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# Inspired by https://gist.github.com/JeffPaine/3083347
# Access full state names using us_states.keys()
# Access all state abbreviations using us_states.values()
us_states = {
	'Alabama': 'AL',
	'Alaska': 'AK',
	'Arizona': 'AZ',
	'Arkansas': 'AR',
	'California': 'CA',
	'Colorado': 'CO',

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import os
import napari
from skimage.io import imread, imsave

def napari_count_tfpn(screenshot_file, point_size=20, save_screenshot=True):
    """
    Opens a napari window and initializes three points layers:
        - "True Positives" with green points
        - "False Positives" with red points
        - "False Negatives" with yellow points

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def copy_lut(from_layer, to_layer, opacity=False):
    """
    Copies the visualization properties from one layer to another in napari.
    
    Only the colormap, contrast limits, and gamma values are included by default.
    
    The opacity can be optionally copied as well.
    
    Blending and interpolation are ignored.
    

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// Requirements:
// Trainable Weka Segmentation

// The list of files to use for training must be described in a text file,
// with each line containing the full path to one image.

listOfImages = File.openDialog("Choose a file with a list of images:");
imagesString = File.openAsString(listOfImages);
images = split(imagesString, "\n");
for (i=0; i<images.length; i++) {

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for /r %%f in (*MP4) do (

ffmpeg -i %%f -q:a 0 -map a "%%~nf.wav"

)

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def reject_outliers(arr, window_size, threshold):
    """
    Given a 1D signal, replace outliers with the average of the surrounding points.

    Does the following for every point:
        1. Computes the median of the sliding window
        2. Computes the percentage difference between the current point and the
           sliding window median
        3. If that deviation exceeds the given threshold, replaces that point with
           the average of the surrounding two points.

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import os
import numpy as np
import matplotlib.pyplot as plt
from skimage import io
from skimage.measure import regionprops

# ----------------------------------------------------------
# Input
wdir = os.path.dirname('<DIRECTORY_CONTAINING_IMAGES>')
fname = '<LABELED_IMAGE_NAME>.tif'

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import numpy as np

x = np.array([0, 1, 2, 3, 4, 5])
y = np.array([4, 5, 6, 7, 8, 9])

displacement = np.sqrt(np.diff(x, prepend=x[0])**2 + np.diff(y, prepend=y[0])**2)

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def sarcastify(my_string):
    """
    Returns a sarcastic version of the provided string.
    
    Example:
    >>> sarcastify('Do you really think that\'s a good idea?')
    "dO YoU ReAlLy tHiNk tHaT'S A GoOd iDeA?"
    
    Useful for arguing online or replying all to that email that accidentally
    went to the whole company.

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# Copy/paste into the Avizo/Amira Python console.
# Alternatively, save to a file, add that file to your path, and import.
# See docstring for usage.
def get_cell_contents(tablelike_object,row,column,table=0):
    """
    Retrieves contents of cell i,j in a table through
    HxSpreadSheetInterface.
    
    You must use the object handle, not just the name of the object! For example,
    to get the contents of row 4 from column 5 of a HxLabelAnalysis data object