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Generate a starting mean and scale from uniform distributions. Use those to\n # generate a sample from the normal distribution. Then shift that sample by -5.0.\n loc_node = NumpyRandomFunc(\"uniform\", low=10.0, high=20.0)\n scale_node = NumpyRandomFunc(\"uniform\", low=0.5, high=1.0)\n norm_node = NumpyRandomFunc(\"normal\", loc=loc_node, scale=scale_node)\n val_node = FunctionNode(_add_func, a=norm_node, b=-5.0)\n\n # Generate 100,000 samples.\n _ = val_node.sample_parameters(num_samples=100_000)", "min_run_count": 2, "name": "benchmarks.time_chained_evaluate", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "b4af9f0021b65861c69db8702dd0a230a74916ac2d379702d0c9d1f047d711f2", "warmup_time": -1}, "benchmarks.time_x1_from_hostmass": {"code": "def time_x1_from_hostmass():\n \"\"\"Time the generation of random numbers from the X1 function.\"\"\"\n x1_func = HostmassX1Func(11.0)\n _ = x1_func.sample_parameters(num_samples=10)", "min_run_count": 2, "name": "benchmarks.time_x1_from_hostmass", "number": 0, "param_names": [], 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"build": "", "packaging": "", "branch": "HEAD"}], "benchmarks": {"benchmarks.time_chained_evaluate": {"code": "def time_chained_evaluate():\n \"\"\"Time the generation of random numbers with an numpy generation node.\"\"\"\n\n def _add_func(a, b):\n return a + b\n\n # Generate a starting mean and scale from uniform distributions. Use those to\n # generate a sample from the normal distribution. Then shift that sample by -5.0.\n loc_node = NumpyRandomFunc(\"uniform\", low=10.0, high=20.0)\n scale_node = NumpyRandomFunc(\"uniform\", low=0.5, high=1.0)\n norm_node = NumpyRandomFunc(\"normal\", loc=loc_node, scale=scale_node)\n val_node = FunctionNode(_add_func, a=norm_node, b=-5.0)\n\n # Generate 100,000 samples.\n _ = val_node.sample_parameters(num_samples=100_000)", "min_run_count": 2, "name": "benchmarks.time_chained_evaluate", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "b4af9f0021b65861c69db8702dd0a230a74916ac2d379702d0c9d1f047d711f2", "warmup_time": -1}, "benchmarks.time_x1_from_hostmass": {"code": "def time_x1_from_hostmass():\n \"\"\"Time the generation of random numbers from the X1 function.\"\"\"\n x1_func = HostmassX1Func(11.0)\n _ = x1_func.sample_parameters(num_samples=10)", "min_run_count": 2, "name": "benchmarks.time_x1_from_hostmass", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "884f0102518d908729fb55e5e29a036490aedd2a1590976b46d3492ed5cf99a1", "warmup_time": -1}}, "machines": {"gh-runner": {"machine": "gh-runner", "version": 1}}, "tags": {}, "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 0b3e5c56..0ae58665 100644 --- a/info.json +++ b/info.json @@ -1,4 +1,4 @@ { "asv-version": "0.6.1", - "timestamp": 1726505440947 + "timestamp": 1726673540554 } \ No newline at end of file diff --git a/regressions.xml b/regressions.xml index 789c65b9..df444036 100644 --- a/regressions.xml +++ b/regressions.xml @@ -1,2 +1,2 @@ -tag:tdastro.asv,1970-01-01:/ebf359c79517f805bb81f390ee0edbc35610f7474e3a5cd79486c82d8dd189a2Airspeed Velocitytdastro performance regressions2024-09-16T16:50:40Z \ No newline at end of file +tag:tdastro.asv,1970-01-01:/ebf359c79517f805bb81f390ee0edbc35610f7474e3a5cd79486c82d8dd189a2Airspeed Velocitytdastro performance regressions2024-09-18T15:32:20Z \ No newline at end of file