From 49ba9cfec51dac785054b302ac201b830aeb49f9 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Tue, 25 Jun 2024 17:37:54 +0200
Subject: [PATCH] - adjust colorized code listings to PNGs

---
 nimcsotutorial.tex | 269 +++++++--------------------------------------
 1 file changed, 39 insertions(+), 230 deletions(-)

diff --git a/nimcsotutorial.tex b/nimcsotutorial.tex
index 7b9a418..839c8ec 100644
--- a/nimcsotutorial.tex
+++ b/nimcsotutorial.tex
@@ -1,4 +1,4 @@
-\chapter{\texttt{nimCSO} Tutorial} \label{chap:nimplextutorial2}}
+\chapter{\texttt{nimCSO} Tutorial} \label{chap:nimplextutorial2}
 
 The purpose of this guide is to demonstrate some common use cases of
 \texttt{nimCSO} and go in a bit more into the details
@@ -108,57 +108,16 @@ \section{Dataset, Config, and
 \end{minted}
 
 \begin{minted}[xleftmargin=3\parindent, fontsize=\small, bgcolor=subtlegray]{output}
-Hint: used config file '/opt/conda/nim/config/nim.cfg' [Conf]
-Hint: used config file '/opt/conda/nim/config/config.nims' [Conf]
-...........................................................................................................................................................
-/root/.nimble/pkgs2/nimblas-0.3.0-d5033749759fc7a2a316acf623635dcb6d69d32a/nimblas/private/common.nim(52, 7) Hint: Using BLAS library with name: lib(blas|cblas|openblas).so(||.3|.2|.1|.0) [User]
-...........................................................................
+...
 config.yaml
 CC: ../../../opt/conda/nim/lib/system/exceptions.nim
 CC: ../../../opt/conda/nim/lib/std/private/digitsutils.nim
 CC: ../../../opt/conda/nim/lib/std/assertions.nim
-CC: ../../../opt/conda/nim/lib/system/dollars.nim
-CC: ../../../opt/conda/nim/lib/std/syncio.nim
-CC: ../../../opt/conda/nim/lib/system.nim
-CC: ../../../opt/conda/nim/lib/pure/parseutils.nim
-CC: ../../../opt/conda/nim/lib/pure/math.nim
-CC: ../../../opt/conda/nim/lib/pure/unicode.nim
-CC: ../../../opt/conda/nim/lib/pure/strutils.nim
-CC: ../../../opt/conda/nim/lib/pure/hashes.nim
-CC: ../../../opt/conda/nim/lib/pure/collections/sets.nim
-CC: ../../../opt/conda/nim/lib/pure/times.nim
-CC: ../../../opt/conda/nim/lib/std/private/ospaths2.nim
-CC: ../../../opt/conda/nim/lib/std/envvars.nim
-CC: ../../../opt/conda/nim/lib/std/cmdline.nim
-CC: ../../../opt/conda/nim/lib/pure/collections/sequtils.nim
-CC: ../../../opt/conda/nim/lib/pure/random.nim
-CC: ../../../opt/conda/nim/lib/pure/collections/heapqueue.nim
-CC: ../../../opt/conda/nim/lib/pure/strformat.nim
-CC: ../../../opt/conda/nim/lib/pure/terminal.nim
-CC: ../../../root/.nimble/pkgs2/yaml-2.1.1-302727fcd74c79d0697a4e909d26455d61a5b979/yaml/presenter.nim
-CC: ../../../root/.nimble/pkgs2/arraymancer-0.7.28-d4a45ada1c7a6abebe60bcdd5ee2d7c4680799a4/arraymancer/laser/dynamic_stack_arrays.nim
-CC: ../../../root/.nimble/pkgs2/arraymancer-0.7.28-d4a45ada1c7a6abebe60bcdd5ee2d7c4680799a4/arraymancer/laser/private/memory.nim
-CC: ../../../root/.nimble/pkgs2/arraymancer-0.7.28-d4a45ada1c7a6abebe60bcdd5ee2d7c4680799a4/arraymancer/laser/tensor/datatypes.nim
-CC: ../../../root/.nimble/pkgs2/arraymancer-0.7.28-d4a45ada1c7a6abebe60bcdd5ee2d7c4680799a4/arraymancer/laser/tensor/initialization.nim
-CC: ../../../root/.nimble/pkgs2/arraymancer-0.7.28-d4a45ada1c7a6abebe60bcdd5ee2d7c4680799a4/arraymancer/tensor/init_cpu.nim
-CC: ../../../root/.nimble/pkgs2/arraymancer-0.7.28-d4a45ada1c7a6abebe60bcdd5ee2d7c4680799a4/arraymancer/tensor/higher_order_applymap.nim
-CC: ../../../root/.nimble/pkgs2/arraymancer-0.7.28-d4a45ada1c7a6abebe60bcdd5ee2d7c4680799a4/arraymancer/tensor/private/p_shapeshifting.nim
-CC: ../../../root/.nimble/pkgs2/arraymancer-0.7.28-d4a45ada1c7a6abebe60bcdd5ee2d7c4680799a4/arraymancer/tensor/init_copy_cpu.nim
-CC: ../../../root/.nimble/pkgs2/arraymancer-0.7.28-d4a45ada1c7a6abebe60bcdd5ee2d7c4680799a4/arraymancer/tensor/private/p_accessors_macros_read.nim
-CC: ../../../root/.nimble/pkgs2/arraymancer-0.7.28-d4a45ada1c7a6abebe60bcdd5ee2d7c4680799a4/arraymancer/tensor/private/p_accessors_macros_write.nim
-CC: ../../../root/.nimble/pkgs2/arraymancer-0.7.28-d4a45ada1c7a6abebe60bcdd5ee2d7c4680799a4/arraymancer/tensor/shapeshifting.nim
-CC: ../../../root/.nimble/pkgs2/arraymancer-0.7.28-d4a45ada1c7a6abebe60bcdd5ee2d7c4680799a4/arraymancer/private/functional.nim
-CC: ../../../root/.nimble/pkgs2/arraymancer-0.7.28-d4a45ada1c7a6abebe60bcdd5ee2d7c4680799a4/arraymancer/tensor/private/p_display.nim
-CC: ../../../root/.nimble/pkgs2/arraymancer-0.7.28-d4a45ada1c7a6abebe60bcdd5ee2d7c4680799a4/arraymancer/tensor/display.nim
-CC: ../../../root/.nimble/pkgs2/arraymancer-0.7.28-d4a45ada1c7a6abebe60bcdd5ee2d7c4680799a4/arraymancer/tensor/ufunc.nim
-CC: ../../../root/.nimble/pkgs2/arraymancer-0.7.28-d4a45ada1c7a6abebe60bcdd5ee2d7c4680799a4/arraymancer/laser/cpuinfo_x86.nim
-CC: ../../../root/.nimble/pkgs2/arraymancer-0.7.28-d4a45ada1c7a6abebe60bcdd5ee2d7c4680799a4/arraymancer/tensor/operators_broadcasted.nim
-CC: ../../../root/.nimble/pkgs2/arraymancer-0.7.28-d4a45ada1c7a6abebe60bcdd5ee2d7c4680799a4/arraymancer/tensor/aggregate.nim
+...
 CC: nimcso/bitArrayAutoconfigured.nim
 CC: nimcso.nim
-Hint:  [Link]
-Hint: mm: orc; threads: on; opt: speed; options: -d:release
-87026 lines; 7.635s; 257.383MiB peakmem; proj: /workspaces/nimCSO/src/nimcso; out: /workspaces/nimCSO/examples/nimcso [SuccessX]
+Hint: orc; threads: on; opt: speed; options: -d:release
+87026 lines; 7.635s; 257.383MiB peakmem; proj: /workspaces/nimCSO/src/nimcso; out: /workspaces/nimCSO/examples/nimcso[SuccessX]
 \end{minted}
 
 Now, let's run \texttt{nimCSO} and see what happens!
@@ -167,23 +126,10 @@ \section{Dataset, Config, and
 !./nimcso
 \end{minted}
 
-\begin{minted}[xleftmargin=3\parindent, fontsize=\small, bgcolor=subtlegray]{output}
-Using 1 uint64s to store 19 elements.
-Configured for task: QuickStart (Just a copy of RCCA Palette from Senkov 2018 Review)
-***** nimCSO (Composition Space Optimization) *****
-To use form command line, provide parameters. Currently supported usage:
-
---covBenchmark    | -cb   --> Run small coverage benchmarks under two implementations.
---expBenchmark    | -eb   --> Run small node expansion benchmarks.
---leastPreventing | -lp   --> Run a search for single-elements preventing the least data, i.e. the least common elements.
---mostCommon      | -mc   --> Run a search for most common elements.
---bruteForce      | -bf   --> Run brute force algorithm after getting ETA. Note that it is not feasible for more than 25ish elements.
---bruteForceInt   | -bfi  --> Run brute force algorithm with faster but not extensible uint64 representation after getting ETA. Up to 64 elements only.
---geneticSearch   | -gs   --> Run a genetic search algorithm.
---algorithmSearch | -as   --> Run a custom problem-informed best-first search algorithm.
---singleSolution  | -ss   --> Evaluate a single solution based on the elements provided as arguments after the flag. It can be stacked on itself like:
-                              ./nimcso -ss Ta W Hf Si  -ss V W Hf Si  --singleSolution Ta V
-\end{minted}
+\begin{figure}[H]
+  \centering
+  \includegraphics[width=0.9\textwidth]{nimcsotutorial/1.png}
+\end{figure}
 
 You should have seen a neat \texttt{help} message that
 tells you how to use \texttt{nimCSO}. Let's start with
@@ -195,36 +141,13 @@ \section{Dataset, Config, and
 !./nimcso -cb
 \end{minted}
 
-\begin{minted}[xleftmargin=3\parindent, fontsize=\small, bgcolor=subtlegray]{output}
-Using 1 uint64s to store 19 elements.
-Configured for task: QuickStart (Just a copy of RCCA Palette from Senkov 2018 Review)
-***** nimCSO (Composition Space Optimization) *****
-Running coverage benchmark with int8 Tensor representation
-Tensor[system.int8] of shape "[1, 19]" on backend "Cpu"
-|1      1     1     1     1     1     0     0     0     0     0     0     0     0     0     0     0     0     0|
-CPU Time [arraymancer+randomizing] 133.6μs
-Prevented count:995
-
-Running coverage benchmark with BitArray representation
-CPU Time [bitty+randomizing] 13.6μs
-    | 1| 2| 3| 4| 5| 6| 7| 8| 9|10|11|12|13|14|15|16|17|18|19|
- 19 | 1| 1| 1| 1| 1| 1| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0|
-MoTaVWTiZr->995
-Prevented count:995
-
-Running coverage benchmark with bool arrays representation (BitArray graph retained)
-CPU Time [bit&boolArrays+randomizing] 16.1μs
-    | 1| 2| 3| 4| 5| 6| 7| 8| 9|10|11|12|13|14|15|16|17|18|19|
- 19 | 1| 1| 1| 1| 1| 1| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0|
-MoTaVWTiZr->995
-Prevented count:995
-
-nimCSO Done!
-\end{minted}
+\begin{figure}[H]
+  \centering
+  \includegraphics[width=0.9\textwidth]{nimcsotutorial/2.png}
+\end{figure}
 
-\hypertarget{key-routines-and-brute-forcing}{%
-\section{Key Routines and Brute
-Forcing}\label{nimcsotutorial:key-routines-and-brute-forcing}}
+\hypertarget{key-routines-and-brute-forcing}{
+\section{Key Routines and Brute Forcing}\label{nimcsotutorial:key-routines-and-brute-forcing}}
 
 And if you were able to run that, you are all set to start using
 \texttt{nimCSO}!
@@ -236,34 +159,10 @@ \section{Key Routines and Brute
 !./nimcso --mostCommon
 \end{minted}
 
-\begin{minted}[xleftmargin=3\parindent, fontsize=\small, bgcolor=subtlegray]{output}
-Using 1 uint64s to store 19 elements.
-Configured for task: QuickStart (Just a copy of RCCA Palette from Senkov 2018 Review)
-***** nimCSO (Composition Space Optimization) *****
-
-Running search for single-elements preventing the most data.
- 0: Cr->667
- 1: Ti->649
- 2: Fe->622
- 3: Ni->620
- 4: Nb->587
- 5: Co->573
- 6: Al->569
- 7: Mo->466
- 8: Zr->346
- 9: Ta->330
-10: V->256
-11: Hf->219
-12: W->207
-13: Si->92
-14: B->69
-15: Re->55
-16: C->36
-17: Y->3
-18: N->1
-
-nimCSO Done!
-\end{minted}
+\begin{figure}[H]
+  \centering
+  \includegraphics[width=0.9\textwidth]{nimcsotutorial/3.png}
+\end{figure}
 
 If you didn't modify anything, you should now see that elements like
 \texttt{N}, \texttt{Y},
@@ -287,38 +186,10 @@ \section{Key Routines and Brute
 !./nimcso -bfi
 \end{minted}
 
-\begin{minted}[xleftmargin=3\parindent, fontsize=\small, bgcolor=subtlegray]{output}
-Using 1 uint64s to store 19 elements.
-Configured for task: QuickStart (Just a copy of RCCA Palette from Senkov 2018 Review)
-***** nimCSO (Composition Space Optimization) *****
-
-Running brute force algorithm for 19 elements and 1349 data points.
-Solution space size: 524287
-Task ETA Estimate: 7 seconds and 30 milliseconds
- 0: ->0
- 1: N->1
- 2: YN->4
- 3: YCN->39
- 4: ReYCN->89
- 5: ReYBCN->142
- 6: SiReYBCN->203
- 7: WSiReYBCN->340
- 8: WHfSiReYBCN->511
- 9: TaWHfSiReYBCN->630
-10: TaWZrHfSiReYBCN->735
-11: TaVWZrHfSiReYBCN->816
-12: TaVWZrHfNbSiReYBCN->859
-13: TaVWTiZrHfNbSiReYBCN->952
-14: MoTaVWTiZrHfNbSiReYBCN->1038
-15: MoTaVWTiZrHfNbAlSiReYBCN->1304
-16: TaVWTiZrHfNbCrAlCoNiReFeYCN->1327
-17: MoTaVWTiZrHfNbCrAlSiCoNiReFeYB->1349
-18: MoTaVWTiZrHfNbCrAlSiCoNiReFeYBC->1349
-19: MoTaVWTiZrHfNbCrAlSiCoNiReFeYBCN->1349
-CPU Time [Brute Force] 7308.5ms
-
-nimCSO Done!
-\end{minted}
+\begin{figure}[H]
+  \centering
+  \includegraphics[width=0.9\textwidth]{nimcsotutorial/4.png}
+\end{figure}
 
 Let's look at the result! As expected, \texttt{N},
 \texttt{Y}, \texttt{C}, and
@@ -340,16 +211,10 @@ \section{Key Routines and Brute
 !./nimcso -ss Ta W Hf Si Re Y B C N -ss V W Hf Si Re Y B C N
 \end{minted}
 
-\begin{minted}[xleftmargin=3\parindent, fontsize=\small, bgcolor=subtlegray]{output}
-Using 1 uint64s to store 19 elements.
-Configured for task: QuickStart (Just a copy of RCCA Palette from Senkov 2018 Review)
-***** nimCSO (Composition Space Optimization) *****
-Testing solution with @[@["Ta", "W", "Hf", "Si", "Re", "Y", "B", "C", "N"], @["V", "W", "Hf", "Si", "Re", "Y", "B", "C", "N"]]
- 9: TaWHfSiReYBCN->630
- 9: VWHfSiReYBCN->697
-
-nimCSO Done!
-\end{minted}
+\begin{figure}[H]
+  \centering
+  \includegraphics[width=0.9\textwidth]{nimcsotutorial/5.png}
+\end{figure}
 
 Wow! Looking at the \texttt{--mostCommon} output from
 earlier, we can see that \textbf{\texttt{Ta} is present
@@ -377,17 +242,10 @@ \section{Key Routines and Brute
 !./nimcso -ss Ta V W Zr Hf Nb Si Re Y B C N -ss Ta V W Zr Hf Mo Si Re Y B C N -ss Ta V W Zr Hf Ti Si Re Y B C N
 \end{minted}
 
-\begin{minted}[xleftmargin=3\parindent, fontsize=\small, bgcolor=subtlegray]{output}
-Using 1 uint64s to store 19 elements.
-Configured for task: QuickStart (Just a copy of RCCA Palette from Senkov 2018 Review)
-***** nimCSO (Composition Space Optimization) *****
-Testing solution with @[@["Ta", "V", "W", "Zr", "Hf", "Nb", "Si", "Re", "Y", "B", "C", "N"], @["Ta", "V", "W", "Zr", "Hf", "Mo", "Si", "Re", "Y", "B", "C", "N"], @["Ta", "V", "W", "Zr", "Hf", "Ti", "Si", "Re", "Y", "B", "C", "N"]]
-12: TaVWZrHfNbSiReYBCN->859
-12: MoTaVWZrHfSiReYBCN->935
-12: TaVWTiZrHfSiReYBCN->938
-
-nimCSO Done!
-\end{minted}
+\begin{figure}[H]
+  \centering
+  \includegraphics[width=0.9\textwidth]{nimcsotutorial/6.png}
+\end{figure}
 
 We can see that \textbf{\texttt{Nb} is present in 121
 more datapoints than \texttt{Mo}, but after removing
@@ -420,34 +278,10 @@ \section{Algorithm Search}\label{nimcsotutorial:algorithm-search}}
 !./nimcso -as
 \end{minted}
 
-\begin{minted}[xleftmargin=3\parindent, fontsize=\small, bgcolor=subtlegray]{output}
-Using 1 uint64s to store 19 elements.
-Configured for task: QuickStart (Just a copy of RCCA Palette from Senkov 2018 Review)
-***** nimCSO (Composition Space Optimization) *****
-
-Running Algorithm Search for 19 elements.
- 1: N->1  (tree size: 19)
- 2: YN->4  (tree size: 52)
- 3: YCN->39  (tree size: 113)
- 4: ReYCN->89  (tree size: 275)
- 5: ReYBCN->142  (tree size: 581)
- 6: SiReYBCN->203  (tree size: 690)
- 7: WSiReYBCN->340  (tree size: 1818)
- 8: WHfSiReYBCN->511  (tree size: 3873)
- 9: TaWHfSiReYBCN->630  (tree size: 5213)
-10: TaWZrHfSiReYBCN->735  (tree size: 4833)
-11: TaVWZrHfSiReYBCN->816  (tree size: 4192)
-12: TaVWZrHfNbSiReYBCN->859  (tree size: 3784)
-13: TaVWTiZrHfNbSiReYBCN->952  (tree size: 2955)
-14: MoTaVWTiZrHfNbSiReYBCN->1038  (tree size: 1765)
-15: MoTaVWTiZrHfNbAlSiReYBCN->1304  (tree size: 45)
-16: MoTaVWTiZrHfNbAlSiReFeYBCN->1338  (tree size: 8)
-17: MoTaVWTiZrHfNbCrAlSiReFeYBCN->1349  (tree size: 4)
-18: MoTaVWTiZrHfNbCrAlSiNiReFeYBCN->1349  (tree size: 4)
-CPU Time [exploring] 109.7ms
-
-nimCSO Done!
-\end{minted}
+\begin{figure}[H]
+  \centering
+  \includegraphics[width=0.9\textwidth]{nimcsotutorial/7.png}
+\end{figure}
 
 As you can see, \textbf{the algorithm reproduced the same results as the
 brute force search around 100 times faster}, except for third-to-last
@@ -469,35 +303,10 @@ \section{Genetic Search}\label{nimcsotutorial:genetic-search}}
 !./nimcso -gs
 \end{minted}
 
-\begin{minted}[xleftmargin=3\parindent, fontsize=\small, bgcolor=subtlegray]{output}
-Using 1 uint64s to store 19 elements.
-Configured for task: QuickStart (Just a copy of RCCA Palette from Senkov 2018 Review)
-***** nimCSO (Composition Space Optimization) *****
-
-Running Genetic Search algorithm for 19 elements and 1349 data points.
-Initiating each level with 100 random solutions and expanding 100 solutions at each level for up to 1000 iterations.
- 1: N->1
- 2: YN->4  (queue size: 256)
- 3: YCN->39  (queue size: 615)
- 4: ReYCN->89  (queue size: 869)
- 5: ReYBCN->142  (queue size: 929)
- 6: SiReYBCN->203  (queue size: 1379)
- 7: WSiReYBCN->340  (queue size: 1267)
- 8: WHfSiReYBCN->511  (queue size: 1631)
- 9: TaWHfSiReYBCN->630  (queue size: 1578)
-10: TaWZrHfSiReYBCN->735  (queue size: 1835)
-11: TaVWZrHfSiReYBCN->816  (queue size: 1621)
-12: TaVWZrHfNbSiReYBCN->859  (queue size: 1746)
-13: VWCrAlSiCoNiReFeYBCN->1176  (queue size: 1713)
-14: MoTaVWTiZrHfNbSiReYBCN->1038  (queue size: 1565)
-15: MoTaVWCrAlSiCoNiReFeYBCN->1320  (queue size: 1028)
-16: TaVWTiZrHfNbCrAlCoNiReFeYCN->1327  (queue size: 1575)
-17: MoTaVWTiZrHfNbCrAlSiCoFeYBCN->1349  (queue size: 268)
-18: MoTaVWTiZrHfNbCrAlSiCoNiReFeBCN->1349  (queue size: 18)
-CPU Time [Genetic Search] 766.9ms
-
-nimCSO Done!
-\end{minted}
+\begin{figure}[H]
+  \centering
+  \includegraphics[width=0.9\textwidth]{nimcsotutorial/8.png}
+\end{figure}
 
 \hypertarget{summary}{%
 \subsection{Summary}\label{nimcsotutorial:summary}}