def download_dip_rates(request, dataset_id):
try:
dataset = HTSDataset.objects.get(pk=dataset_id, deleted_date=None)
except HTSDataset.DoesNotExist:
raise Http404()
_assert_has_perm(request, dataset, 'download_data')
if not license_accepted(request, dataset):
return _plain_response('You must accept the dataset license to '
'download this file')
try:
full_path = _generate_dip_rates(dataset)
except NoDataException:
return _plain_response('No data found for this request')
output_filename = '{}_dip_rates.tsv'.format(dataset.name)
return serve_file(request,
full_path,
rename_to=output_filename,
content_type='text/tab-separated-values')
def download_fit_params(request, dataset_id, stat_type):
file_type = 'fit_params_{}_tsv'.format(stat_type)
file_name = 'fit_params_{}_{}.tsv'.format(stat_type, dataset_id)
file_type_protocol = 1
param_names = {
'dip': ('aa', 'aa_obs', 'emax', 'emax_rel', 'emax_obs', 'emax_obs_rel',
'einf', 'ec50', 'ic50', 'hill'),
'viability':
('aa', 'aa_obs', 'emax', 'emax_obs', 'einf', 'ec50', 'ic50', 'hill')
}
try:
dataset = HTSDataset.objects.get(pk=dataset_id, deleted_date=None)
except (HTSDataset.DoesNotExist, ValueError):
return _plain_response('This dataset does not exist, or you do not '
'have permission to access it.')
_assert_has_perm(request, dataset, 'download_data')
if not license_accepted(request, dataset):
return _plain_response('You must accept the dataset license to '
'download this file')
mod_date = timezone.now()
file = _cached_file(dataset, file_type, file_type_protocol)
# Additional cache invalidation: curve fits were generated after the
# cached file
if file:
if file.creation_date < CurveFitSet.objects.get(
dataset_id=dataset_id, stat_type=stat_type).calculation_end:
file = None
if file:
full_path = file.file.name
else:
try:
# Fetch the DIP rates from the DB
base_params = df_curve_fits(dataset.id,
stat_type,
drug_ids=None,
cell_line_ids=None)
except NoDataException:
return _plain_response(
'The requested parameter set does not exist for the '
'specified dataset')
# Fit Hill curves and compute parameters
fp = fit_params_from_base(base_params,
custom_ic_concentrations={50},
custom_ec_concentrations={50},
include_auc=False,
include_aa=True,
include_hill=True,
include_emax=True,
include_einf=True,
include_response_values=False)
fp.reset_index('dataset_id', drop=True, inplace=True)
# Remove -ve AA values
fp.loc[fp['aa'] < 0.0, 'aa'] = np.nan
# Filter for the default list of parameters only
fp = fp.filter(items=param_names[stat_type])
full_path = os.path.join(settings.DOWNLOADS_ROOT, file_name)
fp.to_csv(full_path, sep='\t')
df, created = HTSDatasetFile.objects.get_or_create(
dataset=dataset,
file_type=file_type,
defaults={
'file_type_protocol': file_type_protocol,
'file': full_path
})
if not created:
df.file_type_protocol = file_type_protocol
df.file = full_path
df.creation_date = mod_date
df.save()
output_filename = '{}_{}_params.tsv'.format(dataset.name, stat_type)
return serve_file(request,
full_path,
rename_to=output_filename,
content_type='text/tab-separated-values')