def multiline(feature, extent=None):
"""Process cartopy feature"""
if extent is None:
geometries = feature.geometries()
else:
geometries = feature.intersecting_geometries(extent)
return xs_ys(cut(iterlines(geometries), 180))
def plot_3D(self, event=None):
'''Plot observation QC values on a global 2D map
Keyword arguments:
event -- an argument passed by any tkiner menu event. Has no influence on output but
tkinter requires it to be passed to any method called by a menu event
'''
qc = [
np.int64(self.qc_menu.get(val).split(": ", 1)[1][0])
for val in self.qc_menu.curselection()
]
#make figure and canvas to draw on
fig = Figure(figsize=(12, 8))
#ax = fig.add_axes([0.01, 0.01, 0.98, 0.98], projection = ccrs.PlateCarree())
canvas = FigureCanvasTkAgg(fig, master=self.main_frame)
canvas.get_tk_widget().grid(column=1,
row=1,
rowspan=4,
sticky="N, S, E, W")
#have to set up a separate toolbar frame because toolbar doesn't like gridding with others
self.toolbar_frame = ttk.Frame(self.main_frame)
self.toolbar = NavigationToolbar2TkAgg(canvas, self.toolbar_frame)
self.toolbar_frame.grid(column=1, row=5, sticky="N, S, E, W")
self.toolbar.grid(column=1, row=1, sticky="N, S, E, W")
#resizing
self.toolbar_frame.grid_columnconfigure(1, weight=1)
self.toolbar_frame.grid_rowconfigure(1, weight=1)
#disable part of the coordinate display functionality, else everything flickers (may need for smaller window)
#ax.format_coord = lambda x, y: ''
data = self.plotter.filter(self.data_qc, ('qc_DART', qc))
target_projection = ccrs.PlateCarree()
#weird redundant line that works for transformation
transform = target_projection.transform_points(target_projection,
data.lons.values,
data.lats.values)
lons, lats = transform[:, 0], transform[:, 1]
concat = lambda iterable: list(itertools.chain.from_iterable(iterable))
feature = cartopy.feature.NaturalEarthFeature('Physical', 'land',
'110m')
geoms = feature.geometries()
geoms = [
target_projection.project_geometry(geom, feature.crs)
for geom in geoms
]
paths = concat(geos_to_path(geom) for geom in geoms)
polys = concat(path.to_polygons() for path in paths)
ax = Axes3D(fig, [0.02, 0.02, 0.97, 0.97],
xlim=[-180, 180],
ylim=[-90, 90])
z_max = max(data.z.values)
#deal with orientation of graph for different vert types
if data.vert_types.values[0] < 0 or data.vert_types.values[0] == 3:
#surface, heights, or unknown units where up is positive direction
#don't need to do 3D polygons since it will plot 2D polygons at z = 0 by default
lc = PolyCollection(polys,
edgecolor='black',
facecolor='green',
closed=False)
ax.set_zlim(bottom=0, top=z_max)
ax.add_collection3d(lc)
else:
#level or pressure units where down is positive direction
polys = [[(point[0], point[1], z_max) for point in shape]
for shape in polys]
lc = Poly3DCollection(polys,
edgecolor='black',
facecolor='green',
closed=False)
ax.set_zlim(bottom=z_max, top=0)
ax.add_collection3d(lc)
plot_values = None
cmap = None
max_value = None
min_value = None
#colormap for QC values
if self.val_type.get() == 'QC':
cmap = plt.get_cmap('gist_ncar', 9)
plot_values = data.qc_DART
max_value = 8
min_value = 0
elif self.val_type.get() == 'Observation value':
cmap = plt.get_cmap('jet', np.unique(data.values).size)
plot_values = data.values.ravel()
max_value = max(plot_values)
min_value = min(plot_values)
#plot each observation type separately to get differing edge colors and markers
ax.scatter(lons,
lats,
data.z,
c=plot_values,
cmap=cmap,
vmin=min_value,
vmax=max_value,
s=35,
alpha=0.5)
#make color bar
sm = plt.cm.ScalarMappable(cmap=cmap,
norm=plt.Normalize(0, max_value + 1))
sm._A = []
cbar = plt.colorbar(sm, ax=ax, orientation='horizontal', pad=0.04)
#set up color bar title according to plot type
if self.val_type.get() == 'QC':
cbar.ax.set_xlabel('DART QC Value')
#also center colorbar ticks and labels
labels = np.arange(0, max_value + 1, 1)
loc = labels + 0.5
cbar.set_ticks(loc)
cbar.set_ticklabels(labels)
elif self.val_type.get() == 'Observation value':
#get observation name
cbar.ax.set_xlabel(
self.obs_menu.get(self.obs_menu.curselection()).split(
" : ", 1)[1])
ax.set_aspect('auto')
#ax.margins(0.5, tight = False)
s = ttk.Style()
s.theme_use('clam')
plotter = plot_2D_obs('../obs_series/obs_epoch_001.nc')
fig = plt.figure()
a = ccrs.PlateCarree().transform_points(ccrs.PlateCarree(), plotter.data.lons[1:10000].values, plotter.data.lats[1:10000].values)
lons, lats = a[:, 0], a[:, 1]
ax = Axes3D(fig, xlim = [-180, 180], ylim = [-90, 90])
zmax = max(plotter.data.z.values)
concat = lambda iterable: list(itertools.chain.from_iterable(iterable))
target_projection = ccrs.PlateCarree()
feature = cartopy.feature.NaturalEarthFeature('Physical', 'land', '110m')
geoms = feature.geometries()
geoms = [target_projection.project_geometry(geom, feature.crs) for geom in geoms]
paths = concat(geos_to_path(geom) for geom in geoms)
polys = concat(path.to_polygons() for path in paths)
print(type(polys[0][0]))
#print(polys)
#print([[[point] for point in shape] for shape in polys])
polys = [[(point[0], point[1], zmax) for point in shape] for shape in polys]
#print(polys.shape)
#polys = [x, y, zmax for x,y
lc = Poly3DCollection(polys, edgecolor = 'black', facecolor = 'green', closed = False)