From 00666bb2edbce23a1575910c0118e40a6e7003d4 Mon Sep 17 00:00:00 2001
From: Baptiste Vandecrux <35140661+BaptisteVandecrux@users.noreply.github.com>
Date: Mon, 13 May 2024 12:48:16 +0200
Subject: [PATCH] removing count_consecutive_true + fixed types in
 duration_consecutive_true

---
 src/pypromice/process/L1toL2.py |  2 +-
 src/pypromice/qc/persistence.py | 40 +++++----------------------------
 2 files changed, 6 insertions(+), 36 deletions(-)

diff --git a/src/pypromice/process/L1toL2.py b/src/pypromice/process/L1toL2.py
index 9b74f3ef..73ca1a7d 100644
--- a/src/pypromice/process/L1toL2.py
+++ b/src/pypromice/process/L1toL2.py
@@ -272,7 +272,7 @@ def smoothTilt(da: xr.DataArray, threshold=0.2):
     xarray.DataArray
         either X or Y smoothed tilt inclinometer measurements
     '''
-    # we caluclate the moving standard deviation over a 3-day sliding window
+    # we calculate the moving standard deviation over a 3-day sliding window
     # hourly resampling is necessary to make sure the same threshold can be used 
     # for 10 min and hourly data
     moving_std_gap_filled = da.to_series().resample('H').median().rolling(
diff --git a/src/pypromice/qc/persistence.py b/src/pypromice/qc/persistence.py
index e0a41830..20ceb60c 100644
--- a/src/pypromice/qc/persistence.py
+++ b/src/pypromice/qc/persistence.py
@@ -133,44 +133,14 @@ def count_consecutive_persistent_values(
 ) -> pd.Series:
     diff = data.ffill().diff().abs()  # forward filling all NaNs!
     mask: pd.Series = diff < max_diff
-    # return count_consecutive_true(mask)
     return duration_consecutive_true(mask)
 
 
-def count_consecutive_true(
-    series: Union[pd.Series, pd.DataFrame]
-) -> Union[pd.Series, pd.DataFrame]:
-    """
-    Convert boolean series to integer series where the values represent the number of connective true values.
-
-    Examples
-    --------
-    >>> count_consecutive_true(pd.Series([False, True, False, False, True, True, True, False, True]))
-    pd.Series([0, 1, 0, 0, 1, 2, 3, 0, 1])
-
-    Parameters
-    ----------
-    series
-        Boolean pandas Series or DataFrame
-
-    Returns
-    -------
-    consecutive_true_count
-        Integer pandas Series or DataFrame with values representing the number of connective true values.
-
-    """
-    # assert series.dtype == bool
-    cumsum = series.cumsum()
-    is_first = series.astype("int").diff() == 1
-    offset = (is_first * cumsum).replace(0, np.nan).fillna(method="ffill").fillna(0)
-    return ((cumsum - offset + 1) * series).astype("int")
-
-
 def duration_consecutive_true(
-    series: Union[pd.Series, pd.DataFrame]
-) -> Union[pd.Series, pd.DataFrame]:
+    series: pd.Series,
+) -> pd.Series:
     """
-    Froma a boolean series, calculates the duration, in hours, of the periods with connective true values.
+    From a boolean series, calculates the duration, in hours, of the periods with connective true values.
 
     Examples
     --------
@@ -179,12 +149,12 @@ def duration_consecutive_true(
 
     Parameters
     ----------
-    series
+    pd.Series
         Boolean pandas Series or DataFrame
 
     Returns
     -------
-    consecutive_true_duration
+    pd.Series
         Integer pandas Series or DataFrame with values representing the number of connective true values.
 
     """