adding first script

code/analaysis.py

@@ -0,0 +1,106 @@
+from thunderfish.dataloader import DataLoader as open_data
+from thunderfish.powerspectrum import spectrogram, decibel
+from IPython import embed
+from audioio import play
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+from scipy.ndimage import gaussian_filter1d
+from modules.filters import bandpass_filter
+
+
+def main(folder):
+    file = os.path.join(folder, 'traces-grid.raw')
+    data = open_data(folder, 60.0, 0, channel=-1)
+    time = np.load(folder + 'times.npy', allow_pickle=True)
+    freq = np.load(folder + 'fund_v.npy', allow_pickle=True)
+    ident = np.load(folder + 'ident_v.npy', allow_pickle=True)
+    idx = np.load(folder + 'idx_v.npy', allow_pickle=True)
+
+    t0 = 3*60*60 + 6*60 + 43.5
+    dt = 60
+    data_oi = data[t0 * data.samplerate: (t0+dt)*data.samplerate, :]
+
+    for i in [10]:
+        # getting the spectogramm
+        spec_power, spec_freqs, spec_times = spectrogram(
+            data_oi[:, i], ratetime=data.samplerate, freq_resolution=50, overlap_frac=0.0)
+        fig, ax = plt.subplots(figsize=(20/2.54, 12/2.54))
+        ax.pcolormesh(spec_times, spec_freqs, decibel(
+            spec_power), vmin=-100, vmax=-50)
+
+        for track_id in np.unique(ident):
+            # window_index for time array in time window 
+            window_index = np.arange(len(idx))[(ident == track_id) &
+                                        (time[idx] >= t0) &
+                                        (time[idx] <= (t0+dt))]
+            freq_temp = freq[window_index]
+            time_temp = time[idx[window_index]] 
+            #mean_freq = np.mean(freq_temp)
+            #fdata = bandpass_filter(data_oi[:, track_id], data.samplerate, mean_freq-5, mean_freq+200)
+            ax.plot(time_temp - t0, freq_temp)
+
+    ax.set_ylim(500, 1000)
+    plt.show()
+    # filter plot 
+    id = 10.
+    i = 10
+    window_index = np.arange(len(idx))[(ident == id) &
+                                        (time[idx] >= t0) &
+                                        (time[idx] <= (t0+dt))] 
+    freq_temp = freq[window_index]
+    time_temp = time[idx[window_index]] 
+    mean_freq = np.mean(freq_temp)
+    fdata = bandpass_filter(data_oi[:, i], rate=data.samplerate, lowf=mean_freq-5, highf=mean_freq+200)
+    fig, ax = plt.subplots()
+    ax.plot(np.arange(len(fdata))/data.samplerate, fdata, marker='*')
+    #plt.show()
+    #freqency analyis of filtered data
+    
+    time_fdata = np.arange(len(fdata))/data.samplerate
+    roll_fdata = np.roll(fdata, shift=1)
+    period_index = np.arange(len(fdata))[(roll_fdata < 0) & (fdata>=0)]
+
+    plt.plot(time_fdata, fdata)
+    plt.scatter(time_fdata[period_index], fdata[period_index], c='r')
+    plt.scatter(time_fdata[period_index-1], fdata[period_index-1], c='r')
+    
+    upper_bound = np.abs(fdata[period_index])
+    lower_bound = np.abs(fdata[period_index-1])
+    
+    upper_times = np.abs(time_fdata[period_index])
+    lower_times = np.abs(time_fdata[period_index-1])
+
+    lower_ratio = lower_bound/(lower_bound+upper_bound)
+    upper_ratio = upper_bound/(lower_bound+upper_bound)
+
+    time_delta = upper_times-lower_times
+    true_zero = lower_times + time_delta*lower_ratio
+
+    plt.scatter(true_zero, np.zeros(len(true_zero)))
+    
+    # calculate the frequency
+    inst_freq = 1 / np.diff(true_zero)
+    filtered_inst_freq = gaussian_filter1d(inst_freq, 0.005)
+    fig, ax  = plt.subplots()
+    ax.plot(filtered_inst_freq, marker='.')
+    # in 5 sekunden welcher fisch auf einer elektrode am 
+
+
+
+    
+    embed()
+    exit()
+
+    # data of intrests
+
+    # first look at the raw data, channel 11 is important
+    # fig, ax = plt.subplots(figsize=(20/2.54, 12/2.54))
+    # ax.plot(np.arange(len(data_oi[:, i])), data_oi[:, i])
+
+
+    pass
+
+
+if __name__ == '__main__':
+    main('/Users/acfw/Documents/uni_tuebingen/chirpdetection/gp_benda/data/2022-06-02-10_00/')