Python scipy 模块，log10() 实例源码

def fin(size,signal):
    fil = sp.zeros(size,sp.float32)
    for i in xrange(size):
        ratio=sp.log10((i+1)/float(size)*10+1.0)
        if ratio>1.0:
            ratio=1.0
        fil[i] = ratio
    return fil*signal

def fout(size,signal):
    fil = sp.zeros(size,sp.float32)
    for i in xrange(size):
        ratio = sp.log10((size-i)/float(size)*10+1.0)
        if ratio>1.0:
            ratio = 1.0
        fil[i] = ratio
    return fil*signal

def create_labeled_data(aud_sample, nasal=0):

    num_windows = (len(aud_sample) - WINDOW_SIZE)/WINDOW_STRIDE

    features = np.zeros((num_windows, WINDOW_SIZE))
    labels = np.zeros(num_windows)

    idx = 0
    for i in range(0, len(aud_sample), WINDOW_STRIDE):

        window = aud_sample[i:i+WINDOW_SIZE]
        for j in range(len(window), WINDOW_SIZE):
            window = np.append(window,0)

        if is_periodic(window) is False:
           continue

        # FFT to shift to frequency domain - use frequency spectrum as features
        fft_values = abs(fft(window))

        feat = 20*scipy.log10(fft_values)

        features[idx:, ] = feat
        labels[idx] = nasal
        idx += 1
    return features[0:idx, ], labels[0:idx]

def get_price_paid_coords():
    postcodes = load_postcodes()
    price_paid = load_price_paid()[['price', 'date', 'postcode']]
    merged = pd.merge(price_paid, postcodes, left_on='postcode', right_index=True)

    london = filter_to_london(merged).copy()
    london['price'] = sp.log10(london['price'])
    return london.drop('postcode', 1)

def get_base(unit ='bit'):
    if unit == 'bit':
        log = sp.log2
    elif unit == 'nat':
        log = sp.log 
    elif unit in ('digit', 'dit'):
        log = sp.log10  
    else:
        raise ValueError('The "unit" "%s" not understood' % unit)
    return log

def svpice( t) :
    '''
    Returns saturation vapor pressure over ice, in hPa, given temperature in K.
    The Goff-Gratch equation (Smithsonian Met. Tables,  5th ed., pp. 350, 1984)
    '''
    a = 273.16 / t
    exponent = -9.09718 * (a - 1.) - 3.56654 * log10(a) + 0.876793 * (1. - 1./a) + log10(6.1071)

    return 10.0**exponent

def plot_resid(d,savename='resfig1.png'):
    """
        Plots the residual frequency after the first wipe using the TLE velocity.
    """
    flim = [-2.e3, 2.e3]
    t = d['tvec']

    dates = [dt.datetime.fromtimestamp(ts) for ts in t]
    datenums = md.date2num(dates)
    xfmt = md.DateFormatter('%Y-%m-%d %H:%M:%S')

    fig1 = plt.figure(figsize=(7, 9))
    doppler_residual = sp.interpolate.interp1d(d['tvec'],d['dopfit'])
    fvec = d["fvec"]
    res0 = d["res0"]
    res1 = d["res1"]
    plt.subplot(211)
    mesh = plt.pcolormesh(datenums, fvec, sp.transpose(10.*sp.log10(res0+1e-12)), vmin=-5, vmax=25)
    plt.plot(datenums, (150.0/400.0)*doppler_residual(t), "r--", label="doppler resid")
    ax = plt.gca()
    ax.xaxis.set_major_formatter(xfmt)
    plt.ylim(flim)
    plt.subplots_adjust(bottom=0.2)
    plt.xticks(rotation=25)
    plt.xlabel("UTC")
    plt.ylabel("Frequency (Hz)")
    plt.title("Power ch0 (dB) %1.2f MHz"%(150.012))
    plt.legend()
    plt.colorbar(mesh, ax=ax)

     # quicklook spectra of residuals spectra along with measured Doppler residual from second channel.
    plt.subplot(212)
    mesh = plt.pcolormesh(datenums, fvec, sp.transpose(10.*sp.log10(res1+1e-12)), vmin=-5, vmax=25)
    plt.plot(datenums, doppler_residual(t), "r--", label="doppler resid")
    ax = plt.gca()
    ax.xaxis.set_major_formatter(xfmt)
    plt.ylim(flim)
    plt.xlabel("UTC")
    plt.ylabel("Frequency (Hz)")
    plt.title("Power ch1 (dB), %1.2f MHz"%(400.032))
    plt.subplots_adjust(bottom=0.2)
    plt.xticks(rotation=25)
    plt.legend()
    plt.colorbar(mesh, ax=ax)

    plt.tight_layout()
    print('Saving residual plots: '+savename)
    plt.savefig(savename, dpi=300)
    plt.close(fig1)