Python pyplot.title函数代码示例

    def make_bar(
        x,
        y,
        f_name,
        title=None,
        legend=None,
        x_label=None,
        y_label=None,
        x_ticks=None,
        y_ticks=None,
    ):
        fig = plt.figure()

        if title is not None:
            plt.title(title, fontsize=16)
        if x_label is not None:
            plt.ylabel(x_label)
        if y_label is not None:
            plt.xlabel(y_label)
        if x_ticks is not None:
            plt.xticks(x, x_ticks)
        if y_ticks is not None:
            plt.yticks(y_ticks)

        plt.bar(x, y, align="center")

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

        plt.savefig(f_name)
        plt.close(fig)

def showOverlap(mode, modes, *args, **kwargs):
    """Show overlap :func:`~matplotlib.pyplot.bar`.

    :arg mode: a single mode/vector
    :type mode: :class:`.Mode`, :class:`.Vector`
    :arg modes: multiple modes
    :type modes: :class:`.ModeSet`, :class:`.ANM`, :class:`.GNM`, :class:`.PCA`
    """

    import matplotlib.pyplot as plt
    if not isinstance(mode, (Mode, Vector)):
        raise TypeError('mode must be Mode or Vector, not {0}'
                        .format(type(mode)))
    if not isinstance(modes, (NMA, ModeSet)):
        raise TypeError('modes must be NMA or ModeSet, not {0}'
                        .format(type(modes)))
    overlap = abs(calcOverlap(mode, modes))
    if isinstance(modes, NMA):
        arange = np.arange(0.5, len(modes)+0.5)
    else:
        arange = modes.getIndices() + 0.5
    show = plt.bar(arange, overlap, *args, **kwargs)
    plt.title('Overlap with {0}'.format(str(mode)))
    plt.xlabel('{0} mode index'.format(modes))
    plt.ylabel('Overlap')
    if SETTINGS['auto_show']:
        showFigure()
    return show

def showNormDistFunct(model, coords, *args, **kwargs):
    """Show normalized distance fluctuation matrix using 
    :func:`~matplotlib.pyplot.imshow`. By default, *origin=lower* 
    keyword  arguments are passed to this function, 
    but user can overwrite these parameters."""

    import math
    import matplotlib
    import matplotlib.pyplot as plt
    normdistfunct = model.getNormDistFluct(coords)

    if not 'origin' in kwargs:
        kwargs['origin'] = 'lower'
        
    matplotlib.rcParams['font.size'] = '14'
    fig = plt.figure(num=None, figsize=(10,8), dpi=100, facecolor='w')
    show = plt.imshow(normdistfunct, *args, **kwargs), plt.colorbar()
    plt.clim(math.floor(np.min(normdistfunct[np.nonzero(normdistfunct)])), \
                                           round(np.amax(normdistfunct),1))
    plt.title('Normalized Distance Fluctution Matrix')
    plt.xlabel('Indices', fontsize='16')
    plt.ylabel('Indices', fontsize='16')
    if SETTINGS['auto_show']:
        showFigure()
    return 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 show_plot(X, y, n_neighbors=10, h=0.2):
    # Create color maps
    cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF','#FFAAAA', '#AAFFAA', '#AAAAFF','#FFAAAA', '#AAFFAA', '#AAAAFF','#AAAAFF'])
    cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF','#FF0000','#FF0000','#FF0000','#FF0000','#FF0000','#FF0000','#FF0000',])

    for weights in ['uniform', 'distance']:
        # we create an instance of Neighbours Classifier and fit the data.
        clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
        clf.fit(X, y)
        clf.n_neighbors = n_neighbors

        # Plot the decision boundary. For that, we will assign a color to each
        # point in the mesh [x_min, x_max]x[y_min, y_max].
        x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
        y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
        xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                             np.arange(y_min, y_max, h))
        Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

        # Put the result into a color plot
        Z = Z.reshape(xx.shape)
        plt.figure()
        plt.pcolormesh(xx, yy, Z, cmap=cmap_light)

        # Plot also the training points
        plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
        plt.xlim(xx.min(), xx.max())
        plt.ylim(yy.min(), yy.max())
        plt.title("3-Class classification (k = %i, weights = '%s')"
                  % (n_neighbors, weights))

    plt.show()

def plot_scenario(strategies, names, scenario_id=1):
    probabilities = get_scenario(scenario_id)

    plt.figure(figsize=(6, 4.5))

    ax = plt.subplot(111)
    ax.spines["top"].set_visible(False)
    ax.spines["bottom"].set_visible(False)
    ax.spines["right"].set_visible(False)
    ax.spines["left"].set_visible(False)

    ax.get_xaxis().tick_bottom()
    ax.get_yaxis().tick_left()

    plt.yticks(fontsize=14)
    plt.xticks(fontsize=14)
    plt.xlim((0, 1300))

    # Remove the tick marks; they are unnecessary with the tick lines we just plotted.
    plt.tick_params(axis="both", which="both", bottom="on", top="off",
                    labelbottom="on", left="off", right="off", labelleft="on")

    for rank, (strategy, name) in enumerate(zip(strategies, names)):
        plot_strategy(probabilities, strategy, name, rank)

    plt.title("Bandits: " + str(probabilities), fontweight='bold')
    plt.xlabel('Number of Trials', fontsize=14)
    plt.ylabel('Cumulative Regret', fontsize=14)
    plt.legend(names)
    plt.show()

 def default_run(self):
     """
     Plots the results, saves the figure, and finally displays it from simulating codewords with Sum-prod and Max-prod
     algorithms across variance levels. This combines the results in one plot.
     :return:
     """
     if not os.path.exists("./graphs"):
         os.makedirs("./graphs")
     self.save_time = str(int(time.time()))
     self.simulate(Decoder.SUM_PROD)
     self.compute_error()
     plt.plot([math.log10(x) for x in self.variance_levels], [math.log10(y) for y in self.bit_error_probability],
              "ro-", label="Sum-Prod")
     self.simulate(Decoder.MAX_PROD)
     self.compute_error()
     plt.plot([math.log10(x) for x in self.variance_levels], [math.log10(y) for y in self.bit_error_probability],
              "g^--", label="Max-Prod")
     plt.legend(loc=2)
     plt.title("Hamming Decoder Factor Graph Simulation Results\n" +
               r"$\log_{10}(\sigma^2)$ vs. $\log_{10}(P_e)$" + " for Max-Prod & Sum-Prod Algorithms\n" +
               "Sample Size n = %(codewords)s Codewords \n Variance Levels = %(levels)s"
               % {"codewords": str(self.iterations), "levels": str(self.variance_levels)})
     plt.xlabel("$\log_{10}(\sigma^2)$")
     plt.ylabel(r"$\log_{10}(P_e)$")
     plt.savefig("graphs/%(time)s-max-prod-sum-prod-%(num_codewords)s-codewords-variance-bit_error_probability.png" %
                 {"time": self.save_time,
                  "num_codewords": str(self.iterations)}, bbox_inches="tight")
     plt.show()

def visualize(segmentation, expression, visualize=None, store=None, title=None, legend=False):
    notes = []
    onsets = []
    values = []
    param = ['Dynamics', 'Articulation', 'Tempo']
    converter = NoteList()
    converter.bpm = 100
    if not visualize:
        visualize = selectSubset(param)
    for segment, expr in zip(segmentation, expression):
        for note in segment:
            onsets.append(converter.ticks_to_milliseconds(note.on)/1000.0)
            values.append([expr[i] for i in visualize])
    import matplotlib.pyplot as plt
    fig = plt.figure(figsize=(12, 4))
    for i in visualize:
        plt.plot(onsets, [v[i] for v in values], label=param[i])
    plt.ylabel('Deviation')
    plt.xlabel('Score time (seconds)')
    if legend:
        plt.legend(bbox_to_anchor=(0., 1), loc=2, borderaxespad=0.)

    if title:
        plt.title(title)
    #dplot = fig.add_subplot(111)
    #sodplot = fig.add_subplot(111)
    #dplot.plot([i for i in range(len(deltas[0]))], deltas[0])
    #sodplot.plot([i for i in range(len(sodeltas[0]))], sodeltas[0])
    if store:
        fig.savefig('plots/{0}.png'.format(store))
    else:
        plt.show()

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 predicted_probabilities(y_true, y_pred, n_groups=30):
    """Plots the distribution of predicted probabilities.

    Parameters
    ----------
    y_true : array_like
        Observed labels, either 0 or 1.
    y_pred : array_like
        Predicted probabilities, floats on [0, 1].
    n_groups : int, optional
        The number of groups to create. The default value is 30.

    Notes
    -----
    .. plot:: pyplots/predicted_probabilities.py
    """
    plt.hist(y_pred, n_groups)
    plt.xlim([0, 1])
    plt.xlabel('Predicted Probability')
    plt.ylabel('Count')

    title = 'Distribution of Predicted Probabilities (n = {})'
    plt.title(title.format(len(y_pred)))

    plt.tight_layout()

def roc_plot(y_true, y_pred):
    """Plots a receiver operating characteristic.

    Parameters
    ----------
    y_true : array_like
        Observed labels, either 0 or 1.
    y_pred : array_like
        Predicted probabilities, floats on [0, 1].

    Notes
    -----
    .. plot:: pyplots/roc_plot.py

    References
    ----------
    .. [1] Pedregosa, F. et al. "Scikit-learn: Machine Learning in Python."
       *Journal of Machine Learning Research* 12 (2011): 2825–2830.
    .. [2] scikit-learn developers. "Receiver operating characteristic (ROC)."
       Last modified August 2013.
       http://scikit-learn.org/stable/auto_examples/plot_roc.html.
    """
    fpr, tpr, __ = roc_curve(y_true, y_pred)
    roc_auc = auc(fpr, tpr)

    plt.plot(fpr, tpr, label='ROC curve (area = {:0.2f})'.format(roc_auc))
    plt.plot([0, 1], [0, 1], 'k--')
    plt.xlim([0, 1])
    plt.ylim([0, 1])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver Operating Characteristic')
    plt.legend(loc='lower right')

def ecdf_by_observed_label(y_true, y_pred):
    """Plots the empirical cumulative density functions by observed label.

    Parameters
    ----------
    y_true : array_like
        Observed labels, either 0 or 1.
    y_pred : array_like
        Predicted probabilities, floats on [0, 1].

    Notes
    -----
    .. plot:: pyplots/ecdf_by_observed_label.py
    """
    x = np.linspace(0, 1)

    ecdf = ECDF(y_pred[y_true == 0])
    y_0 = ecdf(x)

    ecdf = ECDF(y_pred[y_true == 1])
    y_1 = ecdf(x)

    plt.step(x, y_0, label='Observed label 0')
    plt.step(x, y_1, label='Observed label 1')
    plt.xlabel('Predicted Probability')
    plt.ylabel('Proportion')
    plt.title('Empirical Cumulative Density Functions by Observed Label')
    plt.legend(loc='lower right')

    def _plot(self,names,title,style,when=0,showLegend=True):
        if isinstance(names,str):
            names = [names]
        assert isinstance(names,list)

        legend = []
        for name in names:
            assert isinstance(name,str)
            legend.append(name)

            # if it's a differential state
            if name in self.xNames:
                index = self.xNames.index(name)
                ys = np.squeeze(self._log['x'])[:,index]
                ts = np.arange(len(ys))*self.Ts
                plt.plot(ts,ys,style)
                
            if name in self.outputNames:
                index = self.outputNames.index(name)
                ys = np.squeeze(self._log['outputs'][name])
                ts = np.arange(len(ys))*self.Ts
                plt.plot(ts,ys,style)

        if title is not None:
            assert isinstance(title,str), "title must be a string"
            plt.title(title)
        plt.xlabel('time [s]')
        if showLegend is True:
            plt.legend(legend)
        plt.grid()

    def make_line(
        x,
        y,
        f_name,
        title=None,
        legend=None,
        x_label=None,
        y_label=None,
        x_ticks=None,
        y_ticks=None,
    ):
        fig = plt.figure()

        if title is not None:
            plt.title(title, fontsize=16)
        if x_label is not None:
            plt.ylabel(x_label)
        if y_label is not None:
            plt.xlabel(y_label)
        if x_ticks is not None:
            plt.xticks(x, x_ticks)
        if y_ticks is not None:
            plt.yticks(y_ticks)

        if isinstance(y[0], list):
            for data in y:
                plt.plot(x, data)
        else:
            plt.plot(x, y)

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

        plt.savefig(f_name)
        plt.close(fig)

def plot_precision_recall_n(y_true, y_scores, model_name):
    '''
    Takes the model, plots precision and recall curves
    '''

    precision_curve, recall_curve, pr_thresholds = precision_recall_curve(y_true, y_scores)
    precision_curve = precision_curve[:-1]
    recall_curve = recall_curve[:-1]
    pct_above_per_thresh = []
    number_scored = len(y_scores)

    for value in pr_thresholds:
        num_above_thresh = len(y_scores[y_scores >= value])
        pct_above_thresh = num_above_thresh / float(number_scored)
        pct_above_per_thresh.append(pct_above_thresh)

    pct_above_per_thresh = np.array(pct_above_per_thresh)
    plt.clf()
    fig, ax1 = plt.subplots()
    ax1.plot(pct_above_per_thresh, precision_curve, 'b')
    ax1.set_xlabel('percent of population')
    ax1.set_ylabel('precision', color='b')
    ax2 = ax1.twinx()
    ax2.plot(pct_above_per_thresh, recall_curve, 'r')
    ax2.set_ylabel('recall', color='r')
    name = model_name
    plt.title(name)
    plt.savefig("Eval/{}.png".format(name))