import os
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import matplotlib

directory = '<path-to>/results'
#filename = 'exp1002_uc3_75000_1_totallag.csv'
filename = 'exp1002_uc3_50000_2_totallag.csv'
warmup_sec = 60
threshold = 2000 #slope

df = pd.read_csv(os.path.join(directory, filename))

input = df.iloc[::3]
#print(input)
input['sec_start'] = input.loc[0:, 'timestamp'] - input.iloc[0]['timestamp']
#print(input)
#print(input.iloc[0, 'timestamp'])
regress = input.loc[input['sec_start'] >= warmup_sec] # Warm-Up
#regress = input

#input.plot(kind='line',x='timestamp',y='value',color='red')
#plt.show()

X = regress.iloc[:, 4].values.reshape(-1, 1)  # values converts it into a numpy array
Y = regress.iloc[:, 3].values.reshape(-1, 1)  # -1 means that calculate the dimension of rows, but have 1 column
linear_regressor = LinearRegression()  # create object for the class
linear_regressor.fit(X, Y)  # perform linear regression
Y_pred = linear_regressor.predict(X)  # make predictions

print(linear_regressor.coef_)

plt.style.use('ggplot')
plt.rcParams['axes.facecolor']='w'
plt.rcParams['axes.edgecolor']='555555'
#plt.rcParams['ytick.color']='black'
plt.rcParams['grid.color']='dddddd'
plt.rcParams['axes.spines.top']='false'
plt.rcParams['axes.spines.right']='false'
plt.rcParams['legend.frameon']='true'
plt.rcParams['legend.framealpha']='1'
plt.rcParams['legend.edgecolor']='1'
plt.rcParams['legend.borderpad']='1'


#filename = f"exp{exp_id}_{benchmark}_{dim_value}_{instances}"


t_warmup = input.loc[input['sec_start'] <= warmup_sec].iloc[:, 4].values
y_warmup = input.loc[input['sec_start'] <= warmup_sec].iloc[:, 3].values

plt.figure()
#plt.figure(figsize=(4, 3))

plt.plot(X, Y, c="#348ABD", label="observed")
#plt.plot(t_warmup, y_warmup)

plt.plot(X, Y_pred, c="#E24A33", label="trend") # color='red')

#348ABD, 7A68A6, A60628, 467821, CF4457, 188487, E24A33

plt.gca().yaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, pos: '%1.0fK' % (x * 1e-3)))
plt.ylabel('queued messages')
plt.xlabel('seconds since start')
plt.legend()
#ax.set_ylim(ymin=0)
#ax.set_xlim(xmin=0)

plt.savefig("plot.pdf", bbox_inches='tight')