Forked from
empyre / empyre
343 commits behind the upstream repository.
-
Jan Caron authoredcolormap: Added TransparentColormap. magdata: Added get_magslice method, to_emd and from_emd. phasemap: Added to_emd and from_emd. gui: New GUI "PhaseMap Creator". tests: Moved from ./tests to ./pyramid/tests. Not a package anymore!
Jan Caron authoredcolormap: Added TransparentColormap. magdata: Added get_magslice method, to_emd and from_emd. phasemap: Added to_emd and from_emd. gui: New GUI "PhaseMap Creator". tests: Moved from ./tests to ./pyramid/tests. Not a package anymore!
magdata.py 39.67 KiB
# -*- coding: utf-8 -*-
# Copyright 2014 by Forschungszentrum Juelich GmbH
# Author: J. Caron
#
"""This module provides the :class:`~.MagData` class for storing of magnetization data."""
from __future__ import division
import os
import numpy as np
from numpy.linalg import norm
from scipy.ndimage.interpolation import zoom
from numbers import Number
import netCDF4
import matplotlib.pyplot as plt
import matplotlib.cm as cmx
from matplotlib.ticker import MaxNLocator
from pyramid import fft
from pyramid.colormap import DirectionalColormap
import logging
__all__ = ['MagData']
class MagData(object):
'''Class for storing magnetization data.
Represents 3-dimensional magnetic distributions with 3 components which are stored as a
2-dimensional numpy array in `magnitude`, but which can also be accessed as a vector via
`mag_vec`. :class:`~.MagData` objects support negation, arithmetic operators
(``+``, ``-``, ``*``) and their augmented counterparts (``+=``, ``-=``, ``*=``), with numbers
and other :class:`~.MagData` objects, if their dimensions and grid spacings match. It is
possible to load data from NetCDF4 or LLG (.txt) files or to save the data in these formats.
Plotting methods are also provided.
Attributes
----------
a: float
The grid spacing in nm.
dim: tuple (N=3)
Dimensions (z, y, x) of the grid.
magnitude: :class:`~numpy.ndarray` (N=4)
The `x`-, `y`- and `z`-component of the magnetization vector for every 3D-gridpoint
as a 4-dimensional numpy array (first dimension has to be 3, because of the 3 components).
mag_amp: :class:`~numpy.ndarray` (N=3)
The length (amplitude) of the magnetization vectors as a 3D-array.
mag_vec: :class:`~numpy.ndarray` (N=1)
Vector containing the magnetic distribution.
'''
_log = logging.getLogger(__name__+'.MagData')
@property
def a(self):
return self._a
@a.setter
def a(self, a):
assert isinstance(a, Number), 'Grid spacing has to be a number!'
assert a >= 0, 'Grid spacing has to be a positive number!'
self._a = float(a)
@property
def dim(self):
return self._dim
@property
def magnitude(self):
return self._magnitude
@magnitude.setter
def magnitude(self, magnitude):
assert isinstance(magnitude, np.ndarray), 'Magnitude has to be a numpy array!'
assert len(magnitude.shape) == 4, 'Magnitude has to be 4-dimensional!'
assert magnitude.shape[0] == 3, 'First dimension of the magnitude has to be 3!'
self._magnitude = np.asarray(magnitude, dtype=fft.FLOAT)
self._dim = magnitude.shape[1:]
@property
def mag_amp(self):
return np.sqrt(np.sum(self.magnitude**2, axis=0))
@property
def mag_vec(self):
return np.reshape(self.magnitude, -1)
@mag_vec.setter
def mag_vec(self, mag_vec):
mag_vec = np.asarray(mag_vec, dtype=fft.FLOAT)
assert np.size(mag_vec) == 3*np.prod(self.dim), \
'Vector has to match magnitude dimensions! {} {}'.format(mag_vec.shape,
3*np.prod(self.dim))
self.magnitude = mag_vec.reshape((3,)+self.dim)
def __init__(self, a, magnitude):
self._log.debug('Calling __init__')
self.a = a
self.magnitude = magnitude
self._log.debug('Created '+str(self))
def __repr__(self):
self._log.debug('Calling __repr__')
return '%s(a=%r, magnitude=%r)' % (self.__class__, self.a, self.magnitude)
def __str__(self):
self._log.debug('Calling __str__')
return 'MagData(a=%s, dim=%s)' % (self.a, self.dim)
def __neg__(self): # -self
self._log.debug('Calling __neg__')
return MagData(self.a, -self.magnitude)
def __add__(self, other): # self + other
self._log.debug('Calling __add__')
assert isinstance(other, (MagData, Number)), \
'Only MagData objects and scalar numbers (as offsets) can be added/subtracted!'
if isinstance(other, MagData):
self._log.debug('Adding two MagData objects')
assert other.a == self.a, 'Added phase has to have the same grid spacing!'
assert other.magnitude.shape == (3,)+self.dim, \
'Added magnitude has to have the same dimensions!'
return MagData(self.a, self.magnitude+other.magnitude)
else: # other is a Number
self._log.debug('Adding an offset')
return MagData(self.a, self.magnitude+other)
def __sub__(self, other): # self - other
self._log.debug('Calling __sub__')
return self.__add__(-other)
def __mul__(self, other): # self * other
self._log.debug('Calling __mul__')
assert isinstance(other, Number), 'MagData objects can only be multiplied by numbers!'
return MagData(self.a, other*self.magnitude)
def __radd__(self, other): # other + self
self._log.debug('Calling __radd__')
return self.__add__(other)
def __rsub__(self, other): # other - self
self._log.debug('Calling __rsub__')
return -self.__sub__(other)
def __rmul__(self, other): # other * self
self._log.debug('Calling __rmul__')
return self.__mul__(other)
def __iadd__(self, other): # self += other
self._log.debug('Calling __iadd__')
return self.__add__(other)
def __isub__(self, other): # self -= other
self._log.debug('Calling __isub__')
return self.__sub__(other)
def __imul__(self, other): # self *= other
self._log.debug('Calling __imul__')
return self.__mul__(other)
def copy(self):
'''Returns a copy of the :class:`~.MagData` object
Parameters
----------
None
Returns
-------
mag_data: :class:`~.MagData`
A copy of the :class:`~.MagData`.
'''
self._log.debug('Calling copy')
return MagData(self.a, self.magnitude.copy())
def scale_down(self, n=1):
'''Scale down the magnetic distribution by averaging over two pixels along each axis.
Parameters
----------
n : int, optional
Number of times the magnetic distribution is scaled down. The default is 1.
Returns
-------
None
Notes
-----
Acts in place and changes dimensions and grid spacing accordingly.
Only possible, if each axis length is a power of 2!
'''
self._log.debug('Calling scale_down')
assert n > 0 and isinstance(n, (int, long)), 'n must be a positive integer!'
self.a = self.a * 2**n
for t in range(n):
# Pad if necessary:
pz, py, px = self.dim[0] % 2, self.dim[1] % 2, self.dim[2] % 2
if pz != 0 or py != 0 or px != 0:
self.magnitude = np.pad(self.magnitude, ((0, 0), (0, pz), (0, py), (0, px)),
mode='constant')
# Create coarser grid for the magnetization:
self.magnitude = self.magnitude.reshape(
3, self.dim[0]/2, 2, self.dim[1]/2, 2, self.dim[2]/2, 2).mean(axis=(6, 4, 2))
def scale_up(self, n=1, order=0):
'''Scale up the magnetic distribution using spline interpolation of the requested order.
Parameters
----------
n : int, optional
Power of 2 with which the grid is scaled. Default is 1, which means every axis is
increased by a factor of ``2**1 = 2``.
order : int, optional
The order of the spline interpolation, which has to be in the range between 0 and 5
and defaults to 0.
Returns
-------
None
Notes
-----
Acts in place and changes dimensions and grid spacing accordingly.
'''
self._log.debug('Calling scale_up')
assert n > 0 and isinstance(n, (int, long)), 'n must be a positive integer!'
assert 5 > order >= 0 and isinstance(order, (int, long)), \
'order must be a positive integer between 0 and 5!'
self.a = self.a / 2**n
self.magnitude = np.array((zoom(self.magnitude[0], zoom=2**n, order=order),
zoom(self.magnitude[1], zoom=2**n, order=order),
zoom(self.magnitude[2], zoom=2**n, order=order)))
def pad(self, pad_values):
'''Pad the current magnetic distribution with zeros for each individual axis.
Parameters
----------
pad_values : tuple of int
Number of zeros which should be padded. Provided as a tuple where each entry
corresponds to an axis. An entry can be one int (same padding for both sides) or again
a tuple which specifies the pad values for both sides of the corresponding axis.
Returns
-------
None
Notes
-----
Acts in place and changes dimensions accordingly.
'''
self._log.debug('Calling pad')
assert len(pad_values) == 3, 'Pad values for each dimension have to be provided!'
pv = np.zeros(6, dtype=np.int)
for i, values in enumerate(pad_values):
assert np.shape(values) in [(), (2,)], 'Only one or two values per axis can be given!'
pv[2*i:2*(i+1)] = values
self.magnitude = np.pad(self.magnitude,
((0, 0), (pv[0], pv[1]), (pv[2], pv[3]), (pv[4], pv[5])),
mode='constant')
def crop(self, crop_values):
'''Crop the current magnetic distribution with zeros for each individual axis.
Parameters
----------
crop_values : tuple of int
Number of zeros which should be cropped. Provided as a tuple where each entry
corresponds to an axis. An entry can be one int (same cropping for both sides) or again
a tuple which specifies the crop values for both sides of the corresponding axis.
Returns
-------
None
Notes
-----
Acts in place and changes dimensions accordingly.
'''
self._log.debug('Calling crop')
assert len(crop_values) == 3, 'Crop values for each dimension have to be provided!'
cv = np.zeros(6, dtype=np.int)
for i, values in enumerate(crop_values):
assert np.shape(values) in [(), (2,)], 'Only one or two values per axis can be given!'
cv[2*i:2*(i+1)] = values
cv *= np.resize([1, -1], len(cv))
cv = np.where(cv == 0, None, cv)
self.magnitude = self.magnitude[:, cv[0]:cv[1], cv[2]:cv[3], cv[4]:cv[5]]
def get_mask(self, threshold=0):
'''Mask all pixels where the amplitude of the magnetization lies above `threshold`.
Parameters
----------
threshold : float, optional
A pixel only gets masked, if it lies above this threshold . The default is 0.
Returns
-------
mask : :class:`~numpy.ndarray` (N=3, boolean)
Mask of the pixels where the amplitude of the magnetization lies above `threshold`.
'''
self._log.debug('Calling get_mask')
return self.mag_amp > threshold
def get_vector(self, mask):
'''Returns the magnetic components arranged in a vector, specified by a mask.
Parameters
----------
mask : :class:`~numpy.ndarray` (N=3, boolean)
Masks the pixels from which the components should be taken.
Returns
-------
vector : :class:`~numpy.ndarray` (N=1)
The vector containing magnetization components of the specified pixels.
Order is: first all `x`-, then all `y`-, then all `z`-components.
'''
self._log.debug('Calling get_vector')
if mask is not None:
return np.reshape([self.magnitude[0][mask],
self.magnitude[1][mask],
self.magnitude[2][mask]], -1)
else:
return self.mag_vec
def set_vector(self, vector, mask=None):
'''Set the magnetic components of the masked pixels to the values specified by `vector`.
Parameters
----------
mask : :class:`~numpy.ndarray` (N=3, boolean), optional
Masks the pixels from which the components should be taken.
vector : :class:`~numpy.ndarray` (N=1)
The vector containing magnetization components of the specified pixels.
Order is: first all `x`-, then all `y-, then all `z`-components.
Returns
-------
None
'''
self._log.debug('Calling set_vector')
vector = np.asarray(vector, dtype=fft.FLOAT)
assert np.size(vector) % 3 == 0, 'Vector has to contain all 3 components for every pixel!'
count = np.size(vector)//3
if mask is not None:
self.magnitude[0][mask] = vector[:count] # x-component
self.magnitude[1][mask] = vector[count:2*count] # y-component
self.magnitude[2][mask] = vector[2*count:] # z-component
else:
self.mag_vec = vector
def get_magslice(self, ax_slice=0, proj_axis='z', mode='complex'):
'''Extract a slice from the :class:`~.MagData` object.
Parameters
----------
proj_axis : {'z', 'y', 'x'}, optional
The axis, from which the slice is taken. The default is 'z'.
ax_slice : int, optional
The slice-index of the axis specified in `proj_axis`. Defaults to zero (first slice).
mode : {'complex', 'amplitude'}, optional
Determines if the 2D magnetization is returned as complex values or if the amplitude
of the two components is calculated.
Returns
-------
mag_slice : :class:`~numpy.ndarray` (N=2)
The extracted magnetization slice.
'''
self._log.debug('Calling get_mag_slice')
# Find slice:
assert proj_axis == 'z' or proj_axis == 'y' or proj_axis == 'x', \
'Axis has to be x, y or z (as string).'
if proj_axis == 'z': # Slice of the xy-plane with z = ax_slice
self._log.debug('proj_axis == z')
u_mag = np.copy(self.magnitude[0][ax_slice, ...]) # x-component
v_mag = np.copy(self.magnitude[1][ax_slice, ...]) # y-component
elif proj_axis == 'y': # Slice of the xz-plane with y = ax_slice
self._log.debug('proj_axis == y')
u_mag = np.copy(self.magnitude[0][:, ax_slice, :]) # x-component
v_mag = np.copy(self.magnitude[2][:, ax_slice, :]) # z-component
elif proj_axis == 'x': # Slice of the yz-plane with x = ax_slice
self._log.debug('proj_axis == x')
u_mag = np.swapaxes(np.copy(self.magnitude[2][..., ax_slice]), 0, 1) # z-component
v_mag = np.swapaxes(np.copy(self.magnitude[1][..., ax_slice]), 0, 1) # y-component
# Create data field:
if mode == 'complex':
return u_mag + 1j*v_mag
elif mode == 'amplitude':
return np.hypot(u_mag, v_mag)
else:
raise ValueError('Given mode not understood!')
def flip(self, axis='x'):
'''Flip/mirror the magnetization around the specified axis.
Parameters
----------
axis: {'x', 'y', 'z'}, optional
The axis around which the magnetization is flipped.
Returns
-------
mag_data_flip: :class:`~.MagData`
A flipped copy of the :class:`~.MagData` object.
'''
self._log.debug('Calling flip')
if axis == 'x':
mag_x, mag_y, mag_z = self.magnitude[:, :, :, ::-1]
magnitude_flip = np.array((-mag_x, mag_y, mag_z))
elif axis == 'y':
mag_x, mag_y, mag_z = self.magnitude[:, :, ::-1, :]
magnitude_flip = np.array((mag_x, -mag_y, mag_z))
elif axis == 'z':
mag_x, mag_y, mag_z = self.magnitude[:, ::-1, :, :]
magnitude_flip = np.array((mag_x, mag_y, -mag_z))
else:
raise ValueError("Wrong input! 'x', 'y', 'z' allowed!")
return MagData(self.a, magnitude_flip)
def rot90(self, axis='x'):
'''Rotate the magnetization 90° around the specified axis (right hand rotation).
Parameters
----------
axis: {'x', 'y', 'z'}, optional
The axis around which the magnetization is rotated.
Returns
-------
mag_data_rot: :class:`~.MagData`
A rotated copy of the :class:`~.MagData` object.
'''
self._log.debug('Calling rot90')
if axis == 'x':
magnitude_rot = np.zeros((3, self.dim[1], self.dim[0], self.dim[2]))
for i in range(self.dim[2]):
mag_x, mag_y, mag_z = self.magnitude[:, :, :, i]
mag_xrot, mag_yrot, mag_zrot = np.rot90(mag_x), np.rot90(mag_y), np.rot90(mag_z)
magnitude_rot[:, :, :, i] = np.array((mag_xrot, mag_zrot, -mag_yrot))
elif axis == 'y':
magnitude_rot = np.zeros((3, self.dim[2], self.dim[1], self.dim[0]))
for i in range(self.dim[1]):
mag_x, mag_y, mag_z = self.magnitude[:, :, i, :]
mag_xrot, mag_yrot, mag_zrot = np.rot90(mag_x), np.rot90(mag_y), np.rot90(mag_z)
magnitude_rot[:, :, i, :] = np.array((mag_zrot, mag_yrot, -mag_xrot))
elif axis == 'z':
magnitude_rot = np.zeros((3, self.dim[0], self.dim[2], self.dim[1]))
for i in range(self.dim[0]):
mag_x, mag_y, mag_z = self.magnitude[:, i, :, :]
mag_xrot, mag_yrot, mag_zrot = np.rot90(mag_x), np.rot90(mag_y), np.rot90(mag_z)
magnitude_rot[:, i, :, :] = np.array((mag_yrot, -mag_xrot, mag_zrot))
else:
raise ValueError("Wrong input! 'x', 'y', 'z' allowed!")
return MagData(self.a, magnitude_rot)
def save_to_netcdf4(self, filename='magdata.nc'):
'''Save magnetization data in a file with NetCDF4-format.
Parameters
----------
filename : string, optional
The name of the NetCDF4-file in which to store the magnetization data.
Standard format is '\*.nc'.
Returns
-------
None
'''
self._log.debug('Calling save_to_netcdf4')
# Construct path if filename isn't already absolute:
if not os.path.isabs(filename):
from pyramid import DIR_FILES
directory = os.path.join(DIR_FILES, 'magdata')
if not os.path.exists(directory):
os.makedirs(directory)
filename = os.path.join(directory, filename)
# Save data to file:
mag_file = netCDF4.Dataset(filename, 'w', format='NETCDF4')
mag_file.a = self.a
mag_file.createDimension('comp', 3) # Number of components
mag_file.createDimension('z_dim', self.dim[0])
mag_file.createDimension('y_dim', self.dim[1])
mag_file.createDimension('x_dim', self.dim[2])
magnitude = mag_file.createVariable('magnitude', 'f', ('comp', 'z_dim', 'y_dim', 'x_dim'))
magnitude[...] = self.magnitude
mag_file.close()
@classmethod
def load_from_netcdf4(cls, filename):
'''Construct :class:`~.DataMag` object from NetCDF4-file.
Parameters
----------
filename : string
The name of the NetCDF4-file from which to load the data. Standard format is '\*.nc'.
Returns
-------
mag_data: :class:`~.MagData`
A :class:`~.MagData` object containing the loaded data.
'''
cls._log.debug('Calling load_from_netcdf4')
# Construct path if filename isn't already absolute:
if not os.path.isabs(filename):
from pyramid import DIR_FILES
directory = os.path.join(DIR_FILES, 'magdata')
if not os.path.exists(directory):
os.makedirs(directory)
filename = os.path.join(directory, filename)
# Load data from file:
mag_file = netCDF4.Dataset(filename, 'r', format='NETCDF4')
a = mag_file.a
magnitude = mag_file.variables['magnitude'][...]
mag_file.close()
return MagData(a, magnitude)
def save_to_llg(self, filename='magdata.txt'):
'''Save magnetization data in a file with LLG-format.
Parameters
----------
filename : string, optional
The name of the LLG-file in which to store the magnetization data.
The default is '..\output\magdata_output.txt'.
Returns
-------
None
'''
self._log.debug('Calling save_to_llg')
SCALE = 1.0E-9 / 1.0E-2 # from nm to cm
# Create 3D meshgrid and reshape it and the magnetization into a list where x varies first:
zz, yy, xx = self.a * SCALE * (np.indices(self.dim)+0.5).reshape(3, -1)
x_vec, y_vec, z_vec = self.magnitude.reshape(3, -1)
data = np.array([xx, yy, zz, x_vec, y_vec, z_vec]).T
# Construct path if filename isn't already absolute:
if not os.path.isabs(filename):
from pyramid import DIR_FILES
directory = os.path.join(DIR_FILES, 'magdata')
if not os.path.exists(directory):
os.makedirs(directory)
filename = os.path.join(directory, filename)
# Save data to file:
with open(filename, 'w') as mag_file:
mag_file.write('LLGFileCreator: %s\n' % filename)
mag_file.write(' %d %d %d\n' % (self.dim[2], self.dim[1], self.dim[0]))
mag_file.writelines('\n'.join(' '.join('{:7.6e}'.format(cell)
for cell in row) for row in data))
@classmethod
def load_from_llg(cls, filename):
'''Construct :class:`~.MagData` object from LLG-file.
Parameters
----------
filename : string
The name of the LLG-file from which to load the data.
Returns
-------
mag_data: :class:`~.MagData`
A :class:`~.MagData` object containing the loaded data.
'''
cls._log.debug('Calling load_from_llg')
SCALE = 1.0E-9 / 1.0E-2 # From cm to nm
# Construct path if filename isn't already absolute:
if not os.path.isabs(filename):
from pyramid import DIR_FILES
directory = os.path.join(DIR_FILES, 'magdata')
if not os.path.exists(directory):
os.makedirs(directory)
filename = os.path.join(directory, filename)
# Load data from file:
data = np.genfromtxt(filename, skip_header=2)
dim = tuple(np.genfromtxt(filename, dtype=int, skip_header=1, skip_footer=len(data[:, 0])))
a = (data[1, 0] - data[0, 0]) / SCALE
magnitude = data[:, 3:6].T.reshape((3,)+dim)
return MagData(a, magnitude)
def save_to_x3d(self, filename='magdata.x3d', maximum=1):
'''Output the magnetization in the .x3d format for the Fraunhofer InstantReality Player.
Parameters
----------
filename : string, optional
The name of the NetCDF4-file in which to store the magnetization data.
Standard format is '\*.x3d'.
maximum: float, optional
Maximum value to which the arrow color is scaled. Default is 1.
Returns
-------
None
'''
self._log.debug('Calling save_to_x3d')
from lxml import etree
dim = self.dim
# Create points and vector components as lists:
zz, yy, xx = (np.indices(dim)-0.5).reshape(3, -1)
x_mag = np.reshape(self.magnitude[0], (-1))
y_mag = np.reshape(self.magnitude[1], (-1))
z_mag = np.reshape(self.magnitude[2], (-1))
# Load template, load tree and write viewpoint information:
template = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'template.x3d')
parser = etree.XMLParser(remove_blank_text=True)
tree = etree.parse(template, parser)
scene = tree.find('Scene')
etree.SubElement(scene, 'Viewpoint', position='0 0 {}'.format(1.5*dim[0]),
fieldOfView='1')
# Write each "spin"-tag separately:
for i in range(np.prod(dim)):
mag = np.sqrt(x_mag[i]**2+y_mag[i]**2+z_mag[i]**2)
if mag != 0:
spin_position = (xx[i]-dim[2]/2., yy[i]-dim[1]/2., zz[i]-dim[0]/2.)
sx_ref = 0
sy_ref = 1
sz_ref = 0
rot_x = sy_ref*z_mag[i] - sz_ref*y_mag[i]
rot_y = sz_ref*x_mag[i] - sx_ref*z_mag[i]
rot_z = sx_ref*y_mag[i] - sy_ref*x_mag[i]
angle = np.arccos(y_mag[i]/mag)
if norm((rot_x, rot_y, rot_z)) < 1E-10:
rot_x, rot_y, rot_z = 1, 0, 0
spin_rotation = (rot_x, rot_y, rot_z, angle)
spin_color = cmx.RdYlGn(mag/maximum)[:3]
spin_scale = (1., 1., 1.)
spin = etree.SubElement(scene, 'ProtoInstance',
DEF='Spin {}'.format(i), name='Spin_Proto')
etree.SubElement(spin, 'fieldValue', name='spin_position',
value='{} {} {}'.format(*spin_position))
etree.SubElement(spin, 'fieldValue', name='spin_rotation',
value='{} {} {} {}'.format(*spin_rotation))
etree.SubElement(spin, 'fieldValue', name='spin_color',
value='{} {} {}'.format(*spin_color))
etree.SubElement(spin, 'fieldValue', name='spin_scale',
value='{} {} {}'.format(*spin_scale))
# Construct path if filename isn't already absolute:
if not os.path.isabs(filename):
from pyramid import DIR_FILES
directory = os.path.join(DIR_FILES, 'x3d')
if not os.path.exists(directory):
os.makedirs(directory)
filename = os.path.join(directory, filename)
# Write the tree into the file in pretty print format:
tree.write(filename, pretty_print=True)
def to_emd(self, user={}, microscope={}, sample={}, comments={}):
'''Convert :class:`~.MagData` data into ERCpys EMD-format.
Parameters
----------
user: dict, optional
Dictionary defining user metadata.
microscope: dict, optional
Dictionary defining microscope metadata.
sample: dict, optional
Dictionary defining sample metadata.
comments: dict, optional
Dictionary defining comment metadata.
Returns
-------
emd: :class:`~ercpy.EMD`
Representation of the :class:`~.MagData` object as an :class:`~ercpy.EMD` object.
Notes
-----
This method recquires the ercpy package!
'''
self._log.debug('Calling to_emd')
# Try importing ERCpy:
try:
import ercpy
import hyperspy.api as hp
except ImportError:
self._log.error('Could not load ercpy package!')
return
# Create signals:
magnitude = hp.signals.Signal(np.rollaxis(self.magnitude, 0, 4))
# Set axes:
magnitude.axes_manager[0].name = 'x-axis'
magnitude.axes_manager[0].units = 'nm'
magnitude.axes_manager[0].scale = self.a
magnitude.axes_manager[1].name = 'y-axis'
magnitude.axes_manager[1].units = 'nm'
magnitude.axes_manager[1].scale = self.a
magnitude.axes_manager[2].name = 'z-axis'
magnitude.axes_manager[2].units = 'nm'
magnitude.axes_manager[2].scale = self.a
magnitude.axes_manager[3].name = 'components (x,y,z)'
magnitude.axes_manager[3].units = ''
# Set metadata:
magnitude.metadata.Signal.add_dictionary({'name': 'magnitude'})
# Create and return EMD:
signals = {'magnitude': magnitude}
return ercpy.EMD(signals, user, microscope, sample, comments)
@classmethod
def from_emd(cls, emd):
'''Convert a :class:`~ercpy.EMD` object to a :class:`~.MagData` object.
Parameters
----------
emd: :class:`~ercpy.EMD`
The :class:`~ercpy.EMD` object which should be converted to :class:`~.MagData`.
Returns
-------
mag_data: :class:`~.MagData`
A :class:`~.MagData` object containing the loaded data.
Notes
-----
This method recquires the ercpy package!
'''
cls._log.debug('Calling to_emd')
# Extract signals:
try:
magnitude = np.rollaxis(emd['magnitude'], 3, 0)
except KeyError as e:
cls._log.error(str(e))
# Extract properties:
a = emd.signals['magnitude'].axes_manager[0].scale
return MagData(a, magnitude)
def quiver_plot(self, title='Magnetization Distribution', axis=None, proj_axis='z',
coloring='angle', ar_dens=1, ax_slice=None, log=False, scaled=True,
scale=1., show_mask=True):
'''Plot a slice of the magnetization as a quiver plot.
Parameters
----------
title : string, optional
The title for the plot.
axis : :class:`~matplotlib.axes.AxesSubplot`, optional
Axis on which the graph is plotted. Creates a new figure if none is specified.
proj_axis : {'z', 'y', 'x'}, optional
The axis, from which a slice is plotted. The default is 'z'.
coloring : string
Color coding mode of the arrows. Use 'angle' (default), 'amplitude' or 'uniform'.
ar_dens: int (optional)
Number defining the arrow density which is plotted. A higher ar_dens number skips more
arrows (a number of 2 plots every second arrow). Default is 1.
ax_slice : int, optional
The slice-index of the axis specified in `proj_axis`. Is set to the center of
`proj_axis` if not specified.
log : boolean, optional
Takes the Default is False.
scaled : boolean, optional
Normalizes the plotted arrows in respect to the highest one. Default is True.
scale: float, optional
Additional multiplicative factor scaling the arrow length. Default is 1
(no further scaling).
show_mask: boolean
Default is True. Shows the outlines of the mask slice if available.
Returns
-------
axis: :class:`~matplotlib.axes.AxesSubplot`
The axis on which the graph is plotted.
'''
self._log.debug('Calling quiver_plot')
assert proj_axis == 'z' or proj_axis == 'y' or proj_axis == 'x', \
'Axis has to be x, y or z (as string).'
if proj_axis == 'z': # Slice of the xy-plane with z = ax_slice
self._log.debug('proj_axis == z')
if ax_slice is None:
self._log.debug('ax_slice is None')
ax_slice = self.dim[0] // 2
plot_u = np.copy(self.magnitude[0][ax_slice, ...]) # x-component
plot_v = np.copy(self.magnitude[1][ax_slice, ...]) # y-component
u_label = 'x [px]'
v_label = 'y [px]'
submask = self.get_mask()[ax_slice, ...]
elif proj_axis == 'y': # Slice of the xz-plane with y = ax_slice
self._log.debug('proj_axis == y')
if ax_slice is None:
self._log.debug('ax_slice is None')
ax_slice = self.dim[1] // 2
plot_u = np.copy(self.magnitude[0][:, ax_slice, :]) # x-component
plot_v = np.copy(self.magnitude[2][:, ax_slice, :]) # z-component
u_label = 'x [px]'
v_label = 'z [px]'
submask = self.get_mask()[:, ax_slice, :]
elif proj_axis == 'x': # Slice of the yz-plane with x = ax_slice
self._log.debug('proj_axis == x')
if ax_slice is None:
self._log.debug('ax_slice is None')
ax_slice = self.dim[2] // 2
plot_u = np.swapaxes(np.copy(self.magnitude[2][..., ax_slice]), 0, 1) # z-component
plot_v = np.swapaxes(np.copy(self.magnitude[1][..., ax_slice]), 0, 1) # y-component
u_label = 'z [px]'
v_label = 'y [px]'
submask = self.get_mask()[..., ax_slice]
# Prepare quiver (select only used arrows if ar_dens is specified):
dim_uv = plot_u.shape
vv, uu = np.indices(dim_uv) + 0.5 # shift to center of pixel
uu = uu[::ar_dens, ::ar_dens]
vv = vv[::ar_dens, ::ar_dens]
plot_u = plot_u[::ar_dens, ::ar_dens]
plot_v = plot_v[::ar_dens, ::ar_dens]
amplitudes = np.hypot(plot_u, plot_v)
angles = np.angle(plot_u+1j*plot_v, deg=True).tolist()
# Calculate the arrow colors:
if coloring == 'angle':
self._log.debug('Encoding angles')
colorinds = (1 + np.arctan2(plot_v, plot_u)/np.pi) / 2 # in-plane color index (0 - 1)
cmap = DirectionalColormap()
elif coloring == 'amplitude':
self._log.debug('Encoding amplitude')
colorinds = amplitudes / amplitudes.max()
cmap = 'jet'
elif coloring == 'uniform':
self._log.debug('No color encoding')
colorinds = np.zeros_like(plot_u) # use black arrows!
cmap = 'gray'
else:
raise AttributeError("Invalid coloring mode! Use 'angles', 'amplitude' or 'uniform'!")
# If no axis is specified, a new figure is created:
if axis is None:
self._log.debug('axis is None')
fig = plt.figure(figsize=(8.5, 7))
axis = fig.add_subplot(1, 1, 1)
axis.set_aspect('equal')
# Take the logarithm of the arrows to clearly show directions (if specified):
if log:
cutoff = 10
amp = np.round(amplitudes, decimals=cutoff)
min_value = amp[np.nonzero(amp)].min()
plot_u = np.round(plot_u, decimals=cutoff) / min_value
plot_u = np.log10(np.abs(plot_u)+1) * np.sign(plot_u)
plot_v = np.round(plot_v, decimals=cutoff) / min_value
plot_v = np.log10(np.abs(plot_v)+1) * np.sign(plot_v)
amplitudes = np.hypot(plot_u, plot_v) # Recalculate!
# Scale the magnitude of the arrows to the highest one (if specified):
if scaled:
plot_u /= amplitudes.max() + 1E-30
plot_v /= amplitudes.max() + 1E-30
axis.quiver(uu, vv, plot_u, plot_v, colorinds, cmap=cmap, clim=(0, 1), angles=angles,
pivot='middle', units='xy', scale_units='xy', scale=scale/ar_dens,
minlength=0.25, headwidth=6, headlength=7)
if show_mask and not np.all(submask): # Plot mask if desired and not trivial!
vv, uu = np.indices(dim_uv) + 0.5 # shift to center of pixel
axis.contour(uu, vv, submask, levels=[0.5], colors='k', linestyles='dotted')
axis.set_xlim(0, dim_uv[1])
axis.set_ylim(0, dim_uv[0])
axis.set_title(title, fontsize=18)
axis.set_xlabel(u_label, fontsize=15)
axis.set_ylabel(v_label, fontsize=15)
axis.tick_params(axis='both', which='major', labelsize=14)
if dim_uv[0] >= dim_uv[1]:
u_bin, v_bin = np.max((2, np.floor(9*dim_uv[1]/dim_uv[0]))), 9
else:
u_bin, v_bin = 9, np.max((2, np.floor(9*dim_uv[0]/dim_uv[1])))
axis.xaxis.set_major_locator(MaxNLocator(nbins=u_bin, integer=True))
axis.yaxis.set_major_locator(MaxNLocator(nbins=v_bin, integer=True))
# Return plotting axis:
return axis
def quiver_plot3d(self, title='Magnetization Distribution', limit=None, cmap='jet',
ar_dens=1, mode='arrow', coloring='angle', show_pipeline=False):
'''Plot the magnetization as 3D-vectors in a quiverplot.
Parameters
----------
title : string, optional
The title for the plot.
limit : float, optional
Plotlimit for the magnetization arrow length used to scale the colormap.
cmap : string, optional
String describing the colormap which is used (default is 'cool').
ar_dens: int (optional)
Number defining the arrow density which is plotted. A higher ar_dens number skips more
arrows (a number of 2 plots every second arrow). Default is 1.
mode: string, optional
Mode, determining the glyphs used in the 3D plot. Default is 'arrow', which corresponds
to 3D arrows. For large amounts of arrows, '2darrow' should be used.
coloring : string
Color coding mode of the arrows. Use 'angle' (default) or 'amplitude'.
show_pipeline : boolean, optional
If True, the mayavi pipeline, a GUI used for image manipulation is shown. The default
is False.
Returns
-------
plot : :class:`mayavi.modules.vectors.Vectors`
The plot object.
'''
self._log.debug('Calling quiver_plot3D')
from mayavi import mlab
a = self.a
dim = self.dim
if limit is None:
limit = np.max(self.mag_amp)
ad = ar_dens
# Create points and vector components as lists:
zz, yy, xx = (np.indices(dim)-a/2).reshape((3,)+dim)
zz = zz[::ad, ::ad, ::ad].flatten()
yy = yy[::ad, ::ad, ::ad].flatten()
xx = xx[::ad, ::ad, ::ad].flatten()
x_mag = self.magnitude[0][::ad, ::ad, ::ad].flatten()
y_mag = self.magnitude[1][::ad, ::ad, ::ad].flatten()
z_mag = self.magnitude[2][::ad, ::ad, ::ad].flatten()
# Plot them as vectors:
mlab.figure(size=(750, 700))
plot = mlab.quiver3d(xx, yy, zz, x_mag, y_mag, z_mag, mode=mode, colormap=cmap)
if coloring == 'angle': # Encodes the full angle via colorwheel and saturation
self._log.debug('Encoding full 3D angles')
from tvtk.api import tvtk
rgb = DirectionalColormap.rgb_from_direction(x_mag, y_mag, z_mag)
colors = map(tuple, rgb) # convert to list of tuples!
sc = tvtk.UnsignedCharArray() # Used to hold the colors
sc.from_array(colors)
plot.mlab_source.dataset.point_data.scalars = sc
plot.mlab_source.dataset.modified()
plot.glyph.color_mode = 'color_by_scalar'
elif coloring == 'amplitude': # Encodes the amplitude of the arrows with the jet colormap
self._log.debug('Encoding amplitude')
mlab.colorbar(label_fmt='%.2f')
mlab.colorbar(orientation='vertical')
else:
raise AttributeError('Coloring mode not supported!')
plot.glyph.glyph_source.glyph_position = 'center'
plot.module_manager.vector_lut_manager.data_range = np.array([0, limit])
mlab.outline(plot)
mlab.axes(plot)
mlab.title(title, height=0.95, size=0.35)
mlab.orientation_axes()
if show_pipeline:
mlab.show_pipeline()
return plot