daltare

# This example shows one way to access subsets of CEDEN data via the California Open Data Portal API  (it's not 
# necessarily the only or best way though) - if you find any problems or have questions, please contact: david.altare@waterboards.ca.gov

# This example applies to the following CEDEN datasets, which have been split into separate
# resources by year (due to file size limitations)
    # Water Chemistry: https://data.ca.gov/dataset/surface-water-chemistry-results
    # Habitat: https://data.ca.gov/dataset/surface-water-habitat-results
    # Tissue: https://data.ca.gov/dataset/surface-water-aquatic-organism-tissue-sample-results

# It assumes that you have your own API key saved in a system environment variable 

daltare

# This example shows how to work with CEDEN data in the Apache parquet file format, using the arrow package in R 
# The data (in parquet file format) for each type of CEDEN data is available on the California Open Data Portal at the 
# following links: 
# Water Chemistry: https://data.ca.gov/dataset/surface-water-chemistry-results/resource/f4aa224d-4a59-403d-aad8-187955aa2e38
# Habitat: https://data.ca.gov/dataset/surface-water-habitat-results/resource/0184c4d0-1e1d-4a33-92ad-e967b5491274
# Tissue: https://data.ca.gov/dataset/surface-water-aquatic-organism-tissue-sample-results/resource/dea5e450-4196-4a8a-afbb-e5eb89119516
# Toxicity: https://data.ca.gov/dataset/surface-water-toxicity-results/resource/a6c91662-d324-43c2-8166-a94dddd22982
# Benthic Macroinvertebrates: https://data.ca.gov/dataset/surface-water-benthic-macroinvertebrate-results/resource/eb61f9a1-b1c6-4840-99c7-420a2c494a43

# load packages

raulqf

How to install Tensorflow2 with GPU support in Ubuntu 20.04

Installation of Tensorflow2 with GPU support is easy and the only complication can be arisen from the CUDA compability which in turns depends on the Nvidia driver version. Before going farther, please check if your Nvidia Video Card is compatible with the required versions that are defined in this gist, use this link.

Tensorflow offers in its website a table of the compatibility between libraries for the target OS. You can visit that website in the following COMPATIBILITY TABLE that points to the Tensorflow installation from source for linux. For the time writing this gist, Tensorflow2 v2.5.0 requires CUDA v11.2 and CUDNN v8.2. It is really important to match the exact version, otherwise tensorflow will have problems loading the shared libraries as not finding the correct version.

CUDA version also requires for a minimum Nvidia driver version

IanColdwater

Mute these words in your settings here: https://twitter.com/settings/muted_keywords

ActivityTweet
generic_activity_highlights
generic_activity_momentsbreaking
RankedOrganicTweet
suggest_activity
suggest_activity_feed
suggest_activity_highlights
suggest_activity_tweet

aparrish

opieters

#!/usr/bin/env python

# install imagesize: pip install imagesize pyyaml

__author__ = 'Olivier Pieters'
__author_email__ = 'me@olivierpieters.be'
__license__ = 'BSD-3-Clause'

import yaml, imagesize
from os import listdir, rename

gbaman

Raspberry Pi Zero OTG Mode

Simple guide for setting up OTG modes on the Raspberry Pi Zero - By Andrew Mulholland (gbaman).

The Raspberry Pi Zero (and model A and A+) support USB On The Go, given the processor is connected directly to the USB port, unlike on the B, B+ or Pi 2 B, which goes via a USB hub.
Because of this, if setup to, the Pi can act as a USB slave instead, providing virtual serial (a terminal), virtual ethernet, virtual mass storage device (pendrive) or even other virtual devices like HID, MIDI, or act as a virtual webcam!
It is important to note that, although the model A and A+ can support being a USB slave, they are missing the ID pin (is tied to ground internally) so are unable to dynamically switch between USB master/slave mode. As such, they default to USB master mode. There is no easy way to change this right now.
It is also important to note, that a USB to UART serial adapter is not needed for any of these guides, as may be documented elsewhere across the int

hfreire

# Install QEMU OSX port with ARM support
sudo port install qemu +target_arm
export QEMU=$(which qemu-system-arm)

# Dowload kernel and export location
curl -OL \
https://github.com/dhruvvyas90/qemu-rpi-kernel/blob/master/kernel-qemu-4.1.7-jessie
export RPI_KERNEL=./kernel-qemu-4.1.7-jessie

# Download filesystem and export location

andrewheiss

library(httr)
library(jsonlite)

# API documentation: https://github.com/pelias/pelias-doc/blob/master/search.md
base_url <- "https://search.mapzen.com/v1/search"
res <- GET(base_url, query = list(text = "Durham, NC", api_key = "PUT KEY HERE"))
json <- content(res, as = "text")
results <- fromJSON(json)$features

head(results)

flakas

/* HC-SR04 Sensor
   https://www.dealextreme.com/p/hc-sr04-ultrasonic-sensor-distance-measuring-module-133696
  
   This sketch reads a HC-SR04 ultrasonic rangefinder and returns the
   distance to the closest object in range. To do this, it sends a pulse
   to the sensor to initiate a reading, then listens for a pulse 
   to return.  The length of the returning pulse is proportional to 
   the distance of the object from the sensor.
     
   The circuit: