RenSys

RenSys

RenSys

RenSys

radulucaciu

import numpy as np
import pandas as pd
import pylab as pl
import os

from sklearn.neighbors import KNeighborsClassifier
from IPython.core.pylabtools import figsize
from PIL import Image

def load_image(filename):

RenSys

RenSys

fish2000

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
# 
#       asscat.py
# 
#       Generate a properly scaled 1x/2x/3x set of PNGs, optionally with
#       generated JSON metadata and/or subfolders, for each single given image.
#       For use with, like, all those Xcode asset catalogs and shit.
#       Requires the Pillow and docopt modules; optionally makes use of six.
# 

costa

UPDATED!!

TL;DR Wanna work with a remote Docker container in local PyCharm? Now you can!

NB This walkthrough requires a bit of experience with both PyCharm and Docker, plus a *NIX dev env.

At the moment of writing, PyCharm doesn't support development process with a remote Docker host, which is quite an obstacle for serious and comfortable data-analysis work. Fortunately, present-day PyCharm does support "remote interpreters" via SSH, so here I describe how to setup a containerised project appropriately. This takes a little more than 5 minutes, but in the end you will have the comfort of the local PyCharm plus security and performance of a remote host (plus the evident interoperability of Docker).

KelSolaar

import os
import numpy as np

import colour
from colour_hdri import *

RESOURCES_DIRECTORY = '/Users/kelsolaar/Downloads/Tiff'

TIFF_FILES = filter_files(RESOURCES_DIRECTORY, ('tiff',))