ryanjdillon

import seaborn

def light_response(light, EC50, hillslop=2.5):
    '''Typical response function for EC50 tests'''
    import numpy
    # http://www.sigmaplot.co.uk/splot/products/sigmaplot/productuses/prod-uses43.php

    # Response to concentration
    dose_min = light.min()
    dose_max = light.max()

ryanjdillon

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'])) + \

ryanjdillon

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

ryanjdillon

'''
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

ryanjdillon

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

ryanjdillon

# 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

ryanjdillon

"""
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
"""

ryanjdillon

# 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
}

ryanjdillon

[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"}

ryanjdillon

#!/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