Fixing typos

Author: Asanto32

README.md

@@ -25,7 +25,7 @@ The package currently supports the following formats:
 | BIN | GENEActiv | GENEActiv | ✅ |
 
 **Special Note**
-    The `idle_sleep_mode` for Actigraph watches will lead to uneven sampling rates during periods of no motion (read about this [here](https://actigraphcorp.my.site.com/support/s/article/Idle-Sleep-Mode-Explained)). Consequently, this causes issues when implementing wristpy's non-wear and sleep detection. As of this moment, we fill in the missing acceleration data with the assumption that the watch is perfeclty idle in the face-up position (Acceleration vector = [0, 0, -1]). The data is filled in at the same sampling rate as the raw acceleration data. In the special circumstance when acceleration samples are not evenly spaced, the data is resampled to the highest effective sampling rate to ensure linearly sampled data.
+    The `idle_sleep_mode` for Actigraph watches will lead to uneven sampling rates during periods of no motion (read about this [here](https://actigraphcorp.my.site.com/support/s/article/Idle-Sleep-Mode-Explained)). Consequently, this causes issues when implementing wristpy's non-wear and sleep detection. As of this moment, we fill in the missing acceleration data with the assumption that the watch is perfectly idle in the face-up position (Acceleration vector = [0, 0, -1]). The data is filled in at the same sampling rate as the raw acceleration data. In the special circumstance when acceleration samples are not evenly spaced, the data is resampled to the highest effective sampling rate to ensure linearly sampled data.
 
 ## Processing pipeline implementation
 
@@ -37,7 +37,7 @@ The main processing pipeline of the wristpy module can be described as follows:
 - **Metrics Calculation**: Calculates various metrics on the calibrated data, namely ENMO (Euclidean norm , minus one) and angle-Z (angle of acceleration relative to the *x-y* axis).
 - **Non-wear detection**: We find periods of non-wear based on the acceleration data. Specifically, the standard deviation of the acceleration values in a given time window, along each axis, is used as a threshold to decide `wear` or `not wear`.
 - **Sleep Detection**: Using the HDCZ<sup>1</sup> and HSPT<sup>2</sup> algorithms to analyze changes in arm angle we are able to find periods of sleep. We find the sleep onset-wakeup times for all sleep windows detected.
-- **Physical activity levels**: Using the enmo data (aggreagated into epoch 1 time bins, 5 second default) we compute activity levels into the following categories: inactivity, light activity, moderate activity, vigorous activity. The default threshold values have been chosen based on the values presented in the Hildenbrand 2014 study<sup>3</sup>.
+- **Physical activity levels**: Using the enmo data (aggregated into epoch 1 time bins, 5 second default) we compute activity levels into the following categories: inactivity, light activity, moderate activity, vigorous activity. The default threshold values have been chosen based on the values presented in the Hildenbrand 2014 study<sup>3</sup>.
 
 
 ## Installation
@@ -109,7 +109,7 @@ results_dict = orchestrator.run(
 )
 
 
-#Data available in dictionry of results.
+#Data available in dictionary of results.
 subject1 = results_dict['subject1']
 
 physical_activity_metric = subject1.physical_activity_metric

src/wristpy/processing/metrics.py

@@ -252,15 +252,15 @@ def detect_nonwear(
 
 
     Args:
-        acceleration: The Measurment instance that contains the calibrated acceleration
+        acceleration: The Measurement instance that contains the calibrated acceleration
             data.
         short_epoch_length: The short window size, in seconds.
         n_short_epoch_in_long_epoch: Number of short epochs that makeup one long epoch.
         std_criteria: Threshold criteria for standard deviation.
 
 
     Returns:
-        A new Measurment instance with the non-wear flag and corresponding timestamps.
+        A new Measurement instance with the non-wear flag and corresponding timestamps.
     """
     logger.debug("Detecting non-wear data.")
     acceleration_grouped_by_short_window = _group_acceleration_data_by_time(
@@ -296,13 +296,13 @@ def combined_temp_accel_detect_nonwear(
     "non-wear", here denoted by 0 and 1, respectively.
 
     Args:
-        acceleration: The Measurment instance that contains the calibrated acceleration.
-        temperature: The Measurment instance that contains the temperature data.
+        acceleration: The Measurement instance that contains the calibrated acceleration.
+        temperature: The Measurement instance that contains the temperature data.
         std_criteria: Threshold criteria for standard deviation.
         temperature_threshold: The temperature threshold for non-wear detection.
 
     Returns:
-        A new Measurment instance with the non-wear flag and corresponding timestamps.
+        A new Measurement instance with the non-wear flag and corresponding timestamps.
         The temporal resolutions is one minute.
 
     References:
@@ -363,12 +363,12 @@ def detach_nonwear(
     The nonwear array is downsampled to 60 second resolution.
 
     Args:
-        acceleration: The Measurment instance that contains the calibrated acceleration.
-        temperature: The Measurment instance that contains the temperature data.
+        acceleration: The Measurement instance that contains the calibrated acceleration.
+        temperature: The Measurement instance that contains the temperature data.
         std_criteria: The acceleration STD threshold for non-wear detection.
 
     Returns:
-        A new Measurment instance with the non-wear flag and corresponding timestamps.
+        A new Measurement instance with the non-wear flag and corresponding timestamps.
         The temporal resolutions is one minute.
 
     References:
@@ -425,7 +425,7 @@ def _group_acceleration_data_by_time(
     """Helper function to group the acceleration data by short windows.
 
     Args:
-        acceleration: The Measurment instance that contains the calibrated acceleration.
+        acceleration: The Measurement instance that contains the calibrated acceleration.
         window_length: The window size, in seconds.
 
     Returns:
@@ -562,8 +562,8 @@ def _pre_process_temperature(
     intervals and then low-pass filters the data by applying a 2 minute rolling mean.
 
     Args:
-        temperature: The Measurment instance that contains the temperature data.
-        acceleration: The Measurment instance that contains the acceleration data.
+        temperature: The Measurement instance that contains the temperature data.
+        acceleration: The Measurement instance that contains the acceleration data.
 
     Returns:
         A polars DataFrame with the pre-processed temperature data.