def image_host_resource(doc_path, hosts_metrics, host):
options = opts.InitOpts(js_host=os.path.join(os.getcwd(), "scripts/javascript/"),
animation_opts=opts.AnimationOpts(animation=False))
line1 = (
Line(options)
.add_xaxis(hosts_metrics[host['ip']]['metrics']['datetime'])
.add_yaxis("CPU", hosts_metrics[host['ip']]['metrics']['cpu'], is_symbol_show=False, is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(color=utils.JsCode(
"new echarts.graphic.LinearGradient(0,0,0,1,[{offset:0,color:'rgb(255,158,68)'},{offset:1,color:'rgb(255,70,131)'}])"),
opacity=0.5))
.add_yaxis("Memory", hosts_metrics[host['ip']]['metrics']['memory'], is_symbol_show=False, is_smooth=True)
.set_series_opts(linestyle_opts=opts.LineStyleOpts(width=2))
.set_global_opts(
title_opts=opts.TitleOpts(title="主机资源使用率 (%s)" % (host['ip']),
title_textstyle_opts=opts.TextStyleOpts(font_size=13),
subtitle=" 数据来源-健康度平台"),
xaxis_opts=opts.AxisOpts(type_="time"),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(formatter=utils.JsCode("function(val){return val + '%';}")))
)
)
line2 = (
Line(options)
.add_xaxis(hosts_metrics[host['ip']]['metrics']['datetime'])
.add_yaxis("Process", hosts_metrics[host['ip']]['metrics']['process'], is_symbol_show=False, is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(color=utils.JsCode(
"new echarts.graphic.LinearGradient(0,0,0,1,[{offset:0,color:'#fff'},{offset:1,color:'#61a0a8'}])"),
opacity=0.5)
)
.set_series_opts(linestyle_opts=opts.LineStyleOpts(width=2))
.set_global_opts(
xaxis_opts=opts.AxisOpts(type_="time", position="top", axislabel_opts=opts.LabelOpts(is_show=False)),
yaxis_opts=opts.AxisOpts(is_inverse=True),
title_opts=opts.TitleOpts(title="应用运行数量(所有)", pos_bottom="0%",
title_textstyle_opts=opts.TextStyleOpts(font_size=13)),
legend_opts=opts.LegendOpts(pos_bottom="0%")
)
)
grid = (
Grid(options)
.add(line1, grid_opts=opts.GridOpts(pos_top=50, pos_left=50, pos_right=50, pos_bottom="50%"))
.add(line2, grid_opts=opts.GridOpts(pos_top="58%", pos_left=50, pos_right=50, pos_bottom=30))
)
make_snapshot(snapshot, grid.render(os.path.join(doc_path, "render_%s.html" % (host['ip'].replace(".", "_")))),
os.path.join(doc_path, "host_res_%s.png" % (host['ip'].replace(".", "_"))), pixel_ratio=1, delay=1,
is_remove_html=True)
def map_visualmap(area_list, job_num_list) -> Map:
c = (
Map(init_opts=opts.InitOpts(theme=ThemeType.ROMA,))
.add(
"",
[list(z) for z in zip(area_list, job_num_list)],
"china",
tooltip_opts = opts.TooltipOpts(formatter=utils.JsCode(
"""function (params){
return params.name + ' : ' + params.value + '<br/>';
}"""
))
)
.set_global_opts(
title_opts=opts.TitleOpts(title="{}岗位全国热度一览".format(jobkey)),
legend_opts = opts.LegendOpts(pos_left = 'center',),
visualmap_opts=opts.VisualMapOpts( # 视觉映射配置项
is_piecewise=True, # 是否分段
max_= max(job_num_list), # 分段的最大值
# 自定义分段信息
pieces = [
{"min": 0, "max": int(max(job_num_list)/5), "color": "steelblue"},
{"min": int(max(job_num_list)/5), "max": int(max(job_num_list)/5)*2},
{"min": int(max(job_num_list)/5)*2, "max": int(max(job_num_list)/5)*3},
{"min": int(max(job_num_list)/5)*3, "max": int(max(job_num_list)/5)*4},
{"min": int(max(job_num_list)/5)*4, "max": max(job_num_list)},
]
)
)
)
return c
def bar(self,df,legend,title,path):
'''
柱状图
:param df: 要操作的数据
:param legend: 图例
:param title: 报表标题
:param path: 保存路径
:return:
'''
tmp = df.value_counts()
x = list(tmp.index)
y = list(tmp)
bar = Bar()
bar.add_xaxis(x)
bar.add_yaxis(legend, y, category_gap="60%")
bar.set_series_opts(itemstyle_opts={
"normal": {
"color": utils.JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{
offset: 0,
color: 'rgba(0, 244, 255, 1)'
}, {
offset: 1,
color: 'rgba(0, 77, 167, 1)'
}], false)"""),
"barBorderRadius": [30, 30, 30, 30],
"shadowColor": 'rgb(0, 160, 221)',
}})
bar.set_global_opts(title_opts=opts.TitleOpts(title=title))
bar.render(path + '/' + title + '.html')
def make_scatter1(x, y, name, fill_color, border_color):
'''制作基本元素周期表'''
p = (Scatter().add_xaxis(x).add_yaxis(
name,
y,
symbol="rect",
symbol_size=[50, 50],
label_opts=opts.LabelOpts(is_show=True,
position="insideLeft",
formatter=utils.JsCode('''function(params){
return ['{an_s|'+params.value[2]+' '+params.value[3]+'}',
'{n|'+params.value[4]+'}',
'{am|'+params.value[5]+'}'
].join('\\n')
}'''),
rich={
"an_s": {
"fontSize": 11,
"color": "black",
"textBorderWidth": 0
},
"n": {
"fontSize": 20,
"fontWeight": "bold",
"color": "black",
"textBorderWidth": 0
},
"am": {
"fontSize": 7,
"color": "black",
"textBorderWidth": 0
}
}),
itemstyle_opts=opts.ItemStyleOpts(color=fill_color,
border_color=border_color,
border_width=1),
tooltip_opts=opts.TooltipOpts(
is_show=True,
formatter=utils.JsCode('''function(params){
return ['元素序号：'+params.value[2],
'元素名称：'+params.value[4],
'元素符号：'+params.value[3],
'元素族类：'+params.seriesName,
'相对原子质量：'+params.value[5]
].join('<br/>')
}'''))))
return p
def make_scatter3():
'''制作镧系锕系元素周期表'''
p = (Scatter().add_xaxis(list(range(0, 15)) * 2).add_yaxis(
"过渡金属",
[data_lana.loc[i, "period":].to_list() for i in data_lana.index],
symbol="rect",
symbol_size=[50, 50],
label_opts=opts.LabelOpts(is_show=True,
position="insideLeft",
formatter=utils.JsCode('''function(params){
return ['{an_s|'+params.value[2]+' '+params.value[3]+'}',
'{n|'+params.value[4]+'}',
'{am|'+params.value[5]+'}'
].join('\\n')
}'''),
rich={
"an_s": {
"fontSize": 11,
"color": "black",
"textBorderWidth": 0
},
"n": {
"fontSize": 20,
"fontWeight": "bold",
"color": "black",
"textBorderWidth": 0
},
"am": {
"fontSize": 7,
"color": "black",
"textBorderWidth": 0
}
}),
itemstyle_opts=opts.ItemStyleOpts(color=color_map["过渡金属"][0],
border_color=color_map["过渡金属"][1],
border_width=1),
tooltip_opts=opts.TooltipOpts(
is_show=True,
formatter=utils.JsCode('''function(params){
return ['元素序号：'+params.value[2],
'元素名称：'+params.value[4],
'元素符号：'+params.value[3],
'元素族类：'+params.seriesName,
'相对原子质量：'+params.value[5]
].join('<br/>')
}'''))))
return p
def make_scatter2():
'''填补镧系锕系元素的空格'''
p = (Scatter().add_xaxis([2, 2]).add_yaxis(
"过渡金属",
[["Ⅵ", "57-71", "La-Lu", "镧系"], ["Ⅶ", "89-103", "Ac-Lr", "锕系"]],
symbol="rect",
symbol_size=[50, 50],
label_opts=opts.LabelOpts(is_show=True,
position="insideLeft",
formatter=utils.JsCode('''function(params){
return ['{an|'+params.value[2]+'}',
'{s|'+params.value[3]+'}',
'{n|'+params.value[4]+'}'
].join('\\n')
}'''),
rich={
"an": {
"fontSize": 11,
"color": "red",
"textBorderWidth": 0
},
"s": {
"fontSize": 11,
"color": "red",
"textBorderWidth": 0
},
"n": {
"fontSize": 20,
"fontWeight": "bold",
"color": "red",
"textBorderWidth": 0
}
}),
itemstyle_opts=opts.ItemStyleOpts(color=color_map["过渡金属"][0],
border_color=color_map["过渡金属"][1],
border_width=1),
tooltip_opts=opts.TooltipOpts(
formatter=utils.JsCode('''function(params){
return params.value[4]
}'''))))
return p
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 timeline_map() -> Timeline:
allMaps = []
# 共有 12 个地图
for (months, index) in zip(yearMonth, range(0, 11)):
# 计算得出当前颜色域的范围, 根据个数平均分成10个组。
sliptNum = int(len(list(np.array(rowData)[:,index+1])) / 10)
splitBoundaryIndex = [i for i in range(0, len(list(np.array(rowData)[:,index+1])), sliptNum)]
splitBoundaryIndex.append(len(list(np.array(rowData)[:,index+1])) - 1) # 以防万一，将最后一个也加进去。
tmpSort = [int(i) for i in list(np.array(rowData)[:,index+1])]
tmpSort.sort()
splitBoundary = [tmpSort[i] for i in splitBoundaryIndex] # ex: [1, 4, 9, 16, 33, 54, 125, 201, 317, 538, 2663]
# 转化为 json
jsonSplitBoundary = '['
for i in range(len(splitBoundary) - 1):
jsonSplitBoundary += "{\"min\": " + str(splitBoundary[i]) + ", \"max\": " + str(splitBoundary[i+1]) + "},"
jsonSplitBoundary +='$]'
jsonSplitBoundary = jsonSplitBoundary.replace(",$", "")
jsonSplitBoundary = json.loads(jsonSplitBoundary)
map = (
Geo()
.add_schema(maptype="四川+重庆")
.add_coordinate_json(
json_file='./myjson.json'
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="实验四-每月的销售最高Brand-county"),
# list 转 int, 获取 max range, min default 0
visualmap_opts=opts.VisualMapOpts(
max_= max([int(x) for x in list(np.array(rowData)[:,index+1])]),
is_piecewise=True,
pieces = jsonSplitBoundary
),
)
)
# 通过地点 name 与 该点的数值 添加地理点
# 这里通过循环遍历、因为他这个我真的没办法添加品牌。。。，这里用 series_name 来存放 brand
for z in zip(list(np.array(rowData)[:,0]), list(np.array(rowData)[:,index+1]), list(np.array(rowBrand)[:,index+1])):
map.add(
series_name = z[2],
data_pair = [list(z[0:2])],
type_=ChartType.EFFECT_SCATTER,
large_threshold = 2000,
label_opts = opts.LabelOpts(formatter = utils.JsCode("""
function (params) {
return params.seriesName
}
""")
)
)
allMaps.append(map)
# 将allMaps 与时间轴绑定
tl = (
Timeline().add_schema(
play_interval = 2000,
is_auto_play = True
)
)
for (itemTime, itemMap) in zip(yearMonth, allMaps):
tl.add(itemMap, itemTime)
return tl
def test_js_code():
fn = "function() { console.log('test_js_code') }"
js_code = utils.JsCode(fn)
assert_equal(js_code.js_code, "--x_x--0_0--{}--x_x--0_0--".format(fn))
def pyechart_Graph_plot(self, filedata, para): # 指定文件名，包括扩展名
from pyecharts.charts import Graph
from pyecharts.globals import CurrentConfig
print(CurrentConfig.ONLINE_HOST)
file_name = '关系图.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
#print(path_plotly)
costumeTheme = self.themedict[para['theme']]
df = pd.read_excel(filedata, sheet_name='Sheet1')
Ename = df['name'].dropna().values.tolist()
Esource = df['source'].dropna().values.tolist()
Etarget = df['target'].dropna().values.tolist()
Elabel = df['relation'].dropna().values.tolist()
Evalue = df['value'].dropna().values.tolist()
print(Elabel)
Esymbol_size = df['size'].dropna().values.tolist()
Ecategory = df['category'].dropna().values.tolist()
#print(Ecategory)
nodes_data = []
links_data = []
category = []
for i in range(0, len(Ename)):
nodes_data.append({
'name': Ename[i],
'symbolSize': int(Esymbol_size[i]),
'symbol':
"circle", ## ECharts 提供的标记类型包括 'circle', 'rect', 'roundRect', 'triangle', 'diamond', 'pin', 'arrow', 'none'
'category': Ecategory[i]
})
category.append({'name': Ecategory[i]})
#print(nodes_data)
for i in range(0, len(Esource)):
links_data.append({
'source': Esource[i],
'target': Etarget[i],
'value': Evalue[i]
})
a = str(Elabel)
GEO = "function (params) { a= " + a + ";return a[params.dataIndex];}"
#GEO = "function (params) { return params.dataIndex;}" #数据索引 #renderer为svg时清晰一点，但是无法保存为图片
c = (
Graph(init_opts=opts.InitOpts(theme=costumeTheme,
renderer="canvs",
width=para['width'],
height=para['height'])) #
.add(
"", #series_name, # 系列名称，用于 tooltip 的显示，legend 的图例筛选。
nodes=nodes_data,
links=links_data,
categories=category,
gravity=para["gravity"],
repulsion=para["repulsion"],
edge_symbol=["", "arrow"], #可以单一指定
is_draggable=True, #允许拖动，只在力引导有用
layout=para['layout'], ## 'circular' 采用环形布局。# 'force' 采用力引导布局
is_rotate_label=True,
linestyle_opts=opts.LineStyleOpts(
color="source",
curve=0.3), #标签在上方显示，以防止被遮挡 top/middle,edge的上方是箭头的起点
label_opts=opts.LabelOpts(is_show=True,
color=para["color"],
position="top",
font_size=para["font_size"],
font_family=para["font_family"]),
edge_label=opts.LabelOpts(is_show=True,
color=para["color"],
position="middle",
font_size=para["font_size"],
font_family=para["font_family"],
formatter=utils.JsCode(GEO)),
))
chart = c
self.Common_code(chart, path_plotly, para)
return path_plotly # 返回该HTML文件路径
