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0fa16a20ec
- Note: process_time gives CPU time used and perf_counter is absolute time used. - Looks to have noise of about 1-2%.
98 lines
4.9 KiB
Python
98 lines
4.9 KiB
Python
from __future__ import print_function
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import time
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adverbs = [
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"moderately", "really", "slightly", "very"
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]
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adjectives = [
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"abandoned", "able", "absolute", "academic", "acceptable", "acclaimed",
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"accomplished", "accurate", "aching", "acidic", "acrobatic", "active",
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"actual", "adept", "admirable", "admired", "adolescent", "adorable", "adored",
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"advanced", "adventurous", "affectionate", "afraid", "aged", "aggravating",
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"aggressive", "agile", "agitated", "agonizing", "agreeable", "ajar",
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"alarmed", "alarming", "alert", "alienated", "alive", "all", "altruistic",
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"amazing", "ambitious", "ample", "amused", "amusing", "anchored", "ancient",
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"angelic", "angry", "anguished", "animated", "annual", "another", "antique",
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"anxious", "any", "apprehensive", "appropriate", "apt", "arctic", "arid",
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"aromatic", "artistic", "ashamed", "assured", "astonishing", "athletic",
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"attached", "attentive", "attractive", "austere", "authentic", "authorized",
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"automatic", "avaricious", "average", "aware", "awesome", "awful", "awkward",
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"babyish", "back", "bad", "baggy", "bare", "barren", "basic", "beautiful",
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"belated", "beloved", "beneficial", "best", "better", "bewitched", "big",
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"big-hearted", "biodegradable", "bite-sized", "bitter", "black",
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"black-and-white", "bland", "blank", "blaring", "bleak", "blind", "blissful",
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"blond", "blue", "blushing", "bogus", "boiling", "bold", "bony", "boring",
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"bossy", "both", "bouncy", "bountiful", "bowed", "brave", "breakable",
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"brief", "bright", "brilliant", "brisk", "broken", "bronze", "brown",
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"bruised", "bubbly", "bulky", "bumpy", "buoyant", "burdensome", "burly",
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"bustling", "busy", "buttery", "buzzing", "calculating", "calm", "candid",
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"canine", "capital", "carefree", "careful", "careless", "caring", "cautious",
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"cavernous", "celebrated", "charming", "cheap", "cheerful", "cheery", "chief",
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"chilly", "chubby", "circular", "classic", "clean", "clear", "clear-cut",
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"clever", "close", "closed", "cloudy", "clueless", "clumsy", "cluttered",
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"coarse", "cold", "colorful", "colorless", "colossal", "comfortable",
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"common", "compassionate", "competent", "complete", "complex", "complicated",
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"composed", "concerned", "concrete", "confused", "conscious", "considerate",
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"constant", "content", "conventional", "cooked", "cool", "cooperative",
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"coordinated", "corny", "corrupt", "costly", "courageous", "courteous",
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"crafty"
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]
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animals = [
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"aardvark", "african buffalo", "albatross", "alligator", "alpaca", "ant",
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"anteater", "antelope", "ape", "armadillo", "baboon", "badger", "barracuda",
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"bat", "bear", "beaver", "bee", "bison", "black panther", "blue jay", "boar",
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"butterfly", "camel", "capybara", "carduelis", "caribou", "cassowary", "cat",
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"caterpillar", "cattle", "chamois", "cheetah", "chicken", "chimpanzee",
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"chinchilla", "chough", "clam", "cobra", "cockroach", "cod", "cormorant",
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"coyote", "crab", "crane", "crocodile", "crow", "curlew", "deer", "dinosaur",
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"dog", "dolphin", "domestic pig", "donkey", "dotterel", "dove", "dragonfly",
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"duck", "dugong", "dunlin", "eagle", "echidna", "eel", "elephant seal",
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"elephant", "elk", "emu", "falcon", "ferret", "finch", "fish", "flamingo",
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"fly", "fox", "frog", "gaur", "gazelle", "gerbil", "giant panda", "giraffe",
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"gnat", "goat", "goldfish", "goose", "gorilla", "goshawk", "grasshopper",
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"grouse", "guanaco", "guinea fowl", "guinea pig", "gull", "hamster", "hare",
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"hawk", "hedgehog", "heron", "herring", "hippopotamus", "hornet", "horse",
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"human", "hummingbird", "hyena", "ibex", "ibis", "jackal", "jaguar", "jay",
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"jellyfish", "kangaroo", "kingfisher", "koala", "komodo dragon", "kookabura",
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"kouprey", "kudu", "lapwing", "lark", "lemur", "leopard", "lion", "llama",
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"lobster", "locust", "loris", "louse", "lyrebird", "magpie", "mallard",
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"manatee", "mandrill", "mantis", "marten", "meerkat", "mink", "mole",
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"mongoose", "monkey", "moose", "mosquito", "mouse", "mule", "narwhal", "newt",
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"nightingale", "octopus", "okapi", "opossum", "oryx", "ostrich", "otter",
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"owl", "oyster", "parrot", "partridge", "peafowl", "pelican", "penguin",
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"pheasant", "pigeon", "pinniped", "polar bear", "pony", "porcupine",
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"porpoise", "prairie dog", "quail", "quelea", "quetzal", "rabbit", "raccoon",
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"ram", "rat", "raven", "red deer", "red panda", "reindeer", "rhinoceros",
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"rook", "salamander", "salmon", "sand dollar", "sandpiper", "sardine",
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"scorpion", "sea lion", "sea urchin", "seahorse", "shark", "sheep", "shrew",
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"skunk", "snail", "snake", "sparrow", "spider", "spoonbill", "squid",
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"wallaby", "wildebeest"
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]
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keys = []
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for animal in animals:
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for adjective in adjectives:
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for adverb in adverbs:
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keys.append(adverb + " " + adjective + " " + animal)
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start = time.process_time()
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map = {}
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i = 0
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for key in keys:
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map[key] = i
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i += 1
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sum = 0
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for key in keys:
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sum = sum + map[key]
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for key in keys:
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del map[key]
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print(sum)
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print("elapsed: " + str(time.process_time() - start))
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