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| import json | |
| import joblib | |
| import pandas as pd | |
| from sklearn.preprocessing import StandardScaler | |
| from pickle import load | |
| import numpy as np | |
| class RTUPipeline: | |
| scaler = None | |
| def __init__(self, scaler_path=None): | |
| self.output_col_names = [ | |
| "hp_hws_temp", | |
| "rtu_003_sa_temp", | |
| "rtu_003_oadmpr_pct", | |
| "rtu_003_ra_temp", | |
| "rtu_003_oa_temp", | |
| "rtu_003_ma_temp", | |
| "rtu_003_sf_vfd_spd_fbk_tn", | |
| "rtu_003_rf_vfd_spd_fbk_tn", | |
| "rtu_004_sa_temp", | |
| "rtu_004_oadmpr_pct", | |
| "rtu_004_ra_temp", | |
| "rtu_004_oa_temp", | |
| "rtu_004_ma_temp", | |
| "rtu_004_sf_vfd_spd_fbk_tn", | |
| "rtu_004_rf_vfd_spd_fbk_tn", | |
| "rtu_001_sa_temp", | |
| "rtu_001_oadmpr_pct", | |
| "rtu_001_ra_temp", | |
| "rtu_001_oa_temp", | |
| "rtu_001_ma_temp", | |
| "rtu_001_sf_vfd_spd_fbk_tn", | |
| "rtu_001_rf_vfd_spd_fbk_tn", | |
| "rtu_002_sa_temp", | |
| "rtu_002_oadmpr_pct", | |
| "rtu_002_ra_temp", | |
| "rtu_002_oa_temp", | |
| "rtu_002_ma_temp", | |
| "rtu_002_sf_vfd_spd_fbk_tn", | |
| "rtu_002_rf_vfd_spd_fbk_tn", | |
| # "rtu_004_sat_sp_tn", | |
| # "rtu_003_sat_sp_tn", | |
| # "rtu_001_sat_sp_tn", | |
| # "rtu_002_sat_sp_tn", | |
| # "air_temp_set_1", | |
| # "air_temp_set_2", | |
| # "dew_point_temperature_set_1d", | |
| # "relative_humidity_set_1", | |
| # "solar_radiation_set_1", | |
| ] | |
| self.input_col_names = [ | |
| "air_temp_set_1", | |
| "air_temp_set_2", | |
| "dew_point_temperature_set_1d", | |
| "relative_humidity_set_1", | |
| "solar_radiation_set_1", | |
| ] | |
| self.num_inputs = len(self.input_col_names) | |
| self.num_outputs = len(self.output_col_names) | |
| self.column_names = self.output_col_names + self.input_col_names | |
| if scaler_path: | |
| self.scaler = self.get_scaler(scaler_path) | |
| self.df = pd.DataFrame(columns=self.column_names) | |
| def get_scaler(self, scaler_path): | |
| return joblib.load(scaler_path) | |
| def get_window(self, df): | |
| len_df = len(df) | |
| if len_df > 30: | |
| return df[len_df - 31 : len_df].astype("float32") | |
| else: | |
| return None | |
| def transform_window(self, df_window): | |
| return self.scaler.transform(df_window) | |
| def prepare_input(self, df_trans): | |
| return df_trans[:30, :].reshape((1, 30, len(self.column_names))) | |
| def extract_data_from_message(self, message): | |
| payload = json.loads(message.payload.decode()) | |
| len_df = len(self.df) | |
| # self.df.loc[len_df] = {'hp_hws_temp':payload['hp_hws_temp'], | |
| # 'rtu_003_sa_temp':payload['rtu_003_sa_temp'], | |
| # 'rtu_003_oadmpr_pct': payload["rtu_003_oadmpr_pct"], | |
| # 'rtu_003_ra_temp':payload["rtu_003_ra_temp"], | |
| # 'rtu_003_oa_temp': payload["rtu_003_oa_temp"], | |
| # 'rtu_003_ma_temp': payload["rtu_003_ma_temp"], | |
| # 'rtu_003_sf_vfd_spd_fbk_tn': payload["rtu_003_sf_vfd_spd_fbk_tn"], | |
| # 'rtu_003_rf_vfd_spd_fbk_tn':payload["rtu_003_rf_vfd_spd_fbk_tn"], | |
| # 'rtu_004_sa_temp':payload["rtu_004_sa_temp"], | |
| # 'rtu_004_oadmpr_pct':payload["rtu_004_oadmpr_pct"], | |
| # 'rtu_004_ra_temp':payload["rtu_004_ra_temp"], | |
| # 'rtu_004_oa_temp':payload["rtu_004_oa_temp"], | |
| # 'rtu_004_ma_temp':payload["rtu_004_ma_temp"], | |
| # 'rtu_004_sf_vfd_spd_fbk_tn':payload["rtu_004_sf_vfd_spd_fbk_tn"], | |
| # 'rtu_004_rf_vfd_spd_fbk_tn':payload["rtu_004_rf_vfd_spd_fbk_tn"], | |
| # 'rtu_001_sa_temp':payload["rtu_001_sa_temp"], | |
| # 'rtu_001_oadmpr_pct': payload["rtu_001_oadmpr_pct"], | |
| # 'rtu_001_ra_temp':payload["rtu_001_ra_temp"], | |
| # 'rtu_001_oa_temp': payload["rtu_001_oa_temp"], | |
| # 'rtu_001_ma_temp': payload["rtu_001_ma_temp"], | |
| # 'rtu_001_sf_vfd_spd_fbk_tn': payload["rtu_001_sf_vfd_spd_fbk_tn"], | |
| # 'rtu_001_rf_vfd_spd_fbk_tn':payload["rtu_001_rf_vfd_spd_fbk_tn"], | |
| # 'rtu_002_sa_temp':payload["rtu_002_sa_temp"], | |
| # 'rtu_002_oadmpr_pct':payload["rtu_002_oadmpr_pct"], | |
| # 'rtu_002_ra_temp':payload["rtu_002_ra_temp"], | |
| # 'rtu_002_oa_temp':payload["rtu_002_oa_temp"], | |
| # 'rtu_002_ma_temp':payload["rtu_002_ma_temp"], | |
| # 'rtu_002_sf_vfd_spd_fbk_tn':payload["rtu_002_sf_vfd_spd_fbk_tn"], | |
| # 'rtu_002_rf_vfd_spd_fbk_tn':payload["rtu_002_rf_vfd_spd_fbk_tn"], | |
| # 'rtu_004_sat_sp_tn':payload["rtu_004_sat_sp_tn"], | |
| # 'rtu_003_sat_sp_tn' :payload["rtu_003_sat_sp_tn"], | |
| # 'rtu_001_sat_sp_tn':payload["rtu_001_sat_sp_tn"], | |
| # 'rtu_002_sat_sp_tn':payload["rtu_002_sat_sp_tn"], | |
| # 'air_temp_set_1':payload["air_temp_set_1"], | |
| # 'air_temp_set_2':payload["air_temp_set_2"], | |
| # 'dew_point_temperature_set_1d':payload["dew_point_temperature_set_1d"], | |
| # 'relative_humidity_set_1':payload["relative_humidity_set_1"], | |
| # 'solar_radiation_set_1':payload["solar_radiation_set_1"]} | |
| k = {} | |
| for col in self.column_names: | |
| k[col] = payload[col] | |
| self.df.loc[len_df] = k | |
| return self.df | |
| def fit(self, message): | |
| df = self.extract_data_from_message(message) | |
| df_window = self.get_window(df) | |
| if df_window is not None: | |
| df_trans = self.transform_window(df_window) | |
| df_new = self.prepare_input(df_trans) | |
| else: | |
| df_new = None | |
| df_trans = None | |
| return df_new, df_trans | |