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2d color-bar map plot

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Dear all,
I am bit new to the python/pyplot.
This might be simple, but I guess I am missing something here.
I have data file as follows:

2.1576318858 -1.8651195165 4.2333428278
2.1681875208 -1.9229968780 4.1989176884
2.3387636157 -2.0376253255 2.4460899122
2.1696565965 -2.6186941271 4.4172007912
2.0848862071 -2.1708981985 3.3404520962
2.0824347942 -1.9142798955 3.3629290206
2.0281685821 -1.8103363482 2.5446721669
2.3309993378 -1.8721153619 2.7006893016
2.0957461483 -1.5379071451 4.5228264441
2.2761376261 -2.5935979811 3.9231744717
.
.
.
(total of 200 lines)

Columns 1,2,3 corresponds to x,y,z axis data points.
This is not a continuous data. I wish to make a plot as a 2D with 3rd
dimension (i.e z-axis data) as a color map with color bar on right hand
side.

As a beginner, I tried to follow tutorial with some modification as
follows:
http://matplotlib.org/examples/pylab_examples/tricontour_vs_griddata.html

# Read data from file:
fl1 = open('flooding-psiphi.dat','r').readlines()
xs = ys = zs = []
for line in fl1:
    line = line.split()
    xs.append(float(line[0]))
    ys.append(float(line[1]))
    zs.append(float(line[2]))

print xs[0], ys[0], zs[0]
xi = np.mgrid[-5.0:5.0:200j]
yi = np.mgrid[-5.0:5.0:200j]
zi = griddata((x, y), z, (xi, yi), method='cubic')
plt.subplot(221)

plt.contour(xi, yi, zi, 15, linewidths=0.5, colors='k')
plt.contourf(xi, yi, zi, 15, cmap=plt.cm.rainbow,
norm=plt.Normalize(vmax=abs(zi).max(), vmin=-abs(zi).max()))
plt.colorbar()  # draw colorbar
plt.plot(x, y, 'ko', ms=3)
plt.xlim(-5, 5)
plt.ylim(-5, 5)
plt.title('griddata and contour (%d points, %d grid points)' %
          (npts, ngridx*ngridy))
#print ('griddata and contour seconds: %f' % (time.clock() - start))
plt.gcf().set_size_inches(6, 6)
plt.show()


However, I failed and getting long error as follows:

QH6154 qhull precision error: initial facet 1 is coplanar with the interior
point
ERRONEOUS FACET:
- f1
    - flags: bottom simplicial upperDelaunay flipped
    - normal:    0.7071  -0.7071        0
    - offset:         -0
    - vertices: p600(v2) p452(v1) p304(v0)
    - neighboring facets: f2 f3 f4

While executing:  | qhull d Qz Qbb Qt
Options selected for Qhull 2010.1 2010/01/14:
  run-id 1531309415  delaunay  Qz-infinity-point  Qbbound-last  Qtriangulate
  _pre-merge  _zero-centrum  Pgood  _max-width 8.8  Error-roundoff 1.2e-14
  _one-merge 8.6e-14  _near-inside 4.3e-13  Visible-distance 2.5e-14
  U-coplanar-distance 2.5e-14  Width-outside 4.9e-14  _wide-facet 1.5e-13

precision problems (corrected unless 'Q0' or an error)
      2 flipped facets

The input to qhull appears to be less than 3 dimensional, or a
computation has overflowed.

Qhull could not construct a clearly convex simplex from points:
- p228(v3):   2.4   2.4   1.4
- p600(v2):   1.4   1.4   8.8
- p452(v1):   5.7   5.7     8
- p304(v0):  -3.1  -3.1   2.4

The center point is coplanar with a facet, or a vertex is coplanar
with a neighboring facet.  The maximum round off error for
computing distances is 1.2e-14.  The center point, facets and distances
to the center point are as follows:

center point    1.595    1.595    5.173

facet p600 p452 p304 distance=    0
facet p228 p452 p304 distance=    0
facet p228 p600 p304 distance=    0
facet p228 p600 p452 distance=    0

These points either have a maximum or minimum x-coordinate, or
they maximize the determinant for k coordinates.  Trial points
are first selected from points that maximize a coordinate.

The min and max coordinates for each dimension are:
  0:    -3.134     5.701  difference= 8.835
  1:    -3.134     5.701  difference= 8.835
  2:  -2.118e-22     8.835  difference= 8.835

If the input should be full dimensional, you have several options that
may determine an initial simplex:
  - use 'QJ'  to joggle the input and make it full dimensional
  - use 'QbB' to scale the points to the unit cube
  - use 'QR0' to randomly rotate the input for different maximum points
  - use 'Qs'  to search all points for the initial simplex
  - use 'En'  to specify a maximum roundoff error less than 1.2e-14.
  - trace execution with 'T3' to see the determinant for each point.

If the input is lower dimensional:
  - use 'QJ' to joggle the input and make it full dimensional
  - use 'Qbk:0Bk:0' to delete coordinate k from the input.  You should
    pick the coordinate with the least range.  The hull will have the
    correct topology.
  - determine the flat containing the points, rotate the points
    into a coordinate plane, and delete the other coordinates.
  - add one or more points to make the input full dimensional.
Traceback (most recent call last):
  File "./scatter.py", line 43, in <module>
    zi = griddata((x, y), z, (xi, yi), method='linear')
  File "/usr/lib/python2.7/dist-packages/scipy/interpolate/ndgriddata.py",
line 183, in griddata
    ip = LinearNDInterpolator(points, values, fill_value=fill_value)
  File "interpnd.pyx", line 192, in
scipy.interpolate.interpnd.LinearNDInterpolator.__init__
(scipy/interpolate/interpnd.c:2598)
  File "qhull.pyx", line 948, in scipy.spatial.qhull.Delaunay.__init__
(scipy/spatial/qhull.c:4121)
  File "qhull.pyx", line 172, in scipy.spatial.qhull._construct_delaunay
(scipy/spatial/qhull.c:1314)
RuntimeError: Qhull error


Could anyone help me to point out what exactly I am missing here.
I just wish to plot 2D map with color bar for Z-axis.

Thank you in advance.


-- DJ

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2d color-bar map plot Dhananjay <dhananjay.c.joshi@gmail.com> - 2014-10-17 14:28 +0800
  Re: 2d color-bar map plot Peter Pearson <pkpearson@nowhere.invalid> - 2014-10-17 15:51 +0000

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