def subplot_data(fig: matplotlib.figure.Figure,
subplot_params: tuple,
timestamps: list,
data: np.array,
x_label: str,
y_label: str,
event_timestamps: list = None,
data_label: str = None,
fontsize: int = 30,
event_annotation_color='g') -> None:
'''Plots a given time series data on a subplot.
Keyword arguments:
fig -- matplotlib figure handle
subplot_params -- a tuple with three subplot parameters: number of rows,
number of columns, and the number of the current subplot
timestamps -- a list of timestamps (plotted over the x-axis)
data -- a one-dimensional numpy array of data items (plotted over the y-axis)
x_label -- x axis label
y_label -- y axis label
event_timestamps -- optional list of timestamps for data
annotation; the annotations are plotted as
lines at the given timestamps
(default None, in which case there are no annotations)
data_label -- optional label for the plotted data; if a label is given,
a legend is added to the plot (default None)
fontsize -- fontsize for the x and y axes labels (default 30)
event_annotation_color -- color of event annotation lines (default 'g')
'''
fig.add_subplot(*subplot_params)
# we plot the data
if data_label:
plt.plot(timestamps, data, label=data_label)
else:
plt.plot(timestamps, data)
# we add event annotations to the plot if there are any events
if event_timestamps is not None and len(event_timestamps) > 0:
plt.plot([event_timestamps, event_timestamps],
[np.nanmin(data), np.nanmax(data)],
event_annotation_color)
plt.xlabel(x_label, fontsize=fontsize)
plt.ylabel(y_label, fontsize=fontsize)
# we add a legend only if a data label is assigned
if data_label:
plt.legend()
def hfss_plot_convergences_report(convergence_t: pd.core.frame.DataFrame,
convergence_f: pd.core.frame.DataFrame,
fig: mpl.figure.Figure = None,
_display=True):
"""Plot convergence frequency vs. pass number if fig is None.
Plot delta frequency and solved elements vs. pass number.
Plot delta frequency vs. solved elements.
Args:
convergence_t (pandas.core.frame.DataFrame): Convergence vs pass number of the eigenemode freqs.
convergence_f (pandas.core.frame.DataFrame): Convergence vs pass number of the eigenemode freqs.
fig (matplotlib.figure.Figure, optional): A mpl figure. Defaults to None.
_display (bool, optional): Display the plot? Defaults to True.
"""
if fig is None:
fig = plt.figure(figsize=(11, 3.))
# Grid spec and axes; height_ratios=[4, 1], wspace=0.5
gs = mpl.gridspec.GridSpec(1, 3, width_ratios=[1.2, 1.5, 1])
axs = [fig.add_subplot(gs[i]) for i in range(3)]
ax0t = axs[1].twinx()
plot_convergence_f_vspass(axs[0], convergence_f)
plot_convergence_max_df(axs[1], convergence_t.iloc[:, 1])
plot_convergence_solved_elem(ax0t, convergence_t.iloc[:, 0])
plot_convergence_maxdf_vs_sol(axs[2], convergence_t.iloc[:, 1],
convergence_t.iloc[:, 0])
fig.tight_layout(w_pad=0.1) # pad=0.0, w_pad=0.1, h_pad=1.0)
def setup_ax(fig: matplotlib.figure.Figure,
pos: tuple[int, int, int] = (1, 1, 1),
) -> matplotlib.axes.Axes:
'''
Args
- fig: matplotlib.figure.Figure
- pos: 3-tuple of int, axes position (nrows, ncols, index)
Returns: matplotlib.axes.Axes
'''
ax = fig.add_subplot(*pos, projection=ccrs.PlateCarree())
# draw land (better version of cfeature.LAND)
# land = cfeature.NaturalEarthFeature(
# category='physical', name='land', scale='10m',
# edgecolor='face', facecolor=cfeature.COLORS['land'], zorder=-1)
ax.add_feature(cfeature.LAND)
# draw borders of countries (better version of cfeature.BORDERS)
countries = cfeature.NaturalEarthFeature(
category='cultural', name='admin_0_boundary_lines_land', scale='10m',
edgecolor='black', facecolor='none')
ax.add_feature(countries)
# draw coastline (better version of cfeature.COASTLINE)
coastline = cfeature.NaturalEarthFeature(
category='physical', name='coastline', scale='10m',
edgecolor='black', facecolor='none')
ax.add_feature(coastline)
# draw lakes (better version of cfeature.LAKES)
lakes = cfeature.NaturalEarthFeature(
category='physical', name='lakes', scale='10m',
edgecolor='face', facecolor=cfeature.COLORS['water'])
ax.add_feature(lakes)
# draw ocean (better version of cfeature.OCEAN)
ocean = cfeature.NaturalEarthFeature(
category='physical', name='ocean', scale='50m',
edgecolor='face', facecolor=cfeature.COLORS['water'], zorder=-1)
ax.add_feature(ocean)
# draw rivers (better version of cfeature.RIVERS)
rivers = cfeature.NaturalEarthFeature(
category='physical', name='rivers_lake_centerlines', scale='10m',
edgecolor=cfeature.COLORS['water'], facecolor='none')
ax.add_feature(rivers)
# draw borders of states/provinces internal to a country
states_provinces = cfeature.NaturalEarthFeature(
category='cultural', name='admin_1_states_provinces_lines', scale='50m',
edgecolor='gray', facecolor='none')
ax.add_feature(states_provinces)
ax.set_aspect('equal')
gridliner = ax.gridlines(draw_labels=True)
gridliner.top_labels = False
gridliner.right_labels = False
return ax
def plot_convergences(self,
convergence_t: pd.DataFrame = None,
convergence_f: pd.DataFrame = None,
fig: mpl.figure.Figure = None,
_display: bool = True):
"""Creates 3 plots, useful to determin the convergence achieved by the renderer:
* convergence frequency vs. pass number if fig is None.
* delta frequency and solved elements vs. pass number.
* delta frequency vs. solved elements.
Args:
convergence_t (pd.DataFrame): Convergence vs pass number of the eigenemode freqs.
convergence_f (pd.DataFrame): Convergence vs pass number of the eigenemode freqs.
fig (matplotlib.figure.Figure, optional): A mpl figure. Defaults to None.
_display (bool, optional): Display the plot? Defaults to True.
"""
if convergence_t is None:
convergence_t = self.convergence_t
if convergence_f is None:
convergence_f = self.convergence_f
if fig is None:
fig = plt.figure(figsize=(11, 3.))
# Grid spec and axes; height_ratios=[4, 1], wspace=0.5
gspec = mpl.gridspec.GridSpec(1, 3, width_ratios=[1.2, 1.5, 1])
axs = [fig.add_subplot(gspec[i]) for i in range(3)]
ax0t = axs[1].twinx()
plot_convergence_f_vspass(axs[0], convergence_f)
plot_convergence_max_df(axs[1], convergence_t.iloc[:, 1])
plot_convergence_solved_elem(ax0t, convergence_t.iloc[:, 0])
plot_convergence_maxdf_vs_sol(axs[2], convergence_t.iloc[:, 1],
convergence_t.iloc[:, 0])
fig.tight_layout(w_pad=0.1) # pad=0.0, w_pad=0.1, h_pad=1.0)
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 subplot_data_lists(fig: matplotlib.figure.Figure,
subplot_params: tuple,
timestamps: list,
data: np.array,
x_label: str,
y_label: str,
event_timestamps: list = None,
data_labels: list = None,
data_colors: list = None,
fontsize: int = 30,
event_annotation_color='g') -> None:
'''Plots multiple time series on a single subplot.
Keyword arguments:
fig -- matplotlib figure handle
subplot_params -- a tuple with three subplot parameters: number of rows,
number of columns, and the number of the current subplot
timestamps -- a list of timestamps (plotted over the x-axis)
data -- a 2D numpy array of time series (plotted over the y-axis), where
each column represents a single time series
x_label -- x axis label
y_label -- y axis label
event_timestamps -- optional list of timestamps for data
annotation; the annotations are plotted as
green lines at the given timestamps
(default None, in which case there are no annotations)
data_labels -- a list of optional labels for the plotted data, where the size
of the list should match the number of columns in "data";
if this parameter is passed, a legend is added to the plot
(default None)
data_colors -- a list of optional colors for the plotted data, where the size
of the list should match the number of columns in "data"
fontsize -- fontsize for the x and y axes labels (default 30)
event_annotation_color -- color of event annotation lines (default 'g')
'''
if data_labels:
try:
assert data.shape[1] == len(data_labels)
except AssertionError:
print(
'The length of data_labels should match the number of columns in data'
)
return
if data_colors:
try:
assert data.shape[1] == len(data_colors)
except AssertionError:
print(
'The length of data_colors should match the number of columns in data'
)
return
fig.add_subplot(*subplot_params)
# we plot the data
if data_labels and data_colors:
for i in range(data.shape[1]):
plt.plot(timestamps,
data[:, i],
label=data_labels[i],
color=data_colors[i])
elif data_labels:
for i in range(data.shape[1]):
plt.plot(timestamps, data[:, i], label=data_labels[i])
elif data_colors:
for i in range(data.shape[1]):
plt.plot(timestamps, data[:, i], color=data_colors[i])
else:
for i in range(data.shape[1]):
plt.plot(timestamps, data[:, i])
# we add event annotations to the plot if there are any events
if event_timestamps is not None and len(event_timestamps) > 0:
plt.plot([event_timestamps, event_timestamps],
[np.nanmin(data), np.nanmax(data)],
event_annotation_color)
plt.xlabel(x_label, fontsize=fontsize)
plt.ylabel(y_label, fontsize=fontsize)
# we add a legend only if a data label is assigned
if data_labels:
plt.legend()
