def ELS_maxflux_anode(elsdata, starttime, endtime):
startslice, endslice = CAPS_slicenumber(elsdata, starttime), CAPS_slicenumber(elsdata, endtime)
dataslice = ELS_backgroundremoval(elsdata, startslice, endslice)
anodesums = np.sum(np.sum(dataslice, axis=2), axis=0)
maxflux_anode = np.argmax(anodesums)
#print(anodesums, maxflux_anode)
return maxflux_anode
def CAPS_ramtimes(elsdata, starttime, endtime):
ramtimes = []
startslice, endslice = CAPS_slicenumber(elsdata, starttime), CAPS_slicenumber(elsdata, endtime)
for i in np.arange(startslice, endslice, 1):
actuatorangle = elsdata['actuator'][i]
ramangle = caps_ramdirection_azielv(elsdata['times_utc'][i])[0]
if abs(actuatorangle - ramangle) < 1.5:
ramtimes.append(caps_ramdirection_time(elsdata, elsdata['times_utc'][i]))
for counter, i in enumerate(ramtimes):
ramtimes[counter] = i.replace(microsecond=0)
ramtimes = sorted(set(ramtimes))
return ramtimes
def heavy_ion_finder_ibs(ibsdata, startenergy, starttime, endtime):
startslice, endslice = CAPS_slicenumber(ibsdata,
starttime), CAPS_slicenumber(
ibsdata, endtime)
energybin = CAPS_energyslice("ibs", startenergy, startenergy)[0]
tempslice = (ibsdata['ibsdata'][:, 1, startslice:endslice] /
(ibscalib['ibsgeom'] * 1e-4))
maxindices = np.unravel_index(tempslice.argmax(), tempslice.shape)
prominence = tempslice[maxindices] / 10
#print("prominence", prominence)
# dataslice = ibsdata['ibsdata'][energybin, 1, startslice:endslice]
singlepeakslice = (ibsdata['ibsdata'][energybin, 1, startslice:endslice] /
(ibscalib['ibsgeom'] * 1e-4))
# peaks, properties = find_peaks(dataslice, height=5e2, prominence=prominence,distance=15)
# print(peaks)
# if peaks.size == 0:
# raise ValueError("No Peaks")
# while len(peaks) == 1:
while max(singlepeakslice) > prominence:
recentpeakslice = singlepeakslice
recentenergybin = energybin
recentpeaks = np.argmax(recentpeakslice)
energybin += 1
singlepeakslice = (
ibsdata['ibsdata'][energybin, 1, startslice:endslice] /
(ibscalib['ibsgeom'] * 1e-4))
# peaks, properties = find_peaks(singlepeakslice, height=5e2, prominence=prominence)
zerocounter = 0
i = 0
while i == 0:
i = int(ibsdata['ibsdata'][zerocounter, 1, startslice + recentpeaks])
zerocounter += 1
numberofsteps = (255 - (recentenergybin - zerocounter))
# print("Counter", zerocounter)
# print("RecentEnergyBin", recentenergybin)
# print("Number of steps", numberofsteps)
times = ibsdata['times_utc'][startslice:endslice]
times_adjusted = [
d + numberofsteps * datetime.timedelta(seconds=7.813e-3) for d in times
]
timestamps_adjusted = [
datetime.datetime.timestamp(d) for d in times_adjusted
]
weightedtime = datetime.datetime.fromtimestamp(
np.average(timestamps_adjusted, weights=recentpeakslice))
# return times_adjusted[np.argmax(recentpeakslice)], ibscalib['ibsearray'][energybin]
return weightedtime, ibscalib['ibsearray'][energybin]
def heavy_ion_finder(elsdata, startenergy, anode, starttime, endtime):
startslice, endslice = CAPS_slicenumber(elsdata, starttime), CAPS_slicenumber(elsdata, endtime)
tempslice = ELS_backgroundremoval(elsdata, startslice, endslice)[:, anode, :]
maxindices = np.unravel_index(tempslice.argmax(), tempslice.shape)
#print("Max DEF", tempslice[maxindices])
prominence = tempslice[maxindices] / 40
#print("prominence", prominence)
energybin = CAPS_energyslice("els", startenergy, startenergy)[0]
dataslice = ELS_backgroundremoval(elsdata, startslice, endslice)[energybin, anode, :]
peaks, properties = find_peaks(dataslice, height=3e9, prominence=prominence, width=0.5, rel_height=0.5,
distance=15)
results = peak_widths(dataslice, peaks, rel_height=0.97)
recentpeakslice = []
left = int(results[2][0])
right = int(results[3][0]) + 1
tempenergybin = energybin
singlepeakslice = ELS_backgroundremoval(elsdata, startslice, endslice)[tempenergybin, anode, left:right]
temppeaks, tempproperties = find_peaks(singlepeakslice, height=1e9, prominence=prominence, width=0.5,
rel_height=0.5,
distance=5)
if temppeaks.size == 0:
raise ValueError("No Peaks")
while len(temppeaks) == 1:
recentpeakslice = singlepeakslice
recentenergybin = tempenergybin
tempenergybin += 1
singlepeakslice = ELS_backgroundremoval(elsdata, startslice, endslice)[tempenergybin, anode, left:right]
temppeaks, tempproperties = find_peaks(singlepeakslice, height=5e9, prominence=prominence, width=0.5,
rel_height=0.5,
distance=5)
times = elsdata['times_utc'][startslice:endslice][left:right]
times_adjusted = [d + (63 - recentenergybin) * datetime.timedelta(seconds=31.25e-3) for d in
times]
timestamps_adjusted = [datetime.datetime.timestamp(d) for d in times_adjusted]
testtime = np.average(timestamps_adjusted, weights=recentpeakslice)
# plt.plot(times_adjusted, recentpeakslice)
# plt.vlines(times_adjusted[np.argmax(recentpeakslice)],min(recentpeakslice),max(recentpeakslice))
# plt.vlines(datetime.datetime.fromtimestamp(testtime), min(recentpeakslice), max(recentpeakslice),color='m')
# print("currenttime", times_adjusted[np.argmax(recentpeakslice)])
# print("testtime", datetime.datetime.fromtimestamp(testtime))
# return times_adjusted[np.argmax(recentpeakslice)], elscalib['earray'][tempenergybin]
return datetime.datetime.fromtimestamp(testtime), elscalib['earray'][tempenergybin]
ax.set_ylabel("CAPS ELS \nAnode {0} \n eV/q ".format(anode))
flyby = "t32"
anodes = [4, 5]
lowerenergy = 2
upperenergy = 500
elsdata = readsav("data/els/elsres_" + filedates_times[flyby][0] + ".dat")
generate_mass_bins(elsdata, flyby, "els")
starttime = flyby_datetimes[flyby][0]
endtime = flyby_datetimes[flyby][1]
alts = []
startslice, endslice = CAPS_slicenumber(elsdata, starttime), CAPS_slicenumber(
elsdata, endtime)
for i in np.arange(startslice, endslice, 1):
alt, lat, lon = cassini_titan_altlatlon(elsdata['times_utc'][i])
alts.append(alt)
fig, axes = plt.subplots(len(anodes) + 1, figsize=(18, 6), sharex='all')
for anode, ax in zip(anodes, axes[:-1]):
ELS_spectrogram(elsdata,
anode,
filedates_times[flyby][1],
600,
ax=ax,
fig=fig)
axes[-1].plot(elsdata['times_utc'][startslice:endslice], alts)
axes[-1].set_ylabel("Altitude [/km]")
def heavy_ion_finder_old(elsdata,
startenergy,
anode,
starttime,
endtime,
prominence=1e10):
startslice, endslice = CAPS_slicenumber(elsdata,
starttime), CAPS_slicenumber(
elsdata, endtime)
energybin = CAPS_energyslice("els", startenergy, startenergy)[0]
dataslice = ELS_backgroundremoval(elsdata, startslice, endslice)[energybin,
anode, :]
peaks, properties = find_peaks(dataslice,
height=3e9,
prominence=prominence,
width=0.5,
rel_height=0.4,
distance=15)
results = peak_widths(dataslice, peaks, rel_height=0.97)
recentpeakslice = []
left = int(results[2][0])
right = int(results[3][0]) + 1
tempenergybin = energybin
singlepeakslice = ELS_backgroundremoval(elsdata, startslice,
endslice)[tempenergybin, anode,
left:right]
temppeaks, tempproperties = find_peaks(singlepeakslice,
height=1e9,
prominence=prominence,
width=0.5,
rel_height=0.5,
distance=5)
# if temppeaks == []:
# raise ValueError("No Peaks")
while len(temppeaks) == 1:
recentpeaks = temppeaks
recentproperties = tempproperties
recentpeakslice = singlepeakslice
recentenergybin = tempenergybin
tempenergybin += 1
singlepeakslice = ELS_backgroundremoval(elsdata, startslice,
endslice)[tempenergybin, anode,
left:right]
temppeaks, tempproperties = find_peaks(singlepeakslice,
height=5e9,
prominence=prominence,
width=0.5,
rel_height=0.5,
distance=5)
print(
"Energy Bin time", elsdata['times_utc'][startslice + recentpeaks[0]] +
(63 - recentenergybin) * datetime.timedelta(seconds=31.25e-3))
# print(elsdata['times_utc'][startslice + left], elsdata['times_utc'][startslice + right])
# print(anode, startslice, left, right)
# print(recentpeakslice)
times = elsdata['times_utc'][startslice:endslice][left:right]
timestamps = [
datetime.datetime.timestamp(d)
for d in elsdata['times_utc'][startslice:endslice][left:right]
]
times_adjusted = [
d + (63 - recentenergybin) * datetime.timedelta(seconds=31.25e-3)
for d in times
]
timestamps_adjusted = [
datetime.datetime.timestamp(d + (63 - recentenergybin) *
datetime.timedelta(seconds=31.25e-3))
for d in times
]
peak_timestamps_lower = timestamps_adjusted[int(
np.floor(recentproperties['left_ips'][0]))]
peak_timestamps_upper = timestamps_adjusted[int(
np.ceil(recentproperties['right_ips'][0]))]
stddev = (peak_timestamps_upper - peak_timestamps_lower) / 2
time_init = models.Gaussian1D(amplitude=max(recentpeakslice),
mean=timestamps_adjusted[recentpeaks[0]],
stddev=stddev,
bounds={
"amplitude":
(max(recentpeakslice),
max(recentpeakslice) * 1.2),
"mean": (peak_timestamps_lower,
peak_timestamps_upper),
"stddev": (stddev / 1.5, stddev * 4)
})
timefit = fitting.LevMarLSQFitter()(time_init, timestamps_adjusted,
recentpeakslice)
timestamp_linspace = np.linspace(timestamps_adjusted[0],
timestamps_adjusted[-1], 100)
actuatorangle = elsdata['actuator'][startslice:endslice][left:right]
actuatorangle_adjusted = np.interp(timestamps_adjusted, timestamps,
actuatorangle)
print(timestamps, actuatorangle)
print(timestamps_adjusted, actuatorangle_adjusted)
act_init = models.Gaussian1D(
amplitude=max(recentpeakslice),
mean=actuatorangle_adjusted[recentpeaks[0]],
stddev=1,
bounds={
"amplitude": (max(recentpeakslice), max(recentpeakslice) * 1.2),
"mean": (actuatorangle_adjusted[recentpeaks[0]] - 1,
actuatorangle_adjusted[recentpeaks[0]] + 1),
"stddev": (1, 3)
})
actfit = fitting.LevMarLSQFitter()(act_init, actuatorangle,
recentpeakslice)
ramtime = caps_ramdirection_time(
elsdata, datetime.datetime.fromtimestamp(timefit.mean.value))
ramangle = caps_ramdirection_azielv(
caps_ramdirection_time(
elsdata, datetime.datetime.fromtimestamp(timefit.mean.value)))[0]
print("Ram Time", ramtime, ramangle)
print(
"Time Mean", datetime.datetime.fromtimestamp(timefit.mean.value),
CAPS_actuation(elsdata,
datetime.datetime.fromtimestamp(timefit.mean.value)),
CAPS_actuation(elsdata,
datetime.datetime.fromtimestamp(timefit.mean.value)) -
ramangle)
# fig, ax = plt.subplots()
# ax.plot(elsdata['actuator'][startslice:endslice],timestamps)
if elsdata['actuator'][startslice:endslice][0] > elsdata['actuator'][
startslice:endslice][-1]:
testtimes = np.interp(
actfit.mean.value,
np.flip(elsdata['actuator'][startslice:endslice][left:right]),
np.flip(timestamps))
else:
testtimes = np.interp(
actfit.mean.value,
elsdata['actuator'][startslice:endslice][left:right], timestamps)
# ax.vlines(actfit.mean.value,min(timestamps),max(timestamps),color='k')
# ax.hlines(testtimes, min(elsdata['actuator'][startslice:endslice]), max(elsdata['actuator'][startslice:endslice]), color='k')
print("Act Mean", datetime.datetime.fromtimestamp(testtimes),
actfit.mean.value, actfit.mean.value - ramangle)
print(
"No Fit Mean", times_adjusted[np.argmax(recentpeakslice)],
CAPS_actuation(elsdata, times_adjusted[np.argmax(recentpeakslice)]),
CAPS_actuation(elsdata, times_adjusted[np.argmax(recentpeakslice)]) -
ramangle)
if actuatorangle_adjusted[0] > actuatorangle_adjusted[-1]:
print("Negative Actuation")
act_linspace = np.linspace(actuatorangle_adjusted[-1],
actuatorangle_adjusted[0], 100)
else:
act_linspace = np.linspace(actuatorangle_adjusted[0],
actuatorangle_adjusted[-1], 100)
fig, axes = plt.subplots(2)
axes[0].plot(times_adjusted, recentpeakslice, label="data")
axes[0].plot(
[datetime.datetime.fromtimestamp(d) for d in timestamp_linspace],
timefit(timestamp_linspace),
linestyle='--',
label="Gaussian fit")
axes[0].vlines(caps_ramdirection_time(
elsdata, datetime.datetime.fromtimestamp(timefit.mean.value)),
min(recentpeakslice),
max(recentpeakslice),
color='k')
axes[1].plot(actuatorangle_adjusted, recentpeakslice, label="data")
axes[1].plot(act_linspace, actfit(act_linspace), label="interp")
axes[1].vlines(caps_ramdirection_azielv(
caps_ramdirection_time(
elsdata, datetime.datetime.fromtimestamp(timefit.mean.value)))[0],
min(recentpeakslice),
max(recentpeakslice),
color='k')
axes[0].set_xlabel("Time")
axes[0].set_ylabel("DEF")
axes[1].set_xlabel("Actuator Angle")
axes[1].set_ylabel("DEF")
axes[0].legend()
axes[1].legend()
return datetime.datetime.fromtimestamp(
timefit.mean.value), elscalib['earray'][tempenergybin]
anode,
starttime,
endtime,
peaks=True,
prominence=1e10,
ax=ax)
IBS_intensityplot(ibsdata,
55,
starttime,
endtime,
peaks=True,
prominence=300,
ax=ax2)
fig.legend()
startslice_els, endslice_els = CAPS_slicenumber(elsdata,
starttime), CAPS_slicenumber(
elsdata, endtime)
dataslice_els = ELS_backgroundremoval(elsdata, startslice_els,
endslice_els)[:, anode - 1, :]
startslice_ibs, endslice_ibs = CAPS_slicenumber(ibsdata,
starttime), CAPS_slicenumber(
ibsdata, endtime)
ax.plot(elsdata['times_utc'][startslice_els:endslice_els],
np.sum(dataslice_els, axis=0),
linestyle='--',
label="ELS Flux Sum",
color='C0')
ax2.plot(ibsdata['times_utc'][startslice_ibs:endslice_ibs],
np.sum(ibsdata['ibsdata'][:, 1, startslice_ibs:endslice_ibs], axis=0),
linestyle='--',