def _get_viability_scores(datasets, drug_id, cell_line_id, viability_time):
try:
df_data = df_doses_assays_controls(dataset=datasets,
drug_id=drug_id,
cell_line_id=cell_line_id,
assay=None,
use_dataset_names=True)
except NoDataException:
return HttpResponse(
'No viability data found for this request. '
'This drug/cell line/time point combination '
'may not exist.',
status=400)
try:
expt_resp_data, ctrl_resp_data = viability(df_data,
time_hrs=viability_time,
include_controls=True)
except NotImplementedError as e:
return HttpResponse(e, status=400)
if expt_resp_data['viability'].isnull().values.all():
return HttpResponse(
'No viability for this time point. The '
'nearest time point to the time entered '
'is '
'used, but there must be control well '
'measurements from the same time.',
status=400)
return expt_resp_data, ctrl_resp_data
def _generate_dataset_hdf5(dataset, regenerate_cache=False):
file_name = 'dataset_{}.h5'.format(dataset.id)
file_type = 'dataset_hdf5'
file_type_protocol = 1
mod_date = timezone.now()
file = _cached_file(dataset, file_type, file_type_protocol)
if file and not regenerate_cache:
full_path = file.file.name
else:
df_data = df_doses_assays_controls(dataset=dataset,
drug_id=None,
cell_line_id=None,
assay=None,
for_export=True)
full_path = os.path.join(settings.DOWNLOADS_ROOT, file_name)
write_hdf(df_data, full_path)
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()
return full_path
def ajax_get_plot(request, file_type='json'):
if file_type == 'csv':
permission_required = 'download_data'
else:
permission_required = 'view_plots'
try:
plot_type = request.GET['plotType']
template = request.GET.get('theme', default_plotly_template)
if template not in ALLOWED_TEMPLATES:
return HttpResponse('Please select an allowed template',
status=400)
dataset_id = int(request.GET['datasetId'])
dataset2_id = request.GET.get('dataset2Id', None)
if dataset2_id == "":
dataset2_id = None
if dataset2_id is not None:
dataset2_id = int(dataset2_id)
cell_line_id = request.GET.getlist('c')
drug_id = request.GET.getlist('d')
cell_line_id = [int(cl) for cl in cell_line_id]
drug_ids = []
for dr in drug_id:
try:
drug_ids.append(int(dr))
except ValueError:
drug_ids.append([int(d) for d in dr.split(",")])
drug_id = drug_ids
assay = request.GET.get('assayId')
yaxis = request.GET.get('logTransform', 'None')
except (KeyError, ValueError):
raise Http404()
try:
dataset = HTSDataset.objects.get(pk=dataset_id)
except HTSDataset.DoesNotExist:
raise Http404()
_assert_has_perm(request, dataset, permission_required)
if not license_accepted(request, dataset):
return HttpResponse(LICENSE_UNSIGNED.format(dataset.name), status=400)
if plot_type == 'tc':
if len(drug_id) != 1 or len(cell_line_id) != 1:
return HttpResponse(
'Please select exactly one cell line and '
'drug for time course plot',
status=400)
try:
df_data = df_doses_assays_controls(dataset=dataset,
drug_id=drug_id,
cell_line_id=cell_line_id,
assay=assay)
except NoDataException:
return HttpResponse(
'No data found for this request. This '
'drug/cell line/assay combination may not '
'exist.',
status=400)
if assay is None:
assay = df_data.assays.index.get_level_values('assay')[0]
overlay_dip_fit = request.GET.get('overlayDipFit', 'false') == 'true'
if overlay_dip_fit and assay != df_data.dip_assay_name:
return HttpResponse(
'Can only overlay DIP rate on cell '
'proliferation assays',
status=400)
plot_fig = plot_time_course(df_data,
log_yaxis=yaxis == 'log2',
assay_name=assay,
show_dip_fit=overlay_dip_fit,
subtitle=dataset.name,
template=template)
elif plot_type in ('drc', 'drpar'):
if all(isinstance(d, int) for d in drug_id):
plot_fig = _dose_response_plot(request, dataset, dataset2_id,
permission_required, drug_id,
cell_line_id, plot_type, template)
else:
if dataset2_id is not None:
return HttpResponse(
'Please select a single dataset at a time to view drug '
'combination heat plots',
status=400)
plot_fig = _drug_combination_heatmap(request, dataset, drug_id,
cell_line_id, template)
if isinstance(plot_fig, HttpResponse):
return plot_fig
elif plot_type == 'qc':
qc_view = request.GET.get('qcView', None)
if qc_view == 'ctrldipbox':
ctrl_dip_data = df_ctrl_dip_rates(dataset_id)
if ctrl_dip_data is None:
return HttpResponse(
'No control wells with DIP rates '
'available were detected in this '
'dataset.',
status=400)
plot_fig = plot_ctrl_dip_by_plate(ctrl_dip_data, template=template)
elif qc_view == 'ctrlcellbox':
# Try to fetch from cache
cache_key = f'dataset_{dataset_id}_plot_ctrlcellbox'
cur_plotver = 1
plot_cached = cache.get(cache_key)
if plot_cached:
plot_fig = plot_cached['plot_fig']
if plot_cached is None or plot_cached['dataset_last_modified'] < dataset.modified_date or \
plot_cached['plot_version'] < cur_plotver:
# Create plot
groupings = dataset_groupings(dataset)
if not groupings['singleTimepoint']:
return HttpResponse(
'This plot type is only available for '
'single time-point datasets',
status=400)
try:
df_data = df_control_wells(dataset_id=dataset, assay=assay)
except NoDataException:
return HttpResponse('No data found for this request.',
status=400)
if (df_data['value'] == 100.0).all():
return HttpResponse(
'The raw data for this dataset is given as relative viability, so no control '
'wells are available',
status=400)
plot_fig = plot_ctrl_cell_counts_by_plate(
df_data, subtitle=dataset.name, template=template)
# Push to cache
cache.set(
cache_key, {
'dataset_last_modified': dataset.modified_date,
'plot_version': cur_plotver,
'plot_fig': plot_fig
})
elif qc_view == 'dipplatemap':
plate_id = request.GET.get('plateId', None)
try:
plate_id = int(plate_id)
except ValueError:
return HttpResponse('Integer plateId required', status=400)
pl_data = ajax_load_plate(request,
plate_id,
return_as_platedata=True,
use_names=True)
plot_fig = plot_plate_map(pl_data,
color_by='dip_rates',
template=template)
else:
return HttpResponse('Unimplemented QC view: {}'.format(qc_view),
status=400)
else:
return HttpResponse('Unimplemented plot type: %s' % plot_type,
status=400)
as_attachment = request.GET.get('download', '0') == '1'
if file_type == 'json':
j = json.dumps(plot_fig, cls=PlotlyJSONEncoder)
response = HttpResponse(j, content_type='application/json')
elif file_type == 'csv':
response = HttpResponse(plotly_to_dataframe(plot_fig).to_csv(),
content_type='text/csv')
elif file_type == 'html':
template = 'plotly_plot{}.html'.format(
'_standalone' if as_attachment else '')
context = {
'data': json.dumps(plot_fig, cls=PlotlyJSONEncoder),
'page_title': strip_tags(plot_fig['layout']['title']['text'])
}
if as_attachment:
context['plotlyjs'] = get_plotlyjs()
response = render(request, template, context)
else:
return HttpResponse('Unknown file type: %s' % file_type, status=400)
if as_attachment:
try:
title = plot_fig['layout']['title']['text']
except KeyError:
title = 'Plot'
response['Content-Disposition'] = \
'attachment; filename="{}.{}"'.format(strip_tags(title), file_type)
return response