def draw_map_world(data, to_file, svg_name, label_name, number_max):
"""
画地图
:param data:
:param to_file:
:param svg_name:
:param label_name: 图例名称
:param number_max: 最大值
:return:
"""
geo = Map(init_opts=opts.InitOpts(width="800px", height="600px", bg_color='rgb(255, 255, 255)')) \
.add(label_name, data, maptype="world") \
.set_series_opts(label_opts=opts.LabelOpts(is_show=False),
showLegendSymbol=False) \
.set_global_opts(legend_opts=opts.LegendOpts(item_width=50,
item_height=30,
textstyle_opts=opts.TextStyleOpts(font_size=30)),
visualmap_opts=opts.VisualMapOpts(min_=0,
max_=int(number_max),
background_color='rgb(255, 255, 255)',
is_piecewise=True,
item_width=50,
item_height=30,
textstyle_opts=opts.TextStyleOpts(font_size=30)),
toolbox_opts=opts.ToolboxOpts(
feature=opts.ToolBoxFeatureOpts(
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
# brush=opts.ToolBoxFeatureBrushOpts(is_show=False),
)
),
)
# geo.render(to_file)
make_snapshot(snapshot, geo.render(to_file), svg_name) # 生成svg图片
def pyechart_Sunburst_plot(self, filedata, para):
from pyecharts.charts import Sunburst
file_name = '旭日图.html'
path_plotly = self.path_dir_plotly_html + os.sep + file_name # 文件路径，前面是文件夹后面是文件名
costumeTheme = self.themedict[para['theme']]
# -----------------------------------------------------------------------
df = pd.read_excel(filedata, sheet_name='Sheet1')
dic = self.Tree_dict(df)
sunburst = (
Sunburst(init_opts=opts.InitOpts(theme=costumeTheme,
width=para['width'],
height=para['height'])
) # costumeTheme=self.themedict[para['theme']]
.add(series_name="", data_pair=dic, radius=[
0, "90%"
]).set_global_opts(
title_opts=opts.TitleOpts(title=para["title"],
subtitle=para["subtitle"]),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
background_color="white"))),
#visualmap_opts=opts.VisualMapOpts(),
).set_series_opts(label_opts=opts.LabelOpts(
is_show=True,
color=para["color"],
position="inside",
font_size=para["font_size"],
font_family=para["font_family"])).render(path_plotly))
return path_plotly # 返回该HTML文件路径
def Line_charts(users, xaxis_data, yaxis_data, date) -> Line:
"""定义一个Line_charts函数"""
c = Line()
c.add_xaxis(xaxis_data=xaxis_data)
# 设置图例信息
c.add_yaxis(series_name=str(users) + "users" + " " + "date: " + date,
y_axis=yaxis_data,
is_smooth=True)
# c.add_yaxis(series_name='400users', y_axis=y2)
data_zoom = {
"show": False,
"title": {
"zoom": "data zoom",
"back": "data zoom restore"
}
}
# 数据项设置,全局只设置一次
c.set_global_opts(
# 设置标题
title_opts=opts.TitleOpts(title="不同users对应的TPM值"),
# 设置图例is_show=False是 不显示图例
legend_opts=opts.LegendOpts(is_show=True),
# 设置提示项
tooltip_opts=opts.TooltipOpts(trigger='axis',
axis_pointer_type='cross'),
# 工具箱的设置
toolbox_opts=opts.ToolboxOpts(
is_show=False,
feature=opts.ToolBoxFeatureOpts(data_zoom=data_zoom)))
return c
def Common_code(self, chart, path_plotly, para):
datazoom_opts = []
toolbox_opts = []
visualmap_opts = []
if para['DataZoom']:
datazoom_opts = opts.DataZoomOpts()
if para['ToolBox']:
toolbox_opts = opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
background_color="white", pixel_ratio=2)))
if para['VisualMap']:
visualmap_opts = opts.VisualMapOpts()
chart.set_global_opts(
title_opts=opts.TitleOpts(title=para["title"],
subtitle=para["subtitle"]),
legend_opts=opts.LegendOpts(item_width=50,
item_height=25), # 图例，默认25/14
visualmap_opts=visualmap_opts,
datazoom_opts=datazoom_opts,
toolbox_opts=toolbox_opts,
yaxis_opts=opts.AxisOpts(name=para['YAxis']),
xaxis_opts=opts.AxisOpts(name=para['XAxis']),
)
#chart.set_global_opts( visualmap_opts=opts.VisualMapOpts(orient="horizontal"))
chart.set_series_opts(
label_opts=opts.LabelOpts(is_show=True,
position=para['positon'],
font_size=para["font_size"],
font_family=para["font_family"]),
linestyle_opts=opts.LineStyleOpts(width=para['line_width'],
opacity=para['opacity'],
curve=para['curve']))
if not para['TimeLineOn']:
chart.render(path_plotly)
def pie_plot(data):
_p_data = data.copy()
pie = (Pie(init_opts=opts.InitOpts(width=fig_width, height=fig_height)).add(
series_name="占比",
data_pair=[list(z) for z in zip(_p_data.index, (_p_data.values).round(2).tolist())],
radius=["30%", "50%"],
# center=["30%","35%"],
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(legend_opts=opts.LegendOpts(is_show=True,pos_left="2%", orient="vertical",pos_top="10%"),
title_opts=opts.TitleOpts(title="",subtitle="数据来源：WIND"),
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",
),
)
)
) \
.set_series_opts(
tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"
),
label_opts=opts.LabelOpts(formatter= "{b}: {c}（{d}%）")
))
return pie
def __init__(self, width="1200px", height="800px"):
self.graph = Graph(init_opts=opts.InitOpts(
width=width, height=height,
bg_color='rgba(255,255,255)')).set_global_opts(
# 设置显示工具栏
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
name='networks', # 设置工具栏图片保存的文件名
), )))
self.line_width = 1
def draw_river_picture_right_legend(series_name,
data,
to_file,
svg_name,
pos_right,
width='800px'):
"""
主题河流图
:param width:
:param pos_right:
:param series_name: [str]
:param data: [[date, value, series_name]]
:param svg_name: svg文件
:param to_file: 结果文件
:return:
"""
bar = ThemeRiver(init_opts=opts.InitOpts(width="800px", height="600px", bg_color='white')) \
.add(
series_name=series_name,
data=data,
singleaxis_opts=opts.SingleAxisOpts(
pos_top="50",
pos_bottom="50",
type_="time",
name_textstyle_opts=opts.TextStyleOpts(font_size=20),
axislabel_opts=opts.LabelOpts(font_size=20)
),
label_opts=opts.LabelOpts(is_show=False),
) \
.set_global_opts(
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="line"),
toolbox_opts=opts.ToolboxOpts(
feature=opts.ToolBoxFeatureOpts(
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
# brush=opts.ToolBoxFeatureBrushOpts(is_show=False),
)
),
legend_opts=opts.LegendOpts(pos_right=pos_right,
pos_top="2%",
orient="vertical",
item_width=30,
item_height=14,
textstyle_opts=opts.TextStyleOpts(font_size=18)
),
)
grid = (Grid(init_opts=opts.InitOpts(
width=width, height="600px", bg_color='rgb(255, 255, 255)')).add(
bar,
grid_opts=opts.GridOpts(pos_right='10%'),
is_control_axis_index=True))
make_snapshot(snapshot, grid.render(to_file), svg_name) # 生成svg图片
def heat_plot(data):
n, m = data.shape
x, y = np.arange(0, n, 1), np.arange(0, m, 1)
x, y = np.meshgrid(x, y, indexing='ij')
z = data.values.flatten()
x = m - 1 - x
_data = list(zip(y.flatten(), x.flatten(), z))
_data = [[int(d[0]), int(d[1]), np.round(d[2], 4)] for d in _data]
h = (
HeatMap(
init_opts=opts.InitOpts(width="1000px", height="300px")).add_xaxis(
xaxis_data=data.columns.astype(str).tolist()).add_yaxis(
series_name="",
yaxis_data=data.index.astype(str).tolist()[::-1],
value=_data,
label_opts=opts.LabelOpts(position='inside',
is_show=True,
font_size=15),
).set_series_opts().set_global_opts(
# toolbox_opts=opts.ToolboxOpts(is_show=True),
legend_opts=opts.LegendOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=True, ),
xaxis_opts=opts.AxisOpts(
type_="category",
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
yaxis_opts=opts.AxisOpts(
type_="category",
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
visualmap_opts=opts.VisualMapOpts(min_=z.min(),
max_=z.max(),
is_calculable=True,
orient="vertical",
pos_left="left"),
toolbox_opts=opts.ToolboxOpts(
is_show=True,
feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
background_color='white',
connected_background_color="white",
pixel_ratio=2,
name="pic",
), )),
))
return h
def chart(f):
x = [time_str(i["Now"]) for i in f]
y = [i["Price"] for i in f]
l = (
Line(init_opts=opts.InitOpts(theme=ThemeType.LIGHT, width="1366px"))
.add_xaxis(x)
.add_yaxis("v", y, is_connect_nones=True)
.set_global_opts(
title_opts=opts.TitleOpts(title="MC"),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts()),
visualmap_opts=opts.VisualMapOpts(min_=500, max_=1200),
)
)
return l.render_embed()
def pyechart_Pie_plot(self, filedata, para):
from pyecharts.charts import Pie
file_name = '南丁格尔玫瑰图.html'
path_plotly = self.path_dir_plotly_html + os.sep + file_name # 文件路径，前面是文件夹后面是文件名
costumeTheme = self.themedict[para['theme']]
# -----------------------------------------------------------------------
# 准备数据
df = pd.read_excel(filedata, sheet_name='sheet1')
# # 提取数据
v = df['provinces'].values.tolist()
d = df['num'].values.tolist()
color_series = df['color_series'].values.tolist()
# 降序排序
df.sort_values(by='num', ascending=False, inplace=True)
# 实例化Pie类
pie1 = Pie(init_opts=opts.InitOpts(
theme=costumeTheme, width=para['width'], height=para['height']))
# 设置颜色
pie1.set_colors(color_series)
# 添加数据，设置饼图的半径，是否展示成南丁格尔图
pie1.add("", [list(z) for z in zip(v, d)],
radius=["10%", "135%"],
center=["50%", "65%"],
rosetype="area")
# 设置全局配置项
pie1.set_global_opts(
title_opts=opts.TitleOpts(title=para["title"],
subtitle=para["subtitle"]),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
background_color="white")))
#visualmap_opts=opts.VisualMapOpts(),
)
# 设置系列配置项
pie1.set_series_opts(
label_opts=opts.LabelOpts(
is_show=True,
position="inside",
font_size=12,
formatter="{b}:{c}天",
font_style="normal", # css的格式
font_weight="normal",
font_family="宋体"), )
# 生成html文档
pie1.render(path_plotly)
return path_plotly # 返回该HTML文件路径
def scatter3d_base(f) -> Scatter3D:
data = [
[time_m_d(i.now), time_h_m(i.now), i.price]
for i in f
]
name = ["d", "h", "v"]
c = (
Scatter3D(init_opts=opts.InitOpts(theme=ThemeType.LIGHT, width="1366px"))
.add("", data, )
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(min_=300, max_=1200),
title_opts=opts.TitleOpts(title="MC"),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts()),
)
)
return c.render_embed()
def draw_river_picture(series_name, data, to_file, svg_name):
"""
主题河流图
:param series_name: [str]
:param data: [[date, value, series_name]]
:param svg_name: svg文件
:param to_file: 结果文件
:return:
"""
bar = ThemeRiver(init_opts=opts.InitOpts(width="800px", height="600px", bg_color='white')) \
.add(
series_name=series_name,
data=data,
singleaxis_opts=opts.SingleAxisOpts(
pos_top="2%",
pos_bottom="10%",
pos_right="20%",
type_="time",
name_textstyle_opts=opts.TextStyleOpts(font_size=25),
axislabel_opts=opts.LabelOpts(font_size=25)
),
label_opts=opts.LabelOpts(is_show=False, position='bottom', distance='200px'),
) \
.set_global_opts(
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="line"),
toolbox_opts=opts.ToolboxOpts(
feature=opts.ToolBoxFeatureOpts(
# data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
# brush=opts.ToolBoxFeatureBrushOpts(is_show=False),
)
),
legend_opts=opts.LegendOpts(type_="scroll",
pos_left="82%",
pos_top="18%",
orient="vertical",
backgroundColor='rgb(255, 255, 255)',
item_width=40,
item_height=20,
textstyle_opts=opts.TextStyleOpts(font_size=25)
)
)
make_snapshot(snapshot, bar.render(to_file), svg_name) # 生成svg图片
def to_treemap(data, proj, version):
complexity = {'name': f'{proj}-{version}', 'children': []}
extract(complexity, 'complexity', data)
maintainability = {'name': f'{proj}-{version}', 'children': []}
extract(maintainability, 'maintainability', data)
testability = {'name': f'{proj}-{version}', 'children': []}
extract(testability, 'testability', data)
readability = {'name': f'{proj}-{version}', 'children': []}
extract(readability, 'readability', data)
reusability = {'name': f'{proj}-{version}', 'children': []}
extract(reusability, 'reusability', data)
inheritance = {'name': f'{proj}-{version}', 'children': []}
extract(inheritance, 'inheritance', data)
treemap = TreeMap()
for indicator in [
'complexity', 'maintainability', 'testability', 'readability',
'reusability', 'inheritance'
]:
treemap = treemap.add(series_name=indicator,
data=[locals()[indicator]],
leaf_depth=2,
roam=False,
label_opts=opts.LabelOpts(position='inside'))
treemap = (treemap.set_global_opts(
tooltip_opts=opts.TooltipOpts(formatter='{b}<br/>{a}: {c}'),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
magic_type=opts.ToolBoxFeatureMagicTypeOpts(is_show=False),
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
brush=opts.ToolBoxFeatureBrushOpts(type_='clear'))),
legend_opts=opts.LegendOpts(is_show=True,
selected_mode='single',
pos_top='7%',
orient='horizontal',
padding=0),
title_opts=opts.TitleOpts(
title=f'Code Quality Treemap of {proj}-{version}',
pos_left='center')))
grid = Grid()
grid.add(treemap, grid_opts=opts.GridOpts(pos_top='100%'))
return grid
def to_linechart(data, proj):
versions = [version['version_name'] for version in data]
linechart = Line().add_xaxis(xaxis_data=versions)
for indicator in [
'complexity', 'maintainability', 'testability', 'readability',
'reusability', 'inheritance'
]:
linechart = linechart.add_yaxis(
series_name=indicator,
y_axis=[round(version[indicator], 2) for version in data],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
)
linechart = (linechart.set_global_opts(
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
xaxis_opts=opts.AxisOpts(boundary_gap=False),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(formatter="{value}"),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
magic_type=opts.ToolBoxFeatureMagicTypeOpts(is_show=False),
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
brush=opts.ToolBoxFeatureBrushOpts(type_='clear'))),
legend_opts=opts.LegendOpts(is_show=True,
pos_top='middle',
pos_left='1%',
orient='vertical',
padding=0),
datazoom_opts=opts.DataZoomOpts(type_='slider',
range_start=0,
range_end=100),
title_opts=opts.TitleOpts(title=f'Code Quality Linechart of {proj}',
pos_left='center')))
grid = Grid()
grid.add(linechart, grid_opts=opts.GridOpts(pos_left='150'))
return grid
def dayloss_line_base(self, x, y, t, st):
c = (Line(
init_opts=opts.InitOpts(width="1000px", height="500px")
).add_xaxis(x).add_yaxis("线损率(%)", y, is_smooth=True).set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=True,
trigger="axis",
axis_pointer_type="cross"),
title_opts=opts.TitleOpts(title=t, subtitle=st),
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=-90),
max_interval=366,
type_="category",
axispointer_opts=opts.AxisPointerOpts(is_show=True,
type_="shadow"),
),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(formatter="{value} %"),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
pixel_ratio=2, background_color='white'),
restore=opts.ToolBoxFeatureRestoreOpts(is_show=False),
data_view=opts.ToolBoxFeatureDataViewOpts(is_show=False),
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
magic_type=opts.ToolBoxFeatureMagicTypeOpts(is_show=False),
brush=opts.ToolBoxFeatureBrushOpts(type_='clear'),
))).set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markline_opts=opts.MarkLineOpts(
data=[
opts.MarkLineItem(y=6),
],
label_opts=opts.LabelOpts(is_show=False),
),
))
return c
def word_clouds(weights):
w = WordCloud(init_opts=opts.InitOpts(width=fig_width, height=fig_height)).add(series_name="热点分析",
data_pair=weights,
word_size_range=[6, 66],
shape="circle",
)\
.set_global_opts(
title_opts=opts.TitleOpts(
title="", title_textstyle_opts=opts.TextStyleOpts(font_size=23),subtitle="数据来源：WIND"
),
tooltip_opts=opts.TooltipOpts(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",
),
)
)
)
return w
def __init__(self, title: str, output: str):
self.title = title
self.output = output
self.datas = []
self.chart = (Line(init_opts=opts.InitOpts(
page_title=self.title,
theme=ThemeType.DARK,
width="1280px",
height="720px",
)).set_global_opts(
title_opts=opts.TitleOpts(title=self.title),
xaxis_opts=opts.AxisOpts(type_="value",
is_scale=True,
split_number=10,
name="bitrate/kbps"),
yaxis_opts=opts.AxisOpts(type_="value", is_scale=True,
name="vmaf"),
toolbox_opts=opts.ToolboxOpts(
is_show=True,
orient="vertical",
pos_left="right",
feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
title="save as image", is_show=True),
restore=opts.ToolBoxFeatureRestoreOpts(is_show=False),
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
data_view=opts.ToolBoxFeatureDataViewOpts(
is_show=False, is_read_only=True, title="data"),
magic_type=opts.ToolBoxFeatureMagicTypeOpts(is_show=False),
brush=opts.ToolBoxFeatureBrushOpts(type_=[]))),
legend_opts=opts.LegendOpts(pos_top="bottom"),
tooltip_opts=opts.TooltipOpts(
is_show=True,
formatter=pyecharts_utils.JsCode(
"function(x) {return x.seriesName + '<br/>bitrate '+ x.data[0] + ' kbps<br/>vmaf ' + x.data[1];}"
))))
def draw_line_sentiment(lists):
print(lists)
c=Line(init_opts=opts.InitOpts(width='2000px',height='600px'))
c.add_xaxis(xaxis_data=list(range(1,len(lists)+1)))
# c.add_xaxis(xaxis_data=content)
c.add_yaxis(series_name="",y_axis=lists)
data_zoom = {
"show": True,
"title": {"zoom": "data zoom", "back": "data zoom restore"}
}
c.set_global_opts(
# 设置标题
title_opts=opts.TitleOpts(title="情感分析得分走势图"),
# 设置图例is_show=False是不显示图例
legend_opts=opts.LegendOpts(is_show=True),
# 设置提示项
tooltip_opts=opts.TooltipOpts(trigger='axis', axis_pointer_type='cross'),
# 工具箱的设置
toolbox_opts=opts.ToolboxOpts(is_show=True, feature=opts.ToolBoxFeatureOpts(data_zoom=data_zoom))
)
c.set_series_opts()
c.render('line_sentiments.html')
def issuer_industry_plot(data):
name = data.columns[0]
data = data.sort_values(by=name)
x_data = data.index
value = np.round(data[name],2).tolist()
bar = (
Bar()
.add_xaxis(
xaxis_data=x_data.tolist(),
)
.add_yaxis(
series_name="",
yaxis_data=value,
yaxis_index=0,
color="#36648B",
)
.set_global_opts(
yaxis_opts=opts.AxisOpts(
type_="value",
name=name,
position="left",
# offset=80,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts()
),
axislabel_opts=opts.LabelOpts(formatter="{value}"),
),
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=-90),
type_="category",
name="行业",
),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
title_opts=opts.TitleOpts(subtitle="数据来源：WIND"),
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",
),
)
)
)
)
pie = (Pie(init_opts=opts.InitOpts()).add(
series_name="",
data_pair=[list(z) for z in zip(x_data, (data['占比']*100).round(2).tolist())],
radius=["25%", "35%"],
center=["30%","35%"],
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False,pos_left="legft", orient="vertical")) \
.set_series_opts(
tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="{b}: {c}%"
),
label_opts=opts.LabelOpts(formatter="{b}: {c}%"),
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",
),
)
)
))
grid_chart = (
Grid(init_opts=opts.InitOpts(width=fig_width, height=fig_height))
.add(
bar,
grid_opts=opts.GridOpts(
pos_left="10%", pos_top="10%"
),
)
.add(pie, grid_opts=opts.GridOpts())
)
return grid_chart
def rate_histogram_plot(data,bins = 10):
m, bins = np.histogram(data.dropna(), bins)
m = m.astype(float)
s = m.sum()
x = bins.round(2).astype(str).tolist() + [""]
bar = (
Bar(init_opts=opts.InitOpts(width=fig_width, height=fig_height))
.add_xaxis(xaxis_data=bins.round(2).tolist(),
)
.extend_axis(xaxis_data=x,
xaxis=opts.AxisOpts(
position="bottom",
is_show=True,
# ,
is_scale = True,
boundary_gap = False,
# type_="value",
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
axisline_opts=opts.AxisLineOpts(
on_zero_axis_index = 0,
is_on_zero=False, linestyle_opts=opts.LineStyleOpts()
),
axispointer_opts=opts.AxisPointerOpts(
is_show=False, label=opts.LabelOpts()
),
),
)
.add_yaxis(
xaxis_index =0,
series_name="",
yaxis_data=m.astype(float).tolist(),
# yaxis_index=0,
# color=colors[1],
category_gap=0,
gap =5,
itemstyle_opts=opts.ItemStyleOpts(border_color=None),
label_opts=opts.LabelOpts(
formatter=JsCode(
"""function(params) {
return params.data + '(' + ((Math.round(params.data / %s *10000)/100)) + '%s)' ;
}"""%(s,"%")
)
),
color="#36648B",
)
.set_global_opts(
title_opts=opts.TitleOpts(subtitle="数据来源:WIND"),
yaxis_opts=opts.AxisOpts(
type_="value",
name="频数",
position="left",
# offset=80,
axisline_opts=opts.AxisLineOpts(
is_on_zero=True,linestyle_opts=opts.LineStyleOpts()
),
axislabel_opts=opts.LabelOpts(
# # if ('value' in params.data) {
# }
# formatter="{value}"
formatter = JsCode(
"""function(params) {
return (params);
}"""
)
),
),
xaxis_opts=opts.AxisOpts(
is_show=False
),
tooltip_opts=opts.TooltipOpts(is_show=False),
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",
),
)
)
))
return bar
def province_analysis_plot(data):
def format_data(data):
data['占比'] = (data['占比'] * 100).round(4)
_ = data.reset_index().values
data = list(zip([d[0] for d in _], _.tolist()))
data = [list(d) for d in data]
return data
data = format_data(data)
min_data, max_data = (
min([d[1][1] for d in data]),
max([d[1][1] for d in data]),
)
map_chart = (
Map(init_opts=opts.InitOpts(
width=fig_width, height=fig_height,
page_title="中国各省疫情防控债发行情况"))
.add(
series_name="",
data_pair=data,
# label_opts=opts.LabelOpts(is_show=False,
# # formatter="{b}:{@占比}"
# ),
is_map_symbol_show=False,
).set_series_opts(
label_opts=opts.LabelOpts(is_show=True,
formatter=JsCode("""
function(params){
if (typeof(params.data) == 'undefined') {
return params.value[2];
} else {
return params.data.name + params.data.value[2] + '%' ;
}
}"""
))
)
.set_global_opts(
title_opts=opts.TitleOpts(
# title="中国各省疫情防控债发行情况",
subtitle="数据来源：WIND",
pos_left="center",
pos_top="top",
title_textstyle_opts=opts.TextStyleOpts(
font_size=25, color="rgba(255,255,255, 0.9)"
),
),
tooltip_opts=opts.TooltipOpts(
is_show=True,
formatter=JsCode(
"""function(params) {
if ('value' in params.data) {
return params.data.value[0] + '<br/>占比：' + params.data.value[2]+'%<br/>数量：'+params.data.value[1];
}
}"""
),
),
visualmap_opts=opts.VisualMapOpts(
is_calculable=True,
dimension=1,
pos_left="10",
pos_top="center",
range_text=["High", "Low"],
range_color=["lightskyblue", "yellow", "orangered"],
textstyle_opts=opts.TextStyleOpts(color="#ddd"),
min_=min_data,
max_=max_data,
),
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",
),
)
)
)
)
return map_chart
feature=opts.ToolBoxFeatureOpts(
# 保存工具
opts.ToolBoxFeatureSaveAsImageOpts(
# 是否显示
is_show=True,
# 提示语
title="保存为图片",
),
# 还原工具
opts.ToolBoxFeatureRestoreOpts(
# 是否显示该工具
is_show=True,
# 提示语
title="还原",
),
# 数据视图工具
opts.ToolBoxFeatureDataViewOpts(
# 是否显示该工具
is_show=True,
# 提示语
title="数据视图",
# 是否不可编辑
is_read_only=False,
),
# 缩放工具配置项，直角坐标系适用
opts.ToolBoxFeatureDataZoomOpts(
# 是否显示该工具。
is_show=False,
# 提示语
zoom_title="区域缩放",
# 提示语
back_title="区域缩放还原",
),
# 图表类型切换，适用于直角坐标系
opts.ToolBoxFeatureMagicTypeOpts(
# 是否显示该工具
is_show=False,
# 启用的动态类型
type_=['stack', 'line', 'bar', 'tiled'],
),
# 工具箱选框组件配置项
opts.ToolBoxFeatureBrushOpts(
# 选择显示哪些选框
type_=[]),
)),
def bar():
cursor = connection.cursor()
cursor.execute("""SELECT
DATE_FORMAT( rev.create_time, '%Y-%m-%d' ) create_time,
revt.`name`,
count( * )
FROM
resource_event rev
LEFT JOIN resource_eventtype revt ON rev.event_type_id = revt.id
GROUP BY
create_time,
event_type_id""")
rows = cursor.fetchall()
name_list = []
event_type_list = []
for row in rows:
name = row[0]
if name not in name_list:
name_list.append(name)
event_type = row[1]
if event_type not in event_type_list:
event_type_list.append(event_type)
bar = Bar(init_opts=opts.InitOpts(height="350px"))
bar.add_xaxis(name_list)
for event_type in event_type_list:
tmp_list = [0 for i in range(len(name_list))]
for row in rows:
if event_type == row[1]:
index = name_list.index(row[0])
tmp_list[index] = row[2]
bar.add_yaxis(event_type, tmp_list)
bar.set_global_opts(title_opts=opts.TitleOpts(title="违章记录统计"), toolbox_opts=opts.ToolboxOpts(is_show=False,
feature=opts.ToolBoxFeatureOpts(
restore=None,
data_zoom=None)))
return bar.render_embed()
def plot_ground_truth_analysis(
all_score_columns: typing.List[str],
title: str,
output_path: str,
df: pd.DataFrame,
add_wrong_candidates: bool = False,
wrong_candidates_score_column: str = None,
) -> None:
"""
use pyechart to plot html interactive figure
"""
df_processed = copy.deepcopy(df)
for each_col in df_processed.columns:
df_processed[each_col] = pd.to_numeric(df_processed[each_col],
errors='ignore')
xaxis_labels = []
memo = defaultdict(list)
groupby_res = df_processed[df_processed["evaluation_label"] ==
1].groupby(["column", "row"])
for key, each_group in reversed(tuple(groupby_res)):
if add_wrong_candidates:
df_wrong_examples = df_processed[(df_processed["column"] == key[0]) &
(df_processed["row"] == key[1]) &
(df_processed["evaluation_label"] == -1)] \
.sort_values(by=[wrong_candidates_score_column], ascending=False).iloc[:3, :]
# add wrong candidate information
for _, each_row in df_wrong_examples.iterrows():
longest_string = max(each_row["kg_labels"].split("|"),
key=len)
xaxis_labels.append(each_row["label_clean"] +
" \n({})".format(longest_string))
for each_score_column in all_score_columns:
memo[each_score_column].append("{:.2f}".format(
each_row[each_score_column]))
# add ground truth information
xaxis_labels.append(each_group["label_clean"].iloc[0])
for each_score_column in all_score_columns:
memo[each_score_column].append("{:.2f}".format(
each_group[each_score_column].iloc[0]))
# build figure
bar = Bar()
bar.add_xaxis(xaxis_labels)
for k, v in memo.items():
bar.add_yaxis(k, v)
# set the global options
bar.set_global_opts(
title_opts=opts.TitleOpts(title=title, pos_left='40%'),
legend_opts=opts.LegendOpts(pos_left="center",
pos_top="bottom",
orient='horizontal'),
brush_opts=opts.BrushOpts(),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
title="save as image"),
magic_type=opts.ToolBoxFeatureMagicTypeOpts(
line_title="switch to line chart",
bar_title="switch to bar chart",
stack_title="switch to stacked values",
tiled_title="switch to tiled values"),
data_zoom=opts.ToolBoxFeatureDataZoomOpts(
zoom_title="zoom in", back_title="zoom reset"),
restore=opts.ToolBoxFeatureRestoreOpts(title="reset"),
data_view=opts.ToolBoxFeatureDataViewOpts(
title="Data table view",
lang=["Table view", "Close", "Refresh"],
),
brush=opts.ToolBoxFeatureBrushOpts(
rect_title="rectangle choice",
polygon_title="polygon choice",
clear_title="clear choices",
keep_title="keep choices"))),
datazoom_opts=opts.DataZoomOpts(orient="vertical"),
# yaxis_opts=opts.AxisOpts(name='labels', name_gap=5000, name_rotate=15),
tooltip_opts=opts.TooltipOpts(is_show=True,
trigger="axis",
axis_pointer_type="shadow"),
xaxis_opts=opts.AxisOpts(axistick_opts=opts.AxisTickOpts(
is_inside=True,
length=850,
linestyle_opts=opts.LineStyleOpts(type_="dotted",
opacity=0.5))))
# do not shown bar label values
bar.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
bar.reversal_axis()
grid = (Grid(init_opts=opts.InitOpts(
width="1500px",
height="1000px",
page_title="Table Linker visualization page")).add(
bar,
grid_opts=opts.GridOpts(pos_top='5%',
pos_bottom="10%",
pos_right='5%',
pos_left="20%")))
grid.render(output_path + ".html")
def draw_heat_map_picture(grid_data,
x_list,
max_number,
to_file,
svg_name,
height='1200px'):
"""
grid堆叠热力图
:param height:
:param x_list: 横轴列表
:param max_number: 最大值
:param grid_data: [{}] key包括 series_name, y_name, sub_title, value
:param to_file:
:param svg_name:
:return:
"""
grid_number = len(grid_data)
grid = (Grid(init_opts=opts.InitOpts(
width="900px", height=height, bg_color='rgb(255, 255, 255)')))
i = 0
all_top = 5
for grid_data_one in grid_data:
# max_number_list = [i[2] for i in grid_data_one['value'][3:]]
# max_number = max(max_number_list)
length = int(len(grid_data_one['y_name'])) * 1.5
top_position = all_top
bottom_position = 100 - top_position - length
all_top = top_position + length + 2
# print(top_position)
# print(bottom_position)
# print(all_top)
c = HeatMap() \
.add_xaxis(x_list) \
.add_yaxis(grid_data_one['series_name'], grid_data_one['y_name'], grid_data_one['value'],
label_opts=opts.LabelOpts(is_show=True, position="inside"),
)
if i == grid_number - 1:
c = c.set_global_opts(
title_opts=opts.TitleOpts(
title="",
subtitle=grid_data_one['sub_title'],
pos_top=str(top_position) + "%",
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
min_=0,
max_=max_number,
range_color=['#fff799', '#ae4130']),
xaxis_opts=opts.AxisOpts(
is_show=True,
axislabel_opts=opts.LabelOpts(interval=0, rotate=30),
splitarea_opts=opts.SplitAreaOpts(
is_show=False,
areastyle_opts=opts.AreaStyleOpts(opacity=0))),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(font_size=14, interval=0),
splitarea_opts=opts.SplitAreaOpts(
is_show=False,
areastyle_opts=opts.AreaStyleOpts(opacity=0))),
legend_opts=opts.LegendOpts(pos_top="120%"),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
# brush=opts.ToolBoxFeatureBrushOpts(is_show=False),
)),
)
else:
c = c.set_global_opts(
title_opts=opts.TitleOpts(
title="",
subtitle=grid_data_one['sub_title'],
pos_top=str(top_position - 2) + "%",
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
min_=0,
max_=max_number,
range_color=['#fff799', '#ae4130']),
xaxis_opts=opts.AxisOpts(
is_show=False,
splitarea_opts=opts.SplitAreaOpts(
is_show=False,
areastyle_opts=opts.AreaStyleOpts(opacity=0))),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(font_size=14, interval=0),
splitarea_opts=opts.SplitAreaOpts(
is_show=False,
areastyle_opts=opts.AreaStyleOpts(opacity=0))),
legend_opts=opts.LegendOpts(pos_top="120%"),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
# brush=opts.ToolBoxFeatureBrushOpts(is_show=False),
)),
)
# 加到grid
grid = grid.add(c,
grid_opts=opts.GridOpts(
pos_bottom=str(bottom_position) + "%",
pos_top=str(top_position) + "%",
pos_left="25%",
pos_right="10%"))
i = i + 1
make_snapshot(snapshot, grid.render(to_file), svg_name) # 生成svg图片
def draw_bar_reverse_picture(xaxis_data,
data,
to_file,
unit,
svg_name,
stack,
y_name,
pos_left='25%'):
"""
多个y列，是否堆叠要确认下
:param pos_left: "25%"
:param is_reverse: xy轴是否反转
:param y_name: y轴名称
:param stack: boolean, 是否要堆叠
:param xaxis_data: x轴
:param data: {series_name, data}
:param svg_name: svg文件
:param to_file: 结果文件
:param unit: 横轴的数值的单位（加在每个横轴值后面，比如 月）
:return:
"""
xaxis_data = [str(i) + unit for i in xaxis_data]
bar = Bar(init_opts=opts.InitOpts(
width="800px", height="600px", bg_color='white')).add_xaxis( # 1250px
xaxis_data=xaxis_data,
# add_xaxis=opts.LabelOpts(formatter="{value}" + unit),
)
for series_name, y_axis in data.items():
if stack:
bar = bar.add_yaxis(
series_name=series_name,
stack='1',
# color="#d14a61",
y_axis=y_axis,
yaxis_index=0,
label_opts=opts.LabelOpts(is_show=False),
)
else:
bar = bar.add_yaxis(
series_name=series_name,
color="#61a0a8",
category_gap='50%',
y_axis=y_axis,
yaxis_index=0,
label_opts=opts.LabelOpts(is_show=True, position='right'),
)
bar = bar.set_global_opts(
yaxis_opts=opts.AxisOpts(
type_="category",
# min_=0,
# max_=250,
position="left",
offset=0,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts()),
axislabel_opts=opts.LabelOpts(formatter="{value}", font_size=20),
),
xaxis_opts=opts.AxisOpts(
name=y_name,
name_textstyle_opts=opts.TextStyleOpts(font_size=20),
axislabel_opts=opts.LabelOpts(formatter="{value}", font_size=20),
),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
# brush=opts.ToolBoxFeatureBrushOpts(is_show=False),
)),
legend_opts=opts.LegendOpts(is_show=False),
)
bar = bar.reversal_axis()
grid = (
Grid(init_opts=opts.InitOpts(width="800px",
height="600px",
bg_color='rgb(255, 255, 255)')) # 800px
.add(bar,
grid_opts=opts.GridOpts(pos_left=pos_left),
is_control_axis_index=True))
make_snapshot(snapshot, grid.render(to_file), svg_name) # 生成svg图片
def draw_line_with_two_y(xaxis_data, y1_series_name, y1_axis, y2_series_name,
y2_axis, y3_series_name, y3_axis, y1_name, y2_name,
to_file, unit, svg_name):
"""
左边是 两条线； 右边是 一条线。
:param svg_name: svg文件
:param xaxis_data: 横轴
:param y1_series_name: 左边第一条线的系列名
:param y1_axis: 左边第一条线的数值
:param y2_series_name: 左边第二条线的系列名
:param y2_axis: 左边第二条线的数值
:param y3_series_name: 右边第一条线的系列名
:param y3_axis: 右边第一条线的数值
:param y1_name: 左边的纵轴名称
:param y2_name: 右边的纵轴名称
:param to_file: 结果文件
:param unit: 横轴的数值的单位（加在每个横轴值后面，比如 月）
:return:
"""
xaxis_data = [str(i) + unit for i in xaxis_data]
bar = Line(init_opts=opts.InitOpts(
width="800px", height="600px", bg_color='white'
)).add_xaxis(
xaxis_data=xaxis_data,
# add_xaxis=opts.LabelOpts(formatter="{value}" + unit),
).add_yaxis(
series_name=y1_series_name,
is_smooth=True,
symbol="circle",
symbol_size=8,
is_symbol_show=True,
# color="#d14a61",
y_axis=y1_axis,
yaxis_index=0,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
).add_yaxis(
series_name=y2_series_name,
is_smooth=True,
symbol="circle",
symbol_size=8,
is_symbol_show=True,
# color="#d14a61",
y_axis=y2_axis,
yaxis_index=0,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
).add_yaxis(
series_name=y3_series_name,
is_smooth=True,
symbol="circle",
symbol_size=8,
is_symbol_show=True,
# color="#d14a61",
y_axis=y3_axis,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
).extend_axis(yaxis=opts.AxisOpts(
type_="value",
name=y2_name,
# min_=0,
# max_=25,
position="right",
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts()),
axislabel_opts=opts.LabelOpts(formatter="{value}", font_size=15),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1)),
name_textstyle_opts=opts.TextStyleOpts(font_size=15))).set_global_opts(
yaxis_opts=opts.AxisOpts(
type_="value",
name=y1_name,
# min_=0,
# max_=250,
position="left",
offset=0,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts()),
axislabel_opts=opts.LabelOpts(formatter="{value}",
font_size=15),
name_textstyle_opts=opts.TextStyleOpts(font_size=15)),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
formatter="{value}", font_size=15), ),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
# brush=opts.ToolBoxFeatureBrushOpts(is_show=False),
)),
legend_opts=opts.LegendOpts(
item_width=25,
item_height=10,
textstyle_opts=opts.TextStyleOpts(font_size=15)),
)
make_snapshot(snapshot, bar.render(to_file), svg_name) # 生成svg图片
def draw_line_picture(xaxis_data, data, to_file, unit, svg_name, stack,
y_name):
"""
多条线，是否堆叠要确认下
:param label_right:
:param y_name: y轴名称
:param stack: boolean, 是否要堆叠
:param xaxis_data: x轴
:param data: {series_name, data}
:param svg_name: svg文件
:param to_file: 结果文件
:param unit: 横轴的数值的单位（加在每个横轴值后面，比如 月）
:return:
"""
xaxis_data = [str(i) + unit for i in xaxis_data]
bar = Line(init_opts=opts.InitOpts(
width="800px", height="600px", bg_color='white')).add_xaxis(
xaxis_data=xaxis_data,
# add_xaxis=opts.LabelOpts(formatter="{value}" + unit),
)
for series_name, y_axis in data.items():
if stack:
bar = bar.add_yaxis(
series_name=series_name,
is_smooth=True,
symbol="circle",
# symbol_size=8,
stack='1',
is_symbol_show=False,
# color="#d14a61",
y_axis=y_axis,
yaxis_index=0,
label_opts=opts.LabelOpts(is_show=False),
# linestyle_opts=opts.LineStyleOpts(width=2),
areastyle_opts=opts.AreaStyleOpts(opacity=1))
else:
bar = bar.add_yaxis(
series_name=series_name,
is_smooth=True,
symbol="circle",
symbol_size=8,
is_symbol_show=True,
# color="#d14a61",
y_axis=y_axis,
yaxis_index=0,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
)
bar = bar.set_global_opts(
yaxis_opts=opts.AxisOpts(
type_="value",
name=y_name,
name_textstyle_opts=opts.TextStyleOpts(font_size=25),
# min_=0,
# max_=250,
position="left",
offset=0,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts()),
axislabel_opts=opts.LabelOpts(formatter="{value}", font_size=25),
),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
formatter="{value}", font_size=25, interval=0), ),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
# brush=opts.ToolBoxFeatureBrushOpts(is_show=False),
)),
legend_opts=opts.LegendOpts(
pos_left="12%",
pos_top="10%",
orient="vertical",
# backgroundColor='rgb(255, 255, 255)',
item_width=40,
item_height=20,
textstyle_opts=opts.TextStyleOpts(font_size=25)),
)
make_snapshot(snapshot, bar.render(to_file), svg_name) # 生成svg图片
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_bar_picture(xaxis_data, data, to_file, unit, svg_name, stack, y_name):
"""
多个y列，是否堆叠要确认下
:param is_reverse: xy轴是否反转
:param y_name: y轴名称
:param stack: boolean, 是否要堆叠
:param xaxis_data: x轴
:param data: {series_name, data}
:param svg_name: svg文件
:param to_file: 结果文件
:param unit: 横轴的数值的单位（加在每个横轴值后面，比如 月）
:return:
"""
xaxis_data = [str(i) + unit for i in xaxis_data]
bar = Bar(init_opts=opts.InitOpts(
width="900px", height="600px", bg_color='white')).add_xaxis(
xaxis_data=xaxis_data,
# add_xaxis=opts.LabelOpts(formatter="{value}" + unit),
)
for series_name, y_axis in data.items():
if stack:
bar = bar.add_yaxis(
series_name=series_name,
stack='1',
# color="#d14a61",
y_axis=y_axis,
yaxis_index=0,
label_opts=opts.LabelOpts(is_show=False),
)
else:
bar = bar.add_yaxis(
series_name=series_name,
# color="#d14a61",
y_axis=y_axis,
yaxis_index=0,
label_opts=opts.LabelOpts(is_show=False),
)
bar = bar.set_global_opts(
yaxis_opts=opts.AxisOpts(
type_="value",
name=y_name,
name_textstyle_opts=opts.TextStyleOpts(font_size=15),
# min_=0,
# max_=250,
position="left",
offset=0,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts()),
axislabel_opts=opts.LabelOpts(formatter="{value}", font_size=15),
),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
formatter="{value}", font_size=15, interval=0), ),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
# brush=opts.ToolBoxFeatureBrushOpts(is_show=False),
)),
legend_opts=opts.LegendOpts(
item_width=25,
item_height=15,
textstyle_opts=opts.TextStyleOpts(font_size=15)),
)
make_snapshot(snapshot, bar.render(to_file), svg_name) # 生成svg图片
