petebankhead

/**
 * Add a 'small' image overlay to the image currently open in the QuPath viewer.
 * Here, 'small' indicates something that ImageIO can handle, and which isn't pyramidal.
 *
 * @author Pete Bankhead
 * 
 * Updated 1/2023 to be compatible with QuPath v0.4.x
 */

okld

import streamlit as st
from persist import persist, load_widget_state


def main():
    if "page" not in st.session_state:
        # Initialize session state.
        st.session_state.update({
            # Default page.
            "page": "home",

dmontagu

# Based on the example from https://www.activestate.com/blog/dash-vs-bokeh/
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import plotly.graph_objs as obj
import uvicorn as uvicorn
from dash.dependencies import Input, Output
from fastapi import FastAPI
from starlette.middleware.wsgi import WSGIMiddleware

johnhw

### JHW 2018
import numpy as np
import umap



# This code from the excellent module at:
# https://stackoverflow.com/questions/4643647/fast-prime-factorization-module
    
import random

whizzzkid

# Instructions for 4.14 and cuda 9.1
# If upgrading from 4.13 and cuda 9.0
$ sudo apt-get purge --auto-remove libcud*
$ sudo apt-get purge --auto-remove cuda*
$ sudo apt-get purge --auto-remove nvidia*
# also remove the container directory direcotory at /usr/local/cuda-9.0/

# Important libs required with 4.14.x with Cuda 9.X
$ sudo apt install libelf1 libelf-dev

conormm

R to python data wrangling snippets

The dplyr package in R makes data wrangling significantly easier. The beauty of dplyr is that, by design, the options available are limited. Specifically, a set of key verbs form the core of the package. Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).

dplyr is organised around six key verbs: