def selector(self,
array_kind='TWELVE-M',
prj_status=("Ready", "InProgress"),
sb_status=("Ready", "Suspended", "Running", "CalibratorCheck",
"Waiting"),
cycle=("2013.A", "2013.1", "2015.1", "2015.A"),
letterg=("A", "B", "C"),
bands=("ALMA_RB_03", "ALMA_RB_04", "ALMA_RB_06", "ALMA_RB_07",
"ALMA_RB_08", "ALMA_RB_09", "ALMA_RB_10"),
check_count=True,
conf=None,
calc_blratio=False,
numant=None,
array_id=None,
horizon=20.,
minha=-3.,
maxha=3.,
pwv=0.):
"""
:param array_kind:
:param prj_status:
:param sb_status:
:param cycle:
:param letterg:
:param bands:
:param check_count:
:param conf:
:param calc_blratio:
:param numant:
:param array_id:
:param horizon:
:param minha:
:param maxha:
:param pwv:
:param mintrans:
:return:
"""
if float(pwv) > 8:
pwv = 8.0
print self._time_astropy
self._aggregate_dfs()
self.master_wto_df['array'] = self.master_wto_df.apply(
lambda x: 'SEVEN-M' if x['array'] == "ACA" else
x['array'], axis=1
)
self.selection_df = self.master_wto_df[['SB_UID']].copy()
# select array kind
self.selection_df['selArray'] = (
self.master_wto_df['array'] == array_kind)
# select valid Prj States
self.selection_df['selPrjState'] = (
self.master_wto_df.apply(
lambda x: True if x['PRJ_STATUS'] in prj_status else False,
axis=1))
# select valid SB States
self.selection_df['selSBState'] = (
self.master_wto_df.apply(
lambda x: True if x['SB_STATE'] in sb_status else False,
axis=1))
# select By grades
self.selection_df['selGrade'] = (
self.master_wto_df.apply(
lambda x: True if
x['CYCLE'] in cycle and x['DC_LETTER_GRADE'] in letterg else
False, axis=1)
)
# select by band
self.selection_df['selBand'] = (
self.master_wto_df.apply(
lambda x: True if x['band'] in bands else False,
axis=1
)
)
# select if still some observations are left
self.selection_df['selCount'] = True
if check_count:
self.selection_df['selCount'] = (
self.master_wto_df.EXECOUNT > self.master_wto_df.Observed)
self.selection_df['selConf'] = True
# Array Configuraton Selection (12m)
if array_kind == "TWELVE-M":
self.master_wto_df['blmax'] = self.master_wto_df.apply(
lambda row: rUV.compute_bl(row['minAR'] / 0.8, 100.), axis=1)
self.master_wto_df['blmin'] = self.master_wto_df.apply(
lambda row: rUV.compute_bl(row['LAScor'], 100., las=True),
axis=1)
if conf:
qstring = ''
l = len(conf) - 1
for i, c in enumerate(conf):
col = c.replace('-', '_')
if i == l:
qstring += '%s == "%s"' % (col, c)
else:
qstring += '%s == "%s" or ' % (col, c)
sbs_sel = self.master_wto_df.query(qstring).SB_UID.unique()
self.selection_df['selConf'] = self.selection_df.apply(
lambda x: True if x['SB_UID'] in sbs_sel else False,
axis=1
)
self.master_wto_df['bl_ratio'] = 1.
self.master_wto_df['array_ar_cond'] = self.master_wto_df.apply(
lambda x: CONFRES[x['BestConf']] if x['BestConf'] in conf
else pd.np.NaN,
axis=1
)
self.master_wto_df['num_bl_use'] = 630.
if calc_blratio:
try:
array_id = self.bl_arrays.iloc[0, 3]
except AttributeError:
self._query_array()
array_id = self.bl_arrays.iloc[0, 3]
array_ar, num_bl, num_ant, ruv = self._get_bl_prop(array_id)
self.master_wto_df[['array_ar_cond', 'num_bl_use']] = (
self.master_wto_df.apply(
lambda x: self._get_sbbased_bl_prop(
ruv, x['blmin'] * 0.9, x['blmax'] * 1.1),
axis=1)
)
self.master_wto_df['bl_ratio'] = self.master_wto_df.apply(
lambda x: calc_bl_ratio(
x['array'], x['CYCLE'], x['num_bl_use'],
self.selection_df.ix[x.name, 'selConf']),
axis=1
)
else:
if array_id == 'last':
self._query_array()
array_id = self.bl_arrays.iloc[0, 3]
ar, numbl, numant, ruv = self._get_bl_prop(array_id)
self.master_wto_df[['array_ar_cond', 'num_bl_use']] = (
self.master_wto_df.apply(
lambda x: self._get_sbbased_bl_prop(
ruv, x['blmin'] * 0.9, x['blmax'] * 1.1), axis=1)
)
self.selection_df['selConf'] = self.master_wto_df.apply(
lambda x: True if (x['array_ar_cond'] > x['minAR']) and
(x['array_ar_cond'] < x['maxAR']) else
False, axis=1
)
self.master_wto_df['bl_ratio'] = self.master_wto_df.apply(
lambda x: calc_bl_ratio(
x['array'], x['CYCLE'], x['num_bl_use'],
self.selection_df.ix[x.name, 'selConf']),
axis=1
)
# Array Configuration Selection (7m or ACA)
elif array_kind == "SEVEN-M":
if numant is None:
numant = 10.
self.selection_df['selConf'] = self.master_wto_df.apply(
lambda x: True if x['array'] == "SEVEN-M" else
False, axis=1
)
self.master_wto_df['blmax'] = pd.np.NaN
self.master_wto_df['blmin'] = pd.np.NaN
self.master_wto_df['array_ar_cond'] = pd.np.NaN
self.master_wto_df['num_bl_use'] = pd.np.NaN
self.master_wto_df['bl_ratio'] = self.master_wto_df.apply(
lambda x: calc_bl_ratio(
x['array'], x['CYCLE'], x['num_bl_use'],
self.selection_df.ix[x.name, 'selConf'], numant=numant),
axis=1
)
# Array Configuration selection (TP)
else:
if numant is None:
numant = 10.
self.selection_df['selConf'] = self.master_wto_df.apply(
lambda x: True if x['array'] == "TP-Array" else
False, axis=1
)
self.master_wto_df['blmax'] = pd.np.NaN
self.master_wto_df['blmin'] = pd.np.NaN
self.master_wto_df['array_ar_cond'] = pd.np.NaN
self.master_wto_df['num_bl_use'] = pd.np.NaN
self.master_wto_df['bl_ratio'] = 1.
# select observable: elev, ha, moon & sun distance
try:
c = SkyCoord(
ra=self.master_wto_df.RA*u.degree,
dec=self.master_wto_df.DEC*u.degree,
location=ALMA, obstime=self._time_astropy)
except IndexError:
print("Nothing to observe? %s" % len(self.master_wto_df))
self._availableobs = False
return
ha = self._time_astropy.sidereal_time('apparent') - c.ra
self.master_wto_df['HA'] = ha.wrap_at(180*u.degree).value
self.master_wto_df['RAh'] = c.ra.hour
self.master_wto_df['elev'] = c.transform_to(
AltAz(obstime=self._time_astropy, location=ALMA)).alt.value
corr_el = ((self.master_wto_df.ephem != 'N/A') &
(self.master_wto_df.ephem != 'OK'))
self.master_wto_df.ix[corr_el, 'elev'] = -90.
self.master_wto_df.ix[corr_el, 'HA'] = -24.
self.selection_df['selElev'] = (
self.master_wto_df.elev >= horizon)
self.selection_df['selHA'] = (
(self.master_wto_df.HA >= minha) &
(self.master_wto_df.HA <= maxha)
)
# Sel Conditions, exec. frac
ind1 = pd.np.around(self.master_wto_df.repfreq, decimals=1)
pwv_str = (str(int(pwv / 0.05) * 0.05 +
(0.05 if (pwv % 0.05) > 0.02 else 0.)))
self.master_wto_df['transmission'] = self.pwvdata.ix[
ind1, pwv_str].values
self.master_wto_df['tau'] = self.tau.ix[ind1, pwv_str].values
self.master_wto_df['tsky'] = self.tsky.ix[ind1, pwv_str].values
self.master_wto_df['airmass'] = self.master_wto_df.apply(
lambda x: calc_airmass(x['elev'], transit=False), axis=1)
self.master_wto_df['tsys'] = (
self.master_wto_df.apply(
lambda x: calc_tsys(x['band'], x['tsky'], x['tau'],
x['airmass']), axis=1))
self.master_wto_df['tsys_ratio'] = self.master_wto_df.apply(
lambda x: (x['tsys'] / x['tsys_ot'])**2. if x['tsys'] <= 25000. else
pd.np.inf, axis=1)
self.master_wto_df['Exec. Frac'] = self.master_wto_df.apply(
lambda x: 1 / (x['bl_ratio'] * x['tsys_ratio']) if
(x['bl_ratio'] * x['tsys_ratio']) <= 100. else 0., axis=1)
self.master_wto_df.set_index('SB_UID', drop=False, inplace=True)
self.selection_df.set_index('SB_UID', drop=False, inplace=True)
savedate = ALMA1.date
savehoriz = ALMA1.horizon
ALMA1.horizon = 0.0
lstdate = str(ALMA1.sidereal_time()).split(':')
lstdate0 = dt.datetime.strptime(
'2014-12-31 ' + str(lstdate[0]) + ':' +
str(lstdate[1]), '%Y-%m-%d %H:%M')
lstdate1 = dt.datetime.strptime(
'2015-01-01 ' + str(lstdate[0]) + ':' +
str(lstdate[1]), '%Y-%m-%d %H:%M')
lstdate2 = dt.datetime.strptime(
'2015-01-02 ' + str(lstdate[0]) + ':' +
str(lstdate[1]), '%Y-%m-%d %H:%M')
sunrisedate = ALMA1.previous_rising(ephem.Sun())
ALMA1.date = sunrisedate
sunriselst = str(ALMA1.sidereal_time()).split(':')
sunriselst_h = dt.datetime.strptime(
'2015-01-01 ' + str(sunriselst[0]) + ':' +
str(sunriselst[1]), '%Y-%m-%d %H:%M')
sunsetdate = ALMA1.next_setting(ephem.Sun())
ALMA1.date = sunsetdate
sunsetlst = str(ALMA1.sidereal_time()).split(':')
sunsetlst_h = dt.datetime.strptime(
'2015-01-01 ' + str(sunsetlst[0]) + ':' +
str(sunriselst[1]), '%Y-%m-%d %H:%M')
self.inputs = pd.DataFrame(
pd.np.array([lstdate0, lstdate1, lstdate2,
sunsetlst_h - dt.timedelta(1), sunriselst_h,
sunsetlst_h, sunriselst_h + dt.timedelta(1)]),
index=['lst0', 'lst1', 'lst2', 'set1', 'rise1', 'set2', 'rise2'],
columns=['2013.A']).transpose()
self.inputs.ix['2013.1', :] = self.inputs.ix['2013.A', :]
self.inputs.ix['2015.A', :] = self.inputs.ix['2013.A', :]
self.inputs.ix['2015.1', :] = self.inputs.ix['2013.A', :]
ALMA1.date = savedate
ALMA1.horizon = savehoriz
def selector(self,
array_kind='TWELVE-M',
prj_status=("Ready", "InProgress"),
sb_status=("Ready", "Suspended", "Running", "CalibratorCheck",
"Waiting"),
letterg=("A", "B", "C"),
bands=("ALMA_RB_03", "ALMA_RB_04", "ALMA_RB_06", "ALMA_RB_07",
"ALMA_RB_08", "ALMA_RB_09", "ALMA_RB_10"),
check_count=True,
conf=None,
calc_blratio=False,
numant=None,
array_id=None,
horizon=20.,
minha=-3.,
maxha=3.,
pwv=0.,
sim=False):
"""
Args:
array_kind:
prj_status:
sb_status:
letterg:
bands:
check_count:
conf:
calc_blratio:
numant:
array_id:
horizon:
minha:
maxha:
pwv:
sim:
"""
if float(pwv) > 8:
pwv = 8.0
# print self._time_astropy
if sim:
self._aggregate_dfs(sim=True)
else:
self._aggregate_dfs()
self.master_dsa_df['array'] = self.master_dsa_df.apply(
lambda x: 'SEVEN-M' if x['array'] == "ACA" else
x['array'], axis=1
)
self.master_dsa_df['isToo'] = self.master_dsa_df.apply(
lambda x: True if str(x['CODE']).endswith('.T') else
False, axis=1
)
self.selection_df = self.master_dsa_df[['SB_UID']].copy()
# select array kind
self.selection_df['selArray'] = (
self.master_dsa_df['array'] == array_kind)
# select valid Prj States
self.selection_df['selPrjState'] = (
self.master_dsa_df.apply(
lambda x: True if x['PRJ_STATUS'] in prj_status else False,
axis=1))
# select valid SB States
self.selection_df['selSBState'] = (
self.master_dsa_df.apply(
lambda x: True if x['SB_STATE'] in sb_status else False,
axis=1))
# select By grades
sbuid_ex = self.exception_df.SB_UID.unique()
self.selection_df['selGrade'] = (
self.master_dsa_df.apply(
lambda x: True if
(x['CYCLE'] in
["2015.1", "2015.A"] and x['DC_LETTER_GRADE'] in letterg) or
(x['CYCLE'] not in
["2015.1", "2015.A"] and x['DC_LETTER_GRADE'] == "A") or
(x['SB_UID'] in sbuid_ex) else
False, axis=1)
)
# select by band
self.selection_df['selBand'] = (
self.master_dsa_df.apply(
lambda x: True if x['band'] in bands else False,
axis=1
)
)
if not sim:
self.selection_df = self.selection_df.query(
'selArray == True and selPrjState == True and '
'selSBState == True and selGrade == True and selBand == True')
sb_fsel = self.selection_df.SB_UID.unique()
self.master_dsa_df = self.master_dsa_df.query('SB_UID in @sb_fsel')
# select if still some observations are left
self.selection_df['selCount'] = True
if check_count:
self.selection_df['selCount'] = (
self.master_dsa_df.EXECOUNT > self.master_dsa_df.Observed)
self.selection_df['selConf'] = True
# Array Configuration Selection (12m)
if array_kind == "TWELVE-M":
self.master_dsa_df['blmax'] = self.master_dsa_df.apply(
lambda row: rUV.compute_bl(row['minAR'] / 0.8, 100.), axis=1)
self.master_dsa_df['blmin'] = self.master_dsa_df.apply(
lambda row: rUV.compute_bl(row['LAScor'], 100., las=True) if
((row['LAScor'] > row['minAR']) and
(row['LAScor'] > 5 * row['ARcordec']))
else rUV.compute_bl(10., 100., las=True),
axis=1)
if conf:
# Not Working For Exceptions!!!
qstring = ''
l = len(conf) - 1
for i, c in enumerate(conf):
col = c.replace('-', '_')
if i == l:
qstring += '%s == "%s"' % (col, c)
else:
qstring += '%s == "%s" or ' % (col, c)
sbs_sel = self.master_dsa_df.query(qstring).SB_UID.unique()
self.selection_df['selConf'] = self.selection_df.apply(
lambda x: True if (x['SB_UID'] in sbs_sel) or
((x['lenconf'] > 0) and
(x['minAR'] <= self.ar <= x['maxAR']))
else False,
axis=1
)
self.ar = CONFRES[conf[0]]
self.master_dsa_df['bl_ratio'] = 1.
self.master_dsa_df['array_ar_cond'] = self.master_dsa_df.apply(
lambda x: CONFRES[x['BestConf']] if x['BestConf'] in conf
else pd.np.NaN,
axis=1
)
self.master_dsa_df['num_bl_use'] = 630.
if calc_blratio:
self._query_array()
array_id = self.arrays.iloc[0, 3]
array_ar, num_bl, num_ant, ruv = self._get_bl_prop(array_id)
self.master_dsa_df[['array_ar_cond', 'num_bl_use']] = (
self.master_dsa_df.apply(
lambda x: self._get_sbbased_bl_prop(
ruv, x['blmin'] * 0.9, x['blmax'] * 1.1,
x['array']),
axis=1)
)
self.master_dsa_df['bl_ratio'] = self.master_dsa_df.apply(
lambda x: 1. / calc_bl_ratio(
x['array'], x['CYCLE'], x['num_bl_use'],
self.selection_df.ix[x.name, 'selConf']),
axis=1
)
else:
self._query_array()
try:
a = self._last_array_used
except AttributeError:
self._last_array_used = ''
if array_id == 'last':
array_id = self.arrays.iloc[0, 3]
if self._last_array_used == array_id:
self.master_dsa_df['array_ar_cond'] = \
self.arr_cache['array_ar_cond']
self.master_dsa_df['num_bl_use'] = \
self.arr_cache['num_bl_use']
else:
self.ar, numbl, numant, ruv = self._get_bl_prop(array_id)
self.master_dsa_df[['array_ar_cond', 'num_bl_use']] = (
self.master_dsa_df.apply(
lambda x: self._get_sbbased_bl_prop(
ruv, x['blmin'] * 0.9, x['blmax'] * 1.1,
x['array']),
axis=1)
)
self.arr_cache = self.master_dsa_df[
['array_ar_cond', 'num_bl_use']].copy()
self._last_array_used = array_id
self.selection_df['selConf'] = self.master_dsa_df.apply(
lambda x: True if
((x['array_ar_cond'] > x['minAR']) and
(self.ar > 0.5 * x['ARcordec']) and
(x['array_ar_cond'] < (x['maxAR'] * 1.3)) and
(x['array_ar_cond'] < (x['ARcordec'] * 1.15))) or
((x['lenconf'] > 2) and
(x['minAR'] <= self.ar) and
(self.ar <= x['maxAR']) and
(x['array'] == "TWELVE-M"))
else False, axis=1)
self.master_dsa_df['bl_ratio'] = self.master_dsa_df.apply(
lambda x: 1. / calc_bl_ratio(
x['array'], x['CYCLE'], x['num_bl_use'],
self.selection_df.ix[x.name, 'selConf']),
axis=1
)
# Array Configuration Selection (7m or ACA)
elif array_kind == "SEVEN-M":
if numant is None:
numant = 10.
self.selection_df['selConf'] = self.master_dsa_df.apply(
lambda x: True if x['array'] == "SEVEN-M" else
False, axis=1
)
self.master_dsa_df['blmax'] = pd.np.NaN
self.master_dsa_df['blmin'] = pd.np.NaN
self.master_dsa_df['array_ar_cond'] = pd.np.NaN
self.master_dsa_df['num_bl_use'] = pd.np.NaN
self.master_dsa_df['bl_ratio'] = self.master_dsa_df.apply(
lambda x: 1. / calc_bl_ratio(
x['array'], x['CYCLE'], x['num_bl_use'],
self.selection_df.ix[x.name, 'selConf'], numant=numant),
axis=1
)
# Array Configuration selection (TP)
else:
if numant is None:
numant = 2.
# Until we have a clear idea on how to handle TP ampcals, removing
# them from the DSA output
# noinspection PyUnusedLocal
selsb = self.master_dsa_df.query(
'array == "TP-Array"').SB_UID.unique()
selsb1 = self.master_dsa_df[
self.master_dsa_df.sbNote.str.contains('TP ampcal')
].SB_UID.unique()
selsb2 = pd.merge(
pd.merge(
self.data.orderedtar.query('SB_UID in @selsb'),
self.data.target,
on=['SB_UID', 'targetId']),
self.data.fieldsource,
on=['SB_UID', 'fieldRef']).query(
'name_y == "Amplitude"').SB_UID.unique()
self.selection_df['selConf'] = self.master_dsa_df.apply(
lambda x: True if (x['array'] == "TP-Array") and
((x['SB_UID'] not in selsb1) and
(x['SB_UID'] not in selsb2)) else
False, axis=1
)
self.master_dsa_df['blmax'] = pd.np.NaN
self.master_dsa_df['blmin'] = pd.np.NaN
self.master_dsa_df['array_ar_cond'] = pd.np.NaN
self.master_dsa_df['num_bl_use'] = pd.np.NaN
self.master_dsa_df['bl_ratio'] = 1.
# select observable: elev, ha, moon & sun distance
polarization = self.master_dsa_df.query('PolCalibrator != ""').copy()
try:
cpol = SkyCoord(
ra=polarization.RA_pol * u.degree,
dec=polarization.DEC_pol * u.degree,
location=ALMA, obstime=self._time_astropy)
except IndexError:
cpol = polarization.copy()
try:
c = SkyCoord(
ra=self.master_dsa_df.RA * u.degree,
dec=self.master_dsa_df.DEC * u.degree,
location=ALMA, obstime=self._time_astropy)
except IndexError:
print("Nothing to observe? %s" % len(self.master_dsa_df))
self._availableobs = False
return
ha = self._time_astropy.sidereal_time('apparent') - c.ra
self.master_dsa_df['HA'] = ha.wrap_at(180 * u.degree).value
self.master_dsa_df['RAh'] = c.ra.hour
self.master_dsa_df['elev'] = c.transform_to(
AltAz(obstime=self._time_astropy, location=ALMA)).alt.value
corr_el = ((self.master_dsa_df.ephem != 'N/A') &
(self.master_dsa_df.ephem != 'OK'))
self.master_dsa_df.ix[corr_el, 'elev'] = -90.
self.master_dsa_df.ix[corr_el, 'HA'] = -24.
self.selection_df['selElev'] = (
(self.master_dsa_df.elev >= horizon) &
(self.master_dsa_df.RA != 0) &
(self.master_dsa_df.DEC != 0)
)
self.selection_df.set_index('SB_UID', drop=False, inplace=True)
if len(cpol) > 0:
ha = self._time_astropy.sidereal_time('apparent') - cpol.ra
polarization['HA_pol'] = ha.wrap_at(180 * u.degree).value
polarization['RAh_pol'] = cpol.ra.hour
polarization['elev_pol'] = cpol.transform_to(
AltAz(obstime=self._time_astropy, location=ALMA)).alt.value
parall = pd.np.arctan(
pd.np.sin(ha.radian) /
(pd.np.tan(ALMA.latitude.radian) *
pd.np.cos(cpol.dec.radian) -
pd.np.sin(cpol.dec.radian) * pd.np.cos(ha.radian)
))
polarization['parallactic'] = pd.np.degrees(parall)
corr_el = ((polarization.ephem != 'N/A') &
(polarization.ephem != 'OK'))
polarization.ix[corr_el, 'elev_pol'] = -90.
polarization.ix[corr_el, 'HA_pol'] = -24.
self.polarization = polarization
self.selection_df.loc[
polarization[polarization.elev_pol < 20].SB_UID.values,
'selElev'] = False
self.selection_df['selHA'] = (
(self.master_dsa_df.set_index('SB_UID').HA >= minha) &
(self.master_dsa_df.set_index('SB_UID').HA <= maxha)
)
# Sel Conditions, exec. frac
ind1 = pd.np.around(self.master_dsa_df.repfreq, decimals=1)
pwv_str = (str(int(pwv / 0.05) * 0.05 +
(0.05 if (int(pwv * 100) % 5) > 2 else 0.)))
self.master_dsa_df['transmission'] = self.pwvdata.ix[
ind1, pwv_str].values
self.master_dsa_df['tau'] = self.tau.ix[ind1, pwv_str].values
self.master_dsa_df['tsky'] = self.tsky.ix[ind1, pwv_str].values
self.master_dsa_df['airmass'] = self.master_dsa_df.apply(
lambda x: calc_airmass(x['elev'], transit=False), axis=1)
self.master_dsa_df['tsys'] = (
self.master_dsa_df.apply(
lambda x: calc_tsys(x['band'], x['tsky'], x['tau'],
x['airmass']), axis=1))
self.master_dsa_df['tsys_ratio'] = self.master_dsa_df.apply(
lambda x: 1. / (x['tsys'] / x['tsys_ot'])**2.
if x['tsys'] <= 25000. else
0., axis=1)
self.master_dsa_df['Exec. Frac'] = self.master_dsa_df.apply(
lambda x: (x['bl_ratio'] * x['tsys_ratio']) if
(x['bl_ratio'] * x['tsys_ratio']) <= 100. else 0., axis=1)
self.selection_df['selCond'] = self.master_dsa_df.set_index(
'SB_UID').apply(
lambda x: True if x['Exec. Frac'] >= 0.70 else False,
axis=1
)
self.master_dsa_df.set_index('SB_UID', drop=False, inplace=True)
self.selection_df.set_index('SB_UID', drop=False, inplace=True)
savedate = ALMA1.date
savehoriz = ALMA1.horizon
ALMA1.horizon = 0.0
lstdate = str(ALMA1.sidereal_time()).split(':')
lstdate0 = dt.datetime.strptime(
'2014-12-31 ' + str(lstdate[0]) + ':' +
str(lstdate[1]), '%Y-%m-%d %H:%M')
lstdate1 = dt.datetime.strptime(
'2015-01-01 ' + str(lstdate[0]) + ':' +
str(lstdate[1]), '%Y-%m-%d %H:%M')
lstdate2 = dt.datetime.strptime(
'2015-01-02 ' + str(lstdate[0]) + ':' +
str(lstdate[1]), '%Y-%m-%d %H:%M')
sunrisedate = ALMA1.previous_rising(ephem.Sun())
ALMA1.date = sunrisedate
sunriselst = str(ALMA1.sidereal_time()).split(':')
sunriselst_h = dt.datetime.strptime(
'2015-01-01 ' + str(sunriselst[0]) + ':' +
str(sunriselst[1]), '%Y-%m-%d %H:%M')
sunsetdate = ALMA1.next_setting(ephem.Sun())
ALMA1.date = sunsetdate
sunsetlst = str(ALMA1.sidereal_time()).split(':')
sunsetlst_h = dt.datetime.strptime(
'2015-01-01 ' + str(sunsetlst[0]) + ':' +
str(sunriselst[1]), '%Y-%m-%d %H:%M')
self.inputs = pd.DataFrame(
pd.np.array([lstdate0, lstdate1, lstdate2,
sunsetlst_h, sunriselst_h,
sunsetlst_h + dt.timedelta(1),
sunriselst_h + dt.timedelta(1)]),
index=['lst0', 'lst1', 'lst2', 'set1', 'rise1', 'set2', 'rise2'],
columns=['2013.A']).transpose()
self.inputs.ix['2013.1', :] = self.inputs.ix['2013.A', :]
self.inputs.ix['2015.A', :] = self.inputs.ix['2013.A', :]
self.inputs.ix['2015.1', :] = self.inputs.ix['2013.A', :]
ALMA1.date = savedate
ALMA1.horizon = savehoriz
