dtw_applier

 1from dtw import *
 2
 3def comparePatterns(first_pattern, second_pattern):
 4    """
 5    Given two lists, compares them using dtw algorithm  
 6    
 7    Args:  
 8        first_pattern (List[float]): First pattern to be compared
 9        second_pattern (List[float]): Second pattern to be compared  
10    Returns:  
11        float: Distance between the two patterns
12    """
13    alignment_result = dtw(first_pattern, second_pattern, keep_internals=True)
14    return alignment_result.distance
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37# first_dataframe = pd.read_csv(sys.argv[1], index_col=0)
38# second_dataframe = pd.read_csv(sys.argv[2], index_col=0)
39
40# first_data_array = []
41# second_data_array = []
42
43# for index,row in first_dataframe.iterrows():
44#     first_data_array.append(row[0])
45
46# for index,row in second_dataframe.iterrows():
47#     second_data_array.append(row[0])
48
49# print(first_data_array)
50# alignment = dtw(first_data_array, second_data_array, keep_internals=True)
51# #alignment, cost_matrix, acc_cost_matrix, path = dtw(first_data_array, second_data_array, keep_internals=True, step_pattern=rabinerJuangStepPattern(6, "c"))
52# #print(dir(alignment))
53# print("Sequence x: ")
54# print(alignment.index1)
55# print("Sequence y: ")
56# print(alignment.index2)
57# print(alignment.distance)
58# alignment.plot(type='twoway', offset=-2)
59#print(alignment.index2)
60# plt.imshow(alignment.costMatrix)
61# plt.show()
62
63#print(first_dataframe.values.tolist())
64
65#dtw(query, template, keep_internals=True,step_pattern=rabinerJuangStepPattern(6, "c")).plot(type="twoway",offset=-2)