ecoscope.analysis.UD.etd_range#

Module Contents#

ecoscope.analysis.UD.etd_range.logger#
ecoscope.analysis.UD.etd_range.__etd__(_, a)#
ecoscope.analysis.UD.etd_range._etd#
class ecoscope.analysis.UD.etd_range.WeibullPDF[source]#
static fit(data, floc=0)[source]#
static pdf(data, shape, location=0, scale=1)[source]#
static cdf(data, shape, location=0, scale=1)[source]#
static nelder_mead(func, x0, args=(), **kwargs)[source]#
static expected_func(speed, shape, scale, time, distance)[source]#
class ecoscope.analysis.UD.etd_range.Weibull2Parameter[source]#

Bases: WeibullPDF

shape: float = 1.0#
scale: float = 1.0#
class ecoscope.analysis.UD.etd_range.Weibull3Parameter[source]#

Bases: WeibullPDF

shape: float = 1.0#
a: float = 1.0#
b: float = 1.0#
c: float = 1.0#
ecoscope.analysis.UD.etd_range.calculate_etd_range(trajectory, raster_profile, output_path=None, max_speed_kmhr=0.0, max_speed_percentage=0.9999, expansion_factor=1.3, weibull_pdf=Weibull2Parameter(), grid_threshold=100)[source]#

The ETDRange class provides a trajectory-based, nonparametric approach to estimate the utilization distribution (UD) of an animal, using model parameters derived directly from the movement behaviour of the species.get_displacement max_speed_percentage : 0.999 raster_profile : raster.RasterProfile expansion_factor : float weibull_pdf : Weibull2Parameter or Weibull3Parameter grid_threshold: int

Returns:

output_path

Return type:

str

Parameters:

  • trajectory (ecoscope.Trajectory | geopandas.GeoDataFrame)

  • raster_profile (ecoscope.io.raster.RasterProfile)

  • output_path (Union[str, bytes, os.PathLike, None])

  • max_speed_kmhr (float)

  • max_speed_percentage (float)

  • expansion_factor (float)

  • weibull_pdf (Weibull2Parameter)

  • grid_threshold (int)

ecoscope.analysis.UD.etd_range.grid_size_from_geographic_extent(gdf, scale_factor=100)[source]#

Intended for use as input to create_meshgrid and RasterProfile. Uses pyproj.geod.inv to determine the distance of the diagonal across the bounds of a gdf, and divides by the scale_factor to determine a ‘sensible’ grid size in meters

Parameters:

  • gdf (geopandas.GeoDataFrame)

  • scale_factor (int)

Return type:

int