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b/dimaoshibie/segformer.py @@ -34,30 +34,29 @@ class SegFormer_Segmentation(object): # 颜色设置 self.colors = [ - (0, 0, 0), # Background (黑色) 背景 - (252, 250, 205), # Cropland (淡黄色) 农田 - (0, 123, 79), # Forest (深绿色) 森林 - (157, 221, 106), # Grass (浅绿色) 草地 - (77, 208, 159), # Shrub (浅蓝绿色) 灌木 - (111, 208, 242), # Wetland (浅蓝色) 湿地 - (10, 78, 151), # Water (深蓝色) 水 - (92, 106, 55), # Tundra (土黄色) 苔原 - (155, 36, 22), # Impervious surface (红色) 不透明水面 - (205, 205, 205), # Bareland (灰色) 裸地 - (211, 242, 255) # Ice/snow (浅天蓝色) 冰雪 + (0, 0, 0), # Background (黑色) + (252, 250, 205), # Cropland (淡黄色) + (0, 123, 79), # Forest (深绿色) + (157, 221, 106), # Grass (浅绿色) + (77, 208, 159), # Shrub (浅蓝绿色) + (111, 208, 242), # Wetland (浅蓝色) + (10, 78, 151), # Water (深蓝色) + (92, 106, 55), # Tundra (土黄色) + (155, 36, 22), # Impervious surface (红色) + (205, 205, 205), # Bareland (灰色) + (211, 242, 255) # Ice/snow (浅天蓝色) ] # 如果类别数小于等于11，使用预设颜色；否则动态生成颜色 if self.num_classes <= 11: # 10个标签 + 背景 self.colors = self.colors[:self.num_classes] - else: # 随机生成颜色 + else: hsv_tuples = [(x / self.num_classes, 1., 1.) for x in range(self.num_classes)] self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples)) self.colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors)) # 初始化模型 self.generate() - # 打印参数配置 show_config(**self._defaults) def generate(self, onnx=False): @@ -67,8 +66,8 @@ class SegFormer_Segmentation(object): """ self.net = SegFormer(num_classes=self.num_classes, phi=self.phi, pretrained=False) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - self.net.load_state_dict(torch.load(self.model_path, map_location=device)) # 加载权重 - self.net = self.net.eval() # 评估模式 + self.net.load_state_dict(torch.load(self.model_path, map_location=device)) + self.net = self.net.eval() print('{} model, and classes loaded.'.format(self.model_path)) if not onnx: if self.cuda: @@ -123,7 +122,7 @@ class SegFormer_Segmentation(object): # 混合原图和分割图 seg_img = np.reshape(np.array(self.colors, np.uint8)[np.reshape(pr, [-1])], [orininal_h, orininal_w, -1]) image = Image.fromarray(np.uint8(seg_img)) - image = Image.blend(old_img, image, 1) + image = Image.blend(old_img, image, 1.0) elif self.mix_type == 1: # 仅显示分割图 seg_img = np.reshape(np.array(self.colors, np.uint8)[np.reshape(pr, [-1])], [orininal_h, orininal_w, -1]) diff --git a/dimaoshibie/utils/__pycache__/__init__.cpython-310.pyc b/dimaoshibie/utils/__pycache__/__init__.cpython-310.pyc index 4f1e8dd..770bac8 100644 Binary files a/dimaoshibie/utils/__pycache__/__init__.cpython-310.pyc and b/dimaoshibie/utils/__pycache__/__init__.cpython-310.pyc differ diff --git a/dimaoshibie/utils/__pycache__/__init__.cpython-39.pyc b/dimaoshibie/utils/__pycache__/__init__.cpython-39.pyc index 002cdfe..f046168 100644 Binary files a/dimaoshibie/utils/__pycache__/__init__.cpython-39.pyc and b/dimaoshibie/utils/__pycache__/__init__.cpython-39.pyc differ diff --git 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eg:D:\\project\\ai_station\\wudingpv\\tmp\\99f06853-4788-4608-8884-2a3b7bc33768 +# 地貌识别 - 图形分割 +# path 这里需要每次上传，出现一个uuid命名的文件夹，防止文件冲突 eg:D:\\project\\ai_station\\wudingpv\\tmp\\99f06853-4788-4608-8884-2a3b7bc33768 # path = "/home/xiazj/ai-station/ai_station_merge/tmp/dimaoshibie/crop_9_14.png" # result = model_deal.dimaoshibie_pic(dimaoshibie_SegFormer,path,param.dmsb_count,param.dmsb_name_classes) # print(result) @@ -82,7 +82,7 @@ ch4_model_gas = joblib.load(os.path.join(current_dir,'jiawanyuce/qixiangnongdu_m """屋顶识别""" -# # 屋顶识别- 图形分割 +# 屋顶识别- 图形分割 # path = "/home/xiazj/ai-station/ai_station_merge/tmp/wuding/99f06853-4788-4608-8884-2a3b7bc32131/taihuyuan_7-8.png" # result = model_deal.roof_pic(roof_model,path,param.wdpv_palette) # print(result) @@ -97,12 +97,12 @@ ch4_model_gas = joblib.load(os.path.join(current_dir,'jiawanyuce/qixiangnongdu_m """ 光伏识别 """ # 公用一个接口即可,url进行区别 -# # 光伏识别- 图形分割 +# 光伏识别- 图形分割 # path = "/home/xiazj/ai-station/ai_station_merge/tmp/pv/99f06853-4788-4608-8884-2a3b7bc32131/taihuyuan_7-8.png" # result = model_deal.roof_pic(pv_model,path,param.wdpv_palette) # print(result) -# 光伏识别 - 像素面积计算 +# # 光伏识别 - 像素面积计算 # path = "/home/xiazj/ai-station/ai_station_merge/tmp/pv/99f06853-4788-4608-8884-2a3b7bc32131/taihuyuan_7-8.png" # scale_m = 0.92*0.92 # Z18代表的，像素大小 # result = model_deal.roof_area(path,scale_m,param.wdpv_colors) @@ -111,7 +111,7 @@ ch4_model_gas = joblib.load(os.path.join(current_dir,'jiawanyuce/qixiangnongdu_m """ 屋顶光伏识别 """ -# 屋顶光伏识别- 图形分割 +# # 屋顶光伏识别- 图形分割 # path = "/home/xiazj/ai-station/ai_station_merge/tmp/wudingpv/99f06853-4788-4608-8884-2a3b7bc32131/taihuyuan_7-8.png" # result = model_deal.roofpv_pic(roof_model,pv_model,path,param.wdpv_palette) # file_list = result['reason'] @@ -138,18 +138,21 @@ ch4_model_gas = joblib.load(os.path.join(current_dir,'jiawanyuce/qixiangnongdu_m # 3. 上传数据的预测 # 返回pred_data，true_data，通过时间戳为x轴，进行展示结果即可,没有的话就是没有，有的话就是有 -# test_data_path = "/home/xiazj/ai-station/ai_station_merge/tmp/guangfufadian/99f06853-4788-4608-8884-2a3b7bc32131/run_test.csv" +test_data_path = "/home/xiazj/ai-station-code/guangfufadian/datasets/run_test.csv" # predictions = pvfd_model.run_inference(test_data_path) +# print(len(predictions)) # predictions = np.array(predictions).flatten() # pred_data, true_data = prepare_data.result_merge_guangfufadian(test_data_path,predictions) # print(pred_data) # print(true_data) - +# start_time = '2019-05-23 03:15:00' +# end_time = '2019-05-25 03:30:00' +# print(model_deal.start_pvelectric_predict_endpoint(pvfd_model,test_data_path,start_time,end_time,True)) """风力发电预测 """ -# # 1. 读取训练文件，截取部分数据，并针对点击列进行数据前端展示 -# # 展示某一列，并进行返回 +# 1. 读取训练文件，截取部分数据，并针对点击列进行数据前端展示 +# 展示某一列，并进行返回 # tmp = prepare_data.show_data_windfd("Wind speed at height of 10 meters (m/s)") # print(tmp) @@ -160,51 +163,52 @@ ch4_model_gas = joblib.load(os.path.join(current_dir,'jiawanyuce/qixiangnongdu_m # # 3. 上传数据的预测 -# test_data_path = "/home/xiazj/ai-station/ai_station_merge/tmp/fenglifadian/99f06853-4788-4608-8884-2a3b7bc32131/Wind_farm_test.csv" -# predictions = windfd_model.run_inference(test_data_path) -# predictions = np.array(predictions).flatten() -# pred_data, true_data = prepare_data.result_merge_fenglifadian(test_data_path,predictions) -# print(pred_data) -# print(true_data) +test_data_path = "/home/xiazj/ai-station-code/fenglifadian/datasets/Wind_farm_test.csv" +predictions = windfd_model.run_inference(test_data_path) +predictions = np.array(predictions).flatten() +print(len(predictions)) +pred_data, true_data = prepare_data.result_merge_fenglifadian(test_data_path,predictions) +print(pred_data) +print(true_data) """甲烷产量预测""" -#1、读取训练文件，截取部分数据，并针对点击列进行数据前端展示 +# 1、读取训练文件，截取部分数据，并针对点击列进行数据前端展示 # test_data_path = "/home/xiazj/ai-station/ai_station_merge/tmp/jiawanyuce/jiawan_test.csv" # tmp = prepare_data.show_data_jiawanyuce("X_ch") # print(tmp) -# #2、进行预测，预测接口 -# test_data_path = "/home/xiazj/ai-station/ai_station_merge/tmp/jiawanyuce/jiawan_test.csv" -# tmp = model_deal.start_predict_endpoint(ch4_model_flow,ch4_model_gas,test_data_path,"2023-01-01 02:30:00","2023-01-03 02:30:00",2,False) +# 2、进行预测，预测接口 +# test_data_path = "/home/xiazj/ai-station-code/jiawanyuce/data/jiawan_test.csv" +# tmp = model_deal.start_predict_endpoint(ch4_model_flow,ch4_model_gas,test_data_path,"2023-01-01 02:30:00","2023-01-03 02:30:00",2,True) # print(tmp) -""" 煤热解 """ -meirejie_test_content = { - 'A': 11.92, - 'V' : 51.16, - 'FC': 48.84, - 'C': 83.22 , - 'H': 3.89, - 'N': 2.72, - 'S': 0.45, - 'O':20.21, - 'H/C':0.56, - 'O/C':0.18, - 'N/C':0.03, - 'Rt':40.00, - 'Hr':5.00, - 'dp':0.20, - 'T':510.00 -} -meirejie_test_content = pd.DataFrame([meirejie_test_content]) -meirejie_result_content = { - 'Tar':11.71, - 'Gas':10.81 , - 'Char':75.31, - 'Water':2.17 -} -#1、 tar 焦油 +# """ 煤热解 """ +# meirejie_test_content = { +# 'A': 11.92, +# 'V' : 51.16, +# 'FC': 48.84, +# 'C': 83.22 , +# 'H': 3.89, +# 'N': 2.72, +# 'S': 0.45, +# 'O':20.21, +# 'H/C':0.56, +# 'O/C':0.18, +# 'N/C':0.03, +# 'Rt':40.00, +# 'Hr':5.00, +# 'dp':0.20, +# 'T':510.00 +# } +# meirejie_test_content = pd.DataFrame([meirejie_test_content]) +# meirejie_result_content = { +# 'Tar':11.71, +# 'Gas':10.81 , +# 'Char':75.31, +# 'Water':2.17 +# } +# #1、 tar 焦油 #1.1 单独预测 # tar_result = model_deal.pred_single_tar(meirejie_test_content) # print(tar_result) @@ -245,28 +249,28 @@ meirejie_result_content = { -"""煤基碳材料""" -meijiegou_test_content = { - 'A': 10.43, - 'VM' : 35.88, - 'K/C': 4, - 'MM': 0 , - 'AT': 800, - 'At': 1, - 'Rt': 5 -} -# meijiegou_test_content = pd.DataFrame([meijiegou_test_content]) +# """煤基碳材料""" +# meijiegou_test_content = { +# 'A': 10.43, +# 'VM' : 35.88, +# 'K/C': 4, +# 'MM': 0 , +# 'AT': 800, +# 'At': 1, +# 'Rt': 5 +# } +# # meijiegou_test_content = pd.DataFrame([meijiegou_test_content]) -meicailiao_test_content = { - "SSA":1141.8, - "TPV":0.46, - "N":1.74, - "O":3.84, - "ID/IG":1.102, - "J":0.5 -} +# meicailiao_test_content = { +# "SSA":1141.8, +# "TPV":0.46, +# "N":1.74, +# "O":3.84, +# "ID/IG":1.102, +# "J":0.5 +# } -meicailiao_test_content = pd.DataFrame([meicailiao_test_content]) +# meicailiao_test_content = pd.DataFrame([meicailiao_test_content]) # 活性炭总表面积预测分析 # # # # 1、 单独预测 @@ -324,17 +328,18 @@ meicailiao_test_content = pd.DataFrame([meicailiao_test_content]) # "KC_min" : None, "KC_max": None, "KC_step": None,"MM_min" : None, "MM_max": None, "MM_step": None, # "AT_min" : None, "AT_max": None, "AT_step": None,"At_min" : None, "At_max": None, "At_step": None, # "Rt_min" : None, "Rt_max": None, "Rt_step": None} -params = {'A_min': 4.0, 'A_max': 48.0, 'A_step': 4.0, 'VM_min': 5.0, 'VM_max': 50.0, 'VM_step': 5.0, 'KC_min': 1.0, 'KC_max': 4.0, 'KC_step': 0.5, 'MM_min': 0.0, 'MM_max': 1.0, 'MM_step': 1.0, 'AT_min': 600.0, 'AT_max': 900.0, 'AT_step': 50.0, 'At_min': 0.5, 'At_max': 2.0, 'At_step': 0.5, 'Rt_min': 5.0, 'Rt_max': 10.0, 'Rt_step': 5.0} -params = prepare_data.get_params(params) -pred_data = prepare_data.create_pred_data(params) -result = model_deal.pred_func("xgb",pred_data) -sorted_result = result.sort_values(by=['SSA', 'TPV'], ascending=[False, False]) -# 保留条数 -num = 6 -if num is None: - print(sorted_result.head()) # 返回全部 -else: - print(sorted_result.head(6)) # 返回所需条数 +# params = {'A_min': 4.0, 'A_max': 48.0, 'A_step': 4.0, 'VM_min': 5.0, 'VM_max': 50.0, 'VM_step': 5.0, 'KC_min': 1.0, 'KC_max': 4.0, 'KC_step': 0.5, 'MM_min': 0.0, 'MM_max': 1.0, 'MM_step': 1.0, 'AT_min': 600.0, 'AT_max': 900.0, 'AT_step': 50.0, 'At_min': 0.5, 'At_max': 2.0, 'At_step': 0.5, 'Rt_min': 5.0, 'Rt_max': 10.0, 'Rt_step': 5.0} +# params = prepare_data.get_params(params) +# pred_data = prepare_data.create_pred_data(params) +# result = model_deal.pred_func("xgb",pred_data) +# sorted_result = result.sort_values(by=['SSA', 'TPV'], ascending=[False, False]) +# sorted_result.to_csv('/home/xiazj/ai-station-code/meijitancailiao/data/moni.csv') +# # 保留条数 +# num = 6 +# if num is None: +# print(sorted_result.head()) # 返回全部 +# else: +# print(sorted_result.head(6)) # 返回所需条数 """数据界面获取""" diff --git a/fenglifadian/__pycache__/model_base.cpython-310.pyc b/fenglifadian/__pycache__/model_base.cpython-310.pyc index 68bd33d..fd9ca2d 100644 Binary files a/fenglifadian/__pycache__/model_base.cpython-310.pyc and b/fenglifadian/__pycache__/model_base.cpython-310.pyc differ diff --git a/fenglifadian/__pycache__/model_base.cpython-39.pyc b/fenglifadian/__pycache__/model_base.cpython-39.pyc index 2b099b6..cf5d8f7 100644 Binary files a/fenglifadian/__pycache__/model_base.cpython-39.pyc and b/fenglifadian/__pycache__/model_base.cpython-39.pyc differ diff --git a/fenglifadian/cross_exp/__pycache__/__init__.cpython-310.pyc b/fenglifadian/cross_exp/__pycache__/__init__.cpython-310.pyc index cb00868..e336bea 100644 Binary files a/fenglifadian/cross_exp/__pycache__/__init__.cpython-310.pyc and b/fenglifadian/cross_exp/__pycache__/__init__.cpython-310.pyc differ 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meters (?)', - 'Wind speed - at the height of wheel hub (m/s)', - 'Wind speed - at the height of wheel hub (?)', - 'Air temperature (буC) ', 'Atmosphere (hpa)', 'Relative humidity (%)', - 'Power(MW)'] + check_list = ['date', 'WS_10', + 'WD_10', + 'WS_30', + 'WD_30', + 'WS_50', + 'WD_50', + 'WS_hub', + 'WD_hub', + 'Air temperature', 'Atmosphere', 'Relative humidity', + 'Power'] columns_match = list(data.columns) == check_list if columns_match: if length > fenglifadian_args.in_len: @@ -90,10 +88,10 @@ class ModelInference: input_data = np.array(input_data) input_data = input_data[:fenglifadian_args.in_len, 1:].astype(np.float32) input_tensor = torch.tensor(input_data, dtype=torch.float32).unsqueeze(0) # 增加batch维度 - return input_tensor + return {'status':True, 'reason':input_tensor } else: print("文件不匹配，请检查上传文件与模版是否一致") - return None + return {'status':False, 'reason':'文件不匹配，请检查上传文件与模版是否一致'} # 数据归一化 def preprocess_data(self, input_tensor): @@ -106,11 +104,14 @@ class ModelInference: # return predictions.cpu().numpy() # 返回 NumPy 数组 return predictions # 推理过程 - def run_inference(self, file_path): - raw_data = self.load_pred_data(file_path) - input_tensor = self.preprocess_data(raw_data) - predictions = self.predict(input_tensor) - predictions = self.scaler.inverse_transform(predictions) - predictions = predictions.squeeze(0) - predictions = predictions.cpu().numpy() - return predictions[:,-1:] \ No newline at end of file + def run_inference(self, data): + raw_data = self.load_pred_data(data) + if raw_data['status'] == False: + return {'status':False, 'reason':'文件不匹配，请检查上传文件与模版是否一致'} + else: + input_tensor = self.preprocess_data(raw_data['reason']) + predictions = self.predict(input_tensor) + predictions = self.scaler.inverse_transform(predictions) + predictions = predictions.squeeze(0) + predictions = predictions.cpu().numpy() + return {'status':True, 'reason':predictions[:,-1:]} \ No newline at end of file diff --git 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-date_time,nwp_globalirrad,nwp_directirrad,nwp_temperature,nwp_humidity,nwp_windspeed,nwp_winddirection,nwp_pressure,lmd_totalirrad,lmd_diffuseirrad,lmd_temperature,lmd_pressure,lmd_winddirection,lmd_windspeed,power -2019-05-23 03:15:00,0.0,0.0,20.98,17.05,7.09,268.51,947.19,0,0,24.1,946.700012,258,3.4,0.0 -2019-05-23 03:30:00,0.0,0.0,21.12,16.75,7.05,269.86,947.05,0,0,23.9,946.799988,253,3.3,0.0 -2019-05-23 03:45:00,0.0,0.0,21.27,16.43,6.99,271.05,947.15,0,0,23.9,946.599976,278,3.2,0.0 -2019-05-23 04:00:00,0.0,0.0,21.39,16.14,6.91,272.47,947.27,0,0,24.299999,946.599976,275,4.5,0.0 -2019-05-23 04:15:00,0.0,0.0,21.42,15.93,6.84,273.84,947.27,0,0,23.6,946.5,250,5.1,0.0 -2019-05-23 04:30:00,0.0,0.0,21.35,15.85,6.78,273.76,947.3,0,0,23.299999,946.599976,185,2.5,0.0 -2019-05-23 04:45:00,0.0,0.0,21.2,15.86,6.73,272.42,947.47,0,0,23.4,946.799988,278,5.0,0.0 -2019-05-23 05:00:00,0.0,0.0,20.96,15.98,6.7,270.74,947.5,0,0,23.6,946.700012,236,4.1,0.0 -2019-05-23 05:15:00,0.0,0.0,20.7,16.15,6.67,268.99,947.54,0,0,23.5,946.900024,236,5.3,0.0 -2019-05-23 05:30:00,16.12,8.55,20.47,16.77,6.64,267.36,947.63,6,4,23.4,947.0,239,5.7,0.10132 -2019-05-23 05:45:00,59.11,42.07,20.4,18.11,6.61,265.27,947.74,16,11,23.200001,947.200012,255,7.7,0.286004 -2019-05-23 06:00:00,92.58,71.04,20.71,19.16,6.59,262.93,947.71,24,17,23.200001,947.299988,222,3.4,0.499151 -2019-05-23 06:15:00,146.51,119.79,21.59,19.47,6.59,261.02,947.73,31,23,23.299999,947.299988,278,5.2,0.779324 -2019-05-23 06:30:00,184.0,154.52,22.75,19.15,6.59,260.08,947.81,38,28,23.5,947.5,210,6.5,1.2091379999999998 -2019-05-23 06:45:00,241.53,208.58,24.17,18.26,6.65,258.62,947.96,74,33,23.9,947.599976,261,8.1,1.76781 -2019-05-23 07:00:00,280.33,245.41,25.48,17.07,6.83,256.79,948.16,124,38,24.1,947.700012,289,5.6,2.484841 -2019-05-23 07:15:00,338.68,301.16,26.6,15.88,7.11,255.64,948.42,177,42,24.299999,947.700012,272,4.1,3.308445 -2019-05-23 07:30:00,377.39,338.34,27.36,15.43,7.35,253.68,948.61,236,46,24.700001,948.0,239,5.2,4.247745 -2019-05-23 07:45:00,434.78,393.69,28.1,15.18,7.49,252.33,948.66,298,49,25.1,948.0,264,4.9,5.251472 -2019-05-23 08:00:00,472.37,430.05,28.88,14.56,7.51,253.68,948.69,355,51,26.0,948.0,196,5.1,6.242268 -2019-05-23 08:15:00,527.39,483.39,29.66,13.81,7.44,254.82,948.71,411,55,26.4,948.0,323,7.2,7.204602 -2019-05-23 08:30:00,563.0,517.99,30.31,13.31,7.27,255.13,948.74,467,59,27.200001,948.0,255,5.1,8.168957 -2019-05-23 08:45:00,614.48,568.07,31.02,12.92,6.93,255.75,948.82,520,63,27.799999,948.099976,230,6.7,9.058953 -2019-05-23 09:00:00,647.35,600.08,31.75,12.24,6.47,257.45,948.9,575,65,28.4,948.200012,196,5.7,9.863159 -2019-05-23 09:15:00,694.23,645.81,32.39,11.4,5.94,259.75,948.9,624,68,29.299999,948.200012,295,3.0,10.61015 -2019-05-23 09:30:00,723.74,674.6,32.94,10.92,5.29,258.12,948.81,671,70,30.200001,948.099976,230,4.1,11.31974 -2019-05-23 09:45:00,765.15,715.07,33.46,10.14,4.48,255.65,948.74,719,67,31.1,948.099976,261,5.0,11.93025 -2019-05-23 10:00:00,790.73,740.07,33.85,9.36,3.57,250.97,948.66,771,68,32.099998,948.099976,213,5.8,12.4637 -2019-05-23 10:15:00,825.7,774.27,34.15,8.75,2.72,236.32,948.57,821,67,32.400002,948.200012,64,3.8,12.90708 -2019-05-23 10:30:00,846.62,794.75,34.37,8.29,2.16,212.24,948.58,866,65,33.599998,948.099976,236,4.7,13.3642 -2019-05-23 10:45:00,874.29,821.81,34.59,8.01,2.01,187.37,948.64,899,66,34.0,948.0,247,6.2,13.65609 -2019-05-23 11:00:00,890.2,837.39,34.8,7.9,2.09,177.01,948.63,937,66,35.0,947.900024,253,3.1,13.88812 -2019-05-23 11:15:00,910.24,857.01,35.02,7.72,2.2,175.58,948.59,964,65,35.400002,947.799988,224,3.5,14.202179999999998 -2019-05-23 11:30:00,920.93,867.48,35.22,7.59,2.32,179.46,948.55,977,64,36.0,947.599976,196,1.6,14.27024 -2019-05-23 11:45:00,932.97,879.28,35.44,7.39,2.54,187.69,948.45,1014,64,36.700001,947.299988,227,3.6,14.58771 -2019-05-23 12:00:00,938.36,884.59,35.63,7.17,2.94,198.01,948.33,1016,67,36.900002,947.200012,241,3.1,14.63442 -2019-05-23 12:15:00,942.58,888.69,35.81,7.0,3.44,205.26,948.27,1003,70,35.900002,947.0,191,0.8,14.49719 -2019-05-23 12:30:00,942.71,888.84,35.98,6.86,3.94,209.65,948.25,1000,82,36.599998,947.0,233,2.3,14.55544 -2019-05-23 12:45:00,938.94,885.14,36.09,6.74,4.3,212.75,948.12,998,85,36.099998,946.700012,247,2.7,14.22331 -2019-05-23 13:00:00,933.6,879.93,36.18,6.66,4.48,215.34,947.9,1006,103,36.700001,946.5,272,3.0,14.41867 -2019-05-23 13:15:00,921.25,867.84,36.27,6.64,4.54,217.21,947.71,972,101,36.799999,946.299988,250,2.8,14.14035 -2019-05-23 13:30:00,910.03,856.86,36.35,6.66,4.51,217.93,947.52,977,89,37.400002,946.099976,284,5.7,14.022570000000002 -2019-05-23 13:45:00,888.83,836.09,36.43,6.73,4.47,217.26,947.36,926,89,37.200001,945.900024,84,1.2,13.35658 -2019-05-23 14:00:00,871.96,819.58,36.51,6.84,4.43,214.63,947.23,942,87,37.599998,945.700012,264,3.5,13.52902 -2019-05-23 14:15:00,842.84,791.07,36.57,6.97,4.42,211.04,947.08,918,103,37.599998,945.599976,222,1.7,13.01729 -2019-05-23 14:30:00,821.0,769.7,36.61,7.12,4.41,208.37,946.92,848,120,37.0,945.299988,194,1.5,12.18924 -2019-05-23 14:45:00,784.91,734.39,36.63,7.28,4.35,208.3,946.78,834,140,37.700001,945.200012,284,4.0,11.89244 -2019-05-23 15:00:00,758.65,708.72,36.65,7.45,4.26,210.43,946.65,797,146,37.099998,945.0,261,4.2,11.66711 -2019-05-23 15:15:00,715.76,666.8,36.66,7.72,4.32,211.2,946.53,730,107,37.200001,944.900024,56,0.6,10.51391 -2019-05-23 15:30:00,684.97,636.72,36.65,8.19,4.76,205.78,946.44,338,125,37.299999,944.900024,270,3.0,5.716215 -2019-05-23 15:45:00,636.31,589.26,36.52,8.83,5.64,197.05,946.43,597,160,37.299999,944.599976,129,0.9,9.436924 -2019-05-23 16:00:00,602.98,556.77,36.31,9.38,6.56,191.87,946.49,452,160,37.700001,944.299988,253,4.1,7.061485 -2019-05-23 16:15:00,552.1,507.28,36.06,9.63,7.06,189.97,946.43,372,144,37.5,944.400024,28,0.0,5.140048999999999 -2019-05-23 16:30:00,517.4,473.6,35.9,9.55,7.11,187.68,946.35,290,147,37.599998,944.5,255,2.9,4.654119000000001 -2019-05-23 16:45:00,463.61,421.48,35.76,9.4,6.97,183.17,946.27,340,161,37.299999,944.299988,258,0.5,5.033188 -2019-05-23 17:00:00,426.69,385.79,35.66,9.33,6.75,177.54,946.19,220,147,37.099998,944.200012,227,5.8,3.616041000000001 -2019-05-23 17:15:00,370.08,331.22,35.55,9.38,6.24,173.95,946.09,163,112,36.0,944.099976,286,2.8,2.6656 -2019-05-23 17:30:00,331.83,294.5,35.53,9.46,5.34,177.17,945.97,153,110,35.700001,944.099976,278,4.1,2.467118 -2019-05-23 17:45:00,274.34,239.58,35.42,9.6,4.59,197.16,945.83,111,80,35.099998,944.400024,267,6.7,1.811398 -2019-05-23 18:00:00,236.29,203.5,35.15,9.89,4.61,225.21,945.74,89,71,34.700001,944.299988,281,4.0,1.493179 -2019-05-23 18:15:00,179.93,150.66,34.42,10.54,5.02,235.8,945.74,79,55,34.299999,944.400024,244,5.4,1.316404 -2019-05-23 18:30:00,143.43,116.86,33.52,11.47,5.17,236.82,945.81,63,52,33.700001,944.5,258,9.8,1.057303 -2019-05-23 18:45:00,88.81,67.28,32.36,12.77,5.08,236.18,945.95,42,33,33.299999,944.799988,233,3.9,0.671365 -2019-05-23 19:00:00,57.48,40.56,31.19,14.07,5.03,236.28,946.12,17,10,32.799999,945.0,222,3.5,0.272553 -2019-05-23 19:15:00,15.33,8.0,29.93,14.97,5.09,237.12,946.32,7,4,32.200001,945.200012,219,2.1,0.1109609999999999 -2019-05-23 19:30:00,0.0,0.0,28.93,15.25,5.15,239.21,946.5,0,0,31.700001,945.200012,239,1.6,0.0 -2019-05-23 19:45:00,0.0,0.0,28.13,15.55,5.18,242.16,946.66,0,0,31.1,945.400024,213,2.3,0.0 -2019-05-23 20:00:00,0.0,0.0,27.49,15.91,5.17,245.72,946.85,0,0,30.799999,945.599976,286,1.6,0.0 -2019-05-23 20:15:00,0.0,0.0,26.95,16.34,5.14,249.8,947.03,0,0,30.799999,945.799988,216,2.0,0.0 -2019-05-23 20:30:00,0.0,0.0,26.49,16.84,5.11,254.32,947.19,0,0,30.4,945.900024,224,2.0,0.0 -2019-05-23 20:45:00,0.0,0.0,26.09,17.37,5.08,257.82,947.34,0,0,29.799999,946.200012,289,0.8,0.0 -2019-05-23 21:00:00,0.0,0.0,25.76,17.96,5.07,259.95,947.48,0,0,29.1,946.400024,300,0.3,0.0 -2019-05-23 21:15:00,0.0,0.0,25.51,18.6,5.08,261.26,947.63,0,0,28.5,946.5,284,0.3,0.0 -2019-05-23 21:30:00,0.0,0.0,25.27,19.36,5.08,262.37,947.77,0,0,28.5,946.599976,16,0.2,0.0 -2019-05-23 21:45:00,0.0,0.0,25.05,20.21,5.1,264.01,947.92,0,0,28.1,946.599976,28,0.3,0.0 -2019-05-23 22:00:00,0.0,0.0,24.83,21.22,5.21,265.1,948.04,0,0,28.0,946.700012,351,0.0,0.0 -2019-05-23 22:15:00,0.0,0.0,24.68,22.33,5.47,265.38,948.25,0,0,28.0,946.700012,255,0.2,0.0 -2019-05-23 22:30:00,0.0,0.0,24.64,23.42,5.88,265.97,948.43,0,0,28.5,946.799988,216,0.2,0.0 -2019-05-23 22:45:00,0.0,0.0,24.73,24.02,6.36,264.64,948.57,0,0,28.200001,946.700012,329,0.8,0.0 -2019-05-23 23:00:00,0.0,0.0,24.74,24.2,6.74,263.69,948.7,0,0,27.9,946.700012,326,1.0,0.0 -2019-05-23 23:15:00,0.0,0.0,24.71,24.03,6.97,263.0,948.84,0,0,28.1,946.700012,250,2.0,0.0 -2019-05-23 23:30:00,0.0,0.0,24.61,23.8,7.09,262.7,948.98,0,0,27.4,946.900024,239,1.3,0.0 -2019-05-23 23:45:00,0.0,0.0,24.46,23.74,7.17,263.17,949.14,0,0,27.200001,946.799988,253,0.5,0.0 -2019-05-24 00:00:00,0.0,0.0,22.21,28.53,5.36,278.74,947.86,0,0,27.1,946.599976,247,0.0,0.0 -2019-05-24 00:15:00,0.0,0.0,22.13,28.49,5.41,278.24,947.91,0,0,26.5,946.5,19,0.0,0.0 -2019-05-24 00:30:00,0.0,0.0,21.98,28.52,5.52,278.31,947.96,0,0,26.200001,946.599976,267,1.1,0.0 -2019-05-24 00:45:00,0.0,0.0,21.91,28.36,5.65,278.29,948.0,0,0,26.200001,946.700012,298,0.3,0.0 -2019-05-24 01:00:00,0.0,0.0,21.82,28.18,5.7,276.61,947.76,0,0,25.9,946.5,303,0.3,0.0 -2019-05-24 01:15:00,0.0,0.0,21.7,28.08,5.71,274.62,947.61,0,0,25.6,946.5,264,0.0,0.0 -2019-05-24 01:30:00,0.0,0.0,21.6,27.96,5.7,274.52,947.66,0,0,25.200001,946.5,295,0.4,0.0 -2019-05-24 01:45:00,0.0,0.0,21.48,27.95,5.73,275.36,947.94,0,0,25.299999,946.400024,185,0.2,0.0 -2019-05-24 02:00:00,0.0,0.0,21.38,27.98,5.78,275.26,948.04,0,0,25.299999,946.299988,213,0.0,0.0 -2019-05-24 02:15:00,0.0,0.0,21.29,28.1,5.8,275.69,948.04,0,0,25.200001,946.200012,11,0.0,0.0 -2019-05-24 02:30:00,0.0,0.0,21.17,28.32,5.77,276.26,948.03,0,0,24.9,946.200012,30,0.4,0.0 -2019-05-24 02:45:00,0.0,0.0,21.05,28.61,5.75,275.82,947.92,0,0,24.9,946.099976,340,0.5,0.0 -2019-05-24 03:00:00,0.0,0.0,20.93,28.94,5.72,275.49,947.73,0,0,25.0,946.0,16,0.5,0.0 -2019-05-24 03:15:00,0.0,0.0,20.83,29.28,5.7,275.45,947.6,0,0,24.700001,945.900024,0,0.9,0.0 -2019-05-24 03:30:00,0.0,0.0,20.66,29.74,5.72,274.81,947.43,0,0,25.1,945.799988,345,0.9,0.0 -2019-05-24 03:45:00,0.0,0.0,20.68,29.85,5.8,274.23,947.45,0,0,25.1,945.700012,2,1.8,0.0 -2019-05-24 04:00:00,0.0,0.0,20.65,29.99,5.91,273.47,947.56,0,0,24.5,945.700012,345,2.0,0.0 -2019-05-24 04:15:00,0.0,0.0,20.68,30.01,6.03,272.43,947.55,0,0,24.200001,945.599976,2,1.4,0.0 -2019-05-24 04:30:00,0.0,0.0,20.72,29.99,6.16,270.87,947.4,0,0,24.4,945.700012,11,0.7,0.0 -2019-05-24 04:45:00,0.0,0.0,20.66,30.14,6.3,270.39,947.58,0,0,24.200001,945.799988,5,1.3,0.0 -2019-05-24 05:00:00,0.0,0.0,20.44,30.58,6.4,270.51,947.67,0,0,24.700001,945.799988,334,1.2,0.0 -2019-05-24 05:15:00,0.0,0.0,20.17,31.14,6.42,271.43,947.79,0,0,24.6,946.0,233,0.0,0.0 -2019-05-24 05:30:00,14.12,7.27,19.96,31.95,6.39,273.05,947.98,5,3,24.200001,945.700012,216,0.6,0.0734929999999999 -2019-05-24 05:45:00,49.81,33.88,19.96,32.82,6.33,274.56,947.98,22,17,24.0,946.0,149,0.7,0.368792 -2019-05-24 06:00:00,81.21,61.31,20.25,33.14,6.28,275.12,947.84,37,34,24.0,946.0,180,1.2,0.660454 -2019-05-24 06:15:00,130.42,105.85,20.89,32.69,6.3,274.34,947.75,37,29,23.700001,946.0,90,0.5,0.685913 -2019-05-24 06:30:00,166.49,139.24,21.64,31.71,6.36,272.76,947.55,56,43,23.4,946.099976,126,0.3,1.036116 -2019-05-24 06:45:00,219.95,189.95,22.49,30.34,6.39,270.27,947.58,71,60,23.6,946.200012,157,0.1,1.327835 -2019-05-24 07:00:00,254.05,222.32,23.23,29.19,6.4,266.9,947.78,87,69,24.1,946.299988,253,1.7,1.611819 -2019-05-24 07:15:00,303.96,270.24,23.98,27.85,6.44,262.5,947.98,117,90,24.700001,946.299988,233,1.8,2.124651 -2019-05-24 07:30:00,332.99,295.73,24.6,26.7,6.49,258.2,948.07,115,88,25.0,946.400024,222,2.5,2.124017 -2019-05-24 07:45:00,380.55,344.44,25.25,25.52,6.53,254.23,948.07,131,107,25.1,946.299988,216,1.9,2.470755 -2019-05-24 08:00:00,407.73,369.11,25.81,24.37,6.55,251.55,948.04,115,96,25.299999,946.400024,233,1.4,2.208017 -2019-05-24 08:15:00,445.64,401.8,26.37,23.5,6.46,248.55,948.02,92,62,24.9,946.5,250,1.6,1.765559 -2019-05-24 08:30:00,473.64,429.44,26.93,22.7,6.29,245.35,948.06,148,93,24.9,946.5,239,0.9,2.804388 -2019-05-24 08:45:00,520.96,477.52,27.42,21.54,6.08,241.72,948.0,188,124,25.1,946.400024,275,1.0,3.491398 -2019-05-24 09:00:00,551.69,501.43,27.9,21.06,5.76,237.75,947.89,466,230,25.6,946.400024,154,1.3,7.727365 -2019-05-24 09:15:00,591.47,548.47,28.42,20.21,5.37,232.06,947.75,341,255,26.799999,946.5,227,0.8,6.248734 -2019-05-24 09:30:00,603.17,557.18,28.82,19.62,5.04,227.31,947.56,146,104,26.9,946.0,216,2.4,2.809699 -2019-05-24 09:45:00,624.09,575.34,29.26,19.03,4.81,224.27,947.31,282,210,27.5,946.0,210,3.7,5.099175 -2019-05-24 10:00:00,628.72,580.69,29.68,18.37,4.72,221.37,947.0,322,232,27.700001,946.0,236,1.1,5.879827 -2019-05-24 10:15:00,626.35,543.45,30.07,17.73,4.72,219.58,946.86,473,250,28.1,946.099976,292,3.8,7.433452000000001 -2019-05-24 10:30:00,608.57,514.54,30.43,17.08,4.76,216.3,946.81,372,257,29.1,946.0,244,1.4,6.322285 -2019-05-24 10:45:00,622.88,581.48,30.84,16.56,4.74,213.04,946.8,826,349,29.299999,946.099976,196,2.4,12.15367 -2019-05-24 11:00:00,552.53,445.58,31.11,16.3,4.67,208.8,946.76,1045,278,29.299999,945.900024,255,4.7,16.25565 -2019-05-24 11:15:00,421.88,272.1,31.22,16.3,4.69,202.14,946.63,1219,310,30.299999,945.799988,298,1.7,16.52508 -2019-05-24 11:30:00,392.07,320.34,31.53,16.13,4.83,198.63,946.63,887,258,30.299999,945.900024,196,3.5,13.80319 -2019-05-24 11:45:00,206.33,133.68,31.08,16.73,5.05,195.72,946.68,294,233,29.799999,945.599976,154,1.3,5.337551 -2019-05-24 12:00:00,261.79,142.03,31.43,16.76,5.51,193.66,946.64,342,266,30.1,945.599976,202,3.4,6.440347 -2019-05-24 12:15:00,180.82,112.54,31.01,17.57,6.22,194.07,946.66,1025,275,30.6,945.5,244,1.9,15.52713 -2019-05-24 12:30:00,201.7,100.64,30.97,18.01,6.9,194.98,946.68,318,234,30.799999,945.099976,219,3.5,5.495908999999999 -2019-05-24 12:45:00,149.92,88.58,30.51,18.77,7.26,194.31,946.53,378,219,30.299999,944.900024,168,5.2,6.033394 -2019-05-24 13:00:00,124.31,72.79,30.15,19.49,7.45,191.68,946.37,812,273,29.700001,944.5,168,4.3,14.23589 -2019-05-24 13:15:00,156.61,123.75,30.29,19.53,7.63,188.71,946.18,340,217,30.700001,944.299988,165,1.7,5.763612 -2019-05-24 13:30:00,272.17,247.16,30.88,19.0,7.76,185.41,945.99,1086,200,31.299999,944.099976,253,1.8,15.61292 -2019-05-24 13:45:00,306.6,283.91,31.15,18.96,7.86,181.69,945.79,1003,327,30.6,944.200012,202,3.6,16.333579999999998 -2019-05-24 14:00:00,338.29,317.27,31.47,19.0,8.06,179.12,945.57,872,211,30.799999,944.099976,202,5.9,11.52492 -2019-05-24 14:15:00,332.57,311.45,31.61,19.29,8.37,177.69,945.37,713,155,31.5,943.700012,134,1.4,10.58435 -2019-05-24 14:30:00,330.07,308.75,31.69,19.65,8.75,176.86,945.18,888,133,32.299999,943.400024,199,2.2,12.95199 -2019-05-24 14:45:00,319.47,298.23,31.7,19.9,9.1,176.27,945.01,503,261,31.4,943.299988,118,4.7,7.650524000000001 -2019-05-24 15:00:00,309.39,288.37,31.69,19.99,9.36,176.12,944.9,699,193,31.799999,943.299988,208,4.6,9.340569 -2019-05-24 15:15:00,291.88,271.3,31.64,20.07,9.51,176.79,944.8,201,90,31.200001,943.299988,140,3.9,8.49445 -2019-05-24 15:30:00,283.07,262.57,31.65,19.95,9.57,177.73,944.71,671,87,32.400002,943.0,171,3.8,10.11193 -2019-05-24 15:45:00,275.49,254.6,31.6,19.76,9.6,177.87,944.64,635,93,31.799999,942.799988,182,4.7,9.481666 -2019-05-24 16:00:00,283.88,261.59,31.52,20.18,9.59,177.12,944.64,594,105,31.299999,942.799988,163,2.7,8.852041 -2019-05-24 16:15:00,290.91,266.7,31.36,20.76,9.52,176.28,944.53,526,92,31.6,942.5,163,6.0,7.797799 -2019-05-24 16:30:00,308.53,281.72,31.19,21.13,9.41,176.14,944.42,177,93,31.6,942.900024,146,3.3,6.29873 -2019-05-24 16:45:00,311.15,282.07,30.97,21.26,9.32,176.13,944.32,451,105,31.299999,942.5,149,4.0,5.853386 -2019-05-24 17:00:00,318.26,286.86,30.82,21.06,9.27,176.34,944.26,370,96,31.299999,942.5,191,3.2,4.284115 -2019-05-24 17:15:00,301.64,268.99,30.58,21.33,9.24,176.34,944.16,329,101,30.700001,942.5,16,1.7,4.775473000000001 -2019-05-24 17:30:00,290.04,256.37,30.31,22.14,9.17,176.44,944.06,247,76,30.700001,942.400024,174,3.2,3.427027 -2019-05-24 17:45:00,251.31,218.37,29.94,23.21,9.04,177.1,943.91,201,80,30.1,942.5,174,1.9,2.801674 -2019-05-24 18:00:00,222.29,190.28,29.56,24.31,8.87,178.48,943.81,135,56,29.9,942.599976,154,3.9,1.88593 -2019-05-24 18:15:00,170.92,141.88,29.1,25.59,8.66,179.96,943.72,92,49,29.6,942.799988,92,2.5,1.406235 -2019-05-24 18:30:00,136.35,109.9,28.65,26.89,8.43,181.66,943.75,60,40,29.1,942.900024,154,3.1,0.969608 -2019-05-24 18:45:00,85.9,64.5,28.13,28.3,8.22,183.25,943.83,39,29,28.799999,943.0,115,1.8,0.610111 -2019-05-24 19:00:00,54.78,37.8,27.61,29.8,7.99,184.87,943.92,24,18,28.4,943.099976,174,1.7,0.396965 -2019-05-24 19:15:00,15.36,7.6,27.06,31.14,7.72,186.57,943.97,6,3,27.9,943.200012,199,1.3,0.1029359999999999 -2019-05-24 19:30:00,0.0,0.0,26.62,32.13,7.41,188.59,944.06,0,0,27.1,943.5,171,0.7,0.0 -2019-05-24 19:45:00,0.0,0.0,26.26,33.09,7.04,191.71,944.24,0,0,26.299999,943.400024,182,1.2,0.0 -2019-05-24 20:00:00,0.0,0.0,25.92,34.1,6.61,195.03,944.39,0,0,26.200001,943.799988,171,1.6,0.0 -2019-05-24 20:15:00,0.0,0.0,25.58,35.13,6.12,199.26,944.57,0,0,26.200001,943.900024,149,2.1,0.0 -2019-05-24 20:30:00,0.0,0.0,25.2,36.21,5.53,204.28,944.74,0,0,25.6,944.0,160,0.9,0.0 -2019-05-24 20:45:00,0.0,0.0,24.77,37.35,4.84,210.93,944.86,0,0,25.6,944.200012,137,1.3,0.0 -2019-05-24 21:00:00,0.0,0.0,24.3,38.47,4.17,222.5,944.98,0,0,24.9,944.200012,250,0.3,0.0 -2019-05-24 21:15:00,0.0,0.0,23.89,39.35,3.66,238.81,945.13,0,0,24.4,944.299988,16,0.2,0.0 -2019-05-24 21:30:00,0.0,0.0,23.5,40.14,3.38,255.47,945.18,0,0,24.200001,944.400024,160,0.5,0.0 -2019-05-24 21:45:00,0.0,0.0,23.22,40.66,3.34,271.08,945.3,0,0,23.200001,944.400024,337,0.6,0.0 -2019-05-24 22:00:00,0.0,0.0,22.99,41.12,3.43,283.36,945.4,0,0,23.200001,944.400024,5,1.2,0.0 -2019-05-24 22:15:00,0.0,0.0,22.78,41.68,3.57,291.06,945.55,0,0,23.5,944.5,22,1.5,0.0 -2019-05-24 22:30:00,0.0,0.0,22.58,42.37,3.69,294.68,945.65,0,0,22.299999,944.400024,16,0.8,0.0 -2019-05-24 22:45:00,0.0,0.0,22.4,43.22,3.79,296.78,945.72,0,0,22.4,944.5,354,2.0,0.0 -2019-05-24 23:00:00,0.0,0.0,22.24,44.23,3.88,297.76,945.78,0,0,22.4,944.200012,289,1.2,0.0 -2019-05-24 23:15:00,0.0,0.0,22.09,45.45,3.92,297.58,945.83,0,0,21.700001,944.200012,343,1.1,0.0 -2019-05-24 23:30:00,0.0,0.0,21.96,46.88,3.94,295.31,945.86,0,0,21.6,944.299988,326,1.8,0.0 -2019-05-24 23:45:00,0.0,0.0,21.82,48.45,3.97,293.76,945.95,0,0,21.799999,944.099976,8,2.1,0.0 -2019-05-25 00:00:00,0.0,0.0,20.71,47.15,4.23,285.61,945.14,0,0,22.5,944.099976,59,0.2,0.0 -2019-05-25 00:15:00,0.0,0.0,20.71,47.84,4.4,283.5,945.13,0,0,22.5,944.0,343,0.5,0.0 -2019-05-25 00:30:00,0.0,0.0,20.77,48.59,4.66,282.49,945.27,0,0,22.0,944.0,0,1.4,0.0 -2019-05-25 00:45:00,0.0,0.0,20.87,49.5,4.95,281.02,945.09,0,0,21.0,943.900024,314,0.4,0.0 -2019-05-25 01:00:00,0.0,0.0,20.95,50.59,5.22,280.1,945.0,0,0,22.0,943.799988,326,0.8,0.0 -2019-05-25 01:15:00,0.0,0.0,21.02,51.72,5.43,280.52,944.73,0,0,21.9,943.700012,222,1.0,0.0 -2019-05-25 01:30:00,0.0,0.0,21.08,52.81,5.55,281.29,944.82,0,0,22.0,943.599976,216,1.1,0.0 -2019-05-25 01:45:00,0.0,0.0,21.1,53.85,5.6,282.45,945.04,0,0,22.0,943.599976,250,1.8,0.0 -2019-05-25 02:00:00,0.0,0.0,21.06,54.85,5.61,283.17,945.12,0,0,22.0,943.599976,329,0.4,0.0 -2019-05-25 02:15:00,0.0,0.0,20.99,55.78,5.64,283.79,945.0,0,0,21.9,943.5,351,0.0,0.0 -2019-05-25 02:30:00,0.0,0.0,20.92,56.57,5.7,284.32,944.86,0,0,21.9,943.299988,244,1.1,0.0 -2019-05-25 02:45:00,0.0,0.0,20.87,57.17,5.78,284.82,944.93,0,0,21.799999,943.299988,292,0.4,0.0 -2019-05-25 03:00:00,0.0,0.0,20.78,57.68,5.87,285.87,944.77,0,0,22.6,943.200012,241,2.1,0.0 -2019-05-25 03:15:00,0.0,0.0,20.69,58.05,5.96,286.88,944.63,0,0,22.6,943.200012,216,1.1,0.0 -2019-05-25 03:30:00,0.0,0.0,20.6,58.3,6.03,287.64,944.58,0,0,22.299999,943.0,244,0.8,0.0 -2019-05-25 03:45:00,0.0,0.0,20.51,58.41,6.02,288.36,944.64,0,0,22.1,943.0,281,0.6,0.0 -2019-05-25 04:00:00,0.0,0.0,20.38,58.59,5.94,288.89,944.56,0,0,22.200001,943.099976,267,0.4,0.0 -2019-05-25 04:15:00,0.0,0.0,20.24,58.78,5.84,289.5,944.31,0,0,22.200001,943.099976,295,0.0,0.0 -2019-05-25 04:30:00,0.0,0.0,20.12,58.83,5.73,289.56,944.09,0,0,21.4,943.099976,345,0.6,0.0 -2019-05-25 04:45:00,0.0,0.0,20.07,58.63,5.63,289.2,944.27,0,0,21.1,943.299988,14,0.0,0.0 -2019-05-25 05:00:00,0.0,0.0,20.0,58.5,5.49,289.45,944.27,0,0,21.5,943.400024,345,1.4,0.0 -2019-05-25 05:15:00,0.05,0.0,19.9,58.5,5.33,289.69,944.27,0,0,20.299999,943.5,306,0.5,0.0 -2019-05-25 05:30:00,16.46,8.32,19.92,58.26,5.11,289.84,944.31,7,5,20.6,943.599976,8,1.2,0.118986 -2019-05-25 05:45:00,56.28,39.03,20.21,57.5,4.68,289.6,944.26,17,14,20.6,943.700012,14,0.3,0.280346 -2019-05-25 06:00:00,87.5,65.89,20.63,56.59,3.92,288.35,944.18,28,23,20.9,943.900024,2,1.4,0.575588 -2019-05-25 06:15:00,138.06,111.42,21.39,54.69,2.88,289.66,944.11,37,30,20.700001,943.799988,281,0.3,0.86979 -2019-05-25 06:30:00,173.35,143.99,22.28,52.57,1.67,293.29,944.25,48,39,21.200001,944.0,59,0.6,1.247819 -2019-05-25 06:45:00,227.67,194.92,23.31,50.09,0.73,298.19,944.55,77,47,22.299999,944.099976,73,0.0,1.7254349999999998 -2019-05-25 07:00:00,264.48,229.79,24.09,47.17,0.58,91.95,944.9,116,53,23.200001,944.200012,146,0.0,2.335373 -2019-05-25 07:15:00,320.09,282.83,24.72,45.62,1.1,111.5,945.06,159,60,24.9,944.5,208,0.1,3.024289 -2019-05-25 07:30:00,357.13,318.38,25.28,43.28,1.73,112.11,945.22,206,66,25.200001,944.5,205,0.0,3.844083 -2019-05-25 07:45:00,412.17,371.4,25.73,41.89,2.21,115.47,945.25,257,72,25.200001,944.799988,165,0.8,4.681369 -2019-05-25 08:00:00,448.26,406.27,26.24,39.93,2.56,115.14,945.25,313,77,25.700001,944.5,163,1.7,5.576908 -2019-05-25 08:15:00,501.13,457.48,26.73,37.85,2.84,109.73,945.27,365,84,26.1,944.799988,146,1.3,6.45426 -2019-05-25 08:30:00,535.37,490.71,27.17,36.78,3.06,111.37,945.34,409,89,26.299999,944.900024,272,1.2,7.267761 -2019-05-25 08:45:00,584.94,538.9,27.67,34.96,3.21,111.21,945.34,460,96,26.5,945.0,326,0.3,8.027109 -2019-05-25 09:00:00,616.63,569.76,28.01,33.55,3.35,108.86,945.25,504,104,26.799999,945.0,213,1.7,8.775027 -2019-05-25 09:15:00,661.88,613.86,28.37,32.59,3.43,109.04,945.14,555,107,27.299999,945.099976,236,0.6,9.491481 -2019-05-25 09:30:00,690.37,641.65,28.7,31.49,3.4,106.4,945.12,600,109,27.6,944.900024,326,0.4,10.20661 -2019-05-25 09:45:00,730.35,680.68,29.03,30.59,3.28,102.96,944.97,644,114,28.200001,944.799988,199,2.5,10.7916 -2019-05-25 10:00:00,755.08,704.85,29.3,29.98,3.16,100.72,944.75,695,117,27.799999,944.799988,129,0.9,11.34098 -2019-05-25 10:15:00,788.99,737.99,29.62,29.4,3.13,98.82,944.69,748,118,28.299999,944.599976,146,1.4,11.94266 -2019-05-25 10:30:00,809.32,757.88,29.96,28.84,3.18,97.86,944.69,793,118,28.700001,944.5,196,2.2,12.38189 -2019-05-25 10:45:00,836.3,784.28,30.32,28.29,3.26,97.06,944.7,820,124,28.700001,944.5,143,2.4,12.73844 -2019-05-25 11:00:00,851.88,799.52,30.67,27.75,3.34,97.57,944.75,857,126,29.299999,944.5,213,1.3,13.08097 -2019-05-25 11:15:00,871.49,818.72,31.02,27.25,3.34,100.05,944.71,896,126,29.299999,944.299988,177,1.8,13.37407 -2019-05-25 11:30:00,882.03,829.05,31.27,26.91,3.32,106.2,944.62,893,129,29.5,944.099976,196,3.4,13.59137 -2019-05-25 11:45:00,893.74,840.5,31.57,26.66,3.5,111.2,944.55,894,147,30.1,944.200012,171,0.8,13.53405 -2019-05-25 12:00:00,898.67,845.34,31.86,26.51,3.86,115.74,944.5,907,157,30.200001,944.099976,163,3.8,13.57146 -2019-05-25 12:15:00,901.73,848.31,32.14,26.51,4.22,120.66,944.42,920,149,30.1,944.0,137,4.3,13.83477 -2019-05-25 12:30:00,900.91,847.5,32.36,26.65,4.5,125.98,944.34,914,151,30.6,943.799988,73,1.2,13.6162 -2019-05-25 12:45:00,895.57,842.27,32.55,26.97,4.74,129.4,944.2,922,153,30.799999,943.5,171,3.7,13.4889 -2019-05-25 13:00:00,889.48,836.3,32.73,27.11,4.94,131.82,944.04,909,145,31.299999,943.400024,90,2.4,13.72075 -2019-05-25 13:15:00,876.65,823.74,32.87,27.19,5.09,134.67,943.86,943,142,31.200001,943.200012,174,3.1,13.69333 -2019-05-25 13:30:00,865.59,812.92,32.97,27.36,5.22,136.75,943.71,863,163,31.6,943.200012,14,2.7,13.06515 -2019-05-25 13:45:00,845.41,793.15,33.06,27.37,5.34,138.51,943.64,826,170,31.9,943.099976,95,1.5,12.70744 -2019-05-25 14:00:00,829.69,777.78,33.16,27.07,5.38,140.34,943.52,845,178,31.799999,943.099976,157,3.8,12.34182 -2019-05-25 14:15:00,803.04,751.72,33.26,26.47,5.27,141.58,943.37,758,179,32.799999,942.900024,241,1.6,11.83927 -2019-05-25 14:30:00,783.23,732.36,33.38,25.68,5.09,141.9,943.22,716,177,32.400002,942.700012,120,4.8,10.84125 -2019-05-25 14:45:00,750.23,700.12,33.5,24.91,4.93,142.24,943.1,665,188,32.200001,942.5,143,5.7,9.825459 -2019-05-25 15:00:00,725.96,676.43,33.62,24.2,4.85,143.17,943.09,718,205,32.200001,942.5,115,4.8,10.49763 -2019-05-25 15:15:00,686.44,637.86,33.71,23.6,4.83,144.86,943.03,594,180,32.299999,942.400024,168,2.5,9.114202 -2019-05-25 15:30:00,658.2,610.3,33.8,23.06,4.84,146.66,942.99,563,172,31.700001,942.400024,123,3.6,8.315826 -2019-05-25 15:45:00,613.23,566.48,33.83,22.73,4.88,148.77,942.91,485,158,31.799999,942.200012,151,2.8,8.039521 -2019-05-25 16:00:00,581.71,535.8,33.87,22.53,4.94,150.5,942.88,510,147,31.4,942.0,180,2.4,7.585171000000001 -2019-05-25 16:15:00,532.46,487.93,33.84,22.53,5.01,150.75,942.72,428,140,31.5,942.099976,146,4.1,6.605402000000001 -2019-05-25 16:30:00,498.38,454.86,33.82,22.67,5.09,150.45,942.6,402,132,31.0,942.0,115,2.7,5.760957 -2019-05-25 16:45:00,445.68,403.81,33.74,23.02,5.15,149.84,942.52,341,119,31.1,942.0,112,2.6,5.116322 -2019-05-25 17:00:00,409.71,369.05,33.68,23.48,5.19,149.05,942.46,281,116,30.9,942.099976,146,3.9,4.192783 -2019-05-25 17:15:00,354.88,316.24,33.56,24.08,5.24,148.72,942.4,221,99,30.9,942.0,81,2.7,2.876264 -2019-05-25 17:30:00,317.99,280.84,33.44,24.7,5.33,149.28,942.35,179,88,30.4,942.099976,157,3.6,2.661674 -2019-05-25 17:45:00,262.63,228.03,33.26,25.36,5.41,150.52,942.27,139,73,30.299999,942.0,22,1.2,1.989386 -2019-05-25 18:00:00,226.05,193.38,33.08,25.93,5.4,151.8,942.2,104,58,29.9,942.200012,137,2.0,1.506804 -2019-05-25 18:15:00,172.15,142.85,32.81,26.62,5.18,152.07,942.15,72,45,29.700001,942.400024,59,5.0,1.068734 -2019-05-25 18:30:00,137.21,110.61,32.47,27.52,4.7,152.12,942.19,48,33,29.6,942.5,154,0.9,0.7407590000000001 -2019-05-25 18:45:00,87.13,65.53,31.84,29.15,4.04,152.12,942.35,33,23,29.1,942.599976,140,2.7,0.488124 -2019-05-25 19:00:00,56.17,38.9,31.04,31.22,3.42,151.91,942.53,18,12,28.9,942.900024,216,1.4,0.262969 -2019-05-25 19:15:00,16.63,8.4,30.14,33.05,2.89,153.34,942.64,7,4,28.6,942.900024,126,3.3,0.1036869999999999 -2019-05-25 19:30:00,0.14,0.0,29.65,33.66,2.36,162.51,942.71,0,0,28.5,943.099976,134,2.9,0.0 -2019-05-25 19:45:00,0.0,0.0,29.19,34.57,1.93,183.25,942.8,0,0,28.200001,943.299988,104,1.5,0.0 -2019-05-25 20:00:00,0.0,0.0,28.75,35.48,1.79,211.75,942.96,0,0,27.9,943.400024,149,1.5,0.0 -2019-05-25 20:15:00,0.0,0.0,28.31,36.36,1.94,238.51,943.15,0,0,27.6,943.5,134,1.3,0.0 -2019-05-25 20:30:00,0.0,0.0,27.9,37.14,2.23,259.29,943.26,0,0,27.5,943.5,115,2.6,0.0 -2019-05-25 20:45:00,0.0,0.0,27.52,37.85,2.53,274.7,943.33,0,0,27.200001,943.799988,95,0.8,0.0 -2019-05-25 21:00:00,0.0,0.0,27.2,38.41,2.67,283.3,943.4,0,0,27.200001,944.0,106,0.8,0.0 -2019-05-25 21:15:00,0.0,0.0,26.96,38.83,2.55,284.54,943.51,0,0,26.9,944.0,87,0.6,0.0 -2019-05-25 21:30:00,0.0,0.0,26.81,39.07,2.27,280.88,943.7,0,0,26.700001,944.0,61,0.8,0.0 -2019-05-25 21:45:00,0.0,0.0,26.71,39.26,2.12,272.16,943.85,0,0,26.4,944.200012,73,0.4,0.0 -2019-05-25 22:00:00,0.0,0.0,26.65,39.72,2.32,254.2,943.98,0,0,26.700001,944.099976,33,0.5,0.0 -2019-05-25 22:15:00,0.0,0.0,26.68,40.95,2.9,230.19,944.09,0,0,26.5,944.0,87,1.0,0.0 -2019-05-25 22:30:00,0.0,0.0,26.45,43.55,3.64,214.0,944.31,0,0,26.4,944.0,56,0.8,0.0 -2019-05-25 22:45:00,0.0,0.0,26.12,46.71,4.32,207.0,944.64,0,0,26.1,943.900024,236,0.0,0.0 -2019-05-25 23:00:00,0.0,0.0,25.91,49.09,4.69,214.5,944.8,0,0,25.200001,943.900024,5,0.0,0.0 -2019-05-25 23:15:00,0.0,0.0,25.64,50.16,4.65,225.4,945.02,0,0,25.0,944.0,348,0.0,0.0 -2019-05-25 23:30:00,0.0,0.0,25.31,50.48,4.41,239.91,945.26,0,0,24.4,943.900024,25,1.7,0.0 -2019-05-25 23:45:00,0.0,0.0,24.97,50.35,4.19,261.84,945.34,0,0,24.200001,943.700012,300,1.5,0.0 -2019-05-26 00:00:00,0.0,0.0,23.44,54.07,2.89,296.7,945.03,0,0,23.9,943.700012,340,2.1,0.0 -2019-05-26 00:15:00,0.0,0.0,23.32,54.41,3.01,296.2,945.17,0,0,23.9,943.799988,348,1.2,0.0 -2019-05-26 00:30:00,0.0,0.0,23.24,54.6,3.24,295.02,945.56,0,0,23.799999,943.700012,236,1.0,0.0 -2019-05-26 00:45:00,0.0,0.0,23.12,54.89,3.41,293.5,945.36,0,0,24.1,943.700012,331,0.9,0.0 -2019-05-26 01:00:00,0.0,0.0,22.92,55.44,3.4,293.01,945.05,0,0,23.700001,943.5,16,0.7,0.0 -2019-05-26 01:15:00,0.0,0.0,22.61,56.36,3.27,293.59,944.65,0,0,23.6,943.5,345,1.0,0.0 -2019-05-26 01:30:00,0.0,0.0,22.38,57.07,3.22,296.94,944.78,0,0,23.200001,943.599976,2,1.2,0.0 -2019-05-26 01:45:00,0.0,0.0,22.14,57.81,3.36,297.5,944.96,0,0,23.299999,943.700012,345,0.8,0.0 -2019-05-26 02:00:00,0.0,0.0,21.88,58.61,3.63,297.32,944.83,0,0,23.0,943.599976,11,1.4,0.0 -2019-05-26 02:15:00,0.0,0.0,21.56,59.64,3.87,293.85,944.61,0,0,23.200001,943.599976,11,1.1,0.0 -2019-05-26 02:30:00,0.0,0.0,21.3,60.48,4.02,288.79,944.69,0,0,23.200001,943.400024,264,0.1,0.0 -2019-05-26 02:45:00,0.0,0.0,21.25,60.56,4.21,283.97,944.72,0,0,22.5,943.400024,343,1.8,0.0 -2019-05-26 03:00:00,0.0,0.0,21.35,60.41,4.48,280.96,944.64,0,0,22.299999,943.400024,343,1.0,0.0 +date_time,nwp_globalirrad,nwp_directirrad,nwp_temperature,nwp_humidity,nwp_windspeed,nwp_winddirection,nwp_pressure,lmd_totalirrad,lmd_diffuseirrad,lmd_temperature,lmd_pressure,lmd_winddirection,lmd_windspeed,power +5/23/2019 3:15,0,0,20.98,17.05,7.09,268.51,947.19,0,0,24.1,946.700012,258,3.4,0 +5/23/2019 3:30,0,0,21.12,16.75,7.05,269.86,947.05,0,0,23.9,946.799988,253,3.3,0 +5/23/2019 3:45,0,0,21.27,16.43,6.99,271.05,947.15,0,0,23.9,946.599976,278,3.2,0 +5/23/2019 4:00,0,0,21.39,16.14,6.91,272.47,947.27,0,0,24.299999,946.599976,275,4.5,0 +5/23/2019 4:15,0,0,21.42,15.93,6.84,273.84,947.27,0,0,23.6,946.5,250,5.1,0 +5/23/2019 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11:00,552.53,445.58,31.11,16.3,4.67,208.8,946.76,1045,278,29.299999,945.900024,255,4.7,16.25565 +5/24/2019 11:15,421.88,272.1,31.22,16.3,4.69,202.14,946.63,1219,310,30.299999,945.799988,298,1.7,16.52508 +5/24/2019 11:30,392.07,320.34,31.53,16.13,4.83,198.63,946.63,887,258,30.299999,945.900024,196,3.5,13.80319 +5/24/2019 11:45,206.33,133.68,31.08,16.73,5.05,195.72,946.68,294,233,29.799999,945.599976,154,1.3,5.337551 +5/24/2019 12:00,261.79,142.03,31.43,16.76,5.51,193.66,946.64,342,266,30.1,945.599976,202,3.4,6.440347 +5/24/2019 12:15,180.82,112.54,31.01,17.57,6.22,194.07,946.66,1025,275,30.6,945.5,244,1.9,15.52713 +5/24/2019 12:30,201.7,100.64,30.97,18.01,6.9,194.98,946.68,318,234,30.799999,945.099976,219,3.5,5.495908999999999 +5/24/2019 12:45,149.92,88.58,30.51,18.77,7.26,194.31,946.53,378,219,30.299999,944.900024,168,5.2,6.033394 +5/24/2019 13:00,124.31,72.79,30.15,19.49,7.45,191.68,946.37,812,273,29.700001,944.5,168,4.3,14.23589 +5/24/2019 13:15,156.61,123.75,30.29,19.53,7.63,188.71,946.18,340,217,30.700001,944.299988,165,1.7,5.763612 +5/24/2019 13:30,272.17,247.16,30.88,19,7.76,185.41,945.99,1086,200,31.299999,944.099976,253,1.8,15.61292 +5/24/2019 13:45,306.6,283.91,31.15,18.96,7.86,181.69,945.79,1003,327,30.6,944.200012,202,3.6,16.333579999999998 +5/24/2019 14:00,338.29,317.27,31.47,19,8.06,179.12,945.57,872,211,30.799999,944.099976,202,5.9,11.52492 +5/24/2019 14:15,332.57,311.45,31.61,19.29,8.37,177.69,945.37,713,155,31.5,943.700012,134,1.4,10.58435 +5/24/2019 14:30,330.07,308.75,31.69,19.65,8.75,176.86,945.18,888,133,32.299999,943.400024,199,2.2,12.95199 +5/24/2019 14:45,319.47,298.23,31.7,19.9,9.1,176.27,945.01,503,261,31.4,943.299988,118,4.7,7.650524000000001 +5/24/2019 15:00,309.39,288.37,31.69,19.99,9.36,176.12,944.9,699,193,31.799999,943.299988,208,4.6,9.340569 +5/24/2019 15:15,291.88,271.3,31.64,20.07,9.51,176.79,944.8,201,90,31.200001,943.299988,140,3.9,8.49445 +5/24/2019 15:30,283.07,262.57,31.65,19.95,9.57,177.73,944.71,671,87,32.400002,943,171,3.8,10.11193 +5/24/2019 15:45,275.49,254.6,31.6,19.76,9.6,177.87,944.64,635,93,31.799999,942.799988,182,4.7,9.481666 +5/24/2019 16:00,283.88,261.59,31.52,20.18,9.59,177.12,944.64,594,105,31.299999,942.799988,163,2.7,8.852041 +5/24/2019 16:15,290.91,266.7,31.36,20.76,9.52,176.28,944.53,526,92,31.6,942.5,163,6,7.797799 +5/24/2019 16:30,308.53,281.72,31.19,21.13,9.41,176.14,944.42,177,93,31.6,942.900024,146,3.3,6.29873 +5/24/2019 16:45,311.15,282.07,30.97,21.26,9.32,176.13,944.32,451,105,31.299999,942.5,149,4,5.853386 +5/24/2019 17:00,318.26,286.86,30.82,21.06,9.27,176.34,944.26,370,96,31.299999,942.5,191,3.2,4.284115 +5/24/2019 17:15,301.64,268.99,30.58,21.33,9.24,176.34,944.16,329,101,30.700001,942.5,16,1.7,4.775473000000001 +5/24/2019 17:30,290.04,256.37,30.31,22.14,9.17,176.44,944.06,247,76,30.700001,942.400024,174,3.2,3.427027 +5/24/2019 17:45,251.31,218.37,29.94,23.21,9.04,177.1,943.91,201,80,30.1,942.5,174,1.9,2.801674 +5/24/2019 18:00,222.29,190.28,29.56,24.31,8.87,178.48,943.81,135,56,29.9,942.599976,154,3.9,1.88593 +5/24/2019 18:15,170.92,141.88,29.1,25.59,8.66,179.96,943.72,92,49,29.6,942.799988,92,2.5,1.406235 +5/24/2019 18:30,136.35,109.9,28.65,26.89,8.43,181.66,943.75,60,40,29.1,942.900024,154,3.1,0.969608 +5/24/2019 18:45,85.9,64.5,28.13,28.3,8.22,183.25,943.83,39,29,28.799999,943,115,1.8,0.610111 +5/24/2019 19:00,54.78,37.8,27.61,29.8,7.99,184.87,943.92,24,18,28.4,943.099976,174,1.7,0.396965 +5/24/2019 19:15,15.36,7.6,27.06,31.14,7.72,186.57,943.97,6,3,27.9,943.200012,199,1.3,0.1029359999999999 +5/24/2019 19:30,0,0,26.62,32.13,7.41,188.59,944.06,0,0,27.1,943.5,171,0.7,0 +5/24/2019 19:45,0,0,26.26,33.09,7.04,191.71,944.24,0,0,26.299999,943.400024,182,1.2,0 +5/24/2019 20:00,0,0,25.92,34.1,6.61,195.03,944.39,0,0,26.200001,943.799988,171,1.6,0 +5/24/2019 20:15,0,0,25.58,35.13,6.12,199.26,944.57,0,0,26.200001,943.900024,149,2.1,0 +5/24/2019 20:30,0,0,25.2,36.21,5.53,204.28,944.74,0,0,25.6,944,160,0.9,0 +5/24/2019 20:45,0,0,24.77,37.35,4.84,210.93,944.86,0,0,25.6,944.200012,137,1.3,0 +5/24/2019 21:00,0,0,24.3,38.47,4.17,222.5,944.98,0,0,24.9,944.200012,250,0.3,0 +5/24/2019 21:15,0,0,23.89,39.35,3.66,238.81,945.13,0,0,24.4,944.299988,16,0.2,0 +5/24/2019 21:30,0,0,23.5,40.14,3.38,255.47,945.18,0,0,24.200001,944.400024,160,0.5,0 +5/24/2019 21:45,0,0,23.22,40.66,3.34,271.08,945.3,0,0,23.200001,944.400024,337,0.6,0 +5/24/2019 22:00,0,0,22.99,41.12,3.43,283.36,945.4,0,0,23.200001,944.400024,5,1.2,0 +5/24/2019 22:15,0,0,22.78,41.68,3.57,291.06,945.55,0,0,23.5,944.5,22,1.5,0 +5/24/2019 22:30,0,0,22.58,42.37,3.69,294.68,945.65,0,0,22.299999,944.400024,16,0.8,0 +5/24/2019 22:45,0,0,22.4,43.22,3.79,296.78,945.72,0,0,22.4,944.5,354,2,0 +5/24/2019 23:00,0,0,22.24,44.23,3.88,297.76,945.78,0,0,22.4,944.200012,289,1.2,0 +5/24/2019 23:15,0,0,22.09,45.45,3.92,297.58,945.83,0,0,21.700001,944.200012,343,1.1,0 +5/24/2019 23:30,0,0,21.96,46.88,3.94,295.31,945.86,0,0,21.6,944.299988,326,1.8,0 +5/24/2019 23:45,0,0,21.82,48.45,3.97,293.76,945.95,0,0,21.799999,944.099976,8,2.1,0 +5/25/2019 0:00,0,0,20.71,47.15,4.23,285.61,945.14,0,0,22.5,944.099976,59,0.2,0 +5/25/2019 0:15,0,0,20.71,47.84,4.4,283.5,945.13,0,0,22.5,944,343,0.5,0 +5/25/2019 0:30,0,0,20.77,48.59,4.66,282.49,945.27,0,0,22,944,0,1.4,0 +5/25/2019 0:45,0,0,20.87,49.5,4.95,281.02,945.09,0,0,21,943.900024,314,0.4,0 +5/25/2019 1:00,0,0,20.95,50.59,5.22,280.1,945,0,0,22,943.799988,326,0.8,0 +5/25/2019 1:15,0,0,21.02,51.72,5.43,280.52,944.73,0,0,21.9,943.700012,222,1,0 +5/25/2019 1:30,0,0,21.08,52.81,5.55,281.29,944.82,0,0,22,943.599976,216,1.1,0 +5/25/2019 1:45,0,0,21.1,53.85,5.6,282.45,945.04,0,0,22,943.599976,250,1.8,0 +5/25/2019 2:00,0,0,21.06,54.85,5.61,283.17,945.12,0,0,22,943.599976,329,0.4,0 +5/25/2019 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5:15,0.05,0,19.9,58.5,5.33,289.69,944.27,0,0,20.299999,943.5,306,0.5,0 +5/25/2019 5:30,16.46,8.32,19.92,58.26,5.11,289.84,944.31,7,5,20.6,943.599976,8,1.2,0.118986 +5/25/2019 5:45,56.28,39.03,20.21,57.5,4.68,289.6,944.26,17,14,20.6,943.700012,14,0.3,0.280346 +5/25/2019 6:00,87.5,65.89,20.63,56.59,3.92,288.35,944.18,28,23,20.9,943.900024,2,1.4,0.575588 +5/25/2019 6:15,138.06,111.42,21.39,54.69,2.88,289.66,944.11,37,30,20.700001,943.799988,281,0.3,0.86979 +5/25/2019 6:30,173.35,143.99,22.28,52.57,1.67,293.29,944.25,48,39,21.200001,944,59,0.6,1.247819 +5/25/2019 6:45,227.67,194.92,23.31,50.09,0.73,298.19,944.55,77,47,22.299999,944.099976,73,0,1.7254349999999998 +5/25/2019 7:00,264.48,229.79,24.09,47.17,0.58,91.95,944.9,116,53,23.200001,944.200012,146,0,2.335373 +5/25/2019 7:15,320.09,282.83,24.72,45.62,1.1,111.5,945.06,159,60,24.9,944.5,208,0.1,3.024289 +5/25/2019 7:30,357.13,318.38,25.28,43.28,1.73,112.11,945.22,206,66,25.200001,944.5,205,0,3.844083 +5/25/2019 7:45,412.17,371.4,25.73,41.89,2.21,115.47,945.25,257,72,25.200001,944.799988,165,0.8,4.681369 +5/25/2019 8:00,448.26,406.27,26.24,39.93,2.56,115.14,945.25,313,77,25.700001,944.5,163,1.7,5.576908 +5/25/2019 8:15,501.13,457.48,26.73,37.85,2.84,109.73,945.27,365,84,26.1,944.799988,146,1.3,6.45426 +5/25/2019 8:30,535.37,490.71,27.17,36.78,3.06,111.37,945.34,409,89,26.299999,944.900024,272,1.2,7.267761 +5/25/2019 8:45,584.94,538.9,27.67,34.96,3.21,111.21,945.34,460,96,26.5,945,326,0.3,8.027109 +5/25/2019 9:00,616.63,569.76,28.01,33.55,3.35,108.86,945.25,504,104,26.799999,945,213,1.7,8.775027 +5/25/2019 9:15,661.88,613.86,28.37,32.59,3.43,109.04,945.14,555,107,27.299999,945.099976,236,0.6,9.491481 +5/25/2019 9:30,690.37,641.65,28.7,31.49,3.4,106.4,945.12,600,109,27.6,944.900024,326,0.4,10.20661 +5/25/2019 9:45,730.35,680.68,29.03,30.59,3.28,102.96,944.97,644,114,28.200001,944.799988,199,2.5,10.7916 +5/25/2019 10:00,755.08,704.85,29.3,29.98,3.16,100.72,944.75,695,117,27.799999,944.799988,129,0.9,11.34098 +5/25/2019 10:15,788.99,737.99,29.62,29.4,3.13,98.82,944.69,748,118,28.299999,944.599976,146,1.4,11.94266 +5/25/2019 10:30,809.32,757.88,29.96,28.84,3.18,97.86,944.69,793,118,28.700001,944.5,196,2.2,12.38189 +5/25/2019 10:45,836.3,784.28,30.32,28.29,3.26,97.06,944.7,820,124,28.700001,944.5,143,2.4,12.73844 +5/25/2019 11:00,851.88,799.52,30.67,27.75,3.34,97.57,944.75,857,126,29.299999,944.5,213,1.3,13.08097 +5/25/2019 11:15,871.49,818.72,31.02,27.25,3.34,100.05,944.71,896,126,29.299999,944.299988,177,1.8,13.37407 +5/25/2019 11:30,882.03,829.05,31.27,26.91,3.32,106.2,944.62,893,129,29.5,944.099976,196,3.4,13.59137 +5/25/2019 11:45,893.74,840.5,31.57,26.66,3.5,111.2,944.55,894,147,30.1,944.200012,171,0.8,13.53405 +5/25/2019 12:00,898.67,845.34,31.86,26.51,3.86,115.74,944.5,907,157,30.200001,944.099976,163,3.8,13.57146 +5/25/2019 12:15,901.73,848.31,32.14,26.51,4.22,120.66,944.42,920,149,30.1,944,137,4.3,13.83477 +5/25/2019 12:30,900.91,847.5,32.36,26.65,4.5,125.98,944.34,914,151,30.6,943.799988,73,1.2,13.6162 +5/25/2019 12:45,895.57,842.27,32.55,26.97,4.74,129.4,944.2,922,153,30.799999,943.5,171,3.7,13.4889 +5/25/2019 13:00,889.48,836.3,32.73,27.11,4.94,131.82,944.04,909,145,31.299999,943.400024,90,2.4,13.72075 +5/25/2019 13:15,876.65,823.74,32.87,27.19,5.09,134.67,943.86,943,142,31.200001,943.200012,174,3.1,13.69333 +5/25/2019 13:30,865.59,812.92,32.97,27.36,5.22,136.75,943.71,863,163,31.6,943.200012,14,2.7,13.06515 +5/25/2019 13:45,845.41,793.15,33.06,27.37,5.34,138.51,943.64,826,170,31.9,943.099976,95,1.5,12.70744 +5/25/2019 14:00,829.69,777.78,33.16,27.07,5.38,140.34,943.52,845,178,31.799999,943.099976,157,3.8,12.34182 +5/25/2019 14:15,803.04,751.72,33.26,26.47,5.27,141.58,943.37,758,179,32.799999,942.900024,241,1.6,11.83927 +5/25/2019 14:30,783.23,732.36,33.38,25.68,5.09,141.9,943.22,716,177,32.400002,942.700012,120,4.8,10.84125 +5/25/2019 14:45,750.23,700.12,33.5,24.91,4.93,142.24,943.1,665,188,32.200001,942.5,143,5.7,9.825459 +5/25/2019 15:00,725.96,676.43,33.62,24.2,4.85,143.17,943.09,718,205,32.200001,942.5,115,4.8,10.49763 +5/25/2019 15:15,686.44,637.86,33.71,23.6,4.83,144.86,943.03,594,180,32.299999,942.400024,168,2.5,9.114202 +5/25/2019 15:30,658.2,610.3,33.8,23.06,4.84,146.66,942.99,563,172,31.700001,942.400024,123,3.6,8.315826 +5/25/2019 15:45,613.23,566.48,33.83,22.73,4.88,148.77,942.91,485,158,31.799999,942.200012,151,2.8,8.039521 +5/25/2019 16:00,581.71,535.8,33.87,22.53,4.94,150.5,942.88,510,147,31.4,942,180,2.4,7.585171000000001 +5/25/2019 16:15,532.46,487.93,33.84,22.53,5.01,150.75,942.72,428,140,31.5,942.099976,146,4.1,6.605402000000001 +5/25/2019 16:30,498.38,454.86,33.82,22.67,5.09,150.45,942.6,402,132,31,942,115,2.7,5.760957 +5/25/2019 16:45,445.68,403.81,33.74,23.02,5.15,149.84,942.52,341,119,31.1,942,112,2.6,5.116322 +5/25/2019 17:00,409.71,369.05,33.68,23.48,5.19,149.05,942.46,281,116,30.9,942.099976,146,3.9,4.192783 +5/25/2019 17:15,354.88,316.24,33.56,24.08,5.24,148.72,942.4,221,99,30.9,942,81,2.7,2.876264 +5/25/2019 17:30,317.99,280.84,33.44,24.7,5.33,149.28,942.35,179,88,30.4,942.099976,157,3.6,2.661674 +5/25/2019 17:45,262.63,228.03,33.26,25.36,5.41,150.52,942.27,139,73,30.299999,942,22,1.2,1.989386 +5/25/2019 18:00,226.05,193.38,33.08,25.93,5.4,151.8,942.2,104,58,29.9,942.200012,137,2,1.506804 +5/25/2019 18:15,172.15,142.85,32.81,26.62,5.18,152.07,942.15,72,45,29.700001,942.400024,59,5,1.068734 +5/25/2019 18:30,137.21,110.61,32.47,27.52,4.7,152.12,942.19,48,33,29.6,942.5,154,0.9,0.7407590000000001 +5/25/2019 18:45,87.13,65.53,31.84,29.15,4.04,152.12,942.35,33,23,29.1,942.599976,140,2.7,0.488124 +5/25/2019 19:00,56.17,38.9,31.04,31.22,3.42,151.91,942.53,18,12,28.9,942.900024,216,1.4,0.262969 +5/25/2019 19:15,16.63,8.4,30.14,33.05,2.89,153.34,942.64,7,4,28.6,942.900024,126,3.3,0.1036869999999999 +5/25/2019 19:30,0.14,0,29.65,33.66,2.36,162.51,942.71,0,0,28.5,943.099976,134,2.9,0 +5/25/2019 19:45,0,0,29.19,34.57,1.93,183.25,942.8,0,0,28.200001,943.299988,104,1.5,0 +5/25/2019 20:00,0,0,28.75,35.48,1.79,211.75,942.96,0,0,27.9,943.400024,149,1.5,0 +5/25/2019 20:15,0,0,28.31,36.36,1.94,238.51,943.15,0,0,27.6,943.5,134,1.3,0 +5/25/2019 20:30,0,0,27.9,37.14,2.23,259.29,943.26,0,0,27.5,943.5,115,2.6,0 +5/25/2019 20:45,0,0,27.52,37.85,2.53,274.7,943.33,0,0,27.200001,943.799988,95,0.8,0 +5/25/2019 21:00,0,0,27.2,38.41,2.67,283.3,943.4,0,0,27.200001,944,106,0.8,0 +5/25/2019 21:15,0,0,26.96,38.83,2.55,284.54,943.51,0,0,26.9,944,87,0.6,0 +5/25/2019 21:30,0,0,26.81,39.07,2.27,280.88,943.7,0,0,26.700001,944,61,0.8,0 +5/25/2019 21:45,0,0,26.71,39.26,2.12,272.16,943.85,0,0,26.4,944.200012,73,0.4,0 +5/25/2019 22:00,0,0,26.65,39.72,2.32,254.2,943.98,0,0,26.700001,944.099976,33,0.5,0 +5/25/2019 22:15,0,0,26.68,40.95,2.9,230.19,944.09,0,0,26.5,944,87,1,0 +5/25/2019 22:30,0,0,26.45,43.55,3.64,214,944.31,0,0,26.4,944,56,0.8,0 +5/25/2019 22:45,0,0,26.12,46.71,4.32,207,944.64,0,0,26.1,943.900024,236,0,0 +5/25/2019 23:00,0,0,25.91,49.09,4.69,214.5,944.8,0,0,25.200001,943.900024,5,0,0 +5/25/2019 23:15,0,0,25.64,50.16,4.65,225.4,945.02,0,0,25,944,348,0,0 +5/25/2019 23:30,0,0,25.31,50.48,4.41,239.91,945.26,0,0,24.4,943.900024,25,1.7,0 +5/25/2019 23:45,0,0,24.97,50.35,4.19,261.84,945.34,0,0,24.200001,943.700012,300,1.5,0 +5/26/2019 0:00,0,0,23.44,54.07,2.89,296.7,945.03,0,0,23.9,943.700012,340,2.1,0 +5/26/2019 0:15,0,0,23.32,54.41,3.01,296.2,945.17,0,0,23.9,943.799988,348,1.2,0 +5/26/2019 0:30,0,0,23.24,54.6,3.24,295.02,945.56,0,0,23.799999,943.700012,236,1,0 +5/26/2019 0:45,0,0,23.12,54.89,3.41,293.5,945.36,0,0,24.1,943.700012,331,0.9,0 +5/26/2019 1:00,0,0,22.92,55.44,3.4,293.01,945.05,0,0,23.700001,943.5,16,0.7,0 +5/26/2019 1:15,0,0,22.61,56.36,3.27,293.59,944.65,0,0,23.6,943.5,345,1,0 +5/26/2019 1:30,0,0,22.38,57.07,3.22,296.94,944.78,0,0,23.200001,943.599976,2,1.2,0 +5/26/2019 1:45,0,0,22.14,57.81,3.36,297.5,944.96,0,0,23.299999,943.700012,345,0.8,0 +5/26/2019 2:00,0,0,21.88,58.61,3.63,297.32,944.83,0,0,23,943.599976,11,1.4,0 +5/26/2019 2:15,0,0,21.56,59.64,3.87,293.85,944.61,0,0,23.200001,943.599976,11,1.1,0 +5/26/2019 2:30,0,0,21.3,60.48,4.02,288.79,944.69,0,0,23.200001,943.400024,264,0.1,0 +5/26/2019 2:45,0,0,21.25,60.56,4.21,283.97,944.72,0,0,22.5,943.400024,343,1.8,0 +5/26/2019 3:00,0,0,21.35,60.41,4.48,280.96,944.64,0,0,22.299999,943.400024,343,1,0 +5/26/2019 3:15,0,0,21.43,60.97,4.82,279.04,944.43,0,0,21.9,943.599976,303,0,0 +5/26/2019 3:30,0,0,21.53,62.09,5.2,279.21,944.35,0,0,22.200001,943.400024,351,1.1,0 +5/26/2019 3:45,0,0,21.64,63.43,5.47,278.48,944.32,0,0,22.4,943.400024,19,0,0 +5/26/2019 4:00,0,0,21.69,64.77,5.58,277.75,944.41,0,0,22.9,943.299988,320,0,0 +5/26/2019 4:15,0,0,21.67,66.03,5.52,277.09,944.24,0,0,23.1,943.299988,323,0,0 +5/26/2019 4:30,0,0,21.57,67.19,5.34,275.24,943.97,0,0,22.9,943.299988,5,0,0 +5/26/2019 4:45,0,0,21.45,68.23,5.11,274,944.09,0,0,22.700001,943.400024,14,0,0 +5/26/2019 5:00,0,0,21.33,69.12,4.89,273.5,944.31,0,0,22.299999,943.400024,343,0,0 +5/26/2019 5:15,0.22,0,21.14,70.11,4.63,273.17,944.4,0,0,22.4,943.400024,343,1,0 +5/26/2019 5:30,7.34,3.67,21.09,70.57,4.44,273.4,944.44,4,2,22.5,943.700012,317,0,0.03233 +5/26/2019 5:45,23.02,15.84,21.43,69.54,4.34,273.27,944.35,12,8,22.5,943.599976,343,0,0.154317 +5/26/2019 6:00,34.64,25.93,22.04,67.58,4.26,270.23,944.12,34,29,22.5,943.5,0,0.1,0.5440659999999999 +5/26/2019 6:15,54,43.38,22.99,64.41,4.25,265.22,944.09,40,38,22.799999,943.5,0,0.2,0.705485 +5/26/2019 6:30,65.29,42.81,23.92,61.23,4.34,260.2,944.18,64,55,23.1,943.5,2,0.5,1.271835 +5/26/2019 6:45,55.76,17.99,24.51,59.07,4.37,257.17,944.43,92,74,23.200001,943.599976,129,0.1,1.7649240000000002 +5/26/2019 7:00,44.36,0,24.75,58.23,4.21,252.33,944.68,121,90,23.299999,943.900024,191,0.4,2.282491 +5/26/2019 7:15,45.6,0,24.95,57.63,4.04,245.37,944.78,168,116,24.4,943.900024,115,0.1,3.101246 +5/26/2019 7:30,72.48,12.96,25.45,56.1,4.07,238.65,944.71,185,115,25.1,943.900024,160,1,2.934285 +5/26/2019 7:45,144.65,126.91,26.31,53.41,4.33,234.81,944.62,220,128,24.9,944,241,0,3.938648 +5/26/2019 8:00,170.92,154,27.14,50.84,4.65,232.09,944.61,256,141,25.700001,943.900024,227,0,4.568676 +5/26/2019 8:15,173.74,153.07,27.69,49.05,4.84,229.23,944.72,309,157,26.299999,944,253,1,5.454169 +5/26/2019 8:30,179.13,152.56,28.23,47.63,4.84,225.21,944.79,355,174,26.200001,944,14,0.2,6.154227 +5/26/2019 8:45,208.55,183.2,28.87,44.99,4.76,225,944.78,395,187,27,943.900024,132,1.1,6.770631 +5/26/2019 9:00,236.95,217.67,29.41,43.61,4.6,221.65,944.63,453,209,27.4,943.900024,151,0.8,7.692495 +5/26/2019 9:15,254.49,233.71,29.97,41.73,4.39,218.36,944.5,349,226,27.700001,943.700012,182,0.8,5.816783 +5/26/2019 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b/guangfufadian/model_base.py index 7ec02d4..3bc62db 100644 --- a/guangfufadian/model_base.py +++ b/guangfufadian/model_base.py @@ -60,9 +60,7 @@ class ModelInference: 数据长度满足192，截取前192 数据长度不满足192，前拼接满足数量级 """ - def load_pred_data(self,data_path): - - data = pd.read_csv(data_path) + def load_pred_data(self,data): raw_data = data.values length = raw_data.shape[0] # 顺序，必须一致才能进行下一步 @@ -84,10 +82,10 @@ class ModelInference: input_data = np.array(input_data) input_data = input_data[:guangfufadian_args.in_len, 1:].astype(np.float32) input_tensor = torch.tensor(input_data, dtype=torch.float32).unsqueeze(0) # 增加batch维度 - return input_tensor + return {'status':True, 'reason':input_tensor } else: print("文件不匹配，请检查上传文件与模版是否一致") - return None + return {'status':False, 'reason':'文件不匹配，请检查上传文件与模版是否一致'} # 数据归一化 def preprocess_data(self, input_tensor): @@ -100,11 +98,14 @@ class ModelInference: # return predictions.cpu().numpy() # 返回 NumPy 数组 return predictions # 推理过程 - def run_inference(self, file_path): - raw_data = self.load_pred_data(file_path) - input_tensor = self.preprocess_data(raw_data) - predictions = self.predict(input_tensor) - predictions = self.scaler.inverse_transform(predictions) - predictions = predictions.squeeze(0) - predictions = predictions.cpu().numpy() - return predictions[:,-1:] \ No newline at end of file + def run_inference(self, data): + raw_data = self.load_pred_data(data) + if raw_data['status'] == False: + return {'status':False, 'reason':'文件不匹配，请检查上传文件与模版是否一致'} + else: + input_tensor = self.preprocess_data(raw_data['reason']) + predictions = self.predict(input_tensor) + predictions = self.scaler.inverse_transform(predictions) + predictions = predictions.squeeze(0) + predictions = predictions.cpu().numpy() + return {'status':True, 'reason':predictions[:,-1:]} \ No newline at end of file diff --git a/guangfufadian/utils/__pycache__/__init__.cpython-310.pyc b/guangfufadian/utils/__pycache__/__init__.cpython-310.pyc index 3270c26..4855497 100644 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a/imgs/screen/pv-app.png b/imgs/screen/pv-app.png new file mode 100644 index 0000000..defa725 Binary files /dev/null and b/imgs/screen/pv-app.png differ diff --git a/imgs/screen/pvekectric-app.png b/imgs/screen/pvekectric-app.png new file mode 100644 index 0000000..55b5bda Binary files /dev/null and b/imgs/screen/pvekectric-app.png differ diff --git a/imgs/screen/roof-app.png b/imgs/screen/roof-app.png new file mode 100644 index 0000000..cbb7e70 Binary files /dev/null and b/imgs/screen/roof-app.png differ diff --git a/imgs/screen/roofpv-app.png b/imgs/screen/roofpv-app.png new file mode 100644 index 0000000..c5dfe4c Binary files /dev/null and b/imgs/screen/roofpv-app.png differ diff --git a/imgs/screen/wind-electric-app.png b/imgs/screen/wind-electric-app.png new file mode 100644 index 0000000..6d580e8 Binary files /dev/null and b/imgs/screen/wind-electric-app.png differ diff --git a/main.py b/main.py index b7cf934..9f49de2 100644 --- a/main.py +++ b/main.py @@ -12,4 +12,4 @@ app.mount("/files", StaticFiles(directory="/root/app"), name="files") @app.get("/") async def read_root(): message = f"Hello world! From FastAPI running on Uvicorn with Gunicorn. Using Python {version}" - return {"message": message} + return {"message": message} \ No newline at end of file diff --git a/meijitancailiao/data/moni.csv b/meijitancailiao/data/moni.csv new file mode 100644 index 0000000..5e1eed8 --- /dev/null +++ b/meijitancailiao/data/moni.csv @@ -0,0 +1,94081 @@ +,A,VM,K/C,MM,AT,At,Rt,SSA,TPV +26458,16.0,20.0,3.5,0.0,750.0,1.0,5.0,3599.8777,2.3781126 +27242,16.0,25.0,3.5,0.0,750.0,1.0,5.0,3599.8777,2.358298 +26570,16.0,20.0,4.0,0.0,750.0,1.0,5.0,3599.8777,2.3404977 +27354,16.0,25.0,4.0,0.0,750.0,1.0,5.0,3599.8777,2.3206832 +26456,16.0,20.0,3.5,0.0,750.0,0.5,5.0,3599.8777,2.2717328 +27240,16.0,25.0,3.5,0.0,750.0,0.5,5.0,3599.8777,2.2519183 +26568,16.0,20.0,4.0,0.0,750.0,0.5,5.0,3599.8777,2.2314482 +27352,16.0,25.0,4.0,0.0,750.0,0.5,5.0,3599.8777,2.2116337 +18618,12.0,20.0,3.5,0.0,750.0,1.0,5.0,3594.3574,2.3819473 +19402,12.0,25.0,3.5,0.0,750.0,1.0,5.0,3594.3574,2.3621328 +18730,12.0,20.0,4.0,0.0,750.0,1.0,5.0,3594.3574,2.3175538 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+7917,8.0,5.0,1.0,1.0,700.0,1.5,10.0,183.84554,0.4025403 +7861,8.0,5.0,1.0,0.0,700.0,1.5,10.0,183.84554,0.39805758 +7915,8.0,5.0,1.0,1.0,700.0,1.0,10.0,183.84554,0.28752026 +7859,8.0,5.0,1.0,0.0,700.0,1.0,10.0,183.84554,0.287337 diff --git a/meijitancailiao/meishuxing_SSA.py b/meijitancailiao/meishuxing_SSA.py index 04b7389..113af2e 100644 --- a/meijitancailiao/meishuxing_SSA.py +++ b/meijitancailiao/meishuxing_SSA.py @@ -5,7 +5,7 @@ import numpy as np import xgboost as xgb from sklearn.svm import SVR from sklearn.tree import DecisionTreeRegressor -from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor +from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor,GradientBoostingRegressor from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler, MinMaxScaler,RobustScaler from sklearn.linear_model import LinearRegression,Ridge,LogisticRegression, Lasso, ElasticNet @@ -634,60 +634,101 @@ XGB """ # 创建Pipeline -base_estimator = DecisionTreeRegressor(max_depth=3) +# base_estimator = DecisionTreeRegressor(max_depth=3) +# pipeline = Pipeline([ +# ('scaler', StandardScaler()), # 可选 +# ('xgb', xgb.XGBRegressor(objective='reg:squarederror', +# random_state=42, +# n_jobs=-1)) +# ]) +# # 定义超参数网格 +# param_grid = { +# 'xgb__n_estimators': np.arange(1,150), # 树的数量 +# # 'xgb__max_depth': [3, 4, 5], # 树的最大深度 +# # 'xgb__learning_rate': [0.01, 0.05, 0.1], # 学习率 +# # 'xgb__subsample': [0.6, 0.8, 1.0], # 样本采样比例 +# # 'xgb__colsample_bytree': [0.6, 0.8, 1.0], # 特征采样比例 +# # 'xgb__gamma': [0, 0.1, 0.2], # 最小分裂损失 +# # 'xgb__reg_alpha': [0, 0.1, 1], # L1正则化 +# # 'xgb__reg_lambda': [0.1, 1, 10] # L2正则化 +# } + +# # 设置5折交叉验证 +# kf = KFold(n_splits=5, shuffle=True, random_state=42) + +# # 网格搜索（优化超参数） +# grid_search = GridSearchCV( +# pipeline, +# param_grid, +# cv=kf, +# scoring='neg_mean_squared_error', # 最小化MSE +# n_jobs=-1, # 使用所有CPU核心 +# verbose=2 # 打印进度 +# ) + +# # 训练模型 +# print("开始网格搜索优化...") +# grid_search.fit(train_x, train_y) +# print("\n最佳参数组合:", grid_search.best_params_) + +# # 使用最佳模型进行预测 +# best_model = grid_search.best_estimator_ + +# # 保存模型 +# dump(best_model, './model/SSA_XGB.joblib') + +# pred_y = best_model.predict(valid_x) + +# # 验证 +# acc = evaluate_model_accuracy(pred_y,valid_y) +# print(acc) +# # 画图保存 +# draw_picture(valid_y,pred_y,acc,'./pic/ssa','XGB') + +# # 加载模型 +# loaded_pipeline = load('./model/SSA_XGB.joblib') + +# # 使用模型预测 +# pred_y = loaded_pipeline.predict(valid_x) +# print(pred_y) + + + +"""GBDT""" pipeline = Pipeline([ - ('scaler', StandardScaler()), # 可选 - ('xgb', xgb.XGBRegressor(objective='reg:squarederror', - random_state=42, - n_jobs=-1)) -]) -# 定义超参数网格 + ('scaler', StandardScaler()), # 可替换为MinMaxScaler() + ('gbdt', GradientBoostingRegressor(random_state=42)) + ]) param_grid = { - 'xgb__n_estimators': np.arange(1,150), # 树的数量 - # 'xgb__max_depth': [3, 4, 5], # 树的最大深度 - # 'xgb__learning_rate': [0.01, 0.05, 0.1], # 学习率 - # 'xgb__subsample': [0.6, 0.8, 1.0], # 样本采样比例 - # 'xgb__colsample_bytree': [0.6, 0.8, 1.0], # 特征采样比例 - # 'xgb__gamma': [0, 0.1, 0.2], # 最小分裂损失 - # 'xgb__reg_alpha': [0, 0.1, 1], # L1正则化 - # 'xgb__reg_lambda': [0.1, 1, 10] # L2正则化 + 'gbdt__n_estimators': [20, 30, 40, 50, 60, 70, 80,100], # 树的数量 + 'gbdt__learning_rate': [0.01, 0.1, 0.2], # 学习率 + 'gbdt__max_depth': [3, 5, 7], # 树的最大深度 + 'gbdt__min_samples_split': [2, 5], # 分裂所需最小样本数 + 'gbdt__min_samples_leaf': [1, 2,3,4,5], # 叶节点最小样本数 + 'gbdt__max_features': ['sqrt', 'log2'], # 特征选择方式 } - -# 设置5折交叉验证 + # 设置K折交叉验证和网格搜索 kf = KFold(n_splits=5, shuffle=True, random_state=42) - -# 网格搜索（优化超参数） -grid_search = GridSearchCV( - pipeline, - param_grid, - cv=kf, - scoring='neg_mean_squared_error', # 最小化MSE - n_jobs=-1, # 使用所有CPU核心 - verbose=2 # 打印进度 -) +grid_search = GridSearchCV(pipeline, param_grid, cv=kf, scoring='neg_mean_squared_error', + n_jobs=-1, verbose=2) # 训练模型 -print("开始网格搜索优化...") +print("开始网格搜索...") grid_search.fit(train_x, train_y) + print("\n最佳参数组合:", grid_search.best_params_) -# 使用最佳模型进行预测 +#使用最佳模型进行预测 best_model = grid_search.best_estimator_ -# 保存模型 -dump(best_model, './model/SSA_XGB.joblib') +dump(best_model, './model/SSA_GDBT.joblib') pred_y = best_model.predict(valid_x) - -# 验证 acc = evaluate_model_accuracy(pred_y,valid_y) +print(pred_y) +print("\n") print(acc) -# 画图保存 -draw_picture(valid_y,pred_y,acc,'./pic/ssa','XGB') - -# 加载模型 -loaded_pipeline = load('./model/SSA_XGB.joblib') - -# 使用模型预测 +draw_picture(valid_y,pred_y,acc,'./pic/SSA','GDBT') +loaded_pipeline = load('./model/SSA_GDBT.joblib') pred_y = loaded_pipeline.predict(valid_x) print(pred_y) \ No newline at end of file diff --git a/meijitancailiao/model/SSA_GDBT.joblib b/meijitancailiao/model/SSA_GDBT.joblib new file mode 100644 index 0000000..89fb042 Binary files /dev/null and b/meijitancailiao/model/SSA_GDBT.joblib differ diff --git a/meijitancailiao/pic/SSA/GDBT.png b/meijitancailiao/pic/SSA/GDBT.png new file mode 100644 index 0000000..6e9bd9b Binary files /dev/null and b/meijitancailiao/pic/SSA/GDBT.png differ diff --git a/run copy.py b/run-0506.py similarity index 100% rename from run copy.py rename to run-0506.py diff --git a/run.py b/run.py index f9218f4..68e5d9e 100644 --- a/run.py +++ b/run.py @@ -1,13 +1,14 @@ import sys -from fastapi import FastAPI,File, UploadFile,Form +from fastapi import FastAPI,File, UploadFile,Form,Query from fastapi.staticfiles import StaticFiles +from fastapi import FastAPI, HTTPException +from fastapi.responses import FileResponse import sys import os import shutil from pydantic import BaseModel, validator from typing import List -from fastapi import FastAPI, HTTPException import asyncio import pandas as pd import numpy as np @@ -29,11 +30,52 @@ from work_util.logger import logger import joblib import mysql.connector import uuid +import json +import zipfile +from pathlib import Path version = f"{sys.version_info.major}.{sys.version_info.minor}" app = FastAPI() +param = params.ModelParams() +def get_roof_model(): + model_roof = roof_resUnetpamcarb() + model_path_roof = os.path.join(current_dir,'wudingpv/models/roof_best.pth') + model_dict_roof = torch.load(model_path_roof, map_location=torch.device('cpu')) + model_roof.load_state_dict(model_dict_roof['net']) + logger.info("屋顶识别权重加载成功") + model_roof.eval() + model_roof.cuda() + return model_roof + + +def get_pv_model(): + model_roof = roof_resUnetpamcarb() + model_path_roof = os.path.join(current_dir,'wudingpv/models/pv_best.pth') + model_dict_roof = torch.load(model_path_roof, map_location=torch.device('cpu')) + model_roof.load_state_dict(model_dict_roof['net']) + logger.info("屋顶识别权重加载成功") + model_roof.eval() + model_roof.cuda() + return model_roof + +# 模型实例 +dimaoshibie_SegFormer = segformer.SegFormer_Segmentation() +# 模型实例 +dimaoshibie_SegFormer = segformer.SegFormer_Segmentation() +roof_model = get_roof_model() +pv_model = get_pv_model() +ch4_model_flow = joblib.load(os.path.join(current_dir,'jiawanyuce/liuliang_model/xgb_model_liuliang.pkl')) +ch4_model_gas = joblib.load(os.path.join(current_dir,'jiawanyuce/qixiangnongdu_model/xgb_model_qixiangnongdu.pkl')) + +pvfd_param = guangfufadian_model_base.guangfufadian_Args() +pvfd_model_path = os.path.join(pvfd_param.checkpoints,'Crossformer_station08_il192_ol96_sl6_win2_fa10_dm256_nh4_el3_itr0/checkpoint.pth') # 修改为实际模型路径 +pvfd_model = guangfufadian_model_base.ModelInference(pvfd_model_path, pvfd_param) + +windfd_args = fenglifadian_model_base.fenglifadian_Args() +windfd_model_path = os.path.join(windfd_args.checkpoints,'Crossformer_Wind_farm_il192_ol12_sl6_win2_fa10_dm256_nh4_el3_itr0/checkpoint.pth') # 修改为实际模型路径 +windfd_model = fenglifadian_model_base.ModelInference(windfd_model_path, windfd_args) # 将 /root/app 目录挂载为静态文件 app.mount("/files", StaticFiles(directory="/root/app"), name="files") @@ -46,7 +88,7 @@ async def read_root(): # 获取数据界面资源信息 -@app.get("/api/data_source/show") +@app.get("/ai-station-api/data_source/show") async def get_data_source_info(): sql = "SELECT id,application_name, task_type, sample_name, img_url, time, download_url FROM data_samples" data = data_util.fetch_data(sql) @@ -64,7 +106,7 @@ async def get_data_source_info(): # 获取应用界面资源信息 -@app.get("/api/app_source/show") +@app.get("/ai-station-api/app_source/show") async def get_app_source_info(): sql = "SELECT id,application_name, task_type, sample_name, img_url, time FROM app_samples" data = data_util.fetch_data(sql) @@ -83,7 +125,7 @@ async def get_app_source_info(): # 获取煤基碳材料界面资源信息 -@app.get("/api/mjtcl_source/show") +@app.get("/ai-station-api/mjtcl_source/show") async def get_mjtcl_source_info(): sql = "SELECT id,application_name, task_type, sample_name, img_url, time FROM meijitancailiao_samples" data = data_util.fetch_data(sql) @@ -101,7 +143,7 @@ async def get_mjtcl_source_info(): # 获取煤热解界面资源信息 -@app.get("/api/mrj_source/show") +@app.get("/ai-station-api/mrj_source/show") async def get_mrj_source_info(): sql = "SELECT id,application_name, task_type, sample_name, img_url, time FROM meirejie_samples" data = data_util.fetch_data(sql) @@ -121,12 +163,12 @@ async def get_mrj_source_info(): """煤基碳材料输入特征说明接口列表 type = {zongkongtiji,zongbiaomianji,tancailiao,meiliqing,moniqi} """ -@app.get("/api/mjt_feature/show") +@app.get("/ai-station-api/mjt_feature/show") async def get_mjt_feature_info(type:str = None): # if type in ["tancailiao","meiliqing"]: # sql = "SELECT type, chinese_name, col_name, data_type, unit FROM meijitancailiao_features where use_type = %s;" # else: - sql = "SELECT type, chinese_name, col_name, data_type, unit, data_scale FROM meijitancailiao_features where use_type = %s;" + sql = "SELECT type, chinese_name, col_name, data_type, unit, data_scale,best_data FROM meijitancailiao_features where use_type = %s;" data = data_util.fetch_data_with_param(sql,(type,)) if data is None: return { @@ -144,7 +186,7 @@ async def get_mjt_feature_info(type:str = None): """煤热解材料输入特征说明接口列表 type = {tar,char,gas,water} """ -@app.get("/api/mrj_feature/show") +@app.get("/ai-station-api/mrj_feature/show") async def get_mrj_feature_info(type:str = None): sql = "SELECT type, chinese_name, col_name, data_type, unit, data_scale FROM meirejie_features where use_type = %s;" data = data_util.fetch_data_with_param(sql,(type,)) @@ -160,7 +202,7 @@ async def get_mrj_feature_info(type:str = None): "data":data} #========================================煤基碳材料 ================================== -@app.post("/api/mjt/predict") +@app.post("/ai-station-api/mjt/predict") async def mjt_models_predict(content: post_model.Zongbiaomianji): # 处理接收到的字典数据 meijiegou_test_content = pd.DataFrame([content.model_dump()]) @@ -185,7 +227,7 @@ async def mjt_models_predict(content: post_model.Zongbiaomianji): """ 模型综合分析 """ -@app.post("/api/mjt_models_ssa/predict") +@app.post("/ai-station-api/mjt_models_ssa/predict") async def mjt_models_predict_ssa(content: post_model.Zongbiaomianji): # 处理接收到的字典数据 meijiegou_test_content = pd.DataFrame([content.model_dump()]) @@ -209,7 +251,7 @@ async def mjt_models_predict_ssa(content: post_model.Zongbiaomianji): """ 上传文件 """ -@app.post("/api/document/upload") +@app.post("/ai-station-api/document/upload") async def upload_file(file: UploadFile = File(...),type: str = Form(...), ): if not file.filename.endswith('.csv'): raise HTTPException(status_code=400, detail="文件类型有误，必须是 CSV 文件") @@ -228,7 +270,7 @@ async def upload_file(file: UploadFile = File(...),type: str = Form(...), ): """ 批量预测接口 """ -@app.get("/api/mjt_multi_ssa/predict") +@app.get("/ai-station-api/mjt_multi_ssa/predict") async def mjt_multi_ssa_pred(model:str = None, path:str = None): data = model_deal.get_excel_ssa(model, path) return {"success":True, @@ -241,7 +283,7 @@ async def mjt_multi_ssa_pred(model:str = None, path:str = None): """ 模型综合分析 """ -@app.post("/api/mjt_models_tpv/predict") +@app.post("/ai-station-api/mjt_models_tpv/predict") async def mjt_models_predict_tpv(content: post_model.Zongbiaomianji): # 处理接收到的字典数据 meijiegou_test_content = pd.DataFrame([content.model_dump()]) @@ -263,7 +305,7 @@ async def mjt_models_predict_tpv(content: post_model.Zongbiaomianji): """ 批量预测接口 """ -@app.get("/api/mjt_multi_tpv/predict") +@app.get("/ai-station-api/mjt_multi_tpv/predict") async def mjt_multi_tpv_pred(model:str = None, path:str = None): data = model_deal.get_excel_tpv(model, path) return {"success":True, @@ -271,7 +313,7 @@ async def mjt_multi_tpv_pred(model:str = None, path:str = None): "data":data} #==========================煤基碳材料-煤炭材料应用细节接口================================== -@app.post("/api/mjt_models_meitan/predict") +@app.post("/ai-station-api/mjt_models_meitan/predict") async def mjt_models_predict_meitan(content: post_model.Meitan): # 处理接收到的字典数据 meijiegou_test_content = pd.DataFrame([content.model_dump()]) @@ -294,7 +336,7 @@ async def mjt_models_predict_meitan(content: post_model.Meitan): """ 批量预测接口 """ -@app.get("/api/mjt_multi_meitan/predict") +@app.get("/ai-station-api/mjt_multi_meitan/predict") async def mjt_multi_meitan_pred(model:str = None, path:str = None): data = model_deal.get_excel_meitan(model, path) return {"success":True, @@ -303,7 +345,7 @@ async def mjt_multi_meitan_pred(model:str = None, path:str = None): #==========================煤基碳材料-煤沥青应用细节接口================================== -@app.post("/api/mjt_models_meiliqing/predict") +@app.post("/ai-station-api/mjt_models_meiliqing/predict") async def mjt_models_predict_meiliqing(content: post_model.Meitan): # 处理接收到的字典数据 meijiegou_test_content = pd.DataFrame([content.model_dump()]) @@ -326,7 +368,7 @@ async def mjt_models_predict_meiliqing(content: post_model.Meitan): """ 批量预测接口 """ -@app.get("/api/mjt_multi_meiliqing/predict") +@app.get("/ai-station-api/mjt_multi_meiliqing/predict") async def mjt_multi_meiliqing_pred(model:str = None, path:str = None): data = model_deal.get_excel_meiliqing(model, path) return {"success":True, @@ -337,7 +379,8 @@ async def mjt_multi_meiliqing_pred(model:str = None, path:str = None): #=============================煤基炭材料-制备模拟器================================================ -@app.post("/api/mnq_model/analysis") +# 正向模拟器 +@app.post("/ai-station-api/mnq_model/analysis") async def mnq_model_predict(return_count: int, # 返回条数 model_choice: str, # 模型选择标记 form_data: post_model.FormData # 表单数据 @@ -347,21 +390,622 @@ async def mnq_model_predict(return_count: int, # 返回条数 pred_data = prepare_data.create_pred_data(params) result = model_deal.pred_func(model_choice,pred_data) sorted_result = result.sort_values(by=['SSA', 'TPV'], ascending=[False, False]) + upload_dir = os.path.join(current_dir,'tmp','moniqi', str(uuid.uuid4())) + if not os.path.exists(upload_dir): + os.makedirs(upload_dir ) + # 将文件保存到指定目录 + file_name = model_choice + "_moniqi.csv" + file_location = os.path.join(upload_dir , file_name) + sorted_result.to_csv(file_location, index = False) # 保留条数 if return_count is None: return {"success":True, "msg":"获取信息成功", - "data":{"result": sorted_result.to_json(orient='index')}} + "data":{"result": sorted_result.to_dict(orient='records'), "path":file_location}} + else: + data = sorted_result.iloc[:return_count] + # logger.info(data.to_dict(orient="records")) + return {"success":True, + "msg":"获取信息成功", + "data":{"result":data.to_dict(orient="records"), "path":file_location} + } + +# 反向模拟器 +@app.get("/ai-station-api/mnq_model/reverse_analysis") +async def mjt_multi_meiliqing_pred(model:str = None, path:str = None, type:int = 1, scale:float=None, num:int=5): + result = prepare_data.moniqi_data_prepare(model,path,type,scale,num) + if result['status'] == False: + return { + "success":False, + "msg":result['reason'], + "data":None + } + else: + return { + "success":True, + "msg":"获取信息成功", + "data":{"data": result['reason']} + } + + +#================================ 地貌识别 ==================================================== +""" +上传图片 , type = dimaoshibie +""" +@app.post("/ai-station-api/image/upload") +async def upload_image(file: UploadFile = File(...),type: str = Form(...), ): + if not data_util.allowed_file(file.filename): + return { + "success":False, + "msg":"图片必须以 '.jpg', '.jpeg', '.png', '.tif' 结尾", + "data":None + } + if file.size > param.MAX_FILE_SIZE: + return { + "success":False, + "msg":"图片大小不能大于100MB", + "data":None + } + upload_dir = os.path.join(current_dir,'tmp',type, str(uuid.uuid4())) + if not os.path.exists(upload_dir): + os.makedirs(upload_dir ) + # 将文件保存到指定目录 + file_location = os.path.join(upload_dir , file.filename) + with open(file_location, "wb") as buffer: + shutil.copyfileobj(file.file, buffer) + file_location = model_deal.get_pic_url(file_location) + return {"success":True, + "msg":"获取信息成功", + "data":{"location": file_location}} + +""" +图片地貌识别 +""" +@app.get("/ai-station-api/dmsb_image/analysis") +async def dmsb_image_analysis(path:str = None): + path = model_deal.get_pic_path(path) + result = model_deal.dimaoshibie_pic(dimaoshibie_SegFormer,path,param.dmsb_count,param.dmsb_name_classes) + if result['status'] == False: + return {"success":False, + "msg":result['reason'], + "data":None} + else: + path = result['reason'] + path= model_deal.get_pic_url(path[0]) # 是一个列表 + return {"success":True, + "msg":"获取信息成功", + "data":{"result": path}} + + +""" +图片地貌 - 分割结果计算 ,图片demo的是z17 +""" +@app.get("/ai-station-api/dmsb_image/calculate") +async def dmsb_image_calculate(path:str = None, scale:float = 1.92*1.92): + path = model_deal.get_pic_path(path) + result = model_deal.dimaoshibie_area(path,scale,param.dmsb_colors) + logger.info(result) + if result['status'] == True: + res = result['reason'] + translated_dict = {param.dmsb_type[key]: value for key, value in res.items()} + # json_data = json.dumps(translated_dict) + return {"success":True, + "msg":"获取信息成功", + "data":{"result": translated_dict}} + else: + return {"success":False, + "msg":result['reason'], + "data":None} + + +""" +下载 +""" +@app.get("/ai-station-api/download") +async def download_zip(path:str = None): + path = model_deal.get_pic_path(path) + zip_filename = "download.zip" + zip_filepath = Path(zip_filename) + dir_path = os.path.dirname(path) + # 创建 ZIP 文件 + try: + with zipfile.ZipFile(zip_filepath, 'w') as zip_file: + # 遍历文件夹中的所有文件 + for foldername, subfolders, filenames in os.walk(dir_path): + for filename in filenames: + file_path = os.path.join(foldername, filename) + # 将文件写入 ZIP 文件 + zip_file.write(file_path, os.path.relpath(file_path, dir_path)) + except Exception as e: + raise HTTPException(status_code=500, detail=f"Error creating zip file: {str(e)}") + + # 返回 ZIP 文件作为响应 + return FileResponse(zip_filepath, media_type='application/zip', filename=zip_filename) +#============================屋顶识别====================================== +""" +上传图片 type = roof +""" + +""" +图片分析 +""" +@app.get("/ai-station-api/roof_image/analysis") +async def roof_image_analysis(path:str = None): + path = model_deal.get_pic_path(path) + result = model_deal.roof_pic(roof_model,path,param.wdpv_palette) + + if result['status'] == False: + return {"success":False, + "msg":result['reason'], + "data":None} + else: + path = result['reason'] + path= model_deal.get_pic_url(path[0]) + return {"success":True, + "msg":"获取信息成功", + "data":{"result": path}} + + +""" +屋顶面积 图 - 分割结果计算 ,图片demo的是z18 +""" +@app.get("/ai-station-api/roof_image/calculate") +async def roof_image_calculate(path:str = None, scale:float = 0.92*0.92): + path = model_deal.get_pic_path(path) + result = model_deal.roof_area(path,scale,param.wdpv_colors) + logger.info(result) + if result['status'] == True: + res = result['reason'] + translated_dict = {param.wd_type[key]: value for key, value in res.items()} + del translated_dict['其他'] + return {"success":True, + "msg":"获取信息成功", + "data":{"result": translated_dict}} + else: + return {"success":False, + "msg":result['reason'], + "data":None} + + + +#============================光伏识别====================================== +""" +上传图片 type = pv +""" +""" +图片分析 +""" +@app.get("/ai-station-api/pv_image/analysis") +async def pv_image_analysis(path:str = None): + path = model_deal.get_pic_path(path) + result = model_deal.roof_pic(pv_model,path,param.wdpv_palette) + if result['status'] == False: + return {"success":False, + "msg":result['reason'], + "data":None} + else: + path = result['reason'] + path= model_deal.get_pic_url(path[0]) + return {"success":True, + "msg":"获取信息成功", + "data":{"result": path}} + +""" +光伏面积 图 - 分割结果计算 ,图片demo的是z18 +""" +@app.get("/ai-station-api/pv_image/calculate") +async def pv_image_calculate(path:str = None, scale:float = 0.92*0.92): + path = model_deal.get_pic_path(path) + result = model_deal.roof_area(path,scale,param.wdpv_colors) + logger.info(result) + if result['status'] == True: + res = result['reason'] + translated_dict = {param.pv_type[key]: value for key, value in res.items()} + del translated_dict['其他'] + return {"success":True, + "msg":"获取信息成功", + "data":{"result": translated_dict}} + else: + return {"success":False, + "msg":result['reason'], + "data":None} + +#============================屋顶光伏识别====================================== +""" +上传图片 type = roofpv +""" + +""" +图片分析 +""" +@app.get("/ai-station-api/roofpv_image/analysis") +async def roofpv_image_analysis(path:str = None): + path = model_deal.get_pic_path(path) + result = model_deal.roofpv_pic(roof_model,pv_model,path,param.wdpv_palette) + if result['status'] == False: + return {"success":False, + "msg":result['reason'], + "data":None} + else: + file_list = result['reason'] + final_path = prepare_data.merge_binary(file_list) + final_path = model_deal.get_pic_url(final_path) + return {"success":True, + "msg":"获取信息成功", + "data":{"result": final_path}} + + +""" +光伏面积 图 - 分割结果计算 ,图片demo的是z18 +""" +@app.get("/ai-station-api/roofpv_image/calculate") +async def roofpv_image_calculate(path:str = None, scale:float = 0.92*0.92): + path = model_deal.get_pic_path(path) + result = model_deal.roof_area_roofpv(path,scale,param.wdpv_colors) + logger.info(result) + if result['status'] == True: + res = result['reason'] + translated_dict = {param.wdpv_type[key]: value for key, value in res.items()} + del translated_dict['其他'] + return {"success":True, + "msg":"获取信息成功", + "data":{"result": translated_dict}} + else: + return {"success":False, + "msg":result['reason'], + "data":None} + +# ====================================时序预测类============================================ + +# ====================================甲烷预测============================================== + +""" +返回显示列表 +""" +@app.get("/ai-station-api/ch4_features/show") +async def get_ch4_features_info(): + sql = "SELECT chinese_name, col_name,type,data_type,unit,data_sample FROM ch4_features;" + data = data_util.fetch_data(sql) + if data is None: + return { + "success":False, + "msg":"获取信息列表失败", + "data":None + } else: return {"success":True, "msg":"获取信息成功", - "data":{"result": sorted_result.head(return_count).to_json(orient='index')}} + "data":data} - -#=================================================================================================== - -# @app.post("/api/items/") + +@app.get("/ai-station-api/ch4_pic_table/show") +async def get_ch4_pic_table_info(): + sql = "SELECT chinese_name, col_name FROM ch4_features where col_name != 'date_time';" + data = data_util.fetch_data(sql) + if data is None: + return { + "success":False, + "msg":"获取信息列表失败", + "data":None + } + else: + return {"success":True, + "msg":"获取信息成功", + "data":data} + + +""" +针对列表，返回数据，供画图 +""" +@app.get("/ai-station-api/ch4_feature_pic/select") +async def get_ch4_feature_pic_select(type:str = None, path:str =None): + if type == 'date_time': + return { + "success":False, + "msg":"时间列无法展示，请重新选择", + "data":None + } + data = prepare_data.show_data_jiawanyuce(type,path) + if data is None: + return { + "success":False, + "msg":"获取信息列表失败", + "data":None + } + else: + return {"success":True, + "msg":"获取信息成功", + "data":data} + +""" +上传数据分页展示 +""" +@app.get("/ai-station-api/ch4_data/show") +async def get_ch4_data(path:str = None, page: int = Query(1, ge=1), page_size: int = Query(10, ge=1)): + data = prepare_data.get_jiawanyuce_data(path) + total_items = len(data) # 获取总条数 + total_pages = (total_items + page_size - 1) // page_size # 计算总页数 + # 检查请求的页码是否超出范围 + if page > total_pages: + raise HTTPException(status_code=404, detail="Page not found") + # 计算起始和结束索引 + start_idx = (page - 1) * page_size + end_idx = start_idx + page_size + + # 获取当前页的数据 + items = data.iloc[start_idx:end_idx].to_dict(orient="records") + + # 返回数据，包括总条数、总页数和当前页的数据 + return { + "success":True, + "msg":"获取信息成功", + "data":{ + "total_items": total_items, + "total_pages": total_pages, + "current_page": page, + "page_size": page_size, + "items": items + } + } + +""" +csv上传 +""" +# @app.post("/ai-station-api/document/upload") type = "ch4" + +""" +预测, type +""" +@app.get("/ai-station-api/ch4_data/predict") +async def get_ch4_predict(path:str=None,start_time:str=None, end_time:str = None,type:int=1,is_show:bool=True): + data = model_deal.start_predict_endpoint(ch4_model_flow,ch4_model_gas,path,start_time,end_time,type,is_show) + if data['status'] ==False: + return { + "success":False, + "msg":data['reason'], + "data":None + } + else: + return {"success":True, + "msg":"获取信息成功", + "data":data['reason']} + + +#========================================光伏预测========================================================== +""" +返回显示列表 +""" +@app.get("/ai-station-api/pvelectric_features/show") +async def get_pvelectric_features_info(): + sql = "SELECT chinese_name, col_name,type,data_type,unit,data_sample FROM pv_electric_features;" + data = data_util.fetch_data(sql) + if data is None: + return { + "success":False, + "msg":"获取信息列表失败", + "data":None + } + else: + return {"success":True, + "msg":"获取信息成功", + "data":data} + + +@app.get("/ai-station-api/pvelectric_pic_table/show") +async def get_pvelectric_pic_table_info(): + sql = "SELECT chinese_name, col_name FROM pv_electric_features where col_name != 'date_time';" + data = data_util.fetch_data(sql) + if data is None: + return { + "success":False, + "msg":"获取信息列表失败", + "data":None + } + else: + return {"success":True, + "msg":"获取信息成功", + "data":data} + + +""" +针对列表，返回数据，供画图 +""" +@app.get("/ai-station-api/pvelectric_feature_pic/select") +async def get_pvelectric_feature_pic_select(type:str = None, path:str =None): + if type == 'date_time': + return { + "success":False, + "msg":"时间列无法展示，请重新选择", + "data":None + } + data = prepare_data.show_data_pvfd(path,type) + if data is None: + return { + "success":False, + "msg":"获取信息列表失败", + "data":None + } + else: + return {"success":True, + "msg":"获取信息成功", + "data":data} + + +""" +上传数据分页展示 +""" +@app.get("/ai-station-api/pvelectric_data/show") +async def get_pvelectric_data(path:str = None, page: int = Query(1, ge=1), page_size: int = Query(10, ge=1)): + data = prepare_data.show_testdata_pvfd(path) + total_items = len(data) # 获取总条数 + total_pages = (total_items + page_size - 1) // page_size # 计算总页数 + # 检查请求的页码是否超出范围 + if page > total_pages: + raise HTTPException(status_code=404, detail="Page not found") + # 计算起始和结束索引 + start_idx = (page - 1) * page_size + end_idx = start_idx + page_size + + # 获取当前页的数据 + items = data.iloc[start_idx:end_idx].to_dict(orient="records") + + # 返回数据，包括总条数、总页数和当前页的数据 + return { + "success":True, + "msg":"获取信息成功", + "data":{ + "total_items": total_items, + "total_pages": total_pages, + "current_page": page, + "page_size": page_size, + "items": items + } + } + +""" +csv上传 +""" +# @app.post("/ai-station-api/document/upload") type = "pvelectric" + +""" +预测, type +""" +@app.get("/ai-station-api/pvelectric_data/predict") +async def get_pvelectri_predict(path:str=None,start_time:str=None, end_time:str = None,is_show:bool=True): + data = model_deal.start_pvelectric_predict_endpoint(pvfd_model,path,start_time,end_time,is_show) + if data['status'] ==False: + return { + "success":False, + "msg":data['reason'], + "data":None + } + else: + return {"success":True, + "msg":"获取信息成功", + "data":data['reason']} + + +#========================================风力发电预测========================================================== +""" +返回显示列表 +""" +@app.get("/ai-station-api/wind_electric_features/show") +async def get_wind_electric_features_info(): + sql = "SELECT chinese_name, col_name,type,data_type,unit,data_sample FROM wind_electric_features;" + data = data_util.fetch_data(sql) + if data is None: + return { + "success":False, + "msg":"获取信息列表失败", + "data":None + } + else: + return {"success":True, + "msg":"获取信息成功", + "data":data} + + +@app.get("/ai-station-api/wind_electric_pic_table/show") +async def get_wind_electric_pic_table_info(): + sql = "SELECT chinese_name, col_name FROM wind_electric_features where col_name != 'date';" + data = data_util.fetch_data(sql) + if data is None: + return { + "success":False, + "msg":"获取信息列表失败", + "data":None + } + else: + return {"success":True, + "msg":"获取信息成功", + "data":data} + + +""" +针对列表，返回数据，供画图 +""" +@app.get("/ai-station-api/wind_electric_feature_pic/select") +async def get_wind_electric_feature_pic_select(type:str = None, path:str =None): + if type == 'date': + return { + "success":False, + "msg":"时间列无法展示，请重新选择", + "data":None + } + data = prepare_data.show_data_windfd(path,type) + if data is None: + return { + "success":False, + "msg":"获取信息列表失败", + "data":None + } + else: + return {"success":True, + "msg":"获取信息成功", + "data":data} + + +""" +上传数据分页展示 +""" +@app.get("/ai-station-api/wind_electric_data/show") +async def get_wind_electric_data(path:str = None, page: int = Query(1, ge=1), page_size: int = Query(10, ge=1)): + data = prepare_data.show_testdata_windfd(path) + total_items = len(data) # 获取总条数 + total_pages = (total_items + page_size - 1) // page_size # 计算总页数 + # 检查请求的页码是否超出范围 + if page > total_pages: + raise HTTPException(status_code=404, detail="Page not found") + # 计算起始和结束索引 + start_idx = (page - 1) * page_size + end_idx = start_idx + page_size + + # 获取当前页的数据 + items = data.iloc[start_idx:end_idx].to_dict(orient="records") + + # 返回数据，包括总条数、总页数和当前页的数据 + return { + "success":True, + "msg":"获取信息成功", + "data":{ + "total_items": total_items, + "total_pages": total_pages, + "current_page": page, + "page_size": page_size, + "items": items + } + } + +""" +csv上传 +""" +# @app.post("/ai-station-api/document/upload") type = "wind_electric" + +""" +预测, type +""" +@app.get("/ai-station-api/wind_electric_data/predict") +async def get_wind_electri_predict(path:str=None,start_time:str=None, end_time:str = None,is_show:bool=True): + data = model_deal.start_wind_electric_predict_endpoint(windfd_model,path,start_time,end_time,is_show) + if data['status'] ==False: + return { + "success":False, + "msg":data['reason'], + "data":None + } + else: + return {"success":True, + "msg":"获取信息成功", + "data":data['reason']} + + + + + + + +# @app.post("/ai-station-api/items/") # async def create_item(item: post_model.Zongbiaomianji): # try: # data = { diff --git a/work_util/__pycache__/__init__.cpython-310.pyc b/work_util/__pycache__/__init__.cpython-310.pyc index 128c4b1..f8ab42c 100644 Binary files a/work_util/__pycache__/__init__.cpython-310.pyc and b/work_util/__pycache__/__init__.cpython-310.pyc differ diff --git a/work_util/__pycache__/__init__.cpython-39.pyc b/work_util/__pycache__/__init__.cpython-39.pyc index acf0e47..dd162d2 100644 Binary files a/work_util/__pycache__/__init__.cpython-39.pyc and b/work_util/__pycache__/__init__.cpython-39.pyc differ diff --git a/work_util/__pycache__/data_util.cpython-310.pyc b/work_util/__pycache__/data_util.cpython-310.pyc index 6ac1e47..b268b2b 100644 Binary files a/work_util/__pycache__/data_util.cpython-310.pyc and b/work_util/__pycache__/data_util.cpython-310.pyc differ diff --git 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edd324a..a10b470 100644 --- a/work_util/data_util.py +++ b/work_util/data_util.py @@ -123,116 +123,121 @@ def generate_json_meirejie_source(data): result.append(app_data) return {'result': result} +# 图片 +def allowed_file(filename: str) -> bool: + """检查图片扩展名是否被允许""" + param = params.ModelParams() + return any(filename.endswith(ext) for ext in param.ALLOWED_EXTENSIONS) -data_info ={ - "result": [ - { - "name":"应用数据", - "list": - [ - { - "name_col": "回归预测任务", - "list" : [ - { - "name": "煤基功能碳材料样例数据", - "img": "http://124.16.151.196:13432/files/imgs/data/meijitancailiao.png", - "time": "2025-04-28", - "download":"http://124.16.151.196:13432/files/datas/meijitancailiao.zip" - }, - { - "name": "煤热解样例数据", - "img": "http://124.16.151.196:13432/files/imgs/data/meirejie.png", - "time": "2025-04-28", - "download":"http://124.16.151.196:13432/files/datas/meirejie.zip" - } - ]}, - { - "name_col": "时序预测任务", - "list" : [ - { - "name": "光伏出力预测样例数据", - "img": "http://124.16.151.196:13432/files/imgs/data/pv_forecast.png", - "time": "2025-04-28", - "download":"http://124.16.151.196:13432/files/datas/pv_forecast.zip" - }, - { - "name": "生物制甲烷产量预测样例数据", - "img": "http://124.16.151.196:13432/files/imgs/data/jiwanyuce.png", - "time": "2025-04-28", - "download":"http://124.16.151.196:13432/files/datas/jiwanyuce.zip" - }, - { - "name": "风力发电预测样例数据", - "img": "http://124.16.151.196:13432/files/imgs/data/wind_power_forecasting.png", - "time": "2025-04-28", - "download":"http://124.16.151.196:13432/files/datas/wind_power_forecasting.zip" - } - ]}, +# data_info ={ +# "result": [ +# { +# "name":"应用数据", +# "list": +# [ +# { +# "name_col": "回归预测任务", +# "list" : [ +# { +# "name": "煤基功能碳材料样例数据", +# "img": "http://124.16.151.196:13432/files/imgs/data/meijitancailiao.png", +# "time": "2025-04-28", +# "download":"http://124.16.151.196:13432/files/datas/meijitancailiao.zip" +# }, +# { +# "name": "煤热解样例数据", +# "img": "http://124.16.151.196:13432/files/imgs/data/meirejie.png", +# "time": "2025-04-28", +# "download":"http://124.16.151.196:13432/files/datas/meirejie.zip" +# } +# ]}, +# { +# "name_col": "时序预测任务", +# "list" : [ +# { +# "name": "光伏出力预测样例数据", +# "img": "http://124.16.151.196:13432/files/imgs/data/pv_forecast.png", +# "time": "2025-04-28", +# "download":"http://124.16.151.196:13432/files/datas/pv_forecast.zip" +# }, +# { +# "name": "生物制甲烷产量预测样例数据", +# "img": "http://124.16.151.196:13432/files/imgs/data/jiwanyuce.png", +# "time": "2025-04-28", +# "download":"http://124.16.151.196:13432/files/datas/jiwanyuce.zip" +# }, +# { +# "name": "风力发电预测样例数据", +# "img": "http://124.16.151.196:13432/files/imgs/data/wind_power_forecasting.png", +# "time": "2025-04-28", +# "download":"http://124.16.151.196:13432/files/datas/wind_power_forecasting.zip" +# } +# ]}, - { - "name_col": "计算机视觉任务", - "list" : [ - { - "name": "光伏识别样例数据", - "img": "http://124.16.151.196:13432/files/imgs/data/pv.png", - "time": "2025-04-28", - "download":"http://124.16.151.196:13432/files/datas/pv.zip" - }, - { - "name": "屋顶识别样例数据", - "img": "http://124.16.151.196:13432/files/imgs/data/roof.png", - "time": "2025-04-28", - "download":"http://124.16.151.196:13432/files/datas/roof.zip" - }, - { - "name": "屋顶光伏识别样例数据", - "img": "http://124.16.151.196:13432/files/imgs/data/roofpv.png", - "time": "2025-04-28", - "download":"http://124.16.151.196:13432/files/datas/roofpv.zip" - }, - { - "name": "地貌语义识别样例数据", - "img": "http://124.16.151.196:13432/files/imgs/data/dimaoshibie.png", - "time": "2025-04-28", - "download":"http://124.16.151.196:13432/files/datas/dimaoshibie.zip" - }, - ]}, +# { +# "name_col": "计算机视觉任务", +# "list" : [ +# { +# "name": "光伏识别样例数据", +# "img": "http://124.16.151.196:13432/files/imgs/data/pv.png", +# "time": "2025-04-28", +# "download":"http://124.16.151.196:13432/files/datas/pv.zip" +# }, +# { +# "name": "屋顶识别样例数据", +# "img": "http://124.16.151.196:13432/files/imgs/data/roof.png", +# "time": "2025-04-28", +# "download":"http://124.16.151.196:13432/files/datas/roof.zip" +# }, +# { +# "name": "屋顶光伏识别样例数据", +# "img": "http://124.16.151.196:13432/files/imgs/data/roofpv.png", +# "time": "2025-04-28", +# "download":"http://124.16.151.196:13432/files/datas/roofpv.zip" +# }, +# { +# "name": "地貌语义识别样例数据", +# "img": "http://124.16.151.196:13432/files/imgs/data/dimaoshibie.png", +# "time": "2025-04-28", +# "download":"http://124.16.151.196:13432/files/datas/dimaoshibie.zip" +# }, +# ]}, - { - "name_col": "强化学习任务", - "list" : [ - ]}, - { - "name_col": "自然语言处理任务", - "list" : [ - ]}, - { - "name_col": "算法标注工具", - "list" : [ - { - "name": "SAM样例数据集", - "img": "http://124.16.151.196:13432/files/imgs/data/sam.png", - "time": "2025-04-28", - "download":"http://124.16.151.196:13432/files/datas/sam.zip" - } - ]} - ] - }, - { - "name": "教学案例数据", - "list" : [ - {"name_col": "chapter1", - "list" : [] - }, - {"name_col": "chapter2", - "list" : [] - }, - {"name_col": "chapter3", - "list" : [] - }, - ] - } - ] - } +# { +# "name_col": "强化学习任务", +# "list" : [ +# ]}, +# { +# "name_col": "自然语言处理任务", +# "list" : [ +# ]}, +# { +# "name_col": "算法标注工具", +# "list" : [ +# { +# "name": "SAM样例数据集", +# "img": "http://124.16.151.196:13432/files/imgs/data/sam.png", +# "time": "2025-04-28", +# "download":"http://124.16.151.196:13432/files/datas/sam.zip" +# } +# ]} +# ] +# }, +# { +# "name": "教学案例数据", +# "list" : [ +# {"name_col": "chapter1", +# "list" : [] +# }, +# {"name_col": "chapter2", +# "list" : [] +# }, +# {"name_col": "chapter3", +# "list" : [] +# }, +# ] +# } +# ] +# } -name_list = ["应用数据","'教学案例数据'"] \ No newline at end of file +# name_list = ["应用数据","'教学案例数据'"] \ No newline at end of file diff --git a/work_util/model_deal.py b/work_util/model_deal.py index 90c9638..684cfc1 100644 --- a/work_util/model_deal.py +++ b/work_util/model_deal.py @@ -69,7 +69,11 @@ def dimaoshibie_cal(model,path,count=None, name_classes=None): def dimaoshibie_pic(model,path,count=None, name_classes=None): single = prepare_data.cut_big_image(path) if single == False: - return {"status":False, "reason":"图片大小不合规，请检查图片大小是否符合尺寸大于512*512，文件大小小于10MB"} + return { + "success": False, + "msg": "图片大小不合规，请检查图片大小是否符合尺寸大于512*512，文件大小小于10MB", + "data": None + } else: file_directory = os.path.dirname(path) folder_path = os.path.join(file_directory,'ori') @@ -87,22 +91,34 @@ def dimaoshibie_pic(model,path,count=None, name_classes=None): r_image.save(final_path) # 替换为你想保存的路径 # 图片合并 - sataus = prepare_data.merge_pic_binary(path,['binary']) - if sataus['status'] == False: - return {"status":False, "reason":"图片合并失败"} - target_path = sataus['path'] + status = prepare_data.merge_pic_binary(path,['binary']) + if status['status'] == False: + return { + "success": False, + "msg": "图片合并失败，请稍后再试", + "data": None + } + target_path = status['path'] # 临时文件删除 tmp = prepare_data.delete_folder(folder_path) if tmp==False: - return {"status":False, "reason":"临时文件删除失败"} + return { + "success": False, + "msg": "临时文件删除失败", + "data": None + } tmp = prepare_data.delete_folder(output_floder_path) if tmp==False: - return {"status":False, "reason":"临时文件删除失败"} - + return { + "success": False, + "msg": "临时文件删除失败", + "data": None + } # 返回图片地址 return {"status":True, "reason":target_path} - + + """ 计算每一类地貌的面积大小 1、读取前面的图片分割结果 @@ -131,11 +147,14 @@ def dimaoshibie_area(path,scale,colors=None): for k,v in color_count.items(): color_count[k] = v * scale result = {color: color_count[color] for color in colors} - return {"status": True, "reason":"","result": result} + return {"status": True, "reason": result} except Exception as e: return {"status": False, "reason": str(e)} + + + """ 1. 图片分割成规定大小 2. 遍历分割图片，进行预测 @@ -185,10 +204,10 @@ def roof_pic(net,path,palette): # 图片合并 - sataus = prepare_data.merge_pic_binary(path,['binary']) - if sataus['status'] == False: + status = prepare_data.merge_pic_binary(path,['binary']) + if status['status'] == False: return {"status":False, "reason":"图片合并失败"} - target_path = sataus['path'] + target_path = status['path'] # 临时文件删除 tmp = prepare_data.delete_folder(folder_path) @@ -230,7 +249,7 @@ def roof_area(path,scale,colors=None): for k,v in color_count.items(): color_count[k] = v * scale result = {color: color_count[color] for color in colors} - return {"status": True, "reason":"","result": result} + return {"status": True, "reason":result} except Exception as e: return {"status": False, "reason": str(e)} @@ -238,7 +257,6 @@ def roof_area_roofpv(path,scale,colors=None): file_directory = os.path.dirname(path) file_name = os.path.basename(path) file_path = os.path.join(file_directory,"merge_roofpv_binary_"+file_name) - print(file_path) if not os.path.exists(file_path): return {"status": False, "reason": "没有找到对应文件，请先进行图像分割", "result":""} @@ -257,7 +275,7 @@ def roof_area_roofpv(path,scale,colors=None): for k,v in color_count.items(): color_count[k] = v * scale result = {color: color_count[color] for color in colors} - return {"status": True, "reason":"","result": result} + return {"status": True, "reason":result} except Exception as e: return {"status": False, "reason": str(e)} @@ -348,24 +366,30 @@ def roofpv_pic(net_roof,net_pv,path,palette): return {"status":True, "reason":target_path} - +# 甲烷预测 def start_predict_endpoint(ch4_model_flow,ch4_model_gas,data_path, start_index, end_index, type, is_show): try: data = pd.read_csv(data_path) data['date_time'] = pd.to_datetime(data['date_time']) max_date = data['date_time'].max() + min_date = data['date_time'].min() + start_index = pd.to_datetime(start_index) + end_index = pd.to_datetime(end_index) + if max_date < end_index : + return {"reason": "结束日期填写错误，超过上传数据最大日期","status": False} + if min_date > start_index : + return {"reason": "开始日期填写错误，小于上传数据最小日期","status": False} end_index_dt = pd.to_datetime(end_index) end_index_plus_one_hour = end_index_dt + pd.Timedelta(hours=1) filtered_data = data[(data['date_time'] >= start_index) & (data['date_time'] <= end_index)] - if is_show: if len(filtered_data) < 96: - return {"info": "截取步长应该超过96个步长","result": None} + return {"reason": "日期填写错误，截取步长应该超过96个步长","status": False} if max_date < end_index_plus_one_hour: - return {"info": "显示真实值需要保留最终后四节点作为展示信息，请调整结束日期","result": None} + return {"reason": "选择显示真实值，需要保留最终后四节点作为展示信息，请调整结束日期","status": False} else: if len(filtered_data) < 96: - return {"info": "上传信息长度应大于96","result": None} + return {"reason": "上传文件中有效信息长度应大于96","status": False} train_data = prepare_data.get_pred_data(data,start_index,end_index) del train_data['date_time'] @@ -406,12 +430,139 @@ def start_predict_endpoint(ch4_model_flow,ch4_model_gas,data_path, start_index, }) history = pd.concat([history, new_data], ignore_index=True) - return {"info": "","result":history.to_json(orient='records', lines=True)} + return {"status": True,"reason":history.to_dict(orient='records')} except Exception as e: - return{"info": str(e),"result":None} + return{"reason": str(e),"status":False} +# 光伏出力预测 - 预测长度96 +def start_pvelectric_predict_endpoint(pvfd_model,data_path, start_index, end_index, is_show): + try: + data = pd.read_csv(data_path) + data['date_time'] = pd.to_datetime(data['date_time']) + max_date = data['date_time'].max() + min_date = data['date_time'].min() + start_index = pd.to_datetime(start_index) + end_index = pd.to_datetime(end_index) + if max_date < end_index : + return {"reason": "结束日期填写错误，超过上传数据最大日期","status": False} + if min_date > start_index : + return {"reason": "开始日期填写错误，小于上传数据最小日期","status": False} + end_index_dt = pd.to_datetime(end_index) + end_index_plus_one_day = end_index_dt + pd.Timedelta(hours=24) + filtered_data = data[(data['date_time'] >= start_index) & (data['date_time'] <= end_index)] + if is_show: + if len(filtered_data) < 192: + return {"reason": "日期填写错误，截取步长应该超过192个步长","status": False} + if max_date < end_index_plus_one_day: + return {"reason": "选择显示真实值，需要保留最终一天数据96点作为展示信息，请调整结束日期","status": False} + else: + if len(filtered_data) < 192: + return {"reason": "上传文件中有效信息长度应大于192","status": False} + + predictions = pvfd_model.run_inference(filtered_data) + if predictions['status'] == True: + predictions_value = np.array(predictions['reason']).flatten() + predictions_value = [max(0, x) for x in predictions_value] + target = "power" + if is_show: + history = data[(data['date_time'] >= start_index) & (data['date_time'] <= end_index_plus_one_day)] + cols = ['date_time'] + cols.append(target) + history = history[cols] + history.rename(columns={target: 'true_data'}, inplace=True) + history['pred_data'] = 0 + total_rows = len(history) + history.reset_index(drop=True, inplace=True) + history.loc[total_rows - 96:total_rows - 1, 'pred_data'] = predictions_value + else: + history = data[(data['date_time'] >= start_index) & (data['date_time'] <= end_index)] + history.reset_index(drop=True, inplace=True) + cols = ['date_time'] + cols.append(target) + history = history[cols] + history.rename(columns={target: 'true_data'}, inplace=True) + history['pred_data'] = 0 + last_date = history['date_time'].iloc[-1] + # 创建新的日期和对应的值 + new_dates = [last_date + pd.Timedelta(minutes=15 * (i + 1)) for i in range(96)] + new_data = pd.DataFrame({ + 'date_time': new_dates, + 'true_data':[0]*96, + 'pred_data': predictions_value + }) + history = pd.concat([history, new_data], ignore_index=True) + + return {"status": True,"reason":history.to_dict(orient='records')} + except Exception as e: + return{"reason": str(e),"status":False} + + + +# 风力发电出力预测 - 预测长度12 +def start_wind_electric_predict_endpoint(windfd_model,data_path, start_index, end_index, is_show): + # try: + data = pd.read_csv(data_path) + data['date'] = pd.to_datetime(data['date']) + max_date = data['date'].max() + min_date = data['date'].min() + start_index = pd.to_datetime(start_index) + end_index = pd.to_datetime(end_index) + if max_date < end_index : + return {"reason": "结束日期填写错误，超过上传数据最大日期","status": False} + if min_date > start_index : + return {"reason": "开始日期填写错误，小于上传数据最小日期","status": False} + end_index_dt = pd.to_datetime(end_index) + end_index_plus_one_day = end_index_dt + pd.Timedelta(hours=3) + filtered_data = data[(data['date'] >= start_index) & (data['date'] <= end_index)] + if is_show: + if len(filtered_data) < 192: + return {"reason": "日期填写错误，截取步长应该超过192个步长","status": False} + if max_date < end_index_plus_one_day: + return {"reason": "选择显示真实值，需要保留最终一天数据12个点作为展示信息，请调整结束日期","status": False} + else: + if len(filtered_data) < 192: + return {"reason": "上传文件中有效信息长度应大于192","status": False} + + predictions = windfd_model.run_inference(filtered_data) + if predictions['status'] == True: + predictions_value = np.array(predictions['reason']).flatten() + predictions_value = [max(0, x) for x in predictions_value] + target = "Power" + if is_show: + history = data[(data['date'] >= start_index) & (data['date'] <= end_index_plus_one_day)] + cols = ['date'] + cols.append(target) + history = history[cols] + history.rename(columns={target: 'true_data'}, inplace=True) + history['pred_data'] = 0 + total_rows = len(history) + history.reset_index(drop=True, inplace=True) + history.loc[total_rows - 12:total_rows - 1, 'pred_data'] = predictions_value + else: + history = data[(data['date'] >= start_index) & (data['date'] <= end_index)] + history.reset_index(drop=True, inplace=True) + cols = ['date'] + cols.append(target) + history = history[cols] + history.rename(columns={target: 'true_data'}, inplace=True) + history['pred_data'] = 0 + last_date = history['date'].iloc[-1] + # 创建新的日期和对应的值 + new_dates = [last_date + pd.Timedelta(minutes=15 * (i + 1)) for i in range(12)] + new_data = pd.DataFrame({ + 'date': new_dates, + 'true_data':[0]*12, + 'pred_data': predictions_value + }) + history = pd.concat([history, new_data], ignore_index=True) + + return {"status": True,"reason":history.to_dict(orient='records')} + # except Exception as e: + # return{"reason": str(e),"status":False} + + def pred_single_tar(test_content): tar_pred = [] current_directory = os.getcwd() @@ -660,4 +811,27 @@ def pred_func(func_name, pred_data): result = pd.concat([pred_data,result], axis=1) - return result \ No newline at end of file + return result + + + +def get_pic_url(path): + # 1. 移除本地根目录 `/root/app` + logger.info(path) + relative_path = path.replace("/root/app", "", 1) + # 2. 拼接服务器地址和端口 + server = "http://124.16.151.196:13432" + # 3. 组合成完整 URL + url = f"{server}/files{relative_path}" + return url + +def get_pic_path(url): + # 1. 移除协议和服务器地址 + path_without_server = url.replace("http://124.16.151.196:13432", "") + + # 2. 移除 /files 前缀 + relative_path = path_without_server.replace("/files", "", 1) + + # 3. 添加本地根目录 + local_path = f"/root/app{relative_path}" + return local_path \ No newline at end of file diff --git a/work_util/params.py b/work_util/params.py index 42ed682..0cea832 100644 --- a/work_util/params.py +++ b/work_util/params.py @@ -47,6 +47,20 @@ class ModelParams(): ] + dmsb_type = { + (0, 0, 0) : "背景", # Background (黑色) + (252, 250, 205) : "农田", # Cropland (淡黄色) + (0, 123, 79): "森林", # Forest (深绿色) + (157, 221, 106): "草地", # Grass (浅绿色) + (77, 208, 159): "灌木", # Shrub (浅蓝绿色) + (111, 208, 242): "湿地", # Wetland (浅蓝色) + (10, 78, 151): "水体", # Water (深蓝色) + (92, 106, 55): "苔原", # Tundra (土黄色) + (155, 36, 22): "建筑", # Impervious surface (红色) + (205, 205, 205): "裸地", # Bareland (灰色) + (211, 242, 255): "冰雪" # Ice/snow (浅天蓝色) + } + # 前缀 wdpv 屋顶光伏 wdpv_palette = [0, 0, 0, 255, 0, 0, 0, 255, 0] @@ -55,6 +69,23 @@ class ModelParams(): (255, 0, 0), # 红色 (0, 255, 0)] # 绿色 + wd_type = { + (0, 0, 0):"背景", + (255, 0, 0): "屋顶", + (0, 255, 0):'其他' + } + + pv_type = { + (0, 0, 0):"背景", + (255, 0, 0): "光伏", + (0, 255, 0):'其他' + } + + wdpv_type = { + (0, 0, 0):"背景", + (255, 0, 0): "屋顶光伏", + (0, 255, 0):'其他' + } # 煤热解 meirejie_model_dict = { @@ -209,14 +240,14 @@ class ModelParams(): columns_meitan = ['XGBoost', 'Linear Regression', 'Ridge Regression', 'Gaussian Process Regression', 'ElasticNet Regression', 'Gradient Boosting Regression', 'Support Vector Regression', 'Decision Tree Regression', 'Random Forest Regression', 'AdaBoost Regression'] - meijitancailiao_model_list_meitan = ['xgb_meitan', 'lr_meitan', 'ridge_meitan', 'gp_meitan', 'kn_meitan', 'gdbt_meitan', 'svr_meitan', 'dtr_meitan', 'rfr_meitan', 'adb_meitan'] + meijitancailiao_model_list_meitan = ['xgb_meitan', 'lr_meitan', 'ridge_meitan', 'gp_meitan', 'en_meitan', 'gdbt_meitan', 'svr_meitan', 'dtr_meitan', 'rfr_meitan', 'adb_meitan'] meijitancailiao_meitan_mae = [8.17, 37.61, 37.66, 13.41, 20.96, 8.03, 14.89, 19.48, 12.53, 15.6] meijitancailiao_meitan_r2 = [0.96, 0.19, 0.19, 0.91, 0.8, 0.96, 0.88, 0.86, 0.91, 0.91] columns_meiliqing = ['XGBoost', 'Linear Regression', 'Ridge Regression', 'Gaussian Process Regression', 'ElasticNet Regression', 'Gradient Boosting Regression', 'Support Vector Regression', 'Decision Tree Regression', 'Random Forest Regression', 'AdaBoost Regression'] - meijitancailiao_model_list_meiliqing = ['xgb_meiliqing', 'lr_meiliqing', 'ridge_meiliqing', 'gp_meiliqing', 'kn_meiliqing', 'gdbt_meiliqing', 'svr_meiliqing', 'dtr_meiliqing', 'rfr_meiliqing', 'adb_meiliqing'] + meijitancailiao_model_list_meiliqing = ['xgb_meiliqing', 'lr_meiliqing', 'ridge_meiliqing', 'gp_meiliqing', 'en_meiliqing', 'gdbt_meiliqing', 'svr_meiliqing', 'dtr_meiliqing', 'rfr_meiliqing', 'adb_meiliqing'] meijitancailiao_meiliqing_mae = [8.38, 35.02, 35.1, 11.02, 13.58, 7.04, 13.13, 13.13, 11.25, 9.99] meijitancailiao_meiliqing_r2 = [0.95, 0.33, 0.33, 0.94, 0.91, 0.97, 0.88, 0.89, 0.92, 0.94] @@ -230,16 +261,19 @@ class ModelParams(): simu_model_dict = { - "adb" : ["model/SSA_ADB.joblib","model/TPV_ADB.joblib"], - "dtr": ["model/SSA_DTR.joblib","model/TPV_DTR.joblib"], - "en":["model/SSA_ElasticNet.joblib","model/TPV_ElasticNet.joblib"], - "gp":["model/SSA_GaussianProcessRegressor.joblib","model/TPV_GaussianProcessRegressor.joblib"], - "kn":["model/SSA_KNeighborsRegressor.joblib","model/TPV_KNeighborsRegressor.joblib"], - "lasso":["model/SSA_Lasso.joblib","model/TPV_Lasso.joblib"], - "lr":["model/SSA_LinearRegression.joblib","model/TPV_LinearRegression.joblib"], - "rfr":["model/SSA_RFR.joblib","model/TPV_RFR.joblib"], - "ridge":["model/SSA_Ridge.joblib","model/TPV_Ridge.joblib"], - "svr":["model/SSA_SVR.joblib","model/TPV_SVR.joblib"], - "xgb":["model/SSA_XGB.joblib","model/TPV_XGB.joblib"], - "gdbt":["model/SSA_GDBT.joblib","model/TPV_GDBT.joblib"] -} \ No newline at end of file + "adb" : ["model/SSA_ADB.joblib","model/TPV_ADB.joblib"], + "dtr": ["model/SSA_DTR.joblib","model/TPV_DTR.joblib"], + "en":["model/SSA_ElasticNet.joblib","model/TPV_ElasticNet.joblib"], + "gp":["model/SSA_GaussianProcessRegressor.joblib","model/TPV_GaussianProcessRegressor.joblib"], + "kn":["model/SSA_KNeighborsRegressor.joblib","model/TPV_KNeighborsRegressor.joblib"], + "lasso":["model/SSA_Lasso.joblib","model/TPV_Lasso.joblib"], + "lr":["model/SSA_LinearRegression.joblib","model/TPV_LinearRegression.joblib"], + "rfr":["model/SSA_RFR.joblib","model/TPV_RFR.joblib"], + "ridge":["model/SSA_Ridge.joblib","model/TPV_Ridge.joblib"], + "svr":["model/SSA_SVR.joblib","model/TPV_SVR.joblib"], + "xgb":["model/SSA_XGB.joblib","model/TPV_XGB.joblib"], + "gdbt":["model/SSA_GDBT.joblib","model/TPV_GDBT.joblib"] + } + + ALLOWED_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.tif'} + MAX_FILE_SIZE = 100 * 1024 * 1024 # 100 MB \ No newline at end of file diff --git a/work_util/prepare_data.py b/work_util/prepare_data.py index 7418ac8..4a95d81 100644 --- a/work_util/prepare_data.py +++ b/work_util/prepare_data.py @@ -207,23 +207,22 @@ def merge_binary(path_list): return final_path -def show_data_pvfd(col): - current_directory = os.getcwd() - train_data_path = os.path.join(current_directory,"guangfufadian","datasets","station08.csv") - data = pd.read_csv(train_data_path) +def show_data_pvfd(path, type): + data = pd.read_csv(path) # print(data.columns) cols = ['date_time'] - cols.append(col) + cols.append(type) # print(cols) result = data[cols] - result = result.iloc[23362:23553] - json_result = result.to_json(orient='records', lines=True) + # result = result.iloc[:192] + json_result = result.to_dict(orient='records') return json_result + + def show_testdata_pvfd(test_data_path): data = pd.read_csv(test_data_path) - json_result = data.to_json(orient='records', lines=True) - return json_result + return data @@ -279,23 +278,21 @@ def result_merge_guangfufadian(data_path, predictions): """ 风力预测发电 """ -def show_data_windfd(col): - current_directory = os.getcwd() - train_data_path = os.path.join(current_directory,"fenglifadian","datasets","Wind_farm.csv") - data = pd.read_csv(train_data_path) +def show_data_windfd(path,type): + data = pd.read_csv(path) # print(data.columns) cols = ['date'] - cols.append(col) + cols.append(type) # print(cols) result = data[cols] - result = result.iloc[21600:21600+96*2] - json_result = result.to_json(orient='records', lines=True) + # result = result.iloc[:192] + json_result = result.to_dict(orient='records') return json_result + def show_testdata_windfd(test_data_path): data = pd.read_csv(test_data_path) - json_result = data.to_json(orient='records', lines=True) - return json_result + return data @@ -334,21 +331,26 @@ def result_merge_fenglifadian(data_path, predictions): true_data = true_data.to_json(orient='records') return pred_data, true_data - -def show_data_jiawanyuce(col): - current_directory = os.getcwd() - train_data_path = os.path.join(current_directory,"tmp","jiawanyuce","jiawan_test.csv") - data = pd.read_csv(train_data_path) +# 返回所有的数据 +def show_data_jiawanyuce(col,path): + data = pd.read_csv(path) # print(data.columns) cols = ['date_time'] cols.append(col) # print(cols) result = data[cols] - result = result.iloc[:192] - json_result = result.to_json(orient='records', lines=True) + # result = result.iloc[:192] + json_result = result.to_dict(orient='records') return json_result + +# 返回所有数据 +def get_jiawanyuce_data(path): + data = pd.read_csv(path) + return data + + def data_feature(data,x_cols): for name in x_cols: @@ -449,4 +451,47 @@ def create_pred_data(params): sequence_Rt = list(float_range(params['Rt_min'], params['Rt_max'], params['Rt_step'])) training_samples = list(itertools.product(sequence_A, sequence_VM, sequence_KC, sequence_MM, sequence_AT, sequence_At, sequence_Rt)) - return training_samples \ No newline at end of file + return training_samples + + +def moniqi_data_prepare(model,path,type,scale,num): + if model in path: + data = pd.read_csv(path) + if type == 1: + col = 'SSA' + else: + col = "TPV" + max_data = data[col].max() + min_data = data[col].min() + + if scale < min_data: + return { + "status":False, + "reason":"您输入的"+col+'取值范围过小，模拟器运行数据结果最小值结果为'+str(min_data) + } + elif scale > max_data: + return { + "status":False, + "reason":"您输入的"+col+'取值范围过大，模拟器运行数据结果最大值结果为'+str(max_data) + } + else: + if type == 1: + data['difference'] = abs(data['SSA'] - scale) + else: + data = data[data['TPV'] > 0] 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