fix fitness and constraint plots

.github/workflows/docs.yml

@@ -83,7 +83,7 @@ jobs:
       - name: Build Docs
         run: make -C docs/ html
 
-      - uses: actions/upload-artifact@v3
+      - uses: actions/upload-artifact@v4
         with:
           name: ${{ env.REPOSITORY_NAME }}-docs
           path: docs/build/html/

README.rst

@@ -6,11 +6,11 @@ blop
    :target: https://github.com/NSLS-II/blop/actions/workflows/testing.yml
 
 
-.. image:: https://img.shields.io/pypi/v/bloptools.svg
-        :target: https://pypi.python.org/pypi/bloptools
+.. image:: https://img.shields.io/pypi/v/blop.svg
+        :target: https://pypi.python.org/pypi/blop
 
-.. image:: https://img.shields.io/conda/vn/conda-forge/bloptools.svg
-        :target: https://anaconda.org/conda-forge/bloptools
+.. image:: https://img.shields.io/conda/vn/conda-forge/blop.svg
+        :target: https://anaconda.org/conda-forge/blop
 
 Beamline Optimization Tools
 

pyproject.toml

@@ -3,7 +3,7 @@ requires = ["hatchling", "hatch-vcs", "setuptools_scm"]
 build-backend = "hatchling.build"
 
 [project]
-name = "bloptools"
+name = "blop"
 description = "Beamline optimization with machine learning"
 readme = { file = "README.rst", content-type = "text/x-rst" }
 authors = [
@@ -22,7 +22,7 @@ dependencies = [
     "gpytorch",
     "h5py",
     "matplotlib",
-    "numpy<2",
+    "numpy",
     "ophyd",
     "ortools",
     "scipy",

src/blop/plotting.py

@@ -161,6 +161,8 @@ def _plot_objs_many_dofs(agent, axes=(0, 1), shading="nearest", cmap=DEFAULT_COL
         targets = agent.train_targets()[obj.name].numpy()
 
         values = obj._untransform(targets)
+        # mask does not generate properly when values is a tensor (returns values of 0 instead of booleans)
+        values = np.array(values)
 
         val_vmin, val_vmax = np.nanquantile(values, q=[0.01, 0.99])
         val_norm = (
@@ -170,7 +172,13 @@ def _plot_objs_many_dofs(agent, axes=(0, 1), shading="nearest", cmap=DEFAULT_COL
         obj_vmin, obj_vmax = np.nanquantile(targets, q=[0.01, 0.99])
         obj_norm = mpl.colors.Normalize(obj_vmin, obj_vmax)
 
-        val_ax = agent.obj_axes[obj_index, 0].scatter(x_values, y_values, c=values, s=size, norm=val_norm, cmap=cmap)
+        # mask for nan values, uses unfilled o marker
+        mask = np.isnan(values)
+
+        val_ax = agent.obj_axes[obj_index, 0].scatter(
+            x_values[~mask], y_values[~mask], c=values[~mask], s=size, norm=val_norm, cmap=cmap
+        )
+        agent.obj_axes[obj_index, 0].scatter(x_values[mask], y_values[mask], marker="o", ec="k", fc="w", s=size)
 
         # mean and sigma will have shape (*input_shape,)
         test_posterior = obj.model.posterior(model_inputs)
@@ -180,9 +188,15 @@ def _plot_objs_many_dofs(agent, axes=(0, 1), shading="nearest", cmap=DEFAULT_COL
         # test_values = obj.fitness_inverse(test_mean) if obj.kind == "fitness" else test_mean
 
         test_constraint = None
-        if obj.constraint is not None:
+        if obj.constraint is None:
+            # test_constraint = obj.constraint_probability(model_inputs).detach().squeeze().numpy()
+            test_constraint = agent.constraint(model_inputs).squeeze().numpy()
+        else:
             test_constraint = obj.constraint_probability(model_inputs).detach().squeeze().numpy()
 
+        fitness_ax = None
+        fit_err_ax = None
+
         if gridded:
             # _ = agent.obj_axes[obj_index, 1].pcolormesh(
             #     test_x,
@@ -192,22 +206,22 @@ def _plot_objs_many_dofs(agent, axes=(0, 1), shading="nearest", cmap=DEFAULT_COL
             #     cmap=cmap,
             #     norm=val_norm,
             # )
-            if obj.constraint is not None:
+            if obj.constraint is None:
                 fitness_ax = agent.obj_axes[obj_index, 1].pcolormesh(
                     test_x,
                     test_y,
                     test_mean,
                     shading=shading,
-                    cmap=cmap,
                     norm=obj_norm,
+                    cmap=cmap,
                 )
                 fit_err_ax = agent.obj_axes[obj_index, 2].pcolormesh(
                     test_x,
                     test_y,
                     test_sigma,
                     shading=shading,
-                    cmap=cmap,
                     norm=mpl.colors.LogNorm(),
+                    cmap=cmap,
                 )
 
             if test_constraint is not None:
@@ -260,7 +274,7 @@ def _plot_objs_many_dofs(agent, axes=(0, 1), shading="nearest", cmap=DEFAULT_COL
         val_cbar = agent.obj_fig.colorbar(val_ax, ax=agent.obj_axes[obj_index, 0], location="bottom", aspect=32, shrink=0.8)
         val_cbar.set_label(f"{obj.units or ''}")
 
-        if obj.constraint is not None:
+        if obj.constraint is None:
             _ = agent.obj_fig.colorbar(fitness_ax, ax=agent.obj_axes[obj_index, 1], location="bottom", aspect=32, shrink=0.8)
             _ = agent.obj_fig.colorbar(fit_err_ax, ax=agent.obj_axes[obj_index, 2], location="bottom", aspect=32, shrink=0.8)