Source code for indra.assemblers.html.assembler

Format a set of INDRA Statements into an HTML-formatted report which also
supports curation.

import re
import uuid
import logging
import itertools
from collections import OrderedDict
from os.path import abspath, dirname, join

from jinja2 import Environment, FileSystemLoader

from indra.statements import *
from indra.assemblers.english import EnglishAssembler
from indra.databases import get_identifiers_url
from indra.util.statement_presentation import group_and_sort_statements, \
    make_top_level_label_from_names_key, make_stmt_from_sort_key

logger = logging.getLogger(__name__)
HERE = dirname(abspath(__file__))

loader = FileSystemLoader(join(HERE, 'templates'))
env = Environment(loader=loader)

default_template = env.get_template('indra/statements_view.html')

color_schemes = {
    'dark': ['#b2df8a', '#000099', '#6a3d9a', '#1f78b4', '#fdbf6f', '#ff7f00',
             '#cab2d6', '#fb9a99', '#a6cee3', '#33a02c', '#b15928', '#e31a1c'],
    'light': ['#bc80bd', '#fccde5', '#b3de69', '#80b1d3', '#fb8072', '#bebada',
              '#fdb462', '#8dd3c7', '#ffffb3', '#d9d9d9', '#ccebc5', '#ffed6f']

def color_gen(scheme):
    while True:
        for color in color_schemes[scheme]:
            yield color

    ('databases', {'color': 'black',
                   'sources': dict(zip(['phosphosite', 'cbn', 'pc11',
                                        'biopax', 'bel_lc',
                                        'signor', 'biogrid', 'tas',
                                        'lincs_drug', 'hprd', 'trrust'],
    ('reading', {'color': 'white',
                 'sources': dict(zip(['geneways', 'tees', 'isi', 'trips',
                                      'rlimsp', 'medscan', 'sparser', 'reach'],

SRC_KEY_DICT = {src: src for _, d in SOURCE_COLORS
                for src in d['sources'].keys()}

[docs]class HtmlAssembler(object): """Generates an HTML-formatted report from INDRA Statements. The HTML report format includes statements formatted in English (by the EnglishAssembler), text and metadata for the Evidence object associated with each Statement, and a Javascript-based curation interface linked to the INDRA database (access permitting). The interface allows for curation of statements at the evidence level by letting the user specify type of error and (optionally) provide a short description of of the error. Parameters ---------- statements : Optional[list[indra.statements.Statement]] A list of INDRA Statements to be added to the assembler. Statements can also be added using the add_statements method after the assembler has been instantiated. summary_metadata : Optional[dict] Dictionary of statement corpus metadata such as that provided by the INDRA REST API. Default is None. Each value should be a concise summary of O(1), not of order the length of the list, such as the evidence totals. The keys should be informative human-readable strings. ev_totals : Optional[dict] A dictionary of the total evidence available for each statement indexed by hash. Default: None source_counts : Optional[dict] A dictionary of the itemized evidence counts, by source, available for each statement, indexed by hash. Default: None. title : str The title to be printed at the top of the page. db_rest_url : Optional[str] The URL to a DB REST API to use for links out to further evidence. If given, this URL will be prepended to links that load additional evidence for a given Statement. One way to obtain this value is from the configuration entry indra.config.get_config('INDRA_DB_REST_URL'). If None, the URLs are constructed as relative links. Default: None Attributes ---------- statements : list[indra.statements.Statement] A list of INDRA Statements to assemble. model : str The HTML report formatted as a single string. metadata : dict Dictionary of statement list metadata such as that provided by the INDRA REST API. ev_totals : dict A dictionary of the total evidence available for each statement indexed by hash. db_rest_url : str The URL to a DB REST API. """ def __init__(self, statements=None, summary_metadata=None, ev_totals=None, source_counts=None, curation_dict=None, title='INDRA Results', db_rest_url=None): self.title = title self.statements = [] if statements is None else statements self.metadata = {} if summary_metadata is None \ else summary_metadata self.ev_totals = {} if ev_totals is None else ev_totals self.source_counts = {} if source_counts is None else source_counts self.curation_dict = {} if curation_dict is None else curation_dict self.db_rest_url = db_rest_url self.model = None
[docs] def add_statements(self, statements): """Add a list of Statements to the assembler. Parameters ---------- statements : list[indra.statements.Statement] A list of INDRA Statements to be added to the assembler. """ self.statements += statements
[docs] def make_json_model(self, with_grouping=True): """Return the JSON used to create the HTML display. Parameters ---------- with_grouping : bool If True, statements will be grouped under multiple sub-headings. If False, all headings will be collapsed into one on every level, with all statements placed under a single heading. Returns ------- json : dict A complexly structured JSON dict containing grouped statements and various metadata. """ # Get an iterator over the statements, carefully grouped. stmt_rows = group_and_sort_statements( self.statements, self.ev_totals if self.ev_totals else None, self.source_counts if self.source_counts else None) # Do some extra formatting. stmts = OrderedDict() agents = {} previous_stmt_set = set() for row in stmt_rows: # Distinguish between the cases with source counts and without. if self.source_counts: key, verb, stmts_group, tl_counts, src_counts = row else: key, verb, stmts_group = row src_counts = None tl_counts = None curr_stmt_set = {s.get_hash() for s in stmts_group} if curr_stmt_set == previous_stmt_set: continue else: previous_stmt_set = curr_stmt_set # We will keep track of some of the meta data for this stmt group. # NOTE: Much of the code relies heavily on the fact that the Agent # objects in `meta_agents` are references to the Agent's in the # Statement object `meta_stmts`. meta_agents = [] meta_stmt = make_stmt_from_sort_key(key, verb, meta_agents) meta_agent_dict = { ag for ag in meta_agents if ag is not None} # This will now be ordered by prevalence and entity pairs. stmt_info_list = [] for stmt in stmts_group: stmt_hash = stmt.get_hash(shallow=True) # Try to accumulate db refs in the meta agents. for ag in stmt.agent_list(): if ag is None: continue # Get the corresponding meta-agent meta_ag = meta_agent_dict.get( if not meta_ag: continue _cautiously_merge_refs(ag, meta_ag) # Format some strings nicely. ev_list = _format_evidence_text(stmt, self.curation_dict) english = _format_stmt_text(stmt) if self.ev_totals: tot_ev = self.ev_totals.get(int(stmt_hash), '?') if tot_ev == '?': logger.warning('The hash %s was not found in the ' 'evidence totals dict.' % stmt_hash) evidence_count_str = '%s / %s' % (len(ev_list), tot_ev) else: evidence_count_str = str(len(ev_list)) stmt_info_list.append({ 'hash': str(stmt_hash), 'english': english, 'evidence': ev_list, 'evidence_count': evidence_count_str, 'source_count': self.source_counts.get(stmt_hash)}) # Clean out invalid fields from the meta agents. for ag in meta_agents: if ag is None: continue for dbn, dbid in list(ag.db_refs.items()): if isinstance(dbid, set):"Removing %s from refs due to too many " "matches: %s" % (dbn, dbid)) del ag.db_refs[dbn] # Update the top level grouping. tl_names = key[1] if with_grouping: tl_key = '-'.join([str(name) for name in tl_names]) tl_agents = {name: Agent(name) for name in tl_names if name is not None} for ag in tl_agents.values(): meta_ag = meta_agent_dict.get( if meta_ag is None: continue ag.db_refs.update(meta_ag.db_refs) tl_label = None else: tl_key = 'all-statements' tl_label = 'All Statements' tl_agents = None if tl_key not in stmts.keys(): agents[tl_key] = tl_agents stmts[tl_key] = {'html_key': str(uuid.uuid4()), 'source_counts': tl_counts, 'stmts_formatted': [], 'names': tl_names} if tl_label: stmts[tl_key]['label'] = tl_label elif with_grouping: for name, existing_ag in agents[tl_key].items(): new_ag = tl_agents.get(name) if new_ag is None: continue _cautiously_merge_refs(new_ag, existing_ag) # Generate the short name for the statement and a unique key. existing_list = stmts[tl_key]['stmts_formatted'] if with_grouping or not existing_list: if with_grouping: # See note above: this is where the work on meta_agents is # applied because the agents are references. short_name = _format_stmt_text(meta_stmt) short_name_key = str(uuid.uuid4()) else: short_name = "All Statements Sub Group" short_name_key = "all-statements-sub-group" new_dict = {'short_name': short_name, 'short_name_key': short_name_key, 'stmt_info_list': stmt_info_list, 'src_counts': src_counts} existing_list.append(new_dict) else: existing_list[0]['stmt_info_list'].extend(stmt_info_list) if src_counts: existing_list[0]['src_counts'].update(src_counts) # Add labels for each top level group (tlg). if with_grouping: for tl_key, tlg in stmts.items(): tl_agents = list(agents[tl_key].values()) for ag in tl_agents: for dbn, dbid in list(ag.db_refs.items()): if isinstance(dbid, set):"Removing %s from top level refs " "due to multiple matches: %s" % (dbn, dbid)) del ag.db_refs[dbn] tl_label = make_top_level_label_from_names_key(tlg['names']) tl_label = re.sub("<b>(.*?)</b>", r"\1", tl_label) tl_label = tag_agents(tl_label, tl_agents) tlg['label'] = tl_label return stmts
[docs] def make_model(self, template=None, with_grouping=True, **template_kwargs): """Return the assembled HTML content as a string. Parameters ---------- template : a Template object Manually pass a Jinja template to be used in generating the HTML. The template is responsible for rendering essentially the output of `make_json_model`. with_grouping : bool If True, statements will be grouped under multiple sub-headings. If False, all headings will be collapsed into one on every level, with all statements placed under a single heading. All other keyword arguments are passed along to the template. If you are using a custom template with args that are not passed below, this is how you pass them. Returns ------- str The assembled HTML as a string. """ tl_stmts = self.make_json_model(with_grouping) metadata = {k.replace('_', ' ').title(): v for k, v in self.metadata.items() if not isinstance(v, list) and not isinstance(v, dict)} if self.db_rest_url and not self.db_rest_url.endswith('statements'): db_rest_url = self.db_rest_url + '/statements' else: db_rest_url = None # Fill the template. if template is None: template = default_template if self.source_counts and 'source_key_dict' not in template_kwargs: template_kwargs['source_key_dict'] = SRC_KEY_DICT if 'source_colors' not in template_kwargs: template_kwargs['source_colors'] = SOURCE_COLORS self.model = template.render(stmt_data=tl_stmts, metadata=metadata, title=self.title, db_rest_url=db_rest_url, **template_kwargs) return self.model
[docs] def append_warning(self, msg): """Append a warning message to the model to expose issues.""" assert self.model is not None, "You must already have run make_model!" addendum = ('\t<span style="color:red;">(CAUTION: %s occurred when ' 'creating this page.)</span>' % msg) self.model = self.model.replace(self.title, self.title + addendum) return self.model
[docs] def save_model(self, fname): """Save the assembled HTML into a file. Parameters ---------- fname : str The path to the file to save the HTML into. """ if self.model is None: self.make_model() with open(fname, 'wb') as fh: fh.write(self.model.encode('utf-8'))
def _format_evidence_text(stmt, curation_dict=None): """Returns evidence metadata with highlighted evidence text. Parameters ---------- stmt : indra.Statement The Statement with Evidence to be formatted. Returns ------- list of dicts List of dictionaries corresponding to each Evidence object in the Statement's evidence list. Each dictionary has keys 'source_api', 'pmid' and 'text', drawn from the corresponding fields in the Evidence objects. The text entry of the dict includes `<span>` tags identifying the agents referenced by the Statement. """ if curation_dict is None: curation_dict = {} def get_role(ag_ix): if isinstance(stmt, Complex) or \ isinstance(stmt, SelfModification) or \ isinstance(stmt, ActiveForm) or isinstance(stmt, Conversion) or\ isinstance(stmt, Translocation): return 'other' else: assert len(stmt.agent_list()) == 2, (len(stmt.agent_list()), type(stmt)) return 'subject' if ag_ix == 0 else 'object' ev_list = [] for ix, ev in enumerate(stmt.evidence): # Expand the source api to include the sub-database if ev.source_api == 'biopax' and \ 'source_sub_id' in ev.annotations and \ ev.annotations['source_sub_id']: source_api = '%s:%s' % (ev.source_api, ev.annotations['source_sub_id']) else: source_api = ev.source_api # Prepare the evidence text if ev.text is None: format_text = None else: indices = [] for ix, ag in enumerate(stmt.agent_list()): if ag is None: continue # If the statement has been preassembled, it will have # this entry in annotations try: ag_text = ev.annotations['agents']['raw_text'][ix] if ag_text is None: raise KeyError # Otherwise we try to get the agent text from db_refs except KeyError: ag_text = ag.db_refs.get('TEXT') if ag_text is None: continue role = get_role(ix) # Get the tag with the correct badge tag_start = '<span class="badge badge-%s">' % role tag_close = '</span>' # Build up a set of indices indices += [(m.start(), m.start() + len(ag_text), ag_text, tag_start, tag_close) for m in re.finditer(re.escape(ag_text), ev.text)] format_text = tag_text(ev.text, indices) curation_key = (stmt.get_hash(), ev.source_hash) num_curations = len(curation_dict.get(curation_key, [])) ev_list.append({'source_api': source_api, 'pmid': ev.pmid, 'text_refs': ev.text_refs, 'text': format_text, 'source_hash': str(ev.source_hash), 'num_curations': num_curations}) return ev_list def _format_stmt_text(stmt): # Get the English assembled statement ea = EnglishAssembler([stmt]) english = ea.make_model() if not english: english = str(stmt) return tag_agents(english, stmt.agent_list()) def _cautiously_merge_refs(from_ag, to_ag): # Check the db refs for this agent against the meta agent for dbn, dbid in from_ag.db_refs.items(): if dbn == 'TEXT': continue meta_dbid = to_ag.db_refs.get(dbn) if isinstance(meta_dbid, set): # If we've already marked this one add to the set. to_ag.db_refs[dbn].add(dbid) elif meta_dbid is not None and meta_dbid != dbid: # If we've seen it before and don't agree, mark it. to_ag.db_refs[dbn] = {to_ag.db_refs[dbn], dbid} elif meta_dbid is None: # Otherwise, add it. to_ag.db_refs[dbn] = dbid def tag_agents(english, agents): indices = [] for ag in agents: if ag is None or not continue url = id_url(ag) if url is None: continue # Build up a set of indices tag_start = "<a href='%s' target='_blank'>" % url tag_close = "</a>" found = False for m in re.finditer(re.escape(, english): index = (m.start(), m.start() + len(,, tag_start, tag_close) indices.append(index) found = True if not found and \ english.startswith(re.escape( index = (0, len(,, tag_start, tag_close) indices.append(index) return tag_text(english, indices) def id_url(ag): # Return identifier URLs in a prioritized order for db_name in ('FPLX', 'HGNC', 'UP', 'GO', 'MESH', 'CHEBI', 'PUBCHEM', 'HMDB', 'IP', 'PF', 'NXPFA', 'MIRBASEM', 'MIRBASE', 'NCIT', 'UN', 'HUME', 'CWMS', 'SOFIA'): if db_name in ag.db_refs: # Handle a special case where a list of IDs is given if isinstance(ag.db_refs[db_name], list): db_id = ag.db_refs[db_name][0] if db_name == 'CHEBI': if not db_id.startswith('CHEBI'): db_id = 'CHEBI:%s' % db_id elif db_name in ('UN', 'HUME'): db_id = db_id[0] else: db_id = ag.db_refs[db_name] return get_identifiers_url(db_name, db_id)
[docs]def tag_text(text, tag_info_list): """Apply start/end tags to spans of the given text. Parameters ---------- text : str Text to be tagged tag_info_list : list of tuples Each tuple refers to a span of the given text. Fields are `(start_ix, end_ix, substring, start_tag, close_tag)`, where substring, start_tag, and close_tag are strings. If any of the given spans of text overlap, the longest span is used. Returns ------- str String where the specified substrings have been surrounded by the given start and close tags. """ # Check to tags for overlap and if there is any, return the subsumed # range. Return None if no overlap. def overlap(t1, t2): if range(max(t1[0], t2[0]), min(t1[1]-1, t2[1]-1)+1): if t1[1] - t1[0] >= t2[1] - t2[0]: return t2 else: return t1 else: return None # Remove subsumed tags for t1, t2 in list(itertools.combinations(tag_info_list, 2)): subsumed_tag = overlap(t1, t2) if subsumed_tag is not None: # Delete the subsumed tag from the list try: tag_ix = tag_info_list.index(subsumed_tag) del tag_info_list[tag_ix] # Ignore case where tag has already been deleted except ValueError: pass # Sort the indices by their start position tag_info_list.sort(key=lambda x: x[0]) # Now, add the marker text for each occurrence of the strings format_text = '' start_pos = 0 for i, j, ag_text, tag_start, tag_close in tag_info_list: # Add the text before this agent, if any format_text += text[start_pos:i] # Add wrapper for this entity format_text += tag_start + ag_text + tag_close # Now set the next start position start_pos = j # Add the last section of text format_text += text[start_pos:] return format_text