def xlim2tlim(left, right):
left_t = spice_tools.et2pydatetime(
spice_tools.time_at_pos(1187 * left, mccomas=True))
right_t = spice_tools.et2pydatetime(
spice_tools.time_at_pos(1187 * right, mccomas=True))
return left_t, right_t
def plot(filename, title, espec, rehearsal=False):
plt.style.use('pluto-paper')
rcParams[
'figure.autolayout'] = False # autolayout (default True in pluto-paper style) breaks these plots
fig, ax = plt.subplots(figsize=figaspect(0.3))
if rehearsal:
cbar_CH4 = None
cbar_He, cbar_H = N_colorbars(fig, ax, 2, fraction=0.1)
else:
cbar_CH4, cbar_He, cbar_H = N_colorbars(fig, ax, 3, fraction=0.1)
plot_espec(fig,
ax,
cbar_H,
cbar_He,
cbar_CH4,
espec,
mccomas=True,
rehearsal=rehearsal)
ax.set_ylabel('Energy/Q (eV/q)')
if rehearsal:
ax.set_title(title)
ax.set_xlabel('Time (UTC)')
ax.xaxis.set_major_locator(HourLocator())
ax.xaxis.set_major_formatter(DateFormatter('%H:%M'))
ax.xaxis.set_minor_locator(MinuteLocator(byminute=range(0, 60, 10)))
else:
ax.set_title(title, pad=50)
ax.set_xlabel('Time (UTC)')
ax.set_xlabel('X ($R_p$)')
ax.xaxis.set_major_locator(MultipleLocator(50))
ax.xaxis.set_minor_locator(MultipleLocator(10))
ax.set_xlim([-20, 105])
time_axis = ax.twiny()
time_axis.set_xlim(spice_tools.et2pydatetime(espec['times'][0]),
spice_tools.et2pydatetime(espec['times'][-1]))
time_axis.set_xlabel('Time (UTC)')
time_axis.xaxis.set_major_locator(HourLocator())
time_axis.xaxis.set_major_formatter(DateFormatter('%H:%M'))
time_axis.xaxis.set_minor_locator(
MinuteLocator(byminute=range(0, 60, 10)))
if args.show:
plt.show()
else:
fig.savefig(filename, bbox_inches='tight')
def load_and_plot(name):
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import matplotlib.dates as mdates
spec = np.load(name)
times = [st.et2pydatetime(t) for t in spec['times']]
responses = spec['responses']
bins = spec['bins']
centers = 0.5 * (bins[0, 0, 1:] + bins[0, 0, :-1])
total_resps = np.sum(responses, axis=1)
plt.pcolormesh(times, centers, total_resps.T, norm=LogNorm())
plt.yscale('log')
hours = mdates.HourLocator()
hours_fmt = mdates.DateFormatter('%H:%M')
minutes = mdates.MinuteLocator(byminute=[10, 20, 30, 40, 50])
plt.gca().xaxis.set_major_locator(hours)
plt.gca().xaxis.set_major_formatter(hours_fmt)
plt.gca().xaxis.set_minor_locator(minutes)
plt.figure()
n = 23
plt.plot(times, total_resps[:, n])
plt.gca().xaxis.set_major_formatter(hours_fmt)
plt.show()
def find_fit_file(start_et):
start_date = et2pydatetime(start_et)
if start_date < datetime(year=2015, month=1, day=15):
mission_phase = cruise_folder
else:
mission_phase = encounter_folder
date_folders = [
os.path.join(mission_phase, d) for d in os.listdir(mission_phase)
if os.path.isdir(os.path.join(mission_phase, d))
]
date_folders.sort(key=_extract_date)
found = _find_nearest_before(date_folders, start_date)
data_file, = [
os.path.join(found, f) for f in os.listdir(found) if f.endswith('.fit')
]
return fits.open(data_file)
def plot_espec(fig,
ax,
cbar_H,
cbar_He,
cbar_CH4,
espec,
mccomas=False,
rehearsal=False):
if rehearsal: # Use time axis instead of position
X = [spice_tools.et2pydatetime(et) for et in espec['times']]
else:
X = espec['trajectory'][:, 0] / 1187.
if mccomas and not rehearsal:
X = -X
Hhist, Hehist, CH4hist = color_coded_espec_pcolormesh(ax, X, espec)
Hcb = fig.colorbar(Hhist, cax=cbar_H)
Hecb = fig.colorbar(Hehist, cax=cbar_He, format="")
if not rehearsal: # There are no heavies in the rehearsal
CH4cb = fig.colorbar(CH4hist, cax=cbar_CH4, format="")
# add a colorbar title
cb_pos = Hecb.ax.get_position()
if rehearsal:
center = cb_pos.xmax
else:
center = (cb_pos.xmin + cb_pos.xmax) / 2
fig.text(center + 0.005,
cb_pos.ymax + 0.01,
"SCEM (Hz)",
horizontalalignment='center',
fontsize=15)
if not mccomas:
ax.invert_xaxis()
ax.set_yscale('log')
import numpy as np
import cPickle
import spice_tools
from swap import find_fit_file, bin_centers
# New Horizons Data
data = find_fit_file(spice_tools.rehearsal_start)
_t = data['time_label_spect'].data
raw_scem = data['scem_spect_hz'].data
times = [spice_tools.et2pydatetime(tt) for tt in _t['middle_et']]
positions = [
spice_tools.pos_at_time(tt, mccomas=True) / 1187. for tt in _t['middle_et']
]
energies = data['energy_label_spect'].data[0][2:]
_sample_duration = (_t['stop_et'] - _t['start_et']) / len(energies)
assert np.allclose(_sample_duration, _sample_duration[0])
sample_duration = _sample_duration[0]
# SCEM counts with bins that have less than 3 counts per sample zeroed
hz_threshold = 3.0 / sample_duration
scem = np.where(raw_scem > hz_threshold, raw_scem, 0.)
# Simulation
with open('rehearsal_espec.pickle') as f:
espec = cPickle.load(f)
sim_scem = (espec['H'] + espec['He']).T
sim_times = [spice_tools.et2pydatetime(tt) for tt in espec['times']]
sim_positions = espec['trajectory']
import numpy as np
import NH_tools
import spice_tools
from scipy.signal import convolve2d
from swap import find_fit_file
from espec import N_colorbars
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--save', nargs='?', const='synthetic_spectrograms.png')
args = parser.parse_args()
savename = args.save
data = find_fit_file(spice_tools.rehearsal_start)
t = data['time_label_spect'].data
times = [spice_tools.et2pydatetime(tt) for tt in t['middle_et']]
positions = [
spice_tools.pos_at_time(tt, mccomas=True) / 1187. for tt in t['middle_et']
]
energies = data['energy_label_spect'].data[0][2:]
# suppress error and poor statistics
s_raw = data['scem_spect_hz'].data
sample_duration = (t['stop_et'] - t['start_et']) / len(energies)
s_sample = s_raw * sample_duration
samp = 3
s = np.where(s_sample > samp, s_raw, 0)
s = np.ma.masked_where(s == 0, s)