Episode: Function Level Profiling (#6)

Closes #3

episodes/files/snakeviz-worked-example/example.py

@@ -0,0 +1,36 @@
+import time
+
+"""
+This is a synthetic program intended to produce a clear profile with cProfile/snakeviz
+Method names, constructed from a hex digit and a number clearly denote their position in the hierarchy.
+"""
+def a_1():
+    for i in range(3):
+        b_1()
+    time.sleep(1)
+    b_2()
+
+
+def b_1():
+    c_1()
+    c_2()
+
+def b_2():
+    time.sleep(1)
+
+def c_1():
+    time.sleep(0.5)
+
+
+def c_2():
+    time.sleep(0.3)
+    d_1()
+
+def d_1():
+    time.sleep(0.1)
+
+
+
+
+# Entry Point
+a_1()

episodes/files/travelling-sales/travellingsales.py

@@ -0,0 +1,48 @@
+"""
+Naive brute force travelling salesperson
+python travellingsales.py <cities>
+"""
+
+import itertools
+import math
+import random
+import sys
+
+def distance(point1, point2):
+    return math.sqrt((point2[0] - point1[0])**2 + (point2[1] - point1[1])**2)
+
+def total_distance(points, order):
+    total = 0
+    for i in range(len(order) - 1):
+        total += distance(points[order[i]], points[order[i + 1]])
+    return total + distance(points[order[-1]], points[order[0]])
+
+def traveling_salesman_brute_force(points):
+    min_distance = float('inf')
+    min_path = None
+    for order in itertools.permutations(range(len(points))):
+        d = total_distance(points, order)
+        if d < min_distance:
+            min_distance = d
+            min_path = order
+    return min_path, min_distance
+
+# Argument parsing
+if len(sys.argv) != 2:
+    print("Script expects 1 positive integer argument, %u found."%(len(sys.argv) - 1))
+    sys.exit()
+cities_len = int(sys.argv[1])
+if cities_len < 1:
+    print("Script expects 1 positive integer argument, %s converts < 1."%(sys.argv[1]))
+    sys.exit()
+# Define the cities as (x, y) coordinates
+random.seed(12) # Fixed random for consistency
+cities = [(0,0)]
+for i in range(cities_len):
+    cities.append((random.uniform(-1, 1), random.uniform(-1, 1)))
+
+# Find the shortest path
+shortest_path, min_distance = traveling_salesman_brute_force(cities)
+print("Cities:", cities_len)
+print("Shortest Path:", shortest_path)
+print("Shortest Distance:", min_distance)

episodes/profiling-introduction.md

@@ -115,7 +115,7 @@ Function-level profiling analyses where time is being spent with respect to func
 This allows functions that occupy a disproportionate amount of the total runtime to be quickly identified and investigated.
 
 <!-- We will be covering -->
-In this course we will cover the usage of the function-level profiler `cprofile` and how it's output can be visualised with `snakeviz`.
+In this course we will cover the usage of the function-level profiler `cProfile` and how it's output can be visualised with `snakeviz`.
 
 ### Line-Level Profiling
 <!-- Context -->
@@ -205,7 +205,7 @@ Write a short plan of the approach you would take to investigate and confirm whe
 - Profiling is a relatively quick process to analyse where time is being spent and bottlenecks during a program's execution.
 - Code should be profiled when ready for deployment if it will be running for more than a few minutes during it's lifetime.
 - There are several types of profiler each with slightly different purposes.
-    - function-level: `cprofile` (visualised with `snakeviz`)
+    - function-level: `cProfile` (visualised with `snakeviz`)
     - line-level: `line_profiler`
     - timeline: `viztracer`
 - A representative test-case should be profiled, that is large enough to amplify any bottlenecks whilst executing to completion quickly.

learners/setup.md

@@ -24,7 +24,7 @@ This course uses Python and was developed using Python 3.11, therefore it is rec
 
 The non-core Python packages required by the course are `snakeviz`, `line_profiler` and `viztracer` which can be installed via `pip`.
 
-```input
+```sh
 pip install snakeviz line_profiler[all] viztracer[full]
 ```