![]() There is never any need to uninstall any earlier version of Rainmeter to use the newest version. You can safely install the new 4.0 beta version right over top of your existing Rainmeter 3.3 or earlier installation, and nothing you have done will be lost or changed. Your initial approach was running in about 6 minutes on my machine, while my approach works out in about 7 seconds.The following are the changes for the 4.0 beta version of Rainmeter. "new" values: instead of locating/sorting the index of each consecutive slice of values, operate on raw numpy arrays reversing each slice of values with.weights: instead of loop/append/indexing create a numpy array of weights using list comprehension with calculating each weight on the fly.This is really slow, and I know that my solution is very bad, but I couldn't come up with a better clean solution. Xt = (df._index(ascending=False).reset_index(drop=True) * weights).sum() ![]() Then, I compute the new series new_values = Weights.append(get_next_weight(weights, idx, 0.1)) Then, I create a function that computes the weights iteratively def get_next_weight(weight, k, d): Can anyone suggest me a better way to do this computation?įirst of all, I create a random dataset import pandas as pdĭf = pd.DataFrame(np.random.randint(0,100,size=(100000, 1)), columns=) I have tried to implement this computation in python, but it is terribly slow and I am sure I am not using the full potential of pandas/numpy. For example, for the last two terms of the time series, I will compute Of course, given that my series is not infinite, this sum must be truncated. Where the weights w_k are defined by a recursive relationĪnd d is a float parameter between 0 and 1. įor each t, I would like to compute the following series The time series is the sequence where t is the time index that in my case it is truncated, i.e. ![]() I have a time series stored in a pandas dataframe. ![]()
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