Merge branch 'byebye_cython' into 'master'

Replace direct smoothing code by pure Python

See merge request !96

.gitlab-ci.yml

@@ -13,7 +13,7 @@ before_script:
   - apt-get update
   - >
     apt-get install -y build-essential python python-pip python-dev git
-    autoconf gsl-bin libgsl-dev wget python-numpy cython
+    autoconf gsl-bin libgsl-dev wget python-numpy
   - pip install --upgrade -r ci/requirements_base.txt
   - chmod +x ci/*.sh
 

README.md

@@ -15,7 +15,7 @@ Summary
 a versatile library designed to enable the development of signal
 inference algorithms that operate regardless of the underlying spatial
 grid and its resolution. Its object-oriented framework is written in
-Python, although it accesses libraries written in Cython, C++, and C for
+Python, although it accesses libraries written in C++ and C for
 efficiency.
 
 NIFTY offers a toolkit that abstracts discretized representations of
@@ -71,7 +71,6 @@ Installation
 
 -   [Python](http://www.python.org/) (v2.7.x)
     -   [NumPy](http://www.numpy.org/)
-    -   [Cython](http://cython.org/)
 
 ### Download
 
@@ -95,7 +94,7 @@ Starting with a fresh Ubuntu installation move to a folder like
 
 -   Using pip install numpy etc...:
 
-        sudo pip install numpy cython
+        sudo pip install numpy
 
 -   Install pyHealpix:
 
@@ -147,10 +146,9 @@ MacPorts, missing ones need to be installed manually. It may also be
 mentioned that one should only use one package manager, as multiple ones
 may cause trouble.
 
--   Install basic packages numpy and cython:
+-   Install numpy:
 
         sudo port install py27-numpy
-        sudo port install py27-cython
 
 -   Install pyHealpix:
 

ci/requirements.txt

 numpy
-cython
 mpi4py
 matplotlib
 plotly

nifty/operators/__init__.py

@@ -24,7 +24,7 @@ from diagonal_operator import DiagonalOperator
 
 from endomorphic_operator import EndomorphicOperator
 
-from smoothing_operator import SmoothingOperator
+from smoothing_operator import *
 
 from fft_operator import *
 

nifty/operators/linear_operator/linear_operator.py

@@ -75,7 +75,8 @@ class LinearOperator(Loggable, object):
     def __init__(self, default_spaces=None):
         self.default_spaces = default_spaces
 
-    def _parse_domain(self, domain):
+    @staticmethod
+    def _parse_domain(domain):
         return utilities.parse_domain(domain)
 
     @abc.abstractproperty

nifty/operators/response_operator/response_operator.py

@@ -6,6 +6,7 @@ from nifty.operators.smoothing_operator import SmoothingOperator
 from nifty.operators.composed_operator import ComposedOperator
 from nifty.operators.diagonal_operator import DiagonalOperator
 
+
 class ResponseOperator(LinearOperator):
     """ NIFTy ResponseOperator (example)
 

nifty/operators/smoothing_operator/__init__.py

@@ -16,4 +16,4 @@
 # You should have received a copy of the GNU General Public License
 # along with this program.  If not, see <http://www.gnu.org/licenses/>.
 
-from smoothing_operator import SmoothingOperator
+from .smoothing_operator import SmoothingOperator

nifty/operators/smoothing_operator/direct_smoothing_operator.py

+# -*- coding: utf8 -*-
+
+import numpy as np
+
+from d2o import STRATEGIES
+
+from .smoothing_operator import SmoothingOperator
+
+
+class DirectSmoothingOperator(SmoothingOperator):
+    def __init__(self, domain, sigma, log_distances=False,
+                 default_spaces=None):
+        super(DirectSmoothingOperator, self).__init__(domain,
+                                                      sigma,
+                                                      log_distances,
+                                                      default_spaces)
+        self.effective_smoothing_width = 3.01
+
+    def _precompute(self, x, sigma, dxmax=None):
+        """ Does precomputations for Gaussian smoothing on a 1D irregular grid.
+
+        Parameters
+        ----------
+        x: 1D floating point array or list containing the individual grid
+            positions. Points must be given in ascending order.
+
+        sigma: The sigma of the Gaussian with which the function living on x
+            should be smoothed, in the same units as x.
+        dxmax: (optional) The maximum distance up to which smoothing is
+            performed, in the same units as x. Default is 3.01*sigma.
+
+        Returns
+        -------
+        ibegin: integer array of the same size as x
+            ibegin[i] is the minimum grid index to consider when computing the
+            smoothed value at grid index i
+        nval: integer array of the same size as x
+            nval[i] is the number of indices to consider when computing the
+            smoothed value at grid index i.
+        wgt: list with the same number of entries as x
+            wgt[i] is an array with nval[i] entries containing the
+            normalized smoothing weights.
+        """
+
+        if dxmax is None:
+            dxmax = self.effective_smoothing_width*sigma
+
+        x = np.asarray(x)
+
+        ibegin = np.searchsorted(x, x-dxmax)
+        nval = np.searchsorted(x, x+dxmax) - ibegin
+
+        wgt = []
+        expfac = 1. / (2. * sigma*sigma)
+        for i in range(x.size):
+            t = x[ibegin[i]:ibegin[i]+nval[i]]-x[i]
+            t = np.exp(-t*t*expfac)
+            t *= 1./np.sum(t)
+            wgt.append(t)
+
+        return ibegin, nval, wgt
+
+    def _apply_kernel_along_array(self, power, startindex, endindex,
+                                  distances, smooth_length, smoothing_width,
+                                  ibegin, nval, wgt):
+
+        if smooth_length == 0.0:
+            return power[startindex:endindex]
+
+        p_smooth = np.zeros(endindex-startindex, dtype=power.dtype)
+        for i in xrange(startindex, endindex):
+            imin = max(startindex, ibegin[i])
+            imax = min(endindex, ibegin[i]+nval[i])
+            p_smooth[imin:imax] += (power[i] *
+                                    wgt[i][imin-ibegin[i]:imax-imin+ibegin[i]])
+
+        return p_smooth
+
+    def _apply_along_axis(self, axis, arr, startindex, endindex, distances,
+                          smooth_length, smoothing_width):
+
+        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))
+        ibegin, nval, wgt = self._precompute(
+                distances, smooth_length, smooth_length*smoothing_width)
+
+        ind = np.zeros(nd-1, dtype=np.int)
+        i = np.zeros(nd, dtype=object)
+        shape = arr.shape
+        indlist = np.asarray(range(nd))
+        indlist = np.delete(indlist, axis)
+        i[axis] = slice(None, None)
+        outshape = np.asarray(shape).take(indlist)
+
+        i.put(indlist, ind)
+
+        Ntot = np.product(outshape)
+        holdshape = outshape
+        slicedArr = arr[tuple(i.tolist())]
+        res = self._apply_kernel_along_array(
+                    slicedArr, startindex, endindex, distances,
+                    smooth_length, smoothing_width, ibegin, nval, wgt)
+
+        outshape = np.asarray(arr.shape)
+        outshape[axis] = endindex - startindex
+        outarr = np.zeros(outshape, dtype=arr.dtype)
+        outarr[tuple(i.tolist())] = res
+        k = 1
+        while k < Ntot:
+            # increment the index
+            ind[nd-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)
+            slicedArr = arr[tuple(i.tolist())]
+            res = self._apply_kernel_along_array(
+                    slicedArr, startindex, endindex, distances,
+                    smooth_length, smoothing_width, ibegin, nval, wgt)
+            outarr[tuple(i.tolist())] = res
+            k += 1
+
+        return outarr
+
+    def _smooth(self, x, spaces, inverse):
+        # infer affected axes
+        # we rely on the knowledge, that `spaces` is a tuple with length 1.
+        affected_axes = x.domain_axes[spaces[0]]
+
+        if len(affected_axes) > 1:
+            raise ValueError("By this implementation only one-dimensional "
+                             "spaces can be smoothed directly.")
+
+        affected_axis = affected_axes[0]
+
+        distance_array = x.domain[spaces[0]].get_distance_array(
+            distribution_strategy='not')
+        distance_array = distance_array.get_local_data(copy=False)
+
+        if self.log_distances:
+            np.log(distance_array, out=distance_array)
+
+        # collect the local data + ghost cells
+        local_data_Q = False
+
+        if x.distribution_strategy == 'not':
+            local_data_Q = True
+        elif x.distribution_strategy in STRATEGIES['slicing']:
+            # infer the local start/end based on the slicing information of
+            # x's d2o. Only gets non-trivial for axis==0.
+            if 0 != affected_axis:
+                local_data_Q = True
+            else:
+                start_index = x.val.distributor.local_start
+                start_distance = distance_array[start_index]
+                augmented_start_distance = \
+                    (start_distance -
+                     self.effective_smoothing_width*self.sigma)
+                augmented_start_index = \
+                    np.searchsorted(distance_array, augmented_start_distance)
+                true_start = start_index - augmented_start_index
+                end_index = x.val.distributor.local_end
+                end_distance = distance_array[end_index-1]
+                augmented_end_distance = \
+                    (end_distance + self.effective_smoothing_width*self.sigma)
+                augmented_end_index = \
+                    np.searchsorted(distance_array, augmented_end_distance)
+                true_end = true_start + x.val.distributor.local_length
+                augmented_slice = slice(augmented_start_index,
+                                        augmented_end_index)
+
+                augmented_data = x.val.get_data(augmented_slice,
+                                                local_keys=True,
+                                                copy=False)
+                augmented_data = augmented_data.get_local_data(copy=False)
+
+                augmented_distance_array = distance_array[augmented_slice]
+
+        else:
+            raise ValueError("Direct smoothing not implemented for given"
+                             "distribution strategy.")
+
+        if local_data_Q:
+            # if the needed data resides on the nodes already, the necessary
+            # are the same; no matter what the distribution strategy was.
+            augmented_data = x.val.get_local_data(copy=False)
+            augmented_distance_array = distance_array
+            true_start = 0
+            true_end = x.shape[affected_axis]
+
+        # perform the convolution along the affected axes
+        # currently only one axis is supported
+        data_axis = affected_axes[0]
+
+        if inverse:
+            true_sigma = 1. / self.sigma
+        else:
+            true_sigma = self.sigma
+
+        local_result = self._apply_along_axis(
+                              data_axis,
+                              augmented_data,
+                              startindex=true_start,
+                              endindex=true_end,
+                              distances=augmented_distance_array,
+                              smooth_length=true_sigma,
+                              smoothing_width=self.effective_smoothing_width)
+
+        result = x.copy_empty()
+        result.val.set_local_data(local_result, copy=False)
+        return result

nifty/operators/smoothing_operator/fft_smoothing_operator.py

+# -*- coding: utf-8 -*-
+import numpy as np
+
+from nifty.operators.fft_operator import FFTOperator
+
+from .smoothing_operator import SmoothingOperator
+
+
+class FFTSmoothingOperator(SmoothingOperator):
+
+    def _smooth(self, x, spaces, inverse):
+        Transformator = FFTOperator(x.domain[spaces[0]])
+
+        # transform to the (global-)default codomain and perform all remaining
+        # steps therein
+        transformed_x = Transformator(x, spaces=spaces)
+        codomain = transformed_x.domain[spaces[0]]
+        coaxes = transformed_x.domain_axes[spaces[0]]
+
+        # create the kernel using the knowledge of codomain about itself
+        axes_local_distribution_strategy = \
+            transformed_x.val.get_axes_local_distribution_strategy(axes=coaxes)
+
+        kernel = codomain.get_distance_array(
+            distribution_strategy=axes_local_distribution_strategy)
+
+        if self.log_distances:
+            kernel.apply_scalar_function(np.log, inplace=True)
+
+        kernel.apply_scalar_function(
+            codomain.get_fft_smoothing_kernel_function(self.sigma),
+            inplace=True)
+
+        # now, apply the kernel to transformed_x
+        # this is done node-locally utilizing numpys reshaping in order to
+        # apply the kernel to the correct axes
+        local_transformed_x = transformed_x.val.get_local_data(copy=False)
+        local_kernel = kernel.get_local_data(copy=False)
+
+        reshaper = [transformed_x.shape[i] if i in coaxes else 1
+                    for i in xrange(len(transformed_x.shape))]
+        local_kernel = np.reshape(local_kernel, reshaper)
+
+        # apply the kernel
+        if inverse:
+            local_transformed_x /= local_kernel
+        else:
+            local_transformed_x *= local_kernel
+
+        transformed_x.val.set_local_data(local_transformed_x, copy=False)
+
+        smoothed_x = Transformator.adjoint_times(transformed_x, spaces=spaces)
+
+        result = x.copy_empty()
+        result.set_val(smoothed_x, copy=False)
+
+        return result

nifty/operators/smoothing_operator/smooth_util.pyx

-#cython: nonecheck=False
-#cython: boundscheck=True
-#cython: wraparound=False
-#cython: cdivision=True
-
-import numpy as np
-cimport numpy as np
-#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
-
-cdef int SIGMA_RANGE = 3
-
-cpdef np.float32_t gaussianKernel_f(np.ndarray[np.float32_t, ndim=1] mpower,
-                                    np.ndarray[np.float32_t, ndim=1] mk,
-                                    np.float32_t mu,
-                                    np.float32_t smooth_length):
-    cdef np.ndarray[np.float32_t, ndim=1] C = \
-        np.exp(-(mk - mu) ** 2 / (2. * smooth_length ** 2))
-    return np.sum(C * mpower) / np.sum(C)
-
-cpdef np.float64_t gaussianKernel(np.ndarray[np.float64_t, ndim=1] mpower,
-                                  np.ndarray[np.float64_t, ndim=1] mk,
-                                  np.float64_t mu,
-                                  np.float64_t smooth_length):
-    cdef np.ndarray[np.float64_t, ndim=1] C = \
-        np.exp(-(mk - mu) ** 2 / (2. * smooth_length ** 2))
-    return np.sum(C * mpower) / np.sum(C)
-
-
-cpdef np.ndarray[np.float64_t, ndim=1] apply_kernel_along_array(
-                                    np.ndarray[np.float64_t, ndim=1] power,
-                                    np.int_t startindex,
-                                    np.int_t endindex,
-                                    np.ndarray[np.float64_t, ndim=1] distances,
-                                    np.float64_t smooth_length,
-                                    np.float64_t smoothing_width):
-
-    if smooth_length == 0.0:
-        return power[startindex:endindex]
-
-    if (smooth_length is None) or (smooth_length < 0):
-        smooth_length = distances[1]-distances[0]
-
-    cdef np.ndarray[np.float64_t, ndim=1] p_smooth = \
-        np.empty(endindex-startindex, dtype=np.float64)
-    cdef np.int64_t i, l, u
-    for i in xrange(startindex, endindex):
-        current_location = distances[i]
-        l = np.searchsorted(distances,
-                            current_location - smoothing_width*smooth_length)
-        u = np.searchsorted(distances,
-                            current_location + smoothing_width*smooth_length)
-        p_smooth[i-startindex] = gaussianKernel(power[l:u],
-                                                distances[l:u],
-                                                distances[i],
-                                                smooth_length)
-
-    return p_smooth
-
-cpdef np.ndarray[np.float32_t, ndim=1] apply_kernel_along_array_f(
-                                    np.ndarray[np.float32_t, ndim=1] power,
-                                    np.int_t startindex,
-                                    np.int_t endindex,
-                                    np.ndarray[np.float32_t, ndim=1] distances,
-                                    np.float64_t smooth_length,
-                                    np.float64_t smoothing_width):
-
-    if smooth_length == 0.0:
-        return power[startindex:endindex]
-
-    if (smooth_length is None) or (smooth_length < 0):
-        smooth_length = distances[1]-distances[0]
-
-    cdef np.ndarray[np.float32_t, ndim=1] p_smooth = \
-        np.empty(endindex-startindex, dtype=np.float64)
-    cdef np.int64_t i, l, u
-    for i in xrange(startindex, endindex):
-        current_location = distances[i]
-        l = np.searchsorted(distances,
-                            current_location - smoothing_width*smooth_length)
-        u = np.searchsorted(distances,
-                            current_location + smoothing_width*smooth_length)
-        p_smooth[i-startindex] = gaussianKernel(power[l:u],
-                                                distances[l:u],
-                                                distances[i],
-                                                smooth_length)
-
-    return p_smooth
-
-def getShape(a):
-    return tuple(a.shape)
-
-cpdef np.ndarray[np.float64_t, ndim=1] apply_along_axis(
-                                    np.int_t axis,
-                                    np.ndarray arr,
-                                    np.int_t startindex,
-                                    np.int_t endindex,
-                                    np.ndarray[np.float64_t, ndim=1] distances,
-                                    np.float64_t smooth_length,
-                                    np.float64_t smoothing_width):
-    cdef np.int_t 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[np.int_t, ndim=1] ind = np.zeros(nd-1, dtype=np.int)
-    cdef np.ndarray i = np.zeros(nd, dtype=object)
-    cdef tuple shape = getShape(arr)
-    cdef np.ndarray[np.int_t, ndim=1] indlist = np.asarray(range(nd))
-    indlist = np.delete(indlist,axis)
-    i[axis] = slice(None, None)
-    cdef np.ndarray[np.int_t, ndim=1] outshape = \
-        np.asarray(shape).take(indlist)
-
-    i.put(indlist, ind)
-
-    cdef np.ndarray[np.float64_t, ndim=1] slicedArr
-    cdef np.ndarray[np.float64_t, ndim=1] res
-
-    cdef np.int_t Ntot = np.product(outshape)
-    cdef np.ndarray[np.int_t, ndim=1] holdshape = outshape
-    slicedArr = arr[tuple(i.tolist())]
-
-    res = apply_kernel_along_array(slicedArr,
-                                   startindex,
-                                   endindex,
-                                   distances,
-                                   smooth_length,
-                                   smoothing_width)
-
-    outshape = np.asarray(getShape(arr))
-    outshape[axis] = endindex - startindex
-    outarr = np.zeros(outshape, dtype=np.float64)
-    outarr[tuple(i.tolist())] = res
-    cdef np.int_t n, k = 1
-    while k < Ntot:
-        # increment the index
-        ind[nd-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)
-        slicedArr = arr[tuple(i.tolist())]
-        res = apply_kernel_along_array(slicedArr,
-                                       startindex,
-                                       endindex,
-                                       distances,
-                                       smooth_length,
-                                       smoothing_width)
-        outarr[tuple(i.tolist())] = res
-        k += 1
-
-    return outarr
-
-
-cpdef np.ndarray[np.float32_t, ndim=1] apply_along_axis_f(
-                                    np.int_t axis,
-                                    np.ndarray arr,
-                                    np.int_t startindex,
-                                    np.int_t endindex,
-                                    np.ndarray[np.float32_t, ndim=1] distances,
-                                    np.float64_t smooth_length,
-                                    np.float64_t smoothing_width):
-    cdef np.int_t 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[np.int_t, ndim=1] ind = np.zeros(nd-1, dtype=np.int)
-    cdef np.ndarray i = np.zeros(nd, dtype=object)
-    cdef tuple shape = getShape(arr)
-    cdef np.ndarray[np.int_t, ndim=1] indlist = np.asarray(range(nd))
-    indlist = np.delete(indlist,axis)
-    i[axis] = slice(None, None)
-    cdef np.ndarray[np.int_t, ndim=1] outshape = \
-        np.asarray(shape).take(indlist)
-
-    i.put(indlist, ind)
-
-    cdef np.ndarray[np.float32_t, ndim=1] slicedArr
-    cdef np.ndarray[np.float32_t, ndim=1] res
-
-    cdef np.int_t Ntot = np.product(outshape)
-    cdef np.ndarray[np.int_t, ndim=1] holdshape = outshape
-    slicedArr = arr[tuple(i.tolist())]
-
-    res = apply_kernel_along_array_f(slicedArr,
-                                     startindex,
-                                     endindex,
-                                     distances,
-                                     smooth_length,
-                                     smoothing_width)
-
-    outshape = np.asarray(getShape(arr))
-    outshape[axis] = endindex - startindex
-    outarr = np.zeros(outshape, dtype=np.float32)
-    outarr[tuple(i.tolist())] = res
-    cdef np.int_t n, k = 1
-    while k < Ntot:
-        # increment the index
-        ind[nd-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)
-        slicedArr = arr[tuple(i.tolist())]
-        res = apply_kernel_along_array(slicedArr,
-                                       startindex,