Skip to content

Fix RuntimeWarning: More than 20 figures ... #94

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
daavid00 merged 1 commit into from
Jul 23, 2026
Merged

Fix RuntimeWarning: More than 20 figures ... #94

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
52 changes: 21 additions & 31 deletions src/pycopm/template_scripts/common/plot_post.mako
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -155,8 +155,6 @@ def visualizeData():
fmass["FWPT"] = [ ]
param = [[[[ ] for _ in range(num_satn)] for _ in range(num_para)] for _ in range(I)]

figs = []
axs = []
FO = [[ ] for _ in range(N)]
error_standard = 0
error_ens = []
Expand Down Expand Up @@ -187,8 +185,8 @@ def visualizeData():
if np.sum(smspec[type+"H:"+well])>0:
wells[type].append(type+":"+well)
j += 1
linei = [[ ] for _ in range(j)]
linef = [[ ] for _ in range(j)]
linei = []
linef = []
meant = 0
n_e = [0 for _ in range(I)]
smsp_dates = 86400.0 * smspec["TIME"]
Expand All @@ -213,16 +211,11 @@ def visualizeData():
for d_1, d_2 in zip(data[n_t:], datah[n_t:]):
rcum[type][-1] += dens*abs(d_1-d_2)

fig, ax = plt.subplots()
j = 0
k = 0
for type in ["WOPR","WGPR","WWPR"]:
for i in range(len(wells[type])):
data = smspec[wells[type][i]]
datah = smspec[wells[type][i][:4]+"H"+ wells[type][i][4:]]
axs.append(ax)
figs.append(fig)
figs[j], axs[j] = plt.subplots()
for d_1, d_2 in zip(data[n_t:], datah[n_t:]):
error_standard += ((d_1-d_2)/max(minerr[type],var[type]*d_2))**2
k += 1
Expand All @@ -244,22 +237,20 @@ def visualizeData():
if smsp_dates[i] > training:
n_t = i
break
j = 0
k = 0
error_hist[0].append(0)
for type, ftype, dens in zip(["WWPR","WOPR","WGPR"],["FWPT","FOPT","FGPT"],[999.04100, 852.95669, 0.90358]):
fcum[ftype][0].append(dens*abs(smspec[ftype][-1]-smspec[ftype+"H"][-1]))
cum[type][0].append(0)
for i in range(len(wells[type])):
data = smspec[wells[type][i]]
linei[j], = axs[j].plot(smsp_dates, data, color=[51 / 255.0, 153 / 255.0, 255 / 255.0])
linei.append([smsp_dates, data])
datah = smspec[wells[type][i][:4]+"H"+wells[type][i][4:]]
for d_1, d_2 in zip(data[n_t:], datah[n_t:]):
error_ens[0][r] += ((d_1-d_2)/max(minerr[type],var[type]*d_2))**2
error_hist[0][-1] += ((d_1-d_2)/max(minerr[type],var[type]*d_2))**2
cum[type][0][-1] += dens*abs(d_1-d_2)
k += 1
j += 1
error_ens[0][r] /= (2.*k)
error_hist[0][-1] /= (2.*k)
simTime = f"{output_folder}/output/simulations/realisation-{r}/iter-0/time_sim.txt"
Expand Down Expand Up @@ -334,23 +325,21 @@ def visualizeData():
if smsp_dates[i] > training:
n_t = i
break
j = 0
k = 0
error_hist[-1].append(0)
for type, ftype, dens in zip(["WWPR","WOPR","WGPR"],["FWPT","FOPT","FGPT"],[999.04100, 852.95669, 0.90358]):
fcum[ftype][-1].append(dens*abs(smspec[ftype][-1]-smspec[ftype+"H"][-1]))
cum[type][-1].append(0)
for i in range(len(wells[type])):
data = smspec[wells[type][i]]
linef[j], = axs[j].plot(smsp_dates, data, color=[0 / 255.0, 204 / 255.0, 0 / 255.0])
linef.append([smsp_dates, data])
datah = smspec[wells[type][i][:4]+"H"+wells[type][i][4:]]
for d_1, d_2 in zip(data[n_t:], datah[n_t:]):
error_ens[-1][r] += ((d_1-d_2)/max(minerr[type],var[type]*d_2))**2
error_hist[-1][-1] += ((d_1-d_2)/max(minerr[type],var[type]*d_2))**2
cum[type][-1][-1] += dens*abs(d_1-d_2)
k += 1
FO[r].append([smsp_dates,data])
j += 1
error_ens[-1][r] /= (2.*k)
error_hist[-1][-1] /= (2.*k)
simTime = f"{output_folder}/output/simulations/realisation-{r}/iter-{I-1}/time_sim.txt"
Expand Down Expand Up @@ -400,30 +389,31 @@ def visualizeData():
j = 0
for type in ["WWPR","WOPR","WGPR"]:
for i in range(len(wells[type])):
fig, ax = plt.subplots()
if N > 1 and I == 1:
linef[j].set_label('Ensemble')
axs[j].plot(FO[eobs[0][0][0]][j][0], FO[eobs[0][0][0]][j][1], color=[255 / 255.0, 87 / 255.0, 51 / 255.0], lw=1.5 , label='Closest to all obs')
ax.plot(linef[j][0], linef[j][1], color=[0 / 255.0, 204 / 255.0, 0 / 255.0], label='Ensemble')
ax.plot(FO[eobs[0][0][0]][j][0], FO[eobs[0][0][0]][j][1], color=[255 / 255.0, 87 / 255.0, 51 / 255.0], lw=1.5 , label='Closest to all obs')
elif I > 1:
linei[j].set_label('Initial ensemble')
linef[j].set_label('Final ensemble')
axs[j].plot(FO[eobs[-1][0][0]][j][0], FO[eobs[-1][0][0]][j][1], color=[255 / 255.0, 87 / 255.0, 51 / 255.0], lw=1.5 , label='Closest to all obs')
axs[j].axvline(x=training, color="black", ls="--", lw=1)
ax.plot(linei[j][0], linei[j][1], color=[51 / 255.0, 153 / 255.0, 255 / 255.0], label='Initial ensemble')
ax.plot(linef[j][0], linef[j][1], color=[0 / 255.0, 204 / 255.0, 0 / 255.0], label='Final ensemble')
ax.plot(FO[eobs[-1][0][0]][j][0], FO[eobs[-1][0][0]][j][1], color=[255 / 255.0, 87 / 255.0, 51 / 255.0], lw=1.5 , label='Closest to all obs')
ax.axvline(x=training, color="black", ls="--", lw=1)
else:
linef[j].set_label('Single run')
ax.plot(linef[j][0], linef[j][1], color=[0 / 255.0, 204 / 255.0, 0 / 255.0], label='Single run')
data = smspec[wells[type][i]]
datah = smspec[wells[type][i][:4]+"H"+wells[type][i][4:]]
axs[j].plot(smsp_dates, data, color='m', label = 'opm-tests')
ax.plot(smsp_dates, data, color='m', label = 'opm-tests')
if np.sum(datah>0):
axs[j].errorbar(smsp_dates, datah, yerr= [max(minerr[type],var[type]*d_2) for d_2 in datah], color=[128 / 255.0, 128 / 255.0, 128 / 255.0], markersize='.5', elinewidth=.5, fmt='o', linestyle='', label = 'Data')
axs[j].set_ylabel(f'{wells[type][i]} [SM3/day]', fontsize=12)
axs[j].set_xlabel('Time [years]', fontsize=12)
axs[j].xaxis.set_tick_params(size=6, rotation=45)
axs[j].legend()
axs[j].set_ylim(bottom=0)
ax.errorbar(smsp_dates, datah, yerr= [max(minerr[type],var[type]*d_2) for d_2 in datah], color=[128 / 255.0, 128 / 255.0, 128 / 255.0], markersize='.5', elinewidth=.5, fmt='o', linestyle='', label = 'Data')
ax.set_ylabel(f'{wells[type][i]} [SM3/day]', fontsize=12)
ax.set_xlabel('Time [years]', fontsize=12)
ax.xaxis.set_tick_params(size=6, rotation=45)
ax.legend()
ax.set_ylim(bottom=0)
if np.sum(datah>0):
figs[j].savefig(f"{output_folder}/postprocessing/wells/HISTO_DATA_{wells[type][i][:4]}_{wells[type][i][5:]}.png", bbox_inches="tight")
fig.savefig(f"{output_folder}/postprocessing/wells/HISTO_DATA_{wells[type][i][:4]}_{wells[type][i][5:]}.png", bbox_inches="tight")
else:
figs[j].savefig(f"{output_folder}/postprocessing/wells/{wells[type][i][:4]}_{wells[type][i][5:]}.png", bbox_inches="tight")
fig.savefig(f"{output_folder}/postprocessing/wells/{wells[type][i][:4]}_{wells[type][i][5:]}.png", bbox_inches="tight")
plt.close()
j += 1
if N == 1:
Expand Down
Loading