def heated_barplot(
data: pd.Series, desat: float = 0.6, ax: Axes = None, figsize: tuple = (8, 10)
) -> Axes:
"""Plot a sharply divided ranking of positive and negative values.
Args:
data (pd.Series): Data to plot.
desat (float, optional): Saturation of bar colors. Defaults to 0.6.
ax (Axes, optional): Axes to plot on. Defaults to None.
figsize (tuple, optional): Figure size. Defaults to (8, 10).
Returns:
Axes: Axes for the plot.
"""
if not ax:
fig, ax = plt.subplots(figsize=figsize)
data.sort_values(ascending=False, inplace=True)
blues = sns.color_palette("Blues", (data <= 0).sum(), desat=desat)
reds = sns.color_palette("Reds_r", (data > 0).sum(), desat=desat)
palette = reds + blues
ax = sns.barplot(
x=data.values, y=data.index, palette=palette, orient="h", ec="gray", ax=ax
)
ax.axvline(0.0, color="gray", lw=1, ls="-")
return ax
def decorate_azimuth_ax(
ax: Axes,
label: str,
length_array: np.ndarray,
set_array: np.ndarray,
set_names: Tuple[str, ...],
set_ranges: SetRangeTuple,
axial: bool,
visualize_sets: bool,
append_azimuth_set_text: bool = False,
):
"""
Decorate azimuth rose plot ax.
"""
# Title is the name of the target area or group
prop_title = dict(boxstyle="square",
facecolor="linen",
alpha=1,
linewidth=2)
# title = "\n".join(wrap(f"{label}", 10))
title = fill(label, 10)
ax.set_title(
title,
x=0.94 if axial else 1.15,
y=0.8 if axial else 1.0,
fontsize="large",
fontweight="bold",
fontfamily="DejaVu Sans",
va="top",
bbox=prop_title,
transform=ax.transAxes,
ha="center",
)
prop = dict(boxstyle="square", facecolor="linen", alpha=1, pad=0.45)
# text = f"n ={len(set_array)}\n"
text = f"n ={len(set_array)}"
if append_azimuth_set_text:
text += "\n"
text = text + _create_azimuth_set_text(length_array, set_array,
set_names)
ax.text(
x=0.96 if axial else 1.1,
y=0.3 if axial else 0.15,
s=text,
transform=ax.transAxes,
fontsize="medium",
weight="roman",
bbox=prop,
fontfamily="DejaVu Sans",
va="top",
ha="center",
)
# Add lines to denote azimuth set edges
if visualize_sets:
for set_range in set_ranges:
for edge in set_range:
ax.axvline(np.deg2rad(edge), linestyle="dashed", color="black")
def all_traces(record_file: File, ax: Axes):
"""plot full traces of all neurons and trial onsets"""
lever_trajectory = load_mat(record_file["response"])
calcium_trace = _scale(DataFrame.load(record_file["measurement"]).values)
time = np.linspace(0, lever_trajectory.shape[1] / lever_trajectory.sample_rate, lever_trajectory.shape[1])
ax.plot(time, _scale(lever_trajectory.values[0]) - 5, COLORS[1])
for idx, row in enumerate(calcium_trace):
ax.plot(time, row + idx * 5)
for point in lever_trajectory.timestamps / lever_trajectory.sample_rate: # trial onsets
ax.axvline(x=point, color=COLORS[2])
def draw_cves(
axis: axes.Axes, project: tp.Type[Project],
revisions: tp.List[FullCommitHash], cve_line_width: int, cve_color: str,
label_size: int, vertical_alignment: str
) -> None:
"""
Annotates CVEs for a project in an existing plot.
Args:
axis: the axis to use for the plot
project: the project to add CVEs for
revisions: a list of revisions included in the plot in the order they
appear on the x-axis
cve_line_width: the line width of CVE annotations
cve_color: the color of CVE annotations
label_size: the label size of CVE annotations
vertical_alignment: the vertical alignment of CVE annotations
"""
cmap = create_lazy_commit_map_loader(project.NAME)()
revision_time_ids = [cmap.time_id(rev) for rev in revisions]
cve_provider = CVEProvider.get_provider_for_project(project)
for revision, cves in cve_provider.get_revision_cve_tuples():
cve_time_id = cmap.time_id(revision)
if cve_time_id in revision_time_ids:
index = float(revisions.index(revision))
else:
# revision not in sample; draw line between closest samples
index = len([x for x in revision_time_ids if x < cve_time_id]) - 0.5
transform = axis.get_xaxis_transform()
for cve in cves:
axis.axvline(
index,
label=cve.cve_id,
linewidth=cve_line_width,
color=cve_color
)
axis.text(
index + 0.1,
0,
cve.cve_id,
transform=transform,
rotation=90,
size=label_size,
color=cve_color,
va=vertical_alignment
)
def plot_peaks(ax: Axes, peak_list: List[Peak.Peak], label: str = "Peaks", style: str = 'o') -> List[Line2D]:
"""
Plots the locations of peaks as found by PyMassSpec.
:param ax: The axes to plot the peaks on
:param peak_list: List of peaks to plot
:param label: label for plot legend.
:param style: The marker style. See `https://matplotlib.org/3.1.1/api/markers_api.html` for a complete list
:return: A list of Line2D objects representing the plotted data.
"""
if not is_peak_list(peak_list):
raise TypeError("'peak_list' must be a list of Peak objects")
time_list = []
height_list = []
if "line" in style.lower():
lines = []
for peak in peak_list:
lines.append(ax.axvline(x=peak.rt, color="lightgrey", alpha=0.8, linewidth=0.3))
return lines
else:
for peak in peak_list:
time_list.append(peak.rt)
height_list.append(sum(peak.mass_spectrum.intensity_list))
# height_list.append(peak.height)
# print(peak.height - sum(peak.mass_spectrum.intensity_list))
# print(sum(peak.mass_spectrum.intensity_list))
return ax.plot(time_list, height_list, style, label=label)
def draw_bugs(axis: axes.Axes, project: tp.Type[Project],
revisions: tp.List[FullCommitHash], bug_line_width: int,
bug_color: str, label_size: int,
vertical_alignment: str) -> None:
"""
Annotates bugs for a project in an existing plot.
Args:
axis: the axis to use for the plot
project: the project to add bugs for
revisions: a list of revisions included in the plot in the order they
appear on the x-axis
bug_line_width: the line width of bug annotations
bug_color: the color of bug annotations
label_size: the label size of bug annotations
vertical_alignment: the vertical alignment of bug annotations
"""
cmap = create_lazy_commit_map_loader(project.NAME)()
revision_time_ids = [cmap.time_id(rev) for rev in revisions]
bug_provider = BugProvider.get_provider_for_project(project)
for rawbug in bug_provider.find_raw_bugs():
bug_time_id = cmap.time_id(rawbug.fixing_commit)
if bug_time_id in revision_time_ids:
index = float(revisions.index(rawbug.fixing_commit))
else:
# revision not in sample; draw line between closest samples
index = len([x
for x in revision_time_ids if x < bug_time_id]) - 0.5
label = " ".join([f"#{rawbug.issue_id}"])
transform = axis.get_xaxis_transform()
axis.axvline(index,
label=label,
linewidth=bug_line_width,
color=bug_color)
axis.text(index + 0.1,
0,
label,
transform=transform,
rotation=90,
size=label_size,
color=bug_color,
va=vertical_alignment)
def add_stat_line(
ax: Axes,
series_input: SeriesPlotIn,
stat: Callable[[Iterable[float]], float] = np.mean,
vert: bool = True,
**kwargs
) -> Axes:
if "linestyle" not in kwargs.keys():
kwargs.update({"linestyle": "--"})
stat_val: float = stat(series_input.data)
if vert:
ax.axvline(x=stat_val, color=series_input.color, **kwargs)
else:
ax.axhline(y=stat_val, color=series_input.color, **kwargs)
return ax
def example_traces(ax: Axes, record_file: File, start: float, end: float, cells: Set[int]):
"""Visualize calcium trace of cells and the lever trajectory"""
lever_trajectory = load_mat(record_file["response"])
calcium_trace = DataFrame.load(record_file["measurement"])
neuron_rate = record_file.attrs['frame_rate']
l_start, l_end = np.rint(np.multiply([start, end], lever_trajectory.sample_rate)).astype(np.int_)
c_start, c_end = np.rint(np.multiply([start, end], neuron_rate)).astype(np.int_)
ax.plot(np.linspace(0, l_end - l_start, l_end - l_start), # lever trajectory
_scale(lever_trajectory.values[0][l_start: l_end]), COLORS[1])
time = np.linspace(0, calcium_trace.shape[1] / neuron_rate, lever_trajectory.shape[1])
spacing = iter(range(0, 500, 2))
for idx, row in enumerate(calcium_trace.values):
if idx in cells:
ax.plot(time[c_start: c_end] - l_start, _scale(row[c_start: c_end]) + next(spacing))
stim_onsets = lever_trajectory.timestamps[
(lever_trajectory.timestamps > l_start) & (lever_trajectory.timestamps < l_end)]\
/ lever_trajectory.sample_rate - l_start
for x in stim_onsets:
ax.axvline(x=x, color=COLORS[2])
def _set_figure(cls, ax: axes.Axes, energy_range: Sequence, dos_range: Sequence):
"""set figure and axes for plotting
:params ax: matplotlib.axes.Axes object
:params dos_range: range of dos
:params energy_range: range of energy
"""
# y-axis
if dos_range:
ax.set_ylim(dos_range[0], dos_range[1])
ax.set_ylabel("DOS")
# x-axis
if energy_range:
ax.set_xlim(energy_range[0], energy_range[1])
# others
ax.axvline(0, linestyle="--", c='b', lw=1.0)
ax.legend()
def growth_curve(ax: Axes,
plate: Plate,
scatter_color: str,
line_color: str = None,
growth_params: bool = True):
"""
Add a growth curve scatter plot, with median, to an axis
:param ax: a Matplotlib Axes object to add a plot to
:param plate: a Plate instance
:param scatter_color: a Colormap color
:param line_color: a Colormap color for the median
"""
from statistics import median
if line_color is None:
line_color = scatter_color
for colony in plate.items:
ax.scatter(
# Matplotlib does not yet support timedeltas so we have to convert manually to float
[
td.total_seconds() / 3600
for td in sorted(colony.growth_curve.data.keys())
],
list(colony.growth_curve.data.values()),
color=scatter_color,
marker="o",
s=1,
alpha=0.25)
# Plot the median
ax.plot([
td.total_seconds() / 3600
for td in sorted(plate.growth_curve.data.keys())
], [median(val) for _, val in sorted(plate.growth_curve.data.items())],
color=line_color,
label="Median" if growth_params else f"Plate {plate.id}",
linewidth=2)
if growth_params:
# Plot lag, vmax and carrying capacity lines
if plate.growth_curve.lag_time.total_seconds() > 0:
line = ax.axvline(plate.growth_curve.lag_time.total_seconds() /
3600,
color="grey",
linestyle="dashed",
alpha=0.5)
line.set_label("Lag time")
if plate.growth_curve.carrying_capacity > 0:
line = ax.axhline(plate.growth_curve.carrying_capacity,
color="blue",
linestyle="dashed",
alpha=0.5)
line.set_label("Carrying\ncapacity")
if plate.growth_curve.growth_rate > 0:
y0, y1 = 0, plate.growth_curve.carrying_capacity
x0 = plate.growth_curve.lag_time.total_seconds() / 3600
x1 = ((y1 - y0) / (plate.growth_curve.growth_rate * 3600)) + x0
ax.plot([x0, x1], [y0, y1],
color="red",
linestyle="dashed",
alpha=0.5,
label="Maximum\ngrowth rate")
def show_agent_opinions(
self,
t=-1,
direction=True,
sort=False,
ax: Axes = None,
fig: Figure = None,
colorbar: bool = True,
title: str = "Agent opinions",
show_middle=True,
**kwargs,
) -> Tuple[Figure, Axes]:
cmap = kwargs.pop("cmap", OPINIONS_CMAP)
idx = get_time_point_idx(self.sn.result.t, t)
opinions = self.sn.result.y[:, idx]
agents = np.arange(self.sn.N)
if not direction:
# only magnitude
opinions = np.abs(opinions)
if np.iterable(sort) or sort:
if isinstance(sort, np.ndarray):
# sort passed as indices
ind = sort
else:
logger.warning(
"sorting opinions for `show_agent_opinions` means agent indices are jumbled"
)
# sort by opinion
ind = np.argsort(opinions)
opinions = opinions[ind]
v = self._get_equal_opinion_limits()
sm = ScalarMappable(norm=Normalize(*v), cmap=cmap)
color = sm.to_rgba(opinions)
ax.barh(
agents,
opinions,
color=color,
edgecolor="None",
linewidth=0, # remove bar borders
height=1, # per agent
**kwargs,
)
ax.axvline(x=0, ls="-", color="k", alpha=0.5, lw=1)
if (np.iterable(sort) or sort) and show_middle:
min_idx = np.argmin(np.abs(opinions))
ax.hlines(
y=min_idx,
xmin=v[0],
xmax=v[1],
ls="--",
color="k",
alpha=0.5,
lw=1,
)
ax.annotate(
f"{min_idx}",
xy=(np.min(opinions), min_idx),
fontsize="small",
color="k",
alpha=0.5,
va="bottom",
ha="left",
)
if colorbar:
# create colorbar axes without stealing from main ax
cbar = colorbar_inset(sm,
"outer bottom",
size="5%",
pad=0.01,
ax=ax)
sns.despine(ax=ax, bottom=True)
ax.tick_params(axis="x", bottom=False, labelbottom=False)
cbar.set_label(OPINION_SYMBOL)
ax.set_ylim(0, self.sn.N)
ax.set_xlim(*v)
if not colorbar:
# xlabel not part of colorbar
ax.set_xlabel(OPINION_SYMBOL)
ax.set_ylabel("Agent $i$")
ax.yaxis.set_major_locator(MaxNLocator(5))
if title:
ax.set_title(title)
return fig, ax
class Bars:
"""This class represents a complex horizontal bar chart.
This class extends (by composition) the functionality provided
by Matplotlib.
The chart is automatically rendered in Jupyter notebooks and can
be saved on disk.
The chart can be tailored to a great extent by passing keyword
arguments to the constructor. (SEE the class attribute **Bars.conf**
for listing the other optional **kwargs**).
If it is not enough, the **conf.py** module in the Catbars package
gives users full control over "rcParams".
Parameters
-----------
numbers : iterable container
The numbers specifying the width of each bar. First numbers are
converted into bars appearing on the top of the figure.
left_labels : iterable container or str, optional
Labels associated with the bars on the left.
The "rank" option creates one-based indices.
The "proportion" option creates labels representing the
relative proportion of each bar in percents.
"rank" and "proportion" labels depend on the "slice" unless
"global_view" is True.
right_labels : iterable container or str, optional
Labels associated with the bars on the right. It accepts the same
values as "left_labels".
colors : iterable container, optional
The container items can be of any type. Bar colors are
automatically inferred in function of the available "tints" (SEE
Bars.conf) and the most common items in the "slice" (unless
"global_view is True). If there are more distinct items than
available "tints", "default_color" and "default_label" are used with
residual items. The automatic color selection can be overriden
by "color_dic".
line_dic : dict, optional
This dictionary has to contain three keys: "number", "color" and
"label". It describes an optional vertical line to
draw.
sort : bool, optional
If True, "numbers" are sorted in descending order. Optional labels
and the "colors" parameter are sorted in the same way. The default
value is False.
slice : tuple : (start,stop), optional
start and stop are one-based indices. Slicing precedes sorting unless
"global_view" is True.
global_view : bool, optional
If True, the whole dataset is considered instead of the optional
slice when sorting, coloring, setting x bounds and creating
"rank" and "proportion" labels. The default value is False.
auto_scale : bool, optional
If True, the logarithmic scale is used when it seems better for
readability. The default value is False.
color_dic : dict, optional
A dictionary mapping "colors" items (keys) to Matplotlib colors
(values)."colors" items which are not specified by the dic. are
treated as residual items (SEE "colors").
title : str, optional
Figure title.
xlabel : str, optional
The Matplotlib xlabel.
ylabel : str, optional
The Matplotlib ylabel.
legend_title : str, optional
legend_visible : bool, optional
The default value is True.
figsize : (width, height), optional
The Matplotlib figsize. The default value is (6,5).
dpi : number, optional
The Matplotlib dpi. The default value is 100.
file_name : str or path-like or file-like object
The path of the png file to write to. (SEE the method print_pdf()
for writing pdf files).
Returns
--------
catbars.bars.Bars
A Bars instance. It encapsulates useful Matplotlib objects.
Attributes
-----------
conf : dict
This class attribute contains the advanced optional
constructor parameters along with their current values.
In particular, it contains the "fig_size", "dpi", "tints",
"default_color" and "default_label" values.
fig : matplotlib.figure.Figure
ax : matplotlib.axes.Axes
canvas : matplotlib.backends.backend_agg.FigureCanvasAgg
data : catbars.models.AbstractModel
The Bars class delegates to another class data processing tasks.
Methods
-------
print_png(file_name)
To write png files.
print_pdf(file_name)
To write pdf files.
"""
conf = Conf.conf
def __init__(self,
numbers,
left_labels = None,
right_labels = None, # 'proportion' 'rank'
colors = None,
line_dic = None,
sort = False,
slice = None, # one-based indexing
global_view = False,
auto_scale = False,
color_dic = None,
title = None,
xlabel = None,
ylabel = None,
legend_title = None,
legend_visible = True,
file_name = None,
**kwargs):
"""
The data space can adapt to long labels but only to
some extent because the long label sizes are fixed.
This class moves the edges of the axes to make room
for labels (SEE Matplotlib HOW-TOs).
"""
if 'log_level' in kwargs:
logging.basicConfig(format='{levelname}:\n{message}',
level= getattr(logging, kwargs['log_level']),
style = '{')
# Configuration: matplotlibrc is decorated by conf.py.
self.conf = Conf.change_conf(kwargs)
# Data formatted by the model.
self.data = None
# Core Matplotlib objects.
self.fig = None
self.ax = None
self.canvas = None
self.vertical_line = None
self.bars = None # BarContainer.
self._virtual_bars = None # For global_view.
# Helper attributes.
self._left_label_data = None
self._right_label_texts = None
# Titles.
self.title = title
self.xlabel = xlabel
self.ylabel = ylabel
self.legend_title = legend_title
self._global_view = global_view
self.legend = None
self._legend_width = 0
self.legend_visible = legend_visible
# The vertical line.
self.line_x = None
self.line_label = None
self.line_color = None
# Original position of the axes edges in the figure.
self._x0 = 0
self._y0 = 0
self._width = 1
self._height = 1
# To deal with not square figures,
# only x sizes are adapted.
self._x_coeff = 1
#########################################################
# Model.
factory = ModelFactory(
numbers,
global_view = global_view,
left_labels = left_labels,
right_labels = right_labels,
colors = colors,
sort = sort,
slice = slice,
default_label = self.conf['default_label'],
color_dic = color_dic,
tints = self.conf['tints'],
default_color = self.conf['default_color'])
self.data = factory.model
self.fig = Figure(figsize = self.conf['figsize'],
dpi = self.conf['dpi'])
self.canvas = FigureCanvasAgg(self.fig)
self.ax = Axes(self.fig,
[self._x0,
self._y0,
self._width,
self._height])
self.fig.add_axes(self.ax)
self.canvas.draw()
w, h = self.fig.get_size_inches()
self._x_coeff = h / w
# margin.
margin = self.conf['margin']
self._x0 = self._x_coeff * margin
self._y0 = margin
self._width = self._width - 2 * self._x_coeff * margin
self._height = self._height - 2 * margin
self._set_position()
self.ax.tick_params(axis = 'y',
length = 0)
self.ax.grid(b = True,
axis = 'x',
which = 'both',
color = 'black',
alpha = 0.3)
for name in ['top', 'right']:
self.ax.spines[name].set_visible(False)
# xscale.
if auto_scale is True:
# To improve clarity.
if self.data.spread > 1 or self.data.maximum > 1e6:
self.ax.set(xscale = 'log')
else:
default_formatter = self.ax.get_xaxis().get_major_formatter()
custom_formatter = self.build_formatter(default_formatter)
formatter = matplotlib.ticker.FuncFormatter(custom_formatter)
self.ax.get_xaxis().set_major_formatter(formatter)
# Title.
if self.title is not None:
self._manage_title()
_kwargs = dict()
# Left labels.
if self.data.left_labels is not None:
_kwargs['tick_label'] = self.data.left_labels
else:
_kwargs['tick_label'] = ''
# colors.
if self.data.actual_colors is not None:
_kwargs['color'] = self.data.actual_colors
else:
_kwargs['color'] = self.data.default_color
# bars.
self.bars = self.ax.barh(list(range(self.data.length)),
self.data.numbers,
height = 1,
edgecolor = 'white',
linewidth = 1, # 0.4
alpha = self.conf['color_alpha'],
**_kwargs)
# To fix x bounds, virtual bars are used.
if self._global_view is True:
self._virtual_bars = self.ax.barh(
[0, 0],
[self.data.minimum,
self.data.maximum],
height = 0.5,
edgecolor = 'white',
linewidth = 1, # 0.4
alpha = self.conf['color_alpha'],
visible = False)
# The vertical line.
if line_dic is not None:
self._set_line(line_dic)
if (self.line_x is not None and
self.data.minimum <= self.line_x <= self.data.maximum):
#
self.vertical_line = self.ax.axvline(
self.line_x,
ymin = 0,
ymax = 1,
color = self.line_color,
linewidth = 2,
alpha = self.conf['color_alpha'])
# Left label constraint solving.
self._make_room_for_left_labels()
# ylabel.
if self.ylabel is not None:
self._manage_ylabel()
# Legend.
if (self.legend_visible is True and
self.data.colors is not None):
#
self._draw_legend()
self._make_room_for_legend()
# Right labels.
if self.data.right_labels is not None:
self._draw_right_labels()
self._make_room_for_right_labels()
min_tick_y = self._clean_x_ticklabels()
# xlabel.
if self.xlabel is not None:
self._manage_xlabel(min_tick_y)
else:
delta_y0 = abs(self._y0 - min_tick_y)
self._y0 = self._y0 + delta_y0
self._height = self._height - delta_y0
self._set_position()
self.canvas.draw()
# Printing.
if file_name is not None:
self.canvas.print_png(file_name)
#############################################################
def _set_line(self, line_dic):
try:
self.line_x = line_dic['number']
self.line_label = line_dic['label']
self.line_color = line_dic['color']
except Exception:
text = """
"line_dic" has to define three keys: 'number', 'label' and 'color'.
"""
raise TypeError(text.strip())
def _manage_title(self):
pad_in_points = self.fig_coord_to_points(self.fig,
self.conf['title_pad'],
axis = 'y')
title_label = self.ax.set_title(
self.title,
pad = pad_in_points,
fontsize = self.conf['title_font_size'],
fontweight = 'bold')
self.canvas.draw()
h = title_label.get_window_extent(
renderer = self.canvas.get_renderer()
).height
h_in_fig_coord = self.disp_to_fig_coord(self.fig,
h,
axis = 'y')
total_h = (h_in_fig_coord +
self.conf['title_pad'])
self._height = self._height - total_h
self._set_position()
def _make_room_for_left_labels(self):
"""
Constraint solving for left labels.
"left_label_data" is stored for further processing and
will be used to align left and right labels.
"""
left_label_data = [] # To align left and right labels.
min_x = None
self.canvas.draw()
for left_label in self.ax.get_yticklabels():
x, y = left_label.get_position()
va = left_label.get_va()
bbox = left_label.get_window_extent(
renderer = self.canvas.get_renderer()
)
inv = self.fig.transFigure.inverted()
lab_x, _ = inv.transform((bbox.x0, bbox.y0))
if min_x is None or lab_x < min_x:
min_x = lab_x # In pixels.
left_label_data.append((y, va))
delta_x0 = abs(self._x0 - min_x)
self._x0 = self._x0 + delta_x0
self._width = self._width - delta_x0
self._set_position()
self._left_label_data = left_label_data
def _manage_ylabel(self):
"""
"""
pad = self.fig_coord_to_points(self.fig,
self._x_coeff * self.conf['pad'])
y_label = self.ax.set_ylabel(
self.ylabel,
labelpad = pad,
fontweight = 'bold',
fontsize = self.conf['axis_title_font_size'])
self.canvas.draw()
bbox = y_label.get_window_extent(
renderer = self.canvas.get_renderer()
)
w_in_fig_coord = self.disp_to_fig_coord(self.fig,
bbox.width)
delta_x0 = (w_in_fig_coord +
self._x_coeff * self.conf['pad'])
self._x0 = self._x0 + delta_x0
self._width = self._width - delta_x0
self._set_position()
def _draw_legend(self):
artists = []
labels = []
for i, color in enumerate(self.data.legend_colors):
# Proxy artists.
patch = mpatches.Patch(facecolor = color,
alpha = self.conf['color_alpha'])
artists.append(patch)
labels.append(self.data.legend_labels[i])
if self.vertical_line is not None:
artists.append(self.vertical_line)
labels.append(self.line_label)
kwargs = dict()
if self.legend_title is not None:
kwargs['title'] = self.legend_title
lgd = self.fig.legend(artists,
labels,
loc ='center left',
frameon = False,
labelspacing = 0.25,
borderpad = 0,
borderaxespad = 0,
prop = {
'size' : self.conf['axis_title_font_size']},
**kwargs)
lgd.get_title().set_fontsize(self.conf['axis_title_font_size'])
lgd.get_title().set_fontweight('bold')
lgd.get_title().set_multialignment('center')
self.canvas.draw()
# Constraint solving.
lgd_width = (lgd.get_window_extent(
renderer = self.canvas.get_renderer()
).width)
lgd_width_in_fig_coord = self.disp_to_fig_coord(self.fig,
lgd_width)
self.legend = lgd
self._legend_width = lgd_width_in_fig_coord
logging.info('legend width in pixels {}\n'.format(lgd_width))
def _make_room_for_legend(self):
self.legend.set_bbox_to_anchor((1 -
self._legend_width -
self._x_coeff * self.conf['margin'],
0.5))
self._width = (self._width -
self._legend_width -
self._x_coeff *self.conf['pad'])
self._set_position()
def _draw_right_labels(self):
"""
Right labels.
"""
right_label_texts = []
for i, bar in enumerate(self.bars):
y, va = self._left_label_data[i]
w = bar.get_width()
t = None
if self.data.right_labels is not None:
a_right_label = self.data.right_labels[i]
text = ' {}'.format(a_right_label)
t = self.ax.text(w, y,
text,
verticalalignment = va,
fontweight = 'normal',
zorder = 10)
right_label_texts.append(t)
self.canvas.draw()
self._right_label_texts = right_label_texts
def _make_room_for_right_labels(self):
"""
Constraint solving in figure coordinates.
A bisection technique is used.
"""
def _objective_function(coeff_array,
label_array,
x):
#
return max(x, max(coeff_array * x + label_array))
bar_coeff = []
text_widths = []
for i, bar in enumerate(self.bars):
bar_coeff.append(self._get_bar_coeff(bar))
t = self._right_label_texts[i]
text_widths.append(self._get_text_width(t))
coeff_array = np.array(bar_coeff)
label_array = np.array(text_widths)
f = partial(_objective_function,
coeff_array,
text_widths)
min_w = self.conf['min_ax_width']
max_it = self.conf['right_label_max_it']
tolerance = self.conf['right_label_solver_tolerance']
# Two special cases.
if f(self._width) == self._width:
pass
# To check whether a solution exists.
elif f(min_w) < self._width:
w_b = self._width
w_a = min_w
i = 0
# To prevent from infinite loops.
while abs(w_b - w_a) > tolerance and i < max_it:
new_w = w_a + (w_b - w_a) / 2
if f(new_w) < self._width:
w_a = new_w
else:
w_b = new_w
logging.info('w_a {}\nw_b {}\n'.format(w_a, w_b))
i += 1
self._width = w_a
else:
self._width = min_w
self._set_position()
if i == max_it:
logging.warning("""
right_label_max_it {} has been hit.
""".format(max_it))
def _get_bar_coeff(self, bar):
"""
bar_width_in_ax_coord can't be greater than 0.95 if
xmargin = 0.05.
"""
data_x_one = bar.get_bbox().x1 # Assuming that x0 = 0.
disp_x_one, _ = self.ax.transData.transform((data_x_one, 0))
inv = self.ax.transAxes.inverted()
bar_width_in_ax_coord, _ = inv.transform((disp_x_one, _))
return bar_width_in_ax_coord
def _get_text_width(self, t):
t_width = t.get_window_extent(
renderer = self.canvas.get_renderer()
).width # In pixels.
return self.disp_to_fig_coord(self.fig, t_width)
def _clean_x_ticklabels(self):
"""
To discard overlaps.
"""
self.canvas.draw()
labels = self.get_visible_ticklabels(
self.ax,
self.ax.xaxis.get_ticklabels(which = 'both')
)
label_bboxes = [lab.get_window_extent(
renderer = self.canvas.get_renderer()
)
for lab in labels]
current_bbox = label_bboxes[-1]
min_tick_y = current_bbox.y0
for i in range(len(label_bboxes) - 1,
0,
-1):
if label_bboxes[i-1].overlaps(current_bbox):
labels[i-1].set_visible(False)
else:
current_bbox = label_bboxes[i-1]
if current_bbox.y0 < min_tick_y:
min_tick_y = current_bbox.y0
inv = self.fig.transFigure.inverted()
_, tick_y = inv.transform((0, min_tick_y))
return tick_y
def _manage_xlabel(self,
min_tick_y):
"""
min_tick_y is negative.
"""
pad = self.fig_coord_to_points(self.fig,
self.conf['pad'],
axis = 'y')
x_label = self.ax.set_xlabel(
self.xlabel,
labelpad = pad,
fontweight = 'bold',
fontsize = self.conf['axis_title_font_size'])
self.canvas.draw()
bbox = x_label.get_window_extent(
renderer = self.canvas.get_renderer()
)
h = self.disp_to_fig_coord(self.fig,
bbox.height,
axis = 'y')
delta_y0 = abs(self._y0 - min_tick_y) + h + self.conf['pad']
self._y0 = self._y0 + delta_y0
self._height = self._height - delta_y0
self._set_position()
self.canvas.draw()
def _set_position(self):
self.ax.set_position([self._x0,
self._y0,
self._width,
self._height])
positions = ['x0', 'y0', 'width', 'height']
text = 'Position of the Axes instance edges\n'
for pos in positions:
text = text + '{} {}\n'.format(pos, getattr(self, '_'+pos))
logging.info(text)
def disp_to_fig_coord(self,
fig,
dist,
axis = 'x'):
"""
Conversion of a distance from display coordinates
to figure coordinates.
"""
w, h = fig.get_size_inches()
if axis == 'x':
return dist / (fig.dpi * w)
else:
return dist / (fig.dpi * h)
def points_to_fig_coord(self,
fig,
points,
axis = 'x'):
"""
axis = 'x' refers to the X axis ('y' corresponds to the Y axis).
There are 72 points per inch.
"""
w, h = fig.get_size_inches()
if axis == 'x':
return (points * 1 / 72) / w
else:
return (points * 1 / 72) / h
def fig_coord_to_points(self,
fig,
fraction,
axis = 'x'):
"""
axis = 'x' refers to the X axis ('y' corresponds to the Y axis).
Conversion from figure coordinates to points.
"""
w, h = fig.get_size_inches()
if axis == 'x':
return fraction * w * 72
else:
return fraction * h * 72
def get_visible_ticklabels(self,
ax,
labels):
"""
Only a part of the built labels are displayed by
the Matplotlib machinary.
"""
visible_labels = []
x_min, x_max = ax.get_xlim()
for label in labels:
x = label.get_position()[0]
if x_min <= x <= x_max:
if label.get_visible() and label.get_text():
visible_labels.append(label)
return visible_labels
def build_formatter(self, default_formatter):
"""
Custom scientific notation.
"""
def f(default_f, x, pos):
if x > 1e6 or x < 1e-3:
text = '{:.1e}'.format(x)
n, e = text.split('e')
if float(n) == 0:
return 0
e = '{'+ e.lstrip('0+') + '}'
label = r'${} \times 10^{}$'.format(n, e)
return label
else:
return default_f(x, pos)
return partial(f, default_formatter)
def print_pdf(self, file_name):
from matplotlib.backends.backend_pdf import PdfPages
pp = PdfPages(file_name)
pp.savefig(figure = self.fig)
pp.close()
def print_png(self, file_name):
self.canvas.print_png(file_name)
def _repr_png_(self):
"""
For notebook integration.
"""
w, h = self.fig.get_size_inches()
buf = BytesIO() # In-memory bytes buffer.
self.canvas.print_png(buf)
return (buf.getvalue(),
{'width' : str(w * self.fig.dpi),
'height': str(h * self.fig.dpi)})
def plot_pianoroll(
ax: Axes,
pianoroll: ndarray,
is_drum: bool = False,
resolution: Optional[int] = None,
downbeats: Optional[Sequence[int]] = None,
preset: str = "full",
cmap: str = "Blues",
xtick: str = "auto",
ytick: str = "octave",
xticklabel: bool = True,
yticklabel: str = "auto",
tick_loc: Sequence[str] = ("bottom", "left"),
tick_direction: str = "in",
label: str = "both",
grid_axis: str = "both",
grid_linestyle: str = ":",
grid_linewidth: float = 0.5,
**kwargs,
):
"""
Plot a piano roll.
Parameters
----------
ax : :class:`matplotlib.axes.Axes`
Axes to plot the piano roll on.
pianoroll : ndarray, shape=(?, 128), (?, 128, 3) or (?, 128, 4)
Piano roll to plot. For a 3D piano-roll array, the last axis can
be either RGB or RGBA.
is_drum : bool
Whether it is a percussion track. Defaults to False.
resolution : int
Time steps per quarter note. Required if `xtick` is 'beat'.
downbeats : list
Boolean array that indicates whether the time step contains a
downbeat (i.e., the first time step of a bar).
preset : {'full', 'frame', 'plain'}
Preset theme. For 'full' preset, ticks, grid and labels are on.
For 'frame' preset, ticks and grid are both off. For 'plain'
preset, the x- and y-axis are both off. Defaults to 'full'.
cmap : str or :class:`matplotlib.colors.Colormap`
Colormap. Will be passed to :func:`matplotlib.pyplot.imshow`.
Only effective when `pianoroll` is 2D. Defaults to 'Blues'.
xtick : {'auto', 'beat', 'step', 'off'}
Tick format for the x-axis. For 'auto' mode, set to 'beat' if
`resolution` is given, otherwise set to 'step'. Defaults to
'auto'.
ytick : {'octave', 'pitch', 'off'}
Tick format for the y-axis. Defaults to 'octave'.
xticklabel : bool
Whether to add tick labels along the x-axis.
yticklabel : {'auto', 'name', 'number', 'off'}
Tick label format for the y-axis. For 'name' mode, use pitch
name as tick labels. For 'number' mode, use pitch number. For
'auto' mode, set to 'name' if `ytick` is 'octave' and 'number'
if `ytick` is 'pitch'. Defaults to 'auto'.
tick_loc : sequence of {'bottom', 'top', 'left', 'right'}
Tick locations. Defaults to `('bottom', 'left')`.
tick_direction : {'in', 'out', 'inout'}
Tick direction. Defaults to 'in'.
label : {'x', 'y', 'both', 'off'}
Whether to add labels to x- and y-axes. Defaults to 'both'.
grid_axis : {'x', 'y', 'both', 'off'}
Whether to add grids to the x- and y-axes. Defaults to 'both'.
grid_linestyle : str
Grid line style. Will be passed to
:meth:`matplotlib.axes.Axes.grid`.
grid_linewidth : float
Grid line width. Will be passed to
:meth:`matplotlib.axes.Axes.grid`.
**kwargs
Keyword arguments to be passed to
:meth:`matplotlib.axes.Axes.imshow`.
"""
# Plot the piano roll
if pianoroll.ndim == 2:
transposed = pianoroll.T
elif pianoroll.ndim == 3:
transposed = pianoroll.transpose(1, 0, 2)
else:
raise ValueError("`pianoroll` must be a 2D or 3D numpy array")
img = ax.imshow(
transposed,
cmap=cmap,
aspect="auto",
vmin=0,
vmax=1 if pianoroll.dtype == np.bool_ else 127,
origin="lower",
interpolation="none",
**kwargs,
)
# Format ticks and labels
if xtick == "auto":
xtick = "beat" if resolution is not None else "step"
elif xtick not in ("beat", "step", "off"):
raise ValueError(
"`xtick` must be one of 'auto', 'beat', 'step' or 'off', not "
f"{xtick}.")
if yticklabel == "auto":
yticklabel = "name" if ytick == "octave" else "number"
elif yticklabel not in ("name", "number", "off"):
raise ValueError(
"`yticklabel` must be one of 'auto', 'name', 'number' or 'off', "
f"{yticklabel}.")
if preset == "full":
ax.tick_params(
direction=tick_direction,
bottom=("bottom" in tick_loc),
top=("top" in tick_loc),
left=("left" in tick_loc),
right=("right" in tick_loc),
labelbottom=xticklabel,
labelleft=(yticklabel != "off"),
labeltop=False,
labelright=False,
)
elif preset == "frame":
ax.tick_params(
direction=tick_direction,
bottom=False,
top=False,
left=False,
right=False,
labelbottom=False,
labeltop=False,
labelleft=False,
labelright=False,
)
elif preset == "plain":
ax.axis("off")
else:
raise ValueError(
f"`preset` must be one of 'full', 'frame' or 'plain', not {preset}"
)
# Format x-axis
if xtick == "beat" and preset != "frame":
if resolution is None:
raise ValueError(
"`resolution` must not be None when `xtick` is 'beat'.")
n_beats = pianoroll.shape[0] // resolution
ax.set_xticks(resolution * np.arange(n_beats) - 0.5)
ax.set_xticklabels("")
ax.set_xticks(resolution * (np.arange(n_beats) + 0.5) - 0.5,
minor=True)
ax.set_xticklabels(np.arange(1, n_beats + 1), minor=True)
ax.tick_params(axis="x", which="minor", width=0)
# Format y-axis
if ytick == "octave":
ax.set_yticks(np.arange(0, 128, 12))
if yticklabel == "name":
ax.set_yticklabels(["C{}".format(i - 2) for i in range(11)])
elif ytick == "step":
ax.set_yticks(np.arange(0, 128))
if yticklabel == "name":
if is_drum:
ax.set_yticklabels(
[note_number_to_drum_name(i) for i in range(128)])
else:
ax.set_yticklabels(
[note_number_to_name(i) for i in range(128)])
elif ytick != "off":
raise ValueError(
f"`ytick` must be one of 'octave', 'pitch' or 'off', not {ytick}.")
# Format axis labels
if label not in ("x", "y", "both", "off"):
raise ValueError(
f"`label` must be one of 'x', 'y', 'both' or 'off', not {label}.")
if label in ("x", "both"):
if xtick == "step" or not xticklabel:
ax.set_xlabel("time (step)")
else:
ax.set_xlabel("time (beat)")
if label in ("y", "both"):
if is_drum:
ax.set_ylabel("key name")
else:
ax.set_ylabel("pitch")
# Plot the grid
if grid_axis not in ("x", "y", "both", "off"):
raise ValueError(
"`grid` must be one of 'x', 'y', 'both' or 'off', not "
f"{grid_axis}.")
if grid_axis != "off":
ax.grid(
axis=grid_axis,
color="k",
linestyle=grid_linestyle,
linewidth=grid_linewidth,
)
# Plot downbeat boundaries
if downbeats is not None:
for downbeat in downbeats:
ax.axvline(x=downbeat, color="k", linewidth=1)
return img
def show_perf_vs_size(
x_list: List[np.ndarray],
y_list: List[np.ndarray],
label_list: List[str],
*,
xlabel: str = None,
ylabel: str = None,
title: str = None,
ax: Axes = None,
xticks=(0, 25, 50, 75, 100),
yticks=(0, 0.5, 1),
xlim=(0, 100),
ylim=(0, 1),
xticklabels=('0', '25', '50', '75', '100'),
yticklabels=('0', '0.5', '1'),
style_list=None,
linewidth=1,
show_legend=True,
legend_param=None,
vline=None,
hline=None,
xlabel_param=None,
# letter=None,
):
"""x being model size, number of parameter, dataset size, etc.
y being performance.
"""
if style_list is None:
# should give a default set
raise NotImplementedError
if xlabel_param is None:
xlabel_param = dict()
# if letter is not None:
# ax.text(0, 1, letter, horizontalalignment='left', verticalalignment='top',
# transform=ax.get_figure().transFigure, fontweight='bold')
assert len(x_list) == len(y_list) == len(label_list)
for idx, (x_this, y_this,
label_this) in enumerate(zip(x_list, y_list, label_list)):
linestyle, color, marker = style_list[idx]
ax.plot(x_this,
y_this,
linestyle=linestyle,
color=color,
marker=marker,
label=label_this,
linewidth=linewidth)
if vline is not None:
# color maybe adjusted later
ax.axvline(vline, color='black', linewidth=linewidth, linestyle='--')
if hline is not None:
# color maybe adjusted later
ax.axhline(hline, color='black', linewidth=linewidth, linestyle='--')
# ax.set_xlim(0, 1)
ax.set_xlim(*xlim)
ax.set_ylim(*ylim)
ax.set_xticks(xticks)
ax.set_yticks(yticks)
ax.set_xticklabels(xticklabels, **xlabel_param)
ax.set_yticklabels(yticklabels)
if xlabel is not None:
ax.set_xlabel(xlabel)
if ylabel is not None:
ax.set_ylabel(ylabel)
if title is not None:
ax.set_title(title)
if show_legend:
if legend_param is None:
ax.legend()
else:
ax.legend(**legend_param)
def plot_z_trend_histogram(self,
axis: Axes = None,
polar: bool = True,
normed: bool = True) -> None:
if axis is None:
axis = self.figure.add_subplot(111)
cluster = Cluster(simulation_name=self.simulation.simulation_name,
clusterID=0,
redshift='z000p000')
aperture_float = self.get_apertures(cluster)[
self.aperture_id] / cluster.r200
if not os.path.isfile(
os.path.join(
self.path,
f'redshift_rot0rot4_histogram_aperture_{self.aperture_id}.npy'
)):
warnings.warn(
f"File redshift_rot0rot4_histogram_aperture_{self.aperture_id}.npy not found."
)
print("self.make_simhist() activated.")
self.make_simhist()
print(
f"Retrieving npy files: redshift_rot0rot4_histogram_aperture_{self.aperture_id}.npy"
)
sim_hist = np.load(os.path.join(
self.path,
f'redshift_rot0rot4_histogram_aperture_{self.aperture_id}.npy'),
allow_pickle=True)
sim_hist = np.asarray(sim_hist)
if normed:
norm_factor = np.sum(self.simulation.sample_completeness)
sim_hist[2] /= norm_factor
sim_hist[3] /= norm_factor
y_label = r"Sample fraction"
else:
y_label = r"Number of samples"
items_labels = f""" REDSHIFT TRENDS - HISTOGRAM
Number of clusters: {self.simulation.totalClusters:d}
$z$ = 0.0 - 1.8
Total samples: {np.sum(self.simulation.sample_completeness):d} $\equiv N_\mathrm{{clusters}} \cdot N_\mathrm{{redshifts}}$
Aperture radius = {aperture_float:.2f} $R_{{200\ true}}$"""
print(items_labels)
sim_colors = {
'ceagle': 'pink',
'celr_e': 'lime',
'celr_b': 'orange',
'macsis': 'aqua',
}
axis.axvline(90, linestyle='--', color='k', alpha=0.5, linewidth=2)
axis.step(sim_hist[0],
sim_hist[2],
color=sim_colors[self.simulation.simulation_name],
where='mid')
axis.fill_between(sim_hist[0],
sim_hist[2] + sim_hist[3],
sim_hist[2] - sim_hist[3],
step='mid',
color=sim_colors[self.simulation.simulation_name],
alpha=0.2,
edgecolor='none',
linewidth=0)
axis.set_ylabel(y_label, size=25)
axis.set_xlabel(
r"$\Delta \theta \equiv (\mathbf{L}_\mathrm{gas},\mathrm{\widehat{CoP}},\mathbf{L}_\mathrm{stars})$\quad[degrees]",
size=25)
axis.set_xlim(0, 180)
axis.set_ylim(0, 0.1)
axis.text(0.03,
0.97,
items_labels,
horizontalalignment='left',
verticalalignment='top',
transform=axis.transAxes,
size=15)
if polar:
inset_axis = self.figure.add_axes([0.75, 0.65, 0.25, 0.25],
projection='polar')
inset_axis.patch.set_alpha(0) # Transparent background
inset_axis.set_theta_zero_location('N')
inset_axis.set_thetamin(0)
inset_axis.set_thetamax(180)
inset_axis.set_xticks(np.pi / 180. *
np.linspace(0, 180, 5, endpoint=True))
inset_axis.set_yticks([])
inset_axis.step(sim_hist[0] / 180 * np.pi,
sim_hist[2],
color=sim_colors[self.simulation.simulation_name],
where='mid')
inset_axis.fill_between(
sim_hist[0] / 180 * np.pi,
sim_hist[2] + sim_hist[3],
sim_hist[2] - sim_hist[3],
step='mid',
color=sim_colors[self.simulation.simulation_name],
alpha=0.2,
edgecolor='none',
linewidth=0)
patch_ceagle = Patch(facecolor=sim_colors['ceagle'],
label='C-EAGLE',
edgecolor='k',
linewidth=1)
patch_celre = Patch(facecolor=sim_colors['celr_e'],
label='CELR-E',
edgecolor='k',
linewidth=1)
patch_celrb = Patch(facecolor=sim_colors['celr_b'],
label='CELR-B',
edgecolor='k',
linewidth=1)
patch_macsis = Patch(facecolor=sim_colors['macsis'],
label='MACSIS',
edgecolor='k',
linewidth=1)
leg2 = axis.legend(
handles=[patch_ceagle, patch_celre, patch_celrb, patch_macsis],
loc='lower center',
handlelength=1,
fontsize=20)
axis.add_artist(leg2)
