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')