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
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)
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
)
