Merge branch 'dev' into new-modifs

new modifs.

EEGToolkit.egg-info/PKG-INFO

@@ -0,0 +1,72 @@
+Metadata-Version: 2.1
+Name: EEGToolkit
+Version: 2.0.0
+Summary: A package for EEG data analysis of reaction time-delay experiments.
+Home-page: https://github.com/AxelGiottonini/AP-EEG.git
+Author: Axel Giottonini, Noah Kleinschmidt, Kalvin Dobler
+Author-email: [email protected], [email protected], [email protected]
+Classifier: Programming Language :: Python :: 3
+Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
+Classifier: Operating System :: OS Independent
+Classifier: Topic :: Scientific/Engineering :: Information Analysis
+Classifier: Topic :: Scientific/Engineering :: Visualization
+Requires-Python: >=3.6
+Description-Content-Type: text/markdown
+
+# AP-EEG
+
+## Project description
+> ## EEG data analysis
+> Students who choose this task ill be provided with the raw EEG recording of one channel, sampled at $500 [Hz]$ which was recorded from a participant presented with auditory stimuli. The students will also receive an events file describing when during the recording one out of two possible sounds were presented to the subject.
+> ### Task 1
+> Extract the time-period from $0.1 [sec]$ before to $1 [sec]$ after each event for the EEG signal. Store these time-periods in two arrays, one for each of the two event-/sound-types.
+> ### Task 2
+> Plot the average response for each type of event, with shading for the standard error. On the plot, also indicate $t=0$, the amplitude of the signal in microvolts (Y-axis), and the time relative to sound onset in $[ms]$ (X-axis).
+> ### Task 3
+> For each sound type, for each time-point after sound onset, test wheter it is significantly different across trials from the baseline. Also test at what time-points after sound onset the EEG signals for the two sound-types are different from each other. Produce one plot containing the average EEG signal of each sound type, along with lines indicating the significant time-periods.
+
+## Milestones
+> - **Part 1: Project management and first deliverable** (*Deadline: April 28*)
+> - **Part 2: GitHub and second deliverable** (*Deadline: May 5*)
+> - **Part 3: Classes and refactoring** (*Deadline: May 12*)
+> - **Part 4: Unit tests and issues** (*Deadline: May 19*)
+> - **Part 5: Third deliverable, presentation and virtual environment** (*Deadline: June 2*)
+
+## Project roadmap
+
+### Main Objective
+The goal is to develop a small package with CLI to evaluate EEG data for reaction delays given different auditory stimuli.
+While the outset data is given as a binary stimulus experiment, extending to an arbitrary number of different stimuli would be 
+a desirable extention. 
+
+
+### Implementation
+
+#### General TODOs 
+- [x] proper **Docstrings** and comments in general ...
+- [x] re-factoring for private methods ... 
+- [x] requirements.txt
+- [x] setup.py 
+    - [ ] (optional but kinda cool) adding EEGData to PYTHONPATH to allow direct CLI calling...
+- [ ] `testpypi` distribution
+- [x] HTML documentation 
+
+#### CLI TODOs:
+- [x] Core is pretty much finished already...
+- [x] Change the `argparse` settings to allow defaults for parameters like x_scale to allow easier usage
+
+#### Task TODOs:
+##### Task 1: 
+- [x] Event extraction is pretty much finished already 
+> Possible additions: <br>
+> - [x] split reading the files and extracting data, thereby allowing different filetypes to be processed. In case this is done, we might add support for `txt` / `tsv` / `csv` files for more general applicability of the software. 
+
+
+##### Task 2: 
+- [x] plotting is pretty much finished already...
+- [ ] Add units to the figure axes labels
+- [x] (optional) adjust color scheme ...
+
+##### Task 3: 
+- [x] What about the intra-signal baseline-comparison ? 
+- [x] signal-comparison is finished already...

EEGToolkit.egg-info/SOURCES.txt

@@ -0,0 +1,15 @@
+README.md
+setup.py
+EEGToolkit/__init__.py
+EEGToolkit/main.py
+EEGToolkit.egg-info/PKG-INFO
+EEGToolkit.egg-info/SOURCES.txt
+EEGToolkit.egg-info/dependency_links.txt
+EEGToolkit.egg-info/entry_points.txt
+EEGToolkit.egg-info/top_level.txt
+EEGToolkit/EEGData/EEGData.py
+EEGToolkit/EEGData/__init__.py
+EEGToolkit/EEGStats/EEGStats.py
+EEGToolkit/EEGStats/__init__.py
+EEGToolkit/auxiliary/__init__.py
+EEGToolkit/auxiliary/auxiliary.py

EEGToolkit.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+

EEGToolkit.egg-info/entry_points.txt

@@ -0,0 +1,2 @@
+[console_scripts]
+EEGToolkit = EEGToolkit.EEGData:main

EEGToolkit.egg-info/top_level.txt

@@ -0,0 +1 @@
+EEGToolkit

EEGToolkit/EEGData.py → EEGToolkit/EEGData/EEGData.py

@@ -5,13 +5,25 @@
 describing event metadata as a 2D array, describing both the timepoints and the type of event in two columns.
 
 """
+import sys
+import os 
 
-import os
+import subprocess
+import inspect
 import argparse
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
-from EEGStats import plot_signal, difference_plot
+
+try:
+    from EEGToolkit.EEGStats import plot_signal, difference_plot
+except ImportError:
+    abspath = os.path.abspath(__file__)
+    dname = os.path.dirname(os.path.dirname(abspath))
+    sys.path.append(dname)
+
+    from EEGStats import plot_signal, difference_plot
+
 
 supported_filetypes = [ "npy", "tsv", "csv", "txt" ]
 class EEGData():
@@ -156,7 +168,8 @@ def read( self, signal_path : str = None , event_path : str = None ) -> None:
     def extract(self,
                 start_sec:float,
                 stop_sec:float,
-                event_type : ( int or tuple or list or np.ndarray ) = None) -> np.ndarray:
+                event_type : ( int or tuple or list or np.ndarray ) = None, 
+                **kwargs ) -> np.ndarray:
         """
         Extracts data for a specific (set of) event(s) from the loaded data. 
         And returns the data as numpy ndarrays (or a list thereof, in case of 
@@ -420,7 +433,8 @@ def summary(self,
         n = len(data)
 
         # generate a new figure
-        fig, ax = plt.subplots(n,n)
+        figsize = kwargs.pop( "figsize", ( 3*n,2*n ) )
+        fig, ax = plt.subplots(n,n, figsize = figsize )
 
         # setup a baseline reference, either with the computed
         # baselines or None ...
@@ -439,6 +453,7 @@ def summary(self,
                     x_scale, y_scale,
                     baseline = baseline[i],
                     make_legend = make_legend,
+                    significance_level = significance_level,
                     ax = ax[i,i] )
 
             ax[i,i].set_title(f"Signal {signals[i]}")
@@ -647,12 +662,12 @@ def main():
     formatter_class=argparse.RawDescriptionHelpFormatter,description = descr1, epilog = descr2 )
     parser.add_argument(
                             "--eeg_path", "--eeg", 
-                            type=str, required=True, 
+                            type=str,
                             help = f"A file containing EEG signal data. Supported filetypes are {supported_filetypes}" 
                     )
     parser.add_argument(
                             "--event_path", "--event", 
-                            type=str, required=True,
+                            type=str,
                             help = f"A file containing event metadata for the signal file. Supported filetypes are {supported_filetypes}"
                     )
     parser.add_argument(
@@ -662,7 +677,7 @@ def main():
                     )
     parser.add_argument(
                             "--sampling_frequency", "--freq", "-f", 
-                            type=float, required=True,
+                            type=float,
                             help = "The frequency at which the EEG signal data was recorded (in Hertz)."
                     )
     parser.add_argument(
@@ -672,12 +687,12 @@ def main():
                     )
     parser.add_argument(
                             "--start_sec", "--start", "-s", 
-                            type=float, required=True,
+                            type=float,
                             help = "The upstream time-padding for event extraction (in seconds)."
                     )
     parser.add_argument(
                             "--stop_sec", "--stop", "-e", 
-                            type=float, required=True,
+                            type=float,
                             help = "The downstream time-padding for event extraction (in seconds)."
                     )
 
@@ -696,23 +711,57 @@ def main():
                             type=float, default = 1000,
                             help = "A scaling factor for the signal-scale (y-values) from volts to some other unit. Default is 1000 (= millivolts)."
                     )
+
+    parser.add_argument(
+                            "--viewer", "-i", 
+                            action="store_true",
+                            default = False,
+                            help = "Open the EEGToolKit Viewer GUI in a web browser."
+                    )
 
     args = parser.parse_args()
 
-    # the main program (reading datafiles, extracting, and summarizing)
-    data = EEGData(args.eeg_path, args.event_path, args.sampling_frequency)
-    data.extract( args.start_sec, args.stop_sec )
-    if args.baseline:
-        data.baseline()
-    data.summary(
-                    significance_level = args.p_value,
-                    x_scale = args.x_scale,
-                    y_scale = args.y_scale,
-                    output = args.output
-                )
-
-    if args.output is not None: 
-        print( f"Output saved successfully to: '{args.output}'" ) 
+    # if the viewer is being called then we want to just open the 
+    # viewer and nothing else
+    if args.viewer:
+        # first we need to get the relative location of the main.
+        # py file within the package. 
+        directory = os.path.dirname( 
+                                            inspect.getfile( plot_signal ) 
+                                        )
+        directory = os.path.dirname( directory )
+        main_file = f"{directory}/main.py"
+
+        # then we call the web interface
+        print( "Starting the \033[94mEEGToolKit \033[96mViewer" )
+        subprocess.run( f"streamlit run {main_file}", shell = True )
+
+    else: 
+
+        # the main program (reading datafiles, extracting, and summarizing)
+        try:
+            data = EEGData(args.eeg_path, args.event_path, args.sampling_frequency)
+            data.extract( args.start_sec, args.stop_sec )
+            if args.baseline:
+                data.baseline()
+            data.summary(
+                            significance_level = args.p_value,
+                            x_scale = args.x_scale,
+                            y_scale = args.y_scale,
+                            output = args.output
+                        )
+
+            if args.output is not None: 
+                print( f"Output saved successfully to: '{args.output}'" )
+        except FileNotFoundError as e:
+            print(e)
+            return
+        except TypeError as e:
+            print(e)
+            return
+        except ValueError as e:
+            print(e)
+            return
 
 if __name__ == "__main__":
 

EEGToolkit/EEGData/__init__.py

@@ -0,0 +1 @@
+from .EEGData import *