When you start working with Python it is great practice to create isolated Python environments to work on your specific projects.
The standard python environment is used by a large number of system scripts and therefore best to leave alone. In addition, when working on different projects, those projects may have different and conflicting dependencies and therefore should ideally be installed in their own python environments. The ability to create different python environments can also be really beneficial when developing your own python packages and thereby test its installation and performance in different versions of python.
Below I guide you through the basic steps of installing and working with python virtual environments.
Installing OpenCV has never been easy and always required a lot of careful usage of the command line to build from source. This was especially painful when working with a Raspberry Pi as building and installing OpenCV took a lot of time on the RPi, especially on the older models. Luckily this has changed very recently as it is now possible to install OpenCV with pip!
Below I guide you through the basic steps that I think are necessary to get opencv to work nicely on Mac, Ubuntu and Raspberry Pi. If you want more background information, see the excellent article by Adrian Rosebrock from PyImageSearch.com.
In my previous post I showed a fully interactive online graph of one of the plots in my recent paper on leadership in sticklebacks. In this follow-up post I will explain how to easily create such an interactive plot yourself. To be able to do this you will need some experience with the R-language and ideally with ggplot2.
First create an account at plot.ly, which is free. After you have created your account, go to “settings” and click on “generate API key”. You will need your username and this key to link your account to R.
Now you have your account ready start-up R and set-up the R workspace:
# Install the necessary packages
# Now load the packages
# Set your Plotly user credentials