def sandiandayinziti():
'''散点打印字体'''
scatter = Scatter("散点图示例")
v1, v2 = scatter.draw("./img.PNG")
scatter.add("散点图打印文字", v1, v2, is_random=True)
scatter.render('./info/散点图打印文字.html')
def test_scatter_xaxis_type_category():
scatter = Scatter("散点图示例")
scatter.add("A", ["a", "b", "c", "d", "e", "f"], v2)
scatter.add("B", ["a", "b", "c", "d", "e", "f"],
v1[::-1],
xaxis_type="category")
assert '"type": "category"' in scatter._repr_html_()
def test_scatter_xaxis_type_category():
scatter = Scatter("散点图示例")
scatter.add("A", ["a", "b", "c", "d", "e", "f"], v2)
scatter.add(
"B", ["a", "b", "c", "d", "e", "f"], v1[::-1], xaxis_type="category"
)
assert '"type": "category"' in scatter._repr_html_()
def test_scatter_extra_name():
def custom_formatter(params):
return params.value[3]
data = [[28604, 77, 17096], [31163, 77.4, 27662], [1516, 68, 11546]]
x_lst = [v[0] for v in data]
y_lst = [v[1] for v in data]
extra_data = [v[2] for v in data]
extra_name = ["point A", "point B", "point C"]
sc = Scatter()
sc.add(
"scatter",
x_lst,
y_lst,
extra_data=extra_data,
extra_name=extra_name,
is_visualmap=True,
visual_dimension=2,
visual_orient="horizontal",
visual_type="size",
visual_range=[17000, 28000],
visual_text_color="#000",
tooltip_formatter=custom_formatter,
)
assert "point A" in sc._repr_html_()
assert "point B" in sc._repr_html_()
assert "point C" in sc._repr_html_()
def tu_Gussian(self,dataname="None",X=None,TrainData=None,choice=12):
#测试数据集
if X==None:
X,y=make_blobs(n_samples=100,n_features=3,centers=[[3,3, 3], [0,0,0], [1,1,1], [2,2,2]], cluster_std=[0.2, 0.1, 0.2, 0.2],
random_state =9)
n=X.shape[1]
m=X.shape[0]
if TrainData==None:
col=np.random.randint(0,1,(98,1))
col2=np.random.randint(1,2,(2,1))
TrainData=np.column_stack((X,np.row_stack((col,col2))))
#高斯模型
mu1,sigms1=self.estimateGaussion(X)
if choice==1:
px_one=gaussian(sigms1,X,mu1)
if n<=10:
scatter=Scatter("featurn")
for j in range(0,n):
scatter.add(str(j),X[:,j],px_one[:,j])
else:
px_one=self.multivariateGaussian(X,mu1,sigms1)
scatter=Scatter("featurn")
def f(x):
y=1
for i in range(n):
y=y*x[i]
return y
scatter.add("总体分布",map(f,X),px_one)
#交叉验证，取得最好epsilon
len = TrainData.shape[1]
Xval=TrainData[:,0:-1]
Yval=TrainData[:,-1]
pvals=[] #各个特征值的概率相乘
if choice==1:
pval=gaussian(sigms1,Xval,mu1)
for i in range(0,m):
pvals.append(reduce(mul,pval[i,:]))
else:
pvals=self.multivariateGaussian(Xval,mu1,sigms1)
epsilon,F1=self.selectThreshold(Yval,pvals)
yc=[0] #异常点为0
def filteryc(x):
return x[n-1] in yc
newdata=filter(filteryc,X)
save_helper.save_txt_helper(newdata,dataname)
outliers=np.where(px_one<epsilon)
scatter2=ksh.ksh_scatter("离散点异常分布图","正常点",X,"FG","异常点",X[outliers])
self.page.add(scatter)
self.page.add(scatter2)
save_helper.save_tu_helper(self.page,dataname)
def test_grid_four_direction():
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720, width=1200, title_pos="65%")
bar.add("商家A", CLOTHES, v1, is_stack=True)
bar.add("商家B", CLOTHES, v2, is_stack=True, legend_pos="80%")
line = Line("折线图示例")
line.add("最高气温", WEEK, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"], mark_line=["average"])
line.add("最低气温", WEEK, [1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"], mark_line=["average"],
legend_pos="20%")
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter("散点图示例", title_top="50%", title_pos="65%")
scatter.add("scatter", v1, v2, legend_top="50%", legend_pos="80%")
es = EffectScatter("动态散点图示例", title_top="50%")
es.add("es", [11, 11, 15, 13, 12, 13, 10], [1, -2, 2, 5, 3, 2, 0],
effect_scale=6, legend_top="50%", legend_pos="20%")
grid = Grid()
grid.add(bar, grid_bottom="60%", grid_left="60%")
grid.add(line, grid_bottom="60%", grid_right="60%")
grid.add(scatter, grid_top="60%", grid_left="60%")
grid.add(es, grid_top="60%", grid_right="60%")
grid.render()
def main():
dutPl0 = []
dutpl1 = []
maxCntPl0 = []
maxCntPl1 = []
page = Page("Cluster Padding Figure")
txtFiles = [name for name in os.listdir('./') if name.endswith('.txt')]
for textLogName in txtFiles:
waferNum = re.split(r'_', textLogName)
with open(textLogName, 'rt') as clusterFile:
for line in clusterFile:
if re.match(r'IncomingBBKCount[A-Za-z0-9_.\+\-\*\/\s]*BBKP', line):
splitColon = re.split(r'\s*[:]\s*', line)
splitColon1 = splitColon[0]
splitColon2 = splitColon[1]
splitColon3 = splitColon[2]
listBlk = re.split(r'\s*[\s]\s*', splitColon3)
padBlk = paddingBlk(listBlk)
maxCnt = clusterBlk(padBlk)
splitSpace = re.split(r'\s*[\s]\s*', splitColon1)
intDut = int(splitSpace[1].replace('DUT', ''))
if splitSpace[2] == "BBKP0":
dutPl0.append(intDut)
maxCntPl0.append(maxCnt)
else:
dutpl1.append(intDut)
maxCntPl1.append(maxCnt)
scatter = Scatter(waferNum[2])
scatter.add("PL0", dutPl0, maxCntPl0, xaxis_name = "DUT", yaxis_name = "Max Padding BB", is_more_utils = True)
scatter.add("PL1", dutpl1, maxCntPl1, xaxis_name = "DUT", yaxis_name = "Max Padding BB", is_more_utils = True)
page.add(scatter)
page.render()
def Scatter_creater():
max_axis_value = max(max(ControlSample), max(KnockOutSample))
scatter = Scatter('OMG', width=800, height=600)
scatter.add('gene',
ControlSample,
KnockOutSample,
extra_name=gene_id,
xaxis_max=max_axis_value,
yaxis_max=max_axis_value,
is_label_emphasis=True,
label_formatter=lable_formatter,
symbol_size=8,
is_datazoom_show=True,
is_datazoom_extra_show=True,
datazoom_range=[0, 100],
datazoom_extra_range=[0, 100],
datazoom_type='both',
datazoom_extra_type='both',
tooltip_formatter=tooltip_formatter,
extra_data=LogFC,
is_visualmap=True,
visual_dimension=2,
visual_orient='vertical',
visual_range=[-1, 1],
visual_range_color=['#50a3ba', '#d94e5d'],
visual_pos=-5,
is_toolbox_show=False)
scatter.show_config()
return scatter
def test_grid_four_direction():
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720, width=1200, title_pos="65%")
bar.add("商家A", attr, v1, is_stack=True)
bar.add("商家B", attr, v2, is_stack=True, legend_pos="80%")
line = Line("折线图示例")
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"], mark_line=["average"])
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0], mark_point=["max", "min"],
mark_line=["average"], legend_pos="20%")
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter("散点图示例", title_top="50%", title_pos="65%")
scatter.add("scatter", v1, v2, legend_top="50%", legend_pos="80%")
es = EffectScatter("动态散点图示例", title_top="50%")
es.add("es", [11, 11, 15, 13, 12, 13, 10], [1, -2, 2, 5, 3, 2, 0],
effect_scale=6, legend_top="50%", legend_pos="20%")
grid = Grid()
grid.add(bar, grid_bottom="60%", grid_left="60%")
grid.add(line, grid_bottom="60%", grid_right="60%")
grid.add(scatter, grid_top="60%", grid_left="60%")
grid.add(es, grid_top="60%", grid_right="60%")
grid.render()
def visual_scatter_group_data_by_col(group_data, col_name, page):
x_value = list(group_data[col_name])
y_value = list(group_data['rate'])
scatter = Scatter()
scatter.add(col_name, x_value, y_value)
page.add(scatter)
return page
def test_scatter_multi_dimension():
data = [
[28604, 77, 17096869],
[31163, 77.4, 27662440],
[1516, 68, 1154605773],
[13670, 74.7, 10582082],
[28599, 75, 4986705],
[29476, 77.1, 56943299],
[31476, 75.4, 78958237],
[28666, 78.1, 254830],
[1777, 57.7, 870601776],
[29550, 79.1, 122249285],
[2076, 67.9, 20194354],
[12087, 72, 42972254],
[24021, 75.4, 3397534],
[43296, 76.8, 4240375],
[10088, 70.8, 38195258],
[19349, 69.6, 147568552],
[10670, 67.3, 53994605],
[26424, 75.7, 57110117],
[37062, 75.4, 252847810]
]
x_lst = [v[0] for v in data]
y_lst = [v[1] for v in data]
extra_data = [v[2] for v in data]
sc = Scatter()
sc.add("scatter", x_lst, y_lst, extra_data=extra_data, is_visualmap=True,
visual_dimension=2, visual_orient='horizontal',
visual_type='size', visual_range=[254830, 1154605773],
visual_text_color='#000')
sc.render()
def scatter(title, range_def, data):
from pyecharts import Scatter
# import random
# data = [random.randint(0, 100) for _ in range(80)]
range_color = [
'#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',
'#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026'
]
scatter = Scatter(
title,
width=400,
height=300,
background_color='#b0bab9',
)
scatter.add("",
range(len(data)),
data,
visual_range=range_def,
is_visualmap=True,
visual_range_color=range_color,
mark_line=['average'],
mark_point=['max', 'min'])
return scatter.render_embed()
def test_scatter_multi_dimension():
data = [[28604, 77, 17096869], [31163, 77.4, 27662440],
[1516, 68, 1154605773], [13670, 74.7, 10582082],
[28599, 75, 4986705], [29476, 77.1, 56943299],
[31476, 75.4, 78958237], [28666, 78.1, 254830],
[1777, 57.7, 870601776], [29550, 79.1, 122249285],
[2076, 67.9, 20194354], [12087, 72, 42972254],
[24021, 75.4, 3397534], [43296, 76.8, 4240375],
[10088, 70.8, 38195258], [19349, 69.6, 147568552],
[10670, 67.3, 53994605], [26424, 75.7, 57110117],
[37062, 75.4, 252847810]]
x_lst = [v[0] for v in data]
y_lst = [v[1] for v in data]
extra_data = [v[2] for v in data]
sc = Scatter()
sc.add("scatter",
x_lst,
y_lst,
extra_data=extra_data,
is_visualmap=True,
visual_dimension=2,
visual_orient='horizontal',
visual_type='size',
visual_range=[254830, 1154605773],
visual_text_color='#000')
sc.render()
def scatter(title, range_def, data, size=None):
# import random
# data = [random.randint(0, 100) for _ in range(80)]
range_color = ['#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',
'#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026']
if size is None:
scatter = Scatter(title,
width=320,
height=180,
title_pos="center",
background_color='#b0bab9',
)
else:
scatter = Scatter(title,
width=size['width'],
height=size['height'],
title_pos="center",
background_color='#b0bab9',
)
scatter.add("", range(len(data)), data,
visual_range_size=range_def,
is_visualmap=False,
visual_range_color=range_color,
mark_line=['average'],
mark_point=['max', 'min'])
scatter.options['toolbox']['show'] = False
return scatter.render_embed()
def test_scatter_default():
scatter = Scatter("散点图示例")
scatter.add("A", v1, v2)
scatter.add("B", v1[::-1], v2)
html_content = scatter._repr_html_()
assert '"type": "value"' in html_content
assert '"type": "category"' not in html_content
def test_scatter_default():
scatter = Scatter("散点图示例")
scatter.add("A", v1, v2)
scatter.add("B", v1[::-1], v2)
html_content = scatter._repr_html_()
assert '"type": "value"' in html_content
assert '"type": "category"' not in html_content
def draw(generation):
s = []
for i in generation:
s.append(i[1])
scatter = Scatter('牛顿法')
# 因为牛顿法收敛很快，所以用横坐标表示不明显，故用次数表示
scatter.add('', list(range(len(s))), s)
scatter.render('牛顿法.html')
def sandiantu():
'''散点图'''
v1 = [10, 20, 30, 40, 50, 60]
v2 = [10, 20, 30, 40, 50, 60]
scatter = Scatter("散点图示例")
scatter.add("A", v1, v2)
scatter.add("B", v1[::-1], v2)
scatter.render('./info/散点图.html')
def DrawScatter(title, data, savepath='./results'):
if not os.path.exists(savepath):
os.mkdir(savepath)
scatter = Scatter(title)
attrs = [i for i, j in data.items()]
values = [j for i, j in data.items()]
scatter.add('', attrs, values, is_visualmap=True)
scatter.render(os.path.join(savepath, '%s.html' % title))
def DraScatter(self, title, data, savepath='./result'):
if (os.path.exists(savepath) is None):
os.mkdir(savepath)
scatter = Scatter(title)
attrs = [data[i][0] for i in range(len(data))]
vals = [data[i][1] for i in range(len(data))]
scatter.add('', attrs, vals, is_visualmap=True)
scatter.render(os.path.join(savepath, '%s.html' % title))
def test_scatter_visualmap_type_size():
scatter = Scatter("散点图示例")
scatter.add("B",
v1[::-1],
v2,
is_visualmap=True,
visual_type='size',
visual_range_size=[20, 80])
scatter.render()
def draw(generator, name):
scatter = Scatter(name)
s1 = []
s2 = []
for i in generator:
s1.append(i[0])
s2.append(i[1])
scatter.add('', s1, s2)
scatter.render(name + '.html')
def draw(generation):
scatter = Scatter('定点法')
s = []
s1 = []
for i in generation:
s.append(i[0])
s1.append(i[1])
scatter.add('', s, s1)
scatter.render('定点迭代法.html')
def draw(generation):
s1 = []
s2 = []
for i in generation:
s1.append(i[0])
s2.append(i[1])
scatter = Scatter('弦截法')
scatter.add('', s1, s2)
scatter.render('弦截法.html')
def scatter():
from pyecharts import Scatter
v1 = [10, 20, 30, 40, 50, 60]
v2 = [10, 20, 30, 40, 50, 60]
scatter = Scatter("散点图示例")
scatter.add("A", v1, v2)
scatter.add("B", v1[::-1], v2)
return template(scatter)
def tmp():
es = Scatter()
x = []
y = []
for item in data:
x.append(item[2])
y.append(item[3])
es.add('', x, y)
es.render('1.html')
return render_template('1.html')
def drawlog(dic):
"""
:param his: keras fit返回的的history对象字典
:return:
"""
line = Scatter("log")
for label, value in dic.items():
line.add(label, [i for i in range(len(value))], value, symbol_size=3)
line.render(path="output/temp.html")
def test_scatter_visualmap_type_size():
scatter = Scatter("散点图示例")
scatter.add(
"B",
v1[::-1],
v2,
is_visualmap=True,
visual_type="size",
visual_range_size=[20, 80],
)
scatter.render()
def test_scatter_option(patched):
fixture = "scatter_options.json"
patched.return_value = "1"
v1 = [10, 20, 30, 40, 50, 60]
v2 = [10, 20, 30, 40, 50, 60]
scatter = Scatter("scatter test")
scatter.add("A", v1, v2)
scatter.add("B", v1[::-1], v2)
actual_options = dumps_actual_options(scatter.options)
expected = get_fixture_content(fixture)
for a, b in zip(actual_options.split("\n"), expected.split("\n")):
eq_(a.strip(), b.strip())
def show(self, class_key: dict, title: str, image_name):
# print(self.show_coordinates)
scatter = Scatter(title=title, background_color='#ffe')
for i in range(0, self.show_coordinates.shape[0]):
point = self.show_coordinates.iloc[i]['point']
# print(point, class_key[point])
coordinates = self.show_coordinates.iloc[i]['vector']
# print(type(coordinates), type(class_key[point]))
scatter.add(class_key[point], [coordinates[0]], [coordinates[1]])
temp_path = 'html/render.html'
scatter.render(path=temp_path)
make_a_snapshot(temp_path, image_name)
def scatter_demo(self):
# 散点图
from pyecharts import Scatter
scatter = Scatter('散点图', '一年的降水量与蒸发量')
# xais_name是设置横坐标名称，这里由于显示问题，还需要将y轴名称与y轴的距离进行设置
scatter.add("降水量与蒸发量的散点分布",
self.data1,
self.data2,
xaxis_name="降水量",
yaxis_name="蒸发量",
yaxis_name_gap=40)
scatter.render('sactter.html')
def scatter(self):
cmtnum = []
likenum = []
timeS = []
for item in self.data:
cmtnum.append(item[0])
likenum.append(item[1])
d = datetime.fromtimestamp(item[2])
timeS.append(d.year + d.month / 12)
y_min = datetime.fromtimestamp(self.createtime_min).year
y_max = datetime.fromtimestamp(self.createtime_max).year
scatter = Scatter('本人说说点赞数与评论数的时间序列（二维）')
scatter.add('评论数',
timeS,
cmtnum,
extra_data=cmtnum,
xaxis_max=y_max,
xaxis_min=y_min,
is_datazoom_extra_show=True,
datazoom_extra_type='inside',
legend_pos='right',
xaxis_name='年份',
is_visualmap=True,
visual_dimension=2,
visual_orient="horizontal",
visual_type="size",
visual_range=[0, 205],
visual_range_size=[5, 20],
visual_text_color="#000")
scatter.add('点赞数',
timeS,
likenum,
extra_data=likenum,
xaxis_max=y_max,
xaxis_min=y_min,
is_datazoom_extra_show=True,
datazoom_extra_type='inside',
legend_pos='right',
xaxis_name='年份',
is_visualmap=True,
visual_dimension=2,
visual_orient="horizontal",
visual_type="size",
visual_range=[0, 205],
visual_range_size=[5, 20],
visual_text_color="#000")
scatter.render(self.path + 'scatter2d_' + str(int(time.time())) +
'.html')
webbrowser.open_new_tab(self.path + 'scatter2d_' +
str(int(time.time())) + '.html')
self.ins.analysis_pb['value'] = 100
self.ins.analysis_lb.configure(text='完毕')
def scatter_echar_AvgArea_price(self):
x, y = self.getPriceInterval_avg()
scatter = Scatter("广州市租房平均面积与平均价格关系图")
scatter.add(
"平均面积-平均价格",
x,
y,
yaxis_name="平均价格(元)",
xaxis_name="平均面积(平米)",
yaxis_name_gap=50,
)
scatter.render('scatter_avgAreaPrice.html')
def test_scatter_option(patched):
patched.return_value = "1"
v1 = [10, 20, 30, 40, 50, 60]
v2 = [10, 20, 30, 40, 50, 60]
scatter = Scatter("scatter test")
scatter.add("A", v1, v2)
scatter.add("B", v1[::-1], v2)
actual_options = json.dumps(
scatter.options, sort_keys=True, indent=4, cls=DefaultJsonEncoder
)
expected = get_fixture_content("scatter_options.json")
for a, b in zip(actual_options.split("\n"), expected.split("\n")):
eq_(a.strip(), b.strip())
def test_grid_left_right():
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter(width=1200)
scatter.add("散点图示例", v1, v2, legend_pos="70%")
es = EffectScatter()
es.add("动态散点图示例", [11, 11, 15, 13, 12, 13, 10], [1, -2, 2, 5, 3, 2, 0],
effect_scale=6, legend_pos="20%")
grid = Grid()
grid.add(scatter, grid_left="60%")
grid.add(es, grid_right="60%")
grid.render()
def test_grid_left_right():
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter(width=1200)
scatter.add("散点图示例", v1, v2, legend_pos="70%")
es = EffectScatter()
es.add("动态散点图示例", [11, 11, 15, 13, 12, 13, 10], [1, -2, 2, 5, 3, 2, 0],
effect_scale=6, legend_pos="20%")
grid = Grid()
grid.add(scatter, grid_left="60%")
grid.add(es, grid_right="60%")
grid.render()
def create_scatter(df):
"""
三维度散点图
"""
# 生成数据列表
data = [list(i) for i in zip(df['videos'], df['fans'], df['likes'])]
print(data)
# 生成散点图
x_lst = [v[0] for v in data]
y_lst = [v[1] for v in data]
extra_data = [v[2] for v in data]
sc = Scatter("抖音大V视频数粉丝数点赞数三维度", title_pos='center', title_top='5', width=800, height=400)
sc.add("", x_lst, y_lst, extra_data=extra_data, is_visualmap=True, visual_dimension=2, visual_orient="horizontal", visual_type="size", visual_range=[0, 500000000], visual_text_color="#000", visual_range_size=[5, 30])
sc.render('抖音大V视频数粉丝数点赞数三维度.html')
def test_scatter_option(patched):
patched.return_value = "1"
v1 = [10, 20, 30, 40, 50, 60]
v2 = [10, 20, 30, 40, 50, 60]
scatter = Scatter("scatter test")
scatter.add("A", v1, v2)
scatter.add("B", v1[::-1], v2)
actual_options = json.dumps(scatter.options,
sort_keys=True,
indent=4,
cls=DefaultJsonEncoder)
expected = get_fixture_content("scatter_options.json")
for a, b in zip(actual_options.split("\n"), expected.split("\n")):
eq_(a.strip(), b.strip())
def test_overlap_es_scatter():
v1 = [10, 20, 30, 40, 50, 60]
v2 = [30, 30, 30, 30, 30, 30]
v3 = [50, 50, 50, 50, 50, 50]
v4 = [10, 10, 10, 10, 10, 10]
es = EffectScatter("Scatter-EffectScatter 示例")
es.add("es", v1, v2)
scatter = Scatter()
scatter.add("scatter", v1, v3)
es_1 = EffectScatter()
es_1.add("es_1", v1, v4, symbol='pin', effect_scale=5)
overlap = Overlap()
overlap.add(es)
overlap.add(scatter)
overlap.add(es_1)
overlap.render()
def test_scatter_draw_sexy_black_bra():
scatter = Scatter("散点图示例", width=1000, height=480)
v1, v2 = scatter.draw("../images/cup.png")
scatter.add("Cup", v1, v2, label_color=["#000"])
scatter.render()
def test_page_grid_timeline_overlap():
# Grid
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720, width=1200, title_pos="65%")
bar.add("商家A", CLOTHES, v1, is_stack=True)
bar.add("商家B", CLOTHES, v2, is_stack=True, legend_pos="80%")
line = Line("折线图示例")
line.add(
"最高气温",
WEEK,
[11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
)
line.add(
"最低气温",
WEEK,
[1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
legend_pos="20%",
)
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter("散点图示例", title_top="50%", title_pos="65%")
scatter.add("scatter", v1, v2, legend_top="50%", legend_pos="80%")
es = EffectScatter("动态散点图示例", title_top="50%")
es.add(
"es",
[11, 11, 15, 13, 12, 13, 10],
[1, -2, 2, 5, 3, 2, 0],
effect_scale=6,
legend_top="50%",
legend_pos="20%",
)
grid = Grid()
grid.add(bar, grid_bottom="60%", grid_left="60%")
grid.add(line, grid_bottom="60%", grid_right="60%")
grid.add(scatter, grid_top="60%", grid_left="60%")
grid.add(es, grid_top="60%", grid_right="60%")
# Timeline
bar_1 = Bar("2012 年销量", "数据纯属虚构")
bar_1.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2 = Bar("2013 年销量", "数据纯属虚构")
bar_2.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3 = Bar("2014 年销量", "数据纯属虚构")
bar_3.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4 = Bar("2015 年销量", "数据纯属虚构")
bar_4.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5 = Bar("2016 年销量", "数据纯属虚构", height=720, width=1200)
bar_5.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5.add(
"冬季",
CLOTHES,
[randint(10, 100) for _ in range(6)],
is_legend_show=True,
)
timeline = Timeline(is_auto_play=True, timeline_bottom=0)
timeline.add(bar_1, "2012 年")
timeline.add(bar_2, "2013 年")
timeline.add(bar_3, "2014 年")
timeline.add(bar_4, "2015 年")
timeline.add(bar_5, "2016 年")
# Overlap
attr = ["{}月".format(i) for i in range(1, 13)]
v1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]
v2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]
v3 = [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2]
bar = Bar(height=720, width=1200)
bar.add("蒸发量", attr, v1)
bar.add("降水量", attr, v2, yaxis_formatter=" ml", yaxis_max=250)
line = Line()
line.add("平均温度", attr, v3, yaxis_formatter=" °C")
overlap = Overlap()
overlap.add(bar)
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
page = Page()
page.add(grid)
page.add(timeline)
page.add(overlap)
page.render()
def test_scatter_markline_coords():
scatter = Scatter("散点图示例")
scatter.add("A", v1, v2, mark_line_coords=[[10, 10], [30, 30]])
assert '"coord": [' in scatter._repr_html_()
def test_scatter_visualmap_default():
scatter = Scatter("散点图示例")
scatter.add("A", v1, v2)
scatter.add("B", v1[::-1], v2, is_visualmap=True)
scatter.render()
'legend_top': 'center',
'is_datazoom_show': True,
'datazoom_type': 'both',
'datazoom_range':[0,40],
}
total_num = 0
for i in genre:
total_num += len(genre[i]['rate'])
# print(i)
# print(len(genre[i]['boxoffice']), genre[i]['boxoffice'])
# print(len(genre[i]['rate']), genre[i]['rate'])
# print(len(genre[i]['title']), genre[i]['title'])
# print(len(genre[i]['rate_quantity']), genre[i]['rate_quantity'])
# print('===============================')
scatter.add(i, genre[i]['boxoffice'], genre[i]['rate'], **other_setting, extra_name=genre[i]['title'],
xaxis_name='票房（亿）', yaxis_name='评分', yaxis_name_gap=20,yaxis_min=2, symbol_size=5,
label_formatter='{c}', is_label_emphasis=True, is_toolbox_show=False)# xaxis_name_gap=80,
print('该图电影总数',total_num)
scatter#.render('电影评分-票房.html')
# 上图是 电影豆瓣评分-票房 散点图，用点的颜色表现了不同电影类型，包括了2018年国内上映电影中同时有豆瓣评分和票房数据的291部。
#
# 由于很多类型的电影数量较少，因此将部分类型定为‘其它’。一部电影通常具有多个类型，在分析时，电影类型的确定方法为：按照每一部电影豆瓣上类型的先后顺序，选择顺序最前且不是‘其它’类型的类型；若该电影所有类型都在‘其它’类型中，将此电影归类为其它。
#
# 电影票房较高，超过10亿时，其豆瓣评分都在五分及以上；而电影票房较少的电影（小于1亿）豆瓣评分和票房基本无关。
# In[10]:
count = 0
def test_scatter_draw__hot_red_bra():
scatter = Scatter("散点图示例", width=1000, height=480)
v1, v2 = scatter.draw("../images/cup.png")
scatter.add("Cup", v1, v2)
scatter.render()
def test_grid():
# grid_0
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720, is_grid=True)
bar.add("商家A", attr, v1, is_stack=True, grid_bottom="60%")
bar.add("商家B", attr, v2, is_stack=True, grid_bottom="60%")
line = Line("折线图示例", title_top="50%")
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10], mark_point=["max", "min"], mark_line=["average"])
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0], mark_point=["max", "min"],
mark_line=["average"], legend_top="50%")
bar.grid(line.get_series(), grid_top="60%")
bar.show_config()
bar.render()
# grid_1
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter(width=1200, is_grid=True)
scatter.add("散点图示例", v1, v2, grid_left="60%", legend_pos="70%")
es = EffectScatter()
es.add("动态散点图示例", [11, 11, 15, 13, 12, 13, 10], [1, -2, 2, 5, 3, 2, 0],
effect_scale=6, legend_pos="20%")
scatter.grid(es.get_series(), grid_right="60%")
scatter.show_config()
scatter.render()
# grid_2
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720, width=1200, title_pos="65%", is_grid=True)
bar.add("商家A", attr, v1, is_stack=True, grid_bottom="60%", grid_left="60%")
bar.add("商家B", attr, v2, is_stack=True, grid_bottom="60%", grid_left="60%", legend_pos="80%")
line = Line("折线图示例")
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10], mark_point=["max", "min"], mark_line=["average"])
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0], mark_point=["max", "min"],
mark_line=["average"], legend_pos="20%")
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter("散点图示例", title_top="50%", title_pos="65%")
scatter.add("scatter", v1, v2, legend_top="50%", legend_pos="80%")
es = EffectScatter("动态散点图示例", title_top="50%")
es.add("es", [11, 11, 15, 13, 12, 13, 10], [1, -2, 2, 5, 3, 2, 0], effect_scale=6,
legend_top="50%", legend_pos="20%")
bar.grid(line.get_series(), grid_bottom="60%", grid_right="60%")
bar.grid(scatter.get_series(), grid_top="60%", grid_left="60%")
bar.grid(es.get_series(), grid_top="60%", grid_right="60%")
bar.show_config()
bar.render()
# grid_3
line = Line("折线图示例", width=1200, is_grid=True)
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10], mark_point=["max", "min"],
mark_line=["average"], grid_right="65%")
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0], mark_point=["max", "min"],
mark_line=["average"], legend_pos="20%")
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图示例", title_pos="45%")
pie.add("", attr, v1, radius=[30, 55], legend_pos="65%", legend_orient='vertical')
line.grid(pie.get_series(), grid_left="60%")
line.show_config()
line.render()
# grid_4
line = Line("折线图示例", width=1200, is_grid=True)
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10], mark_point=["max", "min"],
mark_line=["average"], grid_right="60%")
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0], mark_point=["max", "min"],
mark_line=["average"], legend_pos="20%", grid_right="60%")
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
value = [20, 40, 60, 80, 100, 120]
v1 = [[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22]]
kline = Kline("K 线图示例", title_pos="60%")
kline.add("日K", ["2017/7/{}".format(i + 1) for i in range(31)], v1, legend_pos="80%")
line.grid(kline.get_series(), grid_left="55%")
line.show_config()
line.render()
# grid_5
import random
x_axis = ["12a", "1a", "2a", "3a", "4a", "5a", "6a", "7a", "8a", "9a", "10a", "11a",
"12p", "1p", "2p", "3p", "4p", "5p", "6p", "7p", "8p", "9p", "10p", "11p"]
y_aixs = ["Saturday", "Friday", "Thursday", "Wednesday", "Tuesday", "Monday", "Sunday"]
data = [[i, j, random.randint(0, 50)] for i in range(24) for j in range(7)]
heatmap = HeatMap("热力图示例", height=700, is_grid=True)
heatmap.add("热力图直角坐标系", x_axis, y_aixs, data, is_visualmap=True, visual_top="45%",
visual_text_color="#000", visual_orient='horizontal', grid_bottom="60%")
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", title_top="52%")
bar.add("商家A", attr, v1, is_stack=True)
bar.add("商家B", attr, v2, is_stack=True, legend_top="50%")
heatmap.grid(bar.get_series(), grid_top="60%")
heatmap.show_config()
heatmap.render()
def test_scatter():
# scatter_0
v1 = [10, 20, 30, 40, 50, 60]
v2 = [10, 20, 30, 40, 50, 60]
scatter = Scatter("散点图示例")
scatter.add("A", v1, v2)
scatter.add("B", v1[::-1], v2)
scatter.show_config()
scatter.render()
# scatter_0_1
scatter = Scatter("散点图示例")
scatter.add("A", v1, v2)
scatter.add("B", v1[::-1], v2, is_visualmap=True)
scatter.show_config()
scatter.render()
# scatter_0_2
scatter = Scatter("散点图示例")
scatter.add("A", v1, v2)
scatter.add("B", v1[::-1], v2, is_visualmap=True, visual_type='size', visual_range_size=[20, 80])
scatter.show_config()
scatter.render()
# scatter_1
scatter = Scatter("散点图示例")
v1, v2 = scatter.draw("../images/pyecharts-0.png")
scatter.add("pyecharts", v1, v2, is_random=True)
scatter.show_config()
scatter.render()
# scatter_2
scatter = Scatter("散点图示例", width=800, height=480)
v1, v2 = scatter.draw("../images/love.png")
scatter.add("Love", v1, v2)
scatter.render()
# scatter_3
scatter = Scatter("散点图示例", width=1000, height=480)
v1, v2 = scatter.draw("../images/cup.png")
scatter.add("Cup", v1, v2)
scatter.render()
# scatter_4
scatter = Scatter("散点图示例", width=1000, height=480)
v1, v2 = scatter.draw("../images/cup.png")
scatter.add("Cup", v1, v2, label_color=["#000"])
scatter.render()
def test_scatter_draw_pyecharts():
scatter = Scatter("散点图示例")
v1, v2 = scatter.draw("../images/pyecharts-0.png")
scatter.add("pyecharts", v1, v2, is_random=True)
scatter.render()
#散点图适合表现大量样本的多个属性的分布规律。散点图的每个点表示一个样本，每个坐标维度表示一个属性。
from pyecharts import Scatter
import pandas as pd
dfboy = pd.DataFrame()
dfboy['weight'] = [56,67,65,70,57,60,80,85,76,64]
dfboy['height'] = [162,170,168,172,168,172,180,176,178,170]
dfgirl = pd.DataFrame()
dfgirl['weight'] = [50,62,60,70,57,45,62,65,70,56]
dfgirl['height'] = [155,162,165,170,166,158,160,170,172,165]
scatter = Scatter(title = "体格数据",width = 600,height = 420)
scatter.add(name = "boy", x_axis = dfboy['weight'], y_axis = dfboy['height'])
scatter.add(name = "girl", x_axis = dfgirl['weight'], y_axis = dfgirl['height'],
yaxis_min = 130,yaxis_max = 200,xaxis_min = 30,xaxis_max = 100)
scatter.render("result.散点图示范.html")
scatter
#当样本属性维度多于2个时，散点图可以使用点的颜色或大小等方式来表达更多属性维度。下面示范使用点的大小表示第3个维度。
def custom_formatter(params):
return (params.value[3] + ':' +
str(params.value[0]) +','
+str(params.value[1]) + ','
+str(params.value[2]))
df = pd.DataFrame()
def test_scatter_draw_love():
scatter = Scatter("散点图示例", width=800, height=480)
v1, v2 = scatter.draw("../images/love.png")
scatter.add("Love", v1, v2)
scatter.render()
