def plot_boundary(model, x, y, **kwargs):
assert (x.shape[-1] == 2)
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])
if 'h' in kwargs:
h = kwargs['h']
else:
h = 0.1
x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
x_grid, y_grid = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
Z = model.predict(np.c_[x_grid.ravel(), y_grid.ravel()])
# Put the result into a color plot
Z = Z.reshape(x_grid.shape)
plt.figure()
plt.pcolormesh(x_grid, y_grid, Z, cmap=cmap_light)
# Plot also the training points
plt.scatter(x[:, 0], x[:, 1], c=y, cmap=cmap_bold, edgecolor='k', s=20)
plt.xlim(x_grid.min(), x_grid.max())
plt.ylim(y_grid.min(), y_grid.max())
if 'title' in kwargs:
plt.suptitle(kwargs['title'])
if 'accuracy' in kwargs:
plt.title("Accuracy: %.1f%%" % (kwargs['accuracy'] * 100), fontsize=10)
plt.show()
def forward(self, x):
# polar plot
dic = creatRealDictionary(self.T, self.rr, self.theta, self.gid)
sparsecode = fista(dic, x, self.lam, 100, self.gid)
if random.randint(1, 20) == 1:
plt.figure()
plt.pcolormesh(sparsecode[0, :, :].data.cpu().detach().numpy(),
cmap='RdBu')
plt.colorbar()
plt.savefig('C_evolution_subsIni.png') #, dpi=200
# plt.show()
plt.close()
rr = self.rr.data.cpu().detach().numpy()
theta = self.theta.data.cpu().detach().numpy()
ax = plt.subplot(1, 1, 1, projection='polar')
ax.scatter(0, 1, c='black')
# unactive poles
ax.scatter(theta, rr)
ax.scatter(-theta, rr)
ax.scatter(np.pi - theta, rr)
ax.scatter(theta - np.pi, rr)
#
ax.set_rmax(1.2)
ax.set_title("Dictionary", va='bottom')
plt.savefig('usedPolesDCT.png')
# plt.show()
plt.close()
return Variable(sparsecode)
def estimate_coord_plot(feten):
lon_res = 13
lat_res = 9
nz = 26
lat_step = 0.5
lon_step = 0.5
lat_start = 44
lat_end = lat_start + lat_step * (lat_res - 1) # calculas lat final
lon_start = -123
lon_end = lon_start + lon_step * (
lon_res - 1) # calculas lon final - con esto puedes construir mesh
lat = np.linspace(start=lat_start,
stop=lat_end,
endpoint=lat_end,
num=lat_res)
lon = np.linspace(start=lon_start,
stop=lon_end,
endpoint=lon_end,
num=lon_res) #
lon, lat = np.meshgrid(lon, lat)
Z = feten.reshape(lat_res, lon_res)
ptos = np.hstack((lat.reshape((lat.size, 1)), lon.reshape((lon.size, 1))))
fig = plt.figure(figsize=(12, 10))
im = plt.pcolormesh(lat, lon,
Z) # Asignas valores a su posición en el mapa
return plt.colorbar(mappable=im)
def lets_paint_the_world(file):
filee = open(filename)
filee
lon = []
lat = []
depth = []
counter = 0
for line in filee.readlines(): #set a counter because computer couldn't run the whole file
if counter < 1000:
each_line = line.split()
lon.append(float(each_line[0][:]))
lat.append(float(each_line[1][:]))
depth.append(float(each_line[2][:]))
counter += 1
m = Basemap(projection = 'tmerc',
llcrnrlon= -180,
urcrnrlon = 180,
llcrnrlat= -90,
urcrnrlat = 90,
lat_0= 0,
lon_0= 0)
#creates the graticule based on the coor
x, y = m(*np.meshgrid(lon,lat))#<----------BREAKING POINT
#plot commands
fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
#draws
m.fillcontinents(color='coral', lake_color='aqua')
m.drawcoastlines(linewidth = .25)
m.drawcountries(linewidth = .25)
m.drawmeridians(np.arange(0, 360, 15))
m.drawparallels(np.arange(-90, 90, 15))
plt.pcolormesh(x,y,depth, [-1000,0,1000], cmap=plt.cm.RdBu_r, vmin=-100, vmax = 100)
plt.show()
cb = m.colorbar(pcm)
cb.set_label('Soil FOO (mm)')
plt.show()
pylab.figure(num=None, figsize=(20,10), dpi=100)
plt.title("Importance of Different Colormaps")
pylab.subplot(2,2,1)
plotpanel(x, y, soil)
pcm = plt.pcolormesh(x, y, soil, cmap=plt.cm.nipy_spectral)
cb = m.colorbar(pcm)
cb.set_label('Soil FOO (mm)')
pylab.title("nipy_spectral (best)")
pylab.subplot(2,2,2)
plotpanel(x, y, soil)
pylab.title("Accent (okay)")
pcm = plt.pcolormesh(x, y, soil, cmap=plt.cm.Accent)
cb = m.colorbar(pcm)
cb.set_label('Soil FOO (mm)')
pylab.subplot(2,2,4)
plotpanel(x, y, soil)