From ba898f2490fc85835ed53e7541c6505257183343 Mon Sep 17 00:00:00 2001
From: David Robert Verelst <dave@dtu.dk>
Date: Tue, 7 Feb 2017 16:05:27 +0100
Subject: [PATCH] remove code/function duplication in dlcplots, small
improvements
---
wetb/prepost/dlcplots.py | 468 +++++++++++++++------------------------
1 file changed, 180 insertions(+), 288 deletions(-)
diff --git a/wetb/prepost/dlcplots.py b/wetb/prepost/dlcplots.py
index 60d5048..71d8e4e 100644
--- a/wetb/prepost/dlcplots.py
+++ b/wetb/prepost/dlcplots.py
@@ -51,6 +51,92 @@ plt.rc('legend', fontsize=11)
plt.rc('legend', numpoints=1)
plt.rc('legend', borderaxespad=0)
+
+def merge_sim_ids(post_dirs, sim_ids, post_dir_save=False):
+ """
+ """
+ # map the run_dir to the same order as the post_dirs, labels
+ run_dirs = []
+
+ # 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)
+ df_stats, _, _ = cc.load_stats(columns=None, leq=False)
+
+ # stats has only a few columns identifying the different cases
+ # add some more for selecting them
+ dfc = cc.cases2df()
+ if '[wsp]' in dfc.columns:
+ wsp = '[wsp]'
+ else:
+ wsp = '[Windspeed]'
+ dfc = dfc[['[case_id]', '[run_dir]', wsp, '[res_dir]',
+ '[wdir]', '[DLC]']]
+ df_stats = pd.merge(df_stats, dfc, on='[case_id]')
+ df_stats.rename(columns={wsp:'[Windspeed]'}, inplace=True)
+
+ # map the run_dir to the same order as the post_dirs, labels
+ run_dirs.append(df_stats['[run_dir]'].unique()[0])
+
+ print('%s Cases loaded.' % sim_id)
+
+ # if specified, save the merged sims elsewhere
+ if post_dir_save:
+ fpath = os.path.join(post_dir_save, '-'.join(sim_ids) + '.h5')
+ try:
+ os.makedirs(post_dir_save)
+ except OSError:
+ pass
+ else:
+ fpath = os.path.join(post_dir, '-'.join(sim_ids) + '.h5')
+ if ii == 0:
+ # and save somewhere so we can add the second data frame on
+ # disc
+ df_stats.to_hdf(fpath, 'table', mode='w', format='table',
+ complevel=9, complib='blosc')
+ print('%s merged stats written to: %s' % (sim_id, fpath))
+ else:
+ # instead of doing a concat in memory, add to the hdf store
+ df_stats.to_hdf(fpath, 'table', mode='r+', format='table',
+ complevel=9, complib='blosc', append=True)
+ print('%s merging stats into: %s' % (sim_id, fpath))
+# df_stats = pd.concat([df_stats, df_stats2], ignore_index=True)
+# df_stats2 = None
+ # we might run into memory issues
+ del df_stats, _, cc
+ gc.collect()
+ # and load the reduced combined set
+ print('loading merged stats: %s' % fpath)
+ df_stats = pd.read_hdf(fpath, 'table')
+ else:
+ sim_id = sim_ids
+ sim_ids = [sim_id]
+ post_dir = post_dirs
+ cc = sim.Cases(post_dir, sim_id, rem_failed=True)
+ df_stats, _, _ = cc.load_stats(leq=False)
+ run_dirs = [df_stats['[run_dir]'].unique()[0]]
+
+ # stats has only a few columns identifying the different cases
+ # add some more for selecting them
+ dfc = cc.cases2df()
+ if 'wsp' in dfc.columns:
+ wsp = '[wsp]'
+ else:
+ wsp = '[Windspeed]'
+ dfc = dfc[['[case_id]', '[run_dir]', wsp, '[res_dir]',
+ '[wdir]', '[DLC]']]
+ df_stats = pd.merge(df_stats, dfc, on='[case_id]')
+ df_stats.rename(columns={wsp:'[Windspeed]'}, inplace=True)
+
+ return run_dirs, df_stats
+
+
# =============================================================================
### STAT PLOTS
# =============================================================================
@@ -287,108 +373,50 @@ def plot_stats(sim_ids, post_dirs, fig_dir_base=None):
print('saved: %s' % fig_path)
-def plot_stats2(sim_ids, post_dirs, fig_dir_base=None, labels=None,
- post_dir_save=False, dlc_ignore=['00']):
+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)):
"""
Map which channels have to be compared
"""
- plot_chans = {}
-
- plot_chans['B1_{flap}'] = ['setbeta-bladenr-1-flapnr-1']
- plot_chans['B2_{flap}'] = ['setbeta-bladenr-2-flapnr-1']
- plot_chans['B3_{flap}'] = ['setbeta-bladenr-3-flapnr-1']
- plot_chans['M_x B1_{root}'] = ['blade1-blade1-node-003-momentvec-x',
- 'blade1-blade1-node-004-momentvec-x']
- plot_chans['M_y B1_{root}'] = ['blade1-blade1-node-003-momentvec-y',
- 'blade1-blade1-node-004-momentvec-y']
- plot_chans['M_z B1_{root}'] = ['blade1-blade1-node-003-momentvec-z',
- 'blade1-blade1-node-004-momentvec-z']
- plot_chans['B3_{pitch}'] = ['bearing-pitch3-angle-deg']
- plot_chans['RPM'] = ['bearing-shaft_rot-angle_speed-rpm']
- plot_chans['P_e'] = ['DLL-2-inpvec-2']
- plot_chans['P_{mech}'] = ['stats-shaft-power']
- plot_chans['M_x B3_{root}'] = ['blade3-blade3-node-003-momentvec-x',
- 'blade3-blade3-node-004-momentvec-x']
- plot_chans['M_y B3_{root}'] = ['blade3-blade3-node-003-momentvec-y',
- 'blade3-blade3-node-004-momentvec-y']
- plot_chans['M_z B3_{root}'] = ['blade3-blade3-node-003-momentvec-z',
- 'blade3-blade3-node-004-momentvec-z']
- plot_chans['B2_{pitch}'] = ['bearing-pitch2-angle-deg']
-
- plot_chans['B3 U_y'] = ['global-blade3-elem-018-zrel-1.00-State pos-y']
- plot_chans['M_z B2_{root}'] = ['blade2-blade2-node-003-momentvec-z',
- 'blade2-blade2-node-004-momentvec-z']
- plot_chans['M_x B2_{root}'] = ['blade2-blade2-node-003-momentvec-x',
- 'blade2-blade2-node-004-momentvec-x']
- plot_chans['M_y B2_{root}'] = ['blade2-blade2-node-003-momentvec-y',
- 'blade2-blade2-node-004-momentvec-y']
- plot_chans['B1_{pitch}'] = ['bearing-pitch1-angle-deg']
- plot_chans['M_x T_B'] = ['tower-tower-node-001-momentvec-x']
- plot_chans['M_y T_B'] = ['tower-tower-node-001-momentvec-y']
- plot_chans['M_z T_B'] = ['tower-tower-node-001-momentvec-z']
- plot_chans['tower clearance'] = ['DLL-5-inpvec-1']
- plot_chans['M_z T_T'] = ['tower-tower-node-008-momentvec-z']
- plot_chans['M_y Shaft_{MB}'] = ['shaft-shaft-node-004-momentvec-y']
- plot_chans['M_x Shaft_{MB}'] = ['shaft-shaft-node-004-momentvec-x']
- plot_chans['M_z Shaft_{MB}'] = ['shaft-shaft-node-004-momentvec-z']
-
# reduce required memory, only use following columns
cols = ['[run_dir]', '[DLC]', 'channel', '[res_dir]', '[Windspeed]',
'mean', 'max', 'min', 'std', '[wdir]']
- # 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)
- df_stats, _, _ = cc.load_stats(columns=cols, leq=False)
- print('%s Cases loaded.' % sim_id)
+ run_dirs, df_stats = merge_sim_ids(sim_ids, post_dirs,
+ post_dir_save=post_dir_save)
+
+ plot_stats_df(df_stats, plot_chans, fig_dir_base, labels=labels,
+ figsize=figsize, dlc_ignore=dlc_ignore)
- # if specified, save the merged sims elsewhere
- if post_dir_save:
- fpath = os.path.join(post_dir_save, '-'.join(sim_ids) + '.h5')
- try:
- os.makedirs(post_dir_save)
- except OSError:
- pass
- else:
- fpath = os.path.join(post_dir, '-'.join(sim_ids) + '.h5')
- if ii == 0:
- # and save somewhere so we can add the second data frame on
- # disc
- df_stats.to_hdf(fpath, 'table', mode='w', format='table',
- complevel=9, complib='blosc')
- print('%s merged stats written to: %s' % (sim_id, fpath))
- else:
- # instead of doing a concat in memory, add to the hdf store
- df_stats.to_hdf(fpath, 'table', mode='r+', format='table',
- complevel=9, complib='blosc', append=True)
- print('%s merging stats into: %s' % (sim_id, fpath))
-# df_stats = pd.concat([df_stats, df_stats2], ignore_index=True)
-# df_stats2 = None
- # we might run into memory issues
- del df_stats, _, cc
- gc.collect()
- # and load the reduced combined set
- print('loading merged stats: %s' % fpath)
- df_stats = pd.read_hdf(fpath, 'table')
- else:
- sim_id = sim_ids
- sim_ids = [sim_id]
- post_dir = post_dirs
- cc = sim.Cases(post_dir, sim_id, rem_failed=True)
- df_stats, _, _ = cc.load_stats(leq=False)
+
+def plot_stats_df(df_stats, plot_chans, fig_dir_base, labels=None,
+ figsize=(8,6), dlc_ignore=['00'], run_dirs=None,
+ sim_ids=[]):
+ """
+ Same as plot_stats2, but give a df with the stats of that sim_id and the
+ relevant channels.
+
+ df_stats columns:
+ * [DLC]
+ * [run_dir]
+ * channel
+ * stat parameters
+ """
mfcs1 = ['k', 'w']
mfcs2 = ['b', 'w']
mfcs3 = ['r', 'w']
stds = ['r', 'b']
+ if run_dirs is None:
+ run_dirs = df_stats['[run_dir]'].unique()
+
+ if not sim_ids:
+ sim_ids = []
+ for run_dir in run_dirs:
+ sim_ids.append(run_dir.split(os.path.sep)[-2])
+
# first, take each DLC appart
for dlc_name, gr_dlc in df_stats.groupby(df_stats['[DLC]']):
# do not plot the stats for dlc00
@@ -418,36 +446,40 @@ def plot_stats2(sim_ids, post_dirs, fig_dir_base=None, labels=None,
# if not len(df_chan.channel.unique()) == len(ch_names):
# continue
lens = []
- for key, gr_ch_dlc_sid in df_chan.groupby(df_chan['[run_dir]']):
- lens.append(len(gr_ch_dlc_sid))
+ # instead of groupby, select the run_dir in the same order as
+ # occuring in the labels and post_dirs lists
+ for run_dir in run_dirs:
+ lens.append(len(df_chan[df_chan['[run_dir]']==run_dir]))
+# for key, gr_ch_dlc_sid in df_chan.groupby(df_chan['[run_dir]']):
+# lens.append(len(gr_ch_dlc_sid))
# when the channel is simply not present
if len(lens) == 0:
continue
# when only one of the channels was present, but the set is still
# complete.
# FIXME: what if both channels are present?
- if len(ch_names) > 1 and (lens[0] < 1 or lens[1] < 1):
+ if len(ch_names) > 1 and (lens[0] < 1) or (lens[1] < 1):
continue
print('start plotting: %s %s' % (str(dlc_name).ljust(7), ch_dscr))
fig, axes = mplutils.make_fig(nrows=1, ncols=1,
- figsize=(11,7.15), dpi=120)
+ figsize=figsize, dpi=120)
ax = axes[0,0]
# seperate figure for the standard deviations
- fig2, axes2 = mplutils.make_fig(nrows=1, ncols=1,
- figsize=(11,7.15), dpi=120)
- ax2 = axes2[0,0]
+# fig2, axes2 = mplutils.make_fig(nrows=1, ncols=1,
+# figsize=figsize, dpi=120)
+# ax2 = axes2[0,0]
if fig_dir_base is None and len(sim_ids) < 2:
res_dir = df_chan['[res_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):
+ elif fig_dir_base is None and len(sim_ids) > 0:
fig_dir = os.path.join(fig_dir_base, '-'.join(sim_ids))
# elif fig_dir_base and len(sim_ids) < 2:
# res_dir = df_chan['[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:
+ elif fig_dir_base is not None:
# create the compare directory if not defined
fig_dir = fig_dir_base
@@ -455,14 +487,25 @@ def plot_stats2(sim_ids, post_dirs, fig_dir_base=None, labels=None,
# 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 df_chan.groupby(df_chan['[run_dir]']):
+# sid_names = []
+ # for clarity, set off-set on wind speed when comparing two DLB's
+ if len(lens)==2:
+ windoffset = [-0.2, 0.2]
+ else:
+ windoffset = [0]
+ # instead of groupby, select the run_dir in the same order as
+ # occuring in the labels, post_dirs lists
+ for ii, run_dir in enumerate(run_dirs):
+ gr_ch_dlc_sid = df_chan[df_chan['[run_dir]']==run_dir]
+ if len(gr_ch_dlc_sid) < 1:
+ print('no data for run_dir:', run_dir)
+ continue
+# for run_dir, gr_ch_dlc_sid in df_chan.groupby(df_chan['[run_dir]']):
if labels is None:
- sid_name = run_dir.split(os.path.sep)[-2]
+ sid_name = sim_ids[ii]
else:
sid_name = labels[ii]
- sid_names.append(sid_name)
+# sid_names.append(sid_name)
print(' sim_id/label:', sid_name)
# FIXME: will this go wrong in PY3?
if str(dlc_name) in ['61', '62']:
@@ -470,14 +513,14 @@ def plot_stats2(sim_ids, post_dirs, fig_dir_base=None, labels=None,
xlabel = 'wind direction [deg]'
xlims = [0, 360]
else:
- xdata = gr_ch_dlc_sid['[Windspeed]'].values
+ xdata = gr_ch_dlc_sid['[Windspeed]'].values + windoffset[ii]
xlabel = 'Wind speed [m/s]'
xlims = [3, 27]
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:
+ if len(sim_ids)==1:
lab1 = 'mean'
lab2 = 'min'
lab3 = 'max'
@@ -491,16 +534,14 @@ def plot_stats2(sim_ids, post_dirs, fig_dir_base=None, labels=None,
mfc2 = mfcs2[ii]
mfc3 = mfcs3[ii]
ax.errorbar(xdata, dmean, mec='k', marker='o', mfc=mfc1, ls='',
- label=lab1, alpha=0.7, yerr=dstd)
+ label=lab1, alpha=0.7, yerr=dstd, ecolor='k')
ax.plot(xdata, dmin, mec='b', marker='^', mfc=mfc2, ls='',
label=lab2, alpha=0.7)
ax.plot(xdata, dmax, mec='r', marker='v', mfc=mfc3, ls='',
label=lab3, alpha=0.7)
- ax2.plot(xdata, dstd, mec=stds[ii], marker='s', mfc=stds[ii],
- ls='', label=lab4, alpha=0.7)
-
- ii += 1
+# ax2.plot(xdata, dstd, mec=stds[ii], marker='s', mfc=stds[ii],
+# ls='', label=lab4, alpha=0.7)
# for wind, gr_wind in gr_ch_dlc.groupby(df_stats['[Windspeed]']):
# wind = gr_wind['[Windspeed]'].values
@@ -513,198 +554,49 @@ def plot_stats2(sim_ids, post_dirs, fig_dir_base=None, labels=None,
# 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(xlims)
- leg = ax.legend(loc='best', ncol=3)
- 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()
- fig.subplots_adjust(top=0.92)
- if not sim_ids:
- fig_path = os.path.join(fig_dir, fname_base + '.png')
- else:
- sids = '_'.join(sid_names)
- fname = '%s_%s.png' % (fname_base, 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')
- fig.clear()
- print('saved: %s' % fig_path)
-
- ax2.grid()
- ax2.set_xlim(xlims)
- leg = ax2.legend(loc='best', ncol=3)
- leg.get_frame().set_alpha(0.7)
- ax2.set_title(r'{DLC%s} $%s$' % (dlc_name, ch_dscr))
- 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, fname_base + '_std.png')
- else:
- sids = '_'.join(sid_names)
- fname = '%s_std_%s.png' % (fname_base, 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')
- fig2.clear()
- print('saved: %s' % fig_path)
-
-def plot_stats3(df_stats, plot_chans, fig_dir_base, labels=None):
- """
- Same as plot_stats2, but give a df with the stats of that sim_id and the
- relevant channels.
-
- df_stats columns:
- * [DLC]
- * [run_dir]
- * channel
- * stat parameters
- """
-
- mfcs1 = ['k', 'w']
- mfcs2 = ['b', 'w']
- mfcs3 = ['r', 'w']
- stds = ['r', 'b']
-
- # first, take each DLC appart
- for dlc_name, gr_dlc in df_stats.groupby(df_stats['[DLC]']):
- # cycle through all the target plot channels
- for ch_dscr, ch_names in plot_chans.items():
- # second, group per channel. Note that when the channel names are not
- # identical, we need to manually pick them.
- # figure file name will be the first channel
- if isinstance(ch_names, list):
- df_chan = gr_dlc[gr_dlc.channel == ch_names[0]]
- fname_base = ch_names[0].replace(' ', '_')
- try:
- df2 = gr_dlc[gr_dlc.channel == ch_names[1]]
- df_chan = pd.concat([df_chan, df2], ignore_index=True)
- except IndexError:
- pass
- else:
- ch_name = ch_names
- ch_names = [ch_name]
- df_chan = gr_dlc[gr_dlc.channel == ch_names]
- fname_base = ch_names.replace(' ', '_')
-
- # if not, than we are missing a channel description, or the channel
- # is simply not available in the given result set
-# if not len(df_chan.channel.unique()) == len(ch_names):
-# continue
- lens = []
- for key, gr_ch_dlc_sid in df_chan.groupby(df_chan['[run_dir]']):
- lens.append(len(gr_ch_dlc_sid))
- # when the channel is simply not present
- if len(lens) == 0:
- continue
- # when only one of the channels was present, but the set is still
- # complete.
- # FIXME: what if both channels are present?
- if len(ch_names) > 1 and (lens[0] < 1 or lens[1] < 1):
- continue
-
- print('start plotting: %s %s' % (str(dlc_name).ljust(7), ch_dscr))
-
- fig, axes = mplutils.make_fig(nrows=1, ncols=1,
- figsize=(11,7.15), dpi=120)
- ax = axes[0,0]
- # seperate figure for the standard deviations
- fig2, axes2 = mplutils.make_fig(nrows=1, ncols=1,
- figsize=(11,7.15), dpi=120)
- ax2 = axes2[0,0]
+# if str(dlc_name) not in ['61', '62']:
+# ax.set_xticks(gr_ch_dlc_sid['[Windspeed]'].values)
- # 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 df_chan.groupby(df_chan['[run_dir]']):
- if labels is None:
- sid_name = run_dir.split(os.path.sep)[-2]
- else:
- sid_name = labels[ii]
- sid_names.append(sid_name)
- print(' sim_id/label:', sid_name)
- # FIXME: will this go wrong in PY3?
- if str(dlc_name) in ['61', '62']:
- xdata = gr_ch_dlc_sid['[wdir]'].values
- xlabel = 'wind direction [deg]'
- xlims = [0, 360]
- else:
- xdata = gr_ch_dlc_sid['[Windspeed]'].values
- xlabel = 'Wind speed [m/s]'
- xlims = [3, 27]
- 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
- 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.errorbar(xdata, dmean, mec='k', marker='o', mfc=mfc1, ls='',
- label=lab1, alpha=0.7, yerr=dstd)
- ax.plot(xdata, dmin, mec='b', marker='^', mfc=mfc2, ls='',
- label=lab2, alpha=0.7)
- ax.plot(xdata, dmax, mec='r', marker='v', mfc=mfc3, ls='',
- label=lab3, alpha=0.7)
-
- ax2.plot(xdata, 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(xlims)
leg = ax.legend(loc='best', ncol=3)
leg.get_frame().set_alpha(0.7)
- ax.set_title(r'{DLC%s} $%s$' % (dlc_name, ch_dscr))
+ ax.set_title(r'{DLC%s} %s' % (dlc_name, ch_dscr))
ax.set_xlabel(xlabel)
fig.tight_layout()
fig.subplots_adjust(top=0.92)
-
- fig_path = os.path.join(fig_dir_base, 'dlc%s/' % dlc_name)
+ fig_path = os.path.join(fig_dir, 'dlc%s' % dlc_name)
+ if len(sim_ids)==1:
+ fname = fname_base + '.png'
+ else:
+ fname = '%s_%s.png' % (fname_base, '_'.join(sim_ids))
if not os.path.exists(fig_path):
os.makedirs(fig_path)
- fname = os.path.join(fig_path, fname_base + '.png')
- fig.savefig(fname)#.encode('latin-1')
+ fig_path = os.path.join(fig_path, fname)
+ fig.savefig(fig_path)#.encode('latin-1')
fig.clear()
- print('saved: %s' % fname)
-
- ax2.grid()
- ax2.set_xlim(xlims)
- leg = ax2.legend(loc='best', ncol=3)
- leg.get_frame().set_alpha(0.7)
- ax2.set_title(r'{DLC%s} $%s$' % (dlc_name, ch_dscr))
- ax2.set_xlabel('Wind speed [m/s]')
- fig2.tight_layout()
- fig2.subplots_adjust(top=0.92)
+ print('saved: %s' % fig_path)
- fname = os.path.join(fig_path, fname_base + '_std.png')
- fig2.savefig(fname)#.encode('latin-1')
- fig2.clear()
- print('saved: %s' % fname)
+# ax2.grid()
+# ax2.set_xlim(xlims)
+# leg = ax2.legend(loc='best', ncol=3)
+# leg.get_frame().set_alpha(0.7)
+# ax2.set_title(r'{DLC%s} $%s$' % (dlc_name, ch_dscr))
+# 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, fname_base + '_std.png')
+# else:
+# sids = '_'.join(sid_names)
+# fname = '%s_std_%s.png' % (fname_base, 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')
+# fig2.clear()
+# print('saved: %s' % fig_path)
class PlotStats(object):
--
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