def build_statistics_channels(self, windows, statistics, name=""):
channel_list = []
for stat in statistics:
#print(stat)
channel_names = pampro_utilities.design_variable_names(self.name, stat)
#print(channel_names)
for cn in channel_names:
channel_list.append(Channel.Channel(cn))
num_expected_results = len(channel_list)
for window in windows:
results = self.window_statistics(window.start_timestamp, window.end_timestamp, statistics)
if len(results) != num_expected_results:
raise Exception("Incorrect number of statistics yielded. {} expected, {} given. Channel: {}. Statistics: {}.".format(num_expected_results, len(results), self.name, statistics))
for i in range(len(results)):
#print len(results)
channel_list[i].append_data(window.start_timestamp, results[i])
for channel in channel_list:
channel.calculate_timeframe()
channel.data = np.array(channel.data)
channel.timestamps = np.array(channel.timestamps)
ts = Time_Series.Time_Series(name)
ts.add_channels(channel_list)
return ts
def build_statistics_channels(self, windows, statistics, name=""):
""" Describe the contents of this channel in the given time windows using the given statistics """
using_indices = True
if str(type(windows[0])) == "<class 'pampro.Bout.Bout'>":
using_indices = False
channel_list = []
for stat in statistics:
# For each statistic, decide what should it be called
channel_names = pampro_utilities.design_variable_names(self.name, stat)
# Create a Channel for each output
for cn in channel_names:
channel_list.append(Channel(cn))
num_expected_results = len(channel_list)
for window in windows:
if using_indices:
results = self.window_statistics(window[0], window[1], statistics)
else:
results = self.window_statistics(window.start_timestamp, window.end_timestamp, statistics)
if len(results) != num_expected_results:
raise Exception("Incorrect number of statistics yielded. {} expected, {} given. Channel: {}. Statistics: {}.".format(num_expected_results, len(results), self.name, statistics))
if using_indices:
for i in range(len(results)):
channel_list[i].append_data(window[0], results[i])
else:
for i in range(len(results)):
channel_list[i].append_data(window.start_timestamp, results[i])
for channel in channel_list:
channel.missing_value = -1
channel.data = np.array(channel.data)
channel.timestamps = np.array(channel.timestamps)
channel.calculate_timeframe()
ts = Time_Series.Time_Series(name)
ts.add_channels(channel_list)
return ts
