From c191ca38c8ffbeadf7fadff442726650b4ef1058 Mon Sep 17 00:00:00 2001
From: sahadeva <sudeep.sahadevan@embl.de>
Date: Mon, 21 Jun 2021 16:11:52 +0200
Subject: [PATCH] session 12 first commit
---
...ional_programming_tools_introduction.ipynb | 1380 +
.../README.md | 14 +
.../Toolz-use-case_1_airway.ipynb | 513 +
.../data/airway.csv | 64102 ++++++++++++++++
.../fp.png | Bin 0 -> 1798643 bytes
.../reduce_diagram.png | Bin 0 -> 16891 bytes
6 files changed, 66009 insertions(+)
create mode 100644 meetings/2021/session_12_functional_programming_with_toolz/Functional_programming_tools_introduction.ipynb
create mode 100644 meetings/2021/session_12_functional_programming_with_toolz/README.md
create mode 100644 meetings/2021/session_12_functional_programming_with_toolz/Toolz-use-case_1_airway.ipynb
create mode 100644 meetings/2021/session_12_functional_programming_with_toolz/data/airway.csv
create mode 100644 meetings/2021/session_12_functional_programming_with_toolz/fp.png
create mode 100644 meetings/2021/session_12_functional_programming_with_toolz/reduce_diagram.png
diff --git a/meetings/2021/session_12_functional_programming_with_toolz/Functional_programming_tools_introduction.ipynb b/meetings/2021/session_12_functional_programming_with_toolz/Functional_programming_tools_introduction.ipynb
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@@ -0,0 +1,1380 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "a9fc6c12",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Functional programming\n",
+ "\n",
+ "\n",
+ "![fp diagram](fp.png \"Functional programming\")\n",
+ "Image taken from: [So You Want to be a Functional Programmer (Part 1)](https://cscalfani.medium.com/so-you-want-to-be-a-functional-programmer-part-1-1f15e387e536)\n",
+ "\n",
+ ">**Functional programming** a programming paradigm where programs are constructed by applying and composing functions\n",
+ "\n",
+ "[Wikipedia](https://en.wikipedia.org/wiki/Functional_programming)\n",
+ "\n",
+ "* **Declarative** $\\rightarrow$ What result do I want ?\n",
+ "* **Imperative** $\\rightarrow$ What steps do I take to solve the problem ? (Python)\n",
+ "\n",
+ "#### Non comprehensive list of functional progamming concepts:\n",
+ "* Immutability \n",
+ "* Pure functions\n",
+ "* Higher order functions\n",
+ "\n",
+ "#### Why ?\n",
+ "* Code becomes concise and simple to understand\n",
+ "* Supports lazy evaluation\n",
+ "* Easier debugging and testing\n",
+ "* Easier parallelism/concurrency"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b8068921",
+ "metadata": {},
+ "source": [
+ "### Immutability\n",
+ "\n",
+ "Data cannot be changed once it is generated"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "4e30c928",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "num_list = [1,2,3,4,5,6]\n",
+ "num_tup = (1,2,3,4,5,6)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "7528bc37",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "First element in list : 1\n",
+ "Changed first element : 0\n",
+ "First element in tuple : 1\n"
+ ]
+ },
+ {
+ "ename": "TypeError",
+ "evalue": "'tuple' object does not support item assignment",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-2-a2a0e40ac0b7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# first element in tuple\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'First element in tuple : '\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnum_tup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mnum_tup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Changed first element : '\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnum_tup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
+ ]
+ }
+ ],
+ "source": [
+ "# first element in list\n",
+ "print('First element in list :',num_list[0])\n",
+ "num_list[0] = 0\n",
+ "print('Changed first element : ',num_list[0])\n",
+ "# first element in tuple\n",
+ "print('First element in tuple : ',num_tup[0])\n",
+ "num_tup[0] = 0\n",
+ "print('Changed first element : ',num_tup[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d4b152b5",
+ "metadata": {},
+ "source": [
+ "### Pure functions\n",
+ "* Given the same input always produce the same output\n",
+ "* Do not change value of inputs or any variable that exists outside the scope of the function"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "214bcf3c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def add(x,y):\n",
+ " return x+y\n",
+ "\n",
+ "counter = 0\n",
+ "def add_counter(x,y):\n",
+ " global counter\n",
+ " counter +=1\n",
+ " return x+y\n",
+ "\n",
+ "p = 1\n",
+ "def power(x):\n",
+ " return p**x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "b57bfc8b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "adding : 41\n",
+ "adding counter (first call): 41 1\n",
+ "adding counter (second call): 41 2\n",
+ "power (p = 1): 1\n",
+ "power (p = 2): 32\n"
+ ]
+ }
+ ],
+ "source": [
+ "# sum --> pure\n",
+ "print('adding : ',add(18,23))\n",
+ "# sum_counter --> impure\n",
+ "print('adding counter (first call): ',add_counter(18,23),counter)\n",
+ "print('adding counter (second call): ',add_counter(18,23),counter)\n",
+ "# power --> impure\n",
+ "print('power (p = 1):',power(5))\n",
+ "p = 2\n",
+ "print('power (p = 2):',power(5))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a340ce34",
+ "metadata": {},
+ "source": [
+ "### Higher order functions\n",
+ "\n",
+ "Functions can either accept functions as input or can return functions as output.\n",
+ "\n",
+ "Examples from [Higher Order Functions in Python](https://www.geeksforgeeks.org/higher-order-functions-in-python/)\n",
+ "\n",
+ "#### Functions accepting functions as input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "a15d07ad",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "shout : HELLO WORLD!\n",
+ "whisper : hello world!\n"
+ ]
+ }
+ ],
+ "source": [
+ "def shout(text):\n",
+ " return text.upper()\n",
+ "\n",
+ "def whisper(text):\n",
+ " return text.lower()\n",
+ "\n",
+ "def greet(func):\n",
+ " greeting = func('Hello World!')\n",
+ " print(func.__name__,' : ',greeting)\n",
+ "\n",
+ "greet(shout)\n",
+ "greet(whisper) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21d47da1",
+ "metadata": {},
+ "source": [
+ "#### Functions returning functions as output\n",
+ "\n",
+ "**GTF** and **GFF** attribute column parser"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "46349b39",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import re\n",
+ "\n",
+ "def attrib_parser(fname):\n",
+ " \n",
+ " # gff parser\n",
+ " def gff(attrib_str):\n",
+ " return re.findall(r'(\\w+)\\=([^;]+)', attrib_str , re.IGNORECASE)\n",
+ " \n",
+ " # gtf parser\n",
+ " def gtf(attrib_str):\n",
+ " return re.findall(r'(\\w+)\\s{1,}\"([^\"]+)\"', attrib_str ,re.IGNORECASE)\n",
+ " \n",
+ " # return a parser based on file name extension\n",
+ " if re.match('^.*gff.*', fname, re.IGNORECASE): # gff formatted input\n",
+ " return gff\n",
+ " elif re.match('.*gtf.*', fname, re.IGNORECASE): # gtf formatted input\n",
+ " return gtf\n",
+ " else:\n",
+ " raise NotImplementedError('Cannot detect {} file format!'.format(fname))\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "da4efbfa",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "GFF formatted attributes\n",
+ "('ID', 'ENSMUST00000193812.1')\n",
+ "('Parent', 'ENSMUSG00000102693.1')\n",
+ "('gene_id', 'ENSMUSG00000102693.1')\n",
+ "('transcript_id', 'ENSMUST00000193812.1')\n",
+ "('gene_type', 'TEC')\n",
+ "('gene_name', 'RP23-271O17.1')\n",
+ "('transcript_type', 'TEC')\n",
+ "('transcript_name', 'RP23-271O17.1-001')\n",
+ "('level', '2')\n",
+ "('transcript_support_level', 'NA')\n",
+ "('tag', 'basic')\n",
+ "('havana_gene', 'OTTMUSG00000049935.1')\n",
+ "('havana_transcript', 'OTTMUST00000127109.1')\n"
+ ]
+ }
+ ],
+ "source": [
+ "# gff attribute column\n",
+ "attrib_gff = 'ID=ENSMUST00000193812.1;Parent=ENSMUSG00000102693.1;gene_id=ENSMUSG00000102693.1;transcript_id=ENSMUST00000193812.1;gene_type=TEC;gene_name=RP23-271O17.1;transcript_type=TEC;transcript_name=RP23-271O17.1-001;level=2;transcript_support_level=NA;tag=basic;havana_gene=OTTMUSG00000049935.1;havana_transcript=OTTMUST00000127109.1'\n",
+ "\n",
+ "# parsing gff formatted file\n",
+ "file_name = 'some_file_name.gff'\n",
+ "\n",
+ "parser = attrib_parser(file_name)\n",
+ "print('GFF formatted attributes')\n",
+ "\n",
+ "for key_value in parser(attrib_gff):\n",
+ " print(key_value)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "0aa49f71",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "GTF formatted attributes \n",
+ "('gene_id', 'ENSMUSG00000102693.1')\n",
+ "('transcript_id', 'ENSMUST00000193812.1')\n",
+ "('gene_type', 'TEC')\n",
+ "('gene_name', 'RP23-271O17.1')\n",
+ "('transcript_type', 'TEC')\n",
+ "('transcript_name', 'RP23-271O17.1-001')\n",
+ "('transcript_support_level', 'NA')\n",
+ "('tag', 'basic')\n",
+ "('havana_gene', 'OTTMUSG00000049935.1')\n",
+ "('havana_transcript', 'OTTMUST00000127109.1')\n"
+ ]
+ }
+ ],
+ "source": [
+ "# gtf attribute column\n",
+ "attrib_gtf = 'gene_id \"ENSMUSG00000102693.1\"; transcript_id \"ENSMUST00000193812.1\"; gene_type \"TEC\"; gene_name \"RP23-271O17.1\"; transcript_type \"TEC\"; transcript_name \"RP23-271O17.1-001\"; level 2; transcript_support_level \"NA\"; tag \"basic\"; havana_gene \"OTTMUSG00000049935.1\"; havana_transcript \"OTTMUST00000127109.1\";'\n",
+ "\n",
+ "# parsing gtf formatted file\n",
+ "file_name = 'some_file_name.gtf'\n",
+ "\n",
+ "parser = attrib_parser(file_name)\n",
+ "print('GTF formatted attributes ')\n",
+ "\n",
+ "for key_value in parser(attrib_gtf):\n",
+ " print(key_value)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f560944f",
+ "metadata": {},
+ "source": [
+ "### Built-in higher order functions: `map`, `filter` and `reduce`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "085a88c1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def square(x):\n",
+ " return x ** 2\n",
+ "\n",
+ "def iseven(n):\n",
+ " return n % 2 == 0\n",
+ "\n",
+ "def multiple3(n):\n",
+ " return n % 3 == 0\n",
+ "\n",
+ "def text_filter(x):\n",
+ " return len(x) > 3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "717f703d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data = [1,2,3,4,5,6,7,8,9,10]\n",
+ "text = ['this', 'is', 'Some', 'TEXT']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "77c11d58",
+ "metadata": {},
+ "source": [
+ "#### Map"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "74da56c1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "list(map(square, data))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "f3f60f05",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "[square(x) for x in data]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "e82d677a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['This', 'Is', 'Some', 'Text']"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "list(map(str.title, text))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ec7e0b99",
+ "metadata": {},
+ "source": [
+ "#### Filter"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "a9a99457",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[2, 4, 6, 8, 10]"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "list(filter(iseven, data))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "8c8bf214",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[2, 4, 6, 8, 10]"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "[ x for x in data if iseven(x) ]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "6d35fb18",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['this', 'Some', 'TEXT']"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "list(filter(text_filter, text))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "429d14f6",
+ "metadata": {},
+ "source": [
+ "#### Reduce\n",
+ "\n",
+ "Upto Python 2, `reduce()` was a built-in function, but in Python 3 it is demoted to `functools`\n",
+ "\n",
+ "> I received an email from a compatriot lamenting the planned demise of `reduce()` and `lambda` in **Python 3000**. After a few exchanges I think even he agreed that they can go. Here's a summary, including my reasons for dropping `lambda`, `map()` and `filter()`. I expect tons of disagreement in the feedback, all from ex-Lisp-or-Scheme folks. :-) \n",
+ "\n",
+ "[Guido van Rossum](https://www.artima.com/weblogs/viewpost.jsp?thread=98196), _March 12, 2005_\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "fd96efc0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from functools import reduce"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "f3e79aed",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "113"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "reduce(add,[47, 11, 42, 13])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "894e8c0e",
+ "metadata": {},
+ "source": [
+ "![reduce diagram](reduce_diagram.png \"Reduce\")\n",
+ "Image taken from: [Reducing a list](https://www.python-course.eu/lambda.php)\n",
+ "\n",
+ "### Additional materials\n",
+ "\n",
+ "* [Python functional programming how to](https://docs.python.org/3/howto/functional.html)\n",
+ "* [Joel Grus: Learning Data Science Using Functional Python](https://www.youtube.com/watch?v=ThS4juptJjQ)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "53a406e0",
+ "metadata": {},
+ "source": [
+ "## Toolz - functional Python\n",
+ "\n",
+ "\n",
+ "A set of utility functions for iterators, functions and dictionaries, and contains several functions (originally from `itertoolz` and `functoolz` libraries) that are absent in the standard [`itertools`](https://docs.python.org/3/library/itertools.html) and [`functools`](https://docs.python.org/3/library/functools.html) libraries.\n",
+ "\n",
+ "Toolz functions have the following properties:\n",
+ "\n",
+ "* composability: interoperable due to use of core data structures\n",
+ "* functional purity: do not rely on external state or change input\n",
+ "* laziness: lazy evaluation\n",
+ "\n",
+ "```bash\n",
+ "$ pip install toolz\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "cd348c7b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import itertools"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8b07be5b",
+ "metadata": {},
+ "source": [
+ "### A sample set of `itertoolz` functions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "e252174a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from toolz.itertoolz import take, drop, take_nth"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "062c6ce7",
+ "metadata": {},
+ "source": [
+ "#### Operations on `n` elements of a sequence\n",
+ "\n",
+ "wrapper for `itertools.islice`\n",
+ "\n",
+ "take first `n` elements"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "80222b27",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "take : [1, 2, 3]\n",
+ "islice: [1, 2, 3]\n"
+ ]
+ }
+ ],
+ "source": [
+ "take3 = list(take(3,[1,2,3,4,5,6,7]))\n",
+ "print('take : ',take3)\n",
+ "# similar to itertools.islice(iterable,start)\n",
+ "islice3 = list(itertools.islice([1,2,3,4,5,6,7],3))\n",
+ "print('islice:',islice3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0528bf37",
+ "metadata": {},
+ "source": [
+ "drop first `n` elements"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "f352e1c3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "drop : [4, 5, 6, 7]\n",
+ "islice: [4, 5, 6, 7]\n"
+ ]
+ }
+ ],
+ "source": [
+ "drop3 = list(drop(3,[1,2,3,4,5,6,7]))\n",
+ "print('drop : ', drop3)\n",
+ "# similar to itertools.islice(iterable,start,None)\n",
+ "islice_drop_3 = list(itertools.islice([1,2,3,4,5,6,7],3,None))\n",
+ "print('islice:', islice_drop_3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1cba4f65",
+ "metadata": {},
+ "source": [
+ "take every `n`th element"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "ef9c95fc",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "take_nth: [1, 4, 7]\n",
+ "islice : [1, 4, 7]\n"
+ ]
+ }
+ ],
+ "source": [
+ "ntake = list(take_nth(3,[1,2,3,4,5,6,7]))\n",
+ "print('take_nth:', ntake)\n",
+ "# similar to itertools.islice(iterable,0,None,step)\n",
+ "itertools_ntake = list(itertools.islice([1,2,3,4,5,6,7],0,None,3))\n",
+ "print('islice :', itertools_ntake)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f6932baa",
+ "metadata": {},
+ "source": [
+ "### Toolz complement\n",
+ "\n",
+ "Convert a function into its logical compelement"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "0aa9e0f7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from toolz.functoolz import complement"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "ef5df16a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[1, 3, 5, 7, 9]"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "isodd = complement(iseven)\n",
+ "list(filter(isodd,data))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5d3c647c",
+ "metadata": {},
+ "source": [
+ "### Itertoolz groupby\n",
+ "\n",
+ "Group a collection by a key (function)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "f814f0b5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from toolz.itertoolz import groupby"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "005781ab",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[2, 4, 6, 8, 10]"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "list(filter(iseven, data))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a4077325",
+ "metadata": {},
+ "source": [
+ "**toolz groupby**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "4baefdd1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{False: [1, 3, 5, 7, 9], True: [2, 4, 6, 8, 10]}\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(groupby(iseven,data))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "42f768c7",
+ "metadata": {},
+ "source": [
+ "**itertools groupby**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "1d272af7",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "False [1]\n",
+ "True [2]\n",
+ "False [3]\n",
+ "True [4]\n",
+ "False [5]\n",
+ "True [6]\n",
+ "False [7]\n",
+ "True [8]\n",
+ "False [9]\n",
+ "True [10]\n"
+ ]
+ }
+ ],
+ "source": [
+ "for key,group in itertools.groupby(data,iseven):\n",
+ " print(key,list(group))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "56f3dec8",
+ "metadata": {},
+ "source": [
+ "A couple of more examples using `toolz groupby`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "12ce1dac",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{True: ['this', 'Some', 'TEXT'], False: ['is']}"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "list(filter(text_filter, text))\n",
+ "groupby(text_filter,text)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "2f7812c3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def text_len(x):\n",
+ " return len(x)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "f292e668",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{4: ['this', 'Some', 'TEXT'], 2: ['is']}"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "groupby(text_len,text)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "689a914c",
+ "metadata": {},
+ "source": [
+ "#### countby\n",
+ "\n",
+ "Count elements in a collection by a key function"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "598fb73e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from toolz.recipes import countby"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "51e1dbe6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{False: 5, True: 5}"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "countby(iseven, data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "dc95d58a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{4: 3, 2: 1}"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "countby(len, text)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "433f05e4",
+ "metadata": {},
+ "source": [
+ "### compose and pipe\n",
+ "\n",
+ "\n",
+ "> **function composition** is an act or mechanism to combine simple functions to build more complicated ones \n",
+ "\n",
+ "_[Wikipedia](https://en.wikipedia.org/wiki/Function_composition_\\(computer_science\\))_\n",
+ "\n",
+ "#### compose\n",
+ "\n",
+ "`compose(f, g, h)(x, y)` is the same as `f(g(h(x, y)))`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "45f4b438",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from toolz.functoolz import compose"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "a5c11408",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "935"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "int(square(sum([10.123,20.456])))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "dcf87ac8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "935"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "compose(int, square, sum)([10.123,20.456])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69eea1a7",
+ "metadata": {},
+ "source": [
+ "#### pipe\n",
+ "\n",
+ "Pass value(s) through a sequence of functions\n",
+ "\n",
+ "```bash\n",
+ "$ cat file.txt| grep | awk\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "bbba4124",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from toolz.functoolz import pipe"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "id": "e0cafff7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "935"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pipe([10.123,20.456], sum, square, int)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6bb079e3",
+ "metadata": {},
+ "source": [
+ "**Note** : difference in how `compose` and `pipe` are called:\n",
+ "\n",
+ "``` python\n",
+ ">>> compose(..., f3, f2, f1)(data)\n",
+ ">>> pipe(data, f1, f2, f3,...)\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "32b6cedb",
+ "metadata": {},
+ "source": [
+ "### Partial (function) application and currying\n",
+ "\n",
+ "How to pass parameters/additional arguments to functions used in `compose` or `pipe` ?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "id": "6917c712",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_str = '1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d6722020",
+ "metadata": {},
+ "source": [
+ "`split` the string into integers, `filter` for even numbers and find multiples of 3 ?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "a14ae694",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Even multiples of 3 : [6, 12, 18]\n"
+ ]
+ }
+ ],
+ "source": [
+ "result = list(filter(multiple3, (filter(iseven, map(int, data_str.split(','))))))\n",
+ "print('Even multiples of 3 : ',result)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0457a5c3",
+ "metadata": {},
+ "source": [
+ "How do we use `compose` or `pipe`\n",
+ "\n",
+ "#### partial application\n",
+ "\n",
+ "> **partial application** (or **partial function application**) refers to the process of fixing a number of arguments to a function, producing another function with smaller number of arguments\n",
+ "\n",
+ "[Wikipedia](https://en.wikipedia.org/wiki/Partial_application)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "id": "b48f7b56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from functools import partial"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "id": "864251a9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# map(int, list)\n",
+ "# map_int = partial(function, first_argument)\n",
+ "map_int = partial(map,int)\n",
+ "\n",
+ "# filter(even, list)\n",
+ "filter_even = partial(filter,iseven)\n",
+ "\n",
+ "# filter(multiple3, list)\n",
+ "filter_multiple3 = partial(filter, multiple3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "49d0bfd7",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Even multiples of 3 : [6, 12, 18]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# result = pipe(data, f1, f2, f3,...)\n",
+ "result = pipe(data_str.split(','), map_int, filter_even, filter_multiple3, list)\n",
+ "print('Even multiples of 3 : ',result)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "591a9ac0",
+ "metadata": {},
+ "source": [
+ "#### currying\n",
+ "\n",
+ "> the technique of converting a function that takes multiple arguments into a sequence of functions that each takes a single argument\n",
+ "\n",
+ "[Wikipedia](https://en.wikipedia.org/wiki/Currying)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "id": "e0a03b09",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from toolz import curry"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "id": "f112e9c2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def sum_three(a,b,c):\n",
+ " return a+b+c"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "id": "d6466abf",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " curried sum function : 50\n"
+ ]
+ }
+ ],
+ "source": [
+ "sum_curried = curry(sum_three)\n",
+ "print(' curried sum function :',sum_curried(10)(15)(25))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "id": "3a33706f",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "TypeError",
+ "evalue": "sum_three() missing 2 required positional arguments: 'b' and 'c'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-49-38974e030e18>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0msum_partial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpartial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msum_three\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msum_partial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mTypeError\u001b[0m: sum_three() missing 2 required positional arguments: 'b' and 'c'"
+ ]
+ }
+ ],
+ "source": [
+ "sum_partial = partial(sum_three)\n",
+ "sum_partial(10)(15)(25)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a79593f6",
+ "metadata": {},
+ "source": [
+ "#### curried namespace\n",
+ "\n",
+ "A curried version of all functions in the `toolz` namespace and standard python higer order functions like `map` and `filter` are available in `toolz.curried` namespace.\n",
+ "\n",
+ "Revisiting `split` the string into integers, `filter` for even numbers and find multiples of 3 \n",
+ "with curried versions of `map` and `filter`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "id": "8de94db8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from toolz.curried import map as mapc\n",
+ "from toolz.curried import filter as filterc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "id": "58b1203d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_str = '1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "id": "7e436183",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Even multiples of 3 : [6, 12, 18]\n"
+ ]
+ }
+ ],
+ "source": [
+ "result = pipe(data_str.split(','), mapc(int), filterc(iseven), filterc(multiple3), list)\n",
+ "print('Even multiples of 3 : ',result)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5deb4f05",
+ "metadata": {},
+ "source": [
+ "Roughly similar to [R tidyverse `pipe`](https://style.tidyverse.org/pipes.html)\n",
+ "```R\n",
+ "\n",
+ "result <- data_str %>% split(',')\n",
+ " %>% mapc(int)\n",
+ " %>% filterc(iseven)\n",
+ " %>% filterc(multiple3)\n",
+ " %>% list()\n",
+ "\n",
+ "```"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/meetings/2021/session_12_functional_programming_with_toolz/README.md b/meetings/2021/session_12_functional_programming_with_toolz/README.md
new file mode 100644
index 0000000..206fef7
--- /dev/null
+++ b/meetings/2021/session_12_functional_programming_with_toolz/README.md
@@ -0,0 +1,14 @@
+# Functional programming with python core data structures
+
+### Depenencies
+To run the notebooks in this repo, please make sure that you have the following packages installed:
+
+* [toolz](https://pypi.org/project/toolz/0.1/)
+* [pandas](https://pypi.org/project/pandas/)
+
+
+### Additional resources
+
+* [A curated list of functional programming related projects in python](https://github.com/sfermigier/awesome-functional-python)
+* [Matthew Rocklins' (toolz author) talk on toolz](https://vimeo.com/80096814)
+* [Currying in Python - Dustin Haffner](https://www.youtube.com/watch?v=L3G7BH7n0K0)
diff --git a/meetings/2021/session_12_functional_programming_with_toolz/Toolz-use-case_1_airway.ipynb b/meetings/2021/session_12_functional_programming_with_toolz/Toolz-use-case_1_airway.ipynb
new file mode 100644
index 0000000..df52c04
--- /dev/null
+++ b/meetings/2021/session_12_functional_programming_with_toolz/Toolz-use-case_1_airway.ipynb
@@ -0,0 +1,513 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "7ca3358f",
+ "metadata": {},
+ "source": [
+ "## Functional programming and `toolz` use cases\n",
+ "\n",
+ "### Streaming read count data and filtering\n",
+ "\n",
+ "This example is a adapted from Juan Nunez-Iglesias' [Linux conference Australia talk in 2016](https://www.youtube.com/watch?v=INAvD4Kmdbc). Refer to [his github repo](https://github.com/jni/functional-programming-in-python) for additional examples.\n",
+ "\n",
+ "#### Airway dataset\n",
+ "\n",
+ "Himes BE, Jiang X, Wagner P, Hu R, Wang Q, Klanderman B, Whitaker RM, Duan Q, Lasky-Su J, Nikolos C, Jester W, Johnson M, Panettieri R Jr, Tantisira KG, Weiss ST, Lu Q. “RNA-Seq Transcriptome Profiling Identifies CRISPLD2 as a Glucocorticoid Responsive Gene that Modulates Cytokine Function in Airway Smooth Muscle Cells.†[PLoS One. 2014 Jun 13;9(6):e99625](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0099625)\n",
+ "\n",
+ "Airway smooth muscle cells were treated with dexamethasone, a synthetic steroid with anti-inflammatory effects and expression levels were measured using RNA-seq."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "cb5b2da3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "17d48aeb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "airway = pd.read_csv('data/airway.csv',sep=\"\\t\", header=None, index_col = 0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "aa45fc7b",
+ "metadata": {},
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+ "ENSG00000000003 679 448 873 408 1138 1047 770 572\n",
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+ "ENSG00000000460 60 55 40 35 78 63 76 60\n",
+ "... ... ... ... ... ... ... ... ...\n",
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+ "LRG_99 0 0 0 0 0 0 0 0\n",
+ "\n",
+ "[64102 rows x 8 columns]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "airway"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3e57eebe",
+ "metadata": {},
+ "source": [
+ "### Find all genes with mean read count $\\ge$ 50\n",
+ "\n",
+ "#### Using `pandas`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "5d5ed72f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0\n",
+ "ENSG00000000003 741.875\n",
+ "ENSG00000000419 534.875\n",
+ "ENSG00000000457 242.000\n",
+ "ENSG00000000460 58.375\n",
+ "ENSG00000000971 6034.750\n",
+ " ... \n",
+ "ENSG00000273179 174.875\n",
+ "ENSG00000273230 119.500\n",
+ "ENSG00000273270 137.375\n",
+ "ENSG00000273290 168.500\n",
+ "ENSG00000273344 65.500\n",
+ "Length: 12671, dtype: float64"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "airway_mean = airway.mean(axis=1)\n",
+ "airway_mean[airway_mean>=50]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "06b09f77",
+ "metadata": {},
+ "source": [
+ "#### Using `for` loop and `map`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "4e46b096",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from statistics import mean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "6de40095",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "airway_genes = []\n",
+ "with open('data/airway.csv','r') as fh:\n",
+ " for f in fh:\n",
+ " seq_data = f.strip().split('\\t')\n",
+ " count_data = map(int, seq_data[1:])\n",
+ " count_mean = mean(count_data)\n",
+ " if count_mean > 50.0:\n",
+ " airway_genes.append((seq_data[0],count_mean))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "6b4c2415",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Genes after filtering: 12666\n",
+ "First 3 from list: [('ENSG00000000003', 741.875), ('ENSG00000000419', 534.875), ('ENSG00000000457', 242)]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Genes after filtering: ',len(airway_genes))\n",
+ "print('First 3 from list: ', airway_genes[:3])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8a30ccc7",
+ "metadata": {},
+ "source": [
+ "#### Rewriting using `pipe` and curried versions of `reduce` and `filter`\n",
+ "\n",
+ "Data flow: `open` $\\rightarrow$ `split` $\\rightarrow$ `map` $\\rightarrow$ `reduce` $\\rightarrow$ `filter`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "2403784d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from toolz.curried import map as mapc\n",
+ "from toolz.curried import filter as filterc\n",
+ "from toolz.functoolz import pipe, curry"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "b4ba3e35",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def read_data(fin):\n",
+ " with open(fin,'r') as fh:\n",
+ " for l in fh:\n",
+ " yield l.strip().split('\\t')\n",
+ "\n",
+ "def map_int_mean(raw_data):\n",
+ " count_mean = pipe(raw_data[1:], mapc(int), mean)\n",
+ " return (raw_data[0],count_mean)\n",
+ "\n",
+ "def filter_mean(gene_tuple):\n",
+ " return gene_tuple[1] >= 50.00"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "72de5559",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "airway_genes_50 = pipe('data/airway.csv',read_data, mapc(map_int_mean), filterc(filter_mean))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "372e9613",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "12671"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(list(airway_genes_50))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7a8c580b",
+ "metadata": {},
+ "source": [
+ "#### Rewriting `filter_mean` as a curried version\n",
+ "\n",
+ "Adding an additional input `thresh` for `filter_mean` "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "55b4cb77",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "@curry # <--- using curry function as a decorator\n",
+ "def filter_mean_thresh(gene_tuple,thresh=0.0):\n",
+ " return gene_tuple[1] >= thresh"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "fc9f6e5e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "airway_genes_100 = pipe('data/airway.csv',read_data, mapc(map_int_mean), filterc(filter_mean_thresh(thresh=100.0)),list)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "d4c65bee",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "11244"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(airway_genes_100)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "b543592a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[('ENSG00000000003', 741.875),\n",
+ " ('ENSG00000000419', 534.875),\n",
+ " ('ENSG00000000457', 242)]"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "airway_genes_100[:3]"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/meetings/2021/session_12_functional_programming_with_toolz/data/airway.csv b/meetings/2021/session_12_functional_programming_with_toolz/data/airway.csv
new file mode 100644
index 0000000..d0de6c4
--- /dev/null
+++ b/meetings/2021/session_12_functional_programming_with_toolz/data/airway.csv
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literal 0
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--
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