Source code for indra.explanation.model_checker.pysb

import numbers
import scipy.stats
import kappy
import logging
import itertools
import networkx as nx
from copy import deepcopy
from collections import Counter
from pysb import WILD, export, Observable, ComponentSet
from pysb.core import as_complex_pattern, ComponentDuplicateNameError
from . import ModelChecker, PathResult
from indra.explanation.reporting import stmt_from_rule
from indra.statements import *
from indra.assemblers.pysb import assembler as pa
from indra.assemblers.pysb.kappa_util import im_json_to_graph


logger = logging.getLogger(__name__)


[docs]class PysbModelChecker(ModelChecker): """Check a PySB model against a set of INDRA statements. Parameters ---------- model : pysb.Model A PySB model to check. statements : Optional[list[indra.statements.Statement]] A list of INDRA Statements to check the model against. agent_obs: Optional[list[indra.statements.Agent]] A list of INDRA Agents in a given state to be observed. do_sampling : bool Whether to use breadth-first search or weighted sampling to generate paths. Default is False (breadth-first search). seed : int Random seed for sampling (optional, default is None). model_stmts : list[indra.statements.Statement] A list of INDRA statements used to assemble PySB model. """ def __init__(self, model, statements=None, agent_obs=None, do_sampling=False, seed=None, model_stmts=None): super().__init__(model, statements, do_sampling, seed) if agent_obs: self.agent_obs = agent_obs else: self.agent_obs = [] self.model_stmts = model_stmts if model_stmts else [] # Influence map self._im = None # Map from statements to associated observables self.stmt_to_obs = {} # Map from agents to associated observables self.agent_to_obs = {} # Map between rules and downstream observables self.rule_obs_dict = {}
[docs] 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
[docs] 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')
[docs] 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
[docs] def get_graph(self, prune_im=True, prune_im_degrade=True, prune_im_subj_obj=False): """Get influence map and convert it to a graph with signed nodes.""" if self.graph: return self.graph im = self.get_im(force_update=True) if prune_im: self.prune_influence_map() if prune_im_degrade: self.prune_influence_map_degrade_bind_positive(self.model_stmts) if prune_im_subj_obj: self.prune_influence_map_subj_obj() self.graph = self.signed_edges_to_signed_nodes( im, prune_nodes=False, edge_signs={'pos': 1, 'neg': -1}) return self.graph
[docs] def process_statement(self, stmt): self.get_im() # Check if this is one of the statement types that we can check if not isinstance(stmt, (Modification, RegulateAmount, RegulateActivity, Influence)): logger.info('Statement type %s not handled' % stmt.__class__.__name__) return (None, None, 'STATEMENT_TYPE_NOT_HANDLED') # Get the polarity for the statement if isinstance(stmt, Modification): target_polarity = 1 if isinstance(stmt, RemoveModification) else 0 elif isinstance(stmt, RegulateActivity): target_polarity = 0 if stmt.is_activation else 1 elif isinstance(stmt, RegulateAmount): target_polarity = 1 if isinstance(stmt, DecreaseAmount) else 0 elif isinstance(stmt, Influence): target_polarity = 1 if stmt.overall_polarity() == -1 else 0 # 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: return (None, None, 'SUBJECT_MONOMERS_NOT_FOUND') 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.info("No observables for stmt %s, returning False" % stmt) return (None, None, 'OBSERVABLES_NOT_FOUND') obs_signed = [(obs, target_polarity) for obs in obs_names] result_code = None return subj_mps, obs_signed, result_code
[docs] def process_subject(self, subj_mp): if subj_mp is None: input_set_signed = None else: input_rule_set = self._get_input_rules(subj_mp) if not input_rule_set: logger.info('Input rules not found for %s' % subj_mp) return (None, 'INPUT_RULES_NOT_FOUND') input_set_signed = {(rule, 0) for rule in input_rule_set} return input_set_signed, None
def _get_input_rules(self, subj_mp): if subj_mp is None: raise ValueError("Cannot take None as an argument for subj_mp.") input_rules = _match_lhs(subj_mp, self.model.rules) logger.debug('Found %s input rules matching %s' % (len(input_rules), str(subj_mp))) # Filter to include only rules where the subj_mp is actually the # subject (i.e., don't pick up upstream rules where the subject # is itself a substrate/object) # FIXME: Note that this will eliminate rules where the subject # being checked is included on the left hand side as # a bound condition rather than as an enzyme. subj_rules = pa.rules_with_annotation(self.model, subj_mp.monomer.name, 'rule_has_subject') logger.debug('%d rules with %s as subject' % (len(subj_rules), subj_mp.monomer.name)) input_rule_set = set([r.name for r in input_rules]).intersection( set([r.name for r in subj_rules])) logger.debug('Final input rule set contains %d rules' % len(input_rule_set)) return input_rule_set def _sample_paths(self, input_rule_set, obs_name, target_polarity, max_paths=1, max_path_length=5): if max_paths == 0: raise ValueError("max_paths cannot be 0 for path sampling.") # Convert path polarity representation from 0/1 to 1/-1 def convert_polarities(path_list): return [tuple((n[0], 0 if n[1] > 0 else 1) for n in path) for path in path_list] pg_polarity = 0 if target_polarity > 0 else 1 nx_graph = self._im_to_signed_digraph(self.get_im()) # Add edges from dummy node to input rules source_node = 'SOURCE_NODE' for rule in input_rule_set: nx_graph.add_edge(source_node, rule, sign=0) # ------------------------------------------------- # Create combined paths_graph f_level, b_level = pg.get_reachable_sets(nx_graph, source_node, obs_name, max_path_length, signed=True) pg_list = [] for path_length in range(1, max_path_length+1): cfpg = pg.CFPG.from_graph( nx_graph, source_node, obs_name, path_length, f_level, b_level, signed=True, target_polarity=pg_polarity) pg_list.append(cfpg) combined_pg = pg.CombinedCFPG(pg_list) # Make sure the combined paths graph is not empty if not combined_pg.graph: pr = PathResult( False, 'NO_PATHS_FOUND', max_paths, max_path_length) pr.path_metrics = None pr.paths = [] return pr # Get a dict of rule objects rule_obj_dict = {} for ann in self.model.annotations: if ann.predicate == 'rule_has_object': rule_obj_dict[ann.subject] = ann.object # Get monomer initial conditions ic_dict = {} for mon in self.model.monomers: # FIXME: A hack that depends on the _0 convention ic_name = '%s_0' % mon.name # TODO: Wrap this in try/except? ic_param = self.model.parameters[ic_name] ic_value = ic_param.value ic_dict[mon.name] = ic_value # Set weights in PG based on model initial conditions for cur_node in combined_pg.graph.nodes(): edge_weights = {} rule_obj_list = [] edge_weights_by_gene = {} for u, v in combined_pg.graph.out_edges(cur_node): v_rule = v[1][0] # Get the object of the rule (a monomer name) rule_obj = rule_obj_dict.get(v_rule) if rule_obj: # Add to list so we can count instances by gene rule_obj_list.append(rule_obj) # Get the abundance of rule object from the initial # conditions # TODO: Wrap in try/except? ic_value = ic_dict[rule_obj] else: ic_value = 1.0 edge_weights[(u, v)] = ic_value edge_weights_by_gene[rule_obj] = ic_value # Get frequency of different rule objects rule_obj_ctr = Counter(rule_obj_list) # Normalize results by weight sum and gene frequency at this level edge_weight_sum = sum(edge_weights_by_gene.values()) edge_weights_norm = {} for e, v in edge_weights.items(): v_rule = e[1][1][0] rule_obj = rule_obj_dict.get(v_rule) if rule_obj: rule_obj_count = rule_obj_ctr[rule_obj] else: rule_obj_count = 1 edge_weights_norm[e] = ((v / float(edge_weight_sum)) / float(rule_obj_count)) # Add edge weights to paths graph nx.set_edge_attributes(combined_pg.graph, name='weight', values=edge_weights_norm) # Sample from the combined CFPG paths = combined_pg.sample_paths(max_paths) # ------------------------------------------------- if paths: pr = PathResult(True, 'PATHS_FOUND', max_paths, max_path_length) pr.path_metrics = None # Convert path polarity representation from 0/1 to 1/-1 pr.paths = convert_polarities(paths) # Strip off the SOURCE_NODE prefix pr.paths = [p[1:] for p in pr.paths] else: assert False pr = PathResult( False, 'NO_PATHS_FOUND', max_paths, max_path_length) pr.path_metrics = None pr.paths = [] return pr
[docs] 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
[docs] 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 successors 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)
[docs] 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)) logger.info('Removing %d edges from influence map' % len(edges_to_prune)) im.remove_edges_from(edges_to_prune)
[docs] def prune_influence_map_degrade_bind_positive(self, model_stmts): """Prune positive edges between X degrading and X forming a complex with Y.""" im = self.get_im() edges_to_prune = [] for r1, r2, data in im.edges(data=True): s1 = stmt_from_rule(r1, self.model, model_stmts) s2 = stmt_from_rule(r2, self.model, model_stmts) # Make sure this is a degradation/binding combo s1_is_degrad = (s1 and isinstance(s1, DecreaseAmount)) s2_is_bind = (s2 and isinstance(s2, Complex) and 'bind' in r2) if not s1_is_degrad or not s2_is_bind: continue # Make sure what is degraded is part of the complex if s1.obj.name not in [m.name for m in s2.members]: continue # Make sure we're dealing with a positive influence if data['sign'] == 1: edges_to_prune.append((r1, r2)) logger.info('Removing %d edges from influence map' % len(edges_to_prune)) im.remove_edges_from(edges_to_prune)
def _im_to_signed_digraph(self, im): edges = [] for e in im.edges(): edge_sign = _get_edge_sign(im, e) polarity = 0 if edge_sign > 0 else 1 edges.append((e[0], e[1], {'sign': polarity})) dg = nx.DiGraph() dg.add_edges_from(edges) return dg
def _find_sources_sample(im, target, sources, polarity, rule_obs_dict, agent_to_obs, agents_values): # Build up dict mapping observables to values obs_dict = {} for ag, val in agents_values.items(): obs_list = agent_to_obs[ag] for obs in obs_list: obs_dict[obs] = val sigma = 0.2 def obs_model(x): return scipy.stats.norm(x, sigma) def _sample_pred(im, target, rule_obs_dict, obs_model): preds = list(_get_signed_predecessors(im, target, 1)) if not preds: return None pred_scores = [] for pred, sign in preds: pred_score = 0 for affected_obs, rule_obs_sign in rule_obs_dict[pred]: pred_sign = sign * rule_obs_sign # Check to see if this observable is in the data logger.info('%s %s: effect %s %s' % (pred, sign, affected_obs, pred_sign)) measured_val = obs_dict.get(affected_obs) if measured_val: logger.info('Actual: %s' % measured_val) # The tail probability of the real value being above 1 tail_prob = obs_model(measured_val).cdf(1) pred_score += (tail_prob if pred_sign == 1 else 1-tail_prob) pred_scores.append(pred_score) # Normalize scores pred_scores = np.array(pred_scores) / np.sum(pred_scores) pred_idx = np.random.choice(range(len(preds)), p=pred_scores) pred = preds[pred_idx] return pred preds = [] for i in range(100): pred = _sample_pred(im, target, rule_obs_dict, obs_model) preds.append(pred[0])
[docs]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
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) 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() 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']) 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 def _add_activity_to_agent(agent, act_type, is_active): # Default to active, and return polarity if it's an inhibition new_act = ActivityCondition(act_type, True) # Check if this state already exists if agent.activity is not None and agent.activity.equals(new_act): return agent new_agent = deepcopy(agent) new_agent.activity = new_act polarity = 1 if is_active else -1 return (new_agent, polarity) 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 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 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 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))