def plot_vars(f_step, projection, load_all=False):
# The one employed for the figure name when exported
variable_name = 'gph_t_850'
# Build the name of the output image
run_string, _ = get_run()
if load_all:
f_steps = list(range(0, 79)) + list(range(81, 121, 3))
else:
f_steps = [f_step]
filenames = ['/tmp/' + projection + '_' + variable_name +
'_%s_%03d.png' % (run_string, f_step) for f_step in f_steps]
test_filenames = [os.path.exists(f) for f in filenames]
if all(test_filenames): # means the files already exist
return filenames
# otherwise do the plots
dset = get_dset(vars_3d=['t@850', 'fi@500'], f_times=f_steps).squeeze()
# Add a fictictious 1-D time dimension just to avoid problems
if 'step' not in dset.dims.keys():
dset = dset.expand_dims('step')
#
dset = subset_arrays(dset, projection)
time = pd.to_datetime(dset.valid_time.values)
cum_hour = dset.step.values.astype(int)
temp_850 = dset['t'] - 273.15
z_500 = dset['z']
gph_500 = mpcalc.geopotential_to_height(z_500)
gph_500 = xr.DataArray(gph_500.magnitude, coords=z_500.coords,
attrs={'standard_name': 'geopotential height',
'units': gph_500.units})
levels_temp = np.arange(-30., 30., 1.)
levels_gph = np.arange(4700., 6000., 70.)
lon, lat = get_coordinates(temp_850)
lon2d, lat2d = np.meshgrid(lon, lat)
cmap = get_colormap('temp')
args = dict(filenames=filenames, projection=projection, levels_temp=levels_temp,
cmap=cmap, lon2d=lon2d, lat2d=lat2d, lon=lon, lat=lat, temp_850=temp_850.values,
gph_500=gph_500.values, levels_gph=levels_gph, time=time, run_string=run_string)
if load_all:
single_plot_param = partial(single_plot, **args)
iterator = range(0, len(f_steps))
pool = Pool(cpu_count())
results = pool.map(single_plot_param, iterator)
pool.close()
pool.join()
else:
results = single_plot(0, **args)
return results
def rview(img, seg=None, overlay=None, colors=None, binary="rview"):
"""
Launches rview.
"""
if isinstance(img, Image):
if overlay is not None and seg is not None:
print "You cannot specify both seg and overlay"
return
if overlay is not None and colors is None:
colors = 'jet' # default colormap
if colors is not None and overlay is None:
overlay = img.rescale() # we want to see img in colors
img = zeros(img.get_header(), dtype='int16')
if isinstance(colors, str):
colors = utils.get_colormap(colors)
if seg is not None and colors is None:
colors = utils.random_colormap(seg.max())
# now, seg == overlay
if overlay is not None:
seg = overlay
if seg is not None and not isinstance(seg, Image):
seg = Image(seg, img.get_header())
handle, filename = tempfile.mkstemp(suffix=".nii")
garbage.append(filename)
imwrite(filename, img.astype('int16')) # rview reads in "short" anyway
args = [binary, filename]
if seg is not None:
handle, seg_filename = tempfile.mkstemp(suffix=".nii")
garbage.append(seg_filename)
handle, colors_filename = tempfile.mkstemp(suffix=".txt")
garbage.append(colors_filename)
imwrite(seg_filename, seg.astype('int16'))
args.extend(["-seg", seg_filename])
utils.colormap(colors, colors_filename)
args.extend(["-lut", colors_filename])
if overlay is not None:
args.append("-labels")
# launch rview as an independant process
subprocess.Popen(args)
def rview( img, seg=None, overlay=None, colors=None, binary="rview" ):
"""
Launches rview.
"""
if isinstance( img, Image ):
if overlay is not None and seg is not None:
print "You cannot specify both seg and overlay"
return
if overlay is not None and colors is None:
colors = 'jet' # default colormap
if colors is not None and overlay is None:
overlay = img.rescale() # we want to see img in colors
img = zeros(img.get_header(), dtype='int16')
if isinstance( colors, str ):
colors = utils.get_colormap( colors )
if seg is not None and colors is None:
colors = utils.random_colormap(seg.max())
# now, seg == overlay
if overlay is not None:
seg = overlay
if seg is not None and not isinstance(seg, Image):
seg = Image( seg, img.get_header() )
handle,filename = tempfile.mkstemp(suffix=".nii")
garbage.append(filename)
imwrite( filename, img.astype('int16') ) # rview reads in "short" anyway
args = [ binary, filename ]
if seg is not None:
handle,seg_filename = tempfile.mkstemp(suffix=".nii")
garbage.append(seg_filename)
handle,colors_filename = tempfile.mkstemp(suffix=".txt")
garbage.append(colors_filename)
imwrite( seg_filename, seg.astype('int16') )
args.extend( ["-seg", seg_filename])
utils.colormap( colors, colors_filename )
args.extend( ["-lut", colors_filename])
if overlay is not None:
args.append("-labels")
# launch rview as an independant process
subprocess.Popen( args )
def thumbnail(self,
seg=None,
overlay=None,
colors=None,
opacity=0.5,
step=2,
unroll=False,
index=None):
"""
Create a thumbnail.
"""
img = self.copy('int')
if overlay is not None and seg is not None:
print "You cannot specify both seg and overlay"
return
if seg is not None:
seg = seg.astype('int')
if overlay is not None and colors is None:
colors = 'jet' # default colormap
if colors is not None and seg is None and overlay is None:
overlay = img.copy() # we want to see img in colors
img = zeros(img.get_header(), dtype='uint8')
opacity = 1.0
if isinstance(colors, str):
colors = utils.get_colormap(colors)
if seg is not None and colors is None:
colors = utils.random_colormap(seg.max())
if index is None:
index = np.array(img.shape).astype('int32') / 2
# now, seg == overlay
if overlay is not None:
seg = overlay
if len(img.shape) == 2:
if seg is not None:
data = img.get_data('uint8')
data = data.reshape(data.shape[0], data.shape[1], 1)
data = np.concatenate([data, data, data], axis=2)
rgb_overlay = utils.remap(seg, colors)
# op = (1-opacity) * (seg != 0).astype('float') + (seg == 0).astype('float')
# op = op.reshape(op.shape[0],
# op.shape[1],
# 1)
# op = np.concatenate( [ op,
# op,
# op ], axis=2 )
# img = op * data + opacity*rgb_overlay
img = (1 - opacity) * data + opacity * rgb_overlay
return img.astype('uint8')
elif len(img.shape) == 3:
if not unroll:
shape = np.array(img.shape).max()
if seg is None:
output = np.ones((shape, shape * 3 + step * (3 - 1))) * 255
else:
output = np.ones(
(shape, shape * 3 + step * (3 - 1), 3)) * 255
offset1 = int((shape - img.shape[1]) / 2)
offset2 = int((shape - img.shape[2]) / 2)
if seg is None:
tmp_img = img[index[0], :, :]
else:
tmp_img = Image(img[index[0], :, :]).thumbnail(
seg[index[0], :, :], colors=colors, opacity=opacity)
output[offset1:offset1 + img.shape[1],
offset2:offset2 + img.shape[2]] = tmp_img
offset1 = int((shape - img.shape[0]) / 2)
offset2 = int(shape + step + (shape - img.shape[2]) / 2)
if seg is None:
tmp_img = img[:, index[1], :]
else:
tmp_img = Image(img[:, index[1], :]).thumbnail(
seg[:, index[1], :], colors=colors, opacity=opacity)
output[offset1:offset1 + img.shape[0],
offset2:offset2 + img.shape[2]] = tmp_img
offset1 = int((shape - img.shape[0]) / 2)
offset2 = int(2 * shape + 2 * step +
(shape - img.shape[1]) / 2)
if seg is None:
tmp_img = img[:, :, index[2]]
else:
tmp_img = Image(img[:, :,
index[2]]).thumbnail(seg[:, :,
index[2]],
colors=colors,
opacity=opacity)
output[offset1:offset1 + img.shape[0],
offset2:offset2 + img.shape[1]] = tmp_img
return output.astype('uint8')
else: # unroll is True
if seg is None:
output = np.ones(
(self.shape[1], self.shape[2] * self.shape[0] + 2 *
(self.shape[0] - 1))) * 255
else:
output = np.ones(
(self.shape[1], self.shape[2] * self.shape[0] + 2 *
(self.shape[0] - 1), 3)) * 255
for k in xrange(self.shape[0]):
if seg is None:
tmp_img = img[k, :, :]
else:
tmp_img = Image(img[k, :, :]).thumbnail(
seg[k, :, :], colors=colors, opacity=opacity)
output[:, k * self.shape[2] +
2 * k:(k + 1) * self.shape[2] + 2 * k] = tmp_img
return output.astype('uint8')
else:
raise "Wrong number of dimensions for thumbnail: " + str(
len(self.shape))
def thumbnail( self,
seg=None,
overlay=None,
colors=None,
opacity=0.5,
step=2,
unroll=False ):
"""
Create a thumbnail.
"""
img = self.copy('int')
if overlay is not None and seg is not None:
print "You cannot specify both seg and overlay"
return
if seg is not None:
seg = seg.astype('int')
if overlay is not None and colors is None:
colors = 'jet' # default colormap
if colors is not None and seg is None and overlay is None:
overlay = img.copy() # we want to see img in colors
img = zeros(img.get_header(), dtype='uint8')
opacity = 1.0
if isinstance( colors, str ):
colors = utils.get_colormap( colors )
if seg is not None and colors is None:
colors = utils.random_colormap(seg.max())
# now, seg == overlay
if overlay is not None:
seg = overlay
if len(img.shape) == 2:
if seg is not None:
data = img.get_data('uint8')
data = data.reshape(data.shape[0],
data.shape[1],
1)
data = np.concatenate( [ data,
data,
data ], axis=2 )
rgb_overlay = utils.remap( seg, colors )
op = (1-opacity) * (seg != 0).astype('float') + (seg == 0).astype('float')
op = op.reshape(op.shape[0],
op.shape[1],
1)
op = np.concatenate( [ op,
op,
op ], axis=2 )
img = op * data + opacity*rgb_overlay
return img.astype('uint8')
elif len(img.shape) == 3:
if not unroll:
shape = np.array(img.shape).max()
if seg is None:
output = np.ones((shape,shape*3+step*(3-1)))*255
else:
output = np.ones((shape,shape*3+step*(3-1),3))*255
offset1 = (shape - img.shape[1])/2
offset2 = (shape - img.shape[2])/2
if seg is None:
tmp_img = img[img.shape[0]/2,:,:]
else:
tmp_img = Image(img[img.shape[0]/2,:,:]).thumbnail( seg[img.shape[0]/2,:,:], colors=colors, opacity=opacity )
output[offset1:offset1+img.shape[1],
offset2:offset2+img.shape[2]] = tmp_img
offset1 = (shape - img.shape[0])/2
offset2 = shape + step + (shape - img.shape[2])/2
if seg is None:
tmp_img = img[:,img.shape[1]/2,:]
else:
tmp_img = Image(img[:,img.shape[1]/2,:]).thumbnail( seg[:,img.shape[1]/2,:], colors=colors, opacity=opacity)
output[offset1:offset1+img.shape[0],
offset2:offset2+img.shape[2]] = tmp_img
offset1 = (shape - img.shape[0])/2
offset2 = 2*shape + 2*step + (shape - img.shape[1])/2
if seg is None:
tmp_img = img[:,:,img.shape[2]/2]
else:
tmp_img = Image(img[:,:,img.shape[2]/2]).thumbnail( seg[:,:,img.shape[2]/2], colors=colors, opacity=opacity )
output[offset1:offset1+img.shape[0],
offset2:offset2+img.shape[1]] = tmp_img
return output.astype('uint8')
else: # unroll is True
if seg is None:
output = np.ones( ( self.shape[1],
self.shape[2]*self.shape[0]+2*(self.shape[0]-1) )
) * 255
else:
output = np.ones( ( self.shape[1],
self.shape[2]*self.shape[0]+2*(self.shape[0]-1),
3 )
) * 255
for k in xrange(self.shape[0]):
if seg is None:
tmp_img = img[k,:,:]
else:
tmp_img = Image(img[k,:,:]).thumbnail( seg[k,:,:], colors=colors, opacity=opacity )
output[:,
k*self.shape[2]+2*k:(k+1)*self.shape[2]+2*k] = tmp_img
return output.astype('uint8')
else:
raise "Wrong number of dimensions for thumbnail: " + str(len(self.shape))
def plot_var(f_step, projection):
# NOTE!
# If we are inside this function it means that the picture does not exist
# The one employed for the figure name when exported
variable_name = 'gph_t_850'
# Build the name of the output image
run_string, _ = get_run()
filename = '/tmp/' + projection + '_' + \
variable_name + '_%s_%03d.png' % (run_string, f_step)
"""In the main function we basically read the files and prepare the variables to be plotted.
This is not included in utils.py as it can change from case to case."""
dset = get_dset(vars_3d=['t@850', 'fi@500'], f_times=f_step).squeeze()
dset = subset_arrays(dset, projection)
time = pd.to_datetime(dset.valid_time.values)
cum_hour = dset.step.values.astype(int)
temp_850 = dset['t'] - 273.15
z_500 = dset['z']
gph_500 = mpcalc.geopotential_to_height(z_500)
gph_500 = xr.DataArray(gph_500.magnitude, coords=z_500.coords,
attrs={'standard_name': 'geopotential height',
'units': gph_500.units})
levels_temp = np.arange(-30., 30., 1.)
levels_gph = np.arange(4700., 6000., 70.)
cmap = get_colormap('temp')
fig = plt.figure(figsize=(figsize_x, figsize_y))
ax = plt.gca()
lon, lat = get_coordinates(temp_850)
lon2d, lat2d = np.meshgrid(lon, lat)
ax = get_projection_cartopy(plt, projection, compute_projection=True)
if projection == 'euratl':
norm = BoundaryNorm(levels_temp, ncolors=cmap.N)
cs = ax.pcolormesh(lon2d, lat2d, temp_850, cmap=cmap, norm=norm)
else:
cs = ax.contourf(lon2d, lat2d, temp_850, extend='both',
cmap=cmap, levels=levels_temp)
c = ax.contour(lon2d, lat2d, gph_500, levels=levels_gph,
colors='white', linewidths=1.)
labels = ax.clabel(c, c.levels, inline=True, fmt='%4.0f', fontsize=6)
maxlabels = plot_maxmin_points(ax, lon, lat, gph_500,
'max', 80, symbol='H', color='royalblue', random=True)
minlabels = plot_maxmin_points(ax, lon, lat, gph_500,
'min', 80, symbol='L', color='coral', random=True)
an_fc = annotation_forecast(ax, time)
an_var = annotation(
ax, 'Geopotential height @500hPa [m] and temperature @850hPa [C]', loc='lower left', fontsize=6)
an_run = annotation_run(ax, time)
plt.colorbar(cs, orientation='horizontal',
label='Temperature', pad=0.03, fraction=0.04)
plt.savefig(filename, **options_savefig)
plt.clf()
return filename
