source: trunk/python/asapmath.py@ 116

Last change on this file since 116 was 113, checked in by mar637, 20 years ago

version 0.1a

  • Property svn:eol-style set to native
  • Property svn:keywords set to Author Date Id Revision
File size: 4.3 KB
Line 
1from scantable import scantable
2
3def average_time(*args, **kwargs):
4 """
5 Return the (time) average of a scan or list of scans. [in channels only]
6 Parameters:
7 one scan or comma separated scans
8 mask: an optional mask
9 Example:
10 # return a time averaged scan from scana and scanb
11 # without using a mask
12 scanav = average_scans(scana,scanb)
13 # return the (time) averaged scan, i.e. the average of
14 # all correlator cycles
15 scanav = average_time(scan)
16
17 """
18 lst = args
19 if len(args) < 2:
20 if type(args[0]) is list:
21 if len(args[0]) < 2:
22 print "Please give at least two scantables"
23 return
24 else:
25 s = args[0]
26 if s.nrow() > 1:
27 from asap._asap import average as _av
28 return scantable(_av(s))
29 else:
30 print "Given scantable is already time averaged"
31 return
32 lst = tuple(args[0])
33 else:
34 lst = tuple(args)
35 from asap._asap import averages as _avs
36 d = [lst[0].nbeam(),lst[0].nif(),lst[0].npol(),lst[0].nchan()]
37 for s in lst:
38 if not isinstance(s,scantable):
39 print "Please give a list of scantables"
40 return
41 dim = [s.nbeam(),s.nif(),s.npol(),s.nchan()]
42 if (dim != d):
43 print "All scans have to have the same numer of Beams/IFs/Pols/Chans"
44 return
45 if kwargs.has_key('mask'):
46 return scantable(_avs(lst, kwargs.get('mask')))
47 else:
48 from numarray import ones
49 mask = tuple(ones(d[3]))
50 return scantable(_avs(lst, mask))
51
52def quotient(source, reference):
53 """
54 Return the quotient of a 'source' scan and a 'reference' scan
55 Parameters:
56 source: the 'on' scan
57 reference: the 'off' scan
58 """
59 from asap._asap import quotient as _quot
60 return scantable(_quot(source, reference))
61
62def scale(scan, factor):
63 """
64 Return a scan where all spectra are scaled by the give 'factor'
65 Parameters:
66 scan: a scantable
67 factor: the scaling factor
68 Note:
69 This currently applies the all beams/IFs/pols
70 """
71 from asap._asap import scale as _scale
72 return scantable(_scale(scan, factor))
73
74def add(scan, offset):
75 """
76 Return a scan where the offset is added.
77 Parameters:
78 scan: a scantable
79 offset: the value to add
80 Note:
81 This currently applies the all beams/IFs/pols
82 """
83 from asap._asap import add as _add
84 return scantable(_add(scan, offset))
85
86
87def bin(scan, binwidth=5):
88 """
89 """
90 from asap._asap import bin as _bin
91 return scantable(_bin(scan, binwidth))
92
93def average_pol(scan, mask=None):
94 """
95 Average the Polarisations together.
96 Parameters:
97 scan - a scantable
98 mask - an optional mask defining the region, where
99 the averaging will be applied. The output
100 will have all specified points masked.
101 The dimension won't be reduced and
102 all polarisations will contain the
103 averaged spectrum.
104 Example:
105 polav = average_pols(myscan)
106 """
107 from asap._asap import averagepol as _avpol
108 from numarray import ones
109 if mask is None:
110 mask = tuple(ones(scan.nchan()))
111 return scantable(_avpol(scan, mask))
112
113def hanning(scan):
114 """
115 Hanning smooth the channels.
116 Parameters:
117 scan - the input scan
118 Example:
119 none
120 """
121 from asap._asap import hanning as _han
122 return scantable(_han(scan))
123
124
125def poly_baseline(scan, mask=None, order=0):
126 """
127 Return a scan which has been baselined by a polynomial.
128 Parameters:
129 scan: a scantable
130 mask: an optional mask
131 order: the order of the polynomial (default is 0)
132 Example:
133 # return a scan baselined by a third order polynomial,
134 # not using a mask
135 bscan = poly_baseline(scan, order=3)
136 """
137 from asap.asapfitter import fitter
138 if mask is None:
139 from numarray import ones
140 mask = tuple(ones(scan.nchan()))
141 f = fitter()
142 f._verbose(True)
143 f.set_scan(scan, mask)
144 f.set_function(poly=order)
145 sf = f.auto_fit()
146 return sf
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