The first step is to identify a folder location where you would like to work in a development environment. We suggest a location that will be able to easily access streamflow predictions to make for easy evaluation of your model. Using the command prompt, change your working directory to this folder and git clone Snow-Extrapolation
git clone https://github.com/geo-smart/Snow-Extrapolation
It is a best practice to create a virtual environment when starting a new project, as a virtual environment essentially creates an isolated working copy of Python for a particular project. I.e., each environment can have its own dependencies or even its own Python versions. Creating a Python virtual environment is useful if you need different versions of Python or packages for different projects. Lastly, a virtual environment keeps things tidy, makes sure your main Python installation stays healthy and supports reproducible and open science.
Go to home directory
cd ~
Create a envs directory
mkdir envs
Create .condarc file and link it to a text file
touch .condarc
ln -s .condarc condarc.txt
Add the below lines to the condarc.txt file
# .condarc
envs_dirs:
- ~/envs
Restart your server
Since we will be using Jupyter Notebooks for this exercise, we will use the Anaconda command prompt to create our virtual environment. In the command line type:
conda create -n NSM_env python=3.9.12
For this example, we will be using Python version 3.9.12, specify this version when setting up your new virtual environment. After Anaconda finishes setting up your NSM_env , activate it using the activate function.
conda activate NSM_env
You should now be working in your new NSM_env within the command prompt. However, we will want to work in this environment within our Jupyter Notebook and need to create a kernel to connect them. We begin by installing the ipykernel python package:
pip install --user ipykernel
With the package installed, we can connect the NSM_env to our Python Notebook
python -m ipykernel install --user --name=NSM_env
Under contributors, there is a start to finish example to get participants up to speed on the modeling workflow. To double check you have the correct working environment, open the up the Methods file, click the kernel tab on the top toolbar, and select the NSM_env. The NSM_env should show up on the top right of the Jupyter Notebook.
We will now be installing the packages needed to use NSM_env, as well as other tools to accomplish data science tasks. Enter the following code block in your Anaconda Command Prompt to get the required dependencies with the appropriate versions, note, you must be in the correct working directory:
pip install -r requirements.txt
The objective of the project is to optimize the NSM, or SSM in this case. To do, the next step is to explore the NSM Example.