Python numpy.log10方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log10 [as 別名]
def get_audio_data(self):
        frames = self.rec.get_frames()
        result = [0] * self.bins
        if len(frames) > 0:
            # keeps only the last frame
            current_frame = frames[-1]
            # plots the time signal
            # self.line_top.set_data(self.time_vect, current_frame)
            # computes and plots the fft signal
            fft_frame = np.fft.rfft(current_frame)
            if self.auto_gain:
                fft_frame /= np.abs(fft_frame).max()
            else:
                fft_frame *= (1 + self.gain) / 5000000.

            fft_frame = np.abs(fft_frame)
            if self.log_scale:
                fft_frame = np.log10(np.add(1, np.multiply(10, fft_frame)))

            result = [min(int(max(i, 0.) * 1023), 1023) for i in fft_frame][0:self.bins]

        return result 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log10 [as 別名]
def _interp_ebv(self, datum, dist):
        """
        Calculate samples of E(B-V) at an arbitrary distance (in kpc) for one
        test coordinate.
        """
        dm = 5. * (np.log10(dist) + 2.)
        idx_ceil = np.searchsorted(datum['DM_bin_edges'], dm)
        if idx_ceil == 0:
            dist_0 = 10.**(datum['DM_bin_edges'][0]/5. - 2.)
            return dist/dist_0 * datum['samples'][:,0]
        elif idx_ceil == len(datum['DM_bin_edges']):
            return datum['samples'][:,-1]
        else:
            dm_ceil = datum['DM_bin_edges'][idx_ceil]
            dm_floor = datum['DM_bin_edges'][idx_ceil-1]
            a = (dm_ceil - dm) / (dm_ceil - dm_floor)
            return (
                (1.-a) * datum['samples'][:,idx_ceil]
                +    a * datum['samples'][:,idx_ceil-1]
            ) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log10 [as 別名]
def logSE(x1, x2=-1):
    """
    10 * log10(e**2)    
    This function accepts two series of data or directly
    one series with error.

    **Args:**

    * `x1` - first data series or error (1d array)

    **Kwargs:**

    * `x2` - second series (1d array) if first series was not error directly,\\
        then this should be the second series

    **Returns:**

    * `e` - logSE of error (1d array) obtained directly from `x1`, \\
        or as a difference of `x1` and `x2`. The values are in dB!

    """
    e = get_valid_error(x1, x2)
    return 10*np.log10(e**2) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log10 [as 別名]
def speech_aug_scheduler(step, s_r, s_i, s_f, peak_lr):
    # Starting from 0, ramp-up to set LR and  converge to 0.01*LR, w/ exp. decay
    final_lr_ratio = 0.01
    exp_decay_lambda = -np.log10(final_lr_ratio)/(s_f-s_i) # Approx. w/ 10-based
    cur_step = step+1

    if cur_step<s_r:
        # Ramp-up
        return peak_lr*float(cur_step)/s_r
    elif cur_step<s_i:
        # Hold
        return peak_lr
    elif cur_step<=s_f:
        # Decay
        return peak_lr*np.power(10,-exp_decay_lambda*(cur_step-s_i))
    else:
        # Converge
        return peak_lr*final_lr_ratio 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log10 [as 別名]
def test_dist_sma(self):
        """
        Test that smas outside of the range have zero probability

        """

        for mod in self.allmods:
            if 'dist_sma' in mod.__dict__:
                with RedirectStreams(stdout=self.dev_null):
                    pp = mod(**self.spec)

                a = np.logspace(np.log10(pp.arange[0].to('AU').value/10.),np.log10(pp.arange[1].to('AU').value*10.),100)

                fa = pp.dist_sma(a)
                self.assertTrue(np.all(fa[a < pp.arange[0].to('AU').value] == 0),'dist_sma high bound failed for %s'%mod.__name__)
                self.assertTrue(np.all(fa[a > pp.arange[1].to('AU').value] == 0),'dist_sma low bound failed for %s'%mod.__name__)
                self.assertTrue(np.all(fa[(a >= pp.arange[0].to('AU').value) & (a <= pp.arange[1].to('AU').value)] >= 0.),'dist_sma generates negative densities within range for %s'%mod.__name__) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log10 [as 別名]
def test_dist_radius(self):
        """
        Test that radii outside of the range have zero probability

        """
        for mod in self.allmods:
            if 'dist_radius' in mod.__dict__:
                with RedirectStreams(stdout=self.dev_null):
                    pp = mod(**self.spec)

                Rp = np.logspace(np.log10(pp.Rprange[0].to('earthRad').value/10.),np.log10(pp.Rprange[1].to('earthRad').value*100.),100) 

                fr = pp.dist_radius(Rp)
                self.assertTrue(np.all(fr[Rp < pp.Rprange[0].to('earthRad').value] == 0),'dist_radius high bound failed for %s'%mod.__name__)
                self.assertTrue(np.all(fr[Rp > pp.Rprange[1].to('earthRad').value] == 0),'dist_radius high bound failed for %s'%mod.__name__)
                self.assertTrue(np.all(fr[(Rp >= pp.Rprange[0].to('earthRad').value) & (Rp <= pp.Rprange[1].to('earthRad').value)] > 0),'dist_radius generates zero probabilities within range for %s'%mod.__name__) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log10 [as 別名]
def test_dist_mass(self):
        """
        Test that masses outside of the range have zero probability

        """

        for mod in self.allmods:
            if 'dist_mass' in mod.__dict__:
                with RedirectStreams(stdout=self.dev_null):
                    pp = mod(**self.spec)

                Mp = np.logspace(np.log10(pp.Mprange[0].value/10.),np.log10(pp.Mprange[1].value*100.),100) 

                fr = pp.dist_mass(Mp)
                self.assertTrue(np.all(fr[Mp < pp.Mprange[0].value] == 0),'dist_mass high bound failed for %s'%mod.__name__)
                self.assertTrue(np.all(fr[Mp > pp.Mprange[1].value] == 0),'dist_mass high bound failed for %s'%mod.__name__)
                self.assertTrue(np.all(fr[(Mp >= pp.Mprange[0].value) & (Mp <= pp.Mprange[1].value)] > 0)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log10 [as 別名]
def test_dist_sma_radius(self):
        """
        Test that sma and radius values outside of the range have zero probability
        """
        
        for mod in self.allmods:
            if 'dist_sma_radius' in mod.__dict__:
                with RedirectStreams(stdout=self.dev_null):
                    pp = mod(**self.spec)
                
                a = np.logspace(np.log10(pp.arange[0].value/10.),np.log10(pp.arange[1].value*100),100)
                Rp = np.logspace(np.log10(pp.Rprange[0].value/10.),np.log10(pp.Rprange[1].value*100),100)
                
                aa, RR = np.meshgrid(a,Rp)
                
                fr = pp.dist_sma_radius(aa,RR)
                self.assertTrue(np.all(fr[aa < pp.arange[0].value] == 0),'dist_sma_radius low bound failed on sma for %s'%mod.__name__)
                self.assertTrue(np.all(fr[aa > pp.arange[1].value] == 0),'dist_sma_radius high bound failed on sma for %s'%mod.__name__)
                self.assertTrue(np.all(fr[RR < pp.Rprange[0].value] == 0),'dist_sma_radius low bound failed on radius for %s'%mod.__name__)
                self.assertTrue(np.all(fr[RR > pp.Rprange[1].value] == 0),'dist_sma_radius high bound failed on radius for %s'%mod.__name__)
                self.assertTrue(np.all(fr[(aa > pp.arange[0].value) & (aa < pp.arange[1].value) & (RR > pp.Rprange[0].value) & (RR < pp.Rprange[1].value)] > 0),'dist_sma_radius is improper pdf for %s'%mod.__name__) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log10 [as 別名]
def maxdmag(self, s):
        """Calculates the maximum value of dMag for projected separation
        
        Args:
            s (float):
                Projected separation (AU)
        
        Returns:
            maxdmag (float):
                Maximum planet delta magnitude
        
        """
        
        if s == 0.0:
            maxdmag = self.cdmax - 2.5*np.log10(self.Phi(np.pi))
        elif s < self.rmax:
            maxdmag = self.cdmax - 2.5*np.log10(np.abs(self.Phi(np.pi-np.arcsin(s/self.rmax))))
        else:
            maxdmag = self.cdmax - 2.5*np.log10(self.Phi(np.pi/2.0))

        return maxdmag 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log10 [as 別名]
def calclogf(self):
        """
        # wavelength dependence, from Table 19 in Leinert et al 1998
        # interpolated w/ a quadratic in log-log space
        Returns:
            interpolant (object):
                a 1D quadratic interpolant of intensity vs wavelength

        """
        self.zodi_lam = np.array([0.2, 0.3, 0.4, 0.5, 0.7, 0.9, 1.0, 1.2, 2.2, 3.5,
                4.8, 12, 25, 60, 100, 140]) # um
        self.zodi_Blam = np.array([2.5e-8, 5.3e-7, 2.2e-6, 2.6e-6, 2.0e-6, 1.3e-6,
                1.2e-6, 8.1e-7, 1.7e-7, 5.2e-8, 1.2e-7, 7.5e-7, 3.2e-7, 1.8e-8,
                3.2e-9, 6.9e-10]) # W/m2/sr/um
        x = np.log10(self.zodi_lam)
        y = np.log10(self.zodi_Blam)
        return interp1d(x, y, kind='quadratic') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log10 [as 別名]
def deltaMag(p,Rp,d,Phi):
    """ Calculates delta magnitudes for a set of planets, based on their albedo, 
    radius, and position with respect to host star.
    
    Args:
        p (ndarray):
            Planet albedo
        Rp (astropy Quantity array):
            Planet radius in units of km
        d (astropy Quantity array):
            Planet-star distance in units of AU
        Phi (ndarray):
            Planet phase function
    
    Returns:
        dMag (ndarray):
            Planet delta magnitudes
    
    """
    dMag = -2.5*np.log10(p*(Rp/d).decompose()**2*Phi).value
    
    return dMag 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log10 [as 別名]
def test_amplitude_to_decibel():
    """test for backend_keras.amplitude_to_decibel"""
    from kapre.backend_keras import amplitude_to_decibel

    x = np.array([[1e-20, 1e-5, 1e-3, 5e-2], [0.3, 1.0, 20.5, 9999]])  # random positive numbers

    amin = 1e-5
    dynamic_range = 80.0

    x_decibel = 10 * np.log10(np.maximum(x, amin))
    x_decibel = x_decibel - np.max(x_decibel, axis=(1,), keepdims=True)
    x_decibel_ref = np.maximum(x_decibel, -1 * dynamic_range)

    x_var = K.variable(x)
    x_decibel_kapre = amplitude_to_decibel(x_var, amin, dynamic_range)

    assert np.allclose(K.eval(x_decibel_kapre), x_decibel_ref, atol=TOL) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log10 [as 別名]
def plotstft(audiopath, binsize=2**10, plotpath=None, colormap="gray", channel=0, name='tmp.png', alpha=1, offset=0):
    samplerate, samples = wav.read(audiopath)
    samples = samples[:, channel]
    s = stft(samples, binsize)

    sshow, freq = logscale_spec(s, factor=1, sr=samplerate, alpha=alpha)
    sshow = sshow[2:, :]
    ims = 20.*np.log10(np.abs(sshow)/10e-6) # amplitude to decibel
    timebins, freqbins = np.shape(ims)
    
    ims = np.transpose(ims)
    # ims = ims[0:256, offset:offset+768] # 0-11khz, ~9s interval
    ims = ims[0:256, :] # 0-11khz, ~10s interval
    #print "ims.shape", ims.shape
    
    image = Image.fromarray(ims) 
    image = image.convert('L')
    image.save(name) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log10 [as 別名]
def gain_Pratio(p1, p2):
    """
    Antenna gain [dB] via power ratio.

    From Rinehart (1997), Eqn 2.1

    Parameters
    ----------
    p1 : float or array
        Power on the beam axis [W]
    p2 : float or array
        Power from an isotropic antenna [W]

    Notes
    -----
    Ensure that both powers have the same units!
    If arrays are used, either for one okay, or both must be the same length.
    """
    return 10. * np.log10(np.asarray(p1) / np.asarray(p2)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import log10 [as 別名]
def zdr(z_h, z_v):
    """
    Differential reflectivity [dB].

    From Rinehart (1997), Eqn 10.3 and Seliga and Bringi (1976)

    Parameters
    ----------
    z_h : float or array
        Horizontal reflectivity [mm^6/m^3]
    z_v : float or array
        Vertical reflectivity [mm^6/m^3]

    Notes
    -----
    Ensure that both powers have the same units!

    Alternating horizontally and linearly polarized pulses are averaged.

    Notes
    -----
    If arrays are used, either for one okay, or both must be the same length.
    """
    return 10. * np.log10(np.asarray(z_h) / np.asarray(z_v))