Finding specific points in data array

[lee403]

I have this really noisy data and I'm wanting to plot Temperature v. Time. I used this function to calculate a moving average. Here's some sample code.

BA1='ANP-Heat-BA1.csv'

#Time Trial 1
time1=pd.read_csv(BA1,skiprows=0)
time1=time1['Time(s)']

#Temperature 
tmp1=pd.read_csv(BA1,skiprows=0)
tmp1=tmp1['Temp']

def movingaverage (values, window):
    weights = np.repeat(1.0, window)/window
    sma = np.convolve(values, weights, 'valid')
    return sma
   
tmp1MA=movingaverage(tmp1, 10)

p.plot(time1[len(time1)-len(tmp1MA):],tmp1MA)

The moving average function seems to work fine at removing some of the random noise, but now I want to find the point in the array tmp1MA that corresponds to a given time1 value. For example, I would like be able to find the averaged temperature at time 500.

 

[chiro]

Hey lee403.

You'd have to define a model and then look at the expectation of that random variable.

What model are you using?

 

[FactChecker]

You have the moving average values, MA_i for a specific set of time points t_i. Any particular value of time, t, might be exactly one of the t_i values, or in between two t_i values or above all of the t_is or below all of the t_is.

There are a few things you need to decide. Do you want to extrapolate above or use the value of the highest t_i? Same question for t below the lowest t_i. Do you want to linearly interpolate for between t_is or fit a polynomial and use that? You can also do a spline fit to the values and evaluate the spline at t.