Python pyplot.clf函数代码示例

def plot_by_groups(df, plot_dir, af_key, config):
    """Plot allele frequencies of grouped/paired samples.
    """
    out_file = os.path.join(plot_dir, "cohort-group-af-comparison.pdf")
    df["sample_label"] = df.apply(lambda row: "%s\n%s" % (row["group_class"], row["sample"]), axis=1)
    sns.despine()
    sns.set(style="white")
    with PdfPages(out_file) as pdf_out:
        for (cohort, group), cur_df in df.groupby(["cohort", "group"]):
            labels = sorted(list(cur_df["sample_label"].unique()))
            labels.reverse()
            cur_df["sample_label"].categories = labels
            g = sns.violinplot(x=af_key, y="sample_label", data=cur_df, inner=None, bw=.1)
            #sns.swarmplot(x=af_key, y="sample_label", data=cur_df, color="w", alpha=.5)
            try:
                group = int(group)
            except ValueError:
                pass
            g.set_title("%s: %s" % (cohort, group))
            g = _af_violinplot_shared(g)
            pdf_out.savefig(g.figure)
            if config and (cohort, group) in config.group_detailed:
                out_dir = utils.safe_makedir(os.path.join(plot_dir, "detailed"))
                out_file = os.path.join(out_dir, "group-%s-%s.png" % (cohort, group))
                g.figure.savefig(out_file)
            plt.clf()
    return out_file

def run_test(fld, seeds, plot2d=True, plot3d=True, add_title="",
             view_kwargs=None, show=False, scatter_mpl=False, mesh_mvi=True):
    interpolated_fld = viscid.interp_trilin(fld, seeds)
    seed_name = seeds.__class__.__name__
    if add_title:
        seed_name += " " + add_title

    try:
        if not plot2d:
            raise ImportError
        from viscid.plot import vpyplot as vlt
        from matplotlib import pyplot as plt
        plt.clf()
        # plt.plot(seeds.get_points()[2, :], fld)
        mpl_plot_kwargs = dict()
        if interpolated_fld.is_spherical():
            mpl_plot_kwargs['hemisphere'] = 'north'
        vlt.plot(interpolated_fld, **mpl_plot_kwargs)
        plt.title(seed_name)

        plt.savefig(next_plot_fname(__file__, series='2d'))
        if show:
            plt.show()

        if scatter_mpl:
            plt.clf()
            vlt.plot2d_line(seeds.get_points(), fld, symdir='z', marker='o')
            plt.savefig(next_plot_fname(__file__, series='2d'))
            if show:
                plt.show()
    except ImportError:
        pass

    try:
        if not plot3d:
            raise ImportError
        from viscid.plot import vlab

        _ = get_mvi_fig(offscreen=not show)

        try:
            if mesh_mvi:
                mesh = vlab.mesh_from_seeds(seeds, scalars=interpolated_fld)
                mesh.actor.property.backface_culling = True
        except RuntimeError:
            pass

        pts = seeds.get_points()
        p = vlab.points3d(pts[0], pts[1], pts[2], interpolated_fld.flat_data,
                          scale_mode='none', scale_factor=0.02)
        vlab.axes(p)
        vlab.title(seed_name)
        if view_kwargs:
            vlab.view(**view_kwargs)

        vlab.savefig(next_plot_fname(__file__, series='3d'))
        if show:
            vlab.show(stop=True)
    except ImportError:
        pass

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 LinRegTest(XTrain, YTrain, close, filename):
	'''
	Using RandomForest learner to predict how much the price will change in 5 days
	@filename: the file's true name is ML4T-filename
	@XTrain: the train data for feature
	@YTrain: the train data for actual price after 5 days
	@close: the actual close price of Test data set
	@k: the number of trees in the forest
	'''
	
	XTest, YTest = TestGenerator(close)

	#plot thge feature
	plt.clf()
	fig = plt.figure()
	fig.suptitle('The value of features')
	plt.plot(range(100), XTest[0:100, 0], 'b', label = 'One day price change')
	plt.plot(range(100), XTest[0:100, 1], 'r', label = 'difference between two day price change')
	plt.legend(loc = 4)
	plt.ylabel('Price')
	filename4 = 'feature' + filename + '.pdf'
	fig.savefig(filename4, format = 'pdf')

	LRL = LinRegLearner()
	cof = LRL.addEvidence(XTrain, YTrain)
	YLearn = LRL.query(XTest, cof)
	return YLearn

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_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_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 display_graph_by_specific_mac(self, mac_address):

        G = nx.Graph()

        count = 0
        edges = set()
        edges_list = []


        for pkt in self.pcap_file:

            src = pkt[Dot11].addr1
            dst = pkt[Dot11].addr2

            if mac_address in [src, dst]:
                edges_list.append((src, dst))
                edges.add(src)
                edges.add(dst)

        plt.clf()
        plt.suptitle('Communicating with ' + str(mac_address), fontsize=14, fontweight='bold')
        plt.title("\n Number of Communicating Users: " + str(int(len(edges))))
        plt.rcParams.update({'font.size': 10})
        G.add_edges_from(edges_list)
        nx.draw(G, with_labels=True, node_color=MY_COLORS)
        plt.show()

    def graph_by_sender(self):

        mac_adresses = {}  # new dictionary
        for pkt in self.pcap_file:
            mac_adresses.update({pkt[Dot11].addr2: 0})
        for pkt in self.pcap_file:
            mac_adresses[pkt[Dot11].addr2] += 1

        MA = []
        for ma in mac_adresses:
            MA.append(mac_adresses[ma])

        plt.clf()
        plt.suptitle('Number of packets of every sender', fontsize=14, fontweight='bold')
        plt.bar(range(len(mac_adresses)), sorted(MA), align='center', color=MY_COLORS)

        plt.xticks(range(len(mac_adresses)), sorted(mac_adresses.keys()))

        plt.rcParams.update({'font.size': 10})

        plt.xlabel('Senders mac addresses')
        plt.ylabel('Number of packets')

        # Set tick colors:
        ax = plt.gca()
        ax.tick_params(axis='x', colors='k')
        ax.tick_params(axis='y', colors='r')
        ax.set_xticklabels(ax.xaxis.get_majorticklabels(), rotation=45)

        plt.show()

    def display_channel_efficiency(self):

        size = 0

        start_time = self.pcap_file[0].time
        end_time = self.pcap_file[len(self.pcap_file) - 1].time

        duration = (end_time - start_time)/1000

        for i in range(len(self.pcap_file) - 1):
            size += len(self.pcap_file[i])
        ans = (((size * 8) / duration) / BW_STANDARD_WIFI) * 100
        ans = float("%.2f" % ans)
        labels = ['utilized', 'unutilized']
        sizes = [ans, 100.0 - ans]
        colors = ['g', 'r']

        # Make a pie graph
        plt.clf()
        plt.figure(num=1, figsize=(8, 6))
        plt.axes(aspect=1)
        plt.suptitle('Channel efficiency', fontsize=14, fontweight='bold')
        plt.title("Bits/s: " + str(float("%.2f" % ((size*8)/duration))),fontsize = 12)
        plt.rcParams.update({'font.size': 17})
        plt.pie(sizes, labels=labels, autopct='%.2f%%', startangle=60, colors=colors, pctdistance=0.7, labeldistance=1.2)

        plt.show()

    def display_PER(self):

        number_of_pkts = len(self.pcap_file)
        retransmission_pkts = 0

        for pkt in self.pcap_file:

            if (pkt[Dot11].FCfield & 0x8) != 0:
                retransmission_pkts += 1

        ans = (retransmission_pkts / number_of_pkts)*100
        ans = float("%.2f" % ans)
        labels = ['Standard packets', 'Retransmitted packets']
        sizes = [100.0 - ans,ans]


        colors = ['g', 'firebrick']

        # Make a pie graph
        plt.clf()
        plt.figure(num=1, figsize=(8, 6))
        plt.axes(aspect=1)
        plt.suptitle('Retransmitted packet', fontsize=14, fontweight='bold')
        plt.rcParams.update({'font.size': 13})
        plt.pie(sizes, labels=labels, autopct='%.2f%%', startangle=60, colors=colors, pctdistance=0.7, labeldistance=1.2)

        plt.show()

    def display_graph(self):

        G = nx.Graph()

        count = 0
        edges = set()
        edges_list = []

        for pkt in self.pcap_file:
            if pkt.haslayer(Dot11Elt):
                src = pkt[Dot11].addr1
                dst = pkt[Dot11].addr2

                edges_list.append((src, dst))
                edges.add(src)
                edges.add(dst)


        plt.clf()
        filepath = os.path.splitext(self.path)[0]
        filename = basename(filepath)
        plt.suptitle('Connection Map of: '+ str(filename), fontsize=14, fontweight='bold')
        plt.title("\n Number of Users: " + str(int(len(edges))))
        plt.rcParams.update({'font.size': 10})
        G.add_edges_from(edges_list)
        nx.draw(G, with_labels=True, node_color=MY_COLORS)
        plt.show()

    def ensemble_pca(self, ref_ensemble=None, ref_first=True):
        data = prepare_pca_input(self._cgs)
        pca = PCA(n_components=2)
        if ref_ensemble:
            ref_data = prepare_pca_input(ref_ensemble)
            if ref_first:
                pca.fit(ref_data)
        if not ref_ensemble or not ref_first:
            pca.fit(data)
        reduced_data = pca.transform(data)
        if ref_ensemble:
            reduced_ref = pca.transform(ref_data)
            plt.scatter(reduced_ref[:, 0], reduced_ref[:, 1],
                        color="green", label="background")
        plt.scatter(reduced_data[:, 0], reduced_data[:,
                                                     1], color="blue", label="sampling")
        if self._reference_cg:
            data_true = prepare_pca_input([self._reference_cg])
            reduced_true = pca.transform(data_true)
            plt.scatter(reduced_true[:, 0], reduced_true[:,
                                                         1], color="red", label="reference")

        plt.xlabel("First principal component")
        plt.ylabel("Second principal component")
        figname = "pca_{}_rf{}.svg".format(self._cgs[0].name, ref_first)
        plt.savefig(figname)
        log.info("Figure {} created".format(figname))
        plt.clf()
        plt.close()

    def view_delta_rmsd_vs_steps(self):
        self._calculate_complete_rmsd_matrix()
        fig, axes = plt.subplots(2)
        a_rmsd = np_nans(len(self._cg_sequence) // 2)
        min_rmsd = np_nans(len(self._cg_sequence) // 2)
        max_rmsd = np_nans(len(self._cg_sequence) // 2)
        for d in range(len(a_rmsd)):
            l = [self._rmsd[self._cg_sequence[i], self._cg_sequence[i + d]]
                 for i in range(len(self._cg_sequence) - d)]
            a_rmsd[d] = sum(l) / len(l)
            min_rmsd[d] = min(l)
            max_rmsd[d] = max(l)
        for ax in axes:
            ax.set_xlabel("Steps apart")
            ax.set_ylabel("Average RMSD")
            ax.plot(list(range(len(a_rmsd))), a_rmsd, label="Average RMSD")
            ax.plot(list(range(len(min_rmsd))), min_rmsd, label="Minimal RMSD")
            ax.plot(list(range(len(max_rmsd))), max_rmsd, label="Maximal RMSD")
            ax.plot([0, len(max_rmsd)], [np.max(self._rmsd), np.max(
                self._rmsd)], "-.", label="Maximal RMSD in whole simulation")
            ax.plot([0, len(max_rmsd)], [np.mean(self._rmsd), np.mean(
                self._rmsd)], "-.", label="Average RMSD in whole simulation")
            ax.legend(prop={'size': 6})
        axes[1].set_xlim([0, 50])

        plt.savefig("rmsd_steps_apart_{}.svg".format(self._cgs[0].name))

        plt.clf()
        plt.close()

def disc_norm():
    x = np.linspace(-3,3,100)
    y = st.norm.pdf(x,0,1)
    fig, ax = plt.subplots()
    fig.canvas.draw()
    
    ax.plot(x,y)
    
    fill1_x = np.linspace(-2,-1.5,100)
    fill1_y = st.norm.pdf(fill1_x,0,1)
    fill2_x = np.linspace(-1.5,-1,100)
    fill2_y = st.norm.pdf(fill2_x,0,1)
    ax.fill_between(fill1_x,0,fill1_y,facecolor = 'blue', edgecolor = 'k',alpha = 0.75)
    ax.fill_between(fill2_x,0,fill2_y,facecolor = 'blue', edgecolor = 'k',alpha = 0.75)
    for label in ax.get_yticklabels():
        label.set_visible(False)
    for tick in ax.get_xticklines():
        tick.set_visible(False)
    for tick in ax.get_yticklines():
        tick.set_visible(False)
    
    plt.rc("font", size = 16)
    plt.xticks([-2,-1.5,-1])
    labels = [item.get_text() for item in ax.get_xticklabels()]
    labels[0] = r"$v_k$"
    labels[1] = r"$\varepsilon_k$"
    labels[2] = r"$v_{k+1}$"
    ax.set_xticklabels(labels)
    plt.ylim([0, .45])

    
    plt.savefig('discnorm.pdf')
    plt.clf()