Python pylab.xlabel方法代码示例

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import xlabel [as 别名]
def plot_confusion_matrix(y_true, y_pred, size=None, normalize=False):
    """plot_confusion_matrix."""
    cm = confusion_matrix(y_true, y_pred)
    fmt = "%d"
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        fmt = "%.2f"
    xticklabels = list(sorted(set(y_pred)))
    yticklabels = list(sorted(set(y_true)))
    if size is not None:
        plt.figure(figsize=(size, size))
    heatmap(cm, xlabel='Predicted label', ylabel='True label',
            xticklabels=xticklabels, yticklabels=yticklabels,
            cmap=plt.cm.Blues, fmt=fmt)
    if normalize:
        plt.title("Confusion matrix (norm.)")
    else:
        plt.title("Confusion matrix")
    plt.gca().invert_yaxis() 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import xlabel [as 别名]
def plot_roc_curve(y_true, y_score, size=None):
    """plot_roc_curve."""
    false_positive_rate, true_positive_rate, thresholds = roc_curve(
        y_true, y_score)
    if size is not None:
        plt.figure(figsize=(size, size))
        plt.axis('equal')
    plt.plot(false_positive_rate, true_positive_rate, lw=2, color='navy')
    plt.plot([0, 1], [0, 1], color='gray', lw=1, linestyle='--')
    plt.xlabel('False positive rate')
    plt.ylabel('True positive rate')
    plt.ylim([-0.05, 1.05])
    plt.xlim([-0.05, 1.05])
    plt.grid()
    plt.title('Receiver operating characteristic AUC={0:0.2f}'.format(
        roc_auc_score(y_true, y_score))) 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import xlabel [as 别名]
def plot_learning_curve(train_sizes, train_scores, test_scores):
    """plot_learning_curve."""
    plt.figure(figsize=(15, 5))
    plt.title('Learning Curve')
    plt.xlabel("Training examples")
    plt.ylabel("AUC ROC")
    tr_ys = compute_stats(train_scores)
    te_ys = compute_stats(test_scores)
    plot_stats(train_sizes, tr_ys,
               label='Training score',
               color='navy')
    plot_stats(train_sizes, te_ys,
               label='Cross-validation score',
               color='orange')
    plt.grid(linestyle=":")
    plt.legend(loc="best")
    plt.show() 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import xlabel [as 别名]
def modBev_plot(ax, rangeX = [-10, 10 ], rangeXpx= [0, 400], numDeltaX = 5, rangeZ= [8,48 ], rangeZpx= [0, 800], numDeltaZ = 9, fontSize = None, xlabel = 'x [m]', ylabel = 'z [m]'):
    '''

    @param ax:
    '''
    #TODO: Configureabiltiy would be nice!
    if fontSize==None:
        fontSize = 8
 
    ax.set_xlabel(xlabel, fontsize=fontSize)
    ax.set_ylabel(ylabel, fontsize=fontSize)
        
    zTicksLabels_val = np.linspace(rangeZpx[0], rangeZpx[1], numDeltaZ)
    ax.set_yticks(zTicksLabels_val)
    #ax.set_yticks([0, 100, 200, 300, 400, 500, 600, 700, 800])
    xTicksLabels_val = np.linspace(rangeXpx[0], rangeXpx[1], numDeltaX)
    ax.set_xticks(xTicksLabels_val)
    xTicksLabels_val = np.linspace(rangeX[0], rangeX[1], numDeltaX)
    zTicksLabels = map(lambda x: str(int(x)), xTicksLabels_val)
    ax.set_xticklabels(zTicksLabels,fontsize=fontSize)
    zTicksLabels_val = np.linspace(rangeZ[1],rangeZ[0], numDeltaZ)
    zTicksLabels = map(lambda x: str(int(x)), zTicksLabels_val)
    ax.set_yticklabels(zTicksLabels,fontsize=fontSize) 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import xlabel [as 别名]
def generate(self, filename, show=True):
        '''Generate a sample sequence, plot the resulting piano-roll and save
        it as a MIDI file.
        filename : string
            A MIDI file will be created at this location.
        show : boolean
            If True, a piano-roll of the generated sequence will be shown.'''

        piano_roll = self.generate_function()
        midiwrite(filename, piano_roll, self.r, self.dt)
        if show:
            extent = (0, self.dt * len(piano_roll)) + self.r
            pylab.figure()
            pylab.imshow(piano_roll.T, origin='lower', aspect='auto',
                         interpolation='nearest', cmap=pylab.cm.gray_r,
                         extent=extent)
            pylab.xlabel('time (s)')
            pylab.ylabel('MIDI note number')
            pylab.title('generated piano-roll') 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import xlabel [as 别名]
def plot(self):
        """
        Plot startup data.
        """
        import pylab

        print("Plotting result...", end="")
        avg_data = self.average_data()
        avg_data = self.__sort_data(avg_data, False)
        if len(self.raw_data) > 1:
            err = self.stdev_data()
            sorted_err = [err[k] for k in list(zip(*avg_data))[0]]
        else:
            sorted_err = None
        pylab.barh(range(len(avg_data)), list(zip(*avg_data))[1],
                   xerr=sorted_err, align='center', alpha=0.4)
        pylab.yticks(range(len(avg_data)), list(zip(*avg_data))[0])
        pylab.xlabel("Average startup time (ms)")
        pylab.ylabel("Plugins")
        pylab.show()
        print(" done.") 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import xlabel [as 别名]
def plot_it():
    '''
    helper function to gain insight on provided data sets background,
    using pylab
    '''
    data1 = [[1.0, 1], [2.25, 3.5], [3.58333333333, 7.5], [4.95833333333, 13.0], [6.35833333333, 20.0], [7.775, 28.5], [9.20357142857, 38.5], [10.6410714286, 50.0], [12.085515873, 63.0], [13.535515873, 77.5]]
    data2 = [[1.0, 1], [1.75, 2.5], [2.41666666667, 4.5], [3.04166666667, 7.0], [3.64166666667, 10.0], [4.225, 13.5], [4.79642857143, 17.5], [5.35892857143, 22.0], [5.91448412698, 27.0], [6.46448412698, 32.5], [7.00993867244, 38.5], [7.55160533911, 45.0], [8.09006687757, 52.0], [8.62578116328, 59.5], [9.15911449661, 67.5], [9.69036449661, 76.0], [10.2197762613, 85.0], [10.7475540391, 94.5], [11.2738698286, 104.5], [11.7988698286, 115.0]]
    time1 = [item[0] for item in data1]
    resource1 = [item[1] for item in data1]
    time2 = [item[0] for item in data2]
    resource2 = [item[1] for item in data2]
    
    # plot in pylab (total resources over time)
    pylab.plot(time1, resource1, 'o')
    pylab.plot(time2, resource2, 'o')
    pylab.title('Silly Homework')
    pylab.legend(('Data Set no.1', 'Data Set no.2'))
    pylab.xlabel('Current Time')
    pylab.ylabel('Total Resources Generated')
    pylab.show()

#plot_it() 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import xlabel [as 别名]
def plot_question2():
    '''
    graph of total resources generated as a function of time,
    for four various upgrade_cost_increment values
    '''
    for upgrade_cost_increment in [0.0, 0.5, 1.0, 2.0]:
        data = resources_vs_time(upgrade_cost_increment, 5)
        time = [item[0] for item in data]
        resource = [item[1] for item in data]
    
        # plot in pylab (total resources over time for each constant)
        pylab.plot(time, resource, 'o')
        
    pylab.title('Silly Homework')
    pylab.legend(('0.0', '0.5', '1.0', '2.0'))
    pylab.xlabel('Current Time')
    pylab.ylabel('Total Resources Generated')
    pylab.show()

#plot_question2()   


# Question 3 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import xlabel [as 别名]
def plot_question3():
    '''
    graph of total resources generated as a function of time;
    for upgrade_cost_increment == 0
    '''
    data = resources_vs_time(0.0, 100)
    time = [item[0] for item in data]
    resource = [item[1] for item in data]

    # plot in pylab on logarithmic scale (total resources over time for upgrade growth 0.0)
    pylab.loglog(time, resource)
        
    pylab.title('Silly Homework')
    pylab.legend('0.0')
    pylab.xlabel('Current Time')
    pylab.ylabel('Total Resources Generated')
    pylab.show()

#plot_question3()


# Question 4 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import xlabel [as 别名]
def plot_question7():
    '''
    graph of total resources generated as a function of time,
    for upgrade_cost_increment == 1
    '''
    data = resources_vs_time(1.0, 50)
    time = [item[0] for item in data]
    resource = [item[1] for item in data]
    a, b, c = pylab.polyfit(time, resource, 2)
    print 'polyfit with argument \'2\' fits the data, thus the degree of the polynomial is 2 (quadratic)'

    # plot in pylab on logarithmic scale (total resources over time for upgrade growth 0.0)
    #pylab.loglog(time, resource, 'o')

    # plot fitting function
    yp = pylab.polyval([a, b, c], time)
    pylab.plot(time, yp)
    pylab.scatter(time, resource)
    pylab.title('Silly Homework, Question 7')
    pylab.legend(('Resources for increment 1', 'Fitting function' + ', slope: ' + str(a)))
    pylab.xlabel('Current Time')
    pylab.ylabel('Total Resources Generated')
    pylab.grid()
    pylab.show() 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import xlabel [as 别名]
def plot_entropy(self):
        """

        Returns:

        """
        try:
            import pylab as plt
        except ImportError:
            import matplotlib.pyplot as plt
        plt.plot(
            self.temperatures,
            self.eV_to_J_per_mol / self.num_atoms * self.get_entropy_p(),
            label="S$_p$",
        )
        plt.plot(
            self.temperatures,
            self.eV_to_J_per_mol / self.num_atoms * self.get_entropy_v(),
            label="S$_V$",
        )
        plt.legend()
        plt.xlabel("Temperature [K]")
        plt.ylabel("Entropy [J K$^{-1}$ mol-atoms$^{-1}$]") 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import xlabel [as 别名]
def contour_entropy(self):
        """

        Returns:

        """
        try:
            import pylab as plt
        except ImportError:
            import matplotlib.pyplot as plt
        s_coeff = np.polyfit(self.volumes, self.entropy.T, deg=self._fit_order)
        s_grid = np.array([np.polyval(s_coeff, v) for v in self.volumes]).T
        x, y = self.meshgrid()
        plt.contourf(x, y, s_grid)
        plt.plot(self.get_minimum_energy_path(), self.temperatures)
        plt.xlabel("Volume [$\AA^3$]")
        plt.ylabel("Temperature [K]") 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import xlabel [as 别名]
def plot_contourf(self, ax=None, show_min_erg_path=False):
        """

        Args:
            ax:
            show_min_erg_path:

        Returns:

        """
        try:
            import pylab as plt
        except ImportError:
            import matplotlib.pyplot as plt
        x, y = self.meshgrid()
        if ax is None:
            fig, ax = plt.subplots(1, 1)
        ax.contourf(x, y, self.energies)
        if show_min_erg_path:
            plt.plot(self.get_minimum_energy_path(), self.temperatures, "w--")
        plt.xlabel("Volume [$\AA^3$]")
        plt.ylabel("Temperature [K]")
        return ax 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import xlabel [as 别名]
def plot_min_energy_path(self, *args, ax=None, **qwargs):
        """

        Args:
            *args:
            ax:
            **qwargs:

        Returns:

        """
        try:
            import pylab as plt
        except ImportError:
            import matplotlib.pyplot as plt
        if ax is None:
            fig, ax = plt.subplots(1, 1)
            ax.xlabel("Volume [$\AA^3$]")
            ax.ylabel("Temperature [K]")
        ax.plot(self.get_minimum_energy_path(), self.temperatures, *args, **qwargs)
        return ax 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import xlabel [as 别名]
def pcolor(self, xname, yname, zname, *args, **kwargs):
        """Plot the results from the experiment.data pandas dataframe in a pcolor graph.
        Store the plots in a plots list attribute."""
        title = self.title
        x, y, z = self._data[xname], self._data[yname], self._data[zname]
        shape = (len(y.unique()), len(x.unique()))
        diff = shape[0] * shape[1] - len(z)
        Z = np.concatenate((z.values, np.zeros(diff))).reshape(shape)
        df = pd.DataFrame(Z, index=y.unique(), columns=x.unique())
        ax = sns.heatmap(df)
        pl.title(title)
        pl.xlabel(xname)
        pl.ylabel(yname)
        ax.invert_yaxis()
        pl.plt.show()
        self.plots.append(
            {'type': 'pcolor', 'x': xname, 'y': yname, 'z': zname, 'args': args, 'kwargs': kwargs,
             'ax': ax})
        if ax.get_figure() not in self.figs:
            self.figs.append(ax.get_figure())