id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 51 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
|---|---|---|---|---|---|---|---|---|---|---|---|
19,300 | sorgerlab/indra | rest_api/api.py | filter_grounded_only | def filter_grounded_only():
"""Filter to grounded Statements only."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
score_threshold = body.get('score_threshold')
if score_threshold is not None:
score_threshold = float(score_threshold)
stmts = stmts_from_json(stmts_json)
stmts_out = ac.filter_grounded_only(stmts, score_threshold=score_threshold)
return _return_stmts(stmts_out) | python | def filter_grounded_only():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
score_threshold = body.get('score_threshold')
if score_threshold is not None:
score_threshold = float(score_threshold)
stmts = stmts_from_json(stmts_json)
stmts_out = ac.filter_grounded_only(stmts, score_threshold=score_threshold)
return _return_stmts(stmts_out) | [
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19,301 | sorgerlab/indra | rest_api/api.py | filter_belief | def filter_belief():
"""Filter to beliefs above a given threshold."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
belief_cutoff = body.get('belief_cutoff')
if belief_cutoff is not None:
belief_cutoff = float(belief_cutoff)
stmts = stmts_from_json(stmts_json)
stmts_out = ac.filter_belief(stmts, belief_cutoff)
return _return_stmts(stmts_out) | python | def filter_belief():
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
belief_cutoff = body.get('belief_cutoff')
if belief_cutoff is not None:
belief_cutoff = float(belief_cutoff)
stmts = stmts_from_json(stmts_json)
stmts_out = ac.filter_belief(stmts, belief_cutoff)
return _return_stmts(stmts_out) | [
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19,302 | sorgerlab/indra | indra/util/get_version.py | get_git_info | def get_git_info():
"""Get a dict with useful git info."""
start_dir = abspath(curdir)
try:
chdir(dirname(abspath(__file__)))
re_patt_str = (r'commit\s+(?P<commit_hash>\w+).*?Author:\s+'
r'(?P<author_name>.*?)\s+<(?P<author_email>.*?)>\s+Date:\s+'
r'(?P<date>.*?)\n\s+(?P<commit_msg>.*?)(?:\ndiff.*?)?$')
show_out = check_output(['git', 'show']).decode('ascii')
revp_out = check_output(['git', 'rev-parse', '--abbrev-ref', 'HEAD'])
revp_out = revp_out.decode('ascii').strip()
m = re.search(re_patt_str, show_out, re.DOTALL)
assert m is not None, \
"Regex pattern:\n\n\"%s\"\n\n failed to match string:\n\n\"%s\"" \
% (re_patt_str, show_out)
ret_dict = m.groupdict()
ret_dict['branch_name'] = revp_out
finally:
chdir(start_dir)
return ret_dict | python | def get_git_info():
start_dir = abspath(curdir)
try:
chdir(dirname(abspath(__file__)))
re_patt_str = (r'commit\s+(?P<commit_hash>\w+).*?Author:\s+'
r'(?P<author_name>.*?)\s+<(?P<author_email>.*?)>\s+Date:\s+'
r'(?P<date>.*?)\n\s+(?P<commit_msg>.*?)(?:\ndiff.*?)?$')
show_out = check_output(['git', 'show']).decode('ascii')
revp_out = check_output(['git', 'rev-parse', '--abbrev-ref', 'HEAD'])
revp_out = revp_out.decode('ascii').strip()
m = re.search(re_patt_str, show_out, re.DOTALL)
assert m is not None, \
"Regex pattern:\n\n\"%s\"\n\n failed to match string:\n\n\"%s\"" \
% (re_patt_str, show_out)
ret_dict = m.groupdict()
ret_dict['branch_name'] = revp_out
finally:
chdir(start_dir)
return ret_dict | [
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19,303 | sorgerlab/indra | indra/util/get_version.py | get_version | def get_version(with_git_hash=True, refresh_hash=False):
"""Get an indra version string, including a git hash."""
version = __version__
if with_git_hash:
global INDRA_GITHASH
if INDRA_GITHASH is None or refresh_hash:
with open(devnull, 'w') as nul:
try:
ret = check_output(['git', 'rev-parse', 'HEAD'],
cwd=dirname(__file__), stderr=nul)
except CalledProcessError:
ret = 'UNHASHED'
INDRA_GITHASH = ret.strip().decode('utf-8')
version = '%s-%s' % (version, INDRA_GITHASH)
return version | python | def get_version(with_git_hash=True, refresh_hash=False):
version = __version__
if with_git_hash:
global INDRA_GITHASH
if INDRA_GITHASH is None or refresh_hash:
with open(devnull, 'w') as nul:
try:
ret = check_output(['git', 'rev-parse', 'HEAD'],
cwd=dirname(__file__), stderr=nul)
except CalledProcessError:
ret = 'UNHASHED'
INDRA_GITHASH = ret.strip().decode('utf-8')
version = '%s-%s' % (version, INDRA_GITHASH)
return version | [
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19,304 | sorgerlab/indra | indra/assemblers/cx/assembler.py | _fix_evidence_text | def _fix_evidence_text(txt):
"""Eliminate some symbols to have cleaner supporting text."""
txt = re.sub('[ ]?\( xref \)', '', txt)
# This is to make [ xref ] become [] to match the two readers
txt = re.sub('\[ xref \]', '[]', txt)
txt = re.sub('[\(]?XREF_BIBR[\)]?[,]?', '', txt)
txt = re.sub('[\(]?XREF_FIG[\)]?[,]?', '', txt)
txt = re.sub('[\(]?XREF_SUPPLEMENT[\)]?[,]?', '', txt)
txt = txt.strip()
return txt | python | def _fix_evidence_text(txt):
txt = re.sub('[ ]?\( xref \)', '', txt)
# This is to make [ xref ] become [] to match the two readers
txt = re.sub('\[ xref \]', '[]', txt)
txt = re.sub('[\(]?XREF_BIBR[\)]?[,]?', '', txt)
txt = re.sub('[\(]?XREF_FIG[\)]?[,]?', '', txt)
txt = re.sub('[\(]?XREF_SUPPLEMENT[\)]?[,]?', '', txt)
txt = txt.strip()
return txt | [
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19,305 | sorgerlab/indra | indra/assemblers/cx/assembler.py | CxAssembler.make_model | def make_model(self, add_indra_json=True):
"""Assemble the CX network from the collected INDRA Statements.
This method assembles a CX network from the set of INDRA Statements.
The assembled network is set as the assembler's cx argument.
Parameters
----------
add_indra_json : Optional[bool]
If True, the INDRA Statement JSON annotation is added to each
edge in the network. Default: True
Returns
-------
cx_str : str
The json serialized CX model.
"""
self.add_indra_json = add_indra_json
for stmt in self.statements:
if isinstance(stmt, Modification):
self._add_modification(stmt)
if isinstance(stmt, SelfModification):
self._add_self_modification(stmt)
elif isinstance(stmt, RegulateActivity) or \
isinstance(stmt, RegulateAmount):
self._add_regulation(stmt)
elif isinstance(stmt, Complex):
self._add_complex(stmt)
elif isinstance(stmt, Gef):
self._add_gef(stmt)
elif isinstance(stmt, Gap):
self._add_gap(stmt)
elif isinstance(stmt, Influence):
self._add_influence(stmt)
network_description = ''
self.cx['networkAttributes'].append({'n': 'name',
'v': self.network_name})
self.cx['networkAttributes'].append({'n': 'description',
'v': network_description})
cx_str = self.print_cx()
return cx_str | python | def make_model(self, add_indra_json=True):
self.add_indra_json = add_indra_json
for stmt in self.statements:
if isinstance(stmt, Modification):
self._add_modification(stmt)
if isinstance(stmt, SelfModification):
self._add_self_modification(stmt)
elif isinstance(stmt, RegulateActivity) or \
isinstance(stmt, RegulateAmount):
self._add_regulation(stmt)
elif isinstance(stmt, Complex):
self._add_complex(stmt)
elif isinstance(stmt, Gef):
self._add_gef(stmt)
elif isinstance(stmt, Gap):
self._add_gap(stmt)
elif isinstance(stmt, Influence):
self._add_influence(stmt)
network_description = ''
self.cx['networkAttributes'].append({'n': 'name',
'v': self.network_name})
self.cx['networkAttributes'].append({'n': 'description',
'v': network_description})
cx_str = self.print_cx()
return cx_str | [
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Parameters
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add_indra_json : Optional[bool]
If True, the INDRA Statement JSON annotation is added to each
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Returns
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19,306 | sorgerlab/indra | indra/assemblers/cx/assembler.py | CxAssembler.print_cx | def print_cx(self, pretty=True):
"""Return the assembled CX network as a json string.
Parameters
----------
pretty : bool
If True, the CX string is formatted with indentation (for human
viewing) otherwise no indentation is used.
Returns
-------
json_str : str
A json formatted string representation of the CX network.
"""
def _get_aspect_metadata(aspect):
count = len(self.cx.get(aspect)) if self.cx.get(aspect) else 0
if not count:
return None
data = {'name': aspect,
'idCounter': self._id_counter,
'consistencyGroup': 1,
'elementCount': count}
return data
full_cx = OrderedDict()
full_cx['numberVerification'] = [{'longNumber': 281474976710655}]
aspects = ['nodes', 'edges', 'supports', 'citations', 'edgeAttributes',
'edgeCitations', 'edgeSupports', 'networkAttributes',
'nodeAttributes', 'cartesianLayout']
full_cx['metaData'] = []
for aspect in aspects:
metadata = _get_aspect_metadata(aspect)
if metadata:
full_cx['metaData'].append(metadata)
for k, v in self.cx.items():
full_cx[k] = v
full_cx['status'] = [{'error': '', 'success': True}]
full_cx = [{k: v} for k, v in full_cx.items()]
if pretty:
json_str = json.dumps(full_cx, indent=2)
else:
json_str = json.dumps(full_cx)
return json_str | python | def print_cx(self, pretty=True):
def _get_aspect_metadata(aspect):
count = len(self.cx.get(aspect)) if self.cx.get(aspect) else 0
if not count:
return None
data = {'name': aspect,
'idCounter': self._id_counter,
'consistencyGroup': 1,
'elementCount': count}
return data
full_cx = OrderedDict()
full_cx['numberVerification'] = [{'longNumber': 281474976710655}]
aspects = ['nodes', 'edges', 'supports', 'citations', 'edgeAttributes',
'edgeCitations', 'edgeSupports', 'networkAttributes',
'nodeAttributes', 'cartesianLayout']
full_cx['metaData'] = []
for aspect in aspects:
metadata = _get_aspect_metadata(aspect)
if metadata:
full_cx['metaData'].append(metadata)
for k, v in self.cx.items():
full_cx[k] = v
full_cx['status'] = [{'error': '', 'success': True}]
full_cx = [{k: v} for k, v in full_cx.items()]
if pretty:
json_str = json.dumps(full_cx, indent=2)
else:
json_str = json.dumps(full_cx)
return json_str | [
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Returns
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json_str : str
A json formatted string representation of the CX network. | [
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19,307 | sorgerlab/indra | indra/assemblers/cx/assembler.py | CxAssembler.save_model | def save_model(self, file_name='model.cx'):
"""Save the assembled CX network in a file.
Parameters
----------
file_name : Optional[str]
The name of the file to save the CX network to. Default: model.cx
"""
with open(file_name, 'wt') as fh:
cx_str = self.print_cx()
fh.write(cx_str) | python | def save_model(self, file_name='model.cx'):
with open(file_name, 'wt') as fh:
cx_str = self.print_cx()
fh.write(cx_str) | [
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file_name : Optional[str]
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19,308 | sorgerlab/indra | indra/assemblers/cx/assembler.py | CxAssembler.set_context | def set_context(self, cell_type):
"""Set protein expression data and mutational status as node attribute
This method uses :py:mod:`indra.databases.context_client` to get
protein expression levels and mutational status for a given cell type
and set a node attribute for proteins accordingly.
Parameters
----------
cell_type : str
Cell type name for which expression levels are queried.
The cell type name follows the CCLE database conventions.
Example: LOXIMVI_SKIN, BT20_BREAST
"""
node_names = [node['n'] for node in self.cx['nodes']]
res_expr = context_client.get_protein_expression(node_names,
[cell_type])
res_mut = context_client.get_mutations(node_names,
[cell_type])
res_expr = res_expr.get(cell_type)
res_mut = res_mut.get(cell_type)
if not res_expr:
msg = 'Could not get protein expression for %s cell type.' % \
cell_type
logger.warning(msg)
if not res_mut:
msg = 'Could not get mutational status for %s cell type.' % \
cell_type
logger.warning(msg)
if not res_expr and not res_mut:
return
self.cx['networkAttributes'].append({'n': 'cellular_context',
'v': cell_type})
counter = 0
for node in self.cx['nodes']:
amount = res_expr.get(node['n'])
mut = res_mut.get(node['n'])
if amount is not None:
node_attribute = {'po': node['@id'],
'n': 'expression_amount',
'v': int(amount)}
self.cx['nodeAttributes'].append(node_attribute)
if mut is not None:
is_mutated = 1 if mut else 0
node_attribute = {'po': node['@id'],
'n': 'is_mutated',
'v': is_mutated}
self.cx['nodeAttributes'].append(node_attribute)
if mut is not None or amount is not None:
counter += 1
logger.info('Set context for %d nodes.' % counter) | python | def set_context(self, cell_type):
node_names = [node['n'] for node in self.cx['nodes']]
res_expr = context_client.get_protein_expression(node_names,
[cell_type])
res_mut = context_client.get_mutations(node_names,
[cell_type])
res_expr = res_expr.get(cell_type)
res_mut = res_mut.get(cell_type)
if not res_expr:
msg = 'Could not get protein expression for %s cell type.' % \
cell_type
logger.warning(msg)
if not res_mut:
msg = 'Could not get mutational status for %s cell type.' % \
cell_type
logger.warning(msg)
if not res_expr and not res_mut:
return
self.cx['networkAttributes'].append({'n': 'cellular_context',
'v': cell_type})
counter = 0
for node in self.cx['nodes']:
amount = res_expr.get(node['n'])
mut = res_mut.get(node['n'])
if amount is not None:
node_attribute = {'po': node['@id'],
'n': 'expression_amount',
'v': int(amount)}
self.cx['nodeAttributes'].append(node_attribute)
if mut is not None:
is_mutated = 1 if mut else 0
node_attribute = {'po': node['@id'],
'n': 'is_mutated',
'v': is_mutated}
self.cx['nodeAttributes'].append(node_attribute)
if mut is not None or amount is not None:
counter += 1
logger.info('Set context for %d nodes.' % counter) | [
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Parameters
----------
cell_type : str
Cell type name for which expression levels are queried.
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] | 79a70415832c5702d7a820c7c9ccc8e25010124b | https://github.com/sorgerlab/indra/blob/79a70415832c5702d7a820c7c9ccc8e25010124b/indra/assemblers/cx/assembler.py#L212-L265 |
19,309 | sorgerlab/indra | indra/databases/biogrid_client.py | get_publications | def get_publications(gene_names, save_json_name=None):
"""Return evidence publications for interaction between the given genes.
Parameters
----------
gene_names : list[str]
A list of gene names (HGNC symbols) to query interactions between.
Currently supports exactly two genes only.
save_json_name : Optional[str]
A file name to save the raw BioGRID web service output in. By default,
the raw output is not saved.
Return
------
publications : list[Publication]
A list of Publication objects that provide evidence for interactions
between the given list of genes.
"""
if len(gene_names) != 2:
logger.warning('Other than 2 gene names given.')
return []
res_dict = _send_request(gene_names)
if not res_dict:
return []
if save_json_name is not None:
# The json module produces strings, not bytes, so the file should be
# opened in text mode
with open(save_json_name, 'wt') as fh:
json.dump(res_dict, fh, indent=1)
publications = _extract_publications(res_dict, gene_names)
return publications | python | def get_publications(gene_names, save_json_name=None):
if len(gene_names) != 2:
logger.warning('Other than 2 gene names given.')
return []
res_dict = _send_request(gene_names)
if not res_dict:
return []
if save_json_name is not None:
# The json module produces strings, not bytes, so the file should be
# opened in text mode
with open(save_json_name, 'wt') as fh:
json.dump(res_dict, fh, indent=1)
publications = _extract_publications(res_dict, gene_names)
return publications | [
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19,310 | sorgerlab/indra | indra/assemblers/pysb/common.py | _n | def _n(name):
"""Return valid PySB name."""
n = name.encode('ascii', errors='ignore').decode('ascii')
n = re.sub('[^A-Za-z0-9_]', '_', n)
n = re.sub(r'(^[0-9].*)', r'p\1', n)
return n | python | def _n(name):
n = name.encode('ascii', errors='ignore').decode('ascii')
n = re.sub('[^A-Za-z0-9_]', '_', n)
n = re.sub(r'(^[0-9].*)', r'p\1', n)
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19,311 | sorgerlab/indra | indra/sources/indra_db_rest/processor.py | IndraDBRestProcessor.get_hash_statements_dict | def get_hash_statements_dict(self):
"""Return a dict of Statements keyed by hashes."""
res = {stmt_hash: stmts_from_json([stmt])[0]
for stmt_hash, stmt in self.__statement_jsons.items()}
return res | python | def get_hash_statements_dict(self):
res = {stmt_hash: stmts_from_json([stmt])[0]
for stmt_hash, stmt in self.__statement_jsons.items()}
return res | [
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19,312 | sorgerlab/indra | indra/sources/indra_db_rest/processor.py | IndraDBRestProcessor.merge_results | def merge_results(self, other_processor):
"""Merge the results of this processor with those of another."""
if not isinstance(other_processor, self.__class__):
raise ValueError("Can only extend with another %s instance."
% self.__class__.__name__)
self.statements.extend(other_processor.statements)
if other_processor.statements_sample is not None:
if self.statements_sample is None:
self.statements_sample = other_processor.statements_sample
else:
self.statements_sample.extend(other_processor.statements_sample)
self._merge_json(other_processor.__statement_jsons,
other_processor.__evidence_counts)
return | python | def merge_results(self, other_processor):
if not isinstance(other_processor, self.__class__):
raise ValueError("Can only extend with another %s instance."
% self.__class__.__name__)
self.statements.extend(other_processor.statements)
if other_processor.statements_sample is not None:
if self.statements_sample is None:
self.statements_sample = other_processor.statements_sample
else:
self.statements_sample.extend(other_processor.statements_sample)
self._merge_json(other_processor.__statement_jsons,
other_processor.__evidence_counts)
return | [
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19,313 | sorgerlab/indra | indra/sources/indra_db_rest/processor.py | IndraDBRestProcessor.wait_until_done | def wait_until_done(self, timeout=None):
"""Wait for the background load to complete."""
start = datetime.now()
if not self.__th:
raise IndraDBRestResponseError("There is no thread waiting to "
"complete.")
self.__th.join(timeout)
now = datetime.now()
dt = now - start
if self.__th.is_alive():
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"statement load to complete." % dt.total_seconds())
ret = False
else:
logger.info("Waited %0.3f seconds for statements to finish loading."
% dt.total_seconds())
ret = True
return ret | python | def wait_until_done(self, timeout=None):
start = datetime.now()
if not self.__th:
raise IndraDBRestResponseError("There is no thread waiting to "
"complete.")
self.__th.join(timeout)
now = datetime.now()
dt = now - start
if self.__th.is_alive():
logger.warning("Timed out after %0.3f seconds waiting for "
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ret = False
else:
logger.info("Waited %0.3f seconds for statements to finish loading."
% dt.total_seconds())
ret = True
return ret | [
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19,314 | sorgerlab/indra | indra/sources/indra_db_rest/processor.py | IndraDBRestProcessor._merge_json | def _merge_json(self, stmt_json, ev_counts):
"""Merge these statement jsons with new jsons."""
# Where there is overlap, there _should_ be agreement.
self.__evidence_counts.update(ev_counts)
for k, sj in stmt_json.items():
if k not in self.__statement_jsons:
self.__statement_jsons[k] = sj # This should be most of them
else:
# This should only happen rarely.
for evj in sj['evidence']:
self.__statement_jsons[k]['evidence'].append(evj)
if not self.__started:
self.statements_sample = stmts_from_json(
self.__statement_jsons.values())
self.__started = True
return | python | def _merge_json(self, stmt_json, ev_counts):
# Where there is overlap, there _should_ be agreement.
self.__evidence_counts.update(ev_counts)
for k, sj in stmt_json.items():
if k not in self.__statement_jsons:
self.__statement_jsons[k] = sj # This should be most of them
else:
# This should only happen rarely.
for evj in sj['evidence']:
self.__statement_jsons[k]['evidence'].append(evj)
if not self.__started:
self.statements_sample = stmts_from_json(
self.__statement_jsons.values())
self.__started = True
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19,315 | sorgerlab/indra | indra/sources/indra_db_rest/processor.py | IndraDBRestProcessor._run_queries | def _run_queries(self, agent_strs, stmt_types, params, persist):
"""Use paging to get all statements requested."""
self._query_over_statement_types(agent_strs, stmt_types, params)
assert len(self.__done_dict) == len(stmt_types) \
or None in self.__done_dict.keys(), \
"Done dict was not initiated for all stmt_type's."
# Check if we want to keep going.
if not persist:
self._compile_statements()
return
# Get the rest of the content.
while not self._all_done():
self._query_over_statement_types(agent_strs, stmt_types, params)
# Create the actual statements.
self._compile_statements()
return | python | def _run_queries(self, agent_strs, stmt_types, params, persist):
self._query_over_statement_types(agent_strs, stmt_types, params)
assert len(self.__done_dict) == len(stmt_types) \
or None in self.__done_dict.keys(), \
"Done dict was not initiated for all stmt_type's."
# Check if we want to keep going.
if not persist:
self._compile_statements()
return
# Get the rest of the content.
while not self._all_done():
self._query_over_statement_types(agent_strs, stmt_types, params)
# Create the actual statements.
self._compile_statements()
return | [
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19,316 | sorgerlab/indra | indra/literature/pubmed_client.py | get_ids | def get_ids(search_term, **kwargs):
"""Search Pubmed for paper IDs given a search term.
Search options can be passed as keyword arguments, some of which are
custom keywords identified by this function, while others are passed on
as parameters for the request to the PubMed web service
For details on parameters that can be used in PubMed searches, see
https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.ESearch Some useful
parameters to pass are db='pmc' to search PMC instead of pubmed reldate=2
to search for papers within the last 2 days mindate='2016/03/01',
maxdate='2016/03/31' to search for papers in March 2016.
PubMed, by default, limits returned PMIDs to a small number, and this
number can be controlled by the "retmax" parameter. This function
uses a retmax value of 100,000 by default that can be changed via the
corresponding keyword argument.
Parameters
----------
search_term : str
A term for which the PubMed search should be performed.
use_text_word : Optional[bool]
If True, the "[tw]" string is appended to the search term to constrain
the search to "text words", that is words that appear as whole
in relevant parts of the PubMed entry (excl. for instance the journal
name or publication date) like the title and abstract. Using this
option can eliminate spurious search results such as all articles
published in June for a search for the "JUN" gene, or journal names
that contain Acad for a search for the "ACAD" gene.
See also: https://www.nlm.nih.gov/bsd/disted/pubmedtutorial/020_760.html
Default : True
kwargs : kwargs
Additional keyword arguments to pass to the PubMed search as
parameters.
"""
use_text_word = kwargs.pop('use_text_word', True)
if use_text_word:
search_term += '[tw]'
params = {'term': search_term,
'retmax': 100000,
'retstart': 0,
'db': 'pubmed',
'sort': 'pub+date'}
params.update(kwargs)
tree = send_request(pubmed_search, params)
if tree is None:
return []
if tree.find('ERROR') is not None:
logger.error(tree.find('ERROR').text)
return []
if tree.find('ErrorList') is not None:
for err in tree.find('ErrorList').getchildren():
logger.error('Error - %s: %s' % (err.tag, err.text))
return []
count = int(tree.find('Count').text)
id_terms = tree.findall('IdList/Id')
if id_terms is None:
return []
ids = [idt.text for idt in id_terms]
if count != len(ids):
logger.warning('Not all ids were retrieved for search %s;\n'
'limited at %d.' % (search_term, params['retmax']))
return ids | python | def get_ids(search_term, **kwargs):
use_text_word = kwargs.pop('use_text_word', True)
if use_text_word:
search_term += '[tw]'
params = {'term': search_term,
'retmax': 100000,
'retstart': 0,
'db': 'pubmed',
'sort': 'pub+date'}
params.update(kwargs)
tree = send_request(pubmed_search, params)
if tree is None:
return []
if tree.find('ERROR') is not None:
logger.error(tree.find('ERROR').text)
return []
if tree.find('ErrorList') is not None:
for err in tree.find('ErrorList').getchildren():
logger.error('Error - %s: %s' % (err.tag, err.text))
return []
count = int(tree.find('Count').text)
id_terms = tree.findall('IdList/Id')
if id_terms is None:
return []
ids = [idt.text for idt in id_terms]
if count != len(ids):
logger.warning('Not all ids were retrieved for search %s;\n'
'limited at %d.' % (search_term, params['retmax']))
return ids | [
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For details on parameters that can be used in PubMed searches, see
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search_term : str
A term for which the PubMed search should be performed.
use_text_word : Optional[bool]
If True, the "[tw]" string is appended to the search term to constrain
the search to "text words", that is words that appear as whole
in relevant parts of the PubMed entry (excl. for instance the journal
name or publication date) like the title and abstract. Using this
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Additional keyword arguments to pass to the PubMed search as
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19,317 | sorgerlab/indra | indra/literature/pubmed_client.py | get_id_count | def get_id_count(search_term):
"""Get the number of citations in Pubmed for a search query.
Parameters
----------
search_term : str
A term for which the PubMed search should be performed.
Returns
-------
int or None
The number of citations for the query, or None if the query fails.
"""
params = {'term': search_term,
'rettype': 'count',
'db': 'pubmed'}
tree = send_request(pubmed_search, params)
if tree is None:
return None
else:
count = tree.getchildren()[0].text
return int(count) | python | def get_id_count(search_term):
params = {'term': search_term,
'rettype': 'count',
'db': 'pubmed'}
tree = send_request(pubmed_search, params)
if tree is None:
return None
else:
count = tree.getchildren()[0].text
return int(count) | [
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19,318 | sorgerlab/indra | indra/literature/pubmed_client.py | get_ids_for_gene | def get_ids_for_gene(hgnc_name, **kwargs):
"""Get the curated set of articles for a gene in the Entrez database.
Search parameters for the Gene database query can be passed in as
keyword arguments.
Parameters
----------
hgnc_name : string
The HGNC name of the gene. This is used to obtain the HGNC ID
(using the hgnc_client module) and in turn used to obtain the Entrez
ID associated with the gene. Entrez is then queried for that ID.
"""
# Get the HGNC ID for the HGNC name
hgnc_id = hgnc_client.get_hgnc_id(hgnc_name)
if hgnc_id is None:
raise ValueError('Invalid HGNC name.')
# Get the Entrez ID
entrez_id = hgnc_client.get_entrez_id(hgnc_id)
if entrez_id is None:
raise ValueError('Entrez ID not found in HGNC table.')
# Query the Entrez Gene database
params = {'db': 'gene',
'retmode': 'xml',
'id': entrez_id}
params.update(kwargs)
tree = send_request(pubmed_fetch, params)
if tree is None:
return []
if tree.find('ERROR') is not None:
logger.error(tree.find('ERROR').text)
return []
# Get all PMIDs from the XML tree
id_terms = tree.findall('.//PubMedId')
if id_terms is None:
return []
# Use a set to remove duplicate IDs
ids = list(set([idt.text for idt in id_terms]))
return ids | python | def get_ids_for_gene(hgnc_name, **kwargs):
# Get the HGNC ID for the HGNC name
hgnc_id = hgnc_client.get_hgnc_id(hgnc_name)
if hgnc_id is None:
raise ValueError('Invalid HGNC name.')
# Get the Entrez ID
entrez_id = hgnc_client.get_entrez_id(hgnc_id)
if entrez_id is None:
raise ValueError('Entrez ID not found in HGNC table.')
# Query the Entrez Gene database
params = {'db': 'gene',
'retmode': 'xml',
'id': entrez_id}
params.update(kwargs)
tree = send_request(pubmed_fetch, params)
if tree is None:
return []
if tree.find('ERROR') is not None:
logger.error(tree.find('ERROR').text)
return []
# Get all PMIDs from the XML tree
id_terms = tree.findall('.//PubMedId')
if id_terms is None:
return []
# Use a set to remove duplicate IDs
ids = list(set([idt.text for idt in id_terms]))
return ids | [
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Search parameters for the Gene database query can be passed in as
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Parameters
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hgnc_name : string
The HGNC name of the gene. This is used to obtain the HGNC ID
(using the hgnc_client module) and in turn used to obtain the Entrez
ID associated with the gene. Entrez is then queried for that ID. | [
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19,319 | sorgerlab/indra | indra/literature/pubmed_client.py | get_article_xml | def get_article_xml(pubmed_id):
"""Get the XML metadata for a single article from the Pubmed database.
"""
if pubmed_id.upper().startswith('PMID'):
pubmed_id = pubmed_id[4:]
params = {'db': 'pubmed',
'retmode': 'xml',
'id': pubmed_id}
tree = send_request(pubmed_fetch, params)
if tree is None:
return None
article = tree.find('PubmedArticle/MedlineCitation/Article')
return article | python | def get_article_xml(pubmed_id):
if pubmed_id.upper().startswith('PMID'):
pubmed_id = pubmed_id[4:]
params = {'db': 'pubmed',
'retmode': 'xml',
'id': pubmed_id}
tree = send_request(pubmed_fetch, params)
if tree is None:
return None
article = tree.find('PubmedArticle/MedlineCitation/Article')
return article | [
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19,320 | sorgerlab/indra | indra/literature/pubmed_client.py | get_abstract | def get_abstract(pubmed_id, prepend_title=True):
"""Get the abstract of an article in the Pubmed database."""
article = get_article_xml(pubmed_id)
if article is None:
return None
return _abstract_from_article_element(article, prepend_title) | python | def get_abstract(pubmed_id, prepend_title=True):
article = get_article_xml(pubmed_id)
if article is None:
return None
return _abstract_from_article_element(article, prepend_title) | [
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19,321 | sorgerlab/indra | indra/literature/pubmed_client.py | get_metadata_from_xml_tree | def get_metadata_from_xml_tree(tree, get_issns_from_nlm=False,
get_abstracts=False, prepend_title=False,
mesh_annotations=False):
"""Get metadata for an XML tree containing PubmedArticle elements.
Documentation on the XML structure can be found at:
- https://www.nlm.nih.gov/bsd/licensee/elements_descriptions.html
- https://www.nlm.nih.gov/bsd/licensee/elements_alphabetical.html
Parameters
----------
tree : xml.etree.ElementTree
ElementTree containing one or more PubmedArticle elements.
get_issns_from_nlm : boolean
Look up the full list of ISSN number for the journal associated with
the article, which helps to match articles to CrossRef search results.
Defaults to False, since it slows down performance.
get_abstracts : boolean
Indicates whether to include the Pubmed abstract in the results.
prepend_title : boolean
If get_abstracts is True, specifies whether the article title should
be prepended to the abstract text.
mesh_annotations : boolean
If True, extract mesh annotations from the pubmed entries and include
in the returned data. If false, don't.
Returns
-------
dict of dicts
Dictionary indexed by PMID. Each value is a dict containing the
following fields: 'doi', 'title', 'authors', 'journal_title',
'journal_abbrev', 'journal_nlm_id', 'issn_list', 'page'.
"""
# Iterate over the articles and build the results dict
results = {}
pm_articles = tree.findall('./PubmedArticle')
for art_ix, pm_article in enumerate(pm_articles):
medline_citation = pm_article.find('./MedlineCitation')
article_info = _get_article_info(medline_citation,
pm_article.find('PubmedData'))
journal_info = _get_journal_info(medline_citation, get_issns_from_nlm)
context_info = _get_annotations(medline_citation)
# Build the result
result = {}
result.update(article_info)
result.update(journal_info)
result.update(context_info)
# Get the abstracts if requested
if get_abstracts:
abstract = _abstract_from_article_element(
medline_citation.find('Article'),
prepend_title=prepend_title
)
result['abstract'] = abstract
# Add to dict
results[article_info['pmid']] = result
return results | python | def get_metadata_from_xml_tree(tree, get_issns_from_nlm=False,
get_abstracts=False, prepend_title=False,
mesh_annotations=False):
# Iterate over the articles and build the results dict
results = {}
pm_articles = tree.findall('./PubmedArticle')
for art_ix, pm_article in enumerate(pm_articles):
medline_citation = pm_article.find('./MedlineCitation')
article_info = _get_article_info(medline_citation,
pm_article.find('PubmedData'))
journal_info = _get_journal_info(medline_citation, get_issns_from_nlm)
context_info = _get_annotations(medline_citation)
# Build the result
result = {}
result.update(article_info)
result.update(journal_info)
result.update(context_info)
# Get the abstracts if requested
if get_abstracts:
abstract = _abstract_from_article_element(
medline_citation.find('Article'),
prepend_title=prepend_title
)
result['abstract'] = abstract
# Add to dict
results[article_info['pmid']] = result
return results | [
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tree : xml.etree.ElementTree
ElementTree containing one or more PubmedArticle elements.
get_issns_from_nlm : boolean
Look up the full list of ISSN number for the journal associated with
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Indicates whether to include the Pubmed abstract in the results.
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19,322 | sorgerlab/indra | indra/literature/pubmed_client.py | get_metadata_for_ids | def get_metadata_for_ids(pmid_list, get_issns_from_nlm=False,
get_abstracts=False, prepend_title=False):
"""Get article metadata for up to 200 PMIDs from the Pubmed database.
Parameters
----------
pmid_list : list of PMIDs as strings
Can contain 1-200 PMIDs.
get_issns_from_nlm : boolean
Look up the full list of ISSN number for the journal associated with
the article, which helps to match articles to CrossRef search results.
Defaults to False, since it slows down performance.
get_abstracts : boolean
Indicates whether to include the Pubmed abstract in the results.
prepend_title : boolean
If get_abstracts is True, specifies whether the article title should
be prepended to the abstract text.
Returns
-------
dict of dicts
Dictionary indexed by PMID. Each value is a dict containing the
following fields: 'doi', 'title', 'authors', 'journal_title',
'journal_abbrev', 'journal_nlm_id', 'issn_list', 'page'.
"""
if len(pmid_list) > 200:
raise ValueError("Metadata query is limited to 200 PMIDs at a time.")
params = {'db': 'pubmed',
'retmode': 'xml',
'id': pmid_list}
tree = send_request(pubmed_fetch, params)
if tree is None:
return None
return get_metadata_from_xml_tree(tree, get_issns_from_nlm, get_abstracts,
prepend_title) | python | def get_metadata_for_ids(pmid_list, get_issns_from_nlm=False,
get_abstracts=False, prepend_title=False):
if len(pmid_list) > 200:
raise ValueError("Metadata query is limited to 200 PMIDs at a time.")
params = {'db': 'pubmed',
'retmode': 'xml',
'id': pmid_list}
tree = send_request(pubmed_fetch, params)
if tree is None:
return None
return get_metadata_from_xml_tree(tree, get_issns_from_nlm, get_abstracts,
prepend_title) | [
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Look up the full list of ISSN number for the journal associated with
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Indicates whether to include the Pubmed abstract in the results.
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dict of dicts
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19,323 | sorgerlab/indra | indra/literature/pubmed_client.py | get_issns_for_journal | def get_issns_for_journal(nlm_id):
"""Get a list of the ISSN numbers for a journal given its NLM ID.
Information on NLM XML DTDs is available at
https://www.nlm.nih.gov/databases/dtd/
"""
params = {'db': 'nlmcatalog',
'retmode': 'xml',
'id': nlm_id}
tree = send_request(pubmed_fetch, params)
if tree is None:
return None
issn_list = tree.findall('.//ISSN')
issn_linking = tree.findall('.//ISSNLinking')
issns = issn_list + issn_linking
# No ISSNs found!
if not issns:
return None
else:
return [issn.text for issn in issns] | python | def get_issns_for_journal(nlm_id):
params = {'db': 'nlmcatalog',
'retmode': 'xml',
'id': nlm_id}
tree = send_request(pubmed_fetch, params)
if tree is None:
return None
issn_list = tree.findall('.//ISSN')
issn_linking = tree.findall('.//ISSNLinking')
issns = issn_list + issn_linking
# No ISSNs found!
if not issns:
return None
else:
return [issn.text for issn in issns] | [
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19,324 | sorgerlab/indra | indra/explanation/model_checker.py | remove_im_params | def remove_im_params(model, im):
"""Remove parameter nodes from the influence map.
Parameters
----------
model : pysb.core.Model
PySB model.
im : networkx.MultiDiGraph
Influence map.
Returns
-------
networkx.MultiDiGraph
Influence map with the parameter nodes removed.
"""
for param in model.parameters:
# If the node doesn't exist e.g., it may have already been removed),
# skip over the parameter without error
try:
im.remove_node(param.name)
except:
pass | python | def remove_im_params(model, im):
for param in model.parameters:
# If the node doesn't exist e.g., it may have already been removed),
# skip over the parameter without error
try:
im.remove_node(param.name)
except:
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19,325 | sorgerlab/indra | indra/explanation/model_checker.py | _get_signed_predecessors | def _get_signed_predecessors(im, node, polarity):
"""Get upstream nodes in the influence map.
Return the upstream nodes along with the overall polarity of the path
to that node by account for the polarity of the path to the given node
and the polarity of the edge between the given node and its immediate
predecessors.
Parameters
----------
im : networkx.MultiDiGraph
Graph containing the influence map.
node : str
The node (rule name) in the influence map to get predecessors (upstream
nodes) for.
polarity : int
Polarity of the overall path to the given node.
Returns
-------
generator of tuples, (node, polarity)
Each tuple returned contains two elements, a node (string) and the
polarity of the overall path (int) to that node.
"""
signed_pred_list = []
for pred in im.predecessors(node):
pred_edge = (pred, node)
yield (pred, _get_edge_sign(im, pred_edge) * polarity) | python | def _get_signed_predecessors(im, node, polarity):
signed_pred_list = []
for pred in im.predecessors(node):
pred_edge = (pred, node)
yield (pred, _get_edge_sign(im, pred_edge) * polarity) | [
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19,326 | sorgerlab/indra | indra/explanation/model_checker.py | _get_edge_sign | def _get_edge_sign(im, edge):
"""Get the polarity of the influence by examining the edge sign."""
edge_data = im[edge[0]][edge[1]]
# Handle possible multiple edges between nodes
signs = list(set([v['sign'] for v in edge_data.values()
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if len(signs) > 1:
logger.warning("Edge %s has conflicting polarities; choosing "
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sign = 1
else:
sign = signs[0]
if sign is None:
raise Exception('No sign attribute for edge.')
elif abs(sign) == 1:
return sign
else:
raise Exception('Unexpected edge sign: %s' % edge.attr['sign']) | python | def _get_edge_sign(im, edge):
edge_data = im[edge[0]][edge[1]]
# Handle possible multiple edges between nodes
signs = list(set([v['sign'] for v in edge_data.values()
if v.get('sign')]))
if len(signs) > 1:
logger.warning("Edge %s has conflicting polarities; choosing "
"positive polarity by default" % str(edge))
sign = 1
else:
sign = signs[0]
if sign is None:
raise Exception('No sign attribute for edge.')
elif abs(sign) == 1:
return sign
else:
raise Exception('Unexpected edge sign: %s' % edge.attr['sign']) | [
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19,327 | sorgerlab/indra | indra/explanation/model_checker.py | _add_modification_to_agent | def _add_modification_to_agent(agent, mod_type, residue, position):
"""Add a modification condition to an Agent."""
new_mod = ModCondition(mod_type, residue, position)
# Check if this modification already exists
for old_mod in agent.mods:
if old_mod.equals(new_mod):
return agent
new_agent = deepcopy(agent)
new_agent.mods.append(new_mod)
return new_agent | python | def _add_modification_to_agent(agent, mod_type, residue, position):
new_mod = ModCondition(mod_type, residue, position)
# Check if this modification already exists
for old_mod in agent.mods:
if old_mod.equals(new_mod):
return agent
new_agent = deepcopy(agent)
new_agent.mods.append(new_mod)
return new_agent | [
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19,328 | sorgerlab/indra | indra/explanation/model_checker.py | _match_lhs | def _match_lhs(cp, rules):
"""Get rules with a left-hand side matching the given ComplexPattern."""
rule_matches = []
for rule in rules:
reactant_pattern = rule.rule_expression.reactant_pattern
for rule_cp in reactant_pattern.complex_patterns:
if _cp_embeds_into(rule_cp, cp):
rule_matches.append(rule)
break
return rule_matches | python | def _match_lhs(cp, rules):
rule_matches = []
for rule in rules:
reactant_pattern = rule.rule_expression.reactant_pattern
for rule_cp in reactant_pattern.complex_patterns:
if _cp_embeds_into(rule_cp, cp):
rule_matches.append(rule)
break
return rule_matches | [
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19,329 | sorgerlab/indra | indra/explanation/model_checker.py | _cp_embeds_into | def _cp_embeds_into(cp1, cp2):
"""Check that any state in ComplexPattern2 is matched in ComplexPattern1.
"""
# Check that any state in cp2 is matched in cp1
# If the thing we're matching to is just a monomer pattern, that makes
# things easier--we just need to find the corresponding monomer pattern
# in cp1
if cp1 is None or cp2 is None:
return False
cp1 = as_complex_pattern(cp1)
cp2 = as_complex_pattern(cp2)
if len(cp2.monomer_patterns) == 1:
mp2 = cp2.monomer_patterns[0]
# Iterate over the monomer patterns in cp1 and see if there is one
# that has the same name
for mp1 in cp1.monomer_patterns:
if _mp_embeds_into(mp1, mp2):
return True
return False | python | def _cp_embeds_into(cp1, cp2):
# Check that any state in cp2 is matched in cp1
# If the thing we're matching to is just a monomer pattern, that makes
# things easier--we just need to find the corresponding monomer pattern
# in cp1
if cp1 is None or cp2 is None:
return False
cp1 = as_complex_pattern(cp1)
cp2 = as_complex_pattern(cp2)
if len(cp2.monomer_patterns) == 1:
mp2 = cp2.monomer_patterns[0]
# Iterate over the monomer patterns in cp1 and see if there is one
# that has the same name
for mp1 in cp1.monomer_patterns:
if _mp_embeds_into(mp1, mp2):
return True
return False | [
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19,330 | sorgerlab/indra | indra/explanation/model_checker.py | _mp_embeds_into | def _mp_embeds_into(mp1, mp2):
"""Check that conditions in MonomerPattern2 are met in MonomerPattern1."""
sc_matches = []
if mp1.monomer.name != mp2.monomer.name:
return False
# Check that all conditions in mp2 are met in mp1
for site_name, site_state in mp2.site_conditions.items():
if site_name not in mp1.site_conditions or \
site_state != mp1.site_conditions[site_name]:
return False
return True | python | def _mp_embeds_into(mp1, mp2):
sc_matches = []
if mp1.monomer.name != mp2.monomer.name:
return False
# Check that all conditions in mp2 are met in mp1
for site_name, site_state in mp2.site_conditions.items():
if site_name not in mp1.site_conditions or \
site_state != mp1.site_conditions[site_name]:
return False
return True | [
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19,331 | sorgerlab/indra | indra/explanation/model_checker.py | _monomer_pattern_label | def _monomer_pattern_label(mp):
"""Return a string label for a MonomerPattern."""
site_strs = []
for site, cond in mp.site_conditions.items():
if isinstance(cond, tuple) or isinstance(cond, list):
assert len(cond) == 2
if cond[1] == WILD:
site_str = '%s_%s' % (site, cond[0])
else:
site_str = '%s_%s%s' % (site, cond[0], cond[1])
elif isinstance(cond, numbers.Real):
continue
else:
site_str = '%s_%s' % (site, cond)
site_strs.append(site_str)
return '%s_%s' % (mp.monomer.name, '_'.join(site_strs)) | python | def _monomer_pattern_label(mp):
site_strs = []
for site, cond in mp.site_conditions.items():
if isinstance(cond, tuple) or isinstance(cond, list):
assert len(cond) == 2
if cond[1] == WILD:
site_str = '%s_%s' % (site, cond[0])
else:
site_str = '%s_%s%s' % (site, cond[0], cond[1])
elif isinstance(cond, numbers.Real):
continue
else:
site_str = '%s_%s' % (site, cond)
site_strs.append(site_str)
return '%s_%s' % (mp.monomer.name, '_'.join(site_strs)) | [
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19,332 | sorgerlab/indra | indra/explanation/model_checker.py | _stmt_from_rule | def _stmt_from_rule(model, rule_name, stmts):
"""Return the INDRA Statement corresponding to a given rule by name."""
stmt_uuid = None
for ann in model.annotations:
if ann.predicate == 'from_indra_statement':
if ann.subject == rule_name:
stmt_uuid = ann.object
break
if stmt_uuid:
for stmt in stmts:
if stmt.uuid == stmt_uuid:
return stmt | python | def _stmt_from_rule(model, rule_name, stmts):
stmt_uuid = None
for ann in model.annotations:
if ann.predicate == 'from_indra_statement':
if ann.subject == rule_name:
stmt_uuid = ann.object
break
if stmt_uuid:
for stmt in stmts:
if stmt.uuid == stmt_uuid:
return stmt | [
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19,333 | sorgerlab/indra | indra/explanation/model_checker.py | ModelChecker.generate_im | def generate_im(self, model):
"""Return a graph representing the influence map generated by Kappa
Parameters
----------
model : pysb.Model
The PySB model whose influence map is to be generated
Returns
-------
graph : networkx.MultiDiGraph
A MultiDiGraph representing the influence map
"""
kappa = kappy.KappaStd()
model_str = export.export(model, 'kappa')
kappa.add_model_string(model_str)
kappa.project_parse()
imap = kappa.analyses_influence_map(accuracy='medium')
graph = im_json_to_graph(imap)
return graph | python | def generate_im(self, model):
kappa = kappy.KappaStd()
model_str = export.export(model, 'kappa')
kappa.add_model_string(model_str)
kappa.project_parse()
imap = kappa.analyses_influence_map(accuracy='medium')
graph = im_json_to_graph(imap)
return graph | [
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model : pysb.Model
The PySB model whose influence map is to be generated
Returns
-------
graph : networkx.MultiDiGraph
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19,334 | sorgerlab/indra | indra/explanation/model_checker.py | ModelChecker.draw_im | def draw_im(self, fname):
"""Draw and save the influence map in a file.
Parameters
----------
fname : str
The name of the file to save the influence map in.
The extension of the file will determine the file format,
typically png or pdf.
"""
im = self.get_im()
im_agraph = nx.nx_agraph.to_agraph(im)
im_agraph.draw(fname, prog='dot') | python | def draw_im(self, fname):
im = self.get_im()
im_agraph = nx.nx_agraph.to_agraph(im)
im_agraph.draw(fname, prog='dot') | [
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fname : str
The name of the file to save the influence map in.
The extension of the file will determine the file format,
typically png or pdf. | [
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19,335 | sorgerlab/indra | indra/explanation/model_checker.py | ModelChecker.get_im | def get_im(self, force_update=False):
"""Get the influence map for the model, generating it if necessary.
Parameters
----------
force_update : bool
Whether to generate the influence map when the function is called.
If False, returns the previously generated influence map if
available. Defaults to True.
Returns
-------
networkx MultiDiGraph object containing the influence map.
The influence map can be rendered as a pdf using the dot layout
program as follows::
im_agraph = nx.nx_agraph.to_agraph(influence_map)
im_agraph.draw('influence_map.pdf', prog='dot')
"""
if self._im and not force_update:
return self._im
if not self.model:
raise Exception("Cannot get influence map if there is no model.")
def add_obs_for_agent(agent):
obj_mps = list(pa.grounded_monomer_patterns(self.model, agent))
if not obj_mps:
logger.debug('No monomer patterns found in model for agent %s, '
'skipping' % agent)
return
obs_list = []
for obj_mp in obj_mps:
obs_name = _monomer_pattern_label(obj_mp) + '_obs'
# Add the observable
obj_obs = Observable(obs_name, obj_mp, _export=False)
obs_list.append(obs_name)
try:
self.model.add_component(obj_obs)
except ComponentDuplicateNameError as e:
pass
return obs_list
# Create observables for all statements to check, and add to model
# Remove any existing observables in the model
self.model.observables = ComponentSet([])
for stmt in self.statements:
# Generate observables for Modification statements
if isinstance(stmt, Modification):
mod_condition_name = modclass_to_modtype[stmt.__class__]
if isinstance(stmt, RemoveModification):
mod_condition_name = modtype_to_inverse[mod_condition_name]
# Add modification to substrate agent
modified_sub = _add_modification_to_agent(stmt.sub,
mod_condition_name, stmt.residue,
stmt.position)
obs_list = add_obs_for_agent(modified_sub)
# Associate this statement with this observable
self.stmt_to_obs[stmt] = obs_list
# Generate observables for Activation/Inhibition statements
elif isinstance(stmt, RegulateActivity):
regulated_obj, polarity = \
_add_activity_to_agent(stmt.obj, stmt.obj_activity,
stmt.is_activation)
obs_list = add_obs_for_agent(regulated_obj)
# Associate this statement with this observable
self.stmt_to_obs[stmt] = obs_list
elif isinstance(stmt, RegulateAmount):
obs_list = add_obs_for_agent(stmt.obj)
self.stmt_to_obs[stmt] = obs_list
elif isinstance(stmt, Influence):
obs_list = add_obs_for_agent(stmt.obj.concept)
self.stmt_to_obs[stmt] = obs_list
# Add observables for each agent
for ag in self.agent_obs:
obs_list = add_obs_for_agent(ag)
self.agent_to_obs[ag] = obs_list
logger.info("Generating influence map")
self._im = self.generate_im(self.model)
#self._im.is_multigraph = lambda: False
# Now, for every rule in the model, check if there are any observables
# downstream; alternatively, for every observable in the model, get a
# list of rules.
# We'll need the dictionary to check if nodes are observables
node_attributes = nx.get_node_attributes(self._im, 'node_type')
for rule in self.model.rules:
obs_list = []
# Get successors of the rule node
for neighb in self._im.neighbors(rule.name):
# Check if the node is an observable
if node_attributes[neighb] != 'variable':
continue
# Get the edge and check the polarity
edge_sign = _get_edge_sign(self._im, (rule.name, neighb))
obs_list.append((neighb, edge_sign))
self.rule_obs_dict[rule.name] = obs_list
return self._im | python | def get_im(self, force_update=False):
if self._im and not force_update:
return self._im
if not self.model:
raise Exception("Cannot get influence map if there is no model.")
def add_obs_for_agent(agent):
obj_mps = list(pa.grounded_monomer_patterns(self.model, agent))
if not obj_mps:
logger.debug('No monomer patterns found in model for agent %s, '
'skipping' % agent)
return
obs_list = []
for obj_mp in obj_mps:
obs_name = _monomer_pattern_label(obj_mp) + '_obs'
# Add the observable
obj_obs = Observable(obs_name, obj_mp, _export=False)
obs_list.append(obs_name)
try:
self.model.add_component(obj_obs)
except ComponentDuplicateNameError as e:
pass
return obs_list
# Create observables for all statements to check, and add to model
# Remove any existing observables in the model
self.model.observables = ComponentSet([])
for stmt in self.statements:
# Generate observables for Modification statements
if isinstance(stmt, Modification):
mod_condition_name = modclass_to_modtype[stmt.__class__]
if isinstance(stmt, RemoveModification):
mod_condition_name = modtype_to_inverse[mod_condition_name]
# Add modification to substrate agent
modified_sub = _add_modification_to_agent(stmt.sub,
mod_condition_name, stmt.residue,
stmt.position)
obs_list = add_obs_for_agent(modified_sub)
# Associate this statement with this observable
self.stmt_to_obs[stmt] = obs_list
# Generate observables for Activation/Inhibition statements
elif isinstance(stmt, RegulateActivity):
regulated_obj, polarity = \
_add_activity_to_agent(stmt.obj, stmt.obj_activity,
stmt.is_activation)
obs_list = add_obs_for_agent(regulated_obj)
# Associate this statement with this observable
self.stmt_to_obs[stmt] = obs_list
elif isinstance(stmt, RegulateAmount):
obs_list = add_obs_for_agent(stmt.obj)
self.stmt_to_obs[stmt] = obs_list
elif isinstance(stmt, Influence):
obs_list = add_obs_for_agent(stmt.obj.concept)
self.stmt_to_obs[stmt] = obs_list
# Add observables for each agent
for ag in self.agent_obs:
obs_list = add_obs_for_agent(ag)
self.agent_to_obs[ag] = obs_list
logger.info("Generating influence map")
self._im = self.generate_im(self.model)
#self._im.is_multigraph = lambda: False
# Now, for every rule in the model, check if there are any observables
# downstream; alternatively, for every observable in the model, get a
# list of rules.
# We'll need the dictionary to check if nodes are observables
node_attributes = nx.get_node_attributes(self._im, 'node_type')
for rule in self.model.rules:
obs_list = []
# Get successors of the rule node
for neighb in self._im.neighbors(rule.name):
# Check if the node is an observable
if node_attributes[neighb] != 'variable':
continue
# Get the edge and check the polarity
edge_sign = _get_edge_sign(self._im, (rule.name, neighb))
obs_list.append((neighb, edge_sign))
self.rule_obs_dict[rule.name] = obs_list
return self._im | [
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Returns
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networkx MultiDiGraph object containing the influence map.
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19,336 | sorgerlab/indra | indra/explanation/model_checker.py | ModelChecker.check_model | def check_model(self, max_paths=1, max_path_length=5):
"""Check all the statements added to the ModelChecker.
Parameters
----------
max_paths : Optional[int]
The maximum number of specific paths to return for each Statement
to be explained. Default: 1
max_path_length : Optional[int]
The maximum length of specific paths to return. Default: 5
Returns
-------
list of (Statement, PathResult)
Each tuple contains the Statement checked against the model and
a PathResult object describing the results of model checking.
"""
results = []
for stmt in self.statements:
result = self.check_statement(stmt, max_paths, max_path_length)
results.append((stmt, result))
return results | python | def check_model(self, max_paths=1, max_path_length=5):
results = []
for stmt in self.statements:
result = self.check_statement(stmt, max_paths, max_path_length)
results.append((stmt, result))
return results | [
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max_path_length : Optional[int]
The maximum length of specific paths to return. Default: 5
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19,337 | sorgerlab/indra | indra/explanation/model_checker.py | ModelChecker.check_statement | def check_statement(self, stmt, max_paths=1, max_path_length=5):
"""Check a single Statement against the model.
Parameters
----------
stmt : indra.statements.Statement
The Statement to check.
max_paths : Optional[int]
The maximum number of specific paths to return for each Statement
to be explained. Default: 1
max_path_length : Optional[int]
The maximum length of specific paths to return. Default: 5
Returns
-------
boolean
True if the model satisfies the Statement.
"""
# Make sure the influence map is initialized
self.get_im()
# Check if this is one of the statement types that we can check
if not isinstance(stmt, (Modification, RegulateAmount,
RegulateActivity, Influence)):
return PathResult(False, 'STATEMENT_TYPE_NOT_HANDLED',
max_paths, max_path_length)
# Get the polarity for the statement
if isinstance(stmt, Modification):
target_polarity = -1 if isinstance(stmt, RemoveModification) else 1
elif isinstance(stmt, RegulateActivity):
target_polarity = 1 if stmt.is_activation else -1
elif isinstance(stmt, RegulateAmount):
target_polarity = -1 if isinstance(stmt, DecreaseAmount) else 1
elif isinstance(stmt, Influence):
target_polarity = -1 if stmt.overall_polarity() == -1 else 1
# Get the subject and object (works also for Modifications)
subj, obj = stmt.agent_list()
# Get a list of monomer patterns matching the subject FIXME Currently
# this will match rules with the corresponding monomer pattern on it.
# In future, this statement should (possibly) also match rules in which
# 1) the agent is in its active form, or 2) the agent is tagged as the
# enzyme in a rule of the appropriate activity (e.g., a phosphorylation
# rule) FIXME
if subj is not None:
subj_mps = list(pa.grounded_monomer_patterns(self.model, subj,
ignore_activities=True))
if not subj_mps:
logger.debug('No monomers found corresponding to agent %s' %
subj)
return PathResult(False, 'SUBJECT_MONOMERS_NOT_FOUND',
max_paths, max_path_length)
else:
subj_mps = [None]
# Observables may not be found for an activation since there may be no
# rule in the model activating the object, and the object may not have
# an "active" site of the appropriate type
obs_names = self.stmt_to_obs[stmt]
if not obs_names:
logger.debug("No observables for stmt %s, returning False" % stmt)
return PathResult(False, 'OBSERVABLES_NOT_FOUND',
max_paths, max_path_length)
for subj_mp, obs_name in itertools.product(subj_mps, obs_names):
# NOTE: Returns on the path found for the first enz_mp/obs combo
result = self._find_im_paths(subj_mp, obs_name, target_polarity,
max_paths, max_path_length)
# If a path was found, then we return it; otherwise, that means
# there was no path for this observable, so we have to try the next
# one
if result.path_found:
return result
# If we got here, then there was no path for any observable
return PathResult(False, 'NO_PATHS_FOUND',
max_paths, max_path_length) | python | def check_statement(self, stmt, max_paths=1, max_path_length=5):
# Make sure the influence map is initialized
self.get_im()
# Check if this is one of the statement types that we can check
if not isinstance(stmt, (Modification, RegulateAmount,
RegulateActivity, Influence)):
return PathResult(False, 'STATEMENT_TYPE_NOT_HANDLED',
max_paths, max_path_length)
# Get the polarity for the statement
if isinstance(stmt, Modification):
target_polarity = -1 if isinstance(stmt, RemoveModification) else 1
elif isinstance(stmt, RegulateActivity):
target_polarity = 1 if stmt.is_activation else -1
elif isinstance(stmt, RegulateAmount):
target_polarity = -1 if isinstance(stmt, DecreaseAmount) else 1
elif isinstance(stmt, Influence):
target_polarity = -1 if stmt.overall_polarity() == -1 else 1
# Get the subject and object (works also for Modifications)
subj, obj = stmt.agent_list()
# Get a list of monomer patterns matching the subject FIXME Currently
# this will match rules with the corresponding monomer pattern on it.
# In future, this statement should (possibly) also match rules in which
# 1) the agent is in its active form, or 2) the agent is tagged as the
# enzyme in a rule of the appropriate activity (e.g., a phosphorylation
# rule) FIXME
if subj is not None:
subj_mps = list(pa.grounded_monomer_patterns(self.model, subj,
ignore_activities=True))
if not subj_mps:
logger.debug('No monomers found corresponding to agent %s' %
subj)
return PathResult(False, 'SUBJECT_MONOMERS_NOT_FOUND',
max_paths, max_path_length)
else:
subj_mps = [None]
# Observables may not be found for an activation since there may be no
# rule in the model activating the object, and the object may not have
# an "active" site of the appropriate type
obs_names = self.stmt_to_obs[stmt]
if not obs_names:
logger.debug("No observables for stmt %s, returning False" % stmt)
return PathResult(False, 'OBSERVABLES_NOT_FOUND',
max_paths, max_path_length)
for subj_mp, obs_name in itertools.product(subj_mps, obs_names):
# NOTE: Returns on the path found for the first enz_mp/obs combo
result = self._find_im_paths(subj_mp, obs_name, target_polarity,
max_paths, max_path_length)
# If a path was found, then we return it; otherwise, that means
# there was no path for this observable, so we have to try the next
# one
if result.path_found:
return result
# If we got here, then there was no path for any observable
return PathResult(False, 'NO_PATHS_FOUND',
max_paths, max_path_length) | [
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Parameters
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The Statement to check.
max_paths : Optional[int]
The maximum number of specific paths to return for each Statement
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max_path_length : Optional[int]
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19,338 | sorgerlab/indra | indra/explanation/model_checker.py | ModelChecker.score_paths | def score_paths(self, paths, agents_values, loss_of_function=False,
sigma=0.15, include_final_node=False):
"""Return scores associated with a given set of paths.
Parameters
----------
paths : list[list[tuple[str, int]]]
A list of paths obtained from path finding. Each path is a list
of tuples (which are edges in the path), with the first element
of the tuple the name of a rule, and the second element its
polarity in the path.
agents_values : dict[indra.statements.Agent, float]
A dictionary of INDRA Agents and their corresponding measured
value in a given experimental condition.
loss_of_function : Optional[boolean]
If True, flip the polarity of the path. For instance, if the effect
of an inhibitory drug is explained, set this to True.
Default: False
sigma : Optional[float]
The estimated standard deviation for the normally distributed
measurement error in the observation model used to score paths
with respect to data. Default: 0.15
include_final_node : Optional[boolean]
Determines whether the final node of the path is included in the
score. Default: False
"""
obs_model = lambda x: scipy.stats.norm(x, sigma)
# Build up dict mapping observables to values
obs_dict = {}
for ag, val in agents_values.items():
obs_list = self.agent_to_obs[ag]
if obs_list is not None:
for obs in obs_list:
obs_dict[obs] = val
# For every path...
path_scores = []
for path in paths:
logger.info('------')
logger.info("Scoring path:")
logger.info(path)
# Look at every node in the path, excluding the final
# observable...
path_score = 0
last_path_node_index = -1 if include_final_node else -2
for node, sign in path[:last_path_node_index]:
# ...and for each node check the sign to see if it matches the
# data. So the first thing is to look at what's downstream
# of the rule
# affected_obs is a list of observable names alogn
for affected_obs, rule_obs_sign in self.rule_obs_dict[node]:
flip_polarity = -1 if loss_of_function else 1
pred_sign = sign * rule_obs_sign * flip_polarity
# Check to see if this observable is in the data
logger.info('%s %s: effect %s %s' %
(node, sign, affected_obs, pred_sign))
measured_val = obs_dict.get(affected_obs)
if measured_val:
# For negative predictions use CDF (prob that given
# measured value, true value lies below 0)
if pred_sign <= 0:
prob_correct = obs_model(measured_val).logcdf(0)
# For positive predictions, use log survival function
# (SF = 1 - CDF, i.e., prob that true value is
# above 0)
else:
prob_correct = obs_model(measured_val).logsf(0)
logger.info('Actual: %s, Log Probability: %s' %
(measured_val, prob_correct))
path_score += prob_correct
if not self.rule_obs_dict[node]:
logger.info('%s %s' % (node, sign))
prob_correct = obs_model(0).logcdf(0)
logger.info('Unmeasured node, Log Probability: %s' %
(prob_correct))
path_score += prob_correct
# Normalized path
#path_score = path_score / len(path)
logger.info("Path score: %s" % path_score)
path_scores.append(path_score)
path_tuples = list(zip(paths, path_scores))
# Sort first by path length
sorted_by_length = sorted(path_tuples, key=lambda x: len(x[0]))
# Sort by probability; sort in reverse order to large values
# (higher probabilities) are ranked higher
scored_paths = sorted(sorted_by_length, key=lambda x: x[1],
reverse=True)
return scored_paths | python | def score_paths(self, paths, agents_values, loss_of_function=False,
sigma=0.15, include_final_node=False):
obs_model = lambda x: scipy.stats.norm(x, sigma)
# Build up dict mapping observables to values
obs_dict = {}
for ag, val in agents_values.items():
obs_list = self.agent_to_obs[ag]
if obs_list is not None:
for obs in obs_list:
obs_dict[obs] = val
# For every path...
path_scores = []
for path in paths:
logger.info('------')
logger.info("Scoring path:")
logger.info(path)
# Look at every node in the path, excluding the final
# observable...
path_score = 0
last_path_node_index = -1 if include_final_node else -2
for node, sign in path[:last_path_node_index]:
# ...and for each node check the sign to see if it matches the
# data. So the first thing is to look at what's downstream
# of the rule
# affected_obs is a list of observable names alogn
for affected_obs, rule_obs_sign in self.rule_obs_dict[node]:
flip_polarity = -1 if loss_of_function else 1
pred_sign = sign * rule_obs_sign * flip_polarity
# Check to see if this observable is in the data
logger.info('%s %s: effect %s %s' %
(node, sign, affected_obs, pred_sign))
measured_val = obs_dict.get(affected_obs)
if measured_val:
# For negative predictions use CDF (prob that given
# measured value, true value lies below 0)
if pred_sign <= 0:
prob_correct = obs_model(measured_val).logcdf(0)
# For positive predictions, use log survival function
# (SF = 1 - CDF, i.e., prob that true value is
# above 0)
else:
prob_correct = obs_model(measured_val).logsf(0)
logger.info('Actual: %s, Log Probability: %s' %
(measured_val, prob_correct))
path_score += prob_correct
if not self.rule_obs_dict[node]:
logger.info('%s %s' % (node, sign))
prob_correct = obs_model(0).logcdf(0)
logger.info('Unmeasured node, Log Probability: %s' %
(prob_correct))
path_score += prob_correct
# Normalized path
#path_score = path_score / len(path)
logger.info("Path score: %s" % path_score)
path_scores.append(path_score)
path_tuples = list(zip(paths, path_scores))
# Sort first by path length
sorted_by_length = sorted(path_tuples, key=lambda x: len(x[0]))
# Sort by probability; sort in reverse order to large values
# (higher probabilities) are ranked higher
scored_paths = sorted(sorted_by_length, key=lambda x: x[1],
reverse=True)
return scored_paths | [
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A list of paths obtained from path finding. Each path is a list
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polarity in the path.
agents_values : dict[indra.statements.Agent, float]
A dictionary of INDRA Agents and their corresponding measured
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loss_of_function : Optional[boolean]
If True, flip the polarity of the path. For instance, if the effect
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Default: False
sigma : Optional[float]
The estimated standard deviation for the normally distributed
measurement error in the observation model used to score paths
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include_final_node : Optional[boolean]
Determines whether the final node of the path is included in the
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19,339 | sorgerlab/indra | indra/explanation/model_checker.py | ModelChecker.prune_influence_map | def prune_influence_map(self):
"""Remove edges between rules causing problematic non-transitivity.
First, all self-loops are removed. After this initial step, edges are
removed between rules when they share *all* child nodes except for each
other; that is, they have a mutual relationship with each other and
share all of the same children.
Note that edges must be removed in batch at the end to prevent edge
removal from affecting the lists of rule children during the comparison
process.
"""
im = self.get_im()
# First, remove all self-loops
logger.info('Removing self loops')
edges_to_remove = []
for e in im.edges():
if e[0] == e[1]:
logger.info('Removing self loop: %s', e)
edges_to_remove.append((e[0], e[1]))
# Now remove all the edges to be removed with a single call
im.remove_edges_from(edges_to_remove)
# Remove parameter nodes from influence map
remove_im_params(self.model, im)
# Now compare nodes pairwise and look for overlap between child nodes
logger.info('Get successorts of each node')
succ_dict = {}
for node in im.nodes():
succ_dict[node] = set(im.successors(node))
# Sort and then group nodes by number of successors
logger.info('Compare combinations of successors')
group_key_fun = lambda x: len(succ_dict[x])
nodes_sorted = sorted(im.nodes(), key=group_key_fun)
groups = itertools.groupby(nodes_sorted, key=group_key_fun)
# Now iterate over each group and then construct combinations
# within the group to check for shared sucessors
edges_to_remove = []
for gix, group in groups:
combos = itertools.combinations(group, 2)
for ix, (p1, p2) in enumerate(combos):
# Children are identical except for mutual relationship
if succ_dict[p1].difference(succ_dict[p2]) == set([p2]) and \
succ_dict[p2].difference(succ_dict[p1]) == set([p1]):
for u, v in ((p1, p2), (p2, p1)):
edges_to_remove.append((u, v))
logger.debug('Will remove edge (%s, %s)', u, v)
logger.info('Removing %d edges from influence map' %
len(edges_to_remove))
# Now remove all the edges to be removed with a single call
im.remove_edges_from(edges_to_remove) | python | def prune_influence_map(self):
im = self.get_im()
# First, remove all self-loops
logger.info('Removing self loops')
edges_to_remove = []
for e in im.edges():
if e[0] == e[1]:
logger.info('Removing self loop: %s', e)
edges_to_remove.append((e[0], e[1]))
# Now remove all the edges to be removed with a single call
im.remove_edges_from(edges_to_remove)
# Remove parameter nodes from influence map
remove_im_params(self.model, im)
# Now compare nodes pairwise and look for overlap between child nodes
logger.info('Get successorts of each node')
succ_dict = {}
for node in im.nodes():
succ_dict[node] = set(im.successors(node))
# Sort and then group nodes by number of successors
logger.info('Compare combinations of successors')
group_key_fun = lambda x: len(succ_dict[x])
nodes_sorted = sorted(im.nodes(), key=group_key_fun)
groups = itertools.groupby(nodes_sorted, key=group_key_fun)
# Now iterate over each group and then construct combinations
# within the group to check for shared sucessors
edges_to_remove = []
for gix, group in groups:
combos = itertools.combinations(group, 2)
for ix, (p1, p2) in enumerate(combos):
# Children are identical except for mutual relationship
if succ_dict[p1].difference(succ_dict[p2]) == set([p2]) and \
succ_dict[p2].difference(succ_dict[p1]) == set([p1]):
for u, v in ((p1, p2), (p2, p1)):
edges_to_remove.append((u, v))
logger.debug('Will remove edge (%s, %s)', u, v)
logger.info('Removing %d edges from influence map' %
len(edges_to_remove))
# Now remove all the edges to be removed with a single call
im.remove_edges_from(edges_to_remove) | [
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19,340 | sorgerlab/indra | indra/explanation/model_checker.py | ModelChecker.prune_influence_map_subj_obj | def prune_influence_map_subj_obj(self):
"""Prune influence map to include only edges where the object of the
upstream rule matches the subject of the downstream rule."""
def get_rule_info(r):
result = {}
for ann in self.model.annotations:
if ann.subject == r:
if ann.predicate == 'rule_has_subject':
result['subject'] = ann.object
elif ann.predicate == 'rule_has_object':
result['object'] = ann.object
return result
im = self.get_im()
rules = im.nodes()
edges_to_prune = []
for r1, r2 in itertools.permutations(rules, 2):
if (r1, r2) not in im.edges():
continue
r1_info = get_rule_info(r1)
r2_info = get_rule_info(r2)
if 'object' not in r1_info or 'subject' not in r2_info:
continue
if r1_info['object'] != r2_info['subject']:
logger.info("Removing edge %s --> %s" % (r1, r2))
edges_to_prune.append((r1, r2))
im.remove_edges_from(edges_to_prune) | python | def prune_influence_map_subj_obj(self):
def get_rule_info(r):
result = {}
for ann in self.model.annotations:
if ann.subject == r:
if ann.predicate == 'rule_has_subject':
result['subject'] = ann.object
elif ann.predicate == 'rule_has_object':
result['object'] = ann.object
return result
im = self.get_im()
rules = im.nodes()
edges_to_prune = []
for r1, r2 in itertools.permutations(rules, 2):
if (r1, r2) not in im.edges():
continue
r1_info = get_rule_info(r1)
r2_info = get_rule_info(r2)
if 'object' not in r1_info or 'subject' not in r2_info:
continue
if r1_info['object'] != r2_info['subject']:
logger.info("Removing edge %s --> %s" % (r1, r2))
edges_to_prune.append((r1, r2))
im.remove_edges_from(edges_to_prune) | [
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19,341 | sorgerlab/indra | indra/tools/reading/util/reporter.py | Reporter.add_section | def add_section(self, section_name):
"""Create a section of the report, to be headed by section_name
Text and images can be added by using the `section` argument of the
`add_text` and `add_image` methods. Sections can also be ordered by
using the `set_section_order` method.
By default, text and images that have no section will be placed after
all the sections, in the order they were added. This behavior may be
altered using the `sections_first` attribute of the `make_report`
method.
"""
self.section_headings.append(section_name)
if section_name in self.sections:
raise ValueError("Section %s already exists." % section_name)
self.sections[section_name] = []
return | python | def add_section(self, section_name):
self.section_headings.append(section_name)
if section_name in self.sections:
raise ValueError("Section %s already exists." % section_name)
self.sections[section_name] = []
return | [
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19,342 | sorgerlab/indra | indra/tools/reading/util/reporter.py | Reporter.set_section_order | def set_section_order(self, section_name_list):
"""Set the order of the sections, which are by default unorderd.
Any unlisted sections that exist will be placed at the end of the
document in no particular order.
"""
self.section_headings = section_name_list[:]
for section_name in self.sections.keys():
if section_name not in section_name_list:
self.section_headings.append(section_name)
return | python | def set_section_order(self, section_name_list):
self.section_headings = section_name_list[:]
for section_name in self.sections.keys():
if section_name not in section_name_list:
self.section_headings.append(section_name)
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19,343 | sorgerlab/indra | indra/tools/reading/util/reporter.py | Reporter.add_text | def add_text(self, text, *args, **kwargs):
"""Add text to the document.
Text is shown on the final document in the order it is added, either
within the given section or as part of the un-sectioned list of content.
Parameters
----------
text : str
The text to be added.
style : str
Choose the style of the text. Options include 'Normal', 'Code',
'Title', 'h1'. For others, see `getSampleStyleSheet` from
`reportlab.lib.styles`.
space : tuple (num spaces, font size)
The number and size of spaces to follow this section of text.
Default is (1, 12).
fontsize : int
The integer font size of the text (e.g. 12 for 12 point font).
Default is 12.
alignment : str
The alignment of the text. Options include 'left', 'right', and
'center'. Default is 'left'.
section : str
(This must be a keyword) Select a section in which to place this
text. Default is None, in which case the text will be simply be
added to a default list of text and images.
"""
# Pull down some kwargs.
section_name = kwargs.pop('section', None)
# Actually do the formatting.
para, sp = self._preformat_text(text, *args, **kwargs)
# Select the appropriate list to update
if section_name is None:
relevant_list = self.story
else:
relevant_list = self.sections[section_name]
# Add the new content to list.
relevant_list.append(para)
relevant_list.append(sp)
return | python | def add_text(self, text, *args, **kwargs):
# Pull down some kwargs.
section_name = kwargs.pop('section', None)
# Actually do the formatting.
para, sp = self._preformat_text(text, *args, **kwargs)
# Select the appropriate list to update
if section_name is None:
relevant_list = self.story
else:
relevant_list = self.sections[section_name]
# Add the new content to list.
relevant_list.append(para)
relevant_list.append(sp)
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19,344 | sorgerlab/indra | indra/tools/reading/util/reporter.py | Reporter.add_image | def add_image(self, image_path, width=None, height=None, section=None):
"""Add an image to the document.
Images are shown on the final document in the order they are added,
either within the given section or as part of the un-sectioned list of
content.
Parameters
----------
image_path : str
A path to the image on the local file system.
width : int or float
The width of the image in the document in inches.
height : int or float
The height of the image in the document in incehs.
section : str
(This must be a keyword) Select a section in which to place this
image. Default is None, in which case the image will be simply be
added to a default list of text and images.
"""
if width is not None:
width = width*inch
if height is not None:
height = height*inch
im = Image(image_path, width, height)
if section is None:
self.story.append(im)
else:
self.sections[section].append(im)
return | python | def add_image(self, image_path, width=None, height=None, section=None):
if width is not None:
width = width*inch
if height is not None:
height = height*inch
im = Image(image_path, width, height)
if section is None:
self.story.append(im)
else:
self.sections[section].append(im)
return | [
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19,345 | sorgerlab/indra | indra/tools/reading/util/reporter.py | Reporter.make_report | def make_report(self, sections_first=True, section_header_params=None):
"""Create the pdf document with name `self.name + '.pdf'`.
Parameters
----------
sections_first : bool
If True (default), text and images with sections are presented first
and un-sectioned content is appended afterword. If False, sectioned
text and images will be placed before the sections.
section_header_params : dict or None
Optionally overwrite/extend the default formatting for the section
headers. Default is None.
"""
full_story = list(self._preformat_text(self.title, style='Title',
fontsize=18, alignment='center'))
# Set the default section header parameters
if section_header_params is None:
section_header_params = {'style': 'h1', 'fontsize': 14,
'alignment': 'center'}
# Merge the sections and the rest of the story.
if sections_first:
full_story += self._make_sections(**section_header_params)
full_story += self.story
else:
full_story += self.story
full_story += self._make_sections(**section_header_params)
fname = self.name + '.pdf'
doc = SimpleDocTemplate(fname, pagesize=letter,
rightMargin=72, leftMargin=72,
topMargin=72, bottomMargin=18)
doc.build(full_story)
return fname | python | def make_report(self, sections_first=True, section_header_params=None):
full_story = list(self._preformat_text(self.title, style='Title',
fontsize=18, alignment='center'))
# Set the default section header parameters
if section_header_params is None:
section_header_params = {'style': 'h1', 'fontsize': 14,
'alignment': 'center'}
# Merge the sections and the rest of the story.
if sections_first:
full_story += self._make_sections(**section_header_params)
full_story += self.story
else:
full_story += self.story
full_story += self._make_sections(**section_header_params)
fname = self.name + '.pdf'
doc = SimpleDocTemplate(fname, pagesize=letter,
rightMargin=72, leftMargin=72,
topMargin=72, bottomMargin=18)
doc.build(full_story)
return fname | [
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19,346 | sorgerlab/indra | indra/tools/reading/util/reporter.py | Reporter._make_sections | def _make_sections(self, **section_hdr_params):
"""Flatten the sections into a single story list."""
sect_story = []
if not self.section_headings and len(self.sections):
self.section_headings = self.sections.keys()
for section_name in self.section_headings:
section_story = self.sections[section_name]
line = '-'*20
section_head_text = '%s %s %s' % (line, section_name, line)
title, title_sp = self._preformat_text(section_head_text,
**section_hdr_params)
sect_story += [title, title_sp] + section_story
return sect_story | python | def _make_sections(self, **section_hdr_params):
sect_story = []
if not self.section_headings and len(self.sections):
self.section_headings = self.sections.keys()
for section_name in self.section_headings:
section_story = self.sections[section_name]
line = '-'*20
section_head_text = '%s %s %s' % (line, section_name, line)
title, title_sp = self._preformat_text(section_head_text,
**section_hdr_params)
sect_story += [title, title_sp] + section_story
return sect_story | [
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19,347 | sorgerlab/indra | indra/tools/reading/util/reporter.py | Reporter._preformat_text | def _preformat_text(self, text, style='Normal', space=None, fontsize=12,
alignment='left'):
"""Format the text for addition to a story list."""
if space is None:
space=(1,12)
ptext = ('<para alignment=\"%s\"><font size=%d>%s</font></para>'
% (alignment, fontsize, text))
para = Paragraph(ptext, self.styles[style])
sp = Spacer(*space)
return para, sp | python | def _preformat_text(self, text, style='Normal', space=None, fontsize=12,
alignment='left'):
if space is None:
space=(1,12)
ptext = ('<para alignment=\"%s\"><font size=%d>%s</font></para>'
% (alignment, fontsize, text))
para = Paragraph(ptext, self.styles[style])
sp = Spacer(*space)
return para, sp | [
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19,348 | sorgerlab/indra | indra/databases/mesh_client.py | get_mesh_name_from_web | def get_mesh_name_from_web(mesh_id):
"""Get the MESH label for the given MESH ID using the NLM REST API.
Parameters
----------
mesh_id : str
MESH Identifier, e.g. 'D003094'.
Returns
-------
str
Label for the MESH ID, or None if the query failed or no label was
found.
"""
url = MESH_URL + mesh_id + '.json'
resp = requests.get(url)
if resp.status_code != 200:
return None
mesh_json = resp.json()
try:
label = mesh_json['@graph'][0]['label']['@value']
except (KeyError, IndexError) as e:
return None
return label | python | def get_mesh_name_from_web(mesh_id):
url = MESH_URL + mesh_id + '.json'
resp = requests.get(url)
if resp.status_code != 200:
return None
mesh_json = resp.json()
try:
label = mesh_json['@graph'][0]['label']['@value']
except (KeyError, IndexError) as e:
return None
return label | [
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19,349 | sorgerlab/indra | indra/databases/mesh_client.py | get_mesh_name | def get_mesh_name(mesh_id, offline=False):
"""Get the MESH label for the given MESH ID.
Uses the mappings table in `indra/resources`; if the MESH ID is not listed
there, falls back on the NLM REST API.
Parameters
----------
mesh_id : str
MESH Identifier, e.g. 'D003094'.
offline : bool
Whether to allow queries to the NLM REST API if the given MESH ID is not
contained in INDRA's internal MESH mappings file. Default is False
(allows REST API queries).
Returns
-------
str
Label for the MESH ID, or None if the query failed or no label was
found.
"""
indra_mesh_mapping = mesh_id_to_name.get(mesh_id)
if offline or indra_mesh_mapping is not None:
return indra_mesh_mapping
# Look up the MESH mapping from NLM if we don't have it locally
return get_mesh_name_from_web(mesh_id) | python | def get_mesh_name(mesh_id, offline=False):
indra_mesh_mapping = mesh_id_to_name.get(mesh_id)
if offline or indra_mesh_mapping is not None:
return indra_mesh_mapping
# Look up the MESH mapping from NLM if we don't have it locally
return get_mesh_name_from_web(mesh_id) | [
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19,350 | sorgerlab/indra | indra/databases/mesh_client.py | get_mesh_id_name | def get_mesh_id_name(mesh_term, offline=False):
"""Get the MESH ID and name for the given MESH term.
Uses the mappings table in `indra/resources`; if the MESH term is not
listed there, falls back on the NLM REST API.
Parameters
----------
mesh_term : str
MESH Descriptor or Concept name, e.g. 'Breast Cancer'.
offline : bool
Whether to allow queries to the NLM REST API if the given MESH term is
not contained in INDRA's internal MESH mappings file. Default is False
(allows REST API queries).
Returns
-------
tuple of strs
Returns a 2-tuple of the form `(id, name)` with the ID of the
descriptor corresponding to the MESH label, and the descriptor name
(which may not exactly match the name provided as an argument if it is
a Concept name). If the query failed, or no descriptor corresponding to
the name was found, returns a tuple of (None, None).
"""
indra_mesh_id = mesh_name_to_id.get(mesh_term)
if indra_mesh_id is not None:
return indra_mesh_id, mesh_term
indra_mesh_id, new_term = \
mesh_name_to_id_name.get(mesh_term, (None, None))
if indra_mesh_id is not None:
return indra_mesh_id, new_term
if offline:
return None, None
# Look up the MESH mapping from NLM if we don't have it locally
return get_mesh_id_name_from_web(mesh_term) | python | def get_mesh_id_name(mesh_term, offline=False):
indra_mesh_id = mesh_name_to_id.get(mesh_term)
if indra_mesh_id is not None:
return indra_mesh_id, mesh_term
indra_mesh_id, new_term = \
mesh_name_to_id_name.get(mesh_term, (None, None))
if indra_mesh_id is not None:
return indra_mesh_id, new_term
if offline:
return None, None
# Look up the MESH mapping from NLM if we don't have it locally
return get_mesh_id_name_from_web(mesh_term) | [
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19,351 | sorgerlab/indra | indra/tools/machine/cli.py | make | def make(directory):
"""Makes a RAS Machine directory"""
if os.path.exists(directory):
if os.path.isdir(directory):
click.echo('Directory already exists')
else:
click.echo('Path exists and is not a directory')
sys.exit()
os.makedirs(directory)
os.mkdir(os.path.join(directory, 'jsons'))
copy_default_config(os.path.join(directory, 'config.yaml')) | python | def make(directory):
if os.path.exists(directory):
if os.path.isdir(directory):
click.echo('Directory already exists')
else:
click.echo('Path exists and is not a directory')
sys.exit()
os.makedirs(directory)
os.mkdir(os.path.join(directory, 'jsons'))
copy_default_config(os.path.join(directory, 'config.yaml')) | [
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19,352 | sorgerlab/indra | indra/tools/machine/cli.py | run_with_search | def run_with_search(model_path, config, num_days):
"""Run with PubMed search for new papers."""
from indra.tools.machine.machine import run_with_search_helper
run_with_search_helper(model_path, config, num_days=num_days) | python | def run_with_search(model_path, config, num_days):
from indra.tools.machine.machine import run_with_search_helper
run_with_search_helper(model_path, config, num_days=num_days) | [
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19,353 | sorgerlab/indra | indra/tools/machine/cli.py | run_with_pmids | def run_with_pmids(model_path, pmids):
"""Run with given list of PMIDs."""
from indra.tools.machine.machine import run_with_pmids_helper
run_with_pmids_helper(model_path, pmids) | python | def run_with_pmids(model_path, pmids):
from indra.tools.machine.machine import run_with_pmids_helper
run_with_pmids_helper(model_path, pmids) | [
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19,354 | sorgerlab/indra | indra/literature/pmc_client.py | id_lookup | def id_lookup(paper_id, idtype=None):
"""This function takes a Pubmed ID, Pubmed Central ID, or DOI
and use the Pubmed ID mapping
service and looks up all other IDs from one
of these. The IDs are returned in a dictionary."""
if idtype is not None and idtype not in ('pmid', 'pmcid', 'doi'):
raise ValueError("Invalid idtype %s; must be 'pmid', 'pmcid', "
"or 'doi'." % idtype)
if paper_id.upper().startswith('PMC'):
idtype = 'pmcid'
# Strip off any prefix
if paper_id.upper().startswith('PMID'):
paper_id = paper_id[4:]
elif paper_id.upper().startswith('DOI'):
paper_id = paper_id[3:]
data = {'ids': paper_id}
if idtype is not None:
data['idtype'] = idtype
try:
tree = pubmed_client.send_request(pmid_convert_url, data)
except Exception as e:
logger.error('Error looking up PMID in PMC: %s' % e)
return {}
if tree is None:
return {}
record = tree.find('record')
if record is None:
return {}
doi = record.attrib.get('doi')
pmid = record.attrib.get('pmid')
pmcid = record.attrib.get('pmcid')
ids = {'doi': doi,
'pmid': pmid,
'pmcid': pmcid}
return ids | python | def id_lookup(paper_id, idtype=None):
if idtype is not None and idtype not in ('pmid', 'pmcid', 'doi'):
raise ValueError("Invalid idtype %s; must be 'pmid', 'pmcid', "
"or 'doi'." % idtype)
if paper_id.upper().startswith('PMC'):
idtype = 'pmcid'
# Strip off any prefix
if paper_id.upper().startswith('PMID'):
paper_id = paper_id[4:]
elif paper_id.upper().startswith('DOI'):
paper_id = paper_id[3:]
data = {'ids': paper_id}
if idtype is not None:
data['idtype'] = idtype
try:
tree = pubmed_client.send_request(pmid_convert_url, data)
except Exception as e:
logger.error('Error looking up PMID in PMC: %s' % e)
return {}
if tree is None:
return {}
record = tree.find('record')
if record is None:
return {}
doi = record.attrib.get('doi')
pmid = record.attrib.get('pmid')
pmcid = record.attrib.get('pmcid')
ids = {'doi': doi,
'pmid': pmid,
'pmcid': pmcid}
return ids | [
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19,355 | sorgerlab/indra | indra/literature/pmc_client.py | get_xml | def get_xml(pmc_id):
"""Returns XML for the article corresponding to a PMC ID."""
if pmc_id.upper().startswith('PMC'):
pmc_id = pmc_id[3:]
# Request params
params = {}
params['verb'] = 'GetRecord'
params['identifier'] = 'oai:pubmedcentral.nih.gov:%s' % pmc_id
params['metadataPrefix'] = 'pmc'
# Submit the request
res = requests.get(pmc_url, params)
if not res.status_code == 200:
logger.warning("Couldn't download %s" % pmc_id)
return None
# Read the bytestream
xml_bytes = res.content
# Check for any XML errors; xml_str should still be bytes
tree = ET.XML(xml_bytes, parser=UTB())
xmlns = "http://www.openarchives.org/OAI/2.0/"
err_tag = tree.find('{%s}error' % xmlns)
if err_tag is not None:
err_code = err_tag.attrib['code']
err_text = err_tag.text
logger.warning('PMC client returned with error %s: %s'
% (err_code, err_text))
return None
# If no error, return the XML as a unicode string
else:
return xml_bytes.decode('utf-8') | python | def get_xml(pmc_id):
if pmc_id.upper().startswith('PMC'):
pmc_id = pmc_id[3:]
# Request params
params = {}
params['verb'] = 'GetRecord'
params['identifier'] = 'oai:pubmedcentral.nih.gov:%s' % pmc_id
params['metadataPrefix'] = 'pmc'
# Submit the request
res = requests.get(pmc_url, params)
if not res.status_code == 200:
logger.warning("Couldn't download %s" % pmc_id)
return None
# Read the bytestream
xml_bytes = res.content
# Check for any XML errors; xml_str should still be bytes
tree = ET.XML(xml_bytes, parser=UTB())
xmlns = "http://www.openarchives.org/OAI/2.0/"
err_tag = tree.find('{%s}error' % xmlns)
if err_tag is not None:
err_code = err_tag.attrib['code']
err_text = err_tag.text
logger.warning('PMC client returned with error %s: %s'
% (err_code, err_text))
return None
# If no error, return the XML as a unicode string
else:
return xml_bytes.decode('utf-8') | [
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19,356 | sorgerlab/indra | indra/literature/pmc_client.py | extract_paragraphs | def extract_paragraphs(xml_string):
"""Returns list of paragraphs in an NLM XML.
Parameters
----------
xml_string : str
String containing valid NLM XML.
Returns
-------
list of str
List of extracted paragraphs in an NLM XML
"""
tree = etree.fromstring(xml_string.encode('utf-8'))
paragraphs = []
# In NLM xml, all plaintext is within <p> tags, and is the only thing
# that can be contained in <p> tags. To handle to possibility of namespaces
# uses regex to search for tags either of the form 'p' or '{<namespace>}p'
for element in tree.iter():
if isinstance(element.tag, basestring) and \
re.search('(^|})[p|title]$', element.tag) and element.text:
paragraph = ' '.join(element.itertext())
paragraphs.append(paragraph)
return paragraphs | python | def extract_paragraphs(xml_string):
tree = etree.fromstring(xml_string.encode('utf-8'))
paragraphs = []
# In NLM xml, all plaintext is within <p> tags, and is the only thing
# that can be contained in <p> tags. To handle to possibility of namespaces
# uses regex to search for tags either of the form 'p' or '{<namespace>}p'
for element in tree.iter():
if isinstance(element.tag, basestring) and \
re.search('(^|})[p|title]$', element.tag) and element.text:
paragraph = ' '.join(element.itertext())
paragraphs.append(paragraph)
return paragraphs | [
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19,357 | sorgerlab/indra | indra/literature/pmc_client.py | filter_pmids | def filter_pmids(pmid_list, source_type):
"""Filter a list of PMIDs for ones with full text from PMC.
Parameters
----------
pmid_list : list of str
List of PMIDs to filter.
source_type : string
One of 'fulltext', 'oa_xml', 'oa_txt', or 'auth_xml'.
Returns
-------
list of str
PMIDs available in the specified source/format type.
"""
global pmids_fulltext_dict
# Check args
if source_type not in ('fulltext', 'oa_xml', 'oa_txt', 'auth_xml'):
raise ValueError("source_type must be one of: 'fulltext', 'oa_xml', "
"'oa_txt', or 'auth_xml'.")
# Check if we've loaded this type, and lazily initialize
if pmids_fulltext_dict.get(source_type) is None:
fulltext_list_path = os.path.join(os.path.dirname(__file__),
'pmids_%s.txt' % source_type)
with open(fulltext_list_path, 'rb') as f:
fulltext_list = set([line.strip().decode('utf-8')
for line in f.readlines()])
pmids_fulltext_dict[source_type] = fulltext_list
return list(set(pmid_list).intersection(
pmids_fulltext_dict.get(source_type))) | python | def filter_pmids(pmid_list, source_type):
global pmids_fulltext_dict
# Check args
if source_type not in ('fulltext', 'oa_xml', 'oa_txt', 'auth_xml'):
raise ValueError("source_type must be one of: 'fulltext', 'oa_xml', "
"'oa_txt', or 'auth_xml'.")
# Check if we've loaded this type, and lazily initialize
if pmids_fulltext_dict.get(source_type) is None:
fulltext_list_path = os.path.join(os.path.dirname(__file__),
'pmids_%s.txt' % source_type)
with open(fulltext_list_path, 'rb') as f:
fulltext_list = set([line.strip().decode('utf-8')
for line in f.readlines()])
pmids_fulltext_dict[source_type] = fulltext_list
return list(set(pmid_list).intersection(
pmids_fulltext_dict.get(source_type))) | [
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19,358 | sorgerlab/indra | indra/sources/cwms/util.py | get_example_extractions | def get_example_extractions(fname):
"Get extractions from one of the examples in `cag_examples`."
with open(fname, 'r') as f:
sentences = f.read().splitlines()
rdf_xml_dict = {}
for sentence in sentences:
logger.info("Reading \"%s\"..." % sentence)
html = tc.send_query(sentence, 'cwms')
try:
rdf_xml_dict[sentence] = tc.get_xml(html, 'rdf:RDF',
fail_if_empty=True)
except AssertionError as e:
logger.error("Got error for %s." % sentence)
logger.exception(e)
return rdf_xml_dict | python | def get_example_extractions(fname):
"Get extractions from one of the examples in `cag_examples`."
with open(fname, 'r') as f:
sentences = f.read().splitlines()
rdf_xml_dict = {}
for sentence in sentences:
logger.info("Reading \"%s\"..." % sentence)
html = tc.send_query(sentence, 'cwms')
try:
rdf_xml_dict[sentence] = tc.get_xml(html, 'rdf:RDF',
fail_if_empty=True)
except AssertionError as e:
logger.error("Got error for %s." % sentence)
logger.exception(e)
return rdf_xml_dict | [
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19,359 | sorgerlab/indra | indra/sources/cwms/util.py | make_example_graphs | def make_example_graphs():
"Make graphs from all the examples in cag_examples."
cag_example_rdfs = {}
for i, fname in enumerate(os.listdir('cag_examples')):
cag_example_rdfs[i+1] = get_example_extractions(fname)
return make_cag_graphs(cag_example_rdfs) | python | def make_example_graphs():
"Make graphs from all the examples in cag_examples."
cag_example_rdfs = {}
for i, fname in enumerate(os.listdir('cag_examples')):
cag_example_rdfs[i+1] = get_example_extractions(fname)
return make_cag_graphs(cag_example_rdfs) | [
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19,360 | sorgerlab/indra | indra/assemblers/english/assembler.py | _join_list | def _join_list(lst, oxford=False):
"""Join a list of words in a gramatically correct way."""
if len(lst) > 2:
s = ', '.join(lst[:-1])
if oxford:
s += ','
s += ' and ' + lst[-1]
elif len(lst) == 2:
s = lst[0] + ' and ' + lst[1]
elif len(lst) == 1:
s = lst[0]
else:
s = ''
return s | python | def _join_list(lst, oxford=False):
if len(lst) > 2:
s = ', '.join(lst[:-1])
if oxford:
s += ','
s += ' and ' + lst[-1]
elif len(lst) == 2:
s = lst[0] + ' and ' + lst[1]
elif len(lst) == 1:
s = lst[0]
else:
s = ''
return s | [
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19,361 | sorgerlab/indra | indra/assemblers/english/assembler.py | _assemble_activeform | def _assemble_activeform(stmt):
"""Assemble ActiveForm statements into text."""
subj_str = _assemble_agent_str(stmt.agent)
if stmt.is_active:
is_active_str = 'active'
else:
is_active_str = 'inactive'
if stmt.activity == 'activity':
stmt_str = subj_str + ' is ' + is_active_str
elif stmt.activity == 'kinase':
stmt_str = subj_str + ' is kinase-' + is_active_str
elif stmt.activity == 'phosphatase':
stmt_str = subj_str + ' is phosphatase-' + is_active_str
elif stmt.activity == 'catalytic':
stmt_str = subj_str + ' is catalytically ' + is_active_str
elif stmt.activity == 'transcription':
stmt_str = subj_str + ' is transcriptionally ' + is_active_str
elif stmt.activity == 'gtpbound':
stmt_str = subj_str + ' is GTP-bound ' + is_active_str
return _make_sentence(stmt_str) | python | def _assemble_activeform(stmt):
subj_str = _assemble_agent_str(stmt.agent)
if stmt.is_active:
is_active_str = 'active'
else:
is_active_str = 'inactive'
if stmt.activity == 'activity':
stmt_str = subj_str + ' is ' + is_active_str
elif stmt.activity == 'kinase':
stmt_str = subj_str + ' is kinase-' + is_active_str
elif stmt.activity == 'phosphatase':
stmt_str = subj_str + ' is phosphatase-' + is_active_str
elif stmt.activity == 'catalytic':
stmt_str = subj_str + ' is catalytically ' + is_active_str
elif stmt.activity == 'transcription':
stmt_str = subj_str + ' is transcriptionally ' + is_active_str
elif stmt.activity == 'gtpbound':
stmt_str = subj_str + ' is GTP-bound ' + is_active_str
return _make_sentence(stmt_str) | [
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19,362 | sorgerlab/indra | indra/assemblers/english/assembler.py | _assemble_modification | def _assemble_modification(stmt):
"""Assemble Modification statements into text."""
sub_str = _assemble_agent_str(stmt.sub)
if stmt.enz is not None:
enz_str = _assemble_agent_str(stmt.enz)
if _get_is_direct(stmt):
mod_str = ' ' + _mod_process_verb(stmt) + ' '
else:
mod_str = ' leads to the ' + _mod_process_noun(stmt) + ' of '
stmt_str = enz_str + mod_str + sub_str
else:
stmt_str = sub_str + ' is ' + _mod_state_stmt(stmt)
if stmt.residue is not None:
if stmt.position is None:
mod_str = 'on ' + ist.amino_acids[stmt.residue]['full_name']
else:
mod_str = 'on ' + stmt.residue + stmt.position
else:
mod_str = ''
stmt_str += ' ' + mod_str
return _make_sentence(stmt_str) | python | def _assemble_modification(stmt):
sub_str = _assemble_agent_str(stmt.sub)
if stmt.enz is not None:
enz_str = _assemble_agent_str(stmt.enz)
if _get_is_direct(stmt):
mod_str = ' ' + _mod_process_verb(stmt) + ' '
else:
mod_str = ' leads to the ' + _mod_process_noun(stmt) + ' of '
stmt_str = enz_str + mod_str + sub_str
else:
stmt_str = sub_str + ' is ' + _mod_state_stmt(stmt)
if stmt.residue is not None:
if stmt.position is None:
mod_str = 'on ' + ist.amino_acids[stmt.residue]['full_name']
else:
mod_str = 'on ' + stmt.residue + stmt.position
else:
mod_str = ''
stmt_str += ' ' + mod_str
return _make_sentence(stmt_str) | [
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19,363 | sorgerlab/indra | indra/assemblers/english/assembler.py | _assemble_association | def _assemble_association(stmt):
"""Assemble Association statements into text."""
member_strs = [_assemble_agent_str(m.concept) for m in stmt.members]
stmt_str = member_strs[0] + ' is associated with ' + \
_join_list(member_strs[1:])
return _make_sentence(stmt_str) | python | def _assemble_association(stmt):
member_strs = [_assemble_agent_str(m.concept) for m in stmt.members]
stmt_str = member_strs[0] + ' is associated with ' + \
_join_list(member_strs[1:])
return _make_sentence(stmt_str) | [
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19,364 | sorgerlab/indra | indra/assemblers/english/assembler.py | _assemble_complex | def _assemble_complex(stmt):
"""Assemble Complex statements into text."""
member_strs = [_assemble_agent_str(m) for m in stmt.members]
stmt_str = member_strs[0] + ' binds ' + _join_list(member_strs[1:])
return _make_sentence(stmt_str) | python | def _assemble_complex(stmt):
member_strs = [_assemble_agent_str(m) for m in stmt.members]
stmt_str = member_strs[0] + ' binds ' + _join_list(member_strs[1:])
return _make_sentence(stmt_str) | [
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19,365 | sorgerlab/indra | indra/assemblers/english/assembler.py | _assemble_autophosphorylation | def _assemble_autophosphorylation(stmt):
"""Assemble Autophosphorylation statements into text."""
enz_str = _assemble_agent_str(stmt.enz)
stmt_str = enz_str + ' phosphorylates itself'
if stmt.residue is not None:
if stmt.position is None:
mod_str = 'on ' + ist.amino_acids[stmt.residue]['full_name']
else:
mod_str = 'on ' + stmt.residue + stmt.position
else:
mod_str = ''
stmt_str += ' ' + mod_str
return _make_sentence(stmt_str) | python | def _assemble_autophosphorylation(stmt):
enz_str = _assemble_agent_str(stmt.enz)
stmt_str = enz_str + ' phosphorylates itself'
if stmt.residue is not None:
if stmt.position is None:
mod_str = 'on ' + ist.amino_acids[stmt.residue]['full_name']
else:
mod_str = 'on ' + stmt.residue + stmt.position
else:
mod_str = ''
stmt_str += ' ' + mod_str
return _make_sentence(stmt_str) | [
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19,366 | sorgerlab/indra | indra/assemblers/english/assembler.py | _assemble_regulate_activity | def _assemble_regulate_activity(stmt):
"""Assemble RegulateActivity statements into text."""
subj_str = _assemble_agent_str(stmt.subj)
obj_str = _assemble_agent_str(stmt.obj)
if stmt.is_activation:
rel_str = ' activates '
else:
rel_str = ' inhibits '
stmt_str = subj_str + rel_str + obj_str
return _make_sentence(stmt_str) | python | def _assemble_regulate_activity(stmt):
subj_str = _assemble_agent_str(stmt.subj)
obj_str = _assemble_agent_str(stmt.obj)
if stmt.is_activation:
rel_str = ' activates '
else:
rel_str = ' inhibits '
stmt_str = subj_str + rel_str + obj_str
return _make_sentence(stmt_str) | [
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19,367 | sorgerlab/indra | indra/assemblers/english/assembler.py | _assemble_regulate_amount | def _assemble_regulate_amount(stmt):
"""Assemble RegulateAmount statements into text."""
obj_str = _assemble_agent_str(stmt.obj)
if stmt.subj is not None:
subj_str = _assemble_agent_str(stmt.subj)
if isinstance(stmt, ist.IncreaseAmount):
rel_str = ' increases the amount of '
elif isinstance(stmt, ist.DecreaseAmount):
rel_str = ' decreases the amount of '
stmt_str = subj_str + rel_str + obj_str
else:
if isinstance(stmt, ist.IncreaseAmount):
stmt_str = obj_str + ' is produced'
elif isinstance(stmt, ist.DecreaseAmount):
stmt_str = obj_str + ' is degraded'
return _make_sentence(stmt_str) | python | def _assemble_regulate_amount(stmt):
obj_str = _assemble_agent_str(stmt.obj)
if stmt.subj is not None:
subj_str = _assemble_agent_str(stmt.subj)
if isinstance(stmt, ist.IncreaseAmount):
rel_str = ' increases the amount of '
elif isinstance(stmt, ist.DecreaseAmount):
rel_str = ' decreases the amount of '
stmt_str = subj_str + rel_str + obj_str
else:
if isinstance(stmt, ist.IncreaseAmount):
stmt_str = obj_str + ' is produced'
elif isinstance(stmt, ist.DecreaseAmount):
stmt_str = obj_str + ' is degraded'
return _make_sentence(stmt_str) | [
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19,368 | sorgerlab/indra | indra/assemblers/english/assembler.py | _assemble_translocation | def _assemble_translocation(stmt):
"""Assemble Translocation statements into text."""
agent_str = _assemble_agent_str(stmt.agent)
stmt_str = agent_str + ' translocates'
if stmt.from_location is not None:
stmt_str += ' from the ' + stmt.from_location
if stmt.to_location is not None:
stmt_str += ' to the ' + stmt.to_location
return _make_sentence(stmt_str) | python | def _assemble_translocation(stmt):
agent_str = _assemble_agent_str(stmt.agent)
stmt_str = agent_str + ' translocates'
if stmt.from_location is not None:
stmt_str += ' from the ' + stmt.from_location
if stmt.to_location is not None:
stmt_str += ' to the ' + stmt.to_location
return _make_sentence(stmt_str) | [
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19,369 | sorgerlab/indra | indra/assemblers/english/assembler.py | _assemble_gap | def _assemble_gap(stmt):
"""Assemble Gap statements into text."""
subj_str = _assemble_agent_str(stmt.gap)
obj_str = _assemble_agent_str(stmt.ras)
stmt_str = subj_str + ' is a GAP for ' + obj_str
return _make_sentence(stmt_str) | python | def _assemble_gap(stmt):
subj_str = _assemble_agent_str(stmt.gap)
obj_str = _assemble_agent_str(stmt.ras)
stmt_str = subj_str + ' is a GAP for ' + obj_str
return _make_sentence(stmt_str) | [
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19,370 | sorgerlab/indra | indra/assemblers/english/assembler.py | _assemble_gef | def _assemble_gef(stmt):
"""Assemble Gef statements into text."""
subj_str = _assemble_agent_str(stmt.gef)
obj_str = _assemble_agent_str(stmt.ras)
stmt_str = subj_str + ' is a GEF for ' + obj_str
return _make_sentence(stmt_str) | python | def _assemble_gef(stmt):
subj_str = _assemble_agent_str(stmt.gef)
obj_str = _assemble_agent_str(stmt.ras)
stmt_str = subj_str + ' is a GEF for ' + obj_str
return _make_sentence(stmt_str) | [
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19,371 | sorgerlab/indra | indra/assemblers/english/assembler.py | _assemble_conversion | def _assemble_conversion(stmt):
"""Assemble a Conversion statement into text."""
reactants = _join_list([_assemble_agent_str(r) for r in stmt.obj_from])
products = _join_list([_assemble_agent_str(r) for r in stmt.obj_to])
if stmt.subj is not None:
subj_str = _assemble_agent_str(stmt.subj)
stmt_str = '%s catalyzes the conversion of %s into %s' % \
(subj_str, reactants, products)
else:
stmt_str = '%s is converted into %s' % (reactants, products)
return _make_sentence(stmt_str) | python | def _assemble_conversion(stmt):
reactants = _join_list([_assemble_agent_str(r) for r in stmt.obj_from])
products = _join_list([_assemble_agent_str(r) for r in stmt.obj_to])
if stmt.subj is not None:
subj_str = _assemble_agent_str(stmt.subj)
stmt_str = '%s catalyzes the conversion of %s into %s' % \
(subj_str, reactants, products)
else:
stmt_str = '%s is converted into %s' % (reactants, products)
return _make_sentence(stmt_str) | [
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19,372 | sorgerlab/indra | indra/assemblers/english/assembler.py | _assemble_influence | def _assemble_influence(stmt):
"""Assemble an Influence statement into text."""
subj_str = _assemble_agent_str(stmt.subj.concept)
obj_str = _assemble_agent_str(stmt.obj.concept)
# Note that n is prepended to increase to make it "an increase"
if stmt.subj.delta['polarity'] is not None:
subj_delta_str = ' decrease' if stmt.subj.delta['polarity'] == -1 \
else 'n increase'
subj_str = 'a%s in %s' % (subj_delta_str, subj_str)
if stmt.obj.delta['polarity'] is not None:
obj_delta_str = ' decrease' if stmt.obj.delta['polarity'] == -1 \
else 'n increase'
obj_str = 'a%s in %s' % (obj_delta_str, obj_str)
stmt_str = '%s causes %s' % (subj_str, obj_str)
return _make_sentence(stmt_str) | python | def _assemble_influence(stmt):
subj_str = _assemble_agent_str(stmt.subj.concept)
obj_str = _assemble_agent_str(stmt.obj.concept)
# Note that n is prepended to increase to make it "an increase"
if stmt.subj.delta['polarity'] is not None:
subj_delta_str = ' decrease' if stmt.subj.delta['polarity'] == -1 \
else 'n increase'
subj_str = 'a%s in %s' % (subj_delta_str, subj_str)
if stmt.obj.delta['polarity'] is not None:
obj_delta_str = ' decrease' if stmt.obj.delta['polarity'] == -1 \
else 'n increase'
obj_str = 'a%s in %s' % (obj_delta_str, obj_str)
stmt_str = '%s causes %s' % (subj_str, obj_str)
return _make_sentence(stmt_str) | [
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19,373 | sorgerlab/indra | indra/assemblers/english/assembler.py | _make_sentence | def _make_sentence(txt):
"""Make a sentence from a piece of text."""
#Make sure first letter is capitalized
txt = txt.strip(' ')
txt = txt[0].upper() + txt[1:] + '.'
return txt | python | def _make_sentence(txt):
#Make sure first letter is capitalized
txt = txt.strip(' ')
txt = txt[0].upper() + txt[1:] + '.'
return txt | [
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19,374 | sorgerlab/indra | indra/assemblers/english/assembler.py | _get_is_hypothesis | def _get_is_hypothesis(stmt):
'''Returns true if there is evidence that the statement is only
hypothetical. If all of the evidences associated with the statement
indicate a hypothetical interaction then we assume the interaction
is hypothetical.'''
for ev in stmt.evidence:
if not ev.epistemics.get('hypothesis') is True:
return True
return False | python | def _get_is_hypothesis(stmt):
'''Returns true if there is evidence that the statement is only
hypothetical. If all of the evidences associated with the statement
indicate a hypothetical interaction then we assume the interaction
is hypothetical.'''
for ev in stmt.evidence:
if not ev.epistemics.get('hypothesis') is True:
return True
return False | [
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19,375 | sorgerlab/indra | indra/assemblers/english/assembler.py | EnglishAssembler.make_model | def make_model(self):
"""Assemble text from the set of collected INDRA Statements.
Returns
-------
stmt_strs : str
Return the assembled text as unicode string. By default, the text
is a single string consisting of one or more sentences with
periods at the end.
"""
stmt_strs = []
for stmt in self.statements:
if isinstance(stmt, ist.Modification):
stmt_strs.append(_assemble_modification(stmt))
elif isinstance(stmt, ist.Autophosphorylation):
stmt_strs.append(_assemble_autophosphorylation(stmt))
elif isinstance(stmt, ist.Association):
stmt_strs.append(_assemble_association(stmt))
elif isinstance(stmt, ist.Complex):
stmt_strs.append(_assemble_complex(stmt))
elif isinstance(stmt, ist.Influence):
stmt_strs.append(_assemble_influence(stmt))
elif isinstance(stmt, ist.RegulateActivity):
stmt_strs.append(_assemble_regulate_activity(stmt))
elif isinstance(stmt, ist.RegulateAmount):
stmt_strs.append(_assemble_regulate_amount(stmt))
elif isinstance(stmt, ist.ActiveForm):
stmt_strs.append(_assemble_activeform(stmt))
elif isinstance(stmt, ist.Translocation):
stmt_strs.append(_assemble_translocation(stmt))
elif isinstance(stmt, ist.Gef):
stmt_strs.append(_assemble_gef(stmt))
elif isinstance(stmt, ist.Gap):
stmt_strs.append(_assemble_gap(stmt))
elif isinstance(stmt, ist.Conversion):
stmt_strs.append(_assemble_conversion(stmt))
else:
logger.warning('Unhandled statement type: %s.' % type(stmt))
if stmt_strs:
return ' '.join(stmt_strs)
else:
return '' | python | def make_model(self):
stmt_strs = []
for stmt in self.statements:
if isinstance(stmt, ist.Modification):
stmt_strs.append(_assemble_modification(stmt))
elif isinstance(stmt, ist.Autophosphorylation):
stmt_strs.append(_assemble_autophosphorylation(stmt))
elif isinstance(stmt, ist.Association):
stmt_strs.append(_assemble_association(stmt))
elif isinstance(stmt, ist.Complex):
stmt_strs.append(_assemble_complex(stmt))
elif isinstance(stmt, ist.Influence):
stmt_strs.append(_assemble_influence(stmt))
elif isinstance(stmt, ist.RegulateActivity):
stmt_strs.append(_assemble_regulate_activity(stmt))
elif isinstance(stmt, ist.RegulateAmount):
stmt_strs.append(_assemble_regulate_amount(stmt))
elif isinstance(stmt, ist.ActiveForm):
stmt_strs.append(_assemble_activeform(stmt))
elif isinstance(stmt, ist.Translocation):
stmt_strs.append(_assemble_translocation(stmt))
elif isinstance(stmt, ist.Gef):
stmt_strs.append(_assemble_gef(stmt))
elif isinstance(stmt, ist.Gap):
stmt_strs.append(_assemble_gap(stmt))
elif isinstance(stmt, ist.Conversion):
stmt_strs.append(_assemble_conversion(stmt))
else:
logger.warning('Unhandled statement type: %s.' % type(stmt))
if stmt_strs:
return ' '.join(stmt_strs)
else:
return '' | [
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Return the assembled text as unicode string. By default, the text
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19,376 | sorgerlab/indra | indra/assemblers/sbgn/assembler.py | SBGNAssembler.add_statements | def add_statements(self, stmts):
"""Add INDRA Statements to the assembler's list of statements.
Parameters
----------
stmts : list[indra.statements.Statement]
A list of :py:class:`indra.statements.Statement`
to be added to the statement list of the assembler.
"""
for stmt in stmts:
if not self.statement_exists(stmt):
self.statements.append(stmt) | python | def add_statements(self, stmts):
for stmt in stmts:
if not self.statement_exists(stmt):
self.statements.append(stmt) | [
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stmts : list[indra.statements.Statement]
A list of :py:class:`indra.statements.Statement`
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19,377 | sorgerlab/indra | indra/assemblers/sbgn/assembler.py | SBGNAssembler.make_model | def make_model(self):
"""Assemble the SBGN model from the collected INDRA Statements.
This method assembles an SBGN model from the set of INDRA Statements.
The assembled model is set as the assembler's sbgn attribute (it is
represented as an XML ElementTree internally). The model is returned
as a serialized XML string.
Returns
-------
sbgn_str : str
The XML serialized SBGN model.
"""
ppa = PysbPreassembler(self.statements)
ppa.replace_activities()
self.statements = ppa.statements
self.sbgn = emaker.sbgn()
self._map = emaker.map()
self.sbgn.append(self._map)
for stmt in self.statements:
if isinstance(stmt, Modification):
self._assemble_modification(stmt)
elif isinstance(stmt, RegulateActivity):
self._assemble_regulateactivity(stmt)
elif isinstance(stmt, RegulateAmount):
self._assemble_regulateamount(stmt)
elif isinstance(stmt, Complex):
self._assemble_complex(stmt)
elif isinstance(stmt, ActiveForm):
#self._assemble_activeform(stmt)
pass
else:
logger.warning("Unhandled Statement type %s" % type(stmt))
continue
sbgn_str = self.print_model()
return sbgn_str | python | def make_model(self):
ppa = PysbPreassembler(self.statements)
ppa.replace_activities()
self.statements = ppa.statements
self.sbgn = emaker.sbgn()
self._map = emaker.map()
self.sbgn.append(self._map)
for stmt in self.statements:
if isinstance(stmt, Modification):
self._assemble_modification(stmt)
elif isinstance(stmt, RegulateActivity):
self._assemble_regulateactivity(stmt)
elif isinstance(stmt, RegulateAmount):
self._assemble_regulateamount(stmt)
elif isinstance(stmt, Complex):
self._assemble_complex(stmt)
elif isinstance(stmt, ActiveForm):
#self._assemble_activeform(stmt)
pass
else:
logger.warning("Unhandled Statement type %s" % type(stmt))
continue
sbgn_str = self.print_model()
return sbgn_str | [
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This method assembles an SBGN model from the set of INDRA Statements.
The assembled model is set as the assembler's sbgn attribute (it is
represented as an XML ElementTree internally). The model is returned
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Returns
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sbgn_str : str
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19,378 | sorgerlab/indra | indra/assemblers/sbgn/assembler.py | SBGNAssembler.print_model | def print_model(self, pretty=True, encoding='utf8'):
"""Return the assembled SBGN model as an XML string.
Parameters
----------
pretty : Optional[bool]
If True, the SBGN string is formatted with indentation (for human
viewing) otherwise no indentation is used. Default: True
Returns
-------
sbgn_str : bytes (str in Python 2)
An XML string representation of the SBGN model.
"""
return lxml.etree.tostring(self.sbgn, pretty_print=pretty,
encoding=encoding, xml_declaration=True) | python | def print_model(self, pretty=True, encoding='utf8'):
return lxml.etree.tostring(self.sbgn, pretty_print=pretty,
encoding=encoding, xml_declaration=True) | [
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19,379 | sorgerlab/indra | indra/assemblers/sbgn/assembler.py | SBGNAssembler.save_model | def save_model(self, file_name='model.sbgn'):
"""Save the assembled SBGN model in a file.
Parameters
----------
file_name : Optional[str]
The name of the file to save the SBGN network to.
Default: model.sbgn
"""
model = self.print_model()
with open(file_name, 'wb') as fh:
fh.write(model) | python | def save_model(self, file_name='model.sbgn'):
model = self.print_model()
with open(file_name, 'wb') as fh:
fh.write(model) | [
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Parameters
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file_name : Optional[str]
The name of the file to save the SBGN network to.
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19,380 | sorgerlab/indra | indra/assemblers/sbgn/assembler.py | SBGNAssembler._glyph_for_complex_pattern | def _glyph_for_complex_pattern(self, pattern):
"""Add glyph and member glyphs for a PySB ComplexPattern."""
# Make the main glyph for the agent
monomer_glyphs = []
for monomer_pattern in pattern.monomer_patterns:
glyph = self._glyph_for_monomer_pattern(monomer_pattern)
monomer_glyphs.append(glyph)
if len(monomer_glyphs) > 1:
pattern.matches_key = lambda: str(pattern)
agent_id = self._make_agent_id(pattern)
complex_glyph = \
emaker.glyph(emaker.bbox(**self.complex_style),
class_('complex'), id=agent_id)
for glyph in monomer_glyphs:
glyph.attrib['id'] = agent_id + glyph.attrib['id']
complex_glyph.append(glyph)
return complex_glyph
return monomer_glyphs[0] | python | def _glyph_for_complex_pattern(self, pattern):
# Make the main glyph for the agent
monomer_glyphs = []
for monomer_pattern in pattern.monomer_patterns:
glyph = self._glyph_for_monomer_pattern(monomer_pattern)
monomer_glyphs.append(glyph)
if len(monomer_glyphs) > 1:
pattern.matches_key = lambda: str(pattern)
agent_id = self._make_agent_id(pattern)
complex_glyph = \
emaker.glyph(emaker.bbox(**self.complex_style),
class_('complex'), id=agent_id)
for glyph in monomer_glyphs:
glyph.attrib['id'] = agent_id + glyph.attrib['id']
complex_glyph.append(glyph)
return complex_glyph
return monomer_glyphs[0] | [
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19,381 | sorgerlab/indra | indra/assemblers/sbgn/assembler.py | SBGNAssembler._glyph_for_monomer_pattern | def _glyph_for_monomer_pattern(self, pattern):
"""Add glyph for a PySB MonomerPattern."""
pattern.matches_key = lambda: str(pattern)
agent_id = self._make_agent_id(pattern)
# Handle sources and sinks
if pattern.monomer.name in ('__source', '__sink'):
return None
# Handle molecules
glyph = emaker.glyph(emaker.label(text=pattern.monomer.name),
emaker.bbox(**self.monomer_style),
class_('macromolecule'), id=agent_id)
# Temporarily remove this
# Add a glyph for type
#type_glyph = emaker.glyph(emaker.label(text='mt:prot'),
# class_('unit of information'),
# emaker.bbox(**self.entity_type_style),
# id=self._make_id())
#glyph.append(type_glyph)
for site, value in pattern.site_conditions.items():
if value is None or isinstance(value, int):
continue
# Make some common abbreviations
if site == 'phospho':
site = 'p'
elif site == 'activity':
site = 'act'
if value == 'active':
value = 'a'
elif value == 'inactive':
value = 'i'
state = emaker.state(variable=site, value=value)
state_glyph = \
emaker.glyph(state, emaker.bbox(**self.entity_state_style),
class_('state variable'), id=self._make_id())
glyph.append(state_glyph)
return glyph | python | def _glyph_for_monomer_pattern(self, pattern):
pattern.matches_key = lambda: str(pattern)
agent_id = self._make_agent_id(pattern)
# Handle sources and sinks
if pattern.monomer.name in ('__source', '__sink'):
return None
# Handle molecules
glyph = emaker.glyph(emaker.label(text=pattern.monomer.name),
emaker.bbox(**self.monomer_style),
class_('macromolecule'), id=agent_id)
# Temporarily remove this
# Add a glyph for type
#type_glyph = emaker.glyph(emaker.label(text='mt:prot'),
# class_('unit of information'),
# emaker.bbox(**self.entity_type_style),
# id=self._make_id())
#glyph.append(type_glyph)
for site, value in pattern.site_conditions.items():
if value is None or isinstance(value, int):
continue
# Make some common abbreviations
if site == 'phospho':
site = 'p'
elif site == 'activity':
site = 'act'
if value == 'active':
value = 'a'
elif value == 'inactive':
value = 'i'
state = emaker.state(variable=site, value=value)
state_glyph = \
emaker.glyph(state, emaker.bbox(**self.entity_state_style),
class_('state variable'), id=self._make_id())
glyph.append(state_glyph)
return glyph | [
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19,382 | sorgerlab/indra | indra/databases/go_client.py | load_go_graph | def load_go_graph(go_fname):
"""Load the GO data from an OWL file and parse into an RDF graph.
Parameters
----------
go_fname : str
Path to the GO OWL file. Can be downloaded from
http://geneontology.org/ontology/go.owl.
Returns
-------
rdflib.Graph
RDF graph containing GO data.
"""
global _go_graph
if _go_graph is None:
_go_graph = rdflib.Graph()
logger.info("Parsing GO OWL file")
_go_graph.parse(os.path.abspath(go_fname))
return _go_graph | python | def load_go_graph(go_fname):
global _go_graph
if _go_graph is None:
_go_graph = rdflib.Graph()
logger.info("Parsing GO OWL file")
_go_graph.parse(os.path.abspath(go_fname))
return _go_graph | [
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go_fname : str
Path to the GO OWL file. Can be downloaded from
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Returns
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rdflib.Graph
RDF graph containing GO data. | [
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19,383 | sorgerlab/indra | indra/databases/go_client.py | update_id_mappings | def update_id_mappings(g):
"""Compile all ID->label mappings and save to a TSV file.
Parameters
----------
g : rdflib.Graph
RDF graph containing GO data.
"""
g = load_go_graph(go_owl_path)
query = _prefixes + """
SELECT ?id ?label
WHERE {
?class oboInOwl:id ?id .
?class rdfs:label ?label
}
"""
logger.info("Querying for GO ID mappings")
res = g.query(query)
mappings = []
for id_lit, label_lit in sorted(res, key=lambda x: x[0]):
mappings.append((id_lit.value, label_lit.value))
# Write to file
write_unicode_csv(go_mappings_file, mappings, delimiter='\t') | python | def update_id_mappings(g):
g = load_go_graph(go_owl_path)
query = _prefixes + """
SELECT ?id ?label
WHERE {
?class oboInOwl:id ?id .
?class rdfs:label ?label
}
"""
logger.info("Querying for GO ID mappings")
res = g.query(query)
mappings = []
for id_lit, label_lit in sorted(res, key=lambda x: x[0]):
mappings.append((id_lit.value, label_lit.value))
# Write to file
write_unicode_csv(go_mappings_file, mappings, delimiter='\t') | [
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RDF graph containing GO data. | [
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19,384 | sorgerlab/indra | indra/databases/ndex_client.py | get_default_ndex_cred | def get_default_ndex_cred(ndex_cred):
"""Gets the NDEx credentials from the dict, or tries the environment if None"""
if ndex_cred:
username = ndex_cred.get('user')
password = ndex_cred.get('password')
if username is not None and password is not None:
return username, password
username = get_config('NDEX_USERNAME')
password = get_config('NDEX_PASSWORD')
return username, password | python | def get_default_ndex_cred(ndex_cred):
if ndex_cred:
username = ndex_cred.get('user')
password = ndex_cred.get('password')
if username is not None and password is not None:
return username, password
username = get_config('NDEX_USERNAME')
password = get_config('NDEX_PASSWORD')
return username, password | [
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19,385 | sorgerlab/indra | indra/databases/ndex_client.py | send_request | def send_request(ndex_service_url, params, is_json=True, use_get=False):
"""Send a request to the NDEx server.
Parameters
----------
ndex_service_url : str
The URL of the service to use for the request.
params : dict
A dictionary of parameters to send with the request. Parameter keys
differ based on the type of request.
is_json : bool
True if the response is in json format, otherwise it is assumed to be
text. Default: False
use_get : bool
True if the request needs to use GET instead of POST.
Returns
-------
res : str
Depending on the type of service and the is_json parameter, this
function either returns a text string or a json dict.
"""
if use_get:
res = requests.get(ndex_service_url, json=params)
else:
res = requests.post(ndex_service_url, json=params)
status = res.status_code
# If response is immediate, we get 200
if status == 200:
if is_json:
return res.json()
else:
return res.text
# If there is a continuation of the message we get status 300, handled below.
# Otherwise we return None.
elif status != 300:
logger.error('Request returned with code %d' % status)
return None
# In case the response is not immediate, a task ID can be used to get
# the result.
task_id = res.json().get('task_id')
logger.info('NDEx task submitted...')
time_used = 0
try:
while status != 200:
res = requests.get(ndex_base_url + '/task/' + task_id)
status = res.status_code
if status != 200:
time.sleep(5)
time_used += 5
except KeyError:
next
return None
logger.info('NDEx task complete.')
if is_json:
return res.json()
else:
return res.text | python | def send_request(ndex_service_url, params, is_json=True, use_get=False):
if use_get:
res = requests.get(ndex_service_url, json=params)
else:
res = requests.post(ndex_service_url, json=params)
status = res.status_code
# If response is immediate, we get 200
if status == 200:
if is_json:
return res.json()
else:
return res.text
# If there is a continuation of the message we get status 300, handled below.
# Otherwise we return None.
elif status != 300:
logger.error('Request returned with code %d' % status)
return None
# In case the response is not immediate, a task ID can be used to get
# the result.
task_id = res.json().get('task_id')
logger.info('NDEx task submitted...')
time_used = 0
try:
while status != 200:
res = requests.get(ndex_base_url + '/task/' + task_id)
status = res.status_code
if status != 200:
time.sleep(5)
time_used += 5
except KeyError:
next
return None
logger.info('NDEx task complete.')
if is_json:
return res.json()
else:
return res.text | [
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ndex_service_url : str
The URL of the service to use for the request.
params : dict
A dictionary of parameters to send with the request. Parameter keys
differ based on the type of request.
is_json : bool
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19,386 | sorgerlab/indra | indra/databases/ndex_client.py | update_network | def update_network(cx_str, network_id, ndex_cred=None):
"""Update an existing CX network on NDEx with new CX content.
Parameters
----------
cx_str : str
String containing the CX content.
network_id : str
UUID of the network on NDEx.
ndex_cred : dict
A dictionary with the following entries:
'user': NDEx user name
'password': NDEx password
"""
server = 'http://public.ndexbio.org'
username, password = get_default_ndex_cred(ndex_cred)
nd = ndex2.client.Ndex2(server, username, password)
try:
logger.info('Getting network summary...')
summary = nd.get_network_summary(network_id)
except Exception as e:
logger.error('Could not get NDEx network summary.')
logger.error(e)
return
# Update network content
try:
logger.info('Updating network...')
cx_stream = io.BytesIO(cx_str.encode('utf-8'))
nd.update_cx_network(cx_stream, network_id)
except Exception as e:
logger.error('Could not update NDEx network.')
logger.error(e)
return
# Update network profile
ver_str = summary.get('version')
new_ver = _increment_ndex_ver(ver_str)
profile = {'name': summary.get('name'),
'description': summary.get('description'),
'version': new_ver,
}
logger.info('Updating NDEx network (%s) profile to %s',
network_id, profile)
profile_retries = 5
for _ in range(profile_retries):
try:
time.sleep(5)
nd.update_network_profile(network_id, profile)
break
except Exception as e:
logger.error('Could not update NDEx network profile.')
logger.error(e)
set_style(network_id, ndex_cred) | python | def update_network(cx_str, network_id, ndex_cred=None):
server = 'http://public.ndexbio.org'
username, password = get_default_ndex_cred(ndex_cred)
nd = ndex2.client.Ndex2(server, username, password)
try:
logger.info('Getting network summary...')
summary = nd.get_network_summary(network_id)
except Exception as e:
logger.error('Could not get NDEx network summary.')
logger.error(e)
return
# Update network content
try:
logger.info('Updating network...')
cx_stream = io.BytesIO(cx_str.encode('utf-8'))
nd.update_cx_network(cx_stream, network_id)
except Exception as e:
logger.error('Could not update NDEx network.')
logger.error(e)
return
# Update network profile
ver_str = summary.get('version')
new_ver = _increment_ndex_ver(ver_str)
profile = {'name': summary.get('name'),
'description': summary.get('description'),
'version': new_ver,
}
logger.info('Updating NDEx network (%s) profile to %s',
network_id, profile)
profile_retries = 5
for _ in range(profile_retries):
try:
time.sleep(5)
nd.update_network_profile(network_id, profile)
break
except Exception as e:
logger.error('Could not update NDEx network profile.')
logger.error(e)
set_style(network_id, ndex_cred) | [
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Parameters
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cx_str : str
String containing the CX content.
network_id : str
UUID of the network on NDEx.
ndex_cred : dict
A dictionary with the following entries:
'user': NDEx user name
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19,387 | sorgerlab/indra | indra/databases/ndex_client.py | set_style | def set_style(network_id, ndex_cred=None, template_id=None):
"""Set the style of the network to a given template network's style
Parameters
----------
network_id : str
The UUID of the NDEx network whose style is to be changed.
ndex_cred : dict
A dictionary of NDEx credentials.
template_id : Optional[str]
The UUID of the NDEx network whose style is used on the
network specified in the first argument.
"""
if not template_id:
template_id = "ea4ea3b7-6903-11e7-961c-0ac135e8bacf"
server = 'http://public.ndexbio.org'
username, password = get_default_ndex_cred(ndex_cred)
source_network = ndex2.create_nice_cx_from_server(username=username,
password=password,
uuid=network_id,
server=server)
source_network.apply_template(server, template_id)
source_network.update_to(network_id, server=server, username=username,
password=password) | python | def set_style(network_id, ndex_cred=None, template_id=None):
if not template_id:
template_id = "ea4ea3b7-6903-11e7-961c-0ac135e8bacf"
server = 'http://public.ndexbio.org'
username, password = get_default_ndex_cred(ndex_cred)
source_network = ndex2.create_nice_cx_from_server(username=username,
password=password,
uuid=network_id,
server=server)
source_network.apply_template(server, template_id)
source_network.update_to(network_id, server=server, username=username,
password=password) | [
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ndex_cred : dict
A dictionary of NDEx credentials.
template_id : Optional[str]
The UUID of the NDEx network whose style is used on the
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19,388 | sorgerlab/indra | indra/assemblers/pysb/bmi_wrapper.py | BMIModel.initialize | def initialize(self, cfg_file=None, mode=None):
"""Initialize the model for simulation, possibly given a config file.
Parameters
----------
cfg_file : Optional[str]
The name of the configuration file to load, optional.
"""
self.sim = ScipyOdeSimulator(self.model)
self.state = numpy.array(copy.copy(self.sim.initials)[0])
self.time = numpy.array(0.0)
self.status = 'initialized' | python | def initialize(self, cfg_file=None, mode=None):
self.sim = ScipyOdeSimulator(self.model)
self.state = numpy.array(copy.copy(self.sim.initials)[0])
self.time = numpy.array(0.0)
self.status = 'initialized' | [
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19,389 | sorgerlab/indra | indra/assemblers/pysb/bmi_wrapper.py | BMIModel.update | def update(self, dt=None):
"""Simulate the model for a given time interval.
Parameters
----------
dt : Optional[float]
The time step to simulate, if None, the default built-in time step
is used.
"""
# EMELI passes dt = -1 so we need to handle that here
dt = dt if (dt is not None and dt > 0) else self.dt
tspan = [0, dt]
# Run simulaton with initials set to current state
res = self.sim.run(tspan=tspan, initials=self.state)
# Set the state based on the result here
self.state = res.species[-1]
self.time += dt
if self.time > self.stop_time:
self.DONE = True
print((self.time, self.state))
self.time_course.append((self.time.copy(), self.state.copy())) | python | def update(self, dt=None):
# EMELI passes dt = -1 so we need to handle that here
dt = dt if (dt is not None and dt > 0) else self.dt
tspan = [0, dt]
# Run simulaton with initials set to current state
res = self.sim.run(tspan=tspan, initials=self.state)
# Set the state based on the result here
self.state = res.species[-1]
self.time += dt
if self.time > self.stop_time:
self.DONE = True
print((self.time, self.state))
self.time_course.append((self.time.copy(), self.state.copy())) | [
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19,390 | sorgerlab/indra | indra/assemblers/pysb/bmi_wrapper.py | BMIModel.set_value | def set_value(self, var_name, value):
"""Set the value of a given variable to a given value.
Parameters
----------
var_name : str
The name of the variable in the model whose value should be set.
value : float
The value the variable should be set to
"""
if var_name in self.outside_name_map:
var_name = self.outside_name_map[var_name]
print('%s=%.5f' % (var_name, 1e9*value))
if var_name == 'Precipitation':
value = 1e9*value
species_idx = self.species_name_map[var_name]
self.state[species_idx] = value | python | def set_value(self, var_name, value):
if var_name in self.outside_name_map:
var_name = self.outside_name_map[var_name]
print('%s=%.5f' % (var_name, 1e9*value))
if var_name == 'Precipitation':
value = 1e9*value
species_idx = self.species_name_map[var_name]
self.state[species_idx] = value | [
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19,391 | sorgerlab/indra | indra/assemblers/pysb/bmi_wrapper.py | BMIModel.get_value | def get_value(self, var_name):
"""Return the value of a given variable.
Parameters
----------
var_name : str
The name of the variable whose value should be returned
Returns
-------
value : float
The value of the given variable in the current state
"""
if var_name in self.outside_name_map:
var_name = self.outside_name_map[var_name]
species_idx = self.species_name_map[var_name]
return self.state[species_idx] | python | def get_value(self, var_name):
if var_name in self.outside_name_map:
var_name = self.outside_name_map[var_name]
species_idx = self.species_name_map[var_name]
return self.state[species_idx] | [
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19,392 | sorgerlab/indra | indra/assemblers/pysb/bmi_wrapper.py | BMIModel.get_input_var_names | def get_input_var_names(self):
"""Return a list of variables names that can be set as input.
Returns
-------
var_names : list[str]
A list of variable names that can be set from the outside
"""
in_vars = copy.copy(self.input_vars)
for idx, var in enumerate(in_vars):
if self._map_in_out(var) is not None:
in_vars[idx] = self._map_in_out(var)
return in_vars | python | def get_input_var_names(self):
in_vars = copy.copy(self.input_vars)
for idx, var in enumerate(in_vars):
if self._map_in_out(var) is not None:
in_vars[idx] = self._map_in_out(var)
return in_vars | [
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19,393 | sorgerlab/indra | indra/assemblers/pysb/bmi_wrapper.py | BMIModel.get_output_var_names | def get_output_var_names(self):
"""Return a list of variables names that can be read as output.
Returns
-------
var_names : list[str]
A list of variable names that can be read from the outside
"""
# Return all the variables that aren't input variables
all_vars = list(self.species_name_map.keys())
output_vars = list(set(all_vars) - set(self.input_vars))
# Re-map to outside var names if needed
for idx, var in enumerate(output_vars):
if self._map_in_out(var) is not None:
output_vars[idx] = self._map_in_out(var)
return output_vars | python | def get_output_var_names(self):
# Return all the variables that aren't input variables
all_vars = list(self.species_name_map.keys())
output_vars = list(set(all_vars) - set(self.input_vars))
# Re-map to outside var names if needed
for idx, var in enumerate(output_vars):
if self._map_in_out(var) is not None:
output_vars[idx] = self._map_in_out(var)
return output_vars | [
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19,394 | sorgerlab/indra | indra/assemblers/pysb/bmi_wrapper.py | BMIModel.make_repository_component | def make_repository_component(self):
"""Return an XML string representing this BMI in a workflow.
This description is required by EMELI to discover and load models.
Returns
-------
xml : str
String serialized XML representation of the component in the
model repository.
"""
component = etree.Element('component')
comp_name = etree.Element('comp_name')
comp_name.text = self.model.name
component.append(comp_name)
mod_path = etree.Element('module_path')
mod_path.text = os.getcwd()
component.append(mod_path)
mod_name = etree.Element('module_name')
mod_name.text = self.model.name
component.append(mod_name)
class_name = etree.Element('class_name')
class_name.text = 'model_class'
component.append(class_name)
model_name = etree.Element('model_name')
model_name.text = self.model.name
component.append(model_name)
lang = etree.Element('language')
lang.text = 'python'
component.append(lang)
ver = etree.Element('version')
ver.text = self.get_attribute('version')
component.append(ver)
au = etree.Element('author')
au.text = self.get_attribute('author_name')
component.append(au)
hu = etree.Element('help_url')
hu.text = 'http://github.com/sorgerlab/indra'
component.append(hu)
for tag in ('cfg_template', 'time_step_type', 'time_units',
'grid_type', 'description', 'comp_type', 'uses_types'):
elem = etree.Element(tag)
elem.text = tag
component.append(elem)
return etree.tounicode(component, pretty_print=True) | python | def make_repository_component(self):
component = etree.Element('component')
comp_name = etree.Element('comp_name')
comp_name.text = self.model.name
component.append(comp_name)
mod_path = etree.Element('module_path')
mod_path.text = os.getcwd()
component.append(mod_path)
mod_name = etree.Element('module_name')
mod_name.text = self.model.name
component.append(mod_name)
class_name = etree.Element('class_name')
class_name.text = 'model_class'
component.append(class_name)
model_name = etree.Element('model_name')
model_name.text = self.model.name
component.append(model_name)
lang = etree.Element('language')
lang.text = 'python'
component.append(lang)
ver = etree.Element('version')
ver.text = self.get_attribute('version')
component.append(ver)
au = etree.Element('author')
au.text = self.get_attribute('author_name')
component.append(au)
hu = etree.Element('help_url')
hu.text = 'http://github.com/sorgerlab/indra'
component.append(hu)
for tag in ('cfg_template', 'time_step_type', 'time_units',
'grid_type', 'description', 'comp_type', 'uses_types'):
elem = etree.Element(tag)
elem.text = tag
component.append(elem)
return etree.tounicode(component, pretty_print=True) | [
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"na... | Return an XML string representing this BMI in a workflow.
This description is required by EMELI to discover and load models.
Returns
-------
xml : str
String serialized XML representation of the component in the
model repository. | [
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] | 79a70415832c5702d7a820c7c9ccc8e25010124b | https://github.com/sorgerlab/indra/blob/79a70415832c5702d7a820c7c9ccc8e25010124b/indra/assemblers/pysb/bmi_wrapper.py#L336-L391 |
19,395 | sorgerlab/indra | indra/assemblers/pysb/bmi_wrapper.py | BMIModel._map_in_out | def _map_in_out(self, inside_var_name):
"""Return the external name of a variable mapped from inside."""
for out_name, in_name in self.outside_name_map.items():
if inside_var_name == in_name:
return out_name
return None | python | def _map_in_out(self, inside_var_name):
for out_name, in_name in self.outside_name_map.items():
if inside_var_name == in_name:
return out_name
return None | [
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19,396 | sorgerlab/indra | indra/tools/reading/pmid_reading/read_pmids.py | read_pmid | def read_pmid(pmid, source, cont_path, sparser_version, outbuf=None,
cleanup=True):
"Run sparser on a single pmid."
signal.signal(signal.SIGALRM, _timeout_handler)
signal.alarm(60)
try:
if (source is 'content_not_found'
or source.startswith('unhandled_content_type')
or source.endswith('failure')):
logger.info('No content read for %s.' % pmid)
return # No real content here.
if cont_path.endswith('.nxml') and source.startswith('pmc'):
new_fname = 'PMC%s%d.nxml' % (pmid, mp.current_process().pid)
os.rename(cont_path, new_fname)
try:
sp = sparser.process_nxml_file(
new_fname,
outbuf=outbuf,
cleanup=cleanup
)
finally:
if cleanup and os.path.exists(new_fname):
os.remove(new_fname)
elif cont_path.endswith('.txt'):
content_str = ''
with open(cont_path, 'r') as f:
content_str = f.read()
sp = sparser.process_text(
content_str,
outbuf=outbuf,
cleanup=cleanup
)
signal.alarm(0)
except Exception as e:
logger.error('Failed to process data for %s.' % pmid)
logger.exception(e)
signal.alarm(0)
return
if sp is None:
logger.error('Failed to run sparser on pmid: %s.' % pmid)
return
# At this point, we rewrite the PMID in the Evidence of Sparser
# Statements according to the actual PMID that was read.
sp.set_statements_pmid(pmid)
s3_client.put_reader_output('sparser', sp.json_stmts, pmid,
sparser_version, source)
return sp.statements | python | def read_pmid(pmid, source, cont_path, sparser_version, outbuf=None,
cleanup=True):
"Run sparser on a single pmid."
signal.signal(signal.SIGALRM, _timeout_handler)
signal.alarm(60)
try:
if (source is 'content_not_found'
or source.startswith('unhandled_content_type')
or source.endswith('failure')):
logger.info('No content read for %s.' % pmid)
return # No real content here.
if cont_path.endswith('.nxml') and source.startswith('pmc'):
new_fname = 'PMC%s%d.nxml' % (pmid, mp.current_process().pid)
os.rename(cont_path, new_fname)
try:
sp = sparser.process_nxml_file(
new_fname,
outbuf=outbuf,
cleanup=cleanup
)
finally:
if cleanup and os.path.exists(new_fname):
os.remove(new_fname)
elif cont_path.endswith('.txt'):
content_str = ''
with open(cont_path, 'r') as f:
content_str = f.read()
sp = sparser.process_text(
content_str,
outbuf=outbuf,
cleanup=cleanup
)
signal.alarm(0)
except Exception as e:
logger.error('Failed to process data for %s.' % pmid)
logger.exception(e)
signal.alarm(0)
return
if sp is None:
logger.error('Failed to run sparser on pmid: %s.' % pmid)
return
# At this point, we rewrite the PMID in the Evidence of Sparser
# Statements according to the actual PMID that was read.
sp.set_statements_pmid(pmid)
s3_client.put_reader_output('sparser', sp.json_stmts, pmid,
sparser_version, source)
return sp.statements | [
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19,397 | sorgerlab/indra | indra/tools/reading/pmid_reading/read_pmids.py | get_stmts | def get_stmts(pmids_unread, cleanup=True, sparser_version=None):
"Run sparser on the pmids in pmids_unread."
if sparser_version is None:
sparser_version = sparser.get_version()
stmts = {}
now = datetime.now()
outbuf_fname = 'sparser_%s_%s.log' % (
now.strftime('%Y%m%d-%H%M%S'),
mp.current_process().pid,
)
outbuf = open(outbuf_fname, 'wb')
try:
for pmid, result in pmids_unread.items():
logger.info('Reading %s' % pmid)
source = result['content_source']
cont_path = result['content_path']
outbuf.write(('\nReading pmid %s from %s located at %s.\n' % (
pmid,
source,
cont_path
)).encode('utf-8'))
outbuf.flush()
some_stmts = read_pmid(pmid, source, cont_path, sparser_version,
outbuf, cleanup)
if some_stmts is not None:
stmts[pmid] = some_stmts
else:
continue # We didn't get any new statements.
except KeyboardInterrupt as e:
logger.exception(e)
logger.info('Caught keyboard interrupt...stopping. \n'
'Results so far will be pickled unless '
'Keyboard interupt is hit again.')
finally:
outbuf.close()
print("Sparser logs may be found in %s" % outbuf_fname)
return stmts | python | def get_stmts(pmids_unread, cleanup=True, sparser_version=None):
"Run sparser on the pmids in pmids_unread."
if sparser_version is None:
sparser_version = sparser.get_version()
stmts = {}
now = datetime.now()
outbuf_fname = 'sparser_%s_%s.log' % (
now.strftime('%Y%m%d-%H%M%S'),
mp.current_process().pid,
)
outbuf = open(outbuf_fname, 'wb')
try:
for pmid, result in pmids_unread.items():
logger.info('Reading %s' % pmid)
source = result['content_source']
cont_path = result['content_path']
outbuf.write(('\nReading pmid %s from %s located at %s.\n' % (
pmid,
source,
cont_path
)).encode('utf-8'))
outbuf.flush()
some_stmts = read_pmid(pmid, source, cont_path, sparser_version,
outbuf, cleanup)
if some_stmts is not None:
stmts[pmid] = some_stmts
else:
continue # We didn't get any new statements.
except KeyboardInterrupt as e:
logger.exception(e)
logger.info('Caught keyboard interrupt...stopping. \n'
'Results so far will be pickled unless '
'Keyboard interupt is hit again.')
finally:
outbuf.close()
print("Sparser logs may be found in %s" % outbuf_fname)
return stmts | [
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19,398 | sorgerlab/indra | indra/tools/reading/pmid_reading/read_pmids.py | run_sparser | def run_sparser(pmid_list, tmp_dir, num_cores, start_index, end_index,
force_read, force_fulltext, cleanup=True, verbose=True):
'Run the sparser reader on the pmids in pmid_list.'
reader_version = sparser.get_version()
_, _, _, pmids_read, pmids_unread, _ =\
get_content_to_read(
pmid_list, start_index, end_index, tmp_dir, num_cores,
force_fulltext, force_read, 'sparser', reader_version
)
logger.info('Adjusting num cores to length of pmid_list.')
num_cores = min(len(pmid_list), num_cores)
logger.info('Adjusted...')
if num_cores is 1:
stmts = get_stmts(pmids_unread, cleanup=cleanup)
stmts.update({pmid: get_stmts_from_cache(pmid)[pmid]
for pmid in pmids_read.keys()})
elif num_cores > 1:
logger.info("Starting a pool with %d cores." % num_cores)
pool = mp.Pool(num_cores)
pmids_to_read = list(pmids_unread.keys())
N = len(pmids_unread)
dn = int(N/num_cores)
logger.info("Breaking pmids into batches.")
batches = []
for i in range(num_cores):
batches.append({
k: pmids_unread[k]
for k in pmids_to_read[i*dn:min((i+1)*dn, N)]
})
get_stmts_func = functools.partial(
get_stmts,
cleanup=cleanup,
sparser_version=reader_version
)
logger.info("Mapping get_stmts onto pool.")
unread_res = pool.map(get_stmts_func, batches)
logger.info('len(unread_res)=%d' % len(unread_res))
read_res = pool.map(get_stmts_from_cache, pmids_read.keys())
logger.info('len(read_res)=%d' % len(read_res))
pool.close()
logger.info('Multiprocessing pool closed.')
pool.join()
logger.info('Multiprocessing pool joined.')
stmts = {
pmid: stmt_list for res_dict in unread_res + read_res
for pmid, stmt_list in res_dict.items()
}
logger.info('len(stmts)=%d' % len(stmts))
return (stmts, pmids_unread) | python | def run_sparser(pmid_list, tmp_dir, num_cores, start_index, end_index,
force_read, force_fulltext, cleanup=True, verbose=True):
'Run the sparser reader on the pmids in pmid_list.'
reader_version = sparser.get_version()
_, _, _, pmids_read, pmids_unread, _ =\
get_content_to_read(
pmid_list, start_index, end_index, tmp_dir, num_cores,
force_fulltext, force_read, 'sparser', reader_version
)
logger.info('Adjusting num cores to length of pmid_list.')
num_cores = min(len(pmid_list), num_cores)
logger.info('Adjusted...')
if num_cores is 1:
stmts = get_stmts(pmids_unread, cleanup=cleanup)
stmts.update({pmid: get_stmts_from_cache(pmid)[pmid]
for pmid in pmids_read.keys()})
elif num_cores > 1:
logger.info("Starting a pool with %d cores." % num_cores)
pool = mp.Pool(num_cores)
pmids_to_read = list(pmids_unread.keys())
N = len(pmids_unread)
dn = int(N/num_cores)
logger.info("Breaking pmids into batches.")
batches = []
for i in range(num_cores):
batches.append({
k: pmids_unread[k]
for k in pmids_to_read[i*dn:min((i+1)*dn, N)]
})
get_stmts_func = functools.partial(
get_stmts,
cleanup=cleanup,
sparser_version=reader_version
)
logger.info("Mapping get_stmts onto pool.")
unread_res = pool.map(get_stmts_func, batches)
logger.info('len(unread_res)=%d' % len(unread_res))
read_res = pool.map(get_stmts_from_cache, pmids_read.keys())
logger.info('len(read_res)=%d' % len(read_res))
pool.close()
logger.info('Multiprocessing pool closed.')
pool.join()
logger.info('Multiprocessing pool joined.')
stmts = {
pmid: stmt_list for res_dict in unread_res + read_res
for pmid, stmt_list in res_dict.items()
}
logger.info('len(stmts)=%d' % len(stmts))
return (stmts, pmids_unread) | [
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19,399 | sorgerlab/indra | indra/statements/statements.py | get_all_descendants | def get_all_descendants(parent):
"""Get all the descendants of a parent class, recursively."""
children = parent.__subclasses__()
descendants = children[:]
for child in children:
descendants += get_all_descendants(child)
return descendants | python | def get_all_descendants(parent):
children = parent.__subclasses__()
descendants = children[:]
for child in children:
descendants += get_all_descendants(child)
return descendants | [
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] | 79a70415832c5702d7a820c7c9ccc8e25010124b | https://github.com/sorgerlab/indra/blob/79a70415832c5702d7a820c7c9ccc8e25010124b/indra/statements/statements.py#L2454-L2460 |
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