index.json

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1566455044000, "1825": 1566818165000, "1826": 1566818363000, "1827": 1566819364000, "1828": 1566882750000, "1829": 1566960590000, "1830": 1567061235000, "1831": 1567655501000, "1832": 1567658469000, "1840": 1568238425000, "1841": 1568860593000, "1843": 1568884079000, "1847": 1569027875000, "1848": 1571558717000, "1849": 1571614692000, "1851": 1572120032000, "1854": 1572283176000, "1858": 1572462593000, "1860": 1572726778000, "1868": 1572758393000, "1870": 1572820280000, "1871": 1572840255000, "1873": 1573160664000, "1874": 1574262173000, "1875": 1574833188000, "1876": 1574981127000, "1877": 1574987152000, "1878": 1575435171000, "1879": 1575491925000, "1880": 1575508260000, "1884": 1575510891000, "1886": 1575511034000, "1887": 1575516702000, "1890": 1576194518000, "1892": 1576357920000, "1894": 1576361571000, "1896": 1576528841000, "1897": 1576562827000, "1898": 1576562938000, "1903": 1576614861000, "1904": 1576638977000, "1906": 1576641626000, "1908": 1577329247000, "1910": 1577329393000, "1916": 1577492217000, "1918": 1577497717000}, "params": {"arch": ["x86_64"], "cpu": ["Intel(R) Core(TM) i5-6500 CPU @ 3.20GHz"], "machine": ["192-168-1-103.tpgi.com.au"], "num_cpu": ["4"], "os": ["Darwin 19.2.0"], "ram": ["17179869184"], "python": ["3.7"], "Cython": [""], "numpy": [""], "pip+ptemcee": [""], "branch": ["master"]}, "graph_param_list": [{"arch": "x86_64", "cpu": "Intel(R) Core(TM) i5-6500 CPU @ 3.20GHz", "machine": "192-168-1-103.tpgi.com.au", "num_cpu": "4", "os": "Darwin 19.2.0", "ram": "17179869184", "python": "3.7", "Cython": "", "numpy": "", "pip+ptemcee": "", "branch": "master"}], "benchmarks": {"analysis.curvefitter.time_sampler": {"code": "class curvefitter:\n    def time_sampler(self):\n        # to get an idea of how fast the actual sampling is.\n        # i.e. the overhead of objective.lnprob, objective.lnprior, etc\n        self.mcfitter.sampler.run_mcmc(self.mcfitter._state, 100)\n\n    def setup(self):\n        # Reproducible results!\n        np.random.seed(123)\n    \n        m_true = -0.9594\n        b_true = 4.294\n        f_true = 0.534\n        m_ls = -1.1040757010910947\n        b_ls = 5.4405552502319505\n    \n        # Generate some synthetic data from the model.\n        N = 50\n        x = np.sort(10 * np.random.rand(N))\n        y_err = 0.1 + 0.5 * np.random.rand(N)\n        y = m_true * x + b_true\n        y += np.abs(f_true * y) * np.random.randn(N)\n        y += y_err * np.random.randn(N)\n    \n        data = Data1D(data=(x, y, y_err))\n    \n        p = Parameter(b_ls, 'b', vary=True, bounds=(-100, 100))\n        p |= Parameter(m_ls, 'm', vary=True, bounds=(-100, 100))\n    \n        model = Model(p, fitfunc=line)\n        self.objective = Objective(model, data)\n        self.mcfitter = CurveFitter(self.objective)\n        self.mcfitter_t = CurveFitter(self.objective, ntemps=20)\n    \n        self.mcfitter.initialise('prior')\n        self.mcfitter_t.initialise('prior')", "min_run_count": 2, "name": "analysis.curvefitter.time_sampler", "number": 0, "param_names": [], "params": [], "processes": 2, "repeat": 3, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "b9033268a72333826e2a44df97866213108aac3912fcd4c9ff8ac7baa6a43d1e", "warmup_time": -1}, "analysis.curvefitter.time_sampler_pool": {"code": "class curvefitter:\n    def time_sampler_pool(self):\n        # see how the multiprocessing in curvefitter performs\n        # automatically use all the cores available\n        self.mcfitter_t.sample(20, pool=-1)\n\n    def setup(self):\n        # Reproducible results!\n        np.random.seed(123)\n    \n        m_true = -0.9594\n        b_true = 4.294\n        f_true = 0.534\n        m_ls = -1.1040757010910947\n        b_ls = 5.4405552502319505\n    \n        # Generate some synthetic data from the model.\n        N = 50\n        x = np.sort(10 * np.random.rand(N))\n        y_err = 0.1 + 0.5 * np.random.rand(N)\n        y = m_true * x + b_true\n        y += np.abs(f_true * y) * np.random.randn(N)\n        y += y_err * np.random.randn(N)\n    \n        data = Data1D(data=(x, y, y_err))\n    \n        p = Parameter(b_ls, 'b', vary=True, bounds=(-100, 100))\n        p |= Parameter(m_ls, 'm', vary=True, bounds=(-100, 100))\n    \n        model = Model(p, fitfunc=line)\n        self.objective = Objective(model, data)\n        self.mcfitter = CurveFitter(self.objective)\n        self.mcfitter_t = CurveFitter(self.objective, ntemps=20)\n    \n        self.mcfitter.initialise('prior')\n        self.mcfitter_t.initialise('prior')", "min_run_count": 2, "name": "analysis.curvefitter.time_sampler_pool", "number": 0, "param_names": [], "params": [], "processes": 2, "repeat": 3, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "80b57c9e0516d3713e934c5ae33eca4190726c11182654dc590bd44f2ff418c4", "warmup_time": -1}, "reflect.Abeles.time_abeles": {"code": "class Abeles:\n    def time_abeles(self):\n        abeles(self.q, self.layers)\n\n    def setup(self):\n        self.q = np.linspace(0.005, 0.5, 50000)\n        self.layers = np.array([[0, 2.07, 0, 3],\n                                [50, 3.47, 0.0001, 4],\n                                [200, -0.5, 1e-5, 5],\n                                [50, 1, 0, 3],\n                                [0, 6.36, 0, 3]])\n        self.repeat=20\n        self.number=10", "min_run_count": 2, "name": "reflect.Abeles.time_abeles", "number": 0, "param_names": [], "params": [], "processes": 2, "repeat": 0, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "72f7d2a7b548c541b1deb0f714cfa4caab18cd0c8f3f78487bc75d5d9a2d321b", "warmup_time": -1}, "reflect.Abeles.time_cabeles": {"code": "class Abeles:\n    def time_cabeles(self):\n        c_abeles(self.q, self.layers)\n\n    def setup(self):\n        self.q = np.linspace(0.005, 0.5, 50000)\n        self.layers = np.array([[0, 2.07, 0, 3],\n                                [50, 3.47, 0.0001, 4],\n                                [200, -0.5, 1e-5, 5],\n                                [50, 1, 0, 3],\n                                [0, 6.36, 0, 3]])\n        self.repeat=20\n        self.number=10", "min_run_count": 2, "name": "reflect.Abeles.time_cabeles", "number": 0, "param_names": [], "params": [], "processes": 2, "repeat": 0, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "2505a32e0cc68c7420c4d94accda2f528dfa9c1f84907ca9996357ab1c81b1b0", "warmup_time": -1}, "reflect.Abeles.time_reflectivity": {"code": "class Abeles:\n    def time_reflectivity(self):\n        reflectivity(self.q, self.layers)\n\n    def setup(self):\n        self.q = np.linspace(0.005, 0.5, 50000)\n        self.layers = np.array([[0, 2.07, 0, 3],\n                                [50, 3.47, 0.0001, 4],\n                                [200, -0.5, 1e-5, 5],\n                                [50, 1, 0, 3],\n                                [0, 6.36, 0, 3]])\n        self.repeat=20\n        self.number=10", "min_run_count": 2, "name": "reflect.Abeles.time_reflectivity", "number": 0, "param_names": [], "params": [], "processes": 2, "repeat": 0, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "3bc383d6ab84102273e394c305e83b224a69c853934fdda6163cc25e0989f07e", "warmup_time": -1}, "reflect.Reflect.time_reflect_emcee": {"code": "class Reflect:\n    def time_reflect_emcee(self):\n        self.fitter.sampler.run_mcmc(self.fitter._state, 50)\n\n    def setup(self):\n        pth = os.path.dirname(os.path.abspath(refnx.reflect.__file__))\n        e361 = RD(os.path.join(pth, 'test', 'e361r.txt'))\n    \n        sio2 = SLD(3.47, name='SiO2')\n        si = SLD(2.07, name='Si')\n        d2o = SLD(6.36, name='D2O')\n        polymer = SLD(1, name='polymer')\n    \n        # e361 is an older dataset, but well characterised\n        structure361 = si | sio2(10, 4) | polymer(200, 3) | d2o(0, 3)\n        model361 = ReflectModel(structure361, bkg=2e-5)\n    \n        model361.scale.vary = True\n        model361.bkg.vary = True\n        model361.scale.range(0.1, 2)\n        model361.bkg.range(0, 5e-5)\n        model361.dq = 5.\n    \n        # d2o\n        structure361[-1].sld.real.vary = True\n        structure361[-1].sld.real.range(6, 6.36)\n    \n        structure361[1].thick.vary = True\n        structure361[1].thick.range(5, 20)\n        structure361[2].thick.vary = True\n        structure361[2].thick.range(100, 220)\n    \n        structure361[2].sld.real.vary = True\n        structure361[2].sld.real.range(0.2, 1.5)\n    \n        # e361.x_err = None\n        objective = Objective(model361,\n                              e361)\n        self.fitter = CurveFitter(objective, nwalkers=200)\n        self.fitter.initialise('jitter')", "min_run_count": 2, "name": "reflect.Reflect.time_reflect_emcee", "number": 0, "param_names": [], "params": [], "processes": 2, "repeat": 2, "sample_time": 0.01, "timeout": 120.0, "type": "time", "unit": "seconds", "version": "527ee263336a16b57a3f3f561fe3312ba896327d3c99cb2e14c0cdc6e80fa7f3", "warmup_time": -1}}, "machines": {"192-168-1-103.tpgi.com.au": {"arch": "x86_64", "cpu": "Intel(R) Core(TM) i5-6500 CPU @ 3.20GHz", "machine": "192-168-1-103.tpgi.com.au", "num_cpu": "4", "os": "Darwin 19.2.0", "ram": "17179869184", "version": 1}}, "tags": {"v0.0.1": 555, "v0.0.10": 943, "v0.0.11": 948, "v0.0.12": 1016, "v0.0.13": 1150, "v0.0.14": 1193, "v0.0.15": 1203, "v0.0.16": 1325, "v0.0.17": 1327, "v0.0.3": 618, "v0.0.4": 687, "v0.0.5": 723, "v0.0.6": 735, "v0.0.7": 824, "v0.0.8": 828, "v0.0.9": 936, "v0.1.0": 1357, "v0.1.1": 1399, "v0.1.10": 1879, "v0.1.2": 1475, "v0.1.3": 1576, "v0.1.4": 1605, "v0.1.5": 1662, "v0.1.6": 1678, "v0.1.7": 1732, "v0.1.8": 1778, "v0.1.9": 1818}, "pages": [["", "Grid view", "Display as a agrid"], ["summarylist", "List view", "Display as a list"], ["regressions", "Show regressions", "Display information about recent regressions"]]}