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https://github.com/costrouc/pysrim
Automation, Analysis, and Plotting of SRIM Calculations
https://github.com/costrouc/pysrim
ion-cascades numpy simulation srim
Last synced: about 2 months ago
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Automation, Analysis, and Plotting of SRIM Calculations
- Host: GitHub
- URL: https://github.com/costrouc/pysrim
- Owner: costrouc
- License: mit
- Created: 2018-05-28T22:46:12.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-09-26T10:23:57.000Z (about 2 years ago)
- Last Synced: 2024-10-12T00:24:51.294Z (2 months ago)
- Topics: ion-cascades, numpy, simulation, srim
- Language: Python
- Homepage: https://pysrim.readthedocs.io/en/latest/
- Size: 45.7 MB
- Stars: 37
- Watchers: 8
- Forks: 22
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# pysrim: Automation, Analysis, and Plotting of SRIM Calculations
`pysrim` is a python package that aims to wrap and extend SRIM a
popular tool for simulating ions traveling through a material. There
are many pain points to SRIM and this package aims to address
them. These include compatibility with all OS's, automation and crash
recovery of SRIM calculations, parsing of all output files, and
publication quality plots.
There is now a docker image
[costrouc/pysrim](https://hub.docker.com/r/costrouc/pysrim/tags/) for
running pysrim and SRIM! **No setup necissary and does not require a
display so it is server ready**. If you would like to try it run the
short command below (obviously requires docker). All output files will
be stored in `/tmp/output` for this example. [Benchmarks
show](https://pysrim.readthedocs.io/en/latest/benchmarks.html) the
docker container is around 50-60% faster. I believe this is due to
using [xvfb](https://linux.die.net/man/1/xvfb)
``` bash
docker run -v $PWD/examples/docker:/opt/pysrim/ \
-v /tmp/output:/tmp/output \
-it costrouc/pysrim sh -c "xvfb-run -a python3.6 /opt/pysrim/ni.py"
ls /tmp/output
```
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# Documentation
Link to [documentation on readthedocs](https://pysrim.readthedocs.io/en/latest/)
# Features
## Automate running SRIM and TRIM on all operating systems
While TRIM is a great code it has many downsides regarding
automation. The `TRIM.IN` input file is tedious to write yourself and
the gui that constructs the `TRIM.IN` will crash at unexpeced moments.
One of these crashes everyone has encountered is the fact that a float
text field can never be empty. TRIM also has a tendancy to crash
becuase it stores all cascades in memory. Meaning that for large runs
with full cascades greater than 1,000 ions it will run out of
memory. `pysrim` addresses all of these issues by providing a simple
API wrapper for the input file (supporting all of the features),
ability to run on all operating systems (using
[wine](https://appdb.winehq.org/objectManager.php?sClass=version&iId=13202)
for linux and OSX), and allowing batch runs of calculations see [this
notebook
example](https://gitlab.com/costrouc/pysrim/blob/master/examples/notebooks/SiC.ipynb).
Below is a hello world example of using `pysrim` for running a TRIM
calcualtion. Note that `/tmp/srim` is the path to the SRIM executable
directory (`SRIM.exe` should reside in this directory). `pysrim` will
add all the necessary input files. If this ran successfully for you a
SRIM window will popup and start the calculation.
``` python
from srim import Ion, Layer, Target, TRIM
# Construct a 3MeV Nickel ion
ion = Ion('Ni', energy=3.0e6)
# Construct a layer of nick 20um thick with a displacement energy of 30 eV
layer = Layer({
'Ni': {
'stoich': 1.0,
'E_d': 30.0,
'lattice': 0.0,
'surface': 3.0
}}, density=8.9, width=20000.0)
# Construct a target of a single layer of Nickel
target = Target([layer])
# Initialize a TRIM calculation with given target and ion for 25 ions, quick calculation
trim = TRIM(target, ion, number_ions=25, calculation=1)
# Specify the directory of SRIM.exe
# For windows users the path will include C://...
srim_executable_directory = '/tmp/srim'
# takes about 10 seconds on my laptop
results = trim.run(srim_executable_directory)
# If all went successfull you should have seen a TRIM window popup and run 25 ions!
# results is `srim.output.Results` and contains all output files parsed
```
See [documentation](https://pysrim.readthedocs.io/en/latest/) for all available options.
## Copy SRIM output files to directory
After a SRIM calculation has completed run `copy_output_files` to take
all of the output files and move them to a directory of your liking.
``` python
from srim import TRIM
TRIM.copy_output_files('/tmp/srim', '/home/costrouc/scratch/srim')
```
## Post processes SRIM output as numpy arrays
By far the hardest part about running TRIM calculations is analyzing
the output files. `pysrim` comes with parsers for
[IONIZ.txt](https://pysrim.readthedocs.io/en/latest/source/srim.html#srim.output.Ioniz),
[VACANCY.txt](https://pysrim.readthedocs.io/en/latest/source/srim.html#srim.output.Vacancy),
[NOVAC.txt](https://pysrim.readthedocs.io/en/latest/source/srim.html#srim.output.NoVacancy),
[E2RECOIL.txt](https://pysrim.readthedocs.io/en/latest/source/srim.html#srim.output.EnergyToRecoils),
[PHONON.txt](https://pysrim.readthedocs.io/en/latest/source/srim.html#srim.output.Phonons),
[RANGE.txt](https://pysrim.readthedocs.io/en/latest/source/srim.html#srim.output.Range),
and
[COLLISON.txt](https://pysrim.readthedocs.io/en/latest/source/srim.html#srim.output.Collision). The
COLLISON.txt file can get quite large so the `Collision` parser uses a
buffered reader that can handle any file size. Additinally a class
[srim.output.Results](https://pysrim.readthedocs.io/en/latest/source/srim.html#srim.output.Results)
will processes all output files in a directory and provide a
dictionary of each parsed output file. `pysrim` comes with some
helpful plotting utilities such a plotting the DPA vs depth. However,
`pysrim's` most powerful feature is that all of the text files are
exposed as numpy arrays. The example below shows how to plot DPA using
a simple math and numpy. This enables the user to seamlessly use TRIM
and do analysis.
``` python
from srim.output import Phonons, Ioniz
def plot_damage_energy(folder, ax):
phon = Phonons(folder)
dx = max(phon.depth) / 100.0
energy_damage = (phon.ions + phon.recoils) * dx
ax.plot(phon.depth, energy_damage / phon.num_ions, label='{}'.format(folder))
return sum(energy_damage)
def plot_ionization(folder, ax):
ioniz = Ioniz(folder)
dx = max(ioniz.depth) / 100.0
ax.plot(ioniz.depth, ioniz.ions, label='Ionization from Ions')
ax.plot(ioniz.depth, ioniz.recoils, label='Ionization from Recoils')
```
Set `folders` to list of directories to SRIM outputs. See
[Analysis](https://gitlab.com/costrouc/pysrim/blob/master/examples/notebooks/Analysis.ipynb)
for detailed example. Notice how there is a python class for each SRIM
output file and gives simple access to each column. This did require
some complex regex to get working just right.
``` python
folders = ['test_files/2', 'test_files/4']
image_directory = 'examples/images'
fig, axes = plt.subplots(1, len(folders), sharey=True, sharex=True)
for ax, folder in zip(np.ravel(axes), folders):
plot_damage_energy(folder, ax)
plot_ionization(folder, ax)
ax.legend()
ax.set_ylabel('eV')
ax.set_xlabel('Depth [Angstroms]')
fig.suptitle('Ionization Energy vs Depth', fontsize=15)
fig.set_size_inches((20, 6))
fig.savefig(os.path.join(image_directory, 'ionizationvsdepth.png'), transparent=True)
```
See [jupyter
notebook](