def design_comparison(self, filenames):
'''
Compare design runs
Parameters:
----------
filenames: list
list of lists, where the inner lists are of equal length.
Returns:
--------
stats: dict
dictionary of summary statistics data
load_rankings: dict
dictionary of load rankings
'''
# Make sure datasets are the same length
ds_len = len(filenames[0])
if any(len(dataset) != ds_len for dataset in filenames):
raise ValueError(
'The datasets for filenames corresponding to the design comparison should all be the same size.'
)
fnames = np.array(filenames).T.tolist()
# Setup FAST_Analysis preferences
loads_analysis = Analysis.Loads_Analysis()
loads_analysis.verbose = self.verbose
loads_analysis.t0 = self.t0
loads_analysis.tf = self.tf
loads_analysis.ranking_vars = self.ranking_vars
loads_analysis.ranking_stats = self.ranking_stats
loads_analysis.DEL_info = self.DEL_info
if self.parallel_analysis: # run analysis in parallel
# run analysis
pool = mp.Pool(self.parallel_cores)
stats_separate = pool.map(
partial(loads_analysis.full_loads_analysis,
get_load_ranking=False), fnames)
pool.close()
pool.join()
# Re-sort into the more "standard" dictionary/dataframe format we like
stats = [pdTools.dict2df(ss).unstack() for ss in stats_separate]
dft = pd.DataFrame(stats)
dft = dft.reorder_levels([2, 0, 1], axis=1).sort_index(axis=1,
level=0)
stats = pdTools.df2dict(dft)
# Get load rankings after stats are loaded
load_rankings = loads_analysis.load_ranking(
stats, names=self.dataset_names)
else: # run analysis in serial
stats = []
load_rankings = []
for file_sets in filenames:
st, lr = loads_analysis.full_loads_analysis(
file_sets, get_load_ranking=True, names=self.dataset_names)
stats.append(st)
load_rankings.append(lr)
return stats, load_rankings
def load_ranking(self, stats, names=[], get_df=False):
'''
Find load rankings for desired signals
Parameters:
-------
stats: dict, list, pd.DataFrame
summary statistic information
ranking_stats: list
desired statistics to rank for load ranking (e.g. ['max', 'std'])
ranking_vars: list
desired variables to for load ranking (e.g. ['GenTq', ['RootMyb1', 'RootMyb2', 'RootMyb3']])
names: list of strings, optional
names corresponding to each dataset
get_df: bool, optional
Return pd.DataFrame of data?
Returns:
-------
load_ranking: dict
dictionary containing load rankings
load_ranking_df: pd.DataFrame
pandas DataFrame containing load rankings
'''
# Make sure stats is in pandas df
if isinstance(stats, dict):
stats_df = pdTools.dict2df([stats], names=names)
elif isinstance(stats, list):
stats_df = pdTools.dict2df(stats, names=names)
elif isinstance(stats, pd.DataFrame):
stats_df = stats
else:
raise TypeError('Input stats is must be a dictionary, list, or pd.DataFrame containing OpenFAST output statistics.')
# Ensure naming consitency
if not names:
names = list(stats_df.columns.levels[0])
if self.verbose:
print('Calculating load rankings.')
# Column names to search in stats_df
# - [name, variable, stat], i.e.['DLC1.1','TwrBsFxt','max']
cnames = [pd.MultiIndex.from_product([names, var, [stat]])
for var, stat in zip(self.ranking_vars, self.ranking_stats)]
rank__ascending = False
# Collect load rankings
collected_rankings = []
for col in cnames:
# Set column names for dataframe
mi_name = list(col.levels[0])
mi_stat = col.levels[2] # length = 1
mi_idx = col.levels[2][0] + '_case_idx'
if len(col.levels[1]) > 1:
mi_var = [col.levels[1][0][:-1]]
else:
mi_var = list(col.levels[1])
mi_colnames = pd.MultiIndex.from_product([mi_name, mi_var, [mi_idx, mi_stat[0]]])
# Check for valid stats
for c in col:
if c not in list(stats_df.columns.values):
print('WARNING: {} does not exist in statistics.'.format(c))
col = col.drop(c)
# raise ValueError('{} does not exist in statistics'.format(c))
# Go to next case if no [stat, var] exists in this set
if len(col) == 0:
continue
# Extract desired variables from stats dataframe
if mi_stat in ['max', 'abs']:
var_df = stats_df[col].max(axis=1, level=0)
rank__ascending = False
elif mi_stat in ['min']:
var_df = stats_df[col].min(axis=1, level=0)
rank__ascending = True
elif mi_stat in ['mean', 'std']:
var_df = stats_df[col].mean(axis=1, level=0)
rank__ascending = False
# Combine ranking dataframes for each dataset
var_df_list = [var_df[column].sort_values(
ascending=rank__ascending).reset_index() for column in var_df.columns]
single_lr = pd.concat(var_df_list, axis=1)
single_lr.columns = mi_colnames
collected_rankings.append(single_lr)
# Combine dataframes for each case
load_ranking_df = pd.concat(collected_rankings, axis=1).sort_index(axis=1)
# Generate dict of info
load_ranking = pdTools.df2dict(load_ranking_df)
if get_df:
return load_ranking, load_ranking_df
else:
return load_ranking
def batch_processing(self):
'''
Run a full batch processing case!
'''
# ------------------ Input consistancy checks ------------------ #
# Do we have a list of data?
N = len(self.OpenFAST_outfile_list)
if N == 0:
raise ValueError(
'Output files not defined! Populate: "FastPost.OpenFAST_outfile_list". \n Quitting FAST_Processing.'
)
# Do all the files exist?
files_exist = True
for i, flist in enumerate(self.OpenFAST_outfile_list):
if isinstance(flist, str):
if not os.path.exists(flist):
print('Warning! File "{}" does not exist.'.format(flist))
self.OpenFAST_outfile_list.remove(flist)
elif isinstance(flist, list):
for fname in flist:
if not os.path.exists(fname):
files_exist = False
if len(self.dataset_names) > 0:
print('Warning! File "{}" from {} does not exist.'.
format(fname, self.dataset_names[i]))
flist.remove(fname)
else:
print(
'Warning! File "{}" from dataset {} of {} does not exist.'
.format(fname, i + 1, N))
flist.remove(fname)
# # load case matrix data to get descriptive case naming
# if self.fname_case_matrix == '':
# print('Warning! No case matrix file provided, no case descriptions will be provided.')
# self.case_desc = ['Case ID %d' % i for i in range(M)]
# else:
# cases = load_case_matrix(self.fname_case_matrix)
# self.case_desc = get_dlc_label(cases, include_seed=True)
# get unique file namebase for datasets
self.namebase = []
if len(self.dataset_names) > 0:
# use filename safe version of dataset names
self.namebase = [
"".join([
c for c in name
if c.isalpha() or c.isdigit() or c in ['_', '-']
]).rstrip() for i, name in zip(range(N), self.dataset_names)
]
elif len(self.OpenFAST_outfile_list) > 0:
# use out file naming
if isinstance(self.OpenFAST_outfile_list[0], list):
self.namebase = [
'_'.join(os.path.split(flist[0])[1].split('_')[:-1])
for flist in self.OpenFAST_outfile_list
]
else:
self.namebase = [
'_'.join(os.path.split(flist)[1].split('_')[:-1])
for flist in self.OpenFAST_outfile_list
]
# check that names are unique
if not len(self.namebase) == len(set(self.namebase)):
self.namebase = []
# as last resort, give generic name
if not self.namebase:
if isinstance(self.OpenFAST_outfile_list[0], str):
# Just one dataset name for single dataset
self.namebase = ['dataset1']
else:
self.namebase = [
'dataset' + ('{}'.format(i)).zfill(len(str(N - 1)))
for i in range(N)
]
# Run design comparison if filenames list has multiple lists
if (len(self.OpenFAST_outfile_list) > 1) and (isinstance(
self.OpenFAST_outfile_list[0], list)):
# Load stats and load rankings for design comparisons
stats, load_rankings = self.design_comparison(
self.OpenFAST_outfile_list)
else:
# Initialize Analysis
loads_analysis = Analysis.Loads_Analysis()
loads_analysis.verbose = self.verbose
loads_analysis.t0 = self.t0
loads_analysis.tf = self.tf
loads_analysis.DEL_info = self.DEL_info
loads_analysis.ranking_stats = self.ranking_stats
loads_analysis.ranking_vars = self.ranking_vars
# run analysis in parallel
if self.parallel_analysis:
pool = mp.Pool(self.parallel_cores)
try:
stats_separate = pool.map(
partial(loads_analysis.full_loads_analysis,
get_load_ranking=False),
self.OpenFAST_outfile_list)
except:
stats_separate = pool.map(
partial(loads_analysis.full_loads_analysis,
get_load_ranking=False),
self.OpenFAST_outfile_list[0])
pool.close()
pool.join()
# Re-sort into the more "standard" dictionary/dataframe format we like
stats = [
pdTools.dict2df(ss).unstack() for ss in stats_separate
]
dft = pd.DataFrame(stats)
dft = dft.reorder_levels([2, 0, 1], axis=1).sort_index(axis=1,
level=0)
stats = pdTools.df2dict(dft)
# Get load rankings after stats are loaded
load_rankings = loads_analysis.load_ranking(
stats, names=self.dataset_names, get_df=False)
# run analysis in serial
else:
# Initialize Analysis
loads_analysis = Analysis.Loads_Analysis()
loads_analysis.verbose = self.verbose
loads_analysis.t0 = self.t0
loads_analysis.tf = self.tf
loads_analysis.ranking_stats = self.ranking_stats
loads_analysis.ranking_vars = self.ranking_vars
loads_analysis.DEL_info = self.DEL_info
stats, load_rankings = loads_analysis.full_loads_analysis(
self.OpenFAST_outfile_list, get_load_ranking=True)
if self.save_SummaryStats:
if isinstance(stats, dict):
fname = self.namebase[0] + '_stats.yaml'
if self.verbose:
print('Saving {}'.format(fname))
save_yaml(self.results_dir, fname, stats)
else:
for namebase, st in zip(self.namebase, stats):
fname = namebase + '_stats.yaml'
if self.verbose:
print('Saving {}'.format(fname))
save_yaml(self.results_dir, fname, st)
if self.save_LoadRanking:
if isinstance(load_rankings, dict):
fname = self.namebase[0] + '_LoadRanking.yaml'
if self.verbose:
print('Saving {}'.format(fname))
save_yaml(self.results_dir, fname, load_rankings)
else:
for namebase, lr in zip(self.namebase, load_rankings):
fname = namebase + '_LoadRanking.yaml'
if self.verbose:
print('Saving {}'.format(fname))
save_yaml(self.results_dir, fname, lr)
return stats, load_rankings
