def load_from_HDF5_legacy(filename_or_fileobject):
data = {}
if isinstance(filename_or_fileobject, h5py.File):
f = filename_or_fileobject
else:
f = h5py.File(filename_or_fileobject, 'r')
for groupname in f:
# Reconstruct the descrciptor
descriptor = DataStreamDescriptor()
g = f[groupname]
axis_refs = g['descriptor']
for ref in reversed(axis_refs):
ax = g[ref]
if not 'unit' in ax.attrs:
# Unstructured
names = [k for k in ax.dtype.fields.keys()]
units = [ax.attrs['unit_'+name] for name in names]
points = ax[:]
points = points.view(np.float32).reshape(points.shape + (-1,))
descriptor.add_axis(DataAxis(names, points=points, unit=units))
else:
# Structured
name = ax.attrs['NAME'].decode('UTF-8')
unit = ax.attrs['unit']
points = ax[:]
descriptor.add_axis(DataAxis(name, points=points, unit=unit))
for attr_name in axis_refs.attrs.keys():
descriptor.metadata[attr_name] = axis_refs.attrs[attr_name]
data[groupname] = g['data'][:]
f.close()
return data, descriptor
def update_descriptors(self):
logger.debug("Updating Plotter %s descriptors based on input descriptor %s", self.filter_name, self.sink.descriptor)
self.stream = self.sink.input_streams[0]
self.descriptor = self.sink.descriptor
try:
self.time_pts = self.descriptor.axes[self.descriptor.axis_num("time")].points
self.record_length = len(self.time_pts)
except ValueError:
raise ValueError("Single shot filter sink does not appear to have a time axis!")
self.num_averages = len(self.sink.descriptor.axes[self.descriptor.axis_num("averages")].points)
self.num_segments = len(self.sink.descriptor.axes[self.descriptor.axis_num("segment")].points)
self.ground_data = np.zeros((self.record_length, self.num_averages), dtype=np.complex)
self.excited_data = np.zeros((self.record_length, self.num_averages), dtype=np.complex)
self.total_points = self.num_segments*self.record_length*self.num_averages # Total points BEFORE sweep axes
output_descriptor = DataStreamDescriptor()
output_descriptor.axes = [_ for _ in self.descriptor.axes if type(_) is SweepAxis]
output_descriptor._exp_src = self.sink.descriptor._exp_src
output_descriptor.dtype = np.complex128
if len(output_descriptor.axes) == 0:
output_descriptor.add_axis(DataAxis("Fidelity", [1]))
for os in self.fidelity.output_streams:
os.set_descriptor(output_descriptor)
os.end_connector.update_descriptors()
def get_descriptor(self, source_instr_settings, channel_settings):
# Create a channel
channel = X6Channel(channel_settings)
descrip = DataStreamDescriptor()
# If it's an integrated stream, then the time axis has already been eliminated.
# Otherwise, add the time axis.
if channel_settings['stream_type'] == 'Raw':
samp_time = 4.0e-9
descrip.add_axis(
DataAxis(
"time",
samp_time *
np.arange(source_instr_settings['record_length'] // 4)))
descrip.dtype = np.float64
elif channel_settings['stream_type'] == 'Demodulated':
samp_time = 32.0e-9
descrip.add_axis(
DataAxis(
"time",
samp_time *
np.arange(source_instr_settings['record_length'] // 32)))
descrip.dtype = np.complex128
elif channel_settings['stream_type'] == 'Integrated':
descrip.dtype = np.complex128
elif channel_settings[
'stream_type'] == 'Correlated': # Same as integrated
descrip.dtype = np.complex128
elif channel_settings['stream_type'] == 'State':
descrip.dtype = np.complex128
return channel, descrip
def get_descriptor(self, source_instr_settings, channel_settings):
channel = AlazarChannel(channel_settings)
# Add the time axis
samp_time = 1.0/source_instr_settings['sampling_rate']
descrip = DataStreamDescriptor()
descrip.add_axis(DataAxis("time", samp_time*np.arange(source_instr_settings['record_length'])))
return channel, descrip
def init_streams(self):
# Add a "base" data axis: say we are averaging 5 samples per trigger
descrip = DataStreamDescriptor()
descrip.data_name = 'voltage'
if self.is_complex:
descrip.dtype = np.complex128
descrip.add_axis(DataAxis("samples", list(range(self.samples))))
self.voltage.set_descriptor(descrip)
def init_streams(self):
# Baked in data axes
descrip = DataStreamDescriptor()
descrip.data_name = 'voltage'
descrip.add_axis(
DataAxis("sample",
range(int(self.integration_time * self.ai_clock))))
descrip.add_axis(DataAxis("attempt", range(self.attempts)))
self.voltage.set_descriptor(descrip)
def init_streams(self):
descrip = DataStreamDescriptor()
descrip.data_name='current_input'
descrip.add_axis(DataAxis("time", np.arange(int(self.sample_rate*self.num_bursts/self.frequency))/self.sample_rate))
self.current_input.set_descriptor(descrip)
descrip = DataStreamDescriptor()
descrip.data_name='voltage_sample'
descrip.add_axis(DataAxis("time", np.arange(int(self.sample_rate*self.num_bursts/self.frequency))/self.sample_rate))
self.voltage_sample.set_descriptor(descrip)
def get_descriptor(self, stream_selector, receiver_channel):
"""Get the axis descriptor corresponding to this stream selector. For the Alazar cards this
is always just a time axis."""
samp_time = 1.0 / receiver_channel.receiver.sampling_rate
descrip = DataStreamDescriptor()
descrip.add_axis(
DataAxis(
"time",
samp_time *
np.arange(receiver_channel.receiver.record_length)))
return descrip
def load_from_HDF5(filename_or_fileobject,
reshape=True,
return_structured_array=True):
data = {}
descriptors = {}
if isinstance(filename_or_fileobject, h5py.File):
f = filename_or_fileobject
else:
f = h5py.File(filename_or_fileobject, 'r')
for groupname in f:
# Reconstruct the descrciptor
descriptor = DataStreamDescriptor()
g = f[groupname]
axis_refs = g['descriptor']
for ref in reversed(axis_refs):
ax = g[ref]
if ax.attrs['unstructured']:
# The entry for the main unstructured axis contains
# references to the constituent axes.
# The DataAxis expects points as tuples coordinates
# in the form [(x1, y1), (x2, y2), ...].
points = np.vstack([g[e] for e in ax[:]]).T
names = [g[e].attrs["name"] for e in ax[:]]
units = [g[e].attrs["unit"] for e in ax[:]]
descriptor.add_axis(DataAxis(names, points=points, unit=units))
else:
name = ax.attrs['name']
unit = ax.attrs['unit']
points = ax[:]
descriptor.add_axis(DataAxis(name, points=points, unit=unit))
for attr_name in axis_refs.attrs.keys():
descriptor.metadata[attr_name] = axis_refs.attrs[attr_name]
col_names = list(g['data'].keys())
if return_structured_array:
dtype = [(g['data'][n].attrs['name'], g['data'][n].dtype.char)
for n in col_names]
length = g['data'][col_names[0]].shape[0]
group_data = np.empty((length, ), dtype=dtype)
for cn in col_names:
group_data[cn] = g['data'][cn]
else:
group_data = {n: g['data'][n][:] for n in col_names}
if reshape:
group_data = group_data.reshape(descriptor.dims())
data[groupname] = group_data
descriptors[groupname] = descriptor
if not isinstance(filename_or_fileobject, h5py.File):
f.close()
return data, descriptors
def test_copy_descriptor(self):
dsd = DataStreamDescriptor()
dsd.add_axis(DataAxis("One", [1, 2, 3, 4]))
dsd.add_axis(DataAxis("Two", [1, 2, 3, 4, 5]))
self.assertTrue(len(dsd.axes) == 2)
self.assertTrue("One" in [a.name for a in dsd.axes])
dsdc = copy(dsd)
self.assertTrue(dsd.axes == dsdc.axes)
ax = dsdc.pop_axis("One")
self.assertTrue(ax.name == "One")
self.assertTrue(len(dsdc.axes) == 1)
self.assertTrue(dsdc.axes[0].name == "Two")
def init_streams(self):
# Baked in data axes
descrip = DataStreamDescriptor()
descrip.add_axis(DataAxis("time", 1e-9 * np.arange(self.samples)))
if len(self.gate_durs) > 1:
descrip.add_axis(DataAxis("gate_pulse_duration", self.gate_durs))
descrip.add_axis(DataAxis("gate_pulse_amplitude", self.gate_amps))
descrip.add_axis(DataAxis("attempt", range(self.attempts)))
self.voltage.set_descriptor(descrip)
def init_streams(self):
descrip = DataStreamDescriptor()
descrip.data_name = 'voltage_input'
descrip.add_axis(DataAxis("index", np.arange(self.num_points + 2)))
descrip.add_axis(DataAxis("repeat", np.arange(self.repeat)))
self.voltage_input.set_descriptor(descrip)
descrip = DataStreamDescriptor()
descrip.data_name = 'voltage_sample'
descrip.add_axis(DataAxis("index", np.arange(self.num_points + 2)))
descrip.add_axis(DataAxis("repeat", np.arange(self.repeat)))
self.voltage_sample.set_descriptor(descrip)
def init_streams(self):
descrip = DataStreamDescriptor()
descrip.data_name = 'voltage'
descrip.add_axis(DataAxis("sample", range(self.samps_per_trig)))
descrip.add_axis(DataAxis("state", range(2)))
descrip.add_axis(DataAxis("attempt", range(self.attempts)))
self.voltage.set_descriptor(descrip)
def init_streams(self):
# Baked in data axes
descrip = DataStreamDescriptor()
descrip.add_axis(DataAxis("sample", range(self.samps_per_trig)))
descrip.add_axis(DataAxis("state", range(2)))
descrip.add_axis(DataAxis("attempt", range(self.attempts.value)))
self.voltage.set_descriptor(descrip)
def init_streams(self):
# Baked in data axes
descrip = DataStreamDescriptor()
descrip.data_name = 'voltage'
descrip.add_axis(DataAxis("sample", range(self.samps_per_trig)))
descrip.add_axis(DataAxis("amplitude", self.amplitudes))
descrip.add_axis(DataAxis("repeat", range(self.repeats)))
self.voltage.set_descriptor(descrip)
def init_streams(self):
descrip = DataStreamDescriptor()
descrip.add_axis(
DataAxis("samples", 2e-9 * np.arange(self.num_samples)))
descrip.add_axis(DataAxis("delay", self.delays))
descrip.add_axis(DataAxis("round_robins",
np.arange(self.round_robins)))
self.voltage.set_descriptor(descrip)
def get_descriptor(self, stream_selector, receiver_channel):
"""Get the axis descriptor corresponding to this stream selector. If it's an integrated stream,
then the time axis has already been eliminated. Otherswise, add the time axis."""
descrip = DataStreamDescriptor()
if stream_selector.stream_type == 'raw':
samp_time = 4.0e-9
descrip.add_axis(
DataAxis(
"time",
samp_time *
np.arange(receiver_channel.receiver.record_length // 4)))
descrip.dtype = np.float64
elif stream_selector.stream_type == 'demodulated':
samp_time = 32.0e-9
descrip.add_axis(
DataAxis(
"time",
samp_time *
np.arange(receiver_channel.receiver.record_length // 32)))
descrip.dtype = np.complex128
else: # Integrated
descrip.dtype = np.complex128
return descrip
def refine_func(sweep_axis):
points, mean = sw.load_switching_data(wr.filename)
new_points = refine.refine_scalar_field(points,
mean,
all_points=False,
criterion="integral",
threshold="one_sigma")
if len(points) + len(new_points) > max_points:
print("Reached maximum points ({}).".format(max_points))
return False
print("Reached {} points.".format(len(points) + len(new_points)))
sweep_axis.add_points(new_points)
# Plot previous mesh
x = [list(el) for el in points[mesh.simplices, 0]]
y = [list(el) for el in points[mesh.simplices, 1]]
val = [np.mean(vals) for vals in mean[mesh.simplices]]
desc = DataStreamDescriptor()
desc.add_axis(sweep_axis)
exp.push_to_plot(fig1, desc, points)
time.sleep(1)
return True
def init_streams(self):
descrip = DataStreamDescriptor()
descrip.data_name='voltage'
descrip.add_axis(DataAxis("sample", range(int(self.meas_duration*self.ai_clock))))
descrip.add_axis(DataAxis("attempt", range(self.attempts)))
self.voltage.set_descriptor(descrip)
