Metadata-Version: 2.1
Name: gvec-to-python
Version: 1.2.2
Summary: Parse GVEC output in Python
Author: Tin Kei Cheng, Florian Hindenlang, Stefan Possanner
Author-email: stefan.possanner@ipp.mpg.de, florian.hindenlang@ipp.mpg.de
License: Copyright 2021 (c) T.K. Cheng, F. Hindenlang, S. Possanner | Max Planck Institute for Plasma Physics
        
        Permission is hereby granted, free of charge, to any person obtaining a copy of this software 
        and associated documentation files (the "Software"), to deal in the Software without restriction, 
        including without limitation the rights to use, copy, modify, merge, publish, distribute, 
        sublicense, and/or sell copies of the Software, and to permit persons to whom the Software 
        is furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all copies 
        or substantial portions of the Software.
        
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        INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR 
        PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE 
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        ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Project-URL: repository, https://gitlab.mpcdf.mpg.de/gvec-group/gvec_to_python
Project-URL: Bug Tracker, https://gitlab.mpcdf.mpg.de/gvec-group/gvec_to_python/-/issues
Keywords: plasma physics,fusion,numerical modeling,mhd equilibrium
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: pyccel
Requires-Dist: scipy
Requires-Dist: tqdm
Requires-Dist: vtk
Requires-Dist: argcomplete

# 3D GVEC equilibria in Python

PyPI install:

```
pip install gvec_to_python
```
Or from source:
```
git clone git@gitlab.mpcdf.mpg.de:gvec-group/gvec_to_python.git
cd gvec_to_python
pip install -e .
```
Compile kernels for faster evaluations:
```
compile-gvec-tp
```

# Usage

The [Galerkin Variational Equilibrium Code (GVEC)](https://gitlab.mpcdf.mpg.de/gvec-group/gvec) calculates magneto-hydrodynamic (MHD) equilibria for Tokamaks and Stellarators. `gvec_to_python` parses the GVEC output file (`.dat`) and collects the data in a `.json` file: 
```
from gvec_to_python.reader.gvec_reader import create_GVEC_json

create_GVEC_json(dat_file_in, json_file_out)  # give absolute paths to the files
```
In a second step, callables of MHD equilibrium quantities are created as methods of the GVEC class:
```
from gvec_to_python import GVEC

gvec = GVEC(json_file_out, mapping='gvec')
```
Profiles can be evaluated via
```
gvec.profiles.profile(s, name='phi')        # toroidal flux profile (radial coordinate s~sqrt(phi_norm)
gvec.profiles.profile(s, name='chi')        # poloidal flux profile
gvec.profiles.profile(s, name='iota')       # iota profile
gvec.profiles.profile(s, name='pressure')   # pressure profile
```
where the radial coordinate `s` is the square-root of the normalized toroidal flux. Profile derivatives are callable via
```
gvec.profiles.profile(s, name='phi', der='s') # first derivative
gvec.profiles.profile(s, name='phi', der='ss') # second derivative
```

![Profiles](notebooks/profiles.png "Title")

The mapping and metric coefficients are called via
```
gvec.f(s, a1, a2)       # mapping
gvec.df(s, a1, a2)      # Jacobian matrix
gvec.det_df(s, a1, a2)  # Jacobian determinant
gvec.df_inv(s, a1, a2)  # inverse Jacobian matrix
gvec.g(s, a1, a2)       # metric tensor
gvec.g_inv(s, a1, a2)   # inverse metric tensor
```
The **radial coordinate** denotes `s`  is **always the square-root of the normalized toroidal flux**, `a1` is the poloidal angle and `a2` denotes the toroidal angle.

![Profiles](notebooks/poloidal.png "Title")
![](notebooks/topview.png)

Five different mappings can be invoked by the `mapping.setter`:
```
# gvec standard coordinates: (s, th, ze) -> (x, y, z) 
#   from (s,a1,a2)=(s,theta,zeta) in [0,1],[0,2pi],[0,2pi] to cartesian coordinates (x,y,z)
gvec.mapping = 'gvec'

# gvec straight-field-line mapping (PEST) (s, theta*, zeta*) -> (x, y, z) 
#   from (s,a1,a2)=(s,theta*,zeta*) in [0,1],[0,2pi],[0,2pi] to cartesian coordinates (x,y,z)
gvec.mapping = 'pest'

# gvec with unit cube as logical domain  (s, u, v) -> (x,y,z)
#   from (s,a1,a2)=(s,u,v) in [0,1],[0,1],[0,1] to cartesian coordinates (x,y,z)
gvec.mapping = 'unit'

# gvec straight-field-line (PEST)  with unit cube as logical domain  (s, u*, v*) -> (x,y,z)
#   from (s,a1,a2)=(s,u*,v*) in [0,1],[0,1],[0,1] to cartesian coordinates (x,y,z)
gvec.mapping = 'unit_pest'

# gvec without hmap (s,th,ze) -> (X1,X2,zeta) 
#   from (s,a1,a2)=(s,theta,zeta) in [0,1],[0,2pi],[0,2pi] to GVECs internal coordinates (X1,X2,zeta)
#   if default torus (hmap=1) is used in GVEC, then (R,Z,phi)=(X1,X2,-zeta)
gvec.mapping = 'wo_hmap'
```

![](notebooks/dtheta.png)
![](notebooks/det_df.png)

The MHD quantities are called via
```
gvec.p0(s, a1, a2)      # pressure as 0-form
gvec.p3(s, a1, a2)      # pressure as 3-form

gvec.bv(s, a1, a2)      # contra-variant B-field
gvec.b1(s, a1, a2)      # co-variant B-field (1-form)
gvec.b2(s, a1, a2)      # 2-form B-field
gvec.b_cart(s, a1, a2)  # Cartesian B-field

gvec.av(s, a1, a2)      # contra-variant vector potential
gvec.a1(s, a1, a2)      # co-variant vector potential (1-form)
gvec.a2(s, a1, a2)      # 2-form vector potential
gvec.a_cart(s, a1, a2)  # Cartesian vector potential

gvec.jv(s, a1, a2)      # contra-variant current 
gvec.j1(s, a1, a2)      # co-variant current (1-form)
gvec.j2(s, a1, a2)      # 2-form current
gvec.j_cart(s, a1, a2)  # Cartesian current
```

![](notebooks/pressure.png)

![](notebooks/absB.png)




