This section applies to machines with Secure Boot, such as ThinkPad.
| Title | Description
| ''' | |
| minimal avplayer example | |
| ''' | |
| from objc_util import * | |
| AVPlayerItem=ObjCClass('AVPlayerItem') | |
| AVPlayer=ObjCClass('AVPlayer') | |
| AVPlayerLayer=ObjCClass('AVPlayerLayer') | |
| import photos | |
| def pick_asset(): | |
| assets = photos.get_assets(media_type='video') |
| -- This creates an athena table that can parse ALB logs. | |
| -- Advantage of this over others are this works when the log ends with a trailing space | |
| -- plus it also breaks the http request into route and params for easier grouping | |
| CREATE EXTERNAL TABLE IF NOT EXISTS alb_logs ( | |
| type string, | |
| timestamp string, | |
| elb string, | |
| client_ip string, | |
| client_port int, |
More details - http://blog.gbaman.info/?p=791
For this method, alongside your Pi Zero, MicroUSB cable and MicroSD card, only an additional computer is required, which can be running Windows (with Bonjour, iTunes or Quicktime installed), Mac OS or Linux (with Avahi Daemon installed, for example Ubuntu has it built in).
1. Flash Raspbian Jessie full or Raspbian Jessie Lite onto the SD card.
2. Once Raspbian is flashed, open up the boot partition (in Windows Explorer, Finder etc) and add to the bottom of the config.txt file dtoverlay=dwc2 on a new line, then save the file.
3. If using a recent release of Jessie (Dec 2016 onwards), then create a new file simply called ssh in the SD card as well. By default SSH i
| jupyter: | |
| image: jupyter/datascience-notebook | |
| environment: | |
| - PASSWORD=${PASSWORD} | |
| nginx: | |
| image: nginx | |
| links: | |
| - jupyter |
| -- Table information like sortkeys, unsorted percentage | |
| -- see http://docs.aws.amazon.com/redshift/latest/dg/r_SVV_TABLE_INFO.html | |
| SELECT * FROM svv_table_info; | |
| -- Table sizes in GB | |
| SELECT t.name, COUNT(tbl) / 1000.0 AS gb | |
| FROM ( | |
| SELECT DISTINCT datname, id, name | |
| FROM stv_tbl_perm | |
| JOIN pg_database ON pg_database.oid = db_id |
Mouseover to focus on rides to or from a single neighborhood.
The thickness of links between neighborhoods encodes the relative frequency of rides between two neighborhoods: thicker links represent more frequent rides. Only flows that represent more than 1000 trips are represented here to avoid too many small flows.
Links are directed: for example, while 0.5% of rides go from Midtown to the Flatiron District, only 0.4% go in the opposite direction. Links are colored by the more frequent origin.
This may not be the best visualisation for this type of data, particularly given how dense the connectivity matrix is. However, it does present a quick and fun way to checkout the CitiBike data at an aggregate level.
| # Shows the location of the last photo in the canera roll in the Maps app. | |
| # (thanks to @HyShai for pointing out that the latitude/longitude refs are necessary) | |
| import photos | |
| import webbrowser | |
| meta = photos.get_metadata(-1) | |
| gps = meta.get('{GPS}') | |
| if gps: | |
| latitude = str(gps.get('Latitude', 0.0)) + gps.get('LatitudeRef', '') |
| #!/usr/bin/ruby | |
| # SIFTTTER 1.5: An IFTTT-to-Day One Logger by Craig Eley 2014 <http://craigeley.com> | |
| # Based on tp-dailylog.rb by Brett Terpstra 2012 <http://brettterpstra.com> | |
| # Multiple Date Function by Paul Hayes 2014 <http://paulrhayes.com> | |
| # | |
| # Notes: | |
| # * Uses `mdfind` to locate a specific folder of IFTTT-generated text files changed in the last day | |
| # * The location of your folder should be hardcoded in line 67, and the location of your Day One in line 66 | |
| # * Scans leading timestamps in each line matching the selected dates | |
| # * Does not alter text files in any way |