def add_interpolated_colorbar(da, colors, direction):
"""
Add 'rastered' colorbar to DrawingArea
"""
# Special case that arises due to not so useful
# aesthetic mapping.
if len(colors) == 1:
colors = [colors[0], colors[0]]
# Number of horizontal egdes(breaks) in the grid
# No need to create more nbreak than colors, provided
# no. of colors = no. of breaks = no. of cmap colors
# the shading does a perfect interpolation
nbreak = len(colors)
if direction == 'vertical':
mesh_width = 1
mesh_height = nbreak-1
linewidth = da.height/mesh_height
# Construct rectangular meshgrid
# The values(Z) at each vertex are just the
# normalized (onto [0, 1]) vertical distance
x = np.array([0, da.width])
y = np.arange(0, nbreak) * linewidth
X, Y = np.meshgrid(x, y)
Z = Y/y.max()
else:
mesh_width = nbreak-1
mesh_height = 1
linewidth = da.width/mesh_width
x = np.arange(0, nbreak) * linewidth
y = np.array([0, da.height])
X, Y = np.meshgrid(x, y)
Z = X/x.max()
# As a 2D coordinates array
coordinates = np.zeros(
((mesh_width+1)*(mesh_height+1), 2),
dtype=float)
coordinates[:, 0] = X.ravel()
coordinates[:, 1] = Y.ravel()
cmap = ListedColormap(colors)
coll = mcoll.QuadMesh(mesh_width, mesh_height,
coordinates,
antialiased=False,
shading='gouraud',
linewidth=0,
cmap=cmap,
array=Z.ravel())
da.add_artist(coll)
def drawColouredQuadMaps(plt, decoUser, (nelem, npoin, ndp, nplan), (x, y,
ikle, z)):
# ~~> Focus on current subplot / axes instance
crax = plt.gca()
# ~~> Plot data
colourmap = cm.jet
#if 'cmapPlot' in geometry:
# colourmap = LinearSegmentedColormap('User', getColourMap(geometry['cmapPlot']))
zmin = np.min(z)
zmax = np.max(z)
nx = npoin - nelem
ny = int(npoin / nx)
mesh = np.column_stack((x, y))
msh = collections.QuadMesh(nx - 1, ny - 1, mesh, True, shading='gouraud')
#msh.set_array(np.zeros(self.x_cells*self.y_cells))
#msh.set_array(np.array(self.FD.GetTimestepData(0)))
#msh.set_clim(0.0, 1.0)
#axis.axis([0, self.x_max, 0, self.y_top])
#plt.colorbar(self.cax)
###!!! I have tried cax and msh, and various combos
#toolbar.show()
#canvas.draw()
msh.set_array(z)
crax.add_collection(msh)
#cs = plt.tricontour(x,y,ikle, z, linewidths=0.5, colors='k')
#ex: colors='k' or colors=('r', 'g', 'b', (1,1,0), '#afeeee', '1')
#plt.tricontourf(x,y,ikle, z, cmap=colourmap)
# adds numbers along the iso-contours
def exemplo_8():
Fs = 250.0
Ts = 1 / Fs
start = 2000.0
end = 3000.0
depth = np.arange(start, end + Ts, Ts)
freq = 60.0
amp = 20
data2 = amp * np.sin(depth * 2 * np.pi * freq)
print(depth, len(depth))
t02 = 2200.0
t12 = 2400.0
time2 = np.arange(t02, t12, Ts)
i, = np.where(depth == t02)
j, = np.where(depth == t12)
print(i, j)
f2 = 100.0 #/128.0
x2 = np.sin(time2 * 2 * np.pi * f2)
print(len(x2))
# data2[50000:100000] = 1#+= x2
print('\nWaveletTransform')
c = Chronometer()
factor = 1
Dj = 0.500 / factor
wa = WaveletTransform(data2, dt=Ts, dj=Dj, time=depth) #dt=Ts)
print(c.end())
"""
# Phase
wt = np.copy(wa.wavelet_transform)
threshold = np.max(np.abs(wt))/10
wt[np.where(np.abs(wt) < threshold)] = 0
to_plot = np.unwrap(np.angle(wt))
#to_plot = np.angle(wt)
"""
"""
# Magnitude
to_plot = np.abs(wa.wavelet_transform)
"""
#"""
# Power
to_plot = wa.wavelet_power
#"""
print('\nmeshgrid')
c = Chronometer()
x_axis = wa.fourier_frequencies
y_axis = wa.time
mesh_x, mesh_y = np.meshgrid(x_axis, y_axis)
print(c.end())
'''
for x in mesh_x:
print
print x, len(x)
print '\n\n\n'
for y in mesh_y:
print
print y, len(y)
'''
print('\ncontourf')
c = Chronometer()
fig, ax = plt.subplots(ncols=1, figsize=(8, 4))
#####
import matplotlib.collections as mcoll
import matplotlib.colors as mcolors
import matplotlib.transforms as mtransforms
kwargs = {}
alpha = None
norm = None
cmap = plt.cm.jet
vmin = None
vmax = None
shading = 'flat'
antialiased = False
kwargs.setdefault('edgecolors', 'None')
allmatch = False
X, Y, C = _pcolorargs('pcolormesh',
mesh_x,
mesh_y,
to_plot.T,
allmatch=allmatch)
Ny, Nx = X.shape
# convert to one dimensional arrays
C = C.ravel()
X = X.ravel()
Y = Y.ravel()
#print X
#print Y
#raise Exception()
# unit conversion allows e.g. datetime objects as axis values
ax._process_unit_info(xdata=X, ydata=Y, kwargs=kwargs)
X = ax.convert_xunits(X)
Y = ax.convert_yunits(Y)
'''
print '\n'
for x in X:
print x
print '\n'
'''
print('\n')
print(X[0], X[-1], len(X))
print('\n')
coords = np.zeros(((Nx * Ny), 2), dtype=float)
coords[:, 0] = X
coords[:, 1] = Y
collection = mcoll.QuadMesh(Nx - 1,
Ny - 1,
coords,
antialiased=antialiased,
shading=shading,
**kwargs)
collection.set_alpha(alpha)
collection.set_array(C)
if norm is not None and not isinstance(norm, mcolors.Normalize):
msg = "'norm' must be an instance of 'mcolors.Normalize'"
raise ValueError(msg)
collection.set_cmap(cmap)
collection.set_norm(norm)
collection.set_clim(vmin, vmax)
collection.autoscale_None()
ax.grid(False)
"""
# Transform from native to data coordinates?
t = collection._transform
print '\nt:', t
if (not isinstance(t, mtransforms.Transform) and hasattr(t, '_as_mpl_transform')):
print 'E1'
t = t._as_mpl_transform(ax.axes)
if t and any(t.contains_branch_seperately(ax.transData)):
trans_to_data = t - ax.transData
pts = np.vstack([X, Y]).T.astype(np.float)
transformed_pts = trans_to_data.transform(pts)
X = transformed_pts[..., 0]
Y = transformed_pts[..., 1]
"""
ax.add_collection(collection, autolim=False)
minx = np.amin(X)
maxx = np.amax(X)
miny = np.amin(Y)
maxy = np.amax(Y)
collection.sticky_edges.x[:] = [minx, maxx]
collection.sticky_edges.y[:] = [miny, maxy]
corners = (minx, miny), (maxx, maxy)
print(corners)
ax.update_datalim(corners)
ax.autoscale_view()
ret = collection
#####
#ax2.pcolormesh(x, y, np.flipud(data))
#ret = ax.pcolormesh(mesh_x, mesh_y, to_plot.T, cmap=plt.cm.jet)
#ret = plt.contourf(mesh_x, mesh_y, to_plot.T, 20, cmap=plt.cm.jet)#, 100, cmap=plt.cm.jet)
print(c.end())
print(ret)
#plt.xscale('log')
#plt.xlim(x_axis[-1], x_axis[0])
plt.ylim(y_axis[-1], y_axis[0])
#plt.grid(True)
plt.show()
def pcolormesh(self, *args, **kwargs):
import warnings
import numpy as np
import numpy.ma as ma
import matplotlib as mpl
import matplotlib.cbook as cbook
import matplotlib.colors as mcolors
import matplotlib.cm as cm
from matplotlib import docstring
import matplotlib.transforms as transforms
import matplotlib.artist as artist
from matplotlib.artist import allow_rasterization
import matplotlib.backend_bases as backend_bases
import matplotlib.path as mpath
import matplotlib.mlab as mlab
import matplotlib.collections as mcoll
if not self._hold: self.cla()
alpha = kwargs.pop('alpha', None)
norm = kwargs.pop('norm', None)
cmap = kwargs.pop('cmap', None)
vmin = kwargs.pop('vmin', None)
vmax = kwargs.pop('vmax', None)
shading = kwargs.pop('shading', 'flat').lower()
antialiased = kwargs.pop('antialiased', False)
kwargs.setdefault('edgecolors', 'None')
X, Y, C = self._pcolorargs('pcolormesh', *args)
Ny, Nx = X.shape
# convert to one dimensional arrays
if shading != 'gouraud':
C = ma.ravel(C[0:Ny-1, 0:Nx-1]) # data point in each cell is value at
# lower left corner
else:
C = C.ravel()
X = X.ravel()
Y = Y.ravel()
coords = np.zeros(((Nx * Ny), 2), dtype=float)
coords[:, 0] = X
coords[:, 1] = Y
collection = mcoll.QuadMesh(
Nx - 1, Ny - 1, coords,
antialiased=antialiased, shading=shading, **kwargs)
collection.set_alpha(alpha)
collection.set_array(C)
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
collection.set_cmap(cmap)
collection.set_norm(norm)
collection.set_clim(vmin, vmax)
collection.autoscale_None()
self.grid(False)
# Transform from native to data coordinates?
t = collection._transform
if (not isinstance(t, mtransforms.Transform)
and hasattr(t, '_as_mpl_transform')):
t = t._as_mpl_transform(self.axes)
if t and any(t.contains_branch_seperately(self.transData)):
trans_to_data = t - self.transData
pts = np.vstack([X, Y]).T.astype(np.float)
transformed_pts = trans_to_data.transform(pts)
X = transformed_pts[..., 0]
Y = transformed_pts[..., 1]
# XXX Not a mpl 1.2 thing...
no_inf = (X != np.inf) & (Y != np.inf)
X = X[no_inf]
Y = Y[no_inf]
minx = np.amin(X)
maxx = np.amax(X)
miny = np.amin(Y)
maxy = np.amax(Y)
corners = (minx, miny), (maxx, maxy)
self.update_datalim( corners)
self.autoscale_view()
self.add_collection(collection)
return collection
