update training process

workspace/2-data_csv.ipynb

@@ -4,37 +4,35 @@
 import csv
 import numpy as np
 import pandas as pd
 from tqdm import tqdm
 import utils.samplefiles
-```
-
-%% Cell type:code id: tags:
-
-``` python
-train_wnum = 50
-train_nnum = 50
-test_wnum = 50
-test_nnum = 50
+import h5py
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 data = utils.samplefiles.SampleFile()
 data.read_hdf('./output/train.hdf')
-```
-
-%% Cell type:code id: tags:
 
-``` python
 wave, noise = data.as_dataframe(injection_parameters=True, 
                   static_arguments=False, 
                   command_line_arguments=False, 
                   split_injections_noise=True)
 ```
 
+%% Cell type:code id: tags:
+
+``` python
+wave['h1_strain'][0].size
+```
+
+%%%% Output: execute_result
+
+    4096
+
 %% Cell type:markdown id: tags:
 
 Turn strain into multi-dimension array
 
 %% Cell type:code id: tags:
@@ -44,23 +42,48 @@
 h1n = noise['h1_strain'].tolist()
 wary = np.array(h1w)
 nary = np.array(h1n)
 ```
 
+%% Cell type:code id: tags:
+
+``` python
+h1w[0].size
+```
+
+%%%% Output: execute_result
+
+    4096
+
 %% Cell type:markdown id: tags:
 
 Split train and test set
 
 %% Cell type:code id: tags:
 
 ``` python
+train_wnum = 50
+train_nnum = 50
+test_wnum = 50
+test_nnum = 50
+
 wtrain = wary[:train_wnum,:]
 ntrain = nary[:train_nnum,:]
 wtest = wary[train_wnum:,:]
 ntest = nary[train_nnum:,:]
 ```
 
+%% Cell type:code id: tags:
+
+``` python
+wtrain.shape
+```
+
+%%%% Output: execute_result
+
+    (50, 4096)
+
 %% Cell type:markdown id: tags:
 
 Insert label
 
 %% Cell type:code id: tags:
@@ -72,17 +95,17 @@
 ntest = np.insert(ntest, 0, values=0, axis=1)
 ```
 
 %% Cell type:markdown id: tags:
 
-Training set name
+# Training set
 
 %% Cell type:code id: tags:
 
 ``` python
 train_name = []
-num = 50
+num = wtrain.shape[1]-1 # 4096
 train_name.append('label')
 for i in tqdm(range(0,num)):
     train_name.append('point{s1}'.format(s1=i))
 ```
 
@@ -97,19 +120,35 @@
     for i in tqdm(range(0,train_wnum)):
         writer.writerow(wtrain[i])
         writer.writerow(ntrain[i])
 ```
 
+%% Cell type:code id: tags:
+
+``` python
+train_set = pd.read_csv("./output/train.csv", dtype=np.float32)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+train_set.shape
+```
+
+%%%% Output: execute_result
+
+    (100, 4097)
+
 %% Cell type:markdown id: tags:
 
-testing set name
+# Testing set
 
 %% Cell type:code id: tags:
 
 ``` python
 test_name = []
-num = 50
+num = wtrain.shape[1]-1 # 4096
 test_name.append('label')
 for i in tqdm(range(0,num)):
     test_name.append('point{s1}'.format(s1=i))
 ```
 
@@ -127,7 +166,23 @@
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
+test_set = pd.read_csv("./output/test.csv", dtype=np.float32)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+test_set.shape
+```
+
+%%%% Output: execute_result
+
+    (100, 4097)
+
+%% Cell type:code id: tags:
+
+``` python
 
 ```

workspace/config_files/default.json

@@ -8,5 +8,5 @@
   "n_injection_samples": 100,
   "n_noise_samples": 100,
   "n_processes": 4,
-  "output_file_name": "train.hdf"
+  "output_file_name": "test.hdf"
 }

workspace/config_files/waveform_params.ini

@@ -13,7 +13,7 @@ injection_snr =
 [static_args]
 approximant = SEOBNRv4
 domain = time
-f_lower = 18
+f_lower = 10
 distance = 100
 waveform_length = 128
 
@@ -31,8 +31,8 @@ target_sampling_rate = 2048
 ; Define parameters for the whitening procedure. See documentation of the
 ; pycbc.types.TimeSeries.whiten() method for an explanation of what these
 ; values exactly mean.
-whitening_segment_duration = 4
-whitening_max_filter_duration = 4
+whitening_segment_duration = 1
+whitening_max_filter_duration = 1
 
 ; Define the lower and upper bound for the bandpass filter (in Hertz)
 bandpass_lower = 20
@@ -41,8 +41,8 @@ bandpass_upper = 2048
 ; Define how to align the sample around the event time. By convention, the
 ; event time is the H1 time!
 ; The sum of these values will be the the sample_length!
-seconds_before_event = 5.5
-seconds_after_event = 2.5
+seconds_before_event = 1.5
+seconds_after_event = 0.5
 
 ; alpha for the Tukey window that is used to "fade on" the waveforms
 ; It represents the fraction of the window inside the cosine tapered region.