Track the Top Trending Communities, Worlds, Lands, and Owners of the Metaverse

At WeMeta, we deal with a lot of data. You could say, “a metaverse full.”

The data comes in all shapes & sizes from an array of APIs & virtual worlds, and we’ve figured out that maintaining useful metrics on the metaverse is not an easy or straightforward task.

Because of this, we wanted to create a simple interface where anyone can gain meaningful insight on what’s happening in the metaverse.

Introducing Metaverse Leaderboards

The Metaverse Leaderboards are a collection of rank-ordered leaderboards covering 4 key questions:

  1. What are the most in-demand worlds?
  2. Which are the most expensive…


Blockchain | Data Science

High Level Dapp and Account Specific Transaction Flow

Screen Shot 2021–06–23 at 4.55.15 AM

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);

'''
select…


Blockchain | Data Science

Connecting to NEAR Indexer for Explorer with psycopg2

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.


Neighbor to Neighbor Real Estate Transactions

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…


Free on Google Colab — Time series forecast with Prophet

Open in Colab: https://colab.research.google.com/github/gumdropsteve/intro_to_prophet/blob/master/prophet_yfinance.ipynb
Open in Colab: https://colab.research.google.com/github/gumdropsteve/intro_to_prophet/blob/master/prophet_yfinance.ipynb

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

Pull & Prep Data

Let’s do the New York Times Company;

Forecast with Prophet


Getting started with Facebook Prophet’s R API (real data + code)

“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
  • 4.2 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…


No camera required. (Built on Jetson Nano.)

Code to Reproduce this Display (original video source)

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;

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


From single image to Dask Delayed (Python)

Looks like we’re stuck in RGB.

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

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…


3 Step Set up and get started (+ test code)

Step 1 — Install PyCUDA

Install PyCUDA with PIP;

pip install pycuda

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
export PATH=${PATH}:/usr/local/cuda/bin
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda/lib64
source ~/.bashrc
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()


(Python) Set up Chromium Chromedriver and get started. (+ sample code)

Step 1 — Install Selenium with pip

sudo pip install selenium
sudo pip3 install selenium

Step 2 — Install Chromium Webdriver

sudo apt-get install chromium-chromedriver

Step 3 — Simple Test

Paste the following into your favorite editor or python terminal, and if it runs you’re good to go!

Fin

Thanks for reading! Please feel free to respond with any questions.

Continued Reading

Winston Robson

Friend links: https://gumdropsteve.github.io/blog — “Energy may be likened to the bending of a crossbow, decision to the releasing of a trigger.”

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