# -*- coding: utf-8 -*-
"""
Created on Fri Dec  9 08:51:35 2022

@author: Daniel Häfliger

This file shows an example of how to use the Influxclient and Influxdataframe.
The example try to fit an prediction of a temperature of a specific measure
with time and a given prediction of the temperature as features. 

There is no data cleaning ect. in this example, its just to show how the lib
works...

Be aware that you need to add a influx- and weatherapi-token and aviable datas 
to the config file. Otherwise the example, as the lib to, wont work.

"""

# -*- coding: utf-8 -*-
"""
Created on Fri Dec  2 08:51:19 2022

@author: Daniel Häfliger
"""
import pandas as pd
import Influxdataframe as Idf
import Influxclient as I
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import r2_score
from sklearn.neural_network import MLPRegressor


# Get a dataframe from the influxDB with the specified values in the config.xml
df = Idf.Get_Df()


# Do a train test split and scale the datas
train_df, test_df = train_test_split(df, test_size=0.3, random_state=42)

scaler = MinMaxScaler()

train_df = pd.DataFrame(scaler.fit_transform(
    train_df), columns=train_df.columns, index=train_df.index)
test_df = pd.DataFrame(scaler.transform(
    test_df), columns=test_df.columns, index=test_df.index)

X = test_df[["y_r", "y_i", "d_r", "d_i", "P_AT_5M_0"]]
y = test_df[["W_AT_5M_0"]]

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42)


# Train data and print r2
dt = MLPRegressor(activation='relu')
dt.fit(X_train, y_train)
print(r2_score(y_test, dt.predict(X_test)))


# Get a df for the current prediciton with values from weather api
pred = I.GetPrediction(48, 5, "temp_c")
data = {
         
   "y_r": pred[0],
   "y_i": pred[1],
   "d_r": pred[2],
   "d_i": pred[3],
   "P_AT_5M_0": pred[5],
   "W_AT_5M_0": pred[5],
}
preddf = pd.DataFrame((data))


preddf = pd.DataFrame(scaler.transform(
    preddf), columns=preddf.columns, index=preddf.index)

preddf = preddf[["y_r", "y_i", "d_r", "d_i", "P_AT_5M_0"]]


# Predict temprature for the values returned by the api
ATpred = dt.predict(preddf)
Prog24 = pd.DataFrame(ATpred)

df_all_cols = pd.concat([preddf, Prog24], axis=1)

Prog24 = scaler.inverse_transform(df_all_cols)

# Rewrite datas to get it into a df
data = {

    "W_AT_5M_0": Prog24[:, 5].tolist(),
    "Timestamp": pred[4],

}

prognose = pd.DataFrame(data)
prognose.plot(x='Timestamp', y='W_AT_5M_0')