pabsan-0

## https://unix.stackexchange.com/a/22215

## find_up filename
find_up () {
  path=$(pwd)
  while [[ "$path" != "" && ! -e "$path/$1" ]]; do
    path=${path%/*}
  done
  echo "$path"
}

pabsan-0

#! /usr/bin/env -vS tmuxinator start -p

# rename to .tmuxinator.yml and run `tmuxinator start`
# OR run `tmuxinator start -p custom_filename.yml`
# OR make executable and run with shebang

name: my-session
on_project_start: cd ~
on_project_exit: tmux kill-session -t my-session
windows:

pabsan-0

from sklearn.datasets import load_iris
import pandas as pd
import numpy as np
from sklearn.model_selection import KFold
from sklearn import metrics
from sklearn.linear_model import LinearRegression

df = load_iris(return_X_y=True, as_frame=True)

X = df[0]

pabsan-0

OPENCV_VER="master"
TMPDIR=$(mktemp -d)

# Build and install OpenCV from source.
cd "${TMPDIR}"
git clone --branch ${OPENCV_VER} --depth 1 --recurse-submodules --shallow-submodules https://github.com/opencv/opencv-python.git opencv-python-${OPENCV_VER}
cd opencv-python-${OPENCV_VER}
export ENABLE_CONTRIB=1
export ENABLE_HEADLESS=0
# We want GStreamer support enabled.

pabsan-0

#! /usr/bin/bash
#
# This bash quickie lists your connected v4l2 cameras by parsing the output of `v4l2-ctl --list-devices`.
# Useful when playing w/ external cameras since replugging will usually modify its /dev/videoX location
#
# Example output
#  ```
#  C:
#
#  #define CAM_0 "/dev/video2"

pabsan-0

#! /usr/bin/bash

# This bash scripts takes the stderr from RidgeRun's Gst-Shark interlatency
# tracer and parses its output to build some tables averaging results.
#
# Install gst shark and wrap your pipeline as:
#   $ GST_DEBUG="GST_TRACER:7" GST_TRACERS="interlatency"  \
#       gst-launch-1.0 videotestsrc ! videoconvert ! autovideosink \
#       2> tracer.log
#

pabsan-0

#! /usr/bin/bash

# This script to plot an array of 4 cameras with gstreamer
#
# To have them in sequence, define d0-d3 in increasing order, then circle your
# hand through all cameras. Change d0-d3 to the order your hand appears in.
# Then, you'll get a spatially coherent display of cameras next to each other.

# Ids from /dev/videoXX, get with `$ gst-device-monitor-1.0 | grep v4l2src`
d0=7

pabsan-0

'''
Actor critic learning of linearized mountain car.

Critic: Linear action-value with function approximation, TD(0).
Actor: gradient ascent with softmax policy, based on action-value function.
'''

import matplotlib
import numpy as np
import gym