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Francesco G. Brundufbrundu

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I may be slow to respond.
Last active Oct 12, 2020
Calculate hypergeometric probability with Python SciPy
View hyper.md

A poker hand consists of 5 cards dealt at random without replacement from a standard deck of 52 cards of which 26 are red and the rest black. A poker hand is dealt. Find the chance that the hand contains three red cards and two black cards.

To achieve it, we use the [hypergeometric][1] probability mass function. We want 3 cards from the set of 26 red cards and 2 from the set of 26. So the parameters for the hypergeometric function are:

``````M = 52  # Total number of cards
n = 26  # Number of Type I cards (e.g. red cards)
N = 5   # Number of draws (5 cards dealt in one poker hand)
k = 3   # Number of Type I cards we want in one hand
``````
Last active Oct 5, 2019
Calculate binomial probability in Python with SciPy
View binom.md

If you bet on "red" at roulette, you have chance 18/38 of winning. Suppose you make a sequence of independent bets on “red” at roulette, with the decision that you will stop playing once you have won 5 times. What is the chance that after 15 bets you are still playing?

We use [binomial][1] probability mass function. To calculate the probability, you have to estimate the probability of having up to 4 successful bets after the 15th. So the final probability will be the sum of the probability to get 0 successful bets in 15 bets, plus the probability to get 1 successful bet, ..., to the probability of having 4 successful bets in 15 bets.

To achieve it:

``````import scipy.stats as ss

n = 15         # Number of total bets
p = 18./38     # Probability of getting "red" at the roulette
``````
Created Apr 5, 2017
Use local R installation
View makeenv.sh
 alias R="/R-X.Y.Z/bin/R" export R_LIBS="/packages" export PATH="/R-X.Y.Z/bin:\${PATH}"
Created Mar 27, 2017
Retrieve TCGA gene expression data using GDC api
View gdc_tcga.py
 # -*- coding: utf-8 -*- import logging as log import pandas as pd import requests as rq class TCGA: def __init__(self, gdc_url='https://gdc-api.nci.nih.gov', per_page=100,
Created Mar 8, 2017
one dark vivid macOS terminal scheme
View onedark-vivid.terminal
 ANSIBlackColor YnBsaXN0MDDUAQIDBAUGFRZYJHZlcnNpb25YJG9iamVjdHNZJGFyY2hpdmVyVCR0b3AS AAGGoKMHCA9VJG51bGzTCQoLDA0OVU5TUkdCXE5TQ29sb3JTcGFjZVYkY2xhc3NPECcw LjExNzY0NzA1ODggMC4xMjk0MTE3NjQ3IDAuMTUyOTQxMTc2NQAQAYAC0hAREhNaJGNs YXNzbmFtZVgkY2xhc3Nlc1dOU0NvbG9yohIUWE5TT2JqZWN0XxAPTlNLZXllZEFyY2hp
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)
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)]
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
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'
Created Apr 6, 2015
Custom css for ipython3 notebook
View custom.css
 .CodeMirror, div.prompt.input_prompt, div.prompt.output_prompt, pre { font-family: "Inconsolata for Powerline"; font-size: 100%; }