# -*- coding: utf-8 -*-
"""
Created on Tue Sep 16 10:21:11 2014
@author: dave
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
from __future__ import absolute_import
from builtins import str
from future import standard_library
standard_library.install_aliases()
#print(*objects, sep=' ', end='\n', file=sys.stdout)
import os
# import socket
import gc
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
#from matplotlib.figure import Figure
#from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigCanvas
#from scipy import interpolate as interp
#from scipy.optimize import fmin_slsqp
#from scipy.optimize import minimize
#from scipy.interpolate import interp1d
#import scipy.integrate as integrate
#http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html
import pandas as pd
#import openpyxl as px
#import numpy as np
#import windIO
from wetb.prepost import mplutils
from wetb.prepost import Simulations as sim
from wetb.prepost import dlcdefs
plt.rc('font', family='serif')
plt.rc('xtick', labelsize=10)
plt.rc('ytick', labelsize=10)
plt.rc('axes', labelsize=12)
# do not use tex on Gorm and or Jess
#if not socket.gethostname()[:2] in ['g-', 'je', 'j-']:
# plt.rc('text', usetex=True)
plt.rc('legend', fontsize=11)
plt.rc('legend', numpoints=1)
plt.rc('legend', borderaxespad=0)
def merge_sim_ids(sim_ids, post_dirs, post_dir_save=False):
"""
"""
cols_extra = ['[run_dir]', '[res_dir]', '[wdir]', '[DLC]', '[Case folder]']
# map the run_dir to the same order as the post_dirs, labels
run_dirs = []
# avoid saving merged cases if there is only one!
if type(sim_ids).__name__ == 'list' and len(sim_ids) == 1:
sim_ids = sim_ids[0]
# 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]'
# columns we want to add from cc.cases (cases dict) to stats
cols_cc = set(cols_extra + [wsp])
# do not add column twice, some might already be in df stats
add_cols = list(cols_cc - set(df_stats.columns))
add_cols.append('[case_id]')
dfc = dfc[add_cols]
df_stats = pd.merge(df_stats, dfc, on='[case_id]')
# FIXME: this is very messy, we can end up with both [wsp] and
# [Windspeed] columns
if '[Windspeed]' in df_stats.columns and '[wsp]' in df_stats.columns:
df_stats.drop('[wsp]', axis=1, inplace=True)
if wsp != '[Windspeed]':
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')
fmerged = fpath.replace('.h5', '_statistics.h5')
if ii == 0:
# and save somewhere so we can add the second data frame on
# disc
df_stats.to_hdf(fmerged, '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(fmerged, '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' % fmerged)
df_stats = pd.read_hdf(fmerged, 'table')
else:
sim_id = sim_ids
sim_ids = [sim_id]
post_dir = post_dirs
if isinstance(post_dirs, list):
post_dir = post_dirs[0]
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]'
# columns we want to add from cc.cases (cases dict) to stats
cols_cc = set(cols_extra + [wsp])
# do not add column twice, some might already be in df stats
add_cols = list(cols_cc - set(df_stats.columns))
add_cols.append('[case_id]')
dfc = dfc[add_cols]
df_stats = pd.merge(df_stats, dfc, on='[case_id]')
if '[Windspeed]' in df_stats.columns and '[wsp]' in df_stats.columns:
df_stats.drop('[wsp]', axis=1, inplace=True)
if wsp != '[Windspeed]':
df_stats.rename(columns={wsp:'[Windspeed]'}, inplace=True)
return run_dirs, df_stats
# =============================================================================
### STAT PLOTS
# =============================================================================
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),
eps=False, ylabels=None, title=True, chans_ms_1hz={}):
"""
Map which channels have to be compared
"""
# reduce required memory, only use following columns
cols = ['[run_dir]', '[DLC]', 'channel', '[res_dir]', '[Windspeed]',
'mean', 'max', 'min', 'std', '[wdir]', '[Case folder]']
run_dirs, df_stats = merge_sim_ids(sim_ids, post_dirs,
post_dir_save=post_dir_save)
plot_dlc_stats(df_stats, plot_chans, fig_dir_base, labels=labels,
figsize=figsize, dlc_ignore=dlc_ignore, eps=eps,
ylabels=ylabels, title=title, chans_ms_1hz=chans_ms_1hz)
def plot_dlc_stats(df_stats, plot_chans, fig_dir_base, labels=None,
figsize=(8,6), dlc_ignore=['00'], run_dirs=None,
sim_ids=[], eps=False, ylabels=None, title=True,
chans_ms_1hz={}):
"""Create for each DLC an overview plot of the statistics.
df_stats required columns:
* [DLC]
* [run_dir]
* [wdir]
* [Windspeed]
* channel
* stat parameters
Parameters
----------
df_stats : pandas.DataFrame
plot_chans : dict
Dictionary of channels to be plotted. Key is used for the plot title,
value is a list of unique channel names that will be included for
the statistic values. For example,
plot_chans['$B123 M_x$'] = ['blade1-blade1-node-003-momentvec-x',
'blade2-blade2-node-003-momentvec-x']
fig_dir_base : str
Base directory of where to store all the figures. A new sub-directory
will be created for each DLC.
labels : list, default=None
Labels used in the legend when comparing various DLB's
figsize : tuple, default=(8,6)
dlc_ignore : list, default=['dlc00']
By default all but dlc00 (stair case, wind ramp) are plotted. Add
more dlc numbers here if necessary.
run_dirs : list, default=None
If run_dirs is not defined it will be taken from the unique values
in the DataFrame. The order of the elements in this list needs to be
consistent with labels, sim_ids (if defined).
sim_ids : list, default=[]
Only used when creating the file name of the figures: appended at
the end of the file name (which starts with the unique channel name).
chans_ms_1hz : dict, default={}
Key/value pairs of channel and list of to be plotten m values. Channel
refers to plot title/label as used as the key value in plot_chans.
"""
def fig_epilogue(fig, ax, fname_base):
ax.grid()
ax.set_xlim(xlims)
leg = ax.legend(bbox_to_anchor=(1, 1), loc='lower right', ncol=3)
leg.get_frame().set_alpha(0.7)
title_space = 0.0
if title:
fig_title = '%s %s' % (dlc_name, ch_dscr)
# FIXME: dlc_name is assumed to be not in math mode ($$), so
# escape underscores to avoid latex going bananas
if mpl.rcParams['text.usetex']:
fig_title = '%s %s' % (dlc_name.replace('_', '\\_'), ch_dscr)
fig.suptitle(fig_title)
title_space = 0.02
ax.set_xlabel(xlabel)
if ylabels is not None:
ax.set_ylabel(ylabels[ch_name])
fig.tight_layout()
spacing = 0.94 - title_space - (0.065 * (ii + 1))
fig.subplots_adjust(top=spacing)
fig_path = os.path.join(fig_dir, 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)
fig_path = os.path.join(fig_path, fname)
fig.savefig(fig_path)#.encode('latin-1')
if eps:
fig.savefig(fig_path.replace('.png', '.eps'))
fig.clear()
print('saved: %s' % fig_path)
mfcs1 = ['k', 'w']
mfcs2 = ['b', 'w']
mfcs3 = ['r', 'w']
mfcs4 = ['k', 'b']
mark4 = ['s', 'o', '<', '>']
mfls4 = ['-', '--']
required = ['[DLC]', '[run_dir]', '[wdir]', '[Windspeed]', '[res_dir]',
'[Case folder]']
cols = df_stats.columns
for col in required:
if col not in cols:
print('plot_dlc_stats requires DataFrame with following columns:')
print(required)
print('following column is missing in stats DataFrame:', col)
return
if run_dirs is None:
run_dirs = df_stats['[run_dir]'].unique()
if not sim_ids:
sim_ids = []
for run_dir in run_dirs:
# in case this is a windows path:
tmp = run_dir.replace('\\', '/').replace(':', '')
sim_ids.append(tmp.split('/')[-2])
# first, take each DLC appart
for gr_name, gr_dlc in df_stats.groupby(df_stats['[Case folder]']):
dlc_name = gr_name
if dlc_name[:3].lower() == 'dlc':
# FIXME: this is messy since this places a hard coded dependency
# between [Case folder] and [Case id.] when the tag [DLC] is
# defined in dlcdefs.py
dlc_name = gr_name.split('_')[0]
# do not plot the stats for dlc00
if dlc_name.lower() in dlc_ignore:
continue
# 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):
ch_name = ch_names[0]
fname_base = ch_names[0]#.replace(' ', '_')
df_chan = gr_dlc[gr_dlc.channel.isin(ch_names)]
else:
ch_name = ch_names
ch_names = [ch_names]
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 = []
# 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):
continue
elif len(ch_names) > 1 and len(lens)==2 and lens[1] < 1:
continue
print('start plotting: %s %s' % (dlc_name.ljust(10), ch_dscr))
fig, axes = mplutils.make_fig(nrows=1, ncols=1,
figsize=figsize, dpi=120)
ax = axes[0,0]
# seperate figure for the mean of the 1Hz equivalent loads
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 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 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
# sid_names = []
# for clarity, set off-set on wind speed when comparing two DLB's
if len(lens)==2:
windoffset = [-0.2, 0.2]
dirroffset = [-5, 5]
else:
windoffset = [0]
dirroffset = [0]
# in case of a fully empty plot xlims will remain None and there
# is no need to save the plot
xlims = None
# 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 = sim_ids[ii]
else:
sid_name = labels[ii]
# sid_names.append(sid_name)
print(' sim_id/label:', sid_name)
# FIXME: will this go wrong in PY3?
if dlc_name.lower() in ['dlc61', 'dlc62']:
key = '[wdir]'
xdata = gr_ch_dlc_sid[key].values + dirroffset[ii]
xlabel = 'wind direction [deg]'
xlims = [0, 360]
else:
key = '[Windspeed]'
xdata = gr_ch_dlc_sid[key].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 len(sim_ids)==1:
lab1 = 'mean'
lab2 = 'min'
lab3 = 'max'
lab4 = '1Hz EqL'
else:
lab1 = 'mean %s' % sid_name
lab2 = 'min %s' % sid_name
lab3 = 'max %s' % sid_name
lab4 = '1Hz EqL %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, 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)
# mean of 1Hz equivalent loads
ms = []
if ch_dscr in chans_ms_1hz:
ms = chans_ms_1hz[ch_dscr]
for im, m in enumerate(ms):
# average over seed and possibly yaw angles
# wind speed or yaw inflow according to dlc case
gr_key = gr_ch_dlc_sid[key]
d1hz = gr_ch_dlc_sid[m].groupby(gr_key).mean()
ax2.plot(d1hz.index, d1hz.values, mec=mfcs4[ii], alpha=0.7,
marker=mark4[im], ls=mfls4[ii], mfc=mfc1,
label=lab4, color=mfcs4[ii])
# 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)
# if str(dlc_name) not in ['61', '62']:
# ax.set_xticks(gr_ch_dlc_sid['[Windspeed]'].values)
# don't save empyt plots
if xlims is None:
continue
fig_epilogue(fig, ax, fname_base)
# don't save empty plots
if len(ms) < 1:
continue
fig_epilogue(fig2, ax2, fname_base + '_1hz_eql')
class PlotStats(object):
def __init__(self):
pass
def load_stats(self, sim_ids, post_dirs, post_dir_save=False):
self.sim_ids = sim_ids
self.post_dirs = post_dirs
# 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)
# 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))
# 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)
return df_stats
def select_extremes_blade_radial(self, df):
"""
For each radial position of the blade, find the extremes
"""
def selector(x):
"""
select following channels:
'local-blade%1i-node-%03i-momentvec-x'
'blade2-blade2-node-003-momentvec-y'
"""
if x[:11] == 'local-blade' and x[22:31] == 'momentvec':
return True
else:
return False
# find all blade local load channels
criteria = df.channel.map(lambda x: selector(x))
df = df[criteria]
# split channel columns so we can select channels properly
df = df.join(df.channel.apply(lambda x: pd.Series(x.split('-'))))
df_ext = {'dlc':[], 'case':[], 'node':[], 'max':[], 'min':[], 'comp':[]}
def fillvalues(x, ii, maxmin):
x['node'].append(m_group[3].ix[ii])
x['dlc'].append(m_group['[DLC]'].ix[ii])
x['case'].append(m_group['[case_id]'].ix[ii])
x['comp'].append(m_group[5].ix[ii])
if maxmin == 'max':
x['max'].append(m_group['max'].ix[ii])
x['min'].append(np.nan)
else:
x['max'].append(np.nan)
x['min'].append(m_group['min'].ix[ii])
return x
# we take blade1, blade2, and blade3
df_b2 = df[df[0]=='local']
# df_b2 = df_b2[df_b2[1]=='blade2']
df_b2 = df_b2[df_b2[4]=='momentvec']
# df_b2 = df_b2[df_b2[5]=='x']
# make sure we only have blade1, 2 and 3
assert set(df_b2[1].unique()) == set(['blade3', 'blade2', 'blade1'])
# # number of nodes
# nrnodes = len(df_b2[3].unique())
# group by node number, and take the max
for nodenr, group in df_b2.groupby(df_b2[3]):
print(nodenr, end=' ')
for comp, m_group in df_b2.groupby(group[5]):
print(comp)
imax = m_group['max'].argmax()
imin = m_group['min'].argmin()
df_ext = fillvalues(df_ext, imax, 'max')
df_ext = fillvalues(df_ext, imin, 'min')
df_ext = pd.DataFrame(df_ext)
df_ext.sort(columns='node', inplace=True)
return df_ext
def plot_extremes_blade_radial(self, df_ext, fpath):
nrows = 2
ncols = 2
figsize = (11,7.15)
fig, axes = mplutils.make_fig(nrows=nrows, ncols=ncols, figsize=figsize)
# self.col = ['r', 'k']
# self.alf = [1.0, 0.7]
# self.i = 0
mx_max = df_ext['max'][df_ext.comp == 'x'].dropna()
mx_min = df_ext['min'][df_ext.comp == 'x'].dropna()
my_max = df_ext['max'][df_ext.comp == 'y'].dropna()
my_min = df_ext['min'][df_ext.comp == 'y'].dropna()
# nodes = df_ext.node.ix[mx_max.index]
axes[0,0].plot(mx_max, 'r^', label='$M_{x_{max}}$')
axes[0,1].plot(mx_min, 'bv', label='$M_{x_{min}}$')
axes[1,0].plot(my_max, 'r^', label='$M_{y_{max}}$')
axes[1,1].plot(my_min, 'bv', label='$M_{y_{min}}$')
axs = axes.ravel()
for ax in axs:
ax.grid()
ax.legend(loc='best')
# axs[0].set_xticklabels([])
# axs[1].set_xticklabels([])
# axs[2].set_xticklabels([])
# axs[-1].set_xlabel('time [s]')
fig.tight_layout()
fig.subplots_adjust(hspace=0.06)
fig.subplots_adjust(top=0.98)
fdir = os.path.dirname(fpath)
# fname = os.path.basename(fpath)
if not os.path.exists(fdir):
os.makedirs(fdir)
print('saving: %s ...' % fpath, end='')
fig.savefig(fpath)#.encode('latin-1')
print('done')
fig.clear()
# save as tables
df_ext.ix[mx_max.index].to_excel(fpath.replace('.png', '_mx_max.xls'))
df_ext.ix[mx_min.index].to_excel(fpath.replace('.png', '_mx_min.xls'))
df_ext.ix[my_max.index].to_excel(fpath.replace('.png', '_my_max.xls'))
df_ext.ix[my_min.index].to_excel(fpath.replace('.png', '_my_min.xls'))
def extract_leq_blade_radial(self, df_leq, fpath):
def selector(x):
"""
select following channels:
'local-blade%1i-node-%03i-momentvec-x'
'blade2-blade2-node-003-momentvec-y'
"""
if x[:11] == 'local-blade' and x[22:31] == 'momentvec':
return True
else:
return False
# find all blade local load channels
criteria = df_leq.channel.map(lambda x: selector(x))
df = df_leq[criteria]
# split channel columns so we can select channels properly
df = df.join(df.channel.apply(lambda x: pd.Series(x.split('-'))))
df.sort(columns=3, inplace=True)
assert set(df[1].unique()) == set(['blade3', 'blade2', 'blade1'])
leqs = list(df.keys())[1:10]
df_ext = {leq:[] for leq in leqs}
df_ext['node'] = []
df_ext['comp'] = []
for nodenr, group in df.groupby(df[3]):
print(nodenr, end=' ')
for comp, m_group in df.groupby(group[5]):
print(comp)
for leq in leqs:
df_ext[leq].append(m_group[leq].max())
df_ext['node'].append(nodenr)
df_ext['comp'].append(comp)
df_ext = pd.DataFrame(df_ext)
df_ext.sort(columns='node', inplace=True)
df_ext[df_ext.comp=='x'].to_excel(fpath.replace('.xls', '_x.xls'))
df_ext[df_ext.comp=='y'].to_excel(fpath.replace('.xls', '_y.xls'))
df_ext[df_ext.comp=='z'].to_excel(fpath.replace('.xls', '_z.xls'))
return df_ext
class PlotPerf(object):
def __init__(self, nrows=4, ncols=1, figsize=(14,11)):
self.fig, self.axes = mplutils.make_fig(nrows=nrows, ncols=ncols,
figsize=figsize)
# self.axs = self.axes.ravel()
self.col = ['r', 'k']
self.alf = [1.0, 0.7]
self.i = 0
def plot(self, res, label_id):
"""
"""
i = self.i
sim_id = label_id
time = res.sig[:,0]
self.t0, self.t1 = time[0], time[-1]
# find the wind speed
for channame, chan in res.ch_dict.items():
if channame.startswith('windspeed-global-Vy-0.00-0.00'):
break
wind = res.sig[:,chan['chi']]
chi = res.ch_dict['bearing-shaft_rot-angle_speed-rpm']['chi']
rpm = res.sig[:,chi]
chi = res.ch_dict['bearing-pitch1-angle-deg']['chi']
pitch = res.sig[:,chi]
chi = res.ch_dict['tower-tower-node-001-momentvec-x']['chi']
tx = res.sig[:,chi]
chi = res.ch_dict['tower-tower-node-001-momentvec-y']['chi']
ty = res.sig[:,chi]
chi = res.ch_dict['DLL-2-inpvec-2']['chi']
power = res.sig[:,chi]
try:
chi = res.ch_dict['Tors_e-1-100.11']['chi']
except KeyError:
chi = res.ch_dict['Tors_e-1-86.50']['chi']
tors_1 = res.sig[:,chi]
# try:
# chi = res.ch_dict['Tors_e-1-96.22']['chi']
# except:
# chi = res.ch_dict['Tors_e-1-83.13']['chi']
# tors_2 = res.sig[:,chi]
try:
chi = res.ch_dict['Tors_e-1-84.53']['chi']
except:
chi = res.ch_dict['Tors_e-1-73.21']['chi']
tors_3 = res.sig[:,chi]
ax = self.axes.ravel()
ax[0].plot(time, wind, self.col[i]+'--', label='%s wind speed' % sim_id,
alpha=self.alf[i])
ax[0].plot(time, pitch, self.col[i]+'-.', label='%s pitch' % sim_id,
alpha=self.alf[i])
ax[0].plot(time, rpm, self.col[i]+'-', label='%s RPM' % sim_id,
alpha=self.alf[i])
ax[1].plot(time, tx, self.col[i]+'--', label='%s Tower FA' % sim_id,
alpha=self.alf[i])
ax[1].plot(time, ty, self.col[i]+'-', label='%s Tower SS' % sim_id,
alpha=self.alf[i])
ax[2].plot(time, power/1e6, self.col[i]+'-', alpha=self.alf[i],
label='%s El Power' % sim_id)
ax[3].plot(time, tors_1, self.col[i]+'--', label='%s torsion tip' % sim_id,
alpha=self.alf[i])
# ax[3].plot(time, tors_2, self.col[i]+'-.', label='%s torsion 96 pc' % sim_id,
# alpha=self.alf[i])
# ax[3].plot(time, tors_3, self.col[i]+'-', label='%s torsion 84 pc' % sim_id,
# alpha=self.alf[i])
self.i += 1
def final(self, fig_path, fig_name):
axs = self.axes.ravel()
for ax in axs:
ax.set_xlim([self.t0, self.t1])
ax.grid()
ax.legend(loc='best')
axs[0].set_xticklabels([])
axs[1].set_xticklabels([])
axs[2].set_xticklabels([])
axs[-1].set_xlabel('time [s]')
self.fig.tight_layout()
self.fig.subplots_adjust(hspace=0.06)
self.fig.subplots_adjust(top=0.98)
if not os.path.exists(fig_path):
os.makedirs(fig_path)
fname = os.path.join(fig_path, fig_name)
print('saving: %s ...' % fname, end='')
self.fig.savefig(fname)#.encode('latin-1')
print('done')
self.fig.clear()
def plot_dlc01_powercurve(sim_ids, post_dirs, run_dirs, fig_dir_base):
"""
Create power curve based on steady DLC01 results
Use the same format as for HS2 for easy comparison!
"""
def plot_dlc00(sim_ids, post_dirs, run_dirs, fig_dir_base=None, labels=None,
cnames=['dlc00_stair_wsp04_25_noturb.htc',
'dlc00_ramp_wsp04_25_04_noturb.htc'], figsize=(14,11)):
"""
This version is an update over plot_staircase.
"""
stairs = []
# if sim_id is a list, combine the two dataframes into one
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
stairs.append(sim.Cases(post_dir, sim_id, rem_failed=True))
else:
post_dir = post_dirs
stairs.append(sim.Cases(post_dir, sim_id, rem_failed=True))
for cname in cnames:
fp = PlotPerf(figsize=figsize)
for i, cc in enumerate(stairs):
if isinstance(cname, list):
_cname = cname[i]
else:
_cname = cname
if _cname in cc.cases_fail:
print('no result for %s' % cc.sim_id)
continue
cc.change_results_dir(run_dirs[i])
try:
res = cc.load_result_file(cc.cases[_cname])
except KeyError:
for k in sorted(cc.cases.keys()):
print(k)
print('-'*79)
print(cc.sim_id, _cname)
print('-'*79)
raise KeyError
if labels is not None:
label = labels[i]
else:
label = cc.sim_id
fp.plot(res, label)
dlcf = 'dlc' + cc.cases[_cname]['[DLC]']
fig_path = os.path.join(fig_dir_base, dlcf)
fp.final(fig_path, _cname.replace('.htc', '.png'))
def plot_staircase(sim_ids, post_dirs, run_dirs, fig_dir_base=None,
cname='dlc00_stair_wsp04_25_noturb.htc'):
"""
Default stair and ramp names:
dlc00_stair_wsp04_25_noturb
dlc00_ramp_wsp04_25_04_noturb
"""
stairs = []
col = ['r', 'k']
alf = [1.0, 0.7]
# if sim_id is a list, combine the two dataframes into one
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
stairs.append(sim.Cases(post_dir, sim_id, rem_failed=True))
else:
sim_id = sim_ids
sim_ids = [sim_id]
post_dir = post_dirs
stairs.append(sim.Cases(post_dir, sim_id, rem_failed=True))
fig, axes = mplutils.make_fig(nrows=3, ncols=1, figsize=(14,10))
ax = axes.ravel()
for i, cc in enumerate(stairs):
if cname in cc.cases_fail:
print('no result for %s' % cc.sim_id)
continue
cc.change_results_dir(run_dirs[i])
res = cc.load_result_file(cc.cases[cname])
respath = cc.cases[cname]['[run_dir]']
fname = os.path.join(respath, cname)
df_respost = pd.read_hdf(fname + '_postres.h5', 'table')
sim_id = cc.sim_id
time = res.sig[:,0]
t0, t1 = time[0], time[-1]
# find the wind speed
for channame, chan in res.ch_dict.items():
if channame.startswith('windspeed-global-Vy-0.00-0.00'):
break
wind = res.sig[:,chan['chi']]
chi = res.ch_dict['bearing-pitch1-angle-deg']['chi']
pitch = res.sig[:,chi]
chi = res.ch_dict['bearing-shaft_rot-angle_speed-rpm']['chi']
rpm = res.sig[:,chi]
chi = res.ch_dict['bearing-pitch1-angle-deg']['chi']
pitch = res.sig[:,chi]
chi = res.ch_dict['tower-tower-node-001-momentvec-x']['chi']
tx = res.sig[:,chi]
chi = res.ch_dict['tower-tower-node-001-momentvec-y']['chi']
ty = res.sig[:,chi]
chi = res.ch_dict['DLL-2-inpvec-2']['chi']
power = res.sig[:,chi]
chi = res.ch_dict['DLL-2-inpvec-2']['chi']
power_mech = df_respost['stats-shaft-power']
ax[0].plot(time, wind, col[i]+'--', label='%s wind speed' % sim_id,
alpha=alf[i])
ax[0].plot(time, pitch, col[i]+'-.', label='%s pitch' % sim_id,
alpha=alf[i])
ax[0].plot(time, rpm, col[i]+'-', label='%s RPM' % sim_id,
alpha=alf[i])
ax[1].plot(time, tx, col[i]+'--', label='%s Tower FA' % sim_id,
alpha=alf[i])
ax[1].plot(time, ty, col[i]+'-', label='%s Tower SS' % sim_id,
alpha=alf[i])
ax[2].plot(time, power/1e6, col[i]+'-', label='%s El Power' % sim_id,
alpha=alf[i])
ax[2].plot(time, power_mech/1e3, col[i]+'-', alpha=alf[i],
label='%s Mech Power' % sim_id)
ax[0].set_xlim([t0, t1])
ax[0].grid()
ax[0].legend(loc='best')
ax[0].set_xticklabels([])
# ax[0].set_xlabel('time [s]')
ax[1].set_xlim([t0, t1])
ax[1].grid()
ax[1].legend(loc='best')
ax[1].set_xticklabels([])
# ax[1].set_xlabel('time [s]')
ax[2].set_xlim([t0, t1])
ax[2].grid()
ax[2].legend(loc='best')
ax[2].set_xlabel('time [s]')
fig.tight_layout()
fig.subplots_adjust(hspace=0.06)
fig.subplots_adjust(top=0.92)
if not os.path.exists(fig_dir_base):
os.makedirs(fig_dir_base)
fig_path = os.path.join(fig_dir_base, '-'.join(sim_ids) + '_stair.png')
print('saving: %s ...' % fig_path, end='')
fig.savefig(fig_path)#.encode('latin-1')
print('done')
fig.clear()
if __name__ == '__main__':
# auto configure directories: assume you are running in the root of the
# relevant HAWC2 model
# and assume we are in a simulation case of a certain turbine/project
P_RUN, P_SOURCE, PROJECT, sim_id, P_MASTERFILE, MASTERFILE, POST_DIR \
= dlcdefs.configure_dirs()
# -------------------------------------------------------------------------
# # manually configure all the dirs
# p_root_remote = '/mnt/hawc2sim'
# p_root_local = '/home/dave/DTU/Projects/AVATAR/'
# # project name, sim_id, master file name
# PROJECT = 'DTU10MW'
# sim_id = 'C0014'
# MASTERFILE = 'dtu10mw_master_C0014.htc'
# # MODEL SOURCES, exchanche file sources
# P_RUN = os.path.join(p_root_remote, PROJECT, sim_id+'/')
# P_SOURCE = os.path.join(p_root_local, PROJECT)
# # location of the master file
# P_MASTERFILE = os.path.join(p_root_local, PROJECT, 'htc', '_master/')
# # location of the pre and post processing data
# POST_DIR = os.path.join(p_root_remote, PROJECT, 'python-prepost-data/')
# force_dir = P_RUN
# -------------------------------------------------------------------------
# PLOT STATS, when comparing cases
sim_ids = [sim_id]
run_dirs = [P_RUN]
figdir = os.path.join(P_RUN, '..', 'dlcplots/%s' % sim_id)
print('='*79)
print(' P_RUN: %s' % P_RUN)
print('P_SOURCE: %s' % P_SOURCE)
print(' PROJECT: %s' % PROJECT)
print(' sim_id: %s' % sim_id)
print(' master: %s' % MASTERFILE)
print(' figdir: %s' % figdir)
print('='*79)
plot_stats2(sim_ids, POST_DIR, fig_dir_base=figdir)
plot_dlc00(sim_ids, POST_DIR, run_dirs, fig_dir_base=figdir)