def load_power_input(input_fmt, power_or_path, fmt='on', normalize=True):
if isinstance(power_or_path, str):
power = Power(fmt=fmt)
power.load_power(power_or_path, fmt=fmt)
power.extend_lp_info(real_only=False)
else:
power = power_or_path
values = np.empty(input_fmt.shape[0], dtype=np.float32) + np.nan
for i, (idx, etype, dtype, name) in \
enumerate(input_fmt[['etype', 'dtype', 'name']].itertuples()):
if etype == 'ed':
# TODO: deal with ed
continue
else:
try:
v = power.data[etype].loc[power.data[etype]['name'] == name,
dtype]
if len(v) > 0:
values[i] = v.values[0]
# values.append(v.values[0] if len(v) > 0 else np.nan)
except KeyError:
# print(etype, dtype, name)
# values.append(np.nan)
continue
if normalize:
values = mm_normalize(values, input_fmt['min'].values,
input_fmt['max'].values)
values[np.isnan(values)] = -1.
return values
def restore_power_input(input_fmt,
power_or_path,
fmt='on',
normalize=True,
clip=True):
if isinstance(power_or_path, str):
power = Power(fmt=fmt)
power.load_power(power_or_path, fmt=fmt)
power.extend_lp_info(real_only=False)
else:
power = power_or_path
if normalize:
input_fmt['value'] = mm_denormalize(input_fmt['value'].values,
input_fmt['min'].values,
input_fmt['max'].values)
if clip:
input_fmt['value'] = np.clip(input_fmt['value'], input_fmt['min'],
input_fmt['max'])
groups = input_fmt.groupby(['etype', 'dtype'], sort=False)
for (e, d), sub in groups:
values = sub[['name', 'value']].set_index('name')
values = values.reindex(power.data[e]['name'])
na_idx = np.isnan(values['value'])
values[na_idx] = power.data[e][d].values[na_idx]
set_values(power.data, e, d, values['value'].values)
return power
def restore_power_input(input_fmt, power_or_path, fmt='on', normalize=True):
if isinstance(power_or_path, str):
power = Power(fmt=fmt)
power.load_power(power_or_path, fmt=fmt)
power.extend_lp_info(real_only=False)
else:
power = power_or_path
if normalize:
input_fmt['value'] = mm_denormalize(input_fmt['value'].values,
input_fmt['min'].values,
input_fmt['max'].values)
group = input_fmt.groupby(['etype', 'dtype'], sort=False)
for (e, d), idx in group.indices.items():
values = power.data[e][[d, 'name']].copy()
indices = values.index
values.set_index('name', inplace=True)
values = input_fmt.loc[idx, ['name', 'value']].set_index('name')
values.index = indices
set_values(power.data, e, d, values['value'])
return power