def update_config_records(filename, new_cfg):
"""
All records of config file are updated exept records_to_save.
"""
if os.path.isfile(filename):
cfg = misc.obj_from_file(filename, filetype='yaml')
cfg.update(new_cfg)
print(cfg)
misc.obj_to_file(new_cfg, "test_" + filename, filetype='yaml')
def delete_config_records(filename, records_to_save=[]):
"""
All records of config file are deleted except records_to_save.
"""
if os.path.isfile(filename):
cfg = misc.obj_from_file(filename, filetype='yaml')
new_cfg = subdict(cfg, records_to_save)
print cfg
misc.obj_to_file(new_cfg, "test_" + filename , filetype='yaml')
def delete_config_records(filename, records_to_save=[]):
"""
All records of config file are deleted except records_to_save.
"""
if os.path.isfile(filename):
cfg = misc.obj_from_file(filename, filetype='yaml')
new_cfg = subdict(cfg, records_to_save)
print(cfg)
misc.obj_to_file(new_cfg, "test_" + filename, filetype='yaml')
def update_config_records(filename, new_cfg):
"""
All records of config file are updated exept records_to_save.
"""
if os.path.isfile(filename):
cfg = misc.obj_from_file(filename, filetype='yaml')
cfg.update(new_cfg)
print cfg
misc.obj_to_file(new_cfg, "test_" + filename , filetype='yaml')
def check_config_version_and_remove_old_records(filename, version,
records_to_save):
"""
Check if config file version is ok. If it is not all records except
records_to_save are deleted and config_version in file is set to version.
It is used to update user configuration.
"""
if os.path.isfile(filename):
cfg = misc.obj_from_file(filename, filetype='yaml')
if ('config_version' in cfg and (cfg['config_version'] == version)):
# everything is ok, no need to panic
return
else:
# older version of config file
cfg = misc.obj_from_file(filename, filetype='yaml')
misc.obj_to_file(cfg, filename + '.old', filetype='yaml')
print 'cfg ', cfg
new_cfg = subdict(cfg, records_to_save)
new_cfg['config_version'] = version
print 'ncfg ', new_cfg
misc.obj_to_file(new_cfg, filename, filetype='yaml')
def test_obj_to_and_from_file_yaml_with_ndarray_to_yaml(self):
testdata = np.random.random([4, 4, 3])
test_object = {'a': 1, 'data': testdata, "lst": [1, 2, 3]}
filename = 'test_obj_to_and_from_file.yaml'
misc.obj_to_file(test_object, filename, 'yaml', ndarray_to_list=True)
saved_object = misc.obj_from_file(filename, 'yaml')
self.assertTrue(saved_object['a'] == 1)
self.assertTrue(saved_object['data'][1][1][1] == testdata[1, 1, 1])
os.remove(filename)
def check_config_version_and_remove_old_records(filename, version,
records_to_save):
"""
Check if config file version is ok. If it is not all records except
records_to_save are deleted and config_version in file is set to version.
It is used to update user configuration.
"""
if os.path.isfile(filename):
cfg = misc.obj_from_file(filename, filetype='yaml')
if ('config_version' in cfg and (cfg['config_version'] == version)):
# everything is ok, no need to panic
return
else:
# older version of config file
cfg = misc.obj_from_file(filename, filetype='yaml')
misc.obj_to_file(cfg, filename + '.old', filetype='yaml')
print('cfg ', cfg)
new_cfg = subdict(cfg, records_to_save)
new_cfg['config_version'] = version
print('ncfg ', new_cfg)
misc.obj_to_file(new_cfg, filename, filetype='yaml')
def test_obj_to_and_from_file_pklz_with_auto(self):
testdata = np.random.random([4, 4, 3])
test_object = {'a': 1, 'data': testdata}
filename = 'test_obj_to_and_from_file_with_auto.pklz'
misc.obj_to_file(test_object, filename, 'auto')
saved_object = misc.obj_from_file(filename, 'auto')
self.assertTrue(saved_object['a'] == 1)
self.assertTrue(saved_object['data'][1, 1, 1] == testdata[1, 1, 1])
os.remove(filename)
def test_obj_to_and_from_file_pickle(self):
testdata = np.random.random([4, 4, 3])
test_object = {'a': 1, 'data': testdata}
filename = 'test_obj_to_and_from_file.pkl'
misc.obj_to_file(test_object, filename, 'pickle')
saved_object = misc.obj_from_file(filename, 'pickle')
self.assertTrue(saved_object['a'] == 1)
self.assertTrue(saved_object['data'][1, 1, 1] == testdata[1, 1, 1])
os.remove(filename)
def test_obj_to_and_from_file_yaml_with_ndarray_to_yaml(self):
testdata = np.random.random([4, 4, 3])
test_object = {'a': 1, 'data': testdata, "lst":[1, 2, 3]}
filename = 'test_obj_to_and_from_file.yaml'
misc.obj_to_file(test_object, filename, 'yaml', ndarray_to_list=True)
saved_object = misc.obj_from_file(filename, 'yaml')
self.assertTrue(saved_object['a'] == 1)
self.assertTrue(saved_object['data'][1][1][1] == testdata[1, 1, 1])
os.remove(filename)
def test_obj_to_and_from_file_with_directories(self):
import shutil
testdata = np.random.random([4, 4, 3])
test_object = {'a': 1, 'data': testdata}
dirname = '__test_write_and_read'
filename = '__test_write_and_read/test_obj_to_and_from_file.pkl'
misc.obj_to_file(test_object, filename, 'pickle')
saved_object = misc.obj_from_file(filename, 'pickle')
self.assertTrue(saved_object['a'] == 1)
self.assertTrue(saved_object['data'][1, 1, 1] == testdata[1, 1, 1])
shutil.rmtree(dirname)
def test_obj_to_and_from_file_with_directories(self):
import shutil
testdata = np.random.random([4, 4, 3])
test_object = {'a': 1, 'data': testdata}
dirname = '__test_write_and_read'
filename = '__test_write_and_read/test_obj_to_and_from_file.pkl'
misc.obj_to_file(test_object, filename, 'pickle')
saved_object = misc.obj_from_file(filename, 'pickle')
self.assertTrue(saved_object['a'] == 1)
self.assertTrue(saved_object['data'][1, 1, 1] == testdata[1, 1, 1])
shutil.rmtree(dirname)
def get_config(filename, default_cfg):
"""
Looks config file and update default_cfg values.
If file does not exist it is created.
"""
if os.path.isfile(filename):
cfg = misc.obj_from_file(filename, filetype='yaml')
default_cfg.update(cfg)
cfg_out = default_cfg
else:
misc.obj_to_file(default_cfg, filename, filetype='yaml')
cfg_out = default_cfg
# default config
return cfg_out
def get_config(filename, default_cfg):
"""
Looks config file and update default_cfg values.
If file does not exist it is created.
"""
if os.path.isfile(filename):
cfg = misc.obj_from_file(filename, filetype='yaml')
default_cfg.update(cfg)
cfg_out = default_cfg
else:
misc.obj_to_file(default_cfg, filename, filetype='yaml')
cfg_out = default_cfg
# default config
return cfg_out
def report(eval_files, labels, markers, show=True, output_prefix='',
use_plt=True, pklz_dirs=None, experiment_name='',
filename_separator=''):
"""
based on
:eval_files: list of filenames with input data without extension. If it is
None, based on pklz_disr the default eval_list is generated.
:use_plt: can supress using of matplotlib
:pklz_dirs: is used for generating eval_files if eval_files is None.
Obsolete
"""
# TODO image_basename generovat obrazky
expn = np.array(range(0, len(markers)))
expn_labels = labels
dp_params = {
'markers': markers,
'labels': labels,
'loc': 0,
'show': show,
'filename': output_prefix,
'filename_separator': filename_separator
}
sp_params = {
'expn': expn,
'expn_labels': expn_labels,
'show': show,
'filename': output_prefix,
'use_plt': use_plt,
'filename_separator': filename_separator
}
# return
# yaml_files = [os.path.normpath(path) + '.yaml' for path in pklz_dirs]
# logger.debug(str(yaml_files))
if eval_files is None:
if pklz_dirs is not None:
eval_files = [
os.path.normpath(path) + '_eval' for path in pklz_dirs]
else:
logger.error('pklz_dirs and eval_files is None')
raise Exception('pklz_dirs and eval_files is None')
logger.debug(str(eval_files))
data = [misc.obj_from_file(fname + '.pkl', filetype='pkl')
for fname in eval_files]
df_all = create_data_frame(data, labels, eval_files, experiment_name)
__df_to_csv_and_latex(df_all, output_prefix)
if use_plt:
logger.debug("first plot")
dataplot(data, 'voe', 'Volume Difference Error [%]', **dp_params)
dataplot(data, 'vd', 'Total Volume Difference [%]', **dp_params)
dataplot(data, 'processing_time', 'Processing time [s]', **dp_params)
dataplot(data, 'maxd', 'MaxD [mm]', **dp_params)
dataplot(data, 'avgd', 'AvgD [mm]', **dp_params)
dataplot(data, 'rmsd', 'RMSD [mm]', **dp_params)
# dataplot(data, 'rmsd', 'RMSD [mm]', **dp_params)
logger.debug("Souhrn měření")
vd_mn, tmp = sumplot(data, 'vd', 'Total Volume Difference', **sp_params)
voe_mn, tmp = sumplot(data, 'voe', 'Volume Difference Error',
**sp_params)
avgd_mn, tmp = sumplot(data, 'avgd', 'Average Distance', **sp_params)
maxd_mn, tmp = sumplot(data, 'maxd', 'Maxiamal Distance', **sp_params)
rmsd_mn, tmp = sumplot(data, 'rmsd', 'Square Distance', **sp_params)
logger.info("\n")
logger.info('vd ' + str(vd_mn))
logger.info("voe " + str(voe_mn))
logger.info('maxd ' + str(maxd_mn))
logger.info('avgd ' + str(avgd_mn))
logger.info('rmsd ' + str(rmsd_mn))
logger.info("Přepočteno na skóre")
# print tables[0].shape
# pandas.set_option('display.max_columns', None)
scoreTotal, scoreMetrics, scoreAll = sliverScoreAll(data)
tables, indexes, columns = scoreTableEvaluation(scoreMetrics)
dataframes = __create_data_frames(tables, indexes, columns)
__save_data_frames(dataframes, labels, output_prefix)
# df = pandas.DataFrame(tables[0], index=indexes[0], columns=columns[0])
# print df.to_string()
if use_plt:
dataplot(scoreAll, 'voe', 'Volume Difference Error [points]',
**dp_params)
dataplot(scoreAll, 'vd', 'Total Volume Difference [points]',
**dp_params)
dataplot(scoreAll, 'maxd', 'MaxD [points]', **dp_params)
dataplot(scoreAll, 'avgd', 'AvgD [points]', **dp_params)
dataplot(scoreAll, 'rmsd', 'RMSD [points]', **dp_params)
# tohle je zoufale propletani ruznych dat
dataplot(data, 'score_pts', 'Score [points]', **dp_params)
vd_mn, tmp = sumplot(scoreAll, 'vd', 'Total Volume Difference', **sp_params)
voe_mn, tmp = sumplot(scoreAll, 'voe', 'Volume Difference Error',
**sp_params)
avgd_mn, tmp = sumplot(scoreAll, 'avgd', 'Average Distance', **sp_params)
maxd_mn, tmp = sumplot(scoreAll, 'maxd', 'Maxiamal Distance', **sp_params)
rmsd_mn, tmp = sumplot(scoreAll, 'rmsd', 'Square Distance', **sp_params)
logger.info("Overall score")
scoreTotal, scoreMetrics, scoreAll = sliverScoreAll(data)
logger.info('Overall score: ' + str(scoreTotal))
if use_plt:
plot_total(scoreMetrics, labels=labels, err_scale=0.05, show=show,
filename=output_prefix)
return df_all
def processIt(pklz_dirs, sliver_dir, yaml_files, eval_files, markers, labels):
import misc
"""
Funkce vypíše report o celém experimentu.
"""
data = [misc.obj_from_file(fname + '.pkl', filetype='pkl')
for fname in eval_files]
print("Jednotlivá měření")
dataplot(data, 'voe', 'Volume Difference Error [%]', markers=markers,
labels=labels, loc=0)
dataplot(data, 'vd', 'Total Volume Difference [%]', markers=markers,
labels=labels, loc=0)
dataplot(data, 'processing_time',
'Processing time [s]', markers=markers, labels=labels, loc=0)
dataplot(data, 'maxd', 'MaxD [mm]', markers=markers, labels=labels, loc=0)
dataplot(data, 'avgd', 'AvgD [mm]', markers=markers, labels=labels, loc=0)
dataplot(data, 'rmsd', 'RMSD [mm]', markers=markers, labels=labels, loc=0)
print("Souhrn měření")
# import "experiment_support.ipynb"
expn = np.array(range(0, len(labels)))
expn_labels = labels
print(expn_labels)
print(expn)
vd_mn, tmp = sumplot(
data, 'vd', 'Total Volume Difference', expn, expn_labels)
voe_mn, tmp = sumplot(
data, 'voe', 'Volume Difference Error', expn, expn_labels)
avgd_mn, tmp = sumplot(data, 'avgd', 'Average Distance', expn, expn_labels)
maxd_mn, tmp = sumplot(
data, 'maxd', 'Maxiamal Distance', expn, expn_labels)
rmsd_mn, tmp = sumplot(data, 'rmsd', 'Square Distance', expn, expn_labels)
print('vd ', vd_mn)
print("voe ", voe_mn)
print('maxd ', maxd_mn)
print('avgd ', avgd_mn)
print('rmsd ', rmsd_mn)
print("Přepočteno na skóre")
import pandas
# print tables[0].shape
# pandas.set_option('display.max_columns', None)
scoreTotal, scoreMetrics, scoreAll = sliverScoreAll(data)
tables, indexes, columns = scoreTableEvaluation(scoreMetrics)
df = pandas.DataFrame(tables[0], index=indexes[0], columns=columns[0])
print(df.to_string())
dataplot(scoreAll, 'voe', 'Volume Difference Error [points]',
markers=markers, labels=labels, loc=0)
dataplot(scoreAll, 'vd', 'Total Volume Difference [points]',
markers=markers, labels=labels, loc=0)
dataplot(scoreAll, 'maxd',
'MaxD [mm]', markers=markers, labels=labels, loc=0)
dataplot(scoreAll, 'avgd',
'AvgD [mm]', markers=markers, labels=labels, loc=0)
dataplot(scoreAll, 'rmsd',
'RMSD [mm]', markers=markers, labels=labels, loc=0)
vd_mn, tmp = sumplot(
scoreAll, 'vd', 'Total Volume Difference', expn, expn_labels)
voe_mn, tmp = sumplot(
scoreAll, 'voe', 'Volume Difference Error', expn, expn_labels)
avgd_mn, tmp = sumplot(
scoreAll, 'avgd', 'Average Distance', expn, expn_labels)
maxd_mn, tmp = sumplot(
scoreAll, 'maxd', 'Maxiamal Distance', expn, expn_labels)
rmsd_mn, tmp = sumplot(
scoreAll, 'rmsd', 'Square Distance', expn, expn_labels)
# scoreTotal, scoreMetrics, scoreAll =
# volumetry_evaluation.sliverScoreAll(data)
scoreTotal, scoreMetrics, scoreAll = sliverScoreAll(data)
print('Score total: ', scoreTotal)
plot_total(scoreMetrics, labels=labels, err_scale=0.05)
return df
def report(eval_files,
labels,
markers,
show=True,
output_prefix='',
use_plt=True,
pklz_dirs=None,
experiment_name='',
filename_separator=''):
"""
based on
:eval_files: list of filenames with input data without extension. If it is
None, based on pklz_disr the default eval_list is generated.
:use_plt: can supress using of matplotlib
:pklz_dirs: is used for generating eval_files if eval_files is None.
Obsolete
"""
# TODO image_basename generovat obrazky
expn = np.array(range(0, len(markers)))
expn_labels = labels
dp_params = {
'markers': markers,
'labels': labels,
'loc': 0,
'show': show,
'filename': output_prefix,
'filename_separator': filename_separator
}
sp_params = {
'expn': expn,
'expn_labels': expn_labels,
'show': show,
'filename': output_prefix,
'use_plt': use_plt,
'filename_separator': filename_separator
}
# return
# yaml_files = [os.path.normpath(path) + '.yaml' for path in pklz_dirs]
# logger.debug(str(yaml_files))
if eval_files is None:
if pklz_dirs is not None:
eval_files = [
os.path.normpath(path) + '_eval' for path in pklz_dirs
]
else:
logger.error('pklz_dirs and eval_files is None')
raise Exception('pklz_dirs and eval_files is None')
logger.debug(str(eval_files))
data = [
misc.obj_from_file(fname + '.pkl', filetype='pkl')
for fname in eval_files
]
df_all = create_data_frame(data, labels, eval_files, experiment_name)
__df_to_csv_and_latex(df_all, output_prefix)
if use_plt:
logger.debug("first plot")
dataplot(data, 'voe', 'Volume Difference Error [%]', **dp_params)
dataplot(data, 'vd', 'Total Volume Difference [%]', **dp_params)
dataplot(data, 'processing_time', 'Processing time [s]', **dp_params)
dataplot(data, 'maxd', 'MaxD [mm]', **dp_params)
dataplot(data, 'avgd', 'AvgD [mm]', **dp_params)
dataplot(data, 'rmsd', 'RMSD [mm]', **dp_params)
# dataplot(data, 'rmsd', 'RMSD [mm]', **dp_params)
logger.debug("Souhrn měření")
vd_mn, tmp = sumplot(data, 'vd', 'Total Volume Difference', **sp_params)
voe_mn, tmp = sumplot(data, 'voe', 'Volume Difference Error', **sp_params)
avgd_mn, tmp = sumplot(data, 'avgd', 'Average Distance', **sp_params)
maxd_mn, tmp = sumplot(data, 'maxd', 'Maxiamal Distance', **sp_params)
rmsd_mn, tmp = sumplot(data, 'rmsd', 'Square Distance', **sp_params)
logger.info("\n")
logger.info('vd ' + str(vd_mn))
logger.info("voe " + str(voe_mn))
logger.info('maxd ' + str(maxd_mn))
logger.info('avgd ' + str(avgd_mn))
logger.info('rmsd ' + str(rmsd_mn))
logger.info("Přepočteno na skóre")
# print tables[0].shape
# pandas.set_option('display.max_columns', None)
scoreTotal, scoreMetrics, scoreAll = sliverScoreAll(data)
tables, indexes, columns = scoreTableEvaluation(scoreMetrics)
dataframes = __create_data_frames(tables, indexes, columns)
__save_data_frames(dataframes, labels, output_prefix)
# df = pandas.DataFrame(tables[0], index=indexes[0], columns=columns[0])
# print df.to_string()
if use_plt:
dataplot(scoreAll, 'voe', 'Volume Difference Error [points]',
**dp_params)
dataplot(scoreAll, 'vd', 'Total Volume Difference [points]',
**dp_params)
dataplot(scoreAll, 'maxd', 'MaxD [points]', **dp_params)
dataplot(scoreAll, 'avgd', 'AvgD [points]', **dp_params)
dataplot(scoreAll, 'rmsd', 'RMSD [points]', **dp_params)
# tohle je zoufale propletani ruznych dat
dataplot(data, 'score_pts', 'Score [points]', **dp_params)
vd_mn, tmp = sumplot(scoreAll, 'vd', 'Total Volume Difference',
**sp_params)
voe_mn, tmp = sumplot(scoreAll, 'voe', 'Volume Difference Error',
**sp_params)
avgd_mn, tmp = sumplot(scoreAll, 'avgd', 'Average Distance', **sp_params)
maxd_mn, tmp = sumplot(scoreAll, 'maxd', 'Maxiamal Distance', **sp_params)
rmsd_mn, tmp = sumplot(scoreAll, 'rmsd', 'Square Distance', **sp_params)
logger.info("Overall score")
scoreTotal, scoreMetrics, scoreAll = sliverScoreAll(data)
logger.info('Overall score: ' + str(scoreTotal))
if use_plt:
plot_total(scoreMetrics,
labels=labels,
err_scale=0.05,
show=show,
filename=output_prefix)
return df_all
def processIt(pklz_dirs, sliver_dir, yaml_files, eval_files, markers, labels):
import misc
"""
Funkce vypíše report o celém experimentu.
"""
data = [
misc.obj_from_file(fname + '.pkl', filetype='pkl')
for fname in eval_files
]
print("Jednotlivá měření")
dataplot(data,
'voe',
'Volume Difference Error [%]',
markers=markers,
labels=labels,
loc=0)
dataplot(data,
'vd',
'Total Volume Difference [%]',
markers=markers,
labels=labels,
loc=0)
dataplot(data,
'processing_time',
'Processing time [s]',
markers=markers,
labels=labels,
loc=0)
dataplot(data, 'maxd', 'MaxD [mm]', markers=markers, labels=labels, loc=0)
dataplot(data, 'avgd', 'AvgD [mm]', markers=markers, labels=labels, loc=0)
dataplot(data, 'rmsd', 'RMSD [mm]', markers=markers, labels=labels, loc=0)
print("Souhrn měření")
# import "experiment_support.ipynb"
expn = np.array(range(0, len(labels)))
expn_labels = labels
print(expn_labels)
print(expn)
vd_mn, tmp = sumplot(data, 'vd', 'Total Volume Difference', expn,
expn_labels)
voe_mn, tmp = sumplot(data, 'voe', 'Volume Difference Error', expn,
expn_labels)
avgd_mn, tmp = sumplot(data, 'avgd', 'Average Distance', expn, expn_labels)
maxd_mn, tmp = sumplot(data, 'maxd', 'Maxiamal Distance', expn,
expn_labels)
rmsd_mn, tmp = sumplot(data, 'rmsd', 'Square Distance', expn, expn_labels)
print('vd ', vd_mn)
print("voe ", voe_mn)
print('maxd ', maxd_mn)
print('avgd ', avgd_mn)
print('rmsd ', rmsd_mn)
print("Přepočteno na skóre")
import pandas
# print tables[0].shape
# pandas.set_option('display.max_columns', None)
scoreTotal, scoreMetrics, scoreAll = sliverScoreAll(data)
tables, indexes, columns = scoreTableEvaluation(scoreMetrics)
df = pandas.DataFrame(tables[0], index=indexes[0], columns=columns[0])
print(df.to_string())
dataplot(scoreAll,
'voe',
'Volume Difference Error [points]',
markers=markers,
labels=labels,
loc=0)
dataplot(scoreAll,
'vd',
'Total Volume Difference [points]',
markers=markers,
labels=labels,
loc=0)
dataplot(scoreAll,
'maxd',
'MaxD [mm]',
markers=markers,
labels=labels,
loc=0)
dataplot(scoreAll,
'avgd',
'AvgD [mm]',
markers=markers,
labels=labels,
loc=0)
dataplot(scoreAll,
'rmsd',
'RMSD [mm]',
markers=markers,
labels=labels,
loc=0)
vd_mn, tmp = sumplot(scoreAll, 'vd', 'Total Volume Difference', expn,
expn_labels)
voe_mn, tmp = sumplot(scoreAll, 'voe', 'Volume Difference Error', expn,
expn_labels)
avgd_mn, tmp = sumplot(scoreAll, 'avgd', 'Average Distance', expn,
expn_labels)
maxd_mn, tmp = sumplot(scoreAll, 'maxd', 'Maxiamal Distance', expn,
expn_labels)
rmsd_mn, tmp = sumplot(scoreAll, 'rmsd', 'Square Distance', expn,
expn_labels)
# scoreTotal, scoreMetrics, scoreAll =
# volumetry_evaluation.sliverScoreAll(data)
scoreTotal, scoreMetrics, scoreAll = sliverScoreAll(data)
print('Score total: ', scoreTotal)
plot_total(scoreMetrics, labels=labels, err_scale=0.05)
return df
