Ryan J. Dillon ryanjdillon

CDK implementation utilizing Nitro Enclave for ACM SSL cert handling

This synth's, but it wasn't tested as it turns out you need an instance class with vCPU==4+, which turns out to be quite expensive.

There is some additional nginx config to be done on the instance following provisioning, outlined in these AWS docs starting at point #7: https://docs.aws.amazon.com/enclaves/latest/user/nitro-enclave-refapp.html#install-acm

This was for a personal project, so I don't particularly see the usefulness of this at this price-point in comparison to using a load balancer, or Letsencrypt with a micro/nano instance.

I'd be interested to hear what applications this might have for those of you looking into this.

	#!/bin/bash

	usage () {
	cat <<HELP_USAGE

	$0 <area> <outputs-path>

	outputs-path: directory weather grib and radar gif should be saved
	area: weather area to collect for, try "n-northsea" or "west_norway". For
	more options see https://api.met.no/weatherapi/gribfiles/1.1/available

	[build-system]
	requires = ["setuptools", "setuptools-scm"]
	build-backend = "setuptools.build_meta"

	[project]
	name = "pytest-myplugin"
	description = "My pytest plugin"
	readme = "README.rst"
	requires-python = ">=3.5"
	license = {text = "MIT"}

	# Functions that push and pull to a singleton working directory...a bare-bones pushd/popd.
	# For namespace clartiy, Norwegian words were use: skyv (push) and trekk (pull).
	# Adapted from here https://stackoverflow.com/a/9916812

	directory_stack=$HOME/.directory_singleton
	function skyv() {
	# Save only one skyv path, overwriting the last
	echo $(pwd) > $directory_stack
	}

	"""
	Load test MLFlow logging to AzureML Workspace (as a tracking backend)

	Warning: Azure charges apply!

	The following environment variables must be set:
	* SUBSCRIPTION_ID - The Azure Subscription ID the Workspace is created under
	* RESOURCE_GROUP - The Azure ResourceGroup ID the Workspace is created under
	* WORKSPACE_NAME - The name of the AzureML Worskpace to log to
	"""

	# Install the aws cli
	source your-venv/bin/activate
	pip install awscli

	# Setup your credentials
	aws configure

	# Check out what is going to get transfered first
	aws s3 sync ./path/to/data s3://bucket-name --exclude "" --include ".nc" --dryrun

	def perc_bc_from_lipid(perc_lipid, perc_water=None):
	'''Calculate body composition component percentages based on % lipid

	Calculation of percent protein and percent ash are based on those presented
	in Reilly and Fedak (1990).

	Args
	----
	perc_lipid: ndarray
	1D array of percent lipid values from which to calculate body composition

	'''
	A Package for retrieving Kartverket image data for specified areas.

	Exampl URLs
	-----------
	GetCapabilities:
	http://opencache.statkart.no/gatekeeper/gk/gk.open?Version=1.0.0&service=wms&request=getcapabilities

	NOAA (not active/testable):
	http://porter.pmel.noaa.gov:8922/wms/wms_servlet?VERSION=1.1.1&REQUEST=GetMap&LAYERS=coads_climatology_cdf:airt&STYLES=ferret_default&WIDTH=640&HEIGHT=320&FORMAT=image/png&SRS=EPSG:4326&BBOX=-180.0,-90.0,180.0,90.0&EXCEPTION=application/vnd.ogc.se_xml&DIM_COADS_CLIMATOLOGY_CDF_TIME=15-Jan

	def calc_mod_density(mass_kg, dens_kgm3, n_blocks, block_type):
	'''Calculate the modified density with attached blocks

	Args
	----
	mass_kg: float
	mass of the seal (kg)
	dens_kgm3: float
	mass of the seal (kg/m^3)
	block_type: str


	def lip2dens(p_lipid, lipid_dens=0.9007, prot_dens=1.34, water_dens=0.994, a_dens=2.3):
	'''Derive tissue density from lipids

	Translated to python from R code written by Martin Biuw
	'''

	p_comps = perc_bc_from_lipid(p_lipid)

	p_comps['density'] = (lipid_dens * (0.01 * p_comps['perc_lipid'])) + \