def plot_speeds(
body_speed,
start: int = 0,
end: int = -1,
is_moving: np.ndarray = None,
ax: plt.axis = None,
show: bool = True,
**other_speeds,
):
"""
Just plot some speeds for debugging stuff
"""
if ax is None:
f, ax = plt.subplots(figsize=(14, 8))
ax.plot(body_speed[start:end], label="body", color="salmon")
for name, speed in other_speeds.items():
ax.plot(speed[start:end], label=name)
if is_moving is not None:
ax.plot(is_moving, lw=4, color="k")
ax.legend()
ax.set(xlabel="time (frames)", ylabel="speed (cm/s)")
if show:
plt.show()
def mpl_plot(self,
ax: plt.axis = None,
temp_unit: str = "GK",
**kwargs) -> plt.axis:
ax = ax or plt.gca()
t = np.logspace(-2, 1, 1000)
if temp_unit is "GK":
ax.loglog(
t,
self.rate(t),
color=self.color,
label="Reaclib-" + self.label + " " + self.__str__(),
**kwargs,
)
elif temp_unit is "KeV":
ax.loglog(
Temperature(t).kev,
self.rate(t),
color=self.color,
label=self.label + " " + self.__str__(),
**kwargs,
)
ax.legend()
ax = super().mpl_plot(ax=ax, temp_unit=temp_unit)
return ax
def plot_on(
self,
ax: plt.axis,
draw_legend: bool = True,
legend_inside: bool = True,
legend_kwargs: dict = {},
yaxis_scale=1.3,
hide_labels: bool = False,
) -> plt.axis:
for component in self.components:
if component.style == 'point':
ax.errorbar(
component.data.x_values,
component.data.y_values,
yerr=component.data.y_errors,
xerr=component.data.x_errors,
label=component.label,
color=component.color,
ls=component.ls,
marker=component.marker,
)
elif component.style == 'box':
ax.bar(component.data.x_values,
2 * component.data.y_errors,
width=2 * component.data.x_errors,
bottom=component.data.y_values -
component.data.y_errors,
label=component.label,
color=component.color,
alpha=0.5)
else:
raise NotImplementedError(
"Options: point|box. If you require a new kind of plot, report a feature request"
)
if not hide_labels:
ax.set_xlabel(self.variable.x_label, plot_style.xlabel_pos)
ax.set_ylabel(self.variable.y_label, plot_style.ylabel_pos)
if draw_legend:
if legend_inside:
ax.legend(frameon=False, **legend_kwargs)
ylims = ax.get_ylim()
ax.set_ylim(ylims[0], yaxis_scale * ylims[1])
else:
ax.legend(frameon=False,
bbox_to_anchor=(1, 1),
**legend_kwargs)
return ax
def _sub_plot_multiple(self, axis:plt.axis, columns:tuple):
'''
Create a sub-plot for the given columns.
Parameters
----------
ax : plt.axis
figure axis
columns : tuple
column names
'''
title = ''
for col in columns:
axis.plot(self.m_data_df[col], label=col,linestyle='-',linewidth=0.1)
title += col + ', '
axis.set_title(title)
axis.set_xlabel('Rollouts')
axis.legend()
def mpl_plot(self, ax: plt.axis = None, temp_unit: str = "GK", **kwargs):
ax = ax or plt.gca()
if temp_unit is "GK":
ax.errorbar(
Temperature(np.array(self.temperature),
unit=self.temp_unit).gk,
self.rr,
yerr=self.err,
color=self.colour,
label="{0} {1}".format(self.label, self.__str__()),
**kwargs,
)
ax.scatter(
Temperature(np.array(self.temperature),
unit=self.temp_unit).gk,
self.rr,
color=self.colour,
)
elif temp_unit is "KeV":
ax.errorbar(
Temperature(np.array(self.temperature),
unit=self.temp_unit).kev,
self.rr,
yerr=self.err,
color=self.colour,
label="{0} {1}".format(self.label, self.__str__()),
**kwargs,
)
ax.scatter(
Temperature(np.array(self.temperature),
unit=self.temp_unit).kev,
self.rr,
color=self.colour,
)
ax.set_yscale("log")
ax.legend()
ax = super().mpl_plot(ax, temp_unit=temp_unit)
return ax
def add_cut_to_axis(ax: plt.axis,
cut_left: Optional[float] = None,
cut_right: Optional[float] = None,
cut_window: Optional[Tuple[float, float]] = None,
keep_window: Optional[Tuple[float, float]] = None,
color: str = 'white'):
"""
Adds a "cut" to a given axis. The cut is shown as shaded area with the color
given in the parameter color.
:param ax: Axis to plot on.
:param cut_left: Upper x value of the cut. If set, the area with
x < cut_left is indicated to be cut away. Default is None
:param cut_right: Lower x value of the cut. If set, the area with
x > cut_right is indicated to be cut away. Default is None
:param cut_window:
:param keep_window:
:param color: Color of the overlay of the area which is indicated to be
cut away. Default is 'white'.
"""
x_lim_low, x_lim_high = ax.get_xlim()
if cut_left is not None:
ax.axvspan(x_lim_low, cut_left, facecolor=color, alpha=0.7)
ax.axvline(cut_left, color='black', linestyle='dashed', lw=1.5, label='Cut')
elif cut_right is not None:
ax.axvspan(cut_right, x_lim_high, facecolor=color, alpha=0.7)
ax.axvline(cut_right, color='black', linestyle='dashed', lw=1.5, label='Cut')
elif cut_window is not None:
ax.axvspan(cut_window[0], cut_window[1], facecolor=color, alpha=0.7)
ax.axvline(cut_window[0], color='black', linestyle='dashed', lw=1.5, label='Cut')
ax.axvline(cut_window[1], color='black', linestyle='dashed', lw=1.5)
elif keep_window is not None:
ax.axvspan(x_lim_low, keep_window[0], facecolor=color, alpha=0.7)
ax.axvline(keep_window[0], color='black', linestyle='dashed', lw=1.5, label='Cut')
ax.axvspan(keep_window[1], x_lim_high, facecolor=color, alpha=0.7)
ax.axvline(keep_window[1], color='black', linestyle='dashed', lw=1.5, label='Cut')
ax.legend(frameon=False, bbox_to_anchor=(1, 1))
def plot_on(self,
ax: plt.axis,
ylabel="Events",
draw_legend=True,
legend_inside=True,
hide_labels: bool = False):
bin_edges, bin_mids, bin_width = self._get_bin_edges()
self._bin_edges = bin_edges
self._bin_mids = bin_mids
self._bin_width = bin_width
ax.hist(
x=[comp.data for comp in self._mc_components['stacked']],
bins=bin_edges,
weights=[comp.weights for comp in self._mc_components['stacked']],
stacked=True,
edgecolor="black",
lw=0.3,
color=[comp.color for comp in self._mc_components['stacked']],
label=[comp.label for comp in self._mc_components['stacked']],
histtype='stepfilled')
if not hide_labels:
ax.set_xlabel(self._variable.x_label, plot_style.xlabel_pos)
y_label = self._get_y_label(False, bin_width, ylabel)
ax.set_ylabel(y_label, plot_style.ylabel_pos)
if draw_legend:
if legend_inside:
ax.legend(frameon=False)
ylims = ax.get_ylim()
ax.set_ylim(ylims[0], 1.4 * ylims[1])
else:
ax.legend(frameon=False, bbox_to_anchor=(1, 1))
return ax
def plot_on(
self,
ax1: plt.axis,
ax2,
style="stacked",
ylabel="Events",
sum_color=plot_style.KITColors.kit_purple,
draw_legend: bool = True,
legend_inside: bool = True,
):
bin_edges, bin_mids, bin_width = self._get_bin_edges()
self._bin_edges = bin_edges
self._bin_mids = bin_mids
self._bin_width = bin_width
sum_w = np.sum(np.array([
binned_statistic(comp.data,
comp.weights,
statistic="sum",
bins=bin_edges)[0]
for comp in self._mc_components["MC"]
]),
axis=0)
sum_w2 = np.sum(np.array([
binned_statistic(comp.data,
comp.weights**2,
statistic="sum",
bins=bin_edges)[0]
for comp in self._mc_components["MC"]
]),
axis=0)
hdata, _ = np.histogram(self._data_component.data, bins=bin_edges)
if style.lower() == "stacked":
ax1.hist(
x=[comp.data for comp in self._mc_components['MC']],
bins=bin_edges,
weights=[comp.weights for comp in self._mc_components['MC']],
stacked=True,
edgecolor="black",
lw=0.3,
color=[comp.color for comp in self._mc_components['MC']],
label=[comp.label for comp in self._mc_components['MC']],
histtype='stepfilled')
ax1.bar(x=bin_mids,
height=2 * np.sqrt(sum_w2),
width=self.bin_width,
bottom=sum_w - np.sqrt(sum_w2),
color="black",
hatch="///////",
fill=False,
lw=0,
label="MC stat. unc.")
if style.lower() == "summed":
ax1.bar(x=bin_mids,
height=2 * np.sqrt(sum_w2),
width=self.bin_width,
bottom=sum_w - np.sqrt(sum_w2),
color=sum_color,
lw=0,
label="MC")
ax1.errorbar(x=bin_mids,
y=hdata,
yerr=np.sqrt(hdata),
ls="",
marker=".",
color="black",
label=self._data_component.label)
y_label = self._get_y_label(False, bin_width, evts_or_cand=ylabel)
# ax1.legend(loc=0, bbox_to_anchor=(1,1))
ax1.set_ylabel(y_label, plot_style.ylabel_pos)
if draw_legend:
if legend_inside:
ax1.legend(frameon=False)
ylims = ax1.get_ylim()
ax1.set_ylim(ylims[0], 1.4 * ylims[1])
else:
ax1.legend(frameon=False, bbox_to_anchor=(1, 1))
ax2.set_ylabel(r"$\frac{\mathrm{Data - MC}}{\mathrm{Data}}$")
ax2.set_xlabel(self._variable.x_label, plot_style.xlabel_pos)
ax2.set_ylim((-1, 1))
try:
uhdata = unp.uarray(hdata, np.sqrt(hdata))
uhmc = unp.uarray(sum_w, np.sqrt(sum_w2))
ratio = (uhdata - uhmc) / uhdata
ax2.axhline(y=0, color=plot_style.KITColors.dark_grey, alpha=0.8)
ax2.errorbar(bin_mids,
unp.nominal_values(ratio),
yerr=unp.std_devs(ratio),
ls="",
marker=".",
color=plot_style.KITColors.kit_black)
except ZeroDivisionError:
ax2.axhline(y=0, color=plot_style.KITColors.dark_grey, alpha=0.8)
plt.subplots_adjust(hspace=0.08)
def plot_on(self,
ax: plt.axis,
draw_legend: bool = True,
legend_inside: bool = True,
yaxis_scale=1.3,
normed: bool = False,
ylabel="Events",
hide_labels: bool = False) -> plt.axis:
"""
Plots the component on a given matplotlib.pyplot.axis
:param ax: matplotlib.pyplot.axis where the histograms will be drawn
on.
:param draw_legend: Draw legend on axis if True.
:param normed: If true the histograms are normalized.
:return: matplotlib.pyplot.axis with histogram drawn on it
"""
bin_edges, bin_mids, bin_width = self._get_bin_edges()
self._bin_edges = bin_edges
self._bin_mids = bin_mids
self._bin_width = bin_width
for component in self._mc_components['single']:
if component.histtype == 'stepfilled':
alpha = 0.6
edge_color = 'black'
else:
edge_color = None
alpha = 1.0
ax.hist(x=component.data,
bins=bin_edges,
density=normed,
weights=component.weights,
histtype=component.histtype,
label=component.label,
edgecolor=edge_color
if edge_color is not None else component.color,
alpha=alpha,
lw=1.5,
ls=component.ls,
color=component.color)
if not hide_labels:
ax.set_xlabel(self._variable.x_label, plot_style.xlabel_pos)
y_label = self._get_y_label(normed=normed,
bin_width=bin_width,
evts_or_cand=ylabel)
ax.set_ylabel(y_label, plot_style.ylabel_pos)
if draw_legend:
if legend_inside:
ax.legend(frameon=False)
ylims = ax.get_ylim()
ax.set_ylim(ylims[0], yaxis_scale * ylims[1])
else:
ax.legend(frameon=False, bbox_to_anchor=(1, 1))
return ax
def word_group_visualization(
transformed_word_embeddings: np.ndarray,
words: np.ndarray,
word_groups: dict,
xlabel: str,
ylabel: str,
emphasis_words: list = None,
alpha: float = 1,
non_group_words_color: str = "#ccc",
scatter_set_rasterized: bool = False,
rasterization_threshold: int = 1000,
ax: plt.axis = None,
show_plot: bool = True,
) -> None:
"""
Visualizes one or more word groups by plotting its word embeddings in 2D.
Parameters
----------
transformed_word_embeddings : np.ndarray
Transformed word embeddings.
words : np.ndarray
Numpy array containing all words from vocabulary.
word_groups : dict
Dictionary containing word groups to visualize.
xlabel : str
X-axis label.
ylabel : str
Y-axis label.
emphasis_words : list, optional
List representing words to emphasize in the visualization (defaults to None).
Entries can be either be strings (words) or tuples, consisting of the word, x-offset
and y-offset.
alpha : float
Scatter plot alpha value (defaults to 1).
non_group_words_color : str
Color for words outside groups (defaults to #ccc).
scatter_set_rasterized : bool
Whether or not to enable rasterization on scatter plotting (defaults to False).
rasterization_threshold : int
The least number of data points to enable rasterization, given that
`scatter_set_rasterized` is set to True (defaults to 1000).
ax : plt.axis
Axis (defaults to None).
show_plot : bool
Whether or not to call plt.show() (defaults to True).
"""
# Filter and restrict words in word groups
word_group_words_restricted = {}
for group_key, group_data in word_groups.items():
group_words = group_data["words"]
group_words = np.array([word for word in group_words if word in words])
group_words_indices = np.array(
[np.where(words == word)[0][0] for word in group_words])
group_word_embeddings = transformed_word_embeddings[
group_words_indices]
boundaries = group_data.get("boundaries", {})
if boundaries.get("xmin") is None:
boundaries["xmin"] = group_word_embeddings[:, 0].min()
if boundaries.get("xmax") is None:
boundaries["xmax"] = group_word_embeddings[:, 0].max()
if boundaries.get("ymin") is None:
boundaries["ymin"] = group_word_embeddings[:, 1].min()
if boundaries.get("ymax") is None:
boundaries["ymax"] = group_word_embeddings[:, 1].max()
group_word_embeddings_boundaries_mask = [
(boundaries["xmin"] <= word_vec[0] <= boundaries["xmax"])
and (boundaries["ymin"] <= word_vec[1] <= boundaries["ymax"])
for i, word_vec in enumerate(group_word_embeddings)
]
word_group_words_restricted[group_key] = group_words[
group_word_embeddings_boundaries_mask]
# Find words not in groups
words_not_in_groups_mask = [
i for i, word in enumerate(words)
for group_words in word_group_words_restricted.values()
if word not in group_words
]
if ax is None:
_, ax = plt.subplots(figsize=(12, 7))
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
# Plot non-group words
non_grp_scatter_handle = ax.scatter(
x=transformed_word_embeddings[words_not_in_groups_mask][:, 0],
y=transformed_word_embeddings[words_not_in_groups_mask][:, 1],
s=10,
alpha=alpha,
c=non_group_words_color,
)
if (scatter_set_rasterized
and len(words_not_in_groups_mask) >= rasterization_threshold):
non_grp_scatter_handle.set_rasterized(True)
# Plot group words
for group_key, group_words in word_group_words_restricted.items():
group_words_indices = np.array(
[np.where(words == word)[0][0] for word in group_words])
group_word_embeddings = transformed_word_embeddings[
group_words_indices]
grp_scatter_handle = ax.scatter(
x=group_word_embeddings[:, 0],
y=group_word_embeddings[:, 1],
s=15,
alpha=alpha,
c=word_groups[group_key]["color"],
label=word_groups[group_key]["label"],
)
if (scatter_set_rasterized
and len(group_word_embeddings) >= rasterization_threshold):
grp_scatter_handle.set_rasterized(True)
# Visualize emphasized words
if emphasis_words is not None:
emphasis_words = [(entry, 0, 0) if type(entry) == str else entry
for entry in emphasis_words]
for emphasis_word, x_offset, y_offset in emphasis_words:
word_group_key = None
for group_key, group_data in word_groups.items():
if emphasis_word in group_data["words"]:
word_group_key = group_key
break
if word_group_key is None:
word_color = non_group_words_color
else:
word_color = word_groups[group_key]["color"]
word_idx = [
i for i, word in enumerate(words) if word == emphasis_word
][0]
emphasis_scatter_handle = ax.scatter(
x=transformed_word_embeddings[word_idx, 0],
y=transformed_word_embeddings[word_idx, 1],
s=40,
alpha=alpha,
c=word_color,
)
if (scatter_set_rasterized
and len(emphasis_words) >= rasterization_threshold):
emphasis_scatter_handle.set_rasterized(True)
# Annotate emphasis word with a text box
offsetbox = TextArea(emphasis_word)
ab = AnnotationBbox(
offsetbox,
tuple(transformed_word_embeddings[word_idx]),
xybox=(x_offset, 40 + y_offset),
xycoords="data",
boxcoords="offset points",
arrowprops=dict(arrowstyle="->", color="black", linewidth=2),
)
ax.add_artist(ab)
ax.legend()
if show_plot:
plt.show()
def visualize_word_cluster_groups(
transformed_word_embeddings: np.ndarray,
words: np.ndarray,
word_groups: dict,
visualize_non_group_words: bool,
xlabel: str,
ylabel: str,
non_group_words_color: str = "#ccc",
ax: plt.axis = None,
show_plot: bool = True,
alpha: float = 1,
interactive: bool = False,
) -> None:
"""
Visualizes word cluster groups.
Parameters
----------
transformed_word_embeddings : np.ndarray
Transformed word embeddings.
words : np.ndarray
Numpy array containing all words from vocabulary.
word_groups : dict
Dictionary containing word groups to visualize.
visualize_non_group_words : bool
Whether or not to visualize words outside word groups
xlabel : str
X-axis label
ylabel : str
Y-axis label
non_group_words_color : str
Color for words outside groups (defaults to #ccc)
ax : plt.axis
Matplotlib axis (defaults to None)
show_plot : bool
Whether or not to call plt.show() (defaults to True)
alpha : float
Scatter plot alpha value (defaults to 1)
interactive : bool
Whether or not to make the visualization interactive
using Plotly (defaults to False).
"""
if ax is None and not interactive:
_, ax = plt.subplots(figsize=(12, 7))
if interactive:
fig = go.Figure(
layout=dict(xaxis=dict(title=xlabel), yaxis=dict(title=ylabel)))
if visualize_non_group_words:
# Create boolean mask for words outside groups
words_in_groups = []
for group_name in word_groups.keys():
words_in_groups.extend(word_groups[group_name]["words"])
words_not_in_groups_mask = [
word not in words_in_groups for word in words
]
words_not_in_groups_sorted = [
word for word in words if word not in words_in_groups
]
# Plot words outside word group
if interactive:
fig.add_trace(
go.Scatter(
x=transformed_word_embeddings[words_not_in_groups_mask][:,
0],
y=transformed_word_embeddings[words_not_in_groups_mask][:,
1],
mode="markers",
marker=dict(color=non_group_words_color),
hovertext=words_not_in_groups_sorted,
hoverinfo="x+y+text",
name="Non group words",
opacity=alpha,
))
else:
ax.scatter(
x=transformed_word_embeddings[words_not_in_groups_mask][:, 0],
y=transformed_word_embeddings[words_not_in_groups_mask][:, 1],
c=non_group_words_color,
alpha=alpha,
)
# Visualize words in groups
for group_name, word_group in word_groups.items():
words_in_group = word_group["words"]
words_in_group_mask = [word in words_in_group for word in words]
words_in_group_sorted = [
word for word in words if word in words_in_group
]
word_group_color = word_group["color"]
# Plot words inside word group
if interactive:
fig.add_trace(
go.Scatter(
x=transformed_word_embeddings[words_in_group_mask][:, 0],
y=transformed_word_embeddings[words_in_group_mask][:, 1],
mode="markers",
marker=dict(color=word_group_color),
hovertext=words_in_group_sorted,
hoverinfo="x+y+text",
name=f"Words in {group_name}",
opacity=alpha,
))
else:
ax.scatter(
x=transformed_word_embeddings[words_in_group_mask][:, 0],
y=transformed_word_embeddings[words_in_group_mask][:, 1],
c=word_group_color,
alpha=alpha,
)
if interactive:
fig.show()
else:
ax_legends = ["Non group words"]
ax_legends.extend([
f"Words which are {group_name}"
for group_name in word_groups.keys()
])
ax.legend(ax_legends)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
if show_plot:
plt.show()