def load_hgram_data_to_json():
from d3_clustergram_class import Network
from copy import deepcopy
# generate object to hold all data
hgram = deepcopy(Network())
# get instance of Network
hgram = Network()
print(hgram.__doc__)
print('\n\tload matrix clustergram')
# use hgram method to load Andrew data
hgram.load_hgram('hgram_data_latest/gene_dataset_cumulprobs_20150814.txt')
# export dictionary and save to file
hgram_data_json = hgram.write_json_to_file('dat', 'hgram_data_latest/hgram_latest.json')
def main():
import json
from d3_clustergram_class import Network
net = Network()
mat_info = {}
mat_info[str((1, 1))] = 1
print(mat_info[str((1, 1))])
print(mat_info)
print(type(mat_info))
tmp = json.dumps(mat_info)
print(tmp)
print(type(tmp))
def make_ldr_clust():
import json_scripts
import numpy as np
import d3_clustergram
from d3_clustergram_class import Network
from ast import literal_eval
# load LDR data - stored as:
# released status (rl)
# nodes, and mat
ldr = json_scripts.load_to_dict('ldr_mat.json')
print('\nload ldr_mat.json with perts')
print(ldr.keys())
ldr['mat'] = np.asarray(ldr['mat'])
ldr['rl']['t'] = np.asarray(ldr['rl']['t'])
ldr['rl']['f'] = np.asarray(ldr['rl']['f'])
print( 'sum all \t' + str(np.sum(ldr['mat'])) )
print( 'sum yes \t' + str(np.sum(ldr['rl']['t'])) )
print( 'sum no \t' + str(np.sum(ldr['rl']['f'])) )
print(len(ldr['nodes']['as']))
print(len(ldr['nodes']['cl']))
print(ldr['mat'].shape)
print('\n')
print( 'size all \t' + str(ldr['mat'].shape) )
print( 'size yes \t' + str(ldr['rl']['t'].shape) )
print( 'size no \t' + str(ldr['rl']['f'].shape) )
print('\n')
print( 'sum all \t' + str(np.sum(ldr['mat'])) )
print( 'sum yes \t' + str(np.sum(ldr['rl']['t'])) )
print( 'sum no \t' + str(np.sum(ldr['rl']['f'])) )
print( 'total yes/no:\t' + str( np.sum(ldr['rl']['t']) + np.sum(ldr['rl']['f']) ) )
# define nodes: unfiltered
nodes_uf = {}
nodes_uf['row'] = ldr['nodes']['as']
nodes_uf['col'] = ldr['nodes']['cl']
# initialize a new network class
##################################
net = Network()
net.dat['nodes']['row'] = nodes_uf['row']
net.dat['nodes']['col'] = nodes_uf['col']
# net.dat['mat'] = ldr['mat']
# net.dat['mat_up'] = ldr['rl']['t']
# net.dat['mat_dn'] = -ldr['rl']['f']
# only include released data in visualization
net.dat['mat'] = ldr['rl']['t']
# add perts as mat_info
############################
print('\nperts')
net.dat['mat_info'] = {}
# initialize mat_info
for i in range(len(net.dat['nodes']['row'])):
for j in range(len(net.dat['nodes']['col'])):
tmp_tuple = str((i,j))
# initialize info
net.dat['mat_info'][tmp_tuple] = {}
for inst_pert in ldr['perts']:
pert_data = ldr['perts'][inst_pert]
inst_pert = literal_eval(inst_pert)
# assay
inst_row = inst_pert[0]
# cell line
inst_col = inst_pert[1]
# assay
index_row = net.dat['nodes']['row'].index(inst_row)
# cell line
index_col = net.dat['nodes']['col'].index(inst_col)
# save to mat_info
tmp_tuple = str((index_row, index_col))
net.dat['mat_info'][str(tmp_tuple)] = pert_data
# filter the matrix using cutoff and min_num_meet
###################################################
# filtering matrix
cutoff_meet = 1
min_num_meet = 1
net.filter_network_thresh( cutoff_meet, min_num_meet )
# cluster
#############
cutoff_comp = 3
min_num_comp = 4
net.cluster_row_and_col('cos', cutoff_comp, min_num_comp, dendro=False)
# export data visualization to file
######################################
net.write_json_to_file('viz', 'static/networks/LDR_as_cl_released_only.json','indent')
