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{
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  "signature": "sha256:0feedd0a35b31c9754c41d8a4b1cc4d08d77967657305f0b8f43b24c397cd132"
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 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [

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import blosc

data = [
    0x93, 0xb0, 0x49, 0xaf, 0x62, 0xad, 0xe3, 0xaa, 0xe4, 0xa5, 0x43, 0x20,
    0x24, 0x29, 0xc9, 0xaf, 0xee, 0xad, 0x0b, 0xac, 0x3d, 0xa8, 0x1f, 0x99,
    0x53, 0x27, 0xb6, 0x2b, 0x16, 0xb0, 0x5f, 0xae, 0x89, 0xac, 0x51, 0xa9,
    0xfc, 0xa1, 0xc9, 0x24, 0x59, 0x2a, 0x2f, 0x2d, 0xb4, 0xae, 0xeb, 0xac,
    0x2f, 0xaa, 0xec, 0xa4, 0x53, 0x21, 0x31, 0x29, 0x8f, 0x2c, 0x8e, 0x2e,
    0x31, 0xad, 0xd6, 0xaa, 0x6d, 0xa6, 0xad, 0x1b, 0x3e, 0x28, 0x0a, 0x2c,
    0xfd, 0x2d, 0xf8, 0x2f, 0x45, 0xab, 0x81, 0xa7, 0x1f, 0x95, 0x02, 0x27,

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zsh» git init example
Initialized empty Git repository in /tmp/example/.git/
zsh» cd example
zsh» cp ../objektmodell-beispiel/hello.py .
zsh» cat hello.py
#! /usr/bin/env python

""" Hello World! """

print 'Hello World!'

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import boto3
import moto


@moto.mock_ec2
def expose():
    ec2 = boto3.resource('ec2')
    blue, green = ec2.create_instances(
        ImageId='ANY_ID', MinCount=2, MaxCount=2)
    ec2.create_tags(Resources=[blue.instance_id],

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# import the main interface for reading
from gulpio import GulpDirectory
# instantiate the GulpDirectory
gulp_directory = GulpDirectory('/tmp/something_something_gulps')
# iterate over all chunks
for chunk in gulp_directory:
    # for each 'video' get the metadata and all frames
    for frames, meta in chunk:
        # do something with the metadata
        for i, f in enumerate(frames):

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from gulpio.dataset import GulpImageDataset
from gulpio.loader import DataLoader
from gulpio.transforms import Scale, CenterCrop, Compose, UnitNorm

# define data augmentations. Notice that there are different functions for videos and images
transforms = Compose([
                      Scale(28),  # resize image by the shortest edge
                      CenterCrop(28),
                      UnitNorm(),  # instance wise mean and std norm
                    ])

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class AbstractDatasetAdapter(ABC):
    """ Base class adapter for gulping (video) datasets.
    Inherit from this class and implement the `iter_data` method. This method
    should iterate over your entire dataset and for each element return a
    dictionary with the following fields:
        id     : a unique(?) ID for the element.
        frames : a list of frames (PIL images, numpy arrays..)
        meta   : a dictionary with arbitrary metadata (labels, start_time...)
    For examples, see the custom adapters below.
    """