Python pyplot.show函数代码示例

    def get_gabors(self, rf):
        lams =  float(rf[0])/self.sfs # lambda = 1./sf  #1./np.array([.1,.25,.4])
        sigma = rf[0]/2./np.pi
        # rf = [100,100]
        gabors = np.zeros(( len(oris),len(phases),len(lams), rf[0], rf[1] ))

        i = np.arange(-rf[0]/2+1,rf[0]/2+1)
        #print i
        j = np.arange(-rf[1]/2+1,rf[1]/2+1)
        ii,jj = np.meshgrid(i,j)
        for o, theta in enumerate(self.oris):
            x = ii*np.cos(theta) + jj*np.sin(theta)
            y = -ii*np.sin(theta) + jj*np.cos(theta)

            for p, phase in enumerate(self.phases):
                for s, lam in enumerate(lams):
                    fxx = np.cos(2*np.pi*x/lam + phase) * np.exp(-(x**2+y**2)/(2*sigma**2))
                    fxx -= np.mean(fxx)
                    fxx /= np.linalg.norm(fxx)

                    #if p==0:
                        #plt.subplot(len(oris),len(lams),count+1)
                        #plt.imshow(fxx,cmap=mpl.cm.gray,interpolation='bicubic')
                        #count+=1

                    gabors[o,p,s,:,:] = fxx
        plt.show()
        return gabors

def plot_figure(data, row_labels, col_labels, abs_val, plot_bin, labels, time, logInterval):
  colors = ['#0077FF', '#FF0000', '#00FF00', 'magenta']
  cmaps = []
  for i in colors:
    cmaps.append(mpl.colors.LinearSegmentedColormap.from_list('m1',['black',i]))
  dim, rows, cols = data.shape
  vmax = np.amax(data)
  vmin = np.amin(data)
  fig, ax = plt.subplots()
  c = np.zeros([rows, cols, 4])
  for i in range(dim):
    c = np.add(c, cmaps[i]((data[i]-vmin)/(vmax-vmin)))
  c = np.clip(c, 0, 1)
  pc = ax.imshow(c, aspect='auto', interpolation='none')
  #ax.set_title(labels[0])
  fig.text(0.5, 0.04, 'Bin # along rod length', ha='center',
      fontsize=fontsize)
  fig.text(0.0, 0.5, 'Time (s)', va='center', rotation='vertical',
      fontsize=fontsize)
  #ax.add_patch(Rectangle((0.5, time/logInterval), cols-1, 10, edgecolor='w',
  #  facecolor='none'))
  #ax.add_patch(Rectangle((bin_id, 0.5/logInterval), 1, rows-0.5/logInterval,
  #  edgecolor='w', facecolor='none'))
  #plt.savefig('kymo.png', bbox_inches='tight')
  plt.savefig('3rods_1long_kymo.pdf')
  plt.show()

    def plot_predict_is(self,h=5,**kwargs):
        """ Plots forecasts with the estimated model against data
            (Simulated prediction with data)

        Parameters
        ----------
        h : int (default : 5)
            How many steps to forecast

        Returns
        ----------
        - Plot of the forecast against data 
        """     

        figsize = kwargs.get('figsize',(10,7))

        plt.figure(figsize=figsize)
        date_index = self.index[-h:]
        predictions = self.predict_is(h)
        data = self.data[-h:]

        t_params = self.transform_z()

        plt.plot(date_index,np.abs(data-t_params[-1]),label='Data')
        plt.plot(date_index,predictions,label='Predictions',c='black')
        plt.title(self.data_name)
        plt.legend(loc=2)   
        plt.show()          

def plotResults(datasetName, sampleSizes, foldsSet, cvScalings, sampleMethods, fileNameSuffix):
    """
    Plots the errors for a particular dataset on a bar graph. 
    """

    for k in range(len(sampleMethods)):
        outfileName = outputDir + datasetName + sampleMethods[k] + fileNameSuffix + ".npz"
        data = numpy.load(outfileName)

        errors = data["arr_0"]
        meanMeasures = numpy.mean(errors, 0)

        for i in range(sampleSizes.shape[0]):
            plt.figure(k*len(sampleMethods) + i)
            plt.title("n="+str(sampleSizes[i]) + " " + sampleMethods[k])

            for j in range(errors.shape[3]):
                plt.plot(foldsSet, meanMeasures[i, :, j])
                plt.xlabel("Folds")
                plt.ylabel('Error')

            labels = ["VFCV", "PenVF+"]
            labels.extend(["VFP s=" + str(x) for x in cvScalings])
            plt.legend(tuple(labels))
    plt.show()

def main():

    # http://scikit-learn.org/stable/tutorial/basic/tutorial.html#loading-an-example-dataset
    # "A dataset is a dictionary-like object that holds all the data and some
    # metadata about the data. This data is stored in the .data member, which
    # is a n_samples, n_features array. In the case of supervised problem, one
    # or more response variables are stored in the .target member."

    # Toy datasets

    iris = datasets.load_iris()         # The iris dataset (classification)
    digits = datasets.load_digits()     # The digits dataset (classification)

    #boston = datasets.load_boston()     # The boston house-prices dataset (regression)
    #diabetes = datasets.load_diabetes() # The diabetes dataset (regression)
    #linnerud = datasets.load_linnerud() # The linnerud dataset (multivariate regression)

    print(iris.feature_names)
    print(iris.data)
    print(iris.target_names)
    print(iris.target)

    print(digits.images[0])
    print(digits.target_names)
    print(digits.target)

    plt.imshow(digits.images[0], cmap='gray', interpolation='nearest')
    plt.show()

    def work(self):
        self.worked = True
        kwargs = dict(
                weights=self.weights,
                mus=self.mus,
                sigmas=self.sigmas,
                low=self.low,
                high=self.high,
                q=self.q,
                )
        samples = GMM1(rng=self.rng,
                size=(self.n_samples,),
                **kwargs)
        samples = np.sort(samples)
        edges = samples[::self.samples_per_bin]
        #print samples

        pdf = np.exp(GMM1_lpdf(edges[:-1], **kwargs))
        dx = edges[1:] - edges[:-1]
        y = 1 / dx / len(dx)

        if self.show:
            plt.scatter(edges[:-1], y)
            plt.plot(edges[:-1], pdf)
            plt.show()
        err = (pdf - y) ** 2
        print np.max(err)
        print np.mean(err)
        print np.median(err)
        if not self.show:
            assert np.max(err) < .1
            assert np.mean(err) < .01
            assert np.median(err) < .01

def filterFunc():
    rects = []
    hsv_planes = [[[]]]
    if os.path.isfile(Image_File):
        BGR=cv2.imread(Image_File)
        gray = cv2.cvtColor(BGR, cv2.COLOR_BGR2GRAY)
        img = gray
        f = np.fft.fft2(img)
        fshift = np.fft.fftshift(f)
        magnitude_spectrum = 20*np.log(np.abs(fshift))
        
        plt.subplot(221),plt.imshow(img, cmap = 'gray')
        plt.title('Input Image'), plt.xticks([]), plt.yticks([])
        
        plt.subplot(222),plt.imshow(magnitude_spectrum, cmap = 'gray')
        plt.title('Magnitude Spectrum'), plt.xticks([]), plt.yticks([])
        
        FiltzeredFFT = HighPassFilter(fshift, 60)
        plt.subplot(223),plt.imshow(np.abs(FiltzeredFFT), cmap = 'gray')
        plt.title('Filtered'), plt.xticks([]), plt.yticks([])
        
		
        f_ishift = np.fft.ifftshift(FiltzeredFFT)
        img_back = np.fft.ifft2(f_ishift)
        img_back = np.abs(img_back)
        plt.subplot(224),plt.imshow(np.abs(img_back), cmap = 'gray')
        plt.title('Filtered Image'), plt.xticks([]), plt.yticks([])
        plt.show()

def plotIterationResult(train_err_list):
    x = range(1,len(train_err_list) + 1)
    fig = plt.figure()
    plt.plot(x,train_err_list)
    plt.xlabel('iterations')
    plt.ylabel('training error')
    plt.show()

def draw(data, classes, model, resolution=100):
    mycm = mpl.cm.get_cmap('Paired')
    
    one_min, one_max = data[:, 0].min()-0.1, data[:, 0].max()+0.1
    two_min, two_max = data[:, 1].min()-0.1, data[:, 1].max()+0.1
    xx1, xx2 = np.meshgrid(np.arange(one_min, one_max, (one_max-one_min)/resolution),
                     np.arange(two_min, two_max, (two_max-two_min)/resolution))
    
    inputs = np.c_[xx1.ravel(), xx2.ravel()]
    z = []
    for i in range(len(inputs)):
        z.append(predict(model, inputs[i])[0])
    result = np.array(z).reshape(xx1.shape)
    
    plt.contourf(xx1, xx2, result, cmap=mycm)
    plt.scatter(data[:, 0], data[:, 1], s=50, c=classes, cmap=mycm)
    
    t = np.zeros(15)
    for i in range(15):
        if i < 5:
            t[i] = 0
        elif i < 10:
            t[i] = 1
        else:
            t[i] = 2
    plt.scatter(model[:, 0], model[:, 1], s=150, c=t, cmap=mycm)
    
    plt.xlim([0, 10])
    plt.ylim([0, 10])
    
    plt.show()

def plotTestData(tree):
	plt.figure()
	plt.axis([0,1,0,1])
	plt.xlabel("X axis")
	plt.ylabel("Y axis")
	plt.title("Green: Class1, Red: Class2, Blue: Class3, Yellow: Class4")
	for value in class1:
		plt.plot(value[0],value[1],'go')
	plt.hold(True)
	for value in class2:
		plt.plot(value[0],value[1],'ro')
	plt.hold(True)
	for value in class3:
		plt.plot(value[0],value[1],'bo')
	plt.hold(True)
	for value in class4:
		plt.plot(value[0],value[1],'yo')
	plotRegion(tree)
	for value in classPlot1:
		plt.plot(value[0],value[1],'g.',ms=3.0)
	plt.hold(True)
	for value in classPlot2:
		plt.plot(value[0],value[1],'r.', ms=3.0)
	plt.hold(True)
	for value in classPlot3:
		plt.plot(value[0],value[1],'b.', ms=3.0)
	plt.hold(True)
	for value in classPlot4:
		plt.plot(value[0],value[1],'y.', ms=3.0)
	plt.grid(True)
	plt.show()

def plotJ(J_history,num_iters):
    x = np.arange(1,num_iters+1)
    plt.plot(x,J_history)
    plt.xlabel(u"迭代次数",fontproperties=font) # 注意指定字体，要不然出现乱码问题
    plt.ylabel(u"代价值",fontproperties=font)
    plt.title(u"代价随迭代次数的变化",fontproperties=font)
    plt.show()

    def zplane(self, title="", fontsize=18):
        """ Display filter in the complex plane

        Parameters
        ----------

        """
        rb = self.z
        ra = self.p

        t = np.arange(0, 2 * np.pi + 0.1, 0.1)
        plt.plot(np.cos(t), np.sin(t), "k")

        plt.plot(np.real(ra), np.imag(ra), "x", color="r")
        plt.plot(np.real(rb), np.imag(rb), "o", color="b")
        M1 = -10000
        M2 = -10000
        if len(ra) > 0:
            M1 = np.max([np.abs(np.real(ra)), np.abs(np.imag(ra))])
        if len(rb) > 0:
            M2 = np.max([np.abs(np.real(rb)), np.abs(np.imag(rb))])
        M = 1.6 * max(1.2, M1, M2)
        plt.axis([-M, M, -0.7 * M, 0.7 * M])
        plt.title(title, fontsize=fontsize)
        plt.show()

    def one_file_features(self, im, demo=False):
        """
        Zde je kontruován vektor příznaků pro klasfikátor
        """
        # color processing
        fd = np.array([])

        img = skimage.color.rgb2gray(im)
        # graylevel
        if self.hogFeatures:
            pass

        if self.grayLevelFeatures:
            imr = skimage.transform.resize(img, [9, 9])
            glfd = imr.reshape(-1)
            fd = np.append(fd, glfd)

            if demo:
                plt.imshow(imr)
                plt.show()

        #fd.append(hsvft[:])
        if self.colorFeatures:
            #fd = np.append(fd, colorft)
            pass

        #print hog_image
        return fd

def display(spectrum):
	template = np.ones(len(spectrum))

	#Get the plot ready and label the axes
	pyp.plot(spectrum)
	max_range = int(math.ceil(np.amax(spectrum) / standard_deviation))
	for i in range(0, max_range):
		pyp.plot(template * (mean + i * standard_deviation))
	pyp.xlabel('Units?')
	pyp.ylabel('Amps Squared')    
	pyp.title('Mean Normalized Power Spectrum')
	if 'V' in Options:
		pyp.show()
	if 'v' in Options:
		tokens = sys.argv[-1].split('.')
		filename = tokens[0] + ".png"
		input = ''
		if os.path.isfile(filename):
			input = input("Error: Plot file already exists! Overwrite? (y/n)\n")
			while input != 'y' and input != 'n':
				input = input("Please enter either \'y\' or \'n\'.\n")
			if input == 'y':
				pyp.savefig(filename) 
			else:
				print("Plot not written.")
		else:
			pyp.savefig(filename) 

def vis_result(image, seg, gt, title1='Segmentation', title2='Ground truth', savefile=None):
    indices = np.where(seg >= 0.5)
    indices_gt = np.where(gt >= 0.5)

    im_norm = image / image.max()
    rgb_image = color.gray2rgb(im_norm)
    multiplier = [0., 1., 1.]
    multiplier_gt = [1., 1., 0.]

    im_seg = rgb_image.copy()
    im_gt = rgb_image.copy()
    im_seg[indices[0], indices[1], :] *= multiplier
    im_gt[indices_gt[0], indices_gt[1], :] *= multiplier_gt

    fig = plt.figure()
    a = fig.add_subplot(1, 2, 1)
    plt.imshow(im_seg)
    a.set_title(title1)
    a = fig.add_subplot(1, 2, 2)
    plt.imshow(im_gt)
    a.set_title(title2)

    if savefile is None:
        plt.show()
    else:
        plt.savefig(savefile)
    plt.close()