The following code is just to understand the context. My question doesn't require much understanding of this. It need a simple translation of one line of MATLAB code in to Python.
us = np.linspace(-(1023)/2,(1023)/2,1024) vs = np.linspace(-(1023)/2,(1023)/2,1024) uu,vv = np.meshgrid(-us,vs) pu = ((((rx*SDD)/(ry+SOD))+us[0])/(-du))+1 xs = np.linspace(-(360-1)/2,(nx-1)/2,360) ys = np.linspace(-(360-1)/2,(ny-1)/2,360) zs = np.linspace(-(360-1)/2,(ny-1)/2,360) xx,yy = np.meshgrid(xs,ys) angle_rad = np.linspace(0,359,360) angle_rad = angle_rad*np.pi/180 for i in range(0,360) : vol = np.zeros((360,360,360)) rx = xx*np.cos(angle_rad[i]-np.pi/2) + yy*np.sin(angle_rad[i]-np.pi/2) ry = -xx*np.sin(angle_rad[i]-np.pi/2) + yy*np.cos(angle_rad[i]-np.pi/2) pu = ((((rx*370)/(ry+9))+us[0])/(-51.2/1024))+1 for iz in range(0,360) : pv = ((((zs[iz]*370)/(ry+9))-vs[0])/(51.2/1024)) +1
So after this step the code should do interpolation and in MATLAB it's like this:
vol(:,:,iz) = interp2(uu,vv ,proj',pu,pv,'linear'); This is in MATLAB
My proj, uu and vv are (1024,1024) and pu, pv are (360,360). I need to convert the above line to Python. I tried using scipy.interpolate but it gives the following errors on trying these :
vol[:,:,iz] = Ratio*(interp2d(uu,vv,proj,pu,pv,'cubic'))
TypeError: unhashable type: 'numpy.ndarray'
vol[:,:,iz] = Ratio*(interp2d(uu,vv,proj,'cubic'))
OverflowError: Too many data points to interpolate
vol[:,:,iz] = Ratio*(interp2d(proj,pu,pv,'cubic'))
ValueError: x and y must have equal lengths for non rectangular grid
vol[:,:,iz] = Ratio*(interp2d(pu,pv,proj,'cubic'))
ValueError: Invalid length for input z for non rectangular grid
I have read all the scipy.interpolate documentations and none seemed to help. Could anyone figure out what's wrong?
The problem on a wide scale is that you're looking at the documentation but you're not trying to understand it. If you're using a specific function interp2d in the module scipy.interpolate, then look at the function's documentation, as @pv also suggested in a comment. The fact that you're throwing the arguments all around the place clearly demonstrates that you're trying to use a function based on guesswork. It won't work: a function was implemented with a given syntax, and it will only work like that.
So look at the function's signature:
class scipy.interpolate.interp2d(x, y, z, kind='linear', copy=True, bounds_error=False, fill_value=nan)
The meaning of the parameters is explained afterwards. You can clearly see that there are 3 mandatory parameters: x, y, z. No other array-valued inputs are allowed This is because interp2d only constructs an interpolating function, which you should then use to compute the interpolated values on a mesh (unlike MATLAB, where interp2d gives you the interpolated values directly). So you can call
myfun = interp2(uu,vv,proj,'linear')
to get an interpolating function. You can then substitute at the given values, but note that the function myfun will expect 1d input, and it will construct a mesh internally. So assuming that your output mesh is constructed as
puu,puv = np.meshgrid(puu_vec,puv_vec)
(which is probably not the case, but I'll get back to this later), you need
vol[:,:,iz] = Ratio*myfun(puu_vec,puv_vec)
to obtain the output you need.
However, there are some important points you should note.
- In MATLAB you have
interp2d(uu,vv,proj',...), but make sure that for scipy the elements of uu, vv, and proj are in the same order. To be on the safe side: in case of an asymmetric mesh size, the shape of uu, vv and proj should all be the same. - In MATLAB you're using
'linear' interpolation, while in python you're using 'cubic'. I'm not sure this is really what you want, if you're porting your code to a new language. - It seems to me that your output mesh is not defined by a rectangular grid as if from
meshgrid, which suggests that interp2d might not be suitable for your case. Anyway, I've had some odd experiences with interp2d, and I wouldn't trust it. So if it's not suitable for your expected output, I'd strongly suggest using scipy.interpolate.griddata instead. This function gives you interpolated points directly: I suggest that you try to figure out its use based on the manual:) My linked answer can also help. You can set the kind of interpolation in the same way, but your output can be any set of scattered points if you like.
