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diag_B.py
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diag_B.py
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'''
Created on Apr 06, 2012
@author: Michael Kraus ([email protected])
'''
import argparse
import numpy as np
import matplotlib
#matplotlib.use('Cairo')
matplotlib.use('AGG')
#matplotlib.use('PDF')
import matplotlib.pyplot as plt
from matplotlib import cm, colors, gridspec
from matplotlib.ticker import MultipleLocator, FormatStrFormatter, ScalarFormatter
from imhd.diagnostics import Diagnostics
class PlotMHD2D(object):
'''
classdocs
'''
def __init__(self, diagnostics, filename, ntMax=0, nPlot=1, plotRef=False, write=False):
'''
Constructor
'''
# matplotlib.rc('text', usetex=True)
matplotlib.rc('font', family='sans-serif', size='28')
matplotlib.rcParams['contour.negative_linestyle'] = 'solid'
self.prefix = filename
self.ntMax = diagnostics.nt
if self.ntMax > ntMax and ntMax > 0:
self.ntMax = ntMax
self.nPlot = nPlot
self.iTime = -1
self.diagnostics = diagnostics
self.x = np.zeros(diagnostics.nx+1)
self.y = np.zeros(diagnostics.ny+1)
self.x[0:-1] = self.diagnostics.xGrid
self.x[ -1] = self.x[-2] + self.diagnostics.hx
self.y[0:-1] = self.diagnostics.yGrid
self.y[ -1] = self.y[-2] + self.diagnostics.hy
self.A = np.zeros((diagnostics.nx+1, diagnostics.ny+1))
self.J = np.zeros((diagnostics.nx+1, diagnostics.ny+1))
self.PB = np.zeros((diagnostics.nx+1, diagnostics.ny+1))
# set up figure/window size
self.figure = plt.figure(num=None, figsize=(10,10))
# set up plot margins
plt.subplots_adjust(hspace=0.25, wspace=0.2)
plt.subplots_adjust(left=0.1, right=0.95, top=0.9, bottom=0.1)
# set up plot title
self.title = self.figure.text(0.5, 0.95, 't = 0.0', horizontalalignment='center', fontsize=30)
# set up tick formatter
majorFormatter = ScalarFormatter(useOffset=False)
## -> limit to 1.1f precision
majorFormatter.set_powerlimits((-1,+1))
majorFormatter.set_scientific(True)
# create axes
self.axes = plt.subplot(1,1,1)
# add data for zero timepoint and compute boundaries
self.add_timepoint()
self.read_data()
self.update_boundaries()
self.A0 = self.A.copy()
# create contour plot
if plotRef:
self.conts_ref = self.axes.contour(self.x[1:-1], self.y[1:-1], self.A.T[1:-1,1:-1], self.ATicks, extend='neither', colors='r', linestyles='dashed')
# create contour plot
self.conts = self.axes.contour(self.x[1:-1], self.y[1:-1], self.A.T[1:-1,1:-1], self.ATicks, extend='neither', colors='b', linestyle='solid')
# self.conts = self.axes.contour(self.x, self.y, self.PB.T, levels=self.PBTicks, extend='neither')
# self.conts = self.axes.contourf(self.x, self.y, self.J.T, 51, norm=self.Jnorm)
for tick in self.axes.xaxis.get_major_ticks():
tick.set_pad(12)
for tick in self.axes.yaxis.get_major_ticks():
tick.set_pad(8)
# plot
self.update()
def read_data(self):
self.A[0:-1, 0:-1] = self.diagnostics.A[:,:]
self.A[ -1, 0:-1] = self.diagnostics.A[0,:]
self.A[ :, -1] = self.A[:,0]
self.J[0:-1, 0:-1] = self.diagnostics.J[:,:]
self.J[ -1, 0:-1] = self.diagnostics.J[0,:]
self.J[ :, -1] = self.J[:,0]
self.PB[0:-1, 0:-1] = self.diagnostics.e_magnetic[:,:]
self.PB[ -1, 0:-1] = self.diagnostics.e_magnetic[0,:]
self.PB[ :, -1] = self.PB[:,0]
def update_boundaries(self):
Jmin = min(np.array(self.diagnostics.J).min(), -np.array(self.diagnostics.J).max())
Jmax = min(np.array(self.diagnostics.J).max(), -np.array(self.diagnostics.J).min())
Jdiff = (Jmax - Jmin)
if Jmin == Jmax:
Jmin -= 1.
Jmax += 1.
self.Jnorm = colors.Normalize(vmin=Jmin - 0.2*Jdiff, vmax=Jmax + 0.2*Jdiff)
self.JTicks = np.linspace(Jmin - 0.2*Jdiff, Jmax + 0.2*Jdiff, 51, endpoint=True)
PBmin = min(np.array(self.diagnostics.e_magnetic).min(), -np.array(self.diagnostics.e_magnetic).max())
PBmax = min(np.array(self.diagnostics.e_magnetic).max(), -np.array(self.diagnostics.e_magnetic).min())
PBdiff = (PBmax - PBmin)
if PBmin == PBmax:
PBmin -= .1 * PBmin
PBmax += .1 * PBmax
self.PBnorm = colors.Normalize(vmin=PBmin - 0.2*PBdiff, vmax=PBmax + 0.2*PBdiff)
self.PBTicks = np.linspace(PBmin - 0.2*PBdiff, PBmax + 0.2*PBdiff, 51, endpoint=True)
Amin = min(np.array(self.diagnostics.A).min(), -np.array(self.diagnostics.A).max())
Amax = max(np.array(self.diagnostics.A).max(), -np.array(self.diagnostics.A).min())
Adiff = Amax - Amin
# self.Anorm = colors.Normalize(vmin=Amin - 0.2*Adiff, vmax=Amax + 0.2*Adiff)
self.ATicks = np.linspace(Amin + 0.01 * Adiff, Amax - 0.01 * Adiff, 31, endpoint=True)
self.ATicks = np.linspace(Amin + 0.01 * Adiff, Amax - 0.01 * Adiff, 51, endpoint=True)
def update(self):
if not (self.iTime == 0 or (self.iTime) % self.nPlot == 0 or self.iTime == self.ntMax):
return
self.read_data()
for coll in self.conts.collections:
self.axes.collections.remove(coll)
self.conts = self.axes.contour(self.x[1:-1], self.y[1:-1], self.A.T[1:-1,1:-1], self.ATicks, extend='neither', colors='b')
# self.conts = self.axes.contour(self.x, self.y, self.PB.T, levels=self.PBTicks, extend='neither')
# self.conts = self.axes.contourf(self.x, self.y, self.J.T, 51, norm=self.Jnorm)
plt.draw()
# filename = self.prefix + str('_B_%06d' % self.iTime) + '.png'
# plt.savefig(filename, dpi=300)
filename = self.prefix + str('_B_%06d' % self.iTime) + '.pdf'
plt.savefig(filename)
def add_timepoint(self):
self.iTime += 1
self.title.set_text('t = %1.2f' % (self.diagnostics.tGrid[self.iTime]))
class Plot(object):
'''
'''
def __init__(self, hdf5_file, nPlot=1, ntMax=0, plotRef=False):
'''
Constructor
'''
self.diagnostics = Diagnostics(hdf5_file)
if ntMax > 0 and ntMax < self.diagnostics.nt:
self.nt = ntMax
else:
self.nt = self.diagnostics.nt
self.plot = PlotMHD2D(self.diagnostics, args.hdf5_file.replace(".hdf5", ""), self.nt, nPlot, plotRef)
def update(self, itime):
self.diagnostics.read_from_hdf5(itime)
self.diagnostics.update_invariants(itime)
if itime > 0:
self.plot.add_timepoint()
self.plot.update()
def run(self):
for itime in range(1, self.nt+1):
print("it = %4i" % (itime))
self.update(itime)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Ideal MHD Solver in 2D :: Magnetic Field Diagnostics')
parser.add_argument('hdf5_file', metavar='<run.hdf5>', type=str,
help='Run HDF5 File')
parser.add_argument('-np', metavar='i', type=int, default=1,
help='plot every i\'th frame')
parser.add_argument('-ntmax', metavar='i', type=int, default=0,
help='limit to i points in time')
parser.add_argument('-ref', action='store_true', required=False,
help='plot reference solution')
args = parser.parse_args()
print
print("Replay run with " + args.hdf5_file)
print
pyvp = Plot(args.hdf5_file, ntMax=args.ntmax, nPlot=args.np, plotRef=args.ref)
pyvp.run()
print
print("Replay finished.")
print