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import tarfile |
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from pathlib import Path |
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import numpy as np |
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from vcelldata.mesh import CartesianMesh |
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from vcelldata.postprocessing import ImageMetadata, PostProcessing, VariableInfo, StatisticType |
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from vcelldata.simdata_models import PdeDataSet, DataFunctions, NamedFunction, VariableType |
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test_data_dir = (Path(__file__).parent.parent / "test_data").absolute() |
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def extract_simdata() -> None: |
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if (test_data_dir / "SimID_946368938_0_.log").exists(): |
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return |
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with tarfile.open(test_data_dir / "SimID_946368938_simdata.tgz", 'r:gz') as tar: |
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tar.extractall(path=test_data_dir) |
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def test_parse_vcelldata(): |
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extract_simdata() |
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pde_dataset = PdeDataSet(base_dir=test_data_dir, log_filename="SimID_946368938_0_.log") |
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pde_dataset.read() |
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expected_times = [0.0, 0.25, 0.5, 0.75, 1.0] |
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assert pde_dataset.times() == expected_times |
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expected_variables = [ |
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'cytosol::C_cyt', |
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'cytosol::Ran_cyt', |
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'cytosol::RanC_cyt', |
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'Nucleus::RanC_nuc', |
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'vcRegionVolume', |
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'vcRegionArea', |
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'vcRegionVolume_ec', |
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'vcRegionVolume_cytosol', |
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'vcRegionVolume_Nucleus' |
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] |
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assert [v.var_name for v in pde_dataset.variables_block_headers()] == expected_variables |
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expected_shapes = [(126025,), (126025,), (126025,), (126025,), (6,), (5,), (5,), (5,), (5,)] |
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assert [pde_dataset.get_data(v.var_name, 0.0).shape for v in pde_dataset.variables_block_headers()] == expected_shapes |
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expected_min_C_cyt = [0.0, 0.0, 0.0, 0.0, 0.0] |
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assert [np.min(pde_dataset.get_data('cytosol::C_cyt', t)) for t in pde_dataset.times()] == expected_min_C_cyt |
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expected_max_C_cyt = [0.0, 1.6578610937269188e-05, 3.688810690038264e-05, 5.838639163921412e-05, 7.973304853048764e-05] |
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assert [np.max(pde_dataset.get_data('cytosol::C_cyt', t)) for t in pde_dataset.times()] == expected_max_C_cyt |
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for t in pde_dataset.times(): |
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for v in pde_dataset.variables_block_headers(): |
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data = pde_dataset.get_data(v.var_name, t) |
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if data.size > 0 and v == "cytosol::RanC_cyt": |
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print(f"v={v}, t={t}, shape={data.shape}, min={np.min(data)}, max={np.max(data)}") |
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def test_function_parse(): |
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extract_simdata() |
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data_functions = DataFunctions(function_file=test_data_dir / "SimID_946368938_0_.functions") |
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data_functions.read() |
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expected_functions = [ |
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NamedFunction(name="Nucleus_cytosol_membrane::J_flux0", |
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vcell_expression="(2.0 * (RanC_cyt - RanC_nuc))", |
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variable_type=VariableType.MEMBRANE), |
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NamedFunction(name="cytosol::J_r0", |
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vcell_expression="(RanC_cyt - (1000.0 * C_cyt * Ran_cyt))", |
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variable_type=VariableType.VOLUME), |
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NamedFunction(name="cytosol_ec_membrane::s2", |
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vcell_expression="0.0", |
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variable_type=VariableType.MEMBRANE_REGION), |
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NamedFunction(name="cytosol::Size_cyt", |
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vcell_expression="vcRegionVolume('cytosol')", |
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variable_type=VariableType.VOLUME_REGION), |
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NamedFunction(name="ec::Size_EC", |
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vcell_expression="vcRegionVolume('ec')", |
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variable_type=VariableType.VOLUME_REGION), |
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NamedFunction(name="Nucleus_cytosol_membrane::Size_nm", |
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vcell_expression="vcRegionArea('Nucleus_cytosol_membrane')", |
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variable_type=VariableType.MEMBRANE_REGION), |
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NamedFunction(name="Nucleus::Size_nuc", |
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vcell_expression="vcRegionVolume('Nucleus')", |
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variable_type=VariableType.VOLUME_REGION), |
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NamedFunction(name="cytosol_ec_membrane::Size_pm", |
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vcell_expression="vcRegionArea('cytosol_ec_membrane')", |
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variable_type=VariableType.MEMBRANE_REGION), |
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NamedFunction(name="cytosol_ec_membrane::sobj_cytosol1_ec0_size", |
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vcell_expression = "vcRegionArea('cytosol_ec_membrane')", |
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variable_type=VariableType.MEMBRANE_REGION), |
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NamedFunction(name="Nucleus_cytosol_membrane::sobj_Nucleus2_cytosol1_size", |
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vcell_expression = "vcRegionArea('Nucleus_cytosol_membrane')", |
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variable_type=VariableType.MEMBRANE_REGION), |
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NamedFunction(name="cytosol::vobj_cytosol1_size", |
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vcell_expression = "vcRegionVolume('cytosol')", |
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variable_type=VariableType.VOLUME_REGION), |
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NamedFunction(name="ec::vobj_ec0_size", |
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vcell_expression="vcRegionVolume('ec')", |
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variable_type=VariableType.VOLUME_REGION), |
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NamedFunction(name="Nucleus::vobj_Nucleus2_size", |
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vcell_expression="vcRegionVolume('Nucleus')", |
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variable_type=VariableType.VOLUME_REGION) |
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] |
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assert [nf.name for nf in data_functions.named_functions] == [nf.name for nf in expected_functions] |
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assert [nf.vcell_expression for nf in data_functions.named_functions] == [nf.vcell_expression for nf in expected_functions] |
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assert [nf.variable_type for nf in data_functions.named_functions] == [nf.variable_type for nf in expected_functions] |
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def test_function_eval(): |
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extract_simdata() |
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pde_dataset = PdeDataSet(base_dir=test_data_dir, log_filename="SimID_946368938_0_.log") |
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pde_dataset.read() |
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data_functions = DataFunctions(function_file=test_data_dir / "SimID_946368938_0_.functions") |
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data_functions.read() |
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volume_functions: list[NamedFunction] = [nf for nf in data_functions.named_functions if nf.variable_type == VariableType.VOLUME] |
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assert len(volume_functions) == 1 |
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function_J_r0 = volume_functions[0] |
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assert function_J_r0.name == "cytosol::J_r0" |
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assert function_J_r0.name.split("::")[1] == "J_r0" |
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assert function_J_r0.variables == ['RanC_cyt', 'Ran_cyt', 'C_cyt'] |
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assert function_J_r0.python_expression == "(RanC_cyt - (1000.0 * C_cyt * Ran_cyt))" |
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min_values = [] |
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max_values = [] |
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for t in pde_dataset.times(): |
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RanC_cyt: np.ndarray = pde_dataset.get_data("cytosol::RanC_cyt", t) |
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C_cyt: np.ndarray = pde_dataset.get_data("cytosol::C_cyt", t) |
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Ran_cyt: np.ndarray = pde_dataset.get_data("cytosol::Ran_cyt", t) |
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bindings = {"RanC_cyt": RanC_cyt, "C_cyt": C_cyt, "Ran_cyt": Ran_cyt} |
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J_r0: np.ndarray = function_J_r0.evaluate(bindings) |
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assert J_r0.shape == (126025,) |
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assert np.allclose(J_r0, RanC_cyt - (1000.0 * C_cyt * Ran_cyt)) |
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min_values.append(np.min(J_r0)) |
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max_values.append(np.max(J_r0)) |
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assert min_values == [0.0, 0.0, 0.0, 0.0, 0.0] |
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assert max_values == [0.0, 0.00013838468860665845, 0.0001493452037574851, 0.0001484768688158302, 0.00014236316719653776] |
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def test_mesh_parse(): |
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extract_simdata() |
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mesh = CartesianMesh(mesh_file=test_data_dir / "SimID_946368938_0_.mesh") |
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mesh.read() |
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def test_post_processing_parse(): |
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extract_simdata() |
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post_processing = PostProcessing(postprocessing_hdf5_path=test_data_dir / "SimID_946368938_0_.hdf5") |
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post_processing.read() |
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expected_times = [0.0, 0.25, 0.5, 0.75, 1.0] |
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assert np.allclose(post_processing.times, expected_times) |
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expected_variables = [ |
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VariableInfo(stat_var_name="C_cyt_average", stat_var_unit="uM", stat_channel=0, var_index=0), |
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VariableInfo(stat_var_name="C_cyt_total", stat_var_unit="molecules", stat_channel=1, var_index=0), |
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VariableInfo(stat_var_name="C_cyt_min", stat_var_unit="uM", stat_channel=2, var_index=0), |
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VariableInfo(stat_var_name="C_cyt_max", stat_var_unit="uM", stat_channel=3, var_index=0), |
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VariableInfo(stat_var_name="Ran_cyt_average", stat_var_unit="uM", stat_channel=4, var_index=1), |
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VariableInfo(stat_var_name="Ran_cyt_total", stat_var_unit="molecules", stat_channel=5, var_index=1), |
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VariableInfo(stat_var_name="Ran_cyt_min", stat_var_unit="uM", stat_channel=6, var_index=1), |
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VariableInfo(stat_var_name="Ran_cyt_max", stat_var_unit="uM", stat_channel=7, var_index=1), |
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VariableInfo(stat_var_name="RanC_cyt_average", stat_var_unit="uM", stat_channel=8, var_index=2), |
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VariableInfo(stat_var_name="RanC_cyt_total", stat_var_unit="molecules", stat_channel=9, var_index=2), |
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VariableInfo(stat_var_name="RanC_cyt_min", stat_var_unit="uM", stat_channel=10, var_index=2), |
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VariableInfo(stat_var_name="RanC_cyt_max", stat_var_unit="uM", stat_channel=11, var_index=2), |
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VariableInfo(stat_var_name="RanC_nuc_average", stat_var_unit="uM", stat_channel=12, var_index=3), |
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VariableInfo(stat_var_name="RanC_nuc_total", stat_var_unit="molecules", stat_channel=13, var_index=3), |
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VariableInfo(stat_var_name="RanC_nuc_min", stat_var_unit="uM", stat_channel=14, var_index=3), |
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VariableInfo(stat_var_name="RanC_nuc_max", stat_var_unit="uM", stat_channel=15, var_index=3) |
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] |
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for i, v in enumerate(post_processing.variables): |
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expected = expected_variables[i] |
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assert v.stat_var_name == expected.stat_var_name |
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assert v.stat_var_unit == expected.stat_var_unit |
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assert v.stat_channel == expected.stat_channel |
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assert v.var_index == expected.var_index |
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stats_table_from_vcell_plot = """ |
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t C_cyt_average C_cyt_total C_cyt_min C_cyt_max Ran_cyt_average Ran_cyt_total Ran_cyt_min Ran_cyt_max RanC_cyt_average RanC_cyt_total RanC_cyt_min RanC_cyt_max RanC_nuc_average RanC_nuc_total RanC_nuc_min RanC_nuc_max |
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0.0 0.0 0.0 0.0 2.2250738585072014E-308 0.0 0.0 0.0 2.2250738585072014E-308 0.0 0.0 0.0 2.2250738585072014E-308 4.500000000000469E-4 995.2639514331112 4.5E-4 4.5E-4 |
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0.25 1.7242715861760507E-6 15.424394116394046 0.0 1.6578610937269188E-5 1.7242715861760507E-6 15.424394116394046 0.0 1.6578610937269188E-5 1.207791769681895E-5 108.04247089303067 0.0 1.386595389472678E-4 3.941755233129602E-4 871.7970864237174 2.618557008421516E-4 4.4784349464175205E-4 |
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0.5 5.558151571952027E-6 49.720195525909354 0.0 3.688810690038264E-5 5.558151571952027E-6 49.720195525909354 0.0 3.688810690038264E-5 1.8059551583630324E-5 161.55090846739807 0.0 1.5070593618817915E-4 3.5447559498153353E-4 783.992847439835 2.1160422746379327E-4 4.325308047580796E-4 |
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0.75 1.047028801407123E-5 93.66149169073127 0.0 5.838639163921412E-5 1.047028801407123E-5 93.66149169073127 0.0 5.838639163921412E-5 2.1237498474550513E-5 189.9790898046723 0.0 1.518858395444779E-4 3.217543607511082E-4 711.6233699377424 1.8374881412946774E-4 4.084354763369491E-4 |
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1.0 1.5889249586954565E-5 142.1365693245928 0.0 7.973304853048764E-5 1.5889249586954565E-5 142.1365693245928 0.0 7.973304853048764E-5 2.277124484748409E-5 203.69914917373157 0.0 1.4872052622450286E-4 2.9363336670624725E-4 649.4282329348212 1.6516455078631075E-4 3.8171626169331387E-4 |
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""" |
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stats_lines = stats_table_from_vcell_plot.strip().split("\n") |
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stats_data = [] |
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for line in stats_lines[1:]: |
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parts = line.split("\t") |
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stats_data.append([float(v) for v in parts]) |
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stats_data = np.array(stats_data) |
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stats_data = stats_data[:, 1:] |
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stats_data = stats_data.reshape((5, 4, 4)) |
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assert np.allclose(post_processing.statistics, stats_data) |
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assert post_processing.statistics.shape == (5, 4, 4) |
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expected_C_cyt_average = np.array([[0.00000000e+00, 1.72427159e-06, 5.55815157e-06, 1.04702880e-05, 1.58892496e-05]]) |
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C_cyt_average = post_processing.statistics[:, 0, int(StatisticType.AVERAGE)] |
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assert np.allclose(C_cyt_average, expected_C_cyt_average) |
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expected_C_cyt_total = np.array([[[[ 0., 15.42439412, 49.72019553, 93.66149169, 142.13656932]]]]) |
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C_cyt_total = post_processing.statistics[:, 0, int(StatisticType.TOTAL)] |
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assert np.allclose(C_cyt_total, expected_C_cyt_total) |
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expected_C_cyt_min = np.array([[0., 0., 0., 0., 0.]]) |
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C_cyt_min = post_processing.statistics[:, 0, int(StatisticType.MIN)] |
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assert np.allclose(C_cyt_min, expected_C_cyt_min) |
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expected_C_cyt_max = np.array([[[2.22507386e-308, 1.65786109e-005, 3.68881069e-005, 5.83863916e-005, 7.97330485e-005]]]) |
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C_cyt_max = post_processing.statistics[:, 0, int(StatisticType.MAX)] |
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assert np.allclose(C_cyt_max, expected_C_cyt_max) |
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fluorescence: ImageMetadata = post_processing.image_metadata[0] |
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assert fluorescence.name == "fluor" |
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assert fluorescence.shape == (71, 71) |
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assert np.allclose(fluorescence.extents, np.array([74.24, 74.24, 26.0])) |
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assert np.allclose(fluorescence.origin, np.array([0.0, 0.0, 0.0])) |
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assert fluorescence.group_path == "/PostProcessing/fluor" |
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fluorescence_data_0 = post_processing.read_image_data(image_metadata=fluorescence, time_index=0) |
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assert fluorescence_data_0.shape == (71, 71) |
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assert fluorescence_data_0.dtype == np.float64 |
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assert np.min(fluorescence_data_0) == 0.0 |
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assert np.max(fluorescence_data_0) == 0.0 |
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fluorescence_data_4 = post_processing.read_image_data(image_metadata=fluorescence, time_index=4) |
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assert fluorescence_data_4.shape == (71, 71) |
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assert fluorescence_data_4.dtype == np.float64 |
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assert np.min(fluorescence_data_4) == 0.0 |
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assert np.allclose(np.max(fluorescence_data_4), 0.7147863306841433) |
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