def test_readAndParseTbuff(self):
'''flaghelper: compare the read and parse and apply tbuff'''
print ''
# MJD in seconds of timeranges are these
# <startTime>4891227930515540000 <endTime>4891227932453838000
# <startTime>4891228473545856000 <endTime>4891228473731891000
# <startTime>4891226924455911000 <endTime>4891226927502314000
# <startTime>4891228838164987000 <endTime>4891228838418996000
# <startTime>4891228609440808000 <endTime>4891228612489617000
online = ["antenna='DV03&&*' timerange='2013/11/15/10:25:30.516~2013/11/15/10:25:32.454'",
"antenna='DA44&&*' timerange='2013/11/15/10:34:33.546~2013/11/15/10:34:33.732'",
"antenna='DA46&&*' timerange='2013/11/15/10:08:44.456~2013/11/15/10:08:47.502'",
"antenna='DV09&&*' timerange='2013/11/15/10:18:11.798~2013/11/15/10:18:13.837'",
"antenna='DV05&&*' timerange='2013/11/15/10:40:38.165~2013/11/15/10:40:38.419'"]
myinput = "antenna='DV03&&*' timerange='2013/11/15/10:25:30.516~2013/11/15/10:25:32.454'\n"\
"antenna='DA44&&*' timerange='2013/11/15/10:34:33.546~2013/11/15/10:34:33.732'\n"\
"antenna='DA46&&*' timerange='2013/11/15/10:08:44.456~2013/11/15/10:08:47.502'\n"\
"antenna='DV09&&*' timerange='2013/11/15/10:18:11.798~2013/11/15/10:18:13.837'\n"\
"antenna='DV05&&*' timerange='2013/11/15/10:40:38.165~2013/11/15/10:40:38.419'"
filename1 = 'flaghelperonline2.txt'
create_input(myinput, filename1)
# First timerange from online before padding
origt = timerange='2013/11/15/10:25:30.516~2013/11/15/10:25:32.454'
# Apply tbuff to timeranges
timebuffer = 1.1
dlist1 = fh.readAndParse([filename1], tbuff=timebuffer)
self.assertEqual(len(dlist1), 5)
# Get the first padded timerange from output
padt = dlist1[0]['timerange']
# Revert the tbuff application manually
t0,t1 = padt.split('~',1)
startTime = qa.totime(t0)['value']
startTimeSec = float((startTime * 24 * 3600) + timebuffer)
startTimeSec = qa.quantity(startTimeSec, 's')
paddedT0 = qa.time(startTimeSec,form='ymd',prec=9)[0]
# end time
endTime = qa.totime(t1)['value']
endTimeSec = float((endTime * 24 * 3600) - timebuffer)
endTimeSec = qa.quantity(endTimeSec, 's')
paddedT1 = qa.time(endTimeSec,form='ymd',prec=9)[0]
newtimerange = paddedT0+'~'+paddedT1
# Compare with the original
self.assertEqual(origt, newtimerange)
# Compare with original values from Flag.xml
xmlt0 = float(4891227930515540000) * 1.0E-9
xmlt1 = float(4891227932453838000) * 1.0E-9
self.assertAlmostEqual(xmlt0, startTimeSec['value'], places=3)
self.assertAlmostEqual(xmlt1, endTimeSec['value'], places=3)
def test_readAndParse(self):
'''flaghelper: compare the read and parse from a file and from a list of strings'''
print ''
# <startTime>4891227930515540000 <endTime>4891227932453838000
# <startTime>4891228473545856000 <endTime>4891228473731891000
# <startTime>4891226924455911000 <endTime>4891226927502314000
# <startTime>4891228838164987000 <endTime>4891228838418996000
# <startTime>4891228609440808000 <endTime>4891228612489617000
online = [
"antenna='DV03&&*' timerange='2013/11/15/10:25:30.516~2013/11/15/10:25:32.454'",
"antenna='DA44&&*' timerange='2013/11/15/10:34:33.546~2013/11/15/10:34:33.732'",
"antenna='DA46&&*' timerange='2013/11/15/10:08:44.456~2013/11/15/10:08:47.502'",
"antenna='DV09&&*' timerange='2013/11/15/10:18:11.798~2013/11/15/10:18:13.837'",
"antenna='DV05&&*' timerange='2013/11/15/10:40:38.165~2013/11/15/10:40:38.419'"
]
myinput = "antenna='DV03&&*' timerange='2013/11/15/10:25:30.516~2013/11/15/10:25:32.454'\n"\
"antenna='DA44&&*' timerange='2013/11/15/10:34:33.546~2013/11/15/10:34:33.732'\n"\
"antenna='DA46&&*' timerange='2013/11/15/10:08:44.456~2013/11/15/10:08:47.502'\n"\
"antenna='DV09&&*' timerange='2013/11/15/10:18:11.798~2013/11/15/10:18:13.837'\n"\
"antenna='DV05&&*' timerange='2013/11/15/10:40:38.165~2013/11/15/10:40:38.419'"
filename1 = 'flaghelperonline1.txt'
create_input(myinput, filename1)
dlist1 = fh.readAndParse([filename1])
self.assertEqual(len(dlist1), 5)
# Use the list instead of the file
dlist2 = fh.readAndParse(online)
self.assertListEqual(dlist1, dlist2)
# Compare with the original Flag.xml, second row
orig_time_start = float(4891228473545856000) * 1.0E-9
orig_time_end = float(4891228473731891000) * 1.0E-9
proc_time = dlist2[1]['timerange']
t0, t1 = proc_time.split('~', 1)
startTime = qa.totime(t0)['value']
startTimeSec = float(startTime * 24 * 3600)
endTime = qa.totime(t1)['value']
endTimeSec = float(endTime * 24 * 3600)
self.assertAlmostEqual(orig_time_start, startTimeSec, places=3)
self.assertAlmostEqual(orig_time_end, endTimeSec, places=3)
def test_readAndParse(self):
'''flaghelper: compare the read and parse from a file and from a list of strings'''
print ''
# <startTime>4891227930515540000 <endTime>4891227932453838000
# <startTime>4891228473545856000 <endTime>4891228473731891000
# <startTime>4891226924455911000 <endTime>4891226927502314000
# <startTime>4891228838164987000 <endTime>4891228838418996000
# <startTime>4891228609440808000 <endTime>4891228612489617000
online = ["antenna='DV03&&*' timerange='2013/11/15/10:25:30.516~2013/11/15/10:25:32.454'",
"antenna='DA44&&*' timerange='2013/11/15/10:34:33.546~2013/11/15/10:34:33.732'",
"antenna='DA46&&*' timerange='2013/11/15/10:08:44.456~2013/11/15/10:08:47.502'",
"antenna='DV09&&*' timerange='2013/11/15/10:18:11.798~2013/11/15/10:18:13.837'",
"antenna='DV05&&*' timerange='2013/11/15/10:40:38.165~2013/11/15/10:40:38.419'"]
myinput = "antenna='DV03&&*' timerange='2013/11/15/10:25:30.516~2013/11/15/10:25:32.454'\n"\
"antenna='DA44&&*' timerange='2013/11/15/10:34:33.546~2013/11/15/10:34:33.732'\n"\
"antenna='DA46&&*' timerange='2013/11/15/10:08:44.456~2013/11/15/10:08:47.502'\n"\
"antenna='DV09&&*' timerange='2013/11/15/10:18:11.798~2013/11/15/10:18:13.837'\n"\
"antenna='DV05&&*' timerange='2013/11/15/10:40:38.165~2013/11/15/10:40:38.419'"
filename1 = 'flaghelperonline1.txt'
create_input(myinput, filename1)
dlist1 = fh.readAndParse([filename1])
self.assertEqual(len(dlist1), 5)
# Use the list instead of the file
dlist2 = fh.readAndParse(online)
self.assertListEqual(dlist1, dlist2)
# Compare with the original Flag.xml, second row
orig_time_start = float(4891228473545856000) * 1.0E-9
orig_time_end = float(4891228473731891000) * 1.0E-9
proc_time = dlist2[1]['timerange']
t0,t1 = proc_time.split('~',1)
startTime = qa.totime(t0)['value']
startTimeSec = float(startTime * 24 * 3600)
endTime = qa.totime(t1)['value']
endTimeSec = float(endTime * 24 * 3600)
self.assertAlmostEqual(orig_time_start, startTimeSec, places=3)
self.assertAlmostEqual(orig_time_end, endTimeSec, places=3)
def datestrs_to_MJDs(cdsdict):
"""
All of the date strings must have the same reference frame (i.e. UT).
"""
datestrlist = cdsdict["data"]
# Convert to FITS format, otherwise qa.totime() will silently drop the hours.
datestrlist = [d.replace(" ", "T") for d in datestrlist]
timeq = {}
# Do first conversion to get unit.
firsttime = qa.totime(datestrlist[0])
timeq["unit"] = firsttime["unit"]
timeq["value"] = [firsttime["value"]]
for datestr in datestrlist[1:]:
timeq["value"].append(qa.totime(datestr)["value"])
return {"unit": timeq["unit"], "value": scipy.array(timeq["value"])}
def datestrs_to_MJDs(cdsdict):
"""
All of the date strings must have the same reference frame (i.e. UT).
"""
datestrlist = cdsdict['data']
# Convert to FITS format, otherwise qa.totime() will silently drop the hours.
datestrlist = [d.replace(' ', 'T') for d in datestrlist]
timeq = {}
# Do first conversion to get unit.
firsttime = qa.totime(datestrlist[0])
timeq['unit'] = firsttime['unit']
timeq['value'] = [firsttime['value']]
for datestr in datestrlist[1:]:
timeq['value'].append(qa.totime(datestr)['value'])
return {'unit': timeq['unit'], 'value': scipy.array(timeq['value'])}
def datestrs_to_MJDs(cdsdict):
"""
All of the date strings must have the same reference frame (i.e. UT).
"""
datestrlist = cdsdict['data']
# Convert to FITS format, otherwise qa.totime() will silently drop the hours.
datestrlist = [d.replace(' ', 'T') for d in datestrlist]
timeq = {}
# Do first conversion to get unit.
firsttime = qa.totime(datestrlist[0])
timeq['unit'] = firsttime['unit']
timeq['value'] = [firsttime['value']]
for datestr in datestrlist[1:]:
timeq['value'].append(qa.totime(datestr)['value'])
return {'unit': timeq['unit'],
'value': scipy.array(timeq['value'])}
def test_readAndParseIrregularTbuff(self):
'''flaghelper: compare the read and parse and apply of irregular tbuff'''
print ''
# MJD in seconds of timeranges are these
# <startTime>4891227930515540000 <endTime>4891227932453838000
# <startTime>4891228473545856000 <endTime>4891228473731891000
# <startTime>4891226924455911000 <endTime>4891226927502314000
# <startTime>4891228838164987000 <endTime>4891228838418996000
# <startTime>4891228609440808000 <endTime>4891228612489617000
online = ["antenna='DV03&&*' timerange='2013/11/15/10:25:30.516~2013/11/15/10:25:32.454'",
"antenna='DA44&&*' timerange='2013/11/15/10:34:33.546~2013/11/15/10:34:33.732'",
"antenna='DA46&&*' timerange='2013/11/15/10:08:44.456~2013/11/15/10:08:47.502'",
"antenna='DV09&&*' timerange='2013/11/15/10:18:11.798~2013/11/15/10:18:13.837'",
"antenna='DV05&&*' timerange='2013/11/15/10:40:38.165~2013/11/15/10:40:38.419'"]
myinput = "antenna='DV03&&*' timerange='2013/11/15/10:25:30.516~2013/11/15/10:25:32.454'\n"\
"antenna='DA44&&*' timerange='2013/11/15/10:34:33.546~2013/11/15/10:34:33.732'\n"\
"antenna='DA46&&*' timerange='2013/11/15/10:08:44.456~2013/11/15/10:08:47.502'\n"\
"antenna='DV09&&*' timerange='2013/11/15/10:18:11.798~2013/11/15/10:18:13.837'\n"\
"antenna='DV05&&*' timerange='2013/11/15/10:40:38.165~2013/11/15/10:40:38.419'"
filename1 = 'flaghelperonline2.txt'
create_input(myinput, filename1)
# timeranges from online before padding, for comparison later
timeranges=[]
for cmd in online:
a,b = cmd.split(' ')
b = b.lstrip('timerange=')
timeranges.append(b.strip("'"))
# Apply 2 values of tbuff to timeranges
timebuffer = [0.4, 0.7]
dlist1 = fh.readAndParse([filename1], tbuff=timebuffer)
self.assertEqual(len(dlist1), 5)
# check the padded time ranges before and after the application
n = 0
for cmd in dlist1:
padt = cmd['timerange']
# padt = dlist1[0]['timerange']
# Revert the tbuff application manually
t0,t1 = padt.split('~',1)
startTime = qa.totime(t0)['value']
startTimeSec = float((startTime * 24 * 3600) + timebuffer[0])
startTimeSec = qa.quantity(startTimeSec, 's')
paddedT0 = qa.time(startTimeSec,form='ymd',prec=9)[0]
# end time
endTime = qa.totime(t1)['value']
endTimeSec = float((endTime * 24 * 3600) - timebuffer[1])
endTimeSec = qa.quantity(endTimeSec, 's')
paddedT1 = qa.time(endTimeSec,form='ymd',prec=9)[0]
newtimerange = paddedT0+'~'+paddedT1
# Compare with the original
self.assertEqual(timeranges[n], newtimerange)
n += 1
def readJPLephem(fmfile, version=''):
"""
Reads a JPL Horizons text file (see
http://ssd.jpl.nasa.gov/horizons.cgi#top ) for a solar system object and
returns various quantities in a dictionary. The dict will be blank ({}) if
there is a failure.
"""
retdict = {}
casalog.origin('readJPLephem')
# Try opening fmfile now, because otherwise there's no point continuing.
try:
ephem = open(fmfile, 'rb')
print("opened the file=", fmfile)
lines = ephem.readlines()
# skip this, handle by rstrip later
#crCount=0
#newlines=''
#newln=''
#for ln in lines:
# n = ln.count('\r')
# if n > 0:
# newln=ln.replace('\r','\n')
# crCount+=n
# newlines += newln
#if crCount > 0:
# print("The input file appears to contains the carriage return code, \'^M\', will replace it with \'\\n\'...")
# raw_input('pause0')
# ephem.close()
# ephem = open('temp_ephem_data.txt','w+')
# ephem.write(newlines)
ephem.seek(0)
except IOError:
casalog.post("Could not open ephemeris file " + fmfile,
priority="SEVERE")
return {}
# reset to default search pattern for MJD
cols['MJD']['pat'] = r'(?P<MJD>\d+-\w+-\d+ \d+:\d+)'
# Setup the regexps.
# Headers (one time only things)
# Dictionary of quantity label: regexp pattern pairs that will be searched
# for once. The matching quantity will go in retdict[label]. Only a
# single quantity (group) will be retrieved per line.
headers = {
'NAME': {
'pat': r'^[>\s]*Target body name:\s+\d*\s*(\w+)'
}, # object name, w.o. number
'ephtype': {
'pat': r'\?s_type=1#top>\]\s*:\s+\*(\w+)'
}, # e.g. OBSERVER
'obsloc': {
'pat': r'^[>\s]*Center-site name:\s+(\w+)'
}, # e.g. GEOCENTRIC
# Catch either an explicit mean radius or a solitary target radius.
'meanrad': {
'pat':
r'(?:Mean radius \(km\)\s*=|^Target radii\s*:)\s*([0-9.]+)(?:\s*km)?\s*$',
'unit': 'km'
},
# Triaxial target radii
#'radii': {'pat': r'Target radii\s*:\s*([0-9.]+\s*x\s*[0-9.]+\s*x\s*[0-9.]+)\s*km.*Equator, meridian, pole',
'radii': {
'pat':
r'Target radii\s*:\s*([0-9.]+\s*x\s*[0-9.]+\s*x\s*[0-9.]+)\s*km.*Equator, meridian, pole|Target radii\s*:\s*([0-9.]+)\s*km\s*',
'unit': 'km'
},
'T_mean': {
'pat': r'Mean Temperature \(K\)\s*=\s*([0-9.]+)',
'unit': 'K'
},
# Figure out the units later.
'rot_per': {
'pat':
r'(?i)(?<!Inferred )\b(rot(ation(al)?|\.)?\s*per.*=\s*([-0-9.]+\s*[dhr]*|Synchronous))'
},
'orb_per': {
'pat':
r'Orbital period((, days)?\s*=\s*[-0-9.]+\s*[dhr](\s*\(?R\)?)?)'
},
# MeasComet does not read units for these! E-lon(deg), Lat(deg), Alt(km)
'GeoLong': {
'pat':
r'^[>\s]*Center geodetic\s*: ([-+0-9.]+,\s*[-+0-9.]+,\s*[-+0-9.]+)'
},
'dMJD': {
'pat': r'^[>\s]*Step-size\s*:\s*(.+)'
},
# request method v wday mth mday hh mm ss yyyy
'VS_CREATE': {
'pat':
r'^[>\s]*Ephemeris / \w+ \w+ (\w+\s+\d+\s+\d+:\d+:\d+\s+\d+)'
}
}
for hk in headers:
headers[hk]['pat'] = re.compile(headers[hk]['pat'])
# Data ("the rows of the table")
# need date, r (heliocentric distance), delta (geocentric distance), and phang (phase angle).
# (Could use the "dot" time derivatives for Doppler shifting, but it's
# likely unnecessary.)
#datapat = r'^[>\s]*'
datapat = r'^\s*'
stoppat = r'[>\s]*\$\$EOE$' # Signifies the end of data.
# Read fmfile into retdict.
num_cols = 0
in_data = False
comp_mismatches = []
print_datapat = False
# define interpretation of invalid values ('n.a.')
invalid = -999.
for origline in ephem:
line = origline.rstrip('\r\n')
if in_data:
if re.match(stoppat, line):
break
matchobj = re.search(datapat, line)
if matchobj:
#print("matchobj!")
gdict = matchobj.groupdict()
#print("gdict=",gdict)
for col in gdict:
if gdict[col] == 'n.a.':
gdict[col] = invalid
# print("cols.key=",cols.keys())
if not cols[col].get('unwanted'):
retdict['data'][col]['data'].append(gdict[col])
if len(gdict) < num_cols:
print("Partially mismatching line:")
print(line)
print("Found:")
print(gdict)
print_datapat = True
raw_input("pause0")
else:
print_datapat = True
# Chomp trailing whitespace.
comp_mismatches.append(re.sub(r'\s*$', '', line))
elif re.match(
r'^[>\s]*' + cols['MJD']['header'] + r'\s+' +
cols['RA']['header'], line):
# need to modify regex to search for the header name for MJD containing second digits
if re.match(r'^[>\s]*Date__\(UT\)__HR:MN:SC.fff', line):
cols['MJD']['pat'] = r'(?P<MJD>\d+-\w+-\d+ \d+:\d+:\d+.\d+)'
# See what columns are present, and finish setting up datapat and
# retdict.
havecols = []
# extract coordinate ref info
m = re.match(r'(^[>\s]*)(\S+)(\s+)(' + cols['RA']['header'] + ')',
line)
coordref = m.group(4).split('(')[-1]
cols['RA']['comment'] += '(' + coordref + ')'
cols['DEC']['comment'] += '(' + coordref + ')'
#print("cols['RA']['comment']=", cols['RA']['comment'])
# Chomp trailing whitespace.
myline = re.sub(r'\s*$', '', line)
titleline = myline
remaining_cols = cols.keys()
found_col = True
# This loop will terminate one way or another.
while myline and remaining_cols and found_col:
found_col = False
#print("myline = '%s'" % myline)
#print("remaining_cols =", ', '.join(remaining_cols))
for col in remaining_cols:
if re.match(r'^[>\s]*' + cols[col]['header'], myline):
#print("Found", col)
havecols.append(col)
remaining_cols.remove(col)
myline = re.sub(r'^[>\s]*' + cols[col]['header'], '',
myline)
found_col = True
break
datapat += r'\s+'.join([cols[col]['pat'] for col in havecols])
sdatapat = datapat
casalog.post("Found columns: " + ', '.join(havecols))
datapat = re.compile(datapat)
retdict['data'] = {}
for col in havecols:
if not cols[col].get('unwanted'):
retdict['data'][col] = {
'comment': cols[col]['comment'],
'data': []
}
num_cols = len(retdict['data'])
#elif re.match(r'^\$\$SOE\s*$', line): # Start of ephemeris
elif re.match(r'^[>\s]*\$\$SOE\s*$', line): # Start of ephemeris
casalog.post("Starting to read data.", priority='INFO2')
in_data = True
else:
#print("line =", line)
#print("looking for",)
for hk in headers:
#print("hk=",hk)
if not retdict.has_key(hk):
matchobj = re.search(headers[hk]['pat'], line)
if matchobj:
if hk == 'radii':
mobjs = matchobj.groups()
for gp in mobjs:
if gp != None:
retdict[hk] = gp
break
break
else:
retdict[hk] = matchobj.group(
1) # 0 is the whole line
break
ephem.close()
# clean up the temp file if exists
#if os.path.exists('temp_ephem_data.txt'):
# os.remove('temp_ephem_data.txt')
# If there were errors, provide debugging info.
if comp_mismatches:
print("Completely mismatching lines:")
#print("\n".join(comp_mismatches))
if print_datapat:
print("The apparent title line is:")
print(titleline)
print("datapat = r'%s'" % sdatapat)
# Convert numerical strings into actual numbers.
try:
retdict['earliest'] = datestr_to_epoch(
retdict['data']['MJD']['data'][0])
retdict['latest'] = datestr_to_epoch(
retdict['data']['MJD']['data'][-1])
except Exception as e:
print("Error!")
if retdict.has_key('data'):
if retdict['data'].has_key('MJD'):
if retdict['data']['MJD'].has_key('data'):
#print("retdict['data']['MJD']['data'] =", retdict['data']['MJD']['data'])
print("retdict['data'] =", retdict['data'])
else:
print("retdict['data']['MJD'] has no 'data' key.")
print("retdict['data']['MJD'].keys() =",
retdict['data']['MJD'].keys())
else:
print("retdict['data'] has no 'MJD' key.")
print("retdict['data'].keys() =", retdict['data'].keys())
else:
print("retdict has no 'data' key.")
raise e
for hk in headers:
if retdict.has_key(hk):
if headers[hk].has_key('unit'):
if hk == 'radii':
radii = retdict[hk].split('x')
if len(radii) == 1:
a = float(radii[0])
retdict[hk] = {
'unit': headers[hk]['unit'],
'value': (a, a, a)
}
retdict['meanrad'] = {
'unit': headers[hk]['unit'],
'value': a
}
else:
a, b, c = [float(r) for r in radii]
retdict[hk] = {
'unit': headers[hk]['unit'],
'value': (a, b, c)
}
retdict['meanrad'] = {
'unit': headers[hk]['unit'],
'value': mean_radius(a, b, c)
}
else:
try:
# meanrad might already have been converted.
if type(retdict[hk]) != dict:
retdict[hk] = {
'unit': headers[hk]['unit'],
'value': float(retdict[hk])
}
except Exception as e:
print("Error converting header", hk, "to a Quantity.")
print("retdict[hk] =", retdict[hk])
raise e
elif hk == 'GeoLong':
long_lat_alt = retdict[hk].split(',')
retdict['GeoLong'] = float(long_lat_alt[0])
retdict['GeoLat'] = float(long_lat_alt[1])
retdict['GeoDist'] = float(long_lat_alt[2])
elif hk == 'dMJD':
retdict[hk] = qa.convert(
qa.totime(retdict[hk].replace('minutes', 'min')),
'd')['value']
elif hk == 'orb_per':
unit = 'h'
retrograde = False
if 'd' in retdict[hk].lower():
unit = 'd' # Actually this is most common.
if 'r' in retdict[hk].lower():
retrograde = True
value = get_num_from_str(retdict[hk], 'orbital period')
if value != False:
if retrograde and value > 0.0:
value = -value
retdict[hk] = {'unit': unit, 'value': value}
else:
del retdict[hk]
# The rotation period might depend on the orbital period ("Synchronous"),
# so handle it after all the other headers have been done.
if 'rot_per' in retdict:
rpstr = retdict['rot_per']
if 'ROTPER' in rpstr: # Asteroid
retdict['rot_per'] = {
'unit': 'h', # Always seems to be for asteroids.
'value': get_num_from_str(rpstr, 'rotation period')
}
elif 'Synchronous' in rpstr:
retdict['rot_per'] = retdict['orb_per']
else: # Most likely a planet.
match = re.search(r'(\d+)h\s*(\d+)m\s*([0-9.]+)s', rpstr)
if match:
hms = [float(match.group(i)) for i in range(1, 4)]
retdict['rot_per'] = {
'unit': 'h',
'value': hms[0] + (hms[1] + hms[2] / 60.0) / 60.0
}
else:
# DON'T include the optional r in hr! qa.totime can't handle it.
try:
match = re.search(r'([-0-9.]+)(?:\s*\+-[0-9.]+)?\s*([dh])',
rpstr)
if match:
retdict['rot_per'] = {
'unit': match.group(2),
'value': float(match.group(1))
}
except:
print("Error parsing the rotation period from")
print(rpstr)
if retdict['data'].has_key('ang_sep'):
retdict['data']['obs_code'] = {'comment': 'Obscuration code'}
for dk in retdict['data']:
if dk == 'obs_code':
continue
if cols[dk].has_key('unit'):
retdict['data'][dk]['data'] = {
'unit':
cols[dk]['unit'],
'value':
scipy.array([float(s) for s in retdict['data'][dk]['data']])
}
if dk == 'RadVel':
# Convert from km/s to AU/d. Blame MeasComet, not me.
retdict['data'][dk]['data']['unit'] = 'AU/d'
kmps_to_AUpd = qa.convert('1km/s', 'AU/d')['value']
retdict['data'][dk]['data']['value'] *= kmps_to_AUpd
if re.match(r'.*(RA|DEC)$', dk):
retdict['data'][dk] = convert_radec(retdict['data'][dk])
elif dk == 'MJD':
retdict['data']['MJD'] = datestrs_to_MJDs(retdict['data']['MJD'])
elif dk == 'ang_sep':
angseps = []
obscodes = []
for asoc in retdict['data'][dk]['data']:
angsep, obscode = asoc.split('/')
angseps.append(float(angsep))
obscodes.append(obscode)
retdict['data'][dk]['data'] = {
'unit': 'arcseconds',
'value': angseps
}
retdict['data']['obs_code']['data'] = obscodes
if len(retdict.get('radii', {'value': []})['value']) == 3 \
and retdict['data'].has_key('NP_RA') and retdict['data'].has_key('NP_DEC'):
# Do a better mean radius estimate using the actual theta.
retdict['meanrad']['value'] = mean_radius_with_known_theta(retdict)
# To be eventually usable as a MeasComet table, a few more keywords are needed.
retdict['VS_TYPE'] = 'Table of comet/planetary positions'
if version == '':
version = '0003.0001'
#retdict['VS_VERSION'] = '0003.0001'
retdict['VS_VERSION'] = version
if retdict.has_key('VS_CREATE'):
dt = time.strptime(retdict['VS_CREATE'], "%b %d %H:%M:%S %Y")
else:
casalog.post(
"The ephemeris creation date was not found. Using the current time.",
priority="WARN")
dt = time.gmtime()
retdict['VS_CREATE'] = time.strftime('%Y/%m/%d/%H:%M', dt)
# VS_DATE is required by MeasComet, but it doesn't seem to be actually used.
retdict['VS_DATE'] = time.strftime('%Y/%m/%d/%H:%M', time.gmtime())
if retdict['data'].has_key('MJD'):
#casalog.post("retdict.keys=%s" % retdict.keys())
retdict['MJD0'] = retdict['data']['MJD']['value'][0] - retdict['dMJD']
else:
print(
"The table will not be usable with me.framecomet because it lacks MJD."
)
# adding posrefsys keyword
if cols['RA']['comment'].count('J2000'):
retdict['posrefsys'] = 'ICRF/J2000.0'
if cols['RA']['comment'].count('B1950'):
retdict['posrefsys'] = 'FK4/B1950.0'
return retdict
def subvs(vis=None,
outputvis=None,
timerange=None,
spw=None,
subtime1=None,
subtime2=None,
splitsel=True,
reverse=False,
overwrite=False):
"""Perform vector subtraction for visibilities
Keyword arguments:
vis -- Name of input visibility file (MS)
default: none; example: vis='ngc5921.ms'
outputvis -- Name of output uv-subtracted visibility file (MS)
default: none; example: outputvis='ngc5921_src.ms'
timerange -- Time range of performing the UV subtraction:
default='' means all times. examples:
timerange = 'YYYY/MM/DD/hh:mm:ss~YYYY/MM/DD/hh:mm:ss'
timerange = 'hh:mm:ss~hh:mm:ss'
spw -- Select spectral window/channel.
default = '' all the spectral channels. Example: spw='0:1~20'
subtime1 -- Time range 1 of the background to be subtracted from the data
default='' means all times. format:
timerange = 'YYYY/MM/DD/hh:mm:ss~YYYY/MM/DD/hh:mm:ss'
timerange = 'hh:mm:ss~hh:mm:ss'
subtime2 -- Time range 2 of the backgroud to be subtracted from the data
default='' means all times. examples:
timerange = 'YYYY/MM/DD/hh:mm:ss~YYYY/MM/DD/hh:mm:ss'
timerange = 'hh:mm:ss~hh:mm:ss'
splitsel -- True or False. default = False. If splitsel = False, then the entire input
measurement set is copied as the output measurement set (outputvis), with
background subtracted at selected timerange and spectral channels.
If splitsel = True,then only the selected timerange and spectral channels
are copied into the output measurement set (outputvis).
reverse -- True or False. default = False. If reverse = False, then the times indicated
by subtime1 and/or subtime2 are treated as background and subtracted; If reverse
= True, then reverse the sign of the background-subtracted data. The option can
be used for mapping absorptive structure.
overwrite -- True or False. default = False. If overwrite = True and
outputvis already exists, the selected subtime and spw in the
output measurment set will be replaced with background subtracted
visibilities
"""
#check the visbility ms
if not outputvis or outputvis.isspace():
raise ValueError, 'Please specify outputvis'
if os.path.exists(outputvis):
if overwrite:
print "The already existing output measurement set will be updated."
else:
raise ValueError, "Output MS %s already exists - will not overwrite." % outputvis
else:
if not splitsel:
shutil.copytree(vis, outputvis)
else:
ms.open(vis, nomodify=True)
ms.split(outputvis, spw=spw, time=timerange, whichcol='DATA')
ms.close()
#define and check the time ranges
if subtime1 and (type(subtime1) == str):
[bsubtime1, esubtime1] = subtime1.split('~')
bsubtime1sec = qa.getvalue(qa.convert(qa.totime(bsubtime1), 's'))
esubtime1sec = qa.getvalue(qa.convert(qa.totime(esubtime1), 's'))
timebin1sec = esubtime1sec - bsubtime1sec
if timebin1sec < 0:
raise Exception, 'Negative timebin! Please check the "subtime1" parameter.'
casalog.post('Selected timerange 1: ' + subtime1 +
' as background for uv subtraction.')
else:
raise Exception, 'Please enter at least one timerange as the background'
if subtime2 and (type(subtime2) == str):
[bsubtime2, esubtime2] = subtime2.split('~')
bsubtime2sec = qa.getvalue(qa.convert(qa.totime(bsubtime2), 's'))
esubtime2sec = qa.getvalue(qa.convert(qa.totime(esubtime2), 's'))
timebin2sec = esubtime2sec - bsubtime2sec
if timebin2sec < 0:
raise Exception, 'Negative timebin! Please check the "subtime2" parameter.'
timebin2 = str(timebin2sec) + 's'
casalog.post('Selected timerange 2: ' + subtime2 +
' as background for uv subtraction.')
#plus 1s is to ensure averaging over the entire timerange
else:
casalog.post(
'Timerange 2 not selected, using only timerange 1 as background')
if timerange and (type(timerange) == str):
[btimeo, etimeo] = timerange.split('~')
btimeosec = qa.getvalue(qa.convert(qa.totime(btimeo), 's'))
etimeosec = qa.getvalue(qa.convert(qa.totime(etimeo), 's'))
timebinosec = etimeosec - btimeosec
if timebinosec < 0:
raise Exception, 'Negative timebin! Please check the "timerange" parameter.'
casalog.post('Selected timerange: ' + timerange +
' as the time for UV subtraction.')
else:
casalog.post(
'Output timerange not specified, using the entire timerange')
if spw and (type(spw) == str):
[spwid, chanran] = spw.split(':')
[bchan, echan] = chanran.split('~')
else:
casalog.post('spw not specified, use all frequency channels')
#Select the background indicated by subtime1
ms.open(vis, nomodify=True)
#Select the spw id
ms.msselect({'time': subtime1})
if spw and (type(spw) == str):
ms.selectinit(datadescid=int(spwid))
nchan = int(echan) - int(bchan) + 1
ms.selectchannel(nchan, int(bchan), 1, 1)
rec1 = ms.getdata(['data', 'time', 'axis_info'], ifraxis=True)
#print 'shape of the frequency matrix ',rec1['axis_info']['freq_axis']['chan_freq'].shape
sz1 = rec1['data'].shape
print 'dimension of selected background 1', rec1['data'].shape
#the data shape is (n_pol,n_channel,n_baseline,n_time), no need to reshape
#rec1['data']=rec1['data'].reshape(sz1[0],sz1[1],sz1[2],nspw,sz1[3]/nspw,order='F')
#print 'reshaped rec1 ', rec1['data'].shape
rec1avg = np.average(rec1['data'], axis=3)
casalog.post('Averaging the visibilities in subtime1: ' + subtime1)
ms.close()
if subtime2 and (type(subtime2) == str):
ms.open(vis, nomodify=True)
#Select the spw id
ms.msselect({'time': subtime2})
if spw and (type(spw) == str):
ms.selectinit(datadescid=0)
nchan = int(echan) - int(bchan) + 1
ms.selectchannel(nchan, int(bchan), 1, 1)
rec2 = ms.getdata(['data', 'time', 'axis_info'], ifraxis=True)
sz2 = rec2['data'].shape
print 'dimension of selected background 2', rec2['data'].shape
#rec2['data']=rec2['data'].reshape(sz2[0],sz2[1],sz2[2],nspw,sz2[3]/nspw,order='F')
#print 'reshaped rec1 ', rec2['data'].shape
rec2avg = np.average(rec2['data'], axis=3)
ms.close()
casalog.post('Averaged the visibilities in subtime2: ' + subtime2)
#do UV subtraction, according to timerange and spw
ms.open(outputvis, nomodify=False)
if not splitsel:
#outputvis is identical to input visibility, do the selection
if timerange and (type(timerange == str)):
ms.msselect({'time': timerange})
if spw and (type(spw) == str):
ms.selectinit(datadescid=int(spwid))
nchan = int(echan) - int(bchan) + 1
ms.selectchannel(nchan, int(bchan), 1, 1)
else:
#outputvis is splitted, selections have already applied, select all the data
ms.selectinit(datadescid=0)
orec = ms.getdata(['data', 'time', 'axis_info'], ifraxis=True)
b_rows = orec['data'].shape[2]
nchan = orec['data'].shape[1]
#szo=orec['data'].shape
print 'dimension of output data', orec['data'].shape
#orec['data']=orec['data'].reshape(szo[0],szo[1],szo[2],nspw,szo[3]/nspw,order='F')
#print 'reshaped rec1 ', orec['data'].shape
t_rows = orec['data'].shape[3]
casalog.post('Number of baselines: ' + str(b_rows))
casalog.post('Number of spectral channels: ' + str(nchan))
casalog.post('Number of time pixels: ' + str(t_rows))
if subtime1 and (not subtime2):
casalog.post(
'Only "subtime1" is defined, subtracting background defined in subtime1: '
+ subtime1)
t1 = (np.amax(rec1['time']) + np.amin(rec1['time'])) / 2.
print 't1: ', qa.time(qa.quantity(t1, 's'), form='ymd', prec=10)
for i in range(t_rows):
orec['data'][:, :, :, i] -= rec1avg
if reverse:
orec['data'][:, :, :, i] = -orec['data'][:, :, :, i]
if subtime1 and subtime2 and (type(subtime2) == str):
casalog.post(
'Both subtime1 and subtime2 are specified, doing linear interpolation between "subtime1" and "subtime2"'
)
t1 = (np.amax(rec1['time']) + np.amin(rec1['time'])) / 2.
t2 = (np.amax(rec2['time']) + np.amin(rec2['time'])) / 2.
touts = orec['time']
print 't1: ', qa.time(qa.quantity(t1, 's'), form='ymd', prec=10)
print 't2: ', qa.time(qa.quantity(t2, 's'), form='ymd', prec=10)
for i in range(t_rows):
tout = touts[i]
if tout > np.amax([t1, t2]):
tout = np.amax([t1, t2])
elif tout < np.amin([t1, t2]):
tout = np.amin([t1, t2])
orec['data'][:, :, :,
i] -= (rec2avg - rec1avg) * (tout -
t1) / (t2 - t1) + rec1avg
if reverse:
orec['data'][:, :, :, i] = -orec['data'][:, :, :, i]
#orec['data']=orec['data'].reshape(szo[0],szo[1],szo[2],szo[3],order='F')
#put the modified data back into the output visibility set
del orec['time']
del orec['axis_info']
ms.putdata(orec)
ms.close()
def test_readAndParseTbuff(self):
'''flaghelper: compare the read and parse and apply tbuff'''
print ''
# MJD in seconds of timeranges are these
# <startTime>4891227930515540000 <endTime>4891227932453838000
# <startTime>4891228473545856000 <endTime>4891228473731891000
# <startTime>4891226924455911000 <endTime>4891226927502314000
# <startTime>4891228838164987000 <endTime>4891228838418996000
# <startTime>4891228609440808000 <endTime>4891228612489617000
online = [
"antenna='DV03&&*' timerange='2013/11/15/10:25:30.516~2013/11/15/10:25:32.454'",
"antenna='DA44&&*' timerange='2013/11/15/10:34:33.546~2013/11/15/10:34:33.732'",
"antenna='DA46&&*' timerange='2013/11/15/10:08:44.456~2013/11/15/10:08:47.502'",
"antenna='DV09&&*' timerange='2013/11/15/10:18:11.798~2013/11/15/10:18:13.837'",
"antenna='DV05&&*' timerange='2013/11/15/10:40:38.165~2013/11/15/10:40:38.419'"
]
myinput = "antenna='DV03&&*' timerange='2013/11/15/10:25:30.516~2013/11/15/10:25:32.454'\n"\
"antenna='DA44&&*' timerange='2013/11/15/10:34:33.546~2013/11/15/10:34:33.732'\n"\
"antenna='DA46&&*' timerange='2013/11/15/10:08:44.456~2013/11/15/10:08:47.502'\n"\
"antenna='DV09&&*' timerange='2013/11/15/10:18:11.798~2013/11/15/10:18:13.837'\n"\
"antenna='DV05&&*' timerange='2013/11/15/10:40:38.165~2013/11/15/10:40:38.419'"
filename1 = 'flaghelperonline2.txt'
create_input(myinput, filename1)
# First timerange from online before padding
origt = timerange = '2013/11/15/10:25:30.516~2013/11/15/10:25:32.454'
# Apply tbuff to timeranges
timebuffer = 1.1
dlist1 = fh.readAndParse([filename1], tbuff=timebuffer)
self.assertEqual(len(dlist1), 5)
# Get the first padded timerange from output
padt = dlist1[0]['timerange']
# Revert the tbuff application manually
t0, t1 = padt.split('~', 1)
startTime = qa.totime(t0)['value']
startTimeSec = float((startTime * 24 * 3600) + timebuffer)
startTimeSec = qa.quantity(startTimeSec, 's')
paddedT0 = qa.time(startTimeSec, form='ymd', prec=9)[0]
# end time
endTime = qa.totime(t1)['value']
endTimeSec = float((endTime * 24 * 3600) - timebuffer)
endTimeSec = qa.quantity(endTimeSec, 's')
paddedT1 = qa.time(endTimeSec, form='ymd', prec=9)[0]
newtimerange = paddedT0 + '~' + paddedT1
# Compare with the original
self.assertEqual(origt, newtimerange)
# Compare with original values from Flag.xml
xmlt0 = float(4891227930515540000) * 1.0E-9
xmlt1 = float(4891227932453838000) * 1.0E-9
self.assertAlmostEqual(xmlt0, startTimeSec['value'], places=3)
self.assertAlmostEqual(xmlt1, endTimeSec['value'], places=3)
def test_readAndParseIrregularTbuff(self):
'''flaghelper: compare the read and parse and apply of irregular tbuff'''
print ''
# MJD in seconds of timeranges are these
# <startTime>4891227930515540000 <endTime>4891227932453838000
# <startTime>4891228473545856000 <endTime>4891228473731891000
# <startTime>4891226924455911000 <endTime>4891226927502314000
# <startTime>4891228838164987000 <endTime>4891228838418996000
# <startTime>4891228609440808000 <endTime>4891228612489617000
online = [
"antenna='DV03&&*' timerange='2013/11/15/10:25:30.516~2013/11/15/10:25:32.454'",
"antenna='DA44&&*' timerange='2013/11/15/10:34:33.546~2013/11/15/10:34:33.732'",
"antenna='DA46&&*' timerange='2013/11/15/10:08:44.456~2013/11/15/10:08:47.502'",
"antenna='DV09&&*' timerange='2013/11/15/10:18:11.798~2013/11/15/10:18:13.837'",
"antenna='DV05&&*' timerange='2013/11/15/10:40:38.165~2013/11/15/10:40:38.419'"
]
myinput = "antenna='DV03&&*' timerange='2013/11/15/10:25:30.516~2013/11/15/10:25:32.454'\n"\
"antenna='DA44&&*' timerange='2013/11/15/10:34:33.546~2013/11/15/10:34:33.732'\n"\
"antenna='DA46&&*' timerange='2013/11/15/10:08:44.456~2013/11/15/10:08:47.502'\n"\
"antenna='DV09&&*' timerange='2013/11/15/10:18:11.798~2013/11/15/10:18:13.837'\n"\
"antenna='DV05&&*' timerange='2013/11/15/10:40:38.165~2013/11/15/10:40:38.419'"
filename1 = 'flaghelperonline2.txt'
create_input(myinput, filename1)
# timeranges from online before padding, for comparison later
timeranges = []
for cmd in online:
a, b = cmd.split(' ')
b = b.lstrip('timerange=')
timeranges.append(b.strip("'"))
# Apply 2 values of tbuff to timeranges
timebuffer = [0.4, 0.7]
dlist1 = fh.readAndParse([filename1], tbuff=timebuffer)
self.assertEqual(len(dlist1), 5)
# check the padded time ranges before and after the application
n = 0
for cmd in dlist1:
padt = cmd['timerange']
# padt = dlist1[0]['timerange']
# Revert the tbuff application manually
t0, t1 = padt.split('~', 1)
startTime = qa.totime(t0)['value']
startTimeSec = float((startTime * 24 * 3600) + timebuffer[0])
startTimeSec = qa.quantity(startTimeSec, 's')
paddedT0 = qa.time(startTimeSec, form='ymd', prec=9)[0]
# end time
endTime = qa.totime(t1)['value']
endTimeSec = float((endTime * 24 * 3600) - timebuffer[1])
endTimeSec = qa.quantity(endTimeSec, 's')
paddedT1 = qa.time(endTimeSec, form='ymd', prec=9)[0]
newtimerange = paddedT0 + '~' + paddedT1
# Compare with the original
self.assertEqual(timeranges[n], newtimerange)
n += 1
else:
try:
# meanrad might already have been converted.
if type(retdict[hk]) != dict:
retdict[hk] = {"unit": headers[hk]["unit"], "value": float(retdict[hk])}
except Exception, e:
print "Error converting header", hk, "to a Quantity."
print "retdict[hk] =", retdict[hk]
raise e
elif hk == "GeoLong":
long_lat_alt = retdict[hk].split(",")
retdict["GeoLong"] = float(long_lat_alt[0])
retdict["GeoLat"] = float(long_lat_alt[1])
retdict["GeoDist"] = float(long_lat_alt[2])
elif hk == "dMJD":
retdict[hk] = qa.convert(qa.totime(retdict[hk].replace("minutes", "min")), "d")["value"]
elif hk == "orb_per":
unit = "h"
retrograde = False
if "d" in retdict[hk].lower():
unit = "d" # Actually this is most common.
if "r" in retdict[hk].lower():
retrograde = True
value = get_num_from_str(retdict[hk], "orbital period")
if value != False:
if retrograde and value > 0.0:
value = -value
retdict[hk] = {"unit": unit, "value": value}
else:
del retdict[hk]
def imreg(vis=None,
ephem=None,
msinfo=None,
imagefile=None,
timerange=None,
reftime=None,
fitsfile=None,
beamfile=None,
offsetfile=None,
toTb=None,
scl100=None,
verbose=False,
p_ang=False,
overwrite=True,
usephacenter=True,
deletehistory=False):
'''
main routine to register CASA images
Required Inputs:
vis: STRING. CASA measurement set from which the image is derived
imagefile: STRING or LIST. name of the input CASA image
timerange: STRING or LIST. timerange used to generate the CASA image, must have the same length as the input images.
Each element should be in CASA standard time format, e.g., '2012/03/03/12:00:00~2012/03/03/13:00:00'
Optional Inputs:
msinfo: DICTIONARY. CASA MS information, output from read_msinfo. If not provided, generate one from the supplied vis
ephem: DICTIONARY. solar ephem, output from read_horizons.
If not provided, query JPL Horizons based on time info of the vis (internet connection required)
fitsfile: STRING or LIST. name of the output registered fits files
reftime: STRING or LIST. Each element should be in CASA standard time format, e.g., '2012/03/03/12:00:00'
offsetfile: optionally provide an offset with a series of solar x and y offsets with timestamps
toTb: Bool. Convert the default Jy/beam to brightness temperature?
scl100: Bool. If True, scale the image values up by 100 (to compensate VLA 20 dB attenuator)
verbose: Bool. Show more diagnostic info if True.
usephacenter: Bool -- if True, correct for the RA and DEC in the ms file based on solar empheris.
Otherwise assume the phasecenter is correctly pointed to the solar disk center
(EOVSA case)
'''
ia = iatool()
if deletehistory:
msclearhistory(vis)
if verbose:
import time
t0 = time.time()
prtidx = 1
print('point {}: {}'.format(prtidx, time.time() - t0))
prtidx += 1
if not imagefile:
raise ValueError, 'Please specify input image'
if not timerange:
raise ValueError, 'Please specify timerange of the input image'
if type(imagefile) == str:
imagefile = [imagefile]
if type(timerange) == str:
timerange = [timerange]
if not fitsfile:
fitsfile = [img + '.fits' for img in imagefile]
if type(fitsfile) == str:
fitsfile = [fitsfile]
nimg = len(imagefile)
if len(timerange) != nimg:
raise ValueError, 'Number of input images does not equal to number of timeranges!'
if len(fitsfile) != nimg:
raise ValueError, 'Number of input images does not equal to number of output fits files!'
nimg = len(imagefile)
if verbose:
print str(nimg) + ' images to process...'
if verbose:
print('point {}: {}'.format(prtidx, time.time() - t0))
prtidx += 1
if reftime: # use as reference time to find solar disk RA and DEC to register the image, but not the actual timerange associated with the image
if type(reftime) == str:
reftime = [reftime] * nimg
if len(reftime) != nimg:
raise ValueError, 'Number of reference times does not match that of input images!'
helio = ephem_to_helio(vis,
ephem=ephem,
msinfo=msinfo,
reftime=reftime,
usephacenter=usephacenter)
else:
# use the supplied timerange to register the image
helio = ephem_to_helio(vis,
ephem=ephem,
msinfo=msinfo,
reftime=timerange,
usephacenter=usephacenter)
if verbose:
print('point {}: {}'.format(prtidx, time.time() - t0))
prtidx += 1
for n, img in enumerate(imagefile):
if verbose:
print 'processing image #' + str(n)
fitsf = fitsfile[n]
timeran = timerange[n]
# obtain duration of the image as FITS header exptime
try:
[tbg0, tend0] = timeran.split('~')
tbg_d = qa.getvalue(qa.convert(qa.totime(tbg0), 'd'))[0]
tend_d = qa.getvalue(qa.convert(qa.totime(tend0), 'd'))[0]
tdur_s = (tend_d - tbg_d) * 3600. * 24.
dateobs = qa.time(qa.quantity(tbg_d, 'd'), form='fits', prec=10)[0]
except:
print 'Error in converting the input timerange: ' + str(
timeran) + '. Proceeding to the next image...'
continue
if verbose:
print('point {}: {}'.format(prtidx, time.time() - t0))
prtidx += 1
hel = helio[n]
if not os.path.exists(img):
raise ValueError, 'Please specify input image'
if os.path.exists(fitsf) and not overwrite:
raise ValueError, 'Specified fits file already exists and overwrite is set to False. Aborting...'
else:
p0 = hel['p0']
ia.open(img)
imr = ia.rotate(pa=str(-p0) + 'deg')
imr.tofits(fitsf, history=False, overwrite=overwrite)
imr.close()
imsum = ia.summary()
ia.close()
if verbose:
print('point {}: {}'.format(prtidx, time.time() - t0))
prtidx += 1
# construct the standard fits header
# RA and DEC of the reference pixel crpix1 and crpix2
(imra, imdec) = (imsum['refval'][0], imsum['refval'][1])
# find out the difference of the image center to the CASA phase center
# RA and DEC difference in arcseconds
ddec = degrees((imdec - hel['dec_fld'])) * 3600.
dra = degrees((imra - hel['ra_fld']) * cos(hel['dec_fld'])) * 3600.
# Convert into image heliocentric offsets
prad = -radians(hel['p0'])
dx = (-dra) * cos(prad) - ddec * sin(prad)
dy = (-dra) * sin(prad) + ddec * cos(prad)
if offsetfile:
try:
offset = np.load(offsetfile)
except:
raise ValueError, 'The specified offsetfile does not exist!'
reftimes_d = offset['reftimes_d']
xoffs = offset['xoffs']
yoffs = offset['yoffs']
timg_d = hel['reftime']
ind = bisect.bisect_left(reftimes_d, timg_d)
xoff = xoffs[ind - 1]
yoff = yoffs[ind - 1]
else:
xoff = hel['refx']
yoff = hel['refy']
if verbose:
print 'offset of image phase center to visibility phase center (arcsec): ', dx, dy
print 'offset of visibility phase center to solar disk center (arcsec): ', xoff, yoff
(crval1, crval2) = (xoff + dx, yoff + dy)
# update the fits header to heliocentric coordinates
if verbose:
print('point {}: {}'.format(prtidx, time.time() - t0))
prtidx += 1
hdu = pyfits.open(fitsf, mode='update')
if verbose:
print('point {}: {}'.format(prtidx, time.time() - t0))
prtidx += 1
header = hdu[0].header
(cdelt1,
cdelt2) = (-header['cdelt1'] * 3600., header['cdelt2'] * 3600.
) # Original CDELT1, 2 are for RA and DEC in degrees
header['cdelt1'] = cdelt1
header['cdelt2'] = cdelt2
header['cunit1'] = 'arcsec'
header['cunit2'] = 'arcsec'
header['crval1'] = crval1
header['crval2'] = crval2
header['ctype1'] = 'HPLN-TAN'
header['ctype2'] = 'HPLT-TAN'
header['date-obs'] = dateobs # begin time of the image
if not p_ang:
hel['p0'] = 0
try:
# this works for pyfits version of CASA 4.7.0 but not CASA 4.6.0
if tdur_s:
header.set('exptime', tdur_s)
else:
header.set('exptime', 1.)
header.set('p_angle', hel['p0'])
header.set('dsun_obs',
sun.sunearth_distance(Time(dateobs)).to(u.meter).value)
header.set(
'rsun_obs',
sun.solar_semidiameter_angular_size(Time(dateobs)).value)
header.set('rsun_ref', sun.constants.radius.value)
header.set('hgln_obs', 0.)
header.set('hglt_obs',
sun.heliographic_solar_center(Time(dateobs))[1].value)
except:
# this works for astropy.io.fits
if tdur_s:
header.append(('exptime', tdur_s))
else:
header.append(('exptime', 1.))
header.append(('p_angle', hel['p0']))
header.append(
('dsun_obs',
sun.sunearth_distance(Time(dateobs)).to(u.meter).value))
header.append(
('rsun_obs',
sun.solar_semidiameter_angular_size(Time(dateobs)).value))
header.append(('rsun_ref', sun.constants.radius.value))
header.append(('hgln_obs', 0.))
header.append(
('hglt_obs',
sun.heliographic_solar_center(Time(dateobs))[1].value))
if verbose:
print('point {}: {}'.format(prtidx, time.time() - t0))
prtidx += 1
# update intensity units, i.e. to brightness temperature?
if toTb:
# get restoring beam info
(bmajs, bmins, bpas, beamunits,
bpaunits) = getbeam(imagefile=imagefile, beamfile=beamfile)
bmaj = bmajs[n]
bmin = bmins[n]
beamunit = beamunits[n]
data = hdu[
0].data # remember the data order is reversed due to the FITS convension
dim = data.ndim
sz = data.shape
keys = header.keys()
values = header.values()
# which axis is frequency?
faxis = keys[values.index('FREQ')][-1]
faxis_ind = dim - int(faxis)
if header['BUNIT'].lower() == 'jy/beam':
header['BUNIT'] = 'K'
header['BTYPE'] = 'Brightness Temperature'
for i in range(sz[faxis_ind]):
nu = header['CRVAL' + faxis] + header['CDELT' + faxis] * (
i + 1 - header['CRPIX' + faxis])
if header['CUNIT' + faxis] == 'KHz':
nu *= 1e3
if header['CUNIT' + faxis] == 'MHz':
nu *= 1e6
if header['CUNIT' + faxis] == 'GHz':
nu *= 1e9
if len(bmaj) > 1: # multiple (per-plane) beams
bmajtmp = bmaj[i]
bmintmp = bmin[i]
else: # one single beam
bmajtmp = bmaj[0]
bmintmp = bmin[0]
if beamunit == 'arcsec':
bmaj0 = np.radians(bmajtmp / 3600.)
bmin0 = np.radians(bmajtmp / 3600.)
if beamunit == 'arcmin':
bmaj0 = np.radians(bmajtmp / 60.)
bmin0 = np.radians(bmintmp / 60.)
if beamunit == 'deg':
bmaj0 = np.radians(bmajtmp)
bmin0 = np.radians(bmintmp)
if beamunit == 'rad':
bmaj0 = bmajtmp
bmin0 = bmintmp
beam_area = bmaj0 * bmin0 * np.pi / (4. * log(2.))
k_b = qa.constants('k')['value']
c_l = qa.constants('c')['value']
factor = 2. * k_b * nu**2 / c_l**2 # SI unit
jy_to_si = 1e-26
# print nu/1e9, beam_area, factor
factor2 = 1.
if scl100:
factor2 = 100.
if faxis == '3':
data[:,
i, :, :] *= jy_to_si / beam_area / factor * factor2
if faxis == '4':
data[
i, :, :, :] *= jy_to_si / beam_area / factor * factor2
if verbose:
print('point {}: {}'.format(prtidx, time.time() - t0))
prtidx += 1
hdu.flush()
hdu.close()
if verbose:
print('point {}: {}'.format(prtidx, time.time() - t0))
prtidx += 1
def datestr_to_epoch(datestr):
"""
Given a UT date like "2010-May-01 00:00", returns an epoch measure.
"""
return me.epoch(rf='UTC', v0=qa.totime(datestr))
def subvs(vis=None,
outputvis=None,
timerange=None,
spw=None,
timoffset=4,
windowlen=10,
windowtype='flat',
splitsel=True,
reverse=False,
overwrite=False):
"""Vector-subtraction in UV using selected time ranges and spectral channels as background
subvs is a function to do UV vector-subtraction. By selecting gliding averaging window,
only the low-frequency signals corresponding to the background continuum emission remain.
As a result, a uv subtraction of original dynamic spectrum from the background can improve
fine stucture such as fibers. Subvs can be used to subtract the background
continuum emission to separate the time-dependent emission, e.g. solar
coherent radio bursts.
Keyword arguments:
vis -- Name of input visibility file
default: none; example: vis='sun_type3.ms'
outputvis -- Name of output visibility file
default: none; example: outputvis='sun_type3.sub.ms'
timerange -- Select the time range in the data to be subtracted from.
timerange = 'YYYY/MM/DD/hh:mm:ss~YYYY/MM/DD/hh:mm:ss'
Note: if YYYY/MM/DD is missing date, timerange defaults to the
first day in the dataset
timerange='09:14:0~09:54:0' picks 40 min on first day
timerange='25:00:00~27:30:00' picks 1 hr to 3 hr 30min
on next day
spw -- Select spectral window/channel.
default = '' all the spectral channels. Example: spw='0:1~20'
timoffset -- After the convolution, each single channel of smoothed uv vectors
in time series are misaligned from original data. Setting the
timoffset allow you to shift by the specifed amount of data points.
windowlen -- Choose the width the gliding window for smoothing.
windowtype -- Choose the type o gliding window. Options available are 'flat',
'hanning', 'hamming', 'bartlett', 'blackman'
splitsel -- True of False. default = False. If splitsel = False, then the entire input
measurement set is copied as the output measurement set (outputvis), with
background subtracted at selected timerange and spectral channels.
If splitsel = True,then only the selected timerange and spectral channels
are copied into the output measurement set (outputvis).
reverse -- True or False. default = False. If reverse = False, then the times indicated
by subtime1 and/or subtime2 are treated as background and subtracted; If reverse
= True, then reverse the sign of the background-subtracted data. The option can
be used for mapping absorptive structure.
overwrite -- True or False. default = False. If overwrite = True and outputvis
already exists, the selected subtime and spw in the already existing
output measurement set will be replaced with subtracted visibilities
"""
#check the visbility ms
if not outputvis or outputvis.isspace():
raise ValueError, 'Please specify outputvis.'
if os.path.exists(outputvis):
if overwrite:
print "The already existing output measurement set will be updated."
else:
raise ValueError, "Output MS %s already exists - will not overwrite." % outputvis
else:
if not splitsel:
shutil.copytree(vis, outputvis)
else:
ms.open(vis, nomodify=True)
ms.split(outputvis, spw=spw, time=timerange, whichcol='DATA')
ms.close()
# check and specify time range and channel
if timerange and (type(timerange) == str):
[btimeo, etimeo] = timerange.split('~')
btimeosec = qa.getvalue(qa.convert(qa.totime(btimeo), 's'))
etimeosec = qa.getvalue(qa.convert(qa.totime(etimeo), 's'))
timebinosec = etimeosec - btimeosec
if timebinosec < 0:
raise Exception, 'Negative timebin! Please check the "timerange" parameter.'
casalog.post('Selected timerange: ' + timerange +
' as the time for UV subtraction.')
else:
casalog.post(
'Output timerange not specified, using the entire timerange')
if spw and (type(spw) == str):
[spwid, chanran] = spw.split(':')
[bchan, echan] = chanran.split('~')
nchan = int(echan) - int(bchan) + 1
else:
casalog.post('spw not specified, use all frequency channels')
#select data range to be smoothed
ms.open(vis, nomodify=True)
#Select the spw id
ms.msselect({'time': timerange})
ms.selectinit(datadescid=int(spwid))
ms.selectchannel(nchan, int(bchan), 1, 1)
rec = ms.getdata(['data', 'time', 'axis_info'], ifraxis=True)
#print 'shape of the frequency matrix ',rec1['axis_info']['freq_axis']['chan_freq'].shape
sz = rec['data'].shape
print 'dimension of selected background for smoothing', rec['data'].shape
#the data shape is (n_pol,n_channel,n_baseline,n_time), no need to reshape
#rec1['data']=rec1['data'].reshape(sz1[0],sz1[1],sz1[2],nspw,sz1[3]/nspw,order='F')
#print 'reshaped rec1 ', rec1['data'].shape
if not (timoffset and (type(timoffset) == int)):
timoffset = int(4)
for i in range(rec['data'].shape[0]):
for j in range(rec['data'].shape[1]):
for k in range(rec['data'].shape[2]):
rec['data'][i, j,
k, :] = task_smooth.smooth(rec['data'][i, j, k, :],
timoffset, windowlen,
windowtype)
casalog.post('Smoothing the visibilities in timerange: ' + timerange)
ms.close()
#do UV subtraction, according to timerange and spw
ms.open(outputvis, nomodify=False)
if not splitsel:
#outputvis is identical to input visibility, do the selection
if timerange and (type(timerange == str)):
ms.msselect({'time': timerange})
if spw and (type(spw) == str):
ms.selectinit(datadescid=int(spwid))
nchan = int(echan) - int(bchan) + 1
ms.selectchannel(nchan, int(bchan), 1, 1)
else:
#outputvis is splitted, selections have already applied, select all the data
ms.selectinit(datadescid=0)
orec = ms.getdata(['data', 'time', 'axis_info'], ifraxis=True)
b_rows = orec['data'].shape[2]
nchan = orec['data'].shape[1]
#szo=orec['data'].shape
print 'dimension of output data', orec['data'].shape
#orec['data']=orec['data'].reshape(szo[0],szo[1],szo[2],nspw,szo[3]/nspw,order='F')
#print 'reshaped rec1 ', orec['data'].shape
t_rows = orec['data'].shape[3]
casalog.post('Number of baselines: ' + str(b_rows))
casalog.post('Number of spectral channels: ' + str(nchan))
casalog.post('Number of time pixels: ' + str(t_rows))
casalog.post('Subtracting background defined in timerange: ' + timerange)
for i in range(t_rows):
orec['data'][:, :, :, i] -= rec['data'][:, :, :, i]
if reverse:
orec['data'][:, :, :, i] = -orec['data'][:, :, :, i]
del orec['time']
del orec['axis_info']
ms.putdata(orec)
ms.close()
def datestr_to_epoch(datestr):
"""
Given a UT date like "2010-May-01 00:00", returns an epoch measure.
"""
return me.epoch(rf='UTC', v0=qa.totime(datestr))
try:
# meanrad might already have been converted.
if type(retdict[hk]) != dict:
retdict[hk] = {'unit': headers[hk]['unit'],
'value': float(retdict[hk])}
except Exception, e:
print "Error converting header", hk, "to a Quantity."
print "retdict[hk] =", retdict[hk]
raise e
elif hk == 'GeoLong':
long_lat_alt = retdict[hk].split(',')
retdict['GeoLong'] = float(long_lat_alt[0])
retdict['GeoLat'] = float(long_lat_alt[1])
retdict['GeoDist'] = float(long_lat_alt[2])
elif hk == 'dMJD':
retdict[hk] = qa.convert(qa.totime(retdict[hk].replace('minutes', 'min')),
'd')['value']
elif hk == 'orb_per':
unit = 'h'
retrograde = False
if 'd' in retdict[hk].lower():
unit = 'd' # Actually this is most common.
if 'r' in retdict[hk].lower():
retrograde = True
value = get_num_from_str(retdict[hk], 'orbital period')
if value != False:
if retrograde and value > 0.0:
value = -value
retdict[hk] = {'unit': unit, 'value': value}
else:
del retdict[hk]
def subvs2(vis=None,
outputvis=None,
timerange='',
spw='',
mode=None,
subtime1=None,
subtime2=None,
smoothaxis=None,
smoothtype=None,
smoothwidth=None,
splitsel=None,
reverse=None,
overwrite=None):
"""Perform vector subtraction for visibilities
Keyword arguments:
vis -- Name of input visibility file (MS)
default: none; example: vis='ngc5921.ms'
outputvis -- Name of output uv-subtracted visibility file (MS)
default: none; example: outputvis='ngc5921_src.ms'
timerange -- Time range of performing the UV subtraction:
default='' means all times. examples:
timerange = 'YYYY/MM/DD/hh:mm:ss~YYYY/MM/DD/hh:mm:ss'
timerange = 'hh:mm:ss~hh:mm:ss'
spw -- Select spectral window/channel.
default = '' all the spectral channels. Example: spw='0:1~20'
mode -- operation mode
default 'linear'
mode = 'linear': use a linear fit for the background to be subtracted
mode = 'lowpass': act as a lowpass filter---smooth the data using different
smooth types and window sizes. Can be performed along either time
or frequency axis
mode = 'highpass': act as a highpass filter---smooth the data first, and
subtract the smoothed data from the original. Can be performed along
either time or frequency axis
mode = 'linear' expandable parameters:
subtime1 -- Time range 1 of the background to be subtracted from the data
default='' means all times. format:
timerange = 'YYYY/MM/DD/hh:mm:ss~YYYY/MM/DD/hh:mm:ss'
timerange = 'hh:mm:ss~hh:mm:ss'
subtime2 -- Time range 2 of the backgroud to be subtracted from the data
default='' means all times. examples:
timerange = 'YYYY/MM/DD/hh:mm:ss~YYYY/MM/DD/hh:mm:ss'
timerange = 'hh:mm:ss~hh:mm:ss'
mode = 'lowpass' or 'highpass' expandable parameters:
smoothaxis -- axis of smooth
Default: 'time'
smoothaxis = 'time': smooth is along the time axis
smoothaxis = 'freq': smooth is along the frequency axis
smoothtype -- type of the smooth depending on the convolving kernel
Default: 'flat'
smoothtype = 'flat': convolving kernel is a flat rectangle,
equivalent to a boxcar moving smooth
smoothtype = 'hanning': Hanning smooth kernel. See numpy.hanning
smoothtype = 'hamming': Hamming smooth kernel. See numpy.hamming
smoothtype = 'bartlett': Bartlett smooth kernel. See numpy.bartlett
smoothtype = 'blackman': Blackman smooth kernel. See numpy.blackman
smoothwidth -- width of the smooth kernel
Default: 5
Examples: smoothwidth=5, meaning the width is 5 pixels
splitsel -- True or False. default = False. If splitsel = False, then the entire input
measurement set is copied as the output measurement set (outputvis), with
background subtracted at selected timerange and spectral channels.
If splitsel = True,then only the selected timerange and spectral channels
are copied into the output measurement set (outputvis).
reverse -- True or False. default = False. If reverse = False, then the times indicated
by subtime1 and/or subtime2 are treated as background and subtracted; If reverse
= True, then reverse the sign of the background-subtracted data. The option can
be used for mapping absorptive structure.
overwrite -- True or False. default = False. If overwrite = True and
outputvis already exists, the selected subtime and spw in the
output measurment set will be replaced with background subtracted
visibilities
"""
# check the visbility ms
casalog.post('input parameters:')
casalog.post('vis: ' + vis)
casalog.post('outputvis: ' + outputvis)
casalog.post('smoothaxis: ' + smoothaxis)
casalog.post('smoothtype: ' + smoothtype)
casalog.post('smoothwidth: ' + str(smoothwidth))
if not outputvis or outputvis.isspace():
raise (ValueError, 'Please specify outputvis')
if os.path.exists(outputvis):
if overwrite:
print(
"The already existing output measurement set will be updated.")
else:
raise (ValueError,
"Output MS %s already exists - will not overwrite." %
outputvis)
else:
if not splitsel:
shutil.copytree(vis, outputvis)
else:
ms.open(vis, nomodify=True)
ms.split(outputvis, spw=spw, time=timerange, whichcol='DATA')
ms.close()
if timerange and (type(timerange) == str):
[btimeo, etimeo] = timerange.split('~')
btimeosec = qa.getvalue(qa.convert(qa.totime(btimeo), 's'))
etimeosec = qa.getvalue(qa.convert(qa.totime(etimeo), 's'))
timebinosec = etimeosec - btimeosec
if timebinosec < 0:
raise Exception(
'Negative timebin! Please check the "timerange" parameter.')
casalog.post('Selected timerange: ' + timerange +
' as the time for UV subtraction.')
else:
casalog.post(
'Output timerange not specified, using the entire timerange')
if spw and (type(spw) == str):
spwlist = spw.split(';')
else:
casalog.post('spw not specified, use all frequency channels')
# read the output data
datams = mstool()
datams.open(outputvis, nomodify=False)
datamsmd = msmdtool()
datamsmd.open(outputvis)
spwinfod = datams.getspectralwindowinfo()
spwinfok = spwinfod.keys()
spwinfok.sort(key=int)
spwinfol = [spwinfod[k] for k in spwinfok]
for s, spi in enumerate(spwinfol):
print('processing spectral window {}'.format(spi['SpectralWindowId']))
datams.selectinit(reset=True)
staql = {'time': '', 'spw': ''}
if not splitsel:
# outputvis is identical to input visibility, do the selection
if timerange and (type(timerange == str)):
staql['time'] = timerange
if spw and (type(spw) == str):
staql['spw'] = spwlist[s]
if not spw and not timerange:
# data selection is not made
print('selecting all spws and times')
staql['spw'] = str(spi['SpectralWindowId'])
else:
# outputvis is splitted, selections have already applied, select all the data
print('split the selected spws and times')
staql['spw'] = str(spi['SpectralWindowId'])
datams.msselect(staql)
orec = datams.getdata(['data', 'time', 'axis_info'], ifraxis=True)
npol, nchan, nbl, ntim = orec['data'].shape
print('dimension of output data', orec['data'].shape)
casalog.post('Number of baselines: ' + str(nbl))
casalog.post('Number of spectral channels: ' + str(nchan))
casalog.post('Number of time pixels: ' + str(ntim))
try:
if mode == 'linear':
# define and check the background time ranges
if subtime1 and (type(subtime1) == str):
[bsubtime1, esubtime1] = subtime1.split('~')
bsubtime1sec = qa.getvalue(
qa.convert(qa.totime(bsubtime1), 's'))
esubtime1sec = qa.getvalue(
qa.convert(qa.totime(esubtime1), 's'))
timebin1sec = esubtime1sec - bsubtime1sec
if timebin1sec < 0:
raise Exception(
'Negative timebin! Please check the "subtime1" parameter.'
)
casalog.post('Selected timerange 1: ' + subtime1 +
' as background for uv subtraction.')
else:
raise Exception(
'Please enter at least one timerange as the background'
)
if subtime2 and (type(subtime2) == str):
[bsubtime2, esubtime2] = subtime2.split('~')
bsubtime2sec = qa.getvalue(
qa.convert(qa.totime(bsubtime2), 's'))
esubtime2sec = qa.getvalue(
qa.convert(qa.totime(esubtime2), 's'))
timebin2sec = esubtime2sec - bsubtime2sec
if timebin2sec < 0:
raise Exception(
'Negative timebin! Please check the "subtime2" parameter.'
)
timebin2 = str(timebin2sec) + 's'
casalog.post('Selected timerange 2: ' + subtime2 +
' as background for uv subtraction.')
# plus 1s is to ensure averaging over the entire timerange
else:
casalog.post(
'Timerange 2 not selected, using only timerange 1 as background'
)
# Select the background indicated by subtime1
ms.open(vis, nomodify=True)
# Select the spw id
# ms.msselect({'time': subtime1})
staql0 = {'time': subtime1, 'spw': ''}
if spw and (type(spw) == str):
staql0['spw'] = spwlist[s]
else:
staql0['spw'] = staql['spw']
ms.msselect(staql0)
rec1 = ms.getdata(['data', 'time', 'axis_info'], ifraxis=True)
# print('shape of the frequency matrix ',rec1['axis_info']['freq_axis']['chan_freq'].shape)
sz1 = rec1['data'].shape
print('dimension of selected background 1', rec1['data'].shape)
# the data shape is (n_pol,n_channel,n_baseline,n_time), no need to reshape
# rec1['data']=rec1['data'].reshape(sz1[0],sz1[1],sz1[2],nspw,sz1[3]/nspw,order='F')
# print('reshaped rec1 ', rec1['data'].shape)
rec1avg = np.average(rec1['data'], axis=3)
casalog.post('Averaging the visibilities in subtime1: ' +
subtime1)
ms.close()
if subtime2 and (type(subtime2) == str):
ms.open(vis, nomodify=True)
# Select the spw id
staql0 = {'time': subtime2, 'spw': ''}
if spw and (type(spw) == str):
staql0['spw'] = spwlist[s]
else:
staql0['spw'] = staql['spw']
ms.msselect(staql0)
rec2 = ms.getdata(['data', 'time', 'axis_info'],
ifraxis=True)
sz2 = rec2['data'].shape
print('dimension of selected background 2',
rec2['data'].shape)
# rec2['data']=rec2['data'].reshape(sz2[0],sz2[1],sz2[2],nspw,sz2[3]/nspw,order='F')
# print('reshaped rec1 ', rec2['data'].shape)
rec2avg = np.average(rec2['data'], axis=3)
ms.close()
casalog.post('Averaged the visibilities in subtime2: ' +
subtime2)
if subtime1 and (not subtime2):
casalog.post(
'Only "subtime1" is defined, subtracting background defined in subtime1: '
+ subtime1)
t1 = (np.amax(rec1['time']) + np.amin(rec1['time'])) / 2.
print('t1: ',
qa.time(qa.quantity(t1, 's'), form='ymd', prec=10))
for i in range(ntim):
orec['data'][:, :, :, i] -= rec1avg
if reverse:
orec['data'][:, :, :,
i] = -orec['data'][:, :, :, i]
if subtime1 and subtime2 and (type(subtime2) == str):
casalog.post(
'Both subtime1 and subtime2 are specified, doing linear interpolation between "subtime1" and "subtime2"'
)
t1 = (np.amax(rec1['time']) + np.amin(rec1['time'])) / 2.
t2 = (np.amax(rec2['time']) + np.amin(rec2['time'])) / 2.
touts = orec['time']
print('t1: ',
qa.time(qa.quantity(t1, 's'), form='ymd', prec=10))
print('t2: ',
qa.time(qa.quantity(t2, 's'), form='ymd', prec=10))
for i in range(ntim):
tout = touts[i]
if tout > np.amax([t1, t2]):
tout = np.amax([t1, t2])
elif tout < np.amin([t1, t2]):
tout = np.amin([t1, t2])
orec['data'][:, :, :, i] -= (rec2avg - rec1avg) * (
tout - t1) / (t2 - t1) + rec1avg
if reverse:
orec['data'][:, :, :,
i] = -orec['data'][:, :, :, i]
elif mode == 'highpass':
if smoothtype != 'flat' and smoothtype != 'hanning' and smoothtype != 'hamming' and smoothtype != 'bartlett' and smoothtype != 'blackman':
raise Exception('Unknown smoothtype ' + str(smoothtype))
if smoothaxis == 'time':
if smoothwidth <= 0 or smoothwidth >= ntim:
raise Exception(
'Specified smooth width is <=0 or >= the total number of '
+ smoothaxis)
else:
for i in range(orec['data'].shape[0]):
for j in range(nchan):
for k in range(nbl):
orec['data'][i, j,
k, :] -= signalsmooth.smooth(
orec['data'][i, j, k, :],
smoothwidth, smoothtype)
if smoothaxis == 'freq':
if smoothwidth <= 0 or smoothwidth >= nchan:
raise Exception(
'Specified smooth width is <=0 or >= the total number of '
+ smoothaxis)
else:
for i in range(orec['data'].shape[0]):
for j in range(nbl):
for k in range(ntim):
orec['data'][i, :, j,
k] -= signalsmooth.smooth(
orec['data'][i, :, j, k],
smoothwidth, smoothtype)
elif mode == 'lowpass':
if smoothtype != 'flat' and smoothtype != 'hanning' and smoothtype != 'hamming' and smoothtype != 'bartlett' and smoothtype != 'blackman':
raise Exception('Unknown smoothtype ' + str(smoothtype))
if smoothaxis == 'time':
if smoothwidth <= 0 or smoothwidth >= ntim:
raise Exception(
'Specified smooth width is <=0 or >= the total number of '
+ smoothaxis)
else:
for i in range(orec['data'].shape[0]):
for j in range(nchan):
for k in range(nbl):
orec['data'][i, j,
k, :] = signalsmooth.smooth(
orec['data'][i, j, k, :],
smoothwidth, smoothtype)
if smoothaxis == 'freq':
if smoothwidth <= 0 or smoothwidth >= nchan:
raise Exception(
'Specified smooth width is <=0 or >= the total number of '
+ smoothaxis)
else:
for i in range(orec['data'].shape[0]):
for j in range(nbl):
for k in range(ntim):
orec['data'][i, :, j,
k] = signalsmooth.smooth(
orec['data'][i, :, j, k],
smoothwidth, smoothtype)
else:
raise Exception('Unknown mode' + str(mode))
except Exception as instance:
print('*** Error ***', instance)
# orec['data']=orec['data'].reshape(szo[0],szo[1],szo[2],szo[3],order='F')
# put the modified data back into the output visibility set
del orec['time']
del orec['axis_info']
# ms.open(outputvis,nomodify=False)
# if not splitsel:
# outputvis is identical to input visibility, do the selection
# if timerange and (type(timerange==str)):
# datams.msselect({'time':timerange})
# if spw and (type(spw)==str):
# datams.selectinit(datadescid=int(spwid))
# nchan=int(echan)-int(bchan)+1
# datams.selectchannel(nchan,int(bchan),1,1)
# if not spw and not timerange:
# data selection is not made
# datams.selectinit(datadescid=0)
# else:
# outputvis is splitted, selections have already applied, select all the data
# datams.selectinit(datadescid=0)
datams.putdata(orec)
datams.close()
datamsmd.done()
retdict[hk] = {
'unit': headers[hk]['unit'],
'value': float(retdict[hk])
}
except Exception, e:
print "Error converting header", hk, "to a Quantity."
print "retdict[hk] =", retdict[hk]
raise e
elif hk == 'GeoLong':
long_lat_alt = retdict[hk].split(',')
retdict['GeoLong'] = float(long_lat_alt[0])
retdict['GeoLat'] = float(long_lat_alt[1])
retdict['GeoDist'] = float(long_lat_alt[2])
elif hk == 'dMJD':
retdict[hk] = qa.convert(
qa.totime(retdict[hk].replace('minutes', 'min')),
'd')['value']
elif hk == 'orb_per':
unit = 'h'
retrograde = False
if 'd' in retdict[hk].lower():
unit = 'd' # Actually this is most common.
if 'r' in retdict[hk].lower():
retrograde = True
value = get_num_from_str(retdict[hk], 'orbital period')
if value != False:
if retrograde and value > 0.0:
value = -value
retdict[hk] = {'unit': unit, 'value': value}
else:
del retdict[hk]
def ephem_to_helio(vis=None,
ephem=None,
msinfo=None,
reftime=None,
polyfit=None,
usephacenter=False):
'''1. Take a solar ms database, read the scan and field information, find out the pointings (in RA and DEC)
2. Compare with the ephemeris of the solar disk center (in RA and DEC)
3. Generate VLA pointings in heliocentric coordinates
inputs:
msinfo: CASA MS information, output from read_msinfo
ephem: solar ephem, output from read_horizons
reftime: list of reference times (e.g., used for imaging)
CASA standard time format, either a single time (e.g., '2012/03/03/12:00:00'
or a time range (e.g., '2012/03/03/12:00:00~2012/03/03/13:00:00'. If the latter,
take the midpoint of the timerange for reference. If no date specified, take
the date of the first scan
polyfit: ONLY works for MS database with only one source with continously tracking;
not recommanded unless scan length is too long and want to have very high accuracy
usephacenter: Bool -- if True, correct for the RA and DEC in the ms file based on solar empheris.
Otherwise assume the phasecenter is correctly pointed to the solar disk center
(EOVSA case)
return value:
helio: a list of VLA pointing information
reftimestr: reference time, in FITS format string
reftime: reference time, in mjd format
ra: actual RA of phasecenter in the ms file at the reference time (interpolated)
dec: actual DEC of phasecenter in the ms file at the reference time (interpolated)
# CASA uses only RA and DEC of the closest field (e.g. in clean) #
ra_fld: right ascention of the CASA reference pointing direction
dec_fld: declination of the CASA reference pointing direction
raoff: RA offset of the phasecenter in the ms file to solar center
decoff: DEC offset of the phasecenter in the ms file to solar center
refx: heliocentric X offset of the phasecenter in the ms file to solar center
refy: heliocentric Y offset of the phasecenter in the ms file to solar center
######## Example #########
msfile='sun_C_20140910T221952-222952.10s.cal.ms'
ephemfile='horizons_sun_20140910.radecp'
ephem=vla_prep.read_horizons(ephemfile=ephemfile)
msinfo=vla_prep.read_msinfo(msfile=msfile)
polyfit=0
reftime = '22:25:20~22:25:40'
'''
if not vis or not os.path.exists(vis):
raise ValueError('Please provide information of the MS database!')
if not ephem:
ephem = read_horizons(vis=vis)
if not msinfo:
msinfo0 = read_msinfo(vis)
else:
if isinstance(msinfo, str):
try:
msinfo0 = np.load(msinfo)
except:
raise ValueError(
'The specified input msinfo file does not exist!')
elif isinstance(msinfo, dict):
msinfo0 = msinfo
else:
raise ValueError(
'msinfo should be either a numpy npz or a dictionary')
print('msinfo is derived from: {0:s}'.format(msinfo0['vis']))
scans = msinfo0['scans']
fieldids = msinfo0['fieldids']
btimes = msinfo0['btimes']
inttimes = msinfo0['inttimes']
ras = msinfo0['ras']
decs = msinfo0['decs']
if 'observatory' in msinfo0.keys():
if msinfo0['observatory'] == 'EOVSA':
usephacenter = False
if type(ras) is list:
ra_rads = [ra['value'] for ra in ras]
elif type(ras) is dict:
ra_rads = ras['value']
else:
print('Type of msinfo0["ras"] unrecognized.')
return 0
if type(decs) is list:
dec_rads = [dec['value'] for dec in decs]
elif type(decs) is dict:
dec_rads = decs['value']
else:
print('Type of msinfo0["decs"] unrecognized.')
# fit 2nd order polynomial fits to the RAs and DECs #
if polyfit:
cra = np.polyfit(btimes, ra_rads, 2)
cdec = np.polyfit(btimes, dec_rads, 2)
# find out phase center infomation in ms according to the input time or timerange #
if not reftime:
raise ValueError('Please specify a reference time of the image')
if type(reftime) == str:
reftime = [reftime]
if (not isinstance(reftime, list)):
print('input "reftime" is not a valid list. Abort...')
nreftime = len(reftime)
helio = []
for reftime0 in reftime:
helio0 = dict.fromkeys([
'reftimestr', 'reftime', 'ra', 'dec', 'ra_fld', 'dec_fld', 'raoff',
'decoff', 'refx', 'refy', 'p0'
])
helio0['reftimestr'] = reftime0
if '~' in reftime0:
# if reftime0 is specified as a timerange
try:
[tbg0, tend0] = reftime0.split('~')
tbg_d = qa.getvalue(qa.convert(qa.totime(tbg0), 'd'))[0]
tend_d = qa.getvalue(qa.convert(qa.totime(tend0), 'd'))[0]
tdur_s = (tend_d - tbg_d) * 3600. * 24.
# if no date is specified, add up the date of the first scan
if tend_d < 1.:
if tend_d >= tbg_d:
tend_d += int(btimes[0])
else:
tend_d += int(btimes[0]) + 1
if tbg_d < 1.:
tbg_d += int(btimes[0])
tref_d = (tbg_d + tend_d) / 2.
except:
print('Error in converting the input reftime: ' +
str(reftime0) + '. Aborting...')
else:
# if reftime0 is specified as a single value
try:
tref_d = qa.getvalue(qa.convert(qa.totime(reftime0), 'd'))[0]
# if no date is specified, add up the date of the first scan
if tref_d < 1.:
tref_d += int(btimes[0])
tbg_d = tref_d
# use the intergration time
# ind = bisect.bisect_left(btimes, tref_d)
# if msinfo0['etimes']:
# tdur_s = inttimes[ind - 1]
# else:
# tdur_s = np.mean(inttimes)
tdur_s = 1.
except:
print('Error in converting the input reftime: ' +
str(reftime0) + '. Aborting...')
helio0['reftime'] = tref_d
# helio0['date-obs'] = qa.time(qa.quantity(tbg_d, 'd'), form='fits', prec=10)[0]
# helio0['exptime'] = tdur_s
# find out phase center RA and DEC in the measurement set according to the reference time
# if polyfit, then use the 2nd order polynomial coeffs
inttime = np.nanmean(inttimes)
ind = bisect.bisect_left(btimes, tref_d)
if ind > 1:
dt = tref_d - btimes[ind - 1]
if ind < len(btimes):
scanlen = btimes[ind] - btimes[ind - 1]
(ra_b, ra_e) = (ra_rads[ind - 1], ra_rads[ind])
(dec_b, dec_e) = (dec_rads[ind - 1], dec_rads[ind])
if ind >= len(btimes):
scanlen = btimes[ind - 1] - btimes[ind - 2]
(ra_b, ra_e) = (ra_rads[ind - 2], ra_rads[ind - 1])
(dec_b, dec_e) = (dec_rads[ind - 2], dec_rads[ind - 1])
if ind == 1: # only one scan exists (e.g., imported from AIPS)
ra_b = ra_rads[ind - 1]
ra_e = ra_b
dec_b = dec_rads[ind - 1]
dec_e = dec_b
scanlen = 10. # radom value
dt = 0.
if ind < 1:
if np.abs((tref_d - btimes[0]) * 24 * 3600) < inttime / 2.0:
ind = 1
ra_b = ra_rads[ind - 1]
ra_e = ra_b
dec_b = dec_rads[ind - 1]
dec_e = dec_b
scanlen = 10. # radom value
dt = 0.
else:
raise ValueError(
'Reference time does not fall into the scan list!')
if polyfit:
ra = cra[0] * tref_d**2. + cra[1] * tref_d + cra[2]
dec = cdec[0] * tref_d**2. + cdec[1] * tref_d + cdec[2]
# if not, use linearly interpolated RA and DEC at the beginning of this scan and next scan
else:
ra = ra_b + (ra_e - ra_b) / scanlen * dt
dec = dec_b + (dec_e - dec_b) / scanlen * dt
if ra < 0:
ra += 2. * np.pi
if ra_b < 0:
ra_b += 2. * np.pi
# compare with ephemeris from JPL Horizons
time0 = ephem['time']
ra0 = ephem['ra']
dec0 = ephem['dec']
p0 = ephem['p0']
delta0 = ephem['delta']
if len(time0) > 1:
ind = bisect.bisect_left(time0, tref_d)
dt0 = time0[ind] - time0[ind - 1]
dt_ref = tref_d - time0[ind - 1]
dra0 = ra0[ind] - ra0[ind - 1]
ddec0 = dec0[ind] - dec0[ind - 1]
dp0 = p0[ind] - p0[ind - 1]
ddelta0 = delta0[ind] - delta0[ind - 1]
ra0 = ra0[ind - 1] + dra0 / dt0 * dt_ref
dec0 = dec0[ind - 1] + ddec0 / dt0 * dt_ref
p0 = p0[ind - 1] + dp0 / dt0 * dt_ref
delta0 = delta0[ind - 1] + ddelta0 / dt0 * dt_ref
else:
try:
ra0 = ra0[0]
dec0 = dec0[0]
p0 = p0[0]
delta0 = delta0[0]
except:
print("Error in retrieving info from ephemeris!")
if ra0 < 0:
ra0 += 2. * np.pi
# RA and DEC offset in arcseconds
decoff = degrees((dec - dec0)) * 3600.
raoff = degrees((ra - ra0) * cos(dec)) * 3600.
# Convert into heliocentric offsets
prad = -radians(p0)
refx = (-raoff) * cos(prad) - decoff * sin(prad)
refy = (-raoff) * sin(prad) + decoff * cos(prad)
helio0['ra'] = ra # ra of the actual pointing
helio0['dec'] = dec # dec of the actual pointing
helio0[
'ra_fld'] = ra_b # ra of the field, used as the reference in e.g., clean
helio0[
'dec_fld'] = dec_b # dec of the field, used as the refenrence in e.g., clean
helio0['raoff'] = raoff
helio0['decoff'] = decoff
if usephacenter:
helio0['refx'] = refx
helio0['refy'] = refy
else:
helio0['refx'] = 0.
helio0['refy'] = 0.
helio0['p0'] = p0
# helio['r_sun']=np.degrees(R_sun.value/(au.value*delta0))*3600. #in arcsecs
helio.append(helio0)
return helio
def imreg(vis=None,
ephem=None,
msinfo=None,
imagefile=None,
timerange=None,
reftime=None,
fitsfile=None,
beamfile=None,
offsetfile=None,
toTb=None,
sclfactor=1.0,
verbose=False,
p_ang=False,
overwrite=True,
usephacenter=True,
deletehistory=False,
subregion=[],
docompress=False):
'''
main routine to register CASA images
Required Inputs:
vis: STRING. CASA measurement set from which the image is derived
imagefile: STRING or LIST. name of the input CASA image
timerange: STRING or LIST. timerange used to generate the CASA image, must have the same length as the input images.
Each element should be in CASA standard time format, e.g., '2012/03/03/12:00:00~2012/03/03/13:00:00'
Optional Inputs:
msinfo: DICTIONARY. CASA MS information, output from read_msinfo. If not provided, generate one from the supplied vis
ephem: DICTIONARY. solar ephem, output from read_horizons.
If not provided, query JPL Horizons based on time info of the vis (internet connection required)
fitsfile: STRING or LIST. name of the output registered fits files
reftime: STRING or LIST. Each element should be in CASA standard time format, e.g., '2012/03/03/12:00:00'
offsetfile: optionally provide an offset with a series of solar x and y offsets with timestamps
toTb: Bool. Convert the default Jy/beam to brightness temperature?
sclfactor: scale the image values up by its value (to compensate VLA 20 dB attenuator)
verbose: Bool. Show more diagnostic info if True.
usephacenter: Bool -- if True, correct for the RA and DEC in the ms file based on solar empheris.
Otherwise assume the phasecenter is correctly pointed to the solar disk center
(EOVSA case)
subregion: Region selection. See 'help par.region' for details.
Usage:
>>> from suncasa.utils import helioimage2fits as hf
>>> hf.imreg(vis='mydata.ms', imagefile='myimage.image', fitsfile='myimage.fits',
timerange='2017/08/21/20:21:10~2017/08/21/20:21:18')
The output fits file is 'myimage.fits'
History:
BC (sometime in 2014): function was first wrote, followed by a number of edits by BC and SY
BC (2019-07-16): Added checks for stokes parameter. Verified that for converting from Jy/beam to brightness temperature,
the convention of 2*k_b*T should always be used. I.e., for unpolarized source, stokes I, RR, LL, XX, YY,
etc. in the output CASA images from (t)clean should all have same values of radio intensity
(in Jy/beam) and brightness temperature (in K).
'''
if deletehistory:
ms_clearhistory(vis)
if not imagefile:
raise ValueError('Please specify input image')
if not timerange:
raise ValueError('Please specify timerange of the input image')
if type(imagefile) == str:
imagefile = [imagefile]
if type(timerange) == str:
timerange = [timerange]
if not fitsfile:
fitsfile = [img + '.fits' for img in imagefile]
if type(fitsfile) == str:
fitsfile = [fitsfile]
nimg = len(imagefile)
if len(timerange) != nimg:
raise ValueError(
'Number of input images does not equal to number of timeranges!')
if len(fitsfile) != nimg:
raise ValueError(
'Number of input images does not equal to number of output fits files!'
)
nimg = len(imagefile)
if verbose:
print(str(nimg) + ' images to process...')
if reftime: # use as reference time to find solar disk RA and DEC to register the image, but not the actual timerange associated with the image
if type(reftime) == str:
reftime = [reftime] * nimg
if len(reftime) != nimg:
raise ValueError(
'Number of reference times does not match that of input images!'
)
helio = ephem_to_helio(vis,
ephem=ephem,
msinfo=msinfo,
reftime=reftime,
usephacenter=usephacenter)
else:
# use the supplied timerange to register the image
helio = ephem_to_helio(vis,
ephem=ephem,
msinfo=msinfo,
reftime=timerange,
usephacenter=usephacenter)
if toTb:
(bmajs, bmins, bpas, beamunits,
bpaunits) = getbeam(imagefile=imagefile, beamfile=beamfile)
for n, img in enumerate(imagefile):
if verbose:
print('processing image #' + str(n) + ' ' + img)
fitsf = fitsfile[n]
timeran = timerange[n]
# obtain duration of the image as FITS header exptime
try:
[tbg0, tend0] = timeran.split('~')
tbg_d = qa.getvalue(qa.convert(qa.totime(tbg0), 'd'))[0]
tend_d = qa.getvalue(qa.convert(qa.totime(tend0), 'd'))[0]
tdur_s = (tend_d - tbg_d) * 3600. * 24.
dateobs = qa.time(qa.quantity(tbg_d, 'd'), form='fits', prec=10)[0]
except:
print('Error in converting the input timerange: ' + str(timeran) +
'. Proceeding to the next image...')
continue
hel = helio[n]
if not os.path.exists(img):
warnings.warn('{} does not existed!'.format(img))
else:
if os.path.exists(fitsf) and not overwrite:
raise ValueError(
'Specified fits file already exists and overwrite is set to False. Aborting...'
)
else:
p0 = hel['p0']
tb.open(img + '/logtable', nomodify=False)
nobs = tb.nrows()
tb.removerows([i + 1 for i in range(nobs - 1)])
tb.close()
ia.open(img)
imr = ia.rotate(pa=str(-p0) + 'deg')
if subregion is not []:
imr = imr.subimage(region=subregion)
imr.tofits(fitsf, history=False, overwrite=overwrite)
imr.close()
imsum = ia.summary()
ia.close()
ia.done()
# construct the standard fits header
# RA and DEC of the reference pixel crpix1 and crpix2
(imra, imdec) = (imsum['refval'][0], imsum['refval'][1])
# find out the difference of the image center to the CASA phase center
# RA and DEC difference in arcseconds
ddec = degrees((imdec - hel['dec_fld'])) * 3600.
dra = degrees((imra - hel['ra_fld']) * cos(hel['dec_fld'])) * 3600.
# Convert into image heliocentric offsets
prad = -radians(hel['p0'])
dx = (-dra) * cos(prad) - ddec * sin(prad)
dy = (-dra) * sin(prad) + ddec * cos(prad)
if offsetfile:
try:
offset = np.load(offsetfile)
except:
raise ValueError(
'The specified offsetfile does not exist!')
reftimes_d = offset['reftimes_d']
xoffs = offset['xoffs']
yoffs = offset['yoffs']
timg_d = hel['reftime']
ind = bisect.bisect_left(reftimes_d, timg_d)
xoff = xoffs[ind - 1]
yoff = yoffs[ind - 1]
else:
xoff = hel['refx']
yoff = hel['refy']
if verbose:
print(
'offset of image phase center to visibility phase center (arcsec): dx={0:.2f}, dy={1:.2f}'
.format(dx, dy))
print(
'offset of visibility phase center to solar disk center (arcsec): dx={0:.2f}, dy={1:.2f}'
.format(xoff, yoff))
(crval1, crval2) = (xoff + dx, yoff + dy)
# update the fits header to heliocentric coordinates
hdu = pyfits.open(fitsf, mode='update')
hdu[0].verify('fix')
header = hdu[0].header
dshape = hdu[0].data.shape
ndim = hdu[0].data.ndim
(cdelt1,
cdelt2) = (-header['cdelt1'] * 3600., header['cdelt2'] * 3600.
) # Original CDELT1, 2 are for RA and DEC in degrees
header['cdelt1'] = cdelt1
header['cdelt2'] = cdelt2
header['cunit1'] = 'arcsec'
header['cunit2'] = 'arcsec'
header['crval1'] = crval1
header['crval2'] = crval2
header['ctype1'] = 'HPLN-TAN'
header['ctype2'] = 'HPLT-TAN'
header['date-obs'] = dateobs # begin time of the image
if not p_ang:
hel['p0'] = 0
try:
# this works for pyfits version of CASA 4.7.0 but not CASA 4.6.0
if tdur_s:
header.set('exptime', tdur_s)
else:
header.set('exptime', 1.)
header.set('p_angle', hel['p0'])
header.set('hgln_obs', 0.)
header.set('rsun_ref', sun.constants.radius.value)
if sunpyver <= 1:
header.set(
'dsun_obs',
sun.sunearth_distance(Time(dateobs)).to(u.meter).value)
header.set(
'rsun_obs',
sun.solar_semidiameter_angular_size(
Time(dateobs)).value)
header.set(
'hglt_obs',
sun.heliographic_solar_center(Time(dateobs))[1].value)
else:
header.set(
'dsun_obs',
sun.earth_distance(Time(dateobs)).to(u.meter).value)
header.set('rsun_obs',
sun.angular_radius(Time(dateobs)).value)
header.set('hglt_obs', sun.L0(Time(dateobs)).value)
except:
# this works for astropy.io.fits
if tdur_s:
header.append(('exptime', tdur_s))
else:
header.append(('exptime', 1.))
header.append(('p_angle', hel['p0']))
header.append(('hgln_obs', 0.))
header.append(('rsun_ref', sun.constants.radius.value))
if sunpyver <= 1:
header.append(
('dsun_obs', sun.sunearth_distance(Time(dateobs)).to(
u.meter).value))
header.append(('rsun_obs',
sun.solar_semidiameter_angular_size(
Time(dateobs)).value))
header.append(('hglt_obs',
sun.heliographic_solar_center(
Time(dateobs))[1].value))
else:
header.append(
('dsun_obs',
sun.earth_distance(Time(dateobs)).to(u.meter).value))
header.append(
('rsun_obs', sun.angular_radius(Time(dateobs)).value))
header.append(('hglt_obs', sun.L0(Time(dateobs)).value))
# check if stokes parameter exist
exist_stokes = False
stokes_mapper = {
'I': 1,
'Q': 2,
'U': 3,
'V': 4,
'RR': -1,
'LL': -2,
'RL': -3,
'LR': -4,
'XX': -5,
'YY': -6,
'XY': -7,
'YX': -8
}
if 'CRVAL3' in header.keys():
if header['CTYPE3'] == 'STOKES':
stokenum = header['CRVAL3']
exist_stokes = True
if 'CRVAL4' in header.keys():
if header['CTYPE4'] == 'STOKES':
stokenum = header['CRVAL4']
exist_stokes = True
if exist_stokes:
if stokenum in stokes_mapper.values():
stokesstr = list(stokes_mapper.keys())[list(
stokes_mapper.values()).index(stokenum)]
else:
print('Stokes parameter {0:d} not recognized'.format(
stokenum))
if verbose:
print('This image is in Stokes ' + stokesstr)
else:
print(
'STOKES Information does not seem to exist! Assuming Stokes I'
)
stokenum = 1
# intensity units to brightness temperature
if toTb:
# get restoring beam info
bmaj = bmajs[n]
bmin = bmins[n]
beamunit = beamunits[n]
data = hdu[
0].data # remember the data order is reversed due to the FITS convension
keys = list(header.keys())
values = list(header.values())
# which axis is frequency?
faxis = keys[values.index('FREQ')][-1]
faxis_ind = ndim - int(faxis)
# find out the polarization of this image
k_b = qa.constants('k')['value']
c_l = qa.constants('c')['value']
# Always use 2*kb for all polarizations
const = 2. * k_b / c_l**2
if header['BUNIT'].lower() == 'jy/beam':
header['BUNIT'] = 'K'
header['BTYPE'] = 'Brightness Temperature'
for i in range(dshape[faxis_ind]):
nu = header['CRVAL' +
faxis] + header['CDELT' + faxis] * (
i + 1 - header['CRPIX' + faxis])
if header['CUNIT' + faxis] == 'KHz':
nu *= 1e3
if header['CUNIT' + faxis] == 'MHz':
nu *= 1e6
if header['CUNIT' + faxis] == 'GHz':
nu *= 1e9
if len(bmaj) > 1: # multiple (per-plane) beams
bmajtmp = bmaj[i]
bmintmp = bmin[i]
else: # one single beam
bmajtmp = bmaj[0]
bmintmp = bmin[0]
if beamunit == 'arcsec':
bmaj0 = np.radians(bmajtmp / 3600.)
bmin0 = np.radians(bmintmp / 3600.)
if beamunit == 'arcmin':
bmaj0 = np.radians(bmajtmp / 60.)
bmin0 = np.radians(bmintmp / 60.)
if beamunit == 'deg':
bmaj0 = np.radians(bmajtmp)
bmin0 = np.radians(bmintmp)
if beamunit == 'rad':
bmaj0 = bmajtmp
bmin0 = bmintmp
beam_area = bmaj0 * bmin0 * np.pi / (4. * log(2.))
factor = const * nu**2 # SI unit
jy_to_si = 1e-26
# print(nu/1e9, beam_area, factor)
factor2 = sclfactor
# if sclfactor:
# factor2 = 100.
if faxis == '3':
data[:,
i, :, :] *= jy_to_si / beam_area / factor * factor2
if faxis == '4':
data[
i, :, :, :] *= jy_to_si / beam_area / factor * factor2
header = fu.headerfix(header)
hdu.flush()
hdu.close()
if ndim - np.count_nonzero(np.array(dshape) == 1) > 3:
docompress = False
'''
Caveat: only 1D, 2D, or 3D images are currently supported by
the astropy fits compression. If a n-dimensional image data array
does not have at least n-3 single-dimensional entries,
force docompress to be False
'''
print(
'warning: The fits data contains more than 3 non squeezable dimensions. Skipping fits compression..'
)
if docompress:
fitsftmp = fitsf + ".tmp.fits"
os.system("mv {} {}".format(fitsf, fitsftmp))
hdu = pyfits.open(fitsftmp)
hdu[0].verify('fix')
header = hdu[0].header
data = hdu[0].data
fu.write_compressed_image_fits(fitsf,
data,
header,
compression_type='RICE_1',
quantize_level=4.0)
os.system("rm -rf {}".format(fitsftmp))
if deletehistory:
ms_restorehistory(vis)
return fitsfile
def subvs(vis='',
outputvis='',
timerange='',
spw='',
timoffset=4,
windowlen=5,
windowtype='hamming',
splitsel=True,
reverse=False,
overwrite=False):
"""Perform vector subtraction for visibilities
Keyword arguments:
vis -- Name of input visibility file (MS)
default: none; example: vis='ngc5921.ms'
outputvis -- Name of output uv-subtracted visibility file (MS)
default: none; example: outputvis='ngc5921_src.ms'
timerange -- Time range of performing the UV subtraction:
default='' means all times. examples:
timerange = 'YYYY/MM/DD/hh:mm:ss~YYYY/MM/DD/hh:mm:ss'
timerange = 'hh:mm:ss~hh:mm:ss'
spw -- Select spectral window/channel.
windowlen -- Specify the width of window for smoothing
windowtype --The type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'
flat window will produce a moving average smoothing.
splitsel -- True or False. default = False. If splitsel = False, then the entire input
measurement set is copied as the output measurement set (outputvis), with
background subtracted at selected timerange and spectral channels.
If splitsel = True,then only the selected timerange and spectral channels
are copied into the output measurement set (outputvis).
reverse -- True or False. default = False. If reverse = False, then the times indicated
by subtime1 and/or subtime2 are treated as background and subtracted; If reverse
= True, then reverse the sign of the background-subtracted data. The option can
be used for mapping absorptive structure.
overwrite -- True or False. default = False. If overwrite = True and
outputvis already exists, the selected subtime and spw in the
output measurment set will be replaced with background subtracted
visibilities
"""
# Get the time and frequency axis of the input ms
# Open the ms and plot dynamic spectrum
print 'using window length: ', windowlen
print 'using window type: ', windowtype
ms.open(vis, nomodify=True)
# ms.selectinit(datadescid=0)
timfreq = ms.getdata(['time', 'axis_info'], ifraxis=True)
tim = timfreq['time']
# check timerange input; default: entire timerange
if timerange and (type(timerange) == str):
[btimeo, etimeo] = timerange.split('~')
btimeosec = qa.getvalue(qa.convert(qa.totime(btimeo), 's'))
etimeosec = qa.getvalue(qa.convert(qa.totime(etimeo), 's'))
timebinosec = etimeosec - btimeosec
if timebinosec < 0:
raise Exception, 'Negative timebin! Please check the "timerange" parameter.'
else:
casalog.post('Selected timerange: ' + timerange +
' as the time for UV subtraction.')
else:
casalog.post(
'Output timerange not specified, using the entire timerange')
timerange = str(qa.time(qa.quantity(
tim[0], 's'), prec=8)[0]) + '~' + str(
qa.time(qa.quantity(tim[-1], 's'), prec=8)[0])
print 'Output timerange not specified, using the entire timerange', timerange
# check spectral window input; default: entire channels of spectral window 0
if spw and (type(spw) == str):
[spwid, chanran] = spw.split(':')
[bchan, echan] = chanran.split('~')
nchan = int(echan) - int(bchan) + 1
else:
casalog.post('spw not specified, use all frequency channels')
freq = timfreq['axis_info']['freq_axis']['chan_freq'].flatten()
nchan = len(freq)
spwid = '0'
bchan = '0'
echan = str(nchan - 1)
print 'spw not specified, use all frequency channels', spwid + ':' + bchan + '~' + str(
nchan - 1)
ntimergn = len(timerange)
# To avoid memory error, split the channel into smaller segements for smoothing
cellstep = 2
chancell = int(nchan / cellstep)
l = range(nchan)
chunks = [l[x:x + cellstep] for x in xrange(0, len(l), cellstep)]
#spwrange='0:0~'+str(chancell)
ms.close()
if not (timoffset and (type(timoffset) == int)):
timoffset = int(4)
for i in range(len(chunks)):
spwrange = spwid + ':' + str(int(bchan) + min(chunks[i])) + '~' + str(
int(bchan) + max(chunks[i]))
print 'Subtracting visibility from spectral range: ', spwrange
result2 = task_subvs_lv1.subvs(vis, outputvis, timerange, spwrange,
timoffset, windowlen, windowtype,
splitsel, False, True)
