Spaces:
Sleeping
Sleeping
File size: 3,010 Bytes
3b66598 fff2149 3b66598 fff2149 767e14d fff2149 767e14d dbd7ac0 3b66598 4a389dc dbd7ac0 767e14d 66977cd 767e14d 66977cd 767e14d dbd7ac0 66977cd 4a389dc 3b66598 767e14d 4a389dc 3b66598 66977cd 767e14d 4a389dc fff2149 cea29ef 3b66598 767e14d fff2149 4a389dc 767e14d 4a389dc 767e14d 3b66598 767e14d 4a389dc 767e14d 4a389dc 767e14d 3b66598 767e14d 66977cd 4a389dc 66977cd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 |
import json
import joblib
import pandas as pd
from sklearn.preprocessing import StandardScaler
from pickle import load
import numpy as np
class RTUPipeline:
scaler1 = None # RTU 1,2
scaler2 = None # RTU 3,4
def __init__(self, rtus=[1, 2, 3, 4], scaler1_path=None, scaler2_path=None):
outputs = [
"sa_temp",
"oadmpr_pct",
"ra_temp",
"oa_temp",
"ma_temp",
"sf_vfd_spd_fbk_tn",
"rf_vfd_spd_fbk_tn",
]
self.output_col_names = [
"hp_hws_temp",
]
for rtu in rtus:
for output in outputs:
self.output_col_names.append(f"rtu_00{rtu}_{output}")
self.input_col_names = []
for rtu in rtus:
self.input_col_names.append(f"rtu_00{rtu}_sat_sp_tn")
self.input_col_names = 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) - 2
self.num_outputs = len(self.output_col_names) - 14
self.column_names = self.output_col_names + self.input_col_names
if scaler1_path:
self.scaler1 = self.get_scaler(scaler1_path)
if scaler2_path:
self.scaler2 = self.get_scaler(scaler2_path)
self.df = pd.DataFrame(columns=self.column_names)
def get_scaler(self, scaler_path):
return joblib.load(scaler_path)
def transform_window(self, df_window):
columns_scaler1 = [0] + list(range(1, 15)) + [29, 30] + list(range(33, 38))
columns_scaler2 = [0] + list(range(15, 29)) + [31, 32] + list(range(33, 38))
return self.scaler1.transform(
df_window.iloc[:, columns_scaler1].values
), self.scaler2.transform(df_window.iloc[:, columns_scaler2].values)
def prepare_input(self, df_trans):
return df_trans[:30, :].reshape((1, 30, len(self.column_names) - 16))
def extract_data_from_message(self, df: pd.DataFrame):
df = df[self.column_names]
len_df = len(self.df)
if len(self.df) != 0:
self.df = pd.concat([self.df, df], axis=0)
else:
self.df = df
if len_df > 31:
self.df = self.df.iloc[len_df - 31 : len_df]
self.df.loc[len_df] = self.df.mean()
return self.df
else:
return None
def fit(self, df: pd.DataFrame):
df_window = self.extract_data_from_message(df)
if df_window is not None:
df_trans1, df_trans2 = self.transform_window(df_window)
df_new1 = self.prepare_input(df_trans1)
df_new2 = self.prepare_input(df_trans2)
else:
df_new1 = None
df_trans1 = None
df_new2 = None
df_trans2 = None
return df_new1, df_trans1, df_new2, df_trans2
|