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dlma/scripts/df_vs_ns_plots.py
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df_vs_ns_plots.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Jun 6 21:19:54 2021
@author: samarth
"""
import pandas as pd
import os
from matplotlib import pyplot as plt
from pathlib import Path
import time
from sklearn.linear_model import LinearRegression
import numpy as np
parent_folder = './results/N_s/'
df_vs_seed = pd.DataFrame(columns=['N_s','d_f'])
r_min = 5
rog_prefactor = 0.8
N = 2048
model = LinearRegression()
for N_s in range(1,21):
seed_folder=parent_folder+str(N_s)+'/'
rog_df = pd.DataFrame(columns=['rog'])
pair_df = pd.DataFrame()
for rng_seed in range(10):
rog_filename=seed_folder+'radius_of_gyration_'+str(N_s)+'_'+str(rng_seed)+'.csv'
rog_temp_df = pd.read_csv(rog_filename)
rog_df.loc[len(rog_df),'rog'] = rog_temp_df.iloc[0]['Radius_of_Gyration']
rog_mean = rog_df['rog'].mean()
r_cutoff = rog_prefactor * rog_mean
pair_filename=seed_folder+'pair_correlation_'+str(N_s)+'_0.csv'
pair_temp_df=pd.read_csv(pair_filename)
pair_temp_df=pair_temp_df[(pair_temp_df['r'] >= r_min) & (pair_temp_df['r'] <= r_cutoff)]
pair_df['r']=pair_temp_df['r']
for rng_seed in range(10):
pair_filename=seed_folder+'pair_correlation_'+str(N_s)+'_'+str(rng_seed)+'.csv'
pair_temp_df=pd.read_csv(pair_filename)
pair_temp_df=pair_temp_df[(pair_temp_df['r'] >= r_min) & (pair_temp_df['r'] <= r_cutoff)]
colname = 'col_'+str(rng_seed)
pair_df[colname] = pair_temp_df['F(r)']
pair_df['F(r)'] = pair_df.iloc[:,1:].mean(axis=1)
pair_df = pair_df.iloc[::5]
x = np.log(pair_df['r'].values)
x = x.reshape(-1,1)
y = np.log(pair_df['F(r)'].values)
model.fit(x,y)
df_vs_seed.loc[len(df_vs_seed)] = [N_s,model.coef_[0]]
plt.figure(dpi=300)
plt.scatter(df_vs_seed['N_s'], df_vs_seed['d_f'])
plt.xlabel(r'$N_s$')
plt.ylabel(r'$d_f$')
plt.text(15,1.6,'N=2048')
plt.savefig('./plots/df_vs_ns.pdf', format='pdf')
plt.clf()
y_preds=model.predict(x)
plt.scatter(x,y_preds,c='red')
plt.plot(x,y)
plt.savefig('./plots/fit.pdf', format='pdf')
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