Readme update - Acknowledgement

README.rst

@@ -33,22 +33,22 @@ This toolbox was developed during multiple projects we appreciate you acknowledg
 The toolbox as a whole was developed during the following projects:
 
 | F. Mivalt et V. Kremen et al., “Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans,” J. Neural Eng., vol. 19, no. 1, p. 016019, Feb. 2022, doi: 10.1088/1741-2552/ac4bfd.
-
+|
 | V. Sladky et al., “Distributed brain co-processor for tracking spikes, seizures and behaviour during electrical brain stimulation,” Brain Commun., vol. 4, no. 3, May 2022, doi: 10.1093/braincomms/fcac115.
-
-For different modules, please see below.
+|
+For individual modules, please see below.
 
 Sleep classification and feature extraction
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 | F. Mivalt et V. Kremen et al., “Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans,” J. Neural Eng., vol. 19, no. 1, p. 016019, Feb. 2022, doi: 10.1088/1741-2552/ac4bfd.
-
+|
 | F. Mivalt et V. Sladky et al., “Automated sleep classification with chronic neural implants in freely behaving canines,” J. Neural Eng., vol. 20, no. 4, p. 046025, Aug. 2023, doi: 10.1088/1741-2552/aced21.
-
+|
 | Gerla, V., Kremen, V., Macas, M., Dudysova, D., Mladek, A., Sos, P., & Lhotska, L. (2019). Iterative expert-in-the-loop classification of sleep PSG recordings using a hierarchical clustering. Journal of Neuroscience Methods, 317(February), 61?70. https://doi.org/10.1016/j.jneumeth.2019.01.013
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 | Kremen, V., Brinkmann, B. H., Van Gompel, J. J., Stead, S. (Matt) M., St Louis, E. K., & Worrell, G. A. (2018). Automated Unsupervised Behavioral State Classification using Intracranial Electrophysiology. Journal of Neural Engineering. https://doi.org/10.1088/1741-2552/aae5ab
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+|
 | Kremen, V., Duque, J. J., Brinkmann, B. H., Berry, B. M., Kucewicz, M. T., Khadjevand, F., G.A. Worrell, G. A. (2017). Behavioral state classification in epileptic brain using intracranial electrophysiology. Journal of Neural Engineering, 14(2), 026001. https://doi.org/10.1088/1741-2552/aa5688
 
 
@@ -71,7 +71,8 @@ Evoked Response Potential Analysis
 Acknowledgement
 """""""""""""""""""
 
-BEST was developed under projects supported by NIH Brain Initiative UH2&3 NS095495 Neurophysiologically-Based Brain State Tracking & Modulation in Focal Epilepsy, DARPA HR0011-20-2-0028 Manipulating and Optimizing Brain Rhythms for Enhancement of Sleep (Morpheus).
+BEST was developed under projects supported by NIH Brain Initiative UH2&3 NS095495 Neurophysiologically-Based Brain State Tracking & Modulation in Focal Epilepsy, NIH U01-NS128612 An Ecosystem of Techmology and Protocols for Adaptive Neuromodulation Research in Humans, DARPA HR0011-20-2-0028 Manipulating and Optimizing Brain Rhythms for Enhancement of Sleep (Morpheus).
+
 Filip Mivalt was also partially supported by the grant FEKT-K-22-7649 realized within the project Quality Internal Grants of the Brno University of Technology (KInG BUT), Reg. No. CZ.02.2.69/0.0/0.0/19_073/0016948, which is financed from the OP RDE.
 
 

best/cloud/db.py

@@ -25,7 +25,6 @@
 from datetime import datetime
 
 
-
 class DatabaseHandler:
     __version__ = '0.0.1'
     def __init__(self, sql_db_name, sql_host=None, sql_user=None, sql_pwd=None, sql_port=None, ssh_host=None, ssh_user=None, ssh_pwd=None, ssh_port=None):
@@ -131,17 +130,16 @@ def db_name(self, name):
         self.check_connection()
 
 
-
 class SessionFinder(DatabaseHandler):
     #TODO: Enable searching for signals between multiple session
     __version__ = '0.0.1'
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
     def find_mef_session(self, patient_id, uutc_start, uutc_stop):
-        if not isinstance(uutc_start, (int, float, np.int64, np.float)):
+        if not isinstance(uutc_start, (int, float, np.int64, np.float32, np.float16, np.float64)):
             raise TypeError('uutc_start has to be of a number type - int or float. Data type ' + type(uutc_start) + ' found instead.')
-        if not isinstance(uutc_stop, (int, float, np.int64, np.float)):
+        if not isinstance(uutc_stop, (int, float, np.int64, np.float32, np.float16, np.float64)):
             raise TypeError('uutc_stop has to be of a number type - int or float. Data type ' + type(uutc_stop) + ' found instead.')
 
         uutc_start = int(round(uutc_start*1e6))
@@ -162,9 +160,9 @@ def find_mef_session_bulk(self, patient_id, df):
 
         for row in tqdm(list(df.iterrows())):
             row = row[1]
-            if not isinstance(row['start'], (int, float, np.int64, np.float)):
+            if not isinstance(row['start'], (int, float, np.int64, np.float32, np.float16, np.float64)):
                 raise TypeError('start column has to be of a number type - int or float. Data type ' + type(row['start']) + ' found instead.')
-            if not isinstance(row['end'], (int, float, np.int64, np.float)):
+            if not isinstance(row['end'], (int, float, np.int64, np.float32, np.float16, np.float64)):
                 raise TypeError('end column has to be of a number type - int or float. Data type ' + type(row['end']) + ' found instead.')
 
             uutc_start = int(round(row['start']*1e6))
@@ -194,7 +192,6 @@ def data_range(self, patient_id):
         return unique_ids.values[0]
 
 
-
 class SleepClassificationModelDBHandler(DatabaseHandler):
     keys_pickle = ['classifier']
     keys_unalter = ['pid', 'kappa_score', 'name', 'class_name', 'package', 'package_version']
@@ -684,7 +681,6 @@ def get_spike_n_detections(self,  patient_id=None, channel=None, start_timestamp
         self._close_sql()
         return df['COUNT(start_uutc)'][0]
 
-
     def get_spike_detections(self,  patient_id=None, channel=None, start_timestamp=None, stop_timestamp=None):
         if isinstance(start_timestamp, type(None)):
             start_timestamp = 0