Merge pull request #16 from sanjaymjoshi/notebooks

Bug fix and notebook update
sanjaymjoshi · Apr 6, 2024 · b2a425e · b2a425e
2 parents d456d4f + 08b2f7c
commit b2a425e
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diff --git a/README.rst b/README.rst
@@ -33,6 +33,9 @@ Concepts
 This library provides methods to calculate these statistics for infinite and finite
 population sizes.
 
+It also includes interactive notebooks that you can access on Google colab:
+1. [Reliability and Confidence](https://colab.research.google.com/github/sanjaymjoshi/relistats/blob/main/examples/01_reliability_confidence.ipynb)
+
 Example usage in a python file:
 
 .. code-block:: python
@@ -41,7 +44,7 @@ Example usage in a python file:
    
    n = 22
    a = assurance(n, 0) or 0
-   print(f"Assurance at {n} good samples: {a*100:.1f}%")
+   print(f"Assurance at {n} good samples: {a:.1%}%")
 
 References
 ----------

diff --git a/example/01_reliability_confidence.ipynb → examples/01_reliability_confidence.ipynb b/example/01_reliability_confidence.ipynb → examples/01_reliability_confidence.ipynb
diff --git a/example/explore_median_confidence.ipynb → examples/explore_median_confidence.ipynb b/example/explore_median_confidence.ipynb → examples/explore_median_confidence.ipynb
diff --git a/example/explore_percentile.ipynb → examples/explore_percentile.ipynb b/example/explore_percentile.ipynb → examples/explore_percentile.ipynb
diff --git a/example/explore_tolerance_interval.ipynb → examples/explore_tolerance_interval.ipynb b/example/explore_tolerance_interval.ipynb → examples/explore_tolerance_interval.ipynb
diff --git a/relistats/binom_fin.py b/relistats/binom_fin.py
@@ -95,23 +95,21 @@ def reli_fin(n: int, f: int, c: float, m: int) -> tuple:
     # This line is never reached in pytest!
 
 
-def assur_fin(n: int, f: int, m: int, tol=0.001) -> tuple:
+def assur_fin(n: int, f: int, m: int) -> tuple:
     """Assurance [0, 1], i.e., confidence = reliability.
     Returns tuple of assurance and actual values of reliability
     and confidence used for computations.
 
     :param n: number of samples
     :type n: int, >=0
     :param f: number of failures
-    :type f: int, >=0
+    :type f: int, 0 <= f <= n
     :param m: remaining samples in population
     :type m: int, >= 0
-    :param tol: accuracy tolerance
-    :type tol: float, optional
     :return: (Assurance, reliability, confidence)
     :rtype: tuple
     """
-    if n <= 0 or f < 0:
+    if n <= 0 or f < 0 or n < f:
         return (None, 0, 0)
 
     # Calculate confidence for each case of remaining failures

diff --git a/test/test_binom_fin.py b/test/test_binom_fin.py
@@ -48,6 +48,10 @@ def test_conf_fin() -> None:
     assert conf_fin(2, 0, 2, -2) == (None, None)
     assert conf_fin(2, 0, -2, -2) == (None, None)
 
+    assert conf_fin(20, 21, 20, 2) == pytest.approx(
+        (0.0, 0.425), abs=ABS_TOL_CONFIDENCE
+    )
+
 
 def test_reli_fin() -> None:
     ABS_TOL_RELIABILITY = 0.001
@@ -89,3 +93,4 @@ def test_assurance() -> None:
 
     assert assur_fin(2, -2, 2) == (None, 0, 0)
     assert assur_fin(-2, 0, 2) == (None, 0, 0)
+    assert assur_fin(20, 21, 8) == (None, 0, 0)
diff --git a/test/test_binomial.py b/test/test_binomial.py
@@ -106,6 +106,7 @@ def test_assurance() -> None:
     assert assurance(22, 2) == pytest.approx(0.812, abs=0.001)
     assert assurance(59, 6) == pytest.approx(0.842, abs=0.001)
     assert assurance(59, 10, 0.0001) == pytest.approx(0.7798, abs=0.0001)
+    assert assurance(20, 21) == pytest.approx(0.0, abs=0.001)
 
     assert assurance(2, -2) is None
     assert assurance(-2, 0) is None