Blockchain | Data Science

In this story we’re going to start understanding account relationships on the NEAR Protocol by exploring transactions between accounts.

Overview

• Fetch Transaction History
• Adding Time
• Initial EDA — Most Popular?
• Findings — What’s Popular?
• Graphistry — Explore Relationships
• Berry Club
• Visualize Transaction Volume
• Prophet — Forecast Transaction Volume
• Resources

Fetch Transaction History

Let’s start by pulling all the transactions from mainnet.

This is easy, just connect to NEAR Indexer for Explorer with mainnet credentials (explained);

Define a helper function to present query results in a pandas DataFrame;

Write a query to pull the signer and receiver of all transactions;

'''
select…

Blockchain | Data Science

NEAR Indexer for Explorer is a public database built on top of NEAR Indexer microframework to watch the network and store all the events in PostgreSQL.

In layman's terms, this database contains everything that’s happened on the NEAR Protocol (blockchain), both mainnet and testnet.

Selling a home can be a lot of fun. Selling a home can be a lot of not fun as well. Often, the difference is the ability of the most interested parties (Buyer and Seller) to know what is going on, and where they stand.

So, how can we improve the odds they know those things?

Well, as a ERC-721 Non-Fungible Token contract, SmartRPA enables secure transparency for both the Homeowner (Seller) and Potential Buyers by integrating Chainlink time enforcement (Alarm Clock) with the trust of blockchain and existing document management systems (e.g. DocuSign).

That’s a lot of words, let’s…

Install yfinance

With the Yahoo! Finance market data downloader (yfinance), we can pull historical data on virtually any stock with a single line of code.

You can install yfinance with pip;

pip install yfinance

From there, simply import the library, and pull a .Ticker().

Pull & Prep Data

Let’s do the New York Times Company;

That outputs a yfinance.Ticker object, which holds historical $NYT data accessible with .history(). Setting period=’max’ will return all data;

Prophet expects input data to have 2 columns, ds and y, so let’s just copy the historical dates (hist.index) and adjusted closing prices (hist[‘Close’]) to a new DataFrame.

Forecast with Prophet

…

“Time series forecasting is the use of a model to predict future values based on previously observed values.”

— Wikipeida

In this story, we’ll break down and examine the R API of Prophet, a procedure for forecasting time series data open-sourced by Facebook in February 2017 with v0.6 released in March 2020.

Outline

1. What is Facebook Prophet ?
2. How does Prophet work?
3. How does Prophet work
4. Practice with Prophet

• 4.1 Installation & Imports
• 4.2 Data & Prep
• 4.3 Making a Forecast
• 4.4 Breaking Down a Forecast
• 4.5 Forecast Quality Evaluation

What is Facebook Prophet?

While advancements in data science often increase the infamous “skills…

cv.cuda

OpenCV’s CUDA python module is a lot of fun, but it’s a work in progress.

For starters, we have to load in the video on CPU before passing it (frame-by-frame) to GPU. cv.cuda.imread() has not been built yet.

Step 1 — .upload()

cv.VideoCapture() can be used to load and iterate through video frames on CPU. Let’s read the corn.mp4 file with it;

After .read()ing the 1st image, we’re ready to make a GPU matrix (picture frame) so that image can be .upload()ed to our GPU.

Great! But what about the 2nd image?

Well, you probably noticed .read() output 2 variables, ret…

Outline

• On a Single Image
• On a Series of Images
• On Series of Images in Parallel with Dask Delayed

On a Single Image

First, we need to create GPU space (gpu_frame) to hold an image (as a picture frame holds a picture) before we can upload our image to the GPU.

Step 1: Upload

Next, load the image into memory with CPU (screenshot), and .upload() it to the gpu_frame (frame the image);

Image now in frame, we can start having fun.

Step 2: Have Fun

OpenCV CUDA functions return cv2.cuda_GpuMat (GPU matrices), so each result can be operated on without the user having to re-.upload().

Let’s convert the image…

Step 1 — Install PyCUDA

Install PyCUDA with PIP;

pip install pycuda

If you don’t have pip, get pip.

Step 2 — Set nvcc Path

Nvcc comes preinstalled, but your Nano isn’t exactly told about it.. Use sudo to open your bashrc file;

sudo gedit ~/.bashrc

Add a blank, then these 2 lines (letting your Nano know where CUDA is) to the bottom of the file;

export PATH=${PATH}:/usr/local/cuda/bin
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda/lib64

Save, close, then (back in Terminal) source the bashrc file

source ~/.bashrc

You can now check your nvcc version with;

nvcc --version

Step 3 — Test with Code

By using PyCUDA’s SourceModule to create a function (add_them) with CUDA C code, we can simply .get_function() …