Ejemplo n.º 1
0
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
Ejemplo n.º 2
0
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()
Ejemplo n.º 3
0
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()
Ejemplo n.º 4
0
    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!')