path: root/Code/html_network.py

# Usage: python html_network.py -f edges.txt -r parameter_for_buildRmatrix.txt -c parameter_for_buildCmatrix.txt -n parameter_for_net.txt
# Purpose: make a summary.html plus its associated files (stored in folder edges) given an edge file (edges.txt).  These files will be served as static files online.  The total volumn of these static files can be quite large, as we get one file for each edge.
#
#          This program is used in update_network.py.
#
# Created on 26 Feb 2017, SLCU, Hui
# Last modified 24 Mar 2017, SLCU, Hui
# Last modified 21 Apr 2017, SLCU, Hui [w2ui for regulatee and regulator tables]
# Last modified 19 Jun 2017, SLCU, Hui [changed text_to_dict to fit the updated RNA_SEQ_INFO_DATABASE]
# Last modified 29 Jun 2017, SLCU, Hui [added key 'sample_id' in text_to_dict]
# Last reviewed 01 Fen 2019, Hui [code review]

import sys, os
import networkx as nx # Run this command on MacOS: export PYTHONPATH="/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages
import numpy as np
from optparse import OptionParser
from itertools import islice
import operator
from datetime import datetime
import collections, re, glob
from geneid2name import make_gene_name_AGI_map_dict
from param4net import make_global_param_dict

## Global variables
REGENERATE_ALL_EDGE_FILES  = 'YES'
INDEX_PAGE                 = '../Webapp/static/summary.html' # change
DIR_NAME                   = '../Webapp/static/edges'        # change

RNA_SEQ_INFO_DATABASE   = '../Data/information/rnaseq_info_database.txt'
RNA_SEQ_INFO_DATABASE_JSON   = '../Data/information/rnaseq_info_database.json'
RNA_SEQ_INFO_HTML_PAGE  = 'rnaseqinfo.html'

GENE_ID_TO_GENE_NAME    = '../Data/information/AGI-to-gene-names_v2.txt'
CHIP_SEQ_INFO_HTML_PAGE = 'chipseqinfo.html'

RAKE_STOPLIST_FILE      = '../Data/information/SmartStoplist.txt'

JSON_DIR                = '../Data/history/expr/json'   # move this directory to the same place as this file html_network.py, for gene expression scatterplot
JSON_DIR2               = '../Data/history/bind/json2'  # for displaying binding plots
C3_DIR                  = './depend/c3'
W2UI_DIR                = './depend/w2ui'
C3_FILES                = ['c3.min.css', 'c3.min.js', 'd3.min.js', 'scatterplot.js', 'barchart.js'] # for displaying scatterplots and binding strength
W2UI_FILES              = ['jquery.min.for.w2ui.js', 'w2ui.min.js', 'w2ui.min.css']
ALPHA                   = 0.6 # weight indicating the importance of number of RNA-seq experiments
## function definitions

### RAKE rapid automatic keyphrase extraction (NOT USED).  Skip it and jump to my function.

def is_number(s):
    try:
        float(s) if '.' in s else int(s)
        return True
    except ValueError:
        return False


def load_stop_words(stop_word_file):
    """
    Utility function to load stop words from a file and return as a list of words
    @param stop_word_file Path and file name of a file containing stop words.
    @return list A list of stop words.
    """
    stop_words = []
    for line in open(stop_word_file):
        if line.strip()[0:1] != "#":
            for word in line.split():  # in case more than one per line
                stop_words.append(word)
    return stop_words


def separate_words(text, min_word_return_size):
    """
    Utility function to return a list of all words that are have a length greater than a specified number of characters.
    @param text The text that must be split in to words.
    @param min_word_return_size The minimum no of characters a word must have to be included.
    """
    splitter = re.compile('[^a-zA-Z0-9_\\+\\-/]')
    words = []
    for single_word in splitter.split(text):
        current_word = single_word.strip().lower()
        #leave numbers in phrase, but don't count as words, since they tend to invalidate scores of their phrases
        if len(current_word) > min_word_return_size and current_word != '' and not is_number(current_word):
            words.append(current_word)
    return words


def split_sentences(text):
    """
    Utility function to return a list of sentences.
    @param text The text that must be split in to sentences.
    """
    sentence_delimiters = re.compile(u'[.!?,;:\t\\\\"\\(\\)\\\'\u2019\u2013]|\\s\\-\\s')
    sentences = sentence_delimiters.split(text)
    return sentences


def build_stop_word_regex(stop_word_file_path):
    stop_word_list = load_stop_words(stop_word_file_path)
    stop_word_regex_list = []
    for word in stop_word_list:
        word_regex = r'\b' + word + r'(?![\w-])'  # added look ahead for hyphen
        stop_word_regex_list.append(word_regex)
    stop_word_pattern = re.compile('|'.join(stop_word_regex_list), re.IGNORECASE)
    return stop_word_pattern


def generate_candidate_keywords(sentence_list, stopword_pattern):
    phrase_list = []
    for s in sentence_list:
        tmp = re.sub(stopword_pattern, '|', s.strip())
        phrases = tmp.split("|")
        for phrase in phrases:
            phrase = phrase.strip().lower()
            if phrase != "":
                phrase_list.append(phrase)
    return phrase_list


def calculate_word_scores(phraseList):
    word_frequency = {}
    word_degree = {}
    for phrase in phraseList:
        word_list = separate_words(phrase, 0)
        word_list_length = len(word_list)
        word_list_degree = word_list_length - 1
        #if word_list_degree > 3: word_list_degree = 3 #exp.
        for word in word_list:
            word_frequency.setdefault(word, 0)
            word_frequency[word] += 1
            word_degree.setdefault(word, 0)
            word_degree[word] += word_list_degree  #orig.
            #word_degree[word] += 1/(word_list_length*1.0) #exp.
    for item in word_frequency:
        word_degree[item] = word_degree[item] + word_frequency[item]

    # Calculate Word scores = deg(w)/frew(w)
    word_score = {}
    for item in word_frequency:
        word_score.setdefault(item, 0)
        word_score[item] = word_degree[item] / (word_frequency[item] * 1.0)  #orig.
    #word_score[item] = word_frequency[item]/(word_degree[item] * 1.0) #exp.
    return word_score


def generate_candidate_keyword_scores(phrase_list, word_score):
    keyword_candidates = {}
    for phrase in phrase_list:
        keyword_candidates.setdefault(phrase, 0)
        word_list = separate_words(phrase, 0)
        candidate_score = 0
        for word in word_list:
            candidate_score += word_score[word]
        keyword_candidates[phrase] = candidate_score
    return keyword_candidates


class Rake(object):
    def __init__(self, stop_words_path):
        self.stop_words_path = stop_words_path
        self.__stop_words_pattern = build_stop_word_regex(stop_words_path)

    def run(self, text):
        sentence_list = split_sentences(text)

        phrase_list = generate_candidate_keywords(sentence_list, self.__stop_words_pattern)

        word_scores = calculate_word_scores(phrase_list)

        keyword_candidates = generate_candidate_keyword_scores(phrase_list, word_scores)

        sorted_keywords = sorted(keyword_candidates.iteritems(), key=operator.itemgetter(1), reverse=True)
        return sorted_keywords


### my functions

def get_id(s):
    lst = s.split(' ')
    return lst[0]

def get_name(s, agi2name_dict):
    s = s.strip()
    if s == '':
        return '???'
    if s in agi2name_dict:
        name = agi2name_dict[s]
        lst = name.split(';')
        return lst[0]
    else:
        return s

def show_path(G, lst, options):
    s = ''
    n = len(lst)
    count = 0
    for i in range(n-1):
        u = lst[i]
        v = lst[i+1]
        e = G.get_edge_data(u, v)
        padding = ''
        if e['weight'] > 0:
            s += padding + '%s\t(%s,%2.2f)\t-> ' % (u, e['color'], e['weight']) + ('[%s]\n' % (e['condition']) if options.cond==True else '\n')
        else:
            s += padding + '%s\t(%s,%2.2f)\t|| ' % (u, e['color'], e['weight']) + ('[%s]\n' % (e['condition']) if options.cond==True else '\n')
        count += 4
    print(s + v)
    print('')


def k_shortest_paths(G, source, target, k, weight=None):
    return list(islice(nx.shortest_simple_paths(G, source, target, weight=weight), k))

def not_bad_line(s):
    if s.strip() == '':
        return False
    if 'WARNING' in s:
        return False
    if 'number' in s:
        return False
    if 'Need' in s:
        return False
    if 'Error' in s:
        return False
    if 'Too' in s:
        return False
    if not s.startswith('AT'): # need modification for other organisms
        return False
    return True

def build_network_from_file(fname):
    ''' build the network from the big edge file, edges.txt. '''
    MG = nx.MultiDiGraph(max_rsubset_size=1400) # maximum size of conditionR list

    max_rsize = 0
    
    f = open(fname)
    cond_list = []
    for line in f:
        line = line.strip()
        if not_bad_line(line):
            lst = line.split('\t')
            g1 = lst[0].split()[0] # target gene id
            g2 = lst[1].split()[0] # source gene id
            MG.add_node(g1)
            MG.add_node(g2)
            edge_type = lst[3] # all or mix

            condR_lst = []
            condC_lst = []
            model_fit_measure = '?'
            if len(lst) > 6:
                condR = lst[4]
                condR_lst = lst[4].split()
                condC = lst[5]
                condC_lst = lst[5].split()
                model_fit_measure = lst[6]
                if model_fit_measure == '.' and edge_type == 'mix':
                    model_fit_measure = '-1000.0'  # RNA-seq samples were selected using post.translation.3.  Search '-1000.0' in QUICKSTART.html for more detail.
                if '=' in model_fit_measure: # in early days, the log likelihood field looks like loglik=-1234.2
                    model_fit_measure = model_fit_measure.split('=')[1] # remove 'loglik='

            size_condR = len(condR_lst)
            if size_condR > max_rsize:
                max_rsize = size_condR

            create_date = '20161201' # default 2016-12-01
            if len(lst) > 7: # has date information, date information is the 8th column
                create_date = lst[7]

            metric = float(lst[8])    # appended by update_network.py
            tissue_or_method = lst[9] # appended by update_network.py

            score = float(lst[2]) # strength of various kinds of relationship.
            
            # Not sure why I distinguished 'all' and 'mix', as the add_edge statements are the same.
            if edge_type == 'all':
                if score > 0:
                    MG.add_edge(g2, g1, action='>', weight=score, metric=metric, conditionR=condR_lst, conditionC=condC_lst, rmse=model_fit_measure, edge_date=create_date, subset=tissue_or_method)
                elif score < 0:
                    MG.add_edge(g2, g1, action='X', weight=score, metric=metric, conditionR=condR_lst, conditionC=condC_lst, rmse=model_fit_measure, edge_date=create_date, subset=tissue_or_method)
            if edge_type == 'mix':
                if score > 0:
                    MG.add_edge(g2, g1, action='>', weight=score, metric=metric, conditionR=condR_lst, conditionC=condC_lst, rmse=model_fit_measure, edge_date=create_date, subset=tissue_or_method)
                elif score < 0:
                    MG.add_edge(g2, g1, action='X', weight=score, metric=metric, conditionR=condR_lst, conditionC=condC_lst, rmse=model_fit_measure, edge_date=create_date, subset=tissue_or_method)

    f.close()

    MG.graph['max_rsubset_size'] = max_rsize

    return MG


def get_value(s, delimit):
    ''' Get the value after the first delimit. '''
    lst = s.split(delimit, 1) # split by the first delimit
    return lst[1].strip()

def text_to_dict(fname, ignore_first_line=True):
    ''' fname is RNA_SEQ_INFO_DATABASE (see above).  '''
    if not os.path.exists(fname):
        print('html_network.py: you must provide %s. See parse_ena_xml.py on how to make it.' % (fname))
        sys.exit()

    d = {}
    f = open(fname)
    lines = f.readlines()
    if ignore_first_line == True:
        lines = lines[1:]
    f.close()
    for line in lines:
        line = line.strip()
        lst = line.split('\t')
        run_id = lst[0]
        d[run_id] = {}  # run_id is ENA/SRA run id
        d[run_id]['experiment_id'] = lst[2]
        if len(lst) < 5:
            continue
        d[run_id]['project_id'] = lst[4]
        d[run_id]['sample_id'] = lst[1].split('...')[0]
        d[run_id]['description'] = '\t'.join(lst[5:])
    return d

def get_true_run_id(s):
    s = s[2:] # s looks like R0SRR1548701XX, so 2 is the position of 'S'.
    index = s.find('X')
    if index >= 0:  # we don't need X
        return s[0:index]
    return s

def make_rna_seq_info_dict(fname):
    db_dict = text_to_dict(RNA_SEQ_INFO_DATABASE)
    f = open(fname)
    d = {}
    for line in f:
        line = line.strip()
        if line.startswith('@'):
            run_id = line[1:] # run_id is sth like R0SRR1548701XX
            run_id2 = get_true_run_id(run_id)
            if run_id2 in db_dict:
                d[run_id] = db_dict[run_id2]
            else:
                d[run_id] = {'project_id':'#', 'experiment_id':'#', 'sample_id':'#', 'description':'NA'}
            
    f.close()
    return d


def make_rna_seq_info_html_page(fname, d):
    f = open(fname, 'w')
    f.write('<html><head><style> body {font-family:\"HelveticaNeue-Light\", \"Helvetica Neue Light\", \"Helvetica neue\"} table {table-layout: fixed; width: 800px;}</style></head><body>')
    for k in sorted(d.keys()):
        run_link = 'http://www.ebi.ac.uk/ena/data/view/%s' % (get_true_run_id(k))
        s = '<p><a href=\"%s\" name=\'%s\'>%s</a></p>' % (run_link, k, k)
        d2 = d[k]
        s += '<table>'
        project_link    = 'http://www.ebi.ac.uk/ena/data/view/%s' % (d2['project_id'])
        experiment_link = 'http://www.ebi.ac.uk/ena/data/view/%s' % (d2['experiment_id'])
        biosample_link  = 'http://www.ebi.ac.uk/biosamples/samples/%s' % (d2['sample_id'])
        description     = d2['description']
        s += '<tr> <td><b>%s</b></td> <td><a href=\"%s\">%s</a> / <a href=\"%s\">%s</a> / <a href=\"%s\">%s</a></td>  </tr>' % ('External links', project_link, d2['project_id'], experiment_link, d2['experiment_id'], biosample_link, d2['sample_id'])
        s += '<tr> <td><b>%s</b></td> <td>%s</td> </tr>' % ('Description', description)
        s += '</table><br>\n'
        f.write(s)
    f.write('</body></html>')
    f.close()

def make_chip_seq_info_dict(fname):
    '''  See QUICKSTART.html#parameter-for-buildcmatrix '''
    f = open(fname)
    d = {}
    for line in f:
        line = line.strip()
        if line.startswith('@'):
            experiment_id = line[1:]
            d[experiment_id] = {}
        if line.startswith('PROTEIN_ID'):
            d[experiment_id]['PROTEIN_ID'] = get_value(line, ':')
        if line.startswith('PROTEIN_NAME'):
            d[experiment_id]['PROTEIN_NAME'] = get_value(line, ':')
        if line.startswith('DATA_NAME'):
            d[experiment_id]['DATA_NAME'] = get_value(line, ':')
        if line.startswith('DESCRIPTION'):
            d[experiment_id]['DESCRIPTION'] = get_value(line, ':')
        if line.startswith('LOCATION'):
            d[experiment_id]['LOCATION'] = get_value(line, ':')
        if line.startswith('NOTE'):
            d[experiment_id]['NOTE'] = get_value(line, ':')

    f.close()
    return d


def make_chip_seq_info_html_page(fname, d):
    f = open(fname, 'w')
    f.write('<html><head><style> body {font-family:\"HelveticaNeue-Light\", \"Helvetica Neue Light\", \"Helvetica neue\"} table {table-layout: fixed; width: 800px;}</style></head><body>')
    for k in sorted(d.keys()):
        s = '<p><a name=\'%s\'>%s</a></p>' % (k, k)
        d2 = d[k]
        s += '<table>'
        for k2 in sorted(d2.keys()):
            s += '<tr> <td>%s</td> <td>%s</td> </tr>' % (k2, d2[k2])
        s += '</table><br>\n'
        f.write(s)
    f.write('</body></html>')
    f.close()


def make_link_string_for_cond(s, type):
    ''' s is a string of RNA-seq IDs or ChIP IDs. '''
    lst = s.split()
    result = ''
    for x in lst:
        if type == 'rnaseq':
            path = '%s#%s' % (RNA_SEQ_INFO_HTML_PAGE, x)
        else:
            path = '%s#%s' % (CHIP_SEQ_INFO_HTML_PAGE, x)
        result += '<a href=\'%s\'>%s</a> ' % (path, x)
    return result


def get_chip_signal(s, d):
    ''' extract signal information, and return the words ordered by frequency '''    
    lst = s.split()
    result = ''
    for x in lst:
        desc = d[x]['DESCRIPTION']
        lst2 = desc.split('\t')
        for y in lst2:
            if y.startswith('SIGNAL='):
                result += ';' + y[7:] # 7 means after the '=' in 'SIGNAL='
                break
    return word_freq(result)


def get_chip_phenotype(s, d):
    ''' extract phenotype information, and return the words ordered by frequency '''
    lst = s.split()
    result = ''
    for x in lst:
        desc = d[x]['DESCRIPTION']
        lst2 = desc.split('\t')
        for y in lst2:
            if y.startswith('PHENOTYPE='):
                result += ';' + y[10:]  # 10 means after the '=' in 'PHENOTYPE='
                break
    return word_freq(result)


def word_freq(s): # for ChIP-seq data
    ''' s is string.  return a string of words ordered by frequency '''
    if s == '':
        return ''
    
    lst = s.split(';')
    d = {}
    for x in lst:
        lst2 = x.split()
        for y in lst2:
            #k = y.lower()
            k = y
            k = k.strip(',')
            k = k.strip('.')
            k = k.strip(')')
            k = k.strip('(')            
            if not k.lower() in ['at', 'in', 'to', 'with', ',', '.', ':', '-']: # exclude these words
                if not k in d:
                    d[k] = 1
                else:
                    d[k] += 1

    sorted_tuples =  sorted(d.items(), key=operator.itemgetter(1), reverse=True)
    first_items = [x[0] for x in sorted_tuples]
    return ' '.join(first_items)


def word_freq2(lst): # for RNA-seq data
    ''' s is string.  return a string of words ordered by frequency '''

    if lst == []:
        return ''
    
    d = {}
    for x in lst: # each description
        lst2 = x.split()
        for y in lst2: # each word
            k = y
            k = k.strip(',') # remove superfluous charaters, if any
            k = k.strip('.')
            k = k.strip(')')
            k = k.strip('(')
            k = k.strip(';')            
            if not k.startswith('SRR') and not k.startswith('ERR') and not k.startswith('DRR') and not k.isdigit() and not ':' in k and len(k) > 1 and not k.lower() in ['just', 'library', 'libraries',  'dna', 'nextseq', 'nextseq500', 'sequencing', 'end', 'al;', 'which', 'analyse', 'analyze', 'analyzer', 'whole-genome', 'thus', 'plant', 'plants', 'future', 'such', 'not', 'alone', 'most', 'within', 'into', 'but', 'between', 'we', 'is', 'or', 'also', 'was', 'can', 'be', 'use', 'kit', 'used', 'et', 'al', 'by', 'this', 'the', 'their', 'at', 'in', 'to', 'on', 'with', ',', '.', ':', '-', 'rna-seq', 'rnaseq', 'of', 'hiseq', 'hiseq2000', 'illumina', 'arabidopsis', 'thaliana', 'from', '<br><br>[title]', '<br><br>[description]', 'using', 'were', 'are', 'and', 'under', 'a', 'an', 'one', 'two', 'three', 'as', 'for', 'after', 'none', 'mapping', 'na', 'whole', 'chip-seq', 'paired']: # exclude these strings
                if not k in d:
                    d[k] = 1
                else:
                    d[k] += 1

    sorted_tuples =  sorted(d.items(), key=operator.itemgetter(1), reverse=True)
    first_items = [x[0] + ' (' + str(x[1]) + ')' for x in sorted_tuples]
    return '<br>'.join(first_items)


def word_freq3(lst): # for RNA-seq data, bag-of-words model
    ''' similar to word_freq2, but may be faster  '''
    if lst == []:
        return ''
    
    bow = [collections.Counter(re.findall(r'\w+', s)) for s in lst] # bag of words
    d = sum(bow, collections.Counter()) # frequency of each word
    sorted_tuples = d.most_common(len(d))
    exclude_lst = ['basis', 'requires', 'population', 'resolution', 'via', 'overall', 'elements', 'grown', 'expression', 'appears', 'total', 'have', 'here', 'of', 'just', 'type', 'transcriptomes', 'transcriptome', 'transcriptomic', 'transcription', 'transcriptional', 'report', 'during', 'diversity', 'investigated', 'library', 'per', 'libraries', '2500', '2000', '1210', '1001', '1107', 'dna', 'nextseq', 'nextseq500', 'seq', 'sequencing', 'sequencing;', 'end', 'al;', 'whereas', 'which', 'analyse', 'analyze', 'analyzer', 'quality', 'analysis', 'analyses', 'whole-genome', 'thus', 'plant', 'plants', 'future', 'such', 'not', 'alone', 'most', 'molecular', 'within', 'into', 'but', 'however', 'between', 'we', 'is', 'origin', 'or', 'also', 'was', 'can', 'be', 'been', 'use', 'kit', 'used', 'et', 'al', 'by', 'this', 'that', 'these', 'the', 'their', 'at', 'in', 'to', 'on', 'with', 'mrna', 'rna', 'rnas', 'rna-seq', 'rnaseq', 'of', 'hiseq', 'hiseq2000', 'illumina', 'arabidopsis', 'thaliana', 'from', 'roles', 'title', 'description', 'using', 'were', 'are', 'and', 'unknown', 'under', 'a', 'an', 'one', 'two', 'three', 'as', 'for', 'found', 'after', 'none', 'mapping', 'na', 'whole', 'chip-seq', 'play', 'paired', 'br', 'future', 'rowan', 'study', 'studies', 'may', 'sample', 'truseq', 'until', 'gene', 'genes', 'genetic', 'genome', 'genomes', 'units', 'its', 'yelina', 'data', 'set', 'tube', 'single-base', 'size', 'room', 'along', 'before', 'several', 'less', 'protocol', 'profiling', 'profiles', 'conditions', 'collection', 'complete', 'reveal', 'given', 'ii', 'isolated', 'described', 'describe', 'na', 'worldwide', 'accessions', 'identify', 'identification'] # exclude these words
    first_items = [x[0] + ' (' + str(x[1]) + ')' for x in sorted_tuples if x[1] > 2 and len(x[0]) > 1 and not x[0].startswith('SRR') and not x[0].startswith('ERR') and not x[0].startswith('DRR') and not x[0].isdigit() and not ':' in x[0] and not x[0].lower() in exclude_lst]
    return ' '.join(first_items)


def get_rna_signal(s, d):
    ''' extract RNA-seq signal information, and return the words ordered by frequency '''    
    lst = s.split()
    result = []
    MAX_WORDS = 60
    if lst[0] == '.': # all RNA samples
        return 'all available signals'
    for x in lst: # x is an RNA sample ID, words by frequency
        if x in d:
            desc = d[x]['description']
            desc_lst = re.split('<br>', desc)
            short_lst = []
            for x in desc_lst:
                short_lst.extend(x.split())
                if len(short_lst) > MAX_WORDS: # average english words 5.1, take the first 100 words, should be informative enough. Longer desc require more computation time.
                    short_lst = short_lst[:MAX_WORDS]
                    break
            # index = desc.find('<br>')
            # if index > 0:
            #     desc = desc[:index]
            result.append((' '.join(short_lst)).strip())
    return word_freq3(result)


def get_rna_signal2(s, d): # not very successful, and slow, so NOT used
    ''' extract RNA-seq signal information, and return the words ordered by frequency '''    

    lst = s.split()

    if lst[0] == '.': # all RNA samples
        return 'all available signals'

    text = ''
    for x in lst: # x is an RNA sample ID, words by frequency
        if x in d:
            desc = d[x]['description']
            text += desc.strip().rstrip('.') + '. '

    rake = Rake(RAKE_STOPLIST_FILE)
    keywords = rake.run(text)
    return '<br>'.join( [ t[0] + ' (' + str(int(t[1])) + ')' for t in keywords ] )


def replace_old_html_page(fname, edge_date):
    ''' If the file fname needs updating, return True.  '''
    if not os.path.exists(fname): # if the file does not exist, it needs updating
        return True

    # Check all files AT2G43790_AT1G03080_0.html,  AT2G43790_AT1G03080_1.html, AT2G43790_AT1G03080_2.html, etc.  If any of them is too old, create a new one.    
    index = fname.rfind('_') 
    if index < 0:
        print('html_network.py: %s has no underscore.' % (fname))
        sys.exit()
    fname_part = fname[:index]
    for fn in glob.glob(os.path.join(fname_part, '*.html')):
        file_date = datetime.fromtimestamp(os.path.getmtime(fn)).strftime('%Y%m%d')
        if int(edge_date) - int(file_date) > 1: # edge_date is at least 1 day newer than edge file date
            return True

    return False


def format_date(s):
    ''' s in the form of 20170419.  Return 2017-04-19 '''
    s = s.strip()
    if len(s) != 8:
        return s
    return s[0:4] + '-' + s[4:6] + '-' + s[6:]


def make_html_page_for_condition(fname, tf_name, target_name, condRstr, condCstr, edge_date, subset): # important page ***

    ### if the page already exists, and its information is up-to-date, then don't create it again (to save time)
    if REGENERATE_ALL_EDGE_FILES == 'NO' and not replace_old_html_page(fname, edge_date):
        return
    
    d3_library =  '<link href=\"./c3.min.css\" rel=\"stylesheet\" /><script src=\"./d3.min.js\"></script><script src=\"./c3.min.js\"></script><script src=\"./scatterplot.js\"></script><script src=\"./barchart.js\"></script>'
    f = open(fname, 'w')
    f.write('<html><head> %s  <style> body {font-family:\"HelveticaNeue-Light\", \"Helvetica Neue Light\", \"Helvetica neue\"} </style></head><body>' % (d3_library))

    ### RNA-seq
    f.write('<h2>RNA-seq experiments</h2>')
    part = os.path.splitext( os.path.basename(fname) )[0] # get file name without extension
    id_lst = part.split('_')
    gene1_file = os.path.join('json', id_lst[0] + '.json') # TF
    gene2_file = os.path.join('json', id_lst[1] + '.json') # target

    f.write('<p>TF is %s %s.  Target is %s %s.  Edge made on %s.  Method: %s.</p>'% (id_lst[0], '' if tf_name == id_lst[0] else tf_name, id_lst[1], '' if target_name == id_lst[1] else target_name, format_date(edge_date), subset))
    cond_lst_str = str(condRstr.split()) # insert to javascript function call code
    rnaseq_info_file = os.path.basename(RNA_SEQ_INFO_DATABASE_JSON)
    s = '<p><a id=\"myLink\" href=\"javascript:void(0);\" onclick=\"drawScatterPlot(\'%s\',\'%s\', \'%s\', %s);\">Click for gene expression scatter-plot</a></p>  <p id=\"chart\"></p>' % (gene1_file, gene2_file, rnaseq_info_file, cond_lst_str)
    f.write(s)

    global glb_rna_seq_info_dict
    #s = get_rna_signal(condRstr, glb_rna_seq_info_dict) # DISABLED since this is SLOWEST part
    # if s.startswith('all available'):
    #     f.write('<h3>Signal</h3>' + '<p>' + s + '</p>')
    # else:
    #     f.write('<h3>Signal</h3> <p>Note: words are ordered by frequency.</p>' + '<p>' + s + '</p>')
    
    # f.write('<p>%s<p>' % (make_link_string_for_cond(condRstr, 'rnaseq')))
    
    ### ChIP-seq
    f.write('<h2>ChIP-seq experiments</h2>')
    gene1_file = os.path.join('json2', id_lst[0] + '.json') # TF
    gene2_file = os.path.join('json2', id_lst[1] + '.json' ) # target
    cond_lst_str = str(condCstr.split())
    s = '<a id=\"myLink2\" href=\"javascript:void(0);\" onclick=\"drawBarChart(\'%s\',%s);\">Click for plot</a>  <p id=\"chart_bind\"></p>' % (gene2_file, cond_lst_str) # display binding strength
    f.write(s)

    global glb_chip_seq_info_dict
    s = get_chip_signal(condCstr, glb_chip_seq_info_dict)
    if s != '':
        f.write('<h3>Signal</h3> <p>Note: words are ordered by frequency.</p>' + '<p>' + s + '</p>')
    else:
        f.write('<h3>Signal</h3>' + '<p>None.</p>')

    s = get_chip_phenotype(condCstr, glb_chip_seq_info_dict)
    f.write('<h3>Phenotype</h3>' + '<p>' + s + '</p>')
    
    f.write('<p>%s</p>' % (make_link_string_for_cond(condCstr, 'chipseq')))
    f.write('</body></html>')   
    f.close()


def make_w2ui_table_page(fname, gene_str, download_str, dict_lst_regulates, dict_lst_regulatedby):
    ''' each element in dict_lst_* must have the form  {'strength': '', 'metric': '', 'geneid': '', 'genename': ''} '''
    start_part = '''
    <html>
      <head>
        <title>%s</title>
        <script src="./jquery.min.for.w2ui.js"></script>
        <script src="./w2ui.min.js"></script>
        <link rel="stylesheet" type="text/css" href="./w2ui.min.css" />    
        <script>
            $(function() {
    ''' % (
        gene_str)
    
    # the first table showing targets of a TF
    grid1 = '''
        $('#grid1').w2grid({ 
            name:'grid1', 
            header:'%s regulates',
            show:{ footer:true, toolbar:true, header:true },
            columns:[	
                { field:'recid', caption:'No.', size:'50px', sortable:true, resizable:true},
                { field:'strength', caption:'Corr', size:'150px', sortable:true, resizable:true, searchable:true },
                { field:'metric', caption:'Metric', size:'150px', sortable:true, resizable:true, searchable:true },
                { field:'geneid', caption:'Gene ID', size:'150px', sortable:true, resizable:true, searchable:true },
                { field:'genename', caption:'Gene name', size:'150px', sortable:true, resizable:true, searchable:true }
            ],
           records: 
    ''' % (
        gene_str)

    grid1 += '[\n'
    i = 1
    for d in dict_lst_regulates:
        grid1 += '    {recid:%d, strength:\'%s\', metric:\'%s\', geneid:\'%s\', genename:\'%s\'},\n' % (i, d['strength'], d['metric'], d['geneid'], d['genename'])
        i += 1
    grid1 = grid1.rstrip('\n').rstrip(',')
    grid1 += ']\n'
    grid1 += '});\n'

    # the second table showing TF's regulators
    grid2 = '''
        $('#grid2').w2grid({ 
            name:'grid2', 
            header:'%s is regulated by',
            show:{ footer:true, toolbar:true, header:true },
            columns:[	
                { field:'recid', caption:'No.', size:'50px', sortable:true, resizable:true},
                { field:'strength', caption:'Corr', size:'150px', sortable:true, resizable:true, searchable:true },
                { field:'metric', caption:'Metric', size:'150px', sortable:true, resizable:true, searchable:true },
                { field:'geneid', caption:'Gene ID', size:'150px', sortable:true, resizable:true, searchable:true },
                { field:'genename', caption:'Gene name', size:'150px', sortable:true, resizable:true, searchable:true }
            ],
           records: 
    ''' % (
        gene_str)

    grid2 += '[\n'
    i = 1
    for d in dict_lst_regulatedby:
        grid2 += '    {recid:%d, strength:\'%s\', metric:\'%s\', geneid:\'%s\', genename:\'%s\'},\n' % (i, d['strength'], d['metric'], d['geneid'], d['genename'])        
        i += 1
    grid2 = grid2.rstrip('\n').rstrip(',')
    grid2 += ']\n'
    grid2 += '});\n'

    end_part = '''
    });
        </script>
      </head>
      <body>
        <div id="grid1" style="position:absolute; left:0px; width:49.9%%; height:99%%;">regulatee table</div>
        <div id="grid2" style="position:absolute; right:0px; width:49.9%%; height:99%%;">regulator table</div>
        <br/>
        <div id="download">%s</div>
      </body>
    </html>
    ''' % (
        download_str)
    
    result = start_part + grid1 + grid2 + end_part
    # minify html 
    lst = re.split(r'\s{2,}', result)
    result = ''.join(lst)
    f = open(fname, 'w')
    f.write(result)
    f.close()


def make_html_page(node, G, fname, agi2name_dict):
    ''' Make html pages for node's successors and predecessors.  '''
    #f.write('<p><a href=%s>Go to index page</a></p>' % ('../summary.html'))
    #download_str = '<a href=\'%s\'>Download all edges</a>' % ('./edges.txt.zip') add in future
    download_str = ''
    gname = get_name(node, agi2name_dict)
    if node.strip() == gname.strip(): # id only
        gene_str = node
    else:
        gene_str = '%s' % (node + ' ' + gname)
    N = G.graph['max_rsubset_size']
    
    predecessors = G.predecessors(node)
    successors = G.successors(node)

    d1 = {}
    d2 = {}
    for n in successors:
        name = n.split()[0] + '.html'
        d = G.get_edge_data(node, n) # n is node's target
        for k in d.keys(): # can have multiple edges between two nodes
            t = d[k]['action']
            t = int(np.abs(d[k]['weight'])*10) * t # edge strength
            R = '  '.join(d[k]['conditionR'])
            C = '  '.join(d[k]['conditionC'])
            RMSE = d[k]['rmse']
            edge_date = d[k]['edge_date']
            subset = d[k]['subset']
            info_page = get_id(node) + '_' + get_id(n) + '_' + str(k) + '.html' # node is TF, n is target
            info_page_path = os.path.join(os.path.dirname(fname), info_page)
            tf_name = get_name(node, agi2name_dict)
            target_name = get_name(n, agi2name_dict)
            make_html_page_for_condition(info_page_path, tf_name, target_name, R, C, edge_date, subset)  # ***

            d1[info_page] = float(d[k]['metric'])
            display_name = n + ' ' + ('' if target_name == n else target_name)
            d2[info_page] = (t, name, display_name, RMSE)

    # order edges by strength
    regulatee_dict_lst = []
    for tpl in sorted(d1.items(), key=operator.itemgetter(1), reverse=True):        
        k = tpl[0]
        info_page = k
        t = d2[k][0]
        name = d2[k][1]
        n = d2[k][2] # display name
        RMSE = d2[k][3]
        #s1 += '<a href=\'%s\' title=\'%s\'>%s</a> <a href=\'%s\'>%s</a><br/>' % (info_page, RMSE, t.rjust(12, '_'), name, n)
        lst = n.split()
        geneid = lst[0]
        genename = '-'
        if len(lst) > 1:
            genename = lst[1]
        regulatee_dict_lst.append({'strength': '<a href=%s title=%s>%s</a>' % (info_page, RMSE, t.rjust(12, '_')), 'geneid': '<a href=%s>%s</a>' % (name, geneid), 'genename': '%s' % (genename), 'metric': '%4.2f' % (d1[k])})


    d1 = {}
    d2 = {}
    for n in predecessors:
        name = n.split()[0] + '.html'
        d = G.get_edge_data(n, node)
        for k in d.keys():
            t = d[k]['action']
            t = int(np.abs(d[k]['weight'])*10) * t # edge strength
            R = '  '.join(d[k]['conditionR'])
            C = '  '.join(d[k]['conditionC'])
            RMSE = d[k]['rmse']
            edge_date = d[k]['edge_date']
            subset = d[k]['subset']            
            info_page = get_id(n) + '_' + get_id(node) + '_' + str(k) + '.html' # n is TF, node is target
            info_page_path = os.path.join(os.path.dirname(fname), info_page)
            tf_name = get_name(n, agi2name_dict)
            target_name = get_name(node, agi2name_dict)            
            #if not os.path.exists(info_page_path):  # tf->target may already exits, if so don't need to make it again
            make_html_page_for_condition(info_page_path, tf_name, target_name, R, C, edge_date, subset)  # CHANGE ***

            d1[info_page]  = float(d[k]['metric'])
            display_name = n + ' ' + ('' if tf_name == n else tf_name)
            d2[info_page] = (t, name, display_name, RMSE)

    # order edges by strength
    regulator_dict_lst = []
    for tpl in sorted(d1.items(), key=operator.itemgetter(1), reverse=True):        
        k = tpl[0]
        info_page = k
        t = d2[k][0]
        name = d2[k][1]
        n = d2[k][2]
        RMSE = d2[k][3]
        lst = n.split()
        geneid = lst[0]
        genename = '-'
        if len(lst) > 1:
            genename = lst[1]
        regulator_dict_lst.append({'strength': '<a href=%s title=%s>%s</a>' % (info_page, RMSE, t.rjust(12, '_')), 'geneid': '<a href=%s>%s</a>' % (name, geneid), 'genename': '%s' % (genename), 'metric': '%4.2f' % (d1[k])})

    make_w2ui_table_page(fname, gene_str, download_str, regulatee_dict_lst, regulator_dict_lst) # ***


def num_lines(fname):
    ''' Return number of lines in file fname. '''
    f = open(fname)
    n = len(f.readlines())
    f.close()
    return n


## main program
parser = OptionParser()
parser.add_option('-f', '--file', dest='edge_file', help='edge file', metavar='FILE')
parser.add_option('-r', '--rnaseqinfo', dest='rna_seq_info_file', default='', help='RNA-seq information file', metavar='FILE')
parser.add_option('-c', '--chipseqinfo', dest='chip_seq_info_file', default='', help='ChIP-seq information file', metavar='FILE')
parser.add_option('-n', '--networkpara', dest='network_para_file', default='', help='Network parameter file', metavar='FILE')
parser.add_option('-i', '--includeedgetype', dest='include',  default='all', help='include edge types')
parser.add_option('-s', '--showcondition', dest='cond',  action="store_true", default=False, help='show correlated conditions')
(options, args) = parser.parse_args()

glb_param_dict = make_global_param_dict(options.network_para_file)

agi2name_dict = make_gene_name_AGI_map_dict(glb_param_dict['GENE_ID_AND_GENE_NAME'])

total_num_edges = num_lines(options.edge_file)


# Make summary.html page
G = build_network_from_file(options.edge_file)
if not os.path.isdir(DIR_NAME):
    os.makedirs(DIR_NAME)

# Make RNA-seq information page
if options.rna_seq_info_file != '':
    glb_rna_seq_info_dict = make_rna_seq_info_dict(options.rna_seq_info_file)
    make_rna_seq_info_html_page(os.path.join(DIR_NAME, RNA_SEQ_INFO_HTML_PAGE), glb_rna_seq_info_dict)

# Make ChIP-seq information page
if options.chip_seq_info_file != '':
    glb_chip_seq_info_dict = make_chip_seq_info_dict(options.chip_seq_info_file)
    make_chip_seq_info_html_page(os.path.join(DIR_NAME, CHIP_SEQ_INFO_HTML_PAGE), glb_chip_seq_info_dict)

# Fill in static index page
findex = open(INDEX_PAGE, 'w')
findex.write('<html><head><style> body {font-family:\"HelveticaNeue-Light\", \"Helvetica Neue Light\", \"Helvetica neue\"} table {table-layout: fixed; width: 800px;}</style></head><body>')
curr_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
s = '<h2>All genes considered</h2>'
s +=  '<p>Last updated at %s. A total of %d edges.</p>' % (curr_time, total_num_edges)
for n in sorted(G.nodes()): # for each node in the network, find its neighbours.
    t = n.split()[0] + '.html'
    filepath = os.path.join(DIR_NAME, t)

    successors = G.successors(n)
    predecessors = G.predecessors(n)

    s1 = ''
    for sn in successors:
        t1 = sn.split()[0] + '.html'
        filepath1 = os.path.join(DIR_NAME.split('/')[-1], t1)
        s1 += '<a href=\'%s\'>%s</a><br>' % (filepath1, sn)

    s2 = ''
    for pn in predecessors:
        t2 = pn.split()[0] + '.html'
        filepath2 = os.path.join(DIR_NAME.split('/')[-1], t2)
        s2 += '<a href=\'%s\'>%s</a><br>' % (filepath2, pn)
    
    s += '<p>Gene:<a href=\'%s\'>%s</a><br>' % (filepath, n)
    s += '<table border=1><tr><td width=400px>Regulated by %d</td><td width=400px>Regulates %d</td></tr>' % (len(predecessors), len(successors))
    s += '<tr> <td valign=\"top\">%s</td> <td valign=\"top\">%s</td></tr>' % (s2, s1)
    s += '</table>'
    s += '</p>'

    make_html_page(n, G, filepath, agi2name_dict)

findex.write(s)
findex.write('</body></html>')
findex.close()

# copy auxiliary folders and files
if os.path.isdir(JSON_DIR):
    cmd = 'cp -r %s %s' % (JSON_DIR, DIR_NAME)
    os.system(cmd)
else:
    print('[WARNING] html_network.py: Omit JSON directory (for displaying gene expression).')

if os.path.isdir(JSON_DIR2):
    cmd = 'cp -r %s %s' % (JSON_DIR2, DIR_NAME)
    os.system(cmd)
else:
    print('[WARNING] html_network.py: Omit JSON directory 2 (for displaying binding).')

if os.path.exists(RNA_SEQ_INFO_DATABASE_JSON):
    cmd = 'cp %s %s' % (RNA_SEQ_INFO_DATABASE_JSON, DIR_NAME)
    os.system(cmd)
else:
    print('[WARNING] html_network.py: %s does not exists. Scatterplots may not work properly.' % (RNA_SEQ_INFO_DATABASE_JSON))

for fname in C3_FILES:
    fpath = os.path.join(C3_DIR, fname)
    if os.path.exists(fpath):
        cmd = 'cp %s %s' % (fpath, DIR_NAME)
        os.system(cmd)
    else:
        print('[WARNING] html_network.py: Omitted %s. Scatter plot may not work without this file. ' % (fpath))

for fname in W2UI_FILES:
    fpath = os.path.join(W2UI_DIR, fname)
    if os.path.exists(fpath):
        cmd = 'cp %s %s' % (fpath, DIR_NAME)
        os.system(cmd)
    else:
        print('[WARNING] html_network.py: Omit %s. Table may not work without this file. ' % (fpath))
        
#print('html_network.py done!')