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add stack trace

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@ -11,6 +11,8 @@ An automated Bitcoin wallet collider that brute forces random wallet addresses
<a href="https://www.python.org/downloads/">Python 3.6</a> or higher
Python modules listed in the <a href="/requirements.txt">requirements.txt<a/>
Minimum <a href="#memory-consumption">RAM requirements</a>
# Installation
@ -40,7 +42,7 @@ Private keys are generated randomly to create a 32 byte hexidecimal string using
The private keys are converted into their respective public keys using the `starkbank-ecdsa` Python module. Then the public keys are converted into their Bitcoin wallet addresses using the `binascii` and `hashlib` standard libraries.
A pre-calculated database of every Bitcoin address with a positive balance is included in this project. The generated address is searched within the database, and if it is found that the address has a balance, then the private key, public key and wallet address are saved to the text file `plutus.txt` on the user's hard drive.
A pre-calculated database of every P2PKH Bitcoin address with a positive balance is included in this project. The generated address is searched within the database, and if it is found that the address has a balance, then the private key, public key and wallet address are saved to the text file `plutus.txt` on the user's hard drive.
This program also utilizes multiprocessing through the `multiprocessing.Process()` function in order to make concurrent calculations.
@ -67,6 +69,14 @@ However, if a balance is found, then all necessary information about the wallet
>public key: 04393B30BC950F358326062FF28D194A5B28751C1FF2562C02CA4DFB2A864DE63280CC140D0D540EA1A5711D1E519C842684F42445C41CB501B7EA00361699C320<br/>
>address: 1Kz2CTvjzkZ3p2BQb5x5DX6GEoHX2jFS45<br/>
# Memory Consumption
This program uses approximately 2GB of RAM per CPU. Becuase this program uses multiprocessing, some data gets shared between threads, making it difficult to accurately measure RAM usage. But the stack trace below is as precise as I could get it:
![Imgur](https://i.imgur.com/9Cq0yf3.png)
The memory consumption stack trace was made by using <a href="https://pypi.org/project/memory-profiler/">mprof</a> to monitor this program brute force 10,000 addresses on a 4 logical processor machine with 8GB of RAM. As a result, 4 child processes were created, each comsuming 2100MiB of RAM (~2GB).
# Recent Improvements & TODO
<a href="https://github.com/Isaacdelly/Plutus/issues">Create an issue</a> so I can add more stuff to improve