forked from mrlan/EnglishPal
89 lines
2.5 KiB
Python
89 lines
2.5 KiB
Python
###########################################################################
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# Copyright 2019 (C) Hui Lan <hui.lan@cantab.net>
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# Written permission must be obtained from the author for commercial uses.
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###########################################################################
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# Purpose: dictionary & pickle as a simple means of database.
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# Task: incorporate the functions into wordfreqCMD.py such that it will also show cumulative frequency.
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import pickle
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from datetime import datetime
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def lst_to_dict(lst, d):
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"""
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Store the information in list lst to dictionary d.
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Note: nothing is returned.
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"""
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for x in lst:
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word = x[0]
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freq = x[1]
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if word not in d:
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d[word] = freq
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else:
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d[word] += freq
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def dict_to_lst(d):
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return list(d.items()) # a list of (key, value) pairs
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def merge_frequency(list1, list2):
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d = {}
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lst_to_dict(list1, d)
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lst_to_dict(list2, d)
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return d
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def load_record(pickle_fname):
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f = open(pickle_fname, 'rb')
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d = pickle.load(f)
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f.close()
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return d
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def save_frequency_to_pickle(d, pickle_fname):
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f = open(pickle_fname, 'wb')
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#exclusion_lst = ['one', 'no', 'has', 'had', 'do', 'that', 'have', 'by', 'not', 'but', 'we', 'this', 'my', 'him', 'so', 'or', 'as', 'are', 'it', 'from', 'with', 'be', 'can', 'for', 'an', 'if', 'who', 'whom', 'whose', 'which', 'the', 'to', 'a', 'of', 'and', 'you', 'i', 'he', 'she', 'they', 'me', 'was', 'were', 'is', 'in', 'at', 'on', 'their', 'his', 'her', 's', 'said', 'all', 'did', 'been', 'w']
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exclusion_lst = []
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d2 = {}
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for k in d:
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if not k in exclusion_lst and not k.isnumeric() and len(k) > 1:
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d2[k] = d[k]
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pickle.dump(d2, f)
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f.close()
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def unfamiliar(path, word):
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f = open(path, "rb")
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dic = pickle.load(f)
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dic[word] += [datetime.now().strftime('%Y%m%d%H%M')]
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fp = open(path, "wb")
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pickle.dump(dic, fp)
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def familiar(path, word):
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f = open(path, "rb")
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dic = pickle.load(f)
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if len(dic[word]) > 1:
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del dic[word][0]
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else:
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dic.pop(word)
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fp = open(path, "wb")
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pickle.dump(dic, fp)
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if __name__ == '__main__':
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lst1 = [('apple', 2), ('banana', 1)]
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d = {}
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lst_to_dict(lst1, d) # d will change
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save_frequency_to_pickle(d, 'frequency.p') # frequency.p is our database
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lst2 = [('banana', 2), ('orange', 4)]
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d = load_record('frequency.p')
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lst1 = dict_to_lst(d)
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d = merge_frequency(lst2, lst1)
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print(d)
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