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test_vbdataframe.py
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test_vbdataframe.py
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from ..util import BaseCase
import pygsti
import pandas
import os
import numpy as np
from pygsti.protocols import vbdataframe as _vbdataframe
class TestVBDataFrame(BaseCase):
def test_vbdataframe_1(self):
df = pandas.read_csv(os.path.join(os.path.dirname(__file__),'test_dataframe_1.csv'))
depths = [0, 4, 8, 12, 20, 28, 40, 56, 80, 112, 160, 224, 316]
vbdf = _vbdataframe.VBDataFrame(df, x_values=depths)
# ///// Tests filtering data ///// #
vbdf1 = vbdf.filter_data('Qubits', metric='polarization', statistic='mean', threshold=1/np.e,
indep_x=False)
# Nothing in this test checks that the correct output is produced here.
vbdf2 = vbdf.filter_data('Qubits', metric='success_probabilities', statistic='max', threshold=1/np.e,
indep_x=True)
qubits = set(vbdf1.dataframe['Qubits'])
# Checks the correct qubits are selected.
qubits == {"('Q0', 'Q1')", "('Q0', 'Q1', 'Q2', 'Q3', 'Q4')", "('Q0', 'Q1', 'Q2', 'Q4')", "('Q0',)"}
# ///// Tests extract VB data ///// #
vbdata1 = vbdf1.vb_data(metric='polarization', statistic='mean', lower_cutoff=0., no_data_action='discard')
correct_vbdata1 = {(0, 1): 0.851416015625,
(0, 2): 0.7064453125,
(0, 4): 0.5225,
(0, 5): 0.351335685483871,
(4, 1): 0.822509765625,
(4, 2): 0.6943359375,
(4, 4): 0.44955729166666664,
(4, 5): 0.3045866935483871,
(8, 1): 0.830419921875,
(8, 2): 0.6907877604166666,
(8, 4): 0.42536458333333327,
(8, 5): 0.2916834677419355,
(12, 1): 0.838671875,
(12, 2): 0.6174479166666667,
(12, 4): 0.4035677083333333,
(12, 5): 0.25471270161290327,
(20, 1): 0.796875,
(20, 2): 0.6025065104166667,
(20, 4): 0.33934895833333334,
(20, 5): 0.21519657258064515,
(28, 1): 0.765869140625,
(28, 2): 0.5246744791666667,
(28, 4): 0.2816927083333334,
(28, 5): 0.16771673387096775,
(40, 1): 0.775,
(40, 2): 0.45244140625,
(40, 4): 0.18473958333333332,
(40, 5): 0.12220262096774193,
(80, 1): 0.659375,
(80, 2): 0.3126953125,
(80, 4): 0.058776041666666654,
(80, 5): 0.030317540322580643}
self.assertTrue(set(correct_vbdata1.keys()) == set(vbdata1.keys()))
for key in vbdata1.keys():
self.assertAlmostEqual(vbdata1[key], correct_vbdata1[key])
# Nothing in this test checks that the correct output is produced here.
vbdata2 = vbdf1.vb_data(metric='success_probabilities', statistic='max', lower_cutoff=0., no_data_action='nan')
vbdata3 = vbdf1.vb_data(metric='success_probabilities', statistic='min', lower_cutoff=0.1, no_data_action='min')
vbdata4 = vbdf1.vb_data(metric='success_probabilities', statistic='monotonic_min', lower_cutoff=0., no_data_action='discard')
vbdata5 = vbdf1.vb_data(metric='success_probabilities', statistic='monotonic_max', lower_cutoff=0., no_data_action='discard')
def test_vbdataframe_2(self):
df = pandas.read_csv(os.path.join(os.path.dirname(__file__),'test_dataframe_2.csv'))
vbdf = _vbdataframe.VBDataFrame(df)
vbdf1 = vbdf.select_column_value('Pass', 'pass1')
capreg1 = vbdf1.capability_regions(metric='polarization', threshold=1/np.e, significance=0.05, monotonic=True,
nan_data_action='discard')
correct_capreg1 = {(0, 1): 2,
(0, 2): 2,
(0, 3): 2,
(0, 4): 2,
(0, 5): 2,
(4, 1): 2,
(4, 2): 2,
(4, 3): 2,
(4, 4): 2,
(4, 5): 1,
(8, 1): 2,
(8, 2): 2,
(8, 3): 2,
(8, 4): 2,
(8, 5): 1,
(16, 1): 2,
(16, 2): 2,
(16, 3): 2,
(16, 4): 1,
(16, 5): 1,
(32, 1): 2,
(32, 2): 2,
(32, 3): 1,
(32, 4): 1,
(32, 5): 1,
(64, 1): 2,
(64, 2): 1,
(64, 3): 1,
(64, 4): 1,
(64, 5): 0,
(128, 1): 2,
(128, 2): 1,
(128, 3): 1,
(128, 4): 0,
(128, 5): 0,
(256, 1): 2,
(256, 2): 1,
(256, 3): 0,
(256, 4): 0,
(256, 5): 0,
(512, 1): 1,
(512, 2): 0,
(512, 3): 0}
self.assertTrue(set(correct_capreg1.keys()) == set(capreg1.keys()))
for key in capreg1.keys():
self.assertAlmostEqual(capreg1[key], correct_capreg1[key])
capreg2 = vbdf1.capability_regions(metric='success_probability', threshold=2/3, significance=0.01, monotonic=False,
nan_data_action='none')