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1316 lines (1129 loc) · 47.3 KB
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"""Plot results using the matplotlib library."""
import copy
import types
from collections.abc import Callable
from functools import partial, wraps
from math import ceil, floor, sqrt
from statistics import stdev
from typing import Literal
import matplotlib
import matplotlib.figure
import matplotlib.pyplot as plt
import matplotlib.transforms as mtransforms
import numpy as np
from matplotlib.axes._axes import Axes
from scipy.ndimage import gaussian_filter1d
from scipy.stats import gaussian_kde, lognorm, norm
import ratapi
import ratapi.inputs
import ratapi.outputs
from ratapi.rat_core import PlotEventData, makeSLDProfile
def _extract_plot_data(event_data: PlotEventData, q4: bool, show_error_bar: bool, shift_value: float):
"""Extract the plot data for the sld, ref, error plot lines.
Parameters
----------
event_data : PlotEventData
The plot event data that contains all the information
to generate the ref and sld plots
q4 : bool, default: False
Controls whether Q^4 is plotted on the reflectivity plot
show_error_bar : bool, default: True
Controls whether the error bars are shown
shift_value : float
A value between 0 and 100 that controls the spacing between the reflectivity plots for each of the contrasts
Returns
-------
plot_values : dict
A dict containing the data for the sld, ref, error plot lines.
"""
results = {"ref": [], "error": [], "sld": [], "sld_resample": []}
if shift_value < 0 or shift_value > 100:
raise ValueError("Parameter `shift_value` must be between 0 and 100")
for i, (r, data, sld) in enumerate(
zip(event_data.reflectivity, event_data.shiftedData, event_data.sldProfiles, strict=False)
):
# Calculate the divisor
div = 10 ** (i / 100 * shift_value)
q4_data = 1 if not q4 or not event_data.dataPresent[i] else data[:, 0] ** 4
mult = q4_data / div
# Plot the reflectivity on plot (1,1)
results["ref"].append([r[:, 0], r[:, 1] * mult])
results["error"].append([])
if event_data.dataPresent[i]:
sd_x = data[:, 0]
sd_y, sd_e = map(lambda x: x * mult, (data[:, 1], data[:, 2]))
errors = np.zeros(len(sd_e))
if show_error_bar:
valid = sd_y - sd_e >= 0
errors[valid] = sd_e[valid]
valid |= sd_y < 0
else:
valid = np.ones(len(sd_e)).astype(bool)
sd_e = errors
results["error"][-1].extend([sd_x[valid], sd_y[valid], sd_e[valid]])
results["sld"].append([])
for j in range(len(sld)):
results["sld"][-1].append([sld[j][:, 0], sld[j][:, 1]])
results["sld_resample"].append([])
if event_data.resample[i] == 1 or event_data.modelType == "custom xy":
layers = event_data.resampledLayers[i][0]
for j in range(len(event_data.resampledLayers[i])):
layer = event_data.resampledLayers[i][j]
if layers.shape[1] == 4:
layer = np.delete(layer, 2, 1)
new_profile = makeSLDProfile(
layers[0, 1], # Bulk In
layers[-1, 1], # Bulk Out
layer,
event_data.subRoughs[i], # roughness
1,
)
results["sld_resample"][-1].append([new_profile[:, 0] - 49, new_profile[:, 1]])
return results
def plot_ref_sld_helper(
data: PlotEventData,
fig: matplotlib.figure.Figure,
delay: bool = True,
confidence_intervals: dict | None = None,
linear_x: bool = False,
q4: bool = False,
show_error_bar: bool = True,
show_grid: bool = False,
show_legend: bool = True,
shift_value: float = 100,
animated=False,
align_profile=False,
):
"""Clear the previous plots and updates the ref and SLD plots.
Parameters
----------
data : PlotEventData
The plot event data that contains all the information
to generate the ref and sld plots
fig : matplotlib.figure.Figure
The figure object that has two subplots
delay : bool, default: True
Controls whether to delay 0.005s after plot is created
confidence_intervals : dict or None, default None
The Bayesian confidence intervals for reflectivity and SLD.
Only relevant if the procedure used is Bayesian (NS or DREAM)
linear_x : bool, default: False
Controls whether the x-axis on reflectivity plot uses the linear scale
q4 : bool, default: False
Controls whether Q^4 is plotted on the reflectivity plot
show_error_bar : bool, default: True
Controls whether the error bars are shown
show_grid : bool, default: False
Controls whether the grid is shown
show_legend : bool, default: True
Controls whether the legend is shown
shift_value : float, default: 100
A value between 0 and 100 that controls the spacing between the reflectivity plots for each of the contrasts
animated : bool, default: False
Controls whether the animated property of foreground plot elements should be set.
"""
preserve_zoom = False
if len(fig.axes) != 2:
fig.clf()
fig.subplots(1, 2)
fig.subplots_adjust(wspace=0.3, hspace=0)
ref_plot: plt.Axes = fig.axes[0]
sld_plot: plt.Axes = fig.axes[1]
if ref_plot.lines and fig.canvas.toolbar is not None:
preserve_zoom = True
fig.canvas.toolbar.push_current()
# Clears the previous plots
ref_plot.cla()
sld_plot.cla()
if align_profile:
_align_profiles(data, confidence_intervals)
plot_data = _extract_plot_data(data, q4, show_error_bar, shift_value)
for i, name in enumerate(data.contrastNames):
ref_plot.plot(plot_data["ref"][i][0], plot_data["ref"][i][1], label=name, linewidth=1, animated=animated)
color = ref_plot.get_lines()[-1].get_color()
# Plot confidence intervals if required
if confidence_intervals is not None:
# Calculate the divisor
div = 10 ** (i / 100 * shift_value)
ref_min, ref_max = confidence_intervals["reflectivity"][i]
mult = (1 if not q4 else plot_data["ref"][i][0] ** 4) / div
ref_plot.fill_between(plot_data["ref"][i][0], ref_min * mult, ref_max * mult, alpha=0.6, color="grey")
if data.dataPresent[i]:
# Plot the errorbars
ref_plot.errorbar(
x=plot_data["error"][i][0],
y=plot_data["error"][i][1],
yerr=plot_data["error"][i][2],
elinewidth=1,
ecolor=color,
marker=".",
markersize=3,
linestyle="none",
color=color,
capsize=0,
animated=animated,
)
# Plot the slds on plot (1,2)
for j in range(len(plot_data["sld"][i])):
label = name if len(plot_data["sld"][i]) == 1 else f"{name} Domain {j + 1}"
sld_plot.plot(
plot_data["sld"][i][j][0], plot_data["sld"][i][j][1], label=label, linewidth=1, animated=animated
)
# Plot confidence intervals if required
if confidence_intervals is not None:
sld_min, sld_max = confidence_intervals["sld"][i][j]
sld_plot.fill_between(plot_data["sld"][i][j][0], sld_min, sld_max, alpha=0.6, color="grey")
for j in range(len(plot_data["sld_resample"][i])):
sld_plot.plot(
plot_data["sld_resample"][i][j][0],
plot_data["sld_resample"][i][j][1],
color=color,
linewidth=1,
animated=animated,
)
# Format the axis
ref_plot.set_yscale("log")
if not linear_x:
ref_plot.set_xscale("log")
ref_plot.set_xlabel("$Q_{z} (\u00c5^{-1})$")
ref_plot.set_ylabel("Reflectivity")
sld_plot.set_xlabel("$Z (\u00c5)$")
sld_plot.set_ylabel("$SLD (\u00c5^{-2})$", labelpad=1)
if show_legend:
ref_plot.legend()
sld_plot.legend()
if show_grid:
ref_plot.grid()
sld_plot.grid()
if preserve_zoom:
fig.canvas.toolbar.back()
if delay:
plt.pause(0.005)
def _align_profiles(data: PlotEventData, confidence_intervals: dict | None = None):
"""Align SLD profiles and resampled layers.
Aligns the A/L SLD profiles so that the substrates line up by padding the
start of any shorter than the longest profile.
Parameters
----------
data : PlotEventData
The plot event data that contains all the information
to generate the ref and sld plots
confidence_intervals : dict or None, default None
The Bayesian confidence intervals for reflectivity and SLD.
Only relevant if the procedure used is Bayesian (NS or DREAM)
"""
slds = data.sldProfiles
size = (len(slds), len(slds[0]))
# Find the length of the longest profile.
lengths = [[sld.shape[0] for sld in sld_row] for sld_row in slds]
max_value = np.max(lengths)
max_index = np.unravel_index(np.argmax(lengths), shape=size)
max_x = slds[max_index[0]][max_index[1]][:, 0]
max_x_value = max_x[-1]
for i in range(size[0]):
for j in range(size[1]):
cur_sld = slds[i][j]
diff = max_value - cur_sld.shape[0]
if diff:
pad = np.zeros(diff)
max_y = np.concatenate((pad, cur_sld[:, 1]))
slds[i][j] = np.column_stack((max_x, max_y))
cur_resample_layer = data.resampledLayers[i][j]
if not np.all(cur_resample_layer):
total_length = sum(cur_resample_layer[:, 0])
offset = max_x_value - total_length
data.resampledLayers[i][j] = np.vstack(([offset, 0, 0], cur_resample_layer))
if confidence_intervals is not None:
cur_ci = confidence_intervals["sld"][i][j]
inter_a = np.concatenate((pad, cur_ci[0]))
inter_b = np.concatenate((pad, cur_ci[1]))
confidence_intervals["sld"][i][j] = (inter_a, inter_b)
def plot_ref_sld(
project: ratapi.Project,
results: ratapi.outputs.Results | ratapi.outputs.BayesResults,
block: bool = False,
fig: matplotlib.figure.Figure | None = None,
return_fig: bool = False,
bayes: Literal[65, 95, None] = None,
linear_x: bool = False,
q4: bool = False,
show_error_bar: bool = True,
show_grid: bool = False,
show_legend: bool = True,
shift_value: float = 100,
) -> plt.Figure | None:
"""Plot the reflectivity and SLD profiles.
Parameters
----------
project : Project
An instance of the Project class
results : Union[Results, BayesResults]
The result from the calculation
block : bool, default: False
Indicates the plot should block until it is closed
fig : matplotlib.figure.Figure, optional
The figure object that has two subplots
return_fig : bool, default False
If True, return the figure instead of displaying it.
bayes : 65, 95 or None, default None
Whether to shade Bayesian confidence intervals. Can be `None`
(if no intervals), `65` to show 65% confidence intervals,
and `95` to show 95% confidence intervals.
linear_x : bool, default: False
Controls whether the x-axis on reflectivity plot uses the linear scale
q4 : bool, default: False
Controls whether Q^4 is plotted on the reflectivity plot
show_error_bar : bool, default: True
Controls whether the error bars are shown
show_grid : bool, default: False
Controls whether the grid is shown
show_legend : bool, default: True
Controls whether the legend is shown
shift_value : float, default: 100
A value between 0 and 100 that controls the spacing between the reflectivity plots for each of the contrasts
Returns
-------
Figure or None
Returns Figure if `return_fig` is True, else returns nothing.
"""
data = PlotEventData()
# We need to take a copy of reflectivity and SLD in case we are plotting a
# shaded plot and will therefore change the plotted data to that from the
# centre of the Bayesian distribution
data.modelType = project.model
data.reflectivity = copy.deepcopy(results.reflectivity)
data.shiftedData = results.shiftedData
data.sldProfiles = copy.deepcopy(results.sldProfiles)
data.resampledLayers = copy.deepcopy(results.resampledLayers)
data.dataPresent = ratapi.inputs.make_data_present(project)
data.subRoughs = results.contrastParams.subRoughs
data.resample = ratapi.inputs.make_resample(project)
data.contrastNames = [contrast.name for contrast in project.contrasts]
if bayes:
if isinstance(results, ratapi.outputs.BayesResults):
# the predictionIntervals data consists of 5 rows:
# row 0: min with 95% confidence
# row 1: min with 65% confidence
# row 2: mean
# row 3: max with 65% confidence
# row 4: max with 95% confidence
if bayes == 95:
interval = [0, 4]
elif bayes == 65:
interval = [1, 3]
else:
raise ValueError("Parameter `bayes` must be 95, 65 or None")
confidence_intervals = {
"reflectivity": [
(ref_inter[interval[0]], ref_inter[interval[1]])
for ref_inter in results.predictionIntervals.reflectivity
],
"sld": [
[(sld_inter[interval[0]], sld_inter[interval[1]]) for sld_inter in sld]
for sld in results.predictionIntervals.sld
],
}
# For a shaded plot, use the mean values from predictionIntervals
for reflectivity, mean_reflectivity in zip(
data.reflectivity, results.predictionIntervals.reflectivity, strict=False
):
reflectivity[:, 1] = mean_reflectivity[2]
for sldProfile, mean_sld_profile in zip(data.sldProfiles, results.predictionIntervals.sld, strict=False):
for sld, mean_sld in zip(sldProfile, mean_sld_profile, strict=False):
sld[:, 1] = mean_sld[2]
else:
raise ValueError(
"Shaded confidence intervals are only available for the results of Bayesian analysis (NS or DREAM)"
)
else:
confidence_intervals = None
if fig is None:
fig = plt.subplots(1, 2)[0]
elif len(fig.axes) != 2:
fig.clf()
fig.subplots(1, 2)
plot_ref_sld_helper(
data,
fig,
confidence_intervals=confidence_intervals,
linear_x=linear_x,
q4=q4,
show_error_bar=show_error_bar,
show_grid=show_grid,
show_legend=show_legend,
shift_value=shift_value,
align_profile=(project.geometry == "air/substrate" and project.model != "custom xy"),
)
if return_fig:
return fig
plt.show(block=block)
class BlittingSupport:
"""Create a SLD plot that uses blitting to get faster draws.
The blit plot stores the background from an
initial draw then updates the foreground (lines and error bars) if the background is not changed.
Parameters
----------
data : PlotEventData
The plot event data that contains all the information
to generate the ref and sld plots
fig : matplotlib.figure.Figure, optional
The figure class that has two subplots
linear_x : bool, default: False
Controls whether the x-axis on reflectivity plot uses the linear scale
q4 : bool, default: False
Controls whether Q^4 is plotted on the reflectivity plot
show_error_bar : bool, default: True
Controls whether the error bars are shown
show_grid : bool, default: False
Controls whether the grid is shown
show_legend : bool, default: True
Controls whether the legend is shown
shift_value : float, default: 100
A value between 0 and 100 that controls the spacing between the reflectivity plots for each of the contrasts
"""
def __init__(
self,
data,
fig=None,
linear_x: bool = False,
q4: bool = False,
show_error_bar: bool = True,
show_grid: bool = False,
show_legend: bool = True,
shift_value: float = 100,
):
self.figure = fig
self.linear_x = linear_x
self.q4 = q4
self.show_error_bar = show_error_bar
self.show_grid = show_grid
self.show_legend = show_legend
self.shift_value = shift_value
self.update_plot(data)
self.event_id = self.figure.canvas.mpl_connect("resize_event", self.resizeEvent)
def __del__(self):
self.figure.canvas.mpl_disconnect(self.event_id)
def resizeEvent(self, _event):
"""Ensure the background is updated after a resize event."""
self.__background_changed = True
def update(self, data):
"""Update the foreground, if background has not changed otherwise it updates full plot.
Parameters
----------
data : PlotEventData
The plot event data that contains all the information
to generate the ref and sld plots
"""
if self.__background_changed:
self.update_plot(data)
else:
self.update_foreground(data)
def __setattr__(self, name, value):
old_value = getattr(self, name, None)
if value == old_value:
return
super().__setattr__(name, value)
if name in ["figure", "linear_x", "q4", "show_error_bar", "show_grid", "show_legend", "shift_value"]:
self.__background_changed = True
def set_animated(self, is_animated: bool):
"""Set the animated property of foreground plot elements.
Parameters
----------
is_animated : bool
Indicates if the animated property should be set.
"""
for line in self.figure.axes[0].lines:
line.set_animated(is_animated)
for line in self.figure.axes[1].lines:
line.set_animated(is_animated)
for container in self.figure.axes[0].containers:
container[2][0].set_animated(is_animated)
def adjust_error_bar(self, error_bar_container, x, y, y_error):
"""Adjust the error bar data.
Parameters
----------
error_bar_container : Tuple
Tuple containing the artist of the errorbar i.e. (data line, cap lines, bar lines)
x : np.ndarray
The shifted data x axis data
y : np.ndarray
The shifted data y axis data
y_error : np.ndarray
The shifted data y axis error data
"""
line, _, (bars_y,) = error_bar_container
line.set_data(x, y)
x_base = x
y_base = y
y_error_top = y_base + y_error
y_error_bottom = y_base - y_error
new_segments_y = [
np.array([[x, yt], [x, yb]]) for x, yt, yb in zip(x_base, y_error_top, y_error_bottom, strict=False)
]
bars_y.set_segments(new_segments_y)
def update_plot(self, data):
"""Update the full plot.
Parameters
----------
data : PlotEventData
The plot event data that contains all the information
to generate the ref and sld plots
"""
if self.figure is not None:
self.figure.clf()
self.figure.tight_layout()
plot_ref_sld_helper(
data,
self.figure,
linear_x=self.linear_x,
q4=self.q4,
show_error_bar=self.show_error_bar,
show_grid=self.show_grid,
show_legend=self.show_legend,
shift_value=self.shift_value,
animated=True,
)
self.figure.canvas.draw()
self.bg = self.figure.canvas.copy_from_bbox(self.figure.bbox)
for line in self.figure.axes[0].lines:
self.figure.axes[0].draw_artist(line)
for line in self.figure.axes[1].lines:
self.figure.axes[1].draw_artist(line)
for container in self.figure.axes[0].containers:
self.figure.axes[0].draw_artist(container[2][0])
self.figure.canvas.blit(self.figure.bbox)
self.set_animated(False)
self.__background_changed = False
def update_foreground(self, data):
"""Update the plot foreground only.
Parameters
----------
data : PlotEventData
The plot event data that contains all the information
to generate the ref and sld plots
"""
self.set_animated(True)
self.figure.canvas.restore_region(self.bg)
plot_data = _extract_plot_data(data, self.q4, self.show_error_bar, self.shift_value)
offset = 0
for i in range(len(data.contrastNames)):
for _ in range(int(data.dataPresent[i]) + 1):
self.figure.axes[0].lines[offset].set_data(plot_data["ref"][i][0], plot_data["ref"][i][1])
self.figure.axes[0].draw_artist(self.figure.axes[0].lines[offset])
offset += 1
i = 0
for j in range(len(plot_data["sld"])):
for sld in plot_data["sld"][j]:
self.figure.axes[1].lines[i].set_data(sld[0], sld[1])
self.figure.axes[1].draw_artist(self.figure.axes[1].lines[i])
i += 1
for resampled in plot_data["sld_resample"][j]:
self.figure.axes[1].lines[i].set_data(resampled[0], resampled[1])
self.figure.axes[1].draw_artist(self.figure.axes[1].lines[i])
i += 1
i = 0
for error in plot_data["error"]:
if error:
container = self.figure.axes[0].containers[i]
self.adjust_error_bar(container, error[0], error[1], error[2])
self.figure.axes[0].draw_artist(container[2][0])
self.figure.axes[0].draw_artist(container[0])
i += 1
self.figure.canvas.blit(self.figure.bbox)
self.figure.canvas.flush_events()
self.set_animated(False)
class LivePlot:
"""Create a plot that gets updates from the plot event during a calculation.
Parameters
----------
block : bool, default: False
Indicates the plot should block until it is closed
"""
def __init__(self, block=False):
self.block = block
self.closed = False
self.blit_plot = None
def __enter__(self):
self.figure = plt.subplots(1, 2)[0]
self.figure.canvas.mpl_connect("close_event", self._setCloseState)
self.figure.show()
ratapi.events.register(ratapi.events.EventTypes.Plot, self.plotEvent)
return self.figure
def _setCloseState(self, _):
"""Close event handler."""
self.closed = True
def plotEvent(self, event):
"""Plot the figure from plot event data.
This is a callback for the plot event.
Parameters
----------
event: PlotEventData
The plot event data.
"""
if not self.closed and self.figure.number in plt.get_fignums():
if self.blit_plot is None:
self.blit_plot = BlittingSupport(event, self.figure)
else:
self.blit_plot.update(event)
def __exit__(self, _exc_type, _exc_val, _traceback):
ratapi.events.clear(ratapi.events.EventTypes.Plot, self.plotEvent)
if not self.closed and self.figure.number in plt.get_fignums():
plt.show(block=self.block)
def assert_bayesian(name: str):
"""Ensure the results passed to a function are Bayesian.
This is a decorator.
Parameters
----------
name : str
The name of the plot for the error message.
"""
def decorator(func: Callable):
@wraps(func)
def inner(results, *args, **kwargs):
if isinstance(results, ratapi.outputs.BayesResults):
return func(results, *args, **kwargs)
raise ValueError(f"{name} plots are only available for the results of Bayesian analysis (NS or DREAM)")
return inner
return decorator
def name_to_index(param: str | int, names: list[str]):
"""Convert parameter names to indices."""
if isinstance(param, str):
if param not in names:
raise ValueError(f"Parameter {param} is not in this analysis.")
param = names.index(param)
elif isinstance(param, int):
if param > len(names) or param < 0:
raise IndexError(f"Index {param} has been given, but indices must be between zero and {len(names)}.")
else:
raise ValueError(f"Parameters must be given as indices or names, not {type(param)}.")
return param
@assert_bayesian("Corner")
def plot_corner(
results: ratapi.outputs.BayesResults,
params: list[int | str] | None = None,
smooth: bool = True,
block: bool = False,
fig: matplotlib.figure.Figure | None = None,
return_fig: bool = False,
hist_kwargs: dict | None = None,
hist2d_kwargs: dict | None = None,
progress_callback: Callable[[int, int], None] | None = None,
):
"""Create a corner plot from a Bayesian analysis.
Parameters
----------
results : BayesResults
The results from a Bayesian calculation.
params : list[int or str], default None
The indices or names of a subset of parameters if required.
If None, uses all indices.
smooth : bool, default True
Whether to apply Gaussian smoothing to the corner plot.
block : bool, default False
Whether Python should block until the plot is closed.
fig : matplotlib.figure.Figure, optional
The figure object to use for plot.
return_fig: bool, default False
If True, return the figure as an object instead of showing it.
hist_kwargs : dict
Extra keyword arguments to pass to the 1d histograms.
Default is {'density': True, 'bins': 25}
hist2d_kwargs : dict
Extra keyword arguments to pass to the 2d histograms.
Default is {'density': True, 'bins': 25}
progress_callback: Union[Callable[[int, int], None], None]
Callback function for providing progress during plot creation
First argument is current completed sub plot and second is total number of sub plots
Returns
-------
Figure or None
If `return_fig` is True, return the figure - otherwise, return nothing.
"""
fitname_to_index = partial(name_to_index, names=results.fitNames)
if params is None:
params = range(0, len(results.fitNames))
else:
params = list(map(fitname_to_index, params))
# defaults are applied inside each function - just pass blank dicts for now
if hist_kwargs is None:
hist_kwargs = {}
if hist2d_kwargs is None:
hist2d_kwargs = {}
num_params = len(params)
total_count = num_params + (num_params**2 - num_params) // 2
if fig is None:
fig, axes = plt.subplots(num_params, num_params, figsize=(11, 10), subplot_kw={"visible": False})
else:
fig.clf()
axes = fig.subplots(num_params, num_params, subplot_kw={"visible": False})
# i is row, j is column
current_count = 0
for i in range(num_params):
for j in range(i + 1):
row_param = params[i]
col_param = params[j]
current_axes: Axes = axes if isinstance(axes, matplotlib.axes.Axes) else axes[i][j]
current_axes.tick_params(which="both", labelsize="medium")
current_axes.xaxis.offsetText.set_fontsize("small")
current_axes.yaxis.offsetText.set_fontsize("small")
current_axes.set_visible(True)
if i == j: # diagonal: histograms
plot_one_hist(results, param=row_param, smooth=smooth, axes=current_axes, **hist_kwargs)
elif i > j: # lower triangle: 2d histograms
plot_contour(
results, x_param=col_param, y_param=row_param, smooth=smooth, axes=current_axes, **hist2d_kwargs
)
# remove label if on inside of corner plot
if j != 0:
current_axes.get_yaxis().set_visible(False)
if i != len(params) - 1:
current_axes.get_xaxis().set_visible(False)
# make labels invisible as titles cover that
current_axes.yaxis._update_offset_text_position = types.MethodType(
_y_update_offset_text_position, current_axes.yaxis
)
current_axes.yaxis.offset_text_position = "center"
current_axes.set_ylabel("")
current_axes.set_xlabel("")
if progress_callback is not None:
current_count += 1
progress_callback(current_count, total_count)
if return_fig:
return fig
plt.show(block=block)
@assert_bayesian("Histogram")
def plot_one_hist(
results: ratapi.outputs.BayesResults,
param: int | str,
smooth: bool = True,
window_size: int = 8,
estimated_density: Literal["normal", "lognor", "kernel", None] = None,
axes: Axes | None = None,
block: bool = False,
return_fig: bool = False,
**hist_settings,
):
"""Plot the marginalised posterior for a parameter of a Bayesian analysis.
Parameters
----------
results : BayesResults
The results from a Bayesian calculation.
param : Union[int, str]
Either the index or name of a parameter.
block : bool, default False
Whether Python should block until the plot is closed.
smooth : bool, default True
Whether to apply Gaussian smoothing to the histogram.
Defaults to True.
window_size : int, default 8
The width of the smoothing window centered around the element being averaged.
The window moves down the length of the data, computing an average over the elements within each window.
estimated_density : 'normal', 'lognor', 'kernel' or None, default None
If None (default), ignore. Else, add an estimated density
of the given form on top of the histogram by the following estimations:
'normal': normal Gaussian.
'lognor': Log-normal probability density.
'kernel': kernel density estimation.
axes: Axes or None, default None
If provided, plot on the given Axes object.
block : bool, default False
Whether Python should block until the plot is closed.
return_fig: bool, default False
If True, return the figure as an object instead of showing it.
**hist_settings :
Settings passed to `np.histogram`. By default, the settings
passed are `bins = 25` and `density = True`.
Returns
-------
Figure or None
If `return_fig` is True, return the figure - otherwise, return nothing.
"""
chain = results.chain
param = name_to_index(param, results.fitNames)
if axes is None:
fig, axes = plt.subplots(1, 1)
else:
fig = None
# apply default settings if not set by user
default_settings = {"bins": 25, "density": True}
hist_settings = {**default_settings, **hist_settings}
parameter_chain = chain[:, param]
counts, bins = np.histogram(parameter_chain, **hist_settings)
mean_y = np.mean(parameter_chain)
sd_y = np.std(parameter_chain)
if smooth:
counts = moving_average(counts, window_size=window_size)
axes.hist(
bins[:-1],
bins,
weights=counts,
edgecolor="black",
linewidth=1.2,
color="white",
)
axes.set_title(results.fitNames[param], loc="left", fontsize="medium")
if estimated_density:
dx = bins[1] - bins[0]
if estimated_density == "normal":
t = np.linspace(mean_y - 3.5 * sd_y, mean_y + 3.5 * sd_y)
axes.plot(t, norm.pdf(t, loc=mean_y, scale=sd_y**2))
elif estimated_density == "lognor":
t = np.linspace(bins[0] - 0.5 * dx, bins[-1] + 2 * dx)
axes.plot(t, lognorm.pdf(t, np.mean(np.log(parameter_chain)), np.std(np.log(parameter_chain))))
elif estimated_density == "kernel":
t = np.linspace(bins[0] - 2 * dx, bins[-1] + 2 * dx, 200)
kde = gaussian_kde(parameter_chain)
axes.plot(t, kde.evaluate(t))
else:
raise ValueError(
f"{estimated_density} is not a supported estimated density function."
" Supported functions are 'normal' 'lognor' or 'kernel'."
)
# adding the estimated density extends the figure range - reset it to histogram range
x_range = hist_settings.get("range", (parameter_chain.min(), parameter_chain.max()))
axes.set_xlim(x_range)
if fig is not None:
if return_fig:
return fig
plt.show(block=block)
def _y_update_offset_text_position(axis, _bboxes, bboxes2):
"""Update the position of the Y axis offset text using the provided bounding boxes.
Adapted from https://github.com/matplotlib/matplotlib/issues/4476#issuecomment-105627334.
Parameters
----------
axis : matplotlib.axis.YAxis
Y axis to update.
_bboxes : List
list of bounding boxes
bboxes2 : List
list of bounding boxes
"""
x, y = axis.offsetText.get_position()
if axis.offset_text_position == "left":
# y in axes coords, x in display coords
axis.offsetText.set_transform(
mtransforms.blended_transform_factory(axis.axes.transAxes, mtransforms.IdentityTransform())
)
top = axis.axes.bbox.ymax
y = top + axis.OFFSETTEXTPAD * axis.figure.dpi / 72.0
else:
# x & y in display coords
axis.offsetText.set_transform(mtransforms.IdentityTransform())
# Northwest of upper-right corner of right-hand extent of tick labels
if bboxes2:
bbox = mtransforms.Bbox.union(bboxes2)
else:
bbox = axis.axes.bbox
center = bbox.ymin + (bbox.ymax - bbox.ymin) / 2
x = bbox.xmin - axis.OFFSETTEXTPAD * axis.figure.dpi / 72.0
y = center
x_offset = 110
axis.offsetText.set_position((x - x_offset, y))
@assert_bayesian("Contour")
def plot_contour(
results: ratapi.outputs.BayesResults,
x_param: int | str,
y_param: int | str,
smooth: bool = True,
sigma: tuple[float] | None = None,
axes: Axes | None = None,
block: bool = False,
return_fig: bool = False,
**hist2d_settings,
):
"""Plot a 2D histogram of two indexed chain parameters, with contours.
Parameters
----------
results : ratapi.outputs.BayesResults
The results of a Bayesian analysis.
x_param : int
The index or name of the parameter on the x-axis.
y_param : int
The index or name ofthe parameter on the y-axis.
smooth : bool, default True
If True, apply Gaussian smoothing to the histogram.
sigma : tuple[float] or None, default None
If given, is used as parameters for Gaussian smoothing in x and y direction respectively.
If None, defaults to the standard deviation of the parameter chain in either direction.
axes: Axes or None, default None
If provided, plot on the given Axes object.
block : bool, default False
Whether Python should block until the plot is closed.
return_fig: bool, default False
If True, return the figure as an object instead of showing it.
**hist2d_settings:
Settings passed to `np.histogram2d`.
Default settings are `bins = 25` and `density = True`.
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