"""
A plotting mechanism based on the idea of a "window" into the data.
"""
from __future__ import print_function
#-----------------------------------------------------------------------------
# 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 base64
import numpy as np
import matplotlib
import types
import six
import sys
from distutils.version import LooseVersion
from matplotlib.mathtext import MathTextParser
from numbers import Number
from ._mpl_imports import FigureCanvasAgg
from .image_writer import apply_colormap
from .base_plot_types import ImagePlotMPL
from .fixed_resolution import \
FixedResolutionBuffer, \
ObliqueFixedResolutionBuffer, \
OffAxisProjectionFixedResolutionBuffer
from .plot_modifications import callback_registry
from .plot_container import \
ImagePlotContainer, \
log_transform, linear_transform, symlog_transform, \
get_log_minorticks, get_symlog_minorticks, \
invalidate_data, invalidate_plot, apply_callback
from .base_plot_types import CallbackWrapper
from yt.data_objects.time_series import \
DatasetSeries
from yt.data_objects.image_array import \
ImageArray
from yt.extern.six.moves import \
StringIO
from yt.extern.six import string_types
from yt.funcs import \
mylog, iterable, ensure_list, \
fix_axis, validate_width_tuple, \
fix_unitary
from yt.units.unit_object import \
Unit
from yt.units.unit_registry import \
UnitParseError
from yt.units.unit_lookup_table import \
prefixable_units, latex_prefixes
from yt.units.yt_array import \
YTArray, YTQuantity
from yt.utilities.png_writer import \
write_png_to_string
from yt.utilities.definitions import \
formatted_length_unit_names
from yt.utilities.math_utils import \
ortho_find
from yt.utilities.orientation import \
Orientation
from yt.utilities.exceptions import \
YTUnitNotRecognized, \
YTInvalidWidthError, \
YTCannotParseUnitDisplayName, \
YTUnitConversionError, \
YTPlotCallbackError
# Some magic for dealing with pyparsing being included or not
# included in matplotlib (not in gentoo, yes in everything else)
# Also accounting for the fact that in 1.2.0, pyparsing got renamed.
try:
if LooseVersion(matplotlib.__version__) < LooseVersion("1.2.0"):
from matplotlib.pyparsing import ParseFatalException
else:
if sys.version_info[0] == 3:
from matplotlib.pyparsing_py3 import ParseFatalException
else:
from matplotlib.pyparsing_py2 import ParseFatalException
except ImportError:
from pyparsing import ParseFatalException
def get_window_parameters(axis, center, width, ds):
if ds.geometry in ("cartesian", "spectral_cube"):
width = ds.coordinates.sanitize_width(axis, width, None)
center, display_center = ds.coordinates.sanitize_center(center, axis)
elif ds.geometry in ("polar", "cylindrical"):
# Set our default width to be the full domain
axis_name = ds.coordinates.axis_name[axis]
center, display_center = ds.coordinates.sanitize_center(center, axis)
# Note: regardless of axes, these are set up to give consistent plots
# when plotted, which is not strictly a "right hand rule" for axes.
r_ax, theta_ax, z_ax = (ds.coordinates.axis_id[ax]
for ax in ('r', 'theta', 'z'))
if axis_name == "r": # soup can label
width = [2.0*np.pi * ds.domain_width.uq, ds.domain_width[z_ax]]
elif axis_name == "theta":
width = [ds.domain_right_edge[r_ax], ds.domain_width[z_ax]]
elif axis_name == "z":
width = [2.0*ds.domain_right_edge[r_ax],
2.0*ds.domain_right_edge[r_ax]]
elif ds.geometry == "spherical":
center, display_center = ds.coordinates.sanitize_center(center, axis)
if axis == 0:
# latitude slice
width = ds.arr([2*np.pi, np.pi], "code_length")
elif axis == 1:
width = [2.0*ds.domain_right_edge[0], 2.0*ds.domain_right_edge[0]]
elif axis == 2:
width = [ds.domain_right_edge[0], 2.0*ds.domain_right_edge[0]]
elif ds.geometry == "geographic":
center, display_center = ds.coordinates.sanitize_center(center, axis)
if axis == 0:
width = [2.0*(ds.domain_right_edge[2] + ds.surface_height),
2.0*(ds.domain_right_edge[2] + ds.surface_height)]
elif axis == 1:
width = [(ds.domain_left_edge[2] + ds.domain_width[2] + ds.surface_height),
2.0*(ds.domain_right_edge[2] + ds.surface_height)]
elif axis == 2:
# latitude slice
width = ds.arr([360, 180], "code_length")
else:
raise NotImplementedError
xax = ds.coordinates.x_axis[axis]
yax = ds.coordinates.y_axis[axis]
bounds = (display_center[xax]-width[0] / 2,
display_center[xax]+width[0] / 2,
display_center[yax]-width[1] / 2,
display_center[yax]+width[1] / 2)
return (bounds, center, display_center)
def get_oblique_window_parameters(normal, center, width, ds, depth=None):
display_center, center = ds.coordinates.sanitize_center(center, 4)
width = ds.coordinates.sanitize_width(normal, width, depth)
if len(width) == 2:
# Transforming to the cutting plane coordinate system
center = (center - ds.domain_left_edge)/ds.domain_width - 0.5
(normal,perp1,perp2) = ortho_find(normal)
mat = np.transpose(np.column_stack((perp1,perp2,normal)))
center = np.dot(mat,center)
w = tuple(el.in_units('unitary') for el in width)
bounds = tuple(((2*(i % 2))-1)*w[i//2]/2 for i in range(len(w)*2))
return (bounds, center)
def get_axes_unit(width, ds):
r"""
Infers the axes unit names from the input width specification
"""
if ds.no_cgs_equiv_length:
return ("code_length",)*2
if iterable(width):
if isinstance(width[1], string_types):
axes_unit = (width[1], width[1])
elif iterable(width[1]):
axes_unit = (width[0][1], width[1][1])
elif isinstance(width[0], YTArray):
axes_unit = (str(width[0].units), str(width[1].units))
else:
axes_unit = None
else:
if isinstance(width, YTArray):
axes_unit = (str(width.units), str(width.units))
else:
axes_unit = None
return axes_unit
[docs]class PlotWindow(ImagePlotContainer):
r"""
A ploting mechanism based around the concept of a window into a
data source. It can have arbitrary fields, each of which will be
centered on the same viewpoint, but will have individual zlimits.
The data and plot are updated separately, and each can be
invalidated as the object is modified.
Data is handled by a FixedResolutionBuffer object.
Parameters
----------
data_source : :class:`yt.data_objects.construction_data_containers.YTQuadTreeProjBase` or :class:`yt.data_objects.selection_data_containers.YTSliceBase`
This is the source to be pixelized, which can be a projection or a
slice. (For cutting planes, see
`yt.visualization.fixed_resolution.ObliqueFixedResolutionBuffer`.)
bounds : sequence of floats
Bounds are the min and max in the image plane that we want our
image to cover. It's in the order of (xmin, xmax, ymin, ymax),
where the coordinates are all in the appropriate code units.
buff_size : sequence of ints
The size of the image to generate.
antialias : boolean
This can be true or false. It determines whether or not sub-pixel
rendering is used during data deposition.
window_size : float
The size of the window on the longest axis (in units of inches),
including the margins but not the colorbar.
"""
[docs] def __init__(self, data_source, bounds, buff_size=(800,800), antialias=True,
periodic=True, origin='center-window', oblique=False,
window_size=8.0, fields=None, fontsize=18, aspect=None,
setup=False):
if not hasattr(self, "ds"):
self.ds = data_source.ds
ts = self._initialize_dataset(self.ds)
self.ts = ts
self._axes_unit_names = None
self.center = None
self._periodic = periodic
self.oblique = oblique
self.buff_size = buff_size
self.antialias = antialias
self.aspect = aspect
skip = list(FixedResolutionBuffer._exclude_fields) + data_source._key_fields
if fields is None:
fields = []
else:
fields = ensure_list(fields)
self.override_fields = list(set(fields).intersection(set(skip)))
self.fields = [f for f in fields if f not in skip]
super(PlotWindow, self).__init__(data_source, window_size, fontsize)
self._set_window(bounds) # this automatically updates the data and plot
self.origin = origin
if self.data_source.center is not None and oblique == False:
ax = self.data_source.axis
xax = self.ds.coordinates.x_axis[ax]
yax = self.ds.coordinates.y_axis[ax]
center, display_center = self.ds.coordinates.sanitize_center(
self.data_source.center, ax)
center = [display_center[xax], display_center[yax]]
self.set_center(center)
for field in self.data_source._determine_fields(self.fields):
finfo = self.data_source.ds._get_field_info(*field)
if finfo.take_log:
self._field_transform[field] = log_transform
else:
self._field_transform[field] = linear_transform
self.setup_callbacks()
self._setup_plots()
def _initialize_dataset(self, ts):
if not isinstance(ts, DatasetSeries):
if not iterable(ts): ts = [ts]
ts = DatasetSeries(ts)
return ts
def __iter__(self):
for ds in self.ts:
mylog.warning("Switching to %s", ds)
self._switch_ds(ds)
yield self
[docs] def piter(self, *args, **kwargs):
for ds in self.ts.piter(*args, **kwargs):
self._switch_ds(ds)
yield self
_frb = None
def frb():
doc = "The frb property."
def fget(self):
if self._frb is None:
self._recreate_frb()
return self._frb
def fset(self, value):
self._frb = value
def fdel(self):
del self._frb
self._frb = None
return locals()
frb = property(**frb())
def _recreate_frb(self):
old_fields = None
# If we are regenerating an frb, we want to know what fields we had before
if self._frb is not None:
old_fields = list(self.frb.keys())
old_units = [str(self.frb[of].units) for of in old_fields]
# Set the bounds
if hasattr(self,'zlim'):
bounds = self.xlim+self.ylim+self.zlim
else:
bounds = self.xlim+self.ylim
if self._frb_generator is ObliqueFixedResolutionBuffer:
bounds = np.array([b.in_units('code_length') for b in bounds])
# Generate the FRB
self._frb = self._frb_generator(self.data_source, bounds,
self.buff_size, self.antialias,
periodic=self._periodic)
# At this point the frb has the valid bounds, size, aliasing, etc.
self._data_valid = True
if old_fields is None:
self._frb._get_data_source_fields()
else:
# Restore the old fields
for key, unit in zip(old_fields, old_units):
self._frb[key]
self._frb[key].convert_to_units(unit)
# Restore the override fields
for key in self.override_fields:
self._frb[key]
@property
def width(self):
Wx = self.xlim[1] - self.xlim[0]
Wy = self.ylim[1] - self.ylim[0]
return (Wx, Wy)
@property
def bounds(self):
return self.xlim+self.ylim
@invalidate_data
[docs] def zoom(self, factor):
r"""This zooms the window by *factor*.
Parameters
----------
factor : float
multiplier for the current width
"""
Wx, Wy = self.width
centerx = self.xlim[0] + Wx*0.5
centery = self.ylim[0] + Wy*0.5
nWx, nWy = Wx/factor, Wy/factor
self.xlim = (centerx - nWx*0.5, centerx + nWx*0.5)
self.ylim = (centery - nWy*0.5, centery + nWy*0.5)
return self
@invalidate_data
[docs] def pan(self, deltas):
r"""Pan the image by specifying absolute code unit coordinate deltas.
Parameters
----------
deltas : Two-element sequence of floats, quantities, or (float, unit)
tuples.
(delta_x, delta_y). If a unit is not supplied the unit is assumed
to be code_length.
"""
if len(deltas) != 2:
raise RuntimeError(
"The pan function accepts a two-element sequence.\n"
"Received %s." % (deltas, ))
if isinstance(deltas[0], Number) and isinstance(deltas[1], Number):
deltas = (self.ds.quan(deltas[0], 'code_length'),
self.ds.quan(deltas[1], 'code_length'))
elif isinstance(deltas[0], tuple) and isinstance(deltas[1], tuple):
deltas = (self.ds.quan(deltas[0][0], deltas[0][1]),
self.ds.quan(deltas[1][0], deltas[1][1]))
elif isinstance(deltas[0], YTQuantity) and isinstance(deltas[1], YTQuantity):
pass
else:
raise RuntimeError(
"The arguments of the pan function must be a sequence of floats,\n"
"quantities, or (float, unit) tuples. Received %s." % (deltas, ))
self.xlim = (self.xlim[0] + deltas[0], self.xlim[1] + deltas[0])
self.ylim = (self.ylim[0] + deltas[1], self.ylim[1] + deltas[1])
return self
@invalidate_data
[docs] def pan_rel(self, deltas):
r"""Pan the image by specifying relative deltas, to the FOV.
Parameters
----------
deltas : sequence of floats
(delta_x, delta_y) in *relative* code unit coordinates
"""
Wx, Wy = self.width
self.xlim = (self.xlim[0] + Wx*deltas[0], self.xlim[1] + Wx*deltas[0])
self.ylim = (self.ylim[0] + Wy*deltas[1], self.ylim[1] + Wy*deltas[1])
return self
@invalidate_plot
[docs] def set_unit(self, field, new_unit):
"""Sets a new unit for the requested field
parameters
----------
field : string or field tuple
The name of the field that is to be changed.
new_unit : string or Unit object
The name of the new unit.
"""
field = self.data_source._determine_fields(field)[0]
field = ensure_list(field)
new_unit = ensure_list(new_unit)
if len(field) > 1 and len(new_unit) != len(field):
raise RuntimeError(
"Field list {} and unit "
"list {} are incompatible".format(field, new_unit))
for f, u in zip(field, new_unit):
self.frb[f].convert_to_units(u)
return self
@invalidate_plot
[docs] def set_origin(self, origin):
"""Set the plot origin.
Parameters
----------
origin : string or length 1, 2, or 3 sequence of strings
The location of the origin of the plot coordinate system. This is
represented by '-' separated string or a tuple of strings. In the
first index the y-location is given by 'lower', 'upper', or 'center'.
The second index is the x-location, given as 'left', 'right', or
'center'. Finally, the whether the origin is applied in 'domain'
space, plot 'window' space or 'native' simulation coordinate system
is given. For example, both 'upper-right-domain' and ['upper',
'right', 'domain'] place the origin in the upper right hand
corner of domain space. If x or y are not given, a value is inferred.
For instance, 'left-domain' corresponds to the lower-left hand corner
of the simulation domain, 'center-domain' corresponds to the center
of the simulation domain, or 'center-window' for the center of the
plot window. Further examples:
================================== ============================
format example
================================== ============================
'{space}' 'domain'
'{xloc}-{space}' 'left-window'
'{yloc}-{space}' 'upper-domain'
'{yloc}-{xloc}-{space}' 'lower-right-window'
('{space}',) ('window',)
('{xloc}', '{space}') ('right', 'domain')
('{yloc}', '{space}') ('lower', 'window')
('{yloc}', '{xloc}', '{space}') ('lower', 'right', 'window')
================================== ============================
"""
self.origin = origin
return self
@invalidate_data
def _set_window(self, bounds):
"""Set the bounds of the plot window.
This is normally only called internally, see set_width.
Parameters
----------
bounds : a four element sequence of floats
The x and y bounds, in the format (x0, x1, y0, y1)
"""
if self.center is not None:
dx = bounds[1] - bounds[0]
dy = bounds[3] - bounds[2]
self.xlim = (self.center[0] - dx/2., self.center[0] + dx/2.)
self.ylim = (self.center[1] - dy/2., self.center[1] + dy/2.)
else:
self.xlim = tuple(bounds[0:2])
self.ylim = tuple(bounds[2:4])
if len(bounds) == 6:
self.zlim = tuple(bounds[4:6])
mylog.info("xlim = %f %f" % self.xlim)
mylog.info("ylim = %f %f" % self.ylim)
if hasattr(self,'zlim'):
mylog.info("zlim = %f %f" % self.zlim)
@invalidate_data
[docs] def set_width(self, width, unit = None):
"""set the width of the plot window
parameters
----------
width : float, array of floats, (float, unit) tuple, or tuple of
(float, unit) tuples.
Width can have four different formats to support windows with
variable x and y widths. They are:
================================== =======================
format example
================================== =======================
(float, string) (10,'kpc')
((float, string), (float, string)) ((10,'kpc'),(15,'kpc'))
float 0.2
(float, float) (0.2, 0.3)
================================== =======================
For example, (10, 'kpc') requests a plot window that is 10
kiloparsecs wide in the x and y directions,
((10,'kpc'),(15,'kpc')) requests a window that is 10 kiloparsecs
wide along the x axis and 15 kiloparsecs wide along the y axis.
In the other two examples, code units are assumed, for example
(0.2, 0.3) requests a plot that has an x width of 0.2 and a y
width of 0.3 in code units. If units are provided the resulting
plot axis labels will use the supplied units.
unit : str
the unit the width has been specified in. If width is a tuple, this
argument is ignored. Defaults to code units.
"""
if isinstance(width, Number):
if unit is None:
width = (width, 'code_length')
else:
width = (width, fix_unitary(unit))
axes_unit = get_axes_unit(width, self.ds)
width = self.ds.coordinates.sanitize_width(
self.frb.axis, width, None)
centerx = (self.xlim[1] + self.xlim[0])/2.
centery = (self.ylim[1] + self.ylim[0])/2.
self.xlim = (centerx - width[0]/2, centerx + width[0]/2)
self.ylim = (centery - width[1]/2, centery + width[1]/2)
if hasattr(self,'zlim'):
centerz = (self.zlim[1] + self.zlim[0])/2.
mw = self.ds.arr(width).max()
self.zlim = (centerz - mw/2.,
centerz + mw/2.)
self.set_axes_unit(axes_unit)
return self
@invalidate_data
[docs] def set_center(self, new_center, unit = 'code_length'):
"""Sets a new center for the plot window
parameters
----------
new_center : two element sequence of floats
The coordinates of the new center of the image in the
coordinate system defined by the plot axes. If the unit
keyword is not specified, the coordinates are assumed to
be in code units.
unit : string
The name of the unit new_center is given in. If new_center is a
YTArray or tuple of YTQuantities, this keyword is ignored.
"""
error = RuntimeError(
"\n"
"new_center must be a two-element list or tuple of floats \n"
"corresponding to a coordinate in the plot relative to \n"
"the plot coordinate system.\n"
)
if new_center is None:
self.center = None
elif iterable(new_center):
if len(new_center) != 2:
raise error
for el in new_center:
if not isinstance(el, Number) and not isinstance(el, YTQuantity):
raise error
if isinstance(new_center[0], Number):
new_center = [self.ds.quan(c, unit) for c in new_center]
self.center = new_center
else:
raise error
self._set_window(self.bounds)
return self
@invalidate_data
[docs] def set_antialias(self,aa):
self.antialias = aa
@invalidate_data
[docs] def set_buff_size(self, size):
"""Sets a new buffer size for the fixed resolution buffer
parameters
----------
size : int or two element sequence of ints
The number of data elements in the buffer on the x and y axes.
If a scalar is provided, then the buffer is assumed to be square.
"""
if iterable(size):
self.buff_size = size
else:
self.buff_size = (size, size)
return self
[docs] def set_window_size(self, size):
"""This calls set_figure_size to adjust the size of the plot window.
This is equivalent to set_figure_size but it still available to maintain
backwards compatibility.
"""
self.set_figure_size(size)
return self
@invalidate_plot
[docs] def set_axes_unit(self, unit_name):
r"""Set the unit for display on the x and y axes of the image.
Parameters
----------
unit_name : string or two element tuple of strings
A unit, available for conversion in the dataset, that the
image extents will be displayed in. If set to None, any previous
units will be reset. If the unit is None, the default is chosen.
If unit_name is '1', 'u', or 'unitary', it will not display the
units, and only show the axes name. If unit_name is a tuple, the
first element is assumed to be the unit for the x axis and the
second element the unit for the y axis.
Raises
------
YTUnitNotRecognized
If the unit is not known, this will be raised.
Examples
--------
>>> from yt import load
>>> ds = load("IsolatedGalaxy/galaxy0030/galaxy0030")
>>> p = ProjectionPlot(ds, "y", "Density")
>>> p.set_axes_unit("kpc")
"""
# blind except because it could be in conversion_factors or units
if unit_name is not None:
if isinstance(unit_name, string_types):
unit_name = (unit_name, unit_name)
for un in unit_name:
try:
self.ds.length_unit.in_units(un)
except (YTUnitConversionError, UnitParseError):
raise YTUnitNotRecognized(un)
self._axes_unit_names = unit_name
return self
class PWViewerMPL(PlotWindow):
"""Viewer using matplotlib as a backend via the WindowPlotMPL.
"""
_current_field = None
_frb_generator = None
_plot_type = None
_data_valid = False
def __init__(self, *args, **kwargs):
if self._frb_generator is None:
self._frb_generator = kwargs.pop("frb_generator")
if self._plot_type is None:
self._plot_type = kwargs.pop("plot_type")
self._splat_color = kwargs.pop("splat_color", None)
PlotWindow.__init__(self, *args, **kwargs)
def _setup_origin(self):
origin = self.origin
axis_index = self.data_source.axis
if isinstance(origin, string_types):
origin = tuple(origin.split('-'))[:3]
if 1 == len(origin):
origin = ('lower', 'left') + origin
elif 2 == len(origin) and origin[0] in set(['left','right','center']):
o0map = {'left': 'lower', 'right': 'upper', 'center': 'center'}
origin = (o0map[origin[0]],) + origin
elif 2 == len(origin) and origin[0] in set(['lower','upper','center']):
origin = (origin[0], 'center', origin[-1])
assert origin[-1] in ['window', 'domain', 'native']
if origin[2] == 'window':
xllim, xrlim = self.xlim
yllim, yrlim = self.ylim
elif origin[2] == 'domain':
xax = self.ds.coordinates.x_axis[axis_index]
yax = self.ds.coordinates.y_axis[axis_index]
xllim = self.ds.domain_left_edge[xax]
xrlim = self.ds.domain_right_edge[xax]
yllim = self.ds.domain_left_edge[yax]
yrlim = self.ds.domain_right_edge[yax]
elif origin[2] == 'native':
return (self.ds.quan(0.0, 'code_length'),
self.ds.quan(0.0, 'code_length'))
else:
mylog.warn("origin = {0}".format(origin))
msg = \
('origin keyword "{0}" not recognized, must declare "domain" '
'or "center" as the last term in origin.').format(self.origin)
raise RuntimeError(msg)
if origin[0] == 'lower':
yc = yllim
elif origin[0] == 'upper':
yc = yrlim
elif origin[0] == 'center':
yc = (yllim + yrlim)/2.0
else:
mylog.warn("origin = {0}".format(origin))
msg = ('origin keyword "{0}" not recognized, must declare "lower" '
'"upper" or "center" as the first term in origin.')
msg = msg.format(self.origin)
raise RuntimeError(msg)
if origin[1] == 'left':
xc = xllim
elif origin[1] == 'right':
xc = xrlim
elif origin[1] == 'center':
xc = (xllim + xrlim)/2.0
else:
mylog.warn("origin = {0}".format(origin))
msg = ('origin keyword "{0}" not recognized, must declare "left" '
'"right" or "center" as the second term in origin.')
msg = msg.format(self.origin)
raise RuntimeError(msg)
return xc, yc
def _setup_plots(self):
if self._plot_valid: return
self._colorbar_valid = True
for f in list(set(self.data_source._determine_fields(self.fields))):
axis_index = self.data_source.axis
xc, yc = self._setup_origin()
if self._axes_unit_names is None:
unit = self.ds.get_smallest_appropriate_unit(
self.xlim[1] - self.xlim[0])
(unit_x, unit_y) = (unit, unit)
else:
(unit_x, unit_y) = self._axes_unit_names
# For some plots we may set aspect by hand, such as for spectral cube data.
# This will likely be replaced at some point by the coordinate handler
# setting plot aspect.
if self.aspect is None:
self.aspect = float((self.ds.quan(1.0, unit_y) /
self.ds.quan(1.0, unit_x)).in_cgs())
extentx = [(self.xlim[i] - xc).in_units(unit_x) for i in (0, 1)]
extenty = [(self.ylim[i] - yc).in_units(unit_y) for i in (0, 1)]
extent = extentx + extenty
if f in self.plots.keys():
zlim = (self.plots[f].zmin, self.plots[f].zmax)
else:
zlim = (None, None)
image = self.frb[f]
if self._field_transform[f] == log_transform:
msg = None
use_symlog = False
if zlim != (None, None):
pass
elif np.nanmax(image) == np.nanmin(image):
msg = "Plot image for field %s has zero dynamic " \
"range. Min = Max = %f." % (f, np.nanmax(image))
elif np.nanmax(image) <= 0:
msg = "Plot image for field %s has no positive " \
"values. Max = %f." % (f, np.nanmax(image))
elif not np.any(np.isfinite(image)):
msg = "Plot image for field %s is filled with NaNs." % (f,)
elif np.nanmax(image) > 0. and np.nanmin(image) < 0:
msg = "Plot image for field %s has both positive "\
"and negative values. Min = %f, Max = %f."\
% (f, np.nanmin(image), np.nanmax(image))
use_symlog = True
if msg is not None:
mylog.warning(msg)
if use_symlog:
mylog.warning("Switching to symlog colorbar scaling "\
"unless linear scaling is specified later")
self._field_transform[f] = symlog_transform
self._field_transform[f].func = None
else:
mylog.warning("Switching to linear colorbar scaling.")
self._field_transform[f] = linear_transform
font_size = self._font_properties.get_size()
fig = None
axes = None
cax = None
draw_colorbar = True
draw_axes = True
if f in self.plots:
draw_colorbar = self.plots[f]._draw_colorbar
draw_axes = self.plots[f]._draw_axes
if self.plots[f].figure is not None:
fig = self.plots[f].figure
axes = self.plots[f].axes
cax = self.plots[f].cax
# This is for splatting particle positions with a single
# color instead of a colormap
if self._splat_color is not None:
# make image a rgba array, using the splat color
greyscale_image = self.frb[f]
ia = np.zeros((greyscale_image.shape[0],
greyscale_image.shape[1],
4))
ia[:, :, 3] = 0.0 # set alpha to 0.0
locs = greyscale_image > 0.0
to_rgba = matplotlib.colors.colorConverter.to_rgba
color_tuple = to_rgba(self._splat_color)
ia[locs] = color_tuple
ia = ImageArray(ia)
else:
ia = image
self.plots[f] = WindowPlotMPL(
ia, self._field_transform[f].name,
self._field_transform[f].func,
self._colormaps[f], extent, zlim,
self.figure_size, font_size,
self.aspect, fig, axes, cax)
axes_unit_labels = ['', '']
comoving = False
hinv = False
for i, un in enumerate((unit_x, unit_y)):
unn = None
if hasattr(self.ds.coordinates, "image_units"):
# This *forces* an override
unn = self.ds.coordinates.image_units[axis_index][i]
elif hasattr(self.ds.coordinates, "default_unit_label"):
axax = getattr(self.ds.coordinates,
"%s_axis" % ("xy"[i]))[axis_index]
unn = self.ds.coordinates.default_unit_label.get(axax,
None)
if unn is not None:
axes_unit_labels[i] = r'\ \ \left('+unn+r'\right)'
continue
# Use sympy to factor h out of the unit. In this context 'un'
# is a string, so we call the Unit constructor.
expr = Unit(un, registry=self.ds.unit_registry).expr
h_expr = Unit('h', registry=self.ds.unit_registry).expr
# See http://docs.sympy.org/latest/modules/core.html#sympy.core.expr.Expr
h_power = expr.as_coeff_exponent(h_expr)[1]
# un is now the original unit, but with h factored out.
un = str(expr*h_expr**(-1*h_power))
if str(un).endswith('cm') and un != 'cm':
comoving = True
un = un[:-2]
# no length units besides code_length end in h so this is safe
if h_power == -1:
hinv = True
elif h_power != 0:
# It doesn't make sense to scale a position by anything
# other than h**-1
raise RuntimeError
if un in formatted_length_unit_names:
un = formatted_length_unit_names[un]
pp = un[0]
if pp in latex_prefixes:
symbol_wo_prefix = un[1:]
if symbol_wo_prefix in prefixable_units:
un = un.replace(pp, "{"+latex_prefixes[pp]+"}", 1)
if un not in ['1', 'u', 'unitary']:
if hinv:
un = un + '\,h^{-1}'
if comoving:
un = un + '\,(1+z)^{-1}'
axes_unit_labels[i] = '\ \ ('+un+')'
if self.oblique:
labels = [r'$\rm{Image\ x'+axes_unit_labels[0]+'}$',
r'$\rm{Image\ y'+axes_unit_labels[1]+'}$']
else:
coordinates = self.ds.coordinates
axis_names = coordinates.image_axis_name[axis_index]
xax = coordinates.x_axis[axis_index]
yax = coordinates.y_axis[axis_index]
if hasattr(coordinates, "axis_default_unit_name"):
axes_unit_labels = \
[coordinates.axis_default_unit_name[xax],
coordinates.axis_default_unit_name[yax]]
labels = [r'$\rm{'+axis_names[0]+axes_unit_labels[0] + r'}$',
r'$\rm{'+axis_names[1]+axes_unit_labels[1] + r'}$']
if hasattr(coordinates, "axis_field"):
if xax in coordinates.axis_field:
xmin, xmax = coordinates.axis_field[xax](
0, self.xlim, self.ylim)
else:
xmin, xmax = [float(x) for x in extentx]
if yax in coordinates.axis_field:
ymin, ymax = coordinates.axis_field[yax](
1, self.xlim, self.ylim)
else:
ymin, ymax = [float(y) for y in extenty]
self.plots[f].image.set_extent((xmin, xmax, ymin, ymax))
self.plots[f].axes.set_aspect("auto")
x_label, y_label, colorbar_label = self._get_axes_labels(f)
if x_label is not None:
labels[0] = x_label
if y_label is not None:
labels[1] = y_label
self.plots[f].axes.set_xlabel(labels[0])
self.plots[f].axes.set_ylabel(labels[1])
# Determine the units of the data
units = Unit(self.frb[f].units, registry=self.ds.unit_registry)
units = units.latex_representation()
if colorbar_label is None:
colorbar_label = image.info['label']
if hasattr(self, 'projected'):
colorbar_label = "$\\rm{Projected }$ %s" % colorbar_label
if units is None or units == '':
pass
else:
colorbar_label += r'$\ \ \left('+units+r'\right)$'
parser = MathTextParser('Agg')
try:
parser.parse(colorbar_label)
except ParseFatalException as err:
raise YTCannotParseUnitDisplayName(f, colorbar_label, str(err))
self.plots[f].cb.set_label(colorbar_label)
# x-y axes minorticks
if f not in self._minorticks:
self._minorticks[f] = True
if self._minorticks[f] is True:
self.plots[f].axes.minorticks_on()
else:
self.plots[f].axes.minorticks_off()
# colorbar minorticks
if f not in self._cbar_minorticks:
self._cbar_minorticks[f] = True
if self._cbar_minorticks[f] is True:
if self._field_transform[f] == linear_transform:
self.plots[f].cax.minorticks_on()
else:
vmin = np.float64( self.plots[f].cb.norm.vmin )
vmax = np.float64( self.plots[f].cb.norm.vmax )
if self._field_transform[f] == log_transform:
mticks = self.plots[f].image.norm( get_log_minorticks(vmin, vmax) )
else: # symlog_transform
flinthresh = 10**np.floor( np.log10( self.plots[f].cb.norm.linthresh ) )
mticks = self.plots[f].image.norm( get_symlog_minorticks(flinthresh, vmin, vmax) )
self.plots[f].cax.yaxis.set_ticks(mticks, minor=True)
else:
self.plots[f].cax.minorticks_off()
if draw_axes is False:
self.plots[f]._toggle_axes(draw_axes)
if draw_colorbar is False:
self.plots[f]._toggle_colorbar(draw_colorbar)
self._set_font_properties()
self._plot_valid = True
def setup_callbacks(self):
for key in callback_registry:
ignored = ['PlotCallback']
if self._plot_type.startswith('OffAxis'):
ignored += ['ParticleCallback','ClumpContourCallback',
'GridBoundaryCallback']
if self._plot_type == 'OffAxisProjection':
ignored += ['VelocityCallback','MagFieldCallback',
'QuiverCallback','CuttingQuiverCallback',
'StreamlineCallback']
if self._plot_type == 'Particle':
ignored += ['HopCirclesCallback','HopParticleCallback',
'ParticleCallback','ClumpContourCallback',
'GridBoundaryCallback', 'VelocityCallback',
'MagFieldCallback', 'QuiverCallback',
'CuttingQuiverCallback', 'StreamlineCallback',
'ContourCallback', ]
if key in ignored:
continue
cbname = callback_registry[key]._type_name
CallbackMaker = callback_registry[key]
callback = invalidate_plot(apply_callback(CallbackMaker))
callback.__doc__ = CallbackMaker.__doc__
self.__dict__['annotate_'+cbname] = types.MethodType(callback,self)
def annotate_clear(self, index=None):
"""
Clear callbacks from the plot. If index is not set, clear all
callbacks. If index is set, clear that index (ie 0 is the first one
created, 1 is the 2nd one created, -1 is the last one created, etc.)
"""
if index is None:
self._callbacks = []
else:
del self._callbacks[index]
self.setup_callbacks()
def run_callbacks(self):
for f in self.fields:
keys = self.frb.keys()
for name, (args, kwargs) in self._callbacks:
cbw = CallbackWrapper(self, self.plots[f], self.frb, f,
self._font_properties, self._font_color)
CallbackMaker = callback_registry[name]
callback = CallbackMaker(*args[1:], **kwargs)
try:
callback(cbw)
except Exception as e:
six.reraise(YTPlotCallbackError,
YTPlotCallbackError(callback._type_name, e),
sys.exc_info()[2])
for key in self.frb.keys():
if key not in keys:
del self.frb[key]
def hide_colorbar(self, field=None):
"""
Hides the colorbar for a plot and updates the size of the
plot accordingly. Defaults to operating on all fields for a
PlotWindow object.
Parameters
----------
field : string, field tuple, or list of strings or field tuples (optional)
The name of the field(s) that we want to hide the colorbar. If None
is provided, will default to using all fields available for this
object.
Examples
--------
This will save an image with no colorbar.
>>> import yt
>>> ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
>>> s = SlicePlot(ds, 2, 'density', 'c', (20, 'kpc'))
>>> s.hide_colorbar()
>>> s.save()
This will save an image with no axis or colorbar.
>>> import yt
>>> ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
>>> s = SlicePlot(ds, 2, 'density', 'c', (20, 'kpc'))
>>> s.hide_axes()
>>> s.hide_colorbar()
>>> s.save()
"""
if field is None:
field = self.fields
field = ensure_list(field)
for f in field:
self.plots[f].hide_colorbar()
def show_colorbar(self, field=None):
"""
Shows the colorbar for a plot and updates the size of the
plot accordingly. Defaults to operating on all fields for a
PlotWindow object. See hide_colorbar().
Parameters
----------
field : string, field tuple, or list of strings or field tuples (optional)
The name of the field(s) that we want to show the colorbar.
"""
if field is None:
field = self.fields
field = ensure_list(field)
for f in field:
self.plots[f].show_colorbar()
def hide_axes(self, field=None):
"""
Hides the axes for a plot and updates the size of the
plot accordingly. Defaults to operating on all fields for a
PlotWindow object.
Parameters
----------
field : string, field tuple, or list of strings or field tuples (optional)
The name of the field(s) that we want to hide the axes.
Examples
--------
This will save an image with no axes.
>>> import yt
>>> ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
>>> s = SlicePlot(ds, 2, 'density', 'c', (20, 'kpc'))
>>> s.hide_axes()
>>> s.save()
This will save an image with no axis or colorbar.
>>> import yt
>>> ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
>>> s = SlicePlot(ds, 2, 'density', 'c', (20, 'kpc'))
>>> s.hide_axes()
>>> s.hide_colorbar()
>>> s.save()
"""
if field is None:
field = self.fields
field = ensure_list(field)
for f in field:
self.plots[f].hide_axes()
def show_axes(self, field=None):
"""
Shows the axes for a plot and updates the size of the
plot accordingly. Defaults to operating on all fields for a
PlotWindow object. See hide_axes().
Parameters
----------
field : string, field tuple, or list of strings or field tuples (optional)
The name of the field(s) that we want to show the axes.
"""
if field is None:
field = self.fields
field = ensure_list(field)
for f in field:
self.plots[f].show_axes()
[docs]class AxisAlignedSlicePlot(PWViewerMPL):
r"""Creates a slice plot from a dataset
Given a ds object, an axis to slice along, and a field name
string, this will return a PWViewerMPL object containing
the plot.
The plot can be updated using one of the many helper functions
defined in PlotWindow.
Parameters
----------
ds : `Dataset`
This is the dataset object corresponding to the
simulation output to be plotted.
axis : int or one of 'x', 'y', 'z'
An int corresponding to the axis to slice along (0=x, 1=y, 2=z)
or the axis name itself
fields : string
The name of the field(s) to be plotted.
center : A sequence of floats, a string, or a tuple.
The coordinate of the center of the image. If set to 'c', 'center' or
left blank, the plot is centered on the middle of the domain. If set to
'max' or 'm', the center will be located at the maximum of the
('gas', 'density') field. Centering on the max or min of a specific
field is supported by providing a tuple such as ("min","temperature") or
("max","dark_matter_density"). Units can be specified by passing in *center*
as a tuple containing a coordinate and string unit name or by passing
in a YTArray. If a list or unitless array is supplied, code units are
assumed.
width : tuple or a float.
Width can have four different formats to support windows with variable
x and y widths. They are:
================================== =======================
format example
================================== =======================
(float, string) (10,'kpc')
((float, string), (float, string)) ((10,'kpc'),(15,'kpc'))
float 0.2
(float, float) (0.2, 0.3)
================================== =======================
For example, (10, 'kpc') requests a plot window that is 10 kiloparsecs
wide in the x and y directions, ((10,'kpc'),(15,'kpc')) requests a
window that is 10 kiloparsecs wide along the x axis and 15
kiloparsecs wide along the y axis. In the other two examples, code
units are assumed, for example (0.2, 0.3) requests a plot that has an
x width of 0.2 and a y width of 0.3 in code units. If units are
provided the resulting plot axis labels will use the supplied units.
axes_unit : A string
The name of the unit for the tick labels on the x and y axes.
Defaults to None, which automatically picks an appropriate unit.
If axes_unit is '1', 'u', or 'unitary', it will not display the
units, and only show the axes name.
origin : string or length 1, 2, or 3 sequence of strings
The location of the origin of the plot coordinate system. This is
represented by '-' separated string or a tuple of strings. In the
first index the y-location is given by 'lower', 'upper', or 'center'.
The second index is the x-location, given as 'left', 'right', or
'center'. Finally, whether the origin is applied in 'domain'
space, plot 'window' space or 'native' simulation coordinate system
is given. For example, both 'upper-right-domain' and ['upper',
'right', 'domain'] place the origin in the upper right hand
corner of domain space. If x or y are not given, a value is inferred.
For instance, the default location 'center-window' corresponds to
the center of the plot window, 'left-domain' corresponds to the
lower-left hand corner of the simulation domain, or 'center-domain'
for the center of the simulation domain. Further examples:
================================== ============================
format example
================================== ============================
'{space}' 'domain'
'{xloc}-{space}' 'left-window'
'{yloc}-{space}' 'upper-domain'
'{yloc}-{xloc}-{space}' 'lower-right-window'
('{space}',) ('window',)
('{xloc}', '{space}') ('right', 'domain')
('{yloc}', '{space}') ('lower', 'window')
('{yloc}', '{xloc}', '{space}') ('lower', 'right', 'window')
================================== ============================
fontsize : integer
The size of the fonts for the axis, colorbar, and tick labels.
field_parameters : dictionary
A dictionary of field parameters than can be accessed by derived
fields.
data_source : YTSelectionContainer Object
Object to be used for data selection. Defaults to a region covering
the entire simulation.
Examples
--------
This will save an image in the file 'sliceplot_Density.png'
>>> from yt import load
>>> ds = load('IsolatedGalaxy/galaxy0030/galaxy0030')
>>> p = SlicePlot(ds, 2, 'density', 'c', (20, 'kpc'))
>>> p.save('sliceplot')
"""
_plot_type = 'Slice'
_frb_generator = FixedResolutionBuffer
[docs] def __init__(self, ds, axis, fields, center='c', width=None, axes_unit=None,
origin='center-window', fontsize=18, field_parameters=None,
window_size=8.0, aspect=None, data_source=None):
# this will handle time series data and controllers
ts = self._initialize_dataset(ds)
self.ts = ts
ds = self.ds = ts[0]
axis = fix_axis(axis, ds)
(bounds, center, display_center) = \
get_window_parameters(axis, center, width, ds)
if field_parameters is None:
field_parameters = {}
slc = ds.slice(axis, center[axis], field_parameters=field_parameters,
center=center, data_source=data_source)
slc.get_data(fields)
PWViewerMPL.__init__(self, slc, bounds, origin=origin,
fontsize=fontsize, fields=fields,
window_size=window_size, aspect=aspect)
if axes_unit is None:
axes_unit = get_axes_unit(width, ds)
self.set_axes_unit(axes_unit)
[docs]class ProjectionPlot(PWViewerMPL):
r"""Creates a projection plot from a dataset
Given a ds object, an axis to project along, and a field name
string, this will return a PWViewerMPL object containing
the plot.
The plot can be updated using one of the many helper functions
defined in PlotWindow.
Parameters
----------
ds : `Dataset`
This is the dataset object corresponding to the
simulation output to be plotted.
axis : int or one of 'x', 'y', 'z'
An int corresponding to the axis to slice along (0=x, 1=y, 2=z)
or the axis name itself
fields : string
The name of the field(s) to be plotted.
center : A sequence of floats, a string, or a tuple.
The coordinate of the center of the image. If set to 'c', 'center' or
left blank, the plot is centered on the middle of the domain. If set to
'max' or 'm', the center will be located at the maximum of the
('gas', 'density') field. Centering on the max or min of a specific
field is supported by providing a tuple such as ("min","temperature") or
("max","dark_matter_density"). Units can be specified by passing in *center*
as a tuple containing a coordinate and string unit name or by passing
in a YTArray. If a list or unitless array is supplied, code units are
assumed.
width : tuple or a float.
Width can have four different formats to support windows with variable
x and y widths. They are:
================================== =======================
format example
================================== =======================
(float, string) (10,'kpc')
((float, string), (float, string)) ((10,'kpc'),(15,'kpc'))
float 0.2
(float, float) (0.2, 0.3)
================================== =======================
For example, (10, 'kpc') requests a plot window that is 10 kiloparsecs
wide in the x and y directions, ((10,'kpc'),(15,'kpc')) requests a
window that is 10 kiloparsecs wide along the x axis and 15
kiloparsecs wide along the y axis. In the other two examples, code
units are assumed, for example (0.2, 0.3) requests a plot that has an
x width of 0.2 and a y width of 0.3 in code units. If units are
provided the resulting plot axis labels will use the supplied units.
axes_unit : A string
The name of the unit for the tick labels on the x and y axes.
Defaults to None, which automatically picks an appropriate unit.
If axes_unit is '1', 'u', or 'unitary', it will not display the
units, and only show the axes name.
origin : string or length 1, 2, or 3 sequence of strings
The location of the origin of the plot coordinate system. This is
represented by '-' separated string or a tuple of strings. In the
first index the y-location is given by 'lower', 'upper', or 'center'.
The second index is the x-location, given as 'left', 'right', or
'center'. Finally, whether the origin is applied in 'domain'
space, plot 'window' space or 'native' simulation coordinate system
is given. For example, both 'upper-right-domain' and ['upper',
'right', 'domain'] place the origin in the upper right hand
corner of domain space. If x or y are not given, a value is inferred.
For instance, 'left-domain' corresponds to the lower-left hand corner
of the simulation domain, 'center-domain' corresponds to the center
of the simulation domain, or 'center-window' for the center of the
plot window. Further examples:
================================== ============================
format example
================================== ============================
'{space}' 'domain'
'{xloc}-{space}' 'left-window'
'{yloc}-{space}' 'upper-domain'
'{yloc}-{xloc}-{space}' 'lower-right-window'
('{space}',) ('window',)
('{xloc}', '{space}') ('right', 'domain')
('{yloc}', '{space}') ('lower', 'window')
('{yloc}', '{xloc}', '{space}') ('lower', 'right', 'window')
================================== ============================
data_source : YTSelectionContainer Object
Object to be used for data selection. Defaults to a region covering
the entire simulation.
weight_field : string
The name of the weighting field. Set to None for no weight.
max_level: int
The maximum level to project to.
fontsize : integer
The size of the fonts for the axis, colorbar, and tick labels.
method : string
The method of projection. Valid methods are:
"integrate" with no weight_field specified : integrate the requested
field along the line of sight.
"integrate" with a weight_field specified : weight the requested
field by the weighting field and integrate along the line of sight.
"mip" : pick out the maximum value of the field in the line of sight.
"sum" : This method is the same as integrate, except that it does not
multiply by a path length when performing the integration, and is
just a straight summation of the field along the given axis. WARNING:
This should only be used for uniform resolution grid datasets, as other
datasets may result in unphysical images.
proj_style : string
The method of projection--same as method keyword. Deprecated as of
version 3.0.2. Please use method instead.
window_size : float
The size of the window in inches. Set to 8 by default.
aspect : float
The aspect ratio of the plot. Set to None for 1.
field_parameters : dictionary
A dictionary of field parameters than can be accessed by derived
fields.
Examples
--------
Create a projection plot with a width of 20 kiloparsecs centered on the
center of the simulation box:
>>> from yt import load
>>> ds = load('IsolateGalaxygalaxy0030/galaxy0030')
>>> p = ProjectionPlot(ds, "z", "density", width=(20, "kpc"))
"""
_plot_type = 'Projection'
_frb_generator = FixedResolutionBuffer
[docs] def __init__(self, ds, axis, fields, center='c', width=None, axes_unit=None,
weight_field=None, max_level=None, origin='center-window',
fontsize=18, field_parameters=None, data_source=None,
method = "integrate", proj_style = None, window_size=8.0,
aspect=None):
ts = self._initialize_dataset(ds)
self.ts = ts
ds = self.ds = ts[0]
axis = fix_axis(axis, ds)
# proj_style is deprecated, but if someone specifies then it trumps
# method.
if proj_style is not None:
method = proj_style
# If a non-weighted integral projection, assure field-label reflects that
if weight_field is None and method == "integrate":
self.projected = True
(bounds, center, display_center) = \
get_window_parameters(axis, center, width, ds)
if field_parameters is None: field_parameters = {}
proj = ds.proj(fields, axis, weight_field=weight_field,
center=center, data_source=data_source,
field_parameters = field_parameters, method = method)
PWViewerMPL.__init__(self, proj, bounds, fields=fields, origin=origin,
fontsize=fontsize, window_size=window_size,
aspect=aspect)
if axes_unit is None:
axes_unit = get_axes_unit(width, ds)
self.set_axes_unit(axes_unit)
[docs]class OffAxisSlicePlot(PWViewerMPL):
r"""Creates an off axis slice plot from a dataset
Given a ds object, a normal vector defining a slicing plane, and
a field name string, this will return a PWViewerMPL object
containing the plot.
The plot can be updated using one of the many helper functions
defined in PlotWindow.
Parameters
----------
ds : :class:`yt.data_objects.api.Dataset`
This is the dataset object corresponding to the
simulation output to be plotted.
normal : a sequence of floats
The vector normal to the slicing plane.
fields : string
The name of the field(s) to be plotted.
center : A sequence of floats, a string, or a tuple.
The coordinate of the center of the image. If set to 'c', 'center' or
left blank, the plot is centered on the middle of the domain. If set to
'max' or 'm', the center will be located at the maximum of the
('gas', 'density') field. Centering on the max or min of a specific
field is supported by providing a tuple such as ("min","temperature") or
("max","dark_matter_density"). Units can be specified by passing in *center*
as a tuple containing a coordinate and string unit name or by passing
in a YTArray. If a list or unitless array is supplied, code units are
assumed.
width : tuple or a float.
Width can have four different formats to support windows with variable
x and y widths. They are:
================================== =======================
format example
================================== =======================
(float, string) (10,'kpc')
((float, string), (float, string)) ((10,'kpc'),(15,'kpc'))
float 0.2
(float, float) (0.2, 0.3)
================================== =======================
For example, (10, 'kpc') requests a plot window that is 10 kiloparsecs
wide in the x and y directions, ((10,'kpc'),(15,'kpc')) requests a
window that is 10 kiloparsecs wide along the x axis and 15
kiloparsecs wide along the y axis. In the other two examples, code
units are assumed, for example (0.2, 0.3) requests a plot that has an
x width of 0.2 and a y width of 0.3 in code units. If units are
provided the resulting plot axis labels will use the supplied units.
axes_unit : A string
The name of the unit for the tick labels on the x and y axes.
Defaults to None, which automatically picks an appropriate unit.
If axes_unit is '1', 'u', or 'unitary', it will not display the
units, and only show the axes name.
north-vector : a sequence of floats
A vector defining the 'up' direction in the plot. This
option sets the orientation of the slicing plane. If not
set, an arbitrary grid-aligned north-vector is chosen.
fontsize : integer
The size of the fonts for the axis, colorbar, and tick labels.
field_parameters : dictionary
A dictionary of field parameters than can be accessed by derived
fields.
data_source : YTSelectionContainer Object
Object to be used for data selection. Defaults to a region covering
the entire simulation.
"""
_plot_type = 'OffAxisSlice'
_frb_generator = ObliqueFixedResolutionBuffer
[docs] def __init__(self, ds, normal, fields, center='c', width=None,
axes_unit=None, north_vector=None, fontsize=18,
field_parameters=None, data_source=None):
(bounds, center_rot) = get_oblique_window_parameters(normal,center,width,ds)
if field_parameters is None:
field_parameters = {}
cutting = ds.cutting(normal, center, north_vector=north_vector,
field_parameters=field_parameters,
data_source=data_source)
cutting.get_data(fields)
# Hard-coding the origin keyword since the other two options
# aren't well-defined for off-axis data objects
PWViewerMPL.__init__(self, cutting, bounds, fields=fields,
origin='center-window',periodic=False,
oblique=True, fontsize=fontsize)
if axes_unit is None:
axes_unit = get_axes_unit(width, ds)
self.set_axes_unit(axes_unit)
class OffAxisProjectionDummyDataSource(object):
_type_name = 'proj'
_key_fields = []
def __init__(self, center, ds, normal_vector, width, fields,
interpolated, resolution = (800,800), weight=None,
volume=None, no_ghost=False, le=None, re=None,
north_vector=None, method="integrate"):
self.center = center
self.ds = ds
self.axis = 4 # always true for oblique data objects
self.normal_vector = normal_vector
self.width = width
self.dd = ds.all_data()
fields = self.dd._determine_fields(fields)
self.fields = fields
self.interpolated = interpolated
self.resolution = resolution
self.weight_field = weight
self.volume = volume
self.no_ghost = no_ghost
self.le = le
self.re = re
self.north_vector = north_vector
self.method = method
self.orienter = Orientation(normal_vector, north_vector=north_vector)
def _determine_fields(self, *args):
return self.dd._determine_fields(*args)
[docs]class OffAxisProjectionPlot(PWViewerMPL):
r"""Creates an off axis projection plot from a dataset
Given a ds object, a normal vector to project along, and
a field name string, this will return a PWViewerMPL object
containing the plot.
The plot can be updated using one of the many helper functions
defined in PlotWindow.
Parameters
----------
ds : :class:`yt.data_objects.api.Dataset`
This is the dataset object corresponding to the
simulation output to be plotted.
normal : a sequence of floats
The vector normal to the slicing plane.
fields : string
The name of the field(s) to be plotted.
center : A sequence of floats, a string, or a tuple.
The coordinate of the center of the image. If set to 'c', 'center' or
left blank, the plot is centered on the middle of the domain. If set to
'max' or 'm', the center will be located at the maximum of the
('gas', 'density') field. Centering on the max or min of a specific
field is supported by providing a tuple such as ("min","temperature") or
("max","dark_matter_density"). Units can be specified by passing in *center*
as a tuple containing a coordinate and string unit name or by passing
in a YTArray. If a list or unitless array is supplied, code units are
assumed.
width : tuple or a float.
Width can have four different formats to support windows with variable
x and y widths. They are:
================================== =======================
format example
================================== =======================
(float, string) (10,'kpc')
((float, string), (float, string)) ((10,'kpc'),(15,'kpc'))
float 0.2
(float, float) (0.2, 0.3)
================================== =======================
For example, (10, 'kpc') requests a plot window that is 10 kiloparsecs
wide in the x and y directions, ((10,'kpc'),(15,'kpc')) requests a
window that is 10 kiloparsecs wide along the x axis and 15
kiloparsecs wide along the y axis. In the other two examples, code
units are assumed, for example (0.2, 0.3) requests a plot that has an
x width of 0.2 and a y width of 0.3 in code units. If units are
provided the resulting plot axis labels will use the supplied units.
depth : A tuple or a float
A tuple containing the depth to project through and the string
key of the unit: (width, 'unit'). If set to a float, code units
are assumed
weight_field : string
The name of the weighting field. Set to None for no weight.
max_level: int
The maximum level to project to.
axes_unit : A string
The name of the unit for the tick labels on the x and y axes.
Defaults to None, which automatically picks an appropriate unit.
If axes_unit is '1', 'u', or 'unitary', it will not display the
units, and only show the axes name.
north-vector : a sequence of floats
A vector defining the 'up' direction in the plot. This
option sets the orientation of the slicing plane. If not
set, an arbitrary grid-aligned north-vector is chosen.
fontsize : integer
The size of the fonts for the axis, colorbar, and tick labels.
method : string
The method of projection. Valid methods are:
"integrate" with no weight_field specified : integrate the requested
field along the line of sight.
"integrate" with a weight_field specified : weight the requested
field by the weighting field and integrate along the line of sight.
"sum" : This method is the same as integrate, except that it does not
multiply by a path length when performing the integration, and is
just a straight summation of the field along the given axis. WARNING:
This should only be used for uniform resolution grid datasets, as other
datasets may result in unphysical images.
"""
_plot_type = 'OffAxisProjection'
_frb_generator = OffAxisProjectionFixedResolutionBuffer
[docs] def __init__(self, ds, normal, fields, center='c', width=None,
depth=(1, '1'), axes_unit=None, weight_field=None,
max_level=None, north_vector=None, volume=None, no_ghost=False,
le=None, re=None, interpolated=False, fontsize=18, method="integrate"):
(bounds, center_rot) = \
get_oblique_window_parameters(normal,center,width,ds,depth=depth)
fields = ensure_list(fields)[:]
oap_width = ds.arr((bounds[1] - bounds[0],
bounds[3] - bounds[2],
bounds[5] - bounds[4]))
OffAxisProj = OffAxisProjectionDummyDataSource(
center_rot, ds, normal, oap_width, fields, interpolated,
weight=weight_field, volume=volume, no_ghost=no_ghost,
le=le, re=re, north_vector=north_vector, method=method)
# If a non-weighted, integral projection, assure field-label reflects that
if weight_field is None and OffAxisProj.method == "integrate":
self.projected = True
# Hard-coding the origin keyword since the other two options
# aren't well-defined for off-axis data objects
PWViewerMPL.__init__(
self, OffAxisProj, bounds, fields=fields, origin='center-window',
periodic=False, oblique=True, fontsize=fontsize)
if axes_unit is None:
axes_unit = get_axes_unit(width, ds)
self.set_axes_unit(axes_unit)
def _recreate_frb(self):
super(OffAxisProjectionPlot, self)._recreate_frb()
_metadata_template = """
%(ds)s<br>
<br>
Field of View: %(x_width)0.3f %(axes_unit_names)s<br>
Minimum Value: %(mi)0.3e %(colorbar_unit)s<br>
Maximum Value: %(ma)0.3e %(colorbar_unit)s<br>
Central Point: (data coords)<br>
%(xc)0.14f<br>
%(yc)0.14f<br>
%(zc)0.14f
"""
class PWViewerExtJS(PlotWindow):
"""A viewer for the web interface.
"""
_ext_widget_id = None
_current_field = None
_widget_name = "plot_window"
_frb_generator = FixedResolutionBuffer
_contour_info = None
_vector_info = None
def _setup_plots(self):
from yt.gui.reason.bottle_mods import PayloadHandler
ph = PayloadHandler()
if self._current_field is not None \
and self._ext_widget_id is not None:
fields = [self._current_field]
addl_keys = {'type': 'widget_payload',
'widget_id': self._ext_widget_id}
else:
fields = self.frb.data.keys()
addl_keys = {}
if self._colorbar_valid is False:
addl_keys['colorbar_image'] = self._get_cbar_image()
self._colorbar_valid = True
min_zoom = 200*self.ds.index.get_smallest_dx() * self.ds['unitary']
for field in fields:
to_plot = apply_colormap(self.frb[field],
func = self._field_transform[field],
cmap_name = self._colormaps[field])
pngs = self._apply_modifications(to_plot)
img_data = base64.b64encode(pngs)
# We scale the width between 200*min_dx and 1.0
x_width = self.xlim[1] - self.xlim[0]
zoom_fac = np.log10(x_width*self.ds['unitary'])/np.log10(min_zoom)
zoom_fac = 100.0*max(0.0, zoom_fac)
ticks = self.get_ticks(field)
payload = {'type':'png_string',
'image_data':img_data,
'metadata_string': self.get_metadata(field),
'zoom': zoom_fac,
'ticks': ticks}
payload.update(addl_keys)
ph.add_payload(payload)
def _apply_modifications(self, img):
if self._contour_info is None and self._vector_info is None:
return write_png_to_string(img)
from matplotlib.figure import Figure
vi, vj, vn = img.shape
# Now we need to get our field values
fig = Figure((vi/100.0, vj/100.0), dpi = 100)
fig.figimage(img)
# Add our contour
ax = fig.add_axes([0.0, 0.0, 1.0, 1.0], frameon=False)
ax.patch.set_alpha(0.0)
# Now apply our modifications
self._apply_contours(ax, vi, vj)
self._apply_vectors(ax, vi, vj)
canvas = FigureCanvasAgg(fig)
f = StringIO()
canvas.print_figure(f)
f.seek(0)
img = f.read()
return img
def _apply_contours(self, ax, vi, vj):
if self._contour_info is None: return
plot_args = {}
field, number, colors, logit = self._contour_info
if colors is not None: plot_args['colors'] = colors
raw_data = self.frb.data_source
b = self.frb.bounds
xi, yi = np.mgrid[b[0]:b[1]:(vi / 8) * 1j,
b[2]:b[3]:(vj / 8) * 1j]
x = raw_data['px']
y = raw_data['py']
z = raw_data[field]
if logit: z = np.log10(z)
if LooseVersion(matplotlib.__version__) < LooseVersion("1.4.0"):
from matplotlib.delaunay.triangulate import Triangulation as triang
fvals = triang(x,y).nn_interpolator(z)(xi,yi).transpose()[::-1,:]
else:
from matplotlib.tri import Triangulation, LinearTriInterpolator
t = Triangulation(x, y)
fvals = LinearTriInterpolator(t, z)(xi, yi).transpose()[::-1,:]
ax.contour(fvals, number, colors='w')
def _apply_vectors(self, ax, vi, vj):
if self._vector_info is None: return
skip, scale = self._vector_info
nx = self.frb.buff_size[0]/skip
ny = self.frb.buff_size[1]/skip
new_frb = FixedResolutionBuffer(self.frb.data_source,
self.frb.bounds, (nx,ny))
axis = self.frb.data_source.axis
xax = self.frb.data_source.ds.coordinates.x_axis[axis]
yax = self.frb.data_source.ds.coordinates.y_axis[axis]
axis_names = self.frb.data_source.ds.coordinates.axis_name
fx = "velocity_%s" % (axis_names[xax])
fy = "velocity_%x" % (axis_names[yax])
px = new_frb[fx][::-1,:]
py = new_frb[fy][::-1,:]
x = np.mgrid[0:vi-1:ny*1j]
y = np.mgrid[0:vj-1:nx*1j]
# Always normalize, then we scale
nn = ((px**2.0 + py**2.0)**0.5).max()
px /= nn
py /= nn
print(scale, px.min(), px.max(), py.min(), py.max())
ax.quiver(x, y, px, py, scale=float(vi)/skip)
def get_ticks(self, field, height = 400):
# This will eventually change to work with non-logged fields
ticks = []
transform = self._field_transform[field]
mi, ma = self.frb[field].min(), self.frb[field].max()
tick_locs = transform.ticks(mi, ma)
mi, ma = transform((mi, ma))
for v1,v2 in zip(tick_locs, transform(tick_locs)):
if v2 < mi or v2 > ma: continue
p = height - height * (v2 - mi)/(ma - mi)
ticks.append((p,v1,v2))
return ticks
def _get_cbar_image(self, height = 400, width = 40, field = None):
if field is None: field = self._current_field
cmap_name = self._colormaps[field]
vals = np.mgrid[1:0:height * 1j] * np.ones(width)[:,None]
vals = vals.transpose()
to_plot = apply_colormap(vals, cmap_name = cmap_name)
pngs = write_png_to_string(to_plot)
img_data = base64.b64encode(pngs)
return img_data
# This calls an invalidation routine from within
def scroll_zoom(self, value):
# We accept value from 0..100, and assume it has been set from the
# scroll bar. In that case, we undo the logic for calcualting
# 'zoom_fac' from above.
min_val = 200*self.ds.index.get_smallest_dx()
unit = self.ds['unitary']
width = (min_val**(value/100.0))/unit
self.set_width(width)
def image_recenter(self, img_x, img_y, img_size_x, img_size_y):
dx = (self.xlim[1] - self.xlim[0]) / img_size_x
dy = (self.ylim[1] - self.ylim[0]) / img_size_y
new_x = img_x * dx + self.xlim[0]
new_y = img_y * dy + self.ylim[0]
print(img_x, img_y, dx, dy, new_x, new_y)
self.set_center((new_x, new_y))
def get_field_units(self, field, strip_mathml = True):
source = self.data_source
field = self._check_field(field)
finfo = source.ds._get_field_info(*field)
if source._type_name in ("slice", "cutting"):
units = finfo.get_units()
elif source._type_name == "proj":
if source.weight_field is not None or source.method in ("mip", "sum"):
units = finfo.get_units()
else:
units = finfo.get_projected_units()
else:
units = ""
if strip_mathml:
units = units.replace(r"\rm{", "").replace("}","")
return units
def get_metadata(self, field, strip_mathml = True, return_string = True):
fval = self.frb[field]
mi = fval.min()
ma = fval.max()
x_width = self.xlim[1] - self.xlim[0]
y_width = self.ylim[1] - self.ylim[0]
if self._axes_unit_names is None:
unit = self.ds.get_smallest_appropriate_unit(x_width)
unit = (unit, unit)
else:
unit = self._axes_unit_names
units = self.get_field_units(field, strip_mathml)
center = getattr(self.frb.data_source, "center", None)
xax = self.ds.coordinates.x_axis[self.frb.axis]
yax = self.ds.coordinates.y_axis[self.frb.axis]
if center is None or self.frb.axis == 4:
xc, yc, zc = -999, -999, -999
else:
center[xax] = 0.5 * (
self.xlim[0] + self.xlim[1])
center[yax] = 0.5 * (
self.ylim[0] + self.ylim[1])
xc, yc, zc = center
if return_string:
md = _metadata_template % dict(
ds = self.ds,
x_width = x_width*self.ds[unit[0]],
y_width = y_width*self.ds[unit[1]],
axes_unit_names = unit[0], colorbar_unit = units,
mi = mi, ma = ma, xc = xc, yc = yc, zc = zc)
else:
md = dict(ds = self.ds,
x_width = x_width*self.ds[unit[0]],
y_width = y_width*self.ds[unit[1]],
axes_unit_names = unit, colorbar_unit = units,
mi = mi, ma = ma, xc = xc, yc = yc, zc = zc)
return md
@invalidate_plot
def set_contour_info(self, field_name, n_cont = 8, colors = None,
logit = True):
if field_name == "None" or n_cont == 0:
self._contour_info = None
return
self._contour_info = (field_name, n_cont, colors, logit)
@invalidate_plot
def set_vector_info(self, skip, scale = 1):
self._vector_info = (skip, scale)
@invalidate_data
def set_current_field(self, field):
field = self._check_field(field)
self._current_field = field
self.frb[field]
finfo = self.data_source.ds._get_field_info(*field)
if finfo.take_log:
self._field_transform[field] = log_transform
else:
self._field_transform[field] = linear_transform
[docs]class WindowPlotMPL(ImagePlotMPL):
"""A container for a single PlotWindow matplotlib figure and axes"""
[docs] def __init__(self, data, cbname, cblinthresh, cmap, extent, zlim, figure_size,
fontsize, aspect, figure, axes, cax):
self._draw_colorbar = True
self._draw_axes = True
self._fontsize = fontsize
self._figure_size = figure_size
# Compute layout
fontscale = float(fontsize) / 18.0
if fontscale < 1.0:
fontscale = np.sqrt(fontscale)
if iterable(figure_size):
fsize = figure_size[0]
else:
fsize = figure_size
self._cb_size = 0.0375*fsize
self._ax_text_size = [1.2*fontscale, 0.9*fontscale]
self._top_buff_size = 0.30*fontscale
self._aspect = ((extent[1] - extent[0])/(extent[3] - extent[2])).in_cgs()
self._unit_aspect = aspect
size, axrect, caxrect = self._get_best_layout()
super(WindowPlotMPL, self).__init__(
size, axrect, caxrect, zlim, figure, axes, cax)
self._init_image(data, cbname, cblinthresh, cmap, extent, aspect)
self.image.axes.ticklabel_format(scilimits=(-2, 3))
if cbname == 'linear':
self.cb.formatter.set_scientific(True)
self.cb.formatter.set_powerlimits((-2, 3))
self.cb.update_ticks()
[docs]def SlicePlot(ds, normal=None, fields=None, axis=None, *args, **kwargs):
r"""
A factory function for
:class:`yt.visualization.plot_window.AxisAlignedSlicePlot`
and :class:`yt.visualization.plot_window.OffAxisSlicePlot` objects. This
essentially allows for a single entry point to both types of slice plots,
the distinction being determined by the specified normal vector to the
slice.
The returned plot object can be updated using one of the many helper
functions defined in PlotWindow.
Parameters
----------
ds : :class:`yt.data_objects.api.Dataset`
This is the dataset object corresponding to the
simulation output to be plotted.
normal : int or one of 'x', 'y', 'z', or sequence of floats
This specifies the normal vector to the slice. If given as an integer
or a coordinate string (0=x, 1=y, 2=z), this function will return an
:class:`AxisAlignedSlicePlot` object. If given as a sequence of floats,
this is interpretted as an off-axis vector and an
:class:`OffAxisSlicePlot` object is returned.
fields : string
The name of the field(s) to be plotted.
axis : int or one of 'x', 'y', 'z'
An int corresponding to the axis to slice along (0=x, 1=y, 2=z)
or the axis name itself. If specified, this will replace normal.
The following are nominally keyword arguments passed onto the respective
slice plot objects generated by this function.
center : A sequence floats, a string, or a tuple.
The coordinate of the center of the image. If set to 'c', 'center' or
left blank, the plot is centered on the middle of the domain. If set to
'max' or 'm', the center will be located at the maximum of the
('gas', 'density') field. Centering on the max or min of a specific
field is supported by providing a tuple such as ("min","temperature") or
("max","dark_matter_density"). Units can be specified by passing in *center*
as a tuple containing a coordinate and string unit name or by passing
in a YTArray. If a list or unitless array is supplied, code units are
assumed.
width : tuple or a float.
Width can have four different formats to support windows with variable
x and y widths. They are:
================================== =======================
format example
================================== =======================
(float, string) (10,'kpc')
((float, string), (float, string)) ((10,'kpc'),(15,'kpc'))
float 0.2
(float, float) (0.2, 0.3)
================================== =======================
For example, (10, 'kpc') requests a plot window that is 10 kiloparsecs
wide in the x and y directions, ((10,'kpc'),(15,'kpc')) requests a
window that is 10 kiloparsecs wide along the x axis and 15
kiloparsecs wide along the y axis. In the other two examples, code
units are assumed, for example (0.2, 0.3) requests a plot that has an
x width of 0.2 and a y width of 0.3 in code units. If units are
provided the resulting plot axis labels will use the supplied units.
axes_unit : A string
The name of the unit for the tick labels on the x and y axes.
Defaults to None, which automatically picks an appropriate unit.
If axes_unit is '1', 'u', or 'unitary', it will not display the
units, and only show the axes name.
origin : string or length 1, 2, or 3 sequence of strings
The location of the origin of the plot coordinate system for
`AxisAlignedSlicePlot` objects; for `OffAxisSlicePlot` objects,
this parameter is discarded. This is represented by '-' separated
string or a tuple of strings. In the first index the y-location is
given by 'lower', 'upper', or 'center'. The second index is the
x-location, given as 'left', 'right', or 'center'. Finally,
whether the origin is applied in 'domain' space, plot 'window' space
or 'native' simulation coordinate system is given. For example, both
'upper-right-domain' and ['upper', 'right', 'domain'] place the
origin in the upper right hand corner of domain space. If x or y are
not given, a value is inferred. For instance, 'left-domain'
corresponds to the lower-left hand corner of the simulation domain,
'center-domain' corresponds to the center of the simulation domain,
or 'center-window' for the center of the plot window. Further
examples:
================================== ============================
format example
================================== ============================
'{space}' 'domain'
'{xloc}-{space}' 'left-window'
'{yloc}-{space}' 'upper-domain'
'{yloc}-{xloc}-{space}' 'lower-right-window'
('{space}',) ('window',)
('{xloc}', '{space}') ('right', 'domain')
('{yloc}', '{space}') ('lower', 'window')
('{yloc}', '{xloc}', '{space}') ('lower', 'right', 'window')
================================== ============================
north-vector : a sequence of floats
A vector defining the 'up' direction in the `OffAxisSlicePlot`; not
used in `AxisAlignedSlicePlot`. This option sets the orientation of the
slicing plane. If not set, an arbitrary grid-aligned north-vector is
chosen.
fontsize : integer
The size of the fonts for the axis, colorbar, and tick labels.
field_parameters : dictionary
A dictionary of field parameters than can be accessed by derived
fields.
data_source : YTSelectionContainer Object
Object to be used for data selection. Defaults to a region covering
the entire simulation.
Raises
------
AssertionError
If a proper normal axis is not specified via the normal or axis
keywords, and/or if a field to plot is not specified.
Examples
--------
>>> from yt import load
>>> ds = load("IsolatedGalaxy/galaxy0030/galaxy0030")
>>> slc = SlicePlot(ds, "x", "density", center=[0.2,0.3,0.4])
>>>
>>> slc = SlicePlot(ds, [0.4, 0.2, -0.1], "pressure",
... north_vector=[0.2,-0.3,0.1])
"""
# Make sure we are passed a normal
# we check the axis keyword for backwards compatibility
if normal is None: normal = axis
if normal is None:
raise AssertionError("Must pass a normal vector to the slice!")
# to keep positional ordering we had to make fields a keyword; make sure
# it is present
if fields is None:
raise AssertionError("Must pass field(s) to plot!")
# use an AxisAlignedSlicePlot where possible, e.g.:
# maybe someone passed normal=[0,0,0.2] when they should have just used "z"
if iterable(normal) and not isinstance(normal, string_types):
if np.count_nonzero(normal) == 1:
normal = ("x","y","z")[np.nonzero(normal)[0][0]]
else:
normal = np.array(normal, dtype='float64')
np.divide(normal, np.dot(normal,normal), normal)
# by now the normal should be properly set to get either a On/Off Axis plot
if iterable(normal) and not isinstance(normal, string_types):
# OffAxisSlicePlot has hardcoded origin; remove it if in kwargs
if 'origin' in kwargs:
msg = "Ignoring 'origin' keyword as it is ill-defined for " \
"an OffAxisSlicePlot object."
mylog.warn(msg)
del kwargs['origin']
return OffAxisSlicePlot(ds, normal, fields, *args, **kwargs)
else:
# north_vector not used in AxisAlignedSlicePlots; remove it if in kwargs
if 'north_vector' in kwargs:
msg = "Ignoring 'north_vector' keyword as it is ill-defined for " \
"an AxisAlignedSlicePlot object."
mylog.warn(msg)
del kwargs['north_vector']
return AxisAlignedSlicePlot(ds, normal, fields, *args, **kwargs)