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## extending_napari_viewer.ipynb

      
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                DragaDoncila
                / extending_napari_viewer.ipynb
            
            
              Last active
              June 29, 2022 16:14
            
              
                Extending the napari viewer with a widget, mousebinding and event callback
              
          
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## environment.yml
name: tracking-challenge-demo
channels:
  - conda
dependencies:
  - python=3.8
  - pip
  - pip:
    - napari[all]
    - cookiecutter
    - scikit-image

## shield.json
{
    "color": "#0074B8",
    "label": "napari hub",
    "logoSvg": "<svg width=\"512\" height=\"512\" viewBox=\"0 0 512 512\" fill=\"none\" xmlns=\"http://www.w3.org/2000/svg\"><circle cx=\"256.036\" cy=\"256\" r=\"85.3333\" fill=\"white\" stroke=\"white\" stroke-width=\"56.8889\"/><circle cx=\"256.036\" cy=\"42.6667\" r=\"42.6667\" fill=\"white\"/><circle cx=\"256.036\" cy=\"469.333\" r=\"42.6667\" fill=\"white\"/><path d=\"M256.036 28.4445L256.036 142.222\" stroke=\"white\" stroke-width=\"56.8889\" stroke-linecap=\"round\" stroke-linejoin=\"round\"/><path d=\"M256.036 369.778L256.036 483.556\" stroke=\"white\" stroke-width=\"56.8889\" stroke-linecap=\"round\" stroke-linejoin=\"round\"/><circle cx=\"71.2838\" cy=\"149.333\" r=\"42.6667\" transform=\"rotate(-60 71.2838 149.333)\" fill=\"white\"/><circle cx=\"440.788\" cy=\"362.667\" r=\"42.6667\" transform=\"rotate(-60 440.788 362.667)\" fill=\"white\"/><path d=\"M58.967 142.222L157.501 199.111\" stroke=\"white\" stroke-width=\"56.8889\" stroke-linecap=\"rou

## shield.json
{
    "schemaVersion": 1,
    "label": "hello",
    "message": "my-napari-plugin",
    "color": "orange"
}

## knn_classify.py
from matplotlib.colors import ListedColormap
from sklearn import neighbors
from imblearn.under_sampling import RandomUnderSampler
from sklearn.model_selection import cross_val_score
from sklearn.metrics import confusion_matrix

import itertools
import pandas as pd
import os
import re

## file_processes.py
import pandas as pd
import os
import re

from sklearn.decomposition import PCA

from microscopium import io
from microscopium.preprocess import montage_stream
from microscopium.preprocess import correct_multiimage_illumination
from microscopium.preprocess import find_background_illumination

## integer_sum.py
"""
Script takes a target sum from the user, along with a list of integers, and displays the pairs of numbers in the list
which sum to the target given.
"""
target_sum = int(input("Enter target sum: "))

addends = []
user_input = input("Please enter a number (Q to stop): ").lower()
# get integers from user until they choose to stop (assume user enters either integers or q for simplicity)
while user_input != "q":
	name: tracking-challenge-demo
	channels:
	- conda
	dependencies:
	- python=3.8
	- pip
	- pip:
	- napari[all]
	- cookiecutter
	- scikit-image
	{
	"color": "#0074B8",
	"label": "napari hub",
	"logoSvg": "<svg width=\"512\" height=\"512\" viewBox=\"0 0 512 512\" fill=\"none\" xmlns=\"http://www.w3.org/2000/svg\"><circle cx=\"256.036\" cy=\"256\" r=\"85.3333\" fill=\"white\" stroke=\"white\" stroke-width=\"56.8889\"/><circle cx=\"256.036\" cy=\"42.6667\" r=\"42.6667\" fill=\"white\"/><circle cx=\"256.036\" cy=\"469.333\" r=\"42.6667\" fill=\"white\"/><path d=\"M256.036 28.4445L256.036 142.222\" stroke=\"white\" stroke-width=\"56.8889\" stroke-linecap=\"round\" stroke-linejoin=\"round\"/><path d=\"M256.036 369.778L256.036 483.556\" stroke=\"white\" stroke-width=\"56.8889\" stroke-linecap=\"round\" stroke-linejoin=\"round\"/><circle cx=\"71.2838\" cy=\"149.333\" r=\"42.6667\" transform=\"rotate(-60 71.2838 149.333)\" fill=\"white\"/><circle cx=\"440.788\" cy=\"362.667\" r=\"42.6667\" transform=\"rotate(-60 440.788 362.667)\" fill=\"white\"/><path d=\"M58.967 142.222L157.501 199.111\" stroke=\"white\" stroke-width=\"56.8889\" stroke-linecap=\"rou
	{
	"schemaVersion": 1,
	"label": "hello",
	"message": "my-napari-plugin",
	"color": "orange"
	}
	from matplotlib.colors import ListedColormap
	from sklearn import neighbors
	from imblearn.under_sampling import RandomUnderSampler
	from sklearn.model_selection import cross_val_score
	from sklearn.metrics import confusion_matrix

	import itertools
	import pandas as pd
	import os
	import re
	"""
	Script takes a target sum from the user, along with a list of integers, and displays the pairs of numbers in the list
	which sum to the target given.
	"""
	target_sum = int(input("Enter target sum: "))

	addends = []
	user_input = input("Please enter a number (Q to stop): ").lower()
	# get integers from user until they choose to stop (assume user enters either integers or q for simplicity)
	while user_input != "q":