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- '''
- 基于某个周期（一天，一周...）的指标统计库
- '''
- __author__ = 'wlm'
- import CONFIGURE.PathSetting as PathSetting
- import sys
- sys.path.append(PathSetting.backend_path)
- sys.path.append(PathSetting.middle_path)
- import datetime
- import Tools
- import pandas as pd
- import numpy as np
- import IndexStaByOneCycle
- class IndexStaByPeriod():
- def __init__(self):
- self.indexStaByOneCycle = IndexStaByOneCycle.IndexStaByOneCycle()
- pass
- def drive_odo_sta(self, df_bms, df_gps):
- '''
- 计算周期内行车累积行驶里程
- ---------输入参数------------
- df_bms : 一段周期内的预处理后的bms数据
- df_gps : 一段周期内的预处理后的gps数据
- ---------输出参数------------
- sum_odo ： 累积里程， 如果该周期内gps均无效，则返回None
- invalid_rate : 该周期内gps无效的bms数据行所占比例
- '''
- invalid_count = 0
- total_count = 0
- sum_odo = 0
- data_number_list = sorted(list(set(df_bms[(df_bms['data_status'].isin(['drive']))]['data_split_by_status_after_combine'])))
- if len(data_number_list) == 0:
- return {'sum_odo':0, 'invalid_rate':0}
- for data_number in data_number_list[:]:
- df_sel_bms = df_bms[df_bms['data_split_by_status_after_combine'] == data_number]
- df_sel_bms = df_sel_bms.reset_index(drop=True)
- total_count += len(df_sel_bms)
- if df_sel_bms.loc[0, 'gps_rely'] != 1:
- invalid_count += len(df_sel_bms)
- continue
- else:
- df_sel_gps = df_gps[(df_gps['时间戳']>df_sel_bms.loc[0,'时间戳']) & (df_gps['时间戳']<df_sel_bms.loc[len(df_sel_bms)-1,'时间戳'])]
- df_sel_gps = df_sel_gps.reset_index(drop=True)
- odo = self.indexStaByOneCycle.odo_sta(np.array(df_sel_gps['odo']))
- if not pd.isnull(odo):
- sum_odo += odo
- invalid_rate = invalid_count/total_count
- return {'sum_odo':sum_odo, 'invalid_rate':invalid_rate}
-
-
- #该函数未完成， TODO!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
- def _energy_consump_sta(self, cap, df_bms, df_gps):
- '''
- 计算周期内百公里能耗
- ---------输入参数------------
- df_bms : 本周期内的bms数据
- df_gps : 本周期内的gps数据
- ---------输出参数------------
- 本周期内的百公里能耗
- '''
- if not df_bms.empty and not df_gps.empty:
- # 计算能耗
- energy_sum = 0
- data_number_list = sorted(list(set(df_bms[(df_bms['data_status'].isin(['drive']))]['data_split_by_status_time'])))
- for data_number in data_number_list[:]:
- df_sel_bms = df_bms[df_bms['data_split_by_status_time'] == data_number]
- df_sel_bms = df_sel_bms.reset_index(drop=True)
- soc_array = np.array(df_sel_bms['SOC[%]'])
- soh_array = np.array(df_sel_bms['SOH[%]'])
- volt_array = np.array(df_sel_bms['总电压[V]'])
- energy = self.indexStaByOneCycle.energy_sta(cap, soc_array, soh_array, volt_array)
- if not pd.isnull(energy):
- energy_sum += energy
- # 计算里程
- pass # TODO!!!!!!!!!!!!!!!!!!!!!
- return 0
- else:
- return None
- def drive_soc_sta(self, df_bms):
- '''
- 计算周期内行车净累积soc
- ---------输入参数------------
- cap : 标称容量
- df_bms : 一段周期内的预处理后的bms数据
- df_gps : 一段周期内的预处理后的gps数据
- ---------输出参数------------
- sum_ah : 本周期的净累积soc
- '''
- sum_soc = 0
- data_number_list = sorted(list(set(df_bms[(df_bms['data_status'].isin(['drive']))]['data_split_by_status_time'])))
- if len(data_number_list) == 0:
- return sum_soc
- for data_number in data_number_list[:]:
- df_sel_bms = df_bms[df_bms['data_split_by_status_time'] == data_number]
- df_sel_bms = df_sel_bms.reset_index(drop=True)
- sum_soc += abs(df_sel_bms.loc[0, 'SOC[%]'] - df_sel_bms.loc[len(df_sel_bms)-1, 'SOC[%]'])
- return sum_soc
- def drive_time_sta(self, df_bms):
- '''
- 计算周期内累计行车时长/h
- ---------输入参数------------
- cap : 标称容量
- df_bms : 一段周期内的预处理后的bms数据
- df_gps : 一段周期内的预处理后的gps数据
- ---------输出参数------------
- sum_ah : 本周期的累计行车时长
- '''
- sum_time = 0
- data_number_list = sorted(list(set(df_bms[(df_bms['data_status'].isin(['drive']))]['data_split_by_status_time'])))
- if len(data_number_list) == 0:
- return sum_time
- for data_number in data_number_list[:]:
- df_sel_bms = df_bms[df_bms['data_split_by_status_time'] == data_number]
- df_sel_bms = df_sel_bms.reset_index(drop=True)
- sum_time += (df_sel_bms.loc[len(df_sel_bms)-1, '时间戳'] - df_sel_bms.loc[0, '时间戳']).total_seconds()
- return sum_time / 3600.0
- def drive_capacity_sta(self, cap, df_bms):
- '''
- 计算周期内行车净累积ah
- ---------输入参数------------
- cap : 标称容量
- df_bms : 一段周期内的预处理后的bms数据
- df_gps : 一段周期内的预处理后的gps数据
- ---------输出参数------------
- sum_ah : 本周期的净累积ah
- '''
- sum_ah = 0
- data_number_list = sorted(list(set(df_bms[(df_bms['data_status'].isin(['drive']))]['data_split_by_status_time'])))
- if len(data_number_list) == 0:
- return sum_ah
- for data_number in data_number_list[:]:
- df_sel_bms = df_bms[df_bms['data_split_by_status_time'] == data_number]
- df_sel_bms = df_sel_bms.reset_index(drop=True)
- soc_array = np.array(df_sel_bms['SOC[%]'])
- soh_array = np.array(df_sel_bms['SOH[%]'])
- sum_ah += self.indexStaByOneCycle.capacity_sta(cap, soc_array, soh_array)
- return sum_ah
- def drive_energy_sta(self, cap, df_bms):
- '''
- 计算周期内行车净累积能量
- ---------输入参数------------
- cap : 标称容量
- df_bms : 一段周期内的预处理后的bms数据
- df_gps : 一段周期内的预处理后的gps数据
- ---------输出参数------------
- sum_ah : 本周期的净累积能量
- '''
- sum_energy = 0
- data_number_list = sorted(list(set(df_bms[(df_bms['data_status'].isin(['drive']))]['data_split_by_status_time'])))
- if len(data_number_list) == 0:
- return sum_energy
- for data_number in data_number_list[:]:
- df_sel_bms = df_bms[df_bms['data_split_by_status_time'] == data_number]
- df_sel_bms = df_sel_bms.reset_index(drop=True)
- soc_array = np.array(df_sel_bms['SOC[%]'])
- soh_array = np.array(df_sel_bms['SOH[%]'])
- volt_array = np.array(df_sel_bms['总电压[V]'])
- sum_energy += self.indexStaByOneCycle.energy_sta(cap, soc_array, soh_array, volt_array)
- return sum_energy
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