123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131 |
- import os
- import pandas as pd
- import plotly.graph_objects as go
- from algorithmContract.confBusiness import *
- from algorithmContract.contract import Contract
- from behavior.analystWithGoodPoint import AnalystWithGoodPoint
- class TSRTrendAnalyst(AnalystWithGoodPoint):
- """
- 风电机组叶尖速比时序分析
- """
- def typeAnalyst(self):
- return "tsr_trend"
- def selectColumns(self):
- return [Field_DeviceCode,Field_Time,Field_WindSpeed,Field_ActiverPower,Field_RotorSpeed,Field_GeneratorSpeed]
- def processDateTime(self, dataFrame: pd.DataFrame, fieldTime:str = None):
- super().processDateTime(dataFrame,Field_YearMonthDay)
- def turbinesAnalysis(self, outputAnalysisDir, conf: Contract, turbineCodes):
- dictionary = self.processTurbineData(turbineCodes,conf,self.selectColumns())
- dataFrameMerge = self.userDataFrame(dictionary,conf.dataContract.configAnalysis,self)
- return self.drawTSRTrend(dataFrameMerge, outputAnalysisDir, conf)
- def drawTSRTrend(self,dataFrameMerge:pd.DataFrame, outputAnalysisDir, conf: Contract):
- # 检查所需列是否存在
- required_columns = {Field_TSR, Field_YearMonthDay}
- if not required_columns.issubset(dataFrameMerge.columns):
- raise ValueError(f"DataFrame缺少必要的列。需要的列有: {required_columns}")
- # 按设备名分组数据
- grouped = dataFrameMerge.groupby([Field_NameOfTurbine, Field_CodeOfTurbine])
- result_rows = []
- for name, group in grouped:
- # 计算四分位数和IQR
- Q1 = group[Field_TSR].quantile(0.25)
- Q3 = group[Field_TSR].quantile(0.75)
- IQR = Q3 - Q1
- # 定义离群值的范围
- lower_bound = Q1 - 1.5 * IQR
- upper_bound = Q3 + 1.5 * IQR
- # 筛选掉离群值
- filtered_group = group[(group[Field_TSR] >= lower_bound) & (group[Field_TSR] <= upper_bound)]
- # 创建箱线图
- fig = go.Figure()
- fig.add_trace(go.Box(
- x=filtered_group[Field_YearMonthDay], # 设置x轴数据为日期
- y=filtered_group[Field_TSR], # 设置y轴数据为风能利用系数
- # boxpoints='outliers', # 显示异常值(偏离值),不显示数据的所有点(只显示异常值)
- boxpoints=False, # 不显示偏离值
- marker=dict(color='lightgoldenrodyellow', size=1), # 设置偏离值的颜色和大小
- line=dict(color='lightgray', width=2), # 设置箱线和须线的颜色为灰色,粗细为2
- fillcolor='rgba(200, 200, 200, 0.5)', # 设置箱体的填充颜色和透明度
- name='叶尖速比' # 图例名称
- ))
- # 对于每个箱线图的中位数,绘制一个蓝色点
- medians = filtered_group.groupby(filtered_group[Field_YearMonthDay])[Field_TSR].median()
- fig.add_trace(go.Scatter(
- x=medians.index,
- y=medians.values,
- mode='markers',
- marker=dict(color='orange', size=3),
- name='叶尖速比中位数' # 中位数标记的图例名称
- ))
- # 设置图表的标题和轴标签
- fig.update_layout(
- title={
- 'text': f'机组: {name[0]}',
- #'x':0.5,
- },
- xaxis_title='时间',
- yaxis_title='叶尖速比',
- xaxis=dict(
- tickmode='auto', # 自动设置x轴刻度,以适应日期数据
- tickformat='%Y-%m-%d', # 设置x轴时间格式
- showgrid=True, # 显示网格线
- gridcolor='lightgray', # setting y-axis gridline color to black
- tickangle=-45,
- linecolor='black', # 设置y轴坐标系线颜色为黑色
- ticklen=5, # 设置刻度线的长度
- ),
- yaxis=dict(
- range=[self.axisLowerLimitTSR, self.axisUpperLimitTSR],
- dtick=self.axisStepTSR,
- showgrid=True, # 显示网格线
- gridcolor='lightgray', # setting y-axis gridline color to black
- linecolor='black', # 设置y轴坐标系线颜色为黑色
- ticklen=5, # 设置刻度线的长度
- ),
- paper_bgcolor='white', # 设置纸张背景颜色为白色
- plot_bgcolor='white', # 设置图表背景颜色为白色
- margin=dict(t=50, b=10) # t为顶部(top)间距,b为底部(bottom)间距
- )
- # 保存图像
- filePathOfImage = os.path.join(outputAnalysisDir, f"{name[0]}.png")
- fig.write_image(filePathOfImage, scale=3)
- filePathOfHtml = os.path.join(outputAnalysisDir, f"{name[0]}.html")
- fig.write_html(filePathOfHtml)
- result_rows.append({
- Field_Return_TypeAnalyst: self.typeAnalyst(),
- Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
- Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
- Field_CodeOfTurbine: name[1],
- Field_Return_FilePath: filePathOfImage,
- Field_Return_IsSaveDatabase: False
- })
- result_rows.append({
- Field_Return_TypeAnalyst: self.typeAnalyst(),
- Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
- Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
- Field_CodeOfTurbine: name[1],
- Field_Return_FilePath: filePathOfHtml,
- Field_Return_IsSaveDatabase: True
- })
- result_df = pd.DataFrame(result_rows)
- return result_df
|