Merge pull request #279 from carterbox/revise-logging

DOC: Make logging less verbose

src/tike/lamino/solvers/bucket.py

@@ -13,7 +13,7 @@ def _estimate_step_length(obj, fwd_data, theta, grid, op, comm, s):
     proper order of magnitude.
 
     """
-    logger.info('Estimate step length from forward adjoint operations.')
+    logger.debug('Estimate step length from forward adjoint operations.')
 
     def reduce_norm(data, workers):
 

src/tike/lamino/solvers/cgrad.py

@@ -13,7 +13,7 @@ def _estimate_step_length(obj, theta, op):
     proper order of magnitude.
 
     """
-    logger.info('Estimate step length from forward adjoint operations.')
+    logger.debug('Estimate step length from forward adjoint operations.')
     outnback = op.adj(
         data=op.fwd(u=obj, theta=theta),
         theta=theta,

src/tike/opt.py

@@ -76,7 +76,7 @@ def momentum(g, v, m, vdecay=None, mdecay=0.9):
     eps : float
         A tiny constant to prevent zero division.
     """
-    logger.info("Momentum decay m=%+.3e", mdecay)
+    logger.debug("Momentum decay m=%+.3e", mdecay)
     m = 0 if m is None else m
     m = mdecay * m + (1 - mdecay) * g
     return m, None, m
@@ -203,7 +203,7 @@ def adam(
     Kingma, Diederik P., and Jimmy Ba. "Adam: A method for stochastic
     optimization." arXiv preprint arXiv:1412.6980 (2014).
     """
-    logger.info("ADAM decay m=%+.3e, v=%+.3e; eps=%+.3e", mdecay, vdecay, eps)
+    logger.debug("ADAM decay m=%+.3e, v=%+.3e; eps=%+.3e", mdecay, vdecay, eps)
     v = np.zeros_like(g.real) if v is None else v
     m = np.zeros_like(g) if m is None else m
     m = mdecay * m + (1 - mdecay) * g
@@ -272,7 +272,7 @@ def line_search(
             break
         step_count += 1
 
-    logger.info("line_search: %d backtracks; %.3e -> %.3e; cost %.6e",
+    logger.debug("line_search: %d backtracks; %.3e -> %.3e; cost %.6e",
                 step_count, first_step, step_length, fxsd)
 
     return step_length, fxsd, xsd
@@ -372,7 +372,7 @@ def conjugate_gradient(
             )
         else:
             x = update_multi(x, step_length, dir_list)
-            logger.info("Blind update; length %.3e", step_length)
+            logger.debug("Blind update; length %.3e", step_length)
 
     if __debug__ and num_search < num_iter:
         cost = cost_function(x)

src/tike/ptycho/io.py

@@ -48,8 +48,11 @@ def position_units_to_pixels(
         (detector_pixel_width * detector_pixel_count) /
         (detector_distance * wavelength(photon_energy / 1000) / 100))
     logger.info(
-        "Based on detector distance and photon energy,"
-        f" reconstruction pixel size will be {1 / pixel_per_meter:.3e} m.")
+        f"For a detector of {detector_pixel_count:d} pixels"
+        f" each {detector_pixel_width:.3e} m wide"
+        f" with propagation distance {detector_distance:.3e} m"
+        f" and photon energy {photon_energy:.3e} eV;"
+        f" the reconstruction pixel size will be {1 / pixel_per_meter:.3e} m.")
     return positions * pixel_per_meter
 
 

src/tike/ptycho/position.py

@@ -625,7 +625,7 @@ def update_positions_pd(operator, data, psi, probe, scan,
 
     check_allowed_positions(scan, psi, probe.shape)
     cost = operator.cost(data=data, psi=psi, scan=scan, probe=probe).get()
-    logger.info('%10s cost is %+12.5e', 'position', cost)
+    logger.debug('%10s cost is %+12.5e', 'position', cost)
     return scan, cost
 
 

src/tike/ptycho/ptycho.py

@@ -476,8 +476,7 @@ def iterate(self, num_iter: int) -> None:
                 self.parameters.psi = self.comm.pool.map(
                     tike.ptycho.object.smoothness_constraint,
                     self.parameters.psi,
-                    a=self.parameters.object_options
-                    .smoothness_constraint,
+                    a=self.parameters.object_options.smoothness_constraint,
                 )
 
             if self.parameters.object_options.clip_magnitude:
@@ -487,9 +486,11 @@ def iterate(self, num_iter: int) -> None:
                     a_max=1.0,
                 )
 
-            if (self.parameters.algorithm_options.name != 'dm'
-                    and self.parameters.object_options.preconditioner is not None
-                    and (len(self.parameters.algorithm_options.costs) % 10 == 1)):
+            if (
+                self.parameters.algorithm_options.name != 'dm'
+                and self.parameters.object_options.preconditioner is not None
+                and len(self.parameters.algorithm_options.costs) % 10 == 1
+            ):  # yapf: disable
                 (
                     self.parameters.psi,
                     self.parameters.probe,
@@ -537,6 +538,12 @@ def iterate(self, num_iter: int) -> None:
                 )
                 break
 
+            logger.info(
+                '%10s cost is %+1.3e',
+                self.parameters.exitwave_options.noise_model,
+                np.mean(self.parameters.algorithm_options.costs[-1]),
+            )
+
     def get_result(self):
         """Return the current parameter estimates."""
         reorder = np.argsort(np.concatenate(self.comm.order))
@@ -734,7 +741,10 @@ def make_certain_args_constant(
         _,
     ) -> typing.Tuple[npt.NDArray]:
 
-        (data, scan,) = ind_args
+        (
+            data,
+            scan,
+        ) = ind_args
         (sums,) = mod_args
 
         intensity, _ = operator._compute_intensity(
@@ -747,12 +757,19 @@ def make_certain_args_constant(
         sums[0] += cp.sum(data[:, measured_pixels], dtype=np.double)
         sums[1] += cp.sum(intensity[:, measured_pixels], dtype=np.double)
 
-        return [sums,]
+        return [
+            sums,
+        ]
 
     result = tike.communicators.stream.stream_and_modify(
         f=make_certain_args_constant,
-        ind_args=[data, scan,],
-        mod_args=[sums,],
+        ind_args=[
+            data,
+            scan,
+        ],
+        mod_args=[
+            sums,
+        ],
         streams=streams,
     )
 

src/tike/ptycho/solvers/dm.py

@@ -87,7 +87,6 @@ def dm(
         )))
 
         cost = comm.Allreduce_mean(cost, axis=None).get()
-        logger.info('%10s cost is %+12.5e', 'farplane', cost)
         batch_cost.append(cost)
 
     (

src/tike/ptycho/solvers/lstsq.py

@@ -356,7 +356,6 @@ def _update_nearplane(
 ):
     m = 0
     if eigen_weights[0] is not None:
-        logger.info('Updating eigen probes')
 
         eigen_weights = comm.pool.map(
             _get_coefs_intensity,
@@ -864,8 +863,6 @@ def _update_position(
         alpha * max(position_update_denominator.max(), 1e-6))
 
     if position_options.use_adaptive_moment:
-        logger.info(
-            "position correction with ADAptive Momemtum acceleration enabled.")
         (
             step,
             position_options.v,

src/tike/ptycho/solvers/rpie.py

@@ -137,7 +137,6 @@ def rpie(
         )))
 
         batch_cost.append(comm.Allreduce_mean(cost, axis=None).get())
-        logger.info('%10s cost is %+12.5e', 'farplane', batch_cost[-1])
 
         if algorithm_options.batch_method != 'compact':
             (
@@ -564,8 +563,6 @@ def _update_position(
         alpha * max(position_update_denominator.max(), 1e-6))
 
     if position_options.use_adaptive_moment:
-        logger.info(
-            "position correction with ADAptive Momemtum acceleration enabled.")
         (
             step,
             position_options.v,