Mailman 3 July 2014 - SciPy-User

How to display a RGB image (MFISH)?
by jp April 3, 2023

April 3, 2023

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 July 28, 2022

July 28, 2022

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Help required regding Eigenvectors
by Sudheer Phani March 14, 2022

March 14, 2022

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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Normalization for optimization in python
by Yuxiang Wang April 27, 2015

April 27, 2015

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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how to share weave.inline generated files accross machines?
by Jadhav, Alok Aug. 17, 2014

Aug. 17, 2014

according to the scipy.weave.inline documentation, weave stores the catalogue of already compiled code strings in an on-disk cache. What happens if the location of weave generated pyd files is commong between 2 machines (on a network drive), will the catalogue of this data shared between 2 machines? To be more precise, if 1 machine generates a pyd file and if we copy this pyd file to a different machine and run the same weave.inline function will the other machine try to recompile the code? My question is because other machine is not allowed to install gcc on it. I am wondering if just copying pyd files will work or not? Thanks. Alok Jadhav CREDIT SUISSE AG SMG - Systematic Market Making Asia IT, KFVA 463 International Commerce Centre | Hong Kong | Hong Kong Phone +852 2101 6274 | Mobile +852 9169 7172 alok.jadhav(a)credit-suisse.com | www.credit-suisse.com =============================================================================== Please access the attached hyperlink for an important electronic communications disclaimer: http://www.credit-suisse.com/legal/en/disclaimer_email_ib.html ===============================================================================

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integrate.ode sets t0 values outside of my data range
by camille chambon Aug. 8, 2014

Aug. 8, 2014

Hello, I would like to solve the ODE dy/dt = -2y + data(t), between t=0..4, for y(t=0)=1. I wrote the following code: import numpy as np from scipy.integrate import odeint from scipy.interpolate import interp1d t = np.linspace(0, 3, 4) data = [1, 2, 3, 4] linear_interpolation = interp1d(t, data) def func(y, t0): print 't0', t0 return -2*y + linear_interpolation(t0) soln = odeint(func, 1, t) When I run this code, I get several errors: ValueError: A value in x_new is above the interpolation range. odepack.error: Error occurred while calling the Python function named func My interpolation range is between 0.0 and 3.0. Printing the value of t0 in func, I realized that t0 is actually sometimes above my interpolation range: 3.07634612585, 3.0203768998, 3.00638459329, ... I have a few questions: - how does integrate.ode makes t0 vary? Why does it make t0 exceed the infimum (3.0) of my interpolation range? - in spite of these errors, integrate.ode returns an array which seems to contain correct value. So, should I just catch and ignore these errors? - if I shouldn't ignore these errors, what is the best way to avoid them? 2 suggestions for the last question: - in interp1d, I could set bounds_error=False and fill_value=data[-1] since the t0 outside of my interpolation range seem to be closed to t[-1]: linear_interpolation = interp1d(t, data, bounds_error=False, fill_value=data[-1]) But first I would like to be sure that with any other func and any other data the t0 will always remain closed to t[-1]. For example, if integrate.ode chooses a t0 below my interpolation range, the fill_value would be still data[-1], which would not be correct.Maybe to know how integrate.ode makes t0 vary would help me to be sure of that (see my first question). - in func, I could enclose the linear_interpolation call in a try/except block, and, when I catch a ValueError, I recall linear_interpolation but with t0 truncated: def func(y, t0): try: interpolated_value = linear_interpolation(t0) except ValueError: interpolated_value = linear_interpolation(int(t0)) # truncate t0 return -2*y + interpolated_value At least this solution permits linear_interpolation to still raise an exception if integrate.ode makes a t0 above 4.0 or below -1.0. I can then be alerted of incoherent behavior.But it is not really readable and the truncation seems to me a little arbitrary by now. Maybe I'm just overthinking about these errors.Please let me know. Thanks in advance. Cheers, Camille

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matlab "uigetfile" analog for ipython
by Antonelli Maria Rosaria Aug. 6, 2014

Aug. 6, 2014

Hi, Is there any way to select interactively the folder/directory where are stored the data that I want to analyse in my notebook. Matlab command should be 'uigetfile'. This open a window and I select the file I want from this window. Thank you very much. Best, Rosa

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Spatio-temporal interpolation
by Abrham Melesse Aug. 1, 2014

Aug. 1, 2014

Dear users, I am new to python and trying to work hard in to it. I am trying to interpolate daily station climate data to the defined grid points. I have more than 20 years of daily climate data for 50 stations that I want to interpolate to the daily grid point values. I could handle how to interpolate one spatial data in to grid points. My data is very bulky. I organize my station data in one excel file with 50 sheets, each sheet is daily climate data for the station. The data in each sheet has 4 columns ( date, latitude, longitude, value/data) My grid points are given in another excel file that has 3 columns (Id, latitude, longitude) I want the interpolation output be one excel/text file in the form as below. I prefer kriging interpolation if someone of you already have the code. date value1 value2 ….... valueN Here Value 1 ...valueN are grid point interpolated values for each grid point Id from 1 to N. Thank you, Abraham -- Abrham Melesse Arba Minch University Water Resource and Irrigation Engineering Department PO Box 2552 Cell Phone +251 912491406 Arba Minch Ethiopia

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Matplotlib axis label spacing
by Barrett B July 31, 2014

July 31, 2014

(Not sure if seeking help for matplotlib is appropriate here but I thought I would give it a try; if not, please direct me to somewhere I can get that assistance. Anyway...) Alright, I'm trying to run the code listed below. It results in the following color plot: http://s14.postimg.org/m16s2mmox/figure_1.png I need help resolving the x-axis labels, which are the right number but in the wrong location. I need them to align evenly with the columns, not be scaled proportionally to their actual value. I.e., if I were to plot 0.5, 0.6, 0.9, and 2.0 on a real number line from 0 to 4, that is exactly where they would land, but I don't want that. How do I get them to space evenly? Also, once I scale this up to a much, much finer resolution (i.e., many more rows and columns), which I will do once I've debugged this, it's not going to try to plot every single value on the x-axis, will it? If so, how would I fix that to print, say, every 10th value, or a total of only 5 values? Here is the code. Portions of this simulator not used for this run have been removed for brevity: ##SIMULATOR import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt from scipy.integrate import odeint #Network constants tau = 20; tau_S = 10000 g_Ca = 3.6; g_K = 10; g_S = 4 #Siemens E_Ca = 25; E_K = -75; E_S = -75 #mV E_half_m = -20; E_half_n = -16; E_half_S = -35.245 #mV E_exc = 0; E_inh = -75 #mV k_m = 12; k_n = 5.6; k_S = 10 #(unitless) Theta_syn = -40 #mV #Initial conditions V0_0 = -60 n0 = np.array([0.6, 0.6]) S0 = np.array([0.1, 0.1]) #Simulation constants stopTime = 30000/100; timeStep = 4 #ms tStartTrack = 2000/100 #Time when Lyapunov exponent calculation begins. nStartTrack = int(tStartTrack/timeStep) #init step for Lyap expo #Plotting constants doNorm = True; V1VsV2 = False; nVsV = False; timeSeries = True pigments = ['r', 'g', 'b'] #Plotting colors. toPlot = [0, 1] #Indeces of the neurons to plot, starting at 0. #Heaviside function. Currently approximated; can be optimised. #Continuous equation: Gamma(x) = 1.0/(1 + np.exp(-100*(x - Theta_syn))) def Heaviside(x): mult = -100.0; diff = mult*(x - Theta_syn); output = np.ones_like(x) #Initialized to 1's. output[x < Theta_syn] = 0 #0 <= x < Theta_syn. output[abs(diff) < 50] = 1.0/(1 + np.exp(diff[abs(diff) < 50])) return output #ODE for Sherman model (Belykh and Reimbayev) def f(X, t, params): N = len(X)/3 V = X[:N]; n = X[N:2*N]; S = X[2*N:3*N] m_inf_E = 1/(1 + np.exp((E_half_m - V)/k_m)) n_inf_E = 1/(1 + np.exp((E_half_n - V)/k_n)) S_inf_E = 1/(1 + np.exp((E_half_S - V)/k_S)) I_Ca = g_Ca * m_inf_E * (V - E_Ca) I_K = g_K * n * (V - E_K) I_S = g_S * S * (V - E_S) dV = (-I_Ca - I_K - I_S)/tau +\ g_inh*(E_inh - V)*Heaviside(np.dot(V, connex))/tau dn = (n_inf_E - n)/tau dS = (S_inf_E - S)/tau_S return np.concatenate((dV, dn, dS)) # else: #if not useForLoops # g_inhibitory = params[0]; V1 = params[1] #Connection matrix. Simulator starts here. connex = np.array([[-1,1], [1,-1]]) inhibID = 7 #Used for below to avoid commenting out code. #List of the inhibitory conductivities (g_inh) to track: if inhibID == 7: inhibConductivity = np.array([0.5, 0.6, 0.9, 2.0]) voltageID = 5 #Used for below to avoid commenting out code. #Starting V1: if voltageID == 5: startingVoltage1 = np.arange(V0_0 + 10, V0_0 + 30, 5) Vsize = startingVoltage1.size #Whether the g_inh and V1 arrays have just one element: justOneRun = (Vsize == 1 and inhibConductivity.size == 1) t = np.arange(0, stopTime, timeStep) #useForLoops = True #if useForLoops: normVsVSpread = np.zeros((Vsize, inhibConductivity.size)) for i in range(inhibConductivity.size): g_inh = inhibConductivity[i] for j in range(Vsize): pctComplete = 100*((1.*i + j/Vsize)/inhibConductivity.size) #Integration and initial conditions V0 = np.array([V0_0, startingVoltage1[j]]); N = len(V0) #Solver soln = odeint(f, np.concatenate((V0, n0, S0)), t, args=(None,)) E = np.transpose(soln[:, :N]) n = np.transpose(soln[:, N:N*2]) VArray = E[:2, nStartTrack:] maxVSpread = np.amax(VArray) - np.amin(VArray) maxNorm = np.amax(abs(VArray[1] - VArray[0])) normVsVSpread[j, i] = maxNorm/maxVSpread #else: #DO NOT USE YET # gInhVsV1 = np.meshgrid(inhibConductivity, startingVoltage1) # V0 = np.array([V0_0, None]) #V1 will be dealt with inside the ODE # soln = odeint(f, np.concatenate((V0, n0, S0)), t, args=(gInhVsV1,)) #Single-run plotting strParameters = 'V0_0 = ' + str(V0[0]) + ' mV; n0 = ' + str(n0) +\ '; S0 = ' + str(S0) mpl.rcParams['image.cmap'] = 'gist_heat' fig = plt.subplots() plt.title('|V1 - V0|/(Vmax - Vmin).\n' + strParameters) plt.xlabel('g_inh (Siemens)') plt.ylabel('V1 (mV)') # xticks = np.linspace(np.amin(inhibConductivity),\ # np.amax(inhibConductivity), num = 4) # plt.xticks(xticks) plt.xticks(inhibConductivity) # plt.yticks(startingVoltage1) plt.pcolormesh(normVsVSpread) plt.colorbar(shrink=0.8) plt.show()

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convolution using numpy/scipy using MKL libraries
by Sai Rajeshwar July 28, 2014

July 28, 2014

hi all, Im trying to implement 3d convolutional networks.. for which I wanted to use convolve function from scipy.signal.convolve or fftconvolve.. but looks like both of them doesnot use MKL libraries.. is there any implementation of convolutoin which uses MKL libraries or MKL-threaded so that code runs faster. thanks a lot in advance *with regards..* *M. Sai Rajeswar* *M-tech Computer Technology* *IIT Delhi----------------------------------Cogito Ergo Sum---------*

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