debug:decoding Unicode is not supported - python-2.7

Question

My code can run,but when I debug,it can enter the Subroutine.And the error is:"decoding Unicode is not supported".
I use anaconda.When I open the untitled0.py,the encoding is UTF-8 at the bottom of the screen,but when I open the fhmm_exact.py,the encoding is UTF-8-GUESSED.
Traceback (most recent call last):
File "<ipython-input-1-f6910c2dfa77>", line 1, in <module>
debugfile('/home/wenwu/untitled0.py', wdir='/home/wenwu')
File "/home/wenwu/anaconda/lib/python2.7/site-packages/spyderlib/widgets/externalshell/sitecustomize.py", line 702, in debugfile
debugger.run("runfile(%r, args=%r, wdir=%r)" % (filename, args, wdir))
File "/home/wenwu/anaconda/lib/python2.7/bdb.py", line 400, in run
exec cmd in globals, locals
File "<string>", line 1, in <module>
File "/home/wenwu/anaconda/lib/python2.7/site-packages/spyderlib/widgets/externalshell/sitecustomize.py", line 682, in runfile
execfile(filename, namespace)
File "/home/wenwu/anaconda/lib/python2.7/site-packages/spyderlib/widgets/externalshell/sitecustomize.py", line 78, in execfile
builtins.execfile(filename, *where)
File "/home/wenwu/untitled0.py", line 37, in <module>
fhmm.disaggregate(test_elec.mains(),output,sample_period = 60)
File "/home/wenwu/nilmtk/nilmtk/disaggregate/fhmm_exact.py", line 287, in disaggregate
mains_data_location = '{}/elec/meter1'.format(building_path)
File "/home/wenwu/nilmtk/nilmtk/disaggregate/fhmm_exact.py", line 287, in disaggregate
mains_data_location = '{}/elec/meter1'.format(building_path)
File "/home/wenwu/anaconda/lib/python2.7/bdb.py", line 49, in trace_dispatch
return self.dispatch_line(frame)
File "/home/wenwu/anaconda/lib/python2.7/bdb.py", line 67, in dispatch_line
self.user_line(frame)
File "/home/wenwu/anaconda/lib/python2.7/pdb.py", line 158, in user_line
self.interaction(frame, None)
File "/home/wenwu/anaconda/lib/python2.7/site-packages/spyderlib/widgets/externalshell/sitecustomize.py", line 488, in interaction
self.notify_spyder(frame) #-----Spyder-specific-------------------------
File "/home/wenwu/anaconda/lib/python2.7/site-packages/spyderlib/widgets/externalshell/sitecustomize.py", line 432, in notify_spyder
fname = unicode(fname, "utf-8")
TypeError: decoding Unicode is not supported
The following is code.
untitled0.py*
from matplotlib import rcParams
import matplotlib.pyplot as plt
rcParams['figure.figsize'] = (13,6)
plt.style.use('ggplot')
from nilmtk import DataSet,TimeFrame,MeterGroup,HDFDataStore
train = DataSet('/home/wenwu/redd.h5')
test = DataSet('/home/wenwu/redd.h5')
building = 1
train.set_window(end = '30-4-2011')
test.set_window(start = '30-4-2011')
train_elec = train.buildings[1].elec
test_elec = test.buildings[1].elec
fridge_meter = train_elec['fridge']
fridge_df = fridge_meter.load().next()
fridge_df.head()
mains = train_elec.mains()
mains_df = mains.load().next()
top_5_train_elec = train_elec.submeters().select_top_k(k = 5)
from nilmtk.disaggregate import fhmm_exact
from nilmtk.metrics import f1_score
fhmm = fhmm_exact.FHMM()
fhmm.train(top_5_train_elec,sample_period = 60)
disag_filename = '/home/wenwu/redd-disag-fhmm.h5'
output = HDFDataStore(disag_filename,'w')
fhmm.disaggregate(test_elec.mains(),output,sample_period = 60)
output.close()
disag_fhmm = DataSet(disag_filename)
disag_fhmm_elec = disag_fhmm.buildings[building].elec
f1_fhmm = f1_score(disag_fhmm_elec,test_elec)
f1_fhmm.plot(kind = 'barh')
disaggreate part of fhmm_exact.py
def disaggregate(self, mains, output_datastore, **load_kwargs):
'''Disaggregate mains according to the model learnt previously.
Parameters
----------
mains : nilmtk.ElecMeter or nilmtk.MeterGroup
output_datastore : instance of nilmtk.DataStore subclass
For storing power predictions from disaggregation algorithm.
output_name : string, optional
The `name` to use in the metadata for the `output_datastore`.
e.g. some sort of name for this experiment. Defaults to
"NILMTK_FHMM_<date>"
resample_seconds : number, optional
The desired sample period in seconds.
**load_kwargs : key word arguments
Passed to `mains.power_series(**kwargs)`
'''
import warnings
warnings.filterwarnings("ignore", category=Warning)
MIN_CHUNK_LENGTH = 100
if not self.model:
raise RuntimeError(
"The model needs to be instantiated before"
" calling `disaggregate`. For example, the"
" model can be instantiated by running `train`.")
# Extract optional parameters from load_kwargs
date_now = datetime.now().isoformat().split('.')[0]
output_name = load_kwargs.pop('output_name', 'NILMTK_FHMM_' + date_now)
resample_seconds = load_kwargs.pop('resample_seconds', 60)
resample_rule = '{:d}S'.format(resample_seconds)
timeframes = []
building_path = '/building{}'.format(mains.building())
mains_data_location = '{}/elec/meter1'.format(building_path)
data_is_available = False
for chunk in mains.power_series(**load_kwargs):
# Check that chunk is sensible size before resampling
if len(chunk) < MIN_CHUNK_LENGTH:
continue
# Record metadata
timeframes.append(chunk.timeframe)
measurement = chunk.name
chunk = chunk.resample(rule=resample_rule)
# Check chunk size *again* after resampling
if len(chunk) < MIN_CHUNK_LENGTH:
continue
# Start disaggregation
predictions = self.disaggregate_chunk(chunk)
for meter in predictions.columns:
data_is_available = True
meter_instance = meter.instance()
cols = pd.MultiIndex.from_tuples([chunk.name])
predicted_power = predictions[[meter]]
output_df = pd.DataFrame(predicted_power)
output_df.columns = pd.MultiIndex.from_tuples([chunk.name])
output_datastore.append('{}/elec/meter{}'
.format(building_path, meter_instance),
output_df)
# Copy mains data to disag output
output_datastore.append(key=mains_data_location,
value=pd.DataFrame(chunk, columns=cols))
if not data_is_available:
return
##################################
# Add metadata to output_datastore
# TODO: `preprocessing_applied` for all meters
# TODO: split this metadata code into a separate function
# TODO: submeter measurement should probably be the mains
# measurement we used to train on, not the mains measurement.
# DataSet and MeterDevice metadata:
meter_devices = {
'FHMM': {
'model': 'FHMM',
'sample_period': resample_seconds,
'max_sample_period': resample_seconds,
'measurements': [{
'physical_quantity': measurement[0],
'type': measurement[1]
}]
},
'mains': {
'model': 'mains',
'sample_period': resample_seconds,
'max_sample_period': resample_seconds,
'measurements': [{
'physical_quantity': measurement[0],
'type': measurement[1]
}]
}
}
merged_timeframes = merge_timeframes(timeframes, gap=resample_seconds)
total_timeframe = TimeFrame(merged_timeframes[0].start,
merged_timeframes[-1].end)
dataset_metadata = {'name': output_name, 'date': date_now,
'meter_devices': meter_devices,
'timeframe': total_timeframe.to_dict()}
output_datastore.save_metadata('/', dataset_metadata)
# Building metadata
# Mains meter:
elec_meters = {
1: {
'device_model': 'mains',
'site_meter': True,
'data_location': mains_data_location,
'preprocessing_applied': {}, # TODO
'statistics': {
'timeframe': total_timeframe.to_dict()
}
}
}
# TODO: FIX THIS! Ugly hack for now
# Appliances and submeters:
appliances = []
for i, meter in enumerate(self.meters):
meter_instance = meter.instance()
for app in meter.appliances:
appliance = {
'meters': [meter_instance],
'type': app.identifier.type,
'instance': app.identifier.instance
# TODO this `instance` will only be correct when the
# model is trained on the same house as it is tested on.
# https://github.com/nilmtk/nilmtk/issues/194
}
appliances.append(appliance)
elec_meters.update({
meter_instance: {
'device_model': 'FHMM',
'submeter_of': 1,
'data_location': ('{}/elec/meter{}'
.format(building_path, meter_instance)),
'preprocessing_applied': {}, # TODO
'statistics': {
'timeframe': total_timeframe.to_dict()
}
}
})
# Setting the name if it exists
if meter.name:
if len(meter.name) > 0:
elec_meters[meter_instance]['name'] = meter.name
building_metadata = {
'instance': mains.building(),
'elec_meters': elec_meters,
'appliances': appliances
}
output_datastore.save_metadata(building_path, building_metadata)