thouis

import requests
import time

# https://github.com/twitterdev/enterprise-scripts-python/blob/main/Engagement-API/generate_user_access_tokens.py
from generate_user_access_tokens import request_token, get_user_authorization, get_user_access_tokens, CONSUMER_KEY, CONSUMER_SECRET

MY_ID = "PUT_YOUR_TWITTER_ID_NUMBER_HERE"


def create_url():

thouis

aahed
aalii
aargh
aarti
abaca
abaci
aback
abacs
abaft
abaka

thouis

import os
from urllib import request 
import numpy as np
import pandas as pd

# grab a large corpus of words, sorted by usage counts (Peter Norvig)
if not os.path.isfile('count_1w.txt'):
    request.urlretrieve("https://norvig.com/ngrams/count_1w.txt",
                        "count_1w.txt")

thouis

thouis

import sys
import twitter
import time

# see https://python-twitter.readthedocs.io/en/latest/getting_started.html

api = twitter.Api(consumer_key=CONSUMER_KEY,
                  consumer_secret=CONSUMER_SECRET,
                  access_token_key=ACCESS_TOKEN,
                  access_token_secret=ACCESS_SECRET)

thouis

thouis

Keybase proof

I hereby claim:

• I am thouis on github.
• I am thouis (https://keybase.io/thouis) on keybase.
• I have a public key whose fingerprint is A4F6 3AF2 C0E4 8CAE 7501 2BDE 2904 0D09 3557 95A4

To claim this, I am signing this object:

thouis

def depth_to_space(x, blocksize=2):
    b, k, d, r, c = x.shape
    r1 = x.reshape((b, k // (blocksize ** 2), blocksize, blocksize, d, r, c))
    r2 = r1.transpose(0, 1, 4, 5, 2, 6, 3)
    return r2.reshape((b, k // (blocksize ** 2), d, r * blocksize, c * blocksize))

thouis

# coding=utf-8

import numpy as np
import scipy.sparse as sparse

# Evaluation code courtesy of Juan Nunez-Iglesias, taken from
# https://github.com/janelia-flyem/gala/blob/master/gala/evaluate.py

def adapted_rand(seg, gt, all_stats=False):
    """Compute Adapted Rand error as defined by the SNEMI3D contest [1]

thouis

import tensorflow as tf
from tensorflow.python import control_flow_ops

# from http://stackoverflow.com/a/34634291
def batch_norm(x, n_out, phase_train, scope='bn', affine=True):
    """
    Batch normalization on convolutional maps.
    Args:
        x:           Tensor, 4D BHWD input maps
        n_out:       integer, depth of input maps