import matplotlib

matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import numpy as np
from numpy import mean
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure
from tkinter import *
from tkinter import filedialog as FD
from scipy import stats


filename = FD.askopenfilename()
print(filename)
# opening *into an object* file to read
file = open(filename, "r")

# file.readline = reading the file line by line (.read is everything)
title = file.readline()
# assign title to substring
title = title[7:]

file.readline()
# read date
date = file.readline()
date = date[16:]

file.readline()
file.readline()
file.readline()

print(title)
print(date)

# make blank list, read
blanks = []

keep_going = True
current_line = file.readline()

while keep_going == True:
    if current_line == "\n":
        keep_going = False
    else:
        blanks.append(float(current_line))
        current_line = file.readline()

print(blanks)

# reads the blanks
file.readline()
file.readline()

keep_going = True
current_line = file.readline()

# making two arrays
x = []
y = []

# making a loop for the table
while keep_going:
    if current_line == "":
        keep_going = False
    else:
        li = current_line.split("\t")

        xi = float(li[0])
        for yi in li[2:]:
            x.append(xi)
            y.append(float(yi))
        current_line = file.readline()

x = np.array(x, dtype=np.float64)
y = np.array(y, dtype=np.float64)
print(x)
print(y)

# generate linear fit
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
line = slope * x + intercept


# setting up graph
main = Tk()
main.geometry("266x208+300+300")
main.title("geometry")
#fig = Figure(figsize=(5, 4), dpi=100)
#ax = fig.add_subplot(111)
fig, ax = plt.subplots(2, 2, figsize=(15, 15))
#ax.scatter(x, y)

#plt.plot(x, y, 'o', x, line)
#main.title('Linear Fit with MatPlotLib')

# print(blankData)
print("blank deviation=", np.std(blanks))
print("LOD = ", np.std(blanks) * 3)

def best_fit_slope_and_intercept(xArray, yArray):
    m = (((mean(xArray) * mean(yArray)) - mean(yArray * xArray)) /
         ((mean(xArray) * mean(xArray)) - mean(xArray * xArray)))

    b = mean(yArray) - m * mean(xArray)

    return m, b


print("best_fit result: ", best_fit_slope_and_intercept(x, y))
m, b = best_fit_slope_and_intercept(x, y)

Fit_line = [(m * xpt) + b for xpt in x]
plt.scatter(x, y, color='#003F72')
plt.plot(x, Fit_line)

plt.show()
main.mainloop()