Mailman 3 March 2015 - SciPy-User

How to display a RGB image (MFISH)?
by jp 02 Apr '23

02 Apr '23

Hi, I have written a script to try to combine five images into a three channels RGB images<http://dip4fish.blogspot.com/2011/05/trying-to-combine-mfish-images-into-rg…> . The resulting RGB image looks like a grey scale image instead of a color one. I have three np.array Rnorm, Gnorm, Bnorm which are copied into a rgb array: *rgb = np.zeros((shape[0],shape[1],3),dtype=float) mxr=np.max(R) mxg=np.max(G) mxb=np.max(B) Rnorm=np.uint8((255*(R/mxr)))Gnorm=np.uint8((255*(R/mxg)))Bnorm=np.uint8((255*(R/mxb)))#copy each RGB component in an RGB array rgb[:,:,0]=Rnorm rgb[:,:,1]=Gnorm rgb[:,:,2]=Bnorm* *pylab.subplot(224, aspect='equal',frameon=False, xticks=[], yticks=[]) pylab.imshow(rgb) pylab.show()* Any advice? Thank you Jean-Patrick

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Invitation to connect on LinkedIn
by Jelle Feringa via LinkedIn 28 Jul '22

28 Jul '22

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Help required regding Eigenvectors
by Sudheer Phani 14 Mar '22

14 Mar '22

Hello All, I have been encountering some strange problems with the calculation of eigen vectors using python. The problem is with the sign of the eigen vectors calculated. On Very few occasions the eigen vectors calculated using python have an opposite sign than the actual eigenvectors, though the magnitude is correct. Can any one of you plz help me in this regard and suggest me some solution if you have encoutered similar problems . Any help in this regd is appreciated Thanks in Advace regds Sudheer

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Playing numpy array over speakers
by Todd 17 Oct '15

17 Oct '15

Is anyone aware of a well-maintained, simple, cross-platform python package that can play a numpy array as sound over speakers? I am aware of https://wiki.python.org/moin/Audio/. However, in all the cases there, as far as I can find they either do not support numpy arrays, are not cross-platform, cannot playback sound at all, or are unmaintained. There is also PySoundCard, which would do what I need but also appears to be unmaintained (no release in over a year, and no commits in 5 months, no release with serious bugfixes mentioned in commits).

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Normalization for optimization in python
by Yuxiang Wang 27 Apr '15

27 Apr '15

Dear all, During optimization, it is often helpful to normalize the input parameters to make them on the same order of magnitude, so the convergence can be much better. For example, if we want to minimize f(x), while a reasonable approximation is x0=[1e3, 1e-4], it might be helpful to normalize x0[0] and x0[1] to about the same order of magnitude (often O(1)). My question is, I have been using scipy.optimize and specifically, the L-BFGS-B algorithm. I was wondering that, do I need to normalize that manually by writing a function, or the algorithm already did it for me? Thank you! -Shawn -- Yuxiang "Shawn" Wang Gerling Research Lab University of Virginia yw5aj(a)virginia.edu +1 (434) 284-0836 https://sites.google.com/a/virginia.edu/yw5aj/

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Re: [SciPy-User] Is it good practice to use IPython notebooks as your Python IDE?
by Ghislain Vaillant 24 Apr '15

24 Apr '15

On Saturday, March 14, 2015, Brian Merchant <bhmerchant(a)gmail.com> wrote: > I need to write about 2000 lines worth of code (based on my last > implementation of the project I am working on). I anticipate that I'll be > using profilers (including line profilers), numpy, Numba, possibly Cython > and almost definitely matplotlib animations. I will need to write my own > classes, so my program isn't just going to be one long script. > > > Previously, I used Spyder for development, but I am wondering if it makes > sense for one to use an IPython notebook alone. I am considering that > option so that I force myself to program "literately" -- lots of headings, > and nice comments. Graphs embedded to show the effects of optimizations, > and so forth. > > So far, I know that IPython might have some trouble with integrating in > matplotlib animations, but that too is possible with a little research. > Otherwise, I should be able to do everything else quite easily, including > debugging, according to the IPython documentation. In fact, writing > parallel code in IPython might be a very nice idea given how it seems to > offer a lot of functionality in that direction. > > > Do people use IPython (notebooks) as a complete IDE often? If not, why not? > > Kind regards, > Brian > Hi Brian, Do you find anything wrong with Spyder ? I use both IEP [1] and Spyder extensively and find them pretty good for the use case you describe. However if you're looking for more project-style features like refactoring tools, project views, VCS integration, etc..., a full-featured IDE may be more appropriate, like PyCharm as kindly suggested by Alistair. Cheers, Ghis [1] http://www.iep-project.org/

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Re: [SciPy-User] strange error when creating csr_matrix
by Rok Roskar 31 Mar '15

31 Mar '15

Hi Pauli, thanks for the idea. I think the arrays are int64 by default, so this doesn't seem make a difference: In [23]: csr_matrix((vals.astype(np.int64), (np.zeros(10,dtype=np.int64), inds.astype(np.int64))), shape = (1, max_index+1)) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <snip> ValueError: could not convert integer scalar Does it work on your machine? > Message: 5 > Date: Mon, 30 Mar 2015 16:51:15 +0200 > From: Rok Roskar <rokroskar(a)gmail.com> > Subject: [SciPy-User] strange error when creating csr_matrix > To: scipy-user(a)scipy.org > Message-ID: > < > CAOufH8WVUzZSHc_RJSPequqe3YA+YH75Rb08j++yjS1s9PRsWA(a)mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > I am trying to create a csr_matrix with very large indices (> 2 x 10^9). > This works most of the time, but occasionally I get an error "could not > convert integer scalar". > > Here is a small snippet that reproduces the problem: > > In [145]: inds > > Out[145]: > array([ 827969148, 996833913, 1968345558, 898183169, 1811744124, > 2101454109, 133039182, 898183170, 919293479, 133039089]) > > In [146]: vals > > Out[146]: > array([ 1., 1., 1., 1., 1., 2., 1., 1., 1., 1.]) > > In [147]: max_index > > Out[147]: > 2337713000 > > In [143]: csr_matrix((vals, (np.zeros(10), inds)), shape = (1, > max_index+1)) > ... > > 996 fn = _sparsetools.csr_sum_duplicates > 997 M,N = self._swap(self.shape) > --> 998 fn(M, N, self.indptr, self.indices, self.data) > 999 > 1000 self.prune() # nnz may have changed > > ValueError: could not convert integer scalar > > > > I thought it might be that the csr_matrix uses 32-bit integers for index > arrays, but this works fine: > > In [11]: csr_matrix(([1,1], ([0,0], [1,2**33])), shape = (1, > 2**34)).nonzero() > Out[11]: (array([0, 0]), array([ 1, 8589934592])) > > > I would appreciate any ideas as to what might be the problem -- I have also > opened a question on stackoverflow: > > http://stackoverflow.com/questions/29168699/cryptic-scipy-could-not-convert… > > > Thanks! >

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How to curve fit a set of fitting parameters in Python?
by nxkryptor nxkr 30 Mar '15

30 Mar '15

Dear SciPy users, I am trying to curve fit a data (arbitrary) set as given below: # Data T Z X 1 X 2 X 3 X 4 X 5 100.000 1.000 1.000 1.478 1.304 1.162 0.805 200.000 1.500 2.000 2.314 2.168 2.086 1.801 300.000 2.250 3.000 3.246 3.114 3.058 2.798 400.000 3.375 4.000 4.211 4.087 4.044 3.780 500.000 5.063 5.000 5.189 5.070 5.035 4.780 At first it is curve fit using a quadratic expression Z = a * x ^ 2 + b * x + c along a constant value of T i.e. along each rows, which gives as fit parameters for each T as given below (as an example): T a b c 100.00 1.00 2.10 10.02 200.00 4.00 6.20 10.06 300.00 9.00 12.30 10.12 400.00 16.00 20.40 10.20 500.00 25.00 30.50 10.31 Now I would like to fit each fit parameters along T so that I get the equations of the form a = p * T ^ 2 + q * T + q, b = s * T + u, etc. I tried applying this using the code: from __future__ import division from scipy import optimize import matplotlib.pyplot as plt import numpy as np data = open('data.dat', "r") line = data.readline() while line.startswith('#'): line = data.readline() data_header = line.split("\t") data_header[-1] = data_header[-1].strip() _data_ = np.genfromtxt('data.dat', skiprows=2, delimiter='\t', names = data_header, dtype = None, unpack = True).transpose() data = np.array(_data_.tolist()) m = data.shape[0] n = data.shape[1] - 2 print m, n y_data = np.empty(shape=(m, n)) for i in range(0, m): for j in range(0, n): y_data[i, j] = (data[i, j+2]) x = _data_['X'] z = _data_['Z'] def quadratic_fit(x, a, b, c): return a * x ** 2 + b * x + c fit_a = np.empty(shape = (m, 1)) fit_b = np.empty(shape = (m, 1)) fit_c = np.empty(shape = (m, 1)) z_fit = np.empty(shape=(m, len(z))) for j in range(m): x_fit = y_data[j, :] y_fit = z fit_a[j], fit_b[j], fit_c[j] = optimize.curve_fit(quadratic_fit, x_fit, y_fit)[0] fit_a_fit_a, fit_a_fit_b, fit_a_fit_c, = optimize.curve_fit(quadratic_fit, x, fit_a)[0] fit_b_fit_a, fit_b_fit_b, fit_b_fit_c, = optimize.curve_fit(quadratic_fit, x, fit_b)[0] fit_c_fit_a, fit_c_fit_b, fit_c_fit_c, = optimize.curve_fit(quadratic_fit, x, fit_c)[0] fit_a = fit_a_fit_a * x ** 2 + fit_a_fit_b * x + fit_a_fit_c fit_b = fit_b_fit_a * x ** 2 + fit_b_fit_b * x + fit_b_fit_c fit_c = fit_c_fit_a * x ** 2 + fit_c_fit_b * x + fit_c_fit_c for j in range(m): z_fit[j, :] = (fit_a[j] * x_fit ** 2) + (fit_b[j] * x_fit) + fit_c[j] This question has already been posted on Stackoverflow <http://stackoverflow.com/questions/29352734/how-to-curve-fit-a-set-of-fitti…> . Thank you. nxkr

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(no subject)
by nxkryptor nxkr 30 Mar '15

30 Mar '15

1 from __future__ import division 2 from scipy import optimize 3 import matplotlib.pyplot as plt 4 import numpy as np 5 6 data = open('data.dat', "r") 7 line = data.readline() 8 while line.startswith('#'): 9 line = data.readline()10 data_header = line.split("\t")11 data_header[-1] = data_header[-1].strip()12 13 14 _data_ = np.genfromtxt('data.dat', skiprows=2, delimiter='\t', names = data_header, dtype = None, unpack = True).transpose()15 data = np.array(_data_.tolist())16 m = data.shape[0]17 n = data.shape[1] - 218 print m, n19 y_data = np.empty(shape=(m, n))20 for i in range(0, m):21 for j in range(0, n):22 y_data[i, j] = (data[i, j+2])23 x = _data_['X']24 z = _data_['Z']25 26 def quadratic_fit(x, a, b, c):27 return a * x ** 2 + b * x + c28 29 fit_a = np.empty(shape = (m, 1))30 fit_b = np.empty(shape = (m, 1))31 fit_c = np.empty(shape = (m, 1)) z_fit = np.empty(shape=(m, len(z)))for j in range(m): x_fit = y_data[j, :] y_fit = z fit_a[j], fit_b[j], fit_c[j] = optimize.curve_fit(quadratic_fit, x_fit, y_fit)[0] fit_a_fit_a, fit_a_fit_b, fit_a_fit_c, = optimize.curve_fit(quadratic_fit, x, fit_a)[0] fit_b_fit_a, fit_b_fit_b, fit_b_fit_c, = optimize.curve_fit(quadratic_fit, x, fit_b)[0] fit_c_fit_a, fit_c_fit_b, fit_c_fit_c, = optimize.curve_fit(quadratic_fit, x, fit_c)[0] fit_a = fit_a_fit_a * x ** 2 + fit_a_fit_b * x + fit_a_fit_c fit_b = fit_b_fit_a * x ** 2 + fit_b_fit_b * x + fit_b_fit_c fit_c = fit_c_fit_a * x ** 2 + fit_c_fit_b * x + fit_c_fit_cfor j in range(m): z_fit[j, :] = (fit_a[j] * x_fit ** 2) + (fit_b[j] * x_fit) + fit_c[j]

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strange error when creating csr_matrix
by Rok Roskar 30 Mar '15

30 Mar '15

I am trying to create a csr_matrix with very large indices (> 2 x 10^9). This works most of the time, but occasionally I get an error "could not convert integer scalar". Here is a small snippet that reproduces the problem: In [145]: inds Out[145]: array([ 827969148, 996833913, 1968345558, 898183169, 1811744124, 2101454109, 133039182, 898183170, 919293479, 133039089]) In [146]: vals Out[146]: array([ 1., 1., 1., 1., 1., 2., 1., 1., 1., 1.]) In [147]: max_index Out[147]: 2337713000 In [143]: csr_matrix((vals, (np.zeros(10), inds)), shape = (1, max_index+1)) ... 996 fn = _sparsetools.csr_sum_duplicates 997 M,N = self._swap(self.shape) --> 998 fn(M, N, self.indptr, self.indices, self.data) 999 1000 self.prune() # nnz may have changed ValueError: could not convert integer scalar I thought it might be that the csr_matrix uses 32-bit integers for index arrays, but this works fine: In [11]: csr_matrix(([1,1], ([0,0], [1,2**33])), shape = (1, 2**34)).nonzero() Out[11]: (array([0, 0]), array([ 1, 8589934592])) I would appreciate any ideas as to what might be the problem -- I have also opened a question on stackoverflow: http://stackoverflow.com/questions/29168699/cryptic-scipy-could-not-convert… Thanks!

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