def curve_plot(self, variable, t_max):
print("Doing curve plot for variable '{}'.".format(variable))
var = Variable(name=variable)
if var.data is None:
self.extract_single_dimension(var, t_max=t_max)
x = np.arange(t_max)
mean = var.data["mean"]
std = var.data["std"]
plt.plot(x, mean, c='black', lw=2)
plt.plot(x, mean + std, c='black', lw=.1)
plt.plot(x, mean - std, c='black', lw=.1)
plt.fill_between(x, mean + std, mean - std, color='black', alpha=.1)
plt.xlabel("t")
plt.ylabel(self.format_label(variable))
plt.savefig("{}/curve_plot_{}.pdf".format(self.fig_folder, variable))
if self.display:
plt.show()
plt.close()
def plot(x,
y,
y_std,
x_label,
y_label,
fig_title,
fig_folder,
fig_name,
comment=None):
plt.figure(figsize=(10, 10))
plt.plot(x, y, c='b', lw=2)
plt.plot(x, y + y_std, c='b', lw=.5)
plt.plot(x, y - y_std, c='b', lw=.5)
plt.fill_between(x, y + y_std, y - y_std, color='b', alpha=.1)
plt.xlabel("\n{}".format(x_label), fontsize=12)
plt.ylabel("{}\n".format(y_label), fontsize=12)
if comment:
plt.title("{}\n({})\n".format(fig_title, comment))
else:
plt.title("{}\n".format(fig_title))
# if comment:
#
# plt.text(x=min(x) + (max(x) - min(x)) * 0.5, y=min(y) + (max(y) - min(y)) * 0.5,
# s="{}".format(comment))
plt.xlim(min(x), max(x))
plt.ylim(-0.001, 0.1)
if not path.exists(fig_folder):
mkdir(fig_folder)
plt.savefig("{}/{}".format(fig_folder, fig_name))
plt.close()
def analyse_profits(pool_backup, file_name="", folder=None):
"""
Expected running_mode is 'discrete'
:param pool_backup:
:param file_name:
:param folder:
:return:
"""
if folder is None:
folder = "data/figures"
os.makedirs(folder, exist_ok=True)
def f_cond(xx):
if xx < 0.25:
return 0
elif xx < 0.5:
return 1
elif xx < 0.75:
return 2
else:
return 3
n_pools = 4
labels = [
"0 <= FoV < 0.25", "0.25 <= FoV < 0.50", "0.50 <= FoV < 0.75",
"0.75 <= FoV <= 1"
]
parameters = pool_backup.parameters
backups = pool_backup.backups
assert backups[
0].running_mode == "discrete", "Expected running_mode is 'discrete'"
profit_max = parameters.n_positions * parameters.n_prices * parameters.unit_value
x = range(parameters.t_max)
for_y = [[[] for j in range(parameters.t_max)] for i in range(n_pools)]
for i, b in tqdm.tqdm(enumerate(backups), total=len(backups)):
cond = f_cond(b.field_of_view)
for t in range(parameters.t_max):
for_y[cond][t].append(np.mean(b.profits[t, :]) / profit_max)
for i in range(n_pools):
y = np.array([np.mean(for_y[i][t]) for t in range(parameters.t_max)])
y_err = np.array(
[np.std(for_y[i][t]) for t in range(parameters.t_max)])
plt.plot(x, y, label=labels[i])
plt.fill_between(x,
y - (y_err / 2),
y + (y_err / 2),
color="C{}".format(i),
alpha=.25)
plt.legend()
plt.xlabel("t")
plt.ylabel("Mean profit")
if file_name:
plt.title(file_name)
plt.tight_layout()
if file_name:
plt.savefig("{}/{}_mean_profit_{}_cat.pdf".format(
folder, file_name, n_pools))
plt.show()
plt.ylabel('rmse | spread [$m$]')
sns.despine()
plt.title(f'n_ens:{n_ens}')
plt.ylim(ymax=120)
plt.savefig(
f'{plotdir}/plasimt42_leadtime_vs_skill_and_spread_best_n_ens{n_ens}.svg')
# difference mean forecast error and spread vs leadtime plus uncertainty, for fixed n_ens
plt.figure(figsize=figsize)
plt.plot(sub_df['leadtime'],
sub_df['errordiff_svd_rand'],
label='rmse svd-rand',
color='#1b9e77')
plt.fill_between(sub_df['leadtime'],
sub_df['errordiff_svd_rand_lower'],
sub_df['errordiff_svd_rand_upper'],
color='#1b9e77',
alpha=0.5)
plt.plot(sub_df['leadtime'],
sub_df['errordiff_svd_netens'],
label='rmse svd-netens',
color='#7570b3')
plt.fill_between(sub_df['leadtime'],
sub_df['errordiff_svd_netens_lower'],
sub_df['errordiff_svd_netens_upper'],
color='#7570b3',
alpha=0.5)
plt.plot(sub_df['leadtime'],
sub_df['errordiff_rand_netens'],
label='rmse rand-netens',
color='#d95f02')
mean_percentage_Rco = np.nanmean(percentage_Rco, axis=0)
mean_percentage_Raslip = np.nanmean(percentage_Raslip, axis=0)
total_percentage = mean_percentage_Easlip + mean_percentage_Rco +mean_percentage_Raslip
################################
ts = []
for t1, t2 in zip(epochs[0:-1],epochs[1:]):
ts += [t1,t2]
ys1 = []
for yi in mean_percentage_Easlip:
ys1 += [yi,yi]
plt.fill_between(ts, ys1, np.zeros_like(ys1), color='blue')
ys2 = []
for yi in mean_percentage_Rco:
ys2 += [1-yi, 1-yi]
plt.fill_between(ts, ys2, np.ones_like(ys2), color='green')
obj = plt.fill_between(ts, ys1, ys2, color='red')
plt.grid('off')
label_patch1 = mpatches.Patch(color='green')
label_patch2 = mpatches.Patch(color='red')
label_patch3 = mpatches.Patch(color='blue')
plt.legend([label_patch1, label_patch2, label_patch3],
[r'$R^{\bf{co}}$', r'$R^{\bf{aslip}}$',r'$E^{\bf{aslip}}$'],
def plot_slip_overview(slip,
output_file,
if_x_log=False,
xlim=[0, 1344],
ylim = [0,100],
yticks = [20, 40, 60],
xticks = [1, 10, 100, 1000],
xticklabels = [r'$10^0$', r'$10^1$', r'$10^2$', r'$10^3$'],
rotation = 45,
fontsize = 10,
):
num_subflts_strike = slip.num_subflt_along_strike
num_subflts_dip = slip.num_subflt_along_dip
epochs = slip.get_epochs()
fig, axes = plt.subplots(num_subflts_dip,
num_subflts_strike,
sharex=True, sharey=True)
for ii in range(num_subflts_dip):
for jj in range(num_subflts_strike):
ax = axes[ii][jj]
slip_subflt = slip.get_cumu_slip_at_subfault(ii,jj)
plt.sca(ax)
plt.fill_between(x=epochs, y1=slip_subflt, y2=0, color='r')
if if_x_log:
ax.set_xscale('log')
plt.xlim(xlim)
plt.ylim(ylim)
plt.grid('on')
plt.box('on')
plt.tick_params(axis='both',which='both',
bottom='off', top='off', left='off', right='off',
labelbottom='off', labeltop='off', labelleft='off', labelright='off')
fig.subplots_adjust(hspace=0, wspace=0)
for ax in axes[-1,::2]:
plt.sca(ax)
plt.tick_params(axis='x',which='major',
bottom='on', top='off', left='off', right='off',
labelbottom='on', labeltop='off', labelleft='off', labelright='off')
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels, rotation=rotation, fontsize=fontsize)
plt.xlabel('day')
for ax in axes[0,1::2]:
plt.sca(ax)
plt.tick_params(axis='x',which='major',
bottom='off', top='on', left='off', right='off',
labelbottom='off', labeltop='on', labelleft='off', labelright='off')
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels, rotation=rotation, fontsize=fontsize)
plt.xlabel('day')
for ax in axes[::2,0]:
plt.sca(ax)
plt.tick_params(axis='y',which='major',
bottom='off', top='off', left='on', right='off',
labelbottom='off', labeltop='off', labelleft='on', labelright='off')
ax.set_yticks(yticks)
#ax.set_yticklabels(range(0,100,20))
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(10)
tick.label.set_rotation('horizontal')
plt.ylabel('slip/m')
for ax in axes[::2,-1]:
plt.sca(ax)
plt.tick_params(axis='y',which='major',
bottom='off', top='off', left='off', right='on',
labelbottom='off', labeltop='off', labelleft='off', labelright='on')
ax.set_yticks(yticks)
#ax.set_yticklabels(range(0,100,20))
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(10)
tick.label.set_rotation('horizontal')
plt.ylabel('slip/m')
ax.yaxis.set_label_position("right")
fig.set_size_inches(33,10)
plt.savefig(output_file)
plt.close()
