[SciPy-User] re[SciPy-user] moving for loops...

mdekauwe mdekauwe@gmail....
Tue Jun 8 10:53:38 CDT 2010


OK still haven't quite solved this in my head, so with a slightly different
array where the makeup is...

array[nummonths, numvars, numrows, numcols]

where nummonths = 12, numvars = 1, numrows = 180, numcols = 360.

However lets say I only want to extract some of the elements in the array,
in a practical sense land points and ignore sea points.

So I construct two arrays r and c which contain the rows and column indicies
for these points, such that their length is numpts = 15238.

If I wanted to just output these points into a new array (dims = nummonths x
15238), is there a good way to do this. Not quite sure what I am doing
wrong, I guess it relates to a size mismatch?

out_array = np.zeros((nummonths, numpts), dtype=np.float32)
month = 0
out_array[month,:] = array[xrange(month, nummonth), 0, r, c]

Thanks.


josef.pktd wrote:
> 
> On Fri, May 28, 2010 at 4:14 PM, mdekauwe <mdekauwe@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@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@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@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@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@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@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:
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