From 6e1fe500ce03a5aa43f636f8aa96b72173d57251 Mon Sep 17 00:00:00 2001 From: "Jens H. Nielsen" Date: Thu, 21 Jul 2022 22:31:11 +0200 Subject: [PATCH 1/6] Add __init__.py files to data driver_examples Otherwise a deprecation warning is triggered when building the package. See https://setuptools.pypa.io/en/latest/userguide/datafiles.html for details. There seems to be a few ways to do this but adding __init__ files seems to be the easiest way. --- qcodes/monitor/dist/__init__.py | 0 qcodes/monitor/dist/css/__init__.py | 0 qcodes/monitor/dist/js/__init.py | 0 qcodes/monitor/dist/js/__init__.py | 0 .../fixtures/2018-01-17/#001_testsweep_15-42-57/__init__.py | 0 .../dataset/fixtures/2018-01-17/#002_2D_test_15-43-14/__init__.py | 0 qcodes/tests/dataset/fixtures/2018-01-17/__init__.py | 0 qcodes/tests/dataset/fixtures/__init__.py | 0 qcodes/tests/delegate/data/__init__.py | 0 9 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 qcodes/monitor/dist/__init__.py create mode 100644 qcodes/monitor/dist/css/__init__.py create mode 100644 qcodes/monitor/dist/js/__init.py create mode 100644 qcodes/monitor/dist/js/__init__.py create mode 100644 qcodes/tests/dataset/fixtures/2018-01-17/#001_testsweep_15-42-57/__init__.py create mode 100644 qcodes/tests/dataset/fixtures/2018-01-17/#002_2D_test_15-43-14/__init__.py create mode 100644 qcodes/tests/dataset/fixtures/2018-01-17/__init__.py create mode 100644 qcodes/tests/dataset/fixtures/__init__.py create mode 100644 qcodes/tests/delegate/data/__init__.py diff --git a/qcodes/monitor/dist/__init__.py b/qcodes/monitor/dist/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/qcodes/monitor/dist/css/__init__.py b/qcodes/monitor/dist/css/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/qcodes/monitor/dist/js/__init.py b/qcodes/monitor/dist/js/__init.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/qcodes/monitor/dist/js/__init__.py b/qcodes/monitor/dist/js/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/qcodes/tests/dataset/fixtures/2018-01-17/#001_testsweep_15-42-57/__init__.py b/qcodes/tests/dataset/fixtures/2018-01-17/#001_testsweep_15-42-57/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/qcodes/tests/dataset/fixtures/2018-01-17/#002_2D_test_15-43-14/__init__.py b/qcodes/tests/dataset/fixtures/2018-01-17/#002_2D_test_15-43-14/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/qcodes/tests/dataset/fixtures/2018-01-17/__init__.py b/qcodes/tests/dataset/fixtures/2018-01-17/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/qcodes/tests/dataset/fixtures/__init__.py b/qcodes/tests/dataset/fixtures/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/qcodes/tests/delegate/data/__init__.py b/qcodes/tests/delegate/data/__init__.py new file mode 100644 index 00000000000..e69de29bb2d From 05f3d3fa5a622320fc7e465de8aadeab362cc6ae Mon Sep 17 00:00:00 2001 From: "Jens H. Nielsen" Date: Fri, 22 Jul 2022 07:46:49 +0200 Subject: [PATCH 2/6] rename fixtures to make them valid python modules --- .../#001_testsweep_15-42-57 => data_2018_01_17}/__init__.py | 0 .../data_001_testsweep_15_42_57}/__init__.py | 0 .../data_001_testsweep_15_42_57}/dac_ch1_set.dat | 0 .../data_001_testsweep_15_42_57}/snapshot.json | 0 .../data_002_2D_test_15_43_14}/__init__.py | 0 .../data_002_2D_test_15_43_14}/dac_ch1_set_dac_ch2_set.dat | 0 .../data_002_2D_test_15_43_14}/snapshot.json | 0 .../dataset/measurement/test_measurement_context_manager.py | 4 ++-- 8 files changed, 2 insertions(+), 2 deletions(-) rename qcodes/tests/dataset/fixtures/{2018-01-17/#001_testsweep_15-42-57 => data_2018_01_17}/__init__.py (100%) rename qcodes/tests/dataset/fixtures/{2018-01-17/#002_2D_test_15-43-14 => data_2018_01_17/data_001_testsweep_15_42_57}/__init__.py (100%) rename qcodes/tests/dataset/fixtures/{2018-01-17/#001_testsweep_15-42-57 => data_2018_01_17/data_001_testsweep_15_42_57}/dac_ch1_set.dat (100%) rename qcodes/tests/dataset/fixtures/{2018-01-17/#001_testsweep_15-42-57 => data_2018_01_17/data_001_testsweep_15_42_57}/snapshot.json (100%) rename qcodes/tests/dataset/fixtures/{2018-01-17 => data_2018_01_17/data_002_2D_test_15_43_14}/__init__.py (100%) rename qcodes/tests/dataset/fixtures/{2018-01-17/#002_2D_test_15-43-14 => data_2018_01_17/data_002_2D_test_15_43_14}/dac_ch1_set_dac_ch2_set.dat (100%) rename qcodes/tests/dataset/fixtures/{2018-01-17/#002_2D_test_15-43-14 => data_2018_01_17/data_002_2D_test_15_43_14}/snapshot.json (100%) diff --git a/qcodes/tests/dataset/fixtures/2018-01-17/#001_testsweep_15-42-57/__init__.py b/qcodes/tests/dataset/fixtures/data_2018_01_17/__init__.py similarity index 100% rename from qcodes/tests/dataset/fixtures/2018-01-17/#001_testsweep_15-42-57/__init__.py rename to qcodes/tests/dataset/fixtures/data_2018_01_17/__init__.py diff --git a/qcodes/tests/dataset/fixtures/2018-01-17/#002_2D_test_15-43-14/__init__.py b/qcodes/tests/dataset/fixtures/data_2018_01_17/data_001_testsweep_15_42_57/__init__.py similarity index 100% rename from qcodes/tests/dataset/fixtures/2018-01-17/#002_2D_test_15-43-14/__init__.py rename to qcodes/tests/dataset/fixtures/data_2018_01_17/data_001_testsweep_15_42_57/__init__.py diff --git a/qcodes/tests/dataset/fixtures/2018-01-17/#001_testsweep_15-42-57/dac_ch1_set.dat b/qcodes/tests/dataset/fixtures/data_2018_01_17/data_001_testsweep_15_42_57/dac_ch1_set.dat similarity index 100% rename from qcodes/tests/dataset/fixtures/2018-01-17/#001_testsweep_15-42-57/dac_ch1_set.dat rename to qcodes/tests/dataset/fixtures/data_2018_01_17/data_001_testsweep_15_42_57/dac_ch1_set.dat diff --git a/qcodes/tests/dataset/fixtures/2018-01-17/#001_testsweep_15-42-57/snapshot.json b/qcodes/tests/dataset/fixtures/data_2018_01_17/data_001_testsweep_15_42_57/snapshot.json similarity index 100% rename from qcodes/tests/dataset/fixtures/2018-01-17/#001_testsweep_15-42-57/snapshot.json rename to qcodes/tests/dataset/fixtures/data_2018_01_17/data_001_testsweep_15_42_57/snapshot.json diff --git a/qcodes/tests/dataset/fixtures/2018-01-17/__init__.py b/qcodes/tests/dataset/fixtures/data_2018_01_17/data_002_2D_test_15_43_14/__init__.py similarity index 100% rename from qcodes/tests/dataset/fixtures/2018-01-17/__init__.py rename to qcodes/tests/dataset/fixtures/data_2018_01_17/data_002_2D_test_15_43_14/__init__.py diff --git a/qcodes/tests/dataset/fixtures/2018-01-17/#002_2D_test_15-43-14/dac_ch1_set_dac_ch2_set.dat b/qcodes/tests/dataset/fixtures/data_2018_01_17/data_002_2D_test_15_43_14/dac_ch1_set_dac_ch2_set.dat similarity index 100% rename from qcodes/tests/dataset/fixtures/2018-01-17/#002_2D_test_15-43-14/dac_ch1_set_dac_ch2_set.dat rename to qcodes/tests/dataset/fixtures/data_2018_01_17/data_002_2D_test_15_43_14/dac_ch1_set_dac_ch2_set.dat diff --git a/qcodes/tests/dataset/fixtures/2018-01-17/#002_2D_test_15-43-14/snapshot.json b/qcodes/tests/dataset/fixtures/data_2018_01_17/data_002_2D_test_15_43_14/snapshot.json similarity index 100% rename from qcodes/tests/dataset/fixtures/2018-01-17/#002_2D_test_15-43-14/snapshot.json rename to qcodes/tests/dataset/fixtures/data_2018_01_17/data_002_2D_test_15_43_14/snapshot.json diff --git a/qcodes/tests/dataset/measurement/test_measurement_context_manager.py b/qcodes/tests/dataset/measurement/test_measurement_context_manager.py index 076b4fe767a..9cb3c2bfe8c 100644 --- a/qcodes/tests/dataset/measurement/test_measurement_context_manager.py +++ b/qcodes/tests/dataset/measurement/test_measurement_context_manager.py @@ -2250,7 +2250,7 @@ def test_parameter_inference(channel_array_instrument): @pytest.mark.usefixtures("experiment") def test_load_legacy_files_2D(): - location = '../fixtures/2018-01-17/#002_2D_test_15-43-14' + location = "../fixtures/data_2018_01_17/data_002_2D_test_15_43_14" directory = os.path.dirname(__file__) full_location = os.path.join(directory, location) run_ids = import_dat_file(full_location) @@ -2277,7 +2277,7 @@ def test_load_legacy_files_2D(): @pytest.mark.usefixtures("experiment") def test_load_legacy_files_1D(): - location = '../fixtures/2018-01-17/#001_testsweep_15-42-57' + location = "../fixtures/data_2018_01_17/data_001_testsweep_15_42_57" dir = os.path.dirname(__file__) full_location = os.path.join(dir, location) run_ids = import_dat_file(full_location) From 43d6def0cdd66194f65494ff47bfe66de9258e0b Mon Sep 17 00:00:00 2001 From: "Jens H. Nielsen" Date: Fri, 22 Jul 2022 08:05:26 +0200 Subject: [PATCH 3/6] update fixtures in examples --- .../import-data-from-legacy-dat-files.ipynb | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/docs/examples/DataSet/import-data-from-legacy-dat-files.ipynb b/docs/examples/DataSet/import-data-from-legacy-dat-files.ipynb index b87c70e6730..97a46c3c52b 100644 --- a/docs/examples/DataSet/import-data-from-legacy-dat-files.ipynb +++ b/docs/examples/DataSet/import-data-from-legacy-dat-files.ipynb @@ -51,8 +51,8 @@ "metadata": {}, "outputs": [], "source": [ - "location2d = '../../../qcodes/tests/dataset/fixtures/2018-01-17/#002_2D_test_15-43-14'\n", - "location1d = '../../../qcodes/tests/dataset/fixtures/2018-01-17/#001_testsweep_15-42-57'" + "location2d = '../../../qcodes/tests/dataset/fixtures/data_2018_01_17/data_002_2D_test_15_43_14'\n", + "location1d = '../../../qcodes/tests/dataset/fixtures/data_2018_01_17/data_001_testsweep_15_42_57'" ] }, { @@ -64,12 +64,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Starting experimental run with id: 741\n" + "Starting experimental run with id: 212. \n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -94,12 +94,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Starting experimental run with id: 742\n" + "Starting experimental run with id: 213. \n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -125,7 +125,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -139,7 +139,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.9.7" }, "toc": { "base_numbering": 1, @@ -185,5 +185,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From 9e2c456959fc8a0f001c3a8590556e9ffbdc783c Mon Sep 17 00:00:00 2001 From: "Jens H. Nielsen" Date: Fri, 22 Jul 2022 08:59:40 +0200 Subject: [PATCH 4/6] move legacy tests to their own module --- .../measurement/test_load_legacy_data.py | 73 +++++++++++++++++++ .../test_measurement_context_manager.py | 55 -------------- 2 files changed, 73 insertions(+), 55 deletions(-) create mode 100644 qcodes/tests/dataset/measurement/test_load_legacy_data.py diff --git a/qcodes/tests/dataset/measurement/test_load_legacy_data.py b/qcodes/tests/dataset/measurement/test_load_legacy_data.py new file mode 100644 index 00000000000..8d4711d77eb --- /dev/null +++ b/qcodes/tests/dataset/measurement/test_load_legacy_data.py @@ -0,0 +1,73 @@ +import json +import os + +import pytest + +from qcodes.dataset import import_dat_file, load_by_id +from qcodes.dataset.data_set import DataSet + + +@pytest.mark.usefixtures("experiment") +def test_load_legacy_files_2d(): + location = "../fixtures/data_2018_01_17/data_002_2D_test_15_43_14" + directory = os.path.dirname(__file__) + full_location = os.path.join(directory, location) + run_ids = import_dat_file(full_location) + run_id = run_ids[0] + data = load_by_id(run_id) + assert isinstance(data, DataSet) + assert data.parameters == "dac_ch1_set,dac_ch2_set,dmm_voltage" + assert data.number_of_results == 36 + expected_names = ["dac_ch1_set", "dac_ch2_set", "dmm_voltage"] + expected_labels = ["Gate ch1", "Gate ch2", "Gate voltage"] + expected_units = ["V", "V", "V"] + expected_depends_on = ["", "", "dac_ch1_set, dac_ch2_set"] + for i, parameter in enumerate(data.get_parameters()): + assert parameter.name == expected_names[i] + assert parameter.label == expected_labels[i] + assert parameter.unit == expected_units[i] + assert parameter.depends_on == expected_depends_on[i] + assert parameter.type == "numeric" + snapshot = json.loads(data.get_metadata("snapshot")) + assert sorted(list(snapshot.keys())) == [ + "__class__", + "arrays", + "formatter", + "io", + "location", + "loop", + "station", + ] + + +@pytest.mark.usefixtures("experiment") +def test_load_legacy_files_1d(): + location = "../fixtures/data_2018_01_17/data_001_testsweep_15_42_57" + dir = os.path.dirname(__file__) + full_location = os.path.join(dir, location) + run_ids = import_dat_file(full_location) + run_id = run_ids[0] + data = load_by_id(run_id) + assert isinstance(data, DataSet) + assert data.parameters == "dac_ch1_set,dmm_voltage" + assert data.number_of_results == 201 + expected_names = ["dac_ch1_set", "dmm_voltage"] + expected_labels = ["Gate ch1", "Gate voltage"] + expected_units = ["V", "V"] + expected_depends_on = ["", "dac_ch1_set"] + for i, parameter in enumerate(data.get_parameters()): + assert parameter.name == expected_names[i] + assert parameter.label == expected_labels[i] + assert parameter.unit == expected_units[i] + assert parameter.depends_on == expected_depends_on[i] + assert parameter.type == "numeric" + snapshot = json.loads(data.get_metadata("snapshot")) + assert sorted(list(snapshot.keys())) == [ + "__class__", + "arrays", + "formatter", + "io", + "location", + "loop", + "station", + ] diff --git a/qcodes/tests/dataset/measurement/test_measurement_context_manager.py b/qcodes/tests/dataset/measurement/test_measurement_context_manager.py index 9cb3c2bfe8c..d879d9dda17 100644 --- a/qcodes/tests/dataset/measurement/test_measurement_context_manager.py +++ b/qcodes/tests/dataset/measurement/test_measurement_context_manager.py @@ -18,7 +18,6 @@ from qcodes.dataset.descriptions.param_spec import ParamSpecBase from qcodes.dataset.experiment_container import new_experiment from qcodes.dataset.export_config import DataExportType -from qcodes.dataset.legacy_import import import_dat_file from qcodes.dataset.measurements import Measurement from qcodes.dataset.sqlite.connection import atomic_transaction from qcodes.parameters import Parameter, expand_setpoints_helper @@ -2248,60 +2247,6 @@ def test_parameter_inference(channel_array_instrument): 'array') == 'array' -@pytest.mark.usefixtures("experiment") -def test_load_legacy_files_2D(): - location = "../fixtures/data_2018_01_17/data_002_2D_test_15_43_14" - directory = os.path.dirname(__file__) - full_location = os.path.join(directory, location) - run_ids = import_dat_file(full_location) - run_id = run_ids[0] - data = load_by_id(run_id) - assert isinstance(data, DataSet) - assert data.parameters == 'dac_ch1_set,dac_ch2_set,dmm_voltage' - assert data.number_of_results == 36 - expected_names = ['dac_ch1_set', 'dac_ch2_set', 'dmm_voltage'] - expected_labels = ['Gate ch1', 'Gate ch2', 'Gate voltage'] - expected_units = ['V', 'V', 'V'] - expected_depends_on = ['', '', 'dac_ch1_set, dac_ch2_set'] - for i, parameter in enumerate(data.get_parameters()): - assert parameter.name == expected_names[i] - assert parameter.label == expected_labels[i] - assert parameter.unit == expected_units[i] - assert parameter.depends_on == expected_depends_on[i] - assert parameter.type == 'numeric' - snapshot = json.loads(data.get_metadata('snapshot')) - assert sorted(list(snapshot.keys())) == ['__class__', 'arrays', - 'formatter', 'io', 'location', - 'loop', 'station'] - - -@pytest.mark.usefixtures("experiment") -def test_load_legacy_files_1D(): - location = "../fixtures/data_2018_01_17/data_001_testsweep_15_42_57" - dir = os.path.dirname(__file__) - full_location = os.path.join(dir, location) - run_ids = import_dat_file(full_location) - run_id = run_ids[0] - data = load_by_id(run_id) - assert isinstance(data, DataSet) - assert data.parameters == 'dac_ch1_set,dmm_voltage' - assert data.number_of_results == 201 - expected_names = ['dac_ch1_set', 'dmm_voltage'] - expected_labels = ['Gate ch1', 'Gate voltage'] - expected_units = ['V', 'V'] - expected_depends_on = ['', 'dac_ch1_set'] - for i, parameter in enumerate(data.get_parameters()): - assert parameter.name == expected_names[i] - assert parameter.label == expected_labels[i] - assert parameter.unit == expected_units[i] - assert parameter.depends_on == expected_depends_on[i] - assert parameter.type == 'numeric' - snapshot = json.loads(data.get_metadata('snapshot')) - assert sorted(list(snapshot.keys())) == ['__class__', 'arrays', - 'formatter', 'io', 'location', - 'loop', 'station'] - - @pytest.mark.parametrize("bg_writing", [True, False]) @pytest.mark.usefixtures("experiment") def test_adding_parents(bg_writing, DAC): From 3353b3cfceabb8c26a6cf3b31a80a5460516aa09 Mon Sep 17 00:00:00 2001 From: "Jens H. Nielsen" Date: Fri, 22 Jul 2022 09:06:01 +0200 Subject: [PATCH 5/6] use pathlib in legacy import tests --- .../measurement/test_load_legacy_data.py | 24 ++++++++++++------- 1 file changed, 15 insertions(+), 9 deletions(-) diff --git a/qcodes/tests/dataset/measurement/test_load_legacy_data.py b/qcodes/tests/dataset/measurement/test_load_legacy_data.py index 8d4711d77eb..62bbc67d282 100644 --- a/qcodes/tests/dataset/measurement/test_load_legacy_data.py +++ b/qcodes/tests/dataset/measurement/test_load_legacy_data.py @@ -1,5 +1,5 @@ import json -import os +from pathlib import Path import pytest @@ -9,10 +9,13 @@ @pytest.mark.usefixtures("experiment") def test_load_legacy_files_2d(): - location = "../fixtures/data_2018_01_17/data_002_2D_test_15_43_14" - directory = os.path.dirname(__file__) - full_location = os.path.join(directory, location) - run_ids = import_dat_file(full_location) + full_location = ( + Path(__file__).parent.parent + / "fixtures" + / "data_2018_01_17" + / "data_002_2D_test_15_43_14" + ) + run_ids = import_dat_file(str(full_location)) run_id = run_ids[0] data = load_by_id(run_id) assert isinstance(data, DataSet) @@ -42,10 +45,13 @@ def test_load_legacy_files_2d(): @pytest.mark.usefixtures("experiment") def test_load_legacy_files_1d(): - location = "../fixtures/data_2018_01_17/data_001_testsweep_15_42_57" - dir = os.path.dirname(__file__) - full_location = os.path.join(dir, location) - run_ids = import_dat_file(full_location) + full_location = ( + Path(__file__).parent.parent + / "fixtures" + / "data_2018_01_17" + / "data_001_testsweep_15_42_57" + ) + run_ids = import_dat_file(str(full_location)) run_id = run_ids[0] data = load_by_id(run_id) assert isinstance(data, DataSet) From f1a762e4a436043261a4e9c9f08bee4332a30009 Mon Sep 17 00:00:00 2001 From: "Jens H. Nielsen" Date: Fri, 22 Jul 2022 09:23:19 +0200 Subject: [PATCH 6/6] update fixture location --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 3920edd8f15..1de822e6a15 100644 --- a/setup.cfg +++ b/setup.cfg @@ -13,7 +13,7 @@ qcodes = monitor/dist/css/* configuration/*.json instrument/sims/*.yaml - tests/dataset/fixtures/2018-01-17/*/* + tests/dataset/fixtures/data_2018_01_17/*/* tests/delegate/data/*.yml tests/drivers/auxiliary_files/* py.typed