def __init__(self, orig=None, description='unknown'):
if orig is None:
pyobs.check_type(description, 'text', str)
self.description = description
self.www = [
pwd.getpwuid(os.getuid())[0],
os.uname()[1],
datetime.datetime.now().strftime('%c')
]
self.shape = []
self.size = 0
self.mean = []
self.ename = []
self.delta = {}
self.cdata = {}
else:
if isinstance(orig, observable):
self.description = orig.description
self.www = orig.www
self.shape = orig.shape
self.size = numpy.prod(self.shape)
self.mean = numpy.array(orig.mean) # or orig.mean.copy()
self.ename = [e for e in orig.ename]
self.delta = {}
for key in orig.delta:
self.delta[key] = orig.delta[key].copy()
self.cdata = {}
for key in orig.cdata:
self.cdata[key] = cdata(orig.cdata[key].cov)
pyobs.memory.add(self)
else:
raise pyobs.PyobsError('Unexpected orig argument')
pyobs.memory.add(self)
def add_syst_err(self, name, err):
"""
Add a systematic error to the observable
Parameters:
name (str): label that uniquely identifies the syst. error
err (array): array with the same dimensions of the observable
with the systematic error
Examples:
>>> data = [0.198638, 0.403983, 1.215960, 1.607684, 0.199049, ... ]
>>> vec = pyobs.observable(description='vector')
>>> vec.create('A',data,shape=(4,))
>>> print(vec)
0.201(13) 0.399(26) 1.199(24) 1.603(47)
>>> vec.add_syst_err('syst.err',[0.05,0.05,0,0])
>>> print(vec)
0.201(52) 0.399(56) 1.199(24) 1.603(47)
"""
pyobs.check_type(name, 'name', str)
if name in self.cdata:
raise pyobs.PyobsError(f'Label {name} already used')
if numpy.shape(err) != self.shape:
raise pyobs.PyobsError(
f'Unexpected error, dimensions do not match {self.shape}')
cov = numpy.reshape(numpy.array(err)**2, (self.size, ))
self.cdata[name] = cdata(cov)
pyobs.memory.update(self)
def create_from_cov(self, cname, value, covariance):
"""
Create observables based on covariance matrices
Parameters:
cname (str): label that uniquely identifies the data set
value (array): central value of the observable; only 1-D arrays are supported
covariance (array): a 2-D covariance matrix; if `covariance` is a 1-D array of
same length as `value`, a diagonal covariance matrix is assumed.
Examples:
>>> mpi = pyobs.observable(description='pion masses, charged and neutral')
>>> mpi.create_cd('mpi-pdg18',[139.57061,134.9770],[0.00023**2,0.0005**2])
>>> print(mpi)
139.57061(23) 134.97700(50)
"""
if isinstance(value, (int, float, numpy.float64, numpy.float32)):
self.mean = numpy.reshape(value, (1, ))
cov = numpy.reshape(covariance, (1, ))
else:
self.mean = numpy.array(value)
cov = numpy.array(covariance)
self.shape = numpy.shape(self.mean)
if numpy.ndim(self.shape) != 1:
raise pyobs.PyobsError(
f'Unexpected value, only 1-D arrays are supported')
self.size = numpy.prod(self.shape)
if cov.shape != (self.size, ) and cov.shape != (self.size, self.size):
raise pyobs.PyobsError(
f'Unexpected shape for covariance {cov.shape}')
pyobs.check_type(cname, 'cname', str)
self.cdata[cname] = cdata(cov)
pyobs.memory.update(self)
def __call__(self, yobs, p0=None, min_search=None):
if len(self.csq) > 1:
pyobs.check_type(yobs, 'yobs', list)
else:
if isinstance(yobs, pyobs.observable):
yobs = [yobs]
if len(yobs) != len(self.csq):
raise pyobs.PyobsError(
f'Unexpected number of observables for {len(self.csq)} fits')
if p0 is None:
p0 = [1.0] * len(self.pdict)
if min_search is None:
min_search = lm
def csq(p0):
res = 0.0
for i in range(len(yobs)):
self.csq[i].set_pars(self.pdict, p0)
res += self.csq[i](yobs[i].mean)
return res
dcsq = lambda x: sum([
self.csq[i].grad(yobs[i].mean, self.pdict)
for i in range(len(yobs))
])
ddcsq = lambda x: sum([
self.csq[i].hess(yobs[i].mean, self.pdict)
for i in range(len(yobs))
])
t0 = time()
res = min_search(csq, p0, jac=dcsq, hess=ddcsq)
# properly create gradients
H = self.csq[0].Hmat(self.pdict, res.x)
for i in range(1, len(yobs)):
H += self.csq[i].Hmat(self.pdict, res.x)
Hinv = numpy.linalg.inv(H)
g = []
for i in range(len(yobs)):
tmp = self.csq[i].gvec(self.pdict, res.x)
g.append(pyobs.gradient(Hinv @ tmp))
if pyobs.is_verbose('mfit.run') or pyobs.is_verbose('mfit'):
print(f'chisquare = {res.fun}')
print(f'minimizer iterations = {res.nit}')
print(f'minimizer status: {res.message}')
print(f'mfit.run executed in {time()-t0:g} secs')
return pyobs.derobs(yobs, res.x, g)
def error_bias4(x, f):
pyobs.check_type(x, 'x', pyobs.observable)
[x0, dx0] = x.error()
bias4 = numpy.zeros((x.size, ))
hess = num_hess(x0, f)
for key in x.delta:
oid = numpy.array(x.delta[key].mask)
idx = numpy.ix_(oid, oid)
d2 = numpy.einsum('abc,bj,cj->aj', hess[:, idx[0], idx[1]],
x.delta[key].delta, x.delta[key].delta)
dd2 = numpy.sum(d2, axis=1)
bias4 += dd2**2 / x.delta[key].n**4
return numpy.sqrt(bias4)
def eval(self, xax, pars):
"""
Evaluates the function on a list of coordinates using the parameters
obtained from a :math:`\chi^2` minimization.
Parameters:
xax (array,list of arrays) : the coordinates :math:`x_i^\mu` where
the function must be evaluated. For combined fits, a list of
arrays must be passed, one for each fit.
pars (obs) : the observable returned by calling this class
Returns:
list of obs : observables corresponding to the functions evaluated
at the coordinates `xax`.
Examples:
>>> fit1 = mfit(xax,W,f,df)
>>> pars = fit1(yobs1)
>>> print(pars)
0.925(35) 2.050(19)
>>> xax = numpy.arange(0,10,0.2)
>>> yeval = fit1.eval(xax, pars)
"""
if not type(xax) is list:
xax = [xax]
pyobs.check_type(pars, 'pars', pyobs.observable)
N = len(xax)
if N != len(self.csq):
raise pyobs.PyobsError(
f'Coordinates and Paramters do not match number of internal functions'
)
out = []
for ic in self.csq:
[m, g] = self.csq[ic].eval(xax[ic], self.pdict, pars.mean)
out.append(pyobs.derobs([pars], m, [pyobs.gradient(g)]))
return out
def derobs(inps, mean, grads, description=None):
t0 = time()
pyobs.check_type(inps, 'inps', list)
pyobs.check_type(mean, 'mean', numpy.ndarray, int, float, numpy.float32,
numpy.float64)
pyobs.check_type(grads, 'grads', list)
if len(inps) != len(grads):
raise pyobs.PyobsError('Incompatible inps and grads')
if description is None:
description = ', '.join(set([i.description for i in inps]))
res = pyobs.observable(description=description)
res.set_mean(mean)
allkeys = []
for i in inps:
for dn in i.delta:
if not dn in allkeys:
allkeys.append(dn)
for key in allkeys:
new_idx = []
new_mask = []
lat = None
for i in range(len(inps)):
if key in inps[i].delta:
data = inps[i].delta[key]
h = grads[i].get_mask(data.mask)
if not h is None:
new_mask += h
if not new_idx:
new_idx = data.idx
else:
new_idx = merge_idx(new_idx, data.idx)
if lat is None:
lat = data.lat
else:
if numpy.any(lat != data.lat): # pragma: no cover
raise pyobs.PyobsError(
f'Unexpected lattice size for master fields with same tag'
)
if len(new_mask) > 0:
res.delta[key] = delta(list(set(new_mask)), new_idx, lat=lat)
for i in range(len(inps)):
if key in inps[i].delta:
res.delta[key].axpy(grads[i], inps[i].delta[key])
res.ename = []
for key in res.delta:
name = key.split(':')[0]
if not name in res.ename:
res.ename.append(name)
res.cdata = {}
allkeys = []
for i in inps:
for cd in i.cdata:
if not cd in allkeys:
allkeys.append(cd)
for key in allkeys:
for i in range(len(inps)):
if key in inps[i].cdata:
if not key in res.cdata:
res.cdata[key] = cdata(numpy.zeros(res.size))
res.cdata[key].axpy(grads[i], inps[i].cdata[key])
pyobs.memory.update(res)
if pyobs.is_verbose('derobs'):
print(f'derobs executed in {time()-t0:g} secs')
return res
def create(self,
ename,
data,
icnfg=None,
rname=None,
shape=(1, ),
lat=None):
"""
Create an observable
Parameters:
ename (str): label of the ensemble
data (array, list of arrays): the data generated from a single
or multiple replica
icnfg (array of ints or list of arrays of ints, optional): indices
of the configurations corresponding to data; if not passed the
measurements are assumed to be contiguous
rname (str or list of str, optional): identifier of the replica; if
not passed integers from 0 are automatically assigned
shape (tuple, optional): shape of the observable, data must be passed accordingly
lat (list of ints, optional): the size of each dimension of the master-field;
if passed data is assumed to be obtained from observables measured at different
sites and `icnfg` is re-interpreted as the index labeling the sites; if `icnfg`
is not passed data is assumed to be contiguous on all sites.
Note:
For data and icnfg array can mean either a list or a 1-D numpy.array.
If the observable has already been created, calling create again will add
a new replica to the same ensemble.
Examples:
>>> data = [0.43, 0.42, ... ] # a scalar observable
>>> a = pyobs.observable(description='test')
>>> a.create('EnsembleA',data)
>>> data0 = [0.43,0.42, ... ] # replica 0
>>> data1 = [0.40,0.41, ... ] # replica 1
>>> a = pyobs.observable(description='test')
>>> a.create('EnsembleA',[data0,data1],rname=['r0','r1'])
>>> data = [0.43, 0.42, 0.44, ... ]
>>> icnfg= [ 10, 11, 13, ... ]
>>> a = pyobs.observable(description='test')
>>> a.create('EnsembleA',data,icnfg=icnfg)
>>> data = [1.0, 2.0, 3.0, 4.0, ... ]
>>> a = pyobs.observable(description='matrix')
>>> a.create('EnsembleA',data,shape=(2,2))
Examples:
>>> data = [0.43, 0.42, 0.44, ... ]
>>> lat = [64,32,32,32]
>>> a = pyobs.observable(description='test-mf')
>>> a.create('EnsembleA',data,lat=lat)
>>> data = [0.43, 0.42, 0.44, ... ]
>>> idx = [0, 2, 4, 6, ...] # measurements on all even points of time-slice
>>> lat = [32, 32, 32]
>>> a = pyobs.observable(description='test-mf')
>>> a.create('EnsembleA',data,lat=lat,icnfg=idx)
"""
t0 = time()
pyobs.check_type(ename, 'ename', str)
if ':' in ename:
raise pyobs.PyobsError(
f'Column symbol not allowed in ename {ename}')
pyobs.check_type(shape, 'shape', tuple)
self.shape = shape
self.size = numpy.prod(shape)
mask = range(self.size)
if not ename in self.ename:
self.ename.append(ename)
if isinstance(data[0], (list, numpy.ndarray)):
R = len(data)
elif isinstance(data[0], (int, float, numpy.float64, numpy.float32)):
R = 1
else:
raise pyobs.PyobsError(f'Unexpected data type')
if R == 1:
pyobs.check_type(data, f'data', list, numpy.ndarray)
nc = int(len(data) / self.size)
if rname is None:
rname = 0
else:
pyobs.check_not_type(rname, 'rname', list)
if icnfg is None:
icnfg = range(nc)
else:
pyobs.check_type(icnfg, 'icnfg', list, range)
pyobs.check_type(icnfg[0], 'icnfg[:]', int, numpy.int32,
numpy.int64)
if len(icnfg) * self.size != len(data):
raise pyobs.PyobsError(
f'Incompatible icnfg and data, for shape={shape}')
if numpy.size(self.mean) != 0:
N0 = sum([self.delta[key].n for key in self.delta])
mean0 = numpy.reshape(self.mean, (self.size, ))
mean1 = numpy.mean(numpy.reshape(data, (nc, self.size)), 0)
self.mean = (N0 * mean0 + nc * mean1) / (N0 + nc)
shift = nc * (mean0 - mean1) / (N0 + nc)
for key in self.delta:
self.delta[key].delta += shift[:, None]
else:
self.mean = numpy.mean(numpy.reshape(data, (nc, self.size)), 0)
key = f'{ename}:{rname}'
self.delta[key] = delta(mask, icnfg, data, self.mean, lat)
else:
if numpy.size(self.mean) != 0:
raise pyobs.PyobsError(
'Only a single replica can be added to existing observables'
)
for ir in range(R):
pyobs.check_type(data[ir], f'data[{ir}]', list, numpy.ndarray)
self.mean = numpy.zeros((self.size, ))
nt = 0
for dd in data:
nc = int(len(dd) / self.size)
self.mean += numpy.sum(numpy.reshape(dd, (nc, self.size)), 0)
nt += nc
self.mean *= 1.0 / float(nt)
if rname is None:
rname = range(R)
else:
pyobs.check_type(rname, 'rname', list)
if len(rname) != R:
raise pyobs.PyobsError('Incompatible rname and data')
if not icnfg is None:
pyobs.check_type(icnfg, 'icnfg', list)
if icnfg is None:
icnfg = []
for ir in range(len(data)):
nc = int(len(data[ir]) / self.size)
icnfg.append(range(nc))
else:
for ir in range(len(data)):
if len(icnfg[ir]) * self.size != len(data[ir]):
raise pyobs.PyobsError(
f'Incompatible icnfg[{ir}] and data[{ir}], for shape={shape}'
)
for ir in range(len(data)):
key = f'{ename}:{rname[ir]}'
self.delta[key] = delta(mask, icnfg[ir], data[ir], self.mean,
lat)
self.mean = numpy.reshape(self.mean, self.shape)
pyobs.memory.update(self)
if pyobs.is_verbose('create'):
print(f'create executed in {time()-t0:g} secs')
def diff(f, x, dx):
"""
Utility function to compute the gradient and hessian of a function using symbolic calculus
Parameters:
f (string): the reference function
x (string): variables that are not differentiated; different variables
must be separated by a comma
dx (string): variables that are differentiated; different variables
must be separated by a comma
Returns:
lambda : (scalar) function `f`
lambda : (vector) function of the gradient of `f`
lambda : (matrix) function of the hessian of `f`
Notes:
The symbolic manipulation is based on the library `sympy` and the user
must follow the `sympy` syntax when passing the argument `f`. The analytic
form of the gradient and hessian can be printed by activating the 'diff'
verbose flag.
Examples:
>>> res = diff('a+b*x','x','a,b') # differentiate wrt to a and b
a + b*x
[1, x]
[[0, 0], [0, 0]]
>>> for i in range(3):
>>> print(res[i](0.4,1.,2.))
1.8
[1, 0.4]
[[0, 0], [0, 0]]
"""
pyobs.check_type(f, 'f', str)
pyobs.check_type(x, 'x', str)
pyobs.check_type(dx, 'dx', str)
sym = {}
for y in dx.rsplit(','):
sym[y] = sympy.Symbol(y)
expr = parse_expr(f, local_dict=sym)
allvars = f'{x},{dx}'
func = sympy.lambdify(allvars, expr, 'numpy')
dexpr = []
ddexpr = []
for y in sym:
dexpr.append(sympy.diff(expr, sym[y]))
tmp = []
for z in sym:
tmp.append(sympy.diff(dexpr[-1], sym[z]))
ddexpr.append(tmp)
if pyobs.is_verbose('diff') or pyobs.is_verbose('symbolic.diff'):
display(expr)
display(dexpr)
display(ddexpr)
dfunc = sympy.lambdify(allvars, dexpr, 'numpy')
ddfunc = sympy.lambdify(allvars, ddexpr, 'numpy')
return [func, dfunc, ddfunc]
