Install PINGMapper

Get PINGMapper up and running on your computer.

PINGMapper PINGWizard PINGVerter PINGInstaller

As of v4.0, the PINGMapper installation process has been dramatically improved and simplified compared to the old version

PINGMapper is a software (i.e. package) written in Python. PINGMapper uses a variety of Python packages (NumPy, Pandas, Tensorflow, etc.), or dependencies, that allow you to process Humminbird® sonar recordings and generate a variety of GIS datasets.

You do not need to know Python to use PINGMapper! After issuing a few simple commands, all interactions with PINGMapper will be point-and-click.

PINGMapper uses conda to ensure the installation is configured correctly. Specifically, conda is used to create a virtual environments called ping, a container storing all the correct versions of of the required dependencies, that ensures PINGMapper runs as expected.

Conda comes in several flavors, however, we will use Miniforge as it is free for anyone to use.

Existing users may have previously used Miniconda or Anaconda. Due to changes is licensing, it is recommended that you transition to Miniforge, an open-source alternative.

This tutorial will demonstrate how to install and configure PINGMapper. After installing Miniforge, we will install and run PINGInstaller. PINGInstaller automatically creates the ping environment, installs the appropriate packages from the PING Ecosystem (PINGMapper, PINGWizard, PINGVerter, etc.), and other necessary dependencies.

Let’s get started!

Step 1 - Install Miniforge

Go to the Miniforge Website and download the software. Choose the appropriate installer for your computer’s operation system. This tutorial was made on a Windows machine but the process should be similar on other operation systems. Click the file and it will download to your Downloads folder, or you can right-click and select “Save Link As…” and choose an alternative location to save the install file.

Double click the file to begin the installation file. This will open an installation window:

Click Next and you will see the license agreement:

After reviewing the license agreement, you must select I Agree to continue with the installation. After you agree, you will have an option to install Miniconda for Just Me or All Users.

You want to install Miniforge in your user folder so that you have the necessary permissions to install the Python dependencies, so select Just Me and click Next.

Accept the default installation location and click Next. This will open the Advanced Installation Options window.

We will accept the default options, shown above, and click Next. Once installation is complete, you will see the following window indicating Miniconda was successfully installed:

Click Finish to close the window.

Step 2

Now for the scary part! We are going to open a command prompt so that we can submit a series of commands to Miniforge. If you want to gain some familiarity with navigating with the prompt, you can watch this video:

Miniforge is a command prompt that we will use to install and run PINGMapper. On Windows, click the start button and scroll through your installed applications until you find Miniforge Prompt.

Click the icon to open the prompt.

Step 2

Installing PINGInstaller in the base environment will only download the PINGInstaller package and make no further changes.

A package called PINGInstaller is used to install and setup PINGMapper. We will install PINGInstaller with the following command and pressing Enter:

pip install pinginstaller -U

Step 3

By running PINGInstaller, a new conda environment called ping will be created, and all dependencies will be installed into ping. Add the following command and press Enter:

python -m pinginstaller

Installation will take approximately 5-10 minutes. You should see something similar to:

At the end of the install process, a window will prompt you where to save the bat or sh shortcut file. Browse to the desired location and click Submit.

That’s It!

PINGMapper is now ready to go. Launch PINGWizard, a new interface for PINGMapper, to start processing data.

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This study was made possible by a partnership between the U.S. Fish and Wildlife Service and Northern Arizona University. Funding for this work was provided by the Open Ocean Trustee Implementation Group to restore natural resources injured by the 2010 Deepwater Horizon oil spill in the Gulf of Mexico. The contents of this website are those of the authors and do not necessarily represent the views of the U.S. Fish and Wildlife Service or Northern Arizona University. Copyright © 2024 Cameron Bodine.