def write_experiment_runs_to_text_files_conv_vs_restless(starttime,endtime,maxlength,filename_gateseq,number_of_gateseq):
"""
Function for writing experiment runs to text files. At the moment only
works for an experiment which is repeated for same parameters several times in time. It also assumes that one starts with conventional tuning, then restless etc. It's specialised for only experiment and is more of a One-fit.
----------------------------------------------------------------------------------------
Parameters:
starttime:string
Formatted in the standard form as used in PycQED_py3, in the form of e.g. 20160614_000000.
endtime:string
Formatted in the standard form as used in PycQED_py3, in the form of e.g.20160615_235959
maxlength: integer
Maximum length of germ power sequence
filename_gateseq: string
Text file containing the list of experiments/gatesequences
number_of_gateseq:
the number of gatesequences in each experiment
"""
from pycqed.analysis import measurement_analysis as ma
from pycqed.analysis import analysis_toolbox as a_tools
import measurement.pulse_sequences.gate_set_tomography as _gst
from importlib import reload
import h5py
reload(_gst)
experiment_timestamp_strings = a_tools.get_timestamps_in_range(starttime, endtime, label = 'GST')
file_path_names = []
for i in range(len(experiment_timestamp_strings)):
file_path_names.append(a_tools.data_from_time(experiment_timestamp_strings[i]))
file_path_names_conventional = file_path_names[0::2]
file_path_names_restless = file_path_names[1::2]
for i in range(len(file_path_names_conventional)):
fn = file_path_names_conventional[i]
suffix = fn[len(a_tools.datadir)+10:]
filename_input = file_path_names_conventional[i]+'\\'+suffix+'.hdf5'
filename_output = 'GST_data_paper_L%s_conv_%s_run%s_17june' %(maxlength,suffix, i) +'.txt'
_gst.write_textfile_data_for_GST_input_adapt_develop(filename_input,
filename_output,filename_gateseq,
number_of_gateseq,zero_one_inverted = 'automatic')
for i in range(len(file_path_names_restless)):
fn = file_path_names_restless[i]
suffix = fn[len(a_tools.datadir)+10:]
filename_input = file_path_names_restless[i]+'\\'+suffix+'.hdf5'
filename_output = 'GST_data_paper_L%s_rest_%s_run%s_17june' %(maxlength,suffix, i) +'.txt'
_gst.write_textfile_data_for_GST_input_adapt_develop(filename_input,
filename_output,filename_gateseq,
number_of_gateseq,zero_one_inverted = 'automatic')
def _fill_experimental_values_with_ReparkingRamsey(experimental_values,
timestamp, qubit):
"""
Fills the experimental_values dictionary with the experimental values
from ReparkingRamsey experiments.
NOTE if there are multiple measurements of the same transition at the
same voltage, the (chronological) last one will be used.
Arguments:
experimental_values: dictionary to fill with experimental data
timestamp: timestamp
qubit: qubit (qubit object) or qubit name (str)
"""
path = a_tools.data_from_time(timestamp)
filepath = a_tools.measurement_filename(path)
data = h5py.File(filepath, "r")
if "_ge_" in filepath:
transition = "ge"
elif "_ef_" in filepath:
transition = "ef"
else:
log.warning(
"Transition not recognized. Only ge and ef transitions "
"are supported.")
return
if type(qubit) is str:
qubit_name = qubit
else:
qubit_name = qubit.name
if not qubit_name in filepath:
return
try:
dc_voltages = np.array(data["Experimental Data"]
["Experimental Metadata"]["dc_voltages"])
for i in range(len(dc_voltages)):
freq = float(
np.array(data["Analysis"]["Processed data"]
["analysis_params_dict"][
qubit_name + f"_"
f"{i}"]["exp_decay"].attrs["new_qb_freq"]))
freq_std = float(
np.array(data["Analysis"]["Processed data"]
["analysis_params_dict"][
qubit_name + f"_"
f"{i}"]["exp_decay"].attrs["new_qb_freq"]))
voltage = dc_voltages[i]
HamiltonianFittingAnalysis._fill_experimental_values(
experimental_values, voltage, transition, freq)
except:
log.warning(
"Could not get reparking data from file {}".format(filepath))
def write_experiment_runs_to_text_files_conv_vs_restless(starttime,endtime,maxlength,filename_gateseq,number_of_gateseq):
"""
Function for writing experiment runs to text files. At the moment only
works for an experiment which is repeated for same parameters several times in time. It also assumes that one starts with conventional tuning, then restless etc. It's specialised for only experiment and is more of a One-fit.
----------------------------------------------------------------------------------------
Parameters:
starttime:string
Formatted in the standard form as used in PycQED_py3, in the form of e.g. 20160614_000000.
endtime:string
Formatted in the standard form as used in PycQED_py3, in the form of e.g.20160615_235959
maxlength: integer
Maximum length of germ power sequence
filename_gateseq: string
Text file containing the list of experiments/gatesequences
number_of_gateseq:
the number of gatesequences in each experiment
"""
from pycqed.analysis import measurement_analysis as ma
from pycqed.analysis import analysis_toolbox as a_tools
import measurement.pulse_sequences.gate_set_tomography as _gst
from importlib import reload
import h5py
reload(_gst)
experiment_timestamp_strings = a_tools.get_timestamps_in_range(starttime, endtime, label = 'GST')
file_path_names = []
for i in range(len(experiment_timestamp_strings)):
file_path_names.append(a_tools.data_from_time(experiment_timestamp_strings[i]))
file_path_names_conventional = file_path_names[0::2]
file_path_names_restless = file_path_names[1::2]
for i in range(len(file_path_names_conventional)):
fn = file_path_names_conventional[i]
suffix = fn[len(a_tools.datadir)+10:]
filename_input = file_path_names_conventional[i]+'\\'+suffix+'.hdf5'
filename_output = 'GST_data_paper_L%s_conv_%s_run%s_17june' %(maxlength,suffix, i) +'.txt'
_gst.write_textfile_data_for_GST_input_adapt_develop(filename_input,
filename_output,filename_gateseq,
number_of_gateseq,zero_one_inverted = 'automatic')
for i in range(len(file_path_names_restless)):
fn = file_path_names_restless[i]
suffix = fn[len(a_tools.datadir)+10:]
filename_input = file_path_names_restless[i]+'\\'+suffix+'.hdf5'
filename_output = 'GST_data_paper_L%s_rest_%s_run%s_17june' %(maxlength,suffix, i) +'.txt'
_gst.write_textfile_data_for_GST_input_adapt_develop(filename_input,
filename_output,filename_gateseq,
number_of_gateseq,zero_one_inverted = 'automatic')
def test_save_quantities_of_interest(self):
# Test based on test below to get a dummy dataset
ts = '20161124_162604'
a = ba.BaseDataAnalysis()
a.proc_data_dict['quantities_of_interest'] = {'a': 5}
a.timestamps = [ts]
a.save_quantities_of_interest()
fn = a_tools.measurement_filename(a_tools.data_from_time(ts))
with h5py.File(fn, 'r') as file:
saved_val = float(file['Analysis']['quantities_of_interest'].attrs['a'])
assert saved_val == 5
def _fill_experimental_values_with_Ramsey(experimental_values, timestamp,
qubit, fluxlines_dict):
"""
Fills the experimental_values dictionary with the experimental values
from Ramsey experiments.
NOTE if there are multiple measurements of the same transition at the
same voltage, the (chronological) last one will be used.
Arguments:
experimental_values: dictionary containing the experimental values
timestamp: timestamp
qubit: qubit (qubit object) or qubit name (str)
fluxlines_dict: dictionary containing the fluxline ids (necessary
to determine voltage)
"""
path = a_tools.data_from_time(timestamp)
filepath = a_tools.measurement_filename(path)
data = h5py.File(filepath, "r")
if "_ge_" in filepath:
transition = "ge"
elif "_ef_" in filepath:
transition = "ef"
else:
raise ValueError("Transition not recognized. Only ge and ef "
"transitions are supported.")
if type(qubit) is str:
qubit_name = qubit
else:
qubit_name = qubit.name
if not qubit_name in filepath:
return
try:
freq = data["Analysis"]["Processed data"]["analysis_params_dict"][
qubit_name]["exp_decay"].attrs["new_qb_freq"]
freq_std = data["Analysis"]["Processed data"][
"analysis_params_dict"][qubit_name]["exp_decay"].attrs[
"new_qb_freq_stderr"]
voltage = float(data["Instrument settings"]["DCSource"].attrs[
fluxlines_dict[qubit_name].name])
HamiltonianFittingAnalysis._fill_experimental_values(
experimental_values, voltage, transition, freq)
except KeyError:
log.warning(f"Could not get ramsey data from file {filepath}")
def test_save_fit_results(self):
# strictly speaking an integration test as it relies on the cond
# oscillation analysis, but the only thing tested here is
# if the value of the fit_result is saved.
ts = '20181126_131143'
a = ma2.Conditional_Oscillation_Analysis(t_start=ts)
exp_val = a.fit_res['cos_fit_off'].params['amplitude'].value
fn = a_tools.measurement_filename(a_tools.data_from_time(ts))
with h5py.File(fn, 'r') as file:
saved_val = float(file['Analysis']['cos_fit_off']['params']
['amplitude'].attrs['value'])
a.fit_res = {}
a.save_fit_results()
assert exp_val == saved_val
def get_experimental_values_from_timestamps(qubit, fluxlines_dict,
timestamps, **kw):
"""
Gets the experimental values from the database from a list of timestamps
and returns them in the usual format.
Arguments:
timestamps: timestamps
qubit: qubit object or qubit name (str)
transition: transition
Keyword Arguments:
include_reparkings: Boolean to include reparkings in the
datadir: path to the directory containing the desired data.
"""
experimental_values = {}
# datadirectory
default_datadir = a_tools.datadir
a_tools.datadir = kw.get("datadir", default_datadir)
include_reparkings = kw.get("include_reparkings", False)
for timestamp in timestamps:
path = a_tools.data_from_time(timestamp)
if "_Ramsey_" in path:
if fluxlines_dict is None:
raise ValueError(
"fluxlines_dict must be specified for to"
"read the experimental values from Ramsey experiments")
HamiltonianFittingAnalysis._fill_experimental_values_with_Ramsey(
experimental_values, timestamp, qubit, fluxlines_dict)
elif ("_ReparkingRamsey_" in path) and include_reparkings:
HamiltonianFittingAnalysis._fill_experimental_values_with_ReparkingRamsey(
experimental_values, timestamp, qubit)
a_tools.datadir = default_datadir
return experimental_values
def plot(self, sequences=0, segments=0, qubits=None,
save=False, legend=True, **plot_kwargs):
"""
Plots (a subset of) sequences / segments of the QuantumExperiment
:param sequences (int, list, "all"): sequences to plot. Can be "all"
(plot all sequences),
an integer (index of sequence to plot), or a list of
integers/str. If strings are in the list, then plots only sequences
with the corresponding name.
:param segments (int, list, "all"): Segments to be plotted.
If a single index i is provided, then the ith segment will be plot-
ted for each sequence in `sequences`. Otherwise a list of list of
indices must be provided: the outer list corresponds to each
sequence and the inner list to the indices of the segments to plot.
E.g. segments=[[0,1],[3]] will plot segment 0 and 1 of
sequence 0 and segment 3 of sequence 1.
If the string 'all' is provided, then all segments are plotted.
Plots segment 0 by default.
:param qubits (list): list of qubits to plot.
Defaults to self.meas_objs. Qubits can be specified as qubit names
or qubit objects.
:param save (bool): whether or not to save the figures in the
measurement folder.
:param legend (bool): whether or not to show the legend.
:param plot_kwargs: kwargs passed on to segment.plot(). By default,
channel_map is taken from dev.get_channel_map(qubits) if available.
:return:
"""
plot_kwargs = deepcopy(plot_kwargs)
if sequences == "all":
# plot all sequences
sequences = self.sequences
# if the provided sequence is not it a list or tuple, make it a list
if np.ndim(sequences) == 0:
sequences = [sequences]
# get sequence objects from sequence name or index
sequences = np.ravel([[s for i, s in enumerate(self.sequences)
if i == ind or s.name == ind]
for ind in sequences])
if qubits is None:
qubits = self.meas_objs
qubits, _ = self.get_qubits(qubits) # get qubit objects
default_ch_map = \
self.dev.get_channel_map(qubits) if self.dev is not None else \
{qb.name: qb.get_channels() for qb in qubits}
plot_kwargs.update(dict(channel_map=plot_kwargs.pop('channel_map',
default_ch_map)))
plot_kwargs.update(dict(legend=legend))
if segments == "all":
# plot all segments
segments = [range(len(seq.segments)) for seq in sequences]
elif isinstance(segments, int):
# single segment from index
segments = [[segments] for _ in sequences]
figs_and_axs = []
for seq, segs in zip(sequences, segments):
for s in segs:
s = list(seq.segments.keys())[s]
if save:
try:
from pycqed.analysis import analysis_toolbox as a_tools
folder = a_tools.data_from_time(self.timestamp,
folder=self.MC.datadir(),
auto_fetch=False)
except:
log.warning('Could not determine folder of current '
'experiment. Sequence plot will be saved in '
'current directory.')
folder = "."
import os
save_path = os.path.join(folder,
"_".join((seq.name, s)) + ".png")
save_kwargs = dict(fname=save_path,
bbox_inches="tight")
plot_kwargs.update(dict(save_kwargs=save_kwargs,
savefig=True))
figs_and_axs.append(seq.segments[s].plot(**plot_kwargs))
# avoid returning a list of Nones (if show_and_close is True)
return [v for v in figs_and_axs if v is not None] or None