fixes doc strings

paat/calibration.py

@@ -9,7 +9,36 @@
 import pandas as pd
 
 
-def estimate_calibration_coefficents(data):
+def estimate_calibration_coefficents(acc):
+    """
+    .. warning::
+        This function is not implemented yet
+    
+    Estimates the calibration correction coefficients based on the method proposed 
+    by Van Hees et al. (2014)
+
+    References
+    ----------
+    
+    Van Hees, V. T., Fang, Z., Langford, J., Assah, F., Mohammad, A., da Silva, I. C. M., 
+    Trenell, M. I., White, T., Wareham, N. J., & Brage, S. (2014). Autocalibration of 
+    accelerometer data for free-living physical activity assessment using local gravity 
+    and temperature: An evaluation on four continents. *Journal of Applied Physiology*, 
+    117(7), 738–744. https://doi.org/10.1152/japplphysiol.00421.2014
+    
+    Parameters
+    ----------
+    acc : array_like
+        numpy array with acceleration data
+
+    Returns
+    -------
+    scale : array_like
+        numpy array with the scale factors
+    offset : array_like
+        numpy array with the offset factors
+    
+    """
     raise NotImplementedError("Autocalibration is not implemented yet. Please use GGIR to estimate the calibration coefficients.")
 
 

paat/wear_time.py

@@ -322,14 +322,11 @@ def _group_episodes(episodes, distance_in_min=3, correction=3, hz=100, training=
 def detect_non_wear_time_syed2021(data, sample_freq, cnn_model_file=None, std_threshold=0.004, distance_in_min=5, episode_window_sec=7, edge_true_or_false=True,
                                   start_stop_label_decision='and', min_segment_length=1, sliding_window=1, verbose=False):
     """
-    Infer non-wear time from raw 100Hz triaxial data. Data at different sample frequencies will be resampled to 100hz.
-
-    Paper:
-    A novel algorithm to detect non-wear time from raw accelerometer data using convolutional neural networks
+    Infer non-wear time from raw 100Hz triaxial data based on the method proposed by Syed et al. (2021). 
+    Data at different sample frequencies will be resampled to 100hz.
 
     The steps are described in the paper but are summarized here too:
 
-
     Detect candidate non-wear episodes:
         Perform a forward pass through the raw acceleration signal and calculate the SD for each 1-minute interval and for each individual axis.
         If the standard deviation is <= 0.004 g for all axes, record this 1-minute interval as a candidate non-wear interval. After all 1-minute
@@ -355,6 +352,11 @@ def detect_non_wear_time_syed2021(data, sample_freq, cnn_model_file=None, std_th
         those candidate non-wear episodes do not have a preceding or following window to extract features from—classify the start or stop
         as non-wear time by default.
 
+    References
+    ----------
+
+    Syed, S., Morseth, B., Hopstock, L. A., & Horsch, A. (2021). A novel algorithm to detect non-wear time from raw 
+    accelerometer data using deep convolutional neural networks. *Scientific Reports*, 11(1), 8832. https://doi.org/10.1038/s41598-021-87757-z
 
     Parameters
     ----------
@@ -545,18 +547,30 @@ def detect_non_wear_time_syed2021(data, sample_freq, cnn_model_file=None, std_th
 def detect_non_wear_time_hees2011(data, sample_freq, min_non_wear_time_window=60, window_overlap=15, std_mg_threshold=3.0, std_min_num_axes=2,
                                   value_range_mg_threshold=50.0, value_range_min_num_axes=2):
     """
-    Estimation of non-wear time periods based on Hees 2013 paper
-
-    Estimation of Daily Energy Expenditure in Pregnant and Non-Pregnant Women Using a Wrist-Worn Tri-Axial Accelerometer
-    Vincent T. van Hees  , Frida Renström , Antony Wright, Anna Gradmark, Michael Catt, Kong Y. Chen, Marie Löf, Les Bluck, Jeremy Pomeroy, Nicholas J. Wareham, Ulf Ekelund, Søren Brage, Paul W. Franks
-    Published: July 29, 2011 https://doi.org/10.1371/journal.pone.0022922
+    Estimation of non-wear time periods based on Van Hees et al. (2011, 2013)
 
     Accelerometer non-wear time was estimated on the basis of the standard deviation and the value range of each accelerometer axis, calculated for consecutive blocks of 30 minutes.
     A block was classified as non-wear time if the standard deviation was less than 3.0 mg (1 mg = 0.00981 m·s−2) for at least two out of the three axes or if the value range, for
     at least two out of three axes, was less than 50 mg.
 
     Important to note that the default encoding for this function of non-wear time = 0, and that of wear time is 1.
 
+
+    References
+    ----------
+
+    Van Hees, V. T., Renström, F., Wright, A., Gradmark, A., Catt, M., Chen, K. Y., 
+    Löf, M., Bluck, L., Pomeroy, J., Wareham, N. J., Ekelund, U., Brage, S., & Franks, 
+    P. W. (2011). Estimation of Daily Energy Expenditure in Pregnant and Non-Pregnant 
+    Women Using a Wrist-Worn Tri-Axial Accelerometer. *PLOS ONE*, 6(7), 7. 
+    https://doi.org/10.1371/journal.pone.0022922
+
+    Hees, V. T. van, Gorzelniak, L., León, E. C. D., 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
+    
     Parameters
     ----------
     data : DataFrame