fitomad

#!/bin/zsh

# Close Xcode, then run this script
xcrun simctl --set previews delete all

fitomad

Screenshots

Hacer una captuta de pantalla del simulator encendido

xcrun simctl io booted screenshot [filename].[extension]

Vídeo

fitomad

Crear un branch local y subirlo a remoto

git branch NEW-BRANCH-NAME
git push -u origin <branch>

Renombrar un branch local

git branch -m nuevo_nombre

fitomad

#!/bin/zsh

# The right time
xcrun simctl status_bar booted override --time 09:41
# WiFi & Cellular network
xcrun simctl status_bar booted override --dataNetwork wifi
xcrun simctl status_bar booted override --wifiBars 3
xcrun simctl status_bar booted override --cellularBars 4
# 100% battery level
xcrun simctl status_bar booted override --batteryLevel 100

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

shivshank

"""
    This script is designed to export a mass amount of MagicaVoxel .vox files
    to .obj. Unlike Magica's internal exporter, this exporter preserves the
    voxel vertices for easy manipulating in a 3d modeling program like Blender.
    
    Various meshing algorithms are included (or to be included). MagicaVoxel
    uses monotone triangulation (I think). The algorithms that will (or do)
    appear in this script will use methods to potentially reduce rendering
    artifacts that could be introduced by triangulation of this nature.