generated from mwc/lab_scatter
I completed defining the functions in transform.py.
I think it went well. The errors were kind of easy to fix because it simple math, so seeing what was expected and what was returned helped me to fix the errors. The one I had the most trouble with was actually the ratio function, because I kept gettinga negative value even when I first tried to include the clamp function. Eventually I figured to return clamp with r as the value, instead of clamp and return r.
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transform.py
52
transform.py
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@ -6,38 +6,74 @@
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def maximum(data):
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"Returns the largest number in data"
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raise NotImplementedError
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highest = None
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for number in data:
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if highest is None:
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highest = number
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if number > highest:
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highest = number
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return highest
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def minimum(data):
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"Returns the smallest number in data"
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raise NotImplementedError
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lowest = None
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for number in data:
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if lowest is None:
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lowest = number
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if number < lowest:
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lowest = number
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return lowest
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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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raise NotImplementedError
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lowest = None
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for number in data:
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if lowest is None:
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lowest = number
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if number < lowest:
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lowest = number
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highest = None
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for number in data:
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if highest is None:
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highest = number
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if number > highest:
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highest = number
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return [lowest, highest]
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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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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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"""
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raise NotImplementedError
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if low < value < high:
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return value
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if value < low:
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return low
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if value > high:
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return high
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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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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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"""
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raise NotImplementedError
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r = (value - start) / (end - start)
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return clamp(r, 0, 1)
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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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raise NotImplementedError
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return (range_min + ratio(value, domain_min, domain_max) * (range_max - range_min))
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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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raise NotImplementedError
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x = []
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for point in points:
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x.append(point[0])
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return x
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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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raise NotImplementedError
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y = []
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for point in points:
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y.append(point[1])
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return y
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