Commit 47ed014e by csongor

### WIP - cythonify

parent 879c7be9
 import numpy as np def smooth_power_2s(power, k, exclude=1, smooth_length=None): if smooth_length == 0: # No smoothing requested, just return the input array. return power if (exclude > 0): k = k[exclude:] excluded_power = np.copy(power[:exclude]) power = power[exclude:] if (smooth_length is None) or (smooth_length < 0): smooth_length = k[1]-k[0] nmirror = int(5*smooth_length/(k[1]-k[0]))+2 print "nmirror", nmirror mpower = np.r_[np.exp(2*np.log(power[0])-np.log(power[1:nmirror][::-1])),power,np.exp(2*np.log(power[-1])-np.log(power[-nmirror:-1][::-1]))] mk = np.r_[(2*k[0]-k[1:nmirror][::-1]),k,(2*k[-1]-k[-nmirror:-1][::-1])] mdk = np.r_[0.5*(mk[1]-mk[0]),0.5*(mk[2:]-mk[:-2]),0.5*(mk[-1]-mk[-2])] print "mpower", mpower p_smooth = np.empty(mpower.shape) print "p_smooth", p_smooth for i in xrange(len(p_smooth)): l = i-int(2*smooth_length/mdk[i])-1 l = max(l,0) u = i+int(2*smooth_length/mdk[i])+2 u = min(u,len(p_smooth)) print "i", i, "l", l, "u", u C = np.exp(-(mk[l:u]-mk[i])**2/(2.*smooth_length**2))*mdk[l:u] p_smooth[i] = np.sum(C*mpower[l:u])/np.sum(C) # print "p_smooth[",i,"] = ",p_smooth[i] print "p_smooth2", " all ", p_smooth p_smooth = p_smooth[nmirror - 1:-nmirror + 1] print "p_smooth3", p_smooth print "p_smooth length ", len(p_smooth) if (exclude > 0): p_smooth = np.r_[excluded_power,p_smooth] return p_smooth def GaussianKernel(mpower, mk, mu,smooth_length): C = np.exp(-(mk - mu) ** 2 / (2. * smooth_length ** 2)) return np.sum(C * mpower) / np.sum(C) def smoothie(power, startindex, endindex, k, exclude=1, smooth_length=None): if smooth_length == 0: # No smoothing requested, just return the input array. return power excluded_power = [] if (exclude > 0): k = k[exclude:] excluded_power = np.copy(power[:exclude]) power = power[exclude:] if (smooth_length is None) or (smooth_length < 0): smooth_length = k[1]-k[0] p_smooth = np.empty(endindex-startindex) for i in xrange(startindex, endindex): l = max(i-int(2*smooth_length)-1,0) u = min(i+int(2*smooth_length)+2,len(p_smooth)) p_smooth[i-startindex] = GaussianKernel(power[l:u], k[l:u], k[i], smooth_length) if (exclude > 0): p_smooth = np.r_[excluded_power,p_smooth] return p_smooth def smooth_something(datablock, axis=0, startindex=None, endindex=None, kernelfunction=lambda x:x, k=None, sigma=None): if startindex == None: startindex=0 if endindex == None: endindex=len(datablock) print kernelfunction return np.apply_along_axis(smoothie, axis, datablock, startindex=startindex, endindex=endindex, k=k, smooth_length=sigma) \ No newline at end of file
 import numpy as np cimport numpy as np ctypedef fused FUSED_TYPES_t: np.int32_t np.int64_t np.float32_t np.float64_t np.complex64_t np.complex128_t cpdef np.ndarray[FUSED_TYPES_t, ndim=1] GaussianKernel(np.ndarray[FUSED_TYPES_t, ndim=1] mpower,np.ndarray[FUSED_TYPES_t, ndim=1] mk,np.ndarray[FUSED_TYPES_t, ndim=1] mu,np.float smooth_length): cdef np.ndarray[FUSED_TYPES_t, ndim=1] C = np.exp(-(mk - mu) ** 2 / (2. * smooth_length ** 2)) return np.sum(C * mpower) / np.sum(C) cpdef np.ndarray[FUSED_TYPES_t, ndim=1] apply_kernel_along_array(np.ndarray[FUSED_TYPES_t, ndim=1] power, np.int startindex,np.int endindex,np.ndarray[FUSED_TYPES_t, ndim=1] k,np.int exclude=1,np.float smooth_length=None): if smooth_length == 0: # No smoothing requested, just return the input array. return power cdef np.ndarray[FUSED_TYPES_t, ndim=1] excluded_power = np.array([]) if (exclude > 0): k = k[exclude:] excluded_power = np.copy(power[:exclude]) power = power[exclude:] if (smooth_length is None) or (smooth_length < 0): smooth_length = k[1]-k[0] cdef np.ndarray[FUSED_TYPES_t, ndim=1] p_smooth = np.empty(endindex-startindex) cdef np.ndarray[FUSED_TYPES_t, ndim=1] l,u cdef np.int i for i in xrange(startindex, endindex): l = max(i-int(2*smooth_length)-1,0) u = min(i+int(2*smooth_length)+2,len(p_smooth)) p_smooth[i-startindex] = GaussianKernel(power[l:u], k[l:u], k[i], smooth_length) if (exclude > 0): p_smooth = np.r_[excluded_power,p_smooth] return p_smooth cpdef np.ndarray[FUSED_TYPES_t] apply_along_axis(tuple axis,np.ndarray[FUSED_TYPES_t] arr, np.int startindex,np.int endindex,np.ndarray[FUSED_TYPES_t, ndim=1] distances,np.int exclude=1,np.float smooth_length=None): cdef tuple nd = arr.ndim if axis < 0: axis += nd if (axis >= nd): raise ValueError("axis must be less than arr.ndim; axis=%d, rank=%d." % (axis, nd)) cdef np.ndarray ind = np.zeros(nd-1) cdef np.ndarray i = np.zeros(nd, 'O') indlist = list(range(nd)) indlist.remove(axis) i[axis] = slice(None, None) cdef np.ndarray outshape = np.asarray(arr.shape).take(indlist) i.put(indlist, ind) cdef np.ndarray res = apply_kernel_along_array(arr[tuple(i.tolist())], startindex=startindex,endindex=endindex,k=distances,exclude=exclude,smooth_length=smooth_length) cdef np.int Ntot = np.product(outshape) holdshape = outshape outshape = list(arr.shape) outshape[axis] = len(res) outarr = np.zeros(outshape, np.asarray(res).dtype) outarr[tuple(i.tolist())] = res cdef np.int k = 1 cdef np.int n while k < Ntot: # increment the index ind[-1] += 1 n = -1 while (ind[n] >= holdshape[n]) and (n > (1-nd)): ind[n-1] += 1 ind[n] = 0 n -= 1 i.put(indlist, ind) res = apply_kernel_along_array(arr[tuple(i.tolist())], startindex=startindex,endindex=endindex,k=distances, exclude=exclude,smooth_length=smooth_length) outarr[tuple(i.tolist())] = res k += 1 return outarr \ No newline at end of file
 import numpy as np def apply_along_axis(func1d, axis, arr, *args, **kwargs): """ Apply a function to 1-D slices along the given axis. Execute `func1d(a, *args)` where `func1d` operates on 1-D arrays and `a` is a 1-D slice of `arr` along `axis`. Parameters ---------- func1d : function This function should accept 1-D arrays. It is applied to 1-D slices of `arr` along the specified axis. axis : integer Axis along which `arr` is sliced. arr : ndarray Input array. args : any Additional arguments to `func1d`. kwargs: any Additional named arguments to `func1d`. .. versionadded:: 1.9.0 Returns ------- apply_along_axis : ndarray The output array. The shape of `outarr` is identical to the shape of `arr`, except along the `axis` dimension, where the length of `outarr` is equal to the size of the return value of `func1d`. If `func1d` returns a scalar `outarr` will have one fewer dimensions than `arr`. """ arr = np.asarray(arr) nd = arr.ndim if axis < 0: axis += nd if (axis >= nd): raise ValueError("axis must be less than arr.ndim; axis=%d, rank=%d." % (axis, nd)) ind = [0]*(nd-1) i = np.zeros(nd, 'O') indlist = list(range(nd)) indlist.remove(axis) i[axis] = slice(None, None) outshape = np.asarray(arr.shape).take(indlist) i.put(indlist, ind) res = func1d(arr[tuple(i.tolist())], *args, **kwargs) Ntot = np.product(outshape) holdshape = outshape outshape = list(arr.shape) outshape[axis] = len(res) outarr = np.zeros(outshape, np.asarray(res).dtype) outarr[tuple(i.tolist())] = res k = 1 while k < Ntot: # increment the index ind[-1] += 1 n = -1 while (ind[n] >= holdshape[n]) and (n > (1-nd)): ind[n-1] += 1 ind[n] = 0 n -= 1 i.put(indlist, ind) res = func1d(arr[tuple(i.tolist())], *args, **kwargs) outarr[tuple(i.tolist())] = res k += 1 return outarr \ No newline at end of file
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