chbrandt

"""
RC Database Server code sample

> Before your interview, write a program that runs a server that is accessible 
> on `http://localhost:4000/`. When your server receives a request 
> on `http://localhost:4000/set?somekey=somevalue` it should store 
> the passed key and value in memory. When it receives a request 
> on `http://localhost:4000/get?key=somekey` it should return 
> the value stored at `somekey`.
> During your interview, you will pair on saving the data to a file. 

chbrandt

chbrandt

chbrandt

{
"type": "FeatureCollection",
"features": [
{ "type": "Feature", "properties": { "index": "0", "Name": "Viking 2", "Space Agency": "NASA", "Type": "Lander", "Travel distance (km)": null, "Launch Date": "1975-09-09", "Landing Date": "1976-09-03", "Region": "Utopia Planitia", "Min Lat": 46.84, "Max Lat": 48.46, "Min Lon": 133.36, "Max Lon": 135.07, "Landing Lat": 47.643, "Landing Lon": 134.28799438476562, "Elevation": -4495.0, "Status": "Not Active" }, "geometry": { "type": "Point", "coordinates": [ 134.287994384765625, 47.643 ] } },
{ "type": "Feature", "properties": { "index": "1", "Name": "Perseverance", "Space Agency": "NASA", "Type": "Rover", "Travel distance (km)": 0.2, "Launch Date": "2020-07-30", "Landing Date": "2021-02-18", "Region": "Jazero Crater", "Min Lat": 17.9, "Max Lat": 19.13, "Min Lon": 76.92, "Max Lon": 78.32, "Landing Lat": 18.4386, "Landing Lon": 77.503097534179688, "Elevation": -2640.0, "Status": "Active" }, "geometry": { "type": "Point", "coordinates": [ 77.503097534179688, 18.4386 ] } 

chbrandt

#!/usr/bin/env python
"""
Interactive execution with automatic history, tries to mimic Mathematica's
prompt system. This environment's main features are:

- Numbered prompts (In/Out) similar to Mathematica. Only actions that produce
  output (NOT assingments, for example) affect the counter and cache.

- The following GLOBAL variables always exist (so don't overwrite them!):
  _p: stores previous result which generated printable output.

chbrandt

Visualizing image in 3D with Matplotlib

This visualization was created to address a request for better visualization of the effects of blurring (smoothing). The (petri dish) image used and the exact context of the discussion can be recovered from the corresponding Carpentries' lesson and related discussion:

• datacarpentry/image-processing#221
• https://datacarpentry.org/image-processing/06-blurring.html

chbrandt

#!/bin/bash -ue

# This is script is meant to be sourced:
# > source git-check.sh
#
# It defines an equally named function 'git-check' that accepts one argument: 
# - the path to where git repositories live in your system.
# If none is given, the current working directory ($PWD) is used.
#
# Example:

chbrandt

#!/bin/bash

# Use docker container from https://github.com/abraunegg/onedrive
# Detailed instructions at https://github.com/abraunegg/onedrive/blob/master/docs/Docker.md

ONEDRIVE_DATA_DIR="${HOME}/OneDrive"
ONEDRIVE_UID=`id -u`
ONEDRIVE_GID=`id -g`

mkdir -p ${ONEDRIVE_DATA_DIR} &> /dev/null

chbrandt

#!/bin/bash

awk '/>>> conda initialize >>>/ {p=1}; /<<< conda initialize <<</ {p=0; next}; {if (p==0) print $0}' .bashrc

chbrandt

from http.server import HTTPServer, ThreadingHTTPServer, BaseHTTPRequestHandler

from urllib.parse import urlparse, parse_qs


# Dictionary-db storing user key-value pairs.
#
_db = {}

_database_file = '/tmp/dataserver.txt'