monte_carlo_from_cdf_lookup¶

halotools.utils.
monte_carlo_from_cdf_lookup
(x_table, y_table, mc_input='random', num_draws=None, seed=None)[source] [edit on github]¶ Randomly draw a set of
num_draws
points from any arbitrary input distribution function. The input distribution is specified in terms of its CDF, which is defined by the values of the inputy_table
of the CDF evaluated at an input set of control pointsx_table
. The inputx_table
andy_table
can be calculated from any input dataset using the functionbuild_cdf_lookup
.Parameters:  x_table : ndarray
Numpy array of shape (npts, ) providing the control points at which the input CDF has been tabulated.
 y_table : ndarray
Numpy array of shape (npts, ) providing the values of the random variable associated with each input control point.
 mc_input : ndarray, optional
Input array of shape (num_desired_draws, ) storing values between 0 and 1 specifying the values of the CDF for which associated values of
y
are desired. Ifmc_input
is left unspecified, thenum_draws
must be specified; in this case, the CDF will be randomly sampled. num_draws : int, optional
Desired number of random draws from the input CDF.
num_draws
must be specified ifmc_input
is left unspecified. seed : int, optional
Random number seed used in the Monte Carlo realization.
Returns:  mc_realization : ndarray
Lengthnum_draws array of random draws from the input CDF.
Notes
See the Transformation of Probability tutorial for pedagogical derivations associated with inverse transformation sampling.
Examples
In this first example, we’ll start by creating some fake data,
y
, drawn from a weighted combination of a Gaussian and a power law. We’ll think of the fake datay
as if it came from some external dataset that we want to model, treating the fake data as our input distribution. We will use thebuild_cdf_lookup
function to build the necessary lookup tablesx_table
andy_table
, and then use themonte_carlo_from_cdf_lookup
function to generate a Monte Carlo realization of the datay
.>>> npts = int(1e4) >>> y = 0.1*np.random.normal(size=npts) + 0.9*np.random.power(2, size=npts) >>> x_table, y_table = build_cdf_lookup(y) >>> result = monte_carlo_from_cdf_lookup(x_table, y_table, num_draws=5000)
The returned array
result
is a stochastic Monte Carlo realization of the distribution specified byy
.Now let’s consider a second example where, rather than specifying an integer number of purely random Monte Carlo draws, instead we pass in an array of uniform random numbers.
>>> uniform_randoms = np.random.rand(npts) >>> result2 = monte_carlo_from_cdf_lookup(x_table, y_table, mc_input=uniform_randoms)
This alternative call to
monte_carlo_from_cdf_lookup
provides completely equivalent results as the first, because we have passed in uniform randoms. Sinceuniform_randoms
just stores random values, then random values from the input distribution will be returned.However, this alternative form of input can be exploited for other applications. Suppose that instead of purely random draws, you wish to draw from the input distribution
y
in such a way that you introduce a correlation between the drawn values and the values stored in some other array,x
. You can accomplish this by passing in the rankorder percentile values ofx
instead of uniform randoms. This is the basis of the conditional abundance matching technique implemented by theconditional_abunmatch_bin_based
function.To see an example of how this works, let’s create some fake data for some property x that we wish to model as being correlated with Monte Carlo realizations of y while preserving the PDF of the realization:
>>> x = np.sort(10**np.random.normal(loc=10, size=5000)) >>> x_percentile = (1. + np.arange(len(x)))/float(len(x)+1) >>> correlated_result = monte_carlo_from_cdf_lookup(x_table, y_table, mc_input=x_percentile) >>> uniform_randoms = np.random.rand(npts) >>> uncorrelated_result = monte_carlo_from_cdf_lookup(x_table, y_table, mc_input=uniform_randoms)
We can use the
noisy_percentile
to introduce variable levels of noise in the correlation.>>> from halotools.empirical_models import noisy_percentile >>> noisy_x_percentile = noisy_percentile(x_percentile, correlation_coeff=0.75) >>> weakly_correlated_result = monte_carlo_from_cdf_lookup(x_table, y_table, mc_input=noisy_x_percentile)