Source code for yt.visualization.plot_window

"""
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> &nbsp;&nbsp;&nbsp;%(xc)0.14f<br> &nbsp;&nbsp;&nbsp;%(yc)0.14f<br> &nbsp;&nbsp;&nbsp;%(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)