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a/graphs/summary/benchmarks.TimeSuite.time_load_passbands.json b/graphs/summary/benchmarks.TimeSuite.time_load_passbands.json index 1ae4c14..bfc1344 100644 --- a/graphs/summary/benchmarks.TimeSuite.time_load_passbands.json +++ b/graphs/summary/benchmarks.TimeSuite.time_load_passbands.json @@ -1 +1 @@ -[[482, 0.008654529749833273], [487, 0.00865646474994719], [493, 0.008704267750090366], [494, 0.008728466999968987], [495, 0.0086581614999659], [498, 0.008653211000023475], [501, 0.008686638750020848], [502, 0.008785136250082815], [504, 0.008634901749985602], [511, 0.008658131000004232], [516, 0.008726190749996476], [520, 0.008675339999996368], [521, 0.008790280749991553], [526, 0.008679843749987981], [528, 0.008572821749993409], [533, 0.008817021999988128], [534, 0.008930302250000466], [535, 0.008691104999996924], [546, 0.009058125250007265], [547, 0.008926490499987949], [548, 0.00898575924999534], [552, 0.009014556749995961], [558, 0.008857631750004202], [561, 0.00907106124999757], [566, 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a/graphs/summary/benchmarks.TimeSuite.time_make_x1_from_hostmass.json b/graphs/summary/benchmarks.TimeSuite.time_make_x1_from_hostmass.json index 964a52d..f9dba6f 100644 --- a/graphs/summary/benchmarks.TimeSuite.time_make_x1_from_hostmass.json +++ b/graphs/summary/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, 1.393926288000003], [535, 1.3992387199999996], [546, 1.3595635774999835], [547, 1.3525861589999977], [548, 1.3150194404999809], [552, 1.3799381715000152], [558, 1.333085834000002], [561, 1.3576806640000143], [566, 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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": "678d58e603bac97e57e5c20344d46af7acaa6c33", "3": "7519ef02db3e46d29645eb024787c4780fd90e49", "4": "7bef979448ce46b5f1dcbf448f0aea941b69aeb9", "12": "3c83d1c92e7876354587e153fd6c51690cb7670b", "15": "939017a041aba63d4c1c58482442e764c79c44d1", "20": "a0462763b4e9ed6ffc8103e7b8bea746b4ae4b35", "23": "5b0a26f3a91226a095a430b47ebd8ce1ea7a2306", "27": "072c8858689e910947d32207882af6a35e7295f1", "45": "0c31e23bbc9ff156236432b7d37ca2d8429a692e", "49": "6c5f5834a43b53e27991fe8ddb0dfffbff96216a", "52": "aafcfe8f6a632ac01b1e70cc067f8c35dabbc9d7", "55": "ea30c944a7dc805fa4ab00a573cc07d5022473e5", "56": "c49b8e1208d42f6f1b30eb1bed36f98a213fd85c", "59": "01463ec2090d7a38975ec8a391f01270d519a575", "69": "40d83aa2a9294b38ee7e889d6dbe8178e44df7c8", "72": "9b287e05eb1e6d0ac9d3b11b06a4e34243592f17", "73": "eee128a6f6967a5b949582a900964648aad899fc", "77": "2bc1e09400c9c39bca72e4217c417cccf9ebeaca", "81": "da499449d7f7ae63485074401eabde47259a9c9b", "83": "ac94f790ad4e7416910d75e827a4ec5dd05eff8c", "94": 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"24474dc1fcf9936eb2b9a0c373cc8259bf843073", "293": "7a7b08ac346160e0507d2d5cf761aa9fc910746b", "294": "25de82cc6b08b306dfca6e78be42d994a49e0947", "300": "9ee112e9fe652dcb99168b98bdfb4d2e22c9a115", "303": "8a9444bfe2464e3cf6c7a3e2fea30b1976447551", "304": "aa81e3f282f3738f045de593dde3be79a1b5cf01", "307": "4a87261cff013be89f0e5c4a036cb36b3afd9178", "315": "9fcdf6dd2cbaec98eb42696876a3b057a6f8422f", "321": "6cbbbfa3779751414208a4b9ce1e6a88855f80d5", "328": "da0c370dae56e09f8e987fd61cae0ebb69755412", "329": "96494ce286d9c456225331e47f8598546aeeca32", "332": "0c25e49a24b27b83292808889cf1d81bb4d5a24f", "338": "91f974fa983ea52cc4e436d0107ee2880ae3bda9", "348": "1b2bc95cf15887619499ea06f0865ddb90ee1253", "356": "c3ff90fc5b0a8dadd7b998741d113741c301a5b3", "358": "a8470898e73d0a380ede24cfb40222684e7686c5", "366": "088ddc10364ac68d61eb36437cd8aa36112fe9ef", "369": "21311734e771e20fe0558c293a2cf7bf3bcc8d85", "371": "079e2e0fca5f873a101fe378bf7f88f96670c6c6", "376": 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1728392437000, "495": 1728392512000, "498": 1728407191000, "501": 1728494355000, "502": 1728501921000, "504": 1728575746000, "511": 1728917431000, "516": 1728997713000, "520": 1729009230000, "521": 1729012143000, "526": 1729023224000, "528": 1729027028000, "533": 1729081509000, "534": 1729081522000, "535": 1729083673000, "546": 1729264175000, "547": 1729268216000, "548": 1729269585000, "552": 1729888358000, "558": 1730127258000, "561": 1730128044000, "566": 1730229976000, "570": 1730385716000, "572": 1730388286000}, "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 4801939..209a82f 100644 --- a/info.json +++ b/info.json @@ -1,4 +1,4 @@ { "asv-version": "0.6.4", - "timestamp": 1730385962908 + "timestamp": 1730388523925 } \ No newline at end of file diff --git a/regressions.json b/regressions.json index c5aaa10..523a24b 100644 --- a/regressions.json +++ b/regressions.json @@ -1 +1 @@ -{"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, 0.004949983166667948, 0.0040748664999531075, [[null, 487, 0.0040748664999531075, 0.004949983166667948]]]]} \ 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, 0.0047467268333321044, 0.0040748664999531075, [[null, 487, 0.0040748664999531075, 0.0047467268333321044]]]]} \ No newline at end of file diff --git a/regressions.xml b/regressions.xml index 2e20ac9..358f6c6 100644 --- a/regressions.xml +++ b/regressions.xml @@ -1,5 +1,5 @@ -tag:tdastro.asv,1970-01-01:/ebf359c79517f805bb81f390ee0edbc35610f7474e3a5cd79486c82d8dd189a2Airspeed Velocitytdastro performance regressions2024-10-15T20:55:55Ztag:tdastro.asv,2024-10-07:/16355e5158d0c63b3b16aa22b8ee4e49d044a04d50d10dab0d3701cf45cd57b621.48% benchmarks.TimeSuite.time_evaluate_salt3_passbands2024-10-15T20:55:55Z<a href="index.html#benchmarks.TimeSuite.time_evaluate_salt3_passbands?commits=3033e07d6238822d258931ac5af152e64847db1e">21.48% 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: 4.95ms, old value: 4.07ms.<br> - Latest value: 4.95ms (21.48% worse +tag:tdastro.asv,1970-01-01:/ebf359c79517f805bb81f390ee0edbc35610f7474e3a5cd79486c82d8dd189a2Airspeed Velocitytdastro performance regressions2024-10-15T20:55:55Ztag:tdastro.asv,2024-10-07:/16355e5158d0c63b3b16aa22b8ee4e49d044a04d50d10dab0d3701cf45cd57b616.49% benchmarks.TimeSuite.time_evaluate_salt3_passbands2024-10-15T20:55:55Z<a href="index.html#benchmarks.TimeSuite.time_evaluate_salt3_passbands?commits=3033e07d6238822d258931ac5af152e64847db1e">16.49% 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: 4.75ms, old value: 4.07ms.<br> + Latest value: 4.75ms (16.49% worse than best value 4.07ms). \ No newline at end of file