def visualize_front_ascii(u, t, fps=10):
"""
Plot u and the exact solution vs t line by line in a
terminal window (only using ascii characters).
Makes it easy to plot very long time series.
"""
from scitools.avplotter import Plotter
import time
umin = 1.2*u.min(); umax = -umin
p = Plotter(ymin=umin, ymax=umax, width=60, symbols='+o')
for n in range(len(u)):
print p.plot(t[n], u[n]), '%.2f' % (t[n])
time.sleep(1/float(fps))
def visualize_front_ascii(u, t, fps=10):
"""
Plot u and the exact solution vs t line by line in a
terminal window (only using ascii characters).
Makes it easy to plot very long time series.
"""
from scitools.avplotter import Plotter
import time
umin = 1.2*u.min(); umax = -umin
p = Plotter(ymin=umin, ymax=umax, width=60, symbols='+o')
for n in range(len(u)):
print p.plot(t[n], u[n]), '%.2f' % (t[n])
time.sleep(1/float(fps))
def visualize_front_ascii(u, t, U, omega, fps=10): """ Строится график зависимостей приближенного и точного решений от t построчно в окне терминала (используются только символы ascii). """ from scitools.avplotter import Plotter import time from math import pi P = 2*pi/omega umin = 1.2*u.min(); umax = -umin p = Plotter(ymin=umin, ymax=umax, width=60, symbols='+o') for n in range(len(u)): print p.plot(t[n], u[n], U*np.cos(omega*t[n])), \ '%.1f' % (t[n]/P) time.sleep(1/float(fps))
def visualize_front_ascii(u, t, I, w, fps=10):
"""
Plot u and the exact solution vs t line by line in a
terminal window (only using ascii characters).
Makes it easy to plot very long time series.
"""
from scitools.avplotter import Plotter
import time
from math import pi
P = 2 * pi / w
umin = 1.2 * u.min()
umax = -umin
p = Plotter(ymin=umin, ymax=umax, width=60, symbols="+o")
for n in range(len(u)):
print p.plot(t[n], u[n], I * cos(w * t[n])), "%.1f" % (t[n] / P)
time.sleep(1 / float(fps))
def read_and_plot(filename, u_min, u_max):
"""
Read file and plot u vs t line by line in a
terminal window (only using ascii characters).
"""
from scitools.avplotter import Plotter
import time
umin = 1.2 * u_min
umax = 1.2 * u_max
p = Plotter(ymin=umin, ymax=umax, width=60, symbols='+o')
fps = 10
infile = open(filename, 'r')
# read and treat one line at a time
infile.readline() # skip header line
for line in infile:
time_and_pos = line.split() # gives list with 2 elements
t = float(time_and_pos[0])
u = float(time_and_pos[1])
#print 'time: %g position: %g' % (time, pos)
print p.plot(t, u), '%.2f' % (t)
time.sleep(1 / float(fps))
for i in range(len(h)):
plot(x[:m], h[i][:m])
legend(['Curve 0', 'Curve 1', 'Curve 2'])
title('Curves in class pysketcher.StochasticWavyCurve')
savefig('tmp.png')
savefig('tmp.pdf')
def write_array(a):
s = 'np.array(['
for e in a:
s += '%.4f, ' % e
s += '])'
return s
f = open('tmp.py', 'w')
f.write("""
x = %s
y = [None]*3
y[0] = %s
y[1] = %s
y[2] = %s
""" % (write_array(x[:m]), write_array(h[0][:m]), write_array(h[1][:m]), write_array(h[2][:m])))
f.close()
from scitools.avplotter import Plotter
p = Plotter(-0.15, 0.17, width=70)
for i in range(3):
print '-'*70
for x_, h_ in zip(x[:m:5], h[i][:m:5]):
print p.plot(x_, h_)
show()
title('Curves in class pysketcher.StochasticWavyCurve')
savefig('tmp.png')
savefig('tmp.pdf')
def write_array(a):
s = 'np.array(['
for e in a:
s += '%.4f, ' % e
s += '])'
return s
f = open('tmp.py', 'w')
f.write("""
x = %s
y = [None]*3
y[0] = %s
y[1] = %s
y[2] = %s
""" % (write_array(x[:m]), write_array(h[0][:m]), write_array(
h[1][:m]), write_array(h[2][:m])))
f.close()
from scitools.avplotter import Plotter
p = Plotter(-0.15, 0.17, width=70)
for i in range(3):
print '-' * 70
for x_, h_ in zip(x[:m:5], h[i][:m:5]):
print p.plot(x_, h_)
show()