如何从python中的zip显示生成器对象值？

Question

Im trying to hash WAV audio file in Jupyter Notebook..i had no issues with creating spectrogram..but while hashing using the peak points..as a zip(frequency_values,time_values).

def generate_hashes(peaks, fan_value=DEFAULT_FAN_VALUE):
if PEAK_SORT:
  peaks.sort(key=itemgetter(1))

# bruteforce all peaks
for i in range(len(peaks)):
  for j in range(1, fan_value):
    if (i + j) < len(peaks):

      # take current & next peak frequency value
      freq1 = peaks[i][IDX_FREQ_I]
      freq2 = peaks[i + j][IDX_FREQ_I]

      # take current & next -peak time offset
      t1 = peaks[i][IDX_TIME_J]
      t2 = peaks[i + j][IDX_TIME_J]

      # get diff of time offsets
      t_delta = t2 - t1

      # check if delta is between min & max
      if t_delta >= MIN_HASH_TIME_DELTA and t_delta <= MAX_HASH_TIME_DELTA:
        h = hashlib.sha1("%s|%s|%s" % (str(freq1), str(freq2), str(t_delta)))
        yield (h.hexdigest()[0:FINGERPRINT_REDUCTION], t1)
h=generate_hashes(local_maxima, fan_value=DEFAULT_FAN_VALUE)
h.__next__()

ERROR:(ps:ignore my formatting..new to site)

---

 AttributeError Traceback (most recent call last) <ipython-input-61-755dd1339124> in <module> ----> 1 h.__next__() <ipython-input-59-a612d64209d2> in generate_hashes(peaks, fan_value) 1 def generate_hashes(peaks, fan_value=DEFAULT_FAN_VALUE): 2 if PEAK_SORT: ----> 3 peaks.sort(key=itemgetter(1)) 4 5 # bruteforce all peaks AttributeError: 'zip' object has no attribute 'sort'

---

And my full code is here:

import hashlib
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt

from scipy.ndimage.filters import maximum_filter
from scipy.ndimage.morphology import (generate_binary_structure, iterate_structure, binary_erosion)
from operator import itemgetter

from scipy.io import wavfile
from scipy import signal
from pydub import AudioSegment
import os


IDX_FREQ_I = 0
IDX_TIME_J = 1
DEFAULT_FS = 44100
DEFAULT_WINDOW_SIZE = 4096
DEFAULT_OVERLAP_RATIO = 0.5
DEFAULT_FAN_VALUE = 15
DEFAULT_AMP_MIN = 10
PEAK_NEIGHBORHOOD_SIZE = 20
MIN_HASH_TIME_DELTA = 0
MAX_HASH_TIME_DELTA = 200
PEAK_SORT = True
FINGERPRINT_REDUCTION = 20
plots=False


file_name=input("Enter the audio file location ::")
if not file_name.lower().endswith('.wav'):
    print("Please specify a WAV file")
if os.path.exists(file_name)==False:
    print("Please enter the path of existing WAV file")


sampling_frequency , data = wavfile.read(file_name)


plt.plot(data,color='B')
plt.xlabel('Sample')
plt.title('%d samples' % len(data))
plt.ylabel('Amplitude')
plt.show
plt.gca().invert_yaxis()


if plots :
    plt.specgram(data[:,0],Fs=44100)
    plt.xlabel('Time')
    plt.ylabel('Frequency')
    plt.show


arr2D = mlab.specgram(data[:,0],Fs=44100,window=mlab.window_hanning,NFFT=4096,noverlap=int(4096 * 0.5))[0]


arr2D


arr2D=np.asarray(arr2D)
arr2D


if plots :
    plt.plot(arr2D)
    plt.title('FFT')
    plt.show()


arr2D = 10 * np.log10(arr2D) 
arr2D[arr2D == -np.inf]
arr2D


def get_2D_peaks(arr2D, plot=False, amp_min=DEFAULT_AMP_MIN):
    struct = generate_binary_structure(2, 1)
    neighborhood = iterate_structure(struct, PEAK_NEIGHBORHOOD_SIZE)
    local_max = maximum_filter(arr2D, footprint=neighborhood) == arr2D
    background = (arr2D == 0)
    eroded_background = binary_erosion(background, structure=neighborhood,
                                       border_value=1)
    detected_peaks = local_max ^ eroded_background
    amps = arr2D[detected_peaks]
    j, i = np.where(detected_peaks)
    amps = amps.flatten()
    peaks = zip(i, j, amps)
    peaks_filtered = [x for x in peaks if x[2] > amp_min]
    frequency_idx = [x[1] for x in peaks_filtered]
    time_idx = [x[0] for x in peaks_filtered]
    if plot:
      fig, ax = plt.subplots()
      ax.imshow(arr2D)
      ax.scatter(time_idx, frequency_idx)
      ax.set_xlabel('Time')
      ax.set_ylabel('Frequency')
      ax.set_title("Spectrogram")
      plt.gca().invert_yaxis()
      plt.show()

    return zip(frequency_idx, time_idx)

local_maxima = get_2D_peaks(arr2D, plot=plots, amp_min=DEFAULT_AMP_MIN)
local_maxima

x=list(local_maxima)
y=len(x)

msg = "  local_maxima: "+str(y)+" of frequency & time pairs"
print ( msg ) 


def generate_hashes(peaks, fan_value=DEFAULT_FAN_VALUE):
    if PEAK_SORT:
      peaks.sort(key=itemgetter(1))

    # bruteforce all peaks
    for i in range(len(peaks)):
      for j in range(1, fan_value):
        if (i + j) < len(peaks):

          # take current & next peak frequency value
          freq1 = peaks[i][IDX_FREQ_I]
          freq2 = peaks[i + j][IDX_FREQ_I]

          # take current & next -peak time offset
          t1 = peaks[i][IDX_TIME_J]
          t2 = peaks[i + j][IDX_TIME_J]

          # get diff of time offsets
          t_delta = t2 - t1

          # check if delta is between min & max
          if t_delta >= MIN_HASH_TIME_DELTA and t_delta <= MAX_HASH_TIME_DELTA:
            h = hashlib.sha1("%s|%s|%s" % (str(freq1), str(freq2), str(t_delta)))
            yield (h.hexdigest()[0:FINGERPRINT_REDUCTION], t1)

h=generate_hashes(local_maxima, fan_value=DEFAULT_FAN_VALUE)

h.__next__()

I hope your answer will help me fix it.. :-)

Answer 1

A zip object/generator does not have a sort method. You can use sorted to generate a new sorted list from the zip

if PEAK_SORT:
    peaks = sorted(peaks, key=itemgetter(1))