def f(time): ''' time: float index ''' vels = interpolated_velocities( sgrid, points, timeobj, time, sgrid.u, sgrid.v, # u_alphas, # v_alphas, # u_ind, # v_ind, ) u_rot = vels[:, 0] v_rot = vels[:, 1] u_rot, v_rot = rotate_vectors(u_rot, v_rot, angles) u_rot = u_rot.reshape(600, -1) v_rot = v_rot.reshape(600, -1) uv_vector_sum = vector_sum(u_rot, v_rot) return uv_vector_sum
def gen_map(k): global t, index, cs, qv, tl, timeobj tindex = t[index] if cs is not None: cs.remove() qv.remove() time_str = timeobj.time_str(tindex) tl.set_text(time_str) mscale = 1 vscale = 15 scale = 0.04 lon_data = lons lat_data = lats print(tindex) print(time_str) u_rot, v_rot = interpolated_velocities(sgrid, points, ind, timeobj, tindex, sgrid.u, sgrid.v) u_rot, v_rot = rotate_vectors(u_rot, v_rot, angles) u_rot = u_rot.reshape(600, -1) v_rot = v_rot.reshape(600, -1) uv_vector_sum = vector_sum(u_rot, v_rot) kw = dict(scale=1.0 / scale, pivot='middle', width=0.003, color='black') cs = plt.pcolormesh(lon_data[::mscale, ::mscale], lat_data[::mscale, ::mscale], uv_vector_sum[::mscale, ::mscale], zorder=1, cmap=plt.cm.rainbow) qv = plt.quiver(lon_data[::vscale, ::vscale], lat_data[::vscale, ::vscale], u_rot[::vscale, ::vscale], v_rot[::vscale, ::vscale], zorder=2, **kw) index += 1 return cs, qv, tl
def gen_map(k): global t, index, cs, qv, tl, timeobj tindex = t[index] if cs is not None: cs.remove() qv.remove() time_str = timeobj.time_str(tindex) tl.set_text(time_str) mscale = 1 vscale = 15 scale = 0.04 lon_data = lons lat_data = lats print tindex print time_str u_rot, v_rot = interpolated_velocities(sgrid, points, ind, timeobj, tindex, sgrid.u, sgrid.v) u_rot, v_rot = rotate_vectors(u_rot, v_rot, angles) u_rot = u_rot.reshape(600, -1) v_rot = v_rot.reshape(600, -1) uv_vector_sum = vector_sum(u_rot, v_rot) kw = dict(scale=1.0 / scale, pivot='middle', width=0.003, color='black') cs = plt.pcolormesh(lon_data[::mscale, ::mscale], lat_data[::mscale, ::mscale], uv_vector_sum[::mscale, ::mscale], zorder=1, cmap=plt.cm.rainbow) qv = plt.quiver(lon_data[::vscale, ::vscale], lat_data[::vscale, ::vscale], u_rot[::vscale, ::vscale], v_rot[::vscale, ::vscale], zorder=2, **kw) index += 1 return cs, qv, tl
def f(time): ''' time: float index ''' vels = interpolated_velocities( sgrid, points, timeobj, time, sgrid.u, sgrid.v, u_alphas, v_alphas, u_ind, v_ind) u_rot = vels[:, 0] v_rot = vels[:, 1] u_rot, v_rot = rotate_vectors(u_rot, v_rot, angles) u_rot = u_rot.reshape(600, -1) v_rot = v_rot.reshape(600, -1) uv_vector_sum = vector_sum(u_rot, v_rot) return uv_vector_sum
time_idx = 0 v_idx = 0 interp_u = sgrid.interpolate_var_to_points( points, sgrid.u[time_idx, v_idx], slices=None) interp_v = sgrid.interpolate_var_to_points( points, sgrid.v, slices=[time_idx, v_idx]) ind = sgrid.locate_faces(points) ang_ind = ind + [1, 1] angles = sgrid.angles[:][ang_ind[:, 0], ang_ind[:, 1]] u_rot, v_rot = rotate_vectors(interp_u, interp_v, angles) u_rot = u_rot.reshape(600, -1) v_rot = v_rot.reshape(600, -1) uv_vector_sum = vector_sum(u_rot, v_rot) def make_map(projection=ccrs.PlateCarree(), figsize=(20, 20)): fig, ax = plt.subplots(figsize=figsize, subplot_kw=dict(projection=projection)) gl = ax.gridlines(draw_labels=True) gl.xlabels_top = gl.ylabels_right = False gl.xformatter = LONGITUDE_FORMATTER gl.yformatter = LATITUDE_FORMATTER return fig, ax mscale = 1 vscale = 10 scale = 0.03
# In[7]: from pysgrid.processing_2d import rotate_vectors angles = nc.variables[sgrid.angle.variable][sgrid.angle.center_slicing] u, v = rotate_vectors(u, v, angles) # - vector_sum # In[8]: from pysgrid.processing_2d import vector_sum speed = vector_sum(u, v) # We need to get the grid cell centers before plotting. # A high level object could use pysgrid's API to get the cell centers and its coordinates variable names. # In[9]: grid_cell_centers = sgrid.centers lon_var_name, lat_var_name = sgrid.face_coordinates sg_lon = getattr(sgrid, lon_var_name) sg_lat = getattr(sgrid, lat_var_name)
def test_vector_sum(): x_vector = np.array([3, 5, 9, 11]) y_vector = np.array([4, 12, 40, 60]) sum_result = vector_sum(x_vector, y_vector) expected = np.array([5, 13, 41, 61]) np.testing.assert_almost_equal(sum_result, expected)
interp_v = sgrid.interpolate_var_to_points(points, sgrid.v, slices=[time_idx, v_idx]) # rotation is still ugly... from pysgrid.processing_2d import rotate_vectors, vector_sum from pysgrid.processing_2d import vector_sum ind = sgrid.locate_faces(points) ang_ind = ind + [1, 1] angles = sgrid.angles[:][ang_ind[:, 0], ang_ind[:, 1]] u_rot, v_rot = rotate_vectors(interp_u, interp_v, angles) u_rot = u_rot.reshape(600, -1) v_rot = v_rot.reshape(600, -1) uv_vector_sum = vector_sum(u_rot, v_rot) import numpy as np import matplotlib.pyplot as plt import cartopy.crs as ccrs from cartopy.io import shapereader from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER def make_map(projection=ccrs.PlateCarree(), figsize=(20, 20)): fig, ax = plt.subplots(figsize=figsize, subplot_kw=dict(projection=projection)) gl = ax.gridlines(draw_labels=True) gl.xlabels_top = gl.ylabels_right = False gl.xformatter = LONGITUDE_FORMATTER
v_idx = 0 # surface time_idx = -1 # Last time step. u = u_velocity[time_idx, v_idx, u_var.center_slicing[-2], u_var.center_slicing[-1]] v = v_velocity[time_idx, v_idx, v_var.center_slicing[-2], v_var.center_slicing[-1]] u = avg_to_cell_center(u, u_var.center_axis) v = avg_to_cell_center(v, v_var.center_axis) angles = nc.variables[sgrid.angle.variable][sgrid.angle.center_slicing] u, v = rotate_vectors(u, v, angles) speed = vector_sum(u, v) \*\* CF convention does describe the angle variable for grids that needs rotation, but there is no action expected. For example, in the formula_terms, pysgrid must be improved to abstract that action when needed via a simpler method. ```xml <entry id="angle_of_rotation_from_east_to_x"> <canonical_units>degree</canonical_units> <grib></grib> <amip></amip> <description>The quantity with standard name angle_of_rotation_from_east_to_x is the angle, anticlockwise reckoned positive, between due East and (dr/di)jk, where r(i,j,k) is the vector 3D position of the point with coordinate indices (i,j,k). It could be used for rotating vector fields between model space and latitude-longitude space.</description> </entry> ``` lon_var_name, lat_var_name = sgrid.face_coordinates sg_lon = getattr(sgrid, lon_var_name)
vertical_index, vertical = nearest_z(raw_var1, canon_dataset, z) print('Vertical: {0}'.format(vertical)) var1_trimmed = raw_var1[time_idx, vertical_index, cached_var1.center_slicing[2], cached_var1.center_slicing[3]] var2_trimmed = raw_var2[time_idx, vertical_index, cached_var2.center_slicing[2], cached_var2.center_slicing[3]] var1_avg = avg_to_cell_center(var1_trimmed, cached_var1.center_axis) var2_avg = avg_to_cell_center(var2_trimmed, cached_var2.center_axis) if cached_var1.center_axis == 1 and cached_var2.center_axis == 0: x_var = var1_avg y_var = var2_avg else: x_var = var2_avg y_var = var1_avg x_rot, y_rot = rotate_vectors(x_var, y_var, angles) subset_x_rot = subset_data(x_rot, subset_idx) subset_y_rot = subset_data(y_rot, subset_idx) xy_vector_sum = vector_sum(subset_x_rot, subset_y_rot) # start experimental quiver_response section # end experimental quiver_response section fig = plt.figure(figsize=(12, 12)) plt.subplot(111, aspect=(1.0/np.cos(np.mean(subset_lat)*np.pi/180.0))) q = plt.quiver(subset_lon[::SUB], subset_lat[::SUB], subset_x_rot[::SUB], subset_y_rot[::SUB], xy_vector_sum[::SUB], scale=1.0/SCALE, pivot='middle', zorder=1e35, width=0.003
cell_centers = sg.centers lon_var_name, lat_var_name = sg.face_coordinates lon_data = cell_centers[:, :, 0] lat_data = cell_centers[:, :, 1] lon_var_obj = getattr(sg, lon_var_name) lat_var_obj = getattr(sg, lat_var_name) lon_subset = lon_data[lon_var_obj.center_slicing] lat_subset = lat_data[lat_var_obj.center_slicing] angles = sg.angles[lon_var_obj.center_slicing] u_data_trimmed = u_var[TIME_INDEX, VERTICAL_INDEX, sg_u.center_slicing[2], sg_u.center_slicing[3]] v_data_trimmed = v_var[TIME_INDEX, VERTICAL_INDEX, sg_v.center_slicing[2], sg_v.center_slicing[3]] u_data_avg = avg_to_cell_center(u_data_trimmed, sg_u.center_axis) v_data_avg = avg_to_cell_center(v_data_trimmed, sg_v.center_axis) u_rotated, v_rotated = rotate_vectors(u_data_avg, v_data_avg, angles) uv_vector_sum = vector_sum(u_rotated, v_rotated) fig = plt.figure(figsize=(12, 12)) plt.subplot(111, aspect=(1.0/np.cos(np.mean(lat_subset)*np.pi/180.0))) q = plt.quiver(lon_subset[::SUB, ::SUB], lat_subset[::SUB, ::SUB], u_rotated[::SUB, ::SUB], v_rotated[::SUB, ::SUB], uv_vector_sum[::SUB, ::SUB], scale=1.0/SCALE, pivot='middle', zorder=1e35, width=0.003 )