def hinton(W, maxWeight=None):
"""
Draws a Hinton diagram for visualizing a weight matrix.
Temporarily disables matplotlib interactive mode if it is on,
otherwise this takes forever.
"""
reenable = False
if P.isinteractive():
P.ioff()
P.clf()
height, width = W.shape
if not maxWeight:
maxWeight = 2**np.ceil(np.log(np.max(np.abs(W))) / np.log(2))
P.fill(np.array([0, width, width, 0]), np.array([0, 0, height, height]),
'gray')
P.axis('off')
P.axis('equal')
for x in range(width):
for y in range(height):
w = W[y, x]
weight = w / maxWeight if w > 0 else min(1, -w / maxWeight)
color = 'white' if w > 0 else 'black'
_blob((x + 1) - 0.5, height - (y + 1) + 0.5, weight, color)
if reenable:
P.ion()
P.show()
def ploterrorbars(x, y, dy, ymax=None, color='k', xfac=1,
ax=None, xlog=False, **other):
if ax is None:
ax = pylab.gca()
if ymax is None:
ymin = y - dy
ymax = y + dy
else:
ymin = dy
ymax = ymax
if xlog:
dxlog = 0.005 * xfac * (np.log10(xlim()[1]) - np.log10(xlim()[0]))
print('dxlog', dxlog)
dx = dxlog * x / np.log10(np.e)
else:
dx = 0.005 * xfac * (xlim()[1] - xlim()[0])
xtemp = xlim()
ytemp = ylim()
pylab.ioff()
for i in range(len(x)):
ax.plot([x[i], x[i]], [ymin[i], ymax[i]], color=color, **other)
ax.plot([x[i] - dx, x[i] + dx],
[ymax[i], ymax[i]], color=color, **other)
ax.plot([x[i] - dx, x[i] + dx],
[ymin[i], ymin[i]], color=color, **other)
if pylab.isinteractive():
pylab.ion()
pylab.show()
ax.set_xlim(xtemp[0], xtemp[1])
ax.set_ylim(ytemp[0], ytemp[1])
def hinton(W, maxWeight=None):
"""
Source: http://wiki.scipy.org/Cookbook/Matplotlib/HintonDiagrams
Draws a Hinton diagram for visualizing a weight matrix.
Temporarily disables matplotlib interactive mode if it is on,
otherwise this takes forever.
"""
reenable = False
if pl.isinteractive():
pl.ioff()
pl.clf()
height, width = W.shape
if not maxWeight:
maxWeight = 2**np.ceil(np.log(np.max(np.abs(W)))/np.log(2))
pl.fill(np.array([0,width,width,0]),np.array([0,0,height,height]),'gray')
pl.axis('off')
pl.axis('equal')
for x in xrange(width):
for y in xrange(height):
_x = x+1
_y = y+1
w = W[y,x]
if w > 0:
_blob(_x - 0.5, height - _y + 0.5, min(1,w/maxWeight),'white')
elif w < 0:
_blob(_x - 0.5, height - _y + 0.5, min(1,-w/maxWeight),'black')
if reenable:
pl.ion()
pl.show()
def hinton(W, maxWeight=None):
"""
Draws a Hinton diagram for visualizing a weight matrix.
Temporarily disables matplotlib interactive mode if it is on,
otherwise this takes forever.
"""
import pylab
reenable = False
if pylab.isinteractive():
pylab.ioff()
pylab.clf()
height, width = W.shape
if not maxWeight:
maxWeight = 2**np.ceil(np.log(np.max(np.abs(W))) / np.log(2))
pylab.fill(np.array([0, width, width, 0]), np.array([0, 0, height,
height]), 'gray')
pylab.axis('off')
pylab.axis('equal')
for x in range(width):
for y in range(height):
_x = x + 1
_y = y + 1
w = W[y, x]
if w > 0:
_blob(_x - 0.5, height - _y + 0.5, min(1, w / maxWeight),
'white')
elif w < 0:
_blob(_x - 0.5, height - _y + 0.5, min(1, -w / maxWeight),
'black')
if reenable:
pylab.ion()
pylab.show()
def hinton(W,filename="hinton.pdf", maxWeight=None):
"""
Draws a Hinton diagram for visualizing a weight matrix.
Temporarily disables matplotlib interactive mode if it is on,
otherwise this takes forever.
"""
reenable = False
if P.isinteractive():
P.ioff()
P.clf()
ax=P.subplot(111)
height, width = W.shape
if not maxWeight:
maxWeight = 2**np.ceil(np.log(np.max(np.abs(W)))/np.log(2))
P.fill(np.array([0,width,width,0]),np.array([0,0,height,height]),'gray')
P.axis('off')
#P.axis('equal')
ax.set_yticklabels(['25','20','15','10','5','0'])
for x in xrange(width):
for y in xrange(height):
_x = x+1
_y = y+1
w = W[y,x]
if w > 0:
_blob(_x - 0.5, height - _y + 0.5, min(1,w/maxWeight),'white')
elif w < 0:
_blob(_x - 0.5, height - _y + 0.5, min(1,-w/maxWeight),'black')
if reenable:
P.ion()
P.title(filename)
P.savefig(filename)
def __init__(self, parent=None, pid=None, figure_or_canvas=None):
super().__init__(parent, pid, 'plot')
self.fileMenu = self.menuBar().addMenu("&File")
self.addMenuItem(self.fileMenu, 'Open...', self.openFigure)
self.addMenuItem(self.fileMenu, 'Save As...', self.saveFigure)
self.addMenuItem(self.fileMenu,
'Close',
self.close_me_from_menu,
icon='cross.png')
self.addMenuItem(self.fileMenu, 'Close Others', self.close_others)
self.editMenu = CheckMenu("&Edit", self.menuBar())
self.menuBar().addMenu(self.editMenu)
self.addMenuItem(self.editMenu,
'Copy',
self.copyToCb,
statusTip="Copy to Clipboard",
icon='page_copy.png')
self.viewMenu = CheckMenu("&View", self.menuBar())
self.menuBar().addMenu(self.viewMenu)
self.addMenuItem(self.viewMenu,
'Refresh',
self.refresh,
statusTip="Refresh the plot",
icon='update.png')
self.addMenuItem(self.viewMenu,
'Grid',
self.grid,
statusTip="Show/hide grid",
icon='layer_grid.png')
self.addMenuItem(
self.viewMenu,
'Tight',
self.tight,
statusTip="Adjust the padding between and around subplots",
icon='canvas_size.png')
self.addMenuItem(self.viewMenu,
'Interactive',
self.toggle_interactive,
checkcall=lambda: pylab.isinteractive())
self.addBaseMenu()
if hasattr(figure_or_canvas, 'canvas'):
self.figure = figure_or_canvas
self.canvas = figure_or_canvas.canvas
else:
self.figure = figure_or_canvas.figure
self.canvas = figure_or_canvas
self.setCentralWidget(self.canvas)
self.nav = NavigationToolbar(self.canvas, self)
self.nav.setIconSize(QtCore.QSize(20, 20))
self.addToolBar(self.nav)
self.statusBar().hide()
def hinton(W, out_file=None, maxWeight=None):
"""
Draws a Hinton diagram for visualizing a weight matrix.
Temporarily disables matplotlib interactive mode if it is on,
otherwise this takes forever.
"""
reenable = False
if P.isinteractive():
P.ioff()
P.clf()
height, width = W.shape
if not maxWeight:
maxWeight = 2**N.ceil(N.log(N.max(N.abs(W)))/N.log(2))
P.gca().set_position([0, 0, 1, 1])
P.fill(N.array([0,width,width,0]),N.array([0,0,height,height]),'gray')
P.axis('off')
P.axis('equal')
for x in xrange(width):
for y in xrange(height):
_x = x+1
_y = y+1
w = W[y,x]
if w > 0:
_blob(_x - 0.5, height - _y + 0.5, min(1,w/maxWeight),'white')
elif w < 0:
_blob(_x - 0.5, height - _y + 0.5, min(1,-w/maxWeight),'black')
if reenable:
P.ion()
#P.show()
if out_file:
#P.savefig(out_file, format='png', bbox_inches='tight', pad_inches=0)
fig = P.gcf()
fig.subplots_adjust()
fig.savefig(out_file)
def hinton(W, filename="hinton.pdf", maxWeight=None):
"""
Draws a Hinton diagram for visualizing a weight matrix.
Temporarily disables matplotlib interactive mode if it is on,
otherwise this takes forever.
"""
reenable = False
if P.isinteractive():
P.ioff()
P.clf()
ax = P.subplot(111)
height, width = W.shape
if not maxWeight:
maxWeight = 2**np.ceil(np.log(np.max(np.abs(W))) / np.log(2))
P.fill(np.array([0, width, width, 0]), np.array([0, 0, height, height]),
'gray')
P.axis('off')
#P.axis('equal')
ax.set_yticklabels(['25', '20', '15', '10', '5', '0'])
for x in xrange(width):
for y in xrange(height):
_x = x + 1
_y = y + 1
w = W[y, x]
if w > 0:
_blob(_x - 0.5, height - _y + 0.5, min(1, w / maxWeight),
'white')
elif w < 0:
_blob(_x - 0.5, height - _y + 0.5, min(1, -w / maxWeight),
'black')
if reenable:
P.ion()
P.title(filename)
P.savefig(filename)
def __init__(self, features, **kwargs):
self.features = features
self.fc_function = None
self.lw_function = None
wasinteractive = pylab.isinteractive()
if wasinteractive:
pylab.ioff()
self.shapes = []
for i, feature in enumerate(self.features):
if isinstance(feature.shape, g.MultiPolygon):
self.shapes.append([])
for shape in feature.shape.geoms:
x, y = numpy.asarray(shape.exterior)[:, :2].swapaxes(0, 1)
self.shapes[-1].append(pylab.fill(x, y, **kwargs)[0])
if isinstance(feature.shape, g.Polygon):
x, y = numpy.asarray(feature.shape.exterior)[:, :2].swapaxes(
0, 1)
self.shapes.append(pylab.fill(x, y, **kwargs))
elif isinstance(feature.shape, g.MultiLineString):
self.shapes.append([])
for shape in feature.shape.geoms:
x, y = numpy.asarray(shape)[:, :2].swapaxes(0, 1)
self.shapes[-1].append(pylab.plot(x, y, **kwargs)[0])
elif isinstance(feature.shape, g.LineString):
x, y = numpy.asarray(feature.shape).swapaxes(0, 1)
self.shapes.append(pylab.plot(x, y, **kwargs))
elif isinstance(feature.shape, g.Point):
x, y = feature.shape.x, feature.shape.y
self.shapes.append(pylab.plot([x], [y], **kwargs)[0])
pylab.axis('equal')
if wasinteractive:
pylab.ion()
pylab.draw()
def hinton(W, name, maxWeight=None):
"""
Draws a Hinton diagram for visualizing a weight matrix.
Temporarily disables matplotlib interactive mode if it is on,
otherwise this takes forever.
"""
reenable = False
if P.isinteractive():
P.ioff()
P.clf()
height, width = W.shape
if not maxWeight:
maxWeight = 2**N.ceil(N.log(N.max(N.abs(W))) / N.log(2))
P.fill(N.array([0, width, width, 0]), N.array([0, 0, height, height]),
'gray')
P.axis('off')
P.axis('equal')
for x in range(width):
for y in range(height):
_x = x + 1
_y = y + 1
w = W[y, x]
if w > 0:
_blob(_x - 0.5, height - _y + 0.5, min(1, w / maxWeight),
'white')
elif w < 0:
_blob(_x - 0.5, height - _y + 0.5, min(1, -w / maxWeight),
'black')
if reenable:
P.ion()
P.savefig('hinton' + name + '.png')
P.show()
def __call__(self,output_fn,init_time=0,final_time=None,**params):
p=ParamOverrides(self,params)
if final_time is None:
final_time=topo.sim.time()
attrs = p.attrib_names if len(p.attrib_names)>0 else output_fn.attrib_names
for a in attrs:
pylab.figure(figsize=(6,4))
isint=pylab.isinteractive()
pylab.ioff()
pylab.grid(True)
ylabel=p.ylabel
pylab.ylabel(a+" "+ylabel)
pylab.xlabel('Iteration Number')
coords = p.units if len(p.units)>0 else output_fn.units
for coord in coords:
y_data=[y for (x,y) in output_fn.values[a][coord]]
x_data=[x for (x,y) in output_fn.values[a][coord]]
if p.raw==True:
plot_data=zip(x_data,y_data)
pylab.save(normalize_path(p.filename+a+'(%.2f, %.2f)' %(coord[0], coord[1])),plot_data,fmt='%.6f', delimiter=',')
pylab.plot(x_data,y_data, label='Unit (%.2f, %.2f)' %(coord[0], coord[1]))
(ymin,ymax)=p.ybounds
pylab.axis(xmin=init_time,xmax=final_time,ymin=ymin,ymax=ymax)
if isint: pylab.ion()
pylab.legend(loc=0)
p.title=topo.sim.name+': '+a
p.filename_suffix=a
self._generate_figure(p)
def hinton(W, max_weight=None, names=(names, worst_names)):
"""
Draws a Hinton diagram for visualizing a weight matrix.
Temporarily disables matplotlib interactive mode if it is on,
otherwise this takes forever.
"""
reenable = False
if P.isinteractive():
P.ioff()
P.clf()
height, width = W.shape
if not max_weight:
max_weight = 2**np.ceil(np.log(np.max(np.abs(W)))/np.log(2))
P.fill(np.array([0, width, width, 0]), np.array([0, 0, height, height]), 'gray')
P.axis('off')
P.axis('equal')
cmap = plt.get_cmap('RdYlGn')
for x in range(width):
if names:
plt.text(-0.5, x, names[0][x], fontsize=7, ha='right', va='bottom')
plt.text(x, height+0.5, names[1][height-x-1], fontsize=7, va='bottom', rotation='vertical', ha='left')
for y in range(height):
_x = x+1
_y = y+1
w = W[y, x]
if w > 0:
_blob(_x - 0.5, height - _y + 0.5, min(1, w/max_weight), color=cmap(w/max_weight))
elif w < 0:
_blob(_x - 0.5, height - _y + 0.5, min(1, -w/max_weight), 'black')
if reenable:
P.ion()
P.show()
def graticule(dpar=None,dmer=None,coord=None,local=None,**kwds):
"""Create a graticule, either on an existing mollweide map or not.
Parameters:
- dpar, dmer: interval in degrees between meridians and between parallels
- coord: the coordinate system of the graticule (make rotation if needed,
using coordinate system of the map if it is defined)
- local: True if local graticule (no rotation is performed)
Return:
None
"""
f = pylab.gcf()
wasinteractive = pylab.isinteractive()
pylab.ioff()
try:
if len(f.get_axes()) == 0:
ax=PA.HpxMollweideAxes(f,(0.02,0.05,0.96,0.9),coord=coord)
f.add_axes(ax)
ax.text(0.86,0.05,ax.proj.coordsysstr,fontsize=14,
fontweight='bold',transform=ax.transAxes)
for ax in f.get_axes():
if isinstance(ax,PA.SphericalProjAxes):
ax.graticule(dpar=dpar,dmer=dmer,coord=coord,
local=local,**kwds)
finally:
pylab.draw()
if wasinteractive:
pylab.ion()
def graticule(dpar=None, dmer=None, coord=None, local=None, **kwds):
"""Create a graticule, either on an existing mollweide map or not.
Parameters:
- dpar, dmer: interval in degrees between meridians and between parallels
- coord: the coordinate system of the graticule (make rotation if needed,
using coordinate system of the map if it is defined)
- local: True if local graticule (no rotation is performed)
Return:
None
"""
f = pylab.gcf()
wasinteractive = pylab.isinteractive()
pylab.ioff()
try:
if len(f.get_axes()) == 0:
ax = PA.HpxMollweideAxes(f, (0.02, 0.05, 0.96, 0.9), coord=coord)
f.add_axes(ax)
ax.text(0.86,
0.05,
ax.proj.coordsysstr,
fontsize=14,
fontweight='bold',
transform=ax.transAxes)
for ax in f.get_axes():
if isinstance(ax, PA.SphericalProjAxes):
ax.graticule(dpar=dpar,
dmer=dmer,
coord=coord,
local=local,
**kwds)
finally:
pylab.draw()
if wasinteractive:
pylab.ion()
def hinton(self, maxWeight=None):
"""
Draws a Hinton diagram for visualizing a weight matrix.
Temporarily disables matplotlib interactive mode if it is on,
otherwise this takes forever.
Source: http://wiki.scipy.org/Cookbook/Matplotlib/HintonDiagrams
"""
reenable = False
if P.isinteractive():
P.ioff()
P.clf()
height, width = self.sigma_w.shape
if not maxWeight:
maxWeight = 2**np.ceil(
np.log(np.max(np.abs(self.sigma_w))) / np.log(2))
P.fill(np.array([0, width, width, 0]), np.array([0, 0, height,
height]), 'gray')
P.axis('off')
P.axis('equal')
for x in xrange(width):
for y in xrange(height):
_x = x + 1
_y = y + 1
w = self.sigma_w[y, x]
if w > 0:
self._blob(_x - 0.5, height - _y + 0.5,
min(1, w / maxWeight), 'white')
elif w < 0:
self._blob(_x - 0.5, height - _y + 0.5,
min(1, -w / maxWeight), 'black')
if reenable:
P.ion()
P.show()
def hinton(W, maxWeight=None):
"""
Draws a Hinton diagram for visualizing a weight matrix.
Temporarily disables matplotlib interactive mode if it is on,
otherwise this takes forever.
"""
reenable = False
if P.isinteractive():
P.ioff()
P.clf()
height, width = W.shape
if not maxWeight:
maxWeight = 2**N.ceil(N.log(N.max(N.abs(W)))/N.log(2))
P.fill(N.array([0,width,width,0]),N.array([0,0,height,height]),'gray')
P.axis('off')
P.axis('equal')
for x in xrange(width):
for y in xrange(height):
_x = x+1
_y = y+1
w = W[y,x]
if w > 0:
_blob(_x - 0.5, height - _y + 0.5, min(1,w/maxWeight),'white')
elif w < 0:
_blob(_x - 0.5, height - _y + 0.5, min(1,-w/maxWeight),'black')
if reenable:
P.ion()
P.show()
def draw(self, axes=None, loc=2):
if axes == None:
axes = p.gca()
if self.box != None and self.box in axes.artists:
del axes.artists[ axes.artists.index(self.box) ]
#key_areas = [ mpl.offsetbox.TextArea(k, textprops=self.textprops) for k in self.datadict.keys() ]
#val_areas = [ mpl.offsetbox.TextArea(self.datadict[k], textprops=self.textprops) for k in self.datadict.keys() ]
key_areas = [ mpl.offsetbox.TextArea(k, textprops=self.textprops) for k in list(self.datadict.keys()) ]
val_areas = [ mpl.offsetbox.TextArea(self.datadict[k], textprops=self.textprops) for k in list(self.datadict.keys()) ]
key_vpack = mpl.offsetbox.VPacker(children=key_areas, align="left", pad=0, sep=0)
val_vpack = mpl.offsetbox.VPacker(children=val_areas, align="right", pad=0, sep=0)
hpack = mpl.offsetbox.HPacker(children=[key_vpack, val_vpack], align="top", pad=0,sep=4)
globchildren = []
if self.title != None:
#titlearea = mpl.offsetbox.TextArea(self.title, textprops=self.titleprops)
titlearea = mpl.offsetbox.TextArea(self.title)
globchildren.append(titlearea)
globchildren.append(hpack)
globvpack = mpl.offsetbox.VPacker(children=globchildren, align="center", pad=0, sep=1)
self.box = mpl.offsetbox.AnchoredOffsetbox(loc=loc, child=globvpack)
self.box.patch.set_facecolor(self.facecolor)
self.box.patch.set_edgecolor(self.edgecolor)
self.box.patch.set_alpha(self.alpha)
axes.add_artist( self.box )
if p.isinteractive():
p.gcf().canvas.draw()
def showslice2(thedata,thelabel,minval,maxval,colormap):
theshape=thedata.shape
numslices=theshape[0]
ysize=theshape[1]
xsize=theshape[2]
slicesqrt=int(np.ceil(np.sqrt(numslices)))
theslice=np.zeros((ysize*slicesqrt,xsize*slicesqrt))
for i in range(0,numslices):
ypos=int(i/slicesqrt)*ysize
xpos=int(i%slicesqrt)*xsize
theslice[ypos:ypos+ysize,xpos:xpos+xsize]=thedata[i,:,:]
if P.isinteractive():
P.ioff()
P.axis('off')
P.axis('equal')
P.subplots_adjust(hspace=0.0)
P.axes([0,0,1,1], frameon = False)
if (colormap==0):
thecmap=P.cm.gray
else:
mycmdata1 = {
'red' : ((0., 0., 0.), (0.5, 1.0, 0.0), (1., 1., 1.)),
'green': ((0., 0., 0.), (0.5, 1.0, 1.0), (1., 0., 0.)),
'blue' : ((0., 0., 0.), (0.5, 1.0, 0.0), (1., 0., 0.))
}
thecmap = P.matplotlib.colors.LinearSegmentedColormap('mycm', mycmdata1)
#thecmap=P.cm.spectral
theimptr = P.imshow(theslice, vmin=minval, vmax=maxval, interpolation='nearest', label=thelabel, aspect='equal', cmap=thecmap)
#P.colorbar()
return()
def saveHintonDiagram(W, directory):
maxWeight = None
#print "Weight: ", W
"""
Draws a Hinton diagram for visualizing a weight matrix.
Temporarily disables matplotlib interactive mode if it is on,
otherwise this takes forever.
"""
reenable = False
if pylab.isinteractive():
pylab.ioff()
pylab.clf()
height, width = W.shape
if not maxWeight:
maxWeight = 2**numpy.ceil(numpy.log(numpy.max(numpy.abs(W)))/numpy.log(2))
pylab.fill(numpy.array([0,width,width,0]),numpy.array([0,0,height,height]),'gray')
pylab.axis('off')
pylab.axis('equal')
for x in xrange(width):
for y in xrange(height):
_x = x+1
_y = y+1
w = W[y,x]
if w > 0:
_blob(_x - 0.5, height - _y + 0.5, min(1,w/maxWeight),'white')
elif w < 0:
_blob(_x - 0.5, height - _y + 0.5, min(1,-w/maxWeight),'black')
if reenable:
pylab.ion()
#pylab.show()
pylab.savefig(directory)
def __call__(self,**params):
p=ParamOverrides(self,params)
sheet = p.sheet
for coordinate in p.coords:
i_value,j_value=sheet.sheet2matrixidx(coordinate[0],coordinate[1])
pylab.figure(figsize=(7,7))
isint=pylab.isinteractive()
pylab.ioff()
pylab.ylabel('Response',fontsize='large')
pylab.xlabel('%s (%s)' % (p.x_axis.capitalize(),p.unit),fontsize='large')
pylab.title('Sheet %s, coordinate(x,y)=(%0.3f,%0.3f) at time %s' %
(sheet.name,coordinate[0],coordinate[1],topo.sim.timestr()))
p.title='%s: %s Tuning Curve' % (topo.sim.name,p.x_axis.capitalize())
self.first_curve=True
for curve_label in sorted(sheet.curve_dict[p.x_axis].keys()):
x_values,y_values,ticks=self._curve_values(i_value,j_value,sheet.curve_dict[p.x_axis][curve_label])
x_tick_values,ticks = self._reduce_ticks(ticks)
labels = [self._format_x_tick_label(x) for x in ticks]
pylab.xticks(x_tick_values, labels,fontsize='large')
pylab.yticks(fontsize='large')
p.plot_type(x_values, y_values, label=curve_label,lw=3.0)
self.first_curve=False
if isint: pylab.ion()
if p.legend: pylab.legend(loc=2)
self._generate_figure(p)
def plot(self):
fig = self._doPlot()
if not pylab.isinteractive():
pylab.show()
else:
pylab.draw()
pylab.close(fig)
def showlines(vs, size=(4.1,2), **kwargs):
# {{{
'''
Plot line plots of a list of 1D variables on the same plot.
Parameters
----------
v : list of :class:`Var`
The variables to plot. Should all have 1 non-degenerate axis.
'''
Z = [v.squeeze() for v in vs]
for z in Z:
assert z.naxes == 1, 'Variable %s has %d non-generate axes; must have 1.' % (z.name, z.naxes)
fig = kwargs.pop('fig', None)
ax = wr.AxesWrapper(size=size)
ydat = []
for v in vs:
vplot(v, axes=ax, label=v.name, )
ydat.append(ax.find_plot(wr.Plot).plot_args[1])
ylim = (np.min([np.min(y) for y in ydat]), np.max([np.max(y) for y in ydat]))
kwargs.update(dict(ylim=ylim))
ax.legend(loc='best', frameon=False)
ax.setp(**kwargs)
import pylab as pyl
if pyl.isinteractive(): ax.render(fig)
return ax
def read_text_pyfusion(files, target='^Shot .*', ph_dtype=None, plot=pl.isinteractive(), ms=100, hold=0, debug=0, quiet=1, exception = Exception):
""" Accepts a file or a list of files, returns a list of structured arrays
See merge ds_list to merge and convert types (float -> pyfusion.prec_med
"""
st = seconds(); last_update=seconds()
file_list = files
if len(np.shape(files)) == 0: file_list = [file_list]
f='f8'
if ph_dtype == None: ph_dtype = [('p12',f),('p23',f),('p34',f),('p45',f),('p56',f)]
#ph_dtype = [('p12',f)]
ds_list =[]
comment_list =[]
count = 0
for (i,filename) in enumerate(file_list):
if seconds() - last_update > 30:
last_update = seconds()
print('reading {n}/{t}: {f}'
.format(f=filename, n=i, t=len(file_list)))
try:
if pl.is_string_like(target):
skip = 1+find_data(filename, target,debug=debug)
else:
skip = target
if quiet == 0:
print('{t:.1f} sec, loading data from line {s} of {f}'
.format(t = seconds()-st, s=skip, f=filename))
# this little bit to determine layout of data
# very inefficient to read twice, but in a hurry!
txt = np.loadtxt(fname=filename, skiprows=skip-1, dtype=str,
delimiter='FOOBARWOOBAR')
header_toks = txt[0].split()
# is the first character of the 2nd last a digit?
if header_toks[-2][0] in '0123456789':
if pyfusion.VERBOSE > 0:
print('found new header including number of phases')
n_phases = int(header_toks[-2])
ph_dtype = [('p{n}{np1}'.format(n=n,np1=n+1), f) for n in range(n_phases)]
if 'frlow' in header_toks: # add the two extra fields
fs_dtype= [ ('shot','i8'), ('t_mid','f8'),
('_binary_svs','i8'),
('freq','f8'), ('amp', 'f8'), ('a12','f8'),
('p', 'f8'), ('H','f8'),
('frlow','f8'), ('frhigh', 'f8'),('phases',ph_dtype)]
else:
fs_dtype= [ ('shot','i8'), ('t_mid','f8'),
('_binary_svs','i8'),
('freq','f8'), ('amp', 'f8'), ('a12','f8'),
('p', 'f8'), ('H','f8'), ('phases',ph_dtype)]
ds_list.append(
np.loadtxt(fname=filename, skiprows = skip,
dtype= fs_dtype)
)
count += 1
comment_list.append(filename)
except ValueError, info:
print('Conversion error while reading {f} with loadtxt - {info}'.format(f=filename, info=info))
def showcol(vs, size=(4.1,2), **kwargs):
# {{{
'''
Plot variable, showing a contour plot for 2d variables or a line plot for 1d variables.
Parameters
----------
v : list of lists of :class:`Var`
The variables to plot. Should have either 1 or 2 non-degenerate axes.
Notes
-----
This function is intended as the simplest way to display the contents of a variable,
choosing appropriate parameter values as automatically as possible.
'''
Z = [v.squeeze() for v in vs]
assert Z[0].naxes in [1, 2], 'Variables %s has %d non-generate axes; must have 1 or 2.' % (Z.name, Z.naxes)
for z in Z[1:]:
assert Z[0].naxes == z.naxes, 'All variables must have the same number of non-generate dimensions'
#assert all([a == b for a, b in zip(Z[0].axes, z.axes)])
fig = kwargs.pop('fig', None)
if Z[0].naxes == 1:
axs = []
ydat = []
for v in vs:
lblx = (v is vs[-1])
ax = vplot(v, lblx = lblx, **kwargs)
ax.size = size
axs.append([ax])
ydat.append(ax.find_plot(wr.Plot).plot_args[1])
Ax = wr.grid(axs)
ylim = (np.min([np.min(y) for y in ydat]), np.max([np.max(y) for y in ydat]))
Ax.setp(ylim = ylim, children=True)
elif Z[0].naxes == 2:
axs = []
for v in vs:
lblx = (v is vs[-1])
ax = vcontour(v, lblx = lblx, **kwargs)
ax.size = size
axs.append([ax])
Ax = wr.grid(axs)
cbar = kwargs.pop('colorbar', dict(orientation='vertical'))
cf = Ax.axes[0].find_plot(wr.Contourf)
if cbar and cf is not None:
Ax = wr.colorbar(Ax, cf, **cbar)
import pylab as pyl
if pyl.isinteractive(): Ax.render(fig)
return Ax
def refresh(self):
for i, f in enumerate(self.features):
for s in self.shapes[i]:
if self.fc_function and hasattr(s, 'set_fc'):
s.set_fc(self.fc_function(f))
if self.lw_function and hasattr(s, 'set_lw'):
s.set_lw(self.lw_function(f))
if pylab.isinteractive():
pylab.draw()
def plot_rectangular(x,
y,
x_label=None,
y_label=None,
title=None,
show_legend=True,
axis='tight',
ax=None,
*args,
**kwargs):
'''
plots rectangular data and optionally label axes.
Parameters
------------
z : array-like, of complex data
data to plot
x_label : string
x-axis label
y_label : string
y-axis label
title : string
plot title
show_legend : Boolean
controls the drawing of the legend
ax : :class:`matplotlib.axes.AxesSubplot` object
axes to draw on
*args,**kwargs : passed to pylab.plot
'''
if ax is None:
ax = plb.gca()
my_plot = ax.plot(x, y, *args, **kwargs)
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)
if show_legend:
# only show legend if they provide a label
if 'label' in kwargs:
ax.legend()
if axis is not None:
ax.autoscale(True, 'x', True)
ax.autoscale(True, 'y', False)
if plb.isinteractive():
plb.draw()
return my_plot
def showslice(theslice):
if P.isinteractive():
P.ioff()
P.axis('off')
P.axis('equal')
P.axis('tight')
P.imshow(theslice, interpolation='nearest', aspect='equal', cmap=P.cm.gray)
P.colorbar()
return()
def plot_uncertainty_bounds_s(self, multiplier=200, *args, **kwargs):
'''
Plots complex uncertainty bounds plot on smith chart.
This function plots the complex uncertainty of a NetworkSet
as circles on the smith chart. At each frequency a circle
with radii proportional to the complex standard deviation
of the set at that frequency is drawn. Due to the fact that
the `markersize` argument is in pixels, the radii can scaled by
the input argument `multiplier`.
default kwargs are
{
'marker':'o',
'color':'b',
'mew':0,
'ls':'',
'alpha':.1,
'label':None,
}
Parameters
-------------
multipliter : float
controls the circle sizes, by multiples of the standard
deviation.
'''
default_kwargs = {
'marker': 'o',
'color': 'b',
'mew': 0,
'ls': '',
'alpha': .1,
'label': None,
}
default_kwargs.update(**kwargs)
if plb.isinteractive():
was_interactive = True
plb.interactive(0)
else:
was_interactive = False
[
self.mean_s[k].plot_s_smith(*args,
ms=self.std_s[k].s_mag * multiplier,
**default_kwargs)
for k in range(len(self[0]))
]
if was_interactive:
plb.interactive(1)
plb.draw()
plb.show()
def plot_polar(
theta, r, x_label=None, y_label=None, title=None, show_legend=True, axis_equal=False, ax=None, *args, **kwargs
):
"""
plots polar data on a polar plot and optionally label axes.
Parameters
------------
theta : array-like
data to plot
r : array-like
x_label : string
x-axis label
y_label : string
y-axis label
title : string
plot title
show_legend : Boolean
controls the drawing of the legend
ax : :class:`matplotlib.axes.AxesSubplot` object
axes to draw on
*args,**kwargs : passed to pylab.plot
See Also
----------
plot_rectangular : plots rectangular data
plot_complex_rectangular : plot complex data on complex plane
plot_polar : plot polar data
plot_complex_polar : plot complex data on polar plane
plot_smith : plot complex data on smith chart
"""
if ax is None:
ax = plb.gca(polar=True)
ax.plot(theta, r, *args, **kwargs)
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)
if show_legend:
# only show legend if they provide a label
if "label" in kwargs:
ax.legend()
if axis_equal:
ax.axis("equal")
if plb.isinteractive():
plb.draw()
def plot_smith(z, smith_r=1, chart_type='z', x_label='Real',
y_label='Imaginary', title='Complex Plane', show_legend=True,
axis='equal', ax=None, force_chart = False, *args, **kwargs):
'''
plot complex data on smith chart
Parameters
------------
z : array-like, of complex data
data to plot
smith_r : number
radius of smith chart
chart_type : ['z','y']
Contour type for chart.
* *'z'* : lines of constant impedance
* *'y'* : lines of constant admittance
x_label : string
x-axis label
y_label : string
y-axis label
title : string
plot title
show_legend : Boolean
controls the drawing of the legend
axis_equal: Boolean
sets axis to be equal increments (calls axis('equal'))
force_chart : Boolean
forces the re-drawing of smith chart
ax : :class:`matplotlib.axes.AxesSubplot` object
axes to draw on
*args,**kwargs : passed to pylab.plot
See Also
----------
plot_rectangular : plots rectangular data
plot_complex_rectangular : plot complex data on complex plane
plot_polar : plot polar data
plot_complex_polar : plot complex data on polar plane
plot_smith : plot complex data on smith chart
'''
if ax is None:
ax = plb.gca()
# test if smith chart is already drawn
if not force_chart:
if len(ax.patches) == 0:
smith(ax=ax, smithR = smith_r, chart_type=chart_type)
plot_complex_rectangular(z, x_label=x_label, y_label=y_label,
title=title, show_legend=show_legend, axis=axis,
ax=ax, *args, **kwargs)
ax.axis(smith_r*npy.array([-1.1, 1.1, -1.1, 1.1]))
if plb.isinteractive():
plb.draw()
def plot_polar(theta, r, x_label=None, y_label=None, title=None,
show_legend=True, axis_equal=False, ax=None, *args, **kwargs):
'''
plots polar data on a polar plot and optionally label axes.
Parameters
------------
theta : array-like
data to plot
r : array-like
x_label : string
x-axis label
y_label : string
y-axis label
title : string
plot title
show_legend : Boolean
controls the drawing of the legend
ax : :class:`matplotlib.axes.AxesSubplot` object
axes to draw on
*args,**kwargs : passed to pylab.plot
See Also
----------
plot_rectangular : plots rectangular data
plot_complex_rectangular : plot complex data on complex plane
plot_polar : plot polar data
plot_complex_polar : plot complex data on polar plane
plot_smith : plot complex data on smith chart
'''
if ax is None:
ax = plb.gca(polar=True)
ax.plot(theta, r, *args, **kwargs)
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)
if show_legend:
# only show legend if they provide a label
if 'label' in kwargs:
ax.legend()
if axis_equal:
ax.axis('equal')
if plb.isinteractive():
plb.draw()
def plot(self):
"""
Draw this layout to a matplotlib canvas.
"""
fig = self._doPlot()
if not pylab.isinteractive():
pylab.show()
else:
pylab.draw()
pylab.close(fig)
def plot(self):
"""
Draw this layout to a matplotlib canvas.
"""
fig = self._doPlot()
if not pylab.isinteractive():
pylab.show()
else:
pylab.draw()
pylab.close(fig)
def showlines(vs,
fmts=None,
labels=None,
size=(4.1, 2),
lblx=True,
lbly=True,
**kwargs):
# {{{
'''
Produce line plots of a list of 1D variables on a single figure.
Parameters
----------
vs : list of :class:`Var`
The variables to plot. Should all have 1 non-degenerate axis.
'''
Z = [v.squeeze() for v in vs]
for z in Z:
assert z.naxes == 1, 'Variable %s has %d non-generate axes; must have 1.' % (
z.name, z.naxes)
fig = kwargs.pop('fig', None)
ax = wr.AxesWrapper(size=size)
ydat = []
for i, v in enumerate(vs):
if fmts is None:
fmt = ''
elif hasattr(fmts, '__len__'):
fmt = fmts[i]
else:
fmt = fmts
if labels is None:
lbl = v.name
elif hasattr(labels, '__len__'):
lbl = labels[i]
else:
lbl = labels
vplot(v, axes=ax, fmt=fmt, label=lbl)
ydat.append(ax.find_plot(wr.Plot).plot_args[1])
#ylim = (np.min([np.min(y) for y in ydat]), np.max([np.max(y) for y in ydat]))
#kwargs.update(dict(ylim=ylim))
kwleg = kwargs.pop('legend', dict(loc='best', frameon=False))
ax.legend(**kwleg)
ax.setp(**kwargs)
import pylab as pyl
if pyl.isinteractive(): ax.render(fig)
return ax
def draw_selection(self, lbl, replace=True):
""" draw a given subset """
if (lbl not in self._draws) | (replace is True):
sample = self.selections[lbl]
self._draws[lbl] = sample.draw(self.x, self.y, ax=self.axes)
if plt.isinteractive():
self.draw()
return sample, self._draws[lbl]
def draw_selection(self, lbl, replace=True):
""" draw a given subset """
if (lbl not in self._draws) | (replace is True):
sample = self.selections[lbl]
self._draws[lbl] = sample.draw(self.x, self.y, ax=self.axes)
if plt.isinteractive():
self.draw()
return sample, self._draws[lbl]
def plot_uncertainty_bounds_s(self, multiplier =200, *args, **kwargs):
'''
Plots complex uncertainty bounds plot on smith chart.
This function plots the complex uncertainty of a NetworkSet
as circles on the smith chart. At each frequency a circle
with radii proportional to the complex standard deviation
of the set at that frequency is drawn. Due to the fact that
the `markersize` argument is in pixels, the radii can scaled by
the input argument `multiplier`.
default kwargs are
{
'marker':'o',
'color':'b',
'mew':0,
'ls':'',
'alpha':.1,
'label':None,
}
Parameters
-------------
multipliter : float
controls the circle sizes, by multiples of the standard
deviation.
'''
default_kwargs = {
'marker':'o',
'color':'b',
'mew':0,
'ls':'',
'alpha':.1,
'label':None,
}
default_kwargs.update(**kwargs)
if plb.isinteractive():
was_interactive = True
plb.interactive(0)
else:
was_interactive = False
[self.mean_s[k].plot_s_smith(*args, ms = self.std_s[k].s_mag*multiplier, **default_kwargs) for k in range(len(self[0]))]
if was_interactive:
plb.interactive(1)
plb.draw()
plb.show()
def wrapper(*args, **kwds):
interactive_mode = P.isinteractive()
if interactive_mode:
#logging.debug('Turning pylab interactive off.')
P.ioff()
try:
return fn(*args, **kwds)
finally:
if interactive_mode:
#logging.debug('Turning pylab interactive on.')
P.ion()
def __call__(self, recalc_range=False):
if recalc_range:
self.maxvalue = max((self.f(c) for c in self.polygons.iterkeys()))
self.minvalue = min((self.f(c) for c in self.polygons.iterkeys()))
for cell, poly in self.polygons.iteritems():
v = self.f(cell)
c = self.cmap(
(v - self.minvalue) / (self.maxvalue - self.minvalue))
poly.set_fc(c)
if pylab.isinteractive():
pylab.draw()
def delgraticules():
f = pylab.gcf()
wasinteractive = pylab.isinteractive()
pylab.ioff()
try:
for ax in f.get_axes():
if isinstance(ax,PA.SphericalProjAxes):
ax.delgraticules()
finally:
pylab.draw()
if wasinteractive:
pylab.ion()
def Trace_diag(LH,Lna,LT,LHR,Lair,fignum) :
figure(fignum)
if isinteractive() :
ioff()
lna_H,lH_H=Return_Hs(LH,Lna)
Plotlines(lna_H,lH_H,lineprops)
lna_T,lH_T=Return_Ts(LT,Lna)
Plotlines(lna_T,lH_T,lineprops)
lna_HR,lH_HR=Return_HRs(Lna,LHR)
PlotHR(lna_HR,lH_HR,LHR,lineprops_HR)
Ttick=Transf_xy(Lna[0],array(lH_T[0:-1:2]))[1]
Tlabel=map(str,LT)
yticks(Ttick,Tlabel)
xlabel(r'$\rm{Teneur\ en\ eau\ (na)}\ [\ g_{H_2O}\ /\ g_{AS}\ ]$')
ylabel(r'$\rm{Temp\'erature\ (T)}\ [\ ^{\circ}C\ ]$')
title("Diagramme de l'air humide")
if not(isinteractive()) :
ion()
axis([x1,x3,y1,y3])
setp(gca(),autoscale_on=False)
draw()
def delgraticules():
f = pylab.gcf()
wasinteractive = pylab.isinteractive()
pylab.ioff()
try:
for ax in f.get_axes():
if isinstance(ax, PA.SphericalProjAxes):
ax.delgraticules()
finally:
pylab.draw()
if wasinteractive:
pylab.ion()
def __init__(self,
cells,
value_function,
cmap=pylab.cm.jet,
hold=True,
vmin=None,
vmax=None,
**kwargs):
self.cells = cells
self.__f = value_function
was_interactive = pylab.isinteractive()
if was_interactive: pylab.ioff()
geos = []
self.maxvalue = -1e300 if vmax is None else vmax
self.minvalue = 1e300 if vmin is None else vmin
self.polygons = {}
# Generate image array, which is filled with generated values from self.f
self._A = []
for cell in cells:
shape = geoms[cell]
if hasattr(shape, "geoms"):
shapes = shape.geoms
else:
shapes = [shape]
value = self.f(cell)
self._A.append(value)
if vmax is None:
self.maxvalue = max(value, self.maxvalue)
if vmin is None:
self.minvalue = min(value, self.minvalue)
for s in shapes:
geos.append((cell, s, value))
if self.minvalue >= self.maxvalue: self.minvalue = self.maxvalue - 1
for cell, s, v in geos:
if hasattr(s, "exterior"):
s = s.exterior
c = cmap(
float(v - self.minvalue) /
float(self.maxvalue - self.minvalue))
a = pylab.asarray(s)
self.polygons[cell] = pylab.fill(a[:, 0],
a[:, 1],
fc=c,
hold=hold,
**kwargs)[0]
hold = 1
pylab.axis('equal')
norm = pylab.matplotlib.colors.Normalize(self.minvalue, self.maxvalue)
pylab.matplotlib.cm.ScalarMappable.__init__(self, norm, cmap)
if was_interactive:
pylab.draw()
pylab.ion()
def draw(self, axes=None, loc=2):
if axes == None:
axes = p.gca()
if self.box != None and self.box in axes.artists:
del axes.artists[axes.artists.index(self.box)]
#key_areas = [ mpl.offsetbox.TextArea(k, textprops=self.textprops) for k in self.datadict.keys() ]
#val_areas = [ mpl.offsetbox.TextArea(self.datadict[k], textprops=self.textprops) for k in self.datadict.keys() ]
key_areas = [
mpl.offsetbox.TextArea(k, textprops=self.textprops)
for k in list(self.datadict.keys())
]
val_areas = [
mpl.offsetbox.TextArea(self.datadict[k], textprops=self.textprops)
for k in list(self.datadict.keys())
]
key_vpack = mpl.offsetbox.VPacker(children=key_areas,
align="left",
pad=0,
sep=0)
val_vpack = mpl.offsetbox.VPacker(children=val_areas,
align="right",
pad=0,
sep=0)
hpack = mpl.offsetbox.HPacker(children=[key_vpack, val_vpack],
align="top",
pad=0,
sep=4)
globchildren = []
if self.title != None:
#titlearea = mpl.offsetbox.TextArea(self.title, textprops=self.titleprops)
titlearea = mpl.offsetbox.TextArea(self.title)
globchildren.append(titlearea)
globchildren.append(hpack)
globvpack = mpl.offsetbox.VPacker(children=globchildren,
align="center",
pad=0,
sep=1)
self.box = mpl.offsetbox.AnchoredOffsetbox(loc=loc, child=globvpack)
self.box.patch.set_facecolor(self.facecolor)
self.box.patch.set_edgecolor(self.edgecolor)
self.box.patch.set_alpha(self.alpha)
axes.add_artist(self.box)
if p.isinteractive():
p.gcf().canvas.draw()
def histogram_p_values(p_values, tag):
import pylab as P
interactive_mode = P.isinteractive()
P.ioff()
try:
P.figure()
P.hist(p_values)
P.title('%s p-values for transcriptional programs' % tag)
P.xlabel('p-values')
P.savefig(os.path.join(output_dir, 'p-values-for-%ss.png' % tag), format='PNG')
P.close()
finally:
if interactive_mode:
P.ion()
def graticule(dpar=None, dmer=None, coord=None, local=None, **kwds):
"""Draw a graticule on the current Axes.
Parameters
----------
dpar, dmer : float, scalars
Interval in degrees between meridians and between parallels
coord : {'E', 'G', 'C'}
The coordinate system of the graticule (make rotation if needed,
using coordinate system of the map if it is defined).
local : bool
If True, draw a local graticule (no rotation is performed, useful for
a gnomonic view, for example)
Notes
-----
Other keyword parameters will be transmitted to the projplot function.
See Also
--------
delgraticules
"""
import pylab
f = pylab.gcf()
wasinteractive = pylab.isinteractive()
pylab.ioff()
try:
if len(f.get_axes()) == 0:
ax = PA.HpxMollweideAxes(f, (0.02, 0.05, 0.96, 0.9), coord=coord)
f.add_axes(ax)
ax.text(
0.86,
0.05,
ax.proj.coordsysstr,
fontsize=14,
fontweight="bold",
transform=ax.transAxes,
)
for ax in f.get_axes():
if isinstance(ax, PA.SphericalProjAxes):
ax.graticule(dpar=dpar,
dmer=dmer,
coord=coord,
local=local,
**kwds)
finally:
pylab.draw()
if wasinteractive:
pylab.ion()
def plot_rectangular(x, y, x_label=None, y_label=None, title=None,
show_legend=True, axis='tight', ax=None, *args, **kwargs):
'''
plots rectangular data and optionally label axes.
Parameters
------------
z : array-like, of complex data
data to plot
x_label : string
x-axis label
y_label : string
y-axis label
title : string
plot title
show_legend : Boolean
controls the drawing of the legend
ax : :class:`matplotlib.axes.AxesSubplot` object
axes to draw on
*args,**kwargs : passed to pylab.plot
'''
if ax is None:
ax = plb.gca()
my_plot = ax.plot(x, y, *args, **kwargs)
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)
if show_legend:
# only show legend if they provide a label
if 'label' in kwargs:
ax.legend()
if axis is not None:
ax.autoscale(True, 'x', True)
ax.autoscale(True, 'y', False)
if plb.isinteractive():
plb.draw()
return my_plot
def histogram_p_values(p_values, tag):
import pylab as P
interactive_mode = P.isinteractive()
P.ioff()
try:
P.figure()
P.hist(p_values)
P.title('%s p-values for transcriptional programs' % tag)
P.xlabel('p-values')
P.savefig(os.path.join(output_dir, 'p-values-for-%ss.png' % tag),
format='PNG')
P.close()
finally:
if interactive_mode:
P.ion()
def projtext(*args,**kwds):
f = pylab.gcf()
wasinteractive = pylab.isinteractive()
pylab.ioff()
ret = None
try:
for ax in f.get_axes():
if isinstance(ax,PA.SphericalProjAxes):
ret = ax.projtext(*args,**kwds)
finally:
pylab.draw()
if wasinteractive:
pylab.ion()
#pylab.show()
return ret
def projtext(*args, **kwds):
f = pylab.gcf()
wasinteractive = pylab.isinteractive()
pylab.ioff()
ret = None
try:
for ax in f.get_axes():
if isinstance(ax, PA.SphericalProjAxes):
ret = ax.projtext(*args, **kwds)
finally:
pylab.draw()
if wasinteractive:
pylab.ion()
#pylab.show()
return ret
def showvar(var, *args, **kwargs):
# {{{
'''
Plot variable, showing a contour plot for 2d variables or a line plot for 1d variables.
Parameters
----------
var : :class:`Var`
The variable to plot. Should have either 1 or 2 non-degenerate axes.
*args, **kwargs : arguments to pass on to underlying plotting routines, see
Notes.
Notes
-----
This function is intended as the simplest way to display the contents of a
variable, choosing appropriate parameter values as automatically as possible.
For 1d variables it calls :func:`Var.vplot()`, and for 2d variables
:func:`Var.vcontour`. In the latter case, if filled contours were produced, it
calls `AxesWrapper.colorbar()'. A dictionary ``colorbar`` can be provided to
pass arguments through. Setting ``colorbar`` to ``False`` suppresses the
colorbar.
See Also
--------
vplot, vcontour, colorbar
'''
Z = var.squeeze()
assert Z.naxes in [1, 2], 'Variable %s has %d non-generate axes; must have 1 or 2.' % (var.name, Z.naxes)
fig = kwargs.pop('fig', None)
if Z.naxes == 1:
ax = vplot(var, *args, **kwargs)
elif Z.naxes == 2:
ax = vcontour(var, *args, **kwargs)
cbar = kwargs.pop('colorbar', dict(orientation='vertical'))
cf = ax.find_plot(wr.Contourf)
if cbar and cf is not None:
ax = wr.colorbar(ax, cf, **cbar)
import pylab as pyl
if pyl.isinteractive():
ax.render(fig)
return ax
def test_analytic_phase(verbose=3):
"""
>>> test_analytic_phase()
"""
t=array(range(10000))/100.
x=sin(t+t**2/30)
y=cos(t+t**2/30)
if len(analytic_phase(x)) < len(x):
print('shortened from {0} to {1}'
.format(len(x),len(analytic_phase(x))))
if verbose>2:
xx0=pl.plot(smooth(analytic_phase(x) - analytic_phase(y), 10), hold=0)
xx1=pl.plot(smooth(analytic_phase(x) - analytic_phase(y+100), 10),
'k--', hold=1)
pl.title('black dash and blue should overlay well')
if not(pl.isinteractive()): pl.show()
def bar_timeseries(data, **kwargs):
try:
step = kwargs.pop('step')
ts = data.reduce_avg(data.begin - data.begin % step, step)
except KeyError:
ts = data
x = __x_from_ts(ts)
was_inter = pylab.isinteractive()
pylab.ioff()
bars = pylab.bar(x, ts, ts.step / cmf.day, **kwargs)
ax = pylab.gca()
ax.xaxis_date()
if was_inter:
pylab.draw()
pylab.ion()
return bars
def test_analytic_phase(verbose=3):
"""
>>> test_analytic_phase()
"""
t=array(range(10000))/100.
x=sin(t+t**2/30)
y=cos(t+t**2/30)
if len(analytic_phase(x)) < len(x):
print('shortened from {0} to {1}'
.format(len(x),len(analytic_phase(x))))
if verbose>2:
xx0=pl.plot(smooth(analytic_phase(x) - analytic_phase(y), 10), hold=0)
xx1=pl.plot(smooth(analytic_phase(x) - analytic_phase(y+100), 10),
'k--', hold=1)
pl.title('black dash and blue should overlay well')
if not(pl.isinteractive()): pl.show()
def graticule(dpar=None, dmer=None, coord=None, local=None, **kwds):
"""Draw a graticule on the current Axes.
Parameters
----------
dpar, dmer : float, scalars
Interval in degrees between meridians and between parallels
coord : {'E', 'G', 'C'}
The coordinate system of the graticule (make rotation if needed,
using coordinate system of the map if it is defined).
local : bool
If True, draw a local graticule (no rotation is performed, useful for
a gnomonic view, for example)
Notes
-----
Other keyword parameters will be transmitted to the projplot function.
See Also
--------
delgraticules
"""
import pylab
f = pylab.gcf()
wasinteractive = pylab.isinteractive()
pylab.ioff()
try:
if len(f.get_axes()) == 0:
ax = PA.HpxMollweideAxes(f, (0.02, 0.05, 0.96, 0.9), coord=coord)
f.add_axes(ax)
ax.text(
0.86,
0.05,
ax.proj.coordsysstr,
fontsize=14,
fontweight="bold",
transform=ax.transAxes,
)
for ax in f.get_axes():
if isinstance(ax, PA.SphericalProjAxes):
ax.graticule(dpar=dpar, dmer=dmer, coord=coord, local=local, **kwds)
finally:
pylab.draw()
if wasinteractive:
pylab.ion()
def hinton(W, labels=['', ''], axes=[[], []], name="", maxWeight=None):
"""
Draws a Hinton diagram for visualizing a weight matrix.
Temporarily disables matplotlib interactive mode if it is on,
otherwise this takes forever.
"""
reenable = False
if P.isinteractive():
P.ioff()
P.clf()
height, width = W.shape
if not maxWeight:
maxWeight = 2**N.ceil(N.log(N.max(N.abs(W))) / N.log(2))
P.fill(N.array([0, width, width, 0]), N.array([0, 0, height, height]),
'gray')
#P.axis('off')
P.axis('equal')
P.axis('image')
P.xlabel(labels[0])
P.ylabel(labels[1])
#fig = P.figure()
#ax = fig.add_subplot(111)
#ax.set_xticklabels(axes[0])
#ax.set_yticklabels(axes[1])
P.xticks(N.arange(len(axes[0])) + 0.5, axes[0], fontsize=10)
P.yticks(N.arange(len(axes[1])) + 0.5, axes[1], fontsize=10)
for x in xrange(width):
for y in xrange(height):
_x = x + 1
_y = y + 1
w = W[y, x]
if w > 0:
_blob(_x - 0.5, height - _y + 0.5, min(1, w / maxWeight),
'white')
elif w < 0:
_blob(_x - 0.5, height - _y + 0.5, min(1, -w / maxWeight),
'black')
if reenable:
P.ion()
#P.show()
if name == "":
P.savefig("hinton.png")
else:
P.savefig(name + ".png")
def plot_tseries(dtimes, data, ax=None, labels=None, datefmt='%b %d, %H:%M', colors=None, ylabel="",
title="", fontsize="medium", saveto="", **kwargs):
"""Plot time series data (e.g. gage recordings)
Parameters
----------
dtimes : array of datetime objects (time steps)
data : 2D array of shape ( num time steps, num data series )
labels : list of strings (names of data series)
title : string
kwargs : keyword arguments related to pylab.plot
"""
if ax is None:
returnax = False
fig = pl.figure()
ax = fig.add_subplot(1, 1, 1, title=title)
else:
returnax = True
# if labels==None:
# labels = ["series%d"%i for i in range(1, data.shape[1]+1)]
# for i, label in enumerate(labels):
# ax.plot_date(mpl.dates.date2num(dtimes),data[:,i],label=label, color=colors[i], **kwargs)
ax.plot_date(mpl.dates.date2num(dtimes), data, **kwargs)
ax.xaxis.set_major_formatter(mdates.DateFormatter(datefmt))
pl.setp(ax.get_xticklabels(), visible=True)
pl.setp(ax.get_xticklabels(), rotation=-30, horizontalalignment='left')
ax.set_ylabel(ylabel, size=fontsize)
ax = set_ticklabel_size(ax, fontsize)
ax.legend(loc='best')
if returnax:
return ax
if saveto == "":
# show plot
pl.show()
if not pl.isinteractive():
# close figure explicitely if pylab is not in interactive mode
pl.close()
else:
# save plot to file
if (path.exists(path.dirname(saveto))) or (path.dirname(saveto) == ''):
pl.savefig(saveto)
pl.close()
