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.

Adding icon for node shape using networkx and pyvis (python)

I am new to netwrokx and pyvis and am making a small network to display the different shapes possible for each node. I managed to use all the shapes except for icons. I searched a lot but I couldn't find anything useful and the examples available did not work with my code I would appreciate it if anyone could help me figure this out.
here is my code:
import networkx as nx
import xlrd #used to access the external excel file
import pyvis
from pyvis.network import Network
import pandas as pd
import textwrap
df = pd.read_csv("Visualizer\Data\EECS2311\shapes.csv",encoding='cp1252')
G=nx.Graph()
nodes = []
p1 = df['person1']
p2 = df['person2']
p3 = df['person3']
p4 = df['person4']
p5 = df['person5']
p6 = df['person6']
p7 = df['person7']
p8 = df['person8']
p9 = df['person9']
p10 = df['person10']
p11 = df['person11']
p12 = df['person12']
p13 = df['person13']
p14 = df['person14']
data = zip(p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, p14)
for e in data:
person1 = e[0]
G.add_node(person1, shape="ellipse")
person2 = e[1]
G.add_node(person2, shape="circle")
person3 = e[2]
G.add_node(person3, shape="database")
person4 = e[3]
G.add_node(person4, shape="box")
person5 = e[4]
G.add_node(person5, shape="text")
person6 = e[5]
G.add_node(person6, shape="image", image="https://image.shutterstock.com/image-vector/hello-funny-person-simple-cartoon-260nw-1311467669.jpg")
person7 = e[6]
G.add_node(person7, shape="circularImage", image="https://image.shutterstock.com/image-vector/hello-funny-person-simple-cartoon-260nw-1311467669.jpg")
person8 = e[7]
G.add_node(person8, shape="diamond")
person9 = e[8]
G.add_node(person9, shape="dot")
person10 = e[9]
G.add_node(person10, shape="star")
person11 = e[10]
G.add_node(person11, shape="triangle")
person12 = e[11]
G.add_node(person12, shape="triangleDown")
person13 = e[12]
G.add_node(person13, shape="square")
person14 = e[13]
G.add_node(person14, shape="icon", icon="https://image.shutterstock.com/image-vector/hello-funny-person-simple-cartoon-260nw-1311467669.jpg")
nodes.append((person1, person2))
nodes.append((person2, person3))
nodes.append((person3, person4))
nodes.append((person4, person5))
nodes.append((person5, person6))
nodes.append((person6, person7))
nodes.append((person7, person8))
nodes.append((person8, person9))
nodes.append((person9, person10))
nodes.append((person10, person11))
nodes.append((person11, person12))
nodes.append((person12, person13))
nodes.append((person13, person14))
options = {
"layout": {
"hierarchical": {
"enabled": True,
"levelSeparation": 300,
"nodeSpacing": 165,
"treeSpacing": 305,
"direction": "LR"
}
},
"physics": {
"hierarchicalRepulsion": {
"centralGravity": 0,
"nodeDistance": 110,
},
"minVelocity": 0.75,
"solver": "hierarchicalRepulsion"
}
}
G.add_edges_from(nodes)
G2 = Network(height="800px", width="100%", bgcolor="#222222", font_color="white", select_menu=True, filter_menu=True, directed=True)
G2.from_nx(G)
G2.options = options
neighbor_map = G2.get_adj_list()
for node in G2.nodes:
node["value"] = len(neighbor_map[node["id"]])
#to wrap long labels:
id_string = node["label"]
width = 20
wrapped_strings = textwrap.wrap(id_string, width)
wrapped_id ="";
for line in wrapped_strings:
wrapped_id = textwrap.fill(id_string, width)
node["label"] = wrapped_id
#G2.show_buttons()
G2.show("shapes.html")
and here is my .csv file:
person1,person2,person3,person4,person5,person6,person7,person8,person9,person10,person11,person12,person13,person14
ellipse, circle, database,box,text,image, circularImage,diamond,dot,star,triangle,triangleDown,square,icon
"ellipse shape displays label inside the shape. To use this simply set shape =""ellipse""","circle shape displays label inside the shape. To use this simply set shape =""circle""","database shape displays label inside the shape. To use this simply set shape =""database""","box shape displays label inside the shape. To use this simply set shape =""box""","only displays text. To use this simply set shape =""text""","image displays a image with label outside. To use set shape=""image"", image=""url"". Note: requires link to image","circularImage displays a circular image with label outside. To use set shape="" circularImage"", image=""url"". Note: requires link to image","diamond shape displays label outside the shape. To use this simply set shape =""diamond""","dot shape displays label outside the shape. To use this simply set shape =""dot""","star shape displays label outside the shape. To use this simply set shape =""star""","triangle shape displays label outside the shape. To use this simply set shape =""triangle""","triangleDown shape displays label outside the shape. To use this simply set shape =""triangleDown""","square shape displays label outside the shape. To use this simply set shape =""square""","icon displays a circular image with label outside. To use set shape="" icon"", image=""url"". Note: requires link to image"
ps. forgive the heading for the csv file :)

This doesn't answer your question, I just want to help you shrink your code so you can debug it more easily.
Use the DataFrame directly
You're doing a ton of extra work to get at your data, assigning to temporary variables, then zipping them together. They are already together! To loop over the things in row 0 of the DataFrame try this:
for item in df.loc[0]:
print(item)
There's also a function in NetworkX, nx.from_pandas_dataframe(), that will create a network directly from a DataFrame... but you can only add edge attributes with that, not node attributes.
Then again...
Maybe dn't even use a DataFrame
Pandas is a convenient way to load CSVs, but your data isn't all that well-suited to this data structure. A dict would be better. It's a kind of mapping, in your case from node names to a node attribute.
Fortunately, there's a fairly easy way to get a dict from your DataFrame:
df.T.to_dict()[0]
This 'transposes' the DataFrame (turns the rows into columns) then turns the result into a dict. Then the [0] gives you the only column in the data.
This way you can avoid needing to repeat all your data (the mapping from person number to symbol) in your code.
Then again...
Maybe don't even use a dictionary
Any time you are mapping from a continuous set of numbers to some other objects (like person1, person2, etc) you might as well just use a list. Everything is indexed by position, which is basically what you have already. So you could just store your data like ['ellipse', 'circle', 'dot'] etc.
Then again...
Maybe don't even store the data
It turns out all these symbols are already defined in matplotlib. Have a look at:
from matplotlib.lines import Line2D
Line2D.markers
It's a dictionary of all the markers! If you want to try all of them, then you can just use these, no need to define anything.
Use zip to add your edges
zip is great for combining two or more lists, or combining a list with itself but with some offset. You can step over the nodes and make edges like so:
nodes = list(G.nodes)
for u, v in zip(nodes, nodes[1:]):
G.add_edge(u, v)
General advice
Try to avoid using tools like pandas just to load data. In my experience, it often introduces a bunch of complexity you don't need.
Get something small and simple working before making it more complex, e.g. with URLs of images.
You can store dictionaries easily as JSON text files. Check out the json module.
Again, sorry for not directly answering your question. But I feel like all this should help get your code down to something that is much easier to debug.

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)
...

what space is this Matrix in?

In Blender 2.6 API, PoseBone is used for animating a bone. PoseBone.matrix is one way to do that. API says PoseBone.matrix is in "object space".
http://www.blender.org/documentation/blender_python_api_2_63_5/bpy.types.PoseBone.html#bpy.types.PoseBone.matrix
PoseBone.matrix is nothing I've seen at all.
I still can't get my animation importer working.
What's the deal with PoseBone.matrix? In Blender 2.4 API there were two matrices: one in local space, one in armature space.
But the new PoseBone.matrix is neither! It's not a local matrix:
Position isn't local, it's global.
But rotation
<Euler (x=1.5708, y=-0.7854, z=-0.0000), order='XYZ'>
is.
So what is this "object space" that the API says PoseBone.matrix is in?
I'm trying to assign my armature-space matrices to a hierarchial armature and I can't get them right.
I tried decomposing the matrices I have, undoing parent rotations then recomposing the matrix again before setting it as "PoseBone.matrix".
It just doesn't work.
oldmatrix = myMatrix
loc, rot, scale = oldmatrix.decompose()
#rot = rot * pose.bones[bonename].parent.rotation_quaternion.conjugated()
for i in pose.bones[bonename].parent_recursive:
rot = rot * i.conjugated()
newmatrix = rot.to_matrix().to_4x4()
newmatrix[0][3] = loc.x
newmatrix[1][3] = loc.y
newmatrix[2][3] = loc.z
pose.bones[bonename].matrix = newmatrix

EDIT:
I found the solution that transforms these Object Space transforms to Bone Local Space.
To cut the long story short - here's a code snippet that accomplishes that task:
poseBone = C.object.pose.bones[2] # <----- set your bone here
# poseBone.matrix is in object space - we need to convert it to local space
if poseBone.parent is not None:
parentRefPoseMtx = poseBone.parent.bone.matrix_local
boneRefPoseMtx = poseBone.bone.matrix_local
parentPoseMtx = poseBone.parent.matrix
bonePoseMtx = poseBone.matrix
boneLocMtx = ( parentRefPoseMtx.inverted() * boneRefPoseMtx ).inverted() * ( parentPoseMtx.inverted() * bonePoseMtx )
else:
boneRefPoseMtx = poseBone.bone.matrix_local
bonePoseMtx = poseBone.matrix
boneLocMtx = boneRefPoseMtx.inverted() * bonePoseMtx
loc, rot, scale = boneLocMtx.decompose()
MY OLD QUESTION:
Have you found the solution yet?
I'm dealing with exactly the same problem.
Object space matrix should be the bone local space matrix expressed with respect to the parent's object space matrix, so:
boneOS = parentOS * boneLS
But as you noticed - it doesn't give the correct solution. The bone is still offset.
One thing that came to my mind was that it's offset by the rest pose transform, so I tried rolling it back, but that didn't work either, but perhaps the equations I used were wrong.
Here's how I tried calculating the local space transform of a pose bone ( let's assume bone 1 is the bone of my interest, and bone 0 is its parent, and that 0 doesn't have a parent ):
boneOS = C.object.pose.bones[2].matrix
parentBoneOS = C.object.pose.bones[2].parent.matrix
boneRP = C.object.pose.bones[2].bone.matrix_local # rest pose matrix in bone local space
parentBoneRP = C.object.pose.bones[1].bone.matrix_local # parent bone's rest pose matrix in bone local space
boneLS = ( parentBoneRP * boneRP ).inverted() * parentOS.inverted() * boneOS

The matrix is in object-space and it should behave like you expect from your code. What's happening is that the bones' matrices aren't being updated immediately after changing the matrix. So far I've found a hack-solution of updating the entire scene after every bone with
bpy.context.scene.update()