def save_dict_to_json(inst_dict):
print('save to cell_line_info_dict.json\n')
from clustergrammer import Network
net = Network()
net.save_dict_to_json(inst_dict,
'cell_line_info_dict.json',
indent='indent')
def mock_g2e_json(gl):
import enrichr_functions as enr_fun
from clustergrammer import Network
'''
A json of signatures from g2e, for enrichment vectoring, should look like this
{
"signature_ids":[
{"col_title":"title 1", "enr_id_up":###, "enr_id_dn":###},
{"col_title":"title 2", "enr_id_up":###, "enr_id_dn":###}
],
"background_type":"ChEA_2015"
}
'''
net = Network()
g2e_post = {}
sig_ids = []
# I have to get user_list_ids from Enrichr
tmp = 1
for inst_gl in gl:
inst_sig = {}
inst_sig['col_title'] = 'Sig-'+str(tmp)
tmp = tmp+1
# submit to enrichr and get user_list_ids
for inst_updn in inst_gl:
inst_list = inst_gl[inst_updn]
inst_id = enr_fun.enrichr_post_request(inst_list)
inst_sig['enr_id_'+inst_updn] = inst_id
sig_ids.append(inst_sig)
g2e_post['signature_ids'] = sig_ids
g2e_post['background_type'] = 'ChEA_2015'
net.save_dict_to_json(g2e_post,'json/g2e_enr_vect.json','indent')
def make_json():
from clustergrammer import Network
net = Network()
row_num = 200
num_columns = 20
# make up all names for all data
row_names = make_up_names(row_num)
# initialize vect_post
vect_post = {}
vect_post['title'] = 'Some-Clustergram'
vect_post['link'] = 'some-link'
vect_post['filter'] = 'N_row_sum'
vect_post['is_up_down'] = False
vect_post['columns'] = []
split = True
# fraction of rows in each column - 1 means all columns have all rows
inst_prob = 1
# make column data
for col_num in range(num_columns):
inst_col = {}
col_name = 'Col-' + str(col_num + 1) + ' make name longer'
inst_col['col_name'] = col_name
inst_col['link'] = 'col-link'
if col_num < 5:
inst_col['cat'] = 'brain'
else:
inst_col['cat'] = 'lung'
# save to columns
inst_col['data'] = [] #vector
# get random subset of row_names
vect_rows = get_subset_rows(row_names, inst_prob)
# generate vectors
for inst_row in vect_rows:
# genrate values
##################
# add positive/negative values
if random.random() > 0.5:
value_up = 10 * random.random()
else:
value_up = 0
if random.random() > 0.5:
value_dn = -10 * random.random()
else:
value_dn = 0
value = value_up + value_dn
# # generate vector component
# #############################
# vector.append([ inst_row, value ])
# vector_up.append([ inst_row, value_up ])
# vector_dn.append([ inst_row, value_dn ])
# define row object - within column
row_obj = {}
row_obj['row_name'] = inst_row
row_obj['val'] = value
row_obj['val_up'] = value_up
row_obj['val_dn'] = value_dn
inst_col['data'].append(row_obj)
# if split:
# inst_col['vector_up'] = vector_up
# inst_col['vector_dn'] = vector_dn
# save columns to vect_post
vect_post['columns'].append(inst_col)
net.save_dict_to_json(vect_post, 'fake_vect_post.json', indent='indent')
def make_json():
from clustergrammer import Network
net = Network()
row_num = 200
num_columns = 20
# make up all names for all data
row_names = make_up_names(row_num)
# initialize vect_post
vect_post = {}
vect_post['title'] = 'Some-Clustergram'
vect_post['link'] = 'some-link'
vect_post['filter'] = 'N_row_sum'
vect_post['is_up_down'] = False
vect_post['columns'] = []
split = True
# fraction of rows in each column - 1 means all columns have all rows
inst_prob = 1
# make column data
for col_num in range(num_columns):
inst_col = {}
col_name = 'Col-' + str( col_num+1 ) + ' make name longer'
inst_col['col_name'] = col_name
inst_col['link'] = 'col-link'
if col_num < 5:
inst_col['cat'] = 'brain'
else:
inst_col['cat'] = 'lung'
# save to columns
inst_col['data'] = [] #vector
# get random subset of row_names
vect_rows = get_subset_rows(row_names, inst_prob)
# generate vectors
for inst_row in vect_rows:
# genrate values
##################
# add positive/negative values
if random.random() > 0.5:
value_up = 10*random.random()
else:
value_up = 0
if random.random() > 0.5:
value_dn = -10*random.random()
else:
value_dn = 0
value = value_up + value_dn
# # generate vector component
# #############################
# vector.append([ inst_row, value ])
# vector_up.append([ inst_row, value_up ])
# vector_dn.append([ inst_row, value_dn ])
# define row object - within column
row_obj = {}
row_obj['row_name'] = inst_row
row_obj['val'] = value
row_obj['val_up'] = value_up
row_obj['val_dn'] = value_dn
inst_col['data'].append(row_obj)
# if split:
# inst_col['vector_up'] = vector_up
# inst_col['vector_dn'] = vector_dn
# save columns to vect_post
vect_post['columns'].append(inst_col)
net.save_dict_to_json(vect_post, 'fake_vect_post.json', indent='indent')
def save_dict_to_json(inst_dict):
print('save to cell_line_info_dict.json\n')
from clustergrammer import Network
net = Network()
net.save_dict_to_json(inst_dict, 'cell_line_info_dict.json', indent='indent')
def main():
from clustergrammer import Network
net = Network()
row_num = 200
num_columns = 20
# make up all names for all data
row_names = make_up_names(row_num)
# initialize vect_post
vect_post = {}
vect_post['title'] = 'Some-Clustergram'
vect_post['link'] = 'some-link'
vect_post['filter'] = 'N_row_sum'
vect_post['is_up_down'] = True
vect_post['columns'] = []
# fraction of rows in each column - 1 means all columns have all rows
inst_prob = 1
# make column data
for col_num in range(num_columns):
inst_col = {}
if col_num < 5:
col_name = "('Columns: Col-" + str( col_num+1 ) + "', 'tissue: brain')"
else:
col_name = "('Columns: Col-" + str( col_num+1 ) + "', 'tissue: lung')"
inst_col['col_name'] = col_name
inst_col['link'] = 'col-link'
# save to columns
inst_col['data'] = [] #vector
# get random subset of row_names
vect_rows = get_subset_rows(row_names, inst_prob)
# generate vectors
for inst_row in vect_rows:
# genrate values
##################
# add positive/negative values
if random.random() > 0.5:
value_up = 10*random.random()
else:
value_up = 0
if random.random() > 0.5:
value_dn = -10*random.random()
else:
value_dn = 0
value = value_up + value_dn
# define row object - within column
row_obj = {}
row_obj['row_name'] = inst_row
row_obj['val'] = value
row_obj['val_up'] = value_up
row_obj['val_dn'] = value_dn
inst_col['data'].append(row_obj)
# save columns to vect_post
vect_post['columns'].append(inst_col)
net.save_dict_to_json(vect_post, 'json/fake_vect_post.json', indent='indent')
