Python math.log10() Examples

def getTicks(self, regionID):
        ticks = []
        start = 0
        width = self.scale.partsToLengths[regionID]
        end = start + width

        res = (10 ** round(math.log10(end - start))) / 10.0
        if width / res > 15:
            res *= 2.5
        elif width / res < 5:
            res /= 2.0

        roundStart = start - (start%res)

        for i in range(int(roundStart), end, int(res)):
            ticks.append(i)

        return ticks 

def test(ctx):
    val_data.reset()
    avg_psnr = 0
    batches = 0
    for batch in val_data:
        batches += 1
        data = gluon.utils.split_and_load(batch.data[0], ctx_list=ctx, batch_axis=0)
        label = gluon.utils.split_and_load(batch.label[0], ctx_list=ctx, batch_axis=0)
        outputs = []
        for x in data:
            outputs.append(net(x))
        metric.update(label, outputs)
        avg_psnr += 10 * math.log10(1/metric.get()[1])
        metric.reset()
    avg_psnr /= batches
    print('validation avg psnr: %f'%avg_psnr) 

def _fade_in(self, waveform_length: int) -> Tensor:
        fade = torch.linspace(0, 1, self.fade_in_len)
        ones = torch.ones(waveform_length - self.fade_in_len)

        if self.fade_shape == "linear":
            fade = fade

        if self.fade_shape == "exponential":
            fade = torch.pow(2, (fade - 1)) * fade

        if self.fade_shape == "logarithmic":
            fade = torch.log10(.1 + fade) + 1

        if self.fade_shape == "quarter_sine":
            fade = torch.sin(fade * math.pi / 2)

        if self.fade_shape == "half_sine":
            fade = torch.sin(fade * math.pi - math.pi / 2) / 2 + 0.5

        return torch.cat((fade, ones)).clamp_(0, 1) 

def _fade_out(self, waveform_length: int) -> Tensor:
        fade = torch.linspace(0, 1, self.fade_out_len)
        ones = torch.ones(waveform_length - self.fade_out_len)

        if self.fade_shape == "linear":
            fade = - fade + 1

        if self.fade_shape == "exponential":
            fade = torch.pow(2, - fade) * (1 - fade)

        if self.fade_shape == "logarithmic":
            fade = torch.log10(1.1 - fade) + 1

        if self.fade_shape == "quarter_sine":
            fade = torch.sin(fade * math.pi / 2 + math.pi / 2)

        if self.fade_shape == "half_sine":
            fade = torch.sin(fade * math.pi + math.pi / 2) / 2 + 0.5

        return torch.cat((ones, fade)).clamp_(0, 1) 

def forward(self, waveform: Tensor) -> Tensor:
        r"""
        Args:
            waveform (Tensor): Tensor of audio of dimension (..., time).

        Returns:
            Tensor: Tensor of audio of dimension (..., time).
        """
        if self.gain_type == "amplitude":
            waveform = waveform * self.gain

        if self.gain_type == "db":
            waveform = F.gain(waveform, self.gain)

        if self.gain_type == "power":
            waveform = F.gain(waveform, 10 * math.log10(self.gain))

        return torch.clamp(waveform, -1, 1) 

def get_weight(follower_count=1, following_count=1, status_count=1, listed_in_count=1, is_verified=False):
    """
    (Follower_count / Following_count) + status_count + listed_in_count
    """
    if not follower_count and not following_count and not status_count:
        return 0
    if not follower_count:
        follower_count = 1
    if not following_count:
        following_count = 1
    if not status_count:
        status_count = 1
    if not listed_in_count:
        listed_in_count = 1
    return int((follower_count / following_count) *
               math.log10(follower_count) +
               2 * listed_in_count) + 50 * bool(is_verified) 

def test_custom_bode_default(self):
        ct.config.defaults['bode.dB'] = True
        ct.config.defaults['bode.deg'] = True
        ct.config.defaults['bode.Hz'] = True

        # Generate a Bode plot
        plt.figure()
        omega = np.logspace(-3, 3, 100)
        ct.bode_plot(self.sys, omega, dB=True)
        mag_x, mag_y = (((plt.gcf().axes[0]).get_lines())[0]).get_data()
        np.testing.assert_almost_equal(mag_y[0], 20*log10(10), decimal=3)

        # Override defaults
        plt.figure()
        ct.bode_plot(self.sys, omega, Hz=True, deg=False, dB=True)
        mag_x, mag_y = (((plt.gcf().axes[0]).get_lines())[0]).get_data()
        phase_x, phase_y = (((plt.gcf().axes[1]).get_lines())[0]).get_data()
        np.testing.assert_almost_equal(mag_x[0], 0.001 / (2*pi), decimal=6)
        np.testing.assert_almost_equal(mag_y[0], 20*log10(10), decimal=3)
        np.testing.assert_almost_equal(phase_y[-1], -pi, decimal=2)

        ct.reset_defaults() 

def PlotTimeResp(self, u, t, fig, clear=True, label='model', mysub=111):
        ax = fig.add_subplot(mysub)
        if clear:
            ax.cla()
        try:
            y = self.lsim(u, t)
        except:
            y = self.lsim2(u, t)
        ax.plot(t, y, label=label)
        return ax


##     def BodePlot(self, f, fig, clear=False):
##         mtf = self.FreqResp(
##         ax1 = fig.axes[0]
##         ax1.semilogx(modelf,20*log10(abs(mtf)))
##         mphase = angle(mtf, deg=1)
##         ax2 = fig.axes[1]
##         ax2.semilogx(modelf, mphase) 

def ELO(wins, losses, draws):

    def _elo(x):
        if x <= 0 or x >= 1: return 0.0
        return -400*math.log10(1/x-1)

    # win/loss/draw ratio
    N = wins + losses + draws;
    if N == 0: return (0, 0, 0)
    w = float(wins)  / N
    l = float(losses)/ N
    d = float(draws) / N

    # mu is the empirical mean of the variables (Xi), assumed i.i.d.
    mu = w + d/2

    # stdev is the empirical standard deviation of the random variable (X1+...+X_N)/N
    stdev = math.sqrt(w*(1-mu)**2 + l*(0-mu)**2 + d*(0.5-mu)**2) / math.sqrt(N)

    # 95% confidence interval for mu
    mu_min = mu + phi_inv(0.025) * stdev
    mu_max = mu + phi_inv(0.975) * stdev

    return (_elo(mu_min), _elo(mu), _elo(mu_max)) 

def simplify_surd(value, sample_args, context=None):
  """E.g., "Simplify (2 + 5*sqrt(3))**2."."""
  del value  # unused
  if context is None:
    context = composition.Context()

  entropy, sample_args = sample_args.peel()

  while True:
    base = random.randint(2, 20)
    if sympy.Integer(base).is_prime:
      break
  num_primes_less_than_20 = 8
  entropy -= math.log10(num_primes_less_than_20)
  exp = _sample_surd(base, entropy, max_power=2, multiples_only=False)
  simplified = sympy.expand(sympy.simplify(exp))

  template = random.choice([
      'Simplify {exp}.',
  ])
  return example.Problem(
      question=example.question(context, template, exp=exp),
      answer=simplified) 

def _semi_prime(entropy):
  """Generates a semi-prime with the given entropy."""
  # Add on extra entropy to account for the sparsity of the primes; we don't
  # actually use the integers sampled, but rather a random prime close to them;
  # thus some entropy is lost, which we must account for
  entropy += math.log10(max(1, entropy * math.log(10)))

  # We intentionally uniformy sample the "entropy" (i.e., approx number digits)
  # of the two factors.
  entropy_1, entropy_2 = entropy * np.random.dirichlet([1, 1])

  # Need >= 2 for randprime to always work (Betrand's postulate).
  approx_1 = number.integer(entropy_1, signed=False, min_abs=2)
  approx_2 = number.integer(entropy_2, signed=False, min_abs=2)

  factor_1 = sympy.ntheory.generate.randprime(approx_1 / 2, approx_1 * 2)
  factor_2 = sympy.ntheory.generate.randprime(approx_2 / 2, approx_2 * 2)

  return factor_1 * factor_2 

def calculate_psnr(img1, img2, border=0):
    # img1 and img2 have range [0, 255]
    #img1 = img1.squeeze()
    #img2 = img2.squeeze()
    if not img1.shape == img2.shape:
        raise ValueError('Input images must have the same dimensions.')
    h, w = img1.shape[:2]
    img1 = img1[border:h-border, border:w-border]
    img2 = img2[border:h-border, border:w-border]

    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)
    mse = np.mean((img1 - img2)**2)
    if mse == 0:
        return float('inf')
    return 20 * math.log10(255.0 / math.sqrt(mse))


# --------------------------------------------
# SSIM
# -------------------------------------------- 

def calc_psnr(sr, hr, scale, rgb_range, dataset=None):
    if hr.nelement() == 1: return 0

    diff = (sr - hr) / rgb_range
    if dataset and dataset.dataset.benchmark:
        shave = scale
        # print('Ycbcr PSNR.')
        if diff.size(1) > 1:
            gray_coeffs = [65.738, 129.057, 25.064]
            convert = diff.new_tensor(gray_coeffs).view(1, 3, 1, 1) / 256
            diff = diff.mul(convert).sum(dim=1)
    else:
        shave = scale + 6

    valid = diff[..., shave:-shave, shave:-shave]
    mse = valid.pow(2).mean()

    return -10 * math.log10(mse) 

def calc_psnr(sr, hr, scale, rgb_range, dataset=None):
    if hr.nelement() == 1: return 0

    diff = (sr - hr) / rgb_range
    if dataset and dataset.dataset.benchmark:
        shave = scale
        # print('Ycbcr PSNR.')
        if diff.size(1) > 1:
            gray_coeffs = [65.738, 129.057, 25.064]
            convert = diff.new_tensor(gray_coeffs).view(1, 3, 1, 1) / 256
            diff = diff.mul(convert).sum(dim=1)
    else:
        shave = scale + 6

    valid = diff[..., shave:-shave, shave:-shave]
    mse = valid.pow(2).mean()

    return -10 * math.log10(mse) 

def calc_psnr(sr, hr, scale, rgb_range, dataset=None):
    if hr.nelement() == 1: return 0

    diff = (sr - hr) / rgb_range
    if dataset and dataset.dataset.benchmark:
        shave = scale
        # print('Ycbcr PSNR.')
        if diff.size(1) > 1:
            gray_coeffs = [65.738, 129.057, 25.064]
            convert = diff.new_tensor(gray_coeffs).view(1, 3, 1, 1) / 256
            diff = diff.mul(convert).sum(dim=1)
    else:
        shave = scale + 6

    valid = diff[..., shave:-shave, shave:-shave]
    mse = valid.pow(2).mean()

    return -10 * math.log10(mse) 

def calc_psnr(sr, hr, scale, rgb_range, dataset=None):
    if hr.nelement() == 1: return 0

    diff = (sr - hr) / rgb_range
    if dataset and dataset.dataset.benchmark:
        shave = scale
        # print('Ycbcr PSNR.')
        if diff.size(1) > 1:
            gray_coeffs = [65.738, 129.057, 25.064]
            convert = diff.new_tensor(gray_coeffs).view(1, 3, 1, 1) / 256
            diff = diff.mul(convert).sum(dim=1)
    else:
        shave = scale + 6

    valid = diff[..., shave:-shave, shave:-shave]
    mse = valid.pow(2).mean()

    return -10 * math.log10(mse) 

def calc_psnr(sr, hr, scale, rgb_range, dataset=None):
    if hr.nelement() == 1: return 0

    diff = (sr - hr) / rgb_range
    if dataset and dataset.dataset.benchmark:
        shave = scale
        # print('Ycbcr PSNR.')
        if diff.size(1) > 1:
            gray_coeffs = [65.738, 129.057, 25.064]
            convert = diff.new_tensor(gray_coeffs).view(1, 3, 1, 1) / 256
            diff = diff.mul(convert).sum(dim=1)
    else:
        shave = scale + 6

    valid = diff[..., shave:-shave, shave:-shave]
    mse = valid.pow(2).mean()

    return -10 * math.log10(mse) 

def percent_to_millibel(percent, raspberry_mod=False):
    if not raspberry_mod:
        from math import log10

        multiplier = 2.5

        percent *= multiplier
        percent = min(percent, 100. * multiplier)
        percent = max(percent, 0.000001)

        millibel = 1000 * log10(percent / 100.)

    else:
        # special case for mute
        if percent == 0:
            return -11000

        min_allowed = -4000
        max_allowed = 400
        percent = percent / 100.
        millibel = min_allowed + (max_allowed - min_allowed) * percent

    return int(millibel) 

def calculate_decibel(data):
    """
    Calculates the volume level in decibel of the given data

    :param data: A bytestring used to calculate the decibel level
    :return db: The calculated volume level in decibel
    """

    count = len(data) / 2
    form = "%dh" % count
    shorts = struct.unpack(form, data)
    sum_squares = 0.0
    for sample in shorts:
        n = sample * (1.0 / 32768)
        sum_squares += n * n
    rms = math.sqrt(sum_squares / count) + 0.0001
    db = 20 * math.log10(rms)
    return db 

def test_logs(self):
        LOG10E = math.log10(math.e)

        for exp in range(10) + [100, 1000, 10000]:
            value = 10 ** exp
            log10 = math.log10(value)
            self.assertAlmostEqual(log10, exp)

            # log10(value) == exp, so log(value) == log10(value)/log10(e) ==
            # exp/LOG10E
            expected = exp / LOG10E
            log = math.log(value)
            self.assertAlmostEqual(log, expected)

        for bad in -(1L << 10000), -2L, 0L:
            self.assertRaises(ValueError, math.log, bad)
            self.assertRaises(ValueError, math.log10, bad) 

def test_math_subclass(self):
        """verify subtypes of float/long work w/ math functions"""
        import math
        class myfloat(float): pass
        class mylong(long): pass

        mf = myfloat(1)
        ml = mylong(1)

        for x in math.log, math.log10, math.log1p, math.asinh, math.acosh, math.atanh, math.factorial, math.trunc, math.isinf:
            try:
                resf = x(mf)
            except ValueError:
                resf = None
            try:
                resl = x(ml)
            except ValueError:
                resl = None
            self.assertEqual(resf, resl) 

def test_log():
    import math
    
    zeros = [-1, -1.0, -1L, 0, 0.0, 0L]
    nonzeros = [2, 2.0, 2L]
    ones = [1, 1.0, 1L]
    
    AreNotEqual(type(zeros[0]), type(zeros[2]))
    
    for z0 in zeros:
        AssertError(ValueError, math.log, z0)
        AssertError(ValueError, math.log10, z0)
        for z in zeros:
            AssertError(ValueError, math.log, z0, z)
        for n in nonzeros + ones:
            AssertError(ValueError, math.log, z0, n)
            AssertError(ValueError, math.log, n, z0)
    
    for one in ones:
        for n in nonzeros:
            AssertError(ZeroDivisionError, math.log, n, one) 

def _parse_aircraft_data(self, a, time):
        alt = a.get('Alt', 0)
        dist = -1
        az = 0
        el = 0
        if 'Lat' in a and 'Long' in a:
            rec_pos = (receiver_latitude, receiver_longitude)
            ac_pos = (a['Lat'], a['Long'])
            dist = geomath.distance(rec_pos, ac_pos)
            az = geomath.bearing(rec_pos, ac_pos)
            el = math.degrees(math.atan(alt / (dist * 5280)))
        speed = 0
        if 'Spd' in a:
            speed = geomath.knot2mph(a['Spd'])
        if 'PosTime' in a:
            last_seen_time = datetime.fromtimestamp(a['PosTime'] / 1000.0)
            seen = (time - last_seen_time).total_seconds()
        else:
            seen = 0
        ac_data = AirCraftData(
            a.get('Icao', None).upper(),
            a.get('Sqk', None),
            a.get('Call', None),
            a.get('Reg', None),
            a.get('Lat', None),
            a.get('Long', None),
            alt,
            a.get('Vsi', 0),
            a.get('Trak', None),
            speed,
            a.get('CMsgs', None),
            seen,
            a.get('Mlat', False),
            None,  # NUCP
            None,  # Seen pos
            10.0 * math.log10(a.get('Sig', 0) / 255.0 + 1e-5),
            dist,
            az,
            el,
            time)
        return ac_data 

def __init__(
		self, precision, otu_separator="\t", element_separator=",", iid_gid_mapping=None,
		logfile=None, verbose=False, debug=False):
		"""
		Constructor

		@param precision: Cluster are made in steps: 10: 0.1, 100: 0.01, 1000: 0.001
		@type precision: int | long
		@param otu_separator: Character separating otu cluster from another
		@type otu_separator: str | unicode
		@param element_separator: Character separating elements within otu cluster from another
		@type element_separator: str | unicode
		@param iid_gid_mapping: Map of internal ids to genome ids
		@type iid_gid_mapping: dict[str|unicode, str|unicode]
		@param logfile: File handler or file path to a log file
		@type logfile: file | FileIO | StringIO | basestring
		@param verbose: Not verbose means that only warnings and errors will be past to stream
		@type verbose: bool
		@param debug: Display debug messages
		@type debug: bool
		"""
		assert isinstance(precision, int)
		assert self.validate_number(precision, minimum=0)
		assert isinstance(otu_separator, basestring)
		assert isinstance(element_separator, basestring)
		assert iid_gid_mapping is None or isinstance(iid_gid_mapping, dict)
		super(MothurCluster, self).__init__(logfile=logfile, verbose=verbose, debug=debug)
		self._precision = int(math.log10(precision))
		self._cluster_separator = otu_separator
		self._element_separator = element_separator
		self._cutoff_to_cluster = {}
		self._gid_to_cluster_index_list = {}
		self._unique_threshold = "unique"
		self._iid_gid = {}
		if iid_gid_mapping is not None:
			self._iid_gid = iid_gid_mapping 

def human_size(self, nbytes):
		rank = int((math.log10(nbytes)) / 3)
		rank = min(rank, len(self.suffixes) - 1)
		human = nbytes / (1024.0 ** rank)
		f = ('%.2f' % human).rstrip('0').rstrip('.')
		return '%s %s' % (f, self.suffixes[rank]) 

def __init__(self, stype: str = 'power', top_db: Optional[float] = None) -> None:
        super(AmplitudeToDB, self).__init__()
        self.stype = stype
        if top_db is not None and top_db < 0:
            raise ValueError('top_db must be positive value')
        self.top_db = top_db
        self.multiplier = 10.0 if stype == 'power' else 20.0
        self.amin = 1e-10
        self.ref_value = 1.0
        self.db_multiplier = math.log10(max(self.amin, self.ref_value)) 

def amplitude_to_DB(
        x: Tensor,
        multiplier: float,
        amin: float,
        db_multiplier: float,
        top_db: Optional[float] = None
) -> Tensor:
    r"""Turn a tensor from the power/amplitude scale to the decibel scale.

    This output depends on the maximum value in the input tensor, and so
    may return different values for an audio clip split into snippets vs. a
    a full clip.

    Args:
        x (Tensor): Input tensor before being converted to decibel scale
        multiplier (float): Use 10. for power and 20. for amplitude
        amin (float): Number to clamp ``x``
        db_multiplier (float): Log10(max(reference value and amin))
        top_db (float or None, optional): Minimum negative cut-off in decibels. A reasonable number
            is 80. (Default: ``None``)

    Returns:
        Tensor: Output tensor in decibel scale
    """
    x_db = multiplier * torch.log10(torch.clamp(x, min=amin))
    x_db -= multiplier * db_multiplier

    if top_db is not None:
        x_db = x_db.clamp(min=x_db.max().item() - top_db)

    return x_db 

def test_DB_to_amplitude(self):
        # Make some noise
        x = torch.rand(1000)
        spectrogram = torchaudio.transforms.Spectrogram()
        spec = spectrogram(x)

        amin = 1e-10
        ref = 1.0
        db_multiplier = math.log10(max(amin, ref))

        # Waveform amplitude -> DB -> amplitude
        multiplier = 20.
        power = 0.5

        db = F.amplitude_to_DB(torch.abs(x), multiplier, amin, db_multiplier, top_db=None)
        x2 = F.DB_to_amplitude(db, ref, power)

        torch.testing.assert_allclose(x2, torch.abs(x), atol=5e-5, rtol=1e-5)

        # Spectrogram amplitude -> DB -> amplitude
        db = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db=None)
        x2 = F.DB_to_amplitude(db, ref, power)

        torch.testing.assert_allclose(x2, spec, atol=5e-5, rtol=1e-5)

        # Waveform power -> DB -> power
        multiplier = 10.
        power = 1.

        db = F.amplitude_to_DB(x, multiplier, amin, db_multiplier, top_db=None)
        x2 = F.DB_to_amplitude(db, ref, power)

        torch.testing.assert_allclose(x2, torch.abs(x), atol=5e-5, rtol=1e-5)

        # Spectrogram power -> DB -> power
        db = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db=None)
        x2 = F.DB_to_amplitude(db, ref, power)

        torch.testing.assert_allclose(x2, spec, atol=5e-5, rtol=1e-5) 

def config_axes(self, xlog, ylog):
        if hasattr(self, '_rng'):
            (i1, j1, i2, j2) = self.visible_area()
            zoomed=1
        else:
            zoomed=0
            
        self._xlog = xlog
        self._ylog = ylog
        if xlog: self._rng = [log10(x) for x in self._original_rng]
        else: self._rng = self._original_rng
        if ylog: self._vals = [log10(x) for x in self._original_vals]
        else: self._vals = self._original_vals
            
        self._imin = min(self._rng)
        self._imax = max(self._rng)
        if self._imax == self._imin:
            self._imin -= 1
            self._imax += 1
        self._jmin = min(self._vals)
        self._jmax = max(self._vals)
        if self._jmax == self._jmin:
            self._jmin -= 1
            self._jmax += 1

        if zoomed:
            self.zoom(i1, j1, i2, j2)
        else:
            self.zoom(self._imin, self._jmin, self._imax, self._jmax) 

def human_readable_download_number(download_number_string):
	download_number_string = download_number_string.split("-")[0].replace(',','').replace('+','').strip()
	n = float(download_number_string)
	millidx = max(0,min(len(millnames)-1,
		int(math.floor(0 if n == 0 else math.log10(abs(n))/3))))
	
	return '{:.0f}{}'.format(n / 10**(3 * millidx), millnames[millidx])