def add_points_on_rings(self, areas):
for polygon in areas:
for ring in (polygon.exterior, ) + tuple(polygon.interiors):
for linestring in assert_multilinestring(
ring.intersection(self.clear_geometry)):
coords = tuple(linestring.coords)
if len(coords) == 2:
path = Path(coords)
length = abs(
np.linalg.norm(path.vertices[0] -
path.vertices[1]))
for coord in tuple(
path.interpolated(int(length / 1.0 +
1)).vertices):
self.add_point(coord)
continue
start = 0
for segment in zip(coords[:-1], coords[1:]):
path = Path(segment)
length = abs(
np.linalg.norm(path.vertices[0] -
path.vertices[1]))
if length < 1.0:
coords = (path.vertices[1 if start == 0 else 0], )
else:
coords = tuple(
path.interpolated(int(length / 1.0 +
0.5)).vertices)[start:]
for coord in coords:
self.add_point(coord)
start = 1
def transform_path_non_affine(self, path):
# Adaptive interpolation:
# we keep adding control points, till all control points
# have an error of less than 0.01 (about 1%)
# or if the number of control points is > 80.
ra0 = self.ra0
path = path.cleaned(curves=False)
v = path.vertices
diff = v[:, 0] - v[0, 0]
v00 = v[0][0] - ra0
while v00 > 180:
v00 -= 360
while v00 < -180:
v00 += 360
v00 += ra0
v[:, 0] = v00 + diff
nonstop = path.codes > 0
path = Path(v[nonstop], path.codes[nonstop])
isteps = int(path._interpolation_steps * 1.5)
if isteps < 10: isteps = 10
while True:
ipath = path.interpolated(isteps)
tiv = self.transform(ipath.vertices)
itv = Path(self.transform(
path.vertices)).interpolated(isteps).vertices
if np.mean(np.abs(tiv - itv)) < 0.01:
break
if isteps > 20:
break
isteps = int(isteps * 1.5)
return Path(tiv, ipath.codes)
def transform_path_non_affine(self, path):
# Adaptive interpolation:
# we keep adding control points, till all control points
# have an error of less than 0.01 (about 1%)
# or if the number of control points is > 80.
ra0 = self.ra0
path = path.cleaned(curves=False)
v = path.vertices
diff = v[:, 0] - v[0, 0]
v00 = v[0][0] - ra0
while v00 > 180: v00 -= 360
while v00 < -180: v00 += 360
v00 += ra0
v[:, 0] = v00 + diff
nonstop = path.codes > 0
path = Path(v[nonstop], path.codes[nonstop])
isteps = path._interpolation_steps * 2
while True:
ipath = path.interpolated(isteps)
tiv = self.transform(ipath.vertices)
itv = Path(self.transform(path.vertices)).interpolated(isteps).vertices
if np.mean(np.abs(tiv - itv)) < 0.01:
break
if isteps > 20:
break
isteps = isteps * 2
return Path(tiv, ipath.codes)
plot_data[cell.y - 1][cell.x - 1] = (cell[0] - cell[1])
figure, axis = plot.subplots(figsize=(6, 7))
newset = trackset.get_tracks_random(1)
max_biomass = numpy.amax(newset.biomasses())
track = newset[0]
area = numpy.pi * (5)**2 # dot radius of 5
plot.scatter(track.x[0]/25, track.y[0]/25, c="green", s=area, zorder=3)
plot.scatter(track.x[-1]/25, track.y[-1]/25, c="red", s=area, zorder=3)
path = Path(numpy.column_stack([track.x/25, track.y/25]))
verts = path.interpolated(steps=3).vertices
x, y = verts[:, 0], verts[:, 1]
data = numpy.true_divide(track.biomasses, max_biomass)
axis.add_collection(colorline(x, y, data))
axis.set_title("Lifetime - Biomass")
axis.set_xlim([0, 100])
axis.set_ylim([0, 100])
figure.subplots_adjust(bottom=0.235)
colorbar_axis = figure.add_axes([0.15, .12, .73, .05])
grid_image = axis.imshow(plot_data, interpolation='none', origin="lower", cmap=plot.get_cmap("Blues_r"), vmin=-1, vmax=1, extent=[0, 100, 0, 100], aspect="equal")
colorbar = plot.colorbar(grid_image, cax=colorbar_axis, orientation='horizontal')
colorbar.set_ticks([-1, 0, 1])
colorbar.set_ticklabels([-1, 0, 1])
# ax.plot(longitudes, latitudes, altitudes)
# ax.set_xlabel('Longitude')
# ax.set_ylabel('Latitude')
# ax.set_zlabel('Altitude')
# fig = plt.figure()
# ax = fig.gca(projection='3d')
# N = len(longitudes)
# for i in range(N - 1):
# ax.plot(longitudes[i:i + 2], latitudes[i:i + 2], altitudes[i:i + 2], color=plt.cm.jet(int(255 * i / N)))
# ax.set_xlabel('Longitude')
# ax.set_ylabel('Latitude')
# ax.set_zlabel('Altitude')
codes = [Path.MOVETO] + [Path.LINETO] * (len(vertices) - 1)
path = Path(vertices, codes)
verts = path.interpolated(steps=3).vertices
x, y = verts[:, 0], verts[:, 1]
fig, ax = plt.subplots()
ax.set_xlim(min(longitudes) - 1, max(longitudes) + 1)
ax.set_ylim(min(latitudes) - 1, max(latitudes) + 1)
z = np.linspace(0, 1, len(x))
colorline(x, y, z, cmap=plt.get_cmap('jet'), linewidth=2)
plt.figure()
plt.xlabel('Timestamp')
plt.ylabel('Altitude')
plt.plot(timestamps, altitudes, '.', markersize=0.5)
plt.show()
