Introduce utils.simulate()

enterprise/signals/gp_signals.py

@@ -13,9 +13,9 @@
 from sksparse.cholmod import cholesky
 
 from enterprise.signals import parameter, selections, signal_base, utils
+from enterprise.signals.signal_base import KernelMatrix
 from enterprise.signals.parameter import function
 from enterprise.signals.selections import Selection
-from enterprise.signals.utils import KernelMatrix
 
 # logging.basicConfig(format="%(levelname)s: %(name)s: %(message)s", level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -225,11 +225,11 @@ def get_timing_model_basis(use_svd=False, normed=True):
         return utils.unnormed_tm_basis()
 
 
-def TimingModel(coefficients=False, name="linear_timing_model", use_svd=False, normed=True):
+def TimingModel(coefficients=False, name="linear_timing_model", use_svd=False, normed=True, prior_variance=1e40):
     """Class factory for marginalized linear timing model signals."""
 
     basis = get_timing_model_basis(use_svd, normed)
-    prior = utils.tm_prior()
+    prior = utils.tm_prior(variance=prior_variance)
 
     BaseClass = BasisGP(prior, basis, coefficients=coefficients, name=name)
 
@@ -848,6 +848,7 @@ def MNMMNF(self, T):
 
     # we're ignoring logdet = True for two-dimensional cases, but OK
     def solve(self, right, left_array=None, logdet=False):
+        # compute generalized version of r+ N^-1 r
         if right.ndim == 1 and left_array is right:
             res = right
 
@@ -856,11 +857,13 @@ def solve(self, right, left_array=None, logdet=False):
             MNr = self.MNr(res)
             ret = rNr - np.dot(MNr, self.cf(MNr))
             return (ret, logdet_N + self.cf.logdet() + self.Mprior) if logdet else ret
+        # compute generalized version of T+ N^-1 r
         elif right.ndim == 1 and left_array is not None and left_array.ndim == 2:
             res, T = right, left_array
 
             TNr = self.Nmat.solve(res, left_array=T)
             return TNr - np.tensordot(self.MNMMNF(T), self.MNr(res), (0, 0))
+        # compute generalized version of T+ N^-1 T
         elif right.ndim == 2 and left_array is right:
             T = right
 

enterprise/signals/signal_base.py

@@ -24,7 +24,6 @@
 from enterprise.signals.parameter import Function  # noqa: F401
 from enterprise.signals.parameter import function  # noqa: F401
 from enterprise.signals.parameter import ConstantParameter
-from enterprise.signals.utils import KernelMatrix
 
 from enterprise import __version__
 from sys import version
@@ -1212,6 +1211,89 @@ def solve(self, other, left_array=None, logdet=False):
         return (ret, self._get_logdet()) if logdet else ret
 
 
+class KernelMatrix(np.ndarray):
+    def __new__(cls, init):
+        if isinstance(init, int):
+            ret = np.zeros(init, "d").view(cls)
+        else:
+            ret = init.view(cls)
+
+        if ret.ndim == 2:
+            ret._cliques = -1 * np.ones(ret.shape[0])
+            ret._clcount = 0
+
+        return ret
+
+    # see PTA._setcliques
+    def _setcliques(self, idxs):
+        allidx = set(self._cliques[idxs])
+        maxidx = max(allidx)
+
+        if maxidx == -1:
+            self._cliques[idxs] = self._clcount
+            self._clcount = self._clcount + 1
+        else:
+            self._cliques[idxs] = maxidx
+            if len(allidx) > 1:
+                self._cliques[np.in1d(self._cliques, allidx)] = maxidx
+
+    def add(self, other, idx):
+        if other.ndim == 2 and self.ndim == 1:
+            self = KernelMatrix(np.diag(self))
+
+        if self.ndim == 1:
+            self[idx] += other
+        else:
+            if other.ndim == 1:
+                self[idx, idx] += other
+            else:
+                self._setcliques(idx)
+                idx = (idx, idx) if isinstance(idx, slice) else (idx[:, None], idx)
+                self[idx] += other
+
+        return self
+
+    def set(self, other, idx):
+        if other.ndim == 2 and self.ndim == 1:
+            self = KernelMatrix(np.diag(self))
+
+        if self.ndim == 1:
+            self[idx] = other
+        else:
+            if other.ndim == 1:
+                self[idx, idx] = other
+            else:
+                self._setcliques(idx)
+                idx = (idx, idx) if isinstance(idx, slice) else (idx[:, None], idx)
+                self[idx] = other
+
+        return self
+
+    def inv(self, logdet=False):
+        if self.ndim == 1:
+            inv = 1.0 / self
+
+            if logdet:
+                return inv, np.sum(np.log(self))
+            else:
+                return inv
+        else:
+            try:
+                cf = sl.cho_factor(self)
+                inv = sl.cho_solve(cf, np.identity(cf[0].shape[0]))
+                if logdet:
+                    ld = 2.0 * np.sum(np.log(np.diag(cf[0])))
+            except np.linalg.LinAlgError:
+                u, s, v = np.linalg.svd(self)
+                inv = np.dot(u / s, u.T)
+                if logdet:
+                    ld = np.sum(np.log(s))
+            if logdet:
+                return inv, ld
+            else:
+                return inv
+
+
 class ShermanMorrison(object):
     """Custom container class for Sherman-morrison array inversion."""
 

enterprise/signals/utils.py

@@ -18,6 +18,7 @@
 import enterprise
 from enterprise import constants as const
 from enterprise import signals as sigs  # noqa: F401
+from enterprise.signals.signal_base import ShermanMorrison
 from enterprise.signals.gp_bases import (  # noqa: F401
     createfourierdesignmatrix_dm,
     createfourierdesignmatrix_env,
@@ -31,6 +32,73 @@
 logger = logging.getLogger(__name__)
 
 
+def simulate(pta, params, sparse_cholesky=True):
+    """Simulate residuals for all pulsars in `pta` by sampling all white-noise
+    and GP objects for parameters `params`. Requires GPs to have `combine=False`,
+    and will run faster with GP ECORR. If `pta` includes a `TimingModel`, that
+    should be created with a small `prior_variance`. This function can be used
+    with `utils.set_residuals` to replace residuals in a `Pulsar` object.
+    Note that any PTA built from that `Pulsar` may nevertheless cache residuals
+    internally, so it is safer to rebuild the PTA with the modified `Pulsar`."""
+
+    delays, ndiags, fmats, phis = (
+        pta.get_delay(params=params),
+        pta.get_ndiag(params=params),
+        pta.get_basis(params=params),
+        pta.get_phi(params=params),
+    )
+
+    gpresiduals = []
+    if pta._commonsignals:
+        if sparse_cholesky:
+            cf = cholesky(sps.csc_matrix(phis))
+            gp = np.zeros(phis.shape[0])
+            gp[cf.P()] = np.dot(cf.L().toarray(), np.random.randn(phis.shape[0]))
+        else:
+            gp = np.dot(sl.cholesky(phis, lower=True), np.random.randn(phis.shape[0]))
+
+        i = 0
+        for fmat in fmats:
+            j = i + fmat.shape[1]
+            gpresiduals.append(np.dot(fmat, gp[i:j]))
+            i = j
+
+        assert len(gp) == i
+    else:
+        for fmat, phi in zip(fmats, phis):
+            if phi is None:
+                gpresiduals.append(0)
+            elif phi.ndim == 1:
+                gpresiduals.append(np.dot(fmat, np.sqrt(phi) * np.random.randn(phi.shape[0])))
+            else:
+                raise NotImplementedError
+
+    whiteresiduals = []
+    for delay, ndiag in zip(delays, ndiags):
+        if ndiag is None:
+            whiteresiduals.append(0)
+        elif isinstance(ndiag, ShermanMorrison):
+            # this code is very slow...
+            n = np.diag(ndiag._nvec)
+            for j, s in zip(ndiag._jvec, ndiag._slices):
+                n[s, s] += j
+            whiteresiduals.append(delay + np.dot(sl.cholesky(n, lower=True), np.random.randn(n.shape[0])))
+        elif ndiag.ndim == 1:
+            whiteresiduals.append(delay + np.sqrt(ndiag) * np.random.randn(ndiag.shape[0]))
+        else:
+            raise NotImplementedError
+
+    return [np.array(g + w) for g, w in zip(gpresiduals, whiteresiduals)]
+
+
+def set_residuals(psr, y):
+    if isinstance(psr, list):
+        for p, r in zip(psr, y):
+            p._residuals[p._isort] = r
+    else:
+        psr._residuals[psr._isort] = y
+
+
 class ConditionalGP:
     def __init__(self, pta, phiinv_method="cliques"):
         """This class allows the computation of conditional means and
@@ -208,89 +276,6 @@ def get_coefficients(pta, params, n=1, phiinv_method="cliques", variance=True, c
         return ret[0] if n == 1 else ret
 
 
-class KernelMatrix(np.ndarray):
-    def __new__(cls, init):
-        if isinstance(init, int):
-            ret = np.zeros(init, "d").view(cls)
-        else:
-            ret = init.view(cls)
-
-        if ret.ndim == 2:
-            ret._cliques = -1 * np.ones(ret.shape[0])
-            ret._clcount = 0
-
-        return ret
-
-    # see PTA._setcliques
-    def _setcliques(self, idxs):
-        allidx = set(self._cliques[idxs])
-        maxidx = max(allidx)
-
-        if maxidx == -1:
-            self._cliques[idxs] = self._clcount
-            self._clcount = self._clcount + 1
-        else:
-            self._cliques[idxs] = maxidx
-            if len(allidx) > 1:
-                self._cliques[np.in1d(self._cliques, allidx)] = maxidx
-
-    def add(self, other, idx):
-        if other.ndim == 2 and self.ndim == 1:
-            self = KernelMatrix(np.diag(self))
-
-        if self.ndim == 1:
-            self[idx] += other
-        else:
-            if other.ndim == 1:
-                self[idx, idx] += other
-            else:
-                self._setcliques(idx)
-                idx = (idx, idx) if isinstance(idx, slice) else (idx[:, None], idx)
-                self[idx] += other
-
-        return self
-
-    def set(self, other, idx):
-        if other.ndim == 2 and self.ndim == 1:
-            self = KernelMatrix(np.diag(self))
-
-        if self.ndim == 1:
-            self[idx] = other
-        else:
-            if other.ndim == 1:
-                self[idx, idx] = other
-            else:
-                self._setcliques(idx)
-                idx = (idx, idx) if isinstance(idx, slice) else (idx[:, None], idx)
-                self[idx] = other
-
-        return self
-
-    def inv(self, logdet=False):
-        if self.ndim == 1:
-            inv = 1.0 / self
-
-            if logdet:
-                return inv, np.sum(np.log(self))
-            else:
-                return inv
-        else:
-            try:
-                cf = sl.cho_factor(self)
-                inv = sl.cho_solve(cf, np.identity(cf[0].shape[0]))
-                if logdet:
-                    ld = 2.0 * np.sum(np.log(np.diag(cf[0])))
-            except np.linalg.LinAlgError:
-                u, s, v = np.linalg.svd(self)
-                inv = np.dot(u / s, u.T)
-                if logdet:
-                    ld = np.sum(np.log(s))
-            if logdet:
-                return inv, ld
-            else:
-                return inv
-
-
 def create_stabletimingdesignmatrix(designmat, fastDesign=True):
     """
     Stabilize the timing-model design matrix.
@@ -885,8 +870,8 @@ def svd_tm_basis(Mmat):
 
 
 @function
-def tm_prior(weights):
-    return weights * 1e40
+def tm_prior(weights, variance=1e40):
+    return weights * variance
 
 
 # Physical ephemeris model utility functions

tests/test_utils.py

@@ -14,8 +14,8 @@
 
 import enterprise.constants as const
 from enterprise.pulsar import Pulsar
+from enterprise.signals import utils, parameter, signal_base, white_signals, gp_signals
 from enterprise.signals import anis_coefficients as anis
-from enterprise.signals import utils
 from tests.enterprise_test_data import datadir
 
 
@@ -26,6 +26,7 @@ def setUpClass(cls):
 
         # initialize Pulsar class
         cls.psr = Pulsar(datadir + "/B1855+09_NANOGrav_9yv1.gls.par", datadir + "/B1855+09_NANOGrav_9yv1.tim")
+        cls.psr2 = Pulsar(datadir + "/J1909-3744_NANOGrav_9yv1.gls.par", datadir + "/J1909-3744_NANOGrav_9yv1.tim")
 
         cls.F, _ = utils.createfourierdesignmatrix_red(cls.psr.toas, nmodes=30)
 
@@ -35,6 +36,28 @@ def setUpClass(cls):
 
         cls.Mm = utils.create_stabletimingdesignmatrix(cls.psr.Mmat)
 
+    def test_simulate(self):
+        ef = white_signals.MeasurementNoise()
+
+        ec = gp_signals.EcorrBasisModel()
+
+        pl = utils.powerlaw(log10_A=parameter.Uniform(-16, -13), gamma=parameter.Uniform(1, 7))
+        orf = utils.hd_orf()
+        crn = gp_signals.FourierBasisCommonGP(pl, orf, components=20, name="GW")
+
+        m = ef + ec + crn
+
+        pta = signal_base.PTA([m(self.psr), m(self.psr2)])
+
+        ys = utils.simulate(pta, params=parameter.sample(pta.params))
+
+        msg = "Simulated residuals shape incorrect"
+        assert ys[0].shape == self.psr.residuals.shape, msg
+        assert ys[1].shape == self.psr2.residuals.shape, msg
+
+        msg = "Simulated residuals shape not a number"
+        assert np.all(~np.isnan(np.concatenate(ys))), msg
+
     def test_createstabletimingdesignmatrix(self):
         """Timing model design matrix shape."""