class Profile(Observable, Validatable):
"""Observed and calculated profile container.
Profile is an Observable. The xpar, ypar and dypar attributes are observed
by the Profile, which can in turn be observed by some other object.
Attributes
_xobs -- A numpy array of the observed independent variable (default
None)
xobs -- Read-only property of _xobs.
_yobs -- A numpy array of the observed signal (default None)
yobs -- Read-only property of _yobs.
_dyobs -- A numpy array of the uncertainty of the observed signal (default
None, optional).
dyobs -- Read-only property of _dyobs.
x -- A numpy array of the calculated independent variable (default
None, property for xpar accessors).
y -- The profile over the calculation range (default None, property
for ypar accessors).
dy -- The uncertainty in the profile over the calculation range
(default None, property for dypar accessors).
ycalc -- A numpy array of the calculated signal (default None).
xpar -- A Parameter that stores x (named "x").
ypar -- A Parameter that stores y (named "y").
dypar -- A Parameter that stores dy (named "dy").
ycpar -- A Parameter that stores ycalc (named "ycalc"). This is
not observed by the profile, but it is present so it can be
constrained to.
meta -- A dictionary of metadata. This is only set if provided by a
parser.
"""
def __init__(self):
"""Initialize the attributes."""
Observable.__init__(self)
self._xobs = None
self._yobs = None
self._dyobs = None
self.xpar = Parameter("x")
self.ypar = Parameter("y")
self.dypar = Parameter("dy")
self.ycpar = Parameter("ycalc")
self.meta = {}
# Observable
self.xpar.addObserver(self._flush)
self.ypar.addObserver(self._flush)
self.dypar.addObserver(self._flush)
return
# We want x, y, ycalc and dy to stay in-sync with xpar, ypar and dypar
x = property( lambda self : self.xpar.getValue(),
lambda self, val : self.xpar.setValue(val) )
y = property( lambda self : self.ypar.getValue(),
lambda self, val : self.ypar.setValue(val) )
dy = property( lambda self : self.dypar.getValue(),
lambda self, val : self.dypar.setValue(val) )
ycalc = property( lambda self : self.ycpar.getValue(),
lambda self, val : self.ycpar.setValue(val) )
# We want xobs, yobs and dyobs to be read-only
xobs = property( lambda self: self._xobs )
yobs = property( lambda self: self._yobs )
dyobs = property( lambda self: self._dyobs )
def loadParsedData(self, parser):
"""Load parsed data from a ProfileParser.
This sets the xobs, yobs, dyobs arrays as well as the metadata.
"""
x, y, junk, dy = parser.getData()
self.meta = dict(parser.getMetaData())
self.setObservedProfile(x, y, dy)
return
def setObservedProfile(self, xobs, yobs, dyobs = None):
"""Set the observed profile.
Arguments
xobs -- Numpy array of the independent variable
yobs -- Numpy array of the observed signal.
dyobs -- Numpy array of the uncertainty in the observed signal. If
dyobs is None (default), it will be set to 1 at each
observed xobs.
Raises ValueError if len(yobs) != len(xobs)
Raises ValueError if dyobs != None and len(dyobs) != len(xobs)
"""
if len(yobs) != len(xobs):
raise ValueError("xobs and yobs are different lengths")
if dyobs is not None and len(dyobs) != len(xobs):
raise ValueError("xobs and dyobs are different lengths")
self._xobs = numpy.asarray(xobs, dtype=float)
self._yobs = numpy.asarray(yobs, dtype=float)
if dyobs is None:
self._dyobs = numpy.ones_like(xobs)
else:
self._dyobs = numpy.asarray(dyobs, dtype=float)
# Set the default calculation points
if self.x is None:
self.setCalculationPoints(self._xobs)
else:
self.setCalculationPoints(self.x)
return
def setCalculationRange(self, xmin = None, xmax = None, dx = None):
"""Set the calculation range
Arguments
xmin -- The minimum value of the independent variable.
If xmin is None (default), the minimum observed value will
be used. This is clipped to the minimum observed x.
xmax -- The maximum value of the independent variable.
If xmax is None (default), the maximum observed value will
be used. This is clipped to the maximum observed x.
dx -- The sample spacing in the independent variable. If dx is
None (default), then the spacing in the observed points
will be preserved.
Note that xmin is always inclusive (unless clipped). xmax is inclusive
if it is within the bounds of the observed data.
raises AttributeError if there is no observed profile
raises ValueError if xmin > xmax
raises ValueError if dx > xmax-xmin
raises ValueError if dx <= 0
"""
clip = dx is None
if self.xobs is None:
raise AttributeError("No observed profile")
if xmin is None and xmax is None and dx is None:
self.x = self.xobs
self.y = self.yobs
self.dy = self.dyobs
return
if xmin is None:
xmin = self.xobs[0]
else:
xmin = float(xmin)
if xmax is None:
xmax = self.xobs[-1]
else:
xmax = float(xmax)
if dx is None:
dx = (self.xobs[-1] - self.xobs[0]) / len(self.xobs)
else:
dx = float(dx)
if xmin > xmax:
raise ValueError("xmax must be greater than xmin")
if dx > xmax - xmin:
raise ValueError("dx must be less than xmax-xmin")
if dx <= 0:
raise ValueError("dx must be positive")
if clip:
x = self.xobs
indices = numpy.logical_and( xmin - epsilon <= x , x <= xmax +
epsilon )
self.x = self.xobs[indices]
self.y = self.yobs[indices]
self.dy = self.dyobs[indices]
else:
self.setCalculationPoints(numpy.arange(xmin, xmax+0.5*dx, dx))
return
def setCalculationPoints(self, x):
"""Set the calculation points.
Arguments
x -- A non-empty numpy array containing the calculation points. If
xobs exists, the bounds of x will be limited to its bounds.
This will create y and dy on the specified grid if xobs, yobs and
dyobs exist.
"""
x = numpy.asarray(x)
if self.xobs is not None:
x = x[ x >= self.xobs[0] - epsilon ]
x = x[ x <= self.xobs[-1] + epsilon ]
self.x = x
if self.yobs is not None:
self.y = rebinArray(self.yobs, self.xobs, self.x)
if self.dyobs is not None:
# work around for interpolation issue making some of these non-1
if (self.dyobs == 1).all():
self.dy = numpy.ones_like(self.x)
else:
# FIXME - This does not follow error propogation rules and it
# introduces (more) correlation between the data points.
self.dy = rebinArray(self.dyobs, self.xobs, self.x)
return
def loadtxt(self, *args, **kw):
"""Use numpy.loadtxt to load data.
Arguments are passed to numpy.loadtxt.
unpack = True is enforced.
The first two arrays returned by numpy.loadtxt are assumed to be x and
y. If there is a third array, it is assumed to by dy. Any other arrays
are ignored. These are passed to setObservedProfile.
Raises ValueError if the call to numpy.loadtxt returns fewer than 2
arrays.
Returns the x, y and dy arrays loaded from the file
"""
if len(args) == 8 and not args[-1]:
args = list(args)
args[-1] = True
else:
kw["unpack"] = True
cols = numpy.loadtxt(*args, **kw)
x = y = dy = None
# Due to using 'unpack', a single column will come out as a single
# array, thus the second check.
if len(cols) < 2 or not isinstance(cols[0], numpy.ndarray):
raise ValueError("numpy.loadtxt returned fewer than 2 arrays")
x = cols[0]
y = cols[1]
if len(cols) > 2:
dy = cols[2]
self.setObservedProfile(x, y, dy)
return x, y, dy
def savetxt(self, fname, fmt='%.18e', delimiter=' '):
"""Call numpy.savetxt with x, ycalc, y, dy
Arguments are passed to numpy.savetxt.
"""
x = self.x
ycalc = self.ycalc
if ycalc is None:
raise AttributeError("ycalc is None")
y = self.y
dy = self.dy
# Add the header
if not hasattr(fname, 'write'):
fname = file(fname, 'w')
if fname.closed:
raise ValueError("I/O operation on closed file")
header = "# x ycalc y dy\n"
fname.write(header)
numpy.savetxt(fname, zip(x, ycalc, y, dy), fmt, delimiter)
return
def _flush(self, other):
"""Invalidate cached state.
This will force any observer to invalidate its state.
"""
self.ycalc = None
self.notify(other)
return
def _validate(self):
"""Validate my state.
This validates that x, y, dy, xobx, yobs and dyobs are not None.
This validates that x, y, and dy are the same length.
Raises SrFitError if validation fails.
"""
datanotset = any(v is None for v in
[self.x, self.y, self.dy, self.xobs, self.yobs, self.dyobs])
if datanotset:
raise SrFitError("Missing data")
if len(self.x) != len(self.y) or len(self.x) != len(self.dy):
raise SrFitError("Data are different lengths")
return
class Profile(Observable, Validatable):
"""Observed and calculated profile container.
Profile is an Observable. The xpar, ypar and dypar attributes are observed
by the Profile, which can in turn be observed by some other object.
Attributes
_xobs -- A numpy array of the observed independent variable (default
None)
xobs -- Read-only property of _xobs.
_yobs -- A numpy array of the observed signal (default None)
yobs -- Read-only property of _yobs.
_dyobs -- A numpy array of the uncertainty of the observed signal (default
None, optional).
dyobs -- Read-only property of _dyobs.
x -- A numpy array of the calculated independent variable (default
None, property for xpar accessors).
y -- The profile over the calculation range (default None, property
for ypar accessors).
dy -- The uncertainty in the profile over the calculation range
(default None, property for dypar accessors).
ycalc -- A numpy array of the calculated signal (default None).
xpar -- A Parameter that stores x (named "x").
ypar -- A Parameter that stores y (named "y").
dypar -- A Parameter that stores dy (named "dy").
ycpar -- A Parameter that stores ycalc (named "ycalc"). This is
not observed by the profile, but it is present so it can be
constrained to.
meta -- A dictionary of metadata. This is only set if provided by a
parser.
"""
def __init__(self):
"""Initialize the attributes."""
Observable.__init__(self)
self._xobs = None
self._yobs = None
self._dyobs = None
self.xpar = Parameter("x")
self.ypar = Parameter("y")
self.dypar = Parameter("dy")
self.ycpar = Parameter("ycalc")
self.meta = {}
# Observable
self.xpar.addObserver(self._flush)
self.ypar.addObserver(self._flush)
self.dypar.addObserver(self._flush)
return
# We want x, y, ycalc and dy to stay in-sync with xpar, ypar and dypar
x = property(lambda self: self.xpar.getValue(),
lambda self, val: self.xpar.setValue(val))
y = property(lambda self: self.ypar.getValue(),
lambda self, val: self.ypar.setValue(val))
dy = property(lambda self: self.dypar.getValue(),
lambda self, val: self.dypar.setValue(val))
ycalc = property(lambda self: self.ycpar.getValue(),
lambda self, val: self.ycpar.setValue(val))
# We want xobs, yobs and dyobs to be read-only
xobs = property(lambda self: self._xobs)
yobs = property(lambda self: self._yobs)
dyobs = property(lambda self: self._dyobs)
def loadParsedData(self, parser):
"""Load parsed data from a ProfileParser.
This sets the xobs, yobs, dyobs arrays as well as the metadata.
"""
x, y, junk, dy = parser.getData()
self.meta = dict(parser.getMetaData())
self.setObservedProfile(x, y, dy)
return
def setObservedProfile(self, xobs, yobs, dyobs=None):
"""Set the observed profile.
Arguments
xobs -- Numpy array of the independent variable
yobs -- Numpy array of the observed signal.
dyobs -- Numpy array of the uncertainty in the observed signal. If
dyobs is None (default), it will be set to 1 at each
observed xobs.
Raises ValueError if len(yobs) != len(xobs)
Raises ValueError if dyobs != None and len(dyobs) != len(xobs)
"""
if len(yobs) != len(xobs):
raise ValueError("xobs and yobs are different lengths")
if dyobs is not None and len(dyobs) != len(xobs):
raise ValueError("xobs and dyobs are different lengths")
self._xobs = numpy.asarray(xobs, dtype=float)
self._yobs = numpy.asarray(yobs, dtype=float)
if dyobs is None:
self._dyobs = numpy.ones_like(xobs)
else:
self._dyobs = numpy.asarray(dyobs, dtype=float)
# Set the default calculation points
if self.x is None:
self.setCalculationPoints(self._xobs)
else:
self.setCalculationPoints(self.x)
return
def setCalculationRange(self, xmin=None, xmax=None, dx=None):
"""Set epsilon-inclusive calculation range.
Adhere to the observed ``xobs`` points when ``dx`` is the same
as in the data. ``xmin`` and ``xmax`` are clipped at the bounds
of the observed data.
Parameters
----------
xmin : float or "obs", optional
The minimum value of the independent variable. Keep the
current minimum when not specified. If specified as "obs"
reset to the minimum observed value.
xmax : float or "obs", optional
The maximum value of the independent variable. Keep the
current maximum when not specified. If specified as "obs"
reset to the maximum observed value.
dx : float or "obs", optional
The sample spacing in the independent variable. When different
from the data, resample the ``x`` as anchored at ``xmin``.
Note that xmin is always inclusive (unless clipped). xmax is inclusive
if it is within the bounds of the observed data.
Raises
------
AttributeError
If there is no observed data.
ValueError
When xmin > xmax or if dx <= 0. Also if dx > xmax - xmin.
"""
if self.xobs is None:
raise AttributeError("No observed profile")
# local helper function
def _isobs(a):
if not isinstance(a, six.string_types):
return False
if a != 'obs':
raise ValueError('Must be either float or "obs".')
return True
# resolve new low and high bounds for x
lo = (self.x[0] if xmin is None else
self.xobs[0] if _isobs(xmin) else float(xmin))
lo = max(lo, self.xobs[0])
hi = (self.x[-1] if xmax is None else
self.xobs[-1] if _isobs(xmax) else float(xmax))
hi = min(hi, self.xobs[-1])
# determine if we need to clip the original grid
clip = True
step = None
ncur = len(self.x)
stepcur = (1 if ncur < 2 else (self.x[-1] - self.x[0]) / (ncur - 1.0))
nobs = len(self.xobs)
stepobs = (1 if nobs < 2 else
(self.xobs[-1] - self.xobs[0]) / (nobs - 1.0))
if dx is None:
# check if xobs overlaps with x
i0 = numpy.fabs(self.xobs - self.x[0]).argmin()
n0 = min(len(self.x), len(self.xobs) - i0)
if not numpy.allclose(self.xobs[i0:i0 + n0], self.x[:n0]):
clip = False
step = stepcur if ncur > 1 else stepobs
elif _isobs(dx):
assert clip and step is None
elif numpy.allclose(stepobs, dx):
assert clip and step is None
else:
clip = False
step = float(dx)
# verify that we either clip or have the step defined.
assert clip or step is not None
# hi, lo, step, clip all resolved here.
# validate arguments
if lo > hi:
raise ValueError("xmax must be greater than xmin.")
if not clip:
if step > hi - lo:
raise ValueError("dx must be less than (xmax - xmin).")
if step <= 0:
raise ValueError("dx must be positive.")
# determine epsilon extensions to the lower and upper bounds.
epslo = abs(lo) * epsilon + epsilon
epshi = abs(hi) * epsilon + epsilon
# process the new grid.
if clip:
indices = (lo - epslo <= self.xobs) & (self.xobs <= hi + epshi)
self.x = self.xobs[indices]
self.y = self.yobs[indices]
self.dy = self.dyobs[indices]
else:
x1 = numpy.arange(lo, hi + epshi, step)
self.setCalculationPoints(x1)
return
def setCalculationPoints(self, x):
"""Set the calculation points.
Arguments
x -- A non-empty numpy array containing the calculation points. If
xobs exists, the bounds of x will be limited to its bounds.
This will create y and dy on the specified grid if xobs, yobs and
dyobs exist.
"""
x = numpy.asarray(x)
if self.xobs is not None:
x = x[x >= self.xobs[0] - epsilon]
x = x[x <= self.xobs[-1] + epsilon]
self.x = x
if self.yobs is not None:
self.y = rebinArray(self.yobs, self.xobs, self.x)
if self.dyobs is not None:
# work around for interpolation issue making some of these non-1
if (self.dyobs == 1).all():
self.dy = numpy.ones_like(self.x)
else:
# FIXME - This does not follow error propogation rules and it
# introduces (more) correlation between the data points.
self.dy = rebinArray(self.dyobs, self.xobs, self.x)
return
def loadtxt(self, *args, **kw):
"""Use numpy.loadtxt to load data.
Arguments are passed to numpy.loadtxt.
unpack = True is enforced.
The first two arrays returned by numpy.loadtxt are assumed to be x and
y. If there is a third array, it is assumed to by dy. Any other arrays
are ignored. These are passed to setObservedProfile.
Raises ValueError if the call to numpy.loadtxt returns fewer than 2
arrays.
Returns the x, y and dy arrays loaded from the file
"""
if len(args) == 8 and not args[-1]:
args = list(args)
args[-1] = True
else:
kw["unpack"] = True
cols = numpy.loadtxt(*args, **kw)
x = y = dy = None
# Due to using 'unpack', a single column will come out as a single
# array, thus the second check.
if len(cols) < 2 or not isinstance(cols[0], numpy.ndarray):
raise ValueError("numpy.loadtxt returned fewer than 2 arrays")
x = cols[0]
y = cols[1]
if len(cols) > 2:
dy = cols[2]
self.setObservedProfile(x, y, dy)
return x, y, dy
def savetxt(self, fname, **kwargs):
"""Call `numpy.savetxt` with x, ycalc, y, dy.
Parameters
----------
fname : filename or file handle
This is passed to `numpy.savetxt`.
**kwargs
The keyword arguments that are passed to `numpy.savetxt`.
We preset file header "x ycalc y dy". Use ``header=''``
to save data without any header.
Raises
------
SrFitError
When `self.ycalc` has not been set.
See also
--------
numpy.savetxt
"""
x = self.x
ycalc = self.ycalc
if ycalc is None:
raise SrFitError("ycalc is None")
y = self.y
dy = self.dy
kwargs.setdefault('header', 'x ycalc y dy')
data = numpy.transpose([x, ycalc, y, dy])
numpy.savetxt(fname, data, **kwargs)
return
def _flush(self, other):
"""Invalidate cached state.
This will force any observer to invalidate its state.
"""
self.ycalc = None
self.notify(other)
return
def _validate(self):
"""Validate my state.
This validates that x, y, dy, xobx, yobs and dyobs are not None.
This validates that x, y, and dy are the same length.
Raises SrFitError if validation fails.
"""
datanotset = any(
v is None for v in
[self.x, self.y, self.dy, self.xobs, self.yobs, self.dyobs])
if datanotset:
raise SrFitError("Missing data")
if len(self.x) != len(self.y) or len(self.x) != len(self.dy):
raise SrFitError("Data are different lengths")
return
class Profile(Observable, Validatable):
"""Observed and calculated profile container.
Profile is an Observable. The xpar, ypar and dypar attributes are observed
by the Profile, which can in turn be observed by some other object.
Attributes
_xobs -- A numpy array of the observed independent variable (default
None)
xobs -- Read-only property of _xobs.
_yobs -- A numpy array of the observed signal (default None)
yobs -- Read-only property of _yobs.
_dyobs -- A numpy array of the uncertainty of the observed signal (default
None, optional).
dyobs -- Read-only property of _dyobs.
x -- A numpy array of the calculated independent variable (default
None, property for xpar accessors).
y -- The profile over the calculation range (default None, property
for ypar accessors).
dy -- The uncertainty in the profile over the calculation range
(default None, property for dypar accessors).
ycalc -- A numpy array of the calculated signal (default None).
xpar -- A Parameter that stores x (named "x").
ypar -- A Parameter that stores y (named "y").
dypar -- A Parameter that stores dy (named "dy").
ycpar -- A Parameter that stores ycalc (named "ycalc"). This is
not observed by the profile, but it is present so it can be
constrained to.
meta -- A dictionary of metadata. This is only set if provided by a
parser.
"""
def __init__(self):
"""Initialize the attributes."""
Observable.__init__(self)
self._xobs = None
self._yobs = None
self._dyobs = None
self.xpar = Parameter("x")
self.ypar = Parameter("y")
self.dypar = Parameter("dy")
self.ycpar = Parameter("ycalc")
self.meta = {}
# Observable
self.xpar.addObserver(self._flush)
self.ypar.addObserver(self._flush)
self.dypar.addObserver(self._flush)
return
# We want x, y, ycalc and dy to stay in-sync with xpar, ypar and dypar
x = property( lambda self : self.xpar.getValue(),
lambda self, val : self.xpar.setValue(val) )
y = property( lambda self : self.ypar.getValue(),
lambda self, val : self.ypar.setValue(val) )
dy = property( lambda self : self.dypar.getValue(),
lambda self, val : self.dypar.setValue(val) )
ycalc = property( lambda self : self.ycpar.getValue(),
lambda self, val : self.ycpar.setValue(val) )
# We want xobs, yobs and dyobs to be read-only
xobs = property( lambda self: self._xobs )
yobs = property( lambda self: self._yobs )
dyobs = property( lambda self: self._dyobs )
def loadParsedData(self, parser):
"""Load parsed data from a ProfileParser.
This sets the xobs, yobs, dyobs arrays as well as the metadata.
"""
x, y, junk, dy = parser.getData()
self.meta = dict(parser.getMetaData())
self.setObservedProfile(x, y, dy)
return
def setObservedProfile(self, xobs, yobs, dyobs = None):
"""Set the observed profile.
Arguments
xobs -- Numpy array of the independent variable
yobs -- Numpy array of the observed signal.
dyobs -- Numpy array of the uncertainty in the observed signal. If
dyobs is None (default), it will be set to 1 at each
observed xobs.
Raises ValueError if len(yobs) != len(xobs)
Raises ValueError if dyobs != None and len(dyobs) != len(xobs)
"""
if len(yobs) != len(xobs):
raise ValueError("xobs and yobs are different lengths")
if dyobs is not None and len(dyobs) != len(xobs):
raise ValueError("xobs and dyobs are different lengths")
self._xobs = numpy.asarray(xobs, dtype=float)
self._yobs = numpy.asarray(yobs, dtype=float)
if dyobs is None:
self._dyobs = numpy.ones_like(xobs)
else:
self._dyobs = numpy.asarray(dyobs, dtype=float)
# Set the default calculation points
if self.x is None:
self.setCalculationPoints(self._xobs)
else:
self.setCalculationPoints(self.x)
return
def setCalculationRange(self, xmin=None, xmax=None, dx=None):
"""Set epsilon-inclusive calculation range.
Adhere to the observed ``xobs`` points when ``dx`` is the same
as in the data. ``xmin`` and ``xmax`` are clipped at the bounds
of the observed data.
Parameters
----------
xmin : float or "obs", optional
The minimum value of the independent variable. Keep the
current minimum when not specified. If specified as "obs"
reset to the minimum observed value.
xmax : float or "obs", optional
The maximum value of the independent variable. Keep the
current maximum when not specified. If specified as "obs"
reset to the maximum observed value.
dx : float or "obs", optional
The sample spacing in the independent variable. When different
from the data, resample the ``x`` as anchored at ``xmin``.
Note that xmin is always inclusive (unless clipped). xmax is inclusive
if it is within the bounds of the observed data.
Raises
------
AttributeError
If there is no observed data.
ValueError
When xmin > xmax or if dx <= 0. Also if dx > xmax - xmin.
"""
if self.xobs is None:
raise AttributeError("No observed profile")
# local helper function
def _isobs(a):
if not isinstance(a, six.string_types):
return False
if a != 'obs':
raise ValueError('Must be either float or "obs".')
return True
# resolve new low and high bounds for x
lo = (self.x[0] if xmin is None else
self.xobs[0] if _isobs(xmin) else float(xmin))
lo = max(lo, self.xobs[0])
hi = (self.x[-1] if xmax is None else
self.xobs[-1] if _isobs(xmax) else float(xmax))
hi = min(hi, self.xobs[-1])
# determine if we need to clip the original grid
clip = True
step = None
ncur = len(self.x)
stepcur = (1 if ncur < 2
else (self.x[-1] - self.x[0]) / (ncur - 1.0))
nobs = len(self.xobs)
stepobs = (1 if nobs < 2
else (self.xobs[-1] - self.xobs[0]) / (nobs - 1.0))
if dx is None:
# check if xobs overlaps with x
i0 = numpy.fabs(self.xobs - self.x[0]).argmin()
n0 = min(len(self.x), len(self.xobs) - i0)
if not numpy.allclose(self.xobs[i0 : i0 + n0], self.x[:n0]):
clip = False
step = stepcur if ncur > 1 else stepobs
elif _isobs(dx):
assert clip and step is None
elif numpy.allclose(stepobs, dx):
assert clip and step is None
else:
clip = False
step = float(dx)
# verify that we either clip or have the step defined.
assert clip or step is not None
# hi, lo, step, clip all resolved here.
# validate arguments
if lo > hi:
raise ValueError("xmax must be greater than xmin.")
if not clip:
if step > hi - lo:
raise ValueError("dx must be less than (xmax - xmin).")
if step <= 0:
raise ValueError("dx must be positive.")
# determine epsilon extensions to the lower and upper bounds.
epslo = abs(lo) * epsilon + epsilon
epshi = abs(hi) * epsilon + epsilon
# process the new grid.
if clip:
indices = (lo - epslo <= self.xobs) & (self.xobs <= hi + epshi)
self.x = self.xobs[indices]
self.y = self.yobs[indices]
self.dy = self.dyobs[indices]
else:
x1 = numpy.arange(lo, hi + epshi, step)
self.setCalculationPoints(x1)
return
def setCalculationPoints(self, x):
"""Set the calculation points.
Arguments
x -- A non-empty numpy array containing the calculation points. If
xobs exists, the bounds of x will be limited to its bounds.
This will create y and dy on the specified grid if xobs, yobs and
dyobs exist.
"""
x = numpy.asarray(x)
if self.xobs is not None:
x = x[ x >= self.xobs[0] - epsilon ]
x = x[ x <= self.xobs[-1] + epsilon ]
self.x = x
if self.yobs is not None:
self.y = rebinArray(self.yobs, self.xobs, self.x)
if self.dyobs is not None:
# work around for interpolation issue making some of these non-1
if (self.dyobs == 1).all():
self.dy = numpy.ones_like(self.x)
else:
# FIXME - This does not follow error propogation rules and it
# introduces (more) correlation between the data points.
self.dy = rebinArray(self.dyobs, self.xobs, self.x)
return
def loadtxt(self, *args, **kw):
"""Use numpy.loadtxt to load data.
Arguments are passed to numpy.loadtxt.
unpack = True is enforced.
The first two arrays returned by numpy.loadtxt are assumed to be x and
y. If there is a third array, it is assumed to by dy. Any other arrays
are ignored. These are passed to setObservedProfile.
Raises ValueError if the call to numpy.loadtxt returns fewer than 2
arrays.
Returns the x, y and dy arrays loaded from the file
"""
if len(args) == 8 and not args[-1]:
args = list(args)
args[-1] = True
else:
kw["unpack"] = True
cols = numpy.loadtxt(*args, **kw)
x = y = dy = None
# Due to using 'unpack', a single column will come out as a single
# array, thus the second check.
if len(cols) < 2 or not isinstance(cols[0], numpy.ndarray):
raise ValueError("numpy.loadtxt returned fewer than 2 arrays")
x = cols[0]
y = cols[1]
if len(cols) > 2:
dy = cols[2]
self.setObservedProfile(x, y, dy)
return x, y, dy
def savetxt(self, fname, **kwargs):
"""Call `numpy.savetxt` with x, ycalc, y, dy.
Parameters
----------
fname : filename or file handle
This is passed to `numpy.savetxt`.
**kwargs
The keyword arguments that are passed to `numpy.savetxt`.
We preset file header "x ycalc y dy". Use ``header=''``
to save data without any header.
Raises
------
SrFitError
When `self.ycalc` has not been set.
See also
--------
numpy.savetxt
"""
x = self.x
ycalc = self.ycalc
if ycalc is None:
raise SrFitError("ycalc is None")
y = self.y
dy = self.dy
kwargs.setdefault('header', 'x ycalc y dy')
data = numpy.transpose([x, ycalc, y, dy])
numpy.savetxt(fname, data, **kwargs)
return
def _flush(self, other):
"""Invalidate cached state.
This will force any observer to invalidate its state.
"""
self.ycalc = None
self.notify(other)
return
def _validate(self):
"""Validate my state.
This validates that x, y, dy, xobx, yobs and dyobs are not None.
This validates that x, y, and dy are the same length.
Raises SrFitError if validation fails.
"""
datanotset = any(v is None for v in
[self.x, self.y, self.dy, self.xobs, self.yobs, self.dyobs])
if datanotset:
raise SrFitError("Missing data")
if len(self.x) != len(self.y) or len(self.x) != len(self.dy):
raise SrFitError("Data are different lengths")
return