def emissions(df_exp, df_ref, test_cfg, filename_student_test=''):
'''
Perform emissions test for each variable of dataframe df_b
:param df_a: reference datframe, containing big sample
:param df_b: datframe containing data to test
:param filename_student_test: filename for writing
result of t-test result into a csv file
:return: result of the emissions test in a dataframe
'''
row_list_df = []
# select base reference for emission
df_ref = df_ref.loc[df_ref['exp'] == 'emis_base_ref']
for var in df_exp.keys():
if 'exp' in var:
continue
log.debug("Emissions test for {}".format(var))
abs_deviation = abs(df_exp[var].iloc[0] - df_ref[var].iloc[0])
rel_deviation = abs_deviation / df_ref[var].iloc[0] * 100
# append results for construction datframe df_result
dict1 = {'variable': var, test_cfg.metric: rel_deviation}
row_list_df.append(dict1)
# construction dataframe
df_result = pd.DataFrame(row_list_df,
columns=['variable', test_cfg.metric])
# sort per p value
df_result.sort_values(by=[test_cfg.metric], inplace=True)
return (df_result)
def download_ref_to_stages_if_required(f_pattern_ref, p_stages,
f_vars_to_extract, test):
# no ref-file passed as argument of process_data
if f_pattern_ref == paths.rootdir:
log.info('Download reference file from ftp-server')
filename_ftp_link = f_vars_to_extract.replace('.csv', '.txt').replace(
'vars_', 'ftp_')
path_to_ftp_link = os.path.join(paths.p_f_vars_proc, test)
file_with_ftp_link = utils.clean_path(path_to_ftp_link,
filename_ftp_link)
output_file = os.path.join(p_stages, 'ftp_ref_pattern.nc')
cmd = ('wget --input-file={} '
'--output-document={}'.format(file_with_ftp_link, output_file))
log.debug('ftp-command: {}'.format(cmd))
utils.shell_cmd(cmd, py_routine=__name__)
f_pattern_ref = output_file
else:
log.info('Using user-defined reference file for test '
'{}'.format(test))
return f_pattern_ref
def rmse(df_exp, test_cfg):
'''
Perform rmse test for each variable fo dataframe df_b
:param df_a: reference datframe, containing big sample
:param df_b: datframe containing data to test
:return: result of the pattern correlation in a dataframe
'''
row_list_df = []
for var in df_exp.keys():
if 'exp' in var:
continue
log.debug("Rmse test for {}".format(var))
# append results for construction datframe df_result
dict1 = {'variable': var, test_cfg.metric: df_exp[var].iloc[0]}
row_list_df.append(dict1)
# construction dataframe
df_result = pd.DataFrame(row_list_df,
columns=['variable', test_cfg.metric])
df_result.sort_values(by=[test_cfg.metric], inplace=True)
return (df_result)
def determine_actions_for_data_processing(exp, tests, p_stages, lforce):
actions = {'standard_postproc': {}, 'test_postproc': {}}
if lforce:
log.warning('Redo all processing steps')
# see if standard-postprocessing is needed
for test in tests:
standard_proc_nc = os.path.join(
p_stages, 'standard_postproc_{}_{}.nc'.format(test, exp))
if (not os.path.isfile(standard_proc_nc) or lforce):
action_needed = True
else:
action_needed = False
actions['standard_postproc'][test] = action_needed
test_specific_csv = os.path.join(
p_stages, 'test_postproc_{}_{}.csv'.format(test, exp))
if (not os.path.isfile(test_specific_csv) or lforce
or actions['standard_postproc'][test]):
action_needed = True
else:
action_needed = False
actions['test_postproc'][test] = action_needed
log.debug('actions: {}'.format(actions))
return (actions)
def shell_cmd(cmd, py_routine, lowarn=False):
"""
Send shell command through subprocess.Popen and returns a string
containing the cmd output
lowarn = True -> only a warning is written, no exit (To use with caution!)
"""
# send cmd to be executed
p = subprocess.Popen(cmd,
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
universal_newlines=True)
# gets the output of the cmd
out, err = p.communicate()
# initailisation output status
out_status = 0
# check if cmd was executed properly
if p.returncode != 0:
log.debug("{} (shell_cmd): ERROR in the command: \n {}".format(
py_routine, cmd))
if lowarn:
log.warning("Shell command failed, but explicitly "
"keep program alive: \n {}".format(err))
out_status = 1
else:
log.error("Error returned: {}".format(err))
return (out_status, str(out))
def timeser_proc_nc_to_df(exp, filename, p_stages, already_a_timeseries=False):
'''
Arguments:
exp = experiment name
filename = filename of the netCDF returned by function standard_postproc
p_stages = directory where processing steps are stored
returns:
dataframe with processed data for welchstest
'''
test = 'welch'
if not already_a_timeseries:
timeser_filename = 'test_postproc_{}_{}.nc'.format(test, exp)
cdo_cmd = 'cdo -L yearmean -fldmean -vertsum {} {}'.format(
filename, timeser_filename)
utils.shell_cmd(cdo_cmd, py_routine=__name__)
else:
log.debug('Skipping CDO-processing step')
timeser_filename = filename
# list of variables in the timeserie netcdf
# file to drop (not to put into the dataframe)
vars_to_drop = []
log.info('Processing netCDF: {}'.format(timeser_filename))
# open dataset
data = xr.open_dataset(timeser_filename)
# Delete variables
# useless variable time_bnds
if ('time_bnds' in data.keys()):
data = data.drop('time_bnds')
# 3D vars
if len(vars_to_drop) > 0:
data.drop(labels=vars_to_drop)
# removed degenerated dimensions
data = data.squeeze(drop=True)
# transforms into dataframe
df_data = data.to_dataframe()
# export in a file
os.makedirs(p_stages, exist_ok=True)
csv_filename = os.path.join(p_stages,
'test_postproc_{}_{}.csv'.format(test, exp))
df_data.to_csv(csv_filename, index=None, header=True, sep=';')
log.info('CSV file can be found here: {}'.format(csv_filename))
log.info('Finished {} for file {}'.format(__name__, timeser_filename))
return (df_data)
def normalize_data(dataset):
log.info('Normalize fields in {} with mean and '
'standard deviation'.format(dataset))
data = dataset.replace('.nc', '')
std_data = '{}_std.nc'.format(data)
std_data_enlarged = '{}_std_enlarged.nc'.format(data)
mean_data = '{}_mean.nc'.format(data)
mean_data_enlarged = '{}_enlarged.nc'.format(data)
sub_data = '{}_sub.nc'.format(data)
normalized_data = '{}_normalized.nc'.format(data)
log.debug('Clean intermediate files for normalization')
shell_cmd = 'rm {} {} {} {} {} {}'.format(std_data, mean_data,
std_data_enlarged,
mean_data_enlarged, sub_data,
normalized_data)
utils.shell_cmd(shell_cmd, py_routine=__name__, lowarn=True)
cdo_cmd = 'cdo -L fldstd {} {}'.format(dataset, std_data)
utils.shell_cmd(cdo_cmd, py_routine=__name__)
cdo_cmd = 'cdo -L fldmean {} {}'.format(dataset, mean_data)
utils.shell_cmd(cdo_cmd, py_routine=__name__)
#cdo_cmd = 'cdo -L sub {} -enlarge,{} {} {}'.format(dataset,
cdo_cmd = 'cdo -L -enlarge,{} {} {}'.format(dataset, mean_data,
mean_data_enlarged)
utils.shell_cmd(cdo_cmd, py_routine=__name__)
cdo_cmd = 'cdo -L -enlarge,{} {} {}'.format(dataset, std_data,
std_data_enlarged)
utils.shell_cmd(cdo_cmd, py_routine=__name__)
cdo_cmd = 'cdo -L sub {} {} {}'.format(dataset, mean_data_enlarged,
sub_data)
utils.shell_cmd(cdo_cmd, py_routine=__name__)
cdo_cmd = 'cdo -L div {} {} {}'.format(sub_data, std_data_enlarged,
normalized_data)
utils.shell_cmd(cdo_cmd, py_routine=__name__)
return normalized_data
def welch_test(df_a, df_b, filename_student_test=''):
'''
Perform Welch t-test for each variable fo dataframe df_b
:param df_a: reference datframe, containing big sample
:param df_b: datframe containing data to test
:param filename_student_test: filename for writing result
of t-test result into a csv file
:return: result of the student test in a dataframe
'''
row_list_df = []
for var in df_b.keys():
if 'exp' in var:
continue
log.debug("Welch's t-test for {}".format(var))
# Welch's t-test
t, p = stats.ttest_ind(df_a[var],
df_b[var],
equal_var=False,
nan_policy='omit')
# append results for construction datframe df_result
dict1 = {'variable': var, 't-value': t, 'p-value': p}
row_list_df.append(dict1)
# construction dataframe
df_result = pd.DataFrame(row_list_df,
columns=['variable', 't-value', 'p-value'])
# sort per p value
df_result.sort_values(by=['p-value'], inplace=True)
# if a filename is given, write the student-stest
# result into the file named filename_student_test
if len(filename_student_test) > 0:
log.info('Write result to {}'.format(filename_student_test))
df_result.to_csv(filename_student_test, sep=',')
return (df_result)
def plt_welchstest(df_tot, new_exp, df_result, p_stages=paths.p_stages):
'''
:param df_tot: Dataframe containing containing all
global annual mean (reference & new_exp)
:param new_exp: Name of the new exp which is analysed
:param df_result: Dataframe containing the results of the Welch's test
:param p_stages : path to save the figures
:return: None, but the figure is saved in p_stages
'''
# simple statistics, sort by exp
# to be sure the order is the same in both dataframe
df_tot_mean = df_tot.groupby(['exp']).mean()\
.sort_values(['exp']).reset_index()
# for std, the panda std has a bug
# cf https://github.com/pandas-dev/pandas/issues/16799
df_tot_std = df_tot.groupby(['exp']).std()\
.sort_values(['exp']).reset_index()
# ensure new exp to be the last line
iexp = df_tot_mean.index[df_tot_mean['exp'] == new_exp]
new_order = df_tot_mean.index.drop(iexp).append(iexp)
df_tot_mean = df_tot_mean.reindex(new_order)
df_tot_std = df_tot_std.reindex(new_order)
# number col/rows per page
nlin = 3
ncol = 3
nplot = nlin * ncol
# needed for multipage pdf file
filename_mean_std_figures = 'glob_means_{}.pdf'.format((new_exp))
p_pdf_file_var = os.path.join(p_stages,filename_mean_std_figures)
pp = PdfPages(p_pdf_file_var)
# loop over all variables
for ivar,var in enumerate(df_result.variable):
log.debug('Create plot for {}'.format(var))
# subplot preparation
# ------------------------------------------------------------------------
# number of plot
iplot = np.mod(ivar,nplot)
# set the plotting frame
if (iplot == 0):
fig, plt_nbr = plt.subplots(nlin,
ncol,
sharex='col',
figsize=(12, 12))
# subplot coordinate
icol = np.mod(iplot, ncol)
ilin = np.int(np.floor(iplot / ncol))
# actual plot
act_plt = plt_nbr[ilin, icol]
# x-axis
xaxis = np.arange(df_tot_mean.shape[0])
# plotting
# ---------------------------------------------------------------------------
nmisval = df_tot_mean[var].isna().sum()
i_newexp = len(xaxis) - nmisval - 1
# define colors
colors = len(xaxis) * ['k']
colors[i_newexp:len(xaxis)] = 'k'
# define thickness
thickness = len(xaxis) * [1.5]
thickness[i_newexp] = 3
# plot mean and std for each variable
act_plt.errorbar(xaxis, df_tot_mean[var], yerr=df_tot_std[var],
fmt='+k',ecolor=colors, elinewidth=thickness)
# plot average reference experiments (grey band)
m_ref = df_tot[df_tot.exp != new_exp][var].mean()
s_ref = df_tot[df_tot.exp != new_exp][var].std()
act_plt.axhline(m_ref, c='k')
act_plt.fill_between([-1, max(xaxis) - 0.5],
m_ref - s_ref, m_ref + s_ref,
facecolor='grey',alpha=0.6)
# plot color background
color_graph = df_result.loc[df_result.variable == var]['col-graph']\
.values[0]
act_plt.set_facecolor('{}'.format(color_graph))
# manage labels/titel/etc
# -------------------------------------------------------------------------------
# label settings
act_plt.xaxis.set_ticks(xaxis)
act_plt.set_xticklabels(df_tot_mean['exp'],rotation=90)
# title settings
pvalue = float(df_result[df_result.variable == var]['p-value'])
act_plt.set_title('{}, p-value = {:.2%}'.format(var,pvalue))
# Saving page & increase number var
# --------------------------------------------------------------------------------
# save full page
if (iplot == (nplot - 1)):
pp.savefig()
# save and close odf file file
fig.savefig(pp, format='pdf')
pp.close()
log.info('Detailed plots of mean and standard deviation per variable '
'can be found in the file {}'.format(p_pdf_file_var))
def main(exp,
tests,
p_stages=paths.p_stages,
p_ref_csv_files=paths.p_ref_csv_files,
ltestsuite=False,
lverbose=False):
# initialisation
new_branch_name = 'test_add_{}'.format(exp)
files_to_commit = []
# fill up file 'Exps_description.csv' with additional
# information via user input
f_exp_descr = os.path.join(p_ref_csv_files, 'Exps_description.csv')
if not ltestsuite:
add_line_descr_f(exp=exp, f_exp_descr=f_exp_descr)
files_to_commit.append(f_exp_descr)
for test in tests:
test_cfg = get_config_of_current_test(test)
csv_file = utils.clean_path(
p_stages, 'test_postproc_{}_{}.csv'.format(test, exp))
# what is the filename in the reference pool
filename_in_ref_dir = '{}_{}.csv'.format(test_cfg.ref_name, exp)
# what is the location to store that file
place_for_reference = os.path.join(p_ref_csv_files, test,
filename_in_ref_dir)
log.debug('Copy {} to {}'.format(csv_file, place_for_reference))
if not ltestsuite:
shutil.copy(csv_file, place_for_reference)
files_to_commit.append(place_for_reference)
# copy pdf with bar-plots from Welch's-test
if test == 'welch':
pdf_file = utils.clean_path(
p_stages, '{}_{}.pdf'.format(test_cfg.ref_name, exp))
# what is the name of the pdf in the reference pool
filename_in_ref_dir = '{}_plots.pdf'.format(test_cfg.ref_name)
# what is the location to store that file
place_for_reference = os.path.join(p_ref_csv_files, test,
filename_in_ref_dir)
log.debug('Copy {} to {}'.format(csv_file, place_for_reference))
files_to_commit.append(place_for_reference)
if not ltestsuite:
shutil.copy(pdf_file, place_for_reference)
# root is important to not fail during git commands
os.chdir(paths.rootdir)
# checkout new branch
if not ltestsuite:
log.info('Create and checkout new branch {}'.format(new_branch_name))
git_cmd = 'git checkout -B {}'.format(new_branch_name)
utils.shell_cmd(git_cmd, py_routine='add_exp_to_ref.py')
# commit all modified files prior in the function to git
for file in files_to_commit:
git_cmd = 'git add {}'.format(file)
log.debug(git_cmd)
utils.shell_cmd(git_cmd, py_routine=__name__)
log.debug('Commit files {}'.format(files_to_commit))
commit_message = input('Please type your commit message :')
git_cmd = 'git commit -m "{}"'.format(commit_message)
utils.shell_cmd(git_cmd, py_routine=__name__)
# Finish
log.info(
Style.GREEN(
'Files are added in the new branch: '
'{} in your local git repository.'.format(new_branch_name)))
log.info('To add the file to the official repository, '
'please perform the following steps:')
log.info('1. Push the new branch into the official repo:')
log.info(' git push --set-upstream origin {}'.format(new_branch_name))
log.info('2. On the Open Web interface (GitHub) , open a Pull Request.')
log.banner('End add_exp_to_ref for experiment {}'.format(exp))
return ()
def standard_postproc(exp, test, spinup, p_raw_files, raw_f_subfold, p_stages,
f_vars_to_extract):
'''
Perfom standard post-processing using cdo
Arguments:
exp = experiment name
test = name of current test to process data
spinup = number of files (from begining of simulation)
to ignore du to model spinup
p_raw_files = path to raw model output
raw_f_subfold = subfolder in p_raw_files with model output
[p_raw_files]/[raw_f_subfold]
p_stages = directory where processing steps are stored
f_vars_to_extract = csv file containg the variables to proceed
returns:
netCDF filename containing the fields as defined in f_vars_to_extract
'''
log.info('Postprocess data using CDO for test {}'.format(test))
# check that exp is defined
if exp is None:
log.error('Experiment is not defined.\n exp = {}'.format(exp))
# get variables to process:
p_test_vars_proc = os.path.join(paths.p_f_vars_proc, test)
full_p_f_vars = utils.clean_path(p_test_vars_proc, f_vars_to_extract)
df_vars = pd.read_csv(full_p_f_vars, sep=',')
# define expressions
df_vars['expr'] = df_vars['var'] + '=' + df_vars['formula']
# name of output file
ofile_tot = os.path.join(p_stages,
'standard_postproc_{}_{}.nc'.format(test, exp))
# initialisation
files_error = [] # list files giving error
files_proceed = [] # list of files where data are collected
# sometimes data is stored in a folder called Raw
p_raw_folder = os.path.join(p_raw_files, exp, raw_f_subfold)
# SPECIAL CASE, echam specific :
# if the folder containing the Raw files have been deleted,
# but folder 'Data' contains already global annual means
if not os.path.isdir(p_raw_folder):
log.warning('The folder containing the raw data '
'has been deleted : {}'.format(p_raw_folder))
p_altern_timeser_fold = os.path.join(p_raw_files, exp, 'Data')
if test == 'welch':
time_series_altern_fold = glob.glob(
os.path.join(p_altern_timeser_fold, 'timeser_daint_*.nc'))
if test == 'fldcor' or test == 'rmse':
time_series_altern_fold = glob.glob(
os.path.join(p_altern_timeser_fold, 'multi_annual_means_*.nc'))
if test == 'emi':
time_series_altern_fold = glob.glob(
os.path.join(p_altern_timeser_fold, 'emi_*.nc'))
if len(time_series_altern_fold) < 1:
log.error('Could not find files in alternative directory '
'{}'.format(time_series_altern_fold))
else:
log.info('The alternative folder has been found instead: '
'{}'.format(p_altern_timeser_fold))
log.warning('This section of code is only tested for ECHAM! '
'It is not recommended to use it for other cases')
if len(time_series_altern_fold) == 1:
index_ts = 0
if len(time_series_altern_fold) > 1:
for (i, item) in enumerate(time_series_altern_fold):
print(i, item)
index_ts = int(
input('Please type the index of the file'
' to use (negative means '
'none of them) : '))
# If index positive, copy the time serie and exit
if index_ts >= 0:
log.info('File used : {}'.format(
time_series_altern_fold[index_ts]))
cdo_cmd = ('cdo -L -chname,CDNC,burden_CDNC '
'-chname,ICNC,burden_ICNC '
'-chname,SCF,SCRE -chname,LCF,LCRE '
'{} {}'.format(time_series_altern_fold[index_ts],
ofile_tot))
utils.shell_cmd(cdo_cmd, py_routine=__name__)
# convert netCDF to dataframe,
# therefore skip next processing step
if test == 'welch':
timeser_proc_nc_to_df(exp,
ofile_tot,
p_stages,
already_a_timeseries=True)
skip_next_steps = True
else:
skip_next_steps = False
log.warning('Leave ECHAM-only code-section! '
'You are save again...')
return (ofile_tot, skip_next_steps)
# NORMAL CASE
else:
log.info('Analyse files in : {}'.format(p_raw_folder))
log.banner('Time for a coffee...')
# loop over output stream
for stream in df_vars['file'].unique():
# extract all lines with file f
df_file = df_vars[df_vars.file == stream]
# list all available files in p_raw_files/exp/raw_f_subfold
#which have stream f
# restart files and {}m.format(stream) e.g. echamm.nc
# files are not considered
final_p_raw_files = os.path.join(p_raw_folder,
'*_*{}*.nc'.format(stream))
ifiles = [
fn for fn in glob.glob(final_p_raw_files) if sum([
s in os.path.basename(fn)
for s in ['stream', '{}m'.format(stream)]
]) == 0
]
if len(ifiles) == 0:
log.warning('No raw files found for stream {} at address : \n'
'{}'.format(stream, final_p_raw_files))
# sort files in chronoligcal order
# (this will be needed for doing yearmean properly)
ifiles.sort()
print_statistics_of_raw_files(ifiles, stream, exp)
# remove spin-up files
log.info('Remove first {} months of data '
'due to model spinup'.format(spinup))
ifiles = ifiles[int(spinup):]
# output file for stream f
ofile_str = '{}_{}.nc'.format(exp, stream)
# variables to extract form netcdf
# files (this is needed for optimization)
variables = variables_to_extract(vars_in_expr=df_file.formula.values)
# Extract variables needed from big files
log.info('Extract variables from file: {}'.format(stream))
# initialization
tmp_selvar_files = [] # list to store the ifiles
for ifile in ifiles:
# basename of ifile
ifile_bsn = os.path.basename(ifile)
log.debug('File {}'.format(ifile_bsn))
tmp_selvar_file = 'tmp_extract_{}'.format(ifile_bsn)
cdo_cmd = 'cdo selvar,{} {} {}'.format(','.join(variables), ifile,
tmp_selvar_file)
out_status, out_mess = utils.shell_cmd(cdo_cmd,
py_routine=__name__,
lowarn=True)
if out_status == 0:
tmp_selvar_files.append(tmp_selvar_file)
else:
files_error.append(ifile_bsn)
# Merge all the monthly files together
log.info('Copy {} files'.format(stream))
tmp_merged = 'tmp_{}_{}.nc'.format(exp, stream)
if os.path.isfile(tmp_merged):
os.remove(tmp_merged)
cdo_cmd = 'cdo -copy {} {}'.format(' '.join(tmp_selvar_files),
tmp_merged)
utils.shell_cmd(cdo_cmd, py_routine=__name__)
# compute needed variables
log.info('Compute variables for file : {}'.format(stream))
if os.path.isfile(ofile_str):
os.remove(ofile_str)
expr_str = ';'.join((df_file.expr.values))
cdo_cmd = 'cdo -L -setctomiss,-9e+33 -expr,"{}" {} {}'.format(
expr_str, tmp_merged, ofile_str)
utils.shell_cmd(cdo_cmd, py_routine=__name__)
# keep trace of output file per stream
files_proceed.append(ofile_str)
# cleaning
[os.remove(f) for f in tmp_selvar_files]
os.remove(tmp_merged)
# merge all stream files
if os.path.isfile(ofile_tot):
os.remove(ofile_tot)
cdo_cmd = 'cdo merge {} {}'.format(' '.join(files_proceed), ofile_tot)
utils.shell_cmd(cdo_cmd, py_routine=__name__)
[os.remove(f) for f in files_proceed]
# Finish
if len(files_error) != 0:
log.warning('Files with a problem: {}'.format(','.join(files_error)))
log.info('Postprocess data using CDO for test {} finished. \n '
'Output here : {}'.format(test, ofile_tot))
# return name of output file
return (ofile_tot, False)
def main(new_exp, results_data_processing, tests, p_stages, p_ref_csv_files,
ltestsuite, f_vars_to_extract):
df_exp = {}
df_ref = {}
p_csv_files = {}
testresult_csv = {}
df_result = {}
for test in tests:
log.info('Prepare references for test {}'.format(test))
test_cfg = get_config_of_current_test(test)
results_data_processing[test]['exp'] = new_exp
# list of paths to all csv files
p_csv_files[test] = glob.glob(
os.path.join(p_ref_csv_files, test,
'{}_*csv'.format(test_cfg.ref_name)))
if len(p_csv_files[test]) == 0:
log.error('No reference files found in {}'.format(p_ref_csv_files))
log.debug('{} reference(s) found for test \
{}'.format(len(p_csv_files[test]), test))
# create big dataframe containing all reference exps
df_ref[test] = create_big_df(test_cfg.ref_name,
list_csv_files=p_csv_files[test])
# Exclude all the non-desired variables (1) var from file, 2) exp)
full_p_f_vars = os.path.join(paths.p_f_vars_proc, test,
f_vars_to_extract)
vars_to_analyse = list(
pd.read_csv(full_p_f_vars, sep=',')['var'].values)
vars_to_analyse.append('exp')
try:
df_ref[test] = df_ref[test][vars_to_analyse]
except KeyError as e:
log.warning(e)
log.error('Variables defined in {} are not contained in reference \
{}'.format(utils.rel_path(f_vars_to_extract),
utils.rel_path(p_ref_csv_files)))
df_exp[test] = results_data_processing[test][vars_to_analyse]
log.info('References for test {} prepared'.format(test))
testresult_csv[test] = os.path.join(
p_stages, 'result_{}_{}.csv'.format(test, new_exp))
if test == 'welch':
log.banner('')
log.banner("Perform Welch's t-test for each variable")
log.banner('')
df_result[test] = welch_test(
df_a=df_ref[test],
df_b=df_exp[test],
filename_student_test=testresult_csv[test])
df_result[test]['p-value [%]'] = df_result[test]['p-value'] * 100.
if test == 'fldcor':
log.banner('')
log.banner("Perform fldcor test for each variable")
log.banner('')
df_result[test] = pattern_correlation(df_exp[test], test_cfg)
if test == 'emi':
log.banner('')
log.banner("Perform emission test for each variable")
log.banner('')
df_result[test] = emissions(df_exp[test], df_ref[test], test_cfg)
if test == 'rmse':
log.banner('')
log.banner("Perform rmse test for each variable")
log.banner('')
df_result[test] = rmse(df_exp[test], test_cfg)
df_result[test] = sort_level_metric(df_result[test],
test_cfg.metric_threshold,
test_cfg.metric)
df_result[test] = add_color_df_result(df_result[test],
test_cfg.metric_threshold)
print_warning_color(df_result[test], test_cfg.metric_threshold,
test_cfg.metric)
if ltestsuite:
for test in tests:
test_cfg = get_config_of_current_test(test)
utils.exit_if_testresult_is_bad(test, df_result[test],
test_cfg.metric_threshold,
test_cfg.metric)
return df_result, df_ref