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# Name our workflow | |
name: build-and-deploy-api-post | |
# Controls when the workflow will run | |
on: | |
# Triggers the workflow on push or pull request events but only for the "main" branch | |
push: | |
branches: [ "main" ] | |
# Allows you to run this workflow manually from the Actions tab |
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const tokenAddresses = [ | |
"0x5d27cFf80dF09f28534bb37d386D43aA60f88e25", //DPX | |
"0xD12d43Cdf498e377D3bfa2c6217f05B466E14228", //FRAX | |
"0xf21Ef887CB667f84B8eC5934C1713A7Ade8c38Cf", //MAGIC | |
"0xeA0a73c17323d1a9457D722F10E7baB22dc0cB83", //plvGLP | |
"0x4C9aAed3b8c443b4b634D1A189a5e25C604768dE", //USDC | |
"0x1ca530f02DD0487cef4943c674342c5aEa08922F", //USDC.e | |
"0x9365181A7df82a1cC578eAE443EFd89f00dbb643", //USDT | |
"0xC37896BF3EE5a2c62Cdbd674035069776f721668", //wBTC | |
"0x4987782da9a63bC3ABace48648B15546D821c720", //DAI |
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// copied from here: https://github.com/Lodestar-Finance/lodestar-staking/blob/main/scripts/create-handler.js | |
const tokenAddresses = [ | |
"0x5d27cFf80dF09f28534bb37d386D43aA60f88e25", //DPX | |
"0xD12d43Cdf498e377D3bfa2c6217f05B466E14228", //FRAX | |
"0xf21Ef887CB667f84B8eC5934C1713A7Ade8c38Cf", //MAGIC | |
"0xeA0a73c17323d1a9457D722F10E7baB22dc0cB83", //plvGLP | |
"0x4C9aAed3b8c443b4b634D1A189a5e25C604768dE", //USDC | |
"0x1ca530f02DD0487cef4943c674342c5aEa08922F", //USDC.e | |
"0x9365181A7df82a1cC578eAE443EFd89f00dbb643", //USDT |
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"Contracts": { | |
"CompoundLens": "0x5bea0da86173D4787f45DF7Ad2701792EBB3a7dB", | |
"JumpRateModel_1": "0xB0249679b9e72F90d9f9adEd98DF25425C4675cC", | |
"JumpRateModel_2": "0x457146F08f35116FEaf2877Da973ee6993128D1e", | |
"JumpRateModel_3": "0x6ef76Cdb3C11791044fE0D687CE7d07114972A8A", | |
"JumpRateModel_4": "0x66fE251097338a16b4ed97d57ef5C5F77C5a2d9A", | |
"JumpRateModel_5": "0xE453e1999A92Af2f02A54Bc984D4ac8b13B828B6", | |
"StdComptroller": "0x4A8eA74650F488612f212dBF1b2b006566bdfea4", | |
"PriceFeed": "0xAA6bD451EECAafad22A2cAd75983C0E225F29CbC", | |
"USDCDelegate": "0x35f9dd0d20193afC5FECd43a2Cf87b98EE9C8A00", |
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raw data dump | |
ports.js:265 cTokenAddress 0x724716Eb6677e738e8259891E76849cA58e03767 | |
ports.js:266 exchangeRateResult 200000000000000000000000000 | |
ports.js:267 supplyRateResult 0 | |
ports.js:268 borrowRateResult 9512937595 | |
ports.js:269 reserveFactorResult 0 | |
ports.js:270 totalBorrowsResult 0 | |
ports.js:271 totalReservesResult 0 | |
ports.js:272 totalSupplyResult 0 | |
ports.js:273 totalCashResult 0 |
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""" | |
Asha and Kelly are programming, but Asha practices more than Kelly. Not wanting to fall behind, Kelly resolves to practice more. | |
They each solve a number of problems in a day. Asha has already solved more problems than Kelly. What is the minimum number of days | |
it will take for Kelly to have solved more problems than Asha? | |
For example, if Asha is 5 problems ahead of Kelly and they solve 3 and 5 problems a day, Asha will be ahead by only 3 after the first day, | |
1 after the second, and Kelly will pass Asha on day 3. | |
------------------------------------------------------------------------------------------------------------------------------------------ | |
Your task is to write a program which: | |
- calculates the number of days needed by Kelly to catch up | |
- return -1 if it's impossible |
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import dash | |
import dash_core_components as dcc | |
import dash_html_components as html | |
import pandas as pd | |
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv') | |
df.head() | |
df['text'] = df['airport'] + '' + df['city'] + ', ' + df['state'] + '' + 'Arrivals: ' + df['cnt'].astype(str) |
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import dash | |
import dash_core_components as dcc | |
import dash_html_components as html | |
from dash.dependencies import Input, Output | |
from textwrap import dedent as d | |
import pandas as pd | |
import plotly.graph_objs as go | |
import psycopg2 | |
import json | |
import datetime |
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import dash | |
import dash_core_components as dcc | |
import dash_html_components as html | |
from dash.dependencies import Input, Output | |
from textwrap import dedent as d | |
import pandas as pd | |
import plotly.graph_objs as go | |
import psycopg2 | |
import json |
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# For some reason eBay make it surprisingly difficult to find every CATEGORY_ID and as a result I put together this script, | |
# so hopefully this script helps a few people who were in my position. All you need is requests and xmltodict. | |
# Cheers -- Cooper. | |
#! /usr/bin/env python | |
import xmltodict as xml | |
import requests | |
url = 'http://open.api.ebay.com/Shopping?callname=GetCategoryInfo&appid={}&siteid={}&CategoryID={}&version=729&IncludeSelector=ChildCategories' | |
app_id = 'YOUR-APP-ID-HERE' |
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