On Wed, Oct 7, 2009 at 12:35, Gökhan Sever wrote:
> Do you know what shortcut name is used for scipy package itself?
I do not recommend using "import scipy" or "import scipy as ...".
Import the subpackages directly (e.g. "from scipy import linalg").
--
Robert Kern
"I have come to believe that
On Wed, Oct 7, 2009 at 12:21 PM, Christopher Barker
wrote:
> Gökhan Sever wrote:
> > > Sorry too much time spent in ipython -pylab :)
>
> > Good reflex. Saves you from making extra explanations. But it works with
> > just typing array why should I type np.array (Ohh my namespacess :)
>
> Beca
Gökhan Sever wrote:
> > Sorry too much time spent in ipython -pylab :)
> Good reflex. Saves you from making extra explanations. But it works with
> just typing array why should I type np.array (Ohh my namespacess :)
Because it shouldn't work that way! I use -pylab, but I've added:
o.
Added as comment in the same entry:
http://projects.scipy.org/numpy/ticket/1253#comment:1
Guessing that this one should be easy to fix :)
On Wed, Oct 7, 2009 at 3:05 AM, Pierre GM wrote:
>
> On Oct 7, 2009, at 2:57 AM, Gökhan Sever wrote:
> > One more example. (I still think the behaviour of f
On Oct 7, 2009, at 2:57 AM, Gökhan Sever wrote:
> One more example. (I still think the behaviour of fill_value is
> inconsistent)
Well, ma.masked_values use `value` to define fill_value,
ma.masked_equal does not. So yes, there's an inconsistency here. Once
again, please fill an enhancement
On Wed, Oct 7, 2009 at 12:47 AM, Pierre GM wrote:
>
> On Oct 7, 2009, at 1:12 AM, Gökhan Sever wrote:
> > One more from me:
> > I[1]: a = np.arange(5)
> > I[2]: mask = 999
> > I[6]: a[3] = 999
> > I[7]: am = ma.masked_equal(a, mask)
> >
> > I[8]: am
> > O[8]:
> > masked_array(data = [0 1 2 -- 4],
On Oct 7, 2009, at 1:12 AM, Gökhan Sever wrote:
> One more from me:
> I[1]: a = np.arange(5)
> I[2]: mask = 999
> I[6]: a[3] = 999
> I[7]: am = ma.masked_equal(a, mask)
>
> I[8]: am
> O[8]:
> masked_array(data = [0 1 2 -- 4],
> mask = [False False False True False],
>fill_val
On Tue, Oct 6, 2009 at 11:33 PM, Pierre GM wrote:
>
> On Oct 7, 2009, at 12:10 AM, Gökhan Sever wrote:
>
> > Created the ticket http://projects.scipy.org/numpy/ticket/1253
>
> Want even more confusion ?
> >>> x = ma.array([1,2,3],mask=[0,1,0], dtype=int)
> >>> x[0].dtype
> dtype('int64')
> >>>
On Oct 7, 2009, at 12:10 AM, Gökhan Sever wrote:
> Created the ticket http://projects.scipy.org/numpy/ticket/1253
Want even more confusion ?
>>> x = ma.array([1,2,3],mask=[0,1,0], dtype=int)
>>> x[0].dtype
dtype('int64')
>>> x[1].dtype
dtype('float64')
>>> x[2].dtype
dtype('int64')
Yet anot
Created the ticket http://projects.scipy.org/numpy/ticket/1253
Could you tell me briefly what was the source of leak in arccos case?
And how do you write a test code for these cases?
On Tue, Oct 6, 2009 at 10:15 PM, Pierre GM wrote:
>
> On Oct 6, 2009, at 10:58 PM, Gökhan Sever wrote:
> >
> >
On Tue, Oct 6, 2009 at 10:15 PM, Pierre GM wrote:
>
> On Oct 6, 2009, at 10:58 PM, Gökhan Sever wrote:
> >
> > I see your points. I don't want to give you extra work, don't
> > worry :) It just seem a bit bizarre:
> >
> > I[27]: c.data['Air_Temp'].fill_value
> > O[27]: 99.005
> >
> >
On Oct 6, 2009, at 10:58 PM, Gökhan Sever wrote:
>
> I see your points. I don't want to give you extra work, don't
> worry :) It just seem a bit bizarre:
>
> I[27]: c.data['Air_Temp'].fill_value
> O[27]: 99.005
>
> I[28]: c.data['Air_Temp'][4].fill_value
> O[28]: 1e+20
>
> As you see
On Tue, Oct 6, 2009 at 9:22 PM, Pierre GM wrote:
>
> On Oct 6, 2009, at 9:54 PM, Gökhan Sever wrote:
> >
> > > Also say, if I want to replace that one element back to its original
> > > state will it use fill_value as 1e+20 or 99.?
> >
> > What do you mean by 'replace back to its original
On Oct 6, 2009, at 9:54 PM, Gökhan Sever wrote:
>
> > Also say, if I want to replace that one element back to its original
> > state will it use fill_value as 1e+20 or 99.?
>
> What do you mean by 'replace back to its original state' ? Using
> `filled`, you mean ?
>
> Yes, in more properly
On Oct 6, 2009, at 6:57 PM, Gökhan Sever wrote:
>
> Seeing a different filling value is causing confusion. Both for
> myself, and when I try to demonstrate the usage of masked array to
> other people. Also say, if I want to replace that one element back
> to its original state will it use fi
On Tue, Oct 6, 2009 at 7:38 PM, Pierre GM wrote:
>
> On Oct 6, 2009, at 6:57 PM, Gökhan Sever wrote:
> > Seeing a different filling value is causing confusion. Both for
> > myself, and when I try to demonstrate the usage of masked array to
> > other people.
>
> Fair enough. I must admit that `fil
On Oct 6, 2009, at 6:57 PM, Gökhan Sever wrote:
> Seeing a different filling value is causing confusion. Both for
> myself, and when I try to demonstrate the usage of masked array to
> other people.
Fair enough. I must admit that `fill_value` is a vestige from the
previous implementation (t
On Tue, Oct 6, 2009 at 4:28 PM, Pierre GM wrote:
>
> On Oct 6, 2009, at 4:42 PM, Gökhan Sever wrote:
>
> > Hello,
> >
> > I have a sample masked array data as shown below.
> >
> > 1-) When I list the whole array I see the fill value correctly.
> > However below that line, when I do access the 5th
On Oct 6, 2009, at 4:42 PM, Gökhan Sever wrote:
> Hello,
>
> I have a sample masked array data as shown below.
>
> 1-) When I list the whole array I see the fill value correctly.
> However below that line, when I do access the 5th element,
> fill_value flies upto 1e+20. What might be wrong he
Hello,
I have a sample masked array data as shown below.
1-) When I list the whole array I see the fill value correctly. However
below that line, when I do access the 5th element, fill_value flies upto
1e+20. What might be wrong here?
I[5]: c.data['Air_Temp']
O[5]:
masked_array(data = [13.1509 1
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