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a/graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.TimeSuite.time_make_new_salt3_model.json b/graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.TimeSuite.time_make_new_salt3_model.json index b8260ef..741665f 100644 --- a/graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.TimeSuite.time_make_new_salt3_model.json +++ b/graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.TimeSuite.time_make_new_salt3_model.json @@ -1 +1 @@ -[[482, 3.34945483373743e-05], [487, 3.392638368536897e-05], [493, 3.3582274242386725e-05], [494, 3.393409451261017e-05], [495, 3.413557339455953e-05], [498, 3.4765329819054456e-05], [501, 3.485683231717092e-05], [502, 3.513310818730745e-05], [504, 3.4870074999915346e-05], [511, 3.2752051428458024e-05], [516, 3.260345402310023e-05], [520, 3.260636297367927e-05], [521, 3.2972995575181305e-05], [526, 3.3965250750739655e-05], [528, 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--git a/graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.TimeSuite.time_make_x1_from_hostmass.json b/graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.TimeSuite.time_make_x1_from_hostmass.json index 8956d13..2992a21 100644 --- a/graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.TimeSuite.time_make_x1_from_hostmass.json +++ b/graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.TimeSuite.time_make_x1_from_hostmass.json @@ -1 +1 @@ -[[482, 1.3937057220000497], [487, 1.3953557534998708], [493, 1.414833739999949], [494, 1.3792914724999719], [495, 1.3707683315000168], [498, 1.387875992999966], [501, 1.3978228775000616], [502, 1.4142060999998876], [504, 1.3874446949998855], [511, 1.3934952245000432], [516, 1.4094450869999946], [520, 1.3838362299999858], [521, 1.3911245805000192], [526, 1.3859135534999893], [528, 1.403044860499989], [533, 1.3921505614999603], [534, 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[""], "branch": ["HEAD"]}, "graph_param_list": [{"machine": "gh-runner", "python": "3.10", "Cython": "", "build": "", "packaging": "", "branch": "HEAD"}], "benchmarks": {"benchmarks.TimeSuite.time_apply_passbands": {"code": "class TimeSuite:\n def time_apply_passbands(self):\n \"\"\"Time applying the (already loaded) passbands to flux.\"\"\"\n _ = self.passbands.fluxes_to_bandfluxes(self.fluxes)\n\n def setup(self):\n \"\"\"Set up items that will be used in multiple tests.\"\"\"\n # Preload the passbands for tests that use them.\n self.passbands = _load_test_passbands()\n \n # Create a model we can use in tests.\n self.redshift = 0.1\n self.hostmass = 8.0\n self.distmod_func = DistModFromRedshift(self.redshift, H0=73.0, Omega_m=0.3)\n self.x1_func = HostmassX1Func(self.hostmass)\n self.x0_func = X0FromDistMod(\n distmod=self.distmod_func,\n x1=self.x1_func,\n c=0.0,\n alpha=0.14,\n beta=3.1,\n m_abs=-19.3,\n )\n \n self.salt3_model = SncosmoWrapperModel(\n \"salt3\",\n t0=0.0,\n x0=self.x0_func,\n x1=self.x1_func,\n c=0.0,\n ra=0.0,\n dec=0.0,\n redshift=self.redshift,\n )\n \n # Create samples that we can use in tests.\n self.times = np.arange(-20.0, 50.0, 0.5)\n self.wavelengths = self.passbands.waves\n self.filter_options = [\"LSST_g\", \"LSST_r\"]\n self.filters = np.array([self.filter_options[i % 2] for i in range(len(self.times))])\n \n self.graph_state = self.salt3_model.sample_parameters()\n self.fluxes = self.salt3_model.evaluate(self.times, self.wavelengths, graph_state=self.graph_state)", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_apply_passbands", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "0cff8deaa503247c6e01aa8a2a73df26174a91e2b9a030472508b28d22a5d5ba", "warmup_time": -1}, "benchmarks.TimeSuite.time_chained_evaluate": {"code": "class TimeSuite:\n def time_chained_evaluate(self):\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)\n\n def setup(self):\n \"\"\"Set up items that will be used in multiple tests.\"\"\"\n # Preload the passbands for tests that use them.\n self.passbands = _load_test_passbands()\n \n # Create a model we can use in tests.\n self.redshift = 0.1\n self.hostmass = 8.0\n self.distmod_func = DistModFromRedshift(self.redshift, H0=73.0, Omega_m=0.3)\n self.x1_func = HostmassX1Func(self.hostmass)\n self.x0_func = X0FromDistMod(\n distmod=self.distmod_func,\n x1=self.x1_func,\n c=0.0,\n alpha=0.14,\n beta=3.1,\n m_abs=-19.3,\n )\n \n self.salt3_model = SncosmoWrapperModel(\n \"salt3\",\n t0=0.0,\n x0=self.x0_func,\n x1=self.x1_func,\n c=0.0,\n ra=0.0,\n dec=0.0,\n redshift=self.redshift,\n )\n \n # Create samples that we can use in tests.\n self.times = np.arange(-20.0, 50.0, 0.5)\n self.wavelengths = self.passbands.waves\n self.filter_options = [\"LSST_g\", \"LSST_r\"]\n self.filters = np.array([self.filter_options[i % 2] for i in range(len(self.times))])\n \n self.graph_state = self.salt3_model.sample_parameters()\n self.fluxes = self.salt3_model.evaluate(self.times, self.wavelengths, graph_state=self.graph_state)", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_chained_evaluate", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "24dbbe12799645d6a395081dd4fa154886941881a71a46750f12c3a1999ee267", "warmup_time": -1}, "benchmarks.TimeSuite.time_evaluate_salt3_model": {"code": "class TimeSuite:\n def time_evaluate_salt3_model(self):\n \"\"\"Time querying a predefined salt3 model.\"\"\"\n _ = self.salt3_model.evaluate(\n self.times,\n self.wavelengths,\n graph_state=self.graph_state,\n )\n\n def setup(self):\n \"\"\"Set up items that will be used in multiple tests.\"\"\"\n # Preload the passbands for tests that use them.\n self.passbands = _load_test_passbands()\n \n # Create a model we can use in tests.\n self.redshift = 0.1\n self.hostmass = 8.0\n self.distmod_func = DistModFromRedshift(self.redshift, H0=73.0, Omega_m=0.3)\n self.x1_func = HostmassX1Func(self.hostmass)\n self.x0_func = X0FromDistMod(\n distmod=self.distmod_func,\n x1=self.x1_func,\n c=0.0,\n alpha=0.14,\n beta=3.1,\n m_abs=-19.3,\n )\n \n self.salt3_model = SncosmoWrapperModel(\n \"salt3\",\n t0=0.0,\n x0=self.x0_func,\n x1=self.x1_func,\n c=0.0,\n ra=0.0,\n dec=0.0,\n redshift=self.redshift,\n )\n \n # Create samples that we can use in tests.\n self.times = np.arange(-20.0, 50.0, 0.5)\n self.wavelengths = self.passbands.waves\n self.filter_options = [\"LSST_g\", \"LSST_r\"]\n self.filters = np.array([self.filter_options[i % 2] for i in range(len(self.times))])\n \n self.graph_state = self.salt3_model.sample_parameters()\n self.fluxes = self.salt3_model.evaluate(self.times, self.wavelengths, graph_state=self.graph_state)", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_evaluate_salt3_model", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "99a8c237be76a988dbcec4e66a5dbe178c2cf3942a23f27757ae1132845a98db", "warmup_time": -1}, "benchmarks.TimeSuite.time_evaluate_salt3_passbands": {"code": "class TimeSuite:\n def time_evaluate_salt3_passbands(self):\n \"\"\"Time evaluate the SALT3 model at the passband level.\"\"\"\n _ = self.salt3_model.get_band_fluxes(\n self.passbands,\n self.times,\n self.filters,\n self.graph_state,\n )\n\n def setup(self):\n \"\"\"Set up items that will be used in multiple tests.\"\"\"\n # Preload the passbands for tests that use them.\n self.passbands = _load_test_passbands()\n \n # Create a model we can use in tests.\n self.redshift = 0.1\n self.hostmass = 8.0\n self.distmod_func = DistModFromRedshift(self.redshift, H0=73.0, Omega_m=0.3)\n self.x1_func = HostmassX1Func(self.hostmass)\n self.x0_func = X0FromDistMod(\n distmod=self.distmod_func,\n x1=self.x1_func,\n c=0.0,\n alpha=0.14,\n beta=3.1,\n m_abs=-19.3,\n )\n \n self.salt3_model = SncosmoWrapperModel(\n \"salt3\",\n t0=0.0,\n x0=self.x0_func,\n x1=self.x1_func,\n c=0.0,\n ra=0.0,\n dec=0.0,\n redshift=self.redshift,\n )\n \n # Create samples that we can use in tests.\n self.times = np.arange(-20.0, 50.0, 0.5)\n self.wavelengths = self.passbands.waves\n self.filter_options = [\"LSST_g\", \"LSST_r\"]\n self.filters = np.array([self.filter_options[i % 2] for i in range(len(self.times))])\n \n self.graph_state = self.salt3_model.sample_parameters()\n self.fluxes = self.salt3_model.evaluate(self.times, self.wavelengths, graph_state=self.graph_state)", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_evaluate_salt3_passbands", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "75a6f92f1475ae05eacee924d4e505191e3d8ae94bb999e0fb9ebe15544c6337", "warmup_time": -1}, "benchmarks.TimeSuite.time_fnu_to_flam": {"code": "class TimeSuite:\n def time_fnu_to_flam(self):\n \"\"\"Time the fnu_to_flam function.\"\"\"\n _ = fnu_to_flam(\n self.fluxes,\n self.wavelengths,\n wave_unit=u.AA,\n flam_unit=u.erg / u.second / u.cm**2 / u.AA,\n fnu_unit=u.nJy,\n )\n\n def setup(self):\n \"\"\"Set up items that will be used in multiple tests.\"\"\"\n # Preload the passbands for tests that use them.\n self.passbands = _load_test_passbands()\n \n # Create a model we can use in tests.\n self.redshift = 0.1\n self.hostmass = 8.0\n self.distmod_func = DistModFromRedshift(self.redshift, H0=73.0, Omega_m=0.3)\n self.x1_func = HostmassX1Func(self.hostmass)\n self.x0_func = X0FromDistMod(\n distmod=self.distmod_func,\n x1=self.x1_func,\n c=0.0,\n alpha=0.14,\n beta=3.1,\n m_abs=-19.3,\n )\n \n self.salt3_model = SncosmoWrapperModel(\n \"salt3\",\n t0=0.0,\n x0=self.x0_func,\n x1=self.x1_func,\n c=0.0,\n ra=0.0,\n dec=0.0,\n redshift=self.redshift,\n )\n \n # Create samples that we can use in tests.\n self.times = np.arange(-20.0, 50.0, 0.5)\n self.wavelengths = self.passbands.waves\n self.filter_options = [\"LSST_g\", \"LSST_r\"]\n self.filters = np.array([self.filter_options[i % 2] for i in range(len(self.times))])\n \n self.graph_state = self.salt3_model.sample_parameters()\n self.fluxes = self.salt3_model.evaluate(self.times, self.wavelengths, graph_state=self.graph_state)", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_fnu_to_flam", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "deaaf9fe7bfa04a19eb655499dcf49508375070fb102112c7dd39ed6f9e8268f", "warmup_time": -1}, "benchmarks.TimeSuite.time_load_passbands": {"code": "class TimeSuite:\n def time_load_passbands(self):\n \"\"\"Time loading the passbands from files.\"\"\"\n _ = _load_test_passbands()\n\n def setup(self):\n \"\"\"Set up items that will be used in multiple tests.\"\"\"\n # Preload the passbands for tests that use them.\n self.passbands = _load_test_passbands()\n \n # Create a model we can use in tests.\n self.redshift = 0.1\n self.hostmass = 8.0\n self.distmod_func = DistModFromRedshift(self.redshift, H0=73.0, Omega_m=0.3)\n self.x1_func = HostmassX1Func(self.hostmass)\n self.x0_func = X0FromDistMod(\n distmod=self.distmod_func,\n x1=self.x1_func,\n c=0.0,\n alpha=0.14,\n beta=3.1,\n m_abs=-19.3,\n )\n \n self.salt3_model = SncosmoWrapperModel(\n \"salt3\",\n t0=0.0,\n x0=self.x0_func,\n x1=self.x1_func,\n c=0.0,\n ra=0.0,\n dec=0.0,\n redshift=self.redshift,\n )\n \n # Create samples that we can use in tests.\n self.times = np.arange(-20.0, 50.0, 0.5)\n self.wavelengths = self.passbands.waves\n self.filter_options = [\"LSST_g\", \"LSST_r\"]\n self.filters = np.array([self.filter_options[i % 2] for i in range(len(self.times))])\n \n self.graph_state = self.salt3_model.sample_parameters()\n self.fluxes = self.salt3_model.evaluate(self.times, self.wavelengths, graph_state=self.graph_state)", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_load_passbands", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "4d744c2f1132c8db040ca424d54e80b828b893b0b362111e59265c74bb951e20", "warmup_time": -1}, "benchmarks.TimeSuite.time_make_new_salt3_model": {"code": "class TimeSuite:\n def time_make_new_salt3_model(self):\n \"\"\"Time creating a new SALT3 model.\"\"\"\n _ = SncosmoWrapperModel(\n \"salt3\",\n t0=0.0,\n x0=self.x0_func,\n x1=self.x1_func,\n c=0.0,\n ra=0.0,\n dec=0.0,\n redshift=self.redshift,\n )\n\n def setup(self):\n \"\"\"Set up items that will be used in multiple tests.\"\"\"\n # Preload the passbands for tests that use them.\n self.passbands = _load_test_passbands()\n \n # Create a model we can use in tests.\n self.redshift = 0.1\n self.hostmass = 8.0\n self.distmod_func = DistModFromRedshift(self.redshift, H0=73.0, Omega_m=0.3)\n self.x1_func = HostmassX1Func(self.hostmass)\n self.x0_func = X0FromDistMod(\n distmod=self.distmod_func,\n x1=self.x1_func,\n c=0.0,\n alpha=0.14,\n beta=3.1,\n m_abs=-19.3,\n )\n \n self.salt3_model = SncosmoWrapperModel(\n \"salt3\",\n t0=0.0,\n x0=self.x0_func,\n x1=self.x1_func,\n c=0.0,\n ra=0.0,\n dec=0.0,\n redshift=self.redshift,\n )\n \n # Create samples that we can use in tests.\n self.times = np.arange(-20.0, 50.0, 0.5)\n self.wavelengths = self.passbands.waves\n self.filter_options = [\"LSST_g\", \"LSST_r\"]\n self.filters = np.array([self.filter_options[i % 2] for i in range(len(self.times))])\n \n self.graph_state = self.salt3_model.sample_parameters()\n self.fluxes = self.salt3_model.evaluate(self.times, self.wavelengths, graph_state=self.graph_state)", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_make_new_salt3_model", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "e4bfaee546a0529dd8587824888393e57090864f28704dffe8ac311b06900295", "warmup_time": -1}, "benchmarks.TimeSuite.time_make_x1_from_hostmass": {"code": "class TimeSuite:\n def time_make_x1_from_hostmass(self):\n \"\"\"Time the creation of the X1 function.\"\"\"\n _ = HostmassX1Func(self.hostmass)\n\n def setup(self):\n \"\"\"Set up items that will be used in multiple tests.\"\"\"\n # Preload the passbands for tests that use them.\n self.passbands = _load_test_passbands()\n \n # Create a model we can use in tests.\n self.redshift = 0.1\n self.hostmass = 8.0\n self.distmod_func = DistModFromRedshift(self.redshift, H0=73.0, Omega_m=0.3)\n self.x1_func = HostmassX1Func(self.hostmass)\n self.x0_func = X0FromDistMod(\n distmod=self.distmod_func,\n x1=self.x1_func,\n c=0.0,\n alpha=0.14,\n beta=3.1,\n m_abs=-19.3,\n )\n \n self.salt3_model = SncosmoWrapperModel(\n \"salt3\",\n t0=0.0,\n x0=self.x0_func,\n x1=self.x1_func,\n c=0.0,\n ra=0.0,\n dec=0.0,\n redshift=self.redshift,\n )\n \n # Create samples that we can use in tests.\n self.times = np.arange(-20.0, 50.0, 0.5)\n self.wavelengths = self.passbands.waves\n self.filter_options = [\"LSST_g\", \"LSST_r\"]\n self.filters = np.array([self.filter_options[i % 2] for i in range(len(self.times))])\n \n self.graph_state = self.salt3_model.sample_parameters()\n self.fluxes = self.salt3_model.evaluate(self.times, self.wavelengths, graph_state=self.graph_state)", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_make_x1_from_hostmass", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "b3e3540b7d214997fa6491b2b924313c65f6ed4f1a95428d9c135ab4a919bd28", "warmup_time": -1}, "benchmarks.TimeSuite.time_sample_x0_from_distmod": {"code": "class TimeSuite:\n def time_sample_x0_from_distmod(self):\n \"\"\"Time the computation of the X0 function.\"\"\"\n _ = self.x0_func.sample_parameters()\n\n def setup(self):\n \"\"\"Set up items that will be used in multiple tests.\"\"\"\n # Preload the passbands for tests that use them.\n self.passbands = _load_test_passbands()\n \n # Create a model we can use in tests.\n self.redshift = 0.1\n self.hostmass = 8.0\n self.distmod_func = DistModFromRedshift(self.redshift, H0=73.0, Omega_m=0.3)\n self.x1_func = HostmassX1Func(self.hostmass)\n self.x0_func = X0FromDistMod(\n distmod=self.distmod_func,\n x1=self.x1_func,\n c=0.0,\n alpha=0.14,\n beta=3.1,\n m_abs=-19.3,\n )\n \n self.salt3_model = SncosmoWrapperModel(\n \"salt3\",\n t0=0.0,\n x0=self.x0_func,\n x1=self.x1_func,\n c=0.0,\n ra=0.0,\n dec=0.0,\n redshift=self.redshift,\n )\n \n # Create samples that we can use in tests.\n self.times = np.arange(-20.0, 50.0, 0.5)\n self.wavelengths = self.passbands.waves\n self.filter_options = [\"LSST_g\", \"LSST_r\"]\n self.filters = np.array([self.filter_options[i % 2] for i in range(len(self.times))])\n \n self.graph_state = self.salt3_model.sample_parameters()\n self.fluxes = self.salt3_model.evaluate(self.times, self.wavelengths, graph_state=self.graph_state)", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_sample_x0_from_distmod", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "83d97c46dfa1a0b88690350683a0dfc3260c41a9e4761822a5467a1b67dba43e", "warmup_time": -1}, "benchmarks.TimeSuite.time_sample_x1_from_hostmass": {"code": "class TimeSuite:\n def time_sample_x1_from_hostmass(self):\n \"\"\"Time the computation of the X1 function.\"\"\"\n _ = self.x1_func.sample_parameters()\n\n def setup(self):\n \"\"\"Set up items that will be used in multiple tests.\"\"\"\n # Preload the passbands for tests that use them.\n self.passbands = _load_test_passbands()\n \n # Create a model we can use in tests.\n self.redshift = 0.1\n self.hostmass = 8.0\n self.distmod_func = DistModFromRedshift(self.redshift, H0=73.0, Omega_m=0.3)\n self.x1_func = HostmassX1Func(self.hostmass)\n self.x0_func = X0FromDistMod(\n distmod=self.distmod_func,\n x1=self.x1_func,\n c=0.0,\n alpha=0.14,\n beta=3.1,\n m_abs=-19.3,\n )\n \n self.salt3_model = SncosmoWrapperModel(\n \"salt3\",\n t0=0.0,\n x0=self.x0_func,\n x1=self.x1_func,\n c=0.0,\n ra=0.0,\n dec=0.0,\n redshift=self.redshift,\n )\n \n # Create samples that we can use in tests.\n self.times = np.arange(-20.0, 50.0, 0.5)\n self.wavelengths = self.passbands.waves\n self.filter_options = [\"LSST_g\", \"LSST_r\"]\n self.filters = np.array([self.filter_options[i % 2] for i in range(len(self.times))])\n \n self.graph_state = self.salt3_model.sample_parameters()\n self.fluxes = self.salt3_model.evaluate(self.times, self.wavelengths, graph_state=self.graph_state)", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_sample_x1_from_hostmass", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "84c2b85effb6b661758348c42460eed4fd580fdd47471bc279ce8f98c7326e7f", "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 +{"project": "tdastro", "project_url": "https://github.com/lincc-frameworks/tdastro", "show_commit_url": "https://github.com/lincc-frameworks/tdastro/commit/", "hash_length": 8, "revision_to_hash": {"0": "98eb15f5c411d9a136d18b0ae5ec9df14e2f6287", "1": "30225d1b54431d19dcfe9552898df5366d798b66", "2": 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1729012143000, "526": 1729023224000, "528": 1729027028000, "533": 1729081509000, "534": 1729081522000, "535": 1729083673000, "546": 1729264175000, "547": 1729268216000, "548": 1729269585000, "550": 1729888358000, "554": 1730127258000}, "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.TimeSuite.time_apply_passbands": {"code": "class TimeSuite:\n def time_apply_passbands(self):\n \"\"\"Time applying the (already loaded) passbands to flux.\"\"\"\n _ = self.passbands.fluxes_to_bandfluxes(self.fluxes)\n\n def setup(self):\n \"\"\"Set up items that will be used in multiple tests.\"\"\"\n # Preload the passbands for tests that use them.\n self.passbands = _load_test_passbands()\n \n # Create a model we can use in tests.\n self.redshift = 0.1\n self.hostmass = 8.0\n self.distmod_func = DistModFromRedshift(self.redshift, H0=73.0, Omega_m=0.3)\n self.x1_func = HostmassX1Func(self.hostmass)\n self.x0_func = X0FromDistMod(\n distmod=self.distmod_func,\n x1=self.x1_func,\n c=0.0,\n alpha=0.14,\n beta=3.1,\n m_abs=-19.3,\n )\n \n self.salt3_model = SncosmoWrapperModel(\n \"salt3\",\n t0=0.0,\n x0=self.x0_func,\n x1=self.x1_func,\n c=0.0,\n ra=0.0,\n dec=0.0,\n redshift=self.redshift,\n )\n \n # Create samples that we can use in tests.\n self.times = np.arange(-20.0, 50.0, 0.5)\n self.wavelengths = self.passbands.waves\n self.filter_options = [\"LSST_g\", \"LSST_r\"]\n self.filters = np.array([self.filter_options[i % 2] for i in range(len(self.times))])\n \n self.graph_state = self.salt3_model.sample_parameters()\n self.fluxes = self.salt3_model.evaluate(self.times, self.wavelengths, graph_state=self.graph_state)", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_apply_passbands", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "0cff8deaa503247c6e01aa8a2a73df26174a91e2b9a030472508b28d22a5d5ba", "warmup_time": -1}, "benchmarks.TimeSuite.time_chained_evaluate": {"code": "class TimeSuite:\n def time_chained_evaluate(self):\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)\n\n def setup(self):\n \"\"\"Set up items that will be used in multiple tests.\"\"\"\n # Preload the passbands for tests that use them.\n self.passbands = _load_test_passbands()\n \n # Create a model we can use in tests.\n self.redshift = 0.1\n self.hostmass = 8.0\n self.distmod_func = DistModFromRedshift(self.redshift, H0=73.0, Omega_m=0.3)\n self.x1_func = HostmassX1Func(self.hostmass)\n self.x0_func = X0FromDistMod(\n distmod=self.distmod_func,\n x1=self.x1_func,\n c=0.0,\n alpha=0.14,\n beta=3.1,\n m_abs=-19.3,\n )\n \n self.salt3_model = SncosmoWrapperModel(\n \"salt3\",\n t0=0.0,\n x0=self.x0_func,\n x1=self.x1_func,\n c=0.0,\n ra=0.0,\n dec=0.0,\n redshift=self.redshift,\n )\n \n # Create samples that we can use in tests.\n self.times = np.arange(-20.0, 50.0, 0.5)\n self.wavelengths = self.passbands.waves\n self.filter_options = [\"LSST_g\", \"LSST_r\"]\n self.filters = np.array([self.filter_options[i % 2] for i in range(len(self.times))])\n \n self.graph_state = self.salt3_model.sample_parameters()\n self.fluxes = self.salt3_model.evaluate(self.times, self.wavelengths, graph_state=self.graph_state)", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_chained_evaluate", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "24dbbe12799645d6a395081dd4fa154886941881a71a46750f12c3a1999ee267", "warmup_time": -1}, "benchmarks.TimeSuite.time_evaluate_salt3_model": {"code": "class TimeSuite:\n def time_evaluate_salt3_model(self):\n \"\"\"Time querying a predefined salt3 model.\"\"\"\n _ = self.salt3_model.evaluate(\n self.times,\n self.wavelengths,\n graph_state=self.graph_state,\n )\n\n def setup(self):\n \"\"\"Set up items that will be used in multiple tests.\"\"\"\n # Preload the passbands for tests that use them.\n self.passbands = _load_test_passbands()\n \n # Create a model we can use in tests.\n self.redshift = 0.1\n self.hostmass = 8.0\n self.distmod_func = DistModFromRedshift(self.redshift, H0=73.0, Omega_m=0.3)\n self.x1_func = HostmassX1Func(self.hostmass)\n self.x0_func = X0FromDistMod(\n distmod=self.distmod_func,\n x1=self.x1_func,\n c=0.0,\n alpha=0.14,\n beta=3.1,\n m_abs=-19.3,\n )\n \n self.salt3_model = SncosmoWrapperModel(\n \"salt3\",\n t0=0.0,\n x0=self.x0_func,\n x1=self.x1_func,\n c=0.0,\n ra=0.0,\n dec=0.0,\n redshift=self.redshift,\n )\n \n # Create samples that we can use in tests.\n self.times = np.arange(-20.0, 50.0, 0.5)\n self.wavelengths = self.passbands.waves\n self.filter_options = [\"LSST_g\", \"LSST_r\"]\n self.filters = np.array([self.filter_options[i % 2] for i in range(len(self.times))])\n \n self.graph_state = self.salt3_model.sample_parameters()\n self.fluxes = self.salt3_model.evaluate(self.times, self.wavelengths, graph_state=self.graph_state)", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_evaluate_salt3_model", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "99a8c237be76a988dbcec4e66a5dbe178c2cf3942a23f27757ae1132845a98db", "warmup_time": -1}, "benchmarks.TimeSuite.time_evaluate_salt3_passbands": {"code": "class TimeSuite:\n def time_evaluate_salt3_passbands(self):\n \"\"\"Time evaluate the SALT3 model at the passband level.\"\"\"\n _ = self.salt3_model.get_band_fluxes(\n self.passbands,\n self.times,\n self.filters,\n self.graph_state,\n )\n\n def setup(self):\n \"\"\"Set up items that will be used in multiple tests.\"\"\"\n # Preload the passbands for tests that use them.\n self.passbands = _load_test_passbands()\n \n # Create a model we can use in tests.\n self.redshift = 0.1\n self.hostmass = 8.0\n self.distmod_func = DistModFromRedshift(self.redshift, H0=73.0, Omega_m=0.3)\n self.x1_func = HostmassX1Func(self.hostmass)\n self.x0_func = X0FromDistMod(\n distmod=self.distmod_func,\n x1=self.x1_func,\n c=0.0,\n alpha=0.14,\n beta=3.1,\n m_abs=-19.3,\n )\n \n self.salt3_model = SncosmoWrapperModel(\n \"salt3\",\n t0=0.0,\n x0=self.x0_func,\n x1=self.x1_func,\n c=0.0,\n ra=0.0,\n dec=0.0,\n redshift=self.redshift,\n )\n \n # Create samples that we can use in tests.\n self.times = np.arange(-20.0, 50.0, 0.5)\n self.wavelengths = self.passbands.waves\n self.filter_options = [\"LSST_g\", \"LSST_r\"]\n self.filters = np.array([self.filter_options[i % 2] for i in range(len(self.times))])\n \n self.graph_state = self.salt3_model.sample_parameters()\n self.fluxes = self.salt3_model.evaluate(self.times, self.wavelengths, graph_state=self.graph_state)", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_evaluate_salt3_passbands", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "75a6f92f1475ae05eacee924d4e505191e3d8ae94bb999e0fb9ebe15544c6337", "warmup_time": -1}, "benchmarks.TimeSuite.time_fnu_to_flam": {"code": "class TimeSuite:\n def time_fnu_to_flam(self):\n \"\"\"Time the fnu_to_flam function.\"\"\"\n _ = fnu_to_flam(\n self.fluxes,\n self.wavelengths,\n wave_unit=u.AA,\n flam_unit=u.erg / u.second / u.cm**2 / u.AA,\n fnu_unit=u.nJy,\n )\n\n def setup(self):\n \"\"\"Set up items that will be used in multiple tests.\"\"\"\n # Preload the passbands for tests that use them.\n self.passbands = _load_test_passbands()\n \n # Create a model we can use in tests.\n self.redshift = 0.1\n self.hostmass = 8.0\n self.distmod_func = DistModFromRedshift(self.redshift, H0=73.0, Omega_m=0.3)\n self.x1_func = HostmassX1Func(self.hostmass)\n self.x0_func = X0FromDistMod(\n distmod=self.distmod_func,\n x1=self.x1_func,\n c=0.0,\n alpha=0.14,\n beta=3.1,\n m_abs=-19.3,\n )\n \n self.salt3_model = SncosmoWrapperModel(\n \"salt3\",\n t0=0.0,\n x0=self.x0_func,\n x1=self.x1_func,\n c=0.0,\n ra=0.0,\n dec=0.0,\n redshift=self.redshift,\n )\n \n # Create samples that we can use in tests.\n self.times = np.arange(-20.0, 50.0, 0.5)\n self.wavelengths = self.passbands.waves\n self.filter_options = [\"LSST_g\", \"LSST_r\"]\n self.filters = np.array([self.filter_options[i % 2] for i in range(len(self.times))])\n \n self.graph_state = self.salt3_model.sample_parameters()\n self.fluxes = self.salt3_model.evaluate(self.times, self.wavelengths, graph_state=self.graph_state)", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_fnu_to_flam", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "deaaf9fe7bfa04a19eb655499dcf49508375070fb102112c7dd39ed6f9e8268f", "warmup_time": -1}, "benchmarks.TimeSuite.time_load_passbands": {"code": "class TimeSuite:\n def time_load_passbands(self):\n \"\"\"Time loading the passbands from files.\"\"\"\n _ = _load_test_passbands()\n\n def setup(self):\n \"\"\"Set up items that will be used in multiple tests.\"\"\"\n # Preload the passbands for tests that use them.\n self.passbands = _load_test_passbands()\n \n # Create a model we can use in tests.\n self.redshift = 0.1\n self.hostmass = 8.0\n self.distmod_func = DistModFromRedshift(self.redshift, H0=73.0, Omega_m=0.3)\n self.x1_func = HostmassX1Func(self.hostmass)\n self.x0_func = X0FromDistMod(\n distmod=self.distmod_func,\n x1=self.x1_func,\n c=0.0,\n alpha=0.14,\n beta=3.1,\n m_abs=-19.3,\n )\n \n self.salt3_model = SncosmoWrapperModel(\n \"salt3\",\n t0=0.0,\n x0=self.x0_func,\n x1=self.x1_func,\n c=0.0,\n ra=0.0,\n dec=0.0,\n redshift=self.redshift,\n )\n \n # Create samples that we can use in tests.\n self.times = np.arange(-20.0, 50.0, 0.5)\n self.wavelengths = self.passbands.waves\n self.filter_options = [\"LSST_g\", \"LSST_r\"]\n self.filters = np.array([self.filter_options[i % 2] for i in range(len(self.times))])\n \n self.graph_state = self.salt3_model.sample_parameters()\n self.fluxes = self.salt3_model.evaluate(self.times, self.wavelengths, graph_state=self.graph_state)", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_load_passbands", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "4d744c2f1132c8db040ca424d54e80b828b893b0b362111e59265c74bb951e20", "warmup_time": -1}, "benchmarks.TimeSuite.time_make_new_salt3_model": {"code": "class TimeSuite:\n def time_make_new_salt3_model(self):\n \"\"\"Time creating a new SALT3 model.\"\"\"\n _ = SncosmoWrapperModel(\n \"salt3\",\n t0=0.0,\n x0=self.x0_func,\n x1=self.x1_func,\n c=0.0,\n ra=0.0,\n dec=0.0,\n redshift=self.redshift,\n )\n\n def setup(self):\n \"\"\"Set up items that will be used in multiple tests.\"\"\"\n # Preload the passbands for tests that use them.\n self.passbands = _load_test_passbands()\n \n # Create a model we can use in tests.\n self.redshift = 0.1\n self.hostmass = 8.0\n self.distmod_func = DistModFromRedshift(self.redshift, H0=73.0, Omega_m=0.3)\n self.x1_func = HostmassX1Func(self.hostmass)\n self.x0_func = X0FromDistMod(\n distmod=self.distmod_func,\n x1=self.x1_func,\n c=0.0,\n alpha=0.14,\n beta=3.1,\n m_abs=-19.3,\n )\n \n self.salt3_model = SncosmoWrapperModel(\n \"salt3\",\n t0=0.0,\n x0=self.x0_func,\n x1=self.x1_func,\n c=0.0,\n ra=0.0,\n dec=0.0,\n redshift=self.redshift,\n )\n \n # Create samples that we can use in tests.\n self.times = np.arange(-20.0, 50.0, 0.5)\n self.wavelengths = self.passbands.waves\n self.filter_options = [\"LSST_g\", \"LSST_r\"]\n self.filters = np.array([self.filter_options[i % 2] for i in range(len(self.times))])\n \n self.graph_state = self.salt3_model.sample_parameters()\n self.fluxes = self.salt3_model.evaluate(self.times, self.wavelengths, graph_state=self.graph_state)", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_make_new_salt3_model", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "e4bfaee546a0529dd8587824888393e57090864f28704dffe8ac311b06900295", "warmup_time": -1}, "benchmarks.TimeSuite.time_make_x1_from_hostmass": {"code": "class TimeSuite:\n def time_make_x1_from_hostmass(self):\n \"\"\"Time the creation of the X1 function.\"\"\"\n _ = HostmassX1Func(self.hostmass)\n\n def setup(self):\n \"\"\"Set up items that will be used in multiple tests.\"\"\"\n # Preload the passbands for tests that use them.\n self.passbands = _load_test_passbands()\n \n # Create a model we can use in tests.\n self.redshift = 0.1\n self.hostmass = 8.0\n self.distmod_func = DistModFromRedshift(self.redshift, H0=73.0, Omega_m=0.3)\n self.x1_func = HostmassX1Func(self.hostmass)\n self.x0_func = X0FromDistMod(\n distmod=self.distmod_func,\n x1=self.x1_func,\n c=0.0,\n alpha=0.14,\n beta=3.1,\n m_abs=-19.3,\n )\n \n self.salt3_model = SncosmoWrapperModel(\n \"salt3\",\n t0=0.0,\n x0=self.x0_func,\n x1=self.x1_func,\n c=0.0,\n ra=0.0,\n dec=0.0,\n redshift=self.redshift,\n )\n \n # Create samples that we can use in tests.\n self.times = np.arange(-20.0, 50.0, 0.5)\n self.wavelengths = self.passbands.waves\n self.filter_options = [\"LSST_g\", \"LSST_r\"]\n self.filters = np.array([self.filter_options[i % 2] for i in range(len(self.times))])\n \n self.graph_state = self.salt3_model.sample_parameters()\n self.fluxes = self.salt3_model.evaluate(self.times, self.wavelengths, graph_state=self.graph_state)", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_make_x1_from_hostmass", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "b3e3540b7d214997fa6491b2b924313c65f6ed4f1a95428d9c135ab4a919bd28", "warmup_time": -1}, "benchmarks.TimeSuite.time_sample_x0_from_distmod": {"code": "class TimeSuite:\n def time_sample_x0_from_distmod(self):\n \"\"\"Time the computation of the X0 function.\"\"\"\n _ = self.x0_func.sample_parameters()\n\n def setup(self):\n \"\"\"Set up items that will be used in multiple tests.\"\"\"\n # Preload the passbands for tests that use them.\n self.passbands = _load_test_passbands()\n \n # Create a model we can use in tests.\n self.redshift = 0.1\n self.hostmass = 8.0\n self.distmod_func = DistModFromRedshift(self.redshift, H0=73.0, Omega_m=0.3)\n self.x1_func = HostmassX1Func(self.hostmass)\n self.x0_func = X0FromDistMod(\n distmod=self.distmod_func,\n x1=self.x1_func,\n c=0.0,\n alpha=0.14,\n beta=3.1,\n m_abs=-19.3,\n )\n \n self.salt3_model = SncosmoWrapperModel(\n \"salt3\",\n t0=0.0,\n x0=self.x0_func,\n x1=self.x1_func,\n c=0.0,\n ra=0.0,\n dec=0.0,\n redshift=self.redshift,\n )\n \n # Create samples that we can use in tests.\n self.times = np.arange(-20.0, 50.0, 0.5)\n self.wavelengths = self.passbands.waves\n self.filter_options = [\"LSST_g\", \"LSST_r\"]\n self.filters = np.array([self.filter_options[i % 2] for i in range(len(self.times))])\n \n self.graph_state = self.salt3_model.sample_parameters()\n self.fluxes = self.salt3_model.evaluate(self.times, self.wavelengths, graph_state=self.graph_state)", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_sample_x0_from_distmod", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "83d97c46dfa1a0b88690350683a0dfc3260c41a9e4761822a5467a1b67dba43e", "warmup_time": -1}, "benchmarks.TimeSuite.time_sample_x1_from_hostmass": {"code": "class TimeSuite:\n def time_sample_x1_from_hostmass(self):\n \"\"\"Time the computation of the X1 function.\"\"\"\n _ = self.x1_func.sample_parameters()\n\n def setup(self):\n \"\"\"Set up items that will be used in multiple tests.\"\"\"\n # Preload the passbands for tests that use them.\n self.passbands = _load_test_passbands()\n \n # Create a model we can use in tests.\n self.redshift = 0.1\n self.hostmass = 8.0\n self.distmod_func = DistModFromRedshift(self.redshift, H0=73.0, Omega_m=0.3)\n self.x1_func = HostmassX1Func(self.hostmass)\n self.x0_func = X0FromDistMod(\n distmod=self.distmod_func,\n x1=self.x1_func,\n c=0.0,\n alpha=0.14,\n beta=3.1,\n m_abs=-19.3,\n )\n \n self.salt3_model = SncosmoWrapperModel(\n \"salt3\",\n t0=0.0,\n x0=self.x0_func,\n x1=self.x1_func,\n c=0.0,\n ra=0.0,\n dec=0.0,\n redshift=self.redshift,\n )\n \n # Create samples that we can use in tests.\n self.times = np.arange(-20.0, 50.0, 0.5)\n self.wavelengths = self.passbands.waves\n self.filter_options = [\"LSST_g\", \"LSST_r\"]\n self.filters = np.array([self.filter_options[i % 2] for i in range(len(self.times))])\n \n self.graph_state = self.salt3_model.sample_parameters()\n self.fluxes = self.salt3_model.evaluate(self.times, self.wavelengths, graph_state=self.graph_state)", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_sample_x1_from_hostmass", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "84c2b85effb6b661758348c42460eed4fd580fdd47471bc279ce8f98c7326e7f", "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 57caeee..79d284c 100644 --- a/info.json +++ b/info.json @@ -1,4 +1,4 @@ { "asv-version": "0.6.4", - "timestamp": 1729888605163 + "timestamp": 1730127495294 } \ No newline at end of file diff --git a/regressions.json b/regressions.json index 8e20176..76fd63f 100644 --- a/regressions.json +++ b/regressions.json @@ -1 +1 @@ -{"regressions": [["benchmarks.TimeSuite.time_fnu_to_flam", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.TimeSuite.time_fnu_to_flam.json", {}, null, 0.0021506331666595697, 0.0014135122500249508, [[null, 546, 0.0014135122500249508, 0.0021506331666595697]]], ["benchmarks.TimeSuite.time_evaluate_salt3_passbands", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.TimeSuite.time_evaluate_salt3_passbands.json", {}, null, 0.005277362000001062, 0.0040748664999531075, [[null, 487, 0.0040748664999531075, 0.005277362000001062]]], ["benchmarks.TimeSuite.time_evaluate_salt3_model", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.TimeSuite.time_evaluate_salt3_model.json", {}, null, 0.009231936000006158, 0.008676093000076435, [[null, 487, 0.008676093000076435, 0.009231936000006158]]]]} \ No newline at end of file +{"regressions": [["benchmarks.TimeSuite.time_evaluate_salt3_passbands", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.TimeSuite.time_evaluate_salt3_passbands.json", {}, null, 0.005268695249995403, 0.0040748664999531075, [[null, 487, 0.0040748664999531075, 0.005268695249995403]]]]} \ No newline at end of file diff --git a/regressions.xml b/regressions.xml index 8fbd581..677d1e2 100644 --- a/regressions.xml +++ b/regressions.xml @@ -1,11 +1,5 @@ -tag:tdastro.asv,1970-01-01:/ebf359c79517f805bb81f390ee0edbc35610f7474e3a5cd79486c82d8dd189a2Airspeed Velocitytdastro performance regressions2024-10-18T15:13:01Ztag:tdastro.asv,2024-10-18:/e072e2df12a39da91b5da03451880eb29c416fe0445c85a4fa5877d201fe167952.15% benchmarks.TimeSuite.time_fnu_to_flam2024-10-18T15:13:01Z<a href="index.html#benchmarks.TimeSuite.time_fnu_to_flam?commits=fa2118b7909565e6c04d747380be000a450e7b79">52.15% regression</a> on 2024-10-18 15:09:35 in commit <a href="https://github.com/lincc-frameworks/tdastro/commit/fa2118b7909565e6c04d747380be000a450e7b79">fa2118b7</a>.<br> - New value: 2.15ms, old value: 1.41ms.<br> - Latest value: 2.15ms (52.15% worse - than best value 1.41ms).tag:tdastro.asv,2024-10-07:/16355e5158d0c63b3b16aa22b8ee4e49d044a04d50d10dab0d3701cf45cd57b629.51% benchmarks.TimeSuite.time_evaluate_salt3_passbands2024-10-15T20:55:55Z<a href="index.html#benchmarks.TimeSuite.time_evaluate_salt3_passbands?commits=3033e07d6238822d258931ac5af152e64847db1e">29.51% regression</a> on 2024-10-07 14:45:25 in commit <a href="https://github.com/lincc-frameworks/tdastro/commit/3033e07d6238822d258931ac5af152e64847db1e">3033e07d</a>.<br> - New value: 5.28ms, old value: 4.07ms.<br> - Latest value: 5.28ms (29.51% worse - than best value 4.07ms).tag:tdastro.asv,2024-10-07:/4aeacdcb8c22c65c0d4ed5350c05202241f26baa200b1a8f6e970d9d2c2ad8606.41% benchmarks.TimeSuite.time_evaluate_salt3_model2024-10-15T20:55:46Z<a href="index.html#benchmarks.TimeSuite.time_evaluate_salt3_model?commits=3033e07d6238822d258931ac5af152e64847db1e">6.41% regression</a> on 2024-10-07 14:45:25 in commit <a href="https://github.com/lincc-frameworks/tdastro/commit/3033e07d6238822d258931ac5af152e64847db1e">3033e07d</a>.<br> - New value: 9.23ms, old value: 8.68ms.<br> - Latest value: 9.23ms (6.41% worse - than best value 8.68ms). \ No newline at end of file +tag:tdastro.asv,1970-01-01:/ebf359c79517f805bb81f390ee0edbc35610f7474e3a5cd79486c82d8dd189a2Airspeed Velocitytdastro performance regressions2024-10-15T20:55:55Ztag:tdastro.asv,2024-10-07:/16355e5158d0c63b3b16aa22b8ee4e49d044a04d50d10dab0d3701cf45cd57b629.30% benchmarks.TimeSuite.time_evaluate_salt3_passbands2024-10-15T20:55:55Z<a href="index.html#benchmarks.TimeSuite.time_evaluate_salt3_passbands?commits=3033e07d6238822d258931ac5af152e64847db1e">29.30% regression</a> on 2024-10-07 14:45:25 in commit <a href="https://github.com/lincc-frameworks/tdastro/commit/3033e07d6238822d258931ac5af152e64847db1e">3033e07d</a>.<br> + New value: 5.27ms, old value: 4.07ms.<br> + Latest value: 5.27ms (29.30% worse + than best value 4.07ms). \ No newline at end of file