def load_resource_real_names():
import json_scripts
print('loading resource real names')
# open text file
filename = 'andrew_data/resource_mapping_names.txt'
f = open(filename,'r')
lines = f.readlines()
f.close()
# make a dictionar of real resource names
rn = {}
# loop through the lines
for inst_line in lines:
# clean the line
inst_line = inst_line.strip().split('\t')
# if there is a real name, keep the resource
if len(inst_line) == 2:
# add the resource and real name to dict - no spaces
rn[inst_line[0]] = inst_line[1].replace(' ','_')
# save dictionary to json
json_scripts.save_to_json(rn,'resource_real_names.json','indent')
def main():
import cookielib, poster, urllib2, json, json_scripts
# make a get request to get the gmt names and meta data from Enrichr
x = urllib2.urlopen('http://amp.pharm.mssm.edu/Enrichr/geneSetLibrary?mode=meta')
response = x.read()
gmt_data = json.loads(response)
# local version
# gmt_data = json_scripts.load_to_dict('enrichr_gmts.json')
# generate list of gmts
gmt_names = []
# get library names
for inst_gmt in gmt_data['libraries']:
# only include active gmts
if inst_gmt['isActive'] == True:
gmt_names.append(inst_gmt['libraryName'])
inst_dict = {}
inst_dict['names'] = gmt_names
# save json with list of gmt names
json_scripts.save_to_json(inst_dict,'gmt_names.json','noindent')
def load_resource_classes():
import json_scripts
print('loading resource classes')
# open text file
filename = 'andrew_data/resource_classes.txt'
f = open(filename,'r')
lines = f.readlines()
f.close()
# add the information into a dictionary
rc = {}
# loop through the lines
for i in range(len(lines)):
# get a list of line components
inst_line = lines[i].split('\t')
# get key names from first row
if i != 0:
# I need dataset name, not resource name
################
# get resource name - no spaces
inst_name = inst_line[1].replace(' ','_')
# initialize dictionary
rc[inst_name] = {}
# # dataset name
# rc[inst_name]['dataset_name'] = inst_line[1]
# description
rc[inst_name]['description'] = inst_line[2]
# data type
rc[inst_name]['data_type'] = inst_line[3]
# data group
rc[inst_name]['data_group'] = inst_line[4]
# association
rc[inst_name]['association'] = inst_line[5]
# attribute type
rc[inst_name]['attribute_type'] = inst_line[6]
# attribute group
rc[inst_name]['attribute_group'] = inst_line[7]
# save resource classes
json_scripts.save_to_json(rc,'resource_classes_harminogram.json','indent')
def load_sigs_to_json():
import glob
print('load')
# normal files
file_names = glob.glob('files_2-17-2017/hdf_day*.txt')
pert_files = glob.glob('files_2-17-2017/Pert*.txt')
file_names = file_names + pert_files
print('\n\n')
print(file_names)
print('\n\n')
# # full char dir files
# file_names = glob.glob('files_2-17-2017/big*.txt')
# store all signatures in a dictionary
exp_sigs = {}
for inst_filename in file_names:
inst_sig = inst_filename.split('.txt')[0].split('/')[1].split(
'_chdir')[0]
# initialize dictionary for signature
exp_sigs[inst_sig] = {}
f = open(inst_filename, 'r')
lines = f.readlines()
for inst_line in lines:
inst_line = inst_line.strip().split(',')
inst_gene = inst_line[0]
inst_value = inst_line[1]
exp_sigs[inst_sig][inst_gene] = inst_value
f.close()
json_scripts.save_to_json(exp_sigs,
'proc_data/exp-pert_sigs.json',
indent='indent')
def main():
'''
I'm working on making similarity matrices for KIN, IC, and GPCR genes based on
data in the Hzome. Here I'm gathering my old (Hgram) gene lists with the
latest list of the 'dark' genes from the KMC 2017 grant. I'm saving these to a
new JSON for later use. The next step is to calculate the similarity matrices
and visualize them in a notebook or webpage.
'''
import json_scripts
hgram_info = json_scripts.load_to_dict(
'../harmonogram_classes/gene_classes_harmonogram.json')
grant_poi = json_scripts.load_to_dict(
'../grant_pois/proteins_of_interest.json')
gene_types = ['KIN', 'IC', 'GPCR']
# make a new json with merged all genes and dark gene info
gene_info = {}
for inst_type in gene_types:
# add any dark genes to all_genes
dark_genes = grant_poi[inst_type]
all_genes = hgram_info[inst_type] + dark_genes
dark_genes = sorted(list(set(dark_genes)))
all_genes = sorted(list(set(all_genes)))
print(inst_type)
print('all: ' + str(len(all_genes)))
print('dark: ' + str(len(dark_genes)))
print(len(list(set(dark_genes) - set(all_genes))))
gene_info[inst_type] = {}
gene_info[inst_type]['all'] = all_genes
gene_info[inst_type]['dark'] = dark_genes
print('\n\n')
json_scripts.save_to_json(gene_info,
'../grant_pois/gene_info_with_dark.json',
indent='indent')
def load_grants_per_gene():
import json_scripts
# open text file
filename = 'andrew_data/grantspergene_weighted_standardized.txt'
f = open(filename,'r')
lines = f.readlines()
f.close()
# grab the column names
col_names = lines[0].strip().split('\t')
# initialize dictionary
grant_gene = {}
# loop through the lines
for i in range(len(lines)):
# grab the data for each gene
if i > 0:
# get inst_line
inst_line = lines[i].strip().split('\t')
# get gene name
inst_name = inst_line[0]
# initialize dictionary for gene
grant_gene[inst_name] = {}
# save information on gene
for j in range(len(col_names)):
# skip first element
if j > 0:
# save column name as dictionary key
grant_gene[inst_name][col_names[j]] = float(inst_line[j])
# print(grant_gene[inst_name])
# print(grant_gene['SELL'])
# save to json
json_scripts.save_to_json(grant_gene, 'andrew_data/grants_per_gene.json', 'indent')
def make_protein_dictionary():
'''
This script makes a python dictionary from the proteins of interest lists
and saves them as a JSON for later use.
'''
print('-- generate dictionary with protein names')
import json_scripts
poi = {}
for inst_type in ['kinase', 'gpcr', 'ion_channel']:
inst_names = load_names(inst_type)
poi[inst_type] = inst_names
json_scripts.save_to_json(poi,
'proteins_of_interest/proteins_of_interest.json',
indent='indent')
def main():
import json_scripts
# load gene list text file
filename = 'example_gene_50.txt'
f = open(filename,'r')
genes_text = f.readlines()
f.close()
# clean gene names
genes_text = [d.strip().upper() for d in genes_text]
# remove duplicates
genes_text = list(set(genes_text))
print(len(genes_text))
# generate dictionary
example_list = {}
example_list['genes'] = genes_text
# save to json
json_scripts.save_to_json(example_list,'example_gene_50.json','no_indent')
def construct_array():
import json_scripts
import scipy
print('\nconstructing array\n')
# load the LDR data is json format
ldr = json_scripts.load_to_dict('LDR/LDR_api.json')
# load cl and as dictionary
as_cl_dict = json_scripts.load_to_dict('as_cl_dict.json')
# get nodes from 'short name' dictionary values
nodes = {}
nodes['as'] = sorted(list(set(as_cl_dict['as'].values())))
nodes['cl'] = list(set(as_cl_dict['cl'].values()))
# add cell-free to list of cell lines
nodes['cl'].append('cell-free')
nodes['cl'] = sorted(nodes['cl'])
# # run once - add back removed as and cl to Avi dictionary
# # find assays and cell lines that were removed from original list
# #####################################################################
# all_nodes = extract_nodes()
# for inst_data in as_cl_dict:
# # get all nodes
# tmp_dict = set( as_cl_dict[inst_data].keys() )
# tmp_all = set( all_nodes[inst_data] )
# not_found = list( tmp_all - tmp_dict )
# print('\n')
# print(inst_data)
# for tmp in not_found:
# print(tmp)
# print('\n')
# make 2d matrix for now
mat = scipy.zeros([ len(nodes['as']), len(nodes['cl']) ])
# generate two released matrices
rl = {}
rl['t'] = scipy.zeros([ len(nodes['as']), len(nodes['cl']) ])
rl['f'] = scipy.zeros([ len(nodes['as']), len(nodes['cl']) ])
# generate perturbation dictionary that will save perturbation
# information for assays and cell lines
perts = {}
total = 0
# loop through the ldf datasets
for inst_ldr in ldr:
# get the inst_assay: put name through dictionary
# print( inst_ldr['datasetName'].strip() )
inst_as = as_cl_dict['as'][ inst_ldr['datasetName'].strip() ]
# print('inst_as: '+ inst_as)
# get the cell line(s)
inst_cls = []
for inst_cl in inst_ldr['metadata']['cellLines']:
if 'name' in inst_cl:
#!! remove cell line 'TBD among cell ...'
if 'TBD among' not in inst_cl['name'].strip():
inst_cls.append( as_cl_dict['cl'][ inst_cl['name'].strip() ] )
# get the perturbations
inst_pts = []
for inst_pt in inst_ldr['metadata']['perturbagens']:
inst_pts.append( inst_pt['name'].strip() )
# if the assay is kinomescan then set cell line to 'cell-free
if inst_as == 'KINOMEscan':
# print('kinomescan')
inst_cls.append( 'cell-free' )
# print(inst_cls)
# print('\n\n\n')
# add information to mat
# get index of assay
index_as = nodes['as'].index(inst_as)
# loop through cell lines
for inst_cl in inst_cls:
# get the index of the cell line
index_cl = nodes['cl'].index(inst_cl)
for inst_pt in inst_pts:
# check if the perturbation represents multiple perturbations
if 'compounds' in inst_pt and 'among' not in inst_pt:
mult_pts = int(inst_pt.split(' ')[0])
else:
mult_pts = 0
# track the number of perturbations and the released status
##############################################################
if mult_pts == 0:
mat[ index_as, index_cl ] = mat[ index_as, index_cl ] + 1
# track number of released
if inst_ldr['released'] == True:
rl['t'][index_as, index_cl] = rl['t'][index_as, index_cl] + 1
else:
rl['f'][index_as, index_cl] = rl['f'][index_as, index_cl] + 1
else:
mat[ index_as, index_cl ] = mat[ index_as, index_cl ] + mult_pts
# track number of released
if inst_ldr['released'] == True:
rl['t'][index_as, index_cl] = rl['t'][index_as, index_cl] + mult_pts
else:
rl['f'][index_as, index_cl] = rl['f'][index_as, index_cl] + mult_pts
# keep track of perturbation information in the dictionary
##############################################################
# genrate as cl tuple
inst_tuple = str((inst_as, inst_cl))
# initailize list if necessary
if inst_tuple not in perts:
perts[inst_tuple] = []
# generate pert_dict
pert_dict = {}
pert_dict['name'] = inst_pt
pert_dict['release'] = inst_ldr['released']
pert_dict['_id'] = inst_ldr['_id']
# add dictionary to list
perts[inst_tuple].append(pert_dict)
# add to total
total = total + 1
# check perts dictionary
print('perts dictionary - the number of found as/cl combinations')
print(len(perts.keys()))
# print(perts)
# print('\n\n'+str(total))
# save the matrix
mat = mat.tolist()
rl['t'] = rl['t'].tolist()
rl['f'] = rl['f'].tolist()
# save the list
ldr_mat = {}
ldr_mat['nodes'] = nodes
ldr_mat['mat'] = mat
ldr_mat['rl'] = rl
ldr_mat['perts'] = perts
json_scripts.save_to_json( ldr_mat, 'ldr_mat.json', 'no-indent' )
def assay_cl_dict():
import json_scripts
f = open('LDR/assays_and_cl_lists_for_Avi-AM.txt', 'r')
lines = f.readlines()
f.close()
# make names dictionary
names = {}
names['as'] = {}
names['cl'] = {}
# will go through assays and cell lines
inst_data = ''
# loop through the lines
for inst_line in lines:
# strip the line
inst_line = inst_line.strip()
if 'assays:' in inst_line:
inst_data = 'as'
# print(inst_data)
if 'cell lines:' in inst_line:
inst_data = 'cl'
# print(inst_data)
# load assays
##############
if inst_data == 'as':
# check if there is a short name
if '\t' in inst_line:
inst_sn = inst_line.split('\t')[0]
inst_ln = inst_line.split('\t')[1]
names[inst_data][inst_ln] = inst_sn
# # add data to dictionary
# if inst_ln not in names[inst_data]:
# # add short name to dictionary
# names[inst_data][inst_key].append(inst_ln)
# if there is no short name add long name as key and value
elif len(inst_line) > 0:
names[inst_data][inst_line] = inst_line
# names[inst_data][inst_line].append(inst_line)
# load cell lines
###################
if inst_data == 'cl':
# check if there is a short name
if '\t' in inst_line:
inst_sn = inst_line.split('\t')[0]
inst_ln = inst_line.split('\t')[1]
names[inst_data][inst_ln] = inst_sn
# # add data to dictionary
# if inst_key not in names[inst_data]:
# names[inst_data][inst_key] = []
# # add short name to dictionary
# names[inst_data][inst_key].append(inst_ln)
# if tehre is no short name add long name as key and value
elif len(inst_line) > 0:
names[inst_data][inst_line] = inst_line
# names[inst_data][inst_line].append(inst_line)
# print(len(names['as'].keys()))
# print('\n')
# print(len(names['cl'].keys()))
# print('\n')
# print( len(list(set(names['cl'].values()))) )
# print('\n')
# print( len(list(set(names['as'].values()))) )
json_scripts.save_to_json(names,'as_cl_dict.json','indent')
def write_json_single_value(nodes, clust_order, mat, full_path, row_class={}, col_class={}, link_hl={} ):
import json
import json_scripts
import d3_clustergram
# initialize dict
d3_json = d3_clustergram.ini_d3_json()
# generate distance cutoffs
all_dist = []
for i in range(11):
all_dist.append(float(i)/10)
#!! generate tmp classes
import random
random.seed(122341)
# append row dicts to array
for i in range(len(nodes['row'])):
inst_dict = {}
inst_dict['name'] = nodes['row'][i]
inst_dict['clust'] = clust_order['clust']['row'].index(i)
# do not need to get index
inst_dict['rank'] = clust_order['rank']['row'][i]
# save group
inst_dict['group'] = []
for inst_dist in all_dist:
inst_dict['group'].append( float(clust_order['group']['row'][inst_dist][i]) )
# save value for bar
inst_dict['value'] = random.random()
# add class information
inst_dict['class'] = row_class[nodes['row'][i]]
# append dictionary
d3_json['row_nodes'].append(inst_dict)
# append col dicts to array
for i in range(len(nodes['col'])):
inst_dict = {}
inst_dict['name'] = nodes['col'][i]
inst_dict['clust'] = clust_order['clust']['col'].index(i)
# do not need to get index
inst_dict['rank'] = clust_order['rank']['col'][i]
# save group data for different cutoffs
inst_dict['group'] = []
for inst_dist in all_dist:
inst_dict['group'].append( float(clust_order['group']['col'][inst_dist][i]) )
# save value for bar
inst_dict['value'] = random.random()
# add class information
inst_dict['class'] = col_class[nodes['col'][i]]
# append dictionary
d3_json['col_nodes'].append(inst_dict)
# links - generate edge list
for i in range(len(nodes['row'])):
for j in range(len(nodes['col'])):
if abs(mat[i,j]) > 0:
inst_dict = {}
inst_dict['source'] = i
inst_dict['target'] = j
inst_dict['value'] = mat[i,j]
# initailize with no highlight
inst_dict['highlight'] = 0
# add highlight if necessary
if len(link_hl) > 0:
# check highlight
if nodes['col'][j] in link_hl:
# check if gene is a known target of the transcription factor
if nodes['row'][i] in link_hl[nodes['col'][j]]:
# highlight
inst_dict['highlight'] = 1
d3_json['links'].append( inst_dict )
# write json
##############
# fw = open(full_path, 'w')
# fw.write( json.dumps( d3_json, indent=2) )
# fw.close()
json_scripts.save_to_json(d3_json, full_path, 'noindent')
def add_grant_num_to_clust():
import json_scripts
import numpy as np
import scipy
print('\n-----------------\nadding grant numbers\n-----------------\n')
# load json of Andrew data
data_json = json_scripts.load_to_dict('andrew_data/cumul_probs.json')
print( '\nthere are ' + str(len(data_json['nodes']['row'])) + ' genes in total' )
print( 'there are ' + str(len(data_json['nodes']['col'])) + ' resources in total\n' )
data_mat = np.asarray(data_json['data_mat'])
print('data_mat shape')
print(data_mat.shape)
print('\ngoing to add grants per gene as a column into the harmonogram\n')
# make an array of zeros that will be added to the matrix as a new column
num_rows = len(data_json['nodes']['row'])
extra_col = scipy.zeros([ num_rows, 1 ])
# #!! temporarily switching to ones from zeros
# extra_col = scipy.ones([ num_rows, 1 ])
print('extra col shape')
print(extra_col.shape)
print(extra_col)
# add the column using hstack
data_mat = np.hstack((data_mat, extra_col))
print('data_mat shape after adding in extra column')
print(data_mat.shape)
# does not need to be done here
######################
# # add extra resource name
# data_json['nodes']['col'].append('Grants_Per_Gene')
print( 'there are ' + str(len(data_json['nodes']['col'])) + ' resources in total after adding grants per gene\n' )
# print(data_json['nodes']['col'])
# add grants data to data_mat
###############################
# load grants_per_gene data
grants_gene = json_scripts.load_to_dict('andrew_data/grants_per_gene.json')
# make list of genes that were not found
genes_not_found = []
genes_found = []
# loop through genes and add grant information into data_mat
for inst_gene in grants_gene:
# get the index if the gene is in rows
if inst_gene in data_json['nodes']['row']:
# get the index of inst_gene
inst_index = data_json['nodes']['row'].index(inst_gene)
# print(inst_index)
# keep track of found genes
genes_found.append(inst_gene)
# save CumulProbWeightSum to the matrix
inst_grants = grants_gene[inst_gene]['CumulProbWeightSum']
# save the number of grants to the last column
# data_mat[inst_index,-1] = inst_grants
## put in fake data
data_mat[inst_index,82] = 1 #inst_grants
else:
# keep track of not found genes
genes_not_found.append(inst_gene)
# print(len(genes_found))
# print(len(genes_not_found))
# print(data_mat.shape)
print('\n-------------\nchecking data_mat\n----------------\n')
print(len(data_mat[:,-1]))
print(data_mat[inst_index,82])
print(data_mat[2,82])
# convert data_mat to list
data_mat = data_mat.tolist()
# add back to json
data_json['data_mat'] = data_mat
# save to json
json_scripts.save_to_json(data_json, 'andrew_data/cumul_probs.json', 'no_indent')
def load_andrew_data():
import json_scripts
import scipy
import numpy as np
# load resource classes
load_resource_classes()
# load resource mapping names
load_resource_real_names()
# load Andrew's data
matrix = json_scripts.load_to_dict('andrew_data/gene_dataset_cumulprobs_20150609.json')
# add grants data to data_mat
###############################
# load grants_per_gene data
grants_gene = json_scripts.load_to_dict('andrew_data/grants_per_gene.json')
# only keep the resources with real names
rn = json_scripts.load_to_dict('resource_real_names.json')
# Andrew data format
######################
# matrix is a list of dictionaries
# each element of the list has a dictionary with two keys: label and entries
# the first element of the list describes the columns of the matrix - label: n.a., entries: resources
# the rest of the rows have gene names and the value of the gene in each resource
# I will convert Andrew's data into
# nodes and data_mat
print('\nstarting to process data')
# save row and column data to nodes
nodes = {}
# initialize a list of genes
nodes['row'] = []
# get the good resources - get the real names
nodes['col'] = rn.values()
# save the column index of grants per gene
col_index_grant = nodes['col'].index('Grants_Per_Gene')
# print('\nlength of nodes col')
# print(len(nodes['col']))
# print('\n')
# get the number of rows in the matrix
# make the matrix smaller by one row
# num_rows = len(matrix)
num_rows = len(matrix) - 1
# print('\nmatrix:')
# print(matrix[0]['label'])
# print(matrix[1]['label'])
# print(matrix[2]['label'])
# print('...')
# print(matrix[-2]['label'])
# print(matrix[-1]['label'])
# print('\n')
# print('there are '+str(num_rows)+' genes in the original data from Andrew')
# initialize data matrix
# rows - genes
# cols - good resources
data_mat = scipy.zeros([ num_rows, len(rn.keys()) ])
print('\n---------------\nadding original data to matrix\n----------------')
# loop through the list
# add one to account for the full length of the matrix
for i in range(num_rows+1):
# get the inst row of the matrix
inst_row = matrix[i]
# grab the gene name
inst_name = inst_row['label']
# grab the list of entries - the actual numerical data
inst_entries = inst_row['entries']
# gather the resource names
if i == 0:
# gather all resource (columns)
all_res = inst_row['entries']
# skip the first line - it has column information
if i > 0:
# save to nodes['row']
nodes['row'].append(inst_name)
# only add data from good resources
######################################
# save values to matrix
for j in range(len(inst_entries)):
# only add data from good resources
if all_res[j] in rn:
# get the inst
inst_data_point = inst_entries[j]
# get the resource index in the list of good resources - nodes['col']
# translate the long name (with underscores) to the real name
inst_index = nodes['col'].index( rn[all_res[j]] )
# fill in the matrix with the entries from row i
# shift the index back one to compensate for first row
matrix_index = i-1
# shift the index to account for first row of colun labels
data_mat[matrix_index,inst_index] = inst_data_point
print('\n---------------\nadding grants to matrix\n----------------')
# add grants per gene to matrix
##################################
for inst_gene in grants_gene:
# get the index of the gene if it is in the original rows
if inst_gene in nodes['row']:
# get the index of inst_gene
inst_index = nodes['row'].index(inst_gene)
# get the number of grants
inst_grants = grants_gene[inst_gene]['CumulProbWeightSum']
# save the number of grants to the appropriate column
data_mat[inst_index,col_index_grant] = inst_grants
# print('i '+str(i))
# print('\n')
# print('shape of data_mat after filling in ')
# print(data_mat.shape)
# print('\n')
# print('length of nodes row')
# print(len(nodes['row']))
# print('nodes')
# print(nodes['row'][0])
# print(nodes['row'][-1])
# print('\n')
# save json of the numpy-ready data
#
# convert numpy array to list
data_mat = data_mat.tolist()
# make one dictionary
inst_dict = {}
inst_dict['nodes'] = nodes
inst_dict['data_mat'] = data_mat
# save to json
json_scripts.save_to_json(inst_dict,'andrew_data/cumul_probs.json','no_indent')
def generate_d3_json():
import json_scripts
import d3_clustergram
import scipy
import numpy as np
print('loading json in generate_d3_json')
# load saved json of andrew data
data_json = json_scripts.load_to_dict('andrew_data/cumul_probs.json')
# get nodes and data_mat
nodes = data_json['nodes']
data_mat = np.asarray(data_json['data_mat'])
print(nodes['col'])
print(data_mat.shape)
print('calculating clustering orders')
# gene and resource classes
#################################
# gene class
gc = json_scripts.load_to_dict('gene_classes_harmonogram.json')
# resource class
rc = json_scripts.load_to_dict('resource_classes_harminogram.json')
# loop through classes
for inst_class in gc:
print(inst_class + '\n')
# initialize class matrix
# class_mat is the subset of data_mat that only has genes of one class, e.g. kinases
class_mat = np.array([])
# initialize class_nodes for export
class_nodes = {}
class_nodes['col'] = nodes['col']
class_nodes['row'] = []
# loop through the rows and check if they are in the class
for i in range(len(nodes['row'])):
# get the index
inst_gs = nodes['row'][i]
# check if in class list
if inst_gs in gc[inst_class]:
# append gene symbol name to row
class_nodes['row'].append(inst_gs)
# initialize class_mat if necesary
if len(class_mat) == 0:
class_mat = data_mat[i,:]
else:
# fill in class_mat
class_mat = np.vstack( (class_mat, data_mat[i,:] ))
# actual clustering
########################
# cluster the matrix, return clust_order
clust_order = d3_clustergram.cluster_row_and_column( class_nodes, class_mat, 'cosine' )
# # mock clustering
# ############################
# print('mock clustering')
# clust_order = {}
# # mock cluster
# clust_order['clust'] = {}
# clust_order['clust']['row'] = range(len(class_nodes['row']))
# clust_order['clust']['col'] = range(len(class_nodes['col']))
# # mock rank
# clust_order['rank'] = {}
# clust_order['rank']['row'] = range(len(class_nodes['row']))
# clust_order['rank']['col'] = range(len(class_nodes['col']))
print('generating d3 json')
# generate d3_clust json: return json
d3_json = d3_clustergram.d3_clust_single_value(class_nodes, clust_order, class_mat )
# add extra information (data_group) to d3_json - add resource class to d3_json['col_nodes']
###############################################################################################
# loop through col_nodes
for inst_col in d3_json['col_nodes']:
# get the inst_res
inst_res = inst_col['name']
# add the resource-class - data_group
inst_col['data_group'] = rc[ inst_res ]['data_group'].replace(' ','_')
# add extra link information about grant: this will be used to color the grant links externally
# from the d3_clustergram code
for inst_link in d3_json['links']:
inst_link['info'] = 0
if d3_json['col_nodes'][inst_link['target']]['name'] == 'Grants_Per_Gene':
inst_link['info'] = 1
print('saving to disk')
# save visualization json
json_scripts.save_to_json(d3_json,'static/networks/'+inst_class+'_cumul_probs.json','no_indent')
def write_json_single_value(nodes, clust_order, LDR, full_path, perts, row_class={}, col_class={}, link_hl={} ):
import json
import json_scripts
import d3_clustergram
print(perts.keys())
#!! special case, encode extra released information for LDR
mat = LDR['mat']
# get release data
rl = LDR['rl']
print('\n\nchecking rl\n\n')
# print(rl['t'])
# initialize dict
d3_json = d3_clustergram.ini_d3_json()
# generate distance cutoffs
all_dist = []
for i in range(11):
all_dist.append(float(i)/10)
#!! generate tmp classes
import random
random.seed(122341)
# append row dicts to array
for i in range(len(nodes['row'])):
inst_dict = {}
inst_dict['name'] = nodes['row'][i]
inst_dict['clust'] = clust_order['clust']['row'].index(i)
# do not need to get index
inst_dict['rank'] = clust_order['rank']['row'][i]
# # save group
# inst_dict['group'] = []
# for inst_dist in all_dist:
# inst_dict['group'].append( float(clust_order['group']['row'][inst_dist][i]) )
# # save value for bar
# inst_dict['value'] = random.random()
# # add class information
# inst_dict['class'] = row_class[nodes['row'][i]]
# append dictionary
d3_json['row_nodes'].append(inst_dict)
# append col dicts to array
for i in range(len(nodes['col'])):
inst_dict = {}
inst_dict['name'] = nodes['col'][i]
inst_dict['clust'] = clust_order['clust']['col'].index(i)
# do not need to get index
inst_dict['rank'] = clust_order['rank']['col'][i]
# # save group data for different cutoffs
# inst_dict['group'] = []
# for inst_dist in all_dist:
# inst_dict['group'].append( float(clust_order['group']['col'][inst_dist][i]) )
# # save value for bar
# inst_dict['value'] = random.random()
# # add class information
# inst_dict['class'] = col_class[nodes['col'][i]]
# append dictionary
d3_json['col_nodes'].append(inst_dict)
# links - generate edge list
for i in range(len(nodes['row'])):
for j in range(len(nodes['col'])):
if abs(mat[i,j]) > 0:
inst_dict = {}
inst_dict['source'] = i
inst_dict['target'] = j
inst_dict['value'] = mat[i,j]
# !! custom change for LDRgram
inst_dict['value_up'] = rl['t'][i,j]
inst_dict['value_dn'] = -rl['f'][i,j]
# print('\tas: '+nodes['row'][i])
# print('\tcl: '+nodes['col'][j])
# add perturbation information
inst_tuple = ( nodes['row'][i], nodes['col'][j] )
# print( perts[inst_tuple] )
# add to dictionary
inst_dict['perts'] = perts[inst_tuple]
d3_json['links'].append( inst_dict )
# write json
##############
json_scripts.save_to_json(d3_json, full_path, 'indent')
def write_json_single_value(nodes,
clust_order,
LDR,
full_path,
perts,
row_class={},
col_class={},
link_hl={}):
import json
import json_scripts
import d3_clustergram
print(perts.keys())
#!! special case, encode extra released information for LDR
mat = LDR['mat']
# get release data
rl = LDR['rl']
print('\n\nchecking rl\n\n')
# print(rl['t'])
# initialize dict
d3_json = d3_clustergram.ini_d3_json()
# generate distance cutoffs
all_dist = []
for i in range(11):
all_dist.append(float(i) / 10)
#!! generate tmp classes
import random
random.seed(122341)
# append row dicts to array
for i in range(len(nodes['row'])):
inst_dict = {}
inst_dict['name'] = nodes['row'][i]
inst_dict['clust'] = clust_order['clust']['row'].index(i)
# do not need to get index
inst_dict['rank'] = clust_order['rank']['row'][i]
# # save group
# inst_dict['group'] = []
# for inst_dist in all_dist:
# inst_dict['group'].append( float(clust_order['group']['row'][inst_dist][i]) )
# # save value for bar
# inst_dict['value'] = random.random()
# # add class information
# inst_dict['class'] = row_class[nodes['row'][i]]
# append dictionary
d3_json['row_nodes'].append(inst_dict)
# append col dicts to array
for i in range(len(nodes['col'])):
inst_dict = {}
inst_dict['name'] = nodes['col'][i]
inst_dict['clust'] = clust_order['clust']['col'].index(i)
# do not need to get index
inst_dict['rank'] = clust_order['rank']['col'][i]
# # save group data for different cutoffs
# inst_dict['group'] = []
# for inst_dist in all_dist:
# inst_dict['group'].append( float(clust_order['group']['col'][inst_dist][i]) )
# # save value for bar
# inst_dict['value'] = random.random()
# # add class information
# inst_dict['class'] = col_class[nodes['col'][i]]
# append dictionary
d3_json['col_nodes'].append(inst_dict)
# links - generate edge list
for i in range(len(nodes['row'])):
for j in range(len(nodes['col'])):
if abs(mat[i, j]) > 0:
inst_dict = {}
inst_dict['source'] = i
inst_dict['target'] = j
inst_dict['value'] = mat[i, j]
# !! custom change for LDRgram
inst_dict['value_up'] = rl['t'][i, j]
inst_dict['value_dn'] = -rl['f'][i, j]
# print('\tas: '+nodes['row'][i])
# print('\tcl: '+nodes['col'][j])
# add perturbation information
inst_tuple = (nodes['row'][i], nodes['col'][j])
# print( perts[inst_tuple] )
# add to dictionary
inst_dict['perts'] = perts[inst_tuple]
d3_json['links'].append(inst_dict)
# write json
##############
json_scripts.save_to_json(d3_json, full_path, 'indent')