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Functions513 in github.com/dfm/george

⨍ Functions 513◇ Types & classes 73 ↳ Endpoints 2

↓ 62 callersMethodget_axis

src/george/include/george/kernels.h:644

↓ 42 callersMethodget_naxes

src/george/include/george/subspace.h:18

↓ 33 callersMethodsize

Parameter vector spec.

src/george/include/george/kernels.h:34

↓ 29 callersMethodvalue

src/george/include/george/kernels.h:78

↓ 20 callersMethodset_parameter_vector

Set the parameter values to the given vector Args: vector (array[vector_size] or array[full_size]): The target

src/george/modeling.py:233

↓ 18 callersMethodExpSquaredKernel

src/george/include/george/kernels.h:1843

↓ 18 callersMethodblock

src/george/kernels.py:423

↓ 18 callersMethodget_axis

src/george/include/george/metrics.h:58

↓ 18 callersMethodget_ndim

src/george/include/george/kernels.h:35

↓ 18 callersMethodget_parameter_vector

Get an array of the parameter values in the correct order Args: include_frozen (Optional[bool]): Should the frozen param

src/george/modeling.py:220

↓ 16 callersMethodget_parameter_names

Get a list of the parameter names Args: include_frozen (Optional[bool]): Should the frozen parameters be

src/george/modeling.py:190

↓ 16 callersMethodset_axis

Axes specification.

src/george/include/george/metrics.h:55

↓ 14 callersMethodcompute

Pre-compute the covariance matrix and factorize it for a set of times and uncertainties. :param x: ``(nsamples,)`` or ``(nsa

src/george/gp.py:303

↓ 13 callersMethod__init__

src/george/kernels.py:208

↓ 13 callersMethodsize

Parameter vector spec.

templates/kernels.h:38

↓ 12 callersMethodget_ndim

templates/kernels.h:39

↓ 12 callersMethodget_parameter

src/george/include/george/kernels.h:37

↓ 12 callersMethodset_axis

src/george/include/george/kernels.h:647

↓ 12 callersMethodx1_gradient

src/george/include/george/kernels.h:94

↓ 11 callersMethodget_value

A synonym for :func:`GP.log_likelihood` provided for consistency with the modeling protocol.

src/george/gp.py:621

↓ 10 callersMethodndim

src/george/kernel_interface.cpp:18

↓ 10 callersMethodset_parameter

src/george/include/george/kernels.h:60

↓ 10 callersMethodsolve

src/george/include/george/hodlr.h:108

↓ 10 callersMethodvalue

templates/kernels.h:82

↓ 9 callersMethodget_value

tests/test_modeling.py:67

↓ 9 callersMethodpredict

Compute the conditional predictive distribution of the model. You must call :func:`GP.compute` before this function. :param

src/george/gp.py:482

↓ 8 callersMethodExpSine2Kernel

src/george/include/george/kernels.h:1478

↓ 8 callersMethodLinearKernel

src/george/include/george/kernels.h:184

↓ 8 callersFunctionbuild_kernel

(metric, **more)

tests/test_kernels.py:95

↓ 8 callersMethodgradient

src/george/include/george/kernels.h:81

↓ 7 callersMethod_call_mean

src/george/gp.py:149

↓ 7 callersFunctioncheck_gradient

(obj, *args, **kwargs)

src/george/utils.py:71

↓ 7 callersMethodget_gradient

A synonym for :func:`GP.grad_log_likelihood` provided for consistency with the modeling protocol.

src/george/gp.py:629

↓ 7 callersFunctiontest_kernel

(kernel, N=20, seed=123, eps=1.32e-6)

tests/test_kernels.py:66

↓ 7 callersFunctiontest_x_gradient_kernel

(kernel, N=20, seed=123, eps=1.32e-6)

tests/test_kernels.py:74

↓ 6 callersMethodapply_inverse

src/george/solvers/_hodlr.cpp:97

↓ 6 callersMethodfreeze_all_parameters

Freeze all parameters of the model

src/george/modeling.py:290

↓ 6 callersMethodget_matrix

Get the covariance matrix at a given set or two of independent coordinates. :param x1: ``(nsamples,)`` or ``(nsamples, ndim)

src/george/gp.py:602

↓ 6 callersMethodget_value

(self, x1, x2=None, diag=False)

src/george/kernels.py:102

↓ 6 callersMethodget_value

src/george/include/george/kernels.h:651

↓ 6 callersMethodrecompute

Re-compute a previously computed model. You might want to do this if the kernel parameters change and the kernel is labeled as ``dirt

src/george/gp.py:339

↓ 6 callersMethodset_metric_parameter

src/george/include/george/kernels.h:640

↓ 5 callersMethodPolynomialKernel

src/george/include/george/kernels.h:2240

↓ 5 callersMethod_check_dimensions

(self, y, check_dim=True)

src/george/gp.py:251

↓ 5 callersMethodapply_inverse

Self-consistently apply the inverse of the computed kernel matrix to some vector or matrix of samples. This method subtracts the mean

src/george/gp.py:277

↓ 5 callersMethodlog_prior

Compute the log prior probability of the current parameters

src/george/modeling.py:323

↓ 5 callersMethodparse_samples

Parse a list of samples to make sure that it has the correct dimensions. :param t: ``(nsamples,)`` or ``(nsamples, ndim)``

src/george/gp.py:223

↓ 4 callersMethodCosineKernel

src/george/include/george/kernels.h:1110

↓ 4 callersMethodLocalGaussianKernel

src/george/include/george/kernels.h:804

↓ 4 callersMethod_apply_to_parameter

(self, func, name, *args)

src/george/modeling.py:434

↓ 4 callersMethodget_ndim

src/george/include/george/metrics.h:61

↓ 4 callersMethodget_parameter

templates/kernels.h:41

↓ 4 callersMethodgrad_log_likelihood

Compute the gradient of :func:`GP.log_likelihood` as a function of the parameters returned by :func:`GP.get_parameter_vector`. You mu

src/george/gp.py:406

↓ 4 callersMethodlnlikelihood

(self, y, quiet=False)

src/george/gp.py:362

↓ 4 callersMethodlog_likelihood

Compute the logarithm of the marginalized likelihood of a set of observations under the Gaussian process model. You must call

src/george/gp.py:369

↓ 4 callersMethodsample

Draw samples from the prior distribution. :param t: ``(ntest, )`` or ``(ntest, ndim)`` (optional) The coordinates where

src/george/gp.py:569

↓ 4 callersMethodvalue

src/george/kernel_interface.cpp:20

↓ 4 callersMethodx1_gradient

templates/kernels.h:98

↓ 3 callersMethodConstantKernel

src/george/include/george/kernels.h:1683

↓ 3 callersMethodDotProductKernel

src/george/include/george/kernels.h:2417

↓ 3 callersMethod_compute_alpha

(self, y, cache)

src/george/gp.py:260

↓ 3 callersFunction_custom_forward_sub

Warning: Herein lie custom Cholesky functions. Use at your own risk!

src/george/include/george/metrics.h:144

↓ 3 callersMethodget_parameter_bounds

Get a list of the parameter bounds Args: include_frozen (Optional[bool]): Should the frozen parameters be

src/george/modeling.py:205

↓ 3 callersMethodgradient

templates/kernels.h:85

↓ 3 callersMethodgradient

src/george/kernel_interface.cpp:21

↓ 3 callersFunctionlnprob

(p, x, y, yerr, **kwargs)

tests/test_tutorial.py:30

↓ 3 callersMethodsize

src/george/kernel_interface.cpp:19

↓ 3 callersMethodthaw_all_parameters

Thaw all parameters of the model

src/george/modeling.py:294

↓ 2 callersMethodRationalQuadraticKernel

src/george/include/george/kernels.h:364

↓ 2 callersMethod__add__

src/george/kernels.py:83

↓ 2 callersMethod__init__

( self, kernel=None, fit_kernel=True, mean=None, fit_mean=None,

src/george/gp.py:63

↓ 2 callersMethod__mul__

src/george/kernels.py:92

↓ 2 callersMethod_call_white_noise

src/george/gp.py:195

↓ 2 callersFunction_general_metric

(metric, N=100, ndim=3)

tests/test_metrics.py:40

↓ 2 callersMethod_get_name

(self, name_or_index)

src/george/modeling.py:140

↓ 2 callersFunction_parse_model

src/george/gp.py:638

↓ 2 callersFunction_test_solver

(Solver, N=300, seed=1234, **kwargs)

tests/test_solvers.py:29

↓ 2 callersMethodcompute

src/george/include/george/hodlr.h:75

↓ 2 callersMethodcompute_gradient

Compute the "gradient" of the model for the current parameters The default implementation computes the gradients numerically using

src/george/modeling.py:107

↓ 2 callersMethodfreeze_parameter

Freeze a parameter by name Args: name: The name of the parameter

src/george/modeling.py:268

↓ 2 callersMethodget_ndim

src/george/include/george/kernels.h:198

↓ 2 callersMethodget_ndim

src/george/include/george/kernels.h:390

↓ 2 callersMethodget_ndim

src/george/include/george/kernels.h:628

↓ 2 callersMethodget_ndim

src/george/include/george/kernels.h:818

↓ 2 callersMethodget_ndim

src/george/include/george/kernels.h:1006

↓ 2 callersMethodget_ndim

src/george/include/george/kernels.h:1122

↓ 2 callersMethodget_ndim

src/george/include/george/kernels.h:1296

↓ 2 callersMethodget_ndim

src/george/include/george/kernels.h:1492

↓ 2 callersMethodget_ndim

src/george/include/george/kernels.h:1695

↓ 2 callersMethodget_ndim

src/george/include/george/kernels.h:1867

↓ 2 callersMethodget_ndim

src/george/include/george/kernels.h:2061

↓ 2 callersMethodget_ndim

src/george/include/george/kernels.h:2254

↓ 2 callersMethodget_ndim

src/george/include/george/kernels.h:2427

↓ 2 callersMethodget_parameter_dict

Get an ordered dictionary of the parameters Args: include_frozen (Optional[bool]): Should the frozen parameters be

src/george/modeling.py:176

↓ 2 callersMethodget_value

Compute the "value" of the model for the current parameters This method should be overloaded by subclasses to implement the actual

src/george/modeling.py:97

↓ 2 callersMethodget_x1_gradient

(self, x1, x2=None)

src/george/kernels.py:129

↓ 2 callersMethodget_x2_gradient

(self, x1, x2=None)

src/george/kernels.py:137

↓ 2 callersFunctionmultivariate_gaussian_samples

Generate samples from a multidimensional Gaussian with a given covariance. :param matrix: ``(k, k)`` The covariance matrix. :pa

src/george/utils.py:11

↓ 2 callersFunctionparse_kernel_spec

src/george/include/george/parser.h:14

↓ 2 callersMethodset_parameter

templates/kernels.h:64

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