diff --git a/wetb/prepost/dlcplots.py b/wetb/prepost/dlcplots.py
index 8594b90e0ab00a14f5d7ffe280178f0c00e4970c..60d50485745a763f72953d7885504f4828ea6ba4 100644
--- a/wetb/prepost/dlcplots.py
+++ b/wetb/prepost/dlcplots.py
@@ -555,6 +555,157 @@ def plot_stats2(sim_ids, post_dirs, fig_dir_base=None, labels=None,
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 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_xlabel(xlabel)
+ fig.tight_layout()
+ fig.subplots_adjust(top=0.92)
+
+ fig_path = os.path.join(fig_dir_base, 'dlc%s/' % dlc_name)
+ 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.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)
+
+ fname = os.path.join(fig_path, fname_base + '_std.png')
+ fig2.savefig(fname)#.encode('latin-1')
+ fig2.clear()
+ print('saved: %s' % fname)
+
class PlotStats(object):