Calcul des kcoefficients pour FSW R3.3

1. Liens utiles
2. Routines principales
3. Calcul des kcoefficients pour usage à bord => FSW
   1. Utilities
   2. SCM TF
   3. ANT TF
   4. Storage for telecommand
   5. Storage for Alexis (default full table)
4. End

#%matplotlib inline
%matplotlib notebook
#%matplotlib widget

import sys
import os

try:
    hostname = os.environ['HOSTNAME']
except KeyError:
    hostname = 'juno'
print('HOSTNAME:', hostname)
#os.path.exists('/home/chust/tch')
    
if hostname == 'juno' and not '/home/chust/tch' in sys.path:
    sys.path.append('/home/chust/tch')
print('sys.path:', sys.path, '\n')

import matplotlib.pyplot as plt
import numpy as np
import glob
from time import time, localtime

from lib_lfr.global_params import *
from lib_lfr.load_routines import load_SCM_TF_comp_CDF, load_LFR_TF_B_CDF, load_LFR_TF_E_CDF
from lib_lfr.interp_routines import (SCM_TF_comp_interp, SCM_TF_mat_interp, load_BIAS_TF_func,
                                     LFR_TF_mean_B_interp, LFR_TF_mean_E_interp)

from lib_maths.conversion_routines import linfromdB
from lib_io.store_routines import save2Dtxt, save2json, json2data
from lib_plot.curves_routines import fig_waveforms

dir_root = '/WIN/Users/chust/DD CHUST' if hostname == 'PC-CHUST2.lpp.polytechnnique.fr' else \
           '/home/chust/DD pc-p-chust' if hostname == 'pc-p-chust' else \
           '/home/thomas/Labo' if hostname == 'pc-tango2' else 'ERROR!'   
dir_DATA = '/DATA1' if hostname == 'PC-CHUST2.lpp.polytechnnique.fr' else \
           '/home/chust/DATA' if hostname in ['pc-p-chust', 'juno'] else \
           '/home/thomas/Labo/DATA' if hostname == 'pc-tango2' else 'ERROR!'
dir_plots = '/home/thomas/Labo/Plots' if hostname == 'pc-tango2' else '/home/chust/Plots'   
dir_storage = '/home/thomas/Labo/Storage' if hostname == 'pc-tango2' else '/home/chust/Storage'   

HOSTNAME: PC-CHUST2.lpp.polytechnnique.fr
sys.path: ['/WIN/Users/chust/DD CHUST/Missions/Solar Orbiter/LFR/Python/Alexis', '/WIN/Users/chust/DD CHUST/Missions/Solar Orbiter/LFR/Python/tch', '/usr/lib64/python39.zip', '/usr/lib64/python3.9', '/usr/lib64/python3.9/lib-dynload', '', '/home/lab-lpp.local/chust/venv_py3/lib64/python3.9/site-packages', '/home/lab-lpp.local/chust/venv_py3/lib/python3.9/site-packages', '/usr/lib64/python3.9/site-packages', '/usr/lib/python3.9/site-packages', '/home/lab-lpp.local/chust/venv_py3/lib/python3.9/site-packages/IPython/extensions', '/home/lab-lpp.local/chust/.ipython'] 

/home/lab-lpp.local/chust/venv_py3/lib64/python3.9/site-packages/spacepy/time.py:2294: UserWarning: Leapseconds may be out of date. Use spacepy.toolbox.update(leapsecs=True)
  warnings.warn('Leapseconds may be out of date.'

Liens utiles previous next

1. TC_LFR_LOAD_KCOEFFICIENTS / TC_LFR_DUMP_K_COEFFICIENTS / TM_LFR_KCOEFFICIENTS_DUMP : https://hephaistos.lpp.polytechnique.fr/redmine/issues/385

   On peut envoyer 36 jeux de coefficients avec les commandes load: 11 pour F0, 13 pour F1 et 12 pour F2. Chaque jeu contient 32 coefficients.
   Lors d'un load, le parametre sy_lfr_kcoeff_frequency permet d'identifier le jeu de coefficients transmis:
   => 0 .. 10, pour les jeux de coefficients F0
   => 11 .. 23, pour les jeux de coefficients F1
   => 24 .. 35, pour les jeux de coefficients F2

   On va rajouter 1 fréquence en bout de chaque bande => 11+1, 13+1, 12+1 = 36+3 fréquences à transmettre
   

2. LFR FSW: https://github.com/LaboratoryOfPlasmaPhysics/LFR_Flight_Software
   
3. shared TCH's Python routine link: https://ao.lpp.polytechnique.fr/index.php/s/gzosYYdLAxxgrCN

Routines principales start next end

# ANT transfer matrix expressed in the SO' coordinate system 

def load_interp_A_E_SOprime(cdffile_ANT_TF_SOprime, freqs):
    """
    Transfer matrix of the electric antennas where the Y'Z' plane 
    is the effective plane of the electric field measurements
    ~ YZ plane SRF (see the ANT_géométrie notebook or Chust et al. 2021, A&A)
    A_E = [ A1Y A1Z     
            A2Y A2Z ]             
    """
    n = len(freqs)
    # up to now we neglect the frequency dependency ...
    A1Y = +3.5 * np.ones(n, dtype=complex) 
    A1Z = -7.0 * np.ones(n, dtype=complex)
    A2Y = -7.0 * np.ones(n, dtype=complex)
    A2Z =  0.0 * np.ones(n, dtype=complex)
    return A1Y, A1Z, A2Y, A2Z  

def load_interp_Atilde_SOprime(cdffile_ANT_TF_SOprime, freqs, echo=True): 
    """
    Normalized transfer matrix of the ANT antennas expressed in the SO' coordinate system
    (see "spectral_matrix_calibration_2021-02-16_V3_corrected.pdf")
    A_E^-1 = [ A2Z  -A1Z
              -A2Y   A1Y ] x (A1Y*A2Z-A1Z*A2Y)^-1
    A_tilde = sqrt(A2Y^2 + A2Z^2) . A_E^-1                 
    """
    A1Y, A1Z, A2Y, A2Z = load_interp_A_E_SOprime(cdffile_ANT_TF_SOprime, freqs)
    k_ant = np.sqrt(abs(A2Y)**2 + abs(A2Z)**2) 
    denominator = A1Y*A2Z - A1Z*A2Y
    atilde_Y1 = +k_ant * A2Z / denominator
    atilde_Y2 = -k_ant * A1Z / denominator
    atilde_Z1 = -k_ant * A2Y / denominator
    atilde_Z2 = +k_ant * A1Y / denominator
    return (atilde_Y1, atilde_Y2, 
            atilde_Z1, atilde_Z2), k_ant

# SCM transfer matrix and transformation into the SO' coordinate system 

def load_interp_Mtilde_SOprime(cdffile_SCM_TF, freqs, echo=True):
    """
    Normalized transfer matrix of the SCM antennas and transformation into the SO' coordinate system 
    (see "BP_2020-06-18_V8_corrected.pdf" or Chust et al. 2021, A&A)
    A_M = C1Y * c
    c^-1 = C1Y * A_M^-1
    M_tilde = M_SOprime_SCM . c^-1
    SOprime ~ SRF (see the SCM_géométrie notebook)
    """
    M_UARF_to_SOprime = np.array([[ 0.50087005,  0.5995206 , -0.62426296],
                                  [ 0.74385771, -0.6669109 , -0.04365272],  
                                  [-0.44249848, -0.44249848, -0.77999372]])
    M_SCM_to_UARF = np.array([[ 0.,  0., 1.],
                              [ 1.,  0., 0.],
                              [ 0.,  1., 0.]])
    M_SCM_to_SOprime = np.dot(M_UARF_to_SOprime, M_SCM_to_UARF)
    if echo: print("cdffile_SCM_TF: ", cdffile_SCM_TF)   
    SCM_TF_comp = load_SCM_TF_comp_CDF(cdffile_SCM_TF, temp=-50., lfr_on=True, echo=echo)       
    A_M_inv = SCM_TF_mat_interp(freqs, SCM_TF_comp, dB=False, reverse=True, UARF=False)  
    A_M = SCM_TF_mat_interp(freqs, SCM_TF_comp, dB=False, reverse=False, UARF=False)
    C1Y = A_M[:, 0, 0]
    n = len(freqs)
    c_inv = np.array([C1Y[ifreq] * A_M_inv[ifreq, ...] for ifreq in range(n)])    
    M_tilde = np.array([np.dot(M_SCM_to_SOprime, c_inv[ifreq, ...]) for ifreq in range(n)])    
    return M_tilde, C1Y

def matrix2comp_Mtilde(M_tilde): 
    dim = M_tilde.ndim
    mtilde_X1 = M_tilde[:, 0, 0] if dim == 3 else M_tilde[0, 0]
    mtilde_X2 = M_tilde[:, 0, 1] if dim == 3 else M_tilde[0, 1]
    mtilde_X3 = M_tilde[:, 0, 2] if dim == 3 else M_tilde[0, 2]
    mtilde_Y1 = M_tilde[:, 1, 0] if dim == 3 else M_tilde[1, 0]
    mtilde_Y2 = M_tilde[:, 1, 1] if dim == 3 else M_tilde[1, 1]
    mtilde_Y3 = M_tilde[:, 1, 2] if dim == 3 else M_tilde[1, 2]
    mtilde_Z1 = M_tilde[:, 2, 0] if dim == 3 else M_tilde[2, 0]
    mtilde_Z2 = M_tilde[:, 2, 1] if dim == 3 else M_tilde[2, 1]
    mtilde_Z3 = M_tilde[:, 2, 2] if dim == 3 else M_tilde[2, 2]
    return (mtilde_X1, mtilde_X2, mtilde_X3,
            mtilde_Y1, mtilde_Y2, mtilde_Y3, 
            mtilde_Z1, mtilde_Z2, mtilde_Z3)

def merge_all_F(kcoeff):
    kcoeff_all = np.concatenate( [kcoeff[2], [np.nan], kcoeff[1], [np.nan], kcoeff[0]] )
    kcoeff_all_arg = np.concatenate( [np.rad2deg(np.unwrap(np.angle(kcoeff[2]))), [np.nan], 
                                      np.rad2deg(np.unwrap(np.angle(kcoeff[1]))), [np.nan], 
                                      np.rad2deg(np.unwrap(np.angle(kcoeff[0])))] )
    return kcoeff_all, kcoeff_all_arg

def plot_kcoeff(kname, freq, kcoeff, figsize=(9,5), title='', ylabel='', fname=None, 
                no_ishow=False, close=False):
    """
    freq and kcoeff are list for F=0, 1, 2
    """
    freq_all = np.concatenate( [freq[2], [np.nan], freq[1], [np.nan], freq[0]] )
    kcoeff_all, kcoeff_all_arg = merge_all_F(kcoeff)
    
    fig, axarr = plt.subplots(3, 1, squeeze=True, figsize=figsize, sharex=True, sharey=False)
    plt.subplots_adjust(hspace=0.0) 
    
    axarr[0].set_title(title, fontsize=14)   
    axarr[0].plot(freq_all, kcoeff_all.real, label='RE', color=None, linewidth=2)
    axarr[0].set_ylim([None, None])
    axarr[0].set_ylabel(ylabel, fontsize=None)
    axarr[0].legend(loc='best')
    axarr[0].semilogx()
    axarr[1].plot(freq_all, kcoeff_all.imag, label='IM', color='darkorange', linewidth=2)
    axarr[1].set_ylabel(ylabel, fontsize=None)
    axarr[1].legend(loc='best')

    axarr[2].plot(freq_all, abs(kcoeff_all), label='ABS', color='k', linewidth=2)
    axarr[2].set_ylabel(ylabel, fontsize=None)
    axarr[2].semilogy([None, None])
    #axarr[2].legend(loc='best')
    axarr[2].legend(loc='lower left')
    axarr[2].set_xlabel('Frequency (Hz)', fontsize=12)
    ax2 = axarr[2].twinx()
    ax2.plot(freq_all, kcoeff_all_arg, label='ARG', color='C3', linewidth=2)
    ax2.set_ylabel('degree', fontsize=None)
    ax2.legend(loc='upper right')
    #ax2.legend(loc='best')
    
    if fname != None: plt.savefig(fname, bbox_inches='tight', pad_inches=0.2) 
    if close: plt.close(fig)
    if no_ishow: plt.ion()      

Calcul des kcoefficients pour usage à bord => FSW start next

Utilities next

# BP frequency table used in NM
print("BP frequency table used in NM")
bp_n_freq = 3*[None]                            
for F in [0,1,2]:
    nfreq_n = [11, 13, 12][F]
    df_n = [8*96., 8*16., 8*1.][F]
    fstart_n = [1968, 152, 10.5][F]        
    bp_n_freq[F] = np.arange(fstart_n, fstart_n+nfreq_n*df_n, df_n) 
    print("F%s: "%(F), bp_n_freq[F])

BP frequency table used in NM
F0:  [1968. 2736. 3504. 4272. 5040. 5808. 6576. 7344. 8112. 8880. 9648.]
F1:  [ 152.  280.  408.  536.  664.  792.  920. 1048. 1176. 1304. 1432. 1560.
 1688.]
F2:  [10.5 18.5 26.5 34.5 42.5 50.5 58.5 66.5 74.5 82.5 90.5 98.5]

# Flight Software Release  
fsw = 'R3.3'

# Full ASM frequency table (for which the calibration coefficients for the onboard calibration are defined)
print()
print("Full ASM frequency table for which the default kcoefficients for onboard calibration are defined")
freq_cal_full = 3*[None]                            
for F in [0,1,2]:          
    nfreq_cal_full = [8*11, 8*13, 8*12][F]
    df_cal_full = [96., 16., 1.][F]
    i0_cal_full = [17, 6, 7][F]        
    freq_cal_full[F] = np.arange(i0_cal_full*df_cal_full, 
                                 i0_cal_full*df_cal_full+nfreq_cal_full*df_cal_full, 
                                 df_cal_full) 
    print("F%s: "%(F), freq_cal_full[F])     
    
# Reduced ASM frequency table (close to the BP frequency table used in NM) corresponding
# to the kcoefficients transmitted for the onboard calibration by linear interpolation 
print()
print("Reduced ASM frequency table for which the kcoefficients to be transmitted for onboard calibration")
print("by linear interpolation are defined (close to the BP frequency table used in NM)")
freq_cal_redu = 3*[None]
freq_cal_sent = 3*[None]                            
for F in [0,1,2]:
    nfreq_cal_redu = [11+1, 13+1, 12+1][F]
    df_cal_redu = [8*96., 8*16., 8*1.][F]
    fstart_cal_redu = [1632, 96, 7][F]        
    freq_cal_redu[F] = np.arange(fstart_cal_redu, fstart_cal_redu+nfreq_cal_redu*df_cal_redu, df_cal_redu) 
    print("F%s: "%(F), freq_cal_redu[F])   
    freq_cal_sent[F] = freq_cal_redu[F][:-1]
    
# kcoefficient indexes 
icoeff = { 
"Mtilde_X1_RE":  0,
"Mtilde_X1_IM":  1,
"Mtilde_X2_RE":  2,
"Mtilde_X2_IM":  3,
"Mtilde_X3_RE":  4,
"Mtilde_X3_IM":  5,
    
"Mtilde_Y1_RE":  6,
"Mtilde_Y1_IM":  7,
"Mtilde_Y2_RE":  8,
"Mtilde_Y2_IM":  9,
"Mtilde_Y3_RE": 10,
"Mtilde_Y3_IM": 11,
    
"Mtilde_Z1_RE": 12,
"Mtilde_Z1_IM": 13,
"Mtilde_Z2_RE": 14,
"Mtilde_Z2_IM": 15,
"Mtilde_Z3_RE": 16,
"Mtilde_Z3_IM": 17,
        
"Atilde_Y1_RE": 18,
"Atilde_Y1_IM": 19,
"Atilde_Y2_RE": 20,    
"Atilde_Y2_IM": 21,
    
"Atilde_Z1_RE": 22,
"Atilde_Z1_IM": 23,
"Atilde_Z2_RE": 24,    
"Atilde_Z2_IM": 25,

"free_1" :      26,  
"free_2" :      27,  
"free_3" :      28,  
"free_4" :      29,  
"free_5" :      30,  
"free_6" :      31,      
}    

# ASM calibration coefficients to be interpolated on board, 
# and transmitted formally as the old "kcoefficients" 
k_coefficients_f0 = np.zeros((11, 32))
k_coefficients_f1 = np.zeros((13, 32))
k_coefficients_f2 = np.zeros((12, 32))
k_coefficients = [k_coefficients_f0, k_coefficients_f1, k_coefficients_f2]

# a numerical constant for the onboard calibrated ASM products to match the digital 
# output dynamics of the LFR, i.e. their magnitudes should be between 2^-27 and 2^37
# (for simplicity, here we have chosen no dependence with F = 0, 1, 2), 
kappa = [1e-4, 1e-4, 1e-4]
print()
print("kappa:", kappa)
print("min output LFR value (2^⁻27): %e"%2**-27)
print("max output LFR value (2^+37): %e"%2**+37)

Full ASM frequency table for which the default kcoefficients for onboard calibration are defined
F0:  [1632. 1728. 1824. 1920. 2016. 2112. 2208. 2304. 2400. 2496. 2592. 2688.
 2784. 2880. 2976. 3072. 3168. 3264. 3360. 3456. 3552. 3648. 3744. 3840.
 3936. 4032. 4128. 4224. 4320. 4416. 4512. 4608. 4704. 4800. 4896. 4992.
 5088. 5184. 5280. 5376. 5472. 5568. 5664. 5760. 5856. 5952. 6048. 6144.
 6240. 6336. 6432. 6528. 6624. 6720. 6816. 6912. 7008. 7104. 7200. 7296.
 7392. 7488. 7584. 7680. 7776. 7872. 7968. 8064. 8160. 8256. 8352. 8448.
 8544. 8640. 8736. 8832. 8928. 9024. 9120. 9216. 9312. 9408. 9504. 9600.
 9696. 9792. 9888. 9984.]
F1:  [  96.  112.  128.  144.  160.  176.  192.  208.  224.  240.  256.  272.
  288.  304.  320.  336.  352.  368.  384.  400.  416.  432.  448.  464.
  480.  496.  512.  528.  544.  560.  576.  592.  608.  624.  640.  656.
  672.  688.  704.  720.  736.  752.  768.  784.  800.  816.  832.  848.
  864.  880.  896.  912.  928.  944.  960.  976.  992. 1008. 1024. 1040.
 1056. 1072. 1088. 1104. 1120. 1136. 1152. 1168. 1184. 1200. 1216. 1232.
 1248. 1264. 1280. 1296. 1312. 1328. 1344. 1360. 1376. 1392. 1408. 1424.
 1440. 1456. 1472. 1488. 1504. 1520. 1536. 1552. 1568. 1584. 1600. 1616.
 1632. 1648. 1664. 1680. 1696. 1712. 1728. 1744.]
F2:  [  7.   8.   9.  10.  11.  12.  13.  14.  15.  16.  17.  18.  19.  20.
  21.  22.  23.  24.  25.  26.  27.  28.  29.  30.  31.  32.  33.  34.
  35.  36.  37.  38.  39.  40.  41.  42.  43.  44.  45.  46.  47.  48.
  49.  50.  51.  52.  53.  54.  55.  56.  57.  58.  59.  60.  61.  62.
  63.  64.  65.  66.  67.  68.  69.  70.  71.  72.  73.  74.  75.  76.
  77.  78.  79.  80.  81.  82.  83.  84.  85.  86.  87.  88.  89.  90.
  91.  92.  93.  94.  95.  96.  97.  98.  99. 100. 101. 102.]

Reduced ASM frequency table for which the kcoefficients to be transmitted for onboard calibration
by linear interpolation are defined (close to the BP frequency table used in NM)
F0:  [ 1632.  2400.  3168.  3936.  4704.  5472.  6240.  7008.  7776.  8544.
  9312. 10080.]
F1:  [  96.  224.  352.  480.  608.  736.  864.  992. 1120. 1248. 1376. 1504.
 1632. 1760.]
F2:  [  7.  15.  23.  31.  39.  47.  55.  63.  71.  79.  87.  95. 103.]

kappa: [0.0001, 0.0001, 0.0001]
min output LFR value (2^⁻27): 7.450581e-09
max output LFR value (2^+37): 1.374390e+11

17*96

SCM Transfer Functions and transformation into the SO' coordinate system (and $\alpha$ factor incorporating LFR tranfer functions) next

#dir_SCM_TF = dir_DATA + '/SO/DATA_CALBUT/Tables_de_cal'
#cdffiles = glob.glob(dir_SCM_TF + '/*RCT-SCM_*V20190519*')
dir_SCM_TF = dir_root + '/Missions/Solar Orbiter/LFR/SCM'
cdffiles = glob.glob(dir_SCM_TF + '/*SOLO*V20200428000000*.cdf')

cdffiles.sort()
for file in cdffiles:
    print(os.path.basename(file))  
    
cdffile_SCM_TF = cdffiles[0]  
SCM_TF_bname = os.path.basename(cdffile_SCM_TF)[:-4]

print()
print(SCM_TF_bname)   

SCM_TF_comp = load_SCM_TF_comp_CDF(cdffile_SCM_TF, temp=-50., lfr_on=True, echo=True)   

freq_range = [1,10000]
fig=plt.figure(figsize=(7,4))
plt.xlabel('Frequency (Hz)', fontsize=12)
plt.ylabel('Gain dB(V/nT)', fontsize=14)
for k in [key for key in SCM_TF_comp.keys() if key[-5:] == 'gains' and key[0] == 'B']:
    #plt.plot(TF_comp['LF-Freqs'], TF_comp[k], label=k)
    plt.plot(SCM_TF_comp['LF-Freqs'], (SCM_TF_comp[k]), label=k, linewidth=2)    
plt.legend(loc='best', fontsize=12) 
plt.xlim(freq_range)
plt.semilogx()
plt.grid(True)
plt.show()

fig=plt.figure(figsize=(7,4))
plt.xlabel('Frequency (Hz)', fontsize=12)
plt.ylabel('Phase (°)', fontsize=14)
for k in [key for key in SCM_TF_comp.keys() if key[-6:] == 'phases' and key[0] == 'B']:
    plt.plot(SCM_TF_comp['LF-Freqs'], SCM_TF_comp[k], label=k)
plt.legend(loc='best', fontsize=12) 
plt.xlim(freq_range)
plt.semilogx()

freq_range = [1,10000]
fig=plt.figure(figsize=(7,4))
plt.xlabel('Frequency (Hz)', fontsize=12)
plt.ylabel('Gain dB(nT/V)', fontsize=14)
for k in [key for key in SCM_TF_comp.keys() if key[-5:] == 'gains' and key[0:2] == 'rB']:
    #plt.plot(SCM_TF_comp['LF-Freqs'], SCM_TF_comp[k], label=k)
    plt.plot(SCM_TF_comp['LF-Freqs'], SCM_TF_comp[k], label=k, linewidth=2)    
plt.legend(loc='best', fontsize=12) 
plt.xlim(freq_range)
#plt.ylim([0,2])
plt.semilogx()
plt.show()

fig=plt.figure(figsize=(7,4))
plt.xlabel('Frequency (Hz)', fontsize=12)
plt.ylabel('Phase (°)', fontsize=14)
for k in [key for key in SCM_TF_comp.keys() if key[-6:] == 'phases' and key[0:2] == 'rB']:
    plt.plot(SCM_TF_comp['LF-Freqs'], SCM_TF_comp[k], label=k)
plt.legend(loc='best', fontsize=12) 
plt.xlim(freq_range)
plt.semilogx()

SOLO_CAL_RPW-SCM_SCM-FS-MEB-PFM_V20200428000000.cdf

SOLO_CAL_RPW-SCM_SCM-FS-MEB-PFM_V20200428000000
PRINT cdf: 
 ANALYZERS_STATUS: CDF_CHAR*40 [2] NRV
B11-LF-gains: CDF_REAL4 [162, 512]
B11-LF-phases: CDF_REAL4 [162, 512]
B12-LF-gains: CDF_REAL4 [162, 512]
B12-LF-phases: CDF_REAL4 [162, 512]
B13-LF-gains: CDF_REAL4 [162, 512]
B13-LF-phases: CDF_REAL4 [162, 512]
B21-LF-gains: CDF_REAL4 [162, 512]
B21-LF-phases: CDF_REAL4 [162, 512]
B22-LF-gains: CDF_REAL4 [162, 512]
B22-LF-phases: CDF_REAL4 [162, 512]
B23-LF-gains: CDF_REAL4 [162, 512]
B23-LF-phases: CDF_REAL4 [162, 512]
B31-LF-gains: CDF_REAL4 [162, 512]
B31-LF-phases: CDF_REAL4 [162, 512]
B32-LF-gains: CDF_REAL4 [162, 512]
B32-LF-phases: CDF_REAL4 [162, 512]
B33-LF-gains: CDF_REAL4 [162, 512]
B33-LF-phases: CDF_REAL4 [162, 512]
B4-MF-gains: CDF_REAL4 [81, 512]
B4-MF-phases: CDF_REAL4 [81, 512]
Comments: CDF_CHAR*40 [162]
LF-Freqs: CDF_REAL4 [162, 512]
MF-Freqs: CDF_REAL4 [81, 512]
SCM_TEMPERATURES: CDF_REAL4 [81] NRV
TF_INDEX: CDF_UINT4 [2, 81] NRV
nLF-Freqs: CDF_UINT4 [162]
nMF-Freqs: CDF_UINT4 [81]
rB11-LF-gains: CDF_REAL4 [162, 512]
rB11-LF-phases: CDF_REAL4 [162, 512]
rB12-LF-gains: CDF_REAL4 [162, 512]
rB12-LF-phases: CDF_REAL4 [162, 512]
rB13-LF-gains: CDF_REAL4 [162, 512]
rB13-LF-phases: CDF_REAL4 [162, 512]
rB21-LF-gains: CDF_REAL4 [162, 512]
rB21-LF-phases: CDF_REAL4 [162, 512]
rB22-LF-gains: CDF_REAL4 [162, 512]
rB22-LF-phases: CDF_REAL4 [162, 512]
rB23-LF-gains: CDF_REAL4 [162, 512]
rB23-LF-phases: CDF_REAL4 [162, 512]
rB31-LF-gains: CDF_REAL4 [162, 512]
rB31-LF-phases: CDF_REAL4 [162, 512]
rB32-LF-gains: CDF_REAL4 [162, 512]
rB32-LF-phases: CDF_REAL4 [162, 512]
rB33-LF-gains: CDF_REAL4 [162, 512]
rB33-LF-phases: CDF_REAL4 [162, 512]
rB4-MF-gains: CDF_REAL4 [81, 512]
rB4-MF-phases: CDF_REAL4 [81, 512] 

temp = -50.0
temp_index = 15, tf_index[0, 15] = 15
LFR ON, TDS MF at -50. degrees          
nLF-Freqs[15]: 400

[]

dir_LFR_TF_PFM2_B_ROC = dir_root + '/Missions/Solar Orbiter/LFR/Ground Segment/Tables de calibration'

#cdffiles_B = glob.glob(dir_LFR_TF_PFM2_B_ROC + '/*SCM_V20180724*.cdf')
cdffiles_B = glob.glob(dir_LFR_TF_PFM2_B_ROC + '/*SCM_V20190123*171020*.cdf')
cdffiles_B.sort()
for file in cdffiles_B:
    print(os.path.basename(file))      
cdffile_LFR_TF_B = file        
    
(LFR_TF_B_ampli, LFR_TF_B_phase, LFR_TF_B_freqs) = load_LFR_TF_B_CDF(cdffile_LFR_TF_B, echo=True)  

LFR_TF_B = 4*[None]
for F in range(4): 
    LFR_TF_B[F] = { 'Freq':  LFR_TF_B_freqs[F], 
                    'Gain':  LFR_TF_B_ampli[F],
                    'Phase': LFR_TF_B_phase[F] }  

#data_version = LFR_TF_PFM2_B1B2B3[0]['Filecomment'][-10:]
data_version = "..."
freq_range = [0.1,12500]
fig=plt.figure(figsize=(10,4))
plt.xlabel('Frequency (Hz)', fontsize=12)
plt.ylabel('Gain (count/V)', fontsize=14)
plt.title("LFR PFM2 transfer functions @F0, F1, F2 and F3 - %s - CDF"%(data_version), fontsize=18)   
for i in range(3):
    for F in range(4): plt.plot(LFR_TF_B_freqs[F], LFR_TF_B_ampli[F][:,i], linewidth=2, color=colors_sm[i], 
                                marker=markers_sm[i], label=chs_sm[i] if F == 0 else None)       
plt.legend(loc='best') 
plt.xlim(freq_range)
plt.ylim([7200,9200])
#plt.ylim([8700,8740])
plt.semilogx()

fig=plt.figure(figsize=(10,4))
plt.xlabel('Frequency (Hz)', fontsize=12)
plt.ylabel('Phase (°)', fontsize=14)
plt.title("LFR PFM2 transfer functions @F0, F1, F2 and F3 - %s - CDF"%(data_version), fontsize=18)   
for i in range(3):
    for F in range(4): plt.plot(LFR_TF_B_freqs[F], LFR_TF_B_phase[F][:,i], linewidth=2, color=colors_sm[i], 
                                marker=markers_sm[i], label=chs_sm[i] if F == 0 else None)       
plt.legend(loc='best') 
plt.xlim(freq_range)
plt.ylim([-360,10])
plt.semilogx()
plt.show()

SOLO_CAL_RCT-LFR-SCM_V20190123171020.cdf
PRINT cdf: 
 Freqs_F0: CDF_FLOAT [1024]
Freqs_F1: CDF_FLOAT [2066]
Freqs_F2: CDF_FLOAT [1035]
Freqs_F3: CDF_FLOAT [89]
TF_B1B2B3_amplitude_F0: CDF_FLOAT [1024, 3]
TF_B1B2B3_amplitude_F1: CDF_FLOAT [2066, 3]
TF_B1B2B3_amplitude_F2: CDF_FLOAT [1035, 3]
TF_B1B2B3_amplitude_F3: CDF_FLOAT [89, 3]
TF_B1B2B3_phase_F0: CDF_FLOAT [1024, 3]
TF_B1B2B3_phase_F1: CDF_FLOAT [2066, 3]
TF_B1B2B3_phase_F2: CDF_FLOAT [1035, 3]
TF_B1B2B3_phase_F3: CDF_FLOAT [89, 3] 

Mtilde_SOprime_redu = 3*[None]
C1Y_redu = 3*[None]
alpha_redu = 3*[None]
Mtilde_X1_redu = 3*[None]
Mtilde_X2_redu = 3*[None]
Mtilde_X3_redu = 3*[None]
Mtilde_Y1_redu = 3*[None]
Mtilde_Y2_redu = 3*[None]
Mtilde_Y3_redu = 3*[None]
Mtilde_Z1_redu = 3*[None]
Mtilde_Z2_redu = 3*[None]
Mtilde_Z3_redu = 3*[None]
Mtilde_Alpha_over_Kappa_X1_redu = 3*[None]
Mtilde_Alpha_over_Kappa_X2_redu = 3*[None]
Mtilde_Alpha_over_Kappa_X3_redu = 3*[None]
Mtilde_Alpha_over_Kappa_Y1_redu = 3*[None]
Mtilde_Alpha_over_Kappa_Y2_redu = 3*[None]
Mtilde_Alpha_over_Kappa_Y3_redu = 3*[None]
Mtilde_Alpha_over_Kappa_Z1_redu = 3*[None]
Mtilde_Alpha_over_Kappa_Z2_redu = 3*[None]
Mtilde_Alpha_over_Kappa_Z3_redu = 3*[None]
for F in [0,1,2]:
    Mtilde_SOprime_redu[F], C1Y_redu[F] = load_interp_Mtilde_SOprime(cdffile_SCM_TF, freq_cal_redu[F], echo=False) 
    alpha_redu[F] = 1 / ( C1Y_redu[F] * LFR_TF_mean_B_interp(freq_cal_redu[F], LFR_TF_B[F]) )
    (Mtilde_X1_redu[F], Mtilde_X2_redu[F], Mtilde_X3_redu[F], 
     Mtilde_Y1_redu[F], Mtilde_Y2_redu[F], Mtilde_Y3_redu[F], 
     Mtilde_Z1_redu[F], Mtilde_Z2_redu[F], Mtilde_Z3_redu[F]) = matrix2comp_Mtilde(Mtilde_SOprime_redu[F])
    Mtilde_Alpha_over_Kappa_X1_redu[F] = Mtilde_X1_redu[F] * alpha_redu[F] / kappa[F]
    Mtilde_Alpha_over_Kappa_X2_redu[F] = Mtilde_X2_redu[F] * alpha_redu[F] / kappa[F]
    Mtilde_Alpha_over_Kappa_X3_redu[F] = Mtilde_X3_redu[F] * alpha_redu[F] / kappa[F]
    Mtilde_Alpha_over_Kappa_Y1_redu[F] = Mtilde_Y1_redu[F] * alpha_redu[F] / kappa[F]
    Mtilde_Alpha_over_Kappa_Y2_redu[F] = Mtilde_Y2_redu[F] * alpha_redu[F] / kappa[F]
    Mtilde_Alpha_over_Kappa_Y3_redu[F] = Mtilde_Y3_redu[F] * alpha_redu[F] / kappa[F]
    Mtilde_Alpha_over_Kappa_Z1_redu[F] = Mtilde_Z1_redu[F] * alpha_redu[F] / kappa[F]
    Mtilde_Alpha_over_Kappa_Z2_redu[F] = Mtilde_Z2_redu[F] * alpha_redu[F] / kappa[F]
    Mtilde_Alpha_over_Kappa_Z3_redu[F] = Mtilde_Z3_redu[F] * alpha_redu[F] / kappa[F]
              
freq_all_redu = np.concatenate( [freq_cal_redu[2], [np.nan], freq_cal_redu[1], [np.nan], freq_cal_redu[0]] )
alpha_all_redu = np.concatenate( [alpha_redu[2], [np.nan], alpha_redu[1], [np.nan], alpha_redu[0]] )
alpha_all_arg_redu = np.concatenate( [np.rad2deg(np.unwrap(np.angle(alpha_redu[2]))), [np.nan], 
                                      np.rad2deg(np.unwrap(np.angle(alpha_redu[1]))), [np.nan], 
                                      np.rad2deg(np.unwrap(np.angle(alpha_redu[0])))] )      

freq_all_redu

Mtilde_SOprime_full = 3*[None]
C1Y_full = 3*[None]
alpha_full = 3*[None]
Mtilde_X1_full = 3*[None]
Mtilde_X2_full = 3*[None]
Mtilde_X3_full = 3*[None]
Mtilde_Y1_full = 3*[None]
Mtilde_Y2_full = 3*[None]
Mtilde_Y3_full = 3*[None]
Mtilde_Z1_full = 3*[None]
Mtilde_Z2_full = 3*[None]
Mtilde_Z3_full = 3*[None]
Mtilde_Alpha_over_Kappa_X1_full = 3*[None]
Mtilde_Alpha_over_Kappa_X2_full = 3*[None]
Mtilde_Alpha_over_Kappa_X3_full = 3*[None]
Mtilde_Alpha_over_Kappa_Y1_full = 3*[None]
Mtilde_Alpha_over_Kappa_Y2_full = 3*[None]
Mtilde_Alpha_over_Kappa_Y3_full = 3*[None]
Mtilde_Alpha_over_Kappa_Z1_full = 3*[None]
Mtilde_Alpha_over_Kappa_Z2_full = 3*[None]
Mtilde_Alpha_over_Kappa_Z3_full = 3*[None]
for F in [0,1,2]:
    Mtilde_SOprime_full[F], C1Y_full[F] = load_interp_Mtilde_SOprime(cdffile_SCM_TF, freq_cal_full[F], echo=False) 
    alpha_full[F] = 1 / ( C1Y_full[F] * LFR_TF_mean_B_interp(freq_cal_full[F], LFR_TF_B[F]) )
    (Mtilde_X1_full[F], Mtilde_X2_full[F], Mtilde_X3_full[F], 
     Mtilde_Y1_full[F], Mtilde_Y2_full[F], Mtilde_Y3_full[F], 
     Mtilde_Z1_full[F], Mtilde_Z2_full[F], Mtilde_Z3_full[F]) = matrix2comp_Mtilde(Mtilde_SOprime_full[F])
    Mtilde_Alpha_over_Kappa_X1_full[F] = Mtilde_X1_full[F] * alpha_full[F] / kappa[F]
    Mtilde_Alpha_over_Kappa_X2_full[F] = Mtilde_X2_full[F] * alpha_full[F] / kappa[F]
    Mtilde_Alpha_over_Kappa_X3_full[F] = Mtilde_X3_full[F] * alpha_full[F] / kappa[F]
    Mtilde_Alpha_over_Kappa_Y1_full[F] = Mtilde_Y1_full[F] * alpha_full[F] / kappa[F]
    Mtilde_Alpha_over_Kappa_Y2_full[F] = Mtilde_Y2_full[F] * alpha_full[F] / kappa[F]
    Mtilde_Alpha_over_Kappa_Y3_full[F] = Mtilde_Y3_full[F] * alpha_full[F] / kappa[F]
    Mtilde_Alpha_over_Kappa_Z1_full[F] = Mtilde_Z1_full[F] * alpha_full[F] / kappa[F]
    Mtilde_Alpha_over_Kappa_Z2_full[F] = Mtilde_Z2_full[F] * alpha_full[F] / kappa[F]
    Mtilde_Alpha_over_Kappa_Z3_full[F] = Mtilde_Z3_full[F] * alpha_full[F] / kappa[F]
              
freq_all_full = np.concatenate( [freq_cal_full[2], [np.nan], freq_cal_full[1], [np.nan], freq_cal_full[0]] )
alpha_all_full = np.concatenate( [alpha_full[2], [np.nan], alpha_full[1], [np.nan], alpha_full[0]] )
alpha_all_arg_full = np.concatenate( [np.rad2deg(np.unwrap(np.angle(alpha_full[2]))), [np.nan], 
                                      np.rad2deg(np.unwrap(np.angle(alpha_full[1]))), [np.nan], 
                                      np.rad2deg(np.unwrap(np.angle(alpha_full[0])))] )    

freq_all_full

fig, axarr = plt.subplots(2, 1, squeeze=True, figsize=(9,5), sharex=True, sharey=False)
plt.subplots_adjust(hspace=0.0) 
axarr[0].set_title(r'alpha = $(TF_{SCM} \times TF_{LFR}^{SCM})^{-1}$ - reduced grid', fontsize=14)   
axarr[0].plot(freq_all_redu, alpha_all_redu.real, label='RE', color=None, linewidth=2)
axarr[0].set_ylim([None, None])
axarr[0].set_ylabel('nT / count', fontsize=None)
axarr[0].semilogx()
axarr[0].plot(freq_all_redu, alpha_all_redu.imag, label='IM', color=None, linewidth=2)
axarr[0].legend(loc='best')

axarr[1].plot(freq_all_redu, abs(alpha_all_redu), label='ABS', color='k', linewidth=2)
#axarr[1].set_ylim([None, None])
axarr[1].set_ylabel('nT / count', fontsize=None)
axarr[1].semilogy([None, None])
#axarr[1].legend(loc='best')
axarr[1].legend(loc='center left')
axarr[1].set_xlabel('Frequency (Hz)', fontsize=12)
ax2 = axarr[1].twinx()
ax2.plot(freq_all_redu, alpha_all_arg_redu, label='ARG', color='C3', linewidth=2)
ax2.set_ylabel('degree', fontsize=None)
ax2.legend(loc='center right')
#ax2.legend(loc='lower right')

fname = dir_plots+ '/kcoefficients-alpha-redu_FSW_%s.png'%(fsw)

plt.savefig(fname, bbox_inches='tight', pad_inches=0.2)

fig, axarr = plt.subplots(2, 1, squeeze=True, figsize=(9,5), sharex=True, sharey=False)
plt.subplots_adjust(hspace=0.0) 
axarr[0].set_title(r'alpha = $(TF_{SCM} \times TF_{LFR}^{SCM})^{-1}$ - full grid', fontsize=14)   
axarr[0].plot(freq_all_full, alpha_all_full.real, label='RE', color=None, linewidth=2)
axarr[0].set_ylim([None, None])
axarr[0].set_ylabel('nT / count', fontsize=None)
axarr[0].semilogx()
axarr[0].plot(freq_all_full, alpha_all_full.imag, label='IM', color=None, linewidth=2)
axarr[0].legend(loc='best')

axarr[1].plot(freq_all_full, abs(alpha_all_full), label='ABS', color='k', linewidth=2)
#axarr[1].set_ylim([None, None])
axarr[1].set_ylabel('nT / count', fontsize=None)
axarr[1].semilogy([None, None])
#axarr[1].legend(loc='best')
axarr[1].legend(loc='center left')
axarr[1].set_xlabel('Frequency (Hz)', fontsize=12)
ax2 = axarr[1].twinx()
ax2.plot(freq_all_full, alpha_all_arg_full, label='ARG', color='C3', linewidth=2)
ax2.set_ylabel('degree', fontsize=None)
ax2.legend(loc='center right')
#ax2.legend(loc='lower right')

fname = dir_plots+ '/kcoefficients-alpha-full_FSW_%s.png'%(fsw)

plt.savefig(fname, bbox_inches='tight', pad_inches=0.2)

print("%.e"%(kappa[0]))

kcoeff_list_redu = (Mtilde_Alpha_over_Kappa_X1_redu, Mtilde_Alpha_over_Kappa_X2_redu, Mtilde_Alpha_over_Kappa_X3_redu, 
                    Mtilde_Alpha_over_Kappa_Y1_redu, Mtilde_Alpha_over_Kappa_Y2_redu, Mtilde_Alpha_over_Kappa_Y3_redu, 
                    Mtilde_Alpha_over_Kappa_Z1_redu, Mtilde_Alpha_over_Kappa_Z2_redu, Mtilde_Alpha_over_Kappa_Z3_redu)
kname_list_redu = ['Mtilde_Alpha_over_Kappa_%s%s_redu'%(i,j) for i in ['X', 'Y', 'Z'] for j in [1, 2, 3]]
title_list_redu = ['Mtilde_Alpha_over_Kappa_%s%s_redu'%(i,j) + \
                               r' = $\left[\,\alpha / \kappa \times \mathbf{M}_\mathit{SCM-SRF}^{-1} '+ \
                               r'\,\cdot\,\mathbf{c}^{-1}\right]_{i\!=\!%s, j\!=\!%s}$'%(i,j) \
                               for i in ['X', 'Y', 'Z'] for j in [1, 2, 3]]
fname_list_redu = [dir_plots+ '/kcoefficients_%s_FSW_%s_kappa=%.e.png'%(kname_list_redu[i], fsw, kappa[0]) \
                   for i in range(len(kname_list_redu))]

for (kname, kcoeff, title, fname) in zip(kname_list_redu, kcoeff_list_redu, title_list_redu, fname_list_redu):
    plot_kcoeff(kname, freq_cal_redu, kcoeff, figsize=(9,5), title=title, ylabel='nT / count', 
                fname=fname, no_ishow=True, close=True)
#    plot_kcoeff(kname, freq_cal_redu, kcoeff, figsize=(9,5), title=title, ylabel='nT / count',
#                fname=fname, no_ishow=True, close=True)

kcoeff_list_full = (Mtilde_Alpha_over_Kappa_X1_full, Mtilde_Alpha_over_Kappa_X2_full, Mtilde_Alpha_over_Kappa_X3_full, 
                    Mtilde_Alpha_over_Kappa_Y1_full, Mtilde_Alpha_over_Kappa_Y2_full, Mtilde_Alpha_over_Kappa_Y3_full, 
                    Mtilde_Alpha_over_Kappa_Z1_full, Mtilde_Alpha_over_Kappa_Z2_full, Mtilde_Alpha_over_Kappa_Z3_full)
kname_list_full = ['Mtilde_Alpha_over_Kappa_%s%s_full'%(i,j) for i in ['X', 'Y', 'Z'] for j in [1, 2, 3]]
title_list_full = ['Mtilde_Alpha_over_Kappa_%s%s_full'%(i,j) + \
                               r' = $\left[\,\alpha / \kappa \times \mathbf{M}_\mathit{SCM-SRF}^{-1} '+ \
                               r'\,\cdot\,\mathbf{c}^{-1}\right]_{i\!=\!%s, j\!=\!%s}$'%(i,j) \
                               for i in ['X', 'Y', 'Z'] for j in [1, 2, 3]]
fname_list_full = [dir_plots+ '/kcoefficients_%s_FSW_%s_kappa=%.e.png'%(kname_list_full[i], fsw, kappa[0]) \
                   for i in range(len(kname_list_full))]

for (kname, kcoeff, title, fname) in zip(kname_list_full, kcoeff_list_full, title_list_full, fname_list_full):
    plot_kcoeff(kname, freq_cal_full, kcoeff, figsize=(9,5), title=title, ylabel='nT / count',  
                fname=fname, no_ishow=True, close=True)
#    plot_kcoeff(kname, freq_cal_full, kcoeff, figsize=(9,5), title=title, ylabel='nT / count',
#                fname=fname, no_ishow=True, close=True)

ANT Transfer Functions expressed in the SO' coordinate system (and $\beta$ factor incorporating BIAS and LFR tranfer functions) storage

#cdffiles=glob.glob(dir_root + '/Missions/Solar Orbiter/LFR/BIAS/*V202003101607*.cdf')
cdffiles=glob.glob(dir_root + '/Missions/Solar Orbiter/LFR/BIAS/*V202011191204*.cdf')

cdffiles.sort()
for file in cdffiles:
    print(os.path.basename(file))   
cdffile_BIAS_TF = file    

BIAS_TF = load_BIAS_TF_func(cdffile_BIAS_TF, echo=True)

bias_conf = ['ac_diff_G5', 'dc_diff_G1']
# R0 R1 R2 (R=0 => BIAS4 BIAS5, R=1 => BIAS2 BIAS3)
r_conf = [0, 0, 0]  
#r_conf = [0, 0, 1]  

r = r_conf[2]
freqs = np.linspace(1, 10000, int(4e4))
fig=plt.figure(figsize=(7,4))
plt.xlabel('Frequency (Hz)', fontsize=12)
plt.ylabel('Gain', fontsize=12)
plt.title('BIAS transfer function', fontsize=14)      
plt.plot(freqs, np.abs( BIAS_TF(freqs, conf=bias_conf[r]) ), linewidth=2)
#plt.legend(loc='best') 
#plt.ylim([10,15])
plt.semilogx()

fig=plt.figure(figsize=(7,4))
plt.xlabel('Frequency (Hz)', fontsize=12)
plt.ylabel('Phase (degree)', fontsize=12)
plt.title('BIAS transfer function', fontsize=14)      
plt.plot(freqs, np.angle(BIAS_TF(freqs, conf=bias_conf[r]), deg=True), linewidth=2)
#plt.legend(loc='best') 
#plt.ylim([10,15])
plt.semilogx()    

SOLO_CAL_RPW-BIAS_V202011191204.cdf
PRINT cdf: 
 BIAS_CURRENT_GAIN: CDF_DOUBLE [3, 1] NRV
BIAS_CURRENT_OFFSET: CDF_DOUBLE [3, 1] NRV
E_LABEL: CDF_UCHAR*3 [3] NRV
E_OFFSET: CDF_DOUBLE [3, 1] NRV
Epoch_H: CDF_TIME_TT2000 [1] NRV
Epoch_L: CDF_TIME_TT2000 [1] NRV
TRANSFER_FUNCTION_COEFFS: CDF_DOUBLE [2, 8, 4] NRV
TRANSFER_FUNCTION_COEFF_LABEL: CDF_UCHAR*3 [8] NRV
TRANSFER_FUNCTION_LABEL: CDF_UCHAR*12 [4] NRV
TRANSFER_FUNCTION_ND_LABEL: CDF_UCHAR*11 [2] NRV
V_LABEL: CDF_UCHAR*2 [3] NRV
V_OFFSET: CDF_DOUBLE [3, 1] NRV 

TRANSFER_FUNCTION_COEFFS: [[[ 5.00900e+20  2.31100e+23  0.00000e+00  0.00000e+00]
  [-8.14800e+14  1.00900e+18  1.33551e+40  6.91633e+41]
  [ 1.04100e+10 -2.66400e+11  1.84960e+34  2.13364e+37]
  [ 0.00000e+00  0.00000e+00 -1.52549e+27  3.89496e+32]
  [ 0.00000e+00  0.00000e+00  2.66744e+19  0.00000e+00]
  [ 0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00]
  [ 0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00]
  [ 0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00]]

 [[ 8.55600e+21  2.32900e+23  1.27572e+41  3.42020e+41]
  [ 2.57800e+17  4.41100e+18  2.62533e+39  7.05355e+39]
  [ 2.04200e+12  7.34400e+12  4.71518e+34  4.33219e+35]
  [ 8.23800e+05  3.86800e+06  3.59175e+28  1.05546e+31]
  [ 1.00000e+00  1.00000e+00  6.98964e+21  9.97355e+25]
  [ 0.00000e+00  0.00000e+00  5.94792e+14  1.71685e+20]
  [ 0.00000e+00  0.00000e+00  1.00000e+00  1.00000e+00]
  [ 0.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00]]]
TRANSFER_FUNCTION_COEFF_LABEL: ['c_0' 'c_1' 'c_2' 'c_3' 'c_4' 'c_5' 'c_6' 'c_7']
TRANSFER_FUNCTION_LABEL: ['DC single   ' 'DC diff     ' 'AC low-gain ' 'AC high-gain']
TRANSFER_FUNCTION_ND_LABEL: ['Numerator  ' 'Denominator']
TRANSFER_FUNCTION_COEFFS : (2, 8, 4)

[]

dir_LFR_TF_E_ROC = dir_root + '/Missions/Solar Orbiter/LFR/Ground Segment/Tables de calibration'

cdffiles_E = glob.glob(dir_LFR_TF_E_ROC + '/*BIAS_V20190123*171020*.cdf')
cdffiles_E.sort()
for file in cdffiles_E:
    print(os.path.basename(file))  
cdffile_LFR_TF_E = file        
    
(LFR_TF_E_ampli, LFR_TF_E_phase, LFR_TF_E_freqs) = load_LFR_TF_E_CDF(cdffile_LFR_TF_E, echo=True)  
LFR_TF_E = 4*[None]
for F in range(4): 
    LFR_TF_E[F] = { 'Freq':  LFR_TF_E_freqs[F], 
                    'Gain':  LFR_TF_E_ampli[F],
                    'Phase': LFR_TF_E_phase[F]}  
    
#data_version = LFR_TF_PFM2_E1E2E3E4E5[0]['Filecomments'][0][-10:]
data_version = "..."
freq_range = [0.1,12500]
fig=plt.figure(figsize=(10,4))
plt.xlabel('Frequency (Hz)', fontsize=12)
plt.ylabel('Gain (count/V)', fontsize=14)
plt.title("LFR PFM2 transfer functions @F0, F1, F2 and F3 - %s - CDF"%(data_version), fontsize=18)   
for F in range(4): 
    for i in range(3 if F==3 else 5):
        plt.plot(LFR_TF_E_freqs[F], LFR_TF_E_ampli[F][:,i], linewidth=2, color=colors_bias[i], 
                                marker=markers_bias[i], label=chs_bias[i] if F == 0 else None)            
plt.legend(loc='best') 
plt.xlim(freq_range)
plt.ylim([6200,8500])
plt.ylim([5000,8500])
plt.semilogx()

fig=plt.figure(figsize=(10,4))
plt.xlabel('Frequency (Hz)', fontsize=12)
plt.ylabel('Phase (°)', fontsize=14)
plt.title("LFR PFM2 transfer functions @F0, F1, F2 and F3 - %s - CDF"%(data_version), fontsize=18)
for F in range(4): 
    for i in range(3 if F==3 else 5):
        plt.plot(LFR_TF_E_freqs[F], LFR_TF_E_phase[F][:,i], linewidth=2, color=colors_bias[i], 
                                marker=markers_bias[i], label=chs_bias[i] if F == 0 else None)            
plt.legend(loc='best') 
plt.xlim(freq_range)
plt.ylim([-360,90])
plt.semilogx()
plt.show()    

SOLO_CAL_RCT-LFR-BIAS_V20190123171020.cdf
PRINT cdf: 
 Freqs_F0: CDF_FLOAT [1024]
Freqs_F1: CDF_FLOAT [2066]
Freqs_F2: CDF_FLOAT [1035]
Freqs_F3: CDF_FLOAT [89]
TF_BIAS_12345_amplitude_F0: CDF_FLOAT [1024, 5]
TF_BIAS_12345_amplitude_F1: CDF_FLOAT [2066, 5]
TF_BIAS_12345_amplitude_F2: CDF_FLOAT [1035, 5]
TF_BIAS_12345_phase_F0: CDF_FLOAT [1024, 5]
TF_BIAS_12345_phase_F1: CDF_FLOAT [2066, 5]
TF_BIAS_12345_phase_F2: CDF_FLOAT [1035, 5]
TF_BIAS_123_amplitude_F3: CDF_FLOAT [89, 3]
TF_BIAS_123_phase_F3: CDF_FLOAT [89, 3] 

Atilde_Y1_redu = 3*[None]                            
Atilde_Y2_redu = 3*[None]                            
Atilde_Z1_redu = 3*[None]                            
Atilde_Z2_redu = 3*[None]  
k_ANT_redu = 3*[None]  
beta_redu = 3*[None]  
Atilde_Beta_over_Kappa_Y1_redu = 3*[None]      
Atilde_Beta_over_Kappa_Y2_redu = 3*[None]      
Atilde_Beta_over_Kappa_Z1_redu = 3*[None]      
Atilde_Beta_over_Kappa_Z2_redu = 3*[None]      

for F in [0,1,2]:
    r = r_conf[F]    
    (Atilde_Y1_redu[F], Atilde_Y2_redu[F], Atilde_Z1_redu[F], Atilde_Z2_redu[F]), k_ANT_redu[F] = \
           load_interp_Atilde_SOprime('cdffile_ANT_TF', freq_cal_redu[F])  
    beta_redu[F] = 1 / ( k_ANT_redu[F] * BIAS_TF(freq_cal_redu[F], conf=bias_conf[r]) * \
                    LFR_TF_mean_E_interp(freq_cal_redu[F], LFR_TF_E[F], R=r) )      
    Atilde_Beta_over_Kappa_Y1_redu[F] = Atilde_Y1_redu[F] * beta_redu[F] / kappa[F]
    Atilde_Beta_over_Kappa_Y2_redu[F] = Atilde_Y2_redu[F] * beta_redu[F] / kappa[F]
    Atilde_Beta_over_Kappa_Z1_redu[F] = Atilde_Z1_redu[F] * beta_redu[F] / kappa[F]
    Atilde_Beta_over_Kappa_Z2_redu[F] = Atilde_Z2_redu[F] * beta_redu[F] / kappa[F]   
    
beta_all_redu = np.concatenate( [beta_redu[2], [np.nan], beta_redu[1], [np.nan], beta_redu[0]] )
beta_all_arg_redu = np.concatenate( [np.rad2deg(np.unwrap(np.angle(beta_redu[2]))), [np.nan], 
                                     np.rad2deg(np.unwrap(np.angle(beta_redu[1]))), [np.nan], 
                                     np.rad2deg(np.unwrap(np.angle(beta_redu[0])))] )    

Atilde_Y1_full = 3*[None]                            
Atilde_Y2_full = 3*[None]                            
Atilde_Z1_full = 3*[None]                            
Atilde_Z2_full = 3*[None]  
k_ANT_full = 3*[None]  
beta_full = 3*[None]  
Atilde_Beta_over_Kappa_Y1_full = 3*[None]      
Atilde_Beta_over_Kappa_Y2_full = 3*[None]      
Atilde_Beta_over_Kappa_Z1_full = 3*[None]      
Atilde_Beta_over_Kappa_Z2_full = 3*[None]      

for F in [0,1,2]:
    r = r_conf[F]    
    (Atilde_Y1_full[F], Atilde_Y2_full[F], Atilde_Z1_full[F], Atilde_Z2_full[F]), k_ANT_full[F] = \
           load_interp_Atilde_SOprime('cdffile_ANT_TF', freq_cal_full[F])  
    beta_full[F] = 1 / ( k_ANT_full[F] * BIAS_TF(freq_cal_full[F], conf=bias_conf[r]) * \
                    LFR_TF_mean_E_interp(freq_cal_full[F], LFR_TF_E[F], R=r) )      
    Atilde_Beta_over_Kappa_Y1_full[F] = Atilde_Y1_full[F] * beta_full[F] / kappa[F]
    Atilde_Beta_over_Kappa_Y2_full[F] = Atilde_Y2_full[F] * beta_full[F] / kappa[F]
    Atilde_Beta_over_Kappa_Z1_full[F] = Atilde_Z1_full[F] * beta_full[F] / kappa[F]
    Atilde_Beta_over_Kappa_Z2_full[F] = Atilde_Z2_full[F] * beta_full[F] / kappa[F]   
    
beta_all_full = np.concatenate( [beta_full[2], [np.nan], beta_full[1], [np.nan], beta_full[0]] )
beta_all_arg_full = np.concatenate( [np.rad2deg(np.unwrap(np.angle(beta_full[2]))), [np.nan], 
                                     np.rad2deg(np.unwrap(np.angle(beta_full[1]))), [np.nan], 
                                     np.rad2deg(np.unwrap(np.angle(beta_full[0])))] )    

fig, axarr = plt.subplots(2, 1, squeeze=True, figsize=(9,5), sharex=True, sharey=False)
plt.subplots_adjust(hspace=0.0) 
axarr[0].set_title(r'beta = $(TF_{ANT} \times TF_{BIAS} \times TF_{LFR}^{BIAS})^{-1}$ reduced grid', fontsize=14)   
axarr[0].plot(freq_all_redu, beta_all_redu.real, label='RE', color=None, linewidth=2)
axarr[0].set_ylim([None, None])
axarr[0].set_ylabel('V/m / count', fontsize=None)
axarr[0].semilogx()
axarr[0].plot(freq_all_redu, beta_all_redu.imag, label='IM', color=None, linewidth=2)
axarr[0].legend(loc='best')

axarr[1].plot(freq_all_redu, abs(beta_all_redu), label='ABS', color='k', linewidth=2)
#axarr[1].set_ylim([None, None])
axarr[1].set_ylabel('V/m / count', fontsize=None)
axarr[1].semilogy([None, None])
axarr[1].legend(loc='best')
axarr[1].legend(loc='upper center')
axarr[1].set_xlabel('Frequency (Hz)', fontsize=12)
ax2 = axarr[1].twinx()
ax2.plot(freq_all_redu, beta_all_arg_redu, label='ARG', color='C3', linewidth=2)
ax2.set_ylabel('degree', fontsize=None)
ax2.legend(loc='lower right')

fname = dir_plots+ '/kcoefficients-beta-redu_FSW_%s_%s_or_%s_R=%s-%s-%s.png'%(fsw, bias_conf[0].upper(), 
                                                                              bias_conf[1].upper(), *r_conf)

plt.savefig(fname, bbox_inches='tight', pad_inches=0.2)

fig, axarr = plt.subplots(2, 1, squeeze=True, figsize=(9,5), sharex=True, sharey=False)
plt.subplots_adjust(hspace=0.0) 
axarr[0].set_title(r'beta = $(TF_{ANT} \times TF_{BIAS} \times TF_{LFR}^{BIAS})^{-1}$ full grid', fontsize=14)   
axarr[0].plot(freq_all_full, beta_all_full.real, label='RE', color=None, linewidth=2)
axarr[0].set_ylim([None, None])
axarr[0].set_ylabel('V/m / count', fontsize=None)
axarr[0].semilogx()
axarr[0].plot(freq_all_full, beta_all_full.imag, label='IM', color=None, linewidth=2)
axarr[0].legend(loc='best')

axarr[1].plot(freq_all_full, abs(beta_all_full), label='ABS', color='k', linewidth=2)
#axarr[1].set_ylim([None, None])
axarr[1].set_ylabel('V/m / count', fontsize=None)
axarr[1].semilogy([None, None])
axarr[1].legend(loc='best')
axarr[1].legend(loc='upper center')
axarr[1].set_xlabel('Frequency (Hz)', fontsize=12)
ax2 = axarr[1].twinx()
ax2.plot(freq_all_full, beta_all_arg_full, label='ARG', color='C3', linewidth=2)
ax2.set_ylabel('degree', fontsize=None)
ax2.legend(loc='lower right')

fname = dir_plots+ '/kcoefficients-beta-full_FSW_%s_%s_or_%s_R=%s-%s-%s.png'%(fsw, bias_conf[0].upper(), 
                                                                 bias_conf[1].upper(), *r_conf)

plt.savefig(fname, bbox_inches='tight', pad_inches=0.2)

kcoeff_list_redu = (Atilde_Beta_over_Kappa_Y1_redu, Atilde_Beta_over_Kappa_Y2_redu,               
                    Atilde_Beta_over_Kappa_Z1_redu, Atilde_Beta_over_Kappa_Z2_redu)
kname_list_redu = ['Atilde_Beta_over_Kappa_%s%s_redu'%(i,j) for i in ['Y', 'Z'] for j in [1,2]]
title_list_redu = ['Atilde_Beta_over_Kappa_%s%s_redu'%(i,j) + \
              r' = $\left[ \,\beta / \kappa \times \tilde{\mathbf{A}}_\mathit{SRF-ANT} '+ \
              r'\right]_{i\!=\!%s, j\!=\!%s}$'%(i,j) for i in ['Y', 'Z'] for j in [1,2]]                                          
fname_list_redu = [dir_plots+ '/kcoefficients_%s_FSW_%s_%s_or_%s_R=%s-%s-%s_kappa=%.e.png'%(kname_list_redu[i], 
                   fsw, bias_conf[0].upper(), bias_conf[1].upper(), 
                   *r_conf, kappa[0]) for i in range(len(kname_list_redu))]

for (kname, kcoeff, title, fname) in zip(kname_list_redu, kcoeff_list_redu, title_list_redu, fname_list_redu):
     plot_kcoeff(kname, freq_cal_redu, kcoeff, figsize=(9,5), title=title, ylabel='V/m / nT', 
                 fname=fname, no_ishow=True, close=True)
#    plot_kcoeff(kname, freq_cal_redu, kcoeff, figsize=(9,5), title=title, ylabel='V/m / nT', 
#                fname=fname, no_ishow=False, close=False)  

kcoeff_list_full = (Atilde_Beta_over_Kappa_Y1_full, Atilde_Beta_over_Kappa_Y2_full,               
                    Atilde_Beta_over_Kappa_Z1_full, Atilde_Beta_over_Kappa_Z2_full)
kname_list_full = ['Atilde_Beta_over_Kappa_%s%s_full'%(i,j) for i in ['Y', 'Z'] for j in [1,2]]
title_list_full = ['Atilde_Beta_over_Kappa_%s%s_full'%(i,j) + \
              r' = $\left[ \,\beta / \kappa \times \tilde{\mathbf{A}}_\mathit{SRF-ANT} '+ \
              r'\right]_{i\!=\!%s, j\!=\!%s}$'%(i,j) for i in ['Y', 'Z'] for j in [1,2]]                                          
fname_list_full = [dir_plots+ '/kcoefficients_%s_FSW_%s_%s_or_%s_R=%s-%s-%s_kappa=%.e.png'%(kname_list_full[i], 
                   fsw, bias_conf[0].upper(), bias_conf[1].upper(), 
                   *r_conf, kappa[0]) for i in range(len(kname_list_full))]

for (kname, kcoeff, title, fname) in zip(kname_list_full, kcoeff_list_full, title_list_full, fname_list_full):
     plot_kcoeff(kname, freq_cal_full, kcoeff, figsize=(9,5), title=title, ylabel='V/m / nT', 
                 fname=fname, no_ishow=True, close=True)
#    plot_kcoeff(kname, freq_cal_full, kcoeff, figsize=(9,5), title=title, ylabel='V/m / nT', 
#                fname=fname, no_ishow=False, close=False)  

Storage in the format useful for telecommand next

for F in [0,1,2]:
    
    for ifreq in range(len(freq_cal_redu[F])-1):        
        k_coefficients[F][ifreq, icoeff["Mtilde_X1_RE"]] = Mtilde_Alpha_over_Kappa_X1_redu[F][ifreq].real
        k_coefficients[F][ifreq, icoeff["Mtilde_X1_IM"]] = Mtilde_Alpha_over_Kappa_X1_redu[F][ifreq].imag
        k_coefficients[F][ifreq, icoeff["Mtilde_X2_RE"]] = Mtilde_Alpha_over_Kappa_X2_redu[F][ifreq].real
        k_coefficients[F][ifreq, icoeff["Mtilde_X2_IM"]] = Mtilde_Alpha_over_Kappa_X2_redu[F][ifreq].imag        
        k_coefficients[F][ifreq, icoeff["Mtilde_X3_RE"]] = Mtilde_Alpha_over_Kappa_X3_redu[F][ifreq].real
        k_coefficients[F][ifreq, icoeff["Mtilde_X3_IM"]] = Mtilde_Alpha_over_Kappa_X3_redu[F][ifreq].imag        
        
        k_coefficients[F][ifreq, icoeff["Mtilde_Y1_RE"]] = Mtilde_Alpha_over_Kappa_Y1_redu[F][ifreq].real
        k_coefficients[F][ifreq, icoeff["Mtilde_Y1_IM"]] = Mtilde_Alpha_over_Kappa_Y1_redu[F][ifreq].imag
        k_coefficients[F][ifreq, icoeff["Mtilde_Y2_RE"]] = Mtilde_Alpha_over_Kappa_Y2_redu[F][ifreq].real
        k_coefficients[F][ifreq, icoeff["Mtilde_Y2_IM"]] = Mtilde_Alpha_over_Kappa_Y2_redu[F][ifreq].imag        
        k_coefficients[F][ifreq, icoeff["Mtilde_Y3_RE"]] = Mtilde_Alpha_over_Kappa_Y3_redu[F][ifreq].real
        k_coefficients[F][ifreq, icoeff["Mtilde_Y3_IM"]] = Mtilde_Alpha_over_Kappa_Y3_redu[F][ifreq].imag        
        
        k_coefficients[F][ifreq, icoeff["Mtilde_Z1_RE"]] = Mtilde_Alpha_over_Kappa_Z1_redu[F][ifreq].real
        k_coefficients[F][ifreq, icoeff["Mtilde_Z1_IM"]] = Mtilde_Alpha_over_Kappa_Z1_redu[F][ifreq].imag
        k_coefficients[F][ifreq, icoeff["Mtilde_Z2_RE"]] = Mtilde_Alpha_over_Kappa_Z2_redu[F][ifreq].real
        k_coefficients[F][ifreq, icoeff["Mtilde_Z2_IM"]] = Mtilde_Alpha_over_Kappa_Z2_redu[F][ifreq].imag        
        k_coefficients[F][ifreq, icoeff["Mtilde_Z3_RE"]] = Mtilde_Alpha_over_Kappa_Z3_redu[F][ifreq].real
        k_coefficients[F][ifreq, icoeff["Mtilde_Z3_IM"]] = Mtilde_Alpha_over_Kappa_Z3_redu[F][ifreq].imag   
        
        k_coefficients[F][ifreq, icoeff["Atilde_Y1_RE"]] = Atilde_Beta_over_Kappa_Y1_redu[F][ifreq].real
        k_coefficients[F][ifreq, icoeff["Atilde_Y1_IM"]] = Atilde_Beta_over_Kappa_Y1_redu[F][ifreq].imag
        k_coefficients[F][ifreq, icoeff["Atilde_Y2_RE"]] = Atilde_Beta_over_Kappa_Y2_redu[F][ifreq].real
        k_coefficients[F][ifreq, icoeff["Atilde_Y2_IM"]] = Atilde_Beta_over_Kappa_Y2_redu[F][ifreq].imag   
        
        k_coefficients[F][ifreq, icoeff["Atilde_Z1_RE"]] = Atilde_Beta_over_Kappa_Z1_redu[F][ifreq].real
        k_coefficients[F][ifreq, icoeff["Atilde_Z1_IM"]] = Atilde_Beta_over_Kappa_Z1_redu[F][ifreq].imag
        k_coefficients[F][ifreq, icoeff["Atilde_Z2_RE"]] = Atilde_Beta_over_Kappa_Z2_redu[F][ifreq].real
        k_coefficients[F][ifreq, icoeff["Atilde_Z2_IM"]] = Atilde_Beta_over_Kappa_Z2_redu[F][ifreq].imag        

    ifreq = -1       
    k_coefficients[F][0, icoeff["free_1"]] = Mtilde_Alpha_over_Kappa_X1_redu[F][ifreq].real
    k_coefficients[F][0, icoeff["free_2"]] = Mtilde_Alpha_over_Kappa_X1_redu[F][ifreq].imag
    k_coefficients[F][0, icoeff["free_3"]] = Mtilde_Alpha_over_Kappa_X2_redu[F][ifreq].real
    k_coefficients[F][0, icoeff["free_4"]] = Mtilde_Alpha_over_Kappa_X2_redu[F][ifreq].imag        
    k_coefficients[F][0, icoeff["free_5"]] = Mtilde_Alpha_over_Kappa_X3_redu[F][ifreq].real
    k_coefficients[F][0, icoeff["free_6"]] = Mtilde_Alpha_over_Kappa_X3_redu[F][ifreq].imag 
    
    k_coefficients[F][1, icoeff["free_1"]] = Mtilde_Alpha_over_Kappa_Y1_redu[F][ifreq].real
    k_coefficients[F][1, icoeff["free_2"]] = Mtilde_Alpha_over_Kappa_Y1_redu[F][ifreq].imag
    k_coefficients[F][1, icoeff["free_3"]] = Mtilde_Alpha_over_Kappa_Y2_redu[F][ifreq].real
    k_coefficients[F][1, icoeff["free_4"]] = Mtilde_Alpha_over_Kappa_Y2_redu[F][ifreq].imag        
    k_coefficients[F][1, icoeff["free_5"]] = Mtilde_Alpha_over_Kappa_Y3_redu[F][ifreq].real
    k_coefficients[F][1, icoeff["free_6"]] = Mtilde_Alpha_over_Kappa_Y3_redu[F][ifreq].imag
    
    k_coefficients[F][2, icoeff["free_1"]] = Mtilde_Alpha_over_Kappa_Z1_redu[F][ifreq].real
    k_coefficients[F][2, icoeff["free_2"]] = Mtilde_Alpha_over_Kappa_Z1_redu[F][ifreq].imag
    k_coefficients[F][2, icoeff["free_3"]] = Mtilde_Alpha_over_Kappa_Z2_redu[F][ifreq].real
    k_coefficients[F][2, icoeff["free_4"]] = Mtilde_Alpha_over_Kappa_Z2_redu[F][ifreq].imag        
    k_coefficients[F][2, icoeff["free_5"]] = Mtilde_Alpha_over_Kappa_Z3_redu[F][ifreq].real
    k_coefficients[F][2, icoeff["free_6"]] = Mtilde_Alpha_over_Kappa_Z3_redu[F][ifreq].imag     

    k_coefficients[F][3, icoeff["free_1"]] = Atilde_Beta_over_Kappa_Y1_redu[F][ifreq].real
    k_coefficients[F][3, icoeff["free_2"]] = Atilde_Beta_over_Kappa_Y1_redu[F][ifreq].imag
    k_coefficients[F][3, icoeff["free_3"]] = Atilde_Beta_over_Kappa_Y2_redu[F][ifreq].real
    k_coefficients[F][3, icoeff["free_4"]] = Atilde_Beta_over_Kappa_Y2_redu[F][ifreq].imag      
    
    k_coefficients[F][4, icoeff["free_1"]] = Atilde_Beta_over_Kappa_Z1_redu[F][ifreq].real
    k_coefficients[F][4, icoeff["free_2"]] = Atilde_Beta_over_Kappa_Z1_redu[F][ifreq].imag
    k_coefficients[F][4, icoeff["free_3"]] = Atilde_Beta_over_Kappa_Z2_redu[F][ifreq].real
    k_coefficients[F][4, icoeff["free_4"]] = Atilde_Beta_over_Kappa_Z2_redu[F][ifreq].imag               

test_Bruno = True

if test_Bruno:
    for F in [0,1,2]:

        for ifreq in range(len(freq_cal_redu[F])-1):        
            k_coefficients[F][ifreq, icoeff["Mtilde_X1_RE"]] = 1.
            k_coefficients[F][ifreq, icoeff["Mtilde_X1_IM"]] = 0.
            k_coefficients[F][ifreq, icoeff["Mtilde_X2_RE"]] = 0.
            k_coefficients[F][ifreq, icoeff["Mtilde_X2_IM"]] = 0.      
            k_coefficients[F][ifreq, icoeff["Mtilde_X3_RE"]] = 0.
            k_coefficients[F][ifreq, icoeff["Mtilde_X3_IM"]] = 0.       

            k_coefficients[F][ifreq, icoeff["Mtilde_Y1_RE"]] = 0.
            k_coefficients[F][ifreq, icoeff["Mtilde_Y1_IM"]] = 0.
            k_coefficients[F][ifreq, icoeff["Mtilde_Y2_RE"]] = 1.
            k_coefficients[F][ifreq, icoeff["Mtilde_Y2_IM"]] = 0.       
            k_coefficients[F][ifreq, icoeff["Mtilde_Y3_RE"]] = 0. 
            k_coefficients[F][ifreq, icoeff["Mtilde_Y3_IM"]] = 0.        

            k_coefficients[F][ifreq, icoeff["Mtilde_Z1_RE"]] = 0.
            k_coefficients[F][ifreq, icoeff["Mtilde_Z1_IM"]] = 0.
            k_coefficients[F][ifreq, icoeff["Mtilde_Z2_RE"]] = 0.
            k_coefficients[F][ifreq, icoeff["Mtilde_Z2_IM"]] = 0.       
            k_coefficients[F][ifreq, icoeff["Mtilde_Z3_RE"]] = 1.
            k_coefficients[F][ifreq, icoeff["Mtilde_Z3_IM"]] = 0. 

            k_coefficients[F][ifreq, icoeff["Atilde_Y1_RE"]] = 1.
            k_coefficients[F][ifreq, icoeff["Atilde_Y1_IM"]] = 0.
            k_coefficients[F][ifreq, icoeff["Atilde_Y2_RE"]] = 0. 
            k_coefficients[F][ifreq, icoeff["Atilde_Y2_IM"]] = 0.   

            k_coefficients[F][ifreq, icoeff["Atilde_Z1_RE"]] = 0.
            k_coefficients[F][ifreq, icoeff["Atilde_Z1_IM"]] = 0. 
            k_coefficients[F][ifreq, icoeff["Atilde_Z2_RE"]] = 1.
            k_coefficients[F][ifreq, icoeff["Atilde_Z2_IM"]] = 0.        

        ifreq = -1       
        k_coefficients[F][0, icoeff["free_1"]] = 1.
        k_coefficients[F][0, icoeff["free_2"]] = 0.
        k_coefficients[F][0, icoeff["free_3"]] = 0.
        k_coefficients[F][0, icoeff["free_4"]] = 0.        
        k_coefficients[F][0, icoeff["free_5"]] = 0. 
        k_coefficients[F][0, icoeff["free_6"]] = 0. 

        k_coefficients[F][1, icoeff["free_1"]] = 0.
        k_coefficients[F][1, icoeff["free_2"]] = 0.
        k_coefficients[F][1, icoeff["free_3"]] = 1.
        k_coefficients[F][1, icoeff["free_4"]] = 0.       
        k_coefficients[F][1, icoeff["free_5"]] = 0.
        k_coefficients[F][1, icoeff["free_6"]] = 0.

        k_coefficients[F][2, icoeff["free_1"]] = 0.
        k_coefficients[F][2, icoeff["free_2"]] = 0.
        k_coefficients[F][2, icoeff["free_3"]] = 0.
        k_coefficients[F][2, icoeff["free_4"]] = 0.        
        k_coefficients[F][2, icoeff["free_5"]] = 1.
        k_coefficients[F][2, icoeff["free_6"]] = 0.     

        k_coefficients[F][3, icoeff["free_1"]] = 1.
        k_coefficients[F][3, icoeff["free_2"]] = 0.
        k_coefficients[F][3, icoeff["free_3"]] = 0.
        k_coefficients[F][3, icoeff["free_4"]] = 0.      

        k_coefficients[F][4, icoeff["free_1"]] = 0.
        k_coefficients[F][4, icoeff["free_2"]] = 0.
        k_coefficients[F][4, icoeff["free_3"]] = 1.
        k_coefficients[F][4, icoeff["free_4"]] = 0.               

time_struc = localtime(time())
date = '{:4}-{:02}-{:02}'.format(time_struc.tm_year, time_struc.tm_mon, time_struc.tm_mday)

if test_Bruno:
    fname = dir_storage + '/kcoefficients_FSW_%s_test1_'%(fsw) + date
else:
    fname = dir_storage + '/kcoefficients_FSW_%s_%s_or_%s_R=%s-%s-%s_kappa=%.e_'%(fsw, bias_conf[0].upper(), 
                                                                                  bias_conf[1].upper(), 
                                                                                  *r_conf, kappa[0]) + date

header = 'Adapted for LFR FSW:            ' + fsw
header += '\nBIAS calibration file used:     ' + os.path.basename(cdffile_BIAS_TF)
header += '\nSCM calibration file used:      ' + os.path.basename(cdffile_SCM_TF)
header += '\nLFR BIAS calibration file used: ' + os.path.basename(cdffile_LFR_TF_E)
header += '\nLFR SCM calibration file used:  ' + os.path.basename(cdffile_LFR_TF_B)
header += '\nBIAS analogue signals used:     ' + bias_conf[0].upper() + ' or ' + bias_conf[1].upper()
header += '\nLFR electric signals used:      R0={:d}, R1={:d}, R2={:d}'.format(*r_conf)
header += '\nConstant kappa used:            k0={:.2e}, k1={:.2e}, k2={:.2e}'.format(*kappa)
header += '\ndate:                           ' + date
header += '\nkcoeff_frequency  kcoeff_frequency'
for i in range(1,32+1,1): 
    header += '{:>14s}'.format('kcoeff_' + str(i))
header += '\n         (index)              (Hz)' + 32*'       (float)' 
fmt = ['%18d','%17.2f'] + 32*['%+13.6e']    
save2Dtxt(np.arange(0,11+13+12,1), np.concatenate(freq_cal_sent), np.concatenate(k_coefficients).T, 
          fname=fname+'.txt', fmt=fmt, header=header, T=False, delimiter=' ')

header = 'kcoeff_frequency;kcoeff_frequency'
for i in range(1,32+1,1): 
    header += '{:s}'.format(';kcoeff_' + str(i))
header += '\n(index);(Hz)' + 32*';(float)' 
fmt = ['%d','%.2f'] + 32*['%+.6e']    
save2Dtxt(np.arange(0,11+13+12,1), np.concatenate(freq_cal_sent), np.concatenate(k_coefficients).T, 
          fname=fname+'.csv', fmt=fmt, header=header, T=False, delimiter=';', comments='')

36*32*4

Storage for Alexis (default full table) end

# full set of ASM calibration coefficients to be used onboard by default

freq_cal_alexis = 3*[None]
B_cal_alexis = 3*[None]
E_cal_alexis = 3*[None]
for F in [0, 1, 2]:
    freq_cal_alexis[F] = np.zeros(128)
    B_cal_alexis[F] = np.zeros([128, 3, 3], dtype=complex)
    E_cal_alexis[F] = np.zeros([128, 2, 2], dtype=complex)
    # in the VHDL code one starts at 1*df and not at 0 Hz as for the L1 CDF ASM data
    i0 = [17-1, 6-1, 7-1][F]     
    nfreq = len(freq_cal_full[F])
    freq_cal_alexis[F][i0:i0+nfreq] = freq_cal_full[F]
    
    B_cal_alexis[F][i0:i0+nfreq, 0, 0] = Mtilde_Alpha_over_Kappa_X1_full[F]
    B_cal_alexis[F][i0:i0+nfreq, 0, 1] = Mtilde_Alpha_over_Kappa_X2_full[F]
    B_cal_alexis[F][i0:i0+nfreq, 0, 2] = Mtilde_Alpha_over_Kappa_X3_full[F]
    B_cal_alexis[F][i0:i0+nfreq, 1, 0] = Mtilde_Alpha_over_Kappa_Y1_full[F]
    B_cal_alexis[F][i0:i0+nfreq, 1, 1] = Mtilde_Alpha_over_Kappa_Y2_full[F]
    B_cal_alexis[F][i0:i0+nfreq, 1, 2] = Mtilde_Alpha_over_Kappa_Y3_full[F]
    B_cal_alexis[F][i0:i0+nfreq, 2, 0] = Mtilde_Alpha_over_Kappa_Z1_full[F]
    B_cal_alexis[F][i0:i0+nfreq, 2, 1] = Mtilde_Alpha_over_Kappa_Z2_full[F]
    B_cal_alexis[F][i0:i0+nfreq, 2, 2] = Mtilde_Alpha_over_Kappa_Z3_full[F]
    
    E_cal_alexis[F][i0:i0+nfreq, 0, 0] = Atilde_Beta_over_Kappa_Y1_full[F]
    E_cal_alexis[F][i0:i0+nfreq, 0, 1] = Atilde_Beta_over_Kappa_Y2_full[F]
    E_cal_alexis[F][i0:i0+nfreq, 1, 0] = Atilde_Beta_over_Kappa_Z1_full[F]
    E_cal_alexis[F][i0:i0+nfreq, 1, 1] = Atilde_Beta_over_Kappa_Z2_full[F]
    
fname = dir_storage + '/ASM_calibration_default-full-tables' + \
                      '_FSW_%s_%s_or_%s_R=%s-%s-%s_kappa=%.e_'%(fsw, bias_conf[0].upper(), 
                                                                bias_conf[1].upper(), 
                                                                *r_conf, kappa[0]) + date +'.json'   

save2json([freq_cal_alexis, B_cal_alexis, E_cal_alexis], fname)
# [freq_cal, B_cal, E_cal] = json2data(fname)

End start

(37 - (1 << 6)) + 1

(1 << 6)