Python matplotlib.pyplot.barh() Examples

def plot_dsc(dsc_dist):
    y_positions = np.arange(len(dsc_dist))
    dsc_dist = sorted(dsc_dist.items(), key=lambda x: x[1])
    values = [x[1] for x in dsc_dist]
    labels = [x[0] for x in dsc_dist]
    labels = ["_".join(l.split("_")[1:-1]) for l in labels]
    fig = plt.figure(figsize=(12, 8))
    canvas = FigureCanvasAgg(fig)
    plt.barh(y_positions, values, align="center", color="skyblue")
    plt.yticks(y_positions, labels)
    plt.xticks(np.arange(0.0, 1.0, 0.1))
    plt.xlim([0.0, 1.0])
    plt.gca().axvline(np.mean(values), color="tomato", linewidth=2)
    plt.gca().axvline(np.median(values), color="forestgreen", linewidth=2)
    plt.xlabel("Dice coefficient", fontsize="x-large")
    plt.gca().xaxis.grid(color="silver", alpha=0.5, linestyle="--", linewidth=1)
    plt.tight_layout()
    canvas.draw()
    plt.close()
    s, (width, height) = canvas.print_to_buffer()
    return np.fromstring(s, np.uint8).reshape((height, width, 4)) 

def plot_preds(image, preds):
    """Displays image and the top-n predicted probabilities in a bar graph
    Args:
        image: PIL image
        preds: list of predicted labels and their probabilities
    """
    
    """# For Spyder
    plt.imshow(image)
    plt.axis('off')"""

    plt.figure()
    labels = ("cat", "dog")
    plt.barh([0, 1], preds, alpha=0.5)
    plt.yticks([0, 1], labels)
    plt.xlabel('Probability')
    plt.xlim(0,1.01)
    plt.tight_layout()
    plt.savefig('out.png') 

def draw_one(self, xls, title, color):
        title = '{} ({})'.format(title, xls.columns[0])
        column_a = xls[xls.columns[0]]
        column_c = xls[xls.columns[2]]

        ticks = [column_a[x] for x in range(3, 16)]
        kbps = [self.float2(column_c[x]) for x in range(3, 16)]
        plt.barh(range(16 - 3), kbps, height=0.2, color=color, alpha=0.8)
        plt.yticks(range(16 - 3), ticks)
        plt.xlim(0, max(kbps) * 1.2)
        plt.xlabel("Speed")
        plt.title(title)
        for x, y in enumerate(kbps):
            plt.text(y + 1000, x - 0.1, '%s KB/s' % y)

        plt.show() 

def altPlotFeaturesImportance(X,y,featureNames,dataName):
    "http://nbviewer.ipython.org/github/cs109/2014/blob/master/homework-solutions/HW5-solutions.ipynb"
    clf = RandomForestClassifier(n_estimators=50)

    clf.fit(X,y)
    importance_list = clf.feature_importances_
    # name_list = df.columns #ORIG
    name_list=featureNames

    importance_list, name_list = zip(*sorted(zip(importance_list, name_list)))
    plt.barh(range(len(name_list)),importance_list,align='center')
    plt.yticks(range(len(name_list)),name_list)
    plt.xlabel('Relative Importance in the Random Forest')
    plt.ylabel('Features')
    plt.title('%s \n Relative Feature Importance' %(dataName))
    plt.grid('off')
    plt.ion()
    plt.show() 

def plot_parameter_statistic(model, layer_types=['Dense', 'Conv2D'], trainable=True, non_trainable=False, outputs=False):
    parameter_count = []
    names = []
    for l in model.layers:
        if l.__class__.__name__ not in layer_types:
            continue
        count = 0
        if outputs:
            count += np.sum([np.sum([np.prod(s[1:]) for s in n.output_shapes]) for n in l._inbound_nodes])
        if trainable:
            count += np.sum([K.count_params(p) for p in set(l.trainable_weights)])
        if non_trainable:
            count += np.sum([K.count_params(p) for p in set(l.non_trainable_weights)])
        parameter_count.append(count)
        names.append(l.name)
    
    y = range(len(names))
    plt.figure(figsize=[12,max(len(y)//4,1)])
    plt.barh(y, parameter_count, align='center')
    plt.yticks(y, names)
    plt.ylim(y[0]-1, y[-1]+1)
    ax = plt.gca()
    ax.invert_yaxis()
    ax.xaxis.tick_top()
    plt.show() 

def plot_topconsumer_bar_chart(self, data):
        x = []
        y = []
        for item in data:
            y.append(item['count'])
            x.append(item['app_name'])
        plt.barh(x, y)
        plt.title("Top consumers", fontsize=10)
        plt.xlabel("Number of API Calls", fontsize=8)
        plt.xticks([])
        plt.ylabel("Consumers", fontsize=8)
        plt.tick_params(axis='y', labelsize=8)
        for i, j in zip(y, x):
            plt.text(i, j, str(i), clip_on=True, ha='center',va='center', fontsize=8)
        plt.tight_layout()
        buf = BytesIO()
        plt.savefig(buf, format='png')
        image_base64 = base64.b64encode(buf.getvalue()).decode('utf-8').replace('\n', '')
        buf.close()
        # Clear the previous plot.
        plt.gcf().clear()
        return image_base64 

def render(self):
        """ Prepare data for plotting
        """

        # init figure
        self.fig, self.ax = plt.subplots()
        self.ax.yaxis.grid(False)
        self.ax.xaxis.grid(True)

        # assemble colors
        colors = []
        for pkg in self.packages:
            colors.append(pkg.color)

        self.barlist = plt.barh(self.yPos, list(self.durations),
                                left=self.start,
                                align='center',
                                height=.5,
                                alpha=1,
                                color=colors)

        # format plot
        self.format()
        self.add_milestones()
        self.add_legend() 

def plot_bar_chart(self, data):
        x = []
        y = []
        for item in data:
            y.append(item['count'])
            x.append(item['Implemented_by_partial_function'])
        plt.barh(x, y)
        plt.title("Top apis", fontsize=10)
        plt.xlabel("Number of API Calls", fontsize=8)
        plt.xticks([])
        plt.ylabel("Partial function", fontsize=8)
        plt.tick_params(axis='y', labelsize=8)
        for i, j in zip(y, x):
            plt.text(i, j, str(i), clip_on=True, ha='center',va='center', fontsize=8)
        plt.tight_layout()
        buf = BytesIO()
        plt.savefig(buf, format='png')
        image_base64 = base64.b64encode(buf.getvalue()).decode('utf-8').replace('\n', '')
        buf.close()
        # Clear the previous plot.
        plt.gcf().clear()
        return image_base64 

def Feature_Importance_plot (est,names):
    'http://nbviewer.ipython.org/github/pprett/pydata-gbrt-tutorial/blob/master/gbrt-tutorial.ipynb'
    fx_imp = pd.Series(est.feature_importances_, index=names)
    fx_imp /= fx_imp.max()  # normalize
    fx_imp.sort()
    fx_imp.plot(kind='barh', figsize=FIGSIZE) 

def plot(effect, how):
    import matplotlib.pyplot as plt
    if how == 1:
        values = [effect.t2, abs(effect.cc), effect.hs, effect.t1]
        bars = ['Tf: %1.2f°C' % effect.t2, 'Convection and Conduction: %1.2f°C' % effect.cc, 'Heat Source Term: %1.2f°C'
                % effect.hs, 'To: %1.2f°C' % effect.t1]
        position = range(4)
        plt.barh(position, values, color=['blue', 'red', 'green', 'blue'])
        if effect.t1 > effect.t2:
            plt.barh(0, abs(effect.cc + effect.hs), color='red', left=effect.t2)
            plt.text(effect.t2, 0, '%1.2f°C' % (effect.cc + effect.hs))
        else:
            plt.barh(0, abs(effect.cc + effect.hs), color='green', left=effect.t1)
            plt.text(effect.t1, 0, '%1.2f°C' % (effect.cc + effect.hs))
        plt.yticks(position, ['Tf', 'CC', 'HS', 'To'])
        plt.title('Temperature contribution of main factors at %1.1fh - %1.1fm' % (effect.time, effect.length),
                  fontweight='bold')
        for i, v in enumerate(values):
            plt.text(v/8, i, bars[i])
        plt.xlabel('Temperature, °C')
        plt.show()

    if how == 2:
        labels = ['pipe rotation in Qp', 'friction in Qp', 'pipe rotation in Qa', 'friction in Qa']
        effects = [effect.ds_rot1, effect.fric1, effect.ds_rot2, effect.fric2]
        plt.pie(effects, startangle=90)
        plt.legend(loc=0, labels=['%s, %1.2f %%' % (l, s) for l, s in zip(labels, effects)])
        title = 'Effect of factors in heat source terms. Qp/Qa = %1.2f' % effect.hsr
        plt.title(title)
        plt.show() 

def analysis_hashtag():
    with sqlite3.connect(db_path) as db:
        conn = db
        c = conn.cursor()
        c.execute("SELECT hashtag from Tweet")
        hashtag_list = []
        for row in c.fetchall():
            if (row != ('',)):
                if " " in ''.join(row):
                    for m in ''.join(row).split(' '):
                        hashtag_list.append(m)
                else:
                    signle_item = ''.join(row)
                    hashtag_list.append(signle_item)

        counter = Counter(hashtag_list).most_common(10)
        pl.rcdefaults()

        keys = []
        performance = []

        for i in counter:
            performance.append(i[1])
            keys.append(i[0])

        pl.rcdefaults()
        y_pos = np.arange(len(keys))
        error = np.random.rand(len(keys))

        pl.barh(y_pos, performance, xerr=error, align='center', alpha=0.4, )
        pl.yticks(y_pos, keys)
        pl.xlabel('quantity')
        pl.title('hashtags')
        print(colored("[INFO] Showing graph of hashtag analysis", "green"))
        pl.show() 

def main():
    newdata = make_data()
    setup_plot()
    allticks = []
    for i,(oracle,data) in enumerate(zip(ORACLES,newdata)):
        pos = i*(max(1,Nsub)+1)+np.array(range(max(1,Nsub)+1))
        handles = []
        for j,dat in enumerate(data):
            h = plt.barh(pos[1]+j,dat,color=COLORS[j],edgecolor=COLORS[j])
            handles.append(h)
        allticks.extend(zip([x for x in pos], ['', oracle['name'],'']))
    finalize_plot(allticks,handles)
    plt.savefig(plotname+'.pdf')
    plt.show() 

def generate_city_pic(self, city_data):
        """
        生成城市数据图片
        因为plt在子线程中执行会出现自动弹出弹框并阻塞主线程的行为，plt行为均放在主线程中
        :param data:
        :return:
        """
        font = {'family': ['xkcd', 'Humor Sans', 'Comic Sans MS'],
                'weight': 'bold',
                'size': 12}
        matplotlib.rc('font', **font)
        cities = city_data['cities']
        city_people = city_data['city_people']

        # 绘制「性别分布」柱状图
        plt.barh(range(len(cities)), width=city_people, align='center', color=self.bar_color, alpha=0.8)
        # 添加轴标签
        plt.xlabel(u'Number of People')
        # 添加标题
        plt.title(u'Top %d Cities of your friends distributed' % len(cities), fontsize=self.title_font_size)
        # 添加刻度标签
        plt.yticks(range(len(cities)), cities)
        # 设置X轴的刻度范围
        plt.xlim([0, city_people[0] * 1.1])

        # 为每个条形图添加数值标签
        for x, y in enumerate(city_people):
            plt.text(y + len(str(y)), x, y, ha='center')

        # 显示图形
        plt.savefig(ALS.result_path + '/4.png')
        # todo 如果调用此处的关闭，就会导致应用本身也被关闭
        # plt.close()
        # plt.show() 

def PlotFeaturesImportance(X,y,featureNames):
    '''

    Plot the relative contribution/importance of the features.
    Best to reduce to top X features first - for interpretability
    Code example from:
    http://bugra.github.io/work/notes/2014-11-22/an-introduction-to-supervised-learning-scikit-learn/
    '''

    gbc = GradientBoostingClassifier(n_estimators=100)
    gbc.fit(X, y)
    # Get Feature Importance from the classifier
    feature_importance = gbc.feature_importances_
    # Normalize The Features
    feature_importance = 100.0 * (feature_importance / feature_importance.max())
    sorted_idx = np.argsort(feature_importance)
    pos = np.arange(sorted_idx.shape[0]) + .5
    plt.figure(figsize=(16, 12))
    plt.barh(pos, feature_importance[sorted_idx], align='center', color='#7A68A6')

    #plt.yticks(pos, np.asanyarray(df.columns.tolist())[sorted_idx]) #ORIG
    plt.yticks(pos, np.asanyarray(featureNames)[sorted_idx])

    plt.xlabel('Relative Importance')
    plt.title('Features Importance')
    plt.show() 

def PlotFeaturesImportance(X,y,featureNames,dataName):
    '''
    Plot the relative contribution/importance of the features.
    Best to reduce to top X features first - for interpretability
    Code example from:
    http://bugra.github.io/work/notes/2014-11-22/an-introduction-to-supervised-learning-scikit-learn/
    '''
    gbc = GradientBoostingClassifier(n_estimators=40)
    gbc.fit(X, y)
    # Get Feature Importance from the classifier
    feature_importance = gbc.feature_importances_
    # Normalize The Features
    feature_importance = 100 * (feature_importance / feature_importance.max())
    sorted_idx = numpy.argsort(feature_importance)
    pos = numpy.arange(sorted_idx.shape[0]) + 4.5
    # pos = numpy.arange(sorted_idx.shape[0])
    # plt.figure(figsize=(16, 12))
    plt.figure(figsize=(14, 9), dpi=250)
    plt.barh(pos, feature_importance[sorted_idx], align='center', color='#7A68A6')
    #plt.yticks(pos, numpy.asanyarray(df.columns.tolist())[sorted_idx]) #ORIG
    plt.yticks(pos, numpy.asanyarray(featureNames)[sorted_idx])

    plt.xlabel('Relative Importance')
    plt.title('%s: Top Features' %(dataName))
    plt.grid('off')
    plt.ion()
    plt.show()
    plt.savefig(str(dataName)+'TopFeatures.png',dpi=200) 

def calc_groups_dist_to_dura(elc_name, output_fol, threshold):
    (electrodes, names, hemis, threshold) = utils.load(op.join(output_fol, '{}_electrodes.pkl'.format(int(threshold))))
    groups, noise = utils.load(op.join(output_fol, '{}_groups.pkl'.format(int(threshold))))
    elc_ind = names.index(elc_name)
    group, in_group_ind = find_electrode_group(elc_ind, groups)

    verts = {}
    for hemi in ['lh', 'rh']:
        # verts[hemi], _ = nib.freesurfer.read_geometry(op.join(subject_fol, 'surf', '{}.dural'.format(hemi)))
        verts[hemi], _ = nib.freesurfer.read_geometry(op.join(subject_fol, 'bem', 'watershed',  'mg105_brain_surface'))

    dists = cdist([electrodes[elc_ind]], verts[hemis[elc_ind]])
    close_verts_dists = np.min(dists, axis=1)
    close_verts_ind = np.argmin(dists, axis=1)
    print('{}: {} ({})'.format(names[elc_ind], close_verts_dists, verts[hemis[elc_ind]][close_verts_ind]))

    mean_dists = []
    for group in groups:
        dists = cdist(electrodes[group], verts[hemis[group[0]]])
        close_verts_dists = np.min(dists, axis=1)
        print('{}-{}: {}'.format(names[group[0]], names[group[-1]], close_verts_dists))
        mean_dists.append(np.max(close_verts_dists))
    plt.barh(np.arange(len(groups)), mean_dists, align='center', alpha=0.5)
    plt.yticks(np.arange(len(groups)), ('{}-{}'.format(names[group[0]], names[group[-1]]) for group in groups))
    plt.title('max groups dist to dural surface')
    plt.show()
    print('asdf') 

def plot_importance(coeff_importances: dict, 
					coeff_sign: dict = {},
					print_legend: bool = True):
	check_types([
		("coeff_importances", coeff_importances, [dict], False),
		("coeff_sign", coeff_sign, [dict], False),
		("print_legend", print_legend, [bool], False)])
	coefficients, importances, signs = [], [], []
	for coeff in coeff_importances:
		coefficients += [coeff]
		importances += [coeff_importances[coeff]]
		signs += [coeff_sign[coeff]] if (coeff in coeff_sign) else [1]
	importances, coefficients, signs = zip( * sorted(zip(importances, coefficients, signs)))
	plt.figure(figsize = (12, int(len(importances) / 2) + 1))
	plt.rcParams['axes.facecolor'] = '#F5F5F5'
	color = []
	for item in signs:
		color += ['#263133'] if (item == 1) else ['#FE5016']
	plt.barh(range(0, len(importances)), importances, 0.9, color = color, alpha = 0.86)
	if (print_legend):
		orange = mpatches.Patch(color = '#FE5016', label = 'sign -')
		blue = mpatches.Patch(color = '#263133', label = 'sign +')
		plt.legend(handles = [orange,blue], loc = "lower right")
	plt.ylabel("Features")
	plt.xlabel("Importance")
	plt.gca().xaxis.grid()
	plt.gca().set_axisbelow(True)
	plt.yticks(range(0, len(importances)), coefficients)
	plt.show()
#---# 

def bar(vdf,
		method: str = "density",
		of = None,
		max_cardinality: int = 6,
		bins: int = 0,
		h: float = 0,
		color: str = '#263133'):
	x, y, z, h, is_categorical = compute_plot_variables(vdf, method = method, of = of, max_cardinality = max_cardinality, bins = bins, h = h)
	plt.figure(figsize = (14, min(len(x), 600))) if isnotebook() else plt.figure(figsize = (10, 6))
	plt.rcParams['axes.facecolor'] = '#F5F5F5'
	plt.barh(x, y, h, color = color, alpha = 0.86)
	plt.ylabel(vdf.alias)
	plt.gca().xaxis.grid()
	plt.gca().set_axisbelow(True)
	if (is_categorical):
		if (vdf.category() == "text"):
			new_z = []
			for item in z:
				new_z += [item[0:47] + "..."] if (len(str(item)) > 50) else [item]
		else:
			new_z = z
		plt.yticks(x,new_z)
		plt.subplots_adjust(left = max(0.1, min(len(max([str(item) for item in z], key = len)), 20) / 80.0))
	if (method == "density"):
		plt.xlabel('Density')
		plt.title('Distribution of {}'.format(vdf.alias))
	elif ((method in ["avg", "min", "max", "sum"] or '%' in method) and (of != None)):
		aggregate = "{}({})".format(method.upper(), of)
		plt.ylabel(aggregate)
		plt.title('{} group by {}'.format(aggregate, vdf.alias))
	else:
		plt.xlabel('Frequency')
		plt.title('Count by {}'.format(vdf.alias))
	plt.show()
#---# 

def draw_bar(labels,quants,indicate_path,xlabel,ylabel,title):
    width=0.35
    plt.figure(figsize=(8,(width+0.1)*len(quants)), dpi=300)
    # Bar Plot
    plt.cla()
    plt.clf()
    plt.barh(range(len(quants)),quants,tick_label=labels)
    plt.grid(True)
    plt.title(title)
    plt.xlabel(xlabel)
    plt.ylabel(ylabel)
    plt.savefig(indicate_path)
    plt.close() 

def draw_compare(self):
        xls = self.oxfs_xls
        column_a = xls[xls.columns[0]]
        column_c = xls[xls.columns[2]]

        oxfs_ticks = [column_a[x] + '- oxfs' for x in range(3, 16)]
        oxfs_kbps = [self.float2(column_c[x]) for x in range(3, 16)]

        xls = self.sshfs_xls
        column_a = xls[xls.columns[0]]
        column_c = xls[xls.columns[2]]

        sshfs_ticks = [column_a[x] + '- sshfs' for x in range(3, 16)]
        sshfs_kbps = [self.float2(column_c[x]) for x in range(3, 16)]

        ticks = []
        kbps = []
        for i in range(0, len(oxfs_kbps)):
            ticks.append(oxfs_ticks[i])
            ticks.append(sshfs_ticks[i])
            kbps.append(oxfs_kbps[i])
            kbps.append(sshfs_kbps[i])

        barlist = plt.barh(range(len(kbps)), kbps, height=0.3, color='coral', alpha=0.8)
        for bar in barlist[1::2]:
            bar.set_color('slateblue')
        plt.yticks(range(len(ticks)), ticks)
        plt.xlim(0, max(kbps) * 1.2)
        for x, y in enumerate(kbps):
            plt.text(y + 1000, x - 0.1, '%s KB/s' % y)

        title = 'Oxfs Vs Sshfs ({})'.format(xls.columns[0])
        plt.title(title)
        plt.xlabel("Speed")

        plt.show() 

def plot(counts):
    labels = map(lambda x: x[0], counts)
    values = map(lambda y: y[1], counts)
    plt.barh(range(len(values)), values, color='green')
    plt.yticks(range(len(values)), labels)
    plt.show() 

def plot_performance(perf, kmax = 100, prec_type = 'LCS_HEIGHT', clip_ahp = None):
    
    import matplotlib.pyplot as plt
    
    plt.figure()
    plt.xlabel('k')
    plt.ylabel('Hierarchical Precision')
    plt.xlim(0, kmax)
    plt.ylim(0, 1)
    plt.grid()
    
    min_prec = 1.0
    for lbl, metrics in perf.items():
        precs = [metrics['P@{} ({})'.format(k, prec_type)] for k in range(1, kmax+1)]
        plt.plot(np.arange(1, kmax + 1), precs, label = lbl)
        min_prec = min(min_prec, min(precs))
    
    min_prec = np.floor(min_prec * 20) / 20
    if min_prec >= 0.3:
        plt.ylim(min_prec, 1)
    
    plt.legend(fontsize = 'x-small')
    
    
    plt.figure()
    plt.xlabel('Mean Average Hierarchical Precision')
    plt.yticks([])
    plt.grid(axis = 'x')
    
    for i, (lbl, metrics) in enumerate(perf.items()):
        mAHP = metrics['AHP{} ({})'.format('@{}'.format(clip_ahp) if clip_ahp else '', prec_type)]
        plt.barh(i + 0.5, mAHP, 0.8)
        plt.text(0.01, i + 0.5, lbl, verticalalignment = 'center', horizontalalignment = 'left', color = 'white', fontsize = 'small')
        plt.text(mAHP - 0.01, i + 0.5, '{:.1%}'.format(mAHP), verticalalignment = 'center', horizontalalignment = 'right', color = 'white')
    
    
    plt.show() 

def barHonGraphics(xLabel,yLabel,xValueList,yValueList,graphicTitle='图例',xWidth=0.5):
    plt.barh(numpy.arange(len(xValueList)), yValueList, alpha=0.4)
    plt.yticks(numpy.arange(len(xValueList)), xValueList,fontproperties=font_set)
    plt.xlabel(yLabel,fontproperties=font_set)
    plt.ylabel(xLabel,fontproperties=font_set)
    plt.title(graphicTitle,fontproperties=font_set)

    plt.show()
	
#折线图:蓝色粗线 

def barHonGraphics(xLabel,yLabel,xValueList,yValueList,graphicTitle='图例',xWidth=0.5):
    plt.barh(numpy.arange(len(xValueList)), yValueList, alpha=0.4)
    plt.yticks(numpy.arange(len(xValueList)), xValueList,fontproperties=font_set)
    plt.xlabel(yLabel,fontproperties=font_set)
    plt.ylabel(xLabel,fontproperties=font_set)
    plt.title(graphicTitle,fontproperties=font_set)

    plt.show()
	
#折线图:蓝色粗线 

def plot_feature_split_proportions(model: SklearnModel, ax=None):
    if ax is None:
        _, ax = plt.subplots(1, 1)
    proportions = feature_split_proportions(model)

    y_pos = np.arange(len(proportions))
    name, count = list(proportions.keys()), list(proportions.values())
    props = pd.DataFrame({"name": name, "counts": count}).sort_values("name", ascending=True)
    plt.barh(y_pos, props.counts, align='center', alpha=0.5)
    plt.yticks(y_pos, props.name)
    plt.xlabel('Proportion of all splits')
    plt.ylabel('Feature')
    plt.title('Proportion of Splits Made on Each Variable')
    return ax 

def barHonGraphics(xLabel,yLabel,xValueList,yValueList,graphicTitle='图例',xWidth=0.5):
    plt.barh(numpy.arange(len(xValueList)), yValueList, alpha=0.4)
    plt.yticks(numpy.arange(len(xValueList)), xValueList,fontproperties=font_set)
    plt.xlabel(yLabel,fontproperties=font_set)
    plt.ylabel(xLabel,fontproperties=font_set)
    plt.title(graphicTitle,fontproperties=font_set)

    plt.show()
	
#折线图:蓝色粗线 

def barHonGraphics(xLabel,yLabel,xValueList,yValueList,graphicTitle='图例',xWidth=0.5):
    plt.barh(numpy.arange(len(xValueList)), yValueList, alpha=0.4)
    plt.yticks(numpy.arange(len(xValueList)), xValueList,fontproperties=font_set)
    plt.xlabel(yLabel,fontproperties=font_set)
    plt.ylabel(xLabel,fontproperties=font_set)
    plt.title(graphicTitle,fontproperties=font_set)

    plt.show()
	
#折线图:蓝色粗线 

def add_grouped_classification(
    fig, df_group, depth_max, depth_min, title_group, num_col, z_NAP
):
    """
    Add to the plot the selected classification.

    :param fig: Original figure.
    :param depth_max: Maximum depth.
    :param depth_min: Minimum depth.
    :return: Complete figure.
    """
    ax = fig.add_subplot(1, num_col, 4)
    df = df_group
    for i, layer in enumerate(df["soil_type"]):
        if z_NAP:
            plt.barh(
                y=df["z_centr_NAP"][i],
                height=df["thickness"][i],
                width=5,
                color=df["colour"][i],
                label=layer,
            )
        else:
            plt.barh(
                y=df["z_centr"][i],
                height=df["thickness"][i],
                width=5,
                color=df["colour"][i],
                label=layer,
            )
    ax.set_xticks([])
    ax.set_title(f"{title_group} classification")
    plt.ylim(depth_max, depth_min)
    return fig 

def get_head_ngram_statistics(questions, correct_model1, correct_model2, correct_model1_and_model2, correct_model1_and_not_model2, correct_model2_and_not_model1, output_dir, num_grams=2, top_count=25):
    # Head ngram statistics
    head_ngrams = get_head_ngrams(questions, num_grams)

    # Get head_ngram_frequencies (hnf)
    hnf_all = get_head_ngram_frequencies(questions, head_ngrams, num_grams)
    hnf_correct_model1 = get_head_ngram_frequencies({qid: questions[qid] for qid in correct_model1}, head_ngrams, num_grams)
    hnf_correct_model2 = get_head_ngram_frequencies({qid: questions[qid] for qid in correct_model2}, head_ngrams, num_grams)
    hnf_correct_model1_and_model2 = get_head_ngram_frequencies({qid: questions[qid] for qid in correct_model1_and_model2}, head_ngrams, num_grams)
    hnf_correct_model1_and_not_model2 = get_head_ngram_frequencies({qid: questions[qid] for qid in correct_model1_and_not_model2}, head_ngrams, num_grams)
    hnf_correct_model2_and_not_model1 = get_head_ngram_frequencies({qid: questions[qid] for qid in correct_model2_and_not_model1}, head_ngrams, num_grams)

    sorted_bigrams_all = sorted(hnf_all.items(), key=lambda x: x[1], reverse=True)
    top_bigrams = [x[0] for x in sorted_bigrams_all[0:top_count]]

    counts_total = [hnf_all[x] for x in top_bigrams]
    counts_model1 = [hnf_correct_model1[x] for x in top_bigrams]
    counts_model2 = [hnf_correct_model2[x] for x in top_bigrams]
    counts_model1_and_model2 = [hnf_correct_model1_and_model2[x] for x in top_bigrams]
    counts_model1_and_not_model2 = [hnf_correct_model1_and_not_model2[x] for x in top_bigrams]
    counts_model2_and_not_model1 = [hnf_correct_model2_and_not_model1[x] for x in top_bigrams]

    top_bigrams_with_counts = []
    for cc in range(len(top_bigrams)):
        top_bigrams_with_counts.append('{0} ({1})'.format(top_bigrams[cc], counts_total[cc]))

    plt.clf()
    fig, ax = plt.subplots(figsize=(6, 10))

    ylocs = list(range(top_count))
    counts_model1_percent = 100 * np.array(counts_model1) / np.array(counts_total)
    plt.barh([top_count - x for x in ylocs], counts_model1_percent, height=0.4, alpha=0.5, color='#EE3224', label=top_bigrams)
    counts_model2_percent = 100 * np.array(counts_model2) / np.array(counts_total)
    plt.barh([top_count - x+0.4 for x in ylocs], counts_model2_percent, height=0.4, alpha=0.5, color='#2432EE', label=top_bigrams  )
    ax.set_yticks([top_count - x + 0.4 for x in ylocs])
    ax.set_yticklabels(top_bigrams_with_counts)
    ax.set_ylim([0.5, top_count+1])
    ax.set_xlim([0, 100])
    plt.subplots_adjust(left=0.28, right=0.9, top=0.9, bottom=0.1)
    plt.xlabel('Percentage of questions with correct answers')
    plt.ylabel('Top N-grams')
    plt.savefig(os.path.join(output_dir, 'ngram_stats_{0}.png'.format(num_grams)))
    plt.close() 

def test(df,ticker,simu_start=100,simu_end=1000,simu_delta=100,**kwargs):
    
    table=pd.DataFrame()
    table['Simulations']=np.arange(simu_start,simu_end+simu_delta,simu_delta)
    table.set_index('Simulations',inplace=True)
    table['Prediction']=0

    #for each simulation
    #we test if the prediction is accurate
    #for instance
    #if the end of testing horizon is larger than the end of training horizon
    #we denote the return direction as +1
    #if both actual and predicted return direction align
    #we conclude the prediction is accurate
    #vice versa
    for i in np.arange(simu_start,simu_end+1,simu_delta):
        print(i)
        
        forecast_horizon,d,pick=monte_carlo(df,simulation=i,**kwargs)
        
        actual_return=np.sign( \
                              df['Close'].iloc[len(df)-forecast_horizon]-df['Close'].iloc[-1])
        
        best_fitted_return=np.sign(d[pick][len(df)-forecast_horizon]-d[pick][-1])
        table.at[i,'Prediction']=np.where(actual_return==best_fitted_return,1,-1)
        
    #we plot the horizontal bar chart 
    #to show the accuracy does not increase over the number of simulations
    ax=plt.figure(figsize=(10,5)).add_subplot(111)
    ax.spines['right'].set_position('center')
    ax.spines['top'].set_visible(False)

    plt.barh(np.arange(1,len(table)*2+1,2),table['Prediction'], \
             color=colorlist[0::int(len(colorlist)/len(table))])

    plt.xticks([-1,1],['Failure','Success'])
    plt.yticks(np.arange(1,len(table)*2+1,2),table.index)
    plt.xlabel('Prediction Accuracy')
    plt.ylabel('Times of Simulation')
    plt.title(f"Prediction accuracy doesn't depend on the numbers of simulation.\nTicker: {ticker}\n")
    plt.show()


# In[6]:

#lets try something extreme, pick ge, the worst performing stock in 2018
#see how monte carlo works for both direction prediction and fat tail simulation
#why the extreme? well if we are risk quants, we care about value at risk, dont we
#if quants only look at one sigma event, the portfolio performance would be devastating