Matching Conditional Numbers

Question

I need to map two very large Numpy arrays (one of them is 20,000 lines, about 100,000 more lines), and I'm trying to create a script to do this efficiently. A simple loop through arrays is incredibly slow, can anyone suggest a better way? Here is what I'm trying to do: array datesSecondDictand array pwfs2Datescontain datetime values, I need to take each datetime value from the array pwfs2Dates(a smaller array) and see if there is a datetime value like this (plus minus 5 minutes) in the array datesSecondDict(maybe more than 1) . If there is one (or more), I populate a new array (the same size as the array pwfs2Dates) with a value (one of the values) from the array valsSecondDict(which is only an array with the corresponding numerical values ​​fordatesSecondDict) Here is a solution from @unutbu and @joaquin that worked for me (thanks guys!):

import time
import datetime as dt
import numpy as np

def combineArs(dict1, dict2):
   """Combine data from 2 dictionaries into a list.
   dict1 contains primary data (e.g. seeing parameter).
   The function compares each timestamp in dict1 to dict2
   to see if there is a matching timestamp record(s)
   in dict2 (plus/minus 5 minutes).
   ==If yes: a list called data gets appended with the
   corresponding parameter value from dict2.
   (Note that if there are more than 1 record matching,
   the first occuring value gets appended to the list).
   ==If no: a list called data gets appended with 0."""
   # Specify the keys to use    
   pwfs2Key = 'pwfs2:dc:seeing'
   dimmKey = 'ws:seeFwhm'

   # Create an iterator for primary dict 
   datesPrimDictIter = iter(dict1[pwfs2Key]['datetimes'])

   # Take the first timestamp value in primary dict
   nextDatePrimDict = next(datesPrimDictIter)

   # Split the second dictionary into lists
   datesSecondDict = dict2[dimmKey]['datetime']
   valsSecondDict  = dict2[dimmKey]['values']

   # Define time window
   fiveMins = dt.timedelta(minutes = 5)
   data = []
   #st = time.time()
   for i, nextDateSecondDict in enumerate(datesSecondDict):
       try:
           while nextDatePrimDict < nextDateSecondDict - fiveMins:
               # If there is no match: append zero and move on
               data.append(0)
               nextDatePrimDict = next(datesPrimDictIter)
           while nextDatePrimDict < nextDateSecondDict + fiveMins:
               # If there is a match: append the value of second dict
               data.append(valsSecondDict[i])
               nextDatePrimDict = next(datesPrimDictIter)
       except StopIteration:
           break
   data = np.array(data)   
   #st = time.time() - st    
   return data

Thanks, Aina.

Answer 1

Are the sorted dates of an array?

• If so, you can speed up your comparisons by breaking the inside when its dates are greater than the date specified by the outer outline. This way you will do a one-pass comparison instead of looping dimValsitems len(pwfs2Vals)times
• If not, perhaps you should convert the current array pwfs2Dates, for example, an array of pairs [(date, array_index),...], and then you can sort by specify all your arrays to make the one-pass comparison above, and at the same time to get the initial indices needed for installationdata[i]

, ( , , ): ( : , pwfs2Dates ):

pdates = iter(enumerate(pwfs2Dates))
i, datei = pdates.next() 

for datej, valuej in zip(dimmDates, dimvals):
    while datei < datej - fiveMinutes:
        i, datei = pdates.next()
    while datei < datej + fiveMinutes:
        data[i] = valuej
        i, datei = pdates.next()

, , :

pwfs2Dates = sorted([(date, idx) for idx, date in enumerate(pwfs2Dates)])
dimmDates = sorted([(date, idx) for idx, date in enumerate(dimmDates)])

:
( : , pwfs2Dates ):

pdates = iter(pwfs2Dates)
datei, i = pdates.next()

for datej, j in dimmDates:
    while datei < datej - fiveMinutes:
        datei, i = pdates.next()
    while datei < datej + fiveMinutes:
        data[i] = dimVals[j]
        datei, i = pdates.next()

!

..

• , dimVals:

  dimVals  = np.array(dict1[dimmKey]['values'])
  

  .

• , , xrange

---

: unutbu . :

• next: next(iterator) iterator.next(). iterator.next() , py3k, iterator.__next__().
• try/except. next() StopIteration. try/except . OP , for . . dict1 dict2 . . : , try/except , .

Answer 2

joaquin:

import datetime as dt
import itertools

def combineArs(dict1, dict2, delta = dt.timedelta(minutes = 5)):
    marks = dict1['datetime']
    values = dict1['values']
    pdates = iter(dict2['datetime'])

    data = []
    datei = next(pdates)
    for datej, val in itertools.izip(marks, values):
        try:
            while datei < datej - delta:
                data.append(0)
                datei = next(pdates)
            while datei < datej + delta:
                data.append(val)
                datei = next(pdates)
        except StopIteration:
            break
    return data

dict1 = { 'ws:seeFwhm':
          {'datetime': [dt.datetime(2011, 12, 19, 12, 0, 0),
                        dt.datetime(2011, 12, 19, 12, 1, 0),
                        dt.datetime(2011, 12, 19, 12, 20, 0),
                        dt.datetime(2011, 12, 19, 12, 22, 0),
                        dt.datetime(2011, 12, 19, 12, 40, 0), ],
           'values': [1, 2, 3, 4, 5] } }
dict2 = { 'pwfs2:dc:seeing':
          {'datetime': [dt.datetime(2011, 12, 19, 12, 9),
                         dt.datetime(2011, 12, 19, 12, 19),
                         dt.datetime(2011, 12, 19, 12, 29),
                         dt.datetime(2011, 12, 19, 12, 39),
                        ], } }

if __name__ == '__main__':
    dimmKey = 'ws:seeFwhm'
    pwfs2Key = 'pwfs2:dc:seeing'    
    print(combineArs(dict1[dimmKey], dict2[pwfs2Key]))

[0, 3, 0, 5]

Answer 3

, :

import datetime
import numpy

# Test data

# Create an array of dates spaced at 1 minute intervals
m = range(1, 21)
n = datetime.datetime.now()
a = numpy.array([n + datetime.timedelta(minutes=i) for i in m])

# A smaller array with three of those dates
m = [5, 10, 15]
b = numpy.array([n + datetime.timedelta(minutes=i) for i in m])

# End of test data

def date_range(date_array, single_date, delta):
    plus = single_date + datetime.timedelta(minutes=delta)
    minus = single_date - datetime.timedelta(minutes=delta)
    return date_array[(date_array < plus) * (date_array > minus)]

dates = []
for i in b:
    dates.append(date_range(a, i, 5))

all_matches = numpy.unique(numpy.array(dates).flatten())

, , ... numpy.argwhere((a < plus) * (a > minus)) .