def test_incremental_write(self):
formatter = GNUPlotFormat()
location = self.locations[0]
location2 = self.locations[1] # use 2nd location for reading back in
data = DataSet1D(name="test_incremental_write", location=location)
path = location + '/x_set.dat'
data_copy = DataSet1D(False)
# empty the data and mark it as unmodified
data.x_set[:] = float('nan')
data.y[:] = float('nan')
data.x_set.modified_range = None
data.y.modified_range = None
# simulate writing after every value comes in, even within
# one row (x comes first, it's the setpoint)
# we'll add a '*' after each write and check that they're
# in the right places afterward, ie we don't write any given
# row until it's done and we never totally rewrite the file
self.stars_before_write = 0
for i, (x, y) in enumerate(zip(data_copy.x_set, data_copy.y)):
data.x_set[i] = x
formatter.write(data, data.io, data.location)
formatter.write(data, data.io, location2)
self.add_star(path)
data.y[i] = y
formatter.write(data, data.io, data.location)
data.x_set.clear_save()
data.y.clear_save()
formatter.write(data, data.io, location2)
self.add_star(path)
# we wrote to a second location without the stars, so we can read
# back in and make sure that we get the right last_saved_index
# for the amount of data we've read.
reread_data = load_data(location=location2,
formatter=formatter,
io=data.io)
self.assertEqual(repr(reread_data.x_set.tolist()),
repr(data.x_set.tolist()))
self.assertEqual(repr(reread_data.y.tolist()),
repr(data.y.tolist()))
self.assertEqual(reread_data.x_set.last_saved_index, i)
self.assertEqual(reread_data.y.last_saved_index, i)
starred_file = '\n'.join([
'# x_set\ty', '# "X"\t"Y"', '# 5', '1\t3', '**2\t4', '**3\t5',
'**4\t6', '**5\t7', '*'
])
with open(path) as f:
self.assertEqual(f.read(), starred_file)
self.assertEqual(self.stars_before_write, 1)
def test_get_live(self):
loc = 'live from New York!'
class MockLive:
pass
live_data = MockLive()
dm = MockDataManager()
dm.location = loc
dm.live_data = live_data
data = load_data(data_manager=dm, location=loc)
self.assertEqual(data, live_data)
for nd in (None, NoData()):
dm.live_data = nd
with self.assertRaises(RuntimeError):
load_data(data_manager=dm, location=loc)
with self.assertRaises(RuntimeError):
load_data(data_manager=dm)
def onOpenFile(self):
options = QFileDialog.Options()
# options |= QFileDialog.DontUseNativeDialog
fileName, _ = QFileDialog.getOpenFileName(
self,
"QFileDialog.getOpenFileName()",
"",
"All Files (*);;Dataset Files (*.dat)",
options=options)
if fileName:
dataset = load_data(location=fileName)
self.data_array_widget.loadDataSet(dataset)
def test_dataset_with_missing_attrs(self):
data1 = new_data(formatter=self.formatter,
location=self.loc_provider,
name='test_missing_attr')
arr = DataArray(array_id='arr', preset_data=np.linspace(0, 10, 21))
data1.add_array(arr)
data1.write()
# data2 = DataSet(location=data1.location, formatter=self.formatter)
# data2.read()
data2 = load_data(location=data1.location, formatter=self.formatter)
# cannot use the check arrays equal as I expect the attributes
# to not be equal
np.testing.assert_array_equal(data2.arrays['arr'], data1.arrays['arr'])
def test_read_write(self):
for f in self.formatters:
print('test formatter %s' % f)
dataset = DataSet2D(name="test_read_write")
dataset.formatter = f()
dataset.add_metadata(self.metadata)
dataset.write(write_metadata=True)
dataset2 = load_data(dataset.location, formatter=f())
self.assertEqual(list(dataset.arrays.keys()),
list(dataset2.arrays.keys()))
# strings should be read and written identically
self.assertEqual(dataset.metadata['string'],
dataset2.metadata['string'])
def import_dat_file(location: str) -> List[int]:
"""
This imports a QCoDeS legacy DataSet
"""
loaded_data = load_data(location)
meas = setup_measurement(loaded_data)
run_ids = []
with meas.run() as datasaver:
datasaver.dataset.add_metadata('snapshot', json.dumps(loaded_data.snapshot()))
for arrayname, array in loaded_data.arrays.items():
if not array.is_setpoint:
run_id = store_array_to_database(datasaver, array)
run_ids.append(run_id)
return run_ids
def data_set_plot(data_set, data_location):
Plot = MatPlot()
raw_data_set = load_data(
location=data_location,
io=NewIO,
)
data_set_P = convert_to_probability(raw_data_set, threshold=0.025)
x_data = data_set_P.arrays['vsg2_frequency_set'].ndarray
P_data = data_set_P.arrays['digitizer'].ndarray.T[0]
x = x_data
y = P_data
plt.plot(x, y)
def import_dat_file(location: str,
exp: Optional[Experiment] = None) -> List[int]:
"""
This imports a QCoDeS legacy DataSet into the database.
Args:
location: Path to file containing legacy dataset
exp: Specify the experiment to store data to.
If None the default one is used. See the
docs of :class:`.Measurement` for more details.
"""
loaded_data = load_data(location)
meas = setup_measurement(loaded_data, exp=exp)
run_ids = []
with meas.run() as datasaver:
datasaver.dataset.add_metadata('snapshot',
json.dumps(loaded_data.snapshot()))
for arrayname, array in loaded_data.arrays.items():
if not array.is_setpoint:
run_id = store_array_to_database(datasaver, array)
run_ids.append(run_id)
return run_ids
def test_get_read(self):
data = load_data(formatter=MockFormatter(), location='here!')
self.assertEqual(data.has_read_data, True)
self.assertEqual(data.has_read_metadata, True)
def test_load_false(self):
with self.assertRaises(ValueError):
load_data(False)
def test_no_saved_data(self):
with self.assertRaises(IOError):
load_data('_no/such/file_')
''' location37 = '2018-02-07/13-18-00/RB_experimentAllXY_sequence' ''' ''' location38 = '2018-02-07/16-03-46/RB_experimentAllXY_sequence' location39 = '2018-02-07/17-14-48/RB_experimentAllXY_sequence' location40 = '2018-02-09/11-56-18/RB_experimentAllXY_sequence' location41 = '2018-02-09/14-18-23/RB_experimentAllXY_sequence' location = location41 ds = load_data(location=location, io=IO, formatter=formatter) #DS = load_data(location = location1, io = IO) #%% ''' ds = DS Qubit = 2 i = 0 if Qubit == 2 else 1 sweep_point = 31 ramsey_point = 11 fitting_point = 18 x = np.linspace(1,fitting_point,fitting_point)
location3 = '2018-04-05/20-26-26/RB_experimentAllXY_sequence' location3 = '2018-04-05/20-55-38/RB_experimentAllXY_sequence' location3 = '2018-04-06/10-55-25/RB_experimentAllXY_sequence' location3 = '2018-04-06/11-26-17/RB_experimentAllXY_sequence' location3 = '2018-04-06/19-53-52/RB_experimentAllXY_sequence' location3 = '2018-06-18/14-01-15/RB_experimentAllXY_sequence' location3 = '2018-06-18/14-19-28/RB_experimentAllXY_sequence' location3 = '2018-06-18/14-50-55/RB_experimentAllXY_sequence' #location3 = '2018-04-04/18-38-25/RB_experimentAllXY_sequence' ds = load_data(location=location3, io=IO_new, formatter=formatter) #%% Qubit = 2 i = 0 if Qubit == 2 else 1 fitting_point = 51 start_point = 0 ''' #x = np.linspace(1e-6, 25e-3, 60)[:fitting_point] x = np.linspace(0, 1.5e-6, 31)[:fitting_point] y = ds.probability_data[:,i,start_point:start_point+fitting_point].mean(axis = 0) pars, pcov = curve_fit(T1_fitting, x, y,)
from qcodes.data.io import DiskIO from qcodes.data.data_set import new_data, DataSet,load_data from qcodes.data.data_array import DataArray import matplotlib.pyplot as plt import matplotlib.widgets as widgets from qcodes.plots.qcmatplotlib import MatPlot from qcodes.plots.pyqtgraph import QtPlot from mpldatacursor import datacursor import numpy as np #%% NewIO = DiskIO(base_location = 'C:\\Users\\LocalAdmin\\Documents') formatter = HDF5FormatMetadata() try_location = '2017-09-04/17-23-05Finding_ResonanceRabi_Sweep' DS = load_data(location = try_location, io = NewIO,) DS_P = convert_to_probability(DS, 0.025) DS_new = new_data(location = try_location, io = NewIO,) x_data = np.linspace(1,10,10) y_data = np.linspace(11,20,10) #z_data = np.linspace(101,201,101) Mplot = MatPlot(x_data,y_data) Qplot = QtPlot(x_data,y_data) Mplot = MatPlot()
location_new2 = '2018-09-06/13-46-30/RB_experimentAllXY_sequence' # Clifford + CZ ''' very high but seems like an even odd effect ''' location_new2 = '2018-09-06/23-39-09/RB_experimentAllXY_sequence' # Clifford + CZ ''' non_interleave new ''' location_new2 = '2018-09-04/11-24-17/RB_experiment2AllXY_sequence' # Clifford without CZ DS4 = load_data(location = location_new2, io = IO_K2, formatter = formatter) #DS = load_data(location = location_new2, io = IO_K, formatter = formatter) #%% character benchmarking without normalization ds = DS fitting_points = 18 #seq_rep_num = 20 seq_rep_num = int(DS.sequence_number_set.shape[0]/16) sequence_number = 16*seq_rep_num #sequence_number = 112 repetition = 100 init_state = ['00', '01', '10', '11',] x = np.array([len(clifford_sets[0][i]) for i in range(fitting_points)])
location_new2 = '2018-07-21/19-03-59/RB_experimentAllXY_sequence' location_new2 = '2018-07-21/23-23-55/RB_experimentAllXY_sequence' location_new2 = '2018-07-22/02-18-53/RB_experimentAllXY_sequence' location_new2 = '2018-07-23/18-36-11/RB_experimentAllXY_sequence' location_new2 = '2018-07-23/19-35-23/RB_experimentAllXY_sequence' #location_new2 = '2018-07-28/22-00-35/RB_experimentAllXY_sequence' #location_new2 = '2018-07-28/23-36-24/RB_experimentAllXY_sequence' location_new2 = '2018-08-19/23-43-55/RB_experimentAllXY_sequence' location_new2 = '2018-08-26/13-44-11/RB_experimentAllXY_sequence' location_new2 = '2018-08-28/16-16-04/RB_experimentAllXY_sequence' DS = load_data(location=location_new2, io=IO_K, formatter=formatter) #%% NewIO = DiskIO(base_location='D:\\Data\\RB_experiment') #DS = load_data(location = location5, io = NewIO) DS = load_data(location=location8, io=IO, formatter=formatter) DS2 = load_data(location=location6, io=IO, formatter=formatter) #%% ds = DS Qubit = 2 i = 0 if Qubit == 2 else 1 ramsey_point = 11 fitting_point = 26
def data_set_plot(data_set, data_location, sweep_type):
Plot = MatPlot()
threshold = 0.025
name = 'Rabi'
raw_data_set = load_data(
location=data_location,
io=data_IO,
)
"""
for 1D sweep outside a sequence, sequence is only one unit sequence
"""
if sweep_type == 1:
# raw_data_set = load_data(location = data_location, io = data_IO,)
data_set_P = convert_to_probability(raw_data_set,
threshold=threshold,
name=name)
x_data = data_set_P.arrays['vsg2_frequency_set'].ndarray
P_data = data_set_P.arrays['digitizer'].ndarray.T[0]
x = x_data
y = P_data
plt.plot(x, y)
datacursor()
"""
for 2D sweep both inside sequence and outside a sequence
"""
if sweep_type == 2:
# raw_data_set = load_data(location = data_location, io = data_IO,)
data_set_P = convert_to_probability(raw_data_set,
threshold=threshold,
name=name)
x_data = data_set_P.arrays['vsg2_frequency_set'].ndarray
y_data = data_set_P.arrays[name + '_set'].ndarray[0]
P_data = data_set_P.arrays['digitizer'].ndarray
X, Y = np.meshgrid(x_data, y_data)
plt.pcolor(X, Y, P_data.T)
datacursor()
"""
for 1D sweep inside a sequence, no qcodes-loop function
"""
if sweep_type == 0:
data_set_P = convert_to_probability(raw_data_set,
threshold=threshold,
name=name)
x_data = data_set_P.arrays[name + '_set'].ndarray[0]
P_data = data_set_P.arrays['digitizer'].ndarray[0]
plt.plot(x_data, P_data)
datacursor()
return 0
formatter = HDF5FormatMetadata() #%% old data location_15crot = '2017-10-25/19-36-23/BillCoish_experimentAllXY_sequence' location_3init = '2017-10-25/20-04-38/BillCoish_experimentAllXY_sequence' #location = '2017-12-12/17-47-41/RB_experimentAllXY_sequence' #%% new data location_15crot = '2017-10-25/19-36-23/BillCoish_experimentAllXY_sequence' location_3init = '2017-10-25/20-04-38/BillCoish_experimentAllXY_sequence' #location = '2017-12-12/17-47-41/RB_experimentAllXY_sequence' #%% ds = load_data(location=location_3init, io=IO, formatter=formatter) #%% average trace x = ds.index3_set[0, 0, 0, 0, :] i = 0 y = ds.raw_data[:, 0, i, :, :].mean(axis=(0, 1)) y = ds.raw_data[10, 0, i, :, :].mean(axis=0) y = ds.raw_data[10, 0, i, 58, :] #%% ''' Qubit = 2 i = 0 if Qubit == 2 else 1 fitting_point = 59 x = np.linspace(1e-6, 25e-3, 60)[:fitting_point]
