restructure problem.evaluate

src/analysers/base.py

@@ -76,7 +76,7 @@ class Analyser(object):
                 isok_mask = num.logical_not(isbad_mask)
             else:
                 isok_mask = None
-            ms = wproblem.evaluate(x, mask=isok_mask)[:, 1]
+            ms = wproblem.misfits(x, mask=isok_mask)[:, 1]
             mss[iiter, :] = ms
 
             isbad_mask = num.isnan(ms)

src/core.py

@@ -250,7 +250,7 @@ def forward(rundir_or_config_path, event_names):
     events = []
     for (problem, x) in payload:
         ds.empty_cache()
-        _, results = problem.evaluate(x, result_mode='full')
+        results = problem.evaluate(x)
 
         event = problem.get_source(x).pyrocko_event()
         events.append(event)
@@ -376,7 +376,7 @@ def check(
             if n_random_synthetics == 0:
                 x = problem.pack(problem.base_source)
                 sources.append(problem.base_source)
-                _, results = problem.evaluate(x, result_mode='full')
+                results = problem.evaluate(x)
                 results_list.append(results)
 
             else:
@@ -391,7 +391,7 @@ def check(
                             pass
 
                     sources.append(problem.get_source(x))
-                    _, results = problem.evaluate(x, result_mode='full')
+                    results = problem.evaluate(x)
                     results_list.append(results)
 
             if show_waveforms:

src/optimizers/highscore/optimizer.py

@@ -435,7 +435,7 @@ class HighScoreOptimizer(Optimizer):
             else:
                 isok_mask = None
 
-            misfits = problem.evaluate(x, mask=isok_mask)
+            misfits = problem.misfits(x, mask=isok_mask)
 
             isbad_mask_new = num.isnan(misfits[:, 0])
             if isbad_mask is not None and num.any(

src/plot.py

@@ -903,7 +903,7 @@ def draw_fits_figures_statics(ds, history, optimizer, plt):
 
     source = problem.get_source(xbest)
 
-    _, results = problem.evaluate(xbest, result_mode='full')
+    results = problem.evaluate(xbest)
 
     figures = []
 
@@ -1059,7 +1059,7 @@ def draw_fits_ensemble_figures(
         model = models[imodel, :]
 
         source = problem.get_source(model)
-        _, results = problem.evaluate(model, result_mode='full')
+        results = problem.evaluate(model)
 
         dtraces.append([])
 
@@ -1420,7 +1420,7 @@ def draw_fits_figures(ds, history, optimizer, plt):
     target_to_result = {}
     all_syn_trs = []
     all_syn_specs = []
-    _, results = problem.evaluate(xbest, result_mode='full')
+    results = problem.evaluate(xbest)
 
     dtraces = []
     for target, result in zip(problem.waveform_targets, results):

src/problems/base.py

@@ -11,8 +11,8 @@ from pyrocko import gf, util, guts
 from pyrocko.guts import Object, String, Bool, List, Dict, Int
 
 from ..meta import ADict, Parameter, GrondError, xjoin
-from ..targets import MisfitTarget, TargetGroup, WaveformMisfitTarget, \
-    SatelliteMisfitTarget
+from ..targets import MisfitResult, MisfitTarget, TargetGroup, \
+    WaveformMisfitTarget, SatelliteMisfitTarget
 
 
 guts_prefix = 'grond'
@@ -353,7 +353,7 @@ class Problem(Object):
 
         return self._family_mask
 
-    def evaluate(self, x, mask=None, result_mode='sparse'):
+    def evaluate(self, x, mask=None, result_mode='full'):
         source = self.get_source(x)
         engine = self.get_engine()
 
@@ -371,16 +371,12 @@ class Problem(Object):
         modelling_results = list(resp.results_list[0])
 
         imt = 0
-        imisfit = 0
-        misfits = num.zeros((self.nmisfits, 2))
-        misfits.fill(None)
         results = []
         for itarget, target in enumerate(self.targets):
             nmt_this = len(t2m_map[target])
             if mask is None or mask[itarget]:
-                misfits[imisfit:imisfit+target.nmisfits, :], result = \
-                    target.finalize_modelling(
-                        modelling_results[imt:imt+nmt_this])
+                result = target.finalize_modelling(
+                    modelling_results[imt:imt+nmt_this])
 
                 imt += nmt_this
             else:
@@ -388,12 +384,21 @@ class Problem(Object):
                     'target was excluded from modelling')
 
             results.append(result)
+
+        return results
+
+    def misfits(self, x, mask=None):
+        results = self.evaluate(x, mask=mask, result_mode='sparse')
+        imisfit = 0
+        misfits = num.zeros((self.nmisfits, 2))
+        misfits.fill(None)
+        for target, result in zip(self.targets, results):
+            if isinstance(result, MisfitResult):
+                misfits[imisfit:imisfit+target.nmisfits, :] = result.misfits
+
             imisfit += target.nmisfits
 
-        if result_mode == 'full':
-            return misfits, results
-        else:
-            return misfits
+        return misfits
 
 
 class InvalidRundir(Exception):

src/targets/base.py

@@ -3,6 +3,7 @@ import copy
 import numpy as num
 
 from pyrocko import gf
+from pyrocko.guts_array import Array
 from pyrocko.guts import Object, Float
 
 
@@ -29,7 +30,9 @@ class TargetAnalysisResult(Object):
 
 
 class MisfitResult(Object):
-    pass
+    misfits = Array.T(
+        shape=(None, 2),
+        dtype=num.float)
 
 
 class MisfitTarget(Object):
@@ -100,7 +103,7 @@ class MisfitTarget(Object):
     def init_modelling(self):
         return []
 
-    def finalize_modelling(self, results):
+    def finalize_modelling(self, modelling_results):
         raise NotImplemented('must be overloaded in subclass')
 
 

src/targets/satellite/target.py

@@ -2,7 +2,7 @@ import logging
 import numpy as num
 
 from pyrocko import gf
-from pyrocko.guts import String, Bool, Dict, List, Object, Float
+from pyrocko.guts import String, Bool, Dict, List, Object
 
 from grond.meta import Parameter
 
@@ -67,8 +67,6 @@ class SatelliteTargetGroup(TargetGroup):
 
 
 class SatelliteMisfitResult(gf.Result, MisfitResult):
-    misfit_value = Float.T()
-    misfit_norm = Float.T()
     statics_syn = Dict.T(optional=True)
     statics_obs = Dict.T(optional=True)
 
@@ -101,8 +99,7 @@ class SatelliteMisfitTarget(gf.SatelliteTarget, MisfitTarget):
                     self._target_ranges.pop(k)
         return self._target_ranges
 
-    @property
-    def id(self):
+    def string_id(self):
         return self.scene_id
 
     def set_dataset(self, ds):
@@ -135,8 +132,7 @@ class SatelliteMisfitTarget(gf.SatelliteTarget, MisfitTarget):
             num.sum((stat_obs * scene.covariance.weight_vector)**2))
 
         result = SatelliteMisfitResult(
-            misfit_value=misfit_value,
-            misfit_norm=misfit_norm)
+            misfits=num.array([[misfit_value, misfit_norm]], dtype=num.float))
 
         if self._result_mode == 'full':
             result.statics_syn = statics

src/targets/waveform/target.py

@@ -149,29 +149,10 @@ class WaveformTargetGroup(TargetGroup):
                 targets.append(target)
 
         if self.limit:
-            return self.weed(origin, targets, self.limit)[0]
+            return weed(origin, targets, self.limit)[0]
         else:
             return targets
 
-    @staticmethod
-    def weed(origin, targets, limit, neighborhood=3):
-        azimuths = num.zeros(len(targets))
-        dists = num.zeros(len(targets))
-        for i, target in enumerate(targets):
-            _, azimuths[i] = target.azibazi_to(origin)
-            dists[i] = target.distance_to(origin)
-
-        badnesses = num.ones(len(targets), dtype=float)
-        deleted, meandists_kept = weeding.weed(
-            azimuths, dists, badnesses,
-            nwanted=limit,
-            neighborhood=neighborhood)
-
-        targets_weeded = [
-            target for (delete, target) in zip(deleted, targets) if not delete]
-
-        return targets_weeded, meandists_kept, deleted
-
 
 class TraceSpectrum(Object):
     network = String.T()
@@ -190,8 +171,6 @@ class TraceSpectrum(Object):
 
 
 class WaveformMisfitResult(gf.Result, MisfitResult):
-    misfit_value = Float.T()
-    misfit_norm = Float.T()
     processed_obs = Trace.T(optional=True)
     processed_syn = Trace.T(optional=True)
     filtered_obs = Trace.T(optional=True)
@@ -217,15 +196,6 @@ class WaveformMisfitTarget(gf.Target, MisfitTarget):
     def string_id(self):
         return '.'.join(x for x in (self.path,) + self.codes if x)
 
-    @property
-    def id(self):
-        return '.'.join(self.codes)
-
-    def get_plain_modelling_targets(self):
-        d = dict(
-            (k, getattr(self, k)) for k in gf.Target.T.propnames)
-        return [gf.Target(**d)]
-
     def get_combined_weight(self, apply_balancing_weights):
         w = self.manual_weight
         if apply_balancing_weights:
@@ -369,16 +339,13 @@ class WaveformMisfitTarget(gf.Target, MisfitTarget):
     def prepare_modelling(self):
         return [self]
 
-    def finalize_modelling(self, results):
-        result = results[0]
-        if isinstance(result, gf.SeismosizerError):
-            misfits = num.array(
-                [[None, None]], dtype=num.float)
+    def finalize_modelling(self, modelling_results):
+        return modelling_results[0]
         else:
-            misfits = num.array(
-                [[result.misfit_value, result.misfit_norm]], dtype=num.float)
+            return targets
+
+
 
-        return misfits, result
 
 
 def misfit(
@@ -472,8 +439,7 @@ tautoshift**2 / tautoshift_max**2``
 
     if result_mode == 'full':
         result = WaveformMisfitResult(
-            misfit_value=float(m),
-            misfit_norm=float(n),
+            misfits=num.array([[m, n]], dtype=num.float),
             processed_obs=tr_proc_obs,
             processed_syn=tr_proc_syn,
             filtered_obs=tr_obs.copy(),
@@ -486,8 +452,7 @@ tautoshift**2 / tautoshift_max**2``
 
     elif result_mode == 'sparse':
         result = WaveformMisfitResult(
-            misfit_value=m,
-            misfit_norm=n)
+            misfits=num.array([[m, n]], dtype=num.float))
     else:
         assert False
 
@@ -568,6 +533,25 @@ def float_or_none(x):
         return float(x)
 
 
+def weed(origin, targets, limit, neighborhood=3):
+    azimuths = num.zeros(len(targets))
+    dists = num.zeros(len(targets))
+    for i, target in enumerate(targets):
+        _, azimuths[i] = target.azibazi_to(origin)
+        dists[i] = target.distance_to(origin)
+
+    badnesses = num.ones(len(targets), dtype=float)
+    deleted, meandists_kept = weeding.weed(
+        azimuths, dists, badnesses,
+        nwanted=limit,
+        neighborhood=neighborhood)
+
+    targets_weeded = [
+        target for (delete, target) in zip(deleted, targets) if not delete]
+
+    return targets_weeded, meandists_kept, deleted
+
+
 __all__ = '''
     WaveformTargetGroup
     WaveformMisfitConfig

src/toy.py

@@ -93,7 +93,7 @@ class ToyProblem(Problem):
                 [t.obs_distance for t in self.targets],
                 dtype=num.float)
 
-    def evaluate(self, x, mask=None):
+    def misfits(self, x, mask=None):
         self._setup_modelling()
         distances = num.sqrt(
             num.sum((x[num.newaxis, :]-self._xtargets)**2, axis=1))
@@ -104,7 +104,7 @@ class ToyProblem(Problem):
             * num.mean(num.abs(self._obs_distances))
         return misfits
 
-    def evaluate_many(self, xs):
+    def misfits_many(self, xs):
         self._setup_modelling()
         distances = num.sqrt(
             num.sum(