업데이트 'README.md'

README.md

@@ -1,3 +1,4 @@
+</br>
 
 # 1. Dataset information
 
@@ -36,7 +37,7 @@ Raw EEG data are saved in EEGLAB dataset format (*.set). Below are the list of f
 **d) Example python scripts**
 
     [analysis_tutorial.ipynb]
-     * written and tested on Google COLAB - Python 3 environment
+     * written and tested on Google Colab - Python 3 environment
     
 
 # 3. How to get started (Python 3 without _gin_)
@@ -49,28 +50,27 @@ As the data are saved in EEGLAB format, you need to install appropriate module t
 
 ### 1-1. Download dataset and MNE-python module
 
-The dataset has been uploaded on G-Node and can be accessed by git command, by typing <code>git clone https://gin.g-node.org/hiobeen/Mouse_hdEEG_ASSR_Hwang_et_al</code>. However, it's currently not functioning because of the large size of each dataset (>100 MB). Instead, you can use *gin* command or custom function written below to copy dataset into your work environment. In *gin* repository, a python script <code>download_sample.py</code> is provided. It doesn't require *git* or *gin* command, simply using <code>request</code> module in Python 3. Try typing <code>python download_sample.py</code> on terminal/command after changing desired directory. In this Notebook document, Demo 1-1 is composed of download_sample.py script. 
+The dataset has been uploaded on G-Node and can be accessed by git command, by typing <code>git clone https://gin.g-node.org/hiobeen/Mouse_hdEEG_ASSR_Hwang_et_al</code>. However, it's currently not functioning because of the large size of each dataset (>100 MB). Instead, you can use *gin* command or custom function written below to copy dataset into your work environment. In *gin* repository, a python script <code>download_sample.py</code> is provided. It doesn't require *git* or *gin* command, simply using <code>request</code> module in Python 3. Try typing <code>python download_sample.py</code> on terminal/command after changing desired directory. Demo 1-1 is composed of download_sample.py script in this Jupyter-Notebook document.
 
 > Warning: Direct cloning using *git clone git@gin.g-node.org:/hiobeen/Mouse_hdEEG_ASSR_Hwang_et_al.git* may not work because of the large size of each dataset (>100 MB).
 
-To download dataset and install MNE-python module into your environment (local machine/COLAB), try running scripts below.
+Also, you need to install *MNE-Python* module using *pip* command to load EEGLAB-formatted EEG data. Install command using *pip* is located at the end of script <code>download_sample.py</code>. To download dataset and install MNE-python module into your environment (local machine/COLAB), try running scripts below.
 
-> Note: Through this step-by-step demonstration, we will use data from one animal (#Animal 2). Unnecessary data files will not be downloaded to prevent long download time. To download whole dataset, change this part; <code>dataset_to_download = [2]</code> into <code>dataset_to_download = [1,2,3,4,5,6]</code>.
-
-Also, you need to install *MNE-Python* module using *pip* command to load EEGLAB-formatted EEG data. Install command using *pip* is located at the end of script <code>download_sample.py</code>.
+> Note: Through this step-by-step demonstration, we will use data from one animal (#Animal 2). Unnecessary data files will not be downloaded to prevent long download time. To download whole dataset, change <code>dataset_to_download = [2]</code> into <code>dataset_to_download = [1,2,3,4,5,6]</code>.
 
 
 ```python
 # Demo 1-1. Setting an enviroment (download_sample.py)
 from os import listdir, mkdir, path, system, getcwd
+import warnings; warnings.simplefilter("ignore")
 dir_origin = dir_origin = getcwd()+'/' # <- Change this in local machine
 dir_dataset= 'dataset/'
 print('\n1)============ Start Downloading =================\n')
 print('Target directory ... => [%s%s]'%(dir_origin,dir_dataset))
 
 #!rm -rf /content/dataset/
-import requests
-def download( dataset_to_download = range(1,7), dir_dataset = dir_dataset ):
+import requests, time
+def download_dataset( dataset_to_download = range(1,7), dir_dataset = dir_dataset ):
   # Check directory
   if not path.isdir('%s%s'%(dir_origin,dir_dataset)):
     mkdir('%s%s'%(dir_origin,dir_dataset))
@@ -83,7 +83,7 @@ def download( dataset_to_download = range(1,7), dir_dataset = dir_dataset ):
     file_ids.append( 'rawdata/epochs_animal%s.fdt'%set_id )
 
   # Request & download
-  repo_url = 'https://gin.g-node.org/hiobeen/Mouse_hdEEG_ASSR_Hwang_et_al/raw/9a35f6b1a53f87a96d76b8b7912738cb7d8d3d36/'
+  repo_url = 'https://gin.g-node.org/hiobeen/Mouse_hdEEG_ASSR_Hwang_et_al/raw/3fa95eeb11021cf740ded020c9948c6aa0d1c904/'
   for file_id in file_ids:
     fname_dest = "%s%s%s"%(dir_origin, dir_dataset, file_id)
     if path.isfile(fname_dest) is False:
@@ -93,13 +93,14 @@ def download( dataset_to_download = range(1,7), dir_dataset = dir_dataset ):
       with open(fname_dest, "wb") as file:
           for block in r.iter_content(chunk_size=1024):
               if block: file.write(block)
+      time.sleep(1) # wait a second to prevent possible errors
     else:
       print('...skipping already existing file [%s]...'%fname_dest)
 
 # Initiate downloading
 dataset_to_download = [2] # Partial download to prevent long download time
-#dataset_to_download = [1,2,3,4,5,6] # Download of whole dataset
-download(dataset_to_download)
+#dataset_to_download = [1,2,3,4,5,6] # Full download 
+download_dataset(dataset_to_download)
 print('\n============= Download finished ==================\n\n')
 
 # List up 'dataset/' directory
@@ -124,10 +125,10 @@ if not path.isdir(dir_fig): mkdir('%s%s'%(dir_origin, dir_fig))
     1)============ Start Downloading =================
     
     Target directory ... => [/content/dataset/]
-    ...skipping already existing file [/content/dataset/meta.csv]...
-    ...skipping already existing file [/content/dataset/montage.csv]...
-    ...skipping already existing file [/content/dataset/rawdata/epochs_animal2.set]...
-    ...skipping already existing file [/content/dataset/rawdata/epochs_animal2.fdt]...
+    ...copying to [/content/dataset/meta.csv]...
+    ...copying to [/content/dataset/montage.csv]...
+    ...copying to [/content/dataset/rawdata/epochs_animal2.set]...
+    ...copying to [/content/dataset/rawdata/epochs_animal2.fdt]...
     
     ============= Download finished ==================
     
@@ -135,7 +136,7 @@ if not path.isdir(dir_fig): mkdir('%s%s'%(dir_origin, dir_fig))
     
     2)=== List of available files in google drive ====
     
-    ['rawdata', 'meta.csv', 'montage.csv']
+    ['meta.csv', 'montage.csv', 'rawdata']
     
     ============= End of the list ==================
     
@@ -162,9 +163,13 @@ File *meta.csv* contains the demographic information of 6 mice. Using <code>read
 ## Demo 1-2. Display meta-data file
 from pandas import read_csv
 meta = read_csv('%s%smeta.csv'%(dir_origin, dir_dataset));
+print('Table 1. Meta-data')
 meta
 ```
 
+    Table 1. Meta-data
+
+
 
 
 
@@ -248,6 +253,26 @@ meta
 
 
 
+
+```python
+# (Optional: Check total number of trials for each experimental condition)
+"""
+# Count number of trials for each experimental condition (all files)
+import pandas as pd
+n_trials = np.zeros((7,8),dtype='int')
+for dataset_idx in range(6):
+  EEG_temp, f_name = get_eeg_data( dataset_idx )
+  for condition in range(1,8): 
+    # Accessing event info: print( EEG.event )
+    trialIdx = np.where(EEG_temp.events[:,2]==condition)[0] 
+    n_trials[dataset_idx,condition-1] = len(trialIdx)
+n_trials[-1,:], n_trials[:,-1] = np.sum(n_trials,axis=0), np.sum(n_trials,axis=1)
+n_trials = pd.DataFrame(n_trials, index=['animal1','animal2','animal3','animal4','animal5','animal6', 'Total'],
+             columns=['In-phase','Out-of-phase','Delayed','Advanced','Continuous','Sound only', 'Light only', 'Total'])
+n_trials
+""";
+```
+
 ### 1-3. Data loading and dimensionality check
 
 Each _*.fdt_ file is consisted of different number of trials. To load dataset, a function <code>get_eeg_data()</code> is defined below. To maintain original dimensionality order (cf. channel-time-trial in EEGLAB of Matlab), <code>np.moveaxis()</code> was applied. 
@@ -258,7 +283,7 @@ Each _*.fdt_ file is consisted of different number of trials. To load dataset, a
 # Demo 1-3. Data loading and dimensionality check
 from mne.io import read_epochs_eeglab as loadeeg
 import numpy as np
-def get_eeg_data(dataset_idx, CAL=1e-6):
+def get_eeg_data(dataset_idx=1, CAL=1e-6):
   f_name = '%s%srawdata/%s'%(dir_origin,dir_dataset,meta.file_name[dataset_idx])
   EEG = loadeeg(f_name, verbose=False)
   EEG.data = np.moveaxis(EEG.get_data(), 0, 2) / CAL
@@ -272,13 +297,11 @@ print('File name  :  [%s]'%f_name)
 print('File contains [%d channels, %4d time points, %3d trials]'%(EEG.data.shape))
 ```
 
-
-
     File name  :  [/content/dataset/rawdata/epochs_animal2.set]
     File contains [38 channels, 5200 time points, 557 trials]
 
 
-Note that voltage calibration value (*CAL*) is set to 1e-6 in 0.11.0 version of [eeglab.py](https://github.com/mne-tools/mne-python/blob/master/mne/io/eeglab/eeglab.py])
+Note that voltage calibration value (*CAL*) is set to 1e-6 in 0.11.0 version of [eeglab.py](https://github.com/mne-tools/mne-python/blob/master/mne/io/eeglab/eeglab.py]).
 
 
 ### 1-4. Getting channel coordinates
@@ -292,7 +315,7 @@ The EEG data are recorded with 38 electrode array, and two of the electrodes wer
 from matplotlib import pyplot as plt; plt.style.use('ggplot')
 plt.rcParams['font.family']='sans-serif'
 plt.rcParams['text.color']='black'; plt.rcParams['axes.labelcolor']='black'
-plt.rcParams['xtick.color']='black' ;plt.rcParams['ytick.color']='black'
+plt.rcParams['xtick.color']='black'; plt.rcParams['ytick.color']='black'
 
 from pandas import read_csv
 montage_table = read_csv('%s%smontage.csv'%(dir_origin, dir_dataset))
@@ -345,7 +368,7 @@ plt.gcf().savefig(dir_fig+'fig1-4.png', format='png', dpi=300);
 ```
 
 
-![png](figures/output_12_0.png)
+![png](figures/output_13_0.png)
 
 
 ## Part 2. Plotting Event-Related Potentials
@@ -385,7 +408,7 @@ plt.gcf().savefig(dir_fig+'fig2-1.png', format='png', dpi=300);
 ```
 
 
-![png](figures/output_15_0.png)
+![png](figures/output_16_0.png)
 
 
 ### 2-2. Visualizing example single-trial trace
@@ -427,7 +450,7 @@ plt.gcf().savefig(dir_fig+'fig2-2.png', format='png', dpi=300);
 ```
 
 
-![png](figures/output_19_0.png)
+![png](figures/output_20_0.png)
 
 
 Note that channels 1 to 36 contain actual EEG data from 36-channel electrode array (from FP1 to PO8), and channel 37 and 38 contain binary stimulus profile (0: no stimulation, 1: stimulation) of light and sound, respectively. 
@@ -450,7 +473,7 @@ plt.gcf().savefig(dir_fig+'fig2-3.png', format='png', dpi=300);
 ```
 
 
-![png](figures/output_22_0.png)
+![png](figures/output_23_0.png)
 
 
 ### 2-4. ERP in frequency domain
@@ -458,55 +481,6 @@ plt.gcf().savefig(dir_fig+'fig2-3.png', format='png', dpi=300);
 To calculate the amplitude of 40-Hz auditory steady-state response, fast Fourier transform can be applied as follow. 
 
 
-```python
-EEG.info['ch_names']
-```
-
-
-
-
-    ['Ch01-FP1',
-     'Ch02-FP2',
-     'Ch03-AF3',
-     'Ch04-AF4',
-     'Ch05-AF7',
-     'Ch06-AF8',
-     'Ch07-F1',
-     'Ch08-F2',
-     'Ch09-F5',
-     'Ch10-F6',
-     'Ch11-FC1',
-     'Ch12-FC2',
-     'Ch13-FC5',
-     'Ch14-FC6',
-     'Ch15-C1',
-     'Ch16-C2',
-     'Ch17-C3',
-     'Ch18-C4',
-     'Ch19-C5',
-     'Ch20-C6',
-     'Ch21-CP1',
-     'Ch22-CP2',
-     'Ch23-CP3',
-     'Ch24-CP4',
-     'Ch25-CP5',
-     'Ch26-CP6',
-     'Ch27-P1',
-     'Ch28-P2',
-     'Ch29-P3',
-     'Ch30-P4',
-     'Ch31-P5',
-     'Ch32-P6',
-     'Ch33-PO3',
-     'Ch34-PO4',
-     'Ch35-PO7',
-     'Ch36-PO8',
-     'LightStim',
-     'SoundStim']
-
-
-
-
 ```python
 # Demo 2-4. Time- and frequency-domain visualization of grand-averaged ERP
 def fft_half(x, Fs=2000): return np.fft.fft(x)[:int(len(x)/2)]/len(x), np.linspace(0,Fs/2,len(x)/2)
@@ -1014,8 +988,7 @@ plt.gcf().savefig(dir_fig+'fig3-3.png', format='png', dpi=300);
 ```
 
 
-
-![png](figures/output_36_1.png)
+![png](figures/output_36_0.png)
 
 
 ### 3-4. Band-power topography: Comparison across various experimental conditions
@@ -1049,9 +1022,7 @@ plt.gcf().savefig(dir_fig+'fig3-4.png', format='png', dpi=300);
 ```
 
 
-
-
-![png](figures/output_38_1.png)
+![png](figures/output_38_0.png)
 
 
 <br>