randerzander

#!/usr/bin/env bash
set -euo pipefail

# Launch the latest vanilla Java Edition with a local/offline username.
# Supported on Linux and macOS (Intel and Apple Silicon).
# Usage: ./start-client.sh [username] [version]
# Overrides: MC_USERNAME=Player, MC_VERSION=release, MC_MEMORY=4G

SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)"
CLIENT_DIR="$SCRIPT_DIR/client"

randerzander

import imageio    
import cv2                                                    
import numpy as np    
                                         
# Load GIF                
gif = imageio.mimread('calvin.gif')                                                                                                                        
frames = [cv2.cvtColor(np.array(frame), cv2.COLOR_RGBA2BGR) for frame in gif]                                                                              
    
def is_panel(box, w_thresh=200, h_thresh=200):    
    x, y, w, h = box           

randerzander

Game {
    height: 3;
    width: 50%;
}

randerzander

from textual.app import App, ComposeResult
from textual.containers import Container
from textual.reactive import reactive
from textual.widgets import Static


class Thing(Static):
    def __init__( self, text ) -> None:
        self._text = text
        super().__init__()

randerzander

# determines whether a GPU is available in your instance and starts an appropriate Dask Cluster
from dask.distributed import Client, LocalCluster
from dask_cuda import LocalCUDACluster
from dask_sql import Context
from pyngrok import ngrok
import dask
import time
import warnings

from IPython.core.magic import needs_local_scope, register_cell_magic

randerzander

from IPython.core.magic import register_cell_magic, needs_local_scope
from sql_formatter.core import format_sql
import time, warnings

warnings.filterwarnings("ignore")

@register_cell_magic
@needs_local_scope
def sql(line, cell, local_ns):
    sql_statement = cell.format(**local_ns)

randerzander

df = spark.createDataFrame(
    [
        (1, 'foo'), # create your data here, be consistent in the types.
        (2, 'bar'),
    ],
    ['id', 'txt'] # add your columns label here
)

df.write.csv('test')
spark.read.csv('test').collect()

randerzander

import sys, os, cgi, _thread
from http.server import SimpleHTTPRequestHandler, HTTPServer

class MyServer(SimpleHTTPRequestHandler):
  #Custom POST handler that saves POSTed file content to local disk
  def do_POST(self):
    post_env = { 
      'REQUEST_METHOD': 'POST',
      'CONTENT_TYPE': self.headers['Content-Type'],
    }   

randerzander

# Generate all substrings of string recursively
def substrings(string):
  #base case
  if len(string) == 0: return ''
  else:
    ret = [string]
    #Slide from left
    for x in range(0, len(string)):
      sub = string[x:-1]
      ret.append(sub)

randerzander

from org.apache.commons.io import IOUtils
from java.nio.charset import StandardCharsets
from org.apache.nifi.processor.io import StreamCallback

import os, sys, imp, traceback, time
from urlparse import urlparse

parser_path = '/home/dev/projects/scripts/parsers/'

class PyStreamCallback(StreamCallback):