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Lucas Verney 2016-03-18 20:45:40 +01:00
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Examples.ipynb
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replot/__init__.py
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@ -1,716 +1,10 @@
"""
The :mod:`replot` module is a (sane) Python plotting module, abstracting on top
of Matplotlib.
The :mod:`replot` module is an attempt at an easier API to plot graphs using
Matplotlib.
"""
import collections
import math
import os
import shutil
from replot.figure import Figure
import cycler
import matplotlib as mpl
# Use "agg" backend automatically if no display is available.
try:
os.environ["DISPLAY"]
except KeyError:
mpl.use("agg")
import matplotlib.animation as animation
import matplotlib.pyplot as plt
import numpy as np
import palettable
from replot import adaptive_sampling
from replot import exceptions as exc
from replot import grid_parser
from replot import tools
__VERSION__ = "0.0.1"
#############
# CONSTANTS #
#############
_DEFAULT_GROUP = "_"
# Default palette is husl palette with length 10 color cycle
def _default_palette(n):
"""
Default palette is a CubeHelix perceptual rainbow palette with length the
number of plots.
:param n: The number of colors in the palette.
:returns: The palette as a list of colors (as RGB tuples).
"""
return palettable.cubehelix.Cubehelix.make(
start_hue=240., end_hue=-300., min_sat=1., max_sat=2.5,
min_light=0.3, max_light=0.8, gamma=.9, n=n).mpl_colors
class Figure():
"""
The main class from :mod:`replot`, representing a figure. Can be used \
directly or in a ``with`` statement.
"""
def __init__(self,
xlabel="", ylabel="", title="",
xrange=None, yrange=None,
palette=_default_palette,
legend=None, savepath=None, grid=None,
custom_mpl_rc=None):
"""
Build a :class:`Figure` object.
:param xlabel: Label for the X axis (optional).
:param ylabel: Label for the Z axis (optional).
:param title: Title of the figure (optional).
:param xrange: Range of the X axis (optional), as a tuple \
representing the interval.
:param yrange: Range of the Y axis (optional), as a tuple \
representing the interval.
:param palette: Color palette to use (optional). Defaults to a safe \
palette with compatibility with colorblindness and black and \
white printing.
:type palette: Either a list of colors (as RGB tuples) or a function \
to call with number of plots as parameter and which returns a \
list of colors (as RGB tuples). You can also pass a Seaborn \
palette directly, or use a Palettable Palette.mpl_colors.
:param legend: Whether to use a legend or not (optional). Defaults to \
no legend, except if labels are found on provided plots. \
``False`` to disable completely. ``None`` for default \
behavior. A string indicating position (:mod:`matplotlib` \
format) to put a legend. ``True`` is a synonym for ``best`` \
position.
:param savepath: A path to save the image to (optional). If set, \
the image will be saved on exiting a `with` statement.
:param grid: A dict containing the width and height of the grid, and \
a description of the grid as a list of subplots. Each subplot \
is a tuple of \
``((y_position, x_position), symbol, (rowspan, colspan))``. \
No check for grid validity is being made. You can set it to \
``False`` to disable it completely.
:param custom_mpl_rc: An optional dict to overload some \
:mod:`matplotlib` rc params.
.. note:: If you use group plotting, ``xlabel``, ``ylabel``, \
``legend``, ``xrange``, ``yrange`` and ``zrange`` will be \
set uniformly for every subplot. If you wish to set \
different properties for every subplots, you \
should pass a dict for these properties, keys being the \
group symbols and values being the value for each subplot.
"""
# Set default values for attributes
self.xlabel = xlabel
self.ylabel = ylabel
self.title = title
self.xrange = xrange
self.yrange = yrange
self.palette = palette
self.legend = legend
self.plots = collections.defaultdict(list) # keys are groups
self.grid = grid
self.savepath = savepath
self.custom_mpl_rc = None
# Working attributes
self.figure = None
self.axes = None
self.animation = {"type": False,
"args": (), "kwargs": {},
"persist": []}
def __enter__(self):
return self
def __exit__(self, exception_type, exception_value, traceback):
# Do not render the figure if an exception was raised
if exception_type is None:
if self.savepath is not None:
self.save()
else:
self.show()
def save(self, *args, **kwargs):
"""
Render and save the figure. Also show the figure if possible.
.. note:: Signature is the same as the one from \
``matplotlib.pyplot.savefig``. You should refer to its \
documentation for lengthy details.
.. note:: Typical use is:
>>> with replot.Figure() as figure: figure.save("SOME_FILENAME")
.. note:: If a ``savepath`` has been provided to the :class:`Figure` \
object, you can call ``figure.save()`` without argument and \
this path will be used.
"""
if len(args) == 0 and self.savepath is not None:
args = (self.savepath,)
self.render()
if self.figure is not None:
self.figure.savefig(*args, **kwargs)
else:
raise exc.InvalidFigure("Invalid figure.")
def show(self):
"""
Render and show the :class:`Figure` object.
"""
self.render()
if self.figure is not None:
self.figure.show()
else:
raise exc.InvalidFigure("Invalid figure.")
def set_grid(self, grid_description=None,
height=None, width=None, ignore_groups=False,
auto=False):
"""
Apply a grid layout on the figure (subplots). Subplots are based on \
defined groups (see ``group`` keyword to \
``replot.Figure.plot``).
:param grid_description: A list of rows. Each row is a string \
containing the groups to display (can be seen as ASCII art). \
Can be a single string in case of a single row.
:param height: An optional ``height`` for the grid, implies \
``auto=True``.
:param width: An optional ``height`` for the grid, implies \
``auto=True``.
:param ignore_groups: (optional, implies ``auto=True``) By default, \
``set_grid`` will use groups to organize plots in different \
subplots. If you want to put every plot in a different \
subplot, regardless of their groups, you can set this \
to ``True``.
:param auto: Whether the grid should be guessed automatically from \
groups or not (optional).
.. note:: Groups are a single unicode character. If a group does not \
contain any plot, the resulting subplot will simply be empty.
.. note:: Note that if you do not include the default group in the \
grid description, it will not be shown.
>>> with replot.Figure() as fig: fig.set_grid(["AAA",
"BBC"
"DEC"])
"""
# Handle auto gridifying
if auto or height is not None or width is not None or ignore_groups:
self._set_auto_grid(height, width, ignore_groups)
return
# Default parameters
if grid_description is None:
grid_description = []
elif grid_description is False:
# Disable the grid and return
self.grid = False
return
elif isinstance(grid_description, str):
# If a single string is provided, enclose it in a list.
grid_description = [grid_description]
# Check that grid is not empty
if len(grid_description) == 0:
raise exc.InvalidParameterError("Grid cannot be an empty list.")
# Check that all rows have the same number of elements
for row in grid_description:
if len(row) != len(grid_description[0]):
raise exc.InvalidParameterError(
"All rows must have the same number of elements.")
# Parse the ASCII art grid
parsed_grid = grid_parser.parse_ascii(grid_description)
if parsed_grid is None:
# If grid is not valid, raise an exception
raise exc.InvalidParameterError(
"Invalid grid provided. You did not use rectangular areas " +
"for each group.")
# Set the grid
self.grid = parsed_grid
def _set_auto_grid(self, height=None, width=None, ignore_groups=False):
"""
Apply an automatic grid on the figure, trying to fit best to the \
number of plots.
.. note:: This method must be called after all the plots \
have been added to the figure, or the grid will miss some \
groups.
.. note:: The grid will be filled by the groups in lexicographic \
order. Unassigned plots go to the last subplot.
:param height: An optional ``height`` for the grid.
:param width: An optional ``height`` for the grid.
:param ignore_groups: By default, ``set_grid`` will use groups to \
organize plots in different subplots. If you want to put \
every plot in a different subplot, regardless of their \
groups, you can set this to ``True``.
"""
if ignore_groups:
# If we want to ignore groups, we will start by creating a new
# group for every existing plot
existing_plots = []
for group_ in self.plots:
existing_plots.extend(self.plots[group_])
self.plots = collections.defaultdict(list,
{chr(i): [existing_plots[i]]
for i in
range(len(existing_plots))})
# Find the optimal layout
nb_groups = len(self.plots)
if height is None and width is not None:
height = math.ceil(nb_groups / width)
elif width is None and height is not None:
width = math.ceil(nb_groups / height)
else:
height, width = _optimal_grid(nb_groups)
# Apply the layout
groups = sorted([k
for k in self.plots.keys()
if k != _DEFAULT_GROUP and len(self.plots[k]) > 0])
if len(self.plots[_DEFAULT_GROUP]) > 0:
# Handle default group separately
groups.append(_DEFAULT_GROUP)
grid_description = ["".join(batch)
for batch in tools.batch(groups, width)]
self.set_grid(grid_description)
def plot(self, *args, **kwargs):
"""
Plot something on the :class:`Figure` object.
.. note:: This function expects ``args`` and ``kwargs`` to support
every possible case. You can either pass it (see examples):
- A single argument, being a series of points or a function.
- Two series of points representing X values and Y values \
(standard :mod:`matplotlib` behavior).
- Two arguments being a function and a list of points at which \
it should be evaluated (X values).
- Two arguments being a function and an interval represented by \
a tuple of its bounds.
.. note:: ``kwargs`` arguments are passed to \
``matplotlib.pyplot.plot``.
.. note:: You can use some :mod:`replot` specific keyword arguments:
- ``group`` which permits to group plots together, in \
subplots (one unicode character maximum). ``group`` \
keyword will not affect the render unless you state \
:mod:`replot` to use subplots. Note that ``_`` is a \
reserved group name which cannot be used.
- ``line`` which can be set to ``True``/``False`` to plot \
broken lines or discrete data series.
- ``logscale`` which can be either ``log`` or ``loglog`` to use \
such scales.
- ``orthonormal`` (boolean) to force axis to be orthonormal.
- ``xlim`` and ``ylim`` which are tuples of intervals on the \
x and y axis.
- ``invert`` (boolean) invert X and Y axis on the plot. Invert \
the axes labels as well.
- ``rotate`` (angle in degrees) rotate the plot by the angle in \
degrees. Leave the labels untouched.
- ``frame`` to specify a frame on which the plot should appear \
when calling ``animate`` afterwards. Default behavior is \
to increase the frame number between each plots. Frame \
count starts at 0.
.. note:: Note that this API call considers list of tuples as \
list of (x, y) coordinates to plot, contrary to standard \
matplotlib API which considers it is two different plots.
>>> with replot.figure() as fig: fig.plot(np.sin, (-1, 1))
>>> with replot.figure() as fig: fig.plot(np.sin, [-1, -0.9, …, 1])
>>> with replot.figure() as fig: fig.plot([1, 2, 3], [4, 5, 6])
>>> with replot.figure() as fig: fig.plot([1, 2, 3],
[4, 5, 6], linewidth=2.0)
>>> with replot.figure() as fig: fig.plot([1, 2, 3],
[4, 5, 6], group="a")
"""
if len(args) == 0:
raise exc.InvalidParameterError(
"You should pass at least one argument to this function.")
# Extract custom kwargs (the ones from replot but not matplotlib) from
# kwargs
if kwargs is not None:
kwargs, custom_kwargs = _handle_custom_plot_arguments(kwargs)
else:
custom_kwargs = {}
if hasattr(args[0], "__call__"):
# We want to plot a function
plot_ = _plot_function(args[0], *(args[1:]), **kwargs)
else:
# Else, it is a point series, and we just have to store it for
# later plotting.
if hasattr(args[0], "__iter__"):
try:
# If we pass it a list of tuples, consider it as a list of
# (x, y) coordinates contrary to the standard matplotlib
# behavior
x_list, y_list = zip(*args[0])
args = (list(x_list),
list(y_list)) + args[1:]
except (TypeError, StopIteration, AssertionError):
pass
plot_ = (args, kwargs)
# Keep track of the custom kwargs
plot_ += (custom_kwargs,)
# Handle inversion
if "invert" in custom_kwargs and custom_kwargs["invert"]:
# Invert X and Y data
plot_ = (
(plot_[0][1], plot_[0][0]) + plot_[0][2:],
plot_[1], plot_[2])
# Handle rotation
if "rotate" in custom_kwargs:
# Rotate X, Y data
# TODO: Not clean
new_X_list = []
new_Y_list = []
for x, y in zip(plot_[0][0], plot_[0][1]):
new_X_list.append(
np.cos(custom_kwargs["rotate"]) * x +
np.sin(custom_kwargs["rotate"]) * y)
new_Y_list.append(
-np.sin(custom_kwargs["rotate"]) * x +
np.cos(custom_kwargs["rotate"]) * y)
plot_ = (
(new_X_list, new_Y_list) + plot_[0][2:],
plot_[1], plot_[2])
# Add the plot to the correct group
if "group" in custom_kwargs:
group_ = custom_kwargs["group"]
else:
group_ = _DEFAULT_GROUP
self.plots[group_].append(plot_)
# Automatically set the legend if label is found
# (only do it if legend is not explicitly suppressed)
if "label" in kwargs and self.legend is None:
self.legend = True
def logplot(self, *args, **kwargs):
"""
Plot something on the :class:`Figure` object, in log scale.
.. note:: See :func:`replot.Figure.plot` for the full documentation.
>>> with replot.figure() as fig: fig.logplot(np.log, (-1, 1))
"""
kwargs["logscale"] = "log"
self.plot(*args, **kwargs)
def loglogplot(self, *args, **kwargs):
"""
Plot something on the :class:`Figure` object, in log-log scale.
.. note:: See :func:`replot.Figure.plot` for the full documentation.
>>> with replot.figure() as fig: fig.logplot(np.log, (-1, 1))
"""
kwargs["logscale"] = "loglog"
self.plot(*args, **kwargs)
def animate(self, *args, **kwargs):
"""
Create an animation.
You can either:
- pass it a function TODO
- use it directly without arguments to create an animation from \
the previously plot commands.
"""
# TODO
self.animation["type"] = "gif"
self.animation["args"] = args
self.animation["kwargs"] = kwargs
def _legend(self, axis, overload_legend=None):
"""
Helper function to handle ``legend`` attribute. It places the legend \
correctly depending on attributes and required plots.
:param axis: The :mod:`matplotlib` axis to put the legend on.
:param overload_legend: An optional legend specification to use \
instead of the ``legend`` attribute.
"""
if overload_legend is None:
overload_legend = self.legend
# If no legend is required, just pass
if overload_legend is None or overload_legend is False:
return
if overload_legend is True:
# If there should be a legend, but no location provided, put it at
# best location.
location = "best"
else:
location = overload_legend
# Create aliases for "upper" / "top" and "lower" / "bottom"
location.replace("top ", "upper ")
location.replace("bottom ", "lower ")
# Avoid warning if no labels were given for plots
nb_labelled_plots = sum(["label" in plt[1]
for group in self.plots.values()
for plt in group])
if nb_labelled_plots > 0:
# Add legend
axis.legend(loc=location)
def _grid(self):
"""
Create subplots according to the grid description.
:returns: A tuple containing the figure object as first element, and \
a dict mapping the symbols of the groups to matplotlib axes \
as second element.
"""
if self.grid is False:
# If grid is disabled, we plot every group in the same sublot.
axes = {}
figure, axis = plt.subplots()
# Set the palette for the subplot
axis.set_prop_cycle(
self._build_cycler_palette(sum([len(i)
for i in self.plots.values()]))
)
# Set the axis for every subplot
for subplot in self.plots:
axes[subplot] = axis
# Set attributes
self.figure = figure
self.axes = axes
# Return
return
elif self.grid is None:
# If no grid is provided, create an auto grid for the figure.
self._set_auto_grid()
# Axes is a dict associating symbols to matplotlib axes
axes = {}
figure = plt.figure()
# Build all the axes
grid_size = (self.grid["height"], self.grid["width"])
for subplot in self.grid["grid"]:
position, symbol, (rowspan, colspan) = subplot
axes[symbol] = plt.subplot2grid(grid_size,
position,
colspan=colspan,
rowspan=rowspan)
# Set the palette for the subplot
axes[symbol].set_prop_cycle(
self._build_cycler_palette(len(self.plots[symbol])))
if _DEFAULT_GROUP not in axes:
# Set the default group axis to None if it is not in the grid
axes[_DEFAULT_GROUP] = None
# Set attributes
self.figure = figure
self.axes = axes
def _set_axes_properties(self, axis, group_):
is_inverted_axis = len(
[i
for i in self.plots[group_]
if "invert" in i[2] and i[2]["invert"]]
) > 0
# Set xlabel
if isinstance(self.xlabel, dict):
try:
if is_inverted_axis:
# Handle axis inversion
axis.set_ylabel(self.xlabel[group_])
else:
axis.set_xlabel(self.xlabel[group_])
except KeyError:
# No entry for this axis in the dict, pass it
pass
else:
if is_inverted_axis:
# Handle axis inversion
axis.set_ylabel(self.xlabel)
else:
axis.set_xlabel(self.xlabel)
# Set ylabel
if isinstance(self.ylabel, dict):
try:
if is_inverted_axis:
# Handle axis inversion
axis.set_xlabel(self.ylabel[group_])
else:
axis.set_ylabel(self.ylabel[group_])
except KeyError:
# No entry for this axis in the dict, pass it
pass
else:
if is_inverted_axis:
# Handle axis inversion
axis.set_xlabel(self.ylabel)
else:
axis.set_ylabel(self.ylabel)
# Set title
if isinstance(self.title, dict):
try:
axis.set_ylabel(self.title[group_])
except KeyError:
# No entry for this axis in the dict, pass it
pass
else:
axis.set_title(self.title)
# Set legend
if isinstance(self.legend, dict):
try:
self._legend(axis, overload_legend=self.legend[group_])
except KeyError:
# No entry for this axis in the dict, use default argument
# That is put a legend except if no plots on this axis.
self._legend(axis, overload_legend=True)
else:
self._legend(axis)
# Set xrange
if isinstance(self.xrange, dict):
try:
if self.xrange[group_] is not None:
axis.set_xlim(*self.xrange[group_])
except KeyError:
# No entry for this axis in the dict, pass it
pass
else:
if self.xrange is not None:
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
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.
"""
# Use custom matplotlib context
with mpl_custom_rc_context(rc=self.custom_mpl_rc):
if self.figure is None or self.axes is None:
# Create figure if necessary
self._grid()
if self.animation["type"] is False:
self._render_no_animation()
elif self.animation["type"] == "gif":
self._render_gif_animation()
elif self.animation["type"] == "animation":
# TODO
return None
else:
return None
self.figure.tight_layout(pad=1)
def _render_gif_animation(self):
"""
Handle the render of a GIF-like animation, cycling through the plots.
:returns: A :mod:`matplotlib` figure.
"""
# Init
# TODO
self.axes[_DEFAULT_GROUP].set_xlim((-2, 2))
self.axes[_DEFAULT_GROUP].set_ylim((-2, 2))
line, = self.axes[_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
default_args = (self.figure, animate)
default_kwargs = {
"frames": 200,
"interval": 20,
"blit": True,
}
# Update with overloaded arguments
default_args = default_args + self.animation["args"]
default_kwargs.update(self.animation["kwargs"])
# Keep track of animation object, as it has to persist
self.animation["persist"] = [
animation.FuncAnimation(*default_args, **default_kwargs)]
return self.figure
def _render_no_animation(self):
"""
Handle the render of the figure when no animation is used.
:returns: A :mod:`matplotlib` figure.
"""
# Add plots
for group_ in self.plots:
# Get the axis corresponding to current group
try:
axis = self.axes[group_]
except KeyError:
# If not found, plot in the default group
axis = self.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")
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_)
return self.figure
__all__ = ["plot", "Figure"]
def plot(data, **kwargs):
@ -753,243 +47,3 @@ def plot(data, **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"]
# Handle "orthonormal" argument
if "orthonormal" in kwargs:
custom_kwargs["orthonormal"] = kwargs["orthonormal"]
del kwargs["orthonormal"]
# Handle "xlim" argument
if "xlim" in kwargs:
custom_kwargs["xlim"] = kwargs["xlim"]
del kwargs["xlim"]
# Handle "ylim" argument
if "ylim" in kwargs:
custom_kwargs["ylim"] = kwargs["ylim"]
del kwargs["ylim"]
# Handle "invert" argument
if "invert" in kwargs:
custom_kwargs["invert"] = kwargs["invert"]
del kwargs["invert"]
# Handle "rotate" argument
if "rotate" in kwargs:
# Convert angle to radians
custom_kwargs["rotate"] = kwargs["rotate"] * np.pi / 180
del kwargs["rotate"]
# Handle "frame" argument
if "frame" in kwargs:
custom_kwargs["frame"] = kwargs["frame"]
del kwargs["frame"]
else:
custom_kwargs["frame"] = 0
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)

8
replot/constants.py Normal file
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@ -0,0 +1,8 @@
"""
Useful constants for :mod:`replot`.
"""
__VERSION__ = "0.0.1"
# Default subplot group (reserved name)
DEFAULT_GROUP = "_"

2
replot/exceptions.py
View File

@ -1,5 +1,5 @@
"""
TODO
Exception classes used in :mod:`replot`.
"""

621
replot/figure.py Normal file
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"""
"""
import collections
import math
import os
import matplotlib as mpl
# Use "agg" backend automatically if no display is available.
try:
os.environ["DISPLAY"]
except KeyError:
mpl.use("agg")
import matplotlib.animation as animation
import matplotlib.pyplot as plt
import numpy as np
from replot import constants
from replot import exceptions as exc
from replot import tools
from replot.grid import layout
from replot.grid import parser as grid_parser
from replot.helpers import custom_kwargs as custom_kwargs_parser
from replot.helpers import custom_mpl
from replot.helpers import palette as rpalette
from replot.helpers import plot as plot_helpers
from replot.helpers import render as render_helpers
class Figure():
"""
The main class from :mod:`replot`, representing a figure. Can be used \
directly or in a ``with`` statement.
"""
def __init__(self,
xlabel="", ylabel="", title="",
xrange=None, yrange=None,
palette=rpalette.default,
legend=None, savepath=None, grid=None,
custom_mpl_rc=None):
"""
Build a :class:`Figure` object.
:param xlabel: Label for the X axis (optional).
:param ylabel: Label for the Z axis (optional).
:param title: Title of the figure (optional).
:param xrange: Range of the X axis (optional), as a tuple \
representing the interval.
:param yrange: Range of the Y axis (optional), as a tuple \
representing the interval.
:param palette: Color palette to use (optional). Defaults to a safe \
palette with compatibility with colorblindness and black and \
white printing.
:type palette: Either a list of colors (as RGB tuples) or a function \
to call with number of plots as parameter and which returns a \
list of colors (as RGB tuples). You can also pass a Seaborn \
palette directly, or use a Palettable Palette.mpl_colors.
:param legend: Whether to use a legend or not (optional). Defaults to \
no legend, except if labels are found on provided plots. \
``False`` to disable completely. ``None`` for default \
behavior. A string indicating position (:mod:`matplotlib` \
format) to put a legend. ``True`` is a synonym for ``best`` \
position.
:param savepath: A path to save the image to (optional). If set, \
the image will be saved on exiting a `with` statement.
:param grid: A dict containing the width and height of the grid, and \
a description of the grid as a list of subplots. Each subplot \
is a tuple of \
``((y_position, x_position), symbol, (rowspan, colspan))``. \
No check for grid validity is being made. You can set it to \
``False`` to disable it completely.
:param custom_mpl_rc: An optional dict to overload some \
:mod:`matplotlib` rc params.
.. note:: If you use group plotting, ``xlabel``, ``ylabel``, \
``legend``, ``xrange``, ``yrange`` and ``zrange`` will be \
set uniformly for every subplot. If you wish to set \
different properties for every subplots, you \
should pass a dict for these properties, keys being the \
group symbols and values being the value for each subplot.
"""
# Set default values for attributes
self.xlabel = xlabel
self.ylabel = ylabel
self.title = title
self.xrange = xrange
self.yrange = yrange
self.palette = palette
self.legend = legend
self.plots = collections.defaultdict(list) # keys are groups
self.grid = grid
self.savepath = savepath
self.custom_mpl_rc = custom_mpl_rc
# Working attributes
self.figure = None
self.axes = None
self.animation = {"type": False,
"args": (), "kwargs": {},
"persist": []}
def __enter__(self): # Allow use in a with statement
return self
def __exit__(self, exception_type, exception_value, traceback):
# Do not render the figure if an exception was raised
if exception_type is None:
if self.savepath is not None:
self.save()
else:
self.show()
def save(self, *args, **kwargs):
"""
Render and save the figure. Also show the figure if possible.
.. note:: Signature is the same as the one from \
``matplotlib.pyplot.savefig``. You should refer to its \
documentation for lengthy details.
.. note:: If a ``savepath`` has been provided to the :class:`Figure` \
object, you can call ``figure.save()`` without argument and \
this path will be used.
>>> with replot.Figure() as figure: figure.save("SOME_FILENAME")
>>> with replot.Figure(savepath="SOME_FILENAME") as figure: pass
"""
if len(args) == 0 and self.savepath is not None:
args = (self.savepath,)
self.render()
if self.figure is not None:
self.figure.savefig(*args, **kwargs)
else:
raise exc.InvalidFigure("Invalid figure.")
def show(self):
"""
Render and show the :class:`Figure` object.
"""
self.render()
if self.figure is not None:
self.figure.show()
else:
raise exc.InvalidFigure("Invalid figure.")
def render(self):
"""
Actually render the figure. Update ``self.figure`` attribute, but do \
not show or save it.
:returns: None.
"""
# Use custom matplotlib context
with plt.rc_context(rc=custom_mpl.custom_rc(rc=self.custom_mpl_rc)):
if self.figure is None or self.axes is None:
# Create figure if necessary
self._render_grid()
# Render depending on animation type
if self.animation["type"] is False:
self._render_no_animation()
elif self.animation["type"] == "gif":
self._render_gif_animation()
elif self.animation["type"] == "animation":
# TODO
return None
else:
return None
# Use tight_layout to optimize layout, use custom padding
self.figure.tight_layout(pad=1)
def set_grid(self, grid_description=None,
height=None, width=None, ignore_groups=False,
auto=None):
"""
Apply a grid layout on the figure (subplots). Subplots are based on \
defined groups (see ``group`` keyword to \
``replot.Figure.plot``).
:param grid_description: A list of rows. Each row is a string \
containing the groups to display (can be seen as ASCII art). \
Can be a single string in case of a single row.
:param height: An optional ``height`` for the grid, implies \
``auto=True``.
:param width: An optional ``height`` for the grid, implies \
``auto=True``.
:param ignore_groups: (optional, implies ``auto=True``) By default, \
``set_grid`` will use groups to organize plots in different \
subplots. If you want to put every plot in a different \
subplot, regardless of their groups, you can set this \
to ``True``.
:param auto: Whether the grid should be guessed automatically from \
groups or not (optional).
:returns: None.
.. note:: Groups are a single unicode character. If a group does not \
contain any plot, the resulting subplot will simply be empty.
.. note:: Note that if you do not include the default group in the \
grid description, it will not be shown.
>>> with replot.Figure() as fig: fig.set_grid(["AAA",
"BBC"
"DEC"])
"""
# Handle incompatible arguments
if((height is not None or width is not None or ignore_groups) and
auto is False):
raise exc.InvalidParameterError(
"auto=False and height/width/ignore_groups arguments are " +
"not compatible.")
# Handle auto gridifying
if auto is None:
auto = False
if auto or height is not None or width is not None or ignore_groups:
self._set_auto_grid(height, width, ignore_groups)
return
if grid_description is False:
# Disable the grid and return
self.grid = False
return
elif isinstance(grid_description, str):
# If a single string is provided, enclose it in a list.
grid_description = [grid_description]
# Check that grid is not empty
if grid_description is None or len(grid_description) == 0:
raise exc.InvalidParameterError("Grid cannot be an empty list.")
# Check that all rows have the same number of elements
for row in grid_description:
if len(row) != len(grid_description[0]):
raise exc.InvalidParameterError(
"All rows must have the same number of elements.")
# Parse the ASCII art grid
parsed_grid = grid_parser.parse_ascii(grid_description)
if parsed_grid is None:
# If grid is not valid, raise an exception
raise exc.InvalidParameterError(
"Invalid grid provided. You did not use rectangular areas " +
"for each group.")
# Set the grid
self.grid = parsed_grid
def plot(self, *args, **kwargs):
"""
Plot something on the :class:`Figure` object.
.. note:: This function expects ``args`` and ``kwargs`` to support
every possible case. You can either pass it (see examples):
- A single argument, being a series of points or a function.
- Two series of points representing X values and Y values \
(standard :mod:`matplotlib` behavior).
- Two arguments being a function and a list of points at which \
it should be evaluated (X values).
- Two arguments being a function and an interval represented by \
a tuple of its bounds.
.. note:: ``kwargs`` arguments are passed to \
``matplotlib.pyplot.plot``.
.. note:: You can use some :mod:`replot` specific keyword arguments:
- ``group`` which permits to group plots together, in \
subplots (one unicode character maximum). ``group`` \
keyword will not affect the render unless you state \
:mod:`replot` to use subplots. Note that ``_`` is a \
reserved group name which cannot be used.
- ``line`` which can be set to ``True``/``False`` to plot \
broken lines or discrete data series.
- ``logscale`` which can be either ``log`` or ``loglog`` to use \
such scales.
- ``orthonormal`` (boolean) to force axis to be orthonormal.
- ``xlim`` and ``ylim`` which are tuples of intervals on the \
x and y axis.
- ``invert`` (boolean) invert X and Y axis on the plot. Invert \
the axes labels as well.
- ``rotate`` (angle in degrees) rotate the plot by the angle in \
degrees. Leave the labels untouched.
- ``frame`` to specify a frame on which the plot should appear \
when calling ``animate`` afterwards. Default behavior is \
to increase the frame number between each plots. Frame \
count starts at 0.
.. note:: Note that this API call considers list of tuples as \
list of (x, y) coordinates to plot, contrary to standard \
matplotlib API which considers it is two different plots.
>>> with replot.figure() as fig: fig.plot(np.sin, (-1, 1))
>>> with replot.figure() as fig: fig.plot(np.sin, [-1, -0.9, …, 1])
>>> with replot.figure() as fig: fig.plot([1, 2, 3], [4, 5, 6])
>>> with replot.figure() as fig: fig.plot([1, 2, 3],
[4, 5, 6], linewidth=2.0)
>>> with replot.figure() as fig: fig.plot([1, 2, 3],
[4, 5, 6], group="a")
"""
if len(args) == 0:
raise exc.InvalidParameterError(
"You should pass at least one argument to this function.")
# Extract custom kwargs (the ones from replot but not matplotlib) from
# kwargs
kwargs, custom_kwargs = custom_kwargs_parser.parse(kwargs)
if hasattr(args[0], "__call__"):
# We want to plot a function
plot_ = plot_helpers.plot_function(args[0], *(args[1:]), **kwargs)
else:
# Else, it is a point series, and we just have to store it for
# later plotting.
if hasattr(args[0], "__iter__"):
try:
# If we pass it a list of tuples, consider it as a list of
# (x, y) coordinates contrary to the standard matplotlib
# behavior
x_list, y_list = zip(*args[0])
args = (list(x_list),
list(y_list)) + args[1:]
except (TypeError, StopIteration, AssertionError):
pass
plot_ = (args, kwargs)
# Apply custom kwargs on plot_
plot_ = custom_kwargs_parser.edit_plot_command(plot_, custom_kwargs)
# Add the plot to the correct group
if "group" in custom_kwargs:
group_ = custom_kwargs["group"]
else:
group_ = constants.DEFAULT_GROUP
self.plots[group_].append(plot_)
# Automatically set the legend if label is found
# (only do it if legend is not explicitly suppressed)
if "label" in kwargs and self.legend is None:
self.legend = True
def logplot(self, *args, **kwargs):
"""
Plot something on the :class:`Figure` object, in log scale.
.. note:: See :func:`replot.Figure.plot` for the full documentation.
.. note:: Side effect of this function is to set the axes to be in \
log scale for the associated subplot.
>>> with replot.figure() as fig: fig.logplot(np.log, (-1, 1))
"""
kwargs["logscale"] = "log"
self.plot(*args, **kwargs)
def loglogplot(self, *args, **kwargs):
"""
Plot something on the :class:`Figure` object, in log-log scale.
.. note:: See :func:`replot.Figure.plot` for the full documentation.
.. note:: Side effect of this function is to set the axes to be in \
log scale for the associated subplot.
>>> with replot.figure() as fig: fig.logplot(np.log, (-1, 1))
"""
kwargs["logscale"] = "loglog"
self.plot(*args, **kwargs)
def animate(self, *args, **kwargs):
"""
Create an animation.
You can either:
- use it directly without arguments to create an animation from \
the previously plot commands, cycling through the plots \
for each subplot.
- TODO: Use an animation function.
"""
self.animation["type"] = "gif"
self.animation["args"] = args
self.animation["kwargs"] = kwargs
###################
# Private methods #
###################
def _set_auto_grid(self, height=None, width=None, ignore_groups=False):
"""
Apply an automatic grid on the figure, trying to fit best to the \
number of plots.
.. note:: This method must be called after all the plots \
have been added to the figure, or the grid will miss some \
groups.
.. note:: The grid will be filled by the groups in lexicographic \
order. Unassigned plots go to the last subplot.
:param height: An optional ``height`` for the grid.
:param width: An optional ``height`` for the grid.
:param ignore_groups: By default, ``set_grid`` will use groups to \
organize plots in different subplots. If you want to put \
every plot in a different subplot, regardless of their \
groups, you can set this to ``True``.
"""
if ignore_groups:
# If we want to ignore groups, we will start by creating a new
# group for every existing plot
existing_plots = []
for group_ in self.plots:
existing_plots.extend(self.plots[group_])
self.plots = collections.defaultdict(list,
{chr(i): [existing_plots[i]]
for i in
range(len(existing_plots))})
# Find the optimal layout
nb_groups = len(self.plots)
if height is None and width is not None:
height = math.ceil(nb_groups / width)
elif width is None and height is not None:
width = math.ceil(nb_groups / height)
else:
height, width = layout.optimal(nb_groups)
# Apply the layout
groups = sorted([k
for k in self.plots.keys()
if k != constants.DEFAULT_GROUP and
len(self.plots[k]) > 0])
if len(self.plots[constants.DEFAULT_GROUP]) > 0:
# Handle default group separately
groups.append(constants.DEFAULT_GROUP)
grid_description = ["".join(batch)
for batch in tools.batch(groups, width)]
self.set_grid(grid_description)
def _legend(self, axis, overload_legend=None):
"""
Helper function to handle ``legend`` attribute. It places the legend \
correctly depending on attributes and required plots.
:param axis: The :mod:`matplotlib` axis to put the legend on.
:param overload_legend: An optional legend specification to use \
instead of the ``legend`` attribute.
"""
if overload_legend is None:
overload_legend = self.legend
# If no legend is required, just pass
if overload_legend is None or overload_legend is False:
return
if overload_legend is True:
# If there should be a legend, but no location provided, put it at
# best location.
location = "best"
else:
location = overload_legend
# Create aliases for "upper" / "top" and "lower" / "bottom"
location.replace("top ", "upper ")
location.replace("bottom ", "lower ")
# Avoid warning if no labels were given for plots
nb_labelled_plots = sum(["label" in plt[1]
for group in self.plots.values()
for plt in group])
if nb_labelled_plots > 0:
# Add legend
axis.legend(loc=location)
def _render_grid(self):
"""
Helper method to fill ``self.figure`` and ``self.axes`` with \
subplots according to the grid description.
:returns: A tuple containing the figure object as first element, and \
a dict mapping the symbols of the groups to matplotlib axes \
as second element.
"""
# If no grid is provided, create an auto grid for the figure.
if self.grid is None:
self._set_auto_grid()
# Axes is a dict associating symbols to matplotlib axes
axes = {}
figure = plt.figure()
# Build all the axes
if self.grid is not False:
grid_size = (self.grid["height"], self.grid["width"])
for subplot in self.grid["grid"]:
position, symbol, (rowspan, colspan) = subplot
axes[symbol] = plt.subplot2grid(grid_size,
position,
colspan=colspan,
rowspan=rowspan)
# Set the palette for the subplot
axes[symbol].set_prop_cycle(
rpalette.build_cycler_palette(self.palette,
len(self.plots[symbol])))
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
axis.set_prop_cycle(
rpalette.build_cycler_palette(
self.palette,
sum([len(i) for i in self.plots.values()]))
)
# Set the axis for every subplot
for subplot in self.plots:
axes[subplot] = axis
# Set attributes
self.figure = figure
self.axes = 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)
# 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
render_helpers.set_axis_property(group_, axis.set_xlim, self.xrange)
# Set yrange
render_helpers.set_axis_property(group_, axis.set_ylim, self.yrange)
def _render_gif_animation(self):
"""
Handle the render of a GIF-like animation, cycling through the plots.
:returns: A :mod:`matplotlib` figure.
"""
# Init
# TODO
self.axes[constants.DEFAULT_GROUP].set_xlim((-2, 2))
self.axes[constants.DEFAULT_GROUP].set_ylim((-2, 2))
line, = self.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
default_args = (self.figure, animate)
default_kwargs = {
"frames": 200,
"interval": 20,
"blit": True,
}
# Update with overloaded arguments
default_args = default_args + self.animation["args"]
default_kwargs.update(self.animation["kwargs"])
# Keep track of animation object, as it has to persist
self.animation["persist"] = [
animation.FuncAnimation(*default_args, **default_kwargs)]
return self.figure
def _render_no_animation(self):
"""
Handle the render of the figure when no animation is used.
:returns: A :mod:`matplotlib` figure.
"""
# Add plots
for group_ in self.plots:
# Get the axis corresponding to current group
try:
axis = self.axes[group_]
except KeyError:
# If not found, plot in the default group
axis = self.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_)
return self.figure

38
replot/grid/layout.py Normal file
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"""
Grid layout functions.
"""
import math
def optimal(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(2)
(1, 2)
>>> _optimal(3)
(1, 3)
>>> _optimal(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)

0
replot/grid_parser.py → replot/grid/parser.py
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102
replot/helpers/custom_kwargs.py Normal file
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"""
Parse custom keyword arguments for ``plot`` command.
"""
import numpy as np
from replot import constants
from replot import exceptions as exc
def parse(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 ``kwargs`` ``dict``.
"""
# Default values
custom_kwargs = {
"frame": 0
}
# 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"] == constants.DEFAULT_GROUP:
raise exc.InvalidParameterError(
"'%s' is a reserved group name." % (constants.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 "xrange" argument, alias for xlim
if "xrange" in kwargs:
kwargs["xlim"] = kwargs["xrange"]
# Handle "yrange" argument, alias for xlim
if "yrange" in kwargs:
kwargs["ylim"] = kwargs["yrange"]
# Handle other arguments
custom_args = [
"frame",
"invert",
"logscale",
"orthonormal",
"rotate",
"xlim",
"ylim"]
for custom_arg in custom_args:
if custom_arg in kwargs:
custom_kwargs[custom_arg] = kwargs[custom_arg]
del kwargs[custom_arg]
return (kwargs, custom_kwargs)
def edit_plot_command(plot_, custom_kwargs):
"""
Edit a plot_ command tuple to take into account custom kwargs, that is \
append them to the plot_ command and edit the command accordingly \
(for axes inversion for instance).
:param plot_: a ``(args, kwargs)`` plot command.
:param custom_kwargs: A dict of custom kwargs.
:returns: a ``(args, kwargs, custom_kwargs)`` plot command.
"""
# Keep track of custom_kwargs in plot_
plot_ += (custom_kwargs,)
# Handle inversion
if "invert" in custom_kwargs and custom_kwargs["invert"]:
# Invert X and Y data
plot_ = (
(plot_[0][1], plot_[0][0]) + plot_[0][2:],
plot_[1], plot_[2])
# Handle rotation
if "rotate" in custom_kwargs:
# Rotate X, Y data
# TODO: Not clean
new_X_list = []
new_Y_list = []
for x, y in zip(plot_[0][0], plot_[0][1]):
angle = np.deg2rad(custom_kwargs["rotate"])
new_X_list.append(
np.cos(angle) * x +
np.sin(angle) * y)
new_Y_list.append(
-np.sin(angle) * x +
np.cos(angle) * y)
plot_ = (
(new_X_list, new_Y_list) + plot_[0][2:],
plot_[1], plot_[2])
return plot_

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replot/helpers/custom_mpl.py Normal file
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"""
Functions to set custom :mod:`matplotlib` parameters.
"""
import shutil
import numpy as np
def custom_rc(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(_rc_scaling())
# Set axes style
custom_rc_.update(_rc_axes_style())
# Overload if necessary
if rc is not None:
custom_rc_.update(rc)
# Return a context object
return custom_rc_
def _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 _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

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replot/helpers/palette.py Normal file
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"""
Palette handling functions.
"""
import cycler
import palettable
def default(n):
"""
Default palette is a CubeHelix perceptual rainbow palette with length the
number of plots.
:param n: The number of colors in the palette.
:returns: The palette as a list of colors (as RGB tuples).
"""
return palettable.cubehelix.Cubehelix.make(
start_hue=240., end_hue=-300., min_sat=1., max_sat=2.5,
min_light=0.3, max_light=0.8, gamma=.9, n=n).mpl_colors
def build_cycler_palette(palette, 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(palette, "__call__"):
return cycler.cycler("color", palette(n))
else:
return cycler.cycler("color", palette)

43
replot/helpers/plot.py Normal file
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"""
Various helper functions for plotting.
"""
import numpy as np
from replot import adaptive_sampling
from replot import exceptions as exc
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)

28
replot/helpers/render.py Normal file
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"""
Various helper functions for plotting.
"""
def set_axis_property(group_, setter, value, default_setter=None):
"""
Set a property on an axis at render time.
:param group_: The subplot for this axis.
:param setter: The setter to use to set the axis property.
:param value: The value for the property, either a value or a dict with \
group as key.
:param default_setter: Default setter to call if ``value`` is a dict but \
has no key for the given subplot.
:returns: None.
"""
if isinstance(value, dict):
try:
if value[group_] is not None:
setter(value[group_])
except KeyError:
# No entry for this axis in the dict, use default argument
if default_setter is not None:
default_setter()
else:
if value is not None:
setter(value)

2
setup.py
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@ -6,7 +6,7 @@ except ImportError:
print('[replot] setuptools not found.')
raise
with open('replot/__init__.py') as fh:
with open('replot/constants.py') as fh:
for line in fh:
line = line.strip()
if line.startswith('__VERSION__'):