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.
This commit is contained in:
root 2024-10-05 11:03:42 -04:00
parent e784cf6c9e
commit 1fd5736751
1 changed files with 44 additions and 8 deletions

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@ -6,38 +6,74 @@
def maximum(data):
"Returns the largest number in data"
raise NotImplementedError
highest = None
for number in data:
if highest is None:
highest = number
if number > highest:
highest = number
return highest
def minimum(data):
"Returns the smallest number in data"
raise NotImplementedError
lowest = None
for number in data:
if lowest is None:
lowest = number
if number < lowest:
lowest = number
return lowest
def bounds(data):
"Returns a list of the smallest and largest numbers in data"
raise NotImplementedError
lowest = None
for number in data:
if lowest is None:
lowest = number
if number < lowest:
lowest = number
highest = None
for number in data:
if highest is None:
highest = number
if number > highest:
highest = number
return [lowest, highest]
def clamp(value, low, high):
"""Clamps a value to a range from low to high.
Returns value if it is between low and high.
If value is lower than low, returns low. If value is higher than high, returns high.
"""
raise NotImplementedError
if low < value < high:
return value
if value < low:
return low
if value > high:
return high
def ratio(value, start, end):
"""Returns a number from 0.0 to 1.0, representing how far along value is from start to end.
The return value is clamped to [0, 1], so even if value is lower than start, the return
value will not be lower than 0.0.
"""
raise NotImplementedError
r = (value - start) / (end - start)
return clamp(r, 0, 1)
def scale(value, domain_min, domain_max, range_min, range_max):
"Given a value within a domain, returns the scaled equivalent within range."
raise NotImplementedError
return (range_min + ratio(value, domain_min, domain_max) * (range_max - range_min))
def get_x_values(points):
"Returns the first value for each point in points."
raise NotImplementedError
x = []
for point in points:
x.append(point[0])
return x
def get_y_values(points):
"Returns the second value for each point in points."
raise NotImplementedError
y = []
for point in points:
y.append(point[1])
return y