def set_basemask(self,masklist=[],invert=False):
"""
Set initial channel mask.
Parameters:
masklist: [[min, max], [min2, max2], ...]
A list of pairs of start/end points (inclusive)
specifying the regions to be masked
invert: optional argument. If specified as True,
return an inverted mask, i.e. the regions
specified are excluded
You can reset the mask selection by running this method with
the default parameters.
"""
# Verify input parameters
if not (isinstance(masklist, list) or isinstance(masklist, tuple)) \
or not isinstance(invert, bool):
msg = 'Invalid mask definition'
raise TypeError(msg)
# Create base mask
if ( len(masklist) > 0 ):
self.mask = self.scan.create_mask(masklist,invert=invert)
elif invert == True:
self.mask = _n_bools(self.scan.nchan(self.scan.getif(0)),False)
else:
self.mask = _n_bools(self.scan.nchan(self.scan.getif(0)),True)
def set_parameters(self, *args, **kwargs):
"""
Set the parameters to be fitted.
Parameters:
params: a vector of parameters
fixed: a vector of which parameters are to be held fixed
(default is none)
component: in case of multiple gaussians/lorentzians/sinusoidals,
the index of the target component
"""
component = None
fixed = None
params = None
if len(args) and isinstance(args[0], dict):
kwargs = args[0]
if kwargs.has_key("fixed"):
fixed = kwargs["fixed"]
if kwargs.has_key("params"):
params = kwargs["params"]
if len(args) == 2 and isinstance(args[1], int):
component = args[1]
if self.fitfunc is None:
msg = "Please specify a fitting function first."
raise RuntimeError(msg)
if (
self.fitfunc == "gauss" or self.fitfunc == "lorentz" or self.fitfunc == "sinusoid"
) and component is not None:
if not self.fitted and sum(self.fitter.getparameters()) == 0:
pars = _n_bools(len(self.components) * 3, False)
fxd = _n_bools(len(pars), False)
else:
pars = list(self.fitter.getparameters())
fxd = list(self.fitter.getfixedparameters())
i = 3 * component
pars[i : i + 3] = params
fxd[i : i + 3] = fixed
params = pars
fixed = fxd
self.fitter.setparameters(params)
if fixed is not None:
self.fitter.setfixedparameters(fixed)
return
def set_parameters(self, *args, **kwargs):
"""
Set the parameters to be fitted.
Parameters:
params: a vector of parameters
fixed: a vector of which parameters are to be held fixed
(default is none)
component: in case of multiple gaussians/lorentzians/sinusoidals,
the index of the target component
"""
component = None
fixed = None
params = None
if len(args) and isinstance(args[0], dict):
kwargs = args[0]
if kwargs.has_key("fixed"): fixed = kwargs["fixed"]
if kwargs.has_key("params"): params = kwargs["params"]
if len(args) == 2 and isinstance(args[1], int):
component = args[1]
if self.fitfunc is None:
msg = "Please specify a fitting function first."
raise RuntimeError(msg)
if (self.fitfunc == "gauss" or self.fitfunc == "lorentz"
or self.fitfunc == "sinusoid") and component is not None:
if not self.fitted and sum(self.fitter.getparameters()) == 0:
pars = _n_bools(len(self.components) * 3, False)
fxd = _n_bools(len(pars), False)
else:
pars = list(self.fitter.getparameters())
fxd = list(self.fitter.getfixedparameters())
i = 3 * component
pars[i:i + 3] = params
fxd[i:i + 3] = fixed
params = pars
fixed = fxd
self.fitter.setparameters(params)
if fixed is not None:
self.fitter.setfixedparameters(fixed)
return
def __init__(self,plotter=None, scan=None):
"""
Create a interactive masking object.
Either or both 'plotter' or/and 'scan' should be defined.
Parameters:
plotter: an ASAP plotter object for interactive selection
scan: a scantable to create a mask interactively
"""
# Return if GUI is not active
if not rcParams['plotter.gui']:
msg = 'GUI plotter is disabled.\n'
msg += 'Exit interactive mode.'
asaplog.push(msg)
asaplog.post("ERROR")
return
# Verify input parameters
if scan is None and plotter is None:
msg = "Either scantable or plotter should be defined."
raise TypeError(msg)
self.scan = None
self.p = None
self.newplot = False
if scan and isinstance(scan, scantable):
self.scan = scan
from asap.asapplotter import asapplotter
if plotter and isinstance(plotter,asapplotter):
self.p = plotter
if self.scan is None and isinstance(self.p._data,scantable):
self.scan = self.p._data
if self.scan is None:
msg = "Invalid scantable."
raise TypeError(msg)
self.mask = _n_bools(self.scan.nchan(self.scan.getif(0)),True)
self.callback = None
self.event = None
self.once = False
self.showmask = True
self.rect = {}
self.xold = None
self.yold = None
self.xdataold = None
self.ydataold = None
self._polygons = []
def _create_flag_from_array(self,x,masklist,invert):
# Return True for channels which should be EXCLUDED (flag)
if len(masklist) <= 1:
asaplog.push()
asaplog.post("masklist should be a list of 2 elements")
asaplog.push("ERROR")
n = len(x)
# Base mask: flag out all channels
mask = _n_bools(n, True)
minval = min(masklist[0:2])
maxval = max(masklist[0:2])
for i in range(n):
if minval <= x[i] <= maxval:
mask[i] = False
if invert:
mask = mask_not(mask)
return mask
def set_scan(self, thescan=None, mask=None):
"""
Set the 'data' (a scantable) of the fitter.
Parameters:
thescan: a scantable
mask: a msk retrieved from the scantable
"""
if not thescan:
msg = "Please give a correct scan"
raise TypeError(msg)
self.fitted = False
self.data = thescan
self.mask = None
if mask is None:
self.mask = _n_bools(self.data.nchan(), True)
else:
self.mask = mask
return
def set_scan(self, thescan=None, mask=None):
"""
Set the 'data' (a scantable) of the fitter.
Parameters:
thescan: a scantable
mask: a msk retrieved from the scantable
"""
if not thescan:
msg = "Please give a correct scan"
raise TypeError(msg)
self.fitted = False
self.data = thescan
self.mask = None
if mask is None:
self.mask = _n_bools(self.data.nchan(), True)
else:
self.mask = mask
return
def set_data(self, xdat, ydat, mask=None):
"""
Set the absissa and ordinate for the fit. Also set the mask
indicating valid points.
This can be used for data vectors retrieved from a scantable.
For scantable fitting use 'fitter.set_scan(scan, mask)'.
Parameters:
xdat: the abcissa values
ydat: the ordinate values
mask: an optional mask
"""
self.fitted = False
self.x = xdat
self.y = ydat
if mask == None:
self.mask = _n_bools(len(xdat), True)
else:
self.mask = mask
return
def set_data(self, xdat, ydat, mask=None):
"""
Set the absissa and ordinate for the fit. Also set the mask
indicating valid points.
This can be used for data vectors retrieved from a scantable.
For scantable fitting use 'fitter.set_scan(scan, mask)'.
Parameters:
xdat: the abcissa values
ydat: the ordinate values
mask: an optional mask
"""
self.fitted = False
self.x = xdat
self.y = ydat
if mask == None:
self.mask = _n_bools(len(xdat), True)
else:
self.mask = mask
return