From bsipocz at gmail.com Fri Aug 3 18:07:20 2018 From: bsipocz at gmail.com (Brigitta Sipocz) Date: Fri, 3 Aug 2018 23:07:20 +0100 Subject: [AstroPy] ANN: astropy bugfix releases v3.0.4 and v2.0.8 (LTS) Message-ID: Dear All, I'm pleased to announce that new bugfix releases have just been made for both the stable (v3.0.4) and LTS (v2.0.8) editions of astropy. The releases are available on PyPI and on the usual conda channels. In addition to various minor bugfixes, these releases are now compatible with Python 3.7. The full list of fixes can be found in the changelog: https://github.com/astropy/astropy/blob/v3.0.4/CHANGES.rst and https://github.com/astropy/astropy/blob/v2.0.8/CHANGES.rst Thank you for everyone who contributed for these releases! Cheers, Brigitta -------------- next part -------------- An HTML attachment was scrubbed... URL: From larry.bradley at gmail.com Fri Aug 10 16:25:58 2018 From: larry.bradley at gmail.com (Larry Bradley) Date: Fri, 10 Aug 2018 16:25:58 -0400 Subject: [AstroPy] [ANN] Photutils v0.5 and v0.4.1 released Message-ID: Dear colleagues, We are very happy to announce the v0.5 and v0.4.1 releases of the Photutils package. Photutils is an astropy coordinated package to perform photometry (both aperture and PSF-fitting), ePSF building, PSF matching, source detection, image segmentation, background estimation, and elliptical isophote analysis. The v0.5 release supports Python 3.5+. New major functionality includes an effective PSF builder following the prescription of Anderson & King (2000; PASP 112, 1360): http://photutils.readthedocs.io/en/latest/epsf.html The v0.4.1 release is a bugfix-only release for those still using Python 2.7. In particular, it provides bugfixes to support Python 2.7 with scikit-image 0.14+ and numpy 1.14+. Many smaller improvements and fixes have also been made. Please see the changelogs of both versions for details. The releases are available on PyPI and on the usual conda channels (astropy, conda-forge, and astroconda). Python 3.7 builds are not yet available on the astropy and conda-forge channels, but in the meantime they are available on PyPI and the astronconda channel. If you use Photutils, please cite the package via its Zenodo record: https://zenodo.org/record/1340699 Code https://github.com/astropy/photutils Documentation https://photutils.readthedocs.io Installation https://photutils.readthedocs.io/en/stable/install.html Many thanks to everyone who has contributed to the package ( https://github.com/astropy/photutils/graphs/contributors). Feedback, contributions, and comments are welcome. Cheers, Larry Bradley on behalf of the Photutils developers -------------- next part -------------- An HTML attachment was scrubbed... URL: From f.s.farimani at gmail.com Fri Aug 10 18:08:45 2018 From: f.s.farimani at gmail.com (Foad Sojoodi Farimani) Date: Sat, 11 Aug 2018 00:08:45 +0200 Subject: [AstroPy] Convolution of NumPy arrays of arbitrary dimension Message-ID: Hello everyone, My first email here, so sorry in advance if I'm violating any rules. I'm trying to implement the convolution of NumPy arrays of arbitrary dimension in order to get Cauchy product of multivariate power series. However I have some issues. Considering that you have already implemented a 1D, 2D 3D convolution (here), I was wondering maybe you can help and this might also be of your interest. I have explained everything here: http://bit.ly/2MhJCex I would appreciate if you could help me figure this out. Thanks in advance. Best, Foad -------------- next part -------------- An HTML attachment was scrubbed... URL: From jakevdp at cs.washington.edu Fri Aug 10 19:51:50 2018 From: jakevdp at cs.washington.edu (Jacob Vanderplas) Date: Fri, 10 Aug 2018 16:51:50 -0700 Subject: [AstroPy] Convolution of NumPy arrays of arbitrary dimension In-Reply-To: References: Message-ID: Hi Foad, I'm sorry if I'm misunderstanding something, but does scipy.ndimage.convolve not address your use case? It implements N-dimensional convolution: from scipy.ndimage import convolveimport numpy as np x = np.random.rand(10, 10, 10, 10) w = np.ones((3, 3, 3, 3)) result = convolve(x, w) Best, Jake Jake VanderPlas Senior Data Science Fellow Director of Open Software University of Washington eScience Institute On Fri, Aug 10, 2018 at 3:08 PM, Foad Sojoodi Farimani < f.s.farimani at gmail.com> wrote: > Hello everyone, > > My first email here, so sorry in advance if I'm violating any rules. > I'm trying to implement the convolution of NumPy arrays of arbitrary > dimension in order to get Cauchy product of multivariate power series. > However I have some issues. Considering that you have already implemented a > 1D, 2D 3D convolution (here), I was wondering maybe you can help and this > might also be of your interest. I have explained everything here: > > http://bit.ly/2MhJCex > > I would appreciate if you could help me figure this out. Thanks in advance. > > Best, > Foad > > _______________________________________________ > AstroPy mailing list > AstroPy at python.org > https://mail.python.org/mailman/listinfo/astropy > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From adam.g.ginsburg at gmail.com Fri Aug 10 19:56:03 2018 From: adam.g.ginsburg at gmail.com (Adam Ginsburg) Date: Fri, 10 Aug 2018 17:56:03 -0600 Subject: [AstroPy] Convolution of NumPy arrays of arbitrary dimension In-Reply-To: References: Message-ID: On Fri, Aug 10, 2018 at 5:51 PM, Jacob Vanderplas wrote: > Hi Foad, > I'm sorry if I'm misunderstanding something, but does > scipy.ndimage.convolve not address your use case? It implements > N-dimensional convolution: > > from scipy.ndimage import convolveimport numpy as np > > x = np.random.rand(10, 10, 10, 10) > w = np.ones((3, 3, 3, 3)) > > result = convolve(x, w) > > For completeness, astropy's convolve_fft supports this same operation since it's doing an nd fft under the hood, but the direct convolution (astropy.convolution.convolve) does not, since we had to hard-code the direct convolution operations in each dimension and so far there has been no demand for an n-dimensional convolution with n>3. -------------- next part -------------- An HTML attachment was scrubbed... URL: From f.s.farimani at gmail.com Sat Aug 11 03:51:30 2018 From: f.s.farimani at gmail.com (Foad Sojoodi Farimani) Date: Sat, 11 Aug 2018 09:51:30 +0200 Subject: [AstroPy] Convolution of NumPy arrays of arbitrary dimension In-Reply-To: References: Message-ID: Hi Adam and Jakub, Thanks a lot for the reply. I have indeed seen scipy.ndimage.convolve and have mentioned it in the OP . but some questions: 1. although there is nothing about the dimension limit of the ndarrays in its official page , but I haven't seen any examples showing it works with higher dimensions. 2. what is the difference between astropy.convolve_fft and scipy.signal.convolve? It seems to me they are for function analysis not array arithmetics. 3. As I can see the term convolution, even for array arithmetics is used for different purposes. For example there is also scipy.ndimage.filters.convolve which apparently calculates different things. My final goal is to do finite multivariate formal power series multiplication (Cauchy product). I think I have figured the formula out here , but I'm not sure if it is correct completely. questions are: 1. is my formula correct? - if not what is the correct one? 2. if yes has this been done before? - if yes where? does any of the above functions do the job? 3. regardless of the correctness of the formula and existence of other implementations, is my implementation correct so far? 4. how to finish the final step to populate the ndarray using the conv function? Thanks a gain and looking forwards to hearing back. Best, Foad On Sat, Aug 11, 2018 at 1:56 AM Adam Ginsburg wrote: > > > On Fri, Aug 10, 2018 at 5:51 PM, Jacob Vanderplas < > jakevdp at cs.washington.edu> wrote: > >> Hi Foad, >> I'm sorry if I'm misunderstanding something, but does >> scipy.ndimage.convolve not address your use case? It implements >> N-dimensional convolution: >> >> from scipy.ndimage import convolveimport numpy as np >> >> x = np.random.rand(10, 10, 10, 10) >> w = np.ones((3, 3, 3, 3)) >> >> result = convolve(x, w) >> >> For completeness, astropy's convolve_fft supports this same operation > since it's doing an nd fft under the hood, but the direct convolution > (astropy.convolution.convolve) does not, since we had to hard-code the > direct convolution operations in each dimension and so far there has been > no demand for an n-dimensional convolution with n>3. > _______________________________________________ > AstroPy mailing list > AstroPy at python.org > https://mail.python.org/mailman/listinfo/astropy > -------------- next part -------------- An HTML attachment was scrubbed... URL: From jakevdp at cs.washington.edu Sat Aug 11 16:11:46 2018 From: jakevdp at cs.washington.edu (Jacob Vanderplas) Date: Sat, 11 Aug 2018 13:11:46 -0700 Subject: [AstroPy] Convolution of NumPy arrays of arbitrary dimension In-Reply-To: References: Message-ID: Hi Foad > Thanks a lot for the reply. I have indeed seen scipy.ndimage.convolve and > have mentioned it in the OP > . > but some questions: > I saw that, but you seemed to imply there that it was limited to 1 and 2 dimensions, when it works for arbitrary dimensions. > > > 1. although there is nothing about the dimension limit of the ndarrays > in its official page > , > but I haven't seen any examples showing it works with higher dimensions. > > I showed an example of a simple four-dimensional convolution in my original response. > > 1. what is the difference between astropy.convolve_fft and > scipy.signal.convolve? It seems to me they are for function analysis > not array arithmetics. > > By design, scipy.signal.convolve and scipy.signal.fftconvolve produce the same results. The difference is that the latter uses an FFT to compute the results, which is generally faster than direct convolution for large inputs. astropy.convolve_fft is similar to scipy.signal.fftconvolve, with a few differences listed in the documentation . > > 1. As I can see the term convolution, even for array arithmetics is > used for different purposes. For example there is > also scipy.ndimage.filters.convolve which apparently calculates > different things. My final goal is to do finite multivariate formal power > series multiplication (Cauchy product). I think I have figured the formula > out here > , > but I'm not sure if it is correct completely. questions are: > 1. is my formula correct? > - if not what is the correct one? > 2. if yes has this been done before? > - if yes where? does any of the above functions do the job? > 3. regardless of the correctness of the formula and existence of > other implementations, is my implementation correct so far? > 4. how to finish the final step to populate the ndarray using the > conv function? > > At a glance, I'm not certain if your formula or implementation is correct, and I don't have the time to dig-in at the moment. Perhaps someone else can help with that part. Best of luck, Jake > Thanks a gain and looking forwards to hearing back. > > Best, > Foad > > On Sat, Aug 11, 2018 at 1:56 AM Adam Ginsburg > wrote: > >> >> >> On Fri, Aug 10, 2018 at 5:51 PM, Jacob Vanderplas < >> jakevdp at cs.washington.edu> wrote: >> >>> Hi Foad, >>> I'm sorry if I'm misunderstanding something, but does >>> scipy.ndimage.convolve not address your use case? It implements >>> N-dimensional convolution: >>> >>> from scipy.ndimage import convolveimport numpy as np >>> >>> x = np.random.rand(10, 10, 10, 10) >>> w = np.ones((3, 3, 3, 3)) >>> >>> result = convolve(x, w) >>> >>> For completeness, astropy's convolve_fft supports this same operation >> since it's doing an nd fft under the hood, but the direct convolution >> (astropy.convolution.convolve) does not, since we had to hard-code the >> direct convolution operations in each dimension and so far there has been >> no demand for an n-dimensional convolution with n>3. >> _______________________________________________ >> AstroPy mailing list >> AstroPy at python.org >> https://mail.python.org/mailman/listinfo/astropy >> > > _______________________________________________ > AstroPy mailing list > AstroPy at python.org > https://mail.python.org/mailman/listinfo/astropy > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From f.s.farimani at gmail.com Mon Aug 13 04:31:53 2018 From: f.s.farimani at gmail.com (Foad Sojoodi Farimani) Date: Mon, 13 Aug 2018 10:31:53 +0200 Subject: [AstroPy] Convolution of NumPy arrays of arbitrary dimension In-Reply-To: References: Message-ID: Dear Jake, Thanks alot for your reply. I just tried the scipy.ndimage.convolve and it doesn't seem to be what I'm looking for. I use the term convolution as used in the context of the Cauchy product of multivariate power series (polynomials). for example from scipy.ndimage import convolve import numpy as np D1=np.array([4,5]) D2=np.array([2,3]) A=np.random.randint(10,size=D1) B=np.random.randint(10,size=D2) print(A) print(B) results: [[9 5 2 0 1] [1 9 8 4 4] [7 9 2 9 6] [5 0 8 8 2]] [[4 6 9] [9 0 8]] I expect the first element of the conve(A,B) to be 36 but the print(convolve(A, B)) results in: [[168 185 185 137 85] [230 205 237 196 209] [212 151 233 198 218] [115 189 152 210 174]] Also the conve(A,B) should be of the shape A.shape+B.shape-1 while scipy.ndimage.convolve result is of the A.shape. the conv I'm looking for is commutative as the Cauchy product of two polynomial is. Best, Foad On Sat, Aug 11, 2018 at 10:11 PM Jacob Vanderplas wrote: > Hi Foad > > >> Thanks a lot for the reply. I have indeed seen scipy.ndimage.convolve and >> have mentioned it in the OP >> . >> but some questions: >> > > I saw that, but you seemed to imply there that it was limited to 1 and 2 > dimensions, when it works for arbitrary dimensions. > > >> >> >> 1. although there is nothing about the dimension limit of the >> ndarrays in its official page >> , >> but I haven't seen any examples showing it works with higher dimensions. >> >> I showed an example of a simple four-dimensional convolution in my > original response. > > >> >> 1. what is the difference between astropy.convolve_fft and >> scipy.signal.convolve? It seems to me they are for function analysis >> not array arithmetics. >> >> By design, scipy.signal.convolve and scipy.signal.fftconvolve produce the > same results. The difference is that the latter uses an FFT to compute the > results, which is generally faster than direct convolution for large > inputs. astropy.convolve_fft is similar to scipy.signal.fftconvolve, with a > few differences listed in the documentation > > . > >> >> 1. As I can see the term convolution, even for array arithmetics is >> used for different purposes. For example there is >> also scipy.ndimage.filters.convolve which apparently calculates different >> things. My final goal is to do finite multivariate formal power series >> multiplication (Cauchy product). I think I have figured the formula out >> here >> , >> but I'm not sure if it is correct completely. questions are: >> 1. is my formula correct? >> - if not what is the correct one? >> 2. if yes has this been done before? >> - if yes where? does any of the above functions do the job? >> 3. regardless of the correctness of the formula and existence >> of other implementations, is my implementation correct so far? >> 4. how to finish the final step to populate the ndarray using the >> conv function? >> >> At a glance, I'm not certain if your formula or implementation is > correct, and I don't have the time to dig-in at the moment. > > Perhaps someone else can help with that part. > > Best of luck, > Jake > > >> Thanks a gain and looking forwards to hearing back. >> >> Best, >> Foad >> >> On Sat, Aug 11, 2018 at 1:56 AM Adam Ginsburg >> wrote: >> >>> >>> >>> On Fri, Aug 10, 2018 at 5:51 PM, Jacob Vanderplas < >>> jakevdp at cs.washington.edu> wrote: >>> >>>> Hi Foad, >>>> I'm sorry if I'm misunderstanding something, but does >>>> scipy.ndimage.convolve not address your use case? It implements >>>> N-dimensional convolution: >>>> >>>> from scipy.ndimage import convolveimport numpy as np >>>> >>>> x = np.random.rand(10, 10, 10, 10) >>>> w = np.ones((3, 3, 3, 3)) >>>> >>>> result = convolve(x, w) >>>> >>>> For completeness, astropy's convolve_fft supports this same operation >>> since it's doing an nd fft under the hood, but the direct convolution >>> (astropy.convolution.convolve) does not, since we had to hard-code the >>> direct convolution operations in each dimension and so far there has been >>> no demand for an n-dimensional convolution with n>3. >>> _______________________________________________ >>> AstroPy mailing list >>> AstroPy at python.org >>> https://mail.python.org/mailman/listinfo/astropy >>> >> >> _______________________________________________ >> AstroPy mailing list >> AstroPy at python.org >> https://mail.python.org/mailman/listinfo/astropy >> >> > _______________________________________________ > AstroPy mailing list > AstroPy at python.org > https://mail.python.org/mailman/listinfo/astropy > -------------- next part -------------- An HTML attachment was scrubbed... URL: From jakevdp at cs.washington.edu Mon Aug 13 11:30:06 2018 From: jakevdp at cs.washington.edu (Jacob Vanderplas) Date: Mon, 13 Aug 2018 08:30:06 -0700 Subject: [AstroPy] Convolution of NumPy arrays of arbitrary dimension In-Reply-To: References: Message-ID: Foad, It sounds like you maybe are looking for a correlation rather than a convolution? >>> from scipy.ndimage import correlate >>> correlate(A, B) array([[36, 28, 14, 4, 2], [20, 24, 24, 14, 9], [16, 26, 28, 23, 23], [24, 21, 19, 27, 25]]) Jake VanderPlas Senior Data Science Fellow Director of Open Software University of Washington eScience Institute On Mon, Aug 13, 2018 at 1:31 AM, Foad Sojoodi Farimani < f.s.farimani at gmail.com> wrote: > Dear Jake, > > Thanks alot for your reply. > I just tried the scipy.ndimage.convolve and it doesn't seem to be what I'm > looking for. I use the term convolution as used in the context of the Cauchy > product of multivariate > power series (polynomials). for example > > from scipy.ndimage import convolve > import numpy as np > > D1=np.array([4,5]) > D2=np.array([2,3]) > A=np.random.randint(10,size=D1) > B=np.random.randint(10,size=D2) > > print(A) > print(B) > > results: > > [[9 5 2 0 1] > [1 9 8 4 4] > [7 9 2 9 6] > [5 0 8 8 2]] > [[4 6 9] > [9 0 8]] > > I expect the first element of the conve(A,B) to be 36 but > the print(convolve(A, B)) results in: > > [[168 185 185 137 85] > [230 205 237 196 209] > [212 151 233 198 218] > [115 189 152 210 174]] > > Also the conve(A,B) should be of the shape A.shape+B.shape-1 > while scipy.ndimage.convolve result is of the A.shape. the conv I'm looking > for is commutative as the Cauchy product of two polynomial is. > > Best, > Foad > > > > On Sat, Aug 11, 2018 at 10:11 PM Jacob Vanderplas < > jakevdp at cs.washington.edu> wrote: > >> Hi Foad >> >> >>> Thanks a lot for the reply. I have indeed seen scipy.ndimage.convolve >>> and have mentioned it in the OP >>> . >>> but some questions: >>> >> >> I saw that, but you seemed to imply there that it was limited to 1 and 2 >> dimensions, when it works for arbitrary dimensions. >> >> >>> >>> >>> 1. although there is nothing about the dimension limit of the >>> ndarrays in its official page >>> , >>> but I haven't seen any examples showing it works with higher dimensions. >>> >>> I showed an example of a simple four-dimensional convolution in my >> original response. >> >> >>> >>> 1. what is the difference between astropy.convolve_fft and >>> scipy.signal.convolve? It seems to me they are for function analysis >>> not array arithmetics. >>> >>> By design, scipy.signal.convolve and scipy.signal.fftconvolve produce >> the same results. The difference is that the latter uses an FFT to compute >> the results, which is generally faster than direct convolution for large >> inputs. astropy.convolve_fft is similar to scipy.signal.fftconvolve, with a >> few differences listed in the documentation >> >> . >> >>> >>> 1. As I can see the term convolution, even for array arithmetics is >>> used for different purposes. For example there is >>> also scipy.ndimage.filters.convolve which apparently calculates >>> different things. My final goal is to do finite multivariate formal power >>> series multiplication (Cauchy product). I think I have figured the formula >>> out here >>> , >>> but I'm not sure if it is correct completely. questions are: >>> 1. is my formula correct? >>> - if not what is the correct one? >>> 2. if yes has this been done before? >>> - if yes where? does any of the above functions do the job? >>> 3. regardless of the correctness of the formula and existence >>> of other implementations, is my implementation correct so far? >>> 4. how to finish the final step to populate the ndarray using the >>> conv function? >>> >>> At a glance, I'm not certain if your formula or implementation is >> correct, and I don't have the time to dig-in at the moment. >> >> Perhaps someone else can help with that part. >> >> Best of luck, >> Jake >> >> >>> Thanks a gain and looking forwards to hearing back. >>> >>> Best, >>> Foad >>> >>> On Sat, Aug 11, 2018 at 1:56 AM Adam Ginsburg >>> wrote: >>> >>>> >>>> >>>> On Fri, Aug 10, 2018 at 5:51 PM, Jacob Vanderplas < >>>> jakevdp at cs.washington.edu> wrote: >>>> >>>>> Hi Foad, >>>>> I'm sorry if I'm misunderstanding something, but does >>>>> scipy.ndimage.convolve not address your use case? It implements >>>>> N-dimensional convolution: >>>>> >>>>> from scipy.ndimage import convolveimport numpy as np >>>>> >>>>> x = np.random.rand(10, 10, 10, 10) >>>>> w = np.ones((3, 3, 3, 3)) >>>>> >>>>> result = convolve(x, w) >>>>> >>>>> For completeness, astropy's convolve_fft supports this same operation >>>> since it's doing an nd fft under the hood, but the direct convolution >>>> (astropy.convolution.convolve) does not, since we had to hard-code the >>>> direct convolution operations in each dimension and so far there has been >>>> no demand for an n-dimensional convolution with n>3. >>>> _______________________________________________ >>>> AstroPy mailing list >>>> AstroPy at python.org >>>> https://mail.python.org/mailman/listinfo/astropy >>>> >>> >>> _______________________________________________ >>> AstroPy mailing list >>> AstroPy at python.org >>> https://mail.python.org/mailman/listinfo/astropy >>> >>> >> _______________________________________________ >> AstroPy mailing list >> AstroPy at python.org >> https://mail.python.org/mailman/listinfo/astropy >> > > _______________________________________________ > AstroPy mailing list > AstroPy at python.org > https://mail.python.org/mailman/listinfo/astropy > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From f.s.farimani at gmail.com Mon Aug 13 12:10:48 2018 From: f.s.farimani at gmail.com (Foad Sojoodi Farimani) Date: Mon, 13 Aug 2018 18:10:48 +0200 Subject: [AstroPy] Convolution of NumPy arrays of arbitrary dimension In-Reply-To: References: Message-ID: Dear Jake, I don't think scipy.ndimage.correlate is what I'm looking for. the result is 5*4 while it must be A.shape+B.shape-1=7*5 Here somebody was able to modify different existing functions astropy.convolution.convolve_fft, astropy.convolution.convolve, scipy.ndimage.filters.convolve, scipy.ndimage.filters.convolve using numpy.pad to generate what I'm looking for. Next step is to automate this step to have something like numpy.convolve for 1D or scipy.signal.convolve2d for 2D. Best, Foad On Mon, Aug 13, 2018 at 5:30 PM Jacob Vanderplas wrote: > Foad, > It sounds like you maybe are looking for a correlation rather than a > convolution? > > >>> from scipy.ndimage import correlate > >>> correlate(A, B) > > array([[36, 28, 14, 4, 2], > [20, 24, 24, 14, 9], > [16, 26, 28, 23, 23], > [24, 21, 19, 27, 25]]) > > > Jake VanderPlas > Senior Data Science Fellow > Director of Open Software > University of Washington eScience Institute > > On Mon, Aug 13, 2018 at 1:31 AM, Foad Sojoodi Farimani < > f.s.farimani at gmail.com> wrote: > >> Dear Jake, >> >> Thanks alot for your reply. >> I just tried the scipy.ndimage.convolve and it doesn't seem to be what >> I'm looking for. I use the term convolution as used in the context of the Cauchy >> product of multivariate >> power series (polynomials). for example >> >> from scipy.ndimage import convolve >> import numpy as np >> >> D1=np.array([4,5]) >> D2=np.array([2,3]) >> A=np.random.randint(10,size=D1) >> B=np.random.randint(10,size=D2) >> >> print(A) >> print(B) >> >> results: >> >> [[9 5 2 0 1] >> [1 9 8 4 4] >> [7 9 2 9 6] >> [5 0 8 8 2]] >> [[4 6 9] >> [9 0 8]] >> >> I expect the first element of the conve(A,B) to be 36 but >> the print(convolve(A, B)) results in: >> >> [[168 185 185 137 85] >> [230 205 237 196 209] >> [212 151 233 198 218] >> [115 189 152 210 174]] >> >> Also the conve(A,B) should be of the shape A.shape+B.shape-1 >> while scipy.ndimage.convolve result is of the A.shape. the conv I'm looking >> for is commutative as the Cauchy product of two polynomial is. >> >> Best, >> Foad >> >> >> >> On Sat, Aug 11, 2018 at 10:11 PM Jacob Vanderplas < >> jakevdp at cs.washington.edu> wrote: >> >>> Hi Foad >>> >>> >>>> Thanks a lot for the reply. I have indeed seen scipy.ndimage.convolve >>>> and have mentioned it in the OP >>>> . >>>> but some questions: >>>> >>> >>> I saw that, but you seemed to imply there that it was limited to 1 and 2 >>> dimensions, when it works for arbitrary dimensions. >>> >>> >>>> >>>> >>>> 1. although there is nothing about the dimension limit of the >>>> ndarrays in its official page >>>> , >>>> but I haven't seen any examples showing it works with higher dimensions. >>>> >>>> I showed an example of a simple four-dimensional convolution in my >>> original response. >>> >>> >>>> >>>> 1. what is the difference between astropy.convolve_fft and >>>> scipy.signal.convolve? It seems to me they are for function analysis >>>> not array arithmetics. >>>> >>>> By design, scipy.signal.convolve and scipy.signal.fftconvolve produce >>> the same results. The difference is that the latter uses an FFT to compute >>> the results, which is generally faster than direct convolution for large >>> inputs. astropy.convolve_fft is similar to scipy.signal.fftconvolve, with a >>> few differences listed in the documentation >>> >>> . >>> >>>> >>>> 1. As I can see the term convolution, even for array arithmetics is >>>> used for different purposes. For example there is >>>> also scipy.ndimage.filters.convolve which apparently calculates different >>>> things. My final goal is to do finite multivariate formal power series >>>> multiplication (Cauchy product). I think I have figured the formula out >>>> here >>>> , >>>> but I'm not sure if it is correct completely. questions are: >>>> 1. is my formula correct? >>>> - if not what is the correct one? >>>> 2. if yes has this been done before? >>>> - if yes where? does any of the above functions do the job? >>>> 3. regardless of the correctness of the formula and existence >>>> of other implementations, is my implementation correct so far? >>>> 4. how to finish the final step to populate the ndarray using >>>> the conv function? >>>> >>>> At a glance, I'm not certain if your formula or implementation is >>> correct, and I don't have the time to dig-in at the moment. >>> >>> Perhaps someone else can help with that part. >>> >>> Best of luck, >>> Jake >>> >>> >>>> Thanks a gain and looking forwards to hearing back. >>>> >>>> Best, >>>> Foad >>>> >>>> On Sat, Aug 11, 2018 at 1:56 AM Adam Ginsburg < >>>> adam.g.ginsburg at gmail.com> wrote: >>>> >>>>> >>>>> >>>>> On Fri, Aug 10, 2018 at 5:51 PM, Jacob Vanderplas < >>>>> jakevdp at cs.washington.edu> wrote: >>>>> >>>>>> Hi Foad, >>>>>> I'm sorry if I'm misunderstanding something, but does >>>>>> scipy.ndimage.convolve not address your use case? It implements >>>>>> N-dimensional convolution: >>>>>> >>>>>> from scipy.ndimage import convolveimport numpy as np >>>>>> >>>>>> x = np.random.rand(10, 10, 10, 10) >>>>>> w = np.ones((3, 3, 3, 3)) >>>>>> >>>>>> result = convolve(x, w) >>>>>> >>>>>> For completeness, astropy's convolve_fft supports this same operation >>>>> since it's doing an nd fft under the hood, but the direct convolution >>>>> (astropy.convolution.convolve) does not, since we had to hard-code the >>>>> direct convolution operations in each dimension and so far there has been >>>>> no demand for an n-dimensional convolution with n>3. >>>>> _______________________________________________ >>>>> AstroPy mailing list >>>>> AstroPy at python.org >>>>> https://mail.python.org/mailman/listinfo/astropy >>>>> >>>> >>>> _______________________________________________ >>>> AstroPy mailing list >>>> AstroPy at python.org >>>> https://mail.python.org/mailman/listinfo/astropy >>>> >>>> >>> _______________________________________________ >>> AstroPy mailing list >>> AstroPy at python.org >>> https://mail.python.org/mailman/listinfo/astropy >>> >> >> _______________________________________________ >> AstroPy mailing list >> AstroPy at python.org >> https://mail.python.org/mailman/listinfo/astropy >> >> > _______________________________________________ > AstroPy mailing list > AstroPy at python.org > https://mail.python.org/mailman/listinfo/astropy > -------------- next part -------------- An HTML attachment was scrubbed... URL: From jakevdp at cs.washington.edu Mon Aug 13 13:10:26 2018 From: jakevdp at cs.washington.edu (Jacob Vanderplas) Date: Mon, 13 Aug 2018 10:10:26 -0700 Subject: [AstroPy] Convolution of NumPy arrays of arbitrary dimension In-Reply-To: References: Message-ID: Foad, Please read through the documentation of correlate(), in particular the mode and cval parameters: those together control how boundary conditions are handled, and thus the size of the output. Jake Jake VanderPlas Senior Data Science Fellow Director of Open Software University of Washington eScience Institute On Mon, Aug 13, 2018 at 9:10 AM, Foad Sojoodi Farimani < f.s.farimani at gmail.com> wrote: > Dear Jake, > > I don't think scipy.ndimage.correlate is what I'm looking for. the result > is 5*4 while it must be A.shape+B.shape-1=7*5 > Here somebody was able to > modify different existing functions astropy.convolution. > convolve_fft, astropy.convolution.convolve, scipy. > ndimage.filters.convolve, scipy.ndimage.filters.convolve using numpy.pad > to generate what I'm looking for. > Next step is to automate this step to have something like numpy.convolve > for 1D or scipy.signal.convolve2d for 2D. > > Best, > Foad > > On Mon, Aug 13, 2018 at 5:30 PM Jacob Vanderplas < > jakevdp at cs.washington.edu> wrote: > >> Foad, >> It sounds like you maybe are looking for a correlation rather than a >> convolution? >> >> >>> from scipy.ndimage import correlate >> >>> correlate(A, B) >> >> array([[36, 28, 14, 4, 2], >> [20, 24, 24, 14, 9], >> [16, 26, 28, 23, 23], >> [24, 21, 19, 27, 25]]) >> >> >> Jake VanderPlas >> Senior Data Science Fellow >> Director of Open Software >> University of Washington eScience Institute >> >> On Mon, Aug 13, 2018 at 1:31 AM, Foad Sojoodi Farimani < >> f.s.farimani at gmail.com> wrote: >> >>> Dear Jake, >>> >>> Thanks alot for your reply. >>> I just tried the scipy.ndimage.convolve and it doesn't seem to be what >>> I'm looking for. I use the term convolution as used in the context of the Cauchy >>> product of multivariate >>> power series (polynomials). for example >>> >>> from scipy.ndimage import convolve >>> import numpy as np >>> >>> D1=np.array([4,5]) >>> D2=np.array([2,3]) >>> A=np.random.randint(10,size=D1) >>> B=np.random.randint(10,size=D2) >>> >>> print(A) >>> print(B) >>> >>> results: >>> >>> [[9 5 2 0 1] >>> [1 9 8 4 4] >>> [7 9 2 9 6] >>> [5 0 8 8 2]] >>> [[4 6 9] >>> [9 0 8]] >>> >>> I expect the first element of the conve(A,B) to be 36 but >>> the print(convolve(A, B)) results in: >>> >>> [[168 185 185 137 85] >>> [230 205 237 196 209] >>> [212 151 233 198 218] >>> [115 189 152 210 174]] >>> >>> Also the conve(A,B) should be of the shape A.shape+B.shape-1 >>> while scipy.ndimage.convolve result is of the A.shape. the conv I'm looking >>> for is commutative as the Cauchy product of two polynomial is. >>> >>> Best, >>> Foad >>> >>> >>> >>> On Sat, Aug 11, 2018 at 10:11 PM Jacob Vanderplas < >>> jakevdp at cs.washington.edu> wrote: >>> >>>> Hi Foad >>>> >>>> >>>>> Thanks a lot for the reply. I have indeed seen scipy.ndimage.convolve >>>>> and have mentioned it in the OP >>>>> . >>>>> but some questions: >>>>> >>>> >>>> I saw that, but you seemed to imply there that it was limited to 1 and >>>> 2 dimensions, when it works for arbitrary dimensions. >>>> >>>> >>>>> >>>>> >>>>> 1. although there is nothing about the dimension limit of the >>>>> ndarrays in its official page >>>>> , >>>>> but I haven't seen any examples showing it works with higher dimensions. >>>>> >>>>> I showed an example of a simple four-dimensional convolution in my >>>> original response. >>>> >>>> >>>>> >>>>> 1. what is the difference between astropy.convolve_fft and >>>>> scipy.signal.convolve? It seems to me they are for function >>>>> analysis not array >>>>> arithmetics. >>>>> >>>>> By design, scipy.signal.convolve and scipy.signal.fftconvolve produce >>>> the same results. The difference is that the latter uses an FFT to compute >>>> the results, which is generally faster than direct convolution for large >>>> inputs. astropy.convolve_fft is similar to scipy.signal.fftconvolve, with a >>>> few differences listed in the documentation >>>> >>>> . >>>> >>>>> >>>>> 1. As I can see the term convolution, even for array arithmetics >>>>> is used for different purposes. For example there is >>>>> also scipy.ndimage.filters.convolve which apparently calculates >>>>> different things. My final goal is to do finite multivariate formal power >>>>> series multiplication (Cauchy product). I think I have figured the formula >>>>> out here >>>>> , >>>>> but I'm not sure if it is correct completely. questions are: >>>>> 1. is my formula correct? >>>>> - if not what is the correct one? >>>>> 2. if yes has this been done before? >>>>> - if yes where? does any of the above functions do the job? >>>>> 3. regardless of the correctness of the formula and >>>>> existence of other implementations, is my implementation correct so far? >>>>> 4. how to finish the final step to populate the ndarray using >>>>> the conv function? >>>>> >>>>> At a glance, I'm not certain if your formula or implementation is >>>> correct, and I don't have the time to dig-in at the moment. >>>> >>>> Perhaps someone else can help with that part. >>>> >>>> Best of luck, >>>> Jake >>>> >>>> >>>>> Thanks a gain and looking forwards to hearing back. >>>>> >>>>> Best, >>>>> Foad >>>>> >>>>> On Sat, Aug 11, 2018 at 1:56 AM Adam Ginsburg < >>>>> adam.g.ginsburg at gmail.com> wrote: >>>>> >>>>>> >>>>>> >>>>>> On Fri, Aug 10, 2018 at 5:51 PM, Jacob Vanderplas < >>>>>> jakevdp at cs.washington.edu> wrote: >>>>>> >>>>>>> Hi Foad, >>>>>>> I'm sorry if I'm misunderstanding something, but does >>>>>>> scipy.ndimage.convolve not address your use case? It implements >>>>>>> N-dimensional convolution: >>>>>>> >>>>>>> from scipy.ndimage import convolveimport numpy as np >>>>>>> >>>>>>> x = np.random.rand(10, 10, 10, 10) >>>>>>> w = np.ones((3, 3, 3, 3)) >>>>>>> >>>>>>> result = convolve(x, w) >>>>>>> >>>>>>> For completeness, astropy's convolve_fft supports this same >>>>>> operation since it's doing an nd fft under the hood, but the direct >>>>>> convolution (astropy.convolution.convolve) does not, since we had to >>>>>> hard-code the direct convolution operations in each dimension and so far >>>>>> there has been no demand for an n-dimensional convolution with n>3. >>>>>> _______________________________________________ >>>>>> AstroPy mailing list >>>>>> AstroPy at python.org >>>>>> https://mail.python.org/mailman/listinfo/astropy >>>>>> >>>>> >>>>> _______________________________________________ >>>>> AstroPy mailing list >>>>> AstroPy at python.org >>>>> https://mail.python.org/mailman/listinfo/astropy >>>>> >>>>> >>>> _______________________________________________ >>>> AstroPy mailing list >>>> AstroPy at python.org >>>> https://mail.python.org/mailman/listinfo/astropy >>>> >>> >>> _______________________________________________ >>> AstroPy mailing list >>> AstroPy at python.org >>> https://mail.python.org/mailman/listinfo/astropy >>> >>> >> _______________________________________________ >> AstroPy mailing list >> AstroPy at python.org >> https://mail.python.org/mailman/listinfo/astropy >> > > _______________________________________________ > AstroPy mailing list > AstroPy at python.org > https://mail.python.org/mailman/listinfo/astropy > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From hessman at astro.physik.uni-goettingen.de Thu Aug 16 06:21:09 2018 From: hessman at astro.physik.uni-goettingen.de (Frederic V. Hessman) Date: Thu, 16 Aug 2018 12:21:09 +0200 Subject: [AstroPy] astropy.table.Table metadata and VOTable Message-ID: <4F012F49-CF16-4874-B17F-EFAA23DF96BC@astro.physik.uni-goettingen.de> I looked at the docs and tried to plow through the old lists but couldn't find anything about Table's and VOTable metadata. Naively, I'd expect >>> import numpy as np >>> from astropy.table import Table >>> t = Table() >>> hdr = t.meta >>> hdr['ONE'] = 1 >>> hdr['MESSAGE'] = 'Hello,world!' >>> hdr['PI'] = np.pi,'this is just pi' >>> t['x'] = np.arange(10) >>> t.write ('nix.xml',format='votable') to produce the following VOTable this is just pi ... (editted out to keep things short) ...
but, in fact, the elements aren't created, i.e. Table doesn't store its metadata when the format is VOTable. I imagine there is a reason for this, since it would have been really simple to implement, but I can't imagine what. If the reason is that Table metadata could conceivably be more complex than a simple dictionary (which is what 99.9% of all uses probably contain), then why not dump non-trivial metadata into an with its python representation? - that at least would preserve the metadata in some form. Or at least complain.... There should at least be a mention in the docs that the Table's VOTable output is broken (unnecessary loss of data). Rick astropy 3.0.1 Python 3.5.5 (default, Mar 29 2018, 16:25:52) [GCC 4.2.1 Compatible Apple LLVM 8.1.0 (clang-802.0.42)] on darwin From aldcroft at head.cfa.harvard.edu Thu Aug 16 08:44:11 2018 From: aldcroft at head.cfa.harvard.edu (Aldcroft, Thomas) Date: Thu, 16 Aug 2018 08:44:11 -0400 Subject: [AstroPy] astropy.table.Table metadata and VOTable In-Reply-To: <4F012F49-CF16-4874-B17F-EFAA23DF96BC@astro.physik.uni-goettingen.de> References: <4F012F49-CF16-4874-B17F-EFAA23DF96BC@astro.physik.uni-goettingen.de> Message-ID: Hi Rick, I am not aware of any purposeful decision to not handle table meta values, I suspect this is just a hole in the implementation. Your point that it should be relatively straightforward is well taken. That said, the original developer of this subpackage is no longer supporting Astropy and there has not been a lot of activity on io.votable. Stefan Becker and Pey Lian Lim are the maintainers now. As a start, you should open an issue on the astropy github issue tracker to put this down as a feature request. From there, you might consider getting involved and adding this feature yourself (see http://www.astropy.org/contribute.html to get started). Regardless of where you are with coding, even a documentation update like you suggested would be welcome. The starting point for writing a table to VOtable is here: https://github.com/astropy/astropy/blob/master/astropy/io/votable/connect.py Cheers, Tom A On Thu, Aug 16, 2018 at 7:32 AM Frederic V. Hessman < hessman at astro.physik.uni-goettingen.de> wrote: > I looked at the docs and tried to plow through the old lists but couldn't > find anything about Table's and VOTable metadata. > > Naively, I'd expect > > >>> import numpy as np > >>> from astropy.table import Table > >>> t = Table() > >>> hdr = t.meta > >>> hdr['ONE'] = 1 > >>> hdr['MESSAGE'] = 'Hello,world!' > >>> hdr['PI'] = np.pi,'this is just pi' > >>> t['x'] = np.arange(10) > >>> t.write ('nix.xml',format='votable') > > to produce the following VOTable > > > > xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" > xsi:noNamespaceSchemaLocation="http://www.ivoa.net/xml/VOTable/v1.3"> > > > > value="3.14159265">this is just pi > > > > > ... (editted out to keep things short) ... > > >
> > > > but, in fact, the elements aren't created, i.e. Table doesn't > store its metadata when the format is VOTable. > > I imagine there is a reason for this, since it would have been really > simple to implement, but I can't imagine what. If the reason is that Table > metadata could conceivably be more complex than a simple dictionary (which > is what 99.9% of all uses probably contain), then why not dump non-trivial > metadata into an with its python representation? - that at least > would preserve the metadata in some form. Or at least complain.... > > There should at least be a mention in the docs that the Table's VOTable > output is broken (unnecessary loss of data). > > Rick > > astropy 3.0.1 > Python 3.5.5 (default, Mar 29 2018, 16:25:52) > [GCC 4.2.1 Compatible Apple LLVM 8.1.0 (clang-802.0.42)] on darwin > > _______________________________________________ > AstroPy mailing list > AstroPy at python.org > https://mail.python.org/mailman/listinfo/astropy > -------------- next part -------------- An HTML attachment was scrubbed... URL: From gomekaratharva at gmail.com Wed Aug 29 14:27:18 2018 From: gomekaratharva at gmail.com (Atharva Gomekar) Date: Wed, 29 Aug 2018 23:57:18 +0530 Subject: [AstroPy] New to Open Source Message-ID: Hi! I'm a newbie in the world of open source but would like to contribute to this organization. Any help regarding how to start is greatly appreciated. -------------- next part -------------- An HTML attachment was scrubbed... URL: From kennethadammiller at gmail.com Wed Aug 29 14:33:30 2018 From: kennethadammiller at gmail.com (Kenneth Adam Miller) Date: Wed, 29 Aug 2018 11:33:30 -0700 Subject: [AstroPy] New to Open Source In-Reply-To: References: Message-ID: Have you seen the tutorials? Are you looking for a project to work on? On Wed, Aug 29, 2018 at 11:27 AM Atharva Gomekar wrote: > Hi! I'm a newbie in the world of open source but would like to contribute > to this organization. Any help regarding how to start is greatly > appreciated. > _______________________________________________ > AstroPy mailing list > AstroPy at python.org > https://mail.python.org/mailman/listinfo/astropy > -------------- next part -------------- An HTML attachment was scrubbed... URL: From gomekaratharva at gmail.com Wed Aug 29 14:39:15 2018 From: gomekaratharva at gmail.com (Atharva Gomekar) Date: Thu, 30 Aug 2018 00:09:15 +0530 Subject: [AstroPy] New to Open Source In-Reply-To: References: Message-ID: Hi Kenneth, I?m currently looking at them. And yes I?m looking for a project to work on. I was working on a personal project and had to handle fits file to plot light curves of stars. And hence my interest in contributing to this organisation. On Thu, 30 Aug 2018 at 12:04 AM, Kenneth Adam Miller < kennethadammiller at gmail.com> wrote: > Have you seen the tutorials? Are you looking for a project to work on? > > On Wed, Aug 29, 2018 at 11:27 AM Atharva Gomekar > wrote: > >> Hi! I'm a newbie in the world of open source but would like to contribute >> to this organization. Any help regarding how to start is greatly >> appreciated. >> _______________________________________________ >> AstroPy mailing list >> AstroPy at python.org >> https://mail.python.org/mailman/listinfo/astropy >> > _______________________________________________ > AstroPy mailing list > AstroPy at python.org > https://mail.python.org/mailman/listinfo/astropy > -------------- next part -------------- An HTML attachment was scrubbed... URL: From kennethadammiller at gmail.com Wed Aug 29 14:44:45 2018 From: kennethadammiller at gmail.com (Kenneth Adam Miller) Date: Wed, 29 Aug 2018 11:44:45 -0700 Subject: [AstroPy] New to Open Source In-Reply-To: References: Message-ID: Oh ok. Well, I'm really interested in trying to map different intervals of light spectrum to images for export to various to media format (including gifs or movies), so that there can be a more robust and automated production of those beautiful hubble images we always see (which are actually made with manual labor and photoshop from the data). I would like to be able to produce a movie or gifs from the data, but also have a programmatic API and interface wrapping this sequence production capability. Does this sound compelling to you? On Wed, Aug 29, 2018 at 11:39 AM Atharva Gomekar wrote: > Hi Kenneth, > > I?m currently looking at them. And yes I?m looking for a project to work > on. > I was working on a personal project and had to handle fits file to plot > light curves of stars. And hence my interest in contributing to this > organisation. > > On Thu, 30 Aug 2018 at 12:04 AM, Kenneth Adam Miller < > kennethadammiller at gmail.com> wrote: > >> Have you seen the tutorials? Are you looking for a project to work on? >> >> On Wed, Aug 29, 2018 at 11:27 AM Atharva Gomekar < >> gomekaratharva at gmail.com> wrote: >> >>> Hi! I'm a newbie in the world of open source but would like to >>> contribute to this organization. Any help regarding how to start is greatly >>> appreciated. >>> _______________________________________________ >>> AstroPy mailing list >>> AstroPy at python.org >>> https://mail.python.org/mailman/listinfo/astropy >>> >> _______________________________________________ >> AstroPy mailing list >> AstroPy at python.org >> https://mail.python.org/mailman/listinfo/astropy >> > _______________________________________________ > AstroPy mailing list > AstroPy at python.org > https://mail.python.org/mailman/listinfo/astropy > -------------- next part -------------- An HTML attachment was scrubbed... URL: From gomekaratharva at gmail.com Wed Aug 29 14:53:08 2018 From: gomekaratharva at gmail.com (Atharva Gomekar) Date: Thu, 30 Aug 2018 00:23:08 +0530 Subject: [AstroPy] New to Open Source In-Reply-To: References: Message-ID: Hi this sounds very interesting. I would definitely like to contribute to this. From where should I begin ? On Thu, 30 Aug 2018 at 12:15 AM, Kenneth Adam Miller < kennethadammiller at gmail.com> wrote: > Oh ok. > > Well, I'm really interested in trying to map different intervals of light > spectrum to images for export to various to media format (including gifs or > movies), so that there can be a more robust and automated production of > those beautiful hubble images we always see (which are actually made with > manual labor and photoshop from the data). I would like to be able to > produce a movie or gifs from the data, but also have a programmatic API and > interface wrapping this sequence production capability. Does this sound > compelling to you? > > On Wed, Aug 29, 2018 at 11:39 AM Atharva Gomekar > wrote: > >> Hi Kenneth, >> >> I?m currently looking at them. And yes I?m looking for a project to work >> on. >> I was working on a personal project and had to handle fits file to plot >> light curves of stars. And hence my interest in contributing to this >> organisation. >> >> On Thu, 30 Aug 2018 at 12:04 AM, Kenneth Adam Miller < >> kennethadammiller at gmail.com> wrote: >> >>> Have you seen the tutorials? Are you looking for a project to work on? >>> >>> On Wed, Aug 29, 2018 at 11:27 AM Atharva Gomekar < >>> gomekaratharva at gmail.com> wrote: >>> >>>> Hi! I'm a newbie in the world of open source but would like to >>>> contribute to this organization. Any help regarding how to start is greatly >>>> appreciated. >>>> _______________________________________________ >>>> AstroPy mailing list >>>> AstroPy at python.org >>>> https://mail.python.org/mailman/listinfo/astropy >>>> >>> _______________________________________________ >>> AstroPy mailing list >>> AstroPy at python.org >>> https://mail.python.org/mailman/listinfo/astropy >>> >> _______________________________________________ >> AstroPy mailing list >> AstroPy at python.org >> https://mail.python.org/mailman/listinfo/astropy >> > _______________________________________________ > AstroPy mailing list > AstroPy at python.org > https://mail.python.org/mailman/listinfo/astropy > -------------- next part -------------- An HTML attachment was scrubbed... URL: From kennethadammiller at gmail.com Wed Aug 29 14:59:55 2018 From: kennethadammiller at gmail.com (Kenneth Adam Miller) Date: Wed, 29 Aug 2018 11:59:55 -0700 Subject: [AstroPy] New to Open Source In-Reply-To: References: Message-ID: Well, I'm getting started on this project as well, and we luckily have a non-zero starting point. There are some examples in the tutorials. There's also some discussion in the email list history where some past project fragments may have some use to our interests. The first step is to install everything. The second is to do some requirements engineering. The third is to grab and run the only tutorial that is useful to us. Then we need to make a plan on how to move forward implementing our vision from what we've already got. Have you got astropy and anaconda installed? On Wed, Aug 29, 2018 at 11:53 AM Atharva Gomekar wrote: > Hi this sounds very interesting. I would definitely like to contribute to > this. From where should I begin ? > > On Thu, 30 Aug 2018 at 12:15 AM, Kenneth Adam Miller < > kennethadammiller at gmail.com> wrote: > >> Oh ok. >> >> Well, I'm really interested in trying to map different intervals of light >> spectrum to images for export to various to media format (including gifs or >> movies), so that there can be a more robust and automated production of >> those beautiful hubble images we always see (which are actually made with >> manual labor and photoshop from the data). I would like to be able to >> produce a movie or gifs from the data, but also have a programmatic API and >> interface wrapping this sequence production capability. Does this sound >> compelling to you? >> >> On Wed, Aug 29, 2018 at 11:39 AM Atharva Gomekar < >> gomekaratharva at gmail.com> wrote: >> >>> Hi Kenneth, >>> >>> I?m currently looking at them. And yes I?m looking for a project to work >>> on. >>> I was working on a personal project and had to handle fits file to plot >>> light curves of stars. And hence my interest in contributing to this >>> organisation. >>> >>> On Thu, 30 Aug 2018 at 12:04 AM, Kenneth Adam Miller < >>> kennethadammiller at gmail.com> wrote: >>> >>>> Have you seen the tutorials? Are you looking for a project to work on? >>>> >>>> On Wed, Aug 29, 2018 at 11:27 AM Atharva Gomekar < >>>> gomekaratharva at gmail.com> wrote: >>>> >>>>> Hi! I'm a newbie in the world of open source but would like to >>>>> contribute to this organization. Any help regarding how to start is greatly >>>>> appreciated. >>>>> _______________________________________________ >>>>> AstroPy mailing list >>>>> AstroPy at python.org >>>>> https://mail.python.org/mailman/listinfo/astropy >>>>> >>>> _______________________________________________ >>>> AstroPy mailing list >>>> AstroPy at python.org >>>> https://mail.python.org/mailman/listinfo/astropy >>>> >>> _______________________________________________ >>> AstroPy mailing list >>> AstroPy at python.org >>> https://mail.python.org/mailman/listinfo/astropy >>> >> _______________________________________________ >> AstroPy mailing list >> AstroPy at python.org >> https://mail.python.org/mailman/listinfo/astropy >> > _______________________________________________ > AstroPy mailing list > AstroPy at python.org > https://mail.python.org/mailman/listinfo/astropy > -------------- next part -------------- An HTML attachment was scrubbed... URL: From gomekaratharva at gmail.com Wed Aug 29 15:04:03 2018 From: gomekaratharva at gmail.com (Atharva Gomekar) Date: Thu, 30 Aug 2018 00:34:03 +0530 Subject: [AstroPy] New to Open Source In-Reply-To: References: Message-ID: Yes I?ve astropy and anaconda installed. I?ll go through the tutorials so that I?ll have a basic idea about what all things are there in astropy. Is that the right thing to do ? On Thu, 30 Aug 2018 at 12:30 AM, Kenneth Adam Miller < kennethadammiller at gmail.com> wrote: > Well, I'm getting started on this project as well, and we luckily have a > non-zero starting point. > > There are some examples in the tutorials. There's also some discussion in > the email list history where some past project fragments may have some use > to our interests. > > The first step is to install everything. The second is to do some > requirements engineering. The third is to grab and run the only tutorial > that is useful to us. Then we need to make a plan on how to move forward > implementing our vision from what we've already got. > > Have you got astropy and anaconda installed? > > On Wed, Aug 29, 2018 at 11:53 AM Atharva Gomekar > wrote: > >> Hi this sounds very interesting. I would definitely like to contribute to >> this. From where should I begin ? >> >> On Thu, 30 Aug 2018 at 12:15 AM, Kenneth Adam Miller < >> kennethadammiller at gmail.com> wrote: >> >>> Oh ok. >>> >>> Well, I'm really interested in trying to map different intervals of >>> light spectrum to images for export to various to media format (including >>> gifs or movies), so that there can be a more robust and automated >>> production of those beautiful hubble images we always see (which are >>> actually made with manual labor and photoshop from the data). I would like >>> to be able to produce a movie or gifs from the data, but also have a >>> programmatic API and interface wrapping this sequence production >>> capability. Does this sound compelling to you? >>> >>> On Wed, Aug 29, 2018 at 11:39 AM Atharva Gomekar < >>> gomekaratharva at gmail.com> wrote: >>> >>>> Hi Kenneth, >>>> >>>> I?m currently looking at them. And yes I?m looking for a project to >>>> work on. >>>> I was working on a personal project and had to handle fits file to plot >>>> light curves of stars. And hence my interest in contributing to this >>>> organisation. >>>> >>>> On Thu, 30 Aug 2018 at 12:04 AM, Kenneth Adam Miller < >>>> kennethadammiller at gmail.com> wrote: >>>> >>>>> Have you seen the tutorials? Are you looking for a project to work on? >>>>> >>>>> On Wed, Aug 29, 2018 at 11:27 AM Atharva Gomekar < >>>>> gomekaratharva at gmail.com> wrote: >>>>> >>>>>> Hi! I'm a newbie in the world of open source but would like to >>>>>> contribute to this organization. Any help regarding how to start is greatly >>>>>> appreciated. >>>>>> _______________________________________________ >>>>>> AstroPy mailing list >>>>>> AstroPy at python.org >>>>>> https://mail.python.org/mailman/listinfo/astropy >>>>>> >>>>> _______________________________________________ >>>>> AstroPy mailing list >>>>> AstroPy at python.org >>>>> https://mail.python.org/mailman/listinfo/astropy >>>>> >>>> _______________________________________________ >>>> AstroPy mailing list >>>> AstroPy at python.org >>>> https://mail.python.org/mailman/listinfo/astropy >>>> >>> _______________________________________________ >>> AstroPy mailing list >>> AstroPy at python.org >>> https://mail.python.org/mailman/listinfo/astropy >>> >> _______________________________________________ >> AstroPy mailing list >> AstroPy at python.org >> https://mail.python.org/mailman/listinfo/astropy >> > _______________________________________________ > AstroPy mailing list > AstroPy at python.org > https://mail.python.org/mailman/listinfo/astropy > -------------- next part -------------- An HTML attachment was scrubbed... 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