From 3434af44be1a8484bd969572f6493fbf9518a981 Mon Sep 17 00:00:00 2001 From: camposandro Date: Thu, 21 Nov 2024 16:34:12 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20astronom?= =?UTF-8?q?y-commons/hats@635816fedf109b5ecf04f3e112fe2742b2202df4=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 +- .../benchmarks.Suite.time_inner_pixel_alignment.json | 2 +- .../benchmarks.Suite.time_outer_pixel_alignment.json | 2 +- .../python-3.10/benchmarks.Suite.time_paths_creation.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 +- ...enchmarks.MetadataSuite.time_load_partition_info_order7.json | 2 +- .../benchmarks.MetadataSuite.time_load_partition_join_info.json | 2 +- graphs/summary/benchmarks.Suite.time_inner_pixel_alignment.json | 2 +- graphs/summary/benchmarks.Suite.time_outer_pixel_alignment.json | 2 +- graphs/summary/benchmarks.Suite.time_paths_creation.json | 2 +- graphs/summary/benchmarks.Suite.time_pixel_tree_creation.json | 2 +- graphs/summary/benchmarks.time_test_alignment_even_sky.json | 2 +- .../benchmarks.time_test_cone_filter_multiple_order.json | 2 +- index.json | 2 +- info.json | 2 +- regressions.json | 2 +- 22 files changed, 22 insertions(+), 22 deletions(-) diff --git a/graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.MetadataSuite.time_load_partition_info_order6.json 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1710856095000, "613": 1710962339000, "614": 1711034607000, "626": 1711541813000, "628": 1712062780000, "634": 1712251497000, "637": 1712252270000, "638": 1712252779000, "651": 1712858741000, "653": 1712863675000, "654": 1712864328000, "657": 1712951644000, "661": 1713373719000, "668": 1713387652000, "669": 1713610175000, "673": 1714564303000, "675": 1714584922000, "677": 1714598787000, "679": 1714675772000, "680": 1714763247000, "688": 1715186696000, "691": 1715196443000, "695": 1715635344000, "696": 1716310934000, "697": 1716312556000, "709": 1716582371000, "711": 1716904416000, "712": 1716993472000, "713": 1717168746000, "717": 1717507460000, "718": 1717699601000, "719": 1718043434000, "720": 1718052056000, "721": 1718114583000, "725": 1718210755000, "726": 1718220018000, "727": 1718639495000, "732": 1719000101000, "734": 1720826684000, "735": 1721058074000, "736": 1721058090000, "737": 1721329153000, "738": 1721412874000, "739": 1721661030000, "744": 1721931051000, "745": 1722016076000, "746": 1722026178000, "749": 1722429827000, "750": 1723592522000, "751": 1723639545000, "754": 1724357254000, "755": 1725370604000, "756": 1726146328000, "757": 1726156432000, "758": 1726243224000, "759": 1726499494000, "760": 1729087283000, "763": 1729173334000, "767": 1729177797000, "769": 1729178583000, "770": 1729183606000, "771": 1729284782000, "772": 1729606385000, "773": 1729622364000, "774": 1729623544000, "775": 1729686726000, "776": 1729706601000, "801": 1729871739000, "802": 1729878040000, "803": 1729878070000, "804": 1730119698000, "807": 1730224350000, "808": 1730394149000, "809": 1730398100000, "810": 1730473652000, "811": 1730483781000, "817": 1730495104000, "822": 1730496550000, "823": 1731596006000, "824": 1731596326000, "826": 1731602039000}, "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 = PixelTree.from_healpix(pixel_list_a)\n tree_b = PixelTree.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:76", "type": "time", "unit": "seconds", "version": "e431b00caf270712fec98bcb3b4b887c36579beca1a9d9cdfbd9571287b975a9", "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 = PixelTree.from_healpix(pixel_list_a)\n tree_b = PixelTree.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:76", "type": "time", "unit": "seconds", "version": "11e7edc14a82f8c063938abf0cca705b6ececf3f9613d27d104ca724895f7c63", "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 = PixelTree.from_healpix(pixel_list_a)\n tree_b = PixelTree.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:76", "type": "time", "unit": "seconds", "version": "c769403a57ec6b1a91c60ffcb9686bf1549195bc2329138d8d69e193dc6a57fd", "warmup_time": -1}, "benchmarks.Suite.time_inner_pixel_alignment": {"code": "class Suite:\n def time_inner_pixel_alignment(self):\n align_trees(self.pixel_tree_1, self.pixel_tree_2)\n\n def setup(self):\n self.pixel_list = [HealpixPixel(8, pixel) for pixel in np.arange(100000)]\n self.pixel_tree_1 = PixelTree.from_healpix(self.pixel_list)\n self.pixel_tree_2 = PixelTree.from_healpix(\n [HealpixPixel(9, pixel) for pixel in np.arange(0, 400000, 4)]\n )", "min_run_count": 2, "name": "benchmarks.Suite.time_inner_pixel_alignment", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "431c5363f39da36f5e31e36d66778137e224f54fb639625a51f62cb4c21d0a44", "warmup_time": -1}, "benchmarks.Suite.time_outer_pixel_alignment": {"code": "class Suite:\n def time_outer_pixel_alignment(self):\n align_trees(self.pixel_tree_1, self.pixel_tree_2, alignment_type=\"outer\")\n\n def setup(self):\n self.pixel_list = [HealpixPixel(8, pixel) for pixel in np.arange(100000)]\n self.pixel_tree_1 = PixelTree.from_healpix(self.pixel_list)\n self.pixel_tree_2 = PixelTree.from_healpix(\n [HealpixPixel(9, pixel) for pixel in np.arange(0, 400000, 4)]\n )", "min_run_count": 2, "name": "benchmarks.Suite.time_outer_pixel_alignment", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "dcc2b55115eedf5805bcb52c36ae0f1f9d3b837e7513b05408ecbc0197b37617", "warmup_time": -1}, "benchmarks.Suite.time_paths_creation": {"code": "class Suite:\n def time_paths_creation(self):\n pixel_catalog_files(\"foo/\", self.pixel_list)\n\n def setup(self):\n self.pixel_list = [HealpixPixel(8, pixel) for pixel in np.arange(100000)]\n self.pixel_tree_1 = PixelTree.from_healpix(self.pixel_list)\n self.pixel_tree_2 = PixelTree.from_healpix(\n [HealpixPixel(9, pixel) for pixel in np.arange(0, 400000, 4)]\n )", "min_run_count": 2, "name": "benchmarks.Suite.time_paths_creation", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "f02f2a47238716628723a290591daaf89362aeb94beace61f47c818ff2d95f1b", "warmup_time": -1}, "benchmarks.Suite.time_pixel_tree_creation": {"code": "class Suite:\n def time_pixel_tree_creation(self):\n PixelTree.from_healpix(self.pixel_list)\n\n def setup(self):\n self.pixel_list = [HealpixPixel(8, pixel) for pixel in np.arange(100000)]\n self.pixel_tree_1 = PixelTree.from_healpix(self.pixel_list)\n self.pixel_tree_2 = PixelTree.from_healpix(\n [HealpixPixel(9, pixel) for pixel in np.arange(0, 400000, 4)]\n )", "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": "76c0cb3b7af2250f83e1c2776004130b91d1ce71867e9fcff69c89068bf13bf9", "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 = TableProperties(\n **{\n \"catalog_name\": \"test_name\",\n \"catalog_type\": \"object\",\n \"total_rows\": 10,\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": "ada9f87a0abf9c0a0b0f512158bb21251f2577b5cf4a131246b2b3c4c1e67a2e", "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, "v0.2.10": 638, "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, "v0.3.0": 669, "v0.3.1": 688, "v0.3.2": 695, "v0.3.3": 697, "v0.3.4": 713, "v0.3.5": 726, "v0.3.6": 736, "v0.3.7": 739, "v0.3.8": 749, "v0.3.9": 754, "v0.4.0": 760, "v0.4.1": 770, "v0.4.2": 807, "v0.4.3": 822}, "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": "hats", "project_url": "https://github.com/astronomy-commons/hats", "show_commit_url": "https://github.com/astronomy-commons/hats/commit", "hash_length": 8, "revision_to_hash": {"0": "75707d8d9990752ef01a997a5699f95c806da559", "9": "8488c6f097e6a13e6fe42e209bc7d1023e1bc7f8", "11": "6bd74733c0eebf9a2275f96fee79f64adc73133b", "13": "05bf21def8039b92607c5b09b393582fc7cca58b", "17": "99f94d7d5eec1c32f3a07fb6de761e822f78a4d8", "19": "220799b48ac3d5dc8c14df19fca3c5cc7a041704", "30": 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1721661030000, "744": 1721931051000, "745": 1722016076000, "746": 1722026178000, "749": 1722429827000, "750": 1723592522000, "751": 1723639545000, "754": 1724357254000, "755": 1725370604000, "756": 1726146328000, "757": 1726156432000, "758": 1726243224000, "759": 1726499494000, "760": 1729087283000, "763": 1729173334000, "767": 1729177797000, "769": 1729178583000, "770": 1729183606000, "771": 1729284782000, "772": 1729606385000, "773": 1729622364000, "774": 1729623544000, "775": 1729686726000, "776": 1729706601000, "801": 1729871739000, "802": 1729878040000, "803": 1729878070000, "804": 1730119698000, "807": 1730224350000, "808": 1730394149000, "809": 1730398100000, "810": 1730473652000, "811": 1730483781000, "817": 1730495104000, "822": 1730496550000, "823": 1731596006000, "824": 1731596326000, "825": 1731602039000, "827": 1732206690000}, "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 = PixelTree.from_healpix(pixel_list_a)\n tree_b = PixelTree.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:76", "type": "time", "unit": "seconds", "version": "e431b00caf270712fec98bcb3b4b887c36579beca1a9d9cdfbd9571287b975a9", "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 = PixelTree.from_healpix(pixel_list_a)\n tree_b = PixelTree.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:76", "type": "time", "unit": "seconds", "version": "11e7edc14a82f8c063938abf0cca705b6ececf3f9613d27d104ca724895f7c63", "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 = PixelTree.from_healpix(pixel_list_a)\n tree_b = PixelTree.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:76", "type": "time", "unit": "seconds", "version": "c769403a57ec6b1a91c60ffcb9686bf1549195bc2329138d8d69e193dc6a57fd", "warmup_time": -1}, "benchmarks.Suite.time_inner_pixel_alignment": {"code": "class Suite:\n def time_inner_pixel_alignment(self):\n align_trees(self.pixel_tree_1, self.pixel_tree_2)\n\n def setup(self):\n self.pixel_list = [HealpixPixel(8, pixel) for pixel in np.arange(100000)]\n self.pixel_tree_1 = PixelTree.from_healpix(self.pixel_list)\n self.pixel_tree_2 = PixelTree.from_healpix(\n [HealpixPixel(9, pixel) for pixel in np.arange(0, 400000, 4)]\n )", "min_run_count": 2, "name": "benchmarks.Suite.time_inner_pixel_alignment", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "431c5363f39da36f5e31e36d66778137e224f54fb639625a51f62cb4c21d0a44", "warmup_time": -1}, "benchmarks.Suite.time_outer_pixel_alignment": {"code": "class Suite:\n def time_outer_pixel_alignment(self):\n align_trees(self.pixel_tree_1, self.pixel_tree_2, alignment_type=\"outer\")\n\n def setup(self):\n self.pixel_list = [HealpixPixel(8, pixel) for pixel in np.arange(100000)]\n self.pixel_tree_1 = PixelTree.from_healpix(self.pixel_list)\n self.pixel_tree_2 = PixelTree.from_healpix(\n [HealpixPixel(9, pixel) for pixel in np.arange(0, 400000, 4)]\n )", "min_run_count": 2, "name": "benchmarks.Suite.time_outer_pixel_alignment", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "dcc2b55115eedf5805bcb52c36ae0f1f9d3b837e7513b05408ecbc0197b37617", "warmup_time": -1}, "benchmarks.Suite.time_paths_creation": {"code": "class Suite:\n def time_paths_creation(self):\n pixel_catalog_files(\"foo/\", self.pixel_list)\n\n def setup(self):\n self.pixel_list = [HealpixPixel(8, pixel) for pixel in np.arange(100000)]\n self.pixel_tree_1 = PixelTree.from_healpix(self.pixel_list)\n self.pixel_tree_2 = PixelTree.from_healpix(\n [HealpixPixel(9, pixel) for pixel in np.arange(0, 400000, 4)]\n )", "min_run_count": 2, "name": "benchmarks.Suite.time_paths_creation", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "f02f2a47238716628723a290591daaf89362aeb94beace61f47c818ff2d95f1b", "warmup_time": -1}, "benchmarks.Suite.time_pixel_tree_creation": {"code": "class Suite:\n def time_pixel_tree_creation(self):\n PixelTree.from_healpix(self.pixel_list)\n\n def setup(self):\n self.pixel_list = [HealpixPixel(8, pixel) for pixel in np.arange(100000)]\n self.pixel_tree_1 = PixelTree.from_healpix(self.pixel_list)\n self.pixel_tree_2 = PixelTree.from_healpix(\n [HealpixPixel(9, pixel) for pixel in np.arange(0, 400000, 4)]\n )", "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": "76c0cb3b7af2250f83e1c2776004130b91d1ce71867e9fcff69c89068bf13bf9", "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 = TableProperties(\n **{\n \"catalog_name\": \"test_name\",\n \"catalog_type\": \"object\",\n \"total_rows\": 10,\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": "ada9f87a0abf9c0a0b0f512158bb21251f2577b5cf4a131246b2b3c4c1e67a2e", "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, "v0.2.10": 638, "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, "v0.3.0": 669, "v0.3.1": 688, "v0.3.2": 695, "v0.3.3": 697, "v0.3.4": 713, "v0.3.5": 726, "v0.3.6": 736, "v0.3.7": 739, "v0.3.8": 749, "v0.3.9": 754, "v0.4.0": 760, "v0.4.1": 770, "v0.4.2": 807, "v0.4.3": 822}, "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 8f34bb5a..aa1ebb2c 100644 --- a/info.json +++ b/info.json @@ -1,4 +1,4 @@ { "asv-version": "0.6.4", - "timestamp": 1731602191440 + "timestamp": 1732206851535 } \ No newline at end of file diff --git a/regressions.json b/regressions.json index 872007d5..bdeb8baa 100644 --- a/regressions.json +++ b/regressions.json @@ -1 +1 @@ -{"regressions": [["benchmarks.Suite.time_pixel_tree_creation", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.Suite.time_pixel_tree_creation.json", {}, null, 0.04590555300001142, 0.04121811200002412, [[null, 773, 0.04121811200002412, 0.04590555300001142]]], ["benchmarks.MetadataSuite.time_load_partition_join_info", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.MetadataSuite.time_load_partition_join_info.json", {}, null, 0.0940049300000112, 0.07140150799997969, [[null, 775, 0.07140150799997969, 0.0940049300000112]]], ["benchmarks.MetadataSuite.time_load_partition_info_order6", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.MetadataSuite.time_load_partition_info_order6.json", {}, null, 0.022892890500003205, 0.01675608549999197, [[null, 775, 0.01675608549999197, 0.022892890500003205]]]]} \ No newline at end of file +{"regressions": [["benchmarks.MetadataSuite.time_load_partition_info_order6", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.MetadataSuite.time_load_partition_info_order6.json", {}, null, 0.022892890500003205, 0.01675608549999197, [[null, 775, 0.01675608549999197, 0.022892890500003205]]], ["benchmarks.Suite.time_pixel_tree_creation", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.Suite.time_pixel_tree_creation.json", {}, null, 0.04590555300001142, 0.04121811200002412, [[null, 773, 0.04121811200002412, 0.04590555300001142]]], ["benchmarks.MetadataSuite.time_load_partition_join_info", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.MetadataSuite.time_load_partition_join_info.json", {}, null, 0.0940049300000112, 0.07140150799997969, [[null, 775, 0.07140150799997969, 0.0940049300000112]]]]} \ No newline at end of file