def prepare_plots_file(n_clicks, file_format_values, width, height,
units, dpi):
opls_data = OPLSModel()
OPLSDashboard.check_clicks(n_clicks)
if not opls_data.results_file_ready:
return 3600000
job = opls_data.download_plots(file_format_values, width, height,
units, dpi)
print(f'Started ')
rds.set_value(f'{OPLSModel.redis_prefix}_job_id', job.id)
return [500]
def set_file_info(self):
rds.set_value(f'{self.redis_prefix}_dataframe_filename',
self._dataframe_filename.encode('utf-8'))
rds.set_value(f'{self.redis_prefix}_results_filename',
self._results_filename.encode('utf-8'))
rds.set_value(f'{self.redis_prefix}_loaded_collection_ids',
msgpack.dumps(self._loaded_collection_ids))
def download_plots(self, file_formats, width, height, units, dpi):
rds.set_value(f'{self.redis_prefix}_image_save_progress', 0)
rds.set_value(f'{self.redis_prefix}_image_save_label', 'Starting job')
rds.set_value(f'{self.redis_prefix}_image_save_progress_fraction',
f'0/0')
if self.results_file_ready:
base_filename = f'opls_results_{self.results_collection_id}'
root_dir = tempfile.mkdtemp()
plot_dir = os.path.join(root_dir, f'{base_filename}_plots')
os.mkdir(plot_dir)
file_formats = file_formats or []
if self.results_file_ready:
groups = h5py.File(self.results_filename).keys()
figure_data = {}
for group in groups:
is_discrimination = 'accuracy' in h5py.File(
self.results_filename)[group].attrs
quality_graph, score_graph = self.get_quality_plot(
group, 'plotly_white', False)
if is_discrimination:
(q_squared_graph, discriminant_q_squared_graph,
accuracy_graph,
roc_auc_graph) = self.get_metric_kde_plot(
group, 'plotly_white', False)
else:
q_squared_graph = self.get_metric_kde_plot(
group, 'plotly_white', False)
discriminant_q_squared_graph = accuracy_graph = roc_auc_graph = None
figure_data.update({
f'{group}_quality_metrics':
quality_graph.to_plotly_json()['props']['figure'],
f'{group}_scores':
score_graph.to_plotly_json()['props']['figure'],
f'{group}_q_squared_kde':
q_squared_graph.to_plotly_json()['props']['figure']
})
if is_discrimination:
figure_data.update({
f'{group}_discriminant_q_squared_kde':
discriminant_q_squared_graph.to_plotly_json()
['props']['figure'],
f'{group}_accuracy_kde':
accuracy_graph.to_plotly_json()['props']['figure'],
f'{group}_roc_auc_kde':
roc_auc_graph.to_plotly_json()['props']['figure']
})
return save_figures.queue(figure_data, file_formats, width,
height, units, dpi, plot_dir,
f'user{current_user.id}',
self.redis_prefix)
raise RuntimeError('Plots not ready!')
def special_numeric_df_label(self, value):
value = value.encode('utf-8') if isinstance(value, str) else value
rds.set_value(f'{self._redis_prefix}_special_numeric_df_label', value)
def region_n_clicks(self, value):
rds.set_value(f'{self._redis_prefix}_region_n_clicks', value)
def baseline_n_clicks(self, value):
rds.set_value(f'{self._redis_prefix}_baseline_n_clicks', value)
def normalize_n_clicks(self, value):
rds.set_value(f'{self._redis_prefix}_normalize_n_clicks', value)
def y_axis_range(self, value):
rds.set_value(f'{self._redis_prefix}_y_axis_range', msgpack.dumps(value))
def processing_log(self, value):
value = value.encode('utf-8') if isinstance(value, str) else value
rds.set_value(f'{self._redis_prefix}_processing_log', value)
def save_figures(figure_data, file_formats, width, height, units, dpi,
output_dir, redis_hash_name, redis_prefix):
archive_name = pathlib.Path(output_dir).with_suffix('.zip')
output_path = pathlib.Path(output_dir)
n_steps = len(figure_data) * len(file_formats) + 1
progress = 0
rds.set_value(f'{redis_prefix}_image_save_progress', 0, redis_hash_name)
rds.set_value(f'{redis_prefix}_image_save_progress_fraction',
f'0/{n_steps}', redis_hash_name)
rds.set_value(f'{redis_prefix}_image_save_label', 'Saving images',
redis_hash_name)
i = 0
for file_format in file_formats:
format_path = output_path.joinpath(file_format)
format_path.mkdir(parents=True, exist_ok=True)
for name, figure in figure_data.items():
path = format_path.joinpath(name).with_suffix(f'.{file_format}')
rds.set_value(f'{redis_prefix}_image_save_label',
f'Saving {path.name}', redis_hash_name)
progress += 100 / n_steps
save_figure(figure, file_format, width, height, units, dpi, path)
i += 1
rds.set_value(f'{redis_prefix}_image_save_progress', progress,
redis_hash_name)
rds.set_value(f'{redis_prefix}_image_save_progress_fraction',
f'{i}/{n_steps}', redis_hash_name)
rds.set_value(f'{redis_prefix}_image_save_label',
f'Creating archive {archive_name}', redis_hash_name)
out_filename = shutil.make_archive(output_path, 'zip', output_path.parent,
output_path.stem)
shutil.rmtree(output_path)
rds.set_value(f'{redis_prefix}_image_save_progress', 100, redis_hash_name)
rds.set_value(f'{redis_prefix}_image_save_progress_fraction',
f'{n_steps}/{n_steps}', redis_hash_name)
rds.set_value(f'{redis_prefix}_image_save_label',
f'Created archive {archive_name}', redis_hash_name)
return out_filename
def set_plot_data(cls, plot_data: Dict[str, List[Dict[str, Any]]]) -> None:
rds.set_value(f'{cls.redis_prefix}_plot_data',
msgpack.dumps(plot_data))
def job_id(self, value):
rds.set_value(f'{self.redis_prefix}_job_id', value)
def results_collection_id(self, value):
rds.set_value(f'{self.redis_prefix}_results_collection_id',
msgpack.dumps(value))
