本文整理汇总了Python中mpl_toolkits.axes_grid.make_axes_locatable函数的典型用法代码示例。如果您正苦于以下问题：Python make_axes_locatable函数的具体用法？Python make_axes_locatable怎么用？Python make_axes_locatable使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了make_axes_locatable函数的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: setup_axes
def setup_axes(fig, header):
from mpl_toolkits.axes_grid import make_axes_locatable
ax0 = pywcsgrid2.subplot(111, wcs=header)
divider = make_axes_locatable(ax0)
gh1 = pywcsgrid2.GridHelperSimple(wcs=header, axis_nums=[0, 2])
ax_v = divider.new_vertical(1.5, pad=0.1, sharex=ax0,
axes_class=pywcsgrid2.Axes,
grid_helper=gh1)
fig.add_axes(ax_v)
gh2 = pywcsgrid2.GridHelperSimple(wcs=header, axis_nums=[2, 1])
ax_h = divider.new_horizontal(1.5, pad=0.1, sharey=ax0,
axes_class=pywcsgrid2.Axes,
grid_helper=gh2)
fig.add_axes(ax_h)
ax_h.axis["left"].toggle(label=False, ticklabels=False)
ax_v.axis["bottom"].toggle(label=False, ticklabels=False)
return ax0, ax_v, ax_h示例2: drawmap
def drawmap(DATA,TITLESTRING,PROD,UNITS):
F = plt.gcf() # Gets the current figure
m.drawstates(color='k', linewidth=1.25)
m.drawcoastlines(color='k')
m.drawcountries(color='k', linewidth=1.25)
#m.readshapefile(shapefile='/data/geog/shapefiles/fe_2007_40_county.shp',name='COUNTY',drawbounds='True')
#m.readshapefile(shapefile='/data/geog/shapefiles/fe_2007_48_county.shp',name='COUNTY',drawbounds='True')
#plt.suptitle('%s' % UNITS, fontsize = 11, x = 0.08, y = 0.105)
plt.title('UW WRF-ARW %s (%s) Valid: %s' % (TITLESTRING, UNITS, curtimestring), \
fontsize=11,bbox=dict(facecolor='white', alpha=0.65),\
x=0.5,y=.95,weight = 'demibold',style='oblique', \
stretch='normal', family='sans-serif')
# Code to make the colorbar outside of the main axis, on the bottom, and lined up
ax = plt.gca() # Gets the current axes
divider = make_axes_locatable(ax) # Lets us move axes around
cax = divider.append_axes("bottom", size="2%",pad=-0.02,axes_class=maxes.Axes) # Adds an axis for the colorbar
F.add_axes(cax) # Adds the new axis to the figure as the current working axis
bar = plt.colorbar(DATA,cax=cax,orientation='horizontal',format='%4.2f',extend='both') # Plots colorbar in new axis
bar.ax.xaxis.set_major_locator(matplotlib.ticker.MultipleLocator(base=1.0)) # Make the colorbars numbers nice
bar.update_ticks()
file_id = '%s_%s_f%02d' % (dom, PROD, time+restart_time)
filename = '%s.png' % (file_id)
plt.savefig(filename,bbox_inches='tight') # Saves the figure with small margins
plt.close()
#if export_flag == 1:
# Convert the figure to a gif file
os.system('convert -render -flatten %s %s.gif' % (filename, file_id))
os.system('rm -f %s' % filename)示例3: plot
def plot(axes, net):
classname = net.__class__.__name__
axes.set_xticks([])
axes.set_yticks([])
divider = make_axes_locatable(axes)
subaxes = divider.new_vertical(1.0, pad=0.4, sharex=axes)
fig.add_axes(subaxes)
subaxes.set_xticks([])
subaxes.yaxis.set_major_locator(matplotlib.ticker.MaxNLocator(2))
subaxes.yaxis.set_ticks_position('right')
subaxes.set_ylabel('Distortion')
subaxes.set_xlabel('Time')
Y = net.distortion[::1]
X = np.arange(len(Y))/float(len(Y)-1)
subaxes.plot(X,Y)
if classname == 'NG':
plt.title('Neural Gas', fontsize=20)
elif classname == 'SOM':
plt.title('Self-Organizing Map', fontsize=20)
elif classname == 'DSOM':
plt.title('Dynamic Self-Organizing Map', fontsize=20)
axes.axis([0,1,0,1])
axes.set_aspect(1)
codebook = net.codebook
axes.imshow(codebook, interpolation='nearest')
#interpolation='bicubic')
if classname == 'NG':
axes.text(0.5, -0.01,
r'$\lambda_i = %.3f,\lambda_f = %.3f, \varepsilon_i=%.3f, \varepsilon_f=%.3f$' % (
net.sigma_i, net.sigma_f, net.lrate_i, net.lrate_f),
fontsize=16,
horizontalalignment='center',
verticalalignment='top',
transform = axes.transAxes)
if classname == 'SOM':
axes.text(0.5, -0.01,
r'$\sigma_i = %.3f,\sigma_f = %.3f, \varepsilon_i=%.3f, \varepsilon_f=%.3f$' % (
net.sigma_i, net.sigma_f, net.lrate_i, net.lrate_f),
fontsize=16,
horizontalalignment='center',
verticalalignment='top',
transform = axes.transAxes)
elif classname == 'DSOM':
axes.text(0.5, -0.01,
r'$elasticity = %.2f$' % (net.elasticity),
fontsize=16,
horizontalalignment='center',
verticalalignment='top',
transform = axes.transAxes)示例4: plot_dist
def plot_dist(self, axes):
''' Plot network on given axes
'''
classname = self.__class__.__name__
fig = plt.gcf()
divider = make_axes_locatable(axes)
axes.axis([0,1,0,.5])
Y = self.distortion[::1]
X = np.arange(len(Y))/float(len(Y)-1)
axes.plot(X,Y)示例5: QC_Chromosome_Plot
def QC_Chromosome_Plot(calls, title=None, outfile=None):
x = np.array(calls.calls["num_probes"].values)
y = np.array(calls.calls["median_svdzrpkm"].values)
fig = plt.figure(1, figsize=(9,9))
from mpl_toolkits.axes_grid import make_axes_locatable
axScatter = plt.subplot(111)
divider = make_axes_locatable(axScatter)
# create a new axes with a height of 1.2 inch above the axScatter
axHistx = divider.new_vertical(1.2, pad=0.1, sharex=axScatter)
# create a new axes with a width of 1.2 inch on the right side of the
# axScatter
axHisty = divider.new_horizontal(1.2, pad=0.1, sharey=axScatter)
fig.add_axes(axHistx)
fig.add_axes(axHisty)
# make some labels invisible
plt.setp(axHistx.get_xticklabels() + axHisty.get_yticklabels(),
visible=False)
# the scatter plot:
#axScatter.scatter(x[x<0], y[x<0], lw=0, alpha=0.3, color="r")
axScatter.scatter(x[x>=0], y[x>=0], lw=0, alpha=0.3, color="b")
#axScatter.set_aspect(1.)
axScatter.set_xscale("symlog")
axHistx.set_xscale("symlog")
axScatter.set_xlabel("Size of call (# of probes)")
axScatter.set_ylabel("Signal Strength (Median SVD-ZRPKM)")
axHistx.hist(x, bins=np.arange(0,np.max(x)), histtype="stepfilled", lw=0,align='left')
axHisty.hist(y, bins=200, orientation='horizontal', histtype="stepfilled", lw=0)
for tl in axHistx.get_xticklabels():
tl.set_visible(False)
axHisty.set_xticklabels(["%d" % i for i in axHisty.get_xticks()], rotation=-90)
if title is not None:
axHistx.set_title(title)
if outfile is not None:
plt.savefig(outfile)示例6: _stabilityassessment
def _stabilityassessment(headers, data1d, dist, fig_correlmatrices, correlmatrixaxes, std_multiplier,
correlmatrix_colormap,
correlmatrix_filename, logarithmic_correlmatrix=True, cormaptest=True):
# calculate and plot correlation matrix
cmatrix, badidx, rowavg = correlmatrix(data1d, std_multiplier, logarithmic_correlmatrix)
rowavgmean = rowavg.mean()
rowavgstd = rowavg.std()
writemarkdown('#### Assessing sample stability')
writemarkdown("- Mean of row averages: " + str(rowavgmean))
writemarkdown("- Std of row averages: " + str(rowavgstd) + ' (%.2f %%)' % (rowavgstd / rowavgmean * 100))
img = correlmatrixaxes.imshow(cmatrix, interpolation='nearest', cmap=matplotlib.cm.get_cmap(correlmatrix_colormap))
cax = make_axes_locatable(correlmatrixaxes).append_axes('right', size="5%", pad=0.1)
fig_correlmatrices.colorbar(img, cax=cax)
fsns = [h.fsn for h in headers]
correlmatrixaxes.set_title('%.2f mm' % dist)
correlmatrixaxes.set_xticks(list(range(len(data1d))))
correlmatrixaxes.set_xticklabels([str(f) for f in fsns], rotation='vertical')
correlmatrixaxes.set_yticks(list(range(len(data1d))))
correlmatrixaxes.set_yticklabels([str(f) for f in fsns])
np.savez_compressed(correlmatrix_filename,
correlmatrix=cmatrix, fsns=np.array(fsns))
# Report table on sample stability
tab = [['FSN', 'Date', 'Discrepancy', 'Relative discrepancy ((x-mean(x))/std(x))', 'Quality', 'Quality (cormap)']]
badfsns = []
badfsns_datcmp = []
if cormaptest:
matC, matp, matpadj, datcmp_ok = datcmp(*data1d)
else:
datcmp_ok = [not x for x in badidx]
for h, bad, discr, dcmp_ok in zip(headers, badidx, rowavg, datcmp_ok):
tab.append([h.fsn, h.date.isoformat(), discr, (discr - rowavgmean) / rowavgstd,
["\u2713", "\u2718\u2718\u2718\u2718\u2718"][bad],
["\u2713", "\u2718\u2718\u2718\u2718\u2718"][dcmp_ok != 1]])
if bad:
badfsns.append(h.fsn)
if (not dcmp_ok and not np.isnan(dcmp_ok)):
badfsns_datcmp.append(h.fsn)
tab = ipy_table.IpyTable(tab)
tab.apply_theme('basic')
return badfsns, badfsns_datcmp, tab, rowavg示例7: plot
def plot(self, axes):
''' Plot network on given axes
'''
classname = self.__class__.__name__
fig = plt.gcf()
divider = make_axes_locatable(axes)
axes.axis([0,1,0,1])
# Plot samples
axes.scatter(self.samples[:,0], self.samples[:,1], s=1, color='g', alpha=0.5)
C = self.adj
Cx,Cy = C[...,0], C[...,1]
if classname != 'SSk':
for i in range(C.shape[0]):
axes.plot (Cx[i,:], Cy[i,:], 'k', alpha=1, lw=1.5)
for i in range(C.shape[1]):
axes.plot (Cx[:,i], Cy[:,i], 'k', alpha=1, lw=1.5)示例8: plot_eigenvectors
def plot_eigenvectors(ax, Y, idx, colormap):
from matplotlib.ticker import MaxNLocator
from mpl_toolkits.axes_grid import make_axes_locatable
divider = make_axes_locatable(ax)
ax2 = divider.new_vertical(size="100%", pad=0.05)
fig1 = ax.get_figure()
fig1.add_axes(ax2)
ax2.set_title("Eigenvectors", fontsize=10)
ax2.scatter(np.arange(0, len(Y)), Y[:, 0], s=10, c=idx, cmap=colormap, alpha=0.9, facecolors="none")
ax2.axhline(0, ls="--", c="k")
ax2.yaxis.set_major_locator(MaxNLocator(4))
ax.yaxis.set_major_locator(MaxNLocator(4))
ax.axhline(0, ls="--", c="k")
ax.scatter(np.arange(0, len(Y)), Y[:, 1], s=10, c=idx, cmap=colormap, alpha=0.9, facecolors="none")
ax.set_xlabel("index", fontsize=8)
ax2.set_ylabel("2nd Smallest", fontsize=8)
ax.set_ylabel("3nd Smallest", fontsize=8)
change_tick_fontsize(ax, 8)
change_tick_fontsize(ax2, 8)
for tl in ax2.get_xticklabels():
tl.set_visible(False)示例9: print
if (num == 0):
Vortensity0 = Vortensity
print("Doing geometric transformation to R-theta plane...")
# create polar-coordinate array
Vortensity_polar = geometric_transform(Vortensity.T, cartesian2polar, output_shape=(Vortensity.T.shape[0], Vortensity.T.shape[0]),
extra_keywords={'inputshape': Vortensity.T.shape, 'origin': (Vortensity.T.shape[0]/2, Vortensity.T.shape[1]/2)})
if (num == 0):
Vortensity0_polar = Vortensity_polar
####################################################################
# plot
if not (skip_cartesian):
ax = fig.add_subplot(1, len(dir_array), count_dir)
divider = make_axes_locatable(ax)
cmap = cm.get_cmap('jet')
im = ax.imshow(np.log10(Vortensity/Vortensity0), origin='lower',
vmin=min_scale, vmax=max_scale,
extent=[rangeX[0], rangeX[1], rangeY[0], rangeY[1]], cmap=cmap)
xlabel("$x$", fontsize=16)
ylabel("$y$", fontsize=16)
ax.set_xlim(rangeX[0], rangeX[1])
ax.set_ylim(rangeY[0], rangeY[1])
xticks, yticks = ax.xaxis.get_majorticklocs(), ax.yaxis.get_majorticklocs()
ax.xaxis.set_ticklabels(['%d' % (xticks[n] - 0.5*BoxX)
for n in range(len(xticks))])
ax.yaxis.set_ticklabels(['%d' % (yticks[n] - 0.5 * BoxY)示例10: show
def show(self, location='right', width=0.2, pad=0.05, ticks=None, labels=True, box=None, box_orientation='vertical'):
'''
Show a colorbar on the side of the image.
Optional Keyword Arguments:
*location*: [ string ]
Where to place the colorbar. Should be one of 'left', 'right', 'top', 'bottom'.
*width*: [ float ]
The width of the colorbar relative to the canvas size.
*pad*: [ float ]
The spacing between the colorbar and the image relative to the canvas size.
*ticks*: [ None or list ]
The position of the ticks on the colorbar.
*labels*: [ True or False ]
Whether to show numerical labels.
*box*: [ list ]
A custom box within which to place the colorbar. This should
be in the form [xmin, ymin, dx, dy] and be in relative figure
units. This overrides the location argument.
*box_orientation* [ str ]
The orientation of the colorbar within the box. Can be
'horizontal' or 'vertical'
'''
self._base_settings['location'] = location
self._base_settings['width'] = width
self._base_settings['pad'] = pad
self._base_settings['ticks'] = ticks
self._base_settings['labels'] = labels
self._base_settings['box'] = box
self._base_settings['box_orientation'] = box_orientation
if self._parent.image:
if self._colorbar_axes:
self._parent._figure.delaxes(self._colorbar_axes)
if box is None:
divider = make_axes_locatable(self._parent._ax1)
if location == 'right':
self._colorbar_axes = divider.new_horizontal(size=width, pad=pad, axes_class=maxes.Axes)
orientation = 'vertical'
elif location == 'top':
self._colorbar_axes = divider.new_vertical(size=width, pad=pad, axes_class=maxes.Axes)
orientation = 'horizontal'
elif location == 'left':
warnings.warn("Left colorbar not fully implemented")
self._colorbar_axes = divider.new_horizontal(size=width, pad=pad, pack_start=True, axes_class=maxes.Axes)
locator = divider.new_locator(nx=0, ny=0)
self._colorbar_axes.set_axes_locator(locator)
orientation = 'vertical'
elif location == 'bottom':
warnings.warn("Bottom colorbar not fully implemented")
self._colorbar_axes = divider.new_vertical(size=width, pad=pad, pack_start=True, axes_class=maxes.Axes)
locator = divider.new_locator(nx=0, ny=0)
self._colorbar_axes.set_axes_locator(locator)
orientation = 'horizontal'
else:
raise Exception("location should be one of: right/top")
self._parent._figure.add_axes(self._colorbar_axes)
else:
self._colorbar_axes = self._parent._figure.add_axes(box)
orientation = box_orientation
self._colorbar = self._parent._figure.colorbar(self._parent.image, cax=self._colorbar_axes, orientation=orientation, ticks=ticks)
if location == 'right':
for tick in self._colorbar_axes.yaxis.get_major_ticks():
tick.tick1On = True
tick.tick2On = True
tick.label1On = False
tick.label2On = labels
elif location == 'top':
for tick in self._colorbar_axes.xaxis.get_major_ticks():
tick.tick1On = True
tick.tick2On = True
tick.label1On = False
tick.label2On = labels
elif location == 'left':
for tick in self._colorbar_axes.yaxis.get_major_ticks():
tick.tick1On = True
tick.tick2On = True
tick.label1On = labels
tick.label2On = False
elif location == 'bottom':
for tick in self._colorbar_axes.xaxis.get_major_ticks():
tick.tick1On = True
tick.tick2On = True
#.........这里部分代码省略.........
示例11: isNum
for str in flist:
str=str.rstrip('\n')
if str.find('Real') != -1:
flag=1
elif flag==1 and str.find('Imag') != -1:
flag=0
elif flag==1 and isNum(str):
x.append(float(str))
elif flag==0 and isNum(str):
y.append(float(str))
fig = plt.figure(1, figsize=(10,10), dpi=50)
axScatter = plt.subplot(111)
divider = make_axes_locatable(axScatter)
axScatter.scatter(x, y)
axScatter.set_aspect(1.)
axes = fig.get_axes()[0]
axes.set_xlim((-3, 3))
axes.set_ylim((-3, 3))
axes.set_aspect('auto', adjustable='box')
plt.draw()
plt.show()
os.remove('/tmp/symbols.txt')
os.remove('/tmp/symbols_noise.txt')示例12: plot
#.........这里部分代码省略.........
self.emptyAxis()
reply = QtGui.QMessageBox.question(self, 'Excessively large plot', 'The resulting plot is too large to display.')
QtGui.QApplication.instance().restoreOverrideCursor()
return
self.fig.set_size_inches(self.imageWidth, self.imageHeight)
# *** Determine width of y-axis labels
yLabelBounds = self.yLabelExtents(features, 8)
# *** Size plots which comprise the extended errorbar plot
self.fig.clear()
spacingBetweenPlots = 0.25 # inches
widthNumSeqPlot = 1.25 # inches
if self.bShowBarPlot == False:
widthNumSeqPlot = 0.0
spacingBetweenPlots = 0.0
widthPvalueLabels = 0.75 # inches
if self.bShowPValueLabels == False:
widthPvalueLabels = 0.1
yPlotOffsetFigSpace = heightBottomLabels / self.imageHeight
heightPlotFigSpace = plotHeight / self.imageHeight
xPlotOffsetFigSpace = yLabelBounds.width + 0.1 / self.imageWidth
pValueLabelWidthFigSpace = widthPvalueLabels / self.imageWidth
widthPlotFigSpace = 1.0 - pValueLabelWidthFigSpace - xPlotOffsetFigSpace
widthErrorBarPlot = widthPlotFigSpace*self.imageWidth - widthNumSeqPlot - spacingBetweenPlots
axInitAxis = self.fig.add_axes([xPlotOffsetFigSpace,yPlotOffsetFigSpace,widthPlotFigSpace,heightPlotFigSpace])
divider = make_axes_locatable(axInitAxis)
divider.get_vertical()[0] = Size.Fixed(len(features)*self.figHeightPerRow)
if self.bShowBarPlot == True:
divider.get_horizontal()[0] = Size.Fixed(widthNumSeqPlot)
axErrorbar = divider.new_horizontal(widthErrorBarPlot, pad=spacingBetweenPlots, sharey=axInitAxis)
self.fig.add_axes(axErrorbar)
else:
divider.get_horizontal()[0] = Size.Fixed(widthErrorBarPlot)
axErrorbar = axInitAxis
# *** Plot of sequences for each subsystem
if self.bShowBarPlot == True:
axNumSeq = axInitAxis
if self.percentageOrSeqCount == 'Proportion (%)':
# plot percentage
axNumSeq.barh(np.arange(len(features))+0.0, percentage1, height = 0.3, color=profile1Colour, zorder=10, ecolor='black')
axNumSeq.barh(np.arange(len(features))-0.3, percentage2, height = 0.3, color=profile2Colour, zorder=10, ecolor='black')
for value in np.arange(-0.5, len(features)-1, 2):
axNumSeq.axhspan(value, value+1, facecolor=highlightColor,edgecolor='none',zorder=1)
axNumSeq.set_xlabel(self.percentageOrSeqCount)
maxPercentage = max(max(percentage1), max(percentage2))
axNumSeq.set_xticks([0, maxPercentage])
axNumSeq.set_xlim([0, maxPercentage*1.05])
maxPercentageStr = '%.1f' % maxPercentage
axNumSeq.set_xticklabels(['0.0', maxPercentageStr])
else:
# plot sequence count
axNumSeq.barh(np.arange(len(features))+0.0, seqs1, height = 0.3, color=profile1Colour, zorder=10, ecolor='black')
axNumSeq.barh(np.arange(len(features))-0.3, seqs2, height = 0.3, color=profile2Colour, zorder=10, ecolor='black')
for value in np.arange(-0.5, len(features)-1, 2):示例13: create_scatterhist
#.........这里部分代码省略.........
raise Exception('Select two different parameters')
#check if parameter and of are really arrays
if not isinstance(parameters, np.ndarray):
raise Exception('parameters need to be numpy ndarray')
if not isinstance(scaled_of, np.ndarray):
raise Exception('objective function need to be numpy ndarray')
# check if objective function is of size 1xN
scaled_of = np.atleast_2d(scaled_of).T
if (len(scaled_of.shape) != 2 ):
raise Exception("Objective function need to be of size (1, N) got %s instead" %(scaled_of.shape))
# check that SSE row length is equal to parameters
if not parameters.shape[0] == scaled_of.shape[0]:
raise Exception("None corresponding size of parameters and OF!")
# Check if threshold is in range of SSE values
if threshold < 0 or threshold > 1:
raise Exception("Threshold outside objective function ranges")
# Select behavioural parameter sets with of lower as threshold
search=np.where(scaled_of < threshold)
behav_par = parameters[search[0]]
behav_obj = selected_of[search[0]].T
print("Number of behavioural parametersets = " + str(behav_obj.shape[0]) + " out of " + str(parameters.shape[0]))
if not behav_par.size > 0:
raise Exception('Threshold to severe, no behavioural sets.')
fig, ax_scatter = plt.subplots(figsize=(8,6))
divider = make_axes_locatable(ax_scatter)
ax_scatter.set_autoscale_on(True)
# create a new axes with above the axScatter
ax_histx = divider.new_vertical(1.5, pad=0.0001, sharex=ax_scatter)
# create a new axes on the right side of the axScatter
ax_histy = divider.new_horizontal(1.5, pad=0.0001, sharey=ax_scatter)
fig.add_axes(ax_histx)
fig.add_axes(ax_histy)
# now determine nice limits by hand:
xmin = np.min(all_parameters[:,parameter1])
xmax = np.max(all_parameters[:,parameter1])
ymin = np.min(all_parameters[:,parameter2])
ymax = np.max(all_parameters[:,parameter2])
ax_histx.set_xlim( (xmin, xmax) )
ax_histy.set_ylim( (ymin, ymax) )
#determine binwidth (pylab examples:scatter_hist.py )
xbinwidth = (xmax-xmin)*xbinwidth
binsx = np.arange(xmin,xmax+xbinwidth,xbinwidth)
ybinwidth = (ymax-ymin)*ybinwidth
binsy = np.arange(ymin,ymax+ybinwidth,ybinwidth)
# create scatter & histogram示例14: show
def show(self, location='right', width=0.2, pad=0.05, ticks=None,
labels=True, log_format=False, box=None,
box_orientation='vertical', axis_label_text=None,
axis_label_rotation=None, axis_label_pad=5):
'''
Show a colorbar on the side of the image.
Parameters
----------
location : str, optional
Where to place the colorbar. Should be one of 'left', 'right', 'top', 'bottom'.
width : float, optional
The width of the colorbar relative to the canvas size.
pad : float, optional
The spacing between the colorbar and the image relative to the
canvas size.
ticks : list, optional
The position of the ticks on the colorbar.
labels : bool, optional
Whether to show numerical labels.
log_format : bool, optional
Whether to format ticks in exponential notation
box : list, optional
A custom box within which to place the colorbar. This should
be in the form [xmin, ymin, dx, dy] and be in relative figure
units. This overrides the location argument.
box_orientation str, optional
The orientation of the colorbar within the box. Can be
'horizontal' or 'vertical'
axis_label_text str, optional
Optional text label of the colorbar.
'''
self._base_settings['location'] = location
self._base_settings['width'] = width
self._base_settings['pad'] = pad
self._base_settings['ticks'] = ticks
self._base_settings['labels'] = labels
self._base_settings['log_format'] = log_format
self._base_settings['box'] = box
self._base_settings['box_orientation'] = box_orientation
self._base_settings['axis_label_text'] = axis_label_text
self._base_settings['axis_label_rotation'] = axis_label_rotation
self._base_settings['axis_label_pad'] = axis_label_pad
if self._parent.image:
if self._colorbar_axes:
self._parent._figure.delaxes(self._colorbar_axes)
if box is None:
divider = make_axes_locatable(self._parent.ax)
if location == 'right':
self._colorbar_axes = divider.new_horizontal(size=width, pad=pad, axes_class=maxes.Axes)
orientation = 'vertical'
elif location == 'top':
self._colorbar_axes = divider.new_vertical(size=width, pad=pad, axes_class=maxes.Axes)
orientation = 'horizontal'
elif location == 'left':
warnings.warn("Left colorbar not fully implemented")
self._colorbar_axes = divider.new_horizontal(size=width, pad=pad, pack_start=True, axes_class=maxes.Axes)
locator = divider.new_locator(nx=0, ny=0)
self._colorbar_axes.set_axes_locator(locator)
orientation = 'vertical'
elif location == 'bottom':
warnings.warn("Bottom colorbar not fully implemented")
self._colorbar_axes = divider.new_vertical(size=width, pad=pad, pack_start=True, axes_class=maxes.Axes)
locator = divider.new_locator(nx=0, ny=0)
self._colorbar_axes.set_axes_locator(locator)
orientation = 'horizontal'
else:
raise Exception("location should be one of: right/top")
self._parent._figure.add_axes(self._colorbar_axes)
else:
self._colorbar_axes = self._parent._figure.add_axes(box)
orientation = box_orientation
if log_format:
format = LogFormatterMathtext()
else:
format = None
self._colorbar = self._parent._figure.colorbar(self._parent.image, cax=self._colorbar_axes,
orientation=orientation, format=format,
ticks=ticks)
if axis_label_text:
#.........这里部分代码省略.........
示例15: plot
def plot(self, axes):
''' Plot network on given axes
:Parameters:
`axes` : matploltlib Axes
axes where to draw network
'''
classname = self.__class__.__name__
# Plot samples
axes.scatter(self.samples[:,0], self.samples[:,1], s=1.0, color='b', alpha=0.25)
fig = plt.gcf()
divider = make_axes_locatable(axes)
# Plot network
C = self.codebook
Cx,Cy = C[...,0], C[...,1]
if classname != 'NG':
for i in range(C.shape[0]):
axes.plot (Cx[i,:], Cy[i,:], 'k', alpha=0.85, lw=1.5)
for i in range(C.shape[1]):
axes.plot (Cx[:,i], Cy[:,i], 'k', alpha=0.85, lw=1.5)
axes.scatter (Cx.flatten(), Cy.flatten(), s=50, c= 'w', edgecolors='k', zorder=10)
axes.axis([0,1,0,1])
axes.set_xticks([])
axes.set_yticks([])
axes.set_aspect(1)
# Plot distortion
subaxes = divider.new_vertical(1.0, pad=0.4, sharex=axes)
fig.add_axes(subaxes)
subaxes.set_xticks([])
subaxes.yaxis.set_major_locator(matplotlib.ticker.MaxNLocator(2))
subaxes.yaxis.set_ticks_position('right')
subaxes.set_ylabel('Distortion')
subaxes.set_xlabel('Time')
#subaxes.axis([0,1,0,1])
Y = self.distortion[::1]
X = np.arange(len(Y))/float(len(Y)-1)
subaxes.plot(X,Y)
axes.axis([0,1,0,1])
if classname == 'NG':
plt.title('Neural Gas', fontsize=20)
elif classname == 'SOM':
plt.title('Self-Organizing Map', fontsize=20)
elif classname == 'DSOM':
plt.title('Dynamic Self-Organizing Map', fontsize=20)
if classname == 'NG':
axes.text(0.5, -0.01,
r'$\lambda_i = %.3f,\lambda_f = %.3f, \varepsilon_i=%.3f, \varepsilon_f=%.3f$' % (
self.sigma_i, self.sigma_f, self.lrate_i, self.lrate_f),
fontsize=16,
horizontalalignment='center',
verticalalignment='top',
transform = axes.transAxes)
if classname == 'SOM':
axes.text(0.5, -0.01,
r'$\sigma_i = %.3f,\sigma_f = %.3f, \varepsilon_i=%.3f, \varepsilon_f=%.3f$' % (
self.sigma_i, self.sigma_f, self.lrate_i, self.lrate_f),
fontsize=16,
horizontalalignment='center',
verticalalignment='top',
transform = axes.transAxes)
elif classname == 'DSOM':
axes.text(0.5, -0.01,
r'$elasticity = %.2f$, $\varepsilon = %.3f$' % (self.elasticity, self.lrate),
fontsize=16,
horizontalalignment='center',
verticalalignment='top',
transform = axes.transAxes)