AngelBerihuete

# vim style tmux config

# use C-a, since it's on the home row and easier to hit than C-b
set-option -g prefix C-a
unbind-key C-a
bind-key C-a send-prefix
set -g base-index 1

# Easy config reload
bind-key R source-file ~/.tmux.conf \; display-message "tmux.conf reloaded."

AngelBerihuete

#!/bin/bash

#------- Descripción del trabajo -------

#SBATCH --job-name='test_cross_gpu'
#SBATCH --qos=debug
#------- Avisos -------

#SBATCH --mail-user=angel.berihuete@uca.es
#SBATCH --mail-type=END,FAIL,TIME_LIMIT_80

AngelBerihuete

VIM KEYBOARD SHORTCUTS

MOVEMENT

h        -   Move left
j        -   Move down
k        -   Move up
l        -   Move right
$        -   Move to end of line
0        -   Move to beginning of line (including whitespace)

AngelBerihuete

# Change keymap layout to spanish
localectl set-x11-keymap es

# Install Spotify
https://snapcraft.io/install/spotify/arch

# After install Anaconda add the line in .zshrc
export PATH="/Users/username/miniconda3/bin:$PATH"
# Because I use the LARBS (Luke Smith)
# https://larbs.xyz/

AngelBerihuete

Nvim-R-Tmux: Neovim-based IDE for R

---

Table of Content

1. Overview
2. Installation
   1. Neovim
3. Tmux

AngelBerihuete

# from https://www.quora.com/How-can-I-ensure-that-my-TensorFlow-is-running-by-GPU-or-not-by-GPU

sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

AngelBerihuete

import numpy as np

import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse

def plot_point_cov(points, nstd=2, ax=None, **kwargs):
    """
    Plots an `nstd` sigma ellipse based on the mean and covariance of a point
    "cloud" (points, an Nx2 array).
    Parameters

AngelBerihuete

import numpy as np

import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse

def plot_point_cov(points, nstd=2, ax=None, **kwargs):
    """
    Plots an `nstd` sigma ellipse based on the mean and covariance of a point
    "cloud" (points, an Nx2 array).
    Parameters

AngelBerihuete

# FROM https://stackoverflow.com/questions/33661064/pymc3-hierarchical-model-with-multiple-obsesrved-variables
import matplotlib.pyplot as plt

model = pm.Model()
with model:
    hyper_mean = pm.Normal('hyper_mean', mu=0, sd=100)
    hyper_sigma = pm.HalfNormal('hyper_sigma', sd=3)

    means = pm.Normal('means', mu=hyper_mean, sd=hyper_sigma, shape=n_individuals)
    sigmas = pm.HalfNormal('sigmas', sd=100)

AngelBerihuete

# This file was obtained from StackOverflow:
#
# How to switch backends in matplotlib / Python
# https://stackoverflow.com/questions/3285193/how-to-switch-backends-in-matplotlib-python


import matplotlib
gui_env = ['TKAgg','GTKAgg','Qt4Agg','WXAgg']
for gui in gui_env:
    try: