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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
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