Add 2021-07 in Python

Signed-off-by: AKU <tom@tdpain.net>

challenges/2021/07-theTreacheryOfWhales/README.md

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+# [Day <n>: <Day Title>](https://adventofcode.com/<year>/day/<n>)
+

challenges/2021/07-theTreacheryOfWhales/benchmark.json

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+{
+  "day": 7,
+  "dir": "challenges/2021/07-theTreacheryOfWhales",
+  "implementations": {
+    "Python": {
+      "part.1.avg": 0.0003524906635284424,
+      "part.1.max": 0.0011348724365234375,
+      "part.1.min": 0.00024008750915527344,
+      "part.2.avg": 0.033716694355010984,
+      "part.2.max": 0.08556342124938965,
+      "part.2.min": 0.025210857391357422
+    }
+  },
+  "numRuns": 1000
+}

challenges/2021/07-theTreacheryOfWhales/info.json

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+{
+    "inputFile": "input.txt",
+    "testCases": {
+        "one": [
+            {
+                "input": "16,1,2,0,4,2,7,1,2,14",
+                "expected": "37"
+            }
+        ],
+        "two": [
+            {
+                "input": "16,1,2,0,4,2,7,1,2,14",
+                "expected": "168"
+            }
+        ]
+    }
+}

challenges/2021/07-theTreacheryOfWhales/py/__init__.py

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+from typing import List
+from aocpy import BaseChallenge
+import math
+
+
+def parse(instr: str) -> List[int]:
+    return list(map(int, instr.strip().split(",")))
+
+
+def increasing_fuel_sum_to_position(crabs: List[int], position: int) -> int:
+    sigma = 0
+    for crab in crabs:
+        dist = abs(position - crab)
+        sigma += sum(i + 1 for i in range(dist))
+    return sigma
+
+
+def linear_fuel_sum_to_position(crabs: List[int], position: int) -> int:
+    sigma = 0
+    for crab in crabs:
+        sigma += abs(position - crab)
+    return sigma
+
+
+def median_distance(crabs: List[int]) -> int:
+    crabs = list(sorted(crabs))
+    num_crabs = len(crabs)
+    n = None
+    if num_crabs % 2 == 0:  # is even, therefore interpolate :(
+        high = crabs[int(num_crabs / 2)]
+        low = crabs[int(num_crabs / 2) - 1]
+        delta = high - low
+        n = low + int(delta / 2)
+    else:  # odd, take raw middle value
+        n = crabs[math.floor(num_crabs / 2)]
+    return n
+
+
+def mean_distance(crabs: List[int]) -> float:
+    return sum(crabs) / len(crabs)
+
+
+class Challenge(BaseChallenge):
+    @staticmethod
+    def one(instr: str) -> int:
+        crab_distances = parse(instr)
+        n = median_distance(crab_distances)
+        return linear_fuel_sum_to_position(crab_distances, n)
+
+    @staticmethod
+    def two(instr: str) -> int:
+        crabs = parse(instr)
+        n = mean_distance(crabs)
+
+        low = increasing_fuel_sum_to_position(crabs, math.floor(n))
+        high = increasing_fuel_sum_to_position(crabs, math.ceil(n))
+
+        return min(low, high)