Solution to problem 16 in Python

src/Year_2015/P16.py

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+# --- Day 16: Aunt Sue ---
+
+# Your Aunt Sue has given you a wonderful gift, and you'd like to send her a
+# thank you card. However, there's a small problem: she signed it "From, Aunt
+# Sue".
+
+# You have 500 Aunts named "Sue".
+
+# So, to avoid sending the card to the wrong person, you need to figure out
+# which Aunt Sue (which you conveniently number 1 to 500, for sanity) gave you
+# the gift. You open the present and, as luck would have it, good ol' Aunt Sue
+# got you a My First Crime Scene Analysis Machine! Just what you wanted. Or
+# needed, as the case may be.
+
+# The My First Crime Scene Analysis Machine (MFCSAM for short) can detect a few
+# specific compounds in a given sample, as well as how many distinct kinds of
+# those compounds there are. According to the instructions, these are what the
+# MFCSAM can detect:
+
+#     children, by human DNA age analysis.
+#     cats. It doesn't differentiate individual breeds.
+#     Several seemingly random breeds of dog: samoyeds, pomeranians, akitas, and
+# vizslas.
+#     goldfish. No other kinds of fish.
+#     trees, all in one group.
+#     cars, presumably by exhaust or gasoline or something.
+#     perfumes, which is handy, since many of your Aunts Sue wear a few kinds.
+
+# In fact, many of your Aunts Sue have many of these. You put the wrapping from
+# the gift into the MFCSAM. It beeps inquisitively at you a few times and then
+# prints out a message on ticker tape:
+
+# children: 3
+# cats: 7
+# samoyeds: 2
+# pomeranians: 3
+# akitas: 0
+# vizslas: 0
+# goldfish: 5
+# trees: 3
+# cars: 2
+# perfumes: 1
+
+# You make a list of the things you can remember about each Aunt Sue. Things
+# missing from your list aren't zero - you simply don't remember the value.
+
+# What is the number of the Sue that got you the gift?
+
+ticker = {
+    "children": 3,
+    "cats": 7,
+    "samoyeds": 2,
+    "pomeranians": 3,
+    "akitas": 0,
+    "vizslas": 0,
+    "goldfish": 5,
+    "trees": 3,
+    "cars": 2,
+    "perfumes": 1,
+}
+
+with open("files/P16.txt") as f:
+    Sues = [line for line in f.read().strip().split("\n")]
+
+
+def is_true_Sue(things):
+    for thing in things:
+        name, value = thing.split(": ")
+        if ticker[name] != int(value):
+            return False
+    return True
+
+
+def part1():
+    for Sue in Sues:
+        aunt = Sue.split(":")[0]
+        things = Sue[len(aunt) + 2 :].split(", ")
+
+        if is_true_Sue(things):
+            print(f"{aunt} gave me the gift")
+            break
+
+
+# --- Part Two ---
+
+# As you're about to send the thank you note, something in the MFCSAM's
+# instructions catches your eye. Apparently, it has an outdated
+# retroencabulator, and so the output from the machine isn't exact values - some
+# of them indicate ranges.
+
+# In particular, the cats and trees readings indicates that there are greater
+# than that many (due to the unpredictable nuclear decay of cat dander and tree
+# pollen), while the pomeranians and goldfish readings indicate that there are
+# fewer than that many (due to the modial interaction of magnetoreluctance).
+
+# What is the number of the real Aunt Sue?
+
+
+def is_real_real_Sue(things):
+    for trait in things:
+        name, value = trait.split(": ")
+
+        if name in ("cats", "trees"):
+            if ticker[name] >= int(value):
+                return False
+        elif name in ("pomeranians", "goldfish"):
+            if ticker[name] <= int(value):
+                return False
+        elif ticker[name] != int(value):
+            return False
+    return True
+
+
+def part2():
+    for Sue in Sues:
+        aunt = Sue.split(":")[0]
+        things = Sue[len(aunt) + 2 :].split(", ")
+
+        if is_real_real_Sue(things):
+            print(f"{aunt} gave me the gift")
+            break
+
+
+if __name__ == "__main__":
+    part1()
+    part2()