We recommend using IPython, a super-powered Python terminal:

pip install ipython


Start an IPython session by copy-pasting the below into your Terminal.

ipython


Let’s randomly generate a dataset according to the Balding-Nichols Model. The dataset has one-hundred variants and ten samples from three populations.

import hail as hl
mt = hl.balding_nichols_model(n_populations=3,
n_samples=10,
n_variants=100)
mt.show()


The last line, mt.show(), displays the dataset in a tabular form.

2020-05-09 19:08:07 Hail: INFO: Coerced sorted dataset
+---------------+------------+------+------+------+------+
| locus         | alleles    | 0.GT | 1.GT | 2.GT | 3.GT |
+---------------+------------+------+------+------+------+
| locus<GRCh37> | array<str> | call | call | call | call |
+---------------+------------+------+------+------+------+
| 1:1           | ["A","C"]  | 0/1  | 1/1  | 0/1  | 0/1  |
| 1:2           | ["A","C"]  | 1/1  | 0/1  | 1/1  | 0/1  |
| 1:3           | ["A","C"]  | 0/1  | 1/1  | 1/1  | 1/1  |
| 1:4           | ["A","C"]  | 0/0  | 0/0  | 0/1  | 1/1  |
| 1:5           | ["A","C"]  | 0/1  | 0/0  | 0/1  | 0/0  |
| 1:6           | ["A","C"]  | 1/1  | 0/1  | 0/1  | 0/1  |
| 1:7           | ["A","C"]  | 0/0  | 0/1  | 0/1  | 0/0  |
| 1:8           | ["A","C"]  | 1/1  | 0/1  | 1/1  | 1/1  |
| 1:9           | ["A","C"]  | 1/1  | 1/1  | 1/1  | 1/1  |
| 1:10          | ["A","C"]  | 1/1  | 0/1  | 1/1  | 0/1  |
| 1:11          | ["A","C"]  | 0/1  | 1/1  | 1/1  | 0/1  |
+---------------+------------+------+------+------+------+
showing top 11 rows
showing the first 4 of 10 columns</code></pre>