Hello all, the github contact section pointed me here. I'd like to open a feature request to build oriented minimum bounding boxes from polygons, like you can do in QGIS. Having trouble implementing it on my own. This this an appropriate issue to open?
Still got some testing to do, but I ended up using https://github.com/dbworth/minimum-area-bounding-rectangle/blob/master/python/min_bounding_rect.py to make follow on operations easier.
It looks like geom.minimum_rotated_rectangle doesn't guarantee right angles? They might be trying to accomplish different things.
Short answer everything works as intended - thank you again. FWIW the github link to min_bounding_rect.py returns identical return as geom.minimum_rotated_rectangle.
Long answer is that the KML writer enforces geographic coordinates, which I did not know. So QGIS' project on the fly visually was not showing a true rectangle versus the underlying (in my case) local UTM. I had done quick tests with min_bounding_rect.py at large scales only, and tests with minimum_rotated_rectangle at small scale only. Obviously at large scale the difference in the projection were not as noticeable so the two calls appeared to have different results. Using the same projections for minimum rectangle and the underlying in a non-KML format works as intended.
Hello everyone :) As a completely freshman to pygeos, I just have simple question on data input format when constructing a geometry in pygeos: is there other input data format possible other than string?
I want to construct polygon, whose boundary points are returned from a list of numbers....it's quite cumbersome to convert it in the desired string format.
I want to construct polygon, whose boundary points are returned from a list of numbers....it's quite cumbersome to convert it in the desired string format.
@Aniwax you can pass arrays of coordinates, see the docs here https://pygeos.readthedocs.io/en/latest/creation.html (for linearrings and polygons)
Hey folks, working on a project and ran into a bunch of "illegalArgumentException: Geometry must be a Point or LineString" when exporting a dataframe to json (or passing it into rasterstats). Any idea what could cause this? The geometry seems valid and plots fine. I'm using Shapely 1.8a1 but unable to reproduce with sample data. This data is a large set of isochrones created using the Mapbox API.
Happy to create a ticket but didn't want to clutter things up if there's something I should try first
The project is analyzing internet connectivity in Kazakhstan for Unicef GIGA
Thanks Martin
Hey Guys, I have the issue of not the same version of GEOS used, when I try to install Shapely and Pygeos in the same environment on Github Actions to run my automated tests. I could resolved this problem on my machine by replacing the geos_c.dill file inside the Shapely package, but I don't know how to resolve it on the cloud. I am using Windows both on the cloud and local environment.
Any suggestions? Appreciated :)
Any suggestions? Appreciated :)
Appreciate any suggestions :)
I am not aware of a ready-made guide for geopandas (apart from the one on my long to-do list). Generally speaking, avoid for loops, avoid
apply and try to rely as much as you can on built-in GeoSeries methods. If needed use the underlying array of pygeos geometries directly with pygeos functions. You can access it as gdf.geometry.values.data. When you can use numpy functions instead of for loops, use them. Look up some vectorization guide for pandas, the rules are fairly similar. And when using spatial index, use query_bulk.
@Aniwax correct: you can use the pygeos functions directly to leverage the speedups (they are already vectorized in most cases). There are a few cases where the pygeos functions operate on singular geometries where you may need to do a bit more to vectorize them (e.g.,
If you run into a case where trying to use pygeos to get what you need isn't obvious, certainly feel free to ask here.
get_geometry), but we've been adding more functions to vectorize the most common operations where you would get these back as ragged arrays (e.g., get_parts), where there are varying numbers of geometries within arrays of multi / geometry collections.If you run into a case where trying to use pygeos to get what you need isn't obvious, certainly feel free to ask here.
I created a minimal test dataset with one feature
Hi there,
I've seen you have already talked about this warning in January.
Is there any "pip install" solution to get rid of the warning besides installing geopandas with conda/conda forge?
I would like to integrate a new geopandas spatial join feature into a package created by poetry and I cannot use a conda.
I've seen you have already talked about this warning in January.
UserWarning: The Shapely GEOS version (3.8.0-CAPI-1.13.1 ) is incompatible with the GEOS version PyGEOS was compiled with (3.9.0-CAPI-1.16.2). Conversions between both will be slow.Is there any "pip install" solution to get rid of the warning besides installing geopandas with conda/conda forge?
I would like to integrate a new geopandas spatial join feature into a package created by poetry and I cannot use a conda.
(it might also be that you can ignore the warning, depending on your use case)
I could ignore the warning but this package is a command-line tool used by non-programmer colleagues to run some reports. It would be confusing for them to see a warning that they cannot understand.
Can you please recommend any resource (link, video, blog post, ...) where I can check how to install from source?
I also tried to use rtree instead of pygeos. Although the warning disappeared, the spatial join was much slower with rtree.
Can you please recommend any resource (link, video, blog post, ...) where I can check how to install from source?
I also tried to use rtree instead of pygeos. Although the warning disappeared, the spatial join was much slower with rtree.
The shapely readme (https://github.com/Toblerity/Shapely/) has some info about how to install from source. I think the main thing is to get an install of GEOS. Once that is available, installing shapely/pygeos from source is not that hard
If it's just for the warning being visible, you could also suppress the warning with the
warnings python package, you can filter that specific UserWarning
Just heads up that we'll be working on centralised XYZ services package (geopandas/contextily#153) today. I am going to create a new repo for that under geopandas org.
Hello all! What is the best way to plot each individual feature from a
GeoDataFrame? I'm trying to quickly make many simple plots at once. I've tried variations on the following, but it feels a bit hacky.for feature in gdf.iterfeatures():
temp = gpd.GeoSeries(Point(*feat['geometry']['coordinates'][0]), crs=gdf.crs)
ax = temp.plot(figsize=(20, 20), color='red', marker='o', alpha=0.9)
ctx.add_basemap(ax=ax)
fig = ax.get_figure()
fig.savefig('.../out/path/...')
And the agenda here https://hackmd.io/pq8MqmtxTFqqaiayFuuEYQ
I see a similar discussion here pygeos/pygeos#150. But before any progress there, is there any quick fix to detect and remove the invalid points?
x_coords = np.array([[1, 2, 3], [4, 5, 6]])
y_coords = np.array([[1, 2, 3], [4, 5, 6]])
pygeos.polygons(pygeos.linearrings(x_coords, y=y_coords))array([<pygeos.Geometry POLYGON ((1 1, 2 2, 3 3, 1 1))>,
<pygeos.Geometry POLYGON ((4 4, 5 5, 6 6, 4 4))>], dtype=object)