Olshansk

// SPDX-License-Identifier: MIT

pragma solidity ^0.8.0;

import "../utils/Context.sol";

/**
 * @dev Contract module which provides a basic access control mechanism, where
 * there is an account (an owner) that can be granted exclusive access to
 * specific functions.

Olshansk

.SILENT:

.PHONY: help
## This help screen
help:
	printf "Available targets\n\n"
	awk '/^[a-zA-Z\-\_0-9]+:/ { \
		helpMessage = match(lastLine, /^## (.*)/); \
		if (helpMessage) { \
			helpCommand = substr($$1, 0, index($$1, ":")-1); \

Olshansk

This gist is a short extension to Neutrollized/free-tier-gke that was discussed in this pull request.

The The information above details out how to use terraform to configure a low cost GKE cluster, but the Google Cloud SDK can be used to achieve a similar solution.

Free Tier Background

According to GCP's free tier, a free f1-micro instance is only available in one of the following regions: us-west1, us-central1, us-east1. Unfortunately, f1-micro instances are too small to run GKE.

Node configurations

Olshansk

import pandas as pd

from datetime import datetime, date
from iexfinance.stocks import get_historical_data

# Assumptions:
# 1. No missing data in existing dataframes in the store; data is complete between (date_min, date_max).
# 2. The requested data is of the same format (URL, params, retrieved columns, etc...).
# Reference for metadata: https://moonbooks.org/Articles/How-to-add-metadata-to-a-data-frame-with-pandas-in-python-/#store-in-a-hdf5-file
def get_historical_data_cached(store, symbol, start_date, end_date, **kwargs):

Olshansk

version: '3.7'
services:
  server:
    container_name: server
    build:
      context: .
      dockerfile: Dockerfile
    volumes:
      - ./src:/usr/src
    ports:

Olshansk

version: '3.7'
services:
  server:
    container_name: server
    build:
      context: .
      dockerfile: Dockerfile
    ports:
      - "8008:8008"
    environment:

Olshansk

FROM tiangolo/uvicorn-gunicorn-fastapi:python3.7

WORKDIR /usr/src/

COPY ./src .

Olshansk

from typing import Optional
from fastapi import FastAPI

app = FastAPI()

@app.get("/")
def read_root():
    return {"Hello2": "World5"}

Olshansk

# https://stackoverflow.com/questions/38931566
def background_gradient(s, m=None, M=None, cmap='Reds', low=0, high=0):
    if m is None:
        m = s.min().min()
    if M is None:
        M = s.max().max()
    rng = M - m
    norm = colors.Normalize(m - (rng * low), M + (rng * high))
    normed = s.apply(lambda x: norm(x.values))
    cm = plt.cm.get_cmap(cmap)

Olshansk

axis = np.linspace(5, 105, NUM_BINS + 1)[:-1]
jp_df = pd.DataFrame(jp_matrix, columns=axis, index=axis)
jp_df = jp_df.applymap(lambda val: 0 if math.isnan(val) else (val / NUM_STUDENTS))
jp_df