def test_timestamps_as_data():
hdr = DataBroker[-1]
events = DataBroker.fetch_events(hdr)
dm = DataMuxer.from_events(events)
data_name = list(dm.sources.keys())
for name in data_name:
dm.include_timestamp_data(name)
assert_true('{}_timestamp'.format(name) in dm._dataframe)
dm.remove_timestamp_data(name)
assert_false('{}_timestamp'.format(name) in dm._dataframe)
def test_attributes():
hdr = DataBroker[-1]
events = DataBroker.fetch_events(hdr)
dm = DataMuxer.from_events(events)
# merely testing that basic usage does not error
for data_key in dm.sources.keys():
getattr(dm, data_key)
dm[data_key]
properties = ['ncols', '_dataframe', 'col_info_by_ndim', 'sources',
'col_info', '_data', '_time', '_timestamps',
'_timestamps_as_data', '_known_events', '_known_descriptors',
'_stale']
for prop in properties:
getattr(dm, prop)
def setUp(self):
self.dm = DataMuxer.from_events(image_and_scalar.run())
def setUp(self):
self.dm = DataMuxer.from_events(multisource_event.run())
self.sparse = 'Troom'
self.dense = 'point_det'
self.agg = np.mean
self.interp = 'linear'
def setUp(self):
self.dm = DataMuxer.from_events(temperature_ramp.run())
self.sparse = 'Tsam'
self.dense = 'point_det'
self.agg = np.mean
self.interp = 'linear'
ns.sort()
# For each sample plot the intra sample temperature curve
for i in ns:
print(i)
save_folder = '../S{}'.format(i)
# Get the folder where the data is
folder = '/mnt/bulk-data/research_data/USC_beamtime/APS_March_2016/S' \
+ str(i) + '/temp_exp'
# Get the run header assocaited with that folder
hdr = db(run_folder=folder)[0]
# Mux the data so that we have the correct Temp->data relationship
dm = DataMuxer()
dm.append_events(get_events(hdr))
df = dm.to_sparse_dataframe()
print(df.keys())
binned = dm.bin_on('img', interpolation={'T': 'linear'})
for plot_type in [
'gr',
'chi'
]:
if plot_type is 'gr':
# Only to the G(r)
key_list = [f for f in os.listdir(folder) if
f.endswith('.gr') and not f.startswith('d')]
# If we are working with G(r) files use these offset and read parameters
offset = .1
