689 lines
29 KiB
Python
689 lines
29 KiB
Python
"""
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"""
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import collections
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import math
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import os
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import matplotlib as mpl
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# Use "agg" backend automatically if no display is available.
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try:
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os.environ["DISPLAY"]
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except KeyError:
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mpl.use("agg")
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import matplotlib.animation as animation
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import matplotlib.pyplot as plt
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import numpy as np
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from replot import constants
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from replot import exceptions as exc
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from replot import tools
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from replot.grid import layout
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from replot.grid import parser as grid_parser
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from replot.helpers import custom_kwargs as custom_kwargs_parser
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from replot.helpers import custom_mpl
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from replot.helpers import palette as rpalette
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from replot.helpers import plot as plot_helpers
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from replot.helpers import render as render_helpers
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PREDEFINED_PALETTES = {
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"colorbrewerq9": rpalette.COLORBREWER_Q9,
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"colorbrewerq10": rpalette.COLORBREWER_Q10,
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"tableau10": rpalette.TABLEAU_10
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}
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class Figure():
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"""
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The main class from :mod:`replot`, representing a figure. Can be used \
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directly or in a ``with`` statement.
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"""
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def __init__(self,
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xlabel="", ylabel="", title="",
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xrange=None, yrange=None,
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palette=None,
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legend=None, savepath=None, grid=None,
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custom_mpl_rc=None):
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"""
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Build a :class:`Figure` object.
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:param xlabel: Label for the X axis (optional).
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:param ylabel: Label for the Z axis (optional).
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:param title: Title of the figure (optional).
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:param xrange: Range of the X axis (optional), as a tuple \
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representing the interval.
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:param yrange: Range of the Y axis (optional), as a tuple \
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representing the interval.
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:param palette: Color palette to use (optional). Defaults to a safe \
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palette with compatibility with colorblindness and black and \
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white printing.
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:type palette: Either a list of colors (as RGB tuples) or a function \
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to call with number of plots as parameter and which returns a \
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list of colors (as RGB tuples). You can also pass a Seaborn \
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palette directly, or use a Palettable ``Palette.mpl_colors``. \
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You can also use ``colorbrewerq10`` (default), \
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``colorbrewerq9`` or ``tableau10`` to use any of these \
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predefined palettes.
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:param legend: Whether to use a legend or not (optional). Defaults to \
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no legend, except if labels are found on provided plots. \
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``False`` to disable completely. ``None`` for default \
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behavior. A string indicating position (:mod:`matplotlib` \
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format) to put a legend. ``True`` is a synonym for ``best`` \
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position.
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:param savepath: A path to save the image to (optional). If set, \
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the image will be saved on exiting a `with` statement.
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:param grid: A dict containing the width and height of the grid, and \
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a description of the grid as a list of subplots. Each subplot \
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is a tuple of \
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``((y_position, x_position), symbol, (rowspan, colspan))``. \
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No check for grid validity is being made. You can set it to \
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``False`` to disable it completely.
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:param custom_mpl_rc: An optional dict to overload some \
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:mod:`matplotlib` rc params.
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.. note:: If you use group plotting, ``xlabel``, ``ylabel``, \
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``legend``, ``xrange``, ``yrange`` and ``zrange`` will be \
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set uniformly for every subplot. If you wish to set \
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different properties for every subplots, you \
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should pass a dict for these properties, keys being the \
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group symbols and values being the value for each subplot.
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"""
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# Set default values for attributes
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self.xlabel = xlabel
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self.ylabel = ylabel
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self.title = title
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self.xrange = xrange
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self.yrange = yrange
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self.palette = palette
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self.legend = legend
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self.plots = collections.defaultdict(list) # keys are groups
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self.grid = grid
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self.savepath = savepath
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self.custom_mpl_rc = custom_mpl_rc
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# Working attributes
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self.animation = {"type": False,
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"args": (), "kwargs": {},
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"persist": []}
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def __enter__(self): # Allow use in a with statement
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return self
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def __exit__(self, exception_type, exception_value, traceback):
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# Do not render the figure if an exception was raised
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if exception_type is None:
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if self.savepath is not None:
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self.save()
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else:
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self.show()
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def save(self, *args, **kwargs):
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"""
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Render and save the figure. Also show the figure if possible.
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.. note:: Signature is the same as the one from \
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``matplotlib.pyplot.savefig``. You should refer to its \
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documentation for lengthy details.
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.. note:: If a ``savepath`` has been provided to the :class:`Figure` \
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object, you can call ``figure.save()`` without argument and \
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this path will be used.
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>>> with replot.Figure() as figure: figure.save("SOME_FILENAME")
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>>> with replot.Figure(savepath="SOME_FILENAME") as figure: pass
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"""
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if len(args) == 0 and self.savepath is not None:
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args = (self.savepath,)
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figure = self.render()
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if figure is not None:
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figure.savefig(*args, **kwargs)
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else:
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raise exc.InvalidFigure("Invalid figure.")
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def show(self):
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"""
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Render and show the :class:`Figure` object.
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"""
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figure = self.render()
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if figure is not None:
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figure.show()
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else:
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raise exc.InvalidFigure("Invalid figure.")
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def render(self):
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"""
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Actually render the figure.
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:returns: A :mod:`matplotlib` figure object.
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"""
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# Use custom matplotlib context
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with plt.rc_context(rc=custom_mpl.custom_rc(rc=self.custom_mpl_rc)):
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# Create figure if necessary
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figure, axes = self._render_grid()
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# Render depending on animation type
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if self.animation["type"] is False:
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self._render_no_animation(axes)
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elif self.animation["type"] == "gif":
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self._render_gif_animation(figure, axes)
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elif self.animation["type"] == "animation":
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# TODO
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return None
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else:
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return None
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# Use tight_layout to optimize layout, use custom padding
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figure.tight_layout(pad=1) # TODO: Messes up animations
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return figure
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def set_grid(self, grid_description=None,
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height=None, width=None, ignore_groups=False,
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auto=None):
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"""
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Apply a grid layout on the figure (subplots). Subplots are based on \
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defined groups (see ``group`` keyword to \
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``replot.Figure.plot``).
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:param grid_description: A list of rows. Each row is a string \
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containing the groups to display (can be seen as ASCII art). \
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Can be a single string in case of a single row.
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:param height: An optional ``height`` for the grid, implies \
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``auto=True``.
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:param width: An optional ``height`` for the grid, implies \
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``auto=True``.
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:param ignore_groups: (optional, implies ``auto=True``) By default, \
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``set_grid`` will use groups to organize plots in different \
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subplots. If you want to put every plot in a different \
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subplot, regardless of their groups, you can set this \
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to ``True``.
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:param auto: Whether the grid should be guessed automatically from \
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groups or not (optional).
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:returns: None.
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.. note:: Groups are a single unicode character. If a group does not \
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contain any plot, the resulting subplot will simply be empty.
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.. note:: Note that if you do not include the default group in the \
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grid description, it will not be shown.
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>>> with replot.Figure() as fig: fig.set_grid(["AAA",
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"BBC"
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"DEC"])
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"""
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# Handle incompatible arguments
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if((height is not None or width is not None or ignore_groups) and
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auto is False):
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raise exc.InvalidParameterError(
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"auto=False and height/width/ignore_groups arguments are " +
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"not compatible.")
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# Handle auto gridifying
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if auto is None:
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auto = False
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if auto or height is not None or width is not None or ignore_groups:
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self._set_auto_grid(height, width, ignore_groups)
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return
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if grid_description is False:
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# Disable the grid and return
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self.grid = False
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return
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elif isinstance(grid_description, str):
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# If a single string is provided, enclose it in a list.
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grid_description = [grid_description]
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# Check that grid is not empty
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if grid_description is None or len(grid_description) == 0:
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raise exc.InvalidParameterError("Grid cannot be an empty list.")
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# Check that all rows have the same number of elements
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for row in grid_description:
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if len(row) != len(grid_description[0]):
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raise exc.InvalidParameterError(
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"All rows must have the same number of elements.")
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# Parse the ASCII art grid
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parsed_grid = grid_parser.parse_ascii(grid_description)
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if parsed_grid is None:
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# If grid is not valid, raise an exception
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raise exc.InvalidParameterError(
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"Invalid grid provided. You did not use rectangular areas " +
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"for each group.")
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# Set the grid
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self.grid = parsed_grid
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def plot(self, *args, **kwargs):
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"""
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Plot something on the :class:`Figure` object.
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.. note:: This function expects ``args`` and ``kwargs`` to support
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every possible case. You can either pass it (see examples):
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- A single argument, being a series of points or a function.
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- Two series of points representing X values and Y values \
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(standard :mod:`matplotlib` behavior).
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- Two arguments being a function and a list of points at which \
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it should be evaluated (X values).
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- Two arguments being a function and an interval represented by \
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a tuple of its bounds.
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.. note:: ``kwargs`` arguments are passed to \
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``matplotlib.pyplot.plot``.
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.. note:: You can use some :mod:`replot` specific keyword arguments:
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- ``group`` which permits to group plots together, in \
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subplots (one unicode character maximum). ``group`` \
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keyword will not affect the render unless you state \
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:mod:`replot` to use subplots. Note that ``_`` is a \
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reserved group name which cannot be used.
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- ``line`` which can be set to ``True``/``False`` to plot \
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broken lines or discrete data series.
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- ``logscale`` which can be either ``log`` or ``loglog`` to use \
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such scales.
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- ``orthonormal`` (boolean) to force axis to be orthonormal.
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- ``xlim`` and ``ylim`` which are tuples of intervals on the \
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x and y axis. You can also use the ``xrange`` / \
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``yrange`` aliases if you find them more convenient.
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- ``invert`` (boolean) invert X and Y axis on the plot. Invert \
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the axes labels as well.
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- ``rotate`` (angle in degrees) rotate the plot by the angle in \
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degrees. Leave the labels untouched.
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- ``frame`` to specify a frame on which the plot should appear \
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when calling ``animate`` afterwards. Default behavior is \
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to increase the frame number between each plots. Frame \
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count starts at 0.
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.. note:: Note that this API call considers list of tuples as \
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list of (x, y) coordinates to plot, contrary to standard \
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matplotlib API which considers it is two different plots.
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>>> with replot.figure() as fig: fig.plot(np.sin, (-1, 1))
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>>> with replot.figure() as fig: fig.plot(np.sin, [-1, -0.9, …, 1])
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>>> with replot.figure() as fig: fig.plot([1, 2, 3], [4, 5, 6])
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>>> with replot.figure() as fig: fig.plot([1, 2, 3],
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[4, 5, 6], linewidth=2.0)
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>>> with replot.figure() as fig: fig.plot([1, 2, 3],
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[4, 5, 6], group="a")
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"""
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if len(args) == 0:
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if "x" in kwargs and "y" in kwargs:
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args = (kwargs["x"], kwargs["y"])
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del kwargs["x"]
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del kwargs["y"]
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else:
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raise exc.InvalidParameterError(
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"You should pass at least one argument to this function.")
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# Extract custom kwargs (the ones from replot but not matplotlib) from
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# kwargs
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kwargs, custom_kwargs = custom_kwargs_parser.parse(kwargs)
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if hasattr(args[0], "__call__"):
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# We want to plot a function
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plot_ = plot_helpers.plot_function(args[0], *(args[1:]), **kwargs)
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else:
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# Else, it is a point series, and we just have to store it for
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# later plotting.
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if hasattr(args[0], "__iter__"):
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try:
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# If we pass it a list of tuples, consider it as a list of
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# (x, y) coordinates contrary to the standard matplotlib
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# behavior
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x_list, y_list = zip(*args[0])
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args = (list(x_list),
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list(y_list)) + args[1:]
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except (TypeError, StopIteration, AssertionError):
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pass
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plot_ = (args, kwargs)
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# Apply custom kwargs on plot_
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plot_ = custom_kwargs_parser.edit_plot_command(plot_, custom_kwargs)
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# Add the plot to the correct group
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if "group" in custom_kwargs:
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group_ = custom_kwargs["group"]
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else:
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group_ = constants.DEFAULT_GROUP
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self.plots[group_].append(plot_)
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# Automatically set the legend if label is found
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# (only do it if legend is not explicitly suppressed)
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if "label" in kwargs and self.legend is None:
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self.legend = True
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def logplot(self, *args, **kwargs):
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"""
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Plot something on the :class:`Figure` object, in log scale.
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.. note:: See :func:`replot.Figure.plot` for the full documentation.
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.. note:: Side effect of this function is to set the axes to be in \
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log scale for the associated subplot.
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>>> with replot.figure() as fig: fig.logplot(np.log, (-1, 1))
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"""
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kwargs["logscale"] = "log"
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self.plot(*args, **kwargs)
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def loglogplot(self, *args, **kwargs):
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"""
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Plot something on the :class:`Figure` object, in log-log scale.
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.. note:: See :func:`replot.Figure.plot` for the full documentation.
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.. note:: Side effect of this function is to set the axes to be in \
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log scale for the associated subplot.
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>>> with replot.figure() as fig: fig.logplot(np.log, (-1, 1))
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"""
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kwargs["logscale"] = "loglog"
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self.plot(*args, **kwargs)
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def animate(self, *args, **kwargs):
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"""
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Create an animation.
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You can either:
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- use it directly without arguments to create an animation from \
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the previously plot commands, cycling through the plots \
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for each subplot.
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- TODO: Use an animation function.
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"""
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self.animation["type"] = "gif"
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self.animation["args"] = args
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self.animation["kwargs"] = kwargs
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###################
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# Private methods #
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###################
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def _set_auto_grid(self, height=None, width=None, ignore_groups=False):
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"""
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Apply an automatic grid on the figure, trying to fit best to the \
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number of plots.
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.. note:: This method must be called after all the plots \
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have been added to the figure, or the grid will miss some \
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groups.
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.. note:: The grid will be filled by the groups in lexicographic \
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order. Unassigned plots go to the last subplot.
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:param height: An optional ``height`` for the grid.
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:param width: An optional ``height`` for the grid.
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:param ignore_groups: By default, ``set_grid`` will use groups to \
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organize plots in different subplots. If you want to put \
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every plot in a different subplot, regardless of their \
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groups, you can set this to ``True``.
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"""
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if ignore_groups:
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# If we want to ignore groups, we will start by creating a new
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# group for every existing plot
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existing_plots = []
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for group_ in self.plots:
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existing_plots.extend(self.plots[group_])
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self.plots = collections.defaultdict(list,
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{chr(i): [existing_plots[i]]
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for i in
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range(len(existing_plots))})
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# Find the optimal layout
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nb_groups = len(self.plots)
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if height is None and width is not None:
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height = math.ceil(nb_groups / width)
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elif width is None and height is not None:
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width = math.ceil(nb_groups / height)
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else:
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height, width = layout.optimal(nb_groups)
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# Apply the layout
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groups = sorted([k
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for k in self.plots.keys()
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if k != constants.DEFAULT_GROUP and
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len(self.plots[k]) > 0])
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if len(self.plots[constants.DEFAULT_GROUP]) > 0:
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# Handle default group separately
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groups.append(constants.DEFAULT_GROUP)
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grid_description = ["".join(batch)
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for batch in tools.batch(groups, width)]
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self.set_grid(grid_description)
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def _legend(self, axis, overload_legend=None):
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"""
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Helper function to handle ``legend`` attribute. It places the legend \
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correctly depending on attributes and required plots.
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:param axis: The :mod:`matplotlib` axis to put the legend on.
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:param overload_legend: An optional legend specification to use \
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instead of the ``legend`` attribute.
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"""
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if overload_legend is None:
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overload_legend = self.legend
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# If no legend is required, just pass
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if overload_legend is None or overload_legend is False:
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return
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if overload_legend is True:
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# If there should be a legend, but no location provided, put it at
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# best location.
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location = "best"
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else:
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location = overload_legend
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# Create aliases for "upper" / "top" and "lower" / "bottom"
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location.replace("top ", "upper ")
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location.replace("bottom ", "lower ")
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# Avoid warning if no labels were given for plots
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nb_labelled_plots = sum(["label" in plt[1]
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for group in self.plots.values()
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for plt in group])
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if nb_labelled_plots > 0:
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# Add legend
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axis.legend(loc=location)
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def _render_grid(self):
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"""
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Helper method to create figure and axes with \
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subplots according to the grid description.
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:returns: A tuple containing the figure object as first element, and \
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a dict mapping the symbols of the groups to matplotlib axes \
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as second element.
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"""
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# If no grid is provided, create an auto grid for the figure.
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if self.grid is None:
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self._set_auto_grid()
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# Axes is a dict associating symbols to matplotlib axes
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axes = {}
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figure = plt.figure()
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# Build all the axes
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if self.grid is not False:
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grid_size = (self.grid["height"], self.grid["width"])
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for subplot in self.grid["grid"]:
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position, symbol, (rowspan, colspan) = subplot
|
|
axes[symbol] = plt.subplot2grid(grid_size,
|
|
position,
|
|
colspan=colspan,
|
|
rowspan=rowspan)
|
|
# Set the palette for the subplot
|
|
palette = None
|
|
if self.palette is not None:
|
|
if hasattr(self.palette, "__call__"):
|
|
palette = self.palette(len(self.plots[symbol]))
|
|
elif isinstance(self.palette, str):
|
|
if self.palette in PREDEFINED_PALETTES:
|
|
palette = PREDEFINED_PALETTES[self.palette]
|
|
else:
|
|
palette = None
|
|
else:
|
|
palette = self.palette
|
|
if palette is not None:
|
|
axes[symbol].set_prop_cycle(
|
|
rpalette.build_cycler_palette(palette))
|
|
if constants.DEFAULT_GROUP not in axes:
|
|
# Set the default group axis to None if it is not in the grid
|
|
axes[constants.DEFAULT_GROUP] = None
|
|
else:
|
|
axis = plt.subplot2grid((1, 1), (0, 0))
|
|
# Set the palette for the subplot
|
|
palette = None
|
|
if self.palette is not None:
|
|
if hasattr(self.palette, "__call__"):
|
|
palette = self.palette(
|
|
sum(
|
|
[len(i) for i in self.plots.values()]))
|
|
elif isinstance(self.palette, str):
|
|
if self.palette in PREDEFINED_PALETTES:
|
|
palette = PREDEFINED_PALETTES[self.palette]
|
|
else:
|
|
palette = None
|
|
else:
|
|
palette = self.palette
|
|
if palette is not None:
|
|
axis.set_prop_cycle(
|
|
rpalette.build_cycler_palette(palette))
|
|
# Set the axis for every subplot
|
|
for subplot in self.plots:
|
|
axes[subplot] = axis
|
|
return (figure, axes)
|
|
|
|
def _set_axes_properties(self, axis, group_):
|
|
"""
|
|
Set the various properties on the axes.
|
|
|
|
:param axis: A :mod:`matplotlib` axis.
|
|
:param group_: The group of plots to use to get the properties to set.
|
|
:returns: None.
|
|
"""
|
|
# Handle "invert" kwarg
|
|
is_inverted_axis = (len(
|
|
[i
|
|
for i in self.plots[group_]
|
|
if "invert" in i[2] and i[2]["invert"]]
|
|
) > 0)
|
|
if is_inverted_axis:
|
|
set_xlabel = axis.set_ylabel
|
|
set_ylabel = axis.set_xlabel
|
|
else:
|
|
set_xlabel = axis.set_xlabel
|
|
set_ylabel = axis.set_ylabel
|
|
# Set xlabel and ylabel
|
|
render_helpers.set_axis_property(group_, set_xlabel, self.xlabel)
|
|
render_helpers.set_axis_property(group_, set_ylabel, self.ylabel)
|
|
# Set title
|
|
render_helpers.set_axis_property(group_, axis.set_title, self.title)
|
|
axis.title.set_position((0.5, 1.05)) # Put padding between title and figure
|
|
# Set legend
|
|
render_helpers.set_axis_property(
|
|
group_,
|
|
lambda v: self._legend(axis, overload_legend=v),
|
|
self.legend,
|
|
lambda: self._legend(axis, overload_legend=True)
|
|
)
|
|
# Set xrange / yrange
|
|
render_helpers.set_axis_property(group_, axis.set_xlim, self.xrange)
|
|
render_helpers.set_axis_property(group_, axis.set_ylim, self.yrange)
|
|
# Note: Extend axes limits to have the full plot, even with large
|
|
# linewidths. This is necessary as we do not clip lines.
|
|
maximum_linewidth = max(
|
|
max([plt[1].get("lw", 0) for plt in self.plots[group_]]),
|
|
max([plt[1].get("linewidth", 0) for plt in self.plots[group_]])
|
|
)
|
|
if maximum_linewidth > 0:
|
|
# Only extend axes limits if linewidths is larger than the default
|
|
# one.
|
|
ticks_position = { # Dump ticks position to restore them afterwards
|
|
"x": (axis.xaxis.get_majorticklocs(),
|
|
axis.xaxis.get_minorticklocs()),
|
|
"y": (axis.yaxis.get_majorticklocs(),
|
|
axis.yaxis.get_minorticklocs())
|
|
}
|
|
# Set xrange
|
|
extra_xrange = render_helpers.data_units_from_points(
|
|
maximum_linewidth,
|
|
axis,
|
|
reference="x")
|
|
xrange = (axis.get_xlim()[0] - extra_xrange / 2,
|
|
axis.get_xlim()[1] + extra_xrange / 2)
|
|
render_helpers.set_axis_property(group_, axis.set_xlim, xrange)
|
|
# Set yrange
|
|
extra_yrange = render_helpers.data_units_from_points(
|
|
maximum_linewidth,
|
|
axis,
|
|
reference="y")
|
|
yrange = (axis.get_ylim()[0] - extra_yrange / 2,
|
|
axis.get_ylim()[1] + extra_yrange / 2)
|
|
render_helpers.set_axis_property(group_, axis.set_ylim, yrange)
|
|
# Restore ticks
|
|
axis.xaxis.set_ticks(ticks_position["x"][0], minor=False)
|
|
axis.xaxis.set_ticks(ticks_position["x"][1], minor=True)
|
|
axis.yaxis.set_ticks(ticks_position["y"][0], minor=False)
|
|
axis.yaxis.set_ticks(ticks_position["y"][1], minor=True)
|
|
|
|
|
|
def _render_gif_animation(self, figure, axes):
|
|
"""
|
|
Handle the render of a GIF-like animation, cycling through the plots.
|
|
|
|
:param figure: A :mod:`matplotlib` figure.
|
|
:param axes: A dict mapping the symbols of the groups to matplotlib \
|
|
axes as second element.
|
|
"""
|
|
# Init
|
|
# TODO
|
|
axes[constants.DEFAULT_GROUP].set_xlim((-2, 2))
|
|
axes[constants.DEFAULT_GROUP].set_ylim((-2, 2))
|
|
line, = axes[constants.DEFAULT_GROUP].plot([], [])
|
|
# Define an animation function (closure)
|
|
def animate(i):
|
|
# TODO
|
|
x = np.linspace(0, 2, 1000)
|
|
y = np.sin(2 * np.pi * (x - 0.01 * i))
|
|
line.set_data(x, y)
|
|
return line,
|
|
# Set default kwargs
|
|
args = (figure, animate)
|
|
kwargs = constants.DEFAULT_ANIMATION_KWARGS
|
|
# Update with overloaded arguments
|
|
args += self.animation["args"]
|
|
kwargs.update(self.animation["kwargs"])
|
|
# Keep track of animation object, as it has to persist
|
|
self.animation["persist"] = [
|
|
animation.FuncAnimation(*args, **kwargs)]
|
|
|
|
def _render_no_animation(self, axes):
|
|
"""
|
|
Handle the render of the figure when no animation is used.
|
|
|
|
:param axes: A dict mapping the symbols of the groups to matplotlib \
|
|
axes as second element.
|
|
"""
|
|
# Add plots
|
|
for group_ in self.plots:
|
|
# Get the axis corresponding to current group
|
|
try:
|
|
axis = axes[group_]
|
|
except KeyError:
|
|
# If not found, plot in the default group
|
|
axis = axes[constants.DEFAULT_GROUP]
|
|
# Skip this plot if the axis is None
|
|
if axis is None:
|
|
continue
|
|
# Plot
|
|
for plot_ in self.plots[group_]:
|
|
tmp_plots = axis.plot(*(plot_[0]), **(plot_[1]))
|
|
# Handle custom kwargs at plotting time
|
|
if "logscale" in plot_[2]:
|
|
if plot_[2]["logscale"] == "log":
|
|
axis.set_xscale("log")
|
|
elif plot_[2]["logscale"] == "loglog":
|
|
axis.set_xscale("log")
|
|
axis.set_yscale("log")
|
|
if "orthonormal" in plot_[2] and plot_[2]["orthonormal"]:
|
|
axis.set_aspect("equal")
|
|
if "xlim" in plot_[2]:
|
|
axis.set_xlim(*plot_[2]["xlim"])
|
|
if "ylim" in plot_[2]:
|
|
axis.set_ylim(*plot_[2]["ylim"])
|
|
# Do not clip line at the axes boundaries to prevent
|
|
# extremas from being cropped.
|
|
for tmp_plot in tmp_plots:
|
|
tmp_plot.set_clip_on(False)
|
|
# Set ax properties
|
|
self._set_axes_properties(axis, group_)
|