Keith Goodman wrote:
>
>
>
> Or maybe this is cleaner:
>
>>> [date.month==1 for date in A[:,0]]
>[True, True, False, True]
>
> which can be used like this:
>
>>> idx = np.array([date.month==1 for date in A[:,0]])
>>> A[idx,:]
>
> array([[2010-01-01, 1],
>[2010-01-02, 2],
>
I have an array with the leading column a series of datetime objects. It
covers several years. What is the most efficient way to pull out all the
'January' dates?
Right now I do this:
A = array with column 0 datetime objects
January = [i for i in A if i[0].month ==1 ]
It works, but I would ra
Pierre GM-2 wrote:
>
> On Dec 8, 2009, at 7:27 PM, John [H2O] wrote:
>> Maybe I should add, I'm looking at this thread:
>> http://old.nabble.com/masked-record-arrays-td26237612.html
>>
>> And, I guess I'm in the same situation as the OP there. It's
Maybe I should add, I'm looking at this thread:
http://old.nabble.com/masked-record-arrays-td26237612.html
And, I guess I'm in the same situation as the OP there. It's not clear to
me, but as best I can tell I am working with structured arrays (that's from
np.rec.fromrecords creates, no?).
Anywa
Pierre GM-2 wrote:
>
>
> Did you check scikits.timeseries ? Might be a solution if you have data
> indexed in time
>
>
>> np.rec.fromrecords(codata_masked,names='datetime,lon,lat,elv,co')
>>return codata, codata_masked
>
> OK, I gonna have to guess again:
> codata is a regular ndarray, n
Pierre GM-2 wrote:
>
>
>
> masked_where is a function that requires 2 arguments.
> If you try to mask a whole record, you can try something like
x = ma.array([('a',1),('b',2)],dtype=[('','|S1'),('',float)])
x[x['f0']=='a'] = ma.masked
> For an individual field, try something like
>>>
This is what I get:
In [74]: type(cd)
Out[74]:
In [75]: type(cd.co)
Out[75]:
In [76]: cd[cd['co']==-.] = np.ma.masked
---
ValueErrorTraceback (most recent call last)
/home/jfb/Research
I see record arrays don't have a masked_where method. How can I achieve the
following for a record array:
cd.masked_where(cd.co == -.)
Or something like this.
Thanks!
--
View this message in context:
http://old.nabble.com/more-recfunctions%2C-structured-array-help-tp26700380p26700380.htm
Hello (Pierre?),
I'm trying to work more with structured arrays, which at times seems great,
and at others (due to my lack of familiarity) very frustrating.
Anyway, right now I'm writing a bit of code to read a series of files with
x,y,z data. I'm creating record arrays for each file a read. Onc
Hello,
I have a bit of code where I create arrays with meaningful names via:
meat = ['beef','lamb','pork']
cut = ['ribs','cutlets']
for m in meat:
for c in cut:
exec("consumed_%s_%s = np.zeros((numxgrid,numygrid,nummeasured))" %
(m,c))
Is this 'pythonic'? Or is it bad practice (and
Hello,
I have a routine that is iterating through a series of directories, loading
files, plotting, then moving on...
It runs very well for the first few iterations, but then slows tremendously
- there is nothing significantly different about the files or directory in
which it slows. I've monito
Hello,
I've started to rely more and more on f2py to create simple modules
utilizing Fortran for efficiency. This is a great tool to have within
Python!
A problem, however, is that unlike python modules, the reload() function
does not seem to update the f2py modules within ipython (which I use
e
I suspect I am trying to do something similar... I would like to create a
mask where I have data. In essence, I need to return True where x,y is equal
to lon,lat
I suppose a setmember solution may somehow be more elegant, but this is what
I've worked up for now... suggestions?
def genData
I have a file containing mixed data types: strings, floats, datetime
output(i.e. strings), and ints. Something like:
#ID, name, date, value
1,sample,2008-07-10 12:34:20,344.56
Presuming I get them nicely into a recarray (see my other
http://www.nabble.com/recarray-and-datetime-objects-td245683
Hello,
I have a file containing mixed data types: strings, floats, datetime
output(i.e. strings), and ints. Something like:
#ID, name, date, value
1,sample,2008-07-10 12:34:20,344.56
And so forth. It seems using recarrays is efficient and a prefered habit to
get into wrg to numpy, so I am tryin
Can someone explain:
x = np.arange(20)
y = np.arange(20)
z = np.vstack((x,y)).T
is equal to:
z = np.column_stack((x,y))
but this does not do the same:
z = np.concatenate((x,y),axis=0) # or with axis=1
Seems I should be able to use concatenate to make a column stack??
Thanks!
--
View this
Also, could someone please explain why:
Tsub = T[ (T[:,0]>t1) & (T[:,0]http://www.nabble.com/Help-with-np.where-and-datetime-functions-tp24389447p24401687.html
Sent from the Numpy-discussion mailing list archive at Nabble.com.
___
NumPy-Discussion mail
nhmc wrote:
>
>
>
> Also, if you don't need the indices, you can just use the conditional
> expression as a boolean mask:
>
condition = (t1 < Y[:,0]) & (Y[:,0] < t2)
Y[:,0][condition]
>
> Neil
>
'condition' is not an index array? Wouldn't it just be the indices as well?
Would i
Pierre GM-2 wrote:
>
>
>
> Would you like to give the scikits.timeseries package a try ? It's
> available at pytseries.sourceforge.net.
> Calculatng the hourly average should be straightforward.
>
I would, in fact I have been investigating it, but I didn't have numpy1.3 up
and running unti
Hello,
I have several issues which require me to iterate through a fairly large
array (30+ records).
The first case is calculating and hourly average from non-regularly sampled
data. The second is screening one array, based on data in the second array.
The functions are defined below, but in
/usr/local/lib/python2.6/dist-packages with whatever directory
> you installed your numpy 1.3 in. This path will be imported before any of
> the other standard directories.
>
>
>
> On Mon, Jul 6, 2009 at 5:08 PM, John [H2O] wrote:
>
>>
>> Hello,
>>
>> I
Hello,
I run Ubuntu 9.04 which has python-numpy 1.2 installed through apt-get. I
would like to upgrade to 1.3 in order to be able to use the
scikits.timeseries package. However, I cannot seem to do it using
apt-get/aptitude/synaptic or at least not that I've discovered.
Currently:
python -c "im
John [H2O] wrote:
>
> Hello, this is probably one of those questions that is going to seem
> simple after reading responses...
>
and a few more minutes of thinking:
def row2shape(row,a):
""" to get indices a 2d array row# to the 3d array
from which i
Hello, this is probably one of those questions that is going to seem simple
after reading responses...
I'm trying to get the original array indices out of a row number from a
reshaped array... shouldn't this be possible somehow?
import numpy as np
a = np.ones((3,5,10))
#for testing, I've done t
hello,
I am trying to calculate the results of a function between two matrices:
>>> F.shape
(170, 2)
>>> T.shape
(170, 481, 2)
Where F contains lat/lon pairs and T contains 481 lat/lon pairs for 170
trajectories of length 481
I want a new array of shape
(170,481)
containing the results of my
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