# RCPANN - Ring Current Proton ANN Model
[](https://nodesource.com/products/nsolid)
The Ring Current Proton Artificial Neural Network (RCPANN) model specifies the ring current proton distribution using artificial neural network (multi-layer perceptron).
This model is based on RBSP/RBSPICE measurements from 2013-2018. The RBSPICE measures proton flux at 14 energy channels from 45 keV to 598 keV, and this model provides proton spin-averaged flux in those 14 energy channels. The data for the training and modeling are available at https://doi.org/10.5281/zenodo.7651736. The training program are available ar https://github.com/jinxingli87/RCPANN.
## How to use the RCPANN model
First, pip install the 'rcpann' package. Tensorflow and Pytorch packages are required.
```sh
pip3 install rcpann
```
## ✨ Example 1: Predict proton flux at a specific moment
Input: iek, coord in shape of either (4,) or (1,4), and tstr in form of 'yyyy-mm-dd hh:mm:ss'
The iek should be a number between 0 and 13, and the corresponding energy is listed below.
|iek|0|1|2|3|4|5|6|7|8|9|10|11|12|13|
| ------ | ------ |------ | ------ |------ | ------ |------ | ------ |------ | ------ |------ | ------ |------ | ------ |------ |
|Energy (keV)|45|55|67|82|99|121|148|181|220|269|328|400|489|598|
```python3
from rcpann import *
iek=1
coord=np.array([3.5, 0.0, 1.0, 0.0]) # L=3.5, cos(theta)=0, sin(theta)=1.0, Lat=0.0, which means MLT = 6h
print(pflux(iek,coord,tstr = '2017-03-01 22:42:00'))
```
## ✨ Example 2. Model the global distribution of proton flux
```python3
from rcpann import *
iek=1
tstr1='2017-03-01 22:42:00'
tstr2='2017-03-04 23:59:00'
rcpann_global_dist(iek,tstr1,tstr2)
```
Please contact jinxing.li.87@gmail.com for support.
## License
MIT
**Free Software, Hell Yeah!**
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"description": "\n# RCPANN - Ring Current Proton ANN Model\n\n[](https://nodesource.com/products/nsolid)\n\nThe Ring Current Proton Artificial Neural Network (RCPANN) model specifies the ring current proton distribution using artificial neural network (multi-layer perceptron).\n\nThis model is based on RBSP/RBSPICE measurements from 2013-2018. The RBSPICE measures proton flux at 14 energy channels from 45 keV to 598 keV, and this model provides proton spin-averaged flux in those 14 energy channels. The data for the training and modeling are available at https://doi.org/10.5281/zenodo.7651736. The training program are available ar https://github.com/jinxingli87/RCPANN.\n\n## How to use the RCPANN model\nFirst, pip install the 'rcpann' package. Tensorflow and Pytorch packages are required. \n```sh\npip3 install rcpann\n```\n\n## \u2728 Example 1: Predict proton flux at a specific moment\nInput: iek, coord in shape of either (4,) or (1,4), and tstr in form of 'yyyy-mm-dd hh:mm:ss'\nThe iek should be a number between 0 and 13, and the corresponding energy is listed below.\n|iek|0|1|2|3|4|5|6|7|8|9|10|11|12|13|\n| ------ | ------ |------ | ------ |------ | ------ |------ | ------ |------ | ------ |------ | ------ |------ | ------ |------ |\n|Energy (keV)|45|55|67|82|99|121|148|181|220|269|328|400|489|598|\n```python3\nfrom rcpann import *\niek=1\ncoord=np.array([3.5, 0.0, 1.0, 0.0]) # L=3.5, cos(theta)=0, sin(theta)=1.0, Lat=0.0, which means MLT = 6h\nprint(pflux(iek,coord,tstr = '2017-03-01 22:42:00'))\n```\n\n## \u2728 Example 2. Model the global distribution of proton flux\n```python3\nfrom rcpann import *\niek=1\ntstr1='2017-03-01 22:42:00'\ntstr2='2017-03-04 23:59:00'\nrcpann_global_dist(iek,tstr1,tstr2)\n```\n\n\n\nPlease contact jinxing.li.87@gmail.com for support.\n\n\n## License\n\nMIT\n\n**Free Software, Hell Yeah!**\n\n\n",
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