'Finished' the cython line-integrator.

Started working on the los response operator.
Switched off a sanity check in the d2o slicingdistributor's advanced indexing routine, because it was extremely slow (assert: is the first dimension ordered?).

nifty_core.py

@@ -2020,8 +2020,10 @@ class field(object):
         """
         if new_val is not None:
             if copy:
-                new_val = self.domain.unary_operation(new_val, 'copy')
-            self.val = self.domain.cast(new_val)
+                new_val = self._map(
+                            lambda z: self.domain.unary_operation(z, 'copy'),
+                            new_val)
+            self.val = self._map(lambda z: self.domain.cast(z), new_val)
         return self.val
 
     def get_val(self):

nifty_mpi_data.py

@@ -1835,11 +1835,11 @@ class _slicing_distributor(distributor):
                     get_full_data(target_rank=j)
                 if j == rank:
                     result = temp_result
-
-            if not all(result[i] <= result[i + 1]
-                       for i in xrange(len(result) - 1)):
-                raise ValueError(about._errors.cstring(
-                    "ERROR: The first dimemnsion of list_key must be sorted!"))
+# TODO: Implement fast check!
+#            if not all(result[i] <= result[i + 1]
+#                       for i in xrange(len(result) - 1)):
+#                raise ValueError(about._errors.cstring(
+#                    "ERROR: The first dimemnsion of list_key must be sorted!"))
             result = [result]
 
             for ii in xrange(1, len(from_list_key)):
@@ -1877,10 +1877,11 @@ class _slicing_distributor(distributor):
             local_selection = greater_than_lower * less_than_upper
 
             result = [local_zeroth_key[local_selection]]
-            if not all(result[0][i] <= result[0][i + 1]
-                       for i in xrange(len(result[0]) - 1)):
-                raise ValueError(about._errors.cstring(
-                    "ERROR: The first dimemnsion of list_key must be sorted!"))
+# TODO: Implement fast check!
+#            if not all(result[0][i] <= result[0][i + 1]
+#                       for i in xrange(len(result[0]) - 1)):
+#                raise ValueError(about._errors.cstring(
+#                    "ERROR: The first dimemnsion of list_key must be sorted!"))
 
             for ii in xrange(1, len(from_list_key)):
                 current = from_list_key[ii]

operators/line_integrator.pyx

+# -*- coding: utf-8 -*-
+#cython: nonecheck=False
+#cython: boundscheck=False
+#cython: wraparound=False
+
+import numpy as np
+cimport numpy as np
+cimport cython
+
+FLOAT = np.float
+ctypedef np.float_t FLOAT_t
+
+INT = np.int
+ctypedef np.int_t INT_t
+
+cdef extern from "numpy/npy_math.h":
+    bint isnan(double x)
+    bint signbit(double x)
+    double ceil(double x)
+    double floor(double x)
+    double sqrt(double x)
+
+cdef FLOAT_t NAN = float("NaN")
+
+
+cdef class line_integrator(object):
+    cdef tuple shape
+    cdef list start
+    cdef list end
+#    cdef FLOAT_t [:] start
+#    cdef FLOAT_t [:] end
+
+    def __init__(self, shape, start, end):
+        self.shape = tuple(shape)
+        self.start = list(start)
+        self.end = list(end)
+        assert(np.all(np.array(self.shape) != 0))
+        assert(len(self.shape) == len(self.start) == len(self.end))
+
+    cpdef tuple integrate(self):
+        if list_equal_Q(self.start, self.end):
+            return self._empty_results()
+        try:
+            projected_start = self._project_to_cuboid('start')
+            projected_end = self._project_to_cuboid('end')
+        except ValueError:
+            return self._empty_results()
+
+        (indices, weights) = self._integrate_through_cuboid(projected_start,
+                                                            projected_end)
+        return (indices, weights)
+
+    def _empty_results(self):
+        return ([np.array([], dtype=INT)] * len(self.shape),
+                np.array([], dtype=FLOAT))
+
+    cpdef list _project_to_cuboid(self, str mode):
+        cdef list a, b, c, p, surface_list, translator_list,\
+                  translator_index_list
+        cdef int ndim, i, s1, s2
+        cdef bint found
+
+        if mode == 'start':
+            a = self.start
+            b = self.end
+        elif mode == 'end':
+            a = self.end
+            b = self.start
+        else:
+            raise ValueError
+
+        if list_all_le([0]*len(a), a) and list_all_le(a, list(self.shape)):
+            return a
+
+        c = list_sub(b, a)
+
+        ndim = len(self.shape)
+        surface_list = [None]*2*ndim
+        for i in xrange(ndim):
+            surface_list[2*i] = [i, 0]
+            surface_list[2*i+1] = [i, self.shape[i]]
+
+        translator_list = []
+
+        for s1, s2 in surface_list:
+            translator_list += [self._get_translator_to_surface(a, c,
+                                                                s1, s2)]
+        # sort the translators according to their norm, save the sorted indices
+        translator_index_list = np.argsort(np.linalg.norm(translator_list,
+                                                          axis=1)).tolist()
+        # iterate through the indices -from short to long translators- and
+        # take the first translator which brings a to the actual surface of
+        # the cuboid and not just to one of the parallel planes
+        found = False
+        for i in translator_index_list:
+            p = list_add(a, translator_list[i])
+            if list_all_le([0]*len(p), p) and list_all_le(p, list(self.shape)):
+                found = True
+                break
+
+        if not found:
+            raise ValueError(
+                "ERROR: Line-of-sight does not go through cuboid.")
+
+        return p
+
+    cdef list _get_translator_to_surface(self,
+                                   list point,
+                                   list full_direction,
+                                   int dimension_index,
+                                   int surface):
+        """
+        translates 'point' along the vector 'direction' such that the
+        dimension with index 'dimension_index' has the value 'surface'
+        """
+        cdef int ndim = len(point)
+        cdef list scaled_direction = [None] * ndim
+        cdef FLOAT_t point_i = point[dimension_index]
+        cdef FLOAT_t full_direction_i = full_direction[dimension_index]
+
+        if full_direction_i == 0:
+            return [NAN]*ndim
+
+        cdef FLOAT_t direction_pre_scaler = surface - point_i
+
+       # here gets checked if the direction_scaler shows in the same direction
+        # and is shorter or of equal length as the full_direction_i.
+        # The implementation avoids divisions in order to exclude errors
+        # from numerical noise
+        if ((abs(direction_pre_scaler) > abs(full_direction_i)) or
+            signbit(direction_pre_scaler) != signbit(full_direction_i)):
+            return [NAN]*ndim
+
+        for i in xrange(ndim):
+            # first multiply, then divide! Otherwise numerical noise will
+            # produce something like: 1003.*(1./1003.) != 1.
+            scaled_direction[i] = ((full_direction[i] * direction_pre_scaler)/
+                                   full_direction_i)
+        return scaled_direction
+
+    cdef tuple _integrate_through_cuboid(self, list start, list end):
+        cdef INT_t i, j, num_estimate
+        cdef list current_position, next_position, floor_current_position
+        cdef FLOAT_t weight
+
+        # estimate the maximum number of cells that could be hit
+        # the current estimator is: norm of the vector times number of dims
+        num_estimate = INT(ceil(list_norm(list_sub(end, start))))*len(start)
+
+        cdef np.ndarray[INT_t, ndim=2] index_list = np.empty((num_estimate,
+                                                              len(start)),
+                                                             dtype=INT)
+        cdef np.ndarray[FLOAT_t, ndim=1] weight_list = np.empty(num_estimate,
+                                                                FLOAT)
+
+
+        current_position = start
+        i = 0
+        while True:
+            next_position, weight = self._get_next_position(current_position,
+                                                            end)
+            floor_current_position = list_floor(current_position)
+            for j in xrange(len(start)):
+                index_list[i, j] = floor_current_position[j]
+            weight_list[i] = weight
+
+            if floor_current_position == list_floor(end):
+                break
+
+            current_position = next_position
+            i += 1
+        return (list(index_list[:i].T), weight_list[:i])
+
+
+    cdef tuple _get_next_position(self,
+                                  list position,
+                                  list end_position):
+
+        cdef list surface_list, translator_list
+        cdef INT_t i, s1, s2, n_surfaces
+        cdef FLOAT_t weight, best_translator_norm, temp_translator_norm
+        cdef list full_direction, best_translator, temp_translator,\
+                  floor_position, next_position
+
+
+        full_direction = list_sub(end_position, position)
+
+        n_surfaces = len(position)
+        surface_list = [None] * n_surfaces
+        for i in xrange(n_surfaces):
+            if signbit(full_direction[i]):
+                surface_list[i] = [i, strong_floor(position[i])]
+            else:
+                surface_list[i] = [i, strong_ceil(position[i])]
+
+        best_translator_norm = NAN
+        best_translator = [NAN] * len(position)
+        for s1, s2 in surface_list:
+            temp_translator = self._get_translator_to_surface(position,
+                                                              full_direction,
+                                                              s1, s2)
+            temp_translator_norm = list_norm(temp_translator)
+            if ((not best_translator_norm <= temp_translator_norm) and
+                    (not isnan(temp_translator_norm))):
+                best_translator_norm = temp_translator_norm
+                best_translator = temp_translator
+        # if the surounding surfaces are not reachable, it must be the case
+        # that the current position is in the same cell as the endpoint
+        if isnan(best_translator_norm):
+            floor_position = list_floor(position)
+            # check if position is in the same cell as the endpoint
+            assert(floor_position == list_floor(end_position))
+            weight = list_norm(list_sub(end_position, position))
+            next_position = position
+        else:
+            next_position = list_add(position, best_translator)
+            weight = list_norm(best_translator)
+        return (next_position, weight)
+
+
+cdef INT_t strong_floor(FLOAT_t x):
+    cdef FLOAT_t floor_x
+    floor_x = floor(x)
+    if floor_x == x:
+        return INT(floor_x - 1)
+    else:
+        return INT(floor_x)
+
+cpdef INT_t strong_ceil(FLOAT_t x):
+    cdef FLOAT_t ceil_x
+    ceil_x = ceil(x)
+    if ceil_x == x:
+        return INT(ceil_x + 1)
+    else:
+        return INT(ceil_x)
+
+cdef list list_floor(list l):
+    cdef unsigned int i, ndim = len(l)
+    cdef list result = [None] * ndim
+    for i in xrange(ndim):
+        result[i] = floor(l[i])
+    return result
+
+cdef list list_ceil(list l):
+    cdef unsigned int i, ndim = len(l)
+    cdef list result = [None] * ndim
+    for i in xrange(ndim):
+        result[i] = ceil(l[i])
+    return result
+
+cdef FLOAT_t list_norm(list l):
+    cdef FLOAT_t d, result = 0.
+    for d in l:
+        result += d**2
+    return sqrt(result)
+
+cdef bint list_equal_Q(list list1, list list2):
+    cdef unsigned int i
+    for i in xrange(len(list1)):
+        if list1[i] != list2[i]:
+            return False
+    return True
+
+cdef bint list_contains_nan_Q(list l):
+    cdef unsigned int i
+    for i in xrange(len(l)):
+        if isnan(l[i]):
+            return True
+    return False
+
+cdef bint list_all_le(list list1, list list2):
+    cdef unsigned int i
+    for i in xrange(len(list1)):
+        if list1[i] <= list2[i]:
+            continue
+        else:
+            return False
+    return True
+
+cdef list list_add(list list1, list list2):
+    cdef int ndim = len(list1)
+    cdef list result = [None]*ndim
+    for i in xrange(ndim):
+        result[i] = list1[i] + list2[i]
+    return result
+
+cdef list list_sub(list list1, list list2):
+    cdef int ndim = len(list1)
+    cdef list result = [None]*ndim
+    for i in xrange(ndim):
+        result[i] = list1[i] - list2[i]
+    return result
+
+
+#def test2():
+#    print ceil(1.5)
+#    print floor(1.5)
+#
+#def test():
+#    l = los_integrator_pure((1000,1000,1000), (-1,-1,-10), (1001, 1002, 1003))
+#    for i in xrange(30000):
+#        l._get_next_position([1.,1.1,1.2], [10.,10.,11.])
+#
+#def test3():
+#    print sqrt(3)
+#
+
+
+
+
+
+
+
+
+
+
+
+

operators/nifty_los.py

 # -*- coding: utf-8 -*-
 
-import pyximport; pyximport.install()
-from nifty_los_implementation import los_integrator
+import numpy as np
+from line_integrator import line_integrator
+
+from nifty.keepers import about
+from nifty.rg import rg_space
+from nifty.operators import operator
+
+
+class los_response(operator):
+
+    def __init__(self, domain, starts, ends, sigmas_low=None, sigmas_up=None,
+                 zero_point=None, error_function=lambda x: 0.5):
+        if not isinstance(domain, rg_space):
+            raise TypeError(about._errors.cstring(
+                "ERROR: The domain must be a rg_space instance."))
+        self.domain = domain
+
+        if not callable(error_function):
+            raise ValueError(about._errors.cstring(
+                "ERROR: error_function must be callable."))
+
+        (self.starts,
+         self.ends,
+         self.sigmas_low,
+         self.sigmas_up,
+         self.zero_point) = self._parse_coordinates(self.domain,
+                                                    starts, ends, sigmas_low,
+                                                    sigmas_up, zero_point)
+
+        self.imp = True
+        self.uni = False
+        self.sym = False
+
+    def _parse_coordinates(self, domain, starts, ends, sigmas_low, sigmas_up,
+                           zero_point):
+        # basic sanity checks
+        if not isinstance(starts, list):
+            raise TypeError(about._errors.cstring(
+                "ERROR: starts must be a list instance."))
+        if not isinstance(ends, list):
+            raise TypeError(about._errors.cstring(
+                "ERROR: ends must be a list instance."))
+        if not (len(domain.get_shape()) == len(starts) == len(ends)):
+            raise ValueError(about._errors.cstring(
+                "ERROR: The length of starts and ends must " +
+                "be the same as the number of dimension of the domain."))
+
+        number_of_dimensions = len(starts)
+
+        if zero_point is None:
+            zero_point = [0.] * number_of_dimensions
+
+        if np.shape(zero_point) != (number_of_dimensions,):
+            raise ValueError(about._errors.cstring(
+                "ERROR: The shape of zero_point must match the length of " +
+                "the starts and ends list"))
+        parsed_zero_point = list(zero_point)
+
+        # extract the number of line-of-sights and by the way check that
+        # all entries of starts and ends have the right shape
+        number_of_los = None
+        for i in xrange(2*number_of_dimensions):
+            if i < number_of_dimensions:
+                temp_entry = starts[i]
+            else:
+                temp_entry = ends[i-number_of_dimensions]
+
+            if isinstance(temp_entry, np.ndarray):
+                if len(np.shape(temp_entry)) != 1:
+                    raise ValueError(about._errors.cstring(
+                        "ERROR: The numpy ndarrays in starts " +
+                        "and ends must be flat."))
+
+                if number_of_los is None:
+                    number_of_los = len(temp_entry)
+                elif number_of_los != len(temp_entry):
+                    raise ValueError(about._errors.cstring(
+                        "ERROR: The length of all numpy ndarrays in starts " +
+                        "and ends must be the same."))
+            elif np.isscalar(temp_entry):
+                pass
+            else:
+                raise TypeError(about._errors.cstring(
+                    "ERROR: The entries of starts and ends must be either " +
+                    "scalar or numpy ndarrays."))
+
+        if number_of_los is None:
+            number_of_los = 1
+            starts = [np.array([x]) for x in starts]
+            ends = [np.array([x]) for x in ends]
+
+        # Parse the coordinate arrays/scalars in the starts and ends list
+        parsed_starts = self._parse_startsends(starts, number_of_los)
+        parsed_ends = self._parse_startsends(ends, number_of_los)
+
+        # check that sigmas_up/lows have the right shape and parse scalars
+        parsed_sigmas_up = self._parse_sigmas_uplows(sigmas_up, number_of_los)
+        parsed_sigmas_low = self._parse_sigmas_uplows(sigmas_low,
+                                                      number_of_los)
+
+        return (parsed_starts, parsed_ends, parsed_sigmas_up,
+                parsed_sigmas_low, parsed_zero_point)
+
+    def _parse_startsends(self, coords, number_of_los):
+        result_coords = [None]*len(coords)
+        for i in xrange(len(coords)):
+            temp_array = np.empty(number_of_los, dtype=np.float)
+            temp_array[:] = coords[i]
+            result_coords[i] = temp_array
+        return result_coords
+
+    def _parse_sigmas_uplows(self, sig, number_of_los):
+        if sig is None:
+            parsed_sig = np.zeros(number_of_los, dtype=np.float)
+        elif isinstance(sig, np.ndarray):
+            if np.shape(sig) != (number_of_los,):
+                    raise ValueError(about._errors.cstring(
+                        "ERROR: The length of sigmas_up/sigmas_low must be " +
+                        " the same as the number of line-of-sights."))
+            parsed_sig = sig.astype(np.float)
+        elif np.isscalar(sig):
+            parsed_sig = np.empty(number_of_los, dtype=np.float)
+            parsed_sig[:] = sig
+        else:
+            raise TypeError(about._errors.cstring(
+                "ERROR: sigmas_up/sigmas_low must either be a scalar or a " +
+                "numpy ndarray."))
+        return parsed_sig
+
+    def convert_indices_to_physical(self, pixel_coordinates):
+        # first of all, compute the phyiscal distance of the given pixel
+        # from the zeroth-pixel
+        phyiscal_distance = np.array(pixel_coordinates) * \
+                            np.array(self.domain.distances)
+        # add the offset of the zeroth pixel with respect to the coordinate
+        # system
+        physical_position = phyiscal_distance + np.array(self.zero_point)
+        return physical_position.tolist()
+
+    def convert_physical_to_indices(self, physical_position):
+        # compute the distance to the zeroth pixel
+        relative_position = np.array(physical_position) - \
+                            np.array(self.zero_point)
+        # rescale the coordinates to the uniform grid
+        pixel_coordinates = relative_position / np.array(self.domain.distances)
+        return pixel_coordinates.tolist()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

operators/nifty_los_implementation.pyx

-# -*- coding: utf-8 -*-
-
-import numpy as np
-cimport numpy as np
-cimport cython
-
-
-class los_integrator(object):
-    def __init__(self, shape, start, end):
-        self.dtype = np.dtype('float')
-
-        self.shape = tuple(shape)
-        self.start = np.array(start, dtype=self.dtype)
-        self.end = np.array(end, dtype=self.dtype)
-
-        assert(np.all(np.array(self.shape) != 0))
-        assert(len(self.shape) == len(self.start) == len(self.end))
-        assert(len(self.start.shape) == len(self.end.shape) == 1)
-
-    def integrate(self):
-        if np.all(self.start == self.end):
-            return self._empty_results()
-        try:
-            projected_start = self._project_to_cuboid(mode='start')
-            projected_end = self._project_to_cuboid(mode='end')
-        except ValueError:
-            return self._empty_results()
-
-        (indices, weights) = self._integrate_through_cuboid(projected_start,
-                                                            projected_end)
-        return (indices, weights)
-
-    def _empty_results(self):
-        return ([np.array([], dtype=np.dtype('int'))] * len(self.shape),
-                np.array([], dtype=np.dtype('float')))
-
-    def _project_to_cuboid(self, mode):
-        if mode == 'start':
-            a = self.start
-            b = self.end
-        elif mode == 'end':
-            a = self.end
-            b = self.start
-        else:
-            raise ValueError
-
-        if np.all(np.zeros_like(a) <= a) and np.all(a <= np.array(self.shape)):
-            return a
-
-        c = b - a
-
-        surface_list = []
-        for i in xrange(len(self.shape)):
-            surface_list += [[i, 0]]
-            surface_list += [[i, self.shape[i]]]