import numpy as np
import MDSplus as mds
import utils.plot_utils as au
import matplotlib
import matplotlib.pyplot as plt
from scipy import integrate
import scipy.interpolate as interp
col, col2, styles, my_cmap, dpi = au.define_colors()
#for now just need tor flux, pol flux, q, j, press
def get_tcv_eq(shot, time):
"""
"""
conn = mds.Connection('tcvdata.epfl.ch')
conn.openTree('tcv_shot', shot)
#t = mds.Tree('tcv_shot', shot)
#iptr_s = t.getNode(r'\magnetics::iplasma:trapeze')
liuqe_flavour = 'LIUQE.M'
_d = {'label': '', 'x': [], 'y': []}
signals = {
'p': dict.copy(_d),
'q': dict.copy(_d),
'torflux': dict.copy(_d),
'i': dict.copy(_d),
'polflux': dict.copy(_d),
'f': dict.copy(_d),
'jbs': dict.copy(_d)
import matplotlib.pyplot as plt
import a4py.classes.ReadEQDSK as ReadEQDSK
import h5py
import a5py.ascot5io.ascot5 as a5
import numpy as np
from utils.plot_utils import common_style, limit_labels, define_colors
import scipy.interpolate as interp
from matplotlib.lines import Line2D
import sys
#sys.path.append('/home/matval/WORK/pythonscripts/python/')
from a5py.ascotpy.ascotpy import Ascotpy
common_style()
_,_,_, my_cmap, _ = define_colors()
class TFripple():
def __init__(self, field_fname='/home/vallar/WORK/JT-60SA/3D/TFripple/ascot_TFcoilout_cocos5.h5',\
coils_fname='/home/vallar/WORK/JT-60SA/3D/JT60SA_3Dfield.h5'):
"""
"""
self.N=18
self.read_file(field_fname, coils_fname)
def read_file(self, field_fname, coils_fname):
"""
coils,f,eqd = read_file()
"""
try:
self.coils = h5py.File(coils_fname, "r")
except:
import a5py.ascot5io.ascot5 as a5
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from utils.plot_utils import common_style, define_colors
import matplotlib.ticker as ticker
common_style()
colours, colours_old, styles, my_cmap, dpi= define_colors()
f=a5.Ascot('/home/vallar/WORK/ASCOT/runs/SA_003/fullpower/production/ascot_fullpower_2d_and_3d.h5')
# run_0766036146 RIPPLE
# run_2107319306, 2D - ACTIVE
data={
'2D':
{'run':f.run_2107319306,
},
'3D':
{'run':f.run_1162028518,
}
}
print('data dict created!')
E_edges=np.linspace(10e3, 500e3, 100)
xi_edges=np.linspace(-1, 1., 100)
for label in data.keys():
endstate = data[label]['run'].endstate
ind_wall = np.where(endstate['endcond']==32)
data[label]['ind_wall'] = ind_wall
E = endstate['energy'][ind_wall] # in J
data[label]['E_end']=E/1.602e-19 # in keV
import pytransp.classes.transp_exp as te
import numpy as np
import matplotlib.pyplot as plt
import utils.plot_utils as pu
colors,_,_,_,_ = pu.define_colors()
def compare_n0(fname_sim=['/home/vallar/TCV/58823/58823V71.CDF'], fname_exp='/home/vallar/TCV/58823/58823n0.dat', t=0.9):
"""
Compares edge density between CDF output of TRANSP and n0 from experiments
Parameters:
fname_sim arr : array of strings with the name of CDF files
fname_exp str : string of name where n0 at edge is stored
t float: time where to compute profile
Returns:
n0_exp dict: dict with time and n0 of experiment
n0_sim dict: dict with time and n0 of CDF files
"""
if type(fname_sim)==str: fname_sim=[fname_sim]
# read data from experiments
n0data = np.loadtxt(fname_exp)
# start plot, it will be needed inside the for loop
f1d = plt.figure(); ax1d=f1d.add_subplot(111)
f=plt.figure(); ax=f.add_subplot(111)
ax.plot(n0data[:,0], n0data[:,1], 'k--', label='EXP')
n0_exp = {}; n0_exp['t']=n0data[:,0]; n0_exp['n0']=n0data[:,1]
n0_sim = {};
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 4 12:03:45 2019
@author: vallar
"""
import a5py.ascot5io.ascot5 as a5
from a5py.ascotpy.ascotpy import Ascotpy
import a5py.marker.evaluate as eval_mrkr
import numpy as np
import matplotlib.pyplot as plt
from utils.plot_utils import common_style, limit_labels, define_colors
import plot_tips
common_style()
col, _, _, _, _ = define_colors()
def evaluate_derived_quant(orb, id_part):
# evaluate derived quantities
ind = np.where(orb['id'] == id_part)[0]
pitch = eval_mrkr.eval_particle('pitch',
mass=2. * 1.60e-27,
charge=None,
R=None,
phi=None,
z=None,
vpar=orb['vpar'][ind],
mu=orb['mu'][ind],
theta=None,
BR=orb['br'][ind],