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Matmul overloaded for correlator class. (#199)
* feat: matmul method added to correlator class. * feat: corr, corr matmul and correlator matrix trace added. * tests: tests for matmul and trace added. * tests: slightly reduced tolerance and good guess bad guess test. * feat: rmatmul added and __array_priority__ set. * tests: additional tests for rmatmul added. * tests: one more tests for rmatmul added. * docs: docstring added to Corr.trace. * tests: associative property test added for complex Corr matmul. * fix: Corr.roll method now also works for correlator matrices by explicitly specifying the axis. Co-authored-by: Matteo Di Carlo <matteo.dicarlo93@gmail.com> * feat: exception type for correlator trace of 1dim correlator changed. * tests: trace N=1 exception tested. --------- Co-authored-by: Matteo Di Carlo <matteo.dicarlo93@gmail.com>
This commit is contained in:
Fabian Joswig
GitHub
parent
7d1858f6c4
commit
f1150f09c8
3 changed files with 185 additions and 6 deletions
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@ -220,7 +220,7 @@ class Corr:
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def anti_symmetric(self):
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"""Anti-symmetrize the correlator around x0=0."""
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if self.N != 1:
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raise Exception('anti_symmetric cannot be safely applied to multi-dimensional correlators.')
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raise TypeError('anti_symmetric cannot be safely applied to multi-dimensional correlators.')
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if self.T % 2 != 0:
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raise Exception("Can not symmetrize odd T")
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@ -242,7 +242,7 @@ class Corr:
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def is_matrix_symmetric(self):
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"""Checks whether a correlator matrices is symmetric on every timeslice."""
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if self.N == 1:
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raise Exception("Only works for correlator matrices.")
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raise TypeError("Only works for correlator matrices.")
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for t in range(self.T):
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if self[t] is None:
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continue
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@ -254,6 +254,18 @@ class Corr:
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return False
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return True
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def trace(self):
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"""Calculates the per-timeslice trace of a correlator matrix."""
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if self.N == 1:
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raise ValueError("Only works for correlator matrices.")
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newcontent = []
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for t in range(self.T):
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if _check_for_none(self, self.content[t]):
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newcontent.append(None)
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else:
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newcontent.append(np.trace(self.content[t]))
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return Corr(newcontent)
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def matrix_symmetric(self):
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"""Symmetrizes the correlator matrices on every timeslice."""
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if self.N == 1:
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@ -405,7 +417,7 @@ class Corr:
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dt : int
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number of timeslices
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"""
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return Corr(list(np.roll(np.array(self.content, dtype=object), dt)))
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return Corr(list(np.roll(np.array(self.content, dtype=object), dt, axis=0)))
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def reverse(self):
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"""Reverse the time ordering of the Corr"""
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@ -1020,6 +1032,8 @@ class Corr:
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# This is because Obs*Corr checks Obs.__mul__ first and does not catch an exception.
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# One could try and tell Obs to check if the y in __mul__ is a Corr and
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__array_priority__ = 10000
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def __add__(self, y):
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if isinstance(y, Corr):
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if ((self.N != y.N) or (self.T != y.T)):
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@ -1076,6 +1090,49 @@ class Corr:
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else:
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raise TypeError("Corr * wrong type")
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def __matmul__(self, y):
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if isinstance(y, np.ndarray):
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if y.ndim != 2 or y.shape[0] != y.shape[1]:
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raise ValueError("Can only multiply correlators by square matrices.")
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if not self.N == y.shape[0]:
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raise ValueError("matmul: mismatch of matrix dimensions")
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newcontent = []
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for t in range(self.T):
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if _check_for_none(self, self.content[t]):
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newcontent.append(None)
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else:
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newcontent.append(self.content[t] @ y)
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return Corr(newcontent)
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elif isinstance(y, Corr):
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if not self.N == y.N:
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raise ValueError("matmul: mismatch of matrix dimensions")
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newcontent = []
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for t in range(self.T):
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if _check_for_none(self, self.content[t]) or _check_for_none(y, y.content[t]):
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newcontent.append(None)
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else:
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newcontent.append(self.content[t] @ y.content[t])
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return Corr(newcontent)
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else:
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return NotImplemented
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def __rmatmul__(self, y):
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if isinstance(y, np.ndarray):
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if y.ndim != 2 or y.shape[0] != y.shape[1]:
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raise ValueError("Can only multiply correlators by square matrices.")
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if not self.N == y.shape[0]:
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raise ValueError("matmul: mismatch of matrix dimensions")
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newcontent = []
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for t in range(self.T):
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if _check_for_none(self, self.content[t]):
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newcontent.append(None)
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else:
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newcontent.append(y @ self.content[t])
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return Corr(newcontent)
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else:
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return NotImplemented
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def __truediv__(self, y):
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if isinstance(y, Corr):
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if not ((self.N == 1 or y.N == 1 or self.N == y.N) and self.T == y.T):
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@ -570,3 +570,125 @@ def test_corr_symmetric():
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assert scorr[1] == scorr[3]
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assert scorr[2] == corr[2]
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assert scorr[0] == corr[0]
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def test_two_matrix_corr_inits():
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T = 4
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rn = lambda : np.random.normal(0.5, 0.1)
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# Generate T random CObs in a list
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list_of_timeslices =[]
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for i in range(T):
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re = pe.pseudo_Obs(rn(), rn(), "test")
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im = pe.pseudo_Obs(rn(), rn(), "test")
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list_of_timeslices.append(pe.CObs(re, im))
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# First option: Correlator of matrix of correlators
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corr = pe.Corr(list_of_timeslices)
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mat_corr1 = pe.Corr(np.array([[corr, corr], [corr, corr]]))
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# Second option: Correlator of list of arrays per timeslice
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list_of_arrays = [np.array([[elem, elem], [elem, elem]]) for elem in list_of_timeslices]
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mat_corr2 = pe.Corr(list_of_arrays)
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for el in mat_corr1 - mat_corr2:
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assert np.all(el == 0)
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def test_matmul_overloading():
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N = 4
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rn = lambda : np.random.normal(0.5, 0.1)
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# Generate N^2 random CObs and assemble them in an array
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ll =[]
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for i in range(N ** 2):
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re = pe.pseudo_Obs(rn(), rn(), "test")
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im = pe.pseudo_Obs(rn(), rn(), "test")
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ll.append(pe.CObs(re, im))
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mat = np.array(ll).reshape(N, N)
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# Multiply with gamma matrix
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corr = pe.Corr([mat] * 4, padding=[0, 1])
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# __matmul__
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mcorr = corr @ pe.dirac.gammaX
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comp = mat @ pe.dirac.gammaX
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for i in range(4):
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assert np.all(mcorr[i] == comp)
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# __rmatmul__
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mcorr = pe.dirac.gammaX @ corr
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comp = pe.dirac.gammaX @ mat
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for i in range(4):
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assert np.all(mcorr[i] == comp)
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test_mat = pe.dirac.gamma5 + pe.dirac.gammaX
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icorr = corr @ test_mat @ np.linalg.inv(test_mat)
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tt = corr - icorr
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for i in range(4):
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assert np.all(tt[i] == 0)
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# associative property
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tt = (corr.real @ pe.dirac.gammaX + corr.imag @ (pe.dirac.gammaX * 1j)) - corr @ pe.dirac.gammaX
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for el in tt:
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if el is not None:
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assert np.all(el == 0)
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corr2 = corr @ corr
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for i in range(4):
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np.all(corr2[i] == corr[i] @ corr[i])
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def test_matrix_trace():
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N = 4
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rn = lambda : np.random.normal(0.5, 0.1)
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# Generate N^2 random CObs and assemble them in an array
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ll =[]
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for i in range(N ** 2):
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re = pe.pseudo_Obs(rn(), rn(), "test")
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im = pe.pseudo_Obs(rn(), rn(), "test")
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ll.append(pe.CObs(re, im))
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mat = np.array(ll).reshape(N, N)
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corr = pe.Corr([mat] * 4)
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# Explicitly check trace
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for el in corr.trace():
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el == np.sum(np.diag(mat))
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# Trace is cyclic
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for one, two in zip((pe.dirac.gammaX @ corr).trace(), (corr @ pe.dirac.gammaX).trace()):
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assert np.all(one == two)
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# Antisymmetric matrices are traceless.
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mat = (mat - mat.T) / 2
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corr = pe.Corr([mat] * 4)
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for el in corr.trace():
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assert el == 0
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with pytest.raises(ValueError):
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corr.item(0, 0).trace()
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def test_corr_roll():
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T = 4
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rn = lambda : np.random.normal(0.5, 0.1)
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ll = []
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for i in range(T):
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re = pe.pseudo_Obs(rn(), rn(), "test")
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im = pe.pseudo_Obs(rn(), rn(), "test")
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ll.append(pe.CObs(re, im))
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# Rolling by T should produce the same correlator
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corr = pe.Corr(ll)
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tt = corr - corr.roll(T)
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for el in tt:
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assert np.all(el == 0)
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mcorr = pe.Corr(np.array([[corr, corr + 0.1], [corr - 0.1, 2 * corr]]))
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tt = mcorr.roll(T) - mcorr
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for el in tt:
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assert np.all(el == 0)
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@ -235,12 +235,12 @@ def test_fit_corr_independent():
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def test_linear_fit_guesses():
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for err in [10, 0.1, 0.001]:
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for err in [1.2, 0.1, 0.001]:
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xvals = []
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yvals = []
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for x in range(1, 8, 2):
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xvals.append(x)
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yvals.append(pe.pseudo_Obs(x + np.random.normal(0.0, err), err, 'test1') + pe.pseudo_Obs(0, err / 100, 'test2', samples=87))
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yvals.append(pe.pseudo_Obs(x + np.random.normal(0.0, err), err, 'test1') + pe.pseudo_Obs(0, err / 97, 'test2', samples=87))
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lin_func = lambda a, x: a[0] + a[1] * x
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with pytest.raises(Exception):
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pe.least_squares(xvals, yvals, lin_func)
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@ -251,7 +251,7 @@ def test_linear_fit_guesses():
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bad_guess = pe.least_squares(xvals, yvals, lin_func, initial_guess=[999, 999])
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good_guess = pe.least_squares(xvals, yvals, lin_func, initial_guess=[0, 1])
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assert np.isclose(bad_guess.chisquare, good_guess.chisquare, atol=1e-8)
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assert np.all([(go - ba).is_zero(atol=1e-6) for (go, ba) in zip(good_guess, bad_guess)])
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assert np.all([(go - ba).is_zero(atol=5e-5) for (go, ba) in zip(good_guess, bad_guess)])
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def test_total_least_squares():
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