def _update3(itr,D,x,soln,t,ax,time):
N = 100
#ax.clear()
buff = 200.0
minx = np.min(x[:,0])
maxx = np.max(x[:,0])
miny = np.min(x[:,1])
maxy = np.max(x[:,1])
ax.set_xlim((minx-buff/2,maxx+buff/2))
ax.set_ylim((miny-buff/2,maxy+buff/2))
square = Polygon([(minx-buff,miny-buff),
(minx-buff,maxy+buff),
(maxx+buff,maxy+buff),
(maxx+buff,miny-buff),
(minx-buff,miny-buff)])
ax.add_artist(PolygonPatch(square.difference(D),alpha=1.0,color='k',zorder=1))
xitp = np.linspace(minx,maxx,N)
yitp = np.linspace(miny,maxy,N)
xgrid,ygrid = np.meshgrid(xitp,yitp)
xflat = xgrid.flatten()
yflat = ygrid.flatten()
ax.images = []
im =NonUniformImage(ax,interpolation='bilinear',
cmap='cubehelix',
extent=(minx,maxx,miny,maxy))
val = soln[itr](zip(xflat,yflat))
val = np.sqrt(np.sum(val**2,1))
im.set_data(xitp,yitp,np.reshape(val,(N,N)))
ax.images.append(im)
t.set_text('t: %s s' % time[itr])
return ax,t
def plot_spectrogram(spec, Xd=(0,1), Yd=(0,1)):
import matplotlib
#matplotlib.use('GTKAgg')
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.image import NonUniformImage
import matplotlib.colors as colo
#
x_min, x_max = Xd
y_min, y_max = Yd
#
fig = plt.figure()
nf = len(spec)
for ch, data in enumerate(spec):
#print ch, data.shape
x = numpy.linspace(x_min, x_max, data.shape[0])
y = numpy.linspace(y_min, y_max, data.shape[1])
#print x[0],x[-1],y[0],y[-1]
ax = fig.add_subplot(nf*100+11+ch)
im = NonUniformImage(ax, interpolation='bilinear', cmap=cm.gray_r,
norm=colo.LogNorm(vmin=.00001))
im.set_data(x, y, data.T)
ax.images.append(im)
ax.set_xlim(x_min, x_max)
ax.set_ylim(y_min, y_max)
ax.set_title('Channel %d' % ch)
#ax.set_xlabel('timeline')
ax.set_ylabel('frequency')
print 'Statistics: max<%.3f> min<%.3f> mean<%.3f> median<%.3f>' % (data.max(), data.min(), data.mean(), numpy.median(data))
#
plt.show()
def _do_plot2(x, y, z, fname, min_pitch):
fig = figure(figsize=(15,7.5+7.5/2))
#fig.suptitle('Narmour')
ax = fig.add_subplot(111)
im = NonUniformImage(ax, interpolation=None, extent=(min(x)-1, max(x)+1, min(y)-1, max(y)+1))
im.set_data(x, y, z)
ax.images.append(im)
ax.set_xlim(min(x)-1, max(x)+1)
ax.set_ylim(min(y)-1, max(y)+1)
def format_pitch(i, pos=None):
if int(i) != i: import ipdb;ipdb.set_trace()
return Note(int(i + min_pitch)%12).get_pitch_name()[:-1]
ax.set_xlabel('Segunda nota')
ax.axes.xaxis.set_major_formatter(ticker.FuncFormatter(format_pitch))
ax.axes.xaxis.set_major_locator(ticker.MultipleLocator(base=1.0))
ax.set_ylabel('Primer nota')
ax.axes.yaxis.set_major_formatter(ticker.FuncFormatter(format_pitch))
ax.axes.yaxis.set_major_locator(ticker.MultipleLocator())
cb = plt.colorbar(im)
pylab.grid(True)
pylab.savefig(fname)
pylab.close()
def sec_show(coord, depth, data, axe, cmap='viridis', coord_type='lon'):
"""
plot data on the axe, mix between imshow and contour
return the colorbar, so user can add a label
"""
depth = -np.abs(depth) # to be sure that depth is negative
im = NonUniformImage(axe, interpolation='nearest', \
extent=(np.nanmin(coord), np.nanmax(coord), \
np.nanmin(depth), np.nanmax(depth)), \
cmap=cmap)
# we need to reverse order of array so everything goes increasing
# xx,yy=np.meshgrid(coord,depth)
# contours = plt.contour(xx,yy, data.T, 3, colors='black')
# plt.clabel(contours, inline=True, fontsize=8)
im.set_data(coord, depth[::-1], data.T[::-1, :])
axe.images.append(im)
axe.set_xlim(np.nanmin(coord), np.nanmax(coord))
axe.set_ylim(np.nanmin(depth), np.nanmax(depth))
axe.set_ylabel('Depth (m)')
axe.set_xlabel(coord_type + u' ($^{\circ}$ E)')
# adding colorbar
cb = plt.colorbar(im, ax=axe, extend='both')
# printing scale with positive depth
axe.set_yticklabels(np.abs([int(i) for i in axe.get_yticks()]))
return cb
def plot_spectrogram(spec,
Xd=(0, 1),
Yd=(0, 1),
norm=colo.LogNorm(vmin=0.000001),
figname=None):
#
x_min, x_max = Xd
y_min, y_max = Yd
#
fig = plt.figure(num=figname)
nf = len(spec)
for ch, data in enumerate(spec):
#print ch, data.shape
x = np.linspace(x_min, x_max, data.shape[0])
y = np.linspace(y_min, y_max, data.shape[1])
#print x[0],x[-1],y[0],y[-1]
ax = fig.add_subplot(nf * 100 + 11 + ch)
im = NonUniformImage(ax,
interpolation='bilinear',
cmap=cm.gray_r,
norm=norm)
im.set_data(x, y, data.T)
ax.images.append(im)
ax.set_xlim(x_min, x_max)
ax.set_ylim(y_min, y_max)
ax.set_title('Channel %d' % ch)
#ax.set_xlabel('timeline')
ax.set_ylabel('frequency')
print 'Statistics: max<%.3f> min<%.3f> mean<%.3f> median<%.3f>' % (
data.max(), data.min(), data.mean(), np.median(data))
#
plt.show()
def plotDensity(ax, x, result):
N = len(result["moments"][0])
y = np.linspace(0, 1, len(result["density"][0]))
z = np.log(1 + np.array(zip(*result["density"])))
im = NonUniformImage(ax, norm=Normalize(0, 5, clip=True), interpolation="nearest", cmap=cm.Greys)
im.set_data(x, y, z)
ax.images.append(im)
def plot_spectrogram(spec, Xd=(0,1), Yd=(0,1), norm=colo.LogNorm(vmin=0.000001), figname=None):
#
x_min, x_max = Xd
y_min, y_max = Yd
#
fig = plt.figure(num=figname)
nf = len(spec)
for ch, data in enumerate(spec):
#print ch, data.shape
x = np.linspace(x_min, x_max, data.shape[0])
y = np.linspace(y_min, y_max, data.shape[1])
#print x[0],x[-1],y[0],y[-1]
ax = fig.add_subplot(nf*100+11+ch)
im = NonUniformImage(ax, interpolation='bilinear', cmap=cm.gray_r,
norm=norm)
im.set_data(x, y, data.T)
ax.images.append(im)
ax.set_xlim(x_min, x_max)
ax.set_ylim(y_min, y_max)
ax.set_title('Channel %d' % ch)
#ax.set_xlabel('timeline')
ax.set_ylabel('frequency')
print 'Statistics: max<%.3f> min<%.3f> mean<%.3f> median<%.3f>' % (data.max(), data.min(), data.mean(), np.median(data))
#
plt.show()
def test_nonuniformimage_setdata():
ax = plt.gca()
im = NonUniformImage(ax)
x = np.arange(3, dtype=float)
y = np.arange(4, dtype=float)
z = np.arange(12, dtype=float).reshape((4, 3))
im.set_data(x, y, z)
x[0] = y[0] = z[0, 0] = 9.9
assert im._A[0, 0] == im._Ax[0] == im._Ay[0] == 0, 'value changed'
def test_nonuniformimage_setdata():
ax = plt.gca()
im = NonUniformImage(ax)
x = np.arange(3, dtype=np.float64)
y = np.arange(4, dtype=np.float64)
z = np.arange(12, dtype=np.float64).reshape((4, 3))
im.set_data(x, y, z)
x[0] = y[0] = z[0, 0] = 9.9
assert im._A[0, 0] == im._Ax[0] == im._Ay[0] == 0, 'value changed'
def NonUnifIm(ax, x, y, z, xlab, ylab, **kwargs):
im = NonUniformImage(ax, **kwargs)
im.set_data(x, y, z)
# im.set_clim(vmin=vmin, vmax=vmax)
ref = ax.images.append(im)
ax.set_xlim([x.min(), x.max()])
ax.set_ylim([y.min(), y.max()])
ax.set_xlabel(xlab)
ax.set_ylabel(ylab)
return ref, im
def test_nonuniform_and_pcolor():
axs = plt.figure(figsize=(3, 3)).subplots(3, sharex=True, sharey=True)
for ax, interpolation in zip(axs, ["nearest", "bilinear"]):
im = NonUniformImage(ax, interpolation=interpolation)
im.set_data(np.arange(3) ** 2, np.arange(3) ** 2,
np.arange(9).reshape((3, 3)))
ax.add_image(im)
axs[2].pcolorfast( # PcolorImage
np.arange(4) ** 2, np.arange(4) ** 2, np.arange(9).reshape((3, 3)))
for ax in axs:
ax.set_axis_off()
# NonUniformImage "leaks" out of extents, not PColorImage.
ax.set(xlim=(0, 10))
def show2(mat):
clf()
ax = gca()
from matplotlib.image import NonUniformImage
#ax = fig.add_subplot(133, title='NonUniformImage: interpolated',
#aspect='equal', xlim=xedges[[0, -1]], ylim=yedges[[0, -1]])
im = NonUniformImage(ax, interpolation='bilinear')
xcenters = (xedges[:-1] + xedges[1:]) / 2
ycenters = (yedges[:-1] + yedges[1:]) / 2
im.set_data(xcenters, ycenters, mat)
ax.images.append(im)
xlim(xedges[[0, -1]])
ylim(yedges[[0, -1]])
def plot_interpolant(D,interp,x,title='',dim=1,ax=None,scatter=False):
if ax is None:
fig,ax = plt.subplots()
plt.gca().set_aspect('equal', adjustable='box')
ax.set_title(title)
buff = 400.0
N = 150
minx = np.min(x[:,0])
maxx = np.max(x[:,0])
miny = np.min(x[:,1])
maxy = np.max(x[:,1])
square = Polygon([(minx-buff,miny-buff),
(minx-buff,maxy+buff),
(maxx+buff,maxy+buff),
(maxx+buff,miny-buff),
(minx-buff,miny-buff)])
ax.add_artist(PolygonPatch(square.difference(D),alpha=1.0,color='k',zorder=1))
ax.set_xlim((minx-buff,maxx+buff))
ax.set_ylim((miny-buff,maxy+buff))
if dim == 1:
xitp = np.linspace(minx,maxx,N)
yitp = np.linspace(miny,maxy,N)
xgrid,ygrid = np.meshgrid(xitp,yitp)
xflat = xgrid.flatten()
yflat = ygrid.flatten()
points = np.zeros((len(xflat),2))
points[:,0] = xflat
points[:,1] = yflat
val = interp(points)
#val[(np.sqrt(xflat**2+yflat**2) > 6371),:] = 0.0
im =NonUniformImage(ax,interpolation='bilinear',cmap='cubehelix_r',extent=(minx,maxx,miny,maxy))
im.set_data(xitp,yitp,np.reshape(val,(N,N)))
ax.images.append(im)
if scatter == True:
p = ax.scatter(x[:,0],
x[:,1],
c='gray',edgecolor='none',zorder=2,s=10)
cbar = plt.colorbar(im)
if dim == 2:
ax.quiver(x[::3,0],x[::3,1],interp(x)[::3,0],interp(x)[::3,1],color='gray',scale=4000.0,zorder=20)
return ax
def execute(self):
pylab.ioff()
self.figure = pylab.figure()
self.figure.canvas.mpl_connect('motion_notify_event', self.dataPrinter)
x = self.fieldContainer.dimensions[-1].data
y = self.fieldContainer.dimensions[-2].data
xmin=scipy.amin(x)
xmax=scipy.amax(x)
ymin=scipy.amin(y)
ymax=scipy.amax(y)
#Support for images with non uniform axes adapted
#from python-matplotlib-doc/examples/pcolor_nonuniform.py
ax = self.figure.add_subplot(111)
vmin = self.fieldContainer.attributes.get('vmin', None)
vmax = self.fieldContainer.attributes.get('vmax', None)
if vmin is not None:
vmin /= self.fieldContainer.unit
if vmax is not None:
vmax /= self.fieldContainer.unit
if MPL_LT_0_98_1 or self.fieldContainer.isLinearlyDiscretised():
pylab.imshow(self.fieldContainer.maskedData,
aspect='auto',
interpolation='nearest',
vmin=vmin,
vmax=vmax,
origin='lower',
extent=(xmin, xmax, ymin, ymax))
pylab.colorbar(format=F(self.fieldContainer), ax=ax)
else:
im = NonUniformImage(ax, extent=(xmin,xmax,ymin,ymax))
if vmin is not None or vmax is not None:
im.set_clim(vmin, vmax)
im.set_data(x, y, self.fieldContainer.maskedData)
else:
im.set_data(x, y, self.fieldContainer.maskedData)
im.autoscale_None()
ax.images.append(im)
ax.set_xlim(xmin,xmax)
ax.set_ylim(ymin,ymax)
pylab.colorbar(im,format=F(self.fieldContainer), ax=ax)
pylab.xlabel(self.fieldContainer.dimensions[-1].shortlabel)
pylab.ylabel(self.fieldContainer.dimensions[-2].shortlabel)
pylab.title(self.fieldContainer.label)
#ax=pylab.gca()
if self.show:
pylab.ion()
pylab.show()
def Graf_Flujo(Vf, K, l, a, h, H):
#Matriz del Marco
Mx = int(l / 0.05)
LadoMx = np.arange(1, Mx + 1, 1)
My = int(a / 0.05) + 1
LadoMy = np.arange(1, My + 1, 1)
M = np.ones([My, Mx])
#Extraigo los datos
mod = modelo(K, l, a, h)
Qs = mod[2]
TsFlujo = mod[4]
Hi = H
Tmin = TsFlujo[Hi - 2]
QsAn = Qs[Hi - 1:Hi + 3]
#Calor en cada celda
Qi = QsAn[0] / (Mx * My)
#Incremento de T
Tadd = Qi / (Vf)
Tper = ((K * Qi) / h)
#Primera columna con Tmin
for i in range(My):
M[i, 0] = Tmin
#print(M)
#Tfinal en cada celda
for j in range(int(My / 2) + 1):
for i in range(1, Mx):
M[j, i] = M[j, i - 1] + Tadd
M[My - 1 - j, i] = M[j, i]
M[j] = M[j] - (Tper * (int(My / 2) - j))
M[My - 1 - j] = M[j]
#print(M)
#grafico del flujo
fig, axs = plt.subplots(figsize=(10, 5))
im = NonUniformImage(axs,
interpolation='bilinear',
extent=(-4, 4, -4, 4),
cmap='plasma')
im.set_data(LadoMx, LadoMy, M)
axs.images.append(im)
axs.set_xlim(0, Mx - 1)
axs.set_ylim(0, My - 1)
axs.set_title('Temperatura del Flujo de Aire dentro del Colector (con K=' +
str(K) + ")",
fontsize=14)
axs.set_xlabel('Longitud (x0.05m)')
axs.set_ylabel('Ancho (x0.05m)')
def sec_show_dens(coord,
depth,
dens,
data,
axe,
cmap='viridis',
coord_type='lon'):
"""
plot data on the axe, mix between imshow and contour
data reordered to plot against density instead of depth
return the colorbar, so user can add a label
"""
depth = -np.abs(depth)
# to be sure that depth is negative
print(dens.shape)
densSort = dens[6, :] * -1
ind = np.argsort(densSort)
densSorted = (densSort[ind] + 1000)
data_along_dens = data[:, ind]
print(np.nanmax(densSorted))
im = NonUniformImage(axe, interpolation='nearest', \
extent=(np.nanmin(coord), np.nanmax(coord), \
np.nanmax(densSorted), np.nanmin(densSorted)), \
cmap=cmap,origin='upper')
# we need to reverse order of array so everything goes increasing
im.set_data(coord, densSorted, data_along_dens.T)
axe.images.append(im)
axe.set_xlim(np.nanmin(coord), np.nanmax(coord))
axe.set_ylim(np.nanmin(densSorted), np.nanmax(densSorted))
axe.set_ylabel('Density (kg/m3)')
axe.set_xlabel(coord_type + u' ($^{\circ}$ N/E)')
# adding colorbar
cb = plt.colorbar(im, ax=axe, extend='both')
# printing scale with positive depth
axe.set_yticklabels(np.abs([int(i) for i in axe.get_yticks()]))
return cb
def plot_interpolant(D,interp,x,title='figure'):
buff = 100.0
fig,ax = plt.subplots()
plt.gca().set_aspect('equal', adjustable='box')
plt.title(title,fontsize=16)
N = 200
minx = np.min(x[:,0])
maxx = np.max(x[:,0])
miny = np.min(x[:,1])
maxy = np.max(x[:,1])
xitp = np.linspace(minx,maxx,N)
yitp = np.linspace(miny,maxy,N)
xgrid,ygrid = np.meshgrid(xitp,yitp)
xflat = xgrid.flatten()
yflat = ygrid.flatten()
points = np.zeros((len(xflat),2))
points[:,0] = xflat
points[:,1] = yflat
val = interp(points)
val[(np.sqrt(xflat**2+yflat**2) > 6371),:] = 0.0
square = Polygon([(minx-buff,miny-buff),
(minx-buff,maxy+buff),
(maxx+buff,maxy+buff),
(maxx+buff,miny-buff),
(minx-buff,miny-buff)])
#help(D)
im =NonUniformImage(ax,interpolation='bilinear',cmap='cubehelix',extent=(minx,maxx,miny,maxy))
im.set_data(xitp,yitp,np.reshape(val,(N,N)))
ax.images.append(im)
ax.add_artist(PolygonPatch(square.difference(D),alpha=1.0,color='k',zorder=1))
p = ax.scatter(x[:,0],
x[:,1],
c='gray',edgecolor='none',zorder=2,s=10)
cbar = plt.colorbar(im)
cbar.ax.set_ylabel(title)
ax.set_xlim((minx-buff,maxx+buff))
ax.set_ylim((miny-buff,maxy+buff))
#fig.colorbar(p)
return fig
def _do_plot(x, y, z, fname, max_interval, reference_note=None):
fig = figure(figsize=(15,7.5+7.5/2))
#fig.suptitle('Narmour')
ax = fig.add_subplot(111)
im = NonUniformImage(ax, interpolation=None, extent=(min(x), max(x), min(y), max(y)))
im.set_data(x, y, z)
ax.images.append(im)
ax.set_xlim(min(x), max(x))
ax.set_ylim(min(y), max(y))
def format_interval_w_ref_note(reference_note):
def format_interval(i, pos=None):
if int(i) != i: import ipdb;ipdb.set_trace()
return Note(int(reference_note.pitch + i - max_interval-1)%12).get_pitch_name()[:-1]
return format_interval
def format_interval_wo_ref_note(x, pos=None):
if int(x) != x: import ipdb;ipdb.set_trace()
return int(x-max_interval-1)
if reference_note is not None:
format_interval= format_interval_w_ref_note(reference_note)
else:
format_interval= format_interval_wo_ref_note
ax.set_xlabel('Intervalo realizado')
ax.axes.xaxis.set_major_formatter(ticker.FuncFormatter(format_interval_wo_ref_note))
ax.axes.xaxis.set_major_locator(ticker.MultipleLocator(base=1.0))
if reference_note is not None:
ax.set_ylabel('Segunda nota')
else:
ax.set_ylabel('Intervalo implicativo')
ax.axes.yaxis.set_major_formatter(ticker.FuncFormatter(format_interval))
ax.axes.yaxis.set_major_locator(ticker.MultipleLocator())
cb = plt.colorbar(im)
pylab.grid(True)
pylab.savefig(fname)
pylab.close()
def plot_nphist2d(ax, hist, bins, **kwargs):
"""Draw 2D numpy histogram as a 2D image
Parameters
----------
ax : Axes
Matplotlib axes on which histogram will be plotted.
hist : ndarray, shape (N,M)
Histogram to be plotted.
bins : (ndarray, ndarray)
Bind edges for the histograms.
kwargs : dict, optional
Additional parameters to pass directly to the matplotlib
NonUniformImage function
Returns
-------
NonUniformImage
Matplotlib NonUniformImage that depicts `hist`.
"""
bins_x = bins[0]
bins_y = bins[1]
range_x = (bins_x[0], bins_x[-1])
range_y = (bins_y[0], bins_y[-1])
im = NonUniformImage(
ax, origin = 'lower', extent = range_x + range_y, **kwargs
)
centers_x = (bins_x[1:] + bins_x[:-1]) / 2
centers_y = (bins_y[1:] + bins_y[:-1]) / 2
im.set_data(centers_x, centers_y, hist.T)
ax.images.append(im)
ax.set_xlim(range_x)
ax.set_ylim(range_y)
return im
def plot_img(img, filename='image.png', xlim=None, ylim=None, title="", xlabel="", ylabel=""):
#
if not xlim: xlim = (0, img.shape[1] - 1)
if not ylim: ylim = (0, img.shape[0] - 1)
x = numpy.linspace(xlim[0], xlim[1], img.shape[1])
y = numpy.linspace(ylim[0], ylim[1], img.shape[0])
#
fig = plt.figure()
ax = fig.add_subplot(111)
im = NonUniformImage(ax, cmap=cm.Greys)#, norm=colo.LogNorm(vmin=.00001))
im.set_data(x, y, img)
ax.images.append(im)
#
ax.set_xlim(*xlim)
ax.set_ylim(*ylim)
if title: ax.set_title(title)
if xlabel: ax.set_xlabel(xlabel)
if ylabel: ax.set_ylabel(ylabel)
#
plt.show()
plt.savefig(filename)
def show_plot(self,
x_data=(),
y_data=(),
z_data=(),
values=(),
x_label=None,
y_label=None,
title=None,
cmap=None,
interpolation="nearest"):
if (len(values) > 0):
assert (len(x_data) == len(y_data) == len(z_data) == 0)
(x_data, y_data, z_data) = convert_xyz_value_list(values)
from matplotlib.image import NonUniformImage
self.figure.clear()
ax = self.figure.add_subplot(111)
im = NonUniformImage(ax, interpolation=interpolation)
if (cmap is not None):
im.set_cmap(get_colormap(cmap))
im.set_data(x_data, y_data, z_data)
ax.images.append(im)
ax.set_xlim(x_data[0], x_data[-1])
ax.set_ylim(y_data[0], y_data[-1])
if (x_label is not None):
ax.set_xlabel(x_label)
if (y_label is not None):
ax.set_ylabel(y_label)
if (title is not None):
ax.set_title(title)
self.canvas.draw()
def plot_stacked_time_series_image(self, fig, ax, x, y, z, title='', ylabel='',
cbar_title='', title_font={}, axis_font={}, tick_font = {},
**kwargs):
'''
This plot is a stacked time series that uses NonUniformImage with regualrly spaced ydata from
a linear interpolation. Designed to support FRF ADCP data.
'''
if not title_font:
title_font = title_font_default
if not axis_font:
axis_font = axis_font_default
# z = np.ma.array(z, mask=np.isnan(z))
h = NonUniformImage(ax, interpolation='bilinear', extent=(min(x), max(x), min(y), max(y)),
cmap=plt.cm.jet)
h.set_data(x, y, z)
ax.images.append(h)
ax.set_xlim(min(x), max(x))
ax.set_ylim(min(y), max(y))
# h = plt.pcolormesh(x, y, z, shading='gouraud', **kwargs)
# h = plt.pcolormesh(x, y, z, **kwargs)
if ylabel:
ax.set_ylabel(ylabel, **axis_font)
if title:
ax.set_title(title, **title_font)
# plt.axis('tight')
ax.xaxis_date()
date_list = mdates.num2date(x)
self.get_time_label(ax, date_list)
fig.autofmt_xdate()
# if invert:
ax.invert_yaxis()
cbar = plt.colorbar(h)
if cbar_title:
cbar.ax.set_ylabel(cbar_title, **axis_font)
ax.grid(True)
if tick_font:
ax.tick_params(**tick_font)
def twoD_scalar(x, y, i0, fig, ax, **kwargs):
reverse = [False, False]
if "reverse_axis" in kwargs.keys():
reverse = kwargs["reverse_axis"]
kwargs.pop("reverse_axis")
i = int(i0 / 2)
j = i0 % 2
x0 = x[0].coordinates.value
x1 = x[1].coordinates.value
y00 = y[i0].components[0].real.astype(np.float64)
extent = [x0[0], x0[-1], x1[0], x1[-1]]
if x[0].type == "linear" and x[1].type == "linear":
# print('uniform')
cs = ax[i][j].imshow(
y00, extent=extent, origin="lower", aspect="auto", **kwargs
)
else:
# print('non-uniform')
cs = NonUniformImage(ax[i][j], interpolation="none", extent=extent, **kwargs)
cs.set_data(x0, x1, y00 / y00.max())
ax[i][j].images.append(cs)
cbar = fig.colorbar(cs, ax=ax[i][j])
cbar.ax.minorticks_off()
cbar.set_label(y[i0].axis_label[0])
ax[i][j].set_xlim([extent[0], extent[1]])
ax[i][j].set_ylim([extent[2], extent[3]])
ax[i][j].set_xlabel(f"{x[0].axis_label} - 0")
ax[i][j].set_ylabel(f"{x[1].axis_label} - 1")
ax[i][j].set_title("{0}".format(y[i0].name))
ax[i][j].grid(color="gray", linestyle="--", linewidth=0.5)
if reverse[0]:
ax[i][j].invert_xaxis()
if reverse[1]:
ax[i][j].invert_yaxis()
def plot_image(x, y, fig, ax):
# if x[0].type != 'labeled':
# extent = [x[0].coordinates[0].value, x[0].coordinates[-1].value,
# x[1].coordinates[0].value, x[1].coordinates[-1].value]
x0 = x[0].coordinates.value
x1 = x[1].coordinates.value
y00 = y.components[0].real.astype(np.float64).real
extent = [x0[0], x0[-1], x1[1], x1[-1]]
if x[0].type == "linear" and x[1].type == "linear":
# print('uniform')
cs = ax.imshow(y00, extent=extent, origin="lower", aspect="auto", cmap="Blues")
else:
# print('non-uniform')
cs = NonUniformImage(ax, interpolation="nearest", extent=extent, cmap="bone_r")
cs.set_data(x0, x1, y00 / y00.max())
ax.images.append(cs)
# cs = ax.imshow(
# y00,
# extent=extent,
# origin='lower',
# aspect='auto',
# cmap='viridis',
# interpolation='none'
# )
cbar = fig.colorbar(cs, ax=ax)
cbar.ax.minorticks_off()
cbar.set_label(y.axis_label[0])
ax.set_xlim([extent[0], extent[1]])
ax.set_ylim([extent[2], extent[3]])
ax.set_xlabel(f"{x[0].axis_label} - 0")
ax.set_ylabel(f"{x[1].axis_label} - 1")
ax.set_title("{0}".format(y.name))
ax.grid(color="gray", linestyle="--", linewidth=0.1)
def plot_time_frequency(spectrum, interpolation='bilinear',
background_color=None, clim=None, dbscale=True, **kwargs):
"""
Time-frequency plot. Modeled after image_nonuniform.py example
spectrum is a dataframe with frequencies in columns and time in rows
"""
if spectrum is None:
return None
times = spectrum.index
freqs = spectrum.columns
if dbscale:
z = 10 * np.log10(spectrum.T)
else:
z = spectrum.T
ax = plt.figure().add_subplot(111)
extent = (times[0], times[-1], freqs[0], freqs[-1])
im = NonUniformImage(ax, interpolation=interpolation, extent=extent)
if background_color:
im.get_cmap().set_bad(kwargs['background_color'])
else:
z[np.isnan(z)] = 0.0 # replace missing values with 0 color
if clim:
im.set_clim(clim)
if 'cmap' in kwargs:
im.set_cmap(kwargs['cmap'])
im.set_data(times, freqs, z)
ax.set_xlim(extent[0], extent[1])
ax.set_ylim(extent[2], extent[3])
ax.images.append(im)
if 'colorbar_label' in kwargs:
plt.colorbar(im, label=kwargs['colorbar_label'])
else:
plt.colorbar(im, label='Power (dB/Hz)')
plt.xlabel('Time (s)')
plt.ylabel('Frequency (Hz)')
return plt.gcf()
def plot2d(x, y, z, ax=None, cmap='RdGy', norm=None, **kw):
""" Plot dataset using NonUniformImage class
Parameters
----------
x : (nx,)
y : (ny,)
z : (nx,nz)
"""
from matplotlib.image import NonUniformImage
if ax is None:
fig = plt.gcf()
ax = fig.add_subplot(111)
xlim = (x.min(), x.max())
ylim = (y.min(), y.max())
im = NonUniformImage(ax,
interpolation='bilinear',
extent=xlim + ylim,
cmap=cmap)
if norm is not None:
im.set_norm(norm)
im.set_data(x, y, z, **kw)
ax.images.append(im)
#plt.colorbar(im)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
def update(z):
return im.set_data(x, y, z, **kw)
return im, update
def plot_probe(data,
ax=None,
show_cbar=True,
make_pretty=True,
fig_kwargs=dict(),
line_kwargs=dict()):
"""
Function to create matplotlib plot from ProbePlot object
:param data: ProbePlot object, either class or dict
:param ax: matplotlib axis to plot on, if None, will create figure
:param show_cbar: whether or not to display colour bar
:param make_pretty: get rid of spines on axis
:param fig_kwargs: dict of matplotlib keywords associcated with plt.subplots e.g can be
fig size, tight layout etc.
:param line_kwargs: dict of matplotlib keywords associated with ax.hlines/ax.vlines
:return: matplotlib axis and figure handles
"""
if not isinstance(data, dict):
data = data.convert2dict()
if not ax:
fig, ax = plt.subplots(figsize=(2, 8), **fig_kwargs)
else:
fig = plt.gcf()
for (x, y, dat) in zip(data['data']['x'], data['data']['y'],
data['data']['c']):
im = NonUniformImage(ax, interpolation='nearest', cmap=data['cmap'])
im.set_clim(data['clim'][0], data['clim'][1])
im.set_data(x, y, dat.T)
ax.images.append(im)
ax.set_xlim(data['xlim'][0], data['xlim'][1])
ax.set_ylim(data['ylim'][0], data['ylim'][1])
ax.set_xlabel(data['labels']['xlabel'])
ax.set_ylabel(data['labels']['ylabel'])
ax.set_title(data['labels']['title'])
if make_pretty:
ax.get_xaxis().set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
if show_cbar:
cbar = fig.colorbar(im, orientation="horizontal", pad=0.02, ax=ax)
cbar.set_label(data['labels']['clabel'])
ax = add_lines(ax, data, **line_kwargs)
plt.show()
return ax, fig
def plot_2d_to_axis(
spectrum: Spectrum2D,
ax: Axes,
cmap: Union[str, Colormap] = 'viridis',
interpolation: str = 'nearest',
norm: Optional[Normalize] = None,
) -> NonUniformImage:
"""Plot Spectrum2D object to Axes
Parameters
----------
spectrum
2D data object for plotting as NonUniformImage. The x_tick_labels
attribute will be used to mark labelled points.
ax
Matplotlib axes to which image will be drawn
cmap
Matplotlib colormap or registered colormap name
interpolation
Interpolation method: 'nearest' or 'bilinear' for a pixellated or
smooth result
norm
Matplotlib normalization object; set this in order to ensure separate
plots are on the same colour scale.
"""
x_unit = spectrum.x_data_unit
y_unit = spectrum.y_data_unit
z_unit = spectrum.z_data_unit
x_bins = spectrum.get_bin_edges('x').to(x_unit).magnitude
y_bins = spectrum.get_bin_edges('y').to(y_unit).magnitude
image = NonUniformImage(ax,
interpolation=interpolation,
extent=(min(x_bins), max(x_bins), min(y_bins),
max(y_bins)),
cmap=cmap)
if norm is not None:
image.set_norm(norm)
image.set_data(
spectrum.get_bin_centres('x').to(x_unit).magnitude,
spectrum.get_bin_centres('y').to(y_unit).magnitude,
spectrum.z_data.to(z_unit).magnitude.T)
ax.images.append(image)
ax.set_xlim(min(x_bins), max(x_bins))
ax.set_ylim(min(y_bins), max(y_bins))
_set_x_tick_labels(ax, spectrum.x_tick_labels, spectrum.x_data)
return image
def plotAnalysis(self, widget):
Xvar = self.cbXAxis.get_active_text()
Yvar = self.cbYAxis.get_active_text()
Zvar = self.cbZAxis.get_active_text()
if Xvar == None or Yvar == None or Zvar == None:
return
if Zvar == "None":
XvarIndex = self.logfile.params().index(Xvar)+1
YvarIndex = self.logfile.params().index(Yvar)+1
rowiter = self.treemodelsorted.get_iter_first()
values = defaultdict(list)
while rowiter != None:
X = self.treemodelsorted.get_value(rowiter,XvarIndex)
Y = self.treemodelsorted.get_value(rowiter,YvarIndex)
values[float(X)].append(float(Y))
rowiter = self.treemodelsorted.iter_next(rowiter)
X = []
Y = []
for k in sorted(values.keys()):
X.append(k)
Y.append(mean(values[k]))
self.axisAN.cla()
self.figureAN.clf()
self.axisAN = self.figureAN.add_subplot(111)
self.axisAN.plot(X,Y, 'k', linewidth=4)
self.axisAN.set_xlabel(Xvar)
self.axisAN.set_ylabel(Yvar)
self.canvasAN.draw()
else:
XvarIndex = self.logfile.params().index(Xvar)+1
YvarIndex = self.logfile.params().index(Yvar)+1
ZvarIndex = self.logfile.params().index(Zvar)+1
rowiter = self.treemodelsorted.get_iter_first()
values = {}
Ykeys = []
while rowiter != None:
X = self.treemodelsorted.get_value(rowiter,XvarIndex)
Y = self.treemodelsorted.get_value(rowiter,YvarIndex)
Z = self.treemodelsorted.get_value(rowiter,ZvarIndex)
Ykeys.append(Y)
values.setdefault(X,defaultdict(list))[Y].append(Z)
rowiter = self.treemodelsorted.iter_next(rowiter)
Ykeys = unique(Ykeys)
XY = []
for k in sorted(values.keys()):
tmp = []
for k2 in sorted(Ykeys):
if values[k].has_key(k2):
tmp.append(mean(values[k][k2]))
else:
tmp.append(0)
XY.append(tmp)
Z = array(XY)
self.axisAN.cla()
self.figureAN.clf()
self.axisAN = self.figureAN.add_subplot(111)
im = NonUniformImage(self.axisAN, interpolation='nearest', extent=(min(values.keys()),max(values.keys()),min(Ykeys),max(Ykeys)))
im.set_data(values.keys(), Ykeys, Z.transpose())
self.axisAN.images.append(im)
self.axisAN.set_xlim(min(values.keys()),max(values.keys()))
self.axisAN.set_ylim(min(Ykeys),max(Ykeys))
self.axisAN.set_xlabel(Xvar)
self.axisAN.set_ylabel(Yvar)
self.axisAN.set_title(Zvar)
self.figureAN.colorbar(im)
self.canvasAN.draw()
def go(self):
'''Plot the evolution of the histogram for iterations self.iter_start to self.iter_stop'''
idim = self.dimensions[0]['idim']
n_iters = self.input_h5['n_iter'][...]
iiter_start = numpy.searchsorted(n_iters, self.iter_start)
iiter_stop = numpy.searchsorted(n_iters, self.iter_stop)
binbounds = self.input_h5['binbounds_{}'.format(idim)][...]
midpoints = self.input_h5['midpoints_{}'.format(idim)][...]
hists_ds = self.input_h5['histograms']
itercount = self.iter_stop - self.iter_start
# We always round down, so that we don't have a dangling partial block at the end
nblocks = itercount // self.iter_step
block_iters = numpy.empty((nblocks, 2), dtype=n_iters.dtype)
blocked_hists = numpy.zeros((nblocks, hists_ds.shape[1 + idim]),
dtype=hists_ds.dtype)
for iblock, istart in enumerate(
xrange(iiter_start, iiter_start + nblocks * self.iter_step,
self.iter_step)):
istop = min(istart + self.iter_step, iiter_stop)
histslice = hists_ds[istart:istop]
# Sum over time
histslice = numpy.add.reduce(histslice, axis=0)
# Sum over other dimensions
blocked_hists[iblock] = sum_except_along(histslice, idim)
# Normalize
normhistnd(blocked_hists[iblock], [binbounds])
block_iters[iblock, 0] = n_iters[istart]
block_iters[iblock, 1] = n_iters[istop - 1] + 1
#enehists = -numpy.log(blocked_hists)
enehists = self._ener_zero(blocked_hists)
log10hists = numpy.log10(blocked_hists)
if self.hdf5_output_filename:
with h5py.File(self.hdf5_output_filename, 'w') as output_h5:
h5io.stamp_creator_data(output_h5)
output_h5.attrs['source_data'] = os.path.abspath(
self.input_h5.filename)
output_h5.attrs['source_dimension'] = idim
output_h5['midpoints'] = midpoints
output_h5['histograms'] = blocked_hists
output_h5['n_iter'] = block_iters
if self.plot_output_filename:
if self.plotscale == 'energy':
plothist = enehists
label = r'$\Delta F(x)\,/\,kT$' + '\n' + r'$\left[-\ln\,P(x)\right]$'
elif self.plotscale == 'log10':
plothist = log10hists
label = r'$\log_{10}\ P(x)$'
else:
plothist = blocked_hists
label = r'$P(x)$'
try:
vmin, vmax = self.plotrange
except TypeError:
vmin, vmax = None, None
pyplot.figure()
norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
ax = pyplot.gca()
nui = NonUniformImage(ax,
extent=(midpoints[0], midpoints[-1],
block_iters[0, -1], block_iters[-1,
-1]),
origin='lower',
norm=norm)
# not sure why plothist works but plothist.T doesn't, and the opposite is true
# for _do_2d_output
nui.set_data(midpoints, block_iters[:, -1], plothist)
ax.images.append(nui)
ax.set_xlim(midpoints[0], midpoints[-1])
ax.set_ylim(block_iters[0, -1], block_iters[-1, -1])
cb = pyplot.colorbar(nui)
cb.set_label(label)
pyplot.xlabel(self.dimensions[0]['label'])
pyplot.xlim(self.dimensions[0].get('lb'),
self.dimensions[0].get('ub'))
pyplot.ylabel('WE Iteration')
if self.plottitle:
pyplot.title(self.plottitle)
if self.postprocess_function:
self.postprocess_function(plothist, midpoints, binbounds)
pyplot.savefig(self.plot_output_filename)
def test_nonuniformimage_setnorm():
ax = plt.gca()
im = NonUniformImage(ax)
im.set_norm(plt.Normalize())
def cross_wavelet(self, signal_1, signal_2, mother='morlet', plot=True):
signal_1 = (signal_1 - signal_1.mean()) / signal_1.std() # Normalizing
signal_2 = (signal_2 - signal_2.mean()) / signal_2.std() # Normalizing
W12, cross_coi, freq, signif = wavelet.xwt(signal_1, signal_2, self.period, dj=1/100, s0=-1, J=-1,
significance_level=0.95, wavelet=mother,
normalize=True)
cross_power = np.abs(W12)**2
cross_sig = np.ones([1, signal_1.size]) * signif[:, None]
cross_sig = cross_power / cross_sig
cross_period = 1/freq
WCT, aWCT, corr_coi, freq, sig = wavelet.wct(signal_1, signal_2, self.period, dj=1/100, s0=-1, J=-1,
sig=False,significance_level=0.95, wavelet=mother,
normalize=True)
cor_sig = np.ones([1, signal_1.size]) * sig[:, None]
cor_sig = np.abs(WCT) / cor_sig
cor_period = 1/freq
angle = 0.5 * np.pi - aWCT
u, v = np.cos(angle), np.sin(angle)
t1 = np.linspace(0,self.period*signal_1.size,signal_1.size)
## indices for stuff
idx = self.find_closest(cor_period,corr_coi.max())
## Into minutes
t1 /= 60
cross_period /= 60
cor_period /= 60
cross_coi /= 60
corr_coi /= 60
fig1, ax1 = plt.subplots(nrows=1,ncols=1, sharex=True, sharey=True, figsize=(12,12))
extent_cross = [t1.min(),t1.max(),0,max(cross_period)]
extent_corr = [t1.min(),t1.max(),0,max(cor_period)]
im1 = NonUniformImage(ax1, interpolation='nearest', extent=extent_cross)
im1.set_cmap('cubehelix')
im1.set_data(t1, cross_period[:idx], cross_power[:idx,:])
ax1.images.append(im1)
ax1.contour(t1, cross_period[:idx], cross_sig[:idx,:], [-99, 1], colors='k', linewidths=2, extent=extent_cross)
ax1.fill(np.concatenate([t1, t1[-1:]+self.period, t1[-1:]+self.period,t1[:1]-self.period, t1[:1]-self.period]),
(np.concatenate([cross_coi,[1e-9], cross_period[-1:], cross_period[-1:], [1e-9]])),
'k', alpha=0.3,hatch='x')
ax1.set_title('Cross-Wavelet')
# ax1.quiver(t1[::3], cross_period[::3], u[::3, ::3],
# v[::3, ::3], units='width', angles='uv', pivot='mid',
# linewidth=1.5, edgecolor='k', headwidth=10, headlength=10,
# headaxislength=5, minshaft=2, minlength=5)
ax1.set_ylim(([min(cross_period), cross_period[idx]]))
ax1.set_xlim(t1.min(),t1.max())
fig2, ax2 = plt.subplots(nrows=1,ncols=1, sharex=True, sharey=True, figsize=(12,12))
fig2.subplots_adjust(right=0.8)
cbar_ax_1 = fig2.add_axes([0.85, 0.05, 0.05, 0.35])
im2 = NonUniformImage(ax2, interpolation='nearest', extent=extent_corr)
im2.set_cmap('cubehelix')
im2.set_data(t1, cor_period[:idx], np.log10(WCT[:idx,:]))
ax2.images.append(im2)
ax2.contour(t1, cor_period[:idx], cor_sig[:idx,:], [-99, 1], colors='k', linewidths=2, extent=extent_corr)
ax2.fill(np.concatenate([t1, t1[-1:]+self.period, t1[-1:]+self.period,t1[:1]-self.period, t1[:1]-self.period]),
(np.concatenate([corr_coi,[1e-9], cor_period[-1:], cor_period[-1:], [1e-9]])),
'k', alpha=0.3,hatch='x')
ax2.set_title('Cross-Correlation')
# ax2.quiver(t1[::3], cor_period[::3], u[::3,::3], v[::3,::3],
# units='height', angles='uv', pivot='mid',linewidth=1.5, edgecolor='k',
# headwidth=10, headlength=10, headaxislength=5, minshaft=2, minlength=5)
ax2.set_ylim(([min(cor_period), cor_period[idx]]))
ax2.set_xlim(t1.min(),t1.max())
fig2.colorbar(im2, cax=cbar_ax_1)
plt.show()
plt.figure(figsize=(12,12))
im3= plt.imshow(np.rad2deg(aWCT), origin='lower',interpolation='nearest', cmap='seismic', extent=extent_corr)
plt.fill(np.concatenate([t1, t1[-1:]+self.period, t1[-1:]+self.period,t1[:1]-self.period, t1[:1]-self.period]),
(np.concatenate([corr_coi,[1e-9], cor_period[-1:], cor_period[-1:], [1e-9]])),
'k', alpha=0.3,hatch='x')
plt.ylim(([min(cor_period), cor_period[idx]]))
plt.xlim(t1.min(),t1.max())
plt.colorbar(im3)
plt.show()
return
# Linear x array for cell centers:
x = np.linspace(-4, 4, 9)
# Highly nonlinear x array:
x2 = x**3
y = np.linspace(-4, 4, 9)
z = np.sqrt(x[np.newaxis, :]**2 + y[:, np.newaxis]**2)
fig, axs = plt.subplots(nrows=2, ncols=2, constrained_layout=True)
fig.suptitle('NonUniformImage class', fontsize='large')
ax = axs[0, 0]
im = NonUniformImage(ax,
interpolation=interp,
extent=(-4, 4, -4, 4),
cmap=cm.Purples)
im.set_data(x, y, z)
ax.images.append(im)
ax.set_xlim(-4, 4)
ax.set_ylim(-4, 4)
ax.set_title(interp)
ax = axs[0, 1]
im = NonUniformImage(ax,
interpolation=interp,
extent=(-64, 64, -4, 4),
cmap=cm.Purples)
im.set_data(x2, y, z)
ax.images.append(im)
ax.set_xlim(-64, 64)
fig = plt.figure(figsize=(7, 3))
ax = fig.add_subplot(131, title='imshow: square bins')
plt.imshow(H,
interpolation='nearest',
origin='lower',
extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])
# <matplotlib.image.AxesImage object at 0x...>
# :func:`pcolormesh <matplotlib.pyplot.pcolormesh>` can display actual edges:
ax = fig.add_subplot(132, title='pcolormesh: actual edges', aspect='equal')
X, Y = np.meshgrid(xedges, yedges)
ax.pcolormesh(X, Y, H)
# <matplotlib.collections.QuadMesh object at 0x...>
# :class:`NonUniformImage <matplotlib.image.NonUniformImage>` can be used to
# display actual bin edges with interpolation:
ax = fig.add_subplot(133,
title='NonUniformImage: interpolated',
aspect='equal',
xlim=xedges[[0, -1]],
ylim=yedges[[0, -1]])
im = NonUniformImage(ax, interpolation='bilinear')
xcenters = (xedges[:-1] + xedges[1:]) / 2
ycenters = (yedges[:-1] + yedges[1:]) / 2
im.set_data(xcenters, ycenters, H)
ax.images.append(im)
plt.show()
def test_nonuniformimage_setnorm():
ax = plt.gca()
im = NonUniformImage(ax)
im.set_norm(plt.Normalize())
power1 = (np.abs(W1)) ** 2 # Normalized wavelet power spectrum
power2 = (np.abs(W2)) ** 2 # Normalized wavelet power spectrum
period1 = 1/freqs1
period2 = 1/freqs2
sig95_1 = np.ones([1, n1]) * signif1[:, None]
sig95_1 = power1 / sig95_1 # Where ratio > 1, power is significant
sig95_2 = np.ones([1, n2]) * signif2[:, None]
sig95_2 = power2 / sig95_2 # Where ratio > 1, power is significant
# First plot is of both CWT
fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, sharex=True)
extent1 = [t1.min(), t1.max(), 0, max(period1)]
extent2 = [t2.min(), t2.max(), 0, max(period2)]
im1 = NonUniformImage(ax1, interpolation='bilinear', extent=extent1)
im1.set_data(t1, period1, power1)
ax1.images.append(im1)
ax1.contour(t1, period1, sig95_1, [-99, 1], colors='k', linewidths=2,
extent=extent1)
ax1.fill(np.concatenate([t1, t1[-1:]+dt, t1[-1:]+dt, t1[:1]-dt, t1[:1]-dt]),
np.concatenate([coi1, [1e-9], period1[-1:], period1[-1:], [1e-9]]),
'k', alpha=0.3, hatch='x')
ax1.set_title('{} Wavelet Power Spectrum ({})'.format(data1['nick'],
mother.name))
im2 = NonUniformImage(ax2, interpolation='bilinear', extent=extent2)
im2.set_data(t2, period2, power2)
ax2.images.append(im2)
ax2.contour(t2, period2, sig95_2, [-99, 1], colors='k', linewidths=2,
extent=extent2)
# First sub-plot, the original time series anomaly.
ax = plt.axes([0.1, 0.75, 0.65, 0.2])
ax.plot(time, iwave, '-', linewidth=1, color=[0.5, 0.5, 0.5])
ax.plot(time, var, 'k', linewidth=1.5)
ax.set_title('a) %s' % (title, ))
if units != '':
ax.set_ylabel(r'%s [$%s$]' % (label, units,))
else:
ax.set_ylabel(r'%s' % (label, ))
extent = [time.min(),time.max(),0,max(period)]
# Second sub-plot, the normalized wavelet power spectrum and significance level
# contour lines and cone of influece hatched area.
bx = plt.axes([0.1, 0.37, 0.65, 0.28], sharex=ax)
im = NonUniformImage(bx, interpolation='bilinear', extent=extent)
im.set_data(time, period, power/scales[:, None])
bx.images.append(im)
bx.contour(time, period, sig95, [-99, 1], colors='k', linewidths=2, extent=extent)
bx.fill(np.concatenate([time, time[-1:]+dt, time[-1:]+dt,time[:1]-dt, time[:1]-dt]),
(np.concatenate([coi,[1e-9], period[-1:], period[-1:], [1e-9]])),
'k', alpha=0.3,hatch='x')
bx.set_title('b) %s Wavelet Power Spectrum (%s)' % (label, mother.name))
bx.set_ylabel('Period (years)')
# Third sub-plot, the global wavelet and Fourier power spectra and theoretical
# noise spectra.
cx = plt.axes([0.77, 0.37, 0.2, 0.28], sharey=bx)
cx.plot(glbl_signif, (period), 'k--')
cx.plot(glbl_power, (period), 'k-', linewidth=1.5)
# Linear x array for cell centers:
x = np.linspace(-4, 4, 9)
# Highly nonlinear x array:
x2 = x**3
y = np.linspace(-4, 4, 9)
z = np.sqrt(x[np.newaxis, :]**2 + y[:, np.newaxis]**2)
fig, axs = plt.subplots(nrows=2, ncols=2)
fig.subplots_adjust(bottom=0.07, hspace=0.3)
fig.suptitle('NonUniformImage class', fontsize='large')
ax = axs[0, 0]
im = NonUniformImage(ax, interpolation=interp, extent=(-4, 4, -4, 4),
cmap=cm.Purples)
im.set_data(x, y, z)
ax.images.append(im)
ax.set_xlim(-4, 4)
ax.set_ylim(-4, 4)
ax.set_title(interp)
ax = axs[0, 1]
im = NonUniformImage(ax, interpolation=interp, extent=(-64, 64, -4, 4),
cmap=cm.Purples)
im.set_data(x2, y, z)
ax.images.append(im)
ax.set_xlim(-64, 64)
ax.set_ylim(-4, 4)
ax.set_title(interp)
def plot(self, data, fmt, xmin, xmax, ymin, ymax, zmin, zmax, **opts):
"""
Plot the data entry.
Raises NeXusError if the data cannot be plotted.
"""
try:
import matplotlib.pyplot as plt
except ImportError:
raise NeXusError("Default plotting package (matplotlib) not available.")
over = opts.pop("over", False)
image = opts.pop("image", False)
log = opts.pop("log", False)
logx = opts.pop("logx", False)
logy = opts.pop("logy", False)
if fmt == '':
fmt = 'o'
if over:
plt.autoscale(enable=False)
else:
plt.autoscale(enable=True)
plt.clf()
signal = data.nxsignal
axes = data.nxaxes
errors = data.nxerrors
title = data.nxtitle
# Provide a new view of the data if there is a dimension of length 1
if 1 in signal.shape:
data, axes = _fixaxes(signal, axes)
else:
data = signal.nxdata
# Find the centers of the bins for histogrammed data
axis_data = centers(data, axes)
#One-dimensional Plot
if len(data.shape) == 1:
if hasattr(signal, 'units'):
if not errors and signal.units == 'counts':
errors = NXfield(np.sqrt(data))
if errors:
ebars = errors.nxdata
plt.errorbar(axis_data[0], data, ebars, fmt=fmt, **opts)
else:
plt.plot(axis_data[0], data, fmt, **opts)
if not over:
ax = plt.gca()
xlo, xhi = ax.set_xlim(auto=True)
ylo, yhi = ax.set_ylim(auto=True)
if xmin: xlo = xmin
if xmax: xhi = xmax
ax.set_xlim(xlo, xhi)
if ymin: ylo = ymin
if ymax: yhi = ymax
ax.set_ylim(ylo, yhi)
if logx: ax.set_xscale('symlog')
if log or logy: ax.set_yscale('symlog')
plt.xlabel(label(axes[0]))
plt.ylabel(label(signal))
plt.title(title)
#Two dimensional plot
else:
from matplotlib.image import NonUniformImage
from matplotlib.colors import LogNorm, Normalize
if len(data.shape) > 2:
slab = []
if image:
for _dim in data.shape[:-3]:
slab.append(0)
slab.extend([slice(None), slice(None), slice(None)])
else:
for _dim in data.shape[:-2]:
slab.append(0)
slab.extend([slice(None), slice(None)])
data = data[slab]
if 0 in slab:
print "Warning: Only the top 2D slice of the data is plotted"
if image:
x, y = axis_data[-2], axis_data[-3]
xlabel, ylabel = label(axes[-2]), label(axes[-3])
else:
x, y = axis_data[-1], axis_data[-2]
xlabel, ylabel = label(axes[-1]), label(axes[-2])
if not zmin:
zmin = np.nanmin(data[data>-np.inf])
if not zmax:
zmax = np.nanmax(data[data<np.inf])
if not image:
if log:
zmin = max(zmin, 0.01)
zmax = max(zmax, 0.01)
opts["norm"] = LogNorm(zmin, zmax)
else:
opts["norm"] = Normalize(zmin, zmax)
ax = plt.gca()
if image:
im = ax.imshow(data, **opts)
ax.set_aspect('equal')
else:
extent = (x[0],x[-1],y[0],y[-1])
im = NonUniformImage(ax, extent=extent, **opts)
im.set_data(x, y, data)
im.get_cmap().set_bad('k', 1.0)
ax.set_xlim(x[0], x[-1])
ax.set_ylim(y[0], y[-1])
ax.set_aspect('auto')
ax.images.append(im)
if not image:
plt.colorbar(im)
if 'origin' in opts and opts['origin'] == 'lower':
image = False
if xmin:
ax.set_xlim(left=xmin)
if xmax:
ax.set_xlim(right=xmax)
if ymin:
if image:
ax.set_ylim(top=ymin)
else:
ax.set_ylim(bottom=ymin)
if ymax:
if image:
ax.set_ylim(bottom=ymax)
else:
ax.set_ylim(top=ymax)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
plt.gcf().canvas.draw_idle()
plt.ion()
plt.show()
z_matrix = np.array([[xi * yi for xi in range(X_MIN, X_MAX, X_STEP)] for yi in range(Y_MIN, Y_MAX, Y_STEP)]) # Plot data ################# fig = plt.figure() ax = fig.add_subplot(111) #interp='nearest' # "raw" (non smooth) map interp = 'bilinear' # "smooth" map # NonUniformImage permet de définir la position des éléments de 'z_matrix' sur # les axes. # Sans NonUniformImage, une matrice 'z_matrix' de taille (sx, sy) serait # dessinée sur un repère avec un axe des abscisses allant de 0 a sx et un axe # des ordonnées allant de 0 a sy. im = NonUniformImage(ax, interpolation=interp, extent=(X_MIN, X_MAX, Y_MIN, Y_MAX), cmap=cm.binary) # im.set_data(x, y, A) # Set the grid for the pixel centers, and the pixel values. # *x* and *y* are 1-D ndarrays of lengths N and M, respectively, specifying pixel centers # *A* is an (M,N) ndarray or masked array of values to be colormapped, or a (M,N,3) RGB array, or a (M,N,4) RGBA array. im.set_data(x, y, z_matrix) ax.images.append(im) ax.set_xlim(X_MIN, X_MAX) ax.set_ylim(Y_MIN, Y_MAX) fig.colorbar(im) # draw colorbar # Save file and plot ########
def wavelet(self, signal, mother='morlet', plot=True):
"""
Takes a 1D signal and perfroms a continous wavelet transform.
Parameters
----------
time: ndarray
The 1D time series for the data
data: ndarray
The actual 1D data
mother: string
The name of the family. Acceptable values are Paul, Morlet, DOG, Mexican_hat
plot: bool
If True, will return a plot of the result.
Returns
-------
Examples
--------
"""
sig_level = 0.95
std2 = signal.std() ** 2
signal_orig = signal[:]
signal = (signal - signal.mean())/ signal.std()
t1 = np.linspace(0,self.period*signal.size,signal.size)
wave, scales, freqs, coi, fft, fftfreqs = wavelet.cwt(signal,
self.period,
wavelet=mother, dj=1/100)
power = (np.abs(wave)) ** 2
period = 1/freqs
# alpha, _, _ = wavelet.ar1(signal)
alpha = 0.0
## (variance=1 for the normalized SST)
signif, fft_theor = wavelet.significance(1.0, self.period, scales, 0, alpha,
significance_level=sig_level, wavelet=mother)
sig95 = np.ones([1, signal.size]) * signif[:, None]
sig95 = power / sig95
glbl_power = std2 * power.mean(axis=1)
dof = signal.size - scales
glbl_signif, tmp = wavelet.significance(std2, self.period, scales, 1, alpha,
significance_level=sig_level, dof=dof, wavelet=mother)
## indices for stuff
idx = self.find_closest(period,coi.max())
## Into minutes
t1 /= 60
period /= 60
coi /= 60
if plot:
plt.figure(figsize=(12,12))
ax = plt.axes([0.1, 0.75, 0.65, 0.2])
ax.plot(t1, signal_orig-signal_orig.mean(), 'k', linewidth=1.5)
extent = [t1.min(),t1.max(),0,max(period)]
bx = plt.axes([0.1, 0.1, 0.65, 0.55], sharex=ax)
im = NonUniformImage(bx, interpolation='nearest', extent=extent)
im.set_cmap('cubehelix')
im.set_data(t1, period[:idx], power[:idx,:])
bx.images.append(im)
bx.contour(t1, period[:idx], sig95[:idx,:], [-99,1], colors='w', linewidths=2, extent=extent)
bx.fill(np.concatenate([t1, t1[-1:]+self.period, t1[-1:]+self.period,t1[:1]-self.period, t1[:1]-self.period]),
(np.concatenate([coi,[1e-9], period[-1:], period[-1:], [1e-9]])),
'k', alpha=0.3,hatch='x', zorder=100)
bx.set_xlim(t1.min(),t1.max())
cx = plt.axes([0.77, 0.1, 0.2, 0.55], sharey=bx)
cx.plot(glbl_signif[:idx], period[:idx], 'k--')
cx.plot(glbl_power[:idx], period[:idx], 'k-', linewidth=1.5)
cx.set_ylim(([min(period), period[idx]]))
plt.setp(cx.get_yticklabels(), visible=False)
plt.show()
return wave, scales, freqs, coi, power
def go(self):
'''Plot the evolution of the histogram for iterations self.iter_start to self.iter_stop'''
idim = self.dimensions[0]['idim']
n_iters = self.input_h5['n_iter'][...]
iiter_start = numpy.searchsorted(n_iters, self.iter_start)
iiter_stop = numpy.searchsorted(n_iters, self.iter_stop)
binbounds = self.input_h5['binbounds_{}'.format(idim)][...]
midpoints = self.input_h5['midpoints_{}'.format(idim)][...]
hists_ds = self.input_h5['histograms']
itercount = self.iter_stop - self.iter_start
# We always round down, so that we don't have a dangling partial block at the end
nblocks = itercount // self.iter_step
block_iters = numpy.empty((nblocks,2), dtype=n_iters.dtype)
blocked_hists = numpy.zeros((nblocks,hists_ds.shape[1+idim]), dtype=hists_ds.dtype)
for iblock, istart in enumerate(xrange(iiter_start, iiter_start+nblocks*self.iter_step, self.iter_step)):
istop = min(istart+self.iter_step, iiter_stop)
histslice = hists_ds[istart:istop]
# Sum over time
histslice = numpy.add.reduce(histslice, axis=0)
# Sum over other dimensions
blocked_hists[iblock] = sum_except_along(histslice, idim)
# Normalize
normhistnd(blocked_hists[iblock], [binbounds])
block_iters[iblock,0] = n_iters[istart]
block_iters[iblock,1] = n_iters[istop-1]+1
#enehists = -numpy.log(blocked_hists)
enehists = self._ener_zero(blocked_hists)
log10hists = numpy.log10(blocked_hists)
if self.hdf5_output_filename:
with h5py.File(self.hdf5_output_filename, 'w') as output_h5:
h5io.stamp_creator_data(output_h5)
output_h5.attrs['source_data'] = os.path.abspath(self.input_h5.filename)
output_h5.attrs['source_dimension'] = idim
output_h5['midpoints'] = midpoints
output_h5['histograms'] = blocked_hists
output_h5['n_iter'] = block_iters
if self.plot_output_filename:
if self.plotscale == 'energy':
plothist = enehists
label = r'$\Delta F(x)\,/\,kT$' +'\n' + r'$\left[-\ln\,P(x)\right]$'
elif self.plotscale == 'log10':
plothist = log10hists
label = r'$\log_{10}\ P(x)$'
else:
plothist = blocked_hists
label = r'$P(x)$'
try:
vmin, vmax = self.plotrange
except TypeError:
vmin, vmax = None, None
pyplot.figure()
norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
ax = pyplot.gca()
nui = NonUniformImage(ax, extent=(midpoints[0], midpoints[-1], block_iters[0,-1], block_iters[-1,-1]),
origin='lower', norm=norm)
# not sure why plothist works but plothist.T doesn't, and the opposite is true
# for _do_2d_output
nui.set_data(midpoints, block_iters[:,-1], plothist)
ax.images.append(nui)
ax.set_xlim(midpoints[0], midpoints[-1])
ax.set_ylim(block_iters[0,-1], block_iters[-1,-1])
cb = pyplot.colorbar(nui)
cb.set_label(label)
pyplot.xlabel(self.dimensions[0]['label'])
pyplot.xlim(self.dimensions[0].get('lb'), self.dimensions[0].get('ub'))
pyplot.ylabel('WE Iteration')
if self.plottitle:
pyplot.title(self.plottitle)
if self.postprocess_function:
self.postprocess_function(plothist, midpoints, binbounds)
pyplot.savefig(self.plot_output_filename)
plt.title('Midplane dust temperature')
plt.show()
#
# The "heatmap" of the 2-D temperature profile
#
iphi = 10 # Azimuthal index at which the temperature structure is shown
cmap = cm.hot
zoomr = 1. # Zoom factor
fig, ax = plt.subplots()
x = np.log10(a.grid.x / au)
y = (np.pi / 2 - a.grid.y)[::-1]
xi = np.log10(a.grid.xi / au)
yi = (np.pi / 2 - a.grid.yi)[::-1]
z = a.dusttemp[:, ::-1, iphi, 0].T
im = NonUniformImage(ax, interpolation='nearest', cmap=cmap)
im.set_data(x, y, z)
ax.images.append(im)
ax.set_xlim((xi[0], (xi[-1] - xi[0]) / zoomr + xi[0]))
ax.set_ylim((yi[0], yi[-1]))
plt.xlabel(r'$^{10}\log(r)\; [\mathrm{AU}]$')
plt.ylabel(r'$\pi/2-\theta$')
norm = colors.Normalize(vmin=z.min(), vmax=z.max())
cbar = fig.colorbar(cm.ScalarMappable(norm=norm, cmap=cmap), ax=ax)
cbar.set_label(r'$T\;[\mathrm{K}]$')
plt.title(r'Dust temperature structure at $\phi=$' +
'{0:3d}'.format(int(a.grid.z[iphi] * 180 / np.pi)) + r'$^{\circ}$')
#
# Compute the RADIAL tau=1 surface for stellar radiation
# We focus only on the small grain component here
pl.xlabel('Slope (deg)')
#pl.ylabel(r'$\frac{Ntrig(<128antennas)}{Nsimulated showers(135)}$',)
#pl.ylabel('Ntrig(<128antennas) / Nsimu showers(=135)',)
pl.ylabel('Trigger rate')
plt.legend(loc='best',fontsize=10,ncol=2)
#pl.legend(bbox_to_anchor=(0., -0.3, 0.75, .102), loc=2, ncol=2, mode="expand", borderaxespad=0.,fontsize=10)
if DISPLAY_2D==1:
dar=(alfa2[-1]-alfa2[0])/(Dd2[-1]-Dd2[0]) *1 #7E-4
ax = plt.subplot(223)
#fig1.subplots_adjust(bottom=0.07, hspace=0.3)
pl.title('Trigger rate - Conservative case')
pl.ylabel('Distance [km]')
pl.xlabel('Slope [deg]')
im = NonUniformImage(ax,interpolation='nearest', extent=(Dd2[0],Dd2[-1],alfa2[0],alfa2[-1]),cmap='jet')
im.set_data(alfa,Dd,Nconfig_ew_cons)
ax.images.append(im)
ax.set_ylim(Dd2[0],Dd2[-1])
ax.set_xlim(alfa2[0],alfa2[-1])
im.set_clim(0,np.max(Nconfig_ew_aggr))
pl.colorbar(im)
#pl.colorbar(im,orientation="horizontal")
#pl.gca().set_aspect(dar,adjustable='box')
ax = plt.subplot(224)
#fig1.subplots_adjust(bottom=0.07, hspace=0.3)
pl.title('Trigger rate - Aggressive case')
pl.ylabel('Distance [km]')
pl.xlabel('Slope [deg]')
def analyzeArea(rasterTransfo, resAnalysis, pLim, newRasters, cfgPath,
cfgFlags):
"""
Compare results to reference.
Compute True positive, False negative... areas.
"""
fname = cfgPath['pressurefileList']
dataPressure = newRasters['newRasterPressure']
scoord = rasterTransfo['s']
lcoord = rasterTransfo['l']
cellarea = rasterTransfo['rasterArea']
indRunoutPoint = rasterTransfo['indRunoutPoint']
# initialize Arrays
nTopo = len(fname)
TP = np.empty((nTopo))
FN = np.empty((nTopo))
FP = np.empty((nTopo))
TN = np.empty((nTopo))
# take first simulation as reference
newMask = copy.deepcopy(dataPressure[0])
# prepare mask for area resAnalysis
newMask[0:indRunoutPoint] = 0
newMask[np.where(np.nan_to_num(newMask) < pLim)] = 0
newMask[np.where(np.nan_to_num(newMask) >= pLim)] = 1
# comparison rasterdata with mask
log.info('{: <15} {: <15} {: <15} {: <15} {: <15}'.format(
'Sim number ', 'TP ', 'FN ', 'FP ', 'TN'))
# rasterinfo
nStart, m_start = np.nonzero(np.nan_to_num(newMask))
nStart = min(nStart)
nTot = len(scoord)
for i in range(nTopo):
rasterdata = dataPressure[i]
"""
area
# true positive: reality(mask)=1, model(rasterdata)=1
# false negative: reality(mask)=1, model(rasterdata)=0
# false positive: reality(mask)=0, model(rasterdata)=1
# true negative: reality(mask)=0, model(rasterdata)=0
"""
# for each pressure-file pLim is introduced (1/3/.. kPa), where the avalanche has stopped
newRasterData = copy.deepcopy(rasterdata)
newRasterData[0:indRunoutPoint] = 0
newRasterData[np.where(np.nan_to_num(newRasterData) < pLim)] = 0
newRasterData[np.where(np.nan_to_num(newRasterData) >= pLim)] = 1
if cfgFlags.getboolean('savePlot') and i > 0:
# read paths
pathResult = cfgPath['pathResult']
projectName = cfgPath['dirName']
outname = ''.join([
pathResult, os.path.sep, 'pics', os.path.sep, projectName, '_',
str(i), '_compToRef', '.pdf'
])
if not os.path.exists(os.path.dirname(outname)):
os.makedirs(os.path.dirname(outname))
fig = plt.figure(figsize=(figW * 2, figH), dpi=figReso)
y_lim = scoord[indRunoutPoint + 20] + resAnalysis['runout'][0, 0]
# for figure: referenz-simulation bei pLim=1
ax1 = plt.subplot(121)
ax1.title.set_text('Reference Peak Presseure in the RunOut area')
cmap = cmapPres
cmap.set_under(color='w')
im = NonUniformImage(ax1,
extent=[
lcoord.min(),
lcoord.max(),
scoord.min(),
scoord.max()
],
cmap=cmap)
im.set_clim(vmin=pLim,
vmax=np.max((dataPressure[0])[nStart:nTot + 1]))
im.set_data(lcoord, scoord, dataPressure[0])
ref0 = ax1.images.append(im)
cbar = ax1.figure.colorbar(im,
extend='both',
ax=ax1,
use_gridspec=True)
cbar.ax.set_ylabel('peak pressure [kPa]')
ax1.set_xlim([lcoord.min(), lcoord.max()])
ax1.set_ylim([scoord[indRunoutPoint - 20], y_lim])
ax1.set_xlabel(r'$l\;[m]$')
ax1.set_ylabel(r'$s\;[m]$')
ax2 = plt.subplot(122)
ax2.title.set_text(
'Difference between current and reference in the RunOut area\n Blue = FN, Red = FP'
)
colorsList = [[0, 0, 1], [1, 1, 1], [1, 0, 0]]
cmap = matplotlib.colors.ListedColormap(colorsList)
cmap.set_under(color='b')
cmap.set_over(color='r')
cmap.set_bad(color='k')
im = NonUniformImage(ax2,
extent=[
lcoord.min(),
lcoord.max(),
scoord.min(),
scoord.max()
],
cmap=cmap)
im.set_clim(vmin=-0.000000001, vmax=0.000000001)
im.set_data(lcoord, scoord, newRasterData - newMask)
ref0 = ax2.images.append(im)
# cbar = ax2.figure.colorbar(im, ax=ax2, extend='both', use_gridspec=True)
# cbar.ax.set_ylabel('peak pressure [kPa]')
ax2.set_xlim([lcoord.min(), lcoord.max()])
ax2.set_ylim([scoord[indRunoutPoint - 20], y_lim])
ax2.set_xlabel(r'$l\;[m]$')
ax2.set_ylabel(r'$s\;[m]$')
plt.subplots_adjust(wspace=0.3)
# fig.tight_layout()
# plt.show()
fig.savefig(outname, transparent=True)
plt.close(fig)
tpInd = np.where((newMask[nStart:nTot + 1] == True)
& (newRasterData[nStart:nTot + 1] == True))
fpInd = np.where((newMask[nStart:nTot + 1] == False)
& (newRasterData[nStart:nTot + 1] == True))
fnInd = np.where((newMask[nStart:nTot + 1] == True)
& (newRasterData[nStart:nTot + 1] == False))
tnInd = np.where((newMask[nStart:nTot + 1] == False)
& (newRasterData[nStart:nTot + 1] == False))
# Teilrasterpunkte
tpCount = len(tpInd[0])
fpCount = len(fpInd[0])
fnCount = len(fnInd[0])
tnCount = len(tnInd[0])
# subareas
tp = sum(cellarea[tpInd[0] + nStart, tpInd[1]])
fp = sum(cellarea[fpInd[0] + nStart, fpInd[1]])
fn = sum(cellarea[fnInd[0] + nStart, fnInd[1]])
tn = sum(cellarea[tnInd[0] + nStart, tnInd[1]])
# take reference (first simulation) as normalizing area
areaSum = tp + fn
TP[i] = tp
FN[i] = fn
FP[i] = fp
TN[i] = tn
log.info('{: <15} {:<15.4f} {:<15.4f} {:<15.4f} {:<15.4f}'.format(
*[i + 1, tp / areaSum, fn / areaSum, fp / areaSum, tn / areaSum]))
resAnalysis['TP'] = TP
resAnalysis['FN'] = FN
resAnalysis['FP'] = FP
resAnalysis['TN'] = TN
return resAnalysis
def _do_2d_output(self, hist, idims, midpoints, binbounds):
enehist = self._ener_zero(hist)
log10hist = numpy.log10(hist)
if self.hdf5_output_filename:
with h5py.File(self.hdf5_output_filename, 'w') as output_h5:
h5io.stamp_creator_data(output_h5)
output_h5.attrs['source_data'] = os.path.abspath(self.input_h5.filename)
output_h5.attrs['source_dimensions'] = numpy.array(idims, numpy.min_scalar_type(max(idims)))
output_h5.attrs['source_dimension_labels'] = numpy.array([dim['label'] for dim in self.dimensions])
for idim in idims:
output_h5['midpoints_{}'.format(idim)] = midpoints
output_h5['histogram'] = hist
if self.plot_output_filename:
if self.plotscale == 'energy':
plothist = enehist
label = r'$\Delta F(\vec{x})\,/\,kT$' +'\n' + r'$\left[-\ln\,P(x)\right]$'
elif self.plotscale == 'log10':
plothist = log10hist
label = r'$\log_{10}\ P(\vec{x})$'
else:
plothist = hist
plothist[~numpy.isfinite(plothist)] = numpy.nan
label = r'$P(\vec{x})$'
try:
vmin, vmax = self.plotrange
except TypeError:
vmin, vmax = None, None
pyplot.figure()
# Transpose input so that axis 0 is displayed as x and axis 1 is displayed as y
# pyplot.imshow(plothist.T, interpolation='nearest', aspect='auto',
# extent=(midpoints[0][0], midpoints[0][-1], midpoints[1][0], midpoints[1][-1]),
# origin='lower', vmin=vmin, vmax=vmax)
# The following reproduces the former calls to imshow and colorbar
norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
ax = pyplot.gca()
nui = NonUniformImage(ax, extent=(midpoints[0][0], midpoints[0][-1], midpoints[1][0], midpoints[1][-1]),
origin='lower', norm=norm)
nui.set_data(midpoints[0], midpoints[1], plothist.T)
ax.images.append(nui)
ax.set_xlim(midpoints[0][0], midpoints[0][-1])
ax.set_ylim(midpoints[1][0], midpoints[1][-1])
cb = pyplot.colorbar(nui)
cb.set_label(label)
pyplot.xlabel(self.dimensions[0]['label'])
pyplot.xlim(self.dimensions[0].get('lb'), self.dimensions[0].get('ub'))
pyplot.ylabel(self.dimensions[1]['label'])
pyplot.ylim(self.dimensions[1].get('lb'), self.dimensions[1].get('ub'))
if self.plottitle:
pyplot.title(self.plottitle)
if self.postprocess_function:
self.postprocess_function(plothist, midpoints, binbounds)
if self.plot_contour:
pyplot.contour(midpoints[0], midpoints[1],plothist.T)
pyplot.savefig(self.plot_output_filename)
def __Plot2D(self, xa, ya, param, con, params, label, kind, fnc_x, fnc_y,
fnc_z, axis_label, fnc_filt, appto, axs, **kwargs):
import pylab
from matplotlib.image import NonUniformImage
from numpy import linspace
from scipy.interpolate import splrep, splev
from mpl_toolkits.mplot3d import Axes3D
if len(params) > 0:
cons = self.lookup.getConditions(con, _asarray(params), fnc_filt,
**kwargs)
else:
cons = _asarray(con)
for i, con in enumerate(cons):
vals = self.lookup.getWhere(con)
if vals <> []:
if self.objects[vals[0]].data.has_key(xa) and \
self.objects[vals[0]].data.has_key(ya):
if not appto:
f = pylab.figure()
if kind in ['imshow', 'contour', 'contourf']:
axs.append(f.add_subplot(111))
else:
axs.append(f.gca(projection='3d'))
else:
if type(appto) in [list, tuple]:
axs = appto
else:
axs.append(appto)
ax_num = i % len(axs)
if self.objects[vals[0]].data.has_key(param):
ma = fnc_z(self.objects[vals[0]].getData(param))
x_axis = fnc_x(self.objects[vals[0]].getData(xa))
y_axis = fnc_y(self.objects[vals[0]].getData(ya))
else:
def generateMatrix(dO, xa, ya, param, kind, ma, xs,
ys):
# generates a matrix for the Image or contoure plot
if dO.data.has_key(xa) and dO.data.has_key(ya) and \
self.header.has_key(param):
ma.append(fnc_z(dO.getData(ya)))
xs.append(fnc_x(dO.getData(xa)))
ys.append(fnc_y(dO.getHeader(param)))
ma = []
xs = []
ys = []
self.map(generateMatrix,
con,
xa=xa,
ya=ya,
param=param,
kind=kind,
ma=ma,
xs=xs,
ys=ys)
x_min, x_max = max(LP_func.transpose(xs)[0]), min(
LP_func.transpose(xs)[-1])
x_axis = linspace(x_min, x_max, 400)
for i in xrange(len(xs)):
ma[i] = splev(x_axis, splrep(xs[i], ma[i], k=3))
xs[i] = x_axis
y_axis = ys
if kind == 'imshow':
im = NonUniformImage(axs[ax_num],
interpolation='bilinear')
im.set_cmap(kwargs.get('cmap', None))
im.set_data(x_axis, y_axis, ma)
if kwargs.has_key('vmin'):
im.set_clim(vmin=kwargs['vmin'])
if kwargs.has_key('vmax'):
im.set_clim(vmax=kwargs['vmax'])
axs[ax_num].images.append(im)
#~ xlabel( r'Wavelength [nm]' )
#~ ylabel( r'Delay [ps]' )
pylab.show()
if kwargs.has_key('bar'):
bar = kwargs['bar']
kwargs.pop('bar')
else:
bar = True
if bar:
axs[ax_num].get_figure().colorbar(im)
axs[ax_num].set_xlim(x_axis[0], x_axis[-1])
axs[ax_num].set_ylim(y_axis[0], y_axis[-1])
pylab.draw()
elif kind == 'contour':
N = kwargs.get('N', 8)
X, Y = pylab.meshgrid(x_axis, y_axis)
CS = axs[ax_num].contour(X, Y, ma, N, **kwargs)
if kwargs.has_key('labels'):
labels = kwargs['labels']
kwargs.pop('labels')
fmt = {}
for l, s in zip(CS.levels, labels):
fmt[l] = s
elif kwargs.has_key('fmt'):
fmt = kwargs('fmt')
else:
fmt = '%1.2f'
if kwargs.has_key('fontsize'):
fontsize = kwargs['fontsize']
else:
fontsize = 12
axs[ax_num].clabel(CS,
CS.levels,
inline=1,
fmt=fmt,
fontsize=fontsize)
pylab.show()
axs[ax_num].set_xlim(x_axis[0], x_axis[-1])
axs[ax_num].set_ylim(y_axis[0], y_axis[-1])
pylab.draw()
elif kind == 'contourf':
N = kwargs.get('N', 8)
X, Y = pylab.meshgrid(x_axis, y_axis)
CS = axs[ax_num].contourf(X, Y, ma, N, **kwargs)
axs[ax_num].get_figure().colorbar(CS)
pylab.show()
axs[ax_num].set_xlim(x_axis[0], x_axis[-1])
axs[ax_num].set_ylim(y_axis[0], y_axis[-1])
pylab.draw()
elif kind == 'surf':
X, Y = pylab.meshgrid(x_axis, y_axis)
CS = axs[ax_num].plot_surface(X, Y, pylab.array(ma),
**kwargs)
#axs[ax_num].get_figure().colorbar(CS, shrink=0.5, aspect=5)
pylab.show()
#axs[ax_num].set_xlim(x_axis[0],x_axis[-1])
#axs[ax_num].set_ylim(y_axis[0],y_axis[-1])
pylab.draw()
elif kind == 'contour3d':
N = kwargs.get('N', 8)
X, Y = pylab.meshgrid(x_axis, y_axis)
CS = axs[ax_num].contourf(X, Y, ma, N, **kwargs)
if kwargs.has_key('labels'):
labels = kwargs['labels']
kwargs.pop('labels')
fmt = {}
for l, s in zip(CS.levels, labels):
fmt[l] = s
elif kwargs.has_key('fmt'):
fmt = kwargs('fmt')
else:
fmt = '%1.2f'
if kwargs.has_key('fontsize'):
fontsize = kwargs['fontsize']
else:
fontsize = 12
axs[ax_num].clabel(CS,
CS.levels,
inline=1,
fmt=fmt,
fontsize=fontsize)
pylab.show()
axs[ax_num].set_xlim(x_axis[0], x_axis[-1])
axs[ax_num].set_ylim(y_axis[0], y_axis[-1])
pylab.draw()
lab = self.lookup.keys_to_string(con)
axs[ax_num].set_title(lab)
pylab.show()
pylab.draw()
scatf1 = abs(fudge*2.0*velocity.value/1.064) # single bounce scatter frequency Virgo Scatter eqn 3
scatf2 = 2.0*scatf1
scatf3 = 3.0*scatf1
scatf4 = 4.0*scatf1
scatf5 = 5.0*scatf1
# print max scatter values and their times
print 'max scatter f1 = ' + str(max(scatf1)) + ' Hz'
tofmax = times[argmax(scatf2)]
tofmaxgps = tofmax + start_time
print 'time of max f2 = ' + str(tofmax) + ' s, GPS=' + str(tofmaxgps)
fig = plt.figure(figsize=(12,12))
ax1 = fig.add_subplot(211)
# Plot Spectrogram
if plotspec==1:
im1 = NonUniformImage(ax1, interpolation='bilinear',extent=(min(t),max(t),10,55),cmap='jet')
im1.set_data(t,freq,20.0*log10(Pxx))
if witness_base=="GDS-CALIB_STRAIN":
print "setting color limits for STRAIN"
im1.set_clim(-1000,-800)
elif witness_base=="ASC-AS_B_RF45_Q_YAW_OUT_DQ" or witness_base=="ASC-AS_B_RF36_Q_PIT_OUT_DQ" or witness_base=="ASC-AS_A_RF45_Q_PIT_OUT_DQ" or witness_base=="LSC-MICH_IN1_DQ":
im1.set_clim(-200,20)
elif witness_base == "OMC-LSC_SERVO_OUT_DQ":
im1.set_clim(-240,-85)
ax1.images.append(im1)
#cbar1 = fig.colorbar(im1)
#cbar1.set_clim(-120,-40)
# plot fringe prediction timeseries
#ax1.plot(times,scatf5, c='blue', linewidth='0.2', label='f5')
ax1.plot(times,scatf4, c='purple', linewidth='0.4', label='f4')
# Plotting
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(figsize=(12, 8), nrows=2, ncols=2)
ax1.plot(time / 60, data, 'k', linewidth=1.5)
ax1.set_xlabel('Time (minutes)')
ax1.set_ylabel('Amplitude (No Units)')
ax1.set_xlim(time.min() / 60, time.max() / 60)
ax2.plot(freq[0:len(time) / 2], fft_power[0:len(time) / 2])
ax2.set_xlabel('Frequency (Hertz)')
ax2.set_ylabel('Power (No Units)')
ax2.axis((0.0, 0.5, 0.0, 0.30000000000000004))
extent = [time.min(), time.max(), 0, max(period)]
im = NonUniformImage(ax3, interpolation='nearest', extent=extent)
im.set_cmap('cubehelix_r')
im.set_data(t1, period[:idx], power[:idx, :])
ax3.images.append(im)
ax3.set_ylabel('Period (minutes)')
ax3.set_xlabel('Time (minutes)')
ax3.contour(t1,
period[:idx],
sig95[:idx, :], [-99, 1],
colors='k',
linewidths=2,
extent=extent)
ax3.fill(np.concatenate(
[t1, t1[-1:] + dt, t1[-1:] + dt, t1[:1] - dt, t1[:1] - dt]),
(np.concatenate([coi, [1e-9], period[-1:], period[-1:], [1e-9]])),
'k',
def tsys_show_dynspec(out,idx=None,ampscl=None,domedian=True,frq='linear'):
''' Given "standard" output of rd_tsys_multi(), possibly
calibrated using calibration.sp_apply_cal() and/or
background-subtracted using calibration.sp_bg_subtract(),
make a nice image plot of the dynamic spectrum. The
plot can contain multiple panels if domedian is False,
or plot a single spectrum representing the median of
multiple antennas. Only linear frequency scale is supported
at this time.
'''
from matplotlib.image import NonUniformImage
from matplotlib.dates import AutoDateLocator, DateFormatter
from matplotlib import colors
from matplotlib.pyplot import locator_params
from util import Time
nant, npol, nf, nt = out['tsys'].shape
if idx is None:
idx_ = np.arange(nt)
else:
idx_ = idx
ut = Time(out['ut_mjd'][idx_],format='mjd')
utd = (ut.mjd - int(ut[0].mjd) + 1).astype('float')
good = np.where(out['fghz'] > 2.0)[0]
if frq == 'linear':
fghz = out['fghz'][good]
else:
fghz = np.log10(out['fghz'][good])
locator = AutoDateLocator(interval_multiples=True) #maxticks=7,minticks=3,
tsys = out['tsys'][:,:,good,idx_]
if domedian:
medtsys = np.nanmedian(np.nanmedian(tsys,0),0)
fig = plt.figure()
fig.suptitle('EOVSA Total Power Data for '+ut[0].iso[:10],fontsize=14)
# Plot X-feed
ax = fig.add_subplot(211)
ax.xaxis_date()
ax.set_ylabel('Frequency [GHz]')
ax.set_title('Median Total Power')
extent=[utd[0],utd[-1],fghz[0],fghz[-1]]
# extent=[ut[0],ut[-1],fghz[0],fghz[-1]]
im = NonUniformImage(ax,extent=extent)
if ampscl != None:
im.set_norm(colors.Normalize(vmin=ampscl[0],vmax=ampscl[1]))
im.set_data(utd,fghz,medtsys)
#fig.colorbar(im,ax)
ax.images.append(im)
ax.set_xlim(extent[0],extent[1])
ax.set_ylim(extent[2],extent[3])
ax.xaxis.set_major_locator(locator)
#ax.xaxis.set_minor_locator(MinuteLocator(interval=10))
# Set up date formatting
fmt = DateFormatter('%H:%M:%S')
ax.xaxis.set_major_formatter(fmt)
labels = (10**ax.get_yticks()).astype('str')
for i in range(len(labels)):
labels[i] = labels[i][:4]
ax.set_yticklabels(labels)
# Repeat for Y-feed
ax = fig.add_subplot(212)
ax.xaxis_date()
ax.set_xlabel('Start time '+ut[0].iso[:19]+' UT')
ax.set_title('Median of Y-poln')
ax.set_ylabel('Frequency [GHz]')
im = NonUniformImage(ax,extent=extent)
if ampscl != None:
im.set_norm(colors.Normalize(vmin=ampscl[0],vmax=ampscl[1]))
im.set_data(utd,fghz,medytsys)
#fig.colorbar(im,ax)
ax.images.append(im)
ax.set_xlim(extent[0],extent[1])
ax.set_ylim(extent[2],extent[3])
ax.xaxis.set_major_locator(locator)
# Set up date formatting
ax.xaxis.set_major_formatter(fmt)
ax.set_yticklabels(labels)
plt.draw()
plt.show()
def plot_3D(
Xdata,
Ydata,
Zdata,
colormap="RdBu_r",
color_list=None,
x_min=None,
x_max=None,
y_min=None,
y_max=None,
z_min=None,
z_max=None,
title="",
xlabel="",
ylabel="",
zlabel="",
xticks=None,
yticks=None,
fig=None,
ax=None,
is_logscale_x=False,
is_logscale_y=False,
is_logscale_z=False,
is_disp_title=True,
type_plot="stem",
is_contour=False,
save_path=None,
is_show_fig=None,
is_switch_axes=False,
win_title=None,
font_name="arial",
font_size_title=12,
font_size_label=10,
font_size_legend=8,
):
"""Plots a 3D graph ("stem", "surf" or "pcolor")
Parameters
----------
Xdata : ndarray
array of x-axis values
Ydata : ndarray
array of y-axis values
Zdata : ndarray
array of z-axis values
colormap : colormap object
colormap prescribed by user
x_min : float
minimum value for the x-axis (no automated scaling in 3D)
x_max : float
maximum value for the x-axis (no automated scaling in 3D)
y_min : float
minimum value for the y-axis (no automated scaling in 3D)
y_max : float
maximum value for the y-axis (no automated scaling in 3D)
z_min : float
minimum value for the z-axis (no automated scaling in 3D)
z_max : float
maximum value for the z-axis (no automated scaling in 3D)
title : str
title of the graph
xlabel : str
label for the x-axis
ylabel : str
label for the y-axis
zlabel : str
label for the z-axis
xticks : list
list of ticks to use for the x-axis
fig : Matplotlib.figure.Figure
existing figure to use if None create a new one
ax : Matplotlib.axes.Axes object
ax on which to plot the data
is_logscale_x : bool
boolean indicating if the x-axis must be set in logarithmic scale
is_logscale_y : bool
boolean indicating if the y-axis must be set in logarithmic scale
is_logscale_z : bool
boolean indicating if the z-axis must be set in logarithmic scale
is_disp_title : bool
boolean indicating if the title must be displayed
type_plot : str
type of 3D graph : "stem", "surf", "pcolor" or "scatter"
is_contour : bool
True to show contour line if type_plot = "pcolor"
save_path : str
full path including folder, name and extension of the file to save if save_path is not None
is_show_fig : bool
True to show figure after plot
is_switch_axes : bool
to switch x and y axes
"""
# Set if figure must be shown if is_show_fig is None
if is_show_fig is None:
is_show_fig = True if fig is None else False
# Set if figure is 3D
if type_plot not in ["pcolor", "pcolormesh", "scatter"]:
is_3d = True
else:
is_3d = False
# Set figure if needed
if fig is None and ax is None:
(fig, ax, _, _) = init_fig(fig=None, shape="rectangle", is_3d=is_3d)
if color_list is None:
color_list = COLORS
# Calculate z limits
if z_min is None:
z_min = np_min(Zdata)
if z_max is None:
z_max = np_max(Zdata)
# Check logscale on z axis
if is_logscale_z:
Zdata = 10 * log10(np_abs(Zdata))
clb_format = "%0.0f"
else:
clb_format = "%.4g"
# Switch axes
if is_switch_axes:
Xdata, Ydata = Ydata, Xdata
if len(Xdata.shape) > 1:
Xdata = Xdata.T
if len(Ydata.shape) > 1:
Ydata = Ydata.T
if len(Zdata.shape) > 1:
Zdata = Zdata.T
x_min, y_min = y_min, x_min
x_max, y_max = y_max, x_max
xlabel, ylabel = ylabel, xlabel
xticks, yticks = yticks, xticks
is_logscale_x, is_logscale_y = is_logscale_y, is_logscale_x
# Plot
if type_plot == "stem":
cmap = matplotlib.cm.get_cmap(colormap)
for xi, yi, zi in zip(Xdata, Ydata, Zdata):
line = art3d.Line3D(*zip((xi, yi, 0), (xi, yi, zi)),
linewidth=2.0,
marker="o",
markersize=3.0,
markevery=(1, 1),
color=cmap(0))
ax.add_line(line)
ax.set_xlim3d(x_max, x_min)
ax.set_ylim3d(y_min, y_max)
ax.set_zlim3d(z_min, z_max)
# set correct angle
ax.view_init(elev=20.0, azim=45)
ax.zaxis.set_rotate_label(False)
ax.set_zlabel(zlabel, rotation=0)
ax.xaxis.labelpad = 5
ax.yaxis.labelpad = 5
ax.zaxis.labelpad = 5
if xticks is not None:
ax.xaxis.set_ticks(xticks)
if yticks is not None:
ax.yaxis.set_ticks(yticks)
# white background
ax.xaxis.pane.fill = False
ax.yaxis.pane.fill = False
ax.zaxis.pane.fill = False
ax.xaxis.pane.set_edgecolor("w")
ax.yaxis.pane.set_edgecolor("w")
ax.zaxis.pane.set_edgecolor("w")
if is_logscale_z:
ax.zscale("log")
elif type_plot == "surf":
ax.plot_surface(Xdata, Ydata, Zdata, cmap=colormap)
ax.set_xlim3d(x_max, x_min)
ax.set_ylim3d(y_min, y_max)
ax.set_zlim3d(z_min, z_max)
ax.zaxis.set_rotate_label(False)
ax.set_zlabel(zlabel, rotation=0)
ax.xaxis.labelpad = 5
ax.yaxis.labelpad = 5
ax.zaxis.labelpad = 5
if xticks is not None:
ax.xaxis.set_ticks(xticks)
if yticks is not None:
ax.yaxis.set_ticks(yticks)
# white background
ax.xaxis.pane.fill = False
ax.yaxis.pane.fill = False
ax.zaxis.pane.fill = False
ax.xaxis.pane.set_edgecolor("w")
ax.yaxis.pane.set_edgecolor("w")
ax.zaxis.pane.set_edgecolor("w")
if is_logscale_z:
ax.zscale("log")
elif type_plot == "pcolor":
Zdata[Zdata < z_min] = z_min
Zdata[Zdata > z_max] = z_max
# Handle descending order axes (e.g. spectrogram of run-down in freq/rpm map)
if Ydata[-1] < Ydata[0]:
Ydata = Ydata[::-1]
Zdata = Zdata[:, ::-1]
if Xdata[-1] < Xdata[0]:
Xdata = Xdata[::-1]
Zdata = Zdata[::-1, :]
im = NonUniformImage(
ax,
interpolation="bilinear",
extent=(x_min, x_max, y_max, y_min),
cmap=colormap,
picker=10,
)
im.set_data(Xdata, Ydata, Zdata.T)
im.set_clim(z_min, z_max)
ax.images.append(im)
if is_contour:
ax.contour(Xdata, Ydata, Zdata.T, colors="black", linewidths=0.8)
clb = fig.colorbar(im, ax=ax, format=clb_format)
clb.ax.set_title(zlabel, fontsize=font_size_legend, fontname=font_name)
clb.ax.tick_params(labelsize=font_size_legend)
for l in clb.ax.yaxis.get_ticklabels():
l.set_family(font_name)
if xticks is not None:
ax.xaxis.set_ticks(xticks)
if yticks is not None:
ax.yaxis.set_ticks(yticks)
ax.set_xlim([x_min, x_max])
ax.set_ylim([y_min, y_max])
elif type_plot == "pcolormesh":
c = ax.pcolormesh(Xdata,
Ydata,
Zdata,
cmap=colormap,
shading="gouraud",
antialiased=True)
clb = fig.colorbar(c, ax=ax)
clb.ax.set_title(zlabel, fontsize=font_size_legend, fontname=font_name)
clb.ax.tick_params(labelsize=font_size_legend)
for l in clb.ax.yaxis.get_ticklabels():
l.set_family(font_name)
if xticks is not None:
ax.xaxis.set_ticks(xticks)
if yticks is not None:
ax.yaxis.set_ticks(yticks)
ax.set_xlim([x_min, x_max])
ax.set_ylim([y_min, y_max])
elif type_plot == "scatter":
c = ax.scatter(
Xdata,
Ydata,
# s=10,
c=Zdata,
marker=".",
cmap=colormap,
vmin=z_min,
vmax=z_max,
picker=True,
pickradius=5,
)
clb = fig.colorbar(c, ax=ax)
clb.ax.set_title(zlabel, fontsize=font_size_legend, fontname=font_name)
clb.ax.tick_params(labelsize=font_size_legend)
for l in clb.ax.yaxis.get_ticklabels():
l.set_family(font_name)
if xticks is not None:
ax.xaxis.set_ticks(xticks)
if yticks is not None:
ax.yaxis.set_ticks(yticks)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
if is_logscale_x:
ax.xscale("log")
if is_logscale_y:
ax.yscale("log")
if is_disp_title:
ax.set_title(title)
if is_3d:
for item in ([ax.xaxis.label, ax.yaxis.label, ax.zaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels() +
ax.get_zticklabels()):
item.set_fontsize(font_size_label)
else:
for item in ([ax.xaxis.label, ax.yaxis.label] + ax.get_xticklabels() +
ax.get_yticklabels()):
item.set_fontsize(font_size_label)
item.set_fontname(font_name)
ax.title.set_fontsize(font_size_title)
ax.title.set_fontname(font_name)
if save_path is not None:
save_path = save_path.replace("\\", "/")
fig.savefig(save_path)
plt.close()
if is_show_fig:
fig.show()
if win_title:
manager = plt.get_current_fig_manager()
if manager is not None:
manager.set_window_title(win_title)
def test_nonuniformimage_setcmap():
ax = plt.gca()
im = NonUniformImage(ax)
im.set_cmap('Blues')
def tsys_show_dynspec(out, idx=None, ampscl=None, domedian=True, frq='linear'):
''' Given "standard" output of rd_tsys_multi(), possibly
calibrated using calibration.sp_apply_cal() and/or
background-subtracted using calibration.sp_bg_subtract(),
make a nice image plot of the dynamic spectrum. The
plot can contain multiple panels if domedian is False,
or plot a single spectrum representing the median of
multiple antennas. Only linear frequency scale is supported
at this time.
'''
from matplotlib.image import NonUniformImage
from matplotlib.dates import AutoDateLocator, DateFormatter
from matplotlib import colors
from matplotlib.pyplot import locator_params
from util import Time
nant, npol, nf, nt = out['tsys'].shape
if idx is None:
idx_ = np.arange(nt)
else:
idx_ = idx
ut = Time(out['ut_mjd'][idx_], format='mjd')
utd = (ut.mjd - int(ut[0].mjd) + 1).astype('float')
good = np.where(out['fghz'] > 2.0)[0]
if frq == 'linear':
fghz = out['fghz'][good]
else:
fghz = np.log10(out['fghz'][good])
locator = AutoDateLocator(interval_multiples=True) #maxticks=7,minticks=3,
tsys = out['tsys'][:, :, good, idx_]
if domedian:
medtsys = np.nanmedian(np.nanmedian(tsys, 0), 0)
fig = plt.figure()
fig.suptitle('EOVSA Total Power Data for ' + ut[0].iso[:10],
fontsize=14)
# Plot X-feed
ax = fig.add_subplot(211)
ax.xaxis_date()
ax.set_ylabel('Frequency [GHz]')
ax.set_title('Median Total Power')
extent = [utd[0], utd[-1], fghz[0], fghz[-1]]
# extent=[ut[0],ut[-1],fghz[0],fghz[-1]]
im = NonUniformImage(ax, extent=extent)
if ampscl != None:
im.set_norm(colors.Normalize(vmin=ampscl[0], vmax=ampscl[1]))
im.set_data(utd, fghz, medtsys)
#fig.colorbar(im,ax)
ax.images.append(im)
ax.set_xlim(extent[0], extent[1])
ax.set_ylim(extent[2], extent[3])
ax.xaxis.set_major_locator(locator)
#ax.xaxis.set_minor_locator(MinuteLocator(interval=10))
# Set up date formatting
fmt = DateFormatter('%H:%M:%S')
ax.xaxis.set_major_formatter(fmt)
labels = (10**ax.get_yticks()).astype('str')
for i in range(len(labels)):
labels[i] = labels[i][:4]
ax.set_yticklabels(labels)
# Repeat for Y-feed
ax = fig.add_subplot(212)
ax.xaxis_date()
ax.set_xlabel('Start time ' + ut[0].iso[:19] + ' UT')
ax.set_title('Median of Y-poln')
ax.set_ylabel('Frequency [GHz]')
im = NonUniformImage(ax, extent=extent)
if ampscl != None:
im.set_norm(colors.Normalize(vmin=ampscl[0], vmax=ampscl[1]))
im.set_data(utd, fghz, medytsys)
#fig.colorbar(im,ax)
ax.images.append(im)
ax.set_xlim(extent[0], extent[1])
ax.set_ylim(extent[2], extent[3])
ax.xaxis.set_major_locator(locator)
# Set up date formatting
ax.xaxis.set_major_formatter(fmt)
ax.set_yticklabels(labels)
plt.draw()
plt.show()
def _do_2d_output(self, hist, idims, midpoints, binbounds):
enehist = self._ener_zero(hist)
log10hist = numpy.log10(hist)
if self.hdf5_output_filename:
with h5py.File(self.hdf5_output_filename, 'w') as output_h5:
h5io.stamp_creator_data(output_h5)
output_h5.attrs['source_data'] = os.path.abspath(
self.input_h5.filename)
output_h5.attrs['source_dimensions'] = numpy.array(
idims, numpy.min_scalar_type(max(idims)))
output_h5.attrs['source_dimension_labels'] = numpy.array(
[dim['label'] for dim in self.dimensions])
for idim in idims:
output_h5['midpoints_{}'.format(idim)] = midpoints
output_h5['histogram'] = hist
if self.plot_output_filename:
if self.plotscale == 'energy':
plothist = enehist
label = r'$\Delta F(\vec{x})\,/\,kT$' + '\n' + r'$\left[-\ln\,P(x)\right]$'
elif self.plotscale == 'log10':
plothist = log10hist
label = r'$\log_{10}\ P(\vec{x})$'
else:
plothist = hist
plothist[~numpy.isfinite(plothist)] = numpy.nan
label = r'$P(\vec{x})$'
try:
vmin, vmax = self.plotrange
except TypeError:
vmin, vmax = None, None
pyplot.figure()
# Transpose input so that axis 0 is displayed as x and axis 1 is displayed as y
# pyplot.imshow(plothist.T, interpolation='nearest', aspect='auto',
# extent=(midpoints[0][0], midpoints[0][-1], midpoints[1][0], midpoints[1][-1]),
# origin='lower', vmin=vmin, vmax=vmax)
# The following reproduces the former calls to imshow and colorbar
norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
ax = pyplot.gca()
nui = NonUniformImage(ax,
extent=(midpoints[0][0], midpoints[0][-1],
midpoints[1][0], midpoints[1][-1]),
origin='lower',
norm=norm)
nui.set_data(midpoints[0], midpoints[1], plothist.T)
ax.images.append(nui)
ax.set_xlim(midpoints[0][0], midpoints[0][-1])
ax.set_ylim(midpoints[1][0], midpoints[1][-1])
cb = pyplot.colorbar(nui)
cb.set_label(label)
pyplot.xlabel(self.dimensions[0]['label'])
pyplot.xlim(self.dimensions[0].get('lb'),
self.dimensions[0].get('ub'))
pyplot.ylabel(self.dimensions[1]['label'])
pyplot.ylim(self.dimensions[1].get('lb'),
self.dimensions[1].get('ub'))
if self.plottitle:
pyplot.title(self.plottitle)
if self.postprocess_function:
self.postprocess_function(plothist, midpoints, binbounds)
if self.plot_contour:
pyplot.contour(midpoints[0], midpoints[1], plothist.T)
pyplot.savefig(self.plot_output_filename)
for i in range(len(var)):
var[i] = np.ma.masked_where(var[i] <= 1.0e-30, var[i])
if output_var == "beta":
mag_press = (bx * bx + by * by) / (8.0 * np.pi)
for i in range(len(var)):
var[i] = var[i] / (mag_press**2)
var[i] = mag_press * var[i]
fig, ax = plt.subplots()
frame = 0
x = X[:, 0]
y = Y[0, :]
if output_var == "rad" or output_var == "beta":
im = NonUniformImage(ax, animated=True, origin='lower', extent=(x_min,x_max,y_min,y_max),\
interpolation='nearest', norm=matplotlib.colors.SymLogNorm(linthresh=1e-5, base=10))
im.set_data(x, y, np.transpose(var[frame]))
im.set_clim(vmin=1e-4)
ax.add_image(im)
ax.set(xlim=(x_min, x_max),
ylim=(y_min, y_max),
xlabel="x (cm)",
ylabel="y (cm)",
title=output_var + ", t=" + str(t[frame]))
var_colorbar = fig.colorbar(im)
var_colorbar.set_label(fullnames[output_var] + " (" +
fullunits[output_var] + ")")
elif output_var == "dt" or output_var == "dt_thermal" or output_var == "dt_rad":
im = NonUniformImage(ax, animated=True, origin='lower', extent=(x_min,x_max,y_min,y_max),\
interpolation='nearest', norm=matplotlib.colors.SymLogNorm(linthresh=1e-10, base=10))
im.set_data(x, y, np.transpose(var[frame]))
def test_nonuniformimage_setcmap():
ax = plt.gca()
im = NonUniformImage(ax)
im.set_cmap('Blues')
def plot(self):
plt.clf()
ax = self.figure.add_subplot(111)
slice_str = '[?]'
# extension = []
if self.view == 'X-Y':
image = self.data[:,:,self.slider.value()]
slice_str = 'z = %f ' % self.b.z[self.slider.value()]
ax.set_ylabel('y-direction')
ax.set_xlabel('x-direction')
# extension = [0, self.param['mx'], 0, self.param['my']]
elif self.view == 'X-Z':
image = self.data[:,self.slider.value(),:]
slice_str = 'y = %f ' % self.b.y[self.slider.value()]
ax.set_ylabel('z-direction')
ax.set_xlabel('x-direction')
# extension = [0, self.param['mx'], 0, self.param['mz']]
elif self.view == 'Y-Z':
image = self.data[self.slider.value(),:,:]
slice_str = 'x = %f ' % self.b.x[self.slider.value()]
ax.set_ylabel('z-direction')
ax.set_xlabel('y-direction')
# extension = [0, self.param['my'], 0, self.param['mz']]
# image = np.fliplr(image)
# image = np.rot90(image,k=3)
label = "Value"
color = cm.get_cmap('jet')
ax.set_title("[%s] %s (Snap: %s) for %s \n[time: %s]" % (self.tag, self.base_name, self.snap_n, slice_str, str(datetime.timedelta(seconds=self.param['t']*self.param['u_t']))))
# ax.xaxis.set_major_locator(ticker.MultipleLocator(int(64)))
# ax.yaxis.set_major_locator(ticker.MultipleLocator(int(64)))
if self.check_si.isChecked():
if self.tag == 'r':
image = image * self.param['u_r']
unit_label = "[g/cm3]"
label = "Value %s" % unit_label
elif (self.tag == 'bx' or self.tag == 'by' or self.tag == 'bz'):
image = image * self.param['u_b']
unit_label = "[G]"
label = "Value %s" % unit_label
elif (self.tag == 'px' or self.tag == 'py' or self.tag == 'pz'):
image = image * self.param['u_p']
unit_label = "[Ba]"
label = "Value %s" % unit_label
elif self.tag == 'e':
image = image * self.param['u_e']
unit_label = "[erg]"
label = "Value %s" % unit_label
if self.check_abs.isChecked():
image = np.absolute(image)
label = "ABS( %s )" % label
if self.check_log.isChecked():
image = np.log10(image)
label = "Log10( %s )" % label
if self.check_bw.isChecked():
# color = cm.get_cmap('gist_yarg')
color = cm.get_cmap('Greys_r') # Mats favorite color palette
if self.view == 'X-Y':
ax.set_ylabel('y-direction [Mm]')
ax.set_xlabel('x-direction [Mm]')
im = NonUniformImage(ax, interpolation='bilinear', extent=(self.b.x.min(),self.b.x.max(),self.b.y.min(),self.b.y.max()), cmap=color)
im.set_data(self.b.x, self.b.y, np.fliplr(zip(*image[::-1])))
ax.images.append(im)
ax.set_xlim(self.b.x.min(),self.b.x.max())
ax.set_ylim(self.b.y.min(),self.b.y.max())
ax.xaxis.set_major_locator(ticker.MultipleLocator(int(4)))
ax.yaxis.set_major_locator(ticker.MultipleLocator(int(4)))
elif self.view == 'X-Z':
ax.set_ylabel('z-direction [Mm]')
ax.set_xlabel('x-direction [Mm]')
im = NonUniformImage(ax, interpolation='bilinear', extent=(self.b.x.min(),self.b.x.max(),self.b.z.min(),self.b.z.max()), cmap=color)
im.set_data(self.b.x, self.b.z[::-1], np.flipud(np.fliplr(zip(*image[::-1]))))
ax.images.append(im)
ax.set_xlim(self.b.x.min(),self.b.x.max())
ax.set_ylim(self.b.z.max(),self.b.z.min())
ax.xaxis.set_major_locator(ticker.MultipleLocator(int(4)))
ax.yaxis.set_major_locator(ticker.MultipleLocator(int(2)))
elif self.view == 'Y-Z':
ax.set_ylabel('z-direction [Mm]')
ax.set_xlabel('y-direction [Mm]')
im = NonUniformImage(ax, interpolation='bilinear', extent=(self.b.y.min(),self.b.y.max(),self.b.z.min(),self.b.z.max()), cmap=color)
im.set_data(self.b.y, self.b.z[::-1], np.flipud(np.fliplr(zip(*image[::-1]))))
ax.images.append(im)
ax.set_xlim(self.b.y.min(),self.b.y.max())
ax.set_ylim(self.b.z.max(),self.b.z.min())
ax.xaxis.set_major_locator(ticker.MultipleLocator(int(4)))
ax.yaxis.set_major_locator(ticker.MultipleLocator(int(2)))
# im = ax.imshow(image, interpolation='none', origin='lower', cmap=color, extent=extension)
# ax.text(0.025, 0.025, (r'$\langle B_{z} \rangle = %2.2e$'+'\n'+r'$\langle |B_{z}| \rangle = %2.2e$') % (np.average(img),np.average(np.absolute(img))), ha='left', va='bottom', transform=ax.transAxes)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
plt.colorbar(im, cax=cax,label=label)
self.canvas.draw()
def imshow_field(field,
grid=None,
ax=None,
vmin=None,
vmax=None,
aspect='equal',
norm=None,
interpolation=None,
non_linear_axes=False,
cmap=None,
mask=None,
mask_color='k',
*args,
**kwargs):
'''Display a two-dimensional image on a matplotlib figure.
This function serves as an easy replacement for the matplotlib.pyplot.imshow() function.
Its signature mostly folows that of matplotlib, with a few minor differences.
Parameters
----------
field : Field or ndarray
The field that we want to display. If this is an ndarray,
then the parameter `grid` needs to be supplied. If the field
is complex, then it will be automatically fed into :func:`complex_field_to_rgb`.
If the field is a vector field with length 3 or 4, these will be
interpreted as an RGB or RGBA field.
grid : Grid or None
If a grid is supplied, it will be used instead of the grid of `field`.
ax : matplotlib axes
The axes which to draw on. If it is not given, the current axes will be used.
vmin : scalar
The minimum value on the colorbar. If it is not given, then the minimum value
of the field will be used.
vmax : scalar
The maximum value on the colorbar. If it is not given, then the maximum value
of the field will be used.
aspect : ['auto', 'equal', scalar]
If 'auto', changes the image aspect ratio to match that of the axes.
If 'equal', changes the axes aspect ratio to match that of the image.
norm : Normalize
A Normalize instance is used to scale the input to the (0, 1) range for
input to the `cmap`. If it is not given, a linear scale will be used.
interpolation : string
The interpolation method used. The default is 'nearest'. Supported values
are {'nearest', 'bilinear'}.
non_linear_axes : boolean
If axes are scaled in a non-linear way, for example on a log plot, then imshow_field
needs to use a more expensive implementation. This parameter is to indicate that this
algorithm needs to be used.
cmap : Colormap
The colormap with which to plot the image. It is ignored if a complex
field or a vector field is supplied.
mask : field or ndarray
If part of the image needs to be masked, this mask is overlayed on top of the image.
This is for example useful when plotting a phase pattern on a certain aperture, which
has no meaning outside of the aperture. Masks can be partially translucent, and will
be automatically scaled between (0, 1). Zero means invisible, one means visible.
mask_color : Color
The color of the mask, if it is used.
Returns
-------
AxesImage
The produced image.
'''
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.image import NonUniformImage
if ax is None:
ax = plt.gca()
ax.set_aspect(aspect)
if grid is None:
grid = field.grid
else:
field = Field(field, grid)
# If field is complex, draw complex
if np.iscomplexobj(field):
f = complex_field_to_rgb(field, rmin=vmin, rmax=vmax, norm=norm)
vmin = None
vmax = None
norm = None
else:
f = field
# Automatically determine vmin, vmax, norm if not overridden
if norm is None and not np.iscomplexobj(field):
if vmin is None:
vmin = np.nanmin(f)
if vmax is None:
vmax = np.nanmax(f)
norm = mpl.colors.Normalize(vmin, vmax)
# Get extent
c_grid = grid.as_('cartesian')
min_x, min_y, max_x, max_y = c_grid.x.min(), c_grid.y.min(), c_grid.x.max(
), c_grid.y.max()
if grid.is_separated and grid.is_('cartesian'):
# We can draw this directly
x, y = grid.coords.separated_coords
z = f.shaped
if np.iscomplexobj(field) or field.tensor_order > 0:
z = np.rollaxis(z, 0, z.ndim)
else:
# We can't draw this directly.
raise NotImplementedError()
if non_linear_axes:
# Use pcolormesh to display
x_mid = (x[1:] + x[:-1]) / 2
y_mid = (y[1:] + y[:-1]) / 2
x2 = np.concatenate(
([1.5 * x[0] - 0.5 * x[1]], x_mid, [1.5 * x[-1] - 0.5 * x[-2]]))
y2 = np.concatenate(
([1.5 * y[0] - 0.5 * y[1]], y_mid, [1.5 * y[-1] - 0.5 * y[-2]]))
X, Y = np.meshgrid(x2, y2)
im = ax.pcolormesh(X,
Y,
z,
*args,
norm=norm,
rasterized=True,
cmap=cmap,
**kwargs)
else:
# Use NonUniformImage to display
im = NonUniformImage(ax,
extent=(min_x, max_x, min_y, max_y),
interpolation=interpolation,
norm=norm,
cmap=cmap,
*args,
**kwargs)
im.set_data(x, y, z)
from matplotlib.patches import Rectangle
patch = Rectangle((min_x, min_y),
max_x - min_x,
max_y - min_y,
facecolor='none')
ax.add_patch(patch)
im.set_clip_path(patch)
ax.images.append(im)
ax.set_xlim(min_x, max_x)
ax.set_ylim(min_y, max_y)
if mask is not None:
one = np.ones(grid.size)
col = mpl.colors.to_rgb(mask_color)
m = np.array([
one * col[0], one * col[1], one * col[2],
1 - mask / np.nanmax(mask)
])
imshow_field(m, grid, ax=ax)
num_rows, num_cols = field.grid.shape
def format_coord(x, y):
col = int(np.round((x - min_x) / (max_x - min_x) * (num_cols - 1)))
row = int(np.round((y - min_y) / (max_y - min_y) * (num_rows - 1)))
if col >= 0 and col < num_cols and row >= 0 and row < num_rows:
z = field.shaped[row, col]
if np.iscomplexobj(z):
return 'x=%0.3g, y=%0.3g, z=%0.3g + 1j * %0.3g = %0.3g * exp(1j * %0.2f)' % (
x, y, z.real, z.imag, np.abs(z), np.angle(z))
else:
return 'x=%0.3g, y=%0.3g, z=%0.3g' % (x, y, z)
return 'x=%0.3g, y=%0.3g' % (x, y)
ax.format_coord = format_coord
ax._sci(im)
return im
def single_plot(data, x, y, axes=None, beta=None, cbar_label='',
cmap=plt.get_cmap('RdBu'), vmin=None, vmax=None,
phase_speeds=True, manual_locations=False, **kwargs):
"""
Plot a single frame Time-Distance Diagram on physical axes.
This function uses mpl NonUniformImage to plot a image using x and y arrays,
it will also optionally over plot in contours beta lines.
Parameters
----------
data: np.ndarray
The 2D image to plot
x: np.ndarray
The x coordinates
y: np.ndarray
The y coordinates
axes: matplotlib axes instance [*optional*]
The axes to plot the data on, if None, use plt.gca().
beta: np.ndarray [*optional*]
The array to contour over the top, default to none.
cbar_label: string [*optional*]
The title label for the colour bar, default to none.
cmap: A matplotlib colour map instance [*optional*]
The colourmap to use, default to 'RdBu'
vmin: float [*optional*]
The min scaling for the image, default to the image limits.
vmax: float [*optional*]
The max scaling for the image, default to the image limits.
phase_speeds : bool
Add phase speed lines to the plot
manual_locations : bool
Array for clabel locations.
Returns
-------
None
"""
if axes is None:
axes = plt.gca()
x = x[:xxlim]
data = data[:,:xxlim]
im = NonUniformImage(axes,interpolation='nearest',
extent=[x.min(),x.max(),y.min(),y.max()],rasterized=False)
im.set_cmap(cmap)
if vmin is None and vmax is None:
lim = np.max([np.nanmax(data),
np.abs(np.nanmin(data))])
im.set_clim(vmax=lim,vmin=-lim)
else:
im.set_clim(vmax=vmax,vmin=vmin)
im.set_data(x,y,data)
im.set_interpolation('nearest')
axes.images.append(im)
axes.set_xlim(x.min(),x.max())
axes.set_ylim(y.min(),y.max())
cax0 = make_axes_locatable(axes).append_axes("right", size="5%", pad=0.05)
cbar0 = plt.colorbar(im, cax=cax0, ticks=mpl.ticker.MaxNLocator(7))
cbar0.set_label(cbar_label)
cbar0.solids.set_edgecolor("face")
kwergs = {'levels': [1., 1/3., 1/5., 1/10., 1/20.]}
kwergs.update(kwargs)
if beta is not None:
ct = axes.contour(x,y,beta[:,:xxlim],colors=['k'], **kwergs)
plt.clabel(ct,fontsize=14,inline_spacing=3, manual=manual_locations,
fmt=mpl.ticker.FuncFormatter(betaswap))
axes.set_xlabel("Time [s]")
axes.set_ylabel("Height [Mm]")