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1720556946000, "193": 1721231912000, "197": 1722030850000, "198": 1722030931000, "204": 1723155498000, "207": 1723230708000, "208": 1723237398000, "212": 1723568086000, "218": 1724096325000, "222": 1724172655000}, "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.AssignSingleDfToNestedSeries.peakmem_run": {"code": "class AssignSingleDfToNestedSeries:\n def peakmem_run(self):\n \"\"\"Benchmark the memory usage of changing a single nested series element.\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment.\"\"\"\n self.new_df = pd.DataFrame(\n {\n \"time\": np.arange(self.n_sources, dtype=np.float64),\n \"flux\": np.linspace(0, 1, self.n_sources),\n \"band\": np.full_like(\"lsstg\", self.n_sources),\n }\n )\n original_df = pd.DataFrame(\n {\n \"time\": np.linspace(0, 1, self.n_sources),\n \"flux\": np.arange(self.n_sources, dtype=np.float64),\n \"band\": np.full_like(\"sdssu\", self.n_sources),\n }\n )\n self.series = pd.Series(\n [original_df] * self.n_objects,\n # When we had NestedExtentionArray inheriting ArrowExtentionArray, it sorted the fields, so we\n # need to order by field name here for backwards compatibility.\n dtype=NestedDtype.from_fields({\"band\": pa.string(), \"flux\": pa.float64(), \"time\": pa.float64()}),\n )", "name": "benchmarks.AssignSingleDfToNestedSeries.peakmem_run", "param_names": [], "params": [], "type": "peakmemory", "unit": "bytes", "version": "6106a8befbead9252b00aef6a759a13bb8654dc82f15c86b5bb6da7262f4571c"}, "benchmarks.AssignSingleDfToNestedSeries.time_run": {"code": "class AssignSingleDfToNestedSeries:\n def time_run(self):\n \"\"\"Benchmark the runtime of changing a single nested series element.\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment.\"\"\"\n self.new_df = pd.DataFrame(\n {\n \"time\": np.arange(self.n_sources, dtype=np.float64),\n \"flux\": np.linspace(0, 1, self.n_sources),\n \"band\": np.full_like(\"lsstg\", self.n_sources),\n }\n )\n original_df = pd.DataFrame(\n {\n \"time\": np.linspace(0, 1, self.n_sources),\n \"flux\": np.arange(self.n_sources, dtype=np.float64),\n \"band\": np.full_like(\"sdssu\", self.n_sources),\n }\n )\n self.series = pd.Series(\n [original_df] * self.n_objects,\n # When we had NestedExtentionArray inheriting ArrowExtentionArray, it sorted the fields, so we\n # need to order by field name here for backwards compatibility.\n dtype=NestedDtype.from_fields({\"band\": pa.string(), \"flux\": pa.float64(), \"time\": pa.float64()}),\n )", "min_run_count": 2, "name": "benchmarks.AssignSingleDfToNestedSeries.time_run", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "561679277ad2275813ce0c17ad97dbe31f41b12cdb6533009f12ae1f7b057caf", "warmup_time": -1}, "benchmarks.NestedFrameAddNested.peakmem_run": {"code": "class NestedFrameAddNested:\n def peakmem_run(self):\n \"\"\"Benchmark the memory usage of adding a nested layer\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n # use provided seed, \"None\" acts as if no seed is provided\n randomstate = np.random.RandomState(seed=1)\n \n # Generate base data\n base_data = {\"a\": randomstate.random(self.n_base), \"b\": randomstate.random(self.n_base) * 2}\n self.base_nf = NestedFrame(data=base_data)\n \n layer_data = {\n \"t\": randomstate.random(self.layer_size * self.n_base) * 20,\n \"flux\": randomstate.random(self.layer_size * self.n_base) * 100,\n \"band\": randomstate.choice([\"r\", \"g\"], size=self.layer_size * self.n_base),\n \"index\": np.arange(self.layer_size * self.n_base) % self.n_base,\n }\n self.layer_nf = NestedFrame(data=layer_data).set_index(\"index\")", "name": "benchmarks.NestedFrameAddNested.peakmem_run", "param_names": [], "params": [], "type": "peakmemory", "unit": "bytes", "version": "7222660c05e164eeabe42a9ea1f18045af2e407dd1e1bb788010eaad7294ba85"}, "benchmarks.NestedFrameAddNested.time_run": {"code": "class NestedFrameAddNested:\n def time_run(self):\n \"\"\"Benchmark the runtime of adding a nested layer\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n # use provided seed, \"None\" acts as if no seed is provided\n randomstate = np.random.RandomState(seed=1)\n \n # Generate base data\n base_data = {\"a\": randomstate.random(self.n_base), \"b\": randomstate.random(self.n_base) * 2}\n self.base_nf = NestedFrame(data=base_data)\n \n layer_data = {\n \"t\": randomstate.random(self.layer_size * self.n_base) * 20,\n \"flux\": randomstate.random(self.layer_size * self.n_base) * 100,\n \"band\": randomstate.choice([\"r\", \"g\"], size=self.layer_size * self.n_base),\n \"index\": np.arange(self.layer_size * self.n_base) % self.n_base,\n }\n self.layer_nf = NestedFrame(data=layer_data).set_index(\"index\")", "min_run_count": 2, "name": "benchmarks.NestedFrameAddNested.time_run", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "51982ce9982d11255339dc387081a0db496f1856294c479e72d65e2a4f5fb506", "warmup_time": -1}, "benchmarks.NestedFrameQuery.peakmem_run": {"code": "class NestedFrameQuery:\n def peakmem_run(self):\n \"\"\"Benchmark the memory usage of applying the two queries\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n self.nf = datasets.generate_data(self.n_base, self.n_nested)", "name": "benchmarks.NestedFrameQuery.peakmem_run", "param_names": [], "params": [], "type": "peakmemory", "unit": "bytes", "version": "cdc2254d41009fb5e493b99ed753b9d677a385388103705b16f1b486c03217c2"}, "benchmarks.NestedFrameQuery.time_run": {"code": "class NestedFrameQuery:\n def time_run(self):\n \"\"\"Benchmark the runtime of applying the two queries\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n self.nf = datasets.generate_data(self.n_base, self.n_nested)", "min_run_count": 2, "name": "benchmarks.NestedFrameQuery.time_run", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "e9944ed6698261a36de55f71c8228b4fcaa8a50b56657fd8132fc6488238f484", "warmup_time": -1}, "benchmarks.NestedFrameReduce.peakmem_run": {"code": "class NestedFrameReduce:\n def peakmem_run(self):\n \"\"\"Benchmark the memory usage of applying the reduce function\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n self.nf = datasets.generate_data(self.n_base, self.n_nested)", "name": "benchmarks.NestedFrameReduce.peakmem_run", "param_names": [], "params": [], "type": "peakmemory", "unit": "bytes", "version": "0dc3aac5f09ea8525a745669cff6fca764fabb7cbe35db18f7d46f7377bfea12"}, "benchmarks.NestedFrameReduce.time_run": {"code": "class NestedFrameReduce:\n def time_run(self):\n \"\"\"Benchmark the runtime of applying the reduce function\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n self.nf = datasets.generate_data(self.n_base, self.n_nested)", "min_run_count": 2, "name": "benchmarks.NestedFrameReduce.time_run", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "5259b45d6e432e2b3eb25050a9581ac63703fbbae1e1fd98cc2fe7b83a9e8b95", "warmup_time": -1}, "benchmarks.ReassignHalfOfNestedSeries.peakmem_run": {"code": "class ReassignHalfOfNestedSeries:\n def peakmem_run(self):\n \"\"\"Benchmark the memory usage of changing a single nested series element.\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment.\"\"\"\n # When we had NestedExtentionArray inheriting ArrowExtentionArray, it sorted the fields, so we need to\n # order by field name here for backwards compatibility.\n dtype = NestedDtype.from_fields({\"band\": pa.string(), \"flux\": pa.float64(), \"time\": pa.float64()})\n original_df = pd.DataFrame(\n {\n \"time\": np.linspace(0, 1, self.n_sources),\n \"flux\": np.arange(self.n_sources, dtype=np.float64),\n \"band\": np.full_like(\"sdssu\", self.n_sources),\n }\n )\n self.series = pd.Series(\n [original_df] * self.n_objects,\n dtype=dtype,\n )\n \n new_df = pd.DataFrame(\n {\n \"time\": np.arange(self.n_sources, dtype=np.float64),\n \"flux\": np.linspace(0, 1, self.n_sources),\n \"band\": np.full_like(\"lsstg\", self.n_sources),\n }\n )\n self.new_series = pd.Series([new_df] * (self.n_objects // 2), dtype=dtype)", "name": "benchmarks.ReassignHalfOfNestedSeries.peakmem_run", "param_names": [], "params": [], "type": "peakmemory", "unit": "bytes", "version": "567fe640051453cd164d11ee74a8aff8c06e2b89a239133ddcc020d9fe426a55"}, "benchmarks.ReassignHalfOfNestedSeries.time_run": {"code": "class ReassignHalfOfNestedSeries:\n def time_run(self):\n \"\"\"Benchmark the runtime of changing a single nested series element.\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment.\"\"\"\n # When we had NestedExtentionArray inheriting ArrowExtentionArray, it sorted the fields, so we need to\n # order by field name here for backwards compatibility.\n dtype = NestedDtype.from_fields({\"band\": pa.string(), \"flux\": pa.float64(), \"time\": pa.float64()})\n original_df = pd.DataFrame(\n {\n \"time\": np.linspace(0, 1, self.n_sources),\n \"flux\": np.arange(self.n_sources, dtype=np.float64),\n \"band\": np.full_like(\"sdssu\", self.n_sources),\n }\n )\n self.series = pd.Series(\n [original_df] * self.n_objects,\n dtype=dtype,\n )\n \n new_df = pd.DataFrame(\n {\n \"time\": np.arange(self.n_sources, dtype=np.float64),\n \"flux\": np.linspace(0, 1, self.n_sources),\n \"band\": np.full_like(\"lsstg\", self.n_sources),\n }\n )\n self.new_series = pd.Series([new_df] * (self.n_objects // 2), dtype=dtype)", "min_run_count": 2, "name": "benchmarks.ReassignHalfOfNestedSeries.time_run", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "9d18ab926df92e4580e7dcbb3760d4206342c672b1e879d7e5a0a3542904b9e4", "warmup_time": -1}}, "machines": {"gh-runner": {"machine": "gh-runner", "version": 1}}, "tags": {"v0.1": 142, "v0.1.1": 165, "v0.1.2": 188, "v0.1.3": 198, "v0.2.0": 218}, "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": "nested-pandas", "project_url": "https://github.com/lincc-frameworks/nested-pandas", "show_commit_url": "https://github.com/lincc-frameworks/nested-pandas/commit/", "hash_length": 8, "revision_to_hash": {"0": "70b520ee45f14a642519b413399edc584795095d", "1": "d8ea9bda0fcb5d729f3ca55b91df33652a409323", "3": "48c6798c90b202dfd3eb325b2e56ac972ab3219a", "4": "bc320bce49750cd3fcb6278e0a060ed57e62bd77", "6": "b78b3b61ce9619716dc74f50bd2dc9994f12123c", "10": "05cc195f6037872a3ac0d49081d89b7374f5cb41", "16": "a1d41363e2f60a4689d512e9d7965b526946d551", "18": "dfb9139c39c4b5c695a8e296098ccfb2f9bc2ec5", "20": "9ca3810ef2729f910f999bd88dd15003f1bf7021", "21": "e04f558b879a6bfd9ae916691fb15fb5f3df8666", "22": "70afe87150f7221c858c90f4125398e4e502f8fc", "28": "00be4642a5dbe446ab85f018fe64ead72bf190a2", "33": "136f2c50571728c795c6e8a08d0df4036353f4d6", "39": "e43b187addfaa16b89d974bee67b59d61ed4017d", "40": "b822a14532f190820039018fc9165af45bd5ec4f", "50": "d63cee9f605c04ae9d8b3c512f4eb98a9fbbe9dd", "53": "39acb912b9fcb12cbcc77e3726ae059e5cd2a7b8", "55": "e5ca3df612c3945db77d580e19e93a1078c62cec", "56": 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1717439852000, "173": 1717706069000, "177": 1718047221000, "181": 1718125788000, "184": 1718651718000, "188": 1720556946000, "193": 1721231912000, "197": 1722030850000, "198": 1722030931000, "204": 1723155498000, "207": 1723230708000, "208": 1723237398000, "212": 1723568086000, "218": 1724096325000, "221": 1724172655000, "224": 1725649586000}, "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.AssignSingleDfToNestedSeries.peakmem_run": {"code": "class AssignSingleDfToNestedSeries:\n def peakmem_run(self):\n \"\"\"Benchmark the memory usage of changing a single nested series element.\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment.\"\"\"\n self.new_df = pd.DataFrame(\n {\n \"time\": np.arange(self.n_sources, dtype=np.float64),\n \"flux\": np.linspace(0, 1, self.n_sources),\n \"band\": np.full_like(\"lsstg\", self.n_sources),\n }\n )\n original_df = pd.DataFrame(\n {\n \"time\": np.linspace(0, 1, self.n_sources),\n \"flux\": np.arange(self.n_sources, dtype=np.float64),\n \"band\": np.full_like(\"sdssu\", self.n_sources),\n }\n )\n self.series = pd.Series(\n [original_df] * self.n_objects,\n # When we had NestedExtentionArray inheriting ArrowExtentionArray, it sorted the fields, so we\n # need to order by field name here for backwards compatibility.\n dtype=NestedDtype.from_fields({\"band\": pa.string(), \"flux\": pa.float64(), \"time\": pa.float64()}),\n )", "name": "benchmarks.AssignSingleDfToNestedSeries.peakmem_run", "param_names": [], "params": [], "type": "peakmemory", "unit": "bytes", "version": "6106a8befbead9252b00aef6a759a13bb8654dc82f15c86b5bb6da7262f4571c"}, "benchmarks.AssignSingleDfToNestedSeries.time_run": {"code": "class AssignSingleDfToNestedSeries:\n def time_run(self):\n \"\"\"Benchmark the runtime of changing a single nested series element.\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment.\"\"\"\n self.new_df = pd.DataFrame(\n {\n \"time\": np.arange(self.n_sources, dtype=np.float64),\n \"flux\": np.linspace(0, 1, self.n_sources),\n \"band\": np.full_like(\"lsstg\", self.n_sources),\n }\n )\n original_df = pd.DataFrame(\n {\n \"time\": np.linspace(0, 1, self.n_sources),\n \"flux\": np.arange(self.n_sources, dtype=np.float64),\n \"band\": np.full_like(\"sdssu\", self.n_sources),\n }\n )\n self.series = pd.Series(\n [original_df] * self.n_objects,\n # When we had NestedExtentionArray inheriting ArrowExtentionArray, it sorted the fields, so we\n # need to order by field name here for backwards compatibility.\n dtype=NestedDtype.from_fields({\"band\": pa.string(), \"flux\": pa.float64(), \"time\": pa.float64()}),\n )", "min_run_count": 2, "name": "benchmarks.AssignSingleDfToNestedSeries.time_run", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "561679277ad2275813ce0c17ad97dbe31f41b12cdb6533009f12ae1f7b057caf", "warmup_time": -1}, "benchmarks.NestedFrameAddNested.peakmem_run": {"code": "class NestedFrameAddNested:\n def peakmem_run(self):\n \"\"\"Benchmark the memory usage of adding a nested layer\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n # use provided seed, \"None\" acts as if no seed is provided\n randomstate = np.random.RandomState(seed=1)\n \n # Generate base data\n base_data = {\"a\": randomstate.random(self.n_base), \"b\": randomstate.random(self.n_base) * 2}\n self.base_nf = NestedFrame(data=base_data)\n \n layer_data = {\n \"t\": randomstate.random(self.layer_size * self.n_base) * 20,\n \"flux\": randomstate.random(self.layer_size * self.n_base) * 100,\n \"band\": randomstate.choice([\"r\", \"g\"], size=self.layer_size * self.n_base),\n \"index\": np.arange(self.layer_size * self.n_base) % self.n_base,\n }\n self.layer_nf = NestedFrame(data=layer_data).set_index(\"index\")", "name": "benchmarks.NestedFrameAddNested.peakmem_run", "param_names": [], "params": [], "type": "peakmemory", "unit": "bytes", "version": "7222660c05e164eeabe42a9ea1f18045af2e407dd1e1bb788010eaad7294ba85"}, "benchmarks.NestedFrameAddNested.time_run": {"code": "class NestedFrameAddNested:\n def time_run(self):\n \"\"\"Benchmark the runtime of adding a nested layer\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n # use provided seed, \"None\" acts as if no seed is provided\n randomstate = np.random.RandomState(seed=1)\n \n # Generate base data\n base_data = {\"a\": randomstate.random(self.n_base), \"b\": randomstate.random(self.n_base) * 2}\n self.base_nf = NestedFrame(data=base_data)\n \n layer_data = {\n \"t\": randomstate.random(self.layer_size * self.n_base) * 20,\n \"flux\": randomstate.random(self.layer_size * self.n_base) * 100,\n \"band\": randomstate.choice([\"r\", \"g\"], size=self.layer_size * self.n_base),\n \"index\": np.arange(self.layer_size * self.n_base) % self.n_base,\n }\n self.layer_nf = NestedFrame(data=layer_data).set_index(\"index\")", "min_run_count": 2, "name": "benchmarks.NestedFrameAddNested.time_run", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "51982ce9982d11255339dc387081a0db496f1856294c479e72d65e2a4f5fb506", "warmup_time": -1}, "benchmarks.NestedFrameQuery.peakmem_run": {"code": "class NestedFrameQuery:\n def peakmem_run(self):\n \"\"\"Benchmark the memory usage of applying the two queries\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n self.nf = datasets.generate_data(self.n_base, self.n_nested)", "name": "benchmarks.NestedFrameQuery.peakmem_run", "param_names": [], "params": [], "type": "peakmemory", "unit": "bytes", "version": "cdc2254d41009fb5e493b99ed753b9d677a385388103705b16f1b486c03217c2"}, "benchmarks.NestedFrameQuery.time_run": {"code": "class NestedFrameQuery:\n def time_run(self):\n \"\"\"Benchmark the runtime of applying the two queries\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n self.nf = datasets.generate_data(self.n_base, self.n_nested)", "min_run_count": 2, "name": "benchmarks.NestedFrameQuery.time_run", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "e9944ed6698261a36de55f71c8228b4fcaa8a50b56657fd8132fc6488238f484", "warmup_time": -1}, "benchmarks.NestedFrameReduce.peakmem_run": {"code": "class NestedFrameReduce:\n def peakmem_run(self):\n \"\"\"Benchmark the memory usage of applying the reduce function\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n self.nf = datasets.generate_data(self.n_base, self.n_nested)", "name": "benchmarks.NestedFrameReduce.peakmem_run", "param_names": [], "params": [], "type": "peakmemory", "unit": "bytes", "version": "0dc3aac5f09ea8525a745669cff6fca764fabb7cbe35db18f7d46f7377bfea12"}, "benchmarks.NestedFrameReduce.time_run": {"code": "class NestedFrameReduce:\n def time_run(self):\n \"\"\"Benchmark the runtime of applying the reduce function\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n self.nf = datasets.generate_data(self.n_base, self.n_nested)", "min_run_count": 2, "name": "benchmarks.NestedFrameReduce.time_run", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "5259b45d6e432e2b3eb25050a9581ac63703fbbae1e1fd98cc2fe7b83a9e8b95", "warmup_time": -1}, "benchmarks.ReassignHalfOfNestedSeries.peakmem_run": {"code": "class ReassignHalfOfNestedSeries:\n def peakmem_run(self):\n \"\"\"Benchmark the memory usage of changing a single nested series element.\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment.\"\"\"\n # When we had NestedExtentionArray inheriting ArrowExtentionArray, it sorted the fields, so we need to\n # order by field name here for backwards compatibility.\n dtype = NestedDtype.from_fields({\"band\": pa.string(), \"flux\": pa.float64(), \"time\": pa.float64()})\n original_df = pd.DataFrame(\n {\n \"time\": np.linspace(0, 1, self.n_sources),\n \"flux\": np.arange(self.n_sources, dtype=np.float64),\n \"band\": np.full_like(\"sdssu\", self.n_sources),\n }\n )\n self.series = pd.Series(\n [original_df] * self.n_objects,\n dtype=dtype,\n )\n \n new_df = pd.DataFrame(\n {\n \"time\": np.arange(self.n_sources, dtype=np.float64),\n \"flux\": np.linspace(0, 1, self.n_sources),\n \"band\": np.full_like(\"lsstg\", self.n_sources),\n }\n )\n self.new_series = pd.Series([new_df] * (self.n_objects // 2), dtype=dtype)", "name": "benchmarks.ReassignHalfOfNestedSeries.peakmem_run", "param_names": [], "params": [], "type": "peakmemory", "unit": "bytes", "version": "567fe640051453cd164d11ee74a8aff8c06e2b89a239133ddcc020d9fe426a55"}, "benchmarks.ReassignHalfOfNestedSeries.time_run": {"code": "class ReassignHalfOfNestedSeries:\n def time_run(self):\n \"\"\"Benchmark the runtime of changing a single nested series element.\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment.\"\"\"\n # When we had NestedExtentionArray inheriting ArrowExtentionArray, it sorted the fields, so we need to\n # order by field name here for backwards compatibility.\n dtype = NestedDtype.from_fields({\"band\": pa.string(), \"flux\": pa.float64(), \"time\": pa.float64()})\n original_df = pd.DataFrame(\n {\n \"time\": np.linspace(0, 1, self.n_sources),\n \"flux\": np.arange(self.n_sources, dtype=np.float64),\n \"band\": np.full_like(\"sdssu\", self.n_sources),\n }\n )\n self.series = pd.Series(\n [original_df] * self.n_objects,\n dtype=dtype,\n )\n \n new_df = pd.DataFrame(\n {\n \"time\": np.arange(self.n_sources, dtype=np.float64),\n \"flux\": np.linspace(0, 1, self.n_sources),\n \"band\": np.full_like(\"lsstg\", self.n_sources),\n }\n )\n self.new_series = pd.Series([new_df] * (self.n_objects // 2), dtype=dtype)", "min_run_count": 2, "name": "benchmarks.ReassignHalfOfNestedSeries.time_run", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "9d18ab926df92e4580e7dcbb3760d4206342c672b1e879d7e5a0a3542904b9e4", "warmup_time": -1}}, "machines": {"gh-runner": {"machine": "gh-runner", "version": 1}}, "tags": {"v0.1": 142, "v0.1.1": 165, "v0.1.2": 188, "v0.1.3": 198, "v0.2.0": 218, "v0.2.1": 221}, "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 95cfed1..8d7c466 100644 --- a/info.json +++ b/info.json @@ -1,4 +1,4 @@ { "asv-version": "0.6.1", - "timestamp": 1724172741480 + "timestamp": 1725649683332 } \ No newline at end of file diff --git a/regressions.json b/regressions.json index df2771c..b863f1d 100644 --- a/regressions.json +++ b/regressions.json @@ -1 +1 @@ -{"regressions": [["benchmarks.AssignSingleDfToNestedSeries.time_run", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.AssignSingleDfToNestedSeries.time_run.json", {}, null, 0.03253270349999582, 0.006260144666669021, [[null, 78, 0.006260144666669021, 0.03253270349999582]]], ["benchmarks.AssignSingleDfToNestedSeries.peakmem_run", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.AssignSingleDfToNestedSeries.peakmem_run.json", {}, null, 257396736.0, 215633920.0, [[null, 78, 215633920.0, 257396736.0]]]]} \ No newline at end of file +{"regressions": [["benchmarks.AssignSingleDfToNestedSeries.time_run", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.AssignSingleDfToNestedSeries.time_run.json", {}, null, 0.03253270349999582, 0.006260144666669021, [[null, 78, 0.006260144666669021, 0.03253270349999582]]], ["benchmarks.AssignSingleDfToNestedSeries.peakmem_run", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.AssignSingleDfToNestedSeries.peakmem_run.json", {}, null, 257411072.0, 215633920.0, [[null, 78, 215633920.0, 257411072.0]]]]} \ No newline at end of file