# Cross-matching catalogs with a common object ID¶

 `crossmatch`(x, y[, skip_bounds_checking]) Finds where the elements of `x` appear in the array `y`, including repeats. `unsorting_indices`(sorting_indices) Return the indexing array that inverts `numpy.argsort`.

# Calculating quantities for objects grouped into a common halo¶

 `group_member_generator`(data, grouping_key, ...) Generator used to loop over grouped data and yield requested properties of members of a group. `compute_richness`(unique_halo_ids, ...) For every ID in unique_halo_ids, calculate the number of times the ID appears in halo_id_of_galaxies.

# Generating Monte Carlo realizations¶

 `monte_carlo_from_cdf_lookup`(x_table, y_table) Randomly draw a set of `num_draws` points from any arbitrary input distribution function. `build_cdf_lookup`(y[, npts_lookup_table]) Compute a lookup table for the cumulative distribution function specified by the input set of `y` values.

# Matching one distribution to another¶

 `distribution_matching_indices`(...[, seed]) Calcuate a set of indices that will resample (with replacement) `input_distribution` so that it matches `output_distribution`. `resample_x_to_match_y`(x, y, bins[, seed]) Return the indices that resample `x` (with replacement) so that the resampled distribution matches the histogram of `y`. `bijective_distribution_matching`(x_in, x_desired) Replace the values in `x_in` with `x_desired`, preserving the rank-order of `x_in`

# Probabilistic binning¶

 `fuzzy_digitize`(x, centroids[, min_counts, seed]) Function assigns each element of the input array `x` to a centroid number.

# Estimating two-dimensional PDFs¶

 `sliding_conditional_percentile`(x, y, ...[, ...]) Estimate the cumulative distribution function Prob(< y | x).