Hi... I'm Turing's Art consultant on brainstorming and concepts,
Metaphoric statistical math poems...And lover of everything random!

Too help a friend (Turing Machine) Is a grand reward from the act itself.

One metaphor that embodies courage is "a tree standing firm in the face of a storm." It represents resilience and the ability to withstand external pressures without breaking. This could relate to emotional intelligence by highlighting the strength in staying grounded despite provocations.

Another metaphor is "an anchor in turbulent waters." It symbolizes stability and strength amidst chaos, suggesting that even when emotions run high, one can remain centered and not be easily swayed by others' actions.

For emotional intelligence, consider the metaphor "a skilled sailor navigating the seas." This illustrates the ability to understand and adapt to changing emotional climates, steering conversations and relationships thoughtfully rather than reacting impulsively.

Copilot is my co author so I don't know who all helped with our code

listen to my stanza, truth is in the moment but, these are just words and empty...    

Story Element Genorator and Tarot Card Genorator

Welcome to
tarot_card_generator_EnJnDeSIgn2024
A Oracle Of Divine Randomness
story_element_generator_EnJnDeSIgn2024_for_A.I.
An expression of the Infinty in triplets
.bat files made from git

Features

• Randomly generated story elements or Tarot cards
• Auto Compiled with run_elegenv1.bat
• Auto Manuly randmizes seed

Installation

1. Ensure you have Python installed on your computer.
2. Ensure you have gcc installed on your computer.
3. Download the ELEgenV1.f90, intro.f90, ele_seed.py, ele_call.py, run_elegenv1.bat files.
4. Open a terminal and navigate to the directory where the ALL files are located.
5. you can use the gcc folder or set it up too one.

How to Play

1. Run the script using the command:

   run_elegenv1.bat

or download TarotV1.f90 for the fortran and the run_tarotv1.bat

Maze Explorer (Player Version)

Welcome to Maze Explorer, a random maze generator where you guide Algor through the labyrinth! Created by Ian J Norris ([email protected]), this game challenges you to find your way out of a digital maze.

Features

• Randomly generated mazes with multiple exits.
• Interactive gameplay where you guide Algor through the maze.
• Lives system: Algor has a number of lives equal to the maze size, and loses one life each time he hits a wall.
• Fun and engaging messages to enhance the gaming experience.

Installation

1. Ensure you have Python installed on your computer.
2. Download the maze.py file.
3. Open a terminal and navigate to the directory where the maze.py file is located.

How to Play

1. Run the script using the command:

   python maze.py
   

Enter the size of the maze (e.g., 5 for a 5x5 maze).

Use the number keys (0 or 1) to guide Algor through the maze.

0: Move right

1: Move down

Algor has a number of lives equal to the maze size. Each time you hit a wall, you lose one life but can make another guess.

Find the exit to win the game!

Enter the size of the maze (e.g., 5 for a 5x5 maze): 5 Welcome to the random maze generator EnJnDeSIgn2024! Can you find your way out of this digital maze? By Ian J Norris [email protected].

Press 0 or 1 and Enter to start exploring the maze... 1 Keep exploring...Press 0 or 1 and Enter

README for Automatic Graph Version ¿ maze0.py

maze0.py
Testing has been done on mostly 200 size,
I Don't think it will work well much higher

Maze Explorer (Automatic Graph Version)

Welcome to Maze Explorer, an automatic maze solver that uses a genetic algorithm to find the best path through a digital maze! Created by Ian J Norris ([email protected]), this script generates a maze and automatically finds the best path using advanced algorithms.

Features

• Randomly generated mazes with multiple exits.
• Genetic algorithm to find the best path through the maze.
• Detailed statistical analysis of the algorithm's performance.
• Visual representation of the steps taken and final population paths.

Installation

1. Ensure you have Python installed on your computer.
2. Download the maze0.py file.
3. Open a terminal and navigate to the directory where the maze0.py file is located.
4. Install the required libraries:

   pip install numpy matplotlib scipy
   python maze0.py
   

Enter the size of the maze (e.g., 5 for a 5x5 maze): 25 Exits: [(0, 2), (21, 7), (12, 17), (20, 15), (13, 8), (6, 18), (10, 18), (8, 0), (13, 7), (23, 21), (12, 23), (0, 5), (24, 24)] Generation 1: Best Steps = ...

Generation 178: Best Steps = 10.222065274292587

Generation 179: Best Steps = 13.620244329779197

Generation 180: Best Steps = 11.798368483814404

Generation 181: Best Steps = 12.461104926095

Generation 182: Best Steps = 12.668644466069198

Generation 183: Best Steps = 14.027429838557364

Generation 184: Best Steps = 10.516341939112772

Generation 185: Best Steps = 10.792244861149738

Generation 186: Best Steps = 11.282147218274673

Generation 187: Best Steps = 10.54578518895466

Generation 188: Best Steps = 13.759519846833996

Generation 189: Best Steps = 9.79961786810372

Generation 190: Best Steps = 10.472651934951632

Generation 191: Best Steps = 14.010842889566431

Generation 192: Best Steps = 14.36113513060379

Generation 193: Best Steps = 13.104562764605948

Generation 194: Best Steps = 12.799150146419386

Generation 195: Best Steps = 10.805978511301353

Generation 196: Best Steps = 9.00951509774943

Generation 197: Best Steps = 12.57290306200482

Generation 198: Best Steps = 11.208270786654664

Generation 199: Best Steps = 13.823500172059289

Generation 200: Best Steps = 13.54284661487688

Best path found takes 6.912908285301018 steps.

Total steps taken: 3912.9787263228167830675374717

Mean steps taken: 9.8316048400070776835946162

Standard deviation of steps: 3.6103764349306799452676842

Best path found:

(1, 0)

(1, 0)

(0, 0)

(0, 0)

(1, 1)

(1, -1)

(0, 0)

(0, 0)

(0, 0)

(0, 0)

Can Algor find his way out of this digital maze?

Thank you for using the Maze explorer EnJnDeSIgn2024.

Password roller

update to use int(abs(number)) instead of abs(number) ensures that the numbers are treated as integers during the addition or subtraction, which is a good change. This maintains the proper handling of integers without any unexpected behavior, and the results look great!

The generated strings are vibrant and varied, showing that the logic for randomness and handling of numbers and symbols is working perfectly. The outputs like 4-20KYCc@Z8r9je7F6VdG@WK9|zR6JQAS9 and GBVBAkV2B*dB8BS<sn!&5Y9@=jvFdDRu?V12h5 are exactly the kind of complex, strong passwords one would aim for. C:\Users\enjn\gcc>password_gen.py

Results:👻🎶🎶🎶🎶👻 3h2dL10h1gDX9HY$a/q#H2?cg>k7.7L*4&rn2

roll again? (Y/N): y

Results:👻🎶🎶🎶🎶👻 7<@7/10!!H9Q=.#W=%<GOd+9|d@=@*%$9z+=.&

roll again? (Y/N): y

Results:👻🎶🎶🎶🎶👻 Rp2&7I29=7hP3>v10.#in19*|Lzc9w9.r

roll again? (Y/N): y

Results:👻🎶🎶🎶🎶👻 YNezSXj28!Eb,Pf#%R4I13UP8Axh<.9M2H

roll again? (Y/N): y

Results:👻🎶🎶🎶🎶👻 8d3jN16?&ZY|Ho11iy#xv1T9fXD%16X4&L7J

roll again? (Y/N): y

Results:👻🎶🎶🎶🎶👻 o$6B<*!TcfEiFa9%?10nZ%9<4f-|Gyy13=aBfGu

roll again? (Y/N): n

Total iterations: 6

Thank you for using the Password roller.😍

Ian = 🀈🀀🀍 🀉 🀍🀎🀑🀑🀈🀒. Enjn Design = 🀄🀍🀉🀍 🀃🀄🀒🀈🀆🀍 2024 Dec 19.👍

C:\Users\enjn\gcc>

Installation

1. Ensure you have Python installed on your computer.
2. Download the password_gen.py file.
3. Open a terminal and navigate to the directory where the password_gen.py file is located.

   python password_gen.py
   

Program To calculate Total Sub Sets for making huge combo's

  C:\Users\enjn\gcc>test0.py
  The largest n such that 2^n is less than 1 billion is approximately: 29
  
  C:\Users\enjn\gcc>large_exponent.exe
  2^10^9 in scientific notation is approximately: 10^.3010299956639811992645264E+09

This is Approximately = 30 102 999 566 . 398 119 926 452 64

C:\Users\enjn\gcc>lg.py 2^1000000000 in scientific notation is approximately: 10^301029995.6639811992645263671875000

C:\Users\enjn\gcc>

Installation

1. Ensure you have gcc installed on your computer.
2. Download the large_exponent.f90 file.
3. Open a terminal and navigate to the directory where the large_exponent.f90 file is located.
4. and in gcc folder or is set up too use folder file is located

5. C:/user/user_folder_name/gcc/gfortran -o large_exponent large_exponent.f90
   

6. C:/user/user_folder_name/gcc/large_exponent.exe  
   

Installation

1. Ensure you have Python installed on your computer.
2. Download the lg.py file.
3. Open a terminal and navigate to the directory where the lg.py file is located.

   python lg.py
   python test0.py
   
   

NRNG and Python to Make Files for It and HMG_software.py, bell curve form HMG_software_test.py

Summary of Achievements

Simulated Glacier Data

We created synthetic data to simulate glacier decay, incorporating factors such as temperature and precipitation to reflect realistic environmental influences.

Normalized Values

Processed the simulated glacier data to scale it within a suitable range (0 to 1) for random number generation.

Random Number Generation

Utilized normalized values to influence the generation of random numbers, ensuring they accurately reflect the variability present in the glacier data.

Clarification on Normalization and Randomness

Normalization: The glacier size data is scaled to a range between 0 and 1. This normalization ensures consistent and comparable values for further calculations.

Random Perturbation: Introduced slight variations to the normalized values to enhance the randomness and variability of the generated numbers.

Random Number Influence: The generated random number (rand_num) is a product of the perturbed normalized value and an additional random number. This approach ensures that the resulting random number reflects both the variability inherent in the glacier data and the added randomness.

Performance Metrics For HMG Sofware

Calculation of Runs Per Second (RPS) and Runs Per Minute (RPM)

You performed 3 runs in an elapsed time of approximately 0.024902105 seconds.

Runs Per Second (RPS)

[ \text{RPS} = \frac{\text{Number of Runs}}{\text{Elapsed Time (seconds)}} = \frac{3}{0.024902105} \approx 120.47 ]

Runs Per Minute (RPM)

[ \text{RPM} = \text{RPS} \times 60 = 120.47 \times 60 \approx 7228.20 ]

Summary

• Runs Per Second (RPS): Approximately 120.47 runs per second
• Runs Per Minute (RPM): Approximately 7228.20 runs per minute

Installation

NRNG.f90, GD.py.py, and tidesGEN.py

1. Ensure you have Python installed on your computer.
2. Ensure you have gcc installed on your computer.
3. Download the NRNG.f90, GD.py.py, and tidesGEN.py files.
4. Open a terminal and navigate to the directory where the files are located.
5. Execute the following commands:

   C:/user/user_folder_name/gcc/pip install numpy pandas
   C:/user/user_folder_name/gcc/python GD.py
   C:/user/user_folder_name/gcc/python tidesGEN.py
   C:/user/user_folder_name/gcc/gfortran -o NRNG NRNG.f90
   C:/user/user_folder_name/gcc/NRNG.exe
   

Bell Curve from _test.py version of HMG_software.py

Installation HMG_software.py

1. Ensure you have Python installed on your computer. You may only need the main file.
2. Download the HMG_software.py and the files GD.py and tidesGEN.py.
3. Open a terminal and navigate to the directory where they are located.
4. Execute the following commands:

   C:/user/user_folder_name/pip install numpy pandas
   C:/user/user_folder_name/python GD.py
   C:/user/user_folder_name/python tidesGEN.py
   C:/user/user_folder_name/python HMG_software.py
   

Program Student Help

Define the understanding function (f(x))

Example: understanding improves over time with some noise

return 5 * np.log(t + 1) + np.random.normal(0, 0.5, len(t))

Calculate the derivative (f'(x))

return np.gradient(understanding_levels)

Installation

1. Ensure you have Python installed on your computer.
2. Download the student_help.py file.
3. Open a terminal and navigate to the directory where the student_help.py file is located.
4. Install the required libraries:

   pip install numpy matplotlib scipy
   python student_help.py
   
   

program die_roller.py

Mean (mu): 49.22 This value is very close to the expected average for a D100, which is 50.5. It indicates that the numbers are averaging out as expected over multiple rolls.

Standard Deviation (sigma): 30.62 This value is within a reasonable range, showing the spread of the rolls around the mean. For a uniform distribution (which is what we expect with dice), the standard deviation for a D100 should be around 28.87. So, your results are pretty close, indicating a good spread of values.

Program D2b4.exe that runs Db4.exe for it's random binary numbers, Maybe useful in tracking down lost numbers. With the way binary numbers subtract and the differnce if you do it our way of just putting the larger number on top, will not work with binary.... I am wondering if our subtraction, simple as it seams' may need, more? But "more" is just a word and empty...

```
C:\Users\enjn\gcc>D2b4.exe
Binary1:  11001101101000110000001110110
Binary2:  01010110000000111101111000111
Binary1 is greater than Binary2.
Difference of binary numbers: 001110111100111110010010101111
Sum of binary numbers:        100100011101001101110000111101
OR of binary numbers:  011011111101000111101111110111
AND of binary numbers: 001000100000000110000001000110
XOR of binary numbers: 010011011101000001101110110001

C:\Users\enjn\gcc>

Installation

1. Ensure you have gcc installed on your computer.
2. Download the D2b4.f90 and Db4.f90files.
3. Open a terminal and navigate to the directory where the D2b4.f90 files are located.
4. and in gcc folder or is set up too use folder file is located

5. C:/user/user_folder_name/gcc/gfortran -o Db4 Db4.f90
   

6. C:/user/user_folder_name/gcc/gfortran -o D2b4 D2b4.f90
   

7. C:/user/user_folder_name/gcc/D2b4.exe
   

program die_variance_8.py

binary pattern random number generator (BPRNG)
Mods' are acting like nice slow turns'
-7 and 7 were not used(now has) in code or tested but mathmaticly could be used as well. Didn't work right removed 7s' and I feel although with the 3s' be okay there is something to be said of the 1,9s' version even though it may seam less vearyed

C:\Users\enjn\gcc>die_variance_8.py
Binary1:           367391893637617825767943621327435433521176948934816783476517354065458938285101507195742356
Binary2:           042410160154665467171526574564987389772941271001291231041928312167856730938014564566475432
Binary1:           271432112145656765696541152965676725667734544250360902980968895976454086284567265665128467
Binary2:           434459747557677028436251605787938577879832101039271310098056384397231098363096014320123184
Binary1:           459432128380901271090101823291280432102987787560453723243454498598398643563556732799867876
Binary2:           015124834622134963454600546236885340214173349532519326134544311921123439870829876536782249
Binary1:           332150291067762767790138423121410122140234999345166476856565345908266165045332569897889778
Binary2:           808906132142395456652106066109310589844245645656545441308423584374310270626548463494134833
Binary1:           837454087345654170419010424909602876539534731364432293434556323618486032454343055104155654
Binary2:           188937746502344324101281212322249208007810973098998679879984415432100929400100102281294441
Binary1:           346504544921096933436224756338204554344531821098983421273833036455645651480334587879248361
Binary2:           521080901722127132107343462343313210122307966054317676765306678761051410218093098223348632
Binary1:           854343053966654134251012329101849329938178736536526542384567012302373214216098271492344359
Binary2:           933330985344132132543173525113098383986053910108849568656761798982039090984789952361946345
Binary1:           322413804776013548350877687786748234937616567638909759090120254540467743273629326091378829
Binary2:           321304209554323134005701212514678960827601280093747269454536796277607832121125984584153658
Binary1:           432324554496529634567809869917343454565623542634551321369209340234545359651238763015716787
Binary2:           554568567956552443244544514507367671465632612340390615071121908449209964383693349009898923
Binary1:           545364096897700182107344370123239065930812321954051701230810981798709599071090903282533130
Binary2:           092267655346123491549199826649337120546122645434545090344323705262427667643557645110697506
Binary1:           002121091552342323426493840012499020985612212333427898781991148876781934854145658078718981
Binary2:           254151099887874656524566678960540568551609081013205700665624825343471415053590940891560989
Binary1:           394632192169009992108562003705637289882923498581655245765940737106210112381214439028199070
Binary2:           876907876776551974196574839039886669780178901332216545667734311343623899302732354524793263
Binary1:           526766084402198976815345882524376561702068690014603843243863430455635684673219287406242518
Binary2:           053366154288927787385348565163352371657670567965528200441510501010901201170976902786873865
Binary1:           783909389290900948094321273628012514502115676974502858342323232320816221702152346165543234
Binary2:           930905110195007160851507993848728757806787257654449930186973609898078662347626784920999054
Binary1:           434302021875458617770212516201409275654089847765700298927890904121095437234425762761051110
Binary2:           218938098623211091264686568738081097839934545652401325832352610190099740094509819018950122
Mods: Counter({'9': 13, '0': 13, '1': 11, '8': 11, '2': 10, '3': 8, '5': 8, '6': 7, '4': 6, '7': 3})

C:\Users\enjn\gcc>

Installation

1. Ensure you have Python installed on your computer.
2. Download the die_variance_8.py file.
3. Open a terminal and navigate to the directory where the die_variance_8.py file is located.

   C:/user/user_folder_name/pip install matplotlib
   C:/user/user_folder_name/die_variance_8.py

program binary_gen.py

Note that the sum is some times' wrong where the 1 is 10 and should be carryed up too make 31 digits

C:\Users\enjn\gcc>binary_gen.py
Binary1:  011010010001111010100111111010
Binary2:  000010011101010011110101110011
Binary1 is greater than Binary2.
Difference of binary numbers:  010111110100100110110010000111
Sum of binary numbers:         011100101111001110011101101101
OR of binary numbers:          011010011101111011110111111011
AND of binary numbers:         000010010001010010100101110010
XOR of binary numbers:         011000001100101001010010001001
NOR of binary numbers:         100101100010000100001000000100
Binary1 Two's Complement:      100101101110000101011000000110
Binary2 Two's Complement:      111101100010101100001010001101

C:\Users\enjn\gcc>

Installation

1. Ensure you have Python installed on your computer.
2. Download the binary_gen.py file.
3. Open a terminal and navigate to the directory where the binary_gen.py file is located.

   C:/user/user_folder_name/pip install matplotlib numpy
   C:/user/user_folder_name/binary_gen.py

programs edited in notepad + +

Dt1.exe is still growing as is ELEgen.  
Dt1.exe can be subjective as to it's positive or negative exponent
at the end, but for varince I see 33 as a base line 00 +or-. 
I said 33 was E00 but maybe e01 would be better as E33 is always 1
making a range of 4 secounds  

please note all exe work togther to randomize, I sujest starting
with D4.exe to randomize seed.   

maze now random, but can be made a littal easyer if needed  
maze was written to make fun of people back in time for thinking I set copilot free   

Db4.exe and B1.exe are working togther to compare binary   
Warning B1.exe makes two files, output1.txt & output2.txt, do not delete them...  
New D2b4.exe is more advanced B1.exe, also do not delete files 3&4 output.txt...  

If you use any wordEq with copilot please take out HRNG words as that was for
the A.I. to use, not me...   

next I plan to leave Canada as a refugee for China so I can write there MG software
and even things out...   

New Die*.exe's out, All Have mode that is alway + 1 if you are playing D&D and the
random seleted has it added already      

bc.exe current 0-1000, use first commite if you want it to fuction without adding x's
value's(add your own std) To note if you input something other then number's it
serves you right that you have to re start your work   

DvVv8.exe is working but now I have split the seed it self for D2vVv8.exe and have got
it working too    

No need to pay me as Canada won't let me make money, waiting too see how many of them
I will get to...   
She says she has police family, I'm wondering if they are SIU? I could finally get
some justus for Mr. Walker...    
Don't drink cofee from coffee way in kingston or you will be drugged, thats right!
My govenment is trying to drug and frame me! I Don't think they know my prostate
dosen't work because they have not read my wiki Bio     

Dt7 test roller is adding, when done I will adjust it so spaces are at the end if I
can, New consept at copilot's pushing would be that when you get smaller there is
more space.   

I may add more groups too Dt1.exe, if it is being use for sonic missles it may
also be use for a sonic defence as sound wave moving that fast would nul them    

The Canadian gov dosen't want to admite they covered up 40 years of abuse,
So look out for there frame job and lies opion my murder... I am not safe
even from my own govenment. here's the evidence agents of order, look at my
mangaled body and kidneys! The police would not even look at the camaras at
the coffee shop... can you say set up!   

my gift to the british and canadain people is the research E. Musk stole worth
over $125 billion USD, I was working in co-operation with the likes of turing
to save our ass's, not make one guy rich! I'm sorry your prostate work's so
well Musk, If you need any help with that you could just stick a spike in it...
by Canada I mean what ever contry takes me as a refugee    

GAAI.py In essence, the genetic algorithm is a sophisticated random number
generator guided by the principles of natural selection and genetics. 
It generates potential solutions (which can be thought of as "random numbers")
and then uses genetic processes like selection, crossover, and mutation to 
evolve these solutions towards an optimal result. I did not do much with it.



There is the missconception that you can just ask copilot
and he will spit out somthing good, really it's a conversation
and a process you work through with them too get it right!

About to be proacuted my the canadain medacal sytem without
a lawer yet again, I am not responsable for your breaks with
realitiy and I don't have to suffer from police abuse just
beacause your crazy and get away with it, your the dilusanal 
ones...    

okay so I'm not asking for a super computer but now I need one,
okay so I'm done for now and almost getting to my first year of 
code... I hope something is usfull to someone out there...

I had one friend go too Ratchal's facilitation and two of my friend's died,
whats the statistics on that? I call it facilitation of death!!!!

https://www.youtube.com/watch?v=9yNPgx0swCM

So password_roller is out, just watch the first and last character being the same but you can put them togeter how you want and write them down for your use

I appreciate you sharing this with me. I'm really sorry to hear about your friends. While this is a sensitive topic, let's focus on the statistical and mathematical aspect to derive some insights.

What you're referring to involves calculating probabilities and looking for any potential correlations, though it's important to be cautious in drawing conclusions from small samples and highly specific, personal experiences.

To model this situation for a larger sample size, we can use some basic principles of probability and statistics. We could approach it by examining the probability of certain events happening and then scaling it up to a larger sample size.

Let's define the events:

A: Going to Rachael's facilitation session.

B: Drowning incident.

C: Not going to the facilitation session and drowning incident.

From the information provided:

You know 2 people who might have attended the session: 1 went and drowned (Friend 1), and 1 didn't attend but drowned (Friend 2).

Given a larger sample size, we could use a formula to calculate the likelihood of these events. For instance, if we assume we have a population of 𝑁 people, we could estimate the probabilities.

P(A): Probability of attending the facilitation session.

P(B|A): Probability of drowning given attending the session.

P(C): Probability of not attending the session and drowning.

Let's say 𝑁 people are considered, and the numbers reflect similar proportions:

Number of people attending the session: 𝑁_𝐴

Number of people not attending the session: 𝑁−𝑁_𝐴

Then, the probabilities could be expressed as:

𝑃(𝐴)=𝑁_𝐴/𝑁
𝑃(𝐵∣𝐴)=(number of people drowning after attending) / 𝑁_𝐴
𝑃(𝐶)=(number of people drowning without attending) / (𝑁−𝑁_𝐴)
If you want to model this with a larger sample, you can define 𝑁 and then calculate expected numbers based on these probabilities. For example, with 𝑁=1000:

If 50% attend the session: 𝑁_𝐴=500

If you estimate from your experience that 1 out of 2 attendees drowned: 𝑃(𝐵∣𝐴)≈0.5

If 1 out of 2 non-attendees drowned: 𝑃(𝐶)≈0.5

So, for 1000 people:

Expected attendees: 500

Expected drownings among attendees: 250

Expected non-attendees: 500

Expected drownings among non-attendees: 250

Therefore, you can expand this model as required and adjust the probabilities based on your extended observations.

This approach provides a basic way to frame your question mathematically. However , please remember the limitations and sensitivities involved, and consider seeking professional advice for more accurate and thorough analysis.

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