The McChicken of Data Analysis. It's not gonna change your life, but it gets the job done.
McNugget is an analysis toolkit that automatically reads and writes data from the MASA Synnax Data Processing Server (no configuration required!), which you can use to analyze MASA's 2023 and later data.
To work with McNugget, you'll need to be connected to the UofM VPN or MWireless. Instructions for installing and using the VPN can be found here.
The next step is to install the Synnax encryption certificates. These allow you to communicate with the database in a secure manner. To install them, run the corresponding command for your operating system:
curl -sfL https://raw.githubusercontent.com/masa-umich/mcnugget/main/scripts/install_certs_macos.sh | sh -Note that you will be prompted for your password.
Invoke-WebRequest -Uri https://raw.githubusercontent.com/masa-umich/mcnugget/main/scripts/install_certs_windows.ps1 -OutFile install_certs_windows.ps1Then, run the following command in powershell:
.\install_certs_windows.ps1The Synnax Console is the tool we use for test ops and data visualization. It's also very useful for analysis. To install it, follow the instructions here. Then, follow the instructions here to connect to the MASA server. Use the following connection parameters:
- Name:
MASA Remote - Host:
synnax.masa.engin.umich.edu - Port:
80 - Username:
synnax - Password:
seldon - Secure:
true
Please note that the console ONLY works on the latest versions of MacOS (Sonoma), Windows 10, and Windows 11
If you find that plots don't display, you may need to udate your version of MacOS or your version of Microsoft Edge on Windows.
To kick things off, you'll need to clone this repository using git. If you don't have git installed, you can find
instructions here. Once you have git installed,
create a new project/folder in your editor. We recommend the
directory ~/Desktop/masa-umich/. Then, run the following command:
git clone https://github.com/masa-umich/mcnugget.gitThis will download the mcnugget source code into the directory ~/Desktop/masa-umich/mcnugget/.
After downloading the source, the next step is to install and set up Python. You'll need version 3.11. If you don't have
Python installed, see the Synnax Python Guide. If you're
still having trouble, send a message in the #software Slack channel.
McNugget uses a virtual environment tool called poetry to manage its dependencies and configuration. To install poetry,
run the following command:
pip install poetryThen, we need to make sure the virtual environment we use is created in the same directory as our project (if you don't know what a virtual environment is, don't worry).
poetry config virtualenvs.in-project true
Now that you have poetry installed, you can install McNugget's dependencies by running the following command:
poetry installTo verify everything installed correctly, you'll want to run an example. The first step is to start a shell. To do this, run the following command:
poetry shellYou'll want to be in a shell whenever you're running mcnugget scripts. Run the following example script:
python examples/simple_line_plot.pyA line plot containing some TPC data should show up.
The first step is to create a new script in the mcnugget directory. Choose the name of this script
to match the type of analysis you're performing. For example, if you're performing a CDA analysis,
name the script cda.py.
There are a few tools we'll need for reading, cleaning, processing, and plotting data. Import them at the top of your file as follows (feel free to copy and paste):
import matplotlib.pyplot as plt
import synnax as sy
from mcnugget.client import clientIf you're interested in what each import does, here's a brief description:
import matplotlib.pyplot as pltis a plotting library that we use to plot data. It's a 1-1 port of the MATLAB plotting library.import synnax as syis a library that we use to define time ranges. We'll use this to define the range of data we want to read.
The best way to find the list of ranges you're interested in is by using the console and following this guide. Once you've found it, you can bring in the range and plot some data on it like so:
import matplotlib.pyplot as plt
import synnax as sy
from mcnugget.client import client
rng = client.ranges.retrieve("My Cool Range")
plt.plot(sy.elapsed_seconds(rng.gse_time), rng.gse_ai_1)
plt.show()If you see an error like the following, screenshot it and send it in the #avionics slack channel:
Traceback (most recent call last):
File "/Users/emilianobonilla/Desktop/masa-umich/mcnugget/mcnugget/query/client.py", line 13, in <module>
CLIENT = Synnax(
^^^^^^^
File "/Users/emilianobonilla/Desktop/synnax/client/py/synnax/synnax.py", line 75, in __init__
self._transport = self._configure_transport(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/emilianobonilla/Desktop/synnax/client/py/synnax/synnax.py", line 119, in _configure_transport
auth.authenticate()
File "/Users/emilianobonilla/Desktop/synnax/client/py/synnax/auth.py", line 98, in authenticate
raise exc
freighter.exceptions.Unreachable: Target http://10.0.0.15:9090/api/v1/auth/login/ unreachable
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/Users/emilianobonilla/Desktop/masa-umich/mcnugget/examples/simple_line_plot.py", line 3, in <module>
from mcnugget.query import read_during_state, ECStates
File "/Users/emilianobonilla/Desktop/masa-umich/mcnugget/mcnugget/query/__init__.py", line 1, in <module>
from .read import *
File "/Users/emilianobonilla/Desktop/masa-umich/mcnugget/mcnugget/query/read.py", line 2, in <module>
from mcnugget.query.client import CLIENT
File "/Users/emilianobonilla/Desktop/masa-umich/mcnugget/mcnugget/query/client.py", line 21, in <module>
raise Exception(
Exception:
Failed to connect to Synnax data processing sever. Screenshot
this error and send it in the #software slack channel.Sometimes we add new dependences to McNugget, to make sure all of your dependencies are up to date, run:
poetry install
in the root directory of the repository.
If you're moving from MATLAB or haven't used an IDE before, you may be wondering what editor to use. For the most MATLAB like experience we recommend using DataSpell. The free version is fine for all of our use cases, but you can also get the pro version for free if you're a student.
If you're looking for a lightweight editor, we recommend VSCode. It's the most widely used editor by far.
GitHub pro for students gives you access to copilot AI autocompletion and JetBrains IDEs for free. You can sign up here.
While VSCode is lightweight and multi-purpose, DataSpell has much better Python syntax highlighting and error checking out of the box. It also gives you really nice debug and run buttons like MATLAB.
If you have github pro, you can get DataSpell and ALL of the JetBrains IDEs for free here.
If you're new to Python or coming from MATLAB, github copilot is a godsend. It's an AI that autocompletes your code for you. You can sign up here for free with your github pro account.
The copilot plugin for DataSpell allows you to use copilot directly in DataSpell. You can
install it by going to File > Settings > Plugins and searching for copilot.
I saw someone eating a chicken nugget while I was trying to come up with a name.