Grid Classes

This platecurie module contains the following Grid classes:

MagGrid
ZtGrid

These classes can be initiatlized with a grid of corresponding data type, and contain methods inherited from Grid for the following functionality:

Performing a wavelet transform using a Morlet wavelet
Obtaining the wavelet scalogram from the wavelet transform
Plotting the input grids, wavelet transform components, and scalograms

MagGrid

class platecurie.classes.MagGrid(grid, dx, dy)

Basic grid class of platecurie for Magnetic anomaly data that inherits from Grid

Contains method to plot the grid data with default title and units using function plot_real_grid().

Additional Attributes

unitsstr: Units of Magnetic data (’\(nTesla\)’)
sg_unitsstr: Units of wavelet PSD (scalogram) (’\(nTesla^2/|k|\)’)
logsg_unitsstr: Units of log of wavelet PSD (log(scalogram)) (’\(log(nTesla^2/|k|)\)’)
title: str: Descriptor for Magnetic anomaly data

>>> import numpy as np
>>> from plateflex import Grid
>>> from platecurie import MagGrid
>>> maggrid = MagGrid(np.zeros((100, 100)), 10, 10)
>>> isinstance(maggrid, Grid)
True

ZtGrid

class platecurie.classes.ZtGrid(grid, dx, dy)

Basic grid class of platecurie for the top of the magnetic layer that inherits from Grid

Contains method to plot the grid data with default title and units using function plot_real_grid().

Additional Attributes

unitsstr: Units of depth (‘m’)
title: str: Descriptor for Depth to top of magnetic layer

Note

This class should only be used to specify the depth to the top of the magnetic layer at each cell location obtained from independent data. Although the Grid methods are still available, they are not useful in this context.

Project Class

This module further contains the class Project, which itself is a container of grid objects (with at least one MagGrid). Methods are available to:

Estimate model parameters at single grid cell
Estimate model parameters at every (or decimated) grid cell
Plot the statistics of the estimated parameters at single grid cell
Plot the fitted power-spectral density of the magnetic anomaly at single grid cell
Plot the final grids of model parameters
Save the results to disk as .csv files

Project

class platecurie.classes.Project(grids=None)

Container for MagGrid and/or ZtGrid objects, with methods to estimate model parameters of the magnetic layer as well as plot various results.

Parameters:: grids (list of Grid, optional) – Initial list of grid objects.

Default Attributes

gridsList: List of MagGrid objects
maskArray: 2D array of boolean values determined independently
initializedBool: Whether or not the project has been initialized and is ready for the estimation steps. By default this parameter is False, unless the method init() has been executed.

Note

A Project can hold a list of any length with any type of Grid or those defined in platecurie - however the estimation will only proceed if the project holds exactly one MagGrid object.

Examples

append(grid)

Append a single grid object to the current :class:`~platecurie.classes.Project object.

Parameters:: grid (Grid) – object to append to project

Example

>>> import numpy as np
>>> from platecurie import MagGrid, Project
>>> nn = 200; dd = 10.
>>> grid = MagGrid(np.random.randn(nn, nn), dd, dd)
>>> project = Project()
>>> project.append(grid)

extend(grid_list)

Extend the current Project object with a list of Grid objects.

Parameters:: trace_list – list of grid objects or Project.

Example

>>> import numpy as np
>>> from platecurie import MagGrid, ZtGrid, Project
>>> nn = 200; dd = 10.
>>> maggrid = MagGrid(np.random.randn(nn, nn), dd, dd)
>>> ztgrid = ZtGrid(np.random.randn(nn, nn), dd, dd)
>>> project = Project()
>>> project.extend(grids=[maggrid, ztgrid])

init(inverse='L2')

Method to initialize a project. This step is required before estimating the model parameters. The method checks that the project contains exactly one MagGrid. It also ensures that all grids have the same shape and sampling intervals. If a grid of type ZtGrid (and possibly SigZtGrid) is present, the project attributes will be updated with data from the grid to be used in the estimation part.

Type:: str, optional
Param:: Type of inversion to perform. By default the type is ‘L2’ for non-linear least-squares. Options are: ‘L2’ or ‘bayes’

Additional Attributes

nxint: Number of grid cells in the x-direction
nyint: Number of grid cells in the y-direction
ns int: Number of wavenumber samples
knp.ndarray: 1D array of wavenumbers
initializedbool: Set to True when method is called successfully

Optional Attributes

ztndarray: Grid of depth to top of magnetic anomaly (km) (shape (nx,ny))
sztndarray: Grid of uncertainty in depth to top of magnetic anomaly (km) (shape (nx,ny))

Example

>>> from platecurie import MagGrid, ZtGrid, Project
>>> nn = 200; dd = 10.
>>> maggrid = MagGrid(np.random.randn(nn, nn), dd, dd)
>>> ztgrid = ZtGrid(np.random.randn(nn, nn), dd, dd)
>>> project = Project[grids=[maggrid, ztgrid]]
>>> project.init()

estimate_cell(cell=(0, 0), fix_beta=None, returned=False)

Method to estimate the parameters of the magnetized layer at a single cell location of the input grid.

Parameters:

cell (tuple) – Indices of cell location within grid
fix_beta (float, optional) – If not None, fix beta parameter - otherwise estimate it
returned (bool, optional) – Whether to use to return the estimates

Additional Attributes

traceMultiTrace: Posterior samples from the MCMC chains
summaryDataFrame: Summary statistics from Posterior distributions
map_estimatedict: Container for Maximum a Posteriori (MAP) estimates
celltuple: Indices of cell location within grid

Results are stored as attributes of Project object.

estimate_grid(nn=10, fix_beta=None, parallel=False)

Method to estimate the parameters of the magnetized layer at all grid point locations. It is also possible to decimate the number of grid cells at which to estimate parameters.

Parameters:

nn (int) – Decimator. If grid shape is (nx, ny), resulting grids will have shape of (int(nx/nn), int(ny/nn)).
fix_beta (float, optional) – If not None, fix beta parameter - otherwise estimate it

Additional Attributes

mean_A_gridndarray: Grid with mean A estimates (shape (nx, ny))
MAP_A_gridndarray: Grid with MAP A estimates (shape (nx, ny))
std_A_gridndarray: Grid with std A estimates (shape (nx, ny))
mean_dz_gridndarray: Grid with mean dz estimates (shape (nx, ny))
MAP_dz_gridndarray: Grid with MAP dz estimates (shape (nx, ny))
std_dz_gridndarray: Grid with std dz estimates (shape (nx, ny))

Optional Additional Attributes

mean_zt_gridndarray: Grid with mean zt estimates (shape (nx, ny))
MAP_zt_gridndarray: Grid with MAP zt estimates (shape (nx, ny))
std_zt_gridndarray: Grid with std zt estimates (shape (nx, ny))
mean_beta_gridndarray: Grid with mean beta estimates (shape (nx, ny))
MAP_beta_gridndarray: Grid with MAP beta estimates (shape (nx, ny))
std_beta_gridndarray: Grid with std beta estimates (shape (nx, ny))

plot_stats(title=None, save=None)

Method to plot the marginal and joint distributions of samples drawn from the posterior distribution as well as the extracted statistics. Calls the function plot_stats() with attributes as arguments.

Parameters:

title (str, optional) – Title of plot
save (str, optional) – Name of file for to save figure

plot_functions(est='mean', title=None, save=None)

Method to plot observed and fitted PSD function using one of MAP or mean estimates. Calls the function plot_fitted() with attributes as arguments.

Parameters:

est (str, optional) – Type of inference estimate to use for predicting th PSD
title (str, optional) – Title of plot
save (str, optional) – Name of file for to save figure

plot_results(mean_A=False, MAP_A=False, std_A=False, mean_zt=False, MAP_zt=False, std_zt=False, mean_dz=False, MAP_dz=False, std_dz=False, mean_beta=False, MAP_beta=False, std_beta=False, chi2=False, mask=False, contours=None, filter=False, sigma=1, save=None, **kwargs)

Method to plot grids of estimated parameters with fixed labels and titles. To have more control over the plot rendering, use the function plot_real_grid() with the relevant quantities and plotting options.

Parameters:: mean/MAP/std_A/zt/dz/beta (bool) – Type of plot to produce. All variables default to False (no plot generated)

save_results(mean_A=False, MAP_A=False, std_A=False, mean_dz=False, MAP_dz=False, std_dz=False, mean_beta=False, MAP_beta=False, std_beta=False, mean_zt=False, MAP_zt=False, std_zt=False, chi2=False, mask=False, contours=None, filter=True, sigma=1, prefix='PlateFlex_')

Method to save grids of estimated parameters with default file names.

Parameters:

mean/MAP/std_A/dz/beta/zt (bool) – Type of grid to save. All variables default to False (no grid saved)
mask (bool) – Whether or not to save the mask
contours (List) – List of contours with coordinate positions
filter (bool) – Whether or not to filter the resulting grid using a Gaussian filter
sigma (int) – Standard deviation of filter (in terms of adjacent cells), if set to True
prefix (str) – Prefix of file name.

Note

It is advisable to save each grid separately using one call per grid, in order to have more control on file names.