def restore_db():
db_file = directory / 'db.idl'
assert (db_file.exists())
idl_command = f"RESTORE, '{db_file.as_posix()}'"
print(f'Executing python command: "{idl_command}"')
mirpyidl.execute(idl_command)
heidbrink_keys = [
'SHOT',
'TIME',
'EFIT',
'ZIP',
'NB',
'EAE',
'TAE',
'RSAE',
'BAE',
'BAAE',
'EGAM',
'FTAE',
'FRSAE',
'FBAE',
'FBAAE',
'FROT',
'TENERGY0',
'TPAS0',
'TENERGYM',
'TPASM',
]
heidbrink_db = {}
for key in heidbrink_keys:
heidbrink_db[key.lower()] = mirpyidl.getVariable(key)
pickle_file = db_file.parent / 'db.pickle'
with pickle_file.open('wb') as f:
pickle.dump(heidbrink_db, f)
return heidbrink_db
def run(crdFormat, aor_crd, filetype, r, rIn, rOut, ap_corr, channel, pixLen, rejection = True):
#initializing table to hold results
result = Table(names = ('AORKEY', 'Target RA', 'Target Dec', 'DateObs', 'Mission', 'Read Mode', 'Workable/Total Files in AOR', 'Cycling DPattern', 'DScale', 'DPosition', 'FTime (sec)', 'Time (MJD)', 'Flux (mJy)', 'Error (mJy)', 'Spread (%)', 'Outliers Rejected'), dtype = ('i4', 'f8', 'f8', 'S25', 'S5', 'S5', 'S10', 'S5', 'S10', 'S5', 'f8', 'f8', 'f8', 'f8', 'f8', 'i4'))
problem = []
#Putting coordinates and filepaths in the correct format
#........................................................
if crdFormat.lower() == 'single_hms':
AORs = glob.glob(aor_crd[0])
skyList = [SkyCoord(aor_crd[1], unit=(u.hourangle, u.deg))]*len(AORs)
aorList = [glob.glob(aor + '/ch%i/bcd/*_%s.fits' % (channel, filetype)) for aor in AORs]
elif crdFormat.lower() == 'single_deg':
AORs = glob.glob(aor_crd[0])
skyList = [SkyCoord(aor_crd[1], unit=u.deg)]*len(AORs)
aorList = [glob.glob(aor + '/ch%i/bcd/*_%s.fits' % (channel, filetype)) for aor in AORs]
elif crdFormat.lower() == 'multiple_hms':
skyTable = ascii.read(aor_crd)
AORs = skyTable['AOR Filepath']
skyList = [SkyCoord(st, unit=(u.hourangle, u.deg)) for st in skyTable['Target Coordinates']]
aorList = [glob.glob(aor + '/ch%i/bcd/*_%s.fits' % (channel, filetype)) for aor in AORs]
elif crdFormat.lower() == 'multiple_deg':
skyTable = ascii.read(aor_crd)
AORs = skyTable['AOR Filepath']
skyList = [SkyCoord(st, unit=u.deg) for st in skyTable['Target Coordinates']]
aorList = [glob.glob(aor + '/ch%i/bcd/*_%s.fits' % (channel, filetype)) for aor in AORs]
else:
print('Please input one of these 4 values for crdFormat: single_hms, single_deg, multiple_hms, multiple_deg')
return
#..........................................................
counter = 0
#Generate data for each aor
for i, (aor, sky) in tqdm(enumerate(zip(aorList, skyList))):
#Performing photometry on every image in an AOR=
if len(aor)>0:
data, header = func.single_target_phot(aor, sky, r, rIn, rOut)
counter += 1
else:
problem.append(AORs[i].split('/r')[1])
print('Empty AOR encountered: %s' % AORs[i].split('/r')[1])
continue
#Cumulating all the data tables from every aor
if counter==1:
cum_data = data
else:
cum_data = vstack([cum_data, data])
#Getting relevant information from data table
Xc = data['Xc'][np.isnan(np.array(data['Xc']).astype('Float64')) == False]
Yc = data['Yc'][np.isnan(np.array(data['Yc']).astype('Float64')) == False]
Res_Flux = data['Res_Flux'][np.isnan(np.array(data['Res_Flux']).astype('Float64')) == False]
#Defining every table element except for flux terms
#..................................................
aKey = header['AORKEY'] #AORKEY
dObs = header['DATE_OBS'] #Date of observation in UT
dPat = 'YES' if 'cycl'in header['AORLABEL'] else 'NO' #Dither pattern
fTim = header['FRAMTIME'] #frame time in sec
time = header['MJD_OBS'] #Date of observation in MJD
mssn = 'CRYO' if (time<=54968) else 'WARM' #Spitzer mission
mode = header['READMODE'] #Readout mode: full or sub
fLen = '%i/%i' % (len(Res_Flux), len(aor)) #workable file/total file
tRA = sky.ra.deg #Target RA
tDEC = sky.dec.deg
try:
dScl = header['DITHSCAL'] #Dither scale: small, medium or large
except KeyError:
dScl = '--'
try:
dPos = str(header['DITHPOS']) #Dither position
except KeyError:
dPos = '--'
#...................................................
#Using IDL to remove systematics
#................................
if len(Res_Flux) > 0:
idl.setVariable('cenX', Xc)
idl.setVariable('cenY', Yc)
idl.setVariable('obsFlux', Res_Flux)
idl.setVariable('ch', channel)
if mode == 'SUB':
if mssn == 'CRYO':
idl.execute('corFlux = IRAC_APHOT_CORR(obsFlux, cenX, cenY, ch, /CRYO, /SUBARRAY)')
else:
idl.execute('corFlux = IRAC_APHOT_CORR(obsFlux, cenX, cenY, ch, /SUBARRAY)')
else:
if mssn =='CRYO':
idl.execute('corFlux = IRAC_APHOT_CORR(obsFlux, cenX, cenY, ch, /CRYO)')
else:
idl.execute('corFlux = IRAC_APHOT_CORR(obsFlux, cenX, cenY, ch)')
corFlux = idl.getVariable('corFlux')
corFlux = np.array([corFlux]) if type(corFlux)==float else np.array(corFlux).astype('Float64')
else:
problem.append(aKey)
result.add_row([aKey, tRA, tDEC, dObs, mssn, mode, fLen, dPat, dScl, dPos, fTim, time, np.nan, np.nan, np.nan, 0])
continue
#.................................
#Rejecting Outliers
#...................
sig = (np.std(corFlux)/np.std(corFlux))*100
if (rejection == False) | (len(corFlux)<=10) | (sig<2):
refFlux = corFlux
else:
refFlux = np.delete(corFlux, [np.argmin(corFlux), np.argmax(corFlux)])
clipped = len(corFlux) - len(refFlux)
#...................
#Final Flux calculations
#........................
#applying aperture correction
flux_bUnit = refFlux*ap_corr #Flux in bad units (MJy/Sr)!
#Fluxes in proper units (mJy)
pixArea = (pixLen**2)/(206265**2) #Steradian
flux_arr = flux_bUnit*(pixArea*(10**9))
#Analysis
flux = np.mean(flux_arr)
error = np.std(flux_arr)
spread = (error/flux)*100
#........................
result.add_row([aKey, tRA, tDEC, dObs, mssn, mode, fLen, dPat, dScl, dPos, fTim, time, flux, error, spread, clipped])
return result, cum_data, problem
import os
#获取当前工作目录
os.getcwd()
#更改当前工作目录
#'/Applications/exelis/idl82/bin/bin.darwin.x86_64/libXm.3.0.2.dylib'
os.chdir('/Applications/exelis/idl82/bin/bin.darwin.x86_64/')
os.getcwd()
# Import mirpyidl.
import mirpyidl as idl
import numpy as np
# Execute a command in IDL.
# This will print 'Hello mom!' from IDL.
idl.execute("PRINT, 'Hello mom!'")
# Create a numpy array in python.
py_array = np.random.normal(size=1000)
# Copy this data to IDL.
idl.setVariable('idl_array', py_array)
# Calculate the standard devation and the mean in IDL.
idl.execute('idl_stddev = STDDEV(idl_array)')
idl.execute('idl_mean = MEAN(idl_array)')
# Copy the results back to python.
py_stddev = idl.getVariable('idl_stddev')
py_mean = idl.getVariable('idl_mean')