{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "249\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "folder_path = \"/home/liuzhongxiao/zlwl-algos/USER/liuzhongxiao/SOC_pre/chracter_qiyuan/chrg_data/\" # 设置相对路径\n",
    "csv_files = [file for file in os.listdir(folder_path) if file.endswith('.csv')]\n",
    "# 遍历所有CSV文件并依次读取它们\n",
    "df_data_all = pd.DataFrame()\n",
    "for csv_file in csv_files:\n",
    "    filename = os.path.join(folder_path, csv_file)\n",
    "    df_data = pd.read_csv(filename, encoding='GB18030')\n",
    "    df_data_all = df_data_all.append(df_data)\n",
    "print(len(df_data_all))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "249\n"
     ]
    }
   ],
   "source": [
    "df_data_all.drop_duplicates(subset=['sn', 'time_st'], keep = 'last', inplace=True)\n",
    "df_data_all.reset_index(drop = True, inplace=True)\n",
    "print(len(df_data_all))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def smooth(a,WSZ):\n",
    "    out0 = np.convolve(a,np.ones(WSZ,dtype=int),'valid')/WSZ\n",
    "    r = np.arange(1,WSZ-1,2)\n",
    "    start = np.cumsum(a[:WSZ-1])[::2]/r\n",
    "    stop = (np.cumsum(a[:-WSZ:-1])[::2]/r)[::-1]\n",
    "    return np.concatenate((start, out0, stop))\n",
    "def find_peak_triangleNum(filter_signal, peak_width):\n",
    "    # 判断是否有凸起\n",
    "    length_data = len(filter_signal)\n",
    "    thre = 0.8*np.percentile(filter_signal, 90)  # 设置阈值高度  95%是前400个点的20个波峰点\n",
    "    botthre = 0.8*np.percentile(filter_signal, 10)  # 设置阈值低度  5%是400个点的后20个波峰点\n",
    "    # 在整个区域内找极值\n",
    "    l = []\n",
    "    bot = []\n",
    "    for i in range(1,length_data-1):\n",
    "        if (filter_signal[i-1] < filter_signal[i]) and (filter_signal[i]>filter_signal[i+1]) and (filter_signal[i]>thre):\n",
    "            l.append(i)\n",
    "        elif (filter_signal[i] == filter_signal[i-1]) and (filter_signal[i]>thre):\n",
    "            l.append(i) # 最高点前后可能有相等的情况\n",
    "        if (filter_signal[i-1] > filter_signal[i]) and (filter_signal[i]<filter_signal[i+1]) and (filter_signal[i]<botthre):\n",
    "            bot.append(i)\n",
    "        elif (filter_signal[i] == filter_signal[i-1]) and (filter_signal[i]<botthre):\n",
    "            bot.append(i) # 最高点前后可能有相等的情况\n",
    "    CC = len(l) # 统计极值得个数\n",
    "    cou = 0\n",
    "    ll = l.copy()\n",
    "    for j in range(1, CC):\n",
    "        if l[j]-l[j-1] < peak_width:  # 此判断用于将位于同一个峰内的极值点去掉\n",
    "            if l[j] > l[j-1]:   # 同一个峰内的数据，将小的值替换成0\n",
    "                ll[j-1] = 0\n",
    "            else:\n",
    "                ll[j] = 0\n",
    "            cou = cou+1\n",
    "    rcou = CC - cou\n",
    "    ll = [i for i in ll if i > 0]  # 去掉0的值\n",
    "    peak_index = []\n",
    "    # 找到每个区间内波峰最大值\n",
    "    # 截断每个区间再求区间最大值的索引\n",
    "    for i in range(len(ll)):\n",
    "        if i == 0:\n",
    "            index_range = np.array(l)[np.array(l) <= ll[i]]\n",
    "        else:\n",
    "            index_range = np.array(l)[(np.array(l)<=ll[i]) & (np.array(l)>ll[i-1])]\n",
    "        # 找到每个区间最大值得索引\n",
    "        if len(index_range) > 0:\n",
    "            peak_index.append(index_range[np.argmax(filter_signal[index_range],axis=0)])\n",
    "    DD = len(bot) # 统计极值得个数\n",
    "    cou = 0\n",
    "    botm = bot.copy()\n",
    "    for j in range(1, DD):\n",
    "        if bot[j]-bot[j-1] < peak_width:  # 此判断用于将位于同一个峰内的极值点去掉\n",
    "            if bot[j] < bot[j-1]:   # 同一个峰内的数据，将大的值替换成0\n",
    "                botm[j] = 0\n",
    "            else:\n",
    "                botm[j-1] = 0\n",
    "            cou = cou+1\n",
    "    Dcou = DD - cou\n",
    "    botm = [i for i in botm if i > 0]  # 去掉0的值\n",
    "    bot_index = []\n",
    "    # 找到每个区间内波峰最大值\n",
    "    # 截断每个区间再求区间最大值的索引\n",
    "    for i in range(len(botm)):\n",
    "        if i == 0:\n",
    "            botindex_range = np.array(bot)[np.array(bot) >= botm[i]]\n",
    "        else:\n",
    "            botindex_range = np.array(bot)[(np.array(bot) >= botm[i]) & (np.array(bot) < botm[i-1])]\n",
    "        # 找到每个区间最小值得索引\n",
    "        if len(botindex_range) > 0:\n",
    "            bot_index.append(botindex_range[np.argmin(filter_signal[botindex_range],axis=0)])\n",
    "    return [rcou, peak_index, Dcou, bot_index]#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3690509/3571068024.py:61: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_datachose['abscrnt'] = list(np.abs(df_datachose['Packcrnt']))\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "from scipy.signal import savgol_filter\n",
    "import matplotlib.pyplot as plt  \n",
    "from pylab import*\n",
    "import matplotlib as mpl\n",
    "from scipy import interpolate\n",
    "plt.rcParams['font.sans-serif']=['simhei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "df_ofc_data = df_data_all.loc[df_data_all['sts_flg'] ==1]\n",
    "df_ofc_data.reset_index(drop = True, inplace=True)\n",
    "dvsocchrcter = []\n",
    "pack_capty = 456#df_param['pack_capacity'].iloc[0]\n",
    "# k = 3\n",
    "def dats_interp(input1, input2, datalen):\n",
    "    cap_frst = list(linspace(input1.iloc[0], input1.iloc[-1], datalen, endpoint = False))#len(df_dataana['chrgah'])\n",
    "    fuc_inter = interpolate.interp1d(input1, input2, kind = 'nearest')#定义差值函数\n",
    "    vlt_splce_temp = list(fuc_inter(cap_frst))\n",
    "    return vlt_splce_temp\n",
    "for item in range(len(df_ofc_data)):#\n",
    "    df_datatp = df_ofc_data.iloc[item]\n",
    "    itemsn = df_datatp['sn']\n",
    "    df_data_temp = pd.DataFrame()\n",
    "    para_lvbo = 7\n",
    "    if (type(df_datatp['celvltmax']) == str):# and (type(df_datatp['secvlt']) == str) and (type(df_datatp['thrdvlt']) == str)\n",
    "        df_data_temp['Time'] = eval(df_datatp['timelst'])#re.findall(r'\\(([\\s\\S]+?)\\)',df_datatp['timelst'])##.iloc[0]\n",
    "        df_data_temp['SOC[%]'] = eval(df_datatp['soc_aray'])#eval(df_datatp['soc_aray'])\n",
    "        df_data_temp['Packcrnt'] = eval(df_datatp['crnt_aray'])\n",
    "        df_data_temp['frst'] = eval(df_datatp['celvltmax'])#eval(df_datatp['celvltmax'])\n",
    "        # df_data_temp['send'] = eval(df_datatp['secvlt'])#eval(df_datatp['secvlt'])\n",
    "        # df_data_temp['thrd'] = eval(df_datatp['thrdvlt'])#eval(df_datatp['thrdvlt'])  \n",
    "        df_data_temp['max_T'] = eval(df_datatp['celtemmax'])#eval(df_datatp['celtemmax'])\n",
    "        df_data_temp['min_T'] = eval(df_datatp['celtemmin'])#eval(df_datatp['celtemmin'])\n",
    "        df_data_temp['odo'] = [df_datatp['odo_st']]*len(df_data_temp['Time'])\n",
    "        df_data_temp['chrgah'] = eval(df_datatp['chrgahlst'])\n",
    "        df_data_temp['packvlt'] = eval(df_datatp['packvlt'])\n",
    "        df_data_temp['Time'] = pd.to_datetime(df_data_temp['Time'])#, utc=True, unit='ms').tz_convert('Asia/Shanghai')\n",
    "        delta_time = round((df_data_temp['Time'].iloc[-1] -df_data_temp['Time'].iloc[0])/pd.Timedelta(1, 'hours'), 3)\n",
    "        delta_soc = round((df_data_temp['SOC[%]'].iloc[-1] - df_data_temp['SOC[%]'].iloc[0]), 3)\n",
    "        rate_chrg = round(delta_soc/(100*delta_time), 2)\n",
    "        df_datachose_temp = df_data_temp[(df_data_temp['SOC[%]'] > 30) & (df_data_temp['SOC[%]'] < 82)]\n",
    "        diff_vlt = df_datachose_temp['frst'].diff()\n",
    "        # 使用 cumsum() 函数标记下降区域\n",
    "        down_regions = (diff_vlt >= 0).cumsum()\n",
    "        # 使用 groupby() 和 transform() 函数计算每个区域的长度\n",
    "        region_sizes = down_regions.groupby(down_regions).transform('size')\n",
    "        # 使用 where() 函数删除连续3次以上下降位置的数据\n",
    "        data_series_filtered = df_datachose_temp.where(region_sizes < 10)\n",
    "        nan_elements = data_series_filtered.isna()\n",
    "\n",
    "        # 使用 np.where() 函数查找所有 NaN 元素的位置\n",
    "        nan_indices = np.where(nan_elements == True)[0]\n",
    "        if len(nan_indices)>0:\n",
    "            last_num = nan_indices[-1]\n",
    "        else:\n",
    "            last_num = 0\n",
    "        \n",
    "        df_datachose = df_datachose_temp.iloc[last_num:]\n",
    "\n",
    "        df_datachose.reset_index(drop= True, inplace=True)\n",
    "        if (rate_chrg > 0.001) and (len(df_datachose) > 10):\n",
    "            df_datachose['abscrnt'] = list(np.abs(df_datachose['Packcrnt']))\n",
    "            crntnum = df_datachose.loc[:,'abscrnt'].value_counts()   #统计电芯数量这一列每个元素出现的个数\n",
    "            crntmaxNums = int(crntnum.idxmax())\n",
    "            df_dataana = df_datachose.loc[(df_datachose['abscrnt'] > crntmaxNums-4) & (df_datachose['abscrnt'] < crntmaxNums+4)]\n",
    "            df_dataana = df_dataana.iloc[3:-2]\n",
    "            df_dataana.reset_index(drop= True, inplace=True)\n",
    "            if (len(df_dataana) > 10):\n",
    "                df_dataana['frst'] = savgol_filter(df_dataana['frst'], para_lvbo, 3)\n",
    "                # df_dataana['send'] = savgol_filter(df_dataana['send'], para_lvbo, 3)\n",
    "                # df_dataana['thrd'] = savgol_filter(df_dataana['thrd'], para_lvbo, 3)\n",
    "                # vlt_frst = list(linspace(df_dataana['frst'].iloc[0], df_dataana['frst'].iloc[-1], len(df_dataana['frst']), endpoint = False))#\n",
    "                # fuc_inter = interpolate.interp1d(df_dataana['frst'], df_dataana['chrgah'], kind = 'nearest')#定义差值函数\n",
    "                # cap_splce = list(fuc_inter(vlt_frst))\n",
    "                split_len = int(1.2*(df_dataana['SOC[%]'].iloc[-1] - df_dataana['SOC[%]'].iloc[0]))\n",
    "                if split_len > 10:\n",
    "                    cap_frst = list(linspace(df_dataana['chrgah'].iloc[0], df_dataana['chrgah'].iloc[-1], split_len, endpoint = False))#len(df_dataana['chrgah'])\n",
    "                    vlt_splce_temp = dats_interp(df_dataana['chrgah'], df_dataana['frst'], split_len)\n",
    "                    soc_splt = dats_interp(df_dataana['chrgah'], df_dataana['SOC[%]'], split_len)\n",
    "                    crnt_splt = dats_interp(df_dataana['chrgah'], df_dataana['Packcrnt'], split_len)\n",
    "                    maxt_splt = dats_interp(df_dataana['chrgah'], df_dataana['max_T'], split_len)\n",
    "                    mint_splt = dats_interp(df_dataana['chrgah'], df_dataana['min_T'], split_len)\n",
    "                    odo_splt = [df_dataana['odo'].iloc[0]]*split_len\n",
    "                    time_splt = dats_interp(df_dataana['chrgah'], df_dataana['Time'], split_len)\n",
    "                    pkvlt_splt_temp = dats_interp(df_dataana['chrgah'], df_dataana['packvlt'], split_len)\n",
    "                    pkvlt_splt = savgol_filter(pkvlt_splt_temp, para_lvbo, 3)\n",
    "                    vlt_splce = savgol_filter(vlt_splce_temp, para_lvbo, 3)\n",
    "                    np_dff_vlt = np.diff(vlt_splce)\n",
    "                    np_dff_cap = np.diff(cap_frst)\n",
    "                    np_dff_dv_dq_temp = np.divide(np_dff_vlt, np_dff_cap)\n",
    "                    np_dff_dv_dq = np.insert(np_dff_dv_dq_temp, 0, np_dff_dv_dq_temp[0])\n",
    "                    \n",
    "                    df_dvdq_data = pd.DataFrame()\n",
    "                    df_dvdq_data['SOC[%]'] = soc_splt\n",
    "                    df_dvdq_data['Packcrnt'] = crnt_splt\n",
    "                    df_dvdq_data['frst'] = vlt_splce\n",
    "                    df_dvdq_data['max_T'] = maxt_splt\n",
    "                    df_dvdq_data['min_T'] = mint_splt\n",
    "                    df_dvdq_data['odo'] = odo_splt\n",
    "                    df_dvdq_data['chrgah'] = cap_frst\n",
    "                    df_dvdq_data['packvlt'] = pkvlt_splt\n",
    "                    df_dvdq_data['Time'] = pd.to_datetime(time_splt)#, utc=True, unit='ms').tz_convert('Asia/Shanghai')\n",
    "                    \n",
    "                    df_dvdq_data['dffsoc'] = df_dvdq_data['SOC[%]'].diff()\n",
    "                    df_dvdq_data['dffah'] = df_dvdq_data['chrgah'].diff()\n",
    "                    df_dvdq_data['dv_dq'] = np_dff_dv_dq\n",
    "                    df_dvdq_data['fr_vlt_dvdq'] = vlt_splce#电压\n",
    "                    df_dvdq_data.fillna(method = 'bfill', inplace = True , axis = 0)\n",
    "                    delta_soc = df_dvdq_data['SOC[%]'].iloc[-1] - df_dvdq_data['SOC[%]'].iloc[0]\n",
    "                    # print('电流稳定区充入SOC：', delta_soc)\n",
    "                    if all(df_dvdq_data['dffsoc'] < 3) and delta_soc > 10:\n",
    "                        df_dvdq_data['df_dq_lvb'] = savgol_filter(df_dvdq_data['dv_dq'], para_lvbo, 3, mode = 'nearest')#\n",
    "                        df_data = df_dvdq_data.copy()#loc[(df_dataana['SOC[%]'] > 30) & (df_dataana['SOC[%]'] < 70)]\n",
    "                        df_data.reset_index(drop = True, inplace = True)\n",
    "                        # fig1 = plt.figure()#figsize=(16,12)\n",
    "                        # # mpl.rcParams['font.sans-serif'] = ['KaiTi']#字体设置为楷体\n",
    "                        # mpl.rcParams['axes.unicode_minus'] = False #用来正常显示负号\n",
    "                        # plt.plot(df_data['SOC[%]'], df_data['df_dq_lvb'], color='k', lw = 2, ls = '-', marker = 'o', ms = 6, label = 'mav_vlt')\n",
    "                        # # plt.plot(df_data['SOC[%]'], df_data['dvsend-dah'], color='b', lw = 2, ls = '-.', marker = 's', ms = 6, label = 'sec_vlt')\n",
    "                        # # plt.plot(df_data['SOC[%]'], df_data['dvthrd-dah'], color='r', lw = 2, ls = '-.', marker = 's', ms = 6, label = 'thrd_vlt')\n",
    "                        # plt.ylabel(\"dv/dq\", fontsize=12)\n",
    "                        # # plt.xlabel(\"Soc(%)\", fontsize=12)\n",
    "                        # # plt.grid(axis = 'y', c = 'gray')\n",
    "                        # plt.xticks(rotation=45, fontsize=12)#\n",
    "                        # plt.yticks(fontsize=12)\n",
    "                        # # plt.ylim([-0.002, 0.02])\n",
    "                        # plt.legend(loc = 'best', fontsize=10)\n",
    "                        # # plt.show()\n",
    "                        # prename = df_datatp['sn'] + '_' + df_datatp['time_st'].replace(':', '-').replace(' ', '-')\n",
    "                        # plt.title(prename + '_dvq')\n",
    "                        # savepath = r'/home/liuzhongxiao/zlwl-algos/USER/liuzhongxiao/SOC_pre/chracter/picture//' + prename + 'dvdq.jpg'\n",
    "                        # plt.savefig(savepath, bbox_inches='tight', dpi=300)\n",
    "                        # plt.close()\n",
    "                        # fig1 = plt.figure()#figsize=(16,12)\n",
    "                        # # mpl.rcParams['font.sans-serif'] = ['KaiTi']#字体设置为楷体\n",
    "                        # mpl.rcParams['axes.unicode_minus'] = False #用来正常显示负号\n",
    "                        # plt.plot(df_data['SOC[%]'], df_data['fr_vlt_dvdq'], color='k', lw = 2, ls = '-', marker = 'o', ms = 6, label = 'mav_vlt')\n",
    "                        # # plt.plot(df_data['SOC[%]'], df_data['send'], color='b', lw = 2, ls = '-.', marker = 's', ms = 6, label = 'sec_vlt')\n",
    "                        # # plt.plot(df_data['SOC[%]'], df_data['thrd'], color='r', lw = 2, ls = '-.', marker = 's', ms = 6, label = 'thrd_vlt')\n",
    "                        # plt.ylabel(\"Volt(V)\", fontsize=12)\n",
    "                        # # plt.xlabel(\"Soc(%)\", fontsize=12)\n",
    "                        # # plt.grid(axis = 'y', c = 'gray')\n",
    "                        # plt.xticks(rotation=45, fontsize=12)#\n",
    "                        # plt.yticks(fontsize=12)\n",
    "                        # # plt.ylim([-0.002, 0.02])\n",
    "                        # plt.legend(loc = 'best', fontsize=10)\n",
    "                        # # plt.show()\n",
    "                        # # prename = df_datatp['sn'] + '-' + df_datatp['time_st'].replace(':', '-').replace(' ', '-')\n",
    "                        # plt.title(prename + '_dvq')\n",
    "                        # savepath = r'/home/liuzhongxiao/zlwl-algos/USER/liuzhongxiao/SOC_pre/chracter/picture//' + prename + 'vlt.jpg'\n",
    "                        # plt.savefig(savepath, bbox_inches='tight', dpi=300)\n",
    "                        # plt.close()\n",
    "                        # df_data.to_csv(r'/home/liuzhongxiao/project/zlwl-algos/USER/liuzhongxiao/SOC_pre/chracter/data//' + prename + 'vlt_data.csv', index=False, encoding='GB18030')\n",
    "                        if len(df_data) > 10:\n",
    "                            cellvltname = ['fr_vlt_dvdq']#, 'send', 'thrd'\n",
    "                            cellvltahname = ['df_dq_lvb']#, 'dffsendvlt', 'dffthdvlt'\n",
    "                            for itemnum in range(0, 1):\n",
    "                                snchrcter = []\n",
    "                                np_vlt_ori = np.array(df_data[cellvltahname[itemnum]])#dffminvlt\n",
    "                                np_vltori_smth = savgol_filter(np_vlt_ori, para_lvbo, 3)\n",
    "                                rcoumax, peak_indexmax, rcoumin, peak_indexmin = find_peak_triangleNum(np_vltori_smth, 7)#, rcoumin, peak_indexmin\n",
    "                                if (len(peak_indexmax) > 0) and (len(peak_indexmin) > 0):\n",
    "                                    peak_fst = np_vltori_smth[peak_indexmax[0]]\n",
    "                                    botm_fst = np_vltori_smth[peak_indexmin[0]]\n",
    "                                    pkbtmratfst = botm_fst/peak_fst\n",
    "                                else:\n",
    "                                    peak_fst = -1\n",
    "                                if (peak_fst>0):#and (botm_fst>0) and (abs(pkbtmratfst) >= 0.2) and (abs(pkbtmratfst) <= 0.8)\n",
    "                                    maxindex = peak_indexmax[0]\n",
    "                                    maxsoc = np.array(df_data['SOC[%]'])[maxindex]\n",
    "                                    snchrcter.append(itemsn)\n",
    "                                    snchrcter.append(df_data.loc[0, 'Time'])\n",
    "                                    snchrcter.append(np.array(df_data[cellvltname[itemnum]])[maxindex])#电压\n",
    "                                    snchrcter.append(maxsoc)\n",
    "                                    snchrcter.append(1)\n",
    "                                    snchrcter.append(np.array(df_data['max_T'])[maxindex])#最大电芯温度     \n",
    "                                    snchrcter.append(np.array(df_data['min_T'])[maxindex])#最大电芯温度\n",
    "                                    snchrcter.append(round(np.array(df_data['Packcrnt'])[maxindex]/pack_capty, 3))#倍率\n",
    "                                    snchrcter.append(df_data['odo'].iloc[0])#里程\n",
    "                                    snchrcter.append(np.array(df_data['packvlt'])[maxindex])#里程\n",
    "                                    dvsocchrcter.append(snchrcter)\n",
    "df_chracter_ofc_temp = pd.DataFrame(dvsocchrcter)\n",
    "if not df_chracter_ofc_temp.empty:\n",
    "    setcolumns = ['sn', 'Time', 'frstvlt', 'frstsoc', 'pkbtmfst', 'frstmaxT', 'frstminT', 'rate', 'odo', 'packvlt']\n",
    "    df_chracter_ofc_temp.columns = setcolumns\n",
    "    df_chracter_ofc = df_chracter_ofc_temp[(df_chracter_ofc_temp['frstsoc'] < 60) & (df_chracter_ofc_temp['frstsoc'] > 40)]\n",
    "    df_chracter_ofc.reset_index(drop = True, inplace=True)\n",
    "    df_chracter_ofc.to_csv('/home/liuzhongxiao/zlwl-algos/USER/liuzhongxiao/SOC_pre/chracter_qiyuan/' + 'SOC_chracter.csv',index=False,encoding='GB18030')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3690509/1026699304.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_chracter_ofc['rate'] = abs(df_chracter_ofc['rate'])\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sys\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# 文件的名字  \n",
    "# 禁用科学计数法  \n",
    "pd.set_option('float_format', lambda x: '%.3f' % x)  \n",
    "np.set_printoptions(suppress=True, threshold=sys.maxsize)  \n",
    "# 得到的DataFrame分别为总价、面积、房间、客厅、年份  \n",
    "# df_chracter_ofc = pd.read_csv('/home/liuzhongxiao/zlwl-algos/USER/liuzhongxiao/SOC_pre/chracter/' + 'SOC_chracterV.csv', encoding='GB18030')\n",
    "df_chracter_ofc['rate'] = abs(df_chracter_ofc['rate'])\n",
    "dataori = df_chracter_ofc.loc[df_chracter_ofc['rate'] > 0.0]#.iloc[50:]#& (datatp['maxsoc'] <=65) & (datatp['maxsoc'] >=50)\n",
    "# dataori2 = datatp.loc[(datatp['rate'] >= 0.4) & (datatp['rate'] < 0.8) & (abs(datatp['maxsoc']) < 50) & (abs(datatp['maxsoc']) > 48)]\n",
    "dataori.reset_index(drop = True, inplace = True)\n",
    "#------------------------最小电压训练集-测试集\n",
    "DataArray = dataori.values  \n",
    "Y = DataArray[:, 3]  #4SOC\n",
    "X = DataArray[:, [2,5,6,7,8,9]] #2,3,5,7,8,9,10#2,7,9,4,10\n",
    "X = np.array(X)#转化为array,自变量  \n",
    "Y = np.array(Y)#转化为array，因变量soc  \n",
    "# data = np.concatenate((X, Y.reshape(-1, 1)), axis=1)\n",
    "\n",
    "# scaler = StandardScaler()\n",
    "# # scaler.fit(X)\n",
    "# data_scaled  = scaler.fit_transform(data)\n",
    "# X_train_nor = data_scaled[:, :-1]\n",
    "# Y_train_nor = data_scaled[:, -1]\n",
    "scaler_x = StandardScaler()\n",
    "scaler_y = StandardScaler()\n",
    "\n",
    "X_train_scaled = scaler_x.fit_transform(X)\n",
    "y_train_scaled = scaler_y.fit_transform(Y.reshape(-1, 1))\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_train_scaled, y_train_scaled, test_size=0.1, random_state=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集长度 60\n",
      "测试集长度 7\n"
     ]
    }
   ],
   "source": [
    "print('训练集长度', len(X_train))\n",
    "print('测试集长度', len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "layer = []\n",
    "sizer = []\n",
    "transcore = []\n",
    "tstscort = []\n",
    "k = 1\n",
    "for i in range(2, 53):#43\n",
    "    for j in range(2, 51):#51\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X_train_scaled, y_train_scaled, test_size=0.1, random_state=j)\n",
    "        \n",
    "        X_train = X_train.astype('float')\n",
    "        y_train = y_train.astype('float')\n",
    "        \n",
    "        pf = MLPRegressor(solver='lbfgs', hidden_layer_sizes=[i,j], random_state=j, activation = 'logistic')#\n",
    "        # print(X_train)\n",
    "        performance = pf.fit(X_train, y_train)\n",
    "\n",
    "        y_pred_train = pf.predict(X_train)\n",
    "        y_pred_test = pf.predict(X_test)\n",
    "\n",
    "        layer.append(i)\n",
    "        sizer.append(j)\n",
    "        trainscore = performance.score(X_train, y_train)\n",
    "        testscore = performance.score(X_test, y_test)\n",
    "        transcore.append(trainscore)\n",
    "        tstscort.append(testscore)\n",
    "        if k % 100 == 0:\n",
    "                print(k)\n",
    "        k += 1\n",
    "# print('运行时间是：', str(time.time() - timest))\n",
    "# print(\"训练集合上R^2 = {:.3f}\".format(performance.score(X_train, y_train)))\n",
    "# print(\"测试集合上R^2 = {:.3f} \".format(performance.score(X_test, y_test)))\n",
    "df_rslt = pd.DataFrame()\n",
    "df_rslt['layer'] = layer\n",
    "df_rslt['sizer'] = sizer\n",
    "df_rslt['transcore'] = transcore\n",
    "df_rslt['testscore'] = tstscort\n",
    "df_good_rlt = df_rslt[df_rslt['testscore'] > 0.8]\n",
    "print(df_good_rlt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      layer  sizer  transcore  testscore\n",
      "918      20     38      0.933      0.826\n",
      "1534     33     17      0.952      0.861\n",
      "1763     37     50      0.998      0.828\n",
      "2057     43     50      0.998      0.814\n"
     ]
    }
   ],
   "source": [
    "print(df_rslt[(df_rslt['transcore'] > 0.9) & (df_rslt['testscore'] > 0.8)])# & "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集合上R^2 = 0.982\n",
      "测试集合上R^2 = 0.925 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/module/anaconda2022-10/envs/py3916/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1623: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/module/anaconda2022-10/envs/py3916/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_train_scaled, y_train_scaled, test_size=0.1, random_state=17)\n",
    "\n",
    "X_train = X_train.astype('float')\n",
    "y_train = y_train.astype('float')\n",
    "\n",
    "pf = MLPRegressor(solver='lbfgs', hidden_layer_sizes=[17,30], random_state=30, activation = 'logistic')#\n",
    "# print(X_train)\n",
    "performance = pf.fit(X_train, y_train)\n",
    "\n",
    "y_pred_train = pf.predict(X_train)\n",
    "y_pred_test = pf.predict(X_test)\n",
    "\n",
    "\n",
    "# print('运行时间是：', str(time.time() - timest))\n",
    "print(\"训练集合上R^2 = {:.3f}\".format(performance.score(X_train, y_train)))\n",
    "print(\"测试集合上R^2 = {:.3f} \".format(performance.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[56.90911065 53.0697147  54.08329847 54.8004438  57.3834528  53.92522077\n",
      " 55.48896625 54.92496211 52.77484024 55.89596635 56.94147584 55.70687068\n",
      " 53.35485482 57.40107689 68.15631435 56.01757857 57.91526574 52.13072402\n",
      " 54.28170063 54.12921399 53.93795154 72.63796994 54.02544896 51.91530927\n",
      " 51.5310249  56.52926692 46.23514279 55.5375045  53.05149805 49.6708511\n",
      " 57.00899946 51.48024391 51.32274605 47.3573692  47.67892069 72.73428624\n",
      " 60.82452072 52.13444316 54.74873266 57.24324697 56.85977915 51.09061541\n",
      " 54.61391662 54.56708363 60.36344977 55.69618691 53.26545049 49.54446821\n",
      " 49.66050769 50.80413343 51.09979423 54.12874298 52.58466123 54.39010084\n",
      " 49.5838364  56.09036285 54.62828142 63.96768212 73.04294384 61.69498831\n",
      " 72.32439957 50.66573596 54.87632132 58.06111749 54.93586051 57.69198526]\n"
     ]
    }
   ],
   "source": [
    "y_pred = scaler_y.inverse_transform(y_pred_train.reshape(-1, 1)).reshape(-1)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/home/liuzhongxiao/zlwl-algos/USER/liuzhongxiao/SOC_Fct/normorlized_y']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(pf, r'/home/liuzhongxiao/zlwl-algos/USER/liuzhongxiao/SOC_Fct/Dnnmodel.pkl')\n",
    "joblib.dump(scaler_x, r'/home/liuzhongxiao/zlwl-algos/USER/liuzhongxiao/SOC_Fct/normorlized_x')\n",
    "joblib.dump(scaler_y, r'/home/liuzhongxiao/zlwl-algos/USER/liuzhongxiao/SOC_Fct/normorlized_y')\n",
    "\n",
    "# predict_soc = joblib.load(r'D:\\Work\\智锂物联\\03项目\\01合众\\03算法开发\\16LFP-soc修正\\02合众数据\\充电过程统计\\2101TS-高压筛选\\test2\\model\\Dnnmodel.pkl')\n",
    "# normalized_data = joblib.load(r'D:\\Work\\智锂物联\\03项目\\01合众\\03算法开发\\16LFP-soc修正\\02合众数据\\充电过程统计\\2101TS-高压筛选\\test2\\model\\normorlized')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
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      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "/opt/module/anaconda2022-10/envs/py3916/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/module/anaconda2022-10/envs/py3916/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/module/anaconda2022-10/envs/py3916/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20540 (\\N{CJK UNIFIED IDEOGRAPH-503C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "/opt/module/anaconda2022-10/envs/py3916/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/module/anaconda2022-10/envs/py3916/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
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      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
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      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
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      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
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      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "/opt/module/anaconda2022-10/envs/py3916/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35823 (\\N{CJK UNIFIED IDEOGRAPH-8BEF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/module/anaconda2022-10/envs/py3916/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 24046 (\\N{CJK UNIFIED IDEOGRAPH-5DEE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: KaiTi\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train = [y_train, y_test]\n",
    "prerlt = [y_pred_train, y_pred_test]\n",
    "y_pred_plt = scaler_y.inverse_transform(y_pred_train.reshape(-1, 1)).reshape(-1)#all_y_trues_max*np.std(all_y_trues) + all_y_trues_mean\n",
    "y_pred_real = scaler_y.inverse_transform(y_train.reshape(-1, 1)).reshape(-1)#ymax_pred*np.std(all_y_trues) + all_y_trues_mean\n",
    "prename = '训练集'\n",
    "import matplotlib.pyplot as plt  \n",
    "from pylab import*\n",
    "fig1 = plt.figure()#figsize=(16,12)\n",
    "mpl.rcParams['font.sans-serif'] = ['KaiTi']#字体设置为楷体\n",
    "mpl.rcParams['axes.unicode_minus'] = False #用来正常显示负号\n",
    "plt.plot(range(len(y_pred_plt)),y_pred_real, color='k', lw = 2, ls = '-', marker = 'o', ms = 6, label = 'SOC真实值')\n",
    "plt.plot(range(len(y_pred_plt)), y_pred_plt, color='b', lw = 2, ls = '-.', marker = 's', ms = 6, label = 'SOC预测值')\n",
    "plt.ylabel(\"Soc(%)\", fontsize=12)\n",
    "# plt.xlabel(\"Soc(%)\", fontsize=12)\n",
    "plt.grid(axis = 'y', c = 'gray')\n",
    "plt.xticks(rotation=45, fontsize=12)#\n",
    "plt.yticks(fontsize=12)\n",
    "# plt.ylim([-0.002, 0.02])\n",
    "plt.legend(loc = 'best', fontsize=10)\n",
    "plt.show()\n",
    "# plt.title(prename + '的真实值与预测值')\n",
    "# savepath = r'D:\\Work\\智锂物联\\03项目\\01合众\\03算法开发\\16LFP-soc修正\\02合众数据\\充电过程统计\\2101TS-高压筛选\\test2\\\\' + prename + '中电压预测与真实值-95.jpg'\n",
    "# plt.savefig(savepath, dpi=300)\n",
    "# plt.close()\n",
    "fig1 = plt.figure()#figsize=(16,12)\n",
    "mpl.rcParams['font.sans-serif'] = ['KaiTi']#字体设置为楷体\n",
    "mpl.rcParams['axes.unicode_minus'] = False #用来正常显示负号\n",
    "plt.plot(range(len(y_pred_plt)), (y_pred_real - y_pred_plt), color='r', lw = 2, ls = '-.', marker = 's', ms = 6, label = 'SOC误差')\n",
    "plt.ylabel(\"Soc(%)\", fontsize=12)\n",
    "# plt.xlabel(\"Soc(%)\", fontsize=12)\n",
    "plt.grid(axis = 'y', c = 'gray')\n",
    "plt.xticks(rotation=45, fontsize=12)#\n",
    "plt.yticks(fontsize=12)\n",
    "# plt.ylim([-0.002, 0.02])\n",
    "plt.legend(loc = 'best', fontsize=10)\n",
    "plt.show()\n",
    "# plt.title(prename + '的预测差值')\n",
    "# savepath = r'D:\\Work\\智锂物联\\03项目\\01合众\\03算法开发\\16LFP-soc修正\\02合众数据\\充电过程统计\\2101TS-高压筛选\\test2\\\\' + prename + '中电压预测值误差-95.jpg'\n",
    "# plt.savefig(savepath, dpi=300)\n",
    "# plt.close()"
   ]
  }
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