def xmlrpc_run(self,
array_kind='TWELVE-M',
prj_status=('Ready', 'InProgress'),
sb_status=('Ready', 'Suspend', 'Running', 'CalibratorCheck',
'Waiting'),
cycle=('2013.A', '2013.1', '2015.1', '2015.A'),
letter_grade=('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,
cal_blratio=False,
numant=None,
array_id=None,
horizon=20,
minha=-3.,
maxha=3.,
pwv=0.5,
timestring=None):
import DsaAlgorithm3 as Wto
import DsaScorers3 as WtoScor
self.data.update_status()
dsa = Wto.WtoAlgorithm3(self.data)
dsa.write_ephem_coords()
dsa.static_param()
if timestring:
dsa.set_time(timestring)
else:
dsa.set_time_now() #Parametrized
dsa.selector(
cycle=['2015.1', '2015.A'], minha=-4., maxha=4., letterg=['A', 'B'],
array_id='last', pwv=0.5)
scorer = dsa.master_wto_df.apply(
lambda x: WtoScor.calc_all_scores(
pwv, x['maxPWVC'], x['Exec. Frac'], x['sbName'], x['array'], x['ARcor'],
x['DEC'], x['array_ar_cond'], x['minAR'], x['maxAR'], x['Observed'],
x['EXECOUNT'], x['PRJ_SCIENTIFIC_RANK'], x['DC_LETTER_GRADE'],
x['CYCLE'], x['HA']), axis=1)
import pandas as pd
fin = pd.merge(pd.merge(dsa.master_wto_df, dsa.selection_df, on='SB_UID'),
scorer.reset_index(), on='SB_UID').set_index(
'SB_UID', drop=False).sort('Score', ascending=0)
return fin.to_json(orient='index');
def rundsa(dsa_instance,
array_kind='TWELVE-M',
array_id='',
conf='',
pwv=0.5,
numant=0,
letterg=("A", "B", "C"),
update=False):
if conf == '' or array_kind != 'TWELVE-M':
conf = None
else:
conf = [conf]
if numant == 0 or array_kind == 'TWELVE-M':
numant = None
dsa_instance.selector(
array_kind=array_kind, minha=-3., maxha=3.,
conf=conf, array_id=array_id, letterg=letterg,
pwv=pwv, horizon=20, numant=numant,
sb_status=("Ready", "Running"), sim=True)
scorer = dsa_instance.master_dsa_df.apply(
lambda x: DsaScore.calc_all_scores(
pwv, x['maxPWVC'], x['Exec. Frac'], x['sbName'], x['array'],
x['ARcor'], x['DEC'], x['array_ar_cond'], x['minAR'],
x['maxAR'], x['Observed'], x['EXECOUNT'],
x['PRJ_SCIENTIFIC_RANK'], x['DC_LETTER_GRADE'], x['CYCLE'],
x['HA']), axis=1)
fin = pd.merge(
pd.merge(
dsa_instance.master_dsa_df[
dsa_instance.selection_df.ix[:, 1:11].sum(axis=1) == 10],
dsa_instance.selection_df, on='SB_UID'),
scorer.reset_index(), on='SB_UID').set_index(
'SB_UID', drop=False).sort_values(by='Score', ascending=0)
return fin
dsa.selection_df["PWV now"] = pwv
dsa.selection_df["PWV now date"] = (
pd.read_sql("pwv_data", engine).date.values[0] + " " + pd.read_sql("pwv_data", engine).time.values[0]
)
dsa.selection_df["date"] = str(dsa._ALMA_ephem.date)
dsa.selection_df["arrayname"] = "TP-Array"
scorer = dsa.master_dsa_df.apply(
lambda x: WtoScor.calc_all_scores(
pwv,
x["maxPWVC"],
x["Exec. Frac"],
x["sbName"],
x["array"],
x["ARcor"],
x["DEC"],
x["array_ar_cond"],
x["minAR"],
x["maxAR"],
x["Observed"],
x["EXECOUNT"],
x["PRJ_SCIENTIFIC_RANK"],
x["DC_LETTER_GRADE"],
x["CYCLE"],
x["HA"],
),
axis=1,
)
dsa.master_dsa_df["allconfs"] = dsa.obs_param.apply(
lambda x: ",".join(
[
str(x["C36_1"]),
def xmlrpc_run(self,
array_kind='TWELVE-M',
bands=('ALMA_RB_03', 'ALMA_RB_04', 'ALMA_RB_06',
'ALMA_RB_07', 'ALMA_RB_08', 'ALMA_RB_09',
'ALMA_RB_10'),
conf='',
cal_blratio=False,
numant=0,
array_id='',
horizon=20,
minha=-3.,
maxha=3.,
pwv=0.5,
timestring=''):
with self.lock:
dsa = Dsa.DsaAlgorithm3(self.data)
if conf == '' or array_kind != 'TWELVE-M':
conf = None
else:
conf = [conf]
if array_id == '' or array_kind != 'TWELVE-M':
array_id = None
elif array_id != '' and array_kind == 'TWELVE-M':
dsa._query_array(array_kind)
if numant == 0 or array_kind == 'TWELVE-M':
numant = None
self.data.update_status() #to be put on thread
if timestring != '':
dsa.set_time(timestring) # YYYY-MM-DD HH:mm:SS
else:
dsa.set_time_now()
dsa.write_ephem_coords()
dsa.static_param()
dsa.selector(array_kind=array_kind, minha=minha, maxha=maxha,
conf=conf, array_id=array_id,
pwv=pwv, horizon=horizon, numant=numant,
bands=bands)
scorer = dsa.master_dsa_df.apply(
lambda x: DsaScore.calc_all_scores(
pwv, x['maxPWVC'], x['Exec. Frac'], x['sbName'], x['array'], x['ARcor'],
x['DEC'], x['array_ar_cond'], x['minAR'], x['maxAR'], x['Observed'],
x['EXECOUNT'], x['PRJ_SCIENTIFIC_RANK'], x['DC_LETTER_GRADE'],
x['CYCLE'], x['HA']), axis=1)
fin = pd.merge(
pd.merge(
dsa.master_dsa_df[
dsa.selection_df.ix[:, 1:11].sum(axis=1) == 10],
dsa.selection_df, on='SB_UID'),
scorer.reset_index(), on='SB_UID').set_index(
'SB_UID', drop=False).sort('Score', ascending=0)
return fin.to_json(orient='index')
dsa.write_ephem_coords()
dsa.static_param()
pwv = pd.read_sql('pwv_data', engine).pwv.values[0]
dsa.selector(
minha=-4., maxha=4., letterg=['A', 'B', 'C'],
array_id='last', pwv=pwv)
dsa.selection_df['PWV now'] = pwv
dsa.selection_df['PWV now date'] = (
pd.read_sql('pwv_data', engine).date.values[0] + ' ' +
pd.read_sql('pwv_data', engine).time.values[0])
dsa.selection_df['date'] = str(dsa._ALMA_ephem.date)
dsa.selection_df['arrayname'] = dsa.arrays.iloc[0, 3]
scorer = dsa.master_dsa_df.apply(
lambda x: WtoScor.calc_all_scores(
pwv, x['maxPWVC'], x['Exec. Frac'], x['sbName'], x['array'], x['ARcor'],
x['DEC'], x['array_ar_cond'], x['minAR'], x['maxAR'], x['Observed'],
x['EXECOUNT'], x['PRJ_SCIENTIFIC_RANK'], x['DC_LETTER_GRADE'],
x['CYCLE'], x['HA']), axis=1)
dsa.master_dsa_df['allconfs'] = dsa.obs_param.apply(
lambda x: ','.join(
[str(x['C36_1']), str(x['C36_2']), str(x['C36_3']), str(x['C36_4']),
str(x['C36_5']), str(x['C36_7']), str(x['C36_8'])]), axis=1)
scorer.to_sql('scorer_wto_test', engine, index_label='SBUID',
if_exists='replace')
print('scorer written')
dsa.inputs.to_sql('inputs_wto_text', engine, index_label='Cycle',
if_exists='replace')
print('inputs written')
dsa.selection_df.to_sql('selection_wto_test', engine, index_label='SBUID',
