Tesselation fails at Generating input point array... with ValueError: need at least one array to concatenate

[bramson]

As the title says, Tesselation fails at the Generating input point array.... step with ValueError: need at least one array to concatenate

My code is simply:

import momepy as mm
tess = mm.Tessellation(edgeDF5, 'communityID', limit=wardsPolygon, shrink=0)

Where wardsPolygon is a <class 'shapely.geometry.multipolygon.MultiPolygon'> of Tokyo's 23 wards and
'communityID' is a column of unique IDs for each polygon in my geopadaframe called edgeDF5, which contains Polygons of neighborhoods in the 'geometry' column (and no other columns).
I don't want space between adjacent polygons; I want a partition (every point is in some polygon), so I set the shrink to 0.

Here's the output:

Traceback (most recent call last):

  File "...3938314878.py", line 1, in <module>
    tess = mm.Tessellation(edgeDF5, 'communityID', limit=wardsPolygon, shrink=0)

  File "...\lib\site-packages\momepy\elements.py", line 252, in __init__
    self.tessellation = self._morphological_tessellation(

  File "...\lib\site-packages\momepy\elements.py", line 276, in _morphological_tessellation
    points, ids = self._dense_point_array(

  File "...\lib\site-packages\momepy\elements.py", line 334, in _dense_point_array
    points = np.vstack(points)

  File "<__array_function__ internals>", line 5, in vstack

  File "...\lib\site-packages\numpy\core\shape_base.py", line 282, in vstack
    return _nx.concatenate(arrs, 0)

  File "<__array_function__ internals>", line 5, in concatenate

ValueError: need at least one array to concatenate

The result of checking the input is:

print(mm.CheckTessellationInput(edgeDF5, shrink=0))
Collapsed features  : 0
Split features      : 0
Overlapping features: 0

So, why isn't this working and what do I need to do to make it work?

[bramson]

While debugging the source code, I found that the geoms array input to _dense_point_array, which a comment says should be an "array of pygeos lines" is actually an array of pygeos polygons.

This is converted within _dense_point_array to linestrings via lines = pygeos.boundary(geoms) so no problem there.

Ah ha!!! I found that the distance parameter is set by the segment parameter in _morphological_tessellation to 0.5.
But my data is in standard lat/lon coords, so the lengths of the boundary linestrings are all less than 0.1.
Points are only added if length > distance, so no points are added to the points array.
So I set segment = 0.01 in the tesselation function call, but...

After fixing that, I get an error

  File "...\lib\site-packages\momepy\elements.py", line 337, in _dense_point_array
    np.linspace(0.1, length - 0.1, num=int((length - 0.1) // distance)),

  File "<__array_function__ internals>", line 5, in linspace

  File "...\lib\site-packages\numpy\core\function_base.py", line 122, in linspace
    raise ValueError("Number of samples, %s, must be non-negative." % num)

ValueError: Number of samples, -6, must be non-negative.

So, essentially, I have come to realize that using this function requires the coordinates to be in some meters-based CRS.
I can do that, but I did not expect that.

[martinfleis]

Hi @bramson,

thank you for the report.

Yes, a lot of operations in Tessellation (including the Voronoi tessellation) are distance-based. With geometries in lat/lon, distance has no meaning so momepy expects projected CRS.

The second error is still related to this, segment = 0.01 is still way too large (by several orders of magnitude).

I would recommend reprojecting your geometries to some local projection, like EPSG:2451.

I did not expect that.

This is a good point. I'll add a note to the documentation and a check to the code, so it emits a warning.

[torresanton]

ValueError: need at least one array to concatenate

Hi, I am getting the same error, but in my case, my dataset was in a projected CRS, EPGS: '3857'. I am working with the Open Building dataset from Google. With polygons with more than 85% confidence (around 300k polygons) is okay, but when I decrease the confidence, and then increase the number of polygons(max number 2M 300k for my research area), I start to get this error.

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[13], line 1
----> 1 tessellation = momepy.Tessellation(Buildings_Lima[:50], "uID", limit, verbose=True, segment=0.1)
      2 tessellation = tessellation.tessellationls

File [~/anaconda3/envs/momepy_env/lib/python3.11/site-packages/momepy/elements.py:259](https://vscode-remote+rs4-002ecode-002ebunsencloud-002ede.vscode-resource.vscode-cdn.net/config/workspace/MoMepy_lima/MorphoMetric_Lima/~/anaconda3/envs/momepy_env/lib/python3.11/site-packages/momepy/elements.py:259), in Tessellation.__init__(self, gdf, unique_id, limit, shrink, segment, verbose, enclosures, enclosure_id, threshold, use_dask, n_chunks)
    252     # add convex hull buffered large distance to eliminate infinity issues
    253     limit = (
    254         gpd.GeoSeries(limit, crs=gdf.crs)
    255         .translate(xoff=-centre_x, yoff=-centre_y)
    256         .array[0]
    257     )
--> 259     self.tessellation = self._morphological_tessellation(
    260         gdf, unique_id, limit, shrink, segment, verbose
    261     )
    263 self.tessellation["geometry"] = self.tessellation["geometry"].translate(
    264     xoff=centre_x, yoff=centre_y
    265 )

File [~/anaconda3/envs/momepy_env/lib/python3.11/site-packages/momepy/elements.py:285](https://vscode-remote+rs4-002ecode-002ebunsencloud-002ede.vscode-resource.vscode-cdn.net/config/workspace/MoMepy_lima/MorphoMetric_Lima/~/anaconda3/envs/momepy_env/lib/python3.11/site-packages/momepy/elements.py:285), in Tessellation._morphological_tessellation(self, gdf, unique_id, limit, shrink, segment, verbose, check)
    282 objects = objects.set_index(unique_id)
    284 print("Generating input point array...") if verbose else None
--> 285 points, ids = self._dense_point_array(
    286     objects.geometry.array, distance=segment, index=objects.index
    287 )
    289 hull = shapely.convex_hull(limit)
    290 bounds = shapely.bounds(hull)

File [~/anaconda3/envs/momepy_env/lib/python3.11/site-packages/momepy/elements.py:343](https://vscode-remote+rs4-002ecode-002ebunsencloud-002ede.vscode-resource.vscode-cdn.net/config/workspace/MoMepy_lima/MorphoMetric_Lima/~/anaconda3/envs/momepy_env/lib/python3.11/site-packages/momepy/elements.py:343), in Tessellation._dense_point_array(self, geoms, distance, index)
    340         points.append(shapely.get_coordinates(pts))
    341         ids += [ix] * len(pts)
--> 343 points = np.vstack(points)
    345 return points, ids

File <__array_function__ internals>:200, in vstack(*args, **kwargs)

File [~/anaconda3/envs/momepy_env/lib/python3.11/site-packages/numpy/core/shape_base.py:296](https://vscode-remote+rs4-002ecode-002ebunsencloud-002ede.vscode-resource.vscode-cdn.net/config/workspace/MoMepy_lima/MorphoMetric_Lima/~/anaconda3/envs/momepy_env/lib/python3.11/site-packages/numpy/core/shape_base.py:296), in vstack(tup, dtype, casting)
    294 if not isinstance(arrs, list):
    295     arrs = [arrs]
--> 296 return _nx.concatenate(arrs, 0, dtype=dtype, casting=casting)

File <__array_function__ internals>:200, in concatenate(*args, **kwargs)

ValueError: need at least one array to concatenate

As you can see I even try with a small sample (50 polygons). The confidence of an object being a polygon is related to its size, as I have learned by exploring the dataset.

Any idea about how to get out of this error?

PS: I can work with polygons with high confidence, but then the number of polygons decreases and my dataset gets less representative of the city.

[martinfleis]

@torresanton Could you dump Buildings_Lima[:50] to a file and share it?

[torresanton]

I'm sharing a pickle file, and I have pypickle 1.1.0 installed
Sample of the dataset that triggers the issue: open_building_65_conf_issue.txt

A similar error happend with momepy.CheckTessellationInput

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[7], line 1
----> 1 momepy.CheckTessellationInput(Lima_buil)

File [~/anaconda3/envs/momepy_env/lib/python3.11/site-packages/momepy/preprocessing.py:376](https://vscode-remote+rs4-002ecode-002ebunsencloud-002ede.vscode-resource.vscode-cdn.net/config/workspace/MoMepy_lima/MorphoMetric_Lima/~/anaconda3/envs/momepy_env/lib/python3.11/site-packages/momepy/preprocessing.py:376), in CheckTessellationInput.__init__(self, gdf, shrink, collapse, split, overlap)
    369 sindex = shrink.sindex
    370 hits = shrink.bounds.apply(
    371     lambda row: list(sindex.intersection(row)), axis=1
    372 )
    373 od_matrix = pd.DataFrame(
    374     {
    375         "origin": np.repeat(hits.index, hits.apply(len)),
--> 376         "dest": np.concatenate(hits.values),
    377     }
    378 )
    379 od_matrix = od_matrix[od_matrix.origin != od_matrix.dest]
    380 duplicated = pd.DataFrame(np.sort(od_matrix, axis=1)).duplicated()

File <__array_function__ internals>:200, in concatenate(*args, **kwargs)

ValueError: need at least one array to concatenate

Thanks very much!!

[martinfleis]

@torresanton you have very tiny geometries. Mean length of perimeter is 0.000349 and mean area 1.0670127274172174e-08. If that is supposed to represent buildings then it is not correct.

In any case, segment=0.1 is way longer than the length of each perimeter, leading to this issue. Dimensions like you have are not expected.

[torresanton]

I suspected something similar. Perhaps the best idea is to remove very tiny polygons and work only over a certain threshold of area and perimeter. At the moment momepy.Tessellation is working with polygons from #OpenBuildingGoogle with a confidence equal to or higher than 0.85. Thanks very much

[martinfleis]

I mean, there are no other than those tiny polygons. This will surely not come from Google's Open Buildings like this. There has been some erroneous coordinate transformation or something like that.

[torresanton]

Yes, @martinfleis is right. When the open_building_dataset.csv is downloaded it doesn't have the crs parameter, and I couldn't set_crs() properly, even after figuring out the right one (<Geographic 2D CRS: EPSG:4326>Name: WGS 84).
Fortunately, there is a CLI tool, apparently developed very recently that can handle appropriately the open_building_dataset.csv, and convert it to gpkg or other formats with proper crs, this is https://opengeos.github.io/open-buildings. After using this tool the crs is correctly assigned from the beginning and then you can change it to a projected one and perform geometry calculations. Thanks again.