def load_EMIC_IMFs_and_dynspec(imf_start, imf_end, IA_filter=None):
'''
Loads IMFs and dynamic spectra
'''
_ti, _dat, _HM_mags, _dt, _xtime, _xfreq, _pow = get_mag_data(_time_start, _time_end,
_probe, low_cut=_band_start, high_cut=_band_end)
sample_rate = 1./_dt
# Calculate IMFs, get instantaneous phase, frequency, amplitude
print('Sifting IMFs and performing HHT')
imfs, IPs, IFs, IAs = [], [], [], []
for ii, lbl in zip(range(3), ['x', 'y', 'z']):
imf = emd.sift.sift(_dat[:, ii], sift_thresh=1e-2)
IP, IF, IA = emd.spectra.frequency_transform(imf, sample_rate, 'hilbert')
print(f'{imf.shape[1]} IMFs found for B{lbl}')
imfs.append(imf)
IPs.append(IP)
IFs.append(IF)
IAs.append(IA)
# Snip IMFs to time
st, en = ascr.boundary_idx64(_ti, imf_start, imf_end)
for ii in range(3):
_imf_t = _ti[st:en]
IAs[ii] = IAs[ii][st:en]
IFs[ii] = IFs[ii][st:en]
IPs[ii] = IPs[ii][st:en]
return _ti, _dat, _HM_mags, _imf_t, IAs, IFs, IPs, _xtime, _xfreq, _pow
def get_pc1_peaks(sfreq, spower, band_start, band_end, npeaks=None):
'''
Returns integrated spower spectrum between band_start and band_end as well
as index locations of the most prominent npeaks
'''
fst, fen = ascr.boundary_idx64(sfreq, band_start, band_end)
pc1_int_power = spower[fst:fen, :].sum(axis=0)
peak_idx = ascr.find_peaks(pc1_int_power, npeaks=npeaks, sortby='prom')
return pc1_int_power, peak_idx
spec)] / (kB * 1e6 * spice_dict['F{}DU_Density'.format(spec)])
this_temp = kB * t_perp_kelvin / q # Convert from K to eV
spice_dens.append(this_dens)
spice_temp.append(this_temp)
spice_anis.append(this_anis)
### COMPARISON PLOTS FOR CHECKING ###
import matplotlib.pyplot as plt
# Plot things :: B0HM, cold density, HOPE/RBSPICE hot densities (4 plots)
fig, axes = plt.subplots(4)
# B0
B0 = np.sqrt(raw_mags[:, 0]**2 + raw_mags[:, 1]**2 + raw_mags[:, 2]**2)
st, en = ascr.boundary_idx64(mag_times, _time_start, _time_end)
_st, _en = ascr.boundary_idx64(_times, _time_start, _time_end)
axes[0].set_title(
'Magnetic field and Cold e/HOPE/RBSPICE ion densities')
axes[0].plot(mag_times[st:en], 1e-9 * B0[st:en], c='k', label='raw')
axes[0].plot(_times[_st:_en],
_B0[_st:_en],
c='r',
label='Filtered + Decimated',
marker='o')
axes[0].legend()
# Cold dens
axes[1].plot(den_times, 1e6 * edens, c='k', label='raw')
axes[1].plot(_times, _cold_dens, c='r', label='decimated')
def plot_velocities_and_energies_single(time_start, time_end, probe='a'):
# Import cutoff-derived composition information
cutoff_dict = epd.read_cutoff_file(cutoff_filename)
# Load particle and field information
time, mag, edens = load_CRRES_data(time_start, time_end, nsec=None,
crres_path='E://DATA//CRRES//')
mag_time, pc1_mags, HM_mags, imf_time, IA, IF, IP, stime, sfreq, spower = \
load_EMIC_IMFs_and_dynspec(time_start, time_end)
spower = spower.sum(axis=0)
cutoff = np.interp(time.astype(np.int64),
cutoff_dict['CUTOFF_TIME'].astype(np.int64),
cutoff_dict['CUTOFF_NORM'])
o_fracs = epd.calculate_o_from_he_and_cutoff(cutoff, he_frac)
# Get velocity/energy data for time
plt.ioff()
for ii in range(time.shape[0]):
# Define time, time index
this_time = time[ii]
maxpwr_tidx = np.where(abs(stime - this_time) == np.min(abs(stime - this_time)))[0][0]
# Find max freq and index
fst, fen = ascr.boundary_idx64(sfreq, _band_start, _band_end)
maxpwr_fidx = spower[maxpwr_tidx, fst:fen].argmax()
maxpwr_fidx += fst
maxpwr_freq = sfreq[maxpwr_fidx]
#date_string = this_time.astype(object).strftime('%Y%m%d')
save_string = this_time.astype(object).strftime('%Y%m%d_%H%M%S')
#clr = mapper.to_rgba(time[ii].astype(np.int64))
print('Doing time:', this_time)
fig, axes = plt.subplots(nrows=4, ncols=2, figsize=(8.0, 0.5*11.00),
gridspec_kw={'width_ratios':[1, 0.01],
'height_ratios':[1, 2, 2, 2]
})
# Spectra/IP
im0 = axes[0, 0].pcolormesh(stime, sfreq, spower.T, cmap='jet',
norm=colors.LogNorm(vmin=1e-4, vmax=1e1))
axes[0, 0].set_ylim(0, _band_end)
axes[0, 0].set_ylabel('$f$ [Hz]', rotation=90)
fig.colorbar(im0, cax=axes[0, 1], extend='both').set_label(
r'$\frac{nT^2}{Hz}$', fontsize=16, rotation=0, labelpad=15)
axes[0, 0].axvline(this_time, color='white', ls='-' , alpha=0.7)
axes[0, 0].set_xlim(time_start, time_end)
axes[0, 0].set_xticklabels([])
axes[0, 0].axvline(this_time, color='white', ls='-', alpha=0.75)
axes[0, 0].axhline(maxpwr_freq, color='white', ls='-', alpha=0.75)
axes[0, 0].set_title(f'Velocities and Energies :: {this_time}')
h_frac = 1. - he_frac - o_fracs[ii]
# Cold plasma params, SI units
B0 = mag[ii]
name = np.array(['H' , 'He' , 'O' ])
mass = np.array([1.0 , 4.0 , 16.0 ]) * PMASS
charge = np.array([1.0 , 1.0 , 1.0 ]) * PCHARGE
density = np.array([h_frac, he_frac, o_fracs[ii] ]) * edens[ii]
ani = np.array([0.0 , 0.0 , 0.0 ])
tpar = np.array([0.0 , 0.0 , 0.0 ])
tper = (ani + 1) * tpar
Species, PP = create_species_array(B0, name, mass, charge, density, tper, ani)
# Frequencies to evaluate, calculate wavenumber (cold approximation)
f_min = _band_start
f_max = _band_end
Nf = 10000
f_vals = np.linspace(f_min, f_max, Nf)
w_vals = 2*np.pi*f_vals
k_vals = nls.get_k_cold(w_vals, Species)
wv_len = 1e-3 * 2*np.pi / k_vals
fidx = np.where(abs(f_vals - maxpwr_freq) == np.min(abs(f_vals - maxpwr_freq)))[0][0]
freq = f_vals[fidx]
axes[1, 0].plot(f_vals, wv_len, c='k')
axes[1, 0].set_ylabel('$\lambda_{EMIC}$ [km]')
axes[1, 0].set_ylim(0, 2000)
# Velocities
Vg, Vp, Vr = nls.get_velocities(w_vals, Species, PP)
axes[2, 0].semilogy(f_vals, Vg*1e-3, c='k', label='$V_g$')
axes[2, 0].semilogy(f_vals, Vp*1e-3, c='r', label='$V_p$')
axes[2, 0].semilogy(f_vals,-Vr*1e-3, c='b', label='$-V_R$')
axes[2, 0].set_ylim(10, 4500)
axes[2, 0].set_ylabel('Velocities [km/s]')
axes[2, 0].legend(bbox_to_anchor=(1.0, 0), loc=3, borderaxespad=0.)
# Energies
ELAND, ECYCL = nls.get_energies(w_vals, k_vals, PP['pcyc_rad'], PMASS)
axes[3, 0].semilogy(f_vals, ELAND*1e-3, c='r', label='$E_0$')
axes[3, 0].semilogy(f_vals, ECYCL*1e-3, c='b', label='$E_1$')
axes[3, 0].set_ylim(1e-1, 1e3)
axes[3, 0].set_ylabel('$E_R$ [keV]')
axes[3, 0].set_xlabel('Freq [Hz]', rotation=0)
axes[3, 0].legend(bbox_to_anchor=(1.0, 0), loc=3, borderaxespad=0.)
# Work out what the energy is at maxpwr_freq
num_landau = ELAND[fidx]*1e-3
num_cyclotron = ECYCL[fidx]*1e-3
axes[3, 0].text(0.8, 0.9, f'$E_0(f) =$ {num_landau:.1f} keV', horizontalalignment='left',
verticalalignment='center', transform=axes[3, 0].transAxes)
axes[3, 0].text(0.8, 0.8, f'$E_1(f) =$ {num_cyclotron:.1f} keV', horizontalalignment='left',
verticalalignment='center', transform=axes[3, 0].transAxes)
axes[0, 0].set_xticklabels([])
axes[1, 0].set_yticks(np.array([0, 500, 1000, 1500]))
for ax in axes[1:, 0]:
ax.set_xlim(_band_start, _band_end)
ax.axvline(freq, color='k', alpha=0.5, ls='--')
if ax != axes[-1, 0]:
ax.set_xticklabels([])
axes[1, 1].set_visible(False)
axes[2, 1].set_visible(False)
axes[3, 1].set_visible(False)
#fig.tight_layout(rect=[0, 0, 0.95, 1])
fig.tight_layout()
fig.subplots_adjust(wspace=0.05, hspace=0)
fig.align_ylabels()
if save_plot == True:
print('Saving plot...')
fig.savefig(_plot_path + 'VELENG_FROMDATA_' + save_string + '.png', dpi=dpi)
plt.close('all')
else:
plt.show()
return
line_VG = np.zeros(time.shape[0], dtype=np.float64)
line_VR = np.zeros(time.shape[0], dtype=np.float64)
max_freqs = np.zeros(time.shape[0], dtype=np.float64)
# Collect info for each time
for ii in range(time.shape[0]):
print('Doing time:', time[ii])
h_frac = 1. - he_frac - o_frac[ii]
# Define time, time index
this_time = time[ii]
maxpwr_tidx = np.where(abs(stime - this_time) == np.min(abs(stime - this_time)))[0][0]
# Find max freq and index
fst, fen = ascr.boundary_idx64(sfreq, _band_start, _band_end)
maxpwr_fidx = spower[maxpwr_tidx, fst:fen].argmax()
maxpwr_fidx += fst
max_freqs[ii] = sfreq[maxpwr_fidx]
# Cold plasma params, SI units
B0 = mag[ii]
name = np.array(['H' , 'He' , 'O' ])
mass = np.array([1.0 , 4.0 , 16.0 ]) * PMASS
charge = np.array([1.0 , 1.0 , 1.0 ]) * PCHARGE
density = np.array([h_frac, he_frac, o_frac[ii] ]) * edens[ii]
ani = np.array([0.0 , 0.0 , 0.0 ])
tpar = np.array([0.0 , 0.0 , 0.0 ])
tper = (ani + 1) * tpar
Species, PP = create_species_array(B0, name, mass, charge, density, tper, ani)