Merge branch 'master' of github.com:Pezz89/BPLabs

da_stim/plot_noise_profiles.py

@@ -49,7 +49,7 @@ plt.semilogy(f, Pxx_den)
 f, Pxx_den = signal.welch(da_x[:, 1], da_fs, nperseg=1024)
 plt.semilogy(f, Pxx_den)
 plt.xlabel('frequency [Hz]')
-plt.ylabel('PSD [V**2/Hz]')
+plt.ylabel('PSD [I**2/Hz]')
 plt.xlim([0, 10000])
 plt.legend(['Pink noise', 'Speech shaped noise', 'Da'])
 plt.savefig('./test.png')

make_portable.py

@@ -0,0 +1,55 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+import dill
+import numpy as np
+import argparse
+from pathtype import PathType
+import sys
+import os
+from loggerops import create_logger
+
+def main(args):
+    file = args.data_file
+    with open(file, 'rb') as pkl:
+        a = dill.load(pkl)
+    del a['participant']
+    np.save(os.path.basename(file)+'-new.npy', a)
+
+if __name__ == '__main__':
+    #peak_pick_test()
+    parser = argparse.ArgumentParser(
+        description='Script for removing BPLabs sepcific objects from participant data'
+    )
+    parser.add_argument(
+        dest='data_file', type=PathType(),
+        help='Configuration file for processing BDF', metavar='CONFIGFILE'
+    )
+    parser.add_argument(
+        '--verbose',
+        '-v',
+        action='count',
+        help='Specifies level of verbosity in output. For example: \'-vvvvv\' '
+        'will output all information. \'-v\' will output minimal information. '
+    )
+    args = parser.parse_args()
+
+    # Set verbosity of logger output based on argument
+    if not args.verbose:
+        args.verbose = 10
+    else:
+        levels = [50, 40, 30, 20, 10]
+        if args.verbose > 5:
+            args.verbose = 5
+        args.verbose -= 1
+        args.verbose = levels[args.verbose]
+
+    # Define path to module for storing log files
+    modpath = sys.argv[0]
+    modpath = os.path.splitext(modpath)[0]+'.log'
+
+    logger = create_logger(
+        logger_streamlevel=args.verbose,
+        log_filename=modpath,
+        logger_filelevel=args.verbose
+    )
+    main(args)

matrix_test/behavioural_stim/gen_noise.py

@@ -1,8 +1,8 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 import sys
-sys.path.insert(0, "../helper_modules")
-sys.path.insert(0, "../matrix_test/helper_modules/")
+#sys.path.insert(0, "../helper_modules")
+#sys.path.insert(0, "../matrix_test/helper_modules/")
 
 import argparse
 import os
@@ -18,25 +18,15 @@ from pysndfile import PySndfile, sndio
 import matplotlib.pyplot as plt
 
 from pathops import dir_must_exist
-try:
-    from signalops import rolling_window_lastaxis, calc_rms, block_process_wav
-except ImportError:
-    from .signalops import rolling_window_lastaxis, block_lfilter, calc_rms, block_process_wav
+from signalops import rolling_window_lastaxis, block_lfilter, calc_rms, block_process_wav
 
 import scipy.signal as sgnl
 from scipy.stats import pearsonr
 
-from pyswarm import pso
 
-try:
-    from lpc import lpc
-except ImportError:
-    from .lpc import lpc
+from lpc import lpc
 
-try:
-    from filesystem import globDir, organiseWavs, prepareOutDir
-except ImportError:
-    from .filesystem import globDir, organiseWavs, prepareOutDir
+from filesystem import globDir, organiseWavs, prepareOutDir
 
 
 def block_lfilter_wav(b, a, x, outfile, fmt, fs, blocksize=8192):
@@ -204,7 +194,7 @@ def calc_spectrum(files, silences, fs=44100, plot=False):
 def gen_noise(OutDir, b, fs, s_rms):
     print("Generating noise...")
     # Generate 10 minutes of white noise
-    x = np.random.randn(int(fs*60.*20.))
+    x = np.random.randn(int(fs*60.*5.))
     x /= x.max()
     noiseDir = os.path.join(OutDir, 'wav')
     noiseRMSDir = os.path.join(OutDir, 'rms')
@@ -212,7 +202,7 @@ def gen_noise(OutDir, b, fs, s_rms):
     noiseDir = os.path.join(noiseDir, 'noise')
     dir_must_exist(noiseDir)
     y, y_max = block_lfilter_wav(b, [1.0], x, os.path.join(noiseDir, 'noise.wav'), 65538, 44100)
-    block_process_wav(os.path.join(noiseDir, 'noise.wav'), os.path.join(noiseDir, 'noise_norm.wav'), lambda x: x / (y_max * 0.95))
+    block_process_wav(os.path.join(noiseDir, 'noise.wav'), os.path.join(noiseDir, 'noise_norm.wav'), lambda x: x / (y_max * 1.05))
     noise_rms_path = os.path.join(noiseRMSDir, 'noise_rms.npy')
     y = y/(np.abs(y).max() * 0.95)
     rms = np.sqrt(np.mean(y**2))

matrix_test/behavioural_stim/gen_stim.py

@@ -45,7 +45,6 @@ def gen_indexes(list_dir, speech_dir):
     component_map = json.loads(json_data)
 
     column_names = ['a', 'b', 'c', 'd', 'e']
-    list_indexes = np.array([])
     sentence_lists = {}
     for list_file in list_files:
         with open(list_file, 'r') as lfile: