Merge pull request #126 from dngoldberg/branch_obs_sensitivity

i did pytests and all seemed ok

Author: dngoldberg

Diff for: example_cases/ismipc_30x30/ismipc_30x30.sh

@@ -13,8 +13,8 @@ cp output_momsolve/ismipc_U_obs.h5 input/
 
 #Run each phase of the model in turn
 RUN_DIR=$FENICS_ICE_BASE_DIR/runs/
-python $RUN_DIR/run_inv.py ismipc_30x30.toml
-python $RUN_DIR/run_forward.py ismipc_30x30.toml
-python $RUN_DIR/run_eigendec.py ismipc_30x30.toml
-python $RUN_DIR/run_errorprop.py ismipc_30x30.toml
-python $RUN_DIR/run_invsigma.py ismipc_30x30.toml
+#python $RUN_DIR/run_inv.py ismipc_30x30.toml
+#python $RUN_DIR/run_forward.py ismipc_30x30.toml
+#python $RUN_DIR/run_eigendec.py ismipc_30x30.toml
+#python $RUN_DIR/run_errorprop.py ismipc_30x30.toml
+mpirun -n 4 python $RUN_DIR/run_obs_sens_prop.py ismipc_30x30.toml

Diff for: example_cases/ismipc_30x30/ismipc_30x30.toml

@@ -32,7 +32,7 @@ random_seed = 0
 [mesh]
 
 mesh_filename = "ismip_mesh.xml"
-periodic_bc = true
+periodic_bc = false
 
 [obs]
 
@@ -67,7 +67,8 @@ sliding_law = 'linear' #budd, linear
 [momsolve.picard_params]
 nonlinear_solver = "newton"
 [momsolve.picard_params.newton_solver]
-linear_solver = "umfpack"
+linear_solver = "cg"
+preconditioner = "hypre_amg"
 maximum_iterations = 200
 absolute_tolerance = 1.0e-0
 relative_tolerance = 1.0e-3
@@ -77,7 +78,9 @@ error_on_nonconvergence =  false
 [momsolve.newton_params]
 nonlinear_solver = "newton"
 [momsolve.newton_params.newton_solver]
-linear_solver = "umfpack"
+#linear_solver = "umfpack"
+linear_solver = "cg"
+preconditioner = "hypre_amg"
 maximum_iterations = 25
 absolute_tolerance = 1.0e-7
 relative_tolerance = 1.0e-8
@@ -128,6 +131,10 @@ qoi = 'h2' #or 'vaf'
     name = "Bottom Edge"
     id = 4
 
+[mass_solve]
+
+use_cg_thickness = true
+
 [testing]
 
 expected_init_alpha = 531.6114524861194

Diff for: fenics_ice/config.py

@@ -124,6 +124,19 @@ def parse(self):
         except KeyError:
             pass
 
+        try:
+            obs_sens_dict = self.config_dict['obs_sens']
+        except KeyError:
+            obs_sens_dict = {}
+        self.obs_sens = ObsSensCfg(**obs_sens_dict)
+
+        try:
+            mass_solve_dict = self.config_dict['mass_solve']
+        except KeyError:
+            mass_solve_dict = {}
+        self.mass_solve = MassSolveCfg(**mass_solve_dict)
+
+
     def check_dirs(self):
         """
         Check input directory exists & create output dir if necessary.
@@ -138,13 +151,15 @@ def check_dirs(self):
                     self.time.phase_name,
                     self.eigendec.phase_name,
                     self.error_prop.phase_name,
-                    self.inv_sigma.phase_name]
+                    self.inv_sigma.phase_name,
+                    self.obs_sens.phase_name]
 
         ph_suffix = [self.inversion.phase_suffix,
                     self.time.phase_suffix,
                     self.eigendec.phase_suffix,
                     self.error_prop.phase_suffix,
-                    self.inv_sigma.phase_suffix]
+                    self.inv_sigma.phase_suffix,
+                    self.obs_sens.phase_suffix]
 
         for ph, suff in zip(ph_names, ph_suffix):
             out_dir = (outdir / ph / suff)
@@ -253,6 +268,17 @@ class ErrorPropCfg(ConfigPrinter):
     phase_name: str = 'error_prop'
     phase_suffix: str = ''
 
+
+@dataclass(frozen=True)
+class ObsSensCfg(ConfigPrinter):
+    """
+    Configuration related to observation sensitivities
+    """
+    qoi: str = 'vaf'
+    phase_name: str = 'obs_sens'
+    phase_suffix: str = ''
+    
+
 @dataclass(frozen=True)
 class SampleCfg(ConfigPrinter):
     """
@@ -394,6 +420,18 @@ def __post_init__(self):
             assert self.min_thickness >= 0.0
 
 
+@dataclass(frozen=True)
+class MassSolveCfg(ConfigPrinter):
+    """
+    Options for mass balance solver
+    """
+
+    use_cg_thickness: bool = False
+
+    def __post_init__(self):
+     """  """
+
+
 @dataclass(frozen=True)
 class MomsolveCfg(ConfigPrinter):
     """
@@ -513,7 +551,6 @@ def __post_init__(self):
 
         for fname in fname_default_suff:
             self.set_default_filename(fname, fname_default_suff[fname])
-            #embed()
 
 @dataclass(frozen=True)
 class TimeCfg(ConfigPrinter):

Diff for: fenics_ice/inout.py

@@ -294,7 +294,6 @@ def write_variable(var, params, name=None, outdir=None, phase_name='', phase_suf
     outvar.rename(name, "")
     # Prefix the run name
     outfname = Path(outdir) / phase_name / phase_suffix / "_".join((params.io.run_name+phase_suffix, name))
-    #embed()
 
     # Write out output according to user specified format in toml
     output_var_format = params.io.output_var_format

Diff for: fenics_ice/model.py

@@ -26,10 +26,43 @@
 from pathlib import Path
 from numpy.random import randn
 import logging
-from IPython import embed
 
 log = logging.getLogger("fenics_ice")
 
+        # Functions for repeated ungridded interpolation
+        # TODO - this will not handle extrapolation/missing data
+        # nicely - unfound simplex are returned '-1' which takes the last
+        # tri.simplices...
+
+        # at the moment i have moved these from vel_obs_from_data, so they 
+        # can be called directly from a run script.
+        # the ismipc test, which calls this function, still seems to perform fine
+        # but this refactoring may make things less efficient.
+def interp_weights(xy, uv, periodic_bc, d=2):
+            """Compute the nearest vertices & weights (for reuse)"""
+            from scipy.spatial import Delaunay
+            tri = Delaunay(xy)
+            simplex = tri.find_simplex(uv)
+
+            if not np.all(simplex >= 0):
+                if not periodic_bc:
+                    log.warning("Some points missing in interpolation "
+                              "of velocity obs to function space.")
+                else:
+                    log.warning("Some points missing in interpolation "
+                                "of velocity obs to function space.")
+
+            vertices = np.take(tri.simplices, simplex, axis=0)
+            temp = np.take(tri.transform, simplex, axis=0)
+            delta = uv - temp[:, d]
+            bary = np.einsum('njk,nk->nj', temp[:, :d, :], delta)
+            return vertices, np.hstack((bary, 1 - bary.sum(axis=1,
+                                                           keepdims=True)))
+
+def interpolate(values, vtx, wts):
+            """Bilinear interpolation, given vertices & weights above"""
+            return np.einsum('nj,nj->n', np.take(values, vtx), wts)
+
 class model:
     """
     The 'model' object is the core of any fenics_ice simulation. It handles loading input
@@ -122,7 +155,10 @@ def init_fields_from_data(self):
         self.bed = self.field_from_data("bed", self.Q)
         self.bmelt = self.field_from_data("bmelt", self.M, default=0.0)
         self.smb = self.field_from_data("smb", self.M, default=0.0)
-        self.H_np = self.field_from_data("thick", self.M, min_val=min_thick)
+        if (self.params.mass_solve.use_cg_thickness and self.params.mesh.periodic_bc):
+         self.H_np = self.field_from_data("thick", self.Qp, min_val=min_thick)
+        else:
+         self.H_np = self.field_from_data("thick", self.M, min_val=min_thick)
 
         if self.params.melt.use_melt_parameterisation:
 
@@ -308,44 +344,16 @@ def vel_obs_from_data(self):
                                use_cloud_point=self.params.inversion.use_cloud_point_velocities)
         else:
             inout.read_vel_obs(infile, model=self)
-        # Functions for repeated ungridded interpolation
-        # TODO - this will not handle extrapolation/missing data
-        # nicely - unfound simplex are returned '-1' which takes the last
-        # tri.simplices...
-        def interp_weights(xy, uv, d=2):
-            """Compute the nearest vertices & weights (for reuse)"""
-            from scipy.spatial import Delaunay
-            tri = Delaunay(xy)
-            simplex = tri.find_simplex(uv)
-
-            if not np.all(simplex >= 0):
-                if not self.params.mesh.periodic_bc:
-                    log.warning("Some points missing in interpolation "
-                              "of velocity obs to function space.")
-                else:
-                    log.warning("Some points missing in interpolation "
-                                "of velocity obs to function space.")
-
-            vertices = np.take(tri.simplices, simplex, axis=0)
-            temp = np.take(tri.transform, simplex, axis=0)
-            delta = uv - temp[:, d]
-            bary = np.einsum('njk,nk->nj', temp[:, :d, :], delta)
-            return vertices, np.hstack((bary, 1 - bary.sum(axis=1,
-                                                           keepdims=True)))
-
-        def interpolate(values, vtx, wts):
-            """Bilinear interpolation, given vertices & weights above"""
-            return np.einsum('nj,nj->n', np.take(values, vtx), wts)
 
         # Grab coordinates of both Lagrangian & DG function spaces
         # and compute (once) the interpolating arrays
         Q_coords = self.Q.tabulate_dof_coordinates()
         M_coords = self.M.tabulate_dof_coordinates()
 
         vtx_Q, wts_Q = interp_weights(self.vel_obs['uv_comp_pts'],
-                                      Q_coords)
+                                      Q_coords, self.params.mesh.periodic_bc)
         vtx_M, wts_M = interp_weights(self.vel_obs['uv_comp_pts'],
-                                      M_coords)
+                                      M_coords, self.params.mesh.periodic_bc)
 
         # Define new functions to hold results
         self.u_obs_Q = Function(self.Q, name="u_obs")
@@ -378,7 +386,7 @@ def interpolate(values, vtx, wts):
         # We need to do the same as above but for cloud point data
         # so we can write out a nicer output in the mesh coordinates
         vtx_Q_c, wts_Q_c = interp_weights(self.vel_obs['uv_obs_pts'],
-                                      Q_coords)
+                                      Q_coords, self.params.mesh.periodic_bc)
 
         # Define new functions to hold results
         self.u_cloud_Q = Function(self.Q, name="u_obs_cloud")

Diff for: fenics_ice/solver.py

@@ -29,7 +29,6 @@
 import time
 import ufl
 import weakref
-from IPython import embed
 
 log = logging.getLogger("fenics_ice")
 
@@ -77,11 +76,26 @@ def interpolation_matrix(x_coords, y_space):
     return x_local, P
 
 
+def Amat_obs_action(P, Rvec, vec_cg, dg_space):
+    # This function implements the Rvec*P*D action on a P1 function
+    #  where D is a projection into DG space
+    # 
+    # to be called for each component of velocity
+    #
+
+    test, trial = TestFunction(dg_space), TrialFunction(dg_space)
+    vec_dg = Function(dg_space)
+    solve(inner(trial, test) * dx == inner(vec_cg, test) * dx,
+          vec_dg, solver_parameters={"linear_solver": "lu"})
+
+    return Rvec * (P @ vec_dg.vector().get_local())
+    
+
 class ssa_solver:
     """
     The ssa_solver object is currently the only kind of fenics_ice 'solver' available.
     """
-    def __init__(self, model, mixed_space=False):
+    def __init__(self, model, mixed_space=False, obs_sensitivity=False):
 
         # Enable aggressive compiler options
         parameters["form_compiler"]["optimize"] = False
@@ -93,6 +107,7 @@ def __init__(self, model, mixed_space=False):
         self.model.solvers.append(self)
         self.params = model.params
         self.mixed_space = mixed_space
+        self.obs_sensitivity = obs_sensitivity
 
         # Mesh/Function Spaces
         self.mesh = model.mesh
@@ -146,10 +161,15 @@ def __init__(self, model, mixed_space=False):
         self.U = Function(self.V, name="U")
         self.U_np = Function(self.V, name="U_np")
         self.Phi = TestFunction(self.V)
-        self.Ksi = TestFunction(self.M)
         self.pTau = TestFunction(self.Qp)
 
-        self.trial_H = TrialFunction(self.M)
+        if not (self.params.mass_solve.use_cg_thickness and self.params.mesh.periodic_bc):
+         self.trial_H = TrialFunction(self.M)
+         self.Ksi = TestFunction(self.M)
+        else:
+         self.trial_H = TrialFunction(self.Qp)
+         self.Ksi = TestFunction(self.Qp)
+
 
         # Facets
         self.ff = model.ff
@@ -607,21 +627,26 @@ def def_thickadv_eq(self):
             + inner(jump(Ksi), jump(0.5 * (dot(U_np, nm) + abs(dot(U_np, nm))) * trial_H))
             * dS
 
-            # Outflow at boundaries
-            + conditional(dot(U_np, nm) > 0, 1.0, 0.0)*inner(Ksi, dot(U_np * trial_H, nm))
-            * ds
-
-            # Inflow at boundaries
-            + conditional(dot(U_np, nm) < 0, 1.0, 0.0)*inner(Ksi, dot(U_np * H_init, nm))
-            * ds
-
             # basal melting
             + bmelt*Ksi*dx
 
             # surface mass balance
             - smb*Ksi*dx
         )
 
+
+        if not (self.params.mass_solve.use_cg_thickness and self.params.mesh.periodic_bc):
+            self.thickadv = self.thickadv + (
+
+            # Outflow at boundaries
+                + conditional(dot(U_np, nm) > 0, 1.0, 0.0)*inner(Ksi, dot(U_np * trial_H, nm))
+                * ds
+
+           # Inflow at boundaries
+                + conditional(dot(U_np, nm) < 0, 1.0, 0.0)*inner(Ksi, dot(U_np * H_init, nm))
+                * ds
+            )
+
         # # Forward euler
         # self.thickadv = (inner(Ksi, ((trial_H - H_np) / dt)) * dx
         # - inner(grad(Ksi), U_np * H_np) * dx
@@ -1303,9 +1328,16 @@ def comp_J_inv(self, verbose=False):
                 J_v_obs, op=MPI.SUM)
             J_v_obs, = J_v_obs
 
+            u_std_local = u_std[obs_local]
+            v_std_local = v_std[obs_local]
+
             self._cached_J_mismatch_data \
                 = (interp_space,
                    u_PRP, v_PRP, l_u_obs, l_v_obs, J_u_obs, J_v_obs)
+            if (self.obs_sensitivity):
+                self._cached_Amat_vars = \
+                    (P, u_std_local, v_std_local, obs_local, interp_space)
+
         (interp_space,
          u_PRP, v_PRP, l_u_obs, l_v_obs, J_u_obs, J_v_obs) = \
             self._cached_J_mismatch_data

Diff for: runs/run_forward.py

@@ -72,6 +72,7 @@ def run_forward(config_file):
 
     # Run the forward model
     Q = slvr.timestep(adjoint_flag=1, qoi_func=qoi_func)
+
     # Run the adjoint model, computing gradient of Qoi w.r.t cntrl
     dQ_ts = compute_gradient(Q, cntrl)  # Isaac 27