Setup a Mach probe processing module. Inputs would be 2 or 6 channels of ion saturation current. Compute Mach number based on standard Mach probe model.

The NRL formulary includes relaxation rates/collision frequencies (pg. 31 in this version) for several collision processes. These are expressed as functions of an integral Phi, which itself is a function of the ratio between the bulk flow energy (1/2 mv^2) and the thermal energy (kT) of the plasma: x.

The next page (pg. 32) expresses these four frequencies for different combinations of ions and electrons in both the fast (x>>1) and slow (x<<1) limits (in which the integral of x is approximately constant). However, numerically it is easy enough to just evaluate the integral Phi and get one expression for both regimes.

It would be great to have the integral Phi and the four expressions for the relaxation rates $\nu$ in the PlasmaPy collisions package! In the fast beam limit these are particularly useful for estimating collisionality in shock experiments.

It would be really helpful to have a Photon class that would include the energy, frequency, wavelength, and momentum of a photon. This would behave similarly to a Particle object from PlasmaPy. To do the conversion between those physical quantities, it might be helpful to look into Astropy equivalencies. There's a bit more information in PlasmaPy/PlasmaPy#1175. Thank you!

This issue outlines the resources we need to put together and include in resources/...

1. Installing Python (pip/Anaconda)
2. Learning Python - @namurphy
3. Outline IDEs
   • Summary of IDEs - @rocco8773
   • Topics
     • Installing
     • git integration
     • add-ons
     • ...
   • PyCharm - @rocco8773
   • VSCode - @wdeshazer @qudsiramiz
   • Spyder (+git and/or GitHub Desktop) - @svincena @dschaffner
4. Terminals (Unix/MacOS/Windows)
   • Unix - @namurphy
   • MacOS - @namurphy
   • Windows - @wdeshazer
5. Virtual Envornments
   • @qudsiramiz @rocco8773
   • base off the 2021 documents: https://github.com/PlasmaPy/hack-week/blob/hack-week-2021/tutorials_and_notebooks/virtual_environments_tutorial.md
6. git & Github
   • probably software-carpentry with any additions from us

Other topics:

1. Jupyter notebooks
2. notebook on PlasmaPy
3. How to structure Python packages.
   • https://packaging.python.org/en/latest/
   • dunders __all__, __init__
   • changing the mindset from writing and running scripts to build a package of tools
4. How to get git...

We already have some optical Thomson Scattering features in PlasmaPy, but it would be good to extend these into the XRTS and warm dense matter regimes, where Compton scattering features, and electron degeneracy, and chemical potentials become important.

In addition to the Biot-Savart law solver proposed in #99 and other one in #100, we should have a way of calculating the electric fields resulting from simple charge distributions using Coulomb's Law. This would include the electric field from an finite/infinite linear charge (which has a simple exact solution) and the electric field resulting from an infinite sheet or dipole (which has a complicated exact solution) for boundary conditions at infinity. There might be other analytical solutions worth including, but these two are probably the most important ones.

This can get added to plasmapy.formulary.electrostatistics

PS: Was suggested by Daniel Prater

I just got the following error when clicking on the binder link:

ERROR: Package 'hack' requires a different Python: 3.7.12 not in '>=3.8'
Removing intermediate container 0fb81a6b4ae4
The command '/bin/sh -c ${KERNEL_PYTHON_PREFIX}/bin/pip install --no-cache-dir .' returned a non-zero code: 1Built image, launching...

I'll try to look into this tomorrow...should be a quick fix.

Create functionality for plotting CMA diagrams. This will likely take two parts:

1. A class or series of functionality to calculated the various parameters (L, P, R, etc.), cutoffs, and resonences of the Stix cold plasma dispersion relation.
2. A plotter to create the figures.

plasmapy's Stix dispersion relation (PR PlasmaPy/PlasmaPy#1511 when merged) can be used to generate the contours.

Code functionality that can predict if Dracula would survive during daylight on any given planet?

• could extend to times of the planetary day Dracula could survive.

• comparison of daylight and moonlight spectra
• Reference paper: Ciocca & Wang (2013)
• https://en.wikipedia.org/wiki/Moonlight

Jupyter notebooks are a great way to use Python to introduce educational concepts. For example, PlasmaPy has a gallery of example notebooks, including a few that use plasmapy.formulary that introduce plasma beta in the solar atmosphere and length scales for reconnection in Earth's magnetosphere.

It would be great to add more educational notebooks using plasmapy.particles, plasmapy.formulary, and other packages in the scientific pythoniverse. If you interested in creating such a notebook, please mention your idea below and/or link to this issue in a pull request (by typing out #61). If you have an idea for a notebook, please feel free to mention it below or to create a new issue.

Thank you!

Create a Jupyter Notebook interface with inputs to calculate a range of plasma parameters given various input values.
Example: Return Alfven speed given magnetic field and density.

Ideal Final version: Put in all inputs and have calculator return all possible calculated components.
Example: If I put in a magnetic field and a density, calculator will return Alfven Speed, plasma frequency, gyrofrequncy, but not ion speed (since T is not given).

A module that would take raw magnetic probe data (usually voltage from a scope or DAQ), into magnetic field. Inputs would be raw data, timescale, loop area. Requires time integration of raw data. Could have optional functionality for mean subtractions, calibration, filtering, integration start.

The plasma power balance in a basic form can be written as:

fusion heating + external heating >= radiation loss + transport loss. (Stacey, Weston "Fusion" 2010)

Relatively simple models for these various elements, many of which can be found in the aforementioned reference, can be collected and implemented in PlamaPy module resources so that plasma power balance studies could be done.

As an example, the fusion heating calculation is based on a fusion reactivity model that is fairly easy to implement (I have my own implementation in Mathcad that could be ported to python). The models exist for several fusion relevant reactions and can be extended for ones that I am not familiar with.

The radiation and transport losses have simple models as well that would not be too hard to implement.

External heating sources such as Neutral Beam Injection (NBI) Electron Cyclotron Resonance Heating (ECRH), and Ion Cyclotron Resonance Heating (ICRH) at a simple level just get modeled as a heat source. The community might have suggestions on how this part could be enriched.

The paper by Voronov (1997) includes practical fit formulae for ionization rate coefficients of atoms and ions by electron impact for hydrogen through nickel (e.g., for atomic numbers 1–28). The main benefit of these formulae is that the ionization rates can be calculated efficiently without needing a full database. The rates are accurate to a few percentage points to the best known data at the time, which is good since errors on atomic data are usually quite a bit larger than that.

The fit parameters are given in Table 1 of Voronov (1997). Fortunately, there are programs that can extract tables from pdf files, like camelot. This would be preferable to typing in the data directly since it'll be a lot less work and typos won't sneak in.

https://hackmd.io/@plasmapy/r1CY9dVj9

The NRL Plasma Formulary (pages ∼54–55) contain expressions the radiative recombination rate and the three-body recombination rate, which could be implemented.

Many photon-based diagnostics (e.g. radiography, x-ray spectroscopy, etc.) require applying corrections at different spatial locations of the instrument. For example, the filters are located in a different spot than the Bragg crystal, and the microchannel plate, the scintillator, the CCD, etc. I was listening to a podcast about AstroConda and one of the speakers mentioned that they have a tool for transforming between different spatial locations within a diagnostic for their telescopes. They can apply calibration corrections, make photometric simulations/estimates, and even apply point-spread functions to simulate the end to end instrument response to a given light source. I think it would be excellent if we could implement a similar type of tool, but more relevant for laboratory plasma experiments.

The critical plasma density for a given frequency of light is the electron density at which the laser frequency equals the electron plasma frequency. This is an important quantity because light will not propagate above the critical density. As a result, laser light is deposited around this density in laser-solid interactions and optical plasma diagnostics only work below it.

It would be great if this formula was added to the PlasmaPy formulary.

Using two point correlation methods, implement a code to produce a spectral density plot (wavenumber vs frequency). Requires computing cross phase from FFT. Takes data from two spatially separated diagnostic points (Isat, Bdot, etc), computes a cross phase, then an estimated wavenumber using crossphase and separation distance, then histogram power as a function of wavenumber and frequency.

Ref paper: https://aip.scitation.org/doi/10.1063/1.1686491

https://youtu.be/ErzQpilPxa0