B d"@s@ddlZdZddlmZddlmZGdddeZdd Z dS) Nz"Kipp Cannon )date)versionc@s^eZdZdZeddZeddZdddZd d Zd d Z d dZ ddZ e ddZ dS) offsetvectorz Subclass of the dict built-in type for storing mappings of instrument to time offset. Examples: >>> x = offsetvector({"H1": 0, "L1": 10, "V1": 20}) >>> x["H1"] 0 >>> not any(x.values()) # check for "zero-lag" False cCs|s tdt|S)zD = min(self) Raises ValueError if the offsetvector is empty. zoffsetvector is empty) ValueErrormin)selfr b/work/yifan.wang/ringdown/master-ringdown-env/lib/python3.7/site-packages/lalburst/offsetvector.pyrefkey9s zoffsetvector.refkeycs(jtfddDS)aT Dictionary of relative offsets. The keys in the result are pairs of keys from the offset vector, (a, b), and the values are the relative offsets, (offset[b] - offset[a]). Raises ValueError if the offsetvector is empty (WARNING: this behaviour might change in the future). Example: >>> x = offsetvector({"H1": 0, "L1": 10, "V1": 20}) >>> assert x.deltas == {('H1', 'L1'): 10, ('H1', 'V1'): 20, ('H1', 'H1'): 0} >>> y = offsetvector({'H1': 100, 'L1': 110, 'V1': 120}) >>> y.deltas == x.deltas True Note that the result always includes a "dummy" entry, giving the relative offset of self.refkey with respect to itself, which is always 0. See also .fromdeltas(). BUGS: I think the keys in each tuple should be reversed. I can't remember why I put them in the way they are. Expect them to change in the future. c3s"|]}|f|fVqdS)Nr ).0key)r refoffsetrr r nsz&offsetvector.deltas..)r dict)rr )r rrr deltasHs$zoffsetvector.deltasFcCs<|r dddt|DSdddt|DS)z Return a human-readable string representation of an offset vector. Example: >>> a = offsetvector({"H1": -10.1234567, "L1": 0.125}) >>> str(a) 'H1 = -10.1234567 s, L1 = +0.125 s' >>> a.__str__(compact = True) 'H1=-10.123,L1=0.125' ,css|]}d|VqdS)z%s=%.5gNr )r xr r r r~sz'offsetvector.__str__..z, css|]}d|VqdS)z %s = %+.16g sNr )r rr r r rs)joinsorteditems)rcompactr r r __str__ps zoffsetvector.__str__cCsd|jjt|fS)a] Return a string representation of the offset vector. Running eval() on the result reconstructs the offsetvector. Example: >>> a = offsetvector({"H1": -10.1234567, "L1": 0.1}) >>> b = eval(repr(a)) >>> b == a True >>> b is a False >>> c = offsetvector({"H1": -10.1234567}) >>> repr(c) "offsetvector({'H1': -10.1234567})" z%s(%s)) __class____name__r__repr__)rr r r rszoffsetvector.__repr__cCst|t|S)z Returns max(offset) - min(offset). Example: >>> abs(offsetvector({"H1": 0.0, "H2": 0.0, "L1": 0.0})) 0.0 >>> abs(offsetvector({"H1": 10.0, "H2": 10.0, "L1": 10.0})) 0.0 >>> abs(offsetvector({'H1': 10.0, 'L1': 0.0, 'V1': -10.0})) 20.0 )maxvaluesr)rr r r __abs__s zoffsetvector.__abs__cs"tfdd|DjjkS)a Returns True if offset vector other can be found in self, False otherwise. An offset vector is "found in" another offset vector if the latter contains all of the former's instruments and the relative offsets among those instruments are equal (the absolute offsets need not be). Example: >>> a = offsetvector({"H1": 10, "L1": 20, "V1": 30}) >>> b = offsetvector({"H1": 20, "V1": 40}) >>> a.contains(b) True Note the distinction between this and the "in" operator: >>> "H1" in a True c3s"|]\}}|kr||fVqdS)Nr )r r offset)otherr r rsz(offsetvector.contains..)rrr)rr r )r r containsszoffsetvector.containscKsVxPt|D]@\}}||kr|||}x |D]}|||7<q4WPqW|S)a Adjust the offsetvector so that a particular instrument has the desired offset. All other instruments have their offsets adjusted so that the relative offsets are preserved. The instrument to noramlize, and the offset one wishes it to have, are provided as a key-word argument. The return value is the time slide dictionary, which is modified in place. If more than one key-word argument is provided the keys are sorted and considered in order until a key is found that is in the offset vector. The offset vector is normalized to that value. This function is a no-op if no key-word argument is found that applies. Example: >>> a = offsetvector({"H1": -10, "H2": -10, "L1": -10}) >>> assert a.normalize(L1 = 0) == offsetvector({'H2': 0, 'H1': 0, 'L1': 0}) >>> a = offsetvector({"H1": -10, "H2": -10}) >>> assert a.normalize(L1 = 0, H2 = 5) == offsetvector({'H2': 5, 'H1': 5}) )rrkeys)rkwargsr rdeltar r r normalizes zoffsetvector.normalizecCs|dd|DS)a Construct an offsetvector from a dictionary of offset deltas as returned by the .deltas attribute. Example: >>> x = offsetvector({"H1": 0, "L1": 10, "V1": 20}) >>> y = offsetvector.fromdeltas(x.deltas) >>> y == x True See also .deltas, .fromkeys() css|]\\}}}||fVqdS)Nr )r r r valuer r r rsz*offsetvector.fromdeltas..)r)clsrr r r fromdeltasszoffsetvector.fromdeltasN)F)r __module__ __qualname____doc__propertyr rrrrr!r% classmethodr(r r r r r,s   ( !rcsfi}xN|D]Fx@tt|D],|ttfddDq Wq Wdd|DS)a Given an iterable of offset vectors, return the shortest list of the unique n-instrument offset vectors from which all the vectors in the input iterable can be constructed. This can be used to determine the minimal set of n-instrument coincs required to construct all of the coincs for all of the requested instrument and offset combinations in a set of offset vectors. It is assumed that the coincs for the vector {"H1": 0, "H2": 10, "L1": 20} can be constructed from the coincs for the vectors {"H1": 0, "H2": 10} and {"H2": 0, "L1": 10}, that is only the relative offsets are significant in determining if two events are coincident, not the absolute offsets. c3s"|]}|dVqdS)rNr )r Z instrument) instrumentsvectr r rsz*component_offsetvectors..cSs(g|] \}}|D]}tt||qqSr )rzip)r r.Z delta_setrr r r sz+component_offsetvectors..) itertools combinationsr setdefaultsetaddtupler)Z offsetvectorsnZ delta_setsr )r.r/r component_offsetvectorss  0r9) r2 __author__Z git_versionr__date__r __version__rrr9r r r r s   J