pygmi.vector package

Submodules

pygmi.vector.dataprep module

Data Preparation for Vector Data.

class pygmi.vector.dataprep.DataGrid(parent=None)

Bases: BasicModule

Grid Point Data.

This class grids point data using a nearest neighbourhood technique.

acceptall()

Accept option.

Updates self.outdata, which is used as input to other modules.

Return type:

None.

dxy_change()

When dxy is changed on the interface, this updates rows and columns.

Return type:

None.

grid_method_change()

When grid method is changed, this updated hidden controls.

Return type:

None.

saveproj()

Save project data from class.

Return type:

None.

settings(nodialog=False)

Entry point into item.

Parameters:

nodialog (bool, optional) – Run settings without a dialog. The default is False.

Returns:

True if successful, False otherwise.

Return type:

bool

setupui()

Set up UI.

Return type:

None.

class pygmi.vector.dataprep.DataReproj(parent=None)

Bases: BasicModule

Reprojections.

This class reprojects datasets using the rasterio routines.

acceptall()

Accept option.

Updates self.outdata, which is used as input to other modules.

Return type:

None.

saveproj()

Save project data from class.

Return type:

None.

settings(nodialog=False)

Entry point into item.

Parameters:

nodialog (bool, optional) – Run settings without a dialog. The default is False.

Returns:

True if successful, False otherwise.

Return type:

bool

setupui()

Set up UI.

Return type:

None.

class pygmi.vector.dataprep.Metadata(parent=None)

Bases: ContextModule

Edit Metadata.

This class allows the editing of the metadata for a vector dataset using a GUI.

banddata

band data

Type:

dictionary

bandid

dictionary of strings containing band names.

Type:

dictionary

acceptall()

Accept option.

Return type:

None.

run()

Entry point to start this routine.

Returns:

tmp – True if successful, False otherwise.

Return type:

bool

setupui()

Set up UI.

Return type:

None.

class pygmi.vector.dataprep.PointCut(parent=None)

Bases: BasicModule

Cut Data using shapefiles.

This class cuts point datasets using a boundary defined by a polygon shapefile.

saveproj()

Save project data from class.

Return type:

None.

settings(nodialog=False)

Entry point into item.

Parameters:

nodialog (bool, optional) – Run settings without a dialog. The default is False.

Returns:

True if successful, False otherwise.

Return type:

bool

class pygmi.vector.dataprep.TextFileSplit(parent=None)

Bases: BasicModule

Split a text file into smaller text files.

acceptall()

Accept option.

Updates self.outdata, which is used as input to other modules.

Return type:

None.

change_method()

Update fields when method changes.

get_ifile()

Get input file information.

Return type:

None.

saveproj()

Save project data from class.

Return type:

None.

settings(nodialog=False)

Entry point into item.

Parameters:

nodialog (bool, optional) – Run settings without a dialog. The default is False.

Returns:

True if successful, False otherwise.

Return type:

bool

setupui()

Set up UI.

Return type:

None.

pygmi.vector.dataprep.blanking(gdat, x, y, bdist, extent, dxy, nullvalue)

Blanks area further than a defined number of cells from input data.

Parameters:

  • gdat (numpy array) – grid data to blank.

  • x (numpy array) – x coordinates.

  • y (numpy array) – y coordinates.

  • bdist (int) – Blanking distance in units for cell.

  • extent (list) – extent of grid.

  • dxy (float) – Cell size.

  • Nullvalue (float) – Null or nodata value.

Returns:

mask – Mask to be used for blanking.

Return type:

numpy array

pygmi.vector.dataprep.cut_point(data, ifile, showlog=<built-in function print>)

Cuts a point dataset.

Cut a point dataset using a shapefile.

Parameters:

  • data (GeoDataFrame) – GeoPandas GeoDataFrame

  • ifile (str) – shapefile used to cut data

Returns:

data – GeoPandas GeoDataFrame

Return type:

GeoDataFrame

pygmi.vector.dataprep.filesplit(ifile, num, mode='bytes', showlog=<built-in function print>, piter=None)

Split an input file into a number of output files.

Parameters:

  • ifile (str) – Input filename.

  • num (int) – Number of bytes or lines to split by.

  • mode (str, optional) – Can be ‘bytes’, ‘files’ or ‘lines’. The default is ‘bytes’.

  • showlog (function, optional) – Display information. The default is print.

  • piter (iter, optional) – Progress iterator. The default is None.

Return type:

None.

pygmi.vector.dataprep.gridxyz(x, y, z, dxy, nullvalue=1e+20, method='Nearest Neighbour', bdist=4.0, showlog=<built-in function print>)

Grid xyz data.

Parameters:

  • x (numpy array) – X coordinate values.

  • y (numpy array) – Y coordinate values.

  • z (numpy array) – Z or data values.

  • dxy (float) – Grid cell size, in distance units.

  • nullvalue (float, optional) – null or nodata value. The default is 1e+20.

  • method (str, optional) – Gridding method. The default is ‘Nearest Neighbour’.

  • bdist (float, optional) – Blacking distance. The default is 4.0.

  • showlog (function, optional) – Display information. The default is print.

Returns:

dat – Output raster dataset.

Return type:

PyGMI Data.

pygmi.vector.dataprep.lltomap(lat, lon)

Convert a latitude and longitude to a 1:50,000 sheet name.

Parameters:

  • lat (float) – Latitude.

  • lon (float) – Longitude.

Returns:

mapsheet – Map sheet number.

Return type:

str

pygmi.vector.dataprep.maptobounds(mapsheet, crs_to=None, showlog=<built-in function print>)

Convert a South African map sheet name to bounds.

Parameters:

  • mapsheet (str) – Map sheet number. Four numbers and up to two letters denoting NE corner in latitude and longitude and quadrants (A to D). Eg, 2928AB is latitude 29, longitude 28, quadrant B of quadrant A.

  • crs_to (CRS, optional) – Destination projection. The default is None.

  • showlog (function, optional) – Display information. The default is print.

Returns:

bounds – output bounds.

Return type:

list

pygmi.vector.dataprep.quickgrid(x, y, z, dxy, numits=4, showlog=<built-in function print>)

Do a quick grid.

Parameters:

  • x (numpy array) – array of x coordinates

  • y (numpy array) – array of y coordinates

  • z (numpy array) – array of z values - this is the column being gridded

  • dxy (float) – cell size for the grid, in both the x and y direction.

  • numits (int) – number of iterations. By default its 4. If this is negative, a maximum will be calculated and used.

  • showlog (function, optional) – Routine to show text messages. The default is print.

Returns:

newz – M x N array of z values

Return type:

numpy array

pygmi.vector.dataprep.reprojxy(x, y, iwkt, owkt, showlog=<built-in function print>)

Reproject x and y coordinates.

Parameters:

  • x (numpy array or float) – x coordinates

  • y (numpy array or float) – y coordinates

  • iwkt (str, int) – Input wkt description or EPSG code (int)

  • owkt (str, int) – Output wkt description or EPSG code (int)

Returns:

  • xout (numpy array) – x coordinates.

  • yout (numpy array) – y coordinates.

pygmi.vector.dataprep.txtlinecnt(filename)

Count lines in text file.

Parameters:

filename (str) – filename of text file.

Returns:

Total number of lines in a file.

Return type:

int

pygmi.vector.graphs module

Plot Vector Data using Matplotlib.

class pygmi.vector.graphs.GraphWindow(parent=None)

Bases: ContextModule

Graph Window - Main QT Dialog class for graphs.

change_band()

Combo box to choose band.

Return type:

None.

class pygmi.vector.graphs.MyMplCanvas(parent=None)

Bases: FigureCanvasQTAgg

MPL Canvas class.

This routine will also allow the picking and movement of nodes of data.

button_release_callback(event)

Mouse button release callback.

Parameters:

event (matplotlib.backend_bases.MouseEvent) – Button release event.

Return type:

None.

format_coord(x, y)

Set format coordinate for correlation coefficient plot.

Parameters:

  • x (float) – x coordinate.

  • y (float) – y coordinate.

Returns:

Output string to display.

Return type:

str

motion_notify_callback(event)

Move mouse callback.

Parameters:

event (matplotlib.backend_bases.MouseEvent) – Motion notify event.

Return type:

None.

onpick(event)

Picker event.

Parameters:

event (matplotlib.backend_bases.PickEvent) – Pick event.

Returns:

Return TRUE if pick succeeded, False otherwise.

Return type:

bool

resizeline(event)

Resize event.

Parameters:

event (matplotlib.backend_bases.ResizeEvent) – Resize event.

Return type:

None.

textresize(axes)

Resize the text on a correlation plot when zooming.

Parameters:

axes (Matplotlib axes) – Current Matplotlib axes.

Return type:

None.

update_ccoef(data, style='Normal')

Update the plot from point data.

Parameters:

  • data (dictionary) – GeoPandas data in a dictionary.

  • style (str) – Style of colour mapping.

Return type:

None.

update_hist(data, col, ylog, iscum)

Update the histogram plot.

Parameters:

  • data (dictionary) – GeoPandas data in a dictionary.

  • col (str) – Label for column to extract.

  • ylog (bool) – Boolean for a log scale on y-axis.

  • iscum (bool) – Boolean for a cumulative distribution.

Return type:

None.

update_lines(r, data)

Update the plot from point data.

Parameters:

  • r (numpy array) – array of distances, for the x-axis

  • data (numpy array) – array of data to be plotted on the y-axis

Return type:

None.

update_lmap(data, ival, scale, uselabels)

Update the plot from line data.

Parameters:

  • data (Pandas dataframe) – Line data

  • ival (dictionary key) – dictionary key representing the line data channel to be plotted.

  • scale (float) – scale of exaggeration for the profile data on the map.

  • uselabels (bool) – boolean choice whether to use labels or not.

Return type:

None.

update_rose(data, rtype, nbins=8, equal=False)

Update the rose diagram plot using vector data.

Parameters:

  • data (dictionary) – GeoPandas data in a dictionary. It should be ‘LineString’

  • rtype (int) – Rose diagram type. Can be either 0 or 1.

  • nbins (int, optional) – Number of bins used in rose diagram. The default is 8.

  • equal (bool, optional) – Option for an equal area rose diagram. The default is False.

Return type:

None.

update_vector(data, col, style=None)

Update the plot from vector data.

Parameters:

  • data (dictionary) – GeoPandas data in a dictionary.

  • col (str) – Label for column to extract.

  • style (str or None) – Style of colour mapping.

Return type:

None.

class pygmi.vector.graphs.PlotCCoef(parent=None)

Bases: GraphWindow

Plot Correlation Coefficients.

change_band()

Combo box to choose band.

Return type:

None.

run()

Entry point to run class.

Return type:

None.

class pygmi.vector.graphs.PlotHist(parent=None)

Bases: ContextModule

Plot Histogram Class.

change_band()

Combo box to choose band.

Return type:

None.

run()

Run.

Return type:

None.

class pygmi.vector.graphs.PlotLineMap(parent=None)

Bases: GraphWindow

Plot Lines Map Class.

change_band()

Combo box to choose band.

Return type:

None.

run()

Entry point to run class.

Return type:

None.

class pygmi.vector.graphs.PlotLines(parent=None)

Bases: GraphWindow

Plot Lines Class.

change_band()

Combo box to choose band.

Return type:

None.

change_line()

Combo to change line number.

Return type:

None.

run()

Entry point to run class.

Return type:

None.

class pygmi.vector.graphs.PlotRose(parent=None)

Bases: GraphWindow

Plot Rose Class.

change_band()

Combo box to choose band.

Return type:

None.

run()

Entry point to run class.

Return type:

None.

class pygmi.vector.graphs.PlotVector(parent=None)

Bases: GraphWindow

Plot Vector Class.

change_band()

Combo box to choose band.

Return type:

None.

run()

Entry point to run class.

Return type:

None.

pygmi.vector.graphs.annotate_heatmap(im, data=None, valfmt='{x:.2f}', textcolors=('black', 'white'), threshold=None, **textkw)

Annotate a heatmap.

From Matplotlib.org.

Parameters:

  • im – The AxesImage to be labelled.

  • data – Data used to annotate. If None, the image’s data is used. Optional.

  • valfmt – The format of the annotations inside the heatmap. This should either use the string format method, e.g. “$ {x:.2f}”, or be a matplotlib.ticker.Formatter. Optional.

  • textcolors – A pair of colours. The first is used for values below a threshold, the second for those above. Optional.

  • threshold – Value in data units according to which the colours from textcolors are applied. If None (the default) uses the middle of the colormap as separation. Optional.

  • **kwargs – All other arguments are forwarded to each call to text used to create the text labels.

pygmi.vector.graphs.heatmap(data, row_labels, col_labels, ax, cbar_kw=None, cbarlabel='', **kwargs)

Create a heatmap from a numpy array and two lists of labels.

From Matplotlib.org

Parameters:

  • data – A 2D numpy array of shape (M, N).

  • row_labels – A list or array of length M with the labels for the rows.

  • col_labels – A list or array of length N with the labels for the columns.

  • ax – A matplotlib.axes.Axes instance to which the heatmap is plotted.

  • cbar_kw – A dictionary with arguments to matplotlib.Figure.colorbar. Optional.

  • cbarlabel – The label for the colorbar. Optional.

  • **kwargs – All other arguments are forwarded to imshow.

pygmi.vector.graphs.histogram(x, y=None, xmin=None, xmax=None, bins=10)

Histogram.

Calculate histogram of a set of data. It is different from a conventional histogram in that instead of summing elements of specific values, this allows the sum of weights/probabilities on a per element basis.

Parameters:

  • x (numpy array) – Input data

  • y (numpy array) – Input data weights. The default is None.

  • xmin (float) – Lower value for the bins. The default is None.

  • xmax (float) – Upper value for the bins. The default is None.

  • bins (int) – number of bins. The default is 10.

Returns:

  • hist (numpy array) – The values of the histogram

  • bin_edges (numpy array) – bin edges of the histogram

pygmi.vector.graphs.rotate(origin, point, angle)

Rotate a point counterclockwise by a given angle around a given origin.

The angle should be given in radians.

Parameters:

  • origin (list) – List containing origin point (ox, oy)

  • point (list) – List containing point to be rotated (px, py)

  • angle (float) – Angle in radians.

Returns:

  • qx (float) – Rotated x-coordinate.

  • qy (float) – Rotated y-coordinate.

pygmi.vector.iodefs module

Import Data.

class pygmi.vector.iodefs.ExportVector(parent=None)

Bases: ContextModule

Export Vector Data.

run()

Run routine.

Returns:

True if successful, False otherwise.

Return type:

bool

class pygmi.vector.iodefs.ExportXYZ(parent=None)

Bases: ContextModule

Export XYZ Data.

run()

Run routine.

Returns:

True if successful, False otherwise.

Return type:

bool

class pygmi.vector.iodefs.ImportVector(parent=None)

Bases: BasicModule

Import Vector Data.

change_bounds()

Change the bounds combo.

get_sfile()

Get the filename and crs and bounds.

saveproj()

Save project data from class.

Return type:

None.

set_bounds(bounds)

Set the bounds.

Parameters:

bounds (list or numpy array) – Bounds defined as (xmin, ymin, xmax, ymax).

Return type:

None.

settings(nodialog=False)

Entry point into item.

Parameters:

nodialog (bool, optional) – Run settings without a dialog. The default is False.

Returns:

True if successful, False otherwise.

Return type:

bool

setupui()

Set up UI.

Return type:

None.

class pygmi.vector.iodefs.ImportXYZ(parent=None)

Bases: BasicModule

Import XYZ Data.

This class imports tabular data.

get_GXYZ()

Get Geosoft XYZ.

Returns:

df – Pandas dataframe.

Return type:

DataFrame

get_delimited(delimiter=',')

Get a delimited file.

Parameters:

delimiter (str, optional) – Delimiter type. The default is ‘,’.

Returns:

gdf – Pandas dataframe.

Return type:

Dataframe

get_excel()

Get an Excel spreadsheet.

Returns:

gdf – Pandas dataframe.

Return type:

Dataframe

saveproj()

Save project data from class.

Return type:

None.

settings(nodialog=False)

Entry point into item.

Parameters:

nodialog (bool, optional) – Run settings without a dialog. The default is False.

Returns:

True if successful, False otherwise.

Return type:

bool

setupui()

Set up UI.

Return type:

None.

pygmi.vector.iodefs.get_GXYZ(ifile, showlog=<built-in function print>, piter=<built-in function iter>)

Get Geosoft XYZ.

Returns:

df2 – Pandas dataframe.

Return type:

DataFrame

pygmi.vector.iodefs.get_intrepid(ifile, showlog=<built-in function print>, piter=<built-in function iter>)

Get Intrepid Database.

Returns:

df – Pandas Dataframe.

Return type:

DataFrame

pygmi.vector.menu module

Vector Menu Routines.

class pygmi.vector.menu.MenuWidget(parent=None)

Bases: object

Widget class to call the main interface.

This widget class creates the vector menus to be found on the main interface. Normal as well as context menus are defined here.

parent

Reference to MainWidget class found in main.py

Type:

pygmi.main.MainWidget

basic_stats()

Display basic statistics.

cut_data()

Cut point data.

export_vector()

Export line data.

export_xyz()

Export XYZ data.

file_split()

Text file split.

grid()

Grid datasets.

import_vector()

Import shape data.

import_xyz()

Import XYZ data.

metadata()

Metadata.

plot_ccoef()

Plot correlation coefficient data.

reproject()

Reproject point data.

scomp()

Structure complexity.

show_hist()

Show histogram.

show_line_data()

Show line data.

show_line_map()

Show line map.

show_rose_diagram()

Show rose diagram.

show_vector_data()

Show vector data.

pygmi.vector.minc module

Minimum Curvature Gridding Routine.

Based on the work by:

Briggs, I. C., 1974, Machine contouring using minimum curvature, Geophysics vol. 39, No. 1, pp. 39-48

pygmi.vector.minc.get_b(e5, n5)

Get b values for input data.

Calculates the b values based on the distance between the data point and the nearest node. Distances are expressed in units of cell.

Parameters:

  • e5 (float) – x distance error.

  • n5 (float) – y distance error.

Returns:

  • b1 (float) – b1 value.

  • b2 (float) – b2 value.

  • b3 (float) – b3 value.

  • b4 (float) – b4 value.

  • b5 (float) – b5 value.

pygmi.vector.minc.mcurv(u, ufixed)

Minimum curvature smoothing.

This routine smooths the data between fixed data nodes.

Parameters:

  • u (numpy array) – 2D grid of z values.

  • ufixed (numpy array) – 2D grid of fixed node values.

Returns:

u – 2D grid of z values.

Return type:

numpy array

pygmi.vector.minc.minc(x, y, z, dxy, showlog=<built-in function print>, extent=None, bdist=None, maxiters=100)

Minimum Curvature Gridding.

Parameters:

  • x (numpy array) – 1D array with x coordinates.

  • y (numpy array) – 1D array with y coordinates.

  • z (numpy array) – 1D array with z coordinates.

  • dxy (float) – Cell x and y dimension.

  • showlog (function, optional) – Routine to show text messages. The default is print.

  • extent (list, optional) – Extent defined as (left, right, bottom, top). The default is None.

  • bdist (float, optional) – Blanking distance in units of cell. The default is None.

  • maxiters (int, optional) – Maximum number of iterations. The default is 100.

Returns:

u – 2D numpy array with gridding z values.

Return type:

numpy array

pygmi.vector.minc.morg(x2, y2, z2, extent, dxy, rows, cols)

Organise coordinates and calculate b values.

Parameters:

  • x2 (numpy array) – 1D array with x coordinates.

  • y2 (numpy array) – 1D array with y coordinates.

  • z2 (numpy array) – 1D array with z coordinates.

  • extent (list) – Extent defined as (left, right, bottom, top).

  • dxy (float) – Cell x and y dimension.

  • rows (int) – Number of rows.

  • cols (int) – Number of columns.

Returns:

  • coords (list) – List containing iint, jint, r and zval.

  • b (list) – List of b values.

pygmi.vector.minc.off_grid(u, i, j, wn, b)

Node value calculation when data value is too far from node.

Parameters:

  • u (numpy array) – 2D grid of z values.

  • i (int) – Current row.

  • j (int) – Current Column.

  • wn (float) – Data value.

  • b (list) – List of b values for calculation.

Returns:

uij – Output value.

Return type:

float

pygmi.vector.minc.u_corner(u)

Minimum curvature smoothing for corner point.

It is defined as:

2*u[i, j]+u[i, j+2] + u[i+2, j] - 2*(u[i, j+1] + u[i+1, j] = 0

Parameters:

u (numpy array) – 2D grid of z values.

Returns:

uij – Smoothed value to replace in master grid.

Return type:

float

pygmi.vector.minc.u_edge(u, i)

Minimum curvature smoothing for edges.

It is defined as:

u[i-2, j] + u[i+2, j] + u[i, j+2] + u[i-1, j+1] + u[i+1, j+1] - 4*(u[i-1, j] + u[i, j+1] + u[i+1, j]) + 7*u[i, j] = 0

Parameters:

  • u (numpy array) – 2D grid of z values.

  • i (int) – Current row.

Returns:

uij – Smoothed value to replace in master grid.

Return type:

float

pygmi.vector.minc.u_edge_next_to_corner(u)

Minimum curvature smoothing for edge next to corner.

It is defined as:

u[i, j+2] + u[i+1, j+1] + u[i-1, j+1] + u[i+2, j] - 2*u[i-1, j] - 4*(u[i+1, j] + u[i, j+1]) + 6*u[i, j] = 0

Parameters:

u (numpy array) – 2D grid of z values.

Returns:

uij – Smoothed value to replace in master grid.

Return type:

float

pygmi.vector.minc.u_next_to_corner(u)

Minimum curvature smoothing for next to corner.

It is defined as:

u[i, j+2] + u[i+2, j] + u[i-1, j+1] + u[i+1, j-1] + 2*u[i+1, j+1] - 8*(u[i, j+1] + u[i+1, j]) - 4*([i, j-1]+u[i-1, j]) + 18*u[i, j] = 0

Parameters:

u (numpy array) – 2D grid of z values.

Returns:

uij – Smoothed value to replace in master grid.

Return type:

float

pygmi.vector.minc.u_normal(u, i, j)

Minimum curvature smoothing for normal cases.

It is defined as:

u[i+2, j] + u[i, j+2] + u[i-2, j] + u[i, j-2] + 2*(u[i+1, j+1] + u[i-1, j+1] + u[i+1, j-1] + u[i-1, j-1]) - 8*(u[i+1, j]+u[i-1, j]+u[i, j+1]+u[i, j-1]) + 20*u[i, j] = 0

Parameters:

  • u (numpy array) – 2D grid of z values.

  • i (int) – Current row.

  • j (int) – Current Column.

Returns:

uij – Smoothed value to replace in master grid.

Return type:

float

pygmi.vector.minc.u_one_row_from_edge(u, i)

Minimum curvature smoothing for one row from edge.

It is defined as:

u[i-2, j] + u[i+2, j] + u[i, j+2] + 2*(u[i-1, j+1] + u[i+1, j+1]) + u[i-1, j-1]+u[i+1, j-1] - 8*([i-1, j]+u[i, j+1]+u[i+1, j]) - 4*u[i, j-1] + 19*u[i, j] = 0

Parameters:

  • u (numpy array) – 2D grid of z values.

  • i (int) – Current row.

Returns:

uij – Smoothed value to replace in master grid.

Return type:

float

pygmi.vector.show_table module

Routine which displays a table graphically with various stats.

class pygmi.vector.show_table.BasicStats(parent=None)

Bases: ContextModule

Show a summary of basic stats.

run()

Run.

Return type:

None.

save()

Save Table.

Returns:

True if successful, False otherwise.

Return type:

bool

setupui()

Set up UI.

Return type:

None.

pygmi.vector.structure module

Structure complexity routines.

class pygmi.vector.structure.StructComp(parent=None)

Bases: BasicModule

Structure complexity.

acceptall()

Accept option.

Updates self.outdata, which is used as input to other modules.

Return type:

None.

method_change()

When method is changed, this updated hidden controls.

Return type:

None.

saveproj()

Save project data from class.

Return type:

None.

settings(nodialog=False)

Entry point into item.

Parameters:

nodialog (bool, optional) – Run settings without a dialog. The default is False.

Returns:

True if successful, False otherwise.

Return type:

bool

setupui()

Set up UI.

Return type:

None.

pygmi.vector.structure.extendlines(gdf, length=500, piter=<built-in function iter>)

Extent lines from GeoPandas dataframe.

Parameters:

  • gdf (GeoDataFrame) – A dataframe containing LINESTRINGs.

  • length (float) – distance in metres to extend the line on either side.

  • piter (iter) – Progressbar iterable.

Returns:

gdf2 – A dataframe containing extended LINESTRINGs.

Return type:

GeoDataFrame

pygmi.vector.structure.feature_circular_stats(gdf, dxy, wsize=3, piter=<built-in function iter>)

Feature circular variance.

Parameters:

  • gdf (GeoDataFrame) – GeoDataframe of linear features.

  • dxy (float) – Raster cell size

  • wsize (int) – Window size (must be odd)

  • piter (iter) – Progressbar iterable.

Returns:

dat – Output raster data

Return type:

PyGMI Data

pygmi.vector.structure.feature_fracdim(gdf, dxy, wsize=21, piter=<built-in function iter>)

Feature fractal dimension.

Parameters:

  • gdf (GeoDataFrame) – GeoDataframe of linear features.

  • dxy (float) – Raster cell size

  • wsize (int) – Window size (must be odd)

  • piter (iter) – Progressbar iterable.

Returns:

dat – Output raster data

Return type:

PyGMI Data

pygmi.vector.structure.feature_intersection_density(gdf, dxy, var, extend=500, piter=<built-in function iter>)

Feature intersection density.

Parameters:

  • gdf (GeoDataFrame) – GeoDataframe of linear features.

  • dxy (float) – Raster cell size

  • var (float) – Variance.

  • extend (float) – Distance to extend linear features.

  • piter (iter) – Progressbar iterable.

Returns:

  • geom2 (GeoDataFrame) – New geometry with intersection points.

  • dat (PyGMi Data) – Output raster data

pygmi.vector.structure.feature_orientation_diversity(gdf, dxy, wsize=3, piter=<built-in function iter>)

Feature orientation diversity.

Parameters:

  • gdf (GeoDataFrame) – GeoDataframe of linear features.

  • dxy (float) – Raster cell size

  • wsize (int) – Window size (must be odd)

  • piter (iter) – Progressbar iterable.

Returns:

dat – Output raster data

Return type:

PyGMI Data

pygmi.vector.structure.fractal_dimension(warray, max_box_size=None, min_box_size=1, n_samples=20, n_offsets=0)

Calculate the fractal dimension of a 3D numpy array.

From: https://github.com/ChatzigeorgiouGroup/FractalDimension

Parameters:

  • warray (np.array) – The array to calculate the fractal dimension of.

  • max_box_size (int, optional) – The largest box size, given as the power of 2 so that 2**max_box_size gives the side length of the largest box. The default is None.

  • min_box_size (int, optional) – The smallest box size, given as the power of 2 so that 2**min_box_size gives the side length of the smallest box. The default is 1.

  • n_samples (int, optional) – number of scales to measure over. The default is 20.

  • n_offsets (int, optional) – number of offsets to search over to find the smallest set N(s) to cover all voxels>0. The default is 0.

Returns:

coeffs[0] – Fractal dimension

Return type:

float

pygmi.vector.structure.linesplit(curve)

Split LineString into segments.

pygmi.vector.structure.segments_to_angles(gdf, piter=<built-in function iter>)

Get line segment angles.

Parameters:

  • gdf (GeoDataFrame) – GeoDataFrame with line segments.

  • piter (iter) – Progressbar iterable.

Returns:

gdf2 – GeoDataFrame with angles added.

Return type:

GeoDataFrame

Module contents

Vector data routines.