Parameter Functions
Creator for parameters and priors for the gaussian model
- class src.magpy_rv.parameters.Gaussian(hparam, mu, sigma)[source]
Gaussian prior computed as:
\[-0.5 \cdot (\frac{(x - \mu)}{\sigma})^2 - 0.5 \cdot log(2\pi \cdot \sigma^2)\]- Parameters
hparam (string) – parameter label
mu (float) – centre of Gaussian Prior
sigma (float) – width of the Gaussian Prior
- class src.magpy_rv.parameters.Jeffrey(hparam, minval, maxval)[source]
Jeffrey prior computed as:
p(x) proportional to \(\frac{1}{x}\) with upper and lower bound to avoid singularity at x = 0
and normalized as:
\(\frac{1}{ln(\frac{maxval}{minval})}\)
- Parameters
hparam (string) – parameter label
minval (float) – minimum allowed value
maxval (float) – maximum allowed value
- class src.magpy_rv.parameters.Modified_Jeffrey(hparam, minval, maxval, kneeval)[source]
Modified Jeffrey prior computed as:
p(x) proportional to \(\frac{1}{x-x_0}\) with upper bound
- Parameters
hparam (string) – parameter label
kneeval (float) – x0, knee of the Jeffrey prior
minval (float) – minimum allowed value
maxval (float) – maximum allowed value
- src.magpy_rv.parameters.PRINTPRIORDER(pri_name=None)[source]
Function to print the information and orders for the prior values
- Parameters
pri_name (string, optional) – name of the desired prior to check Defaults to None
- src.magpy_rv.parameters.PrintPriorList()[source]
Function to print the list of all currently available PRIORS
- class src.magpy_rv.parameters.Prior[source]
Parent class for all priors. All new priors should inherit from this class and follow its structure. Each new prior will require a __init__() method to override the parent class. In the __init__ function, call the neccesary parameters.
- class src.magpy_rv.parameters.Uniform(hparam, minval, maxval)[source]
Uniform prior
- Parameters
hparam (string) – parameter label
minval (float) – minimum allowed value
maxval (float) – maximum allowed value
- src.magpy_rv.parameters.defPriorList()[source]
Function to return the list of all currently available PRIORS
- src.magpy_rv.parameters.par_create(kernel)[source]
Funciton to create the hyperparameters of the kernel to be used in the gp model
- Parameters
kernel (string) – name of the desired kernel
- Returns
hparams – dictionary of necessary hyperparameters for the relevant kernel
- Return type
dict
- class src.magpy_rv.parameters.parameter(value=None, error=None, vary=True)[source]
Object to assign initial values to a parameter and define whether it is allowed to vary in the fitting
- Parameters
value (float, optional) – Assumed initial value of the chosen variable. The default is None.
error (float, optional) – Error on the value. The default is None
vary (True or False, optional) – Is the variable allowed to vary? The default is True.
- src.magpy_rv.parameters.pri_create(param_name, prior, vals=None)[source]
Funciton to generate a set of parameters necessary for the chosen prior
- Parameters
param_name (string) – name of parameter that the prior is being assigned to - should be the same as it appears in the kernel or model
prior (string) – name of the desired prior
vals (list or tuple of floats or ints, optional) – list of floats containing the prior parameters in order specified by the PRINTPRIORDER function. To view which values belong in the list and the format, run the PRINTPRIORDER function.
- Raises
Assertion: – Raised if vals is not None and not a list or a tuple
Assertion: – Raised if vals is not None and not made of floats or ints
Assertion: – Raised if length of the vals list does not match the required length for the prior
Assertion: – Raised if the minval is larger than the maxval for the prior
- Returns
prior_params – dictionary of all prior parameters
- Return type
dictionary