def my_Normalize(img):
# convert into range of [0,1]
min_val = np.min(img.ravel())
max_val = np.max(img.ravel())
output = (img.astype('float') - min_val) / (max_val - min_val)
return output
def draw_single_cluster_plot(plotFilePath, dots):
# print "# creating data from dots"
X = []
Y = []
for x, y in dots:
X.append(float(x))
Y.append(float(y))
nullfmt = NullFormatter() # no labels
# print "# definitions for the axes"
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
bottom_h = left_h = left + width + 0.02
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
# print "start with a rectangular Figure"
plt.figure(1, figsize=(8, 8))
axScatter = plt.axes(rect_scatter)
axHistx = plt.axes(rect_histx)
axHisty = plt.axes(rect_histy)
# print "# no labels"
axHistx.xaxis.set_major_formatter(nullfmt)
axHisty.yaxis.set_major_formatter(nullfmt)
# print "# the scatter plot:"
# axScatter.scatter(X, Y)
axScatter.plot(X, Y, '.')
# print "# now determine nice limits by hand:"
# binwidth = 0.25
# nbBins = 60
# print " -1"
# xymax = np.max( [np.max(np.fabs(X)), np.max(np.fabs(Y))] )
# xymin = np.min( [np.min(np.fabs(X)), np.min(np.fabs(Y))] )
xmax = np.max(X)
ymax = np.max(Y)
xmin = np.min(X)
ymin = np.min(Y)
# taille fixe sur les histo
# xmin=0;xmax=score_max
# ymin=0;ymax=rmsd_max
# print xymax,xymin
# print " -2"
# lim = ( int(xymax/binwidth) + 1) * binwidth
# print " -3"
# axScatter.set_xlim( (-lim, lim) )
# axScatter.set_ylim( (-lim, lim) )
axScatter.set_xlim((xmin * .9, xmax * 1.1))
axScatter.set_ylim((ymin * .9, ymax * 1.1))
# print " -4"
# bins = np.arange(-lim, lim + binwidth, binwidth)
binwidth = (xmax * 1.1 - xmin * .9) / nbBins
xBins = np.arange(xmin * .9, xmax * 1.1 + binwidth, binwidth)
binwidth = (ymax * 1.1 - ymin * .9) / nbBins
yBins = np.arange(ymin * .9, ymax * 1.1 + binwidth, binwidth)
# print " -5"
axHistx.hist(X, bins=xBins)
# print " -6"
axHisty.hist(Y, bins=yBins, orientation='horizontal')
# print "making histograms"
axHistx.set_xlim(axScatter.get_xlim())
axHisty.set_ylim(axScatter.get_ylim())
print "# save plot"
plt.savefig(plotFilePath)
plt.clf()
def plot_2D_and_histos(plotFilePath,dots,tag):
idx=getTagIndex(tag)
color=getTagColor(tag)
print "# creating data from dots"
X=[];Y=[]
for d in dots :
X.append(d[idx]);Y.append(d[-1])
nullfmt = NullFormatter() # no labels
print "# definitions for the axes"
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
bottom_h = left_h = left+width+0.02
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
print "start with a rectangular Figure"
plt.figure(1, figsize=(8,8))
axScatter = plt.axes(rect_scatter)
axHistx = plt.axes(rect_histx)
axHisty = plt.axes(rect_histy)
print "# no labels"
axHistx.xaxis.set_major_formatter(nullfmt)
axHisty.yaxis.set_major_formatter(nullfmt)
print "# the scatter plot:"
# axScatter.scatter(X, Y)
axScatter.plot(X, Y,'.',color=color)
print "# now determine nice limits by hand:"
# binwidth = 0.25
# nbBins = 60
# print " -1"
# xymax = np.max( [np.max(np.fabs(X)), np.max(np.fabs(Y))] )
# xymin = np.min( [np.min(np.fabs(X)), np.min(np.fabs(Y))] )
xmax = np.max(X); ymax = np.max(Y)
xmin = np.min(X); ymin = np.min(Y)
# print xymax,xymin
# print " -2"
# lim = ( int(xymax/binwidth) + 1) * binwidth
# print " -3"
# axScatter.set_xlim( (-lim, lim) )
# axScatter.set_ylim( (-lim, lim) )
axScatter.set_xlim( (xmin*.9, xmax*1.1) )
axScatter.set_ylim( (ymin*.9, ymax*1.1) )
# print " -4"
# bins = np.arange(-lim, lim + binwidth, binwidth)
binwidth = (xmax*1.1-xmin*.9) / nbBins
xBins = np.arange(xmin*.9,xmax*1.1 + binwidth, binwidth)
binwidth = (ymax*1.1-ymin*.9) / nbBins
yBins = np.arange(ymin*.9,ymax*1.1 + binwidth, binwidth)
# print " -5"
axHistx.hist(X, bins=xBins, color = color)
# print " -6"
axHisty.hist(Y, bins=yBins, orientation='horizontal')
print "making histograms"
axHistx.set_xlim( axScatter.get_xlim() )
axHisty.set_ylim( axScatter.get_ylim() )
print "# save plot"
plt.savefig(plotFilePath)
plt.clf()
def initialize(self, image, customize=True, xlim=None, ylim=None, xlabel='', ylabel='', log=True, vmin=None, vmax=None, maxresolution=1280, **kwargs):
'''Display one image, give user a chance to zoom in, and leave everything set for populating with later images.'''
if customize | (xlim is None )| (ylim is None):
self.xlim = [0, image.shape[1]]
self.ylim = [0, image.shape[0]]
else:
self.xlim = xlim
self.ylim = ylim
zoomed = image[self.ylim[0]:self.ylim[1], self.xlim[0]:self.xlim[1]]
# create the figure, using xlim and ylim to determine the scale of the figure
plt.ion()
# create an empty dictionary to store the axes objects
self.ax = {}
# calculate aspect ratio (y/x)
self.ysize, self.xsize = self.ylim[1] - self.ylim[0], self.xlim[1] - self.xlim[0]
aspect = np.float(self.ysize)/self.xsize
# set up the geometry of the plotting figure, including the desired resolution, histograms, and space for labels
scale = 7.5
dpi = maxresolution/np.maximum(scale, scale*aspect)
margin = 0.07
histheight = 0.1
inflate = 2*margin + histheight +1
self.figure = plt.figure(figsize=(scale*inflate, scale*aspect*inflate), dpi=dpi)
gs = plt.matplotlib.gridspec.GridSpec(2,2,width_ratios=[1,histheight], height_ratios=[histheight, 1], top=1-margin, bottom=margin, left=margin, right=1-margin, hspace=0, wspace=0)
# define panes for the image, as well as summed x and y histogram plots
self.ax['image'] = plt.subplot(gs[1,0])
self.ax['xhist'] = plt.subplot(gs[0,0], sharex=self.ax['image'] )
self.ax['yhist'] = plt.subplot(gs[1,1], sharey=self.ax['image'] )
self.ax['navigator'] = plt.subplot(gs[0,1])
# clear the axes labels
for k in self.ax.keys():
plt.setp(self.ax[k].get_xticklabels(), visible=False)
plt.setp(self.ax[k].get_yticklabels(), visible=False)
# set default image display keywords
self.imagekw = dict(cmap='gray',interpolation='nearest',extent=[0, self.xsize, 0, self.ysize], aspect='equal')
# replace any of these, as defined through the input keywords
for k in kwargs.keys():
self.imagekw[k] = kwargs[k]
#if customize:
# self.imagekw['aspect'] = 'auto'
# set the default line plotting keywords
self.linekw = dict(color='black', linewidth=1, alpha=0.5)
# make sure the min and max values for the color scale are set
if vmin is None:
vmin = np.min(image)
if vmax is None:
vmax = np.max(image)
self.vmin, self.vmax = vmin, vmax
# keep track of whether we're using a log scale
self.log = log
# calculate summed histograms
xhist = np.sum(zoomed, 0)
yhist = np.sum(zoomed, 1)
# readjust things if a log scale is set
if self.log:
zoomedtoplot = logify(zoomed)
imagetoplot = logify(image)
self.imagekw['vmin'] = np.log(np.maximum(vmin, 1))
self.imagekw['vmax'] = np.log(vmax)
else:
zoomedtoplot = zoomed
imagetoplot = image
self.imagekw['vmin'] = vmin
self.imagekw['vmax'] = vmax
# keep the navigator like the image, but adjust its extent back to the regular
self.navigatorkw = self.imagekw.copy()
self.navigatorkw['extent'] = [0,image.shape[1], 0, image.shape[0]]
# keep track of the data that goes into each plot
self.current = {}
# plot the histograms, once zoomed in
self.current['xhist'] = self.ax['xhist'].plot(np.arange(len(xhist)), xhist, **self.linekw)[0]
self.current['yhist'] = self.ax['yhist'].plot(yhist, np.arange(len(yhist)), **self.linekw)[0]
self.ax['xhist'].set_xlim(0, zoomed.shape[1]-1)
self.ax['xhist'].set_ylim(vmin*zoomed.shape[0], vmax*zoomed.shape[0])
self.ax['yhist'].set_xlim(vmin*zoomed.shape[1], vmax*zoomed.shape[1])
self.ax['xhist'].set_yscale('log')
self.ax['yhist'].set_xscale('log')
self.ax['yhist'].set_ylim(0, zoomed.shape[0]-1)
# plot the (zoomed) image
self.current['image'] = self.ax['image'].imshow(zoomedtoplot, **self.imagekw)
self.current['navigator'] = self.ax['navigator'].imshow(imagetoplot, **self.navigatorkw)
self.current['rectangle'] = self.ax['navigator'].add_patch(plt.matplotlib.patches.Rectangle((self.xlim[0], self.ylim[0]), self.xlim[1] - self.xlim[0], self.ylim[1]-self.ylim[0], edgecolor='red', facecolor='none', alpha=0.5, linewidth=5))
self.speak('created new image and plots')
if customize:
self.name = self.input('Please zoom to desired limits, enter a label to identify this window, and press return:')
xlim, ylim = np.array(self.ax['image'].get_xlim()), np.array(self.ax['image'].get_ylim())
xlim[0] = np.maximum(xlim[0], 0)
ylim[0] = np.maximum(ylim[0], 0)
xlim[1] = np.minimum(xlim[1], self.xsize)
ylim[1] = np.minimum(ylim[1], self.ysize)
self.initialize(image, customize=False, xlim=xlim, ylim=ylim, log=log, vmin=vmin, vmax=vmax, maxresolution=maxresolution, **kwargs)
self.speak('the display has been fully initialized -- ready for new plots!')