def line3d_base():
data = []
for t in range(0, 25000):
_t = t / 1000
x = (1 + 0.25 * math.cos(75 * _t)) * math.cos(_t)
y = (1 + 0.25 * math.cos(75 * _t)) * math.sin(_t)
z = _t + 2.0 * math.sin(75 * _t)
data.append([x, y, z])
line3d = Line3D()
line3d.add(
"",
data,
xaxis3d_opts=opts.Axis3DOpts(type_="value"),
yaxis3d_opts=opts.Axis3DOpts(type_="value"),
grid3d_opts=opts.Grid3DOpts(width=100, depth=100),
)
line3d.set_global_opts(
title_opts=opts.TitleOpts(title="Line3D-基本示例"),
visualmap_opts=opts.VisualMapOpts(max_=30)
)
line3d.render("./line3d_base.html")
def line3d_base() -> Line3D:
data = []
for t in range(0, 25000):
_t = t / 1000
x = (1 + 0.25 * math.cos(75 * _t)) * math.cos(_t)
y = (1 + 0.25 * math.cos(75 * _t)) * math.sin(_t)
z = _t + 2.0 * math.sin(75 * _t)
data.append([x, y, z])
c = (Line3D().add(
"",
data,
xaxis3d_opts=opts.Axis3DOpts(Faker.clock, type_="value"),
yaxis3d_opts=opts.Axis3DOpts(Faker.week_en, type_="value"),
grid3d_opts=opts.Grid3DOpts(width=100, height=100, depth=100),
).set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=30,
min_=0,
range_color=Faker.visual_color),
title_opts=opts.TitleOpts(title="Line3D-基本示例"),
))
return c
def scatter3d_base() -> Scatter3D:
# data = [
# [random.randint(0, 100), random.randint(0, 200), random.randint(0, 100)]
# for _ in range(10000)
# ]
data, max_rel = read_as_info(
'..\\000LocalData\\as_compare\\as_core_map_data_integrate20191203.csv')
c = (Scatter3D(init_opts=opts.InitOpts(
width="1080px",
height="1080px",
page_title="全球互联网网络3D散点图",
theme=ThemeType.ROMANTIC)).add(
"随机散点",
data,
grid3d_opts=opts.Grid3DOpts(width=300,
height=160,
depth=300,
rotate_speed=5,
is_rotate=True,
rotate_sensitivity=2),
xaxis3d_opts=opts.Axis3DOpts(type_="value",
name="经度",
min_=180.0,
max_=-180.0),
yaxis3d_opts=opts.Axis3DOpts(type_="value",
name="维度",
min_=-180.0,
max_=180.0),
zaxis3d_opts=opts.Axis3DOpts(type_="value",
name="连通度",
min_=0,
max_=max_rel + 1),
itemstyle_opts=opts.ItemStyleOpts()).set_global_opts(
title_opts=opts.TitleOpts("全球互联网网络3D散点图"),
visualmap_opts=opts.VisualMapOpts(
range_color=Faker.visual_color),
))
print(data)
print(Faker.visual_color)
return c
def surface3D_flower() -> Surface3D:
def surface3d_data():
for t0 in range(-30, 30, 1):
y = t0 / 10
for t1 in range(-30, 30, 1):
x = t1 / 10
z = math.sin(x * x + y * y) * x / 3.14
yield [x, y, z]
c = (Surface3D().add(
"",
list(surface3d_data()),
xaxis3d_opts=opts.Axis3DOpts(type_="value"),
yaxis3d_opts=opts.Axis3DOpts(type_="value"),
grid3d_opts=opts.Grid3DOpts(width=100, height=100, depth=100),
).set_global_opts(
title_opts=opts.TitleOpts(title="Surface3D-曲面波图"),
visualmap_opts=opts.VisualMapOpts(max_=1,
min_=-1,
range_color=Faker.visual_color),
))
return c
def test_surface3d_base():
def surface3d_data():
for t0 in range(-60, 60, 1):
y = t0 / 60
for t1 in range(-60, 60, 1):
x = t1 / 60
if math.fabs(x) < 0.1 and math.fabs(y) < 0.1:
z = "-"
else:
z = math.sin(x * math.pi) * math.sin(y * math.pi)
yield [x, y, z]
c = (Surface3D().add(
"",
list(surface3d_data()),
xaxis3d_opts=opts.Axis3DOpts(type_="value"),
yaxis3d_opts=opts.Axis3DOpts(type_="value"),
grid3d_opts=opts.Grid3DOpts(width=100, height=100, depth=100),
).set_global_opts(visualmap_opts=opts.VisualMapOpts(
max_=3, min_=-3, range_color=Faker.visual_color)))
eq_(c.theme, "white")
eq_(c.renderer, "canvas")
c.render()
def bar_3d_fun():
# 三维数据，定义 x、y 长度，z 数值随机
data = [(i, j, random.randint(1, 20)) for i in range(7) for j in range(10)]
print(len(data), data)
Bar3D().add(
# 添加 x、y、z 轴数据，并设置属性
series_name="",
data=data,
xaxis3d_opts=opts.Axis3DOpts(Faker.choose(), type_="category"),
yaxis3d_opts=opts.Axis3DOpts(Faker.choose(), type_="category"),
zaxis3d_opts=opts.Axis3DOpts(Faker.values(), type_="value"),
# 添加旋转效果
grid3d_opts=opts.Grid3DOpts(width=100,
depth=100,
rotate_speed=150,
is_rotate=True),
).set_global_opts(
# 设置图表属性
# 视觉配置
visualmap_opts=opts.VisualMapOpts(max_=20),
title_opts=opts.TitleOpts(title="主标题", subtitle="副标题")).render(
# 设置输出路径
path="D:/temp/pyecharts_bar3d.html")
yield [x, y, z]
data_xyz = list(surface3d_data())
(
Surface3D(init_opts=opts.InitOpts(width="1600px", height="800px"))
.add(
series_name="",
shading="color",
data=list(surface3d_data()),
xaxis3d_opts=opts.Axis3DOpts(type_="value"),
yaxis3d_opts=opts.Axis3DOpts(type_="value"),
grid3d_opts=opts.Grid3DOpts(width=100, height=40, depth=100),
)
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(
dimension=2,
max_=1,
min_=-1,
range_color=[
"#313695",
"#4575b4",
"#74add1",
"#abd9e9",
"#e0f3f8",
"#ffffbf",
"#fee090",
"#fdae61",
]
x_axis = [
"12am", "1am", "2am", "3am", "4am", "5am", "6am", "7am", "8am", "9am",
"10am", "11am", "12pm", "1pm", "2pm", "3pm", "4pm", "5pm", "6pm", "7pm",
"8pm", "9pm", "10pm", "11pm"
]
y_axis = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
bar3d = (Bar3D().add('',
data=data,
shading='realistic',
xaxis3d_opts=opts.Axis3DOpts(x_axis, type_='category'),
yaxis3d_opts=opts.Axis3DOpts(y_axis, type_='category'),
zaxis3d_opts=opts.Axis3DOpts(type_='value'),
grid3d_opts=opts.Grid3DOpts(
width=140, depth=84, is_rotate=True)).set_global_opts(
title_opts=opts.TitleOpts('一周不同时间段的骑行需求'),
visualmap_opts=opts.VisualMapOpts(
is_show=True,
max_=600,
is_piecewise=False,
range_color=Faker.visual_color)))
bar3d.render()
# 天气维度(不同天气状况)
def set_weather(val):
if val == 1:
return 'Good'
elif val == 2:
return 'Normal'
_t = t / 1000
x = (1 + 0.25 * math.cos(75 * _t)) * math.cos(_t)
y = (1 + 0.25 * math.cos(75 * _t)) * math.sin(_t)
z = _t + 2.0 * math.sin(75 * _t)
data.append([x, y, z])
# In[131]:
c = (
Line3D().add(
"",
data,
xaxis3d_opts=opts.Axis3DOpts(Faker.clock, type_="value"),
yaxis3d_opts=opts.Axis3DOpts(Faker.week_en, type_="value"),
grid3d_opts=opts.Grid3DOpts(width=100,
depth=100,
rotate_speed=150,
is_rotate=True),
).set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=30,
min_=0,
range_color=Faker.visual_color),
title_opts=opts.TitleOpts(title="Line3D-旋转的弹簧"),
)
#.render("line3d_autorotate.html")
)
c.render_notebook()
# In[137]:
# vis
import pyecharts.options as opts
def to_map_china(self, area, variate, value, update_time):
# 显示标识栏的颜色分段表示
pieces = [
{
"max": 99999999,
"min": 10000,
"label": '>10000',
"color": '#E64546'
},
{
"max": 9999,
"min": 1000,
"label": '1000-9999',
"color": '#F57567'
},
{
"max": 999,
"min": 100,
"label": '100-999',
"color": '#FF9985'
},
{
"max": 99,
"min": 10,
"label": '10-99',
"color": '#FFC4B3'
},
{
"max": 9,
"min": 1,
"label": '1-9',
"color": '#FFE5DB'
},
{
"max": 0,
"min": 0,
"label": '0',
"color": '#FFFFFF'
},
]
data_tip = [
'累计确诊', '死亡', '治愈', '现有确诊', '累计确诊增量', '死亡增量', '治愈增量', '现有确诊增量'
]
data_area = [
'西藏', '澳门', '青海', '台湾', '香港', '贵州', '吉林', '新疆', '宁夏', '内蒙古', '甘肃',
'天津', '山西', '辽宁', '黑龙江', '海南', '河北', '陕西', '云南', '广西', '福建', '上海',
'北京', '江苏', '四川', '山东', '江西', '重庆', '安徽', '湖南', '河南', '广东', '浙江',
'湖北'
]
# 绘制地图
map = (
Map(init_opts=opts.InitOpts(width='800px',
height='600px')) # 初始化配置项，设置地图大小
.add("累计确诊人数", [
list(z) for z in zip(area, variate)
], "china").set_global_opts(
title_opts=opts.TitleOpts(title="中国疫情地图分布",
subtitle='截止%s 中国疫情分布情况' %
(update_time),
pos_left='center',
pos_top='30px'), #TitleOpts：标题设置
visualmap_opts=opts.VisualMapOpts(max_=200,
is_piecewise=True,
pieces=pieces),
# # max_：设置 visualMapPiecewise 所有取值区间中的最大值，is_piecewise设置数据是否连续，split_number设置为分段数，pices可自定义数据分段
).render("中国疫情地图.html") # 展示提取后的效果
)
# 绘制地理坐标图
geo = (
Geo().add_schema(maptype="china") #地图类型
.add(
"累计确诊人数",
[list(z) for z in zip(area, variate)],
type_=ChartType.EFFECT_SCATTER,
).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)) # label_opts:标签配置项设置，is_show：是否显示视觉映射配置
.set_global_opts(
title_opts=opts.TitleOpts(title="中国疫情地图分布",
subtitle='截止%s 中国疫情分布情况' %
(update_time),
pos_left='center',
pos_top='30px'),
visualmap_opts=opts.VisualMapOpts(max_=200,
is_piecewise=True,
pieces=pieces),
).render("中国疫情新增情况.html"))
# 绘制折线图
line = (
Line().add_xaxis(area) #添加x轴数值
.add_yaxis("累计确诊人数", variate) #添加y轴名称、数值
.add_yaxis("累计治愈人数", value).set_global_opts(
title_opts=opts.TitleOpts(title="国内疫情情况"),
xaxis_opts=opts.AxisOpts(axislabel_opts={
"interval": "0",
"rotate": "45"
}), #设置x轴数值分割间隔为0，且旋转45°
yaxis_opts=opts.AxisOpts(
type_='log',
splitline_opts=opts.SplitLineOpts(is_show=True),
is_scale=True,
) #设置y轴数据类型为“log”，凸显分割线，不会强制包含零刻度
).set_series_opts(label_opts=opts.LabelOpts(is_show=False)) #标签设置项
.render("全国累计趋势折线图.html"))
# 绘制柱状图
bar = (
Bar().add_xaxis(area).add_yaxis(
"累计确诊人数", variate,
stack='stack1') #若y轴设置项“stack”为同一个值，则显示为堆叠型柱状，反之，成分散型
# .add_yaxis("累计治愈人数",value,stack='stack2')
# .add_yaxis("累计死亡人数", value, stack='stack1')
.add_yaxis("累计治愈人数", value, stack='stack1').set_global_opts(
title_opts=opts.TitleOpts(title="全国累计确诊治愈情况"),
xaxis_opts=opts.AxisOpts(axislabel_opts={
"interval": "0",
"rotate": "45"
}),
yaxis_opts=opts.AxisOpts(
type_='log',
splitline_opts=opts.SplitLineOpts(is_show=True),
is_scale=True,
)).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).render("全国累计确诊治愈.html"))
# 绘制饼状图
pie = (
Pie().add(
"",
[list(z) for z in zip(area, variate)],
radius=["40%", "75%", "log"], #设置圆环大小及数据类型
).set_global_opts(
title_opts=opts.TitleOpts(title="国内疫情情况"),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_left="2%"),
#图例配置项：图例列表的布局朝向为水平，图例组件离容器上侧的距离为15%，图例组件离容器左侧的距离为相对于容器高宽的20%
).set_series_opts(
label_opts=opts.LabelOpts(formatter="{b}: {c}")) #设置标签形式
.render("全国累计趋势饼状图.html"))
# 绘制3D柱状图
bar3d = (
Bar3D().add("全国指标",
data,
label_opts=opts.LabelOpts(position='left'),
yaxis3d_opts=opts.Axis3DOpts(data_area),
xaxis3d_opts=opts.Axis3DOpts(
data_tip,
type_="category",
max_=8,
interval=0,
),
zaxis3d_opts=opts.Axis3DOpts(type_="value", min_=0),
grid3d_opts=opts.Grid3DOpts(width="600",
height="100",
is_rotate=True)
#设置三维笛卡尔坐标系组件在三维场景中的宽度，高度，以及是否自动旋转
).set_global_opts(
visualmap_opts=opts.VisualMapOpts(
is_piecewise=True, pieces=pieces),
title_opts=opts.TitleOpts(title="疫情指标"),
).render("疫情指标.html"))
bar3d.add("",
data,
shading="lambert",
xaxis3d_opts=opts.Axis3DOpts(data=x_axis,
type_="category",
name="Date",
name_gap=50,
interval=1),
yaxis3d_opts=opts.Axis3DOpts(data=y_axis,
type_="category",
name="Hours",
name_gap=80,
interval=1),
zaxis3d_opts=opts.Axis3DOpts(type_="value",
name="Incidence"),
grid3d_opts=opts.Grid3DOpts(width=100, height=150,
depth=300))
if n == 0:
max_v = 2
elif n == 1:
max_v = 0.5
elif n == 2:
max_v = 20
elif n == 3:
max_v = 20
bar3d.set_global_opts(title_opts=opts.TitleOpts(title=p,
pos_right='50%',
pos_top='10%'),
visualmap_opts=opts.VisualMapOpts(
max_=max_v,
range_color=range_color,
is_calculable=False,
def surface3d_base() -> Surface3D:
def surface3d_data():
for t0 in range(-60, 60, 1):
y = t0 / 60
for t1 in range(-60, 60, 1):
x = t1 / 60
if math.fabs(x) < 0.1 and math.fabs(y) < 0.1:
z = "-"
else:
z = math.sin(x * math.pi) * math.sin(y * math.pi)
yield [x, y, z]
c = (
Surface3D()
.add(
"",
list(surface3d_data()),
xaxis3d_opts=opts.Axis3DOpts(type_="value"),
yaxis3d_opts=opts.Axis3DOpts(type_="value"),
grid3d_opts=opts.Grid3DOpts(width=100, height=100, depth=100),
)
.set_global_opts(import math
from example.commons import Faker
from pyecharts import options as opts
from pyecharts.charts import Surface3D
def surface3d_base() -> Surface3D:
def surface3d_data():
for t0 in range(-60, 60, 1):
y = t0 / 60
for t1 in range(-60, 60, 1):
x = t1 / 60
if math.fabs(x) < 0.1 and math.fabs(y) < 0.1:
z = "-"
else:
z = math.sin(x * math.pi) * math.sin(y * math.pi)
yield [x, y, z]
c = (
Surface3D()
.add(
"",
list(surface3d_data()),
xaxis3d_opts=opts.Axis3DOpts(type_="value"),
yaxis3d_opts=opts.Axis3DOpts(type_="value"),
grid3d_opts=opts.Grid3DOpts(width=100, height=100, depth=100),
)
.set_global_opts(
title_opts=opts.TitleOpts(title="Surface3D-基本示例"),
visualmap_opts=opts.VisualMapOpts(
max_=3, min_=-3, range_color=Faker.visual_color
),
)
)
return c
title_opts=opts.TitleOpts(title="Surface3D-基本示例"),
visualmap_opts=opts.VisualMapOpts(
max_=3, min_=-3, range_color=Faker.visual_color
),
)
)
def plot_performance_overview(dict_results, plot_tymod, p):
if plot_tymod == "pic":
figs = {}
for k, v in dict_results.items():
v = v.unstack()
series = v.index.get_level_values(0).unique() if isinstance(
v.index, pd.MultiIndex) else [k]
c = 3
r = np.ceil(len(series) / c)
fig = plt.figure(figsize=(8, 6))
plt.subplots_adjust(hspace=0.3)
for i, _s in enumerate(series):
_data = v.loc[_s] if isinstance(v.index, pd.MultiIndex) else v
X, Y = np.meshgrid(_data.index, _data.columns, indexing='ij')
Z = _data.values
pos = int(r * 100 + c * 10 + (i + 1))
ax = fig.add_subplot(pos, projection='3d')
ax.plot_surface(X, Y, Z, color='b')
ax.set_title("%s" % _s, fontdict={"size": 8})
figs[k] = fig
fig.savefig("%s_%s.png" % (p, k))
return figs
elif plot_tymod == 'dyn-3d':
results = {}
for k, perf in dict_results.items():
perf = perf.unstack()
c = (Surface3D(init_opts=opts.InitOpts(
width="1000px", height="500px")).set_global_opts(
title_opts=opts.TitleOpts(title="绩效-曲面波图（%s）" % k),
visualmap_opts=opts.VisualMapOpts(
max_=perf.values.max(),
min_=perf.values.min(),
range_color=Faker.visual_color),
toolbox_opts=opts.ToolboxOpts(is_show=True),
))
series = perf.index.get_level_values(0).unique() if isinstance(
perf.index, pd.MultiIndex) else [k]
for i, _s in enumerate(series):
_data = perf.loc[_s] if isinstance(perf.index,
pd.MultiIndex) else perf
X, Y = np.meshgrid(_data.index, _data.columns)
d = list(
zip(X.flatten().astype(str),
Y.flatten().astype(str),
_data.values.T.flatten().astype(np.float)))
c = c.add(
series_name=str(_s),
shading="color",
data=d,
xaxis3d_opts=opts.Axis3DOpts(type_="category"),
yaxis3d_opts=opts.Axis3DOpts(type_="category"),
grid3d_opts=opts.Grid3DOpts(width=100,
height=50,
depth=100),
)
results[k] = c
tab = Tab()
for k, v in results.items():
tab.add(v, k)
tab.render("%s.html" % p)
elif plot_tymod == "dyn-hm":
tab = Tab()
for k, v in dict_results.items():
v = v.unstack()
_tl = _timeline(v, name=k)
tab.add(_tl, k)
tab.render("%s.html" % p)
def scatter3d_base() -> Scatter3D:
data = [
[random.randint(-10000, 10000), random.randint(-10000, 10000), random.randint(-10000, 10000)]
for _ in range(100)
]
# 求莫比乌斯环数据
# v_mobius = np.linspace(-1.0, 1.0, num=20, endpoint=True)
# u_mobius = np.linspace(0, 2 * np.pi, num=20, endpoint=True)
# x_3d = (1. + v_mobius/2. * np.cos(u_mobius/2.)) * np.cos(u_mobius)
# print(x_3d)
# y_3d = (1. + v_mobius/2. * np.cos(u_mobius/2.)) * np.sin(u_mobius)
# print(y_3d)
# z_3d = v_mobius/2. * np.sin(v_mobius/2.)
# print(z_3d)
# data = [[0, 0, 0], [100, 100, 100], [100, -100, -100]]
# print(data)
# data = []
# temp_list = []
# for iter_cnt in range(0, len(list(x_3d))):
# temp_list.append(x_3d[iter_cnt])
# temp_list.append(y_3d[iter_cnt])
# temp_list.append(x_3d[iter_cnt])
# data.append(temp_list)
# temp_list = []
print(data)
# data, max_rel = read_as_info('..\\000LocalData\\as_compare\\as_core_map_data_integrate20191203.csv')
assert isinstance(
Scatter3D(init_opts=opts.InitOpts(width="1920px", height="960px", page_title="3D散点图", theme=ThemeType.DARK))
.add("3D散点可视化",
data,
grid3d_opts=opts.Grid3DOpts(width=100, height=100, depth=100, rotate_speed=5, is_rotate=False,
rotate_sensitivity=2),
xaxis3d_opts=opts.Axis3DOpts(type_="value", name="x", textstyle_opts=opts.TextStyleOpts(color="white")),
yaxis3d_opts=opts.Axis3DOpts(type_="value", name="y", textstyle_opts=opts.TextStyleOpts(color="white")),
zaxis3d_opts=opts.Axis3DOpts(type_="value", name="z", textstyle_opts=opts.TextStyleOpts(color="white")),
itemstyle_opts=opts.ItemStyleOpts(color="white", border_width=0.01, border_color="red", area_color="blue"),
# label_opts=opts.LabelOpts(color="objectwhite")
)
.set_global_opts, )
c = (
Scatter3D(init_opts=opts.InitOpts(width="1920px", height="960px", page_title="3D散点图", theme=ThemeType.DARK))
.add("3D散点可视化",
data,
grid3d_opts=opts.Grid3DOpts(width=100, height=100, depth=100, rotate_speed=5, is_rotate=False, rotate_sensitivity=2),
xaxis3d_opts=opts.Axis3DOpts(type_="value", name="x", textstyle_opts=opts.TextStyleOpts(color="white")),
yaxis3d_opts=opts.Axis3DOpts(type_="value", name="y", textstyle_opts=opts.TextStyleOpts(color="white")),
zaxis3d_opts=opts.Axis3DOpts(type_="value", name="z", textstyle_opts=opts.TextStyleOpts(color="white")),
itemstyle_opts=opts.ItemStyleOpts(color="white", border_width=0.01, border_color="red", area_color="blue"),
# label_opts=opts.LabelOpts(color="white")
)
.set_global_opts(
title_opts=opts.TitleOpts("3D散点图"),
# visualmap_opts=opts.VisualMapOpts(range_color=Faker.visual_color),
)
)
print(data)
print(Faker.visual_color)
return c
