differences for PR #27

RSE-Sheffield · Mar 28, 2024 · fba9a80 · fba9a80
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diff --git a/config.yaml b/config.yaml
@@ -61,10 +61,12 @@ contact: '[email protected]'
 episodes: 
 - profiling-introduction.md
 - profiling-functions.md
+- short-break1.md
 - profiling-lines.md
 - profiling-conclusion.md
 - optimisation-introduction.md
 - optimisation-data-structures-algorithms.md
+- long-break1.md
 - optimisation-minimise-python.md
 - optimisation-use-latest.md
 - optimisation-memory.md

diff --git a/files/line_profiler-worked-example/fizzbuzz.py b/files/line_profiler-worked-example/fizzbuzz.py
@@ -1,23 +1,10 @@
 n = 100
-a=0
-b=0
-c=0
-d=0
 for i in range(1, n + 1):
     if i % 3 == 0 and i % 5 == 0:
-        a+=1
         print("FizzBuzz")
     elif i % 3 == 0:
-        b+=1
         print("Fizz")
     elif i % 5 == 0:
-        c+=1
         print("Buzz")
     else:
-        d+=1
-        print(i)
-
-print(a)
-print(b)
-print(c)
-print(d)
+        print(i)
diff --git a/files/pred-prey/predprey.py.lprof b/files/pred-prey/predprey.py.lprof
diff --git a/files/pred-prey/predprey_out.prof b/files/pred-prey/predprey_out.prof
diff --git a/long-break1.md b/long-break1.md
@@ -0,0 +1,8 @@
+---
+title: Break
+teaching: 0
+exercises: 0
+break: 60
+---
+
+Take a break. If you can, move around and look at something away from your screen to give your eyes a rest.
diff --git a/md5sum.txt b/md5sum.txt
@@ -1,20 +1,22 @@
 "file" "checksum" "built" "date"
 "CODE_OF_CONDUCT.md" "c93c83c630db2fe2462240bf72552548" "site/built/CODE_OF_CONDUCT.md" "2024-03-20"
 "LICENSE.md" "b24ebbb41b14ca25cf6b8216dda83e5f" "site/built/LICENSE.md" "2024-03-20"
-"config.yaml" "b413b2dfbce4f70e178cae4d6d2d6311" "site/built/config.yaml" "2024-03-20"
+"config.yaml" "6d4d0c5a03448624b21b0eeaf3dd183a" "site/built/config.yaml" "2024-03-28"
 "index.md" "3a6d3683998a6b866c134a818f1bb46e" "site/built/index.md" "2024-03-20"
 "links.md" "8184cf4149eafbf03ce8da8ff0778c14" "site/built/links.md" "2024-03-20"
-"episodes/profiling-introduction.md" "86c0d8691aae9c0aa4b6f23e216a2978" "site/built/profiling-introduction.md" "2024-03-20"
-"episodes/profiling-functions.md" "afdff3941ebb6f5f9121a57eaf6dbf88" "site/built/profiling-functions.md" "2024-03-22"
-"episodes/profiling-lines.md" "c00ccba171a20837aeffc9f8079d9e1d" "site/built/profiling-lines.md" "2024-03-22"
-"episodes/profiling-conclusion.md" "a3c2deb1bc4efaaf4a2a70f966734b71" "site/built/profiling-conclusion.md" "2024-03-20"
-"episodes/optimisation-introduction.md" "2c2bbafab97d4db78aa5735839516c81" "site/built/optimisation-introduction.md" "2024-03-20"
-"episodes/optimisation-data-structures-algorithms.md" "029ce8cc24d92f2d819fadadc06b5999" "site/built/optimisation-data-structures-algorithms.md" "2024-03-20"
-"episodes/optimisation-minimise-python.md" "fa4f1c8cd55a8b4ac2870d5bbc4d23d1" "site/built/optimisation-minimise-python.md" "2024-03-20"
-"episodes/optimisation-use-latest.md" "4c939e9dbde33a1f47fefe5e757ff256" "site/built/optimisation-use-latest.md" "2024-03-20"
-"episodes/optimisation-memory.md" "69eb84dfc419083ff12856a80750a618" "site/built/optimisation-memory.md" "2024-03-20"
-"episodes/optimisation-conclusion.md" "ccd780c447f0b0ce97b8da1b2572b9c1" "site/built/optimisation-conclusion.md" "2024-03-20"
+"episodes/profiling-introduction.md" "e3678e7f2f3ea9bd44e7fc0604cc650b" "site/built/profiling-introduction.md" "2024-03-28"
+"episodes/profiling-functions.md" "94be5829062d7d0691bf192de13708ad" "site/built/profiling-functions.md" "2024-03-28"
+"episodes/short-break1.md" "c7d9988cade5cc12dfbbf6c2a29ff2e9" "site/built/short-break1.md" "2024-03-28"
+"episodes/profiling-lines.md" "7ad4ed55b19e0c874b9474912b8dd532" "site/built/profiling-lines.md" "2024-03-28"
+"episodes/profiling-conclusion.md" "b5687e26387b353ef23c6292f295ca02" "site/built/profiling-conclusion.md" "2024-03-28"
+"episodes/optimisation-introduction.md" "c875757a1d4df74c654322de2261fbab" "site/built/optimisation-introduction.md" "2024-03-28"
+"episodes/optimisation-data-structures-algorithms.md" "ae2d5f70c4649800f35794b24c3cef2b" "site/built/optimisation-data-structures-algorithms.md" "2024-03-28"
+"episodes/long-break1.md" "19a5c42e45032003c36ad8f413f44528" "site/built/long-break1.md" "2024-03-28"
+"episodes/optimisation-minimise-python.md" "37e3a2643171a7e2a432902b420929be" "site/built/optimisation-minimise-python.md" "2024-03-28"
+"episodes/optimisation-use-latest.md" "5948276773890e97b7898292fddbcb39" "site/built/optimisation-use-latest.md" "2024-03-28"
+"episodes/optimisation-memory.md" "d819f88003ffd721bc6b1ec783a23390" "site/built/optimisation-memory.md" "2024-03-28"
+"episodes/optimisation-conclusion.md" "99b7eb75c9c513b54bcf01c321aa0020" "site/built/optimisation-conclusion.md" "2024-03-28"
 "instructors/instructor-notes.md" "cae72b6712578d74a49fea7513099f8c" "site/built/instructor-notes.md" "2024-03-20"
-"learners/setup.md" "3465b1c09e7527d085eb32f647227dc6" "site/built/setup.md" "2024-03-20"
+"learners/setup.md" "4998f740cb34f70b024206224cdbcaf6" "site/built/setup.md" "2024-03-28"
 "learners/acknowledgements.md" "c4064263d442f147d3796cb3dfa7b351" "site/built/acknowledgements.md" "2024-03-20"
 "profiles/learner-profiles.md" "60b93493cf1da06dfd63255d73854461" "site/built/learner-profiles.md" "2024-03-20"
diff --git a/optimisation-conclusion.md b/optimisation-conclusion.md
@@ -1,6 +1,6 @@
 ---
 title: "Optimisation Conclusion"
-teaching: 0
+teaching: 5
 exercises: 0
 ---
 

diff --git a/optimisation-data-structures-algorithms.md b/optimisation-data-structures-algorithms.md
@@ -1,7 +1,7 @@
 ---
 title: "Data Structures & Algorithms"
-teaching: 0
-exercises: 0
+teaching: 30
+exercises: 5
 ---
 
 :::::::::::::::::::::::::::::::::::::: questions

diff --git a/optimisation-introduction.md b/optimisation-introduction.md
@@ -1,6 +1,6 @@
 ---
 title: "Introduction to Optimisation"
-teaching: 0
+teaching: 10
 exercises: 0
 ---
 

diff --git a/optimisation-memory.md b/optimisation-memory.md
@@ -1,6 +1,6 @@
 ---
 title: "Understanding Memory"
-teaching: 0
+teaching: 30
 exercises: 0
 ---
 

diff --git a/optimisation-minimise-python.md b/optimisation-minimise-python.md
@@ -1,6 +1,6 @@
 ---
 title: "Understanding Python (NumPy/Pandas)"
-teaching: 0
+teaching: 30
 exercises: 0
 ---
 
@@ -202,18 +202,18 @@ dis.dis(searchListC)
 
 ## Scope
 
-When Python executes your code, it has to find the variables that you're using.
+When Python executes your code, it has to find the variables and functions that you're using.
 
-This adds an additional cost to accessing variables in Python, which isn't typically seen in compiled languages.
+This adds an additional cost to accessing variables and calling functions in Python, which isn't typically seen in compiled languages.
 
-In particular, it will first check whether the variable has been declared within the current function (local scope), if it can't find it there it will check whether it has been declared in the file (global scope) after which it may even check whether it's from an imported package.
+In particular, it will first check whether the variable or functions has been declared within the current function (local scope), if it can't find it there it will check whether it has been declared in the file (global scope) after which it may even check whether it's from an imported package.
 
-Repeated accesses to variables, will repeat these checks.
+These are not implicitly cached, therefore repeated accesses to variables and functions, will repeat these checks.
 
-The implication, is that as local scope variables are checked first, they will be faster to access.
+The implication, is that as local scope variables and functions are checked first, they will be faster to use.
 
 If you're only accessing a variable once or twice that's nothing to worry about, this is a relatively small cost.
-But if a variable is being accessed regularly, such as within a loop, the impact may become visible.
+But if a variable or functions is being accessed regularly, such as within a loop, the impact may become visible.
 
 The below example provides a small demonstration of this in practice.
 
@@ -242,7 +242,7 @@ print(f"Global Scope: {timeit(test_list_global, number=repeats):.5f}ms")
 print(f"Local Scope: {timeit(test_list_local, number=repeats):.5f}ms")
 ```
 
-This is only a trivial example, but local scope is about 20% faster than global scope!
+This is only a trivial example, whereby `N` has been copied to the local scope `N_local`, but local scope is about 20% faster than global scope!
 
 ```output
 Global Scope: 0.06416ms
@@ -251,6 +251,18 @@ Local Scope: 0.05391ms
 
 Consider copying highly accessed variables into local scope, you can always copy them back to global scope before you return from a function.
 
+Copying functions to local scope works much the same as variables, e.g.
+
+```py
+import numpy as np
+
+def my_function():
+    uniform_local = np.random.uniform
+
+    for i in range(10000):
+        t = uniform_local()
+```
+
 ## Built-in Functions Operators
 
 In order to take advantage of offloading computation to the CPython back-end it's necessary to be aware of what functionality is present. Those available without importing packages are considered [built-in](https://docs.python.org/3/library/functions.html) functions.
@@ -584,26 +596,31 @@ import pandas as pandas
 
 N = 100000  # Number of rows in DataFrame
 
+def genInput():
+    s = pandas.Series({'a' : 1, 'b' : 2})
+    d = {'a' : 1, 'b' : 2}
+    return s, d
+
 def series():
-    x = pandas.Series({'a' : 1, 'b' : 2})
+    s, _ = genInput()
     for i in range(N):
-        y = x['a'] * x['b']
+        y = s['a'] * s['b']
 
 def dictionary():
-    x = {'a' : 1, 'b' : 2}
+    _, d = genInput()
     for i in range(N):
-        y = x['a'] * x['b']
+        y = d['a'] * d['b']
 
 repeats = 1000
 print(f"series: {timeit(series, number=repeats):.2f}ms")
 print(f"dictionary: {timeit(dictionary, number=repeats):.2f}ms")
 ```
 
-69x slower!
+65x slower!
 
 ```output
-series: 236.16ms
-dictionary: 3.42ms
+series: 237.25ms
+dictionary: 3.63ms
 ```
 
 ## Filter Early

diff --git a/optimisation-use-latest.md b/optimisation-use-latest.md
@@ -1,6 +1,6 @@
 ---
 title: "Keep Python & Packages up to Date"
-teaching: 0
+teaching: 10
 exercises: 0
 ---
 

diff --git a/profiling-conclusion.md b/profiling-conclusion.md
@@ -1,6 +1,6 @@
 ---
 title: "Profiling Conclusion"
-teaching: 0
+teaching: 5
 exercises: 0
 ---
 

diff --git a/profiling-functions.md b/profiling-functions.md
@@ -1,7 +1,7 @@
 ---
 title: "Function Level Profiling"
-teaching: 0
-exercises: 0
+teaching: 20
+exercises: 20
 ---
 
 :::::::::::::::::::::::::::::::::::::: questions
@@ -150,9 +150,9 @@ The columns have the following definitions:
 |---------|---------------------------------------------------|
 | `ncalls`  | The number of times the given function was called. |
 | `tottime` | The total time spent in the given function, excluding child function calls. |
-| `percall` | The average tottime per function call (`tottime`/`percall`). |
+| `percall` | The average tottime per function call (`tottime`/`ncalls`). |
 | `cumtime` | The total time spent in the given function, including child function calls. |
-| `percall` | The average cumtime per function call (`cumtime`/`percall`). |
+| `percall` | The average cumtime per function call (`cumtime`/`ncalls`). |
 | `filename:lineno(function)` | The location of the given function's definition and it's name. |
 
 This output can often exceed the terminal's buffer length for large programs and can be unwieldy to parse, so the package `snakeviz` is often utilised to provide an interactive visualisation of the data when exported to file.
@@ -394,7 +394,7 @@ The value of `cities` should be a positive integer, this algorithm has poor scal
 :::::::::::::::::::::::: hint
 
 - If a hotspot isn't visible with the argument `1`, try increasing the value.
-- If you think you identified the hotspot with your first profile, try investigating how the value of `int` affects the hotspot within the profile.
+- If you think you identified the hotspot with your first profile, try investigating how the value of `cities` affects the hotspot within the profile.
 
 :::::::::::::::::::::::::::::::::
 
@@ -415,6 +415,15 @@ Other boxes within the diagram correspond to the initialisation of imports, or i
 
 The default configuration of the Predator Prey model takes around 10 seconds to run, it may be slower on other hardware.
 
+Download the pre-generated `cProfile` output, this can be opened with `snakeviz` to save waiting for the profiler.
+
+
+* <a href="files/pred-prey/predprey_out.prof" download>files/pred-prey/predprey_out.prof</a>
+
+```sh
+python -m snakeviz predprey_out.prof
+```
+
 :::::::::::::::::::::::::::::::::::::::::::::
 ::::::::::::::::::::::::::::::::::::: challenge
 

diff --git a/profiling-introduction.md b/profiling-introduction.md
@@ -1,7 +1,7 @@
 ---
 title: "Introduction to Profiling"
-teaching: 0
-exercises: 0
+teaching: 15
+exercises: 10
 ---
 
 :::::::::::::::::::::::::::::::::::::: questions

diff --git a/profiling-lines.md b/profiling-lines.md
@@ -1,7 +1,7 @@
 ---
 title: "Line Level Profiling"
-teaching: 0
-exercises: 0
+teaching: 20
+exercises: 30
 ---
 
 :::::::::::::::::::::::::::::::::::::: questions
@@ -400,6 +400,18 @@ As this is a reference implementation of a classic sorting algorithm we are unli
 :::::::::::::::::::::::::::::::::
 ::::::::::::::::::::::::::::::::::::::::::::::::
 
+:::::::::::::::::::::::::::::::::: instructor
+
+Download the pre-generated `line_profiler` output, this can be opened be to save waiting for the profiler.
+
+
+* <a href="files/pred-prey/predprey.py.lprof" download>files/pred-prey/predprey.py.lprof</a>
+
+```sh
+python -m line_profiler -rm predprey.py.lprof
+```
+
+:::::::::::::::::::::::::::::::::::::::::::::
 ::::::::::::::::::::::::::::::::::::: challenge 
 
 ## Exercise 2: Predator Prey

diff --git a/setup.md b/setup.md
@@ -20,12 +20,17 @@ Download the [data zip file](https://example.com/FIXME) and unzip it to your Des
 
 This course uses Python and was developed using Python 3.11, therefore it is recommended that you have a Python 3.11 or newer environment.
 
-<!-- Todo suggest using a venv?-->
+You may want to create a new Python virtual environment for the course, this can be done with your preferred Python environment manager (e.g. `conda`, `pipenv`), the required packages can all be installed via `pip`.
+
+<!-- conda create -n pando python
+     conda activate pando -->
 
 The non-core Python packages required by the course are `pytest`, `snakeviz`, `line_profiler`, `numpy` and `matplotlib` which can be installed via `pip`.
 
 ```sh
 pip install pytest snakeviz line_profiler[all] numpy matplotlib
 ```
 
+To complete some of the exercises you will need to use a text-editor or Python IDE, so make sure you have your favourite available.
+
 :::::::::::::::::::::::::::::::::::::::::::::::::::
diff --git a/short-break1.md b/short-break1.md
@@ -0,0 +1,8 @@
+---
+title: Break
+teaching: 0
+exercises: 0
+break: 15
+---
+
+Take a break. If you can, move around and look at something away from your screen to give your eyes a rest and a chance to absorb the content covered so far.