more cleanup

iblrig/misc.py

@@ -80,13 +80,19 @@ def get_task_arguments(parents=None):
     return _post_parse_arguments(**kwargs)
 
 
-def _isdatetime(x: str) -> Optional[bool]:
+def _is_datetime(x: str) -> bool:
     """
-    Check if string is a date in the format YYYY-MM-DD.
+    Check if a string is a date in the format YYYY-MM-DD.
 
-    :param x: The string to check
-    :return: True if the string matches the date format, False otherwise.
-    :rtype: Optional[bool]
+    Parameters
+    ----------
+    x : str
+        The string to check.
+
+    Returns
+    -------
+    bool or None
+        True if the string matches the date format, False otherwise, or None if there's an exception.
     """
     try:
         datetime.strptime(x, "%Y-%m-%d")
@@ -104,7 +110,7 @@ def get_session_path(path: Union[str, Path]) -> Optional[Path]:
         path = Path(path)
     sess = None
     for i, p in enumerate(path.parts):
-        if p.isdigit() and _isdatetime(path.parts[i - 1]):
+        if p.isdigit() and _is_datetime(path.parts[i - 1]):
             sess = Path().joinpath(*path.parts[: i + 1])
 
     return sess
@@ -210,3 +216,30 @@ def draw_contrast(contrast_set: Iterable[float],
         return np.random.choice(contrast_set)
     else:
         raise ValueError("Unsupported probability_type. Use 'skew_zero', 'biased', or 'uniform'.")
+
+
+def online_std(new_sample: float, new_count: int, old_mean: float, old_std: float) -> tuple[float, float]:
+    """
+    Updates the mean and standard deviation of a group of values after a sample update
+
+    Parameters
+    ----------
+    new_sample : float
+        The new sample to be included.
+    new_count : int
+        The new count of samples (including new_sample).
+    old_mean : float
+        The previous mean (N - 1).
+    old_std : float
+        The previous standard deviation (N - 1).
+
+    Returns
+    -------
+    tuple[float, float]
+        Updated mean and standard deviation.
+    """
+    if new_count == 1:
+        return new_sample, 0.0
+    new_mean = (old_mean * (new_count - 1) + new_sample) / new_count
+    new_std = np.sqrt((old_std ** 2 * (new_count - 1) + (new_sample - old_mean) * (new_sample - new_mean)) / new_count)
+    return new_mean, new_std

iblrig/online_plots.py

@@ -22,33 +22,6 @@
 sns.set_style('white')
 
 
-def online_std(new_sample: float, new_count: int, old_mean: float, old_std: float) -> tuple[float, float]:
-    """
-    Updates the mean and standard deviation of a group of values after a sample update
-
-    Parameters
-    ----------
-    new_sample : float
-        The new sample to be included.
-    new_count : int
-        The new count of samples (including new_sample).
-    old_mean : float
-        The previous mean (N - 1).
-    old_std : float
-        The previous standard deviation (N - 1).
-
-    Returns
-    -------
-    tuple[float, float]
-        Updated mean and standard deviation.
-    """
-    if new_count == 1:
-        return new_sample, 0.0
-    new_mean = (old_mean * (new_count - 1) + new_sample) / new_count
-    new_std = np.sqrt((old_std ** 2 * (new_count - 1) + (new_sample - old_mean) * (new_sample - new_mean)) / new_count)
-    return new_mean, new_std
-
-
 class DataModel(object):
     """
     The data model is a pure numpy / pandas container for the choice world task.

iblrig/test/test_misc.py

@@ -4,11 +4,11 @@
 from scipy import stats
 
 from iblrig import misc
+from iblrig.misc import online_std
 
 
 class TestMisc(unittest.TestCase):
     def test_draw_contrast(self):
-
         n_draws = 400
         n_contrasts = 10
         contrast_set = np.linspace(0, 1, n_contrasts)
@@ -31,3 +31,11 @@ def assert_distribution(values: list[int], f_exp: list[float] | None = None) ->
 
         self.assertRaises(ValueError, misc.draw_contrast, [], "incorrect_type")  # assert exception for incorrect type
         self.assertRaises(ValueError, misc.draw_contrast, [0, 1], "biased", 2)  # assert exception for out-of-range index
+
+    def test_online_std(self):
+        n = 41
+        b = np.random.rand(n)
+        a = b[:-1]
+        mu, std = online_std(new_sample=b[-1], new_count=n, old_mean=np.mean(a), old_std=np.std(a))
+        np.testing.assert_almost_equal(std, np.std(b))
+        np.testing.assert_almost_equal(mu, np.mean(b))

iblrig/test/test_online_plots.py

@@ -11,17 +11,6 @@
 matplotlib.use('Agg')  # avoid pyqt testing issues
 
 
-class TestOnlineStd(unittest.TestCase):
-
-    def test_online_std(self):
-        n = 41
-        b = np.random.rand(n)
-        a = b[:-1]
-        mu, std = op.online_std(new_sample=b[-1], count=n, mean=np.mean(a), std=np.std(a))
-        np.testing.assert_almost_equal(std, np.std(b))
-        np.testing.assert_almost_equal(mu, np.mean(b))
-
-
 class TestOnlinePlots(unittest.TestCase):
     @classmethod
     def setUpClass(cls) -> None: