ecoscope.analysis.ecograph#
Module Contents#
- class ecoscope.analysis.ecograph.Ecograph(trajectory, resolution=15, radius=2, cutoff=None, tortuosity_length=3)[source]#
A class that analyzes movement tracking data using Network Theory.
- Parameters:
trajectory (ecoscope.base.Trajectory) – Trajectory dataframe
resolution (float) – Pixel size, in meters
radius (int) – Radius to compute Collective Influence (Default : 2)
cutoff (int) – Cutoff to compute an approximation of betweenness index if the standard algorithm is too slow. Can be useful for very large graphs (Default : None)
tortuosity_length (int) – The number of steps used to compute the two tortuosity metrics (Default : 3 steps)
- graphs#
- trajectory#
- resolution#
- utm_crs#
- features = ['dot_product', 'speed', 'step_length', 'sin_time', 'cos_time', 'weight', 'degree',...#
- xmin#
- ymin#
- xmax#
- ymax#
- transform#
- inverse_transform#
- n_rows#
- n_cols#
- to_csv(output_path)[source]#
Saves the features of all nodes in a CSV file
- Parameters:
output_path (str, Pathlike) – Output path for the CSV file
- to_geotiff(feature, output_path, individual='all', interpolation=None, transform=None)[source]#
Saves a specific node feature as a GeoTIFF
- Parameters:
feature (str) – Feature of interest
output_path (str, Pathlike) – Output path for the GeoTIFF file
individual (str) – The individual for which we want to output the node feature (Default : “all”)
interpolation (str or None) – Whether to interpolate the feature for each step in the trajectory (Default : None). If provided, has to be one of those four types of interpolation : “mean”, “median”, “max” or “min”
transform (sklearn.base.TransformerMixin or None) – A feature transform method (Default : None)