From bbcb81cfc4bfadcc707e9fbfdd3b283882db10ed Mon Sep 17 00:00:00 2001 From: delucchi-cmu <113376043+delucchi-cmu@users.noreply.github.com> Date: Tue, 2 Apr 2024 13:02:07 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20astronom?= =?UTF-8?q?y-commons/hipscat@ed95d6d6b5b07d903bbbe2b475c66079e94b01cd=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...enchmarks.MetadataSuite.time_load_partition_info_order6.json | 2 +- ...enchmarks.MetadataSuite.time_load_partition_info_order7.json | 2 +- .../benchmarks.MetadataSuite.time_load_partition_join_info.json | 2 +- .../python-3.10/benchmarks.Suite.time_pixel_tree_creation.json | 2 +- .../python-3.10/benchmarks.time_test_alignment_even_sky.json | 2 +- .../benchmarks.time_test_cone_filter_multiple_order.json | 2 +- .../build/machine-gh-runner/packaging/python-3.10/summary.json | 2 +- ...enchmarks.MetadataSuite.time_load_partition_info_order6.json | 2 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1709137986000, "590": 1709143523000, "593": 1709158666000, "598": 1709312988000, "599": 1709317202000, "600": 1709323407000, "601": 1709926021000, "602": 1709927289000, "603": 1710168551000, "604": 1710173763000, "605": 1710270755000, "607": 1710273099000, "609": 1710453748000, "610": 1710856095000, "613": 1710962339000, "614": 1711034607000, "616": 1711541813000}, "params": {"machine": ["gh-runner"], "python": ["3.10"], "Cython": [""], "build": [""], "packaging": [""], "branch": ["HEAD"]}, "graph_param_list": [{"machine": "gh-runner", "python": "3.10", "Cython": "", "build": "", "packaging": "", "branch": "HEAD"}], "benchmarks": {"benchmarks.MetadataSuite.time_load_partition_info_order6": {"code": "class MetadataSuite:\n def time_load_partition_info_order6(self, cache):\n PartitionInfo.read_from_dir(cache[1])\n\n def setup_cache(self):\n root_dir = os.getcwd()\n \n ## Create partition info for catalog a (only at order 7)\n pixel_list_a = [HealpixPixel(7, pixel) for pixel in np.arange(100_000)]\n catalog_path_a = os.path.join(root_dir, \"catalog_a\")\n os.makedirs(catalog_path_a, exist_ok=True)\n partition_info = PartitionInfo.from_healpix(pixel_list_a)\n partition_info.write_to_file(os.path.join(catalog_path_a, \"partition_info.csv\"))\n \n ## Create partition info for catalog a (only at order 6)\n pixel_list_b = [HealpixPixel(6, pixel) for pixel in np.arange(25_000)]\n catalog_path_b = os.path.join(root_dir, \"catalog_b\")\n os.makedirs(catalog_path_b, exist_ok=True)\n partition_info = PartitionInfo.from_healpix(pixel_list_b)\n partition_info.write_to_file(os.path.join(catalog_path_b, \"partition_info.csv\"))\n \n ## Fake an association table between the two\n tree_a = PixelTreeBuilder.from_healpix(pixel_list_a)\n tree_b = PixelTreeBuilder.from_healpix(pixel_list_b)\n alignment = align_trees(tree_a, tree_b)\n alignment_df = alignment.pixel_mapping[\n [\n PixelAlignment.PRIMARY_ORDER_COLUMN_NAME,\n PixelAlignment.PRIMARY_PIXEL_COLUMN_NAME,\n PixelAlignment.JOIN_ORDER_COLUMN_NAME,\n PixelAlignment.JOIN_PIXEL_COLUMN_NAME,\n ]\n ]\n alignment_df = alignment_df.rename(\n columns={\n PixelAlignment.PRIMARY_ORDER_COLUMN_NAME: \"Norder\",\n PixelAlignment.PRIMARY_PIXEL_COLUMN_NAME: \"Npix\",\n }\n )\n \n association_catalog_path = os.path.join(root_dir, \"assocation_a_b\")\n os.makedirs(association_catalog_path, exist_ok=True)\n partition_info = PartitionJoinInfo(alignment_df)\n partition_info.write_to_csv(association_catalog_path)\n \n return (catalog_path_a, catalog_path_b, association_catalog_path)", "min_run_count": 2, "name": "benchmarks.MetadataSuite.time_load_partition_info_order6", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "benchmarks:62", "type": "time", "unit": "seconds", "version": "b0aff219fe52d62c8d1712e9bf9fb801ed80d42b6cd7d46e0606affc2916a933", "warmup_time": -1}, "benchmarks.MetadataSuite.time_load_partition_info_order7": {"code": "class MetadataSuite:\n def time_load_partition_info_order7(self, cache):\n PartitionInfo.read_from_dir(cache[0])\n\n def setup_cache(self):\n root_dir = os.getcwd()\n \n ## Create partition info for catalog a (only at order 7)\n pixel_list_a = [HealpixPixel(7, pixel) for pixel in np.arange(100_000)]\n catalog_path_a = os.path.join(root_dir, \"catalog_a\")\n os.makedirs(catalog_path_a, exist_ok=True)\n partition_info = PartitionInfo.from_healpix(pixel_list_a)\n partition_info.write_to_file(os.path.join(catalog_path_a, \"partition_info.csv\"))\n \n ## Create partition info for catalog a (only at order 6)\n pixel_list_b = [HealpixPixel(6, pixel) for pixel in np.arange(25_000)]\n catalog_path_b = os.path.join(root_dir, \"catalog_b\")\n os.makedirs(catalog_path_b, exist_ok=True)\n partition_info = PartitionInfo.from_healpix(pixel_list_b)\n partition_info.write_to_file(os.path.join(catalog_path_b, \"partition_info.csv\"))\n \n ## Fake an association table between the two\n tree_a = PixelTreeBuilder.from_healpix(pixel_list_a)\n tree_b = PixelTreeBuilder.from_healpix(pixel_list_b)\n alignment = align_trees(tree_a, tree_b)\n alignment_df = alignment.pixel_mapping[\n [\n PixelAlignment.PRIMARY_ORDER_COLUMN_NAME,\n PixelAlignment.PRIMARY_PIXEL_COLUMN_NAME,\n PixelAlignment.JOIN_ORDER_COLUMN_NAME,\n PixelAlignment.JOIN_PIXEL_COLUMN_NAME,\n ]\n ]\n alignment_df = alignment_df.rename(\n columns={\n PixelAlignment.PRIMARY_ORDER_COLUMN_NAME: \"Norder\",\n PixelAlignment.PRIMARY_PIXEL_COLUMN_NAME: \"Npix\",\n }\n )\n \n association_catalog_path = os.path.join(root_dir, \"assocation_a_b\")\n os.makedirs(association_catalog_path, exist_ok=True)\n partition_info = PartitionJoinInfo(alignment_df)\n partition_info.write_to_csv(association_catalog_path)\n \n return (catalog_path_a, catalog_path_b, association_catalog_path)", "min_run_count": 2, "name": "benchmarks.MetadataSuite.time_load_partition_info_order7", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "benchmarks:62", "type": "time", "unit": "seconds", "version": "d7da8deb183080047bf91294a4a0b2b964bd81ec0a732c6940f5613f125eb312", "warmup_time": -1}, "benchmarks.MetadataSuite.time_load_partition_join_info": {"code": "class MetadataSuite:\n def time_load_partition_join_info(self, cache):\n PartitionInfo.read_from_dir(cache[2])\n\n def setup_cache(self):\n root_dir = os.getcwd()\n \n ## Create partition info for catalog a (only at order 7)\n pixel_list_a = [HealpixPixel(7, pixel) for pixel in np.arange(100_000)]\n catalog_path_a = os.path.join(root_dir, \"catalog_a\")\n os.makedirs(catalog_path_a, exist_ok=True)\n partition_info = PartitionInfo.from_healpix(pixel_list_a)\n partition_info.write_to_file(os.path.join(catalog_path_a, \"partition_info.csv\"))\n \n ## Create partition info for catalog a (only at order 6)\n pixel_list_b = [HealpixPixel(6, pixel) for pixel in np.arange(25_000)]\n catalog_path_b = os.path.join(root_dir, \"catalog_b\")\n os.makedirs(catalog_path_b, exist_ok=True)\n partition_info = PartitionInfo.from_healpix(pixel_list_b)\n partition_info.write_to_file(os.path.join(catalog_path_b, \"partition_info.csv\"))\n \n ## Fake an association table between the two\n tree_a = PixelTreeBuilder.from_healpix(pixel_list_a)\n tree_b = PixelTreeBuilder.from_healpix(pixel_list_b)\n alignment = align_trees(tree_a, tree_b)\n alignment_df = alignment.pixel_mapping[\n [\n PixelAlignment.PRIMARY_ORDER_COLUMN_NAME,\n PixelAlignment.PRIMARY_PIXEL_COLUMN_NAME,\n PixelAlignment.JOIN_ORDER_COLUMN_NAME,\n PixelAlignment.JOIN_PIXEL_COLUMN_NAME,\n ]\n ]\n alignment_df = alignment_df.rename(\n columns={\n PixelAlignment.PRIMARY_ORDER_COLUMN_NAME: \"Norder\",\n PixelAlignment.PRIMARY_PIXEL_COLUMN_NAME: \"Npix\",\n }\n )\n \n association_catalog_path = os.path.join(root_dir, \"assocation_a_b\")\n os.makedirs(association_catalog_path, exist_ok=True)\n partition_info = PartitionJoinInfo(alignment_df)\n partition_info.write_to_csv(association_catalog_path)\n \n return (catalog_path_a, catalog_path_b, association_catalog_path)", "min_run_count": 2, "name": "benchmarks.MetadataSuite.time_load_partition_join_info", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "benchmarks:62", "type": "time", "unit": "seconds", "version": "b1ee35389cc35c47c97e4b59c1a68b11f92f1d4cc9db3bcd32338400177c72e9", "warmup_time": -1}, "benchmarks.Suite.time_pixel_tree_creation": {"code": "class Suite:\n def time_pixel_tree_creation(self):\n PixelTreeBuilder.from_healpix(self.pixel_list)\n\n def setup(self):\n self.pixel_list = [HealpixPixel(8, pixel) for pixel in np.arange(100000)]", "min_run_count": 2, "name": "benchmarks.Suite.time_pixel_tree_creation", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "b253603a95d873d4a9a8bdf0fd8e8845cfc04dd474b6a5f8f4ae10540eb19006", "warmup_time": -1}, "benchmarks.time_test_alignment_even_sky": {"code": "def time_test_alignment_even_sky():\n \"\"\"Create alignment from an even distribution at order 7\"\"\"\n initial_histogram = np.full(hp.order2npix(7), 40)\n result = hist.generate_alignment(initial_histogram, highest_order=7, threshold=1_000)\n # everything maps to order 5, given the density\n for mapping in result:\n assert mapping[0] == 5", "min_run_count": 2, "name": "benchmarks.time_test_alignment_even_sky", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "c4d4cd79963d85a8084022a7439520a28a2bd2b3f63e581aeb1604b2d4f3d2ac", "warmup_time": -1}, "benchmarks.time_test_cone_filter_multiple_order": {"code": "def time_test_cone_filter_multiple_order():\n \"\"\"Create a catalog cone filter where we have multiple orders in the catalog\"\"\"\n catalog_info = CatalogInfo(\n **{\n \"catalog_name\": \"test_name\",\n \"catalog_type\": \"object\",\n \"total_rows\": 10,\n \"epoch\": \"J2000\",\n \"ra_column\": \"ra\",\n \"dec_column\": \"dec\",\n }\n )\n pixels = [HealpixPixel(6, 30), HealpixPixel(7, 124), HealpixPixel(7, 5000)]\n catalog = Catalog(catalog_info, pixels)\n filtered_catalog = catalog.filter_by_cone(47.1, 6, 30 * 3600)\n assert filtered_catalog.get_healpix_pixels() == [HealpixPixel(6, 30), HealpixPixel(7, 124)]", "min_run_count": 2, "name": "benchmarks.time_test_cone_filter_multiple_order", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "80938a43a838f21dafc45157b1f98adffe1cdea3613d42f2b8e7ad6041ad0a52", "warmup_time": -1}}, "machines": {"gh-runner": {"machine": "gh-runner", "version": 1}}, "tags": {"0.0.2": 35, "0.0.4": 62, "v0.0.10": 217, "v0.0.11": 238, "v0.0.12": 264, "v0.0.13": 306, "v0.0.14": 306, "v0.0.3": 54, "v0.0.5": 103, "v0.0.6": 138, "v0.0.7": 150, "v0.0.8": 153, "v0.0.9": 199, "v0.1": 306, "v0.1.1": 330, "v0.1.2": 355, "v0.1.3": 364, "v0.1.4": 431, "v0.1.5": 476, "v0.1.6": 495, "v0.1.7": 506, "v0.1.8": 508, "v0.2.0": 522, "v0.2.1": 529, "v0.2.2": 538, "v0.2.3": 544, "v0.2.4": 564, "v0.2.5": 572, "v0.2.6": 585, "v0.2.7": 602, "v0.2.8": 607, "v0.2.9": 614}, "pages": [["", "Grid view", "Display as a agrid"], ["summarylist", "List view", "Display as a list"], ["regressions", "Show regressions", "Display information about recent regressions"]]} \ No newline at end of file +{"project": "hipscat", "project_url": "https://github.com/astronomy-commons/hipscat", "show_commit_url": "https://github.com/astronomy-commons/hipscat/commit", "hash_length": 8, "revision_to_hash": {"0": "75707d8d9990752ef01a997a5699f95c806da559", "9": "8488c6f097e6a13e6fe42e209bc7d1023e1bc7f8", "11": "6bd74733c0eebf9a2275f96fee79f64adc73133b", "13": "05bf21def8039b92607c5b09b393582fc7cca58b", "17": "99f94d7d5eec1c32f3a07fb6de761e822f78a4d8", 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"109": 1679068431000, "116": 1679326195000, "117": 1679337359000, "129": 1679424193000, "130": 1679499392000, "136": 1679581972000, "138": 1679586639000, "141": 1679670194000, "142": 1679678035000, "150": 1680007530000, "151": 1680028901000, "152": 1680098144000, "153": 1680109242000, "155": 1680292343000, "158": 1680902918000, "164": 1681227242000, "165": 1681234027000, "173": 1681240202000, "188": 1681319094000, "192": 1681331929000, "194": 1681336756000, "198": 1681411757000, "199": 1681411793000, "205": 1681752637000, "208": 1681836142000, "216": 1681846497000, "217": 1681852609000, "220": 1682009213000, "232": 1682364840000, "238": 1682528996000, "245": 1685533765000, "247": 1685555616000, "248": 1685560235000, "253": 1685646487000, "255": 1685728690000, "259": 1685972195000, "261": 1686057909000, "264": 1686096828000, "266": 1686170689000, "280": 1687279542000, "284": 1687373753000, "285": 1687477898000, "288": 1688151361000, "300": 1689094987000, "306": 1689181731000, "313": 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1710273099000, "609": 1710453748000, "610": 1710856095000, "613": 1710962339000, "614": 1711034607000, "615": 1711541813000, "617": 1712062780000}, "params": {"machine": ["gh-runner"], "python": ["3.10"], "Cython": [""], "build": [""], "packaging": [""], "branch": ["HEAD"]}, "graph_param_list": [{"machine": "gh-runner", "python": "3.10", "Cython": "", "build": "", "packaging": "", "branch": "HEAD"}], "benchmarks": {"benchmarks.MetadataSuite.time_load_partition_info_order6": {"code": "class MetadataSuite:\n def time_load_partition_info_order6(self, cache):\n PartitionInfo.read_from_dir(cache[1])\n\n def setup_cache(self):\n root_dir = os.getcwd()\n \n ## Create partition info for catalog a (only at order 7)\n pixel_list_a = [HealpixPixel(7, pixel) for pixel in np.arange(100_000)]\n catalog_path_a = os.path.join(root_dir, \"catalog_a\")\n os.makedirs(catalog_path_a, exist_ok=True)\n partition_info = PartitionInfo.from_healpix(pixel_list_a)\n partition_info.write_to_file(os.path.join(catalog_path_a, \"partition_info.csv\"))\n \n ## Create partition info for catalog a (only at order 6)\n pixel_list_b = [HealpixPixel(6, pixel) for pixel in np.arange(25_000)]\n catalog_path_b = os.path.join(root_dir, \"catalog_b\")\n os.makedirs(catalog_path_b, exist_ok=True)\n partition_info = PartitionInfo.from_healpix(pixel_list_b)\n partition_info.write_to_file(os.path.join(catalog_path_b, \"partition_info.csv\"))\n \n ## Fake an association table between the two\n tree_a = PixelTreeBuilder.from_healpix(pixel_list_a)\n tree_b = PixelTreeBuilder.from_healpix(pixel_list_b)\n alignment = align_trees(tree_a, tree_b)\n alignment_df = alignment.pixel_mapping[\n [\n PixelAlignment.PRIMARY_ORDER_COLUMN_NAME,\n PixelAlignment.PRIMARY_PIXEL_COLUMN_NAME,\n PixelAlignment.JOIN_ORDER_COLUMN_NAME,\n PixelAlignment.JOIN_PIXEL_COLUMN_NAME,\n ]\n ]\n alignment_df = alignment_df.rename(\n columns={\n PixelAlignment.PRIMARY_ORDER_COLUMN_NAME: \"Norder\",\n PixelAlignment.PRIMARY_PIXEL_COLUMN_NAME: \"Npix\",\n }\n )\n \n association_catalog_path = os.path.join(root_dir, \"assocation_a_b\")\n os.makedirs(association_catalog_path, exist_ok=True)\n partition_info = PartitionJoinInfo(alignment_df)\n partition_info.write_to_csv(association_catalog_path)\n \n return (catalog_path_a, catalog_path_b, association_catalog_path)", "min_run_count": 2, "name": "benchmarks.MetadataSuite.time_load_partition_info_order6", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "benchmarks:62", "type": "time", "unit": "seconds", "version": "b0aff219fe52d62c8d1712e9bf9fb801ed80d42b6cd7d46e0606affc2916a933", "warmup_time": -1}, "benchmarks.MetadataSuite.time_load_partition_info_order7": {"code": "class MetadataSuite:\n def time_load_partition_info_order7(self, cache):\n PartitionInfo.read_from_dir(cache[0])\n\n def setup_cache(self):\n root_dir = os.getcwd()\n \n ## Create partition info for catalog a (only at order 7)\n pixel_list_a = [HealpixPixel(7, pixel) for pixel in np.arange(100_000)]\n catalog_path_a = os.path.join(root_dir, \"catalog_a\")\n os.makedirs(catalog_path_a, exist_ok=True)\n partition_info = PartitionInfo.from_healpix(pixel_list_a)\n partition_info.write_to_file(os.path.join(catalog_path_a, \"partition_info.csv\"))\n \n ## Create partition info for catalog a (only at order 6)\n pixel_list_b = [HealpixPixel(6, pixel) for pixel in np.arange(25_000)]\n catalog_path_b = os.path.join(root_dir, \"catalog_b\")\n os.makedirs(catalog_path_b, exist_ok=True)\n partition_info = PartitionInfo.from_healpix(pixel_list_b)\n partition_info.write_to_file(os.path.join(catalog_path_b, \"partition_info.csv\"))\n \n ## Fake an association table between the two\n tree_a = PixelTreeBuilder.from_healpix(pixel_list_a)\n tree_b = PixelTreeBuilder.from_healpix(pixel_list_b)\n alignment = align_trees(tree_a, tree_b)\n alignment_df = alignment.pixel_mapping[\n [\n PixelAlignment.PRIMARY_ORDER_COLUMN_NAME,\n PixelAlignment.PRIMARY_PIXEL_COLUMN_NAME,\n PixelAlignment.JOIN_ORDER_COLUMN_NAME,\n PixelAlignment.JOIN_PIXEL_COLUMN_NAME,\n ]\n ]\n alignment_df = alignment_df.rename(\n columns={\n PixelAlignment.PRIMARY_ORDER_COLUMN_NAME: \"Norder\",\n PixelAlignment.PRIMARY_PIXEL_COLUMN_NAME: \"Npix\",\n }\n )\n \n association_catalog_path = os.path.join(root_dir, \"assocation_a_b\")\n os.makedirs(association_catalog_path, exist_ok=True)\n partition_info = PartitionJoinInfo(alignment_df)\n partition_info.write_to_csv(association_catalog_path)\n \n return (catalog_path_a, catalog_path_b, association_catalog_path)", "min_run_count": 2, "name": "benchmarks.MetadataSuite.time_load_partition_info_order7", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "benchmarks:62", "type": "time", "unit": "seconds", "version": "d7da8deb183080047bf91294a4a0b2b964bd81ec0a732c6940f5613f125eb312", "warmup_time": -1}, "benchmarks.MetadataSuite.time_load_partition_join_info": {"code": "class MetadataSuite:\n def time_load_partition_join_info(self, cache):\n PartitionInfo.read_from_dir(cache[2])\n\n def setup_cache(self):\n root_dir = os.getcwd()\n \n ## Create partition info for catalog a (only at order 7)\n pixel_list_a = [HealpixPixel(7, pixel) for pixel in np.arange(100_000)]\n catalog_path_a = os.path.join(root_dir, \"catalog_a\")\n os.makedirs(catalog_path_a, exist_ok=True)\n partition_info = PartitionInfo.from_healpix(pixel_list_a)\n partition_info.write_to_file(os.path.join(catalog_path_a, \"partition_info.csv\"))\n \n ## Create partition info for catalog a (only at order 6)\n pixel_list_b = [HealpixPixel(6, pixel) for pixel in np.arange(25_000)]\n catalog_path_b = os.path.join(root_dir, \"catalog_b\")\n os.makedirs(catalog_path_b, exist_ok=True)\n partition_info = PartitionInfo.from_healpix(pixel_list_b)\n partition_info.write_to_file(os.path.join(catalog_path_b, \"partition_info.csv\"))\n \n ## Fake an association table between the two\n tree_a = PixelTreeBuilder.from_healpix(pixel_list_a)\n tree_b = PixelTreeBuilder.from_healpix(pixel_list_b)\n alignment = align_trees(tree_a, tree_b)\n alignment_df = alignment.pixel_mapping[\n [\n PixelAlignment.PRIMARY_ORDER_COLUMN_NAME,\n PixelAlignment.PRIMARY_PIXEL_COLUMN_NAME,\n PixelAlignment.JOIN_ORDER_COLUMN_NAME,\n PixelAlignment.JOIN_PIXEL_COLUMN_NAME,\n ]\n ]\n alignment_df = alignment_df.rename(\n columns={\n PixelAlignment.PRIMARY_ORDER_COLUMN_NAME: \"Norder\",\n PixelAlignment.PRIMARY_PIXEL_COLUMN_NAME: \"Npix\",\n }\n )\n \n association_catalog_path = os.path.join(root_dir, \"assocation_a_b\")\n os.makedirs(association_catalog_path, exist_ok=True)\n partition_info = PartitionJoinInfo(alignment_df)\n partition_info.write_to_csv(association_catalog_path)\n \n return (catalog_path_a, catalog_path_b, association_catalog_path)", "min_run_count": 2, "name": "benchmarks.MetadataSuite.time_load_partition_join_info", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "benchmarks:62", "type": "time", "unit": "seconds", "version": "b1ee35389cc35c47c97e4b59c1a68b11f92f1d4cc9db3bcd32338400177c72e9", "warmup_time": -1}, "benchmarks.Suite.time_pixel_tree_creation": {"code": "class Suite:\n def time_pixel_tree_creation(self):\n PixelTreeBuilder.from_healpix(self.pixel_list)\n\n def setup(self):\n self.pixel_list = [HealpixPixel(8, pixel) for pixel in np.arange(100000)]", "min_run_count": 2, "name": "benchmarks.Suite.time_pixel_tree_creation", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "b253603a95d873d4a9a8bdf0fd8e8845cfc04dd474b6a5f8f4ae10540eb19006", "warmup_time": -1}, "benchmarks.time_test_alignment_even_sky": {"code": "def time_test_alignment_even_sky():\n \"\"\"Create alignment from an even distribution at order 7\"\"\"\n initial_histogram = np.full(hp.order2npix(7), 40)\n result = hist.generate_alignment(initial_histogram, highest_order=7, threshold=1_000)\n # everything maps to order 5, given the density\n for mapping in result:\n assert mapping[0] == 5", "min_run_count": 2, "name": "benchmarks.time_test_alignment_even_sky", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "c4d4cd79963d85a8084022a7439520a28a2bd2b3f63e581aeb1604b2d4f3d2ac", "warmup_time": -1}, "benchmarks.time_test_cone_filter_multiple_order": {"code": "def time_test_cone_filter_multiple_order():\n \"\"\"Create a catalog cone filter where we have multiple orders in the catalog\"\"\"\n catalog_info = CatalogInfo(\n **{\n \"catalog_name\": \"test_name\",\n \"catalog_type\": \"object\",\n \"total_rows\": 10,\n \"epoch\": \"J2000\",\n \"ra_column\": \"ra\",\n \"dec_column\": \"dec\",\n }\n )\n pixels = [HealpixPixel(6, 30), HealpixPixel(7, 124), HealpixPixel(7, 5000)]\n catalog = Catalog(catalog_info, pixels)\n filtered_catalog = catalog.filter_by_cone(47.1, 6, 30 * 3600)\n assert filtered_catalog.get_healpix_pixels() == [HealpixPixel(6, 30), HealpixPixel(7, 124)]", "min_run_count": 2, "name": "benchmarks.time_test_cone_filter_multiple_order", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "80938a43a838f21dafc45157b1f98adffe1cdea3613d42f2b8e7ad6041ad0a52", "warmup_time": -1}}, "machines": {"gh-runner": {"machine": "gh-runner", "version": 1}}, "tags": {"0.0.2": 35, "0.0.4": 62, "v0.0.10": 217, "v0.0.11": 238, "v0.0.12": 264, "v0.0.13": 306, "v0.0.14": 306, "v0.0.3": 54, "v0.0.5": 103, "v0.0.6": 138, "v0.0.7": 150, "v0.0.8": 153, "v0.0.9": 199, "v0.1": 306, "v0.1.1": 330, "v0.1.2": 355, "v0.1.3": 364, "v0.1.4": 431, "v0.1.5": 476, "v0.1.6": 495, "v0.1.7": 506, "v0.1.8": 508, "v0.2.0": 522, "v0.2.1": 529, "v0.2.2": 538, "v0.2.3": 544, "v0.2.4": 564, "v0.2.5": 572, "v0.2.6": 585, "v0.2.7": 602, "v0.2.8": 607, "v0.2.9": 614}, "pages": [["", "Grid view", "Display as a agrid"], ["summarylist", "List view", "Display as a list"], ["regressions", "Show regressions", "Display information about recent regressions"]]} \ No newline at end of file diff --git a/info.json b/info.json index 20a41faa..89c1e934 100644 --- a/info.json +++ b/info.json @@ -1,4 +1,4 @@ { "asv-version": "0.6.1", - "timestamp": 1711541973530 + "timestamp": 1712062926480 } \ No newline at end of file diff --git a/regressions.xml b/regressions.xml index 5254627e..1262bbfb 100644 --- a/regressions.xml +++ b/regressions.xml @@ -1,2 +1,2 @@ -tag:hipscat.asv,1970-01-01:/eae0f09a111aec35b2b6cf0865b5d2d7b3fbc2e0a70ab64f42c3be218c62f12aAirspeed Velocityhipscat performance regressions2024-03-27T12:19:33Z \ No newline at end of file +tag:hipscat.asv,1970-01-01:/eae0f09a111aec35b2b6cf0865b5d2d7b3fbc2e0a70ab64f42c3be218c62f12aAirspeed Velocityhipscat performance regressions2024-04-02T13:02:06Z \ No newline at end of file