| 1 | from asap.scantable import scantable
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| 2 | from asap.parameters import rcParams
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| 3 | from asap.logging import asaplog, asaplog_post_dec
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| 4 | from asap.selector import selector
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| 5 | from asap.asapplotter import new_asaplot
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| 6 | from matplotlib import rc as rcp
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| 7 |
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| 8 | @asaplog_post_dec
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| 9 | def average_time(*args, **kwargs):
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| 10 | """
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| 11 | Return the (time) average of a scan or list of scans. [in channels only]
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| 12 | The cursor of the output scan is set to 0
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| 13 | Parameters:
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| 14 | one scan or comma separated scans or a list of scans
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| 15 | mask: an optional mask (only used for 'var' and 'tsys' weighting)
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| 16 | scanav: True averages each scan separately.
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| 17 | False (default) averages all scans together,
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| 18 | weight: Weighting scheme.
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| 19 | 'none' (mean no weight)
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| 20 | 'var' (1/var(spec) weighted)
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| 21 | 'tsys' (1/Tsys**2 weighted)
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| 22 | 'tint' (integration time weighted)
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| 23 | 'tintsys' (Tint/Tsys**2)
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| 24 | 'median' ( median averaging)
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| 25 | align: align the spectra in velocity before averaging. It takes
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| 26 | the time of the first spectrum in the first scantable
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| 27 | as reference time.
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| 28 | compel: True forces to average overwrapped IFs.
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| 29 | Example:
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| 30 | # return a time averaged scan from scana and scanb
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| 31 | # without using a mask
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| 32 | scanav = average_time(scana,scanb)
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| 33 | # or equivalent
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| 34 | # scanav = average_time([scana, scanb])
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| 35 | # return the (time) averaged scan, i.e. the average of
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| 36 | # all correlator cycles
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| 37 | scanav = average_time(scan, scanav=True)
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| 38 | """
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| 39 | scanav = False
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| 40 | if kwargs.has_key('scanav'):
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| 41 | scanav = kwargs.get('scanav')
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| 42 | weight = 'tint'
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| 43 | if kwargs.has_key('weight'):
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| 44 | weight = kwargs.get('weight')
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| 45 | mask = ()
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| 46 | if kwargs.has_key('mask'):
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| 47 | mask = kwargs.get('mask')
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| 48 | align = False
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| 49 | if kwargs.has_key('align'):
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| 50 | align = kwargs.get('align')
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| 51 | compel = False
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| 52 | if kwargs.has_key('compel'):
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| 53 | compel = kwargs.get('compel')
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| 54 | varlist = vars()
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| 55 | if isinstance(args[0],list):
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| 56 | lst = args[0]
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| 57 | elif isinstance(args[0],tuple):
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| 58 | lst = list(args[0])
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| 59 | else:
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| 60 | lst = list(args)
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| 61 |
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| 62 | del varlist["kwargs"]
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| 63 | varlist["args"] = "%d scantables" % len(lst)
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| 64 | # need special formatting here for history...
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| 65 |
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| 66 | from asap._asap import stmath
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| 67 | stm = stmath()
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| 68 | for s in lst:
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| 69 | if not isinstance(s,scantable):
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| 70 | msg = "Please give a list of scantables"
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| 71 | raise TypeError(msg)
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| 72 | if scanav: scanav = "SCAN"
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| 73 | else: scanav = "NONE"
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| 74 | alignedlst = []
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| 75 | if align:
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| 76 | refepoch = lst[0].get_time(0)
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| 77 | for scan in lst:
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| 78 | alignedlst.append(scan.freq_align(refepoch,insitu=False))
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| 79 | else:
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| 80 | alignedlst = lst
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| 81 | if weight.upper() == 'MEDIAN':
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| 82 | # median doesn't support list of scantables - merge first
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| 83 | merged = None
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| 84 | if len(alignedlst) > 1:
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| 85 | merged = merge(alignedlst)
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| 86 | else:
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| 87 | merged = alignedlst[0]
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| 88 | s = scantable(stm._averagechannel(merged, 'MEDIAN', scanav))
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| 89 | del merged
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| 90 | else:
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| 91 | #s = scantable(stm._average(alignedlst, mask, weight.upper(), scanav))
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| 92 | s = scantable(stm._new_average(alignedlst, compel, mask,
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| 93 | weight.upper(), scanav))
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| 94 | s._add_history("average_time",varlist)
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| 95 |
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| 96 | return s
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| 97 |
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| 98 | @asaplog_post_dec
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| 99 | def quotient(source, reference, preserve=True):
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| 100 | """
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| 101 | Return the quotient of a 'source' (signal) scan and a 'reference' scan.
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| 102 | The reference can have just one scan, even if the signal has many. Otherwise
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| 103 | they must have the same number of scans.
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| 104 | The cursor of the output scan is set to 0
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| 105 | Parameters:
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| 106 | source: the 'on' scan
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| 107 | reference: the 'off' scan
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| 108 | preserve: you can preserve (default) the continuum or
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| 109 | remove it. The equations used are
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| 110 | preserve: Output = Toff * (on/off) - Toff
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| 111 | remove: Output = Toff * (on/off) - Ton
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| 112 | """
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| 113 | varlist = vars()
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| 114 | from asap._asap import stmath
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| 115 | stm = stmath()
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| 116 | stm._setinsitu(False)
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| 117 | s = scantable(stm._quotient(source, reference, preserve))
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| 118 | s._add_history("quotient",varlist)
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| 119 | return s
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| 120 |
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| 121 | @asaplog_post_dec
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| 122 | def dototalpower(calon, caloff, tcalval=0.0):
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| 123 | """
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| 124 | Do calibration for CAL on,off signals.
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| 125 | Adopted from GBTIDL dototalpower
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| 126 | Parameters:
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| 127 | calon: the 'cal on' subintegration
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| 128 | caloff: the 'cal off' subintegration
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| 129 | tcalval: user supplied Tcal value
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| 130 | """
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| 131 | varlist = vars()
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| 132 | from asap._asap import stmath
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| 133 | stm = stmath()
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| 134 | stm._setinsitu(False)
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| 135 | s = scantable(stm._dototalpower(calon, caloff, tcalval))
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| 136 | s._add_history("dototalpower",varlist)
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| 137 | return s
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| 138 |
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| 139 | @asaplog_post_dec
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| 140 | def dosigref(sig, ref, smooth, tsysval=0.0, tauval=0.0):
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| 141 | """
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| 142 | Calculate a quotient (sig-ref/ref * Tsys)
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| 143 | Adopted from GBTIDL dosigref
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| 144 | Parameters:
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| 145 | sig: on source data
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| 146 | ref: reference data
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| 147 | smooth: width of box car smoothing for reference
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| 148 | tsysval: user specified Tsys (scalar only)
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| 149 | tauval: user specified Tau (required if tsysval is set)
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| 150 | """
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| 151 | varlist = vars()
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| 152 | from asap._asap import stmath
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| 153 | stm = stmath()
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| 154 | stm._setinsitu(False)
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| 155 | s = scantable(stm._dosigref(sig, ref, smooth, tsysval, tauval))
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| 156 | s._add_history("dosigref",varlist)
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| 157 | return s
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| 158 |
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| 159 | @asaplog_post_dec
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| 160 | def calps(scantab, scannos, smooth=1, tsysval=0.0, tauval=0.0, tcalval=0.0, verify=False):
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| 161 | """
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| 162 | Calibrate GBT position switched data
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| 163 | Adopted from GBTIDL getps
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| 164 | Currently calps identify the scans as position switched data if source
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| 165 | type enum is pson or psoff. The data must contains 'CAL' signal
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| 166 | on/off in each integration. To identify 'CAL' on state, the source type
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| 167 | enum of poncal and poffcal need to be present.
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| 168 |
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| 169 | Parameters:
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| 170 | scantab: scantable
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| 171 | scannos: list of scan numbers
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| 172 | smooth: optional box smoothing order for the reference
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| 173 | (default is 1 = no smoothing)
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| 174 | tsysval: optional user specified Tsys (default is 0.0,
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| 175 | use Tsys in the data)
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| 176 | tauval: optional user specified Tau
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| 177 | tcalval: optional user specified Tcal (default is 0.0,
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| 178 | use Tcal value in the data)
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| 179 | verify: Verify calibration if true
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| 180 | """
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| 181 | varlist = vars()
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| 182 | # check for the appropriate data
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| 183 | ## s = scantab.get_scan('*_ps*')
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| 184 | ## if s is None:
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| 185 | ## msg = "The input data appear to contain no position-switch mode data."
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| 186 | ## raise TypeError(msg)
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| 187 | s = scantab.copy()
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| 188 | from asap._asap import srctype
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| 189 | sel = selector()
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| 190 | sel.set_types( srctype.pson )
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| 191 | try:
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| 192 | scantab.set_selection( sel )
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| 193 | except Exception, e:
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| 194 | msg = "The input data appear to contain no position-switch mode data."
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| 195 | raise TypeError(msg)
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| 196 | s.set_selection()
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| 197 | sel.reset()
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| 198 | ssub = s.get_scan(scannos)
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| 199 | if ssub is None:
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| 200 | msg = "No data was found with given scan numbers!"
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| 201 | raise TypeError(msg)
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| 202 | #ssubon = ssub.get_scan('*calon')
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| 203 | #ssuboff = ssub.get_scan('*[^calon]')
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| 204 | sel.set_types( [srctype.poncal,srctype.poffcal] )
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| 205 | ssub.set_selection( sel )
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| 206 | ssubon = ssub.copy()
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| 207 | ssub.set_selection()
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| 208 | sel.reset()
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| 209 | sel.set_types( [srctype.pson,srctype.psoff] )
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| 210 | ssub.set_selection( sel )
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| 211 | ssuboff = ssub.copy()
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| 212 | ssub.set_selection()
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| 213 | sel.reset()
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| 214 | if ssubon.nrow() != ssuboff.nrow():
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| 215 | msg = "mismatch in numbers of CAL on/off scans. Cannot calibrate. Check the scan numbers."
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| 216 | raise TypeError(msg)
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| 217 | cals = dototalpower(ssubon, ssuboff, tcalval)
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| 218 | #sig = cals.get_scan('*ps')
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| 219 | #ref = cals.get_scan('*psr')
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| 220 | sel.set_types( srctype.pson )
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| 221 | cals.set_selection( sel )
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| 222 | sig = cals.copy()
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| 223 | cals.set_selection()
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| 224 | sel.reset()
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| 225 | sel.set_types( srctype.psoff )
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| 226 | cals.set_selection( sel )
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| 227 | ref = cals.copy()
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| 228 | cals.set_selection()
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| 229 | sel.reset()
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| 230 | if sig.nscan() != ref.nscan():
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| 231 | msg = "mismatch in numbers of on/off scans. Cannot calibrate. Check the scan numbers."
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| 232 | raise TypeError(msg)
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| 233 |
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| 234 | #for user supplied Tsys
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| 235 | if tsysval>0.0:
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| 236 | if tauval<=0.0:
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| 237 | msg = "Need to supply a valid tau to use the supplied Tsys"
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| 238 | raise TypeError(msg)
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| 239 | else:
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| 240 | sig.recalc_azel()
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| 241 | ref.recalc_azel()
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| 242 | #msg = "Use of user specified Tsys is not fully implemented yet."
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| 243 | #raise TypeError(msg)
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| 244 | # use get_elevation to get elevation and
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| 245 | # calculate a scaling factor using the formula
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| 246 | # -> tsys use to dosigref
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| 247 |
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| 248 | #ress = dosigref(sig, ref, smooth, tsysval)
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| 249 | ress = dosigref(sig, ref, smooth, tsysval, tauval)
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| 250 | ###
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| 251 | if verify:
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| 252 | # get data
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| 253 | import numpy
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| 254 | precal={}
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| 255 | postcal=[]
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| 256 | keys=['ps','ps_calon','psr','psr_calon']
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| 257 | types=[srctype.pson,srctype.poncal,srctype.psoff,srctype.poffcal]
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| 258 | ifnos=list(ssub.getifnos())
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| 259 | polnos=list(ssub.getpolnos())
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| 260 | sel=selector()
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| 261 | for i in range(2):
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| 262 | #ss=ssuboff.get_scan('*'+keys[2*i])
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| 263 | ll=[]
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| 264 | for j in range(len(ifnos)):
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| 265 | for k in range(len(polnos)):
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| 266 | sel.set_ifs(ifnos[j])
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| 267 | sel.set_polarizations(polnos[k])
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| 268 | sel.set_types(types[2*i])
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| 269 | try:
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| 270 | #ss.set_selection(sel)
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| 271 | ssuboff.set_selection(sel)
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| 272 | except:
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| 273 | continue
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| 274 | #ll.append(numpy.array(ss._getspectrum(0)))
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| 275 | ll.append(numpy.array(ssuboff._getspectrum(0)))
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| 276 | sel.reset()
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| 277 | ssuboff.set_selection()
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| 278 | precal[keys[2*i]]=ll
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| 279 | #del ss
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| 280 | #ss=ssubon.get_scan('*'+keys[2*i+1])
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| 281 | ll=[]
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| 282 | for j in range(len(ifnos)):
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| 283 | for k in range(len(polnos)):
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| 284 | sel.set_ifs(ifnos[j])
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| 285 | sel.set_polarizations(polnos[k])
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| 286 | sel.set_types(types[2*i+1])
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| 287 | try:
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| 288 | #ss.set_selection(sel)
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| 289 | ssubon.set_selection(sel)
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| 290 | except:
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| 291 | continue
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| 292 | #ll.append(numpy.array(ss._getspectrum(0)))
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| 293 | ll.append(numpy.array(ssubon._getspectrum(0)))
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| 294 | sel.reset()
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| 295 | ssubon.set_selection()
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| 296 | precal[keys[2*i+1]]=ll
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| 297 | #del ss
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| 298 | for j in range(len(ifnos)):
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| 299 | for k in range(len(polnos)):
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| 300 | sel.set_ifs(ifnos[j])
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| 301 | sel.set_polarizations(polnos[k])
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| 302 | try:
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| 303 | ress.set_selection(sel)
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| 304 | except:
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| 305 | continue
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| 306 | postcal.append(numpy.array(ress._getspectrum(0)))
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| 307 | sel.reset()
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| 308 | ress.set_selection()
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| 309 | del sel
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| 310 | # plot
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| 311 | asaplog.post()
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| 312 | asaplog.push('Plot only first spectrum for each [if,pol] pairs to verify calibration.')
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| 313 | asaplog.post('WARN')
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| 314 | p=new_asaplot()
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| 315 | rcp('lines', linewidth=1)
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| 316 | #nr=min(6,len(ifnos)*len(polnos))
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| 317 | nr=len(ifnos)*len(polnos)
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| 318 | titles=[]
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| 319 | btics=[]
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| 320 | if nr<4:
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| 321 | p.set_panels(rows=nr,cols=2,nplots=2*nr,ganged=False)
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| 322 | for i in range(2*nr):
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| 323 | b=False
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| 324 | if i >= 2*nr-2:
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| 325 | b=True
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| 326 | btics.append(b)
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| 327 | elif nr==4:
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| 328 | p.set_panels(rows=2,cols=4,nplots=8,ganged=False)
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| 329 | for i in range(2*nr):
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| 330 | b=False
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| 331 | if i >= 2*nr-4:
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| 332 | b=True
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| 333 | btics.append(b)
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| 334 | elif nr<7:
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| 335 | p.set_panels(rows=3,cols=4,nplots=2*nr,ganged=False)
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| 336 | for i in range(2*nr):
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| 337 | if i >= 2*nr-4:
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| 338 | b=True
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| 339 | btics.append(b)
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| 340 | else:
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| 341 | asaplog.post()
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| 342 | asaplog.push('Only first 6 [if,pol] pairs are plotted.')
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| 343 | asaplog.post('WARN')
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| 344 | nr=6
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| 345 | for i in range(2*nr):
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| 346 | b=False
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| 347 | if i >= 2*nr-4:
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| 348 | b=True
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| 349 | btics.append(b)
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| 350 | p.set_panels(rows=3,cols=4,nplots=2*nr,ganged=False)
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| 351 | for i in range(nr):
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| 352 | p.subplot(2*i)
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| 353 | p.color=0
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| 354 | title='raw data IF%s POL%s' % (ifnos[int(i/len(polnos))],polnos[i%len(polnos)])
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| 355 | titles.append(title)
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| 356 | #p.set_axes('title',title,fontsize=40)
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| 357 | ymin=1.0e100
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| 358 | ymax=-1.0e100
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| 359 | nchan=s.nchan(ifnos[int(i/len(polnos))])
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| 360 | edge=int(nchan*0.01)
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| 361 | for j in range(4):
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| 362 | spmin=min(precal[keys[j]][i][edge:nchan-edge])
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| 363 | spmax=max(precal[keys[j]][i][edge:nchan-edge])
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| 364 | ymin=min(ymin,spmin)
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| 365 | ymax=max(ymax,spmax)
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| 366 | for j in range(4):
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| 367 | if i==0:
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| 368 | p.set_line(label=keys[j])
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| 369 | else:
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| 370 | p.legend()
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| 371 | p.plot(precal[keys[j]][i])
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| 372 | p.axes.set_ylim(ymin-0.1*abs(ymin),ymax+0.1*abs(ymax))
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| 373 | if not btics[2*i]:
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| 374 | p.axes.set_xticks([])
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| 375 | p.subplot(2*i+1)
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| 376 | p.color=0
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| 377 | title='cal data IF%s POL%s' % (ifnos[int(i/len(polnos))],polnos[i%len(polnos)])
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| 378 | titles.append(title)
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| 379 | #p.set_axes('title',title)
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| 380 | p.legend()
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| 381 | ymin=postcal[i][edge:nchan-edge].min()
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| 382 | ymax=postcal[i][edge:nchan-edge].max()
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| 383 | p.plot(postcal[i])
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| 384 | p.axes.set_ylim(ymin-0.1*abs(ymin),ymax+0.1*abs(ymax))
|
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| 385 | if not btics[2*i+1]:
|
|---|
| 386 | p.axes.set_xticks([])
|
|---|
| 387 | for i in range(2*nr):
|
|---|
| 388 | p.subplot(i)
|
|---|
| 389 | p.set_axes('title',titles[i],fontsize='medium')
|
|---|
| 390 | x=raw_input('Accept calibration ([y]/n): ' )
|
|---|
| 391 | if x.upper() == 'N':
|
|---|
| 392 | p.quit()
|
|---|
| 393 | del p
|
|---|
| 394 | return scantab
|
|---|
| 395 | p.quit()
|
|---|
| 396 | del p
|
|---|
| 397 | ###
|
|---|
| 398 | ress._add_history("calps", varlist)
|
|---|
| 399 | return ress
|
|---|
| 400 |
|
|---|
| 401 | @asaplog_post_dec
|
|---|
| 402 | def calnod(scantab, scannos=[], smooth=1, tsysval=0.0, tauval=0.0, tcalval=0.0, verify=False):
|
|---|
| 403 | """
|
|---|
| 404 | Do full (but a pair of scans at time) processing of GBT Nod data
|
|---|
| 405 | calibration.
|
|---|
| 406 | Adopted from GBTIDL's getnod
|
|---|
| 407 | Parameters:
|
|---|
| 408 | scantab: scantable
|
|---|
| 409 | scannos: a pair of scan numbers, or the first scan number of the pair
|
|---|
| 410 | smooth: box car smoothing order
|
|---|
| 411 | tsysval: optional user specified Tsys value
|
|---|
| 412 | tauval: optional user specified tau value (not implemented yet)
|
|---|
| 413 | tcalval: optional user specified Tcal value
|
|---|
| 414 | verify: Verify calibration if true
|
|---|
| 415 | """
|
|---|
| 416 | varlist = vars()
|
|---|
| 417 | from asap._asap import stmath
|
|---|
| 418 | from asap._asap import srctype
|
|---|
| 419 | stm = stmath()
|
|---|
| 420 | stm._setinsitu(False)
|
|---|
| 421 |
|
|---|
| 422 | # check for the appropriate data
|
|---|
| 423 | ## s = scantab.get_scan('*_nod*')
|
|---|
| 424 | ## if s is None:
|
|---|
| 425 | ## msg = "The input data appear to contain no Nod observing mode data."
|
|---|
| 426 | ## raise TypeError(msg)
|
|---|
| 427 | s = scantab.copy()
|
|---|
| 428 | sel = selector()
|
|---|
| 429 | sel.set_types( srctype.nod )
|
|---|
| 430 | try:
|
|---|
| 431 | s.set_selection( sel )
|
|---|
| 432 | except Exception, e:
|
|---|
| 433 | msg = "The input data appear to contain no Nod observing mode data."
|
|---|
| 434 | raise TypeError(msg)
|
|---|
| 435 | sel.reset()
|
|---|
| 436 | del sel
|
|---|
| 437 | del s
|
|---|
| 438 |
|
|---|
| 439 | # need check correspondance of each beam with sig-ref ...
|
|---|
| 440 | # check for timestamps, scan numbers, subscan id (not available in
|
|---|
| 441 | # ASAP data format...). Assume 1st scan of the pair (beam 0 - sig
|
|---|
| 442 | # and beam 1 - ref...)
|
|---|
| 443 | # First scan number of paired scans or list of pairs of
|
|---|
| 444 | # scan numbers (has to have even number of pairs.)
|
|---|
| 445 |
|
|---|
| 446 | #data splitting
|
|---|
| 447 | scan1no = scan2no = 0
|
|---|
| 448 |
|
|---|
| 449 | if len(scannos)==1:
|
|---|
| 450 | scan1no = scannos[0]
|
|---|
| 451 | scan2no = scannos[0]+1
|
|---|
| 452 | pairScans = [scan1no, scan2no]
|
|---|
| 453 | else:
|
|---|
| 454 | #if len(scannos)>2:
|
|---|
| 455 | # msg = "calnod can only process a pair of nod scans at time."
|
|---|
| 456 | # raise TypeError(msg)
|
|---|
| 457 | #
|
|---|
| 458 | #if len(scannos)==2:
|
|---|
| 459 | # scan1no = scannos[0]
|
|---|
| 460 | # scan2no = scannos[1]
|
|---|
| 461 | pairScans = list(scannos)
|
|---|
| 462 |
|
|---|
| 463 | if tsysval>0.0:
|
|---|
| 464 | if tauval<=0.0:
|
|---|
| 465 | msg = "Need to supply a valid tau to use the supplied Tsys"
|
|---|
| 466 | raise TypeError(msg)
|
|---|
| 467 | else:
|
|---|
| 468 | scantab.recalc_azel()
|
|---|
| 469 | resspec = scantable(stm._donod(scantab, pairScans, smooth, tsysval,tauval,tcalval))
|
|---|
| 470 | ###
|
|---|
| 471 | if verify:
|
|---|
| 472 | # get data
|
|---|
| 473 | import numpy
|
|---|
| 474 | precal={}
|
|---|
| 475 | postcal=[]
|
|---|
| 476 | keys=['','_calon']
|
|---|
| 477 | types=[srctype.nod,srctype.nodcal]
|
|---|
| 478 | ifnos=list(scantab.getifnos())
|
|---|
| 479 | polnos=list(scantab.getpolnos())
|
|---|
| 480 | sel=selector()
|
|---|
| 481 | ss = scantab.copy()
|
|---|
| 482 | for i in range(2):
|
|---|
| 483 | #ss=scantab.get_scan('*'+keys[i])
|
|---|
| 484 | ll=[]
|
|---|
| 485 | ll2=[]
|
|---|
| 486 | for j in range(len(ifnos)):
|
|---|
| 487 | for k in range(len(polnos)):
|
|---|
| 488 | sel.set_ifs(ifnos[j])
|
|---|
| 489 | sel.set_polarizations(polnos[k])
|
|---|
| 490 | sel.set_scans(pairScans[0])
|
|---|
| 491 | sel.set_types(types[i])
|
|---|
| 492 | try:
|
|---|
| 493 | ss.set_selection(sel)
|
|---|
| 494 | except:
|
|---|
| 495 | continue
|
|---|
| 496 | ll.append(numpy.array(ss._getspectrum(0)))
|
|---|
| 497 | sel.reset()
|
|---|
| 498 | ss.set_selection()
|
|---|
| 499 | sel.set_ifs(ifnos[j])
|
|---|
| 500 | sel.set_polarizations(polnos[k])
|
|---|
| 501 | sel.set_scans(pairScans[1])
|
|---|
| 502 | sel.set_types(types[i])
|
|---|
| 503 | try:
|
|---|
| 504 | ss.set_selection(sel)
|
|---|
| 505 | except:
|
|---|
| 506 | ll.pop()
|
|---|
| 507 | continue
|
|---|
| 508 | ll2.append(numpy.array(ss._getspectrum(0)))
|
|---|
| 509 | sel.reset()
|
|---|
| 510 | ss.set_selection()
|
|---|
| 511 | key='%s%s' %(pairScans[0],keys[i])
|
|---|
| 512 | precal[key]=ll
|
|---|
| 513 | key='%s%s' %(pairScans[1],keys[i])
|
|---|
| 514 | precal[key]=ll2
|
|---|
| 515 | #del ss
|
|---|
| 516 | keys=precal.keys()
|
|---|
| 517 | for j in range(len(ifnos)):
|
|---|
| 518 | for k in range(len(polnos)):
|
|---|
| 519 | sel.set_ifs(ifnos[j])
|
|---|
| 520 | sel.set_polarizations(polnos[k])
|
|---|
| 521 | sel.set_scans(pairScans[0])
|
|---|
| 522 | try:
|
|---|
| 523 | resspec.set_selection(sel)
|
|---|
| 524 | except:
|
|---|
| 525 | continue
|
|---|
| 526 | postcal.append(numpy.array(resspec._getspectrum(0)))
|
|---|
| 527 | sel.reset()
|
|---|
| 528 | resspec.set_selection()
|
|---|
| 529 | del sel
|
|---|
| 530 | # plot
|
|---|
| 531 | asaplog.post()
|
|---|
| 532 | asaplog.push('Plot only first spectrum for each [if,pol] pairs to verify calibration.')
|
|---|
| 533 | asaplog.post('WARN')
|
|---|
| 534 | p=new_asaplot()
|
|---|
| 535 | rcp('lines', linewidth=1)
|
|---|
| 536 | #nr=min(6,len(ifnos)*len(polnos))
|
|---|
| 537 | nr=len(ifnos)*len(polnos)
|
|---|
| 538 | titles=[]
|
|---|
| 539 | btics=[]
|
|---|
| 540 | if nr<4:
|
|---|
| 541 | p.set_panels(rows=nr,cols=2,nplots=2*nr,ganged=False)
|
|---|
| 542 | for i in range(2*nr):
|
|---|
| 543 | b=False
|
|---|
| 544 | if i >= 2*nr-2:
|
|---|
| 545 | b=True
|
|---|
| 546 | btics.append(b)
|
|---|
| 547 | elif nr==4:
|
|---|
| 548 | p.set_panels(rows=2,cols=4,nplots=8,ganged=False)
|
|---|
| 549 | for i in range(2*nr):
|
|---|
| 550 | b=False
|
|---|
| 551 | if i >= 2*nr-4:
|
|---|
| 552 | b=True
|
|---|
| 553 | btics.append(b)
|
|---|
| 554 | elif nr<7:
|
|---|
| 555 | p.set_panels(rows=3,cols=4,nplots=2*nr,ganged=False)
|
|---|
| 556 | for i in range(2*nr):
|
|---|
| 557 | if i >= 2*nr-4:
|
|---|
| 558 | b=True
|
|---|
| 559 | btics.append(b)
|
|---|
| 560 | else:
|
|---|
| 561 | asaplog.post()
|
|---|
| 562 | asaplog.push('Only first 6 [if,pol] pairs are plotted.')
|
|---|
| 563 | asaplog.post('WARN')
|
|---|
| 564 | nr=6
|
|---|
| 565 | for i in range(2*nr):
|
|---|
| 566 | b=False
|
|---|
| 567 | if i >= 2*nr-4:
|
|---|
| 568 | b=True
|
|---|
| 569 | btics.append(b)
|
|---|
| 570 | p.set_panels(rows=3,cols=4,nplots=2*nr,ganged=False)
|
|---|
| 571 | for i in range(nr):
|
|---|
| 572 | p.subplot(2*i)
|
|---|
| 573 | p.color=0
|
|---|
| 574 | title='raw data IF%s POL%s' % (ifnos[int(i/len(polnos))],polnos[i%len(polnos)])
|
|---|
| 575 | titles.append(title)
|
|---|
| 576 | #p.set_axes('title',title,fontsize=40)
|
|---|
| 577 | ymin=1.0e100
|
|---|
| 578 | ymax=-1.0e100
|
|---|
| 579 | nchan=scantab.nchan(ifnos[int(i/len(polnos))])
|
|---|
| 580 | edge=int(nchan*0.01)
|
|---|
| 581 | for j in range(4):
|
|---|
| 582 | spmin=min(precal[keys[j]][i][edge:nchan-edge])
|
|---|
| 583 | spmax=max(precal[keys[j]][i][edge:nchan-edge])
|
|---|
| 584 | ymin=min(ymin,spmin)
|
|---|
| 585 | ymax=max(ymax,spmax)
|
|---|
| 586 | for j in range(4):
|
|---|
| 587 | if i==0:
|
|---|
| 588 | p.set_line(label=keys[j])
|
|---|
| 589 | else:
|
|---|
| 590 | p.legend()
|
|---|
| 591 | p.plot(precal[keys[j]][i])
|
|---|
| 592 | p.axes.set_ylim(ymin-0.1*abs(ymin),ymax+0.1*abs(ymax))
|
|---|
| 593 | if not btics[2*i]:
|
|---|
| 594 | p.axes.set_xticks([])
|
|---|
| 595 | p.subplot(2*i+1)
|
|---|
| 596 | p.color=0
|
|---|
| 597 | title='cal data IF%s POL%s' % (ifnos[int(i/len(polnos))],polnos[i%len(polnos)])
|
|---|
| 598 | titles.append(title)
|
|---|
| 599 | #p.set_axes('title',title)
|
|---|
| 600 | p.legend()
|
|---|
| 601 | ymin=postcal[i][edge:nchan-edge].min()
|
|---|
| 602 | ymax=postcal[i][edge:nchan-edge].max()
|
|---|
| 603 | p.plot(postcal[i])
|
|---|
| 604 | p.axes.set_ylim(ymin-0.1*abs(ymin),ymax+0.1*abs(ymax))
|
|---|
| 605 | if not btics[2*i+1]:
|
|---|
| 606 | p.axes.set_xticks([])
|
|---|
| 607 | for i in range(2*nr):
|
|---|
| 608 | p.subplot(i)
|
|---|
| 609 | p.set_axes('title',titles[i],fontsize='medium')
|
|---|
| 610 | x=raw_input('Accept calibration ([y]/n): ' )
|
|---|
| 611 | if x.upper() == 'N':
|
|---|
| 612 | p.quit()
|
|---|
| 613 | del p
|
|---|
| 614 | return scantab
|
|---|
| 615 | p.quit()
|
|---|
| 616 | del p
|
|---|
| 617 | ###
|
|---|
| 618 | resspec._add_history("calnod",varlist)
|
|---|
| 619 | return resspec
|
|---|
| 620 |
|
|---|
| 621 | @asaplog_post_dec
|
|---|
| 622 | def calfs(scantab, scannos=[], smooth=1, tsysval=0.0, tauval=0.0, tcalval=0.0, verify=False):
|
|---|
| 623 | """
|
|---|
| 624 | Calibrate GBT frequency switched data.
|
|---|
| 625 | Adopted from GBTIDL getfs.
|
|---|
| 626 | Currently calfs identify the scans as frequency switched data if source
|
|---|
| 627 | type enum is fson and fsoff. The data must contains 'CAL' signal
|
|---|
| 628 | on/off in each integration. To identify 'CAL' on state, the source type
|
|---|
| 629 | enum of foncal and foffcal need to be present.
|
|---|
| 630 |
|
|---|
| 631 | Parameters:
|
|---|
| 632 | scantab: scantable
|
|---|
| 633 | scannos: list of scan numbers
|
|---|
| 634 | smooth: optional box smoothing order for the reference
|
|---|
| 635 | (default is 1 = no smoothing)
|
|---|
| 636 | tsysval: optional user specified Tsys (default is 0.0,
|
|---|
| 637 | use Tsys in the data)
|
|---|
| 638 | tauval: optional user specified Tau
|
|---|
| 639 | verify: Verify calibration if true
|
|---|
| 640 | """
|
|---|
| 641 | varlist = vars()
|
|---|
| 642 | from asap._asap import stmath
|
|---|
| 643 | from asap._asap import srctype
|
|---|
| 644 | stm = stmath()
|
|---|
| 645 | stm._setinsitu(False)
|
|---|
| 646 |
|
|---|
| 647 | # check = scantab.get_scan('*_fs*')
|
|---|
| 648 | # if check is None:
|
|---|
| 649 | # msg = "The input data appear to contain no Nod observing mode data."
|
|---|
| 650 | # raise TypeError(msg)
|
|---|
| 651 | s = scantab.get_scan(scannos)
|
|---|
| 652 | del scantab
|
|---|
| 653 |
|
|---|
| 654 | resspec = scantable(stm._dofs(s, scannos, smooth, tsysval,tauval,tcalval))
|
|---|
| 655 | ###
|
|---|
| 656 | if verify:
|
|---|
| 657 | # get data
|
|---|
| 658 | ssub = s.get_scan(scannos)
|
|---|
| 659 | #ssubon = ssub.get_scan('*calon')
|
|---|
| 660 | #ssuboff = ssub.get_scan('*[^calon]')
|
|---|
| 661 | sel = selector()
|
|---|
| 662 | sel.set_types( [srctype.foncal,srctype.foffcal] )
|
|---|
| 663 | ssub.set_selection( sel )
|
|---|
| 664 | ssubon = ssub.copy()
|
|---|
| 665 | ssub.set_selection()
|
|---|
| 666 | sel.reset()
|
|---|
| 667 | sel.set_types( [srctype.fson,srctype.fsoff] )
|
|---|
| 668 | ssub.set_selection( sel )
|
|---|
| 669 | ssuboff = ssub.copy()
|
|---|
| 670 | ssub.set_selection()
|
|---|
| 671 | sel.reset()
|
|---|
| 672 | import numpy
|
|---|
| 673 | precal={}
|
|---|
| 674 | postcal=[]
|
|---|
| 675 | keys=['fs','fs_calon','fsr','fsr_calon']
|
|---|
| 676 | types=[srctype.fson,srctype.foncal,srctype.fsoff,srctype.foffcal]
|
|---|
| 677 | ifnos=list(ssub.getifnos())
|
|---|
| 678 | polnos=list(ssub.getpolnos())
|
|---|
| 679 | for i in range(2):
|
|---|
| 680 | #ss=ssuboff.get_scan('*'+keys[2*i])
|
|---|
| 681 | ll=[]
|
|---|
| 682 | for j in range(len(ifnos)):
|
|---|
| 683 | for k in range(len(polnos)):
|
|---|
| 684 | sel.set_ifs(ifnos[j])
|
|---|
| 685 | sel.set_polarizations(polnos[k])
|
|---|
| 686 | sel.set_types(types[2*i])
|
|---|
| 687 | try:
|
|---|
| 688 | #ss.set_selection(sel)
|
|---|
| 689 | ssuboff.set_selection(sel)
|
|---|
| 690 | except:
|
|---|
| 691 | continue
|
|---|
| 692 | ll.append(numpy.array(ss._getspectrum(0)))
|
|---|
| 693 | sel.reset()
|
|---|
| 694 | #ss.set_selection()
|
|---|
| 695 | ssuboff.set_selection()
|
|---|
| 696 | precal[keys[2*i]]=ll
|
|---|
| 697 | #del ss
|
|---|
| 698 | #ss=ssubon.get_scan('*'+keys[2*i+1])
|
|---|
| 699 | ll=[]
|
|---|
| 700 | for j in range(len(ifnos)):
|
|---|
| 701 | for k in range(len(polnos)):
|
|---|
| 702 | sel.set_ifs(ifnos[j])
|
|---|
| 703 | sel.set_polarizations(polnos[k])
|
|---|
| 704 | sel.set_types(types[2*i+1])
|
|---|
| 705 | try:
|
|---|
| 706 | #ss.set_selection(sel)
|
|---|
| 707 | ssubon.set_selection(sel)
|
|---|
| 708 | except:
|
|---|
| 709 | continue
|
|---|
| 710 | ll.append(numpy.array(ss._getspectrum(0)))
|
|---|
| 711 | sel.reset()
|
|---|
| 712 | #ss.set_selection()
|
|---|
| 713 | ssubon.set_selection()
|
|---|
| 714 | precal[keys[2*i+1]]=ll
|
|---|
| 715 | #del ss
|
|---|
| 716 | #sig=resspec.get_scan('*_fs')
|
|---|
| 717 | #ref=resspec.get_scan('*_fsr')
|
|---|
| 718 | sel.set_types( srctype.fson )
|
|---|
| 719 | resspec.set_selection( sel )
|
|---|
| 720 | sig=resspec.copy()
|
|---|
| 721 | resspec.set_selection()
|
|---|
| 722 | sel.reset()
|
|---|
| 723 | sel.set_type( srctype.fsoff )
|
|---|
| 724 | resspec.set_selection( sel )
|
|---|
| 725 | ref=resspec.copy()
|
|---|
| 726 | resspec.set_selection()
|
|---|
| 727 | sel.reset()
|
|---|
| 728 | for k in range(len(polnos)):
|
|---|
| 729 | for j in range(len(ifnos)):
|
|---|
| 730 | sel.set_ifs(ifnos[j])
|
|---|
| 731 | sel.set_polarizations(polnos[k])
|
|---|
| 732 | try:
|
|---|
| 733 | sig.set_selection(sel)
|
|---|
| 734 | postcal.append(numpy.array(sig._getspectrum(0)))
|
|---|
| 735 | except:
|
|---|
| 736 | ref.set_selection(sel)
|
|---|
| 737 | postcal.append(numpy.array(ref._getspectrum(0)))
|
|---|
| 738 | sel.reset()
|
|---|
| 739 | resspec.set_selection()
|
|---|
| 740 | del sel
|
|---|
| 741 | # plot
|
|---|
| 742 | asaplog.post()
|
|---|
| 743 | asaplog.push('Plot only first spectrum for each [if,pol] pairs to verify calibration.')
|
|---|
| 744 | asaplog.post('WARN')
|
|---|
| 745 | p=new_asaplot()
|
|---|
| 746 | rcp('lines', linewidth=1)
|
|---|
| 747 | #nr=min(6,len(ifnos)*len(polnos))
|
|---|
| 748 | nr=len(ifnos)/2*len(polnos)
|
|---|
| 749 | titles=[]
|
|---|
| 750 | btics=[]
|
|---|
| 751 | if nr>3:
|
|---|
| 752 | asaplog.post()
|
|---|
| 753 | asaplog.push('Only first 3 [if,pol] pairs are plotted.')
|
|---|
| 754 | asaplog.post('WARN')
|
|---|
| 755 | nr=3
|
|---|
| 756 | p.set_panels(rows=nr,cols=3,nplots=3*nr,ganged=False)
|
|---|
| 757 | for i in range(3*nr):
|
|---|
| 758 | b=False
|
|---|
| 759 | if i >= 3*nr-3:
|
|---|
| 760 | b=True
|
|---|
| 761 | btics.append(b)
|
|---|
| 762 | for i in range(nr):
|
|---|
| 763 | p.subplot(3*i)
|
|---|
| 764 | p.color=0
|
|---|
| 765 | title='raw data IF%s,%s POL%s' % (ifnos[2*int(i/len(polnos))],ifnos[2*int(i/len(polnos))+1],polnos[i%len(polnos)])
|
|---|
| 766 | titles.append(title)
|
|---|
| 767 | #p.set_axes('title',title,fontsize=40)
|
|---|
| 768 | ymin=1.0e100
|
|---|
| 769 | ymax=-1.0e100
|
|---|
| 770 | nchan=s.nchan(ifnos[2*int(i/len(polnos))])
|
|---|
| 771 | edge=int(nchan*0.01)
|
|---|
| 772 | for j in range(4):
|
|---|
| 773 | spmin=min(precal[keys[j]][i][edge:nchan-edge])
|
|---|
| 774 | spmax=max(precal[keys[j]][i][edge:nchan-edge])
|
|---|
| 775 | ymin=min(ymin,spmin)
|
|---|
| 776 | ymax=max(ymax,spmax)
|
|---|
| 777 | for j in range(4):
|
|---|
| 778 | if i==0:
|
|---|
| 779 | p.set_line(label=keys[j])
|
|---|
| 780 | else:
|
|---|
| 781 | p.legend()
|
|---|
| 782 | p.plot(precal[keys[j]][i])
|
|---|
| 783 | p.axes.set_ylim(ymin-0.1*abs(ymin),ymax+0.1*abs(ymax))
|
|---|
| 784 | if not btics[3*i]:
|
|---|
| 785 | p.axes.set_xticks([])
|
|---|
| 786 | p.subplot(3*i+1)
|
|---|
| 787 | p.color=0
|
|---|
| 788 | title='sig data IF%s POL%s' % (ifnos[2*int(i/len(polnos))],polnos[i%len(polnos)])
|
|---|
| 789 | titles.append(title)
|
|---|
| 790 | #p.set_axes('title',title)
|
|---|
| 791 | p.legend()
|
|---|
| 792 | ymin=postcal[2*i][edge:nchan-edge].min()
|
|---|
| 793 | ymax=postcal[2*i][edge:nchan-edge].max()
|
|---|
| 794 | p.plot(postcal[2*i])
|
|---|
| 795 | p.axes.set_ylim(ymin-0.1*abs(ymin),ymax+0.1*abs(ymax))
|
|---|
| 796 | if not btics[3*i+1]:
|
|---|
| 797 | p.axes.set_xticks([])
|
|---|
| 798 | p.subplot(3*i+2)
|
|---|
| 799 | p.color=0
|
|---|
| 800 | title='ref data IF%s POL%s' % (ifnos[2*int(i/len(polnos))+1],polnos[i%len(polnos)])
|
|---|
| 801 | titles.append(title)
|
|---|
| 802 | #p.set_axes('title',title)
|
|---|
| 803 | p.legend()
|
|---|
| 804 | ymin=postcal[2*i+1][edge:nchan-edge].min()
|
|---|
| 805 | ymax=postcal[2*i+1][edge:nchan-edge].max()
|
|---|
| 806 | p.plot(postcal[2*i+1])
|
|---|
| 807 | p.axes.set_ylim(ymin-0.1*abs(ymin),ymax+0.1*abs(ymax))
|
|---|
| 808 | if not btics[3*i+2]:
|
|---|
| 809 | p.axes.set_xticks([])
|
|---|
| 810 | for i in range(3*nr):
|
|---|
| 811 | p.subplot(i)
|
|---|
| 812 | p.set_axes('title',titles[i],fontsize='medium')
|
|---|
| 813 | x=raw_input('Accept calibration ([y]/n): ' )
|
|---|
| 814 | if x.upper() == 'N':
|
|---|
| 815 | p.quit()
|
|---|
| 816 | del p
|
|---|
| 817 | return scantab
|
|---|
| 818 | p.quit()
|
|---|
| 819 | del p
|
|---|
| 820 | ###
|
|---|
| 821 | resspec._add_history("calfs",varlist)
|
|---|
| 822 | return resspec
|
|---|
| 823 |
|
|---|
| 824 | @asaplog_post_dec
|
|---|
| 825 | def merge(*args, **kwargs):
|
|---|
| 826 | """
|
|---|
| 827 | Merge a list of scanatables, or comma-sperated scantables into one
|
|---|
| 828 | scnatble.
|
|---|
| 829 | Parameters:
|
|---|
| 830 | A list [scan1, scan2] or scan1, scan2.
|
|---|
| 831 | freq_tol: frequency tolerance for merging IFs. numeric values
|
|---|
| 832 | in units of Hz (1.0e6 -> 1MHz) and string ('1MHz')
|
|---|
| 833 | is allowed.
|
|---|
| 834 | Example:
|
|---|
| 835 | myscans = [scan1, scan2]
|
|---|
| 836 | allscans = merge(myscans)
|
|---|
| 837 | # or equivalent
|
|---|
| 838 | sameallscans = merge(scan1, scan2)
|
|---|
| 839 | # with freqtol
|
|---|
| 840 | allscans = merge(scan1, scan2, freq_tol=1.0e6)
|
|---|
| 841 | # or equivalently
|
|---|
| 842 | allscans = merge(scan1, scan2, freq_tol='1MHz')
|
|---|
| 843 | """
|
|---|
| 844 | varlist = vars()
|
|---|
| 845 | if isinstance(args[0],list):
|
|---|
| 846 | lst = tuple(args[0])
|
|---|
| 847 | elif isinstance(args[0],tuple):
|
|---|
| 848 | lst = args[0]
|
|---|
| 849 | else:
|
|---|
| 850 | lst = tuple(args)
|
|---|
| 851 | if kwargs.has_key('freq_tol'):
|
|---|
| 852 | freq_tol = str(kwargs['freq_tol'])
|
|---|
| 853 | else:
|
|---|
| 854 | freq_tol = ''
|
|---|
| 855 | varlist["args"] = "%d scantables" % len(lst)
|
|---|
| 856 | # need special formatting her for history...
|
|---|
| 857 | from asap._asap import stmath
|
|---|
| 858 | stm = stmath()
|
|---|
| 859 | for s in lst:
|
|---|
| 860 | if not isinstance(s,scantable):
|
|---|
| 861 | msg = "Please give a list of scantables"
|
|---|
| 862 | raise TypeError(msg)
|
|---|
| 863 | s = scantable(stm._merge(lst, freq_tol))
|
|---|
| 864 | s._add_history("merge", varlist)
|
|---|
| 865 | return s
|
|---|
| 866 |
|
|---|
| 867 | @asaplog_post_dec
|
|---|
| 868 | def calibrate( scantab, scannos=[], calmode='none', verify=None ):
|
|---|
| 869 | """
|
|---|
| 870 | Calibrate data.
|
|---|
| 871 |
|
|---|
| 872 | Parameters:
|
|---|
| 873 | scantab: scantable
|
|---|
| 874 | scannos: list of scan number
|
|---|
| 875 | calmode: calibration mode
|
|---|
| 876 | verify: verify calibration
|
|---|
| 877 | """
|
|---|
| 878 | import re
|
|---|
| 879 | antname = scantab.get_antennaname()
|
|---|
| 880 | if ( calmode == 'nod' ):
|
|---|
| 881 | asaplog.push( 'Calibrating nod data.' )
|
|---|
| 882 | scal = calnod( scantab, scannos=scannos, verify=verify )
|
|---|
| 883 | elif ( calmode == 'quotient' ):
|
|---|
| 884 | asaplog.push( 'Calibrating using quotient.' )
|
|---|
| 885 | scal = scantab.auto_quotient( verify=verify )
|
|---|
| 886 | elif ( calmode == 'ps' ):
|
|---|
| 887 | asaplog.push( 'Calibrating %s position-switched data.' % antname )
|
|---|
| 888 | if ( antname.find( 'APEX' ) != -1 ):
|
|---|
| 889 | scal = apexcal( scantab, scannos, calmode, verify )
|
|---|
| 890 | elif ( antname.find( 'ALMA' ) != -1 or antname.find( 'OSF' ) != -1
|
|---|
| 891 | or re.match('DV[0-9][0-9]$',antname) is not None
|
|---|
| 892 | or re.match('PM[0-9][0-9]$',antname) is not None
|
|---|
| 893 | or re.match('CM[0-9][0-9]$',antname) is not None
|
|---|
| 894 | or re.match('DA[0-9][0-9]$',antname) is not None ):
|
|---|
| 895 | scal = almacal( scantab, scannos, calmode, verify )
|
|---|
| 896 | else:
|
|---|
| 897 | scal = calps( scantab, scannos=scannos, verify=verify )
|
|---|
| 898 | elif ( calmode == 'fs' or calmode == 'fsotf' ):
|
|---|
| 899 | asaplog.push( 'Calibrating %s frequency-switched data.' % antname )
|
|---|
| 900 | if ( antname.find( 'APEX' ) != -1 ):
|
|---|
| 901 | scal = apexcal( scantab, scannos, calmode, verify )
|
|---|
| 902 | elif ( antname.find( 'ALMA' ) != -1 or antname.find( 'OSF' ) != -1 ):
|
|---|
| 903 | scal = almacal( scantab, scannos, calmode, verify )
|
|---|
| 904 | else:
|
|---|
| 905 | scal = calfs( scantab, scannos=scannos, verify=verify )
|
|---|
| 906 | elif ( calmode == 'otf' ):
|
|---|
| 907 | asaplog.push( 'Calibrating %s On-The-Fly data.' % antname )
|
|---|
| 908 | scal = almacal( scantab, scannos, calmode, verify )
|
|---|
| 909 | else:
|
|---|
| 910 | asaplog.push( 'No calibration.' )
|
|---|
| 911 | scal = scantab.copy()
|
|---|
| 912 |
|
|---|
| 913 | return scal
|
|---|
| 914 |
|
|---|
| 915 | def apexcal( scantab, scannos=[], calmode='none', verify=False ):
|
|---|
| 916 | """
|
|---|
| 917 | Calibrate APEX data
|
|---|
| 918 |
|
|---|
| 919 | Parameters:
|
|---|
| 920 | scantab: scantable
|
|---|
| 921 | scannos: list of scan number
|
|---|
| 922 | calmode: calibration mode
|
|---|
| 923 |
|
|---|
| 924 | verify: verify calibration
|
|---|
| 925 | """
|
|---|
| 926 | from asap._asap import stmath
|
|---|
| 927 | stm = stmath()
|
|---|
| 928 | antname = scantab.get_antennaname()
|
|---|
| 929 | selection=selector()
|
|---|
| 930 | selection.set_scans(scannos)
|
|---|
| 931 | orig = scantab.get_selection()
|
|---|
| 932 | scantab.set_selection(orig+selection)
|
|---|
| 933 | ## ssub = scantab.get_scan( scannos )
|
|---|
| 934 | ## scal = scantable( stm.cwcal( ssub, calmode, antname ) )
|
|---|
| 935 | scal = scantable( stm.cwcal( scantab, calmode, antname ) )
|
|---|
| 936 | scantab.set_selection(orig)
|
|---|
| 937 | return scal
|
|---|
| 938 |
|
|---|
| 939 | def almacal( scantab, scannos=[], calmode='none', verify=False ):
|
|---|
| 940 | """
|
|---|
| 941 | Calibrate ALMA data
|
|---|
| 942 |
|
|---|
| 943 | Parameters:
|
|---|
| 944 | scantab: scantable
|
|---|
| 945 | scannos: list of scan number
|
|---|
| 946 | calmode: calibration mode
|
|---|
| 947 |
|
|---|
| 948 | verify: verify calibration
|
|---|
| 949 | """
|
|---|
| 950 | from asap._asap import stmath
|
|---|
| 951 | stm = stmath()
|
|---|
| 952 | selection=selector()
|
|---|
| 953 | selection.set_scans(scannos)
|
|---|
| 954 | orig = scantab.get_selection()
|
|---|
| 955 | scantab.set_selection(orig+selection)
|
|---|
| 956 | ## ssub = scantab.get_scan( scannos )
|
|---|
| 957 | ## scal = scantable( stm.almacal( ssub, calmode ) )
|
|---|
| 958 | scal = scantable( stm.almacal( scantab, calmode ) )
|
|---|
| 959 | scantab.set_selection(orig)
|
|---|
| 960 | return scal
|
|---|
| 961 |
|
|---|
| 962 | @asaplog_post_dec
|
|---|
| 963 | def splitant(filename, outprefix='',overwrite=False, getpt=True):
|
|---|
| 964 | """
|
|---|
| 965 | Split Measurement set by antenna name, save data as a scantables,
|
|---|
| 966 | and return a list of filename. Note that frequency reference frame
|
|---|
| 967 | is imported as it is in Measurement set.
|
|---|
| 968 | Notice this method can only be available from CASA.
|
|---|
| 969 | Prameter
|
|---|
| 970 | filename: the name of Measurement set to be read.
|
|---|
| 971 | outprefix: the prefix of output scantable name.
|
|---|
| 972 | the names of output scantable will be
|
|---|
| 973 | outprefix.antenna1, outprefix.antenna2, ....
|
|---|
| 974 | If not specified, outprefix = filename is assumed.
|
|---|
| 975 | overwrite If the file should be overwritten if it exists.
|
|---|
| 976 | The default False is to return with warning
|
|---|
| 977 | without writing the output. USE WITH CARE.
|
|---|
| 978 | getpt Whether to import direction from MS/POINTING
|
|---|
| 979 | table or not. Default is True (import direction).
|
|---|
| 980 | """
|
|---|
| 981 | # Import the table toolkit from CASA
|
|---|
| 982 | from taskinit import gentools
|
|---|
| 983 | from asap.scantable import is_ms
|
|---|
| 984 | tb = gentools(['tb'])[0]
|
|---|
| 985 | # Check the input filename
|
|---|
| 986 | if isinstance(filename, str):
|
|---|
| 987 | import os.path
|
|---|
| 988 | filename = os.path.expandvars(filename)
|
|---|
| 989 | filename = os.path.expanduser(filename)
|
|---|
| 990 | if not os.path.exists(filename):
|
|---|
| 991 | s = "File '%s' not found." % (filename)
|
|---|
| 992 | raise IOError(s)
|
|---|
| 993 | # check if input file is MS
|
|---|
| 994 | if not is_ms(filename):
|
|---|
| 995 | s = "File '%s' is not a Measurement set." % (filename)
|
|---|
| 996 | raise IOError(s)
|
|---|
| 997 | else:
|
|---|
| 998 | s = "The filename should be string. "
|
|---|
| 999 | raise TypeError(s)
|
|---|
| 1000 | # Check out put file name
|
|---|
| 1001 | outname=''
|
|---|
| 1002 | if len(outprefix) > 0: prefix=outprefix+'.'
|
|---|
| 1003 | else:
|
|---|
| 1004 | prefix=filename.rstrip('/')
|
|---|
| 1005 | # Now do the actual splitting.
|
|---|
| 1006 | outfiles=[]
|
|---|
| 1007 | tb.open(tablename=filename,nomodify=True)
|
|---|
| 1008 | ant1=tb.getcol('ANTENNA1',0,-1,1)
|
|---|
| 1009 | anttab=tb.getkeyword('ANTENNA').lstrip('Table: ')
|
|---|
| 1010 | tb.close()
|
|---|
| 1011 | tb.open(tablename=anttab,nomodify=True)
|
|---|
| 1012 | nant=tb.nrows()
|
|---|
| 1013 | antnames=tb.getcol('NAME',0,nant,1)
|
|---|
| 1014 | tb.close()
|
|---|
| 1015 | for antid in set(ant1):
|
|---|
| 1016 | scan=scantable(filename,average=False,antenna=int(antid),getpt=getpt)
|
|---|
| 1017 | outname=prefix+antnames[antid]+'.asap'
|
|---|
| 1018 | scan.save(outname,format='ASAP',overwrite=overwrite)
|
|---|
| 1019 | del scan
|
|---|
| 1020 | outfiles.append(outname)
|
|---|
| 1021 | return outfiles
|
|---|
| 1022 |
|
|---|
| 1023 | @asaplog_post_dec
|
|---|
| 1024 | def _array2dOp( scan, value, mode="ADD", tsys=False, insitu=None):
|
|---|
| 1025 | """
|
|---|
| 1026 | This function is workaround on the basic operation of scantable
|
|---|
| 1027 | with 2 dimensional float list.
|
|---|
| 1028 |
|
|---|
| 1029 | scan: scantable operand
|
|---|
| 1030 | value: float list operand
|
|---|
| 1031 | mode: operation mode (ADD, SUB, MUL, DIV)
|
|---|
| 1032 | tsys: if True, operate tsys as well
|
|---|
| 1033 | insitu: if False, a new scantable is returned.
|
|---|
| 1034 | Otherwise, the array operation is done in-sitsu.
|
|---|
| 1035 | """
|
|---|
| 1036 | if insitu is None: insitu = rcParams['insitu']
|
|---|
| 1037 | nrow = scan.nrow()
|
|---|
| 1038 | s = None
|
|---|
| 1039 | from asap._asap import stmath
|
|---|
| 1040 | stm = stmath()
|
|---|
| 1041 | stm._setinsitu(insitu)
|
|---|
| 1042 | if len( value ) == 1:
|
|---|
| 1043 | s = scantable( stm._arrayop( scan, value[0], mode, tsys ) )
|
|---|
| 1044 | elif len( value ) != nrow:
|
|---|
| 1045 | raise ValueError( 'len(value) must be 1 or conform to scan.nrow()' )
|
|---|
| 1046 | else:
|
|---|
| 1047 | from asap._asap import stmath
|
|---|
| 1048 | if not insitu:
|
|---|
| 1049 | s = scan.copy()
|
|---|
| 1050 | else:
|
|---|
| 1051 | s = scan
|
|---|
| 1052 | # insitu must be True as we go row by row on the same data
|
|---|
| 1053 | stm._setinsitu( True )
|
|---|
| 1054 | basesel = s.get_selection()
|
|---|
| 1055 | # generate a new selector object based on basesel
|
|---|
| 1056 | sel = selector(basesel)
|
|---|
| 1057 | for irow in range( nrow ):
|
|---|
| 1058 | sel.set_rows( irow )
|
|---|
| 1059 | s.set_selection( sel )
|
|---|
| 1060 | if len( value[irow] ) == 1:
|
|---|
| 1061 | stm._unaryop( s, value[irow][0], mode, tsys )
|
|---|
| 1062 | else:
|
|---|
| 1063 | #stm._arrayop( s, value[irow], mode, tsys, 'channel' )
|
|---|
| 1064 | stm._arrayop( s, value[irow], mode, tsys )
|
|---|
| 1065 | s.set_selection(basesel)
|
|---|
| 1066 | return s
|
|---|
