From e94f957e069448e172dc5afbbd436be8a793c648 Mon Sep 17 00:00:00 2001 From: hombit Date: Thu, 31 Oct 2024 14:09:49 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20lincc-fr?= =?UTF-8?q?ameworks/nested-pandas@d3796650cb7e1c1f31a388adf1c8c8f235014c1e?= =?UTF-8?q?=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../benchmarks.AssignSingleDfToNestedSeries.peakmem_run.json | 2 +- .../benchmarks.AssignSingleDfToNestedSeries.time_run.json | 2 +- .../benchmarks.NestedFrameAddNested.peakmem_run.json | 2 +- .../python-3.10/benchmarks.NestedFrameAddNested.time_run.json | 2 +- .../python-3.10/benchmarks.NestedFrameQuery.peakmem_run.json | 2 +- .../python-3.10/benchmarks.NestedFrameQuery.time_run.json | 2 +- .../python-3.10/benchmarks.NestedFrameReduce.peakmem_run.json | 2 +- .../python-3.10/benchmarks.NestedFrameReduce.time_run.json | 2 +- .../benchmarks.ReassignHalfOfNestedSeries.peakmem_run.json | 2 +- .../benchmarks.ReassignHalfOfNestedSeries.time_run.json | 2 +- .../machine-gh-runner/packaging/python-3.10/summary.json | 2 +- .../benchmarks.AssignSingleDfToNestedSeries.peakmem_run.json | 2 +- .../benchmarks.AssignSingleDfToNestedSeries.time_run.json | 2 +- .../summary/benchmarks.NestedFrameAddNested.peakmem_run.json | 2 +- graphs/summary/benchmarks.NestedFrameAddNested.time_run.json | 2 +- graphs/summary/benchmarks.NestedFrameQuery.peakmem_run.json | 2 +- graphs/summary/benchmarks.NestedFrameQuery.time_run.json | 2 +- graphs/summary/benchmarks.NestedFrameReduce.peakmem_run.json | 2 +- graphs/summary/benchmarks.NestedFrameReduce.time_run.json | 2 +- .../benchmarks.ReassignHalfOfNestedSeries.peakmem_run.json | 2 +- .../benchmarks.ReassignHalfOfNestedSeries.time_run.json | 2 +- index.json | 2 +- info.json | 2 +- regressions.json | 2 +- regressions.xml | 4 ++-- 25 files changed, 26 insertions(+), 26 deletions(-) diff --git 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1728679593000, "244": 1728924949000, "245": 1728950190000, "246": 1729011990000, "248": 1730238218000}, "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, "v0.2.2": 223}, "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": 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1720556946000, "193": 1721231912000, "197": 1722030850000, "198": 1722030931000, "204": 1723155498000, "207": 1723230708000, "208": 1723237398000, "212": 1723568086000, "218": 1724096325000, "221": 1724172655000, "223": 1725649586000, "225": 1727121784000, "228": 1727718916000, "230": 1727789652000, "233": 1728496944000, "235": 1728928992000, "236": 1728488305000, "237": 1728494277000, "238": 1728502908000, "239": 1728504094000, "240": 1728513201000, "241": 1728580271000, "242": 1728598850000, "243": 1728679593000, "244": 1728924949000, "245": 1728950190000, "246": 1729011990000, "247": 1730238218000, "250": 1730383709000}, "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, "v0.2.2": 223}, "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 e454355..79ccea2 100644 --- a/info.json +++ b/info.json @@ -1,4 +1,4 @@ { "asv-version": "0.6.4", - "timestamp": 1730239695960 + "timestamp": 1730383789036 } \ No newline at end of file diff --git a/regressions.json b/regressions.json index 6499e0f..3043468 100644 --- a/regressions.json +++ b/regressions.json @@ -1 +1 @@ -{"regressions": [["benchmarks.AssignSingleDfToNestedSeries.peakmem_run", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.AssignSingleDfToNestedSeries.peakmem_run.json", {}, null, 257310720.0, 215633920.0, [[null, 78, 215633920.0, 257310720.0]]], ["benchmarks.AssignSingleDfToNestedSeries.time_run", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.AssignSingleDfToNestedSeries.time_run.json", {}, null, 0.024085637000013094, 0.006260144666669021, [[null, 78, 0.006260144666669021, 0.024085637000013094]]], ["benchmarks.NestedFrameReduce.time_run", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.NestedFrameReduce.time_run.json", {}, null, 0.0016103532142831714, 0.0010955252499996958, [[null, 228, 0.0010955252499996958, 0.0016103532142831714]]]]} \ No newline at end of file +{"regressions": [["benchmarks.NestedFrameReduce.time_run", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.NestedFrameReduce.time_run.json", {}, null, 0.0016103532142831714, 0.0010955252499996958, [[null, 228, 0.0010955252499996958, 0.0016103532142831714]]], ["benchmarks.AssignSingleDfToNestedSeries.peakmem_run", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.AssignSingleDfToNestedSeries.peakmem_run.json", {}, null, 257353728.0, 215633920.0, [[null, 78, 215633920.0, 257353728.0]]], ["benchmarks.AssignSingleDfToNestedSeries.time_run", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.AssignSingleDfToNestedSeries.time_run.json", {}, null, 0.024085637000013094, 0.006260144666669021, [[null, 78, 0.006260144666669021, 0.024085637000013094]]]]} \ No newline at end of file diff --git a/regressions.xml b/regressions.xml index 97a225d..a9557ba 100644 --- a/regressions.xml +++ b/regressions.xml @@ -5,7 +5,7 @@ than best value 1.10ms).tag:nested-pandas.asv,2024-05-03:/eb4a3566c42a993e1b46a453dc2359aa50f94f2dd38c656bc38fee9802720670284.75% benchmarks.AssignSingleDfToNestedSeries.time_run2024-05-07T15:14:36Z<a href="index.html#benchmarks.AssignSingleDfToNestedSeries.time_run?commits=109c16c82efcb3b33fc770e552d6e0d1e021c61f">284.75% regression</a> on 2024-05-03 20:18:09 in commit <a href="https://github.com/lincc-frameworks/nested-pandas/commit/109c16c82efcb3b33fc770e552d6e0d1e021c61f">109c16c8</a>.<br> New value: 24.1ms, old value: 6.26ms.<br> Latest value: 24.1ms (284.75% worse - than best value 6.26ms).tag:nested-pandas.asv,2024-05-03:/f78c071dfb5f7e712acfb54ad13cd2af02c44ebbe5cd17e783b61ac64b14a38519.33% benchmarks.AssignSingleDfToNestedSeries.peakmem_run2024-05-07T15:14:35Z<a href="index.html#benchmarks.AssignSingleDfToNestedSeries.peakmem_run?commits=109c16c82efcb3b33fc770e552d6e0d1e021c61f">19.33% regression</a> on 2024-05-03 20:18:09 in commit <a href="https://github.com/lincc-frameworks/nested-pandas/commit/109c16c82efcb3b33fc770e552d6e0d1e021c61f">109c16c8</a>.<br> + than best value 6.26ms).tag:nested-pandas.asv,2024-05-03:/f78c071dfb5f7e712acfb54ad13cd2af02c44ebbe5cd17e783b61ac64b14a38519.35% benchmarks.AssignSingleDfToNestedSeries.peakmem_run2024-05-07T15:14:35Z<a href="index.html#benchmarks.AssignSingleDfToNestedSeries.peakmem_run?commits=109c16c82efcb3b33fc770e552d6e0d1e021c61f">19.35% regression</a> on 2024-05-03 20:18:09 in commit <a href="https://github.com/lincc-frameworks/nested-pandas/commit/109c16c82efcb3b33fc770e552d6e0d1e021c61f">109c16c8</a>.<br> New value: 257M, old value: 216M.<br> - Latest value: 257M (19.33% worse + Latest value: 257M (19.35% worse than best value 216M). \ No newline at end of file