Repository: RSE-Sheffield/pando-python
Pull request #73: ICR RSE: Improvements and Text Simplifications
Author: login: Robadob
Created: 2025-03-21T13:45:15Z | Merged: 2025-03-24T12:43:59Z

Manual merge of the appropriate changes from #51 (cc @msarkis-icr), mostly typographic/simplification changes (those in profiling are mostly directly used, whereas optimisation was heavily conflicted) including a new figure what I've reproduced.

**Todo:**
- [ ] New figure probably needs a bit more iteration (cc @JostMigenda for feedback)
- [x] How should https://github.com/ICR-RSE-Group be added to `learners/acknowledgements.md` (cc @msarkis-icr)
    * e.g. "Members of [The Institute of Cancer Research RSE](https://github.com/ICR-RSE-Group) contributed a large number of typographic corrections and simplifications during the alpha phase of the course's development lifecycle."
    * Happy to also name/tag specific people if preferred.
    * Manual merge has stolen all of your names from git history, if you're keen to rewrite history to change that and submit a new PR go for it (but probably wait for Jost to review the new figure).
- [x] Write a proper commit message
- [ ] Fixup all the correction commits.
    
 Closes #51


Diff:
diff --git a/episodes/fig/list-append.ai b/episodes/fig/list-append.ai
new file mode 100644
index 00000000..f138a85b
--- /dev/null
+++ b/episodes/fig/list-append.ai
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+                           <xmpG:type>PROCESS</xmpG:type>
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+                           <xmpG:green>255</xmpG:green>
+                           <xmpG:blue>255</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>RGB Blue</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>0</xmpG:red>
+                           <xmpG:green>0</xmpG:green>
+                           <xmpG:blue>255</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>RGB Magenta</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>255</xmpG:red>
+                           <xmpG:green>0</xmpG:green>
+                           <xmpG:blue>255</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=193 G=39 B=45</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>193</xmpG:red>
+                           <xmpG:green>39</xmpG:green>
+                           <xmpG:blue>45</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=237 G=28 B=36</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>237</xmpG:red>
+                           <xmpG:green>28</xmpG:green>
+                           <xmpG:blue>36</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=241 G=90 B=36</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>241</xmpG:red>
+                           <xmpG:green>90</xmpG:green>
+                           <xmpG:blue>36</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=247 G=147 B=30</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>247</xmpG:red>
+                           <xmpG:green>147</xmpG:green>
+                           <xmpG:blue>30</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=251 G=176 B=59</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>251</xmpG:red>
+                           <xmpG:green>176</xmpG:green>
+                           <xmpG:blue>59</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=252 G=238 B=33</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>252</xmpG:red>
+                           <xmpG:green>238</xmpG:green>
+                           <xmpG:blue>33</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=217 G=224 B=33</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>217</xmpG:red>
+                           <xmpG:green>224</xmpG:green>
+                           <xmpG:blue>33</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=140 G=198 B=63</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>140</xmpG:red>
+                           <xmpG:green>198</xmpG:green>
+                           <xmpG:blue>63</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=57 G=181 B=74</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>57</xmpG:red>
+                           <xmpG:green>181</xmpG:green>
+                           <xmpG:blue>74</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=0 G=146 B=69</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>0</xmpG:red>
+                           <xmpG:green>146</xmpG:green>
+                           <xmpG:blue>69</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=0 G=104 B=55</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>0</xmpG:red>
+                           <xmpG:green>104</xmpG:green>
+                           <xmpG:blue>55</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=34 G=181 B=115</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>34</xmpG:red>
+                           <xmpG:green>181</xmpG:green>
+                           <xmpG:blue>115</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=0 G=169 B=157</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>0</xmpG:red>
+                           <xmpG:green>169</xmpG:green>
+                           <xmpG:blue>157</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=41 G=171 B=226</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>41</xmpG:red>
+                           <xmpG:green>171</xmpG:green>
+                           <xmpG:blue>226</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=0 G=113 B=188</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>0</xmpG:red>
+                           <xmpG:green>113</xmpG:green>
+                           <xmpG:blue>188</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=46 G=49 B=146</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>46</xmpG:red>
+                           <xmpG:green>49</xmpG:green>
+                           <xmpG:blue>146</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=27 G=20 B=100</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>27</xmpG:red>
+                           <xmpG:green>20</xmpG:green>
+                           <xmpG:blue>100</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=102 G=45 B=145</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>102</xmpG:red>
+                           <xmpG:green>45</xmpG:green>
+                           <xmpG:blue>145</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=147 G=39 B=143</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>147</xmpG:red>
+                           <xmpG:green>39</xmpG:green>
+                           <xmpG:blue>143</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=158 G=0 B=93</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>158</xmpG:red>
+                           <xmpG:green>0</xmpG:green>
+                           <xmpG:blue>93</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=212 G=20 B=90</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>212</xmpG:red>
+                           <xmpG:green>20</xmpG:green>
+                           <xmpG:blue>90</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=237 G=30 B=121</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>237</xmpG:red>
+                           <xmpG:green>30</xmpG:green>
+                           <xmpG:blue>121</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=199 G=178 B=153</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>199</xmpG:red>
+                           <xmpG:green>178</xmpG:green>
+                           <xmpG:blue>153</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=153 G=134 B=117</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>153</xmpG:red>
+                           <xmpG:green>134</xmpG:green>
+                           <xmpG:blue>117</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=115 G=99 B=87</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>115</xmpG:red>
+                           <xmpG:green>99</xmpG:green>
+                           <xmpG:blue>87</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=83 G=71 B=65</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>83</xmpG:red>
+                           <xmpG:green>71</xmpG:green>
+                           <xmpG:blue>65</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=198 G=156 B=109</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>198</xmpG:red>
+                           <xmpG:green>156</xmpG:green>
+                           <xmpG:blue>109</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=166 G=124 B=82</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>166</xmpG:red>
+                           <xmpG:green>124</xmpG:green>
+                           <xmpG:blue>82</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=140 G=98 B=57</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>140</xmpG:red>
+                           <xmpG:green>98</xmpG:green>
+                           <xmpG:blue>57</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=117 G=76 B=36</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>117</xmpG:red>
+                           <xmpG:green>76</xmpG:green>
+                           <xmpG:blue>36</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=96 G=56 B=19</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>96</xmpG:red>
+                           <xmpG:green>56</xmpG:green>
+                           <xmpG:blue>19</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=66 G=33 B=11</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>66</xmpG:red>
+                           <xmpG:green>33</xmpG:green>
+                           <xmpG:blue>11</xmpG:blue>
+                        </rdf:li>
+                     </rdf:Seq>
+                  </xmpG:Colorants>
+               </rdf:li>
+               <rdf:li rdf:parseType="Resource">
+                  <xmpG:groupName>Grays</xmpG:groupName>
+                  <xmpG:groupType>1</xmpG:groupType>
+                  <xmpG:Colorants>
+                     <rdf:Seq>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=0 G=0 B=0</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>0</xmpG:red>
+                           <xmpG:green>0</xmpG:green>
+                           <xmpG:blue>0</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=26 G=26 B=26</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>26</xmpG:red>
+                           <xmpG:green>26</xmpG:green>
+                           <xmpG:blue>26</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=51 G=51 B=51</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>51</xmpG:red>
+                           <xmpG:green>51</xmpG:green>
+                           <xmpG:blue>51</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=77 G=77 B=77</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>77</xmpG:red>
+                           <xmpG:green>77</xmpG:green>
+                           <xmpG:blue>77</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=102 G=102 B=102</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>102</xmpG:red>
+                           <xmpG:green>102</xmpG:green>
+                           <xmpG:blue>102</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=128 G=128 B=128</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>128</xmpG:red>
+                           <xmpG:green>128</xmpG:green>
+                           <xmpG:blue>128</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=153 G=153 B=153</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>153</xmpG:red>
+                           <xmpG:green>153</xmpG:green>
+                           <xmpG:blue>153</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=179 G=179 B=179</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>179</xmpG:red>
+                           <xmpG:green>179</xmpG:green>
+                           <xmpG:blue>179</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=204 G=204 B=204</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>204</xmpG:red>
+                           <xmpG:green>204</xmpG:green>
+                           <xmpG:blue>204</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=230 G=230 B=230</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>230</xmpG:red>
+                           <xmpG:green>230</xmpG:green>
+                           <xmpG:blue>230</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=242 G=242 B=242</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>242</xmpG:red>
+                           <xmpG:green>242</xmpG:green>
+                           <xmpG:blue>242</xmpG:blue>
+                        </rdf:li>
+                     </rdf:Seq>
+                  </xmpG:Colorants>
+               </rdf:li>
+               <rdf:li rdf:parseType="Resource">
+                  <xmpG:groupName>Web Color Group</xmpG:groupName>
+                  <xmpG:groupType>1</xmpG:groupType>
+                  <xmpG:Colorants>
+                     <rdf:Seq>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=63 G=169 B=245</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>63</xmpG:red>
+                           <xmpG:green>169</xmpG:green>
+                           <xmpG:blue>245</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=122 G=201 B=67</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>122</xmpG:red>
+                           <xmpG:green>201</xmpG:green>
+                           <xmpG:blue>67</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=255 G=147 B=30</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>255</xmpG:red>
+                           <xmpG:green>147</xmpG:green>
+                           <xmpG:blue>30</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=255 G=29 B=37</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>255</xmpG:red>
+                           <xmpG:green>29</xmpG:green>
+                           <xmpG:blue>37</xmpG:blue>
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diff --git a/episodes/fig/list-append.png b/episodes/fig/list-append.png
new file mode 100644
index 00000000..2348418e
Binary files /dev/null and b/episodes/fig/list-append.png differ
diff --git a/episodes/optimisation-data-structures-algorithms.md b/episodes/optimisation-data-structures-algorithms.md
index 3a957f2d..14caf8f1 100644
--- a/episodes/optimisation-data-structures-algorithms.md
+++ b/episodes/optimisation-data-structures-algorithms.md
@@ -58,6 +58,11 @@ When an item is appended, the list checks whether it has enough spare space to a
 If it doesn't, it will re-allocate a larger array, copy across the elements, and deallocate the old array.
 The item to be appended is then copied to the end and the counter which tracks the list's length is incremented.
 
+<!-- Based on ICR-RSE's visual note: https://icr-rse-group.github.io/carpentry-pando-python/optimisation-data-structures-algorithms.html#lists -->
+![A visual diagram of list storage.](episodes/fig/list-append.png){alt="A list uses a contiguous block of memory, similar to an array, for storing the pointers to its elements. It is depicted as a series of five adjacent boxes, labelled 'P1' to 'P5', representing pointers to the list's elements.
+It can have additional storage beyond its length to make appends faster. An illustration shows the previous list with two extra empty boxes marked with question marks, indicating spare elements. Below, Python code `len(my_list) == 5` and `my_list.append(6)` is shown. After appending, the first of the previously empty boxes contains 'P6', and the last one remains empty. The length is now `len(my_list) == 6`.&#13;&#10;&#13;&#10;Appending to a full list causes it to grow. This makes some appends slower. An illustration depicts a full list with 'P1' through 'P7' in adjacent boxes and a label &quot;No spare elements!&quot;. Below, Python code `len(my_list) == 7` and `my_list.append(8)` is shown. The result is a new, larger continuous block of memory with 'P1' through 'P8' followed by a question mark in an additional box, indicating one spare element. The label &quot;2 new elements&quot; with curved arrows suggests that when the list grows, it typically allocates more memory than just the space for the new element.
+A concluding note states that a list will typically grow by 12.5%, hence shorter lists will grow more frequently when appending." }
+
 The amount the internal array grows by is dependent on the particular list implementation's growth factor.
 CPython for example uses [`newsize + (newsize >> 3) + 6`](https://github.com/python/cpython/blob/a571a2fd3fdaeafdfd71f3d80ed5a3b22b63d0f7/Objects/listobject.c#L74), which works out to an over allocation of roughly ~12.5%.
 
@@ -152,20 +157,20 @@ Since Python 3.6, the items within a dictionary will iterate in the order that t
 ### Hashing Data Structures
 
 Python's dictionaries are implemented as hashing data structures.
-Explaining how these work will get a bit technical, so let’s start with an analogy:
+Explaining how these work will get a bit technical, so let's start with an analogy:
 
 A Python list is like having a single long bookshelf. When you buy a new book (append a new element to the list), you place it at the far end of the shelf, right after all the previous books.
 
 ![A bookshelf corresponding to a Python list.](episodes/fig/bookshelf_list.jpg){alt="An image of a single long bookshelf, with a large number of books."}
 
-A hashing data structure is more like a bookcase with several shelves, labeled by genre (sci-fi, romance, children’s books, non-fiction, …) and author surname. When you buy a new book by Jules Verne, you might place it on the shelf labeled “Sci-Fi, V–Z”.
-And if you keep adding more books, at some point you’ll move to a larger bookcase with more shelves (and thus more fine-grained sorting), to make sure you don’t have too many books on a single shelf.
+A hashing data structure is more like a bookcase with several shelves, labelled by genre (sci-fi, romance, children's books, non-fiction,&nbsp;…) and author surname. When you buy a new book by Jules Verne, you might place it on the shelf labelled &quot;Sci-Fi, V–Z&quot;.
+And if you keep adding more books, at some point you'll move to a larger bookcase with more shelves (and thus more fine-grained sorting), to make sure you don't have too many books on a single shelf.
 
-![A bookshelf corresponding to a Python dictionary.](episodes/fig/bookshelf_dict.jpg){alt="An image of two bookcases, labelled “Sci-Fi” and “Romance”. Each bookcase contains shelves labelled in alphabetical order, with zero or few books on each shelf."}
+![A bookshelf corresponding to a Python dictionary.](episodes/fig/bookshelf_dict.jpg){alt="An image of two bookcases, labelled &quot;Sci-Fi&quot; and &quot;Romance&quot;. Each bookcase contains shelves labelled in alphabetical order, with zero or few books on each shelf."}
 
-Now, let's say a friend wanted to borrow the book "'—All You Zombies—'" by Robert Heinlein.
+Now, let's say a friend wanted to borrow the book &quot;'—All You Zombies—'&quot; by Robert Heinlein.
 If I had my books arranged on a single bookshelf (in a list), I would have to look through every book I own in order to find it.
-However, if I had a bookcase with several shelves (a hashing data structure), I know immediately that I need to check the shelf “Sci-Fi, G—J”, so I’d be able to find it much more quickly!
+However, if I had a bookcase with several shelves (a hashing data structure), I know immediately that I need to check the shelf &quot;Sci-Fi, G—J&quot;, so I'd be able to find it much more quickly!
 
 ::::::::::::::::::::::::::::::::::::: instructor
 
@@ -199,7 +204,7 @@ To retrieve or check for the existence of a key within a hashing data structure,
 
 ### Keys
 
-Keys will typically be a core Python type such as a number or string. However multiple of these can be combined as a Tuple to form a compound key, or a custom class can be used if the methods `__hash__()` and `__eq__()` have been implemented.
+Keys will typically be a core Python type such as a number or string. However, multiple of these can be combined as a Tuple to form a compound key, or a custom class can be used if the methods `__hash__()` and `__eq__()` have been implemented.
 
 You can implement `__hash__()` by utilising the ability for Python to hash tuples, avoiding the need to implement a bespoke hash function.
 
@@ -301,7 +306,7 @@ Constructing a set with a loop and `add()` (equivalent to a list's `append()`) c
 
 The naive list approach is 2200x times slower than the fastest approach, because of how many times the list is searched. This gap will only grow as the number of items increases.
 
-Sorting the input list reduces the cost of searching the output list significantly, however it is still 8x slower than the fastest approach. In part because around half of it's runtime is now spent sorting the list.
+Sorting the input list reduces the cost of searching the output list significantly, however it is still 8x slower than the fastest approach. In part because around half of its runtime is now spent sorting the list.
 
 ```output
 uniqueSet: 0.30ms
@@ -316,9 +321,9 @@ uniqueListSort: 2.67ms
 
 Independent of the performance to construct a unique set (as covered in the previous section), it's worth identifying the performance to search the data-structure to retrieve an item or check whether it exists.
 
-The performance of a hashing data structure is subject to the load factor and number of collisions. An item that hashes with no collision can be checked almost directly, whereas one with collisions will probe until it finds the correct item or an empty slot. In the worst possible case, whereby all insert items have collided this would mean checking every single item. In practice, hashing data-structures are designed to minimise the chances of this happening and most items should be found or identified as missing with a single access.
+The performance of a hashing data structure is subject to the load factor and number of collisions. An item that hashes with no collision can be accessed almost directly, whereas one with collisions will probe until it finds the correct item or an empty slot. In the worst possible case, whereby all insert items have collided this would mean checking every single item. In practice, hashing data-structures are designed to minimise the chances of this happening and most items should be found or identified as missing on the first attempt (without probing beyond the original hash).
 
-In contrast if searching a list or array, the default approach is to start at the first item and check all subsequent items until the correct item has been found. If the correct item is not present, this will require the entire list to be checked. Therefore the worst-case is similar to that of the hashing data-structure, however it is guaranteed in cases where the item is missing. Similarly, on-average we would expect an item to be found half way through the list, meaning that an average search will require checking half of the items.
+In contrast, if searching a list or array, the default approach is to start at the first item and check all subsequent items until the correct item has been found. If the correct item is not present, this will require the entire list to be checked. Therefore the worst-case is similar to that of the hashing data-structure, however it is guaranteed in cases where the item is missing. Similarly, on-average we would expect an item to be found halfway through the list, meaning that an average search will require checking half of the items.
 
 If however the list or array is sorted, a binary search can be used. A binary search divides the list in half and checks which half the target item would be found in, this continues recursively until the search is exhausted whereby the item should be found or dismissed. This is significantly faster than performing a linear search of the list, checking a total of `log N` items every time.
 
diff --git a/episodes/optimisation-introduction.md b/episodes/optimisation-introduction.md
index cfd8ee6a..af954c98 100644
--- a/episodes/optimisation-introduction.md
+++ b/episodes/optimisation-introduction.md
@@ -27,28 +27,29 @@ We'll talk briefly about some of these external bottlenecks at the end. For now,
 In order to optimise code for performance, it is necessary to have an understanding of what a computer is doing to execute it.
 
 <!-- Goal is to give you a high level understanding of how your code executes. You don't need to be an expert, even a vague general understanding will leave you in a stronger position. -->
-Even a high-level understanding of how you code executes, such as how Python and the most common data-structures and algorithms are implemented, can help you to identify suboptimal approaches when programming. If you have learned to write code informally out of necessity, to get something to work, it's not uncommon to have collected some "unpythonic" habits along the way that may harm your code's performance.
+A high-level understanding of how your code executes, such as how Python and the most common data-structures and algorithms are implemented, can help you identify suboptimal approaches when programming. If you have learned to write code informally out of necessity, to get something to work, it's not uncommon to have collected some "unpythonic" habits along the way that may harm your code's performance.
+
+<!-- This should be considered good practice that you can implement when first writing your code. -->
+These are the first steps in code optimisation, and knowledge you can put into practice by making more informed choices as you write your code and after profiling it.
 
 <!-- This is largely high-level/abstract knowledge applicable to the vast majority of programming languages, applies even more strongly if using compiled Python features like numba -->
 The remaining content is often abstract knowledge, that is transferable to the vast majority of programming languages. This is because the hardware architecture, data-structures and algorithms used are common to many languages and they hold some of the greatest influence over performance bottlenecks.
 
-## Premature Optimisation
+## Performance vs Maintainability
 
 > Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and these attempts at efficiency actually have a strong negative impact when debugging and maintenance are considered. We should forget about small efficiencies, say about 97% of the time: **premature optimization is the root of all evil**. Yet we should not pass up our opportunities in that critical 3%. - Donald Knuth
 
-This classic quote among computer scientists states; when considering optimisation it is important to focus on the potential impact, both to the performance and maintainability of the code.
-
-Profiling is a valuable tool in this cause. Should effort be expended to optimise a component which occupies 1% of the runtime? Or would that time be better spent focusing on the most expensive components?
+This classic quote among computer scientists emphasises the importance of considering both performance and maintainability when optimising code and prioritising your optimisations.
 
-Advanced optimisations, mostly outside the scope of this course, can increase the cost of maintenance by obfuscating what code is doing. Even if you are a solo-developer working on private code, your future self should be able to easily comprehend your implementation.
+While advanced optimisations may boost performance, they often come at the cost of making the code harder to understand and maintain. Even if you're working alone on private code, your future self should be able to easily understand the implementation. Hence, when optimising, always weigh the potential impact on both performance and maintainability. While this course does not cover most advanced optimisations, you may already be familiar with and using some.
 
-Therefore, the balance between the impact to both performance and maintainability should be considered when optimising code.
+Profiling is a valuable tool for prioritising optimisations. Should effort be expended to optimise a component which occupies 1% of the runtime? Or would that time be better spent optimising the most expensive components?
 
-This is not to say, don't consider performance when first writing code. The selection of appropriate algorithms and data-structures covered in this course form good practice, simply don't fret over a need to micro-optimise every small component of the code that you write.
+This doesn't mean you should ignore performance when initially writing code. Choosing the right algorithms and data structures, as we will discuss in this course, is good practice. However, there's no need to obsess over micro-optimising every tiny component of your code—focus on the bigger picture.
 
 ### Performance of Python
 
-If you've read about different programming languages, you may have heard that there’s a difference between “interpreted” languages (like Python) and "compiled" languages (like C). You may have heard that Python is slow *because* it is an interpreted language.
+If you've read about different programming languages, you may have heard that there’s a difference between "interpreted" languages (like Python) and "compiled" languages (like C). You may have heard that Python is slow *because* it is an interpreted language.
 To understand where this comes from (and how to get around it), let's talk a little bit about how Python works.
 
 ![Illustration of integers in C and Python.](episodes/fig/int-c-vs-py.png){alt="A diagram illustrating the difference between integers in C and Python. In C, the integer is a raw number in memory. In Python, it additionally contains a header with metadata."}
@@ -111,22 +112,17 @@ This usually makes your code more readable, too: When someone else reads your co
 ## Ensuring Reproducible Results
 
 <!-- This is also good practice when optimising your code, to ensure mistakes aren't made -->
-When optimising your code, you are making speculative changes. It's easy to make mistakes, many of which can be subtle. Therefore, it's important to have a strategy in place to check that the outputs remain correct.
+When optimising existing code, you're often making speculative changes, which can lead to subtle mistakes. To ensure that your optimisations aren't also introducing errors, it's crucial to have a strategy for checking that the results remain correct.
 
-Testing is hopefully already a seamless part of your research software development process.
-Test can be used to clarify how your software should perform, ensuring that new features work as intended and protecting against unintended changes to old functionality.
-
-There are a plethora of methods for testing code.
+Testing should already be an integral part of your development process. It helps clarify expected behaviour, ensures new features are working as intended, and protects against unintended regressions in previously working functionality. Always verify your changes through testing to ensure that the optimisations don’t compromise the correctness of your code.
 
 ## pytest Overview
 
-Most Python developers use the testing package [pytest](https://docs.pytest.org/en/latest/), it's a great place to get started if you're new to testing code.
+There are a plethora of methods for testing code. Most Python developers use the testing package [pytest](https://docs.pytest.org/en/latest/), it's a great place to get started if you're new to testing code.
 
 Here's a quick example of how a test can be used to check your function's output against an expected value.
 
-Tests should be created within a project's testing directory, by creating files named with the form `test_*.py` or `*_test.py`.
-
-pytest looks for these files, when running the test suite.
+Tests should be created within a project's testing directory, by creating files named with the form `test_*.py` or `*_test.py`. pytest looks for file names with these patterns when running the test suite.
 
 Within the created test file, any functions named in the form `test*` are considered tests that will be executed by pytest.
 
diff --git a/episodes/optimisation-memory.md b/episodes/optimisation-memory.md
index 46d3a947..2650d9c8 100644
--- a/episodes/optimisation-memory.md
+++ b/episodes/optimisation-memory.md
@@ -242,7 +242,6 @@ Within Python memory is not explicitly allocated and deallocated, instead it is
 The below implementation of the [heat-equation](https://en.wikipedia.org/wiki/Heat_equation), reallocates `out_grid`, a large 2 dimensional (500x500) list each time `update()` is called which progresses the model.
 
 ```python
-import time
 grid_shape = (512, 512)
 
 def update(grid, a_dt):
@@ -291,7 +290,6 @@ Line #      Hits         Time  Per Hit   % Time  Line Contents
 If instead `out_grid` is double buffered, such that two buffers are allocated outside the function, which are swapped after each call to update().
 
 ```python
-import time
 grid_shape = (512, 512)
 
 def update(grid, a_dt, out_grid):
diff --git a/episodes/optimisation-numpy.md b/episodes/optimisation-numpy.md
index 9f395285..9d617b99 100644
--- a/episodes/optimisation-numpy.md
+++ b/episodes/optimisation-numpy.md
@@ -35,7 +35,7 @@ It adds restriction via its own [basic numeric types](https://numpy.org/doc/stab
 
 ### NumPy Arrays and Python Lists Live in Two Separate Worlds
 
-NumPy's arrays (not to be confused with the core Python `array` package) are static arrays. Unlike core Python's lists, they do not dynamically resize. Therefore if you wish to append to a NumPy array, you must call `resize()` first. If you treat this like `append()` for a Python list, resizing for each individual append, you will be performing significantly more copies and memory allocations than a Python list.
+NumPy's arrays (not to be confused with the core Python `array` package) are static arrays. Unlike core Python's lists, they do not dynamically resize. Therefore, if you wish to append to a NumPy array, you must call `resize()` first. If you treat this like `append()` for a Python list, resizing for each individual append, you will be performing significantly more copies and memory allocations than a Python list.
 
 The below example sees lists and arrays constructed from `range(100000)`.
 
@@ -225,11 +225,6 @@ print(f"numpy_sum_array: {timeit(np_sum_ar, setup=gen_array, number=repeats):.2f
 print(f"numpy_dot_array: {timeit(np_dot_ar, setup=gen_array, number=repeats):.2f}ms")
 ```
 
-* `python_sum_list` uses list comprehension to perform the multiplication, followed by the Python core `sum()`. This comes out at 46.93ms
-* `python_sum_array` instead directly multiplies the two arrays (taking advantage of NumPy's vectorisation) but uses the core Python `sum()`, this comes in slightly faster at 33.26ms.
-* `numpy_sum_array` again takes advantage of NumPy's vectorisation for the multiplication, and additionally uses NumPy's `sum()` implementation. These two rounds of vectorisation provide a much faster 1.44ms completion.
-* `numpy_dot_array` instead uses NumPy's `dot()` to calculate the dot product in a single operation. This comes out the fastest at 0.29ms, 162x faster than `python_sum_list`. 
-
 ```output
 python_sum_list: 46.93ms
 python_sum_array: 33.26ms
@@ -237,6 +232,12 @@ numpy_sum_array: 1.44ms
 numpy_dot_array: 0.29ms
 ```
 
+* `python_sum_list` uses list comprehension to perform the multiplication, followed by the Python core `sum()`. This comes out at 46.93ms
+* `python_sum_array` instead directly multiplies the two arrays (taking advantage of NumPy's vectorisation) but uses the core Python `sum()`, this comes in slightly faster at 33.26ms.
+* `numpy_sum_array` again takes advantage of NumPy's vectorisation for the multiplication, and additionally uses NumPy's `sum()` implementation. These two rounds of vectorisation provide a much faster 1.44ms completion.
+* `numpy_dot_array` instead uses NumPy's `dot()` to calculate the dot product in a single operation. This comes out the fastest at 0.29ms, 162x faster than `python_sum_list`. 
+
+
 ::::::::::::::::::::::::::::::::::::: callout
 
 ## Parallel NumPy
@@ -248,7 +249,7 @@ A small number of functions are backed by BLAS and LAPACK, enabling even greater
 
 The [supported functions](https://numpy.org/doc/stable/reference/routines.linalg.html) mostly correspond to linear algebra operations like `numpy.dot()`.
 
-The auto-parallelisation of these functions is hardware dependant, so you won't always automatically get the additional benefit of parallelisation.
+The auto-parallelisation of these functions is hardware-dependent, so you won't always automatically get the additional benefit of parallelisation.
 However, HPC systems should be primed to take advantage, so try increasing the number of cores you request when submitting your jobs and see if it improves the performance.
 
 *This might be why `numpy_dot_array` is that much faster than `numpy_sum_array` in the previous example!*
@@ -453,7 +454,7 @@ vectorize: 1.48ms
 
 It won't always be possible to take full advantage of vectorisation, for example you may have conditional logic.
 
-An alternate approach is converting your dataframe to a Python dictionary using `to_dict(orient='index')`. This creates a nested dictionary, where each row of the outer dictionary is an internal dictionary. This can then be processed via list-comprehension:
+An alternate approach is converting your DataFrame to a Python dictionary using `to_dict(orient='index')`. This creates a nested dictionary, where each row of the outer dictionary is an internal dictionary. This can then be processed via list-comprehension:
 
 ```python
 def to_dict():
@@ -470,7 +471,7 @@ Whilst still nearly 100x slower than pure vectorisation, it's twice as fast as `
 to_dict: 131.15ms
 ```
 
-This is because indexing into Pandas' `Series` (rows) is significantly slower than a Python dictionary. There is a slight overhead to creating the dictionary (40ms in this example), however the stark difference in access speed is more than enough to overcome that cost for any large dataframe.
+This is because indexing into Pandas' `Series` (rows) is significantly slower than a Python dictionary. There is a slight overhead to creating the dictionary (40ms in this example), however the stark difference in access speed is more than enough to overcome that cost for any large DataFrame.
 
 ```python
 from timeit import timeit
diff --git a/episodes/optimisation-using-python.md b/episodes/optimisation-using-python.md
index 85e2550c..a7f28b11 100644
--- a/episodes/optimisation-using-python.md
+++ b/episodes/optimisation-using-python.md
@@ -91,7 +91,7 @@ In particular, those which are passed an `iterable` (e.g. lists) are likely to p
 
 ::::::::::::::::::::::::::::::::::::: callout
 
-The built-in functions [`filter()`](https://docs.python.org/3/library/functions.html#filter) and [`map()`](https://docs.python.org/3/library/functions.html#map) can be used for processing iterables. However list-comprehension is likely to be more performant.
+The built-in functions [`filter()`](https://docs.python.org/3/library/functions.html#filter) and [`map()`](https://docs.python.org/3/library/functions.html#map) can be used for processing iterables. However, list-comprehension is likely to be more performant.
 
 <!-- Would this benefit from an example? -->
 
@@ -103,11 +103,11 @@ It is often best to tell the interpreter/library at a high level *what you want*
 
 ## Example: Searching an element in a list
 
-A simple example of this would be to perform a linear search of a list. (Though as we’ll see in the next section, there’s a much more effective way to do this!)
-The below example creates a list of 2500 integers in the inclusive-exclusive range `[0, 5000)`.
-It then searches for all of the even numbers in that range.
-`manualSearch()` is implemented manually, iterating `ls` checking each individual item in Python code.
-`operatorSearch()` in contrast uses the `in` keyword to perform each search, which allows CPython to implement the inner loop in its C back-end.
+A simple example of this is performing a linear search on a list. (Though as we’ll see in the next section, this isn't the most efficient approach!)
+In the following example, we create a list of 2500 integers in the (inclusive-exclusive) range `[0, 5000)`.
+The goal is to search for all even numbers within that range.
+
+The function `manualSearch()` manually iterates through the list (`ls`) and checks each individual item using Python code. On the other hand, `operatorSearch()` uses the `in` operator to perform each search, which allows CPython to implement the inner loop in its C back-end.
 
 ```python
 import random
diff --git a/episodes/profiling-functions.md b/episodes/profiling-functions.md
index 54ba38b6..3bdb227b 100644
--- a/episodes/profiling-functions.md
+++ b/episodes/profiling-functions.md
@@ -46,7 +46,7 @@ As a stack it is a last-in first-out (LIFO) data structure.
 
 ![A diagram of a call stack](fig/stack.png){alt="A greyscale diagram showing a (call)stack, containing 5 stack frame. Two additional stack frames are shown outside the stack, one is marked as entering the call stack with an arrow labelled push and the other is marked as exiting the call stack labelled pop."}
 
-When a function is called, a frame to track it's variables and metadata is pushed to the call stack.
+When a function is called, a frame to track its variables and metadata is pushed to the call stack.
 When that same function finishes and returns, it is popped from the stack and variables local to the function are dropped.
 
 If you've ever seen a stack overflow error, this refers to the call stack becoming too large.
@@ -88,7 +88,7 @@ Hence, this prints the following call stack:
     traceback.print_stack()
 ```
 
-The first line states the file and line number where `a()` was called from (the last line of code in the file shown). The second line states that it was the function `a()` that was called, this could include it's arguments. The third line then repeats this pattern, stating the line number where `b2()` was called inside `a()`. This continues until the call to `traceback.print_stack()` is reached.
+The first line states the file and line number where `a()` was called from (the last line of code in the file shown). The second line states that it was the function `a()` that was called, this could include its arguments. The third line then repeats this pattern, stating the line number where `b2()` was called inside `a()`. This continues until the call to `traceback.print_stack()` is reached.
 
 You may see stack traces like this when an unhandled exception is thrown by your code.
 
@@ -102,7 +102,7 @@ You may see stack traces like this when an unhandled exception is thrown by your
 [`cProfile`](https://docs.python.org/3/library/profile.html#instant-user-s-manual) is a function-level profiler provided as part of the Python standard library.
 
 <!-- How is it used? -->
-It can be called directly within your Python code as an imported package, however it's easier to use it's script interface:
+It can be called directly within your Python code as an imported package, however it's easier to use its script interface:
 
 ```sh
 python -m cProfile -o <output file> <script name> <arguments>
@@ -199,9 +199,9 @@ In the icicle visualization style functions are represented by rectangles. A roo
 -->
 
 The icicle diagram displayed by `snakeviz` represents an aggregate of the call stack during the execution of the profiled code.
-The box which fills the top row represents the the root call, filling the row shows that it occupied 100% of the runtime.
+The box which fills the top row represents the root call, filling the row shows that it occupied 100% of the runtime.
 The second row holds the child methods called from the root, with their widths relative to the proportion of runtime they occupied.
-This continues with each subsequent row, however where a method only occupies 50% of the runtime, it's children can only occupy a maximum of that runtime hence the appearance of "icicles" as each row gets narrower when the overhead of methods with no further children is accounted for.
+This continues with each subsequent row, however where a method only occupies 50% of the runtime, its children can only occupy a maximum of that runtime hence the appearance of "icicles" as each row gets narrower when the overhead of methods with no further children is accounted for.
 
 By clicking a box within the diagram, it will "zoom" making the selected box the root allowing more detail to be explored. The diagram is limited to 10 rows by default ("Depth") and methods with a relatively small proportion of the runtime are hidden ("Cutoff").
 
@@ -211,9 +211,9 @@ As you hover each box, information to the left of the diagram updates specifying
 
 ## snakeviz Inside Notebooks
 
-If you're more familiar with writing Python inside Jupyter notebooks you can still use  `snakeviz` directly from inside notebooks using the notebooks "magic" prefix (`%`) and it will automatically call `cProfile` for you.
+If you're more familiar with writing Python inside Jupyter notebooks you can still use `snakeviz` directly from inside notebooks using the notebooks "magic" prefix (`%`) and it will automatically call `cProfile` for you.
 
-First `snakeviz` must be installed and it's extension loaded.
+First `snakeviz` must be installed and its extension loaded.
 
 ```py
 !pip install snakeviz
@@ -239,7 +239,7 @@ my_function()
 
 In both cases, the full `snakeviz` profile visualisation will appear as an output within the notebook!
 
-*You may wish to right click the top of the output, and select "Disable Scrolling for Outputs" to expand it's box if it begins too small.*
+*You may wish to right click the top of the output, and select "Disable Scrolling for Outputs" to expand its box if it begins too small.*
 
 :::::::::::::::::::::::::::::::::::::::::::::
 
@@ -323,7 +323,7 @@ As `time.sleep()` is a core Python method it is displayed as "built-in method" a
 
 If you hover any boxes representing the methods from the above code, you will see file and line properties completed. The directory property remains empty as the profiled code was in the root of the working directory. A profile of a large project with many files across multiple directories will see this filled.
 
-Find the box representing `c_2()` on the icicle diagram, it's children are unlabelled because they are not wide enough (but they can still be hovered). Clicking `c_2()` zooms in the diagram, showing the children to be `time.sleep()` and `d_1()`.
+Find the box representing `c_2()` on the icicle diagram, its children are unlabelled because they are not wide enough (but they can still be hovered). Clicking `c_2()` zooms in the diagram, showing the children to be `time.sleep()` and `d_1()`.
 
 To zoom back out you can either click the top row, which will zoom out one layer, or click "Reset Zoom" on the left-hand side.
 
diff --git a/episodes/profiling-introduction.md b/episodes/profiling-introduction.md
index ad58dc4e..8321dd44 100644
--- a/episodes/profiling-introduction.md
+++ b/episodes/profiling-introduction.md
@@ -41,7 +41,7 @@ Increasingly, particularly with relation to HPC, attention is being paid to the
 
 Profiling is most relevant to working code, when you have reached a stage that the code works and are considering deploying it.
 
-Any code that will run for more than a few minutes over it's lifetime, that isn't a quick one-shot script can benefit from profiling.
+Any code that will run for more than a few minutes over its lifetime and isn't a quick one-off script can benefit from profiling.
 
 Profiling should be a relatively quick and inexpensive process. If there are no significant bottlenecks in your code you can quickly be confident that your code is reasonably optimised. If you do identify a concerning bottleneck, further work to optimise your code and reduce the bottleneck could see significant improvements to the performance of your code and hence productivity.
 
@@ -124,7 +124,7 @@ Therefore, it is better described as a tool for **benchmarking**.
 Software is typically comprised of a hierarchy of function calls, both functions written by the developer and those used from the language's standard library and third party packages.
 
 <!-- What -->
-Function-level profiling analyses where time is being spent with respect to functions. Typically function-level profiling will calculate the number of times each function is called and the total time spent executing each function, inclusive and exclusive of child function calls.
+Function-level profiling analyses where time is being spent with respect to functions. Typically, function-level profiling will calculate the number of times each function is called and the total time spent executing each function, inclusive and exclusive of child function calls.
 
 <!-- Why -->
 This allows functions that occupy a disproportionate amount of the total runtime to be quickly identified and investigated.
@@ -156,7 +156,7 @@ Line-level profiling can be particularly expensive, a program can execute hundre
 
 `line_profiler` is deterministic, meaning that it tracks every line of code executed. To avoid it being too costly, the profiling is restricted to methods targeted with the decorator `@profile`.
 
-In contrast [`scalene`](https://github.com/plasma-umass/scalene) is a more advanced Python profiler capable of line-level profiling. It uses a sampling based approach, whereby the profiler halts and samples the line of code currently executing thousands of times per second. This reduces the cost of profiling, whilst still maintaining representative metrics for the most expensive components.
+In contrast, [`scalene`](https://github.com/plasma-umass/scalene) is a more advanced Python profiler capable of line-level profiling. It uses a sampling based approach, whereby the profiler halts and samples the line of code currently executing thousands of times per second. This reduces the cost of profiling, whilst still maintaining representative metrics for the most expensive components.
 
 :::::::::::::::::::::::::::::::::::::::::::::
 
@@ -165,7 +165,7 @@ In contrast [`scalene`](https://github.com/plasma-umass/scalene) is a more advan
 Timeline profiling takes a different approach to visualising where time is being spent during execution.
 
 <!-- What -->
-Typically a subset of function-level profiling, the execution of the profiled software is instead presented as a timeline highlighting the order of function execution in addition to the time spent in each individual function call.
+Typically, a subset of function-level profiling, the execution of the profiled software is instead presented as a timeline highlighting the order of function execution in addition to the time spent in each individual function call.
 
 <!-- Why -->
 By highlighting individual functions calls, patterns relating to how performance scales over time can be identified. These would be hidden with the aforementioned aggregate approaches.
@@ -202,7 +202,7 @@ Ideally, it should take no more than a few minutes to run the profiled test-case
 
 <!-- For example -->
 <!-- I don't really like this paragraph -->
-For example, you may have a model which normally simulates a year in hourly timesteps.
+For example, you may have a model which normally simulates a year in hourly time-steps.
 It would be appropriate to begin by profiling the simulation of a single day.
 If the model scales over time, such as due to population growth, it may be pertinent to profile a single day later into a simulation if the model can be resumed or configured.
 A larger population is likely to amplify any bottlenecks that scale with the population, making them easier to identify.
@@ -215,7 +215,7 @@ A larger population is likely to amplify any bottlenecks that scale with the pop
 Think about a project where you've been working with Python.
 Do you know where the time during execution is being spent?
 
-Write a short plan of the approach you would take to investigate and confirm where the majority of time is being spent during it's execution.
+Write a short plan of the approach you would take to investigate and confirm where the majority of time is being spent during its execution.
 
 <!-- TODO should they share this anywhere, should it be discussed within the group? -->
 
@@ -233,7 +233,7 @@ Write a short plan of the approach you would take to investigate and confirm whe
 ::::::::::::::::::::::::::::::::::::: keypoints
 
 - 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.
+- Code should be profiled when ready for deployment if it will be running for more than a few minutes during its lifetime.
 - There are several types of profiler each with slightly different purposes.
     - function-level: `cProfile` (visualised with `snakeviz`)
     - line-level: `line_profiler`
diff --git a/episodes/profiling-lines.md b/episodes/profiling-lines.md
index b0efeead..2efaaf63 100644
--- a/episodes/profiling-lines.md
+++ b/episodes/profiling-lines.md
@@ -27,7 +27,7 @@ exercises: 30
 Whilst profiling, you may find that function-level profiling highlights expensive methods where you can't easily determine the cause of the cost due to their complexity.
 
 <!-- What -->
-Line level profiling allows you to target specific methods to collect more granular metrics, which can help narrow the source of expensive computation further. Typically line-level profiling will calculate the number of times each line is called and the total time spent executing each line. However, with the increased granularity come increased collection costs, which is why it's targeted to specific methods.
+Line level profiling allows you to target specific methods to collect more granular metrics, which can help narrow the source of expensive computation further. Typically, line-level profiling will calculate the number of times each line is called and the total time spent executing each line. However, with the increased granularity come increased collection costs, which is why it's targeted to specific methods.
 
 <!-- Why -->
 This allows lines that occupy a disproportionate amount of the total runtime to be quickly identified and investigated.
@@ -263,7 +263,7 @@ The `-r` argument passed to `kernprof` (or `line_profiler`) enables rich output,
 
 ## line_profiler Inside Notebooks
 
-If you're more familiar with writing Python inside Jupyter notebooks you can, as with `snakeviz`, use `line_profiler` directly from inside notebooks. However it is still necessary for the code you wish to profile to be placed within a function.
+If you're more familiar with writing Python inside Jupyter notebooks you can, as with `snakeviz`, use `line_profiler` directly from inside notebooks. However, it is still necessary for the code you wish to profile to be placed within a function.
 
 First `line_profiler` must be installed and it's extension loaded.
 
@@ -282,7 +282,7 @@ The functions to be line profiled are specified with `-f <function name>`, this
 
 This is followed by calling the function which runs the full code to be profiled.
 
-For the above fizzbuzz example it would be:
+For the above FizzBuzz example it would be:
 
 ```py
 %lprun -f fizzbuzz fizzbuzz(100)
@@ -522,7 +522,7 @@ From the profiling output it can be seen that lines 285-287 occupy over 90% of t
 
 Given that the following line 289 only has a relative 0.6% time, it can be understood that the vast majority of times the condition `prey.life < PREY_HUNGER_THRESH` is evaluated it does not proceed.
 
-Remembering that this method is executed once per each of the 5000 `Grass` agents each step of the model, it could make sense to pre-filter `prey_list` once each timestep before it is passed to `Grass::eaten()`. This would greatly reduce the number of `Prey` iterated, reducing the cost of the method.
+Remembering that this method is executed once per each of the 5000 `Grass` agents each step of the model, it could make sense to pre-filter `prey_list` once each time-step before it is passed to `Grass::eaten()`. This would greatly reduce the number of `Prey` iterated, reducing the cost of the method.
 
 :::::::::::::::::::::::::::::::::
 ::::::::::::::::::::::::::::::::::::::::::::::::
@@ -532,7 +532,7 @@ Remembering that this method is executed once per each of the 5000 `Grass` agent
 
 - Specific methods can be line-level profiled if decorated with `@profile` that is imported from `line_profiler`.
 - `kernprof` executes `line_profiler` via `python -m kernprof -lvr <script name> <arguments>`.
-- Code in global scope must wrapped in a method if it is to be profiled with `line_profiler`.
+- Code in global scope must be wrapped in a method if it is to be profiled with `line_profiler`.
 - The output from `line_profiler` lists the absolute and relative time spent per line for each targeted function.
 
 ::::::::::::::::::::::::::::::::::::::::::::::::
diff --git a/index.md b/index.md
index af9940b6..19650b92 100644
--- a/index.md
+++ b/index.md
@@ -26,7 +26,7 @@ If you would like to register to take the course, check the [registration inform
 <!-- Aim for 3-4 objectives for every 6 hours of training -->
 <!-- SMART Objectives
     - Specific
-    - Measureable
+    - Measurable
     - Attainable (within the span of the course)
     - Relevant
     - Time-bound (implicitly the length of the course)
@@ -35,7 +35,7 @@ If you would like to register to take the course, check the [registration inform
 After attending this training, participants will be able to:
 
 - identify the most expensive functions and lines of code using `cprofile` and `line_profiler`.
-- evaluate code to determine the limiting factors of it's performance.
+- evaluate code to determine the limiting factors of its performance.
 - recognise and implement optimisations for common limiting factors of performance.
 
 ::::::::::::::::::::::::::::::::::::::::::  prereq
diff --git a/learners/acknowledgements.md b/learners/acknowledgements.md
index 66068c0e..27c4506d 100644
--- a/learners/acknowledgements.md
+++ b/learners/acknowledgements.md
@@ -12,6 +12,8 @@ The initial materials were authored by [Robert Chisholm](https://www.sheffield.a
 
 Additional consulting was provided by James Kilbane a close friend (and general rubber duck).
 
+Anastasiia Shcherbakova and Mira Sarkis of [ICR-RSE](https://github.com/ICR-RSE-Group) contributed inspiration for the list append figure and a large number of typographic corrections and simplifications during the alpha phase of the course's development lifecycle.
+
 **Resources**
 
 Most of the content was drawn from the education and experience of the author, however the below resources provided inspiration:
diff --git a/learners/setup.md b/learners/setup.md
index add737a6..b320652b 100644
--- a/learners/setup.md
+++ b/learners/setup.md
@@ -22,8 +22,12 @@ This course uses Python and was developed using Python 3.11, therefore it is rec
 
 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 -->
+If you have `conda` available, you can create and activate a new environment named `py311_env` with Python 3.11 using the following command:
+
+```sh
+conda create --name py311_env python=3.11
+conda activate py311_env
+```
 
 The non-core Python packages required by the course are `pytest`, `snakeviz`, `line_profiler`, `numpy`, `pandas` and `matplotlib` which can be installed via `pip`.
  
@@ -51,13 +55,7 @@ pip install shapely
 If you are unable to install `line_profiler` via `pip` on MacOS. Instead it can be installed via `conda`.
 
 ```sh
-conda install line_profiler
-```
-
-It may first be necessary to enable conda-forge.
-
-```sh
-conda config --add channels conda-forge
+conda install -c conda-forge line_profiler
 ```
 ::::::::::::::::::::::::
 


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Review comments:
path: episodes/fig/list-append.png
user: login: JostMigenda
body: The text in 1. is incorrect (or at least misleading), since it makes lists look like NumPy arrays. (Cf. the figure comparing the two in the NumPy episode!) Lists use a continuous block of memory for storing _the pointers to_ individual elements.

Typos:
* “continous” ➔ “continuous” (in 1.)
* “it’s” ➔ “its” (in 2.)
* no comma after “list” in 3.
created_at: 2025-03-21T15:20:16Z
author_association: COLLABORATOR
diff_hunk: @@ -58,6 +58,9 @@ When an item is appended, the list checks whether it has enough spare space to a
 If it doesn't, it will re-allocate a larger array, copy across the elements, and deallocate the old array.
 The item to be appended is then copied to the end and the counter which tracks the list's length is incremented.
 
+<!-- Based on ICR-RSE's visual note: https://icr-rse-group.github.io/carpentry-pando-python/optimisation-data-structures-algorithms.html#lists -->
+![A visual diagram of list storage.](episodes/fig/list-append.png){alt="1. A list uses an array for storage, a continuous block of memory. (An array with 5 elements) 2. It can have additional storage, beyond it's length. This makes appending fast. (an array with 7 elements, 2 unoccupied elements are marked '?', '7' is then appended leaving 1 unoccupied element marked '?' in a second array) 3. Appending to a full list, causes it to grow. This makes some appends slower. (An array with 7 occupied elements, '9' is appended, the second array has 9 elements with the final element marked '?') A list will typically grow by 12.%, hence shorter lists will grow more frequently when appending." }
path: episodes/optimisation-data-structures-algorithms.md
user: login: JostMigenda
body: ```suggestion
![A visual diagram of list storage.](episodes/fig/list-append.png){alt="1. A list uses an array for storage, a continuous block of memory. (An array with 5 elements) 2. It can have additional storage, beyond its length. This makes appending fast. (an array with 7 elements, 2 unoccupied elements are marked '?', '7' is then appended leaving 1 unoccupied element marked '?' in a second array) 3. Appending to a full list, causes it to grow. This makes some appends slower. (An array with 7 occupied elements, '9' is appended, the second array has 9 elements with the final element marked '?') A list will typically grow by 12.5%, hence shorter lists will grow more frequently when appending." }
```
created_at: 2025-03-21T15:24:21Z
author_association: COLLABORATOR
diff_hunk: @@ -58,6 +58,9 @@ When an item is appended, the list checks whether it has enough spare space to a
 If it doesn't, it will re-allocate a larger array, copy across the elements, and deallocate the old array.
 The item to be appended is then copied to the end and the counter which tracks the list's length is incremented.
 
+<!-- Based on ICR-RSE's visual note: https://icr-rse-group.github.io/carpentry-pando-python/optimisation-data-structures-algorithms.html#lists -->
+![A visual diagram of list storage.](episodes/fig/list-append.png){alt="1. A list uses an array for storage, a continuous block of memory. (An array with 5 elements) 2. It can have additional storage, beyond it's length. This makes appending fast. (an array with 7 elements, 2 unoccupied elements are marked '?', '7' is then appended leaving 1 unoccupied element marked '?' in a second array) 3. Appending to a full list, causes it to grow. This makes some appends slower. (An array with 7 occupied elements, '9' is appended, the second array has 9 elements with the final element marked '?') A list will typically grow by 12.%, hence shorter lists will grow more frequently when appending." }
path: episodes/optimisation-data-structures-algorithms.md
user: login: JostMigenda
body: I think it’s slightly harder than necessary to follow the alt text, because of the offset between the number of elements and their value. It might be a little nicer to start the elements in the illustration with `1`?
created_at: 2025-03-21T15:28:16Z
author_association: COLLABORATOR
diff_hunk: @@ -152,20 +155,20 @@ Since Python 3.6, the items within a dictionary will iterate in the order that t
 ### Hashing Data Structures
 
 Python's dictionaries are implemented as hashing data structures.
-Explaining how these work will get a bit technical, so let’s start with an analogy:
+Explaining how these work will get a bit technical, so let's start with an analogy:
 
 A Python list is like having a single long bookshelf. When you buy a new book (append a new element to the list), you place it at the far end of the shelf, right after all the previous books.
 
 ![A bookshelf corresponding to a Python list.](episodes/fig/bookshelf_list.jpg){alt="An image of a single long bookshelf, with a large number of books."}
 
-A hashing data structure is more like a bookcase with several shelves, labeled by genre (sci-fi, romance, children’s books, non-fiction, …) and author surname. When you buy a new book by Jules Verne, you might place it on the shelf labeled “Sci-Fi, V–Z”.
-And if you keep adding more books, at some point you’ll move to a larger bookcase with more shelves (and thus more fine-grained sorting), to make sure you don’t have too many books on a single shelf.
+A hashing data structure is more like a bookcase with several shelves, labelled by genre (sci-fi, romance, children's books, non-fiction,&nbsp;…) and author surname. When you buy a new book by Jules Verne, you might place it on the shelf labelled "Sci-Fi, V–Z".
+And if you keep adding more books, at some point you'll move to a larger bookcase with more shelves (and thus more fine-grained sorting), to make sure you don't have too many books on a single shelf.
 
-![A bookshelf corresponding to a Python dictionary.](episodes/fig/bookshelf_dict.jpg){alt="An image of two bookcases, labelled “Sci-Fi” and “Romance”. Each bookcase contains shelves labelled in alphabetical order, with zero or few books on each shelf."}
+![A bookshelf corresponding to a Python dictionary.](episodes/fig/bookshelf_dict.jpg){alt="An image of two bookcases, labelled "Sci-Fi" and "Romance". Each bookcase contains shelves labelled in alphabetical order, with zero or few books on each shelf."}
path: episodes/optimisation-data-structures-algorithms.md
user: login: JostMigenda
body: Using straight double quotes in a double quoted string would probably break? Either use single quotes for the alt text or restore typographically correct quotation marks.
created_at: 2025-03-21T15:31:23Z
author_association: COLLABORATOR
diff_hunk: @@ -27,28 +27,29 @@ We'll talk briefly about some of these external bottlenecks at the end. For now,
 In order to optimise code for performance, it is necessary to have an understanding of what a computer is doing to execute it.
 
 <!-- Goal is to give you a high level understanding of how your code executes. You don't need to be an expert, even a vague general understanding will leave you in a stronger position. -->
-Even a high-level understanding of how you code executes, such as how Python and the most common data-structures and algorithms are implemented, can help you to identify suboptimal approaches when programming. If you have learned to write code informally out of necessity, to get something to work, it's not uncommon to have collected some "unpythonic" habits along the way that may harm your code's performance.
+A high-level understanding of how your code executes, such as how Python and the most common data-structures and algorithms are implemented, can help you identify suboptimal approaches when programming. If you have learned to write code informally out of necessity, to get something to work, it's not uncommon to have collected some "unpythonic" habits along the way that may harm your code's performance.
+
+<!-- This should be considered good practice that you can implement when first writing your code. -->
+These are the first steps in code optimisation, and knowledge you can put into practice by making more informed choices as you write your code and after profiling it.
 
 <!-- This is largely high-level/abstract knowledge applicable to the vast majority of programming languages, applies even more strongly if using compiled Python features like numba -->
 The remaining content is often abstract knowledge, that is transferable to the vast majority of programming languages. This is because the hardware architecture, data-structures and algorithms used are common to many languages and they hold some of the greatest influence over performance bottlenecks.
 
-## Premature Optimisation
+## Performance vs Maintainability
 
 > Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and these attempts at efficiency actually have a strong negative impact when debugging and maintenance are considered. We should forget about small efficiencies, say about 97% of the time: **premature optimization is the root of all evil**. Yet we should not pass up our opportunities in that critical 3%. - Donald Knuth
 
-This classic quote among computer scientists states; when considering optimisation it is important to focus on the potential impact, both to the performance and maintainability of the code.
-
-Profiling is a valuable tool in this cause. Should effort be expended to optimise a component which occupies 1% of the runtime? Or would that time be better spent focusing on the most expensive components?
+This classic quote among computer scientists emphasises the importance of considering both performance and maintainability when optimising code and prioritising your optimisations.
 
-Advanced optimisations, mostly outside the scope of this course, can increase the cost of maintenance by obfuscating what code is doing. Even if you are a solo-developer working on private code, your future self should be able to easily comprehend your implementation.
+While advanced optimisations may boost performance, they often come at the cost of making the code harder to understand and maintain. Even if you're working alone on private code, your future self should be able to easily understand the implementation. Hence, when optimising, always weigh the potential impact on both performance and maintainability.
path: episodes/optimisation-introduction.md
user: login: JostMigenda
body: Do we actually have any advanced optimisations in this course that hurt maintainability to improve performance? If there are just 1–2 such examples, maybe it’d be better to move this warning so it’s right next to those examples?

(Just a general note that came to mind while looking through the diff. Not something we need to change right now; but we should probably keep it in mind e.g. when working on #60.)
created_at: 2025-03-21T15:44:32Z
author_association: COLLABORATOR
diff_hunk: @@ -28,14 +28,14 @@ Packages like NumPy and Pandas work similarly: They have been written in compile
 
 [NumPy](https://numpy.org/) is a commonly used package for scientific computing, which provides a wide variety of methods.
 
-It adds restriction via its own [basic numeric types](https://numpy.org/doc/stable/user/basics.types.html) and static arrays to enable even greater performance than that of core Python. However if these restrictions are ignored, the performance can become significantly worse.
+It adds restriction via it's own [basic numeric types](https://numpy.org/doc/stable/user/basics.types.html) and static arrays to enable even greater performance than that of core Python. However if these restrictions are ignored, the performance can become significantly worse.
path: episodes/optimisation-numpy.md
user: login: JostMigenda
body: ```suggestion
It adds restriction via its own [basic numeric types](https://numpy.org/doc/stable/user/basics.types.html) and static arrays to enable even greater performance than that of core Python. However if these restrictions are ignored, the performance can become significantly worse.
```
created_at: 2025-03-21T16:05:11Z
author_association: COLLABORATOR
diff_hunk: @@ -379,7 +380,7 @@ Pandas' methods by default operate on columns. Each column or series can be thou
 
 Following the theme of this episode, iterating over the rows of a data frame using a `for` loop is not advised. The pythonic iteration will be slower than other approaches.
 
-Pandas allows its own methods to be applied to rows in many cases by passing `axis=1`, where available these functions should be preferred over manual loops. Where you can't find a suitable method, `apply()` can be used, which is similar to `map()`, to apply your own function to rows.
+Pandas allows it's own methods to be applied to rows in many cases by passing `axis=1`, where available these functions should be preferred over manual loops. Where you can't find a suitable method, `apply()` can be used, which is similar to `map()`, to apply your own function to rows.
path: episodes/optimisation-numpy.md
user: login: JostMigenda
body: ```suggestion
Pandas allows its own methods to be applied to rows in many cases by passing `axis=1`, where available these functions should be preferred over manual loops. Where you can't find a suitable method, `apply()` can be used, which is similar to `map()`, to apply your own function to rows.
```
created_at: 2025-03-21T16:06:44Z
author_association: COLLABORATOR
diff_hunk: @@ -41,7 +41,7 @@ Increasingly, particularly with relation to HPC, attention is being paid to the
 
 Profiling is most relevant to working code, when you have reached a stage that the code works and are considering deploying it.
 
-Any code that will run for more than a few minutes over it's lifetime, that isn't a quick one-shot script can benefit from profiling.
+Any code that will run for more than a few minutes over its lifetime, that isn't a quick one-shot script can benefit from profiling.
path: episodes/profiling-introduction.md
user: login: JostMigenda
body: ```suggestion
Any code that will run for more than a few minutes over its lifetime and isn't a quick one-off script can benefit from profiling.
```
Probably a matter of taste (and OED says “one-off” is mainly British English, while “one-shot” is mainly American English?), but this feels more natural to me.
created_at: 2025-03-21T16:16:29Z
author_association: COLLABORATOR
diff_hunk: @@ -22,8 +22,12 @@ This course uses Python and was developed using Python 3.11, therefore it is rec
 
 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 -->
+If you have `conda` available, you can create and activate a new environment named `py311_env` with Python 3.11 using the following command:
+
+```sh
+conda create --name py311_env python=3.11
+conda activate py311_env
path: learners/setup.md
user: login: JostMigenda
body: ```suggestion
If you have `conda` available, you can create and activate a new environment named `p_and_o` with Python 3.13 using the following command:

```sh
conda create --name p_and_o python=3.13
conda activate p_and_o
```
* Use the latest version of Python, like we tell learners to do in a later episode
* More meaningful name for the environment
created_at: 2025-03-21T16:24:40Z
author_association: COLLABORATOR
diff_hunk: @@ -152,20 +155,20 @@ Since Python 3.6, the items within a dictionary will iterate in the order that t
 ### Hashing Data Structures
 
 Python's dictionaries are implemented as hashing data structures.
-Explaining how these work will get a bit technical, so let’s start with an analogy:
+Explaining how these work will get a bit technical, so let's start with an analogy:
 
 A Python list is like having a single long bookshelf. When you buy a new book (append a new element to the list), you place it at the far end of the shelf, right after all the previous books.
 
 ![A bookshelf corresponding to a Python list.](episodes/fig/bookshelf_list.jpg){alt="An image of a single long bookshelf, with a large number of books."}
 
-A hashing data structure is more like a bookcase with several shelves, labeled by genre (sci-fi, romance, children’s books, non-fiction, …) and author surname. When you buy a new book by Jules Verne, you might place it on the shelf labeled “Sci-Fi, V–Z”.
-And if you keep adding more books, at some point you’ll move to a larger bookcase with more shelves (and thus more fine-grained sorting), to make sure you don’t have too many books on a single shelf.
+A hashing data structure is more like a bookcase with several shelves, labelled by genre (sci-fi, romance, children's books, non-fiction,&nbsp;…) and author surname. When you buy a new book by Jules Verne, you might place it on the shelf labelled "Sci-Fi, V–Z".
+And if you keep adding more books, at some point you'll move to a larger bookcase with more shelves (and thus more fine-grained sorting), to make sure you don't have too many books on a single shelf.
 
-![A bookshelf corresponding to a Python dictionary.](episodes/fig/bookshelf_dict.jpg){alt="An image of two bookcases, labelled “Sci-Fi” and “Romance”. Each bookcase contains shelves labelled in alphabetical order, with zero or few books on each shelf."}
+![A bookshelf corresponding to a Python dictionary.](episodes/fig/bookshelf_dict.jpg){alt="An image of two bookcases, labelled "Sci-Fi" and "Romance". Each bookcase contains shelves labelled in alphabetical order, with zero or few books on each shelf."}
path: episodes/optimisation-data-structures-algorithms.md
user: login: Robadob
body: Yeah, I've normally swapped the outer quotes for single quotes in these occasions, clearly messed this one up.
created_at: 2025-03-23T16:15:41Z
author_association: MEMBER
diff_hunk: @@ -152,20 +155,20 @@ Since Python 3.6, the items within a dictionary will iterate in the order that t
 ### Hashing Data Structures
 
 Python's dictionaries are implemented as hashing data structures.
-Explaining how these work will get a bit technical, so let’s start with an analogy:
+Explaining how these work will get a bit technical, so let's start with an analogy:
 
 A Python list is like having a single long bookshelf. When you buy a new book (append a new element to the list), you place it at the far end of the shelf, right after all the previous books.
 
 ![A bookshelf corresponding to a Python list.](episodes/fig/bookshelf_list.jpg){alt="An image of a single long bookshelf, with a large number of books."}
 
-A hashing data structure is more like a bookcase with several shelves, labeled by genre (sci-fi, romance, children’s books, non-fiction, …) and author surname. When you buy a new book by Jules Verne, you might place it on the shelf labeled “Sci-Fi, V–Z”.
-And if you keep adding more books, at some point you’ll move to a larger bookcase with more shelves (and thus more fine-grained sorting), to make sure you don’t have too many books on a single shelf.
+A hashing data structure is more like a bookcase with several shelves, labelled by genre (sci-fi, romance, children's books, non-fiction,&nbsp;…) and author surname. When you buy a new book by Jules Verne, you might place it on the shelf labelled "Sci-Fi, V–Z".
+And if you keep adding more books, at some point you'll move to a larger bookcase with more shelves (and thus more fine-grained sorting), to make sure you don't have too many books on a single shelf.
 
-![A bookshelf corresponding to a Python dictionary.](episodes/fig/bookshelf_dict.jpg){alt="An image of two bookcases, labelled “Sci-Fi” and “Romance”. Each bookcase contains shelves labelled in alphabetical order, with zero or few books on each shelf."}
+![A bookshelf corresponding to a Python dictionary.](episodes/fig/bookshelf_dict.jpg){alt="An image of two bookcases, labelled "Sci-Fi" and "Romance". Each bookcase contains shelves labelled in alphabetical order, with zero or few books on each shelf."}
path: episodes/optimisation-data-structures-algorithms.md
user: login: Robadob
body: Done a test build with single quotes and it outputs correctly.

The double quotes are automatically converted to `&quot;` (probably the real solution for nesting double quotes, I'd just forgot that html symbol existed (did remember `&nbsp;` above), given any apostrophes would break single quotes.)
created_at: 2025-03-23T16:22:39Z
author_association: MEMBER
diff_hunk: @@ -27,28 +27,29 @@ We'll talk briefly about some of these external bottlenecks at the end. For now,
 In order to optimise code for performance, it is necessary to have an understanding of what a computer is doing to execute it.
 
 <!-- Goal is to give you a high level understanding of how your code executes. You don't need to be an expert, even a vague general understanding will leave you in a stronger position. -->
-Even a high-level understanding of how you code executes, such as how Python and the most common data-structures and algorithms are implemented, can help you to identify suboptimal approaches when programming. If you have learned to write code informally out of necessity, to get something to work, it's not uncommon to have collected some "unpythonic" habits along the way that may harm your code's performance.
+A high-level understanding of how your code executes, such as how Python and the most common data-structures and algorithms are implemented, can help you identify suboptimal approaches when programming. If you have learned to write code informally out of necessity, to get something to work, it's not uncommon to have collected some "unpythonic" habits along the way that may harm your code's performance.
+
+<!-- This should be considered good practice that you can implement when first writing your code. -->
+These are the first steps in code optimisation, and knowledge you can put into practice by making more informed choices as you write your code and after profiling it.
 
 <!-- This is largely high-level/abstract knowledge applicable to the vast majority of programming languages, applies even more strongly if using compiled Python features like numba -->
 The remaining content is often abstract knowledge, that is transferable to the vast majority of programming languages. This is because the hardware architecture, data-structures and algorithms used are common to many languages and they hold some of the greatest influence over performance bottlenecks.
 
-## Premature Optimisation
+## Performance vs Maintainability
 
 > Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and these attempts at efficiency actually have a strong negative impact when debugging and maintenance are considered. We should forget about small efficiencies, say about 97% of the time: **premature optimization is the root of all evil**. Yet we should not pass up our opportunities in that critical 3%. - Donald Knuth
 
-This classic quote among computer scientists states; when considering optimisation it is important to focus on the potential impact, both to the performance and maintainability of the code.
-
-Profiling is a valuable tool in this cause. Should effort be expended to optimise a component which occupies 1% of the runtime? Or would that time be better spent focusing on the most expensive components?
+This classic quote among computer scientists emphasises the importance of considering both performance and maintainability when optimising code and prioritising your optimisations.
 
-Advanced optimisations, mostly outside the scope of this course, can increase the cost of maintenance by obfuscating what code is doing. Even if you are a solo-developer working on private code, your future self should be able to easily comprehend your implementation.
+While advanced optimisations may boost performance, they often come at the cost of making the code harder to understand and maintain. Even if you're working alone on private code, your future self should be able to easily understand the implementation. Hence, when optimising, always weigh the potential impact on both performance and maintainability.
path: episodes/optimisation-introduction.md
user: login: Robadob
body: > Do we actually have any advanced optimisations in this course that hurt maintainability to improve performance?

It's a good point, the only one that comes to mind is the edge-case pandas data-table unwrapping to a dict of dict. Python isn't really a language with lots of small bit fiddling things for tiny performance boosts.

This paragraph could perhaps be reframed (or the "outside the scope of this course" added back).
created_at: 2025-03-23T16:26:42Z
author_association: MEMBER
diff_hunk: @@ -22,8 +22,12 @@ This course uses Python and was developed using Python 3.11, therefore it is rec
 
 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 -->
+If you have `conda` available, you can create and activate a new environment named `py311_env` with Python 3.11 using the following command:
+
+```sh
+conda create --name py311_env python=3.11
+conda activate py311_env
path: learners/setup.md
user: login: Robadob
body: > More meaningful name for the environment

Agree, the original commented env create was named "pando"

> Use the latest version of Python,

In this case is it not better to specify `python` sans version? (I'm reluctant to be chasing keeping this up to date). Although the comment above does say the course was developed/tested with 3.11 hence most suitable (hence why I was fine with leaving this 3.11 in the manual merge).

https://docs.conda.io/projects/conda/en/latest/commands/install.html

> Conda attempts to install the newest versions of the requested packages.
created_at: 2025-03-23T16:32:31Z
author_association: MEMBER
path: episodes/fig/list-append.png
user: login: Robadob
body: Do you think it would be worth doing something like $P_1$, $P_2$, $P_3$ to be even clearer that they're pointers (without needing to make the figure massive to draw the data storage?)
created_at: 2025-03-23T16:41:17Z
author_association: MEMBER
path: episodes/fig/list-append.png
user: login: JostMigenda
body: I think that might be clearer, yes; let’s try it.

Also, a minor regression in the updated figure: In the red/purple text (“1 spare element”, “2 new elements”, etc.), “spare” and “new” should start with a lower case letter.
created_at: 2025-03-24T11:29:21Z
author_association: COLLABORATOR
diff_hunk: @@ -59,7 +59,7 @@ If it doesn't, it will re-allocate a larger array, copy across the elements, and
 The item to be appended is then copied to the end and the counter which tracks the list's length is incremented.
 
 <!-- Based on ICR-RSE's visual note: https://icr-rse-group.github.io/carpentry-pando-python/optimisation-data-structures-algorithms.html#lists -->
-![A visual diagram of list storage.](episodes/fig/list-append.png){alt="1. A list uses an array for storage, a continuous block of memory. (An array with 5 elements) 2. It can have additional storage, beyond it's length. This makes appending fast. (an array with 7 elements, 2 unoccupied elements are marked '?', '7' is then appended leaving 1 unoccupied element marked '?' in a second array) 3. Appending to a full list, causes it to grow. This makes some appends slower. (An array with 7 occupied elements, '9' is appended, the second array has 9 elements with the final element marked '?') A list will typically grow by 12.%, hence shorter lists will grow more frequently when appending." }
+![A visual diagram of list storage.](episodes/fig/list-append.png){alt="1. A list uses an array for storage, a continuous block of memory. (An array with 5 elements) 2. It can have additional storage, beyond its length. This makes appending fast. (an array with 7 elements, 2 unoccupied elements are marked '?', '7' is then appended leaving 1 unoccupied element marked '?' in a second array) 3. Appending to a full list, causes it to grow. This makes some appends slower. (An array with 7 occupied elements, '9' is appended, the second array has 9 elements with the final element marked '?') A list will typically grow by 12.5%, hence shorter lists will grow more frequently when appending." }
path: episodes/optimisation-data-structures-algorithms.md
user: login: JostMigenda
body: ```suggestion
![A visual diagram of list storage.](episodes/fig/list-append.png){alt="1. A list uses an array for storage, a continuous block of memory. (An array with 5 elements) 2. It can have additional storage, beyond its length. This makes appending fast. (an array with 7 elements, 2 unoccupied elements are marked '?', '6' is then appended leaving 1 unoccupied element marked '?' in a second array) 3. Appending to a full list causes it to grow. This makes some appends slower. (An array with 7 occupied elements, '8' is appended, the second array has 9 elements with the final element marked '?') A list will typically grow by 12.5%, hence shorter lists will grow more frequently when appending." }
```
Also, we should probably change e.g. `6` to `pointer #6` if that works well in the updated figure.
created_at: 2025-03-24T11:31:57Z
author_association: COLLABORATOR
diff_hunk: @@ -27,28 +27,29 @@ We'll talk briefly about some of these external bottlenecks at the end. For now,
 In order to optimise code for performance, it is necessary to have an understanding of what a computer is doing to execute it.
 
 <!-- Goal is to give you a high level understanding of how your code executes. You don't need to be an expert, even a vague general understanding will leave you in a stronger position. -->
-Even a high-level understanding of how you code executes, such as how Python and the most common data-structures and algorithms are implemented, can help you to identify suboptimal approaches when programming. If you have learned to write code informally out of necessity, to get something to work, it's not uncommon to have collected some "unpythonic" habits along the way that may harm your code's performance.
+A high-level understanding of how your code executes, such as how Python and the most common data-structures and algorithms are implemented, can help you identify suboptimal approaches when programming. If you have learned to write code informally out of necessity, to get something to work, it's not uncommon to have collected some "unpythonic" habits along the way that may harm your code's performance.
+
+<!-- This should be considered good practice that you can implement when first writing your code. -->
+These are the first steps in code optimisation, and knowledge you can put into practice by making more informed choices as you write your code and after profiling it.
 
 <!-- This is largely high-level/abstract knowledge applicable to the vast majority of programming languages, applies even more strongly if using compiled Python features like numba -->
 The remaining content is often abstract knowledge, that is transferable to the vast majority of programming languages. This is because the hardware architecture, data-structures and algorithms used are common to many languages and they hold some of the greatest influence over performance bottlenecks.
 
-## Premature Optimisation
+## Performance vs Maintainability
 
 > Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and these attempts at efficiency actually have a strong negative impact when debugging and maintenance are considered. We should forget about small efficiencies, say about 97% of the time: **premature optimization is the root of all evil**. Yet we should not pass up our opportunities in that critical 3%. - Donald Knuth
 
-This classic quote among computer scientists states; when considering optimisation it is important to focus on the potential impact, both to the performance and maintainability of the code.
-
-Profiling is a valuable tool in this cause. Should effort be expended to optimise a component which occupies 1% of the runtime? Or would that time be better spent focusing on the most expensive components?
+This classic quote among computer scientists emphasises the importance of considering both performance and maintainability when optimising code and prioritising your optimisations.
 
-Advanced optimisations, mostly outside the scope of this course, can increase the cost of maintenance by obfuscating what code is doing. Even if you are a solo-developer working on private code, your future self should be able to easily comprehend your implementation.
+While advanced optimisations may boost performance, they often come at the cost of making the code harder to understand and maintain. Even if you're working alone on private code, your future self should be able to easily understand the implementation. Hence, when optimising, always weigh the potential impact on both performance and maintainability.
path: episodes/optimisation-introduction.md
user: login: JostMigenda
body: Yep, a lot of the time, the more performant example code is also cleaner. Can we re-add the “outside the scope of this course”?
created_at: 2025-03-24T11:35:16Z
author_association: COLLABORATOR
diff_hunk: @@ -59,7 +59,7 @@ If it doesn't, it will re-allocate a larger array, copy across the elements, and
 The item to be appended is then copied to the end and the counter which tracks the list's length is incremented.
 
 <!-- Based on ICR-RSE's visual note: https://icr-rse-group.github.io/carpentry-pando-python/optimisation-data-structures-algorithms.html#lists -->
-![A visual diagram of list storage.](episodes/fig/list-append.png){alt="1. A list uses an array for storage, a continuous block of memory. (An array with 5 elements) 2. It can have additional storage, beyond it's length. This makes appending fast. (an array with 7 elements, 2 unoccupied elements are marked '?', '7' is then appended leaving 1 unoccupied element marked '?' in a second array) 3. Appending to a full list, causes it to grow. This makes some appends slower. (An array with 7 occupied elements, '9' is appended, the second array has 9 elements with the final element marked '?') A list will typically grow by 12.%, hence shorter lists will grow more frequently when appending." }
+![A visual diagram of list storage.](episodes/fig/list-append.png){alt="1. A list uses an array for storage, a continuous block of memory. (An array with 5 elements) 2. It can have additional storage, beyond its length. This makes appending fast. (an array with 7 elements, 2 unoccupied elements are marked '?', '7' is then appended leaving 1 unoccupied element marked '?' in a second array) 3. Appending to a full list, causes it to grow. This makes some appends slower. (An array with 7 occupied elements, '9' is appended, the second array has 9 elements with the final element marked '?') A list will typically grow by 12.5%, hence shorter lists will grow more frequently when appending." }
path: episodes/optimisation-data-structures-algorithms.md
user: login: Robadob
body: I've merged this suggestion, but the alt text feels very unweildy.

Would it better to just describe what the figure demonstrates verbaly? (e.g. this was generated by AI)

> A list uses a contiguous block of memory, similar to an array, for storing the pointers to its elements. It is depicted as a series of five adjacent boxes, each containing 'P' with a subscript from 1 to 5, representing pointers to the list's elements.
> 
> It can have additional storage, beyond its length. This makes appending fast. An illustration shows the previous list with two extra empty boxes marked with question marks, indicating spare elements. Below, Python code `len(my_list) == 5` and `my_list.append(6)` is shown. An arrow points to the list after appending, now showing 'P6' in the first of the previously empty boxes, and one remaining empty box. The length is now `len(my_list) == 6`.
> 
> Appending to a full list causes it to grow. This makes some appends slower. An illustration depicts a full list with 'P1' through 'P7' in adjacent boxes and a label &quot;No spare elements!&quot;. Below, Python code `len(my_list) == 7` and `my_list.append(8)` is shown. An arrow indicates the result: a new, larger contiguous block of memory with 'P1' through 'P8' followed by a question mark in an additional box, indicating one spare element. The label &quot;2 new elements&quot; with curved arrows suggests that when the list grows, it typically allocates more memory than just the space for the new element.
> 
> A concluding note states that a list will typically grow by 12.5%, hence shorter lists will grow more frequently when appending.
created_at: 2025-03-24T11:49:39Z
author_association: MEMBER
diff_hunk: @@ -27,28 +27,29 @@ We'll talk briefly about some of these external bottlenecks at the end. For now,
 In order to optimise code for performance, it is necessary to have an understanding of what a computer is doing to execute it.
 
 <!-- Goal is to give you a high level understanding of how your code executes. You don't need to be an expert, even a vague general understanding will leave you in a stronger position. -->
-Even a high-level understanding of how you code executes, such as how Python and the most common data-structures and algorithms are implemented, can help you to identify suboptimal approaches when programming. If you have learned to write code informally out of necessity, to get something to work, it's not uncommon to have collected some "unpythonic" habits along the way that may harm your code's performance.
+A high-level understanding of how your code executes, such as how Python and the most common data-structures and algorithms are implemented, can help you identify suboptimal approaches when programming. If you have learned to write code informally out of necessity, to get something to work, it's not uncommon to have collected some "unpythonic" habits along the way that may harm your code's performance.
+
+<!-- This should be considered good practice that you can implement when first writing your code. -->
+These are the first steps in code optimisation, and knowledge you can put into practice by making more informed choices as you write your code and after profiling it.
 
 <!-- This is largely high-level/abstract knowledge applicable to the vast majority of programming languages, applies even more strongly if using compiled Python features like numba -->
 The remaining content is often abstract knowledge, that is transferable to the vast majority of programming languages. This is because the hardware architecture, data-structures and algorithms used are common to many languages and they hold some of the greatest influence over performance bottlenecks.
 
-## Premature Optimisation
+## Performance vs Maintainability
 
 > Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and these attempts at efficiency actually have a strong negative impact when debugging and maintenance are considered. We should forget about small efficiencies, say about 97% of the time: **premature optimization is the root of all evil**. Yet we should not pass up our opportunities in that critical 3%. - Donald Knuth
 
-This classic quote among computer scientists states; when considering optimisation it is important to focus on the potential impact, both to the performance and maintainability of the code.
-
-Profiling is a valuable tool in this cause. Should effort be expended to optimise a component which occupies 1% of the runtime? Or would that time be better spent focusing on the most expensive components?
+This classic quote among computer scientists emphasises the importance of considering both performance and maintainability when optimising code and prioritising your optimisations.
 
-Advanced optimisations, mostly outside the scope of this course, can increase the cost of maintenance by obfuscating what code is doing. Even if you are a solo-developer working on private code, your future self should be able to easily comprehend your implementation.
+While advanced optimisations may boost performance, they often come at the cost of making the code harder to understand and maintain. Even if you're working alone on private code, your future self should be able to easily understand the implementation. Hence, when optimising, always weigh the potential impact on both performance and maintainability.
path: episodes/optimisation-introduction.md
user: login: Robadob
body: Difficult to add that back into the new paragraph. So I've added the sentence

>  Such advanced optimisations are mostly outside the scope of this course, but you may already know some.

I think this better covers why it's worth mentioning even if we don't teach. (although "know some" doesn't feel too eloquent).

**Edit:**

AI reword to the rescue

>  While this course does not cover most advanced optimisations, you may already be familiar and using with some.

created_at: 2025-03-24T11:59:09Z
author_association: MEMBER
diff_hunk: @@ -22,8 +22,12 @@ This course uses Python and was developed using Python 3.11, therefore it is rec
 
 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 -->
+If you have `conda` available, you can create and activate a new environment named `py311_env` with Python 3.11 using the following command:
+
+```sh
+conda create --name py311_env python=3.11
+conda activate py311_env
path: learners/setup.md
user: login: JostMigenda
body: > In this case is it not better to specify `python` sans version?

Most of the time, yes; however, there’s always a risk in the first weeks after a major Python release that one of the third-party packages with binary extensions used in the course doesn’t have compatible wheels available yet. For example, Python 3.13 came out in October 2024 and `line_profiler` published the first release with compatible wheels [in early December](https://pypi.org/project/line-profiler/4.2.0/#history).
(It’s possible that the sdist of the previous release would compile without issues; however, I wouldn’t want to rely on that working reliably on all learners’ laptops.)

More generally, I think it’d be good to briefly go over the course after every major Python release, to make sure it’s still up to date. (Especially in the next few releases, where free-threading and the JIT will potentially affect performance characteristics significantly.)

If you prefer, we can keep 3.11 here for now and open a new issue (feel free to assign to me!) to update to 3.13 across the board.
created_at: 2025-03-24T12:01:08Z
author_association: COLLABORATOR
diff_hunk: @@ -22,8 +22,12 @@ This course uses Python and was developed using Python 3.11, therefore it is rec
 
 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 -->
+If you have `conda` available, you can create and activate a new environment named `py311_env` with Python 3.11 using the following command:
+
+```sh
+conda create --name py311_env python=3.11
+conda activate py311_env
path: learners/setup.md
user: login: Robadob
body: I'll setup an issue, would be good to have a documented process for updating Python version e.g. if we're keen for things to be tested, may be possible to semi-automate this with CI.
created_at: 2025-03-24T12:05:48Z
author_association: MEMBER
diff_hunk: @@ -59,7 +59,7 @@ If it doesn't, it will re-allocate a larger array, copy across the elements, and
 The item to be appended is then copied to the end and the counter which tracks the list's length is incremented.
 
 <!-- Based on ICR-RSE's visual note: https://icr-rse-group.github.io/carpentry-pando-python/optimisation-data-structures-algorithms.html#lists -->
-![A visual diagram of list storage.](episodes/fig/list-append.png){alt="1. A list uses an array for storage, a continuous block of memory. (An array with 5 elements) 2. It can have additional storage, beyond it's length. This makes appending fast. (an array with 7 elements, 2 unoccupied elements are marked '?', '7' is then appended leaving 1 unoccupied element marked '?' in a second array) 3. Appending to a full list, causes it to grow. This makes some appends slower. (An array with 7 occupied elements, '9' is appended, the second array has 9 elements with the final element marked '?') A list will typically grow by 12.%, hence shorter lists will grow more frequently when appending." }
+![A visual diagram of list storage.](episodes/fig/list-append.png){alt="1. A list uses an array for storage, a continuous block of memory. (An array with 5 elements) 2. It can have additional storage, beyond its length. This makes appending fast. (an array with 7 elements, 2 unoccupied elements are marked '?', '7' is then appended leaving 1 unoccupied element marked '?' in a second array) 3. Appending to a full list, causes it to grow. This makes some appends slower. (An array with 7 occupied elements, '9' is appended, the second array has 9 elements with the final element marked '?') A list will typically grow by 12.5%, hence shorter lists will grow more frequently when appending." }
path: episodes/optimisation-data-structures-algorithms.md
user: login: JostMigenda
body: While longer, I find that easier to understand, yes. Just made a few tweaks of my own to shorten it:

> A list uses a contiguous block of memory, similar to an array, for storing the pointers to its elements. It is depicted as a series of five adjacent boxes, labelled 'P1' to 'P5', representing pointers to the list's elements.
>
> It can have additional storage beyond its length to make appends faster. An illustration shows the previous list with two extra empty boxes marked with question marks, indicating spare elements. Below, Python code `len(my_list) == 5` and `my_list.append(6)` is shown. After appending, the first of the previously empty boxes contains 'P6', and the last one remains empty. The length is now `len(my_list) == 6`.
>
> Appending to a full list causes it to grow. This makes some appends slower. An illustration depicts a full list with 'P1' through 'P7' in adjacent boxes and a label "No spare elements!". Below, Python code `len(my_list) == 7` and `my_list.append(8)` is shown. The result is a new, larger continuous block of memory with 'P1' through 'P8' followed by a question mark in an additional box, indicating one spare element. The label "2 new elements" with curved arrows suggests that when the list grows, it typically allocates more memory than just the space for the new element.
>
> A concluding note states that a list will typically grow by 12.5%, hence shorter lists will grow more frequently when appending.

created_at: 2025-03-24T12:24:57Z
author_association: COLLABORATOR
diff_hunk: @@ -41,7 +41,7 @@ The remaining content is often abstract knowledge, that is transferable to the v
 
 This classic quote among computer scientists emphasises the importance of considering both performance and maintainability when optimising code and prioritising your optimisations.
 
-While advanced optimisations may boost performance, they often come at the cost of making the code harder to understand and maintain. Even if you're working alone on private code, your future self should be able to easily understand the implementation. Hence, when optimising, always weigh the potential impact on both performance and maintainability.
+While advanced optimisations are may boost performance, they often come at the cost of making the code harder to understand and maintain. Even if you're working alone on private code, your future self should be able to easily understand the implementation. Hence, when optimising, always weigh the potential impact on both performance and maintainability. While this course does not cover most advanced optimisations, you may already be familiar and using with some.
path: episodes/optimisation-introduction.md
user: login: JostMigenda
body: ```suggestion
While advanced optimisations may boost performance, they often come at the cost of making the code harder to understand and maintain. Even if you're working alone on private code, your future self should be able to easily understand the implementation. Hence, when optimising, always weigh the potential impact on both performance and maintainability. While this course does not cover most advanced optimisations, you may already be familiar with and using some.
```
created_at: 2025-03-24T12:25:34Z
author_association: COLLABORATOR
diff_hunk: @@ -59,7 +59,7 @@ If it doesn't, it will re-allocate a larger array, copy across the elements, and
 The item to be appended is then copied to the end and the counter which tracks the list's length is incremented.
 
 <!-- Based on ICR-RSE's visual note: https://icr-rse-group.github.io/carpentry-pando-python/optimisation-data-structures-algorithms.html#lists -->
-![A visual diagram of list storage.](episodes/fig/list-append.png){alt="1. A list uses an array for storage, a continuous block of memory. (An array with 5 elements) 2. It can have additional storage, beyond it's length. This makes appending fast. (an array with 7 elements, 2 unoccupied elements are marked '?', '7' is then appended leaving 1 unoccupied element marked '?' in a second array) 3. Appending to a full list, causes it to grow. This makes some appends slower. (An array with 7 occupied elements, '9' is appended, the second array has 9 elements with the final element marked '?') A list will typically grow by 12.%, hence shorter lists will grow more frequently when appending." }
+![A visual diagram of list storage.](episodes/fig/list-append.png){alt="1. A list uses an array for storage, a continuous block of memory. (An array with 5 elements) 2. It can have additional storage, beyond its length. This makes appending fast. (an array with 7 elements, 2 unoccupied elements are marked '?', '7' is then appended leaving 1 unoccupied element marked '?' in a second array) 3. Appending to a full list, causes it to grow. This makes some appends slower. (An array with 7 occupied elements, '9' is appended, the second array has 9 elements with the final element marked '?') A list will typically grow by 12.5%, hence shorter lists will grow more frequently when appending." }
path: episodes/optimisation-data-structures-algorithms.md
user: login: Robadob
body: Hmm, including newlines inside the alt text breaks the website build.

Replacing it with `&#13;&#10;` (`\r\n`) or doubled all gets converted to a single space by chrome.

Alt text *shouldn't* require line breaks.
created_at: 2025-03-24T12:39:58Z
author_association: MEMBER
diff_hunk: @@ -41,7 +41,7 @@ The remaining content is often abstract knowledge, that is transferable to the v
 
 This classic quote among computer scientists emphasises the importance of considering both performance and maintainability when optimising code and prioritising your optimisations.
 
-While advanced optimisations may boost performance, they often come at the cost of making the code harder to understand and maintain. Even if you're working alone on private code, your future self should be able to easily understand the implementation. Hence, when optimising, always weigh the potential impact on both performance and maintainability.
+While advanced optimisations are may boost performance, they often come at the cost of making the code harder to understand and maintain. Even if you're working alone on private code, your future self should be able to easily understand the implementation. Hence, when optimising, always weigh the potential impact on both performance and maintainability. While this course does not cover most advanced optimisations, you may already be familiar and using with some.
path: episodes/optimisation-introduction.md
user: login: Robadob
body: 🤦 thanks.
created_at: 2025-03-24T12:40:33Z
author_association: MEMBER
diff_hunk: @@ -59,7 +59,7 @@ If it doesn't, it will re-allocate a larger array, copy across the elements, and
 The item to be appended is then copied to the end and the counter which tracks the list's length is incremented.
 
 <!-- Based on ICR-RSE's visual note: https://icr-rse-group.github.io/carpentry-pando-python/optimisation-data-structures-algorithms.html#lists -->
-![A visual diagram of list storage.](episodes/fig/list-append.png){alt="1. A list uses an array for storage, a continuous block of memory. (An array with 5 elements) 2. It can have additional storage, beyond it's length. This makes appending fast. (an array with 7 elements, 2 unoccupied elements are marked '?', '7' is then appended leaving 1 unoccupied element marked '?' in a second array) 3. Appending to a full list, causes it to grow. This makes some appends slower. (An array with 7 occupied elements, '9' is appended, the second array has 9 elements with the final element marked '?') A list will typically grow by 12.%, hence shorter lists will grow more frequently when appending." }
+![A visual diagram of list storage.](episodes/fig/list-append.png){alt="1. A list uses an array for storage, a continuous block of memory. (An array with 5 elements) 2. It can have additional storage, beyond its length. This makes appending fast. (an array with 7 elements, 2 unoccupied elements are marked '?', '7' is then appended leaving 1 unoccupied element marked '?' in a second array) 3. Appending to a full list, causes it to grow. This makes some appends slower. (An array with 7 occupied elements, '9' is appended, the second array has 9 elements with the final element marked '?') A list will typically grow by 12.5%, hence shorter lists will grow more frequently when appending." }
path: episodes/optimisation-data-structures-algorithms.md
user: login: Robadob
body: I suppose alt would be to just start each paragraph 1), 2) etc.
created_at: 2025-03-24T12:43:45Z
author_association: MEMBER

PR comments:
user: login: github-actions[bot]
created_at: 2025-03-21T13:45:28Z
body: ## Thank you!

Thank you for your pull request :smiley:

:robot: This automated message can help you check the rendered files in your submission for clarity. If you have any questions, please feel free to [open an issue in {sandpaper}](https://github.com/carpentries/sandpaper/issues/new).

If you have files that automatically render output (e.g. R Markdown), then you should check for the following:

 - :dart: correct output
 - :framed_picture: correct figures
 - :question: new warnings
 - :bangbang: new errors

### Rendered Changes

:mag: Inspect the changes: https://github.com/RSE-Sheffield/pando-python/compare/md-outputs..md-outputs-PR-73


The following changes were observed in the _rendered_ markdown documents:

```
 acknowledgements.md                        |    2 +
 fig/list-append.ai (new)                   | 1827 ++++++++++++++++++++++++++++
 fig/list-append.png (new)                  |  Bin 0 -> 42183 bytes
 index.md                                   |    4 +-
 md5sum.txt                                 |   22 +-
 optimisation-data-structures-algorithms.md |   25 +-
 optimisation-introduction.md               |   32 +-
 optimisation-memory.md                     |    2 -
 optimisation-numpy.md                      |   19 +-
 optimisation-using-python.md               |   12 +-
 profiling-functions.md                     |   18 +-
 profiling-introduction.md                  |   14 +-
 profiling-lines.md                         |   10 +-
 setup.md                                   |   16 +-
 14 files changed, 1915 insertions(+), 88 deletions(-)
```

<details>
<summary>What does this mean?</summary>

If you have source files that require output and figures to be generated (e.g.  R Markdown), then it is important to make sure the generated figures and output are reproducible.

This output provides a way for you to inspect the output in a diff-friendly manner so that it's easy to see the changes that occur due to new software versions or randomisation.

</details>

:stopwatch: Updated at 2025-03-24 12:42:42 +0000

author_association: NONE
user: login: Robadob
created_at: 2025-03-21T19:13:21Z
body: Thanks @JostMigenda for the quick feedback, I'll get this fixed up over the weekend.
author_association: MEMBER
user: login: Robadob
created_at: 2025-03-23T16:43:20Z
body: I've addressed these points, a few responses to clarify changes though.

Thanks again for catching out all my terrible grammar (illustrator spell-check suggested I capitalise Spare/New [in red/purple] as start of sentence following the numbers).
author_association: MEMBER
user: login: msarkis-icr
created_at: 2025-03-23T21:11:51Z
body: > * [ ]  How should https://github.com/ICR-RSE-Group be added to `learners/acknowledgements.md` (cc @msarkis-icr)
>   
>   * e.g. "Members of [The Institute of Cancer Research RSE](https://github.com/ICR-RSE-Group) contributed a large number of typographic corrections and simplifications during the alpha phase of the course's development lifecycle."
>   * Happy to also name/tag specific people if preferred.
>   * Manual merge has stolen all of your names from git history, if you're keen to rewrite history to change that and submit a new PR go for it (but probably wait for Jost to review the new figure).

Hello @Robadob,
We are happy to name both Anastasiia shcherbakova and myself, Mira Sarkis as contributors from ICR-RSE.

author_association: NONE
user: login: Robadob
created_at: 2025-03-24T12:04:06Z
body: @JostMigenda 

**Changes**
- Figure, Removed the 1 New/Spare caps that I previously added
- Figure, Changed elements from the form $1$ to $P_1$
- Figure alt text, updated text to match elements (not mentioning pointers) 
- Intro, added back a note that advanced optimisations are outside the scope of the course.

**Pending**
- Figure alt-text, use AI suggested alt text it's less unwieldy than what I drafted?
- ~~conda python version, specify 3.13/3.11 or none (which should default latest)~~

author_association: MEMBER
user: login: Robadob
created_at: 2025-03-24T12:44:55Z
body: Thanks, next I'll try and sort out the technical appendix (probably tail end of this week).
author_association: MEMBER