def rtree_generator_function(): c = 0 centers = pair_arrays(sg.center_lon, sg.center_lat) for i, axis in enumerate(centers): for j, (x, y) in enumerate(axis): c += 1 yield (c, (x, y, x, y), (i, j))
def wgs84_bounds(self, layer): try: cached_sg = load_grid(self.topology_file) except: pass else: #centers = cached_sg.centers centers = pair_arrays(cached_sg.center_lon, cached_sg.center_lat) longitudes = centers[..., 0] latitudes = centers[..., 1] lon_name, lat_name = cached_sg.face_coordinates lon_var_obj = getattr(cached_sg, lon_name) lat_var_obj = getattr(cached_sg, lat_name) lon_trimmed = longitudes[lon_var_obj.center_slicing] lat_trimmed = latitudes[lat_var_obj.center_slicing] lon_max = lon_trimmed.max() lon_min = lon_trimmed.min() lat_max = lat_trimmed.max() lat_min = lat_trimmed.min() return DotDict(minx=lon_min, miny=lat_min, maxx=lon_max, maxy=lat_max, bbox=(lon_min, lat_min, lon_max, lat_max) )
def test_pair_arrays(): a1, a2, a3 = (1, 2), (3, 4), (5, 6) b1, b2, b3 = (10, 20), (30, 40), (50, 60) a = np.array([a1, a2, a3]) b = np.array([b1, b2, b3]) result = pair_arrays(a, b) expected = np.array([[(1, 10), (2, 20)], [(3, 30), (4, 40)], [(5, 50), (6, 60)]]) np.testing.assert_almost_equal(result, expected, decimal=3)
def getmap(self, layer, request): time_index, time_value = self.nearest_time(layer, request.GET['time']) wgs84_bbox = request.GET['wgs84_bbox'] with self.dataset() as nc: cached_sg = load_grid(self.topology_file) lon_name, lat_name = cached_sg.face_coordinates lon_obj = getattr(cached_sg, lon_name) lat_obj = getattr(cached_sg, lat_name) #centers = cached_sg.centers centers = pair_arrays(cached_sg.center_lon, cached_sg.center_lat) lon = centers[..., 0][lon_obj.center_slicing] lat = centers[..., 1][lat_obj.center_slicing] if isinstance(layer, Layer): data_obj = getattr(cached_sg, layer.access_name) raw_var = nc.variables[layer.access_name] if len(raw_var.shape) == 4: z_index, z_value = self.nearest_z(layer, request.GET['elevation']) raw_data = raw_var[time_index, z_index, data_obj.center_slicing[-2], data_obj.center_slicing[-1]] elif len(raw_var.shape) == 3: raw_data = raw_var[time_index, data_obj.center_slicing[-2], data_obj.center_slicing[-1]] elif len(raw_var.shape) == 2: raw_data = raw_var[data_obj.center_slicing] else: raise BaseException('Unable to trim variable {0} data.'.format(layer.access_name)) # handle edge variables if data_obj.location is not None and 'edge' in data_obj.location: raw_data = avg_to_cell_center(raw_data, data_obj.center_axis) if request.GET['image_type'] == 'pcolor': return mpl_handler.pcolormesh_response(lon, lat, data=raw_data, request=request) elif request.GET['image_type'] in ['filledhatches', 'hatches', 'filledcontours', 'contours']: return mpl_handler.contouring_response(lon, lat, data=raw_data, request=request) else: raise NotImplementedError('Image type "{}" is not supported.'.format(request.GET['image_type'])) elif isinstance(layer, VirtualLayer): x_var = None y_var = None raw_vars = [] for l in layer.layers: data_obj = getattr(cached_sg, l.access_name) raw_var = nc.variables[l.access_name] raw_vars.append(raw_var) if len(raw_var.shape) == 4: z_index, z_value = self.nearest_z(layer, request.GET['elevation']) raw_data = raw_var[time_index, z_index, data_obj.center_slicing[-2], data_obj.center_slicing[-1]] elif len(raw_var.shape) == 3: raw_data = raw_var[time_index, data_obj.center_slicing[-2], data_obj.center_slicing[-1]] elif len(raw_var.shape) == 2: raw_data = raw_var[data_obj.center_slicing] else: raise BaseException('Unable to trim variable {0} data.'.format(l.access_name)) raw_data = avg_to_cell_center(raw_data, data_obj.center_axis) if x_var is None: if data_obj.vector_axis and data_obj.vector_axis.lower() == 'x': x_var = raw_data elif data_obj.center_axis == 1: x_var = raw_data if y_var is None: if data_obj.vector_axis and data_obj.vector_axis.lower() == 'y': y_var = raw_data elif data_obj.center_axis == 0: y_var = raw_data if x_var is None or y_var is None: raise BaseException('Unable to determine x and y variables.') dim_lengths = [ len(v.dimensions) for v in raw_vars ] if len(list(set(dim_lengths))) != 1: raise AttributeError('One or both of the specified variables has screwed up dimensions.') if request.GET['image_type'] == 'vectors': angles = cached_sg.angles[lon_obj.center_slicing] vectorstep = request.GET['vectorstep'] # don't do this if the vectorstep is 1; let's save a microsecond or two # it's identical to getting all the data if vectorstep > 1: data_dim = len(lon.shape) step_slice = (np.s_[::vectorstep],) * data_dim # make sure the vector step is used for all applicable dimensions lon = lon[step_slice] lat = lat[step_slice] x_var = x_var[step_slice] y_var = y_var[step_slice] angles = angles[step_slice] vectorscale = request.GET['vectorscale'] padding_factor = calc_safety_factor(vectorscale) # figure out the average distance between lat/lon points # do the math after taking into the vectorstep if specified spatial_idx_padding = calc_lon_lat_padding(lon, lat, padding_factor) spatial_idx = data_handler.lat_lon_subset_idx(lon, lat, lonmin=wgs84_bbox.minx, latmin=wgs84_bbox.miny, lonmax=wgs84_bbox.maxx, latmax=wgs84_bbox.maxy, padding=spatial_idx_padding ) subset_lon = self._spatial_data_subset(lon, spatial_idx) subset_lat = self._spatial_data_subset(lat, spatial_idx) # rotate vectors x_rot, y_rot = rotate_vectors(x_var, y_var, angles) spatial_subset_x_rot = self._spatial_data_subset(x_rot, spatial_idx) spatial_subset_y_rot = self._spatial_data_subset(y_rot, spatial_idx) return mpl_handler.quiver_response(subset_lon, subset_lat, spatial_subset_x_rot, spatial_subset_y_rot, request, vectorscale ) else: raise NotImplementedError('Image type "{}" is not supported.'.format(request.GET['image_type']))
def minmax(self, layer, request): time_index, time_value = self.nearest_time(layer, request.GET['time']) wgs84_bbox = request.GET['wgs84_bbox'] with self.dataset() as nc: cached_sg = load_grid(self.topology_file) lon_name, lat_name = cached_sg.face_coordinates lon_obj = getattr(cached_sg, lon_name) lat_obj = getattr(cached_sg, lat_name) #centers = cached_sg.centers centers = pair_arrays(cached_sg.center_lon, cached_sg.center_lat) lon = centers[..., 0][lon_obj.center_slicing] lat = centers[..., 1][lat_obj.center_slicing] spatial_idx = data_handler.lat_lon_subset_idx(lon, lat, lonmin=wgs84_bbox.minx, latmin=wgs84_bbox.miny, lonmax=wgs84_bbox.maxx, latmax=wgs84_bbox.maxy) subset_lon = np.unique(spatial_idx[0]) subset_lat = np.unique(spatial_idx[1]) grid_variables = cached_sg.grid_variables vmin = None vmax = None raw_data = None if isinstance(layer, Layer): data_obj = getattr(cached_sg, layer.access_name) raw_var = nc.variables[layer.access_name] if len(raw_var.shape) == 4: z_index, z_value = self.nearest_z(layer, request.GET['elevation']) raw_data = raw_var[time_index, z_index, subset_lon, subset_lat] elif len(raw_var.shape) == 3: raw_data = raw_var[time_index, subset_lon, subset_lat] elif len(raw_var.shape) == 2: raw_data = raw_var[subset_lon, subset_lat] else: raise BaseException('Unable to trim variable {0} data.'.format(layer.access_name)) # handle grid variables if set([layer.access_name]).issubset(grid_variables): raw_data = avg_to_cell_center(raw_data, data_obj.center_axis) vmin = np.nanmin(raw_data).item() vmax = np.nanmax(raw_data).item() elif isinstance(layer, VirtualLayer): x_var = None y_var = None raw_vars = [] for l in layer.layers: data_obj = getattr(cached_sg, l.access_name) raw_var = nc.variables[l.access_name] raw_vars.append(raw_var) if len(raw_var.shape) == 4: z_index, z_value = self.nearest_z(layer, request.GET['elevation']) raw_data = raw_var[time_index, z_index, subset_lon, subset_lat] elif len(raw_var.shape) == 3: raw_data = raw_var[time_index, subset_lon, subset_lat] elif len(raw_var.shape) == 2: raw_data = raw_var[subset_lon, subset_lat] else: raise BaseException('Unable to trim variable {0} data.'.format(l.access_name)) if x_var is None: if data_obj.vector_axis and data_obj.vector_axis.lower() == 'x': x_var = raw_data elif data_obj.center_axis == 1: x_var = raw_data if y_var is None: if data_obj.vector_axis and data_obj.vector_axis.lower() == 'y': y_var = raw_data elif data_obj.center_axis == 0: y_var = raw_data if ',' in layer.var_name and raw_data is not None: # Vectors, so return magnitude data = [ sqrt((u*u) + (v*v)) for (u, v,) in zip(x_var.flatten(), y_var.flatten()) if u != np.nan and v != np.nan] vmin = min(data) vmax = max(data) return gmd_handler.from_dict(dict(min=vmin, max=vmax))