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""" Benchmarking two techniques for compressing numpy arrays with python-blosc.
"""

import numpy
import numpy.random
import time
import blosc
import blosc.blosc_extension as ext

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gc () {
    LBUFFER="git commit -m \""
    RBUFFER="\""
}
zle -N gc gc
bindkey '^gc' gc

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    alias gib="git branch"
    alias branch="git branch"
    alias gil="git log"
    # log is a zsh builtin
    alias gid="git diff"
    alias gidc="git diff --cached"
    alias giw="git wdiff"
    # diff is a unix utility
    alias gis="git status --ignored"
    alias status="git status"

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[color]
	ui = auto
	branch = auto
	diff = auto
	status = auto
[core]
	excludesfile = ~/.gitignore
	whitespace = trailing-space,space-before-tab,cr-at-eol
	abbrev = 10
[merge]

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Repository-Browsers

• goblet
• gitiles
• cgit
• gitweb
• viewgit
• gitalist
• gitlist

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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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adapter = Custom20BNCsvJpegAdapter(input_csv, jpeg_path, output_folder,
                                   shuffle=shuffle,
                                   frame_size=img_size,
                                   shm_dir_path=shm_dir
                                   )
ingestor = GulpIngestor(adapter, output_folder, videos_per_chunk,
                        num_workers=num_workers)
ingestor()

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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.
    """