Metadata-Version: 2.1
Name: pyDEER
Version: 1.0.2
Summary: A Python Package for Fitting Double Electron-Electron Resonance Data
Home-page: https://github.com/tkellerBridge12/pyDEER
Author: Timothy Keller
Author-email: tkeller@bridge12.com
License: UNKNOWN
Project-URL: Source Code, https://github.com/tkellerBridge12/pyDEER
Project-URL: Documentation, https://pydeer.readthedocs.io/
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=2.7, >=3.6
Description-Content-Type: text/markdown
Requires-Dist: scipy
Requires-Dist: numpy

# README #

pyDEER is a python package for Double Electron-Electron Resonance

### Requirements ###

* Python3 (>= 3.6)
* numpy

```console
python -m pip install numpy
```

### Importing ELEXSYS Data ###

```python
import pyDEER as deer
path = 'path/to/deer/data/including/filename'
deer.load_elexsys(path)
```

### Performing Tikhonov Regularization ###

```python
import numpy as np
from matplotlib.pylab import *
import pyDEER as deer

r = np.r_[1.5e-9:10e-9:100j]
t = np.r_[-100e-9:5e-6,500j]

K = deer.kernel(t,r,angles = 1000)

P_gauss = gaussian(r, 0.2e-9, 4-9)

S = np.dot(K,P_gauss)

S = deer.add_noise(S)

P_lambda = deer.tikhonov(K,S)

figure('P(r)')
plot(r*1e9,P_lambda,label = 'Exact')
plot(r*1e9,P_lambda,label = 'Tikhonov')
xlabel('r (nm)')
ylabel('P(r)')
legend()
show()
```


