업데이트 'README.md'

README.md

@@ -266,7 +266,7 @@ plt.savefig( '%sFig_QQ_TutorialSWA.jpg'%(figure_directory), dpi=figure_dpi )
 ```
 
 
-![png](output_12_0.png)
+![png](figures/output_12_0.png)
 
 
 #### 2. Detection of Spindles
@@ -350,7 +350,7 @@ plt.show();
 ```
 
 
-![png](output_14_0.png)
+![png](figures/output_14_0.png)
 
 
 ### 3-3) Time-frequency transformation (Spectrogram)
@@ -433,7 +433,7 @@ for chanIdx in range(1):
     
 
 
-![png](output_16_1.png)
+![png](figures/output_16_1.png)
 
 
 ### 3-4) Topographical reconstruction of HD-EEG 
@@ -476,7 +476,7 @@ plt.savefig( '%sFig_QQ_TutorialLabeling.jpg'%(figure_directory), dpi=figure_dpi
 ```
 
 
-![png](output_18_0.png)
+![png](figures/output_18_0.png)
 
 
 Next, we need to extract band power information of each channel to draw topographical map. To easily extract specific band power with given state, we made a handy function bandpower_state() as below. It slices in temporal and frequency domain for corresponding point of sleep state and frequency bin, respectively. 
@@ -701,7 +701,7 @@ plt.savefig( '%sFig_QQ_TutorialTopography.jpg'%(figure_directory), dpi=figure_dp
 ```
 
 
-![png](output_24_0.png)
+![png](figures/output_24_0.png)
 
 
 ### 3-5) Example trace figures
@@ -782,7 +782,7 @@ plt.show();
     
 
 
-![png](output_26_1.png)
+![png](figures/output_26_1.png)
 
 
 
@@ -839,7 +839,7 @@ plt.show();
     
 
 
-![png](output_27_1.png)
+![png](figures/output_27_1.png)
 
 
 
@@ -883,7 +883,7 @@ plt.show();
 ```
 
 
-![png](output_28_0.png)
+![png](figures/output_28_0.png)
 
 
 ### 3-6) Obtaining grand-averaged results 
@@ -1116,14 +1116,14 @@ plt.show()
     
 
 
-![png](output_33_1.png)
+![png](figures/output_33_1.png)
 
 
     REM theta
     
 
 
-![png](output_33_3.png)
+![png](figures/output_33_3.png)
 
 
 Now, do it on your own!