Python pyplot.pie方法代碼示例

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import pie [as 別名]
def analyseSex(firends):
    sexs = list(map(lambda x:x['Sex'],friends[1:]))
    counts = Counter(sexs).items()
    counts = sorted(counts, key=lambda x:x[0], reverse=False)
    counts = list(map(lambda x:x[1],counts))
    labels = ['Unknow','Male','Female']
    colors = ['red','yellowgreen','lightskyblue']
    plt.figure(figsize=(8,5), dpi=80)
    plt.axes(aspect=1) 
    plt.pie(counts, 
            labels=labels, 
            colors=colors, 
            labeldistance = 1.1, 
            autopct = '%3.1f%%',
            shadow = False, 
            startangle = 90, 
            pctdistance = 0.6 
    )
    plt.legend(loc='upper right',)
    plt.title(u'%s的微信好友性別組成' % friends[0]['NickName'])
    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import pie [as 別名]
def analysis_mobile(self):
        # self.record_result('<strong style="color: black; font-size: 24px;">正在分析該商品不同省份的購買量...</strong>')

        fig_size = plt.rcParams["figure.figsize"]
        plt.figure(figsize = (2.4, 2.4))

        obj = self.data_frame['is_mobile']
        obj = obj.value_counts()

        obj = obj.rename({1: '移動端', 0: 'PC'})
        plt.pie(x = obj.values, autopct = '%.0f%%', radius = 0.7, labels = obj.index, startangle = 180)

        plt.title('該商品移動/ PC 購買比例')

        plt.tight_layout()
        filename = '%s_mobile.png' % self.product_id
        plt.savefig('%s/%s' % (utils.get_save_image_path(), filename))
        plt.figure(figsize = fig_size)
        plt.clf()
        result = utils.get_image_src(filename = filename)
        self.record_result(result, type = 'image')

    # 分析購買後評論的時間分布 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import pie [as 別名]
def visualize_distributions(distro_dict, threshold=10):
    import matplotlib.pyplot as plt
    key_list = sorted(distro_dict.keys())
    threshold_str = '%d+' % (threshold, )
    label_list = [
        threshold_str if key == threshold else str(key)
        for key in key_list
    ]
    size_list = [ distro_dict[key] for key in key_list ]
    color_list = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
    explode = [0.0] + [0.0] * (len(size_list) - 1)

    plt.pie(size_list, explode=explode, labels=label_list, colors=color_list,
            autopct='%1.1f%%', shadow=True, startangle=90)
    plt.axis('equal')
    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import pie [as 別名]
def test_pie_center_radius():
    # The slices will be ordered and plotted counter-clockwise.
    labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
    sizes = [15, 30, 45, 10]
    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
    explode = (0, 0.1, 0, 0)  # only "explode" the 2nd slice (i.e. 'Hogs')

    plt.pie(sizes, explode=explode, labels=labels, colors=colors,
            autopct='%1.1f%%', shadow=True, startangle=90,
            wedgeprops={'linewidth': 0}, center=(1, 2), radius=1.5)

    plt.annotate("Center point", xy=(1, 2), xytext=(1, 1.5),
                 arrowprops=dict(arrowstyle="->",
                                 connectionstyle="arc3"))
    # Set aspect ratio to be equal so that pie is drawn as a circle.
    plt.axis('equal') 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import pie [as 別名]
def test_pie_textprops():
    data = [23, 34, 45]
    labels = ["Long name 1", "Long name 2", "Long name 3"]

    textprops = dict(horizontalalignment="center",
                     verticalalignment="top",
                     rotation=90,
                     rotation_mode="anchor",
                     size=12, color="red")

    _, texts, autopct = plt.gca().pie(data, labels=labels, autopct='%.2f',
                                      textprops=textprops)
    for labels in [texts, autopct]:
        for tx in labels:
            assert tx.get_ha() == textprops["horizontalalignment"]
            assert tx.get_va() == textprops["verticalalignment"]
            assert tx.get_rotation() == textprops["rotation"]
            assert tx.get_rotation_mode() == textprops["rotation_mode"]
            assert tx.get_size() == textprops["size"]
            assert tx.get_color() == textprops["color"] 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import pie [as 別名]
def label_distribution(trainFile):
    labels=[]
    with open(trainFile,'r') as f:
        raw_data=f.readlines()
        for line in raw_data:
            line=line.strip().split("+++$+++")
            label=int(line[2])
            assert label in [0,1],"Invalid label value!"
            labels.append(label)
    neg_count=labels.count(0)
    pos_count=len(labels)-neg_count
    counts=[neg_count,pos_count]
    labels=["negative","positive"]
    fig=plt.figure(figsize=(9,9))
    # 畫餅圖（數據，數據對應的標簽，百分數保留兩位小數點）
    plt.pie(counts, labels=labels, autopct='%1.2f%%')
    plt.title('Train Label Distribution', bbox={'facecolor': '0.8', 'pad': 5})
    plt.show()
    savePath=trainFile.split(".")[0]+"_ld.png"
    fig.savefig(savePath)
    plt.close() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import pie [as 別名]
def pieGraphics(Labels,ValueList,graphicTitle='圖例'):
    # The slices will be ordered and plotted counter-clockwise.
    #labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
    #sizes = [15, 30, 45, 10]
    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral', "blue","green","cyan","magenta"]
    explode = (0, 0.1, 0, 0)  # only "explode" the 2nd slice (i.e. 'Hogs')

    plt.pie(ValueList, labels=Labels, colors=colors,autopct='%1.1f%%', shadow=True, startangle=90)
    # Set aspect ratio to be equal so that pie is drawn as a circle.
    plt.axis('equal')
    plt.show()


#barGraphics('等級','數量',['A','B','C','D','E','F'],[29,30,40,47,38,23],'測試圖例')
#linePlotGraphics("xLabel","yLabel",[1,2,3,4,5,6,7,8,9,10],[1.1,1.9,2.6,3.6,9.8,14,24,40,80,150],graphicTitle='圖例')
#scatterPlotsGraphics("xLabel","yLabel",[1,2,3,4,5,6,7,8,9,10],[1,11.9,2,6.3,6,9.8,14,4,8,5],graphicTitle='圖例') 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import pie [as 別名]
def pieGraphics(Labels,ValueList,graphicTitle='圖例'):
    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral', "blue","green","cyan","magenta"]
    maxdata = max(ValueList)
    explode = []
    for v in ValueList:
        if v == maxdata:
            explode.append(0.1)
        else:
            explode.append(0)
    print(explode)
    patches,l_text,p_text = plt.pie(ValueList, labels=Labels, colors=colors,autopct='%1.1f%%',explode=explode ,shadow=True, startangle=90)
    for font in l_text:
        font.set_fontproperties(FontProperties(fname=PATH_SUFFIX+'SIMLI.TTF'))
    plt.title(graphicTitle,fontproperties=font_set,y=1.05)
    # Set aspect ratio to be equal so that pie is drawn as a circle.
    plt.axis('equal')
    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import pie [as 別名]
def plot(sizes,plotname):
    fig = plt.figure(figsize=(2.0,2.0),facecolor='white')
    ax = plt.subplot(111)
    psizes = ['%1.1f%%' % (x/sum(sizes)*100) for x in sizes]
    labels = [x+'\n'+y for x,y in zip(LABELS,psizes)]
    patches = plt.pie(sizes, colors=COLORS, labels=labels, 
                      shadow=False, startangle=90, labeldistance=0.7, 
                      wedgeprops={'linewidth': 4})
    for pie_wedge in patches[0]:
        pie_wedge.set_edgecolor('white')
    for t in patches[1]:
        t.set_horizontalalignment('center')
    plt.axis('equal')
    plt.tight_layout()
    plt.savefig(plotname)
    print('saved plot to {}'.format(plotname))
    plt.show()


######################################################
# Data processing 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import pie [as 別名]
def graph():
    total = len(os.listdir(test_path))
    affected = pandas.read_csv('output.csv')
    affected = affected['output'].sum()

    healthy = total - affected

    piey = ['Glaucomatous', 'Healthy']
    piex = [affected, healthy]

    plt.axis('equal')
    plt.pie(piex, labels=piey, radius=1.5, autopct='%0.1f%%', explode=[0.2, 0])
    plt.show()


# Frontend GUI 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import pie [as 別名]
def test_pie_frame_grid():
    # The slices will be ordered and plotted counter-clockwise.
    labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
    sizes = [15, 30, 45, 10]
    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
    # only "explode" the 2nd slice (i.e. 'Hogs')
    explode = (0, 0.1, 0, 0)

    plt.pie(sizes, explode=explode, labels=labels, colors=colors,
            autopct='%1.1f%%', shadow=True, startangle=90,
            wedgeprops={'linewidth': 0},
            frame=True, center=(2, 2))

    plt.pie(sizes[::-1], explode=explode, labels=labels, colors=colors,
            autopct='%1.1f%%', shadow=True, startangle=90,
            wedgeprops={'linewidth': 0},
            frame=True, center=(5, 2))

    plt.pie(sizes, explode=explode[::-1], labels=labels, colors=colors,
            autopct='%1.1f%%', shadow=True, startangle=90,
            wedgeprops={'linewidth': 0},
            frame=True, center=(3, 5))
    # Set aspect ratio to be equal so that pie is drawn as a circle.
    plt.axis('equal') 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import pie [as 別名]
def create_pie_chart(elements, suptitle, png, figure_id):
    """
    Create pie chart

    :param elements: dict with elements (dict)
    :param suptitle: name of chart (str)
    :param png: name of output file (str)
    :param figure_id: id of current plot (started with 1) (int)
    :return: None
    """
    values = [value for value in elements.values()]
    keys = [key for key in elements.keys()]
    plt.figure(figure_id)
    plt.subplots_adjust(bottom=.05, left=.01, right=.99, top=.90, hspace=.35)

    explode = [0 for x in range(len(keys))]
    max_value = max(values)
    explode[list(values).index(max_value)] = 0.1

    plt.pie(values, labels=keys,
            autopct=make_autopct(values), explode=explode,
            textprops={'fontsize': PIE_LABEL_FONT_SIZE})
    plt.axis("equal")
    plt.suptitle(suptitle, fontsize=PIE_SUPTITLE_FONT_SIZE)

    plt.gcf().set_dpi(PIE_DPI)
    plt.savefig("{dest}/{png}/{result_file}".format(dest=RESULTS_DIR,
                                                    png=PNG_DIR,
                                                    result_file=png)) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import pie [as 別名]
def plot_recursive(self, attrs, base_folder, base_time, div_number):

		# Creating a folder named aatr+'_children' which contains the plot of the attr children, a plot showing time division at that level

		labels = []
		values = []
		sum_time = 0
		
		for attr in attrs['children']:
			labels.append(attr['name'])
			values.append(attr['time'])
			sum_time += attr['time']

		arg = np.argsort(values)

		labels = np.array(labels)[arg].tolist()
		values = np.array(values)[arg]

		plt.pie(values, labels=labels, autopct='%1.1f%%')#, radius=len(labels)
		plt.xlabel(attrs['name']+'\n'+'Unaccounted Time: '+str((base_time - sum_time))+'\nTotal Time: '+str(base_time))#/div_number

		if not os.path.exists(base_folder+'/'+str(attrs['name'])):
			os.mkdir(base_folder+'/'+str(attrs['name']))

		plt.savefig(base_folder+'/'+str(attrs['name'])+'/'+str(attrs['name'])+'.png')

		plt.clf()

		if len(attrs['children']) != 0:
			for i in attrs['children']:
				if len(i['children']):
					self.plot_recursive(i.copy(), base_folder+'/'+str(attrs['name']), i['time'], div_number)#/div_number 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import pie [as 別名]
def parse_args():
    parser = argparse.ArgumentParser(description=
            "Create a pie chart showing the most active users in a Telegram chat")
    required = parser.add_argument_group('required arguments')
    required.add_argument('-f','--file',
            help='the jsonl chatlog file to analyse',
            required = True
            )
    parser.add_argument(
            '-o', '--output-folder',
            help='the folder to save the pie chart image in.'
            'Using this option will make the graph not display on screen.')
    parser.add_argument(
            '-s','--figure-size',
            help='the size of the figure shown or saved (X and Y size).'
            'Choose an appropriate value for your screen size. Default 12 8.',
            nargs=2,type=int,default = [12,8]
            )
    parser.add_argument(
            '-m','--minimum-percentage',
            help='the minimum percentage of activity a person must contribute '
            'to get their own slice of the pie chart. Default 2',
            type=float,default=2
            )
    parser.add_argument(
            '-d','--date-range',
            help='the range of dates you want to look at data between. '
            'Must be in format YYYY-MM-DD YYYY-MM-DD with the first date '
            'the start of the range, and the second the end. Example: '
            "-d '2017-11-20 2017-05-15'. Make sure you don't put a day "
            'that is too high for the month eg 30th February.',
            default="1000-01-01 4017-01-01"
            #hopefully no chatlogs contain these dates :p
    )

    return parser.parse_args() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import pie [as 別名]
def make_trimmed_ddict(counter,total_datapoints,names,min_percent):
    trimmedCounter = defaultdict(int)
    #find percentile to start adding people to "other" at
    min_chars = (min_percent/100) * total_datapoints
    for person, frequency in counter.items():
        if frequency < min_chars:
            trimmedCounter["other"] += frequency
        else:
            if names[str(person)] == "other":
                print("Someone in this chat is called 'other'. "
                "They will be absorbed into the 'other' pie slice.")
            trimmedCounter[names[str(person)]] = frequency

    return trimmedCounter