generated from mwc/lab_scatter
	I wrote new codes under the comments.
The first few went will for me but when I started to get to the def ratio function everything started getting confusing. When I was stuck I did try to use my notes from CsE 115 which helped me out a little.
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								transform.py
									
									
									
									
									
								
							
							
						
						
									
										78
									
								
								transform.py
									
									
									
									
									
								
							@@ -5,39 +5,79 @@
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# None of them are finished; this is your job!
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					# None of them are finished; this is your job!
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def maximum(data):
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					def maximum(data):
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    "Returns the largest number in data"
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					    #Returns the largest number in data
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    raise NotImplementedError
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					    largest= data[0]
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					    for n in data:
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					        if n > largest:
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					            largest = n
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					    return largest
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def minimum(data):
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					def minimum(data):
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    "Returns the smallest number in data"
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					   #Returns the smallest number in data
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    raise NotImplementedError
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					    smallest= data[0]
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					    for n in data:
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					        if n < smallest:
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					            smallest = n
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					    return smallest
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def bounds(data):
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					def bounds(data):
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    "Returns a list of the smallest and largest numbers in data"
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					#Returns a list of the smallest and largest numbers in data
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    raise NotImplementedError
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					    smallest= data[0]   
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					    largest= data[0]
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					    for n in data:
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					        if n < smallest:
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					            smallest = n 
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					        if n > largest:
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					            largest = n
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					    return [smallest,largest]        
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def clamp(value, low, high):
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					def clamp(value, low, high):
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    """Clamps a value to a range from low to high. 
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					#     """Clamps a value to a range from low to high. 
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    Returns value if it is between low and high.
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					#     Returns value if it is between low and high.
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    If value is lower than low, returns low. If value is higher than high, returns high.
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					#     If value is lower than low, returns low. If value is higher than high, returns high."""
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    """
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					#    
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    raise NotImplementedError
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					    if value > high:
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					        return high
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					    elif value < low:
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					        return low
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					    else:
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					        return value
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def ratio(value, start, end):
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					def ratio(value, start, end):
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    """Returns a number from 0.0 to 1.0, representing how far along value is from start to end.
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					    """Returns a number from 0.0 to 1.0, representing how far along value is from start to end.
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    The return value is clamped to [0, 1], so even if value is lower than start, the return 
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					    The return value is clamped to [0, 1], so even if value is lower than start, the return 
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    value will not be lower than 0.0.
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					    value will not be lower than 0.0.
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    """
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					    """
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    raise NotImplementedError
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					    r= (value - start) / (end - start)   
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					    if start==end:
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					        return 0.0
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					    if r < 0.0:
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					        return 0.0
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					    elif r > 1.0:
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					        return 1.0
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					    else:
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					        return r
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def scale(value, domain_min, domain_max, range_min, range_max):
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					def scale(value, domain_min, domain_max, range_min, range_max):
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    "Given a value within a domain, returns the scaled equivalent within range."
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					#     "Given a value within a domain, returns the scaled equivalent within range."
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    raise NotImplementedError
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					    if domain_min == domain_max:
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					        return range_min
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					    r = (value - domain_min) / (domain_max - domain_min)
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					    return range_min + r * (range_max - range_min) 
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def get_x_values(points):
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					def get_x_values(points):
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    "Returns the first value for each point in points."
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					#     "Returns the first value for each point in points."
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    raise NotImplementedError
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					    x_values=[] 
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					    for i in points:
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					        x_values.append(i[0])
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					    return x_values
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def get_y_values(points):
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					def get_y_values(points):
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    "Returns the second value for each point in points."
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					#     "Returns the second value for each point in points."
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    raise NotImplementedError
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					    y_values = []
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					    for i in points:
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					        y_values.append(i[1]) 
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					    return y_values
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