Statistics str, rich_repr

Author: janpipek

src/physt/_construction.py

@@ -479,13 +479,13 @@ def calculate_1d_frequencies(
         stats = Statistics()
     else:
         stats = Statistics(
-            sum=(data_array * weights_array).sum(),
-            sum2=(data_array**2 * weights_array).sum(),
+            sum=float((data_array * weights_array).sum()),
+            sum2=float((data_array**2 * weights_array).sum()),
             min=float(data_array.min()),
             max=float(data_array.max()),
             weight=float(weights_array.sum()),
             # TODO: Support median with weights?
-            median=np.median(data_array) if equal_weights else np.nan,
+            median=float(np.median(data_array)) if equal_weights else np.nan,
         )
     return frequencies, errors2, underflow, overflow, stats
 

src/physt/statistics.py

@@ -42,14 +42,14 @@ class Statistics:
     def mean(self) -> float:
         """Statistical mean of all values entered into histogram (weighted)."""
         try:
-            return self.sum / self.weight
+            return float(self.sum / self.weight)
         except ZeroDivisionError:
             return np.nan
 
     @property
     def std(self) -> float:
         """Standard deviation of all values entered into histogram."""
-        return np.sqrt(self.variance)
+        return float(np.sqrt(self.variance))
 
     @property
     def variance(self) -> float:
@@ -59,7 +59,7 @@ def variance(self) -> float:
         separate from bin contents.
         """
         if self.weight > 0:
-            return (self.sum2 - self.sum**2 / self.weight) / self.weight
+            return float((self.sum2 - self.sum**2 / self.weight) / self.weight)
         return np.nan
 
     def __add__(self, other: Any) -> Statistics:
@@ -97,6 +97,17 @@ def __eq__(self, other):
             and np.array_equal(self.median, other.median, equal_nan=True)
         )
 
+    def __rich_repr__(self):
+        yield "mean", self.mean
+        yield "std", self.std
+        yield "min", self.min
+        yield "max", self.max
+        yield "total", self.weight
+
+    def __str__(self):
+        rich_str = ", ".join(f"{key}={value}" for key, value in self.__rich_repr__())
+        return f"Statistics({rich_str})"
+
 
 INVALID_STATISTICS: Statistics = Statistics(
     sum=np.nan, sum2=np.nan, min=np.nan, max=np.nan, weight=np.nan

tests/test_stats.py renamed to tests/test_statistics.py

@@ -1,3 +1,5 @@
+import re
+
 import numpy as np
 import pytest
 
@@ -26,7 +28,7 @@ def simple_h1_without_stats(simple_h1: Histogram1D) -> Histogram1D:
 
 @pytest.mark.parametrize("range_", [None, pytest.param((1.5, 2.5), id="limiting")])
 @pytest.mark.parametrize("use_weights", [False, True])
-class TestStatisticsComputation:
+class TestStatistics:
     @pytest.fixture
     def histogram(
         self,
@@ -83,6 +85,12 @@ def test_std(self, histogram, use_weights):
         else:
             assert np.allclose(histogram.statistics.std, np.sqrt(5 / 4))
 
+    def test_str(self, histogram, use_weights):
+        str_repr = str(histogram.statistics)
+        pattern = re.compile(r"^Statistics\(mean=[0-9.\-]+, std=[0-9.\-]+, min=[0-9.\-]+, max=[0-9.\-]+, total=[0-9.\-]+\)$")
+        match = pattern.match(str_repr)
+        x = 7
+
 
 class TestEmptyHistogram:
     def test_zero_statistics(self, empty_h1):