Grid Classes

This platecurie module contains the following Grid classes:

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.