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docs/examples/DataSet/Linking to parent datasets.ipynb.
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Linking to parent datasets
When performing a measurement in QCoDeS, it is possible to annotate the outcome (the dataset) of that measurement as having one or more parent datasets. This is done by adding a link to each parent dataset. This notebook covers the mechanisms to do that by going through a few practical examples.
[1]:
import datetime
import os
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
from qcodes.dataset import (
Measurement,
initialise_or_create_database_at,
load_by_run_spec,
load_or_create_experiment,
plot_dataset,
)
[2]:
now = str(datetime.datetime.now())
tutorial_db_path = os.path.join(os.getcwd(), 'linking_datasets_tutorial.db')
initialise_or_create_database_at(tutorial_db_path)
load_or_create_experiment('tutorial ' + now, 'no sample')
[2]:
tutorial 2023-09-29 10:21:18.657192#no sample#1@/home/runner/work/Qcodes/Qcodes/docs/examples/DataSet/linking_datasets_tutorial.db
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Example 1: Measuring and then fitting
Say we measure some raw data and subsequently do a curve fit to those data. We’d like to save the fit as a separate dataset that has a link to the dataset of the original data. This is achieved in two steps.
Step 1: measure raw data
[3]:
meas = Measurement()
meas.register_custom_parameter(name='time',
label='Time', unit='s',
paramtype='array')
meas.register_custom_parameter(name='signal',
label='Signal', unit='V',
paramtype='array',
setpoints=['time'])
N = 500
with meas.run() as datasaver:
time_data = np.linspace(0, 1, N)
signal_data = np.sin(2*np.pi*time_data) + 0.25*np.random.randn(N)
datasaver.add_result(('time', time_data), ('signal', signal_data))
dataset = datasaver.dataset
Starting experimental run with id: 1.
[4]:
cbs, axs = plot_dataset(dataset)

Step 2: Set up a fit “measurement”
We now load the raw data dataset, set up a new measurement for the fit, register the raw data as a parent and save a fit.
As the very first step, we supply a model to fit to.
[5]:
def fit_func(x, a, b):
return a*np.sin(2*np.pi*x)+b
Next, we set up the fitting measurement.
[6]:
raw_data = load_by_run_spec(captured_run_id=dataset.captured_run_id)
meas = Measurement()
meas.register_custom_parameter('fit_axis',
label='Fit axis', unit='t',
paramtype='array')
meas.register_custom_parameter('fit_curve',
label='Fitted curve', unit='V',
paramtype='array',
setpoints=['fit_axis'])
meas.register_custom_parameter('fit_param_a',
label='Fitted parameter amplitude',
unit='V')
meas.register_custom_parameter('fit_param_b',
label='Fitted parameter offset',
unit='V')
meas.register_parent(parent=raw_data, link_type="curve fit")
[6]:
<qcodes.dataset.measurements.Measurement at 0x7f8fcc8e8d60>
As we now run the measurement, the parent datasets become available via the datasaver. The datasets appear in the order they were registered.
[7]:
with meas.run() as datasaver:
raw = datasaver.parent_datasets[0]
xdata = np.ravel(raw.get_parameter_data()['signal']['time'])
ydata = np.ravel(raw.get_parameter_data()['signal']['signal'])
popt, pcov = opt.curve_fit(fit_func, xdata, ydata, p0=[1, 1])
fit_axis = xdata
fit_curve = fit_func(fit_axis, *popt)
datasaver.add_result(('fit_axis', fit_axis),
('fit_curve', fit_curve),
('fit_param_a', popt[0]),
('fit_param_b', popt[1]))
fit_data = datasaver.dataset
Starting experimental run with id: 2.
[8]:
cbs, axs = plot_dataset(fit_data)

And just for completeness, let us plot both datasets on top of each other.
[9]:
fig, ax = plt.subplots(1)
cbs, axs = plot_dataset(raw_data, axes=ax, label='data')
cbs, axs = plot_dataset(fit_data, axes=ax, label='fit', linewidth=4)
ax.set_xlabel('Time (s)')
ax.set_ylabel('Signal (V)')
plt.legend()
Trying to mark a run completed that was already completed.
[9]:
<matplotlib.legend.Legend at 0x7f8fcc8eb9a0>

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