def get_average_tel_power(t1, t2):
""" Get average telescope power used during period
:param t1: start of period
:param t2: end of period
:return: mean total (PTOTAL) telescope power for period
"""
p = fetch.Msid('DP_PTOTAL',
DateTime(t1).date,
DateTime(t2).date,
stat='daily')
return np.mean(p.vals)
def plot_housing_temperature():
dat = fetch.Msid('aach1t', '2000:001', stat='daily')
plt.close(1)
fig = plt.figure(figsize=(8, 4))
year = Time(dat.times, format='cxcsec').decimalyear
plt.plot(year, dat.vals)
plt.grid()
plt.xlabel('Year')
plt.ylabel('Temperature (degF)')
plt.title('Aspect Camera housing temperature trend')
outroot = os.path.join(opt.data_root, 'aca_housing_temperature')
logger.info('Writing plot files {}.png'.format(outroot))
fig.patch.set_visible(False)
plt.savefig(outroot + '.png', facecolor="none")
def gen_thermal_checklist(filename='thermlist.csv'):
def read_therm_checklist(filename):
def splitline(line):
return line.strip().split(',')
with open(filename, 'r') as fid:
lines = fid.readlines()
header = splitline(lines.pop(0))
thermlist = [splitline(line) for line in lines]
return thermlist, header
thermlist, header = read_therm_checklist(filename)
thermdict = {}
notinarchive = []
missing = []
for i, line in enumerate(thermlist):
ok = False
greta_msid = line[0].strip().lower()
ska_msid = line[1].strip().lower()
try:
# Assume the local archive is not more than 30 days out of date
data = fetch.Msid(ska_msid,
DateTime().secs - 3600 * 24 * 30,
stat='daily')
ok = True
except IOError as e:
missing.append(thermlist[i])
except ValueError as e:
notinarchive.append(thermlist[i])
if ok:
thermdict[ska_msid] = {
'greta_msid': greta_msid,
'owner': line[2].strip(),
'description': line[3].strip()
}
if data.state_codes:
thermdict[ska_msid]['type'] = 'expst'
else:
thermdict[ska_msid]['type'] = 'limit'
with open('thermalmsiddata.pkl', 'w') as fid:
pickle.dump((thermdict, missing, notinarchive), fid, protocol=2)
def plot_dropouts(msid, thresh=-20):
close('all')
x = fetch.Msid(msid, '2012:300', stat='5min')
d_temp = diff(x.vals)
drop = d_temp < thresh
figure()
subplot(2, 1, 1)
title(msid + ' Dropouts w.r.t. Time \n Dropout defined as: T2 - T1 < ' +
str(thresh) + ' deg F')
plot_cxctime(x.times, x.vals, 'b', label='All Temps')
plot_cxctime(x.times[:-1][drop],
x.vals[:-1][drop],
'r.',
alpha=.5,
label='Temp Prior to a Dropout')
ylabel('deg F')
legend(loc=3)
subplot(2, 1, 2)
hist(x.vals,
bins=20,
range=[min(x.vals), max(x.vals)],
normed=True,
color='b',
label='All Temps')
hist(x.vals[:-1][drop],
bins=20,
range=[min(x.vals), max(x.vals)],
normed=True,
color='r',
alpha=.5,
label='Temp Prior to a Dropout')
legend(loc=3)
ylabel('Fraction of 5-min Data Points')
xlabel('deg F')
tight_layout()
savefig(msid + '_dropouts.png')
def check_limit_changes(t1, t2):
db = pylimmon.open_sqlite_file()
cursor = db.cursor()
cursor.execute(
'''SELECT a.msid, a.setkey FROM limits AS a WHERE a.datesec>=?
AND a.datesec <=? ''',
[DateTime(t1).secs, DateTime(t2).secs])
allchanges = cursor.fetchall()
msid_sets = [(d[0], d[1]) for d in allchanges]
msid_sets = set(msid_sets)
changes = {}
for msid, setval in list(msid_sets):
try:
if 'wide' in msid.lower():
skamsid = msid[:-5]
else:
skamsid = msid
data = fetch.Msid(skamsid,
t1,
DateTime(t1).secs + 3600,
stat='5min')
desc = data.tdb.technical_name
except:
desc = None
cursor.execute(
'''SELECT a.msid, a.setkey, a.default_set, a.warning_low,
a.caution_low, a.caution_high, a.warning_high, a.date, a.mlmenable,
a.switchstate, a.mlimsw FROM limits AS a
WHERE a.setkey = ? AND a.msid = ? AND a.datesec < ?
AND a.modversion = (SELECT MAX(b.modversion) FROM limits AS b
WHERE a.msid = b.msid and a.setkey = b.setkey and b.datesec < ?)''',
[setval, msid, DateTime(t1).secs,
DateTime(t1).secs])
b = cursor.fetchone()
if not b:
b = []
cursor.execute(
'''SELECT a.msid, a.setkey, a.default_set, a.warning_low,
a.caution_low, a.caution_high, a.warning_high, a.date, a.mlmenable,
a.switchstate, a.mlimsw FROM limits AS a
WHERE a.setkey = ? AND a.msid = ? AND a.datesec >= ? AND
a.datesec <= ? AND a.modversion = (SELECT MAX(b.modversion)
FROM limits AS b WHERE a.msid = b.msid AND a.setkey = b.setkey
AND b.datesec >= ? AND b.datesec <= ?)''', [
setval, msid,
DateTime(t1).secs,
DateTime(t2).secs,
DateTime(t1).secs,
DateTime(t2).secs
])
a = cursor.fetchone()
changes[(msid, setval)] = {
'before': b,
'after': a,
'description': desc
}
return changes
def process_violations(msid, violations):
"""Add contextual information for any limit/expected state violations.
:param msid: Current mnemonic
:param violations: List of individual violations (list of tuples)
"""
data = fetch.Msid(msid,
violations[0][0][0],
violations[0][0][-1],
stat='5min')
try:
desc = data.tdb.technical_name
except:
desc = 'No Description in TDB'
violation_dict = {}
for v in violations:
limtype = v[-1]
if 'high' in limtype.lower():
if limtype not in violation_dict.keys():
violation_dict.update({
limtype: {
'starttime': v[0][0],
'stoptime': v[0][-1],
'num_excursions': 1,
'extrema': np.max(v[1]),
'limit': v[2][0],
'setid': v[3][0],
'duration': v[0][-1] - v[0][0]
}
})
else:
violation_dict[limtype]['extrema'] = np.max(
(np.max(v[1]), violation_dict[limtype]['extrema']))
violation_dict[limtype]['starttime'] = np.min(
(v[0][0], violation_dict[limtype]['starttime']))
violation_dict[limtype]['stoptime'] = np.max(
(v[0][0], violation_dict[limtype]['stoptime']))
violation_dict[limtype]['num_excursions'] = violation_dict[
limtype]['num_excursions'] + 1
violation_dict[limtype]['duration'] = violation_dict[limtype][
'duration'] + v[0][-1] - v[0][0]
elif 'low' in limtype.lower():
if limtype not in violation_dict.keys():
violation_dict.update({
limtype: {
'starttime': v[0][0],
'stoptime': v[0][-1],
'num_excursions': 1,
'extrema': np.min(v[1]),
'limit': v[2][0],
'setid': v[3][0],
'duration': v[0][-1] - v[0][0]
}
})
else:
violation_dict[limtype]['extrema'] = np.min(
(np.min(v[1]), violation_dict[limtype]['extrema']))
violation_dict[limtype]['starttime'] = np.min(
(v[0][0], violation_dict[limtype]['starttime']))
violation_dict[limtype]['stoptime'] = np.max(
(v[0][0], violation_dict[limtype]['stoptime']))
violation_dict[limtype]['num_excursions'] = violation_dict[
limtype]['num_excursions'] + 1
violation_dict[limtype]['duration'] = violation_dict[limtype][
'duration'] + v[0][-1] - v[0][0]
elif 'state' in limtype.lower():
if limtype not in violation_dict.keys():
violation_dict.update({
limtype: {
'starttime': v[0][0],
'stoptime': v[0][-1],
'num_excursions': 1,
'extrema': v[1][0],
'limit': v[2][0],
'setid': v[3][0],
'duration': v[0][-1] - v[0][0]
}
})
else:
violation_dict[limtype]['starttime'] = np.min(
(v[0][0], violation_dict[limtype]['starttime']))
violation_dict[limtype]['stoptime'] = np.max(
(v[0][0], violation_dict[limtype]['stoptime']))
violation_dict[limtype]['num_excursions'] = violation_dict[
limtype]['num_excursions'] + 1
violation_dict[limtype]['duration'] = violation_dict[limtype][
'duration'] + v[0][-1] - v[0][0]
for limittype in [
'warning_low', 'caution_low', 'caution_high', 'warning_high',
'state'
]:
if limittype in violation_dict.keys():
violation_dict[limittype][
'duration'] = violation_dict[limittype]['duration'] / 3600.
violation_dict[limittype]['description'] = desc
violation_dict[limittype]['startdate'] = DateTime(
violation_dict[limittype]['starttime']).date
violation_dict[limittype]['stopdate'] = DateTime(
violation_dict[limittype]['stoptime']).date
return violation_dict
def remote_fetch(*args, **kwargs):
import Ska.engarchive.fetch_eng as fetch
return fetch.Msid(*args, **kwargs)
# Ephemeris values: position (meters) of Chandra relative to Earth center in
# ECI coordinate frame.
model.comp['orbitephem0_x'].set_data(25000e3) # 25000 km
model.comp['orbitephem0_y'].set_data(25000e3) # 25000 km
model.comp['orbitephem0_z'].set_data(25000e3) # 25000 km
# Normalized attitude quaternions
model.comp['aoattqt1'].set_data(0.0)
model.comp['aoattqt2'].set_data(0.0)
model.comp['aoattqt3'].set_data(0.0)
model.comp['aoattqt4'].set_data(1.0)
# All the usual values here
model.comp['pitch'].set_data(130)
model.comp['eclipse'].set_data(False)
model.comp['sim_z'].set_data(75000)
model.comp['ccd_count'].set_data(6)
model.comp['fep_count'].set_data(6)
model.comp['vid_board'].set_data(1)
model.comp['clocking'].set_data(1)
model.comp['dpa_power'].set_data(0.0)
model.make()
model.calc()
# Note the telemetry MSID is fptemp_11 but the Node name is fptemp
fptemp_11 = fetch_eng.Msid('fptemp_11', start, stop) # DEGC
plot_cxctime(model.times, model.comp['fptemp'].mvals, 'r-')
plot_cxctime(fptemp_11.times, fptemp_11.vals, 'b-')
os.path.join('/proj/sot/ska/data/eng_archive', 'data', content,
'colnames.pickle')))
print('New {}'.format(content))
new = new_colnames - cur_colnames
print(', '.join(sorted(new)))
lost = cur_colnames - new_colnames
if lost:
print('LOST: ', lost)
# Plot representative new vals
d1 = '2016:001'
d2 = '2016:002'
msids = set(['1AHIRADF'])
msids.update(['POLAEV2BT', 'POLINE07T', 'POM2THV1T'])
msids.update([
'OHRTHR35_WIDE', '1OLORADF', '1OHIRADF', '2OLORADF', '2OHIRADF',
'OOBTHR30_WIDE'
])
msids.update(['AOACIIRS', 'AOACISPX', 'AOACIDPX', 'AOACIMSS'])
msids.update(['4OAVOBAT_WIDE', '4OAVHRMT_WIDE'])
msids.update(['TFSSHDT1', 'TFSSHDT2'])
for msid in msids:
m = Ska.tdb.msids[msid]
print(m)
dat = fetch.Msid(msid, d1, d2)
plt.figure()
dat.plot()
plt.title(msid)
def plot_msid_interactive(msid='aacccdpt',
group='sc',
tstart='2001:001',
tstop=None,
stat='daily',
plot_warning_low=False,
plot_caution_low=False,
plot_caution_high=True,
plot_warning_high=True,
remove_bads=True,
maudefill=False):
def add_limit_lines():
if limitquery:
plotdate_dates = cxctime2plotdate(dates)
if plot_warning_low:
_ = ax.step(plotdate_dates,
wL,
where='post',
color='r',
zorder=4)
if plot_caution_low:
_ = ax.step(plotdate_dates,
cL,
where='post',
color='#FFA500',
zorder=6)
if plot_caution_high:
_ = ax.step(plotdate_dates,
ch,
where='post',
color='#FFA500',
zorder=5)
if plot_warning_high:
_ = ax.step(plotdate_dates,
wh,
where='post',
color='r',
zorder=3)
def update_plot_data(fig, ax):
ticklocs, _, _ = plot_cxctime(data.times[good],
data.vals[good],
fmt='-',
fig=fig,
ax=ax,
color='#555555',
zorder=2)
if 'none' not in statstr:
if len(data.vals) > 4000:
indmax, indmaxstart, blocklen = thin_dataset(data.maxes[good],
kind='max')
indmin, indminstart, blocklen = thin_dataset(data.mins[good],
kind='min')
times_max = data.cxctimes[good][indmaxstart]
maxvals = data.maxes[good][indmax]
times_min = data.cxctimes[good][indminstart]
minvals = data.mins[good][indmin]
times = np.sort(np.concatenate((times_max, times_min)))
maxvals = np.interp(times, times_max, maxvals)
minvals = np.interp(times, times_min, minvals)
else:
times = data.cxctimes[good]
maxvals = data.maxes[good]
minvals = data.mins[good]
times = np.repeat(times, 2)[1:]
maxes = np.repeat(maxvals, 2)[:-1]
mins = np.repeat(minvals, 2)[:-1]
_ = ax.fill_between(times, mins, maxes, color='#aaaaaa', zorder=1)
# ax.plot(times_max, maxvals, 'r')
# ax.plot(times_min, minvals, 'r')
# ax.plot(data.cxctimes[good], data.maxes[good], 'b')
# ax.plot(data.cxctimes[good], data.mins[good], 'b')
# Added to fill in MAUDE data
if maudefill and maudedata is not None:
_ = ax.plot(maudedata['data'][0]['cxctimes'],
maudedata['data'][0]['values'],
color='blue',
zorder=2)
add_limit_lines()
def get_msid_limits(msid):
limdict = pylimmon.get_limits(msid)
enabled = np.array(limdict['limsets'][0]['mlmenable']) == 1
last_enabled = np.where(enabled)[0]
if len(last_enabled) > 0:
if last_enabled[-1] < (len(enabled) - 1):
enabled[last_enabled[-1] + 1] = True
wL = np.array(limdict['limsets'][0]['warning_low'])[enabled]
cL = np.array(limdict['limsets'][0]['caution_low'])[enabled]
ch = np.array(limdict['limsets'][0]['caution_high'])[enabled]
wh = np.array(limdict['limsets'][0]['warning_high'])[enabled]
dates = np.array(limdict['limsets'][0]['times'])[enabled]
return wL, cL, ch, wh, dates
if 'none' in str(stat).lower():
stat = None
statstr = str(stat).lower()
msid = msid.lower()
tstart = DateTime(tstart).secs
if 'none' in str(tstop).lower():
tstop = DateTime().secs
else:
tstop = DateTime(tstop).secs
data = fetch.Msid(msid, tstart, tstop, stat=stat)
data.cxctimes = cxctime2plotdate(data.times)
if maudefill:
# This may fail for a large number of reasons, so just use a blanket except.
# This last bit of data is often not critical anyway.
# try:
maudedata = maude.get_msids(msid, data.times[-1])
maudedata['data'][0]['cxctimes'] = cxctime2plotdate(
maudedata['data'][0]['times'])
if remove_bads:
good = keepind(data.times, group, msid, stat)
else:
good = np.array([True] * len(data.times))
good = np.where(good)[0]
try:
# The data object has the `tdb` attribute, however calling that attribute will result
# in an error if there is no entry in the TDB. Simply using the hasattr command will
# trigger the error if the entry is missing.
hasattr(data, 'tdb')
title = '{}: {}'.format(msid.upper(), data.tdb.technical_name)
except:
title = msid.upper()
if data.unit:
units = data.unit
else:
units = ''
wide = [
'OOBTHR02', 'OOBTHR03', 'OOBTHR04', 'OOBTHR05', 'OOBTHR06', 'OOBTHR07',
'OOBTHR08', 'OOBTHR09', 'OOBTHR10', 'OOBTHR11', 'OOBTHR12', 'OOBTHR13',
'OOBTHR14', 'OOBTHR15', 'OOBTHR17', 'OOBTHR18', 'OOBTHR19', 'OOBTHR20',
'OOBTHR21', 'OOBTHR22', 'OOBTHR23', 'OOBTHR24', 'OOBTHR25', 'OOBTHR26',
'OOBTHR27', 'OOBTHR28', 'OOBTHR29', 'OOBTHR30', 'OOBTHR31', 'OOBTHR33',
'OOBTHR34', 'OOBTHR35', 'OOBTHR36', 'OOBTHR37', 'OOBTHR38', 'OOBTHR39',
'OOBTHR40', 'OOBTHR41', 'OOBTHR44', 'OOBTHR45', 'OOBTHR46', 'OOBTHR49',
'OOBTHR50', 'OOBTHR51', 'OOBTHR52', 'OOBTHR53', 'OOBTHR54', '4OAVHRMT',
'4OAVOBAT'
]
limitquery = False
if plot_warning_low or plot_caution_low or plot_caution_high or plot_warning_high:
try:
wL, cL, ch, wh, dates = get_msid_limits(msid)
if msid.upper() in wide:
wL2, cL2, ch2, wh2, dates2 = get_msid_limits(msid.upper() +
'_WIDE')
wL = np.concatenate((wL, wL2))
cL = np.concatenate((cL, cL2))
ch = np.concatenate((ch, ch2))
wh = np.concatenate((wh, wh2))
dates = np.concatenate((dates, dates2))
limitquery = True
except IndexError:
pass
if statstr == 'daily':
xtickfontsize = 20
fig_h_start = 0.23
fig_w_start = 0.15
else:
xtickfontsize = 14
fig_h_start = 0.3
fig_w_start = 0.15
fig_height = 1. - fig_h_start - 0.1
fig_width = 1. - fig_w_start - 0.1
fig = plt.gcf()
fig.clf()
fig.set_label(msid.upper())
ax = fig.add_axes([fig_w_start, fig_h_start, fig_width, fig_height])
_ = ax.set_xlim(cxctime2plotdate(DateTime([tstart, tstop]).secs))
_ = ax.grid(True)
_ = ax.set_ylabel(units, fontsize=22)
_ = ax.set_xlabel('Time (Seconds)', fontsize=22)
_ = ax.set_title(title, fontsize=30, y=1.03)
_ = ax.tick_params(axis='both', which='major', labelsize=20)
plt.setp(ax.xaxis.get_majorticklabels(),
rotation_mode='anchor',
ha='right')
# ax.callbacks.connect('xlim_changed', update_plot_data)
update_plot_data(fig, ax)
# Use MSID values from telemetry to initialize if available
model.comp['1pdeaat'].set_data(20.0)
model.comp['pin1at'].set_data(20.0)
## INPUT DATA COMPONENTS
# These initializations are used needed for predicting into the future.
# For analyzing back-orbit data, do not set any of these and let xija
# grab the right values from telemetry.
# All the usual values here
model.comp['pitch'].set_data(130)
model.comp['sim_z'].set_data(75000)
model.comp['ccd_count'].set_data(6)
model.comp['fep_count'].set_data(6)
model.comp['vid_board'].set_data(1)
model.comp['clocking'].set_data(1)
model.comp['dpa_power'].set_data(0.0)
# Detector housing heater. Set to True for heater ON, False for heater OFF.
model.comp['dh_heater'].set_data(True)
model.make()
model.calc()
# Note the telemetry MSID is fptemp_11 but the Node name is fptemp
pdeaat = fetch_eng.Msid('1pdeaat', start, stop) # DEGC
plot_cxctime(model.times, model.comp['1pdeaat'].mvals, 'r-')
plot_cxctime(pdeaat.times, pdeaat.vals, 'b-')
