from __future__ import division, absolute_import

__copyright__ = "Copyright (C) 2012 Andreas Kloeckner"

__license__ = """

Permission is hereby granted, free of charge, to any person obtaining a copy

of this software and associated documentation files (the "Software"), to deal

in the Software without restriction, including without limitation the rights

to use, copy, modify, merge, publish, distribute, sublicense, and/or sell

copies of the Software, and to permit persons to whom the Software is

furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in

all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER

LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,

OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN

THE SOFTWARE.

"""

__doc__ = """

.. autofunction:: make_field_plotter_from_bbox

.. autoclass:: FieldPlotter

"""

import numpy as np

from six.moves import range

def separate_by_real_and_imag(data, real_only):

for name, field in data:

from pytools.obj_array import log_shape

ls = log_shape(field)

if ls != () and ls[0] > 1:

assert len(ls) == 1

from pytools.obj_array import (

oarray_real_copy, oarray_imag_copy,

with_object_array_or_scalar)

if field[0].dtype.kind == "c":

if real_only:

yield (name,

with_object_array_or_scalar(oarray_real_copy, field))

else:

yield (name+"_r",

with_object_array_or_scalar(oarray_real_copy, field))

yield (name+"_i",

with_object_array_or_scalar(oarray_imag_copy, field))

else:

yield (name, field)

else:

# ls == ()

if field.dtype.kind == "c":

yield (name+"_r", field.real.copy())

yield (name+"_i", field.imag.copy())

else:

yield (name, field)

def make_field_plotter_from_bbox(bbox, h, extend_factor=0):

"""

:arg bbox: a tuple (low, high) of points represented as 1D numpy arrays

indicating the low and high ends of the extent of a bounding box.

:arg h: Either a number or a sequence of numbers indicating the desired

(approximate) grid spacing in all or each of the dimensions. If a

sequence, the length must match the number of dimensions.

:arg extend_factor: A floating point number indicating by what percentage

the plot area should be grown compared to *bbox*.

"""

low, high = bbox

extent = (high-low) * (1 + extend_factor)

center = 0.5*(high+low)

dimensions = len(center)

from numbers import Number

if isinstance(h, Number):

h = (h,)*dimensions

else:

if len(h) != dimensions:

raise ValueError("length of 'h' must match number of dimensions")

from math import ceil

npoints = tuple(

int(ceil(extent[i] / h[i]))

for i in range(dimensions))

return FieldPlotter(center, extent, npoints)

class FieldPlotter(object):

"""

.. automethod:: set_matplotlib_limits

.. automethod:: show_scalar_in_matplotlib

.. automethod:: show_scalar_in_mayavi

.. automethod:: write_vtk_file

"""

def __init__(self, center, extent=1, npoints=1000):

center = np.asarray(center)

self.dimensions, = dim, = center.shape

self.a = a = center-extent*0.5

self.b = b = center+extent*0.5

from numbers import Number

if isinstance(npoints, Number):

npoints = dim*(npoints,)

else:

if len(npoints) != dim:

raise ValueError("length of npoints must match dimension")

for i in range(dim):

if npoints[i] == 1:

a[i] = center[i]

mgrid_index = tuple(

slice(a[i], b[i], 1j*npoints[i])

for i in range(dim))

mgrid = np.mgrid[mgrid_index]

# (axis, point x idx, point y idx, ...)

self.nd_points = mgrid

self.points = self.nd_points.reshape(dim, -1).copy()

from pytools import product

self.npoints = product(npoints)

def _get_nontrivial_dims(self):

return np.array(self.nd_points.shape[1:]) != 1

def _get_squeezed_bounds(self):

nontriv_dims = self._get_nontrivial_dims()

return self.a[nontriv_dims], self.b[nontriv_dims]

def show_scalar_in_matplotlib(self, fld, max_val=None,

func_name="imshow", **kwargs):

squeezed_points = self.points.squeeze()

if len(squeezed_points.shape) != 2:

raise RuntimeError(

"matplotlib plotting requires 2D geometry")

if len(fld.shape) == 1:

fld = fld.reshape(self.nd_points.shape[1:])

squeezed_fld = fld.squeeze()

if max_val is not None:

squeezed_fld[squeezed_fld > max_val] = max_val

squeezed_fld[squeezed_fld < -max_val] = -max_val

squeezed_fld = squeezed_fld[..., ::-1]

a, b = self._get_squeezed_bounds()

kwargs["extent"] = (

# (left, right, bottom, top)

a[0], b[0],

a[1], b[1])

import matplotlib.pyplot as pt

return getattr(pt, func_name)(squeezed_fld.T, **kwargs)

def set_matplotlib_limits(self):

import matplotlib.pyplot as pt

a, b = self._get_squeezed_bounds()

pt.xlim((a[0], b[0]))

pt.ylim((a[1], b[1]))

def show_vector_in_mayavi(self, fld, do_show=True, **kwargs):

c = self.points

from mayavi import mlab

mlab.quiver3d(c[0], c[1], c[2], fld[0], fld[1], fld[2],

**kwargs)

if do_show:

mlab.show()

def write_vtk_file(self, file_name, data, real_only=False):

from pyvisfile.vtk import write_structured_grid

write_structured_grid(file_name, self.nd_points,

point_data=list(separate_by_real_and_imag(data, real_only)))

def show_scalar_in_mayavi(self, fld, max_val=None, **kwargs):

if max_val is not None:

fld[fld > max_val] = max_val

fld[fld < -max_val] = -max_val

if len(fld.shape) == 1:

fld = fld.reshape(self.nd_points.shape[1:])

nd_points = self.nd_points.squeeze()[self._get_nontrivial_dims()]

squeezed_fld = fld.squeeze()

from mayavi import mlab

mlab.surf(nd_points[0], nd_points[1], squeezed_fld, **kwargs)

# vim: foldmethod=marker