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)
