def style_figure(fig: matplotlib.figure.Figure, title: str, bottom: float = 0.2) -> None:
"""Stylize the supplied matplotlib Figure instance."""
fig.tight_layout()
if bottom is not None:
fig.subplots_adjust(bottom=bottom)
fig.suptitle(title)
fig.legend(ncol=10, handlelength=0.75, handletextpad=0.25, columnspacing=0.5, loc='lower left')
def apply(self, fig: matplotlib.figure.Figure) -> None:
# It shouldn't hurt to align the labels if there's only one.
fig.align_ylabels()
# Adjust the layout.
fig.tight_layout()
adjust_default_args = dict(
# Reduce spacing between subplots
hspace=0,
wspace=0,
# Reduce external spacing
left=0.10,
bottom=0.105,
right=0.98,
top=0.98,
)
adjust_default_args.update(self.edge_padding)
fig.subplots_adjust(**adjust_default_args)
def savefig(f: matplotlib.figure.Figure,
fpath: str,
tight: bool = True,
details: str = None,
space: float = 0.0,
**kwargs):
if tight:
if details:
add_parameter_details(f, details, -0.4 + space)
f.savefig(fpath, bbox_inches='tight', **kwargs)
else:
if details:
f.subplots_adjust(bottom=0.2)
add_parameter_details(f, details, 0.1)
f.savefig(fpath, **kwargs)
if fpath.endswith('png'):
add_tags_to_png_file(fpath)
if fpath.endswith('svg'):
add_tags_to_svg_file(fpath)
def plot_avg_decay_data(t_sol: Union[np.ndarray, List[np.array]],
list_sim_data: List[np.array],
list_exp_data: List[np.array] = None,
state_labels: List[str] = None,
concentration: Conc = None,
atol: float = A_TOL,
colors: Union[str, Tuple[ColorMap, ColorMap]] = 'rk',
fig: mpl.figure.Figure = None,
title: str = '') -> None:
''' Plot the list of simulated and experimental data (optional) against time in t_sol.
If concentration is given, the legend will show the concentrations.
colors is a string with two chars. The first is the sim color,
the second the exp data color.
'''
num_plots = len(list_sim_data)
num_rows = 3
num_cols = int(np.ceil(num_plots / 3))
# optional lists default to list of None
list_exp_data = list_exp_data or [None] * num_plots
state_labels = state_labels or [''] * num_plots
list_t_sim = t_sol if len(
t_sol) == num_plots else [t_sol] * num_plots # type: List[np.array]
if concentration:
conc_str = '_' + str(concentration.S_conc) + 'S_' + str(
concentration.A_conc) + 'A'
# state_labels = [label+conc_str for label in state_labels]
else:
conc_str = ''
sim_color = colors[0]
exp_color = colors[1]
exp_size = 2 # marker size
exp_marker = '.'
if fig is None:
fig = plt.figure()
fig.suptitle(title + '. Time in ms.')
list_axes = fig.get_axes() # type: List
if not list_axes:
for num in range(num_plots):
fig.add_subplot(num_rows, num_cols, num + 1)
list_axes = fig.get_axes()
for sim_data, t_sim, exp_data, state_label, axes\
in zip(list_sim_data, list_t_sim, list_exp_data, state_labels, list_axes):
if state_label:
axes.set_title(
state_label.replace('_', ' '), {
'horizontalalignment': 'center',
'verticalalignment': 'center',
'fontweight': 'bold',
'fontsize': 10
})
if sim_data is None or np.isnan(sim_data).any() or not np.any(
sim_data > 0):
continue
# no exp data: either a GS or simply no exp data available
if exp_data is 0 or exp_data is None:
# nonposy='clip': clip non positive values to a very small positive number
axes.semilogy(t_sim * 1000,
sim_data,
color=sim_color,
label=state_label + conc_str)
axes.axis('tight')
axes.set_xlim(left=t_sim[0] * 1000.0)
# add some white space above and below
margin_factor = np.array([0.7, 1.3])
axes.set_ylim(*np.array(axes.get_ylim()) * margin_factor)
if axes.set_ylim()[0] < atol:
axes.set_ylim(bottom=atol) # don't show noise below atol
# detect when the simulation goes above and below atol
above = sim_data > atol
change_indices = np.where(np.roll(above, 1) != above)[0]
# make sure change_indices[-1] happens when the population is going BELOW atol
if change_indices.size > 1 and sim_data[
change_indices[-1]] < atol: # pragma: no cover
# last time it changes
max_index = change_indices[-1]
# show simData until it falls below atol
axes.set_xlim(right=t_sim[max_index] * 1000)
min_y = min(*axes.get_ylim())
max_y = max(*axes.get_ylim())
axes.set_ylim(bottom=min_y, top=max_y)
else: # exp data available
sim_handle, = axes.semilogy(t_sim * 1000,
sim_data,
color=sim_color,
label=state_label + conc_str,
zorder=10)
# convert exp_data time to ms
exp_handle, = axes.semilogy(exp_data[:, 0] * 1000,
exp_data[:, 1] * np.max(sim_data),
color=exp_color,
marker=exp_marker,
linewidth=0,
markersize=exp_size,
zorder=1)
axes.axis('tight')
axes.set_ylim(top=axes.get_ylim()[1] *
1.2) # add some white space on top
tmin = min(exp_data[-1, 0], t_sim[0])
axes.set_xlim(left=tmin * 1000.0, right=exp_data[-1, 0] *
1000) # don't show beyond expData
if conc_str:
list_axes[0].legend(loc="best", fontsize='small')
curr_handles, curr_labels = list_axes[0].get_legend_handles_labels()
new_labels = [
label.replace(state_labels[0] + '_', '').replace('_', ', ')
for label in curr_labels
]
list_axes[0].legend(curr_handles,
new_labels,
markerscale=5,
loc="best",
fontsize='small')
fig.subplots_adjust(top=0.918,
bottom=0.041,
left=0.034,
right=0.99,
hspace=0.275,
wspace=0.12)
def southern_ocean_axes_setup(
ax: matplotlib.axes.Axes,
fig: matplotlib.figure.Figure,
add_gridlines: bool = True,
) -> None:
"""
This function sets up the subplot so that it is a cartopy map of the southern ocean.
returns void as the ax and figure objects are pointers not data.
Args:
ax (matplotlib.axes.Axes): The axis object to add the map to.
fig (matplotlib.figure.Figure): The figure object for the figure in general.
add_gridlines (bool): whether or not to add gridlines to the plot.
"""
carree = ccrs.PlateCarree()
ax.set_extent([-180, 180, -90, -30], carree)
fig.subplots_adjust(bottom=0.05,
top=0.95,
left=0.04,
right=0.95,
wspace=0.02)
def plot_boundary() -> None:
"""
Makes SO plot boundary into a nice circle
of the right size.
"""
theta = np.linspace(0, 2 * np.pi, 100)
center, radius = [0.5, 0.5], 0.45
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
circle = mpath.Path(verts * radius + center)
ax.set_boundary(circle, transform=ax.transAxes)
plot_boundary()
# add coastlines and gridlines
ax.coastlines(resolution="50m", linewidth=0.3)
if add_gridlines:
ax.gridlines(linewidth=0.5)
# Add 2000m isobath (or whatever the max depth is).
@jit(cache=True) # significant performance enhancement.
def find_isobath(tmp_bathymetry: np.ndarray,
crit_depth=cst.MAX_DEPTH) -> List[list]:
"""
Find isobath.
Args:
tmp_bathymetry (np.ndarray): Bathymetry np array.
crit_depth ([type], optional): Critical depth. Defaults to cst.MAX_DEPTH.
Returns:
List[list]: List of index pairs.
"""
isobath_index_list = []
shape_bathymetry = np.shape(tmp_bathymetry)
for i in range(0, shape_bathymetry[0] - 1):
for j in range(0, shape_bathymetry[1] - 1):
if tmp_bathymetry[i, j] >= crit_depth:
if tmp_bathymetry[i - 1, j] < crit_depth:
isobath_index_list.append([i, j])
if tmp_bathymetry[i, j - 1] < crit_depth:
isobath_index_list.append([i, j])
if tmp_bathymetry[i + 1, j] < crit_depth:
isobath_index_list.append([i, j])
if tmp_bathymetry[i, j + 1] < crit_depth:
isobath_index_list.append([i, j])
return isobath_index_list
i_list = find_isobath(
xr.open_dataset(cst.SALT_FILE)[cst.DEPTH_NAME].values,
crit_depth=cst.MAX_DEPTH,
)
index_npa: np.ndarray = np.array(i_list)
lons = xr.open_dataset(cst.SALT_FILE)[cst.X_COORD].values[index_npa[:, 1]]
lats = xr.open_dataset(cst.SALT_FILE)[cst.Y_COORD].values[index_npa[:, 0]]
ax.plot(lons,
lats,
",",
markersize=0.4,
color="grey",
transform=ccrs.Geodetic())
