change minor typos

README.md

@@ -47,8 +47,7 @@ ecg_aligned_80hz = normalizer.transform(ecg)
 * `template`: A template ECG created with `create_template()` method. This template is 
     used as a reference for aligning R-peaks in the ECG signals.
 
-* `select_lead`: Specifies the lead (e.g., 'Lead II', 'Lead V1') for R-peak 
-    and QRST point detection. Different leads can provide varying levels of 
+* `select_lead`: Specifies the lead (e.g., 'Lead II', 'Lead V1') for R-peak detection. Different leads can provide varying levels of 
     clarity for these features. Selection via channel numbers 0,1,... .
 
 * `num_workers`: Determines the number of CPU cores to be utilized for 
@@ -74,6 +73,8 @@ ecg_aligned_80hz = normalizer.transform(ecg)
 * `median_beat`: Calculates the median from a set of aligned beats 
     and returns a single, representative beat.
 
+* `silent`: Disable all warnings.
+
 ## Citation
 Please use the following citation:
 

pyproject.toml

@@ -13,6 +13,7 @@ requires = [
 name='rlign'
 version='1.0'
 description='Beat normalization for ecg data.'
+readme = "README.md"
 requires-python = ">=3.11"
 authors=[
     {name="Lucas Bickmann"},
@@ -35,4 +36,4 @@ classifiers=[
 ]
 
 [project.optional-dependencies]
-docs = ["sphinx"]
+docs = ["sphinx"]

rlign/rlign.py

@@ -145,9 +145,6 @@ def _normalize_interval(
         # switch to defined resample method
         match scale_method:
             case 'linear':
-                # limit number of peaks
-                #source_rpeaks = source_rpeaks[:-1]
-
                 medians = []
                 # iterate over all peaks
                 for idx, (source_st, source_fs) in enumerate(zip(source_rpeaks, source_rpeaks_intervals)):
@@ -166,7 +163,6 @@ def _normalize_interval(
                             target_fs
                     ).transpose(1, 0)
 
-
                     if self.median_beat:
                         if (template_starts[idx] - int(self.template.intervals[0] / 3) < 0 or
                                 template_starts[idx] + self.template.intervals[0] - int(self.template.intervals[0] / 3) > len(
@@ -304,9 +300,7 @@ def _template_transform(
                 return None, 1
 
         try:
-            #hr = ecg_process(ecg_lead, sampling_rate=self.sampling_rate)[0]["ECG_Rate"].mean()
-
-            # just some basic approximation
+            # just some basic heart rate approximation
             min_factor = 60 / (ecg.shape[1] / self.sampling_rate)
             hr = int(len(rpeaks) * min_factor)
         except:
@@ -319,8 +313,7 @@ def _template_transform(
 
             return ecg, 1                
 
-
-        # limit number of rpeaks to the targets
+        # limit number of R-peaks to the targets
         rpeaks = rpeaks[: len(self.template.rpeaks+1)]
 
         # get the interval lengths of found rpeaks

rlign/utils.py

@@ -7,7 +7,7 @@
 import numpy as np
 import pandas as pd
 
-from typing import Optional, Tuple
+from typing import Tuple
 from scipy.signal import resample
 from sklearn.utils import check_array
 from neurokit2 import ecg_clean, ecg_peaks
@@ -66,53 +66,52 @@ def find_rpeaks(
             ecg_lead: np.ndarray,
             sampling_rate: int,
             neurokit_method: str = "neurokit",
-            correct_artifacts: bool = True
-    ) -> Tuple[np.ndarray, dict]:
-        '''
-            Internal function which calls neurokit for rpeak and qrs-complex computation.
+            correct_artifacts: bool = True) -> Tuple[np.ndarray, dict]:
+    """
+        Internal function which calls neurokit for rpeak and qrs-complex computation.
 
-            Parameters:
-                ecg_lead: An array representing a single-lead ECG signal. The input is
-                    expected to be a one-dimensional array of voltage values over time, representing
-                    the electrical activity of the heart as captured by a specific ECG lead.
-
-                sampling_rate: Defines the sampling rate for all ECG recordings.
-
-                neurokit_method: Chooses the algorithm for R-peak detection from the
-                    NeuroKit package. Different algorithms may offer varying performance
-                    based on the ECG signal characteristics.
-
-                correct_artifacts: If set to True, artifact correction is applied
-                    exclusively for R-peak detections, enhancing the accuracy of peak
-                    identification in noisy signals.
-
-            Returns:
-                (rpeaks, qrs_epochs): A pair of elements (rpeaks, qrs_epochs) representing
-                    the outcomes of the R-peak detection and QRS complex computation processes,
-                    respectively. If an error occurs during the processing, the function
-                    returns (None, None), indicating a failure in signal analysis.
-        '''
-        try:
-            # clean the ecg as recommended by neurokit
-            data_ = ecg_clean(
-                ecg_lead,
-                sampling_rate=sampling_rate
-            )
-
-            # caluclate rpeaks
-            _, r_peaks = ecg_peaks(
-                data_,
-                sampling_rate=sampling_rate,
-                method=neurokit_method,
-                correct_artifacts=correct_artifacts
-            )
-            rpeaks = r_peaks['ECG_R_Peaks'].astype(np.int32)
-
-        except Exception as e:
-            logging.warning(f'Failure in neurokit: {e}\n')
-            return None, None
-
-        return rpeaks
+        Parameters:
+            ecg_lead: An array representing a single-lead ECG signal. The input is
+                expected to be a one-dimensional array of voltage values over time, representing
+                the electrical activity of the heart as captured by a specific ECG lead.
+
+            sampling_rate: Defines the sampling rate for all ECG recordings.
+
+            neurokit_method: Chooses the algorithm for R-peak detection from the
+                NeuroKit package. Different algorithms may offer varying performance
+                based on the ECG signal characteristics.
+
+            correct_artifacts: If set to True, artifact correction is applied
+                exclusively for R-peak detections, enhancing the accuracy of peak
+                identification in noisy signals.
+
+        Returns:
+            (rpeaks, qrs_epochs): A pair of elements (rpeaks, qrs_epochs) representing
+                the outcomes of the R-peak detection and QRS complex computation processes,
+                respectively. If an error occurs during the processing, the function
+                returns (None, None), indicating a failure in signal analysis.
+    """
+    try:
+        # clean the ecg as recommended by neurokit
+        data_ = ecg_clean(
+            ecg_lead,
+            sampling_rate=sampling_rate
+        )
+
+        # caluclate rpeaks
+        _, r_peaks = ecg_peaks(
+            data_,
+            sampling_rate=sampling_rate,
+            method=neurokit_method,
+            correct_artifacts=correct_artifacts
+        )
+        rpeaks = r_peaks['ECG_R_Peaks'].astype(np.int32)
+
+    except Exception as e:
+        logging.warning(f'Failure in neurokit: {e}\n')
+        return None, None
+
+    return rpeaks
 
 
 def _resample_multichannel(xs, fs, fs_target):
@@ -160,8 +159,6 @@ def _resample_signal(x, fs, fs_target):
     Note:
         The method have been modified from wfdbs resample_multichan and resample_sig.
     """
-    #t = np.arange(x.shape[0]).astype("float64")
-
     if fs == fs_target:
         return x
 
@@ -181,4 +178,4 @@ def _check_3d_array(X):
     X = check_array(X, ensure_2d=False, allow_nd=True)
     if X.ndim != 3:
         raise ValueError(f"X must be 3-dimensional (got {X.ndim}).")
-    return X
+    return X