def plot(self):
self.chart = Scatter(init_opts=opts.InitOpts(
page_title=self.title, width='600px', height='500px'))
# load data
self.chart.add_xaxis(self.data[0][0])
self.chart.add_yaxis(self.yaxis_name, self.data[0][1])
# set options
self.chart.set_global_opts(
title_opts=opts.TitleOpts(title=self.title),
toolbox_opts=opts.ToolboxOpts(is_show=True),
datazoom_opts=opts.DataZoomOpts(is_show=True,
type_='slider',
range_start=0,
range_end=100),
xaxis_opts=opts.AxisOpts(
name=self.xaxis_name,
type_='time',
name_location='end',
name_gap=15,
splitline_opts=opts.SplitLineOpts(is_show=True),
min_=self.min_x,
max_=self.max_x),
yaxis_opts=opts.AxisOpts(
name=self.yaxis_name,
name_location='center',
name_gap=15,
splitline_opts=opts.SplitLineOpts(is_show=True),
max_=self.max_y),
visualmap_opts=opts.VisualMapOpts(max_=self.max_y))
self.chart.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
return self
def scatter(df) -> Scatter:
if df.empty is False:
scatter = (
Scatter()
.add_xaxis(xaxis_data=df['当前月处方量'])
.add_yaxis(
series_name=df.index.tolist(),
y_axis=df['月累计相关病人数'],
symbol_size=10,
label_opts=opts.LabelOpts(is_show=False),
)
)
scatter.set_series_opts()
scatter.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="value", splitline_opts=opts.SplitLineOpts(is_show=True)
),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
tooltip_opts=opts.TooltipOpts(is_show=True),
title_opts=opts.TitleOpts(title="Scatter-基本示例")
)
else:
scatter = (Scatter())
return scatter
def bar_base(x, y, xline, yline):
scatter = Scatter().add_xaxis(x).add_yaxis(
'真实值', y).set_global_opts(title_opts=opts.TitleOpts(title="相关线"))
xline = xline.reshape(-1)
line = Line().add_xaxis(xaxis_data=xline.tolist()).add_yaxis(
series_name="拟合线", y_axis=yline.tolist())
scatter.overlap(line)
return scatter
def scatter_render_air(self):
c = Scatter()
c.add_xaxis(self.x_value)
c.add_yaxis(
series_name=self.y_name,
y_axis=self.inner_data_pair,
label_opts=opts.LabelOpts(
is_show=self.is_show,
formatter=JsCode(
"function(params){return params.value[2] +' : '+ params.value[1];}"
)))
c.set_global_opts(
title_opts=opts.TitleOpts(title=self.title,
subtitle=self.subtitle),
# tooltip_opts=opts.TooltipOpts(
# formatter=JsCode(
# "function (params) {return params.name + ' : ' + params.value[2];}"
# )
# ),
visualmap_opts=opts.VisualMapOpts(type_="color",
max_=150,
min_=20,
dimension=1),
legend_opts=opts.LegendOpts(pos_left=self.pos_left,
legend_icon=self.legend_icon))
return c
def scatter():
x_data = list(score['人均价格'])
y_data = list(score['综合评分'])
c = (Scatter(init_opts=opts.InitOpts(
width="800px", height="500px", theme=ThemeType.PURPLE_PASSION)).
add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="",
y_axis=y_data,
symbol_size=20,
label_opts=opts.LabelOpts(is_show=False),
).set_series_opts().set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="value",
splitline_opts=opts.SplitLineOpts(is_show=True),
name='人均价格'),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
name='评分'),
tooltip_opts=opts.TooltipOpts(is_show=False),
))
c.render('人均消费与综合评分散点.html')
return c
def scatter_value_xaxis() -> Scatter:
c = (Scatter().add_xaxis(Faker.values()).add_yaxis(
"商家A", Faker.values()).set_global_opts(
title_opts=opts.TitleOpts(title="Scatter-X 数值轴"),
xaxis_opts=opts.AxisOpts(type_="value"),
tooltip_opts=opts.TooltipOpts(formatter="{c}")))
return c
def test_bar_base():
c = (Scatter().add_xaxis(["A", "B",
"C"]).add_yaxis("series0", [1, 2, 4]).add_yaxis(
"series1", [2, 3, 6]))
eq_(c.theme, "white")
eq_(c.renderer, "canvas")
c.render()
def line_base() -> Scatter:
a = (Line().add_xaxis([
"2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008",
"2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017",
"2018"
]).add_yaxis(
"Adults_ages_15_newly_infected_with_HIV",
Adults_ages_15_newly_infected_with_HIV).add_yaxis(
"Children_ages_0_14_newly_infected_with_HIV",
Children_ages_0_14_newly_infected_with_HIV).set_global_opts(
title_opts=opts.TitleOpts(title="HIV新增感染人数")))
c = (Scatter().add_xaxis([
"2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008",
"2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017",
"2018"
]).add_yaxis("Children_0_14_living_with_HIV",
Children_0_14_living_with_HIV).add_yaxis(
"Antiretroviral_therapy_coverage",
Antiretroviral_therapy_coverage).set_global_opts(
title_opts=opts.TitleOpts(title=""),
visualmap_opts=opts.VisualMapOpts(type_="size",
max_=90,
min_=2),
))
return render_template(
"show_pyecharts.html",
bar_data=a.dump_options(),
bar_data2=c.dump_options(),
desc1=
'小结:2000年-2018年，0-14岁群体的艾滋病人数总体呈减少趋势,2000年-2018年，抗逆转录病毒治疗覆盖率正逐年提高。,因此，笔者初步判断，抗病毒治疗对治疗艾滋病具有极大的作用',
desc2=
'小结:从图表中可以看出,在2000年到2018年间，0-14岁的群体中，新增感染HIV的人数是在逐年减少的, 在2000年到2018年间，15岁以上的群体中，新增感染HIV的人数也是在逐年减少的'
)
def scatter_render(self, dic, htmlname):
logger.info(dic)
df = pd.DataFrame(dic)
df.sort_values("文物编号", inplace=True, ascending=True) # 按年龄对数据做升序排序
c = (
Scatter().add_xaxis(df.文物编号.values.tolist()).add_yaxis(
"识别准确率",
df[[
"准确率",
"文物名称",
]].values.tolist(), # 传入信用分与姓名组合，方便js回调函数显示标签
label_opts=opts.LabelOpts(formatter=JsCode(
"function(params){return params.value[2];}" # 通过定义JavaScript回调函数自定义标签
))).set_global_opts(
title_opts=opts.TitleOpts(title="识别准确率分布图"),
xaxis_opts=opts.AxisOpts(
name='文物编号',
type_="value", # x轴数据类型是连续型的
min_=0 # x轴范围最小为20
),
yaxis_opts=opts.AxisOpts(
name='准确率值',
min_=0 # y轴范围最小为700
)))
# xaxis_opts = opts.AxisOpts(name='AC(刷题数)', type_='value', min_=20), # x轴从20开始，原点不为0
# yaxis_opts = opts.AxisOpts(name='ACB(能力值)', min_=100), # y轴起始点的值
c.render('./static/html/%s' % htmlname)
return htmlname
def scatter_base(value, label, title, subtitle):
c = (Scatter()
.add_xaxis(list(range(len(value.numpy().reshape(-1)))))
.add_yaxis(label, value.numpy().reshape(-1).tolist(), symbol_size=2,)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(title_opts=opts.TitleOpts(title=title, subtitle=subtitle)))
return c
def pyechart_Scatter_plot(self, filedata, para):
from pyecharts.charts import Scatter
file_name = '散点图.html'
path_plotly = self.path_dir_plotly_html + os.sep + file_name # 文件路径，前面是文件夹后面是文件名
costumeTheme = self.themedict[para['theme']]
# -----------------------------------------------------------------------
Scatter = (Scatter(init_opts=opts.InitOpts(
theme=costumeTheme, width=para['width'], height=para['height'])))
chart = Scatter
df = pd.read_excel(filedata, sheet_name='Sheet1')
columns = df.columns.values.tolist()
xlabel = columns[0]
xdata = df[xlabel].values.tolist()
chart.add_xaxis(xaxis_data=xdata)
for i in range(1, len(columns)):
ylabel = columns[i]
ydata = df[ylabel].values.tolist()
chart.add_yaxis(series_name=ylabel, y_axis=ydata)
if para['x2y']:
chart.reversal_axis()
self.Common_code(chart, path_plotly, para)
print('成功绘制散点图')
return path_plotly # 返回该HTML文件路径
def grid_horizontal():
scatter = (
Scatter()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values())
.add_yaxis("商家B", Faker.values())
.set_global_opts(
toolbox_opts=opts.ToolboxOpts(is_show=True),
title_opts=opts.TitleOpts(title="Grid-Scatter"),
legend_opts=opts.LegendOpts(pos_left="20%"),
)
)
line = (
Line()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values())
.add_yaxis("商家B", Faker.values())
.set_global_opts(
toolbox_opts=opts.ToolboxOpts(is_show=True),
title_opts=opts.TitleOpts(title="Grid-Line", pos_right="5%"),
legend_opts=opts.LegendOpts(pos_right="20%"),
)
)
grid = (
Grid()
.add(scatter, grid_opts=opts.GridOpts(pos_left="55%"))
.add(line, grid_opts=opts.GridOpts(pos_right="55%")))
return grid
def score_vs_comment_num(movies):
scores = [m.score for m in movies[:10]]
comment_nums = [m.comment_num for m in movies[:10]]
c = (Scatter().add_xaxis(scores).add_yaxis(
"comment_nums", comment_nums).set_global_opts(
title_opts=options.TitleOpts(title="Score VS Comment num")))
c.render('charts/scatter.html')
def dong_scatter(data, info='', path0=''):
from pyecharts import options as opts
from pyecharts.commons.utils import JsCode
from pyecharts.charts import Scatter, Timeline
# endlist = ['canshu', 'celue_name', '预测_s_Time', '未来end_3', '最终预测值1', 'hg最终预测值1']
data['aim'] = data['canshu'] + data['celue_name']
data.rename(columns={
'hg最终预测值1': '预测值',
'未来end_3': '未来=月终收益',
'预测_s_Time': 's_Time'
},
inplace=True)
print(data.columns)
print(data.tail())
title = '动态图'
df = data
# data['预测值'] = data['预测值'].apply(lambda x: int(x))
min_pre = min(data['预测值'].values.tolist())
max_pre = max(data['预测值'].values.tolist())
# df['未来=月终收益']=df['未来=月终收益'].apply(lambda x: int(x))
# df['预测值']=df['预测值'].apply(lambda x: int(x))
df['s_Time'] = pd.to_datetime(
df['s_Time']) #.apply(lambda x:x.strftime(format="%Y-%m-%d"))
df.sort_values(by=['s_Time'], ascending=True, inplace=True)
tl = Timeline()
timelist = list(set(df['s_Time'].tolist()))
print(list(set(df['s_Time'].tolist())))
sorted(timelist)
# df_date = [time.strftime('%Y-%m-%d',time.localtime(i/1000000000) ) for i in timelist]
# print(df_date)
for k, i in enumerate(sorted(timelist)):
# print(k,i)
xdata = df.loc[df['s_Time'] == i, '预测值'].values.tolist()
ydata = df.loc[df['s_Time'] == i, ['未来=月终收益', '预测值']].values.tolist()
# print(ydata)
Scatter0 = (Scatter().add_xaxis(xdata).add_yaxis(
'未来=月终收益', ydata, label_opts=opts.LabelOpts(is_show=False)
).set_series_opts().set_global_opts(
xaxis_opts=opts.AxisOpts(
name='预测值：',
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(
name='真实值：',
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True)),
title_opts=opts.TitleOpts(title=f"{title}==:{i}月份的数据"),
tooltip_opts=opts.TooltipOpts(formatter=JsCode(
"function (params) { return '真实：'+params.value[1] +' == 预测：'+ params.value[2];}"
)),
visualmap_opts=opts.VisualMapOpts(min_=min_pre, max_=max_pre),
))
tl.add(Scatter0, "{}月".format(i))
tl.render(path0 + f"{info}.html")
print(path0 + f"{info}.html")
def scatter_muti_diemnsion_data():
c = (
Scatter()
.add_xaxis(Faker.choose())
.add_yaxis(
"商家A",
[list(z) for z in zip(Faker.values(), Faker.choose())],
label_opts=opts.LabelOpts(
formatter=JsCode(
"function (params) {return params.value[1] +' : '+ params.value[2];}"
)
),
)
.set_global_opts(
toolbox_opts=opts.ToolboxOpts(is_show=True),
tooltip_opts=opts.TooltipOpts(
formatter=JsCode(
"function (params) {return params.name +' : ' +params.value[2];}"
)
),
visualmap_opts=opts.VisualMapOpts(
type_="color", max_=150, min_=20, dimension=1
),
)
)
return c
def scatter_visualmap_color() -> Scatter:
c = (Scatter().add_xaxis(Faker.choose()).add_yaxis(
"商家A", Faker.values()).set_global_opts(
title_opts=opts.TitleOpts(title="Scatter-VisualMap(Color)"),
visualmap_opts=opts.VisualMapOpts(max_=150),
))
return c
def scatter_debug_diff() -> Scatter:
f = open('case_data.json', encoding='utf-8')
res = f.read()
data = json.loads(res)
xy_data = []
tmp = []
x_data = []
y_data = []
# x轴为难度，y轴为debug时间
for k in data.keys():
if data[k]["case_type"] == "字符串":
tmp = [data[k]["final_score"], data[k]["average_interval(min)"]]
xy_data.append(tmp)
xy_data = sorted(xy_data)
for tmp in xy_data:
x_data.append(tmp[0])
y_data.append(tmp[1])
# y_data.append(round(tmp[1]/10,2))
scatter3 = (
Scatter()
.add_xaxis(x_data)
.add_yaxis("debug-time(min)", y_data)
.set_global_opts(
title_opts=opts.TitleOpts(title='scatter debug-diff'),
visualmap_opts=opts.VisualMapOpts(max_=600)
)
)
return scatter3
def draw_uniform_points():
x, y = [i for i in range(100)], [round(uniform(0, 10), 2)
for _ in range(100)]
print(y)
c = (Scatter()
.add_xaxis(x)
.add_yaxis('y', y))
c.render()
def scatter_base() -> Scatter:
c = (
Scatter()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values())
.set_global_opts(title_opts=opts.TitleOpts(title="Scatter-基本示例"))
)
return c
def scatter_visualmap_size() -> Scatter:
c = (Scatter().add_xaxis(Faker.choose()).add_yaxis(
"商家A",
Faker.values()).add_yaxis("商家B", Faker.values()).set_global_opts(
title_opts=opts.TitleOpts(title="Scatter-VisualMap(Size)"),
visualmap_opts=opts.VisualMapOpts(type_="size", max_=150, min_=20),
))
return c
def test_scatter_base(fake_writer):
c = (Scatter().add_xaxis(["A", "B",
"C"]).add_yaxis("series0", [1, 2, 4]).add_yaxis(
"series1", [2, 3, 6]))
c.render()
_, content = fake_writer.call_args[0]
assert_equal(c.theme, "white")
assert_equal(c.renderer, "canvas")
def scatter_spliteline(self, key, value, city, title) -> Scatter:
c = (Scatter().add_xaxis(key).add_yaxis(city, value).set_global_opts(
title_opts=opts.TitleOpts(title=title),
xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)),
yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)),
))
return c
def scatter_rating_likes() -> Scatter:
c = (Scatter().add_xaxis(range(1, 6)).add_yaxis(
"点赞数", likes_new).set_global_opts(
title_opts=opts.TitleOpts(title="评分-点赞图"),
visualmap_opts=opts.VisualMapOpts(type_="size",
max_=100000,
min_=20000),
))
return c
def pycharts_visit():
c = (
Scatter()
.add_yaxis("负面情绪")
.add_xaxis("正面情绪")
.render("散点图.html")
.set_global_opts(title_opts=opts.TitleOpts(title="Scatter-基本示例"))
)
return c
def scatter_spliteline() -> Scatter:
c = (Scatter().add_xaxis(Faker.choose()).add_yaxis(
"商家A", Faker.values()).set_global_opts(
title_opts=opts.TitleOpts(title="Scatter-显示分割线"),
xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)),
yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)),
))
return c
def scatter_base(choose, values, country) -> Scatter:
c = (
Scatter().add_xaxis(choose).add_yaxis(
"%s历年GDP" % country, values).set_global_opts(
title_opts=opts.TitleOpts(title=""),
# datazoom_opts=opts.DataZoomOpts(),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
formatter="{value} /万亿"))).set_series_opts(
label_opts=opts.LabelOpts(is_show=False)))
return c
def test_bar_base():
c = (
Scatter()
.add_xaxis(["A", "B", "C"])
.add_yaxis("series0", [1, 2, 4])
.add_yaxis("series1", [2, 3, 6])
)
assert c.theme == "white"
assert c.renderer == "canvas"
c.render("render.html")
def overlap_line_scatter() -> Line:
x = Faker.choose()
line = (Line().add_xaxis(x).add_yaxis("商家A", Faker.values()).add_yaxis(
"商家B", Faker.values()).set_global_opts(title_opts=opts.TitleOpts(
title="Overlap-line+scatter")))
scatter = (Scatter().add_xaxis(x).add_yaxis("商家A",
Faker.values()).add_yaxis(
"商家B", Faker.values()))
line.overlap(scatter)
return line
def draw_factor_eval(df: pd.DataFrame):
cols = df.columns.tolist()
cols.remove('return_close')
charts = []
for i in range(len(cols)):
dataset_ = [
list(z)
for z in zip(df[cols[i]].tolist(), df[cols[i]].shift(-1).tolist())
]
chart = (Scatter().add_dataset(
dimensions=["factor", "next_factor"],
source=dataset_,
).add_yaxis(
series_name=cols[i],
y_axis=[],
symbol_size=2.5,
xaxis_index=0,
yaxis_index=0,
encode={
"x": "factor",
"y": "next_factor",
"tooltip": [0, 1, 2, 3, 4]
},
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(xaxis_opts=opts.AxisOpts(
type_="value",
grid_index=0,
name="factor",
axislabel_opts=opts.LabelOpts(rotate=50, interval=0),
is_scale=True,
),
yaxis_opts=opts.AxisOpts(type_="value",
grid_index=0,
name="next_factor",
is_scale=True),
title_opts=opts.TitleOpts(title="Factor Eval"),
datazoom_opts=[
opts.DataZoomOpts(range_start=95, range_end=100),
opts.DataZoomOpts(is_show=False,
type_='inside',
range_start=95,
range_end=100)
],
toolbox_opts=opts.ToolboxOpts(),
tooltip_opts=opts.TooltipOpts(
is_show=True,
trigger='axis',
axis_pointer_type='cross',
)))
chart.width = "600PX"
chart.height = "600PX"
charts.append(chart)
return charts
def DIC_scatter() -> Scatter:
c = (Scatter().add_xaxis(DQ).add_yaxis(
"2018年人均可支配收入（十元）",
list(DIPC["2018年"])).add_yaxis("博物馆机构数", BQ2018).set_global_opts(
title_opts=opts.TitleOpts(title="2018年全国各地可支配收入展示"),
datazoom_opts=[
opts.DataZoomOpts(),
opts.DataZoomOpts(orient="vertical"),
opts.DataZoomOpts(type_="inside")
],
))
return c
