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< META-INF/maven/javax.xml.bind/
< META-INF/maven/javax.xml.bind/jaxb-api/
< META-INF/maven/javax.xml.bind/jaxb-api/pom.properties
< META-INF/maven/javax.xml.bind/jaxb-api/pom.xml
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< META-INF/maven/org.apache.spark/
< META-INF/maven/org.apache.spark/spark-catalyst_2.12/
< META-INF/maven/org.apache.spark/spark-catalyst_2.12/pom.properties
< META-INF/maven/org.apache.spark/spark-catalyst_2.12/pom.xml

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from typing import List, Optional, Callable
import hailtop.batch as hb
import math

from hailtop.utils import grouped
from hailtop.utils.utils import digits_needed


def batch_combine2(base_combop: Callable[[hb.job.BashJob, List[str], str], None],
                   combop: Callable[[hb.job.BashJob, List[str], str], None],

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#!/bin/bash

set -ex

SPARK_HOME=${SPARK_HOME:-$(find_spark_home.py)}

if [ ! -e ${SPARK_HOME}/jars/hadoop-azure-3.2.2.jar ]
then
curl -sSL \
     https://search.maven.org/remotecontent?filepath=org/apache/hadoop/hadoop-azure/3.2.2/hadoop-azure-3.2.2.jar \

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#!/bin/bash

set -ex

SPARK_HOME=${SPARK_HOME:-$(find_spark_home.py)}

if [ ! -e ${SPARK_HOME}/jars/hadoop-aws-3.2.0.jar ]
then
curl -sSL \
     https://search.maven.org/remotecontent?filepath=org/apache/hadoop/hadoop-aws/3.2.0/hadoop-aws-3.2.0.jar \

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import uuid
from aiohttp import web
import base64
import ssl
import json
from ssl import Purpose
from hailtop.hail_logging import AccessLogger, configure_logging
from hailtop.config import get_deploy_config

configure_logging()

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import hail as hl
import numpy as np


def tsqr(mt: hl.MatrixTable, field: str, *, block_size: int = 1024):
    A = hl.experimental.mt_to_table_of_ndarray(mt[field], block_size=block_size)
    A = A.add_index('partition_index')
    A = A.annotate(r_and_q = hl.nd.qr(A.ndarray))
    A = A.annotate(q = A.r_and_q[0])
    A = A.annotate(r = A.r_and_q[1])

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import hail as hl

hl.nd.array([1, 2, 3, 4]).reshape((2, 2)).show()

# FIXME: use ndarray sum / fma
def block_product(left, right):
    product = left @ right
    n_rows, n_cols = product.shape
    return hl.struct(
        shape=product.shape,

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I realized I started using lambda without explaining what that is. My apologies.

In math class you probably defined functions by writing something like the following:

f(x) = x * 2

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import hail as hl

# Download a small part of the One Thousand Genomes dataset
hl.utils.get_1kg('data/')
# Read the dataset as a Hail MatrixTable, a tool for exploring two-dimensional data.
mt = hl.read_matrix_table('data/1kg.mt') 
# Show some of the data in a pleasant visual form
mt.show()