raphaelvallat

library(ez)
library(lsmeans)
library(effsize)

# Load the file
df <- read.csv(file="", head=TRUE, sep=",")

# Choose dependant variable (e.g. reaction time)
df$dv <- df$RT

raphaelvallat

import numpy as np

def find_closest_divisor(n, m): 
    """Find the divisor of n closest to m
    """
    divisors = np.array([ i for i in range(1, int(np.sqrt(n)+1)) if n % i == 0 ])
    divisions = n / divisors 
    return divisions[np.argmin(np.abs(m - divisions))]

number, divisor = 1024, 100

raphaelvallat

# Thanks to http://www.jarrodmillman.com/rcsds/lectures/convolution_background.html
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import gamma

def hrf(x):
    """ Return values for HRF at given times """
    # Gamma pdf for the peak
    peak_values = gamma.pdf(x, 6)
    # Gamma pdf for the undershoot

raphaelvallat

"""
Password brute-force algorithm.

List of most probable passwords and english names can be found, respectively, at:
- https://github.com/danielmiessler/SecLists/blob/master/Passwords/probable-v2-top12000.txt
- https://github.com/dominictarr/random-name/blob/master/middle-names.txt

Author: Raphael Vallat
Date: May 2018
Python 3

raphaelvallat

# List unique values in a DataFrame column
# h/t @makmanalp for the updated syntax!
df['Column Name'].unique()

# Convert Series datatype to numeric (will error if column has non-numeric values)
# h/t @makmanalp
pd.to_numeric(df['Column Name'])

# Convert Series datatype to numeric, changing non-numeric values to NaN
# h/t @makmanalp for the updated syntax!

raphaelvallat

raphaelvallat

import numpy as np

def spindles_detect(x, sf, perc_threshold=90, wlt_params={'n_cycles': 7, 'central_freq': 'auto'}):
    """Simple spindles detector based on Morlet wavelet.
    
    Parameters
    ----------
    x : 1D-array
        EEG signal
    sf : float

raphaelvallat

raphaelvallat

'''
Non-parametric computation of entropy and mutual-information

Adapted by G Varoquaux for code created by R Brette, itself
from several papers (see in the code).

These computations rely on nearest-neighbor statistics
'''
import numpy as np

raphaelvallat

import numpy as np
import multiprocessing
from joblib import Parallel, delayed
from scipy.spatial.distance import pdist, squareform

def _dcorr(y, n2, A, dcov2_xx):
    """Helper function for distance correlation bootstrapping.
    """
    # Pairwise Euclidean distances
    b = squareform(pdist(y, metric='euclidean'))