High Performance Computing most generally refers to the practice of aggregating computing power in a way that delivers much higher performance than one could get out of a typical desktop computer or workstation in order to solve large problems in science, engineering, or business(USGS). Supercomputers give us a chance to solve problems too complex for the desktop but every problem has a specific supercomputer configuration that is suitable. We use these configurations from different services to make sure our clients problem is resolved efficiently.
Description:
Points-cli is a small program that takes a given number of randomly generated points in 3-Dimensions and plots them in multiple colors using matplotlib. It then allows the user to obtain various measurements from distances and angles of the points to the van de Waals potential between centers of noble gases' molecules emulated by the random points.
The points are restricted within boundaries of a box. The user interacts with the program through both the command line interface and the matplotlib plot. From the command line interface the user is taken through elaborative steps that describe the proper arguments and options. The plot is a 3D scatter plot with an extra feature that enables the user to hover the mouse over any point to display the index assigned to that point.
After the plot has been displayed an access to a menu appears on the command line with five options: Measure distance, Measure angle, vdW Lennard-Jones, vdW Morse and Exit.
GETTING STARTED
Dependencies:
Python 3.7.6
Numpy 1.18.1
Matplotlib 3.1.3
tqdm 4.47.0
Installing:
pip install Points-cli
Executing program:
Points-cli
Help:
To make the program run faster opt for a smaller number of points depending on the processing power and memory available at your disposal.
You can always use Ctrl+C to prematurely exit the program.
In the current state of the world acquiring a lot of money is not simple and living a life
without the concern for economic welfare is not an option for the majority of the population. As we
observe individuals that are better off economically, they tend to have investments that they
hold valuably(usually stocks). The main goal of such investors is to have their investment return them profits
fairly and reliably to have their retirements early or make life easier for themselves when they
retire.
Therefore, we tried to build a model using pyspark that would try to support stock investors on their
ventures by helping them make better decisions. Due to its high volatility and the technical analysis on
stocks (generally) not working on high intervals, we chose Bitcoin as our reference stock. We
tried to implement an aspect of Technical Analysis (candlestick patterns).