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19,200 | sorgerlab/indra | indra/databases/__init__.py | get_identifiers_url | def get_identifiers_url(db_name, db_id):
"""Return an identifiers.org URL for a given database name and ID.
Parameters
----------
db_name : str
An internal database name: HGNC, UP, CHEBI, etc.
db_id : str
An identifier in the given database.
Returns
-------
url : str
An identifiers.org URL corresponding to the given database name and ID.
"""
identifiers_url = 'http://identifiers.org/'
bel_scai_url = 'https://arty.scai.fraunhofer.de/artifactory/bel/namespace/'
if db_name == 'UP':
url = identifiers_url + 'uniprot/%s' % db_id
elif db_name == 'HGNC':
url = identifiers_url + 'hgnc/HGNC:%s' % db_id
elif db_name == 'IP':
url = identifiers_url + 'interpro/%s' % db_id
elif db_name == 'IPR':
url = identifiers_url + 'interpro/%s' % db_id
elif db_name == 'CHEBI':
url = identifiers_url + 'chebi/%s' % db_id
elif db_name == 'NCIT':
url = identifiers_url + 'ncit/%s' % db_id
elif db_name == 'GO':
if db_id.startswith('GO:'):
url = identifiers_url + 'go/%s' % db_id
else:
url = identifiers_url + 'go/GO:%s' % db_id
elif db_name in ('PUBCHEM', 'PCID'): # Assuming PCID = PubChem compound ID
if db_id.startswith('PUBCHEM:'):
db_id = db_id[8:]
elif db_id.startswith('PCID:'):
db_id = db_id[5:]
url = identifiers_url + 'pubchem.compound/%s' % db_id
elif db_name == 'PF':
url = identifiers_url + 'pfam/%s' % db_id
elif db_name == 'MIRBASEM':
url = identifiers_url + 'mirbase.mature/%s' % db_id
elif db_name == 'MIRBASE':
url = identifiers_url + 'mirbase/%s' % db_id
elif db_name == 'MESH':
url = identifiers_url + 'mesh/%s' % db_id
elif db_name == 'EGID':
url = identifiers_url + 'ncbigene/%s' % db_id
elif db_name == 'HMDB':
url = identifiers_url + 'hmdb/%s' % db_id
elif db_name == 'LINCS':
if db_id.startswith('LSM-'): # Lincs Small Molecule ID
url = identifiers_url + 'lincs.smallmolecule/%s' % db_id
elif db_id.startswith('LCL-'): # Lincs Cell Line ID
url = identifiers_url + 'lincs.cell/%s' % db_id
else: # Assume LINCS Protein
url = identifiers_url + 'lincs.protein/%s' % db_id
elif db_name == 'HMS-LINCS':
url = 'http://lincs.hms.harvard.edu/db/sm/%s-101' % db_id
# Special cases with no identifiers entry
elif db_name == 'SCHEM':
url = bel_scai_url + 'selventa-legacy-chemicals/' + \
'selventa-legacy-chemicals-20150601.belns'
elif db_name == 'SCOMP':
url = bel_scai_url + 'selventa-named-complexes/' + \
'selventa-named-complexes-20150601.belns'
elif db_name == 'SFAM':
url = bel_scai_url + 'selventa-protein-families/' + \
'selventa-protein-families-20150601.belns'
elif db_name == 'FPLX':
url = 'http://identifiers.org/fplx/%s' % db_id
elif db_name == 'LNCRNADB':
if db_id.startswith('ENSG'):
url = 'http://www.lncrnadb.org/search/?q=%s' % db_id
else: # Assmuing HGNC symbol
url = 'http://www.lncrnadb.org/%s/' % db_id
elif db_name == 'NXPFA':
url = 'https://www.nextprot.org/term/FA-%s' % db_id
elif db_name in ('UN', 'WDI', 'FAO'):
url = 'https://github.com/clulab/eidos/wiki/JSON-LD#Grounding/%s' % \
db_id
elif db_name == 'HUME':
url = ('https://github.com/BBN-E/Hume/blob/master/resource/ontologies/'
'hume_ontology/%s' % db_id)
elif db_name == 'CWMS':
url = 'http://trips.ihmc.us/%s' % db_id
elif db_name == 'SIGNOR': # Assuming db_id == Primary ID
url = 'https://signor.uniroma2.it/relation_result.php?id=%s' % db_id
elif db_name == 'SOFIA':
url = 'http://cs.cmu.edu/sofia/%s' % db_id
elif db_name == 'CHEMBL':
if not db_id.startswith('CHEMBL'):
db_id = 'CHEMBL%s' % db_id
url = identifiers_url + 'chembl.compound/%s' % db_id
elif db_name == 'NONCODE':
url = 'http://www.noncode.org/show_gene.php?id=NONHSAG%s' % db_id
elif db_name == 'TEXT':
return None
else:
logger.warning('Unhandled name space %s' % db_name)
url = None
return url | python | def get_identifiers_url(db_name, db_id):
identifiers_url = 'http://identifiers.org/'
bel_scai_url = 'https://arty.scai.fraunhofer.de/artifactory/bel/namespace/'
if db_name == 'UP':
url = identifiers_url + 'uniprot/%s' % db_id
elif db_name == 'HGNC':
url = identifiers_url + 'hgnc/HGNC:%s' % db_id
elif db_name == 'IP':
url = identifiers_url + 'interpro/%s' % db_id
elif db_name == 'IPR':
url = identifiers_url + 'interpro/%s' % db_id
elif db_name == 'CHEBI':
url = identifiers_url + 'chebi/%s' % db_id
elif db_name == 'NCIT':
url = identifiers_url + 'ncit/%s' % db_id
elif db_name == 'GO':
if db_id.startswith('GO:'):
url = identifiers_url + 'go/%s' % db_id
else:
url = identifiers_url + 'go/GO:%s' % db_id
elif db_name in ('PUBCHEM', 'PCID'): # Assuming PCID = PubChem compound ID
if db_id.startswith('PUBCHEM:'):
db_id = db_id[8:]
elif db_id.startswith('PCID:'):
db_id = db_id[5:]
url = identifiers_url + 'pubchem.compound/%s' % db_id
elif db_name == 'PF':
url = identifiers_url + 'pfam/%s' % db_id
elif db_name == 'MIRBASEM':
url = identifiers_url + 'mirbase.mature/%s' % db_id
elif db_name == 'MIRBASE':
url = identifiers_url + 'mirbase/%s' % db_id
elif db_name == 'MESH':
url = identifiers_url + 'mesh/%s' % db_id
elif db_name == 'EGID':
url = identifiers_url + 'ncbigene/%s' % db_id
elif db_name == 'HMDB':
url = identifiers_url + 'hmdb/%s' % db_id
elif db_name == 'LINCS':
if db_id.startswith('LSM-'): # Lincs Small Molecule ID
url = identifiers_url + 'lincs.smallmolecule/%s' % db_id
elif db_id.startswith('LCL-'): # Lincs Cell Line ID
url = identifiers_url + 'lincs.cell/%s' % db_id
else: # Assume LINCS Protein
url = identifiers_url + 'lincs.protein/%s' % db_id
elif db_name == 'HMS-LINCS':
url = 'http://lincs.hms.harvard.edu/db/sm/%s-101' % db_id
# Special cases with no identifiers entry
elif db_name == 'SCHEM':
url = bel_scai_url + 'selventa-legacy-chemicals/' + \
'selventa-legacy-chemicals-20150601.belns'
elif db_name == 'SCOMP':
url = bel_scai_url + 'selventa-named-complexes/' + \
'selventa-named-complexes-20150601.belns'
elif db_name == 'SFAM':
url = bel_scai_url + 'selventa-protein-families/' + \
'selventa-protein-families-20150601.belns'
elif db_name == 'FPLX':
url = 'http://identifiers.org/fplx/%s' % db_id
elif db_name == 'LNCRNADB':
if db_id.startswith('ENSG'):
url = 'http://www.lncrnadb.org/search/?q=%s' % db_id
else: # Assmuing HGNC symbol
url = 'http://www.lncrnadb.org/%s/' % db_id
elif db_name == 'NXPFA':
url = 'https://www.nextprot.org/term/FA-%s' % db_id
elif db_name in ('UN', 'WDI', 'FAO'):
url = 'https://github.com/clulab/eidos/wiki/JSON-LD#Grounding/%s' % \
db_id
elif db_name == 'HUME':
url = ('https://github.com/BBN-E/Hume/blob/master/resource/ontologies/'
'hume_ontology/%s' % db_id)
elif db_name == 'CWMS':
url = 'http://trips.ihmc.us/%s' % db_id
elif db_name == 'SIGNOR': # Assuming db_id == Primary ID
url = 'https://signor.uniroma2.it/relation_result.php?id=%s' % db_id
elif db_name == 'SOFIA':
url = 'http://cs.cmu.edu/sofia/%s' % db_id
elif db_name == 'CHEMBL':
if not db_id.startswith('CHEMBL'):
db_id = 'CHEMBL%s' % db_id
url = identifiers_url + 'chembl.compound/%s' % db_id
elif db_name == 'NONCODE':
url = 'http://www.noncode.org/show_gene.php?id=NONHSAG%s' % db_id
elif db_name == 'TEXT':
return None
else:
logger.warning('Unhandled name space %s' % db_name)
url = None
return url | [
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db_name : str
An internal database name: HGNC, UP, CHEBI, etc.
db_id : str
An identifier in the given database.
Returns
-------
url : str
An identifiers.org URL corresponding to the given database name and ID. | [
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19,201 | sorgerlab/indra | indra/tools/assemble_corpus.py | dump_statements | def dump_statements(stmts, fname, protocol=4):
"""Dump a list of statements into a pickle file.
Parameters
----------
fname : str
The name of the pickle file to dump statements into.
protocol : Optional[int]
The pickle protocol to use (use 2 for Python 2 compatibility).
Default: 4
"""
logger.info('Dumping %d statements into %s...' % (len(stmts), fname))
with open(fname, 'wb') as fh:
pickle.dump(stmts, fh, protocol=protocol) | python | def dump_statements(stmts, fname, protocol=4):
logger.info('Dumping %d statements into %s...' % (len(stmts), fname))
with open(fname, 'wb') as fh:
pickle.dump(stmts, fh, protocol=protocol) | [
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The name of the pickle file to dump statements into.
protocol : Optional[int]
The pickle protocol to use (use 2 for Python 2 compatibility).
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19,202 | sorgerlab/indra | indra/tools/assemble_corpus.py | load_statements | def load_statements(fname, as_dict=False):
"""Load statements from a pickle file.
Parameters
----------
fname : str
The name of the pickle file to load statements from.
as_dict : Optional[bool]
If True and the pickle file contains a dictionary of statements, it
is returned as a dictionary. If False, the statements are always
returned in a list. Default: False
Returns
-------
stmts : list
A list or dict of statements that were loaded.
"""
logger.info('Loading %s...' % fname)
with open(fname, 'rb') as fh:
# Encoding argument not available in pickle for Python 2
if sys.version_info[0] < 3:
stmts = pickle.load(fh)
# Encoding argument specified here to enable compatibility with
# pickle files created with Python 2
else:
stmts = pickle.load(fh, encoding='latin1')
if isinstance(stmts, dict):
if as_dict:
return stmts
st = []
for pmid, st_list in stmts.items():
st += st_list
stmts = st
logger.info('Loaded %d statements' % len(stmts))
return stmts | python | def load_statements(fname, as_dict=False):
logger.info('Loading %s...' % fname)
with open(fname, 'rb') as fh:
# Encoding argument not available in pickle for Python 2
if sys.version_info[0] < 3:
stmts = pickle.load(fh)
# Encoding argument specified here to enable compatibility with
# pickle files created with Python 2
else:
stmts = pickle.load(fh, encoding='latin1')
if isinstance(stmts, dict):
if as_dict:
return stmts
st = []
for pmid, st_list in stmts.items():
st += st_list
stmts = st
logger.info('Loaded %d statements' % len(stmts))
return stmts | [
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The name of the pickle file to load statements from.
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If True and the pickle file contains a dictionary of statements, it
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19,203 | sorgerlab/indra | indra/tools/assemble_corpus.py | map_grounding | def map_grounding(stmts_in, **kwargs):
"""Map grounding using the GroundingMapper.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to map.
do_rename : Optional[bool]
If True, Agents are renamed based on their mapped grounding.
grounding_map : Optional[dict]
A user supplied grounding map which maps a string to a
dictionary of database IDs (in the format used by Agents'
db_refs).
use_deft : Optional[bool]
If True, Deft will be attempted to be used for acronym disambiguation.
Default: True
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of mapped statements.
"""
from indra.preassembler.grounding_mapper import GroundingMapper
from indra.preassembler.grounding_mapper import gm as grounding_map
from indra.preassembler.grounding_mapper import \
default_agent_map as agent_map
logger.info('Mapping grounding on %d statements...' % len(stmts_in))
do_rename = kwargs.get('do_rename')
gm = kwargs.get('grounding_map', grounding_map)
if do_rename is None:
do_rename = True
gm = GroundingMapper(gm, agent_map, use_deft=kwargs.get('use_deft', True))
stmts_out = gm.map_agents(stmts_in, do_rename=do_rename)
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | python | def map_grounding(stmts_in, **kwargs):
from indra.preassembler.grounding_mapper import GroundingMapper
from indra.preassembler.grounding_mapper import gm as grounding_map
from indra.preassembler.grounding_mapper import \
default_agent_map as agent_map
logger.info('Mapping grounding on %d statements...' % len(stmts_in))
do_rename = kwargs.get('do_rename')
gm = kwargs.get('grounding_map', grounding_map)
if do_rename is None:
do_rename = True
gm = GroundingMapper(gm, agent_map, use_deft=kwargs.get('use_deft', True))
stmts_out = gm.map_agents(stmts_in, do_rename=do_rename)
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | [
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Parameters
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A list of statements to map.
do_rename : Optional[bool]
If True, Agents are renamed based on their mapped grounding.
grounding_map : Optional[dict]
A user supplied grounding map which maps a string to a
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If True, Deft will be attempted to be used for acronym disambiguation.
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The name of a pickle file to save the results (stmts_out) into.
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-------
stmts_out : list[indra.statements.Statement]
A list of mapped statements. | [
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19,204 | sorgerlab/indra | indra/tools/assemble_corpus.py | merge_groundings | def merge_groundings(stmts_in):
"""Gather and merge original grounding information from evidences.
Each Statement's evidences are traversed to find original grounding
information. These groundings are then merged into an overall consensus
grounding dict with as much detail as possible.
The current implementation is only applicable to Statements whose
concept/agent roles are fixed. Complexes, Associations and Conversions
cannot be handled correctly.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of INDRA Statements whose groundings should be merged. These
Statements are meant to have been preassembled and potentially have
multiple pieces of evidence.
Returns
-------
stmts_out : list[indra.statements.Statement]
The list of Statements now with groundings merged at the Statement
level.
"""
def surface_grounding(stmt):
# Find the "best" grounding for a given concept and its evidences
# and surface that
for idx, concept in enumerate(stmt.agent_list()):
if concept is None:
continue
aggregate_groundings = {}
for ev in stmt.evidence:
if 'agents' in ev.annotations:
groundings = ev.annotations['agents']['raw_grounding'][idx]
for ns, value in groundings.items():
if ns not in aggregate_groundings:
aggregate_groundings[ns] = []
if isinstance(value, list):
aggregate_groundings[ns] += value
else:
aggregate_groundings[ns].append(value)
best_groundings = get_best_groundings(aggregate_groundings)
concept.db_refs = best_groundings
def get_best_groundings(aggregate_groundings):
best_groundings = {}
for ns, values in aggregate_groundings.items():
# There are 3 possibilities here
# 1. All the entries in the list are scored in which case we
# get unique entries and sort them by score
if all([isinstance(v, (tuple, list)) for v in values]):
best_groundings[ns] = []
for unique_value in {v[0] for v in values}:
scores = [v[1] for v in values if v[0] == unique_value]
best_groundings[ns].append((unique_value, max(scores)))
best_groundings[ns] = \
sorted(best_groundings[ns], key=lambda x: x[1],
reverse=True)
# 2. All the entries in the list are unscored in which case we
# get the highest frequency entry
elif all([not isinstance(v, (tuple, list)) for v in values]):
best_groundings[ns] = max(set(values), key=values.count)
# 3. There is a mixture, which can happen when some entries were
# mapped with scores and others had no scores to begin with.
# In this case, we again pick the highest frequency non-scored
# entry assuming that the unmapped version is more reliable.
else:
unscored_vals = [v for v in values
if not isinstance(v, (tuple, list))]
best_groundings[ns] = max(set(unscored_vals),
key=unscored_vals.count)
return best_groundings
stmts_out = []
for stmt in stmts_in:
if not isinstance(stmt, (Complex, Conversion)):
surface_grounding(stmt)
stmts_out.append(stmt)
return stmts_out | python | def merge_groundings(stmts_in):
def surface_grounding(stmt):
# Find the "best" grounding for a given concept and its evidences
# and surface that
for idx, concept in enumerate(stmt.agent_list()):
if concept is None:
continue
aggregate_groundings = {}
for ev in stmt.evidence:
if 'agents' in ev.annotations:
groundings = ev.annotations['agents']['raw_grounding'][idx]
for ns, value in groundings.items():
if ns not in aggregate_groundings:
aggregate_groundings[ns] = []
if isinstance(value, list):
aggregate_groundings[ns] += value
else:
aggregate_groundings[ns].append(value)
best_groundings = get_best_groundings(aggregate_groundings)
concept.db_refs = best_groundings
def get_best_groundings(aggregate_groundings):
best_groundings = {}
for ns, values in aggregate_groundings.items():
# There are 3 possibilities here
# 1. All the entries in the list are scored in which case we
# get unique entries and sort them by score
if all([isinstance(v, (tuple, list)) for v in values]):
best_groundings[ns] = []
for unique_value in {v[0] for v in values}:
scores = [v[1] for v in values if v[0] == unique_value]
best_groundings[ns].append((unique_value, max(scores)))
best_groundings[ns] = \
sorted(best_groundings[ns], key=lambda x: x[1],
reverse=True)
# 2. All the entries in the list are unscored in which case we
# get the highest frequency entry
elif all([not isinstance(v, (tuple, list)) for v in values]):
best_groundings[ns] = max(set(values), key=values.count)
# 3. There is a mixture, which can happen when some entries were
# mapped with scores and others had no scores to begin with.
# In this case, we again pick the highest frequency non-scored
# entry assuming that the unmapped version is more reliable.
else:
unscored_vals = [v for v in values
if not isinstance(v, (tuple, list))]
best_groundings[ns] = max(set(unscored_vals),
key=unscored_vals.count)
return best_groundings
stmts_out = []
for stmt in stmts_in:
if not isinstance(stmt, (Complex, Conversion)):
surface_grounding(stmt)
stmts_out.append(stmt)
return stmts_out | [
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Each Statement's evidences are traversed to find original grounding
information. These groundings are then merged into an overall consensus
grounding dict with as much detail as possible.
The current implementation is only applicable to Statements whose
concept/agent roles are fixed. Complexes, Associations and Conversions
cannot be handled correctly.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of INDRA Statements whose groundings should be merged. These
Statements are meant to have been preassembled and potentially have
multiple pieces of evidence.
Returns
-------
stmts_out : list[indra.statements.Statement]
The list of Statements now with groundings merged at the Statement
level. | [
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] | 79a70415832c5702d7a820c7c9ccc8e25010124b | https://github.com/sorgerlab/indra/blob/79a70415832c5702d7a820c7c9ccc8e25010124b/indra/tools/assemble_corpus.py#L122-L201 |
19,205 | sorgerlab/indra | indra/tools/assemble_corpus.py | merge_deltas | def merge_deltas(stmts_in):
"""Gather and merge original Influence delta information from evidence.
This function is only applicable to Influence Statements that have
subj and obj deltas. All other statement types are passed through unchanged.
Polarities and adjectives for subjects and objects respectivey are
collected and merged by travesrsing all evidences of a Statement.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of INDRA Statements whose influence deltas should be merged.
These Statements are meant to have been preassembled and potentially
have multiple pieces of evidence.
Returns
-------
stmts_out : list[indra.statements.Statement]
The list of Statements now with deltas merged at the Statement
level.
"""
stmts_out = []
for stmt in stmts_in:
# This operation is only applicable to Influences
if not isinstance(stmt, Influence):
stmts_out.append(stmt)
continue
# At this point this is guaranteed to be an Influence
deltas = {}
for role in ('subj', 'obj'):
for info in ('polarity', 'adjectives'):
key = (role, info)
deltas[key] = []
for ev in stmt.evidence:
entry = ev.annotations.get('%s_%s' % key)
deltas[key].append(entry if entry else None)
# POLARITY
# For polarity we need to work in pairs
polarity_pairs = list(zip(deltas[('subj', 'polarity')],
deltas[('obj', 'polarity')]))
# If we have some fully defined pairs, we take the most common one
both_pols = [pair for pair in polarity_pairs if pair[0] is not None and
pair[1] is not None]
if both_pols:
subj_pol, obj_pol = max(set(both_pols), key=both_pols.count)
stmt.subj.delta['polarity'] = subj_pol
stmt.obj.delta['polarity'] = obj_pol
# Otherwise we prefer the case when at least one entry of the
# pair is given
else:
one_pol = [pair for pair in polarity_pairs if pair[0] is not None or
pair[1] is not None]
if one_pol:
subj_pol, obj_pol = max(set(one_pol), key=one_pol.count)
stmt.subj.delta['polarity'] = subj_pol
stmt.obj.delta['polarity'] = obj_pol
# ADJECTIVES
for attr, role in ((stmt.subj.delta, 'subj'), (stmt.obj.delta, 'obj')):
all_adjectives = []
for adj in deltas[(role, 'adjectives')]:
if isinstance(adj, list):
all_adjectives += adj
elif adj is not None:
all_adjectives.append(adj)
attr['adjectives'] = all_adjectives
stmts_out.append(stmt)
return stmts_out | python | def merge_deltas(stmts_in):
stmts_out = []
for stmt in stmts_in:
# This operation is only applicable to Influences
if not isinstance(stmt, Influence):
stmts_out.append(stmt)
continue
# At this point this is guaranteed to be an Influence
deltas = {}
for role in ('subj', 'obj'):
for info in ('polarity', 'adjectives'):
key = (role, info)
deltas[key] = []
for ev in stmt.evidence:
entry = ev.annotations.get('%s_%s' % key)
deltas[key].append(entry if entry else None)
# POLARITY
# For polarity we need to work in pairs
polarity_pairs = list(zip(deltas[('subj', 'polarity')],
deltas[('obj', 'polarity')]))
# If we have some fully defined pairs, we take the most common one
both_pols = [pair for pair in polarity_pairs if pair[0] is not None and
pair[1] is not None]
if both_pols:
subj_pol, obj_pol = max(set(both_pols), key=both_pols.count)
stmt.subj.delta['polarity'] = subj_pol
stmt.obj.delta['polarity'] = obj_pol
# Otherwise we prefer the case when at least one entry of the
# pair is given
else:
one_pol = [pair for pair in polarity_pairs if pair[0] is not None or
pair[1] is not None]
if one_pol:
subj_pol, obj_pol = max(set(one_pol), key=one_pol.count)
stmt.subj.delta['polarity'] = subj_pol
stmt.obj.delta['polarity'] = obj_pol
# ADJECTIVES
for attr, role in ((stmt.subj.delta, 'subj'), (stmt.obj.delta, 'obj')):
all_adjectives = []
for adj in deltas[(role, 'adjectives')]:
if isinstance(adj, list):
all_adjectives += adj
elif adj is not None:
all_adjectives.append(adj)
attr['adjectives'] = all_adjectives
stmts_out.append(stmt)
return stmts_out | [
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Polarities and adjectives for subjects and objects respectivey are
collected and merged by travesrsing all evidences of a Statement.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of INDRA Statements whose influence deltas should be merged.
These Statements are meant to have been preassembled and potentially
have multiple pieces of evidence.
Returns
-------
stmts_out : list[indra.statements.Statement]
The list of Statements now with deltas merged at the Statement
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19,206 | sorgerlab/indra | indra/tools/assemble_corpus.py | map_sequence | def map_sequence(stmts_in, **kwargs):
"""Map sequences using the SiteMapper.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to map.
do_methionine_offset : boolean
Whether to check for off-by-one errors in site position (possibly)
attributable to site numbering from mature proteins after
cleavage of the initial methionine. If True, checks the reference
sequence for a known modification at 1 site position greater
than the given one; if there exists such a site, creates the
mapping. Default is True.
do_orthology_mapping : boolean
Whether to check sequence positions for known modification sites
in mouse or rat sequences (based on PhosphoSitePlus data). If a
mouse/rat site is found that is linked to a site in the human
reference sequence, a mapping is created. Default is True.
do_isoform_mapping : boolean
Whether to check sequence positions for known modifications
in other human isoforms of the protein (based on PhosphoSitePlus
data). If a site is found that is linked to a site in the human
reference sequence, a mapping is created. Default is True.
use_cache : boolean
If True, a cache will be created/used from the laction specified by
SITEMAPPER_CACHE_PATH, defined in your INDRA config or the environment.
If False, no cache is used. For more details on the cache, see the
SiteMapper class definition.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of mapped statements.
"""
from indra.preassembler.sitemapper import SiteMapper, default_site_map
logger.info('Mapping sites on %d statements...' % len(stmts_in))
kwarg_list = ['do_methionine_offset', 'do_orthology_mapping',
'do_isoform_mapping']
sm = SiteMapper(default_site_map,
use_cache=kwargs.pop('use_cache', False),
**_filter(kwargs, kwarg_list))
valid, mapped = sm.map_sites(stmts_in)
correctly_mapped_stmts = []
for ms in mapped:
correctly_mapped = all([mm.has_mapping() for mm in ms.mapped_mods])
if correctly_mapped:
correctly_mapped_stmts.append(ms.mapped_stmt)
stmts_out = valid + correctly_mapped_stmts
logger.info('%d statements with valid sites' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
del sm
return stmts_out | python | def map_sequence(stmts_in, **kwargs):
from indra.preassembler.sitemapper import SiteMapper, default_site_map
logger.info('Mapping sites on %d statements...' % len(stmts_in))
kwarg_list = ['do_methionine_offset', 'do_orthology_mapping',
'do_isoform_mapping']
sm = SiteMapper(default_site_map,
use_cache=kwargs.pop('use_cache', False),
**_filter(kwargs, kwarg_list))
valid, mapped = sm.map_sites(stmts_in)
correctly_mapped_stmts = []
for ms in mapped:
correctly_mapped = all([mm.has_mapping() for mm in ms.mapped_mods])
if correctly_mapped:
correctly_mapped_stmts.append(ms.mapped_stmt)
stmts_out = valid + correctly_mapped_stmts
logger.info('%d statements with valid sites' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
del sm
return stmts_out | [
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stmts_in : list[indra.statements.Statement]
A list of statements to map.
do_methionine_offset : boolean
Whether to check for off-by-one errors in site position (possibly)
attributable to site numbering from mature proteins after
cleavage of the initial methionine. If True, checks the reference
sequence for a known modification at 1 site position greater
than the given one; if there exists such a site, creates the
mapping. Default is True.
do_orthology_mapping : boolean
Whether to check sequence positions for known modification sites
in mouse or rat sequences (based on PhosphoSitePlus data). If a
mouse/rat site is found that is linked to a site in the human
reference sequence, a mapping is created. Default is True.
do_isoform_mapping : boolean
Whether to check sequence positions for known modifications
in other human isoforms of the protein (based on PhosphoSitePlus
data). If a site is found that is linked to a site in the human
reference sequence, a mapping is created. Default is True.
use_cache : boolean
If True, a cache will be created/used from the laction specified by
SITEMAPPER_CACHE_PATH, defined in your INDRA config or the environment.
If False, no cache is used. For more details on the cache, see the
SiteMapper class definition.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of mapped statements. | [
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] | 79a70415832c5702d7a820c7c9ccc8e25010124b | https://github.com/sorgerlab/indra/blob/79a70415832c5702d7a820c7c9ccc8e25010124b/indra/tools/assemble_corpus.py#L275-L331 |
19,207 | sorgerlab/indra | indra/tools/assemble_corpus.py | run_preassembly | def run_preassembly(stmts_in, **kwargs):
"""Run preassembly on a list of statements.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to preassemble.
return_toplevel : Optional[bool]
If True, only the top-level statements are returned. If False,
all statements are returned irrespective of level of specificity.
Default: True
poolsize : Optional[int]
The number of worker processes to use to parallelize the
comparisons performed by the function. If None (default), no
parallelization is performed. NOTE: Parallelization is only
available on Python 3.4 and above.
size_cutoff : Optional[int]
Groups with size_cutoff or more statements are sent to worker
processes, while smaller groups are compared in the parent process.
Default value is 100. Not relevant when parallelization is not
used.
belief_scorer : Optional[indra.belief.BeliefScorer]
Instance of BeliefScorer class to use in calculating Statement
probabilities. If None is provided (default), then the default
scorer is used.
hierarchies : Optional[dict]
Dict of hierarchy managers to use for preassembly
flatten_evidence : Optional[bool]
If True, evidences are collected and flattened via supports/supported_by
links. Default: False
flatten_evidence_collect_from : Optional[str]
String indicating whether to collect and flatten evidence from the
`supports` attribute of each statement or the `supported_by` attribute.
If not set, defaults to 'supported_by'.
Only relevant when flatten_evidence is True.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
save_unique : Optional[str]
The name of a pickle file to save the unique statements into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of preassembled top-level statements.
"""
dump_pkl_unique = kwargs.get('save_unique')
belief_scorer = kwargs.get('belief_scorer')
use_hierarchies = kwargs['hierarchies'] if 'hierarchies' in kwargs else \
hierarchies
be = BeliefEngine(scorer=belief_scorer)
pa = Preassembler(hierarchies, stmts_in)
run_preassembly_duplicate(pa, be, save=dump_pkl_unique)
dump_pkl = kwargs.get('save')
return_toplevel = kwargs.get('return_toplevel', True)
poolsize = kwargs.get('poolsize', None)
size_cutoff = kwargs.get('size_cutoff', 100)
options = {'save': dump_pkl, 'return_toplevel': return_toplevel,
'poolsize': poolsize, 'size_cutoff': size_cutoff,
'flatten_evidence': kwargs.get('flatten_evidence', False),
'flatten_evidence_collect_from':
kwargs.get('flatten_evidence_collect_from', 'supported_by')
}
stmts_out = run_preassembly_related(pa, be, **options)
return stmts_out | python | def run_preassembly(stmts_in, **kwargs):
dump_pkl_unique = kwargs.get('save_unique')
belief_scorer = kwargs.get('belief_scorer')
use_hierarchies = kwargs['hierarchies'] if 'hierarchies' in kwargs else \
hierarchies
be = BeliefEngine(scorer=belief_scorer)
pa = Preassembler(hierarchies, stmts_in)
run_preassembly_duplicate(pa, be, save=dump_pkl_unique)
dump_pkl = kwargs.get('save')
return_toplevel = kwargs.get('return_toplevel', True)
poolsize = kwargs.get('poolsize', None)
size_cutoff = kwargs.get('size_cutoff', 100)
options = {'save': dump_pkl, 'return_toplevel': return_toplevel,
'poolsize': poolsize, 'size_cutoff': size_cutoff,
'flatten_evidence': kwargs.get('flatten_evidence', False),
'flatten_evidence_collect_from':
kwargs.get('flatten_evidence_collect_from', 'supported_by')
}
stmts_out = run_preassembly_related(pa, be, **options)
return stmts_out | [
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A list of statements to preassemble.
return_toplevel : Optional[bool]
If True, only the top-level statements are returned. If False,
all statements are returned irrespective of level of specificity.
Default: True
poolsize : Optional[int]
The number of worker processes to use to parallelize the
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size_cutoff : Optional[int]
Groups with size_cutoff or more statements are sent to worker
processes, while smaller groups are compared in the parent process.
Default value is 100. Not relevant when parallelization is not
used.
belief_scorer : Optional[indra.belief.BeliefScorer]
Instance of BeliefScorer class to use in calculating Statement
probabilities. If None is provided (default), then the default
scorer is used.
hierarchies : Optional[dict]
Dict of hierarchy managers to use for preassembly
flatten_evidence : Optional[bool]
If True, evidences are collected and flattened via supports/supported_by
links. Default: False
flatten_evidence_collect_from : Optional[str]
String indicating whether to collect and flatten evidence from the
`supports` attribute of each statement or the `supported_by` attribute.
If not set, defaults to 'supported_by'.
Only relevant when flatten_evidence is True.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
save_unique : Optional[str]
The name of a pickle file to save the unique statements into.
Returns
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stmts_out : list[indra.statements.Statement]
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19,208 | sorgerlab/indra | indra/tools/assemble_corpus.py | run_preassembly_duplicate | def run_preassembly_duplicate(preassembler, beliefengine, **kwargs):
"""Run deduplication stage of preassembly on a list of statements.
Parameters
----------
preassembler : indra.preassembler.Preassembler
A Preassembler instance
beliefengine : indra.belief.BeliefEngine
A BeliefEngine instance.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of unique statements.
"""
logger.info('Combining duplicates on %d statements...' %
len(preassembler.stmts))
dump_pkl = kwargs.get('save')
stmts_out = preassembler.combine_duplicates()
beliefengine.set_prior_probs(stmts_out)
logger.info('%d unique statements' % len(stmts_out))
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | python | def run_preassembly_duplicate(preassembler, beliefengine, **kwargs):
logger.info('Combining duplicates on %d statements...' %
len(preassembler.stmts))
dump_pkl = kwargs.get('save')
stmts_out = preassembler.combine_duplicates()
beliefengine.set_prior_probs(stmts_out)
logger.info('%d unique statements' % len(stmts_out))
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | [
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beliefengine : indra.belief.BeliefEngine
A BeliefEngine instance.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of unique statements. | [
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19,209 | sorgerlab/indra | indra/tools/assemble_corpus.py | run_preassembly_related | def run_preassembly_related(preassembler, beliefengine, **kwargs):
"""Run related stage of preassembly on a list of statements.
Parameters
----------
preassembler : indra.preassembler.Preassembler
A Preassembler instance which already has a set of unique statements
internally.
beliefengine : indra.belief.BeliefEngine
A BeliefEngine instance.
return_toplevel : Optional[bool]
If True, only the top-level statements are returned. If False,
all statements are returned irrespective of level of specificity.
Default: True
poolsize : Optional[int]
The number of worker processes to use to parallelize the
comparisons performed by the function. If None (default), no
parallelization is performed. NOTE: Parallelization is only
available on Python 3.4 and above.
size_cutoff : Optional[int]
Groups with size_cutoff or more statements are sent to worker
processes, while smaller groups are compared in the parent process.
Default value is 100. Not relevant when parallelization is not
used.
flatten_evidence : Optional[bool]
If True, evidences are collected and flattened via supports/supported_by
links. Default: False
flatten_evidence_collect_from : Optional[str]
String indicating whether to collect and flatten evidence from the
`supports` attribute of each statement or the `supported_by` attribute.
If not set, defaults to 'supported_by'.
Only relevant when flatten_evidence is True.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of preassembled top-level statements.
"""
logger.info('Combining related on %d statements...' %
len(preassembler.unique_stmts))
return_toplevel = kwargs.get('return_toplevel', True)
poolsize = kwargs.get('poolsize', None)
size_cutoff = kwargs.get('size_cutoff', 100)
stmts_out = preassembler.combine_related(return_toplevel=False,
poolsize=poolsize,
size_cutoff=size_cutoff)
# Calculate beliefs
beliefengine.set_hierarchy_probs(stmts_out)
# Flatten evidence if needed
do_flatten_evidence = kwargs.get('flatten_evidence', False)
if do_flatten_evidence:
flatten_evidences_collect_from = \
kwargs.get('flatten_evidence_collect_from', 'supported_by')
stmts_out = flatten_evidence(stmts_out, flatten_evidences_collect_from)
# Filter to top if needed
stmts_top = filter_top_level(stmts_out)
if return_toplevel:
stmts_out = stmts_top
logger.info('%d top-level statements' % len(stmts_out))
else:
logger.info('%d statements out of which %d are top-level' %
(len(stmts_out), len(stmts_top)))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | python | def run_preassembly_related(preassembler, beliefengine, **kwargs):
logger.info('Combining related on %d statements...' %
len(preassembler.unique_stmts))
return_toplevel = kwargs.get('return_toplevel', True)
poolsize = kwargs.get('poolsize', None)
size_cutoff = kwargs.get('size_cutoff', 100)
stmts_out = preassembler.combine_related(return_toplevel=False,
poolsize=poolsize,
size_cutoff=size_cutoff)
# Calculate beliefs
beliefengine.set_hierarchy_probs(stmts_out)
# Flatten evidence if needed
do_flatten_evidence = kwargs.get('flatten_evidence', False)
if do_flatten_evidence:
flatten_evidences_collect_from = \
kwargs.get('flatten_evidence_collect_from', 'supported_by')
stmts_out = flatten_evidence(stmts_out, flatten_evidences_collect_from)
# Filter to top if needed
stmts_top = filter_top_level(stmts_out)
if return_toplevel:
stmts_out = stmts_top
logger.info('%d top-level statements' % len(stmts_out))
else:
logger.info('%d statements out of which %d are top-level' %
(len(stmts_out), len(stmts_top)))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | [
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... | Run related stage of preassembly on a list of statements.
Parameters
----------
preassembler : indra.preassembler.Preassembler
A Preassembler instance which already has a set of unique statements
internally.
beliefengine : indra.belief.BeliefEngine
A BeliefEngine instance.
return_toplevel : Optional[bool]
If True, only the top-level statements are returned. If False,
all statements are returned irrespective of level of specificity.
Default: True
poolsize : Optional[int]
The number of worker processes to use to parallelize the
comparisons performed by the function. If None (default), no
parallelization is performed. NOTE: Parallelization is only
available on Python 3.4 and above.
size_cutoff : Optional[int]
Groups with size_cutoff or more statements are sent to worker
processes, while smaller groups are compared in the parent process.
Default value is 100. Not relevant when parallelization is not
used.
flatten_evidence : Optional[bool]
If True, evidences are collected and flattened via supports/supported_by
links. Default: False
flatten_evidence_collect_from : Optional[str]
String indicating whether to collect and flatten evidence from the
`supports` attribute of each statement or the `supported_by` attribute.
If not set, defaults to 'supported_by'.
Only relevant when flatten_evidence is True.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of preassembled top-level statements. | [
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19,210 | sorgerlab/indra | indra/tools/assemble_corpus.py | filter_by_type | def filter_by_type(stmts_in, stmt_type, **kwargs):
"""Filter to a given statement type.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to filter.
stmt_type : indra.statements.Statement
The class of the statement type to filter for.
Example: indra.statements.Modification
invert : Optional[bool]
If True, the statements that are not of the given type
are returned. Default: False
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements.
"""
invert = kwargs.get('invert', False)
logger.info('Filtering %d statements for type %s%s...' %
(len(stmts_in), 'not ' if invert else '',
stmt_type.__name__))
if not invert:
stmts_out = [st for st in stmts_in if isinstance(st, stmt_type)]
else:
stmts_out = [st for st in stmts_in if not isinstance(st, stmt_type)]
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | python | def filter_by_type(stmts_in, stmt_type, **kwargs):
invert = kwargs.get('invert', False)
logger.info('Filtering %d statements for type %s%s...' %
(len(stmts_in), 'not ' if invert else '',
stmt_type.__name__))
if not invert:
stmts_out = [st for st in stmts_in if isinstance(st, stmt_type)]
else:
stmts_out = [st for st in stmts_in if not isinstance(st, stmt_type)]
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | [
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Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to filter.
stmt_type : indra.statements.Statement
The class of the statement type to filter for.
Example: indra.statements.Modification
invert : Optional[bool]
If True, the statements that are not of the given type
are returned. Default: False
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements. | [
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19,211 | sorgerlab/indra | indra/tools/assemble_corpus.py | _remove_bound_conditions | def _remove_bound_conditions(agent, keep_criterion):
"""Removes bound conditions of agent such that keep_criterion is False.
Parameters
----------
agent: Agent
The agent whose bound conditions we evaluate
keep_criterion: function
Evaluates removal_criterion(a) for each agent a in a bound condition
and if it evaluates to False, removes a from agent's bound_conditions
"""
new_bc = []
for ind in range(len(agent.bound_conditions)):
if keep_criterion(agent.bound_conditions[ind].agent):
new_bc.append(agent.bound_conditions[ind])
agent.bound_conditions = new_bc | python | def _remove_bound_conditions(agent, keep_criterion):
new_bc = []
for ind in range(len(agent.bound_conditions)):
if keep_criterion(agent.bound_conditions[ind].agent):
new_bc.append(agent.bound_conditions[ind])
agent.bound_conditions = new_bc | [
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Parameters
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agent: Agent
The agent whose bound conditions we evaluate
keep_criterion: function
Evaluates removal_criterion(a) for each agent a in a bound condition
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19,212 | sorgerlab/indra | indra/tools/assemble_corpus.py | _any_bound_condition_fails_criterion | def _any_bound_condition_fails_criterion(agent, criterion):
"""Returns True if any bound condition fails to meet the specified
criterion.
Parameters
----------
agent: Agent
The agent whose bound conditions we evaluate
criterion: function
Evaluates criterion(a) for each a in a bound condition and returns True
if any agents fail to meet the criterion.
Returns
-------
any_meets: bool
True if and only if any of the agents in a bound condition fail to match
the specified criteria
"""
bc_agents = [bc.agent for bc in agent.bound_conditions]
for b in bc_agents:
if not criterion(b):
return True
return False | python | def _any_bound_condition_fails_criterion(agent, criterion):
bc_agents = [bc.agent for bc in agent.bound_conditions]
for b in bc_agents:
if not criterion(b):
return True
return False | [
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Parameters
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agent: Agent
The agent whose bound conditions we evaluate
criterion: function
Evaluates criterion(a) for each a in a bound condition and returns True
if any agents fail to meet the criterion.
Returns
-------
any_meets: bool
True if and only if any of the agents in a bound condition fail to match
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19,213 | sorgerlab/indra | indra/tools/assemble_corpus.py | filter_grounded_only | def filter_grounded_only(stmts_in, **kwargs):
"""Filter to statements that have grounded agents.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to filter.
score_threshold : Optional[float]
If scored groundings are available in a list and the highest score
if below this threshold, the Statement is filtered out.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
remove_bound: Optional[bool]
If true, removes ungrounded bound conditions from a statement.
If false (default), filters out statements with ungrounded bound
conditions.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements.
"""
remove_bound = kwargs.get('remove_bound', False)
logger.info('Filtering %d statements for grounded agents...' %
len(stmts_in))
stmts_out = []
score_threshold = kwargs.get('score_threshold')
for st in stmts_in:
grounded = True
for agent in st.agent_list():
if agent is not None:
criterion = lambda x: _agent_is_grounded(x, score_threshold)
if not criterion(agent):
grounded = False
break
if not isinstance(agent, Agent):
continue
if remove_bound:
_remove_bound_conditions(agent, criterion)
elif _any_bound_condition_fails_criterion(agent, criterion):
grounded = False
break
if grounded:
stmts_out.append(st)
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | python | def filter_grounded_only(stmts_in, **kwargs):
remove_bound = kwargs.get('remove_bound', False)
logger.info('Filtering %d statements for grounded agents...' %
len(stmts_in))
stmts_out = []
score_threshold = kwargs.get('score_threshold')
for st in stmts_in:
grounded = True
for agent in st.agent_list():
if agent is not None:
criterion = lambda x: _agent_is_grounded(x, score_threshold)
if not criterion(agent):
grounded = False
break
if not isinstance(agent, Agent):
continue
if remove_bound:
_remove_bound_conditions(agent, criterion)
elif _any_bound_condition_fails_criterion(agent, criterion):
grounded = False
break
if grounded:
stmts_out.append(st)
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | [
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Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to filter.
score_threshold : Optional[float]
If scored groundings are available in a list and the highest score
if below this threshold, the Statement is filtered out.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
remove_bound: Optional[bool]
If true, removes ungrounded bound conditions from a statement.
If false (default), filters out statements with ungrounded bound
conditions.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements. | [
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19,214 | sorgerlab/indra | indra/tools/assemble_corpus.py | _agent_is_gene | def _agent_is_gene(agent, specific_only):
"""Returns whether an agent is for a gene.
Parameters
----------
agent: Agent
The agent to evaluate
specific_only : Optional[bool]
If True, only elementary genes/proteins evaluate as genes and families
will be filtered out. If False, families are also included.
Returns
-------
is_gene: bool
Whether the agent is a gene
"""
if not specific_only:
if not(agent.db_refs.get('HGNC') or \
agent.db_refs.get('UP') or \
agent.db_refs.get('FPLX')):
return False
else:
if not(agent.db_refs.get('HGNC') or \
agent.db_refs.get('UP')):
return False
return True | python | def _agent_is_gene(agent, specific_only):
if not specific_only:
if not(agent.db_refs.get('HGNC') or \
agent.db_refs.get('UP') or \
agent.db_refs.get('FPLX')):
return False
else:
if not(agent.db_refs.get('HGNC') or \
agent.db_refs.get('UP')):
return False
return True | [
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agent: Agent
The agent to evaluate
specific_only : Optional[bool]
If True, only elementary genes/proteins evaluate as genes and families
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Returns
-------
is_gene: bool
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19,215 | sorgerlab/indra | indra/tools/assemble_corpus.py | filter_genes_only | def filter_genes_only(stmts_in, **kwargs):
"""Filter to statements containing genes only.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to filter.
specific_only : Optional[bool]
If True, only elementary genes/proteins will be kept and families
will be filtered out. If False, families are also included in the
output. Default: False
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
remove_bound: Optional[bool]
If true, removes bound conditions that are not genes
If false (default), filters out statements with non-gene bound
conditions
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements.
"""
remove_bound = 'remove_bound' in kwargs and kwargs['remove_bound']
specific_only = kwargs.get('specific_only')
logger.info('Filtering %d statements for ones containing genes only...' %
len(stmts_in))
stmts_out = []
for st in stmts_in:
genes_only = True
for agent in st.agent_list():
if agent is not None:
criterion = lambda a: _agent_is_gene(a, specific_only)
if not criterion(agent):
genes_only = False
break
if remove_bound:
_remove_bound_conditions(agent, criterion)
else:
if _any_bound_condition_fails_criterion(agent, criterion):
genes_only = False
break
if genes_only:
stmts_out.append(st)
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | python | def filter_genes_only(stmts_in, **kwargs):
remove_bound = 'remove_bound' in kwargs and kwargs['remove_bound']
specific_only = kwargs.get('specific_only')
logger.info('Filtering %d statements for ones containing genes only...' %
len(stmts_in))
stmts_out = []
for st in stmts_in:
genes_only = True
for agent in st.agent_list():
if agent is not None:
criterion = lambda a: _agent_is_gene(a, specific_only)
if not criterion(agent):
genes_only = False
break
if remove_bound:
_remove_bound_conditions(agent, criterion)
else:
if _any_bound_condition_fails_criterion(agent, criterion):
genes_only = False
break
if genes_only:
stmts_out.append(st)
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | [
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Parameters
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stmts_in : list[indra.statements.Statement]
A list of statements to filter.
specific_only : Optional[bool]
If True, only elementary genes/proteins will be kept and families
will be filtered out. If False, families are also included in the
output. Default: False
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
remove_bound: Optional[bool]
If true, removes bound conditions that are not genes
If false (default), filters out statements with non-gene bound
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Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements. | [
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19,216 | sorgerlab/indra | indra/tools/assemble_corpus.py | filter_belief | def filter_belief(stmts_in, belief_cutoff, **kwargs):
"""Filter to statements with belief above a given cutoff.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to filter.
belief_cutoff : float
Only statements with belief above the belief_cutoff will be returned.
Here 0 < belief_cutoff < 1.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements.
"""
dump_pkl = kwargs.get('save')
logger.info('Filtering %d statements to above %f belief' %
(len(stmts_in), belief_cutoff))
# The first round of filtering is in the top-level list
stmts_out = []
# Now we eliminate supports/supported-by
for stmt in stmts_in:
if stmt.belief < belief_cutoff:
continue
stmts_out.append(stmt)
supp_by = []
supp = []
for st in stmt.supports:
if st.belief >= belief_cutoff:
supp.append(st)
for st in stmt.supported_by:
if st.belief >= belief_cutoff:
supp_by.append(st)
stmt.supports = supp
stmt.supported_by = supp_by
logger.info('%d statements after filter...' % len(stmts_out))
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | python | def filter_belief(stmts_in, belief_cutoff, **kwargs):
dump_pkl = kwargs.get('save')
logger.info('Filtering %d statements to above %f belief' %
(len(stmts_in), belief_cutoff))
# The first round of filtering is in the top-level list
stmts_out = []
# Now we eliminate supports/supported-by
for stmt in stmts_in:
if stmt.belief < belief_cutoff:
continue
stmts_out.append(stmt)
supp_by = []
supp = []
for st in stmt.supports:
if st.belief >= belief_cutoff:
supp.append(st)
for st in stmt.supported_by:
if st.belief >= belief_cutoff:
supp_by.append(st)
stmt.supports = supp
stmt.supported_by = supp_by
logger.info('%d statements after filter...' % len(stmts_out))
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | [
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A list of statements to filter.
belief_cutoff : float
Only statements with belief above the belief_cutoff will be returned.
Here 0 < belief_cutoff < 1.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
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stmts_out : list[indra.statements.Statement]
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19,217 | sorgerlab/indra | indra/tools/assemble_corpus.py | filter_gene_list | def filter_gene_list(stmts_in, gene_list, policy, allow_families=False,
**kwargs):
"""Return statements that contain genes given in a list.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to filter.
gene_list : list[str]
A list of gene symbols to filter for.
policy : str
The policy to apply when filtering for the list of genes. "one": keep
statements that contain at least one of the list of genes and
possibly others not in the list "all": keep statements that only
contain genes given in the list
allow_families : Optional[bool]
Will include statements involving FamPlex families containing one
of the genes in the gene list. Default: False
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
remove_bound: Optional[str]
If true, removes bound conditions that are not genes in the list
If false (default), looks at agents in the bound conditions in addition
to those participating in the statement directly when applying the
specified policy.
invert : Optional[bool]
If True, the statements that do not match according to the policy
are returned. Default: False
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements.
"""
invert = kwargs.get('invert', False)
remove_bound = kwargs.get('remove_bound', False)
if policy not in ('one', 'all'):
logger.error('Policy %s is invalid, not applying filter.' % policy)
else:
genes_str = ', '.join(gene_list)
inv_str = 'not ' if invert else ''
logger.info(('Filtering %d statements for ones %scontaining "%s" of: '
'%s...') % (len(stmts_in), inv_str, policy, genes_str))
# If we're allowing families, make a list of all FamPlex IDs that
# contain members of the gene list, and add them to the filter list
filter_list = copy(gene_list)
if allow_families:
for hgnc_name in gene_list:
gene_uri = hierarchies['entity'].get_uri('HGNC', hgnc_name)
parents = hierarchies['entity'].get_parents(gene_uri)
for par_uri in parents:
ns, id = hierarchies['entity'].ns_id_from_uri(par_uri)
filter_list.append(id)
stmts_out = []
if remove_bound:
# If requested, remove agents whose names are not in the list from
# all bound conditions
if not invert:
keep_criterion = lambda a: a.name in filter_list
else:
keep_criterion = lambda a: a.name not in filter_list
for st in stmts_in:
for agent in st.agent_list():
_remove_bound_conditions(agent, keep_criterion)
if policy == 'one':
for st in stmts_in:
found_gene = False
if not remove_bound:
agent_list = st.agent_list_with_bound_condition_agents()
else:
agent_list = st.agent_list()
for agent in agent_list:
if agent is not None:
if agent.name in filter_list:
found_gene = True
break
if (found_gene and not invert) or (not found_gene and invert):
stmts_out.append(st)
elif policy == 'all':
for st in stmts_in:
found_genes = True
if not remove_bound:
agent_list = st.agent_list_with_bound_condition_agents()
else:
agent_list = st.agent_list()
for agent in agent_list:
if agent is not None:
if agent.name not in filter_list:
found_genes = False
break
if (found_genes and not invert) or (not found_genes and invert):
stmts_out.append(st)
else:
stmts_out = stmts_in
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | python | def filter_gene_list(stmts_in, gene_list, policy, allow_families=False,
**kwargs):
invert = kwargs.get('invert', False)
remove_bound = kwargs.get('remove_bound', False)
if policy not in ('one', 'all'):
logger.error('Policy %s is invalid, not applying filter.' % policy)
else:
genes_str = ', '.join(gene_list)
inv_str = 'not ' if invert else ''
logger.info(('Filtering %d statements for ones %scontaining "%s" of: '
'%s...') % (len(stmts_in), inv_str, policy, genes_str))
# If we're allowing families, make a list of all FamPlex IDs that
# contain members of the gene list, and add them to the filter list
filter_list = copy(gene_list)
if allow_families:
for hgnc_name in gene_list:
gene_uri = hierarchies['entity'].get_uri('HGNC', hgnc_name)
parents = hierarchies['entity'].get_parents(gene_uri)
for par_uri in parents:
ns, id = hierarchies['entity'].ns_id_from_uri(par_uri)
filter_list.append(id)
stmts_out = []
if remove_bound:
# If requested, remove agents whose names are not in the list from
# all bound conditions
if not invert:
keep_criterion = lambda a: a.name in filter_list
else:
keep_criterion = lambda a: a.name not in filter_list
for st in stmts_in:
for agent in st.agent_list():
_remove_bound_conditions(agent, keep_criterion)
if policy == 'one':
for st in stmts_in:
found_gene = False
if not remove_bound:
agent_list = st.agent_list_with_bound_condition_agents()
else:
agent_list = st.agent_list()
for agent in agent_list:
if agent is not None:
if agent.name in filter_list:
found_gene = True
break
if (found_gene and not invert) or (not found_gene and invert):
stmts_out.append(st)
elif policy == 'all':
for st in stmts_in:
found_genes = True
if not remove_bound:
agent_list = st.agent_list_with_bound_condition_agents()
else:
agent_list = st.agent_list()
for agent in agent_list:
if agent is not None:
if agent.name not in filter_list:
found_genes = False
break
if (found_genes and not invert) or (not found_genes and invert):
stmts_out.append(st)
else:
stmts_out = stmts_in
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | [
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Parameters
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stmts_in : list[indra.statements.Statement]
A list of statements to filter.
gene_list : list[str]
A list of gene symbols to filter for.
policy : str
The policy to apply when filtering for the list of genes. "one": keep
statements that contain at least one of the list of genes and
possibly others not in the list "all": keep statements that only
contain genes given in the list
allow_families : Optional[bool]
Will include statements involving FamPlex families containing one
of the genes in the gene list. Default: False
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
remove_bound: Optional[str]
If true, removes bound conditions that are not genes in the list
If false (default), looks at agents in the bound conditions in addition
to those participating in the statement directly when applying the
specified policy.
invert : Optional[bool]
If True, the statements that do not match according to the policy
are returned. Default: False
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements. | [
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19,218 | sorgerlab/indra | indra/tools/assemble_corpus.py | filter_by_db_refs | def filter_by_db_refs(stmts_in, namespace, values, policy, **kwargs):
"""Filter to Statements whose agents are grounded to a matching entry.
Statements are filtered so that the db_refs entry (of the given namespace)
of their Agent/Concept arguments take a value in the given list of values.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of Statements to filter.
namespace : str
The namespace in db_refs to which the filter should apply.
values : list[str]
A list of values in the given namespace to which the filter should
apply.
policy : str
The policy to apply when filtering for the db_refs. "one": keep
Statements that contain at least one of the list of db_refs and
possibly others not in the list "all": keep Statements that only
contain db_refs given in the list
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
invert : Optional[bool]
If True, the Statements that do not match according to the policy
are returned. Default: False
match_suffix : Optional[bool]
If True, the suffix of the db_refs entry is matches agains the list
of entries
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered Statements.
"""
invert = kwargs.get('invert', False)
match_suffix = kwargs.get('match_suffix', False)
if policy not in ('one', 'all'):
logger.error('Policy %s is invalid, not applying filter.' % policy)
return
else:
name_str = ', '.join(values)
rev_mod = 'not ' if invert else ''
logger.info(('Filtering %d statements for those with %s agents %s'
'grounded to: %s in the %s namespace...') %
(len(stmts_in), policy, rev_mod, name_str, namespace))
def meets_criterion(agent):
if namespace not in agent.db_refs:
return False
entry = agent.db_refs[namespace]
if isinstance(entry, list):
entry = entry[0][0]
ret = False
# Match suffix or entire entry
if match_suffix:
if any([entry.endswith(e) for e in values]):
ret = True
else:
if entry in values:
ret = True
# Invert if needed
if invert:
return not ret
else:
return ret
enough = all if policy == 'all' else any
stmts_out = [s for s in stmts_in
if enough([meets_criterion(ag) for ag in s.agent_list()
if ag is not None])]
logger.info('%d Statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | python | def filter_by_db_refs(stmts_in, namespace, values, policy, **kwargs):
invert = kwargs.get('invert', False)
match_suffix = kwargs.get('match_suffix', False)
if policy not in ('one', 'all'):
logger.error('Policy %s is invalid, not applying filter.' % policy)
return
else:
name_str = ', '.join(values)
rev_mod = 'not ' if invert else ''
logger.info(('Filtering %d statements for those with %s agents %s'
'grounded to: %s in the %s namespace...') %
(len(stmts_in), policy, rev_mod, name_str, namespace))
def meets_criterion(agent):
if namespace not in agent.db_refs:
return False
entry = agent.db_refs[namespace]
if isinstance(entry, list):
entry = entry[0][0]
ret = False
# Match suffix or entire entry
if match_suffix:
if any([entry.endswith(e) for e in values]):
ret = True
else:
if entry in values:
ret = True
# Invert if needed
if invert:
return not ret
else:
return ret
enough = all if policy == 'all' else any
stmts_out = [s for s in stmts_in
if enough([meets_criterion(ag) for ag in s.agent_list()
if ag is not None])]
logger.info('%d Statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | [
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Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of Statements to filter.
namespace : str
The namespace in db_refs to which the filter should apply.
values : list[str]
A list of values in the given namespace to which the filter should
apply.
policy : str
The policy to apply when filtering for the db_refs. "one": keep
Statements that contain at least one of the list of db_refs and
possibly others not in the list "all": keep Statements that only
contain db_refs given in the list
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
invert : Optional[bool]
If True, the Statements that do not match according to the policy
are returned. Default: False
match_suffix : Optional[bool]
If True, the suffix of the db_refs entry is matches agains the list
of entries
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered Statements. | [
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19,219 | sorgerlab/indra | indra/tools/assemble_corpus.py | filter_human_only | def filter_human_only(stmts_in, **kwargs):
"""Filter out statements that are grounded, but not to a human gene.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to filter.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
remove_bound: Optional[bool]
If true, removes all bound conditions that are grounded but not to human
genes. If false (default), filters out statements with boundary
conditions that are grounded to non-human genes.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements.
"""
from indra.databases import uniprot_client
if 'remove_bound' in kwargs and kwargs['remove_bound']:
remove_bound = True
else:
remove_bound = False
dump_pkl = kwargs.get('save')
logger.info('Filtering %d statements for human genes only...' %
len(stmts_in))
stmts_out = []
def criterion(agent):
upid = agent.db_refs.get('UP')
if upid and not uniprot_client.is_human(upid):
return False
else:
return True
for st in stmts_in:
human_genes = True
for agent in st.agent_list():
if agent is not None:
if not criterion(agent):
human_genes = False
break
if remove_bound:
_remove_bound_conditions(agent, criterion)
elif _any_bound_condition_fails_criterion(agent, criterion):
human_genes = False
break
if human_genes:
stmts_out.append(st)
logger.info('%d statements after filter...' % len(stmts_out))
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | python | def filter_human_only(stmts_in, **kwargs):
from indra.databases import uniprot_client
if 'remove_bound' in kwargs and kwargs['remove_bound']:
remove_bound = True
else:
remove_bound = False
dump_pkl = kwargs.get('save')
logger.info('Filtering %d statements for human genes only...' %
len(stmts_in))
stmts_out = []
def criterion(agent):
upid = agent.db_refs.get('UP')
if upid and not uniprot_client.is_human(upid):
return False
else:
return True
for st in stmts_in:
human_genes = True
for agent in st.agent_list():
if agent is not None:
if not criterion(agent):
human_genes = False
break
if remove_bound:
_remove_bound_conditions(agent, criterion)
elif _any_bound_condition_fails_criterion(agent, criterion):
human_genes = False
break
if human_genes:
stmts_out.append(st)
logger.info('%d statements after filter...' % len(stmts_out))
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | [
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Parameters
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stmts_in : list[indra.statements.Statement]
A list of statements to filter.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
remove_bound: Optional[bool]
If true, removes all bound conditions that are grounded but not to human
genes. If false (default), filters out statements with boundary
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Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements. | [
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19,220 | sorgerlab/indra | indra/tools/assemble_corpus.py | filter_direct | def filter_direct(stmts_in, **kwargs):
"""Filter to statements that are direct interactions
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to filter.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements.
"""
def get_is_direct(stmt):
"""Returns true if there is evidence that the statement is a direct
interaction.
If any of the evidences associated with the statement
indicates a direct interatcion then we assume the interaction
is direct. If there is no evidence for the interaction being indirect
then we default to direct.
"""
any_indirect = False
for ev in stmt.evidence:
if ev.epistemics.get('direct') is True:
return True
elif ev.epistemics.get('direct') is False:
# This guarantees that we have seen at least
# some evidence that the statement is indirect
any_indirect = True
if any_indirect:
return False
return True
logger.info('Filtering %d statements to direct ones...' % len(stmts_in))
stmts_out = []
for st in stmts_in:
if get_is_direct(st):
stmts_out.append(st)
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | python | def filter_direct(stmts_in, **kwargs):
def get_is_direct(stmt):
"""Returns true if there is evidence that the statement is a direct
interaction.
If any of the evidences associated with the statement
indicates a direct interatcion then we assume the interaction
is direct. If there is no evidence for the interaction being indirect
then we default to direct.
"""
any_indirect = False
for ev in stmt.evidence:
if ev.epistemics.get('direct') is True:
return True
elif ev.epistemics.get('direct') is False:
# This guarantees that we have seen at least
# some evidence that the statement is indirect
any_indirect = True
if any_indirect:
return False
return True
logger.info('Filtering %d statements to direct ones...' % len(stmts_in))
stmts_out = []
for st in stmts_in:
if get_is_direct(st):
stmts_out.append(st)
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | [
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Parameters
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stmts_in : list[indra.statements.Statement]
A list of statements to filter.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
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-------
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19,221 | sorgerlab/indra | indra/tools/assemble_corpus.py | filter_no_hypothesis | def filter_no_hypothesis(stmts_in, **kwargs):
"""Filter to statements that are not marked as hypothesis in epistemics.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to filter.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements.
"""
logger.info('Filtering %d statements to no hypothesis...' % len(stmts_in))
stmts_out = []
for st in stmts_in:
all_hypotheses = True
ev = None
for ev in st.evidence:
if not ev.epistemics.get('hypothesis', False):
all_hypotheses = False
break
if ev is None:
all_hypotheses = False
if not all_hypotheses:
stmts_out.append(st)
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | python | def filter_no_hypothesis(stmts_in, **kwargs):
logger.info('Filtering %d statements to no hypothesis...' % len(stmts_in))
stmts_out = []
for st in stmts_in:
all_hypotheses = True
ev = None
for ev in st.evidence:
if not ev.epistemics.get('hypothesis', False):
all_hypotheses = False
break
if ev is None:
all_hypotheses = False
if not all_hypotheses:
stmts_out.append(st)
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | [
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Parameters
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stmts_in : list[indra.statements.Statement]
A list of statements to filter.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
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19,222 | sorgerlab/indra | indra/tools/assemble_corpus.py | filter_evidence_source | def filter_evidence_source(stmts_in, source_apis, policy='one', **kwargs):
"""Filter to statements that have evidence from a given set of sources.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to filter.
source_apis : list[str]
A list of sources to filter for. Examples: biopax, bel, reach
policy : Optional[str]
If 'one', a statement that hase evidence from any of the sources is
kept. If 'all', only those statements are kept which have evidence
from all the input sources specified in source_apis.
If 'none', only those statements are kept that don't have evidence
from any of the sources specified in source_apis.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements.
"""
logger.info('Filtering %d statements to evidence source "%s" of: %s...' %
(len(stmts_in), policy, ', '.join(source_apis)))
stmts_out = []
for st in stmts_in:
sources = set([ev.source_api for ev in st.evidence])
if policy == 'one':
if sources.intersection(source_apis):
stmts_out.append(st)
if policy == 'all':
if sources.intersection(source_apis) == set(source_apis):
stmts_out.append(st)
if policy == 'none':
if not sources.intersection(source_apis):
stmts_out.append(st)
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | python | def filter_evidence_source(stmts_in, source_apis, policy='one', **kwargs):
logger.info('Filtering %d statements to evidence source "%s" of: %s...' %
(len(stmts_in), policy, ', '.join(source_apis)))
stmts_out = []
for st in stmts_in:
sources = set([ev.source_api for ev in st.evidence])
if policy == 'one':
if sources.intersection(source_apis):
stmts_out.append(st)
if policy == 'all':
if sources.intersection(source_apis) == set(source_apis):
stmts_out.append(st)
if policy == 'none':
if not sources.intersection(source_apis):
stmts_out.append(st)
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | [
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Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to filter.
source_apis : list[str]
A list of sources to filter for. Examples: biopax, bel, reach
policy : Optional[str]
If 'one', a statement that hase evidence from any of the sources is
kept. If 'all', only those statements are kept which have evidence
from all the input sources specified in source_apis.
If 'none', only those statements are kept that don't have evidence
from any of the sources specified in source_apis.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements. | [
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19,223 | sorgerlab/indra | indra/tools/assemble_corpus.py | filter_top_level | def filter_top_level(stmts_in, **kwargs):
"""Filter to statements that are at the top-level of the hierarchy.
Here top-level statements correspond to most specific ones.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to filter.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements.
"""
logger.info('Filtering %d statements for top-level...' % len(stmts_in))
stmts_out = [st for st in stmts_in if not st.supports]
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | python | def filter_top_level(stmts_in, **kwargs):
logger.info('Filtering %d statements for top-level...' % len(stmts_in))
stmts_out = [st for st in stmts_in if not st.supports]
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | [
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Here top-level statements correspond to most specific ones.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to filter.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements. | [
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19,224 | sorgerlab/indra | indra/tools/assemble_corpus.py | filter_inconsequential_mods | def filter_inconsequential_mods(stmts_in, whitelist=None, **kwargs):
"""Filter out Modifications that modify inconsequential sites
Inconsequential here means that the site is not mentioned / tested
in any other statement. In some cases specific sites should be
preserved, for instance, to be used as readouts in a model.
In this case, the given sites can be passed in a whitelist.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to filter.
whitelist : Optional[dict]
A whitelist containing agent modification sites whose
modifications should be preserved even if no other statement
refers to them. The whitelist parameter is a dictionary in which
the key is a gene name and the value is a list of tuples of
(modification_type, residue, position). Example:
whitelist = {'MAP2K1': [('phosphorylation', 'S', '222')]}
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements.
"""
if whitelist is None:
whitelist = {}
logger.info('Filtering %d statements to remove' % len(stmts_in) +
' inconsequential modifications...')
states_used = whitelist
for stmt in stmts_in:
for agent in stmt.agent_list():
if agent is not None:
if agent.mods:
for mc in agent.mods:
mod = (mc.mod_type, mc.residue, mc.position)
try:
states_used[agent.name].append(mod)
except KeyError:
states_used[agent.name] = [mod]
for k, v in states_used.items():
states_used[k] = list(set(v))
stmts_out = []
for stmt in stmts_in:
skip = False
if isinstance(stmt, Modification):
mod_type = modclass_to_modtype[stmt.__class__]
if isinstance(stmt, RemoveModification):
mod_type = modtype_to_inverse[mod_type]
mod = (mod_type, stmt.residue, stmt.position)
used = states_used.get(stmt.sub.name, [])
if mod not in used:
skip = True
if not skip:
stmts_out.append(stmt)
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | python | def filter_inconsequential_mods(stmts_in, whitelist=None, **kwargs):
if whitelist is None:
whitelist = {}
logger.info('Filtering %d statements to remove' % len(stmts_in) +
' inconsequential modifications...')
states_used = whitelist
for stmt in stmts_in:
for agent in stmt.agent_list():
if agent is not None:
if agent.mods:
for mc in agent.mods:
mod = (mc.mod_type, mc.residue, mc.position)
try:
states_used[agent.name].append(mod)
except KeyError:
states_used[agent.name] = [mod]
for k, v in states_used.items():
states_used[k] = list(set(v))
stmts_out = []
for stmt in stmts_in:
skip = False
if isinstance(stmt, Modification):
mod_type = modclass_to_modtype[stmt.__class__]
if isinstance(stmt, RemoveModification):
mod_type = modtype_to_inverse[mod_type]
mod = (mod_type, stmt.residue, stmt.position)
used = states_used.get(stmt.sub.name, [])
if mod not in used:
skip = True
if not skip:
stmts_out.append(stmt)
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | [
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in any other statement. In some cases specific sites should be
preserved, for instance, to be used as readouts in a model.
In this case, the given sites can be passed in a whitelist.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to filter.
whitelist : Optional[dict]
A whitelist containing agent modification sites whose
modifications should be preserved even if no other statement
refers to them. The whitelist parameter is a dictionary in which
the key is a gene name and the value is a list of tuples of
(modification_type, residue, position). Example:
whitelist = {'MAP2K1': [('phosphorylation', 'S', '222')]}
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements. | [
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19,225 | sorgerlab/indra | indra/tools/assemble_corpus.py | filter_inconsequential_acts | def filter_inconsequential_acts(stmts_in, whitelist=None, **kwargs):
"""Filter out Activations that modify inconsequential activities
Inconsequential here means that the site is not mentioned / tested
in any other statement. In some cases specific activity types should be
preserved, for instance, to be used as readouts in a model.
In this case, the given activities can be passed in a whitelist.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to filter.
whitelist : Optional[dict]
A whitelist containing agent activity types which should be preserved
even if no other statement refers to them.
The whitelist parameter is a dictionary in which
the key is a gene name and the value is a list of activity types.
Example: whitelist = {'MAP2K1': ['kinase']}
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements.
"""
if whitelist is None:
whitelist = {}
logger.info('Filtering %d statements to remove' % len(stmts_in) +
' inconsequential activations...')
states_used = whitelist
for stmt in stmts_in:
for agent in stmt.agent_list():
if agent is not None:
if agent.activity:
act = agent.activity.activity_type
try:
states_used[agent.name].append(act)
except KeyError:
states_used[agent.name] = [act]
for k, v in states_used.items():
states_used[k] = list(set(v))
stmts_out = []
for stmt in stmts_in:
skip = False
if isinstance(stmt, RegulateActivity):
used = states_used.get(stmt.obj.name, [])
if stmt.obj_activity not in used:
skip = True
if not skip:
stmts_out.append(stmt)
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | python | def filter_inconsequential_acts(stmts_in, whitelist=None, **kwargs):
if whitelist is None:
whitelist = {}
logger.info('Filtering %d statements to remove' % len(stmts_in) +
' inconsequential activations...')
states_used = whitelist
for stmt in stmts_in:
for agent in stmt.agent_list():
if agent is not None:
if agent.activity:
act = agent.activity.activity_type
try:
states_used[agent.name].append(act)
except KeyError:
states_used[agent.name] = [act]
for k, v in states_used.items():
states_used[k] = list(set(v))
stmts_out = []
for stmt in stmts_in:
skip = False
if isinstance(stmt, RegulateActivity):
used = states_used.get(stmt.obj.name, [])
if stmt.obj_activity not in used:
skip = True
if not skip:
stmts_out.append(stmt)
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | [
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Inconsequential here means that the site is not mentioned / tested
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preserved, for instance, to be used as readouts in a model.
In this case, the given activities can be passed in a whitelist.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to filter.
whitelist : Optional[dict]
A whitelist containing agent activity types which should be preserved
even if no other statement refers to them.
The whitelist parameter is a dictionary in which
the key is a gene name and the value is a list of activity types.
Example: whitelist = {'MAP2K1': ['kinase']}
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements. | [
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19,226 | sorgerlab/indra | indra/tools/assemble_corpus.py | filter_enzyme_kinase | def filter_enzyme_kinase(stmts_in, **kwargs):
"""Filter Phosphorylations to ones where the enzyme is a known kinase.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to filter.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements.
"""
logger.info('Filtering %d statements to remove ' % len(stmts_in) +
'phosphorylation by non-kinases...')
path = os.path.dirname(os.path.abspath(__file__))
kinase_table = read_unicode_csv(path + '/../resources/kinases.tsv',
delimiter='\t')
gene_names = [lin[1] for lin in list(kinase_table)[1:]]
stmts_out = []
for st in stmts_in:
if isinstance(st, Phosphorylation):
if st.enz is not None:
if st.enz.name in gene_names:
stmts_out.append(st)
else:
stmts_out.append(st)
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | python | def filter_enzyme_kinase(stmts_in, **kwargs):
logger.info('Filtering %d statements to remove ' % len(stmts_in) +
'phosphorylation by non-kinases...')
path = os.path.dirname(os.path.abspath(__file__))
kinase_table = read_unicode_csv(path + '/../resources/kinases.tsv',
delimiter='\t')
gene_names = [lin[1] for lin in list(kinase_table)[1:]]
stmts_out = []
for st in stmts_in:
if isinstance(st, Phosphorylation):
if st.enz is not None:
if st.enz.name in gene_names:
stmts_out.append(st)
else:
stmts_out.append(st)
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | [
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stmts_in : list[indra.statements.Statement]
A list of statements to filter.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements. | [
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19,227 | sorgerlab/indra | indra/tools/assemble_corpus.py | filter_transcription_factor | def filter_transcription_factor(stmts_in, **kwargs):
"""Filter out RegulateAmounts where subject is not a transcription factor.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to filter.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements.
"""
logger.info('Filtering %d statements to remove ' % len(stmts_in) +
'amount regulations by non-transcription-factors...')
path = os.path.dirname(os.path.abspath(__file__))
tf_table = \
read_unicode_csv(path + '/../resources/transcription_factors.csv')
gene_names = [lin[1] for lin in list(tf_table)[1:]]
stmts_out = []
for st in stmts_in:
if isinstance(st, RegulateAmount):
if st.subj is not None:
if st.subj.name in gene_names:
stmts_out.append(st)
else:
stmts_out.append(st)
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | python | def filter_transcription_factor(stmts_in, **kwargs):
logger.info('Filtering %d statements to remove ' % len(stmts_in) +
'amount regulations by non-transcription-factors...')
path = os.path.dirname(os.path.abspath(__file__))
tf_table = \
read_unicode_csv(path + '/../resources/transcription_factors.csv')
gene_names = [lin[1] for lin in list(tf_table)[1:]]
stmts_out = []
for st in stmts_in:
if isinstance(st, RegulateAmount):
if st.subj is not None:
if st.subj.name in gene_names:
stmts_out.append(st)
else:
stmts_out.append(st)
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | [
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Parameters
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stmts_in : list[indra.statements.Statement]
A list of statements to filter.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements. | [
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19,228 | sorgerlab/indra | indra/tools/assemble_corpus.py | filter_uuid_list | def filter_uuid_list(stmts_in, uuids, **kwargs):
"""Filter to Statements corresponding to given UUIDs
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to filter.
uuids : list[str]
A list of UUIDs to filter for.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
invert : Optional[bool]
Invert the filter to remove the Statements corresponding to the given
UUIDs.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements.
"""
invert = kwargs.get('invert', False)
logger.info('Filtering %d statements for %d UUID%s...' %
(len(stmts_in), len(uuids), 's' if len(uuids) > 1 else ''))
stmts_out = []
for st in stmts_in:
if not invert:
if st.uuid in uuids:
stmts_out.append(st)
else:
if st.uuid not in uuids:
stmts_out.append(st)
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | python | def filter_uuid_list(stmts_in, uuids, **kwargs):
invert = kwargs.get('invert', False)
logger.info('Filtering %d statements for %d UUID%s...' %
(len(stmts_in), len(uuids), 's' if len(uuids) > 1 else ''))
stmts_out = []
for st in stmts_in:
if not invert:
if st.uuid in uuids:
stmts_out.append(st)
else:
if st.uuid not in uuids:
stmts_out.append(st)
logger.info('%d statements after filter...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | [
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... | Filter to Statements corresponding to given UUIDs
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to filter.
uuids : list[str]
A list of UUIDs to filter for.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
invert : Optional[bool]
Invert the filter to remove the Statements corresponding to the given
UUIDs.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of filtered statements. | [
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19,229 | sorgerlab/indra | indra/tools/assemble_corpus.py | expand_families | def expand_families(stmts_in, **kwargs):
"""Expand FamPlex Agents to individual genes.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to expand.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of expanded statements.
"""
from indra.tools.expand_families import Expander
logger.info('Expanding families on %d statements...' % len(stmts_in))
expander = Expander(hierarchies)
stmts_out = expander.expand_families(stmts_in)
logger.info('%d statements after expanding families...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | python | def expand_families(stmts_in, **kwargs):
from indra.tools.expand_families import Expander
logger.info('Expanding families on %d statements...' % len(stmts_in))
expander = Expander(hierarchies)
stmts_out = expander.expand_families(stmts_in)
logger.info('%d statements after expanding families...' % len(stmts_out))
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | [
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Parameters
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A list of statements to expand.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
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stmts_out : list[indra.statements.Statement]
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19,230 | sorgerlab/indra | indra/tools/assemble_corpus.py | reduce_activities | def reduce_activities(stmts_in, **kwargs):
"""Reduce the activity types in a list of statements
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to reduce activity types in.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of reduced activity statements.
"""
logger.info('Reducing activities on %d statements...' % len(stmts_in))
stmts_out = [deepcopy(st) for st in stmts_in]
ml = MechLinker(stmts_out)
ml.gather_explicit_activities()
ml.reduce_activities()
stmts_out = ml.statements
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | python | def reduce_activities(stmts_in, **kwargs):
logger.info('Reducing activities on %d statements...' % len(stmts_in))
stmts_out = [deepcopy(st) for st in stmts_in]
ml = MechLinker(stmts_out)
ml.gather_explicit_activities()
ml.reduce_activities()
stmts_out = ml.statements
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | [
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A list of statements to reduce activity types in.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
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19,231 | sorgerlab/indra | indra/tools/assemble_corpus.py | strip_agent_context | def strip_agent_context(stmts_in, **kwargs):
"""Strip any context on agents within each statement.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements whose agent context should be stripped.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of stripped statements.
"""
logger.info('Stripping agent context on %d statements...' % len(stmts_in))
stmts_out = []
for st in stmts_in:
new_st = deepcopy(st)
for agent in new_st.agent_list():
if agent is None:
continue
agent.mods = []
agent.mutations = []
agent.activity = None
agent.location = None
agent.bound_conditions = []
stmts_out.append(new_st)
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | python | def strip_agent_context(stmts_in, **kwargs):
logger.info('Stripping agent context on %d statements...' % len(stmts_in))
stmts_out = []
for st in stmts_in:
new_st = deepcopy(st)
for agent in new_st.agent_list():
if agent is None:
continue
agent.mods = []
agent.mutations = []
agent.activity = None
agent.location = None
agent.bound_conditions = []
stmts_out.append(new_st)
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | [
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The name of a pickle file to save the results (stmts_out) into.
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19,232 | sorgerlab/indra | indra/tools/assemble_corpus.py | standardize_names_groundings | def standardize_names_groundings(stmts):
"""Standardize the names of Concepts with respect to an ontology.
NOTE: this function is currently optimized for Influence Statements
obtained from Eidos, Hume, Sofia and CWMS. It will possibly yield
unexpected results for biology-specific Statements.
"""
print('Standardize names to groundings')
for stmt in stmts:
for concept in stmt.agent_list():
db_ns, db_id = concept.get_grounding()
if db_id is not None:
if isinstance(db_id, list):
db_id = db_id[0][0].split('/')[-1]
else:
db_id = db_id.split('/')[-1]
db_id = db_id.replace('|', ' ')
db_id = db_id.replace('_', ' ')
db_id = db_id.replace('ONT::', '')
db_id = db_id.capitalize()
concept.name = db_id
return stmts | python | def standardize_names_groundings(stmts):
print('Standardize names to groundings')
for stmt in stmts:
for concept in stmt.agent_list():
db_ns, db_id = concept.get_grounding()
if db_id is not None:
if isinstance(db_id, list):
db_id = db_id[0][0].split('/')[-1]
else:
db_id = db_id.split('/')[-1]
db_id = db_id.replace('|', ' ')
db_id = db_id.replace('_', ' ')
db_id = db_id.replace('ONT::', '')
db_id = db_id.capitalize()
concept.name = db_id
return stmts | [
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19,233 | sorgerlab/indra | indra/tools/assemble_corpus.py | dump_stmt_strings | def dump_stmt_strings(stmts, fname):
"""Save printed statements in a file.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to save in a text file.
fname : Optional[str]
The name of a text file to save the printed statements into.
"""
with open(fname, 'wb') as fh:
for st in stmts:
fh.write(('%s\n' % st).encode('utf-8')) | python | def dump_stmt_strings(stmts, fname):
with open(fname, 'wb') as fh:
for st in stmts:
fh.write(('%s\n' % st).encode('utf-8')) | [
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fname : Optional[str]
The name of a text file to save the printed statements into. | [
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19,234 | sorgerlab/indra | indra/tools/assemble_corpus.py | rename_db_ref | def rename_db_ref(stmts_in, ns_from, ns_to, **kwargs):
"""Rename an entry in the db_refs of each Agent.
This is particularly useful when old Statements in pickle files
need to be updated after a namespace was changed such as
'BE' to 'FPLX'.
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements whose Agents' db_refs need to be changed
ns_from : str
The namespace identifier to replace
ns_to : str
The namespace identifier to replace to
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of Statements with Agents' db_refs changed.
"""
logger.info('Remapping "%s" to "%s" in db_refs on %d statements...' %
(ns_from, ns_to, len(stmts_in)))
stmts_out = [deepcopy(st) for st in stmts_in]
for stmt in stmts_out:
for agent in stmt.agent_list():
if agent is not None and ns_from in agent.db_refs:
agent.db_refs[ns_to] = agent.db_refs.pop(ns_from)
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | python | def rename_db_ref(stmts_in, ns_from, ns_to, **kwargs):
logger.info('Remapping "%s" to "%s" in db_refs on %d statements...' %
(ns_from, ns_to, len(stmts_in)))
stmts_out = [deepcopy(st) for st in stmts_in]
for stmt in stmts_out:
for agent in stmt.agent_list():
if agent is not None and ns_from in agent.db_refs:
agent.db_refs[ns_to] = agent.db_refs.pop(ns_from)
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out | [
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Parameters
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stmts_in : list[indra.statements.Statement]
A list of statements whose Agents' db_refs need to be changed
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The namespace identifier to replace
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The namespace identifier to replace to
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The name of a pickle file to save the results (stmts_out) into.
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stmts_out : list[indra.statements.Statement]
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19,235 | sorgerlab/indra | indra/tools/assemble_corpus.py | align_statements | def align_statements(stmts1, stmts2, keyfun=None):
"""Return alignment of two lists of statements by key.
Parameters
----------
stmts1 : list[indra.statements.Statement]
A list of INDRA Statements to align
stmts2 : list[indra.statements.Statement]
A list of INDRA Statements to align
keyfun : Optional[function]
A function that takes a Statement as an argument
and returns a key to align by. If not given,
the default key function is a tuble of the names
of the Agents in the Statement.
Return
------
matches : list(tuple)
A list of tuples where each tuple has two elements,
the first corresponding to an element of the stmts1
list and the second corresponding to an element
of the stmts2 list. If a given element is not matched,
its corresponding pair in the tuple is None.
"""
def name_keyfun(stmt):
return tuple(a.name if a is not None else None for
a in stmt.agent_list())
if not keyfun:
keyfun = name_keyfun
matches = []
keys1 = [keyfun(s) for s in stmts1]
keys2 = [keyfun(s) for s in stmts2]
for stmt, key in zip(stmts1, keys1):
try:
match_idx = keys2.index(key)
match_stmt = stmts2[match_idx]
matches.append((stmt, match_stmt))
except ValueError:
matches.append((stmt, None))
for stmt, key in zip(stmts2, keys2):
try:
match_idx = keys1.index(key)
except ValueError:
matches.append((None, stmt))
return matches | python | def align_statements(stmts1, stmts2, keyfun=None):
def name_keyfun(stmt):
return tuple(a.name if a is not None else None for
a in stmt.agent_list())
if not keyfun:
keyfun = name_keyfun
matches = []
keys1 = [keyfun(s) for s in stmts1]
keys2 = [keyfun(s) for s in stmts2]
for stmt, key in zip(stmts1, keys1):
try:
match_idx = keys2.index(key)
match_stmt = stmts2[match_idx]
matches.append((stmt, match_stmt))
except ValueError:
matches.append((stmt, None))
for stmt, key in zip(stmts2, keys2):
try:
match_idx = keys1.index(key)
except ValueError:
matches.append((None, stmt))
return matches | [
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A list of INDRA Statements to align
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19,236 | sorgerlab/indra | indra/sources/indra_db_rest/util.py | submit_query_request | def submit_query_request(end_point, *args, **kwargs):
"""Low level function to format the query string."""
ev_limit = kwargs.pop('ev_limit', 10)
best_first = kwargs.pop('best_first', True)
tries = kwargs.pop('tries', 2)
# This isn't handled by requests because of the multiple identical agent
# keys, e.g. {'agent': 'MEK', 'agent': 'ERK'} which is not supported in
# python, but is allowed and necessary in these query strings.
# TODO because we use the API Gateway, this feature is not longer needed.
# We should just use the requests parameters dict.
query_str = '?' + '&'.join(['%s=%s' % (k, v) for k, v in kwargs.items()
if v is not None]
+ list(args))
return submit_statement_request('get', end_point, query_str,
ev_limit=ev_limit, best_first=best_first,
tries=tries) | python | def submit_query_request(end_point, *args, **kwargs):
ev_limit = kwargs.pop('ev_limit', 10)
best_first = kwargs.pop('best_first', True)
tries = kwargs.pop('tries', 2)
# This isn't handled by requests because of the multiple identical agent
# keys, e.g. {'agent': 'MEK', 'agent': 'ERK'} which is not supported in
# python, but is allowed and necessary in these query strings.
# TODO because we use the API Gateway, this feature is not longer needed.
# We should just use the requests parameters dict.
query_str = '?' + '&'.join(['%s=%s' % (k, v) for k, v in kwargs.items()
if v is not None]
+ list(args))
return submit_statement_request('get', end_point, query_str,
ev_limit=ev_limit, best_first=best_first,
tries=tries) | [
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19,237 | sorgerlab/indra | indra/sources/indra_db_rest/util.py | submit_statement_request | def submit_statement_request(meth, end_point, query_str='', data=None,
tries=2, **params):
"""Even lower level function to make the request."""
full_end_point = 'statements/' + end_point.lstrip('/')
return make_db_rest_request(meth, full_end_point, query_str, data, params, tries) | python | def submit_statement_request(meth, end_point, query_str='', data=None,
tries=2, **params):
full_end_point = 'statements/' + end_point.lstrip('/')
return make_db_rest_request(meth, full_end_point, query_str, data, params, tries) | [
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19,238 | sorgerlab/indra | indra/preassembler/__init__.py | render_stmt_graph | def render_stmt_graph(statements, reduce=True, english=False, rankdir=None,
agent_style=None):
"""Render the statement hierarchy as a pygraphviz graph.
Parameters
----------
stmts : list of :py:class:`indra.statements.Statement`
A list of top-level statements with associated supporting statements
resulting from building a statement hierarchy with
:py:meth:`combine_related`.
reduce : bool
Whether to perform a transitive reduction of the edges in the graph.
Default is True.
english : bool
If True, the statements in the graph are represented by their
English-assembled equivalent; otherwise they are represented as
text-formatted Statements.
rank_dir : str or None
Argument to pass through to the pygraphviz `AGraph` constructor
specifying graph layout direction. In particular, a value of 'LR'
specifies a left-to-right direction. If None, the pygraphviz default
is used.
agent_style : dict or None
Dict of attributes specifying the visual properties of nodes. If None,
the following default attributes are used::
agent_style = {'color': 'lightgray', 'style': 'filled',
'fontname': 'arial'}
Returns
-------
pygraphviz.AGraph
Pygraphviz graph with nodes representing statements and edges pointing
from supported statements to supported_by statements.
Examples
--------
Pattern for getting statements and rendering as a Graphviz graph:
>>> from indra.preassembler.hierarchy_manager import hierarchies
>>> braf = Agent('BRAF')
>>> map2k1 = Agent('MAP2K1')
>>> st1 = Phosphorylation(braf, map2k1)
>>> st2 = Phosphorylation(braf, map2k1, residue='S')
>>> pa = Preassembler(hierarchies, [st1, st2])
>>> pa.combine_related() # doctest:+ELLIPSIS
[Phosphorylation(BRAF(), MAP2K1(), S)]
>>> graph = render_stmt_graph(pa.related_stmts)
>>> graph.write('example_graph.dot') # To make the DOT file
>>> graph.draw('example_graph.png', prog='dot') # To make an image
Resulting graph:
.. image:: /images/example_graph.png
:align: center
:alt: Example statement graph rendered by Graphviz
"""
from indra.assemblers.english import EnglishAssembler
# Set the default agent formatting properties
if agent_style is None:
agent_style = {'color': 'lightgray', 'style': 'filled',
'fontname': 'arial'}
# Sets to store all of the nodes and edges as we recursively process all
# of the statements
nodes = set([])
edges = set([])
stmt_dict = {}
# Recursive function for processing all statements
def process_stmt(stmt):
nodes.add(str(stmt.matches_key()))
stmt_dict[str(stmt.matches_key())] = stmt
for sby_ix, sby_stmt in enumerate(stmt.supported_by):
edges.add((str(stmt.matches_key()), str(sby_stmt.matches_key())))
process_stmt(sby_stmt)
# Process all of the top-level statements, getting the supporting statements
# recursively
for stmt in statements:
process_stmt(stmt)
# Create a networkx graph from the nodes
nx_graph = nx.DiGraph()
nx_graph.add_edges_from(edges)
# Perform transitive reduction if desired
if reduce:
nx_graph = nx.algorithms.dag.transitive_reduction(nx_graph)
# Create a pygraphviz graph from the nx graph
try:
pgv_graph = pgv.AGraph(name='statements', directed=True,
rankdir=rankdir)
except NameError:
logger.error('Cannot generate graph because '
'pygraphviz could not be imported.')
return None
for node in nx_graph.nodes():
stmt = stmt_dict[node]
if english:
ea = EnglishAssembler([stmt])
stmt_str = ea.make_model()
else:
stmt_str = str(stmt)
pgv_graph.add_node(node,
label='%s (%d)' % (stmt_str, len(stmt.evidence)),
**agent_style)
pgv_graph.add_edges_from(nx_graph.edges())
return pgv_graph | python | def render_stmt_graph(statements, reduce=True, english=False, rankdir=None,
agent_style=None):
from indra.assemblers.english import EnglishAssembler
# Set the default agent formatting properties
if agent_style is None:
agent_style = {'color': 'lightgray', 'style': 'filled',
'fontname': 'arial'}
# Sets to store all of the nodes and edges as we recursively process all
# of the statements
nodes = set([])
edges = set([])
stmt_dict = {}
# Recursive function for processing all statements
def process_stmt(stmt):
nodes.add(str(stmt.matches_key()))
stmt_dict[str(stmt.matches_key())] = stmt
for sby_ix, sby_stmt in enumerate(stmt.supported_by):
edges.add((str(stmt.matches_key()), str(sby_stmt.matches_key())))
process_stmt(sby_stmt)
# Process all of the top-level statements, getting the supporting statements
# recursively
for stmt in statements:
process_stmt(stmt)
# Create a networkx graph from the nodes
nx_graph = nx.DiGraph()
nx_graph.add_edges_from(edges)
# Perform transitive reduction if desired
if reduce:
nx_graph = nx.algorithms.dag.transitive_reduction(nx_graph)
# Create a pygraphviz graph from the nx graph
try:
pgv_graph = pgv.AGraph(name='statements', directed=True,
rankdir=rankdir)
except NameError:
logger.error('Cannot generate graph because '
'pygraphviz could not be imported.')
return None
for node in nx_graph.nodes():
stmt = stmt_dict[node]
if english:
ea = EnglishAssembler([stmt])
stmt_str = ea.make_model()
else:
stmt_str = str(stmt)
pgv_graph.add_node(node,
label='%s (%d)' % (stmt_str, len(stmt.evidence)),
**agent_style)
pgv_graph.add_edges_from(nx_graph.edges())
return pgv_graph | [
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Parameters
----------
stmts : list of :py:class:`indra.statements.Statement`
A list of top-level statements with associated supporting statements
resulting from building a statement hierarchy with
:py:meth:`combine_related`.
reduce : bool
Whether to perform a transitive reduction of the edges in the graph.
Default is True.
english : bool
If True, the statements in the graph are represented by their
English-assembled equivalent; otherwise they are represented as
text-formatted Statements.
rank_dir : str or None
Argument to pass through to the pygraphviz `AGraph` constructor
specifying graph layout direction. In particular, a value of 'LR'
specifies a left-to-right direction. If None, the pygraphviz default
is used.
agent_style : dict or None
Dict of attributes specifying the visual properties of nodes. If None,
the following default attributes are used::
agent_style = {'color': 'lightgray', 'style': 'filled',
'fontname': 'arial'}
Returns
-------
pygraphviz.AGraph
Pygraphviz graph with nodes representing statements and edges pointing
from supported statements to supported_by statements.
Examples
--------
Pattern for getting statements and rendering as a Graphviz graph:
>>> from indra.preassembler.hierarchy_manager import hierarchies
>>> braf = Agent('BRAF')
>>> map2k1 = Agent('MAP2K1')
>>> st1 = Phosphorylation(braf, map2k1)
>>> st2 = Phosphorylation(braf, map2k1, residue='S')
>>> pa = Preassembler(hierarchies, [st1, st2])
>>> pa.combine_related() # doctest:+ELLIPSIS
[Phosphorylation(BRAF(), MAP2K1(), S)]
>>> graph = render_stmt_graph(pa.related_stmts)
>>> graph.write('example_graph.dot') # To make the DOT file
>>> graph.draw('example_graph.png', prog='dot') # To make an image
Resulting graph:
.. image:: /images/example_graph.png
:align: center
:alt: Example statement graph rendered by Graphviz | [
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19,239 | sorgerlab/indra | indra/preassembler/__init__.py | flatten_stmts | def flatten_stmts(stmts):
"""Return the full set of unique stms in a pre-assembled stmt graph.
The flattened list of statements returned by this function can be
compared to the original set of unique statements to make sure no
statements have been lost during the preassembly process.
Parameters
----------
stmts : list of :py:class:`indra.statements.Statement`
A list of top-level statements with associated supporting statements
resulting from building a statement hierarchy with
:py:meth:`combine_related`.
Returns
-------
stmts : list of :py:class:`indra.statements.Statement`
List of all statements contained in the hierarchical statement graph.
Examples
--------
Calling :py:meth:`combine_related` on two statements results in one
top-level statement; calling :py:func:`flatten_stmts` recovers both:
>>> from indra.preassembler.hierarchy_manager import hierarchies
>>> braf = Agent('BRAF')
>>> map2k1 = Agent('MAP2K1')
>>> st1 = Phosphorylation(braf, map2k1)
>>> st2 = Phosphorylation(braf, map2k1, residue='S')
>>> pa = Preassembler(hierarchies, [st1, st2])
>>> pa.combine_related() # doctest:+ELLIPSIS
[Phosphorylation(BRAF(), MAP2K1(), S)]
>>> flattened = flatten_stmts(pa.related_stmts)
>>> flattened.sort(key=lambda x: x.matches_key())
>>> flattened
[Phosphorylation(BRAF(), MAP2K1()), Phosphorylation(BRAF(), MAP2K1(), S)]
"""
total_stmts = set(stmts)
for stmt in stmts:
if stmt.supported_by:
children = flatten_stmts(stmt.supported_by)
total_stmts = total_stmts.union(children)
return list(total_stmts) | python | def flatten_stmts(stmts):
total_stmts = set(stmts)
for stmt in stmts:
if stmt.supported_by:
children = flatten_stmts(stmt.supported_by)
total_stmts = total_stmts.union(children)
return list(total_stmts) | [
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The flattened list of statements returned by this function can be
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statements have been lost during the preassembly process.
Parameters
----------
stmts : list of :py:class:`indra.statements.Statement`
A list of top-level statements with associated supporting statements
resulting from building a statement hierarchy with
:py:meth:`combine_related`.
Returns
-------
stmts : list of :py:class:`indra.statements.Statement`
List of all statements contained in the hierarchical statement graph.
Examples
--------
Calling :py:meth:`combine_related` on two statements results in one
top-level statement; calling :py:func:`flatten_stmts` recovers both:
>>> from indra.preassembler.hierarchy_manager import hierarchies
>>> braf = Agent('BRAF')
>>> map2k1 = Agent('MAP2K1')
>>> st1 = Phosphorylation(braf, map2k1)
>>> st2 = Phosphorylation(braf, map2k1, residue='S')
>>> pa = Preassembler(hierarchies, [st1, st2])
>>> pa.combine_related() # doctest:+ELLIPSIS
[Phosphorylation(BRAF(), MAP2K1(), S)]
>>> flattened = flatten_stmts(pa.related_stmts)
>>> flattened.sort(key=lambda x: x.matches_key())
>>> flattened
[Phosphorylation(BRAF(), MAP2K1()), Phosphorylation(BRAF(), MAP2K1(), S)] | [
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19,240 | sorgerlab/indra | indra/preassembler/__init__.py | Preassembler.combine_duplicates | def combine_duplicates(self):
"""Combine duplicates among `stmts` and save result in `unique_stmts`.
A wrapper around the static method :py:meth:`combine_duplicate_stmts`.
"""
if self.unique_stmts is None:
self.unique_stmts = self.combine_duplicate_stmts(self.stmts)
return self.unique_stmts | python | def combine_duplicates(self):
if self.unique_stmts is None:
self.unique_stmts = self.combine_duplicate_stmts(self.stmts)
return self.unique_stmts | [
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19,241 | sorgerlab/indra | indra/preassembler/__init__.py | Preassembler._get_stmt_matching_groups | def _get_stmt_matching_groups(stmts):
"""Use the matches_key method to get sets of matching statements."""
def match_func(x): return x.matches_key()
# Remove exact duplicates using a set() call, then make copies:
logger.debug('%d statements before removing object duplicates.' %
len(stmts))
st = list(set(stmts))
logger.debug('%d statements after removing object duplicates.' %
len(stmts))
# Group statements according to whether they are matches (differing
# only in their evidence).
# Sort the statements in place by matches_key()
st.sort(key=match_func)
return itertools.groupby(st, key=match_func) | python | def _get_stmt_matching_groups(stmts):
def match_func(x): return x.matches_key()
# Remove exact duplicates using a set() call, then make copies:
logger.debug('%d statements before removing object duplicates.' %
len(stmts))
st = list(set(stmts))
logger.debug('%d statements after removing object duplicates.' %
len(stmts))
# Group statements according to whether they are matches (differing
# only in their evidence).
# Sort the statements in place by matches_key()
st.sort(key=match_func)
return itertools.groupby(st, key=match_func) | [
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19,242 | sorgerlab/indra | indra/preassembler/__init__.py | Preassembler.combine_duplicate_stmts | def combine_duplicate_stmts(stmts):
"""Combine evidence from duplicate Statements.
Statements are deemed to be duplicates if they have the same key
returned by the `matches_key()` method of the Statement class. This
generally means that statements must be identical in terms of their
arguments and can differ only in their associated `Evidence` objects.
This function keeps the first instance of each set of duplicate
statements and merges the lists of Evidence from all of the other
statements.
Parameters
----------
stmts : list of :py:class:`indra.statements.Statement`
Set of statements to de-duplicate.
Returns
-------
list of :py:class:`indra.statements.Statement`
Unique statements with accumulated evidence across duplicates.
Examples
--------
De-duplicate and combine evidence for two statements differing only
in their evidence lists:
>>> map2k1 = Agent('MAP2K1')
>>> mapk1 = Agent('MAPK1')
>>> stmt1 = Phosphorylation(map2k1, mapk1, 'T', '185',
... evidence=[Evidence(text='evidence 1')])
>>> stmt2 = Phosphorylation(map2k1, mapk1, 'T', '185',
... evidence=[Evidence(text='evidence 2')])
>>> uniq_stmts = Preassembler.combine_duplicate_stmts([stmt1, stmt2])
>>> uniq_stmts
[Phosphorylation(MAP2K1(), MAPK1(), T, 185)]
>>> sorted([e.text for e in uniq_stmts[0].evidence]) # doctest:+IGNORE_UNICODE
['evidence 1', 'evidence 2']
"""
# Helper function to get a list of evidence matches keys
def _ev_keys(sts):
ev_keys = []
for stmt in sts:
for ev in stmt.evidence:
ev_keys.append(ev.matches_key())
return ev_keys
# Iterate over groups of duplicate statements
unique_stmts = []
for _, duplicates in Preassembler._get_stmt_matching_groups(stmts):
ev_keys = set()
# Get the first statement and add the evidence of all subsequent
# Statements to it
duplicates = list(duplicates)
start_ev_keys = _ev_keys(duplicates)
for stmt_ix, stmt in enumerate(duplicates):
if stmt_ix is 0:
new_stmt = stmt.make_generic_copy()
if len(duplicates) == 1:
new_stmt.uuid = stmt.uuid
raw_text = [None if ag is None else ag.db_refs.get('TEXT')
for ag in stmt.agent_list(deep_sorted=True)]
raw_grounding = [None if ag is None else ag.db_refs
for ag in stmt.agent_list(deep_sorted=True)]
for ev in stmt.evidence:
ev_key = ev.matches_key() + str(raw_text) + \
str(raw_grounding)
if ev_key not in ev_keys:
# In case there are already agents annotations, we
# just add a new key for raw_text, otherwise create
# a new key
if 'agents' in ev.annotations:
ev.annotations['agents']['raw_text'] = raw_text
ev.annotations['agents']['raw_grounding'] = \
raw_grounding
else:
ev.annotations['agents'] = \
{'raw_text': raw_text,
'raw_grounding': raw_grounding}
if 'prior_uuids' not in ev.annotations:
ev.annotations['prior_uuids'] = []
ev.annotations['prior_uuids'].append(stmt.uuid)
new_stmt.evidence.append(ev)
ev_keys.add(ev_key)
end_ev_keys = _ev_keys([new_stmt])
if len(end_ev_keys) != len(start_ev_keys):
logger.debug('%d redundant evidences eliminated.' %
(len(start_ev_keys) - len(end_ev_keys)))
# This should never be None or anything else
assert isinstance(new_stmt, Statement)
unique_stmts.append(new_stmt)
return unique_stmts | python | def combine_duplicate_stmts(stmts):
# Helper function to get a list of evidence matches keys
def _ev_keys(sts):
ev_keys = []
for stmt in sts:
for ev in stmt.evidence:
ev_keys.append(ev.matches_key())
return ev_keys
# Iterate over groups of duplicate statements
unique_stmts = []
for _, duplicates in Preassembler._get_stmt_matching_groups(stmts):
ev_keys = set()
# Get the first statement and add the evidence of all subsequent
# Statements to it
duplicates = list(duplicates)
start_ev_keys = _ev_keys(duplicates)
for stmt_ix, stmt in enumerate(duplicates):
if stmt_ix is 0:
new_stmt = stmt.make_generic_copy()
if len(duplicates) == 1:
new_stmt.uuid = stmt.uuid
raw_text = [None if ag is None else ag.db_refs.get('TEXT')
for ag in stmt.agent_list(deep_sorted=True)]
raw_grounding = [None if ag is None else ag.db_refs
for ag in stmt.agent_list(deep_sorted=True)]
for ev in stmt.evidence:
ev_key = ev.matches_key() + str(raw_text) + \
str(raw_grounding)
if ev_key not in ev_keys:
# In case there are already agents annotations, we
# just add a new key for raw_text, otherwise create
# a new key
if 'agents' in ev.annotations:
ev.annotations['agents']['raw_text'] = raw_text
ev.annotations['agents']['raw_grounding'] = \
raw_grounding
else:
ev.annotations['agents'] = \
{'raw_text': raw_text,
'raw_grounding': raw_grounding}
if 'prior_uuids' not in ev.annotations:
ev.annotations['prior_uuids'] = []
ev.annotations['prior_uuids'].append(stmt.uuid)
new_stmt.evidence.append(ev)
ev_keys.add(ev_key)
end_ev_keys = _ev_keys([new_stmt])
if len(end_ev_keys) != len(start_ev_keys):
logger.debug('%d redundant evidences eliminated.' %
(len(start_ev_keys) - len(end_ev_keys)))
# This should never be None or anything else
assert isinstance(new_stmt, Statement)
unique_stmts.append(new_stmt)
return unique_stmts | [
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Statements are deemed to be duplicates if they have the same key
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arguments and can differ only in their associated `Evidence` objects.
This function keeps the first instance of each set of duplicate
statements and merges the lists of Evidence from all of the other
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Parameters
----------
stmts : list of :py:class:`indra.statements.Statement`
Set of statements to de-duplicate.
Returns
-------
list of :py:class:`indra.statements.Statement`
Unique statements with accumulated evidence across duplicates.
Examples
--------
De-duplicate and combine evidence for two statements differing only
in their evidence lists:
>>> map2k1 = Agent('MAP2K1')
>>> mapk1 = Agent('MAPK1')
>>> stmt1 = Phosphorylation(map2k1, mapk1, 'T', '185',
... evidence=[Evidence(text='evidence 1')])
>>> stmt2 = Phosphorylation(map2k1, mapk1, 'T', '185',
... evidence=[Evidence(text='evidence 2')])
>>> uniq_stmts = Preassembler.combine_duplicate_stmts([stmt1, stmt2])
>>> uniq_stmts
[Phosphorylation(MAP2K1(), MAPK1(), T, 185)]
>>> sorted([e.text for e in uniq_stmts[0].evidence]) # doctest:+IGNORE_UNICODE
['evidence 1', 'evidence 2'] | [
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19,243 | sorgerlab/indra | indra/preassembler/__init__.py | Preassembler._get_stmt_by_group | def _get_stmt_by_group(self, stmt_type, stmts_this_type, eh):
"""Group Statements of `stmt_type` by their hierarchical relations."""
# Dict of stmt group key tuples, indexed by their first Agent
stmt_by_first = collections.defaultdict(lambda: [])
# Dict of stmt group key tuples, indexed by their second Agent
stmt_by_second = collections.defaultdict(lambda: [])
# Dict of statements with None first, with second Agent as keys
none_first = collections.defaultdict(lambda: [])
# Dict of statements with None second, with first Agent as keys
none_second = collections.defaultdict(lambda: [])
# The dict of all statement groups, with tuples of components
# or entity_matches_keys as keys
stmt_by_group = collections.defaultdict(lambda: [])
# Here we group Statements according to the hierarchy graph
# components that their agents are part of
for stmt_tuple in stmts_this_type:
_, stmt = stmt_tuple
entities = self._get_entities(stmt, stmt_type, eh)
# At this point we have an entity list
# If we're dealing with Complexes, sort the entities and use
# as dict key
if stmt_type == Complex:
# There shouldn't be any statements of the type
# e.g., Complex([Foo, None, Bar])
assert None not in entities
assert len(entities) > 0
entities.sort()
key = tuple(entities)
if stmt_tuple not in stmt_by_group[key]:
stmt_by_group[key].append(stmt_tuple)
elif stmt_type == Conversion:
assert len(entities) > 0
key = (entities[0],
tuple(sorted(entities[1:len(stmt.obj_from)+1])),
tuple(sorted(entities[-len(stmt.obj_to):])))
if stmt_tuple not in stmt_by_group[key]:
stmt_by_group[key].append(stmt_tuple)
# Now look at all other statement types
# All other statements will have one or two entities
elif len(entities) == 1:
# If only one entity, we only need the one key
# It should not be None!
assert None not in entities
key = tuple(entities)
if stmt_tuple not in stmt_by_group[key]:
stmt_by_group[key].append(stmt_tuple)
else:
# Make sure we only have two entities, and they are not both
# None
key = tuple(entities)
assert len(key) == 2
assert key != (None, None)
# First agent is None; add in the statements, indexed by
# 2nd
if key[0] is None and stmt_tuple not in none_first[key[1]]:
none_first[key[1]].append(stmt_tuple)
# Second agent is None; add in the statements, indexed by
# 1st
elif key[1] is None and stmt_tuple not in none_second[key[0]]:
none_second[key[0]].append(stmt_tuple)
# Neither entity is None!
elif None not in key:
if stmt_tuple not in stmt_by_group[key]:
stmt_by_group[key].append(stmt_tuple)
if key not in stmt_by_first[key[0]]:
stmt_by_first[key[0]].append(key)
if key not in stmt_by_second[key[1]]:
stmt_by_second[key[1]].append(key)
# When we've gotten here, we should have stmt_by_group entries, and
# we may or may not have stmt_by_first/second dicts filled out
# (depending on the statement type).
if none_first:
# Get the keys associated with stmts having a None first
# argument
for second_arg, stmts in none_first.items():
# Look for any statements with this second arg
second_arg_keys = stmt_by_second[second_arg]
# If there are no more specific statements matching this
# set of statements with a None first arg, then the
# statements with the None first arg deserve to be in
# their own group.
if not second_arg_keys:
stmt_by_group[(None, second_arg)] = stmts
# On the other hand, if there are statements with a matching
# second arg component, we need to add the None first
# statements to all groups with the matching second arg
for second_arg_key in second_arg_keys:
stmt_by_group[second_arg_key] += stmts
# Now do the corresponding steps for the statements with None as the
# second argument:
if none_second:
for first_arg, stmts in none_second.items():
# Look for any statements with this first arg
first_arg_keys = stmt_by_first[first_arg]
# If there are no more specific statements matching this
# set of statements with a None second arg, then the
# statements with the None second arg deserve to be in
# their own group.
if not first_arg_keys:
stmt_by_group[(first_arg, None)] = stmts
# On the other hand, if there are statements with a matching
# first arg component, we need to add the None second
# statements to all groups with the matching first arg
for first_arg_key in first_arg_keys:
stmt_by_group[first_arg_key] += stmts
return stmt_by_group | python | def _get_stmt_by_group(self, stmt_type, stmts_this_type, eh):
# Dict of stmt group key tuples, indexed by their first Agent
stmt_by_first = collections.defaultdict(lambda: [])
# Dict of stmt group key tuples, indexed by their second Agent
stmt_by_second = collections.defaultdict(lambda: [])
# Dict of statements with None first, with second Agent as keys
none_first = collections.defaultdict(lambda: [])
# Dict of statements with None second, with first Agent as keys
none_second = collections.defaultdict(lambda: [])
# The dict of all statement groups, with tuples of components
# or entity_matches_keys as keys
stmt_by_group = collections.defaultdict(lambda: [])
# Here we group Statements according to the hierarchy graph
# components that their agents are part of
for stmt_tuple in stmts_this_type:
_, stmt = stmt_tuple
entities = self._get_entities(stmt, stmt_type, eh)
# At this point we have an entity list
# If we're dealing with Complexes, sort the entities and use
# as dict key
if stmt_type == Complex:
# There shouldn't be any statements of the type
# e.g., Complex([Foo, None, Bar])
assert None not in entities
assert len(entities) > 0
entities.sort()
key = tuple(entities)
if stmt_tuple not in stmt_by_group[key]:
stmt_by_group[key].append(stmt_tuple)
elif stmt_type == Conversion:
assert len(entities) > 0
key = (entities[0],
tuple(sorted(entities[1:len(stmt.obj_from)+1])),
tuple(sorted(entities[-len(stmt.obj_to):])))
if stmt_tuple not in stmt_by_group[key]:
stmt_by_group[key].append(stmt_tuple)
# Now look at all other statement types
# All other statements will have one or two entities
elif len(entities) == 1:
# If only one entity, we only need the one key
# It should not be None!
assert None not in entities
key = tuple(entities)
if stmt_tuple not in stmt_by_group[key]:
stmt_by_group[key].append(stmt_tuple)
else:
# Make sure we only have two entities, and they are not both
# None
key = tuple(entities)
assert len(key) == 2
assert key != (None, None)
# First agent is None; add in the statements, indexed by
# 2nd
if key[0] is None and stmt_tuple not in none_first[key[1]]:
none_first[key[1]].append(stmt_tuple)
# Second agent is None; add in the statements, indexed by
# 1st
elif key[1] is None and stmt_tuple not in none_second[key[0]]:
none_second[key[0]].append(stmt_tuple)
# Neither entity is None!
elif None not in key:
if stmt_tuple not in stmt_by_group[key]:
stmt_by_group[key].append(stmt_tuple)
if key not in stmt_by_first[key[0]]:
stmt_by_first[key[0]].append(key)
if key not in stmt_by_second[key[1]]:
stmt_by_second[key[1]].append(key)
# When we've gotten here, we should have stmt_by_group entries, and
# we may or may not have stmt_by_first/second dicts filled out
# (depending on the statement type).
if none_first:
# Get the keys associated with stmts having a None first
# argument
for second_arg, stmts in none_first.items():
# Look for any statements with this second arg
second_arg_keys = stmt_by_second[second_arg]
# If there are no more specific statements matching this
# set of statements with a None first arg, then the
# statements with the None first arg deserve to be in
# their own group.
if not second_arg_keys:
stmt_by_group[(None, second_arg)] = stmts
# On the other hand, if there are statements with a matching
# second arg component, we need to add the None first
# statements to all groups with the matching second arg
for second_arg_key in second_arg_keys:
stmt_by_group[second_arg_key] += stmts
# Now do the corresponding steps for the statements with None as the
# second argument:
if none_second:
for first_arg, stmts in none_second.items():
# Look for any statements with this first arg
first_arg_keys = stmt_by_first[first_arg]
# If there are no more specific statements matching this
# set of statements with a None second arg, then the
# statements with the None second arg deserve to be in
# their own group.
if not first_arg_keys:
stmt_by_group[(first_arg, None)] = stmts
# On the other hand, if there are statements with a matching
# first arg component, we need to add the None second
# statements to all groups with the matching first arg
for first_arg_key in first_arg_keys:
stmt_by_group[first_arg_key] += stmts
return stmt_by_group | [
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19,244 | sorgerlab/indra | indra/preassembler/__init__.py | Preassembler.combine_related | def combine_related(self, return_toplevel=True, poolsize=None,
size_cutoff=100):
"""Connect related statements based on their refinement relationships.
This function takes as a starting point the unique statements (with
duplicates removed) and returns a modified flat list of statements
containing only those statements which do not represent a refinement of
other existing statements. In other words, the more general versions of
a given statement do not appear at the top level, but instead are
listed in the `supports` field of the top-level statements.
If :py:attr:`unique_stmts` has not been initialized with the
de-duplicated statements, :py:meth:`combine_duplicates` is called
internally.
After this function is called the attribute :py:attr:`related_stmts` is
set as a side-effect.
The procedure for combining statements in this way involves a series
of steps:
1. The statements are grouped by type (e.g., Phosphorylation) and
each type is iterated over independently.
2. Statements of the same type are then grouped according to their
Agents' entity hierarchy component identifiers. For instance,
ERK, MAPK1 and MAPK3 are all in the same connected component in the
entity hierarchy and therefore all Statements of the same type
referencing these entities will be grouped. This grouping assures
that relations are only possible within Statement groups and
not among groups. For two Statements to be in the same group at
this step, the Statements must be the same type and the Agents at
each position in the Agent lists must either be in the same
hierarchy component, or if they are not in the hierarchy, must have
identical entity_matches_keys. Statements with None in one of the
Agent list positions are collected separately at this stage.
3. Statements with None at either the first or second position are
iterated over. For a statement with a None as the first Agent,
the second Agent is examined; then the Statement with None is
added to all Statement groups with a corresponding component or
entity_matches_key in the second position. The same procedure is
performed for Statements with None at the second Agent position.
4. The statements within each group are then compared; if one
statement represents a refinement of the other (as defined by the
`refinement_of()` method implemented for the Statement), then the
more refined statement is added to the `supports` field of the more
general statement, and the more general statement is added to the
`supported_by` field of the more refined statement.
5. A new flat list of statements is created that contains only those
statements that have no `supports` entries (statements containing
such entries are not eliminated, because they will be retrievable
from the `supported_by` fields of other statements). This list
is returned to the caller.
On multi-core machines, the algorithm can be parallelized by setting
the poolsize argument to the desired number of worker processes.
This feature is only available in Python > 3.4.
.. note:: Subfamily relationships must be consistent across arguments
For now, we require that merges can only occur if the *isa*
relationships are all in the *same direction for all the agents* in
a Statement. For example, the two statement groups: `RAF_family ->
MEK1` and `BRAF -> MEK_family` would not be merged, since BRAF
*isa* RAF_family, but MEK_family is not a MEK1. In the future this
restriction could be revisited.
Parameters
----------
return_toplevel : Optional[bool]
If True only the top level statements are returned.
If False, all statements are returned. Default: True
poolsize : Optional[int]
The number of worker processes to use to parallelize the
comparisons performed by the function. If None (default), no
parallelization is performed. NOTE: Parallelization is only
available on Python 3.4 and above.
size_cutoff : Optional[int]
Groups with size_cutoff or more statements are sent to worker
processes, while smaller groups are compared in the parent process.
Default value is 100. Not relevant when parallelization is not
used.
Returns
-------
list of :py:class:`indra.statement.Statement`
The returned list contains Statements representing the more
concrete/refined versions of the Statements involving particular
entities. The attribute :py:attr:`related_stmts` is also set to
this list. However, if return_toplevel is False then all
statements are returned, irrespective of level of specificity.
In this case the relationships between statements can
be accessed via the supports/supported_by attributes.
Examples
--------
A more general statement with no information about a Phosphorylation
site is identified as supporting a more specific statement:
>>> from indra.preassembler.hierarchy_manager import hierarchies
>>> braf = Agent('BRAF')
>>> map2k1 = Agent('MAP2K1')
>>> st1 = Phosphorylation(braf, map2k1)
>>> st2 = Phosphorylation(braf, map2k1, residue='S')
>>> pa = Preassembler(hierarchies, [st1, st2])
>>> combined_stmts = pa.combine_related() # doctest:+ELLIPSIS
>>> combined_stmts
[Phosphorylation(BRAF(), MAP2K1(), S)]
>>> combined_stmts[0].supported_by
[Phosphorylation(BRAF(), MAP2K1())]
>>> combined_stmts[0].supported_by[0].supports
[Phosphorylation(BRAF(), MAP2K1(), S)]
"""
if self.related_stmts is not None:
if return_toplevel:
return self.related_stmts
else:
assert self.unique_stmts is not None
return self.unique_stmts
# Call combine_duplicates, which lazily initializes self.unique_stmts
unique_stmts = self.combine_duplicates()
# Generate the index map, linking related statements.
idx_map = self._generate_id_maps(unique_stmts, poolsize, size_cutoff)
# Now iterate over all indices and set supports/supported by
for ix1, ix2 in idx_map:
unique_stmts[ix1].supported_by.append(unique_stmts[ix2])
unique_stmts[ix2].supports.append(unique_stmts[ix1])
# Get the top level statements
self.related_stmts = [st for st in unique_stmts if not st.supports]
logger.debug('%d top level' % len(self.related_stmts))
if return_toplevel:
return self.related_stmts
else:
return unique_stmts | python | def combine_related(self, return_toplevel=True, poolsize=None,
size_cutoff=100):
if self.related_stmts is not None:
if return_toplevel:
return self.related_stmts
else:
assert self.unique_stmts is not None
return self.unique_stmts
# Call combine_duplicates, which lazily initializes self.unique_stmts
unique_stmts = self.combine_duplicates()
# Generate the index map, linking related statements.
idx_map = self._generate_id_maps(unique_stmts, poolsize, size_cutoff)
# Now iterate over all indices and set supports/supported by
for ix1, ix2 in idx_map:
unique_stmts[ix1].supported_by.append(unique_stmts[ix2])
unique_stmts[ix2].supports.append(unique_stmts[ix1])
# Get the top level statements
self.related_stmts = [st for st in unique_stmts if not st.supports]
logger.debug('%d top level' % len(self.related_stmts))
if return_toplevel:
return self.related_stmts
else:
return unique_stmts | [
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This function takes as a starting point the unique statements (with
duplicates removed) and returns a modified flat list of statements
containing only those statements which do not represent a refinement of
other existing statements. In other words, the more general versions of
a given statement do not appear at the top level, but instead are
listed in the `supports` field of the top-level statements.
If :py:attr:`unique_stmts` has not been initialized with the
de-duplicated statements, :py:meth:`combine_duplicates` is called
internally.
After this function is called the attribute :py:attr:`related_stmts` is
set as a side-effect.
The procedure for combining statements in this way involves a series
of steps:
1. The statements are grouped by type (e.g., Phosphorylation) and
each type is iterated over independently.
2. Statements of the same type are then grouped according to their
Agents' entity hierarchy component identifiers. For instance,
ERK, MAPK1 and MAPK3 are all in the same connected component in the
entity hierarchy and therefore all Statements of the same type
referencing these entities will be grouped. This grouping assures
that relations are only possible within Statement groups and
not among groups. For two Statements to be in the same group at
this step, the Statements must be the same type and the Agents at
each position in the Agent lists must either be in the same
hierarchy component, or if they are not in the hierarchy, must have
identical entity_matches_keys. Statements with None in one of the
Agent list positions are collected separately at this stage.
3. Statements with None at either the first or second position are
iterated over. For a statement with a None as the first Agent,
the second Agent is examined; then the Statement with None is
added to all Statement groups with a corresponding component or
entity_matches_key in the second position. The same procedure is
performed for Statements with None at the second Agent position.
4. The statements within each group are then compared; if one
statement represents a refinement of the other (as defined by the
`refinement_of()` method implemented for the Statement), then the
more refined statement is added to the `supports` field of the more
general statement, and the more general statement is added to the
`supported_by` field of the more refined statement.
5. A new flat list of statements is created that contains only those
statements that have no `supports` entries (statements containing
such entries are not eliminated, because they will be retrievable
from the `supported_by` fields of other statements). This list
is returned to the caller.
On multi-core machines, the algorithm can be parallelized by setting
the poolsize argument to the desired number of worker processes.
This feature is only available in Python > 3.4.
.. note:: Subfamily relationships must be consistent across arguments
For now, we require that merges can only occur if the *isa*
relationships are all in the *same direction for all the agents* in
a Statement. For example, the two statement groups: `RAF_family ->
MEK1` and `BRAF -> MEK_family` would not be merged, since BRAF
*isa* RAF_family, but MEK_family is not a MEK1. In the future this
restriction could be revisited.
Parameters
----------
return_toplevel : Optional[bool]
If True only the top level statements are returned.
If False, all statements are returned. Default: True
poolsize : Optional[int]
The number of worker processes to use to parallelize the
comparisons performed by the function. If None (default), no
parallelization is performed. NOTE: Parallelization is only
available on Python 3.4 and above.
size_cutoff : Optional[int]
Groups with size_cutoff or more statements are sent to worker
processes, while smaller groups are compared in the parent process.
Default value is 100. Not relevant when parallelization is not
used.
Returns
-------
list of :py:class:`indra.statement.Statement`
The returned list contains Statements representing the more
concrete/refined versions of the Statements involving particular
entities. The attribute :py:attr:`related_stmts` is also set to
this list. However, if return_toplevel is False then all
statements are returned, irrespective of level of specificity.
In this case the relationships between statements can
be accessed via the supports/supported_by attributes.
Examples
--------
A more general statement with no information about a Phosphorylation
site is identified as supporting a more specific statement:
>>> from indra.preassembler.hierarchy_manager import hierarchies
>>> braf = Agent('BRAF')
>>> map2k1 = Agent('MAP2K1')
>>> st1 = Phosphorylation(braf, map2k1)
>>> st2 = Phosphorylation(braf, map2k1, residue='S')
>>> pa = Preassembler(hierarchies, [st1, st2])
>>> combined_stmts = pa.combine_related() # doctest:+ELLIPSIS
>>> combined_stmts
[Phosphorylation(BRAF(), MAP2K1(), S)]
>>> combined_stmts[0].supported_by
[Phosphorylation(BRAF(), MAP2K1())]
>>> combined_stmts[0].supported_by[0].supports
[Phosphorylation(BRAF(), MAP2K1(), S)] | [
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] | 79a70415832c5702d7a820c7c9ccc8e25010124b | https://github.com/sorgerlab/indra/blob/79a70415832c5702d7a820c7c9ccc8e25010124b/indra/preassembler/__init__.py#L428-L563 |
19,245 | sorgerlab/indra | indra/preassembler/__init__.py | Preassembler.find_contradicts | def find_contradicts(self):
"""Return pairs of contradicting Statements.
Returns
-------
contradicts : list(tuple(Statement, Statement))
A list of Statement pairs that are contradicting.
"""
eh = self.hierarchies['entity']
# Make a dict of Statement by type
stmts_by_type = collections.defaultdict(lambda: [])
for idx, stmt in enumerate(self.stmts):
stmts_by_type[indra_stmt_type(stmt)].append((idx, stmt))
# Handle Statements with polarity first
pos_stmts = AddModification.__subclasses__()
neg_stmts = [modclass_to_inverse[c] for c in pos_stmts]
pos_stmts += [Activation, IncreaseAmount]
neg_stmts += [Inhibition, DecreaseAmount]
contradicts = []
for pst, nst in zip(pos_stmts, neg_stmts):
poss = stmts_by_type.get(pst, [])
negs = stmts_by_type.get(nst, [])
pos_stmt_by_group = self._get_stmt_by_group(pst, poss, eh)
neg_stmt_by_group = self._get_stmt_by_group(nst, negs, eh)
for key, pg in pos_stmt_by_group.items():
ng = neg_stmt_by_group.get(key, [])
for (_, st1), (_, st2) in itertools.product(pg, ng):
if st1.contradicts(st2, self.hierarchies):
contradicts.append((st1, st2))
# Handle neutral Statements next
neu_stmts = [Influence, ActiveForm]
for stt in neu_stmts:
stmts = stmts_by_type.get(stt, [])
for (_, st1), (_, st2) in itertools.combinations(stmts, 2):
if st1.contradicts(st2, self.hierarchies):
contradicts.append((st1, st2))
return contradicts | python | def find_contradicts(self):
eh = self.hierarchies['entity']
# Make a dict of Statement by type
stmts_by_type = collections.defaultdict(lambda: [])
for idx, stmt in enumerate(self.stmts):
stmts_by_type[indra_stmt_type(stmt)].append((idx, stmt))
# Handle Statements with polarity first
pos_stmts = AddModification.__subclasses__()
neg_stmts = [modclass_to_inverse[c] for c in pos_stmts]
pos_stmts += [Activation, IncreaseAmount]
neg_stmts += [Inhibition, DecreaseAmount]
contradicts = []
for pst, nst in zip(pos_stmts, neg_stmts):
poss = stmts_by_type.get(pst, [])
negs = stmts_by_type.get(nst, [])
pos_stmt_by_group = self._get_stmt_by_group(pst, poss, eh)
neg_stmt_by_group = self._get_stmt_by_group(nst, negs, eh)
for key, pg in pos_stmt_by_group.items():
ng = neg_stmt_by_group.get(key, [])
for (_, st1), (_, st2) in itertools.product(pg, ng):
if st1.contradicts(st2, self.hierarchies):
contradicts.append((st1, st2))
# Handle neutral Statements next
neu_stmts = [Influence, ActiveForm]
for stt in neu_stmts:
stmts = stmts_by_type.get(stt, [])
for (_, st1), (_, st2) in itertools.combinations(stmts, 2):
if st1.contradicts(st2, self.hierarchies):
contradicts.append((st1, st2))
return contradicts | [
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Returns
-------
contradicts : list(tuple(Statement, Statement))
A list of Statement pairs that are contradicting. | [
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] | 79a70415832c5702d7a820c7c9ccc8e25010124b | https://github.com/sorgerlab/indra/blob/79a70415832c5702d7a820c7c9ccc8e25010124b/indra/preassembler/__init__.py#L565-L608 |
19,246 | sorgerlab/indra | indra/literature/deft_tools.py | get_text_content_for_pmids | def get_text_content_for_pmids(pmids):
"""Get text content for articles given a list of their pmids
Parameters
----------
pmids : list of str
Returns
-------
text_content : list of str
"""
pmc_pmids = set(pmc_client.filter_pmids(pmids, source_type='fulltext'))
pmc_ids = []
for pmid in pmc_pmids:
pmc_id = pmc_client.id_lookup(pmid, idtype='pmid')['pmcid']
if pmc_id:
pmc_ids.append(pmc_id)
else:
pmc_pmids.discard(pmid)
pmc_xmls = []
failed = set()
for pmc_id in pmc_ids:
if pmc_id is not None:
pmc_xmls.append(pmc_client.get_xml(pmc_id))
else:
failed.append(pmid)
time.sleep(0.5)
remaining_pmids = set(pmids) - pmc_pmids | failed
abstracts = []
for pmid in remaining_pmids:
abstract = pubmed_client.get_abstract(pmid)
abstracts.append(abstract)
time.sleep(0.5)
return [text_content for source in (pmc_xmls, abstracts)
for text_content in source if text_content is not None] | python | def get_text_content_for_pmids(pmids):
pmc_pmids = set(pmc_client.filter_pmids(pmids, source_type='fulltext'))
pmc_ids = []
for pmid in pmc_pmids:
pmc_id = pmc_client.id_lookup(pmid, idtype='pmid')['pmcid']
if pmc_id:
pmc_ids.append(pmc_id)
else:
pmc_pmids.discard(pmid)
pmc_xmls = []
failed = set()
for pmc_id in pmc_ids:
if pmc_id is not None:
pmc_xmls.append(pmc_client.get_xml(pmc_id))
else:
failed.append(pmid)
time.sleep(0.5)
remaining_pmids = set(pmids) - pmc_pmids | failed
abstracts = []
for pmid in remaining_pmids:
abstract = pubmed_client.get_abstract(pmid)
abstracts.append(abstract)
time.sleep(0.5)
return [text_content for source in (pmc_xmls, abstracts)
for text_content in source if text_content is not None] | [
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19,247 | sorgerlab/indra | indra/literature/deft_tools.py | universal_extract_paragraphs | def universal_extract_paragraphs(xml):
"""Extract paragraphs from xml that could be from different sources
First try to parse the xml as if it came from elsevier. if we do not
have valid elsevier xml this will throw an exception. the text extraction
function in the pmc client may not throw an exception when parsing elsevier
xml, silently processing the xml incorrectly
Parameters
----------
xml : str
Either an NLM xml, Elsevier xml or plaintext
Returns
-------
paragraphs : str
Extracted plaintext paragraphs from NLM or Elsevier XML
"""
try:
paragraphs = elsevier_client.extract_paragraphs(xml)
except Exception:
paragraphs = None
if paragraphs is None:
try:
paragraphs = pmc_client.extract_paragraphs(xml)
except Exception:
paragraphs = [xml]
return paragraphs | python | def universal_extract_paragraphs(xml):
try:
paragraphs = elsevier_client.extract_paragraphs(xml)
except Exception:
paragraphs = None
if paragraphs is None:
try:
paragraphs = pmc_client.extract_paragraphs(xml)
except Exception:
paragraphs = [xml]
return paragraphs | [
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function in the pmc client may not throw an exception when parsing elsevier
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Parameters
----------
xml : str
Either an NLM xml, Elsevier xml or plaintext
Returns
-------
paragraphs : str
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19,248 | sorgerlab/indra | indra/literature/deft_tools.py | filter_paragraphs | def filter_paragraphs(paragraphs, contains=None):
"""Filter paragraphs to only those containing one of a list of strings
Parameters
----------
paragraphs : list of str
List of plaintext paragraphs from an article
contains : str or list of str
Exclude paragraphs not containing this string as a token, or
at least one of the strings in contains if it is a list
Returns
-------
str
Plaintext consisting of all input paragraphs containing at least
one of the supplied tokens.
"""
if contains is None:
pattern = ''
else:
if isinstance(contains, str):
contains = [contains]
pattern = '|'.join(r'[^\w]%s[^\w]' % shortform
for shortform in contains)
paragraphs = [p for p in paragraphs if re.search(pattern, p)]
return '\n'.join(paragraphs) + '\n' | python | def filter_paragraphs(paragraphs, contains=None):
if contains is None:
pattern = ''
else:
if isinstance(contains, str):
contains = [contains]
pattern = '|'.join(r'[^\w]%s[^\w]' % shortform
for shortform in contains)
paragraphs = [p for p in paragraphs if re.search(pattern, p)]
return '\n'.join(paragraphs) + '\n' | [
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List of plaintext paragraphs from an article
contains : str or list of str
Exclude paragraphs not containing this string as a token, or
at least one of the strings in contains if it is a list
Returns
-------
str
Plaintext consisting of all input paragraphs containing at least
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19,249 | sorgerlab/indra | indra/statements/resources.py | get_valid_residue | def get_valid_residue(residue):
"""Check if the given string represents a valid amino acid residue."""
if residue is not None and amino_acids.get(residue) is None:
res = amino_acids_reverse.get(residue.lower())
if res is None:
raise InvalidResidueError(residue)
else:
return res
return residue | python | def get_valid_residue(residue):
if residue is not None and amino_acids.get(residue) is None:
res = amino_acids_reverse.get(residue.lower())
if res is None:
raise InvalidResidueError(residue)
else:
return res
return residue | [
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19,250 | sorgerlab/indra | indra/statements/resources.py | get_valid_location | def get_valid_location(location):
"""Check if the given location represents a valid cellular component."""
# If we're given None, return None
if location is not None and cellular_components.get(location) is None:
loc = cellular_components_reverse.get(location)
if loc is None:
raise InvalidLocationError(location)
else:
return loc
return location | python | def get_valid_location(location):
# If we're given None, return None
if location is not None and cellular_components.get(location) is None:
loc = cellular_components_reverse.get(location)
if loc is None:
raise InvalidLocationError(location)
else:
return loc
return location | [
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19,251 | sorgerlab/indra | indra/statements/resources.py | _read_activity_types | def _read_activity_types():
"""Read types of valid activities from a resource file."""
this_dir = os.path.dirname(os.path.abspath(__file__))
ac_file = os.path.join(this_dir, os.pardir, 'resources',
'activity_hierarchy.rdf')
g = rdflib.Graph()
with open(ac_file, 'r'):
g.parse(ac_file, format='nt')
act_types = set()
for s, _, o in g:
subj = s.rpartition('/')[-1]
obj = o.rpartition('/')[-1]
act_types.add(subj)
act_types.add(obj)
return sorted(list(act_types)) | python | def _read_activity_types():
this_dir = os.path.dirname(os.path.abspath(__file__))
ac_file = os.path.join(this_dir, os.pardir, 'resources',
'activity_hierarchy.rdf')
g = rdflib.Graph()
with open(ac_file, 'r'):
g.parse(ac_file, format='nt')
act_types = set()
for s, _, o in g:
subj = s.rpartition('/')[-1]
obj = o.rpartition('/')[-1]
act_types.add(subj)
act_types.add(obj)
return sorted(list(act_types)) | [
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19,252 | sorgerlab/indra | indra/statements/resources.py | _read_cellular_components | def _read_cellular_components():
"""Read cellular components from a resource file."""
# Here we load a patch file in addition to the current cellular components
# file to make sure we don't error with InvalidLocationError with some
# deprecated cellular location names
this_dir = os.path.dirname(os.path.abspath(__file__))
cc_file = os.path.join(this_dir, os.pardir, 'resources',
'cellular_components.tsv')
cc_patch_file = os.path.join(this_dir, os.pardir, 'resources',
'cellular_components_patch.tsv')
cellular_components = {}
cellular_components_reverse = {}
with open(cc_file, 'rt') as fh:
lines = list(fh.readlines())
# We add the patch to the end of the lines list
with open(cc_patch_file, 'rt') as fh:
lines += list(fh.readlines())
for lin in lines[1:]:
terms = lin.strip().split('\t')
cellular_components[terms[1]] = terms[0]
# If the GO -> name mapping doesn't exist yet, we add a mapping
# but if it already exists (i.e. the try doesn't error) then
# we don't add the GO -> name mapping. This ensures that names from
# the patch file aren't mapped to in the reverse list.
try:
cellular_components_reverse[terms[0]]
except KeyError:
cellular_components_reverse[terms[0]] = terms[1]
return cellular_components, cellular_components_reverse | python | def _read_cellular_components():
# Here we load a patch file in addition to the current cellular components
# file to make sure we don't error with InvalidLocationError with some
# deprecated cellular location names
this_dir = os.path.dirname(os.path.abspath(__file__))
cc_file = os.path.join(this_dir, os.pardir, 'resources',
'cellular_components.tsv')
cc_patch_file = os.path.join(this_dir, os.pardir, 'resources',
'cellular_components_patch.tsv')
cellular_components = {}
cellular_components_reverse = {}
with open(cc_file, 'rt') as fh:
lines = list(fh.readlines())
# We add the patch to the end of the lines list
with open(cc_patch_file, 'rt') as fh:
lines += list(fh.readlines())
for lin in lines[1:]:
terms = lin.strip().split('\t')
cellular_components[terms[1]] = terms[0]
# If the GO -> name mapping doesn't exist yet, we add a mapping
# but if it already exists (i.e. the try doesn't error) then
# we don't add the GO -> name mapping. This ensures that names from
# the patch file aren't mapped to in the reverse list.
try:
cellular_components_reverse[terms[0]]
except KeyError:
cellular_components_reverse[terms[0]] = terms[1]
return cellular_components, cellular_components_reverse | [
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19,253 | sorgerlab/indra | indra/statements/resources.py | _read_amino_acids | def _read_amino_acids():
"""Read the amino acid information from a resource file."""
this_dir = os.path.dirname(os.path.abspath(__file__))
aa_file = os.path.join(this_dir, os.pardir, 'resources', 'amino_acids.tsv')
amino_acids = {}
amino_acids_reverse = {}
with open(aa_file, 'rt') as fh:
lines = fh.readlines()
for lin in lines[1:]:
terms = lin.strip().split('\t')
key = terms[2]
val = {'full_name': terms[0],
'short_name': terms[1],
'indra_name': terms[3]}
amino_acids[key] = val
for v in val.values():
amino_acids_reverse[v] = key
return amino_acids, amino_acids_reverse | python | def _read_amino_acids():
this_dir = os.path.dirname(os.path.abspath(__file__))
aa_file = os.path.join(this_dir, os.pardir, 'resources', 'amino_acids.tsv')
amino_acids = {}
amino_acids_reverse = {}
with open(aa_file, 'rt') as fh:
lines = fh.readlines()
for lin in lines[1:]:
terms = lin.strip().split('\t')
key = terms[2]
val = {'full_name': terms[0],
'short_name': terms[1],
'indra_name': terms[3]}
amino_acids[key] = val
for v in val.values():
amino_acids_reverse[v] = key
return amino_acids, amino_acids_reverse | [
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19,254 | sorgerlab/indra | indra/assemblers/pysb/export.py | export_sbgn | def export_sbgn(model):
"""Return an SBGN model string corresponding to the PySB model.
This function first calls generate_equations on the PySB model to obtain
a reaction network (i.e. individual species, reactions). It then iterates
over each reaction and and instantiates its reactants, products, and the
process itself as SBGN glyphs and arcs.
Parameters
----------
model : pysb.core.Model
A PySB model to be exported into SBGN
Returns
-------
sbgn_str : str
An SBGN model as string
"""
import lxml.etree
import lxml.builder
from pysb.bng import generate_equations
from indra.assemblers.sbgn import SBGNAssembler
logger.info('Generating reaction network with BNG for SBGN export. ' +
'This could take a long time.')
generate_equations(model)
sa = SBGNAssembler()
glyphs = {}
for idx, species in enumerate(model.species):
glyph = sa._glyph_for_complex_pattern(species)
if glyph is None:
continue
sa._map.append(glyph)
glyphs[idx] = glyph
for reaction in model.reactions:
# Get all the reactions / products / controllers of the reaction
reactants = set(reaction['reactants']) - set(reaction['products'])
products = set(reaction['products']) - set(reaction['reactants'])
controllers = set(reaction['reactants']) & set(reaction['products'])
# Add glyph for reaction
process_glyph = sa._process_glyph('process')
# Connect reactants with arcs
if not reactants:
glyph_id = sa._none_glyph()
sa._arc('consumption', glyph_id, process_glyph)
else:
for r in reactants:
glyph = glyphs.get(r)
if glyph is None:
glyph_id = sa._none_glyph()
else:
glyph_id = glyph.attrib['id']
sa._arc('consumption', glyph_id, process_glyph)
# Connect products with arcs
if not products:
glyph_id = sa._none_glyph()
sa._arc('production', process_glyph, glyph_id)
else:
for p in products:
glyph = glyphs.get(p)
if glyph is None:
glyph_id = sa._none_glyph()
else:
glyph_id = glyph.attrib['id']
sa._arc('production', process_glyph, glyph_id)
# Connect controllers with arcs
for c in controllers:
glyph = glyphs[c]
sa._arc('catalysis', glyph.attrib['id'], process_glyph)
sbgn_str = sa.print_model().decode('utf-8')
return sbgn_str | python | def export_sbgn(model):
import lxml.etree
import lxml.builder
from pysb.bng import generate_equations
from indra.assemblers.sbgn import SBGNAssembler
logger.info('Generating reaction network with BNG for SBGN export. ' +
'This could take a long time.')
generate_equations(model)
sa = SBGNAssembler()
glyphs = {}
for idx, species in enumerate(model.species):
glyph = sa._glyph_for_complex_pattern(species)
if glyph is None:
continue
sa._map.append(glyph)
glyphs[idx] = glyph
for reaction in model.reactions:
# Get all the reactions / products / controllers of the reaction
reactants = set(reaction['reactants']) - set(reaction['products'])
products = set(reaction['products']) - set(reaction['reactants'])
controllers = set(reaction['reactants']) & set(reaction['products'])
# Add glyph for reaction
process_glyph = sa._process_glyph('process')
# Connect reactants with arcs
if not reactants:
glyph_id = sa._none_glyph()
sa._arc('consumption', glyph_id, process_glyph)
else:
for r in reactants:
glyph = glyphs.get(r)
if glyph is None:
glyph_id = sa._none_glyph()
else:
glyph_id = glyph.attrib['id']
sa._arc('consumption', glyph_id, process_glyph)
# Connect products with arcs
if not products:
glyph_id = sa._none_glyph()
sa._arc('production', process_glyph, glyph_id)
else:
for p in products:
glyph = glyphs.get(p)
if glyph is None:
glyph_id = sa._none_glyph()
else:
glyph_id = glyph.attrib['id']
sa._arc('production', process_glyph, glyph_id)
# Connect controllers with arcs
for c in controllers:
glyph = glyphs[c]
sa._arc('catalysis', glyph.attrib['id'], process_glyph)
sbgn_str = sa.print_model().decode('utf-8')
return sbgn_str | [
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model : pysb.core.Model
A PySB model to be exported into SBGN
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sbgn_str : str
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19,255 | sorgerlab/indra | indra/assemblers/pysb/export.py | export_kappa_im | def export_kappa_im(model, fname=None):
"""Return a networkx graph representing the model's Kappa influence map.
Parameters
----------
model : pysb.core.Model
A PySB model to be exported into a Kappa IM.
fname : Optional[str]
A file name, typically with .png or .pdf extension in which
the IM is rendered using pygraphviz.
Returns
-------
networkx.MultiDiGraph
A graph object representing the influence map.
"""
from .kappa_util import im_json_to_graph
kappa = _prepare_kappa(model)
imap = kappa.analyses_influence_map()
im = im_json_to_graph(imap)
for param in model.parameters:
try:
im.remove_node(param.name)
except:
pass
if fname:
agraph = networkx.nx_agraph.to_agraph(im)
agraph.draw(fname, prog='dot')
return im | python | def export_kappa_im(model, fname=None):
from .kappa_util import im_json_to_graph
kappa = _prepare_kappa(model)
imap = kappa.analyses_influence_map()
im = im_json_to_graph(imap)
for param in model.parameters:
try:
im.remove_node(param.name)
except:
pass
if fname:
agraph = networkx.nx_agraph.to_agraph(im)
agraph.draw(fname, prog='dot')
return im | [
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model : pysb.core.Model
A PySB model to be exported into a Kappa IM.
fname : Optional[str]
A file name, typically with .png or .pdf extension in which
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Returns
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A graph object representing the influence map. | [
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19,256 | sorgerlab/indra | indra/assemblers/pysb/export.py | export_kappa_cm | def export_kappa_cm(model, fname=None):
"""Return a networkx graph representing the model's Kappa contact map.
Parameters
----------
model : pysb.core.Model
A PySB model to be exported into a Kappa CM.
fname : Optional[str]
A file name, typically with .png or .pdf extension in which
the CM is rendered using pygraphviz.
Returns
-------
npygraphviz.Agraph
A graph object representing the contact map.
"""
from .kappa_util import cm_json_to_graph
kappa = _prepare_kappa(model)
cmap = kappa.analyses_contact_map()
cm = cm_json_to_graph(cmap)
if fname:
cm.draw(fname, prog='dot')
return cm | python | def export_kappa_cm(model, fname=None):
from .kappa_util import cm_json_to_graph
kappa = _prepare_kappa(model)
cmap = kappa.analyses_contact_map()
cm = cm_json_to_graph(cmap)
if fname:
cm.draw(fname, prog='dot')
return cm | [
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fname : Optional[str]
A file name, typically with .png or .pdf extension in which
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19,257 | sorgerlab/indra | indra/assemblers/pysb/export.py | _prepare_kappa | def _prepare_kappa(model):
"""Return a Kappa STD with the model loaded."""
import kappy
kappa = kappy.KappaStd()
model_str = export(model, 'kappa')
kappa.add_model_string(model_str)
kappa.project_parse()
return kappa | python | def _prepare_kappa(model):
import kappy
kappa = kappy.KappaStd()
model_str = export(model, 'kappa')
kappa.add_model_string(model_str)
kappa.project_parse()
return kappa | [
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19,258 | sorgerlab/indra | indra/databases/cbio_client.py | send_request | def send_request(**kwargs):
"""Return a data frame from a web service request to cBio portal.
Sends a web service requrest to the cBio portal with arguments given in
the dictionary data and returns a Pandas data frame on success.
More information about the service here:
http://www.cbioportal.org/web_api.jsp
Parameters
----------
kwargs : dict
A dict of parameters for the query. Entries map directly to web service
calls with the exception of the optional 'skiprows' entry, whose value
is used as the number of rows to skip when reading the result data
frame.
Returns
-------
df : pandas.DataFrame
Response from cBioPortal as a Pandas DataFrame.
"""
skiprows = kwargs.pop('skiprows', None)
res = requests.get(cbio_url, params=kwargs)
if res.status_code == 200:
# Adaptively skip rows based on number of comment lines
if skiprows == -1:
lines = res.text.split('\n')
skiprows = 0
for line in lines:
if line.startswith('#'):
skiprows += 1
else:
break
csv_StringIO = StringIO(res.text)
df = pandas.read_csv(csv_StringIO, sep='\t', skiprows=skiprows)
return df
else:
logger.error('Request returned with code %d' % res.status_code) | python | def send_request(**kwargs):
skiprows = kwargs.pop('skiprows', None)
res = requests.get(cbio_url, params=kwargs)
if res.status_code == 200:
# Adaptively skip rows based on number of comment lines
if skiprows == -1:
lines = res.text.split('\n')
skiprows = 0
for line in lines:
if line.startswith('#'):
skiprows += 1
else:
break
csv_StringIO = StringIO(res.text)
df = pandas.read_csv(csv_StringIO, sep='\t', skiprows=skiprows)
return df
else:
logger.error('Request returned with code %d' % res.status_code) | [
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More information about the service here:
http://www.cbioportal.org/web_api.jsp
Parameters
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A dict of parameters for the query. Entries map directly to web service
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df : pandas.DataFrame
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19,259 | sorgerlab/indra | indra/databases/cbio_client.py | get_mutations | def get_mutations(study_id, gene_list, mutation_type=None,
case_id=None):
"""Return mutations as a list of genes and list of amino acid changes.
Parameters
----------
study_id : str
The ID of the cBio study.
Example: 'cellline_ccle_broad' or 'paad_icgc'
gene_list : list[str]
A list of genes with their HGNC symbols.
Example: ['BRAF', 'KRAS']
mutation_type : Optional[str]
The type of mutation to filter to.
mutation_type can be one of: missense, nonsense, frame_shift_ins,
frame_shift_del, splice_site
case_id : Optional[str]
The case ID within the study to filter to.
Returns
-------
mutations : tuple[list]
A tuple of two lists, the first one containing a list of genes, and
the second one a list of amino acid changes in those genes.
"""
genetic_profile = get_genetic_profiles(study_id, 'mutation')[0]
gene_list_str = ','.join(gene_list)
data = {'cmd': 'getMutationData',
'case_set_id': study_id,
'genetic_profile_id': genetic_profile,
'gene_list': gene_list_str,
'skiprows': -1}
df = send_request(**data)
if case_id:
df = df[df['case_id'] == case_id]
res = _filter_data_frame(df, ['gene_symbol', 'amino_acid_change'],
'mutation_type', mutation_type)
mutations = {'gene_symbol': list(res['gene_symbol'].values()),
'amino_acid_change': list(res['amino_acid_change'].values())}
return mutations | python | def get_mutations(study_id, gene_list, mutation_type=None,
case_id=None):
genetic_profile = get_genetic_profiles(study_id, 'mutation')[0]
gene_list_str = ','.join(gene_list)
data = {'cmd': 'getMutationData',
'case_set_id': study_id,
'genetic_profile_id': genetic_profile,
'gene_list': gene_list_str,
'skiprows': -1}
df = send_request(**data)
if case_id:
df = df[df['case_id'] == case_id]
res = _filter_data_frame(df, ['gene_symbol', 'amino_acid_change'],
'mutation_type', mutation_type)
mutations = {'gene_symbol': list(res['gene_symbol'].values()),
'amino_acid_change': list(res['amino_acid_change'].values())}
return mutations | [
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study_id : str
The ID of the cBio study.
Example: 'cellline_ccle_broad' or 'paad_icgc'
gene_list : list[str]
A list of genes with their HGNC symbols.
Example: ['BRAF', 'KRAS']
mutation_type : Optional[str]
The type of mutation to filter to.
mutation_type can be one of: missense, nonsense, frame_shift_ins,
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case_id : Optional[str]
The case ID within the study to filter to.
Returns
-------
mutations : tuple[list]
A tuple of two lists, the first one containing a list of genes, and
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19,260 | sorgerlab/indra | indra/databases/cbio_client.py | get_case_lists | def get_case_lists(study_id):
"""Return a list of the case set ids for a particular study.
TAKE NOTE the "case_list_id" are the same thing as "case_set_id"
Within the data, this string is referred to as a "case_list_id".
Within API calls it is referred to as a 'case_set_id'.
The documentation does not make this explicitly clear.
Parameters
----------
study_id : str
The ID of the cBio study.
Example: 'cellline_ccle_broad' or 'paad_icgc'
Returns
-------
case_set_ids : dict[dict[int]]
A dict keyed to cases containing a dict keyed to genes
containing int
"""
data = {'cmd': 'getCaseLists',
'cancer_study_id': study_id}
df = send_request(**data)
case_set_ids = df['case_list_id'].tolist()
return case_set_ids | python | def get_case_lists(study_id):
data = {'cmd': 'getCaseLists',
'cancer_study_id': study_id}
df = send_request(**data)
case_set_ids = df['case_list_id'].tolist()
return case_set_ids | [
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Within API calls it is referred to as a 'case_set_id'.
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Parameters
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study_id : str
The ID of the cBio study.
Example: 'cellline_ccle_broad' or 'paad_icgc'
Returns
-------
case_set_ids : dict[dict[int]]
A dict keyed to cases containing a dict keyed to genes
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19,261 | sorgerlab/indra | indra/databases/cbio_client.py | get_profile_data | def get_profile_data(study_id, gene_list,
profile_filter, case_set_filter=None):
"""Return dict of cases and genes and their respective values.
Parameters
----------
study_id : str
The ID of the cBio study.
Example: 'cellline_ccle_broad' or 'paad_icgc'
gene_list : list[str]
A list of genes with their HGNC symbols.
Example: ['BRAF', 'KRAS']
profile_filter : str
A string used to filter the profiles to return. Will be one of:
- MUTATION
- MUTATION_EXTENDED
- COPY_NUMBER_ALTERATION
- MRNA_EXPRESSION
- METHYLATION
case_set_filter : Optional[str]
A string that specifices which case_set_id to use, based on a complete
or partial match. If not provided, will look for study_id + '_all'
Returns
-------
profile_data : dict[dict[int]]
A dict keyed to cases containing a dict keyed to genes
containing int
"""
genetic_profiles = get_genetic_profiles(study_id, profile_filter)
if genetic_profiles:
genetic_profile = genetic_profiles[0]
else:
return {}
gene_list_str = ','.join(gene_list)
case_set_ids = get_case_lists(study_id)
if case_set_filter:
case_set_id = [x for x in case_set_ids if case_set_filter in x][0]
else:
case_set_id = study_id + '_all'
# based on looking at the cBioPortal, this is a common case_set_id
data = {'cmd': 'getProfileData',
'case_set_id': case_set_id,
'genetic_profile_id': genetic_profile,
'gene_list': gene_list_str,
'skiprows': -1}
df = send_request(**data)
case_list_df = [x for x in df.columns.tolist()
if x not in ['GENE_ID', 'COMMON']]
profile_data = {case: {g: None for g in gene_list}
for case in case_list_df}
for case in case_list_df:
profile_values = df[case].tolist()
df_gene_list = df['COMMON'].tolist()
for g, cv in zip(df_gene_list, profile_values):
if not pandas.isnull(cv):
profile_data[case][g] = cv
return profile_data | python | def get_profile_data(study_id, gene_list,
profile_filter, case_set_filter=None):
genetic_profiles = get_genetic_profiles(study_id, profile_filter)
if genetic_profiles:
genetic_profile = genetic_profiles[0]
else:
return {}
gene_list_str = ','.join(gene_list)
case_set_ids = get_case_lists(study_id)
if case_set_filter:
case_set_id = [x for x in case_set_ids if case_set_filter in x][0]
else:
case_set_id = study_id + '_all'
# based on looking at the cBioPortal, this is a common case_set_id
data = {'cmd': 'getProfileData',
'case_set_id': case_set_id,
'genetic_profile_id': genetic_profile,
'gene_list': gene_list_str,
'skiprows': -1}
df = send_request(**data)
case_list_df = [x for x in df.columns.tolist()
if x not in ['GENE_ID', 'COMMON']]
profile_data = {case: {g: None for g in gene_list}
for case in case_list_df}
for case in case_list_df:
profile_values = df[case].tolist()
df_gene_list = df['COMMON'].tolist()
for g, cv in zip(df_gene_list, profile_values):
if not pandas.isnull(cv):
profile_data[case][g] = cv
return profile_data | [
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gene_list : list[str]
A list of genes with their HGNC symbols.
Example: ['BRAF', 'KRAS']
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A string that specifices which case_set_id to use, based on a complete
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profile_data : dict[dict[int]]
A dict keyed to cases containing a dict keyed to genes
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19,262 | sorgerlab/indra | indra/databases/cbio_client.py | get_num_sequenced | def get_num_sequenced(study_id):
"""Return number of sequenced tumors for given study.
This is useful for calculating mutation statistics in terms of the
prevalence of certain mutations within a type of cancer.
Parameters
----------
study_id : str
The ID of the cBio study.
Example: 'paad_icgc'
Returns
-------
num_case : int
The number of sequenced tumors in the given study
"""
data = {'cmd': 'getCaseLists',
'cancer_study_id': study_id}
df = send_request(**data)
if df.empty:
return 0
row_filter = df['case_list_id'].str.contains('sequenced', case=False)
num_case = len(df[row_filter]['case_ids'].tolist()[0].split(' '))
return num_case | python | def get_num_sequenced(study_id):
data = {'cmd': 'getCaseLists',
'cancer_study_id': study_id}
df = send_request(**data)
if df.empty:
return 0
row_filter = df['case_list_id'].str.contains('sequenced', case=False)
num_case = len(df[row_filter]['case_ids'].tolist()[0].split(' '))
return num_case | [
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This is useful for calculating mutation statistics in terms of the
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Parameters
----------
study_id : str
The ID of the cBio study.
Example: 'paad_icgc'
Returns
-------
num_case : int
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19,263 | sorgerlab/indra | indra/databases/cbio_client.py | get_cancer_studies | def get_cancer_studies(study_filter=None):
"""Return a list of cancer study identifiers, optionally filtered.
There are typically multiple studies for a given type of cancer and
a filter can be used to constrain the returned list.
Parameters
----------
study_filter : Optional[str]
A string used to filter the study IDs to return. Example: "paad"
Returns
-------
study_ids : list[str]
A list of study IDs.
For instance "paad" as a filter would result in a list
of study IDs with paad in their name like "paad_icgc", "paad_tcga",
etc.
"""
data = {'cmd': 'getCancerStudies'}
df = send_request(**data)
res = _filter_data_frame(df, ['cancer_study_id'],
'cancer_study_id', study_filter)
study_ids = list(res['cancer_study_id'].values())
return study_ids | python | def get_cancer_studies(study_filter=None):
data = {'cmd': 'getCancerStudies'}
df = send_request(**data)
res = _filter_data_frame(df, ['cancer_study_id'],
'cancer_study_id', study_filter)
study_ids = list(res['cancer_study_id'].values())
return study_ids | [
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19,264 | sorgerlab/indra | indra/databases/cbio_client.py | get_cancer_types | def get_cancer_types(cancer_filter=None):
"""Return a list of cancer types, optionally filtered.
Parameters
----------
cancer_filter : Optional[str]
A string used to filter cancer types. Its value is the name or
part of the name of a type of cancer. Example: "melanoma",
"pancreatic", "non-small cell lung"
Returns
-------
type_ids : list[str]
A list of cancer types matching the filter.
Example: for cancer_filter="pancreatic", the result includes
"panet" (neuro-endocrine) and "paad" (adenocarcinoma)
"""
data = {'cmd': 'getTypesOfCancer'}
df = send_request(**data)
res = _filter_data_frame(df, ['type_of_cancer_id'], 'name', cancer_filter)
type_ids = list(res['type_of_cancer_id'].values())
return type_ids | python | def get_cancer_types(cancer_filter=None):
data = {'cmd': 'getTypesOfCancer'}
df = send_request(**data)
res = _filter_data_frame(df, ['type_of_cancer_id'], 'name', cancer_filter)
type_ids = list(res['type_of_cancer_id'].values())
return type_ids | [
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19,265 | sorgerlab/indra | indra/databases/cbio_client.py | get_ccle_mutations | def get_ccle_mutations(gene_list, cell_lines, mutation_type=None):
"""Return a dict of mutations in given genes and cell lines from CCLE.
This is a specialized call to get_mutations tailored to CCLE cell lines.
Parameters
----------
gene_list : list[str]
A list of HGNC gene symbols to get mutations in
cell_lines : list[str]
A list of CCLE cell line names to get mutations for.
mutation_type : Optional[str]
The type of mutation to filter to.
mutation_type can be one of: missense, nonsense, frame_shift_ins,
frame_shift_del, splice_site
Returns
-------
mutations : dict
The result from cBioPortal as a dict in the format
{cell_line : {gene : [mutation1, mutation2, ...] }}
Example:
{'LOXIMVI_SKIN': {'BRAF': ['V600E', 'I208V']},
'SKMEL30_SKIN': {'BRAF': ['D287H', 'E275K']}}
"""
mutations = {cl: {g: [] for g in gene_list} for cl in cell_lines}
for cell_line in cell_lines:
mutations_cl = get_mutations(ccle_study, gene_list,
mutation_type=mutation_type,
case_id=cell_line)
for gene, aa_change in zip(mutations_cl['gene_symbol'],
mutations_cl['amino_acid_change']):
aa_change = str(aa_change)
mutations[cell_line][gene].append(aa_change)
return mutations | python | def get_ccle_mutations(gene_list, cell_lines, mutation_type=None):
mutations = {cl: {g: [] for g in gene_list} for cl in cell_lines}
for cell_line in cell_lines:
mutations_cl = get_mutations(ccle_study, gene_list,
mutation_type=mutation_type,
case_id=cell_line)
for gene, aa_change in zip(mutations_cl['gene_symbol'],
mutations_cl['amino_acid_change']):
aa_change = str(aa_change)
mutations[cell_line][gene].append(aa_change)
return mutations | [
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A list of HGNC gene symbols to get mutations in
cell_lines : list[str]
A list of CCLE cell line names to get mutations for.
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The type of mutation to filter to.
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Example:
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19,266 | sorgerlab/indra | indra/databases/cbio_client.py | get_ccle_lines_for_mutation | def get_ccle_lines_for_mutation(gene, amino_acid_change):
"""Return cell lines with a given point mutation in a given gene.
Checks which cell lines in CCLE have a particular point mutation
in a given gene and return their names in a list.
Parameters
----------
gene : str
The HGNC symbol of the mutated gene in whose product the amino
acid change occurs. Example: "BRAF"
amino_acid_change : str
The amino acid change of interest. Example: "V600E"
Returns
-------
cell_lines : list
A list of CCLE cell lines in which the given mutation occurs.
"""
data = {'cmd': 'getMutationData',
'case_set_id': ccle_study,
'genetic_profile_id': ccle_study + '_mutations',
'gene_list': gene,
'skiprows': 1}
df = send_request(**data)
df = df[df['amino_acid_change'] == amino_acid_change]
cell_lines = df['case_id'].unique().tolist()
return cell_lines | python | def get_ccle_lines_for_mutation(gene, amino_acid_change):
data = {'cmd': 'getMutationData',
'case_set_id': ccle_study,
'genetic_profile_id': ccle_study + '_mutations',
'gene_list': gene,
'skiprows': 1}
df = send_request(**data)
df = df[df['amino_acid_change'] == amino_acid_change]
cell_lines = df['case_id'].unique().tolist()
return cell_lines | [
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19,267 | sorgerlab/indra | indra/databases/cbio_client.py | get_ccle_cna | def get_ccle_cna(gene_list, cell_lines):
"""Return a dict of CNAs in given genes and cell lines from CCLE.
CNA values correspond to the following alterations
-2 = homozygous deletion
-1 = hemizygous deletion
0 = neutral / no change
1 = gain
2 = high level amplification
Parameters
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gene_list : list[str]
A list of HGNC gene symbols to get mutations in
cell_lines : list[str]
A list of CCLE cell line names to get mutations for.
Returns
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profile_data : dict[dict[int]]
A dict keyed to cases containing a dict keyed to genes
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"""
profile_data = get_profile_data(ccle_study, gene_list,
'COPY_NUMBER_ALTERATION', 'all')
profile_data = dict((key, value) for key, value in profile_data.items()
if key in cell_lines)
return profile_data | python | def get_ccle_cna(gene_list, cell_lines):
profile_data = get_profile_data(ccle_study, gene_list,
'COPY_NUMBER_ALTERATION', 'all')
profile_data = dict((key, value) for key, value in profile_data.items()
if key in cell_lines)
return profile_data | [
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19,268 | sorgerlab/indra | indra/databases/cbio_client.py | get_ccle_mrna | def get_ccle_mrna(gene_list, cell_lines):
"""Return a dict of mRNA amounts in given genes and cell lines from CCLE.
Parameters
----------
gene_list : list[str]
A list of HGNC gene symbols to get mRNA amounts for.
cell_lines : list[str]
A list of CCLE cell line names to get mRNA amounts for.
Returns
-------
mrna_amounts : dict[dict[float]]
A dict keyed to cell lines containing a dict keyed to genes
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"""
gene_list_str = ','.join(gene_list)
data = {'cmd': 'getProfileData',
'case_set_id': ccle_study + '_mrna',
'genetic_profile_id': ccle_study + '_mrna',
'gene_list': gene_list_str,
'skiprows': -1}
df = send_request(**data)
mrna_amounts = {cl: {g: [] for g in gene_list} for cl in cell_lines}
for cell_line in cell_lines:
if cell_line in df.columns:
for gene in gene_list:
value_cell = df[cell_line][df['COMMON'] == gene]
if value_cell.empty:
mrna_amounts[cell_line][gene] = None
elif pandas.isnull(value_cell.values[0]):
mrna_amounts[cell_line][gene] = None
else:
value = value_cell.values[0]
mrna_amounts[cell_line][gene] = value
else:
mrna_amounts[cell_line] = None
return mrna_amounts | python | def get_ccle_mrna(gene_list, cell_lines):
gene_list_str = ','.join(gene_list)
data = {'cmd': 'getProfileData',
'case_set_id': ccle_study + '_mrna',
'genetic_profile_id': ccle_study + '_mrna',
'gene_list': gene_list_str,
'skiprows': -1}
df = send_request(**data)
mrna_amounts = {cl: {g: [] for g in gene_list} for cl in cell_lines}
for cell_line in cell_lines:
if cell_line in df.columns:
for gene in gene_list:
value_cell = df[cell_line][df['COMMON'] == gene]
if value_cell.empty:
mrna_amounts[cell_line][gene] = None
elif pandas.isnull(value_cell.values[0]):
mrna_amounts[cell_line][gene] = None
else:
value = value_cell.values[0]
mrna_amounts[cell_line][gene] = value
else:
mrna_amounts[cell_line] = None
return mrna_amounts | [
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19,269 | sorgerlab/indra | indra/databases/cbio_client.py | _filter_data_frame | def _filter_data_frame(df, data_col, filter_col, filter_str=None):
"""Return a filtered data frame as a dictionary."""
if filter_str is not None:
relevant_cols = data_col + [filter_col]
df.dropna(inplace=True, subset=relevant_cols)
row_filter = df[filter_col].str.contains(filter_str, case=False)
data_list = df[row_filter][data_col].to_dict()
else:
data_list = df[data_col].to_dict()
return data_list | python | def _filter_data_frame(df, data_col, filter_col, filter_str=None):
if filter_str is not None:
relevant_cols = data_col + [filter_col]
df.dropna(inplace=True, subset=relevant_cols)
row_filter = df[filter_col].str.contains(filter_str, case=False)
data_list = df[row_filter][data_col].to_dict()
else:
data_list = df[data_col].to_dict()
return data_list | [
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19,270 | sorgerlab/indra | rest_api/api.py | allow_cors | def allow_cors(func):
"""This is a decorator which enable CORS for the specified endpoint."""
def wrapper(*args, **kwargs):
response.headers['Access-Control-Allow-Origin'] = '*'
response.headers['Access-Control-Allow-Methods'] = \
'PUT, GET, POST, DELETE, OPTIONS'
response.headers['Access-Control-Allow-Headers'] = \
'Origin, Accept, Content-Type, X-Requested-With, X-CSRF-Token'
return func(*args, **kwargs)
return wrapper | python | def allow_cors(func):
def wrapper(*args, **kwargs):
response.headers['Access-Control-Allow-Origin'] = '*'
response.headers['Access-Control-Allow-Methods'] = \
'PUT, GET, POST, DELETE, OPTIONS'
response.headers['Access-Control-Allow-Headers'] = \
'Origin, Accept, Content-Type, X-Requested-With, X-CSRF-Token'
return func(*args, **kwargs)
return wrapper | [
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19,271 | sorgerlab/indra | rest_api/api.py | trips_process_text | def trips_process_text():
"""Process text with TRIPS and return INDRA Statements."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
text = body.get('text')
tp = trips.process_text(text)
return _stmts_from_proc(tp) | python | def trips_process_text():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
text = body.get('text')
tp = trips.process_text(text)
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19,272 | sorgerlab/indra | rest_api/api.py | trips_process_xml | def trips_process_xml():
"""Process TRIPS EKB XML and return INDRA Statements."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
xml_str = body.get('xml_str')
tp = trips.process_xml(xml_str)
return _stmts_from_proc(tp) | python | def trips_process_xml():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
xml_str = body.get('xml_str')
tp = trips.process_xml(xml_str)
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19,273 | sorgerlab/indra | rest_api/api.py | reach_process_text | def reach_process_text():
"""Process text with REACH and return INDRA Statements."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
text = body.get('text')
offline = True if body.get('offline') else False
rp = reach.process_text(text, offline=offline)
return _stmts_from_proc(rp) | python | def reach_process_text():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
text = body.get('text')
offline = True if body.get('offline') else False
rp = reach.process_text(text, offline=offline)
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19,274 | sorgerlab/indra | rest_api/api.py | reach_process_json | def reach_process_json():
"""Process REACH json and return INDRA Statements."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
json_str = body.get('json')
rp = reach.process_json_str(json_str)
return _stmts_from_proc(rp) | python | def reach_process_json():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
json_str = body.get('json')
rp = reach.process_json_str(json_str)
return _stmts_from_proc(rp) | [
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19,275 | sorgerlab/indra | rest_api/api.py | reach_process_pmc | def reach_process_pmc():
"""Process PubMedCentral article and return INDRA Statements."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
pmcid = body.get('pmcid')
rp = reach.process_pmc(pmcid)
return _stmts_from_proc(rp) | python | def reach_process_pmc():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
pmcid = body.get('pmcid')
rp = reach.process_pmc(pmcid)
return _stmts_from_proc(rp) | [
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19,276 | sorgerlab/indra | rest_api/api.py | bel_process_pybel_neighborhood | def bel_process_pybel_neighborhood():
"""Process BEL Large Corpus neighborhood and return INDRA Statements."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
genes = body.get('genes')
bp = bel.process_pybel_neighborhood(genes)
return _stmts_from_proc(bp) | python | def bel_process_pybel_neighborhood():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
genes = body.get('genes')
bp = bel.process_pybel_neighborhood(genes)
return _stmts_from_proc(bp) | [
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19,277 | sorgerlab/indra | rest_api/api.py | bel_process_belrdf | def bel_process_belrdf():
"""Process BEL RDF and return INDRA Statements."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
belrdf = body.get('belrdf')
bp = bel.process_belrdf(belrdf)
return _stmts_from_proc(bp) | python | def bel_process_belrdf():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
belrdf = body.get('belrdf')
bp = bel.process_belrdf(belrdf)
return _stmts_from_proc(bp) | [
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19,278 | sorgerlab/indra | rest_api/api.py | biopax_process_pc_pathsbetween | def biopax_process_pc_pathsbetween():
"""Process PathwayCommons paths between genes, return INDRA Statements."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
genes = body.get('genes')
bp = biopax.process_pc_pathsbetween(genes)
return _stmts_from_proc(bp) | python | def biopax_process_pc_pathsbetween():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
genes = body.get('genes')
bp = biopax.process_pc_pathsbetween(genes)
return _stmts_from_proc(bp) | [
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19,279 | sorgerlab/indra | rest_api/api.py | biopax_process_pc_pathsfromto | def biopax_process_pc_pathsfromto():
"""Process PathwayCommons paths from-to genes, return INDRA Statements."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
source = body.get('source')
target = body.get('target')
bp = biopax.process_pc_pathsfromto(source, target)
return _stmts_from_proc(bp) | python | def biopax_process_pc_pathsfromto():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
source = body.get('source')
target = body.get('target')
bp = biopax.process_pc_pathsfromto(source, target)
return _stmts_from_proc(bp) | [
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19,280 | sorgerlab/indra | rest_api/api.py | biopax_process_pc_neighborhood | def biopax_process_pc_neighborhood():
"""Process PathwayCommons neighborhood, return INDRA Statements."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
genes = body.get('genes')
bp = biopax.process_pc_neighborhood(genes)
return _stmts_from_proc(bp) | python | def biopax_process_pc_neighborhood():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
genes = body.get('genes')
bp = biopax.process_pc_neighborhood(genes)
return _stmts_from_proc(bp) | [
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19,281 | sorgerlab/indra | rest_api/api.py | eidos_process_text | def eidos_process_text():
"""Process text with EIDOS and return INDRA Statements."""
if request.method == 'OPTIONS':
return {}
req = request.body.read().decode('utf-8')
body = json.loads(req)
text = body.get('text')
webservice = body.get('webservice')
if not webservice:
response.status = 400
response.content_type = 'application/json'
return json.dumps({'error': 'No web service address provided.'})
ep = eidos.process_text(text, webservice=webservice)
return _stmts_from_proc(ep) | python | def eidos_process_text():
if request.method == 'OPTIONS':
return {}
req = request.body.read().decode('utf-8')
body = json.loads(req)
text = body.get('text')
webservice = body.get('webservice')
if not webservice:
response.status = 400
response.content_type = 'application/json'
return json.dumps({'error': 'No web service address provided.'})
ep = eidos.process_text(text, webservice=webservice)
return _stmts_from_proc(ep) | [
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19,282 | sorgerlab/indra | rest_api/api.py | eidos_process_jsonld | def eidos_process_jsonld():
"""Process an EIDOS JSON-LD and return INDRA Statements."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
eidos_json = body.get('jsonld')
ep = eidos.process_json_str(eidos_json)
return _stmts_from_proc(ep) | python | def eidos_process_jsonld():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
eidos_json = body.get('jsonld')
ep = eidos.process_json_str(eidos_json)
return _stmts_from_proc(ep) | [
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19,283 | sorgerlab/indra | rest_api/api.py | cwms_process_text | def cwms_process_text():
"""Process text with CWMS and return INDRA Statements."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
text = body.get('text')
cp = cwms.process_text(text)
return _stmts_from_proc(cp) | python | def cwms_process_text():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
text = body.get('text')
cp = cwms.process_text(text)
return _stmts_from_proc(cp) | [
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19,284 | sorgerlab/indra | rest_api/api.py | hume_process_jsonld | def hume_process_jsonld():
"""Process Hume JSON-LD and return INDRA Statements."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
jsonld_str = body.get('jsonld')
jsonld = json.loads(jsonld_str)
hp = hume.process_jsonld(jsonld)
return _stmts_from_proc(hp) | python | def hume_process_jsonld():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
jsonld_str = body.get('jsonld')
jsonld = json.loads(jsonld_str)
hp = hume.process_jsonld(jsonld)
return _stmts_from_proc(hp) | [
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19,285 | sorgerlab/indra | rest_api/api.py | sofia_process_text | def sofia_process_text():
"""Process text with Sofia and return INDRA Statements."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
text = body.get('text')
auth = body.get('auth')
sp = sofia.process_text(text, auth=auth)
return _stmts_from_proc(sp) | python | def sofia_process_text():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
text = body.get('text')
auth = body.get('auth')
sp = sofia.process_text(text, auth=auth)
return _stmts_from_proc(sp) | [
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19,286 | sorgerlab/indra | rest_api/api.py | assemble_pysb | def assemble_pysb():
"""Assemble INDRA Statements and return PySB model string."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
export_format = body.get('export_format')
stmts = stmts_from_json(stmts_json)
pa = PysbAssembler()
pa.add_statements(stmts)
pa.make_model()
try:
for m in pa.model.monomers:
pysb_assembler.set_extended_initial_condition(pa.model, m, 0)
except Exception as e:
logger.exception(e)
if not export_format:
model_str = pa.print_model()
elif export_format in ('kappa_im', 'kappa_cm'):
fname = 'model_%s.png' % export_format
root = os.path.dirname(os.path.abspath(fname))
graph = pa.export_model(format=export_format, file_name=fname)
with open(fname, 'rb') as fh:
data = 'data:image/png;base64,%s' % \
base64.b64encode(fh.read()).decode()
return {'image': data}
else:
try:
model_str = pa.export_model(format=export_format)
except Exception as e:
logger.exception(e)
model_str = ''
res = {'model': model_str}
return res | python | def assemble_pysb():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
export_format = body.get('export_format')
stmts = stmts_from_json(stmts_json)
pa = PysbAssembler()
pa.add_statements(stmts)
pa.make_model()
try:
for m in pa.model.monomers:
pysb_assembler.set_extended_initial_condition(pa.model, m, 0)
except Exception as e:
logger.exception(e)
if not export_format:
model_str = pa.print_model()
elif export_format in ('kappa_im', 'kappa_cm'):
fname = 'model_%s.png' % export_format
root = os.path.dirname(os.path.abspath(fname))
graph = pa.export_model(format=export_format, file_name=fname)
with open(fname, 'rb') as fh:
data = 'data:image/png;base64,%s' % \
base64.b64encode(fh.read()).decode()
return {'image': data}
else:
try:
model_str = pa.export_model(format=export_format)
except Exception as e:
logger.exception(e)
model_str = ''
res = {'model': model_str}
return res | [
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] | 79a70415832c5702d7a820c7c9ccc8e25010124b | https://github.com/sorgerlab/indra/blob/79a70415832c5702d7a820c7c9ccc8e25010124b/rest_api/api.py#L307-L342 |
19,287 | sorgerlab/indra | rest_api/api.py | assemble_cx | def assemble_cx():
"""Assemble INDRA Statements and return CX network json."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
stmts = stmts_from_json(stmts_json)
ca = CxAssembler(stmts)
model_str = ca.make_model()
res = {'model': model_str}
return res | python | def assemble_cx():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
stmts = stmts_from_json(stmts_json)
ca = CxAssembler(stmts)
model_str = ca.make_model()
res = {'model': model_str}
return res | [
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19,288 | sorgerlab/indra | rest_api/api.py | share_model_ndex | def share_model_ndex():
"""Upload the model to NDEX"""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_str = body.get('stmts')
stmts_json = json.loads(stmts_str)
stmts = stmts_from_json(stmts_json["statements"])
ca = CxAssembler(stmts)
for n, v in body.items():
ca.cx['networkAttributes'].append({'n': n, 'v': v, 'd': 'string'})
ca.make_model()
network_id = ca.upload_model(private=False)
return {'network_id': network_id} | python | def share_model_ndex():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_str = body.get('stmts')
stmts_json = json.loads(stmts_str)
stmts = stmts_from_json(stmts_json["statements"])
ca = CxAssembler(stmts)
for n, v in body.items():
ca.cx['networkAttributes'].append({'n': n, 'v': v, 'd': 'string'})
ca.make_model()
network_id = ca.upload_model(private=False)
return {'network_id': network_id} | [
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19,289 | sorgerlab/indra | rest_api/api.py | fetch_model_ndex | def fetch_model_ndex():
"""Download model and associated pieces from NDEX"""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
network_id = body.get('network_id')
cx = process_ndex_network(network_id)
network_attr = [x for x in cx.cx if x.get('networkAttributes')]
network_attr = network_attr[0]['networkAttributes']
keep_keys = ['txt_input', 'parser',
'model_elements', 'preset_pos', 'stmts',
'sentences', 'evidence', 'cell_line', 'mrna', 'mutations']
stored_data = {}
for d in network_attr:
if d['n'] in keep_keys:
stored_data[d['n']] = d['v']
return stored_data | python | def fetch_model_ndex():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
network_id = body.get('network_id')
cx = process_ndex_network(network_id)
network_attr = [x for x in cx.cx if x.get('networkAttributes')]
network_attr = network_attr[0]['networkAttributes']
keep_keys = ['txt_input', 'parser',
'model_elements', 'preset_pos', 'stmts',
'sentences', 'evidence', 'cell_line', 'mrna', 'mutations']
stored_data = {}
for d in network_attr:
if d['n'] in keep_keys:
stored_data[d['n']] = d['v']
return stored_data | [
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19,290 | sorgerlab/indra | rest_api/api.py | assemble_graph | def assemble_graph():
"""Assemble INDRA Statements and return Graphviz graph dot string."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
stmts = stmts_from_json(stmts_json)
ga = GraphAssembler(stmts)
model_str = ga.make_model()
res = {'model': model_str}
return res | python | def assemble_graph():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
stmts = stmts_from_json(stmts_json)
ga = GraphAssembler(stmts)
model_str = ga.make_model()
res = {'model': model_str}
return res | [
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19,291 | sorgerlab/indra | rest_api/api.py | assemble_cyjs | def assemble_cyjs():
"""Assemble INDRA Statements and return Cytoscape JS network."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
stmts = stmts_from_json(stmts_json)
cja = CyJSAssembler()
cja.add_statements(stmts)
cja.make_model(grouping=True)
model_str = cja.print_cyjs_graph()
return model_str | python | def assemble_cyjs():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
stmts = stmts_from_json(stmts_json)
cja = CyJSAssembler()
cja.add_statements(stmts)
cja.make_model(grouping=True)
model_str = cja.print_cyjs_graph()
return model_str | [
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19,292 | sorgerlab/indra | rest_api/api.py | assemble_english | def assemble_english():
"""Assemble each statement into """
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
stmts = stmts_from_json(stmts_json)
sentences = {}
for st in stmts:
enga = EnglishAssembler()
enga.add_statements([st])
model_str = enga.make_model()
sentences[st.uuid] = model_str
res = {'sentences': sentences}
return res | python | def assemble_english():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
stmts = stmts_from_json(stmts_json)
sentences = {}
for st in stmts:
enga = EnglishAssembler()
enga.add_statements([st])
model_str = enga.make_model()
sentences[st.uuid] = model_str
res = {'sentences': sentences}
return res | [
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19,293 | sorgerlab/indra | rest_api/api.py | assemble_loopy | def assemble_loopy():
"""Assemble INDRA Statements into a Loopy model using SIF Assembler."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
stmts = stmts_from_json(stmts_json)
sa = SifAssembler(stmts)
sa.make_model(use_name_as_key=True)
model_str = sa.print_loopy(as_url=True)
res = {'loopy_url': model_str}
return res | python | def assemble_loopy():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
stmts = stmts_from_json(stmts_json)
sa = SifAssembler(stmts)
sa.make_model(use_name_as_key=True)
model_str = sa.print_loopy(as_url=True)
res = {'loopy_url': model_str}
return res | [
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19,294 | sorgerlab/indra | rest_api/api.py | get_ccle_mrna_levels | def get_ccle_mrna_levels():
"""Get CCLE mRNA amounts using cBioClient"""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
gene_list = body.get('gene_list')
cell_lines = body.get('cell_lines')
mrna_amounts = cbio_client.get_ccle_mrna(gene_list, cell_lines)
res = {'mrna_amounts': mrna_amounts}
return res | python | def get_ccle_mrna_levels():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
gene_list = body.get('gene_list')
cell_lines = body.get('cell_lines')
mrna_amounts = cbio_client.get_ccle_mrna(gene_list, cell_lines)
res = {'mrna_amounts': mrna_amounts}
return res | [
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19,295 | sorgerlab/indra | rest_api/api.py | get_ccle_mutations | def get_ccle_mutations():
"""Get CCLE mutations
returns the amino acid changes for a given list of genes and cell lines
"""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
gene_list = body.get('gene_list')
cell_lines = body.get('cell_lines')
mutations = cbio_client.get_ccle_mutations(gene_list, cell_lines)
res = {'mutations': mutations}
return res | python | def get_ccle_mutations():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
gene_list = body.get('gene_list')
cell_lines = body.get('cell_lines')
mutations = cbio_client.get_ccle_mutations(gene_list, cell_lines)
res = {'mutations': mutations}
return res | [
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19,296 | sorgerlab/indra | rest_api/api.py | map_grounding | def map_grounding():
"""Map grounding on a list of INDRA Statements."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
stmts = stmts_from_json(stmts_json)
stmts_out = ac.map_grounding(stmts)
return _return_stmts(stmts_out) | python | def map_grounding():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
stmts = stmts_from_json(stmts_json)
stmts_out = ac.map_grounding(stmts)
return _return_stmts(stmts_out) | [
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19,297 | sorgerlab/indra | rest_api/api.py | run_preassembly | def run_preassembly():
"""Run preassembly on a list of INDRA Statements."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
stmts = stmts_from_json(stmts_json)
scorer = body.get('scorer')
return_toplevel = body.get('return_toplevel')
if scorer == 'wm':
belief_scorer = get_eidos_scorer()
else:
belief_scorer = None
stmts_out = ac.run_preassembly(stmts, belief_scorer=belief_scorer,
return_toplevel=return_toplevel)
return _return_stmts(stmts_out) | python | def run_preassembly():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
stmts = stmts_from_json(stmts_json)
scorer = body.get('scorer')
return_toplevel = body.get('return_toplevel')
if scorer == 'wm':
belief_scorer = get_eidos_scorer()
else:
belief_scorer = None
stmts_out = ac.run_preassembly(stmts, belief_scorer=belief_scorer,
return_toplevel=return_toplevel)
return _return_stmts(stmts_out) | [
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19,298 | sorgerlab/indra | rest_api/api.py | map_ontologies | def map_ontologies():
"""Run ontology mapping on a list of INDRA Statements."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
stmts = stmts_from_json(stmts_json)
om = OntologyMapper(stmts, wm_ontomap, scored=True, symmetric=False)
om.map_statements()
return _return_stmts(stmts) | python | def map_ontologies():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
stmts = stmts_from_json(stmts_json)
om = OntologyMapper(stmts, wm_ontomap, scored=True, symmetric=False)
om.map_statements()
return _return_stmts(stmts) | [
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19,299 | sorgerlab/indra | rest_api/api.py | filter_by_type | def filter_by_type():
"""Filter to a given INDRA Statement type."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
stmt_type_str = body.get('type')
stmt_type_str = stmt_type_str.capitalize()
stmt_type = getattr(sys.modules[__name__], stmt_type_str)
stmts = stmts_from_json(stmts_json)
stmts_out = ac.filter_by_type(stmts, stmt_type)
return _return_stmts(stmts_out) | python | def filter_by_type():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
stmt_type_str = body.get('type')
stmt_type_str = stmt_type_str.capitalize()
stmt_type = getattr(sys.modules[__name__], stmt_type_str)
stmts = stmts_from_json(stmts_json)
stmts_out = ac.filter_by_type(stmts, stmt_type)
return _return_stmts(stmts_out) | [
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"filter_by_type",
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"method",
"==",
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"{",
"}",
"response",
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"'utf-8'",
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"loads",... | Filter to a given INDRA Statement type. | [
"Filter",
"to",
"a",
"given",
"INDRA",
"Statement",
"type",
"."
] | 79a70415832c5702d7a820c7c9ccc8e25010124b | https://github.com/sorgerlab/indra/blob/79a70415832c5702d7a820c7c9ccc8e25010124b/rest_api/api.py#L598-L610 |
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