lukegre

"""
Contains a function to quickly plot xarray datasets on a map

Loading the script creates a method for xr.DataArrays that can be used as follows:

da.mean('time').map()

Defaults can also be changed by changing values in the rcMaps dictionary. 
I haven't figured out how this can be changed in notebooks, but you can just 
change these with the **kwargs argument. 

lukegre

import xarray as xr
import numpy as np
import joblib
import os


# SEASONAL CYCLE FITTING # 
def graven2013_seasonal_fit(da, n_jobs=24):
    """
    Fits a seasonal cycle to data using cos and sin functions. 

lukegre

def load_url_as_python_module(url, pkg_name=None, save_dir=None):
    """Imports a python file url as a package. 
    
    Downloads the file to local storage and imports into the global namespace. 
    Can be useful to quickly access gists and github files as functions. 
    
    Warning: this function could be VERY dangerous as a script could be 
        run on your machine without knowing what it does... be very careful
    
    Args:

lukegre

lukegre

def input_limits(return_bool=True, return_info=True, **kwargs):
    """
    This is a function wrapper for functions that have defined input limits. 
    The user can define the valid limits of any of the inputs. 
    
    Parameters
    ----------
    return_bool: array-bool
        returns a boolean array of the original function where valid or not
    return_info: array-str

lukegre

lukegre

import numpy as np

### Implementation of our EMD() function
def emd(data, stop_limit=0.001, spline_order=3):
    """
    EMD as explained by Scott Cole (https://srcole.github.io/2016/01/18/emd/),
    but has been modified by Luke Gregor (https://github.com/luke-gregor) to
    automatically stop when there are no more IMFs in the dataset.
    Additionally, the residual from the stopping point and the original dataset
    is counted as the final IMF.

lukegre

#!/usr/bin/env python

"""
A short script that is used to fill gaps in a time series (gap_filler_with_noise). 
The method uses gaussian processes (aka Kriging) to get the trend (calls gaussian_smoother). 
I recommend that you play around with the theta0 value to find the correct scale for the trend. 

Noise is added to the estimated trend by assessing the noise around the trend. 

Diagnostic plots can be created with this script. 

lukegre

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
This script is written to calculate total alkalinity according to Lee et al. (2006).
Please see function help for more details. 
Please acknowledge use.
"""

__author__ = "Luke Gregor"

lukegre

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