Welcome back! Since you last read, we’ve been fine tuning our Metaverse Leaderboards, diving down rabbit holes in our data & talking to a number of potential investors. Too many actually, we’re thinking about charging them.
Today, WeMeta is excited to (re)release the WeMeta Aggregator!
Surf, find, analyze, and buy land across the metaverse quickly & easily!
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.
The Metaverse Leaderboards are a collection of rank-ordered leaderboards covering 4 key questions:
In this story we’re going to start understanding account relationships on the NEAR Protocol by exploring transactions between accounts.
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
receiver of all
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…
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
Let’s do the New York Times Company;
That outputs a yfinance.Ticker object, which holds historical $NYT data accessible with
period=’max’ will return all data;
Prophet expects input data to have 2 columns,
y, so let’s just copy the historical dates (
hist.index) and adjusted closing prices (
hist[‘Close’]) to a new DataFrame.
“Time series forecasting is the use of a model to predict future values based on previously observed values.”
While advancements in data science often increase the infamous “skills…
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.
cv.VideoCapture() can be used to load and iterate through video frames on CPU. Let’s read the
corn.mp4 file with it;
.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,
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.
Next, load the image into memory with CPU (
.upload() it to the
gpu_frame (frame the image);
Image now in frame, we can start having fun.
OpenCV CUDA functions return
cv2.cuda_GpuMat (GPU matrices), so each result can be operated on without the user having to re-
Let’s convert the image…
Install PyCUDA with PIP;
pip install pycuda
If you don’t have pip, get pip.
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;
Save, close, then (back in Terminal) source the
You can now check your nvcc version with;
By using PyCUDA’s
SourceModule to create a function (
add_them) with CUDA C code, we can simply