[SciPy-User] re[SciPy-user] moving for loops...
Benjamin Root
ben.root at ou.edu
Thu Jun 10 17:08:00 EDT 2010
Good! The -1 in the reshape means "however many it takes" to have a correct
reshaped array.
Of course, as always with developing code that uses reshape operations, do
some "sanity checks" to make sure that your array was reshaped in a manner
you expect. When you have many-dimension arrays, it is very easy to make a
mistake with reshape. Print out a few slices of the array and/or see if the
averages make sense until you are convinced that you coded it correctly.
Ben Root
On Thu, Jun 10, 2010 at 3:36 PM, mdekauwe <mdekauwe at gmail.com> wrote:
>
> OK I think it is clear now!! Although what does the -1 bit do, this is
> surely
> the same as saying 11, 12 or numyears, nummonths?
>
> thanks.
>
>
>
> Benjamin Root-2 wrote:
> >
> > Well, let's try a more direct example. I am going to create a 4d array
> of
> > random values to illustrate. I know the length of the dimensions won't
> be
> > exactly the same as yours, but the example will still be valid.
> >
> > In this example, I will be able to calculate *all* of the monthly
> averages
> > for *all* of the variables for *all* of the grid points without a single
> > loop.
> >
> >> jules = np.random.random((132, 10, 50, 3))
> >> print jules.shape
> > (132, 10, 50, 3)
> >
> >> jules_5d = np.reshape(jules, (-1, 12) + jules.shape[1:])
> >> print jules_5d.shape
> > (11, 12, 10, 50, 3)
> >
> >> jules_5d = np.ma.masked_array(jules_5d, mask=jules_5d < 0.0)
> >
> >> jules_means = np.mean(jules_5d, axis=0)
> >> print jules_means.shape
> > (12, 10, 50, 3)
> >
> > voila! This matrix has a mean for each month across all eleven years for
> > each datapoint in each of the 10 variables at each (I am assuming) level
> > in
> > the atmosphere.
> >
> > So, if you want to operate on a subset of your jules matrix (for example,
> > you need to do special masking for each variable), then you can just work
> > off of a slice of the original matrix, and many of these same concepts in
> > this example and the previous example still applies.
> >
> > Ben Root
> >
> >
> > On Thu, Jun 10, 2010 at 1:08 PM, mdekauwe <mdekauwe at gmail.com> wrote:
> >
> >>
> >> Hi,
> >>
> >> No if I am honest I am a little confused how what you are suggesting
> >> would
> >> work. As I see it the array I am trying to average from has dims
> >> jules[(numyears * nummonths),1,numpts,0]. Where the first dimension
> (132)
> >> is
> >> 12 months x 11 years. And as I said before I would like to average the
> >> jan
> >> from the first, second, third years etc. Then the same for the feb and
> so
> >> on.
> >>
> >> So I don't see how you get to your 2d array that you mention in the
> first
> >> line? I thought what you were suggesting was I could precompute the step
> >> that builds the index for the months e.g
> >>
> >> mth_index = np.zeros(0)
> >> for month in xrange(nummonths):
> >> mth_index = np.append(mth_index, np.arange(month, numyears *
> >> nummonths,
> >> nummonths))
> >>
> >> and use this as my index to skip the for loop. Though I still have a for
> >> loop I guess!
> >>
> >>
> >>
> >>
> >>
> >>
> >> Benjamin Root-2 wrote:
> >> >
> >> > Correction for me as well. To mask out the negative values, use
> masked
> >> > arrays. So we will turn jules_2d into a masked array (second line),
> >> then
> >> > all subsequent commands will still work as expected. It is very
> >> similar
> >> > to
> >> > replacing negative values with nans and using nanmin().
> >> >
> >> >> jules_2d = jules.reshape((-1, 12))
> >> >> jules_2d = np.ma.masked_array(jules_2d, mask=jules_2d < 0.0)
> >> >> jules_monthly = np.mean(jules_2d, axis=0)
> >> >> print jules_monthly.shape
> >> > (12,)
> >> >
> >> > Ben Root
> >> >
> >> > On Tue, Jun 8, 2010 at 7:49 PM, Benjamin Root <ben.root at ou.edu>
> wrote:
> >> >
> >> >> The np.mod in my example caused the data points to stay within [0,
> 11]
> >> in
> >> >> order to illustrate that these are months. In my example, months are
> >> >> column, years are rows. In your desired output, months are rows and
> >> >> years
> >> >> are columns. It makes very little difference which way you have it.
> >> >>
> >> >> Anyway, let's imagine that we have a time series of data "jules". We
> >> can
> >> >> easily reshape this like so:
> >> >>
> >> >> > jules_2d = jules.reshape((-1, 12))
> >> >> > jules_monthly = np.mean(jules_2d, axis=0)
> >> >> > print jules_monthly.shape
> >> >> (12,)
> >> >>
> >> >> voila! You have 12 values in jules_monthly which are means for that
> >> >> month
> >> >> across all years.
> >> >>
> >> >> protip - if you want yearly averages just change the ax parameter in
> >> >> np.mean():
> >> >> > jules_yearly = np.mean(jules_2d, axis=1)
> >> >>
> >> >> I hope that makes my previous explanation clearer.
> >> >>
> >> >> Ben Root
> >> >>
> >> >>
> >> >> On Tue, Jun 8, 2010 at 5:41 PM, mdekauwe <mdekauwe at gmail.com> wrote:
> >> >>
> >> >>>
> >> >>> 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.
> >> >>>
> >> >>> _______________________________________________
> >> >>> 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...-tp28633477p28846602.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...-tp28633477p28848191.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
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