# Licensed under a 3-clause BSD style license - see LICENSE.rst from __future__ import print_function, division, absolute_import """ This file defines the classes used to represent a 'coordinate', which includes axes, ticks, tick labels, and grid lines. """ import numpy as np from matplotlib.ticker import Formatter from matplotlib.transforms import Affine2D, ScaledTranslation from matplotlib.patches import PathPatch from matplotlib import rcParams from ... import units as u from ...extern import six from .formatter_locator import AngleFormatterLocator, ScalarFormatterLocator from .ticks import Ticks from .ticklabels import TickLabels from .axislabels import AxisLabels from .grid_paths import get_lon_lat_path, get_gridline_path __all__ = ['CoordinateHelper'] def wrap_angle_at(values, coord_wrap): # On ARM processors, np.mod emits warnings if there are NaN values in the # array, although this doesn't seem to happen on other processors. with np.errstate(invalid='ignore'): return np.mod(values - coord_wrap, 360.) - (360. - coord_wrap) class CoordinateHelper(object): """ Helper class to control one of the coordinates in the :class:`~astropy.visualization.wcsaxes.WCSAxes`. Parameters ---------- parent_axes : :class:`~astropy.visualization.wcsaxes.WCSAxes` The axes the coordinate helper belongs to. parent_map : :class:`~astropy.visualization.wcsaxes.CoordinatesMap` The :class:`~astropy.visualization.wcsaxes.CoordinatesMap` object this coordinate belongs to. transform : `~matplotlib.transforms.Transform` The transform corresponding to this coordinate system. coord_index : int The index of this coordinate in the :class:`~astropy.visualization.wcsaxes.CoordinatesMap`. coord_type : {'longitude', 'latitude', 'scalar'} The type of this coordinate, which is used to determine the wrapping and boundary behavior of coordinates. Longitudes wrap at ``coord_wrap``, latitudes have to be in the range -90 to 90, and scalars are unbounded and do not wrap. coord_unit : `~astropy.units.Unit` The unit that this coordinate is in given the output of transform. coord_wrap : float The angle at which the longitude wraps (defaults to 360) frame : `~astropy.visualization.wcsaxes.frame.BaseFrame` The frame of the :class:`~astropy.visualization.wcsaxes.WCSAxes`. """ def __init__(self, parent_axes=None, parent_map=None, transform=None, coord_index=None, coord_type='scalar', coord_unit=None, coord_wrap=None, frame=None): # Keep a reference to the parent axes and the transform self.parent_axes = parent_axes self.parent_map = parent_map self.transform = transform self.coord_index = coord_index self.coord_unit = coord_unit self.frame = frame self.set_coord_type(coord_type, coord_wrap) # Initialize ticks self.dpi_transform = Affine2D() self.offset_transform = ScaledTranslation(0, 0, self.dpi_transform) self.ticks = Ticks(transform=parent_axes.transData + self.offset_transform) # Initialize tick labels self.ticklabels = TickLabels(self.frame, transform=None, # display coordinates figure=parent_axes.get_figure()) self.ticks.display_minor_ticks(False) self.minor_frequency = 5 # Initialize axis labels self.axislabels = AxisLabels(self.frame, transform=None, # display coordinates figure=parent_axes.get_figure()) # Initialize container for the grid lines self.grid_lines = [] # Initialize grid style. Take defaults from matplotlib.rcParams. # Based on matplotlib.axis.YTick._get_gridline. # # Matplotlib's gridlines use Line2D, but ours use PathPatch. # Patches take a slightly different format of linestyle argument. lines_to_patches_linestyle = {'-': 'solid', '--': 'dashed', '-.': 'dashdot', ':': 'dotted', 'none': 'none', 'None': 'none', ' ': 'none', '': 'none'} self.grid_lines_kwargs = {'visible': False, 'facecolor': 'none', 'edgecolor': rcParams['grid.color'], 'linestyle': lines_to_patches_linestyle[rcParams['grid.linestyle']], 'linewidth': rcParams['grid.linewidth'], 'alpha': rcParams.get('grid.alpha', 1.0), 'transform': self.parent_axes.transData} def grid(self, draw_grid=True, grid_type='lines', **kwargs): """ Plot grid lines for this coordinate. Standard matplotlib appearance options (color, alpha, etc.) can be passed as keyword arguments. Parameters ---------- draw_grid : bool Whether to show the gridlines grid_type : { 'lines' | 'contours' } Whether to plot the contours by determining the grid lines in world coordinates and then plotting them in world coordinates (``'lines'``) or by determining the world coordinates at many positions in the image and then drawing contours (``'contours'``). The first is recommended for 2-d images, while for 3-d (or higher dimensional) cubes, the ``'contours'`` option is recommended. """ if grid_type in ('lines', 'contours'): self._grid_type = grid_type else: raise ValueError("grid_type should be 'lines' or 'contours'") if 'color' in kwargs: kwargs['edgecolor'] = kwargs.pop('color') self.grid_lines_kwargs.update(kwargs) if self.grid_lines_kwargs['visible']: if not draw_grid: self.grid_lines_kwargs['visible'] = False else: self.grid_lines_kwargs['visible'] = True def set_coord_type(self, coord_type, coord_wrap=None): """ Set the coordinate type for the axis. Parameters ---------- coord_type : str One of 'longitude', 'latitude' or 'scalar' coord_wrap : float, optional The value to wrap at for angular coordinates """ self.coord_type = coord_type if coord_type == 'longitude' and coord_wrap is None: self.coord_wrap = 360 elif coord_type != 'longitude' and coord_wrap is not None: raise NotImplementedError('coord_wrap is not yet supported ' 'for non-longitude coordinates') else: self.coord_wrap = coord_wrap # Initialize tick formatter/locator if coord_type == 'scalar': self._coord_unit_scale = None self._formatter_locator = ScalarFormatterLocator(unit=self.coord_unit) elif coord_type in ['longitude', 'latitude']: if self.coord_unit is u.deg: self._coord_unit_scale = None else: self._coord_unit_scale = self.coord_unit.to(u.deg) self._formatter_locator = AngleFormatterLocator() else: raise ValueError("coord_type should be one of 'scalar', 'longitude', or 'latitude'") def set_major_formatter(self, formatter): """ Set the formatter to use for the major tick labels. Parameters ---------- formatter : str or Formatter The format or formatter to use. """ if isinstance(formatter, Formatter): raise NotImplementedError() # figure out how to swap out formatter elif isinstance(formatter, six.string_types): self._formatter_locator.format = formatter else: raise TypeError("formatter should be a string or a Formatter " "instance") def format_coord(self, value): """ Given the value of a coordinate, will format it according to the format of the formatter_locator. """ if not hasattr(self, "_fl_spacing"): return "" # _update_ticks has not been called yet fl = self._formatter_locator if isinstance(fl, AngleFormatterLocator): # Convert to degrees if needed if self._coord_unit_scale is not None: value *= self._coord_unit_scale if self.coord_type == 'longitude': value = wrap_angle_at(value, self.coord_wrap) value = value * u.degree value = value.to(fl._unit).value spacing = self._fl_spacing string = fl.formatter(values=[value] * fl._unit, spacing=spacing) return string[0] def set_separator(self, separator): """ Set the separator to use for the angle major tick labels. Parameters ---------- separator : The separator between numbers in sexagesimal representation. Can be either a string or a tuple. """ if not (self._formatter_locator.__class__ == AngleFormatterLocator): raise TypeError("Separator can only be specified for angle coordinates") if isinstance(separator, six.string_types) or isinstance(separator, tuple): self._formatter_locator.sep = separator else: raise TypeError("separator should be a string or a tuple") def set_format_unit(self, unit): """ Set the unit for the major tick labels. Parameters ---------- unit : class:`~astropy.units.Unit` The unit to which the tick labels should be converted to. """ if not issubclass(unit.__class__, u.UnitBase): raise TypeError("unit should be an astropy UnitBase subclass") self._formatter_locator.format_unit = unit def set_ticks(self, values=None, spacing=None, number=None, size=None, width=None, color=None, alpha=None, exclude_overlapping=False): """ Set the location and properties of the ticks. At most one of the options from ``values``, ``spacing``, or ``number`` can be specified. Parameters ---------- values : iterable, optional The coordinate values at which to show the ticks. spacing : float, optional The spacing between ticks. number : float, optional The approximate number of ticks shown. size : float, optional The length of the ticks in points color : str or tuple A valid Matplotlib color for the ticks exclude_overlapping : bool, optional Whether to exclude tick labels that overlap over each other. """ if sum([values is None, spacing is None, number is None]) < 2: raise ValueError("At most one of values, spacing, or number should " "be specified") if values is not None: self._formatter_locator.values = values elif spacing is not None: self._formatter_locator.spacing = spacing elif number is not None: self._formatter_locator.number = number if size is not None: self.ticks.set_ticksize(size) if width is not None: self.ticks.set_linewidth(width) if color is not None: self.ticks.set_color(color) if alpha is not None: self.ticks.set_alpha(alpha) self.ticklabels.set_exclude_overlapping(exclude_overlapping) def set_ticks_position(self, position): """ Set where ticks should appear Parameters ---------- position : str The axes on which the ticks for this coordinate should appear. Should be a string containing zero or more of ``'b'``, ``'t'``, ``'l'``, ``'r'``. For example, ``'lb'`` will lead the ticks to be shown on the left and bottom axis. """ self.ticks.set_visible_axes(position) def set_ticks_visible(self, visible): """ Set whether ticks are visible or not. Parameters ---------- visible : bool The visibility of ticks. Setting as ``False`` will hide ticks along this coordinate. """ self.ticks.set_visible(visible) def set_ticklabel(self, **kwargs): """ Set the visual properties for the tick labels. Parameters ---------- kwargs Keyword arguments are passed to :class:`matplotlib.text.Text`. These can include keywords to set the ``color``, ``size``, ``weight``, and other text properties. """ self.ticklabels.set(**kwargs) def set_ticklabel_position(self, position): """ Set where tick labels should appear Parameters ---------- position : str The axes on which the tick labels for this coordinate should appear. Should be a string containing zero or more of ``'b'``, ``'t'``, ``'l'``, ``'r'``. For example, ``'lb'`` will lead the tick labels to be shown on the left and bottom axis. """ self.ticklabels.set_visible_axes(position) def set_ticklabel_visible(self, visible): """ Set whether the tick labels are visible or not. Parameters ---------- visible : bool The visibility of ticks. Setting as ``False`` will hide this coordinate's tick labels. """ self.ticklabels.set_visible(visible) def set_axislabel(self, text, minpad=1, **kwargs): """ Set the text and optionally visual properties for the axis label. Parameters ---------- text : str The axis label text. minpad : float, optional The padding for the label in terms of axis label font size. kwargs Keywords are passed to :class:`matplotlib.text.Text`. These can include keywords to set the ``color``, ``size``, ``weight``, and other text properties. """ self.axislabels.set_text(text) self.axislabels.set_minpad(minpad) self.axislabels.set(**kwargs) def get_axislabel(self): """ Get the text for the axis label Returns ------- label : str The axis label """ return self.axislabels.get_text() def set_axislabel_position(self, position): """ Set where axis labels should appear Parameters ---------- position : str The axes on which the axis label for this coordinate should appear. Should be a string containing zero or more of ``'b'``, ``'t'``, ``'l'``, ``'r'``. For example, ``'lb'`` will lead the axis label to be shown on the left and bottom axis. """ self.axislabels.set_visible_axes(position) @property def locator(self): return self._formatter_locator.locator @property def formatter(self): return self._formatter_locator.formatter def _draw(self, renderer, bboxes, ticklabels_bbox): renderer.open_group('coordinate_axis') self._update_ticks() self.ticks.draw(renderer) self.ticklabels.draw(renderer, bboxes=bboxes, ticklabels_bbox=ticklabels_bbox) if self.grid_lines_kwargs['visible']: if self._grid_type == 'lines': self._update_grid_lines() else: self._update_grid_contour() if self._grid_type == 'lines': frame_patch = self.frame.patch for path in self.grid_lines: p = PathPatch(path, **self.grid_lines_kwargs) p.set_clip_path(frame_patch) p.draw(renderer) elif self._grid is not None: for line in self._grid.collections: line.set(**self.grid_lines_kwargs) line.draw(renderer) renderer.close_group('coordinate_axis') def _draw_axislabels(self, renderer, bboxes, ticklabels_bbox, visible_ticks): renderer.open_group('axis labels') self.axislabels.draw(renderer, bboxes=bboxes, ticklabels_bbox_list=ticklabels_bbox, visible_ticks=visible_ticks) renderer.close_group('axis labels') def _update_ticks(self): # TODO: this method should be optimized for speed # Here we determine the location and rotation of all the ticks. For # each axis, we can check the intersections for the specific # coordinate and once we have the tick positions, we can use the WCS # to determine the rotations. # Find the range of coordinates in all directions coord_range = self.parent_map.get_coord_range() # First find the ticks we want to show tick_world_coordinates, self._fl_spacing = self.locator(*coord_range[self.coord_index]) if self.ticks.get_display_minor_ticks(): minor_ticks_w_coordinates = self._formatter_locator.minor_locator(self._fl_spacing, self.get_minor_frequency(), *coord_range[self.coord_index]) # We want to allow non-standard rectangular frames, so we just rely on # the parent axes to tell us what the bounding frame is. from . import conf frame = self.frame.sample(conf.frame_boundary_samples) self.ticks.clear() self.ticklabels.clear() self.lblinfo = [] self.lbl_world = [] # Look up parent axes' transform from data to figure coordinates. # # See: # http://matplotlib.org/users/transforms_tutorial.html#the-transformation-pipeline transData = self.parent_axes.transData invertedTransLimits = transData.inverted() for axis, spine in six.iteritems(frame): # Determine tick rotation in display coordinates and compare to # the normal angle in display coordinates. pixel0 = spine.data world0 = spine.world[:, self.coord_index] world0 = self.transform.transform(pixel0)[:, self.coord_index] axes0 = transData.transform(pixel0) # Advance 2 pixels in figure coordinates pixel1 = axes0.copy() pixel1[:, 0] += 2.0 pixel1 = invertedTransLimits.transform(pixel1) world1 = self.transform.transform(pixel1)[:, self.coord_index] # Advance 2 pixels in figure coordinates pixel2 = axes0.copy() pixel2[:, 1] += 2.0 if self.frame.origin == 'lower' else -2.0 pixel2 = invertedTransLimits.transform(pixel2) world2 = self.transform.transform(pixel2)[:, self.coord_index] dx = (world1 - world0) dy = (world2 - world0) # Rotate by 90 degrees dx, dy = -dy, dx if self._coord_unit_scale is not None: dx *= self._coord_unit_scale dy *= self._coord_unit_scale if self.coord_type == 'longitude': # Here we wrap at 180 not self.coord_wrap since we want to # always ensure abs(dx) < 180 and abs(dy) < 180 dx = wrap_angle_at(dx, 180.) dy = wrap_angle_at(dy, 180.) tick_angle = np.degrees(np.arctan2(dy, dx)) normal_angle_full = np.hstack([spine.normal_angle, spine.normal_angle[-1]]) with np.errstate(invalid='ignore'): reset = (((normal_angle_full - tick_angle) % 360 > 90.) & ((tick_angle - normal_angle_full) % 360 > 90.)) tick_angle[reset] -= 180. # We find for each interval the starting and ending coordinate, # ensuring that we take wrapping into account correctly for # longitudes. w1 = spine.world[:-1, self.coord_index] w2 = spine.world[1:, self.coord_index] if self._coord_unit_scale is not None: w1 = w1 * self._coord_unit_scale w2 = w2 * self._coord_unit_scale if self.coord_type == 'longitude': w1 = wrap_angle_at(w1, self.coord_wrap) w2 = wrap_angle_at(w2, self.coord_wrap) with np.errstate(invalid='ignore'): w1[w2 - w1 > 180.] += 360 w2[w1 - w2 > 180.] += 360 # For longitudes, we need to check ticks as well as ticks + 360, # since the above can produce pairs such as 359 to 361 or 0.5 to # 1.5, both of which would match a tick at 0.75. Otherwise we just # check the ticks determined above. self._compute_ticks(tick_world_coordinates, spine, axis, w1, w2, tick_angle) if self.ticks.get_display_minor_ticks(): self._compute_ticks(minor_ticks_w_coordinates, spine, axis, w1, w2, tick_angle, ticks='minor') # format tick labels, add to scene text = self.formatter(self.lbl_world * tick_world_coordinates.unit, spacing=self._fl_spacing) for kwargs, txt in zip(self.lblinfo, text): self.ticklabels.add(text=txt, **kwargs) def _compute_ticks(self, tick_world_coordinates, spine, axis, w1, w2, tick_angle, ticks='major'): tick_world_coordinates_values = tick_world_coordinates.value if self.coord_type == 'longitude': tick_world_coordinates_values = np.hstack([tick_world_coordinates_values, tick_world_coordinates_values + 360]) for t in tick_world_coordinates_values: # Find steps where a tick is present. We have to check # separately for the case where the tick falls exactly on the # frame points, otherwise we'll get two matches, one for w1 and # one for w2. with np.errstate(invalid='ignore'): intersections = np.hstack([np.nonzero((t - w1) == 0)[0], np.nonzero(((t - w1) * (t - w2)) < 0)[0]]) # But we also need to check for intersection with the last w2 if t - w2[-1] == 0: intersections = np.append(intersections, len(w2) - 1) # Loop over ticks, and find exact pixel coordinates by linear # interpolation for imin in intersections: imax = imin + 1 if np.allclose(w1[imin], w2[imin], rtol=1.e-13, atol=1.e-13): continue # tick is exactly aligned with frame else: frac = (t - w1[imin]) / (w2[imin] - w1[imin]) x_data_i = spine.data[imin, 0] + frac * (spine.data[imax, 0] - spine.data[imin, 0]) y_data_i = spine.data[imin, 1] + frac * (spine.data[imax, 1] - spine.data[imin, 1]) x_pix_i = spine.pixel[imin, 0] + frac * (spine.pixel[imax, 0] - spine.pixel[imin, 0]) y_pix_i = spine.pixel[imin, 1] + frac * (spine.pixel[imax, 1] - spine.pixel[imin, 1]) delta_angle = tick_angle[imax] - tick_angle[imin] if delta_angle > 180.: delta_angle -= 360. elif delta_angle < -180.: delta_angle += 360. angle_i = tick_angle[imin] + frac * delta_angle if self.coord_type == 'longitude': world = wrap_angle_at(t, self.coord_wrap) else: world = t if ticks == 'major': self.ticks.add(axis=axis, pixel=(x_data_i, y_data_i), world=world, angle=angle_i, axis_displacement=imin + frac) # store information to pass to ticklabels.add # it's faster to format many ticklabels at once outside # of the loop self.lblinfo.append(dict(axis=axis, pixel=(x_pix_i, y_pix_i), world=world, angle=spine.normal_angle[imin], axis_displacement=imin + frac)) self.lbl_world.append(world) else: self.ticks.add_minor(minor_axis=axis, minor_pixel=(x_data_i, y_data_i), minor_world=world, minor_angle=angle_i, minor_axis_displacement=imin + frac) def display_minor_ticks(self, display_minor_ticks): """ Display minor ticks for this coordinate. Parameters ---------- display_minor_ticks : bool Whether or not to display minor ticks. """ self.ticks.display_minor_ticks(display_minor_ticks) def get_minor_frequency(self): return self.minor_frequency def set_minor_frequency(self, frequency): """ Set the frequency of minor ticks per major ticks. Parameters ---------- frequency : int The number of minor ticks per major ticks. """ self.minor_frequency = frequency def _update_grid_lines(self): # For 3-d WCS with a correlated third axis, the *proper* way of # drawing a grid should be to find the world coordinates of all pixels # and drawing contours. What we are doing here assumes that we can # define the grid lines with just two of the coordinates (and # therefore assumes that the other coordinates are fixed and set to # the value in the slice). Here we basically assume that if the WCS # had a third axis, it has been abstracted away in the transformation. coord_range = self.parent_map.get_coord_range() tick_world_coordinates, spacing = self.locator(*coord_range[self.coord_index]) tick_world_coordinates_values = tick_world_coordinates.value n_coord = len(tick_world_coordinates_values) from . import conf n_samples = conf.grid_samples xy_world = np.zeros((n_samples * n_coord, 2)) self.grid_lines = [] for iw, w in enumerate(tick_world_coordinates_values): subset = slice(iw * n_samples, (iw + 1) * n_samples) if self.coord_index == 0: xy_world[subset, 0] = np.repeat(w, n_samples) xy_world[subset, 1] = np.linspace(coord_range[1][0], coord_range[1][1], n_samples) else: xy_world[subset, 0] = np.linspace(coord_range[0][0], coord_range[0][1], n_samples) xy_world[subset, 1] = np.repeat(w, n_samples) # We now convert all the world coordinates to pixel coordinates in a # single go rather than doing this in the gridline to path conversion # to fully benefit from vectorized coordinate transformations. # Currently xy_world is in deg, but transform function needs it in # native units if self._coord_unit_scale is not None: xy_world /= self._coord_unit_scale # Transform line to pixel coordinates pixel = self.transform.inverted().transform(xy_world) # Create round-tripped values for checking xy_world_round = self.transform.transform(pixel) for iw in range(n_coord): subset = slice(iw * n_samples, (iw + 1) * n_samples) self.grid_lines.append(self._get_gridline(xy_world[subset], pixel[subset], xy_world_round[subset])) def _get_gridline(self, xy_world, pixel, xy_world_round): if self.coord_type == 'scalar': return get_gridline_path(xy_world, pixel) else: return get_lon_lat_path(xy_world, pixel, xy_world_round) def _update_grid_contour(self): if hasattr(self, '_grid'): for line in self._grid.collections: line.remove() xmin, xmax = self.parent_axes.get_xlim() ymin, ymax = self.parent_axes.get_ylim() x, y, field = self.transform.get_coord_slices(xmin, xmax, ymin, ymax, 200, 200) coord_range = self.parent_map.get_coord_range() tick_world_coordinates, spacing = self.locator(*coord_range[self.coord_index]) field = field[self.coord_index] # tick_world_coordinates is a Quantities array and we only needs its values tick_world_coordinates_values = tick_world_coordinates.value if self.coord_type == 'longitude': # Find biggest gap in tick_world_coordinates and wrap in middle # For now just assume spacing is equal, so any mid-point will do mid = 0.5 * (tick_world_coordinates_values[0] + tick_world_coordinates_values[1]) field = wrap_angle_at(field, mid) tick_world_coordinates_values = wrap_angle_at(tick_world_coordinates_values, mid) # Replace wraps by NaN reset = (np.abs(np.diff(field[:, :-1], axis=0)) > 180) | (np.abs(np.diff(field[:-1, :], axis=1)) > 180) field[:-1, :-1][reset] = np.nan field[1:, :-1][reset] = np.nan field[:-1, 1:][reset] = np.nan field[1:, 1:][reset] = np.nan if len(tick_world_coordinates_values) > 0: self._grid = self.parent_axes.contour(x, y, field.transpose(), levels=np.sort(tick_world_coordinates_values)) else: self._grid = None