[SciPy-User] re[SciPy-user] moving for loops...
mdekauwe
mdekauwe at gmail.com
Fri May 28 16:42:44 EDT 2010
In my original attempt I was only averaging values greater than 0.0, would
this be the work around, I wonder if it is a bit clumsy...?
for month in xrange(nummonths):
temp[:] = array[month:numyears * nummonths:nummonths,VAR,:,0]
temp = temp[temp>0.0]
data[month, :] = np.mean(temp[:])
mdekauwe 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:
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>>>>>>>>>>>>>>>> Nabble.com.
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