# 错误
import sys, os

# 正确
import sys
import os

from sklearn import metrics, datasets

import matplotlib.pyplot as plt

# 错误：
if x == 4 :print(x ,y);x ,y = y ,x

# 要修改成：
if x == 4: print(x, y); x, y = y, x

# 错误：
spam( ham[ 1 ], { eggs: 2 } )

# 要修改成：
spam(ham[1], {eggs: 2})

# 错误：
dict ['key'] = list [index]

# 要修改成：
dict['key'] = list[index]

# 错误：
x=1
y=2
long_variable=3

# 正确：
x = 1
y = 2
long_variable = 3

# Yes:
i = i  1
submitted = 1
x = x * 2 - 1
hypot2 = x * x  y * y
c = (a  b) * (a – b)

# No:
i=i1
submitted =1
x = x*2 - 1
hypot2 = x*x  y*y
c = (ab) * (a-b)

# 正确
def complex(real, imag=0.0):
    return magic(r=real, i=imag)

# 错误
def complex(real, imag = 0.0):
    return magic(r = real, i = imag)

# 正确:
if foo == 'blah':
    do_blah_thing()
    do_one()
    do_two()
    do_three()

# 错误:
if foo == 'blah': 
    do_blah_thing()do_one(); do_two(); do_three()

# 网站地址（单独一行注释：#一个空格注释内容）
__website__ = "htpp://www.52phm.cn"  # 网站地址（和代码在同一行注释：两个空格#一个空格注释内容）

# 网站地址（单独一行注释：#一个空格注释内容）
# 网站地址
# 网站地址
__website__ = "htpp://www.52phm.cn"

"""
简介：
52phm社区，工业互联网人的技术交流社区
"""
name = "52phm社区，工业互联网人的技术交流社区"

re/time/pymysql/os

MyCat
WeChat

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Spectral feature extraction"""

import numpy as np
import scipy
import scipy.signal
import scipy.fftpack

from .. import util
from .. import filters
from ..util.exceptions import ParameterError
from ..util.decorators import deprecate_positional_args

from ..core.convert import fft_frequencies
from ..core.audio import zero_crossings
from ..core.spectrum import power_to_db, _spectrogram
from ..core.constantq import cqt, hybrid_cqt
from ..core.pitch import estimate_tuning


__all__ = [
    "spectral_centroid",
    "spectral_bandwidth",
    "spectral_contrast",
    "spectral_rolloff",
    "spectral_flatness",
    "poly_features",
    "rms",
    "zero_crossing_rate",
    "chroma_stft",
    "chroma_cqt",
    "chroma_cens",
    "melspectrogram",
    "mfcc",
    "tonnetz",
]


# -- Spectral features -- #
@deprecate_positional_args
def spectral_centroid(
    *,
    y=None,
    sr=22050,
    S=None,
    n_fft=2048,
    hop_length=512,
    freq=None,
    win_length=None,
    window="hann",
    center=True,
    pad_mode="constant",
):
    """Compute the spectral centroid.

    Each frame of a magnitude spectrogram is normalized and treated as a
    distribution over frequency bins, from which the mean (centroid) is
    extracted per frame.

    More precisely, the centroid at frame ``t`` is defined as [#]_::

        centroid[t] = sum_k S[k, t] * freq[k] / (sum_j S[j, t])

    where ``S`` is a magnitude spectrogram, and ``freq`` is the array of
    frequencies (e.g., FFT frequencies in Hz) of the rows of ``S``.

    .. [#] Klapuri, A., & Davy, M. (Eds.). (2007). Signal processing
        methods for music transcription, chapter 5.
        Springer Science & Business Media.

    Parameters
    ----------
    y : np.ndarray [shape=(..., n,)] or None
        audio time series. Multi-channel is supported.

    sr : number > 0 [scalar]
        audio sampling rate of ``y``

    S : np.ndarray [shape=(..., d, t)] or None
        (optional) spectrogram magnitude

    n_fft : int > 0 [scalar]
        FFT window size

    hop_length : int > 0 [scalar]
        hop length for STFT. See `librosa.stft` for details.

    freq : None or np.ndarray [shape=(d,) or shape=(d, t)]
        Center frequencies for spectrogram bins.
        If `None`, then FFT bin center frequencies are used.

        Otherwise, it can be a single array of ``d`` center frequencies,
        or a matrix of center frequencies as constructed by
        `librosa.reassigned_spectrogram`

    win_length : int <= n_fft [scalar]
        Each frame of audio is windowed by `window()`.
        The window will be of length ``win_length`` and then padded
        with zeros to match ``n_fft``.

        If unspecified, defaults to ``win_length = n_fft``.

    window : string, tuple, number, function, or np.ndarray [shape=(n_fft,)]
        - a window specification (string, tuple, or number);
          see `scipy.signal.get_window`
        - a window function, such as `scipy.signal.windows.hann`
        - a vector or array of length ``n_fft``

        .. see also:: `librosa.filters.get_window`

    center : boolean
        - If `True`, the signal ``y`` is padded so that frame
          `t` is centered at ``y[t * hop_length]``.
        - If `False`, then frame ``t`` begins at ``y[t * hop_length]``

    pad_mode : string
        If ``center=True``, the padding mode to use at the edges of the signal.
        By default, STFT uses zero padding.

    Returns
    -------
    centroid : np.ndarray [shape=(..., 1, t)]
        centroid frequencies

    See Also
    --------
    librosa.stft : Short-time Fourier Transform
    librosa.reassigned_spectrogram : Time-frequency reassigned spectrogram

    Examples
    --------
    From time-series input:

    >>> y, sr = librosa.load(librosa.ex('trumpet'))
    >>> cent = librosa.feature.spectral_centroid(y=y, sr=sr)
    >>> cent
    array([[1768.888, 1921.774, ..., 5663.477, 5813.683]])

    From spectrogram input:

    >>> S, phase = librosa.magphase(librosa.stft(y=y))
    >>> librosa.feature.spectral_centroid(S=S)
    array([[1768.888, 1921.774, ..., 5663.477, 5813.683]])

    Using variable bin center frequencies:

    >>> freqs, times, D = librosa.reassigned_spectrogram(y, fill_nan=True)
    >>> librosa.feature.spectral_centroid(S=np.abs(D), freq=freqs)
    array([[1768.838, 1921.801, ..., 5663.513, 5813.747]])

    Plot the result

    >>> import matplotlib.pyplot as plt
    >>> times = librosa.times_like(cent)
    >>> fig, ax = plt.subplots()
    >>> librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max),
    ...                          y_axis='log', x_axis='time', ax=ax)
    >>> ax.plot(times, cent.T, label='Spectral centroid', color='w')
    >>> ax.legend(loc='upper right')
    >>> ax.set(title='log Power spectrogram')
    """

    # input is time domain:y or spectrogam:s
    #

    S, n_fft = _spectrogram(
        y=y,
        S=S,
        n_fft=n_fft,
        hop_length=hop_length,
        win_length=win_length,
        window=window,
        center=center,
        pad_mode=pad_mode,
    )

    if not np.isrealobj(S):
        raise ParameterError(
            "Spectral centroid is only defined " "with real-valued input"
        )
    elif np.any(S < 0):
        raise ParameterError(
            "Spectral centroid is only defined " "with non-negative energies"
        )

    # Compute the center frequencies of each bin
    if freq is None:
        freq = fft_frequencies(sr=sr, n_fft=n_fft)

    if freq.ndim == 1:
        # reshape for broadcasting
        freq = util.expand_to(freq, ndim=S.ndim, axes=-2)

    # Column-normalize S
    return np.sum(freq * util.normalize(S, norm=1, axis=-2), axis=-2, keepdims=True)


@deprecate_positional_args
def zero_crossing_rate(y, *, frame_length=2048, hop_length=512, center=True, **kwargs):
    """Compute the zero-crossing rate of an audio time series.

    Parameters
    ----------
    y : np.ndarray [shape=(..., n)]
        Audio time series. Multi-channel is supported.

    frame_length : int > 0
        Length of the frame over which to compute zero crossing rates

    hop_length : int > 0
        Number of samples to advance for each frame

    center : bool
        If `True`, frames are centered by padding the edges of ``y``.
        This is similar to the padding in `librosa.stft`,
        but uses edge-value copies instead of zero-padding.

    **kwargs : additional keyword arguments
        See `librosa.zero_crossings`

        .. note:: By default, the ``pad`` parameter is set to `False`, which
            differs from the default specified by
            `librosa.zero_crossings`.

    Returns
    -------
    zcr : np.ndarray [shape=(..., 1, t)]
        ``zcr[..., 0, i]`` is the fraction of zero crossings in frame ``i``

    See Also
    --------
    librosa.zero_crossings : Compute zero-crossings in a time-series

    Examples
    --------
    >>> y, sr = librosa.load(librosa.ex('trumpet'))
    >>> librosa.feature.zero_crossing_rate(y)
    array([[0.044, 0.074, ..., 0.488, 0.355]])

    """

    # check if audio is valid
    util.valid_audio(y, mono=False)

    if center:
        padding = [(0, 0) for _ in range(y.ndim)]
        padding[-1] = (int(frame_length // 2), int(frame_length // 2))
        y = np.pad(y, padding, mode="edge")

    y_framed = util.frame(y, frame_length=frame_length, hop_length=hop_length)

    kwargs["axis"] = -2
    kwargs.setdefault("pad", False)

    crossings = zero_crossings(y_framed, **kwargs)

    return np.mean(crossings, axis=-2, keepdims=True)