combine limits at more lower levels

validphys2/src/validphys/dataplots.py

@@ -28,7 +28,7 @@
 from validphys.coredata import KIN_NAMES
 from validphys.plotoptions.core import get_info, kitable, transform_result
 from validphys.results import chi2_stat_labels, chi2_stats
-from validphys.sumrules import POL_LIMS, polarized_sum_rules
+from validphys.sumrules import POL_LIMS, partial_polarized_sum_rules
 from validphys.utils import sane_groupby_iter, scale_from_grid, split_ranges
 
 log = logging.getLogger(__name__)
@@ -980,19 +980,19 @@ def _compute_ellipse(preds_x, preds_y, nstd=3):
 
 
 @figure
-def plot_polarized_momentum(pdf, Q, polarized_sum_rules, angular_momentum=False):
+def plot_polarized_momentum(pdf, Q, partial_polarized_sum_rules, angular_momentum=False):
     """
     Plot the correlated uncertainties for the truncated integrals of the polarized
     gluon and singlet distributions.
     """
     ((xmina, xmaxa), (xminb, xmaxb)) = POL_LIMS
-    predictions = polarized_sum_rules[-1]  # large-x
+    predictions = partial_polarized_sum_rules[-1]  # large-x
 
     if not angular_momentum:
         xpreds = np.array(predictions["g"])
         ypreds = np.array(predictions["singlet"]) / 2.0
     else:
-        preds_low_x = polarized_sum_rules[0]  # small-x
+        preds_low_x = partial_polarized_sum_rules[0]  # small-x
         xpreds = np.array(predictions["g"]) + np.array(predictions["singlet"]) / 2.0
         xpreds = 1 / 2 - xpreds  # substract the proton spin
         ypreds = -(np.array(preds_low_x["g"]) + np.array(preds_low_x["singlet"]) / 2.0)
@@ -1051,11 +1051,11 @@ def plot_polarized_momentum(pdf, Q, polarized_sum_rules, angular_momentum=False)
 
 
 @figure
-def plot_orbital_momentum(pdf, Q, polarized_sum_rules):
+def plot_orbital_momentum(pdf, Q, partial_polarized_sum_rules):
     """In addition to plotting the correlated spin moments as in `plot_polarized_momentum`,
     it also plots the contributions from the Orbital Angular Momentum.
     """
-    return plot_polarized_momentum(pdf, Q, polarized_sum_rules, angular_momentum=True)
+    return plot_polarized_momentum(pdf, Q, partial_polarized_sum_rules, angular_momentum=True)
 
 
 @figuregen

validphys2/src/validphys/sumrules.py

@@ -152,23 +152,30 @@ def _combine_limits(res: list[dict]):
     return {k: np.sum([v[k] for v in res], axis=0) for k in res[0].keys()}
 
 
+@check_positive('Q')
+def partial_polarized_sum_rules(pdf: PDF, Q: numbers.Real, lims: tuple = POL_LIMS):
+    """Compute the partial low- and large-x polarized sum rules. Return a SumRulesGrid
+    object with the list of values for each sum rule. The integration is performed with
+    absolute and relative tolerance of 1e-4."""
+    lpdf = pdf.load()
+    return _sum_rules(POLARIZED_SUM_RULES, lpdf, Q, lims=lims)
+
+
 @check_positive('Q')
 def sum_rules(pdf: PDF, Q: numbers.Real):
     """Compute the momentum, uvalence, dvalence, svalence and cvalence sum rules for
     each member, at the energy scale ``Q``.
     Return a SumRulesGrid object with the list of values for each sum rule.
     The integration is performed with absolute and relative tolerance of 1e-4."""
     lpdf = pdf.load()
-    return _sum_rules(KNOWN_SUM_RULES, lpdf, Q)
+    return _combine_limits(_sum_rules(KNOWN_SUM_RULES, lpdf, Q))
 
 
 @check_positive('Q')
-def polarized_sum_rules(pdf: PDF, Q: numbers.Real, lims: tuple = POL_LIMS):
-    """Compute the polarized sum rules. Return a SumRulesGrid object with the list of
-    values for each sum rule. The integration is performed with absolute and relative
-    tolerance of 1e-4."""
-    lpdf = pdf.load()
-    return _sum_rules(POLARIZED_SUM_RULES, lpdf, Q, lims=lims)
+def polarized_sum_rules(partial_polarized_sum_rules):
+    """Compute the full polarized sum rules. The integration is performed with absolute
+    and relative tolerance of 1e-4."""
+    return _combine_limits(partial_polarized_sum_rules)
 
 
 @check_positive('Q')
@@ -194,12 +201,11 @@ def unknown_sum_rules(pdf: PDF, Q: numbers.Real):
     - T8
     """
     lpdf = pdf.load()
-    return _sum_rules(UNKNOWN_SUM_RULES, lpdf, Q)
+    return _combine_limits(_sum_rules(UNKNOWN_SUM_RULES, lpdf, Q))
 
 
 def _simple_description(d):
     res = {}
-    d = _combine_limits(d)
     for k, arr in d.items():
         res[k] = d = {}
         d["mean"] = np.mean(arr)
@@ -212,7 +218,6 @@ def _simple_description(d):
 
 def _err_mean_table(d, polarized=False):
     res = {}
-    d = _combine_limits(d)
     for k, arr in d.items():
         res[k] = d = {}
         d["mean"] = np.mean(arr)