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Jack Northrup JupyterJones

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@JupyterJones
JupyterJones / backup_jupyter.py
Created February 15, 2023 03:11
Locate and copy all jupyter notebooks in one directory renaming duplicate filenames
#Locate and Copy all Jupyter notebooks in one directory
import os
import shutil
src_dir = '/home/jack' # Replace this with the root directory where you want to search for Jupyter notebooks
dst_dir = '/home/jack/Desktop/HDD500/notebooks/' # Replace this with the destination directory where you want to copy the notebooks
notebooks = {} # Dictionary to keep track of notebook names and sequence numbers
for root, dirs, files in os.walk(src_dir):
for file in files:
if file.endswith('.ipynb'):
@JupyterJones
JupyterJones / blurred-transparent-circles.py
Created December 31, 2022 11:06
Create a removes a circle, blurs the edges and pastes that image on a blank transparent image
from PIL import Image, ImageFilter, ImageDraw
import glob
import time
def circleblur(FILENAME,saveDirectory,count):
im1 = Image.open(FILENAME)
im1 = im1.resize((512,512), Image.BICUBIC)
im2 = Image.new("RGBA",im1.size,(0,0,0,0))
mask = Image.new("L", im1.size, 0)
@JupyterJones
JupyterJones / logo.py
Last active November 25, 2022 23:08 — forked from SuperDoxin/logo.py
Python Logo using bezier curves and the turtle module
import turtle
import math
from PIL import Image
def lerp(a, b, t):
"""Linear interpolation function. returns a when t==0, returns b when t==1
and linearly interpolates for values inbetween"""
return (a * (1 - t)) + (b * t)
@JupyterJones
JupyterJones / Search.py
Created September 25, 2022 04:14
Search Jupyter notebooks for a term then enter the results in an SQLITE database
import glob
import os.path
FILES = []
dir = '.'
files = glob.glob(os.path.join(dir, '*.ipynb'))
for file in files:
print (file)
FILES.append(file)
def insert(data):
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JupyterJones / Working-With-Dates-and-TimeStamps.ipynb
Created May 12, 2022 03:22
Python - Working with Dates, Epochs and Time
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JupyterJones / mybootstrap.css
Created May 1, 2022 11:47
bootstrapcss modified
/*!
* Bootstrap v4.0.0 (https://getbootstrap.com)
* Copyright 2011-2018 The Bootstrap Authors
* Copyright 2011-2018 Twitter, Inc.
* Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
*/
:root {
--blue: #007bff;
--indigo: #6610f2;
--purple: #6f42c1;
@JupyterJones
JupyterJones / mongodb.html
Last active May 1, 2022 11:30
mongodb information
<!doctype html>
<html lang="en">
<head>
<title>MongoDB Information</title>
<meta name="description" content="Information concerning ChatterBot, mongodb, using mongo-compass and the terminal commands to work with Chatterbot statements.">
<!-- Required meta tags -->
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<!-- Bootstrap CSS -->
@JupyterJones
JupyterJones / InitiateDockerSqlserver.sh
Last active April 20, 2022 06:14
Start an SQL server
sudo docker pull microsoft/mssql-server-linux:2017-latest &&
docker run \
-e 'ACCEPT_EULA=Y' \
-e 'MSSQL_SA_PASSWORD=YourSTRONG!Passw0rd' \
-p 1401:1433 \
-n sql1 \
-d microsoft/mssql-server-linux:2017-latest
private static string _connStr = @"
Server=127.0.0.1,1401;
@JupyterJones
JupyterJones / Covid-Chart-USA-States.html
Created July 3, 2020 08:17
JavaScript - Get the twenty five states with most covid deaths Graphed high to low. Live data uploaded. All in one including CSS
<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en">
<head>
<title>Covid-19 Deaths in USA and Territories.</title>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta name="mobile-web-app-capable" content="yes">
<meta name="apple-mobile-web-app-capable" content="yes">
<meta content="Jack Northrup " name="author" />
@JupyterJones
JupyterJones / NormalizeData.py
Created May 29, 2020 00:30
Normalize - Ranges any span of numeric data in a list between 0-1
#!/usr/bin/python
'''
Ranges any span of numeric data in a list between 0-1
Usage:
from NormalizeData import *
DATA = [1,34,546,3985,857463,49495857]
NormalizeData(DATA)
>>> array([0.00000000e+00, 6.66722483e-07, 1.10110228e-05, 8.04915870e-05,
1.73239150e-02, 1.00000000e+00])
'''