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Code_to_Read_Environmental_and_Demogrpahic_Stochasticity.py
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Code_to_Read_Environmental_and_Demogrpahic_Stochasticity.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 18 00:43:19 2019
@author: staubj
"""
import matplotlib.pyplot as plt
import sys
def linear_regression(x_list, y_list):
n = len(x_list)
sigma_y = 0
sigma_x = 0
sigma_xy = 0
sigma_x2 = 0
sigma_y2 = 0
for i in range(len(x_list)):
sigma_y += y_list[i]
sigma_x += x_list[i]
sigma_x2 += ((x_list[i])**2)
sigma_y2 += ((y_list[i])**2)
sigma_xy += (y_list[i] * x_list[i])
a = ( (sigma_y*sigma_x2) - (sigma_x*sigma_xy) ) / ( n*(sigma_x2) - (sigma_x)**2 )
b = ( n*(sigma_xy) - (sigma_x*sigma_y) ) / ( n*(sigma_x2) - (sigma_x)**2 )
num = n*sigma_xy - (sigma_x * sigma_y)
den_1 = (n*sigma_x2 - (sigma_x)**2)**(0.5)
den_2 = (n*sigma_y2 - (sigma_y)**2)**(0.5)
r = num/(den_1 * den_2)
r_squared = r**2.
vals = [b, a, r_squared]
return vals
generations = []
for i in range(0,100):
generations.append(i)
E_homo_A = []
E_uncertainty_homo_A = []
E_hetero = []
E_uncertainty_hetero = []
E_homo_B = []
E_uncertainty_homo_B = []
all_my_populations = []
D_homo_A = []
D_uncertainty_homo_A = []
D_hetero = []
D_uncertainty_hetero = []
D_homo_B = []
D_uncertainty_homo_B = []
all_my_populations = []
file = open('Environmental_Stochasticity_Data.txt', 'r')
rows = file.readlines()
for index in range(len(rows)):
values = rows[index].split(' , ')
E_homo_A.append(float(values[1]))
E_uncertainty_homo_A.append(float(values[2])/10)
E_hetero.append(float(values[3]))
E_uncertainty_hetero.append(float(values[4])/10)
E_homo_B.append(float(values[5]))
E_uncertainty_homo_B.append(float(values[6])/10)
file = open('Demographic_Stochasticity_Data.txt', 'r')
rows = file.readlines()
for index in range(len(rows)):
values = rows[index].split(' , ')
D_homo_A.append(float(values[1]))
D_uncertainty_homo_A.append(float(values[2])/10)
D_hetero.append(float(values[3]))
D_uncertainty_hetero.append(float(values[4])/10)
D_homo_B.append(float(values[5]))
D_uncertainty_homo_B.append(float(values[6])/10)
#### For the linear regression lines ####
E_vals = linear_regression(generations, E_hetero)
D_vals = linear_regression(generations, D_hetero)
slope_E = E_vals[0]
slope_D = D_vals[0]
E_intercept = E_vals[1]
D_intercept = D_vals[1]
E_r = E_vals[2]
D_r = D_vals[2]
print(D_r, E_r)
E_line_x = []
E_line_y = []
D_line_x = []
D_line_y = []
delta = generations[99]/10
index = 0
while index <= generations[99]:
E_line_x.append(index)
D_line_x.append(index)
E_line_y.append( (E_vals[0]*index) + E_vals[1] )
D_line_y.append( (D_vals[0]*index) + D_vals[1] )
index += delta
#### For errorbars ####
E_moded_H = []
E_moded_Unc_H = []
E_gens = []
D_H = []
D_Unc_H = []
D_gens_full = []
D_moded_H = []
D_moded_Unc_H = []
D_gens = []
for i in range(0, len(D_hetero), 2):
D_H.append(D_hetero[i])
D_Unc_H.append(D_uncertainty_hetero[i])
D_gens_full.append(generations[i])
for i in range(0, len(E_homo_A), 10):
E_moded_H.append(E_hetero[i])
E_moded_Unc_H.append(E_uncertainty_hetero[i])
E_gens.append(generations[i])
D_moded_H.append(D_hetero[i])
D_moded_Unc_H.append(D_uncertainty_hetero[i])
D_gens.append(generations[i])
fig, ax = plt.subplots()
fig.set_tight_layout(True)
#ax.scatter(generations, homo_A, color = 'r', label = 'A Homozygous')
#ax.errorbar(generations, homo_A, yerr = uncertainty_homo_A, capsize = 5, linewidth = 1, color = 'k', fmt = ' ')
ax.scatter(generations, E_hetero, color = 'm', s = 60, label = 'Environmental Stochasticity')
ax.errorbar(E_gens, E_moded_H, yerr = E_moded_Unc_H, capsize = 5, linewidth = 1, color = 'k', fmt = ' ')
ax.plot(E_line_x, E_line_y, linestyle = '--', color = 'k')
ax.text(54, .445, 'H = {:.3e} * gen + {:.3f}'.format(slope_E, E_intercept))
ax.text(54, .435, '$R^2$ = {:.3f}'.format(E_vals[2]))
ax.scatter(D_gens_full, D_H, color = 'c', s = 60, label = 'Demographic Stochasticity')
ax.errorbar(D_gens, D_moded_H, yerr = D_moded_Unc_H, capsize = 5, linewidth = 1, color = 'k', fmt = ' ')
ax.plot(D_line_x, D_line_y, linestyle = '--', color = 'k')
ax.text(45, .505, 'H = {:.3e} * gen + {:.3f}'.format(slope_D, D_intercept))
ax.text(45, 0.495, '$R^2$ = {:.3f}'.format(D_vals[2]))
#ax.scatter(generations, homo_B, color = 'b', label = 'B Homozygous')
#ax.errorbar(generations, homo_B, yerr = uncertainty_homo_B, capsize = 5, linewidth = 1, color = 'k', fmt = ' ')
ax.set_xlabel('Generation (years)')
ax.set_ylabel('Percent of Population')
ax.legend( title = 'Stochasticity Event:')
fig.savefig('Full_Stochasticity_Figure_with_errorbars.png', filetype = 'png')
fig.savefig('Full_Stochasticity_Figure_with_errorbars.svg', filetype = 'svg')
plt.show()
fig.show()