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Commit 3adda7db authored by David Verelst's avatar David Verelst
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prepost.dlcplots: prune unused method, do not set legend alpha if no leg

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wetb/prepost/dlcplots.py
+ 3
− 233
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......@@ -150,242 +150,10 @@ def merge_sim_ids(sim_ids, post_dirs, post_dir_save=False):
return run_dirs, df_stats
# =============================================================================
### STAT PLOTS
# =============================================================================
def plot_stats(sim_ids, post_dirs, fig_dir_base=None):
"""
For each wind speed, take the max of the max.
Only one or two sim_ids are supported. When they are from different
models/projects, specify for each sim_id a different post_dir
Parameters
----------
sim_ids : list
list of sim_id's, 1 or 2
post_dirs
list of post_dir's, 1 or 2. If 2, should correspond to sim_ids
fig_dir_base : str, default=None
"""
# if sim_id is a list, combine the two dataframes into one
df_stats = pd.DataFrame()
if type(sim_ids).__name__ == 'list':
for ii, sim_id in enumerate(sim_ids):
if isinstance(post_dirs, list):
post_dir = post_dirs[ii]
else:
post_dir = post_dirs
cc = sim.Cases(post_dir, sim_id, rem_failed=True)
if ii == 0:
df_stats, _, _ = cc.load_stats()
else:
# because there is no unique index, we will ignore it
df_stats, _, _ = pd.concat([df_stats, cc.load_stats()],
ignore_index=True)
else:
sim_id = sim_ids
sim_ids = False
post_dir = post_dirs
cc = sim.Cases(post_dir, sim_id, rem_failed=True)
df_stats, _, _ = cc.load_stats()
# if force_dir:
# cc.change_results_dir(resdir=force_dir)
# for case in cc.cases:
# sim_id = cc.cases[case]['[post_dir]']
# cc.cases[case]['[post_dir]'] = post_dir
# # add DLC category
# f = lambda x: x.split('_')[0]
# df_stats['DLC'] = df_stats['[Case id.]'].map(f)
# fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(12,8), num=1)
# define the number of positions you want to have the color for
N = 22
# select a color map
cmap = mpl.cm.get_cmap('jet', N)
# convert to array
cmap_arr = cmap(np.arange(N))
# color=cmap_arr[icol][0:3]
# make a stastics plot for each channel
gb_ch = df_stats.groupby(df_stats.channel)
# channel selection
plot_chans = {}
plot_chans['DLL-2-inpvec-2'] = 'P_e'
plot_chans['bearing-shaft_rot-angle_speed-rpm'] = 'RPM'
plot_chans['tower-tower-node-001-momentvec-x'] = 'M_x T_B'
plot_chans['tower-tower-node-001-momentvec-y'] = 'M_y T_B'
plot_chans['tower-tower-node-001-momentvec-z'] = 'M_z T_B'
plot_chans['tower-tower-node-008-momentvec-z'] = 'M_x T_T'
plot_chans['tower-tower-node-008-momentvec-z'] = 'M_y T_T'
plot_chans['tower-tower-node-008-momentvec-z'] = 'M_z T_T'
plot_chans['shaft-shaft-node-004-momentvec-x'] = 'M_x Shaft_{MB}'
plot_chans['shaft-shaft-node-004-momentvec-y'] = 'M_y Shaft_{MB}'
plot_chans['shaft-shaft-node-004-momentvec-z'] = 'M_z Shaft_{MB}'
plot_chans['blade1-blade1-node-003-momentvec-x'] = 'M_x B1_{root}'
plot_chans['blade1-blade1-node-003-momentvec-y'] = 'M_y B1_{root}'
plot_chans['blade1-blade1-node-003-momentvec-z'] = 'M_z B1_{root}'
plot_chans['blade2-blade2-node-003-momentvec-x'] = 'M_x B2_{root}'
plot_chans['blade2-blade2-node-003-momentvec-y'] = 'M_y B2_{root}'
plot_chans['blade2-blade2-node-003-momentvec-z'] = 'M_z B2_{root}'
plot_chans['blade3-blade3-node-003-momentvec-x'] = 'M_x B3_{root}'
plot_chans['blade3-blade3-node-003-momentvec-y'] = 'M_y B3_{root}'
plot_chans['blade3-blade3-node-003-momentvec-z'] = 'M_z B3_{root}'
plot_chans['DLL-5-inpvec-1'] = 'Min tower clearance'
plot_chans['bearing-pitch1-angle-deg'] = 'B1_{pitch}'
plot_chans['bearing-pitch2-angle-deg'] = 'B2_{pitch}'
plot_chans['bearing-pitch3-angle-deg'] = 'B3_{pitch}'
plot_chans['setbeta-bladenr-1-flapnr-1'] = 'B1_{flap}'
plot_chans['setbeta-bladenr-2-flapnr-1'] = 'B2_{flap}'
plot_chans['setbeta-bladenr-3-flapnr-1'] = 'B3_{flap}'
mfcs1 = ['k', 'w']
mfcs2 = ['b', 'w']
mfcs3 = ['r', 'w']
stds = ['r', 'b']
for nr, (ch_name, gr_ch) in enumerate(gb_ch):
if ch_name not in plot_chans:
continue
for dlc_name, gr_ch_dlc in gr_ch.groupby(df_stats['[DLC]']):
print('start plotting: %s %s' % (str(dlc_name).ljust(7), ch_name))
fig, axes = mplutils.make_fig(nrows=1, ncols=1, figsize=(7,5))
ax = axes[0,0]
# seperate figure for the standard deviations
fig2, axes2 = mplutils.make_fig(nrows=1, ncols=1, figsize=(7,5))
ax2 = axes2[0,0]
if fig_dir_base is None and not sim_ids:
res_dir = gr_ch_dlc['[res_dir]'][:1].values[0]
run_dir = gr_ch_dlc['[run_dir]'][:1].values[0]
fig_dir = os.path.join(fig_dir_base, res_dir)
elif fig_dir_base is None and isinstance(sim_ids, list):
fig_dir = os.path.join(fig_dir_base, '-'.join(sim_ids))
elif fig_dir_base and not sim_ids:
res_dir = gr_ch_dlc['[res_dir]'][:1].values[0]
fig_dir = os.path.join(fig_dir_base, res_dir)
elif sim_ids and fig_dir_base is not None:
# create the compare directory if not defined
fig_dir = fig_dir_base
# if we have a list of different cases, we also need to group those
# because the sim_id wasn't saved before in the data frame,
# we need to derive that from the run dir
# if there is only one run dir nothing changes
ii = 0
sid_names = []
for run_dir, gr_ch_dlc_sid in gr_ch_dlc.groupby(df_stats['[run_dir]']):
sid_name = run_dir.split('/')[-2]
sid_names.append(sid_name)
print(sid_name)
wind = gr_ch_dlc_sid['[Windspeed]'].values
dmin = gr_ch_dlc_sid['min'].values
dmean = gr_ch_dlc_sid['mean'].values
dmax = gr_ch_dlc_sid['max'].values
dstd = gr_ch_dlc_sid['std'].values
if not sim_ids:
lab1 = 'mean'
lab2 = 'min'
lab3 = 'max'
lab4 = 'std'
else:
lab1 = 'mean %s' % sid_name
lab2 = 'min %s' % sid_name
lab3 = 'max %s' % sid_name
lab4 = 'std %s' % sid_name
mfc1 = mfcs1[ii]
mfc2 = mfcs2[ii]
mfc3 = mfcs3[ii]
ax.plot(wind, dmean, mec='k', marker='o', mfc=mfc1, ls='',
label=lab1, alpha=0.7)
ax.plot(wind, dmin, mec='b', marker='^', mfc=mfc2, ls='',
label=lab2, alpha=0.7)
ax.plot(wind, dmax, mec='r', marker='v', mfc=mfc3, ls='',
label=lab3, alpha=0.7)
ax2.plot(wind, dstd, mec=stds[ii], marker='s', mfc=stds[ii], ls='',
label=lab4, alpha=0.7)
ii += 1
# for wind, gr_wind in gr_ch_dlc.groupby(df_stats['[Windspeed]']):
# wind = gr_wind['[Windspeed]'].values
# dmin = gr_wind['min'].values#.mean()
# dmean = gr_wind['mean'].values#.mean()
# dmax = gr_wind['max'].values#.mean()
## dstd = gr_wind['std'].mean()
# ax.plot(wind, dmean, 'ko', label='mean', alpha=0.7)
# ax.plot(wind, dmin, 'b^', label='min', alpha=0.7)
# ax.plot(wind, dmax, 'rv', label='max', alpha=0.7)
## ax.errorbar(wind, dmean, c='k', ls='', marker='s', mfc='w',
## label='mean and std', yerr=dstd)
ax.grid()
ax.set_xlim([3, 27])
leg = ax.legend(loc='best', ncol=2)
leg.get_frame().set_alpha(0.7)
ax.set_title(r'{DLC%s} $%s$' % (dlc_name, plot_chans[ch_name]))
ax.set_xlabel('Wind speed [m/s]')
fig.tight_layout()
fig.subplots_adjust(top=0.92)
if not sim_ids:
fig_path = os.path.join(fig_dir,
ch_name.replace(' ', '_') + '.png')
else:
sids = '_'.join(sid_names)
# fig_dir = run_dir.split('/')[:-1] + 'figures/'
fname = '%s_%s.png' % (ch_name.replace(' ', '_'), sids)
fig_path = os.path.join(fig_dir, 'dlc%s/' % dlc_name)
if not os.path.exists(fig_path):
os.makedirs(fig_path)
fig_path = fig_path + fname
fig.savefig(fig_path)#.encode('latin-1')
# canvas.close()
fig.clear()
print('saved: %s' % fig_path)
ax2.grid()
ax2.set_xlim([3, 27])
leg = ax2.legend(loc='best', ncol=2)
leg.get_frame().set_alpha(0.7)
ax2.set_title(r'{DLC%s} $%s$' % (dlc_name, plot_chans[ch_name]))
ax2.set_xlabel('Wind speed [m/s]')
fig2.tight_layout()
fig2.subplots_adjust(top=0.92)
if not sim_ids:
fig_path = os.path.join(fig_dir,
ch_name.replace(' ', '_') + '_std.png')
else:
sids = '_'.join(sid_names)
fname = '%s_std_%s.png' % (ch_name.replace(' ', '_'), sids)
fig_path = os.path.join(fig_dir, 'dlc%s/' % dlc_name)
if not os.path.exists(fig_path):
os.makedirs(fig_path)
fig_path = fig_path + fname
fig2.savefig(fig_path)#.encode('latin-1')
# canvas.close()
fig2.clear()
print('saved: %s' % fig_path)
def plot_stats2(sim_ids, post_dirs, plot_chans, fig_dir_base=None, labels=None,
post_dir_save=False, dlc_ignore=['00'], figsize=(8,6)):
"""
......@@ -619,7 +387,9 @@ def plot_dlc_stats(df_stats, plot_chans, fig_dir_base, labels=None,
ax.grid()
ax.set_xlim(xlims)
leg = ax.legend(loc='best', ncol=3)
leg.get_frame().set_alpha(0.7)
# if no data at all was found
if leg is not None:
leg.get_frame().set_alpha(0.7)
ax.set_title(r'{DLC%s} %s' % (dlc_name, ch_dscr))
ax.set_xlabel(xlabel)
fig.tight_layout()
......
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