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"face_bounds.Bounds.time_bounds": { + "code": "class Bounds:\n def time_bounds(self):\n self.uxgrid.bounds\n\n def setup(self):\n self.uxgrid = ux.open_grid(r\"C:\\Users\\chmie\\PycharmProjects\\ncar-uxarray\\uxarray-hongyu\\benchmarks\\oQU480.grid.nc\")", + "min_run_count": 2, + "name": "face_bounds.Bounds.time_bounds", + "number": 0, + "param_names": [], + "params": [], + "repeat": 0, + "rounds": 2, + "sample_time": 0.01, + "type": "time", + "unit": "seconds", + "version": "f0dc6389b413dd90cc4bc89d04e22d4bded407582cf58cebc40a895c3e608dea", + "warmup_time": -1 + }, "face_bounds.FaceBounds.peakmem_face_bounds": { "code": "class FaceBounds:\n def peakmem_face_bounds(self, grid_path):\n \"\"\"Peak memory usage obtain ``Grid.face_bounds.\"\"\"\n face_bounds = self.uxgrid.bounds\n\n def setup(self, grid_path):\n self.uxgrid = ux.open_grid(grid_path)", "name": "face_bounds.FaceBounds.peakmem_face_bounds", @@ -57,7 +72,7 @@ "warmup_time": -1 }, "mpas_ocean.ConnectivityConstruction.time_face_face_connectivity": { - "code": "class ConnectivityConstruction:\n def time_face_face_connectivity(self, resolution):\n ux.grid.connectivity._populate_face_face_connectivity(self.uxds.uxgrid)\n\n def setup(self, resolution):\n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", + "code": "class ConnectivityConstruction:\n def time_face_face_connectivity(self, resolution):\n ux.grid.connectivity._populate_face_face_connectivity(self.uxds.uxgrid)\n\nclass DatasetBenchmark:\n def setup(self, resolution, *args, **kwargs):\n \n \n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", "min_run_count": 2, "name": "mpas_ocean.ConnectivityConstruction.time_face_face_connectivity", "number": 0, @@ -75,11 +90,11 @@ "sample_time": 0.01, "type": "time", "unit": "seconds", - "version": "ae27a3df82c890aa93df87fc5f67ba8e0c4b51f85da767fac702be7ef05bfa92", + "version": "0e86f4796f5762f3d54fbc03de55c71678cd10713a9d855732c8d7f6271fb5d9", "warmup_time": -1 }, "mpas_ocean.ConnectivityConstruction.time_n_nodes_per_face": { - "code": "class ConnectivityConstruction:\n def time_n_nodes_per_face(self, resolution):\n self.uxds.uxgrid.n_nodes_per_face\n\n def setup(self, resolution):\n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", + "code": "class ConnectivityConstruction:\n def time_n_nodes_per_face(self, resolution):\n self.uxds.uxgrid.n_nodes_per_face\n\nclass DatasetBenchmark:\n def setup(self, resolution, *args, **kwargs):\n \n \n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", "min_run_count": 2, "name": "mpas_ocean.ConnectivityConstruction.time_n_nodes_per_face", "number": 0, @@ -97,11 +112,11 @@ "sample_time": 0.01, "type": "time", "unit": "seconds", - "version": "dad5686c3187a018e28621fd2e0ce788e613f508ba3cf7e3cc8136d2423c5fbf", + "version": "048a954b109fd388237d52a513b83fdb053c87fe5b04e39e2b0ed22db26ac7cc", "warmup_time": -1 }, "mpas_ocean.ConstructTreeStructures.time_ball_tree": { - "code": "class ConstructTreeStructures:\n def time_ball_tree(self, resolution):\n self.uxds.uxgrid.get_ball_tree()\n\n def setup(self, resolution):\n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", + "code": "class ConstructTreeStructures:\n def time_ball_tree(self, resolution):\n self.uxds.uxgrid.get_ball_tree()\n\nclass DatasetBenchmark:\n def setup(self, resolution, *args, **kwargs):\n \n \n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", "min_run_count": 2, "name": "mpas_ocean.ConstructTreeStructures.time_ball_tree", "number": 0, @@ -119,11 +134,11 @@ "sample_time": 0.01, "type": "time", "unit": "seconds", - "version": "f1f3fea03a00052ef8c3e75069560761700a091dce616cf32ab1503dfd1cd0b9", + "version": "e6c12f881e09c7410a76b884d48cbd5cfed69c7b1ac45f0a912c25e683644eec", "warmup_time": -1 }, "mpas_ocean.ConstructTreeStructures.time_kd_tree": { - "code": "class ConstructTreeStructures:\n def time_kd_tree(self, resolution):\n self.uxds.uxgrid.get_kd_tree()\n\n def setup(self, resolution):\n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", + "code": "class ConstructTreeStructures:\n def time_kd_tree(self, resolution):\n self.uxds.uxgrid.get_kd_tree()\n\nclass DatasetBenchmark:\n def setup(self, resolution, *args, **kwargs):\n \n \n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", "min_run_count": 2, "name": "mpas_ocean.ConstructTreeStructures.time_kd_tree", "number": 0, @@ -141,11 +156,11 @@ "sample_time": 0.01, "type": "time", "unit": "seconds", - "version": "fc440c582485de0d25b1bbb1e0450bedd0c0c0ebd7b64c4c51022871f790a932", + "version": "5cb806e87ae96ad594fa332873c00f308209beb043243fad87db127a6f58d0d2", "warmup_time": -1 }, "mpas_ocean.GeoDataFrame.peakmem_to_geodataframe": { - "code": "class GeoDataFrame:\n def peakmem_to_geodataframe(self, resolution, exclude_antimeridian):\n gdf = self.uxds[data_var].to_geodataframe(exclude_antimeridian=exclude_antimeridian)\n\n def setup(self, resolution, exclude_antimeridian):\n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", + "code": "class GeoDataFrame:\n def peakmem_to_geodataframe(self, resolution, exclude_antimeridian):\n gdf = self.uxds[data_var].to_geodataframe(exclude_antimeridian=exclude_antimeridian)\n\nclass DatasetBenchmark:\n def setup(self, resolution, *args, **kwargs):\n \n \n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", "name": "mpas_ocean.GeoDataFrame.peakmem_to_geodataframe", "param_names": [ "resolution", @@ -163,10 +178,10 @@ ], "type": "peakmemory", "unit": "bytes", - "version": "aabe445c29aa2068658af57e83dbb13650b81fab0ac0021b7d4d7a6cc255fe82" + "version": "a32a7b0e0300eaefaa7b73c692e3c8a68b847b7dea280712097677e21237c4bc" }, "mpas_ocean.GeoDataFrame.time_to_geodataframe": { - "code": "class GeoDataFrame:\n def time_to_geodataframe(self, resolution, exclude_antimeridian):\n self.uxds[data_var].to_geodataframe(exclude_antimeridian=exclude_antimeridian)\n\n def setup(self, resolution, exclude_antimeridian):\n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", + "code": "class GeoDataFrame:\n def time_to_geodataframe(self, resolution, exclude_antimeridian):\n self.uxds[data_var].to_geodataframe(exclude_antimeridian=exclude_antimeridian)\n\nclass DatasetBenchmark:\n def setup(self, resolution, *args, **kwargs):\n \n \n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", "min_run_count": 2, "name": "mpas_ocean.GeoDataFrame.time_to_geodataframe", "number": 0, @@ -189,11 +204,11 @@ "sample_time": 0.01, "type": "time", "unit": "seconds", - "version": "8fd59dc804740e6ef225af4f52e8814cc380df3ae27baa7bdaf24b6833ac5214", + "version": "5ae84430963547882e3ca35285f39b2c73c17832f240321838d046fe481aff0a", "warmup_time": -1 }, "mpas_ocean.Gradient.peakmem_gradient": { - "code": "class Gradient:\n def peakmem_gradient(self, resolution):\n grad = self.uxds[data_var].gradient()\n\n def setup(self, resolution):\n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", + "code": "class Gradient:\n def peakmem_gradient(self, resolution):\n grad = self.uxds[data_var].gradient()\n\nclass DatasetBenchmark:\n def setup(self, resolution, *args, **kwargs):\n \n \n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", "name": "mpas_ocean.Gradient.peakmem_gradient", "param_names": [ "resolution" @@ -206,10 +221,10 @@ ], "type": "peakmemory", "unit": "bytes", - "version": "d30be97e0fbab7963b25f9549426e13d76cf4305fdf9e21af66690feffc07612" + "version": "ec24e9b4704c46d7ab04c978ae9b9d24c2e8ef6e77498414f29ec74ca6e8a34d" }, "mpas_ocean.Gradient.time_gradient": { - "code": "class Gradient:\n def time_gradient(self, resolution):\n self.uxds[data_var].gradient()\n\n def setup(self, resolution):\n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", + "code": "class Gradient:\n def time_gradient(self, resolution):\n self.uxds[data_var].gradient()\n\nclass DatasetBenchmark:\n def setup(self, resolution, *args, **kwargs):\n \n \n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", "min_run_count": 2, "name": "mpas_ocean.Gradient.time_gradient", "number": 0, @@ -227,11 +242,11 @@ "sample_time": 0.01, "type": "time", "unit": "seconds", - "version": "505e2bee0d40c2459579062763518d05d422328f5e6a4955815f8163789dc86e", + "version": "a6a46ac79f2cff8797ec9dc46acbbdf483ada08ae6701d6631d0382b0051c35d", "warmup_time": -1 }, "mpas_ocean.HoleEdgeIndices.time_construct_hole_edge_indices": { - "code": "class HoleEdgeIndices:\n def time_construct_hole_edge_indices(self, resolution):\n ux.grid.geometry._construct_hole_edge_indices(self.uxds.uxgrid.edge_face_connectivity)\n\n def setup(self, resolution):\n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", + "code": "class HoleEdgeIndices:\n def time_construct_hole_edge_indices(self, resolution):\n ux.grid.geometry._construct_hole_edge_indices(self.uxds.uxgrid.edge_face_connectivity)\n\nclass DatasetBenchmark:\n def setup(self, resolution, *args, **kwargs):\n \n \n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", "min_run_count": 2, "name": "mpas_ocean.HoleEdgeIndices.time_construct_hole_edge_indices", "number": 0, @@ -249,11 +264,11 @@ "sample_time": 0.01, "type": "time", "unit": "seconds", - "version": "9721e8e4eadfbb1cbd9e353e11bb66f2ceb3a946c59a0040419b48cc1258f165", + "version": "40d01110d8192eb24b3d3c7ce52f74afbaa2f8efd645eb650339db1cbfe9e4f9", "warmup_time": -1 }, "mpas_ocean.Integrate.peakmem_integrate": { - "code": "class Integrate:\n def peakmem_integrate(self, resolution):\n integral = self.uxds[data_var].integrate()\n\n def setup(self, resolution):\n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", + "code": "class Integrate:\n def peakmem_integrate(self, resolution):\n integral = self.uxds[data_var].integrate()\n\nclass DatasetBenchmark:\n def setup(self, resolution, *args, **kwargs):\n \n \n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", "name": "mpas_ocean.Integrate.peakmem_integrate", "param_names": [ "resolution" @@ -266,10 +281,10 @@ ], "type": "peakmemory", "unit": "bytes", - "version": "a1d2ab08020dfa02c63a017687b71268bb9ab501a2913e114478d53808e75297" + "version": "c0f563e9fa258207351e560866f616dcbd6c00d73cba40377a8cdfcde2ecfd24" }, "mpas_ocean.Integrate.time_integrate": { - "code": "class Integrate:\n def time_integrate(self, resolution):\n self.uxds[data_var].integrate()\n\n def setup(self, resolution):\n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", + "code": "class Integrate:\n def time_integrate(self, resolution):\n self.uxds[data_var].integrate()\n\nclass DatasetBenchmark:\n def setup(self, resolution, *args, **kwargs):\n \n \n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", "min_run_count": 2, "name": "mpas_ocean.Integrate.time_integrate", "number": 0, @@ -287,11 +302,11 @@ "sample_time": 0.01, "type": "time", "unit": "seconds", - "version": "4884ecd1a6d03e961f2ac877955cd44cbd952b85df5df47398dc92317dfe65a3", + "version": "602926f39f66a5fee325785baf58dba3ae5cad8c95697670b119457ddcdb2461", "warmup_time": -1 }, "mpas_ocean.MatplotlibConversion.time_dataarray_to_polycollection": { - "code": "class MatplotlibConversion:\n def time_dataarray_to_polycollection(self, resolution, periodic_elements):\n self.uxds[data_var].to_polycollection()\n\n def setup(self, resolution, periodic_elements):\n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", + "code": "class MatplotlibConversion:\n def time_dataarray_to_polycollection(self, resolution, periodic_elements):\n self.uxds[data_var].to_polycollection()\n\nclass DatasetBenchmark:\n def setup(self, resolution, *args, **kwargs):\n \n \n self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1])", "min_run_count": 2, "name": "mpas_ocean.MatplotlibConversion.time_dataarray_to_polycollection", "number": 0, @@ -315,7 +330,7 @@ "sample_time": 0.01, "type": "time", "unit": "seconds", - "version": "26e4314dabc51bab6febda69c8d170ce4502991ee109dbf89fde2f038c3a678c", + "version": "f8d69840e549a19644389544b092a40b8b32f29d28a21fc1c35c2fe6578a5977", "warmup_time": -1 }, "mpas_ocean.RemapDownsample.time_inverse_distance_weighted_remapping": {