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@@ -1,13 +1,10 @@
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import os
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from datetime import datetime
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-import numpy as np
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import pandas as pd
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-import plotly.graph_objects as go
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from algorithmContract.confBusiness import *
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from algorithmContract.contract import Contract
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from behavior.analystWithGoodBadPoint import AnalystWithGoodBadPoint
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-from plotly.subplots import make_subplots
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class PowerScatter2DAnalyst(AnalystWithGoodBadPoint):
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@@ -31,14 +28,9 @@ class PowerScatter2DAnalyst(AnalystWithGoodBadPoint):
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dataFrame = self.userDataFrame(dictionary, conf.dataContract.configAnalysis, self)
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turbineInfos = self.common.getTurbineInfos(conf.dataContract.dataFilter.powerFarmID, turbineCodes, self.turbineInfo)
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if len(dataFrame) <= 0:
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- print("After screening for blade pitch angle less than the configured value, plot power curve scatter points without data")
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+ self.logger.info("After screening for blade pitch angle less than the configured value, plot power curve scatter points without data")
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return
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- grouped = self.dataFrameContractOfTurbine.groupby(
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- [Field_PowerFarmCode, Field_MillTypeCode])
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-
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- for groupByKey, contractPowerCurveOfMillType in grouped:
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- break
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- return self.drawOfPowerCurveScatter(dataFrame, turbineInfos,outputAnalysisDir, conf, contractPowerCurveOfMillType)
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+ return self.drawOfPowerCurveScatter(dataFrame, turbineInfos,outputAnalysisDir, conf, self.dataFrameContractOfTurbine)
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def drawOfPowerCurveScatter(self, dataFrame: pd.DataFrame, turbineModelInfo: pd.Series, outputAnalysisDir, conf: Contract, dataFrameGuaranteePowerCurve: pd.DataFrame):
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"""
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@@ -50,9 +42,6 @@ class PowerScatter2DAnalyst(AnalystWithGoodBadPoint):
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outputAnalysisDir (str): 分析输出目录。
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confData (ConfBusiness): 配置
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"""
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- x_name = '风速'
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- y_name = '功率'
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-
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#机型切入风速 series
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cutInWsField = self.turbineModelInfo[Field_CutInWS]
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cut_in_ws = cutInWsField.min() - 1 if cutInWsField.notna().any() else 2
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@@ -64,137 +53,24 @@ class PowerScatter2DAnalyst(AnalystWithGoodBadPoint):
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# 按设备名分组数据
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grouped = dataFrame.groupby([Field_NameOfTurbine, Field_CodeOfTurbine])
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result_rows = []
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-
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- # 定义固定的颜色映射列表
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- fixed_colors = [
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- "#3E409C",
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- "#476CB9",
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- "#3586BF",
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- "#4FA4B5",
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- "#52A3AE",
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- "#60C5A3",
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- "#85D0AE",
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- "#A8DCA2",
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- "#CFEE9E",
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- "#E4F39E",
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- "#EEF9A7",
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- "#FBFFBE",
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- "#FDF1A9",
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- "#FFE286",
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- "#FFC475",
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- "#FCB06C",
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- "#F78F4F",
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- "#F96F4A",
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- "#E4574C",
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- "#CA3756",
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- "#AF254F"
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- ]
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-
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- # 将 fixed_colors 转换为 Plotly 的 colorscale 格式
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- fixed_colorscale = [
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- [i / (len(fixed_colors) - 1), color] for i, color in enumerate(fixed_colors)
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- ]
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- fixed_colors_points = [
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- "#F96F4A",
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- "#FFC475",
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- "#FBFFBE",
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- "#85D0AE",
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- "#3586BF",
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- "#3E409C"
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-
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- ]
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-
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# 遍历每个设备的数据
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for name, group in grouped:
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- fig = make_subplots()
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-
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- # 提取月份
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- group['month'] = group['monthIntTime'].apply(lambda x: datetime.fromtimestamp(x).month)
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- unique_months = group['month'].unique()
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-
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- # 计算时间跨度
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- time_span_months = len(unique_months)
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-
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- if time_span_months >= 6:
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- # 绘制散点图（时间跨度大于等于6个月）
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- scatter = go.Scatter(x=group[Field_WindSpeed],
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- y=group[Field_ActiverPower],
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- mode='markers',
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- marker=dict(
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- color=group['monthIntTime'],
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- colorscale=fixed_colorscale, # 使用自定义的 colorscale
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- size=3,
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- opacity=0.7,
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- colorbar=dict(
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- tickvals=np.linspace(
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- group['monthIntTime'].min(), group['monthIntTime'].max(), 6),
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- ticktext=[datetime.fromtimestamp(ts).strftime(
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- '%Y-%m') for ts in np.linspace(group['monthIntTime'].min(), group['monthIntTime'].max(), 6)],
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- thickness=18,
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- len=1, # 设置颜色条的长度，使其占据整个图的高度
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- outlinecolor='rgba(255,255,255,0)'
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- ),
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- showscale=True
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- ),
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- showlegend=False) # 不显示散点图的 legend，用 colorbar 代替
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- fig.add_trace(scatter)
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- else:
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- # 绘制散点图（时间跨度小于6个月）
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- for i, month in enumerate(unique_months):
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- month_data = group[group['month'] == month]
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- # 使用固定的颜色列表
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- color = fixed_colors_points[i % len(fixed_colors_points)]
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- scatter = go.Scatter(x=month_data[Field_WindSpeed],
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- y=month_data[Field_ActiverPower],
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- mode='markers',
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- marker=dict(
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- color=color,
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- size=3,
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- opacity=0.7
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- ),
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- name=f'{datetime.fromtimestamp(month_data["monthIntTime"].iloc[0]).strftime("%Y-%m")}',
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- showlegend=True)
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- fig.add_trace(scatter)
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-
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- # 绘制合同功率曲线
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- line = go.Scatter(x=dataFrameGuaranteePowerCurve[Field_WindSpeed],
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- y=dataFrameGuaranteePowerCurve[Field_ActiverPower],
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- mode='lines+markers',
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- marker=dict(color='gray', size=7),
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- name='合同功率曲线')
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- fig.add_trace(line, secondary_y=False)
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-
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- # 设置图形布局
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- fig.update_layout(
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- title=f'机组: {name[0]}',
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- xaxis=dict(title=x_name,
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- range=[cut_in_ws, 25],
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- tickmode='linear', tick0=0, dtick=1,
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- tickangle=-45),
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- yaxis=dict(title=y_name,
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- dtick=self.axisStepActivePower,
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- range=[self.axisLowerLimitActivePower,
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- self.axisUpperLimitActivePower]
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- ),
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- legend=dict(yanchor="bottom", y=0, xanchor="right", x=1, font=dict(
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- size=10), bgcolor='rgba(255,255,255,0)')
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- )
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- # 确保从 Series 中提取的是具体的值
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- engineTypeCode = turbineModelInfo.get(Field_MillTypeCode, "")
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- if isinstance(engineTypeCode, pd.Series):
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- engineTypeCode = engineTypeCode.iloc[0]
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-
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- engineTypeName = turbineModelInfo.get(Field_MachineTypeCode, "")
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- if isinstance(engineTypeName, pd.Series):
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- engineTypeName = engineTypeName.iloc[0]
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- # 使用 apply() 对每个元素调用 datetime.fromtimestamp
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+ #获取当前风机信息dataFrame
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+ currentEngineDataFrame = turbineModelInfo[turbineModelInfo[Field_CodeOfTurbine]==name[1]]
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+ #获取当前机型
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+ millTypeCode = currentEngineDataFrame.get(Field_MillTypeCode, "").iloc[0]
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+ #当前机型合同功率曲线dataFrame
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+ currentMillTypePowerDataFrame = dataFrameGuaranteePowerCurve[dataFrameGuaranteePowerCurve[Field_MillTypeCode] == millTypeCode]
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+ # 获取机型的名字（machine_type_code）
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+ engineTypeName = self.common.getTurbineModelByCode(millTypeCode, self.turbineModelInfo)[Field_MachineTypeCode]
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+ # 使用 apply() 对每个元素调用 datetime.fromtimestamp
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group['monthIntTime'] = group['monthIntTime'].apply(lambda x: datetime.fromtimestamp(x).strftime('%Y-%m'))
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# 定义要替换的空值类型
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na_values = {pd.NA, float('nan')}
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# 构建最终的JSON对象
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json_output = {
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"analysisTypeCode": "逐月有功功率散点2D分析",
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- "engineCode": engineTypeCode,
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+ "engineCode": millTypeCode,
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"engineTypeName": engineTypeName,
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"xaixs": "风速(m/s)",
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"yaixs": "有功功率(kW)",
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@@ -214,24 +90,12 @@ class PowerScatter2DAnalyst(AnalystWithGoodBadPoint):
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},
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{# 提取合同功率曲线数据
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"enginName": "合同功率曲线",
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- "xData":dataFrameGuaranteePowerCurve[Field_WindSpeed].replace(na_values, None).tolist(),
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- "yData":dataFrameGuaranteePowerCurve[Field_ActiverPower].replace(na_values, None).tolist(),
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+ "xData":currentMillTypePowerDataFrame[Field_WindSpeed].replace(na_values, None).tolist(),
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+ "yData":currentMillTypePowerDataFrame[Field_ActiverPower].replace(na_values, None).tolist(),
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"zData": [],
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"mode":"lines+markers"
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}]
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}
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-
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- # 保存图像
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- # pngFileName = f"{name[0]}-scatter.png"
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- # pngFilePath = os.path.join(outputAnalysisDir, pngFileName)
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- # fig.write_image(pngFilePath, scale=3)
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-
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- # # 保存HTML
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- # htmlFileName = f"{name[0]}-scatter.html"
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- # htmlFilePath = os.path.join(outputAnalysisDir, htmlFileName)
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- # fig.write_html(htmlFilePath)
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-
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-
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# 将JSON对象保存到文件
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output_json_path = os.path.join(outputAnalysisDir, f"{name[0]}-scatter.json")
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with open(output_json_path, 'w', encoding='utf-8') as f:
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@@ -248,24 +112,6 @@ class PowerScatter2DAnalyst(AnalystWithGoodBadPoint):
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Field_Return_IsSaveDatabase: True
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})
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- # result_rows.append({
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- # Field_Return_TypeAnalyst: self.typeAnalyst(),
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- # Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
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- # Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
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- # Field_CodeOfTurbine: name[1],
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- # Field_Return_FilePath: pngFilePath,
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- # Field_Return_IsSaveDatabase: False
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- # })
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-
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- # result_rows.append({
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- # Field_Return_TypeAnalyst: self.typeAnalyst(),
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- # Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
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- # Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
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- # Field_CodeOfTurbine: name[1],
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- # Field_Return_FilePath: htmlFilePath,
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- # Field_Return_IsSaveDatabase: True
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- # })
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-
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result_df = pd.DataFrame(result_rows)
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return result_df
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