Pseudo Wells#
This section contains documentation for the Pseudo Wells module.
Monte Carlo Simulations#
Determines n Monte Carlo Simulations (MCS) using data1 and data2 as correlated variables [1]_.
Parameters#
- ninteger
Number of simulations to be performed.
- data1array_like
A dataset that represents a given porperty, related to data2.
- data2array_like
A dataset that represents a given porperty, related to data1.
- smooth_data1array_like
A smoothed version of the data1, or its general trend.
- smooth_data2array_like
A smoothed version of the data2, or its general trend.
- covarray_like
Spatial symmetrical covariance matrix representing both data1 and data2.
Returns#
- simulationsarray_like
n Monte Carlo Simulations with correlated variables for data1 and n Monte Carlo Simulations with correlated variables for data2, in this order.
References#
properties. [S.l.]: Cambridge University Press, 2014.
- stoneforge.pseudo_wells.monte_carlo_simulations.MCS_spacial_correlation(n: int, smooth_data: Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], cov: Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes]) ndarray#
Determines n Monte Carlo Simulations (MCS) with spatial correlation [1]_ for a given dataset.
Parameters#
- ninteger
Number of simulations to be performed.
- smooth_dataarray_like
a smoothed version of the data under examination, or its general trend.
- covarray_like
Spatial symmetrical covariance matrix of the data.
Returns#
- simulationsarray_like
n Monte Carlo simulations with spatial correlation for a given property, each line of this matrix represents a different simulation.
References#
properties. [S.l.]: Cambridge University Press, 2014.
- stoneforge.pseudo_wells.monte_carlo_simulations.analytical_variogram(distance: Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], gama: Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], initial_guess: Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes]) ndarray#
Fits the choosen analytical variogram function (model) to the experimental one [1]_, if no model is choosen, determines the best model to fit, comparing the Gaussian, Exponential and Spherical models [2].
Parameters#
- distancearray_like
1D array containing all the possible distances between a pair of points in the dataset.
- gamaarray_like
1D experimental variogram of the data under examination.
- modelstr, optional
- Analytical variogram model to be fitted. Should be one of:
“exponential”: fits the exponential model
“gaussian”: fits the gaussian model
“spherical”: fits the spherical model
“best-fit”: fits the three models above and verifies which one produces the smallest error.
If not given, default method is “best-fit”.
Returns#
- modeled_variogramarray_like
The variogram model that has been choosen, or the variogram model that fits the best the experimental one.
- coeficientsarray_like
The range, sill and nugget optimal values for the modeled variogram.
References#
Examples and Algorithms. India: Wiley Blackwell, 2021.
- stoneforge.pseudo_wells.monte_carlo_simulations.cov_matrix(rho: Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], var: float) ndarray#
Determines the 1D spatial symmetrical covariance matrix [1]_ from a modeled correlation function.
Parameters#
- rhoarray_like
1D modeled correlation function of the data under examination.
- varfloat
Variance (the square of the standard deviation) of the dataset.
Returns#
- covarray_like
Spatial symmetrical covariance matrix of the data.
References#
properties. [S.l.]: Cambridge University Press, 2014.
- stoneforge.pseudo_wells.monte_carlo_simulations.experimental_correlation(data: Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes]) ndarray#
Determines the 1D experimental correlation function [1]_ for a dataset by calculating the Pearson correlation coefficient for each possible separation of samples.
Parameters#
- dataarray_like
1D dataset for which the experimental correlation function must be calculated.
Returns#
- rhoarray_like
1D experimental correlation function of the data under examination.
References#
properties. [S.l.]: Cambridge University Press, 2014.
- stoneforge.pseudo_wells.monte_carlo_simulations.experimental_variogram(data: Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], rho: Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes]) ndarray#
Determines the 1D experimental variogram [1]_ of a dataset.
Parameters#
- dataarray_like
1D dataset for which the experimental variogram must be calculated.
- rhoarray_like
1D experimental correlation function of the data under examination.
Returns#
- gamaarray_like
1D experimental variogram of the data under examination.
References#
properties. [S.l.]: Cambridge University Press, 2014.
- stoneforge.pseudo_wells.monte_carlo_simulations.exponential_variogram_model(distance: Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], correlation_length: float, sill: float, nugget: float = 0) ndarray#
Builds a variogram following the exponential model, using the correlation length, sill and nugget given [1]_.
Parameters#
- distancearray_like
1D array containing all the possible distances between a pair of points in the dataset.
- correlation_lengthfloat
The range of the variogram, or the distance where it loses the correlation
- sillfloat
The maximum value of the variogram, it is equivalent to the variance of the data
- nuggetfloat
The nugget effect, y value where the variogam begins
Returns#
- rhoarray_like
The variogram that follows the exponential model and has the given correlation length, sill and nugget
References#
Examples and Algorithms. India: Wiley Blackwell, 2021.
- stoneforge.pseudo_wells.monte_carlo_simulations.gaussian_variogram_model(distance: Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], correlation_length: float, sill: float, nugget: float = 0) ndarray#
Builds a variogram following the gaussian model, using the correlation length, sill and nugget given [1]_.
Parameters#
- distancearray_like
1D array containing all the possible distances between a pair of points in the dataset.
- correlation_lengthfloat
The range of the variogram, or the distance where it loses the correlation
- sillfloat
The maximum value of the variogram, it is equivalent to the variance of the data
- nuggetfloat
The nugget effect, y value where the variogam begins
Returns#
- rhoarray_like
The variogram that follows the gaussian model and has the given correlation length, sill and nugget
References#
Examples and Algorithms. India: Wiley Blackwell, 2021.
- stoneforge.pseudo_wells.monte_carlo_simulations.modeled_correlation(gama: Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], var: float) ndarray#
Determines the 1D modeled correlation function [1]_ from a variogram model [2].
Parameters#
- gamaarray_like
1D analytical variogram model.
- varfloat
variance (the square of the standard deviation) of the dataset.
Returns#
- rhoarray_like
1D modeled correlation function of the data under examination.
References#
properties. [S.l.]: Cambridge University Press, 2014. .. [2] https://mmaelicke.github.io/scikit-gstat/reference/models.html
- stoneforge.pseudo_wells.monte_carlo_simulations.spherical_variogram_model(distance: Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], correlation_length: float, sill: float, nugget: float = 0) ndarray#
Builds a variogram following the spherical model, using the correlation length, sill and nugget given [1]_.
Parameters#
- distancearray_like
1D array containing all the possible distances between a pair of points in the dataset.
- correlation_lengthfloat
The range of the variogram, or the distance where it loses the correlation
- sillfloat
The maximum value of the variogram, it is equivalent to the variance of the data
- nuggetfloat
The nugget effect, y value where the variogam begins
Returns#
- rhoarray_like
The variogram that follows the spherical model and has the given correlation length, sill and nugget
References#
Examples and Algorithms. India: Wiley Blackwell, 2021.