Source code for yt.visualization.volume_rendering.transfer_function_helper

A helper class to build, display, and modify transfer functions for volume


# Copyright (c) 2013, yt Development Team.
# Distributed under the terms of the Modified BSD License.
# The full license is in the file COPYING.txt, distributed with this software.

import matplotlib
import numpy as np

from distutils.version import LooseVersion

from yt.funcs import mylog
from yt.data_objects.profiles import create_profile
from yt.visualization.volume_rendering.transfer_functions import \
from yt.extern.six import BytesIO

[docs]class TransferFunctionHelper(object): r"""A transfer function helper. This attempts to help set up a good transfer function by finding bounds, handling linear/log options, and displaying the transfer function combined with 1D profiles of rendering quantity. Parameters ---------- ds: A Dataset instance A static output that is currently being rendered. This is used to help set up data bounds. Notes ----- """ profiles = None def __init__(self, ds): self.ds = ds self.field = None self.log = False = None self.bounds = None self.grey_opacity = False self.profiles = {}
[docs] def set_bounds(self, bounds=None): """ Set the bounds of the transfer function. Parameters ---------- bounds: array-like, length 2, optional A length 2 list/array in the form [min, max]. These should be the raw values and not the logarithm of the min and max. If bounds is None, the bounds of the data are calculated from all of the data in the dataset. This can be slow for very large datasets. """ if bounds is None: bounds = self.ds.h.all_data().quantities['Extrema'](self.field, non_zero=True) bounds = [b.ndarray_view() for b in bounds] self.bounds = bounds # Do some error checking. assert(len(self.bounds) == 2) if self.log: assert(self.bounds[0] > 0.0) assert(self.bounds[1] > 0.0) return
[docs] def set_field(self, field): """ Set the field to be rendered Parameters ---------- field: string The field to be rendered. """ if field != self.field: self.log = self.ds._get_field_info(field).take_log self.field = field
[docs] def set_log(self, log): """ Set whether or not the transfer function should be in log or linear space. Also modifies the ds.field_info[field].take_log attribute to stay in sync with this setting. Parameters ---------- log: boolean Sets whether the transfer function should use log or linear space. """ self.log = log
[docs] def build_transfer_function(self): """ Builds the transfer function according to the current state of the TransferFunctionHelper. Parameters ---------- None Returns ------- A ColorTransferFunction object. """ if self.bounds is None:'Calculating data bounds. This may take a while.' + ' Set the TranferFunctionHelper.bounds to avoid this.') self.set_bounds() if self.log: mi, ma = np.log10(self.bounds[0]), np.log10(self.bounds[1]) else: mi, ma = self.bounds = ColorTransferFunction((mi, ma), grey_opacity=self.grey_opacity, nbins=512) return
[docs] def setup_default(self): """Setup a default colormap Creates a ColorTransferFunction including 10 gaussian layers whose colors smaple the 'spectral' colormap. Also attempts to scale the transfer function to produce a natural contrast ratio. """ if LooseVersion(matplotlib.__version__) < LooseVersion('2.0.0'): colormap_name = 'spectral' else: colormap_name = 'nipy_spectral', colormap=colormap_name) factor =[-1].y.size /[-1].y.sum()[-1].y *= 2*factor
[docs] def plot(self, fn=None, profile_field=None, profile_weight=None): """ Save the current transfer function to a bitmap, or display it inline. Parameters ---------- fn: string, optional Filename to save the image to. If None, the returns an image to an IPython session. Returns ------- If fn is None, will return an image to an IPython notebook. """ from yt.visualization._mpl_imports import FigureCanvasAgg from matplotlib.figure import Figure if is None: self.build_transfer_function() self.setup_default() tf = if self.log: xfunc = np.logspace xmi, xma = np.log10(self.bounds[0]), np.log10(self.bounds[1]) else: xfunc = np.linspace # Need to strip units off of the bounds to avoid a recursion error # in matplotlib 1.3.1 xmi, xma = [np.float64(b) for b in self.bounds] x = xfunc(xmi, xma, tf.nbins) y = tf.funcs[3].y w = np.append(x[1:]-x[:-1], x[-1]-x[-2]) colors = np.array([tf.funcs[0].y, tf.funcs[1].y, tf.funcs[2].y, np.ones_like(x)]).T fig = Figure(figsize=[6, 3]) canvas = FigureCanvasAgg(fig) ax = fig.add_axes([0.2, 0.2, 0.75, 0.75]), tf.funcs[3].y, w, edgecolor=[0.0, 0.0, 0.0, 0.0], log=self.log, color=colors, bottom=[0]) if profile_field is not None: try: prof = self.profiles[self.field] except KeyError: self.setup_profile(profile_field, profile_weight) prof = self.profiles[self.field] try: prof[profile_field] except KeyError: prof.add_fields([profile_field]) # Strip units, if any, for matplotlib 1.3.1 xplot = np.array(prof.x) yplot = np.array(prof[profile_field]*tf.funcs[3].y.max() / prof[profile_field].max()) ax.plot(xplot, yplot, color='w', linewidth=3) ax.plot(xplot, yplot, color='k') ax.set_xscale({True: 'log', False: 'linear'}[self.log]) ax.set_xlim(x.min(), x.max()) ax.set_xlabel(self.ds._get_field_info(self.field).get_label()) ax.set_ylabel(r'$\mathrm{alpha}$') ax.set_ylim(y.max()*1.0e-3, y.max()*2) if fn is None: from IPython.core.display import Image f = BytesIO() canvas.print_figure(f) img = return Image(img) else: fig.savefig(fn)
[docs] def setup_profile(self, profile_field=None, profile_weight=None): if profile_field is None: profile_field = 'cell_volume' prof = create_profile(self.ds.all_data(), self.field, profile_field, n_bins=128, extrema={self.field: self.bounds}, weight_field=profile_weight, logs = {self.field: self.log}) self.profiles[self.field] = prof return