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How to smooth signals statistically correct in Python?


By : Kestrel
Date : September 17 2020, 05:00 AM
will help you One approach, originating in the image processing, is the downscale_local_mean from the skimage package.
code :
import matplotlib.pyplot as plt    
from skimage.transform import downscale_local_mean
data=np.genfromtxt(r'example_smoothing_data.txt',delimiter=";")

smoothed=downscale_local_mean(data, (8, 1))

plt.figure()
plt.plot(data[:,0],data[:,1],'b-',label='Original data')
plt.plot(smoothed[:,0],smoothed[:,1],'r-',label='Smoothed data')
plt.legend()
plt.show()


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Are there generic Python libraries that provide 'signals' (event) capability like Django signals?


By : Wessh
Date : March 29 2020, 07:55 AM
I wish this help you There are a number of modules for this. Here are a few options, ordered by what I think their popularity is:
The blinker module provides a signal/event mechanism PyDispatcher gives you event dispatch The PySignals module is the Django signals module without any dependency on Django SpiffSignal implements a signal/event framework, but its GitHub page seems to be missing

Python statistically choose word from dictionary using list


By : user7586876
Date : March 29 2020, 07:55 AM
wish helps you This is a solution, however to maintain order of your dictionary, items should be appended one by one. In such exact order, items will be stored in OrderedDict object.
code :
from collections import OrderedDict
import random

ordered_dict = OrderedDict()
ordered_dict['the'] = 2
ordered_dict['quick'] = 1
ordered_dict['brown'] = 1

generated_index = random.randrange(0, 3)
new_index = ordered_dict.values()[generated_index] + generated_index

resulting_string = ordered_dict.keys()[new_index]
print resulting_string

Use 'groupby' on statistically processed R2 values- python


By : user1825474
Date : March 29 2020, 07:55 AM
this one helps. You can group by 'test_event' column, and apply a custom function to compute the best_r2 value for each group. The custom function is simply a wrapper over your desired logic (here called compute_best_r2).
Following is a working solution:
code :
import numpy, pandas as pd
import copy

df=pd.read_excel("...")

def UniqueCombinations(items, n):
    if n==0:
        yield []
    else:
        for i in range(len(items)):
            for cc in UniqueCombinations(items[i+1:],n-1):
                yield [items[i]]+cc


def compute_best_r2(data):
    xyDataPairs = data[['x', 'y']].values.tolist()
    minDataPoints = len(xyDataPairs)
    bestR2 = 0.0
    bestDataPairCombination = []
    bestParameters = []

    for pairs in UniqueCombinations(xyDataPairs, minDataPoints):
        x = []
        y = []
        for pair in pairs:
            x.append(pair[0])
            y.append(pair[1])
        fittedParameters = numpy.polyfit(x, y, 1) # straight line
        modelPredictions = numpy.polyval(fittedParameters, x)
        absError = modelPredictions - y
        Rsquared = 1.0 - (numpy.var(absError) / numpy.var(y))
        if Rsquared > bestR2:
            bestR2 = Rsquared
            bestDataPairCombination = copy.deepcopy(pairs)
            bestParameters = copy.deepcopy(fittedParameters)
    data['best_r2'] = bestR2
    return data

df_with_best_r2 = df.groupby(['test_event']).apply(compute_best_r2)
result = df_with_best_r2[['test_event', 'best_r2']].groupby(['test_event']).agg(['first']).reset_index()[['test_event', 'best_r2']]
result.columns = result.columns.droplevel(-1)
test_event  x   y
1          1.5  2
1          1    1.8
1          2    4
1          2    6
2          1    1
2          2    2
   test_event   best_r2
0           1  0.705464
1           2  1.000000

How should I analyze web traffic in a statistically correct way?


By : user3846424
Date : March 29 2020, 07:55 AM
wish helps you You can always place a more flexible model on the arrive rate parameter. For instance, make the arrive rate a function of time, or place some time-series style model on it. Whatever makes sense for your data. The literature typically focuses on the core model because extensions are application specific.
In an extended model, you'll almost certainly want to use Bayesian methods. You are interested in the posterior predictive distribution of the object "almost concurrent events." A recent paper in JASA describes nearly your exact problem, applied to call center data:

python function to determine how statistically likely it is that a number belongs to a given list of numbers


By : user224934
Date : March 29 2020, 07:55 AM
To fix the issue you can do I am trying to find a function (in Python, ideally) that will tell me how 'similar' a number is to a given list of numbers. The end goal is to find out which list a given number is more likely to be a member of. , How about using an estimated pdf for both sets?
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