From 3adda7db7a4b52087bfb1183ea3322d53e4bc6c6 Mon Sep 17 00:00:00 2001
From: David Robert Verelst <dave@dtu.dk>
Date: Mon, 3 Jul 2017 12:46:45 +0200
Subject: [PATCH] prepost.dlcplots: prune unused method, do not set legend
alpha if no leg
---
wetb/prepost/dlcplots.py | 236 +--------------------------------------
1 file changed, 3 insertions(+), 233 deletions(-)
diff --git a/wetb/prepost/dlcplots.py b/wetb/prepost/dlcplots.py
index 78fef068..24db6c6b 100644
--- a/wetb/prepost/dlcplots.py
+++ b/wetb/prepost/dlcplots.py
@@ -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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