add BatchPeakToPeak

batchstats/__init__.py

@@ -1,7 +1,8 @@
 from .nanstats import BatchNanMean, BatchNanStat, BatchNanSum
-from .stats import BatchCov, BatchMax, BatchMean, BatchMin, BatchStat, BatchSum, BatchVar
+from .stats import (BatchCov, BatchMax, BatchMean, BatchMin, BatchPeakToPeak,
+                    BatchStat, BatchSum, BatchVar)
 
-__all__ = ['BatchCov', 'BatchMax', 'BatchMean', 'BatchMin', 'BatchStat', 'BatchSum', 'BatchVar',
+__all__ = ['BatchCov', 'BatchMax', 'BatchMean', 'BatchMin', 'BatchPeakToPeak', 'BatchStat', 'BatchSum', 'BatchVar',
            'BatchNanMean', 'BatchNanStat', 'BatchNanSum']
 
-__version__ = '0.3'
+__version__ = '0.3.1'

batchstats/stats.py

@@ -2,7 +2,8 @@
 
 import numpy as np
 
-from ._misc import NoValidSamplesError, UnequalSamplesNumber, any_nan, check_params
+from ._misc import (NoValidSamplesError, UnequalSamplesNumber, any_nan,
+                    check_params)
 
 
 class BatchStat:
@@ -239,6 +240,46 @@ def __call__(self) -> np.ndarray:
             return self.mean.copy()
 
 
+class BatchPeakToPeak(BatchStat):
+    """
+    Class for calculating the peak-to-peak (max - min) of batches of data.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.batchmax = BatchMax()
+        self.batchmin = BatchMin()
+
+    def update_batch(self, batch, assume_valid=False):
+        """
+        Update the peak-to-peak with a new batch of data.
+
+        Args:
+            batch (numpy.ndarray): Input batch.
+            assume_valid (bool, optional): If True, assumes all elements in the batch are valid. Default is False.
+
+        Returns:
+            BatchPeakToPeak: Updated BatchPeakToPeak object.
+
+        """
+        self.batchmax.update_batch(batch, assume_valid=assume_valid)
+        self.batchmin.update_batch(batch, assume_valid=assume_valid)
+        return self
+
+    def __call__(self) -> np.ndarray:
+        """
+        Calculate the peak-to-peak.
+
+        Returns:
+            numpy.ndarray: Peak-to-peak of the batches.
+
+        Raises:
+            NoValidSamplesError: If no valid samples are available.
+
+        """
+        return self.batchmax() - self.batchmin()
+
+
 class BatchVar(BatchMean):
     """
     Class for calculating the variance of batches of data.

tests/test_stats.py

@@ -1,7 +1,8 @@
 import numpy as np
 import pytest
 
-from batchstats import BatchCov, BatchMax, BatchMean, BatchMin, BatchSum, BatchVar
+from batchstats import (BatchCov, BatchMax, BatchMean, BatchMin,
+                        BatchPeakToPeak, BatchSum, BatchVar)
 
 
 @pytest.fixture
@@ -51,6 +52,16 @@ def test_mean(data, n_batches):
     assert np.allclose(true_stat, batch_stat)
 
 
+def test_ptp(data, n_batches):
+    true_stat = np.ptp(data, axis=0)
+
+    batchptp = BatchPeakToPeak()
+    for batch_data in np.array_split(data, n_batches):
+        batchptp.update_batch(batch=batch_data)
+    batch_stat = batchptp()
+    assert np.allclose(true_stat, batch_stat)
+
+
 def test_sum(data, n_batches):
     true_stat = np.sum(data, axis=0)