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docs/examples/DataSet/Offline plotting with categorical data.ipynb.
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Offline plotting with categorical data
This notebook is a collection of plotting examples using the plot_dataset function and caterogical (string-valued) data. The notebook should cover all possible permutations of categorical versus numerical data.
[1]:
%matplotlib inline
from pathlib import Path
import numpy as np
from qcodes.dataset import (
Measurement,
initialise_or_create_database_at,
load_or_create_experiment,
plot_dataset,
)
from qcodes.parameters import Parameter
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[2]:
initialise_or_create_database_at(Path.cwd() / "offline_plotting_example_categorical.db")
exp = load_or_create_experiment('offline_plotting_experiment', 'nosample')
1D plotting
Category is the independent parameter
With the category as the independent parameter, plot_dataset will default to a bar plot as long as there is at most one value per category. If more than one value is found for any category a bar plot is not possible, and the plot_dataset falls back to a scatter plot.
[3]:
voltage = Parameter('voltage',
label='Voltage',
unit='V',
set_cmd=None,
get_cmd=None)
fridge_config = Parameter('config',
label='Fridge configuration',
set_cmd=None,
get_cmd=None)
meas = Measurement(exp=exp)
meas.register_parameter(fridge_config, paramtype='text')
meas.register_parameter(voltage, setpoints=(fridge_config,))
with meas.run() as datasaver:
configurations = ['open', 'outer chamber closed',
'pumping', 'closed']
for configuration in configurations:
datasaver.add_result((fridge_config, configuration),
(voltage, np.random.rand()))
dataset = datasaver.dataset
Starting experimental run with id: 1.
[4]:
_ = plot_dataset(dataset)
[5]:
with meas.run() as datasaver:
configurations = ['open', 'outer chamber closed',
'pumping', 'closed']
for configuration in configurations:
datasaver.add_result((fridge_config, configuration),
(voltage, np.random.rand()))
datasaver.add_result((fridge_config, 'open'),
(voltage, np.random.rand()))
dataset = datasaver.dataset
_ = plot_dataset(dataset)
Starting experimental run with id: 2.
Category is the dependent parameter
With the categories as the dependent variable, i.e., the outcome of a measurement, the plot_dataset defaults to a scatter plot.
Here is an example with made-up parameters and random values.
UNRESOLVED: How do we ensure the y-axis order?
[6]:
voltage = Parameter('voltage',
label='Voltage',
unit='V',
set_cmd=None,
get_cmd=None)
response = Parameter('response',
label='Sample response',
set_cmd=None,
get_cmd=None)
meas = Measurement(exp=exp)
meas.register_parameter(voltage)
meas.register_parameter(response, paramtype='text', setpoints=(voltage,))
with meas.run() as datasaver:
for volt in np.linspace(0, 1, 50):
coinvalue = volt + 0.5*np.random.randn()
if coinvalue < 0:
resp = 'Bad'
elif coinvalue < 0.8:
resp = 'Good'
else:
resp = 'Excellent'
datasaver.add_result((voltage, volt),
(response, resp))
dataset = datasaver.dataset
Starting experimental run with id: 3.
[7]:
_ = plot_dataset(dataset)
Both variables are categorical
For both variables being categorical, the plot_dataset defaults to a scatter plot.
This case would typically be some summary of a large number of measurements.
[8]:
sample = Parameter('sample',
label='Sample',
unit='',
set_cmd=None,
get_cmd=None)
feature = Parameter('feature',
label='Physical feature',
set_cmd=None,
get_cmd=None)
meas = Measurement(exp=exp)
meas.register_parameter(sample, paramtype='text')
meas.register_parameter(feature, paramtype='text', setpoints=(sample,))
with meas.run() as datasaver:
features = ['superconducting', 'qubit',
'clean states', 'high bandwidth']
for samp in ['Nanowire', 'Silicon Chip', 'SQUID', 'Membrane']:
feats = np.random.randint(1, 5)
for _ in range(feats):
datasaver.add_result((sample, samp),
(feature, features[np.random.randint(0, 4)]))
dataset = datasaver.dataset
Starting experimental run with id: 4.
[9]:
_ = plot_dataset(dataset)
2D plotting
Naming convention: the x-axis is horizontal, the y-axis is vertical, and the z-axis is out-of-plane.
Categorical data on the x-axis
Here is an example where different samples are tested for conductivity. The longer the name of the sample, the higher the conductivity.
[10]:
sample = Parameter('sample',
label='Sample',
unit='',
set_cmd=None,
get_cmd=None)
gate_voltage = Parameter('gate_v',
label='Gate voltage',
unit='V',
set_cmd=None,
get_cmd=None)
conductance = Parameter('conductance',
label='Conductance',
unit='e^2/hbar',
set_cmd=None,
get_cmd=None)
meas = Measurement(exp=exp)
meas.register_parameter(sample, paramtype='text')
meas.register_parameter(gate_voltage)
meas.register_parameter(conductance, setpoints=(sample, gate_voltage))
with meas.run() as datasaver:
for samp in ['Nanowire', 'Silicon Chip', 'SQUID', 'Membrane']:
gate_vs = np.linspace(0, 0.075, 75)
for gate_v in gate_vs:
datasaver.add_result((sample, samp),
(gate_voltage, gate_v),
(conductance, len(samp)*gate_v))
dataset = datasaver.dataset
Starting experimental run with id: 5.
[11]:
ax, _ = plot_dataset(dataset)
Categorical data on the y-axis
This situation is very similar to having categorical data on the x-axis. We reuse the same example.
[12]:
sample = Parameter('sample',
label='Sample',
unit='',
set_cmd=None,
get_cmd=None)
gate_voltage = Parameter('gate_v',
label='Gate voltage',
unit='V',
set_cmd=None,
get_cmd=None)
conductance = Parameter('conductance',
label='Conductance',
unit='e^2/hbar',
set_cmd=None,
get_cmd=None)
meas = Measurement(exp=exp)
meas.register_parameter(sample, paramtype='text')
meas.register_parameter(gate_voltage)
meas.register_parameter(conductance, setpoints=(gate_voltage, sample))
with meas.run() as datasaver:
for samp in ['Nanowire', 'Silicon Chip', 'SQUID', 'Membrane']:
gate_vs = np.linspace(0, 0.01, 75)
for gate_v in gate_vs:
datasaver.add_result((sample, samp),
(gate_voltage, gate_v),
(conductance, len(samp)*gate_v))
dataset = datasaver.dataset
Starting experimental run with id: 6.
[13]:
ax, _ = plot_dataset(dataset)
Categorical data on the z-axis
Categorical data on the z-axis behaves similarly to numerical data on the z-axis; what kind of plot we get depends on the structure of the setpoints (i.e. the x-axis and y-axis data). If the setpoints are on a grid, we get a heatmap. If not, we get a scatter plot.
Gridded setpoints
[14]:
bias_voltage = Parameter('bias_v',
label='Bias voltage',
unit='V',
set_cmd=None,
get_cmd=None)
gate_voltage = Parameter('gate_v',
label='Gate voltage',
unit='V',
set_cmd=None,
get_cmd=None)
useful = Parameter('usefulness',
label='Usefulness of region',
set_cmd=None,
get_cmd=None)
meas = Measurement(exp=exp)
meas.register_parameter(gate_voltage)
meas.register_parameter(bias_voltage)
meas.register_parameter(useful, setpoints=(bias_voltage, gate_voltage),
paramtype='text')
# a function to simulate the usefulness of a region
def get_usefulness(x, y):
val = np.sin(x)*np.sin(y)
if val < -0.4:
return 'Useless'
if val < 0:
return 'Bad'
if val <0.5:
return 'Possible'
return 'Good'
with meas.run() as datasaver:
for bias_v in np.linspace(0, 3, 100):
for gate_v in np.linspace(-1, 1, 75):
datasaver.add_result((bias_voltage, bias_v),
(gate_voltage, gate_v),
(useful, get_usefulness(bias_v, gate_v)))
dataset = datasaver.dataset
Starting experimental run with id: 7.
[15]:
ax, cax = plot_dataset(dataset)
Scattered setpoints
The same example as above, but this time with setpoints not on a grid.
[16]:
bias_voltage = Parameter('bias_v',
label='Bias voltage',
unit='V',
set_cmd=None,
get_cmd=None)
gate_voltage = Parameter('gate_v',
label='Gate voltage',
unit='V',
set_cmd=None,
get_cmd=None)
useful = Parameter('usefulness',
label='Usefulness of region',
set_cmd=None,
get_cmd=None)
meas = Measurement(exp=exp)
meas.register_parameter(gate_voltage)
meas.register_parameter(bias_voltage)
meas.register_parameter(useful, setpoints=(bias_voltage, gate_voltage),
paramtype='text')
# a function to simulate the usefulness of a region
def get_usefulness(x, y):
val = np.sin(x)*np.sin(y)
if val < -0.4:
return 'Useless'
if val < 0:
return 'Bad'
if val <0.5:
return 'Possible'
return 'Good'
with meas.run() as datasaver:
for bias_v in 3*(np.random.rand(100)):
for gate_v in 2*(np.random.rand(75)-0.5):
datasaver.add_result((bias_voltage, bias_v),
(gate_voltage, gate_v),
(useful, get_usefulness(bias_v, gate_v)))
dataset = datasaver.dataset
Starting experimental run with id: 8.
[17]:
ax, cax = plot_dataset(dataset)
Categorical data on x-axis and z-axis
For completeness, we include two examples of this situation. One resulting in a grid and one resulting in a scatter plot. We reuse the example with the x- and y-axes having numerical data with just a slight modification.
[18]:
sample = Parameter('sample',
label='Sample',
set_cmd=None,
get_cmd=None)
gate_voltage = Parameter('gate_v',
label='Gate voltage',
unit='V',
set_cmd=None,
get_cmd=None)
useful = Parameter('usefulness',
label='Usefulness of region',
set_cmd=None,
get_cmd=None)
meas = Measurement(exp=exp)
meas.register_parameter(sample, paramtype='text')
meas.register_parameter(gate_voltage)
meas.register_parameter(useful, setpoints=(sample, gate_voltage),
paramtype='text')
samples = ['nanowire', '2DEG', 'spin qubit', 'nanowire_alt']
# a function to simulate the usefulness of a region
def get_usefulness(x, y):
x_num = samples.index(x)*4/len(samples)
val = np.sin(x_num)*np.sin(y)
if val < -0.4:
return 'Useless'
if val < 0:
return 'Bad'
if val <0.5:
return 'Possible'
return 'Good'
with meas.run() as datasaver:
for samp in samples:
for gate_v in np.linspace(-1, 1, 75):
datasaver.add_result((sample, samp),
(gate_voltage, gate_v),
(useful, get_usefulness(samp, gate_v)))
dataset = datasaver.dataset
Starting experimental run with id: 9.
[19]:
ax, cax = plot_dataset(dataset)
[20]:
sample = Parameter('sample',
label='Sample',
set_cmd=None,
get_cmd=None)
gate_voltage = Parameter('gate_v',
label='Gate voltage',
unit='V',
set_cmd=None,
get_cmd=None)
useful = Parameter('usefulness',
label='Usefulness of region',
set_cmd=None,
get_cmd=None)
meas = Measurement(exp=exp)
meas.register_parameter(sample, paramtype='text')
meas.register_parameter(gate_voltage)
meas.register_parameter(useful, setpoints=(sample, gate_voltage),
paramtype='text')
samples = ['nanowire', '2DEG', 'spin qubit', 'nanowire_alt']
# a function to simulate the usefulness of a region
def get_usefulness(x, y):
x_num = samples.index(x)*4/len(samples)
val = np.sin(x_num)*np.sin(y)
if val < -0.4:
return 'Useless'
if val < 0:
return 'Bad'
if val <0.5:
return 'Possible'
return 'Good'
with meas.run() as datasaver:
for samp in samples:
for gate_v in 2*(np.random.rand(75)-0.5):
datasaver.add_result((sample, samp),
(gate_voltage, gate_v),
(useful, get_usefulness(samp, gate_v)))
dataset = datasaver.dataset
Starting experimental run with id: 10.
[21]:
ax, cax = plot_dataset(dataset)
[ ]: