In [1]:

    

import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pathlib
import sys
import scipy.stats
import pathlib

import PaSDqc

%matplotlib inline


    

In [2]:

    

sns.set_context('poster')
sns.set_style('ticks')


    

In [3]:

    

p_1465 = "../data/Lodato_2015/1465/psd/"
p_4643 = "../data/Lodato_2015/4643/psd/"

freq, nd_1465, sl_1465 = PaSDqc.extra_tools.mk_ndarray(p_1465)
freq, nd_4643, sl_4643 = PaSDqc.extra_tools.mk_ndarray(p_4643)


    

In [4]:

    

a1 = np.arange(0, 1000, 100)
a2 = np.arange(1000, 10000, 1000)
a3 = np.arange(10000, 100000, 10000)
a4 = np.arange(100000, 1000000, 100000)
a5 = np.array([1000000])
lags = np.concatenate([a1, a2, a3, a4, a5])


    

In [5]:

    

ACF_1465 = np.array([PaSDqc.extra_tools.PSD_to_ACF(freq, psd, lags) for psd in nd_1465])
ACF_4643 = np.array([PaSDqc.extra_tools.PSD_to_ACF(freq, psd, lags) for psd in nd_4643])

var_1465 = ACF_1465[:, 0]
var_4643 = ACF_4643[:, 0]


    

In [6]:

    

ACF_1465[:, 0]


    

    
Out[6]:





array([ 0.00704312,  0.00714016,  0.00678791,  0.00715435,  0.00698335,
        0.00712423,  0.0071455 ,  0.00755803,  0.00733562,  0.00689233,
        0.0069051 ,  0.00685543,  0.00710103,  0.00767409,  0.00713649,
        0.00738721])

In [7]:

    

for acf in ACF_1465:
    plt.plot(lags[1:], acf[1:])
    plt.xscale('log')


    

In [8]:

    

sns.kdeplot(var_1465, label='1465')
sns.kdeplot(var_4643, label='4643')
plt.legend()


    

    
Out[8]:





<matplotlib.legend.Legend at 0x2ae817ded240>






    

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