def draw_decision_boundary(X, y, model, title):
resolution = 0.01
markers = ('o', 's', '^')
colors = ('lightgreen', 'red', 'blue')
cmap = mpl.colors.ListedColormap(colors)
x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
np.arange(x2_min, x2_max, resolution))
mpd = model.predict(np.array([xx1.ravel(),
xx2.ravel()]).T).reshape(xx1.shape)
plt.contour(xx1, xx2, mpd, cmap=mpl.colors.ListedColormap(['k']))
plt.contourf(xx1, xx2, mpd, alpha=0.4, cmap=cmap)
plt.xlim(xx1.min(), xx1.max())
plt.ylim(xx2.min(), xx2.max())
for idx, cl in enumerate(np.unique(y)):
plt.scatter(x=X[y == cl, 0],
y=X[y == cl, 1],
alpha=0.8,
c=[cmap(idx)],
marker=markers[idx],
s=80,
label=cl)
plt.xlabel(iris.feature_names[2])
plt.ylabel(iris.feature_names[3])
plt.legend(loc='upper left')
plt.title(title)
return mpd
def plot_field(self,x,y,u=None,v=None,F=None,contour=False,outdir=None,plot='quiver',figname='_field',format='eps'):
outdir = self.set_dir(outdir)
p = 64
if F is None: F=self.calc_F(u,v)
plt.close('all')
plt.figure()
#fig, axes = plt.subplots(nrows=1)
if contour:
plt.hold(True)
plt.contourf(x,y,F)
if plot=='quiver':
plt.quiver(x[::p],y[::p],u[::p],v[::p],scale=0.1)
if plot=='pcolor':
plt.pcolormesh(x[::4],y[::4],F[::4],cmap=plt.cm.Pastel1)
plt.colorbar()
if plot=='stream':
speed = F[::16]
plt.streamplot(x[::16], y[::16], u[::16], v[::16], density=(1,1),color='k')
plt.xlabel('$x$ (a.u.)')
plt.ylabel('$y$ (a.u.)')
plt.savefig(os.path.join(outdir,figname+'.'+format),format=format,dpi=320,bbox_inches='tight')
plt.close()
def drown_field(self,data,mask,drown):
''' drown_field is a wrapper around the fortran code fill_msg_grid.
depending on the output geometry, applies land extrapolation on 1 or N levels'''
if self.geometry == 'surface':
for kz in _np.arange(self.nz):
tmpin = data[kz,:,:].transpose()
if self.debug and kz == 0:
tmpin_plt = _np.ma.masked_values(tmpin,self.xmsg)
_plt.figure() ; _plt.contourf(tmpin_plt.transpose(),40) ; _plt.colorbar() ;
_plt.title('normalized before drown')
if drown == 'ncl':
tmpout = _fill.mod_poisson.poisxy1(tmpin,self.xmsg, self.guess, self.gtype, \
self.nscan, self.epsx, self.relc)
elif drown == 'sosie':
tmpout = _mod_drown_sosie.mod_drown.drown(self.kew,tmpin,mask[kz,:,:].T,\
nb_inc=200,nb_smooth=40)
data[kz,:,:] = tmpout.transpose()
if self.debug and kz == 0:
_plt.figure() ; _plt.contourf(tmpout.transpose(),40) ; _plt.colorbar() ;
_plt.title('normalized after drown')
_plt.show()
elif self.geometry == 'line':
tmpin = data[:,:].transpose()
if drown == 'ncl':
tmpout = _fill.mod_poisson.poisxy1(tmpin,self.xmsg, self.guess, self.gtype, \
self.nscan, self.epsx, self.relc)
elif drown == 'sosie':
tmpout = _mod_drown_sosie.mod_drown.drown(self.kew,tmpin,mask[:,:].T,\
nb_inc=200,nb_smooth=40)
data[:,:] = tmpout.transpose()
return data
def plot(x,y,field,filename,c=200):
plt.figure()
# define grid.
xi = np.linspace(min(x),max(x),100)
yi = np.linspace(min(y),max(y),100)
# grid the data.
si_lin = griddata((x, y), field, (xi[None,:], yi[:,None]), method='linear')
si_cub = griddata((x, y), field, (xi[None,:], yi[:,None]), method='linear')
print np.min(field)
print np.max(field)
plt.subplot(211)
# contour the gridded data, plotting dots at the randomly spaced data points.
CS = plt.contour(xi,yi,si_lin,c,linewidths=0.5,colors='k')
CS = plt.contourf(xi,yi,si_lin,c,cmap=plt.cm.jet)
plt.colorbar() # draw colorbar
# plot data points.
# plt.scatter(x,y,marker='o',c='b',s=5)
plt.xlim(min(x),max(x))
plt.ylim(min(y),max(y))
plt.title('Lineaarinen interpolointi')
#plt.tight_layout()
plt.subplot(212)
# contour the gridded data, plotting dots at the randomly spaced data points.
CS = plt.contour(xi,yi,si_cub,c,linewidths=0.5,colors='k')
CS = plt.contourf(xi,yi,si_cub,c,cmap=plt.cm.jet)
plt.colorbar() # draw colorbar
# plot data points.
# plt.scatter(x,y,marker='o',c='b',s=5)
plt.xlim(min(x),max(x))
plt.ylim(min(y),max(y))
plt.title('Kuubinen interpolointi')
plt.savefig(filename)
def perform_interpolation(self,dataextrap,regridme,field_src,field_target,use_locstream):
data = self.allocate()
if self.geometry == 'surface':
for kz in _np.arange(self.nz):
field_src.data[:] = dataextrap[kz,:,:].transpose()
field_target = regridme(field_src, field_target)
if use_locstream:
if self.nx == 1:
data[kz,:,0] = field_target.data.copy()
elif self.ny == 1:
data[kz,0,:] = field_target.data.copy()
else:
data[kz,:,:] = field_target.data.transpose()[self.jmin:self.jmax+1, \
self.imin:self.imax+1]
if self.debug and kz == 0:
data_target_plt = _np.ma.masked_values(data[kz,:,:],self.xmsg)
#data_target_plt = _np.ma.masked_values(field_target.data,self.xmsg)
_plt.figure() ; _plt.contourf(data_target_plt[:,:],40) ; _plt.colorbar() ;
_plt.title('regridded') ; _plt.show()
elif self.geometry == 'line':
field_src.data[:] = dataextrap[:,:].transpose()
field_target = regridme(field_src, field_target)
if use_locstream:
data[:,:] = _np.reshape(field_target.data.transpose(),(self.ny,self.nx))
else:
data[:,:] = field_target.data.transpose()[self.jmin:self.jmax+1,self.imin:self.imax+1]
return data
def main_k_nearest_neighbour(k):
X, y = make_blobs(n_samples=100,
n_features=2,
centers=2,
cluster_std=1.0,
center_box=(-10.0, 10.0))
h = .4
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
z = np.c_[xx.ravel(), yy.ravel()]
z_f = []
for i_z in z:
z_f.append(k_nearest_neighbour(X, y, i_z, k, False))
zz = np.array(z_f).reshape(xx.shape)
plt.figure()
plt.contourf(xx, yy, zz, cmap=plt.cm.Paired)
plt.axis('tight')
plt.scatter(X[:, 0], X[:, 1], c=y)
plt.show()
def drown_field(self, data, mask, drown):
''' drown_field is a wrapper around the fortran code fill_msg_grid.
depending on the output geometry, applies land extrapolation on 1 or N levels'''
if self.geometry == 'surface':
for kz in _np.arange(self.nz):
tmpin = data[kz, :, :].transpose()
if self.debug and kz == 0:
tmpin_plt = _np.ma.masked_values(tmpin, self.xmsg)
_plt.figure()
_plt.contourf(tmpin_plt.transpose(), 40)
_plt.colorbar()
_plt.title('normalized before drown')
if drown == 'ncl':
tmpout = _fill.mod_poisson.poisxy1(tmpin,self.xmsg, self.guess, self.gtype, \
self.nscan, self.epsx, self.relc)
elif drown == 'sosie':
tmpout = _mod_drown_sosie.mod_drown.drown(self.kew,tmpin,mask[kz,:,:].T,\
nb_inc=200,nb_smooth=40)
data[kz, :, :] = tmpout.transpose()
if self.debug and kz == 0:
_plt.figure()
_plt.contourf(tmpout.transpose(), 40)
_plt.colorbar()
_plt.title('normalized after drown')
_plt.show()
elif self.geometry == 'line':
tmpin = data[:, :].transpose()
if drown == 'ncl':
tmpout = _fill.mod_poisson.poisxy1(tmpin,self.xmsg, self.guess, self.gtype, \
self.nscan, self.epsx, self.relc)
elif drown == 'sosie':
tmpout = _mod_drown_sosie.mod_drown.drown(self.kew,tmpin,mask[:,:].T,\
nb_inc=200,nb_smooth=40)
data[:, :] = tmpout.transpose()
return data
def perform_extrapolation(self, datasrc, maskfile, maskvar, missing_value,
drown):
# 2.1 read mask or compute it
if maskvar is not None:
mask = _ncdf.read_field(maskfile, maskvar)
# to do, needs imin/imax_src,...
else:
mask = self.compute_mask_from_missing_value(
datasrc, missing_value=missing_value)
# 2.2 mask the source data
if _np.ma.is_masked(datasrc):
datasrc = datasrc.data
datasrc[_np.where(mask == 0)] = self.xmsg
datamin = datasrc[_np.where(mask == 1)].min()
datamax = datasrc[_np.where(mask == 1)].max()
if self.debug:
datasrc_plt = _np.ma.masked_values(datasrc, self.xmsg)
_plt.figure()
_plt.contourf(datasrc_plt[0, :, :], 40)
_plt.title('original')
_plt.colorbar()
# 2.3 perform land extrapolation on reduced variable
datanorm = self.normalize(datasrc, datamin, datamax, mask)
if self.debug:
print(datanorm.min(), datanorm.max(), datamin, datamax)
datanormextrap = self.drown_field(datanorm, mask, drown)
dataextrap = self.unnormalize(datanormextrap, datamin, datamax)
return dataextrap
def make_2d_surface_contours(x,
y,
z,
title='',
x_axis_name='',
y_axis_name='',
z_axis_name='',
extra_contour_line=None):
fig = plt.figure()
ax = plt.axes()
X, Y, Z = grid(x, y, z, resX=187, resY=187)
if extra_contour_line != None:
contours2 = plt.contour(X,
Y,
Z,
levels=[extra_contour_line],
colors='blue',
linestyles=':')
# plt.plot([0], [0], ls=':',c='blue', label=entry['label'])
ax.scatter(x, y, c='red', s=2, zorder=5)
contours = plt.contour(X, Y, Z, colors='black', vmin=0.5, vmax=max(z))
plt.contourf(X, Y, Z, 100)
ax.set_xlim([min(x), max(x)])
ax.set_ylim([min(y), max(y)])
plt.title(title)
plt.xlabel(x_axis_name)
plt.ylabel(y_axis_name)
cbar = plt.colorbar()
cbar.ax.set_ylabel(z_axis_name)
plt.tight_layout()
plt.show()
plt.savefig('plot.png')
def simple_plot(resource, variable=None, output='plot.png'):
"""
Generates a nice and simple plot.
"""
print("Plotting {} ...".format(resource))
# Create dataset from resource ... a local NetCDF file or a remote OpenDAP URL
ds = Dataset(resource)
# Get the values of the given variable
values = ds.variables[variable]
# Prepare plot with a given size
fig = plt.figure(figsize=(20, 10))
# add projection
ax = plt.axes(projection=ccrs.PlateCarree())
# Render a contour plot for the first timestep
plt.contourf(values[0, :, :])
# add background image with coastlines
ax.stock_img()
ax.coastlines()
# add a colorbar
plt.colorbar()
# Save the plot to filesystem
fig.savefig(output)
plt.close()
print("Plot written to {}".format(output))
return output
def plot_electric_field(self,sp=10,scales = 500000,cont_scale=90,savefigs = False):
fig = plt.figure(figsize=(7.0, 7.0))
xplot = self.x*1e6
yplot = self.y*1e6
X,Y = np.meshgrid(xplot,yplot)
try:
plt.contourf(X,Y,self.mode_field,cont_scale)
except AttributeError:
raise NotImplementedError("interpolate before plotting")
plt.quiver(X[::sp,::sp], Y[::sp,::sp], np.real(self.E[::sp,::sp,0]), np.real(self.E[::sp,::sp,1]),scale = scales,headlength=7)
plt.xlabel(r'$x(\mu m)$')
plt.ylabel(r'$y(\mu m)$')
#plt.title(r'mode$=$'+str(self.mode)+', '+' $n_{eff}=$'+str(self.neff.real)+str(self.neff.imag)+'j')
if savefigs == True:
plt.savefig('mode'+str(self.mode)+'.eps',bbox_inches ='tight')
D = {}
D['X'] = X
D['Y'] = Y
D['Z'] = self.mode_field
D['u'] = np.real(self.E[::sp,::sp,0])
D['v'] = np.real(self.E[::sp,::sp,1])
D['scale'] = scales
D['cont_scale'] = 90
D['sp'] = sp
savemat('mode'+str(self.mode)+'.mat',D)
return None
def example_plot(xy, labels, a, b, title, filename=None):
# Shape
c_shape = ['bs', 'r^']
# 1. Point
for k, (point, label) in enumerate(zip(xy, labels), 1):
x, y = point
plt.plot(x, y, c_shape[label[0]], mec='k')
plt.text(x, y, '$P_{}$'.format(k), size=15, \
verticalalignment='top', horizontalalignment='left')
# 2. Decision line
tmp = np.linspace(0, 1, 500)
decision_line = a * tmp + b
plt.plot(tmp, decision_line, 'k-', linewidth=3)
# 3. Contour
X, Y = np.meshgrid(tmp, tmp)
Z = np.zeros_like(X)
Z[Y > a * X + b] = 1
plt.contourf(X, Y, Z, cmap='coolwarm')
# plt.axis('equal')
plt.grid(linestyle='--', alpha=0.5)
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.title(title)
if filename:
plt.savefig(filename, dpi=300)
plt.show()
def plot_decision_regions(X, y, classifier, resolution=0.02):
maker = ('s', 'x', 'o', 'v')
colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
cmap = ListedColormap(colors[:len(np.unique(y))])
x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max()
x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max()
# print(x1_min,x1_max)
# print(x2_min,x2_max)
xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
np.arange(x2_min, x2_max, resolution))
# print(np.arange(x1_min,x1_max,resolution).shape)
# print(np.arange(x1_min,x1_max,resolution))
# print(xx1.shape)
# print(xx1)
Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
print(xx1.ravel())
print(xx2.ravel())
print(Z)
Z = Z.reshape(xx1.shape)
plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
plt.xlim(xx1.min(), xx1.max())
plt.ylim(xx2.min(), xx2.max())
for idx, cl in enumerate(np.unique(y)):
plt.scatter(x=X[y == cl, 0],
y=X[y == cl, 1],
alpha=0.8,
c=cmap(idx),
marker=maker[idx],
label=cl)
def main():
# get Dimensions of the 2D grid.
OptDB = PETSc.Options()
n = OptDB.getInt('n', 33)
nx = OptDB.getInt('nx', n)
ny = OptDB.getInt('ny', n)
# run solution
pde, time = solvePoisson(nx, ny)
# Plot solution
show_solution = OptDB.getBool('plot_solution', 0)
if show_solution:
pylab.figure()
pylab.contourf(pde.da.getVecArray(pde.x)[:])
pylab.colorbar()
# plot convergence behavior
pylab.figure()
rh = pde.ksp.getConvergenceHistory()
pylab.semilogy(range(len(rh)), rh, 'b-o')
pylab.xlabel('Iterations')
pylab.ylabel('||r||_2')
pylab.title('Convergence Behavior {0}, time={1:8.6f} s'.format(
pde.solver_name, time))
pylab.grid()
pylab.show()
def plot_decision_surface(axes, clusters, X, Y=None):
import matplotlib.pylab as pylab
import numpy as np
def kmeans_predict(clusters, X):
from ..core import distance
dist_m = distance.minkowski_dist(clusters, X)
print 'dist_m:', dist_m.shape
pred = np.argmin(dist_m, axis=0)
print 'pred:', pred.shape
return pred
# step size in the mesh
h = (np.max(X, axis=0) - np.min(X, axis=0)) / 100.0
# create a mesh to plot in
x_min = np.min(X, axis=0) - 1
x_max = np.max(X, axis=0) + 1
xx, yy = np.meshgrid(np.arange(x_min[0], x_max[0], h[0]),
np.arange(x_min[1], x_max[1], h[1]))
Z = kmeans_predict(clusters, np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
pylab.set_cmap(pylab.cm.Paired)
pylab.axes(axes)
pylab.contourf(xx, yy, Z, cmap=pylab.cm.Paired)
pylab.xlim(np.min(xx), np.max(xx))
pylab.ylim(np.min(yy), np.max(yy))
#pylab.axis('off')
# Plot also the training points
if Y is not None:
pylab.scatter(X[:,0], X[:,1], c=Y)
else:
pylab.scatter(X[:,0], X[:,1])
pylab.scatter(clusters[:,0], clusters[:,1], s=200, marker='x', color='white')
def perform_interpolation(self, dataextrap, regridme, field_src,
field_target, use_locstream):
data = self.allocate()
if self.geometry == 'surface':
for kz in _np.arange(self.nz):
field_src.data[:] = dataextrap[kz, :, :].transpose()
field_target = regridme(field_src, field_target)
if use_locstream:
if self.nx == 1:
data[kz, :, 0] = field_target.data.copy()
elif self.ny == 1:
data[kz, 0, :] = field_target.data.copy()
else:
data[kz,:,:] = field_target.data.transpose()[self.jmin:self.jmax+1, \
self.imin:self.imax+1]
if self.debug and kz == 0:
data_target_plt = _np.ma.masked_values(
data[kz, :, :], self.xmsg)
#data_target_plt = _np.ma.masked_values(field_target.data,self.xmsg)
_plt.figure()
_plt.contourf(data_target_plt[:, :], 40)
_plt.colorbar()
_plt.title('regridded')
_plt.show()
elif self.geometry == 'line':
field_src.data[:] = dataextrap[:, :].transpose()
field_target = regridme(field_src, field_target)
if use_locstream:
data[:, :] = _np.reshape(field_target.data.transpose(),
(self.ny, self.nx))
else:
data[:, :] = field_target.data.transpose()[
self.jmin:self.jmax + 1, self.imin:self.imax + 1]
return data
def Contour(x, y, z, title):
X, Y = np.meshgrid(x, y)
plt.contourf(X, Y, z, 30)
plt.xlabel("P1 (S->I)")
plt.ylabel("P3 (R->S)")
plt.title("Phase diagram of " + str(title))
plt.colorbar()
plt.show()
def PlotArray(self):
x = y = np.linspace(0,self.dimension-1,self.dimension)
Y,X = np.meshgrid(x,y)
plt.contourf(X,Y,self.order_array[:,:],30)
plt.title("Contour plot of Steady State Concentration Array")
plt.xlabel("Lattice Position X")
plt.ylabel("Lattice Position Y")
plt.colorbar()
plt.show()
def normal2(fig, ax):
# MISSING: Plot the contours of the two-dimensional standard Normal distribution.
# See https://matplotlib.org/api/_as_gen/matplotlib.pyplot.contour.html
x, y = np.meshgrid(np.linspace(-3, 3, 50), np.linspace(-3, 3, 50))
z = (np.exp(-x**2 - y**2) / 2.0) / (2.0 * np.pi)
plt.contourf(x, y, z)
plt.xlabel('$x$')
plt.ylabel('$y$')
plt.axis('equal')
def visual(self, Data, no_of_points: int = 1000):
'''
'''
x = zeros((2, 1))
X, Y = meshgrid(linspace(0, 1, no_of_points),
linspace(0, 1, no_of_points))
# create grid of points
X1, X2 = array(X.ravel()), array(Y.ravel())
# vectorize
Stack = stack((X1, X2), axis=1)
# left to right stack, columnwise
empty = zeros(X.shape[0] * X.shape[0])
# matrix of values
for i in arange(Stack.shape[0]):
# run over each coordinate
x[0, 0], x[1, 0] = Stack[i, 0], Stack[i, 1]
# input
Predictions = self.predict(x)
# predict
Predictions = array(Predictions[0] >= Predictions[1])
# create booleans
if Predictions[0] == True:
empty[i] = 1
# assign 1 where true
Pred = empty.reshape((X.shape[0], X.shape[0]))
# reshape ready for plotting contour
import matplotlib.pyplot as plt
plt.style.use('Solarize_Light2')
plt.figure()
# plot figure
plt.contourf(X, Y, Pred, cmap='cividis', alpha=0.8)
# plot contour
plt.scatter(Data.xtrain[0, 0:5],
Data.xtrain[1, 0:5],
marker='^',
c='k',
lw=3)
# plot first half of training set
plt.scatter(Data.xtrain[0, 5:],
Data.xtrain[1, 5:],
marker='o',
c='w',
lw=3)
# plot second half of training set
if Data.highamdata:
plt.savefig('higham_planes.png')
# save figure
else:
plt.savefig('planes.png')
# save figure
plt.show()
return
def plot_discontinuity(self,
depth,
colormap='tomo',
res='i',
verbose=False):
""" plot horizontal slices through an ses3d model
plot_slice(self,depth,colormap='tomo',res='i',verbose=False)
depth=depth for discontinuity
colormap='tomo','mono'
res=resolution of the map, admissible values are: c, l, i, h f
"""
#- set up a map and colourmap -----------------------------------------
m = Basemap(projection='merc',
llcrnrlat=np.min(grid_lat),
urcrnrlat=np.max(grid_lat),
llcrnrlon=np.min(grid_lon),
urcrnrlon=np.max(grid_lon),
lat_ts=20,
resolution=res)
m.drawparallels(np.arange(self.lat_min, self.lat_max, 10.),
labels=[0, 0, 0, 0]) #[1,0,0,1])
m.drawmeridians(np.arange(self.lon_min, self.lon_max, 10.),
labels=[0, 0, 0, 0]) #[1,0,0,1])
m.drawcoastlines()
m.drawcountries()
m.drawmapboundary(fill_color=[1.0, 1.0, 1.0])
model = getattr(self, 'Vs')
layer = np.argmin(np.abs(self.depth - depth))
print('True plotting depth is ', self.depth[layer])
xx, yy = np.meshgrid(self.lon, self.lat)
x, y = m(xx.T, yy.T)
topo = np.zeros(np.shape(x))
for i in range((np.shape(topo)[0])):
for j in range((np.shape(topo)[1])):
vel = model[i, j, :]
ind = np.argmax(np.diff(vel[layer - 4:layer + 4])) + layer - 4
topo[i, j] = self.depth[ind] + 5.
minval = np.min(np.min(topo))
maxval = np.max(np.max(topo))
contours = np.round(np.linspace(depth - 35., depth + 35., 8))
plt.contourf(x, y, topo, contours, cmap=plt.cm.get_cmap('jet_r'))
#plt.pcolor(x,y,self.dvs[:,:,layer],vmin=-0.04, vmax=0.04,cmap=plt.cm.get_cmap('jet_r'))
plt.colorbar()
plt.title('Discontinuity at ' + str(depth) + ' km')
def plot_electric(x,y,u,w,beta,a,V,Delta):
ele = np.zeros([3,len(x),len(y)],dtype=np.complex128)
for i,xx in enumerate(x):
for j, yy in enumerate(y):
ele[:,i,j] = electric_field(xx,yy,u,w,beta,a,V,0,0,Delta)
abss = (np.abs(ele[0,:,:])**2 + np.abs(ele[1,:,:])**2 + np.abs(ele[2,:,:])**2)**0.5
X,Y = np.meshgrid(x,y)
fig = plt.figure()
plt.contourf(X,Y,abss)
plt.show()
return 0
def Decision_Surface(data,
target,
model,
surface=True,
probabilities=False,
cell_size=.01,
size=20):
# Get bounds
x_min, x_max = data[data.columns[0]].min(), data[data.columns[0]].max()
y_min, y_max = data[data.columns[1]].min(), data[data.columns[1]].max()
# Create a mesh
xx, yy = np.meshgrid(np.arange(x_min, x_max, cell_size),
np.arange(y_min, y_max, cell_size),
sparse=True)
meshed_data = pd.DataFrame(np.c_[xx.ravel(), yy.ravel()])
# Add interactions
for i in range(data.shape[1]):
if i <= 1:
continue
meshed_data = np.c_[meshed_data, np.power(xx.ravel(), i)]
if model != None:
# Predict on the mesh
if probabilities:
Z = model.predict_proba(meshed_data)[:, 1].reshape(xx.shape)
else:
Z = model.predict(meshed_data).reshape(xx.shape)
# Plot mesh and data
if data.shape[1] > 2:
plt.title("humor^(" + str(range(1, data.shape[1])) +
") and number_pets")
else:
plt.title("humor and number_pets")
plt.xlabel(data.columns[random_column0])
plt.ylabel(data.columns[random_column1])
if surface and model != None:
if probabilities:
cs = plt.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.4)
else:
cs = plt.contourf(xx,
yy,
Z,
levels=[-1, 0, 1],
cmap=plt.cm.coolwarm,
alpha=0.4)
color = ["blue" if t == 0 else "red" for t in target]
plt.scatter(data[data.columns[0]],
data[data.columns[1]],
color=color,
s=size)
def contourplot(objfun, xmin, xmax, ymin, ymax, ncontours=50, fill=True):
x = np.linspace(xmin, xmax, 200)
y = np.linspace(ymin, ymax, 200)
X, Y = np.meshgrid(x,y)
Z = objfun(X,Y)
if fill:
plt.contourf(X,Y,Z,ncontours); # plot the contours
else:
plt.contour(X,Y,Z,ncontours); # plot the contours
plt.scatter(1,1,marker="x",s=50,color="r"); # mark the minimum
def contour_plot(fig_dims, Z, T, tc, L, N, fname):
fig = plt.figure(figsize=fig_dims)
x = np.linspace(T * (tc - 1), T * tc, N)
y = np.linspace(0, L, N)
Y, X = np.meshgrid(x, y)
plt.contourf(X, Y, Z, cmap=cm.viridis)
plt.ylabel('t (s)')
plt.xlabel('z (cm)')
plt.colorbar()
fname = "plots/%s" % (fname)
plt.savefig(fname, dpi=600, bbox_inches='tight')
def potential(x_axis,
y_axis,
cbar,
x_label,
y_label,
cbar_label,
int_pol,
colorMap,
xmin=None,
xmax=None,
ymin=None,
ymax=None,
plot=None):
"""A general plotter allowing for the plotting of a heatmap(primarily used
here for potential function) which takes in relevant data about the
graph. It also allows for the option of overlaying either a quiver or a
streamline plot, or not!
Parameters:
x_axis, y_axis : The corresponding x and y axis data lists of a plot
cbar: The heatmap list data which usually corresponds to x and y axis
x_label, y_label, : The x, y and z label of the graph dtype = string
cbar_label : The colourbar label dtype = string
int_pol: The colour interpolation of the heatmap dtype = integer
colorMap: The style and colour of heatmap dtype = string
xmin, xmax: The minimum and maximum value of the x-axis
ymin, ymax: The minimum and maximum value of the y-axis
plot: "quiver", "stream" allowing for the overlay of quiver or streamline
plot
Returns:
Image : AxesImage
"""
plt.contourf(x_axis, y_axis, cbar, int_pol, cmap=colorMap)
cbar_tag = plt.colorbar()
Z = cbar
plt.xlabel(x_label)
plt.ylabel(y_label)
cbar_tag.set_label(cbar_label, rotation=270)
cbar_tag.set_clim(-1000.0, 1000.0)
E = np.gradient(Z)
E = E / np.sqrt(E[0]**2 + E[1]**2)
if plot is not None:
if plot == "quiver":
print("\nQuiver plot:")
plt.quiver(x_axis, y_axis, E[1], E[0])
if plot == "stream":
print("\nStreamline Plot:")
plt.streamplot(x_axis, y_axis, -E[1], -E[0], color='black')
if xmin is not None and xmax is not None:
plt.xlim(xmin, xmax)
if ymin is not None and ymax is not None:
plt.ylim(ymin, ymax)
plt.show()
def plot(self,key='AOD'):
"""
Create a plot of a variable over the ORACLES study area.
Parameters
----------
key : string
See names for available datasets to plot.
Modification history
--------------------
Written: Michael Diamond, 08/16/2016, Seattle, WA
Modified: Michael Diamond, 08/21/2016
-Added ORACLES routine flight plan, Walvis Bay (orange), and Ascension Island
Modified: Michael Diamond, 09/02/2016, Swakopmund, Namibia
-Updated flight track
"""
plt.clf()
size = 16
font = 'Arial'
m = Basemap(llcrnrlon=self.lon.min(),llcrnrlat=self.lat.min(),urcrnrlon=self.lon.max(),\
urcrnrlat=self.lat.max(),projection='merc',resolution='i')
m.drawparallels(np.arange(-180,180,5),labels=[1,0,0,0],fontsize=size,fontname=font)
m.drawmeridians(np.arange(0,360,5),labels=[1,1,0,1],fontsize=size,fontname=font)
m.drawmapboundary(linewidth=1.5)
m.drawcoastlines()
m.drawcountries()
m.drawmapboundary(fill_color='steelblue')
m.fillcontinents(color='floralwhite',lake_color='steelblue',zorder=0)
if key == 'AOD':
m.pcolormesh(self.lon,self.lat,self.ds['%s' % key],cmap=self.colors['%s' % key],\
latlon=True,vmin=self.v['%s' % key][0],vmax=self.v['%s' % key][1])
cbar = m.colorbar()
cbar.ax.tick_params(labelsize=size-2)
cbar.set_label('[%s]' % self.units['%s' % key],fontsize=size,fontname=font)
elif key == 'ATYP':
m.pcolormesh(self.lon,self.lat,self.ds['%s' % key],cmap=self.colors['%s' % key],\
latlon=True,vmin=self.v['%s' % key][0],vmax=self.v['%s' % key][1])
plt.contourf(np.array(([5,1],[3,2])),cmap=self.colors['%s' % key],levels=[0,1,2,3,4,5])
cbar = m.colorbar(ticks=[0,1,2,3,4,5])
cbar.ax.set_yticklabels(['Sea Salt','Sulphate','Organic C','Black C','Dust'])
cbar.ax.tick_params(labelsize=size-2)
else:
print('Error: Invalid key. Check names for available datasets.')
m.scatter(14.5247,-22.9390,s=250,c='orange',marker='D',latlon=True)
m.scatter(-14.3559,-7.9467,s=375,c='c',marker='*',latlon=True)
m.scatter(-5.7089,-15.9650,s=375,c='chartreuse',marker='*',latlon=True)
m.plot([14.5247,13,0],[-22.9390,-23,-10],c='w',linewidth=5,linestyle='dashed',latlon=True)
m.plot([14.5247,13,0],[-22.9390,-23,-10],c='k',linewidth=3,linestyle='dashed',latlon=True)
plt.title('%s from MSG SEVIRI on %s/%s/%s at %s UTC' % \
(self.names['%s' % key],self.month,self.day,self.year,self.time),fontsize=size+4,fontname=font)
plt.show()
def plot_intensity_sphere_overlap(theta, phi, rho_0x, rho_0y, rho, w, n_0, P):
""" plot intensity profile and sphere outline"""
# theta - sphere theta coordinate, array
# phi - sphere phi coordinate, array
# rho_0x - beam x displacement, scalar
# rho_0y - beam y displacement, scalar
# rho - sphere diameter, scalar
intensity = LG_01_Intensity_sphere_coordinate(theta, phi, rho_0x, rho_0y,
rho, w, n_0, P)
cartesian_coordinates = spherical_to_cartesian_coordinates(
5 * rho, theta, phi)
x = cartesian_coordinates['x']
y = cartesian_coordinates['y']
#x = np.linspace(-3 * rho, 3 * rho, 300)
#y = np.linspace(-3 * rho, 3 * rho, 300)
plt.contourf(x * 10**6,
y * 10**6,
intensity / np.max(intensity),
500,
cmap='Reds')
cbar = plt.colorbar()
cbar.set_ticks([0, 0.2, 0.4, 0.6, 0.8, 1])
plt.xlabel('x(um)', fontsize=20)
plt.ylabel('y(um)', fontsize=20)
plt.xlim(-120, 120)
plt.ylim(120, -120)
'''
plt.figure("beam_sphere_overlap_pcolormesh")
plt.clf()
plt.pcolormesh(x, y, intensity)
'''
# make sure coordiantes not distorted
axes = plt.gca()
axes.set_aspect('equal')
sphere_outline = plt.Circle((0, 0),
radius=rho,
facecolor='None',
edgecolor='k')
axes.add_artist(sphere_outline)
#plt.figure("beam_sphere_overlap_surf")
#plt.su
return
def simple_plot(resource, variable=None, output=None):
output = output or 'plot.png'
ds = Dataset(resource)
values = ds.variables[variable]
fig = plt.figure(figsize=(20, 10))
ax = plt.axes(projection=ccrs.PlateCarree())
plt.contourf(values[0, :, :])
ax.stock_img()
ax.coastlines()
plt.colorbar()
fig.savefig(output)
plt.close()
return output
def read_2d_obc(self,dirin,obcfile,step=0,orientation='NS',precision='single',ntimes=12): filein = dirin + obcfile self.ntime = ntimes # load data if precision == 'single': data_raw = np.fromfile(filein,'>f') elif precision == 'double': data_raw = np.fromfile(filein,'>d') # reshape on dims if orientation == 'NS': data_compact = np.reshape(data_raw,[self.ntime,self.nz,self.nyNS_total]) elif orientation == 'EW': data_compact = np.reshape(data_raw,[self.ntime,self.nz,self.nyEW_total]) del data_raw ## split in faces if orientation == 'NS': data_f1 = data_compact[:,:,:self.nyNS_end[0]] data_f2 = data_compact[:,:,self.nyNS_end[0]:self.nyNS_end[1]] data_f3 = data_compact[:,:,self.nyNS_end[1]:self.nyNS_end[2]] data_f4 = data_compact[:,:,self.nyNS_end[2]:self.nyNS_end[3]] data_f5 = data_compact[:,:,self.nyNS_end[3]:self.nyNS_end[4]] elif orientation == 'EW': data_f1 = data_compact[:,:,:self.nyEW_end[0]] data_f2 = data_compact[:,:,self.nyEW_end[0]:self.nyEW_end[1]] data_f3 = data_compact[:,:,self.nyEW_end[1]:self.nyEW_end[2]] data_f4 = data_compact[:,:,self.nyEW_end[2]:self.nyEW_end[3]] data_f5 = data_compact[:,:,self.nyEW_end[3]:self.nyEW_end[4]] del data_compact plt.figure() plt.subplot(151) if data_f1.shape[-1] != 0: plt.contourf(self.mask_invalid_data(data_f1[step,:])) plt.subplot(152) if data_f2.shape[-1] != 0: plt.contourf(self.mask_invalid_data(data_f2[step,:])) plt.subplot(153) if data_f3.shape[-1] != 0: plt.contourf(self.mask_invalid_data(data_f3[step,:])) plt.subplot(154) if data_f4.shape[-1] != 0: plt.contourf(self.mask_invalid_data(data_f4[step,:])) plt.subplot(155) if data_f5.shape[-1] != 0: plt.contourf(self.mask_invalid_data(data_f5[step,:])) plt.show() return None
def plot_bidensity(self):
"""Plot bivariate density.
"""
ndots = 100
xgrid = np.linspace(-2, 2, ndots)
ygrid = np.linspace(-2, 2, ndots)
xgrid, ygrid = np.meshgrid(xgrid, ygrid)
data = np.vstack((xgrid.flatten(), ygrid.flatten())).T
zvalues = self.pdf(data).reshape((ndots, ndots))
plt.contourf(xgrid, ygrid, zvalues)
plt.axis('square')
plt.title(self.get_name())
plt.show()
def wind_radar_affine(self):
'''
功能是生成仿射，并保存。
把函数直接当属性用的方法：
'''
x = np.round(np.linspace(self.stg_X[0], self.stg_X[1], self.pixel[0]),
4)
y = np.round(np.linspace(self.stg_Y[0], self.stg_Y[1], self.pixel[1]),
4)
[X, Y] = np.meshgrid(x, y)
# 得把时间传进来
start_time = self.real_time
end_time = self.real_time + self.duration
self.time_split = 3 # (end_time - start_time)/self.interval
for i in range(self.time_split):
ptime = self.real_time + (i + 1) * interval
Z = self.read_json_data(self.real_time) # 只读取此刻真实的radar.
feng_x = self.read_nc_data('U', ptime)
feng_y = self.read_nc_data('V', ptime)
model = AffineModel()
fx, fy = model(feng_x, feng_y)
cmaps = colors.LinearSegmentedColormap.from_list('mylist',
self.colors_sys,
N=6)
fig = pb.gcf()
fig.set_size_inches(4, 4)
X += fx * 1 # 这里是interval,按小时计算的interval,数值预报默认是1小时出
Y += fy * 1
pb.contourf(X, Y, Z, radar_sys, cmap=cmaps, extend='both') # 循环的生成
pb.xlim(self.stg_X[0], self.stg_X[1])
pb.ylim(self.stg_Y[0], self.stg_Y[1])
pb.gca().xaxis.set_major_locator(plt.NullLocator())
pb.gca().yaxis.set_major_locator(plt.NullLocator())
pb.subplots_adjust(top=1,
bottom=0,
right=1,
left=0,
hspace=0,
wspace=0)
pb.margins(0, 0)
affine_image_path = self.save_affine_path + self.real_time + ptime + 'affine' + '.png'
fig.savefig(affine_image_path,
format='png',
transparent=True,
dpi=200,
pad_inches=0)
def plot_decision_boundary(pred_func):
# Set min and max values and give it some padding
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
h = 0.01
# Generate a grid of points with distance h between them
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
# Predict the function value for the whole gid
Z = pred_func(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
# Plot the contour and training examples
plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)
def plot_solution(problem_data, solution):
from numpy import linspace
from matplotlib.pylab import contour, colorbar, contourf, xlabel, ylabel, title, show
from matplotlib.mlab import griddata
print(" * Preparing for plotting...")
NN = problem_data["NN"]
# Extract node coordinates seperately for plotting
x, y = [0] * NN, [0] * NN
for i, node in enumerate(problem_data["nodes"]):
x[i] = node[0]
y[i] = node[1]
# Refine the contour plot mesh for a "smoother" image, by generating a
# 200*200 grid
xi = linspace(min(x), max(x), 200)
yi = linspace(min(y), max(y), 200)
# Approximate the mid values from neighbors
zi = griddata(x, y, solution, xi, yi)
print(" * Plotting...")
# Plot the contour lines with black
contour(xi, yi, zi, 15, linewidths=0.5, colors='k')
# Plot the filled contour plot
plot = contourf(xi, yi, zi, 15, antialiased=True)
colorbar(plot, format="%.3f").set_label("T")
xlabel('X')
ylabel('Y')
title("Contour plot of T values for {0}".format(problem_data["title"]))
show()
def Decision_Surface(data, target, model, surface=True, probabilities=False, cell_size=.01):
# Get bounds
x_min, x_max = data[data.columns[0]].min(), data[data.columns[0]].max()
y_min, y_max = data[data.columns[1]].min(), data[data.columns[1]].max()
# Create a mesh
xx, yy = np.meshgrid(np.arange(x_min, x_max, cell_size), np.arange(y_min, y_max, cell_size))
meshed_data = pd.DataFrame(np.c_[xx.ravel(), yy.ravel()])
# Add interactions
for i in range(data.shape[1]):
if i <= 1:
continue
meshed_data = np.c_[meshed_data, np.power(xx.ravel(), i)]
if model != None:
# Predict on the mesh
if probabilities:
Z = model.predict_proba(meshed_data)[:, 1].reshape(xx.shape)
else:
Z = model.predict(meshed_data).reshape(xx.shape)
# Plot mesh and data
if data.shape[1] > 2:
plt.title("humor^(" + str(range(1,data.shape[1])) + ") and number_pets")
else:
plt.title("humor and number_pets")
plt.xlabel("humor")
plt.ylabel("number_pets")
if surface and model != None:
cs = plt.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.4)
color = ["blue" if t == 0 else "red" for t in target]
plt.scatter(data[data.columns[0]], data[data.columns[1]], color=color)
def plot(AB_final,lams,lamp2,title):
X,Y = np.meshgrid(lams,lamp2)
Z = AB_final
fig = plt.figure()
ax = fig.gca(projection='3d')
plt.gca().get_xaxis().get_major_formatter().set_useOffset(False)
plt.gca().get_yaxis().get_major_formatter().set_useOffset(False)
plt.title(title)
ax.set_xlabel(r'$\lambda_p(\mu m)$')
ax.set_ylabel(r'$\lambda_s(\mu m)$')
ax.set_zlabel(r'$P_{idler}(dBm)$')
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet, linewidth=0, antialiased=False)
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()
fig = plt.figure()
axi = plt.contourf(X,Y,Z, cmap=cm.jet)
plt.gca().get_xaxis().get_major_formatter().set_useOffset(False)
plt.gca().get_yaxis().get_major_formatter().set_useOffset(False)
plt.xlabel(r'$\lambda_p(\mu m)$')
plt.ylabel(r'$\lambda_s(\mu m)$')
plt.title(title)
fig.colorbar(axi, shrink=0.5, aspect=5)
plt.show()
return 0
def Decision_Surface(X, target, model, cell_size=.01, surface=True, points=True):
# Get bounds
x_min, x_max = X[:, 0].min(), X[:, 0].max()
y_min, y_max = X[:, 1].min(), X[:, 1].max()
# Create a mesh
xx, yy = np.meshgrid(np.arange(x_min, x_max, cell_size), np.arange(y_min, y_max, cell_size))
meshed_data = pd.DataFrame(np.c_[xx.ravel(), yy.ravel()])
# Add interactions
for i in range(X.shape[1]):
if i <= 1:
continue
meshed_data = np.c_[meshed_data, np.power(xx.ravel(), i)]
Z_flat = model.predict(meshed_data)
Z = Z_flat.reshape(xx.shape)
if surface:
cs = plt.contourf(xx, yy, Z, color=colorizer(Z_flat))
if points:
plt.scatter(X[:, 0], X[:, 1], color=colorizer(target), linewidth=0, s=20)
plt.xlabel("Feature 1",fontsize=20)
plt.ylabel("Feature 2",fontsize=20)
plt.show()
def decision_surface(data, target, model, surface=True, probabilities=False, cell_size=0.01):
plt.rcParams["figure.figsize"] = [10.0, 10.0]
# Get bounds
x_min, x_max = data[data.columns[0]].min(), data[data.columns[0]].max()
y_min, y_max = data[data.columns[1]].min(), data[data.columns[1]].max()
# Create a mesh
xx, yy = np.meshgrid(np.arange(x_min, x_max, cell_size), np.arange(y_min, y_max, cell_size))
meshed_data = pd.DataFrame(np.c_[xx.ravel(), yy.ravel()])
if model != None:
# Predict on the mesh
if probabilities:
Z = model.predict_proba(meshed_data)[:, 1].reshape(xx.shape)
else:
Z = model.predict(meshed_data).reshape(xx.shape)
# Plot mesh and data
plt.title("Decision Surface")
plt.xlabel(data.columns[0])
plt.ylabel(data.columns[1])
if surface and model != None:
cs = plt.contourf(xx, yy, Z, cmap=plt.cm.coolwarm_r, alpha=0.4)
color = ["red" if t == 0 else "blue" for t in target]
plt.scatter(data[data.columns[0]], data[data.columns[1]], color=color)
plt.show()
def contour(X,Y,Z,
extent=None, vrange=None, levels=None, extend='both',
inaxis=None,
cmap=None, addcolorbar=True, clabel=None,
smooth=True, smoothlen=None):
'''Build a super fancy contour image'''
# Build up some nice ranges and levels to be plotted
XX, YY = _getmesh(X,Y,Z)
extent = _getextent(extent, XX, YY)
vrange = _getvrange(vrange, XX,YY,Z, inaxis=inaxis)
levels = _getlevels(levels, vrange)
cmap = _getcmap(cmap)
# Smooth if needed
if smooth:
X,Y,Z = _smooth(X,Y,Z, smoothlen)
cs = pylab.contourf(X, Y, Z, levels,
vmin=vrange[0], vmax=vrange[1],
extent=extent, extend='both',
cmap=cmap)
ccs = pylab.contour(X, Y, Z, levels, vmin=vrange[0], vmax=vrange[1],
cmap=cmap)
# setup a colorbar, add in the lines, and then return it all out.
if addcolorbar:
cb = colorbar(cs, ccs, levels=levels, clabel=clabel)
return cs, ccs, cb
return cs, ccs
def time_freq_plot(t, freqs, data, coefs, xunits='s', yunits=''):
if xunits == 'ms':
t = 1e3 * t
fig = plt.figure(figsize=(12, 6))
plt.subplots_adjust(wspace=.8, hspace=.5, bottom=.2)
# signal plot
plt.subplot2grid((3, 7), (0, 0), colspan=6)
plt.plot(t, data)
plt.ylabel(yunits)
plt.xlim([t[0], t[-1]])
# time frequency power plot
plt.subplot2grid((3, 7), (1, 0), rowspan=2, colspan=6)
c = plt.contourf(t, freqs, coefs, cmap='PRGn', aspect='auto')
plt.xlabel('time (' + xunits + ')')
plt.ylabel('frequency (Hz)')
# mean power plot over intervals
plt.subplot2grid((3, 7), (1, 6), rowspan=2)
plt.xlabel('power')
# max of power over intervals
plt.subplot2grid((3, 8), (1, 7), rowspan=2)
plt.barh(freqs, np.power(coefs,2).mean(axis=1),\
label='mean', height=freqs[-1]-freqs[-2])
# plt.plot(np.power(coefs,2).max(axis=1), freqs,\
# label='max.')
plt.xlabel(' power')
plt.legend(prop={'size': 'small'}, loc=(0.1, 1.1))
return fig
def plot_contour(z,x,y,title="TITLE",xtitle="",ytitle="",xrange=None,yrange=None,plot_points=0,contour_levels=20,
cmap=None,cbar=True,fill=False,cbar_title="",show=1):
fig = plt.figure()
if fill:
fig = plt.contourf(x, y, z.T, contour_levels, cmap=cmap, origin='lower')
else:
fig = plt.contour( x, y, z.T, contour_levels, cmap=cmap, origin='lower')
if cbar:
cbar = plt.colorbar(fig)
cbar.ax.set_ylabel(cbar_title)
plt.title(title)
plt.xlabel(xtitle)
plt.ylabel(ytitle)
# the scatter plot:
if plot_points:
axScatter = plt.subplot(111)
axScatter.scatter( np.outer(x,np.ones_like(y)), np.outer(np.ones_like(x),y))
# set axes range
plt.xlim(xrange)
plt.ylim(yrange)
if show:
plt.show()
return fig
def plotModel2D(self,X=[],Y=[],xLabel="",yLabel="",title="",fName=""):
#fig = plt.figure(figsize=(6,4),dpi=100)
plt.close()
# 真値のプロット（クラスごとにマーカーを変更）
plt.plot(X[Y[:,0]==0,0],X[Y[:,0]==0,1],'cx',markersize=14,label="ラベル0")
plt.plot(X[Y[:,0]==1,0],X[Y[:,0]==1,1],'m.',markersize=14,label="ラベル1")
# 予測値のメッシュの計算
X1,X2 = plt.meshgrid(plt.linspace(np.min(X[:,0]),np.max(X[:,0]),50),plt.linspace(np.min(X[:,1]),np.max(X[:,1]),50))
Xmesh = np.hstack([np.reshape(X1,[-1,1]),np.reshape(X2,[-1,1])])
Pmesh,_,_ = self.predict(Xmesh)
Pmesh = np.reshape(Pmesh,X1.shape)
# 予測値のプロット
CS = plt.contourf(X1,X2,Pmesh,linewidths=2,cmap="bwr",alpha=0.3,vmin=0,vmax=1)
# カラーバー
CB = plt.colorbar(CS)
CB.ax.tick_params(labelsize=14)
# 各軸の範囲とラベルの設定
plt.xlim([np.min(X[:,0]),np.max(X[:,0])])
plt.ylim([np.min(X[:,1]),np.max(X[:,1])])
plt.title(title,fontsize=14)
plt.xlabel(xLabel,fontsize=14)
plt.ylabel(yLabel,fontsize=14)
plt.legend()
# グラフの表示またはファイルへの保存
if len(fName):
plt.savefig(fName)
else:
plt.show()
def time_freq_plot(t, freqs, data, coefs):
"""
a plot to illustrate the output of the wavelet analysis
"""
dt = t[1] - t[0]
import matplotlib.pylab as plt
fig = plt.figure(figsize=(8, 5))
plt.subplots_adjust(wspace=.8, hspace=.5, bottom=.2)
# signal plot
plt.subplot2grid((3, 8), (0, 0), colspan=6)
plt.plot(1e3 * t, data, 'k-', lw=2)
plt.ylabel('signal')
plt.xlim([1e3 * t[0], 1e3 * t[-1]])
# time frequency power plot
ax1 = plt.subplot2grid((3, 8), (1, 0), rowspan=2, colspan=6)
c = plt.contourf(1e3 * t, freqs, coefs, cmap='PRGn', aspect='auto')
plt.xlabel('time (ms)')
plt.ylabel('frequency (Hz)')
# inset with legend
acb = plt.axes([.4, .4, .02, .2])
plt.colorbar(c, cax=acb, label='coeffs (a.u.)', ticks=[-1, 0, 1])
# mean power plot over intervals
plt.subplot2grid((3, 8), (1, 6), rowspan=2)
plt.barh(freqs, np.power(coefs, 2).mean(axis=1) * dt)
plt.xticks([])
plt.xlabel(' mean \n power \n (a.u.)')
# max of power over intervals
plt.subplot2grid((3, 8), (1, 7), rowspan=2)
plt.barh(freqs, np.power(coefs, 2).max(axis=1) * dt)
plt.xticks([])
plt.xlabel(' max. \n power \n (a.u.)')
return fig
def Decision_Surface(X, target, model, cell_size=.01, surface=True, points=True):
# Get bounds
x_min, x_max = X[:, 0].min(), X[:, 0].max()
y_min, y_max = X[:, 1].min(), X[:, 1].max()
# Create a mesh
xx, yy = np.meshgrid(np.arange(x_min, x_max, cell_size), np.arange(y_min, y_max, cell_size))
meshed_data = pd.DataFrame(np.c_[xx.ravel(), yy.ravel()])
# Add interactions
for i in range(X.shape[1]):
if i <= 1:
continue
meshed_data = np.c_[meshed_data, np.power(xx.ravel(), i)]
Z_flat = model.predict(meshed_data)
Z = Z_flat.reshape(xx.shape)
# Plot mesh and data
plt.title("humor and number_pets")
plt.xlabel("humor")
plt.ylabel("number_pets")
if surface:
cs = plt.contourf(xx, yy, Z, color=colorizer(Z_flat))
if points:
plt.scatter(X[:, 0], X[:, 1], color=colorizer(target), linewidth=0, s=20)
plt.xlabel("Feature 1")
plt.xlabel("Feature 2")
plt.show()
def diffDensity(fig, F2, F3, Trange):
T1 = copy.deepcopy(Trange)
T2 = copy.deepcopy(Trange)
X, Y = numpy.meshgrid(T1, T2)
p2 = []
p3 = []
for tau in Trange:
p2.append(F2.pdf(tau))
p3.append(F3.pdf(tau))
p2mat = numpy.matrix(p2)
p3mat = numpy.matrix(p3)
pp2 = p2mat.T * p2mat
pp3 = p3mat.T * p3mat
PXP = numpy.array(numpy.log(pp3) - numpy.log(pp2))
# norm = plt.cm.colors.Normalize(vmin=-0.8,vmax=0.8)
levels = numpy.arange(-2.0, .1, .1)
cmap = plt.cm.get_cmap("PiYG")
cmap.set_under(color='red', alpha=0.3)
cmap.set_over(color='red', alpha=0.3)
cset = plt.contourf(X,
Y,
PXP,
levels=numpy.array([-10.0, 0, .8]),
colors=('r', 'g'),
alpha=.3) #plt.cm.get_cmap(cmap,2))
matplotlib.rcParams['contour.negative_linestyle'] = 'solid'
levels = numpy.arange(-10, .8, .1)
cs2 = plt.contour(X, Y, PXP, levels=levels, colors='k', alpha=.2)
plt.clabel(cs2, levels[91:], fontsize=9, inline=1)
# plt.colorbar(cset)
plt.xlabel("First Opening Time $\\tau_1$")
plt.ylabel("Second Opening Time $\\tau_2$")
plt.title(
"Test Statistic Comparing Models as a Function of Two Observations")
plt.show()
def plot_svc(X, y, mysvc, bounds=None, grid=50):
if bounds is None:
xmin = np.min(X[:, 0], 0)
xmax = np.max(X[:, 0], 0)
ymin = np.min(X[:, 1], 0)
ymax = np.max(X[:, 1], 0)
else:
xmin, ymin = bounds[0], bounds[0]
xmax, ymax = bounds[1], bounds[1]
aspect_ratio = (xmax - xmin) / (ymax - ymin)
xgrid, ygrid = np.meshgrid(np.linspace(xmin, xmax, grid),
np.linspace(ymin, ymax, grid))
plt.gca(aspect=aspect_ratio)
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
plt.xticks([])
plt.yticks([])
plt.hold(True)
plt.plot(X[y == 1, 0], X[y == 1, 1], 'bo')
plt.plot(X[y == -1, 0], X[y == -1, 1], 'ro')
box_xy = np.append(xgrid.reshape(xgrid.size, 1), ygrid.reshape(ygrid.size, 1), 1)
if mysvc is not None:
scores = mysvc.decision_function(box_xy)
else:
print 'You must have a valid SVC object.'
return None;
CS=plt.contourf(xgrid, ygrid, scores.reshape(xgrid.shape), alpha=0.5, cmap='jet_r')
plt.contour(xgrid, ygrid, scores.reshape(xgrid.shape), levels=[0], colors='k', linestyles='solid', linewidths=1.5)
plt.contour(xgrid, ygrid, scores.reshape(xgrid.shape), levels=[-1,1], colors='k', linestyles='dashed', linewidths=1)
plt.plot(mysvc.support_vectors_[:,0], mysvc.support_vectors_[:,1], 'ko', markerfacecolor='none', markersize=10)
CB = plt.colorbar(CS)
def Plot2DwSVM(datax, datay):
"""
plot data and corresponding SVM results.
"""
clf = svm.SVC()
clf.fit(datax, datay)
step = 0.02
x_min, x_max = datax[:,0].min(), datax[:,0].max()
y_min, y_max = datax[:,1].min(), datax[:,1].max()
xx, yy = numpy.meshgrid(numpy.arange(x_min, x_max, step), numpy.arange(y_min, y_max, step))
Z = clf.decision_function(numpy.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
pylab.contourf(xx, yy, Z, 10, cmap=pylab.cm.Oranges)
pylab.scatter(datax[datay == 1,0], datax[datay == 1,1], c='b', s=50)
pylab.scatter(datax[datay != 1,0], datax[datay != 1,1], c='r', s=50)
pylab.show()
def plot(points, col):
n = 256
x = np.linspace(-100, 100, n)
y = np.linspace(-100, 100, n)
X, Y = np.meshgrid(x, y)
xs = []
ys = []
pl.contourf(X, Y, fun([X, Y]), 8, alpha=.75, cmap='jet')
pl.contour(X, Y, fun([X, Y]), 8, colors='black', linewidth=.5)
for i in range(len(points)):
xs.append(points[i][0])
ys.append(points[i][1])
pl.plot(xs, ys, marker='o', linestyle='--', color=str(col), label='Square')
def plot_decision_surface(axes, clf, X, Y):
import numpy as np
h=.02 # step size in the mesh
# create a mesh to plot in
x_min, x_max = X[:,0].min()-1, X[:,0].max()+1
y_min, y_max = X[:,1].min()-1, X[:,1].max()+1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
axes.set_cmap(pylab.cm.Paired)
pylab.axes(axes)
pylab.contourf(xx, yy, Z)
pylab.axis('off')
# Plot also the training points
pylab.scatter(X[:,0], X[:,1], c=Y)
def plot(points):
'''
Plotting 2D function and way search
'''
n = 256
x = np.linspace(-12, 12, n)
y = np.linspace(-12, 12, n)
X, Y = np.meshgrid(x, y)
xs = []
ys = []
pl.contourf(X, Y, fun([X, Y]), 8, alpha=.75, cmap='jet')
C = pl.contour(X, Y, fun([X, Y]), 8, colors='black', linewidth=.5)
for i in range(len(points)):
xs.append(points[i][0])
ys.append(points[i][1])
pl.plot(xs, ys, marker='o', linestyle='--', color='r', label='Square')
def plot(points):
"""
Plotting 2D function and way search
"""
n = 256
x = np.linspace(-12, 12, n)
y = np.linspace(-12, 12, n)
X, Y = np.meshgrid(x, y)
xs = []
ys = []
pl.contourf(X, Y, fun([X, Y]), 8, alpha=0.75, cmap="jet")
C = pl.contour(X, Y, fun([X, Y]), 8, colors="black", linewidth=0.5)
for i in range(len(points)):
xs.append(points[i][0])
ys.append(points[i][1])
pl.plot(xs, ys, marker="o", linestyle="--", color="r", label="Square")
def plt_decision_regions(X, y, classifier, resolution = 0.02):
markers = ('s', 'x', 'o', '^', 'v')
colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
cmap = ListedColormap(colors[:len(np.unique(y))])
#plot decision surface
x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
np.arange(x2_max, x2_max, resolution))
z = classifier.predict(np.array([xx1.reval(), xx2.reval()]).T)
z = z.reshape(xx1.shape)
plt.contourf(xx1, xx2, z, alpha = 0.4, cmap = cmap)
plt.xlim(xx1.min(), xx1.max())
plt.ylim(xx2.min(), xx2.max())
#plot class samples
for idx, c1 in enumerate(np.unique(y)):
plt.scatter(x = X[y == c1, 0], y = X[y == c1, 1],
alpha = 0.8, c = cmap(idx), marker = markers[idx], label = c1)
def plot_decision_surface(axes, clf, X, Y):
import matplotlib.pylab as pylab
import numpy as np
# step size in the mesh
h = (np.max(X, axis=0) - np.min(X, axis=0)) / 100.0
# create a mesh to plot in
x_min = np.min(X, axis=0) - 1
x_max = np.max(X, axis=0) + 1
xx, yy = np.meshgrid(np.arange(x_min[0], x_max[0], h[0]),
np.arange(x_min[1], x_max[1], h[1]))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
pylab.set_cmap(pylab.cm.Paired)
pylab.axes(axes)
pylab.contourf(xx, yy, Z, cmap=pylab.cm.Paired)
#pylab.axis('off')
# Plot also the training points
pylab.scatter(X[:,0], X[:,1], c=Y)
def plot_rough(data=None, use_fb=True, levels=20, colorbars=False):
if data is None: data = read_rough('rough')
(x, y) = ('fb_x', 'fb_y') if use_fb else ('x', 'y')
fig = pl.figure()
#pos = np.genfromtxt("rough_pos")
pos = None
print pos
for n,signal in enumerate(['psd_x', 'psd_y', 'sum_x', 'sum_y']):
pl.subplot(2,2,n+1)
pl.contourf(data[x][:,:,0], data[y][:,:,0], data[signal][:,:,0], levels)
pl.title(signal)
pl.axis('equal')
pl.axis('tight')
if colorbars:
pl.colorbar()
if pos is not None:
pl.axvline(pos[0], alpha=0.3)
pl.axhline(pos[1], alpha=0.3)
fig.show()
def plot_mpl(da, U):
comm = da.getComm()
rank = comm.getRank()
scatter, U0 = PETSc.Scatter.toZero(U)
scatter.scatter(U, U0, False, PETSc.Scatter.Mode.FORWARD)
if rank == 0:
try:
from matplotlib import pylab
except ImportError:
PETSc.Sys.Print("matplotlib not available")
else:
from numpy import mgrid
nx, ny, nz = da.sizes
solution = U0[...].reshape(da.sizes, order='f')
xx, yy = mgrid[0:1:1j*nx,0:1:1j*ny]
pylab.contourf(xx, yy, solution[:, :, nz//2])
pylab.axis('equal')
pylab.xlabel('X')
pylab.ylabel('Y')
pylab.title('Z/2')
pylab.show()
comm.barrier()
def perform_extrapolation(self,datasrc,maskfile,maskvar,missing_value,drown):
# 2.1 read mask or compute it
if maskvar is not None:
mask = _ncdf.read_field(maskfile,maskvar)
# to do, needs imin/imax_src,...
else:
mask = self.compute_mask_from_missing_value(datasrc,missing_value=missing_value)
# 2.2 mask the source data
if _np.ma.is_masked(datasrc):
datasrc = datasrc.data
datasrc[_np.where(mask == 0)] = self.xmsg
datamin = datasrc[_np.where(mask == 1)].min()
datamax = datasrc[_np.where(mask == 1)].max()
if self.debug:
datasrc_plt = _np.ma.masked_values(datasrc,self.xmsg)
_plt.figure() ; _plt.contourf(datasrc_plt[0,:,:],40) ; _plt.title('original') ; _plt.colorbar()
# 2.3 perform land extrapolation on reduced variable
datanorm = self.normalize(datasrc,datamin,datamax,mask)
if self.debug:
print(datanorm.min() , datanorm.max(), datamin, datamax)
datanormextrap = self.drown_field(datanorm,mask,drown)
dataextrap = self.unnormalize(datanormextrap,datamin,datamax)
return dataextrap
def display_fv_solution(grid, field_name, **kwargs):
fig = plt.figure()
plt.axes().set_aspect('equal')
var, = grid.get_cell_fields(field_name)
cell_nodes = grid.cell_nodes
plt.contourf(cell_nodes[:, :, 0], cell_nodes[:, :, 1], var, kwargs.get('contour_levels', 15))
if kwargs.get('show_grid', False):
plt.plot(cell_nodes[:, :, 0], cell_nodes[:, :, 1], '-', color='black')
plt.plot(np.transpose(cell_nodes[:, :, 0]), np.transpose(cell_nodes[:, :, 1]), '-', color='black')
if kwargs.get('mark_cell_centers', False):
plt.plot(cell_nodes[:, :, 0], cell_nodes[:, :, 1], 'o', color='black')
if kwargs.get('show', False):
plt.show()
f = BytesIO()
plt.savefig(f)
return f
def plot2D(x, y, z, zmin= None, zmax= None, xlim=None, ylim=None, nx = 200, ny = 200):
if zmin == None:
zmin = min(z)
if zmax == None:
zmax = max(z)
for i in range(len(z)):
z[i] = min(z[i], zmax)
z[i] = max(z[i], zmin)
xi = np.linspace(min(x), max(x), nx)
yi = np.linspace(min(y), max(y), ny)
zi = plt.mlab.griddata(x, y, z, xi, yi)
plt.contourf(xi, yi, zi, 32, cmap=plt.cm.jet) #, norm=plt.Normalize(zmin, zmax))
plt.contourf(xi, yi, zi, 32, norm=plt.Normalize(zmin, zmax))
if 1 == 1:
if (xlim == None):
plt.xlim(-6.0, +6.0)
else:
plt.xlim(xlim[0], xlim[1]);
if (ylim == None):
plt.ylim(-6.0, +6.0)
else:
plt.ylim(ylim[0], ylim[1]);
else:
plt.xlim(-0.5, +0.5)
plt.ylim(-0.5, +0.5)
plt.colorbar()
plt.show()
def plot_teff_logg(self, fig):
fig.clf()
ax = fig.add_subplot(111)
#grid = ax.imshow(self.teff_logg_grid.transpose(), aspect='auto', extent=(self.teffs.min(), self.teffs.max(), self.loggs.min(), self.loggs.max()))
#fig.colorbar(grid)
ax.set_xlabel('Teff')
ax.set_ylabel('log(g)')
X, Y = np.meshgrid(self.teffs, self.loggs)
contours = ax.contourf(X, Y, self.teff_logg_grid)
fig.colorbar(contours)
ax.plot([self.teff], [self.logg], 'rx', mew=3, ms=20, label='polyfit')
ax.plot([self.teff_min], [self.logg_min], 'gx', mew=3, ms=20, label='min')
ax.set_title('teff = %.2f teff_min = %.2f logg = %.2f logg_min = %.2f' %
(self.teff, self.teff_min, self.logg, self.logg_min))
ax.legend(numpoints=1)
