interpolation

问题 I have a set of 3D points defining a 3D contour. What I want to do is to obtain the minimal surface representation corresponding to this contour (see Minimal Surfaces in Wikipedia). Basically this requires to solve a nonlinear partial differential equation. In Matlab this is almost straightforward using the pdenonlin function (see Matlab's documentation). An example of its usage for solving a minimal surface problem can be found here: Minimal Surface Problem on the Unit Disk. I need to make

问题 I have a set of 3D points defining a 3D contour. What I want to do is to obtain the minimal surface representation corresponding to this contour (see Minimal Surfaces in Wikipedia). Basically this requires to solve a nonlinear partial differential equation. In Matlab this is almost straightforward using the pdenonlin function (see Matlab's documentation). An example of its usage for solving a minimal surface problem can be found here: Minimal Surface Problem on the Unit Disk. I need to make

问题 I have developed Bicubic interpolation for demonstration to some undergraduate students using Python Programming language. The methodology is as explained in wikipedia, The code is working fine except the results I am getting are slightly different than what is obtained when using scipy library. The interpolation code is shown below in the function bicubic_interpolation . import numpy as np import matplotlib.pyplot as plt from mpl_toolkits import mplot3d from scipy import interpolate import

问题 Closed . This question is opinion-based. It is not currently accepting answers. Want to improve this question? Update the question so it can be answered with facts and citations by editing this post. Closed 5 months ago . Improve this question I am building a pyramid of images. First I take a big picture and build a smaller one even smaller, etc. I use interpolation to reduce the image. And I need to understand at what interpolation there will be less lost information between images. This is

问题 I have been attempting to plot a line, along with a spline fitting. The following is a generalised version of my code. 'x_coord' and 'y_coord' are lists containing lists of float values. import matplotlib.pyplot as plt from scipy import interpolate as ipl for a in range(len(x_coord)): plt.plot(x_coord[a],y_coord[a],label='Label') yinterp = ipl.UnivariateSpline(x_coord[a],y_coord[a],s=1e4)(x_coord[a]) plt.plot(x_coord[a],yinterp,label='Spline Fit') While I believe this has worked for me in the

问题 I have been attempting to plot a line, along with a spline fitting. The following is a generalised version of my code. 'x_coord' and 'y_coord' are lists containing lists of float values. import matplotlib.pyplot as plt from scipy import interpolate as ipl for a in range(len(x_coord)): plt.plot(x_coord[a],y_coord[a],label='Label') yinterp = ipl.UnivariateSpline(x_coord[a],y_coord[a],s=1e4)(x_coord[a]) plt.plot(x_coord[a],yinterp,label='Spline Fit') While I believe this has worked for me in the

问题 Currently trying to use scipy's implementation of interpolate to create a uniform cubic B-spline (clamped). When using interpolate.splev() the target (x) value I pass in is changed and the function returns me the x value of a point near but not the same as the target value (and the correct y value for the wrong x value). Anybody got any advice on how I can resolve this problem? Code provided below to recreate problem. Thank you very much in advance :) import numpy as np import random as rd

问题 In order to fill missing values I need to interpolate those missing ones. I have gotten a dataset like the following (example): Country Year Value A 2000 1.5 A 2001 2.5 A 2002 null A 2003 4.5 B 2000 null B 2000 null B 2002 5.3 B 2003 6.3 C 2000 1 C 2001 null C 2002 null C 2003 4 As a result I would excpect: Country Year Value A 2000 1.5 A 2001 2.5 A 2002 3.5 A 2003 4.5 B 2000 3.3 B 2000 4.3 B 2002 5.3 B 2003 6.3 C 2000 1 C 2001 2 C 2002 3 C 2003 4 How can i possible interpolate this values by

问题 I have data which has missing values irregulaly, and I'd like to convert it with a certain interval with liner interpolation using BigQuery Standard SQL. Specifically, I have data like this: # data is missing irregulary +------+-------+ | time | value | +------+-------+ | 1 | 3.0 | | 5 | 5.0 | | 7 | 1.0 | | 9 | 8.0 | | 10 | 4.0 | +------+-------+ and I'd like to convert this table as follows: # interpolated with interval of 1 +------+--------------------+ | time | value_interpolated | +------

问题 I'm having a strange issue using scipy.interpolate.griddata . It's a QhullError . It says center point is coplanar with a facet, or a vertex is coplanar with a neighboring facet. . What does this error mean? How can it be overcome? from scipy.interpolate import griddata import numpy as np def f(x, y): return (1 - x) ** 2 * 10 * (y - x**2) ** 2 X = np.linspace(-3, 3) Y = np.linspace(-3, 3) Z = f(X, Y) xi = np.linspace(X.min(), X.max(), 1000) yi = np.linspace(Y.min(), Y.max(), 1000) zi =