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
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
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
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 gand...@shopzeus.com 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
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)
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 hugad...@gwmail.gwu.edu 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
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
On Mon, Jan 30, 2012 at 10:57 AM, Ted To rainexpec...@theo.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
On Mon, Jan 30, 2012 at 11:31 AM, Ted To rainexpec...@theo.to wrote:
On 01/30/2012 12:13 PM, Brett Olsen wrote:
On Mon, Jan 30, 2012 at 10:57 AM, Ted To rainexpec...@theo.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
On Thu, Aug 25, 2011 at 2:10 PM, Paul Menzel
paulepan...@users.sourceforge.net 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
On Tue, Aug 2, 2011 at 9:44 AM, Jeremy Conlin jlcon...@gmail.com 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
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,
On Tue, Jul 19, 2011 at 11:08 AM, Robert Kern robert.k...@gmail.com wrote:
On Tue, Jul 19, 2011 at 07:38, Andrea Cimatoribus
g.plantagen...@gmail.com 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
On Sat, Apr 16, 2011 at 2:08 PM, Laszlo Nagy gand...@shopzeus.com 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 v5184:
On Tue, Sep 21, 2010 at 6:20 PM, Timothy W. Hilton hil...@meteo.psu.edu 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
On Wed, Sep 15, 2010 at 4:38 PM, Mark Fenner mfen...@gmail.com 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
On Sat, Sep 11, 2010 at 7:45 AM, Massimo Di Stefano
massimodisa...@gmail.com 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
On Sat, Sep 11, 2010 at 4:46 PM, Massimo Di Stefano
massimodisa...@gmail.com 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 :
^^
, False, False, True, False, True, True],
dtype=bool)
N.array(map(lambda x: x in valid, ar))
array([ True, False, True, False, False, True, False, True, True],
dtype=bool)
Is there a numpy-appropriate way to do this?
Thanks,
Brett Olsen
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