Develop Reference

Why Gmsh create more surfaces than expected when I use "gmhs.model.occ.cut()" command?

I am trying to create a volume in Gmsh (using Python API) by cutting some small cylinders from a bigger one.
When I do that, I expect to have one surface for each cutted region, instead, I get the result in the figure. I have highlighted in red the surfaces that give me the problem (some cutted regions behave as expected), as you can see, instead of one surface I get two, that sometimes aren't even equal.
gmsh creates more surfaces than expected:
So, my questions are:
Why gmsh behaves like that?
How can I fix this as I need predictable behavior?
Below is the code I used to generate the geometry.
The code to work requires some parameters such as core_height, core_inner_radius and core_outer_radius, the number of small cylinders and their radius.
gmsh.initialize(sys.argv)
#gmsh.initialize()
gmsh.clear()
gmsh.model.add("circle_extrusion")
inner_cyl_tag = 1
outer_cyl_tag = 2
inner_cyl = gmsh.model.occ.addCylinder(0,0,0, 0, 0, core_height, core_inner_radius, tag = inner_cyl_tag)
outer_cyl = gmsh.model.occ.addCylinder(0,0,0, 0, 0, core_height, core_outer_radius, tag = outer_cyl_tag)
core_tag = 3
cut1 = gmsh.model.occ.cut([(3,outer_cyl)],[(3,inner_cyl)], tag = core_tag)
#create a set of filled cylinders
#set position
angle_vector = np.linspace(0,2*np.pi,number_of_hp+1)
pos_x = hp_radial_position*np.cos(angle_vector)
pos_y = hp_radial_position*np.sin(angle_vector)
pos_z = 0.0
#cut one cylinder at the time and assign the new core tag
for ii in range(0,len(angle_vector)):
old_core_tag = core_tag
heat_pipe = gmsh.model.occ.addCylinder(pos_x[ii], pos_y[ii], pos_z, 0, 0, core_height,hp_outer_radius, tag =-1)
core_tag = heat_pipe+1
core = gmsh.model.occ.cut([(3,old_core_tag)],[(3,heat_pipe)], tag = core_tag)
gmsh.model.occ.synchronize()
#get volume entities and assign physical groups
volumes = gmsh.model.getEntities(dim=3)
solid_marker = 1
gmsh.model.addPhysicalGroup(volumes[0][0], [volumes[0][1]],solid_marker)
gmsh.model.setPhysicalName(volumes[0][0],solid_marker, "solid_volume")
#get surfaces entities and apply physical groups
surfaces = gmsh.model.getEntities(dim=2)
surface_markers= np.arange(1,len(surfaces)+1,1)
for ii in range(0,len(surfaces)):
gmsh.model.addPhysicalGroup(2,[surfaces[ii][1]],tag = surface_markers[ii])
#We finally generate and save the mesh:
gmsh.model.mesh.generate(3)
gmsh.model.mesh.refine()
gmsh.model.mesh.refine()
gmsh.option.setNumber("Mesh.MshFileVersion", 2.2) #save in ASCII 2 format
gmsh.write(mesh_name+".msh")
# Launch the GUI to see the results:
#if '-nopopup' not in sys.argv:
# gmsh.fltk.run()
gmsh.finalize()

I do not think that you have additional surfaces in the sense of gmsh.model.occ surfaces. To me this looks like your volume mesh is sticking out of your surface mesh, i.e. volume and surface mesh do not fit together.
Here is what I did to check your case:
First I added the following lines at the beginning of our code to get a minimum working example:
import gmsh
import sys
import numpy as np
inner_cyl_tag = 1
outer_cyl_tag = 2
core_height = 1
core_inner_radius = 0.1
core_outer_radius = 0.2
number_of_hp = 5
hp_radial_position = 0.1
hp_outer_radius = 0.05
What I get with this code is the following:
To visualize it like this go to "Tools"-->"Options"-->"Mesh" and check "2D element faces", "3D element edges" and "3D element faces".
You can see that there are some purple triangles sticking out of the green/yellowish surfaces triangles of the inner surfaces.
You could try to visualize your case the same way and check <--> uncheck the "3D element faces" a few times.
So here is the solution for this behaviour, I did not know that gmsh behaves like this myself. It seems that when you create your mesh and refine it the refinement will be applied on the 2D surface mesh and the 3D volume mesh seperately, which means that those two meshes are not connected after the refinement anymore. What I did next was to try what happens if you create the 2D mesh only, refine it, and then create the 3D mesh, i.e.:
replace:
gmsh.model.mesh.generate(3)
gmsh.model.mesh.refine()
gmsh.model.mesh.refine()
by:
gmsh.model.mesh.generate(2)
gmsh.model.mesh.refine()
gmsh.model.mesh.refine()
gmsh.model.mesh.generate(3)
The result then looks like this:
I hope that this was actually your problem. But in future it would be good if you could provide us a minimum working example of code that we can copy-paste and get the same visualization you showed us in your image.

Sorting the mesh vertices in Blender

I'm creating an application that requires the vertices array of the mesh to be sorted in a particular way, to access them more easily: from left to right then downwards to upwards (in the xy plane) like this:
[indexes][1]
My code successfully does that, but the resulting mesh is all glitched out:
[resulting mesh][2]
I think it's likely that the new edges are causing the problem, but haven't found a way to fix them. Here's the mesh sorting code I wrote:
# This example assumes we have a mesh object selected
import bpy
import bmesh
#Weights for order. Left>>Right Down>>Up
def order(vector):
return vector[0]+200*vector[1]
# Get the active mesh
me = bpy.context.object.data
verts=[]
# Get a BMesh representation
bm = bmesh.new() # create an empty BMesh
bm.from_mesh(me) # fill it in from a Mesh
#Convert current verts to tuple list (x,y,z)
i=0
for v in bm.verts:
verts.append((v.co.x,v.co.y,v.co.z))
# Sort the verts.
verts.sort(key=order)
#Assign the sorted vertices to the mesh
i=0
for v in bm.verts:
v.co.x,v.co.y,v.co.z = verts[i]
i+=1
#Debugging edges (possible problem?)
for v in bm.edges:
if i<10:
print(v)
i+=1
# Finish up, write the bmesh back to the mesh
bm.verts.index_update()
bm.to_mesh(me)
#bmesh.update_edit_mesh(me)
bm.free() # free and prevent further access
Is there any way to rearrange the edges? Or any post processing trick I can do on the mesh, anything helps.
Thanks in advance.
[1]: https://i.stack.imgur.com/iuoBc.png
[2]: https://i.stack.imgur.com/atxvF.png

How to cut vertices and faces connected to points lower than some value in pyvista?

So when one exports r.out.vtk from Grass GIS we get a bad surface with -99999 points instead of nulls:
I want to remove them, yet a simple clip is not enough:
pd = pv.read('./pid1.vtk')
pd = pd.clip((0,1,1), invert=False).extract_surface()
p.add_mesh(pd ) #add atoms to scene
p.show()
resulting in:
So I wonder how to keep from it only top (> -999) points and connected vertices - in order to get only the top plane (it is curved\not flat actually) using pyvista?
link to example .vtk

There is an easy way to do this and there isn't...
You could use pyvista's threshold filter with all_scalars=True as long as you have only one set of scalars:
import pyvista as pv
pd = pv.read('./pid1.vtk')
pd = pd.threshold(-999, all_scalars=True)
plotter = pv.Plotter()
plotter.add_mesh(pd) #add atoms to scene
plotter.show()
Since all_scalars starts filtering based on every scalar array, this will only do what you'd expect if there are no other scalars. Furthermore, unfortunately there seems to be a bug in pyvista (expected to be fixed in version 0.32.0) which makes the use of this keyword impossible.
What you can do in the meantime (if you don't want to use pyvista's main branch before the fix is released) is to threshold the data yourself using numpy:
import pyvista as pv
pd = pv.read('./pid1.vtk')
scalars = pd.active_scalars
keep_inds = (scalars > -999).nonzero()[0]
pd = pd.extract_points(keep_inds, adjacent_cells=False)
plotter = pv.Plotter()
plotter.add_mesh(pd) #add atoms to scene
plotter.show()
The main point of both all_scalars (in threshold) and adjacent_cells (in extract_points) is to only keep cells where every point satisfies the condition.
With both of the above I get the following figure using your data:

Creating an ASCII art world map

I'd like to render an ASCII art world map given this GeoJSON file.
My basic approach is to load the GeoJSON into Shapely, transform the points using pyproj to Mercator, and then do a hit test on the geometries for each character of my ASCII art grid.
It looks (edit: mostly) OK when centered one the prime meridian:
But centered on New York City (lon_0=-74), and it suddenly goes haywire:
I'm fairly sure I'm doing something wrong with the projections here. (And it would probably be more efficient to transform the ASCII map coordinates to lat/lon than transform the whole geometry, but I am not sure how.)
import functools
import json
import shutil
import sys
import pyproj
import shapely.geometry
import shapely.ops
# Load the map
with open('world-countries.json') as f:
countries = []
for feature in json.load(f)['features']:
# buffer(0) is a trick for fixing polygons with overlapping coordinates
country = shapely.geometry.shape(feature['geometry']).buffer(0)
countries.append(country)
mapgeom = shapely.geometry.MultiPolygon(countries)
# Apply a projection
tform = functools.partial(
pyproj.transform,
pyproj.Proj(proj='longlat'), # input: WGS84
pyproj.Proj(proj='webmerc', lon_0=0), # output: Web Mercator
)
mapgeom = shapely.ops.transform(tform, mapgeom)
# Convert to ASCII art
minx, miny, maxx, maxy = mapgeom.bounds
srcw = maxx - minx
srch = maxy - miny
dstw, dsth = shutil.get_terminal_size((80, 20))
for y in range(dsth):
for x in range(dstw):
pt = shapely.geometry.Point(
(srcw*x/dstw) + minx,
(srch*(dsth-y-1)/dsth) + miny # flip vertically
)
if any(country.contains(pt) for country in mapgeom):
sys.stdout.write('*')
else:
sys.stdout.write(' ')
sys.stdout.write('\n')

I made edit at the bottom, discovering new problem (why there is no Canada and unreliability of Shapely and Pyproj)
Even though its not exactly solving the problem, I believe this attitude has more potential than using pyproc and Shapely and in future, if you will do more Ascii art, will give you more possibilites and flexibility. Firstly I will write pros and cons.
PS: Initialy I wanted to find problem in your code, but I had problems with running it, because pyproj was returning me some error.
PROS
1) I was able to extract all points (Canada is really missing) and rotate image
2) The processing is very fast and therefore you can create Animated Ascii art.
3) Printing is done all at once without need to loop
CONS (known Issues, solvable)
1) This attitude is definetly not translating geo-coordinates correctly - too plane, it should look more spherical
2) I didnt take time to try to find out solution to filling the borders, so only borders has '*'. Therefore this attitude needs to find algorithm to fill the countries. I think it shouldnt be problem since the JSON file contains countries separated
3) You need 2 extra libs beside numpy - opencv(you can use PIL instead) and Colorama, because my example is animated and I needed to 'clean' terminal by moving cursor to (0,0) instead of using os.system('cls')
4) I made it run only in python 3. In python 2 it works too but I am getting error with sys.stdout.buffer
Change font size on terminal to lowest point so the the printed chars fit in terminal. Smaller the font, better resolution
The animation should look like the map is 'rotating'
I used little bit of your code to extract the data. Steps are in the commentaries
import json
import sys
import numpy as np
import colorama
import sys
import time
import cv2
#understand terminal_size as how many letters in X axis and how many in Y axis. Sorry not good name
if len(sys.argv)>1:
terminal_size = (int(sys.argv[1]),int(sys.argv[2]))
else:
terminal_size=(230,175)
with open('world-countries.json') as f:
countries = []
minimal = 0 # This can be dangerous. Expecting negative values
maximal = 0 # Expecting bigger values than 0
for feature in json.load(f)['features']: # getting data - I pretend here, that geo coordinates are actually indexes of my numpy array
indexes = np.int16(np.array(feature['geometry']['coordinates'][0])*2)
if indexes.min()<minimal:
minimal = indexes.min()
if indexes.max()>maximal:
maximal = indexes.max()
countries.append(indexes)
countries = (np.array(countries)+np.abs(minimal)) # Transform geo-coordinates to image coordinates
correction = np.abs(minimal) # because geo-coordinates has negative values, I need to move it to 0 - xaxis
colorama.init()
def move_cursor(x,y):
print ("\x1b[{};{}H".format(y+1,x+1))
move = 0 # 'rotate' the globe
for i in range(1000):
image = np.zeros(shape=[maximal+correction+1,maximal+correction+1]) #creating clean image
move -=1 # you need to rotate with negative values
# because negative one are by numpy understood. Positive one will end up with error
for i in countries: # VERY STRANGE,because parsing the json, some countries has different JSON structure
if len(i.shape)==2:
image[i[:,1],i[:,0]+move]=255 # indexes that once were geocoordinates now serves to position the countries in the image
if len(i.shape)==3:
image[i[0][:,1],i[0][:,0]+move]=255
cut = np.where(image==255) # Bounding box
if move == -1: # creating here bounding box - removing empty edges - from sides and top and bottom - we need space. This needs to be done only once
max_x,min_x = cut[0].max(),cut[0].min()
max_y,min_y = cut[1].max(),cut[1].min()
new_image = image[min_x:max_x,min_y:max_y] # the bounding box
new_image= new_image[::-1] # reverse, because map is upside down
new_image = cv2.resize(new_image,terminal_size) # resize so it fits inside terminal
ascii = np.chararray(shape = new_image.shape).astype('|S4') #create container for asci image
ascii[:,:]='' #chararray contains some random letters - dunno why... cleaning it
ascii[:,-1]='\n' #because I pring everything all at once, I am creating new lines at the end of the image
new_image[:,-1]=0 # at the end of the image can be country borders which would overwrite '\n' created one step above
ascii[np.where(new_image>0)]='*' # transforming image array to chararray. Better to say, anything that has pixel value higher than 0 will be star in chararray mask
move_cursor(0,0) # 'cleaning' the terminal for new animation
sys.stdout.buffer.write(ascii) # print into terminal
time.sleep(0.025) # FPS
Maybe it would be good to explain what is the main algorithm in the code. I like to use numpy whereever I can. The whole thing is that I pretend that coordinates in the image, or whatever it may be (in your case geo-coordinates) are matrix indexes. I have then 2 Matrixes - Real Image and Charray as Mask. I then take indexes of interesting pixels in Real image and for the same indexes in Charray Mask I assign any letter I want. Thanks to this, the whole algorithm doesnt need a single loop.
About Future posibilities
Imagine you will also have information about terrain(altitude). Let say you somehow create grayscale image of world map where gray shades expresses altitude. Such grayscale image would have shape x,y. You will prepare 3Dmatrix with shape = [x,y,256]. For each layer out of 256 in the 3D matrix, you assign one letter ' ....;;;;### and so on' that will express shade.
When you have this prepared, you can take your grayscale image where any pixel will actually have 3 coordinates: x,y and shade value. So you will have 3 arrays of indexes out of your grascale map image -> x,y,shade. Your new charray will simply be extraction of your 3Dmatrix with layer letters, because:
#Preparation phase
x,y = grayscale.shape
3Dmatrix = np.chararray(shape = [x,y,256])
table = ' ......;;;;;;;###### ...'
for i in range(256):
3Dmatrix[:,:,i] = table[i]
x_indexes = np.arange(x*y)
y_indexes = np.arange(x*y)
chararray_image = np.chararray(shape=[x,y])
# Ready to print
...
shades = grayscale.reshape(x*y)
chararray_image[:,:] = 3Dmatrix[(x_indexes ,y_indexes ,shades)].reshape(x,y)
Because there is no loop in this process and you can print chararray all at once, you can actually print movie into terminal with huge FPS
For example if you have footage of rotating earth, you can make something like this - (250*70 letters), render time 0.03658s
You can ofcourse take it into extreme and make super-resolution in your terminal, but resulting FPS is not that good: 0.23157s, that is approximately 4-5 FPS. Interesting to note is, that this attitude FPS is enourmous, but terminal simply cannot handle printing, so this low FPS is due to limitations of terminal and not of calculation as calculation of this high resolution took 0.00693s, that is 144 FPS.
BIG EDIT - contradicting some of above statements
I accidentaly opened raw json file and find out, there is CANADA and RUSSIA with full correct coordinates. I made mistake to rely on the fact that we both didnt have canada in the result, so I expected my code is ok. Inside JSON, the data has different NOT-UNIFIED structure. Russia and Canada has 'Multipolygon', so you need to iterate over it.
What does it mean? Dont rely on Shapely and pyproj. Obviously they cant extract some countries and if they cant do it reliably, you cant expect them to do anything more complicated.
After modifying the code, everything is allright
CODE: This is how to load the file correctly
...
with open('world-countries.json') as f:
countries = []
minimal = 0
maximal = 0
for feature in json.load(f)['features']: # getting data - I pretend here, that geo coordinates are actually indexes of my numpy array
for k in range((len(feature['geometry']['coordinates']))):
indexes = np.int64(np.array(feature['geometry']['coordinates'][k]))
if indexes.min()<minimal:
minimal = indexes.min()
if indexes.max()>maximal:
maximal = indexes.max()
countries.append(indexes)
...

python region growing with polygons performance

I'm trying to create regions of polygons on the condition that they touch. In my example I have an example dataset with 382 polygons that need to be grouped together (but the full dataset contains 6355 polygons). (I would show a picture, but I don't have enough reputation to do that..)
I though of doing this brute force, but of course that takes very long and is not very optimal.
def groupBuildings(blds):
# blds is a list with shapely polygons
groups = []
for bld in blds:
group = []
group.append(bld)
for other in blds:
for any in group:
if any != other and any.intersects(other):
group.append(other)
groups.append(group)
return groups
I learned about region growing and thought that that would be a possible solution, but still the performance is terrible. I've implemented this in the following way:
def groupBuildings(blds):
# blds is a list with shapely polygons
others = blds
groups = []
while blds != []:
done = []
group = []
first = blds.pop(0)
done.append(first)
group.append(first)
for other in others:
if (other in blds) and first.touches(other):
group.append(other)
blds.remove(other)
return groups
But I think the problem here is that I don't have any nearest neighbors, so I still have to iterate over every building twice.
So my question is: are nearest neighbors essential for region growing? Or is there another way of doing this efficiently?

You will be best served using shapely.ops.cascaded_union() (docs here).
from shapely.geometry import Point, Polygon, MultiPolygon
from shapely.ops import cascaded_union
import numpy as np
polygons = [Point(200*x,200*y).buffer(b) for x,y,b in np.random.random((6000,3))]
multi = MultiPolygon(polygons)
unioned = cascaded_union(multi)
%%timeit
unioned = cascaded_union(multi)
# 2.8 seconds for me