Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Add tests for dataset cache xarray and pandas methods #2771

Draft
wants to merge 1 commit into
base: main
Choose a base branch
from
Draft

Add tests for dataset cache xarray and pandas methods #2771

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
186 changes: 186 additions & 0 deletions qcodes/tests/dataset/test_data_set_cache.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,9 @@

import hypothesis.strategies as hst
import numpy as np
import pandas
import pytest
import xarray
from hypothesis import HealthCheck, given, settings

from qcodes.dataset.descriptions.detect_shapes import detect_shape_of_measurement
Expand Down Expand Up @@ -790,6 +792,171 @@ def test_cache_2d_shape(experiment,
clip=cache_size == "too_large")


@pytest.mark.parametrize("bg_writing", [True, False])
@pytest.mark.parametrize("in_memory_cache", [True, False])
@settings(deadline=None, max_examples=5,
suppress_health_check=(HealthCheck.function_scoped_fixture,))
@given(n_points_outer=hst.integers(min_value=1, max_value=11),
n_points_inner=hst.integers(min_value=1, max_value=11))
def test_cache_2d_to_dataframe_dict_and_dataframe(
experiment, DAC, DMM, n_points_outer, n_points_inner, bg_writing,
channel_array_instrument, in_memory_cache
):
meas = Measurement()

meas.register_parameter(DAC.ch1)
meas.register_parameter(DAC.ch2)

meas_parameters = (DMM.v1,
channel_array_instrument.A.dummy_multi_parameter,
channel_array_instrument.A.dummy_scalar_multi_parameter,
channel_array_instrument.A.dummy_2d_multi_parameter,
channel_array_instrument.A.dummy_2d_multi_parameter_2,
channel_array_instrument.A.dummy_array_parameter,
channel_array_instrument.A.dummy_complex_array_parameter,
channel_array_instrument.A.dummy_complex,
channel_array_instrument.A.dummy_parameter_with_setpoints,
channel_array_instrument.A.dummy_parameter_with_setpoints_complex,
)
channel_array_instrument.A.dummy_start(0)
channel_array_instrument.A.dummy_stop(10)
channel_array_instrument.A.dummy_n_points(10)
for param in meas_parameters:
meas.register_parameter(param, setpoints=(DAC.ch1, DAC.ch2))
n_rows_written = 0
with meas.run(
write_in_background=bg_writing,
in_memory_cache=in_memory_cache) as datasaver:
dataset = datasaver.dataset

_assert_dataframe_dicts_are_identical(
dataset.to_pandas_dataframe_dict(),
dataset.cache.to_pandas_dataframe_dict()
)

pandas.testing.assert_frame_equal(
dataset.to_pandas_dataframe(),
dataset.cache.to_pandas_dataframe()
)

for v1 in np.linspace(-1, 1, n_points_outer):
for v2 in np.linspace(-1, 1, n_points_inner):
DAC.ch1.set(v1)
DAC.ch2.set(v2)
meas_vals = [(param, param.get()) for param in meas_parameters[:-2]]
meas_vals += expand_setpoints_helper(meas_parameters[-2])
meas_vals += expand_setpoints_helper(meas_parameters[-1])

datasaver.add_result((DAC.ch1, v1),
(DAC.ch2, v2),
*meas_vals)
datasaver.flush_data_to_database(block=True)
n_rows_written += 1

_assert_dataframe_dicts_are_identical(
dataset.to_pandas_dataframe_dict(),
dataset.cache.to_pandas_dataframe_dict()
)

pandas.testing.assert_frame_equal(
dataset.to_pandas_dataframe(),
dataset.cache.to_pandas_dataframe()
)

_assert_dataframe_dicts_are_identical(
dataset.to_pandas_dataframe_dict(),
dataset.cache.to_pandas_dataframe_dict()
)

pandas.testing.assert_frame_equal(
dataset.to_pandas_dataframe(),
dataset.cache.to_pandas_dataframe()
)


@pytest.mark.parametrize("bg_writing", [True, False])
@pytest.mark.parametrize("in_memory_cache", [True, False])
@settings(deadline=None, max_examples=5,
suppress_health_check=(HealthCheck.function_scoped_fixture,))
@given(n_points_outer=hst.integers(min_value=1, max_value=11),
n_points_inner=hst.integers(min_value=1, max_value=11))
def test_cache_2d_to_xarray_dataarray_dict_and_xarray_dataset(
experiment, DAC, DMM, n_points_outer, n_points_inner, bg_writing,
channel_array_instrument, in_memory_cache
):
meas = Measurement()

meas.register_parameter(DAC.ch1)
meas.register_parameter(DAC.ch2)

meas_parameters = (DMM.v1,
channel_array_instrument.A.dummy_multi_parameter,
channel_array_instrument.A.dummy_scalar_multi_parameter,
channel_array_instrument.A.dummy_2d_multi_parameter,
channel_array_instrument.A.dummy_2d_multi_parameter_2,
channel_array_instrument.A.dummy_array_parameter,
channel_array_instrument.A.dummy_complex_array_parameter,
channel_array_instrument.A.dummy_complex,
channel_array_instrument.A.dummy_parameter_with_setpoints,
channel_array_instrument.A.dummy_parameter_with_setpoints_complex,
)
channel_array_instrument.A.dummy_start(0)
channel_array_instrument.A.dummy_stop(10)
channel_array_instrument.A.dummy_n_points(10)
for param in meas_parameters:
meas.register_parameter(param, setpoints=(DAC.ch1, DAC.ch2))
n_rows_written = 0
with meas.run(
write_in_background=bg_writing,
in_memory_cache=in_memory_cache) as datasaver:
dataset = datasaver.dataset

_assert_xarray_dataarray_dicts_are_identical(
dataset.to_xarray_dataarray_dict(),
dataset.cache.to_xarray_dataarray_dict()
)

xarray.testing.assert_identical(
dataset.to_xarray_dataset(),
dataset.cache.to_xarray_dataset()
)

for v1 in np.linspace(-1, 1, n_points_outer):
for v2 in np.linspace(-1, 1, n_points_inner):
DAC.ch1.set(v1)
DAC.ch2.set(v2)
meas_vals = [(param, param.get()) for param in
meas_parameters[:-2]]
meas_vals += expand_setpoints_helper(meas_parameters[-2])
meas_vals += expand_setpoints_helper(meas_parameters[-1])

datasaver.add_result((DAC.ch1, v1),
(DAC.ch2, v2),
*meas_vals)
datasaver.flush_data_to_database(block=True)
n_rows_written += 1

_assert_xarray_dataarray_dicts_are_identical(
dataset.to_xarray_dataarray_dict(),
dataset.cache.to_xarray_dataarray_dict()
)

xarray.testing.assert_identical(
dataset.to_xarray_dataset(),
dataset.cache.to_xarray_dataset()
)

_assert_xarray_dataarray_dicts_are_identical(
dataset.to_xarray_dataarray_dict(),
dataset.cache.to_xarray_dataarray_dict()
)

xarray.testing.assert_identical(
dataset.to_xarray_dataset(),
dataset.cache.to_xarray_dataset()
)


def _assert_completed_cache_is_as_expected(
cache_data_trees,
param_data_trees,
Expand Down Expand Up @@ -915,6 +1082,25 @@ def _assert_parameter_data_is_identical(
)


def _assert_dataframe_dicts_are_identical(df_dict_left, df_dict_right) -> None:
for param_name, param_df_from_left in df_dict_left.items():
assert param_name in df_dict_right
param_df_from_right = df_dict_right[param_name]
pandas.testing.assert_frame_equal(
param_df_from_left, param_df_from_right
)


def _assert_xarray_dataarray_dicts_are_identical(xr_dict_left, xr_dict_right
) -> None:
for param_name, param_dataarray_from_left in xr_dict_left.items():
assert param_name in xr_dict_right
param_dataarray_from_right = xr_dict_right[param_name]
xarray.testing.assert_identical(
param_dataarray_from_left, param_dataarray_from_right
)


def _array_param_used_in_tree(measurement: Measurement) -> bool:
found_array = False
for paramspecbase in measurement.parameters.values():
Expand Down