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@@ -30,10 +30,20 @@ We use aLIGO zero detuning, high power design sensitivity PSD found at the link
 
 For example, we whiten a signal which generated from "h_m16_L0.18_l2m2_r300.dat".
 First plot the orignal data:   
-![original_signal](uploads/eb18b45e8ebc80c2fc820d4307d603df/original_signal.png)  
+![h_m16_L0.18_l2m2_r300_o](uploads/fecd83a3ab8cb8f8967bd6176cb9d984/h_m16_L0.18_l2m2_r300_o.png)  
+
 Once it get whitened, the plot goes like:  
-![whitened_signal](uploads/8cec6c288e9b83847396dc85d5fcecc7/whitened_signal.png)  
-The resulting time series is no longer in units of strain, now in units of "sigmas" away from the mean.   
+![h_m16_L0.18_l2m2_r300_w](uploads/452bb8b77e4f70a7380b37de91d7af83/h_m16_L0.18_l2m2_r300_w.png)  
+ 
+The resulting time series no longer has an amplitude between the order of magnitude of 10^-24 to 10^-23. Also, we can plot another 4 whitened merging waveforms:  
+![h_m1_L0.9_l2m2_r300_o](uploads/d15614da9bbf6ebe9980e2e4f5ac4d4a/h_m1_L0.9_l2m2_r300_o.png)  
+![h_m1_L0.9_l2m2_r300_w](uploads/1fdbae194e06c3792d0873e718316439/h_m1_L0.9_l2m2_r300_w.png)  
+![h_m2_L0.87_l2m2_r300_o](uploads/1d02c68b655a835140011d4ef2773aa6/h_m2_L0.87_l2m2_r300_o.png)  
+![h_m2_L0.87_l2m2_r300_w](uploads/a8a772001cdf0fa3e31b4e1acad49886/h_m2_L0.87_l2m2_r300_w.png)  
+![h_m4_L0.5_l2m2_r300_o](uploads/0851005a279d9ee27e37b7599d7b624f/h_m4_L0.5_l2m2_r300_o.png)  
+![h_m4_L0.5_l2m2_r300_w](uploads/a0db0bd859f917eb5ef9b83308f30451/h_m4_L0.5_l2m2_r300_w.png)  ![h_m8_L0.35_l2m2_r280_o](uploads/fa5d2833dad93e0e1949d57f0b5ce1e0/h_m8_L0.35_l2m2_r280_o.png)  
+![h_m8_L0.35_l2m2_r280_w](uploads/a52c89a04511f36f9b1088501f8c3322/h_m8_L0.35_l2m2_r280_w.png)  
+   
 In terms of the machine learning side of things, the whitening is primarily done to normalise the data for input to the neural network. So long as you give the network signals with similar noise properties and priors as those it was trained on, it should function properly.    
 
 ## (5)window...?