Allow categorical variables in smpdf plots

nnpdfcpp/data/commondata/PLOTTING_ATLAS_WZ_TOT_13TEV.yaml

@@ -3,8 +3,7 @@ dataset_label: "ATLAS $W,Z$ inclusive 13 TeV"
 x: " "
 
 y_label: $\sigma^{fid}$ (fb)
-figure_by:
-    - boson
+
 extra_labels:
     " ":
         - $W^-$

validphys2/src/validphys/dataplots.py

@@ -387,6 +387,7 @@ def _plot_fancy_impl(
             lb = labellist[normalize_to]
             ax.set_ylabel(f"Ratio to {lb if lb else norm_result.label}")
 
+
         ax.legend().set_zorder(100000)
         ax.set_xlabel(info.xlabel)
         fig.tight_layout()
@@ -851,7 +852,7 @@ def plot_replica_sum_rules(pdf, sum_rules, Q):
 
 
 @figuregen
-def plot_smpdf(pdf, dataset, obs_pdf_correlations, mark_threshold: float = 0.9):
+def plot_smpdf(pdf, dataset, obs_pdf_correlations, mark_threshold:float=0.9):
     """
     Plot the correlations between the change in the observable and the change
     in the PDF in (x,fl) space.
@@ -879,7 +880,6 @@ def plot_smpdf(pdf, dataset, obs_pdf_correlations, mark_threshold: float = 0.9):
     info = get_info(dataset)
 
     table = kitable(dataset, info)
-    figby = sane_groupby_iter(table, info.figure_by)
 
     basis = obs_pdf_correlations.basis
 
@@ -891,54 +891,82 @@ def plot_smpdf(pdf, dataset, obs_pdf_correlations, mark_threshold: float = 0.9):
 
     plotting_var = info.get_xcol(table)
 
-    # TODO: vmin vmax should be global or by figure?
-    vmin, vmax = min(plotting_var), max(plotting_var)
-    if info.x_scale == 'log':
-        norm = mcolors.LogNorm(vmin, vmax)
+    categorical = not np.issubdtype(plotting_var.dtype, np.number)
+    if categorical:
+        # Categorical
+        categorical_keys, values = np.unique(plotting_var, return_inverse=True)
+        plotting_var = values
+        num_categories = len(categorical_keys)
+        if num_categories <= len(cm.Set2.colors):
+            cmap = mcolors.ListedColormap(cm.Set2.colors[:num_categories])
+        else:
+            cmap = cm.viridis.resample(num_categories)
+        bins = np.linspace(0, num_categories, num_categories + 1)
+        norm = mcolors.BoundaryNorm(bins, num_categories)
+
     else:
-        norm = mcolors.Normalize(vmin, vmax)
-    # http://stackoverflow.com/a/11558629/1007990
-    sm = cm.ScalarMappable(cmap=cm.viridis, norm=norm)
+        cmap = cm.viridis
+        #TODO: vmin vmax should be global or by figure?
+        vmin, vmax = min(plotting_var), max(plotting_var)
+        if info.x_scale == 'log':
+            norm = mcolors.LogNorm(vmin, vmax)
+        else:
+            norm = mcolors.Normalize(vmin, vmax)
+
+
+    table["__plotting_var"] = plotting_var
+    sm = cm.ScalarMappable(cmap=cmap, norm=norm)
+
+    figby = sane_groupby_iter(table, info.figure_by)
 
     for same_vals, fb in figby:
-        grid = fullgrid[np.asarray(fb.index), ...]
+        grid = fullgrid[ np.asarray(fb.index),...]
 
-        # Use the maximum absolute correlation for plotting purposes
+
+        #Use the maximum absolute correlation for plotting purposes
         absgrid = np.max(np.abs(grid), axis=0)
-        mark_mask = absgrid > np.max(absgrid) * mark_threshold
+        mark_mask = absgrid > np.max(absgrid)*mark_threshold
 
         label = info.group_label(same_vals, info.figure_by)
-        # TODO: PY36ScalarMappable
-        # TODO Improve title?
-        title = "%s %s\n[%s]" % (info.dataset_label, '(%s)' % label if label else '', pdf.label)
-
-        # Start plotting
-        w, h = mpl.rcParams["figure.figsize"]
-        h *= 2.5
-        fig, axes = plotutils.subplots(nrows=nf, sharex=True, figsize=(w, h), sharey=True)
+        #TODO: PY36ScalarMappable
+        #TODO Improve title?
+        title = f"{info.dataset_label} {label if label else ''}\n[{pdf.label}]"
+
+        #Start plotting
+        w,h = mpl.rcParams["figure.figsize"]
+        h*=2.5
+        fig, axes = plotutils.subplots(nrows=nf, sharex=True, figsize=(w,h), sharey=True)
         fig.suptitle(title)
-        colors = sm.to_rgba(info.get_xcol(fb))
+        colors = sm.to_rgba(fb["__plotting_var"])
         for flindex, (ax, fl) in enumerate(zip(axes, fls)):
-            for i, color in enumerate(colors):
-                ax.plot(x, grid[i, flindex, :].T, color=color)
+            for i,color in enumerate(colors):
+                ax.plot(x, grid[i,flindex,:].T, color=color)
+
 
-            flmask = mark_mask[flindex, :]
+            flmask = mark_mask[flindex,:]
             ranges = split_ranges(x, flmask, filter_falses=True)
             for r in ranges:
                 ax.axvspan(r[0], r[-1], color='#eeeeff')
 
-            ax.set_ylabel("$%s$" % basis.elementlabel(fl))
+            ax.set_ylabel("$%s$"%basis.elementlabel(fl))
             ax.set_xscale(scale_from_grid(obs_pdf_correlations))
-            ax.set_ylim(-1, 1)
+            ax.set_ylim(-1,1)
             ax.set_xlim(x[0], x[-1])
         ax.set_xlabel('$x$')
-        # fig.subplots_adjust(hspace=0)
 
-        fig.colorbar(sm, ax=axes.ravel().tolist(), label=info.xlabel, aspect=100)
-        # TODO: Fix title for this
-        # fig.tight_layout()
-        yield fig
+        cbar = fig.colorbar(
+            sm,
+            ax=axes.ravel().tolist(),
+            label=info.xlabel,
+            aspect=100,
+        )
+        if categorical:
+            cbar.set_ticks(np.linspace(0.5, num_categories - 0.5, num_categories))
+            cbar.ax.set_yticklabels(categorical_keys)
 
+        #TODO: Fix title for this
+        #fig.tight_layout()
+        yield fig
 
 @figure
 def plot_obscorrs(corrpair_datasets, obs_obs_correlations, pdf):
@@ -1225,14 +1253,14 @@ def plot_xq2(
         highlight_datasets = set()
 
     def next_options():
-        # Get the colors
-        prop_settings = mpl.rcParams['axes.prop_cycle']
-        # Apparently calling the object gives us an infinite cycler
+        #Get the colors
+        prop_settings = plt.rcParams['axes.prop_cycle']
+        #Apparently calling the object gives us an infinite cycler
         settings_cycler = prop_settings()
-        # So far, I don't understand how this is done with mpl "cycler"
-        # objects, or wether  I like it. So far this is godd enough
-        for markeropts, settings in zip(plotutils.marker_iter_plot(), settings_cycler):
-            # Override last with first
+        #So far, I don't understand how this is done with mpl "cycler"
+        #objects, or wether  I like it. So far this is godd enough
+        for  markeropts, settings in zip(plotutils.marker_iter_plot(), settings_cycler):
+            #Override last with first
             options = {
                 'linestyle': 'none',
                 **markeropts,
@@ -1243,7 +1271,7 @@ def next_options():
     next_opts = next_options()
     key_options = {}
 
-    for experiment, commondata, fitted, masked, group in dataset_inputs_by_groups_xq2map:
+    for (experiment, commondata, fitted, masked, group) in dataset_inputs_by_groups_xq2map:
         info = get_info(commondata)
         if marker_by == 'process type':
             key = info.process_description

validphys2/src/validphys/tests/conftest.py

@@ -27,6 +27,8 @@ def tmp(tmpdir):
 
 SINGLE_DATASET = {'dataset': 'NMC'}
 
+SINGLE_CATEGICAL = {"dataset": "ATLAS_WZ_TOT_13TEV", 'cfac': ["QCD"]}
+
 DATA = [
     {'dataset': 'NMC'},
     {'dataset': 'ATLASTTBARTOT', 'cfac':['QCD']},
@@ -95,6 +97,16 @@ def single_data_internal_cuts_config(data_internal_cuts_config):
     config_dict.update(dataset_input=DATA[0])
     return config_dict
 
+@pytest.fixture(scope='module')
+def single_data_categorical_internal_cuts_config(data_internal_cuts_config):
+    """Test dataset with categorical plotting variables"""
+    return {
+        **data_internal_cuts_config,
+        'dataset_input': SINGLE_CATEGICAL,
+        # NOTE: The old theory is currently bugged for this dataset
+        'theoryid': THEORYID_NEW,
+    }
+
 @pytest.fixture(scope='module')
 def single_data_single_point_internal_cuts_config(single_data_internal_cuts_config):
     config_dict = dict(single_data_internal_cuts_config)

validphys2/src/validphys/tests/test_plots.py

@@ -40,6 +40,11 @@ def test_dataspecschi2():
 def test_plot_smpdf(single_data_internal_cuts_config):
     return next(API.plot_smpdf(**single_data_internal_cuts_config))
 
+@pytest.mark.linux
+@pytest.mark.mpl_image_compare
+def test_plot_smpdf_categorical(single_data_categorical_internal_cuts_config):
+    return next(API.plot_smpdf(**single_data_categorical_internal_cuts_config))
+
 @pytest.mark.linux
 @pytest.mark.mpl_image_compare
 def test_plot_obscorrs(single_data_internal_cuts_config):