pol_den = confirm_detail.sort_values(by="density", ascending=False)
x_data = pol_den["Region_CN"].tolist()
y_data = pol_den["四月"].tolist()
x_data.pop()
x_data.pop()
y_data.pop()
y_data.pop()
polDensity_line = (Line(init_opts=opts.InitOpts(
width='95vw',
height='95vh',
)).set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=False),
title_opts=opts.TitleOpts(title="人口密度——确诊人数折线图"),
xaxis_opts=opts.AxisOpts(type_="category",
name="人/km²（大~小）",
axislabel_opts={"rotate": 45}),
yaxis_opts=opts.AxisOpts(
type_="value",
name="确诊人数",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
).add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="",
y_axis=y_data,
symbol="emptyCircle",
is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=True),
).render("visual_render/basic_line_chart1.html"))
def vtradecost(self,
cftable,
unitcost=False,
start=None,
end=yesterdayobj(),
rendered=True):
"""
visualization giving the average cost line together with netvalue line as well as buy and sell points
:returns: pyecharts.line
"""
funddata = []
costdata = []
cashdata = [] #kahar add in order to observe the sum of the cash
funddata_test = []
pprice = self.price[self.price["date"] <= end]
pcftable = cftable
if start is not None:
pprice = pprice[pprice["date"] >= start]
pcftable = pcftable[pcftable["date"] >= start]
for _, row in pprice.iterrows():
date = row["date"]
funddata.append(row["netvalue"])
funddata_test.append(3.2)
if unitcost:
cost = 0
cash = 0
if (date - self.cftable.iloc[0].date).days >= 0:
cost = self.unitcost(date)
cash = self.get_datecashvalue(date)
costdata.append(cost)
cashdata.append(cash / 1000000.0)
coords = []
# pcftable = pcftable[abs(pcftable["cash"]) > threhold]
for i, r in pcftable.iterrows():
coords.append(
[r.date, pprice[pprice["date"] <= r.date].iloc[-1]["netvalue"]])
upper = pcftable.cash.abs().max()
lower = pcftable.cash.abs().min()
if upper == lower:
upper = 2 * lower + 1 # avoid zero in denominator
def marker_factory(x, y):
buy = pcftable[pcftable["date"] <= x].iloc[-1]["cash"]
if buy < 0:
color = "#ff7733"
else:
color = "#3366ff"
size = (abs(buy) - lower) / (upper - lower) * 5 + 5
return opts.MarkPointItem(
coord=[x.date(), y],
itemstyle_opts=opts.ItemStyleOpts(color=color),
# this nested itemstyle_opts within MarkPointItem is only supported for pyechart>1.7.1
symbol="circle",
symbol_size=size,
)
line = Line()
line.add_xaxis([d.date() for d in pprice.date])
print("costdata", costdata)
if unitcost:
line.add_yaxis(
series_name="持仓成本",
y_axis=costdata,
is_symbol_show=False,
)
line.add_yaxis(
series_name="持仓总额k6",
y_axis=cashdata,
is_symbol_show=False,
)
line.add_yaxis(
series_name="基金净值",
y_axis=funddata,
is_symbol_show=False,
markpoint_opts=opts.MarkPointOpts(
data=[marker_factory(*c) for c in coords], ),
)
line.set_global_opts(
datazoom_opts=[
opts.DataZoomOpts(is_show=True,
type_="slider",
range_start=50,
range_end=100),
opts.DataZoomOpts(
is_show=True,
type_="slider",
orient="vertical",
range_start=50,
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(
is_show=True,
trigger="axis",
trigger_on="mousemove",
axis_pointer_type="cross",
),
)
if rendered:
return line.render_notebook()
else:
return line
暂无
"""
(
Line(init_opts=opts.InitOpts(width="1280px", height="720px"))
.add_xaxis(xaxis_data=["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"])
.add_yaxis(
series_name="",
y_axis=[120, 200, 150, 80, 70, 110, 130],
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(
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),
)
.render("line_style_and_item_style.html")
)
data = df.loc[df['City'] == 'Delhi', ['Date', 'AQI']]
list1 = []
for date in data['Date']:
year = date.split('-')[0]
list1.append(year)
data['year'] = list1
year_message = data.groupby(['year'])
year_com = year_message['AQI'].agg(['mean'])
year_com.reset_index(inplace=True)
year_com_last = year_com.sort_index()
x = [e for e in year_com_last['year']]
y = [int(e) for e in year_com_last['mean']]
line = Line()
line.add_xaxis(x)
line.add_yaxis('Delhi', y)
line.set_series_opts(
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_='max', name='最大值'),
opts.MarkPointItem(type_='min', name='最小值'),
]),
label_opts=opts.LabelOpts(is_show=False),
)
line.set_global_opts(title_opts=opts.TitleOpts(
title='2015-2020印度德里AQI年均走势图',
pos_left='center',
),
def bar_difficult() -> Bar:
f = open('difficulty_test3.json', encoding='gbk')
res = f.read()
data = json.loads(res)
simple = [0 for i in range(8)]
medium = [0 for i in range(8)]
hard = [0 for i in range(8)]
total = [0 for i in range(8)]
nums = [0 for i in range(8)]
level_distributed = {"0-10": 0, "11-20": 0, "21-30": 0, "31-40": 0, "41-50": 0, "51-60": 0, "61-70": 0, "71-80": 0,
"81-90": 0, "91-100": 0}
# 观察难度情况
for val in data.values():
if val["final_level"] <= 10:
level_distributed["0-10"] += 1
elif 10 < val["final_level"] <= 20:
level_distributed["11-20"] += 1
elif 20 < val["final_level"] <= 30:
level_distributed["21-30"] += 1
elif 30 < val["final_level"] <= 40:
level_distributed["31-40"] += 1
elif 40 < val["final_level"] <= 50:
level_distributed["41-50"] += 1
elif 50 < val["final_level"] <= 60:
level_distributed["51-60"] += 1
elif 60 < val["final_level"] <= 70:
level_distributed["61-70"] += 1
elif 70 < val["final_level"] <= 80:
level_distributed["71-80"] += 1
elif 80 < val["final_level"] <= 90:
level_distributed["81-90"] += 1
else:
level_distributed["91-100"] += 1
print(level_distributed)
# 对数据进行分类
for val in data.values():
if val["case_type"] == "字符串":
nums[0] += 1
total[0] += val["final_level"]
if val["final_level"] <= 30:
simple[0] += 1
elif val["final_level"] > 60:
hard[0] += 1
else:
medium[0] += 1
elif val["case_type"] == "线性表":
nums[1] += 1
total[1] += val["final_level"]
if val["final_level"] <= 30:
simple[1] += 1
elif val["final_level"] > 60:
hard[1] += 1
else:
medium[1] += 1
elif val["case_type"] == "数组":
nums[2] += 1
total[2] += val["final_level"]
if val["final_level"] <= 30:
simple[2] += 1
elif val["final_level"] > 60:
hard[2] += 1
else:
medium[2] += 1
elif val["case_type"] == "查找算法":
nums[3] += 1
total[3] += val["final_level"]
if val["final_level"] <= 30:
simple[3] += 1
elif val["final_level"] > 60:
hard[3] += 1
else:
medium[3] += 1
elif val["case_type"] == "排序算法":
nums[4] += 1
total[4] += val["final_level"]
if val["final_level"] <= 30:
simple[4] += 1
elif val["final_level"] > 60:
hard[4] += 1
else:
medium[4] += 1
elif val["case_type"] == "数字操作":
nums[5] += 1
total[5] += val["final_level"]
if val["final_level"] <= 30:
simple[5] += 1
elif val["final_level"] > 60:
hard[5] += 1
else:
medium[5] += 1
elif val["case_type"] == "图结构":
nums[6] += 1
total[6] += val["final_level"]
if val["final_level"] <= 30:
simple[6] += 1
elif val["final_level"] > 60:
hard[6] += 1
else:
medium[6] += 1
else:
nums[7] += 1
total[7] += val["final_level"]
if val["final_level"] <= 30:
simple[7] += 1
elif val["final_level"] > 60:
hard[7] += 1
else:
medium[7] += 1
list1 = [{"value": hard[i], "percent": hard[i] / nums[i]} for i in range(8)]
list2 = [{"value": medium[i], "percent": medium[i] / nums[i]} for i in range(8)]
list3 = [{"value": simple[i], "percent": simple[i] / nums[i]} for i in range(8)]
average = [round(total[i] / nums[i], 2) for i in range(8)]
xaxis = ["字符串", "线性表", "数组", "查找算法", "排序算法", "数字操作", "图结构", "树结构"]
bar = (
Bar()
.add_xaxis(xaxis)
.add_yaxis("困难", list1, stack="stack1", category_gap="50%")
.add_yaxis("中等", list2, stack="stack1", category_gap="50%")
.add_yaxis("简单", list3, stack="stack1", category_gap="50%")
.extend_axis(
yaxis=opts.AxisOpts(
name="难度评分",
type_="value",
min_=0,
max_=100,
interval=10,
axislabel_opts=opts.LabelOpts(formatter="{value} 分"),
)
)
.set_global_opts(
title_opts=opts.TitleOpts(title="不同类别题目难度展示图"),
toolbox_opts=opts.ToolboxOpts(),
tooltip_opts=opts.TooltipOpts(
is_show=True, trigger="axis", axis_pointer_type="cross"
),
legend_opts=opts.LegendOpts(is_show=True),
# xaxis_opts=opts.AxisOpts(
# type_="category",
# axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow"),
# ),
yaxis_opts=opts.AxisOpts(
name="题目个数",
type_="value",
min_=0,
max_=250,
interval=50,
axislabel_opts=opts.LabelOpts(formatter="{value} 个"),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
)
.set_series_opts(
label_opts=opts.LabelOpts(
position="right",
formatter=JsCode(
"function(x){return Number(x.data.percent * 100).toFixed() + '%';}"
),
)
)
# .render("diff_type_diff_bar.html")
)
line = (
Line()
.add_xaxis(xaxis_data=xaxis)
.add_yaxis(
series_name="平均难度分",
yaxis_index=1,
y_axis=average,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(color="#f05b72", width=2, opacity=0.8)
)
)
return bar.overlap(line)
def daily_issue_plot(daily,cummulative):
import pyecharts.options as opts
from pyecharts.charts import Bar, Line
# color = ["#36648B","tomato"]
daily = daily.round(2)
cummulative = cummulative.round(2)
x_data = daily.index.astype(str).tolist()
bar = (
Bar(init_opts=opts.InitOpts(width=fig_width, height=fig_height))
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="每日发行数量",
yaxis_index=2,
yaxis_data=daily['发行数量'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="每日发行总额",
yaxis_index=3,
yaxis_data=daily['发行总额'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
tooltip_opts=opts.TooltipOpts(
is_show=True, trigger="axis", axis_pointer_type="cross"
),
xaxis_opts=opts.AxisOpts(
type_="category",
axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow"),
),
yaxis_opts=opts.AxisOpts(
name="数量",
type_="value",
# min_=0,
# max_=250,
# interval=50,
axislabel_opts=opts.LabelOpts(formatter="{value}"),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
background_color='white',
connected_background_color="white",
pixel_ratio=pixel_ratio,
name="pic",
),
)
)
)
)
line = (
Line(init_opts=opts.InitOpts(width=fig_width, height=fig_height))
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="累计发行数量",
yaxis_index=0,
y_axis=cummulative['累计发行数量'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_smooth=True,
is_symbol_show=True,
symbol="circle",
symbol_size=6,
linestyle_opts=opts.LineStyleOpts(width=2),
).add_yaxis(
series_name="累计发行总额",
yaxis_index=1,
is_smooth=True,
is_symbol_show=True,
symbol="circle",
symbol_size=6,
linestyle_opts=opts.LineStyleOpts(width=2),
y_axis=cummulative['累计发行总额'].tolist(),
label_opts=opts.LegendOpts(is_show=False),
)
.extend_axis(yaxis=opts.AxisOpts(
name= "总额",
type_="value",
position='right',
offset=60,
axislabel_opts=opts.LabelOpts(formatter="{value}亿",position="right"),
),).extend_axis(
yaxis=opts.AxisOpts(
name="数量",
type_="value",
position="left",
offset=0,
axislabel_opts=opts.LabelOpts(formatter="{value}"),
)
)
.extend_axis(
yaxis=opts.AxisOpts(
name="总额",
type_="value",
position="left",
offset=60,
axislabel_opts=opts.LabelOpts(formatter="{value}亿"),
)
)
.set_global_opts(
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
xaxis_opts=opts.AxisOpts(
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(color="#E8E8E8")
),
),
yaxis_opts=opts.AxisOpts(
name = "数量",
type_="value",
position="right",
offset=0,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(width=1)
),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(color='#E8E8E8')
),
),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
background_color='white',
connected_background_color="white",
pixel_ratio=pixel_ratio,
name="pic",
),
)
),
)
)
line.overlap(bar)
return line
def draw_chart(cls, data):
kline = (
Kline().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="",
y_axis=data["datas"],
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
markline_opts=opts.MarkLineOpts(
label_opts=opts.LabelOpts(position="middle",
color="blue",
font_size=15),
data=cls.split_data_part(data),
symbol=["circle", "none"],
),
).set_series_opts(markarea_opts=opts.MarkAreaOpts(
is_silent=True, data=cls.split_data_part(data))).
set_global_opts(
title_opts=opts.TitleOpts(title="K线周期图表", pos_left="0"),
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True)),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="line"),
datazoom_opts=[
opts.DataZoomOpts(is_show=False,
type_="inside",
xaxis_index=[0,
0],
range_end=100),
opts.DataZoomOpts(is_show=True,
xaxis_index=[0,
1],
pos_top="97%",
range_end=100),
opts.DataZoomOpts(is_show=False,
xaxis_index=[0,
2],
range_end=100),
opts.DataZoomOpts(is_show=False,
xaxis_index=[0,
3],
range_end=100),
],
# 三个图的 axis 连在一块
# axispointer_opts=opts.AxisPointerOpts(
# is_show=True,
# link=[{"xAxisIndex": "all"}],
# label=opts.LabelOpts(background_color="#777"),
# ),
))
kline_line = (
Line().add_xaxis(xaxis_data=data["times"])
# # EMA10
# .add_yaxis(
# series_name="EMA10",
# y_axis=data["ema10"],
# xaxis_index=2,
# yaxis_index=2,
# label_opts=opts.LabelOpts(is_show=False),
# )
# EMA144
# .add_yaxis(
# series_name="EMA144",
# y_axis=data["ema144"],
# xaxis_index=2,
# yaxis_index=2,
# label_opts=opts.LabelOpts(is_show=False),
# )
# boll_median
.add_yaxis(
series_name="median",
y_axis=data["boll_median"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
# boll_upper
.add_yaxis(
series_name="upper",
y_axis=data["boll_upper"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
# boll_lower
.add_yaxis(
series_name="lower",
y_axis=data["boll_lower"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
split_number=3,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
))
# Overlap Kline + Line
overlap_kline_line = kline.overlap(kline_line)
# Bar-1
bar_1 = (
Bar().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="Volumn",
yaxis_data=data["vols"],
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
# 根据 echarts demo 的原版是这么写的
# itemstyle_opts=opts.ItemStyleOpts(
# color=JsCode("""
# function(params) {
# var colorList;
# if (data.datas[params.dataIndex][1]>data.datas[params.dataIndex][0]) {
# colorList = '#ef232a';
# } else {
# colorList = '#14b143';
# }
# return colorList;
# }
# """)
# )
# 改进后在 grid 中 add_js_funcs 后变成如下
itemstyle_opts=opts.ItemStyleOpts(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=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
# Bar-2 (Overlap Bar + Line)
bar_2 = (Bar().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="MACD",
yaxis_data=data["macds"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
legend_opts=opts.LegendOpts(is_show=False),
))
line_2 = (Line().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="DIF",
y_axis=data["difs"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="DEA",
y_axis=data["deas"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False)))
rsi_line = (Line().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="rsi6",
y_axis=data["rsi6"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="rsi12",
y_axis=data["rsi12"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="rsi24",
y_axis=data["rsi24"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False)))
# MACD
overlap_bar_line = bar_2.overlap(line_2)
# 最后的 Grid
grid_chart = Grid(
init_opts=opts.InitOpts(width="1400px", height="800px"))
# 这个是为了把 data.datas 这个数据写入到 html 中,还没想到怎么跨 series 传值
# demo 中的代码也是用全局变量传的
grid_chart.add_js_funcs("var barData = {}".format(data["datas"]))
# K线图和 MA5 的折线图
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="4%",
pos_right="1%",
height="45%"),
)
# Volumn 柱状图
grid_chart.add(
bar_1,
grid_opts=opts.GridOpts(pos_left="4%",
pos_right="1%",
pos_top="56%",
height="10%"),
)
# MACD DIFS DEAS
grid_chart.add(
overlap_bar_line,
grid_opts=opts.GridOpts(pos_left="4%",
pos_right="1%",
pos_top="68%",
height="14%"),
)
# rsi6
grid_chart.add(
rsi_line,
grid_opts=opts.GridOpts(pos_left="4%",
pos_right="1%",
pos_top="84%",
height="10%"),
)
return grid_chart.render_embed()
# radius=[30, 75],
# is_legend_show=False,
# is_label_show=True,
)
pie1.render('评分.html')
#%%
datas['dates'] = datas.date.apply(lambda x:pd.Timestamp(x).date())
datas['time'] = datas.date.apply(lambda x:pd.Timestamp(x).time().hour)
num_date = datas.author.groupby(datas['dates']).count()
# 评论数时间分布
chart = Line("评论数时间分布")
chart.use_theme('dark')
chart.add( '评论数时间分布',num_date.index, num_date.values, is_fill=True, line_opacity=0.2,
area_opacity=0.4, symbol=None)
chart.render('评论时间分布.html')
# 好评字数分布
datalikes = datas.loc[datas.likes>5]
datalikes['num'] = datalikes.content.apply(lambda x:len(x))
chart = Scatter("likes")
chart.use_theme('dark')
chart.add('likes', np.log(datalikes.likes), datalikes.num, is_visualmap=True,
xaxis_name = 'log(评论字数)',
)
Line(opts.InitOpts(bg_color="#1A1835", ))
# 设置图形的全局参数
.set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=False),
legend_opts=opts.LegendOpts(textstyle_opts=opts.TextStyleOpts(
color='#90979c')),
xaxis_opts=opts.AxisOpts(
type_="category",
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(
color="rgba(204,187,225,0.5)")),
splitline_opts=opts.SplitLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False)),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(
color="rgba(204,187,225,0.5)")),
),
datazoom_opts=opts.DataZoomOpts(
is_show=True, )).add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="访问量",
y_axis=y_data,
symbol="circle",
symbol_size=10,
is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color="#6f7de3"),
markpoint_opts=opts.MarkPointOpts(
label_opts=opts.LabelOpts(color='#fff'),
data=[
opts.MarkPointItem(type_='max', name='最大值'),
opts.MarkPointItem(type_='min', name='最小值')
])).add_yaxis(
series_name="订单量",
y_axis=y2_data,
symbol="circle",
symbol_size=10,
is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color="#c257F6"),
markpoint_opts=opts.MarkPointOpts(
label_opts=opts.LabelOpts(color='#fff'),
data=[
opts.MarkPointItem(type_='max', name='最大值'),
opts.MarkPointItem(type_='min', name='最小值')
])).render("basic_line_chart.html"))
xaxis_opts=opts.AxisOpts(
type_="category",
axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow"),
),
yaxis_opts=opts.AxisOpts(
name="水量",
type_="value",
min_=0,
max_=250,
interval=50,
axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
)
)
line = (
Line()
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="平均温度",
yaxis_index=1,
y_axis=[2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2],
label_opts=opts.LabelOpts(is_show=False),
)
)
#
bar.overlap(line).render("mixed_bar_and_line.html")
linestyle_opts=opts.LineStyleOpts(color="#5793f3")
),
axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
),
title_opts=opts.TitleOpts(title="Grid-Overlap-多 X/Y 轴示例"),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
legend_opts=opts.LegendOpts(pos_left="25%"),
)
)
line = (
Line()
.add_xaxis(["{}月".format(i) for i in range(1, 13)])
.add_yaxis(
"平均温度",
[2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2],
yaxis_index=2,
color="#675bba",
label_opts=opts.LabelOpts(is_show=False),
)
)
bar1 = (
Bar()
.add_xaxis(["{}月".format(i) for i in range(1, 13)])
.add_yaxis(
"蒸发量 1",
[2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3],
color="#d14a61",
xaxis_index=1,
yaxis_index=3,
def ka_to_echarts(ka: KlineAnalyze, width="1500px", height='800px'):
# 配置项设置
# ------------------------------------------------------------------------------------------------------------------
bg_color = "#1f212d" # 背景
up_color = "#F9293E"
down_color = "#00aa3b"
init_opts = opts.InitOpts(bg_color=bg_color, width=width, height=height, animation_opts=opts.AnimationOpts(False))
title_opts = opts.TitleOpts(title="{} - {}".format(ka.symbol, ka.name),
subtitle="from {} to {}".format(ka.start_dt, ka.end_dt),
pos_top="1%",
title_textstyle_opts=opts.TextStyleOpts(color=up_color, font_size=20),
subtitle_textstyle_opts=opts.TextStyleOpts(color=down_color, font_size=12))
label_not_show_opts = opts.LabelOpts(is_show=False)
legend_not_show_opts = opts.LegendOpts(is_show=False)
red_item_style = opts.ItemStyleOpts(color=up_color)
green_item_style = opts.ItemStyleOpts(color=down_color)
k_style_opts = opts.ItemStyleOpts(color=up_color, color0=down_color, border_color=up_color,
border_color0=down_color, opacity=0.8)
legend_opts = opts.LegendOpts(is_show=True, pos_top="1%", pos_left="30%", item_width=14, item_height=8,
textstyle_opts=opts.TextStyleOpts(font_size=12, color="#0e99e2"))
brush_opts = opts.BrushOpts(tool_box=["rect", "polygon", "keep", "clear"],
x_axis_index="all", brush_link="all",
out_of_brush={"colorAlpha": 0.1}, brush_type="lineX")
axis_pointer_opts = opts.AxisPointerOpts(is_show=True, link=[{"xAxisIndex": "all"}])
dz_inside = opts.DataZoomOpts(False, "inside", xaxis_index=[0, 1, 2])
dz_slider = opts.DataZoomOpts(True, "slider", xaxis_index=[0, 1, 2], pos_top="96%", pos_bottom="0%")
yaxis_opts = opts.AxisOpts(is_scale=True, axislabel_opts=opts.LabelOpts(color="#c7c7c7", font_size=8, position="inside"))
grid0_xaxis_opts = opts.AxisOpts(type_="category", grid_index=0, axislabel_opts=label_not_show_opts,
split_number=20, min_="dataMin", max_="dataMax",
is_scale=True, boundary_gap=False, axisline_opts=opts.AxisLineOpts(is_on_zero=False))
tool_tip_opts = opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
position=JsCode("""
function (pos, params, el, elRect, size) {
var obj = {top: 10};
obj[['left', 'right'][+(pos[0] < size.viewSize[0] / 2)]] = 30;
return obj;
}
"""),
textstyle_opts=opts.TextStyleOpts(color="#000"),
)
# 数据预处理
# ------------------------------------------------------------------------------------------------------------------
dts = [x['dt'] for x in ka.kline_raw]
k_data = [[x['open'], x['close'], x['low'], x['high']] for x in ka.kline_raw]
ma = ka.ma
vol = []
for row in ka.kline_raw:
item_style = red_item_style if row['close'] > row['open'] else green_item_style
bar = opts.BarItem(value=row['vol'], itemstyle_opts=item_style, label_opts=label_not_show_opts)
vol.append(bar)
macd = []
for row in ka.macd:
item_style = red_item_style if row['macd'] > 0 else green_item_style
bar = opts.BarItem(value=round(row['macd'], 4), itemstyle_opts=item_style, label_opts=label_not_show_opts)
macd.append(bar)
diff = [round(x['diff'], 4) for x in ka.macd]
dea = [round(x['dea'], 4) for x in ka.macd]
# K 线主图
# ------------------------------------------------------------------------------------------------------------------
chart_k = Kline()
chart_k.add_xaxis(xaxis_data=dts)
chart_k.add_yaxis(series_name="Kline", y_axis=k_data, itemstyle_opts=k_style_opts)
chart_k.set_global_opts(
legend_opts=legend_opts,
datazoom_opts=[dz_inside, dz_slider],
yaxis_opts=yaxis_opts,
tooltip_opts=tool_tip_opts,
axispointer_opts=axis_pointer_opts,
brush_opts=brush_opts,
title_opts=title_opts,
xaxis_opts=grid0_xaxis_opts
)
# 均线图
# ------------------------------------------------------------------------------------------------------------------
chart_ma = Line()
chart_ma.add_xaxis(xaxis_data=dts)
ma_keys = [x for x in ma[0].keys() if "ma" in x][:3]
ma_colors = ["#39afe6", "#da6ee8", "#00940b"]
for i, k in enumerate(ma_keys):
y_data = [x[k] for x in ma]
chart_ma.add_yaxis(series_name=k.upper(), y_axis=y_data, is_smooth=True,
is_selected=False, symbol_size=0, label_opts=label_not_show_opts,
linestyle_opts=opts.LineStyleOpts(opacity=0.8, width=1.0, color=ma_colors[i]))
chart_ma.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_ma)
# 缠论结果
# ------------------------------------------------------------------------------------------------------------------
fx_dts = [x['dt'] for x in ka.fx_list]
fx_val = [x['fx'] for x in ka.fx_list]
chart_fx = Scatter()
chart_fx.add_xaxis(fx_dts)
chart_fx.add_yaxis(series_name="FX", y_axis=fx_val, is_selected=False,
symbol="circle", symbol_size=6, label_opts=label_not_show_opts,
itemstyle_opts=opts.ItemStyleOpts(color="rgba(152, 147, 193, 1.0)",))
chart_fx.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_fx)
bi_dts = [x['dt'] for x in ka.bi_list]
bi_val = [x['bi'] for x in ka.bi_list]
chart_bi = Scatter()
chart_bi.add_xaxis(bi_dts)
chart_bi.add_yaxis(series_name="BI", y_axis=bi_val, is_selected=True,
symbol="diamond", symbol_size=10, label_opts=label_not_show_opts,
itemstyle_opts=opts.ItemStyleOpts(color="rgba(184, 117, 225, 1.0)",))
chart_bi.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_bi)
xd_dts = [x['dt'] for x in ka.xd_list]
xd_val = [x['xd'] for x in ka.xd_list]
chart_xd = Scatter()
chart_xd.add_xaxis(xd_dts)
chart_xd.add_yaxis(series_name="XD", y_axis=xd_val, is_selected=True, symbol="triangle", symbol_size=10,
itemstyle_opts=opts.ItemStyleOpts(color="rgba(37, 141, 54, 1.0)",))
chart_xd.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_xd)
# 成交量图
# ------------------------------------------------------------------------------------------------------------------
chart_vol = Bar()
chart_vol.add_xaxis(dts)
chart_vol.add_yaxis(series_name="Volume", y_axis=vol, bar_width='60%')
chart_vol.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=True, font_size=8, color="#9b9da9"),
),
yaxis_opts=yaxis_opts, legend_opts=legend_not_show_opts,
)
# MACD图
# ------------------------------------------------------------------------------------------------------------------
chart_macd = Bar()
chart_macd.add_xaxis(dts)
chart_macd.add_yaxis(series_name="MACD", y_axis=macd, bar_width='60%')
chart_macd.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True, color="#c7c7c7"),
),
legend_opts=opts.LegendOpts(is_show=False),
)
line = Line()
line.add_xaxis(dts)
line.add_yaxis(series_name="DIFF", y_axis=diff, label_opts=label_not_show_opts, is_symbol_show=False,
linestyle_opts=opts.LineStyleOpts(opacity=0.8, width=1.0, color="#da6ee8"))
line.add_yaxis(series_name="DEA", y_axis=dea, label_opts=label_not_show_opts, is_symbol_show=False,
linestyle_opts=opts.LineStyleOpts(opacity=0.8, width=1.0, color="#39afe6"))
chart_macd = chart_macd.overlap(line)
grid0_opts = opts.GridOpts(pos_left="0%", pos_right="1%", pos_top="12%", height="58%")
grid1_opts = opts.GridOpts(pos_left="0%", pos_right="1%", pos_top="74%", height="8%")
grid2_opts = opts.GridOpts(pos_left="0%", pos_right="1%", pos_top="86%", height="10%")
grid_chart = Grid(init_opts)
grid_chart.add(chart_k, grid_opts=grid0_opts)
grid_chart.add(chart_vol, grid_opts=grid1_opts)
grid_chart.add(chart_macd, grid_opts=grid2_opts)
return grid_chart
df.sort_index(inplace=True) # 索引排序，inplace=True表示修改这个索引
df.head()
# In[12]:
# 2、使用pyecharts绘制折线图
from pyecharts.charts import Line
from pyecharts import options as opts
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')
# In[13]:
# 折线图
line = Line() # 折线图对象
# x轴
line.add_xaxis(df.index.to_list()) # x轴数据：列表——日期
# 每个y轴
line.add_yaxis("开盘价", df['open'].round(2).to_list())
line.add_yaxis("收盘价", df['close'].round(2).to_list())
# 图标配置
line.set_global_opts(title_opts=opts.TitleOpts(title='百度股票2019年'),
tooltip_opts=opts.TooltipOpts(trigger='axix',
axis_pointer_type='cross'))
# In[15]:
def trans_data_to_use(dataset: list):
size = len(dataset)
# 元组中可能数据大小不一致，因此先进行转化
dataset = np.array(dataset)
k = 1 / 1024
# 得到测试的时刻
x_data = dataset[:, 10]
y_data_1 = np.array(dataset[:, 1], float) * k
y_data_1 = np.round(y_data_1, decimals=1)
y_data_2 = np.array(dataset[:, 2], float) * k
y_data_2 = np.round(y_data_2, decimals=1)
y_data_3 = np.array(dataset[:, 3], float) * k
y_data_3 = np.round(y_data_3, decimals=1)
y_data_4 = np.array(dataset[:, 4], float) * k
y_data_4 = np.round(y_data_4, decimals=1)
y_data_5 = np.array(dataset[:, 5], float) * k
y_data_5 = np.round(y_data_5, decimals=1)
y_data_6 = np.array(dataset[:, 6], float) * k
y_data_6 = np.round(y_data_6, decimals=1)
y_data_7 = np.array(dataset[:, 7], float) * k
y_data_7 = np.round(y_data_7, decimals=1)
y_data_8 = np.array(dataset[:, 8], float) * k
y_data_8 = np.round(y_data_8, decimals=1)
(Line(init_opts=opts.InitOpts(width="1600px", height="800px")).add_xaxis(
xaxis_data=x_data).add_yaxis(
series_name="java_heap",
y_axis=y_data_1,
linestyle_opts=opts.LineStyleOpts(width=2),
).add_yaxis(
series_name="native_heap",
y_axis=y_data_2,
linestyle_opts=opts.LineStyleOpts(width=2)).add_yaxis(
series_name="code",
y_axis=y_data_3,
linestyle_opts=opts.LineStyleOpts(width=2)).add_yaxis(
series_name="stack",
y_axis=y_data_4,
linestyle_opts=opts.LineStyleOpts(width=2)).add_yaxis(
series_name="graphics",
y_axis=y_data_5,
linestyle_opts=opts.LineStyleOpts(width=2)).add_yaxis(
series_name="private_other",
y_axis=y_data_6,
linestyle_opts=opts.LineStyleOpts(width=2)).
add_yaxis(series_name="system",
y_axis=y_data_7,
linestyle_opts=opts.LineStyleOpts(width=2)).add_yaxis(
series_name="total",
y_axis=y_data_8,
linestyle_opts=opts.LineStyleOpts(width=2)).set_global_opts(
title_opts=opts.TitleOpts(title="memory information",
pos_left="center"),
tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="{a} <br/>{b} : {c}"),
legend_opts=opts.LegendOpts(pos_left="left"),
xaxis_opts=opts.AxisOpts(type_="category", name="x"),
yaxis_opts=opts.AxisOpts(
type_="log",
name="y",
splitline_opts=opts.SplitLineOpts(is_show=True),
is_scale=True,
),
).render("memory.html"))
from pywebio.output import put_html
import pyecharts.options as opts
from pyecharts.charts import Line
from pyecharts.faker import Faker
c = (
Line()
.add_xaxis(Faker.choose())
.add_yaxis(
"商家A",
Faker.values(),
markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")]),
)
.add_yaxis(
"商家B",
Faker.values(),
markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")]),
)
.set_global_opts(title_opts=opts.TitleOpts(title="Line-MarkLine"))
)
c.width = "100%"
put_html(c.render_notebook())
def paint(cls,
result,
file_name,
title="标题",
is_df=False,
indicator_index={}):
"""
绘制K线
:param result: 数据
:param file_name: 保存到指定文件
:param title: 标题
:param is_df: 是否是DataFrame类型数据
:param indicator_index: 指标 {'ma20': 6} -- {'指标': '索引'}
:return:
"""
time_list = []
kline_data = []
line_data = []
# print(result)
if is_df:
# 遍历DataFrame
result = result.values
for item in result:
# 数据
kline_data.append([
float(item[1]), # open
float(item[4]), # close
float(item[3]), # low
float(item[2]), # high
])
# 时间
time_list.append(str(item[0]))
# line
if 'ma20' in indicator_index:
line_data.append(item[indicator_index['ma20']])
else:
for item in result:
# 数据
kline_data.append([
float(item['open']),
float(item['close']),
float(item['low']),
float(item['high']),
])
# 时间
time_list.append(str(item['candle_begin_time']))
# K线
kline = (Kline().add_xaxis(time_list).add_yaxis(
"K线", kline_data).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()],
title_opts=opts.TitleOpts(title=title),
))
# 均线
line = (Line().add_xaxis(time_list).add_yaxis(
"MA30", line_data).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)))
kline.overlap(line)
return kline.render_embed()
暂无
"""
x_data = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
y_data = [820, 932, 901, 934, 1290, 1330, 1320]
(
Line()
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="",
y_axis=y_data,
symbol="emptyCircle",
is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),
areastyle_opts=opts.AreaStyleOpts(opacity=1, color="#C67570"),
)
.set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=False),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
)
# 设置 boundary_gap 的时候一定要放在最后一个配置项里, 不然会被覆盖
.render("basic_area_chart.html")
)
# bar demo2
bar = (Bar(init_opts=opts.InitOpts(theme=ThemeType.CHALK)).add_xaxis(
["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋",
"袜子"]).add_yaxis("商家A", [5, 20, 36, 10, 75, 90]).add_yaxis(
"商家B", [15, 6, 45, 20, 35, 66]).set_global_opts(
title_opts=opts.TitleOpts(title="主标题", subtitle="副标题")))
bar.render()
lin = (
Line(init_opts=opts.InitOpts(theme=ThemeType.CHALK)).add_xaxis([
"衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"
]).add_yaxis("商家A", [5, 20, 36, 10, 75, 90], is_smooth=True).add_yaxis(
"商家B", [15, 6, 45, 20, 35, 66], is_smooth=True).set_global_opts(
title_opts=opts.TitleOpts(title="主标题", subtitle="副标题"),
xaxis_opts=opts.AxisOpts(name='日期'),
yaxis_opts=opts.AxisOpts(
name='数量',
max_=16000,
min_=1,
type_="log", # 坐标轴配置项
splitline_opts=opts.SplitLineOpts(is_show=True), # 分割线配置项
axisline_opts=opts.AxisLineOpts(is_show=True))))
lin.render()
lin.overlap(bar)
lin.render()
get_line()
value = [95.1, 23.2, 43.3, 66.4, 88.5]
attr = ["China", "Canada", "Brazil", "Russia", "United States"]
map = Map("世界地图示例")
from pyecharts import options as opts
from pyecharts.charts import Bar, Grid, Line
from pyecharts.faker import Faker
bar = (Bar().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="Grid-Bar")))
line = (Line().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="Grid-Line", pos_top="48%"),
legend_opts=opts.LegendOpts(pos_top="48%"),
))
grid = (Grid().add(bar, grid_opts=opts.GridOpts(pos_bottom="60%")).add(
line, grid_opts=opts.GridOpts(pos_top="60%")).render("grid_vertical.html"))
c = (Line(init_opts=opts.InitOpts(
bg_color=JsCode(background_color_js))).add_xaxis(
xaxis_data=x_data).add_yaxis(
series_name="注册总量",
y_axis=y_data,
is_smooth=True,
is_symbol_show=True,
symbol="circle",
symbol_size=6,
linestyle_opts=opts.LineStyleOpts(color="#fff"),
label_opts=opts.LabelOpts(is_show=True,
position="top",
color="white"),
itemstyle_opts=opts.ItemStyleOpts(color="red",
border_color="#fff",
border_width=3),
tooltip_opts=opts.TooltipOpts(is_show=False),
areastyle_opts=opts.AreaStyleOpts(color=JsCode(area_color_js),
opacity=1),
).set_global_opts(
title_opts=opts.TitleOpts(
title="OCTOBER 2015",
pos_bottom="5%",
pos_left="center",
title_textstyle_opts=opts.TextStyleOpts(color="#fff",
font_size=16),
),
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=25,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
),
yaxis_opts=opts.AxisOpts(
type_="value",
position="right",
axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=15,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
),
legend_opts=opts.LegendOpts(is_show=False),
))
Line().add_xaxis(date_list).add_yaxis(
'确诊数据',
confirm_list,
is_smooth=True,
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average")])).add_yaxis(
'疑似数据',
suspect_list,
is_smooth=True,
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average")]),
).add_yaxis(
'死亡数据',
dead_list,
is_smooth=True,
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average")]),
).add_yaxis(
'治愈数据',
heal_list,
is_smooth=True,
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average")]),
)
# 隐藏数字 设置面积
.set_series_opts(areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False))
# 设置x轴标签旋转角度
.set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30)),
yaxis_opts=opts.AxisOpts(name='人数', min_=3),
title_opts=opts.TitleOpts(title='2019-nCoV疫情曲线图')))
def grid_mutil_yaxis() -> Grid:
x_data = ["{}月".format(i) for i in range(1, 13)]
bar = (
Bar()
.add_xaxis(x_data)
.add_yaxis(
"蒸发量",
[2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3],
yaxis_index=0,
color="#d14a61",
)
.add_yaxis(
"降水量",
[2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3],
yaxis_index=1,
color="#5793f3",
)
.extend_axis(
yaxis=opts.AxisOpts(
name="蒸发量",
type_="value",
min_=0,
max_=250,
position="right",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="#d14a61")
),
axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
)
)
.extend_axis(
yaxis=opts.AxisOpts(
type_="value",
name="温度",
min_=0,
max_=25,
position="left",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="#675bba")
),
axislabel_opts=opts.LabelOpts(formatter="{value} °C"),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1)
),
)
)
.set_global_opts(
yaxis_opts=opts.AxisOpts(
name="降水量",
min_=0,
max_=250,
position="right",
offset=80,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="#5793f3")
),
axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
),
title_opts=opts.TitleOpts(title="Grid-多 Y 轴示例"),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
)
)
line = (
Line()
.add_xaxis(x_data)
.add_yaxis(
"平均温度",
[2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2],
yaxis_index=2,
color="#675bba",
label_opts=opts.LabelOpts(is_show=False),
)
)
bar.overlap(line)
return Grid().add(
bar, opts.GridOpts(pos_left="5%", pos_right="20%"), is_control_axis_index=True
)
import pyecharts.options as opts
from pyecharts.charts import Line
from pyecharts.faker import Faker
y = Faker.values()
y[3], y[5] = None, None
c = (Line().add_xaxis(Faker.choose()).add_yaxis(
"商家A", y, is_connect_nones=True).set_global_opts(title_opts=opts.TitleOpts(
title="Line-连接空数据")).render("Line_connect_null.html"))
def draw(cls,
data_list: typing.List[ClassifierResult],
report_path: str = None,
data_path: str = None):
# draw line chart
x_axis = [str(i.timestamp) for i in data_list]
y_axis = [i.stage for i in data_list]
line = Line()
line.add_xaxis(x_axis)
line.add_yaxis("stage", y_axis, is_step=True, is_symbol_show=True)
line.set_global_opts(
title_opts=opts.TitleOpts(title='Trend'),
toolbox_opts=opts.ToolboxOpts(is_show=True),
tooltip_opts=opts.TooltipOpts(is_show=True,
trigger='axis',
axis_pointer_type='cross'),
)
# draw bar chart
bar = Bar()
x_axis = sorted(list(set([i.stage for i in data_list])))
bar.add_xaxis(x_axis)
y_axis = list()
offset = data_list[1].timestamp - data_list[0].timestamp
for each_stage_name in x_axis:
each_stage = sorted(
[i for i in data_list if i.stage == each_stage_name],
key=lambda x: x.frame_id)
time_cost = each_stage[-1].timestamp - each_stage[
0].timestamp + offset
y_axis.append(time_cost)
bar.add_yaxis('time cost', y_axis)
bar.set_global_opts(
title_opts=opts.TitleOpts(title="Time Cost"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
)
logger.debug(f'time cost: {dict(zip(x_axis, y_axis))}')
# draw pie chart
pie = Pie()
pie.add('', [list(z) for z in zip(x_axis, y_axis)])
pie.set_global_opts(toolbox_opts=opts.ToolboxOpts(is_show=True), )
# merge charts
page = Page()
page.add(line)
page.add(bar)
page.add(pie)
# insert sample pictures' path
if data_path and os.path.isdir(data_path):
data_path = os.path.abspath(data_path)
stage_list = [
os.path.join(data_path, i) for i in os.listdir(data_path)
]
else:
logger.warning(f'data path {data_path} not existed')
stage_list = []
# save to file
if not report_path:
report_path = f'{toolbox.get_timestamp_str()}.html'
template = Template(TEMPLATE)
template_content = template.render(
chart=Markup(page.render_embed()),
pic_list=stage_list,
)
with open(report_path, "w") as fh:
fh.write(template_content)
logger.info(f'save report to {report_path}')
def plot_kline(ka, bs=None, file_html="kline.html", width="1400px", height="680px"):
"""
:param ka: KlineAnalyze
:param bs: pd.DataFrame
买卖点，包含三个字段 ["操作提示", "交易时间", "交易价格"]
:param file_html: str
:param width: str
:param height: str
:return: None
"""
df = ka.to_df(use_macd=True, ma_params=(5, 20,))
x = df.dt.to_list()
title = "%s | %s 至 %s" % (ka.symbol, ka.start_dt, ka.end_dt)
kline = (
Kline()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="",
y_axis=df[['open', 'close', 'low', 'high']].values.tolist(),
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
)
.set_series_opts(
markarea_opts=opts.MarkAreaOpts(is_silent=True)
)
.set_global_opts(
title_opts=opts.TitleOpts(title=title, pos_left="0"),
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
is_scale=True, splitline_opts=opts.SplitLineOpts(is_show=True),
axislabel_opts=opts.LabelOpts(is_show=True, position="inside")
),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="line"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False, type_="inside", xaxis_index=[0, 0], range_end=100
),
opts.DataZoomOpts(
is_show=True, xaxis_index=[0, 1], pos_top="96%", range_end=100
),
opts.DataZoomOpts(is_show=False, xaxis_index=[0, 2], range_end=100),
],
# 三个图的 axis 连在一块
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{"xAxisIndex": "all"}],
label=opts.LabelOpts(background_color="#777"),
),
)
)
kline_line = (
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="笔",
y_axis=df.bi.tolist(),
is_smooth=False,
is_connect_nones=True,
symbol='diamond',
symbol_size=8,
linestyle_opts=opts.LineStyleOpts(opacity=1, type_='dotted', width=2),
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="线段",
y_axis=df.xd.tolist(),
is_smooth=False,
is_connect_nones=True,
symbol='triangle',
symbol_size=12,
linestyle_opts=opts.LineStyleOpts(opacity=1, type_='solid', width=2),
label_opts=opts.LabelOpts(is_show=True, position='right'),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
split_number=3,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True, position="inside"),
),
)
)
# Overlap Kline + Line
overlap_kline_line = kline.overlap(kline_line)
if isinstance(bs, pd.DataFrame) and len(bs) > 0:
c = (
Scatter()
.add_xaxis(bs['交易时间'].to_list())
.add_yaxis(
"买卖点",
bs['交易价格'].to_list(),
label_opts=opts.LabelOpts(
is_show=True,
position="left",
formatter=JsCode(
"function(params){return bsName[params.dataIndex][0];}"
)
),
))
overlap_kline_line = overlap_kline_line.overlap(c)
# draw volume
bar_1 = (
Bar()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="Volumn",
yaxis_data=df.vol.tolist(),
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
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=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(is_show=True, position='inside')
),
legend_opts=opts.LegendOpts(is_show=False),
)
)
# Bar-2 (Overlap Bar + Line)
bar_2 = (
Bar()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="MACD",
yaxis_data=df.macd.tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
color=JsCode(
"""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
"""
)
),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True, position="inside"),
),
legend_opts=opts.LegendOpts(is_show=False),
)
)
line_2 = (
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="DIF",
y_axis=df['diff'].tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="DEA",
y_axis=df['dea'].tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
)
# draw MACD
overlap_bar_line = bar_2.overlap(line_2)
# 最后的 Grid
grid_chart = Grid(init_opts=opts.InitOpts(width=width, height=height, page_title=title))
grid_chart.add_js_funcs("var barData = {}".format(df[['open', 'close', 'low', 'high']].values.tolist()))
if isinstance(bs, pd.DataFrame) and len(bs) > 0:
grid_chart.add_js_funcs("var bsName = {}".format(bs[["操作提示", "交易价格"]].values.tolist()))
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", height="60%"),
)
grid_chart.add(
bar_1,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", pos_top="71%", height="10%"),
)
grid_chart.add(
overlap_bar_line,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", pos_top="82%", height="14%"),
)
grid_chart.render(path=file_html)
df['dateTime'] = df['date'] + ' ' + df['time']
line = (
Line().add_xaxis(df['dateTime'].tolist()).add_yaxis(
"下載",
df['download'],
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="平均值")
])).set_series_opts(
areastyle_opts=opts.AreaStyleOpts(opacity=0.1),
label_opts=opts.LabelOpts(is_show=False) #顯示資料點
).set_global_opts(
title_opts=opts.TitleOpts(title="FTP流量分析"),
toolbox_opts=opts.ToolboxOpts(),
datazoom_opts=opts.DataZoomOpts(),
xaxis_opts=opts.AxisOpts(
name="日期",
splitline_opts=opts.SplitLineOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(
type_="log",
name="秒",
splitline_opts=opts.SplitLineOpts(is_show=True),
is_scale=True),
#交點輔助線
tooltip_opts=opts.TooltipOpts(
is_show=True,
trigger='axis',
),
))
line.render()
def visual(self, name='model_visual', path=None, gif=False):
"""
Arguments:
name : str, train end save last image or gif file with html format name.
path : str, train end save last image or gif file with html format to path;
save_gif : bool, default False, if save_gif=True, train end save all image to gif;
Return:
a html file path.
"""
if path is not None:
assert tf.io.gfile.exists(path), "`path` not exist."
file = path + '/' + '{}.html'.format(name)
else:
file = '{}.html'.format(name)
page = Page(interval=1, layout=Page.SimplePageLayout)
plot_list = []
width_len = '750px'
height_len = '450px'
for metric_id, metric in enumerate(self.params.metrics):
if not gif:
line = Line(opts.InitOpts(width=width_len, height=height_len))
line = line.add_xaxis(list(range(1, self.params.polt_num + 1)))
line = line.add_yaxis(
'train',
Series(self.params.logs[metric]).round(4).tolist(),
is_smooth=True)
if self.params.valid_fmt.format(metric) in self.params.logs:
line = line.add_yaxis(
self.params.valid_fmt.split('_')[0],
Series(self.params.logs[self.params.valid_fmt.format(
metric)]).round(4).tolist(),
is_smooth=True)
line = line.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_='max', name='max_value'),
opts.MarkPointItem(type_='min', name='min_value')
]))
line = line.set_global_opts(
title_opts=opts.TitleOpts(title=metric),
xaxis_opts=opts.AxisOpts(
name=self.params.xlabel[self.params.mode],
name_location='center',
is_scale=True),
datazoom_opts=[
opts.DataZoomOpts(range_start=0, range_end=100)
],
toolbox_opts=opts.ToolboxOpts())
plot_list.append(line)
else:
timeline = Timeline(
opts.InitOpts(width=width_len,
height=height_len)).add_schema(
play_interval=100, is_auto_play=True)
for i in range(1, self.params.polt_num + 1):
line = Line(
opts.InitOpts(width=width_len, height=height_len))
line = line.add_xaxis(list(range(1, i + 1)))
line = line.add_yaxis(
'train',
Series(self.params.logs[metric])[:i].round(4).tolist(),
is_smooth=True)
if self.params.valid_fmt.format(
metric) in self.params.logs:
line = line.add_yaxis(
self.params.valid_fmt.split('_')[0],
Series(
self.params.logs[self.params.valid_fmt.format(
metric)])[:i].round(4).tolist(),
is_smooth=True)
line = line.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_='max', name='max_value'),
opts.MarkPointItem(type_='min', name='min_value')
]))
line = line.set_global_opts(
title_opts=opts.TitleOpts(title=metric),
xaxis_opts=opts.AxisOpts(
name=self.params.xlabel[self.params.mode],
name_location='center',
is_scale=True))
timeline.add(line, str(i))
plot_list.append(timeline)
page.add(*plot_list).render(file)
return file
from pyecharts import options as opts
# =============================================================================
# # V1 版本开始支持链式调用
# bar = (
# Bar()
# .add_xaxis(["衬衫", "毛衣", "领带", "裤子", "风衣", "高跟鞋", "袜子"])
# .add_yaxis("商家A", [114, 55, 27, 101, 125, 27, 105])
# .add_yaxis("商家B", [57, 134, 137, 129, 145, 60, 49])
# .set_global_opts(title_opts=opts.TitleOpts(title="某商场销售情况"))
# )
# bar.render()
#
# # 不习惯链式调用的开发者依旧可以单独调用方法
# bar = Bar()
# bar.add_xaxis(["衬衫", "毛衣", "领带", "裤子", "风衣", "高跟鞋", "袜子"])
# bar.add_yaxis("商家A", [114, 55, 27, 101, 125, 27, 105])
# bar.add_yaxis("商家B", [57, 134, 137, 129, 145, 60, 49])
# bar.set_global_opts(title_opts=opts.TitleOpts(title="某商场销售情况"))
# bar.render()
#
# =============================================================================
#普通折线图
c = (Line().add_xaxis(["衬衫", "毛衣", "领带", "裤子", "风衣", "高跟鞋", "袜子"]).add_yaxis(
"商家A", [114, 55, 27, 101, 125, 27, 105]).add_yaxis(
"商家B", [57, 134, 137, 129, 145, 60, 49]).set_global_opts(
title_opts=opts.TitleOpts(title="Line-基本示例")))
c.render()
def rendertohtml(self, resultset):
page = Page()
if resultset is None:
print('无数据')
return None
header = [
'日期', '股票代码 ', '股票名称 ', '持股数亿', '占比', '收盘价 ', '当日涨跌幅 ',
'持股市值亿 ', '一日市值变化亿', '五日市值变化亿', '十日市值变化亿'
]
tb = pt.PrettyTable()
tb.field_names = header # 设置表头
tb.align = 'c' # 对齐方式（c:居中，l居左，r:居右）
c = Line()
x = ['持股占比']
name = ''
HDDATELIST = []
SHAREHOLDSUMlist = [] # 持股数
SHARESRATElist = [] # 持股占比
SHAREHOLD = [] # 持股数量
zdflist = []
SHAREHOLDPRICEONElist = []
SHAREHOLDPRICEFIVElist = []
SHAREHOLDPRICETENlsit = []
# 取出占比数据
for data in resultset:
HDDATE = data['HDDATE']
# HDDATE = datetime.datetime.strptime(HDDATE1, '%Y-%m-%d').strftime('%Y%m%d')
HDDATELIST.append(HDDATE)
SCODE = data['SCODE']
SHAREHOLDSUM = data['SHAREHOLDSUM']
SHAREHOLDSUMlist.append(SHAREHOLDSUM)
SNAME = data['SNAME']
SHARESRATE = data['SHARESRATE']
SHARESRATElist.append(SHARESRATE)
CLOSEPRICE = data['CLOSEPRICE']
ZDF = data['ZDF']
zdflist.append(ZDF)
SHAREHOLDPRICE = format(data['SHAREHOLDPRICE'], '.3f')
SHAREHOLDPRICEONE = format(data['SHAREHOLDPRICEONE'], '.3f')
SHAREHOLDPRICEONElist.append(SHAREHOLDPRICEONE)
SHAREHOLDPRICEFIVE = format(data['SHAREHOLDPRICEFIVE'], '.3f')
SHAREHOLDPRICEFIVElist.append(SHAREHOLDPRICEFIVE)
SHAREHOLDPRICETEN = format(data['SHAREHOLDPRICETEN'], '.3f')
SHAREHOLDPRICETENlsit.append(SHAREHOLDPRICETEN)
tb.add_row([
HDDATE, SCODE, SNAME, SHAREHOLDSUM, SHARESRATE, CLOSEPRICE,
ZDF, SHAREHOLDPRICE, SHAREHOLDPRICEONE, SHAREHOLDPRICEFIVE,
SHAREHOLDPRICETEN
])
OUTFILE = '南向资金_' + SNAME + '.html'
# print(SHARESRATE)
x1 = HDDATELIST
y1 = SHARESRATElist # 将占比数据设置为y轴
y2 = SHAREHOLDSUMlist
y3 = zdflist
y4 = SHAREHOLDPRICEONElist
y5 = SHAREHOLDPRICEFIVElist
y6 = SHAREHOLDPRICETENlsit
# y2 = [1000, 300, 500]
# bar = Bar()
# 设置x轴
c.add_xaxis(xaxis_data=x)
c.add_xaxis(xaxis_data=x1)
# 设置y轴
c.add_yaxis(series_name='持股百分比', y_axis=y1)
# c.add_yaxis(series_name='持股数量亿', y_axis=y2)
# c.add_yaxis(series_name='涨跌幅', y_axis=y3)
c.add_yaxis(series_name='1日市值变动亿', y_axis=y4)
c.add_yaxis(series_name='5日市值变动亿', y_axis=y5)
c.add_yaxis(series_name='10日市值变动亿', y_axis=y6)
c.set_global_opts(title_opts=opts.TitleOpts(title='南向资金持股分析: ' +
SNAME))
# 生成html文件
# c.render(path=OUTFILE)
page.add(c)
page.render(path=OUTFILE)
# 如果要输出柱图
'''
bar = Bar()
然后将c 换成bar
'''
#获取港股日线数据并画K线图
getstockdata = self.getHK_stockQuotes(SCODE)
if len(getstockdata) < 90:
getstockdata = getstockdata
else:
getstockdata = getstockdata.tail(90) #只后90行
# print(getstockdata)
if getstockdata.items == None:
kline = ''
else:
# getstockdata['trade_date'] = pd.to_datetime(getstockdata.index) # 设置字段trade_date 为datetime
# getstockdata = getstockdata.set_index('trade_date') # 设置trade_date为索引
index = getstockdata.index.tolist()
# getstockdata.sort_values(by=[getstockdata.index().to_list,'close'],ascending=False)
# 设置四个绘图区域 包括 K线（均线），成交量，MACD
np.seterr(divide='ignore', invalid='ignore') # 忽略warning
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
fig, ax = plt.subplots(figsize=(9, 6)) # 创建fig对象
# 画绘图区域
gs = gridspec.GridSpec(2,
1,
left=0.08,
bottom=0.15,
right=0.99,
top=0.96,
wspace=None,
hspace=0,
height_ratios=[3.5, 1])
# 添加指标
graph_KAV = fig.add_subplot(gs[0, :]) # K线图
graph_VOL = fig.add_subplot(gs[1, :])
# graph_MACD = fig.add_subplot(gs[2, :])
# graph_KDJ = fig.add_subplot(gs[3, :])
mpf.candlestick2_ochl(graph_KAV,
getstockdata.open,
getstockdata.close,
getstockdata.high,
getstockdata.low,
width=0.5,
colorup='r',
colordown='g') # 绘制K线走势
# mpf.plot(getstockdata.iloc[:100],type='candle') # 绘制K线走势
# 绘制移动平均线图
getstockdata['Ma5'] = getstockdata.close.rolling(window=5).mean(
) # pd.rolling_mean(df_stockload.close,window=20)
getstockdata['Ma10'] = getstockdata.close.rolling(window=10).mean(
) # pd.rolling_mean(df_stockload.close,window=30)
getstockdata['Ma20'] = getstockdata.close.rolling(window=20).mean()
graph_KAV.plot(np.arange(0, len(index)),
getstockdata['Ma5'],
'black',
label='M5',
lw=1.0)
graph_KAV.plot(np.arange(0, len(index)),
getstockdata['Ma10'],
'green',
label='M10',
lw=1.0)
graph_KAV.plot(np.arange(0, len(index)),
getstockdata['Ma20'],
'blue',
label='M20',
lw=1.0)
# 添加网格
graph_KAV.grid()
graph_KAV.legend(loc='best')
graph_KAV.set_title(SCODE + ' ' + SNAME + '(日线)')
graph_KAV.set_ylabel(u"价格")
graph_KAV.set_xlim(0, len(index)) # 设置一下x轴的范围
# 绘制成交量图
graph_VOL.bar(
np.arange(0, len(index)),
getstockdata.volume,
color=[
'g'
if getstockdata.open[x] > getstockdata.close[x] else 'r'
for x in range(0, len(index))
])
graph_VOL.set_ylabel(u"成交量")
graph_VOL.set_xlim(0, len(index)) # 设置一下x轴的范围
graph_VOL.set_xticks(range(0, len(index), 1)) # X轴刻度设定 每1天标一个日期
# X-轴每个ticker标签都向右倾斜45度
for label in graph_KAV.xaxis.get_ticklabels():
label.set_visible(False)
for label in graph_VOL.xaxis.get_ticklabels():
label.set_visible(True)
label.set_fontsize(10)
plt.savefig('./Kline.jpg')
kline = '''<img src=./Kline.jpg />'''
# 将数据也输出到文件
s = tb.get_html_string()
with open(OUTFILE, 'a+', encoding='utf-8') as fw:
fw.write(kline)
fw.write(s)
fw.close()
webbrowser.open(OUTFILE) # 调用浏览器打开文件
def generate_mchart(self,lst_tscode=[],lst_macro=['shibor.1w','pimon.nt_yoy','pimon.ppi_yoy','pimon.m1_yoy','pimon.m2_yoy','pimon.allstock_circ_mv_sum'],
lst_findmedian=['pe',()],renderfile=False) -> Grid:
if self.exists == False:
return None
''' testing findmedian with a given index '''
# df=self._dataagent.query('index_weight',index_code='000300.SH',start_date='20200630', end_date='20200630')
# lst_findmedian=['pb',tuple(df['con_code'].values)]
''' ----------test end--------------------'''
lst_tscode=list(set([self.ts_code,*lst_tscode]))
# lst_findmedian[1] = tuple(self._dataagent.registry.loc[self._dataagent.registry['sec_type']=='stk']['ts_code']) if lst_findmedian[1] is None or len(lst_findmedian[1])==0 else lst_findmedian[1]
dat_cursor = self.cursorTick['timeAxis']
int_latestViewDate = self._dataagent.int_findClosestTradeDate(datetime.strftime(dat_cursor,'%Y%m%d') )
df_baseCal = self._dataagent.df_calTbl.loc[self._dataagent.df_calTbl['cal_date']<=int_latestViewDate]['cal_date'].copy()
lst_xaxis = ['{}/{}/{}'.format(i[:4],i[4:6],i[6:]) for i in df_baseCal.values.astype('str')] # xaxis values has to be STRING type, int will not work
linechart = (
Line()
.add_xaxis(lst_xaxis)
)
# 生成主线，即目标证券/指数，及比较用的指数
for str_tscode in lst_tscode:
# str_tscode = DataAgent.formatCode(code)
df_target = self._dataagent.query('load_alldaily',tpl_dateindex=tuple(df_baseCal),ts_code=str_tscode)
if len(df_target)>0:
df_toPlot = pd.merge(left=df_baseCal, right=df_target, how='left', left_on='cal_date', right_on='trade_date')
else:
continue
'''=====主图画图部分包括主标的和副标的价格曲线，在主图中续把主图及后面叠加图的option，y轴等设置好==='''
linename = '{} - {}'.format(str_tscode,self.sec_name)
lst_values = list(df_toPlot['close'])
lst_values = list(libs.normByRange(lst_values))
linechart.add_yaxis(linename, lst_values,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.3),
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
color="#00da3c",
),
)
linechart.extend_axis( # 所有保持原比例的折线都放在linechart2里，使用右侧y轴
yaxis=opts.AxisOpts(
name="倍数",
type_="value",
position="right",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="black")
),
axislabel_opts=opts.LabelOpts(formatter="{value}倍"),
)
)
linechart.set_global_opts(
title_opts=opts.TitleOpts(
title='宏观比较',
),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(name="等比例",
is_scale=True,
),
legend_opts=opts.LegendOpts(is_show=True, pos_top='5%', pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0],
range_start=75,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0],
type_="slider",
pos_top="90%",
range_start=75,
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000",font_size=10),
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{"xAxisIndex": "all"}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
)
'''=============================主图画图部分结束================='''
'''=========叠加图1 尝试读取pe等股票才有的属性，如失败则说明不是股票，循环继续============='''
try:
lst_pe = df_toPlot['pe'].values.tolist()
lst_pb = df_toPlot['pb'].values.tolist()
lst_pepb = [round(lst_pe[index]/value,2) for index, value in enumerate(lst_pb) if value!=0]
linechart.add_yaxis(str_tscode+"_PE", lst_pe, yaxis_index=1, #倍数使用右y轴
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
)
linechart.add_yaxis(str_tscode+"_PE/PB", lst_pepb, yaxis_index=1, #倍数使用右y轴
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
)
except:
continue
'''==========================叠加图1结束================================'''
df_shibor = self._dataagent.query('load_shibor')
# print('Supported shibor columns: {}'.format(df_shibor.columns))
df_aggDay = self._dataagent.query('load_aggDayKPI')
# print('Supported macro eco columns: {}'.format(df_aggDay.columns))
df_pimon = self._dataagent.query('load_piMon')
df_pimon = pd.merge(df_pimon, df_aggDay, how='left', left_on='date', right_on='trade_date') #与aggday按每月末日期合并以获得如“流通市值”等kpi
# print('Supported macro eco columns: {}'.format(df_pimon.columns))
'''=================叠加图2 所有股票的倍数类KPI，使用倍数类y轴============='''
lst_aggDayKPIloop = ['allstock_pe_median','allstock_pe_ttm_median','allstock_pb_median','allstock_pepb_median']
for kpi in lst_aggDayKPIloop:
lst_values = df_aggDay[kpi].values.tolist()
linechart.add_yaxis(kpi, lst_values, yaxis_index=1, #倍数使用右y轴
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
)
'''==========================叠加图2结束================================'''
'''===========叠加图3 宏观数据如shibor，cpi，m1等，按“。”分割类别进行图表生成，使用等比类y轴 ========'''
for item in lst_macro:
try:
topic,colname = item.split('.')
except:
print('WARNING! input code {} not recognized...'.format(item))
if topic=='shibor':
df_target = df_shibor
maxarr = df_target.max(axis=0, skipna=True).values[1:]
minarr = df_target.min(axis=0, skipna=True).values[1:]
deltaRng = max(maxarr)-min(minarr) # overwrite the normalization function delta to put all shibor rates comparable
elif topic=='pimon':
df_target = df_pimon
deltaRng = None
else:
print('WARNING! subject code {} is not recognized...'.format(topic))
if len(df_target)>0:
df_toPlot = pd.merge(df_baseCal, df_target, how='left', left_on='cal_date', right_on='date')
df_toPlot[colname] = df_toPlot[colname].bfill()
else:
continue
linename = item
lst_values = list(df_toPlot[colname])
lst_values = list(libs.normByRange(lst_values,delta=deltaRng))
linechart.add_yaxis(linename, lst_values, # 所有等比例缩放的折线都放在linechart里，使用左侧y轴
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1,opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
'''==========================叠加图3结束================================'''
'''========叠加图4 如参数给出需要寻找中位数的股票列表则读取相应所有本地文件后计算并生成叠加图标========'''
if lst_findmedian[1] is not None and len(lst_findmedian[1])>0 :
str_cat = lst_findmedian[0]
df_result = pd.DataFrame()
for str_tscode in lst_findmedian[1]:
try:
df = self._dataagent.query('load_alldaily',tpl_dateindex=tuple(df_baseCal),ts_code=str_tscode)[['ts_code','trade_date',str_cat]]
print('{} csv file loaded for {} trend analysis...'.format(str_tscode,str_cat),end='\r')
except Exception as e:
print('WARNING! {} csv file not found...'.format(str_tscode))
continue
df_result = pd.concat([df_result,df])
df_result.dropna(subset=[str_cat],inplace=True)
df_result = df_result.loc[df_result['trade_date']>19940301]
# libs.df_csv(cfg.PATH_BUGFILE,(df_result.groupby('trade_date')['ts_code'].count(),))
# print(df_result.groupby('trade_date')['ts_code'].count())
df_target = df_result.groupby('trade_date')[str_cat].median()
if len(df_target)>0:
df_toPlot = pd.merge(left=df_baseCal, right=df_target, how='left', left_on='cal_date', right_on='trade_date')
linename = '{}-median'.format(str_cat)
lst_values = list(df_toPlot[str_cat])
lst_values = list(lst_values)
linechart.add_yaxis(linename, lst_values, yaxis_index=1,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
)
else:
pass
'''==========================叠加图4结束================================'''
# put everything in a grid for easy resizing and positioning
gridChart = Grid()
gridChart.add(
linechart,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top='25%'),
)
fname = '{}{}.html'.format(cfg.PATH_ANAFILE,'macro')
gridChart.render(fname) if renderfile else None
self.mchart = gridChart #输出至可访问对象属性中
return self
