The difference appears to be that the boolean selection pulls out all data
values <= 0.5 whether or not they are masked, and then carries over the
appropriate masks to the new array. So r2010 and bt contain identical
unmasked values but different numbers of masked values. Because the
initial fill
Here's some code implementing the "replace similar values with an
arbitrarily chosen one" (in this case the smallest of the similar values).
I didn't see any way to do this cleverly with strides, so I just did a
simple loop. It's about 100 times slower in pure Python, or a bit under 10
times slow
I can see a couple opportunities for improvements in your algorithm.
Running your code on a single experiment, I get about 2.9 seconds to run.
I get this down to about 1.0 seconds by (1) exploiting the symmetry of the
M matrix and (2) avoiding the costly inner loop over k in favor of array
operati
The example data/method you've provided doesn't do what you describe.
E.g., in your example data you have several 2x2 blocks of NaNs. According
to your description, these should not be replaced (as they all have a
neighbor that is also a NaN). Your example method, however, replaces them
- in fac
a = np.ones(30)
idx = np.array([2, 3, 2])
a += 2 * np.bincount(idx, minlength=len(a))
>>> a
array([ 1., 1., 5., 3., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1.])
As for speed:
def loop(a, idx):
On Wed, Aug 8, 2012 at 9:19 AM, Laszlo Nagy wrote:
> Is there a more efficient way to calculate the "slices" array below?
>
> I do not want to make copies of DATA, because it can be huge. The
> argsort is fast enough. I just need to create slices for different
> dimensions. The above code works, b
This seems to work:
import networkx as nx
import pylab
import numpy as N
M = N.random.random((10, 10))
G = nx.Graph(M)
node_colors = []
for i in xrange(len(M)):
if M[i,0] < 0.5:
node_colors.append('white')
else:
node_colors.append('blue')
nx.draw(G, node_color=node_colors)
pylab.show(
> Another issue to watch out for is if the array is empty. Technically
> speaking, that should be True, but some of the solutions offered so far
> would fail in this case.
Similarly, NaNs or Infs could cause problems: they should signal as
False, but several of the solutions would return True.
On Sat, Feb 18, 2012 at 8:12 PM, Adam Hughes wrote:
> Hey everyone,
>
> I have timeseries data in which the column label is simply a filename from
> which the original data was taken. Here's some sample data:
>
> name1.txt name2.txt name3.txt
> 32 34 953
> 32
The namespace is different. If you want to use numpy.sin(), for
example, you would use:
import numpy as np
np.sin(angle)
or
from numpy import *
sin(angle)
I generally prefer the first option because then I don't need to worry
about multiple imports writing on top of each other (i.e., having te
On Mon, Jan 30, 2012 at 11:31 AM, Ted To wrote:
> On 01/30/2012 12:13 PM, Brett Olsen wrote:
>> On Mon, Jan 30, 2012 at 10:57 AM, Ted To wrote:
>>> Sure thing. To keep it simple suppose I have just a two dimensional
>>> array (time,output):
>>> [(1,2),(2,3
On Mon, Jan 30, 2012 at 10:57 AM, Ted To wrote:
> Sure thing. To keep it simple suppose I have just a two dimensional
> array (time,output):
> [(1,2),(2,3),(3,4)]
> I would like to look at all values of output for which, for example time==2.
>
> My actual application has a six dimensional array a
On Tue, Jan 24, 2012 at 6:22 PM, questions anon
wrote:
> I need some help understanding how to loop through many arrays to calculate
> the 95th percentile.
> I can easily do this by using numpy.concatenate to make one big array and
> then finding the 95th percentile using numpy.percentile but this
On Thu, Aug 25, 2011 at 2:10 PM, Paul Menzel
wrote:
> is there an easy way to also save the indexes of an array (columns, rows
> or both) when outputting it to a text file. For saving an array to a
> file I only found `savetxt()` [1] which does not seem to have such an
> option. Adding indexes man
On Tue, Aug 2, 2011 at 9:44 AM, Jeremy Conlin wrote:
> I am trying to create a numpy array from some text I'm reading from a
> file. Ideally, I'd like to create a structured array with the first
> element as an int and the remaining as floats. I'm currently
> unsuccessful in my attempts. I've copi
This method is probably simpler:
In [1]: import numpy as N
In [2]: A = N.random.random_integers(-10, 10, 25).reshape((5, 5))
In [3]: A
Out[3]:
array([[ -5, 9, 1, 9, -2],
[ -8, 0, 9, 7, -10],
[ 2, -3, -1, 5, -7],
[ 0, -2, -2, 9, 1],
[ -7, -9,
On Tue, Jul 19, 2011 at 11:08 AM, Robert Kern wrote:
> On Tue, Jul 19, 2011 at 07:38, Andrea Cimatoribus
> wrote:
>> Dear all,
>> I would like to avoid the use of a boolean array (mask) in the following
>> statement:
>>
>> mask = (A != 0.)
>> B = A[mask]
>>
>> in order to be able to move th
On Sat, Apr 16, 2011 at 2:08 PM, Laszlo Nagy wrote:
> import numpy as np
> import numpy.random as rnd
>
> def dim_weight(X):
> weights = X[0]
> volumes = X[1]*X[2]*X[3]
> res = np.empty(len(volumes), dtype=np.double)
> for i,v in enumerate(volumes):
> if v>5184:
>
On Tue, Sep 21, 2010 at 6:20 PM, Timothy W. Hilton wrote:
> Hello,
>
> I have an indexing problem which I suspect has a simple solution, but
> I've not been able to piece together various threads I've read on this
> list to solve.
>
> I have an 80x1200x1200 nd.array of floats this_par. I have a
>
On Wed, Sep 15, 2010 at 4:38 PM, Mark Fenner wrote:
> A separate question. Suppose I have a slice for indexing that looks like:
>
> [:, :, 2, :, 5]
>
> How can I get an indexing slice for all OTHER dimension values besides
> those specified. Conceptually, something like:
>
> [:, :, all but 2, :,
On Sat, Sep 11, 2010 at 4:46 PM, Massimo Di Stefano
wrote:
> Thanks Pierre,
>
> i tried it and all works fine and fast.
>
> my apologize :-(
>
> i used a wrong "if" statment to represent my needs
>
> if mydata[i,0] < E or mydata[i,0] > W or mydata[i,1] < N or mydata[i,1] > S :
>
> ^^ totally wrong
On Sat, Sep 11, 2010 at 7:45 AM, Massimo Di Stefano
wrote:
> Hello All,
>
> i need to extract data from an array, that are inside a
> rectangle area defined as :
>
> N, S, E, W = 234560.94503118, 234482.56929822, 921336.53116178, 921185.3779625
>
> the data are in a csv (comma delimited text file,
27;ve come up with a couple
possible methods, but they all seem to be inefficient or kludges:
>>> valid = N.array(("a", "c"))
>>> (ar == valid[0]) | (ar == valid[1])
array([ True, False, True, False, False, True, False, True, True],
dtype=bool)
&g
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