forked from mrlan/EnglishPal
90 lines
3.0 KiB
Python
90 lines
3.0 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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# Note: unlike pick_idea.py, now the second item is not frequency, but a list of dates.
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import pickle
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from datetime import datetime
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def lst2dict(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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dates = x[1]
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if not word in d:
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d[word] = dates
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else:
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d[word] += dates
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def deleteRecord(path,word):
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with open(path, 'rb') as f:
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db = pickle.load(f)
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try:
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db.pop(word)
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except KeyError:
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print("sorry")
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with open(path, 'wb') as ff:
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pickle.dump(db, ff)
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def dict2lst(d):
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if len(d) > 0:
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keys = list(d.keys())
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if isinstance(d[keys[0]], int):
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lst = []
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for k in d:
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lst.append((k, [datetime.now().strftime('%Y%m%d%H%M')]))
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return lst
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elif isinstance(d[keys[0]], list):
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return list(d.items()) # a list of (key, value) pairs
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return []
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def merge_frequency(lst1, lst2):
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d = {}
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lst2dict(lst1, d)
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lst2dict(lst2, 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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d2 = {}
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for k in d:
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if not k in exclusion_lst and not k.isnumeric() and not len(k) < 2:
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d2[k] = list(sorted(d[k])) # This code sorts the values of the dictionary 'd' associated with the key 'k' and assigns the resulting sorted list to the key 'k' in the dictionary 'd2'. However, it does not remove duplicates from the original list, which means that it keeps track of all counts even when the same value is clicked multiple times in a row.
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pickle.dump(d2, f)
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f.close()
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if __name__ == '__main__':
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lst1 = [('apple',['201910251437', '201910251438']), ('banana',['201910251439'])]
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d = {}
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lst2dict(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',['201910251439']), ('orange', ['201910251440', '201910251439'])]
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d = load_record('frequency.p')
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lst1 = dict2lst(d)
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d = merge_frequency(lst2, lst1)
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print(d)
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