Fixed merge conflicts

da_stim/gen_da.py

@@ -22,14 +22,26 @@ def gen_da_stim(n, outpath):
     prestim = np.zeros(int(fs*prestim_size))
     poststim = np.zeros(int(fs*((full_stim_size-prestim_size)-da_size)))
     y_part = np.concatenate([prestim, da_stim, poststim])
+    pdb.set_trace()
     y_part_inv = -y_part
+    loc_part = np.zeros(y_part.size)
+    loc_part[prestim.size+1]=1
 
     y_2part = np.concatenate([y_part, y_part_inv])
+    loc = np.concatenate([loc_part, loc_part])
     y_r = np.tile(y_2part, n)
+    loc = np.tile(loc, n)
+    loc = np.insert(loc, 0, np.zeros(fs))
+    loc = np.where(loc)[0]
+
     y_r = np.insert(y_r, 0, np.zeros(fs))
     y_r = resampy.resample(y_r, fs, 44100)
+    rat = 44100/fs
     fs = 44100
     y_l = np.zeros(y_r.size)
+    loc = loc * rat
+    loc=loc.round().astype(int)
+    np.save('./stimulus/3000_da_locs.npy', loc)
 
     idx = np.arange(y_l.size)
     trigger = gen_trigger(idx, 2., 0.01, fs)

da_stim/plot_noise_profiles.py

@@ -0,0 +1,55 @@
+# coding: utf-8
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy import signal
+import pysndfile.sndio as sndio
+import pandas as pd
+
+def voss(nrows, ncols=16):
+    """Generates pink noise using the Voss-McCartney algorithm.
+
+    nrows: number of values to generate
+    rcols: number of random sources to add
+
+    returns: NumPy array
+    """
+    array = np.empty((nrows, ncols))
+    array.fill(np.nan)
+    array[0, :] = np.random.random(ncols)
+    array[:, 0] = np.random.random(nrows)
+
+    # the total number of changes is nrows
+    n = nrows
+    cols = np.random.geometric(0.5, n)
+    cols[cols >= ncols] = 0
+    rows = np.random.randint(nrows, size=n)
+    array[rows, cols] = np.random.random(n)
+
+    df = pd.DataFrame(array)
+    df.fillna(method='ffill', axis=0, inplace=True)
+    total = df.sum(axis=1)
+
+    return total.values
+
+
+da_x, da_fs, da_enc = sndio.read('./stimulus/wav/10min_da.wav')
+sp_x, sp_fs, sp_enc = sndio.read('./noise_source/male_speech_resamp.wav')
+
+# %load test.ipy
+pink_n = voss(sp_x.size, 1)
+da_rms = np.sqrt(np.mean(da_x**2))
+sp_rms = np.sqrt(np.mean(sp_x**2))
+pink_n_rms = np.sqrt(np.mean(pink_n**2))
+da_x *= sp_rms / da_rms
+pink_n *= sp_rms / pink_n_rms
+f, Pxx_den = signal.welch(pink_n, sp_fs, nperseg=1024)
+plt.semilogy(f, Pxx_den)
+f, Pxx_den = signal.welch(sp_x, sp_fs, nperseg=1024)
+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.xlim([0, 10000])
+plt.legend(['Pink noise', 'Speech shaped noise', 'Da'])
+plt.savefig('./test.png')

matrix_test/long_concat_stim/concatenate_stimuli.py

@@ -74,14 +74,15 @@ def concatenateStimuli(MatrixDir, OutDir, Length, n):
     parts = []
     questions = []
     i = 0
-    for wav in wavFiles:
+    gapSize = np.uniform(0.8, 1.2, len(wavFiles))
+    for wav, gap in zip(wavFiles, gapSize):
         if i == n:
             break
         wavObj = PySndfile(wav)
         fs = wavObj.samplerate()
         size = wavObj.frames()
         totalSize += size
-        totalSize += int(0.1*fs)
+        totalSize += int(gap*fs)
         if (totalSize/fs) > Length:
             # total size + 2 second silence at start
             y.append(np.zeros((totalSize+2*fs, 3)))