import warnings
import geopandas as gpd
import scipy.stats
from scipy.stats._distn_infrastructure import rv_frozen
[docs]def clip_points(points: gpd.GeoDataFrame, by: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
"""Subset a GeoDataFrame of points based on the boundaries of another GeoDataFrame.
Parameters
----------
points : geopandas GeoDataFrame
Point features to be clipped
by : geopandas GeoDataFrame
Boundaries to use for clipping
Returns
-------
geopandas GeoDataFrame
A subset of the original `points`
References
----------
Earth Analytics Python course, https://doi.org/10.5281/zenodo.2209415
"""
poly = by.geometry.unary_union
return points[points.geometry.intersects(poly)]
[docs]def clip_lines_polys(
lines_polys: gpd.GeoDataFrame, by: gpd.GeoDataFrame
) -> gpd.GeoDataFrame:
"""Subset a GeoDataFrame of lines or polygons based on the boundaries of another GeoDataFrame.
Parameters
----------
lines_polys : geopandas GeoDataFrame
Features to be clipped
by : geopandas GeoDataFrame
Boundaries to use for clipping
Returns
-------
geopandas GeoDataFrame
A subset of the original `lines_polys`
References
----------
Earth Analytics Python course, https://doi.org/10.5281/zenodo.2209415
"""
warnings.filterwarnings("ignore", "GeoSeries.notna", UserWarning)
# Create a single polygon object for clipping
poly = by.geometry.unary_union
spatial_index = lines_polys.sindex
# Create a box for the initial intersection
bbox = poly.bounds
# Get a list of id's for each object that overlaps the bounding box and subset the data to just those objects
sidx = list(spatial_index.intersection(bbox))
thing_sub = lines_polys.iloc[sidx]
# Clip the data - with these data
clipped = thing_sub.copy()
clipped["geometry"] = thing_sub.intersection(poly)
# Return the clipped layer with no null geometry values
return clipped[(~clipped.geometry.is_empty) & (clipped.geometry.notna())]
[docs]def beta(a: float, b: float, **kwargs) -> rv_frozen:
"""Create a fixed beta distribution.
Parameters
----------
a, b : float
Shape parameters
Returns
-------
rv_frozen
Fixed beta distribution
"""
return scipy.stats.beta(a=a, b=b, **kwargs)
[docs]def truncnorm(mean: float, sd: float, lower: float, upper: float) -> rv_frozen:
"""Create a truncated normal distribution.
Parameters
----------
mean : float
Mean of distribution
sd : float
Standard deviation of the distribution
lower : float
Lower bound
upper : float
Upper bound
Returns
-------
rv_frozen
Fixed truncated normal distribution
"""
return scipy.stats.truncnorm(
(lower - mean) / sd, (upper - mean) / sd, loc=mean, scale=sd
)
