815 lines
31 KiB
Python
815 lines
31 KiB
Python
"""
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The :mod:`replot` module is a (sane) Python plotting module, abstracting on top
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of Matplotlib.
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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 shutil
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import cycler
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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.pyplot as plt
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import numpy as np
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import palettable
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from replot import adaptive_sampling
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from replot import exceptions as exc
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from replot import grid_parser
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from replot import tools
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__VERSION__ = "0.0.1"
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#############
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# CONSTANTS #
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#############
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_DEFAULT_GROUP = "_"
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# Default palette is husl palette with length 10 color cycle
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def _default_palette(n):
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"""
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Default palette is a CubeHelix perceptual rainbow palette with length the
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number of plots.
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:param n: The number of colors in the palette.
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:returns: The palette as a list of colors (as RGB tuples).
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"""
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return palettable.cubehelix.Cubehelix.make(
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start_hue=240., end_hue=-300., min_sat=1., max_sat=2.5,
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min_light=0.3, max_light=0.8, gamma=.9, n=n).mpl_colors
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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=_default_palette,
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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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: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 = None
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def __enter__(self):
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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:: Typical use is:
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>>> with replot.Figure() as figure: figure.save("SOME_FILENAME")
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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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"""
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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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figure.savefig(*args, **kwargs)
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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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figure.show()
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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=False):
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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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.. 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 auto gridifying
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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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# Default parameters
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if grid_description is None:
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grid_description = []
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elif 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 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 _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 = _optimal_grid(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 != _DEFAULT_GROUP and len(self.plots[k]) > 0])
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if len(self.plots[_DEFAULT_GROUP]) > 0:
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# Handle default group separately
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groups.append(_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 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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.. 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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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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if kwargs is not None:
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kwargs, custom_kwargs = _handle_custom_plot_arguments(kwargs)
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else:
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custom_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_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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# Keep track of the custom kwargs
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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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self.plots[custom_kwargs["group"]].append(plot_)
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else:
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self.plots[_DEFAULT_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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>>> 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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>>> 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 _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 _grid(self):
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"""
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Create 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 self.grid is False:
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# If grid is disabled, we plot every group in the same sublot.
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axes = {}
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figure, axis = plt.subplots()
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# Set the palette for the subplot
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axis.set_prop_cycle(
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self._build_cycler_palette(sum([len(i) for i in self.plots])))
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# Set the axis for every subplot
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for subplot in self.plots:
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axes[subplot] = axis
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return figure, axes
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elif self.grid is None:
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# If no grid is provided, create an auto grid for the figure.
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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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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
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axes[symbol] = plt.subplot2grid(grid_size,
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position,
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colspan=colspan,
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rowspan=rowspan)
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# Set the palette for the subplot
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axes[symbol].set_prop_cycle(
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self._build_cycler_palette(len(self.plots[symbol])))
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if _DEFAULT_GROUP not in axes:
|
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# Set the default group axis to None if it is not in the grid
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axes[_DEFAULT_GROUP] = None
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return figure, axes
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def _set_axes_properties(self, axis, group_):
|
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# Set xlabel
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|
if isinstance(self.xlabel, dict):
|
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try:
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axis.set_xlabel(self.xlabel[group_])
|
|
except KeyError:
|
|
# No entry for this axis in the dict, pass it
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|
pass
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else:
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axis.set_xlabel(self.xlabel)
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|
# Set ylabel
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|
if isinstance(self.ylabel, dict):
|
|
try:
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axis.set_ylabel(self.ylabel[group_])
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|
except KeyError:
|
|
# No entry for this axis in the dict, pass it
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pass
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else:
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axis.set_ylabel(self.ylabel)
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# Set title
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|
if isinstance(self.title, dict):
|
|
try:
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axis.set_ylabel(self.title[group_])
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except KeyError:
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# No entry for this axis in the dict, pass it
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pass
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else:
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axis.set_title(self.title)
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# Set legend
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|
if isinstance(self.legend, dict):
|
|
try:
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self._legend(axis, overload_legend=self.legend[group_])
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|
except KeyError:
|
|
# No entry for this axis in the dict, use default argument
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|
# That is put a legend except if no plots on this axis.
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self._legend(axis, overload_legend=True)
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else:
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self._legend(axis)
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# Set xrange
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|
if isinstance(self.xrange, dict):
|
|
try:
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|
if self.xrange[group_] is not None:
|
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axis.set_xlim(*self.xrange[group_])
|
|
except KeyError:
|
|
# No entry for this axis in the dict, pass it
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pass
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|
else:
|
|
if self.xrange is not None:
|
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axis.set_xlim(*self.xrange)
|
|
# Set yrange
|
|
if isinstance(self.yrange, dict):
|
|
try:
|
|
if self.yrange[group_] is not None:
|
|
axis.set_ylim(*self.yrange[group_])
|
|
except KeyError:
|
|
# No entry for this axis in the dict, pass it
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|
pass
|
|
else:
|
|
if self.yrange is not None:
|
|
axis.set_ylim(*self.yrange)
|
|
|
|
def _build_cycler_palette(self, n):
|
|
"""
|
|
Build a cycler palette for the selected subplot.
|
|
|
|
:param n: number of colors in the palette.
|
|
:returns: a cycler object for the palette.
|
|
"""
|
|
if hasattr(self.palette, "__call__"):
|
|
return cycler.cycler("color", self.palette(n))
|
|
else:
|
|
return cycler.cycler("color", self.palette)
|
|
|
|
def _render(self):
|
|
"""
|
|
Actually render the figure.
|
|
|
|
:returns: A :mod:`matplotlib` figure.
|
|
"""
|
|
figure = None
|
|
# Use custom matplotlib context
|
|
with mpl_custom_rc_context(rc=self.custom_mpl_rc):
|
|
# Create figure
|
|
figure, axes = self._grid()
|
|
# 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[_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")
|
|
# 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_)
|
|
return figure
|
|
|
|
|
|
def plot(data, **kwargs):
|
|
"""
|
|
Helper function to make one-liner plots. Typical use case is:
|
|
|
|
>>> replot.plot([range(10),
|
|
(np.sin, (-5, 5)),
|
|
(lambda x: np.sin(x) + 4, (-10, 10), {"linewidth": 10}),
|
|
(lambda x: np.sin(x) - 4, (-10, 10), {"linewidth": 10}),
|
|
([-i for i in range(5)], {"linewidth": 10})],
|
|
xlabel="some x label",
|
|
ylabel="some y label",
|
|
title="A title for the figure",
|
|
legend="best",
|
|
palette=seaborn.color_palette("husl", 2))
|
|
"""
|
|
# Init new figure
|
|
figure = Figure(**kwargs)
|
|
# data is a list of plotting commands
|
|
for plot_ in data:
|
|
# If we provide a tuple, handle it
|
|
if isinstance(plot_, tuple):
|
|
args = ()
|
|
kwargs = {}
|
|
# First case, only two items provided
|
|
if len(plot_) == 2:
|
|
# Parse args and kwargs according to type of items
|
|
if isinstance(plot_[1], tuple):
|
|
args = (plot_[1],)
|
|
elif isinstance(plot_[1], dict):
|
|
kwargs = plot_[1]
|
|
# Second case, at least 3 items provided
|
|
elif len(plot_) > 2:
|
|
# Then, args and kwargs are well defined
|
|
args = (plot_[1],)
|
|
kwargs = plot_[2]
|
|
# Pass the correct argument to plot function
|
|
figure.plot(plot_[0], *args, **kwargs)
|
|
else:
|
|
figure.plot(plot_)
|
|
figure.show()
|
|
|
|
|
|
def _mpl_custom_rc_scaling():
|
|
"""
|
|
Scale the elements of the figure to get a better rendering.
|
|
|
|
Settings borrowed from
|
|
[Seaborn](https://github.com/mwaskom/seaborn/blob/master/seaborn/rcmod.py#L344).
|
|
|
|
:returns: a :mod:`matplotlib` ``rcParams``-like dict.
|
|
"""
|
|
rc_params = {
|
|
"figure.figsize": np.array([8, 5.5]),
|
|
# Set misc font sizes
|
|
"font.size": 12,
|
|
"axes.labelsize": 11,
|
|
"axes.titlesize": 12,
|
|
"xtick.labelsize": 10,
|
|
"ytick.labelsize": 10,
|
|
"legend.fontsize": 10,
|
|
# Set misc linewidth
|
|
"grid.linewidth": 1,
|
|
"lines.linewidth": 1.75,
|
|
"patch.linewidth": .3,
|
|
"lines.markersize": 7,
|
|
"lines.markeredgewidth": 1.75,
|
|
# Disable ticks
|
|
"xtick.major.width": 0,
|
|
"ytick.major.width": 0,
|
|
"xtick.minor.width": 0,
|
|
"ytick.minor.width": 0,
|
|
# Set ticks padding
|
|
"xtick.major.pad": 7,
|
|
"ytick.major.pad": 7,
|
|
}
|
|
return rc_params
|
|
|
|
|
|
def _mpl_custom_rc_axes_style():
|
|
"""
|
|
Set the style of the plot and the axes. Things like set a grid etc.
|
|
|
|
Settings borrowed from
|
|
[Seaborn](https://github.com/mwaskom/seaborn/blob/master/seaborn/rcmod.py#L344).
|
|
|
|
:returns: a :mod:`matplotlib` ``rcParams``-like dict.
|
|
"""
|
|
# Use dark gray instead of black for better readability on screen
|
|
dark_gray = ".15"
|
|
rc_params = {
|
|
# Colors
|
|
"figure.facecolor": "white",
|
|
"text.color": dark_gray,
|
|
# Legend
|
|
"legend.frameon": False, # No frame around legend
|
|
"legend.numpoints": 1,
|
|
"legend.scatterpoints": 1,
|
|
# Ticks
|
|
"xtick.direction": "out",
|
|
"ytick.direction": "out",
|
|
"xtick.color": dark_gray,
|
|
"ytick.color": dark_gray,
|
|
"lines.solid_capstyle": "round",
|
|
# Axes
|
|
"axes.axisbelow": True,
|
|
"axes.linewidth": 0,
|
|
"axes.labelcolor": dark_gray,
|
|
"axes.grid": True,
|
|
"axes.facecolor": "EAEAF2",
|
|
"axes.edgecolor": "white",
|
|
# Grid
|
|
"grid.linestyle": "-",
|
|
"grid.color": "white",
|
|
# Image
|
|
"image.cmap": "Greys"
|
|
}
|
|
return rc_params
|
|
|
|
|
|
def mpl_custom_rc_context(rc=None):
|
|
"""
|
|
Overload ``matplotlib.rcParams`` to enable advanced features if \
|
|
available. In particular, use LaTeX if available.
|
|
|
|
:param rc: An optional dict to overload some :mod:`matplotlib` rc params.
|
|
:returns: A ``matplotlib.rc_context`` object to use in a ``with`` \
|
|
statement.
|
|
"""
|
|
custom_rc = {}
|
|
# Add LaTeX in rc if available
|
|
if(shutil.which("latex") is not None and
|
|
shutil.which("gs") is not None and
|
|
shutil.which("dvipng") is not None):
|
|
# LateX dependencies are all available
|
|
custom_rc["text.usetex"] = True
|
|
custom_rc["text.latex.unicode"] = True
|
|
# Use LaTeX default font family
|
|
# See https://stackoverflow.com/questions/17958485/matplotlib-not-using-latex-font-while-text-usetex-true
|
|
custom_rc["font.family"] = "serif"
|
|
custom_rc["font.serif"] = "cm"
|
|
# Scale everything
|
|
custom_rc.update(_mpl_custom_rc_scaling())
|
|
# Set axes style
|
|
custom_rc.update(_mpl_custom_rc_axes_style())
|
|
# Overload if necessary
|
|
if rc is not None:
|
|
custom_rc.update(rc)
|
|
# Return a context object
|
|
return plt.rc_context(rc=custom_rc)
|
|
|
|
|
|
def _handle_custom_plot_arguments(kwargs):
|
|
"""
|
|
This method handles custom keyword arguments from plot in \
|
|
:mod:`replot` which are not in :mod:`matplotlib` function.
|
|
|
|
:param kwargs: A dictionary of keyword arguments to handle.
|
|
:return: A tuple of :mod:`matplotlib` compatible keyword arguments \
|
|
and of extra :mod:`replot` keyword arguments, both returned \
|
|
as ``dict``.
|
|
"""
|
|
custom_kwargs = {}
|
|
# Handle "group" argument
|
|
if "group" in kwargs:
|
|
if len(kwargs["group"]) > 1:
|
|
raise exc.InvalidParameterError(
|
|
"Group name cannot be longer than one unicode character.")
|
|
elif kwargs["group"] == _DEFAULT_GROUP:
|
|
raise exc.InvalidParameterError(
|
|
"'%s' is a reserved group name." % (_DEFAULT_GROUP,))
|
|
custom_kwargs["group"] = kwargs["group"]
|
|
del kwargs["group"]
|
|
# Handle "line" argument
|
|
if "line" in kwargs:
|
|
if not kwargs["line"]: # If should not draw lines, set kwargs for it
|
|
kwargs["linestyle"] = "None"
|
|
kwargs["marker"] = "x"
|
|
del kwargs["line"]
|
|
# Handle "logscale" argument
|
|
if "logscale" in kwargs:
|
|
custom_kwargs["logscale"] = kwargs["logscale"]
|
|
del kwargs["logscale"]
|
|
return (kwargs, custom_kwargs)
|
|
|
|
|
|
def _plot_function(data, *args, **kwargs):
|
|
"""
|
|
Helper function to handle plotting of unevaluated functions (trying \
|
|
to evaluate it nicely and rendering the plot).
|
|
|
|
:param data: The function to plot.
|
|
:returns: A tuple of ``(args, kwargs)`` representing the plot.
|
|
|
|
.. seealso:: The documentation of the ``replot.Figure.plot`` method.
|
|
|
|
.. note:: ``args`` is used to handle the interval or point series on \
|
|
which the function should be evaluated. ``kwargs`` are passed \
|
|
directly to ``matplotlib.pyplot.plot`.
|
|
"""
|
|
if len(args) == 0:
|
|
# If no interval specified, raise an issue
|
|
raise exc.InvalidParameterError(
|
|
"You should pass a plotting interval to the plot command.")
|
|
elif isinstance(args[0], tuple):
|
|
# Interval specified, use it and adaptive plotting
|
|
x_values, y_values = adaptive_sampling.sample_function(
|
|
data,
|
|
args[0],
|
|
tol=1e-3)
|
|
elif isinstance(args[0], (list, np.ndarray)):
|
|
# List of points specified, use them and compute values of the
|
|
# function
|
|
x_values = args[0]
|
|
y_values = [data(i) for i in x_values]
|
|
else:
|
|
raise exc.InvalidParameterError(
|
|
"Second parameter in plot command should be a tuple " +
|
|
"specifying plotting interval.")
|
|
return ((x_values, y_values) + args[1:], kwargs)
|
|
|
|
|
|
def _optimal_grid(nb_items):
|
|
"""
|
|
(Naive) attempt to find an optimal grid layout for N elements.
|
|
|
|
:param nb_items: The number of square elements to put on the grid.
|
|
:returns: A tuple ``(height, width)`` containing the number of rows and \
|
|
the number of cols of the resulting grid.
|
|
|
|
>>> _optimal_grid(2)
|
|
(1, 2)
|
|
|
|
>>> _optimal_grid(3)
|
|
(1, 3)
|
|
|
|
>>> _optimal_grid(4)
|
|
(2, 2)
|
|
"""
|
|
# Compute first possibility
|
|
height1 = math.floor(math.sqrt(nb_items))
|
|
width1 = math.ceil(nb_items / height1)
|
|
|
|
# Compute second possibility
|
|
width2 = math.ceil(math.sqrt(nb_items))
|
|
height2 = math.ceil(nb_items / width2)
|
|
|
|
# Minimize the product of height and width
|
|
if height1 * width1 < height2 * width2:
|
|
height, width = height1, width1
|
|
else:
|
|
height, width = height2, width2
|
|
|
|
return (height, width)
|