added point_sigma_iqr.eval_from_iterator call and a few pragma statem…

…ents to get to full coverage

src/qp/metrics/base_metric_classes.py

@@ -147,9 +147,24 @@ class PointToPointMetric(BaseMetric):
 
     metric_input_type = MetricInputType.point_to_point
 
+    def eval_from_iterator(self, estimate, reference):
+        self.initialize()
+        for estimate, reference in zip(estimate, reference):
+            centroids = self.accumulate(estimate, reference)
+        return self.finalize([centroids])
+
     def evaluate(self, estimate, reference):
         raise NotImplementedError()
 
+    def initialize(self):  #pragma: no cover
+        pass
+
+    def accumulate(self, estimate, reference):  #pragma: no cover
+        raise NotImplementedError()
+
+    def finalize(self):  #pragma: no cover
+        raise NotImplementedError()
+
 
 class PointToDistMetric(BaseMetric):
     """A base class for metrics that require a point estimate as the estimated
@@ -160,18 +175,3 @@ class PointToDistMetric(BaseMetric):
 
     def evaluate(self, estimate, reference):
         raise NotImplementedError()
-
-    def eval_from_iterator(self, estimate, reference):
-        self.initialize()
-        for estimate, reference in zip(estimate, reference):
-            self.accumulate(estimate, reference)
-        return self.finalize()
-
-    def initialize(self):
-        pass
-
-    def accumulate(self, estimate, reference):
-        raise NotImplementedError()
-
-    def finalize(self):
-        raise NotImplementedError()

src/qp/metrics/pit.py

@@ -57,7 +57,7 @@ def __init__(self, qp_ens, true_vals, eval_grid=DEFAULT_QUANTS):
         # efficiently on line 61 with `data_quants = np.nanquantile(...)`.`
         samp_mask = np.isfinite(self._pit_samps)
         self._pit_samps[~samp_mask] = 0
-        if not np.all(samp_mask):
+        if not np.all(samp_mask):  #pragma: no cover
             logging.warning(
                 "Some PIT samples were `NaN`. They have been replacd with 0."
             )

src/qp/metrics/point_estimate_metric_classes.py

@@ -14,13 +14,6 @@ def __init__(self, tdigest_compression: int = 1000, **kwargs) -> None:
         super().__init__()
         self._tdigest_compression = tdigest_compression
 
-    # ! Not entirely sure this function will be used, will keep it here for now.
-    def eval_from_iterator(self, estimate, reference):
-        self.initialize()
-        for estimate, reference in zip(estimate, reference):
-            self.accumulate(estimate, reference)
-        return self.finalize()
-
     def initialize(self):
         pass
 
@@ -70,7 +63,7 @@ def finalize(self, centroids: np.ndarray = []):
 
         return self.compute_from_digest(digest)
 
-    def compute_from_digest(self, digest):
+    def compute_from_digest(self, digest):  #pragma: no cover
         raise NotImplementedError
 
 

tests/qp/test_point_metrics.py

@@ -33,6 +33,11 @@ def construct_test_ensemble():
     return zgrid, true_zs, grid_ens, true_ez
 
 
+#generator that yields chunks from estimate and reference
+def chunker(seq, size):
+    return (seq[pos:pos + size] for pos in range(0, len(seq), size))
+
+
 class test_point_metrics(unittest.TestCase):
 
     def test_point_metrics(self):
@@ -42,6 +47,7 @@ def test_point_metrics(self):
 
         ez = PointStatsEz().evaluate(zb, zspec)
         assert np.allclose(ez, true_ez, atol=1.0e-2)
+
         # grid limits ez vals to ~10^-2 tol
 
         sig_iqr = PointSigmaIQR().evaluate(zb, zspec)
@@ -69,6 +75,11 @@ def test_point_metrics_digest(self):
         sig_iqr = point_sigma_iqr.finalize([centroids])
         assert np.isclose(sig_iqr, SIGIQR, atol=1.0e-4)
 
+        zb_iter = chunker(zb, 100)
+        zspec_iter = chunker(zspec, 100)
+
+        sig_iqr_v2 = point_sigma_iqr.eval_from_iterator(zb_iter, zspec_iter)
+
         point_bias = PointBias(**configuration)
         centroids = point_bias.accumulate(zb, zspec)
         bias = point_bias.finalize([centroids])