update readme with howto

README.md

@@ -1,2 +1,17 @@
 # PQM
-Implemenation of PQMass from Lemos et al. 2024
+
+Implementation of the PQMass two sample test from Lemos et al. 2024
+
+## Usage
+
+```python
+from pqm import pqm_pvalue
+import numpy as np
+
+x_sample = np.random.normal(size = (500, 10))
+y_sample = np.random.normal(size = (400, 10))
+
+pvalues = pqm_pvalue(x_sample, y_sample, num_refs = 100, bootstrap = 50)
+
+print(np.mean(pvalues), np.std(pvalues))
+```

src/pqm/pqm.py

@@ -4,10 +4,10 @@
 from scipy.stats import chi2_contingency
 from scipy.spatial import KDTree
 
-__all__ = "get_pqm_pvalue"
+__all__ = "pqm_pvalue"
 
 
-def get_pqm_pvalue(
+def pqm_pvalue(
     x_samples: np.ndarray,
     y_samples: np.ndarray,
     num_refs: int = 100,
@@ -33,9 +33,7 @@ def get_pqm_pvalue(
         pvalue. Null hypothesis that both samples are drawn from the same distribution.
     """
     if bootstrap is not None:
-        return list(
-            get_pqm_pvalue(x_samples, y_samples, num_refs=num_refs) for _ in range(bootstrap)
-        )
+        return list(pqm_pvalue(x_samples, y_samples, num_refs=num_refs) for _ in range(bootstrap))
     if len(y_samples) < num_refs:
         raise ValueError(
             "Number of reference samples must be less than the number of true samples."

src/pqm/test_gaussian.py

@@ -1,5 +1,5 @@
 import numpy as np
-from .pqm import get_pqm_pvalue
+from .pqm import pqm_pvalue
 
 
 def test():
@@ -8,6 +8,6 @@ def test():
         y_samples = np.random.normal(size=(500, 50))
         x_samples = np.random.normal(size=(250, 50))
 
-        new.append(get_pqm_pvalue(x_samples, y_samples))
+        new.append(pqm_pvalue(x_samples, y_samples))
 
     assert np.abs(np.mean(new) - 0.5) < 0.15

tests/test_gaussian.py

@@ -1,5 +1,5 @@
 import numpy as np
-from pqm import get_pqm_pvalue
+from pqm import pqm_pvalue
 
 
 def test():
@@ -8,6 +8,6 @@ def test():
         y_samples = np.random.normal(size=(500, 50))
         x_samples = np.random.normal(size=(250, 50))
 
-        new.append(get_pqm_pvalue(x_samples, y_samples))
+        new.append(pqm_pvalue(x_samples, y_samples))
 
     assert np.abs(np.mean(new) - 0.5) < 0.15