def lefilter(keep=None):
data, filters, _ = get_data()
mask = filters[:,-1] == keep
lefilter = filters[mask][:,:-1]
value = data[:,lefilter.astype(np.bool)[0]]
if keep != 'gender':
value = value.astype(np.integer)
return value
def calc_score(param=None):
data, filters, _ = get_data()
mask = filters[:,-1] == param
lefilter = filters[mask][:,:-1]
values = data[:,lefilter.astype(np.bool)[0]].astype(np.integer)
value = np.sum(values, axis=1)
if param.startswith('tipi'):
value = value / divide[divide[:,0] == 'tipi',:][:,-1].astype(np.integer)
elif param.startswith('ras'):
value = value / divide[divide[:,0] == 'ras',:][:,-1].astype(np.integer)
return value
def reverse_score():
data, filters, _ = get_data()
data_nn = data[0:,:]
lefilter = filters[filters[:,-1] == 'reverse scoring'][0]
soirfilter = filters[filters[:,-1] == 'soi-r'][0]
rasfilter = filters[filters[:,-1] == 'ras'][0]
tipifilter = filters[filters[:,-1] == 'tipi'][0]
# mask = lefilter != '0'
# newfilter = filters[:,mask]
# newdata = data[:,mask[:-1]].astype(np.integer)
for ixr,row in enumerate(data_nn):
for ixe, el in enumerate(row):
if lefilter[ixe] == '1':
if soirfilter[ixe] == '1':
data_nn[ixr,ixe] = soir_max + 1 - el.astype(np.integer)
elif rasfilter[ixe] == '1':
data_nn[ixr,ixe] = ras_max + 1 - el.astype(np.integer)
elif tipifilter[ixe] == '1':
data_nn[ixr,ixe] = tipi_max + 1 - el.astype(np.integer)
return data_nn