441 lines
16 KiB
Python
441 lines
16 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 shutil
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import matplotlib.pyplot as plt
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import numpy as np
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import seaborn.apionly as sns
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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 tools
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__VERSION__ = "0.0.1"
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# Constants
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_DEFAULT_GROUP = "_"
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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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palette="hls", max_colors=10,
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legend=None, savepath=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 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 palette name (``str``) or a built palette.
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:param max_colors: Number of colors to use in the palette (optional). \
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Defaults to 10.
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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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"""
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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.palette = palette
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self.max_colors = max_colors
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self.legend = legend
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self.plots = collections.defaultdict(list) # keys are groups
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self.savepath = savepath
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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 apply_grid(self, grid_description):
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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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.. 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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>>> with replot.Figure() as fig: fig.apply_grid(["AAA",
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"BBC"
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"DEC"])
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"""
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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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# TODO: Parse grid
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def gridify(self, height=None, width=None):
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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 groups of 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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"""
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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 = (
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sorted([k for k in self.plots.keys() if k != _DEFAULT_GROUP]) +
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[_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.apply_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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>>> 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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plot_ = (args, 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 _legend(self, axes):
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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 axes: The :mod:`matplotlib` axes to put the legend on.
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"""
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# If no legend is required, just pass
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if self.legend is None or self.legend is False:
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return
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if self.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 = self.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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axes.legend(loc=location)
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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.
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"""
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figure = None
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# Use custom matplotlib context
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with mpl_custom_rc_context():
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# Tweak matplotlib to use seaborn
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sns.set()
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# Plot using specified color palette
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with sns.color_palette(palette=self.palette,
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n_colors=self.max_colors):
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# Create figure
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figure, axes = plt.subplots()
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# Add plots
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for group_ in self.plots:
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for plot_ in self.plots[group_]:
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tmp_plots = axes.plot(*(plot_[0]), **(plot_[1]))
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# Do not clip line at the axes boundaries to prevent
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# extremas from being cropped.
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for tmp_plot in tmp_plots:
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tmp_plot.set_clip_on(False)
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# Set properties
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axes.set_xlabel(self.xlabel)
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axes.set_ylabel(self.ylabel)
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axes.set_title(self.title)
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self._legend(axes)
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# Do not forget to restore matplotlib state, in order not to
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# interfere with it.
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sns.reset_orig()
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return figure
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def plot(data, **kwargs):
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"""
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Helper function to make one-liner plots. Typical use case is:
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>>> replot.plot([range(10),
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(np.sin, (-5, 5)),
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(lambda x: np.sin(x) + 4, (-10, 10), {"linewidth": 10}),
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(lambda x: np.sin(x) - 4, (-10, 10), {"linewidth": 10}),
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([-i for i in range(5)], {"linewidth": 10})],
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xlabel="some x label",
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ylabel="some y label",
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title="A title for the figure",
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legend="best",
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palette=replot.sns.color_palette("husl", 2))
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"""
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# Init new figure
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figure = Figure(**kwargs)
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# data is a list of plotting commands
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for plot_ in data:
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# If we provide a tuple, handle it
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if isinstance(plot_, tuple):
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args = ()
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kwargs = {}
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# First case, only two items provided
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if len(plot_) == 2:
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# Parse args and kwargs according to type of items
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if isinstance(plot_[1], tuple):
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args = (plot_[1],)
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elif isinstance(plot_[1], dict):
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kwargs = plot_[1]
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# Second case, at least 3 items provided
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elif len(plot_) > 2:
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# Then, args and kwargs are well defined
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args = (plot_[1],)
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kwargs = plot_[2]
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# Pass the correct argument to plot function
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figure.plot(plot_[0], *args, **kwargs)
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else:
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figure.plot(plot_)
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figure.show()
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def mpl_custom_rc_context():
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"""
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Overload ``matplotlib.rcParams`` to enable advanced features if \
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available. In particular, use LaTeX if available.
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:returns: A ``matplotlib.rc_context`` object to use in a ``with`` \
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statement.
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"""
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custom_rc = {}
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# Add LaTeX in rc if available
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if(shutil.which("latex") is not None and
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shutil.which("gs") is not None and
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shutil.which("dvipng") is not None):
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# LateX dependencies are all available
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custom_rc["text.usetex"] = True
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custom_rc["text.latex.unicode"] = True
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return plt.rc_context(rc=custom_rc)
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def _handle_custom_plot_arguments(kwargs):
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"""
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This method handles custom keyword arguments from plot in \
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:mod:`replot` which are not in :mod:`matplotlib` function.
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:param kwargs: A dictionary of keyword arguments to handle.
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:return: A tuple of :mod:`matplotlib` compatible keyword arguments \
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and of extra :mod:`replot` keyword arguments, both returned \
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as ``dict``.
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"""
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custom_kwargs = {}
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# Handle "group" argument
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if "group" in kwargs:
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if len(kwargs["group"]) > 1:
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raise exc.InvalidParameterError(
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"Group name cannot be longer than one unicode character.")
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elif kwargs["group"] == _DEFAULT_GROUP:
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raise exc.InvalidParameterError(
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"'%s' is a reserved group name." % (_DEFAULT_GROUP,))
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custom_kwargs["group"] = kwargs["group"]
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del kwargs["group"]
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return (kwargs, custom_kwargs)
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def _plot_function(data, *args, **kwargs):
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"""
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Helper function to handle plotting of unevaluated functions (trying \
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to evaluate it nicely and rendering the plot).
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:param data: The function to plot.
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:returns: A tuple of ``(args, kwargs)`` representing the plot.
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.. seealso:: The documentation of the ``replot.Figure.plot`` method.
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.. note:: ``args`` is used to handle the interval or point series on \
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which the function should be evaluated. ``kwargs`` are passed \
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directly to ``matplotlib.pyplot.plot`.
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"""
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if len(args) == 0:
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# If no interval specified, raise an issue
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raise exc.InvalidParameterError(
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"You should pass a plotting interval to the plot command.")
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elif isinstance(args[0], tuple):
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# Interval specified, use it and adaptive plotting
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x_values, y_values = adaptive_sampling.sample_function(
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data,
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args[0],
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tol=1e-3)
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elif isinstance(args[0], (list, np.ndarray)):
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# List of points specified, use them and compute values of the
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# function
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x_values = args[0]
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y_values = [data(i) for i in x_values]
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else:
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raise exc.InvalidParameterError(
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"Second parameter in plot command should be a tuple " +
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"specifying plotting interval.")
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return ((x_values, y_values) + args[1:], kwargs)
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def _optimal_grid(nb_items):
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"""
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(Naive) attempt to find an optimal grid layout for N elements.
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:param nb_items: The number of square elements to put on the grid.
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:returns: A tuple ``(height, width)`` containing the number of rows and \
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the number of cols of the resulting grid.
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>>> _optimal_grid(2)
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(1, 2)
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>>> _optimal_grid(3)
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(1, 3)
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>>> _optimal_grid(4)
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(2, 2)
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"""
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# Compute first possibility
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height1 = math.floor(math.sqrt(nb_items))
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width1 = math.ceil(nb_items / height1)
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# Compute second possibility
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width2 = math.ceil(math.sqrt(nb_items))
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height2 = math.ceil(nb_items / width2)
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# Minimize the product of height and width
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if height1 * width1 < height2 * width2:
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height, width = height1, width1
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else:
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height, width = height2, width2
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return (height, width)
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