def load_tsv_to_net(net, file_buffer, filename=None): import pandas as pd import categories import proc_df_labels lines = file_buffer.getvalue().split('\n') num_labels = categories.check_categories(lines) row_arr = range(num_labels['row']) col_arr = range(num_labels['col']) tmp_df = {} # use header if there are col categories if len(col_arr) > 1: tmp_df['mat'] = pd.read_table(file_buffer, index_col=row_arr, header=col_arr) else: tmp_df['mat'] = pd.read_table(file_buffer, index_col=row_arr) tmp_df = proc_df_labels.main(tmp_df) net.df_to_dat(tmp_df) net.dat['filename'] = filename
def load_tsv_to_net(net, file_buffer, filename=None): import pandas as pd import categories import proc_df_labels lines = file_buffer.getvalue().split('\n') num_labels = categories.check_categories(lines) row_arr = range(num_labels['row']) col_arr = range(num_labels['col']) tmp_df = {} # use header if there are col categories if len(col_arr) > 1: tmp_df['mat'] = pd.read_table(file_buffer, index_col=row_arr, header=col_arr) else: tmp_df['mat'] = pd.read_table(file_buffer, index_col=row_arr) tmp_df = proc_df_labels.main(tmp_df) net.df_to_dat(tmp_df) net.dat['filename'] = filename
def main(real_net, vect_post): import numpy as np from copy import deepcopy from __init__ import Network import proc_df_labels net = deepcopy(Network()) sigs = vect_post['columns'] all_rows = [] all_sigs = [] for inst_sig in sigs: all_sigs.append(inst_sig['col_name']) col_data = inst_sig['data'] for inst_row_data in col_data: all_rows.append(inst_row_data['row_name']) all_rows = sorted(list(set(all_rows))) all_sigs = sorted(list(set(all_sigs))) net.dat['nodes']['row'] = all_rows net.dat['nodes']['col'] = all_sigs net.dat['mat'] = np.empty((len(all_rows), len(all_sigs))) net.dat['mat'][:] = np.nan is_up_down = False if 'is_up_down' in vect_post: if vect_post['is_up_down'] is True: is_up_down = True if is_up_down is True: net.dat['mat_up'] = np.empty((len(all_rows), len(all_sigs))) net.dat['mat_up'][:] = np.nan net.dat['mat_dn'] = np.empty((len(all_rows), len(all_sigs))) net.dat['mat_dn'][:] = np.nan for inst_sig in sigs: inst_sig_name = inst_sig['col_name'] col_data = inst_sig['data'] for inst_row_data in col_data: inst_row = inst_row_data['row_name'] inst_value = inst_row_data['val'] row_index = all_rows.index(inst_row) col_index = all_sigs.index(inst_sig_name) net.dat['mat'][row_index, col_index] = inst_value if is_up_down is True: net.dat['mat_up'][row_index, col_index] = inst_row_data['val_up'] net.dat['mat_dn'][row_index, col_index] = inst_row_data['val_dn'] tmp_df = net.dat_to_df() tmp_df = proc_df_labels.main(tmp_df) real_net.df_to_dat(tmp_df)
def main(real_net, vect_post): import numpy as np from copy import deepcopy from __init__ import Network import proc_df_labels net = deepcopy(Network()) sigs = vect_post['columns'] all_rows = [] all_sigs = [] for inst_sig in sigs: all_sigs.append(inst_sig['col_name']) col_data = inst_sig['data'] for inst_row_data in col_data: all_rows.append(inst_row_data['row_name']) all_rows = sorted(list(set(all_rows))) all_sigs = sorted(list(set(all_sigs))) net.dat['nodes']['row'] = all_rows net.dat['nodes']['col'] = all_sigs net.dat['mat'] = np.empty((len(all_rows), len(all_sigs))) net.dat['mat'][:] = np.nan is_up_down = False if 'is_up_down' in vect_post: if vect_post['is_up_down'] is True: is_up_down = True if is_up_down is True: net.dat['mat_up'] = np.empty((len(all_rows), len(all_sigs))) net.dat['mat_up'][:] = np.nan net.dat['mat_dn'] = np.empty((len(all_rows), len(all_sigs))) net.dat['mat_dn'][:] = np.nan for inst_sig in sigs: inst_sig_name = inst_sig['col_name'] col_data = inst_sig['data'] for inst_row_data in col_data: inst_row = inst_row_data['row_name'] inst_value = inst_row_data['val'] row_index = all_rows.index(inst_row) col_index = all_sigs.index(inst_sig_name) net.dat['mat'][row_index, col_index] = inst_value if is_up_down is True: net.dat['mat_up'][row_index, col_index] = inst_row_data['val_up'] net.dat['mat_dn'][row_index, col_index] = inst_row_data['val_dn'] tmp_df = net.dat_to_df() tmp_df = proc_df_labels.main(tmp_df) real_net.df_to_dat(tmp_df)