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def contiguous_regions(condition):

    """
    Finds contiguous True regions of an input array. 
    
    Parameters
    ----------
    condition : `np.ndarray` of dtype "bool" or `np.ndarray` and conditional logic statement to produce the boolean array 
        e.g., arr    or    arr > 5.5

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def plot_image_fft(img_path, n_labels=7, d=1):
    
    '''
    Plot an image alongside its frequency-domain representation.
    
    
    Parameters
    ----------
    img_path: (str) the path to an image
    n_labels: (int) the number of labels to space out along each axis, default=7

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    def contrast_2d(arr, spectrum):

    """
    For an arbitrary 2D spectrogram and a characteristic 1D spectrum,
    find the magnitude of each Bivector rotor separating them in degrees.

    Paramters:
    arr: (`pd.DataFrame`) a 2D (t,f) matrix of spectrogram data with `pd.index.DateTimeIndex`.
    spectrum: (`np.array`) a vector (f) of sound levels representing a characteristic spectrum.

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def cardinal_to_degree(cardinal_direction, bounds=False):
    '''
    Convert cardinal direction (i.e., "ESE") to degrees.
    Output may be returned either as bounds [low, high] or an average bearing.
    
    Inputs
    ------
    cardinal_direction (str): initials of a cardinal (e.g., "N"), intercardinal (e.g. "NW"),
                               or secondary intercardinal (e.g., "NNW") direction.
                               

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def vessel_Lw(octave_f_c, v, v_c, C_B, D, E_num, E_mass, E_mount_spec):
    
    '''
    The Wittekind vessel noise source model
    
       Wittekind, D. K.: A simple model for the underwater
        noise source level of ships, J. Sh. Prod. Des., 30, 1–8,
        https://doi.org/10.5957/JSPD.30.1.120052, 2014.
        
       from equations published in Jalkanen et al. 2018,

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def offset_SPL(d_offset, V, speed_MPH, m, r, n_tires, A, Cd=0.8, ρ=1.1, surface_condition='dry'):
    
    '''
    SOUND LEVEL OF A COMBUSTION-ENGINE DRIVEN ROAD VEHICLE
    PROXIMAL TO THE ROAD SURFACE 
    
    
    T. Priede 1970, 
      "...the main criterion which determines [automotive] noise is the operational speed 
       or how short the time interval is within which the operation of one cycle of events 

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import numpy as np
np.set_printoptions(suppress=True)
import pandas as pd
import matplotlib.pyplot as plt

def get_third_octave_limits():
  
    '''
    Scrape limits of one-third octave bands from Engineering Toolbox HTML table.
    '''

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def Rasterize(geodataframe, inras, outras, field):
    
    '''
    Burn a geodataframe to a raster using an existing raster
    as a template.
    '''
    
    # open the template raster
    with rasterio.open(inras) as src:
        

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# Re-writing the first steps in a beat-matching algorithm described by Roger Dannenberg:
#
#    https://www.cs.cmu.edu/~rbd/papers/dannenberg-ismir2005-beat-tracking.pdf
# 
#
#  Particularly helpful is the function `conditional_threshold` which could be applied
#  more generally in thresholding situations where sections/chunks of a time series have
#  very different peaking behavior. 
#

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# Imports
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt, rcParams, rc
import matplotlib.dates as mdates
import datetime as dt
import sys
import os

# You'll need to clone two Github repositories to use this code