IndexError: tuple index out of range #775

adityabhandwalkar · 2023-01-09T10:45:48Z

adityabhandwalkar
Jan 9, 2023

Hello everyone,

While finding the consensus motif n a time series of length of 550000, I chose the value of m equivalent to the half of the sampling rate so it threw an error as mentioned IndexError: tuple index out of range, I also tried the less values of m but still it is showing the same error.

Thanks

Answered by adityabhandwalkar

Jan 9, 2023

 def osti(self,path):
        #Reading the data from the file
        
        
        df = pd.read_csv(path)
        
        data = np.array(df.iloc[:,0])      
        window_size = input('Enter the window size?\n')
    # using ostinato function in for patterns discovery in stump
        rad, TS_idx, Sub_idx = stumpy.ostinato(data ,int(window_size))
        print(rad)
        print(TS_idx)

and the data is 1d time series

[-0.3288759  -0.32424527 -0.3199583  ... -0.00456399 -0.01842756
 -0.03370683] <class 'numpy.ndarray'>

View full answer

seanlaw · 2023-01-09T11:50:11Z

seanlaw
Jan 9, 2023
Maintainer

@adityabhandwalkar Can you please provide a small code example that produces this error? Otherwise, there would be little to no way for us to help as there would be many potential causes for this error.

0 replies

adityabhandwalkar · 2023-01-09T12:03:45Z

adityabhandwalkar
Jan 9, 2023
Author

 def osti(self,path):
        #Reading the data from the file
        
        
        df = pd.read_csv(path)
        
        data = np.array(df.iloc[:,0])      
        window_size = input('Enter the window size?\n')
    # using ostinato function in for patterns discovery in stump
        rad, TS_idx, Sub_idx = stumpy.ostinato(data ,int(window_size))
        print(rad)
        print(TS_idx)

and the data is 1d time series

[-0.3288759  -0.32424527 -0.3199583  ... -0.00456399 -0.01842756
 -0.03370683] <class 'numpy.ndarray'>

6 replies

seanlaw Jan 9, 2023
Maintainer

That's the problem. If you look at the documentation for the ostinato function, it states the following:

Ts (list) – A list of time series for which to find the most central consensus motif

Thus, the first input, Ts, must be a (Python) list of numpy arrays. Additionally, the main docstring also clearly states that the goal of ostinato is to:

Find the z-normalized consensus motif of multiple time series

Emphasis on "multiple time series" (i.e., not ONE time series).

seanlaw Jan 9, 2023
Maintainer

Also, note that in the upcoming release, an error will be thrown if Ts is not a list

adityabhandwalkar Jan 10, 2023
Author

@seanlaw Thank you very much for your help. It is working now , I also want to ask you how can I use this with
stumpy.ostinatoed(client, Ts, m, normalize=True, p=2.0) If my data set is larger, could you please provide a small example with implementation code. That will be really helpful

seanlaw Jan 10, 2023
Maintainer

@adityabhandwalkar Unfortunately, we are unable to assist you with setting up a Dask cluster as this is beyond the scope of STUMPY. We recommend taking a look at the Dask Documentation and asking questions on their Github Disucssion section.

Once you have a distributed cluster setup, then please post a new/separate question.

adityabhandwalkar Jan 11, 2023
Author

Sure ! Thank you

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