diff options
| -rwxr-xr-x | Code/start_webapp.py | 562 | ||||
| -rwxr-xr-x | Code/util_networkx.py | 151 |
2 files changed, 0 insertions, 713 deletions
diff --git a/Code/start_webapp.py b/Code/start_webapp.py deleted file mode 100755 index 8891384..0000000 --- a/Code/start_webapp.py +++ /dev/null @@ -1,562 +0,0 @@ -# Brain's web app
-# Created in December 2016, slcu, hui
-#
-# Last modified 29 June 2017 [made function get_successors, udpated function get_predecessors]
-# Last modified 3 July 2017 [added method option, so users can input 'AT1G65480 leaf' to get neighours based on RNA-seq samples from leaf tissue. See get_method()]
-# Last modified 25 Jan 2018 [Note that if the 8th field (lst[8] in build_network_from_file()) is all 0.0. Edges will be missing. Search workaround in this file to 'fix' it. The 8th field represents the confidence of the edge.]
-# Last modified 1 Aug 2019. Can I share global variables G and SOURCE_NODES between requests? Failed to do that. The current solution is save two pickle files under the static folder.
-
-import networkx as nx
-from networkx.readwrite import json_graph
-import json
-import util_networkx
-import os
-import re
-import random
-import numpy as np
-import operator
-import math
-from datetime import datetime
-import time
-#import pickle
-import _pickle as cPickle
-import random
-######################################
-BRAIN_APP_PATH = '/var/www/brain/brain/' # the place of the application folder
-EDGE_FILE = '../Data/temp/edges.txt' # [change]
-EDGE_DB = BRAIN_APP_PATH + 'static/edges/edges.sqlite'
-TF_CATEGORY_FILE = '../Data/information/2D_table2.xls' # put TFs into four categories: TI, TN, GI, GN.
-SHOW_N_NODE_EACH_TIME = 50
-TOP_N_NEIGHBOURS = 30
-STRENGTH_TAU = 0.1 # don't show edges with low strength
-ALPHA = 0.6
-######################################
-
-
-def scale_factor(n, N, LL, alpha):
- '''
- n -- number of RNA-seq experiments
- N -- maximum number of RNA-seq experiments
- LL -- total log likelihood
- alpha -- weight for number of RNA-seq experiments
- '''
- #print('DEBUG n=%d N=%d LL=%g' % (n,N,LL))
- return alpha * (1.0*n/N) + (1-alpha)*(np.e**(1.0*min(LL,0)/n))**0.1
-
-def get_method(s):
- s = s.lower()
- recognised_methods = ['root', 'bud', 'meristem', 'aerial', 'leaf', 'stem', 'seedling', 'seed', 'shoot', 'flower', 'mixreg', 'all', 'hclust.group', 'hclust.fixed.group', 'wedge', 'mixtool']
- for x in recognised_methods:
- if x in s:
- return x
- return ''
-
-def contain_whole_word(s, t):
- ''' whole word s is in t. '''
- lst = t.split(',') # t is sth like: 'shoot, all, seedling, hclust, seed, root'
- lst.append('') # handle the case when s is '', to make it true
- return s in lst
-
-def get_successors(G, gene, method):
- ''' Only select successors with good relationship strength with gene '''
- node = gene
- successors = []
- for n in G.successors(node):
- d = G.get_edge_data(node, n)
- max_strength = 0
- for k in d.keys(): # select a best edge
- if d[k]['strength'] > max_strength and contain_whole_word(method, d[k]['method']):
- max_strength = d[k]['strength']
- if max_strength > STRENGTH_TAU:
- successors.append(n)
- return successors
-
-def get_predecessors(G, gene, num, method):
- ''' Get top num predecessors of gene '''
-
- node = gene
- predecessors = []
- # don't include edges with small strength
- for n in G.predecessors(node):
- d = G.get_edge_data(n, node)
- max_strength = 0
- for k in d.keys(): # select a best edge
- #print('DEBUG: %s %4.2f %4.2f' % (d[k]['link'], d[k]['weight'], d[k]['strength'] ) )
- if d[k]['strength'] > max_strength and contain_whole_word(method, d[k]['method']):
- max_strength = d[k]['strength']
- if max_strength > STRENGTH_TAU:
- predecessors.append(n)
-
- #print('DEBUG: len(predecessors)=%d num=%d' % (len(predecessors), num))
- if len(predecessors) < num:
- return predecessors
-
- d1 = {}
- for n in predecessors:
- d = G.get_edge_data(n, node)
- max_strength = 0
- for k in d.keys(): # select a best edge
- if d[k]['strength'] > max_strength:
- max_strength = d[k]['strength']
- if n == 'AT3G50410' or n == 'AT1G52150':
- print('WARNING: %s should not be here. Method:%s.' % (n, method))
- d1[n] = max_strength
-
- sorted_lst = sorted(d1.items(), key=operator.itemgetter(1), reverse=True)
- a, b = zip(*sorted_lst)
- return np.random.choice(a, num, replace=False, p=1.0 * np.array(b)/sum(b)) # choose with probability
-
-
-def get_max_rsubset_size(fname):
- ''' get the max number of RNA-seq experiment '''
- max_rsize = 0
- f = open(fname)
- for line in f:
- line = line.strip()
- lst = line.split('\t')
- condR_lst = []
- if line != '' and len(lst) > 3:
- if len(lst) > 6:
- condR_lst = lst[4].split()
- size_condR = len(condR_lst)
- if size_condR > max_rsize:
- max_rsize = size_condR
- f.close()
- return max_rsize
-
-def build_network_from_file(edge_fname, tf_category_fname):
-
- EDGE_DIR = 'static/edges'
- tf_category_dict = get_tf_category_dict(tf_category_fname)
-
- ## build the network
- N = get_max_rsubset_size(edge_fname)
- G = nx.MultiDiGraph(max_rsubset_size=N)
-
- source_nodes = []
- d = {} # count dict
- f = open(edge_fname)
- for line in f:
- line = line.strip()
- lst = line.split('\t')
- if line != '' and random.randint(1,10) < 10:
- strength = max(abs(float(lst[2])), float(lst[8])) # prevent strength being zero
- #strength = abs(float(lst[2])) # a workaround since lst[8] is now zero as time elapse, check compute_metric() in update_network.py for details
- method_or_tissue = lst[9]
- g1 = lst[0].split()[0] # target gene ID
- g1_label = lst[0].split()[1].split(';')[0] if lst[0].split()[1] != '.' else g1
- g1_name = lst[0].split()[1] if lst[0].split()[1] != '.' else ''
- g2 = lst[1].split()[0] # source gene ID
- g2_label = lst[1].split()[1].split(';')[0] if lst[1].split()[1] != '.' else g2
- g2_name = lst[1].split()[1] if lst[1].split()[1] != '.' else ''
- g1_link = '/' + EDGE_DIR.rstrip('/') + '/' + g1 + '.html'
- g2_link = '/' + EDGE_DIR.rstrip('/') + '/' + g2 + '.html'
- g1_category = tf_category_dict[g1] if g1 in tf_category_dict else [-9.0, 'N'] # -9.0 means not tissue-specific, and N means not inducible
- g1_TG_score = g1_category[0]
- g1_TG_category = TorGorN(g1_TG_score, 2.40) # T/G, 2.40 is selected to satisfy 'GI > TI and TN > GN'
- g1_IN_category = g1_category[1] # I/N
- g2_category = tf_category_dict[g2] if g2 in tf_category_dict else [-9.0, 'N']
- g2_TG_score = g2_category[0]
- g2_TG_category = TorGorN(g2_TG_score, 2.40) # 'T' if g2_TG_score >= 2.40 else 'G'
- g2_IN_category = g2_category[1]
- G.add_node(g1, full_name=g1_name, label=g1_label, link=g1_link, istf='0', tf_category=g1_TG_category+g1_IN_category, tf_TG_score=g1_TG_score, tf_IN_category=g1_IN_category) # if g1 is also a TF, then istf='0' will overwrite it in the following for loop
- G.add_node(g2, full_name=g2_name, label=g2_label, link=g2_link, istf='1', tf_category=g2_TG_category+g2_IN_category, tf_TG_score=g2_TG_score, tf_IN_category=g2_IN_category) # tf_category contains default TF category code. It can be modified later given user's input
-
- if not g2+g1 in d:
- d[g2+g1] = 0
- else:
- d[g2+g1] += 1
-
- link_html_name = '/brain/edges/' + '%s_%s_%d.html' % (g2, g1, d[g2+g1])
-
- G.add_edge(g2, g1, weight=float(lst[2]), link=link_html_name, strength=strength, method=method_or_tissue) # g2 is source, and g1 is target
-
- source_nodes.append(g2)
-
- f.close()
-
- source_nodes = list(set(source_nodes))
- for n in source_nodes:
- G.node[n]['istf'] = '1' # force source nodes to be TFs
-
- return G, source_nodes
-
-
-def is_valid_query_gene(gid, G):
- if not re.match(r'AT\dG\d+', gid):
- return 'Invalid gene ID. Example of a valid gene ID: AT1G80870.'
- if not gid in G.nodes():
- return 'Not in graph.'
- return ''
-
-def allowed_file(filename):
- return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
-
-def validate_chip_peak_file(fname, protein_id, protein_name, status, description):
- protein_id = protein_id.strip().upper()
- if protein_id == 'E.G., AT0G12345' or not protein_id.startswith('AT') or protein_id.find('G') != 3 or len(protein_id) != 9:
- return 'Invalide protein ID: %s.' % (protein_id)
- protein_name = protein_name.strip()
- description = description.strip()
- status = status.strip().upper()
- if status == 'UNSPECIFIED':
- return 'Please select ChIP-seq experiment status.'
-
- f = open(fname)
- lines = f.readlines()
- for line in lines:
- line = line.strip()
- lst = line.split('\t')
- if len(lst) < 10: # check we have at least 10 columns
- return 'The line <code>%s</code> should have at least 10 columns (see <a href=\"https://github.com/taoliu/MACS\">BED6+4 format</a>).' % line
-
- if not lst[0].isdigit():
- c = lst[0].lower()
- if (c.startswith('chr') and c[3:].isdigit()) or (c in ['mt', 'pt', 'ct']):
- pass
- else:
- return 'First column must be a chromosome number. [line: <code>%s</code>].' % line
-
- if not lst[1].isdigit():
- return 'Second column (starting position) must be a number. [line: <code>%s</code>].' % line
- if not lst[2].isdigit():
- return 'Third column (end position) must be a number. [line: <code>%s</code>].' % line
- if not lst[4].isdigit():
- return 'Fourth column (binding strength) must be a number. [line: <code>%s</code>].' % line
-
- f.close()
- return ''
-
-def edit_chip_peak_file(fname, protein_id, protein_name, status, description):
- protein_id = protein_id.strip().upper()
- protein_name = protein_name.strip()
- description = description.strip()
- status = status.strip().upper()
-
- f = open(fname)
- lines = f.readlines()
- f.close()
-
- f = open(fname, 'w')
- f.write('# PROTEIN_ID:%s\n' % protein_id)
- f.write('# PROTEIN_NAME:%s\n' % protein_name.upper())
- f.write('# STATUS:%s\n' % status.upper())
- f.write('# DESCRIPTION:%s\n' % ' '.join([x.strip() for x in description.split('\n')]))
- f.write(''.join(lines))
- f.close()
-
-# Taken from https://stackoverflow.com/questions/354038/how-do-i-check-if-a-string-is-a-number-float
-def is_number(s):
- try:
- float(s)
- return True
- except ValueError:
- return False
-
-
-def validate_and_edit_rnaseq_file(fname, genes):
- protein_id = genes.strip().upper()
- protein_id_lst = protein_id.split()
- for x in protein_id_lst:
- if not x.startswith('AT') or x.find('G') != 3 or len(x) != 9:
- return 'Invalide gene ID in %s: %s.' % (fname, x)
-
- fn = os.path.basename(fname)[16:]
- f = open(fname)
- lines = f.readlines()
- head_line = lines[0].strip()
- head_line_lst = head_line.split()
- if len(head_line_lst) > 2:
- return 'Invalid head line in %s: %s. The head line must contain one or two sample IDs.' % (fn, head_line)
- lineno = 1
- for line in lines[1:]:
- line = line.strip()
- lst = line.split('\t')
- x = lst[0] # gene id
- if not x.startswith('AT') or x.find('G') != 3 or len(x) != 9:
- return 'Invalide gene ID in %s: %s at line %d.' % (fn, x, lineno)
- for x in lst[1:]:
- if not is_number(x):
- return 'Invalid number %s in %s.' % (x, fn)
- lineno += 1
- f.close()
- return ''
-
-def write_gene_list(genes_str, directory, fname):
- lst = genes_str.strip().split()
- keep_lst = []
- for x in lst:
- g = x.strip()
- if g != '' and g != 'AT0G12345':
- keep_lst.append(g)
-
- f = open(os.path.join(directory, fname), 'w')
- if len(keep_lst) > 0:
- f.write('\n'.join(keep_lst))
- else:
- f.write('AT0G12345\n') # ghost gene
- f.close()
-
-def write_log_file(fname, s):
- # in the future, if we see concurrency problems, use the python logging module.
- f = open(fname, 'a')
- curr_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
- f.write('[%s] %s\n' % (curr_time, s))
- f.close()
-
-def number_of_columns(fname):
- ''' Return number of transcriptome columns in fname '''
- f = open(fname)
- lines = f.readlines()
- f.close()
- if len(lines) < 2:
- return 0
- second_line = lines[1]
- lst = second_line.split('\t')
- return len(lst) - 1 # number of transcriptome columns
-
-def map_to_transcriptome_space(fname):
-
- nc = number_of_columns(fname)
- if nc == 1:
- cmd = 'Rscript map_to_transcriptome_space.R %s' % (fname)
- os.system(cmd)
- elif nc == 2:
- cmd = 'Rscript map_to_transcriptome_space2.R %s' % (fname)
- os.system(cmd)
- else:
- return {} # an empty dictionary
-
- head = os.path.basename(fname)[:16]
- static_temp = './static/temp/'
- if not os.path.isdir(static_temp): # make the directory if not exists
- os.makedirs(static_temp)
-
- f = open(static_temp + head + 'result.txt')
- s = f.read()
- f.close()
-
- if nc == 1:
- return {'result': s, 'heatmap': head + 'heatmap.png', 'edges': head + 'edges.txt'}
- if nc == 2:
- return {'head': head, 'result': s, 'heatmap': head + 'heatmap.png', 'edges': head + 'edges.txt', 'diff_expr_genes': head + 'diff_expr_genes.txt'}
-
-
-def peak_to_target_genes(peak_file, protein_id):
- ''' Convert bed file to target gene list. Also make two files, date_time_targets.txt and date_time_tfs.txt. '''
-
- static_temp = './static/temp'
- if not os.path.isdir(static_temp): # make the directory if not exists
- os.makedirs(static_temp)
-
- static_info = './static/info'
- info_bed_file = os.path.join(static_info, 'genesTAIR10.bed')
-
- fname_head = os.path.splitext(os.path.basename(peak_file))[0]
- fname_head = fname_head[:16] # get date and time info
- #fname_head = 'test'
- bedmap_result = fname_head + 'bedmap_result.txt'
- bedmap_result = os.path.join(static_temp, bedmap_result)
-
- cmd = 'bedmap --echo --echo-map-id-uniq --delim \'\t\' ' \
- + '--range 3000' \
- + ' ' + peak_file \
- + ' ' + info_bed_file + ' > %s' % (bedmap_result)
- os.system(cmd)
- print(cmd)
- #cmd = 'cp ./static/temp/20170919_100450_bedmap_result.txt %s' % (bedmap_result) # [to be deleted]
- #os.system(cmd) # [to be deleted]
-
- f = open(bedmap_result)
- lines = f.readlines()
- d = {}
- for line in lines:
- line = line.strip()
- lst = line.split('\t')
- strength = lst[6]
- if len(lst) >= 11:
- genes = lst[10].replace('"','') # remove double-quotes
- for g in genes.split(';'):
- d[g] = strength
- f.close()
-
- tf_file = os.path.join(static_temp, fname_head + 'tfs.txt')
- f = open(tf_file, 'w')
- s = 'gene_id\tbinding_strength\n%s\t0.0\n' % (protein_id)
- f.write(s)
- f.close()
-
- target_file = os.path.join(static_temp, fname_head + 'targets.txt')
- s = 'gene_id\tbinding_strength\n'
- for k in sorted(d.keys()):
- s += '%s\t%s\n' % (k, d[k])
- f = open(target_file, 'w')
- f.write(s)
- f.close()
- tissue_specific_edge_file = os.path.join(static_temp, fname_head + 'tissue_specific_edges.txt') # generated by edge_per_tissue.R
- result_dict = {'targets':target_file, 'tissue_specific_edges':tissue_specific_edge_file}
- return result_dict
-
-def get_go_enrichment(fname):
- # make a list of functional targets (from the tissue specific edge file)
- d = {}
- f = open(fname)
- lines = f.readlines()
- f.close()
- for line in lines[1:]: # skip the head line
- line = line.strip()
- lst = line.split('\t')
- target = (lst[0].split())[0] # gene id only, no gene name
- if not target.startswith('NA'):
- d[target] = 1
- temp_fname = fname + '.temp'
- f = open(temp_fname, 'w')
- f.write('\n'.join(sorted(d.keys())) + '\n')
- f.close()
- # call goatools
- head = os.path.basename(fname)[0:16]
- go_enrichment_file = './static/temp/' + head + 'go_enrichment.txt'
- cmd = 'python /home/hui/software/goatools/goatools/scripts/find_enrichment.py %s /home/hui/software/goatools/goatools/data/population /home/hui/software/goatools/goatools/data/association --obo /home/hui/software/goatools/goatools/scripts/go-basic.obo > %s' % (temp_fname, go_enrichment_file)
- os.system(cmd)
- return go_enrichment_file
-
-def get_tf_category(fname):
- ''' Return a dictionary where key is gene id and value is TI/TN/GI/GN. '''
- d = {}
- f = open(fname)
- lines = f.readlines()
- f.close()
- for line in lines[1:]:
- line = line.strip()
- lst = line.split('\t')
- gene = lst[0]
- category = lst[3]
- if not gene in d:
- d[gene] = category
- return d
-
-def weighted_score_for_tissue(a, b):
- a = a
- b = b # demoninator
- r = a / b
- scale = math.log(b+1, 2) # scale factor
- return scale * r
-
-def weighted_score_for_treatment(a, b):
- if a == 0: # avoid returning 1 in the case of 0/0
- return 0
- a = a + 1
- b = b + 1
- return a/b # avoid dividing by zeros
-
-def TorG(x, tau):
- ''' Phil's version '''
- if (x >= tau):
- return ('T', x)
- else:
- return ('G', x)
-
-def TorGorN(x, tau):
- if (x >= tau):
- return 'T'
- elif x >= 0:
- return 'G'
- else:
- return 'N'
-
-def IorN(lst):
- ''' lst -- a list of tuples of the form [('cold', 2), ('heat', 2), ...]'''
- L = [x[1] for x in lst]
- maxL = max(L)
- if maxL >= 1:
- return ('I', maxL)
- else:
- return ('N', maxL)
-
-def get_tf_category_dict(fname):
- ''' fname is 2D_table2.xls, whose third column tissue.specific.score specifies the strength of being tissue-speficif. '''
- f = open(fname)
- lines = f.readlines()
- f.close()
- d = {} # key is TF and value is category code TI/GI/TN/GN
-
- for line in lines[1:]: # skip the first line
- line = line.strip()
- lst = line.split('\t')
- tissue_score_lst = []
- tname = ['flower', 'leaf', 'meristem', 'root', 'seed', 'seedling', 'silique', 'stem'] # tissue names in fname
- j = 0
- for x in lst[5:13]: # tissue column starts from column 6
- y = x.split('\\')
- t = (tname[j], weighted_score_for_tissue(int(y[0]), int(y[1])))
- j += 1
- tissue_score_lst.append(t)
- #torg = TorG(float(lst[2]), tissue_specific_tau) # tissue_specific_tau = 1.9 would make SEP3 a T
- torg_score = float(lst[2])
-
- treat_score_lst = []
- tname = ['cold', 'heat', 'wounding', 'drought']
- j = 0
- for x in lst[14:]:
- y = x.split('\\')
- t = (tname[j], weighted_score_for_treatment(int(y[0]), int(y[1])))
- j += 1
- treat_score_lst.append(t)
- iorn = IorN(treat_score_lst)
-
- tf_id = lst[0]
- if not tf_id in d:
- d[tf_id] = [torg_score, iorn[0]]
-
- return d
-
-
-def get_tissue_specific_tau(s):
- s = s.upper()
- index = s.find('TISSUE SPECIFIC TAU')
- if index < 0:
- return -9000.0
- s2 = s[index+19:]
- for x in s2.split( ):
- if is_number(x):
- return float(x)
- return -9001.0
-
-
-def get_user_option(s):
- ''' Return a dictionary containing user's input'''
- d = {}
- s = s.upper()
- lst = s.split()
- accepted_options = ['TISSUE SPECIFIC TAU', 'JUST TFS']
- for x in accepted_options:
- if x in s and x == 'TISSUE SPECIFIC TAU':
- d[x] = get_tissue_specific_tau(s)
- elif x in s:
- d[x] = 1
- return d
-
-
-def load_pickle_data(fname):
- with open(fname, 'rb') as f:
- return pickle.load(f)
-
-
-def num_line(fname):
- ''' Return number of lines in file fname. '''
- if not os.path.exists(fname):
- return 0
- f = open(fname)
- lines = f.readlines()
- f.close()
- return len(lines)
-
-if __name__ == "__main__":
- print('Call build_network_from_file ...')
- G, SOURCE_NODES = build_network_from_file(EDGE_FILE, TF_CATEGORY_FILE) # [change] use networkx binary file later
- with open('../Data/temp/SOURCE_NODES.json', 'w') as p:
- json.dump(SOURCE_NODES, p)
- #with open('../Data/temp/G.json', 'w') as p:
- # json.dump(json_graph.node_link_data(G), p)
- nx.write_gpickle(G, '../Data/temp/G.gpickle')
- print('Network updated and tf category loaded.')
diff --git a/Code/util_networkx.py b/Code/util_networkx.py deleted file mode 100755 index b708610..0000000 --- a/Code/util_networkx.py +++ /dev/null @@ -1,151 +0,0 @@ -"""
-Data conversion utility for NetworkX
-=====================================
-
-Convert cytoscape.js style graphs from/to NetworkX object.
-
-https://networkx.github.io/
-
-"""
-
-import networkx as nx
-
-# Special Keys
-ID = 'id'
-NAME = 'name'
-DATA = 'data'
-ELEMENTS = 'elements'
-
-NODES = 'nodes'
-EDGES = 'edges'
-
-SOURCE = 'source'
-TARGET = 'target'
-
-DEF_SCALE = 100
-
-
-def __map_table_data(columns, graph_obj):
- data = {}
- for col in columns:
- if col == 0:
- break
-
- data[col] = graph_obj[col]
-
- return data
-
-
-def __create_node(node, node_id):
- new_node = {}
- node_columns = node.keys()
- data = __map_table_data(node_columns, node)
- # Override special keys
- data[ID] = str(node_id)
- data[NAME] = str(node_id)
-
- if 'position' in node.keys():
- position = node['position']
- new_node['position'] = position
-
- new_node[DATA] = data
- return new_node
-
-
-def __build_multi_edge(edge_tuple, g):
- source = edge_tuple[0]
- target = edge_tuple[1]
- key = edge_tuple[2]
- data = edge_tuple[3]
-
- data['source'] = str(source)
- data['target'] = str(target)
- data['interaction'] = str(key)
- return {DATA: data}
-
-
-def __build_edge(edge_tuple, g):
- source = edge_tuple[0]
- target = edge_tuple[1]
- data = edge_tuple[2]
-
- data['source'] = str(source)
- data['target'] = str(target)
- return {DATA: data}
-
-
-def __build_empty_graph():
- return {
- DATA: {},
- ELEMENTS: {
- NODES: [],
- EDGES: []
- }
- }
-
-
-def from_networkx(g, layout=None, scale=DEF_SCALE):
- # Dictionary Object to be converted to Cytoscape.js JSON
- cygraph = __build_empty_graph()
-
- if layout is not None:
- pos = map(lambda position:
- {'x': position[0]*scale, 'y': position[1]*scale},
- layout.values())
-
- nodes = g.nodes()
- if isinstance(g, nx.MultiDiGraph) or isinstance(g, nx.MultiGraph):
- edges = g.edges(data=True, keys=True)
- edge_builder = __build_multi_edge
- else:
- edges = g.edges(data=True)
- edge_builder = __build_edge
-
- # Map network table data
- cygraph[DATA] = __map_table_data(g.graph.keys(), g.graph)
-
- for i, node_id in enumerate(nodes):
- new_node = __create_node(g.node[node_id], node_id)
- if layout is not None:
- new_node['position'] = pos[i]
-
- cygraph['elements']['nodes'].append(new_node)
-
- for edge in edges:
- cygraph['elements']['edges'].append(edge_builder(edge, g))
-
- return cygraph['elements']
-
-
-def to_networkx(cyjs, directed=True):
- """
- Convert Cytoscape.js-style JSON object into NetworkX object.
-
- By default, data will be handles as a directed graph.
- """
-
- if directed:
- g = nx.MultiDiGraph()
- else:
- g = nx.MultiGraph()
-
- network_data = cyjs[DATA]
- if network_data is not None:
- for key in network_data.keys():
- g.graph[key] = network_data[key]
-
- nodes = cyjs[ELEMENTS][NODES]
- edges = cyjs[ELEMENTS][EDGES]
-
- for node in nodes:
- data = node[DATA]
- g.add_node(data[ID], attr_dict=data)
-
- for edge in edges:
- data = edge[DATA]
- source = data[SOURCE]
- target = data[TARGET]
-
- g.add_edge(source, target, attr_dict=data)
-
- return g
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