[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

docs/source/getting_started.ipynb

@@ -16,7 +16,7 @@
                 "\n",
                 "## Installation\n",
                 "\n",
-                "> ### ⚠️ Important Note for macOS Users\n",
+                "> ### \u26a0\ufe0f Important Note for macOS Users\n",
                 ">\n",
                 "> **wristpy** depends on `libomp`, a system-level dependency that is not always installed by default on macOS. Install it via:\n",
                 ">\n",
@@ -776,30 +776,30 @@
                 "\n",
                 "## Physical Activity Metrics Explained\n",
                 "\n",
-                "### *ENMO— Euclidean Norm Minus One*  \n",
+                "### *ENMO\u2014 Euclidean Norm Minus One*  \n",
                 "ENMO quantifies movement intensity from calibrated 3-axis acceleration by taking the vector magnitude and subtracting 1 g (gravity). Values below zero are set to 0 (they mostly reflect noise or tiny calibration errors). \n",
                 "\n",
                 "**References:**  \n",
-                "- van Hees, V. T., Gorzelniak, L., Dean León, E. C., Eder, M., Pias, M., Taherian, S., Ekelund, U., Renström, F., Franks, P. W., Horsch, A., & Brage, S. (2013). Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity. *PLoS ONE, 8*(4), e61691. https://doi.org/10.1371/journal.pone.0061691  \n",
-                "- Hildebrand, M., van Hees, V. T., Hansen, B. H., & Ekelund, U. (2014). Age group comparability of raw accelerometer output from wrist- and hip-worn monitors. *Medicine & Science in Sports & Exercise, 46*(9), 1816–1824. https://doi.org/10.1249/MSS.0000000000000289  \n",
+                "- van Hees, V. T., Gorzelniak, L., Dean Le\u00f3n, E. C., Eder, M., Pias, M., Taherian, S., Ekelund, U., Renstr\u00f6m, F., Franks, P. W., Horsch, A., & Brage, S. (2013). Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity. *PLoS ONE, 8*(4), e61691. https://doi.org/10.1371/journal.pone.0061691  \n",
+                "- Hildebrand, M., van Hees, V. T., Hansen, B. H., & Ekelund, U. (2014). Age group comparability of raw accelerometer output from wrist- and hip-worn monitors. *Medicine & Science in Sports & Exercise, 46*(9), 1816\u20131824. https://doi.org/10.1249/MSS.0000000000000289  \n",
                 "\n",
                 "\n",
-                "### *MAD — Mean Amplitude Deviation*\n",
-                "MAD summarizes how much the acceleration magnitude fluctuates within an epoch. Specifically it’s the mean of the absolute deviations from the epoch’s mean acceleration magnitude. It correlates well with energy expenditure and is orientation-independent.\n",
+                "### *MAD \u2014 Mean Amplitude Deviation*\n",
+                "MAD summarizes how much the acceleration magnitude fluctuates within an epoch. Specifically it\u2019s the mean of the absolute deviations from the epoch\u2019s mean acceleration magnitude. It correlates well with energy expenditure and is orientation-independent.\n",
                 "\n",
                 "**Reference:**  \n",
-                "- Vähä-Ypyä, H., Vasankari, T., Husu, P., Suni, J., & Sievänen, H. (2015). A universal, accurate intensity-based classification of different physical activities using raw data of accelerometer. *Clinical Physiology and Functional Imaging, 35*(1), 64–70. https://doi.org/10.1111/cpf.12127  \n",
+                "- V\u00e4h\u00e4-Ypy\u00e4, H., Vasankari, T., Husu, P., Suni, J., & Siev\u00e4nen, H. (2015). A universal, accurate intensity-based classification of different physical activities using raw data of accelerometer. *Clinical Physiology and Functional Imaging, 35*(1), 64\u201370. https://doi.org/10.1111/cpf.12127  \n",
                 "\n",
                 "\n",
-                "### *MIMS — Monitor-Independent Summary Units*\n",
-                "MIMS is a standardized, device-agnostic summary unit. The pipeline interpolates to a fixed frequency (100 Hz), extrapolates values outside device range, band-pass filters, integrates area-under-the-curve per axis over each epoch, then combines axes (sum or vector magnitude) with small-value truncation. It’s designed to be comparable across devices and studies. This metric is computationally intensive and will run significantly slower than the other algorithms.\n",
+                "### *MIMS \u2014 Monitor-Independent Summary Units*\n",
+                "MIMS is a standardized, device-agnostic summary unit. The pipeline interpolates to a fixed frequency (100 Hz), extrapolates values outside device range, band-pass filters, integrates area-under-the-curve per axis over each epoch, then combines axes (sum or vector magnitude) with small-value truncation. It\u2019s designed to be comparable across devices and studies. This metric is computationally intensive and will run significantly slower than the other algorithms.\n",
                 "\n",
                 "**Reference:**  \n",
-                "- John, D., Tang, Q., Albinali, F., & Intille, S. (2019). An open-source monitor-independent movement summary for accelerometer data processing. *Journal for the Measurement of Physical Behaviour, 2*(4), 268–281. https://doi.org/10.1123/jmpb.2019-0011  \n",
+                "- John, D., Tang, Q., Albinali, F., & Intille, S. (2019). An open-source monitor-independent movement summary for accelerometer data processing. *Journal for the Measurement of Physical Behaviour, 2*(4), 268\u2013281. https://doi.org/10.1123/jmpb.2019-0011  \n",
                 "\n",
                 "\n",
                 "### *ActiGraph Activity Counts (ag_counts)*\n",
-                "This reproduces the open ActiGraph counts method: resample to 30 Hz, apply the published IIR band-pass filter, scale to device units, threshold, downsample to 10 Hz, and sum within each epoch. The three axes are finally combined  to yield epoch-level “counts.”\n",
+                "This reproduces the open ActiGraph counts method: resample to 30 Hz, apply the published IIR band-pass filter, scale to device units, threshold, downsample to 10 Hz, and sum within each epoch. The three axes are finally combined  to yield epoch-level \u201ccounts.\u201d\n",
                 "\n",
                 "**Reference:**  \n",
                 "- Neishabouri, A., Wilson, K. E., Williams, D. K., Keadle, S. K., Sampson, J., John, D., & Staudenmayer, J. (2022). Quantification of acceleration as activity counts in ActiGraph wearable. *Scientific Reports, 12*(1), 11169. https://doi.org/10.1038/s41598-022-16003-x  \n",