'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/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]]]
-
-        translator_list = map(lambda z:
-                              self._get_translator_to_surface(a, c, *z),
-                              surface_list)
-        # sort the translators according to their norm, save the sorted indices
-        translator_index_list = np.argsort(map(np.linalg.norm,
-                                               translator_list))
-        # 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 = a + translator_list[i]
-            if np.all(np.zeros_like(p) <= p) and \
-                    np.all(p <= np.array(self.shape)):
-                found = True
-                break
-
-        if not found:
-            raise ValueError(
-                "ERROR: Line-of-sight does not go through cuboid.")
-
-        return p
-
-    def _get_translator_to_surface(self, point, full_direction,
-                                   dimension_index, surface):
-        """
-        translates 'point' along the vector 'direction' such that the
-        dimension with index 'dimension_index' has the value 'surface'
-        """
-        direction_scaler = np.divide((surface - point[dimension_index]),
-                                     full_direction[dimension_index])
-        if direction_scaler < 0 or direction_scaler > 1:
-            return point * np.nan
-
-        scaled_direction = full_direction * direction_scaler
-        return scaled_direction
-
-    def _integrate_through_cuboid(self, start, end):
-        # estimate the maximum number of cells that could be hit
-        # the current estimator is: norm of the vector times number of dims
-        num_estimate = np.ceil(np.linalg.norm(end - start))*len(start)
-
-        index_list = np.empty((num_estimate, len(start)),
-                              dtype=np.dtype('int'))
-        weight_list = np.empty((num_estimate), self.dtype)
-
-        current_position = start
-        i = 0
-        while True:
-            next_position, weight = self._get_next_position(current_position,
-                                                            end)
-            floor_current_position = np.floor(current_position)
-            index_list[i] = floor_current_position
-            weight_list[i] = weight
-
-            if np.all(np.floor(current_position) == np.floor(end)):
-                break
-
-            current_position = next_position
-            i += 1
-
-        return list(index_list[:i].T), weight_list[:i]
-
-    def _get_next_position(self, position, end_position):
-        full_direction = end_position - position
-
-        surface_list = []
-        for i in xrange(len(position)):
-            surface_list += [[i, strong_floor(position[i])]]
-            surface_list += [[i, strong_ceil(position[i])]]
-
-        translator_list = map(lambda z: self._get_translator_to_surface(
-                                                              position,
-                                                              full_direction,
-                                                              *z),
-                              surface_list)
-
-#        index_of_best_translator = np.argsort(np.linalg.norm(translator_list,
-#                                                             axis=1))[0]
-        translator_list = np.array(translator_list)
-        index_of_best_translator = np.linalg.norm(translator_list, axis=1)
-        index_of_best_translator = np.argsort(index_of_best_translator)[0]
-
-
-        best_translator = translator_list[index_of_best_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 np.isnan(np.linalg.norm(best_translator)):
-            floor_position = np.floor(position)
-            # check if position is in the same cell as the endpoint
-            assert(np.all(floor_position == np.floor(end_position)))
-            weight = np.linalg.norm(end_position - position)
-            next_position = None
-        else:
-            next_position = position + best_translator
-            weight = np.linalg.norm(best_translator)
-        return (next_position, weight)
-
-
-def strong_floor(x):
-    floor_x = np.floor(x)
-    if floor_x == x:
-        return floor_x-1
-    else:
-        return floor_x
-
-
-def strong_ceil(x):
-    ceil_x = np.ceil(x)
-    if ceil_x == x:
-        return ceil_x+1
-    else:
-        return ceil_x
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-

operators/nifty_operators.py

@@ -2845,7 +2845,8 @@ class response_operator(operator):
             None
         """
         if not isinstance(domain, space):
-            raise TypeError(about._errors.cstring("ERROR: invalid input."))
+            raise TypeError(about._errors.cstring(
+                "ERROR: The domain must be a space instance."))
         self.domain = domain
 
         if self.domain.check_codomain(codomain):

setup.py

@@ -20,8 +20,17 @@
 ## along with this program. If not, see <http://www.gnu.org/licenses/>.
 
 from distutils.core import setup
+#from Cython.Build import cythonize
+from distutils.extension import Extension
+from Cython.Distutils import build_ext
+import sys
 import os
 
+ext_modules=[Extension(
+                   "line_integrator_vector",
+                   ["operators/line_integrator.pyx"])]
+
+
 setup(name="ift_nifty",
       version="1.0.6",
       author="Marco Selig",
@@ -32,6 +41,13 @@ setup(name="ift_nifty",
       url="http://www.mpa-garching.mpg.de/ift/nifty/",
       packages=["nifty", "nifty.demos", "nifty.rg", "nifty.lm",
                 "nifty.operators", "nifty.dummys", "nifty.keepers"],
+      cmdclass={'build_ext': build_ext},
+      ext_modules = ext_modules,
+      #ext_modules=cythonize(["operators/line_integrator_vector.pyx"]),
+
       package_dir={"nifty": ""},
       data_files=[(os.path.expanduser('~') + "/.nifty", ["nifty_config"])],
       license="GPLv3")
+
+
+