jcheong0428

jcheong0428

# This sets the pdf font type so that it imports correctly in Adobe Illustrator. 
# Source: http://jonathansoma.com/lede/data-studio/matplotlib/exporting-from-matplotlib-to-open-in-adobe-illustrator/
import matplotlib
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42

# Generating 2x retina plots 
%config InlineBackend.figure_format = 'retina'

# Set base figure size 

jcheong0428

import numpy as np
import pandas as pd
def parse_triangle(df, condition='upper'):
    '''
    This function grabs the upper triangle of a correlation matrix
    by masking out the bottom triangle (tril) and returns the values. 
    You can use scipy.spatial.distance.squareform to recreate matrix from upper triangle
    
    Args:
      df: pandas or numpy correlation matrix

jcheong0428

# First load rpy2 
%load_ext rpy2.ipython

# Then load packages in a separate cell
%%R
require('lme4')
require('lmerTest')
require('lattice')
require('boot')
require('sjPlot')

jcheong0428

# Correct p-value by adjusting degrees of freedom.
# Degrees of freedom is divided by two and then looked up 
# Reference:
# https://www.ncbi.nlm.nih.gov/pubmed/27751943
# Chen, G., Taylor, P. A., Shin, Y. W., Reynolds, R. C., & Cox, R. W. (2017). Untangling the relatedness among correlations, Part II: Inter-subject correlation group analysis through linear mixed-effects modeling. Neuroimage, 147, 825-840.

from scipy.stats import t
tt = 9.83
df = 17.6/2.
pval = t.sf(np.abs(tt), df)*2 

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jcheong0428

def crosscorr(datax, datay, lag=0):
    """ Lag-N cross correlation. 
    Calculates cross correlations using pandas functionality that can be used in a list comprehension. 
    
    Parameters
    ----------
    lag : int, default 0
    datax, datay : pandas.Series objects of equal length

    Returns

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jcheong0428

# First gist for Hungarian post.
# Brute force estimation of optimal assignment for given preference. 
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_context('talk')
sns.set_style('white')
import itertools
import numpy as np

jcheong0428

%%timeit
# Second gist for Hungarian post.
# Estimates the time needed for brute force optimal assignment for group size 10. 
# Takes about 3 minutes.
maxsums, colixs = [], []
group_size = 10
np.random.seed(0)
preferences = np.random.rand(group_size,group_size)

rowix = list(range(group_size)) # rows don't need to be permuted