[Tutor] using datetime and calculating hourly average

[John [H2O]]

The data is just x,y data where x = datetime objects from the datetime
module. y are just floats. It is bundled in a numpy array.

So the only import statements are:

import datetime as dt
import numpy as np

I pass the array X, where X is a numpy array of shape [n,2] where n is the
number of points in the data.

As for your comment regarding the invariant... would it be:
while hr q:

NOT 

while hr not q: 

The latter makes more sense to me, but I'm not familiar with this
approach...

Thanks,
john

Bob Gailer wrote:
> 
> John [H2O] wrote:
>> Here's a function I wrote to calculate hourly averages:
>>
>> It seems a bit slow, however... any thoughts on how to improve it?
>>
>> def calc_hravg(X):
>>     """Calculates hourly average from input data"""
>>
>>     X_hr = []
>>     minX = X[:,0].min()
>>     hr = dt.datetime(*minX.timetuple()[0:4])
>>     
>>     while hr <= dt.datetime(*X[-1,0].timetuple()[0:4]):
>>         nhr = hr + dt.timedelta(hours=1)
>>         ind = np.where( (X[:,0] > hr) & (X[:,0] < nhr) )
>>         vals = X[ind,1][0].T
>>         try:
>>             #hr_avg = np.sum(vals) / len(vals)
>>             hr_avg = np.average(vals)
>>
>>         except:
>>             hr_avg = np.nan
>>         X_hr.append([hr,hr_avg])
>>         hr = hr + dt.timedelta(hours=1)
>>     
>>     return np.array(X_hr)
>>
>>
>>   
> Someone else may know exactly what data you are working with and what 
> you have imported, but I, for one?, don't.
> 
> Please show us more of the program, including the import statement(s), 
> and some sample input data. What leads you to think is is slow?
> 
> One opportunity for improvement - take the invariant out of the while 
> statement.
> 
>     q =  dt.datetime(*X[-1,0].timetuple()[0:4])
> 
>     while hr q:
> 
> 
> -- 
> Bob Gailer
> Chapel Hill NC
> 919-636-4239
> _______________________________________________
> Tutor maillist  -  Tutor at python.org
> http://mail.python.org/mailman/listinfo/tutor
> 
> 

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