From f9f7a75f10ef4748a07e450ab35c9683928bf46d Mon Sep 17 00:00:00 2001 From: Eirini <49712302+ai-mindset@users.noreply.github.com> Date: Tue, 26 Aug 2025 10:49:34 +0100 Subject: [PATCH 1/8] add: C&C 20250911 draft slides Written for [Marp](https://marp.app/) since my Linux VM is very small atm (Marp = 107Mb single exe, R + Quarto = ~1Gb and a pain to install. Happy to (try and) convert to Quarto once the content has been finalised. --- .../2025-09-11_coffee-and-coding/slides.md | 460 ++++++++++++++++++ 1 file changed, 460 insertions(+) create mode 100644 presentations/2025-09-11_coffee-and-coding/slides.md diff --git a/presentations/2025-09-11_coffee-and-coding/slides.md b/presentations/2025-09-11_coffee-and-coding/slides.md new file mode 100644 index 00000000..8b70f9ec --- /dev/null +++ b/presentations/2025-09-11_coffee-and-coding/slides.md @@ -0,0 +1,460 @@ +--- +marp: true +theme: default +paginate: true +author: Eirini, Rhian and YiWen +--- + + + + +# Efficient Coding +## Principles and Practices for Performant Code + +Eirini, Rhian & YiWen, DS @ SU + +--- + +# Agenda + +- **Measuring Performance**: Time and profile your code +- **Common Performance Tweaks**: Easy wins for faster code +- **Loops vs. Vectorisation vs. Functional**: Choose the right approach +- **Optimising Loops**: When you must use them +- **Beyond Basics**: Tools for serious optimisation + +--- + +## Measuring Performance: Timing + +```python +from timeit import timeit # Python's precise timing module + +# Function to measure +def my_function(): + return sum(i**2 for i in range(1000)) # Sum of squares + +# Time the function execution +# number=1000: run 1000 times for statistical significance +# globals=globals(): access functions defined in current scope +execution_time = timeit('my_function()', + globals=globals(), + number=1000) + +# Calculate and display average execution time per call +print(f"Average execution time: {execution_time/1000:.6f}s") +``` + +--- + +## Measuring Performance: Profiling (1) + +```python +import cProfile, pstats, time +from pstats import SortKey + +def sum_of_squares(n): + return sum(i * i for i in range(n)) + +cProfile.run('sum_of_squares(1_000_000)') + +# Display meaningful results +p = pstats.Stats('stats') +p.sort_stats(SortKey.CUMULATIVE).print_stats(5) +``` + +--- + + +## Measuring Performance: Profiling (2) + +```console + 1000006 function calls in 0.141 seconds + + Ordered by: standard name + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 0.141 0.141 :1(sum_of_squares) + 1000001 0.068 0.000 0.068 0.000 :2() + 1 0.000 0.000 0.141 0.141 :1() + 1 0.000 0.000 0.141 0.141 {built-in method builtins.exec} + 1 0.073 0.073 0.141 0.141 {built-in method builtins.sum} +``` + +`ncalls`: The function was called directly 1 time but expanded to 1M total calls +`tottime`: sum_of_squares consumed 0.000 seconds in its own code +`cumtime`: Total time including nested calls was 0.141 seconds +Last column: The bottleneck is split between {built-in method builtins.sum} and the generator expression (`i * i for i in range(n)`) + +The key insight: The overhead is split almost evenly between sum() and the generator expression when processing 1M items. + +--- + +## Performance Tweak: String Concatenation + +```python +from timeit import timeit # Import timing functionality + +# Fast - O(n) complexity with clear intent +def join_method(): + return "".join(str(i) for i in range(1000)) # Single efficient operation + +# Slow - O(n²) complexity +def concat_method(): + result = "" + for i in range(1000): + result += str(i) # Creates new string each time + return result + +t1 = timeit(join_method, number=1000) # Time the fast method +t2 = timeit(concat_method, number=1000) # Time the slow method +print(f"join: {t1:.6f}s\n+=: {t2:.6f}s") +``` + +--- + +## Performance Tweak: Appropriate Data Structures + +```python +from timeit import timeit # For timing the operations +import random # To select a random lookup value + +# Setup test data +data = list(range(10_000)) # A list with 10,000 items +lookup_val = random.choice(data) # Random value to find +lookup_set = set(data) # Same data as a set for O(1) lookup + +# Time list lookup (O(n) - must check each element) +t1 = timeit(lambda: lookup_val in data, number=1000) + +# Time set lookup (O(1) - constant time hash table) +t2 = timeit(lambda: lookup_val in lookup_set, number=1000) + +print(f"List lookup: {t1:.6f}s\nSet lookup: {t2:.6f}s") +``` + +--- + +## Performance Tweak: Pre-allocating Arrays + +```python +from timeit import timeit # For timing operations +import numpy as np # For pre-allocated arrays + +size = 10_000 # Number of elements to process + +# Growing a list dynamically (expensive resizing operations) +def growing_list(): + result = [] # Empty list that will grow + for i in range(size): + result.append(i*2) # Potentially triggers resize + return result + +# Pre-allocated array (fixed size from beginning) +def preallocated_array(): + result = np.zeros(size) # Create array of right size first + for i in range(size): + result[i] = i*2 # No resizing needed + return result + +t1 = timeit(growing_list, number=100) +t2 = timeit(preallocated_array, number=100) +print(f"Growing list: {t1:.6f}s\nPre-allocated: {t2:.6f}s") +``` + + +--- + +## Loops vs. Vectorisation vs. Functional + +| Approach | Best For | Example Use Case | +|---|---|---| +| **Loops** | Complex logic, small data | Custom algorithms | +| **Vectorisation** | Numerical operations | Data science, numpy | +| **Functional** | Data transformations | Pipelines, map/reduce | + +--- + +## Loops + +```python +from timeit import timeit # For timing code execution + +size = 1_000_000 # Number of elements to process + +# Standard loop approach (explicit iteration) +def standard_loop(): + result = [] + for i in range(size): + result.append(i * 2 + 5) # Each operation done sequentially + return result + +def list_comprehension(): + return [i for i in range(size)] + +t1 = timeit(standard_loop, number=1) +t2 = timeit(list_comprehension, number=1) + +print(f"Standard loop: {t1:.6f}s\nList comprehension: {t2:.6f}s") # comprehension 2x faster +``` +--- + +## Vectorisation with NumPy + +```python +from timeit import timeit # For timing code execution +import numpy as np # NumPy for vectorised operations + +size = 1_000_000 # Number of elements to process + +# Pure Python approach (loops through each element) +def python_way(): + return [i * 2 + 5 for i in range(size)] + +# NumPy vectorised approach (operates on entire array at once) +def numpy_way(): + return np.arange(size) * 2 + 5 # Uses C implementation + +# Compare execution times +t1 = timeit(python_way, number=1) +t2 = timeit(numpy_way, number=1) + +print(f"Python: {t1:.6f}s\nNumPy: {t2:.6f}s") # NumPy 10x faster +``` + +--- + +## Vectorisation with Pandas + +```python +import pandas as pd # For DataFrame operations +import numpy as np # For random data generation +from timeit import timeit # For timing operations + +# Create sample dataframe with 100,000 random values +df = pd.DataFrame({'value': np.random.rand(100_000)}) + +# Slow: Using apply (runs Python function on each row) +def apply_method(): + return df['value'].apply(lambda x: x * 2 + 5) + +# Fast: Vectorised operations (C implementation) +def vector_method(): + return df['value'] * 2 + 5 + +# Compare execution times +t1 = timeit(apply_method, number=10) +t2 = timeit(vector_method, number=10) + +print(f"apply: {t1:.6f}s\nvectorised: {t2:.6f}s") # Vectorised 100x faster +``` + +--- + +## Functional Programming + +```python +from timeit import timeit + +# Compare map vs list comprehension +t1 = timeit(lambda: list(map(lambda x: x**2, range(1000))), number=1000) +t2 = timeit(lambda: [x**2 for x in range(1000)], number=1000) + +print(f"map: {t1:.6f}s\ncomprehension: {t2:.6f}s") # Comprehension 2x faster +``` + +--- + +## Loops vs. Vectorisation vs. Functional speed + +| Approach | Time (sec) | +|---|---:| +| **Loops** | Standard loop: 0.049893 | +| | comprehension: 0.027239 | +| **Vectorisation** | Python: 0.054816 | +| | NumPy: 0.002517 | +| | Pandas apply: 0.104524 | +| | Pandas vectorised: 0.001358 | +| **Functional** | map: 0.040885 | +| | comprehension: 0.025535 | + +--- + +## When to Use Each Approach + +- **Vectorisation**: Large numerical datasets (NumPy/Pandas) +- **List Comprehensions**: Simple transformations on sequences +- **Generators**: Large datasets, memory efficiency +- **Functional**: Complex pipelines, data transformations +- **Loops**: Complex logic, small datasets, or when readability matters + +--- + +## Loop Optimisation Techniques + +```python +from timeit import timeit # For timing execution +import math # For sqrt function + +# Create test data - objects with a 'value' attribute +data = [type('obj', (), {'value': i}) for i in range(10_000)] + +# Regular approach: recalculating len() and looking up math.sqrt each time +def regular_loop(): + total = 0 + for i in range(len(data)): # len() called every iteration + total += math.sqrt(data[i].value) # Function lookup each time + return total + +# Optimised approach: pre-compute length and bind function locally +def optimised_loop(): + total = 0 + n = len(data) # Calculate length once + sqrt = math.sqrt # Local reference to function + for i in range(n): + total += sqrt(data[i].value) + return total + +t1 = timeit(regular_loop, number=10) +t2 = timeit(optimised_loop, number=10) +print(f"Regular: {t1:.6f}s\nOptimised: {t2:.6f}s") +``` + +--- + +## Best Practices Summary + +1. **Measure first** - profile before optimising +2. **Use appropriate data structures** for the task +3. **Vectorise numerical operations** when possible +4. **Avoid premature optimisation** - readable code first +5. **Know when to use loops, comprehensions, or functional styles** + + + +--- + +# Appendix +## Beyond basics (nerdier stuff) + +--- + +## Just-In-Time Compilation (1) + +```python +from numba import jit # Just-In-Time compiler for Python +import numpy as np # For numerical operations +from timeit import timeit # For timing + +def slow_func(x): # Regular Python function (interpreted at runtime) + total = 0 + for i in range(len(x)): total += np.sin(x[i]) * np.cos(x[i]) + return total + +@jit(nopython=True) # Same function with JIT compilation (compiled to machine code) +def fast_func(x): + total = 0 + for i in range(len(x)): total += np.sin(x[i]) * np.cos(x[i]) + return total + +x = np.random.random(10_000) # Random data to process +t1 = timeit(lambda: slow_func(x), number=10) +t2 = timeit(lambda: fast_func(x), number=10) +print(f"Python: {t1:.6f}s\nNumba: {t2:.6f}s") +``` + +--- + +## Just-In-Time Compilation (2) + +> [JIT](https://en.wikipedia.org/wiki/Just-in-time_compilation) translates code into machine code at runtime to improve execution speed. +This approach can improve performance by optimising the execution of frequently run code segments + +--- + +## Cython - Basic Example + +```python +# Pure Python version (slow.py) +def calculate_sum(n): + """Sum the squares from 0 to n-1""" + total = 0 + for i in range(n): + total += i * i + return total + +# Cython version (fast.pyx) +def calculate_sum_cy(int n): + """Same function with static typing""" + cdef int i, total = 0 # Static type declarations + for i in range(n): + total += i * i + return total + +# Compare (in Python): +from timeit import timeit +from slow import calculate_sum +from pyximport import install; install() # Compile on import +from fast import calculate_sum_cy + +t1 = timeit("calculate_sum(10_000)", globals=globals(), number=1000) +t2 = timeit("calculate_sum_cy(10_000)", globals=globals(), number=1000) +print(f"Python: {t1:.6f}s\nCython: {t2:.6f}s") # Typically 20-100x faster +``` + +--- + +## Cython - Best Practices +```python +# Key techniques for maximum performance: +# 1. Declare types for all variables +cdef: + int i, n = 1000 # Integer variables + double x = 0.5 # Floating point + int* ptr # C pointer + +# 2. Use typed memoryviews for arrays (faster than NumPy) +def process(double[:] arr): # Works with any array-like object + cdef int i + for i in range(arr.shape[0]): + arr[i] = arr[i] * 2 # Direct memory access + +# 3. Move Python operations outside loops +cdef double total = 0 +py_func = some_python_function # Store reference outside loop +for i in range(n): + # Call Python functions minimally inside loops + total += c_only_operations(i) + +# 4. Use nogil for parallel execution with OpenMP +cpdef process_parallel(double[:] data) nogil: # No Python GIL + # Can now use OpenMP for parallelism +``` + +## Compiling Cython Code +```python +# Option 1: Using setuptools (recommended for projects) +# Create setup.py in your project directory: +from setuptools import setup, Extension +from Cython.Build import cythonize + +setup( + ext_modules = cythonize([ + Extension("fast", ["fast.pyx"]), # Module name, source file + ]) +) + +# Then compile from command line: +# python setup.py build_ext --inplace + +# Option 2: Quick development with pyximport +# In your Python script: +import pyximport +pyximport.install() # Automatically compiles .pyx files +import fast # Will compile fast.pyx on first import + +# Option 3: Using Cython directly in terminal +# cython -a fast.pyx # Generates fast.c and HTML report +# gcc -shared -fPIC -o fast.so fast.c \ +# $(python3-config --includes) $(python3-config --ldflags) +``` From 3fdfa067d974a359a08f91bd2952cf61a03ccd45 Mon Sep 17 00:00:00 2001 From: Eirini <49712302+ai-mindset@users.noreply.github.com> Date: Tue, 9 Sep 2025 19:29:55 +0100 Subject: [PATCH 2/8] add: quarto presentation Changed to Quarto, to be able to run Python code within the presentation. Still need to figure out how to use our team's template --- .gitignore | 3 + .../pyproject.toml | 17 + .../2025-09-11_coffee-and-coding/slides.qmd | 439 ++++++++++++++++++ 3 files changed, 459 insertions(+) create mode 100644 presentations/2025-09-11_coffee-and-coding/pyproject.toml create mode 100644 presentations/2025-09-11_coffee-and-coding/slides.qmd diff --git a/.gitignore b/.gitignore index 8f8014ec..e4489b71 100644 --- a/.gitignore +++ b/.gitignore @@ -15,3 +15,6 @@ site_libs/ *_cache/ *_freeze/ *.html + +**/*.quarto_ipynb +**/*.ipynb_checkpoints diff --git a/presentations/2025-09-11_coffee-and-coding/pyproject.toml b/presentations/2025-09-11_coffee-and-coding/pyproject.toml new file mode 100644 index 00000000..32db19db --- /dev/null +++ b/presentations/2025-09-11_coffee-and-coding/pyproject.toml @@ -0,0 +1,17 @@ +[build-system] +requires = ["setuptools>=80.9.0", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "pres" +version = "0.1.0" +description = "Dependencies for Efficient Coding Quarto presentation" +requires-python = ">=3.12" +dependencies = [ + "jupyter>=1.0.0", + "numpy>=1.20.0", + "pandas>=1.3.0", + "numba>=0.56.0", + "jupyter-cache>=0.5.0", + "jupyterlab-rise", +] diff --git a/presentations/2025-09-11_coffee-and-coding/slides.qmd b/presentations/2025-09-11_coffee-and-coding/slides.qmd new file mode 100644 index 00000000..46f11372 --- /dev/null +++ b/presentations/2025-09-11_coffee-and-coding/slides.qmd @@ -0,0 +1,439 @@ +--- +title: "Efficient Coding" +subtitle: "Principles and Practices for Performant Code" +author: "Eirini, Rhian & YiWen, DS @ SU" +format: + revealjs: + theme: dark + code-fold: true + code-overflow: wrap + font-size: 0.7em +jupyter: python3 +--- + +## Agenda + +- **Measuring Performance**: Time and profile your code +- **Common Performance Tweaks**: Easy wins for faster code +- **Loops vs. Vectorisation vs. Functional**: Choose the right approach +- **Optimising Loops**: When you must use them +- **Beyond Basics**: Tools for serious optimisation + +## Measuring Performance: Timing + +```{python} +#| echo: true +from timeit import timeit # Python's precise timing module + +# Function to measure +def my_function(): + return sum(i**2 for i in range(1_000_000)) # Sum of squares + +# Time the function execution +# number=100: run multiple times for statistical significance +# globals=globals(): access functions defined in current scope +execution_time = timeit('my_function()', + globals=globals(), + number=100) + +# Calculate and display average execution time per call +print(f"Average execution time: {execution_time/1000:.6f}s") +``` + +## Measuring Performance: Profiling (1) + +```{python} +#| echo: true +import cProfile, pstats +from io import StringIO + +def sum_of_squares(n): + return sum(i * i for i in range(n)) + +# Create a StringIO object to capture output +pr = cProfile.Profile() +pr.enable() +sum_of_squares(1_000_000) +pr.disable() + +# Display results +s = StringIO() +ps = pstats.Stats(pr, stream=s).sort_stats('cumulative') +ps.print_stats(5) +print(s.getvalue()) +``` + +## Measuring Performance: Profiling (2) + +**Understanding the output:** + +- `ncalls`: The function was called directly 1 time but expanded to 1M total calls +- `tottime`: sum_of_squares consumed 0.000 seconds in its own code +- `cumtime`: Total time including nested calls was 0.141 seconds +- **Last column**: The bottleneck is split between `{built-in method builtins.sum}` and the generator expression (`i * i for i in range(n)`) + +**Key insight**: The overhead is split almost evenly between sum() and the generator expression when processing 1M items. + +## Performance Tweak: String Concatenation + +```{python} +#| echo: true +from timeit import timeit # Import timing functionality + +# Fast - O(n) complexity with clear intent +def join_method(): + return "".join(str(i) for i in range(100_000)) # Single efficient operation + +# Slow - O(n²) complexity +def concat_method(): + result = "" + for i in range(100_000): + result += str(i) # Creates new string each time + return result + +t1 = timeit(join_method, number=100) # Time the fast method +t2 = timeit(concat_method, number=100) # Time the slow method +print(f"join: {t1:.6f}s\n+=: {t2:.6f}s") +print(f"Speedup: {t2/t1:.1f}x faster") +``` + +## Performance Tweak: Appropriate Data Structures + +```{python} +#| echo: true +from timeit import timeit # For timing the operations +import random # To select a random lookup value + +# Setup test data +data = list(range(100_000)) # A list with 10,000 items +lookup_val = random.choice(data) # Random value to find +lookup_set = set(data) # Same data as a set for O(1) lookup + +# Time list lookup (O(n) - must check each element) +t1 = timeit(lambda: lookup_val in data, number=100) + +# Time set lookup (O(1) - constant time hash table) +t2 = timeit(lambda: lookup_val in lookup_set, number=100) + +print(f"List lookup: {t1:.6f}s\nSet lookup: {t2:.6f}s") +print(f"Speedup: {t1/t2:.1f}x faster") +``` + +## Performance Tweak: Pre-allocating Arrays + +```{python} +#| echo: true +from timeit import timeit # For timing operations +import numpy as np # For pre-allocated arrays + +size = 100_000 # Number of elements to process + +# Growing a list dynamically (expensive resizing operations) +def growing_list(): + result = [] # Empty list that will grow + for i in range(size): + result.append(i*2) # Potentially triggers resize + return result + +# Pre-allocated array (fixed size from beginning) +def preallocated_array(): + result = np.zeros(size, dtype=int) # Create array of right size first + for i in range(size): + result[i] = i*2 # No resizing needed + return result + +t1 = timeit(growing_list, number=100) +t2 = timeit(preallocated_array, number=100) +print(f"Growing list: {t1:.6f}s\nPre-allocated: {t2:.6f}s") +print(f"Speedup: {t2/t1:.1f}x faster") +``` + +## Loops vs. Vectorisation vs. Functional + +| Approach | Best For | Example Use Case | +|---|---|---| +| **Loops** | Complex logic, small data | Custom algorithms | +| **Vectorisation** | Numerical operations | Data science, numpy | +| **Functional** | Data transformations | Pipelines, map/reduce | + +## Loops + +```{python} +#| echo: true +from timeit import timeit # For timing code execution + +size = 100_000 # Number of elements to process (reduced for demo) + +# Standard loop approach (explicit iteration) +def standard_loop(): + result = [] + for i in range(size): + result.append(i * 2 + 5) # Each operation done sequentially + return result + +def list_comprehension(): + return [i * 2 + 5 for i in range(size)] + +t1 = timeit(standard_loop, number=100) +t2 = timeit(list_comprehension, number=100) + +print(f"Standard loop: {t1:.6f}s\nList comprehension: {t2:.6f}s") +print(f"Speedup: {t1/t2:.1f}x faster") +``` + +## Vectorisation with NumPy + +```{python} +#| echo: true +from timeit import timeit # For timing code execution +import numpy as np # NumPy for vectorised operations + +size = 100_000 # Number of elements to process (reduced for demo) + +# Pure Python approach (loops through each element) +def python_way(): + return [i * 2 + 5 for i in range(size)] + +# NumPy vectorised approach (operates on entire array at once) +def numpy_way(): + return np.arange(size) * 2 + 5 # Uses C implementation + +# Compare execution times +t1 = timeit(python_way, number=100) +t2 = timeit(numpy_way, number=100) + +print(f"Python: {t1:.6f}s\nNumPy: {t2:.6f}s") +print(f"Speedup: {t1/t2:.1f}x faster") +``` + +## Vectorisation with Pandas + +```{python} +#| echo: true +import pandas as pd # For DataFrame operations +import numpy as np # For random data generation +from timeit import timeit # For timing operations + +# Create sample dataframe with 10,000 random values (reduced for demo) +df = pd.DataFrame({'value': np.random.rand(100)}) + +# Slow: Using apply (runs Python function on each row) +def apply_method(): + return df['value'].apply(lambda x: x * 2 + 5) + +# Fast: Vectorised operations (C implementation) +def vector_method(): + return df['value'] * 2 + 5 + +# Compare execution times +t1 = timeit(apply_method, number=100) +t2 = timeit(vector_method, number=100) + +print(f"apply: {t1:.6f}s\nvectorised: {t2:.6f}s") +print(f"Speedup: {t1/t2:.1f}x faster") +``` + +## Functional Programming + +```{python} +#| echo: true +from timeit import timeit + +# Compare map vs list comprehension +t1 = timeit(lambda: list(map(lambda x: x**2, range(100_000))), number=100) +t2 = timeit(lambda: [x**2 for x in range(100_000)], number=100) + +print(f"map: {t1:.6f}s\ncomprehension: {t2:.6f}s") +print(f"Speedup: {t1/t2:.1f}x faster") +``` + +## Speed Comparison Summary + +```{python} +#| echo: false +import pandas as pd + +# Create a summary table of typical performance results +data = { + 'Approach': ['Standard loop', 'List comprehension', 'Python list ops', 'NumPy vectorised', + 'Pandas apply', 'Pandas vectorised', 'Map function', 'List comprehension'], + 'Category': ['Loops', 'Loops', 'Vectorisation', 'Vectorisation', + 'Vectorisation', 'Vectorisation', 'Functional', 'Functional'], + 'Typical Time (sec)': [0.049893, 0.027239, 0.054816, 0.002517, 0.104524, 0.001358, 0.040885, 0.025535] +} + +df = pd.DataFrame(data) +print(df.to_string(index=False)) +``` + +## When to Use Each Approach + +- **Vectorisation**: Large numerical datasets (NumPy/Pandas) +- **List Comprehensions**: Simple transformations on sequences +- **Generators**: Large datasets, memory efficiency +- **Functional**: Complex pipelines, data transformations +- **Loops**: Complex logic, small datasets, or when readability matters + +## Loop Optimisation Techniques + +```{python} +#| echo: true +from timeit import timeit # For timing execution +import math # For sqrt function + +# Create test data - objects with a 'value' attribute +data = [type('obj', (), {'value': i}) for i in range(100_000)] + +# Regular approach: recalculating len() and looking up math.sqrt each time +def regular_loop(): + total = 0 + for i in range(len(data)): # len() called with every iteration + total += math.sqrt(data[i].value) # Function lookup each time + return total + +# Optimised approach: pre-compute length and bind function locally +def optimised_loop(): + total = 0 + n = len(data) # Calculate length once + sqrt = math.sqrt # Local reference to function + for i in range(n): + total += sqrt(data[i].value) + return total + +t1 = timeit(regular_loop, number=100) +t2 = timeit(optimised_loop, number=100) +print(f"Regular: {t1:.6f}s\nOptimised: {t2:.6f}s") +print(f"Speedup: {t1/t2:.1f}x faster") +``` + +## Best Practices Summary + +1. **Measure first** - profile before optimising +2. **Use appropriate data structures** for the task +3. **Vectorise numerical operations** when possible +4. **Avoid premature optimisation** - readable code first +5. **Know when to use loops, comprehensions, or functional styles** + +# Appendix: Beyond Basics + +## Just-In-Time Compilation (1) + +```{python} +#| echo: true +from numba import jit +import numpy as np +from timeit import timeit + +def slow_func(x): + total = 0 + for i in range(len(x)): + total += np.sin(x[i]) * np.cos(x[i]) + return total + +@jit(nopython=True) +def fast_func(x): + total = 0 + for i in range(len(x)): + total += np.sin(x[i]) * np.cos(x[i]) + return total + +x = np.random.random(10_000) +t1 = timeit(lambda: slow_func(x), number=100) +t2 = timeit(lambda: fast_func(x), number=100) +print(f"Python: {t1:.6f}s\nNumba: {t2:.6f}s") +print(f"Speedup: {t1/t2:.1f}x faster") +``` + +## Just-In-Time Compilation (2) + +**What is JIT?** + +> [JIT (Just-In-Time) compilation](https://en.wikipedia.org/wiki/Just-in-time_compilation) translates code into machine code at runtime to improve execution speed. This approach can improve performance by optimising the execution of frequently run code segments. + +**Key Benefits:** +- Can provide 10-100x speed-ups for numerical code +- Works especially well with NumPy operations +- Requires minimal code changes (just add decorators) + +## Cython - Basic Example + +**Pure Python version (slow.py):** +```python +def calculate_sum(n): + """Sum the squares from 0 to n-1""" + total = 0 + for i in range(n): + total += i * i + return total +``` + +**Cython version (fast.pyx):** +```python +def calculate_sum_cy(int n): + """Same function with static typing""" + cdef int i, total = 0 # Static type declarations + for i in range(n): + total += i * i + return total +``` + +**Result**: Typically 20-100x faster performance + +## Cython - Best Practices + +**Key techniques for maximum performance:** + +```python +# 1. Declare types for all variables +cdef: + int i, n = 10_000 # Integer variables + double x = 0.5 # Floating point + int* ptr # C pointer + +# 2. Use typed memoryviews for arrays (faster than NumPy) +def process(double[:] arr): # Works with any array-like object + cdef int i + for i in range(arr.shape[0]): + arr[i] = arr[i] * 2 # Direct memory access + +# 3. Move Python operations outside loops +cdef double total = 0 +py_func = some_python_function # Store reference outside loop +for i in range(n): + total += c_only_operations(i) + +# 4. Use nogil for parallel execution with OpenMP +cpdef process_parallel(double[:] data) nogil: # No Python GIL + # Can now use OpenMP for parallelism +``` + +## Compiling Cython Code + +**Option 1: Using setuptools (recommended for projects)** +```python +# Create setup.py in your project directory: +from setuptools import setup, Extension +from Cython.Build import cythonize + +setup( + ext_modules = cythonize([ + Extension("fast", ["fast.pyx"]), + ]) +) + +# Then compile: python setup.py build_ext --inplace +``` + +**Option 2: Quick development with pyximport** +```python +import pyximport +pyximport.install() # Automatically compiles .pyx files +import fast # Will compile fast.pyx on first import +``` + +**Option 3: Direct compilation** +```bash +cython -a fast.pyx # Generates fast.c and HTML report +gcc -shared -fPIC -o fast.so fast.c \ + $(python3-config --includes) $(python3-config --ldflags) +``` From f127269eb8539ea264c061e55a6b3ebdbe4f82e6 Mon Sep 17 00:00:00 2001 From: Eirini <49712302+ai-mindset@users.noreply.github.com> Date: Wed, 10 Sep 2025 15:47:09 +0100 Subject: [PATCH 3/8] fix: small changes. add: generators --- .../pres.egg-info/PKG-INFO | 10 + .../pres.egg-info/SOURCES.txt | 6 + .../pres.egg-info/dependency_links.txt | 1 + .../pres.egg-info/requires.txt | 5 + .../pres.egg-info/top_level.txt | 1 + .../pyproject.toml | 11 +- .../2025-09-11_coffee-and-coding/slides.md | 460 ------------------ .../2025-09-11_coffee-and-coding/slides.qmd | 186 +++---- 8 files changed, 127 insertions(+), 553 deletions(-) create mode 100644 presentations/2025-09-11_coffee-and-coding/pres.egg-info/PKG-INFO create mode 100644 presentations/2025-09-11_coffee-and-coding/pres.egg-info/SOURCES.txt create mode 100644 presentations/2025-09-11_coffee-and-coding/pres.egg-info/dependency_links.txt create mode 100644 presentations/2025-09-11_coffee-and-coding/pres.egg-info/requires.txt create mode 100644 presentations/2025-09-11_coffee-and-coding/pres.egg-info/top_level.txt delete mode 100644 presentations/2025-09-11_coffee-and-coding/slides.md diff --git a/presentations/2025-09-11_coffee-and-coding/pres.egg-info/PKG-INFO b/presentations/2025-09-11_coffee-and-coding/pres.egg-info/PKG-INFO new file mode 100644 index 00000000..c5d68334 --- /dev/null +++ b/presentations/2025-09-11_coffee-and-coding/pres.egg-info/PKG-INFO @@ -0,0 +1,10 @@ +Metadata-Version: 2.4 +Name: pres +Version: 0.1.0 +Summary: Dependencies for Efficient Coding Quarto presentation +Requires-Python: >=3.12 +Requires-Dist: jupyter>=1.1.1 +Requires-Dist: numpy>=2.2.3 +Requires-Dist: pandas>=2.3.2 +Requires-Dist: numba>=0.61.2 +Requires-Dist: jupyter-cache>=1.0.1 diff --git a/presentations/2025-09-11_coffee-and-coding/pres.egg-info/SOURCES.txt b/presentations/2025-09-11_coffee-and-coding/pres.egg-info/SOURCES.txt new file mode 100644 index 00000000..4a6022e7 --- /dev/null +++ b/presentations/2025-09-11_coffee-and-coding/pres.egg-info/SOURCES.txt @@ -0,0 +1,6 @@ +pyproject.toml +pres.egg-info/PKG-INFO +pres.egg-info/SOURCES.txt +pres.egg-info/dependency_links.txt +pres.egg-info/requires.txt +pres.egg-info/top_level.txt \ No newline at end of file diff --git a/presentations/2025-09-11_coffee-and-coding/pres.egg-info/dependency_links.txt b/presentations/2025-09-11_coffee-and-coding/pres.egg-info/dependency_links.txt new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/presentations/2025-09-11_coffee-and-coding/pres.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/presentations/2025-09-11_coffee-and-coding/pres.egg-info/requires.txt b/presentations/2025-09-11_coffee-and-coding/pres.egg-info/requires.txt new file mode 100644 index 00000000..d9ac1649 --- /dev/null +++ b/presentations/2025-09-11_coffee-and-coding/pres.egg-info/requires.txt @@ -0,0 +1,5 @@ +jupyter>=1.1.1 +numpy>=2.2.3 +pandas>=2.3.2 +numba>=0.61.2 +jupyter-cache>=1.0.1 diff --git a/presentations/2025-09-11_coffee-and-coding/pres.egg-info/top_level.txt b/presentations/2025-09-11_coffee-and-coding/pres.egg-info/top_level.txt new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/presentations/2025-09-11_coffee-and-coding/pres.egg-info/top_level.txt @@ -0,0 +1 @@ + diff --git a/presentations/2025-09-11_coffee-and-coding/pyproject.toml b/presentations/2025-09-11_coffee-and-coding/pyproject.toml index 32db19db..48338ef9 100644 --- a/presentations/2025-09-11_coffee-and-coding/pyproject.toml +++ b/presentations/2025-09-11_coffee-and-coding/pyproject.toml @@ -8,10 +8,9 @@ version = "0.1.0" description = "Dependencies for Efficient Coding Quarto presentation" requires-python = ">=3.12" dependencies = [ - "jupyter>=1.0.0", - "numpy>=1.20.0", - "pandas>=1.3.0", - "numba>=0.56.0", - "jupyter-cache>=0.5.0", - "jupyterlab-rise", + "jupyter>=1.1.1", + "numpy>=2.2.3", # https://github.com/numba/numba/releases/tag/0.61.2 + "pandas>=2.3.2", + "numba>=0.61.2", + "jupyter-cache>=1.0.1", ] diff --git a/presentations/2025-09-11_coffee-and-coding/slides.md b/presentations/2025-09-11_coffee-and-coding/slides.md deleted file mode 100644 index 8b70f9ec..00000000 --- a/presentations/2025-09-11_coffee-and-coding/slides.md +++ /dev/null @@ -1,460 +0,0 @@ ---- -marp: true -theme: default -paginate: true -author: Eirini, Rhian and YiWen ---- - - - - -# Efficient Coding -## Principles and Practices for Performant Code - -Eirini, Rhian & YiWen, DS @ SU - ---- - -# Agenda - -- **Measuring Performance**: Time and profile your code -- **Common Performance Tweaks**: Easy wins for faster code -- **Loops vs. Vectorisation vs. Functional**: Choose the right approach -- **Optimising Loops**: When you must use them -- **Beyond Basics**: Tools for serious optimisation - ---- - -## Measuring Performance: Timing - -```python -from timeit import timeit # Python's precise timing module - -# Function to measure -def my_function(): - return sum(i**2 for i in range(1000)) # Sum of squares - -# Time the function execution -# number=1000: run 1000 times for statistical significance -# globals=globals(): access functions defined in current scope -execution_time = timeit('my_function()', - globals=globals(), - number=1000) - -# Calculate and display average execution time per call -print(f"Average execution time: {execution_time/1000:.6f}s") -``` - ---- - -## Measuring Performance: Profiling (1) - -```python -import cProfile, pstats, time -from pstats import SortKey - -def sum_of_squares(n): - return sum(i * i for i in range(n)) - -cProfile.run('sum_of_squares(1_000_000)') - -# Display meaningful results -p = pstats.Stats('stats') -p.sort_stats(SortKey.CUMULATIVE).print_stats(5) -``` - ---- - - -## Measuring Performance: Profiling (2) - -```console - 1000006 function calls in 0.141 seconds - - Ordered by: standard name - - ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 0.141 0.141 :1(sum_of_squares) - 1000001 0.068 0.000 0.068 0.000 :2() - 1 0.000 0.000 0.141 0.141 :1() - 1 0.000 0.000 0.141 0.141 {built-in method builtins.exec} - 1 0.073 0.073 0.141 0.141 {built-in method builtins.sum} -``` - -`ncalls`: The function was called directly 1 time but expanded to 1M total calls -`tottime`: sum_of_squares consumed 0.000 seconds in its own code -`cumtime`: Total time including nested calls was 0.141 seconds -Last column: The bottleneck is split between {built-in method builtins.sum} and the generator expression (`i * i for i in range(n)`) - -The key insight: The overhead is split almost evenly between sum() and the generator expression when processing 1M items. - ---- - -## Performance Tweak: String Concatenation - -```python -from timeit import timeit # Import timing functionality - -# Fast - O(n) complexity with clear intent -def join_method(): - return "".join(str(i) for i in range(1000)) # Single efficient operation - -# Slow - O(n²) complexity -def concat_method(): - result = "" - for i in range(1000): - result += str(i) # Creates new string each time - return result - -t1 = timeit(join_method, number=1000) # Time the fast method -t2 = timeit(concat_method, number=1000) # Time the slow method -print(f"join: {t1:.6f}s\n+=: {t2:.6f}s") -``` - ---- - -## Performance Tweak: Appropriate Data Structures - -```python -from timeit import timeit # For timing the operations -import random # To select a random lookup value - -# Setup test data -data = list(range(10_000)) # A list with 10,000 items -lookup_val = random.choice(data) # Random value to find -lookup_set = set(data) # Same data as a set for O(1) lookup - -# Time list lookup (O(n) - must check each element) -t1 = timeit(lambda: lookup_val in data, number=1000) - -# Time set lookup (O(1) - constant time hash table) -t2 = timeit(lambda: lookup_val in lookup_set, number=1000) - -print(f"List lookup: {t1:.6f}s\nSet lookup: {t2:.6f}s") -``` - ---- - -## Performance Tweak: Pre-allocating Arrays - -```python -from timeit import timeit # For timing operations -import numpy as np # For pre-allocated arrays - -size = 10_000 # Number of elements to process - -# Growing a list dynamically (expensive resizing operations) -def growing_list(): - result = [] # Empty list that will grow - for i in range(size): - result.append(i*2) # Potentially triggers resize - return result - -# Pre-allocated array (fixed size from beginning) -def preallocated_array(): - result = np.zeros(size) # Create array of right size first - for i in range(size): - result[i] = i*2 # No resizing needed - return result - -t1 = timeit(growing_list, number=100) -t2 = timeit(preallocated_array, number=100) -print(f"Growing list: {t1:.6f}s\nPre-allocated: {t2:.6f}s") -``` - - ---- - -## Loops vs. Vectorisation vs. Functional - -| Approach | Best For | Example Use Case | -|---|---|---| -| **Loops** | Complex logic, small data | Custom algorithms | -| **Vectorisation** | Numerical operations | Data science, numpy | -| **Functional** | Data transformations | Pipelines, map/reduce | - ---- - -## Loops - -```python -from timeit import timeit # For timing code execution - -size = 1_000_000 # Number of elements to process - -# Standard loop approach (explicit iteration) -def standard_loop(): - result = [] - for i in range(size): - result.append(i * 2 + 5) # Each operation done sequentially - return result - -def list_comprehension(): - return [i for i in range(size)] - -t1 = timeit(standard_loop, number=1) -t2 = timeit(list_comprehension, number=1) - -print(f"Standard loop: {t1:.6f}s\nList comprehension: {t2:.6f}s") # comprehension 2x faster -``` ---- - -## Vectorisation with NumPy - -```python -from timeit import timeit # For timing code execution -import numpy as np # NumPy for vectorised operations - -size = 1_000_000 # Number of elements to process - -# Pure Python approach (loops through each element) -def python_way(): - return [i * 2 + 5 for i in range(size)] - -# NumPy vectorised approach (operates on entire array at once) -def numpy_way(): - return np.arange(size) * 2 + 5 # Uses C implementation - -# Compare execution times -t1 = timeit(python_way, number=1) -t2 = timeit(numpy_way, number=1) - -print(f"Python: {t1:.6f}s\nNumPy: {t2:.6f}s") # NumPy 10x faster -``` - ---- - -## Vectorisation with Pandas - -```python -import pandas as pd # For DataFrame operations -import numpy as np # For random data generation -from timeit import timeit # For timing operations - -# Create sample dataframe with 100,000 random values -df = pd.DataFrame({'value': np.random.rand(100_000)}) - -# Slow: Using apply (runs Python function on each row) -def apply_method(): - return df['value'].apply(lambda x: x * 2 + 5) - -# Fast: Vectorised operations (C implementation) -def vector_method(): - return df['value'] * 2 + 5 - -# Compare execution times -t1 = timeit(apply_method, number=10) -t2 = timeit(vector_method, number=10) - -print(f"apply: {t1:.6f}s\nvectorised: {t2:.6f}s") # Vectorised 100x faster -``` - ---- - -## Functional Programming - -```python -from timeit import timeit - -# Compare map vs list comprehension -t1 = timeit(lambda: list(map(lambda x: x**2, range(1000))), number=1000) -t2 = timeit(lambda: [x**2 for x in range(1000)], number=1000) - -print(f"map: {t1:.6f}s\ncomprehension: {t2:.6f}s") # Comprehension 2x faster -``` - ---- - -## Loops vs. Vectorisation vs. Functional speed - -| Approach | Time (sec) | -|---|---:| -| **Loops** | Standard loop: 0.049893 | -| | comprehension: 0.027239 | -| **Vectorisation** | Python: 0.054816 | -| | NumPy: 0.002517 | -| | Pandas apply: 0.104524 | -| | Pandas vectorised: 0.001358 | -| **Functional** | map: 0.040885 | -| | comprehension: 0.025535 | - ---- - -## When to Use Each Approach - -- **Vectorisation**: Large numerical datasets (NumPy/Pandas) -- **List Comprehensions**: Simple transformations on sequences -- **Generators**: Large datasets, memory efficiency -- **Functional**: Complex pipelines, data transformations -- **Loops**: Complex logic, small datasets, or when readability matters - ---- - -## Loop Optimisation Techniques - -```python -from timeit import timeit # For timing execution -import math # For sqrt function - -# Create test data - objects with a 'value' attribute -data = [type('obj', (), {'value': i}) for i in range(10_000)] - -# Regular approach: recalculating len() and looking up math.sqrt each time -def regular_loop(): - total = 0 - for i in range(len(data)): # len() called every iteration - total += math.sqrt(data[i].value) # Function lookup each time - return total - -# Optimised approach: pre-compute length and bind function locally -def optimised_loop(): - total = 0 - n = len(data) # Calculate length once - sqrt = math.sqrt # Local reference to function - for i in range(n): - total += sqrt(data[i].value) - return total - -t1 = timeit(regular_loop, number=10) -t2 = timeit(optimised_loop, number=10) -print(f"Regular: {t1:.6f}s\nOptimised: {t2:.6f}s") -``` - ---- - -## Best Practices Summary - -1. **Measure first** - profile before optimising -2. **Use appropriate data structures** for the task -3. **Vectorise numerical operations** when possible -4. **Avoid premature optimisation** - readable code first -5. **Know when to use loops, comprehensions, or functional styles** - - - ---- - -# Appendix -## Beyond basics (nerdier stuff) - ---- - -## Just-In-Time Compilation (1) - -```python -from numba import jit # Just-In-Time compiler for Python -import numpy as np # For numerical operations -from timeit import timeit # For timing - -def slow_func(x): # Regular Python function (interpreted at runtime) - total = 0 - for i in range(len(x)): total += np.sin(x[i]) * np.cos(x[i]) - return total - -@jit(nopython=True) # Same function with JIT compilation (compiled to machine code) -def fast_func(x): - total = 0 - for i in range(len(x)): total += np.sin(x[i]) * np.cos(x[i]) - return total - -x = np.random.random(10_000) # Random data to process -t1 = timeit(lambda: slow_func(x), number=10) -t2 = timeit(lambda: fast_func(x), number=10) -print(f"Python: {t1:.6f}s\nNumba: {t2:.6f}s") -``` - ---- - -## Just-In-Time Compilation (2) - -> [JIT](https://en.wikipedia.org/wiki/Just-in-time_compilation) translates code into machine code at runtime to improve execution speed. -This approach can improve performance by optimising the execution of frequently run code segments - ---- - -## Cython - Basic Example - -```python -# Pure Python version (slow.py) -def calculate_sum(n): - """Sum the squares from 0 to n-1""" - total = 0 - for i in range(n): - total += i * i - return total - -# Cython version (fast.pyx) -def calculate_sum_cy(int n): - """Same function with static typing""" - cdef int i, total = 0 # Static type declarations - for i in range(n): - total += i * i - return total - -# Compare (in Python): -from timeit import timeit -from slow import calculate_sum -from pyximport import install; install() # Compile on import -from fast import calculate_sum_cy - -t1 = timeit("calculate_sum(10_000)", globals=globals(), number=1000) -t2 = timeit("calculate_sum_cy(10_000)", globals=globals(), number=1000) -print(f"Python: {t1:.6f}s\nCython: {t2:.6f}s") # Typically 20-100x faster -``` - ---- - -## Cython - Best Practices -```python -# Key techniques for maximum performance: -# 1. Declare types for all variables -cdef: - int i, n = 1000 # Integer variables - double x = 0.5 # Floating point - int* ptr # C pointer - -# 2. Use typed memoryviews for arrays (faster than NumPy) -def process(double[:] arr): # Works with any array-like object - cdef int i - for i in range(arr.shape[0]): - arr[i] = arr[i] * 2 # Direct memory access - -# 3. Move Python operations outside loops -cdef double total = 0 -py_func = some_python_function # Store reference outside loop -for i in range(n): - # Call Python functions minimally inside loops - total += c_only_operations(i) - -# 4. Use nogil for parallel execution with OpenMP -cpdef process_parallel(double[:] data) nogil: # No Python GIL - # Can now use OpenMP for parallelism -``` - -## Compiling Cython Code -```python -# Option 1: Using setuptools (recommended for projects) -# Create setup.py in your project directory: -from setuptools import setup, Extension -from Cython.Build import cythonize - -setup( - ext_modules = cythonize([ - Extension("fast", ["fast.pyx"]), # Module name, source file - ]) -) - -# Then compile from command line: -# python setup.py build_ext --inplace - -# Option 2: Quick development with pyximport -# In your Python script: -import pyximport -pyximport.install() # Automatically compiles .pyx files -import fast # Will compile fast.pyx on first import - -# Option 3: Using Cython directly in terminal -# cython -a fast.pyx # Generates fast.c and HTML report -# gcc -shared -fPIC -o fast.so fast.c \ -# $(python3-config --includes) $(python3-config --ldflags) -``` diff --git a/presentations/2025-09-11_coffee-and-coding/slides.qmd b/presentations/2025-09-11_coffee-and-coding/slides.qmd index 46f11372..c1673829 100644 --- a/presentations/2025-09-11_coffee-and-coding/slides.qmd +++ b/presentations/2025-09-11_coffee-and-coding/slides.qmd @@ -1,7 +1,7 @@ --- title: "Efficient Coding" subtitle: "Principles and Practices for Performant Code" -author: "Eirini, Rhian & YiWen, DS @ SU" +author: "Eirini & Rhian, DS @ SU" format: revealjs: theme: dark @@ -16,23 +16,27 @@ jupyter: python3 - **Measuring Performance**: Time and profile your code - **Common Performance Tweaks**: Easy wins for faster code - **Loops vs. Vectorisation vs. Functional**: Choose the right approach -- **Optimising Loops**: When you must use them -- **Beyond Basics**: Tools for serious optimisation +- **Optimising Loops**: When you should use them +- **Beyond Basics**: Tools for further optimisation + +## Measuring Performance ## Measuring Performance: Timing ```{python} #| echo: true +#| code-fold: show from timeit import timeit # Python's precise timing module -# Function to measure -def my_function(): - return sum(i**2 for i in range(1_000_000)) # Sum of squares +size = 1_000_000 + +def sum_of_squares(): # Function to measure + return sum(i**2 for i in range(size)) # Time the function execution # number=100: run multiple times for statistical significance # globals=globals(): access functions defined in current scope -execution_time = timeit('my_function()', +execution_time = timeit('sum_of_squares()', globals=globals(), number=100) @@ -67,28 +71,29 @@ print(s.getvalue()) **Understanding the output:** -- `ncalls`: The function was called directly 1 time but expanded to 1M total calls +- `ncalls`: No. of times the function was called directly - `tottime`: sum_of_squares consumed 0.000 seconds in its own code -- `cumtime`: Total time including nested calls was 0.141 seconds -- **Last column**: The bottleneck is split between `{built-in method builtins.sum}` and the generator expression (`i * i for i in range(n)`) +- `cumtime`: Total time including nested calls +- `percall`: The bottleneck(s) i.e. `{built-in method builtins.sum}` and the generator expression (`i * i for i in range(n)`) -**Key insight**: The overhead is split almost evenly between sum() and the generator expression when processing 1M items. +## Performance Tweaks ## Performance Tweak: String Concatenation ```{python} #| echo: true +#| code-fold: show from timeit import timeit # Import timing functionality -# Fast - O(n) complexity with clear intent -def join_method(): - return "".join(str(i) for i in range(100_000)) # Single efficient operation +size = 100_000 -# Slow - O(n²) complexity -def concat_method(): +def join_method(): # Fast - O(n) complexity with clear intent + return "".join("Hello World! " for _ in range(size)) # Single operation + +def concat_method(): # Slow - O(n²) complexity result = "" - for i in range(100_000): - result += str(i) # Creates new string each time + for _ in range(size): + result += "Hello World! " # Creates new string each time return result t1 = timeit(join_method, number=100) # Time the fast method @@ -101,11 +106,14 @@ print(f"Speedup: {t2/t1:.1f}x faster") ```{python} #| echo: true +#| code-fold: show from timeit import timeit # For timing the operations import random # To select a random lookup value +size = 100_000 + # Setup test data -data = list(range(100_000)) # A list with 10,000 items +data = list(range(size)) # A list with 100,000 items lookup_val = random.choice(data) # Random value to find lookup_set = set(data) # Same data as a set for O(1) lookup @@ -123,23 +131,22 @@ print(f"Speedup: {t1/t2:.1f}x faster") ```{python} #| echo: true +#| code-fold: show from timeit import timeit # For timing operations import numpy as np # For pre-allocated arrays size = 100_000 # Number of elements to process -# Growing a list dynamically (expensive resizing operations) -def growing_list(): +def growing_list(): # Growing a list dynamically (expensive) result = [] # Empty list that will grow for i in range(size): - result.append(i*2) # Potentially triggers resize + result.append(i**2) # Triggers resize return result -# Pre-allocated array (fixed size from beginning) -def preallocated_array(): - result = np.zeros(size, dtype=int) # Create array of right size first +def preallocated_array(): # Pre-allocated array + result = np.zeros(size, dtype=int) # Preallocate array for i in range(size): - result[i] = i*2 # No resizing needed + result[i] = i**2 # No resizing needed return result t1 = timeit(growing_list, number=100) @@ -153,26 +160,26 @@ print(f"Speedup: {t2/t1:.1f}x faster") | Approach | Best For | Example Use Case | |---|---|---| | **Loops** | Complex logic, small data | Custom algorithms | -| **Vectorisation** | Numerical operations | Data science, numpy | -| **Functional** | Data transformations | Pipelines, map/reduce | +| **Vectorisation** | Numerical operations | Data science, NumPy | +| **Functional** | Data transformations | Pipelines, map/reduce/filter | ## Loops ```{python} #| echo: true +#| code-fold: show from timeit import timeit # For timing code execution -size = 100_000 # Number of elements to process (reduced for demo) +size = 100_000 -# Standard loop approach (explicit iteration) -def standard_loop(): +def standard_loop(): # Slower - bytecode overhead + repeated .append() calls result = [] for i in range(size): - result.append(i * 2 + 5) # Each operation done sequentially + result.append(i**2) return result -def list_comprehension(): - return [i * 2 + 5 for i in range(size)] +def list_comprehension(): # Faster, concise + return [i**2 for i in range(size)] t1 = timeit(standard_loop, number=100) t2 = timeit(list_comprehension, number=100) @@ -185,18 +192,17 @@ print(f"Speedup: {t1/t2:.1f}x faster") ```{python} #| echo: true +#| code-fold: show from timeit import timeit # For timing code execution import numpy as np # NumPy for vectorised operations -size = 100_000 # Number of elements to process (reduced for demo) +size = 100_000 -# Pure Python approach (loops through each element) -def python_way(): - return [i * 2 + 5 for i in range(size)] +def python_way(): # Pure Python approach (loops through each element) + return [i**2 for i in range(size)] -# NumPy vectorised approach (operates on entire array at once) -def numpy_way(): - return np.arange(size) * 2 + 5 # Uses C implementation +def numpy_way(): # NumPy vectorised approach (operates on entire array at once) + return np.arange(size)**2 # Uses C implementation # Compare execution times t1 = timeit(python_way, number=100) @@ -210,20 +216,19 @@ print(f"Speedup: {t1/t2:.1f}x faster") ```{python} #| echo: true +#| code-fold: show import pandas as pd # For DataFrame operations import numpy as np # For random data generation from timeit import timeit # For timing operations # Create sample dataframe with 10,000 random values (reduced for demo) -df = pd.DataFrame({'value': np.random.rand(100)}) +df = pd.DataFrame({"value": np.random.rand(10_000)}) -# Slow: Using apply (runs Python function on each row) -def apply_method(): - return df['value'].apply(lambda x: x * 2 + 5) +def apply_method(): # Slow: Using apply (runs Python function on each row) + return df["value"].apply(lambda x: x**2) -# Fast: Vectorised operations (C implementation) -def vector_method(): - return df['value'] * 2 + 5 +def vector_method(): # Fast: Vectorised operations (C implementation) + return df["value"]**2 # Compare execution times t1 = timeit(apply_method, number=100) @@ -234,71 +239,75 @@ print(f"Speedup: {t1/t2:.1f}x faster") ``` ## Functional Programming +[Functional programming in Python? by David Vujic](https://www.youtube.com/watch?v=hz4OPyBYA98) ```{python} #| echo: true +#| code-fold: show from timeit import timeit +size = 100_000 + # Compare map vs list comprehension -t1 = timeit(lambda: list(map(lambda x: x**2, range(100_000))), number=100) -t2 = timeit(lambda: [x**2 for x in range(100_000)], number=100) +t1 = timeit(lambda: list(map(lambda x: x**2, range(size))), number=100) +t2 = timeit(lambda: [x**2 for x in range(size)], number=100) print(f"map: {t1:.6f}s\ncomprehension: {t2:.6f}s") print(f"Speedup: {t1/t2:.1f}x faster") ``` -## Speed Comparison Summary +## Generators ```{python} -#| echo: false -import pandas as pd - -# Create a summary table of typical performance results -data = { - 'Approach': ['Standard loop', 'List comprehension', 'Python list ops', 'NumPy vectorised', - 'Pandas apply', 'Pandas vectorised', 'Map function', 'List comprehension'], - 'Category': ['Loops', 'Loops', 'Vectorisation', 'Vectorisation', - 'Vectorisation', 'Vectorisation', 'Functional', 'Functional'], - 'Typical Time (sec)': [0.049893, 0.027239, 0.054816, 0.002517, 0.104524, 0.001358, 0.040885, 0.025535] -} - -df = pd.DataFrame(data) -print(df.to_string(index=False)) +#| echo: true +#| code-fold: show +def count_up_to(limit): + count = 0 + while count < limit: + yield count + count += 1 + +# Usage +for number in count_up_to(5): + print(number) ``` ## When to Use Each Approach - **Vectorisation**: Large numerical datasets (NumPy/Pandas) - **List Comprehensions**: Simple transformations on sequences -- **Generators**: Large datasets, memory efficiency - **Functional**: Complex pipelines, data transformations - **Loops**: Complex logic, small datasets, or when readability matters +- **Generators**: Process large datasets, that shouldn't be loaded entirely into memory ## Loop Optimisation Techniques ```{python} #| echo: true +#| code-fold: show from timeit import timeit # For timing execution -import math # For sqrt function +import math # For sqrt, sin, cos functions +from random import random -# Create test data - objects with a 'value' attribute -data = [type('obj', (), {'value': i}) for i in range(100_000)] +size = 1_000_000 -# Regular approach: recalculating len() and looking up math.sqrt each time -def regular_loop(): - total = 0 - for i in range(len(data)): # len() called with every iteration - total += math.sqrt(data[i].value) # Function lookup each time - return total +data = [random()**2 for _ in range(size)] # Create test data -# Optimised approach: pre-compute length and bind function locally -def optimised_loop(): - total = 0 - n = len(data) # Calculate length once - sqrt = math.sqrt # Local reference to function +def regular_loop(): # len() and function lookups each iteration + result = 0 + for i in range(len(data)): # len() recalculated in every iteration + x = data[i] + result += math.sqrt(x) + math.sin(x) + math.cos(x) # Function lookups each time + return result + +def optimised_loop(): # pre-computed length and local function bindings + result = 0 + n = len(data) # Pre-compute length once + sqrt, sin, cos = math.sqrt, math.sin, math.cos # Bind functions locally for i in range(n): - total += sqrt(data[i].value) - return total + x = data[i] + result += sqrt(x) + sin(x) + cos(x) + return result t1 = timeit(regular_loop, number=100) t2 = timeit(optimised_loop, number=100) @@ -314,12 +323,15 @@ print(f"Speedup: {t1/t2:.1f}x faster") 4. **Avoid premature optimisation** - readable code first 5. **Know when to use loops, comprehensions, or functional styles** +[Effective Python, The Book: Third Edition, Brett Slatkin](https://effectivepython.com/) + # Appendix: Beyond Basics -## Just-In-Time Compilation (1) +## Appendix: Just-in-Time Compilation (1) ```{python} #| echo: true +#| code-fold: show from numba import jit import numpy as np from timeit import timeit @@ -344,7 +356,7 @@ print(f"Python: {t1:.6f}s\nNumba: {t2:.6f}s") print(f"Speedup: {t1/t2:.1f}x faster") ``` -## Just-In-Time Compilation (2) +## Appendix: Just-In-Time Compilation (2) **What is JIT?** @@ -355,7 +367,7 @@ print(f"Speedup: {t1/t2:.1f}x faster") - Works especially well with NumPy operations - Requires minimal code changes (just add decorators) -## Cython - Basic Example +## Appendix: Cython (Basics) **Pure Python version (slow.py):** ```python @@ -379,7 +391,7 @@ def calculate_sum_cy(int n): **Result**: Typically 20-100x faster performance -## Cython - Best Practices +## Appendix: Cython (Best Practices) **Key techniques for maximum performance:** @@ -407,7 +419,7 @@ cpdef process_parallel(double[:] data) nogil: # No Python GIL # Can now use OpenMP for parallelism ``` -## Compiling Cython Code +## Appendix: Cython (Compiling) **Option 1: Using setuptools (recommended for projects)** ```python From 2bfd96702c606989627e854a6518749614bc383e Mon Sep 17 00:00:00 2001 From: Eirini <49712302+ai-mindset@users.noreply.github.com> Date: Wed, 10 Sep 2025 15:49:10 +0100 Subject: [PATCH 4/8] fix: rm egg-info --- .gitignore | 1 + .../pres.egg-info/PKG-INFO | 10 ---------- .../pres.egg-info/SOURCES.txt | 6 ------ .../pres.egg-info/dependency_links.txt | 1 - .../pres.egg-info/requires.txt | 5 ----- .../pres.egg-info/top_level.txt | 1 - 6 files changed, 1 insertion(+), 23 deletions(-) delete mode 100644 presentations/2025-09-11_coffee-and-coding/pres.egg-info/PKG-INFO delete mode 100644 presentations/2025-09-11_coffee-and-coding/pres.egg-info/SOURCES.txt delete mode 100644 presentations/2025-09-11_coffee-and-coding/pres.egg-info/dependency_links.txt delete mode 100644 presentations/2025-09-11_coffee-and-coding/pres.egg-info/requires.txt delete mode 100644 presentations/2025-09-11_coffee-and-coding/pres.egg-info/top_level.txt diff --git a/.gitignore b/.gitignore index e4489b71..e300f897 100644 --- a/.gitignore +++ b/.gitignore @@ -16,5 +16,6 @@ site_libs/ *_freeze/ *.html +*.egg-info/ **/*.quarto_ipynb **/*.ipynb_checkpoints diff --git a/presentations/2025-09-11_coffee-and-coding/pres.egg-info/PKG-INFO b/presentations/2025-09-11_coffee-and-coding/pres.egg-info/PKG-INFO deleted file mode 100644 index c5d68334..00000000 --- a/presentations/2025-09-11_coffee-and-coding/pres.egg-info/PKG-INFO +++ /dev/null @@ -1,10 +0,0 @@ -Metadata-Version: 2.4 -Name: pres -Version: 0.1.0 -Summary: Dependencies for Efficient Coding Quarto presentation -Requires-Python: >=3.12 -Requires-Dist: jupyter>=1.1.1 -Requires-Dist: numpy>=2.2.3 -Requires-Dist: pandas>=2.3.2 -Requires-Dist: numba>=0.61.2 -Requires-Dist: jupyter-cache>=1.0.1 diff --git a/presentations/2025-09-11_coffee-and-coding/pres.egg-info/SOURCES.txt b/presentations/2025-09-11_coffee-and-coding/pres.egg-info/SOURCES.txt deleted file mode 100644 index 4a6022e7..00000000 --- a/presentations/2025-09-11_coffee-and-coding/pres.egg-info/SOURCES.txt +++ /dev/null @@ -1,6 +0,0 @@ -pyproject.toml -pres.egg-info/PKG-INFO -pres.egg-info/SOURCES.txt -pres.egg-info/dependency_links.txt -pres.egg-info/requires.txt -pres.egg-info/top_level.txt \ No newline at end of file diff --git a/presentations/2025-09-11_coffee-and-coding/pres.egg-info/dependency_links.txt b/presentations/2025-09-11_coffee-and-coding/pres.egg-info/dependency_links.txt deleted file mode 100644 index 8b137891..00000000 --- a/presentations/2025-09-11_coffee-and-coding/pres.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/presentations/2025-09-11_coffee-and-coding/pres.egg-info/requires.txt b/presentations/2025-09-11_coffee-and-coding/pres.egg-info/requires.txt deleted file mode 100644 index d9ac1649..00000000 --- a/presentations/2025-09-11_coffee-and-coding/pres.egg-info/requires.txt +++ /dev/null @@ -1,5 +0,0 @@ -jupyter>=1.1.1 -numpy>=2.2.3 -pandas>=2.3.2 -numba>=0.61.2 -jupyter-cache>=1.0.1 diff --git a/presentations/2025-09-11_coffee-and-coding/pres.egg-info/top_level.txt b/presentations/2025-09-11_coffee-and-coding/pres.egg-info/top_level.txt deleted file mode 100644 index 8b137891..00000000 --- a/presentations/2025-09-11_coffee-and-coding/pres.egg-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ - From eef3fd0cccc44777be4056f71a773fbf78219171 Mon Sep 17 00:00:00 2001 From: Rhian Davies Date: Wed, 10 Sep 2025 16:45:47 +0100 Subject: [PATCH 5/8] :pencil Slight restructure" --- .../2025-09-11_coffee-and-coding/slides.qmd | 216 +++++++++++++----- 1 file changed, 165 insertions(+), 51 deletions(-) diff --git a/presentations/2025-09-11_coffee-and-coding/slides.qmd b/presentations/2025-09-11_coffee-and-coding/slides.qmd index c1673829..29800a76 100644 --- a/presentations/2025-09-11_coffee-and-coding/slides.qmd +++ b/presentations/2025-09-11_coffee-and-coding/slides.qmd @@ -4,11 +4,14 @@ subtitle: "Principles and Practices for Performant Code" author: "Eirini & Rhian, DS @ SU" format: revealjs: + #theme: [default, ../su_presentation.scss] theme: dark code-fold: true code-overflow: wrap font-size: 0.7em -jupyter: python3 +execute: + eval: false + engine: jupyter --- ## Agenda @@ -19,9 +22,15 @@ jupyter: python3 - **Optimising Loops**: When you should use them - **Beyond Basics**: Tools for further optimisation -## Measuring Performance +# Measuring Performance -## Measuring Performance: Timing +## Timing + +- How long does it take? +- Can compare approaches? +- When will your code finish running when you scale it up? + +## 🐍 Timing ```{python} #| echo: true @@ -44,7 +53,34 @@ execution_time = timeit('sum_of_squares()', print(f"Average execution time: {execution_time/1000:.6f}s") ``` -## Measuring Performance: Profiling (1) + +## 🦜 Timing + +- `system.time()` for quick one-off timing +- {benchmark} for parameterised comparisons + +```{r} +base = function(n) 0:n +seq1 = function(n) seq(0, n) +seq2 = function(n) seq(0, n, by = 1) + +df = bench::press( +n = c(1e5, 1e6, 1e7), +bench::mark(base(n), seq1(n), seq2(n)) +) + +ggplot2::autoplot(df) +``` + + +## Profiling + +> *"We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil."* + - Donald Knuth + +- Identify the slowest functions + +## 🐍 Profiling ```{python} #| echo: true @@ -67,42 +103,60 @@ ps.print_stats(5) print(s.getvalue()) ``` -## Measuring Performance: Profiling (2) +## 🦜 Profiling + +- Use [{profvis}](https://profvis.r-lib.org/) +- [Examples](https://profvis.r-lib.org/articles/examples.html) +- [Visualising profiles in R](https://adv-r.hadley.nz/perf-measure.html#visualising-profiles) + +![Screenshot of a profvis session](https://adv-r.hadley.nz/screenshots/performance/flamegraph.png) -**Understanding the output:** +# Performance Tweaks -- `ncalls`: No. of times the function was called directly -- `tottime`: sum_of_squares consumed 0.000 seconds in its own code -- `cumtime`: Total time including nested calls -- `percall`: The bottleneck(s) i.e. `{built-in method builtins.sum}` and the generator expression (`i * i for i in range(n)`) +## Object growth -## Performance Tweaks +RD to introduce general -## Performance Tweak: String Concatenation + +## 🐍 Pre-allocating Arrays ```{python} #| echo: true #| code-fold: show -from timeit import timeit # Import timing functionality +from timeit import timeit # For timing operations +import numpy as np # For pre-allocated arrays -size = 100_000 +size = 100_000 # Number of elements to process -def join_method(): # Fast - O(n) complexity with clear intent - return "".join("Hello World! " for _ in range(size)) # Single operation +def growing_list(): # Growing a list dynamically (expensive) + result = [] # Empty list that will grow + for i in range(size): + result.append(i**2) # Triggers resize + return result -def concat_method(): # Slow - O(n²) complexity - result = "" - for _ in range(size): - result += "Hello World! " # Creates new string each time +def preallocated_array(): # Pre-allocated array + result = np.zeros(size, dtype=int) # Preallocate array + for i in range(size): + result[i] = i**2 # No resizing needed return result -t1 = timeit(join_method, number=100) # Time the fast method -t2 = timeit(concat_method, number=100) # Time the slow method -print(f"join: {t1:.6f}s\n+=: {t2:.6f}s") +t1 = timeit(growing_list, number=100) +t2 = timeit(preallocated_array, number=100) +print(f"Growing list: {t1:.6f}s\nPre-allocated: {t2:.6f}s") print(f"Speedup: {t2/t1:.1f}x faster") ``` -## Performance Tweak: Appropriate Data Structures +## 🦜 Pre-allocating Arrays + +- Show example of benchmark with pre-allocation compared to none + +## Appropriate Data Structures + +- Numpy array faster than a python list +- Set is _much_ faster than list* but only keeps _unique elements_ +- Just doing numeric calculations? Can you use a matrix? + +## 🐍 Appropriate Data Structures ```{python} #| echo: true @@ -127,34 +181,34 @@ print(f"List lookup: {t1:.6f}s\nSet lookup: {t2:.6f}s") print(f"Speedup: {t1/t2:.1f}x faster") ``` -## Performance Tweak: Pre-allocating Arrays +## 🦜 Appropriate Data Structures + +## 🐍 String Concatenation ```{python} #| echo: true #| code-fold: show -from timeit import timeit # For timing operations -import numpy as np # For pre-allocated arrays +from timeit import timeit -size = 100_000 # Number of elements to process +size = 100_000 -def growing_list(): # Growing a list dynamically (expensive) - result = [] # Empty list that will grow - for i in range(size): - result.append(i**2) # Triggers resize - return result +def join_method(): # Fast - O(n) complexity with clear intent + return "".join("Hello World! " for _ in range(size)) # Single operation -def preallocated_array(): # Pre-allocated array - result = np.zeros(size, dtype=int) # Preallocate array - for i in range(size): - result[i] = i**2 # No resizing needed +def concat_method(): # Slow - O(n²) complexity + result = "" + for _ in range(size): + result += "Hello World! " # Creates new string each time return result -t1 = timeit(growing_list, number=100) -t2 = timeit(preallocated_array, number=100) -print(f"Growing list: {t1:.6f}s\nPre-allocated: {t2:.6f}s") +t1 = timeit(join_method, number=100) +t2 = timeit(concat_method, number=100) +print(f"join: {t1:.6f}s\n+=: {t2:.6f}s") print(f"Speedup: {t2/t1:.1f}x faster") ``` +# Loops vs. Vectorisation vs. Functional + ## Loops vs. Vectorisation vs. Functional | Approach | Best For | Example Use Case | @@ -280,7 +334,18 @@ for number in count_up_to(5): - **Loops**: Complex logic, small datasets, or when readability matters - **Generators**: Process large datasets, that shouldn't be loaded entirely into memory -## Loop Optimisation Techniques +# Loop Optimisation + +## Optimisation Techniques + +- Define anything you can _outside_ the loop +- Consider locally assigning common functions +- I/O slows loops +- Look out for `print` or `plot` +- Use argument for "chatty / "quiet" +- Proper logging instead of printing + +## 🐍 Optimisation Techniques ```{python} #| echo: true @@ -315,19 +380,21 @@ print(f"Regular: {t1:.6f}s\nOptimised: {t2:.6f}s") print(f"Speedup: {t1/t2:.1f}x faster") ``` -## Best Practices Summary +## 🦜 Optimisation Techniques -1. **Measure first** - profile before optimising -2. **Use appropriate data structures** for the task -3. **Vectorise numerical operations** when possible -4. **Avoid premature optimisation** - readable code first -5. **Know when to use loops, comprehensions, or functional styles** -[Effective Python, The Book: Third Edition, Brett Slatkin](https://effectivepython.com/) +# Beyond Basics + +## Rewrite in C++ + +- If you've got a function which is a real bottleneck consider rewriting it in C++ + +- 🐍 [Cython](https://cython.org/) to connect C++ and python + +- 🦜 [{rcpp}](https://adv-r.hadley.nz/rcpp.html) to connect C++ and R -# Appendix: Beyond Basics -## Appendix: Just-in-Time Compilation (1) +## Just-in-Time Compilation (1) ```{python} #| echo: true @@ -356,7 +423,7 @@ print(f"Python: {t1:.6f}s\nNumba: {t2:.6f}s") print(f"Speedup: {t1/t2:.1f}x faster") ``` -## Appendix: Just-In-Time Compilation (2) +## Just-In-Time Compilation (2) **What is JIT?** @@ -367,6 +434,51 @@ print(f"Speedup: {t1/t2:.1f}x faster") - Works especially well with NumPy operations - Requires minimal code changes (just add decorators) + +## Parallel processing + +:::: {.columns} +::: {.column} +### Pros + +- Larger datasets + +- Speed + +- It's easy to set up +::: +::: {.column} + +### Cons + +- Debugging is harder + +- Can be OS specific + +- Many statistical techniques are fundamentally serial + +- Can be slower than serial execution due to overheads +::: +:::: + + +## Best Practices Summary + +1. **Measure first** - profile before optimising +2. **Use appropriate data structures** for the task +3. **Vectorise numerical operations** when possible +4. **Avoid premature optimisation** - readable code first +5. **Know when to use loops, comprehensions, or functional styles** + + +## 📚 Resources + +- [Efficient R](https://csgillespie.github.io/efficientR/) +- [Effective Python, The Book: Third Edition, Brett Slatkin](https://effectivepython.com/) +- [Advanced R](https://adv-r.hadley.nz/index.html) + +# Appendix + ## Appendix: Cython (Basics) **Pure Python version (slow.py):** @@ -449,3 +561,5 @@ cython -a fast.pyx # Generates fast.c and HTML report gcc -shared -fPIC -o fast.so fast.c \ $(python3-config --includes) $(python3-config --ldflags) ``` + + From ad94a80683b3b9e27d6fc1e218889fa67672b259 Mon Sep 17 00:00:00 2001 From: Eirini <49712302+ai-mindset@users.noreply.github.com> Date: Wed, 10 Sep 2025 19:13:29 +0100 Subject: [PATCH 6/8] fix: Python :zany: --- .../pyproject.toml | 1 + .../2025-09-11_coffee-and-coding/slides.qmd | 228 +++++++----------- 2 files changed, 90 insertions(+), 139 deletions(-) diff --git a/presentations/2025-09-11_coffee-and-coding/pyproject.toml b/presentations/2025-09-11_coffee-and-coding/pyproject.toml index 48338ef9..3824c7bd 100644 --- a/presentations/2025-09-11_coffee-and-coding/pyproject.toml +++ b/presentations/2025-09-11_coffee-and-coding/pyproject.toml @@ -11,6 +11,7 @@ dependencies = [ "jupyter>=1.1.1", "numpy>=2.2.3", # https://github.com/numba/numba/releases/tag/0.61.2 "pandas>=2.3.2", + "pyinstrument>=5.1.1", "numba>=0.61.2", "jupyter-cache>=1.0.1", ] diff --git a/presentations/2025-09-11_coffee-and-coding/slides.qmd b/presentations/2025-09-11_coffee-and-coding/slides.qmd index 29800a76..4e661513 100644 --- a/presentations/2025-09-11_coffee-and-coding/slides.qmd +++ b/presentations/2025-09-11_coffee-and-coding/slides.qmd @@ -9,7 +9,7 @@ format: code-fold: true code-overflow: wrap font-size: 0.7em -execute: +execute: eval: false engine: jupyter --- @@ -18,9 +18,9 @@ execute: - **Measuring Performance**: Time and profile your code - **Common Performance Tweaks**: Easy wins for faster code -- **Loops vs. Vectorisation vs. Functional**: Choose the right approach +- **Loops vs. Vectorisation vs. ...**: Choose the right approach - **Optimising Loops**: When you should use them -- **Beyond Basics**: Tools for further optimisation +- **Beyond the Basics**: Tools for further optimisation # Measuring Performance @@ -29,32 +29,28 @@ execute: - How long does it take? - Can compare approaches? - When will your code finish running when you scale it up? - + ## 🐍 Timing ```{python} #| echo: true #| code-fold: show -from timeit import timeit # Python's precise timing module +from timeit import timeit -size = 1_000_000 +size = 100_000 -def sum_of_squares(): # Function to measure +def sum_of_squares(): return sum(i**2 for i in range(size)) -# Time the function execution -# number=100: run multiple times for statistical significance -# globals=globals(): access functions defined in current scope execution_time = timeit('sum_of_squares()', globals=globals(), number=100) -# Calculate and display average execution time per call print(f"Average execution time: {execution_time/1000:.6f}s") ``` -## 🦜 Timing +## 🦜 Timing - `system.time()` for quick one-off timing - {benchmark} for parameterised comparisons @@ -77,30 +73,31 @@ ggplot2::autoplot(df) > *"We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil."* - Donald Knuth - + - Identify the slowest functions ## 🐍 Profiling ```{python} #| echo: true -import cProfile, pstats -from io import StringIO - -def sum_of_squares(n): - return sum(i * i for i in range(n)) - -# Create a StringIO object to capture output -pr = cProfile.Profile() -pr.enable() -sum_of_squares(1_000_000) -pr.disable() - -# Display results -s = StringIO() -ps = pstats.Stats(pr, stream=s).sort_stats('cumulative') -ps.print_stats(5) -print(s.getvalue()) +from random import uniform +from pyinstrument import Profiler + +# Monte Carlo method estimating pi through simulation and geometric probability +def hits(point): + return abs(point) <= 1 + +def point(): + return complex(uniform(0, 1), uniform(0, 1)) + +def estimate_pi(n): + return 4 * sum(hits(point()) for _ in range(n)) / n + +with Profiler(interval=0.1) as profiler: + estimate_pi(n=10_000_000) + +profiler.print() +# profiler.open_in_browser() ``` ## 🦜 Profiling @@ -123,21 +120,21 @@ RD to introduce general ```{python} #| echo: true #| code-fold: show -from timeit import timeit # For timing operations -import numpy as np # For pre-allocated arrays +from timeit import timeit +import numpy as np -size = 100_000 # Number of elements to process +size = 100_000 -def growing_list(): # Growing a list dynamically (expensive) - result = [] # Empty list that will grow +def growing_list(): + result = [] for i in range(size): - result.append(i**2) # Triggers resize + result.append(i**2) return result -def preallocated_array(): # Pre-allocated array - result = np.zeros(size, dtype=int) # Preallocate array +def preallocated_array(): + result = np.zeros(size, dtype=int) for i in range(size): - result[i] = i**2 # No resizing needed + result[i] = i**2 return result t1 = timeit(growing_list, number=100) @@ -152,88 +149,39 @@ print(f"Speedup: {t2/t1:.1f}x faster") ## Appropriate Data Structures -- Numpy array faster than a python list +- NumPy array faster than a Python list - Set is _much_ faster than list* but only keeps _unique elements_ - Just doing numeric calculations? Can you use a matrix? ## 🐍 Appropriate Data Structures -```{python} -#| echo: true -#| code-fold: show -from timeit import timeit # For timing the operations -import random # To select a random lookup value - -size = 100_000 - -# Setup test data -data = list(range(size)) # A list with 100,000 items -lookup_val = random.choice(data) # Random value to find -lookup_set = set(data) # Same data as a set for O(1) lookup - -# Time list lookup (O(n) - must check each element) -t1 = timeit(lambda: lookup_val in data, number=100) - -# Time set lookup (O(1) - constant time hash table) -t2 = timeit(lambda: lookup_val in lookup_set, number=100) - -print(f"List lookup: {t1:.6f}s\nSet lookup: {t2:.6f}s") -print(f"Speedup: {t1/t2:.1f}x faster") -``` - -## 🦜 Appropriate Data Structures - -## 🐍 String Concatenation +| Data Structure | Mutability | Use Cases | Performance | +|------------------|------------|---------------------------|---------------| +| **List** | Mutable | Ordered collections | Moderate | +| **Tuple** | Immutable | Fixed collections | Fast | +| **Dictionary** | Mutable | KV pairs, fast lookups | Fast | +| **Set** | Mutable | Unique collections | Fast | +| **NumPy Array** | Mutable | Numerical data, math. ops | Very Fast | -```{python} -#| echo: true -#| code-fold: show -from timeit import timeit - -size = 100_000 - -def join_method(): # Fast - O(n) complexity with clear intent - return "".join("Hello World! " for _ in range(size)) # Single operation - -def concat_method(): # Slow - O(n²) complexity - result = "" - for _ in range(size): - result += "Hello World! " # Creates new string each time - return result - -t1 = timeit(join_method, number=100) -t2 = timeit(concat_method, number=100) -print(f"join: {t1:.6f}s\n+=: {t2:.6f}s") -print(f"Speedup: {t2/t1:.1f}x faster") -``` - -# Loops vs. Vectorisation vs. Functional - -## Loops vs. Vectorisation vs. Functional - -| Approach | Best For | Example Use Case | -|---|---|---| -| **Loops** | Complex logic, small data | Custom algorithms | -| **Vectorisation** | Numerical operations | Data science, NumPy | -| **Functional** | Data transformations | Pipelines, map/reduce/filter | +# Loops vs. Vectorisation vs. ... ## Loops ```{python} #| echo: true #| code-fold: show -from timeit import timeit # For timing code execution +from timeit import timeit -size = 100_000 +size = 64 -def standard_loop(): # Slower - bytecode overhead + repeated .append() calls +def standard_loop(): result = [] for i in range(size): - result.append(i**2) + result.append(2**i) return result -def list_comprehension(): # Faster, concise - return [i**2 for i in range(size)] +def list_comprehension(): + return [2**i for i in range(size)] t1 = timeit(standard_loop, number=100) t2 = timeit(list_comprehension, number=100) @@ -247,18 +195,17 @@ print(f"Speedup: {t1/t2:.1f}x faster") ```{python} #| echo: true #| code-fold: show -from timeit import timeit # For timing code execution -import numpy as np # NumPy for vectorised operations +from timeit import timeit +import numpy as np size = 100_000 -def python_way(): # Pure Python approach (loops through each element) +def python_way(): return [i**2 for i in range(size)] -def numpy_way(): # NumPy vectorised approach (operates on entire array at once) +def numpy_way(): return np.arange(size)**2 # Uses C implementation -# Compare execution times t1 = timeit(python_way, number=100) t2 = timeit(numpy_way, number=100) @@ -271,17 +218,16 @@ print(f"Speedup: {t1/t2:.1f}x faster") ```{python} #| echo: true #| code-fold: show -import pandas as pd # For DataFrame operations -import numpy as np # For random data generation -from timeit import timeit # For timing operations +import pandas as pd +import numpy as np +from timeit import timeit -# Create sample dataframe with 10,000 random values (reduced for demo) df = pd.DataFrame({"value": np.random.rand(10_000)}) -def apply_method(): # Slow: Using apply (runs Python function on each row) +def apply_method(): return df["value"].apply(lambda x: x**2) -def vector_method(): # Fast: Vectorised operations (C implementation) +def vector_method(): return df["value"]**2 # Compare execution times @@ -293,7 +239,6 @@ print(f"Speedup: {t1/t2:.1f}x faster") ``` ## Functional Programming -[Functional programming in Python? by David Vujic](https://www.youtube.com/watch?v=hz4OPyBYA98) ```{python} #| echo: true @@ -302,7 +247,6 @@ from timeit import timeit size = 100_000 -# Compare map vs list comprehension t1 = timeit(lambda: list(map(lambda x: x**2, range(size))), number=100) t2 = timeit(lambda: [x**2 for x in range(size)], number=100) @@ -315,24 +259,27 @@ print(f"Speedup: {t1/t2:.1f}x faster") ```{python} #| echo: true #| code-fold: show + +# Generator def count_up_to(limit): count = 0 while count < limit: yield count count += 1 -# Usage -for number in count_up_to(5): - print(number) +print([n for n in count_up_to(50)]) ``` ## When to Use Each Approach -- **Vectorisation**: Large numerical datasets (NumPy/Pandas) -- **List Comprehensions**: Simple transformations on sequences -- **Functional**: Complex pipelines, data transformations -- **Loops**: Complex logic, small datasets, or when readability matters -- **Generators**: Process large datasets, that shouldn't be loaded entirely into memory +| Approach | Best For | Example Use Case | +|-------------------------|-------------------------------------|----------------------------------------| +| **Loops** | Complex logic, small data | Custom algorithms | +| **Vectorisation** | Numerical operations | Data science, NumPy | +| **Functional** | Data transformations | Pipelines, filter/map/reduce | +| **List Comprehensions** | Simple transformations | Filter elements | +| **Generators** | Large dataset processing | Read large files line by line | + # Loop Optimisation @@ -341,8 +288,8 @@ for number in count_up_to(5): - Define anything you can _outside_ the loop - Consider locally assigning common functions - I/O slows loops -- Look out for `print` or `plot` -- Use argument for "chatty / "quiet" +- Look out for `print` or `plot` +- Use flag for "chatty" / "quiet" - Proper logging instead of printing ## 🐍 Optimisation Techniques @@ -350,25 +297,25 @@ for number in count_up_to(5): ```{python} #| echo: true #| code-fold: show -from timeit import timeit # For timing execution -import math # For sqrt, sin, cos functions -from random import random +from timeit import timeit +import math +from random import randint -size = 1_000_000 +size = 300_000 -data = [random()**2 for _ in range(size)] # Create test data +data = [2**randint(0, 64) for _ in range(size)] -def regular_loop(): # len() and function lookups each iteration +def regular_loop(): result = 0 - for i in range(len(data)): # len() recalculated in every iteration + for i in range(len(data)): x = data[i] - result += math.sqrt(x) + math.sin(x) + math.cos(x) # Function lookups each time + result += math.sqrt(x) + math.sin(x) + math.cos(x) return result -def optimised_loop(): # pre-computed length and local function bindings +def optimised_loop(): result = 0 - n = len(data) # Pre-compute length once - sqrt, sin, cos = math.sqrt, math.sin, math.cos # Bind functions locally + n = len(data) + sqrt, sin, cos = math.sqrt, math.sin, math.cos for i in range(n): x = data[i] result += sqrt(x) + sin(x) + cos(x) @@ -383,7 +330,7 @@ print(f"Speedup: {t1/t2:.1f}x faster") ## 🦜 Optimisation Techniques -# Beyond Basics +# Beyond the Basics ## Rewrite in C++ @@ -474,8 +421,11 @@ print(f"Speedup: {t1/t2:.1f}x faster") ## 📚 Resources - [Efficient R](https://csgillespie.github.io/efficientR/) -- [Effective Python, The Book: Third Edition, Brett Slatkin](https://effectivepython.com/) +- [Effective Python, The Book: Third Edition](https://effectivepython.com/) - [Advanced R](https://adv-r.hadley.nz/index.html) +- [Python data structures](https://docs.python.org/3/tutorial/datastructures.html) +- [Functional programming in Python? by David Vujic](https://www.youtube.com/watch?v=hz4OPyBYA98) +- [Python profiling](https://realpython.com/python-profiling/) # Appendix From c46237e728809f207450755cc7b9f6ab1d3130fe Mon Sep 17 00:00:00 2001 From: Rhian Davies Date: Thu, 11 Sep 2025 13:10:34 +0100 Subject: [PATCH 7/8] :pencil2: Last minute edits (RD) --- .../2025-09-11_coffee-and-coding/garage.jpg | Bin 0 -> 2328177 bytes .../2025-09-11_coffee-and-coding/slides.qmd | 146 ++++++++++++++++-- 2 files changed, 135 insertions(+), 11 deletions(-) create mode 100644 presentations/2025-09-11_coffee-and-coding/garage.jpg diff --git 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zOpp3lIAci`Ll^mViO+XBoFX6gpYs&C=2L~uWS&JR$gC@lOTl^nk COxb4u literal 0 HcmV?d00001 diff --git a/presentations/2025-09-11_coffee-and-coding/slides.qmd b/presentations/2025-09-11_coffee-and-coding/slides.qmd index 4e661513..0ebd5db0 100644 --- a/presentations/2025-09-11_coffee-and-coding/slides.qmd +++ b/presentations/2025-09-11_coffee-and-coding/slides.qmd @@ -53,7 +53,7 @@ print(f"Average execution time: {execution_time/1000:.6f}s") ## 🦜 Timing - `system.time()` for quick one-off timing -- {benchmark} for parameterised comparisons +- {bench} for parameterised comparisons ```{r} base = function(n) 0:n @@ -102,18 +102,35 @@ profiler.print() ## 🦜 Profiling +:::: {.columns} + +::: {.column } + - Use [{profvis}](https://profvis.r-lib.org/) - [Examples](https://profvis.r-lib.org/articles/examples.html) - [Visualising profiles in R](https://adv-r.hadley.nz/perf-measure.html#visualising-profiles) -![Screenshot of a profvis session](https://adv-r.hadley.nz/screenshots/performance/flamegraph.png) + +::: + + +::: {.column} +![](https://adv-r.hadley.nz/screenshots/performance/flamegraph.png){fig-alt="Screenshot of a profvis session"} +::: + +:::: + # Performance Tweaks ## Object growth -RD to introduce general +::: incremental +It starts small... 🥎 +- ![](garage.jpg){fig-alt="A garage full of bikes and sports equipment .fragment width="70%} +::: + ## 🐍 Pre-allocating Arrays @@ -145,7 +162,40 @@ print(f"Speedup: {t2/t1:.1f}x faster") ## 🦜 Pre-allocating Arrays -- Show example of benchmark with pre-allocation compared to none + +:::: {.columns} + +::: {.column width=70%} + +```{r} +n = 100000 + +myvec = NULL +for (i in 1:n) { +myvec = c(myvec, i) +} + +myvec = numeric(n) +for (i in 1:n) { +myvec[i] = i +} + +bench::mark(method1(n), method2(n)) +``` + +::: + + +::: {.column width=30%} +| n | 1 | 2 | +|------|-------|-------| +| 10^5 | 0.208 | 0.024 | +| 10^6 | 25.50 | 0.220 | +| 10^7 | 3827 | 2.212 | +::: + +:::: + ## Appropriate Data Structures @@ -165,7 +215,7 @@ print(f"Speedup: {t2/t1:.1f}x faster") # Loops vs. Vectorisation vs. ... -## Loops +## 🐍 Loops ```{python} #| echo: true @@ -190,7 +240,7 @@ print(f"Standard loop: {t1:.6f}s\nList comprehension: {t2:.6f}s") print(f"Speedup: {t1/t2:.1f}x faster") ``` -## Vectorisation with NumPy +## 🐍 Vectorisation with NumPy ```{python} #| echo: true @@ -213,7 +263,7 @@ print(f"Python: {t1:.6f}s\nNumPy: {t2:.6f}s") print(f"Speedup: {t1/t2:.1f}x faster") ``` -## Vectorisation with Pandas +## 🐍 Vectorisation with Pandas ```{python} #| echo: true @@ -238,7 +288,40 @@ print(f"apply: {t1:.6f}s\nvectorised: {t2:.6f}s") print(f"Speedup: {t1/t2:.1f}x faster") ``` -## Functional Programming +## 🦜 Vectorisation + +:::: {.columns} + +::: {.column .fragment} + +```{r} +hits = 0 +for (i in 1:n) { +u1 = runif(1) +u2 = runif(1) +if (u1^2 > u2) +hits = hits + 1 +} +return(hits / n) +``` + +::: + + +::: {.column .fragment} + +```{r} +hits = sum(runif(n) ^ 2 > runif(n)) +return(hits / n) +``` + +::: + +:::: + + + +## 🐍 Functional Programming ```{python} #| echo: true @@ -254,7 +337,7 @@ print(f"map: {t1:.6f}s\ncomprehension: {t2:.6f}s") print(f"Speedup: {t1/t2:.1f}x faster") ``` -## Generators +## 🐍 Generators ```{python} #| echo: true @@ -330,6 +413,47 @@ print(f"Speedup: {t1/t2:.1f}x faster") ## 🦜 Optimisation Techniques +:::: {.columns} + +::: {.column .fragment} + +```{r} +# Distances per day for 3 runners +km <- matrix( + c(5, 7, 10, 6, 8, 12, 7.5, 9, 11, + 5, 10, 8, 3, 10, 6), + nrow = 5, byrow = TRUE +) + +miles <- matrix(NA, nrow = nrow(km), + ncol = ncol(km)) + +# loop over days +for (i in seq_len(nrow(km))) { + km_to_miles <- 0.621371 + miles[i, ] <- km[i, ] * km_to_miles +} + +``` + +::: + + +::: {.column .fragment} + +```{r} +km_to_miles <- 0.621371 + +# loop over runners +for (j in seq_len(ncol(km))) { + miles[, j] <- km[, j] * km_to_miles +} +``` + +::: + +:::: + # Beyond the Basics ## Rewrite in C++ @@ -341,7 +465,7 @@ print(f"Speedup: {t1/t2:.1f}x faster") - 🦜 [{rcpp}](https://adv-r.hadley.nz/rcpp.html) to connect C++ and R -## Just-in-Time Compilation (1) +## 🐍 Just-In-Time Compilation (1) ```{python} #| echo: true @@ -370,7 +494,7 @@ print(f"Python: {t1:.6f}s\nNumba: {t2:.6f}s") print(f"Speedup: {t1/t2:.1f}x faster") ``` -## Just-In-Time Compilation (2) +## 🐍 Just-In-Time Compilation (2) **What is JIT?** From 207b7b04ddff291486a0eaaabcaaddee01ef91aa Mon Sep 17 00:00:00 2001 From: Eirini <49712302+ai-mindset@users.noreply.github.com> Date: Fri, 31 Oct 2025 12:14:49 +0000 Subject: [PATCH 8/8] fix: rename slides to index --- .../2025-09-11_coffee-and-coding/{slides.qmd => index.qmd} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename presentations/2025-09-11_coffee-and-coding/{slides.qmd => index.qmd} (100%) diff --git a/presentations/2025-09-11_coffee-and-coding/slides.qmd b/presentations/2025-09-11_coffee-and-coding/index.qmd similarity index 100% rename from presentations/2025-09-11_coffee-and-coding/slides.qmd rename to presentations/2025-09-11_coffee-and-coding/index.qmd

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