bigsnarfdude

import networkx as nx

B = nx.Graph()
# Add nodes with the node attribute "bipartite"
B.add_nodes_from([1, 2, 3, 4], bipartite=0)
B.add_nodes_from(["a", "b", "c"], bipartite=1)
# Add edges only between nodes of opposite node sets
B.add_edges_from([(1, "a"), (1, "b"), (2, "b"), (2, "c"), (3, "c"), (4, "a")])

def get_webpage_visitors(webpage, target_graph):

bigsnarfdude

bigsnarfdude

import os
import numpy as np
import tensorflow as tf
import networkx as nx
from collections import Counter

from sklearn.utils import shuffle
from sklearn.preprocessing import LabelEncoder

bigsnarfdude

bigsnarfdude

"""
This example implements the experiments on citation networks from the paper:

Semi-Supervised Classification with Graph Convolutional Networks (https://arxiv.org/abs/1609.02907)
Thomas N. Kipf, Max Welling
"""
import numpy as np
import tensorflow as tf
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.losses import CategoricalCrossentropy

bigsnarfdude

import java.io.PrintWriter
import java.io.File

import com.twitter.algebird._
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.broadcast.Broadcast

bigsnarfdude

import unicodedata
output = unicodedata.normalize('NFD', my_unicode).encode('ascii', 'ignore')

myfoo = u'àà'
myfoo
u'\xe0\xe0'

unicodedata.normalize('NFD', myfoo).encode('ascii', 'ignore')
'aa'

bigsnarfdude

{
    "name": "pipeline1",
    "properties": {
        "activities": [
            {
                "name": "Call dataset refresh",
                "type": "WebActivity",
                "dependsOn": [
                    {
                        "activity": "Get AAD Token",

bigsnarfdude

import sys
import json
import urllib
import urllib2
import time

POWER_BI_RESOURCE_ENDPOINT = "https://analysis.windows.net/powerbi/api"
MICROSOFT_OAUTH2_API_ENDPOINT = "https://login.windows.net/common/oauth2/token/"

WAIT_AFTER_REFRESH_START_SECONDS = 15 

bigsnarfdude

The Blackmagic Pocket Cinema DCI 4K resolution at 30 fps, the bitrates are as follows:

DCI 4K 30p (4096 x 2160) at 272MB/s (2176mb/s) bitrate using CinemaDNG RAW 12-bit
DCI 4K 30p (4096 x 2160) at 129MB/s (1032mb/s) bitrate using CinemaDNG RAW 3:1 12-bit
DCI 4K 30p (4096 x 2160) at 97MB/s (776mb/s) bitrate using CinemaDNG RAW 4:1 12-bit
DCI 4K 30p (4096 x 2160) at 118MB/s (944mb/s) bitrate using ProRes 422 HQ 10-bit
DCI 4K 30p (4096 x 2160) at 78.63MB/s (629mb/s) bitrate using ProRes 422 10-bit
DCI 4K 30p (4096 x 2160) at 54.63MB/s (437mb/s) bitrate using ProRes 422 LT 10-bit
DCI 4K 30p (4096 x 2160) at 24.25MB/s (220mb/s) bitrate using ProRes 422 Proxy 10-bit
The Blackmagic Pocket Cinema DCI 4K resolution at 60 fps, the bitrates are as follows: