arogozhnikov

Notes on edgedb

I’ve made a test evaluation of edgedb for the purposes of a small company.

My order of priorities (starting from most important):

1. correctness of results (data and code)
2. developer convenience
3. minimal maintenance
4. efficiency/scalability

geerlingguy

# Download the pivariety driver install script and make it executable
wget -O install_pivariety_pkgs.sh https://github.com/ArduCAM/Arducam-Pivariety-V4L2-Driver/releases/download/install_script/install_pivariety_pkgs.sh
chmod +x install_pivariety_pkgs.sh

# Install libcamera dev and apps
./install_pivariety_pkgs.sh -p libcamera_dev
./install_pivariety_pkgs.sh -p libcamera_apps

# Install the Hawk-Eye kernel driver
./install_pivariety_pkgs.sh -p 64mp_pi_hawk_eye_kernel_driver

going-digital

;; Native implementations of sin, log and exp functions.
;; sintau: 41 bytes code, 34 bytes shared code, 24 bytes data
;; exp2: 25 bytes code, 34 bytes shared code, 20 bytes data
;; log2: 37 bytes code, 34 bytes shared code, 24 bytes data
;; Total 137 bytes code, 68 bytes data

;; Wasm-opt -Oz tries to optimise out $half by converting to f32.consts, but that actually takes up more space, not less.

;; Polynomial coefficients calculated by accompanying python script.
;; call $evalpoly parameters will need to be manually changed for different length polynomials.

jitran

#!/usr/bin/env python3

from aws_cdk import core

from my_python_sample.my_python_sample_stack import MyPythonSampleStack


app = core.App()
MyPythonSampleStack(app, 'my-python-sample', env={'region': 'ap-southeast-2'})

yang-song

import tensorflow as tf


def gradients(f, x, grad_ys=None):
    '''
    An easier way of computing gradients in tensorflow. The difference from tf.gradients is
        * If f is not connected with x in the graph, it will output 0s instead of Nones. This will be more meaningful
            for computing higher-order gradients.

        * The output will have the same shape and type as x. If x is a list, it will be a list. If x is a Tensor, it

crazyhottommy

An error

I was using TEQC to do quality control of my WES bam files aligned by bwa-mem. My data are paired end, so a function reads2pairs is called to make the paired-end reads to be a single fragment. I then get this error:

> readpairs <- reads2pairs(reads)
Error in reads2pairs(reads) : read pair IDs do not seem to be unique

I asked in the bioconductor support site and went to the source code of that function.

marten-cz

# Run command with the same user ID as current user
# -v $(pwd):/tmp/mount - mount current directory to /tmp/mount/
# --env HOME="/tmp/" - some commands may need to be able to write to your home, se it to temporary folder
docker run -ti --rm -v $(pwd):/tmp/mount —user=$(id -u) --env HOME="/tmp/" debian:jessie

# Mount current users and group and be able to use them
# mount /etc/group and /etc/passwd read only
# set user from $USER
docker run -ti --rm -v $(pwd):/tmp/mount -w /tmp/hx -v /etc/group:/etc/group:ro -v /etc/passwd:/etc/passwd:ro —user=$USER debian:jessie

arogozhnikov

mstaflex

# executing a script for user 'pi' with std. shell

su -c "./home/pi/run_robbi_demo.sh" -s /bin/sh pi

entaroadun

Movies Recommendation:

• MovieLens - Movie Recommendation Data Sets http://www.grouplens.org/node/73
• Yahoo! - Movie, Music, and Images Ratings Data Sets http://webscope.sandbox.yahoo.com/catalog.php?datatype=r
• Jester - Movie Ratings Data Sets (Collaborative Filtering Dataset) http://www.ieor.berkeley.edu/~goldberg/jester-data/
• Cornell University - Movie-review data for use in sentiment-analysis experiments http://www.cs.cornell.edu/people/pabo/movie-review-data/

Music Recommendation:

• Last.fm - Music Recommendation Data Sets http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/index.html