[SciPy-User] re[SciPy-user] moving for loops...
mdekauwe
mdekauwe at gmail.com
Tue Jun 8 18:41:15 EDT 2010
OK...
but if I do...
In [28]: np.mod(np.arange(nummonths*numyears), nummonths).reshape((-1,
nummonths))
Out[28]:
array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]])
When really I would be after something like this I think?
array([ 0, 12, 24, 36, 48, 60, 72, 84, 96, 108, 120],
[ 1, 13, 25, 37, 49, 61, 73, 85, 97, 109, 121],
[ 2, 14, 26, 38, 50, 62, 74, 86, 98, 110, 122]
etc, etc
i.e. so for each month jump across the years.
Not quite sure of this example...this is what I currently have which does
seem to work, though I guess not completely efficiently.
for month in xrange(nummonths):
tmp = jules[xrange(0, numyears * nummonths, nummonths),VAR,:,0]
tmp[tmp < 0.0] = np.nan
data[month,:] = np.mean(tmp, axis=0)
Benjamin Root-2 wrote:
>
> If you want an average for each month from your timeseries, then the
> sneaky
> way would be to reshape your array so that the time dimension is split
> into
> two (month, year) dimensions.
>
> For a 1-D array, this would be:
>
>> dataarray = numpy.mod(numpy.arange(36), 12)
>> print dataarray
> array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4,
> 5, 6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
> 10, 11])
>> datamatrix = dataarray.reshape((-1, 12))
>> print datamatrix
> array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
> [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
> [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]])
>
> Hope that helps.
>
> Ben Root
>
>
> On Fri, May 28, 2010 at 3:28 PM, mdekauwe <mdekauwe at gmail.com> wrote:
>
>>
>> OK so I just need to have a quick loop across the 12 months then, that is
>> fine, just thought there might have been a sneaky way!
>>
>> Really appreciated, getting there slowly!
>>
>>
>>
>> josef.pktd wrote:
>> >
>> > On Fri, May 28, 2010 at 4:14 PM, mdekauwe <mdekauwe at gmail.com> wrote:
>> >>
>> >> ok - something like this then...but how would i get the index for the
>> >> month
>> >> for the data array (where month is 0, 1, 2, 4 ... 11)?
>> >>
>> >> data[month,:] = array[xrange(0, numyears * nummonths,
>> nummonths),VAR,:,0]
>> >
>> > you would still need to start at the right month
>> > data[month,:] = array[xrange(month, numyears * nummonths,
>> > nummonths),VAR,:,0]
>> > or
>> > data[month,:] = array[month: numyears * nummonths : nummonths),VAR,:,0]
>> >
>> > an alternative would be a reshape with an extra month dimension and
>> > then sum only once over the year axis. this might be faster but
>> > trickier to get the correct reshape .
>> >
>> > Josef
>> >
>> >>
>> >> and would that be quicker than making an array months...
>> >>
>> >> months = np.arange(numyears * nummonths)
>> >>
>> >> and you that instead like you suggested x[start:end:12,:]?
>> >>
>> >> Many thanks again...
>> >>
>> >>
>> >> josef.pktd wrote:
>> >>>
>> >>> On Fri, May 28, 2010 at 3:53 PM, mdekauwe <mdekauwe at gmail.com> wrote:
>> >>>>
>> >>>> Ok thanks...I'll take a look.
>> >>>>
>> >>>> Back to my loops issue. What if instead this time I wanted to take
>> an
>> >>>> average so every march in 11 years, is there a quicker way to go
>> about
>> >>>> doing
>> >>>> that than my current method?
>> >>>>
>> >>>> nummonths = 12
>> >>>> numyears = 11
>> >>>>
>> >>>> for month in xrange(nummonths):
>> >>>> for i in xrange(numpts):
>> >>>> for ym in xrange(month, numyears * nummonths, nummonths):
>> >>>> data[month, i] += array[ym, VAR, land_pts_index[i], 0]
>> >>>
>> >>>
>> >>> x[start:end:12,:] gives you every 12th row of an array x
>> >>>
>> >>> something like this should work to get rid of the inner loop, or you
>> >>> could directly put
>> >>> range(month, numyears * nummonths, nummonths) into the array instead
>> >>> of ym and sum()
>> >>>
>> >>> Josef
>> >>>
>> >>>
>> >>>>
>> >>>> so for each point in the array for a given month i am jumping
>> through
>> >>>> and
>> >>>> getting the next years month and so on, summing it.
>> >>>>
>> >>>> Thanks...
>> >>>>
>> >>>>
>> >>>> josef.pktd wrote:
>> >>>>>
>> >>>>> On Wed, May 26, 2010 at 5:03 PM, mdekauwe <mdekauwe at gmail.com>
>> wrote:
>> >>>>>>
>> >>>>>> Could you possibly if you have time explain further your comment
>> re
>> >>>>>> the
>> >>>>>> p-values, your suggesting I am misusing them?
>> >>>>>
>> >>>>> Depends on your use and interpretation
>> >>>>>
>> >>>>> test statistics, p-values are random variables, if you look at
>> several
>> >>>>> tests at the same time, some p-values will be large just by chance.
>> >>>>> If, for example you just look at the largest test statistic, then
>> the
>> >>>>> distribution for the max of several test statistics is not the same
>> as
>> >>>>> the distribution for a single test statistic
>> >>>>>
>> >>>>> http://en.wikipedia.org/wiki/Multiple_comparisons
>> >>>>> http://www.itl.nist.gov/div898/handbook/prc/section4/prc47.htm
>> >>>>>
>> >>>>> we also just had a related discussion for ANOVA post-hoc tests on
>> the
>> >>>>> pystatsmodels group.
>> >>>>>
>> >>>>> Josef
>> >>>>>>
>> >>>>>> Thanks.
>> >>>>>>
>> >>>>>>
>> >>>>>> josef.pktd wrote:
>> >>>>>>>
>> >>>>>>> On Sat, May 22, 2010 at 6:21 AM, mdekauwe <mdekauwe at gmail.com>
>> >>>>>>> wrote:
>> >>>>>>>>
>> >>>>>>>> Sounds like I am stuck with the loop as I need to do the
>> comparison
>> >>>>>>>> for
>> >>>>>>>> each
>> >>>>>>>> pixel of the world and then I have a basemap function call which
>> I
>> >>>>>>>> guess
>> >>>>>>>> slows it down further...hmm
>> >>>>>>>
>> >>>>>>> I don't see much that could be done differently, after a brief
>> look.
>> >>>>>>>
>> >>>>>>> stats.pearsonr could be replaced by an array version using
>> directly
>> >>>>>>> the formula for correlation even with nans. wilcoxon looks slow,
>> and
>> >>>>>>> I
>> >>>>>>> never tried or seen a faster version.
>> >>>>>>>
>> >>>>>>> just a reminder, the p-values are for a single test, when you
>> have
>> >>>>>>> many of them, then they don't have the right size/confidence
>> level
>> >>>>>>> for
>> >>>>>>> an overall or joint test. (some packages report a Bonferroni
>> >>>>>>> correction in this case)
>> >>>>>>>
>> >>>>>>> Josef
>> >>>>>>>
>> >>>>>>>
>> >>>>>>>>
>> >>>>>>>> i.e.
>> >>>>>>>>
>> >>>>>>>> def compareSnowData(jules_var):
>> >>>>>>>> # Extract the 11 years of snow data and return
>> >>>>>>>> outrows = 180
>> >>>>>>>> outcols = 360
>> >>>>>>>> numyears = 11
>> >>>>>>>> nummonths = 12
>> >>>>>>>>
>> >>>>>>>> # Read various files
>> >>>>>>>> fname="world_valid_jules_pts.ascii"
>> >>>>>>>> (numpts, land_pts_index, latitude, longitude, rows, cols) =
>> >>>>>>>> jo.read_land_points_ascii(fname, 1.0)
>> >>>>>>>>
>> >>>>>>>> fname = "globalSnowRun_1985_96.GSWP2.nsmax0.mon.gra"
>> >>>>>>>> jules_data1 = jo.readJulesOutBinary(fname, numrows=15238,
>> >>>>>>>> numcols=1,
>> >>>>>>>> \
>> >>>>>>>> timesteps=132, numvars=26)
>> >>>>>>>> fname = "globalSnowRun_1985_96.GSWP2.nsmax3.mon.gra"
>> >>>>>>>> jules_data2 = jo.readJulesOutBinary(fname, numrows=15238,
>> >>>>>>>> numcols=1,
>> >>>>>>>> \
>> >>>>>>>> timesteps=132, numvars=26)
>> >>>>>>>>
>> >>>>>>>> # grab some space
>> >>>>>>>> data1_snow = np.zeros((nummonths * numyears, numpts),
>> >>>>>>>> dtype=np.float32)
>> >>>>>>>> data2_snow = np.zeros((nummonths * numyears, numpts),
>> >>>>>>>> dtype=np.float32)
>> >>>>>>>> pearsonsr_snow = np.ones((outrows, outcols),
>> dtype=np.float32)
>> *
>> >>>>>>>> np.nan
>> >>>>>>>> wilcoxStats_snow = np.ones((outrows, outcols),
>> dtype=np.float32)
>> >>>>>>>> *
>> >>>>>>>> np.nan
>> >>>>>>>>
>> >>>>>>>> # extract the data
>> >>>>>>>> data1_snow = jules_data1[:,jules_var,:,0]
>> >>>>>>>> data2_snow = jules_data2[:,jules_var,:,0]
>> >>>>>>>> data1_snow = np.where(data1_snow < 0.0, np.nan, data1_snow)
>> >>>>>>>> data2_snow = np.where(data2_snow < 0.0, np.nan, data2_snow)
>> >>>>>>>> #for month in xrange(numyears * nummonths):
>> >>>>>>>> # for i in xrange(numpts):
>> >>>>>>>> # data1 =
>> >>>>>>>> jules_data1[month,jules_var,land_pts_index[i],0]
>> >>>>>>>> # data2 =
>> >>>>>>>> jules_data2[month,jules_var,land_pts_index[i],0]
>> >>>>>>>> # if data1 >= 0.0:
>> >>>>>>>> # data1_snow[month,i] = data1
>> >>>>>>>> # else:
>> >>>>>>>> # data1_snow[month,i] = np.nan
>> >>>>>>>> # if data2 > 0.0:
>> >>>>>>>> # data2_snow[month,i] = data2
>> >>>>>>>> # else:
>> >>>>>>>> # data2_snow[month,i] = np.nan
>> >>>>>>>>
>> >>>>>>>> # exclude any months from *both* arrays where we have dodgy
>> >>>>>>>> data,
>> >>>>>>>> else
>> >>>>>>>> we
>> >>>>>>>> # can't do the correlations correctly!!
>> >>>>>>>> data1_snow = np.where(np.isnan(data2_snow), np.nan,
>> data1_snow)
>> >>>>>>>> data2_snow = np.where(np.isnan(data1_snow), np.nan,
>> data1_snow)
>> >>>>>>>>
>> >>>>>>>> # put data on a regular grid...
>> >>>>>>>> print 'regridding landpts...'
>> >>>>>>>> for i in xrange(numpts):
>> >>>>>>>> # exclude the NaN, note masking them doesn't work in the
>> >>>>>>>> stats
>> >>>>>>>> func
>> >>>>>>>> x = data1_snow[:,i]
>> >>>>>>>> x = x[np.isfinite(x)]
>> >>>>>>>> y = data2_snow[:,i]
>> >>>>>>>> y = y[np.isfinite(y)]
>> >>>>>>>>
>> >>>>>>>> # r^2
>> >>>>>>>> # exclude v.small arrays, i.e. we need just less over 4
>> >>>>>>>> years
>> >>>>>>>> of
>> >>>>>>>> data
>> >>>>>>>> if len(x) and len(y) > 50:
>> >>>>>>>> pearsonsr_snow[((180-1)-(rows[i]-1)),cols[i]-1] =
>> >>>>>>>> (stats.pearsonr(x, y)[0])**2
>> >>>>>>>>
>> >>>>>>>> # wilcox signed rank test
>> >>>>>>>> # make sure we have enough samples to do the test
>> >>>>>>>> d = x - y
>> >>>>>>>> d = np.compress(np.not_equal(d,0), d ,axis=-1) # Keep all
>> >>>>>>>> non-zero
>> >>>>>>>> differences
>> >>>>>>>> count = len(d)
>> >>>>>>>> if count > 10:
>> >>>>>>>> z, pval = stats.wilcoxon(x, y)
>> >>>>>>>> # only map out sign different data
>> >>>>>>>> if pval < 0.05:
>> >>>>>>>> wilcoxStats_snow[((180-1)-(rows[i]-1)),cols[i]-1]
>> =
>> >>>>>>>> np.mean(x - y)
>> >>>>>>>>
>> >>>>>>>> return (pearsonsr_snow, wilcoxStats_snow)
>> >>>>>>>>
>> >>>>>>>>
>> >>>>>>>> josef.pktd wrote:
>> >>>>>>>>>
>> >>>>>>>>> On Fri, May 21, 2010 at 10:14 PM, mdekauwe <mdekauwe at gmail.com>
>> >>>>>>>>> wrote:
>> >>>>>>>>>>
>> >>>>>>>>>> Also I then need to remap the 2D array I make onto another
>> grid
>> >>>>>>>>>> (the
>> >>>>>>>>>> world in
>> >>>>>>>>>> this case). Which again I had am doing with a loop (note
>> numpts
>> >>>>>>>>>> is
>> >>>>>>>>>> a
>> >>>>>>>>>> lot
>> >>>>>>>>>> bigger than my example above).
>> >>>>>>>>>>
>> >>>>>>>>>> wilcoxStats_snow = np.ones((outrows, outcols),
>> dtype=np.float32)
>> >>>>>>>>>> *
>> >>>>>>>>>> np.nan
>> >>>>>>>>>> for i in xrange(numpts):
>> >>>>>>>>>> # exclude the NaN, note masking them doesn't work in
>> the
>> >>>>>>>>>> stats
>> >>>>>>>>>> func
>> >>>>>>>>>> x = data1_snow[:,i]
>> >>>>>>>>>> x = x[np.isfinite(x)]
>> >>>>>>>>>> y = data2_snow[:,i]
>> >>>>>>>>>> y = y[np.isfinite(y)]
>> >>>>>>>>>>
>> >>>>>>>>>> # wilcox signed rank test
>> >>>>>>>>>> # make sure we have enough samples to do the test
>> >>>>>>>>>> d = x - y
>> >>>>>>>>>> d = np.compress(np.not_equal(d,0), d ,axis=-1) # Keep
>> all
>> >>>>>>>>>> non-zero
>> >>>>>>>>>> differences
>> >>>>>>>>>> count = len(d)
>> >>>>>>>>>> if count > 10:
>> >>>>>>>>>> z, pval = stats.wilcoxon(x, y)
>> >>>>>>>>>> # only map out sign different data
>> >>>>>>>>>> if pval < 0.05:
>> >>>>>>>>>>
>> wilcoxStats_snow[((180-1)-(rows[i]-1)),cols[i]-1]
>> >>>>>>>>>> =
>> >>>>>>>>>> np.mean(x - y)
>> >>>>>>>>>>
>> >>>>>>>>>> Now I think I can push the data in one move into the
>> >>>>>>>>>> wilcoxStats_snow
>> >>>>>>>>>> array
>> >>>>>>>>>> by removing the index,
>> >>>>>>>>>> but I can't see how I will get the individual x and y pts for
>> >>>>>>>>>> each
>> >>>>>>>>>> array
>> >>>>>>>>>> member correctly without the loop, this was my attempt which
>> of
>> >>>>>>>>>> course
>> >>>>>>>>>> doesn't work!
>> >>>>>>>>>>
>> >>>>>>>>>> x = data1_snow[:,:]
>> >>>>>>>>>> x = x[np.isfinite(x)]
>> >>>>>>>>>> y = data2_snow[:,:]
>> >>>>>>>>>> y = y[np.isfinite(y)]
>> >>>>>>>>>>
>> >>>>>>>>>> # r^2
>> >>>>>>>>>> # exclude v.small arrays, i.e. we need just less over 4 years
>> of
>> >>>>>>>>>> data
>> >>>>>>>>>> if len(x) and len(y) > 50:
>> >>>>>>>>>> pearsonsr_snow[((180-1)-(rows-1)),cols-1] =
>> (stats.pearsonr(x,
>> >>>>>>>>>> y)[0])**2
>> >>>>>>>>>
>> >>>>>>>>>
>> >>>>>>>>> If you want to do pairwise comparisons with stats.wilcoxon,
>> then
>> >>>>>>>>> you
>> >>>>>>>>> might be stuck with the loop, since wilcoxon takes only two 1d
>> >>>>>>>>> arrays
>> >>>>>>>>> at a time (if I read the help correctly).
>> >>>>>>>>>
>> >>>>>>>>> Also the presence of nans might force the use a loop.
>> stats.mstats
>> >>>>>>>>> has
>> >>>>>>>>> masked array versions, but I didn't see wilcoxon in the list.
>> >>>>>>>>> (Even
>> >>>>>>>>> when vectorized operations would work with regular arrays, nan
>> or
>> >>>>>>>>> masked array versions still have to loop in many cases.)
>> >>>>>>>>>
>> >>>>>>>>> If you have many columns with count <= 10, so that wilcoxon is
>> not
>> >>>>>>>>> calculated then it might be worth to use only array operations
>> up
>> >>>>>>>>> to
>> >>>>>>>>> that point. If wilcoxon is calculated most of the time, then
>> it's
>> >>>>>>>>> not
>> >>>>>>>>> worth thinking too hard about this.
>> >>>>>>>>>
>> >>>>>>>>> Josef
>> >>>>>>>>>
>> >>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>>> thanks.
>> >>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>>> mdekauwe wrote:
>> >>>>>>>>>>>
>> >>>>>>>>>>> Yes as Zachary said index is only 0 to 15237, so both methods
>> >>>>>>>>>>> work.
>> >>>>>>>>>>>
>> >>>>>>>>>>> I don't quite get what you mean about slicing with axis > 3.
>> Is
>> >>>>>>>>>>> there
>> >>>>>>>>>>> a
>> >>>>>>>>>>> link you can recommend I should read? Does that mean given I
>> >>>>>>>>>>> have
>> >>>>>>>>>>> 4dims
>> >>>>>>>>>>> that Josef's suggestion would be more advised in this case?
>> >>>>>>>>>
>> >>>>>>>>> There were several discussions on the mailing lists (fancy
>> slicing
>> >>>>>>>>> and
>> >>>>>>>>> indexing). Your case is safe, but if you run in future into
>> funny
>> >>>>>>>>> shapes, you can look up the details.
>> >>>>>>>>> when in doubt, I use np.arange(...)
>> >>>>>>>>>
>> >>>>>>>>> Josef
>> >>>>>>>>>
>> >>>>>>>>>>>
>> >>>>>>>>>>> Thanks.
>> >>>>>>>>>>>
>> >>>>>>>>>>>
>> >>>>>>>>>>>
>> >>>>>>>>>>> josef.pktd wrote:
>> >>>>>>>>>>>>
>> >>>>>>>>>>>> On Fri, May 21, 2010 at 10:55 AM, mdekauwe <
>> mdekauwe at gmail.com>
>> >>>>>>>>>>>> wrote:
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> Thanks that works...
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> So the way to do it is with np.arange(tsteps)[:,None], that
>> >>>>>>>>>>>>> was
>> >>>>>>>>>>>>> the
>> >>>>>>>>>>>>> step
>> >>>>>>>>>>>>> I
>> >>>>>>>>>>>>> was struggling with, so this forms a 2D array which
>> replaces
>> >>>>>>>>>>>>> the
>> >>>>>>>>>>>>> the
>> >>>>>>>>>>>>> two
>> >>>>>>>>>>>>> for
>> >>>>>>>>>>>>> loops? Do I have that right?
>> >>>>>>>>>>>>
>> >>>>>>>>>>>> Yes, but as Zachary showed, if you need the full index in a
>> >>>>>>>>>>>> dimension,
>> >>>>>>>>>>>> then you can use slicing. It might be faster.
>> >>>>>>>>>>>> And a warning, mixing slices and index arrays with 3 or more
>> >>>>>>>>>>>> dimensions can have some surprise switching of axes.
>> >>>>>>>>>>>>
>> >>>>>>>>>>>> Josef
>> >>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> A lot quicker...!
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> Martin
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> josef.pktd wrote:
>> >>>>>>>>>>>>>>
>> >>>>>>>>>>>>>> On Fri, May 21, 2010 at 8:59 AM, mdekauwe
>> >>>>>>>>>>>>>> <mdekauwe at gmail.com>
>> >>>>>>>>>>>>>> wrote:
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>> Hi,
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>> I am trying to extract data from a 4D array and store it
>> in
>> >>>>>>>>>>>>>>> a
>> >>>>>>>>>>>>>>> 2D
>> >>>>>>>>>>>>>>> array,
>> >>>>>>>>>>>>>>> but
>> >>>>>>>>>>>>>>> avoid my current usage of the for loops for speed, as in
>> >>>>>>>>>>>>>>> reality
>> >>>>>>>>>>>>>>> the
>> >>>>>>>>>>>>>>> arrays
>> >>>>>>>>>>>>>>> sizes are quite big. Could someone also try and explain
>> the
>> >>>>>>>>>>>>>>> solution
>> >>>>>>>>>>>>>>> as
>> >>>>>>>>>>>>>>> well
>> >>>>>>>>>>>>>>> if they have a spare moment as I am still finding it
>> quite
>> >>>>>>>>>>>>>>> difficult
>> >>>>>>>>>>>>>>> to
>> >>>>>>>>>>>>>>> get
>> >>>>>>>>>>>>>>> over the habit of using loops (C convert for my sins). I
>> get
>> >>>>>>>>>>>>>>> that
>> >>>>>>>>>>>>>>> one
>> >>>>>>>>>>>>>>> could
>> >>>>>>>>>>>>>>> precompute the indices's i and j i.e.
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>> i = np.arange(tsteps)
>> >>>>>>>>>>>>>>> j = np.arange(numpts)
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>> but just can't get my head round how i then use them...
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>> Thanks,
>> >>>>>>>>>>>>>>> Martin
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>> import numpy as np
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>> numpts=10
>> >>>>>>>>>>>>>>> tsteps = 12
>> >>>>>>>>>>>>>>> vari = 22
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>> data = np.random.random((tsteps, vari, numpts, 1))
>> >>>>>>>>>>>>>>> new_data = np.zeros((tsteps, numpts), dtype=np.float32)
>> >>>>>>>>>>>>>>> index = np.arange(numpts)
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>> for i in xrange(tsteps):
>> >>>>>>>>>>>>>>> for j in xrange(numpts):
>> >>>>>>>>>>>>>>> new_data[i,j] = data[i,5,index[j],0]
>> >>>>>>>>>>>>>>
>> >>>>>>>>>>>>>> The index arrays need to be broadcastable against each
>> other.
>> >>>>>>>>>>>>>>
>> >>>>>>>>>>>>>> I think this should do it
>> >>>>>>>>>>>>>>
>> >>>>>>>>>>>>>> new_data = data[np.arange(tsteps)[:,None], 5,
>> >>>>>>>>>>>>>> np.arange(numpts),
>> >>>>>>>>>>>>>> 0]
>> >>>>>>>>>>>>>>
>> >>>>>>>>>>>>>> Josef
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>> --
>> >>>>>>>>>>>>>>> View this message in context:
>> >>>>>>>>>>>>>>>
>> http://old.nabble.com/removing-for-loops...-tp28633477p28633477.html
>> >>>>>>>>>>>>>>> Sent from the Scipy-User mailing list archive at
>> Nabble.com.
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>> _______________________________________________
>> >>>>>>>>>>>>>>> SciPy-User mailing list
>> >>>>>>>>>>>>>>> SciPy-User at scipy.org
>> >>>>>>>>>>>>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>> _______________________________________________
>> >>>>>>>>>>>>>> SciPy-User mailing list
>> >>>>>>>>>>>>>> SciPy-User at scipy.org
>> >>>>>>>>>>>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>> >>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> --
>> >>>>>>>>>>>>> View this message in context:
>> >>>>>>>>>>>>>
>> http://old.nabble.com/removing-for-loops...-tp28633477p28634924.html
>> >>>>>>>>>>>>> Sent from the Scipy-User mailing list archive at
>> Nabble.com.
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> _______________________________________________
>> >>>>>>>>>>>>> SciPy-User mailing list
>> >>>>>>>>>>>>> SciPy-User at scipy.org
>> >>>>>>>>>>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>> _______________________________________________
>> >>>>>>>>>>>> SciPy-User mailing list
>> >>>>>>>>>>>> SciPy-User at scipy.org
>> >>>>>>>>>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>> >>>>>>>>>>>>
>> >>>>>>>>>>>>
>> >>>>>>>>>>>
>> >>>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>>> --
>> >>>>>>>>>> View this message in context:
>> >>>>>>>>>>
>> http://old.nabble.com/removing-for-loops...-tp28633477p28640656.html
>> >>>>>>>>>> Sent from the Scipy-User mailing list archive at Nabble.com.
>> >>>>>>>>>>
>> >>>>>>>>>> _______________________________________________
>> >>>>>>>>>> SciPy-User mailing list
>> >>>>>>>>>> SciPy-User at scipy.org
>> >>>>>>>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>> >>>>>>>>>>
>> >>>>>>>>> _______________________________________________
>> >>>>>>>>> SciPy-User mailing list
>> >>>>>>>>> SciPy-User at scipy.org
>> >>>>>>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>> >>>>>>>>>
>> >>>>>>>>>
>> >>>>>>>>
>> >>>>>>>> --
>> >>>>>>>> View this message in context:
>> >>>>>>>>
>> http://old.nabble.com/removing-for-loops...-tp28633477p28642434.html
>> >>>>>>>> Sent from the Scipy-User mailing list archive at Nabble.com.
>> >>>>>>>>
>> >>>>>>>> _______________________________________________
>> >>>>>>>> SciPy-User mailing list
>> >>>>>>>> SciPy-User at scipy.org
>> >>>>>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>> >>>>>>>>
>> >>>>>>> _______________________________________________
>> >>>>>>> SciPy-User mailing list
>> >>>>>>> SciPy-User at scipy.org
>> >>>>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>> >>>>>>>
>> >>>>>>>
>> >>>>>>
>> >>>>>> --
>> >>>>>> View this message in context:
>> >>>>>>
>> http://old.nabble.com/removing-for-loops...-tp28633477p28686356.html
>> >>>>>> Sent from the Scipy-User mailing list archive at Nabble.com.
>> >>>>>>
>> >>>>>> _______________________________________________
>> >>>>>> SciPy-User mailing list
>> >>>>>> SciPy-User at scipy.org
>> >>>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>> >>>>>>
>> >>>>> _______________________________________________
>> >>>>> SciPy-User mailing list
>> >>>>> SciPy-User at scipy.org
>> >>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>> >>>>>
>> >>>>>
>> >>>>
>> >>>> --
>> >>>> View this message in context:
>> >>>> http://old.nabble.com/removing-for-loops...-tp28633477p28711249.html
>> >>>> Sent from the Scipy-User mailing list archive at Nabble.com.
>> >>>>
>> >>>> _______________________________________________
>> >>>> SciPy-User mailing list
>> >>>> SciPy-User at scipy.org
>> >>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>> >>>>
>> >>> _______________________________________________
>> >>> SciPy-User mailing list
>> >>> SciPy-User at scipy.org
>> >>> http://mail.scipy.org/mailman/listinfo/scipy-user
>> >>>
>> >>>
>> >>
>> >> --
>> >> View this message in context:
>> >> http://old.nabble.com/removing-for-loops...-tp28633477p28711444.html
>> >> Sent from the Scipy-User mailing list archive at Nabble.com.
>> >>
>> >> _______________________________________________
>> >> SciPy-User mailing list
>> >> SciPy-User at scipy.org
>> >> http://mail.scipy.org/mailman/listinfo/scipy-user
>> >>
>> > _______________________________________________
>> > SciPy-User mailing list
>> > SciPy-User at scipy.org
>> > http://mail.scipy.org/mailman/listinfo/scipy-user
>> >
>> >
>>
>> --
>> View this message in context:
>> http://old.nabble.com/removing-for-loops...-tp28633477p28711581.html
>> Sent from the Scipy-User mailing list archive at Nabble.com.
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
--
View this message in context: http://old.nabble.com/removing-for-loops...-tp28633477p28824023.html
Sent from the Scipy-User mailing list archive at Nabble.com.
More information about the SciPy-User
mailing list