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Francesco G. Brundu fbrundu

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@fbrundu
fbrundu / check_unique.sh
Last active Aug 29, 2015
Check if a line is composed by unique words.
View check_unique.sh
# line number after ==
# change \\t with the delimiter you want to use instead of tab for splitting to words
words_count=$(awk 'NR==5{print}' data.csv | tr \\t \\n | wc -l)
unique_words_count=$(awk 'NR==5{print}' data.csv | tr \\t \\n | uniq | wc -l)
if [[ "$words_count" -ne "$unique_words_counts" ]]; then
echo "Not unique"
else
echo "Unique"
@fbrundu
fbrundu / .vimrc
Last active Mar 7, 2017
My vimrc
View .vimrc
" no vi-compatible
set nocompatible
let g:python_host_prog=$HOME.'/.pyenv/versions/neovim2/bin/python'
let g:python3_host_prog=$HOME.'/.pyenv/versions/neovim3/bin/python'
" Setting up Vundle - the vim plugin bundler
let iCanHazVundle=1
let vundle_readme=expand('~/.vim/bundle/vundle/README.md')
if !filereadable(vundle_readme)
@fbrundu
fbrundu / jprob_cmatrix.py
Last active Dec 30, 2015
Generation of a joint probability consensus matrix from pandas dataframe
View jprob_cmatrix.py
import numpy as np
import pandas as pd
# load data
mat = pd.read_table('matrix.txt', index_col=0)
# get classes
classes = np.unique(mat.values)
classes = classes[~np.isnan(classes)]
@fbrundu
fbrundu / consensus_array.py
Last active Dec 30, 2015
Generate consensus array from pandas DataFrame (NaN values are ignored)
View consensus_array.py
import pandas as pd
# load data
mat = pd.read_table('class_matrix.txt', index_col=0)
# initialize consensus array
consensus_a = pd.Series(index=mat.index)
# define columns subset on which compute consensus
# in this case all columns are used
@fbrundu
fbrundu / fastcluster_to_k.py
Last active Dec 30, 2015
Get k clusters from pandas dataframe using fastcluster. Use fastcluster to make a hierarchical clustering cropped to k clusters.
View fastcluster_to_k.py
import fastcluster as fc
import pandas as pd
import scipy.cluster.hierarchy as sch
# define total number of cluster to obtain
k = 5
# define matrix path
mat_path = 'matrix.txt'