Added fft1.py test

diff --git a/audio-test/fft1.py b/audio-test/fft1.py
new file mode 100755 (executable)
index 0000000..168ec22
--- /dev/null
+++ b/audio-test/fft1.py
@@ -0,0 +1,306 @@
+#!/usr/bin/python
+#
+# Depends on python-scipy and python-matplotlib
+#
+
+import sys
+import scipy
+import struct
+from numpy import average as npavg, max as npmax, abs as npabs, sum as npsum, fromfile as npfromfile
+from scipy.fftpack import fft, fftfreq
+from scipy.io import wavfile
+
+SAMPLES = {
+  'zero': (
+    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+  ),
+  'noise1': (
+    [9, 6, 7, 6, 6, 5, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
+    [13, 3, 11, 23, 23, 8, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]
+  ),
+  'noise2': (
+    [11, 6, 7, 9, 7, 11, 4, 3, 3, 2, 2, 2, 3, 2, 2, 2, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
+    [12, 3, 11, 22, 16, 8, 3, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1]
+  ),
+  'speech1': (
+    [10, 6, 6, 9, 12, 7, 3, 3, 3, 2, 2, 2, 2, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
+    [8, 4, 10, 21, 19, 9, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+  ),
+  'speech2': (
+    [8, 6, 7, 6, 6, 5, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2],
+    [15, 4, 11, 25, 18, 8, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0]
+  ),
+  'opera1': (
+    [5, 6, 11, 11, 9, 5, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1],
+    [7, 9, 10, 11, 10, 4, 5, 3, 2, 2, 2, 2, 3, 2, 2, 3, 2, 2, 1, 2, 1, 1, 1, 3, 1, 1, 6, 1, 1]
+  ),
+  'sweep1': (
+    [9, 7, 6, 5, 5, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
+    [9, 7, 6, 5, 4, 4, 4, 4, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 2, 2, 2, 2]
+  ),
+  'sine-500': (
+    [4, 47, 2, 1, 1, 1, 4, 1, 1, 1, 1, 9, 1, 1, 1, 1, 7, 1, 1, 1, 1, 3, 1, 1, 1, 1, 4, 2, 1],
+    [1, 45, 0, 0, 0, 0, 17, 1, 0, 1, 1, 10, 0, 0, 0, 0, 5, 1, 0, 0, 1, 7, 0, 0, 0, 0, 4, 1, 0]
+  ),
+  'sine-800': (
+    [2, 2, 6, 18, 2, 1, 1, 2, 1, 2, 6, 9, 1, 2, 1, 1, 2, 1, 6, 9, 2, 1, 2, 3, 1, 1, 5, 6, 1],
+    [1, 0, 3, 47, 1, 0, 1, 1, 0, 1, 3, 14, 1, 1, 0, 0, 1, 0, 3, 9, 1, 0, 1, 1, 0, 1, 3, 5, 1]
+  ),
+  'sine-1000': (
+    [2, 2, 2, 24, 27, 2, 1, 1, 1, 1, 1, 1, 1, 4, 2, 2, 2, 1, 1, 1, 1, 1, 2, 8, 6, 1, 1, 1, 1],
+    [0, 1, 1, 19, 41, 0, 0, 0, 0, 0, 0, 0, 0, 15, 2, 1, 1, 0, 0, 0, 0, 1, 1, 8, 6, 0, 0, 0, 0]
+  ),
+  'sine-5000': (
+    [2, 2, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 31, 29, 2, 1, 2, 1],
+    [1, 1, 0, 6, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 4, 1, 2, 1, 1, 1, 2, 1, 41, 30, 0, 0, 1, 0]
+  )
+}
+
+class WavFileWarning(UserWarning):
+    pass
+
+_big_endian = False
+
+def asbytes(s):
+  return str(s)
+
+# assumes file pointer is immediately
+#  after the 'fmt ' id
+def _read_fmt_chunk(fid):
+    if _big_endian:
+        fmt = '>'
+    else:
+        fmt = '<'
+    res = struct.unpack(fmt+'ihHIIHH',fid.read(20))
+    size, comp, noc, rate, sbytes, ba, bits = res
+    if (comp != 1 or size > 16):
+        warnings.warn("Unfamiliar format bytes", WavFileWarning)
+        if (size>16):
+            fid.read(size-16)
+    return size, comp, noc, rate, sbytes, ba, bits
+
+# assumes file pointer is immediately
+#   after the 'data' id
+def _read_data_chunk(fid, noc, bits):
+    if _big_endian:
+        fmt = '>i'
+    else:
+        fmt = '<i'
+    size = struct.unpack(fmt,fid.read(4))[0]
+    if bits == 8:
+        data = npfromfile(fid, dtype=numpy.ubyte, count=size)
+        if noc > 1:
+            data = data.reshape(-1,noc)
+    else:
+        bytes = bits//8
+        if _big_endian:
+            dtype = '>i%d' % bytes
+        else:
+            dtype = '<i%d' % bytes
+        data = npfromfile(fid, dtype=dtype, count=size//bytes)
+        if noc > 1:
+            data = data.reshape(-1,noc)
+    return data
+
+def _read_riff_chunk(fid):
+    global _big_endian
+    str1 = fid.read(4)
+    if str1 == asbytes('RIFX'):
+        _big_endian = True
+    elif str1 != asbytes('RIFF'):
+        raise ValueError("Not a WAV file.")
+    if _big_endian:
+        fmt = '>I'
+    else:
+        fmt = '<I'
+    fsize = struct.unpack(fmt, fid.read(4))[0] + 8
+    str2 = fid.read(4)
+    if (str2 != asbytes('WAVE')):
+        raise ValueError("Not a WAV file.")
+    if str1 == asbytes('RIFX'):
+        _big_endian = True
+    return fsize
+
+# open a wave-file
+def read_wav0(file):
+    """
+    Return the sample rate (in samples/sec) and data from a WAV file
+
+    Parameters
+    ----------
+    file : file
+        Input wav file.
+
+    Returns
+    -------
+    rate : int
+        Sample rate of wav file
+    data : numpy array
+        Data read from wav file
+
+    Notes
+    -----
+
+    * The file can be an open file or a filename.
+
+    * The returned sample rate is a Python integer
+    * The data is returned as a numpy array with a
+      data-type determined from the file.
+
+    """
+    if hasattr(file,'read'):
+        fid = file
+    else:
+        fid = open(file, 'rb')
+
+    fsize = _read_riff_chunk(fid)
+    noc = 1
+    bits = 8
+    while (fid.tell() < fsize):
+        # read the next chunk
+        chunk_id = fid.read(4)
+        if chunk_id == asbytes('fmt '):
+            size, comp, noc, rate, sbytes, ba, bits = _read_fmt_chunk(fid)
+        elif chunk_id == asbytes('data'):
+            data = _read_data_chunk(fid, noc, bits)
+        else:
+            warnings.warn("chunk not understood", WavFileWarning)
+            data = fid.read(4)
+            if _big_endian:
+                fmt = '>i'
+            else:
+                fmt = '<i'
+            size = struct.unpack(fmt, data)[0]
+            fid.seek(size, 1)
+    fid.close()
+    return rate, data
+
+def read_wav(filename, channel=0):
+  a = read_wav0(filename)
+  samplerate, data = a
+  try:
+    data = map(lambda x: x[channel], data)
+  except:
+    pass
+  m = npmax(npabs(data), axis=0)
+  print '# absolute sample maximum in percent'
+  print 'SAMPLE_MAXIMUM=%s' % int((m * 100 + 32768 / 2) / 32768.0)
+  if m > 0:
+    data /= m 
+  return samplerate, data
+
+def fft_test(args):
+  global SAMPLES
+  graph = False
+  values = False
+  while 1:
+    if args[0] == '-graph':
+      del args[0]
+      graph = True
+      continue
+    if args[0] == '-values':
+      del args[0]
+      values = True
+      continue
+    if args[0] == '-clean':
+      del args[0]
+      a = SAMPLES['zero']
+      SAMPLES = {}
+      SAMPLES['zero'] = a
+      continue
+    if args[0].startswith('-avg='):
+      avg = eval(args[0][5:])
+      del args[0]
+      continue
+    if args[0].startswith('-max='):
+      mex = eval(args[0][5:])
+      del args[0]
+      continue
+    if args[0].startswith('-name='):
+      name = args[0][6:]
+      del args[0]
+      SAMPLES[name] = (avg, mex)
+      print repr(SAMPLES[name])
+    break
+
+  filename = args[0]
+
+  samplerate, data = read_wav(filename)
+
+  windowsize = len(data)
+  lowfreq = 200
+  highfreq = 6000
+  
+  ffty = fft(data[:windowsize])
+  fftx = fftfreq(windowsize, 1.0 / samplerate)
+  l, h = 0, 0
+  for x in fftx:
+    if x >= lowfreq:
+      break
+    l += 1
+  for x in fftx:
+    if x >= highfreq:
+      break
+    h += 1
+  ffty = ffty[l:h]
+  fftx = fftx[l:h]
+  ffty = map(lambda x: (float(abs(x)) * 1000) / windowsize, ffty)
+  a = 200
+  r = {}
+  m = {}
+  totalr = totalm = 0.0
+  for b in range(400, 6000 + 1, 200):
+    l, h = 0, 0
+    for x in fftx:
+      if x >= a:
+        break
+      l += 1
+    for x in fftx:
+      if x >= b:
+        break
+      h += 1
+    ar = ffty[l:h]
+    r[b] = npsum(ar)
+    m[b] = npmax(ar)
+    totalr += r[b]
+    totalm += m[b]
+    a = b
+  l = r.keys()
+  l.sort()
+  if totalr > 0.0:
+    r = map(lambda x: int(((r[x] * 100) + totalr / 2) / totalr), l)
+  else:
+    r = map(lambda x: 0, l)
+  if totalm > 0.0:
+    m = map(lambda x: int(((m[x] * 100) + totalm / 2) / totalm), l)
+  else:
+    m = map(lambda x: 0, l)
+  check = {}
+  for s in SAMPLES:
+    x, y = SAMPLES[s]
+    diffa = diffm = idx = 0
+    for idx in range(len(r)):
+      diffa += abs(r[idx] - x[idx])
+      diffm += abs(m[idx] - y[idx])
+    check[diffa + diffm] = s
+  l = check.keys()
+  l.sort()
+  print '# closest spectral diff and name'
+  print 'SPECTRAL_DIFF=%s' % l[0]
+  print 'SPECTRAL_NAME="%s"' % check[l[0]]
+  if values:
+    print '-avg="%s"' % repr(r)
+    print '-max="%s"' % repr(m)
+  if graph:
+    import matplotlib.pyplot as plt
+    fig = plt.figure()
+    ax = fig.add_subplot(111)
+    ax.plot(abs(fftx), ffty, 'ro')
+    ax.set_xlabel('Frequency in Hz')
+    ax.set_ylabel('Occurence')
+    plt.title(filename)
+    plt.show()
+
+if __name__ == '__main__':
+  fft_test(sys.argv[1:])