Python pylab.legend方法代碼示例

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import legend [as 別名]
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() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import legend [as 別名]
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() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import legend [as 別名]
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 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import legend [as 別名]
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 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import legend [as 別名]
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() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import legend [as 別名]
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$^{-1}$]") 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import legend [as 別名]
def plot_Geweke(parameterdistribution,parametername):
    '''Input:  Takes a list of sampled values for a parameter and his name as a string
       Output: Plot as seen for e.g. in BUGS or PyMC'''
    import matplotlib.pyplot as plt

    # perform the Geweke test
    Geweke_values = _Geweke(parameterdistribution)

    # plot the results
    fig = plt.figure()
    plt.plot(Geweke_values,label=parametername)
    plt.legend()
    plt.title(parametername + '- Geweke_Test')
    plt.xlabel('Subinterval')
    plt.ylabel('Geweke Test')
    plt.ylim([-3,3])

    # plot the delimiting line
    plt.plot( [2]*len(Geweke_values), 'r-.')
    plt.plot( [-2]*len(Geweke_values), 'r-.') 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import legend [as 別名]
def impose_legend_limit(limit=30, axes="gca", **kwargs):
    """
    This will erase all but, say, 30 of the legend entries and remake the legend.
    You'll probably have to move it back into your favorite position at this point.
    """
    if axes=="gca": axes = _pylab.gca()

    # make these axes current
    _pylab.axes(axes)

    # loop over all the lines_pylab.
    for n in range(0,len(axes.lines)):
        if n >  limit-1 and not n==len(axes.lines)-1: axes.lines[n].set_label("_nolegend_")
        if n == limit-1 and not n==len(axes.lines)-1: axes.lines[n].set_label("...")

    _pylab.legend(**kwargs) 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import legend [as 別名]
def _plotFMeasures(fstepsize=.1,  stepsize=0.0005, start = 0.0, end = 1.0):
    """Plots 10 fmeasure Curves into the current canvas."""
    p = sc.arange(start, end, stepsize)[1:]
    for f in sc.arange(0., 1., fstepsize)[1:]:
        points = [(x, _fmeasureCurve(f, x)) for x in p
                  if 0 < _fmeasureCurve(f, x) <= 1.5]
        try:
            xs, ys = zip(*points)
            curve, = pl.plot(xs, ys, "--", color="gray", linewidth=0.8)  # , label=r"$f=%.1f$"%f) # exclude labels, for legend
            # bad hack:
            # gets the 10th last datapoint, from that goes a bit to the left, and a bit down
            datapoint_x_loc = int(len(xs)/2)
            datapoint_y_loc = int(len(ys)/2)
            # x_left = 0.05
            # y_left = 0.035
            x_left = 0.035
            y_left = -0.02
            pl.annotate(r"$f=%.1f$" % f, xy=(xs[datapoint_x_loc], ys[datapoint_y_loc]), xytext=(xs[datapoint_x_loc] - x_left, ys[datapoint_y_loc] - y_left), size="small", color="gray")
        except Exception as e:
            print e 

#colors = "gcmbbbrrryk"
#colors = "yyybbbrrrckgm"  # 7 is a prime, so we'll loop over all combinations of colors and markers, when zipping their cycles 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import legend [as 別名]
def main():

    l2_base_dir = '/media/admin228/00027E210001A5BD/train_pytorch/change_detection/CMU/prediction_cons/l2_5,6,7/roc'
    cos_base_dir = '/media/admin228/00027E210001A5BD/train_pytorch/change_detection/CMU/prediction_cons/dist_cos_new_5,6,7/roc'
    CSF_dir = os.path.join(l2_base_dir)
    CSF_fig_dir = os.path.join(l2_base_dir,'fig.png')
    end_number = 22
    csf_conv5_l2_ls,csf_fc6_l2_ls,csf_fc7_l2_ls,x_l2 = get_csf_ls(l2_base_dir,end_number)
    csf_conv5_cos_ls,csf_fc6_cos_ls,csf_fc7_cos_ls,x_cos = get_csf_ls(cos_base_dir,end_number)
    Fig = pylab.figure()
    setFigLinesBW(Fig)
    #pylab.plot(x,csf_conv4_ls, color='k',label= 'conv4')
    pylab.plot(x_l2,csf_conv5_l2_ls, color='m',label= 'l2:conv5')
    pylab.plot(x_l2,csf_fc6_l2_ls, color = 'b',label= 'l2:fc6')
    pylab.plot(x_l2,csf_fc7_l2_ls, color = 'g',label= 'l2:fc7')
    pylab.plot(x_cos,csf_conv5_cos_ls, color='c',label= 'cos:conv5')
    pylab.plot(x_cos,csf_fc6_cos_ls, color = 'r',label= 'cos:fc6')
    pylab.plot(x_cos,csf_fc7_cos_ls, color = 'y',label= 'cos:fc7')
    pylab.legend(loc='lower right', prop={'size': 10})
    pylab.ylabel('RMS Contrast', fontsize=14)
    pylab.xlabel('Epoch', fontsize=14)
    pylab.savefig(CSF_fig_dir) 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import legend [as 別名]
def plot_sensor_data(self, title, timestamp, x, y, z):
        if not self.to_save and not self.to_show:
            return

        self.big_figure()
        pylab.grid("on")

        pylab.plot(timestamp, x, color='r', label='x')
        pylab.plot(timestamp, y, color='g', label='y')
        pylab.plot(timestamp, z, color='b', label='z')

        pylab.legend()

        pylab.title(title)
        pylab.xlabel('Time')
        pylab.ylabel('Amplitude') 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import legend [as 別名]
def plot_data(self, title, data, xlabel, ylabel, colors=None, labels=None):
        if not self.to_save and not self.to_show:
            return

        self.big_figure()

        for i in range(0, len(data)):
            color = 'm' if colors is None else colors[i]

            if labels is None:
                pylab.plot(data[i], color=color)
            else:
                pylab.plot(data[i], color=color, label=labels[i])

        if labels is not None:
            pylab.legend()

        pylab.title(title)
        pylab.xlabel(xlabel)
        pylab.ylabel(ylabel) 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import legend [as 別名]
def plot_signal_and_label(self, title, timestamp, signal, label_timestamp, label):
        if not self.to_save and not self.to_show:
            return

        pylab.figure()

        pylab.plot(timestamp, signal, color='m', label='signal')

        for i in range(0, len(label_timestamp)):
            pylab.axvline(label_timestamp[i], color="k", label="{}: key {}".format(i, label[i]), ls='dashed')

        pylab.legend()

        pylab.title(title)
        pylab.xlabel('Time')
        pylab.ylabel('Amplitude') 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import legend [as 別名]
def plot_barchart(self, data, labels, colors, xlabel, ylabel, xticks, legendloc=1):
        self.big_figure()

        index = np.arange(len(data[0][0]))
        bar_width = 0.25

        pylab.grid("on", axis='y')
        pylab.ylim([0.5, 1.0])

        for i in range(0, len(data)):
            rects = pylab.bar(bar_width / 2 + index + (i * bar_width), data[i][0], bar_width,
                              alpha=0.5, color=colors[i],
                              yerr=data[i][1],
                              error_kw={'ecolor': '0.3'},
                              label=labels[i])

        pylab.legend(loc=legendloc, prop={'size': 12})

        pylab.xlabel(xlabel)
        pylab.ylabel(ylabel)
        pylab.xticks(bar_width / 2 + index + ((bar_width * (len(data[0]) + 1)) / len(data[0])), xticks) 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import legend [as 別名]
def addqqplotinfo(qnull,M,xl='-log10(P) observed',yl='-log10(P) expected',xlim=None,ylim=None,alphalevel=0.05,legendlist=None,fixaxes=False):    
    distr='log10'
    pl.plot([0,qnull.max()], [0,qnull.max()],'k')
    pl.ylabel(xl)
    pl.xlabel(yl)
    if xlim is not None:
        pl.xlim(xlim)
    if ylim is not None:
        pl.ylim(ylim)        
    if alphalevel is not None:
        if distr == 'log10':
            betaUp, betaDown, theoreticalPvals = _qqplot_bar(M=M,alphalevel=alphalevel,distr=distr)
            lower = -sp.log10(theoreticalPvals-betaDown)
            upper = -sp.log10(theoreticalPvals+betaUp)
            pl.fill_between(-sp.log10(theoreticalPvals),lower,upper,color="grey",alpha=0.5)
            #pl.plot(-sp.log10(theoreticalPvals),lower,'g-.')
            #pl.plot(-sp.log10(theoreticalPvals),upper,'g-.')
    if legendlist is not None:
        leg = pl.legend(legendlist, loc=4, numpoints=1)
        # set the markersize for the legend
        for lo in leg.legendHandles:
            lo.set_markersize(10)

    if fixaxes:
        fix_axes()