' or read[6] == '*': yield read for i in read: if i == 'YT:Z:UP': yield read
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get unmapped reads
[ "get", "unmapped", "reads" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/unmapped.py#L11-L26
train
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() pool.join()
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execute jobs in processes using N threads
[ "execute", "jobs", "in", "processes", "using", "N", "threads" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/parallel.py#L19-L26
train
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 structlog.processors.JSONRenderer() if fpath is not None: return structlog.processors.JSONRenderer() if sys.stderr.isatty() and not quiet: return structlog.dev.ConsoleRenderer() return structlog.processors.JSONRenderer()
python
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 structlog.processors.JSONRenderer() if fpath is not None: return structlog.processors.JSONRenderer() if sys.stderr.isatty() and not quiet: return structlog.dev.ConsoleRenderer() return structlog.processors.JSONRenderer()
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the final log processor that structlog requires to render.
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f7233963c5291530fcb2444a7f45b556e6407b90
https://github.com/deep-compute/basescript/blob/f7233963c5291530fcb2444a7f45b556e6407b90/basescript/log.py#L239-L256
train
deep-compute/basescript
basescript/log.py
_structlog_default_keys_processor
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' % ( datetime.utcnow().strftime('%Y%m%dT%H%M%S'), uuid.uuid1().hex ) if 'type' not in event: event['type'] = 'log' event['host'] = HOSTNAME return event
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' % ( datetime.utcnow().strftime('%Y%m%dT%H%M%S'), uuid.uuid1().hex ) if 'type' not in event: event['type'] = 'log' event['host'] = HOSTNAME return event
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Add unique id, type and hostname
[ "Add", "unique", "id", "type", "and", "hostname" ]
f7233963c5291530fcb2444a7f45b556e6407b90
https://github.com/deep-compute/basescript/blob/f7233963c5291530fcb2444a7f45b556e6407b90/basescript/log.py#L258-L273
train
deep-compute/basescript
basescript/log.py
define_log_processors
def define_log_processors(): """ log processors that structlog executes before final rendering """ # these processors should accept logger, method_name and event_dict # and return a new dictionary which will be passed as event_dict to the next one. return [ structlog.processors.TimeStamper(fmt="iso"), _structlog_default_keys_processor, structlog.stdlib.PositionalArgumentsFormatter(), structlog.processors.StackInfoRenderer(), structlog.processors.format_exc_info, ]
python
def define_log_processors(): """ log processors that structlog executes before final rendering """ # these processors should accept logger, method_name and event_dict # and return a new dictionary which will be passed as event_dict to the next one. return [ structlog.processors.TimeStamper(fmt="iso"), _structlog_default_keys_processor, structlog.stdlib.PositionalArgumentsFormatter(), structlog.processors.StackInfoRenderer(), structlog.processors.format_exc_info, ]
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log processors that structlog executes before final rendering
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f7233963c5291530fcb2444a7f45b556e6407b90
https://github.com/deep-compute/basescript/blob/f7233963c5291530fcb2444a7f45b556e6407b90/basescript/log.py#L352-L364
train
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()) global _GLOBAL_LOG_CONFIGURED if _GLOBAL_LOG_CONFIGURED: return # since the hooks need to run through structlog, need to wrap them like processors def wrap_hook(fn): @wraps(fn) def processor(logger, method_name, event_dict): fn(event_dict) return event_dict return processor processors = define_log_processors() processors.extend( [ wrap_hook(h) for h in pre_hooks ] ) if metric_grouping_interval: processors.append(metrics_grouping_processor) log_renderer = define_log_renderer(fmt, fpath, quiet) stderr_required = (not quiet) pretty_to_stderr = ( stderr_required and ( fmt == "pretty" or (fmt is None and sys.stderr.isatty()) ) ) should_inject_pretty_renderer = ( pretty_to_stderr and not isinstance(log_renderer, structlog.dev.ConsoleRenderer) ) if should_inject_pretty_renderer: stderr_required = False processors.append(StderrConsoleRenderer()) processors.append(log_renderer) processors.extend( [ wrap_hook(h) for h in post_hooks ] ) streams = [] # we need to use a stream if we are writing to both file and stderr, and both are json if stderr_required: streams.append(sys.stderr) if fpath is not None: # TODO handle creating a directory for this log file ? # TODO set mode and encoding appropriately streams.append(open(fpath, 'a')) assert len(streams) != 0, "cannot configure logger for 0 streams" stream = streams[0] if len(streams) == 1 else Stream(*streams) atexit.register(stream.close) # a global level struct log config unless otherwise specified. structlog.configure( processors=processors, context_class=dict, logger_factory=LevelLoggerFactory(stream, level=level), wrapper_class=BoundLevelLogger, cache_logger_on_first_use=True, ) # TODO take care of removing other handlers stdlib_root_log = logging.getLogger() stdlib_root_log.addHandler(StdlibStructlogHandler()) stdlib_root_log.setLevel(level) _GLOBAL_LOG_CONFIGURED = True
python
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()) global _GLOBAL_LOG_CONFIGURED if _GLOBAL_LOG_CONFIGURED: return # since the hooks need to run through structlog, need to wrap them like processors def wrap_hook(fn): @wraps(fn) def processor(logger, method_name, event_dict): fn(event_dict) return event_dict return processor processors = define_log_processors() processors.extend( [ wrap_hook(h) for h in pre_hooks ] ) if metric_grouping_interval: processors.append(metrics_grouping_processor) log_renderer = define_log_renderer(fmt, fpath, quiet) stderr_required = (not quiet) pretty_to_stderr = ( stderr_required and ( fmt == "pretty" or (fmt is None and sys.stderr.isatty()) ) ) should_inject_pretty_renderer = ( pretty_to_stderr and not isinstance(log_renderer, structlog.dev.ConsoleRenderer) ) if should_inject_pretty_renderer: stderr_required = False processors.append(StderrConsoleRenderer()) processors.append(log_renderer) processors.extend( [ wrap_hook(h) for h in post_hooks ] ) streams = [] # we need to use a stream if we are writing to both file and stderr, and both are json if stderr_required: streams.append(sys.stderr) if fpath is not None: # TODO handle creating a directory for this log file ? # TODO set mode and encoding appropriately streams.append(open(fpath, 'a')) assert len(streams) != 0, "cannot configure logger for 0 streams" stream = streams[0] if len(streams) == 1 else Stream(*streams) atexit.register(stream.close) # a global level struct log config unless otherwise specified. structlog.configure( processors=processors, context_class=dict, logger_factory=LevelLoggerFactory(stream, level=level), wrapper_class=BoundLevelLogger, cache_logger_on_first_use=True, ) # TODO take care of removing other handlers stdlib_root_log = logging.getLogger() stdlib_root_log.addHandler(StdlibStructlogHandler()) stdlib_root_log.setLevel(level) _GLOBAL_LOG_CONFIGURED = True
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configures a logger when required write to stderr or a file
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f7233963c5291530fcb2444a7f45b556e6407b90
https://github.com/deep-compute/basescript/blob/f7233963c5291530fcb2444a7f45b556e6407b90/basescript/log.py#L366-L447
train
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 here. """ 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): """ Instead of using a processor, adding basic information like caller, filename etc here. """ f = sys._getframe() level_method_frame = f.f_back caller_frame = level_method_frame.f_back return event_dict
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Instead of using a processor, adding basic information like caller, filename etc here.
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f7233963c5291530fcb2444a7f45b556e6407b90
https://github.com/deep-compute/basescript/blob/f7233963c5291530fcb2444a7f45b556e6407b90/basescript/log.py#L121-L129
train
deep-compute/basescript
basescript/log.py
BoundLevelLogger._proxy_to_logger
def _proxy_to_logger(self, method_name, event, *event_args, **event_kw): """ Propagate a method call to the wrapped logger. This is the same as the superclass implementation, except that it also preserves positional arguments in the `event_dict` so that the stdblib's support for format strings can be used. """ if isinstance(event, bytes): event = event.decode('utf-8') if event_args: event_kw['positional_args'] = event_args return super(BoundLevelLogger, self)._proxy_to_logger(method_name, event=event, **event_kw)
python
def _proxy_to_logger(self, method_name, event, *event_args, **event_kw): """ Propagate a method call to the wrapped logger. This is the same as the superclass implementation, except that it also preserves positional arguments in the `event_dict` so that the stdblib's support for format strings can be used. """ if isinstance(event, bytes): event = event.decode('utf-8') if event_args: event_kw['positional_args'] = event_args return super(BoundLevelLogger, self)._proxy_to_logger(method_name, event=event, **event_kw)
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Propagate a method call to the wrapped logger. This is the same as the superclass implementation, except that it also preserves positional arguments in the `event_dict` so that the stdblib's support for format strings can be used.
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f7233963c5291530fcb2444a7f45b556e6407b90
https://github.com/deep-compute/basescript/blob/f7233963c5291530fcb2444a7f45b556e6407b90/basescript/log.py#L211-L229
train
smdabdoub/phylotoast
bin/core_overlap_plot.py
translate
def translate(rect, x, y, width=1): """ Given four points of a rectangle, translate the rectangle to the specified x and y coordinates and, optionally, change the width. :type rect: list of tuples :param rect: Four points describing a rectangle. :type x: float :param x: The amount to shift the rectangle along the x-axis. :type y: float :param y: The amount to shift the rectangle along the y-axis. :type width: float :param width: The amount by which to change the width of the rectangle. """ return ((rect[0][0]+x, rect[0][1]+y), (rect[1][0]+x, rect[1][1]+y), (rect[2][0]+x+width, rect[2][1]+y), (rect[3][0]+x+width, rect[3][1]+y))
python
def translate(rect, x, y, width=1): """ Given four points of a rectangle, translate the rectangle to the specified x and y coordinates and, optionally, change the width. :type rect: list of tuples :param rect: Four points describing a rectangle. :type x: float :param x: The amount to shift the rectangle along the x-axis. :type y: float :param y: The amount to shift the rectangle along the y-axis. :type width: float :param width: The amount by which to change the width of the rectangle. """ return ((rect[0][0]+x, rect[0][1]+y), (rect[1][0]+x, rect[1][1]+y), (rect[2][0]+x+width, rect[2][1]+y), (rect[3][0]+x+width, rect[3][1]+y))
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Given four points of a rectangle, translate the rectangle to the specified x and y coordinates and, optionally, change the width. :type rect: list of tuples :param rect: Four points describing a rectangle. :type x: float :param x: The amount to shift the rectangle along the x-axis. :type y: float :param y: The amount to shift the rectangle along the y-axis. :type width: float :param width: The amount by which to change the width of the rectangle.
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0b74ef171e6a84761710548501dfac71285a58a3
https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/core_overlap_plot.py#L57-L74
train
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 = [':', ',', '(', ')', ' ', '|', ';', '\''] for c in remove: string = string.replace(c, '_') return string
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remove problem characters from string
[ "remove", "problem", "characters", "from", "string" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/rax.py#L43-L50
train
christophertbrown/bioscripts
ctbBio/rax.py
get_ids
def get_ids(a): """ make copy of sequences with short identifier """ a_id = '%s.id.fa' % (a.rsplit('.', 1)[0]) a_id_lookup = '%s.id.lookup' % (a.rsplit('.', 1)[0]) if check(a_id) is True: return a_id, a_id_lookup a_id_f = open(a_id, 'w') a_id_lookup_f = open(a_id_lookup, 'w') ids = [] for seq in parse_fasta(open(a)): id = id_generator() while id in ids: id = id_generator() ids.append(id) header = seq[0].split('>')[1] name = remove_bad(header) seq[0] = '>%s %s' % (id, header) print('\n'.join(seq), file=a_id_f) print('%s\t%s\t%s' % (id, name, header), file=a_id_lookup_f) return a_id, a_id_lookup
python
def get_ids(a): """ make copy of sequences with short identifier """ a_id = '%s.id.fa' % (a.rsplit('.', 1)[0]) a_id_lookup = '%s.id.lookup' % (a.rsplit('.', 1)[0]) if check(a_id) is True: return a_id, a_id_lookup a_id_f = open(a_id, 'w') a_id_lookup_f = open(a_id_lookup, 'w') ids = [] for seq in parse_fasta(open(a)): id = id_generator() while id in ids: id = id_generator() ids.append(id) header = seq[0].split('>')[1] name = remove_bad(header) seq[0] = '>%s %s' % (id, header) print('\n'.join(seq), file=a_id_f) print('%s\t%s\t%s' % (id, name, header), file=a_id_lookup_f) return a_id, a_id_lookup
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make copy of sequences with short identifier
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/rax.py#L55-L76
train
christophertbrown/bioscripts
ctbBio/rax.py
convert2phylip
def convert2phylip(convert): """ convert fasta to phylip because RAxML is ridiculous """ out = '%s.phy' % (convert.rsplit('.', 1)[0]) if check(out) is False: convert = open(convert, 'rU') out_f = open(out, 'w') alignments = AlignIO.parse(convert, "fasta") AlignIO.write(alignments, out, "phylip") return out
python
def convert2phylip(convert): """ convert fasta to phylip because RAxML is ridiculous """ out = '%s.phy' % (convert.rsplit('.', 1)[0]) if check(out) is False: convert = open(convert, 'rU') out_f = open(out, 'w') alignments = AlignIO.parse(convert, "fasta") AlignIO.write(alignments, out, "phylip") return out
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convert fasta to phylip because RAxML is ridiculous
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/rax.py#L78-L88
train
christophertbrown/bioscripts
ctbBio/rax.py
run_iqtree
def run_iqtree(phy, model, threads, cluster, node): """ run IQ-Tree """ # set ppn based on threads if threads > 24: ppn = 24 else: ppn = threads tree = '%s.treefile' % (phy) if check(tree) is False: if model is False: model = 'TEST' dir = os.getcwd() command = 'iqtree-omp -s %s -m %s -nt %s -quiet' % \ (phy, model, threads) if cluster is False: p = Popen(command, shell = True) else: if node is False: node = '1' qsub = 'qsub -l nodes=%s:ppn=%s -m e -N iqtree' % (node, ppn) command = 'cd /tmp; mkdir iqtree; cd iqtree; cp %s/%s .; %s; mv * %s/; rm -r ../iqtree' \ % (dir, phy, command, dir) re_call = 'cd %s; %s --no-fast --iq' % (dir.rsplit('/', 1)[0], ' '.join(sys.argv)) p = Popen('echo "%s;%s" | %s' % (command, re_call, qsub), shell = True) p.communicate() return tree
python
def run_iqtree(phy, model, threads, cluster, node): """ run IQ-Tree """ # set ppn based on threads if threads > 24: ppn = 24 else: ppn = threads tree = '%s.treefile' % (phy) if check(tree) is False: if model is False: model = 'TEST' dir = os.getcwd() command = 'iqtree-omp -s %s -m %s -nt %s -quiet' % \ (phy, model, threads) if cluster is False: p = Popen(command, shell = True) else: if node is False: node = '1' qsub = 'qsub -l nodes=%s:ppn=%s -m e -N iqtree' % (node, ppn) command = 'cd /tmp; mkdir iqtree; cd iqtree; cp %s/%s .; %s; mv * %s/; rm -r ../iqtree' \ % (dir, phy, command, dir) re_call = 'cd %s; %s --no-fast --iq' % (dir.rsplit('/', 1)[0], ' '.join(sys.argv)) p = Popen('echo "%s;%s" | %s' % (command, re_call, qsub), shell = True) p.communicate() return tree
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run IQ-Tree
[ "run", "IQ", "-", "Tree" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/rax.py#L163-L190
train
christophertbrown/bioscripts
ctbBio/rax.py
fix_tree
def fix_tree(tree, a_id_lookup, out): """ get the names for sequences in the raxml tree """ if check(out) is False and check(tree) is True: tree = open(tree).read() for line in open(a_id_lookup): id, name, header = line.strip().split('\t') tree = tree.replace(id+':', name+':') out_f = open(out, 'w') print(tree.strip(), file=out_f) return out
python
def fix_tree(tree, a_id_lookup, out): """ get the names for sequences in the raxml tree """ if check(out) is False and check(tree) is True: tree = open(tree).read() for line in open(a_id_lookup): id, name, header = line.strip().split('\t') tree = tree.replace(id+':', name+':') out_f = open(out, 'w') print(tree.strip(), file=out_f) return out
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get the names for sequences in the raxml tree
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/rax.py#L192-L203
train
disqus/nydus
nydus/db/__init__.py
create_cluster
def create_cluster(settings): """ Creates a new Nydus cluster from the given settings. :param settings: Dictionary of the cluster settings. :returns: Configured instance of ``nydus.db.base.Cluster``. >>> redis = create_cluster({ >>> 'backend': 'nydus.db.backends.redis.Redis', >>> 'router': 'nydus.db.routers.redis.PartitionRouter', >>> 'defaults': { >>> 'host': 'localhost', >>> 'port': 6379, >>> }, >>> 'hosts': { >>> 0: {'db': 0}, >>> 1: {'db': 1}, >>> 2: {'db': 2}, >>> } >>> }) """ # Pull in our client settings = copy.deepcopy(settings) backend = settings.pop('engine', settings.pop('backend', None)) if isinstance(backend, basestring): Conn = import_string(backend) elif backend: Conn = backend else: raise KeyError('backend') # Pull in our cluster cluster = settings.pop('cluster', None) if not cluster: Cluster = Conn.get_cluster() elif isinstance(cluster, basestring): Cluster = import_string(cluster) else: Cluster = cluster # Pull in our router router = settings.pop('router', None) if not router: Router = BaseRouter elif isinstance(router, basestring): Router = import_string(router) else: Router = router # Build the connection cluster return Cluster( router=Router, backend=Conn, **settings )
python
def create_cluster(settings): """ Creates a new Nydus cluster from the given settings. :param settings: Dictionary of the cluster settings. :returns: Configured instance of ``nydus.db.base.Cluster``. >>> redis = create_cluster({ >>> 'backend': 'nydus.db.backends.redis.Redis', >>> 'router': 'nydus.db.routers.redis.PartitionRouter', >>> 'defaults': { >>> 'host': 'localhost', >>> 'port': 6379, >>> }, >>> 'hosts': { >>> 0: {'db': 0}, >>> 1: {'db': 1}, >>> 2: {'db': 2}, >>> } >>> }) """ # Pull in our client settings = copy.deepcopy(settings) backend = settings.pop('engine', settings.pop('backend', None)) if isinstance(backend, basestring): Conn = import_string(backend) elif backend: Conn = backend else: raise KeyError('backend') # Pull in our cluster cluster = settings.pop('cluster', None) if not cluster: Cluster = Conn.get_cluster() elif isinstance(cluster, basestring): Cluster = import_string(cluster) else: Cluster = cluster # Pull in our router router = settings.pop('router', None) if not router: Router = BaseRouter elif isinstance(router, basestring): Router = import_string(router) else: Router = router # Build the connection cluster return Cluster( router=Router, backend=Conn, **settings )
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Creates a new Nydus cluster from the given settings. :param settings: Dictionary of the cluster settings. :returns: Configured instance of ``nydus.db.base.Cluster``. >>> redis = create_cluster({ >>> 'backend': 'nydus.db.backends.redis.Redis', >>> 'router': 'nydus.db.routers.redis.PartitionRouter', >>> 'defaults': { >>> 'host': 'localhost', >>> 'port': 6379, >>> }, >>> 'hosts': { >>> 0: {'db': 0}, >>> 1: {'db': 1}, >>> 2: {'db': 2}, >>> } >>> })
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9b505840da47a34f758a830c3992fa5dcb7bb7ad
https://github.com/disqus/nydus/blob/9b505840da47a34f758a830c3992fa5dcb7bb7ad/nydus/db/__init__.py#L28-L82
train
dokterbob/django-multilingual-model
multilingual_model/models.py
MultilingualModel._get_translation
def _get_translation(self, field, code): """ Gets the translation of a specific field for a specific language code. This raises ObjectDoesNotExist if the lookup was unsuccesful. As of today, this stuff is cached. As the cache is rather aggressive it might cause rather strange effects. However, we would see the same effects when an ordinary object is changed which is already in memory: the old state would remain. """ if not code in self._translation_cache: translations = self.translations.select_related() logger.debug( u'Matched with field %s for language %s. Attempting lookup.', field, code ) try: translation_obj = translations.get(language_code=code) except ObjectDoesNotExist: translation_obj = None self._translation_cache[code] = translation_obj logger.debug(u'Translation not found in cache.') else: logger.debug(u'Translation found in cache.') # Get the translation from the cache translation_obj = self._translation_cache.get(code) # If this is none, it means that a translation does not exist # It is important to cache this one as well if not translation_obj: raise ObjectDoesNotExist field_value = getattr(translation_obj, field) logger.debug( u'Found translation object %s, returning value %s.', translation_obj, field_value ) return field_value
python
def _get_translation(self, field, code): """ Gets the translation of a specific field for a specific language code. This raises ObjectDoesNotExist if the lookup was unsuccesful. As of today, this stuff is cached. As the cache is rather aggressive it might cause rather strange effects. However, we would see the same effects when an ordinary object is changed which is already in memory: the old state would remain. """ if not code in self._translation_cache: translations = self.translations.select_related() logger.debug( u'Matched with field %s for language %s. Attempting lookup.', field, code ) try: translation_obj = translations.get(language_code=code) except ObjectDoesNotExist: translation_obj = None self._translation_cache[code] = translation_obj logger.debug(u'Translation not found in cache.') else: logger.debug(u'Translation found in cache.') # Get the translation from the cache translation_obj = self._translation_cache.get(code) # If this is none, it means that a translation does not exist # It is important to cache this one as well if not translation_obj: raise ObjectDoesNotExist field_value = getattr(translation_obj, field) logger.debug( u'Found translation object %s, returning value %s.', translation_obj, field_value ) return field_value
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2479b2c3d6f7b697e95aa1e082c8bc8699f1f638
https://github.com/dokterbob/django-multilingual-model/blob/2479b2c3d6f7b697e95aa1e082c8bc8699f1f638/multilingual_model/models.py#L44-L90
train
dokterbob/django-multilingual-model
multilingual_model/models.py
MultilingualModel.unicode_wrapper
def unicode_wrapper(self, property, default=ugettext('Untitled')): """ Wrapper to allow for easy unicode representation of an object by the specified property. If this wrapper is not able to find the right translation of the specified property, it will return the default value instead. Example:: def __unicode__(self): return unicode_wrapper('name', default='Unnamed') """ # TODO: Test coverage! try: value = getattr(self, property) except ValueError: logger.warn( u'ValueError rendering unicode for %s object.', self._meta.object_name ) value = None if not value: value = default return value
python
def unicode_wrapper(self, property, default=ugettext('Untitled')): """ Wrapper to allow for easy unicode representation of an object by the specified property. If this wrapper is not able to find the right translation of the specified property, it will return the default value instead. Example:: def __unicode__(self): return unicode_wrapper('name', default='Unnamed') """ # TODO: Test coverage! try: value = getattr(self, property) except ValueError: logger.warn( u'ValueError rendering unicode for %s object.', self._meta.object_name ) value = None if not value: value = default return value
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Wrapper to allow for easy unicode representation of an object by the specified property. If this wrapper is not able to find the right translation of the specified property, it will return the default value instead. Example:: def __unicode__(self): return unicode_wrapper('name', default='Unnamed')
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2479b2c3d6f7b697e95aa1e082c8bc8699f1f638
https://github.com/dokterbob/django-multilingual-model/blob/2479b2c3d6f7b697e95aa1e082c8bc8699f1f638/multilingual_model/models.py#L202-L228
train
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()]) yield seq
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remove insertion columns from aligned fasta file
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/strip_align_inserts.py#L12-L18
train
cldf/segments
src/segments/tokenizer.py
Tokenizer.transform
def transform(self, word, column=Profile.GRAPHEME_COL, error=errors.replace): """ 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. column : str (default = "Grapheme") The label of the column to transform to. Default it to tokenize with orthography profile. Returns ------- result : list of lists Result of the transformation. """ assert self.op, 'method can only be called with orthography profile.' if column != Profile.GRAPHEME_COL and column not in self.op.column_labels: raise ValueError("Column {0} not found in profile.".format(column)) word = self.op.tree.parse(word, error) if column == Profile.GRAPHEME_COL: return word out = [] for token in word: try: target = self.op.graphemes[token][column] except KeyError: target = self._errors['replace'](token) if target is not None: if isinstance(target, (tuple, list)): out.extend(target) else: out.append(target) return out
python
def transform(self, word, column=Profile.GRAPHEME_COL, error=errors.replace): """ 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. column : str (default = "Grapheme") The label of the column to transform to. Default it to tokenize with orthography profile. Returns ------- result : list of lists Result of the transformation. """ assert self.op, 'method can only be called with orthography profile.' if column != Profile.GRAPHEME_COL and column not in self.op.column_labels: raise ValueError("Column {0} not found in profile.".format(column)) word = self.op.tree.parse(word, error) if column == Profile.GRAPHEME_COL: return word out = [] for token in word: try: target = self.op.graphemes[token][column] except KeyError: target = self._errors['replace'](token) if target is not None: if isinstance(target, (tuple, list)): out.extend(target) else: out.append(target) return out
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9136a4ec89555bf9b574399ffbb07f3cc9a9f45f
https://github.com/cldf/segments/blob/9136a4ec89555bf9b574399ffbb07f3cc9a9f45f/src/segments/tokenizer.py#L231-L270
train
cldf/segments
src/segments/tokenizer.py
Tokenizer.rules
def rules(self, word): """ Function to tokenize input string and return output of str with ortho rules applied. Parameters ---------- word : str The input string to be tokenized. Returns ------- result : str Result of the orthography rules applied to the input str. """ return self._rules.apply(word) if self._rules else word
python
def rules(self, word): """ Function to tokenize input string and return output of str with ortho rules applied. Parameters ---------- word : str The input string to be tokenized. Returns ------- result : str Result of the orthography rules applied to the input str. """ return self._rules.apply(word) if self._rules else word
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Function to tokenize input string and return output of str with ortho rules applied. Parameters ---------- word : str The input string to be tokenized. Returns ------- result : str Result of the orthography rules applied to the input str.
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9136a4ec89555bf9b574399ffbb07f3cc9a9f45f
https://github.com/cldf/segments/blob/9136a4ec89555bf9b574399ffbb07f3cc9a9f45f/src/segments/tokenizer.py#L272-L288
train
cldf/segments
src/segments/tokenizer.py
Tokenizer.combine_modifiers
def combine_modifiers(self, graphemes): """ Given a string that is space-delimited on Unicode grapheme clusters, group Unicode modifier letters with their preceding base characters, deal with tie bars, etc. Parameters ---------- string : str A Unicode string tokenized into grapheme clusters to be tokenized into simple IPA. """ result = [] temp = "" count = len(graphemes) for grapheme in reversed(graphemes): count -= 1 if len(grapheme) == 1 and unicodedata.category(grapheme) == "Lm" \ and not ord(grapheme) in [712, 716]: temp = grapheme + temp # hack for the cases where a space modifier is the first character in the # string if count == 0: result[-1] = temp + result[-1] continue # pragma: no cover # catch and repair stress marks if len(grapheme) == 1 and ord(grapheme) in [712, 716]: result[-1] = grapheme + result[-1] temp = "" continue # combine contour tone marks (non-accents) if len(grapheme) == 1 and unicodedata.category(grapheme) == "Sk": if len(result) == 0: result.append(grapheme) temp = "" continue else: if unicodedata.category(result[-1][0]) == "Sk": result[-1] = grapheme + result[-1] temp = "" continue result.append(grapheme + temp) temp = "" # last check for tie bars segments = result[::-1] i = 0 r = [] while i < len(segments): # tie bars if ord(segments[i][-1]) in [865, 860]: r.append(segments[i] + segments[i + 1]) i += 2 else: r.append(segments[i]) i += 1 return r
python
def combine_modifiers(self, graphemes): """ Given a string that is space-delimited on Unicode grapheme clusters, group Unicode modifier letters with their preceding base characters, deal with tie bars, etc. Parameters ---------- string : str A Unicode string tokenized into grapheme clusters to be tokenized into simple IPA. """ result = [] temp = "" count = len(graphemes) for grapheme in reversed(graphemes): count -= 1 if len(grapheme) == 1 and unicodedata.category(grapheme) == "Lm" \ and not ord(grapheme) in [712, 716]: temp = grapheme + temp # hack for the cases where a space modifier is the first character in the # string if count == 0: result[-1] = temp + result[-1] continue # pragma: no cover # catch and repair stress marks if len(grapheme) == 1 and ord(grapheme) in [712, 716]: result[-1] = grapheme + result[-1] temp = "" continue # combine contour tone marks (non-accents) if len(grapheme) == 1 and unicodedata.category(grapheme) == "Sk": if len(result) == 0: result.append(grapheme) temp = "" continue else: if unicodedata.category(result[-1][0]) == "Sk": result[-1] = grapheme + result[-1] temp = "" continue result.append(grapheme + temp) temp = "" # last check for tie bars segments = result[::-1] i = 0 r = [] while i < len(segments): # tie bars if ord(segments[i][-1]) in [865, 860]: r.append(segments[i] + segments[i + 1]) i += 2 else: r.append(segments[i]) i += 1 return r
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Given a string that is space-delimited on Unicode grapheme clusters, group Unicode modifier letters with their preceding base characters, deal with tie bars, etc. Parameters ---------- string : str A Unicode string tokenized into grapheme clusters to be tokenized into simple IPA.
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9136a4ec89555bf9b574399ffbb07f3cc9a9f45f
https://github.com/cldf/segments/blob/9136a4ec89555bf9b574399ffbb07f3cc9a9f45f/src/segments/tokenizer.py#L290-L349
train
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 = intron[0].split('>')[1].split() Start, End = [int(i) for i in pos.split('-')] if strand != GeneStrand: if strand == '+': strand = '-' else: strand = '+' Start, End = End - 2, Start - 2 Start, End = abs(Start + offset) - 1, abs(End + offset) - 1 gff['#seqname'].append(insertion['ID']) gff['source'].append('Rfam') gff['feature'].append('Catalytic RNA') gff['start'].append(Start) gff['end'].append(End) gff['score'].append('.') gff['strand'].append(strand) gff['frame'].append('.') gff['attribute'].append('ID=%s; Name=%s' % (ID, annot)) return gff
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 for intron in parse_fasta(insertion['intron'].split('|')): ID, annot, strand, pos = intron[0].split('>')[1].split() Start, End = [int(i) for i in pos.split('-')] if strand != GeneStrand: if strand == '+': strand = '-' else: strand = '+' Start, End = End - 2, Start - 2 Start, End = abs(Start + offset) - 1, abs(End + offset) - 1 gff['#seqname'].append(insertion['ID']) gff['source'].append('Rfam') gff['feature'].append('Catalytic RNA') gff['start'].append(Start) gff['end'].append(End) gff['score'].append('.') gff['strand'].append(strand) gff['frame'].append('.') gff['attribute'].append('ID=%s; Name=%s' % (ID, annot)) return gff
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parse catalytic RNAs to gff format
[ "parse", "catalytic", "RNAs", "to", "gff", "format" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/rRNA_insertions_gff.py#L13-L40
train
christophertbrown/bioscripts
ctbBio/rRNA_insertions_gff.py
parse_orf
def parse_orf(insertion, gff): """ parse ORF to gff format """ offset = insertion['offset'] if type(insertion['orf']) is not str: return gff for orf in parse_fasta(insertion['orf'].split('|')): ID = orf[0].split('>')[1].split()[0] Start, End, strand = [int(i) for i in orf[0].split(' # ')[1:4]] if strand == 1: strand = '+' else: strand = '-' GeneStrand = insertion['strand'] if strand != GeneStrand: if strand == '+': strand = '-' else: strand = '+' Start, End = End - 2, Start - 2 Start, End = abs(Start + offset) - 1, abs(End + offset) - 1 annot = orf[0].split()[1] if annot == 'n/a': annot = 'unknown' gff['#seqname'].append(insertion['ID']) gff['source'].append('Prodigal and Pfam') gff['feature'].append('CDS') gff['start'].append(Start) gff['end'].append(End) gff['score'].append('.') gff['strand'].append(strand) gff['frame'].append('.') gff['attribute'].append('ID=%s; Name=%s' % (ID, annot)) return gff
python
def parse_orf(insertion, gff): """ parse ORF to gff format """ offset = insertion['offset'] if type(insertion['orf']) is not str: return gff for orf in parse_fasta(insertion['orf'].split('|')): ID = orf[0].split('>')[1].split()[0] Start, End, strand = [int(i) for i in orf[0].split(' # ')[1:4]] if strand == 1: strand = '+' else: strand = '-' GeneStrand = insertion['strand'] if strand != GeneStrand: if strand == '+': strand = '-' else: strand = '+' Start, End = End - 2, Start - 2 Start, End = abs(Start + offset) - 1, abs(End + offset) - 1 annot = orf[0].split()[1] if annot == 'n/a': annot = 'unknown' gff['#seqname'].append(insertion['ID']) gff['source'].append('Prodigal and Pfam') gff['feature'].append('CDS') gff['start'].append(Start) gff['end'].append(End) gff['score'].append('.') gff['strand'].append(strand) gff['frame'].append('.') gff['attribute'].append('ID=%s; Name=%s' % (ID, annot)) return gff
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parse ORF to gff format
[ "parse", "ORF", "to", "gff", "format" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/rRNA_insertions_gff.py#L42-L76
train
christophertbrown/bioscripts
ctbBio/rRNA_insertions_gff.py
parse_insertion
def parse_insertion(insertion, gff): """ parse insertion to gff format """ offset = insertion['offset'] for ins in parse_fasta(insertion['insertion sequence'].split('|')): strand = insertion['strand'] ID = ins[0].split('>')[1].split()[0] Start, End = [int(i) for i in ins[0].split('gene-pos=', 1)[1].split()[0].split('-')] Start, End = abs(Start + offset), abs(End + offset) if strand == '-': Start, End = End, Start gff['#seqname'].append(insertion['ID']) gff['source'].append(insertion['source']) gff['feature'].append('IVS') gff['start'].append(Start) gff['end'].append(End) gff['score'].append('.') gff['strand'].append(strand) # same as rRNA gff['frame'].append('.') gff['attribute'].append('ID=%s' % (ID)) return gff
python
def parse_insertion(insertion, gff): """ parse insertion to gff format """ offset = insertion['offset'] for ins in parse_fasta(insertion['insertion sequence'].split('|')): strand = insertion['strand'] ID = ins[0].split('>')[1].split()[0] Start, End = [int(i) for i in ins[0].split('gene-pos=', 1)[1].split()[0].split('-')] Start, End = abs(Start + offset), abs(End + offset) if strand == '-': Start, End = End, Start gff['#seqname'].append(insertion['ID']) gff['source'].append(insertion['source']) gff['feature'].append('IVS') gff['start'].append(Start) gff['end'].append(End) gff['score'].append('.') gff['strand'].append(strand) # same as rRNA gff['frame'].append('.') gff['attribute'].append('ID=%s' % (ID)) return gff
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parse insertion to gff format
[ "parse", "insertion", "to", "gff", "format" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/rRNA_insertions_gff.py#L78-L99
train
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 == '-': Start, End = End - 2, Start - 2 pos = (abs(Start + offset) - 1, abs(End + offset) - 1) Start, End = min(pos), max(pos) source = insertion['source'] annot = '%s rRNA' % (source.split('from', 1)[0]) gff['#seqname'].append(insertion['ID']) gff['source'].append(source) gff['feature'].append('rRNA') gff['start'].append(Start) gff['end'].append(End) gff['score'].append('.') gff['strand'].append(strand) gff['frame'].append('.') gff['attribute'].append('Name=%s' % (annot)) return gff
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 == '-': Start, End = End - 2, Start - 2 pos = (abs(Start + offset) - 1, abs(End + offset) - 1) Start, End = min(pos), max(pos) source = insertion['source'] annot = '%s rRNA' % (source.split('from', 1)[0]) gff['#seqname'].append(insertion['ID']) gff['source'].append(source) gff['feature'].append('rRNA') gff['start'].append(Start) gff['end'].append(End) gff['score'].append('.') gff['strand'].append(strand) gff['frame'].append('.') gff['attribute'].append('Name=%s' % (annot)) return gff
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parse rRNA to gff format
[ "parse", "rRNA", "to", "gff", "format" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/rRNA_insertions_gff.py#L122-L147
train
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} for insertion in iTable.iterrows(): insertion = insertion[1] if insertion['ID'] not in fa: continue # rRNA strand strand = insertion['sequence'].split('strand=', 1)[1].split()[0] # set rRNA positions for reporting features on contig or extracted sequence if contig is True: gene = [int(i) for i in insertion['sequence'].split('pos=', 1)[1].split()[0].split('-')] if strand == '-': offset = -1 * (gene[1]) else: offset = gene[0] else: strand = '+' gene = [1, int(insertion['sequence'].split('total-len=', 1)[1].split()[0])] offset = gene[0] insertion['strand'] = strand insertion['offset'] = offset # source for prediction source = insertion['sequence'].split('::model', 1)[0].rsplit(' ', 1)[-1] insertion['source'] = source # rRNA gene geneAnnot = '%s rRNA gene' % (source.split('from', 1)[0]) geneNum = insertion['sequence'].split('seq=', 1)[1].split()[0] gff['#seqname'].append(insertion['ID']) gff['source'].append(source) gff['feature'].append('Gene') gff['start'].append(gene[0]) gff['end'].append(gene[1]) gff['score'].append('.') gff['strand'].append(strand) gff['frame'].append('.') gff['attribute'].append('ID=%s; Name=%s' % (geneNum, geneAnnot)) # rRNA gff = parse_rRNA(insertion, fa[insertion['ID']], gff) # insertions gff = parse_insertion(insertion, gff) # orfs gff = parse_orf(insertion, gff) # catalytic RNAs gff = parse_catalytic(insertion, gff) return pd.DataFrame(gff)[columns].drop_duplicates()
python
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} for insertion in iTable.iterrows(): insertion = insertion[1] if insertion['ID'] not in fa: continue # rRNA strand strand = insertion['sequence'].split('strand=', 1)[1].split()[0] # set rRNA positions for reporting features on contig or extracted sequence if contig is True: gene = [int(i) for i in insertion['sequence'].split('pos=', 1)[1].split()[0].split('-')] if strand == '-': offset = -1 * (gene[1]) else: offset = gene[0] else: strand = '+' gene = [1, int(insertion['sequence'].split('total-len=', 1)[1].split()[0])] offset = gene[0] insertion['strand'] = strand insertion['offset'] = offset # source for prediction source = insertion['sequence'].split('::model', 1)[0].rsplit(' ', 1)[-1] insertion['source'] = source # rRNA gene geneAnnot = '%s rRNA gene' % (source.split('from', 1)[0]) geneNum = insertion['sequence'].split('seq=', 1)[1].split()[0] gff['#seqname'].append(insertion['ID']) gff['source'].append(source) gff['feature'].append('Gene') gff['start'].append(gene[0]) gff['end'].append(gene[1]) gff['score'].append('.') gff['strand'].append(strand) gff['frame'].append('.') gff['attribute'].append('ID=%s; Name=%s' % (geneNum, geneAnnot)) # rRNA gff = parse_rRNA(insertion, fa[insertion['ID']], gff) # insertions gff = parse_insertion(insertion, gff) # orfs gff = parse_orf(insertion, gff) # catalytic RNAs gff = parse_catalytic(insertion, gff) return pd.DataFrame(gff)[columns].drop_duplicates()
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convert iTable to gff file
[ "convert", "iTable", "to", "gff", "file" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/rRNA_insertions_gff.py#L149-L197
train
smdabdoub/phylotoast
bin/biom_phyla_summary.py
summarize_taxa
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']: tot_seqs += amt rtax = biom['rows'][row]['metadata']['taxonomy'] for i, t in enumerate(rtax): t = t.strip() if i == len(rtax)-1 and len(t) > 3 and len(rtax[-1]) > 3: t = 's__'+rtax[i-1].strip().split('_')[-1]+'_'+t.split('_')[-1] tamtcounts[t] += amt lvlData = {lvl: levelData(tamtcounts, tot_seqs, lvl) for lvl in ['k', 'p', 'c', 'o', 'f', 'g', 's']} return tot_seqs, lvlData
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']: tot_seqs += amt rtax = biom['rows'][row]['metadata']['taxonomy'] for i, t in enumerate(rtax): t = t.strip() if i == len(rtax)-1 and len(t) > 3 and len(rtax[-1]) > 3: t = 's__'+rtax[i-1].strip().split('_')[-1]+'_'+t.split('_')[-1] tamtcounts[t] += amt lvlData = {lvl: levelData(tamtcounts, tot_seqs, lvl) for lvl in ['k', 'p', 'c', 'o', 'f', 'g', 's']} return tot_seqs, lvlData
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Given an abundance table, group the counts by every taxonomic level.
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0b74ef171e6a84761710548501dfac71285a58a3
https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/biom_phyla_summary.py#L27-L46
train
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): return image_location return None
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) if os.path.isfile(image_location): return image_location return None
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Returns the path to the custom image set for this game, or None if no image is set
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1eb2254b5235a053a953e596fa7602d0b110245d
https://github.com/scottrice/pysteam/blob/1eb2254b5235a053a953e596fa7602d0b110245d/pysteam/legacy/game.py#L41-L48
train
scottrice/pysteam
pysteam/legacy/game.py
Game.set_image
def set_image(self, user, image_path): """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 an image file on disk""" _, ext = os.path.splitext(image_path) shutil.copy(image_path, 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
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1eb2254b5235a053a953e596fa7602d0b110245d
https://github.com/scottrice/pysteam/blob/1eb2254b5235a053a953e596fa7602d0b110245d/pysteam/legacy/game.py#L50-L54
train
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() id, map = line[0], int(line[1]) if map != 4 and map != 8: list.append(id) return set(list)
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get a list of mapped reads
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/filter_fastq_sam.py#L7-L19
train
christophertbrown/bioscripts
ctbBio/filter_fastq_sam.py
sam_list_paired
def sam_list_paired(sam): """ get a list of mapped reads require that both pairs are mapped in the sam file in order to remove the reads """ list = [] pair = ['1', '2'] prev = '' 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: read = id.rsplit('/')[0] if read == prev: list.append(read) prev = read return set(list)
python
def sam_list_paired(sam): """ get a list of mapped reads require that both pairs are mapped in the sam file in order to remove the reads """ list = [] pair = ['1', '2'] prev = '' 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: read = id.rsplit('/')[0] if read == prev: list.append(read) prev = read return set(list)
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get a list of mapped reads require that both pairs are mapped in the sam file in order to remove the reads
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/filter_fastq_sam.py#L21-L39
train
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: filtered.extend(ids) return set(filtered)
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: pairs[read] = [] pairs[read].append(id) for read in pairs: ids = pairs[read] if len(ids) == 2: filtered.extend(ids) return set(filtered)
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require that both pairs are mapped in the sam file in order to remove the reads
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/filter_fastq_sam.py#L41-L56
train
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]) fastq.append(line[10]) return fastq
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print fastq from sam
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/mapped.py#L13-L22
train
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 in a pair need to pass the mismatch threshold - pair can be False if read does not have a pair - make sure alignment score is not 0, which would indicate that the read was not aligned to the reference """ # if read is not paired, make sure it is mapped and that mm <= thresh if pair is False: mm = count_mismatches(read) if mm is False: return False # if no threshold is supplied, return True if mismatches is False: return True # passes threshold? if mm <= mismatches: return True # paired reads r_mm = count_mismatches(read) p_mm = count_mismatches(pair) # if neither read is mapped, return False if r_mm is False and p_mm is False: return False # if no threshold, return True if mismatches is False: return True # if req_map is True, both reads have to map if req_map is True: if r_mm is False or p_mm is False: return False ## if option is 'one,' only one read has to pass threshold if mm_option == 'one': if (r_mm is not False and r_mm <= mismatches) or (p_mm is not False and p_mm <= mismatches): return True ## if option is 'both,' both reads have to pass threshold if mm_option == 'both': ## if one read in pair does not map to the scaffold, ## make sure the other read passes threshold if r_mm is False: if p_mm <= mismatches: return True elif p_mm is False: if r_mm <= mismatches: return True elif (r_mm is not False and r_mm <= mismatches) and (p_mm is not False and p_mm <= mismatches): return True return False
python
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 in a pair need to pass the mismatch threshold - pair can be False if read does not have a pair - make sure alignment score is not 0, which would indicate that the read was not aligned to the reference """ # if read is not paired, make sure it is mapped and that mm <= thresh if pair is False: mm = count_mismatches(read) if mm is False: return False # if no threshold is supplied, return True if mismatches is False: return True # passes threshold? if mm <= mismatches: return True # paired reads r_mm = count_mismatches(read) p_mm = count_mismatches(pair) # if neither read is mapped, return False if r_mm is False and p_mm is False: return False # if no threshold, return True if mismatches is False: return True # if req_map is True, both reads have to map if req_map is True: if r_mm is False or p_mm is False: return False ## if option is 'one,' only one read has to pass threshold if mm_option == 'one': if (r_mm is not False and r_mm <= mismatches) or (p_mm is not False and p_mm <= mismatches): return True ## if option is 'both,' both reads have to pass threshold if mm_option == 'both': ## if one read in pair does not map to the scaffold, ## make sure the other read passes threshold if r_mm is False: if p_mm <= mismatches: return True elif p_mm is False: if r_mm <= mismatches: return True elif (r_mm is not False and r_mm <= mismatches) and (p_mm is not False and p_mm <= mismatches): return True return False
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/mapped.py#L36-L84
train
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 = [start, start + length - 1] if get_overlap(r, region) > 0: return True return False
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]) r = [start, start + length - 1] if get_overlap(r, region) > 0: return True return False
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determine whether or not reads map to specific region of scaffold
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/mapped.py#L92-L105
train
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 if it does. # If the directory doesnt exist it returns None instead helper = lambda udd: Steam(udd) if os.path.exists(udd) else None # For both OS X and Linux, Steam stores it's userdata in a consistent # location. plat = platform.system() if plat == 'Darwin': return helper(paths.default_osx_userdata_path()) if plat == 'Linux': return helper(paths.default_linux_userdata_path()) # Windows is a bit trickier. The userdata directory is stored in the Steam # installation directory, meaning that theoretically it could be anywhere. # Luckily, Valve stores the installation directory in the registry, so its # still possible for us to figure out automatically if plat == 'Windows': possible_dir = winutils.find_userdata_directory() # Unlike the others, `possible_dir` might be None (if something odd # happened with the registry) return helper(possible_dir) if possible_dir is not None else None # This should never be hit. Windows, OS X, and Linux should be the only # supported platforms. # TODO: Add logging here so that the user (developer) knows that something # odd happened. return None
python
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 if it does. # If the directory doesnt exist it returns None instead helper = lambda udd: Steam(udd) if os.path.exists(udd) else None # For both OS X and Linux, Steam stores it's userdata in a consistent # location. plat = platform.system() if plat == 'Darwin': return helper(paths.default_osx_userdata_path()) if plat == 'Linux': return helper(paths.default_linux_userdata_path()) # Windows is a bit trickier. The userdata directory is stored in the Steam # installation directory, meaning that theoretically it could be anywhere. # Luckily, Valve stores the installation directory in the registry, so its # still possible for us to figure out automatically if plat == 'Windows': possible_dir = winutils.find_userdata_directory() # Unlike the others, `possible_dir` might be None (if something odd # happened with the registry) return helper(possible_dir) if possible_dir is not None else None # This should never be hit. Windows, OS X, and Linux should be the only # supported platforms. # TODO: Add logging here so that the user (developer) knows that something # odd happened. return None
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Returns a Steam object representing the current Steam installation on the users computer. If the user doesn't have Steam installed, returns None.
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1eb2254b5235a053a953e596fa7602d0b110245d
https://github.com/scottrice/pysteam/blob/1eb2254b5235a053a953e596fa7602d0b110245d/pysteam/steam.py#L12-L43
train
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: if ma == m: t_row.append(0) else: t_row.append((i - m)/(ma - m)) t.append(t_row) return t
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 = [] if option != 'table': m, ma = min(row), max(row) for i in row: if ma == m: t_row.append(0) else: t_row.append((i - m)/(ma - m)) t.append(t_row) return t
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normalize from zero to one for row or table
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/transform.py#L18-L36
train
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: t_row.append(0) else: t_row.append(i/total*100) t.append(t_row) return t
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 = [] if option != 'table': total = sum(row) for i in row: if total == 0: t_row.append(0) else: t_row.append(i/total*100) t.append(t_row) return t
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calculate percent of total
[ "calculate", "percent", "of", "total" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/transform.py#L38-L55
train
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_factor = [scale_to/i for i in sums if i != 0] for row in table: t.append([a * b for a,b in zip(row, scale_factor)]) return t
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_factor = [scale_to/i for i in sums if i != 0] for row in table: t.append([a * b for a,b in zip(row, scale_factor)]) return t
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scale table based on the column with the largest sum
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/transform.py#L79-L93
train
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])) return t
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fit to normal distribution
[ "fit", "to", "normal", "distribution" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/transform.py#L95-L105
train
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 + scale for j in i]))) return t
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 else: scale = 0 for i in table: t.append(np.ndarray.tolist(np.log10([j + scale for j in i]))) return t
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log transform each value in table
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/transform.py#L107-L119
train
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 in i]))[0])) return t
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 in i]))[0])) return t
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box-cox transform table
[ "box", "-", "cox", "transform", "table" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/transform.py#L121-L133
train
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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inverse hyperbolic sine transformation
[ "inverse", "hyperbolic", "sine", "transformation" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/transform.py#L135-L142
train
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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from SparCC - "randomly draw from the corresponding posterior Dirichlet distribution with a uniform prior"
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/transform.py#L144-L153
train
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', 'T', 'C', 'G']) def rand_seq(n): return ''.join([rand_base() for _ in range(n)]) # homopolymer filter regex: match if 4 identical bases in a row hpf = re.compile('aaaa|cccc|gggg|tttt', re.IGNORECASE) while True: codes = [rand_seq(codeLen)] if (hpf.search(codes[0]) is None): break idx = 0 while len(codes) < nIds: idx -= 1 if idx < -codeLen: idx = -1 codes.append(rand_seq(codeLen)) else: nc = next_code(rand_base(), codes[-1], idx) if hpf.search(nc) is None: codes.append(nc) codes = list(set(codes)) return codes
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', 'T', 'C', 'G']) def rand_seq(n): return ''.join([rand_base() for _ in range(n)]) # homopolymer filter regex: match if 4 identical bases in a row hpf = re.compile('aaaa|cccc|gggg|tttt', re.IGNORECASE) while True: codes = [rand_seq(codeLen)] if (hpf.search(codes[0]) is None): break idx = 0 while len(codes) < nIds: idx -= 1 if idx < -codeLen: idx = -1 codes.append(rand_seq(codeLen)) else: nc = next_code(rand_base(), codes[-1], idx) if hpf.search(nc) is None: codes.append(nc) codes = list(set(codes)) return codes
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Given a list of sample IDs generate unique n-base barcodes for each. Note that only 4^n unique barcodes are possible.
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0b74ef171e6a84761710548501dfac71285a58a3
https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/sanger_qiimify.py#L94-L128
train
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 outfiles = [osp.split(outF.name)[1]] if qualF: outfiles.append(osp.split(qualF.name)[1]) for item in os.listdir(dataDir): if item in outfiles or not osp.isfile(os.path.join(dataDir, item)): continue # FASTA files if osp.splitext(item)[1] in file_types['fasta']: fh = open_enc(os.path.join(dataDir, item), utf16) records = SeqIO.parse(fh, 'fasta') for record in records: if isinstance(idopt, tuple): sep, field = idopt sampleID = record.id.split(sep)[field - 1] else: sampleID = osp.splitext(item)[0] record.seq = (sampleMap[sampleID].barcode + sampleMap[sampleID].primer + record.seq) SeqIO.write(record, outF, 'fasta') seqcount += 1 fh.close() # QUAL files elif qualF and osp.splitext(item)[1] in file_types['qual']: fh = open_enc(os.path.join(dataDir, item), utf16) records = SeqIO.parse(fh, 'qual') for record in records: mi = sampleMap[sampleMap.keys()[0]] quals = [40 for _ in range(len(mi.barcode) + len(mi.primer))] record.letter_annotations['phred_quality'][0:0] = quals SeqIO.write(record, qualF, 'qual') fh.close() return seqcount
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 and change the description line as needed. """ seqcount = 0 outfiles = [osp.split(outF.name)[1]] if qualF: outfiles.append(osp.split(qualF.name)[1]) for item in os.listdir(dataDir): if item in outfiles or not osp.isfile(os.path.join(dataDir, item)): continue # FASTA files if osp.splitext(item)[1] in file_types['fasta']: fh = open_enc(os.path.join(dataDir, item), utf16) records = SeqIO.parse(fh, 'fasta') for record in records: if isinstance(idopt, tuple): sep, field = idopt sampleID = record.id.split(sep)[field - 1] else: sampleID = osp.splitext(item)[0] record.seq = (sampleMap[sampleID].barcode + sampleMap[sampleID].primer + record.seq) SeqIO.write(record, outF, 'fasta') seqcount += 1 fh.close() # QUAL files elif qualF and osp.splitext(item)[1] in file_types['qual']: fh = open_enc(os.path.join(dataDir, item), utf16) records = SeqIO.parse(fh, 'qual') for record in records: mi = sampleMap[sampleMap.keys()[0]] quals = [40 for _ in range(len(mi.barcode) + len(mi.primer))] record.letter_annotations['phred_quality'][0:0] = quals SeqIO.write(record, qualF, 'qual') fh.close() return seqcount
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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.
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0b74ef171e6a84761710548501dfac71285a58a3
https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/sanger_qiimify.py#L158-L199
train
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 \ derived data files for use with the \ metagenomics analysis program QIIME, by \ extracting Sample ID information, adding\ barcodes and primers to the sequence \ data, and outputting a mapping file and\ single FASTA-formatted sequence file \ formed by concatenating all input data.") parser.add_argument('-i', '--input_dir', required=True, help="The directory containing sequence data files. \ Assumes all data files are placed in this \ directory. For files organized within folders by\ sample, use -s in addition.") parser.add_argument('-m', '--map_file', default='map.txt', help="QIIME-formatted mapping file linking Sample IDs \ with barcodes and primers.") parser.add_argument('-o', '--output', default='output.fasta', metavar='OUTPUT_FILE', help="Single file containing all sequence data found \ in input_dir, FASTA-formatted with barcode and \ primer preprended to sequence. If the -q option \ is passed, any quality data will also be output \ to a single file of the same name with a .qual \ extension.") parser.add_argument('-b', '--barcode_length', type=int, default=12, help="Length of the generated barcode sequences. \ Default is 12 (QIIME default), minimum is 8.") parser.add_argument('-q', '--qual', action='store_true', default=False, help="Instruct the program to look for quality \ input files") parser.add_argument('-u', '--utf16', action='store_true', default=False, help="UTF-16 encoded input files") parser.add_argument('-t', '--treatment', help="Inserts an additional column into the mapping \ file specifying some treatment or other variable\ that separates the current set of sequences \ from any other set of seqeunces. For example:\ -t DiseaseState=healthy") # data input options sidGroup = parser.add_mutually_exclusive_group(required=True) sidGroup.add_argument('-d', '--identifier_pattern', action=ValidateIDPattern, nargs=2, metavar=('SEPARATOR', 'FIELD_NUMBER'), help="Indicates how to extract the Sample ID from \ the description line. Specify two things: \ 1. Field separator, 2. Field number of Sample \ ID (1 or greater). If the separator is a space \ or tab, use \s or \\t respectively. \ Example: >ka-SampleID-2091, use -i - 2, \ indicating - is the separator and the Sample ID\ is field #2.") sidGroup.add_argument('-f', '--filename_sample_id', action='store_true', default=False, help='Specify that the program should\ the name of each fasta file as the Sample ID for use\ in the mapping file. This is meant to be used when \ all sequence data for a sample is stored in a single\ file.') return parser.parse_args()
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 \ derived data files for use with the \ metagenomics analysis program QIIME, by \ extracting Sample ID information, adding\ barcodes and primers to the sequence \ data, and outputting a mapping file and\ single FASTA-formatted sequence file \ formed by concatenating all input data.") parser.add_argument('-i', '--input_dir', required=True, help="The directory containing sequence data files. \ Assumes all data files are placed in this \ directory. For files organized within folders by\ sample, use -s in addition.") parser.add_argument('-m', '--map_file', default='map.txt', help="QIIME-formatted mapping file linking Sample IDs \ with barcodes and primers.") parser.add_argument('-o', '--output', default='output.fasta', metavar='OUTPUT_FILE', help="Single file containing all sequence data found \ in input_dir, FASTA-formatted with barcode and \ primer preprended to sequence. If the -q option \ is passed, any quality data will also be output \ to a single file of the same name with a .qual \ extension.") parser.add_argument('-b', '--barcode_length', type=int, default=12, help="Length of the generated barcode sequences. \ Default is 12 (QIIME default), minimum is 8.") parser.add_argument('-q', '--qual', action='store_true', default=False, help="Instruct the program to look for quality \ input files") parser.add_argument('-u', '--utf16', action='store_true', default=False, help="UTF-16 encoded input files") parser.add_argument('-t', '--treatment', help="Inserts an additional column into the mapping \ file specifying some treatment or other variable\ that separates the current set of sequences \ from any other set of seqeunces. For example:\ -t DiseaseState=healthy") # data input options sidGroup = parser.add_mutually_exclusive_group(required=True) sidGroup.add_argument('-d', '--identifier_pattern', action=ValidateIDPattern, nargs=2, metavar=('SEPARATOR', 'FIELD_NUMBER'), help="Indicates how to extract the Sample ID from \ the description line. Specify two things: \ 1. Field separator, 2. Field number of Sample \ ID (1 or greater). If the separator is a space \ or tab, use \s or \\t respectively. \ Example: >ka-SampleID-2091, use -i - 2, \ indicating - is the separator and the Sample ID\ is field #2.") sidGroup.add_argument('-f', '--filename_sample_id', action='store_true', default=False, help='Specify that the program should\ the name of each fasta file as the Sample ID for use\ in the mapping file. This is meant to be used when \ all sequence data for a sample is stored in a single\ file.') return parser.parse_args()
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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
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0b74ef171e6a84761710548501dfac71285a58a3
https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/sanger_qiimify.py#L202-L271
train
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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Applies the arcsine square root transform to the given BIOM-format table
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0b74ef171e6a84761710548501dfac71285a58a3
https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/transform_biom.py#L78-L88
train
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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parse sam file and check mapping quality
[ "parse", "sam", "file", "and", "check", "mapping", "quality" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/genome_variation.py#L23-L33
train
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'] rc['In'] = pos['In'] rc['Del'] = pos['Del'] rc['ref'] = rc_nucs[pos['ref']] rc['consensus'] = (rc_nucs[pos['consensus'][0]], pos['consensus'][1]) for base, stat in list(pos.items()): if base in rc_nucs: rc[rc_nucs[base]] = stat rcs.append(rc) return rcs
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'] rc['In'] = pos['In'] rc['Del'] = pos['Del'] rc['ref'] = rc_nucs[pos['ref']] rc['consensus'] = (rc_nucs[pos['consensus'][0]], pos['consensus'][1]) for base, stat in list(pos.items()): if base in rc_nucs: rc[rc_nucs[base]] = stat rcs.append(rc) return rcs
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reverse completement stats
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/genome_variation.py#L138-L156
train
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) if n == 3: yield codon codon = []
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) if n == 3: yield codon codon = []
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parse codon nucleotide positions in range start -> end, wrt strand
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/genome_variation.py#L158-L172
train
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)/float(length)
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()): if base in nucs: bases += count return float(bases)/float(length)
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calculate coverage for positions in range start -> end
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/genome_variation.py#L174-L184
train
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] except: continue if feature.type == 'CDS': try: locus = feature.qualifiers['locus_tag'][0] except: pass start = int(feature.location.start) + int(feature.qualifiers['codon_start'][0]) end, strand = int(feature.location.end), feature.location.strand if strand is None: strand = 1 else: strand = -1 contig = record.id # contig = record.id.rsplit('.', 1)[0] yield contig, [locus, \ [start, end, strand], \ feature.qualifiers]
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] except: continue if feature.type == 'CDS': try: locus = feature.qualifiers['locus_tag'][0] except: pass start = int(feature.location.start) + int(feature.qualifiers['codon_start'][0]) end, strand = int(feature.location.end), feature.location.strand if strand is None: strand = 1 else: strand = -1 contig = record.id # contig = record.id.rsplit('.', 1)[0] yield contig, [locus, \ [start, end, strand], \ feature.qualifiers]
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parse gbk file
[ "parse", "gbk", "file" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/genome_variation.py#L186-L213
train
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().split() annots[ID] = [start, end, int(strand)] for fasta in fastas: for seq in parse_fasta(fasta): if ('# ;gc_cont' not in seq[0] and '# ID=' not in seq[0]) and annot_tables is False: print('# specify fasta from Prodigal or annotations table (-t)', file=sys.stderr) exit() if 'ID=' in seq[0]: ID = seq[0].rsplit('ID=', 1)[1].split(';', 1)[0] contig = seq[0].split()[0].split('>')[1].rsplit('_%s' % (ID), 1)[0] else: contig = seq[0].split()[0].split('>')[1].rsplit('_', 1)[0] locus = seq[0].split()[0].split('>')[1] # annotation info from Prodigal if ('# ;gc_cont' in seq[0] or '# ID=' in seq[0]): info = seq[0].split(' # ') start, end, strand = int(info[1]), int(info[2]), info[3] if strand == '1': strand = 1 else: strand = -1 product = [''.join(info[4].split()[1:])] # annotation info from table else: start, end, strand = annots[locus] product = seq[0].split(' ', 1)[1] info = {'transl_table':[trans_table], \ 'translation':[seq[1]], \ 'product':product} yield contig, [locus, [start, end, strand], info]
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().split() annots[ID] = [start, end, int(strand)] for fasta in fastas: for seq in parse_fasta(fasta): if ('# ;gc_cont' not in seq[0] and '# ID=' not in seq[0]) and annot_tables is False: print('# specify fasta from Prodigal or annotations table (-t)', file=sys.stderr) exit() if 'ID=' in seq[0]: ID = seq[0].rsplit('ID=', 1)[1].split(';', 1)[0] contig = seq[0].split()[0].split('>')[1].rsplit('_%s' % (ID), 1)[0] else: contig = seq[0].split()[0].split('>')[1].rsplit('_', 1)[0] locus = seq[0].split()[0].split('>')[1] # annotation info from Prodigal if ('# ;gc_cont' in seq[0] or '# ID=' in seq[0]): info = seq[0].split(' # ') start, end, strand = int(info[1]), int(info[2]), info[3] if strand == '1': strand = 1 else: strand = -1 product = [''.join(info[4].split()[1:])] # annotation info from table else: start, end, strand = annots[locus] product = seq[0].split(' ', 1)[1] info = {'transl_table':[trans_table], \ 'translation':[seq[1]], \ 'product':product} yield contig, [locus, [start, end, strand], info]
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parse gene call information from Prodigal fasta output
[ "parse", "gene", "call", "information", "from", "Prodigal", "fasta", "output" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/genome_variation.py#L215-L252
train
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 annotations: annotations[contig] = [] annotations[contig].append(feature) # fasta format else: for contig, feature in parse_fasta_annotations(annots, annot_tables, trans_table): if contig not in annotations: annotations[contig] = [] annotations[contig].append(feature) return annotations
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: for contig, feature in parse_gbk(annots): if contig not in annotations: annotations[contig] = [] annotations[contig].append(feature) # fasta format else: for contig, feature in parse_fasta_annotations(annots, annot_tables, trans_table): if contig not in annotations: annotations[contig] = [] annotations[contig].append(feature) return annotations
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parse annotations in either gbk or Prodigal fasta format
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/genome_variation.py#L254-L271
train
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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convert codon to amino acid
[ "convert", "codon", "to", "amino", "acid" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/genome_variation.py#L311-L315
train
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 in nucs if nuc in bases]) except: bases['consensus'] = ('N', 'n/a') bases['consensus frequency'] = 'n/a' bases['reference frequency'] = 'n/a' return bases top = [(nuc, bases[nuc]) for nuc in bases if bases[nuc] == top] if top[0][1] == 0: bases['consensus'] = ('n/a', 0) else: bases['consensus'] = random.choice(top) if total == 0: c_freq = 'n/a' ref_freq = 'n/a' else: c_freq = float(bases['consensus'][1]) / float(total) if bases['ref'] not in bases: ref_freq = 0 else: ref_freq = float(bases[bases['ref']]) / float(total) bases['consensus frequency'] = c_freq bases['reference frequency'] = ref_freq return bases
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 in nucs if nuc in bases]) except: bases['consensus'] = ('N', 'n/a') bases['consensus frequency'] = 'n/a' bases['reference frequency'] = 'n/a' return bases top = [(nuc, bases[nuc]) for nuc in bases if bases[nuc] == top] if top[0][1] == 0: bases['consensus'] = ('n/a', 0) else: bases['consensus'] = random.choice(top) if total == 0: c_freq = 'n/a' ref_freq = 'n/a' else: c_freq = float(bases['consensus'][1]) / float(total) if bases['ref'] not in bases: ref_freq = 0 else: ref_freq = float(bases[bases['ref']]) / float(total) bases['consensus frequency'] = c_freq bases['reference frequency'] = ref_freq return bases
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find consensus base based on nucleotide frequencies
[ "find", "consensus", "base", "based", "on", "nucleotide", "frequencies" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/genome_variation.py#L371-L402
train
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.items()): for sample, stats in list(samples.items()): if sample not in cons[genome]: cons[genome][sample] = {} seq = cons[genome][sample][contig] = [] for pos, ps in enumerate(stats['bp_stats'], 1): ref, consensus = ps['ref'], ps['consensus'][0] if consensus == 'n/a': consensus = ref.lower() seq.append(consensus) # print consensus sequences for genome, samples in cons.items(): for sample, contigs in samples.items(): fn = '%s.%s.consensus.fa' % (genome, sample) f = open(fn, 'w') for contig, seq in contigs.items(): print('>%s' % (contig), file = f) print(''.join(seq), file = f) f.close() return cons
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.items()): for sample, stats in list(samples.items()): if sample not in cons[genome]: cons[genome][sample] = {} seq = cons[genome][sample][contig] = [] for pos, ps in enumerate(stats['bp_stats'], 1): ref, consensus = ps['ref'], ps['consensus'][0] if consensus == 'n/a': consensus = ref.lower() seq.append(consensus) # print consensus sequences for genome, samples in cons.items(): for sample, contigs in samples.items(): fn = '%s.%s.consensus.fa' % (genome, sample) f = open(fn, 'w') for contig, seq in contigs.items(): print('>%s' % (contig), file = f) print(''.join(seq), file = f) f.close() return cons
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print consensensus sequences for each genome and sample
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/genome_variation.py#L451-L478
train
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') if line[0].startswith('#'): samples = line[1:] samples = [i.rsplit('/', 1)[-1].split('.', 1)[0] for i in samples] continue scaffold, length = line[0].split(': ') length = float(length) covs = [float(i) for i in line[1:]] bases = [c * length for c in covs] if scaffold not in scaffold2genome: continue genome = scaffold2genome[scaffold] if genome not in size: size[genome] = 0 mapped[genome] = {sample:0 for sample in samples} # keep track of genome size size[genome] += length # keep track of number of mapped bases for sample, count in zip(samples, bases): mapped[genome][sample] += count # calculate coverage from base counts and genome size coverage = {'genome':[], 'genome size (bp)':[], 'sample':[], 'coverage':[]} for genome, length in size.items(): for sample in samples: cov = mapped[genome][sample] / length coverage['genome'].append(genome) coverage['genome size (bp)'].append(length) coverage['sample'].append(sample) coverage['coverage'].append(cov) return pd.DataFrame(coverage)
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') if line[0].startswith('#'): samples = line[1:] samples = [i.rsplit('/', 1)[-1].split('.', 1)[0] for i in samples] continue scaffold, length = line[0].split(': ') length = float(length) covs = [float(i) for i in line[1:]] bases = [c * length for c in covs] if scaffold not in scaffold2genome: continue genome = scaffold2genome[scaffold] if genome not in size: size[genome] = 0 mapped[genome] = {sample:0 for sample in samples} # keep track of genome size size[genome] += length # keep track of number of mapped bases for sample, count in zip(samples, bases): mapped[genome][sample] += count # calculate coverage from base counts and genome size coverage = {'genome':[], 'genome size (bp)':[], 'sample':[], 'coverage':[]} for genome, length in size.items(): for sample in samples: cov = mapped[genome][sample] / length coverage['genome'].append(genome) coverage['genome size (bp)'].append(length) coverage['sample'].append(sample) coverage['coverage'].append(cov) return pd.DataFrame(coverage)
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calculate genome coverage from scaffold coverage table
[ "calculate", "genome", "coverage", "from", "scaffold", "coverage", "table" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/genome_coverage.py#L13-L50
train
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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calculate genome coverage from scaffold coverage
[ "calculate", "genome", "coverage", "from", "scaffold", "coverage" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/genome_coverage.py#L52-L59
train
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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convert s2b files to dictionary
[ "convert", "s2b", "files", "to", "dictionary" ]
83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/genome_coverage.py#L61-L71
train
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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convert fastas to s2b dictionary
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83b2566b3a5745437ec651cd6cafddd056846240
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/genome_coverage.py#L73-L82
train
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 truncating a sequence. :type percent: float :param percent: Defines the overall percentage of N in a sequence that will cause the sequence to be filtered out. """ seqs = [] # Ns = ''.join(['N' for _ in range(repeats)]) count = 0 for record in records: if record.seq.count('N')/float(len(record)) < percent: # pos = record.seq.find(Ns) # if pos >= 0: # record.seq = Seq(str(record.seq)[:pos]) seqs.append(record) count += 1 return seqs, count
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 :param repeats: Defines the number of repeated N that trigger truncating a sequence. :type percent: float :param percent: Defines the overall percentage of N in a sequence that will cause the sequence to be filtered out. """ seqs = [] # Ns = ''.join(['N' for _ in range(repeats)]) count = 0 for record in records: if record.seq.count('N')/float(len(record)) < percent: # pos = record.seq.find(Ns) # if pos >= 0: # record.seq = Seq(str(record.seq)[:pos]) seqs.append(record) count += 1 return seqs, count
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0b74ef171e6a84761710548501dfac71285a58a3
https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/filter_ambiguity.py#L16-L41
train
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 """ try: response = requests.get(PYPI_URL.format(name)) if response.ok: msg = ('[error] "{0}" is registered already in PyPI.\n' '\tSpecify another package name.').format(name) raise Conflict(msg) except (socket.gaierror, Timeout, ConnectionError, HTTPError) as exc: raise BackendFailure(exc)
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 """ try: response = requests.get(PYPI_URL.format(name)) if response.ok: msg = ('[error] "{0}" is registered already in PyPI.\n' '\tSpecify another package name.').format(name) raise Conflict(msg) except (socket.gaierror, Timeout, ConnectionError, HTTPError) as exc: raise BackendFailure(exc)
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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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95d56ed98ef409fd9f019dc352fd1c3711533275
https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/pypi.py#L12-L33
train