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#Water splash png circle license#
float64 ( 6.Pikbest authorizes the User in a non-transferable, non-exclusive manner and on a worldwide basis for the duration of the relevant rights to download, use and modify the Pikbest Content, as expressly permitted by the applicable license and subject to this document. float64 ( 6.0 ) # x-coordinate of centre of splashes y0 = np. """ from _future_ import print_function import matplotlib.pyplot as plt, mpl_toolkits.mplot3d import numpy as np import os import subprocess import time import sys from scipy.interpolate import RectBivariateSpline, UnivariateSpline from lors import LightSource from mayavi import mlab # animation parameters Lx = np. One Gaussian hump released at t=t0 is given by the initial conditions: eta(r,t0) = 2 * exp( -r^2 ), d/dt eta(r,t0) = 0.
#Water splash png circle series#
The initial conditions are a series of Gaussian humps, on an equidistant grid in the (x,y)-plane. 113-122 doi: 10.3970/cmes.2005.010.113 The dimensionless and linear shallow-water equations in polar coordinates are: d/dt^2 eta(r,t) - 1/r * d/dr( r * d/dr eta(r,t) ) = 0. Computer Modelling in Engineering & Sciences, vol. Yeh (2005) Tsunami propagation from a finite source. #!/usr/bin/env python2.7 """ Make an animation of the linear shallow-water equations in 2D Based on the exact solution for axisymmetrical waves in: G. close () input ( '- hit \' Enter \' to close. subplots ( 1 ) # needed to show the last figure in spyder plt. savefig ( 'fig.sol/swe_ani_2D_eta_similarity.pdf', bbox_extra_artists = ( lgd ,), bbox_inches = 'tight' ) #%% ready print ( '= ready' ) plt. set_size_inches ( 12, 5, forward = True ) plt. legend ( loc = 'center left', bbox_to_anchor = ( 1.0, 0.5 ) ) plt. set_title ( r 'self-similar behaviour of surface elevation' ) lgd = ax. Pool ( num_cores ) print ( '- number of cores : $ $\eta(r-t,t)$' ) ax.
exp ( - r ** 2 ) eta = eta0 num_cores = int ( multiprocessing. float64 ) # time coordinate eta0 = 2 * np. linspace ( 0, ( m - 1 ) * dt, num = m, dtype = np. linspace ( 0, ( n - 1 ) * dr, num = n, dtype = np. float64 ) # surface elevation integrated in time r = np. inf, args = ( r_now, t_now ), epsabs = 1.e-5, epsrel = 1.e-5, limit = 100 ) #%% compute time integral of surface evolution as a function of r and t print ( '= compute time integral of surface evolution as a function of r and t' ) start_time = time. exp ( - ( rho ** 2 ) / 4 ) def integrate ( args ): r_now = args t_now = args return quad ( integrand, 0, np. float64 () def integrand ( rho, r, t ): return rho * sp. of elevation snapshots to plot #%% integrand r = np. float64 ( 0.2 ) # time step res = int ( 10 ) # resample factor for 2D interpolation mpl = int ( 11 ) # no. float64 ( 0.2 ) # step in r-direction dt = np. """ #%% import libs from _future_ import print_function from future.builtins import input import numpy as np import scipy.special as sp import matplotlib.pyplot as plt import multiprocessing import time from scipy.integrate import quad from scipy.interpolate import RectBivariateSpline #%% input n = int ( 181 ) # number of points in r-direction m = int ( 151 ) # number of time steps dr = np.
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