Producing publication quality plots for latex

Question

Hello,

is there a good method for producing high quality plots for latex? For example i want to plot a solution for a 3D problem. I'm not taking the 3D plot everytime, so I decide on a specific slice.
Basically I want to plot the 2D slice, preferrably with tikz, so it integrates well with the document.

Has anyone tried this yet for a contour or quiver plot?

Answer 1

I found this previous topic helpful, maybe you want to have a look at it :

http://fenicsproject.org/qa/5795/plotting-dolfin-solutions-in-matplotlib?show=5797#a5797

hope this helps,

Claire

Answer 2

Here's a method I wrote to create a plot for a function defined on a 2D mesh. Note you can always extract a surface from a 3D mesh by creating a BoundaryMesh and a SubMesh.

import numpy as np
import os
from matplotlib import colors
from pylab import plt

def plot_variable(u, name, direc, cmap='gist_yarg', scale='lin', numLvls=12,
                  umin=None, umax=None, tp=False, tpAlpha=0.5, show=True,
                  hide_ax_tick_labels=False, label_axes=True, title='',
                  use_colorbar=True, hide_axis=False, colorbar_loc='right'):
  """
  """
  mesh = u.function_space().mesh()
  v    = u.compute_vertex_values(mesh)
  x    = mesh.coordinates()[:,0]
  y    = mesh.coordinates()[:,1]
  t    = mesh.cells()

  d    = os.path.dirname(direc)
  if not os.path.exists(d):
    os.makedirs(d)

  if umin != None:
    vmin = umin
  else:
    vmin = v.min()
  if umax != None:
    vmax = umax
  else:
    vmax = v.max()

  # countour levels :
  if scale == 'log':
    v[v < vmin] = vmin + 1e-12
    v[v > vmax] = vmax - 1e-12
    from matplotlib.ticker import LogFormatter
    levels      = np.logspace(np.log10(vmin), np.log10(vmax), numLvls)
    formatter   = LogFormatter(10, labelOnlyBase=False)
    norm        = colors.LogNorm()

  elif scale == 'lin':
    v[v < vmin] = vmin + 1e-12
    v[v > vmax] = vmax - 1e-12
    from matplotlib.ticker import ScalarFormatter
    levels    = np.linspace(vmin, vmax, numLvls)
    formatter = ScalarFormatter()
    norm      = None

  elif scale == 'bool':
    from matplotlib.ticker import ScalarFormatter
    levels    = [0, 1, 2]
    formatter = ScalarFormatter()
    norm      = None

  fig = plt.figure(figsize=(5,5))
  ax  = fig.add_subplot(111)

  c = ax.tricontourf(x, y, t, v, levels=levels, norm=norm,
                     cmap=pl.get_cmap(cmap))
  plt.axis('equal')

  if tp == True:
    p = ax.triplot(x, y, t, '-', lw=0.2, alpha=tpAlpha)
  ax.set_xlim([x.min(), x.max()])
  ax.set_ylim([y.min(), y.max()])
  if label_axes:
    ax.set_xlabel(r'$x$')
    ax.set_ylabel(r'$y$')
  if hide_ax_tick_labels:
    ax.set_xticklabels([])
    ax.set_yticklabels([])
  if hide_axis:
    plt.axis('off')

  # include colorbar :
  if scale != 'bool' and use_colorbar:
    divider = make_axes_locatable(plt.gca())
    cax  = divider.append_axes(colorbar_loc, "5%", pad="3%")
    cbar = plt.colorbar(c, cax=cax, format=formatter,
                        ticks=levels)
    tit = plt.title(title)

  if use_colorbar:
    plt.tight_layout(rect=[.03,.03,0.97,0.97])
  else:
    plt.tight_layout()
  plt.savefig(direc + name + '.png', dpi=300)
  if show:
    plt.show()
  plt.close(fig)