misterhay

from pythonosc import udp_client
from time import sleep
client = udp_client.SimpleUDPClient('192.168.1.126', 10023)
for x in range(5):
    for i in range(9):
        client.send_message('/ch/0'+str(i)+'/mix/on', 0)
        client.send_message('/ch/0'+str(i)+'/mix/fader', 0.1)
        sleep(0.5)
        for c in range(16):
            client.send_message('/ch/0'+str(i)+'/config/color', c)

misterhay

%%html
<script>
requirejs.config({
    paths: { 
        'phidget22': ['https://unpkg.com/phidget22@^3.10/browser/phidget22'], 
    },                                         // strip .js ^, require adds it back
});

var usbconn;
require(['phidget22'], function(phidget22) {

misterhay

misterhay

class_code = '5L'
value = 12

from datetime import datetime
import requests
import pandas as pd

ethercalc_id = 'callysto'  # can be the same as class_code, or randomly generated
base_url = 'https://ethercalc.net/'
post_url = base_url+'_/'+ethercalc_id

misterhay

import pandas as pd
import plotly.express as px

url = 'http://ets.aeso.ca/ets_web/ip/Market/Reports/CSDReportServlet'
df = pd.read_html(url, header=0)

updated = df[1].columns[-1].split(': ')[1]
legend = df[1]['Legend'].values.tolist()
notes = df[1]['Legend.1'].dropna().values.tolist()

misterhay

# download csv files from https://climateknowledgeportal.worldbank.org/download-data

import pandas as pd
import os

df = pd.DataFrame()
for root, dirs, files in os.walk('.'):
    for name in files:
        if name.endswith('.csv'):
            df2 = pd.read_csv(os.path.join(root, name), skiprows=1)

misterhay

countries = sorted(['Canada','United States','Mexico','Costa Rica','China'])
year = '2013'

import pandas as pd
co2 = pd.read_csv('https://docs.google.com/spreadsheets/d/1VxxG9XJNG5oFXBo_0TTCP_sQ9wEv3Oy5SEOlR_0du5c/export?gid=1415343215&format=csv')
electricity = pd.read_csv('https://docs.google.com/spreadsheets/d/1L9CAwtcOoG3WfGkrFOYjAByaznce-DGE1dPK-1-23PU/export?gid=1944428591&format=csv')

y1 = co2[co2['country'].isin(countries)][year]
y2 = electricity[electricity['country'].isin(countries)][year]/1000
y1_name = 'Carbon Dioxide Emissions per Person'

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misterhay

import pandas as pd
import plotly.express as px
df = pd.read_html('https://en.wikipedia.org/wiki/List_of_generating_stations_in_Alberta')
categories = ['coal','natural gas','dual fuel','biomass','geothermal','hydroelectric','wind','solar']
values = []
for i, c in enumerate(categories):
  total = int(pd.to_numeric(df[i+1]['Capacity (MW)'], errors='coerce').sum())
  values.append(total)
px.pie(names=categories, values=values)

misterhay

// Return Halo Infinite stats for a gamertag as a json string
// instructions for creating your own endpoint at https://docs.autocode.com/getting-started/creating-your-first-project/creating-an-endpoint/

const lib = require('lib')({token: process.env.STDLIB_SECRET_TOKEN});

module.exports = async (context) => {
  const gamertag = context.params.gamertag;
  // if no gamertag is passed return an error
  if (!gamertag) {
    return {