def store_file_meta_data(
contents, filename, dataset_name
):
desc = ''
subsets = []
row_options = []
col_options = []
if dataset_name is not None:
dataset = datasets[dataset_name]
if dataset['file'].endswith('.tsv'):
desc, row_options, col_options = \
gene_expression_reader.read_tsv_file(
filepath=dataset['file'],
index_column=dataset['row_labels_source'],
ignore_columns=dataset['ignore_columns']
)
subsets = None
elif dataset['file'].endswith('.soft'):
desc, subsets, row_options, col_options = \
gene_expression_reader.read_soft(dataset['file'])
elif contents is not None:
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string).decode('UTF-8')
try:
desc, subsets, row_options, col_options = \
gene_expression_reader.read_soft(
decoded,
is_datafile=False
)
except Exception:
pass
return {
'desc': desc,
'subsets': subsets,
'row_options': row_options,
'col_options': col_options
}
def display_clustergram(_, group_markers, sel_rows, sel_cols, fig_opts,
dataset_name, contents, filename, computed_traces):
ctx = dash.callback_context
adding_grp_marker = ctx.triggered[0]['prop_id'].split(
'.')[0] == 'group-markers'
wrapper_content = ''
curves = None
comp_traces = computed_traces
if len(sel_rows) < 2 or len(sel_cols) < 2 and \
dataset_name is None and contents is None:
wrapper_content = html.Div(
'No data have been selected to display. Please upload a file \
or select a preloaded file from the dropdown, then select at \
least two columns and two rows.',
style={
'padding': '40px',
'font-size': '20pt'
})
return wrapper_content, curves, comp_traces
if fig_opts['cluster'] is None or len(fig_opts['cluster']) == 0:
wrapper_content = html.Div(
'No dimension has been selected along which to perform \
clustering. \
Please select at least one option from the dropdown.',
style={
'padding': '30px',
'font-size': '20pt'
})
return wrapper_content, curves, comp_traces
if dataset_name is not None:
dataset = datasets[dataset_name]
if dataset['file'].endswith('.tsv'):
data = gene_expression_reader.read_tsv_file(
filepath=dataset['file'],
rows=sel_rows,
columns=sel_cols,
ignore_columns=dataset['ignore_columns'],
index_column=dataset['row_labels_source'],
return_filtered_data=True)
elif dataset['file'].endswith('.soft'):
data = gene_expression_reader.read_soft_file(
filepath=dataset['file'],
rows=sel_rows,
columns=sel_cols,
return_filtered_data=True)
elif contents is not None and dataset_name is None:
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string).decode('UTF-8')
try:
data = gene_expression_reader.read_soft_file(
contents=decoded,
rows=sel_rows,
columns=sel_cols,
return_filtered_data=True)
except Exception:
data = None
if group_markers is not None:
fig_opts['row_group_marker'] = group_markers['row_group_marker']
fig_opts['col_group_marker'] = group_markers['col_group_marker']
try:
# don't recompute the dendrogram traces if we're just adding a group
# marker
if adding_grp_marker and computed_traces is not None:
fig, curves = dash_bio.Clustergram(
generate_curves_dict=True,
computed_traces=computed_traces,
data=data,
**fig_opts)
else:
fig, curves, comp_traces = dash_bio.Clustergram(
generate_curves_dict=True,
return_computed_traces=True,
data=data,
**fig_opts)
wrapper_content = dcc.Graph(id='clustergram', figure=fig)
except IndexError:
wrapper_content = "Loading data..."
except ValueError:
wrapper_content = "Loading data..."
except Exception as e:
wrapper_content = "There was an error: {}".format(e)
return wrapper_content, curves, comp_traces