Asked  6 Months ago    Answers:  5   Viewed   76 times

Is there a SciPy function or NumPy function or module for Python that calculates the running mean of a 1D array given a specific window?



For a short, fast solution that does the whole thing in one loop, without dependencies, the code below works great.

mylist = [1, 2, 3, 4, 5, 6, 7]
N = 3
cumsum, moving_aves = [0], []

for i, x in enumerate(mylist, 1):
    cumsum.append(cumsum[i-1] + x)
    if i>=N:
        moving_ave = (cumsum[i] - cumsum[i-N])/N
        #can do stuff with moving_ave here
Tuesday, June 1, 2021
answered 6 Months ago

The agg framework now has $map and $reduce and $range built in so array processing is much more straightfoward. Below is an example of calculating moving average on a set of data where you wish to filter by some predicate. The basic setup is each doc contains filterable criteria and a value, e.g.

{sym: "A", d: ISODate("2018-01-01"), val: 10}
{sym: "A", d: ISODate("2018-01-02"), val: 30}

Here it is:

// This controls the number of observations in the moving average:
days = 4;[

// Filter down to what you want.  This can be anything or nothing at all.
{$match: {"sym": "S1"}}

// Ensure dates are going earliest to latest:
,{$sort: {d:1}}

// Turn docs into a single doc with a big vector of observations, e.g.
//     {sym: "A", d: d1, val: 10}
//     {sym: "A", d: d2, val: 11}
//     {sym: "A", d: d3, val: 13}
// becomes
//     {_id: "A", prx: [ {v:10,d:d1}, {v:11,d:d2},  {v:13,d:d3} ] }
// This will set us up to take advantage of array processing functions!
,{$group: {_id: "$sym", prx: {$push: {v:"$val",d:"$date"}} }}

// Nice additional info.  Note use of dot notation on array to get
// just scalar date at elem 0, not the object {v:val,d:date}:
,{$addFields: {numDays: days, startDate: {$arrayElemAt: [ "$prx.d", 0 ]}} }

// The Juice!  Assume we have a variable "days" which is the desired number
// of days of moving average.
// The complex expression below does this in python pseudocode:
// for z in range(0, size of value vector - # of days in moving avg):
//    seg = vector[n:n+days]
//    values = seg.v
//    dates = seg.d
//    for v in seg:
//        tot += v
//    avg = tot/len(seg)
// Note that it is possible to overrun the segment at the end of the "walk"
// along the vector, i.e. not enough date-values.  So we only run the
// vector to (len(vector) - (days-1).
// Also, for extra info, we also add the number of days *actually* used in the
// calculation AND the as-of date which is the tail date of the segment!
// Again we take advantage of dot notation to turn the vector of
// object {v:val, d:date} into two vectors of simple scalars [v1,v2,...]
// and [d1,d2,...] with $prx.v and $prx.d
,{$addFields: {"prx": {$map: {
    input: {$range:[0,{$subtract:[{$size:"$prx"}, (days-1)]}]} ,
    as: "z",
    in: {
       avg: {$avg: {$slice: [ "$prx.v", "$$z", days ] } },
       d: {$arrayElemAt: [ "$prx.d", {$add: ["$$z", (days-1)] } ]}


This might produce the following output:

    "_id" : "S1",
    "prx" : [
            "avg" : 11.738793632512115,
            "d" : ISODate("2018-09-05T16:10:30.259Z")
            "avg" : 12.420766702631376,
            "d" : ISODate("2018-09-06T16:10:30.259Z")

    "numDays" : 4,
    "startDate" : ISODate("2018-09-02T16:10:30.259Z")
Tuesday, July 6, 2021
answered 5 Months ago

I think you don't need to do fftshift(), and you can pass sampling period to fftfreq():

import scipy
import scipy.fftpack
import pylab
from scipy import pi
t = scipy.linspace(0,120,4000)
acc = lambda t: 10*scipy.sin(2*pi*2.0*t) + 5*scipy.sin(2*pi*8.0*t) + 2*scipy.random.random(len(t))

signal = acc(t)

FFT = abs(scipy.fft(signal))
freqs = scipy.fftpack.fftfreq(signal.size, t[1]-t[0])

pylab.plot(t, signal)

from the graph you can see there are two peak at 2Hz and 8Hz.

enter image description here

Friday, July 9, 2021
answered 5 Months ago

You can use rolling with transform:

df['moving'] = df.groupby('object')['value'].transform(lambda x: x.rolling(10, 1).mean())

The 1 in rolling is for minimum number of periods.

Tuesday, July 13, 2021
answered 5 Months ago

SQL Fiddle

    extract(dow from date),
    case when
        row_number() over (order by date) > 3
            avg(amount) OVER (
                ORDER BY date DESC
                ROWS BETWEEN 1 following AND 3 FOLLOWING
        else null end
from (
    select *
    from ro
    where extract(dow from date) = 4
) s

What is wrong with the OP's query is the frame specification:


Other than that my query avoids unneeded computing by filtering Thursdays before applying the expensive window functions.

If it is necessary to partition by shop_id then obviously add the partition by shop_id to both functions, avg and row_number.

Friday, September 17, 2021
answered 3 Months ago
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