typing and comment updates

master
Jan Petykiewicz 3 years ago
parent 085bb79ed7
commit ecefdff781

@ -1,7 +1,7 @@
from typing import Tuple
import numpy
from typing import Tuple, Optional
import numpy # type: ignore
from numpy import logical_and, diff, floor, ceil, ones, zeros, hstack, full_like, newaxis
from scipy import sparse
from scipy import sparse # type: ignore
def raster(vertices: numpy.ndarray,
@ -18,11 +18,14 @@ def raster(vertices: numpy.ndarray,
Polygons are assumed to have clockwise vertex order; reversing the vertex order is equivalent
to multiplying the result by -1.
:param vertices: 2xN ndarray containing x,y coordinates for each vertex of the polygon
:param grid_x: x-coordinates for the edges of each pixel (ie, the leftmost two columns span
x=grid_x[0] to x=grid_x[1] and x=grid_x[1] to x=grid_x[2])
:param grid_y: y-coordinates for the edges of each pixel (see grid_x)
:return: 2D ndarray with pixel values in the range [0, 1] containing the anti-aliased polygon
Args:
vertices: 2xN ndarray containing `x,y` coordinates for each vertex of the polygon
grid_x: x-coordinates for the edges of each pixel (ie, the leftmost two columns span
`x=grid_x[0]` to `x=grid_x[1]` and `x=grid_x[1]` to `x=grid_x[2]`)
grid_y: y-coordinates for the edges of each pixel (see `grid_x`)
Returns:
2D ndarray with pixel values in the range [0, 1] containing the anti-aliased polygon
"""
vertices = numpy.array(vertices)
grid_x = numpy.array(grid_x)
@ -49,7 +52,7 @@ def find_intersections(
vertices: numpy.ndarray,
grid_x: numpy.ndarray,
grid_y: numpy.ndarray
) -> Tuple[numpy.ndarray]:
) -> Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]:
"""
Find intersections between a polygon and grid lines
"""
@ -126,7 +129,7 @@ def create_vertices(
vertices: numpy.ndarray,
grid_x: numpy.ndarray,
grid_y: numpy.ndarray,
new_vertex_data: Tuple[numpy.ndarray] = None
new_vertex_data: Optional[Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]] = None
) -> sparse.coo_matrix:
"""
Create additional vertices where a polygon crosses gridlines
@ -171,6 +174,7 @@ def create_vertices(
return vertices
def clip_vertices_to_window(
vertices: numpy.ndarray,
min_x: float = -numpy.inf,
@ -201,26 +205,29 @@ def get_raster_parts(
grid_y: numpy.ndarray
) -> sparse.coo_matrix:
"""
This function performs the same task as raster(...), but instead of returning a dense array
This function performs the same task as `raster(...)`, but instead of returning a dense array
of pixel values, it returns a sparse array containing the value
(-area + 1j * cover)
`(-area + 1j * cover)`
for each pixel which contains a line segment, where
cover is the fraction of the pixel's y-length that is traversed by the segment,
multiplied by the sign of (y_final - y_initial)
area is the fraction of the pixel's area covered by the trapezoid formed by
`cover` is the fraction of the pixel's y-length that is traversed by the segment,
multiplied by the sign of `(y_final - y_initial)`
`area` is the fraction of the pixel's area covered by the trapezoid formed by
the line segment's endpoints (clipped to the cell edges) and their projections
onto the pixel's left (i.e., lowest-x) edge, again multiplied by
the sign of (y_final - y_initial)
the sign of `(y_final - y_initial)`
Note that polygons are assumed to be wound clockwise.
The result from raster(...) can be obtained with
raster_result = numpy.real(lines_result) + numpy.imag(lines_result).cumsum(axis=0)
The result from `raster(...)` can be obtained with
`raster_result = numpy.real(lines_result) + numpy.imag(lines_result).cumsum(axis=0)`
:param vertices: 2xN ndarray containing x,y coordinates for each point in the polygon
:param grid_x: x-coordinates for the edges of each pixel (ie, the leftmost two columns span
x=grid_x[0] to x=grid_x[1] and x=grid_x[1] to x=grid_x[2])
:param grid_y: y-coordinates for the edges of each pixel (see grid_x)
:return: Complex sparse COO matrix containing area and cover information
Args:
vertices: 2xN ndarray containing `x, y` coordinates for each point in the polygon
grid_x: x-coordinates for the edges of each pixel (ie, the leftmost two columns span
`x=grid_x[0]` to `x=grid_x[1]` and `x=grid_x[1]` to `x=grid_x[2]`)
grid_y: y-coordinates for the edges of each pixel (see `grid_x`)
Returns:
Complex sparse COO matrix containing area and cover information
"""
if grid_x.size < 2 or grid_y.size < 2:
raise Exception('Grid must contain at least one full pixel')

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