How to create collada objects in pycollada - collada
Goal: I want to create a collada object/file with 5-10 cuboids in a 3-D space.
However, I am finding it very hard to follow the documentation here to create the above object. In particular, I am unable to understand the following:
>>> vert_floats = [-50,50,50,50,50,50,-50,-50,50,50,
-50,50,-50,50,-50,50,50,-50,-50,-50,-50,50,-50,-50]
>>> normal_floats = [0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,
0,1,0,0,1,0,0,1,0,0,-1,0,0,-1,0,0,-1,0,0,-1,0,-1,0,0,
-1,0,0,-1,0,0,-1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,0,-1,
0,0,-1,0,0,-1,0,0,-1]
What are normal_floats and vert_floats variables? What do the elements in these array represent? Why do we have 24 and 72 elements respectively?
>>> indices = numpy.array([0,0,2,1,3,2,0,0,3,2,1,3,0,4,1,5,5,6,0,
... 4,5,6,4,7,6,8,7,9,3,10,6,8,3,10,2,11,0,12,
... 4,13,6,14,0,12,6,14,2,15,3,16,7,17,5,18,3,
... 16,5,18,1,19,5,20,7,21,6,22,5,20,6,22,4,23])
What is the indices variable? Here too, what do these elements mean? These array also has 72 elements
The above code is produces the following cube
Thanks in advance!
The vert_floats are the vertex source data. The normal_floats are the normal source data. The indices index into the arrays. I'd suggest taking some time to familiarize yourself with the Collada spec. This page has a nice overview:
http://www.wazim.com/Collada_Tutorial_1.htm
Related
Vectorize nested for loop in python to find curl
I am trying to find the curl of a 3D vector (with x,y,z components) which has values over a 3D grid of size (1200,1200,400). I was able to find curl using the finite difference method using nested for loops. But only for a section of the data. It's computation time is way higher for the entire set of (1200,1200,400) grid points. So, I tried using a package numba to speed up, but it didn't work. So, I tried vectorizing the whole thing. But the problem is there is something wrong (broadcasting error) with the way I am indexing the vector. NB: I am relatively new to python So, here is my approach: create three 1D arrays x,y,z to represent the grid axis which can be used to index the vector Put the arrays as indices of the vector. For eg: vx[x,y,z] I expect it to give the value of vx in the entire grid. To find the curl I need to add and subtract 1 from the indices (when I use finite difference method). The error I get is IndexError: shape mismatch: indexing arrays could not be broadcast together with shapes (1200,) (1200,) (400,) I tried looking it up. tried changing the shape to (1200,1) instead of (1200,), error remained. This is the function I defined: def curl(rx,ry,rz): curlvx = (vz[rx,np.add(ry,1)%1200,rz] - vy[rx,np.add(ry,-1)%1200%1200,rz])/0.02 - (vy[rx,ry,np.add(rz,1)%400] - vy[rx,ry,np.add(rz,-1)%400])/0.02 curlvy = (vx[rx,ry,np.add(rz,1)%400] - vx[rx,ry,np.add(rz,-1)%400])/0.02 - (vz[np.add(rx,1)%1200,ry,rz] - vz[np.add(rx,-1)%1200,ry,rz])/0.02 curlvz = (vy[np.add(rx,1)%1200,ry,rz] - vy[np.add(rx,-1)%1200,ry,rz])/0.02 - (vx[rx,np.add(ry,1)%1200,rz] - vx[rx,np.add(ry,-1)%1200,rz])/0.02 return [curlvx,curlvy,curlvz] Where I call my function like this: x=np.arange(0,1200) y=np.arange(0,1200) z=np.arange(0,400) curl(x,y,z) This is the line where I'm getting error. curlvx = (vz[rx,np.add(ry,1)%1200,rz] - vy[rx,np.add(ry,-1)%1200%1200,rz])/0.02 - (vy[rx,ry,np.add(rz,1)%400] - vy[rx,ry,np.add(rz,-1)%400])/0.02 The is the part vz[rx,np.add(ry,1)%1200,rz] which is showing error
OSMNx : get coordinates of nodes/corners/edges of polygons/buildings
I am trying to retrieve the coordinates of all nodes/corners/edges of each commercial building in a list. E.g. for the supermarket Aldi in Macclesfield (UK), I can get from the UI 10 nodes (all the corners/edges of the supermarket) but I can only retrieve from osmnx 2 of those 10 nodes. I would need to access to the complete list of nodes but it truncates the results giving only 2 nodes of 10 in this case.Using this code below:
import osmnx as ox
test = ox.geocode_to_gdf('aldi, Macclesfield, Cheshire, GB')
ax = ox.project_gdf(test).plot()
test.geometry
or
gdf = ox.geometries_from_place('Grosvenor, Macclesfield, Cheshire, GB', tags)
gdf.geometry
Both return just two coordinates and truncate other info/results that is available in openStreetMap UI (you can see it in the first column of the image attached geometry>POLYGON>only two coordinates and other results truncated...). I would appreciate some help on this, thanks in advance.
It's hard to guess what you're doing here because you didn't provide a reproducible example (e.g., tags is undefined). But I'll try to guess what you're going for.
I am trying to retrieve the coordinates of all nodes/corners/edges of commercial buildings
Here I retrieve all the tagged commercial building footprints in Macclesfield, then extract the first one's polygon coordinates. You could instead filter these by other attribute values as you see fit if you only want certain kinds of buildings. Proper usage of OSMnx's geometries module is described in the documentation.
import osmnx as ox
# get the building footprints in Macclesfield
place = 'Macclesfield, Cheshire, England, UK'
tags = {'building': 'commercial'}
gdf = ox.geometries_from_place(place, tags)
# how many did we get?
print(gdf.shape) # (57, 10)
# extract the coordinates for the first building's footprint
gdf.iloc[0]['geometry'].exterior.coords
Alternatively, if you want a specific building's footprint, you can look up its OSM ID and tell OSMnx to geocode that value:
gdf = ox.geocode_to_gdf('W251154408', by_osmid=True)
polygon = gdf.iloc[0]['geometry']
polygon.exterior.coords
gdf = ox.geocode_to_gdf('W352332709', by_osmid=True)
polygon = gdf.iloc[0]['geometry']
polygon.exterior.coords
list(polygon.exterior.coords)
How to export "simplices" array from Delaunay triangulation?
I am using the "Delaunay triangulation" module in from "scipy.spatial." I am able to generate an array (actually an ndarray, since I am using x, y and z coordinates) from the "simplices," but unable to export it into any format I can use for further processing. The code is straightforward: tri = Delaunay(points) a = np.array(points[tri.simplices]) What I get looks like this: [[7.02192702e+05, 7.53337067e+06, 1.43116411e+02], [7.02275075e+05, 7.53339801e+06, 1.53508313e+02], [7.02073353e+05, 7.53340902e+06, 1.40979450e+02], [7.02288667e+05, 7.53338498e+06, 1.52185457e+02]], ..., [[7.02038856e+05, 7.53333613e+06, 1.39584833e+02], [7.02069568e+05, 7.53327029e+06, 1.46902739e+02], [7.02062213e+05, 7.53331215e+06, 1.31241316e+02], [7.02040635e+05, 7.53329922e+06, 1.30787203e+02]],... By playing around with it I can export it into an extended string: 702299.971067+7533414.077516+163.2373+... But I would prefer to have it in a .csv file with columns, or convert that extended string into a table or array with a set number of columns. I assume I'm doing something wrong in saving or writing the output, but can't find any obvious solutions to saving/exporting arrays online anywhere. Any ideas? suggestions?
Once it's in an np.ndarray format, just use np.savetxt() to save the array to a .txt file. (see: https://docs.scipy.org/doc/numpy/reference/generated/numpy.savetxt.html). This is the simplest method I know of.
VTK - How to use vtkNetCDFCFReader to read an array or variable array at specific time frame
Im trying to load an array at a specific time frame (for example if it has 50 frames or time units then get an array corresponding to the 2nd time frame) from netCDF files (.nc). Im currently using vtkNetCDFCFReader and getting the data array "vwnd" from the 1st time frame like this:
vtkSmartPointer<vtkNetCDFCFReader> reader = vtkSmartPointer<vtkNetCDFCFReader>::New();
reader->SetFileName(path.c_str());
reader->UpdateMetaData();
vtkSmartPointer<vtkStructuredGridGeometryFilter> geometryFilter = vtkSmartPointer<vtkStructuredGridGeometryFilter>::New();
geometryFilter->SetInputConnection(reader->GetOutputPort());
geometryFilter->Update();
vtkSmartPointer<vtkPolyData> ncPolydata = vtkSmartPointer<vtkPolyData>::New();
ncPolydata = geometryFilter->GetOutput();
vtkSmartPointer<vtkDataArray> dataArray = ncPolydata->GetCellData()->GetArray("vwnd");
Variable Arrays are : lat, lon, time, vwnd (vwnd has dimensions (lat,lon)). Im also interested in getting arrays for lat and lon. Any help would be appreciated.
Thanks in advance
As the dimension of lat/lon is different from vwnd, you will need 2 vtknetCDFreaders to read in data with different dimensions. Just remember to set the dimension after creating the reader.
For example in C++:
vtknetCDFReader* reader = vtknetCDFReader::New();
reader->SetFileName(fileName.c_str());
reader->UpdateMetaData();
//here you specify the dimension of the reader
reader->SetDimension(dim);
reader->SetVariableArrayStatus("lat",1)
reader->SetVariableArrayStatus("lon",1)
reader->Update();
If you are doing it correctly, you could read in any arrays and store it into vtkDataArray.
If you want to read in the vwnd data in the second time step, just skip the first lat*lon values.
Scikit-Learn Linear Regression how to get coefficient's respective features?
I'm trying to perform feature selection by evaluating my regressions coefficient outputs, and select the features with the highest magnitude coefficients. The problem is, I don't know how to get the respective features, as only coefficients are returned form the coef._ attribute. The documentation says: Estimated coefficients for the linear regression problem. If multiple targets are passed during the fit (y 2D), this is a 2D array of shape (n_targets, n_features), while if only one target is passed, this is a 1D array of length n_features. I am passing into my regression.fit(A,B), where A is a 2-D array, with tfidf value for each feature in a document. Example format: "feature1" "feature2" "Doc1" .44 .22 "Doc2" .11 .6 "Doc3" .22 .2 B are my target values for the data, which are just numbers 1-100 associated with each document: "Doc1" 50 "Doc2" 11 "Doc3" 99 Using regression.coef_, I get a list of coefficients, but not their corresponding features! How can I get the features? I'm guessing I need to modfy the structure of my B targets, but I don't know how.
What I found to work was: X = your independent variables coefficients = pd.concat([pd.DataFrame(X.columns),pd.DataFrame(np.transpose(logistic.coef_))], axis = 1) The assumption you stated: that the order of regression.coef_ is the same as in the TRAIN set holds true in my experiences. (works with the underlying data and also checks out with correlations between X and y)
You can do that by creating a data frame: cdf = pd.DataFrame(regression.coef_, X.columns, columns=['Coefficients']) print(cdf)
coefficients = pd.DataFrame({"Feature":X.columns,"Coefficients":np.transpose(logistic.coef_)})
I suppose you are working on some feature selection task. Well using regression.coef_ does get the corresponding coefficients to the features, i.e. regression.coef_[0] corresponds to "feature1" and regression.coef_[1] corresponds to "feature2". This should be what you desire. Well I in its turn recommend tree model from sklearn, which could also be used for feature selection. To be specific, check out here.
Coefficients and features in zip
print(list(zip(X_train.columns.tolist(),logreg.coef_[0])))
Coefficients and features in DataFrame
pd.DataFrame({"Feature":X_train.columns.tolist(),"Coefficients":logreg.coef_[0]})
This is the easiest and most intuitive way: pd.DataFrame(logisticRegr.coef_, columns=x_train.columns) or the same but transposing index and columns pd.DataFrame(logisticRegr.coef_, columns=x_train.columns).T
Suppose your train data X variable is 'df_X' then you can map into a dictionary and feed into pandas dataframe to get the mapping: pd.DataFrame(dict(zip(df_X.columns,model.coef_[0])),index=[0]).T
Try putting them in a series with the data columns names as index: coeffs = pd.Series(model.coef_[0], index=X.columns.values) coeffs.sort_values(ascending = False)