API reference: The pydEXP package

Imaging methods for potential fields.

Implements the DEXP method described in Fedi and Pilkington (2012).

Note

This is part of a larger project aiming at inverting current sources density (see more at: https://icsd-dev.readthedocs.io/en/latest/)

References

Fedi, M., and M. Pilkington (2012), Understanding imaging methods for potential field data, Geophysics, 77(1), G13, doi:10.1190/geo2011-0078.1

dEXP.dEXP(x, y, z, data, shape, zmin, zmax, nlayers, qorder=0, SI=1)[source]

DEXP model (Fedi, 2012). (NOT YET TESTED)

Calculates a physical property distribution given potential field data on a regular grid. Uses depth weights. Parameters:

  • x, y1D-arrays

    The x and y coordinates of the grid points

  • zfloat or 1D-array

    The z coordinate of the grid points

  • data1D-array

    The potential field at the grid points

  • shapetuple = (ny, nx)

    The shape of the grid

  • zmin, zmaxfloat

    The top and bottom, respectively, of the region where the physical property distribution is calculated

  • nlayersint

    The number of layers used to divide the region where the physical property distribution is calculated

  • qorderfloat

    The order of the vertical derivative

  • SIfloat

    The structural index

Returns:

  • meshfatiando.mesher.PrismMesh

    The estimated physical property distribution set in a prism mesh (for easy 3D plotting)

dEXP.dEXP_ratio(x, y, z, data, shape, zmin, zmax, nlayers, qorders=[1, 0])[source]

DEXP ratio model (NOT YET validated) Abbas, M. A., and Fedi, M. (2014). Automatic DEXP imaging of potential fields independent of the structural index. Geophysical Journal International, 199 (3), 1625-1632.

Calculates a physical property distribution given potential field data on a regular grid. Uses depth weights. Parameters:

  • x, y1D-arrays

    The x and y coordinates of the grid points

  • zfloat or 1D-array

    The z coordinate of the grid points

  • data1D-array

    The potential field at the grid points

  • shapetuple = (ny, nx)

    The shape of the grid

  • zmin, zmaxfloat

    The top and bottom, respectively, of the region where the physical property distribution is calculated

  • nlayersint

    The number of layers used to divide the region where the physical property distribution is calculated

  • qorders1D-array

    The order of the derivatives for the ratio calculation

Returns:

  • meshfatiando.mesher.PrismMesh

    The estimated physical property distribution set in a prism mesh (for easy 3D plotting)

dEXP.filter_ridges(dfIf, dfIIf, dfIIIf, minDepth, maxDepth, minlength=3, rmvNaN=False, **kwargs)[source]

Filter non-rectiligne ridges (denoising)

Parameters: dfI: dataframe

contains ridges of type I

dfII: dataframe

contains ridges of type II

dfIII: dataframe

contains ridges of type III

  • minDepth

    Text here

  • maxDepth

  • kwargs

heights: dataframe

contains heights of ridges of type I

Returns:

  • BB :

    Text here

dEXP.fit_ridges(df, rmvOutliers=False)[source]

Fit ridges and return points and fit equations to plot

Parameters:

  • df

    dataframe including all tree types of ridges

Returns:

  • BB :

    points and fit equations to plot

dEXP.peakdet(v, delta, x=None)[source]

Converted from MATLAB script at http://billauer.co.il/peakdet.html

Returns two arrays

function [maxtab, mintab]=peakdet(v, delta, x) %PEAKDET Detect peaks in a vector % [MAXTAB, MINTAB] = PEAKDET(V, DELTA) finds the local % maxima and minima (“peaks”) in the vector V. % MAXTAB and MINTAB consists of two columns. Column 1 % contains indices in V, and column 2 the found values. % % With [MAXTAB, MINTAB] = PEAKDET(V, DELTA, X) the indices % in MAXTAB and MINTAB are replaced with the corresponding % X-values. % % A point is considered a maximum peak if it has the maximal % value, and was preceded (to the left) by a value lower by % DELTA.

% Eli Billauer, 3.4.05 (Explicitly not copyrighted). % This function is released to the public domain; Any use is allowed.

dEXP.profile_extra(x, y, v, point1, point2, size, algorithm='cubic')[source]

Extract a profile between 2 points from spacial data.

Uses interpolation to calculate the data values at the profile points.

Parameters:

  • x, y1D arrays

    Arrays with the x and y coordinates of the data points.

  • v1D array

    Array with the scalar value assigned to the data points.

  • point1, point2lists = [x, y]

    Lists the x, y coordinates of the 2 points between which the profile will be extracted.

  • sizeint

    Number of points along the profile.

  • algorithmstring

    Interpolation algorithm. Either 'cubic', 'nearest', 'linear' (see scipy.interpolate.griddata).

Returns:

  • [xp, yp, distances, vp]1d arrays

    xp and yp are the x, y coordinates of the points along the profile. distances are the distances of the profile points from point1. vp are the data points along the profile.

dEXP.profile_noInter(x, y, v, point1, point2, size=None, **kwargs)[source]

Extract a profile between 2 points from spacial data.

NO interpolation to calculate the data values at the profile points (find nearest point).

Parameters:

  • x, y1D arrays

    Arrays with the x and y coordinates of the data points.

  • v1D array

    Array with the scalar value assigned to the data points.

  • point1, point2lists = [x, y]

    Lists the x, y coordinates of the 2 points between which the profile will be extracted.

  • sizeint

    Number of points along the profile.

Returns:

  • [xp, yp, distances, vp]1d arrays

    xp and yp are the x, y coordinates of the points along the profile. distances are the distances of the profile points from point1. vp are the data points along the profile.

dEXP.ridges_intersection_Z0(fit, ax=None, ridge_nb=None)[source]

Find intersection of ridges (NOT YET IMPLEMENTED)

Parameters:

  • a

    Text here

Returns:

  • BB :

    return intersection by pairs

dEXP.ridges_minmax(x, y, mesh, p1, p2, qorder=0, z=0, label='upwc', fix_peak_nb=None, interp=True, smooth=False, showfig=False, **kwargs)[source]

Form a multiridge set RI and RII : zeros of the first horizontal and first vertical derivatives of the potential field RIII :zeros of the potential field itself

Note

ridges generated by isolated simple sources point, line, sheet, and contact are straight lines defined by the zeros of a potential

field and its horizontal and vertical derivatives at all measured or computed levels

Parameters:

  • x, y1D-arrays

    The x and y coordinates of the grid points

  • meshfatiando.mesher.PrismMesh

    The estimated physical property distribution set in a prism mesh (for easy 3D plotting). The upward continuated field mesh (of order-q derivative). Read the property label ‘upwc’ of the mesh by default

  • p1, p21D-arrays

    The p1 and p2 coordinates of the extracted profile end points

  • qorderint

    The derivative order

  • zfloat or 1D-array

    The z coordinate of the grid points

  • labelstring

    Label of the estimated physical property distribution

  • fix_peak_nbint

    The maximum number of peak to identify

  • interpTrue or False

    If True, will interpolate values between the data points.

  • smoothTrue or False

    If True, will apply a low-pass filter values.

  • showfigTrue or False

    If True, will display the figure.

**kwargs

prominence for peak detection reverse: start peak analysis from bottom to top

Returns:

  • MinMax_peaks :

    Panda dataframe containing ridges RI, RII and RII

dEXP.ridges_minmax_plot(x, y, mesh, p1, p2, qorder=0, z=0, fix_peak_nb=None, label='upwc', interp=True, smooth=False, showfig=False, **kwargs)[source]
  • x, y1D-arrays

    The x and y coordinates of the grid points

  • meshfatiando.mesher.PrismMesh

    The estimated physical property distribution set in a prism mesh (for easy 3D plotting)

  • p1, p21D-arrays

    The p1 and p2 coordinates of the extracted profile end points

  • qorderint

    The derivative order

  • zfloat or 1D-array

    The z coordinate of the grid points

  • fix_peak_nbint

    The maximum number of peak to identify

  • labelstring

    Label of the estimated physical property distribution

  • interpTrue or False

    If True, will interpolate values between the data points.

  • smoothTrue or False

    If True, will apply a low-pass filter values.

  • showfigTrue or False

    If True, will display the figure.

Returns:

dEXP.scalEULER(df, EXTnb=[1], z0=0)[source]

Analysis (Euler deconvolution of ridges, Fedi 2019) (NOT YET IMPLEMENTED)

Parameters:

  • a

    Text here

Returns:

  • BB :

    Text here

dEXP.scalFUN(df, EXTnb=[1], z0=0, **kwargs)[source]

Analysis of ridges (NOT YET IMPLEMENTED)

Parameters:

  • a

    Text here

Returns:

  • BB :

    Text here

dEXP.upwc(x, y, z, data, shape, zmin, zmax, nlayers, qorder=0)[source]

Upward continuation model (Fedi, 2012).

Calculates the upward continuation for given potential field data on a regular grid. Parameters:

  • x, y1D-arrays

    The x and y coordinates of the grid points

  • zfloat or 1D-array

    The z coordinate of the grid points

  • data1D-array

    The potential field at the grid points

  • shapetuple = (ny, nx)

    The shape of the grid

  • zmin, zmaxfloat

    The top and bottom, respectively, of the region where the physical property distribution is calculated

  • nlayersint

    The number of layers used to divide the region where the physical property distribution is calculated

  • qorderfloat

    The order of the vertical derivative

Returns:

  • meshfatiando.mesher.PrismMesh

    The estimated physical property distribution set in a prism mesh (for easy 3D plotting)

plot_dEXP: plotter

Some functions to plot results from DEXP transformation

plot_dEXP.label2unit(label)[source]

Convert label name to unit

Parameters

label : str

Returns

unit : str

plot_dEXP.plot_line(x, y, data, p1, p2, ax=None, interp=True, **kwargs)[source]

Parameters

xTYPE

x coords of the 2d map.

yTYPE

y coords of the 2d map..

dataTYPE

2d map dataset.

p1list

initial point coordinate (x,y) of the extracted profile.

p2list

final point coordinate (x,y) of the extracted profile.

**kwargs

Returns

xxnp.array

interpolated x.

yynp.array

interpolated y.

distancenp.array

profile distance.

profileTYPE

data values along the profile.

plot_dEXP.plot_ridges_harmonic(RI=None, RII=None, RIII=None, ax=None, label=False, legend=True, **kwargs)[source]

Plot ridges in the harmonic domain

Parameters:

  • a

    Text here

Returns:

  • BB

    Text here

plot_dEXP.plot_ridges_sources(df_fit, ax=None, ridge_type=None, ridge_nb=None, z_max_source=None, **kwargs)[source]

Plot ridges in the source domain and observe intersection point

Parameters: * df_fit

dataframe fit

Returns:

  • BB

    Text here

plot_dEXP.plot_scalFUN(points, fit, ax=None, z0=None, label=None)[source]

Plot scalfun function analysis

Parameters:

  • a

    Text here

Returns:

  • BB

    Text here

plot_dEXP.plot_xy(mesh, scaled=0, label=None, ax=None, markerMax=False, **kwargs)[source]

Get vertical xy section of the mesh at max/2 and plot it

Parameters:

  • mesh

    Fatiando mesh type

  • scaled

    Txt here

  • label

    Txt here

  • ax

    Txt here

  • markerMax

    Txt here

plot_dEXP.plot_z(mesh)[source]

Get horizontal sections at z levels and plot it

Parameters:

  • mesh

    Fatiando mesh type

plot_dEXP.slice_mesh(x, y, mesh, label, p1, p2, ax=None, interp=True, **kwargs)[source]

Get vertical xy section of the mesh at max/2 and plot it

Parameters:

  • mesh

    Fatiando mesh type

  • scaled

    Txt here

  • label

    Txt here Txt here

  • markerMax

    Txt here

utils_dexp: utils

Miscellaneous utility functions.

@author: Benjamin

utils_dEXP.cor_field_B(x, y, z, u, B, rho=100, **kwargs)[source]

Calculates the potential field (electric) produced by a current injection in B (return electrode) for a given homogeneous electrical resistivity rho.

Parameters

  • x, y1D-arrays

    The x and y coordinates of the grid points

  • zfloat or 1D-array

    The z coordinate of the grid points

  • u1D-array

    The potential field at the grid points

  • Bu1D-array

    The position of the return electrode B

  • rhoint

    The estimated electrical resistivity of the medium

Returns

u_cor1D-arrays

The corrected from the B return electrode influence potential field at the grid points

utils_dEXP.load_surfer(fname, dtype='float64')[source]

Read data from a Surfer ASCII grid file.

Surfer is a contouring, griding and surface mapping software from GoldenSoftware. The names and logos for Surfer and Golden Software are registered trademarks of Golden Software, Inc.

http://www.goldensoftware.com/products/surfer

Parameters:

  • fnamestr

    Name of the Surfer grid file

  • dtypenumpy dtype object or string

    The type of variable used for the data. Default is numpy.float64. Use numpy.float32 if the data are large and precision is not an issue.

Returns:

  • datadict

    The data in a dictionary with some metadata:

    • 'file'string

      The name of the original data file

    • 'shape'tuple

      (nx, ny), the number of grid points in x (North) and y (East)

    • 'area'tuple

      (x1, x2, y1, y2), the spacial limits of the grid.

    • 'x'1d-array

      Value of the North-South coordinate of each grid point.

    • 'y'1d-array

      Value of the East-West coordinate of each grid point.

    • 'data'1d-array

      Values of the field in each grid point. Field can be for example topography, gravity anomaly, etc. If any field values are >= 1.70141e+38 (Surfers way of marking NaN values), the array will be masked at those values (i.e., 'data' will be a numpy masked array).