Background knowledge: what you need to know to get the most out of this workshop

This workshop is tailored toward researchers with a working knowledge of Python. If you are a total beginner, it would be a good idea to familiarize yourself with some basic programming concepts before you begin. The Software Carpentries website might be a good place to start. You will need to be able to understand and make use of the following Python concepts:

Data can be stored in variables through assignment.

 >>> a = 4
 >>> b = 'test'
 >>> a
 4
 >>> b
 'test'

Variables hold data of different types (e.g., strings, integers, 64-bit floating-point numbers, etc.).
```
 >>> type(a)
 <class 'int'>
 >>> type(b)
 <class 'str'>
```

Data can be stored as lists and these can also be assigned to variables.

 >>> c = [1,2,3,4]
 >>> c
 [1, 2, 3, 4]
 >>> type(c)
 <class 'list'>

Indexing can be used to select specific positions in a list.
```
 >>> c[0]
 1
 >>> c[3]
 4
```

Functions can take one or more arguments, do something with them, and return a value.

 >>> def add(a, b):
 ...     sum = a + b
 ...     return sum
 ...
 >>> add(1 ,3)
 4
 >>> add(34, 92)
 126

Python’s functionality can be extended through packages and modules.

 >>> import matplotlib.pyplot as plt
 >>> import numpy as np
 >>> a = np.array([1,2,3]) #accessing the array function in the np (numpy) package to create an array object
 >>> plt.hist(a)
 (array([1., 0., 0., 0., 0., 1., 0., 0., 0., 1.]), array([1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4, 2.6, 2.8, 3. ]), <a list of 10 Patch objects>)

Data can be represented as objects, which have attributes as well as associated functions called methods.

 >>> a.dtype #accessing the data type of object 'a', which is a numpy array (created in #6 above)
 dtype('int64')
 >>> a.max() #a function that finds the maximum value of 'a'
 3

Note that in Python, dot notation can be used to call an object method, e.g., a.max(), but can also be used to call a function that is part of a package, e.g., np.array().

Tools we will be using

You will need to use three software tools in addition to Python for this workshop: git, conda, and jupyter.

git is only used in this workshop to copy the course notes and code to your local computer, although it is a very powerful tool for managing your projects!

conda is a tool used for two purposes: to create environments for your projects and to manage Python packages. It will be a very good idea to read up on conda before you begin this course, spefically how to use environments.

jupyter is a tool for developing your code in “notebooks” (file extension *.ipynb) in a web browser. The main utility of using notebooks for image analysis is that you can save all of your output on the same page as your code, which helps you and your collaborators to follow the steps of an analysis.

Setting up for the class

Clone the git repository to your computer.
```
 git clone https://github.com/TheJacksonLaboratory/PythonImagingBasic.git
```
Windows Users: You will first need to install git. Then you can use Git Bash to use the command shown above

As an alternative to using git, you can also download the repository as a zip file from Github.
Install Anaconda (or Miniconda)
- This gives you access to the conda Python package management system
- Windows Users: You may want to opt for the full install of Anaconda. This will give you “Anaconda Prompt” which will make it easier for you to follow along here.
Using conda in a terminal window (or Anaconda prompt in Windows), create a virtual environment called ‘ImPyClass’ that uses Python3:
```
 conda create -n ImPyClass python=3
```
Switch to the ImPyClass virtual environment

macOS, Linux: conda activate ImPyClass

Windows (Anaconda Prompt): activate ImPyClass

Install Scikit-Image, Numpy, Jupyter Lab, and NB_Conda:

 conda install scikit-image numpy nb_conda
 conda install -c conda-forge jupyterlab

Launch Jupyter Lab from the Anaconda launcher or command line.

jupyter lab