Пример #1
0
    def _rhov2int(self, phi, r, ra):
        """Compute dimensionless pressure integral for phi, r """

        # Isotropic case first (equation 9, GZ15)
        rhov2r = exp(phi)*gammainc(self.g + 2.5, phi)
        rhov2  = 3*rhov2r
        rhov2t = 2*rhov2r

        # Add anisotropy, add parts depending explicitly on r
        # (see equations 12, 13, and 14 of GZ15)
        if (ra < self.ramax) and (r>0) and (phi>0):
            p, g = r/ra, self.g
            p2 = p**2
            p12 = 1+p2
            g3, g5, g7, fp2 = g+1.5, g+2.5, g+3.5, phi*p2

            P1 = p2*phi**g5/gamma(g7)
            H1 = hyp1f1(1, g7, -fp2) if fp2 <self.max_arg_exp else g5/fp2
            H2 = hyp1f1(2, g7, -fp2) if fp2 <self.max_arg_exp else g5*g3/fp2**2

            rhov2r += P1*H1
            rhov2r /= p12

            rhov2t /= p12
            rhov2t += 2*P1*(H1/p12 + H2)
            rhov2t /= p12

            rhov2 = rhov2r + rhov2t
        return rhov2, rhov2r, rhov2t
Пример #2
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    def _rhov2int(self, phi, r, ra):
        """Compute dimensionless pressure integral for phi, r """

        # Isotropic case first (equation 9, GZ15)
        rhov2r = exp(phi)*gammainc(self.g + 2.5, phi)
        rhov2  = 3*rhov2r
        rhov2t = 2*rhov2r

        # Add anisotropy, add parts depending explicitly on r
        # (see equations 12, 13, and 14 of GZ15)
        if (ra < self.ramax) and (r>0) and (phi>0):
            p, g = r/ra, self.g
            p2 = p**2
            p12 = 1+p2
            g3, g5, g7, fp2 = g+1.5, g+2.5, g+3.5, phi*p2

            P1 = p2*phi**g5/gamma(g7)
            H1 = hyp1f1(1, g7, -fp2) if fp2 <self.max_arg_exp else g5/fp2
            H2 = hyp1f1(2, g7, -fp2) if fp2 <self.max_arg_exp else g5*g3/fp2**2

            rhov2r += P1*H1
            rhov2r /= p12

            rhov2t /= p12
            rhov2t += 2*P1*(H1/p12 + H2)
            rhov2t /= p12

            rhov2 = rhov2r + rhov2t
        return rhov2, rhov2r, rhov2t
Пример #3
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def beta_poisson_pmf(x,lmbd,Phi,N):
    '''
    Evaluate the probability mass function for beta-Poisson distribution.

    Parameters
    ----------
        x : int or array
            point(s) at which to evaluate function
        lmbd : float
        Phi : float
        N : float

    Returns
    -------
        P : float or array
            probability of each point in x

    '''
    if type(x)==int:
        P=spsp.hyp1f1(x+Phi*lmbd,x+Phi*N,-N)
        for n in range(1,x+1): # This loop gives us the N^x/gamma(x+1 term)
            P=(N/n)*P
        for m in range(x): # This loop gives us the term with the two gamma functions in numerator and denominator
            P=((m+Phi*lmbd)/(m+Phi*N))*P
    else:
        P=[]
        for i in range(0,len(x)):
            p=spsp.hyp1f1(x[i]+Phi*lmbd,x[i]+Phi*N,-N)
            for n in range(1,x[i]+1): # This loop gives us the N^x/gamma(x+1 term)
                p=(N/n)*p
            for m in range(x[i]): # This loop gives us the term with the two gamma functions in numerator and denominator
                p=((m+Phi*lmbd)/(m+Phi*N))*p
            P=P+[p]
    return P
Пример #4
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def deviate1(C0, sigma, beta, orig=False):
    """
    Calculate deviate to solve fo C0

    Args:
        C0 (float):
        sigma (float):
        beta (float):
        orig (bool, optional):
            use the original approach.  Not recommended

    Returns:
        float: deviate

    """
    if orig:
        # Calculate <D>
        x = -1 * C0**2 / 18 / sigma**2
        num1 = const1_A * hyp1f1(2 / 3, 3 / 2, x)
        num2 = const2_A * sigma * hyp1f1(1 / 6, 1 / 2, x)
        cerf = erf(C0 / (3 * np.sqrt(2) * sigma))  # Used again below
        denom = const3_A * sigma**(4 / 3) * (cerf + 1)
        avgD = (num1 + num2) / denom
    else:
        PDF = DMcosmic_PDF(Delta_values, C0, sigma=sigma, beta=beta)
        avgD = np.sum(Delta_values * PDF) / np.sum(PDF)
    # Return
    return np.abs(avgD - 1)
Пример #5
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def raw_gaussian_moments_bivar(indices, mu1, mu2, sig1, sig2):
    """
    This function returns raw 2D-Gaussian moments as a function of
    means (mu_1,mu_2) and standard deviations (sigma_1,sigma_2)
    """

    num_moments = len(indices)
    moments = np.zeros(num_moments)
    for i_moment in range(num_moments):
        i = indices[i_moment][0]
        j = indices[i_moment][1]
        moments[i_moment] = (
            (1.0 / math.pi) * 2**((-4.0 + i + j) / 2.0) *
            math.exp(-(mu1**2.0 / (2.0 * sig1**2)) -
                     (mu2**2.0 /
                      (2 * sig2**2.0))) * sig1**(-1.0 + i) * sig2**(-1 + j) *
            (-math.sqrt(2.0) * (-1.0 +
                                (-1.0)**i) * mu1 * sc.gamma(1.0 + i / 2.0) *
             sc.hyp1f1(1 + i / 2.0, 3.0 / 2.0, mu1**2.0 / (2.0 * sig1**2.0)) +
             (1.0 + (-1.0)**i) * sig1 * sc.gamma((1.0 + i) / 2.0) * sc.hyp1f1(
                 (1.0 + i) / 2.0, 1.0 / 2.0, mu1**2.0 / (2.0 * sig1**2.0))) *
            (-math.sqrt(2.0) * (-1 + (-1)**j) * mu2 * sc.gamma(1.0 + j / 2.0) *
             sc.hyp1f1(1.0 + j / 2.0, 3.0 / 2.0, mu2**2.0 /
                       (2.0 * sig2**2.0)) +
             (1.0 + (-1)**j) * sig2 * sc.gamma((1.0 + j) / 2.0) * sc.hyp1f1(
                 (1.0 + j) / 2.0, 1.0 / 2.0, mu2**2.0 / (2.0 * sig2**2.0))))
    return moments
Пример #6
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    def wake(self, z, maxi=25, L=None, bunlen=80e-6, convolved=True):
        gaus_to_si = 120*_c/4  # _Z0 _c / (4 pi)
        L = L or (2 * _np.pi * self.rho)

        # Free Space Term
        inds = z < 0
        W0 = _np.zeros(len(z))
        W0[inds] = (-2/3**(4/3)/self.rho**(2/3) /
                    _np.power((-z[inds]), 4/3)*gaus_to_si*L)

        # Shielding Term
        W1 = _np.zeros(len(z))
        zshield = self.shielding / self.bl * z
        for i in range(1, maxi):
            uai = self._getY(3*zshield/i**(3/2))
            W1 += 8*_np.pi*(-1)**(i+1)/i/i*uai*(3 - uai)/(1 + uai)**3
        W1 *= -1/self.h**2 / (2 * _np.pi) * gaus_to_si * L

        # If want the convolved values
        if convolved:
            # For the free space used analytical formulas of convolution
            bl = bunlen
            C = _Z0*_c/2**(13/6)/_np.pi**(3/2)/(3*self.rho**2*bl**10)**(1/3)*L
            W0 = C*(2**(1/2)*_scyspe.gamma(5/6)*(
                        bl**2*_scyspe.hyp1f1(-1/3, 1/2, -z*z/2/bl**2) -
                        z**2*_scyspe.hyp1f1(2/3, 3/2, -z*z/2/bl**2)) +
                    z*bl*_scyspe.gamma(4/3)*(
                        3*_scyspe.hyp1f1(1/6, 1/2, -z*z/2/bl**2) -
                        2*_scyspe.hyp1f1(1/6, 3/2, -z*z/2/bl**2)))
            # For shielding perform convolution numerically
            bunch = _np.exp(-(z*z/bl**2)/2)/_np.sqrt(2*_np.pi)/bl  # gaussian
            W1 = _scysig.fftconvolve(W1, bunch, mode='same') * (z[1]-z[0])
        return W0, W1
Пример #7
0
    def _rhov2int(self, phi, r, ra):
        """Compute product of density and mean square velocity """

        # Isotropic case first
        rhov2r = exp(phi)*gammainc(self.g + 2.5, phi)
        rhov2  = 3*rhov2r
        rhov2t = 2*rhov2r

        # Add anisotropy
        if (ra < self.ramax)&(r>0)&(phi>0):
            p, g = r/ra, self.g
            p2 = p**2
            p12 = 1+p2
            g3, g5, g7, fp2 = g+1.5, g+2.5, g+3.5, phi*p2

            P1 = p2*phi**g5/gamma(g7)
            H1, H2 = hyp1f1(1, g7, -fp2), hyp1f1(2, g7, -fp2)

            rhov2r += P1*H1
            rhov2r /= p12

            rhov2t /= p12
            rhov2t += 2*P1*(H1/p12 + H2)
            rhov2t /= p12

            rhov2 = rhov2r + rhov2t
        return rhov2, rhov2r, rhov2t
Пример #8
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def part_of_sum(k_on, k_off, mu, n, r, t):

    part_b1 = math.comb(n, r)
    logging.debug(part_b1)
    part_b2 = (-1) ** r * sc.poch(-k_off, r) * sc.poch(1 - k_off, n - r) * np.exp(-r * t)
    logging.debug(part_b2)
    part_b3 = 1 / sc.poch(1 + k_on + k_off, r)
    logging.debug(part_b3)
    part_b4 = 1 / sc.poch(2 - k_on - k_off, n - r)
    logging.debug(part_b4)
    part_b5 = sc.hyp1f1(k_off + r,
                        1 + k_on + k_off + r,
                        mu * np.exp(-t))
    logging.debug(part_b5)

    # NB: some terms in the Pochhammer's reappear in 1F1
    # and may cancel each other and this may avoid dividing by zero?
    part_b6 = sc.hyp1f1(1 - k_off + n - r,
                        2 - k_on - k_off + n - r,
                        -mu)
    logging.debug(part_b6)
    part_b = part_b1 * part_b2 * part_b3 * part_b4 * part_b5 * part_b6

    logging.debug(part_b)

    return part_b
Пример #9
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    def P0(n):
        p = 0.0
        for m in range(n):
            p += sp.comb(n-1,m) * rb**(n-1-m) * (R/Sb)**m * fracrise(a,b,m) * \
                (Sb*(m+a)*sp.hyp1f1(a+m+1, b+m, w0)/(Sb-1) - \
                (m+a+su*rb/R)*sp.hyp1f1(a+m, b+m, w0))

        return p / math.factorial(n)
Пример #10
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def _Z1ba2(b, a, order=10):
    js = np.arange(0, order + 1)

    _arg = 0.25 * (2 * js + 1)

    expr1 = gamma(_arg) * hyp1f1(_arg, 0.5, 0.25 * b**2)
    expr2 = b * gamma(_arg + 0.5) * hyp1f1(_arg + 0.5, 1.5, 0.25 * b**2)
    expr3 = a**(2 * js) / factorial(2 * js)
    return 0.5 * sum((expr1 + expr2) * expr3)
Пример #11
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def mSweepSel(a, mu, s, V, k, m):
    hyperg1 = hyp1f1(k + mu, m + 2.0 * mu, -(1.0 - a) * V + a * V)
    hyperg2 = hyp1f1(k + mu, m + 2.0 * mu, s - (1.0 - a) * V + a * V)
    ret = math.exp(gammaln(k + mu) + gammaln(m - k + mu) - gammaln(m + 2.0 * mu))
    ret *= binom(m, k) * math.exp(-(1.0 - a) * s - a * V) / zSel(a, mu, s)
    ret *= math.exp((1.0 - a) * s) * hyperg1 - hyperg2;
    ret += hitchhikingFractionSel(a, 1, mu, s, V) if k == m else 0.0
    ret += hitchhikingFractionSel(a, 0, mu, s, V) if k == 0 else 0.0
    return ret
Пример #12
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def hitchhikingFractionSel(a, b, mu, s, V):
    hyperg1 = hyp1f1(b + mu, 1.0 + 2.0 * mu, -(1.0 - a) * V + a * V)
    hyperg2 = hyp1f1(b + mu, 1.0 + 2.0 * mu, s - (1.0 - a) * V + a * V)
    ret = math.exp(gammaln(b + mu) + gammaln(1.0 - b + mu) - gammaln(1.0 + 2.0 * mu))
    ret *= math.exp(-(1.0 - a) * s - a * V)
    ret /= zSel(a, mu, s)
    ret *= math.exp((1.0 - a) * s) * hyperg1 - hyperg2
    diff = mSel(a, mu, s, b, 1)
    diff -= ret;
    return diff
Пример #13
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def mSweep(a, mu, V, k, m):
    ret = binom(m, k)
    hyperg1 = hyp1f1(k + mu, m + 2.0 * mu, (2.0 * a - 1.0) * V)
    hyperg2 = hyp1f1(k + mu, 1.0 + m + 2.0 * mu, (2.0 * a - 1.0) * V)
    ret /= zNeutral(mu)
    ret *= (-a * math.exp(-V) + (1.0 - a)) * math.exp(gammaln(k + mu) + gammaln(-k + m + mu) - gammaln(m + 2.0 * mu)) * ((-2.0 * a) * hyperg1 + 2.0 * (-k + m + mu) / (m + 2.0 * mu) * hyperg2)
    ret += hitchhikingFraction(a, 1, mu, V) if k == m else 0.0
    ret += hitchhikingFraction(a, 0, mu, V) if k == 0 else 0.0
    
    return ret;
Пример #14
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def whittM(k, m, z):
    """Evaluates the Whitaker function M(k, m, z) as defined in Abramowitz &
    Stegun, Section 13.1.
    """
    from scipy.special import hyp1f1
    if k is int or m is int:
        return np.exp(-0.5 * z) * np.power(z, 0.5 + float(m)) * hyp1f1(0.5 + float(m - k), 1 + 2 * m, z)
    elif k is int and m is int:
        return np.exp(-0.5 * z) * np.power(z, 0.5 + float(m)) * hyp1f1(0.5 + float(m - k), 1 + 2 * m, z)
    else:
        return np.exp(-0.5 * z) * np.power(z, 0.5 + m) * hyp1f1(0.5 + m - k, 1.0 + 2.0 * m, z)
Пример #15
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def koay_next(t_n, N, r):
    """ returns the n+1 guassian SNR value given an estimate
    @param: t_n estimate of the guassian SNR
    @param: N the number of MRA channels
    @param: r the measure signal to noise ratio
    """
    g_n = g_theta(t_n, N, r)
    b_n = beta_N(N)
    f1_a = hyp1f1(-0.5, N, -0.5*t_n**2.0)
    f1_b = hyp1f1(0.5, N+1, -0.5*t_n**2.0)
    return t_n - (g_n*(g_n - t_n) ) / (t_n*(1.0+r**2.0)*(1.0 - (0.5*b_n**2.0 / N) * f1_a * f1_b) - g_n)
Пример #16
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def hitchhikingFraction(a, b, mu, V):
    hyperg1 = hyp1f1(b + mu, 1.0 + 2.0 * mu, (2.0 * a - 1.0) * V)
    hyperg2 = hyp1f1(b + mu, 2.0 + 2.0 * mu, (2.0 * a - 1.0) * V)
    ret = -a * math.exp(-V) + 1.0 - a
    ret /= zNeutral(mu)
    ret *= math.exp(gammaln(b + mu) + gammaln(1.0 - b + mu) - gammaln(1.0 + 2.0 * mu))
    term = -2.0 * a * hyperg1
    term += 2.0 * (1.0 - b + mu) / (1.0 + 2.0 * mu) * hyperg2
    ret *= term
    diff = mNeutral(a, mu, b, 1) - ret
    return diff
Пример #17
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 def _get_spline(self):
     """Defines a qubic spline to fit concentration parameter."""
     x = np.logspace(-3, np.log10(self.max_concentration),
                     self.spline_markers)
     y = (hyp1f1(2, self.dimension + 1, x) /
          (self.dimension * hyp1f1(1, self.dimension, x)))
     return interp1d(y,
                     x,
                     kind='quadratic',
                     assume_sorted=True,
                     bounds_error=False,
                     fill_value=(0, self.max_concentration))
Пример #18
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def HermiteIndefiniteIntegral(z, n):
  '''
  Integrate[HermiteH[n, z]/E^z^2, z]

  '''
  
  if n == 0:
    return 0.5 * np.sqrt(np.pi) * erf(z)
  elif n == 1:
    return -0.5 * np.exp(-z ** 2)
  else:
    return 2 ** n * np.sqrt(np.pi) * (z * hyp1f1(n / 2. + 1 / 2., 3 / 2., -z ** 2) / gamma(1 / 2. - n / 2.) + \
                                      hyp1f1(n / 2., 1 / 2., -z ** 2) / ( n * gamma(-n / 2.)))
Пример #19
0
def WatsonMeanDirDensity(x, k, p):
    Coeff = gamma(p / 2.0) * (gamma(
        (p - 1.0) / 2.0) * np.sqrt(np.pi) / hyp1f1(1.0 / 2.0, p / 2.0, k))
    y = Coeff * np.exp(k *
                       (np.power(x, 2.0))) * np.power(1.0 - x * x,
                                                      (p - 3.0) / 2.0)
    return y
Пример #20
0
 def dDphi_dz(self, r, phi, phi_q, wavelength):
     """differential contribution to the phase structure function
     """
     return 4.0 * (wavelength / self.wavelength_reference
                   )**2 * self.C_scatt_0 / self.scatt_alpha * (sps.hyp1f1(
                       -self.scatt_alpha / 2.0, 0.5, -r**2 /
                       (4.0 * self.r_in**2) * np.cos(phi - phi_q)**2) - 1.0)
def kummer_log(a, b, x):
    ## First try using the funcion in the library.
    ## If it is 0 or inf then we try to use our own implementation with logs
    ## If it does not converge, then we return None !!

    f = hyp1f1(a, b, x)
    if (np.isinf(f) == True):
        #        warnings.warn("hyp1f1() is 'inf', trying log version,  (a,b,x) = (%f,%f,%f)" %(a,b,x),UserWarning, stacklevel=2)
        f_log = kummer_own_log(a, b, x)
#        print f_log

    elif (f == 0):
        #        warnings.warn("hyp1f1() is '0', trying log version, (a,b,x) = (%f,%f,%f)" %(a,b,x),UserWarning, stacklevel=2)
        raise RuntimeError('Kummer function is 0. Kappa = %f', "Kummer_is_0",
                           x)
#        f_log = kummer_own_log(a,b,x)  # TODO: We cannot do negative x, the functions is in log
    else:
        f_log = np.log(f)


#        print (a,b,x)
#        print f_log

    f_log = float(f_log)
    return f_log
Пример #22
0
def mSel(a, mu, s, k, m):
    hyperg = hyp1f1(k + mu, m + 2.0 * mu, s)
    ret = binom(m, k)
    ret /= zSel(a, mu, s)
    ret *= math.exp(gammaln(k + mu) + gammaln(m - k + mu) - gammaln(m + 2.0 * mu))
    ret *= (1.0 - math.exp(-(1.0 - a) * s) * hyperg)
    return ret
Пример #23
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 def _pdf(self, x, df, nc):
     n = df*1.0
     nc = nc*1.0
     x2 = x*x
     ncx2 = nc*nc*x2
     fac1 = n + x2
     trm1 = n/2.*np.log(n) + special.gammaln(n+1)
     trm1 -= n*np.log(2)+nc*nc/2.+(n/2.)*np.log(fac1)+special.gammaln(n/2.)
     Px = np.exp(trm1)
     valF = ncx2 / (2*fac1)
     trm1 = np.sqrt(2)*nc*x*special.hyp1f1(n/2+1,1.5,valF)
     trm1 /= (fac1*special.gamma((n+1)/2))
     trm2 = special.hyp1f1((n+1)/2,0.5,valF)
     trm2 /= (np.sqrt(fac1)*special.gamma(n/2+1))
     Px *= trm1+trm2
     return Px
Пример #24
0
    def P1(n):
        p = 0.0
        for m in range(n + 1):
            p += sp.comb(n,m) * rb**(n-m) * (R/Sb)**m * fracrise(a,b,m) * \
                sp.hyp1f1(a+m, b+m, w0)

        return p / math.factorial(n)
Пример #25
0
def curve_phantom(curve, direction, kappa,
                  px=(20,20,20), vox_dim=(100,100,100), cyl_rad=0.2, max_l=6,
                  dtype=np.float32):

    # Setup grid
    xyz = np.array(np.meshgrid(
        np.linspace(-(px[0]/2)*vox_dim[0], (px[0]/2)*vox_dim[0], px[0]),
        np.linspace(-(px[1]/2)*vox_dim[1], (px[1]/2)*vox_dim[1], px[1]),
        np.linspace(-(px[2]/2)*vox_dim[2], (px[2]/2)*vox_dim[2], px[2])))
    xyz = np.moveaxis(xyz, [0, 1], [-1, 1])

    # Calculate directions and kappas
    diff = xyz[:,:,:,None,:] - curve
    dist = np.linalg.norm(diff, axis=-1)
    min_dist = np.min(dist, axis=-1) # min dist between grid points and curve
    t_index = np.argmin(dist, axis=-1)
    min_dir = direction[t_index] # directions for closest point on curve
    min_k = kappa[t_index] # kappas
    
    # Calculate watson 
    spang_shape = xyz.shape[0:-1] + (util.maxl2maxj(max_l),)
    spang1 = spang.Spang(np.zeros(spang_shape), vox_dim=vox_dim)
    dot = np.einsum('ijkl,ml->ijkm', min_dir, spang1.sphere.vertices)
    k = min_k[...,None]
    watson = np.exp(k*dot**2)/(4*np.pi*hyp1f1(0.5, 1.5, k))
    watson_sh = np.einsum('ijkl,lm', watson, spang1.B)
    watson_sh = watson_sh/watson_sh[...,None,0] # Normalize

    # Cylinder mask
    mask = min_dist < cyl_rad
    spang1.f = np.einsum('ijkl,ijk->ijkl', watson_sh, mask).astype(dtype)
    
    return spang1
Пример #26
0
def beta_poisson_loglh(data,lmbd,phi,N):
    '''
    Calculate log likelihood of beta-Poisson parameters given data.

    Parameters
    ----------
        data : list
            sample dataset
        lmbd : float
        phi : float
        N : float

    Returns
    -------
        llh : float
            log likelihood of parameters given data
    '''
    llh=0
    for x in data:
        llh+=x*np.log(N)-np.real(spsp.loggamma(x+1))+np.real(spsp.loggamma(phi*N))+np.real(spsp.loggamma(x+phi*lmbd))-np.real(spsp.loggamma(x+phi*N))-np.real(spsp.loggamma(phi*lmbd))
        if x+phi*N<50:
            llh+=np.log(spsp.hyp1f1(x+phi*lmbd,x+phi*N,-N))
        else:
            llh+=np.log(float(hyp1f1_alt(x+phi*lmbd,x+phi*N,-N)))
    return llh
Пример #27
0
 def _pdf(self, x, df, nc):
     n = df*1.0
     nc = nc*1.0
     x2 = x*x
     ncx2 = nc*nc*x2
     fac1 = n + x2
     trm1 = n/2.*np.log(n) + special.gammaln(n+1)
     trm1 -= n*np.log(2)+nc*nc/2.+(n/2.)*np.log(fac1)+special.gammaln(n/2.)
     Px = np.exp(trm1)
     valF = ncx2 / (2*fac1)
     trm1 = np.sqrt(2)*nc*x*special.hyp1f1(n/2+1,1.5,valF)
     trm1 /= (fac1*special.gamma((n+1)/2))
     trm2 = special.hyp1f1((n+1)/2,0.5,valF)
     trm2 /= (np.sqrt(fac1)*special.gamma(n/2+1))
     Px *= trm1+trm2
     return Px
Пример #28
0
def watson_girdle_logp(x, lon_lat, kappa):

    if x[1] < -90. or x[1] > 90.:
        raise ZeroProbability
        return -np.inf

    if np.abs(kappa) < eps:
        return np.log(1. / 4. / np.pi)

    mu = np.array([
        np.cos(lon_lat[1] * d2r) * np.cos(lon_lat[0] * d2r),
        np.cos(lon_lat[1] * d2r) * np.sin(lon_lat[0] * d2r),
        np.sin(lon_lat[1] * d2r)
    ])
    test_point = np.transpose(
        np.array([
            np.cos(x[1] * d2r) * np.cos(x[0] * d2r),
            np.cos(x[1] * d2r) * np.sin(x[0] * d2r),
            np.sin(x[1] * d2r)
        ]))

    normalization = 1. / sp.hyp1f1(0.5, 1.5, kappa) / 4. / np.pi
    logp_elem = np.log( normalization ) + \
        kappa * (np.dot(test_point, mu)**2.)

    logp = logp_elem.sum()
    return logp
Пример #29
0
 def test_geometric_convergence(self, a, b, x, result):
     # Test the region where we are relying on the ratio of
     #
     # (|a| + 1) * |x| / |b|
     #
     # being small. Desired answers computed using Mpmath
     assert_allclose(sc.hyp1f1(a, b, x), result, atol=0, rtol=1e-15)
Пример #30
0
def get_cp(Ndim, kappa):
    gammaValue = gamma(float(Ndim) / 2)
    M = hyp1f1(0.5,
               float(Ndim) / 2,
               kappa)  # Confluent hypergeometric function 1F1(a, b; x)
    cp = gammaValue / (np.power(2 * np.pi, float(Ndim) / 2) * M)
    return cp
Пример #31
0
def boys(n, t):
    """Boys function for the calculation of coulombic integrals.

    Parameters
    ----------
    n : int
        Order of boys function

    t : float
        Varible for boys function.

    Raises
    ------
    TypeError
        If boys function order is not an integer.

    ValueError
        If boys function order n is not a none negative number.
    """
    if not isinstance(n, int):
        raise TypeError("Boys function order n must be an integer")
    if n < 0:
        raise ValueError(
            "Boys function order n must be a none negative number")
    if not isinstance(t, float):
        raise TypeError("Boys function varible t must be integer or float")
    return sc.hyp1f1(n + 0.5, n + 1.5, -t) / (2.0 * n + 1.0)
Пример #32
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def KLD2(x,mu_a_x,mu_a_y,d_r_a,sigma_d_r_a):    
    #sigma_d_r_a = .01
    d = 0
    for i in np.arange(start=0,stop=mu_a_x.__len__(),step=1):
        d = d+ (-2*d_r_a[i]*np.sqrt(x[2]**2*np.pi/2)*
        hyp1f1(-1/2,1,-np.linalg.norm(np.array([x[0]-mu_a_x[i],x[1]-mu_a_y[i]]))**2/(2*x[2]**2))+
        np.linalg.norm(np.array([x[0]-mu_a_x[i],x[1]-mu_a_y[i]]))**2+2*x[2]**2)/(2*sigma_d_r_a[i])
    return d
Пример #33
0
def KLD3(x,mu_m_x,mu_m_y,sigma_m,d_r_m,sigma_d_r_m):    
    
    d = 0
    for i in np.arange(start=0,stop=mu_m_x.__len__(),step=1):
        d = d+(-2*d_r_m[i]*np.sqrt((x[2]**2+sigma_m[i])*np.pi/2)*
        hyp1f1(-1/2,1,-np.linalg.norm(np.array([x[0]-mu_m_x[i],x[1]-mu_m_y[i]]))**2/(2*(x[2]**2+sigma_m[i])))+
        np.linalg.norm(np.array([x[0]-mu_m_x[i],x[1]-mu_m_y[i]]))**2+2*x[2]**2)/(2*sigma_d_r_m[i])
    return d
def KLD3_1(x, mu_m_x, mu_m_y, sigma_m, d_r_m):
    sigma_d_r_m = .01
    return (-2 * d_r_m * np.sqrt((x[2]**2 + sigma_m) * np.pi / 2) * hyp1f1(
        -1 / 2, 1,
        -np.linalg.norm(np.array([x[0] - mu_m_x, x[1] - mu_m_y]))**2 /
        (2 * (x[2]**2 + sigma_m))) +
            np.linalg.norm(np.array([x[0] - mu_m_x, x[1] - mu_m_y]))**2 +
            2 * x[2]**2) / (2 * sigma_d_r_m)
Пример #35
0
 def spline(self):
     """Defines a cubic spline to fit concentration parameter."""
     assert self.dimension is not None, (
         "You need to specify dimension. This can be done at object "
         "instantiation or it can be inferred when using the fit function.")
     x = np.logspace(-3, np.log10(self.max_concentration),
                     self.spline_markers)
     y = hyp1f1(2, self.dimension + 1,
                x) / (self.dimension * hyp1f1(1, self.dimension, x))
     return interp1d(
         y,
         x,
         kind="quadratic",
         assume_sorted=True,
         bounds_error=False,
         fill_value=(0, self.max_concentration),
     )
Пример #36
0
def check_Kummer(Ndim, kappa):
    # This functions checks if the Kummer function will go to inf
    # Returns 1 if Kummer is stable, 0 if unstable
    f = hyp1f1(0.5, float(Ndim) / 2, kappa)
    if (np.isinf(f) == False):
        return 1
    else:
        return 0
Пример #37
0
 def compute_characteristic_function(self, t):
     """
     The characteristic function of the beta distribution is Kummer's confluent hypergeometric function which 
     is implemented by scipy.special.hyp1f1. See: https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.hyp1f1.html
     If Phi_X(t) is the characteristic function of X, then for any constant c, the characteristic function of cX is
     Phi_cX(t) = Phi_X(ct)
     """
     return hyp1f1(self.alpha, self.beta, self.c * t)
Пример #38
0
 def test_hyp1f1_complex(self):
     assert_mpmath_equal(
         _inf_to_nan(lambda a, b, x: sc.hyp1f1(a.real, b.real, x)),
         _exception_to_nan(
             lambda a, b, x: mpmath.hyp1f1(a, b, x, **HYPERKW)),
         [Arg(-1e3, 1e3), Arg(-1e3, 1e3),
          ComplexArg()],
         n=2000)
Пример #39
0
def boys_function(n, x):
    """
    the Boys Function
    :param n: order of the boys function
    :param x: argument
    :return:
    """
    return hyp1f1(n + 0.5, n + 1.5, -x) / (2.0 * n + 1.0)
Пример #40
0
def radial_function(r, n, l, zeta):
    result = 0.0
    for i in range(n + 1):
        result += \
          (- 1)**i * utils.binomial(n + 0.5, n - i) / \
          factorial(i) * 2**(0.5 * l + i - 0.5) * \
          special.gamma(0.5 * l + i + 1.5) * \
          hyp1f1((2 * i + l + 3) * 0.5, l + 1.5, - 2 * np.pi**2 * r**2 * zeta)
    result = result * 4 * (-1)**(0.5 * l) * zeta**(0.5 * l + 1.5) * \
      np.pi**(l + 1.5) * r**l / special.gamma(l + 1.5) * spf.kappa(zeta, n)
    return result
Пример #41
0
    def _rhoint(self, phi, r, ra):
        """ Dimensionless density as a function of phi and r (scalars only) """
        # Isotropic case first
        rho = exp(phi)*gammainc(self.g + 1.5, phi)

        # Add anisotropy
        if (self.ra < self.ramax)&(phi>0)&(r>0):
            p, g = r/ra, self.g
            p2 = p**2
            g3, g5, fp2 = g+1.5, g+2.5, phi*p2
            rho += p2*phi**(g+1.5)*hyp1f1(1, g5, -fp2)/gamma(g5)
            rho /= (1+p2)
        return rho
Пример #42
0
    def _rhoint(self, phi, r, ra):
        """
        Dimensionless density integral as a function of phi and r (scalar)
        """

        # Isotropic case first (equation 8, GZ15)
        rhoint = exp(phi)*gammainc(self.g + 1.5, phi)

        # Anisotropic case, add r-dependent part explicitly (equation 11, GZ15)
        if (self.ra < self.ramax) and (phi>0) and (r>0):
            p, g = r/ra, self.g
            p2 = p**2
            g3, g5, fp2 = g+1.5, g+2.5, phi*p2

            func = hyp1f1(1, g5, -fp2)  if fp2 < self.max_arg_exp else g3/fp2
            rhoint += p2*phi**(g+1.5)*func/gamma(g5)
            rhoint /= (1+p2)
        return rhoint
Пример #43
0
def bingham_pdf(fit):
    """
    From the *Encyclopedia of Paleomagnetism*

    From Onstott, 1980:
    Vector resultant: R is analogous to eigenvectors
    of T.
    Eigenvalues are analogous to |R|/N.
    """
    # Uses eigenvectors of the covariance matrix
    e = fit.hyperbolic_axes #singular_values
    #e = sampling_covariance(fit) # not sure
    e = e[2]**2/e

    kappa = (e-e[2])[:-1]
    kappa /= kappa[-1]
    F = N.sqrt(N.pi)*confluent_hypergeometric_function(*kappa)


    ax = fit.axes

    Z = 1/e
    M = ax
    F = 1/hyp1f1(*1/Z)

    def pdf(coords):
        lon,lat = coords

        I = lat
        D = lon# + N.pi/2
        #D,I = _rotate(N.degrees(D),N.degrees(I),90)

        # Bingham is given in spherical coordinates of inclination
        # and declination in radians

        # From USGS bingham statistics reference

        xhat = N.array(sph2cart(lon,lat)).T

        #return F*expm(dot(xhat.T, M, N.diag(Z), M.T, xhat))

        return 1/(F*N.exp(dot(xhat.T, M, N.diag(Z), M.T, xhat)))

    return pdf
Пример #44
0
    def R_nl(self, r):
        """ The radial part of the wavefunction, R_nl(r).

            Quantum Mechanics of one and two electron atoms,
            H. A. Bethe and E. E. Salpeter 1957

                r in units of the Bohr radius, a_0.

            quantum numbers:
                n, l
        """
        rho = 1.0 * r / self.n           # re-scaled rho by 1/2 from hydrogen
        epsilon = 1.0 / self.n
        c1 = np.sqrt(factorial(self.n + self.l) / (2.0 * self.n * \
                     factorial(self.n - self.l - 1))) * \
            1.0/ (factorial(2.0 * self.l + 1.0))
        c2 = np.power(2.0, -3.0/2.0)    # re-normalise again for Ps
        return c1 * c2 * (2* epsilon)**(3.0/2.0) * np.exp(-0.5* rho) * rho**self.l *\
               hyp1f1(-(self.n - self.l - 1), 2 * self.l + 2, rho)
Пример #45
0
def watson_girdle_logp(x, lon_lat, kappa):

    if x[1] < -90. or x[1] > 90.:
        raise ZeroProbability
        return -np.inf

    if np.abs(kappa) < eps:
        return np.log(1. / 4. / np.pi)

    mu = np.array([np.cos(lon_lat[1] * d2r) * np.cos(lon_lat[0] * d2r),
                   np.cos(lon_lat[1] * d2r) * np.sin(lon_lat[0] * d2r),
                   np.sin(lon_lat[1] * d2r)])
    test_point = np.transpose(np.array([np.cos(x[1] * d2r) * np.cos(x[0] * d2r),
                                        np.cos(x[1] * d2r) *
                                        np.sin(x[0] * d2r),
                                        np.sin(x[1] * d2r)]))

    normalization = 1. / sp.hyp1f1(0.5, 1.5, kappa) / 4. / np.pi
    logp_elem = np.log( normalization ) + \
        kappa * (np.dot(test_point, mu)**2.)

    logp = logp_elem.sum()
    return logp
Пример #46
0
def zSel(a, mu, s):
    hyperg = hyp1f1(mu, 2.0 * mu, s)
    ret = math.exp(2.0 * gammaln(mu) - gammaln(2.0 * mu)) * (1.0 - math.exp(-(1.0 - a) * s) * hyperg)
    return ret
Пример #47
0
def exp_inc_gamma(m, z):
    return 1.0/(2*m+1) * hyp1f1(1, m+1.5, -z)
Пример #48
0
def psi_l(l, b):
    n = l//2
    v = (-b)**n
    v *= gamma(n + 1./2) / gamma(2*n + 3./2)
    v *= hyp1f1(n + 1./2, 2*n + 3./2, -b)
    return v
Пример #49
0
 def test_hyp1f1_complex(self):
     assert_mpmath_equal(_inf_to_nan(lambda a, b, x: sc.hyp1f1(a.real, b.real, x)),
                         _exception_to_nan(lambda a, b, x: mpmath.hyp1f1(a, b, x, **HYPERKW)),
                         [Arg(-1e3, 1e3), Arg(-1e3, 1e3), ComplexArg()],
                         n=2000)
Пример #50
0
def boys(n,T):
    """
    """
    return hyp1f1(n+0.5,n+1.5,-T)/(2.0*n+1.0)
Пример #51
0
    db = pymc.database.pickle.load(dbname)
else:
    pymc.MAP(model).fit()
    mcmc.sample(10000)
    mcmc.db.close()
    db = pymc.database.pickle.load(dbname)

#pymc.Matplot.trace(db.trace('concentration'))
concentration_trace = db.trace('concentration')[:]
plt.hist( concentration_trace )
plt.show()

ax = plt.axes( projection=ccrs.Mollweide(0.) )
ax.set_global()
interval = 1
ax.scatter(lon_lat[::interval,0],lon_lat[::interval,1], transform=ccrs.PlateCarree())
plt.show()

n_samples = len(lon_lat[:,1])
uniform_x = np.linspace(0., np.pi, 100)
uniform_y = np.sin(uniform_x)/2. 
kappa =  -0.78#hidden_concentration
watson_x = np.linspace(0., np.pi, 100)
watson_y = 1./sp.hyp1f1(0.5,1.5,kappa) * np.exp(kappa * np.cos(watson_x)**2.)*np.sin(watson_x)/2. 

bins = np.linspace(-90., 90., 9)
plt.hist( lon_lat[:,1], bins=bins, weights=np.ones_like(lon_lat[:,1])/n_samples, normed=False)
plt.plot( 90.-uniform_x*180./np.pi, uniform_y, 'r', lw=3)
plt.plot( 90.-watson_x*180./np.pi, watson_y, 'g', lw=3)
plt.show()
Пример #52
0
def diallelic_d_helper(n0, n1, g):
    if not g:
        return n0 / float(n0 + n1)
    else:
        return (special.hyp1f1(n0, n0 + n1, g) - 1) / math.expm1(g)
Пример #53
0
def WatsonMeanDirDensity(x, k, p):
    Coeff = special.gamma(p/2.0) * (special.gamma((p - 1.0) / 2.0) * math.sqrt(pi) * hyp1f1(1.0/2.0, p/2.0, k))**(-1.0)
    y = Coeff*np.exp(k*x**2.0)*(1.0-x**2.0)**((p-3.0)/2.0)
    return y
Пример #54
0
def WatsonMeanDirDensity(x, k, p):
    Coeff = gamma(p/2.0) * (gamma((p - 1.0) / 2.0) * np.sqrt(np.pi) / hyp1f1(1.0/2.0, p/2.0, k))
    y = Coeff*np.exp(k*(np.power(x,2.0)))*np.power(1.0-x*x,(p-3.0)/2.0)
    return y
Пример #55
0
def erfi(x):
    """
    Why does scipy.special not include this?
    """
    return 2 * x * special.hyp1f1(0.5, 1.5, x*x) / math.sqrt(math.pi)
Пример #56
0
def pr(u, x):
    if u == 0:
        out = 1.0 * np.exp(-x)
    else:
        out = 1.0 * x * np.exp(2 * -x) * (2 ** -u) * spc.hyp1f1(u + 1, 2, x)
    return out
Пример #57
0
def inc_gamma(m, z):
    return 1.0/(2*m+1) * hyp1f1(m+0.5, m+1.5, -z)
Пример #58
0
 def runTest(self):
     self.assertAlmostEqual(hyp1f1(1,2,3), 6.361845641062556)
     self.assertAlmostEqual(hyp2f1(1,2,3,-4.), 0.2988202609457375)
     self.assertAlmostEqual(hyp2f1(1,2,3,-0.5), 0.7562791351346849)
     self.assertAlmostEqual(hyp2f1(1,2,3,0.7), 2.0570318543915747)
     self.assertAlmostEqual(hyp2f1(1, 0.3, 1.3, -3.0), 0.7113010112875268)