Python pyplot.savefig函数代码示例

def make_fish(zoom=False):
    plt.close(1)
    plt.figure(1, figsize=(6, 4))
    plt.plot(plot_limits['pitch'], plot_limits['rolldev'], '-g', lw=3)
    plt.plot(plot_limits['pitch'], -plot_limits['rolldev'], '-g', lw=3)
    plt.plot(pitch.midvals, roll.midvals, '.b', ms=1, alpha=0.7)

    p, r = make_ellipse()  # pitch, off nominal roll
    plt.plot(p, r, '-c', lw=2)

    gf = -0.08  # Fudge on pitch value for illustrative purposes
    plt.plot(greta['pitch'] + gf, -greta['roll'], '.r', ms=1, alpha=0.7)
    plt.plot(greta['pitch'][-1] + gf, -greta['roll'][-1], 'xr', ms=10, mew=2)

    if zoom:
        plt.xlim(46.3, 56.1)
        plt.ylim(4.1, 7.3)
    else:
        plt.ylim(-22, 22)
        plt.xlim(40, 180)
    plt.xlabel('Sun pitch angle (deg)')
    plt.ylabel('Sun off-nominal roll angle (deg)')
    plt.title('Mission off-nominal roll vs. pitch (5 minute samples)')
    plt.grid()
    plt.tight_layout()
    plt.savefig('fish{}.png'.format('_zoom' if zoom else ''))

def draw_ranges_for_parameters(data, title='', save_path='./pictures/'):
  parameters = data.columns.values.tolist()

  # remove flight name parameter
  for idx, parameter in enumerate(parameters):
    if parameter == 'flight_name':
      del parameters[idx]

  flight_names = np.unique(data['flight_name'])

  print len(flight_names)

  for parameter in parameters:
    plt.figure()

    axis = plt.gca()

    # ax.set_xticks(numpy.arange(0,1,0.1))
    axis.set_yticks(flight_names)
    axis.tick_params(labelright=True)
    axis.set_ylim([94., 130.])
    plt.grid()

    plt.title(title)
    plt.xlabel(parameter)
    plt.ylabel('flight name')

    colors = iter(cm.rainbow(np.linspace(0, 1,len(flight_names))))

    for flight in flight_names:
      temp = data[data.flight_name == flight][parameter]

      plt.plot([np.min(temp), np.max(temp)], [flight, flight], c=next(colors), linewidth=2.0)
    plt.savefig(save_path+title+'_'+parameter+'.jpg')
    plt.close()

def plot_scatter_matrix(df, plotdir):
    "Plot scatter matrix."
    print('plotting scatter matrix, this may take a while')
    plt.clf()
    pd_scatter_matrix(df, figsize=(16,16))
    plt.suptitle("Scatter Matrix", fontsize=14)
    plt.savefig(plotdir + 'scatter_matrix.png')

    def Test(self):
        test_Dir = "Result";        
        if not os.path.exists(test_Dir):
            os.makedirs(test_Dir);

        test_Label_List = [0,1,2,3,4,5,6,7,8,9,0,1,2,3,4,5];
        test_Label_Pattern = np.zeros((16, 10));
        test_Label_Pattern[np.arange(16), test_Label_List] = 1;            
        feed_Dict = {
            self.noise_Placeholder: np.random.uniform(-1., 1., size=[16, self.noise_Size]),
            self.label_for_Fake_Placeholder: test_Label_Pattern,
            self.is_Training_Placeholder: False
            };   #Batch is constant in the test.
        global_Step, mnist_List = self.tf_Session.run(self.test_Tensor_List, feed_dict = feed_Dict);

        fig = plt.figure(figsize=(4, 4))
        gs = gridspec.GridSpec(4, 4)
        gs.update(wspace=0.05, hspace=0.05)

        for index, mnist in enumerate(mnist_List):
            ax = plt.subplot(gs[index])
            plt.axis('off')
            ax.set_xticklabels([])
            ax.set_yticklabels([])
            ax.set_aspect('equal')
            plt.imshow(mnist.reshape(28, 28), cmap='Greys_r')

        plt.savefig('%s/S%d.png' % (test_Dir, global_Step), bbox_inches='tight');
        plt.close();

def do_plot(mode, content, wide):
	global style
	style.apply(mode, content, wide)

	data = np.load("data/prr_AsAu_%s%s.npz"%(content, wide))

	AU, TAU = np.meshgrid(-data["Au_range_dB"], data["tau_range"])
	Zu = data["PRR_U"]
	Zs = data["PRR_S"]

	assert TAU.shape == AU.shape == Zu.shape, "The inputs TAU, AU, PRR_U must have the same shape for plotting!"

	plt.clf()

	if mode in ("sync",):
		# Plot the inverse power ratio, sync signal is stronger for positive ratios
		CSf = plt.contourf(TAU, AU, Zs, levels=(0.0, 0.2, 0.4, 0.6, 0.8, 0.9, 1.0), colors=("1.0", "0.75", "0.5", "0.25", "0.15", "0.0"), origin="lower")
		CS2 = plt.contour(CSf, colors = ("r",)*5+("w",), linewidths=(0.75,)*5+(1.0,), origin="lower", hold="on")
	else:
		CSf  = plt.contourf(TAU, AU, Zs, levels=(0.0, 0.2, 0.4, 0.6, 0.8, 0.9, 1.0), colors=("1.0", "0.75", "0.5", "0.25", "0.15", "0.0"), origin="lower")
		CS2f = plt.contour(CSf, levels=(0.0, 0.2, 0.4, 0.6, 0.8, 1.0), colors=4*("r",)+("w",), linewidths=(0.75,)*4+(1.0,), origin="lower", hold="on")
		#CS2f = plt.contour(TAU, -AU, Zu, levels=(0.9, 1.0), colors=("0.0",), linewidths=(1.0,), origin="lower", hold="on")
		if content in ("unif",):
			CSu  = plt.contourf(TAU, AU, Zu, levels=(0.2, 1.0), hatches=("////",), colors=("0.75",), origin="lower")
			CS2  = plt.contour(CSu, levels=(0.2,), colors = ("r",), linewidths=(1.0,), origin="lower", hold="on")

	style.annotate(mode, content, wide)

	plt.axis([data["tau_range"][0], data["tau_range"][-1], -data["Au_range_dB"][-1], -data["Au_range_dB"][0]])

	plt.ylabel(r"Signal power ratio ($\mathrm{SIR}$)", labelpad=2)
	plt.xlabel(r"Time offset $\tau$ ($/T$)", labelpad=2)

	plt.savefig("pdf/prrc2_%s_%s%s_z.pdf"%(mode, content, wide))

def compare_chebhist(dname, mylambda, c, Nbin = 25):


    if mylambda == 'Do not exist':
        print('--!!Warning: eig file does not exist, can not display compare histgram')
    else:
        mylambda = 1 - mylambda
        lmin = max(min(mylambda), -1)
        lmax = min(max(mylambda),  1)

        # print c
        cheb_file_content = '\n'.join([str(st) for st in c])
        x = np.linspace(lmin, lmax, Nbin + 1)
        y = plot_chebint(c, x)
        u = (x[1:] + x[:-1]) / 2
        v =  y[1:] - y[:-1]

        plt.clf()
        plt.hold(True)
        plt.hist(mylambda,Nbin)
        plt.plot(u, v, "r.", markersize=10)
        plt.hold(False)
        plt.show()
        filename = 'data/' + dname + '.png'
        plt.savefig(filename)

        cheb_filename = 'data/' + dname + '.cheb'
        f = open(cheb_filename, 'w+')
        f.write(cheb_file_content)
        f.close()

 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 main():
    parser = argparse.ArgumentParser(description="""Compute subset of users who rated at
                                     least 10 movies and plot fraction of users satisfied
                                     as a function of inventory size.""")
    parser.add_argument("infilename",
                        help="Read from this file.", type=open)
    args = parser.parse_args()

    ratings = read_inputs(args.infilename)
    ratings = ratings.drop("timestamp", axis=1)
    movie_rankings = find_movie_rankings(ratings)
    ratings = ratings.drop("rating", axis=1)
    user_rankings = find_user_rankings(ratings, movie_rankings)
    num_users = user_rankings.user_id.unique().size
    num_movies = movie_rankings.shape[0]
    user_rankings = clean_rankings(user_rankings)

    us_levels_100 = find_satisfaction(user_rankings, num_users, num_movies)
    us_levels_90 = find_satisfaction(user_rankings, num_users, num_movies, satisfaction_level=0.9)

    rc('text', usetex=True)
    plt.title('Percent of Users Satisfied vs Inventory Size in the MovieLens Dataset')
    plt.xlabel('Inventory Size')
    plt.ylabel('Percent of Users Satisfied')
    plt.plot(us_levels_100, 'b', label=r'$100\% \ satisfaction$')
    plt.plot(us_levels_90, 'r--', label=r'$90\% \ satisfaction$')
    plt.legend()
    d = datetime.datetime.now().isoformat()
    plt.savefig('user_satisfaction_%s.png' % d)

def plot_wav_fft(wav_filename, desc=None):
    plt.clf()
    plt.figure(num=None, figsize=(6, 4))
    sample_rate, X = scipy.io.wavfile.read(wav_filename)
    spectrum = np.fft.fft(X)
    freq = np.fft.fftfreq(len(X), 1.0 / sample_rate)

    plt.subplot(211)
    num_samples = 200.0
    plt.xlim(0, num_samples / sample_rate)
    plt.xlabel("time [s]")
    plt.title(desc or wav_filename)
    plt.plot(np.arange(num_samples) / sample_rate, X[:num_samples])
    plt.grid(True)

    plt.subplot(212)
    plt.xlim(0, 5000)
    plt.xlabel("frequency [Hz]")
    plt.xticks(np.arange(5) * 1000)
    if desc:
        desc = desc.strip()
        fft_desc = desc[0].lower() + desc[1:]
    else:
        fft_desc = wav_filename
    plt.title("FFT of %s" % fft_desc)
    plt.plot(freq, abs(spectrum), linewidth=5)
    plt.grid(True)

    plt.tight_layout()

    rel_filename = os.path.split(wav_filename)[1]
    plt.savefig("%s_wav_fft.png" % os.path.splitext(rel_filename)[0],
                bbox_inches='tight')

def plot_jacobian(A, name, cmap= plt.cm.coolwarm, normalize=True, precision=1e-6):

    """
    Customized visualization of jacobian matrices for observing
    sparsity patterns
    """
    
    plt.figure()
    fig, ax = plt.subplots()
    
    if normalize is True:
        plt.imshow(A, interpolation='none', cmap=cmap,
                   norm = mpl.colors.Normalize(vmin=-1.,vmax=1.))
    else:
        plt.imshow(A, interpolation='none', cmap=cmap)        
    plt.colorbar(format=ticker.FuncFormatter(fmt))
    
    ax.spy(A, marker='.', markersize=0,  precision=precision)
    
    ax.spines['right'].set_visible(True)
    ax.spines['bottom'].set_visible(True)
    ax.xaxis.set_ticks_position('top')
    ax.yaxis.set_ticks_position('left')

    xlabels = np.linspace(0, A.shape[0], 5, True, dtype=int)
    ylabels = np.linspace(0, A.shape[1], 5, True, dtype=int)

    plt.xticks(xlabels)
    plt.yticks(ylabels)

    plt.savefig(name, bbox_inches='tight', pad_inches=0.05)
    
    plt.close()

    return

def plot_dpi_dpr_distribution(args, dpis, dprs, diagnoses):
    print log.INFO, 'Plotting estimate distributions...'
    diagnoses = np.array(diagnoses)
    diagnoses[(0.25 <= diagnoses) & (diagnoses <= 0.75)] = 0.5

    # Setup plot
    fig, ax = plt.subplots()
    pt.setup_axes(plt, ax)

    biomarkers_str = args.method if args.biomarkers is None else ', '.join(args.biomarkers)
    ax.set_title('DP estimation using {0} at {1}'.format(biomarkers_str, ', '.join(args.visits)))
    ax.set_xlabel('DP')
    ax.set_ylabel('DPR')

    plt.scatter(dpis, dprs, c=diagnoses, edgecolor='none', s=25.0,
                vmin=0.0, vmax=1.0, cmap=pt.progression_cmap,
                alpha=0.5)

    # Plot legend
    # noinspection PyUnresolvedReferences
    rects = [mpl.patches.Rectangle((0, 0), 1, 1, fc=pt.color_cn + (0.5,), linewidth=0),
             mpl.patches.Rectangle((0, 0), 1, 1, fc=pt.color_mci + (0.5,), linewidth=0),
             mpl.patches.Rectangle((0, 0), 1, 1, fc=pt.color_ad + (0.5,), linewidth=0)]
    labels = ['CN', 'MCI', 'AD']
    legend = ax.legend(rects, labels, fontsize=10, ncol=len(rects), loc='upper center', framealpha=0.9)
    legend.get_frame().set_edgecolor((0.6, 0.6, 0.6))

    # Draw or save the plot
    plt.tight_layout()
    if args.plot_file is not None:
        plt.savefig(args.plot_file, transparent=True)
    else:
        plt.show()
    plt.close(fig)

def make_entity_plot(filename, title, fixed_noip, fixed_ip, dynamic_noip, dynamic_ip):
    plt.figure(figsize=(12,5))

    plt.title("Settings comparison - " + title)
    
    plt.xlabel('Time (ms)', fontsize=12)
    plt.xlim([0,62000])

    x = 0
    barwidth = 0.5
    bargroupspacing = 1.5

    fixed_noip_mean,fixed_noip_conf = conf_stats(fixed_noip)
    fixed_ip_mean,fixed_ip_conf = conf_stats(fixed_ip)
    dynamic_noip_mean,dynamic_noip_conf = conf_stats(dynamic_noip)
    dynamic_ip_mean,dynamic_ip_conf = conf_stats(dynamic_ip)

    values = [fixed_noip_mean,fixed_ip_mean,dynamic_noip_mean, dynamic_ip_mean]
    errs = [fixed_noip_conf,fixed_ip_conf,dynamic_noip_conf, dynamic_ip_conf]

    y_pos = numpy.arange(len(values))
    plt.barh(y_pos, values, xerr=errs, align='center', color=['r', 'b', 'r', 'b'],  ecolor='black', alpha=0.7)
    plt.yticks(y_pos, ["Fixed | no I.P.", "Fixed | I.P.", "Dynamic | no I.P.", "Dynamic | I.P."])
    plt.savefig(output_file(filename))
    plt.clf()

def make_overview_plot(filename, title, noip_arrs, ip_arrs):
    plt.title("Inner parallelism - " + title)

    
    plt.ylabel('Time (ms)', fontsize=12)

    x = 0
    barwidth = 0.5
    bargroupspacing = 1.5

    for z in zip(noip_arrs, ip_arrs):
        noip,ip = z
        noip_mean,noip_conf = conf_stats(noip)
        ip_mean,ip_conf = conf_stats(ip)

        b_noip = plt.bar(x, noip_mean, barwidth, color='r', yerr=noip_conf, ecolor='black', alpha=0.7)
        x += barwidth

        b_ip = plt.bar(x, ip_mean, barwidth, color='b', yerr=ip_conf, ecolor='black', alpha=0.7)
        x += bargroupspacing

    plt.xticks([0.5, 2.5, 4.5], ['50k', '100k', '200k'], rotation='horizontal')

    fontP = FontProperties()
    fontP.set_size('small')

    plt.legend([b_noip, b_ip], \
        ('no inner parallelism', 'inner parallelism'), \
        prop=fontP, loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True, ncol=2)
   
    plt.ylim([0,62000])
    plt.savefig(output_file(filename))
    plt.clf()

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))

    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)