Merge 3361b47 into ae88990

tests/test_peakdetection.py

@@ -40,7 +40,7 @@ def test_detect_peaks():
 
     assert_raises(ValueError, pd.detect_peaks, data, -1.0)
 
-    assert_raises(IndexError, pd.detect_peaks, data, threshold, time[:len(time) / 2])
+    assert_raises(IndexError, pd.detect_peaks, data, threshold, time[:int(len(time) / 2)])
 
     peaks, troughs = pd.detect_peaks(data, threshold)
     assert_true(np.all(peaks == peak_indices),
@@ -106,7 +106,7 @@ def test_detect_dynamic_peaks():
     assert_raises(ValueError, pd.detect_dynamic_peaks, data, threshold, min_thresh, -1.0, time,
                                              pd.accept_peak_size_threshold)
 
-    assert_raises(IndexError, pd.detect_dynamic_peaks, data, threshold, min_thresh, 0.5, time[:len(time) / 2],
+    assert_raises(IndexError, pd.detect_dynamic_peaks, data, threshold, min_thresh, 0.5, time[:int(len(time) / 2)],
                                              pd.accept_peak_size_threshold)
 
     peaks, troughs = pd.detect_dynamic_peaks(data, threshold, min_thresh, 0.5, time,

thunderfish/Auxiliary.py

@@ -4,7 +4,7 @@
 import os
 import numpy as np
 import wave
-from scipy.signal import butter, filtfil
+from scipy.signal import butter, filtfilt
 try:
     import matplotlib.pyplot as plt
 except ImportError:

thunderfish/fakefish.py

@@ -280,7 +280,7 @@ def generate_pulsefish(frequency=100.0, samplerate=44100., duration=1., noise_st
     data = np.random.randn(len(time)) * noise_std
     period = 1.0/frequency
     jitter_std = period * jitter_cv
-    first_pulse = np.max(pulse_duration, 3.0*jitter_std)
+    first_pulse = np.max([pulse_duration, 3.0*jitter_std])
     pulse_times = np.arange(first_pulse, duration, period )
     pulse_times += np.random.randn(len(pulse_times)) * jitter_std
     pulse_indices = np.round(pulse_times * samplerate).astype(np.int)

thunderfish/harmonicgroups.py

@@ -820,10 +820,9 @@ def fundamental_freqs(group_list):
     list_of_list = False
     for groups in group_list:
         for harmonic_group in groups:
-            if hasattr(harmonic_group, 'shape'):
+            if len(np.shape(harmonic_group)) > 1:
                 list_of_list = True
                 break
-
     if list_of_list:
         fundamentals = []
         for groups in group_list:

thunderfish/time_analysis.py

@@ -1830,7 +1830,7 @@ def main(file_path, hobo_path, show_fish_plot=False, show_combined_fishes=False,
         old_colors = False
 
     for enu, folder in enumerate(sorted(folders)):
-        print folder
+        print(folder)
         # load data
         dat_file = False
         fishes = False
@@ -2128,4 +2128,4 @@ def main(file_path, hobo_path, show_fish_plot=False, show_combined_fishes=False,
     parser.add_argument('hobo_path', nargs='?', default='', type=str, help='folder containing the hobo datalogger files')
 
     args = parser.parse_args()
-    main(args.file_path, args.hobo_path)
+    main(args.file_path, args.hobo_path)

thunderfish/voronoi.py

@@ -608,7 +608,7 @@ def random_points(self, n=None, poisson=True, mode='outer'):
         nn = n
         if poisson:
             nn = np.random.poisson(n)
-        m = nn/2
+        m = nn//2
         if m < 5:
             m = 5
         # get bounding box:
@@ -636,7 +636,7 @@ def random_points(self, n=None, poisson=True, mode='outer'):
             elif mode == 'bbox':
                 points = np.vstack((points, newpoints[np.all(newpoints<max_bound, axis=1),:]))
             else:
-                print('')            
+                print('')
                 print('Voronoi.random_points(): unknown value "%s" for the mode parameter:' % mode)
                 print('Use one of the following values:')
                 print('  bbox: Place points within rectangular bounding box.')