def plot_forecast_ts(df_test_m, df_test):
fontsize = 16
fig = plt.figure(figsize=(12, 5))
gs = gridspec.GridSpec(1, 1, height_ratios=None)
facecolor = 'white'
ax0 = plt.subplot(gs[0], facecolor=facecolor)
# df_test.plot(ax=ax0)
ax0.plot_date(df_test.index,
df_test.iloc[:, 0],
ls='-',
label='Observed',
c='black')
ax0.plot_date(df_test.index,
df_test.iloc[:, 1],
ls='-',
c='red',
label=r'Ridge Regression forecast')
# ax0.set_xticks()
# ax0.set_xticklabels(df_test.index.year,
ax0.set_ylabel('Standardized Soy Yield', fontsize=fontsize)
ax0.tick_params(labelsize=fontsize)
ax0.axhline(y=0, color='black', lw=1)
ax0.legend(fontsize=fontsize)
df_scores = df_test_m.loc[0][df_test_m.columns[0][0]]
Texts1 = []
Texts2 = []
textprops = dict(color='black', fontsize=fontsize + 4, family='serif')
rename_met = {
'RMSE': 'RMSE-SS',
'corrcoef': 'Corr. Coeff.',
'MAE': 'MAE-SS',
'BSS': 'BSS',
'roc_auc_score': 'ROC-AUC',
'r2_score': '$r^2$',
'mean_absolute_percentage_error': 'MAPE'
}
for k in df_scores.index:
label = rename_met[k]
val = round(df_scores[k], 2)
Texts1.append(TextArea(f'{label}', textprops=textprops))
Texts2.append(TextArea(f'{val}', textprops=textprops))
texts_vbox1 = VPacker(children=Texts1, pad=0, sep=4)
texts_vbox2 = VPacker(children=Texts2, pad=0, sep=4)
ann1 = AnnotationBbox(texts_vbox1, (.02, .15),
xycoords=ax0.transAxes,
box_alignment=(0, .5),
bboxprops=dict(facecolor='white',
boxstyle='round',
edgecolor='white'))
ann2 = AnnotationBbox(texts_vbox2, (.21, .15),
xycoords=ax0.transAxes,
box_alignment=(0, .5),
bboxprops=dict(facecolor='white',
boxstyle='round',
edgecolor='white'))
ann1.set_figure(fig)
ann2.set_figure(fig)
fig.artists.append(ann1)
fig.artists.append(ann2)
return
def plot_cell(show=True, save=False):
"""Makes total cell"""
# Get the angles
t_c, t_ld, t_sd, t_d, t_f = get_angles()
# Get the distances - cosmetic
r0 = 0.3
r1 = 0.5
# Define the fig and axes for the graph
fig = plt.figure()
ax = fig.add_subplot(projection='polar')
ax.set_thetamin(0)
ax.set_thetamax(60)
ax.grid(axis='y')
ax.set_title("Layout of KURRI Main Ring", fontsize=16)
t_ticks = [0, 5, 10, 15, 20, 25, 30]
plt.xticks(
np.array(t_ticks) * TO_RAD * 2, [fr'${t}\degree$' for t in t_ticks])
plt.yticks([])
ax.set_ylim(0, r1 + 0.1)
# Make wedge to contain the entire cell
#plot_segment(ax, 0, r1+0.1, 0, 0, ls='--', lw=1.5)
#plot_segment(ax, 0, r1+0.1, t_c, t_c, ls='--', lw=1.5)
# Make the F and D magnets
t_d1_start = t_ld
t_f_start = t_d1_start + t_d + t_sd
t_d2_start = t_f_start + t_f + t_sd
plot_wedge(ax,
r0,
r1,
t_d1_start,
t_d1_start + t_d,
col='royalblue',
label='D Magnet') # First D magnet
plot_wedge(ax,
r0,
r1,
t_f_start,
t_f_start + t_f,
col='firebrick',
label='F Magnet') # F magnet
plot_wedge(ax, r0, r1, t_d2_start, t_d2_start + t_d,
col='royalblue') # Second D magnet
# Plot arrows to show dimensions
plot_ang_arrow(ax, 0.1, 0, t_c, r'$\theta_{C}$', headsize=0)
r_arr = r1 + 0.025
plot_ang_arrow(ax, r_arr, 0, t_ld, r'$\theta_{LD}$')
plot_ang_arrow(ax, r_arr, t_d1_start, t_d1_start + t_d, r'$\theta_{D}$')
plot_ang_arrow(ax, r_arr, t_d1_start + t_d, t_d1_start + t_d + t_sd,
r'$\theta_{SD}$')
plot_ang_arrow(ax, r_arr, t_f_start, t_f_start + t_f / 2,
r'$\theta_{F}/2$')
plot_rad_arrow(ax, 0, r0, t_d2_start + t_d, r'$r_0$')
plot_rad_arrow(ax, 0, r1, t_d2_start + t_d, r'$r_1$')
# Write in the values of angles and lengths using artists
text = [
fr'$\theta_C$', fr'$\theta_{"{LD}"}$', fr'$\theta_D$',
fr'$\theta_{"{SD}"}$', fr'$\theta_F$'
]
val = [rf'$= {v}\degree$' for v in [t_c, t_ld, t_d, t_sd, t_f]]
Texts = [TextArea(t) for t in text]
Vals = [TextArea(v) for v in val]
textbox = VPacker(children=Texts, pad=0.1, sep=0.1)
valsbox = VPacker(children=Vals, pad=0.1,
sep=2.4) # This 'sep' is fine-tuned to make boxes align!
totbox = HPacker(children=[textbox, valsbox], pad=0.1, sep=0.1)
ann = AnnotationBbox(totbox, (0.05, 0.8),
xycoords=ax.transAxes,
box_alignment=(0.05, 0.8),
bboxprops=dict(facecolor='wheat',
boxstyle='round',
color='black'))
ann.set_figure(fig)
fig.artists.append(ann)
text2 = [r'$r_0$', r'$r_1$']
val2 = [r'$= 4.3$m', r'$= 5.4$m']
Texts2 = [TextArea(t) for t in text2]
Vals2 = [TextArea(v) for v in val2]
textbox2 = VPacker(children=Texts2, pad=0.1, sep=0.1)
valsbox2 = VPacker(children=Vals2, pad=0.1,
sep=1) # This 'sep' is fine-tuned to make boxes align!
totbox2 = HPacker(children=[textbox2, valsbox2], pad=0.1, sep=0.1)
ann2 = AnnotationBbox(totbox2, (0.05, 0.5),
xycoords=ax.transAxes,
box_alignment=(0.05, 0.5),
bboxprops=dict(facecolor='wheat',
boxstyle='round',
color='black'))
ann2.set_figure(fig)
fig.artists.append(ann2)
# Legend
ax.legend(loc=(0.75, 0.8), facecolor='lavenderblush', edgecolor='k')
#Misc
fig.tight_layout()
if save:
fig.savefig('figs/KURRI_layout.png')
fig.savefig('figs/KURRI_layout.pdf')
if show:
fig.show()
return fig, ax
texts_vbox1 = VPacker(children=Texts1, pad=0, sep=4)
texts_vbox2 = VPacker(children=Texts2, pad=0, sep=4)
ann1 = AnnotationBbox(texts_vbox1, (.02, .15),
xycoords=ax0.transAxes,
box_alignment=(0, .5),
bboxprops=dict(facecolor='white',
boxstyle='round',
edgecolor='white'))
ann2 = AnnotationBbox(texts_vbox2, (.21, .15),
xycoords=ax0.transAxes,
box_alignment=(0, .5),
bboxprops=dict(facecolor='white',
boxstyle='round',
edgecolor='white'))
ann1.set_figure(fig)
ann2.set_figure(fig)
fig.artists.append(ann1)
fig.artists.append(ann2)
# if save:
# plt.savefig(os.path.join(rg.path_outsub2,
# f'forecast_aCI{alpha_CI}.pdf'),
# bbox_inches='tight')
#%% forecasting
from sklearn.linear_model import RidgeCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegressionCV
from stat_models_cont import ScikitModel