#
# Class for quick plotting of variables from models
#
import os
import numpy as np
import pybamm
from collections import defaultdict
from pybamm.util import import_optional_dependency
class LoopList(list):
"""A list which loops over itself when accessing an
index so that it never runs out"""
def __getitem__(self, i):
# implement looping by calling "(i) modulo (length of list)"
return super().__getitem__(i % len(self))
def ax_min(data):
"""Calculate appropriate minimum axis value for plotting"""
data_min = np.nanmin(data)
data_max = np.nanmax(data)
return data_max - 1.05 * (data_max - data_min)
def ax_max(data):
"""Calculate appropriate maximum axis value for plotting"""
data_min = np.nanmin(data)
data_max = np.nanmax(data)
return data_min + 1.05 * (data_max - data_min)
def split_long_string(title, max_words=None):
"""Get title in a nice format"""
max_words = max_words or pybamm.settings.max_words_in_line
words = title.split()
# Don't split if fits on one line, don't split just for units
if len(words) <= max_words or words[max_words].startswith("["):
return title
else:
first_line = (" ").join(words[:max_words])
second_line = (" ").join(words[max_words:])
return first_line + "\n" + second_line
def close_plots():
"""Close all open figures"""
plt = import_optional_dependency("matplotlib.pyplot")
plt.close("all")
[docs]
class QuickPlot:
"""
Generates a quick plot of a subset of key outputs of the model so that the model
outputs can be easily assessed.
Parameters
----------
solutions: (iter of) :class:`pybamm.Solution` or :class:`pybamm.Simulation`
The numerical solution(s) for the model(s), or the simulation object(s)
containing the solution(s).
output_variables : list of str, optional
List of variables to plot
labels : list of str, optional
Labels for the different models. Defaults to model names
colors : list of str, optional
The colors to loop over when plotting. Defaults to None, in which case the
default color loop defined by matplotlib style sheet or rcParams is used.
linestyles : list of str, optional
The linestyles to loop over when plotting. Defaults to ["-", ":", "--", "-."]
shading : str, optional
The shading to use for 2D plots. Defaults to "auto".
figsize : tuple of floats, optional
The size of the figure to make
n_rows : int, optional
The number of rows to use. If None (default), floor(n // sqrt(n)) is used where
n = len(output_variables) so that the plot is as square as possible
time_unit : str, optional
Format for the time output ("hours", "minutes", or "seconds")
spatial_unit : str, optional
Format for the spatial axes ("m", "mm", or "um")
variable_limits : str or dict of str, optional
How to set the axis limits (for 0D or 1D variables) or colorbar limits (for 2D
variables). Options are:
- "fixed" (default): keep all axes fixes so that all data is visible
- "tight": make axes tight to plot at each time
- dictionary: fine-grain control for each variable, can be either "fixed" or \
"tight" or a specific tuple (lower, upper).
"""
def __init__(
self,
solutions,
output_variables=None,
labels=None,
colors=None,
linestyles=None,
shading="auto",
figsize=None,
n_rows=None,
time_unit=None,
spatial_unit="um",
variable_limits="fixed",
):
solutions = self.preprocess_solutions(solutions)
models = [solution.all_models[0] for solution in solutions]
# Set labels
if labels is None:
self.labels = [model.name for model in models]
else:
if len(labels) != len(models):
raise ValueError(
f"labels '{labels}' have different length to models '{[model.name for model in models]}'"
)
self.labels = labels
# Set colors, linestyles, figsize, axis limits
# call LoopList to make sure list index never runs out
if colors is None:
self.colors = LoopList(colors or ["r", "b", "k", "g", "m", "c"])
else:
self.colors = LoopList(colors)
self.linestyles = LoopList(linestyles or ["-", ":", "--", "-."])
self.shading = shading
# Default output variables for lead-acid and lithium-ion
if output_variables is None:
output_variables = models[0].default_quick_plot_variables
# raise error if still None
if output_variables is None:
raise ValueError(
f"No default output variables provided for {models[0].name}"
)
# check variables have been provided after any serialisation
if any(len(m.variables) == 0 for m in models):
raise AttributeError("No variables to plot")
self.n_rows = n_rows or int(
len(output_variables) // np.sqrt(len(output_variables))
)
self.n_cols = int(np.ceil(len(output_variables) / self.n_rows))
figwidth_default = min(15, 4 * self.n_cols)
figheight_default = min(8, 1 + 3 * self.n_rows)
self.figsize = figsize or (figwidth_default, figheight_default)
# Spatial scales (default to 1 if information not in model)
if spatial_unit == "m":
self.spatial_factor = 1
self.spatial_unit = "m"
elif spatial_unit == "mm":
self.spatial_factor = 1e3
self.spatial_unit = "mm"
elif spatial_unit == "um": # micrometers
self.spatial_factor = 1e6
self.spatial_unit = r"$\mu$m"
else:
raise ValueError(f"spatial unit '{spatial_unit}' not recognized")
# Time parameters
self.ts_seconds = [solution.t for solution in solutions]
min_t = np.min([t[0] for t in self.ts_seconds])
max_t = np.max([t[-1] for t in self.ts_seconds])
# Set timescale
if time_unit is None:
# defaults depend on how long the simulation is
if max_t >= 3600:
time_scaling_factor = 3600 # time in hours
self.time_unit = "h"
else:
time_scaling_factor = 1 # time in seconds
self.time_unit = "s"
elif time_unit == "seconds":
time_scaling_factor = 1
self.time_unit = "s"
elif time_unit == "minutes":
time_scaling_factor = 60
self.time_unit = "min"
elif time_unit == "hours":
time_scaling_factor = 3600
self.time_unit = "h"
else:
raise ValueError(f"time unit '{time_unit}' not recognized")
self.time_scaling_factor = time_scaling_factor
self.min_t = min_t / time_scaling_factor
self.max_t = max_t / time_scaling_factor
# Prepare dictionary of variables
# output_variables is a list of strings or lists, e.g.
# ["var 1", ["variable 2", "var 3"]]
output_variable_tuples = []
self.variable_limits = {}
for variable_list in output_variables:
# Make sure we always have a list of lists of variables, e.g.
# [["var 1"], ["variable 2", "var 3"]]
if isinstance(variable_list, str):
variable_list = [variable_list]
# Store the key as a tuple
variable_tuple = tuple(variable_list)
output_variable_tuples.append(variable_tuple)
# axis limits
if variable_limits in ["fixed", "tight"]:
self.variable_limits[variable_tuple] = variable_limits
else:
# If there is only one variable, extract it
if len(variable_tuple) == 1:
variable = variable_tuple[0]
else:
variable = variable_tuple
try:
self.variable_limits[variable_tuple] = variable_limits[variable]
except KeyError:
# if variable_tuple is not provided, default to "fixed"
self.variable_limits[variable_tuple] = "fixed"
except TypeError as error:
raise TypeError(
"variable_limits must be 'fixed', 'tight', or a dict"
) from error
self.set_output_variables(output_variable_tuples, solutions)
self.reset_axis()
@staticmethod
def preprocess_solutions(solutions):
input_solutions = QuickPlot.check_input_validity(solutions)
processed_solutions = []
for sim_or_sol in input_solutions:
if isinstance(sim_or_sol, pybamm.Simulation):
# 'sim_or_sol' is actually a 'Simulation' object here, so it has a
# 'Solution' attribute
processed_solutions.append(sim_or_sol.solution)
elif isinstance(sim_or_sol, pybamm.Solution):
processed_solutions.append(sim_or_sol)
return processed_solutions
@staticmethod
def check_input_validity(input_solutions):
if not isinstance(input_solutions, (pybamm.Solution, pybamm.Simulation, list)):
raise TypeError(
"Solutions must be 'pybamm.Solution' or 'pybamm.Simulation' or list"
)
elif not isinstance(input_solutions, list):
input_solutions = [input_solutions]
else:
if not input_solutions:
raise TypeError("QuickPlot requires at least 1 solution or simulation.")
return input_solutions
def set_output_variables(self, output_variables, solutions):
# Set up output variables
self.variables = {}
self.spatial_variable_dict = {}
self.first_spatial_variable = {}
self.second_spatial_variable = {}
self.x_first_and_y_second = {}
self.is_y_z = {}
# Calculate subplot positions based on number of variables supplied
self.subplot_positions = {}
for k, variable_tuple in enumerate(output_variables):
# Prepare list of variables
variables = [None] * len(solutions)
# process each variable in variable_list for each model
for i, solution in enumerate(solutions):
# variables lists of lists, so variables[i] is a list
variables[i] = []
for var in variable_tuple:
sol = solution[var]
# Check variable isn't all-nan
if np.all(np.isnan(sol.entries)):
raise ValueError(f"All-NaN variable '{var}' provided")
# If ok, add to the list of solutions
else:
variables[i].append(sol)
# Make sure variables have the same dimensions and domain
# just use the first solution to check this
first_solution = variables[0]
first_variable = first_solution[0]
domain = first_variable.domain
# check all other solutions against the first solution
for idx, variable in enumerate(first_solution):
if variable.domain != domain:
raise ValueError(
"Mismatching variable domains. "
f"'{variable_tuple[0]}' has domain '{domain}', but '{variable_tuple[idx]}' has domain '{variable.domain}'"
)
self.spatial_variable_dict[variable_tuple] = {}
# Set the x variable (i.e. "x" or "r" for any one-dimensional variables)
if first_variable.dimensions == 1:
(spatial_var_name, spatial_var_value) = self.get_spatial_var(
variable_tuple, first_variable, "first"
)
self.spatial_variable_dict[variable_tuple] = {
spatial_var_name: spatial_var_value
}
self.first_spatial_variable[variable_tuple] = (
spatial_var_value * self.spatial_factor
)
elif first_variable.dimensions == 2:
# Don't allow 2D variables if there are multiple solutions
if len(variables) > 1:
raise NotImplementedError(
"Cannot plot 2D variables when comparing multiple solutions, "
f"but '{variable_tuple[0]}' is 2D"
)
# But do allow if just a single solution
else:
# Add both spatial variables to the variable_tuples
(
first_spatial_var_name,
first_spatial_var_value,
) = self.get_spatial_var(variable_tuple, first_variable, "first")
(
second_spatial_var_name,
second_spatial_var_value,
) = self.get_spatial_var(variable_tuple, first_variable, "second")
self.spatial_variable_dict[variable_tuple] = {
first_spatial_var_name: first_spatial_var_value,
second_spatial_var_name: second_spatial_var_value,
}
self.first_spatial_variable[variable_tuple] = (
first_spatial_var_value * self.spatial_factor
)
self.second_spatial_variable[variable_tuple] = (
second_spatial_var_value * self.spatial_factor
)
# different order based on whether the domains
# are x-r, x-z or y-z, etc
if (
first_spatial_var_name in ("r", "R")
and second_spatial_var_name == "x"
):
self.x_first_and_y_second[variable_tuple] = False
self.is_y_z[variable_tuple] = False
elif (
first_spatial_var_name == "y" and second_spatial_var_name == "z"
):
self.x_first_and_y_second[variable_tuple] = True
self.is_y_z[variable_tuple] = True
else:
self.x_first_and_y_second[variable_tuple] = True
self.is_y_z[variable_tuple] = False
# Store variables and subplot position
self.variables[variable_tuple] = variables
self.subplot_positions[variable_tuple] = (self.n_rows, self.n_cols, k + 1)
[docs]
def get_spatial_var(self, key, variable, dimension):
"""Return the appropriate spatial variable(s)"""
# Extract name and value
# Special case for current collector, which is 2D but in a weird way (both
# first and second variables are in the same domain, not auxiliary domain)
if dimension == "first":
spatial_var_name = variable.first_dimension
spatial_var_value = variable.first_dim_pts
domain = variable.domain[0]
elif dimension == "second":
spatial_var_name = variable.second_dimension
spatial_var_value = variable.second_dim_pts
if variable.domain[0] == "current collector":
domain = "current collector"
else:
domain = variable.domains["secondary"][0]
if domain == "current collector":
domain += f" {spatial_var_name}"
return spatial_var_name, spatial_var_value
[docs]
def reset_axis(self):
"""
Reset the axis limits to the default values.
These are calculated to fit around the minimum and maximum values of all the
variables in each subplot
"""
self.axis_limits = {}
for key, variable_lists in self.variables.items():
if variable_lists[0][0].dimensions == 0:
x_min = self.min_t
x_max = self.max_t
elif variable_lists[0][0].dimensions == 1:
x_min = self.first_spatial_variable[key][0]
x_max = self.first_spatial_variable[key][-1]
elif variable_lists[0][0].dimensions == 2:
# different order based on whether the domains are x-r, x-z or y-z, etc
if self.x_first_and_y_second[key] is False:
x_min = self.second_spatial_variable[key][0]
x_max = self.second_spatial_variable[key][-1]
y_min = self.first_spatial_variable[key][0]
y_max = self.first_spatial_variable[key][-1]
else:
x_min = self.first_spatial_variable[key][0]
x_max = self.first_spatial_variable[key][-1]
y_min = self.second_spatial_variable[key][0]
y_max = self.second_spatial_variable[key][-1]
# Create axis for contour plot
self.axis_limits[key] = [x_min, x_max, y_min, y_max]
# Get min and max variable values
if self.variable_limits[key] == "fixed":
# fixed variable limits: calculate "globlal" min and max
spatial_vars = self.spatial_variable_dict[key]
var_min = np.min(
[
ax_min(var(self.ts_seconds[i], **spatial_vars, warn=False))
for i, variable_list in enumerate(variable_lists)
for var in variable_list
]
)
var_max = np.max(
[
ax_max(var(self.ts_seconds[i], **spatial_vars, warn=False))
for i, variable_list in enumerate(variable_lists)
for var in variable_list
]
)
if np.isnan(var_min) or np.isnan(var_max):
raise ValueError(
"The variable limits are set to 'fixed' but the min and max "
"values are NaN"
)
if var_min == var_max:
var_min -= 1
var_max += 1
elif self.variable_limits[key] == "tight":
# tight variable limits: axes will adjust each time
var_min, var_max = None, None
else:
# user-specified axis limits
var_min, var_max = self.variable_limits[key]
if variable_lists[0][0].dimensions in [0, 1]:
self.axis_limits[key] = [x_min, x_max, var_min, var_max]
else:
self.variable_limits[key] = (var_min, var_max)
if (
var_min is not None
and var_max is not None
and (np.isnan(var_min) or np.isnan(var_max))
): # pragma: no cover
raise ValueError(f"Axis limits cannot be NaN for variables '{key}'")
[docs]
def plot(self, t, dynamic=False):
"""Produces a quick plot with the internal states at time t.
Parameters
----------
t : float
Dimensional time (in 'time_units') at which to plot.
dynamic : bool, optional
If True, creates a dynamic plot with a slider.
"""
plt = import_optional_dependency("matplotlib.pyplot")
gridspec = import_optional_dependency("matplotlib.gridspec")
cm = import_optional_dependency("matplotlib", "cm")
colors = import_optional_dependency("matplotlib", "colors")
t_in_seconds = t * self.time_scaling_factor
self.fig = plt.figure(figsize=self.figsize)
self.gridspec = gridspec.GridSpec(self.n_rows, self.n_cols)
self.plots = {}
self.time_lines = {}
self.colorbars = {}
self.axes = QuickPlotAxes()
# initialize empty handles, to be created only if the appropriate plots are made
solution_handles = []
for k, (key, variable_lists) in enumerate(self.variables.items()):
ax = self.fig.add_subplot(self.gridspec[k])
self.axes.add(key, ax)
x_min, x_max, y_min, y_max = self.axis_limits[key]
ax.set_xlim(x_min, x_max)
if y_min is not None and y_max is not None:
ax.set_ylim(y_min, y_max)
ax.xaxis.set_major_locator(plt.MaxNLocator(3))
self.plots[key] = defaultdict(dict)
variable_handles = []
# Set labels for the first subplot only (avoid repetition)
if variable_lists[0][0].dimensions == 0:
# 0D plot: plot as a function of time, indicating time t with a line
ax.set_xlabel(f"Time [{self.time_unit}]")
for i, variable_list in enumerate(variable_lists):
for j, variable in enumerate(variable_list):
if len(variable_list) == 1:
# single variable -> use linestyle to differentiate model
linestyle = self.linestyles[i]
else:
# multiple variables -> use linestyle to differentiate
# variables (color differentiates models)
linestyle = self.linestyles[j]
full_t = self.ts_seconds[i]
(self.plots[key][i][j],) = ax.plot(
full_t / self.time_scaling_factor,
variable(full_t, warn=False),
color=self.colors[i],
linestyle=linestyle,
)
variable_handles.append(self.plots[key][0][j])
solution_handles.append(self.plots[key][i][0])
y_min, y_max = ax.get_ylim()
ax.set_ylim(y_min, y_max)
(self.time_lines[key],) = ax.plot(
[
t_in_seconds / self.time_scaling_factor,
t_in_seconds / self.time_scaling_factor,
],
[y_min, y_max],
"k--",
lw=1.5,
)
elif variable_lists[0][0].dimensions == 1:
# 1D plot: plot as a function of x at time t
# Read dictionary of spatial variables
spatial_vars = self.spatial_variable_dict[key]
spatial_var_name = next(iter(spatial_vars.keys()))
ax.set_xlabel(
f"{spatial_var_name} [{self.spatial_unit}]",
)
for i, variable_list in enumerate(variable_lists):
for j, variable in enumerate(variable_list):
if len(variable_list) == 1:
# single variable -> use linestyle to differentiate model
linestyle = self.linestyles[i]
else:
# multiple variables -> use linestyle to differentiate
# variables (color differentiates models)
linestyle = self.linestyles[j]
(self.plots[key][i][j],) = ax.plot(
self.first_spatial_variable[key],
variable(t_in_seconds, **spatial_vars, warn=False),
color=self.colors[i],
linestyle=linestyle,
zorder=10,
)
variable_handles.append(self.plots[key][0][j])
solution_handles.append(self.plots[key][i][0])
# add lines for boundaries between subdomains
for boundary in variable_lists[0][0].internal_boundaries:
boundary_scaled = boundary * self.spatial_factor
ax.axvline(boundary_scaled, color="0.5", lw=1, zorder=0)
elif variable_lists[0][0].dimensions == 2:
# Read dictionary of spatial variables
spatial_vars = self.spatial_variable_dict[key]
# there can only be one entry in the variable list
variable = variable_lists[0][0]
# different order based on whether the domains are x-r, x-z or y-z, etc
if self.x_first_and_y_second[key] is False:
x_name = list(spatial_vars.keys())[1][0]
y_name = next(iter(spatial_vars.keys()))[0]
x = self.second_spatial_variable[key]
y = self.first_spatial_variable[key]
var = variable(t_in_seconds, **spatial_vars, warn=False)
else:
x_name = next(iter(spatial_vars.keys()))[0]
y_name = list(spatial_vars.keys())[1][0]
x = self.first_spatial_variable[key]
y = self.second_spatial_variable[key]
var = variable(t_in_seconds, **spatial_vars, warn=False).T
ax.set_xlabel(f"{x_name} [{self.spatial_unit}]")
ax.set_ylabel(f"{y_name} [{self.spatial_unit}]")
vmin, vmax = self.variable_limits[key]
# store the plot and the var data (for testing) as cant access
# z data from QuadMesh or QuadContourSet object
if self.is_y_z[key] is True:
self.plots[key][0][0] = ax.pcolormesh(
x,
y,
var,
vmin=vmin,
vmax=vmax,
shading=self.shading,
)
else:
self.plots[key][0][0] = ax.contourf(
x, y, var, levels=100, vmin=vmin, vmax=vmax
)
self.plots[key][0][1] = var
if vmin is None and vmax is None:
vmin = ax_min(var)
vmax = ax_max(var)
self.colorbars[key] = self.fig.colorbar(
cm.ScalarMappable(colors.Normalize(vmin=vmin, vmax=vmax)),
ax=ax,
)
# Set either y label or legend entries
if len(key) == 1:
title = split_long_string(key[0])
ax.set_title(title, fontsize="medium")
else:
ax.legend(
variable_handles,
[split_long_string(s, 6) for s in key],
bbox_to_anchor=(0.5, 1),
loc="lower center",
)
# Set global legend
if len(self.labels) > 1:
fig_legend = self.fig.legend(
solution_handles, self.labels, loc="lower right"
)
# Get the position of the top of the legend in relative figure units
# There may be a better way ...
try:
legend_top_inches = fig_legend.get_window_extent(
renderer=self.fig.canvas.get_renderer()
).get_points()[1, 1]
fig_height_inches = (self.fig.get_size_inches() * self.fig.dpi)[1]
legend_top = legend_top_inches / fig_height_inches
except AttributeError: # pragma: no cover
# When testing the examples we set the matplotlib backend to "Template"
# which means that the above code doesn't work. Since this is just for
# that particular test we can just skip it
legend_top = 0
else:
legend_top = 0
# Fix layout
if dynamic:
slider_top = 0.05
else:
slider_top = 0
bottom = max(legend_top, slider_top)
self.gridspec.tight_layout(self.fig, rect=[0, bottom, 1, 1])
[docs]
def dynamic_plot(self, show_plot=True, step=None):
"""
Generate a dynamic plot with a slider to control the time.
Parameters
----------
step : float, optional
For notebook mode, size of steps to allow in the slider. Defaults to 1/100th
of the total time.
show_plot : bool, optional
Whether to show the plots. Default is True. Set to False if you want to
only display the plot after plt.show() has been called.
"""
if pybamm.is_notebook(): # pragma: no cover
import ipywidgets as widgets
step = step or self.max_t / 100
widgets.interact(
lambda t: self.plot(t, dynamic=False),
t=widgets.FloatSlider(
min=self.min_t, max=self.max_t, step=step, value=self.min_t
),
continuous_update=False,
)
else:
plt = import_optional_dependency("matplotlib.pyplot")
Slider = import_optional_dependency("matplotlib.widgets", "Slider")
# create an initial plot at time self.min_t
self.plot(self.min_t, dynamic=True)
axcolor = "lightgoldenrodyellow"
ax_slider = plt.axes([0.315, 0.02, 0.37, 0.03], facecolor=axcolor)
self.slider = Slider(
ax_slider,
f"Time [{self.time_unit}]",
self.min_t,
self.max_t,
valinit=self.min_t,
color="#1f77b4",
)
self.slider.on_changed(self.slider_update)
if show_plot: # pragma: no cover
plt.show()
[docs]
def slider_update(self, t):
"""
Update the plot in self.plot() with values at new time
"""
from matplotlib import cm, colors
time_in_seconds = t * self.time_scaling_factor
for k, (key, plot) in enumerate(self.plots.items()):
ax = self.axes[k]
if self.variables[key][0][0].dimensions == 0:
self.time_lines[key].set_xdata([t])
elif self.variables[key][0][0].dimensions == 1:
var_min = np.inf
var_max = -np.inf
for i, variable_lists in enumerate(self.variables[key]):
for j, variable in enumerate(variable_lists):
var = variable(
time_in_seconds,
**self.spatial_variable_dict[key],
warn=False,
)
plot[i][j].set_ydata(var)
var_min = min(var_min, ax_min(var))
var_max = max(var_max, ax_max(var))
# update boundaries between subdomains
y_min, y_max = self.axis_limits[key][2:]
if y_min is None and y_max is None:
ax.set_ylim(var_min, var_max)
elif self.variables[key][0][0].dimensions == 2:
# 2D plot: plot as a function of x and y at time t
# Read dictionary of spatial variables
spatial_vars = self.spatial_variable_dict[key]
# there can only be one entry in the variable list
variable = self.variables[key][0][0]
vmin, vmax = self.variable_limits[key]
if self.x_first_and_y_second[key] is False:
x = self.second_spatial_variable[key]
y = self.first_spatial_variable[key]
var = variable(time_in_seconds, **spatial_vars, warn=False)
else:
x = self.first_spatial_variable[key]
y = self.second_spatial_variable[key]
var = variable(time_in_seconds, **spatial_vars, warn=False).T
# store the plot and the var data (for testing) as cant access
# z data from QuadMesh or QuadContourSet object
if self.is_y_z[key] is True:
self.plots[key][0][0] = ax.pcolormesh(
x,
y,
var,
vmin=vmin,
vmax=vmax,
shading=self.shading,
)
else:
self.plots[key][0][0] = ax.contourf(
x, y, var, levels=100, vmin=vmin, vmax=vmax
)
self.plots[key][0][1] = var
if (vmin, vmax) == (None, None):
vmin = ax_min(var)
vmax = ax_max(var)
cb = self.colorbars[key]
cb.update_normal(
cm.ScalarMappable(colors.Normalize(vmin=vmin, vmax=vmax))
)
self.fig.canvas.draw_idle()
[docs]
def create_gif(self, number_of_images=80, duration=0.1, output_filename="plot.gif"):
"""
Generates x plots over a time span of max_t - min_t and compiles them to create
a GIF.
Parameters
----------
number_of_images : int, optional
Number of images/plots to be compiled for a GIF.
duration : float, optional
Duration of visibility of a single image/plot in the created GIF.
output_filename : str, optional
Name of the generated GIF file.
"""
imageio = import_optional_dependency("imageio.v2")
plt = import_optional_dependency("matplotlib.pyplot")
# time stamps at which the images/plots will be created
time_array = np.linspace(self.min_t, self.max_t, num=number_of_images)
images = []
# create images/plots
stub_name = output_filename.split(".")[0]
for val in time_array:
self.plot(val)
temp_name = f"{stub_name}{val}.png"
images.append(temp_name)
self.fig.savefig(temp_name, dpi=300)
plt.close()
# compile the images/plots to create a GIF
with imageio.get_writer(output_filename, mode="I", duration=duration) as writer:
for image in images:
writer.append_data(imageio.imread(image))
# remove the generated images
for image in images:
os.remove(image)
[docs]
class QuickPlotAxes:
"""
Class to store axes for the QuickPlot
"""
def __init__(self):
self._by_variable = {}
self._axes = []
[docs]
def add(self, keys, axis):
"""
Add axis
Parameters
----------
keys : iter
Iterable of keys of variables being plotted on the axis
axis : matplotlib Axis object
The axis object
"""
self._axes.append(axis)
for k in keys:
self._by_variable[k] = axis
def __getitem__(self, index):
"""
Get axis by index
"""
return self._axes[index]
[docs]
def by_variable(self, key):
"""
Get axis by variable name
"""
return self._by_variable[key]
