updated populations

    else:

        return M*(1+z)

def draw_population_params(num, M_min=5e4, M_max=5e7, M_lam=2, z_max=20, spin_mu=0.98, spin_std=0.01, e_min=0.01,e_max=0.3):

# def draw_population_params(num, M_min=5e4, M_max=5e7, M_lam=2, z_max=20, spin_mu=0.98, spin_std=0.01, e_min=0.01,e_max=0.3):

#     '''

#     Draw sets of EMRI parameters from population distributions.

#     :param num: Number of events to draw.

#     :param M_min: Minimum mass for mass population.

#     :param M_max: Maximum mass for mass population.

#     :param M_lam: Spectral index for mass population power law.

#     :param z_max: Maximum redshift for redshift population.

#     :param spin_mu: Mean for spin population.

#     :param spin_std: Standard deviation for spin population.

#     :return: Numpy array of shape (num, 5) that consists of the rows (M, a, d_L, z, M_s)

#     '''

#

#     #initialise populations

#

#     mpop = mass_pop(M_min=M_min, M_max=M_max, lam=M_lam)

#     redpop = redshift_pop(z_max=z_max)

#

#     # spin population with scipy truncnorm

#     l1, l2 = (0 - spin_mu) / spin_std, (1 - spin_mu) / spin_std  # rescale the limits (from scipy.stats.truncnorm docs)

#     spinpop = truncnorm(l1, l2, loc=spin_mu,scale=spin_std)

#

#     masses = mpop.rvs(size=num)

#     redshifts = redpop.rvs(size=num)

#     spins = spinpop.rvs(size=num)

#

#     lum_dists = np.zeros(num)

#     for i, z in enumerate(redshifts):

#         lum_dists[i] = dL(z)

#     red_masses = convert_M(masses, redshifts, to_source=False)

#

#     eccentricities = np.random.uniform(e_min, e_max, num)

#

#     out = np.column_stack((red_masses, spins, lum_dists, redshifts, masses, eccentricities))

#     return out

def draw_population_params(num, M_min=5e4, M_max=5e7, M_lam=2, spin_mu=0.98, spin_std=0.01, e_min=0.01,e_max=0.3):

    '''

    Draw sets of EMRI parameters from population distributions.

    :param num: Number of events to draw.

    #initialise populations

    mpop = mass_pop(M_min=M_min, M_max=M_max, lam=M_lam)

    redpop = redshift_pop(z_max=z_max)

    # spin population with scipy truncnorm

    l1, l2 = (0 - spin_mu) / spin_std, (1 - spin_mu) / spin_std  # rescale the limits (from scipy.stats.truncnorm docs)

    spinpop = truncnorm(l1, l2, loc=spin_mu,scale=spin_std)

    masses = mpop.rvs(size=num)

    redshifts = redpop.rvs(size=num)

    second_ms = 10**np.random.uniform(-0.3,2,num)

    spins = spinpop.rvs(size=num)

    lum_dists = np.zeros(num)

    for i, z in enumerate(redshifts):

        lum_dists[i] = dL(z)

    red_masses = convert_M(masses, redshifts, to_source=False)

    eccentricities = np.random.uniform(e_min, e_max, num)

    out = np.column_stack((red_masses, spins, lum_dists, redshifts, masses, eccentricities))

    out = np.column_stack((masses, second_ms, spins, eccentricities))

    return out

    qS = np.arccos(2*np.random.uniform(0,1,size=num)-1)

    qK = np.arccos(2*np.random.uniform(0,1,size=num)-1)

    phiS = np.random.uniform(0,2*np.pi,size=num)

    Y0 = np.cos(np.arcsin(2*np.random.uniform(0,1,size=num)-1))*0.995  # YLIM = 0.998

    Y0 = np.cos(np.arcsin(np.random.uniform(0,np.sin(np.arccos(0.5/0.99)),size=num)))*0.99  # YLIM = 0.998

    t = np.random.uniform(0,4,size=num)

    out = np.column_stack((Y0,qS,phiS,qK,t))

    return out

#     def _pdf(self,e):

#         return (self.b - e)**(11*(e-self.b))*np.exp(11*(e-self.b))

temp = draw_population_params(100)

print(temp)

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# M, mu, a, e = draw_population_params(1)[0]

# Y0, qS, phiS, qK, tPl = draw_other_params(1)[0]

#

# print(M, mu, a, e, Y0, qS, phiS, qK, tPl)

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