I have a polygon (rectangle or very close it) in a geopandas
dataframe that is at an angle relative the x-axis, i.e. it is neither horizontal not vertical. I have a function that splits polygons into smaller rectangles (isometric) but it only works (as desired) on polygon making an angle that is a multiple of pi/2 with the x-axis.
So, my idea has been to rotate any polygon that does not satisfy my requirements, split it and rotate it back to its original position.
For instance:
polygon =
id geometry
85 POLYGON ((49.37794 51.395203, 49.37794 51.395203, 49.37794 51.395203, 49.37794 51.395203, 49.178337 50.363914, 49.178337 50.363914, 49.178337 50.363914, 49.178337 50.363914, 59.99021 48.733814, 59.99021 48.733814, 59.99021 48.733814, 59.99021 48.733814, 60.223083 49.698566, 60.223083 49.698566, 60.223083 49.698566, 60.223083 49.698566, 49.37794 51.395203))
Now, I determine its angle with the x-axis and rotate it:
polygon = pd.DataFrame(geostore_obstacles_geometry_polygon.loc[85:85,])
polygon['angle'] = polygon.apply(lambda row : polygon_angle(row['geometry']), axis = 1)
polygon = gpd.GeoDataFrame(polygon)
polygon = polygon.set_geometry('geometry')
polygon['rotated'] = polygon.apply(lambda row : shapely.affinity.rotate(row['geometry'], row['angle']), axis = 1)
polygon = polygon.set_geometry('rotated')
This step splits the polygon inte smaller pieces:
polygon['add'] = polygon.apply(lambda row : split_polygon_up(row['rotated'],side_length=side_length, shape="square", thresh=threshold), axis = 1)
polygon = polygon.explode('add')
polygon = polygon.set_geometry('add')
Before I finally rotate it back
polygon['rotated_add'] = polygon.apply(lambda row : shapely.rotate(row['add'], -row['angle']), axis = 1)
polygon = polygon.set_geometry('rotated_add')
But, as you can imagine, this is not what I expect to have (sorry for the very uggly image).
I understand WHY it does this but I cannot solve it. I have some ideas that the one possible solution would be to rotate all the smaller polygons together with the convex hull or envelope of their union, but I struggle using geopandas to do it.
I would be immensely grateful for any help on how to solve this issue. The dataframe obtained after all the transformations can be found here: https://drive.google.com/file/d/1wY7g3jsD7PNpaTkGBjbGvYArpRUr0UIk/view?usp=sharing