adamjstewart

#!/usr/bin/env bash

# Bash function bodies can be delimited by curly braces,
# but they don't have to be. The following are all valid
# function body delimiters:
#
#     arithmetic: ((expression))
#     test:       [[ expression ]]
#     subshell:   (list)
#     group:      { list }

adamjstewart

#!/usr/bin/env bash

# Various methods of using Booleans in Bash

# Credit for most of these techniques comes from:
# http://stackoverflow.com/questions/2953646/

###########################################################
# Using the builtin 'true' and 'false' commands
###########################################################

adamjstewart

#!/usr/bin/env expect

# Automates CVS login to ocr, hwr, and rcr.
# Prompts user for password a single time.

# Turn off timeout
set timeout -1

# Ask user for password
stty -echo;  # make sure password isn't echo'ed

adamjstewart

#!/usr/bin/env bash

# There are many ways to get a path to a file or script.
# This script showcases several of them and their pitfalls.

# Credit for most of these techniques comes from:
# http://stackoverflow.com/questions/4774054/

abs_path='/usr/bin/bash'
rel_path='.'

adamjstewart

SRC := $(wildcard *.dot *.gv)
CMD := dot


.PHONY: help Makefile

# Put it first so that "make" without argument is like "make help".
help:
	@echo Usage:
	@echo

adamjstewart

#!/usr/bin/env bash
  
# Continuously kill processes
# Use Ctrl+C to exit

while true
do
    read -p "kill " pid

    if [[ "$pid" == $$ ]]

adamjstewart

from torch.utils.data import DataLoader
from torchgeo.datasets import CDL, Landsat7, Landsat8, stack_samples
from torchgeo.samplers import RandomGeoSampler

landsat7 = Landsat7(root="...")
landsat8 = Landsat8(root="...", bands=Landsat8.all_bands[1:-2])
landsat = landsat7 | landsat8

adamjstewart

cdl = CDL(root="...", download=True, checksum=True)
dataset = landsat & cdl

adamjstewart

sampler = RandomGeoSampler(dataset, size=256, length=10000)
dataloader = DataLoader(dataset, batch_size=128, sampler=sampler, collate_fn=stack_samples)

adamjstewart

for batch in dataloader:
    image = batch["image"]
    mask = batch["mask"]

    # train a model, or make predictions using a pre-trained model