| #! /bin/bash | |
| # | |
| # rough and ready bash script to download & decrypt a widevine-encrypted video via MPD URL | |
| # this will work with (for example) channel5 UKTV | |
| # | |
| # assumptions: | |
| # 1) the following binaries are in your $PATH: | |
| # curl, ffmpeg, head, jq, mp4decrypt, yt-dlp | |
| # | |
| # this script requires 2x arguments: |
If you're trying to do this, you came to the right place!
Watch this code work in real time: https://twitter.com/CodingDoug/status/942576182276497409
See also this gist for copying in the other direction: https://gist.github.com/CodingDoug/ffc4f050cc489a0280eb7f4cbe36af07
If you're trying to do this, you came to the right place!
Watch this code work in real time: https://twitter.com/CodingDoug/status/940022568089554944
See also this gist for copying in the other direction: https://gist.github.com/CodingDoug/44ad12f4836e79ca9fa11ba5af6955f7
| """ | |
| flask application | |
| """ | |
| import logging | |
| import logging.config | |
| import os | |
| from flask import Flask | |
| import structlog |
| import json | |
| import numpy as np | |
| import keras | |
| import keras.preprocessing.text as kpt | |
| from keras.preprocessing.text import Tokenizer | |
| from keras.models import model_from_json | |
| # we're still going to use a Tokenizer here, but we don't need to fit it | |
| tokenizer = Tokenizer(num_words=3000) | |
| # for human-friendly printing |
This guide has moved to a GitHub repository to enable collaboration and community input via pull-requests.
https://github.com/alexellis/k8s-on-raspbian
Alex
| import logging | |
| from airflow.models import DagBag | |
| def callback_subdag_clear(context): | |
| """Clears a subdag's tasks on retry.""" | |
| dag_id = "{}.{}".format( | |
| context['dag'].dag_id, | |
| context['ti'].task_id, | |
| ) | |
| execution_date = context['execution_date'] |
Visualizing stable/unstable equilibria in K-Means unsupervised learning algorithm. Each run follows the path of the mean converging as shown here
| package com.mallo64.dataflow | |
| import com.google.api.client.json.GenericJson; | |
| import com.google.api.services.bigquery.model.TableCell; | |
| import com.google.api.services.bigquery.model.TableFieldSchema; | |
| import com.google.api.services.bigquery.model.TableRow; | |
| import com.google.api.services.bigquery.model.TableSchema; | |
| import com.google.cloud.dataflow.sdk.transforms.DoFn; | |
| import org.apache.avro.Schema; | |
| import org.apache.avro.specific.SpecificRecord; |
| #!/usr/bin/env python | |
| """ | |
| The script builds OpenCV.framework for iOS. | |
| The built framework is universal, it can be used to build app and run it on either iOS simulator or real device. | |
| Usage: | |
| ./build_framework.py <outputdir> | |
| By cmake conventions (and especially if you work with OpenCV repository), | |
| the output dir should not be a subdirectory of OpenCV source tree. |
