pyfor.cloud module

class pyfor.cloud.Cloud(path)

Bases: object

The cloud object is an API for interacting with .las, .laz, and .ply files in memory, and is generally the starting point for any analysis with pyfor. For a more qualitative assessment of getting started with Cloud please see the user manual.

__init__(path)

Initialize self. See help(type(self)) for accurate signature.

chm(cell_size, interp_method=None, pit_filter=None, kernel_size=3)

Returns a Raster object of the maximum z value in each cell, with optional interpolation (i.e. nan-filling) and pit filter parameters. Currently, only a median pit filter is implemented.

Parameters

  • cell_size – The cell size for the returned raster in the same units as the parent Cloud or las file.

  • interp_method – The interpolation method as a string to fill in NA values of the produced canopy height model, one of either “nearest”, “cubic”, or “linear”. This is an argument to scipy.interpolate.griddata.

  • pit_filter – If “median” passes a median filter over the produced canopy height model.

  • kernel_size – The kernel size of the median filter, must be an odd integer.

Returns

A Raster object of the canopy height model.

clip(polygon)

Clips the point cloud to the provided shapely polygon using a ray casting algorithm. This method calls clip.poly_clip() directly. This returns a new Cloud.

Parameters

polygon – A shapely.geometry.Polygon in the same CRS as the Cloud.

Returns

A new :class:.`Cloud` object clipped to the provided polygon.

property convex_hull

Calculates the convex hull of the cloud projected onto a 2d plane, a wrapper for scipy.spatial.ConvexHull().

Returns

A shapely.geometry.Polygon of the convex hull.

filter(min, max, dim)

Filters a cloud object for a given dimension in place.

Parameters

  • min – Minimum dimension to retain.

  • max – Maximum dimension to retain.

  • dim – The dimension of interest as a string. For example “z”. This corresponds to a column label in self.data.points.

classmethod from_pdal(ins)

Converts a PDAL ins argument from a PDAL filters.python into a Cloud object.

Parameters

ins – The ins argument from PDAL.

grid(cell_size)

Generates a Grid object for the parent object given a cell size. See the documentation for Grid for more information.

Parameters

cell_size – The resolution of the plot in the same units as the input file.

Returns

A Grid object.

normalize(cell_size, classified=False, **kwargs)

Normalize the cloud using the default Zhang et al. (2003) progressive morphological ground filter. Please see the documentation in ground_filter.Zhang2003 for more information and keyword argument definitions. If you want to use a pre-computed DEM to normalize, please see subtract().

Parameters

  • cell_size – The resolution of the intermediate bare earth model.

  • classified – If True and file type is .las or .laz, uses the points classified as ground (i.e. 2) to construct the intermediate bare earth model.

plot(cell_size=1, cmap='viridis', return_plot=False, block=False)

Plots a basic canopy height model of the Cloud object. This is mainly a convenience function for Raster.plot. More robust methods exist for dealing with canopy height models. Please see the user manual.

Parameters

  • clip_size – The resolution of the plot in the same units as the input file.

  • return_plot – If true, returns a matplotlib plt object.

Returns

If return_plot == True, returns matplotlib plt object. Not yet implemented.

plot3d(dim='z', point_size=1, cmap='Spectral_r', max_points=500000.0, n_bin=8, plot_trees=False)

Plots the three dimensional point cloud using a Qt backend. By default, if the point cloud exceeds 5e5 points, then it is downsampled using a uniform random distribution. This is for performance purposes.

Parameters

  • point_size – The size of the rendered points.

  • dim – The dimension upon which to color (i.e. “z”, “intensity”, etc.)

  • cmap – The matplotlib color map used to color the height distribution.

  • max_points – The maximum number of points to render.

standard_metrics(heightbreak=0)
subtract(path)

Normalize using a pre-computed raster file, i.e. “subtract” the heights from the input raster in place. This assumes the raster and the point cloud are in the same coordinate system. :param path: The path to the raster file, must be in a format supported by rasterio. :return:

write(path)

Write to file. The precise mechanisms of this writing will depend on the file input type. For .las files this will be handled by LASData.write(), for .ply files this will be handled by PLYData.write().

Parameters

path – The path of the output file.

class pyfor.cloud.CloudData(points, header)

Bases: object

__init__(points, header)

Initialize self. See help(type(self)) for accurate signature.

class pyfor.cloud.LASData(points, header)

Bases: pyfor.cloud.CloudData

write(path)

Writes the object to file. This is a wrapper for laspy.file.File()

Parameters

path – The path of the ouput file.

class pyfor.cloud.PLYData(points, header)

Bases: pyfor.cloud.CloudData

write(path)

Writes the object to file. This is a wrapper for plyfile.PlyData.write()

Parameters

path – The path of the ouput file.