def test_vector_rotation_complex():
x, y, angles_simple, angles_complex = rotate_vectors_data()
rotated_x, rotated_y = rotate_vectors(x, y, angles_complex)
expected_x = np.array([0.5981, -3.0083, -21.9203, -46.4615])
expected_y = np.array([4.9641, 12.6471, 34.6482, 39.5263])
np.testing.assert_almost_equal(rotated_x, expected_x, decimal=3)
np.testing.assert_almost_equal(rotated_y, expected_y, decimal=3)
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
def test_vector_rotation_complex():
x, y, angles_simple, angles_complex = rotate_vectors_data()
rotated_x, rotated_y = rotate_vectors(x, y, angles_complex)
expected_x = np.array([0.5981, -3.0083, -21.9203, -46.4615])
expected_y = np.array([4.9641, 12.6471, 34.6482, 39.5263])
np.testing.assert_almost_equal(rotated_x, expected_x, decimal=3)
np.testing.assert_almost_equal(rotated_y, expected_y, decimal=3)
def test_vector_rotation_simple():
x, y, angles_simple, angles_complex = rotate_vectors_data()
rotated_x, rotated_y = rotate_vectors(x, y, angles_simple)
expected_x = np.array([3, -12, 9, -60])
expected_y = np.array([4, 5, 40, 11])
np.testing.assert_almost_equal(rotated_x, expected_x, decimal=3)
np.testing.assert_almost_equal(rotated_y, expected_y, decimal=3)
def test_vector_rotation_simple():
x, y, angles_simple, angles_complex = rotate_vectors_data()
rotated_x, rotated_y = rotate_vectors(x, y, angles_simple)
expected_x = np.array([3, -12, 9, -60])
expected_y = np.array([4, 5, 40, 11])
np.testing.assert_almost_equal(rotated_x, expected_x, decimal=3)
np.testing.assert_almost_equal(rotated_y, expected_y, decimal=3)
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
# In[6]: from pysgrid.processing_2d import avg_to_cell_center u = avg_to_cell_center(u, sgrid.u.center_axis) v = avg_to_cell_center(v, sgrid.v.center_axis) # - rotate_vectors # 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]:
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 = from_ncfile(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
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']))
# points, sgrid.u, slices=[time_idx, v_idx])
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])
# 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,
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 = from_ncfile(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
lon = centers[..., 0][lon_obj.center_slicing]
lat = centers[..., 1][lat_obj.center_slicing]
if request.GET['image_type'] == 'vectors':
vectorstep = request.GET['vectorstep']
vectorscale = request.GET['vectorscale']
padding_factor = calc_safety_factor(vectorscale)
spatial_idx_padding = calc_lon_lat_padding(lon, lat, padding_factor)
else:
spatial_idx_padding = 0.18
vectorstep = None
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_x = np.unique(spatial_idx[0])
subset_y = np.unique(spatial_idx[1])
if subset_x.shape == (0, ) and subset_y.shape == (0, ):
return mpl_handler.empty_response() # return an empty tile if subset contains no data
else:
x_min_idx = subset_x.min()
x_max_idx = subset_x.max() + 1
y_min_idx = subset_y.min()
y_max_idx = subset_y.max() + 1
lonlat_mask = np.ones(lon.shape)
lonlat_mask[spatial_idx[0], spatial_idx[1]] = 0
trimmed_lon = ma.masked_array(lon, mask=lonlat_mask).data[x_min_idx:x_max_idx:vectorstep, y_min_idx:y_max_idx:vectorstep]
trimmed_lat = ma.masked_array(lat, mask=lonlat_mask).data[x_min_idx:x_max_idx:vectorstep, y_min_idx:y_max_idx:vectorstep]
if isinstance(layer, Layer):
data_obj = getattr(cached_sg, layer.access_name)
raw_var = nc.variables[layer.access_name]
raw_data = self._retrieve_data(request=request,
nc_variable=raw_var,
sg_variable=data_obj,
layer=layer,
subset_x=subset_x,
subset_y=subset_y,
time_index=time_index,
vectorstep=1
)
# 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(trimmed_lon, trimmed_lat, data=raw_data, request=request)
elif request.GET['image_type'] == 'filledcontours':
return mpl_handler.contourf_response(trimmed_lon, trimmed_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)
raw_data = self._retrieve_data(request=request,
nc_variable=raw_var,
sg_variable=data_obj,
layer=layer,
subset_x=subset_x,
subset_y=subset_y,
time_index=time_index,
vectorstep=vectorstep
)
raw_data = self._avg_to_cell_center(raw_data, data_obj.center_axis, vectorstep)
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]
trimmed_angles = ma.masked_array(angles, mask=lonlat_mask).data[x_min_idx:x_max_idx:vectorstep, y_min_idx:y_max_idx:vectorstep]
# rotate vectors
x_rot, y_rot = rotate_vectors(x_var, y_var, trimmed_angles)
return mpl_handler.quiver_response(trimmed_lon,
trimmed_lat,
x_rot,
y_rot,
request,
vectorscale
)
else:
raise NotImplementedError('Image type "{}" is not supported.'.format(request.GET['image_type']))
points = np.stack((lons, lats), axis=-1)
print(points.shape)
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
# <codecell> from pysgrid.processing_2d import avg_to_cell_center u_avg = avg_to_cell_center(u_data, u_var.center_axis) v_avg = avg_to_cell_center(v_data, v_var.center_axis) # <markdowncell> # ### Rotate vectors by angles # <codecell> from pysgrid.processing_2d import rotate_vectors u_rot, v_rot = rotate_vectors(u_avg, v_avg, angles) # <markdowncell> # ### Speed # <codecell> from pysgrid.processing_2d import vector_sum uv_vector_sum = vector_sum(u_rot, v_rot) # <markdowncell> # ### Lon, lat of the center grid #
u_velocity = nc.variables[u_var.variable]
v_velocity = nc.variables[v_var.variable]
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>
```
cached_var2 = getattr(cached_sg, var2_name)
raw_var1 = canon_dataset.variables[var1_name]
raw_var2 = canon_dataset.variables[var2_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,
s = from_nc_dataset(ds)
u_var = s.u
v_var = s.v
u_velocity = ds.variables['u']
v_velocity = ds.variables['v']
u_data = u_velocity[TIME_INDEX, VERTICAL_INDEX, u_var.center_slicing[2], u_var.center_slicing[3]]
v_data = v_velocity[TIME_INDEX, VERTICAL_INDEX, v_var.center_slicing[2], v_var.center_slicing[3]]
angle_var = s.angle
angle_data = s.angles
angle_data_trimed = angle_data[angle_var.center_slicing]
u_avg = avg_to_cell_center(u_data, u_var.center_axis)
v_avg = avg_to_cell_center(v_data, v_var.center_axis)
u_rot, v_rot = rotate_vectors(u_avg, v_avg, angle_data_trimed)
uv_vector_sum = vector_sum(u_rot, v_rot)
cell_centers = s.centers
lon_var, lat_var = s.face_coordinates
lon_obj = getattr(s, lon_var)
lat_obj = getattr(s, lat_var)
lon_data = cell_centers[:, :, 0]
lat_data = cell_centers[:, :, 1]
lon_subset = lon_data[lon_obj.center_slicing]
lat_subset = lat_data[lat_obj.center_slicing]
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],
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 = from_ncfile(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
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'] == 'filledcontours':
return mpl_handler.contourf_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']))
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
)
