scipy

问题 In most of the below answers for complex matrix differential equations, the odeintw package has been suggested. https://stackoverflow.com/a/45970853/7952027 https://stackoverflow.com/a/26320130/7952027 https://stackoverflow.com/a/26747232/7952027 https://stackoverflow.com/a/26582411/7952027 I want to know the theory behind the manipulations done in the code of odeintw. Like why one has to build that banded jacobian, the idea behind the functions _complex_to_real_jac, _transform_banded_jac,

问题 I'm looking to apply an affine transformation, defined in homogeneous coordinates on images of different resolutions, but I encounter an issue when one ax is of different resolution of the others. Normally, as only the translation part of the affine is dependent of the resolution, I normalize the translation part by the resolution and apply the corresponding affine on the image, using scipy.ndimage.affine_transform. If the resolution of the image is the same for all axes, it works perfectly,

问题 The data I'm using is pasted below. When I apply the basic formula for skewness to my data in R: 3*(mean(data) - median(data))/sd(data) The result is -0.07949198. I get a very similar result in Python. The median is therefore greater than the mean suggesting the left tail is longer. However, when I apply the descdist function from the fitdistrplus package, the skewness is 0.3076471 suggesting the right tail is longer. The Scipy function skew again returns a skewness of 0.303. Can I trust this

问题 With N 1-dimensional data X, I would like to evaluate each point at K cubic B-splines. In R, there is a simple function with an intuitive API, called bs. There is actually a python package patsy which replicates this, but I can't use that package -- only scipy and such. Having looked through the scipy.interpolate documentation on spline-related functions, the closest I can find is BSpline, or BSpline.basis_element, but how to get just the K basis functions is totally mysterious to me. I tried

问题 With N 1-dimensional data X, I would like to evaluate each point at K cubic B-splines. In R, there is a simple function with an intuitive API, called bs. There is actually a python package patsy which replicates this, but I can't use that package -- only scipy and such. Having looked through the scipy.interpolate documentation on spline-related functions, the closest I can find is BSpline, or BSpline.basis_element, but how to get just the K basis functions is totally mysterious to me. I tried

问题 How to fit a non linear data's using scipy.optimize import curve_fit in Python using following 3 methods: Gaussian. Lorentz fit. Langmuir fit. I am just able to link and plot from my data file. from matplotlib import pyplot as plt from matplotlib import style import numpy as np import pylab from scipy.optimize import curve_fit style.use('ggplot') data = np.genfromtxt('D:\csvtrail3.csv', delimiter=',', skiprows=1) x=data[:,0] y=data[:,1] data.fit_lorentzians() plt.plot(x, y) plt.title('Epic

问题 I have the following data: x_old = [ 0.00000000e+00, -5.96880765e-24, -8.04361605e-23, -2.11167774e-22, -2.30386081e-22, -7.86854147e-23, 1.17548440e-22, 1.93009272e-22, 1.49906866e-22, 9.66877465e-23, 1.48495705e-23] y_old = [ 0. , 0.03711505, 0.03780602, 0.02524459, 0.01349815, 0.00964215, 0.00972842, 0.0168793 , 0.02577024, 0.02761626, 0.02141961] z_old = [ 0. , 0.29834302, 0.59805918, 0.89773519, 1.19755092, 1.49749325, 1.79750314, 2.09741402, 2.39727031, 2.69726787, 2.99719479] I want to

问题 I have the following data: x_old = [ 0.00000000e+00, -5.96880765e-24, -8.04361605e-23, -2.11167774e-22, -2.30386081e-22, -7.86854147e-23, 1.17548440e-22, 1.93009272e-22, 1.49906866e-22, 9.66877465e-23, 1.48495705e-23] y_old = [ 0. , 0.03711505, 0.03780602, 0.02524459, 0.01349815, 0.00964215, 0.00972842, 0.0168793 , 0.02577024, 0.02761626, 0.02141961] z_old = [ 0. , 0.29834302, 0.59805918, 0.89773519, 1.19755092, 1.49749325, 1.79750314, 2.09741402, 2.39727031, 2.69726787, 2.99719479] I want to

问题 SciPy can solve ode equations by scipy.integrate.odeint or other packages, but it gives result after the function has been solved completely. However, if the ode function is very complex, the program will take a lot of time(one or two days) to give the whole result. So how can I mointor the step it solve the equations(print out result when the equation hasn't been solved completely)? 回答1: You could split the integration domain and integrate the segments, taking the last value of the previous

问题 SciPy can solve ode equations by scipy.integrate.odeint or other packages, but it gives result after the function has been solved completely. However, if the ode function is very complex, the program will take a lot of time(one or two days) to give the whole result. So how can I mointor the step it solve the equations(print out result when the equation hasn't been solved completely)? 回答1: You could split the integration domain and integrate the segments, taking the last value of the previous