Python matplotlib.pyplot 模块,axis() 实例源码

我们从Python开源项目中,提取了以下50个代码示例,用于说明如何使用matplotlib.pyplot.axis()

项目:lyricswordcloud    作者:qwertyyb    | 项目源码 | 文件源码
def showData(self):
    print('???,????···')
    mask = imread(self.picfile)
    imgcolor = ImageColorGenerator(mask)
    wcc = WordCloud(font_path='./msyhl.ttc', 
    mask=mask, background_color='white', 
    max_font_size=200, 
    max_words=300,
    color_func=imgcolor
    )
    wc = wcc.generate_from_frequencies(self.data)
    plt.figure()
    plt.imshow(wc)
    plt.axis('off')
    print('?????')
    plt.show()
项目:hippylib    作者:hippylib    | 项目源码 | 文件源码
def plot_pts(points, values, colorbar=True, subplot_loc=None, mytitle=None, show_axis='on', vmin=None, vmax=None, xlim=(0,1), ylim=(0,1)):
    if subplot_loc is not None:
        plt.subplot(subplot_loc)

    pp = plt.scatter(points[:,0], points[:,1], c=values.get_local(), marker=",", s=20, vmin=vmin, vmax=vmax)

    plt.axis(show_axis)

    if colorbar:
        plt.colorbar(pp, fraction=.1, pad=0.2)
    else:
        plt.gca().set_aspect('equal')

    if mytitle is not None:
        plt.title(mytitle, fontsize=20)

    if xlim is not None:
        plt.xlim(xlim)

    if ylim is not None:
        plt.ylim(ylim)

    return pp
项目:treecat    作者:posterior    | 项目源码 | 文件源码
def layout_tree(correlation):
    """Layout tree for visualization with e.g. matplotlib.

    Args:
        correlation: A [V, V]-shaped numpy array of latent correlations.

    Returns:
        A [V, 3]-shaped numpy array of spectral positions of vertices.
    """
    assert len(correlation.shape) == 2
    assert correlation.shape[0] == correlation.shape[1]
    assert correlation.dtype == np.float32

    laplacian = -correlation
    np.fill_diagonal(laplacian, 0)
    np.fill_diagonal(laplacian, -laplacian.sum(axis=0))
    evals, evects = scipy.linalg.eigh(laplacian, eigvals=[1, 2, 3])
    assert np.all(evals > 0)
    assert evects.shape[1] == 3
    return evects
项目:snake_game    作者:wing3s    | 项目源码 | 文件源码
def play(self, nb_rounds):
        img_saver = save_image()
        img_saver.next()

        game_cnt = it.count(1)
        for i in xrange(nb_rounds):
            game = self.game(width=self.width, height=self.height)
            screen, _ = game.next()
            img_saver.send(screen)
            frame_cnt = it.count()
            try:
                state = np.asarray([screen] * self.nb_frames)
                while True:
                    frame_cnt.next()
                    act_idx = np.argmax(
                        self.model.predict(state[np.newaxis]), axis=-1)[0]
                    screen, _ = game.send(self.actions[act_idx])
                    state = np.roll(state, 1, axis=0)
                    state[0] = screen
                    img_saver.send(screen)
            except StopIteration:
                print 'Saved %4i frames for game %3i' % (
                    frame_cnt.next(), game_cnt.next())
        img_saver.close()
项目:shenlan    作者:vector-1127    | 项目源码 | 文件源码
def plotGeneratedImages(epoch,example=100,dim=(10,10),figsize=(10,10)):
    noise = np.random.normal(0,1,size=(example,randomDim))
    generatedImage = generator.predict(noise)
    generatedImage = generatedImage.reshape(example,28,28)

    plt.figure(figsize=figsize)

    for i in range(example):
        plt.subplot(dim[0],dim[1],i+1)
        plt.imshow(generatedImage[i],interpolation='nearest',cmap='gray')
        '''drop the x and y axis'''
        plt.axis('off')
    plt.tight_layout()

    if not os.path.exists('generated_image'):
        os.mkdir('generated_image')
    plt.savefig('generated_image/wgan_generated_img_epoch_%d.png' % epoch)
项目:pycma    作者:CMA-ES    | 项目源码 | 文件源码
def alleviate_conditioning_in_coordinates(self, condition=1e8):
        """pass scaling from `C` to `sigma_vec`.

        As a result, `C` is a correlation matrix, i.e., all diagonal
        entries of `C` are `1`.
        """
        if max(self.dC) / min(self.dC) > condition:
            # allows for much larger condition numbers, if axis-parallel
            if hasattr(self, 'sm') and isinstance(self.sm, sampler.GaussFullSampler):
                old_coordinate_condition = max(self.dC) / min(self.dC)
                old_condition = self.sm.condition_number
                factors = self.sm.to_correlation_matrix()
                self.sigma_vec *= factors
                self.pc /= factors
                self._updateBDfromSM(self.sm)
                utils.print_message('\ncondition in coordinate system exceeded'
                                    ' %.1e, rescaled to %.1e, '
                                    '\ncondition changed from %.1e to %.1e'
                                      % (old_coordinate_condition, max(self.dC) / min(self.dC),
                                         old_condition, self.sm.condition_number),
                                    iteration=self.countiter)
项目:pycma    作者:CMA-ES    | 项目源码 | 文件源码
def plot_axes_scaling(self, iabscissa=1):
        from matplotlib import pyplot
        if not hasattr(self, 'D'):
            self.load()
        dat = self
        if np.max(dat.D[:, 5:]) == np.min(dat.D[:, 5:]):
            pyplot.text(0, dat.D[-1, 5],
                        'all axes scaling values equal to %s'
                        % str(dat.D[-1, 5]),
                        verticalalignment='center')
            return self  # nothing interesting to plot
        self._enter_plotting()
        pyplot.semilogy(dat.D[:, iabscissa], dat.D[:, 5:], '-b')
        # pyplot.hold(True)
        pyplot.grid(True)
        ax = array(pyplot.axis())
        # ax[1] = max(minxend, ax[1])
        pyplot.axis(ax)
        pyplot.title('Principle Axes Lengths')
        # pyplot.xticks(xticklocs)
        self._xlabel(iabscissa)
        self._finalize_plotting()
        return self
项目:aesop    作者:BioMoDeL    | 项目源码 | 文件源码
def ddGa_rel(self):
        """Summary
        Calculates and returns the free energy of association relative to the
        parent free energy of association.

        Returns
        -------
        ndarray
            Array of free energies corresponding to the mutant IDs from the
            Alascan.getMutIDs() method.
        """
        Gsolv = self.Gsolv
        Gref = self.Gref
        Gcoul = self.Gcoul

        dGsolv = Gsolv - Gref

        dGsolu = Gsolv[:, 0] - Gsolv[:, 1:].sum(axis=1)
        dGcoul = Gcoul[:, 0] - Gcoul[:, 1:].sum(axis=1)
        ddGsolv = dGsolv[:, 0] - dGsolv[:, 1:].sum(axis=1)

        dGbind = ddGsolv + dGcoul
        dGbind_rel = dGbind - dGbind[0]
        return dGbind_rel
项目:aesop    作者:BioMoDeL    | 项目源码 | 文件源码
def ddGa_rel(self):
        """Summary
        Calculates and returns the free energy of association relative
        to the parent free energy of association.

        Returns
        -------
        ndarray
            Array of free energies corresponding to the mutant IDs from
            the Alascan.getMutIDs() method.
        """
        Gsolv = self.Gsolv
        Gref = self.Gref
        Gcoul = self.Gcoul

        dGsolv = Gsolv - Gref

        dGsolu = Gsolv[:, 0] - Gsolv[:, 1:].sum(axis=1)
        dGcoul = Gcoul[:, 0] - Gcoul[:, 1:].sum(axis=1)
        ddGsolv = dGsolv[:, 0] - dGsolv[:, 1:].sum(axis=1)

        dGbind = ddGsolv + dGcoul
        dGbind_rel = dGbind - dGbind[0]
        return dGbind_rel
项目:bob.bio.base    作者:bioidiap    | 项目源码 | 文件源码
def _plot_cmc(cmcs, colors, labels, title, fontsize=10, position=None):
  if position is None: position = 'lower right'
  # open new page for current plot
  figure = pyplot.figure()

  max_R = 0
  # plot the CMC curves
  for i in range(len(cmcs)):
    probs = bob.measure.cmc(cmcs[i])
    R = len(probs)
    pyplot.semilogx(range(1, R+1), probs, figure=figure, color=colors[i], label=labels[i])
    max_R = max(R, max_R)

  # change axes accordingly
  ticks = [int(t) for t in pyplot.xticks()[0]]
  pyplot.xlabel('Rank')
  pyplot.ylabel('Probability')
  pyplot.xticks(ticks, [str(t) for t in ticks])
  pyplot.axis([0, max_R, -0.01, 1.01])
  pyplot.legend(loc=position, prop = {'size':fontsize})
  pyplot.title(title)

  return figure
项目:bob.bio.base    作者:bioidiap    | 项目源码 | 文件源码
def _plot_epc(scores_dev, scores_eval, colors, labels, title, fontsize=10, position=None):
  if position is None: position = 'upper center'
  # open new page for current plot
  figure = pyplot.figure()

  # plot the DET curves
  for i in range(len(scores_dev)):
    x,y = bob.measure.epc(scores_dev[i][0], scores_dev[i][1], scores_eval[i][0], scores_eval[i][1], 100)
    pyplot.plot(x, y, color=colors[i], label=labels[i])

  # change axes accordingly
  pyplot.xlabel('alpha')
  pyplot.ylabel('HTER')
  pyplot.title(title)
  pyplot.axis([-0.01, 1.01, -0.01, 0.51])
  pyplot.grid(True)
  pyplot.legend(loc=position, prop = {'size':fontsize})
  pyplot.title(title)

  return figure
项目:MLPractices    作者:carefree0910    | 项目源码 | 文件源码
def _opt(self, i, _activation, _delta):
        if not isinstance(self._layers[i], ConvLayer):
            self._weights[i] *= self._regularization_param
            self._weights[i] += self._w_optimizer.run(
                i, _activation.reshape(_activation.shape[0], -1).T.dot(_delta)
            )
            if self._whether_apply_bias:
                self._bias[i] += self._b_optimizer.run(
                    i, np.sum(_delta, axis=0, keepdims=True)
                )
        else:
            self._weights[i] *= self._regularization_param
            if _delta[1] is not None:
                self._weights[i] += self._w_optimizer.run(i, _delta[1])
            if self._whether_apply_bias and _delta[2] is not None:
                self._bias[i] += self._b_optimizer.run(i, _delta[2])

    # API
项目:structured-output-ae    作者:sbelharbi    | 项目源码 | 文件源码
def eval_classificationT( self, y, p_y):
        """Calculate the error (100 - accuracy) of the DNN in the case of classification.

        :type y: vector 
        :param y: vector (r,) of labels

        :type p_y: matrix
        :param p_y: matrix of the output of the network. Each raw is a vector of probailities (probablities of the classes)
        """

        y_ = T.argmax(p_y, axis = 1)
        # Accuracy
        error = 1 - T.mean(T.eq(y_, y) * 1.)
        error = error * 100.

        return error
项目:structured-output-ae    作者:sbelharbi    | 项目源码 | 文件源码
def eval_segmentation_bin( self, y, model_output, th=.5, path='../data/predict/'):
        '''Evaluation the performance of a binary segmentation. The default used threshold

        .5.
        The used evaluation is the mean squarre error.
        '''
        # binarization
        nbr, dim = y.shape
        output_bin = np.float32((model_output > th) * 1.)
        mse = self.MeanSquareError(y, model_output)
        for i in xrange(nbr):
            im_gt = Image.fromarray(np.reshape(np.uint8(y[i,:] *255.), (128,128)))
            im_bin = Image.fromarray(np.reshape(np.uint8(output_bin[i,:] *255.), (128,128)))
            im_gr  = Image.fromarray(np.reshape(np.uint8(model_output[i,:] *255.) , (128,128)))
            temp = np.concatenate((im_gt, im_bin, im_gr), axis=1)
            two_imgs = sc.misc.toimage(temp)
            sc.misc.imsave(path + str(i) +'.png', two_imgs)
            #two_imgs.show()
            #raw_input('Press ENTER to continue...')
        return mse
项目:structured-output-ae    作者:sbelharbi    | 项目源码 | 文件源码
def segment( self, y, model_output, th=.5, path='../data/predict/'):
        '''Segment an image using the output of a neural network. The default used threshold

        .5.
        '''
        # binarization
        nbr, dim = y.shape
        output_bin = np.float32((model_output > th) * 1.)
        for i in xrange(nbr):
            im_gt = Image.fromarray(np.reshape(np.uint8(y[i,:]) *255., (128,128)))
            im_bin = Image.fromarray(np.reshape(np.uint8(output_bin[i,:]) *255., (128,128)))
            im_gr  = Image.fromarray(np.reshape(np.uint8(model_output[i,:] *255.) , (128,128)))
            temp = np.concatenate((im_gt, im_bin, im_gr), axis=1)
            two_imgs = sc.misc.toimage(temp)
            sc.misc.imsave(path + str(i) +'.png', two_imgs)
            #two_imgs.show()
            #raw_input('Press ENTER to continue...')
项目:Wall-EEG    作者:neurotechuoft    | 项目源码 | 文件源码
def updatePlot(self, data):
        """ Update the plot """

        plt.figure(self.fig.number)  
        #assert (data.shape[1] == self.nbCh), 'new data does not have the same number of channels'
        #assert (data.shape[0] == self.nbPoints), 'new data does not have the same number of points'

        data = data - np.mean(data,axis=0)
        std_data = np.std(data,axis=0)
        std_data[np.where(std_data == 0)] = 1
        data = data/std_data*self.chRange/5.0     

        for i, chName in enumerate(self.chNames):
            self.chLinesDict[chName].set_ydata(data[:,i]+self.offsets[i])

        plt.draw()
项目:OpenTDA    作者:outlace    | 项目源码 | 文件源码
def drawComplex(origData, ripsComplex, axes=[-6,8,-6,6]):
  plt.clf()
  plt.axis(axes)
  plt.scatter(origData[:,0],origData[:,1]) #plotting just for clarity
  for i, txt in enumerate(origData):
      plt.annotate(i, (origData[i][0]+0.05, origData[i][1])) #add labels

  #add lines for edges
  for edge in [e for e in ripsComplex if len(e)==2]:
      #print(edge)
      pt1,pt2 = [origData[pt] for pt in [n for n in edge]]
      #plt.gca().add_line(plt.Line2D(pt1,pt2))
      line = plt.Polygon([pt1,pt2], closed=None, fill=None, edgecolor='r')
      plt.gca().add_line(line)

  #add triangles
  for triangle in [t for t in ripsComplex if len(t)==3]:
      pt1,pt2,pt3 = [origData[pt] for pt in [n for n in triangle]]
      line = plt.Polygon([pt1,pt2,pt3], closed=False, color="blue",alpha=0.3, fill=True, edgecolor=None)
      plt.gca().add_line(line)
  plt.show()
项目:OpenTDA    作者:outlace    | 项目源码 | 文件源码
def drawComplex(data, ph, axes=[-6, 8, -6, 6]):
    plt.clf()
    plt.axis(axes)  # axes = [x1, x2, y1, y2]
    plt.scatter(data[:, 0], data[:, 1])  # plotting just for clarity
    for i, txt in enumerate(data):
        plt.annotate(i, (data[i][0] + 0.05, data[i][1]))  # add labels

    # add lines for edges
    for edge in [e for e in ph.ripsComplex if len(e) == 2]:
        # print(edge)
        pt1, pt2 = [data[pt] for pt in [n for n in edge]]
        # plt.gca().add_line(plt.Line2D(pt1,pt2))
        line = plt.Polygon([pt1, pt2], closed=None, fill=None, edgecolor='r')
        plt.gca().add_line(line)

    # add triangles
    for triangle in [t for t in ph.ripsComplex if len(t) == 3]:
        pt1, pt2, pt3 = [data[pt] for pt in [n for n in triangle]]
        line = plt.Polygon([pt1, pt2, pt3], closed=False,
                           color="blue", alpha=0.3, fill=True, edgecolor=None)
        plt.gca().add_line(line)
    plt.show()
项目:OpenTDA    作者:outlace    | 项目源码 | 文件源码
def drawComplex(origData, ripsComplex, axes=[-6,8,-6,6]):
  plt.clf()
  plt.axis(axes)
  plt.scatter(origData[:,0],origData[:,1]) #plotting just for clarity
  for i, txt in enumerate(origData):
      plt.annotate(i, (origData[i][0]+0.05, origData[i][1])) #add labels

  #add lines for edges
  for edge in [e for e in ripsComplex if len(e)==2]:
      #print(edge)
      pt1,pt2 = [origData[pt] for pt in [n for n in edge]]
      #plt.gca().add_line(plt.Line2D(pt1,pt2))
      line = plt.Polygon([pt1,pt2], closed=None, fill=None, edgecolor='r')
      plt.gca().add_line(line)

  #add triangles
  for triangle in [t for t in ripsComplex if len(t)==3]:
      pt1,pt2,pt3 = [origData[pt] for pt in [n for n in triangle]]
      line = plt.Polygon([pt1,pt2,pt3], closed=False, color="blue",alpha=0.3, fill=True, edgecolor=None)
      plt.gca().add_line(line)
  plt.show()
项目:DeepTextSpotter    作者:MichalBusta    | 项目源码 | 文件源码
def vis_square(data):
    """Take an array of shape (n, height, width) or (n, height, width, 3)
       and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)"""

    # normalize data for display
    data = (data - data.min()) / (data.max() - data.min())

    # force the number of filters to be square
    n = int(np.ceil(np.sqrt(data.shape[0])))
    padding = (((0, n ** 2 - data.shape[0]),
               (0, 1), (0, 1))                 # add some space between filters
               + ((0, 0),) * (data.ndim - 3))  # don't pad the last dimension (if there is one)
    data = np.pad(data, padding, mode='constant', constant_values=1)  # pad with ones (white)

    # tile the filters into an image
    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))
    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])
    plt.imshow(data, interpolation='nearest'); plt.axis('off')
项目:DeblurGAN    作者:KupynOrest    | 项目源码 | 文件源码
def __plot_canvas(self, show, save):
        if len(self.result) == 0:
            raise Exception('Please run blur_image() method first.')
        else:
            plt.close()
            plt.axis('off')
            fig, axes = plt.subplots(1, len(self.result), figsize=(10, 10))
            if len(self.result) > 1:
                for i in range(len(self.result)):
                        axes[i].imshow(self.result[i])
            else:
                plt.axis('off')

                plt.imshow(self.result[0])
            if show and save:
                if self.path_to_save is None:
                    raise Exception('Please create Trajectory instance with path_to_save')
                cv2.imwrite(os.path.join(self.path_to_save, self.image_path.split('/')[-1]), self.result[0] * 255)
                plt.show()
            elif save:
                if self.path_to_save is None:
                    raise Exception('Please create Trajectory instance with path_to_save')
                cv2.imwrite(os.path.join(self.path_to_save, self.image_path.split('/')[-1]), self.result[0] * 255)
            elif show:
                plt.show()
项目:Twitter-and-IMDB-Sentimental-Analytics    作者:abhinandanramesh    | 项目源码 | 文件源码
def make_plot(counts):
    """
    Plot the counts for the positive and negative words for each timestep.
    Use plt.show() so that the plot will popup.
    """
    positive = []
    negative = []

    for count in counts:
    for word in count:
        if word[0] == "positive":
            positive.append(word[1])
        else:
            negative.append(word[1])

    plt.axis([-1, len(positive), 0, max(max(positive),max(negative))+100])
    pos, = plt.plot(positive, 'b-', marker = 'o', markersize = 10)
    neg, = plt.plot(negative, 'g-', marker = 'o', markersize = 10)
    plt.legend((pos,neg),('Positive','Negative'),loc=2)
    plt.xticks(np.arange(0, len(positive), 1))
    plt.xlabel("Time Step")
    plt.ylabel("Word Count")
    plt.show()
项目:tieba-zhuaqu    作者:ankanch    | 项目源码 | 文件源码
def pieGraphics(Labels,ValueList,graphicTitle='??'):
    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral', "blue","green","cyan","magenta"]
    maxdata = max(ValueList)
    explode = []
    for v in ValueList:
        if v == maxdata:
            explode.append(0.1)
        else:
            explode.append(0)
    print(explode)
    patches,l_text,p_text = plt.pie(ValueList, labels=Labels, colors=colors,autopct='%1.1f%%',explode=explode ,shadow=True, startangle=90)
    for font in l_text:
        font.set_fontproperties(FontProperties(fname=PATH_SUFFIX+'SIMLI.TTF'))
    plt.title(graphicTitle,fontproperties=font_set,y=1.05)
    # Set aspect ratio to be equal so that pie is drawn as a circle.
    plt.axis('equal')
    plt.show()
项目:tieba-zhuaqu    作者:ankanch    | 项目源码 | 文件源码
def pieGraphics(Labels,ValueList,graphicTitle='??'):
    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral', "blue","green","cyan","magenta"]
    maxdata = max(ValueList)
    explode = []
    for v in ValueList:
        if v == maxdata:
            explode.append(0.1)
        else:
            explode.append(0)
    print(explode)
    patches,l_text,p_text = plt.pie(ValueList, labels=Labels, colors=colors,autopct='%1.1f%%',explode=explode ,shadow=True, startangle=90)
    for font in l_text:
        font.set_fontproperties(FontProperties(fname=PATH_SUFFIX+'SIMLI.TTF'))
    plt.title(graphicTitle,fontproperties=font_set,y=1.05)
    # Set aspect ratio to be equal so that pie is drawn as a circle.
    plt.axis('equal')
    plt.show()
项目:tieba-zhuaqu    作者:ankanch    | 项目源码 | 文件源码
def pieGraphics(Labels,ValueList,graphicTitle='??'):
    # The slices will be ordered and plotted counter-clockwise.
    #labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
    #sizes = [15, 30, 45, 10]
    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral', "blue","green","cyan","magenta"]
    explode = (0, 0.1, 0, 0)  # only "explode" the 2nd slice (i.e. 'Hogs')

    plt.pie(ValueList, labels=Labels, colors=colors,autopct='%1.1f%%', shadow=True, startangle=90)
    # Set aspect ratio to be equal so that pie is drawn as a circle.
    plt.axis('equal')
    plt.show()


#barGraphics('??','??',['A','B','C','D','E','F'],[29,30,40,47,38,23],'????')
#linePlotGraphics("xLabel","yLabel",[1,2,3,4,5,6,7,8,9,10],[1.1,1.9,2.6,3.6,9.8,14,24,40,80,150],graphicTitle='??')
#scatterPlotsGraphics("xLabel","yLabel",[1,2,3,4,5,6,7,8,9,10],[1,11.9,2,6.3,6,9.8,14,4,8,5],graphicTitle='??')
项目:tieba-zhuaqu    作者:ankanch    | 项目源码 | 文件源码
def pieGraphics(Labels,ValueList,graphicTitle='??'):
    # The slices will be ordered and plotted counter-clockwise.
    #labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
    #sizes = [15, 30, 45, 10]
    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral', "blue","green","cyan","magenta"]
    explode = (0, 0.1, 0, 0)  # only "explode" the 2nd slice (i.e. 'Hogs')

    plt.pie(ValueList, labels=Labels, colors=colors,autopct='%1.1f%%', shadow=True, startangle=90)
    # Set aspect ratio to be equal so that pie is drawn as a circle.
    plt.axis('equal')
    plt.show()


#barGraphics('??','??',['A','B','C','D','E','F'],[29,30,40,47,38,23],'????')
#linePlotGraphics("xLabel","yLabel",[1,2,3,4,5,6,7,8,9,10],[1.1,1.9,2.6,3.6,9.8,14,24,40,80,150],graphicTitle='??')
#scatterPlotsGraphics("xLabel","yLabel",[1,2,3,4,5,6,7,8,9,10],[1,11.9,2,6.3,6,9.8,14,4,8,5],graphicTitle='??')
项目:trend_ml_toolkit_xgboost    作者:raymon-tian    | 项目源码 | 文件源码
def compare_pr_auc(measures,fid=1,axis_interval=[0,1,0,1],marker_list=['g-','r-','b-']):
    plt.figure(fid)
    plt.title('P-R Curve')
    plt.xlabel('Recall')
    plt.ylabel('Precision')
    plt.axis(axis_interval)
    for i in range(len(measures['precisions'])):
        plt.plot(measures['recalls'][i],measures['precisions'][i],marker_list[i],label='%s pr_auc '%(measures['model_name'][i]))
        # plt.plot(measures['precisions'][i],measures['recalls'][i],marker_list[i],label='%s pr_auc : %f'%(measures['model_name'][i], measures['pr_auc'][i]))
        # plt.plot(measures['precisions'][0],measures['recalls'][0],'r-',measures['precisions'][0],measures['recalls'][0],'ro',label='%s pr_auc : %f'%(measures['model_name'][0], measures['pr_auc'][0]))
        # plt.plot(measures['precisions'][1], measures['recalls'][1], 'g-', label='%s pr_auc : %f' % (measures['model_name'][1], measures['pr_auc'][1]))
        # plt.plot(measures['precisions'][1], measures['recalls'][1], 'g-', measures['precisions'][1], measures['recalls'][1], 'go',label='%s pr_auc : %f' % (measures['model_name'][1], measures['pr_auc'][1]))
    # plt.legend(loc='lower center', shadow=True, fontsize='x-large')
    plt.legend(loc='lower center', shadow=True)

    plt.show()
项目:geom_rcnn    作者:asbroad    | 项目源码 | 文件源码
def plot_confusion_matrix(self):
        # Calculate and create confusion matrix
        conf_mat = np.zeros((len(self.categories.keys()),len(self.categories.keys())))
        for idx in range(len(self.predictions_int)):
            conf_mat[self.predictions_int[idx]][self.true_ys_int[idx]] += 1

        for idx1 in range(conf_mat.shape[0]):
            total = np.sum(conf_mat, axis=0)[idx1]
            for idx2 in range(conf_mat.shape[1]):
                conf_mat[idx1][idx2] = float(conf_mat[idx1][idx2]/total)

        fig = plt.figure()
        ax = fig.add_subplot(111)

        cax = ax.matshow(conf_mat)
        fig.colorbar(cax)

        ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
        ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
        ax.set_xticklabels([''] + self.inv_categories.values(), rotation='vertical')
        ax.set_yticklabels([''] + self.inv_categories.values())

        plt.show()
项目:geom_rcnn    作者:asbroad    | 项目源码 | 文件源码
def plot_confusion_matrix(self):
        # Calculate and create confusion matrix
        conf_mat = np.zeros((len(self.categories.keys()),len(self.categories.keys())))
        for idx in range(len(self.predictions_int)):
            conf_mat[self.predictions_int[idx]][self.true_ys_int[idx]] += 1

        for idx1 in range(conf_mat.shape[0]):
            total = np.sum(conf_mat, axis=0)[idx1]
            for idx2 in range(conf_mat.shape[1]):
                conf_mat[idx1][idx2] = float(conf_mat[idx1][idx2]/total)

        fig = plt.figure()
        ax = fig.add_subplot(111)

        cax = ax.matshow(conf_mat)
        fig.colorbar(cax)

        ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
        ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
        ax.set_xticklabels([''] + self.inv_categories.values(), rotation='vertical')
        ax.set_yticklabels([''] + self.inv_categories.values())

        plt.show()
项目:third_person_im    作者:bstadie    | 项目源码 | 文件源码
def plot_axes_scaling(self, iabscissa=1):
        if not hasattr(self, 'D'):
            self.load()
        dat = self
        self._enter_plotting()
        pyplot.semilogy(dat.D[:, iabscissa], dat.D[:, 5:], '-b')
        pyplot.hold(True)
        pyplot.grid(True)
        ax = array(pyplot.axis())
        # ax[1] = max(minxend, ax[1])
        pyplot.axis(ax)
        pyplot.title('Principle Axes Lengths')
        # pyplot.xticks(xticklocs)
        self._xlabel(iabscissa)
        self._finalize_plotting()
        return self
项目:FGVC2017    作者:lijiancheng0614    | 项目源码 | 文件源码
def plot(file_path, iterations):
    im = Image.open(file_path)
    im = np.array(im, dtype=np.uint8)

    plt.figure(figsize=(20, 16))
    plt.subplot(121)
    plt.imshow(im)
    plt.axis('off')

    plt.subplot(122)
    plt.imshow(np.zeros((640, 300, 3)))
    height = 14
    for i in range(len(labels)):
        plt.text(0, height * i + height / 2, labels[i], family='Times New Roman', size=14, color='#ffffff')
    plt.axis('off')

    # plt.savefig(idx)
    plt.show()
项目:slitSpectrographBlind    作者:aasensio    | 项目源码 | 文件源码
def cellplot(fs, csf):
    """
    Plots PSF kernels

    --------------------------------------------------------------------------
    Usage:

    Call:  cellplot(fs, csf)

    Input: fs   PSF kernels, i.e. 3d array with kernels indexed by 0th index
           csf  size of kernels in x and y direction

    Output: Shows stack of PSF kernels arranged according to csf
    --------------------------------------------------------------------------

    Copyright (C) 2011 Michael Hirsch
    """    

    mp.clf()
    for i in range(np.prod(csf)):
        mp.subplot(csf[0],csf[1],i+1)
        mp.imshow(fs[i])
        mp.axis('off')
    mp.draw()
项目:base_function    作者:Rockyzsu    | 项目源码 | 文件源码
def hist_test():
    mu, sigma = 100, 15
    x = mu + sigma * np.random.randn(10000)

    # ??????
    n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)


    plt.xlabel('Smarts')
    plt.ylabel('Probability')
    #????
    plt.title('Histogram of IQ')
    #????
    plt.text(60, .025, r'$mu=100, sigma=15$')
    plt.axis([40, 160, 0, 0.03])
    plt.grid(True)
    plt.show()
项目:base_function    作者:Rockyzsu    | 项目源码 | 文件源码
def testcase1():
    #http://www.jianshu.com/p/1ad947f98e4c

    np.random.seed(2000)
    y = np.random.standard_normal((20, 2)).cumsum(axis=0)

    plt.figure(figsize=(7, 4))
    plt.plot(y[:,0], lw=1.5,label='1st')
    plt.plot(y[:,1], lw=1.5,label='2nd')
    plt.plot(y, 'ro')
    plt.grid(True)
    plt.legend(loc=0)
    plt.axis('tight')
    plt.xlabel('index')
    plt.ylabel('value')
    plt.title('A Simple Plot')
    plt.show()
项目:dcss_single_cell    作者:srmcc    | 项目源码 | 文件源码
def plot_svd(sigma_full, sigma_dls, k, plot_loc):
    """ Plot the variance explained by different principal components
    :param n_components: Number of components to show the variance
    :param ylim: y-axis limits
    :param fig: matplotlib Figure object
    :param ax: matplotlib Axis object
    :return: fig, ax
    """
    fig, ax = plt.subplots()
    ax.scatter(range(len(sigma_full)),sigma_full,c='red',s=36,edgecolors='gray',
                    lw = 0.5, label='TCC singular values')
    ax.scatter(range(len(sigma_dls)),sigma_dls,c='blue',s=36,edgecolors='gray',
                    lw = 0.5, label='TCC_dls singular values')
    ax.legend(loc='upper right',bbox_to_anchor=(1.05, 1))
    ax.set_xlabel('Components')
    ax.set_ylabel('Singular Values')
    plt.title('TCC Distribution Singular Values')
    fig.tight_layout()
    plt.savefig(plot_loc+ 'plot_pca_variance_explained_' +str(k) +'.pdf')
项目:dcss_single_cell    作者:srmcc    | 项目源码 | 文件源码
def tru_plot9(X,labels,t,plot_suffix,clust_names,clust_color, plot_loc):
    """
    From clustering_on_transcript_compatibility_counts, see github for MIT license
    """
    unique_labels = np.unique(labels)
    plt.figure(figsize=(15,10))
    for i in unique_labels:
        ind = np.squeeze(labels == i)
        plt.scatter(X[ind,0],X[ind,1],c=clust_color[i],s=36,edgecolors='gray',
                    lw = 0.5, label=clust_names[i])        
    plt.legend(loc='upper right',bbox_to_anchor=(1.1, 1))
    plt.legend(loc='upper right',bbox_to_anchor=(1.19, 1.01))
    plt.title(t)
    plt.xlim([-20,20])
    plt.ylim([-20,20])
    plt.axis('off')
    plt.savefig(plot_loc+ 't-SNE_plot_tru_plot9_'+ plot_suffix +'.pdf', bbox_inches='tight')

    # Plot function with Zeisel's colors corresponding to labels
项目:joysix    作者:niberger    | 项目源码 | 文件源码
def animate(i):
    y.append(j.readRawValues())
    if(len(y) > 100):
        y.popleft()
    x = range(len(y))
    ax1.clear()
    plt.axis([0,100,-2050,2050])
    ax1.plot(x,y)
项目:lang-reps    作者:chaitanyamalaviya    | 项目源码 | 文件源码
def heatmap(src_sent, tgt_sent, att_weights, idx):

    plt.figure(figsize=(8, 6), dpi=80)
    att_probs = np.stack(att_weights, axis=1)

    plt.imshow(att_weights, cmap='gray', interpolation='nearest')
    #src_sent = [ str(s) for s in src_sent]
    #tgt_sent = [ str(s) for s in tgt_sent]
    #plt.xticks(range(0, len(tgt_sent)), tgt_sent, rotation='vertical')
    #plt.yticks(range(0, len(src_sent)), src_sent)
    plt.xticks(range(0, len(tgt_sent)),"")
    plt.yticks(range(0, len(src_sent)),"")
    plt.axis('off')
    plt.savefig("att_matrix_"+str(idx), bbox_inches='tight')
    plt.close()
项目:pyfds    作者:emtpb    | 项目源码 | 文件源码
def show_setup(self, halt=True):
        """Open a plot window that shows the simulation setup including boundaries, outputs and 
        material regions.

        Args:
            halt: Halt script execution until plot window is closed.
        """

        pp.figure()
        self.axes = pp.gca()
        pp.axis('equal')
        self.axes.set_xlim(0, max(self.field.x.vector) / self._x_axis_factor)
        self.axes.set_ylim(0, max(self.field.y.vector) / self._y_axis_factor)
        self.axes.set_xlabel('{0} / {1}m'.format(self.x_label, self._x_axis_prefix))
        self.axes.set_ylabel('{0} / {1}m'.format(self.y_label, self._y_axis_prefix))

        if self.show_materials:
            for mat_region in self.field.material_regions:
                self.plot_region(mat_region.region)

        if self.show_boundaries:
            for name, component in self.field_components.items():
                for boundary in component.boundaries:
                    self.plot_region(boundary.region)

        if self.show_output:
            for name, component in self.field_components.items():
                for output in component.outputs:
                    self.plot_region(output.region)

        if halt:
            pp.show()
项目:treecat    作者:posterior    | 项目源码 | 文件源码
def plot_feature_overlap(df, cmap='binary', method='cluster'):
    """Plot feature-feature presence overlap of a pandas dataframe.

    Args:
        df: A pandas dataframe.
        cmap: A matplotlib colormap.
        method: Method of clustering, one of 'cluster' or 'tree'.
    """
    V = len(df.columns)
    present = (df == df).as_matrix().astype(np.float32)
    overlap = np.dot(present.T, present)
    assert overlap.shape == (V, V)

    # Sort features to make blocks contiguous.
    if method == 'tree':
        # TODO(fritzo) Fix this to not look awful.
        grid = make_complete_graph(V)
        weights = np.empty(grid.shape[1], dtype=np.float32)
        for k, v1, v2 in grid.T:
            weights[k] = overlap[v1, v2]
        edges = estimate_tree(grid, weights)
        order, order_inv = order_vertices(edges)
    elif method == 'cluster':
        distance = scipy.spatial.distance.pdist(overlap)
        clustering = scipy.cluster.hierarchy.complete(distance)
        order_inv = scipy.cluster.hierarchy.leaves_list(clustering)
    else:
        raise ValueError(method)
    overlap = overlap[order_inv, :]
    overlap = overlap[:, order_inv]
    assert overlap.shape == (V, V)

    pyplot.imshow(overlap**0.5, cmap=cmap)
    pyplot.axis('off')
项目:treecat    作者:posterior    | 项目源码 | 文件源码
def contract_positions(XY, edges, stepsize):
    """Perturb vertex positions by an L1-minimizing attractive force.

    This is used to slightly adjust vertex positions to provide a visual
    hint to their grouping.

    Args:
        XY: A [V, 2]-shaped numpy array of the current positions.
        edges: An [E, 2]-shaped numpy array of edges as (vertex,vertex) pairs.

    """
    E = edges.shape[0]
    V = E + 1
    assert edges.shape == (E, 2)
    assert XY.shape == (V, 2)
    old = XY
    new = old.copy()
    heads = edges[:, 0]
    tails = edges[:, 1]
    diff = old[heads] - old[tails]
    distances = (diff**2).sum(axis=1)**0.5
    spacing = distances.min()
    assert spacing > 0
    diff /= distances[:, np.newaxis]
    diff *= spacing
    new[tails] += stepsize * diff
    new[heads] -= stepsize * diff
    return new
项目:hippylib    作者:hippylib    | 项目源码 | 文件源码
def plot(obj, colorbar=True, subplot_loc=None, mytitle=None, show_axis='off', vmin=None, vmax=None, logscale=False):
    if subplot_loc is not None:
        plt.subplot(subplot_loc)
#    plt.gca().set_aspect('equal')
    if isinstance(obj, dl.Function):
        pp = mplot_function(obj, vmin, vmax, logscale)
    elif isinstance(obj, dl.CellFunctionSizet):
        pp = mplot_cellfunction(obj)
    elif isinstance(obj, dl.CellFunctionDouble):
        pp = mplot_cellfunction(obj)
    elif isinstance(obj, dl.CellFunctionInt):
        pp = mplot_cellfunction(obj)
    elif isinstance(obj, dl.Mesh):
        if (obj.geometry().dim() != 2):
            raise AttributeError('Mesh must be 2D')
        pp = plt.triplot(mesh2triang(obj), color='#808080')
        colorbar = False
    else:
        raise AttributeError('Failed to plot %s'%type(obj))

    plt.axis(show_axis)

    if colorbar:
        plt.colorbar(pp, fraction=.1, pad=0.2)
    else:
        plt.gca().set_aspect('equal')

    if mytitle is not None:
        plt.title(mytitle, fontsize=20)

    return pp
项目:ICGan-tensorflow    作者:zhangqianhui    | 项目源码 | 文件源码
def load_mnist(self):

        data_dir = os.path.join("./data", "mnist")

        fd = open(os.path.join(data_dir, 'train-images-idx3-ubyte'))
        loaded = np.fromfile(file=fd , dtype=np.uint8)
        trX = loaded[16:].reshape((60000, 28 , 28 ,  1)).astype(np.float)

        fd = open(os.path.join(data_dir, 'train-labels-idx1-ubyte'))
        loaded = np.fromfile(file=fd, dtype=np.uint8)
        trY = loaded[8:].reshape((60000)).astype(np.float)

        fd = open(os.path.join(data_dir, 't10k-images-idx3-ubyte'))
        loaded = np.fromfile(file=fd, dtype=np.uint8)
        teX = loaded[16:].reshape((10000, 28 , 28 , 1)).astype(np.float)

        fd = open(os.path.join(data_dir, 't10k-labels-idx1-ubyte'))
        loaded = np.fromfile(file=fd, dtype=np.uint8)
        teY = loaded[8:].reshape((10000)).astype(np.float)

        trY = np.asarray(trY)
        teY = np.asarray(teY)

        X = np.concatenate((trX, teX), axis=0)
        y = np.concatenate((trY, teY), axis=0)

        seed = 547
        np.random.seed(seed)
        np.random.shuffle(X)
        np.random.seed(seed)
        np.random.shuffle(y)

        #convert label to one-hot

        y_vec = np.zeros((len(y), 10), dtype=np.float)
        for i, label in enumerate(y):
            y_vec[i, int(y[i])] = 1.0

        return X / 255. , y_vec
项目:ICGan-tensorflow    作者:zhangqianhui    | 项目源码 | 文件源码
def vis_square(visu_path, data, type):
    """Take an array of shape (n, height, width) or (n, height, width , 3)
       and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)"""

    # normalize data for display
    data = (data - data.min()) / (data.max() - data.min())

    # force the number of filters to be square
    n = int(np.ceil(np.sqrt(data.shape[0])))

    padding = (((0, n ** 2 - data.shape[0]),
                (0, 1), (0, 1))  # add some space between filters
               + ((0, 0),) * (data.ndim - 3))  # don't pad the last dimension (if there is one)
    data = np.pad(data, padding, mode='constant', constant_values=1)  # pad with ones (white)

    # tilethe filters into an im age
    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))

    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])

    plt.imshow(data[:, :, 0])
    plt.axis('off')

    if type:
        plt.savefig('./{}/weights.png'.format(visu_path), format='png')
    else:
        plt.savefig('./{}/activation.png'.format(visu_path), format='png')
项目:qqmbr    作者:ischurov    | 项目源码 | 文件源码
def axes4x4(labels=("t","x"),xmin=-4, xmax=4, ymin=-4, ymax=4, fontsize=20):
    """Set axes to [-4,4]×[-4,4] and label them

    args
    ====
    - labels — axes labels (x, y)
    """
    plt.axis([xmin,xmax, ymin, ymax])
    center_spines()
    xscale = (xmax - xmin) / 8.
    yscale = (ymax - ymin) / 8.
    plt.text(xmax - 0.2 * xscale, 0.2 * yscale, "$%s$" % labels[0],
             fontsize=fontsize, verticalalignment='bottom')
    plt.text(0.1 * xscale, ymax - 0.3 * yscale, "$%s$" % labels[1],
             fontsize=fontsize)
项目:qqmbr    作者:ischurov    | 项目源码 | 文件源码
def draw_axes(xmin, xmax, ymin, ymax, labels=("x", "y")):
    plt.axis([xmin, xmax, ymin, ymax])
    center_spines()
    plt.text(xmax, 0, "$%s$" % labels[0],fontsize=20, verticalalignment='bottom', horizontalalignment='right')
    plt.text(0, ymax, "$%s$" % labels[1],fontsize=20, verticalalignment='top', horizontalalignment='right')
项目:qqmbr    作者:ischurov    | 项目源码 | 文件源码
def center_spines(ax=None, centerx=0, centery=0):
    """Centers the axis spines at <centerx, centery> on the axis "ax", and
    places arrows at the end of the axis spines."""
    if ax is None:
        ax = plt.gca()

    # Set the axis's spines to be centered at the given point
    # (Setting all 4 spines so that the tick marks go in both directions)
    ax.spines['left'].set_position(('data', centerx))
    ax.spines['bottom'].set_position(('data', centery))
    ax.spines['right'].set_position(('data', centerx))
    ax.spines['top'].set_position(('data', centery))

    # Hide the line (but not ticks) for "extra" spines
    for side in ['right', 'top']:
        ax.spines[side].set_color('none')

    # On both the x and y axes...
    for axis, center in zip([ax.xaxis, ax.yaxis], [centerx, centery]):
        # Turn on minor and major gridlines and ticks
        axis.set_ticks_position('both')
        axis.grid(True, 'major', ls='solid', lw=0.5, color='gray')
#        axis.grid(True, 'minor', ls='solid', lw=0.1, color='gray')
        axis.set_minor_locator(mpl.ticker.AutoMinorLocator())

        # Hide the ticklabels at <centerx, centery>
        formatter = CenteredFormatter()
        formatter.center = center
        axis.set_major_formatter(formatter)

    # Add offset ticklabels at <centerx, centery> using annotation
    # (Should probably make these update when the plot is redrawn...)
    xlabel, ylabel = map(formatter.format_data, [centerx, centery])
    if centerx != 0 or centery != 0:
        annotation = '(%s, %s)' % (xlabel, ylabel)
    else:
        annotation = xlabel
    ax.annotate(annotation, (centerx, centery),
            xytext=(-4, -4), textcoords='offset points',
            ha='right', va='top')
项目:snake_game    作者:wing3s    | 项目源码 | 文件源码
def save_image(folder='images'):
    """
    Coroutine of image saving
    """
    from matplotlib import pyplot as plt
    from matplotlib import colors

    if folder not in os.listdir('.'):
        os.mkdir(folder)
    frame_cnt = it.count()

    cmap = colors.ListedColormap(['#009688', '#E0F2F1', '#004D40'])
    bounds = [0, 0.25, 0.75, 1]
    norm = colors.BoundaryNorm(bounds, cmap.N)

    while True:
        screen = (yield)
        shape = screen.shape
        plt.imshow(
            screen,
            interpolation='none',
            cmap=cmap,
            norm=norm,
            aspect='equal',
            extent=(0, shape[1], 0, shape[0]))
        plt.grid(True)
        plt.axis('off')
        plt.savefig('%s/frame%06i.png' % (folder, frame_cnt.next()))
项目:HandDetection    作者:YunqiuXu    | 项目源码 | 文件源码
def vis_detections(im, class_name, dets, thresh=0.5):
    """Draw detected bounding boxes."""
    inds = np.where(dets[:, -1] >= thresh)[0]
    if len(inds) == 0:
        return

    im = im[:, :, (2, 1, 0)]
    #fig, ax = plt.subplots(figsize=(12, 12))
    ax.imshow(im, aspect='equal')
    for i in inds:
        bbox = dets[i, :4]
        score = dets[i, -1]

        ax.add_patch(
            plt.Rectangle((bbox[0], bbox[1]),
                          bbox[2] - bbox[0],
                          bbox[3] - bbox[1], fill=False,
                          edgecolor='red', linewidth=3.5)
            )
        ax.text(bbox[0], bbox[1] - 2,
                '{:s} {:.3f}'.format(class_name, score),
                bbox=dict(facecolor='blue', alpha=0.5),
                fontsize=14, color='white')

    ax.set_title(('{} detections with '
                  'p({} | box) >= {:.1f}').format(class_name, class_name,
                                                  thresh),
                  fontsize=14)
    plt.axis('off')
    plt.tight_layout()
    plt.draw()
项目:pybot    作者:spillai    | 项目源码 | 文件源码
def imshow_plt(label, im, block=True):
    global figures
    if label not in figures: 
        figures[label] = plt.imshow(im, interpolation=None, animated=True, label=label)
        plt.tight_layout()
        plt.axis('off')

    figures[label].set_data(im)
    # figures[label].canvas.draw()
    # plt.draw()
    plt.show(block=block)