def bar_histogram_color() -> Bar:
x = Faker.dogs + Faker.animal
xlen = len(x)
y = []
for idx, item in enumerate(x):
if idx <= xlen / 2:
y.append(
opts.BarItem(
name=item,
value=(idx + 1) * 10,
itemstyle_opts=opts.ItemStyleOpts(color="#749f83"),
))
else:
y.append(
opts.BarItem(
name=item,
value=(xlen + 1 - idx) * 10,
itemstyle_opts=opts.ItemStyleOpts(color="#d48265"),
))
c = (Bar().add_xaxis(x).add_yaxis(
"series0", y, category_gap=0,
color=Faker.rand_color()).set_global_opts(title_opts=opts.TitleOpts(
title="Bar-直方图（颜色区分）")))
return c
def tab_fun():
line = Line().add_xaxis(Faker.choose()).add_yaxis(
series_name="xx1",
y_axis=Faker.values(1, 100),
itemstyle_opts=opts.ItemStyleOpts(color=Faker.rand_color())).add_yaxis(
series_name="xx2",
y_axis=Faker.values(1, 100),
itemstyle_opts=opts.ItemStyleOpts(
color=Faker.rand_color())).set_global_opts(
title_opts=opts.TitleOpts(title="主标题",
subtitle="副标题",
pos_left="10%"),
legend_opts=opts.LegendOpts(pos_left="40%"),
)
bar = Bar().add_xaxis(Faker.choose()).add_yaxis(
series_name="柱1",
y_axis=Faker.values(1, 100),
itemstyle_opts=opts.ItemStyleOpts(color=Faker.rand_color())).add_yaxis(
series_name="柱2",
y_axis=Faker.values(1, 100),
itemstyle_opts=opts.ItemStyleOpts(
color=Faker.rand_color())).set_global_opts(
title_opts=opts.TitleOpts(title="主标题",
subtitle="副标题",
pos_left="10%"),
legend_opts=opts.LegendOpts(pos_left="40%"),
)
Tab().add(chart=line, tab_name="选项卡一").add(
chart=bar, tab_name="选项卡二").render(path="D:/temp/pyecharts_tab.html")
def get_dma_chart(xdatas, ddd, ama):
line = Line()
line.add_xaxis(xaxis_data=xdatas)
line.add_yaxis(
series_name='ddd',
y_axis=ddd,
symbol="none",
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.9),
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=Chart.colors[0]),
)
line.add_yaxis(
series_name='ama',
y_axis=ama,
symbol="none",
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.9),
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=Chart.colors[1]),
)
line.set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
return line
def get_turnover_chart(self,df):
xdatas = df.trade_date.to_numpy().tolist()
turnovers = df.turnover_rate.to_numpy().tolist()
volume_ratios = df.volume_ratio.to_numpy().tolist()
line = Line()
line.add_xaxis(xaxis_data=xdatas)
line.add_yaxis(
series_name='turnover_rate',
y_axis=turnovers,
symbol="none",
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.9),
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=Chart.colors[0]),
)
line.add_yaxis(
series_name='volume_ratio',
y_axis=volume_ratios,
symbol="none",
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.9),
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=Chart.colors[1]),
)
line.set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
return line
def get_tanqian(self, tanqian):
xdatas = tanqian['trade_date'].to_numpy().tolist()
line = Line()
line.add_xaxis(xaxis_data=xdatas)
line.add_yaxis(
series_name=tanqian['up']['name'],
y_axis=tanqian['up']['data'].to_numpy().round(2).tolist(),
symbol="none",
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.9),
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=Chart.colors[20]),
)
line.add_yaxis(
series_name=tanqian['down']["name"],
y_axis=tanqian['down']['data'].to_numpy().round(2).tolist(),
symbol="none",
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.9),
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=Chart.colors[22]),
)
line.set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
return line
def bar_custom_bar_color() -> Bar:
color_function = """
function (params) {
if (params.value > 0 && params.value < 50) {
return 'red';
} else if (params.value > 50 && params.value < 100) {
return 'blue';
}
return 'green';
}
"""
c = (Bar().add_xaxis(Faker.choose()).add_yaxis(
"商家A",
Faker.values(),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)),
).add_yaxis(
"商家B",
Faker.values(),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)),
).add_yaxis(
"商家C",
Faker.values(),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)),
).set_global_opts(title_opts=opts.TitleOpts(title="Bar-自定义柱状颜色")))
return c
def draw_tendency(month, day):
dates = ['1-%d' % i for i in range(16, day + 1)]
v0 = [4, 17, 59, 78, 92, 149, 131, 259, 444, 688, 769, 1771, 1459, 1576]
v1 = [4, 17, 59, 77, 72, 105, 69, 105, 180, 323, 371, 1291, 840, 1032]
c = (Line().add_xaxis(dates).add_yaxis(
"全国新增确诊病例",
v0,
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(width=4, color='#B44038'),
itemstyle_opts=opts.ItemStyleOpts(
color='#B44038', border_color="#B44038",
border_width=5)).add_yaxis(
"湖北新增确诊病例",
v1,
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(width=2, color='6FA0A7'),
label_opts=opts.LabelOpts(position='bottom'),
itemstyle_opts=opts.ItemStyleOpts(
color='#6FA0A7', border_color="#6FA0A7",
border_width=3)).set_global_opts(
title_opts=opts.TitleOpts(title=""),
yaxis_opts=opts.AxisOpts(
type_="log",
name="y",
splitline_opts=opts.SplitLineOpts(is_show=True),
is_scale=True,
axisline_opts=opts.AxisLineOpts(is_show=False))))
c.render('charts/%d%d-新增病例趋势图.html' % (month, day))
return c
def bar_histogram():
x = Faker.dogs + Faker.animal
xlen = len(x)
y = []
for idx, item in enumerate(x):
if idx <= xlen / 2:
bar_item = opts.BarItem(
name=item,
value=(idx + 1) * 10,
itemstyle_opts=opts.ItemStyleOpts(color="#749f83")
)
else:
bar_item = opts.BarItem(
name=item,
value=(xlen + 1 - idx) * 10,
itemstyle_opts=opts.ItemStyleOpts(color="#d48265"),
)
y.append(bar_item)
obj_bar = Bar()
obj_bar.add_xaxis(x)
#直方图就是紧挨着的系列柱category_gap=0调节系列柱之间距离，
# color=Faker.rand_color()因为上面设置了柱的颜色，这里只对图例legend的颜色起作用
obj_bar.add_yaxis("series0", y, category_gap=0,color=Faker.rand_color())
obj_bar.set_global_opts(
title_opts=opts.TitleOpts(title="Bar-直方图（颜色区分）", subtitle="副标题")
)
return obj_bar
def draw_tendency(month, day):
dates = ['1-%d' % i for i in range(22, 31 + 1)]
dates.extend(['2-%02d' % i for i in range(1, day)])
v0 = [
131, 259, 444, 688, 769, 1771, 1459, 1737, 1982, 2102, 2590, 2829,
3235, 3887, 3143, 3399, 2656, 3062, 2478, 2015, 15152, 5090, 2641,
2009, 2051, 1886, 1749, 385
]
v1 = [
69, 105, 180, 323, 371, 1291, 840, 1032, 1220, 1347, 1921, 2103, 2345,
3156, 2447, 2841, 2147, 2618, 2097, 1638, 14840, 4823, 2420, 1843,
1933, 1807, 1693, 349
]
v2 = [v0[i] - v1[i] for i in range(len(v0))]
print(v2)
c = (Line(init_opts=opts.InitOpts(
width='1000px', height='500px')).add_xaxis(dates).add_yaxis(
"全国新增确诊病例",
v0,
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(width=4, color='#B44038'),
itemstyle_opts=opts.ItemStyleOpts(
color='#B44038', border_color="#B44038",
border_width=5)).add_yaxis(
"湖北新增确诊病例",
v1,
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(width=2,
color='#4E87ED'),
label_opts=opts.LabelOpts(position='bottom'),
itemstyle_opts=opts.ItemStyleOpts(
color='#4E87ED',
border_color="#4E87ED",
border_width=3)).add_yaxis(
"其他省份新增病例",
v2,
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(width=2,
color='#F1A846'),
label_opts=opts.LabelOpts(position='bottom'),
itemstyle_opts=opts.ItemStyleOpts(
color='#F1A846',
border_color="#F1A846",
border_width=3)).set_global_opts(
title_opts=opts.TitleOpts(title=""),
yaxis_opts=opts.AxisOpts(
max_=16000,
min_=10,
type_="log",
name="y",
splitline_opts=opts.SplitLineOpts(
is_show=True),
is_scale=True,
axisline_opts=opts.AxisLineOpts(
is_show=False))))
root = 'html-charts/%d%d' % (month, day)
create_dir(root)
c.render('%s/新增病例趋势图.html' % root)
def __draw_zs_area(_ka):
lines = []
scatters = []
for _zs in _ka.zs_list:
x_start = _zs['start_point']['dt']
if _zs['zs_finished']:# 中枢完成
x_end = _zs['end_point']['dt']
chart_b = EffectScatter()
if 'buy3' in _zs:
chart_b.add_xaxis([_zs['buy3']['dt']])
chart_b.add_yaxis(series_name=_ka.freq if aggregation else "B", y_axis=[_zs['buy3']['xd']], is_selected=False, symbol="circle", symbol_size=8,
itemstyle_opts=opts.ItemStyleOpts(color="red",))
chart_b.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
scatters.append(chart_b)
elif 'sell3' in _zs:
chart_b.add_xaxis([_zs['sell3']['dt']])
chart_b.add_yaxis(series_name=_ka.freq if aggregation else "S", y_axis=[_zs['sell3']['xd']], is_selected=False, symbol="circle", symbol_size=8,
itemstyle_opts=opts.ItemStyleOpts(color="green",))
chart_b.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
scatters.append(chart_b)
elif len(_zs['points'])>=5:# 中枢成立但未完成，有3笔或段以上
x_end = _zs['points'][-1]['dt']
else: # 中枢未完成，且未确定
continue
ZD = _zs['ZD']
ZG = _zs['ZG']
area_data=[[ {'xAxis': x_start, 'yAxis': ZD, 'value': ZD }, {'xAxis': x_end, 'yAxis': ZG, 'value': ZG }]]
line = (Line()
.add_xaxis([x_start, x_end])
.add_yaxis(series_name=_ka.freq if aggregation else "ZS", y_axis=[ZD, ZG], symbol='none'
"""
, markpoint_opts = opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="ZG"),
opts.MarkPointItem(type_="min", name="ZD"),
]
)
"""
, markline_opts=opts.MarkLineOpts(
label_opts=opts.LabelOpts(
position="middle", color="blue", font_size=15,
),
linestyle_opts=opts.LineStyleOpts(type_="dashed"),
data=area_data,
symbol=["none", "none"],
)
)
.set_series_opts(
markarea_opts=opts.MarkAreaOpts(data=area_data, itemstyle_opts=opts.ItemStyleOpts(color="#dcdcdc",opacity=0.1))
))
lines.append(line)
return lines, scatters
def generate_3d_map(self, provinces_data):
map_3d = Map3D(init_opts=opts.InitOpts(width="1280px", height="720px"))
map_3d.add_schema(
itemstyle_opts=opts.ItemStyleOpts(
color="#172eb2",
opacity=1,
border_width=0.8,
border_color="rgb(76, 96, 255)",
),
map3d_label=opts.Map3DLabelOpts(
is_show=False,
formatter=JsCode("function(data){return data.name + "
" + data.value[2];}"),
),
emphasis_label_opts=opts.LabelOpts(
is_show=False,
color="#fff",
font_size=10,
background_color="rgba(242, 149, 128, 0)",
),
light_opts=opts.Map3DLightOpts(
main_color="#fff",
main_intensity=1.2,
main_shadow_quality="high",
is_main_shadow=True,
main_beta=10,
ambient_intensity=0.3,
),
is_show_ground=True,
ground_color="#FFF",
)
map_3d_data = []
from .data import provinces_coordinates
for item in provinces_data.items():
province_data = (item[0],
[*provinces_coordinates[item[0]], item[1]])
map_3d_data.append(province_data)
map_3d.add(
series_name="Data",
data_pair=map_3d_data,
type_=ChartType.BAR3D,
bar_size=1,
itemstyle_opts=opts.ItemStyleOpts(color="rgb(173, 212, 217)"),
shading="realistic",
label_opts=opts.LabelOpts(
is_show=False,
formatter=JsCode(
"function(data){return data.name + ' ' + data.value[2];}"),
),
)
map_3d.set_global_opts(title_opts=opts.TitleOpts(title="3D Map"))
map_3d.render("map3d_with_bar3d.html")
def page_Sankey(nodes_links, title_name):
# 使用Sankey()函数创建对象赋值给sankey
# 使用InitOpts()，传入参数theme="dark",bg_color="#253441"，赋值给init_opts
sankey = Sankey(init_opts=opts.InitOpts(theme="dark", bg_color="#253441"))
# 为创建的实例增加名字（sankey）、传入实验组节点和信息流列表
sankey.add(
"sankey",
nodes=nodes_links[0],
links=nodes_links[1],
# 使用LineStyleOpts()，传入参数opacity=0.3, curve=0.5, color="source"，赋值给linestyle_opt
linestyle_opt=opts.LineStyleOpts(opacity=0.3,
curve=0.5,
color="source"),
# 使用LabelOpts()，传入参数position="right",color="white",font_size=10，赋值给label_opts
label_opts=opts.LabelOpts(position="right",
color="white",
font_size=10),
# 使用SankeyLevelsOpts()，传入参数depth为0-5、itemstyle_opts，赋值给列表levels
levels=[
opts.SankeyLevelsOpts(
depth=0,
# 使用ItemStyleOpts()，传入参数color="#FF8947",border_color="#FF8947"
itemstyle_opts=opts.ItemStyleOpts(color="#FF8947",
border_color="#FF8947")),
opts.SankeyLevelsOpts(
depth=1,
# 使用ItemStyleOpts()，传入参数color="#96D15C",border_color="#96D15C"
itemstyle_opts=opts.ItemStyleOpts(color="#96D15C",
border_color="#96D15C")),
opts.SankeyLevelsOpts(
depth=2,
# 使用ItemStyleOpts()，传入参数color="#479BED",border_color="#479BED"
itemstyle_opts=opts.ItemStyleOpts(color="#479BED",
border_color="#479BED")),
opts.SankeyLevelsOpts(
depth=3,
# 使用ItemStyleOpts()，传入参数color="#55C4CA",border_color="#55C4CA"
itemstyle_opts=opts.ItemStyleOpts(color="#55C4CA",
border_color="#55C4CA")),
opts.SankeyLevelsOpts(
depth=4,
# 使用ItemStyleOpts()，传入参数color="#E7BF4F",border_color="#E7BF4F"
itemstyle_opts=opts.ItemStyleOpts(color="#E7BF4F",
border_color="#E7BF4F"))
])
# 使用TitleOpts()，传入参数title，赋值给title_opts
# 使用LegendOpts()，传入参数is_show=False，赋值给legend_opts
# 调用set_global_opts()
sankey.set_global_opts(title_opts=opts.TitleOpts(title=title_name),
legend_opts=opts.LegendOpts(is_show=False))
# 使用return返回sankey
return sankey
def buy_sell_line(self, tradelist):
l = Line(init_opts=opts.InitOpts(width=self.width, height=self.height))
l.set_global_opts(datazoom_opts=opts.DataZoomOpts(type_="inside"))
x = self.time
y = [None for i in range(len(x))]
l.add_xaxis(x)
l1 = Line(init_opts=opts.InitOpts(width=self.width, height=self.height))
l2 = Line(init_opts=opts.InitOpts(width=self.width, height=self.height))
l3 = Line(init_opts=opts.InitOpts(width=self.width, height=self.height))
l4 = Line(init_opts=opts.InitOpts(width=self.width, height=self.height))
l1.set_global_opts(datazoom_opts=opts.DataZoomOpts(type_="inside"))
l2.set_global_opts(datazoom_opts=opts.DataZoomOpts(type_="inside"))
l3.set_global_opts(datazoom_opts=opts.DataZoomOpts(type_="inside"))
l4.set_global_opts(datazoom_opts=opts.DataZoomOpts(type_="inside"))
for d in tradelist:
# 多头
if d[6] == 1:
# 多头盈利
if d[7] > 0:
l1.add_xaxis([d[0], d[3]])
l1.add_yaxis("long_win", [d[1], d[4]],
is_connect_nones=True,
linestyle_opts=opts.LineStyleOpts(color="#FFD700", width=5),
itemstyle_opts=opts.ItemStyleOpts(color="#FFD700", opacity=0.6))
# 多头亏损
else:
l2.add_xaxis([d[0], d[3]])
l2.add_yaxis("long_loss", [d[1], d[4]],
is_connect_nones=True,
linestyle_opts=opts.LineStyleOpts(color="#0088F0", width=3),
itemstyle_opts=opts.ItemStyleOpts(color="#0088F0", opacity=0.6))
# 空头
elif d[6] == -1:
# 空头盈利
if d[7] > 0:
l3.add_xaxis([d[0], d[3]])
l3.add_yaxis("short_win", [d[1], d[4]],
is_connect_nones=True,
linestyle_opts=opts.LineStyleOpts(color="#80008f", width=5),
itemstyle_opts=opts.ItemStyleOpts(color="#80008f", opacity=0.6))
# 空头亏损
else:
l4.add_xaxis([d[0], d[3]])
l4.add_yaxis("short_loss", [d[1], d[4]],
is_connect_nones=True,
linestyle_opts=opts.LineStyleOpts(color="#00FA9A", width=3),
itemstyle_opts=opts.ItemStyleOpts(color="#00FA9A", opacity=0.6))
l.overlap(l1)
l.overlap(l2)
l.overlap(l3)
l.overlap(l4)
return l
def createDiffKlines(leftDf, rightDf, leftName, rightName):
kline1_data = [[i[1], i[2], i[3], i[4]] for i in leftDf.to_numpy()]
kline2_data = [[i[1], i[2], i[3], i[4]] for i in rightDf.to_numpy()]
kline1 = (
Kline()
.add_xaxis(leftDf.iloc[:, 0].tolist())
.add_yaxis(
series_name=leftName,
y_axis=kline1_data,
yaxis_index=0,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000", color0="#00da3c"),
)
.extend_axis(yaxis=opts.AxisOpts(position='right'))
.set_global_opts(
title_opts=opts.TitleOpts(title="%s -- %s" % (leftName, rightName)),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
xaxis_opts=opts.AxisOpts(is_scale=True),
datazoom_opts=[
opts.DataZoomOpts(
type_="inside",
range_start=int((len(leftDf)-60)/len(leftDf)*100),
range_end=100,
),
opts.DataZoomOpts(
type_="slider",
range_start=int((len(leftDf)-60)/len(leftDf)*100),
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(trigger='axis', axis_pointer_type='cross'),
)
)
kline2 = (
Kline()
.add_xaxis(rightDf.iloc[:, 0].tolist())
.add_yaxis(
series_name=rightName,
y_axis=kline2_data,
yaxis_index=1,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000", color0="#00da3c"),
)
)
kline1.overlap(kline2)
return kline1
def test_map3d_schema(fake_writer):
c = (Map3D().add_schema(
itemstyle_opts=opts.ItemStyleOpts(),
map3d_label=opts.Map3DLabelOpts(),
light_opts=opts.Map3DLightOpts(),
view_control_opts=opts.Map3DViewControlOpts(),
post_effect_opts=opts.Map3DPostEffectOpts(),
realistic_material_opts=opts.Map3DRealisticMaterialOpts(),
lambert_material_opts=opts.Map3DLambertMaterialOpts(),
color_material_opts=opts.Map3DColorMaterialOpts(),
).add(
series_name="商家A",
data_pair=[list(z) for z in zip(Faker.provinces, Faker.values())],
maptype="china",
type_=ChartType.LINES3D,
effect=opts.Lines3DEffectOpts(),
))
c.render()
_, content = fake_writer.call_args[0]
assert_in("itemStyle", content)
assert_in("label", content)
assert_in("light", content)
assert_in("viewControl", content)
assert_in("postEffect", content)
assert_in("realisticMaterial", content)
assert_in("lambertMaterial", content)
assert_in("colorMaterial", content)
def draw_map():
"""
绘制世界地图
遇到一个很神奇的问题：
两个列表必须写死数据地图才会渲染数据，如果数据是从方法中获得，则地图不渲染数据
:return:
"""
# 修复注释中的问题，原因是 confirmed_count 中的 int 是 numpy 的 int ，需转化为 python 中的 int
# 感谢公众号的 @李康伟 同学提出
countrys_names, confirmed_count = read_csv()
confirmed_count_list = []
for item in confirmed_count:
confirmed_count_list.append(int(item))
# countrys_names = ['United States', 'Brazil', 'Russia', 'Spain', 'United Kingdom', 'Italy', 'France', 'Germany', 'Turkey', 'Iran', 'India', 'Peru', 'Canada', 'Saudi Arabia', 'Mexico', 'Chile', 'Belgium', 'Pakistan', 'Netherlands', 'Qatar', 'Ecuador', 'Belarus', 'Sweden', 'Bangladesh', 'Singapore Rep.', 'Switzerland', 'Portugal', 'United Arab Emirates', 'Ireland', 'Indonesia', 'South Africa', 'Poland', 'Ukraine', 'Kuwait', 'Colombia', 'Romania', 'Israel', 'Japan', 'Egypt', 'Austria', 'Dominican Rep.', 'Philippines', 'Denmark', 'Argentina', 'Korea', 'Serbia', 'Panama', 'Afghanistan', 'Czech Rep.', 'Norway', 'Kazakhstan', 'Algeria', 'Nigeria', 'Morocco', 'Oman', 'Malaysia', 'Australia', 'Moldova', 'Ghana', 'Finland', 'Armenia', 'Bolivia', 'Cameroon', 'Iraq', 'Luxembourg', 'Azerbaijan', 'Honduras', 'Hungary', 'Sudan', 'Guinea', 'Uzbekistan', 'Guatemala', 'Thailand', 'Senegal', 'Greece', 'Tajikistan', 'Bulgaria', "Côte d'Ivoire", 'Djibouti', 'Croatia', 'Gabon', 'Cuba', 'Estonia', 'El Salvador', 'Iceland', 'Lithuania', 'Somalia', 'New Zealand', 'Slovakia', 'Slovenia', 'Kyrgyzstan', 'Kenya', 'Guinea Bissau', 'Lebanon', 'Sri Lanka', 'Tunisia', 'Latvia', 'Mali', 'Venezuela', 'Albania', 'Eq. Guinea', 'Niger', 'Cyprus', 'Zambia', 'Costa Rica', 'Haiti', 'Paraguay', 'Burkina Faso', 'Uruguay', 'Georgia', 'Jordan', 'Chad', 'Sierra Leone', 'Nepal', 'Jamaica', 'Tanzania', 'Ethiopia', 'Madagascar', 'Palestine', 'Togo', 'Vietnam', 'Rwanda', 'Montenegro', 'Nicaragua', 'Liberia', 'Swaziland', 'Mauritania', 'Yemen', 'Myanmar', 'Uganda', 'Mozambique', 'Mongolia', 'Brunei', 'Benin', 'Guyana', 'Cambodia', 'The Bahamas', 'Malawi', 'Libya', 'Syria', 'Angola', 'Zimbabwe', 'Burundi', 'Eritrea', 'Botswana', 'Gambia', 'Bhutan', 'East Timor', 'Namibia', 'Lao PDR', 'Fiji', 'Belize', 'Suriname', 'Papua New Guinea', 'Lesotho']
#
# confirmed_count = [1666828, 347398, 335882, 281904, 258504, 229327, 182036, 179986, 155686, 133521, 131920, 115754, 85151, 70161, 65856, 65393, 56810, 54601, 45265, 42213, 36258, 35244, 33188, 32078, 31068, 30725, 30471, 28704, 24582, 21745, 21343, 20931, 20580, 20464, 20177, 17857, 16712, 16536, 16513, 16486, 14422, 13777, 11487, 11353, 11190, 11092, 10577, 9998, 8890, 8346, 8322, 8113, 7526, 7406, 7257, 7185, 7114, 6994, 6617, 6568, 6302, 5915, 4400, 4272, 3990, 3982, 3743, 3741, 3628, 3176, 3132, 3054, 3040, 2976, 2876, 2738, 2427, 2366, 2270, 2243, 1934, 1931, 1821, 1819, 1804, 1616, 1594, 1504, 1504, 1468, 1403, 1192, 1114, 1097, 1089, 1048, 1046, 1015, 1010, 989, 960, 943, 927, 920, 918, 865, 850, 814, 764, 728, 704, 648, 621, 584, 550, 509, 494, 488, 423, 373, 325, 325, 324, 279, 255, 238, 227, 212, 201, 198, 168, 141, 141, 135, 127, 124, 100, 82, 75, 70, 61, 56, 42, 39, 30, 25, 24, 24, 20, 19, 18, 18, 11, 8, 2]
c = (Map().add(
"确诊人数", [list(z) for z in zip(countrys_names, confirmed_count_list)],
is_map_symbol_show=False,
maptype="world",
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
color="rgb(49,60,72)")).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="全球 2019-nCoV 地图"),
visualmap_opts=opts.VisualMapOpts(max_=1700000),
).render("map_world.html"))
def map3d_china_base() -> Map3D:
c = (Map3D().add_schema(
itemstyle_opts=opts.ItemStyleOpts(
color="rgb(5,101,123)",
opacity=1,
border_width=0.8,
border_color="rgb(62,215,213)",
),
map3d_label=opts.Map3DLabelOpts(
is_show=True,
text_style=opts.TextStyleOpts(color="#fff",
font_size=16,
background_color="rgba(0,0,0,0)"),
),
emphasis_label_opts=opts.LabelOpts(is_show=True),
light_opts=opts.Map3DLightOpts(
main_color="#fff",
main_intensity=1.2,
is_main_shadow=False,
main_alpha=55,
main_beta=10,
ambient_intensity=0.3,
),
).add(series_name="", data_pair="",
maptype=ChartType.MAP3D).set_global_opts(
title_opts=opts.TitleOpts(title="全国行政区划地图-Base"),
visualmap_opts=opts.VisualMapOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=True),
))
return c
def get_salary_city(self):
sql = """
select city,cast(sum((max_salary+min_salary)/2)/count(*) as signed) salary from lagou_job GROUP BY city order by salary desc
"""
cursor.execute(sql)
key = []
values = []
for info in cursor.fetchall():
if not info[0] == '海外':
key.append(info[0])
values.append(info[1])
max = values[0]
geo = Geo(init_opts=opts.InitOpts(width="1600px", height="1000px"))
geo.add_schema(
maptype="china",
itemstyle_opts=opts.ItemStyleOpts(color="#DDF8FF",
border_color="#111"),
)
geo.add("薪资水平", [list(z) for z in zip(key, values)],
type_="effectScatter")
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
geo.set_global_opts(visualmap_opts=opts.VisualMapOpts(max_=max),
title_opts=opts.TitleOpts(title="各城市工资水平,单位K"))
file = data_store + "\\" + "salary.html"
check_path(file)
geo.render(file)
def process_plot_data(self):
for s in self.symbol_list:
# Drop duplicates for x_axis and data
self.x_axis[s] = pd.DataFrame(self.x_axis[s])
self.data[s] = pd.DataFrame(self.data[s])
x_axis_unique = ~self.x_axis[s].duplicated(keep='last')
self.data[s] = self.data[s][x_axis_unique]
self.x_axis[s] = self.x_axis[s].drop_duplicates(keep='last')
# Transform x_axis and data to list
self.x_axis[s] = self.x_axis[s][0].values.tolist()
self.data[s] = self.data[s].values.tolist()
# Transform markpoint to MarkPointItem_list
markpoint = [
opts.MarkPointItem(
coord=[point[0].strftime('%m-%d %H:%M'), point[1]],
value=point[3] + '\n' + str(point[1]),
symbol='pin' if point[3] == 'SELL' else 'pin',
#symbol_size=30
itemstyle_opts=opts.ItemStyleOpts(
color='#911146' if point[3] == 'BUY' else '#1F8A70'))
for point in self.markpoint[s]
]
# Generate markline based on markpoint
self.markline[s] = [
opts.MarkLineItem(
y=point[1], name='SHORT' if point[3] == 'SELL' else 'LONG')
for point in self.markpoint[s]
]
self.markpoint[s] = markpoint
def geo_base() -> Geo:
c = (Geo().add_schema(
maptype="china",
itemstyle_opts=opts.ItemStyleOpts(
color="#323c48", border_color="#111")).add(
"人/平方千米", zip(list(df.地区), list(df.人口密度))).set_series_opts(
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="2018年36个主要城市的人口密度情况"),
visualmap_opts=opts.VisualMapOpts(max_=2306.6,
min_=18.7,
is_piecewise=True,
pieces=[{
'min':
924,
'max':
2306.593060
}, {
'min':
602,
'max':
923.156006
}, {
'min':
362,
'max':
600.689035
}, {
'min':
18.781760,
'max':
361.142947
}])))
return c
def draw_kline(df_list: List[pd.DataFrame],
render='html',
file_name=None) -> Kline:
kline = None
for df in df_list:
security_id = df['security_id'][0]
security_type, _, _ = decode_security_id(security_id)
xdata = [to_time_str(timestamp) for timestamp in df.index]
if security_type == SecurityType.stock:
ydata = df.loc[:, ['qfq_open', 'qfq_close', 'qfq_low', 'qfq_high'
]].values.tolist()
else:
ydata = df.loc[:, ['open', 'close', 'low', 'high']].values.tolist()
current_kline = get_default_kline()
current_kline.add_xaxis(xdata)
current_kline.add_yaxis(security_id,
ydata,
itemstyle_opts=opts.ItemStyleOpts(
color="#ec0000",
color0="#00da3c",
border_color="#8A0000",
border_color0="#008F28"))
if not kline:
kline = current_kline
else:
kline.overlap(current_kline)
if render == 'html':
kline.render(get_ui_path(file_name))
return kline
def grid_volume(self, grid_index=1):
""""""
vol_bar = (Bar().add_xaxis(
xaxis_data=self.bar_data_datetime).add_yaxis(
series_name="成交量",
y_axis=self.bar_data["volume"].tolist(),
xaxis_index=grid_index,
yaxis_index=grid_index,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(opacity=0.8,
color=JsCode("""
function(params) {
var colorList;
if (barData[params.dataIndex][1] >= barData[params.dataIndex][0]) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=grid_index,
),
legend_opts=opts.LegendOpts(is_show=False),
))
return vol_bar
def show_dlt(self):
red_balls = []
blue_balls = []
with open(self.input_file, 'r') as f:
for i in range(Config.PAGES * Config.ITEMS_PER_PAGE):
one_line_data = f.readline().strip()
# print(one_line_data)
red_balls.extend([
int(one_line_data[15 + (2 * i):15 + (2 * (i + 1))])
for i in range(5)
])
blue_balls.append(int(one_line_data[-4:-2]))
blue_balls.append(int(one_line_data[-2:]))
red_counter = Counter(red_balls)
blue_counter = Counter(blue_balls)
# print(red_balls)
# print(blue_balls)
# print(red_counter)
# print(blue_counter)
# print(red_counter.most_common())
# print(blue_counter.most_common())
red_dict = {}
blue_dict = {}
for i in red_counter.most_common():
red_dict['{}'.format(i[0])] = i[1]
for j in blue_counter.most_common():
blue_dict['{}'.format(j[0])] = j[1]
print(red_dict)
print(blue_dict)
red_list = sorted(red_counter.most_common(),
key=lambda number: number[0])
blue_list = sorted(blue_counter.most_common(),
key=lambda number: number[0])
print(blue_list)
print(red_list)
# 红球图表添加
red_bar = Bar()
red_x = ['{}'.format(str(x[0])) for x in red_list]
red_y = ['{}'.format(str(x[1])) for x in red_list]
red_bar.add_xaxis(red_x)
red_bar.add_yaxis('红色球出现的次数', red_y)
# 篮球图表添加
blue_bar = Bar()
blue_x = ['{}'.format(str(x[0])) for x in blue_list]
blue_y = ['{}'.format(str(x[1])) for x in blue_list]
blue_bar.add_xaxis(blue_x)
blue_bar.add_yaxis('蓝色球出现的次数',
blue_y,
itemstyle_opts=opts.ItemStyleOpts(color='blue'))
page = Page(page_title='大乐透数据分析', interval=3)
page.add(red_bar)
page.add(blue_bar)
page.render(self.output_file)
def kline_itemstyle() -> Kline:
c = (
Kline()
.add_xaxis(["2017/7/{}".format(i + 1) for i in range(31)])
.add_yaxis(
"kline",
data,
itemstyle_opts=opts.ItemStyleOpts(
color="#ec0000",
color0="#00da3c",
border_color="#8A0000",
border_color0="#008F28",
),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
datazoom_opts=[opts.DataZoomOpts(type_="inside")],
title_opts=opts.TitleOpts(title="Kline-ItemStyle"),
)
)
return c
def charts():
fp = open('./oldData.txt', 'r')
# Timeline
timeline = Timeline(init_opts=opts.InitOpts(
width="1660px",
height="708px",
))
# 48 bar charts
for i in range(48):
X, Y, Color = getSta(fp)
# y-axle with color
y = []
for j in range(22):
y.append(
opts.BarItem(
name=X[j],
value=Y[j],
itemstyle_opts=opts.ItemStyleOpts(color=Color[j])))
# single bar chart
bar = (
Bar().add_xaxis(X).add_yaxis(
"Youbike 使用率", y).reversal_axis().set_global_opts(
title_opts=opts.TitleOpts(
title="南港區Youbike的一天",
pos_left="center",
),
legend_opts=opts.LegendOpts(pos_left="left"),
graphic_opts=[
opts.GraphicGroup( # add a block
graphic_item=opts.GraphicItem(left="80%",
top="80%"),
children=[
opts.GraphicText( # add text into the block
graphic_item=opts.GraphicItem(
left="center",
top="center",
# z=100
),
graphic_textstyle_opts=opts.
GraphicTextStyleOpts(
text="{}".format(invTime[i]),
font="48px Verdana",
),
)
])
]).set_series_opts( # number showed at right of the bar
label_opts=opts.LabelOpts(position="right",
color="#333")))
timeline.add(bar, "") # add single bar chart into timeline
timeline.add_schema(play_interval=1000,
is_loop_play=True) # play settings
# export into HTML
timeline.render("YoubikeUsingRate.html")
fp.close()
def bar(self, data, label="", xaxis=None, position=0, show_rgb=False):
if not xaxis: xaxis = self.xaxis
rgb_color = {
"itemstyle_opts": opts.ItemStyleOpts(color=JsCode(
"""
function(params) {
var colorList;
if (params.data >= 0) { colorList = '#ef232a'; } else { colorList = '#14b143'; }
return colorList;
}
"""
)
)
}
if not show_rgb: rgb_color = {}
return (Bar()
.add_xaxis(xaxis_data=xaxis)
.add_yaxis(
series_name=label,
y_axis=data,
label_opts=opts.LabelOpts(is_show=False),
**rgb_color
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=position,
axislabel_opts=opts.LabelOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
)
)
def mapProvince(self, data) -> Map:
"""
-- 全国各省火车站数量
:param data:list:[(省份名称，值)]
:return: Map
"""
province_map = (
Map(init_opts=opts.InitOpts(
width='1000px', height='500px', theme=ThemeType.DARK)).add(
'火车站数量', data, "china", zoom=1.2).set_series_opts(
label_opts=opts.LabelOpts(is_show=False), # 关闭各省名称显示
itemstyle_opts=opts.ItemStyleOpts(
color='rgba(255, 255, 255, 0)')) # 去掉省会的点
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(type_='color',
min_=0,
max_=250), # 设置最大，最小值
legend_opts=opts.LegendOpts(is_show=False), # 不显示图例
title_opts=opts.TitleOpts(
title='全国各省火车站数量分布',
title_textstyle_opts=opts.TextStyleOpts(
font_size=20), # 设置标题大小
subtitle='全国3500+个车站，数量最多的前三个省是：{}，{}，{}... {}'.format(
data[0][0], data[1][0], data[2][0],
self.data_source))))
return province_map
def geo_lines_background() -> Geo:
c = (
Geo()
.add_schema(
maptype="china",
itemstyle_opts=opts.ItemStyleOpts(color="#323c48", border_color="#111"),
)
.add(
"",
[("广州", 55), ("北京", 66), ("杭州", 77), ("重庆", 88)],
type_=ChartType.EFFECT_SCATTER,
color="white",
)
.add(
"geo",
[("广州", "上海"), ("广州", "北京"), ("广州", "杭州"), ("广州", "重庆")],
type_=ChartType.LINES,
effect_opts=opts.EffectOpts(
symbol=SymbolType.ARROW, symbol_size=6, color="blue"
),
linestyle_opts=opts.LineStyleOpts(curve=0.2),
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(title_opts=opts.TitleOpts(title="Geo-Lines-background"))
)
return c
def line_itemstyle() -> Line:
c = (
Line()
.add_xaxis(xaxis_data=["1990","1991","1992","1993","1994","1995","1996","1997","1998","1999","2000","2001","2002","2003","2004","2005","2006","2007","2008","2009","2010","2011","2012","2013","2014","2015","2016","2017"])
.add_yaxis(
"吸烟死亡比例",
吸烟,
symbol="triangle",
symbol_size=20,
linestyle_opts=opts.LineStyleOpts(color="green", width=4, type_="dashed"),
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
border_width=3, border_color="yellow", color="blue"
),
)
.set_global_opts(
title_opts=opts.TitleOpts(title="中国因吸烟死亡比例"),
xaxis_opts=opts.AxisOpts(type_="category"),
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=False),
)
)
return render_template('xyzx.html')
def kline_base(mydate, data, name) -> Kline:
kline = (
Kline()
.add_xaxis(mydate)
.add_yaxis("%s" % name, data, markpoint_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="max", value_dim="close")]
), markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="max", value_dim="close")]
),
itemstyle_opts=opts.ItemStyleOpts(
color="#ec0000",
color0="#00da3c",
border_color="#8A0000",
border_color0="#008F28",
),
)
.set_global_opts(
yaxis_opts=opts.AxisOpts(is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
xaxis_opts=opts.AxisOpts(is_scale=True,
axislabel_opts=opts.LabelOpts(rotate=-30)),
title_opts=opts.TitleOpts(title="股票走势"),
datazoom_opts=[opts.DataZoomOpts()],
toolbox_opts=opts.ToolboxOpts(is_show=True),
)
)
return kline
