def df_to_dat(net, df):
import categories
net.dat['mat'] = df['mat'].values
net.dat['nodes']['row'] = df['mat'].index.tolist()
net.dat['nodes']['col'] = df['mat'].columns.tolist()
for inst_rc in ['row', 'col']:
if type(net.dat['nodes'][inst_rc][0]) is tuple:
# get the number of categories from the length of the tuple
# subtract 1 because the name is the first element of the tuple
num_cat = len(net.dat['nodes'][inst_rc][0]) - 1
net.dat['node_info'][inst_rc]['full_names'] = net.dat['nodes']\
[inst_rc]
for inst_rcat in range(num_cat):
net.dat['node_info'][inst_rc]['cat-' + str(inst_rcat)] = \
[i[inst_rcat + 1] for i in net.dat['nodes'][inst_rc]]
net.dat['nodes'][inst_rc] = [i[0] for i in net.dat['nodes'][inst_rc]]
if 'mat_up' in df:
net.dat['mat_up'] = df['mat_up'].values
net.dat['mat_dn'] = df['mat_dn'].values
categories.dict_cat(net)
def df_to_dat(net, df):
import categories
net.dat['mat'] = df['mat'].values
net.dat['nodes']['row'] = df['mat'].index.tolist()
net.dat['nodes']['col'] = df['mat'].columns.tolist()
for inst_rc in ['row', 'col']:
if type(net.dat['nodes'][inst_rc][0]) is tuple:
# get the number of categories from the length of the tuple
# subtract 1 because the name is the first element of the tuple
num_cat = len(net.dat['nodes'][inst_rc][0]) - 1
net.dat['node_info'][inst_rc]['full_names'] = net.dat['nodes']\
[inst_rc]
for inst_rcat in range(num_cat):
net.dat['node_info'][inst_rc]['cat-' + str(inst_rcat)] = \
[i[inst_rcat + 1] for i in net.dat['nodes'][inst_rc]]
net.dat['nodes'][inst_rc] = [
i[0] for i in net.dat['nodes'][inst_rc]
]
if 'mat_up' in df:
net.dat['mat_up'] = df['mat_up'].values
net.dat['mat_dn'] = df['mat_dn'].values
if 'mat_orig' in df:
net.dat['mat_orig'] = df['mat_orig'].values
categories.dict_cat(net)
def main(net, vect_post):
import numpy as np
import categories
sigs = vect_post['columns']
all_rows = []
all_sigs = []
tmp_col_cat = {}
is_col_cat = False
for inst_sig in sigs:
all_sigs.append(inst_sig['col_name'])
if 'cat' in inst_sig:
is_col_cat = True
tmp_col_cat[inst_sig['col_name']] = inst_sig['cat']
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
if is_col_cat:
all_cat = []
for tmp_sig in all_sigs:
all_cat.append(tmp_col_cat[tmp_sig])
net.dat['node_info']['col']['cat-0'] = all_cat
categories.dict_cat(net)
net.dat['node_info']['col']['full_names'] = []
# construct full_names from single column category
# need to improve
for inst_index in range(len(all_cat)):
inst_cat = all_cat[inst_index]
inst_name = net.dat['nodes']['col'][inst_index]
inst_tuple = (inst_name, inst_cat)
net.dat['node_info']['col']['full_names'].append(inst_tuple)
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']
def main(net, vect_post):
import numpy as np
import categories
sigs = vect_post['columns']
all_rows = []
all_sigs = []
tmp_col_cat = {}
is_col_cat = False
for inst_sig in sigs:
all_sigs.append(inst_sig['col_name'])
if 'cat' in inst_sig:
is_col_cat = True
tmp_col_cat[inst_sig['col_name']] = inst_sig['cat']
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
if is_col_cat:
all_cat = []
for tmp_sig in all_sigs:
all_cat.append(tmp_col_cat[tmp_sig])
net.dat['node_info']['col']['cat-0'] = all_cat
categories.dict_cat(net)
net.dat['node_info']['col']['full_names'] = []
# construct full_names from single column category
# need to improve
for inst_index in range(len(all_cat)):
inst_cat = all_cat[inst_index]
inst_name = net.dat['nodes']['col'][inst_index]
inst_tuple = (inst_name, inst_cat)
net.dat['node_info']['col']['full_names'].append(inst_tuple)
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']
