Re: [Numpy-discussion] Fortran order in recarray.

[Kiko]

2017-02-22 16:23 GMT+01:00 Alex Rogozhnikov :

> Hi Francesc,
> thanks a lot for you reply and for your impressive job on bcolz!
>
> Bcolz seems to make stress on compression, which is not of much interest
> for me, but the *ctable*, and chunked operations look very appropriate to
> me now. (Of course, I'll need to test it much before I can say this for
> sure, that's current impression).
>
> The strongest concern with bcolz so far is that it seems to be completely
> non-trivial to install on windows systems, while pip provides binaries for
> most (or all?) OS for numpy.
> I didn't build pip binary wheels myself, but is it hard / impossible to
> cook pip-installabel binaries?
>

http://www.lfd.uci.edu/~gohlke/pythonlibs/#bcolz
Check if the link solves the issue with installing.

>
> ​You can change shapes of numpy arrays, but that usually involves copies
> of the whole container.
>
> sure, but this is ok for me, as I plan to organize column editing in
> 'batches', so this should require seldom copying.
> It would be nice to see an example to understand how deep I need to go
> inside numpy.
>
> Cheers,
> Alex.
>
>
>
>
> 22 февр. 2017 г., в 17:03, Francesc Alted  написал(а):
>
> Hi Alex,
>
> 2017-02-22 12:45 GMT+01:00 Alex Rogozhnikov :
>
>> Hi Nathaniel,
>>
>>
>> pandas
>>
>>
>> yup, the idea was to have minimal pandas.DataFrame-like storage (which I
>> was using for a long time),
>> but without irritating problems with its row indexing and some other
>> problems like interaction with matplotlib.
>>
>> A dict of arrays?
>>
>>
>> that's what I've started from and implemented, but at some point I
>> decided that I'm reinventing the wheel and numpy has something already. In
>> principle, I can ignore this 'column-oriented' storage requirement, but
>> potentially it may turn out to be quite slow-ish if dtype's size is large.
>>
>> Suggestions are welcome.
>>
>
> ​You may want to try bcolz:
>
> https://github.com/Blosc/bcolz
>
> bcolz is a columnar storage, basically as you require, but data is
> compressed by default even when stored in-memory (although you can disable
> compression if you want to).​
>
>
>
>>
>> Another strange question:
>> in general, it is considered that once numpy.array is created, it's shape
>> not changed.
>> But if i want to keep the same recarray and change it's dtype and/or
>> shape, is there a way to do this?
>>
>
> ​You can change shapes of numpy arrays, but that usually involves copies
> of the whole container.  With bcolz you can change length and add/del
> columns without copies.​  If your containers are large, it is better to
> inform bcolz on its final estimated size.  See:
>
> http://bcolz.blosc.org/en/latest/opt-tips.html
>
> ​Francesc​
>
>
>>
>> Thanks,
>> Alex.
>>
>>
>>
>> 22 февр. 2017 г., в 3:53, Nathaniel Smith  написал(а):
>>
>> On Feb 21, 2017 3:24 PM, "Alex Rogozhnikov" 
>> wrote:
>>
>> Ah, got it. Thanks, Chris!
>> I thought recarray can be only one-dimensional (like tables with named
>> columns).
>>
>> Maybe it's better to ask directly what I was looking for:
>> something that works like a table with named columns (but no labelling
>> for rows), and keeps data (of different dtypes) in a column-by-column way
>> (and this is numpy, not pandas).
>>
>> Is there such a magic thing?
>>
>>
>> Well, that's what pandas is for...
>>
>> A dict of arrays?
>>
>> -n
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>
>
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Re: [Numpy-discussion] From Python to Numpy

[Kiko]

2016-12-22 17:44 GMT+01:00 Nicolas P. Rougier :

>
> Dear all,
>
> I've just put online a (kind of) book on Numpy and more specifically about
> vectorization methods. It's not yet finished, has not been reviewed and
> it's a bit rough around the edges. But I think there are some material that
> can be interesting. I'm specifically happy with the boids example that show
> a nice combination of numpy and matplotlib strengths.
>
> Book is online at: http://www.labri.fr/perso/
> nrougier/from-python-to-numpy/
> Sources are available at: https://github.com/rougier/from-python-to-numpy
>
>
> Comments/questions/fixes/ideas are of course welcome.
>

Wow!!! Beautiful.

Thanks for sharing.


>
>
> Nicolas
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Re: [Numpy-discussion] PyData Madrid

[Kiko]

2016-02-20 20:13 GMT+01:00 David Cournapeau <courn...@gmail.com>:

>
>
> On Sat, Feb 20, 2016 at 5:26 PM, Kiko <kikocorre...@gmail.com> wrote:
>
>>
>>
>> 2016-02-20 17:58 GMT+01:00 Ralf Gommers <ralf.gomm...@gmail.com>:
>>
>>>
>>>
>>> On Wed, Feb 17, 2016 at 9:46 PM, Sebastian Berg <
>>> sebast...@sipsolutions.net> wrote:
>>>
>>>> On Mi, 2016-02-17 at 20:59 +0100, Jaime Fernández del Río wrote:
>>>> > Hi all,
>>>> >
>>>> > I just found out there is a PyData Madrid happening in early April,
>>>> > and it would feel wrong not to go, it being my hometown and all.
>>>> >
>>>> > Aside from the usual "Who else is going? We should meet!" I was also
>>>> > thinking of submitting a proposal for a talk.  My idea was to put
>>>> > something together on "The future of NumPy indexing" and use it as an
>>>> > opportunity to raise awareness and hopefully gather feedback from
>>>> > users on the proposed changes, in sort of a "if the mountain won't
>>>> > come to Muhammad" type of thing.
>>>> >
>>>>
>>>> I guess you do know my last name means mountain in german? But if
>>>> Muhammed might come, I should really improve my arabic ;).
>>>>
>>>> In any case sounds good to me if you like to do it, I don't think I
>>>> will go, though it sounds nice.
>>>>
>>>
>>> Sounds like a good idea to me too. I like both the concrete topic, as
>>> well as just having a talk on Numpy at a PyData conference. In general
>>> there are too few (if any) talks on Numpy and other core libraries at
>>> PyData and Scipy confs I think.
>>>
>>
>> +1.
>>
>> It would be great a numpy talk from a core developer. BTW, C4P closes
>> tomorrow!!!
>>
>> Jaime, if you come to Madrid you know you have some beers waiting for you.
>>
>> Disclaimer, I'm one of co-organizers of the PyData Madrid.
>>
>
> Since when does one need disclaimer when offering beers ? That would make
> for a dangerous precedent :)
>

The disclaimer is not for the beers :-P

The beers sentence should be a "P.D.:"


>
> David
>
>>
>> Best.
>>
>>
>>> Ralf
>>>
>>>
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Re: [Numpy-discussion] PyData Madrid

[Kiko]

2016-02-20 17:58 GMT+01:00 Ralf Gommers :

>
>
> On Wed, Feb 17, 2016 at 9:46 PM, Sebastian Berg <
> sebast...@sipsolutions.net> wrote:
>
>> On Mi, 2016-02-17 at 20:59 +0100, Jaime Fernández del Río wrote:
>> > Hi all,
>> >
>> > I just found out there is a PyData Madrid happening in early April,
>> > and it would feel wrong not to go, it being my hometown and all.
>> >
>> > Aside from the usual "Who else is going? We should meet!" I was also
>> > thinking of submitting a proposal for a talk.  My idea was to put
>> > something together on "The future of NumPy indexing" and use it as an
>> > opportunity to raise awareness and hopefully gather feedback from
>> > users on the proposed changes, in sort of a "if the mountain won't
>> > come to Muhammad" type of thing.
>> >
>>
>> I guess you do know my last name means mountain in german? But if
>> Muhammed might come, I should really improve my arabic ;).
>>
>> In any case sounds good to me if you like to do it, I don't think I
>> will go, though it sounds nice.
>>
>
> Sounds like a good idea to me too. I like both the concrete topic, as well
> as just having a talk on Numpy at a PyData conference. In general there are
> too few (if any) talks on Numpy and other core libraries at PyData and
> Scipy confs I think.
>

+1.

It would be great a numpy talk from a core developer. BTW, C4P closes
tomorrow!!!

Jaime, if you come to Madrid you know you have some beers waiting for you.

Disclaimer, I'm one of co-organizers of the PyData Madrid.

Best.


> Ralf
>
>
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Re: [Numpy-discussion] ANN: scipy 0.17.0 release

[Kiko]

is it python3.5 compatible? your message and github don't say the same.

2016-01-23 19:12 GMT+01:00, Charles R Harris :
> 
>
> Congratulations.
>
> Chuck
>
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Re: [Numpy-discussion] ANN: scipy 0.17.0 release

[Kiko]

BTW, congratulations and thanks for the hard work

2016-01-23 20:12 GMT+01:00, Kiko <kikocorre...@gmail.com>:
> is it python3.5 compatible? your message and github don't say the same.
>
> 2016-01-23 19:12 GMT+01:00, Charles R Harris <charlesr.har...@gmail.com>:
>> 
>>
>> Congratulations.
>>
>> Chuck
>>
>
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Re: [Numpy-discussion] Fwd: Numpy for data manipulation

[Kiko]

2015-10-02 9:38 GMT+02:00 Alex Rogozhnikov :

> I would suggest
>>
>> %matplotlib notebook
>>
>> It will still have to a nice png, but you get an interactive figure when
>> it is live.
>>
>
> Amazing, thanks. I was using mpld3 for this.
> (for some strange reason I need to put %matplotlib notebook before each
> plot)
>

You should create a figure before each plot instead of putthon %matplotlib
notebook
plt.figure()



>
> The recommendation of inverting a permutation by argsort'ing it, while it
>> works, is suboptimal, as it takes O(n log(n)) time, and you can do it in
>> linear time:
>>
> Actually, there is (later in post) a linear solution using bincount, but
> your code is definitely better. Thanks!
>
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Re: [Numpy-discussion] Fwd: Numpy for data manipulation

[Kiko]

2015-10-02 9:48 GMT+02:00 Kiko <kikocorre...@gmail.com>:

>
>
> 2015-10-02 9:38 GMT+02:00 Alex Rogozhnikov <alex.rogozhni...@yandex.ru>:
>
>> I would suggest
>>>
>>> %matplotlib notebook
>>>
>>> It will still have to a nice png, but you get an interactive figure when
>>> it is live.
>>>
>>
>> Amazing, thanks. I was using mpld3 for this.
>> (for some strange reason I need to put %matplotlib notebook before each
>> plot)
>>
>
> You should create a figure before each plot instead of putthon %matplotlib
> notebook
> plt.figure()
> 
>

putthon == putting


>
>
>>
>> The recommendation of inverting a permutation by argsort'ing it, while it
>>> works, is suboptimal, as it takes O(n log(n)) time, and you can do it in
>>> linear time:
>>>
>> Actually, there is (later in post) a linear solution using bincount, but
>> your code is definitely better. Thanks!
>>
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Re: [Numpy-discussion] netcdf lat lon to coord - ValueError: need more than 1 value to unpack

[Kiko]

2015-03-24 11:02 GMT+01:00 questions anon questions.a...@gmail.com:

 I would like to find the nearest coord in a netcdf from a given latitude
 and longitude.
 I found some fantastic code that does this -
 http://nbviewer.ipython.org/github/Unidata/unidata-python-workshop/blob/master/netcdf-by-coordinates.ipynb
 but I keep receiving this error - I am receiving a ValueError: need more
 than 1 value to unpack

 I have pasted the code and full error below. Any help will be greatly
 appreciated.




 import numpy as np

 import netCDF4


 def naive_fast(latvar,lonvar,lat0,lon0):

# Read latitude and longitude from file into numpy arrays

latvals = latvar[:]

lonvals = lonvar[:]

ny,nx = latvals.shape

dist_sq = (latvals-lat0)**2 + (lonvals-lon0)**2

minindex_flattened = dist_sq.argmin() # 1D index of min element

iy_min,ix_min = np.unravel_index(minindex_flattened, latvals.shape)

return iy_min,ix_min

 filename = /Users/T_SFC.nc

 ncfile = netCDF4.Dataset(filename, 'r')

 latvar = ncfile.variables['latitude']

 lonvar = ncfile.variables['longitude']


 iy,ix = naive_fast(latvar, lonvar, -38.009, 146.438)

 print 'Closest lat lon:', latvar[iy,ix], lonvar[iy,ix]

 ncfile.close()





 ---
 ValueErrorTraceback (most recent call last)
 /Applications/Canopy.app/appdata/canopy-1.3.0.1715.macosx-x86_64/Canopy.app/Contents/lib/python2.7/site-packages/IPython/utils/py3compat.pyc
 in execfile(fname, *where)
 202 else:
 203 filename = fname
 -- 204 __builtin__.execfile(filename, *where)

 /Users/latlon_to_closestgrid.py in module()
  22 lonvar = ncfile.variables['longitude']
  23
 --- 24 iy,ix = naive_fast(latvar, lonvar, -38.009, 146.438)
  25 print 'Closest lat lon:', latvar[iy,ix], lonvar[iy,ix]
  26 ncfile.close()

 /Users/latlon_to_closestgrid.py in naive_fast(latvar, lonvar, lat0, lon0)
  12 latvals = latvar[:]
  13 lonvals = lonvar[:]
 --- 14 ny,nx = latvals.shape
  15 dist_sq = (latvals-lat0)**2 + (lonvals-lon0)**2
  16 minindex_flattened = dist_sq.argmin()  # 1D index of min
 element

 ValueError: need more than 1 value to unpack



It seems that latvals and lonvals should be a 2D array and you are
providing just a 1D array.
Maybe you could use numpy.meshgrid [1] to get 2D inputs from 1D arrays.

[1] http://docs.scipy.org/doc/numpy/reference/generated/numpy.meshgrid.html






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Re: [Numpy-discussion] Calculation of a hessian

[Kiko]

2014-08-08 11:51 GMT+02:00 Jose Gomez-Dans jgomezd...@gmail.com:

 Your function looks fairly simple to differentiate by hand, but if you
 have access to the gradient (or you estimate it numerically using
 scipy...), this function might do the job:

 def hessian ( x, the_func, epsilon=1e-8):
 Numerical approximation to the Hessian
 Parameters
 
 x: array-like
 The evaluation point
 the_func: function
 The function. We assume that the function returns the function
 value and
 the associated gradient as the second return element
 epsilon: float
 The size of the step
 

 N = x.size
 h = np.zeros((N,N))
 df_0 = the_func ( x )[1]
 for i in xrange(N):
 xx0 = 1.*x[i]
 x[i] = xx0 + epsilon
 df_1 = the_func ( x )[1]
 h[i,:] = (df_1 - df_0)/epsilon
 x[i] = xx0
 return h

 Jose


Hi José,

Thanks for the answer.

My idea would be to generalise the calculation of the Hessian, not just to
differentiate the example I posted and I was wondering if Numpy/Scipy
already had something similar to that provided by NumDiffTools.

Thanks again.



 On 8 August 2014 08:31, Kiko kikocorre...@gmail.com wrote:

 Hi all,

 I am trying to calculate a Hessian. I am using numdifftools for this (
 https://pypi.python.org/pypi/Numdifftools).

 My question is, is it possible to make it using pure numpy?.

 The actual code is like this:


 *import numdifftools as nd*
 *import numpy as np*

 *def log_likelihood(params):*
 *sum1 = 0; sum2 = 0*
 *mu = params[0]; sigma = params[1]; xi = params[2]*
 *for z in data:*
 *x = 1 + xi * ((z-mu)/sigma)*
 *sum1 += np.log(x)*
 *sum2 += x**(-1.0/xi)*
 *return -((-len(data) * np.log(sigma)) - (1 + 1/xi)*sum1 - sum2) #
 negated so we can use 'minimum'*

 *kk = nd.Hessian(log_likelihood)*

 Thanks in advance.

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Re: [Numpy-discussion] Calculation of a hessian

[Kiko]

2014-08-08 16:37 GMT+02:00 Eelco Hoogendoorn hoogendoorn.ee...@gmail.com:

 Do it in pure numpy? How about copying the source of numdifftools?


Of course it is a solution. I was just wondering if it exist something
similar in the numpy/scipy packages so I do not have to use a new third
party library to do that.


 What exactly is the obstacle to using numdifftools? There seem to be no
 licensing issues. In my experience, its a crafty piece of work; and
 calculating a hessian correctly, accounting for all kinds of nasty floating
 point issues, is no walk in the park. Even if an analytical derivative
 isn't too big a pain in the ass to implement, there is a good chance that
 what numdifftools does is more numerically stable (though in all likelihood
 much slower).

 The only good reason for a specialized solution I can think of is speed;
 but be aware what you are trading it in for. If speed is your major concern
 though, you really cant go wrong with Theano.


 http://deeplearning.net/software/theano/library/gradient.html#theano.gradient.hessian


Thanks, it seems that NumDiffTools is the way to go.


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[Numpy-discussion] Calculation of a hessian

[Kiko]

Hi all,

I am trying to calculate a Hessian. I am using numdifftools for this (
https://pypi.python.org/pypi/Numdifftools).

My question is, is it possible to make it using pure numpy?.

The actual code is like this:


*import numdifftools as nd*
*import numpy as np*

*def log_likelihood(params):*
*sum1 = 0; sum2 = 0*
*mu = params[0]; sigma = params[1]; xi = params[2]*
*for z in data:*
*x = 1 + xi * ((z-mu)/sigma)*
*sum1 += np.log(x)*
*sum2 += x**(-1.0/xi)*
*return -((-len(data) * np.log(sigma)) - (1 + 1/xi)*sum1 - sum2) #
negated so we can use 'minimum'*

*kk = nd.Hessian(log_likelihood)*

Thanks in advance.
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Re: [Numpy-discussion] python geospatial package?

[Kiko]

2012/2/23 Vincent Schut sc...@sarvision.nl

 On 02/22/2012 10:45 PM, Chao YUE wrote:
  Hi all,
 
  Is anyone using some python geospatial package that can do jobs like
  intersection, etc.  the job is like you automatically extract a region
  on a global map etc.
 
  thanks and cheers,
 
  Chao


Depending what you want to do:

Shapely, GDAL/OGR, pyproj, Mapnik, Basemap,...
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Re: [Numpy-discussion] Fwd: Re: Creating parallel curves

[Kiko]

2012/2/13 Andrea Gavana andrea.gav...@gmail.com

 -- Forwarded message --
 From: Andrea Gavana andrea.gav...@gmail.com
 Date: Feb 13, 2012 11:31 PM
 Subject: Re: [Numpy-discussion] Creating parallel curves
 To: Jonathan Hilmer jkhil...@gmail.com

 Thank you Jonathan for this, it's exactly what I was looking for. I' ll
 try it tomorrow on the 768 well trajectories I have and I'll let you know
 if I stumble upon any issue.

 If someone could shed some light on my problem number 2 (how to adjust the
 scaling/distance) so that the curves look parallel on a matplotlib graph
 even though the axes scales are different, I'd be more than grateful.

 Thank you in advance.


Hi.
Maybe this could help you as a starting point.

*from Shapely.geometry import LineString
from matplotlib import pyplot

myline = LineString(...)
x, y = myline.xy

xx, yy =  myline.buffer(distancefrommyline).exterior.xy  # coordinates
around myline

pyplot.plot(x, y)
pyplot.plot(xx,yy)
pyplot.show()*

Best.
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Re: [Numpy-discussion] what python module to modify NetCDF data?

[Kiko]

Quoting Chao YUE chaoyue...@gmail.com:


  Dear all,
 
  I want to change some variable values in a series of NetCDF file. Did
  anybody else did this before using python?
  Now I use pupynere for reading data from NetCDF files and making plots.
 but
  the document of pupynere for writing data to NetCDF file is quite simple
 and
  I still feel difficult to do this with pupynere.
 
  the NetCDF file I want to change is a global data (0.5X0.5d resolution,
  360X720grid with 12 time steps) and have approx. 10 variables. I just
 want
  to change some points for a specific
  variable for all 12 time steps. I know it's possible use NCO ncap2
 utility
  to do the job. but now I have some problem in using ncap2 within a shell
  script.
  I guess there is some easy way to use some python module to do the job?
 like
  mainly altering the data that need to change while let the others
 remaining
  intact?
 
  Any idea will be greatly appreciated. I will all a good weekend,
 
  Chao


Hi.

Have a look to [1] and [2].

[1] http://code.google.com/p/netcdf4-python/
[2] http://www.scipy.org/doc/api_docs/SciPy.io.netcdf.html
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[Numpy-discussion] Reading a big netcdf file

[Kiko]

Hi.

I'm trying to read a big netcdf file (445 Mb) using netcdf4-python.

The data are described as:
*The GEBCO gridded data set is stored in NetCDF as a one dimensional array
of 2-byte signed integers that represent integer elevations in metres.
The complete data set gives global coverage. It consists of 21601 x 10801
data values, one for each one minute of latitude and longitude for 233312401
points.
The data start at position 90°N, 180°W and are arranged in bands of 360
degrees x 60 points/degree + 1 = 21601 values. The data range eastward from
180°W longitude to 180°E longitude, i.e. the 180° value is repeated.*

The problem is that it is very slow (or I am quite newbie).

Anyone has a suggestion to get these data in a numpy array in a faster way?

Thanks in advance.
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