\u001b[0m in \u001b[0;36ma_func\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0ma_func\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----> 4\u001b[0;31m \u001b[0ma_var\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma_var\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'[ a_var inside a_func() ]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'a_var' referenced before assignment"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "1 [ a_var outside a_func() ]\n"
- ]
- }
- ],
- "prompt_number": 4
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2. LEG - Local, Enclosed, and Global scope\n",
- "\n",
- "\n",
- "\n",
- "Now, let us introduce the concept of the enclosed (E) scope. Following the order \"Local -> Enclosed -> Global\", can you guess what the following code will print?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Example 2.1**"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a_var = 'global value'\n",
- "\n",
- "def outer():\n",
- " a_var = 'enclosed value'\n",
- " \n",
- " def inner():\n",
- " a_var = 'local value'\n",
- " print(a_var)\n",
- " \n",
- " inner()\n",
- "\n",
- "outer()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 4
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**a)**\n",
- "global value
\n",
- "\n",
- "**b)** \n",
- "enclosed value
\n",
- "\n",
- "**c)** \n",
- "local value
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[go to solution](#solutions)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Here is why:\n",
- "\n",
- "Let us quickly recapitulate what we just did: We called `outer()`, which defined the variable `a_var` locally (next to an existing `a_var` in the global scope). Next, the `outer()` function called `inner()`, which in turn defined a variable with of name `a_var` as well. The `print()` function inside `inner()` searched in the local scope first (L->E) before it went up in the scope hierarchy, and therefore it printed the value that was assigned in the local scope."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Similar to the concept of the `global` keyword, which we have seen in the section above, we can use the keyword `nonlocal` inside the inner function to explicitely access a variable from the outer (enclosed) scope in order to modify its value. \n",
- "Note that the `nonlocal` keyword was added in Python 3.x and is not implemented in Python 2.x (yet)."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a_var = 'global value'\n",
- "\n",
- "def outer():\n",
- " a_var = 'local value'\n",
- " print('outer before:', a_var)\n",
- " def inner():\n",
- " nonlocal a_var\n",
- " a_var = 'inner value'\n",
- " print('in inner():', a_var)\n",
- " inner()\n",
- " print(\"outer after:\", a_var)\n",
- "outer()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "outer before: local value\n",
- "in inner(): inner value\n",
- "outer after: inner value\n"
- ]
- }
- ],
- "prompt_number": 5
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 3. LEGB - Local, Enclosed, Global, Built-in\n",
- "\n",
- "To wrap up the LEGB rule, let us come to the built-in scope. Here, we will define our \"own\" length-funcion, which happens to bear the same name as the in-built `len()` function. What outcome do you excpect if we'd execute the following code?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Example 3**"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a_var = 'global variable'\n",
- "\n",
- "def len(in_var):\n",
- " print('called my len() function')\n",
- " l = 0\n",
- " for i in in_var:\n",
- " l += 1\n",
- " return l\n",
- "\n",
- "def a_func(in_var):\n",
- " len_in_var = len(in_var)\n",
- " print('Input variable is of length', len_in_var)\n",
- "\n",
- "a_func('Hello, World!')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 6
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**a)**\n",
- "raises an error (conflict with in-built `len()` function)
\n",
- "\n",
- "**b)** \n",
- "called my len() function\n",
- "Input variable is of length 13
\n",
- "\n",
- "**c)** \n",
- "Input variable is of length 13
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[go to solution](#solutions)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Here is why:\n",
- "\n",
- "Since the exact same names can be used to map names to different objects - as long as the names are in different name spaces - there is no problem of reusing the name `len` to define our own length function (this is just for demonstration pruposes, it is NOT recommended). As we go up in Python's L -> E -> G -> B hierarchy, the function `a_func()` finds `len()` already in the global scope first before it attempts"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Self-assessment exercise"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now, after we went through a couple of exercises, let us quickly check where we are. So, one more time: What would the following code print out?"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a = 'global'\n",
- "\n",
- "def outer():\n",
- " \n",
- " def len(in_var):\n",
- " print('called my len() function: ', end=\"\")\n",
- " l = 0\n",
- " for i in in_var:\n",
- " l += 1\n",
- " return l\n",
- " \n",
- " a = 'local'\n",
- " \n",
- " def inner():\n",
- " global len\n",
- " nonlocal a\n",
- " a += ' variable'\n",
- " inner()\n",
- " print('a is', a)\n",
- " print(len(a))\n",
- "\n",
- "\n",
- "outer()\n",
- "\n",
- "print(len(a))\n",
- "print('a is', a)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 59
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[go to solution](#solutions)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Conclusion"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "I hope this short tutorial was helpful to understand the basic concept of Python's scope resolution order using the LEGB rule. I want to encourage you (as a little self-assessment exercise) to look at the code snippets again tomorrow and check if you can correctly predict all their outcomes."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### A rule of thumb"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In practice, **it is usually a bad idea to modify global variables inside the function scope**, since it often be the cause of confusion and weird errors that are hard to debug. \n",
- "If you want to modify a global variable via a function, it is recommended to pass it as an argument and reassign the return-value. \n",
- "For example:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a_var = 2\n",
- "\n",
- "def a_func(some_var):\n",
- " return 2**3\n",
- "\n",
- "a_var = a_func(a_var)\n",
- "print(a_var)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "8\n"
- ]
- }
- ],
- "prompt_number": 42
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Solutions\n",
- "\n",
- "In order to prevent you from unintentional spoilers, I have written the solutions in binary format. In order to display the character representation, you just need to execute the following lines of code:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print('Example 1.1:', chr(int('01100011',2)))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 6
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print('Example 1.2:', chr(int('01100001',2)))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 7
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print('Example 2.1:', chr(int('01100011',2)))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 8
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print('Example 3.1:', chr(int('01100010',2)))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 9
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# Execute to run the self-assessment solution\n",
- "\n",
- "sol = \"000010100110111101110101011101000110010101110010001010\"\\\n",
- "\"0000101001001110100000101000001010011000010010000001101001011100110\"\\\n",
- "\"0100000011011000110111101100011011000010110110000100000011101100110\"\\\n",
- "\"0001011100100110100101100001011000100110110001100101000010100110001\"\\\n",
- "\"1011000010110110001101100011001010110010000100000011011010111100100\"\\\n",
- "\"1000000110110001100101011011100010100000101001001000000110011001110\"\\\n",
- "\"1010110111001100011011101000110100101101111011011100011101000100000\"\\\n",
- "\"0011000100110100000010100000101001100111011011000110111101100010011\"\\\n",
- "\"0000101101100001110100000101000001010001101100000101001100001001000\"\\\n",
- "\"0001101001011100110010000001100111011011000110111101100010011000010\"\\\n",
- "\"1101100\"\n",
- "\n",
- "sol_str =''.join(chr(int(sol[i:i+8], 2)) for i in range(0, len(sol), 8))\n",
- "for line in sol_str.split('\\n'):\n",
- " print(line)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 58
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "
\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Warning: For-loop variables \"leaking\" into the global namespace"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In contrast to some other programming languages, `for-loops` will use the scope they exist in and leave their defined loop-variable behind.\n"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "for a in range(5):\n",
- " if a == 4:\n",
- " print(a, '-> a in for-loop')\n",
- "print(a, '-> a in global')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "4 -> a in for-loop\n",
- "4 -> a in global\n"
- ]
- }
- ],
- "prompt_number": 5
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**This also applies if we explicitely defined the `for-loop` variable in the global namespace before!** In this case it will rebind the existing variable:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "b = 1\n",
- "for b in range(5):\n",
- " if b == 4:\n",
- " print(b, '-> b in for-loop')\n",
- "print(b, '-> b in global')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "4 -> b in for-loop\n",
- "4 -> b in global\n"
- ]
- }
- ],
- "prompt_number": 9
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "However, in **Python 3.x**, we can use closures to prevent the for-loop variable to cut into the global namespace. Here is an example (exectuted in Python 3.4):"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "i = 1\n",
- "print([i for i in range(5)])\n",
- "print(i, '-> i in global')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "[0, 1, 2, 3, 4]\n",
- "1 -> i in global\n"
- ]
- }
- ],
- "prompt_number": 1
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Why did I mention \"Python 3.x\"? Well, as it happens, the same code executed in Python 2.x would print:\n",
- "\n",
- "\n",
- "4 -> i in global\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This goes back to a change that was made in Python 3.x and is described in [What\u2019s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:\n",
- "\n",
- "\"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.\""
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [],
- "language": "python",
- "metadata": {},
- "outputs": []
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
diff --git a/.ipynb_checkpoints/timeit_test-checkpoint.ipynb b/.ipynb_checkpoints/timeit_test-checkpoint.ipynb
deleted file mode 100644
index dd46362..0000000
--- a/.ipynb_checkpoints/timeit_test-checkpoint.ipynb
+++ /dev/null
@@ -1,628 +0,0 @@
-{
- "metadata": {
- "name": "",
- "signature": "sha256:5a2264b30b9632e14bd425a887a4455658fbdf9f8102fc5703ad982c3fa09b21"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Sebastian Raschka \n",
- "last updated: 04/14/2014 \n",
- "\n",
- "[Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/timeit_test.ipynb)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "# Python benchmarks via `timeit`"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Sections\n",
- "- [String operations](#string_operations)\n",
- " - [String formatting: .format() vs. binary operator %s](#str_format_bin)\n",
- " - [String reversing: [::-1] vs. `''.join(reversed())`](#str_reverse)\n",
- " - [String concatenation: `+=` vs. `''.join()`](#string_concat)\n",
- " - [Assembling strings](#string_assembly) \n",
- "- [List operations](#list_operations)\n",
- " - [List reversing: [::-1] vs. reverse() vs. reversed()](#list_reverse)\n",
- " - [Creating lists using conditional statements](#create_cond_list)\n",
- "- [Dictionary operations](#dict_ops) \n",
- " - [Adding elements to a dictionary](#adding_dict_elements)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "# String operations"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "## String formatting: `.format()` vs. binary operator `%s`\n",
- "\n",
- "We expect the string .format() method to perform slower than %, because it is doing the formatting for each object itself, where formatting via the binary % is hard-coded for known types. But let's see how big the difference really is..."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "\n",
- "def test_format():\n",
- " return ['{}'.format(i) for i in range(1000000)]\n",
- "\n",
- "def test_binaryop():\n",
- " return ['%s' %i for i in range(1000000)]\n",
- "\n",
- "%timeit test_format()\n",
- "%timeit test_binaryop()\n",
- "\n",
- "#\n",
- "# Python 3.4.0\n",
- "# MacOS X 10.9.2\n",
- "# 2.5 GHz Intel Core i5\n",
- "# 4 GB 1600 Mhz DDR3\n",
- "#"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "1 loops, best of 3: 400 ms per loop\n",
- "1 loops, best of 3: 241 ms per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 3
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "## String reversing: `[::-1]` vs. `''.join(reversed())`"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "\n",
- "def reverse_join(my_str):\n",
- " return ''.join(reversed(my_str))\n",
- " \n",
- "def reverse_slizing(my_str):\n",
- " return my_str[::-1]\n",
- "\n",
- "\n",
- "# Test to show that both work\n",
- "a = reverse_join('abcd')\n",
- "b = reverse_slizing('abcd')\n",
- "assert(a == b and a == 'dcba')\n",
- "\n",
- "%timeit reverse_join('abcd')\n",
- "%timeit reverse_slizing('abcd')\n",
- "\n",
- "# Python 3.4.0\n",
- "# MacOS X 10.9.2\n",
- "# 2.4 GHz Intel Core Duo\n",
- "# 8 GB 1067 Mhz DDR3\n",
- "#"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "1000000 loops, best of 3: 1.28 \u00b5s per loop\n",
- "1000000 loops, best of 3: 337 ns per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 13
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "## String concatenation: `+=` vs. `''.join()`"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Strings in Python are immutable objects. So, each time we append a character to a string, it has to be created \u201cfrom scratch\u201d in memory. Thus, the answer to the question \u201cWhat is the most efficient way to concatenate strings?\u201d is a quite obvious, but the relative numbers of performance gains are nonetheless interesting."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "\n",
- "def string_add(in_chars):\n",
- " new_str = ''\n",
- " for char in in_chars:\n",
- " new_str += char\n",
- " return new_str\n",
- "\n",
- "def string_join(in_chars):\n",
- " return ''.join(in_chars)\n",
- "\n",
- "test_chars = ['a', 'b', 'c', 'd', 'e', 'f']\n",
- "\n",
- "%timeit string_add(test_chars)\n",
- "%timeit string_join(test_chars)\n",
- "\n",
- "#\n",
- "# Python 3.4.0\n",
- "# MacOS X 10.9.2\n",
- "# 2.5 GHz Intel Core i5\n",
- "# 4 GB 1600 Mhz DDR3\n",
- "#"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "1000000 loops, best of 3: 595 ns per loop\n",
- "1000000 loops, best of 3: 269 ns per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 16
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "## Assembling strings\n",
- "\n",
- "Next, I wanted to compare different methods string \u201cassembly.\u201d This is different from simple string concatenation, which we have seen in the previous section, since we insert values into a string, e.g., from a variable."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "\n",
- "def plus_operator():\n",
- " return 'a' + str(1) + str(2) \n",
- " \n",
- "def format_method():\n",
- " return 'a{}{}'.format(1,2)\n",
- " \n",
- "def binary_operator():\n",
- " return 'a%s%s' %(1,2)\n",
- "\n",
- "%timeit plus_operator()\n",
- "%timeit format_method()\n",
- "%timeit binary_operator()\n",
- "\n",
- "#\n",
- "# Python 3.4.0\n",
- "# MacOS X 10.9.2\n",
- "# 2.5 GHz Intel Core i5\n",
- "# 4 GB 1600 Mhz DDR3\n",
- "#"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "1000000 loops, best of 3: 764 ns per loop\n",
- "1000000 loops, best of 3: 494 ns per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "10000000 loops, best of 3: 79.3 ns per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 17
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# List operations"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "## List reversing - `[::-1]` vs. `reverse()` vs. `reversed()`"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "\n",
- "def reverse_func(my_list):\n",
- " new_list = my_list[:]\n",
- " new_list.reverse()\n",
- " return new_list\n",
- " \n",
- "def reversed_func(my_list):\n",
- " return list(reversed(my_list))\n",
- "\n",
- "def reverse_slizing(my_list):\n",
- " return my_list[::-1]\n",
- "\n",
- "%timeit reverse_func([1,2,3,4,5])\n",
- "%timeit reversed_func([1,2,3,4,5])\n",
- "%timeit reverse_slizing([1,2,3,4,5])\n",
- "\n",
- "# Python 3.4.0\n",
- "# MacOS X 10.9.2\n",
- "# 2.4 GHz Intel Core Duo\n",
- "# 8 GB 1067 Mhz DDR3\n",
- "#"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "1000000 loops, best of 3: 930 ns per loop\n",
- "1000000 loops, best of 3: 1.89 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "1000000 loops, best of 3: 775 ns per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 1
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "## Creating lists using conditional statements\n",
- "\n",
- "In this test, I attempted to figure out the fastest way to create a new list of elements that meet a certain criterion. For the sake of simplicity, the criterion was to check if an element is even or odd, and only if the element was even, it should be included in the list. For example, the resulting list for numbers in the range from 1 to 10 would be \n",
- "[2, 4, 6, 8, 10].\n",
- "\n",
- "Here, I tested three different approaches: \n",
- "1) a simple for loop with an if-statement check (`cond_loop()`) \n",
- "2) a list comprehension (`list_compr()`) \n",
- "3) the built-in filter() function (`filter_func()`) \n",
- "\n",
- "Note that the filter() function now returns a generator in Python 3, so I had to wrap it in an additional list() function call."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "\n",
- "def cond_loop():\n",
- " even_nums = []\n",
- " for i in range(100):\n",
- " if i % 2 == 0:\n",
- " even_nums.append(i)\n",
- " return even_nums\n",
- "\n",
- "def list_compr():\n",
- " even_nums = [i for i in range(100) if i % 2 == 0]\n",
- " return even_nums\n",
- " \n",
- "def filter_func():\n",
- " even_nums = list(filter((lambda x: x % 2 != 0), range(100)))\n",
- " return even_nums\n",
- "\n",
- "%timeit cond_loop()\n",
- "%timeit list_compr()\n",
- "%timeit filter_func()\n",
- "\n",
- "#\n",
- "# Python 3.4.0\n",
- "# MacOS X 10.9.2\n",
- "# 2.5 GHz Intel Core i5\n",
- "# 4 GB 1600 Mhz DDR3\n",
- "#"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "100000 loops, best of 3: 14.4 \u00b5s per loop\n",
- "100000 loops, best of 3: 12 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "10000 loops, best of 3: 23.9 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 14
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# Dictionary operations "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "## Adding elements to a Dictionary\n",
- "\n",
- "All three functions below count how often different elements (values) occur in a list. \n",
- "E.g., for the list ['a', 'b', 'a', 'c'], the dictionary would look like this: \n",
- "`my_dict = {'a': 2, 'b': 1, 'c': 1}`"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import random\n",
- "import copy\n",
- "import timeit\n",
- "\n",
- "\n",
- "\n",
- "def add_element_check1(my_dict, elements):\n",
- " for e in elements:\n",
- " if e not in my_dict:\n",
- " my_dict[e] = 1\n",
- " else:\n",
- " my_dict[e] += 1\n",
- " \n",
- "def add_element_check2(my_dict, elements):\n",
- " for e in elements:\n",
- " if e not in my_dict:\n",
- " my_dict[e] = 0\n",
- " my_dict[e] += 1 \n",
- "\n",
- "def add_element_except(my_dict, elements):\n",
- " for e in elements:\n",
- " try:\n",
- " my_dict[e] += 1\n",
- " except KeyError:\n",
- " my_dict[e] = 1\n",
- " \n",
- "\n",
- "random.seed(123)\n",
- "rand_ints = [random.randrange(1, 10) for i in range(100)]\n",
- "empty_dict = {}\n",
- "\n",
- "print('Results for 100 integers in range 1-10') \n",
- "%timeit add_element_check1(copy.deepcopy(empty_dict), rand_ints)\n",
- "%timeit add_element_check2(copy.deepcopy(empty_dict), rand_ints)\n",
- "%timeit add_element_except(copy.deepcopy(empty_dict), rand_ints)\n",
- " \n",
- "print('\\nResults for 1000 integers in range 1-10') \n",
- "rand_ints = [random.randrange(1, 10) for i in range(1000)]\n",
- "empty_dict = {}\n",
- "\n",
- "%timeit add_element_check1(copy.deepcopy(empty_dict), rand_ints)\n",
- "%timeit add_element_check2(copy.deepcopy(empty_dict), rand_ints)\n",
- "%timeit add_element_except(copy.deepcopy(empty_dict), rand_ints)\n",
- "\n",
- "print('\\nResults for 1000 integers in range 1-1000') \n",
- "rand_ints = [random.randrange(1, 10) for i in range(1000)]\n",
- "empty_dict = {}\n",
- "\n",
- "%timeit add_element_check1(copy.deepcopy(empty_dict), rand_ints)\n",
- "%timeit add_element_check2(copy.deepcopy(empty_dict), rand_ints)\n",
- "%timeit add_element_except(copy.deepcopy(empty_dict), rand_ints)\n",
- "\n",
- "#\n",
- "# Python 3.4.0\n",
- "# MacOS X 10.9.2\n",
- "# 2.5 GHz Intel Core i5\n",
- "# 4 GB 1600 Mhz DDR3\n",
- "#"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "Results for 100 integers in range 1-10\n",
- "100000 loops, best of 3: 16.6 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "100000 loops, best of 3: 17.6 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "100000 loops, best of 3: 17.9 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "\n",
- "Results for 1000 integers in range 1-10\n",
- "10000 loops, best of 3: 135 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "10000 loops, best of 3: 125 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "10000 loops, best of 3: 105 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "\n",
- "Results for 1000 integers in range 1-1000\n",
- "10000 loops, best of 3: 122 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "10000 loops, best of 3: 123 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "10000 loops, best of 3: 104 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 13
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Conclusion\n",
- "Interestingly, the `try-except` loop pays off if we have more elements (here: 1000 integers instead of 100) as dictionary keys to check. Also, it doesn't matter much whether the elements exist or do not exist in the dictionary, yet."
- ]
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
diff --git a/.ipynb_checkpoints/timeit_tests-checkpoint.ipynb b/.ipynb_checkpoints/timeit_tests-checkpoint.ipynb
deleted file mode 100644
index d1bc680..0000000
--- a/.ipynb_checkpoints/timeit_tests-checkpoint.ipynb
+++ /dev/null
@@ -1,1883 +0,0 @@
-{
- "metadata": {
- "name": "",
- "signature": "sha256:75d807f509bd9f76b2e14a5a048cb44852a3318bcd0d95afc95d1c9b2904c078"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Sebastian Raschka \n",
- "last updated: 04/14/2014 \n",
- "\n",
- "[Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/benchmarks/timeit_tests.ipynb)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "I am really looking forward to your comments and suggestions to improve and extend this collection! Just send me a quick note \n",
- "via Twitter: [@rasbt](https://twitter.com/rasbt) \n",
- "or Email: [bluewoodtree@gmail.com](mailto:bluewoodtree@gmail.com)\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "# Python benchmarks via `timeit`"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Sections\n",
- "- [String operations](#string_operations)\n",
- " - [String formatting: .format() vs. binary operator %s](#str_format_bin)\n",
- " - [String reversing: [::-1] vs. `''.join(reversed())`](#str_reverse)\n",
- " - [String concatenation: `+=` vs. `''.join()`](#string_concat)\n",
- " - [Assembling strings](#string_assembly) \n",
- " - [Testing if a string is an integer](#is_integer)\n",
- " - [Testing if a string is a number](#is_number)\n",
- "- [List operations](#list_operations)\n",
- " - [List reversing: [::-1] vs. reverse() vs. reversed()](#list_reverse)\n",
- " - [Creating lists using conditional statements](#create_cond_list)\n",
- "- [Dictionary operations](#dict_ops) \n",
- " - [Adding elements to a dictionary](#adding_dict_elements)\n",
- "- [Comprehensions vs. for-loops](#comprehensions)\n",
- "- [Copying files by searching directory trees](#find_copy)\n",
- "- [Returning column vectors slicing through a numpy array](#row_vectors)\n",
- "- [Speed of numpy functions vs Python built-ins and std. lib.](#numpy)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# String operations"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[back to top](#sections)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "## String formatting: `.format()` vs. binary operator `%s`\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[back to top](#sections)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We expect the string .format() method to perform slower than %, because it is doing the formatting for each object itself, where formatting via the binary % is hard-coded for known types. But let's see how big the difference really is..."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "\n",
- "def test_format():\n",
- " return ['{}'.format(i) for i in range(1000000)]\n",
- "\n",
- "def test_binaryop():\n",
- " return ['%s' %i for i in range(1000000)]\n",
- "\n",
- "%timeit test_format()\n",
- "%timeit test_binaryop()\n",
- "\n",
- "#\n",
- "# Python 3.4.0\n",
- "# MacOS X 10.9.2\n",
- "# 2.5 GHz Intel Core i5\n",
- "# 4 GB 1600 Mhz DDR3\n",
- "#"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "1 loops, best of 3: 400 ms per loop\n",
- "1 loops, best of 3: 241 ms per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 3
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "## String reversing: `[::-1]` vs. `''.join(reversed())`"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[back to top](#sections)]"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "\n",
- "def reverse_join(my_str):\n",
- " return ''.join(reversed(my_str))\n",
- " \n",
- "def reverse_slizing(my_str):\n",
- " return my_str[::-1]\n",
- "\n",
- "\n",
- "# Test to show that both work\n",
- "a = reverse_join('abcd')\n",
- "b = reverse_slizing('abcd')\n",
- "assert(a == b and a == 'dcba')\n",
- "\n",
- "%timeit reverse_join('abcd')\n",
- "%timeit reverse_slizing('abcd')\n",
- "\n",
- "# Python 3.4.0\n",
- "# MacOS X 10.9.2\n",
- "# 2.4 GHz Intel Core Duo\n",
- "# 8 GB 1067 Mhz DDR3\n",
- "#"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "1000000 loops, best of 3: 1.28 \u00b5s per loop\n",
- "1000000 loops, best of 3: 337 ns per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 13
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "## String concatenation: `+=` vs. `''.join()`"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[back to top](#sections)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Strings in Python are immutable objects. So, each time we append a character to a string, it has to be created \u201cfrom scratch\u201d in memory. Thus, the answer to the question \u201cWhat is the most efficient way to concatenate strings?\u201d is a quite obvious, but the relative numbers of performance gains are nonetheless interesting."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "\n",
- "def string_add(in_chars):\n",
- " new_str = ''\n",
- " for char in in_chars:\n",
- " new_str += char\n",
- " return new_str\n",
- "\n",
- "def string_join(in_chars):\n",
- " return ''.join(in_chars)\n",
- "\n",
- "test_chars = ['a', 'b', 'c', 'd', 'e', 'f']\n",
- "\n",
- "%timeit string_add(test_chars)\n",
- "%timeit string_join(test_chars)\n",
- "\n",
- "#\n",
- "# Python 3.4.0\n",
- "# MacOS X 10.9.2\n",
- "# 2.5 GHz Intel Core i5\n",
- "# 4 GB 1600 Mhz DDR3\n",
- "#"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "1000000 loops, best of 3: 595 ns per loop\n",
- "1000000 loops, best of 3: 269 ns per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 16
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "## Assembling strings\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[back to top](#sections)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Next, I wanted to compare different methods string \u201cassembly.\u201d This is different from simple string concatenation, which we have seen in the previous section, since we insert values into a string, e.g., from a variable."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "\n",
- "def plus_operator():\n",
- " return 'a' + str(1) + str(2) \n",
- " \n",
- "def format_method():\n",
- " return 'a{}{}'.format(1,2)\n",
- " \n",
- "def binary_operator():\n",
- " return 'a%s%s' %(1,2)\n",
- "\n",
- "%timeit plus_operator()\n",
- "%timeit format_method()\n",
- "%timeit binary_operator()\n",
- "\n",
- "#\n",
- "# Python 3.4.0\n",
- "# MacOS X 10.9.2\n",
- "# 2.5 GHz Intel Core i5\n",
- "# 4 GB 1600 Mhz DDR3\n",
- "#"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "1000000 loops, best of 3: 764 ns per loop\n",
- "1000000 loops, best of 3: 494 ns per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "10000000 loops, best of 3: 79.3 ns per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 17
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "## Testing if a string is an integer"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[back to top](#sections)]"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "\n",
- "def string_is_int(a_str):\n",
- " try:\n",
- " int(a_str)\n",
- " return True\n",
- " except ValueError:\n",
- " return False\n",
- "\n",
- "an_int = '123'\n",
- "no_int = '123abc'\n",
- "\n",
- "%timeit string_is_int(an_int)\n",
- "%timeit string_is_int(no_int)\n",
- "%timeit an_int.isdigit()\n",
- "%timeit no_int.isdigit()\n",
- "\n",
- "#\n",
- "# Python 3.4.0\n",
- "# MacOS X 10.9.2\n",
- "# 2.5 GHz Intel Core i5\n",
- "# 4 GB 1600 Mhz DDR3\n",
- "#"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "1000000 loops, best of 3: 401 ns per loop\n",
- "100000 loops, best of 3: 3.04 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "10000000 loops, best of 3: 92.1 ns per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "10000000 loops, best of 3: 96.3 ns per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 5
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "## Testing if a string is a number"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[back to top](#sections)]"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "\n",
- "def string_is_number(a_str):\n",
- " try:\n",
- " float(a_str)\n",
- " return True\n",
- " except ValueError:\n",
- " return False\n",
- " \n",
- "a_float = '1.234'\n",
- "no_float = '123abc'\n",
- "\n",
- "a_float.replace('.','',1).isdigit()\n",
- "no_float.replace('.','',1).isdigit()\n",
- "\n",
- "%timeit string_is_number(an_int)\n",
- "%timeit string_is_number(no_int)\n",
- "%timeit a_float.replace('.','',1).isdigit()\n",
- "%timeit no_float.replace('.','',1).isdigit()\n",
- "\n",
- "#\n",
- "# Python 3.4.0\n",
- "# MacOS X 10.9.2\n",
- "# 2.5 GHz Intel Core i5\n",
- "# 4 GB 1600 Mhz DDR3\n",
- "#"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "1000000 loops, best of 3: 400 ns per loop\n",
- "1000000 loops, best of 3: 1.15 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "1000000 loops, best of 3: 452 ns per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "1000000 loops, best of 3: 394 ns per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 6
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# List operations"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[back to top](#sections)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "## List reversing - `[::-1]` vs. `reverse()` vs. `reversed()`"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[back to top](#sections)]"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "\n",
- "def reverse_func(my_list):\n",
- " new_list = my_list[:]\n",
- " new_list.reverse()\n",
- " return new_list\n",
- " \n",
- "def reversed_func(my_list):\n",
- " return list(reversed(my_list))\n",
- "\n",
- "def reverse_slizing(my_list):\n",
- " return my_list[::-1]\n",
- "\n",
- "%timeit reverse_func([1,2,3,4,5])\n",
- "%timeit reversed_func([1,2,3,4,5])\n",
- "%timeit reverse_slizing([1,2,3,4,5])\n",
- "\n",
- "# Python 3.4.0\n",
- "# MacOS X 10.9.2\n",
- "# 2.4 GHz Intel Core Duo\n",
- "# 8 GB 1067 Mhz DDR3\n",
- "#"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "1000000 loops, best of 3: 930 ns per loop\n",
- "1000000 loops, best of 3: 1.89 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "1000000 loops, best of 3: 775 ns per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 1
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "## Creating lists using conditional statements\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[back to top](#sections)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "In this test, I attempted to figure out the fastest way to create a new list of elements that meet a certain criterion. For the sake of simplicity, the criterion was to check if an element is even or odd, and only if the element was even, it should be included in the list. For example, the resulting list for numbers in the range from 1 to 10 would be \n",
- "[2, 4, 6, 8, 10].\n",
- "\n",
- "Here, I tested three different approaches: \n",
- "1) a simple for loop with an if-statement check (`cond_loop()`) \n",
- "2) a list comprehension (`list_compr()`) \n",
- "3) the built-in filter() function (`filter_func()`) \n",
- "\n",
- "Note that the filter() function now returns a generator in Python 3, so I had to wrap it in an additional list() function call."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "\n",
- "def cond_loop():\n",
- " even_nums = []\n",
- " for i in range(100):\n",
- " if i % 2 == 0:\n",
- " even_nums.append(i)\n",
- " return even_nums\n",
- "\n",
- "def list_compr():\n",
- " even_nums = [i for i in range(100) if i % 2 == 0]\n",
- " return even_nums\n",
- " \n",
- "def filter_func():\n",
- " even_nums = list(filter((lambda x: x % 2 != 0), range(100)))\n",
- " return even_nums\n",
- "\n",
- "%timeit cond_loop()\n",
- "%timeit list_compr()\n",
- "%timeit filter_func()\n",
- "\n",
- "#\n",
- "# Python 3.4.0\n",
- "# MacOS X 10.9.2\n",
- "# 2.5 GHz Intel Core i5\n",
- "# 4 GB 1600 Mhz DDR3\n",
- "#"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "100000 loops, best of 3: 14.4 \u00b5s per loop\n",
- "100000 loops, best of 3: 12 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "10000 loops, best of 3: 23.9 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 14
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# Dictionary operations "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[back to top](#sections)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "## Adding elements to a Dictionary\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[back to top](#sections)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "All three functions below count how often different elements (values) occur in a list. \n",
- "E.g., for the list ['a', 'b', 'a', 'c'], the dictionary would look like this: \n",
- "`my_dict = {'a': 2, 'b': 1, 'c': 1}`"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import random\n",
- "import timeit\n",
- "from collections import defaultdict\n",
- "\n",
- "\n",
- "def add_element_check1(elements):\n",
- " d = dict()\n",
- " for e in elements:\n",
- " if e not in d:\n",
- " d[e] = 1\n",
- " else:\n",
- " d[e] += 1\n",
- " return d\n",
- " \n",
- "def add_element_check2(elements):\n",
- " d = dict()\n",
- " for e in elements:\n",
- " if e not in d:\n",
- " d[e] = 0\n",
- " d[e] += 1 \n",
- " return d\n",
- " \n",
- "def add_element_except(elements):\n",
- " d = dict()\n",
- " for e in elements:\n",
- " try:\n",
- " d[e] += 1\n",
- " except KeyError:\n",
- " d[e] = 1\n",
- " return d\n",
- " \n",
- "def add_element_defaultdict(elements):\n",
- " d = defaultdict(int)\n",
- " for e in elements:\n",
- " d[e] += 1\n",
- " return d\n",
- "\n",
- "def add_element_get(elements):\n",
- " d = dict()\n",
- " for e in elements:\n",
- " d[e] = d.get(e, 1) + 1\n",
- " return d\n",
- "\n",
- "\n",
- "random.seed(123)\n",
- "\n",
- "print('Results for 100 integers in range 1-10') \n",
- "rand_ints = [random.randrange(1, 10) for i in range(100)]\n",
- "%timeit add_element_check1(rand_ints)\n",
- "%timeit add_element_check2(rand_ints)\n",
- "%timeit add_element_except(rand_ints)\n",
- "%timeit add_element_defaultdict(rand_ints)\n",
- "%timeit add_element_get(rand_ints)\n",
- "\n",
- "print('\\nResults for 1000 integers in range 1-5') \n",
- "rand_ints = [random.randrange(1, 5) for i in range(1000)]\n",
- "%timeit add_element_check1(rand_ints)\n",
- "%timeit add_element_check2(rand_ints)\n",
- "%timeit add_element_except(rand_ints)\n",
- "%timeit add_element_defaultdict(rand_ints)\n",
- "%timeit add_element_get(rand_ints)\n",
- "\n",
- "print('\\nResults for 1000 integers in range 1-1000') \n",
- "rand_ints = [random.randrange(1, 1000) for i in range(1000)]\n",
- "%timeit add_element_check1(rand_ints)\n",
- "%timeit add_element_check2(rand_ints)\n",
- "%timeit add_element_except(rand_ints)\n",
- "%timeit add_element_defaultdict(rand_ints)\n",
- "%timeit add_element_get(rand_ints)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "Results for 100 integers in range 1-10\n",
- "10000 loops, best of 3: 28 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "10000 loops, best of 3: 26.2 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "10000 loops, best of 3: 26.5 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "10000 loops, best of 3: 22.8 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "10000 loops, best of 3: 33.3 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "\n",
- "Results for 1000 integers in range 1-5\n",
- "1000 loops, best of 3: 242 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "1000 loops, best of 3: 239 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "1000 loops, best of 3: 203 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "10000 loops, best of 3: 184 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "1000 loops, best of 3: 350 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "\n",
- "Results for 1000 integers in range 1-1000\n",
- "1000 loops, best of 3: 262 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "1000 loops, best of 3: 370 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "1000 loops, best of 3: 502 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "1000 loops, best of 3: 422 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "1000 loops, best of 3: 373 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 25
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Conclusion"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We see from the results that the `try-except` variant is faster than then the `if element in my_dict` alternative if we have a low number of unique elements (here: 1000 integers in the range 1-5), which makes sense: the `except`-block is skipped if an element is already added as a key to the dictionary. However, in this case the `collections.defaultdict` has even a better performance. \n",
- "However, if we are having a relative large number of unique entries(here: 1000 integers in range 1-1000), the `if element in my_dict` approach outperforms the alternative approaches."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Comprehesions vs. for-loops"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Comprehensions are not only shorter and prettier than ye goode olde for-loop, \n",
- "but they are also up to ~1.2x faster."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "n = 1000\n",
- "\n",
- "#\n",
- "# Python 3.4.0\n",
- "# MacOS X 10.9.2\n",
- "# 2.5 GHz Intel Core i5\n",
- "# 4 GB 1600 Mhz DDR3\n",
- "#"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 19
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Set comprehensions"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "def set_loop(n):\n",
- " a_set = set()\n",
- " for i in range(n):\n",
- " if i % 3 == 0:\n",
- " a_set.add(i)\n",
- " return a_set"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 20
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "def set_compr(n):\n",
- " return {i for i in range(n) if i % 3 == 0}"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 21
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%timeit set_loop(n)\n",
- "%timeit set_compr(n)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "10000 loops, best of 3: 136 \u00b5s per loop\n",
- "10000 loops, best of 3: 113 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 22
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## List comprehensions"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "def list_loop(n):\n",
- " a_list = list()\n",
- " for i in range(n):\n",
- " if i % 3 == 0:\n",
- " a_list.append(i)\n",
- " return a_list"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 23
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "def list_compr(n):\n",
- " return [i for i in range(n) if i % 3 == 0]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 24
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%timeit list_loop(n)\n",
- "%timeit list_compr(n)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "10000 loops, best of 3: 129 \u00b5s per loop\n",
- "10000 loops, best of 3: 111 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 25
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Dictionary comprehensions"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "def dict_loop(n):\n",
- " a_dict = dict()\n",
- " for i in range(n):\n",
- " if i % 3 == 0:\n",
- " a_dict[i] = i\n",
- " return a_dict"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 26
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "def dict_compr(n):\n",
- " return {i:i for i in range(n) if i % 3 == 0}"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 27
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%timeit dict_loop(n)\n",
- "%timeit dict_compr(n)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "10000 loops, best of 3: 121 \u00b5s per loop\n",
- "10000 loops, best of 3: 127 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 28
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Copying files by searching directory trees"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Executing `Unix`/`Linux` shell commands:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import subprocess\n",
- "\n",
- "def subprocess_findcopy(path, search_str, dest): \n",
- " query = 'find %s -name \"%s\" -exec cp {} %s \\;' %(path, search_str, dest)\n",
- " subprocess.call(query, shell=True)\n",
- " return "
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 30
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Using Python's `os.walk()` to search the directory tree recursively and matching patterns via `fnmatch.filter()`"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import shutil\n",
- "import os\n",
- "import fnmatch\n",
- "\n",
- "def walk_findcopy(path, search_str, dest):\n",
- " for path, subdirs, files in os.walk(path):\n",
- " for name in fnmatch.filter(files, search_str):\n",
- " try:\n",
- " shutil.copy(os.path.join(path,name), dest)\n",
- " except NameError:\n",
- " pass\n",
- " return"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 33
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "\n",
- "\n",
- "def findcopy_timeit(inpath, outpath, search_str):\n",
- " \n",
- " shutil.rmtree(outpath)\n",
- " os.mkdir(outpath)\n",
- " print(50*'#')\n",
- " print('subprocsess call')\n",
- " %timeit subprocess_findcopy(inpath, search_str, outpath)\n",
- " print(\"copied %s files\" %len(os.listdir(outpath)))\n",
- " shutil.rmtree(outpath)\n",
- " os.mkdir(outpath)\n",
- " print('\\nos.walk approach')\n",
- " %timeit walk_findcopy(inpath, search_str, outpath)\n",
- " print(\"copied %s files\" %len(os.listdir(outpath)))\n",
- " print(50*'#')\n",
- "\n",
- "print('small tree')\n",
- "inpath = '/Users/sebastian/Desktop/testdir_in'\n",
- "outpath = '/Users/sebastian/Desktop/testdir_out'\n",
- "search_str = '*.png'\n",
- "findcopy_timeit(inpath, outpath, search_str)\n",
- "\n",
- "print('larger tree')\n",
- "inpath = '/Users/sebastian/Dropbox'\n",
- "outpath = '/Users/sebastian/Desktop/testdir_out'\n",
- "search_str = '*.csv'\n",
- "findcopy_timeit(inpath, outpath, search_str)\n"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "small tree\n",
- "##################################################"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "subprocsess call\n",
- "1 loops, best of 3: 268 ms per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "copied 13 files\n",
- "\n",
- "os.walk approach\n",
- "100 loops, best of 3: 12.2 ms per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "copied 13 files\n",
- "##################################################\n",
- "larger tree\n",
- "##################################################\n",
- "subprocsess call\n",
- "1 loops, best of 3: 623 ms per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "copied 105 files\n",
- "\n",
- "os.walk approach\n",
- "1 loops, best of 3: 417 ms per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "copied 105 files\n",
- "##################################################\n"
- ]
- }
- ],
- "prompt_number": 35
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "I have to say that I am really positively surprised. The shell's `find` scales even better than expected!"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "
\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Returning column vectors slicing through a numpy array"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Given a numpy matrix, I want to iterate through it and return each column as a 1-column vector. \n",
- "E.g., if I want to return the 1st column from matrix A below\n",
- "\n",
- "\n",
- "A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])\n",
- ">>> A\n",
- "array([[1, 2, 3],\n",
- " [4, 5, 6],\n",
- " [7, 8, 9]])
\n",
- "\n",
- "I want my result to be:\n",
- "\n",
- "array([[1],\n",
- " [4],\n",
- " [7]])
\n",
- "\n",
- "with `.shape` = `(3,1)`\n",
- "\n",
- "\n",
- "However, the default behavior of numpy is to return the column as a row vector:\n",
- "\n",
- "\n",
- ">>> A[:,0]\n",
- "array([1, 4, 7])\n",
- ">>> A[:,0].shape\n",
- "(3,)\n",
- "
"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import numpy as np\n",
- "\n",
- "# 1st column, e.g., A[:,0,np.newaxis]\n",
- "\n",
- "def colvec_method1(A):\n",
- " for col in A.T:\n",
- " colvec = row[:,np.newaxis]\n",
- " yield colvec"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 83
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# 1st column, e.g., A[:,0:1]\n",
- "\n",
- "def colvec_method2(A):\n",
- " for idx in range(A.shape[1]):\n",
- " colvec = A[:,idx:idx+1]\n",
- " yield colvec"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 82
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# 1st column, e.g., A[:,0].reshape(-1,1)\n",
- "\n",
- "def colvec_method3(A):\n",
- " for idx in range(A.shape[1]):\n",
- " colvec = A[:,idx].reshape(-1,1)\n",
- " yield colvec"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 81
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# 1st column, e.g., np.vstack(A[:,0]\n",
- "\n",
- "def colvec_method4(A):\n",
- " for idx in range(A.shape[1]):\n",
- " colvec = np.vstack(A[:,idx])\n",
- " yield colvec"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 79
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# 1st column, e.g., np.row_stack(A[:,0])\n",
- "\n",
- "def colvec_method5(A):\n",
- " for idx in range(A.shape[1]):\n",
- " colvec = np.row_stack(A[:,idx])\n",
- " yield colvec"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 77
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# 1st column, e.g., np.column_stack((A[:,0],))\n",
- "\n",
- "def colvec_method6(A):\n",
- " for idx in range(A.shape[1]):\n",
- " colvec = np.column_stack((A[:,idx],))\n",
- " yield colvec"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 74
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# 1st column, e.g., A[:,[0]]\n",
- "\n",
- "def colvec_method7(A):\n",
- " for idx in range(A.shape[1]):\n",
- " colvec = A[:,[idx]]\n",
- " yield colvec"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 89
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "def test_method(method, A):\n",
- " for i in method(A): \n",
- " assert i.shape == (A.shape[0],1), \"{}, {}\".format(i.shape, A.shape[0],1)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 69
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "\n",
- "A = np.random.random((300, 3))\n",
- "\n",
- "for method in [\n",
- " colvec_method1, colvec_method2, \n",
- " colvec_method3, colvec_method4, \n",
- " colvec_method5, colvec_method6,\n",
- " colvec_method7]:\n",
- " print('\\nTest:', method.__name__)\n",
- " %timeit test_method(colvec_method2, A)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "Test: colvec_method1\n",
- "100000 loops, best of 3: 16.6 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "\n",
- "Test: colvec_method2\n",
- "10000 loops, best of 3: 16.1 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "\n",
- "Test: colvec_method3\n",
- "100000 loops, best of 3: 16.2 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "\n",
- "Test: colvec_method4\n",
- "100000 loops, best of 3: 16.4 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "\n",
- "Test: colvec_method5\n",
- "100000 loops, best of 3: 16.2 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "\n",
- "Test: colvec_method6\n",
- "100000 loops, best of 3: 16.8 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "\n",
- "Test: colvec_method7\n",
- "100000 loops, best of 3: 16.3 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 91
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Speed of numpy functions vs Python built-ins and std. lib."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import numpy as np\n",
- "import timeit\n",
- "\n",
- "samples = list(range(1000000))\n",
- "\n",
- "%timeit(sum(samples))\n",
- "%timeit(np.sum(samples))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "100 loops, best of 3: 18.3 ms per loop\n",
- "10 loops, best of 3: 136 ms per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 6
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%timeit(list(range(1000000)))\n",
- "%timeit(np.arange(1000000))\n",
- "\n",
- "# note that in Python range() is implemented as xrange()\n",
- "# with lazy evaluation (generator)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "10 loops, best of 3: 82.6 ms per loop\n",
- "100 loops, best of 3: 5.35 ms per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 11
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import statistics\n",
- "\n",
- "%timeit(statistics.mean(samples))\n",
- "%timeit(np.mean(samples))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "1 loops, best of 3: 1.14 s per loop\n",
- "10 loops, best of 3: 141 ms per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 14
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [],
- "language": "python",
- "metadata": {},
- "outputs": []
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
diff --git a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
deleted file mode 100644
index 0f94642..0000000
--- a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
+++ /dev/null
@@ -1,2461 +0,0 @@
-{
- "metadata": {
- "name": "",
- "signature": "sha256:90a74ceed4e3dca395013883669de508e2ef94573e58cf24ce3ba49686ca5fe0"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[Sebastian Raschka](http://www.sebastianraschka.com) \n",
- "last updated: 05/06/2014\n",
- "\n",
- "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb) \n",
- "- [Link to the GitHub repository](https://github.com/rasbt/python_reference)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### The code in this notebook was executed in Python 3.4.0"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "I am really looking forward to your comments and suggestions to improve and \n",
- "extend this little collection! Just send me a quick note \n",
- "via Twitter: [@rasbt](https://twitter.com/rasbt) \n",
- "or Email: [bluewoodtree@gmail.com](mailto:bluewoodtree@gmail.com)\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Implementing the least squares fit method for linear regression and speeding it up via Cython"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#Sections"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "- [Introduction](#introduction)\n",
- "- [Least squares fit implementations](#implementations)\n",
- "- [Generating sample data and benchmarking](#sample_data)\n",
- "- [Compiling the Python code via Cython in the IPython notebook](#cython_nb)\n",
- "- [Performance growth rates for different sample sizes](#sample_sizes)\n",
- "- [Bonus: How to use Cython without the IPython magic](#cython_bonus)\n",
- "- [Appendix I: Cython vs. Numba](#numba)\n",
- "- [Appendix II: Cython with and without type declarations](#type_declarations)\n",
- "- [Appendix III: Cython performance after replacing list comprehensions by explicit for loops](#explicit_loops)\n",
- "- [Final Comparison: Cython vs. NumPy vs. SciPy for least squares fitting](#showdown)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- " \n",
- "(Note that this chart just reflects my rather objective thoughts after experimenting with Cython, and it is not based on real numbers or benchmarks.)\n",
- "
\n",
- "
\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Introduction"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[back to top](#sections)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Linear regression via the least squares method is the simplest approach to performing a regression analysis of a dependent and a explanatory variable. The objective is to find the best-fitting straight line through a set of points that minimizes the sum of the squared offsets from the line. \n",
- "The offsets come in 2 different flavors: perpendicular and vertical - with respect to the line. \n",
- " \n",
- " \n",
- "\n",
- "As Michael Burger summarizes it nicely in his article \"[Problems of Linear Least Square Regression - And Approaches to Handle Them](http://www.arsa-conf.com/archive/?vid=1&aid=2&kid=60101-220)\": \"the perpendicular offset method delivers a more precise result but is are more complicated to handle. Therefore normally the vertical offsets are used.\" \n",
- "Here, we will also use the method of computing the vertical offsets.\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In more mathematical terms, our goal is to compute the best fit to *n* points $(x_i, y_i)$ with $i=1,2,...n,$ via linear equation of the form \n",
- "$f(x) = a\\cdot x + b$. \n",
- "We further have to assume that the y-component is functionally dependent on the x-component. \n",
- "In a cartesian coordinate system, $b$ is the intercept of the straight line with the y-axis, and $a$ is the slope of this line."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In order to obtain the parameters for the linear regression line for a set of multiple points, we can re-write the problem as matrix equation \n",
- "$\\pmb X \\; \\pmb a = \\pmb y$"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "$\\Rightarrow\\Bigg[ \\begin{array}{cc}\n",
- "x_1 & 1 \\\\\n",
- "... & 1 \\\\\n",
- "x_n & 1 \\end{array} \\Bigg]$\n",
- "$\\bigg[ \\begin{array}{c}\n",
- "a \\\\\n",
- "b \\end{array} \\bigg]$\n",
- "$=\\Bigg[ \\begin{array}{c}\n",
- "y_1 \\\\\n",
- "... \\\\\n",
- "y_n \\end{array} \\Bigg]$"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "With a little bit of calculus, we can rearrange the term in order to obtain the parameter vector $\\pmb a = [a\\;b]^T$"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "$\\Rightarrow \\pmb a = (\\pmb X^T \\; \\pmb X)^{-1} \\pmb X^T \\; \\pmb y$"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "The more classic approach to obtain the slope parameter $a$ and y-axis intercept $b$ would be:"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "$a = \\frac{S_{x,y}}{\\sigma_{x}^{2}}\\quad$ (slope)\n",
- "\n",
- "\n",
- "$b = \\bar{y} - a\\bar{x}\\quad$ (y-axis intercept)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "where \n",
- "\n",
- "\n",
- "$S_{xy} = \\sum_{i=1}^{n} (x_i - \\bar{x})(y_i - \\bar{y})\\quad$ (covariance)\n",
- "\n",
- "\n",
- "$\\sigma{_x}^{2} = \\sum_{i=1}^{n} (x_i - \\bar{x})^2\\quad$ (variance)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "## Least squares fit implementations"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[back to top](#sections)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1. The matrix approach"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "First, let us implement the equation:"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "$\\pmb a = (\\pmb X^T \\; \\pmb X)^{-1} \\pmb X^T \\; \\pmb y$"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "which I will refer to as the \"matrix approach\"."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import numpy as np\n",
- "\n",
- "def lin_lstsqr_mat(x, y):\n",
- " \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
- " X = np.vstack([x, np.ones(len(x))]).T\n",
- " return (np.linalg.inv(X.T.dot(X)).dot(X.T)).dot(y)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 1
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2. The classic approach"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Next, we will calculate the parameters separately, using standard library functions in Python only, which I will call the \"classic approach\"."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "$a = \\frac{S_{x,y}}{\\sigma_{x}^{2}}\\quad$ (slope)\n",
- "\n",
- "\n",
- "$b = \\bar{y} - a\\bar{x}\\quad$ (y-axis intercept)"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "def classic_lstsqr(x, y):\n",
- " \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
- " x_avg = sum(x)/len(x)\n",
- " y_avg = sum(y)/len(y)\n",
- " var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
- " cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
- " slope = cov_xy / var_x\n",
- " y_interc = y_avg - slope*x_avg\n",
- " return (slope, y_interc)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 2
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 3. Using the lstsq numpy function"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "For our convenience, `numpy` has a function that can computes the least squares solution of a linear matrix equation. For more information, please refer to the [documentation](http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html)."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "def numpy_lstsqr(x, y):\n",
- " \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
- " X = np.vstack([x, np.ones(len(x))]).T\n",
- " return np.linalg.lstsq(X,y)[0]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 3
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 4. Using the linregress scipy function"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Also scipy has a least squares function, `scipy.stats.linregress()`, which returns a tuple of 5 different attributes, where the 1st value in the tuple is the slope, and the second value is the y-axis intercept, respectively. \n",
- "The documentation for this function can be found [here](http://docs.scipy.org/doc/scipy-0.13.0/reference/generated/scipy.stats.linregress.html)."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import scipy.stats\n",
- "\n",
- "def scipy_lstsqr(x,y):\n",
- " \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
- " return scipy.stats.linregress(x, y)[0:2]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 4
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Teaser"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "The following plot below should give you an impression of how the performance of the 4 different approaches will compare to each other at the end of this article."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Generating sample data and benchmarking"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[back to top](#sections)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In order to test our different least squares fit implementations, we will generate some sample data: \n",
- "- 500 sample points for the x-component within the range [0,500) \n",
- "- 500 sample points for the y-component within the range [100,600) \n",
- "\n",
- "where each sample point is multiplied by a random value within\n",
- "the range [0.8, 12) to simulate some scatter in our dataset."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import random\n",
- "random.seed(12345)\n",
- "\n",
- "x = [x_i*random.randrange(8,12)/10 for x_i in range(500)]\n",
- "y = [y_i*random.randrange(8,12)/10 for y_i in range(100,600)]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 5
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "#### Visualization"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To check how our dataset is distributed, and how the least squares regression line looks like, we will plot the results in a scatter plot. \n",
- "Note that we are only using our \"matrix approach\" to visualize the results - for simplicity. We expect all 4 approaches to produce similar results, which we will confirm after visualizing the data."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%pylab inline"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 7
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "from matplotlib import pyplot as plt\n",
- "\n",
- "slope, intercept = lin_lstsqr_mat(x, y)\n",
- "\n",
- "line_x = [round(min(x)) - 1, round(max(x)) + 1]\n",
- "line_y = [slope*x_i + intercept for x_i in line_x]\n",
- "\n",
- "plt.figure(figsize=(8,8))\n",
- "plt.scatter(x,y)\n",
- "plt.plot(line_x, line_y, color='red', lw='2')\n",
- "\n",
- "plt.ylabel('y')\n",
- "plt.xlabel('x')\n",
- "plt.title('Linear regression via least squares fit')\n",
- "\n",
- "ftext = 'y = ax + b = {:.3f} + {:.3f}x'\\\n",
- " .format(slope, intercept)\n",
- "plt.figtext(.15,.8, ftext, fontsize=11, ha='left')\n",
- "\n",
- "plt.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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l74wWGRlJmTJlsixjzJgxjBkzBtBd0q74tMeqhYUFzs7OALi4uNCnTx+OHDmi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\nESNex9Sp5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44dQ7NmbWAy1QQwG8BiAFuh0ZyFQuEJtdoXg9LCMAOjAABfaDzRdswYVN63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\nYII=\n",
- "text": [
- ""
- ]
- }
- ],
- "prompt_number": 8
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "#### Comparing the results from the different implementations"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "As mentioned above, let us now confirm that the different implementations computed the same parameters (i.e., slope and y-axis intercept) as solution of the linear equation."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import prettytable\n",
- "\n",
- "params = [appr(x,y) for appr in [lin_lstsqr_mat, classic_lstsqr, numpy_lstsqr, scipy_lstsqr]]\n",
- "\n",
- "print(params)\n",
- "\n",
- "fit_table = prettytable.PrettyTable([\"\", \"slope\", \"y-intercept\"])\n",
- "fit_table.add_row([\"matrix approach\", params[0][0], params[0][1]])\n",
- "fit_table.add_row([\"classic approach\", params[1][0], params[1][1]])\n",
- "fit_table.add_row([\"numpy function\", params[2][0], params[2][1]])\n",
- "fit_table.add_row([\"scipy function\", params[3][0], params[3][1]])\n",
- "\n",
- "print(fit_table)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "[array([ 0.95181895, 107.01399744]), (0.95181895319126741, 107.01399744459181), array([ 0.95181895, 107.01399744]), (0.95181895319126764, 107.01399744459175)]\n",
- "+------------------+----------------+---------------+\n",
- "| | slope | y-intercept |\n",
- "+------------------+----------------+---------------+\n",
- "| matrix approach | 0.951818953191 | 107.013997445 |\n",
- "| classic approach | 0.951818953191 | 107.013997445 |\n",
- "| numpy function | 0.951818953191 | 107.013997445 |\n",
- "| scipy function | 0.951818953191 | 107.013997445 |\n",
- "+------------------+----------------+---------------+\n"
- ]
- }
- ],
- "prompt_number": 9
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "#### Initial performance comparison"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To get an initial impression of how the performances of the different least squares implementations compare against each other, let us do a quick benchmark using the `timeit` module via IPython's `%timeit` magic."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "\n",
- "for lab,appr in zip([\"matrix approach\",\"classic approach\",\n",
- " \"numpy function\",\"scipy function\"],\n",
- " [lin_lstsqr_mat, classic_lstsqr, \n",
- " numpy_lstsqr, scipy_lstsqr]):\n",
- " print(\"\\n{}: \".format(lab), end=\"\")\n",
- " %timeit appr(x, y)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "matrix approach: 10000 loops, best of 3: 158 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "\n",
- "classic approach: 1000 loops, best of 3: 1.6 ms per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "\n",
- "numpy function: 1000 loops, best of 3: 217 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "\n",
- "scipy function: 1000 loops, best of 3: 366 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 10
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "The timing above indicates that the \"classic\" approach (Python's standard library functions only) is significantly worse in performance than the other implemenations - roughly by a magnitude of 10. However, we should keep in mind that this experiment was done for a fixed sample size n=500."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "
\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Compiling the Python code via Cython in the IPython notebook"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[back to top](#sections)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Maybe we can speed things up a little bit via [Cython's C-extensions for Python](http://cython.org). Cython is basically a hybrid between C and Python and can be pictured as compiled Python code with type declarations. \n",
- "Since we are working in an IPython notebook here, we can make use of the very convenient *IPython magic*: It will take care of the conversion to C code, the compilation, and eventually the loading of the function. \n",
- "Let us also compile the functions for the other 3 least squares approaches - those already use numpy objects and therefore we don't expect any performance gain here, but it is good to be thorough.\n",
- "\n",
- "**Note** \n",
- "Of course Cython has much more horsepower under its hood - more than I am showing in this section of this article (for example, I am not using Cython's type definitions via `cdef` here, but we will get to it [in a later section - Appendix II](#type_declarations)). \n",
- "The focus of this section should be on how to speed up existing Python code by making only minimal changes to it. "
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%load_ext cythonmagic"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 11
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%%cython\n",
- "import numpy as np\n",
- "import scipy.stats\n",
- "cimport numpy as np\n",
- "\n",
- "def cy_lin_lstsqr_mat(x, y):\n",
- " \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
- " X = np.vstack([x, np.ones(len(x))]).T\n",
- " return (np.linalg.inv(X.T.dot(X)).dot(X.T)).dot(y)\n",
- "\n",
- "def cy_classic_lstsqr(x, y):\n",
- " \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
- " x_avg = sum(x)/len(x)\n",
- " y_avg = sum(y)/len(y)\n",
- " var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
- " cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
- " slope = cov_xy / var_x\n",
- " y_interc = y_avg - slope*x_avg\n",
- " return (slope, y_interc)\n",
- "\n",
- "def cy_numpy_lstsqr(x, y):\n",
- " \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
- " X = np.vstack([x, np.ones(len(x))]).T\n",
- " return np.linalg.lstsq(X,y)[0]\n",
- "\n",
- "def cy_scipy_lstsqr(x,y):\n",
- " \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
- " return scipy.stats.linregress(x, y)[0:2]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 12
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Comparing the compiled Cython code to the original Python code"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "\n",
- "for lab,appr in zip([\"matrix approach\",\"classic approach\",\n",
- " \"numpy function\",\"scipy function\"],\n",
- " [(lin_lstsqr_mat, cy_lin_lstsqr_mat), \n",
- " (classic_lstsqr, cy_classic_lstsqr),\n",
- " (numpy_lstsqr, cy_numpy_lstsqr),\n",
- " (scipy_lstsqr, cy_scipy_lstsqr)]):\n",
- " print(\"\\n\\n{}: \".format(lab))\n",
- " %timeit appr[0](x, y)\n",
- " %timeit appr[1](x, y)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "\n",
- "matrix approach: \n",
- "10000 loops, best of 3: 159 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "10000 loops, best of 3: 159 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "\n",
- "\n",
- "classic approach: \n",
- "1000 loops, best of 3: 1.62 ms per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "10000 loops, best of 3: 126 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "\n",
- "\n",
- "numpy function: \n",
- "1000 loops, best of 3: 218 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "1000 loops, best of 3: 220 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "\n",
- "\n",
- "scipy function: \n",
- "1000 loops, best of 3: 372 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "1000 loops, best of 3: 354 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- }
- ],
- "prompt_number": 13
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "
\n",
- "As we've seen before, our \"classic\" implementation of the least square method is pretty slow compared to the other approaches which use Numpy objects. This is not surprising, since Numpy is highly optimized and uses compiled C/C++ and Fortran code already. This explains why there is no significant difference if we used Cython to compile the numpy-objects-containing functions. \n",
- "However, we were able to speed up the \"classic approach\" quite significantly via Cython, roughly by 1500%.\n",
- "\n",
- "The following 2 code blocks are just to visualize our results in a bar plot."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "\n",
- "funcs = ['classic_lstsqr', 'cy_classic_lstsqr', \n",
- " 'lin_lstsqr_mat', 'numpy_lstsqr', 'scipy_lstsqr']\n",
- "labels = ['classic approach','classic approach (cython)', \n",
- " 'matrix approach', 'numpy function', 'scipy function']\n",
- "\n",
- "times = [timeit.Timer('%s(x,y)' %f, \n",
- " 'from __main__ import %s, x, y' %f).timeit(1000)\n",
- " for f in funcs]\n",
- "\n",
- "times_rel = [times[0]/times[i+1] for i in range(len(times[1:]))]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 14
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import matplotlib.pyplot as plt\n",
- "\n",
- "x_pos = np.arange(len(funcs))\n",
- "plt.bar(x_pos, times, align='center', alpha=0.5)\n",
- "plt.xticks(x_pos, labels, rotation=45)\n",
- "plt.ylabel('time in ms')\n",
- "plt.title('Performance of different least square fit implementations')\n",
- "plt.grid()\n",
- "plt.show()\n",
- "\n",
- "x_pos = np.arange(len(funcs[1:]))\n",
- "plt.bar(x_pos, times_rel, align='center', alpha=0.5, color=\"green\")\n",
- "plt.xticks(x_pos, labels[1:], rotation=45)\n",
- "plt.ylabel('relative performance gain')\n",
- "plt.title('Performance gain compared to the classic least square implementation')\n",
- "plt.grid()\n",
- "plt.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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lDDdr1gzffPONSoNijDFW8yosCKdOncLixYshk8lQUFAAoOihCxcuXFB5cLqC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\nHqKjo5GbmwuBQACRSITy8nIsXLgQ+/fvh4WFhdpO4WtuygJ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HD9ChQwfUrl0bHTt2xKNHj8qz6YwxxgyAbAXPwsICAGBpaYmbN2/CxMQEaWkv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\n6gQJeEIIIeqE/weOwIO8Og7UhQAAAABJRU5ErkJggg==\n",
- "text": [
- ""
- ]
- }
- ],
- "prompt_number": 16
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Performance growth rates for different sample sizes"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[back to top](#sections)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In the previous section, we have seen how the different implemantations perform for a fixed sample size n=500. Now, let us take a look at the effect of different sample sizes on the relative performances for each approach. We will consider the sample sizes 10, 100, 1000, 10000, 100000, and 1000000."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "import random\n",
- "random.seed(12345)\n",
- "\n",
- "funcs = ['cy_classic_lstsqr', \n",
- " 'lin_lstsqr_mat', 'numpy_lstsqr', 'scipy_lstsqr']\n",
- "\n",
- "orders_n = [10**n for n in range(1, 7)]\n",
- "times_n = {f:[] for f in funcs}\n",
- "\n",
- "for n in orders_n:\n",
- " x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
- " y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
- " for f in funcs:\n",
- " times_n[f].append(timeit.Timer('%s(x,y)' %f, \n",
- " 'from __main__ import %s, x, y' %f).timeit(1000))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 17
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import matplotlib.pyplot as plt\n",
- "\n",
- "labels = [('cy_classic_lstsqr', 'Cython implementation'), \n",
- " ('lin_lstsqr_mat', 'numpy matrix equation'),\n",
- " ('numpy_lstsqr', 'numpy.linalg.lstsq()'), \n",
- " ('scipy_lstsqr', 'scipy.stats.linregress()')] \n",
- "\n",
- "matplotlib.rcParams.update({'font.size': 12})\n",
- "\n",
- "fig = plt.figure(figsize=(10,8))\n",
- "for lb in labels:\n",
- " plt.plot(orders_n, times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
- "plt.xlabel('sample size n')\n",
- "plt.ylabel('performance gain relative to the slowest approach')\n",
- "plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
- "plt.legend(loc=2)\n",
- "plt.grid()\n",
- "plt.xscale('log')\n",
- "plt.yscale('log')\n",
- "plt.title('Performance of least square fit implementations for different sample sizes')\n",
- "plt.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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GO0dkZCQiIyOxaNEiuadZG7QzV1HR2Ho6BuPtwtocg8FgVM57uWaOwWAwysLW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\nFgv33ntv01FkoaGhjBkzhpSUFKA5G9Zjz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- "text": [
- ""
- ]
- }
- ],
- "prompt_number": 19
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "In this performance comparison using different sample sizes, we can see that our Cython approach is actually not so fast anymore. However, this is just the simplest approach to using Cython. \n",
- "There are a lot of improvements that can be made. In a [later section](#showdown) we will come back to this comparison and see how the Cython version of our simple least squares implementation holds up against the other approaches, after we tweaked it a little bit.\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Bonus: How to use Cython without the IPython magic"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[back to top](#sections)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "IPython's notebook is really great for explanatory analysis and documentation, but what if we want to compile our Python code via Cython without letting IPython's magic doing all the work? \n",
- "These are the steps you would need."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### 1. Creating a .pyx file containing the the desired code or function."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%%file ccy_classic_lstsqr.pyx\n",
- "\n",
- "def ccy_classic_lstsqr(x, y):\n",
- " \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
- " x_avg = sum(x)/len(x)\n",
- " y_avg = sum(y)/len(y)\n",
- " var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
- " cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
- " slope = cov_xy / var_x\n",
- " y_interc = y_avg - slope*x_avg\n",
- " return (slope, y_interc)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "Writing ccy_classic_lstsqr.pyx\n"
- ]
- }
- ],
- "prompt_number": 11
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### 2. Creating a simple setup file"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%%file setup.py\n",
- "\n",
- "from distutils.core import setup\n",
- "from distutils.extension import Extension\n",
- "from Cython.Distutils import build_ext\n",
- "\n",
- "setup(\n",
- " cmdclass = {'build_ext': build_ext},\n",
- " ext_modules = [Extension(\"ccy_classic_lstsqr\", [\"ccy_classic_lstsqr.pyx\"])]\n",
- ")"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "Writing setup.py\n"
- ]
- }
- ],
- "prompt_number": 12
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "
\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "####3. Building and Compiling"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "!python3 setup.py build_ext --inplace"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "running build_ext\r\n"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "cythoning ccy_classic_lstsqr.pyx to ccy_classic_lstsqr.c\r\n"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "building 'ccy_classic_lstsqr' extension\r\n",
- "creating build\r\n",
- "creating build/temp.macosx-10.6-intel-3.4\r\n",
- "/usr/bin/clang -fno-strict-aliasing -Werror=declaration-after-statement -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -arch i386 -arch x86_64 -g -I/Library/Frameworks/Python.framework/Versions/3.4/include/python3.4m -c ccy_classic_lstsqr.c -o build/temp.macosx-10.6-intel-3.4/ccy_classic_lstsqr.o\r\n"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\u001b[1mccy_classic_lstsqr.c:2040:28: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyObject_AsString'\r\n",
- " [-Wunused-function]\u001b[0m\r\n",
- "static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) {\r\n",
- "\u001b[0;1;32m ^\r\n",
- "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2037:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
- " '__Pyx_PyUnicode_FromString' [-Wunused-function]\u001b[0m\r\n",
- "static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char* c_str) {\r\n",
- "\u001b[0;1;32m ^\r\n",
- "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2104:26: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyObject_IsTrue'\r\n",
- " [-Wunused-function]\u001b[0m\r\n",
- "static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {\r\n",
- "\u001b[0;1;32m ^\r\n",
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- "stream": "stdout",
- "text": [
- "8"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- " warnings generated.\r\n",
- "/usr/bin/clang -bundle -undefined dynamic_lookup -arch i386 -arch x86_64 -g build/temp.macosx-10.6-intel-3.4/ccy_classic_lstsqr.o -o /Users/sebastian/Github/python_reference/benchmarks/ccy_classic_lstsqr.so\r\n"
- ]
- }
- ],
- "prompt_number": 13
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### 4. Importing and running the code"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import ccy_classic_lstsqr\n",
- "\n",
- "%timeit classic_lstsqr(x, y)\n",
- "%timeit cy_classic_lstsqr(x, y)\n",
- "%timeit ccy_classic_lstsqr.ccy_classic_lstsqr(x, y)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "100 loops, best of 3: 2.9 ms per loop\n",
- "1000 loops, best of 3: 212 \u00b5s per loop"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n",
- "1000 loops, best of 3: 207 \u00b5s per loop"
- ]
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- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
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- "prompt_number": 20
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- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Appendix I: Cython vs. Numba"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[back to top](#sections)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Like we did with Cython before, we will use the minimalist approach to Numba and see how the two - Cython and Numba - compare against each other. \n",
- "\n",
- "Numba is using the [LLVM compiler infrastructure](http://llvm.org) for compiling Python code to machine code. Its strength is to work with NumPy arrays to speed-up the code. If you want to read more about Numba, please see refer to the original [website and documentation](http://numba.pydata.org/numba-doc/0.13/index.html)."
- ]
- },
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- "metadata": {},
- "source": [
- "
\n",
- "Here is our \"classic\" approach in Python, where I removed the list comprehensions, since they caused errors in the Numba compilation."
- ]
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- "cell_type": "code",
- "collapsed": false,
- "input": [
- "def lstsqr(x, y):\n",
- " \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
- " x_avg = sum(x)/len(x)\n",
- " y_avg = sum(y)/len(y)\n",
- " var_x = 0\n",
- " for x_i in x:\n",
- " var_x += (x_i - x_avg)**2\n",
- " cov_xy = 0\n",
- " for x_i, y_i in zip(x,y):\n",
- " cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
- " slope = cov_xy / var_x\n",
- " y_interc = y_avg - slope*x_avg\n",
- " return (slope, y_interc)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 22
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The Cython-compiled version of it:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%load_ext cythonmagic"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%%cython\n",
- "\n",
- "def cy_lstsqr(x, y):\n",
- " \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
- " x_avg = sum(x)/len(x)\n",
- " y_avg = sum(y)/len(y)\n",
- " var_x = 0\n",
- " for x_i in x:\n",
- " var_x += (x_i - x_avg)**2\n",
- " cov_xy = 0\n",
- " for x_i, y_i in zip(x,y):\n",
- " cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
- " slope = cov_xy / var_x\n",
- " y_interc = y_avg - slope*x_avg\n",
- " return (slope, y_interc)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 26
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "And now the Numba-compiled version:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "from numba import jit\n",
- "\n",
- "@jit\n",
- "def nmb_lstsqr(x, y):\n",
- " \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
- " x_avg = sum(x)/len(x)\n",
- " y_avg = sum(y)/len(y)\n",
- " var_x = 0\n",
- " for x_i in x:\n",
- " var_x += (x_i - x_avg)**2\n",
- " cov_xy = 0\n",
- " for x_i, y_i in zip(x,y):\n",
- " cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
- " \n",
- " slope = cov_xy / var_x\n",
- " y_interc = y_avg - slope*x_avg\n",
- " return (slope, y_interc)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 27
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "
\n",
- "Now, let us see how the different approaches compare against each other for different sample sizes."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "import random\n",
- "random.seed(12345)\n",
- "\n",
- "funcs = ['lstsqr', 'cy_lstsqr', 'nmb_lstsqr'] \n",
- "orders_n = [10**n for n in range(1, 7)]\n",
- "times_n = {f:[] for f in funcs}\n",
- "\n",
- "for n in orders_n:\n",
- " x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
- " y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
- " for f in funcs:\n",
- " times_n[f].append(timeit.Timer('%s(x,y)' %f, \n",
- " 'from __main__ import %s, x, y' %f).timeit(1000))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 28
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%pylab inline"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import matplotlib.pyplot as plt\n",
- "\n",
- "plt.figure(figsize=(10,8))\n",
- "\n",
- "for f in times_n.keys():\n",
- " plt.plot(orders_n, times_n[f], alpha=0.5, label=f, marker='o', lw=2)\n",
- "\n",
- "plt.xlabel('sample size n')\n",
- "plt.ylabel('time in ms')\n",
- "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
- "plt.legend(loc=2)\n",
- "plt.grid()\n",
- "\n",
- "plt.title('Performance of a simple least square fit implementation')\n",
- "plt.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
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yxz/+EYC7774bAH9/f3x8fNi5cyeZmZn0798ff39/goKCGDdunPl6vvnmG9q1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hNGZB1t9ja+yPuCEyUn8MKLOUPnMDlm8u0jKCoqJPvr6lXZ+0tNlTN/nLAPWFIitV5enlwHBsK0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\n9RDoJxgTb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- "text": [
- ""
- ]
- }
- ],
- "prompt_number": 32
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Without making any modifications to the original Python code - thereby we are not accounting for the particular strengths of Numba (Numpy) and Cython (static type declarations), we see that Cython performs significantly better than Numba."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Appendix II: Cython with and without type declarations"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[back to top](#sections)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In the sections above, we have been using the simplest approach to Cython without using static type declarations and thereby neglecting one of its major strengths. \n",
- "Now, let us see how and if we can further improve the Cython implementation of our \"classic least squares approach\" by adding those type declarations."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Here is our \"classic\" approach in Python:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "def lstsqr(x, y):\n",
- " \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
- " x_avg = sum(x)/len(x)\n",
- " y_avg = sum(y)/len(y)\n",
- " var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
- " cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
- " slope = cov_xy / var_x\n",
- " y_interc = y_avg - slope*x_avg\n",
- " return (slope, y_interc)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 21
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The Cython-compiled version of it:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%load_ext cythonmagic"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 23
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%%cython\n",
- "def cy_lstsqr(x, y):\n",
- " \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
- " x_avg = sum(x)/len(x)\n",
- " y_avg = sum(y)/len(y)\n",
- " var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
- " cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
- " slope = cov_xy / var_x\n",
- " y_interc = y_avg - slope*x_avg\n",
- " return (slope, y_interc)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 24
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "And now, the same code with static type declarations:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%%cython\n",
- "def static_type_lstsqr(x, y):\n",
- " \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
- " cdef double x_avg, y_avg, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
- " x_avg = sum(x)/len(x)\n",
- " y_avg = sum(y)/len(y)\n",
- " var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
- " cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
- " slope = cov_xy / var_x\n",
- " y_interc = y_avg - slope*x_avg\n",
- " return (slope, y_interc)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 25
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "
\n",
- "Now, let us see how the two functions (with and without static type declarations) compare against each other for different sample sizes."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "import random\n",
- "random.seed(12345)\n",
- "\n",
- "funcs = ['cy_lstsqr', 'static_type_lstsqr'] \n",
- "labels = ['simple Cython', 'Cython w. type declarations']\n",
- "orders_n = [10**n for n in range(1, 7)]\n",
- "times_n = {f:[] for f in funcs}\n",
- "\n",
- "for n in orders_n:\n",
- " x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
- " y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
- " for f in funcs:\n",
- " times_n[f].append(timeit.Timer('%s(x,y)' %f, \n",
- " 'from __main__ import %s, x, y' %f).timeit(1000))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 26
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%pylab inline"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import matplotlib.pyplot as plt\n",
- "\n",
- "plt.figure(figsize=(10,8))\n",
- "\n",
- "for f in times_n.keys():\n",
- " plt.plot(orders_n, times_n[f], alpha=0.5, label=f, marker='o', lw=2)\n",
- "\n",
- "plt.xlabel('sample size n')\n",
- "plt.ylabel('time in ms')\n",
- "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
- "plt.legend(loc=2)\n",
- "plt.grid()\n",
- "\n",
- "plt.title('Performance of a simple least square fit implementation')\n",
- "plt.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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jP/j836tVq1bgMSEEFAoFPvroIxw8eBBfffUVnJ2dYWpqitmzZyMtLU1j2S83QysUCqhU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\nO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- "text": [
- ""
- ]
- }
- ],
- "prompt_number": 27
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "
\n",
- "The improvement is pretty significant when static type declarations are used. \n",
- "One more experiment to see by how much we could improve our \"classic least squares\" code via Cython compared to the initial Python implementation:\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import random\n",
- "random.seed(12345)\n",
- "\n",
- "x = [x_i*random.randrange(8,12)/10 for x_i in range(500)]\n",
- "y = [y_i*random.randrange(8,12)/10 for y_i in range(100,600)]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 30
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "\n",
- "funcs = ['classic_lstsqr', 'cy_lstsqr', 'static_type_lstsqr', \n",
- " 'lin_lstsqr_mat', 'numpy_lstsqr', 'scipy_lstsqr']\n",
- "labels = ['classic approach','classic approach (cython)', \n",
- " 'classic approach (cython + type decl.)',\n",
- " 'matrix approach', 'numpy function', 'scipy function']\n",
- "\n",
- "times = [timeit.Timer('%s(x,y)' %f, \n",
- " 'from __main__ import %s, x, y' %f).timeit(1000)\n",
- " for f in funcs]\n",
- "\n",
- "times_rel = [times[0]/times[i+1] for i in range(len(times[1:]))]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 31
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%pylab inline"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import matplotlib.pyplot as plt\n",
- "\n",
- "x_pos = np.arange(len(funcs[1:]))\n",
- "plt.bar(x_pos, times_rel, align='center', alpha=0.5, color=\"green\")\n",
- "plt.xticks(x_pos, labels[1:], rotation=90)\n",
- "plt.ylabel('rel. performance gain')\n",
- "plt.title('Performance gain compared to the classic least square'\\\n",
- " '(n={})'.format(len(x)))\n",
- "plt.grid()\n",
- "plt.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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8wLx58/Dee+/hww8/RH5+PubPny9P98orryAqKgpdunRRMVrGGGMiEWpNFQAW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\nVsKiSkREZCX/D2lbGZgZrg2GAAAAAElFTkSuQmCC\n",
- "text": [
- ""
- ]
- }
- ],
- "prompt_number": 35
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This is a pretty significant performance gain. The \"Cython + type declarations\" approach sped up our initial Python code 25 times."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Appendix III: Cython performance after replacing list comprehensions by explicit for loops"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[[back to top](#sections)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In Python, we have those wonderful list, set, and dictionary comprehensions, which do not only look prettier and are easier to read (at least most of the time) than nested loop structures, but they also additionally come with some small performance benefits. \n",
- "Does this also apply in Cython? Let's check it out."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "This is the code for our \"classic\" least squares approach that we have been using in the previous sections:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "def lstsqr_comprehensions(x, y):\n",
- " \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
- " x_avg = sum(x)/len(x)\n",
- " y_avg = sum(y)/len(y)\n",
- " var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
- " cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
- " slope = cov_xy / var_x\n",
- " y_interc = y_avg - slope*x_avg\n",
- " return (slope, y_interc)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 46
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "And here is a version where I replaced the list comprehensions by for-loops:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "def lstsqr_loops(x, y):\n",
- " \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
- " x_avg = sum(x)/len(x)\n",
- " y_avg = sum(y)/len(y)\n",
- " var_x = 0\n",
- " for x_i in x:\n",
- " var_x += (x_i - x_avg)**2\n",
- " cov_xy = 0\n",
- " for x_i, y_i in zip(x,y):\n",
- " cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
- " slope = cov_xy / var_x\n",
- " y_interc = y_avg - slope*x_avg\n",
- " return (slope, y_interc)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 48
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "Finally, the Cython versions of the two functions (with and without using list comprehensions) that we have defined above:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%load_ext cythonmagic"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%%cython\n",
- "\n",
- "def cy_lstsqr_comprehensions(x, y):\n",
- " \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
- " cdef double x_avg, y_avg, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
- " x_avg = sum(x)/len(x)\n",
- " y_avg = sum(y)/len(y)\n",
- " var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
- " cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
- " slope = cov_xy / var_x\n",
- " y_interc = y_avg - slope*x_avg\n",
- " return (slope, y_interc)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 49
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%%cython\n",
- "\n",
- "def cy_lstsqr_loops(x, y):\n",
- " \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
- " cdef double x_avg, y_avg, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
- " x_avg = sum(x)/len(x)\n",
- " y_avg = sum(y)/len(y)\n",
- " var_x = 0\n",
- " for x_i in x:\n",
- " var_x += (x_i - x_avg)**2\n",
- " cov_xy = 0\n",
- " for x_i, y_i in zip(x,y):\n",
- " cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
- " slope = cov_xy / var_x\n",
- " y_interc = y_avg - slope*x_avg\n",
- " return (slope, y_interc)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 50
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "
\n",
- "We will generate some sample data for different sample sizes and take a look at the results for the regular Python functions, and the Cython code separately."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "import random\n",
- "random.seed(12345)\n",
- "\n",
- "funcs = ['lstsqr_comprehensions', 'lstsqr_loops',\n",
- " 'cy_lstsqr_comprehensions', 'cy_lstsqr_loops'] \n",
- "\n",
- "orders_n = [10**n for n in range(1, 6)]\n",
- "times_n = {f:[] for f in funcs}\n",
- "\n",
- "for n in orders_n:\n",
- " x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
- " y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
- " for f in funcs:\n",
- " times_n[f].append(timeit.Timer('%s(x,y)' %f, \n",
- " 'from __main__ import %s, x, y' %f).timeit(1000))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 52
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "plt.figure(figsize=(8,6))\n",
- "plt.plot(orders_n, times_n['lstsqr_comprehensions'], alpha=0.5, \n",
- " label='list comprehensions', marker='o', lw=2)\n",
- "plt.plot(orders_n, times_n['lstsqr_loops'], alpha=0.5, \n",
- " label='for-loops', marker='o', lw=2)\n",
- "plt.xlabel('sample size n')\n",
- "plt.ylabel('time in ms')\n",
- "plt.legend(loc=2)\n",
- "plt.grid()\n",
- "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
- "plt.title('Performance comparison list comprehensions and for-loops')\n",
- "plt.show()\n",
- "\n",
- "plt.figure(figsize=(8,6))\n",
- "plt.plot(orders_n, times_n['cy_lstsqr_comprehensions'], alpha=0.5, \n",
- " label='list comprehensions (Cython', marker='o', lw=2)\n",
- "plt.plot(orders_n, times_n['cy_lstsqr_loops'], alpha=0.5, \n",
- " label='for-loops (Cython)', marker='o', lw=2)\n",
- "plt.xlabel('sample size n')\n",
- "plt.ylabel('time in ms')\n",
- "plt.legend(loc=2)\n",
- "plt.grid()\n",
- "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
- "plt.title('Performance comparison list comprehensions and for-loops in Cython')\n",
- "plt.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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QSAxrhjnHsVHfAKWhePfddxkyZIjptpnVIddtCyFEzYiJjWHPr3s4d/YcZ9PO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\ne/bw9ttvc/To0Vaf1+l06Ox8OqmU4uT1k6w7u47y+nL8evqxcuRKQt3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8vLwA6Nevn2Ge1ZLWBYpc19bWloSEBGJjYwkKCqJjx45F5u3TTz/F3d0dgOnT\npzN+/HhmzpwJgI2NDdOmTcPa2prevXtTu3ZtYmJiaNu2Lba2tpw4cYLmzZvj5OT0wPuV5/bt24ar\nZoCbN28CULdu3SLfz5EjRzJ48GDmzp0LwPLly3njjTeAos+DTp068dRTTwEwfPhwFi5cCMCBAwe4\ne/euYf2uXbvSt29fVq1axfTp0wEYMGAArVu3BmDYsGFMnjy5yNjKk55rg5ZCcqg5eRK++w4yM8HX\nV+vZne9jWCw951D3V9RZWYUfQk5O6Q8tN7fw15pimLkRI0bw5JNPMmTIEHx8fHj99dcL1HmLq1Nf\nvnyZ+vXrP/B4YmIiWVlZBAT8+UeEv78/V65cMSx7enoafq5VqxYAderUKfBYamqqIQZfX1/Dc/b2\n9ri6uhIfH294LH9Hp7i4OObPn4+Li4vh3+XLlwu8Pq+hvX9fxqz72muvERwcTK9evQgKCuL9998v\nNG/x8fEP5Cb/9t3c3LC2/vO9tbOzM+zjm2++YcuWLQQGBhIaGsqBAwcK3YerqyspKSkFtgmQkJBQ\n6OsB2rVrR61atYiKiuLUqVOcO3eOZ555psjXQ8H30c7Ojnv37pGbm0t8fPwDnc8CAgIMx2llZfXA\nOZB3jELojVLw3//CmjVaI92iBYSHl76R1jvdX1Hb2GhDwd3/x5KHRy6lne560aJcbtx48HFTDDNX\nvXp1pk2bxrRp0wy3shs2bMiYMWNK7Ezm5+fHwYMHH3jc3d0dGxsbYmNjady4MQAXL14s0Ng+DKUU\nly5dMiynpqaSlJRkqL9CwT8o/P39eeutt3jzzTdLvY+89R923fz7rV27NvPmzWPevHmG29Jt27al\na9euBdbx9vZ+IDf5j6U4rVu35vvvvycnJ4dPPvmEwYMHF6jX5wkJCeHDDz80LDds2BA/Pz/WrVvH\nq6++WuT2R40aRWRkJJ6engwaNAjb//V8KexcKO788Pb25tKlSyilDK+Li4ujUaNGpTrOimTp31/V\ng6qcw6wsWL8efv9dq0H36AEdOhRfjy6MnnOo+yvqHj2CyMgo+J3djIxddO8eVKHbKEpUVBTHjx8n\nJycHBwcHbGxsqFatGqBdLZ07d67IdYcNG8bOnTtZu3Yt2dnZ3Lx5k+joaKpVq8bgwYN56623SE1N\nJS4ujg8//LBAD+SHtWXLFvbt20dmZiZvv/027du3N9z2vt+4ceP4/PPPOXjwIEop7t69y+bNm4u9\nYsu7tfubivxFAAAgAElEQVSw6+a/Jbxp0ybOnj2LUgpHR0eqVatW4Mo4T1hYGLNmzSIxMZHExERm\nzpxZqu8jZ2VlsWLFCu7cuUO1atVwcHAwvFf3a9OmDbdv3y5wBbtgwQLeffddIiIiSE5OJjc3l59+\n+onx48cb1hs+fDjffvstK1asYOTIkYbHPT09uXnzJsnJyYUe+/3atWuHnZ0dH3zwAVlZWURFRbFp\n0yaGDBlS4rpC6EVyMixdqjXStrYQFgYdOz58I613um+oGzYMIDw8GA+P3Tg7R+Hhsfuhx3E1xTby\ny9+x6erVqwwaNAgnJyeaNGlCaGioodH4+9//zrp163B1deXll19+YDt+fn5s2bKF+fPn4+bmRqtW\nrTh27BgAn3zyCfb29tSvX59OnToxbNgwRo8e/cD+88dUXLxDhw7lnXfewc3NjSNHjhTo9HT/uo89\n9hhffPEFEydOxNXVlUceeYRly5YVuY/88Riz7tmzZ+nZsycODg506NCBCRMm0KVLlwfWmTp1Kq1b\ntyYkJISQkBBat27N1KlTS5WLyMhI6tWrh5OTE4sXL2bFihWFvs7W1pbw8PACeXruuedYs2YNX375\nJT4+Pnh5eTFt2jT69+9veI2fnx+PPvoo1tbWhj4BAI0aNSIsLIz69evj6upq6PVd1Ptoa2vLxo0b\n2bp1K3Xq1GHixIksX76cBg0aPJC30hx3edLrVYwlqYo5vHxZ6zQWHw8uLvDCC/C/07tM9JxDGUJU\nMHr0aHx9fXn33XfNHYquJCYm0qlTJ44ePVpg0JOSjB07Fh8fH0PnNnOTz5WwNMeOad+Rzs6GwEDt\n+9F2duaOqnzJEKKiWPJLumzc3d05efLkQzXSsbGxfPvtt4wdO7YcI7Msev7+qqWoKjlUCnbuhG+/\n1Rrp1q1hxAjTNNJ6zqE01MIsQ35WRW+//TbNmzdnypQpBXqlCyEgIwNWr4affgJra3j6aW32qyK6\niVQpcutbiCpOPlfC3G7d0ma+un4datXSRhor5JuplVpxn0Pdfz1LCCGEfsXGwtdfQ1oauLvD0KHg\n6mruqCyL3PoWQpQrPdcGLUVlzeFvv2ljdqelwSOPaD27y6uR1nMO5YpaCCFEhcrJge3bIW88pw4d\ntIFMChkWQSA1aiGqPPlciYqUng5r18L581pHsX794H9zDVVpUqMWQghhdjduaJ3GkpLA3h6GDIH7\nhqwXhZAbDSYSExNDy5YtcXR05NNPPzV6e6GhoSxZssQEkZlOWFgY69evL5dtx8bGYm1tTW6u8eOr\n3+8f//gHn3/+ucm3K0pHz7VBS1EZcnjmDPznP1oj7eUFL75YsY20nnMoDbWJfPDBB3Tv3p3k5GTD\nnMHGsLTvNh87doxjx47x7LPPGh5LSEhg7NixeHt74+joSOPGjZkxY0aBea6LEhgYyO7du8szZIN/\n/OMfzJkzxzDntRCi4igF+/fDypXad6WbNIExY8DJydyR6Yc01CYSFxdHkyZNyrRuTk6OiaMxvf/7\nv/8rMOlHUlIS7du3JyMjgwMHDpCcnMwPP/zAnTt3ip1oJE9F1kW9vLxo1KgRGzZsqJD9iYL0PMay\npdBrDrOztZmvduzQGuzQUO070v+bNK5C6TWHUEka6pizMSxas4iFqxeyaM0iYs7GVOg2unXrRlRU\nFBMnTsTR0ZGzZ89y584dRo4ciYeHB4GBgcyePdvQMEVERNCxY0cmT56Mu7s777zzTrHbV0oxa9Ys\nAgMD8fT0ZNSoUQVmWdqwYQNNmzbFxcWFrl27curUKcNzgYGBzJ07l6ZNm+Lq6sqYMWPIyMgAtLGq\n+/bti4uLC25ubnTu3LnIxnPbtm0FJsBYsGABTk5OREZG4u/vD4Cvry8ffvghzZs3Z8KECfzjH/8o\nsI1nnnmGhQsXMnLkSC5evEi/fv1wcHBg3rx5htdERkYSEBBAnTp1mDNnjuHxjIwMXn75ZXx8fPDx\n8eGVV14hMzMT0G5p+fr6smDBAjw9PfH29iYiIqLAvkNDQ9m8eXOxeRZCmE5qKnz1FRw9CjY2WgMd\nGlr1Zr4yBd031DFnY4j4MYIbnje47XWbG543iPgx4qEaWmO3sXv3bjp16sSiRYtITk4mODiYSZMm\nkZKSwoULF9izZw/Lli1j6dKlhnUOHjxIUFAQ169fL3Fu5qVLl/LVV18RFRXF+fPnSU1NNdxeP336\nNEOHDuXjjz8mMTGRPn360K9fP7Kzsw3rr1y5kh07dnDu3DlOnz7NrFmzAJg/fz5+fn4kJiZy/fp1\n3nvvvUJvt9+9e5cLFy7QsGFDw2M7d+5kwIABRcYcHh7OqlWrDA1/YmIiu3btYtiwYSxbtgx/f382\nbdpESkpKgQZ93759nD59ml27djFz5kxiYrT3YPbs2Rw8eJDo6Giio6M5ePCg4TgArl27RnJyMvHx\n8SxZsoQJEyZw584dw/ONGjUiOjq62DyL8qHn2qCl0FsOExLgiy/g0iVwdNRudTdtat6Y9JbD/HTf\n63vnbzup8UgNomKj/nzQBo6tPkabJ9qUahsHfzpImm8axGrLoYGh1HikBrsO76JhcMNi180vr1HK\nyclhzZo1REdHY29vj729Pa+++irLly9nzJgxAHh7ezNhwgQAatasWex2V6xYwauvvkpgYCAA7733\nHs2aNWPp0qWsWbOGvn370r17d0Crx3700Ufs37+fzp07Y2VlxcSJEw1zS7/11ltMmjSJd999F1tb\nWxISEoiNjSUoKIiOHTsWuv/bt28D4ODgYHgsKSmJunXrFhlzmzZtcHJyYteuXfTo0YPVq1fTtWtX\n6tSpU+yxTp8+nRo1ahASEkKLFi2Ijo6mYcOGrFy5kk8//RR3d3fD68aPH2+YgcrGxoZp06ZhbW1N\n7969qV27NjExMbRt29YQe95xCCHKzx9/wHffQVYW+PpqPbtr1zZ3VPqm+yvqLFV4B6EcSl/3zaXw\nnsaZuZkPFUve1WhiYiJZWVkFJl7w9/fnypUrhmW/fN0dX3rpJRwcHHBwcGDu3LkPbDchIeGBbWVn\nZ3Pt2jUSEhIMt57zYvDz8ytyX/7+/sTHxwPw2muvERwcTK9evQgKCuL9998v9LicnZ0BSElJMTzm\n5uZm2E5RRo4caZivOTIy0jAPd3G8vLwMP9vZ2ZGamgpAfHz8AznIv383Nzes842WkH/dvNjzjkNU\nLD3XBi2FHnKoFOzZow0HmpUFLVpAeLjlNNJ6yGFRdH9FbWNlA2hXwfl52Hnw19C/lmobi64t4obn\njQcet7UuW48Hd3d3bGxsiI2NpXHjxgBcvHgRX19fw2vy32L+/PPPi/36kLe3N7GxsYblixcvUr16\ndby8vPD29ub48eOG55RSXLp0yXAFnff6/D97e3sDULt2bebNm8e8efM4ceIE3bp1o02bNnTr1q3A\n/u3t7QkKCiImJoYOHToA0KNHD7777jumT59eZO/04cOH07x5c6Kjozl16hT9+/cv9PhLIy8H+fOZ\ndxylcfLkSVrKqApClIusLPj+ezhxQqtB9+wJ7dtLPdpUdH9F3eOxHmScySjwWMaZDLo/2r1CtwF/\n3vquVq0agwcP5q233iI1NZW4uDg+/PDDAr2mH0ZYWBgffvghsbGxpKam8uabbzJkyBCsra0ZNGgQ\nmzdvZvfu3WRlZTF//nxq1qxpaFCVUnz22WdcuXKFpKQkZs+ezZAhQwDYtGkTZ8+eRSmFo6Mj1apV\no1oRc8r16dOHPXv2GJYnT55McnIyo0aNMvwhcOXKFV599VXDHw6+vr60bt2akSNHMnDgwALzNnt6\nepaqd3j+HMyaNYvExEQSExOZOXNmqa7Q8+zZs4fevXuX+vXCdPRcG7QUlpzDO3fgyy+1RrpGDW1S\njQ4dLK+RtuQclkT3DXXD4IaEdw3H47oHzled8bjuQXjX8IeqLZtiG1DwKvGTTz7B3t6e+vXr06lT\nJ4YNG8bo0aMNr3uYK8oxY8YwYsQIOnfuTP369bGzs+OTTz7RYm/YkMjISCZNmkSdOnXYvHkzGzdu\npHr16oZ9DR061HB7+5FHHmHq1KkAnD17lp49e+Lg4ECHDh2YMGFCgZ7d+b344ousWLHCsOzi4sL+\n/fuxsbGhXbt2ODo60qNHD5ydnQkODja8btSoURw/fvyBRvWf//wns2bNwsXFhQULFjyQv/tNnTqV\n1q1bExISQkhICK1btzYcR0nrJiQkcPLkyQJX9EII412+rHUaS0jQJtN44QVtcg1hWjLWdyVXr149\nlixZ8sDt7LIYNmwYgwcPLjDoSUn27t3L8OHDiYuLM3r/ZfWPf/yD4OBgXnrpJbPFYMnkcyXKIjoa\nNmzQJtioV0/7+pWdnbmj0i8Z61uYRP4r6tLIyspi4cKFjBs3rpwiKp3839MWQhgnNxd27YJ9+7Tl\ntm3hySe1CTZE+dD9rW9hmU6ePImLiwvXrl3j5ZdfNnc4woz0XBu0FJaSw4wMWL1aa6StraFvX+jT\nRx+NtKXksCzkirqSu3Dhgln227hx4wJfjxJC6FtSkjbz1Y0bUKsWDB6s3fIW5U9q1EJUcfK5EiW5\ncEH7fnR6OtSpA2FhWucxYTpSoxZCCFEmhw7B1q1abbpBA3juOe1rWKLiSI1aCFGu9FwbtBTmyGFO\nDmzerP3LzYWOHbXhQPXaSOv5PJQraiGEEAWkpcHatdot72rV4JlntCFBhXnoqkbt6urKrVu3zBCR\nEJWXi4sLSUlJ5g5DWIgbN7ROY0lJ2jjdQ4Zok2uI8lVcjVpXDbUQQojyc+YMrFunfQ2rbl2tkXZy\nMndUVUNx7Z7UqCspPddjLIXk0DQkj8Yr7xwqBfv3w8qVWiPdtKk2h3RlaqT1fB6WW0M9ZswYPD09\nad68ueGxpKQkevbsSYMGDejVq5fMDyyEEGaWna3NfLVjh9Zgd+0KAweCjY25IxN5yu3W9969e6ld\nuzYjR440zKY0ZcoU3N3dmTJlCu+//z63bt0qdP5lufUthBDlLzVVG2ns8mWtYf7LX6BJE3NHVTWZ\nrUYdGxtLv379DA11o0aN2LNnD56enly9epXQ0FBOnTr1UAELIYQwXkKC1mksOVm7xR0WBl5e5o6q\n6rKYGvW1a9fw9PQEtPmIr127VpG7r1L0XI+xFJJD05A8Gs/UOTxxQptDOjkZ/Pxg3LjK30jr+Tw0\n2/eoH3ZOZiGEEMZRCvbsgbw2q1UrePppqC4jali0Cn178m55e3l5kZCQgIeHR5GvDQ8PJzAwEABn\nZ2datmxJaGgo8OdfRrJc/HIeS4lHlqvmct5jlhKPXpfzlHX9Dh1C+f572LJFWx4/PpTHH4c9eyzj\n+Kract7PsbGxlKRCa9RTpkzBzc2N119/nblz53L79m3pTCaEEOXszh2tHn31qjYE6MCB8Mgj5o5K\n5GeWGnVYWBgdOnQgJiYGPz8/li5dyhtvvMEPP/xAgwYN2L17N2+88UZ57b7Ku/+vcPHwJIemIXk0\nnjE5vHQJFi/WGmlXV3jhharZSOv5PCy3W9+rVq0q9PGdO3eW1y6FEELkc/QobNyoTbBRvz4MGqTN\nJS30RYYQFUKISiY3F3bu1EYbA2jbFp58UptgQ1gmmY9aCCGqiHv34JtvtHG7ra2hTx9o3drcUQlj\nyFjflZSe6zGWQnJoGpJH45U2hzdvwn/+ozXSdnYwcqQ00nn0fB7KFbUQQlQC589rc0inp4OHhzbS\nmIuLuaMSpiA1aiGE0DGl4NAh2LZNq003bAgDBmhfwxL6ITVqIYSohHJyYOtW+PVXbfmJJ6BbN602\nLSoPeTsrKT3XYyyF5NA0JI/GKyyHaWmwfLnWSFevrl1F9+ghjXRR9HweyhW1EELozPXr2khjt26B\ngwM8/zz4+po7KlFepEYthBA6EhOjff0qMxO8vWHIEHB0NHdUwlhSoxZCCJ1TCvbtg127tJ+bNYNn\nnwUbG3NHJsqbVDMqKT3XYyyF5NA0JI/G27Uriu++00YbU0rrMPbcc9JIPww9n4dyRS2EEBYsJUX7\n6pW9Pdjawl/+Ao0bmzsqUZGkRi2EEBYqPh5Wr4bkZHB21gYx8fQ0d1SiPEiNWgghdOb332H9esjK\nAn9/rWe3vb25oxLmIDXqSkrP9RhLITk0Dcnjw1EKfvwR1q3TGulHH4WAgChppI2k5/NQGmohhLAQ\nmZnw9dewZw9YWcFTT0G/fjI9ZVUnNWohhLAAt29rg5hcuwY1a8LAgRAcbO6oREWRGrUQQliwixdh\nzRq4exfc3LROY+7u5o5KWAq59V1J6bkeYykkh6YheSzekSPw1VdaIx0UBC+88GAjLTk0np5zKFfU\nQghhBrm58MMP8PPP2vLjj0OvXjKphniQ1KiFEKKC3bun9eo+e1brKPb001rvblF1SY1aCCEsxM2b\nWqexxESws9O+Hx0QYO6ohCWTmyyVlJ7rMZZCcmgaksc/nTsHX3yhNdKenvDii6VrpCWHxtNzDuWK\nWgghyplScPAgbN+u1aYbNdLG7K5Rw9yRCT2QGrUQQpSjnBzYsgV++01b7tRJm/3Kysq8cQnLIjVq\nIYQwg7Q07fvRcXFQvbo2f3Tz5uaOSuiN1KgrKT3XYyyF5NA0qmoer12DxYu1RtrBAUaPLnsjXVVz\naEp6zqFcUQshhImdOgXffquN3e3jA0OGaI21EGUhNWohhDARpeCnn2D3bu3n5s3hmWfAxsbckQlL\nJzVqIYQoZ1lZsGEDHD+uLXfvDk88IZ3GhPGkRl1J6bkeYykkh6ZRFfKYkgIREVojbWur3eru1Ml0\njXRVyGF503MO5YpaCCGMcOUKrF6tNdbOztrMV56e5o5KVCZSoxZCiDI6fhzWr4fsbG2EscGDwd7e\n3FEJPZIatRBCmJBSWoexvXu15Ucf1SbWqFbNvHGJyklq1JWUnusxlkJyaBqVLY8ZGdogJnv3ajXo\n3r2hX7/ybaQrWw7NQc85lCtqIYQopdu3tZmvrl2DmjVh0CAICjJ3VKKykxq1EEKUQlycdiWdlgZu\nbjB0qPa/EKYgNWohhDDC4cOwebM2wUZwMAwcqF1RC1ERpEZdSem5HmMpJIemoec85ubCtm3aQCY5\nOdC+vXYlXdGNtJ5zaCn0nEO5ohZCiEKkp8O6dXDunNZRrG9faNXK3FGJqkhq1EIIcZ/ERK3T2M2b\n2vein38e/P3NHZWozKRGLYQQpXTuHKxdC/fugZeXNhyos7O5oxJVmdSoKyk912MsheTQNPSSR6Xg\nwAGIjNQa6caNYcwYy2ik9ZJDS6bnHMoVtRCiysvJ0Xp1Hz6sLXfpAqGhMvOVsAxSoxZCVGl372rf\nj754EapXh/79oVkzc0clqhqpUQshRCGuXdM6jd2+DY6OWj3a29vcUQlRkNSoKyk912MsheTQNCw1\nj6dOwZIlWiPt4wPjxlluI22pOdQTPefQLA31e++9R9OmTWnevDlDhw4lIyPDHGEIIaogpeC//9Xm\nkM7MhJAQGD0aHBzMHZkQhavwGnVsbCzdunXj5MmT1KhRg+eff54+ffowatSoP4OSGrUQohxkZWnz\nR//+u9ZRrHt36NhROo0J87OoGrWjoyM2NjakpaVRrVo10tLS8PHxqegwhBBVTHKydhUdHw+2tvDc\nc9CwobmjEqJkFX7r29XVlVdffRV/f3+8vb1xdnamR48eFR1GpafneoylkByahiXk8coV+OILrZF2\ncYEXXtBXI20JOdQ7Peewwhvqc+fOsXDhQmJjY4mPjyc1NZUVK1ZUdBhCiCri+HFYuhRSUiAwUOs0\n5uFh7qiEKL0Kv/X966+/0qFDB9z+N5HrgAED2L9/P8OGDSvwuvDwcAIDAwFwdnamZcuWhIaGAn/+\nZSTLxS/nsZR4ZLlqLuc9VtH779IllF27IDJSWx44MJTevWHvXvPmQz7PspwnKiqK2NhYSlLhncmi\no6MZNmwYhw4dombNmoSHh9O2bVsmTJjwZ1DSmUwIYYSMDPj2W4iJAWtreOopaNNGOo0Jy1Vcu1fh\nt75btGjByJEjad26NSEhIQC8+OKLFR1GpXf/X+Hi4UkOTaOi83jrlvb96JgYqFULhg+Htm313UjL\nuWg8PefQLCOTTZkyhSlTpphj10KISiw2Fr7+GtLSwN0dwsLgf1U2IXRLxvoWQlQKv/2mTayRmwvB\nwTBwINSsae6ohCgdi/oetRBCmFJuLmzbBgcPassdOkCPHlptWojKQE7lSkrP9RhLITk0jfLMY3q6\nNn/0wYNQrZo281WvXpWvkZZz0Xh6zqFcUQshdCkxEVauhKQksLfXZr7y8zN3VEKYntSohRC6c+YM\nrFunfQ3Ly0vrNObkZO6ohCg7qVELISoFpeDAAdixQ/u5SRPtdretrbkjE6L8VLJKjsij53qMpZAc\nmoap8pidrc18tX271kh36QKDBlWNRlrORePpOYdyRS2EsHipqbBmDVy6BDY22lV006bmjkqIiiE1\naiGERbt6FVatgjt3wNFRq0fXrWvuqIQwLalRCyF06eRJbczurCzw9dV6dteube6ohKhYUqOupPRc\nj7EUkkPTKEselYI9e7Tb3VlZ0KIFhIdX3UZazkXj6TmHckUthLAoWVnw/fdw4oQ2kUaPHtpoY3qe\nVEMIY0iNWghhMZKTtXp0QgLUqAHPPQcNGpg7KiHKn9SohRAW7/JlWL1a6+Ht6qp1GqtTx9xRCWF+\nUqOupPRcj7EUkkPTKE0eo6MhIkJrpOvVgxdekEY6PzkXjafnHMoVtRDCbHJzYdcu2LdPW27TBp56\nSptgQwihkRq1EMIsMjLgm2/g9GlttqvevbWGWoiqSGrUQgiLcuuWNvPVjRtQqxYMHqzd8hZCPEhq\n1JWUnusxlkJyaBr35/HCBVi8WGuk69SBceOkkS6JnIvG03MO5YpaCFFhfv0VtmzRatMNGmhfv6pR\nw9xRCWHZpEYthCh3OTmwbRscOqQtd+wI3btrtWkhhNSohRBmlJ4OX3+t3fKuVg2eeUYbElQIUTry\n92wlped6jKWQHBrvxg2YMiWKCxe0cbrDw6WRLgs5F42n5xzKFbUQolycOQPr1kFKCjRrps185eRk\n7qiE0B+pUQshTEop+Pln+OEH7eemTeHZZ8HW1tyRCWG5pEYthKgQ2dmwaRMcPaotd+0KnTvLzFdC\nGKPEGnVqaio5OTkAxMTEsGHDBrKysso9MGEcPddjLIXk8OGkpsJXX2mNtI2NNohJly6wZ0+UuUPT\nPTkXjafnHJbYUHfu3JmMjAyuXLnCk08+yfLlywkPD6+A0IQQepGQAF98AZcuaXXosWOhSRNzRyVE\n5VBijbpVq1YcOXKETz75hPT0dKZMmUKLFi2Ijo4uv6CkRi2EbvzxB3z3HWRlgZ8fPP+81sNbCFF6\nRteof/75Z1asWMGSJUsAyM3NNV10QghdUgr27IG8O4otW0LfvlBder4IYVIl3vpeuHAh7733Hn/5\ny19o2rQp586do2vXrhURmzCCnusxlkJyWLTMTFi7VmukraygVy+tZ3dhjbTk0XiSQ+PpOYcl/u3b\npUsXunTpYlgOCgri448/LteghBCW684dWL1aq0vXqAEDB8Ijj5g7KiEqrxJr1IcOHWLOnDnExsaS\nnZ2trWRlxbFjx8ovKKlRC2GRLl3SGum7d8HVFcLCtBmwhBDGKa7dK7GhbtCgAfPmzaNZs2ZY5xtB\nPzAw0KRBFghKGmohLM7Ro7BxozbBRv36MGiQNpe0EMJ4xbV7Jdao69SpwzPPPEP9+vUJDAw0/BOW\nTc/1GEshOdTk5sKOHfD991oj3bYtDBtW+kZa8mg8yaHx9JzDEmvU06dPZ+zYsfTo0QPb/40BaGVl\nxYABA8o9OCGEed27B998o43bbW0NffpA69bmjkqIqqXEW9/Dhg0jJiaGpk2bFrj1vXTp0vILSm59\nC2F2SUmwapU2A1atWtr3o+VmmhDlw6gadcOGDTl16hRWFThYrzTUQpjXhQvaHNLp6eDhoXUac3Ex\nd1RCVF5G1ag7dOjAH3/8YfKgRPnScz3GUlTVHB46BMuXa410gwbacKDGNNJVNY+mJDk0np5zWGKN\n+ueff6Zly5bUq1ePGjVqAOX/9SwhRMXLyYGtW+HXX7XlJ56Abt202rQQwnxKvPUdGxtb6OPy9Swh\nKo+0NO1Wd2ysNrrYM89ASIi5oxKi6jCqRm0O0lALUXGuX9c6jd26pU2mMWQI+PqaOyohqhajatRC\nn/Rcj7EUVSGHp0/DkiVaI+3tDS++aPpGuirksbxJDo2n5xzKPDdCVEFKwf79sHOn9nOzZtqkGjY2\n5o5MCHE/ufUtRBWTna0NBZo3pXy3btCpkzYLlhDCPIy69f3NN9/wyCOP4OjoiIODAw4ODjg6Opo8\nSCFE+UtJgYgIrZG2tdUGMencWRppISxZiQ31lClT2LBhA8nJyaSkpJCSkkJycnJFxCaMoOd6jKWo\nbDmMj4cvvoDLl8HJCcaMgcaNy3+/lS2P5iA5NJ6ec1hiQ+3l5UVjE3+ab9++zcCBA2ncuDFNmjTh\nwIEDJt2+EKKgEydg6VJITgZ/f63TmJeXuaMSQpRGiTXqv//971y9epX+/fubbFKOUaNG0aVLF8aM\nGUN2djZ3797Fycnpz6CkRi2ESSgFUVGwZ4+23KoVPP209l1pIYTlMOp71OHh4YaN5FfWSTnu3LlD\nq1atOH/+fJGvkYZaCONlZsJ338HJk1oN+sknoV07qUcLYYksasCTo0ePMn78eJo0aUJ0dDSPPfYY\nH330EXZ2dn8GJQ210aKioggNDTV3GLqm5xzevg2rV8PVq1CzJgwcCMHB5olFz3m0FJJD41l6Dotr\n94q8Afb+++/z+uuvM2nSpEI3+PHHH5cpmOzsbA4fPsynn35KmzZtePnll5k7dy4zZ84s8Lrw8HDD\nMKXOzs60bNnSkOS8TgGyXPTy0aNHLSoePS7nsZR4Sru8Zk0UP/4IXl6huLmBv38Uly9DcLB54jl6\n9KHJ3WgAACAASURBVKhZ81EZluXzXPk+z3k/FzVMd35FXlFv3LiRfv36ERERUeC2t1IKKysrRo0a\nVeLGC3P16lXat2/PhQsXAPjpp5+YO3cumzZt+jMouaIWokyOHIFNm7QJNurXh0GDtLmkhRCWrUxX\n1P369QP+rFGbipeXF35+fpw+fZoGDRqwc+dOmjZtatJ9CFHV5ObCDz/Azz9ry+3aaTVpaxkkWAjd\nM8vH+JNPPmHYsGG0aNGCY8eO8eabb5ojjErt/ts94uHpJYf37sHKlVojbW0N/fpB796W00jrJY+W\nTHJoPD3n0Cxf0mjRogWHDh0yx66FqFRu3tRmvkpMBDs7baSxgABzRyWEMCUZ61sInTp/HtauhfR0\n8PCAsDBwcTF3VEKIsjBqrO+YmBi6d+9uqCMfO3aMWbNmmTZCIUSpKQUHD0JkpNZIN2wIY8dKIy1E\nZVViQz1u3DjmzJljGJWsefPmrFq1qtwDE8bRcz3GUlhiDnNytF7dW7ZoHcg6dYIhQ6BGDXNHVjRL\nzKPeSA6Np+ccllijTktLo127doZlKysrbGTSWiEqXFoafP01xMZqQ4A++yw0b27uqIQQ5a3EhrpO\nnTqcPXvWsLxu3Trq1q1brkEJ4+V9uV6UnSXl8Pp1rdPYrVvg4KBdRfv4mDuq0rGkPOqV5NB4es5h\niZ3Jzp07x4svvsj+/ftxcXGhXr16rFixwjBqWLkEJZ3JhDCIiYFvvtHG7vb21hppmRJeiMrFqM5k\nQUFB7Nq1i8TERGJiYti3b1+5NtLCNPRcj7EU5s6hUrB3rzZmd2YmNGsGo0frr5E2dx4rA8mh8fSc\nwxJvfd+6dYtly5YRGxtLdnY2YNxY30KIkmVlwYYNcPy4tty9OzzxhMx8JURVVOKt7/bt29O+fXua\nN2+OtbW10WN9lyooufUtqrCUFO0q+soVsLWFAQOgUSNzRyWEKE9GTXP56KOPcvjw4XIJrCjSUIuq\nKj5e6zSWkgLOztogJp6e5o5KCFHejKpRDx06lMWLF5OQkEBSUpLhn7Bseq7HWIqKzuHvv8OXX2qN\ndEAAjBtXORppOReNJzk0np5zWGKNumbNmrz22mvMnj0b6/+N8m9lZcX58+fLPTghqgKl4Mcf4b//\n1ZYffRSefhqqVTNvXEIIy1Dire969epx6NAh3N3dKyomufUtqozMTPj2Wzh1Suso9tRT0LatdBoT\noqop03zUeR555BFqyczzQpjc7dtaPfraNahZEwYNgqAgc0clhLA0Jdao7ezsaNmyJS+++CKTJk1i\n0qRJ/O1vf6uI2IQR9FyPsRTlmcO4OFi8WGuk3dy0enRlbaTlXDSe5NB4es5hiVfU/fv3p3///gUe\ns5L7ckKU2eHDsHmzNsFGcDAMHKhdUQshRGFkPmohKkhuLuzYAQcOaMuPPw69eoF1ife1hBCVXZlq\n1IMGDWLt2rU0L2R6HisrK44dO2a6CIWo5O7dg7Vr4dw5rTd3377QqpW5oxJC6EGRV9Tx8fF4e3sT\nFxf3QCtvZWVFQEBA+QUlV9RGi4qK0vVsMZbAVDm8eRNWrtT+t7eH558Hf3/j49MLOReNJzk0nqXn\nsEwDnnh7ewPw2WefERgYWODfZ599Vj6RClHJnDsHX3yhNdKenlqnsarUSAshjFdijbpVq1YcOXKk\nwGPNmzfneN5sAeURlFxRC51TCn75BbZv135u1Egbs9vW1tyRCSEsUZlq1P/+97/57LPPOHfuXIE6\ndUpKCh07djR9lEJUEjk5Wq/uvCHyO3eGrl1lEBMhRNkUeUV9584dbt26xRtvvMH7779vaOkdHBxw\nc3Mr36Dkitpoll6P0YOy5PDuXfj6a+170tWrQ//+2jzSVZmci8aTHBrP0nNYpitqJycnnJycWL16\ndbkFJkRlcu2aNtLY7dvg4KDNfPW/rh5CCFFm8j1qIUzg1CltzO7MTPDxgSFDtMZaCCFKw6ixvoUQ\nRVMKfvoJdu3SlkNCoF8/sLExb1xCiMpDxkSqpPQ8rq2lKCmHWVnaVfSuXVpHsR494C9/kUb6fnIu\nGk9yaDw951CuqIUog+RkWL0a4uO1r1w99xw0bGjuqIQQlZHUqIV4SFeuaI10Sgo4O8PQoeDhYe6o\nhBB6JjVqIUzk+HFYvx6ysyEwEAYPBjs7c0clhKjMpEZdSem5HmMp8udQKa0W/c03WiP92GMwYoQ0\n0qUh56LxJIfG03MO5YpaiBJkZMB332lfwbK2hqeegjZtZKQxIUTFkBq1EMW4dUsbxOT6dahZU7vV\nXb++uaMSQlQ2UqMWogzi4mDNGkhLA3d3baSxch49VwghHiA16kpKz/UYS/DbbzBjRhRpaRAcDC+8\nII10Wcm5aDzJofH0nEO5ohYin9xcbWrKX37ROpC1bw89e2q1aSGEMAepUQvxP+npsHYtnD8P1apB\n377QqpW5oxJCVAVSoxaiBImJWqexmzfB3h6efx78/c0dlRBCSI260tJzPaainT0L//mP1kh7ecG4\ncVojLTk0Dcmj8SSHxtNzDuWKWlRZSsGBA7Bjh/Zz48bapBq2tuaOTAgh/iQ1alElZWfD5s3/v707\nj26ruvYH/pUseR5kyfEQybFsWXYGJ7HJSPgBDm4IFBICSchQoCF9PEpb2rR9Hd5qu1Zf14KE1RFW\nWavrteW5tIUQhkLCkIYMJpiQAEn8mhJe4siSLc+OZFmWrPme3x+3OUFkwI5s3Stpf/7y1ZGsox3H\n2+fufe8BTp4Uj2++GWhspJuYEEKkQTVqQj7F6xWvj+7qErekXLMGmDNH6lkRQqbCmXNnsP/4foRY\nCGqFGl9Y8AXUVifWVndUo05SiVyPmUr9/cB//7eYpPPzgQcfvHKSphhODopj7CiG1+bMuTNoPtSM\nvml9eN/+PoZKhtB8qBlnzp2RemoTQitqkjI++QR45RUgFAIMBrGzOy9P6lkRQqaC0+fE7/f/HhaN\nBa4uF3wuH2ZhFjLMGThw4kBCraqpRk2SHmPA4cPAoUPi8fz5wKpVgIr+TCUkaUSECLpGutDubMdZ\nx1mcHzuPo61H4Tf4AQAFGQWYVzIPaco0aPo12LZxm8QzjkY1apKyQiFx/+h//lNsFPvCF4Bly6hp\njJBk4A16cc55DmcdZ3HOeQ6BSICPZaoyYcg1QF2khjZLC3Wamo+lKxPr0g7JEnUkEsHChQthMBiw\nZ88eqaaRtFpaWtDY2Cj1NCTldgM7dwK9vUBGBrB2LVBTM/7XUwwnB8UxdhRDEWMMA94BnHWcxVnH\nWfS4e8BwcRU6LXsaanQ1qNHVoLygHO2l7Wg+1Ay1WQ1bmw3GeiMC7QE0LW+S8FNMnGSJ+sknn8Ts\n2bMxOjoq1RRIEuvuFpO0xwMUFoo7XxUXSz0rQshEBSNBWIetOOs4i3ZnO9wBNx9LU6ShsrASZq0Z\nNboaFGYVRr22troWW7AFB04cwHnneRQPFqNpeVNC1acBiWrU3d3d2LJlC370ox/hV7/61SUraqpR\nk1j84x/A7t3itdJGo7iHdHa21LMihIyXy+8SE7OjHVaXFWEhzMfy0vNg1omJuaqwCulpiXUa+0pk\nV6P+9re/jZ///Odwu92f/2RCxkkQgIMHgdZW8XjRIuC228QNNggh8iUwAfYRO28EG/QO8jEFFNDn\n6fkp7dLcUihSrMkk7on69ddfR3FxMRoaGujawCmUajWtQAB4+WXg7FlxS8rbbxcTdSxSLYZTheIY\nu2SMoS/ki2oE84V9fCwjLQMmrQk1uhpUa6uRm54b8/slcgzjnqiPHDmC3bt3480334Tf74fb7cYD\nDzyAZ599Nup5W7ZsgdFoBABoNBrU19fzIF9I8HR85eO2tjZZzWcqj/fsacGBA4BG04isLGDGjBZ4\nvQAQ2/e/QOrPl+jHbW1tsppPIh4nw//nm2++GUNjQ3jh9Rdgd9uRY84BA4OtzQYAWHD9AtToauD4\nxIGSnBI0zWma1Pe/QC7xuPC1zWbD55H0Oup33nkHv/jFL6hGTa6ZzQbs2gWMjQHTpolNY1qt1LMi\nhABAWAhHNYK5/C4+plQoYdQYeSOYLlsn4UylJ7sa9aelWq2BTJ6PPgLefFOsTZvN4uVXmZlSz4qQ\n1OYOuNHuEGvNHcMdCAkhPpajzolqBMtU0X/Y8aA7kyWplgSux3yeSAT4+9+BDz4Qj5ctE29kopzk\nO9cncwzjieIYOznHUGACekd7+bXN/Z7+qPGy3DLeCDY9b7pkizM5xxCQ+YqakInw+YAXXwQ6OsRu\n7lWrgPp6qWdFSGrxh/2wOC38lPZYaIyPpaelo6qwCjW6Gpi1ZuRl0A31Y0UrapIwhoaA558HnE4g\nN1fcVKO8XOpZEZL8GGNw+Bx81dw10gWBCXy8MLNQTMw6M4waI1RKWgNOFK2oScJrbwdeekm8DKus\nDNi4ESgokHpWhCSvsBBGp6uTX9vs9Dn5mFKhREVBBT+lXZRdRP1GU4gSdZKSez1mvBgD3n8fePtt\n8evZs4E1a4D0ONyMKFliKDWKY+ziFUNP0MMbwSzDFgQjQT6WpcrijWCmQhOy1FlTPp/JlMg/h5So\niWyFw8DrrwP/ugwXjY3AzTfTzleETBbGGPo8ffyUdu9ob9R4SU4JXzXr8/VQKia5Y5OMC9WoiSx5\nPMALLwB2O6BWA3ffLa6mCSGxCYQD6Bju4I1gnqCHj6mUKlQVVvFrmwsyqb4UL1SjJgmlr0/c+Wpk\nRKxDb9wo1qUJIdfG6XPyTS5sLhsiLMLHCjIK+CntSk1l1L7NRB4oUSepRK3HnD4N/O1vQCgkdnRv\n2CB2eEshUWMoNxTH2E00hhEhArvbzk9pnx87z8cUUKA8v5yf0i7OKU6JRrBE/jmkRE1kgTHg8GHg\n0CHxuL4euPNOQEU/oYSMizfo5ZtcWIYt8If9fCxTlYlqbTXf5CJbTfu+JhKqURPJhULAq68CH38s\nNoqtWAFcfz01jRFyNYwxDHgH+Kq5x90Dhou/N6dlT+PXNpfnlyNNSfu9yhnVqIlsjYyI9ei+PiAj\nA1i3TrxvNyHkUqFIKKoRzB1w87E0RRoqCyt5I1hhVqGEMyWTiRJ1kkqEeozdLnZ2ezzijlebNok7\nYMlFIsQwEVAcY+Pyu7Bzz07k1ebB6rIiLIT5WF56XtQmF+lpcbjBQIJK5J9DStREEm1twJ494gYb\nlZXA+vVANpXNCIHABHS7u/kp7UHvIGw9NhinGQEA+jw9bwQrzS1NiUawVEc1ahJXggDs3w8cOSIe\nL14MrFwpbrBBSKryhXy8Eeyc8xx8YR8fy0jLgElr4o1guekSXQZBphTVqIksBALi/brb28UtKb/4\nRWDhQqlnRUj8McYwNDbEV832EXtUI5guS8cbwSoKKqgRLMVRok5ScqvHOJ3izldDQ0BWFnDvveIp\nbzmTWwwTFcVRFBbCsA5b+SYXLr+LjykVShgLjPyUti5bF/VaimHsEjmGlKjJlLNagV27xL2kp00T\nm8a0WqlnRcjUcwfcfJOLjuEOhIQQH8tR50Q1gmWqMiWcKZEzqlGTKfXhh8Bbb4m16ZoaYO1a8TIs\nQpKRwAT0jvbyU9r9nv6o8bLcMr5qnp43nRrBCEc1ahJ3kQiwd6+YqAHghhuApiaxNk1IMvGH/bA4\nLfza5rHQGB9TK9UwaU0wa80w68zIz8iXcKYkUVGiTlJS1mPGxoAXXxRPeatUwOrVwLx5kkwlJolc\n05KTZIsjYwwOn4NvctE50gmBCXxck6nhq2ajxgiVMvZfs8kWQykkcgwpUZNJNTQEPPccMDwsbqax\ncSNgMEg9K0JiExbC6HR18kYwp8/Jx5QKJSoKKnhyLsouolPaZFJRjZpMmrNngZdfFi/DKisTm8by\n6UwfSVCeoIc3glmGLQhGgnwsS5XFG8FMhSZkqbMknClJBlSjJlOKMfEGJvv3i1/PmQOsWQOoaVtb\nkkAYY+jz9PFGsN7R3qjxkpwSvmrW5+uhVFDDBYkPStRJKl71mHBYvBXo//6veHzLLcCNNybHzleJ\nXNOSEznHMRAORG1y4Ql6+JhKqUJVYRXf5KIgs0Cyeco5hokikWNIiZpcM49H3Pmqu1tcPd9zDzBr\nltSzIuTqnD4nbwSzuWyIsAgfK8go4Ke0KzWVUKfRaSEiPapRk2vS1yfeacztBgoKxHp0aanUsyLk\nUhEhArvbzk9pnx87z8cUUMCQb+CntItziqkRjEiCatRkUn38MfDqq0AoBMyYAWzYAOTkSD0rQi7y\nBr18kwvLsAX+sJ+PZaoyUa2t5ptcZKtp2zYib5Sok9RU1GMYA1pagHfeEY8bGoA77hCvlU5GiVzT\nkpN4xJExhgHvAF8197h7oja5mJY9jW9yUZ5fnnCbXNDPYuwSOYZJ+iuWTLZgUFxFnz4tNordeiuw\ndGlyNI2RxBSKhNAx3MGvbXYH3HwsTZEGo+biJheFWYUSzpSQ2FCNmnyukRGxHt3fL96ne/16oLpa\n6lmRVOTyu/i1zVaXFWEhzMfy0vOiNrlIT0uXcKaETAzVqMk1s9vFzm6vV9zxavNmoKhI6lmRVCEw\nAd3ubn5Ke9A7GDWuz9PzVXNpbik1gpGkRIk6SU1GPaatTbxGOhIBqqrElXRWCt2AKZFrWnIy0Tj6\nQj7eCHbOeQ6+sI+PZaRlwKQ18Uaw3PTcKZix/NDPYuwSOYaUqMklBEG8y9iRI+LxkiXAypW08xWZ\nGowxDI0N8Wubu0a6ohrBtFlavmquKKhIuEYwQmJFNWoSxe8X79fd3i4m5jvuABYskHpWJNmEhTCs\nw1beCObyu/jYZze50GXrJJwpIfFBNWoyLg6H2DR2/jyQnQ3cey9gNEo9K5Is3AE3bwTrGO5ASAjx\nsRx1TlQjWKYqU8KZEiIvlKiT1ETrMR0d4h7SPh9QXCzeaawwxa9oSeSalhwITEDvaC92vbEL2eZs\n9Hv6o8bLcsv4tc36PD01gl0F/SzGLpFjSIk6xTEGfPghsHevWJuurRXv2Z2RIfXMSCLyh/2wOC28\nEcwb8sI2YIOxzAi1Ug2T1gSz1gyzzoz8DNoDlZDxoBp1CotEgLfeAj76SDy+8UZx9yta2JDxYozB\n4XPwRrDOkU4ITODjmkwNrzUbNUaolLQ2IORyqEZNLjE2BuzaBdhs4i1AV68G5s2TelYkEUSECDpH\nOvm1zU6fk499thGsKLuITmkTEiNK1EnqavWYwUGxaWx4GMjLAzZuBPT6+M4vESRyTWuyeYIe3ghm\nGbYgGAnysSxVFm8EMxWakKWOvtie4hg7imHsEjmGlKhTzJkz4uVXwSAwfbqYpPOpVEg+gzGGPk8f\nXzX3jvZGjZfklPBGMEO+AUoFXWRPyFShGnWKYAx47z3gwAHx67o64K67ALVa6pkRuQiEA1GbXHiC\nHj6mUqpQqankp7QLMgsknCkhyYdq1CkuHAZ27wb+8Q/x+JZbxMYxKh0Sp8/JT2nbXDZEWISP5Wfk\n88RcqamEOo3+qiNECpSok9SFeszoqLipRk8PkJ4O3H03MGuW1LNLDIlc07qSiBCB3W3np7TPj53n\nYwooUJ5fzpNzcU7xpDSCJWMc441iGLtEjiEl6iTW2ysmabcb0GjEm5iUlEg9KxJv3qCXb3JhGbbA\nH/bzsUxVJqq11XyTi2x1toQzJYRcDtWok9Q//wm8+qp42nvGDGDDBiAnR+pZkXhgjGHAO8BXzT3u\nnqhNLqZlT+Nd2uX55bTJBSEyQDXqFMIYcOgQcPiweHzddeLGGmn0uziphSKhqEYwd8DNx9IUaTBq\njPyUdmFWit8blpAEE/dEbbfb8cADD2BwcBAKhQL//u//jm9+85vxnkZSCgaBv/0N+OQTwGZrwVe/\n2oglS6hp7FrJvabl8rt4I5jVZUVYCPOxvPS8qE0u0tPSJZun3OOYCCiGsUvkGMY9UavVavz6179G\nfX09PB4PFixYgBUrVmAWdTjFxOUSb2IyMABkZgIrVgBLl0o9KzKZBCag293NT2kPegejxvV5er5q\nLs0tpTuCEZIkJK9Rr1mzBo8++iiampr4Y1SjnpiuLuCFFwCvF9DpxKaxoiKpZ0Umgy/k441g55zn\n4Av7+Fh6WjqqtdV8k4vc9FwJZ0oIiYVsa9Q2mw0nT57EkiVLpJxGQjtxAnjjDXGDDZMJWLcOyMr6\n/NcReWKMYWhsiG9y0TXSFdUIps3S8lVzRUEFNYIRkgIkS9Qejwfr1q3Dk08+idxcWglMlCAA+/YB\nR4+Kx0uXArfeCij/dSfHRK7HyEW8YhgWwrC5bPyUtsvv4mNKhRLGgouNYLps3ZTPZ7LRz2LsKIax\nS+QYSpKoQ6EQ1q5di/vuuw9r1qy57HO2bNkCo9EIANBoNKivr+dBbmlpAYCUPd63rwXvvAOo1Y1I\nSwNKSlqQmQkolRef39bWJpv5JurxBVPx/b1BL0rrSnHWcRb7D+5HhEVgrDcCAPpP9cNQYMA9t9+D\nqsIqHG09ioArAF25TlbxGe9xW1ubrOaTiMf0/1ne/5+vdT4tLS2w2Wz4PHGvUTPG8OUvfxk6nQ6/\n/vWvLz8pqlFfkcMhNo2dPw9kZ4vXR1dUSD0r8nkEJqB3tJevmvs9/VHjZbllfJMLfZ6eGsEISTFX\ny3txT9Stra246aabMG/ePP7LaPv27bjtttsuTooS9WVZLMCLLwJ+v3iHsU2bxDuOEXnyh/2wOC28\nEcwb8vIxtVKNqsIqnpzzM2gLM0JSmawS9XhQoo7GGPDBB8Df/y7WpmfOBO65R7x395W0JHA9Ri4m\nGkPGGBw+B7+2uXOkEwIT+LgmU8NrzUaNESplatxviH4WY0cxjJ3cYyjbrm/y+SIR4M03gePHxeOb\nbgKWL6ebmMhFRIigc6STn9J2+px8TKlQoqKggifnouwiOqVNCJkwWlHLmNcL7NoFdHYCKpW4f/Tc\nuVLPiniCHr5qtgxbEIwE+ViWKgtmnRlmrRnV2mpkqelaOULI56MVdQIaGBCbxlwuIC8P2LgR0Oul\nnlVqYoyhz9PHr23uGe2JGi/JKeG36zTkG6BUKCWaKSEkGVGilqH/+z/glVfEe3fr9WKSzsub2PeQ\nez1G7gLhAHa+vhOFswpx1nEWnqCHj6mUKlRqKnkjmCaTOvquhn4WY0cxjF0ix5AStYwwBrS2AgcP\nil/PnQusXg2o1VLPLDU4fU5+StvmssFis8CoMQIA8jPyea25UlMJdRr9oxBC4oNq1DIRCgG7dwOn\nTomNYk1NwA03UNPYVIoIEdjddt4Idn7sPB9TQAFDvoEn5+KcYmoEI4RMGapRy9zoKLBzJ9DTI15y\ntXYtUFsr9ayS01hoLKoRzB/287FMVSbf5KJaW42c9BwJZ0oIISJK1BLr6RGT9OioePOSTZvEm5nE\nKpHrMZOJMYYB7wBvBOt2d0dtcjEtexpvBCvPL4/a5IJiODkojrGjGMYukWNIiVpCp04Br70GhMPi\nbUDvvRfIoUVczEKREDqGO9DuFFfO7oCbj6Up0mDUXNzkojCrUMKZEkLI56MatQQYExvG3n1XPF6w\nAPjiF4E02rHwmrn8Ln5K2+qyIiyE+Vhuei5PzFWFVUhPu8ot3QghRAJUo5aRQAD429/ES7CUSmDl\nSmDxYmoamyiBCeh2d/NGsEHvYNS4Pk/PL58qyy2jRjBCSMKiRB1HLpd4E5OBASAzE1i/HjCZpua9\nErkecyW+kA/nnOf4Jhe+sI+Ppael80Yws86M3PTY9zhPxhhKgeIYO4ph7BI5hpSo46SzE3jhBWBs\nDCgqEpvGdDqpZyVvjDEMjQ3xRrCuka6oRjBtlpaf0q4oqIhqBCOEkGRBNeo4OHECeOMNcYON6mpg\n3TpxRU0uFRbCsLls/JS2y+/iY5/d5EKXTX/pEEKSA9WoJSII4taUx46Jx9dfD6xYIdamyUXugJs3\ngnUMdyAkhPhYjjqHXz5VVViFTBX9hUMISS2UqKeIzwe89BJgsYjd3HfeCTQ0xO/95VyPEZiA3tFe\nvmru9/RHjZfllvHkrM/TS9YIJucYJhKKY+wohrFL5BhSop4C58+LTWMOh3hd9IYNwIwZUs9KWv6w\nHxanBe3OdrQ72uENefmYWqlGVWEV79LOz8iXcKaEECIvVKOeZOfOiStpvx8oLRV3vtKk4OZKjDE4\nfA5+SrtzpBMCE/i4JlPDa81GjREqJf3NSAhJXVSjjgPGxFr03/8ufj1rFnD33eK9u1NFRIigc6ST\nn9J2+px87NONYGadGdOyp9G1zYQQMg6UqCdBOCx2dZ88KR7ffDPQ2CjtTUziVY/xBD1RjWCBSICP\nZamyYNaZ+SYXWeqsKZ/PZErkmpacUBxjRzGMXSLHkBJ1jLxe8frori5ApQLWrAHq6qSe1dRhjKHP\n08evbe4Z7YkaL8kp4Y1ghnwDlApqcSeEkFhQjToG/f1i09jICJCfL9ajp0+XelaTLxAORG1y4Ql6\n+JhKqUKlppKf0tZkpmBBnhBCYkQ16kl05kwn9u+3wG5X4vRpATNmmFBfX4ENG4C8PKlnN3mcPic/\npW1z2RBhET6Wn5HPG8EqNZVQp6klnCkhhCQ3StQTcOZMJ5qbz6G/vwlWq/hYb+8BPPIIkJdXIe3k\nPmOi9ZiIEIHdbeeNYOfHzvMxBRQozy/nq+aSnJKUaARL5JqWnFAcY0cxjF0ix5AS9QS89poFn3zS\nBNe/7mpZVQWUlzfhnXcOYs4ceSXq8RgLjfFVs2XYAn/Yz8cyVZl8k4tqbTVy0mmjbEIIkQLVqMeB\nMeDDD4Gf/rQFY2ONUKuBmTMvbqqh0bRg27ZGSec4HowxDHgHeCNYt7s7apOLouwifkq7PL+cNrkg\nhJA4oRp1DJxO4LXXxN2vAAHFxeLGGp++Pjo9XbjSyyUXioRgdVn5KW13wM3H0hRpMGqM/JS2Nksr\n4UwJIYRcDiXqK7hwA5MDB4BQSLwV6Ne/bkJr6wGkpzfx5wUCB9DUVC3hTC/l8ruwc89O5NXmkFMO\nXwAAEZhJREFUweqyIiyE+Vhuei5fNVcVViE9LYXuyDJBiVzTkhOKY+wohrFL5BhSor4Mh0NcRXd1\nicdz5wK33w5kZ1eguho4cOAggkEl0tMFNDVVo7ZW2vq0wAR0u7v5qnnQOwhbjw3GaUYAgD5Pz69t\nLsstS4lGMEIISRZUo/4UQbi4ig6HgdxccdermTPjPpXP5Qv5cM55jm9y4Qv7+Fh6WjpMhSZ+Sjs3\nPVfCmRJCCPk8VKMeh/PnxVW03S4ez58P3HYbkCWTu14yxjA0NsS7tO1ue9QmF9osLT+lPaNgBm1y\nQQghSSLlf5sLAvD++8ChQ+IqOi8PWLUKqKmRemZAWAjD5rLxU9ouv4uPKRVKfkewGl0NdNm6qNcm\ncj1GLiiGk4PiGDuKYewSOYYpnaiHhoBXXwV6/nW76vp6YOVKaVfR7oA7apOLkBDiYznqHL7JhUlr\nQqYqU7qJEkIIiYuUrFELAnDkiLiKjkTE+3SvWgWYzVP2llfEGEPPaA+/trnP0xc1XpZbxhvB9Hl6\nagQjhJAkRDXqTxkcFFfRvb3i8XXXAbfeCmTGcXHqD/thcVp4I5g35OVjaqUaVYVVvBEsPyM/fhMj\nhBAiOymTqCMR4L33gHfeEb8uKBBX0dVxugTaMebgtebOkc6oRjBNpobXmo0a46Q0giVyPUYuKIaT\ng+IYO4ph7BI5himRqAcGxFV037/OKi9cCKxYAWRkTN17RoQIOkc6eXJ2+px8TKlQoqKggq+ap2VP\no1PahBBCLiupa9SRCNDaChw+LH6t0QCrV4ubaUwFT9AT1QgWiAT4WJYqizeCVWurkaWWyXVfhBBC\nJJeSNer+fnEV3d8vHi9aBHzhC7Gvos+cO4P9x/cjxEJQQYV5s+ZBKBDQ7mhHz2hP1HOLc4r5KW1D\nvgFKhTK2NyeEEJJykm5FHYmIK+h33xW7uwsLxVV0ZWXs8zpz7gz+cOAPGDOMwelzwuFzYOzMGOpn\n16NoehFUShW/ttmsM0OTqYn9Ta9RItdj5IJiODkojrGjGMZO7jFMmRV1X5+4ih4YEI+XLAGamqJ3\nupqoiBBBz2gPOoY78PtXf4/eol6woYvBzKnNQcQRweaVm1GpqYQ6TR3jpyCEEEIuSooVdTgsrqJb\nW8VVtFYrrqKNxom/N2MMDp8DHcMdsDgtsLlsvNZ8tPUoAoYA8jPyoc3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- "text": [
- ""
- ]
- }
- ],
- "prompt_number": 71
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "As we can see in the first plot, the list comprehensions lead to a slightly increased performance in regular Python code. \n",
- "But the second plot is quite interesting: List comprehensions in Cython are significantly slower than the regular for-loop structures.\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "Let us do a quick comparison by how much we were able to improve the performance of the simple least square implementation using Cython so far:\n",
- "
"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import random\n",
- "random.seed(12345)\n",
- "\n",
- "x = [x_i*random.randrange(8,12)/10 for x_i in range(500)]\n",
- "y = [y_i*random.randrange(8,12)/10 for y_i in range(100,600)]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 72
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "\n",
- "funcs = ['lstsqr_comprehensions', 'cy_lstsqr_comprehensions', \n",
- " 'cy_lstsqr_loops'] \n",
- "labels = ['list comprehensions', 'list comprehensions (Cython)', \n",
- " 'for-loops (Cython)']\n",
- "\n",
- "times = [timeit.Timer('%s(x,y)' %f, \n",
- " 'from __main__ import %s, x, y' %f).timeit(1000)\n",
- " for f in funcs]\n",
- "\n",
- "times_rel = [times[0]/times[i+1] for i in range(len(times[1:]))]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 73
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "#%pylab inline\n",
- "#import matplotlib.pyplot as plt\n",
- "\n",
- "plt.figure(figsize=(8,6))\n",
- "x_pos = np.arange(len(funcs[1:]))\n",
- "plt.bar(x_pos, times_rel, align='center', alpha=0.5, color=\"green\")\n",
- "plt.xticks(x_pos, labels[1:], rotation=90)\n",
- "plt.ylabel('relative performance gain')\n",
- "plt.title('Performance gain compared to the classic least square implementation')\n",
- "ftext = 'For-loops in Cython are {:.2f}x faster then list comprehensions'\\\n",
- " .format(times[1]/times[2],1)\n",
- "plt.figtext(.15,.8, ftext, fontsize=11, ha='left')\n",
- "plt.xlim([-1,len(funcs[1:])])\n",
- "plt.ylim([0,max(times_rel)*1.2])\n",
- "plt.grid()\n",
- "plt.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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p8+rUqZPJMgs6bxZk6dKlCAoKUuZ34sSJPNu4ILnPV05OTvDw8DA5rgo6Jh/HU52sC+Ln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\nrXeUi4hILVjWJK2RI0eiTZs22LlzJ3bs2IHWrVsjMjJSdCw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- "text": [
- ""
- ]
- }
- ],
- "prompt_number": 88
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "
\n",
- "
\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Final Comparison: Cython vs. NumPy vs. SciPy for least squares fitting"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To wrap it up, let us compare the Cython code of our simple least squares fit implementation to the Numpy and Scipy functions - after we made some improvements by adding static type declarations and explicit for loops."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%load_ext cythonmagic"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 1
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%%cython\n",
- "\n",
- "def cy_lstsqr(x, y):\n",
- " \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
- " cdef double x_avg, y_avg, temp, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
- " x_avg = sum(x)/len(x)\n",
- " y_avg = sum(y)/len(y)\n",
- " var_x = 0\n",
- " for x_i, y_i in zip(x,y):\n",
- " temp = (x_i - x_avg)\n",
- " var_x += temp**2\n",
- " cov_xy += temp*(y_i - y_avg)\n",
- " slope = cov_xy / var_x\n",
- " y_interc = y_avg - slope*x_avg\n",
- " return (slope, y_interc)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 2
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import numpy as np\n",
- "import scipy.stats"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 3
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "def lin_lstsqr_mat(x, y):\n",
- " \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
- " X = np.vstack([x, np.ones(len(x))]).T\n",
- " return (np.linalg.inv(X.T.dot(X)).dot(X.T)).dot(y)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 4
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "def numpy_lstsqr(x, y):\n",
- " \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
- " X = np.vstack([x, np.ones(len(x))]).T\n",
- " return np.linalg.lstsq(X,y)[0]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 5
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "def scipy_lstsqr(x,y):\n",
- " \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
- " return scipy.stats.linregress(x, y)[0:2]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 6
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import timeit\n",
- "import random\n",
- "random.seed(12345)\n",
- "\n",
- "funcs = ['cy_lstsqr', 'lin_lstsqr_mat',\n",
- " 'numpy_lstsqr', 'scipy_lstsqr'] \n",
- "\n",
- "orders_n = [10**n for n in range(1, 6)]\n",
- "times_n = {f:[] for f in funcs}\n",
- "\n",
- "for n in orders_n:\n",
- " x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
- " y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
- " for f in funcs:\n",
- " times_n[f].append(min(timeit.Timer('%s(x,y)' %f, \n",
- " 'from __main__ import %s, x, y' %f)\n",
- " .repeat(repeat=3, number=1000)))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 26
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%pylab inline"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import matplotlib.pyplot as plt\n",
- "\n",
- "labels = [('cy_lstsqr', 'Cython implementation'), \n",
- " ('lin_lstsqr_mat', 'numpy matrix equation'),\n",
- " ('numpy_lstsqr', 'numpy.linalg.lstsq()'), \n",
- " ('scipy_lstsqr', 'scipy.stats.linregress()')] \n",
- "\n",
- "matplotlib.rcParams.update({'font.size': 12})\n",
- "\n",
- "fig = plt.figure(figsize=(10,8))\n",
- "for lb in labels:\n",
- " plt.plot(orders_n, times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
- "plt.xlabel('sample size n')\n",
- "plt.ylabel('performance gain relative to the slowest approach')\n",
- "plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
- "plt.legend(loc=2)\n",
- "plt.grid()\n",
- "plt.xscale('log')\n",
- "plt.yscale('log')\n",
- "plt.title('Performance of least square fit implementations for different sample sizes')\n",
- "plt.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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pHj16VNdqMBgMxltLdYdW66xHThn4+/vDzc2tzJLfx48fs/lzDIYSYcfyMBgM\nhnyio6PfaJj1g+yRYwshGAzl8j7Uuejo6BrZa4yhOMzmyoXZW7mUtvc7t9iBwWAwGAwGg/FmsB45\nBoNR67A6x2AwGBXDeuQYDAaDwWAwPjDea0eOnexQdXx8fNC9e/c6y9/a2hqLFy9WSl5WVlbC3oUf\nKjzPY+vWrXWtxjsBa0uUD7O5cmH2Vi7F9n7Tkx3ee0fufZy4+fDhQ0ybNg2Ojo7Q0tKCqakpunTp\ngrCwMBQUFCgk4/jx4+B5Hrdv3xaFcxxXpysMY2NjMWXKFKXkVddlrSp37twBz/M4evRoldN6eHjA\n19e3THh6ejr69+9fE+oxGAwGoxq4ubm9kSP3Xm8/Ul0SElJw6FAS8vN5qKkVwsPDFg4Olm+FzNTU\nVHTq1Anq6uqYP38+WrRoATU1NZw4cQLff/89XFxc0KxZM4XllR6Pr+t5TEZGRnWa/7tATd4jmUxW\nY7Led97HP4VvO8zmyoXZW7nUlL3f6x656pCQkILg4ETcv98VT5644f79rggOTkRCQspbIXP8+PHI\nz8/H2bNn4e3tDUdHR9ja2mL48OE4e/Ys7OzsEBwcDKlUipycHFHa+fPno1GjRkhOToarqyuAoqFM\nnufRtWtXIR4RYcOGDbC0tISBgQH69OmDzMxMkayQkBA4OTlBQ0MDDRo0wNy5c0W9gW5ubhgzZgwW\nLFgAc3NzGBkZYcSIEcjOzq6wfKWHO62srDBv3jyMGzcOEokEZmZm+Omnn/Dy5UtMmDABhoaGqF+/\nPgIDA0VyeJ7Hjz/+iP79+0NXVxf169fHjz/+WGHe+fn58Pf3h42NDbS0tNCkSRNs2LChjNy1a9di\n0KBB0NXVhZWVFfbs2YPHjx/D29sb+vr6sLW1xe7du0XpMjIy4OPjA5lMBn19fXTq1AnHjh0TrkdH\nR4PneRw6dAiurq7Q0dGBs7MzDhw4IMRp2LAhAMDd3R08z8PGxgYAcOvWLfTr1w8WFhbQ0dFBs2bN\nEB4eLqTz8fHBkSNHEBISAp7nRb16pYdW09LS4OXlBalUCm1tbbi7uyMuLq5KejIYDAZDidB7SkVF\nq+ja2rWHyc+PqEsX8eeTT4rCq/Px9DxcRp6fH1Fg4OEqlenhw4ekoqJCixYtqjBeTk4OSaVSCgkJ\nEcIKCgrI0tKSli9fTgUFBbR3717iOI5iY2MpIyODHj9+TEREI0aMIAMDAxo8eDBduXKFTp06RdbW\n1jRs2DDAEwgXAAAgAElEQVRB1r59+0hFRYWWLl1KN27coO3bt5NUKqW5c+cKcbp06UISiYS++eYb\nSkhIoL///psMDQ1FceRhZWUlKp+lpSVJJBJatWoVJSUl0cKFC4nneerZs6cQtmTJEuJ5nq5evSqk\n4ziODA0Nae3atXTjxg1avXo1qaqq0u+//15uXiNGjCAXFxc6ePAgJScn0/bt20kikdDGjRtFcs3M\nzCg0NJSSkpJo/PjxpKOjQz169KCQkBBKSkqiSZMmkY6ODj18+JCIiF68eEGNGzemAQMGUFxcHCUl\nJdGiRYtIQ0OD4uPjiYgoKiqKOI4jFxcXioyMpMTERPL19SV9fX3h3pw7d444jqM9e/ZQRkYGPXjw\ngIiILl26RIGBgXTx4kW6efMmrVmzhlRVVSkqKoqIiLKyssjV1ZW8vLwoIyODMjIyKC8vTyjPli1b\niIiosLCQ2rZtSy1atKATJ07QpUuXaNCgQSSVSoW8FNFTHu9DU1NsT4byYDZXLszeyqW0vavbTrIe\nuVLk58s3SUFB9U1VWCg/bV5e1WQmJiaisLAQTk5OFcbT1NTEsGHDEBQUJIQdPHgQaWlp8PX1Bc/z\nkEqlAAATExPIZDJIJBJR+uDgYDg5OaF9+/b46quvcOjQIeH60qVLMWDAAEyfPh12dnYYOHAg/P39\n8f333+PVq1dCPCsrK6xYsQKNGjVC9+7dMWjQIJEcRXF3d8fkyZNhY2ODWbNmQVdXFxoaGkLY9OnT\nYWBggCNHjojS9e7dGxMmTICdnR2+/vprDBw4EN9//73cPG7duoWwsDDs2LEDHh4esLS0xMCBAzFl\nyhSsWbNGFNfb2xvDhg2DjY0NAgIC8OLFCzg6OmL48OGwsbHB/Pnz8eLFC/zzzz8AgO3bt+PZs2f4\n9ddf0bJlS6EcHTp0wM8//yyS7e/vjx49esDW1hZLly7Fs2fPcObMGQCAsbExgKIj5mQymTAM3aRJ\nE4wfPx5NmzaFtbU1Jk6ciF69egk9bfr6+lBXV4eWlhZkMhlkMhnU1NTK2ODIkSM4c+YMtm7dig4d\nOqBJkyYIDQ2FpqYm1q1bp7CeDAaDwVAe7/UcufKO6KoINbVCueEqKvLDFYHn5adVV6+aTKrC3Kix\nY8eiSZMmSEhIgIODA4KCgtCnTx/BGagIR0dH0Yve3NwcGRkZwu+rV6/C29tblMbV1RUvX75EUlIS\nHBwcAAAuLi6iOObm5oiMjFS4DEDRgoSScjiOg4mJiWgeIMdxkMlkuH//vijtRx99JPrdoUMHzJs3\nT24+sbGxICK0atVKFP7q1SuoqoqrSUl9jI2NoaKiItJHIpFAXV1dGI4+c+YM0tPTRc4yAOTm5kJH\nR0cU1rx5c+G7TCaDioqKyPbyePHiBebPn499+/YhLS0NeXl5yM3NFQ2XK8KVK1dgZGQER0dHIUxd\nXR3t2rXDlStX3ljPdx02f0j5MJsrF2Zv5VJs7zc9ouu9d+SqioeHLYKDD8PNrZsQlpt7GD4+dnjt\nn1SZhIQimRoaYpndutlVSY69vT14nseVK1fw+eefVxjXyckJnTp1woYNGzB9+nT88ccf2L9/v0L5\nlO6tqc4mhRzHQV1dvUxYYWHVHWJ5+sgLq47sYorTnjp1Ctra2mVkV6RPeToWyywsLETjxo0RERFR\nJl3pvErbrKRu5fG///0Pe/fuxapVq+Dg4ABtbW1MnToVWVlZFaZTFCIqY4Pq6MlgMBiMshR3OAUE\nBFQrPRtaLYWDgyV8fOwgkx2BRBINmezIayeu+qtWa0qmoaEhPD09sXbtWjx9+rTM9fz8fLx48UL4\nPXbsWISGhmLDhg2oX78+PDw8hGvFL2J525VUtiWHs7MzYmJiRGExMTHQ1taGra1tlcpUm5w6dUr0\n++TJk3B2dpYbt7gnLiUlBTY2NqKPtbX1G+nRpk0b3Lx5E3p6emVkm5mZKSynvHt27NgxDB06FAMG\nDBCGVxMSEkT3UV1dXTTsLQ9nZ2c8fPgQ8fHxQlhubi7+/fdfNGnSRGE931fYHlvKh9lcuTB7K5ea\nsjdz5OTg4GCJ8eO7YvJkN4wf3/WNtx6pSZnr1q2DmpoaWrVqhW3btuHq1atITExEeHg42rRpg8TE\nRCHugAEDAAALFy7E6NGjRXIsLS3B8zz279+PzMxMkWNYWe/bzJkz8dtvv2HZsmW4fv06duzYgYCA\nAEydOlUYhiSiam2TUTqNPBmKhu3fvx+BgYG4ceMG1qxZgx07dmDq1Kly09jZ2WHkyJEYM2YMwsPD\nkZiYiAsXLmDTpk1Yvnx5lctRkiFDhsDa2hq9evXCwYMHkZycjH///RdLlizB77//rrAcY2Nj6Orq\nIjIyEunp6Xj8+DEAwMHBAREREThz5gyuXr2KL7/8EmlpaaLyWVtbIy4uDjdv3sSDBw/kOnXdunVD\n27ZtMXjwYJw8eRKXL1/G8OHDkZeXh3Hjxr2RDRgMBoNROzBH7h2jQYMGOHv2LD7//HP4+/ujVatW\n6NixI4KCgjBu3DhRj5OGhgaGDh0KIsLIkSNFckxNTbFkyRIsXboU9erVE4Zqy9skt2SYp6cnNm3a\nhJCQEDRt2hTffPMNJkyYAD8/P1H80nIU2YBXXprK4pQXNm/ePBw6dAjNmzfH0qVL8d1336FPnz7l\nptmwYQOmTJmCRYsWwdnZGR4eHggLC3vjXkYNDQ3ExMSgdevW8PX1hYODA/r374/Y2FhYWVlVWIaS\n8DyPwMBA7NixAw0aNBB6EVetWgVLS0u4u7vDw8MDDRo0wIABA0Typk6dCmNjY7i4uEAmk+HkyZNy\n84iIiICjoyN69eqFtm3bIjMzEwcPHoShoaHCer6vsPlDyofZXLkweyuXmrI3R9XpNnkHqGhe14d0\ngPfAgQNRUFCA3377ra5VUSo8zyM8PByDBw+ua1UY+LDqHIPBYFSH6raTrEfuPeXx48eIjIxERESE\n0o68YjDeZ9j8IeXDbK5cmL2VS03Z+71ftVrV7UfeF1q0aIFHjx5h+vTp6NSpU12rw2AwGAwGQw5v\nuv0IG1plMBi1DqtzDAaDUTFsaJXBYDAYDAbjA4M5cgwGg6EAbP6Q8mE2Vy7M3sqF7SPHYDAYDAaD\n8YHD5sgxGIxah9U5BoPBqBg2R47BYDAYDAbjA4M5cgwGg6EAbP6Q8mE2Vy7M3sqFzZFjMGqB6Oho\n8DyPe/fu1bUqtY6/vz/s7e3rWg0Gg8FgvAHv9Rw5Pz8/uRsCs/k6HxZ2dnYYNmyY6CzY8sjPz8fj\nx49hYmLy3pwpevz4cbi6uiI5ORkNGzYUwrOzs5Gbmys6R7W2YHWOwWAw5FO8IXBAQEC12sn3/mQH\nBkNRh+zVq1dQU1ODTCarZY3qhtINhI6ODnR0dOpIGwaDwWAAEDqcAgICqpWeDa3KISExAYHbA/HD\nrz8gcHsgEhIT3hqZbm5uGDNmDBYsWABzc3MYGRlhxIgRyM7OFuL4+Pige/fuonTh4eHg+f9ud/Gw\n2s6dO2FnZwcdHR30798fz58/x86dO+Hg4AB9fX188cUXePr0aRnZq1atgoWFBXR0dDBw4EA8fvwY\nQNE/C1VVVdy5c0eUf2hoKCQSCXJycuSWq7r6nD17Fp6enjA1NYWenh7atm2LyMhIkb2SkpIQEBAA\nnuehoqKC27dvC0Oof/75Jzp16gQtLS1s3LixzNDq8uXLIZVKkZKSIsicP38+ZDIZ0tPTy71PGRkZ\n8PHxgUwmg76+Pjp16oRjx46J4kRFRaFZs2bQ0tKCi4sLoqKiwPM8tmzZAgBITk4Gz/M4efKkKJ2d\nnZ2owq9evRotWrSAnp4ezM3N4e3tLeiWnJwMV1dXAIC1tTV4nkfXrl1FNi9JSEgInJycoKGhgQYN\nGmDu3LkoKCgQ2bOy5+99hc0fUj7M5sqF2Vu5sDlytURCYgKCo4Jx3/Q+npg9wX3T+wiOCn4jZ66m\nZe7atQtPnjxBTEwMfv31V+zbtw/Lli0TrnMcp1AvVFpaGkJDQxEREYG//voLx44dQ79+/RAcHIxd\nu3YJYYsXLxalO336NGJiYvD333/jzz//xPnz5zFq1CgARS96e3t7bNq0SZQmKCgIQ4YMgZaWVo3q\n8+zZM3h7eyM6Ohrnzp1Dz5498dlnn+HGjRsAgD179sDKygrffvst0tPTkZaWhvr16wvpp06dipkz\nZ+LatWvo3bt3GZ2mTZuGdu3awdvbGwUFBTh69CgWLlyIkJAQmJmZyS1HTk4O3N3dkZ2djQMHDuD8\n+fP45JNP0L17d1y7dg0AcO/ePfTu3Rtt2rTBuXPnsGLFCvzf//0fgMp7EEvfX47jsGLFCly+fBl7\n9uzB7du34eXlBQBo2LAhfv/9dwDAmTNnkJ6ejt27d8uVu3//fowaNQojRozAlStXsGLFCgQGBpb5\nl1jZ88dgMBgM5fFeD61Wh0Nxh6Bhr4Ho5Oj/AtWAi79eRJtObaol8/Tx03hR/wWQ/F+Ym70bDp89\nDAc7hyrLs7KywooVKwAAjRo1wqBBg3Do0CHMnz8fQNEQmiLj7Lm5uQgJCRHmSA0cOBDr169HRkYG\njIyMAABeXl44fPiwKB0RISwsDHp6egCAwMBA9OzZEzdv3oSNjQ2+/PJLrF69GnPnzgXHcbh27RpO\nnDiBtWvX1rg+Xbp0EclYsGAB/vjjD+zcuROzZs2CVCqFiooKdHV15Q6ZzpkzB7169RJ+FzuAJQkN\nDYWLiwsmTZqEffv2YdKkSfD09Cy3HNu3b8ezZ8/w66+/QkVFBQAwa9YsHDp0CD///DNWrVqFdevW\nQSaTISgoCDzPw9HREUuWLMGnn35aoY3k8fXXXwvfLS0tsXbtWrRq1QppaWkwNzeHVCoFAJiYmFQ4\nbLx06VIMGDAA06dPB1DU85eeno4ZM2Zg3rx5UFUtai4qe/7eV0rPtWXUPszmyoXZW7nUlL1Zj1wp\n8ilfbngBCuSGK0IhCuWG5xXmVVkWx3FwcXERhZmbmyMjI6PKsiwsLEQT3U1NTWFmZiY4TcVhmZmZ\nonROTk6CEwcAHTp0AABcvXoVADB8+HBkZmYKQ5y//PILWrduXUbvmtDn/v37GD9+PBo3bgypVAo9\nPT1cuXIFt2/fVsgGbdu2rTSOTCbD5s2bsX79ehgbG1fa+1Tc8yWRSKCnpyd8jh8/jsTERABFtmrb\ntq1ouLtjx44K6Vya6Oho9OzZEw0bNoS+vj46d+4MAKLhYEW4evWqMAxbjKurK16+fImkpCQhrKae\nPwaD8XaQkJiAFVtWYFn4shqbTsRQHsyRK4UapyY3XAUq1ZbJl2NmdV69WvLU1cXpOI5DYeF/ziLP\n82V65PLzyzqoamrisnIcJzespGyg7KT50hgZGWHAgAEICgpCfn4+QkND8eWXX1aYprr6+Pj44MSJ\nE/juu+9w/PhxnD9/Hs2bN0denmJOsqKT/aOjo6GiooKMjAw8efKkwriFhYVo3LgxLly4IPpcu3YN\nQUFBQjkqs2Oxk1fRvbx9+zY++eQT2NjYYPv27YiLi8PevXsBQGEbVAWO4yp9/t5X2Pwh5cNsXvsk\nJCZgw6ENOESHsCd+D+4a333j6UQMxaip55sNrZbCo5UHgqOC4WbvJoTl3siFj5dPtYZBASChftEc\nOQ17DZHMbu7d3lRduZiamuKff/4RhZ09e7bG5MfHx+PZs2dCr1zxZHwnJychztixY+Hu7o7169fj\n5cuX8Pb2rrH8S3Ls2DF89913wvy27OxsJCUloWnTpkIcdXV10YT9qnLo0CGsXLkS+/fvx9y5c+Hj\n44N9+/aVG79NmzbC0LOJiYncOE5OTggLC0NhYaHgsJ04cUIUpzjt3bt3hbDMzEzR7zNnzuDly5f4\n4YcfoKGhIYSVpNjxqswGzs7OiImJwfjx44WwmJgYaGtrw9bWtsK0DAbj3WTfv/twVfcqcl7l4GXB\nS1zMuIhWdq2qPfWHoXxYj1wpHOwc4OPuA1mmDJJ0CWSZMvi4V9+Jq2mZisx/8/DwwLVr17Bu3Tok\nJSUhKCgIO3furK76ZeA4DsOHD8eVK1dw9OhRTJgwAX369IGNjY0Qp2PHjnBwcMD//vc/eHt719o2\nFw4ODggPD8fly5dx/vx5eHt7o7CwUGQja2trHD9+HKmpqXjw4EGV9um5f/8+hg0bhmnTpqFHjx7Y\ntm0bjh07hh9++KHcNEOGDIG1tTV69eqFgwcPIjk5Gf/++y+WLFkiLDwYN24c7t+/jy+//BLx8fE4\nfPgwZs+eLZKjpaWFjh07Yvny5bh48SLi4uIwfPhwwWEDAHt7e3Ach++//x63bt1CREQEFixYIJJj\naWkJnuexf/9+ZGZmIisrS67eM2fOxG+//YZly5bh+vXr2LFjBwICAjB16lRhfpyi8y/fR9j8IeXD\nbF67PM19ihOpJ5Dzqmg3AamjFJYGluA4rlpTfxhVg82Rq0Uc7BwwfuB4TPaajPEDx9fIv5Kakilv\nRWrpsG7dumHhwoVYvHgxmjdvjujoaMybN6/MSsfK5JQX1rZtW3Tq1Andu3eHp6cnXFxcyqxSBYDR\no0cjLy9PoWHV6uqzefNmFBYWom3btujXrx8++eQTtGnTRhQnICAAT548gYODA0xNTZGamirIKk+X\nYnx8fGBtbS1M5LexscH69esxY8YMXLhwQW56DQ0NxMTEoHXr1vD19YWDgwP69++P2NhYWFlZAQDq\n1auHP/74A6dPn0aLFi0wZcoUrFq1qoysTZs2QVdXFx06dMDgwYMxduxYmJubC9ebNWuGNWvW4Oef\nf4azszNWrlyJH374QVQGU1NTLFmyBEuXLkW9evXQt29fubb09PTEpk2bEBISgqZNm+Kbb77BhAkT\nRBspK3qfGAzG283T3KcIPh+Ml69eoiAtG8Z/pcIlKgeaf9/C89sPqj31h6F83uuTHcorGttlvvr4\n+Pjg7t27OHjwYKVxp02bhsOHDyMuLk4Jmr0f8DyP8PBwDB48uK5VqVHehzoXHR3NeoiUDLN57ZD1\nMgshF0LwKOcRUmNvQGVnNCa/UsHZ7AJYtrRG5JMCdJs8D+49yl+dz3hzSj/f1W0n3+seOX9/fzZZ\ntg7IysrCmTNnEBQUhClTptS1OgwGg8F4TdbLLASfD8ajnEcAAFnifSziTGGQowGNHKBB/BNMtm0J\nSrxVx5p+OERHR7/RSVSsR45RJXx9fXH37l38/fff5cZxc3PD6dOn4e3tjY0bNypRu3cf1iPHYDBq\niycvnyDkfAgevyw6iUfnWS4cfzqNT1+8XgjF80CTJoChIaIlErhNnlyH2n54VLedZI4cg8GodVid\nYzDqlicvnyD4fDCevCzaPkn3aS6GXyBcPnURFllZOKStjfz69aGmoQEPbW3ctbND1xIr2Bm1Dxta\nZTAYjFqETdNQPszmNUMZJy7rJUacJ8gKNAGZDAHGxrg+aBBi6tXD/fbtEZCbCziwrUdqG7aPHIPB\nYDAYjAp5nPMYweeDkZVbtO1QkRMHmJAmACBeRweqw4cjJicHzzMzwenpoaGvL66lpaFrXSrOUBg2\ntMpgMGodVucYDOVT2onTf5qL4ecJxoVFThzU1PCtmRliW7YU0mjzPNro60N6+TImV+PsZ0b1YUOr\nDAaDwWAwAACPch6Jnbislxh+rvA/J05dHacGDMBVlf+On9TieTTT1QUHgO0i9+7AHDkGg8FQADZf\nS/kwm1eP0k6cwZOXGHGOYExaAABSV8eR/v0Rqa4OGxsbvIqNha6KCgwvX4YmzyM3Lg7dnJ3rsggf\nBGyOHIPBYDAYDBEPXzxEyIUQPM19CgAweJyD4RcAI7x24jQ08Gffvjjz+gxm4wYN4KmhAZ3kZCSm\npECmr49uLVvCocSRi4y3GzZHjsFg1DqszjEYtc/DFw8RfD4Yz/KeAQCkj19i6AUSnLgCDQ1E9O2L\nSyXPa9bWxkATE6jxbICurmFz5BjvFP7+/rC3txd+BwcHQ01NrcbzqSm5Pj4+6N69ew1o9OYQEVq1\naoWdO3cCAAoKCtC4cWP89ddfdawZg8GoKx68eCB24h7lYNj5QsGJy9fSwvbPPxc5cU10dOAlkzEn\n7h2H3T1GnVHyoHUvLy/cu3evDrWpmKoeDK+qqorQ0NBa0WXr1q3Izc3FF198AQBQUVHB7NmzMX36\n9FrJj1EEm6+lfJjNFePBiwcIOR8iOHGGD3Mw7ALBkNMGALzU0kL4Z5/huqamkKa1nh76mZhApUS7\nxuytXNgcuVokJSEBSYcOgc/PR6GaGmw9PGD5hpsj1obMd52SXciamprQLNHIvG0QUZW6vGtzKPGH\nH37AqFGjRGH9+/fHhAkTEBUVBXd391rJl8FgvH0U98Q9z3sOADB88ALDLgJSvsiJy9bRQXjv3kgr\n0b52lkjQVSKp0p9TxtsL65ErRUpCAhKDg9H1/n24PXmCrvfvIzE4GCkJCXUu083NDWPGjMGCBQtg\nbm4OIyMjjBgxAtnZ2QDkD/+Fh4eDL9FtXjykuXPnTtjZ2UFHRwf9+/fH8+fPsXPnTjg4OEBfXx9f\nfPEFnj59KqQrlr1q1SpYWFhAR0cHAwcOxOPHRWf2RUdHQ1VVFXfu3BHlHxoaColEgpycnArLVnoI\ntPj3yZMn0bJlS+jo6KB169aIjY0VpRszZgzs7Oygra0NW1tbzJ49G3l5eRXmtW3bNtja2kJLSwud\nO3fG/v37wfM8Tp48WWG6kly5cgU9e/aEVCqFrq4unJycEB4eDgCwsrJCQUEBfH19wfM8VF4v73/6\n9Cl8fX1hbm4OTU1NNGzYEFOnThVkvnz5EuPGjYNEIoGhoSHGjx+PmTNnioagr1+/jri4OPTt21ek\nj5aWFj7++GNBB0bN4+bmVtcqfHAwm1fM/ez7IifOqJQTl6Wnh029eomcuB6Ghugmlcp14pi9lUtN\n2Zv1yJUi6dAhdNPQAEp0eXYDcOTiRVi2aVM9madPo9uLF6Kwbm5uOHL4cJV75Xbt2oWRI0ciJiYG\nKSkp8PLygqWlJebPnw8ACv3DSktLQ2hoKCIiIvDo0SMMGDAA/fr1g5qaGnbt2oWnT5+if//+WLx4\nMZYuXSqkO336NHR0dPD333/jwYMHGDNmDEaNGoXdu3fDzc0N9vb22LRpE+bNmyekCQoKwpAhQ6Cl\npVWlcgJAYWEhZs2ahTVr1sDY2BhTpkzBwIEDcePGDaioqICIYGpqim3btsHU1BQXLlzA2LFjoaam\nBn9/f7ky4+LiMHToUMyePRvDhg3D1atXMXny5Cr/M/X29kazZs1w6tQpaGpq4tq1aygoKDp4OjY2\nFubm5li5ciUGDRokpJkzZw7OnTuHvXv3wtzcHKmpqbh69apwfebMmdi9ezfCwsLg4OCAoKAgrFu3\nDqampkKc6OhoGBsbw8rKqoxO7dq1w5o1a6pUDgaD8W5S7MRl5xf9kTd+8AJDLgBSlSIn7oG+PkI/\n/hhPX7e9HMfhUyMjtNTTqzOdGbUDc+RKwefnyw9//ZKulszCQvnhlfQcycPKygorVqwAADRq1AiD\nBg3CoUOHBEdOkeG83NxchISEwNDQEAAwcOBArF+/HhkZGTAyMgJQNGft8OHDonREhLCwMOi9bggC\nAwPRs2dP3Lx5EzY2Nvjyyy+xevVqzJ07FxzH4dq1azhx4gTWrl1b5XIW5/fDDz+gefPmAIp6E9u3\nb4+bN2/C3t4eHMdh4cKFQvyGDRsiMTERP/30U7mO3MqVK9GpUyfBXvb29khPT8e4ceOqpNvt27cx\ndepUODo6AoDIsTI2NgYAGBgYQCaTidK0aNECbV7/Iahfvz4++ugjAEB2djbWr1+PtWvX4tPXu6l/\n9913iI6ORlZWliDj+vXrsLS0lKuTlZUVUlJS8OrVK6iqsqpd00RHR7MeCyXDbC6fzOxMhJwP+c+J\nu5+NoZc4SF47cfckEoT36IEXr504FY5DfxMTOOnoVCiX2Vu51JS93+uhVX9//ypPJiwsZ4VjYYnd\nr6tKYTkrggrVq7Z3NsdxcHFxEYWZm5sjIyOjSnIsLCwEJw4ATE1NYWZmJjhxxWGZmZmidE5OToIT\nBwAdOnQAAKFXafjw4cjMzERkZCQA4JdffkHr1q3L6Kwopctrbm4OAKLyBgUFoV27djAzM4Oenh5m\nzZqF27dvlyszPj4e7du3F4WV/q0I3377LUaPHg13d3cEBATg3LlzlaYZP348du3ahaZNm2Ly5Mk4\ncOCA4HgnJSUhNzdXsGkxHTt2FDnnWVlZ0NXVlStfX18fAPDkyZMql4fBYLwblHbiTDJfO3Gvh1OT\nDQ0RUsKJU+d5DDY1rdSJY9Qd0dHR5XY+KMJ7/be9Ooax9fDA4eBgdCvhJR/OzYWdjw9QzcUJtgkJ\nRTJLLPs+nJsLu27dqixLvZTzx3EcCl/3+PE8X6ZHLl9OD2Pp7Tg4jpMbVliqJ7Gy3j4jIyMMGDAA\nQUFB6NatG0JDQ7F48eKKC1QBPM+LhjyLvxfrtXPnTkycOBHLli1Dly5doK+vjx07dmD27NkVyq2J\nCb5z5szBkCFDcODAARw5cgSLFy/GtGnTsGDBgnLT9OjRA7dv30ZkZCSio6MxdOhQNG3atEzPZ0VI\nJBI8e/ZM7rXinjuJRFK1wjAUgvVUKB9mczEZzzMQciEEL/KLpurIMrIx+PJ/PXHXjYywo1s3vHrt\nxGmpqGCITIb6Ci4kY/ZWLsX2dnNzg5ubGwICAqol573ukasOlg4OsPPxwRGZDNESCY7IZLDz8Xmj\nFaa1IVMeMpmszBYeZ8+erTH58fHxIieieHGAk5OTEDZ27Fj88ccfWL9+PV6+fAlvb+8ay780R48e\nRYsWLTB58mS0aNECtra2uHXrVoVpnJycyixq+OeffxTKr7QDaG1tjXHjxmHnzp0ICAjATz/9JFxT\nV1cX5syVRCqVwsvLC+vXr8f+/fsRExOD+Ph42NraQl1dHSdOnBDFP3HihChfe3t7pKSkyNUvJSUF\nVr/46b0AACAASURBVFZWbFiVwXgPSX+eLnLiTEs5cRdlMvxawonTVVGBj5mZwk4c492FtfhysHRw\nqHEnqyZkVrYFhoeHB5YvX45169ahZ8+eOHLkiLBpbE3AcRyGDx+OhQsX4uHDh5gwYQL69OkDmxJH\nuXTs2BEODg743//+hxEjRkDndXd+t27d0K5duzfqoSuNo6MjNm3ahL1798LZ2Rn79u3Dnj17Kkzz\nzTffoE2bNvDz88OQIUNw7do1rFy5UihfSdmTJk3ChAkThLBi2z9//hzTp0/HgAEDYGVlhSdPnuDA\ngQNwLnE2obW1NY4cOYKePXtCXV0dxsbGmD17Nlq3bg0nJyfwPI/w8HDo6emhYcOG0NHRwVdffYU5\nc+bA1NQUjRo1wsaNG3H9+nXRYocuXbrg4cOHSE5OLrPg4Z9//mH/qGsRNn9I+TCbF5H+PB2hF0L/\nc+LSn2PwFQ4Gr52402Zm+NPVFXjtxEnV1DDc1BTSKm6GzuytXNgcuQ8QeZvSlgzz8PDAwoULsXjx\nYjRv3hzR0dGYN29emeHJimRUFNa2bVt06tQJ3bt3h6enJ1xcXLBp06Yyeo4ePRp5eXn48ssvhbCb\nN28iPT290jwr+l06bOzYsRg2bBh8fX3RsmVLnDlzBv7+/hXKadmyJbZs2YItW7agWbNmWLZsmTAc\nWnIfu+vXr+Phw4dy9VVTU8OTJ08watQoODk54eOPP4a5uTm2bt0qxF+xYgXi4uJgbW0tOGJaWlqY\nN28eWrdujTZt2uDy5cv466+/hHmHS5cuxeeff45hw4ahXbt2ePr0KSZMmCBy3h0cHNC6dWvs3r1b\nVMacnBxERkZi6NChZWzGYDDeXdKepSHk/H89cWbpzzHkCg8DFR0QgJh69UROnExdHSPNzKrsxDHe\nXdhZqwyF8PHxwd27d3Hw4MFK406bNg2HDx9GXFycEjR7c0JDQzFy5Eg8evRIWDDwtuDv748tW7bg\nxo0bQtjWrVuxaNEiXLlyRQgLCwvDd999h4sXL9aFmpXC6hyDUXXSnqUh9EIocl4V7cNpnvYc3lc4\n6KsWOXGR9evjn44dgdd/QutraGCIqSm03mBxHqPuYGetMuqcrKwsnDlzBkFBQZgyZUpdq1Mu33//\nPeLi4nDr1i3s2LEDM2bMwMCBA986J648Bg8eDC0tLdFZq4sXL8by5cvrWDMGg1FT3Ht2T+TE1bv3\nTHDiCgH8bmkpcuJstbQw3MyMOXEfIMyRYyiEImeN9unTB126dEG/fv3e6iG+S5cu4dNPP0Xjxo2F\njYHlDRG/DZRn99jYWNFZq/Hx8fj444+Vrd4HBTuHUvl8qDYv7cRZ3H0Gr6s89FV18IrjsMPaGufb\ntxecOCcdHXjLZFAvZ6srRflQ7V1XsLNWGUpl8+bNlcZ5VxqBkJCQulZBYfz8/ODn51fXajAYDCVx\n9+ldhF0Mw8tXLwEA9e88w6B4HnpqOsjlOPxqbY1bbdsCr7ezaqGnh0+NjMCzc1M/WNgcOQaDUeuw\nOsdgVE4ZJy71KQZdU4Gemg5e8Dy22NjgbuvWghPXwcAA3cs5N5Xx7lHddlLhHrnIyEicP38ez58/\nF2VafNQRg8FgMBiM6nHn6R2EXQhDbkEuAKDB7SwMvK4GPTVtPFVRQZiNDe63aiU4cd2kUnQyMGBO\nHEOxOXITJ07EsGHDcPbsWdy5cwd37txBamoqUlNTa1s/BoPBeCt4V6YOvE98KDZPzUoVOXENb2dh\nYIIq9FS18UhVFZvs7HD/dU8cx3HoZWSEzhJJjTtxH4q93xaUOkduy5YtuHjxIho0aFAjmTIYDAaD\nwShy4sIvhgtOnGVKFr64rgpdNR2kq6sj3MYGz1u0ANTVwXMc+hkbo0k55y0zPkwUmiPXqFEjxMbG\nvjPbMwBsjhyD8TbB6hyDUZbbWbcRfjEceQV5AACr5CwMuKEGXTVtpGpoYIuNDV42bw6oq0OV4zBI\nJoO9tnYda82oLarbTpbryN28eVP4fvDgQezfvx8zZsyAmZmZKF7J45neJpgjx2C8PbA6x2CIKe3E\nWd96gv6J6tBV00ailha2W1sjv3lzQE0NGjyPwaamsGTnpr7X1PiGwHZ2dsJn3Lhx2LdvHzp16iQK\nt7e3fyOlGcojOTkZPM+XOTD+QyE6Oho8z+Pevf9n777DoyrTxo9/ZzLpddIbpEISQg1BmoaqgIIs\noNJEIthe3V3Xjr5Lta2+q/5cdXWtkaqCILIqSklognRIgUBIIxXSe5s5vz+GTDKhTcqUhOdzXbnk\nnJmc88ztZHLnKfeTB4h4tJWXl4ebmxu5ubkAZGRk4ObmxuXLl03cMvMh5g8ZX0+NeVZZlm4Sl16q\nTeKS7ezYEBysTeLsLSyI9fY2ShLXU+Ntrroq3tdN5NRq9U2/VCpVlzRCMLzevXtTUFDAbbfdZpL7\nv/baawQFBbX7+3JycpDL5ezdu7dL22PqeJib5cuXM3v2bPz8/AAICgpixowZYlW6IHSxrLIs1iWu\n0yZxIell3HcliTvm4MCm4GBUgwaBpSXOCgWLfHzwubJSVRCuRa/FDrm5udja2uLq6qo9V1JSQl1d\nHb6+vgZrnKmkpqezMzmZRsASmBgZSVgnh5ANcc32kMvleHp6Gu1+Xa2rh+W6Kh4NDQ1YWVl1QYuu\nplarAU1bDamkpIS1a9de1Tu5aNEiJk2axJtvvomDmFzN2LFjTd2EW05Pi3lmWSbrTq+jUd0IQOiF\nUmamW2NnZcd+Z2d29u4NAweCQoG7pSULvL1xVhivbn9Pi7e566p46/UbYvr06eTk5Oicy8nJYcaM\nGV3SCHOSmp5O3PHjXO7fn7L+/bncvz9xx4+T2mrOoCmvuX//fkaPHo2TkxNOTk4MHjyY3377DYBL\nly7x8MMP4+3tja2tLeHh4dodGdoOJTYfr1u3jgkTJmBnZ0dISAjffvut9l5jx47l8ccf17m/JEmE\nhITw+uuvX9W2N954g5CQEGxsbPD09GTy5MnU1dURFxfHsmXLyMrKQi6XI5fLtT0969evZ/jw4bi4\nuODh4cHUqVN1Nojv3bs3AOPGjUMul2vnZObk5DBr1iw8PDywtbUlJCSEf/7zn3rH8Xrx2LhxI1On\nTsXe3p6QkJCrdoGQy+V88MEHzJs3DxcXFxYuXAho5pGOHj0aOzs7/P39WbRoESUlJTpxe+WVV/Dw\n8MDJyYkHH3yQ999/H0tLS+1zVqxYQZ8+ffjuu+8IDw/H2tqa8+fPU1VVxdNPP42/vz/29vZERUWx\nZcsWvWKvT6w2btyIl5cXQ4YM0bnmyJEjsbe3v+pegiC0X0Zphm4Sl1bCzHRrbC3t2KFU6iRxvtbW\nLPLxMWoSJ3Rfer1Lzp07x8CBA3XODRgwgDNnzhikUaa0MzkZ66FDSSgrazkZEsLpvXsZ1sGaPYf3\n7qVm0CBodc2xQ4eyKympXb1yTU1N3HvvvSxatIjVq1cDmn1D7e3tqa2tZcyYMdjb27N+/XpCQkK4\ncOECRUVFN7zmiy++yD//+U8++eQTVq9ezfz58wkLC2Pw4ME88cQTPPbYY7z77rvY29sDsHv3brKz\ns1m8eLHOdTZv3sxbb73F+vXrGTRoEMXFxezZsweAOXPmkJqayrp16zh69CiA9noNDQ0sW7aMfv36\nUVFRwbJly7jnnntITk7G0tKS48ePExUVxebNmxk1ahQWVzaEfvLJJ6mrq2PXrl24uLiQnp5OYWGh\n3rG8niVLlvDWW2/xr3/9iy+++IJHHnmEUaNG6cwHXblyJatWreL1119HrVaze/du/vSnP/H222+z\nevVqSktLefHFF5k5c6Z2DsR7773HBx98wCeffMKIESP48ccfWbVq1VV1oPLy8vj4449Zs2YNSqUS\nb29vpk2bhkwm47vvvsPX15cdO3YwZ84cfvnlF8aPH3/D2F8vVgUFBdrH9+zZw/Dhw6+KhUwmY/jw\n4ezevZsFCxZ0OrbdXUJCguixMLKeEvOM0gzWJ67XJnF9zpcwI9MGG0s7/uvmxjE/PxgwABQKAm1s\nmOvlhbWBe+KvpafEu7voqnjrlch5enpy/vx5nV9mFy5cwN3dvdMNaK+KigomTpzImTNn+OOPP+jX\nr1+XXr/xOudVnSi8qL7O9za08zqVlZWUlZUxbdo0QkJCALT//eKLL8jMzOTChQva4e6AgICbXvOR\nRx5h7ty5ALz66qvs3r2bd999l9WrVzNjxgz++te/8s0332gTt88//5ypU6detXo5KysLb29vJk2a\nhEKhwN/fn0GDBmkft7e3x8LC4qrhzNjYWJ3jr776Cnd3d44ePcrIkSO17zFXV1ed783OzmbGjBna\nPzCae+466y9/+Qv33XefNh4ffPAB8fHxOu/9GTNm8OSTT2qPFy9ezNNPP81TTz2lPRcXF0dgYCCn\nT59m4MCBvPPOOzz77LPMnz8fgGeeeYbDhw+zadMmnfvX1dWxZs0a/P39Ac0P+qFDhygsLNSW/3n0\n0Uc5ePAgH3zwAePHj79p7G8Wq3PnzjFu3LhrxiMgIIBjx461L4iCIGill6azIXGDNonre66YP2XZ\nYm1px/ceHiT7+GiTuDA7O+7z8MDSBEmc0H3p9W5ZtGgRs2bNYtu2baSkpPDjjz8ya9asq3pljMHO\nzo6ff/6Z++67zyDlDCyvc96iE/eSX+d72zuzSqlU8sgjjzBp0iTuvvtu3nrrLc6dOwfAsWPHiIyM\nbPecxZEjR+ocjx49muTkZACsra2JjY3ls88+A6C4uJgffviBRx999KrrzJ49m8bGRgICAnj44YdZ\nu3atznZu13Py5ElmzJhBcHAwTk5O2uQzKyvrht/3t7/9jTfeeIMRI0awZMkS9u3bp9frvZnBgwdr\n/908j+7SpUs6z2m7QOLIkSO89957ODo6ar8iIyORyWScP3+e8vJy8vPzGTFihM73tT0G8PLy0iZx\nzdduaGjAz89P5/rr1q0jLS0NuHnsbxariooKHB0drxkPJycnylr3Tt/CRE+F8XX3mKeXpuv0xIWn\napI4hZU9G7y8NEncleHUgQ4OPODpadIkrrvHu7vpqnjr1SO3ZMkSLC0tef7558nJyaFXr1488sgj\nPPvss13SiPZQKBQG7QmcGBlJ3LFjjB06VHuu/tgxYmNiCOvAqkuAVEki7vhxrNtcc0JUVLuv9emn\nn/L000/z22+/sWPHDpYuXcqHH37YZXW62l7j8ccf55133iExMZFdu3bh6enJlClTrvo+X19fzp49\nS3x8PLt37+bVV1/lpZde4o8//tBJTFqrqanhrrvuIiYmhri4OLy8vJAkicjISBoabtxfGRsby+TJ\nk9m+fTvx8fFMmTKFGTNmsGbNmo6/eLhq4YJMJtMuOmjWPCzcTJIklixZcs3hRy8vL5qamrTXupm2\n11ar1Tg7O2uHpK/V1pvF/maxcnFxobKy8prtKS8vR6lU3rTdgiDoulBygQ1JG2hSa37+w88WMf2i\nPVjbs8bTk4teXpqeOAsLhjs5MdnVVeybKnSIXqm/XC7nhRdeIDU1lerqas6ePcvzzz9v8NV0phAW\nHExsVBSeSUm4JCXhmZREbFRUp1aYdvU1IyMjeeaZZ/j5559ZvHgxn376KUOHDiUlJUVbB0xfBw8e\n1Dn+/fffiYyM1B6HhIQwfvx4PvvsM7744gsWLVp03Q8bKysrJk2axFtvvUViYiI1NTVs3bpV+1jb\ncjVnzpyhqKiI119/nZiYGMLCwigpKdFJJpuTlWuVuvH29iY2Npavv/6azz//nHXr1unVC9jVoqOj\nSUpKIjg4+Kove3t7nJ2d8fX1vWpV6KFDh2567WHDhlFWVkZtbe1V126dIN8o9nDjWPXp04fMzMxr\n3j8rK4u+fft2ICo9j6ixZXzdNeZpJWk6SVy/FE0Sp7JxIM7bWyeJG+viYjZJXHeNd3dl1L1WQTMp\nPTU1laKiIp1ftOPHj+/QjT/88EPi4uJISkpi7ty52tWVoCmHsHjxYnbs2IG7uztvvvmmdh5Xa4Z6\n44cFB3d5aZCuuOaFCxf49NNPuffee/H39ycvL4+9e/cSHR3N3Llzefvtt7n33nt5++23CQ4OJj09\nneLiYh544IHrXvPLL78kPDycoUOHsnbtWg4dOsRHH32k85zHH3+c+fPno1areeSRRwDYsmULL7/8\nMvHx8fj4+PDFF18gSRLDhg3DxcWFXbt2UVlZqZ3DGBQUREFBAYcOHSI0NBR7e3sCAgKwtrbmX//6\nF88++yyZmZksWbJE5/+ru7s7Dg4O/Prrr0RERGBtbY1SqeTPf/4z99xzD3379qWuro7NmzfTu3dv\nbZmMl19+mSNHjrBz585OxVyfXs5Vq1Zx11138dxzz7FgwQIcHR05f/48mzZt4sMPP8TGxobnnnuO\n5cuXEx4ezrBhw/jpp5/YsWPHTf8YGj9+PBMnTmTmzJm8/fbbDBgwgNLSUn7//XdsbW155JFHbhr7\nm8VqzJgx11yFLEkShw8f5u233+5A5ATh1pRWksY3Sd+0SuIuMy3XgTpbR9Z4eVHi4QH9+4OFBZNd\nXRnh7GziFgvdnqSHffv2Sd7e3pJSqZTkcrmkVColCwsLKSgoSJ9vv6bNmzdLP/zwg/Q///M/Umxs\nrM5jc+bMkebMmSNVV1dL+/fvl5ydnaXk5GSd58TGxkpJSUnXvf6NXpqeL9vs5OfnSzNnzpT8/f0l\na2trydfXV3rsscekiooKSZIkqaCgQHrooYckd3d3ycbGRoqIiJC+/vprSZIkKSMjQ5LL5dKBAwe0\nxzKZTFq7dq00duxYycbGRgoODpY2bNhw1X0bGxslT09PaerUqdpzX331lSSXy6WsrCxJkjT/P0eN\nGiUplUrJzs5OGjBggPTll1/qXGPevHmSq6urJJPJpJUrV0qSJEmbNm2S+vTpI9nY2EhRUVHSnj17\nJIVCoW23JEnS6tWrpaCgIEmhUGjfc0899ZTUt29fydbWVnJzc5OmTp0qpaSkaL8nNjZW5/0ZHx8v\nyeVyKTc397rxaH3cLDQ0VNtWSZIkmUwmrVu37qoY7du3T5o4caLk6Ogo2dvbSxEREdIzzzwjNTU1\nSZIkSWq1Wnr55Zcld3d3ycHBQZo7d670xhtvSI6OjtprrFixQurTp89V166trZWWLFkiBQUFSVZW\nVpK3t7c0ZcoUKT4+Xq/Y3yxWRUVFko2NjXTs2DGd++7fv1+yt7eXKisrr2pTe3XXnzlBaI9zReek\nVQmrpOXxy6Xlu5dJGz96Sqp55UWp8LXXpH9+9pm0/IcfpOVpadLKjAzpZBf8XAk9S0c/J6+712pr\n0dHRzJs3j2effRalUklpaSmrVq3C1taWF154oVOJ5NKlS8nJydH2yFVXV+Pq6kpycjKhoaEALFy4\nEF9fX958800A7r77bk6dOkVAQACPP/64tpZXa2Kv1RvLzMwkODiY/fv3M2rUqBs+t7i4mF69evHt\nt98ybdo0I7Ww51u0aBGJiYkcOXLE1E3hsccew8LCgo8//lh7bvHixdja2vLhhx92+vriZ07o6c4V\nn+PbpG9RSSqQJPonFzG1wJFiOyfWenlR6+4O/fujsLDgPg8PwtvMhxWEjn5O6jW0ev78ef72t78B\nLUNNS5YsITAwsNOJXNtGnzt3DoVCoU3iAAYNGqQzlvzzzz/rde3Y2FgCAwMBzYTuwYMHi1U57dDU\n1ERRURErVqzA399fJHGdkJ+fz+bNmxk3bhwWFhZs27aNNWvWXDWMbSorV66kf//+/P3vf8fPz4+M\njAy2bt3apbUiW9dMav557k7HJ0+e1H4OmkN7boXj5nPm0p7rHa/Zuob4jHh6D+4NkoRsy0mcSu3J\nj+zFBk9PzuXng40NfRUK5np6kvXHHxSYUfu7W7x7yvHJkycpKyu77hxlfenVI9e7d29OnTqFUqmk\nX79+bNy4EXd3d/r27Ut5eXmnGtC2R27fvn088MAD5Ofna5/z2WefsX79euLj4/W+ruiRu7HMzExC\nQkLYt2/fdXvkEhISGD9+PMHBwaxZs+aqUiWC/i5dusTs2bM5ffo0dXV19OnTh7/85S8mKeFjCj3h\nZy5BFEs1uu4Q89SiVL5L/k7bEzco8TJTLjmR6eTKRg8PVG5uEBmJnaUl87288DPjfVO7Q7x7krbx\nNmiP3IwZM/j555+ZP38+ixYtYvz48SgUCm3h1M5o22gHBwcqKip0zpWXl1+3zpXQMYGBgddcCdra\n2LFjryq9IXSMp6dnu/4QEcyP+AVnfOYe87NFZ9mYvLEliTt9iSmXnTnr4s5WNzckd3fo1w8nKysW\neHnhYWWYfZm7irnHu6fpqnjrlci9//772n8///zzDB8+nMrKSiZPntzpBrRdedq3b1+amppIS0vT\nDq+eOnWK/v37d/pegiAIgtAVzhad5bvk71BLapAkhpy6xKQiZ066erLd1RWuJHGuVlY85OWFi+X1\nys0LQue0qxBcdnY2Bw8eJCAggLvvvrtTdeRUKhV1dXU0NTWhUqmor69HpVJhb2/PzJkzWbZsGTU1\nNezfv59t27Z1aK/HFStW6Iz9C4IgdJT4LDE+c435mctndJO4k4VMKnLhoLu3ThLnbW3NIm/vbpPE\nmWu8e6rmeCckJLBixYoOX0evTCw/P58xY8YQGhrKzJkzCQ0NJSYmhry8vA7f+NVXX8XOzo633nqL\ntWvXYmtrq61l9e9//5va2lo8PT158MEH+eSTT4iIiGj3PVasWCG6igVBEIQuk3I5hY0pG7VJXNTJ\nQiYVK9nt6cMeFxfw8IB+/ehta0ustzcOCr3LtQq3qLFjx3YqkdNrscP06dMJCAjgzTffxN7enurq\nal555RUyMjL48ccfO3xzQxKLHQTBfIifOaEnSL6UzPdnvtcmcUOPFzCh1JXt3n6cdnDQJHEREYTa\n2THbxPumCt1PRz8n9Urk3NzcyM/P19mHsr6+Hl9fX4qLi9t9U2O4UUBcXV0pLS01cosE4dalVCop\nKSkxdTMEocOuSuKO5TOu3J0fffw5Z2cHnp4QHk5/BwdmeHhgYQZbbgndS0cTOb3+XHB1dSUlJUXn\n3NmzZ81+M+3rzZFr3s9TfHXtV3x8vMnbcCt9dad494QkTswfMj5ziXnSpSSdJG7Y0XxiKjzY6Ndb\nk8R5eUF4ONFOTszsxkmcucT7VtFVc+T0Grx/8cUXufPOO1m8eDEBAQFkZmby1Vdf8eqrr3b4xsbQ\nmcAIgiAIQmJhIpvPbEZCQqaWiD6Wx6gqb77x8yff2lqTxIWFcYdSyXgXF4PtAS70XGPHjmXs2LGs\nXLmyQ9+v19AqwO7du1m3bh35+fn4+voyd+5cJkyY0KGbGoOYkyMIgiB0RtskbtjRfIbVePOtXy+K\nLC3B2xv69uVONzdGOzuburlCN2ewOXJNTU2EhYWRkpKCtRlXpG5LJHKCIAhCR50uPM2WM1u0Sdxt\nh3MZ1ODHt769KFcowMcHWd++THN3J0oUrBe6gMHmyCkUCuRyObW1tR1qmHDrEPMrjEvE27hEvI3P\nVDE/VXBKJ4kbfjiXfo3+rPPrrU3iLMLCuN/Ts0clceI9blxdFW+95sg988wzzJ49m5dffplevXrp\nzAEIDg7ukoYIgiAIgqmdLDjJ1rNbNUmcSs2Iw/kEq3uz3s+ferkcfH2x7NuXOV5ehNjamrq5gqDf\nHLnr7eAgk8luul+nqchkMpYvX66dRCgIgiAIN9I2iRv5Rx6+BPKDjx9NMhn4+WHTty/zvbzoZWNj\n6uZ2mdTULHbuvEBjoxxLSzUTJ4YQFhZg6mbdMhISEkhISGDlypWGmSPXXYk5coIgCIK+TuSf4MfU\nH7VJ3KhDubhZhPJfL2/UV5I4h7AwFnh749Wqpmp3l5qaRVxcGjU1E5DLwdkZ6ut3ERsbKpI5IzNo\nHblmubm5HDlyhNzc3HbfSOj5xPwK4xLxNi4Rb+MzVsyP5x/XJnFylZrRh3JxsOzLtuYkzt8fZXg4\ni3x8elQSB7Bz5wVqaiaQmAh79iRQVgbW1hPYteuCqZvW43XV+1uvRC47O5s77riDgIAA7rnnHgIC\nArjjjjvIysrqkkYIgiAIgikcyzumk8SNOpiL3CacXz29kGQy6NULzytJnKulpamb2+UKC+UkJoJa\nrfk6dw4kCRoaxPZi3YVe/6ceeughhg4dSnl5OZcuXaKsrIzo6GgWLlxo6PYJ3YiYi2hcIt7GJeJt\nfIaO+bG8Y2w7tw1Ak8T9nkO9fSR73Tw0T+jdG7+ICGJ9fHBU6LU2sFvJzoaTJ9Wo1ZpjT8+x9O8P\nMhlYWalN27hbQFe9v/WaI+fk5ERRUZHOXqsNDQ24ublRWVnZJQ3pamKxgyAIgnA9R/OO8t9z/wVA\n3qRi1MFcyp0GkujsonlCQADB4eHM8fLC6joL/rqzixdhzRrIy8vi5Mk07OwmMHgw2NmJOXLG1tnF\nDnq9O0eMGMHhw4d1zh05coSRI0e2+4bGtGLFCpHEGZGYQ2RcIt7GJeJtfIaK+ZHcI7pJ3O95FLoM\n1kniIvr1Y14PTeJyc2HtWmhoAHf3AEaODGXChN3U1Pw/PD13iyTOSJrf32PHjjX8XqvBwcHcfffd\nTJ06FX9/fy5evMjPP//MvHnzWLp0KaDpAVu1alWHGyIIgiAIhnY49zA/n/8Z0CRxI37P46J7FFl2\n9ponBAYypH9/prm5Ie+B+6bm5Wl64urrNcd2dvDkkwF4egaQkCAXnR/dkF5Dq7GxsS3f0Gp5bHNh\nYEmSkMlkfPXVV4ZpZQeI8iOCIAhCa62TOItGFcMO5pPpGUWBjZ3mCYGBjBw4kLuUSp3C9z1Ffj6s\nXg3NGzXZ2cHCheDlZdp2CRoG22u1uxKJnCAIgtDsj5w/+CXtF0CTxEUfKiDNcyjF1lcK+wYFMX7Q\nIO5wdu6RSVxBAXz9dUsSZ2urSeK8vU3bLqGFwevInTt3jtdee42nnnqK119/nXPnzrX7ZkLPmrOD\nTwAAIABJREFUJuYQGZeIt3GJeBtfV8X8UM6hliSuoYkhhy6R4hWtTeJkwcHcM2QIMS4uPTKJu3RJ\ntyfOxgYeeujqJE68x43LqHXk1q9fT1RUFImJidjb23P69GmioqJYt25dlzRCEARBEAzh4MWDbE/b\nDmiSuIF/FJHiPZRKK2sA5CEhzIyKYpiTkymbaTCXL2t64mpqNMfNSZyPj2nbJXQdvYZWg4KC+Prr\nr4mJidGe27dvHwsWLCAzM9OQ7eswMbQqCIJwa/v94u/8duE3ABQNTUQcKSHVewgNFprCvoqQEB4Y\nOpS+dnambKbBFBVBXBxUVWmOra1hwQLw9zdps4Tr6Gjeoteq1aqqqqtKjYwYMYLq6up239CYmsuP\niFU4giAIt5a2SVzo0TJSvKNQWWh+7VmHhjIvOpoAGxtTNtNgios1PXHNSZyVFTz4oEjizFFzHbmO\n0mto9dlnn+Xll1+m9soAe01NDa+88grPPPNMh29sDKKOnHGJ+RXGJeJtXCLextfRmB/IPtCSxNU3\nEnCsklTvIdokzr5PH2KHDeuxSVxJiSaJa67X35zE9ep14+8T73HjMmoduY8++ojCwkLef/99lEol\npaWlAHh7e/Pxxx8Dmi7B7OzsDjdEEARBEDprf/Z+dqbvBDRJnN/JWtK8BiG7UtjXuW9fHho2DLce\nuG8qQGmpJomrqNAcW1rCvHnQu7dp2yUYjl5z5PTN0s2p90vMkRMEQbi17Mvax66MXQBY1jXifrqB\nXI8I5DJNEuceFsaCYcNw7oH7pgKUlWnmxJWVaY4VCpg/H4KCTNosQU+ijlwbIpETBEG4dezN2svu\njN0AKGobcEpWU+TWV5vE+YSH8+CwYdhbWJiymQZTXq5J4q4MmKFQwNy5EBJi0mYJ7WDQxQ4AJ06c\nYN++fRQXF+vcSGzLJTRLSEgwq17Znk7E27hEvI1P35jvydxDfGY8ABa1DdickekkcQEREcwbNgzr\nHrhvKmiGUb/+uiWJs7CAOXPan8SJ97hxdVW89XpXf/rpp9x+++3Ex8fzj3/8g8TERN555x3S0tI6\n3QBBEARB6KiEzARtEqeobcAiVUGFMkSbxPWNjOTBHpzEVVZqkriSEs2xhQXMng2hoaZtl2A8eg2t\nhoSE8NVXXxETE6Nd7PDLL7+wYcMGVq9ebYx2tpsYWhUEQejZEjITSMhMAMCithHVeSvUjr20SdzA\n/v2ZPnQoFj1wtwbQlBaJi9PUiwOQyzVJXFiYSZsldJBB58g5OTlRcWUJjJubG5cuXUIul+Pq6qpd\nwWpuZDIZy5cvF3XkBEEQehhJkkjITGBP1h4AZDWNNKbbILf30yRxMhm3DRjAlCFDeuSWWwDV1Zok\n7vJlzbFcDvffDxERJm2W0AHNdeRWrlxpuL1W/f39ycjIAKBPnz5s3bqVffv2YW1t3e4bGpOoI2dc\nogaRcYl4G5eIt/FdK+aSJBGfGa9N4qhuojbDDgt7f20SN2bgwB6fxH39tW4SN2tW55M48R43LqPW\nkXvhhRc4c+YMQUFBLF++nFmzZtHQ0MC//vWvDt9YEARBENpDkiR2Z+xmX/Y+ANRVTdRkO2Bv56VJ\n2mQyJg8ezIhBg0zcUsOpqYHVq+HSJc2xTAYzZ0JkpGnbJZhOh8qP1NfX09DQgKOjoyHa1CXEHDlB\nEISeQ5IkdmXsYn/2fgAaq1TUXHTAycYTmUyGDJgeFcXggQNN21ADqq3V9MQVFGiOZTKYMQN68Eu+\npYg6cm2IRE4QBKFnaJvE1VdLVF10wNXaHZlMhgVw/7BhhPfgbqm6Ok1PXF6e5lgmg+nTYfBg07ZL\n6DodzVt65npswSTE/ArjEvE2LhFv40tISECSJHam79QmcTVVUJnjqE3irID5t93W45O4NWtakjiA\ne+/t+iROvMeNq6vi3TP3KREEQRC6PUmS2JG+g98v/g5AZZWc2lx7PKxckclk2EoSD44ciV94uIlb\najj19bB2LeTmtpybNg2GDDFdmwTzIoZWBUEQBLOSmpbKjqM7SLycyMXyiwQHB2Ph5E1Dri2eV5I4\nR0nioVGj8OjBRdMaGjRJXHZ2y7l77oFhw0zXJsFwDDq06urqes3znp6e7b6hIAiCIFxPaloqcfFx\nHLI6RKpjKjX+NRy7UEh5uoU2iXNVqVh8++09Polbt043iZsyRSRxwtX0SuQaGxuveU6lUnV5g4Tu\nS8yvMC4Rb+MS8TaO347+RpZrFjkVOZSeLaNR7Y+XfTCKOhUymQwvlYpFY8bg0qePqZtqMI2NsGED\nZGW1nJs0CYYPN+x9xXvcuIwyR+6OO+4AoLa2VvvvZjk5OYwcObJLGiEIgiAIdU11/JH7B+fJorTE\nhspcN7zqrPFwagAH6NXYyLzx47Ft727w3UhzEnelBj8Ad90F4tetcD03nCMXFxcHwBNPPMF//vMf\n7ditTCbDy8uLCRMmYGlpaZSGtpfYoksQBKH7KKsrY33ier75bjPpKhtkETFYyhxQyCyQJ59molri\n3adfwCooyNRNNZimJvjmG0hLazk3cSLcfrvp2iQYXme36NJrscPZs2cJ72argsRiB0EQhO4hpyKH\nDYkbqG6sZtu2VIqCo/G0skWODJlMRmNZOXeUV/D/Xn3d1E01mKYm+PZbOH++5dz48RATY7o2CcZl\n0MUOx48fJyUlBYDU1FRiYmIYN24cZ8+ebfcNhZ5LzK8wLhFv4xLxNoykS0nEnYyjpLGOs5Ib5ZIS\nP2snFJIFlWfP41VWwQSFFQ2NPXdOtkoFGzfqJnFjxxo/iRPvcePqqnjrlcj9/e9/x83NDYDnnnuO\n2267jZiYGJ588skuaYQgCIJwa5Ekib1Ze/ku5XvS1XYcbfKG7AZcatVYKSyxsbSid30Dgx2dsXNR\nYtXQYOomG0RzEpea2nIuJgbGjDFdm4TuRa+hVScnJyoqKqitrcXX15eCggIsLS1xc3OjtLTUGO1s\nNzG0KgiCYJ6a1E38eHYb8ZfOkYYr6moVvhfL8bV0JTn1PFJpKeGhodgqlWBlRf3x44R5ehL797+b\nuuldSqWC77+HKwNegGY+3IQJmi24hFtLR/MWvXZ28PDw4Pz58yQmJjJs2DCsra2prq4WiZIgCILQ\nLjWNNXx+eiMJlTWUSJ44F5bjXVSPp5073k0qJslkpBcUUCOX02BpiVVTE3YKBePuv9/UTe9SajVs\n2aKbxI0aJZI4of30GlpdunQp0dHRLF68mOeffx6AnTt3Mljs1iu0IuZXGJeIt3GJeHdeTtUlnjvy\nDVsq1ZQ3WOJ1oZBeJU0E2rpzT1k5T5SUEBMby7h//pPwQYMACB84kHF/+QsBPaj4b3MSl5TUcm7E\nCLjzTtMmceI9blxG3Ws1NjaW+++/H5lMhp2dHQAjR45kuKGrEwqCIAjdniRJ/JR/jn+n/UGNWo5t\neQ3u2UV4WDoxtknOhKJc7P39YdYscHYmAAgIC0OekNDjykep1bB1KyQmtpy77TZNwV/REyd0hN57\nrRYXF/PTTz9RUFDAiy++SG5uLpIk4e/vb+g2doiYIycIgmB6F+vq+E/GKQ4UngW1GmV+Kc6XKxks\ns+OB6kZ8GhtbZvfL9Rok6rYkCX78EU6caDk3bBjcfbdI4oSO5y16JXJ79uxh1qxZREdHc+DAASor\nK0lISOCdd95h27ZtHWqwoYlEThAEwXQqmprYUVLC1pwksiuyUdQ14pF1GY/KGubXK7itEWROTjBz\nJgQGmrq5BidJsG0bHD/ecm7oUJg6VSRxgoZB68g9/fTTfPPNN2zfvh2FQjMaO2LECP74449231Do\nucT8CuMS8TYuEW/9NKnV7Csr4/2L2XyTeYTs8iwcSqrodTaHmIvZvFauYngjyMLC4IknbpjE9ZSY\nSxL89JNuEjdkiPklcT0l3t2FUefIZWVlMXHiRJ1zlpaWqFQ9t0CjIAiCoD9JkkitqeHX0lIK6qpJ\nupREVW057heLiczM5K6SEoa7hKCwtNZsHnrbbeaVxRiIJMEvv8DRoy3nBg2CadNuiZcvGIFeQ6uj\nRo1i2bJlTJ48GaVSSWlpKb/99htvvPGG2WbwYmhVEATBOC43NLC9pIQLtbVUNVSTeCkRqaKCPuey\nuP1CCsPkDoQoQ5B5eMB994G3t6mbbBSSBL/+CocOtZwbMABmzOjx0wGFDjBoHbl3332XqVOncvfd\nd1NXV8djjz3Gtm3b2Lp1a7tvKAiCIPQMtSoVCWVlHKmsRC1JFNeWkHIpGWVBEeOPHSP8ch5hrqH4\nOflpxhKnTAErK1M32ygkCXbs0E3i+vcXSZzQ9fR6O40YMYJTp04RGRnJww8/THBwMEeOHOG2224z\ndPuEbsRce2d7KhFv4xLxbqGWJI5WVPBBbi5/VFSgliRyKnJJyT3BiBMneWTHLwwoKmCw1wD8PII1\nvXDTp7c7ieuuMZck2LULfv+95Vy/fpp1HeacxHXXeHdXRp0jV1ZWhp+fHy+99FKX3FQQBEHonrLq\n6viluJiCK3ufSkiklVxAyj7FY3sO4FVWirWFNQN9BmMf1FeTxCmVJm618UgSxMfD/v0t5yIiNCXy\nzDmJE7ovvebI2djYEBERwZgxYxgzZgwxMTG4ubkZo30dJpPJWL58OWPHju1xBSUFQRCMrfxKOZGk\n6mrtuSa1iqzLifQ7upsRRxORAY5WjgzwGoDVmPEwbhxYWJiu0SaQkKD5ahYWBg88cMuFQWiHhIQE\nEhISWLlypeHqyNXW1nLw4EH27NnD3r17OXz4MMHBwcTExPDRRx91qOGGJhY7CIIgdF6jWs3vFRXs\nLy+nUa3WnlepG6jPTGDE9p9xvVwOgIedB+GB0VjMug9CQkzVZJPZuxd272457tMHZs8GhV5jX8Kt\nzqAFgZtVV1dz4MABtm/fzueff46trS2FhYXtvqkxiETO+BJ64HY65kzE27hutXhLksSZmhp+Kymh\nrKlJ5zEfeT2q/XH0TTiColFThirAOYDA6InIZswAB4cuaUN3ivn+/bBzZ8txaCjMmdO9krjuFO+e\noG28Dbpq9cUXX2Tv3r3k5uYyatQoxowZw6FDh4iIiGj3DQVBEATzVnilnEhGba3OeW8rK0IbCylZ\n9w6e53IBkCGjr2c4Pn9aACNH3pLF0Q4c0E3igoNFT5xgPHr1yNnb2+Pj48PixYsZM2YMw4YNw9LS\n0hjt6zDRIycIgtA+tSoV8VfKibT+/LSzsGCciwuNZ3dTtu5T7MprAFDIFUSEjcbtwcfAz89UzTap\ngwc1teKaBQXBvHlg5r8iBTNk0KHVxsZGjhw5wr59+9i7dy8nTpwgMjKSmJgYli5d2qEGG5pI5ARB\nEPSjliSOVVayu6yM2lY79shlMoY5OnKHowPHtryP6tdfkKs1n6u2Clv6TZiD48w5YG1tqqab1B9/\naHZtaBYYqEnibpFSeUIXM8ocuZKSEvbs2cOuXbtYvXo1dXV1NFxZgm5uRCJnfGJ+hXGJeBtXT413\nZm0tv5SUUNjmszzY1pbJrq441lZy+N//i5R6VvuYo6M7kQ89j030cIMOpZpzzI8c0eyf2qx3b3jw\nwe6dxJlzvHsio86R++tf/0pCQgLnz58nOjqaMWPG8P333zNy5Mh231AQBEEwvbLGRn4rLSWlVTkR\nAKWlJZOUSsLs7Cg7e5Kjn6xCKi/VPu4SFMGAJ1di4eFp7CabjWPHdJO4Xr1g/vzuncQJ3ZdePXLN\n9dhGjBiBra2tMdrVaaJHThAE4WqNajX7y8s5UF5OU6vPSEu5nBhnZ0Y6OaEACn76lrQfvqRJ1ah9\njvv4aUTO/SuyW3gC2IkT0Hp3Sn9/WLDglh1dFrqQUYZWs7Ozyc3Nxc/Pj969e7f7ZsYkEjlBEIQW\nkiSRXF3NjtJSytuUExno4MBEpRInhQLKy8n+8j0yTu1BQvMZqrKxJiD2aUJH3G2KppuNkyc1SVzz\nrxZfX3joIbCxMW27hJ6ho3mLXhuG5OfnM2bMGEJDQ5k5cyahoaHExMSQl5fX7hsKPZfYp8+4RLyN\nqzvHu6C+nriCAjZdvqyTxPlYW7PIx4eZHh44KRRIKSmkvfE86acStElcrb83EUv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HAAAg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\n2NIh2T1PT0/s3LkTjz/+uFGLAZNhoqOjs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- "text": [
- ""
- ]
- }
- ],
- "prompt_number": 70
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "So, using a few tweaks, such as static type declarations and explicit for-loops instead of list comprehensions, we managed to increase the performance of our least squares fit implementation quite significantly - it outperforms the alternative functions in Numpy and Scipy now."
- ]
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file