Exemple #1
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    def multi_advect(self, fields, interpolation="LINEAR", dt=1):
        assert isinstance(
            fields,
            (list, tuple)), "first parameter must be either a tuple or list"
        inputs_lists = []
        coords_lists = []
        value_generators = []
        for field in fields:
            if isinstance(field, StaggeredGrid):
                i, c, v = self._mac_block_advection(field.staggered, dt)
            else:
                i, c, v = self._centered_block_advection(field, dt)
            inputs_lists.append(i)
            coords_lists.append(c)
            value_generators.append(v)

        inputs = math.concat(sum(inputs_lists, []), 0)
        coords = math.concat(sum(coords_lists, []), 0)
        all_advected = math.resample(inputs,
                                     coords,
                                     interpolation=interpolation,
                                     boundary="REPLICATE")
        all_advected = math.reshape(all_advected, [self.spatial_rank, -1] +
                                    list(all_advected.shape[1:]))
        all_advected = math.unstack(all_advected)
        results = []
        abs_i = 0
        for i in range(len(inputs_lists)):
            n = len(inputs_lists[0])
            assigned_advected = all_advected[abs_i:abs_i + n]
            results.append(value_generators[i](assigned_advected))
            abs_i += n
        return results
Exemple #2
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 def _padded_resample(self, points):
     data = self.padded([[1, 1]] * self.rank).data
     local_points = self.box.global_to_local(points)
     local_points = local_points * math.to_float(
         self.resolution) + 0.5  # depends on amount of padding
     resampled = math.resample(data,
                               local_points,
                               boundary='replicate',
                               interpolation=self.interpolation)
     return resampled
Exemple #3
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 def _advect_centered_field(self, field, dt, interpolation):
     idx = indices_tensor(field)
     centered_velocity = self.at_centers()[
         ..., ::-1]  # assume right number of components
     sample_coords = idx - centered_velocity * dt
     result = math.resample(field,
                            sample_coords,
                            interpolation=interpolation,
                            boundary="REPLICATE")
     return result
Exemple #4
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 def sample_at(self, points, collapse_dimensions=True):
     if not isinstance(self.extrapolation, six.string_types):
         return self._padded_resample(points)
     local_points = self.box.global_to_local(points)
     local_points = math.mul(local_points, math.to_float(
         self.resolution)) - 0.5
     resampled = math.resample(self.data,
                               local_points,
                               boundary=_pad_mode(self.extrapolation),
                               interpolation=self.interpolation)
     return resampled
Exemple #5
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 def sample_at(self, points):
     local_points = self.box.global_to_local(points)
     local_points = math.mul(local_points, math.to_float(
         self.resolution)) - 0.5
     resampled = math.resample(self.data,
                               local_points,
                               boundary=_pad_mode(self.extrapolation),
                               interpolation=self.interpolation,
                               constant_values=_pad_value(
                                   self.extrapolation_value))
     return resampled
Exemple #6
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 def sample_at(self, points, collapse_dimensions=True):
     if not isinstance(self.extrapolation, six.string_types):
         return self._padded_resample(points)
     local_points = self.box.global_to_local(points)
     local_points = local_points * math.to_float(self.resolution) - 0.5
     if self.extrapolation == 'periodic':
         data = math.pad(self.data,
                         [[0, 0]] + [[0, 1]] * self.rank + [[0, 0]],
                         mode='wrap')
         local_points = local_points % math.to_float(
             math.staticshape(self.data)[1:-1])
         resampled = math.resample(data,
                                   local_points,
                                   interpolation=self.interpolation)
     else:
         boundary = 'replicate' if self.extrapolation == 'boundary' else 'zero'
         resampled = math.resample(self.data,
                                   local_points,
                                   boundary=boundary,
                                   interpolation=self.interpolation)
     return resampled
Exemple #7
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 def sample_at(self, points, collapse_dimensions=True):
     if not isinstance(self.extrapolation, six.string_types):
         return self._padded_resample(points)
     local_points = self.box.global_to_local(points)
     local_points = math.mul(local_points, math.to_float(
         self.resolution)) - 0.5
     boundary = {
         'periodic': 'circular',
         'boundary': 'replicate',
         'constant': 'constant'
     }[self.extrapolation]
     resampled = math.resample(self.data,
                               local_points,
                               boundary=boundary,
                               interpolation=self.interpolation)
     return resampled
Exemple #8
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    def _advect_mac(self, field_mac, dt, interpolation):
        # resample in each dimension
        idx = indices_tensor(self.staggered)
        advected_component_fields = []
        dims = range(len(self.staggered.shape) - 2)

        for d in dims:  # z,y,x
            velocity_at_staggered_points = self.at_faces(len(dims) - d -
                                                         1)[..., ::-1]
            sample_coords = idx - velocity_at_staggered_points * dt
            d_comp = len(dims) - d - 1
            advected = math.resample(field_mac[..., d_comp:d_comp + 1],
                                     sample_coords,
                                     interpolation=interpolation,
                                     boundary="REPLICATE")
            advected_component_fields.append(advected)

        all_advected = math.concat(advected_component_fields[::-1], axis=-1)
        return all_advected