def gradient(f, *varargs):
'''Gradient of array
f -- array
*varargs -- 0, 1, N scalars for sample distance, or (1 or N-d) datasets for sample points
'''
if varargs is None or len(varargs) == 0:
g = _maths.gradient(f)
else:
# check for scalars, etc
from jycore import arange as _ar
vl = len(varargs)
nd = f.getRank()
if vl == 1:
varargs = [varargs[0]]*nd
vl = nd
if vl != nd:
raise ValueError, "Number of arguments must be 0, 1 or rank of f"
xlist = []
for i in range(vl):
x = varargs[i]
xlist.append(x if isinstance(x, _ds) else (_ar(f.shape[i])*x)._jdataset())
g = _maths.gradient(f, xlist)
if len(g) == 1:
return g[0]
return g
def getPolarDataset(self, datasetX, datasetY): datasetRho = DatasetMaths.sqrt( DatasetMaths.power(datasetX, 2) + DatasetMaths.power(datasetY, 2) ); datasetTheta=DatasetMaths.arctan2(datasetY, datasetX); datasetThetaInDegree = DatasetMaths.toDegrees(datasetTheta); return [datasetRho, datasetThetaInDegree];
def gradient(f, *varargs):
'''Gradient of array
f -- array
*varargs -- 0, 1, N scalars for sample distance, or (1 or N-d) datasets for sample points
'''
if varargs is None or len(varargs) == 0:
g = _maths.gradient(f)
else:
# check for scalars, etc
from .jycore import arange as _ar
vl = len(varargs)
nd = f.getRank()
if vl == 1:
varargs = [varargs[0]] * nd
vl = nd
if vl != nd:
raise ValueError("Number of arguments must be 0, 1 or rank of f")
xlist = []
for i in range(vl):
x = varargs[i]
xlist.append(x if isinstance(x, _ds) else (_ar(f.shape[i]) *
x)._jdataset())
g = _maths.gradient(f, xlist)
if len(g) == 1:
return g[0]
return g
def renderShapesOntoDataset(self, targetDataset):
if self.shapesToPaint == {}:
return targetDataset.clone()
a = targetDataset.max()
b = targetDataset.min()
image = self.renderShapes(targetDataset)
image.imultiply(b - (a - b))
return Maths.add(targetDataset, image)
def _process(self,xDataSet, yDataSet):
dyDataSet = Maths.derivative(xDataSet._jdataset(), yDataSet._jdataset(), self.smoothwidth)
minVal, maxVal = dyDataSet.min(), dyDataSet.max()
if maxVal - minVal == 0:
raise ValueError("There is no edge")
labels = [label if label != 'slope' else 'top' for label in self.labelList]
return GaussianPeak(self.name, labels, self.formatString, self.plotPanel)._process(xDataSet, dyDataSet)
def _process(self, xDataSet, yDataSet):
dyDataSet = dnp.array(Maths.derivative(xDataSet._jdataset(), yDataSet._jdataset(), self.smoothwidth))
uposC, ufwhmC, uareaC, dposC, dfwhmC, dareaC = self.coarseProcess(xDataSet, dyDataSet)
gaussian = dnp.fit.function.gaussian
if abs(dareaC) < 0.2 * uareaC:
r = dnp.fit.fit([gaussian], xDataSet, dyDataSet,
[uposC, ufwhmC, uareaC],
bounds=[
(uposC - 2 * ufwhmC, uposC + 2 * ufwhmC),
(0, 2 * ufwhmC),
(0, 2 * uareaC)],
ptol=1e-10, optimizer=self.optimizer)
upos, ufwhm, _uarea = r.parameters
results = {'upos': upos, 'ufwhm': ufwhm, 'area': _uarea, 'uarea': _uarea, 'fwhm': ufwhm}
elif uareaC < 0.2 * abs(dareaC):
r = dnp.fit.fit([gaussian], xDataSet, dyDataSet,
[dposC, dfwhmC, dareaC],
bounds=[
(dposC - 2 * dfwhmC, dposC + 2 * dfwhmC),
(0, 2 * dfwhmC),
(2 * dareaC, 0)],
ptol=1e-10, optimizer=self.optimizer)
dpos, dfwhm, _darea = r.parameters
results = {'dpos': dpos, 'dfwhm': dfwhm, 'area': abs(_darea), 'darea': _darea, 'fwhm': dfwhm}
else:
r = dnp.fit.fit([gaussian, gaussian], xDataSet, dyDataSet,
[uposC, ufwhmC, uareaC,dposC, dfwhmC, dareaC],
bounds=[
(uposC - 2 * ufwhmC, uposC + 2 * ufwhmC),
(0, 2 * ufwhmC),
(0, 2 * uareaC),
(dposC - 2 * dfwhmC, dposC + 2 * dfwhmC),
(0, 2 * dfwhmC),
(2 * dareaC, 0)],
ptol=1e-10, optimizer=self.optimizer)
upos, ufwhm, _uarea, dpos, dfwhm, _darea = r.parameters
results = {'upos': upos,
'dpos': dpos,
'ufwhm': ufwhm,
'dfwhm': dfwhm,
'uarea': _uarea,
'darea': _darea,
'centre': (upos + dpos) / 2.0,
'width': abs(upos - dpos),
'area': (_uarea + abs(_darea)) / 2.0,
'fwhm': (ufwhm + dfwhm) / 2.0}
self.plotResult(r)
results['residual'] = r.residual
return [results.get(label, float('NaN')) for label in self.labelList]
def interp(x, xp, fp, left=None, right=None):
'''Linearly interpolate'''
x = _asarray(x)
xp = _asarray(xp)
fp = _asarray(fp)
if left is None:
left = fp[0]
if right is None:
right = fp[-1]
r = _asarray(_maths.interpolate(xp._jdataset(), fp._jdataset(), x._jdataset(), left, right))
if x.ndim == 0:
return r.item()
return r
def interp(x, xp, fp, left=None, right=None):
'''Linearly interpolate'''
x = _asarray(x)
xp = _asarray(xp)
fp = _asarray(fp)
if left is None:
left = fp[0]
if right is None:
right = fp[-1]
r = _asarray(_maths.interpolate(xp._jdataset(), fp._jdataset(), x._jdataset(), left, right))
if x.ndim == 0:
return r.item()
return r
def square(a, out=None):
'''Square of input'''
return _maths.square(a, out)
def floor(a, out=None):
'''Largest integer smaller or equal to input'''
return _maths.floor(a, out)
def log1p(x, out=None):
'''Natural logarithm of (x+1)'''
return _maths.log1p(x, out)
def expm1(x, out=None):
'''Exponential of (x-1)'''
return _maths.expm1(x, out)
def diff(a, order=1, axis=-1):
'''Difference of input'''
return _maths.difference(a, order, axis)
def log10(a, out=None):
'''Logarithm of input to base 10'''
return _maths.log10(a, out)
def power(a, p, out=None):
'''Input raised to given power'''
return _maths.power(a, p, out)
def sign(a, out=None):
'''Sign of input, indicated by -1 for negative, +1 for positive and 0 for zero'''
return _maths.signum(a, out)
def sqrt(a, out=None):
'''Square root of input'''
return _maths.sqrt(a, out)
def square(a, out=None):
'''Square of input'''
return _maths.square(a, out)
def expm1(x, out=None):
'''Exponential of (x-1)'''
return _maths.expm1(x, out)
def exp(a, out=None):
'''Exponential of input'''
return _maths.exp(a, out)
def log1p(x, out=None):
'''Natural logarithm of (x+1)'''
return _maths.log1p(x, out)
def rint(a, out=None):
'''Round elements of input to nearest integers'''
return _maths.rint(a, out)
def floor(a, out=None):
'''Largest integer smaller or equal to input'''
return _maths.floor(a, out)
def rad2deg(a, out=None):
'''Convert from radian to degree'''
return _maths.toDegrees(a, out)
def phase(a, keepzeros=False):
'''Calculate phase of input by dividing by amplitude
keepzeros -- if True, pass zeros through, else return complex NaNs
'''
return _maths.phaseAsComplexNumber(a, keepzeros)
def clip(a, a_min, a_max, out=None):
'''Clip input to given bounds (replace NaNs with midpoint of bounds)'''
return _maths.clip(a, a_min, a_max, out)
def log(a, out=None):
'''Natural logarithm of input'''
return _maths.log(a, out)
def ceil(a, out=None):
'''Smallest integer greater or equal to input'''
return _maths.ceil(a, out)
def log(a, out=None):
'''Natural logarithm of input'''
return _maths.log(a, out)
def arctanh(a, out=None):
'''Inverse hyperbolic tangent of input'''
return _maths.arctanh(a, out)
def rint(a, out=None):
'''Round elements of input to nearest integers'''
return _maths.rint(a, out)
def log10(a, out=None):
'''Logarithm of input to base 10'''
return _maths.log10(a, out)
def trunc(a, out=None):
'''Truncate elements of input to nearest integers'''
return _maths.truncate(a, out)
def exp(a, out=None):
'''Exponential of input'''
return _maths.exp(a, out)
def rad2deg(a, out=None):
'''Convert from radian to degree'''
return _maths.toDegrees(a, out)
def sqrt(a, out=None):
'''Square root of input'''
return _maths.sqrt(a, out)
def deg2rad(a, out=None):
'''Convert from degree to radian'''
return _maths.toRadians(a, out)
def power(a, p, out=None):
'''Input raised to given power'''
return _maths.power(a, p, out)
def sign(a, out=None):
'''Sign of input, indicated by -1 for negative, +1 for positive and 0 for zero'''
return _maths.signum(a, out)
def ceil(a, out=None):
'''Smallest integer greater or equal to input'''
return _maths.ceil(a, out)
def negative(a, out=None):
'''Negate input'''
return _maths.negative(a, out)
def trunc(a, out=None):
'''Truncate elements of input to nearest integers'''
return _maths.truncate(a, out)
def clip(a, a_min, a_max, out=None):
'''Clip input to given bounds (replace NaNs with midpoint of bounds)'''
return _maths.clip(a, a_min, a_max, out)
def deg2rad(a, out=None):
'''Convert from degree to radian'''
return _maths.toRadians(a, out)
def minimum(a, b, out=None):
'''Item-wise minimum'''
return _maths.minimum(a, b)
def negative(a, out=None):
'''Negate input'''
return _maths.negative(a, out)
def diff(a, order=1, axis=-1):
'''Difference of input'''
return _maths.difference(a, order, axis)
def minimum(a, b, out=None):
'''Item-wise minimum'''
return _maths.minimum(a, b)
def cbrt(a):
'''Cube root of input'''
return _maths.cbrt(a)
def dividez(a, b):
'''Divide one array-like object by another with items that are zero divisors set to zero'''
return _maths.dividez(a, b)
def tanh(a, out=None):
'''Hyperbolic tangent of input'''
return _maths.tanh(a, out)
def abs(a, out=None): #@ReservedAssignment
'''Absolute value of input'''
return _maths.abs(a, out)
def arctanh(a, out=None):
'''Inverse hyperbolic tangent of input'''
return _maths.arctanh(a, out)
def dividez(a, b):
'''Divide one array-like object by another with items that are zero divisors set to zero'''
return _maths.dividez(a, b)
def arccosh(a, out=None):
'''Inverse hyperbolic cosine of input'''
return _maths.arccosh(a, out)
def phase(a, keepzeros=False):
'''Calculate phase of input by dividing by amplitude
keepzeros -- if True, pass zeros through, else return complex NaNs
'''
return _maths.phaseAsComplexNumber(a, keepzeros)
def tanh(a, out=None):
'''Hyperbolic tangent of input'''
return _maths.tanh(a, out)
def cbrt(a):
'''Cube root of input'''
return _maths.cbrt(a)
def arccosh(a, out=None):
'''Inverse hyperbolic cosine of input'''
return _maths.arccosh(a, out)
