def update_all(selected_proc, selected_scan, selected_sess, selected_proj,
selected_time, selected_groupby, n_clicks):
refresh = False
logging.debug('update_all')
# Load our data
# This data will already be merged scans and assessors with
# a row per scan or assessor
ctx = dash.callback_context
if was_triggered(ctx, 'button-qa-refresh'):
# Refresh data if refresh button clicked
logging.debug('refresh:clicks={}'.format(n_clicks))
refresh = True
logging.debug('loading data')
#df = load_data(
# proj_filter=selected_proj,
# proc_filter=selected_proc,
# scan_filter=selected_scan,
# refresh=refresh)
df = load_data(refresh=refresh)
# Update lists of possible options for dropdowns (could have changed)
# make these lists before we filter what to display
proj = utils.make_options(load_proj_options())
scan = utils.make_options(load_scan_options(selected_proj))
sess = utils.make_options(load_sess_options(selected_proj))
proc = utils.make_options(load_proc_options(selected_proj))
# Filter data based on dropdown values
df = data.filter_data(df, selected_proj, selected_proc, selected_scan,
selected_time, selected_sess)
# Get the qa pivot from the filtered data
dfp = qa_pivot(df)
tabs = get_graph_content(dfp, selected_groupby)
# Get the table data
selected_cols = ['SESSION', 'PROJECT', 'DATE', 'SESSTYPE', 'SITE']
if selected_proc:
selected_cols += selected_proc
if selected_scan:
selected_cols += selected_scan
columns = utils.make_columns(selected_cols)
records = dfp.reset_index().to_dict('records')
# TODO: should we only include data for selected columns here,
# to reduce amount of data sent?
# Return table, figure, dropdown options
logging.debug('update_all:returning data')
return [proc, scan, sess, proj, records, columns, tabs]
def update_stats(selected_proc, selected_proj, selected_time,
selected_sesstype, n_clicks):
refresh = False
logging.debug('update_all')
# Load our data
# This data will already be merged scans and assessors with
# a row per scan or assessor
ctx = dash.callback_context
if was_triggered(ctx, 'button-stats-refresh'):
# Refresh data if refresh button clicked
logging.debug('refresh:clicks={}'.format(n_clicks))
refresh = True
# Load data with refresh if requested
df = load_stats(refresh=refresh)
# Update lists of possible options for dropdowns (could have changed)
# make these lists before we filter what to display
proc = utils.make_options(df.TYPE.unique())
proj = utils.make_options(df.PROJECT.unique())
# if none chose, default to the first proc
#if not selected_proc:
# selected_proc = [(df.TYPE.unique())[0]]
# if 'fmri_msit_v2' in df.TYPE.unique():
# selected_proc = ['fmri_msit_v2']
# Filter data based on dropdown values
df = data.filter_data(df, selected_proj, selected_proc, selected_time,
selected_sesstype)
# Get the graph content in tabs (currently only one tab)
tabs = get_graph_content(df)
# Determine columns to be included in the table
_vars = data.get_vars()
selected_cols = list(data.static_columns())
selected_cols.extend(
[x for x in df.columns if (x in _vars and not pd.isnull(df[x]).all())])
# Get the table data
columns = utils.make_columns(selected_cols)
records = df.reset_index().to_dict('records')
# Return table, figure, dropdown options
logging.debug('update_all:returning data')
return [proc, proj, records, columns, tabs]
def __init__(self, width=600, height=300, plugins=(),
toolbars=u_settings.DEFAULT_TOOLBARS,
file_path='', image_path='', scrawl_path='',
image_manager_path='', css='',
options={}, attrs=None, **kwargs):
self.ueditor_options = make_options(width, height, plugins, toolbars,
file_path, image_path, scrawl_path,
image_manager_path, css, options)
super(UEditorWidget, self).__init__(attrs)
def __init__(self, width=600, height=300, plugins=(),
toolbars=u_settings.DEFAULT_TOOLBARS,
file_path='', image_path='', scrawl_path='',
image_manager_path='', css='',
options={}, attrs=None, **kwargs):
self.ueditor_options = make_options(width, height, plugins, toolbars,
file_path, image_path, scrawl_path,
image_manager_path, css, options)
super(UEditorWidget, self).__init__(attrs)
def update_activity(
selected_category,
selected_project,
selected_source,
n_clicks
):
refresh = False
logging.debug('update_activity')
# Load activity data
ctx = dash.callback_context
if was_triggered(ctx, 'button-activity-refresh'):
# Refresh data if refresh button clicked
logging.debug('activity refresh:clicks={}'.format(n_clicks))
refresh = True
logging.debug('loading activity data')
df = load_activity(refresh=refresh)
# Update lists of possible options for dropdowns (could have changed)
# make these lists before we filter what to display
projects = utils.make_options(load_project_options())
categories = utils.make_options(load_category_options())
sources = utils.make_options(load_source_options())
# Filter data based on dropdown values
df = filter_data(
df,
selected_project,
selected_category,
selected_source)
tabs = get_graph_content(df)
# Get the table data
selected_cols = ['ID', 'DATETIME', 'DESCRIPTION']
columns = utils.make_columns(selected_cols)
records = df.reset_index().to_dict('records')
# Return table, figure, dropdown options
logging.debug('update_activity:returning data')
return [categories, projects, sources, records, columns, tabs]
def __init__(self, verbose_name, width=600, height=300,
plugins=(), toolbars='normal', file_path='',
image_path='', scrawl_path='', image_manager_path='',
css='', options={}, **kwargs):
self.ueditor_options = make_options(width, height, plugins, toolbars,
file_path, image_path, scrawl_path,
image_manager_path, css, options)
kwargs["verbose_name"] = verbose_name
super(UEditorField, self).__init__(**kwargs)
def __init__(self, label, width=600, height=300, plugins=(),
toolbars=u_settings.DEFAULT_TOOLBARS, file_path='',
image_path='', scrawl_path='', image_manager_path='',
css='', options={}, *args, **kwargs):
options = make_options(width, height, plugins, toolbars,
file_path, image_path, scrawl_path,
image_manager_path, css, options)
kwargs['widget'] = UEditorWidget(**options)
kwargs['label'] = label
super(UEditorField, self).__init__(*args, **kwargs)
def __init__(self,
label,
width=600,
height=300,
plugins=(),
toolbars=u_settings.DEFAULT_TOOLBARS,
file_path='',
image_path='',
scrawl_path='',
image_manager_path='',
css='',
options={},
*args,
**kwargs):
options = make_options(width, height, plugins, toolbars, file_path,
image_path, scrawl_path, image_manager_path,
css, options)
kwargs['widget'] = UEditorWidget(**options)
kwargs['label'] = label
super(UEditorField, self).__init__(*args, **kwargs)
def update_stats(
selected_proc,
selected_proj,
selected_time,
selected_pivot,
n_clicks
):
refresh = False
logging.debug('update_all')
# Load our data
# This data will already be merged scans and assessors with
# a row per scan or assessor
ctx = dash.callback_context
if was_triggered(ctx, 'button-stats-refresh'):
# Refresh data if refresh button clicked
logging.debug('refresh:clicks={}'.format(n_clicks))
refresh = True
# Load data with refresh if requested
df = load_stats(refresh=refresh)
# Update lists of possible options for dropdowns (could have changed)
# make these lists before we filter what to display
proc = utils.make_options(df.TYPE.unique())
proj = utils.make_options(df.PROJECT.unique())
# Filter data based on dropdown values
selected_sesstype = 'all'
df = data.filter_data(
df,
selected_proj,
selected_proc,
selected_time,
selected_sesstype)
# Get the graph content in tabs (currently only one tab)
tabs = get_graph_content(df)
# TODO: handle multiple of same type for a subject?
if selected_pivot == 'subj':
# Pivot to one row per subject
_index = ['SUBJECT', 'PROJECT', 'AGE', 'SEX', 'DEPRESS', 'SITE']
_vars = data.get_vars()
_vars = [x for x in df.columns if (
x in _vars and not pd.isnull(df[x]).all())]
_cols = []
if len(df.SESSTYPE.unique()) > 1:
# Multiple session types, need prefix to disambiguate
_cols += ['SESSTYPE']
if len(df.TYPE.unique()) > 1:
# Multiple processing types, need prefix to disambiguate
_cols += ['TYPE']
# Drop any duplicates found b/c redcap sync module does not prevent
df = df.drop_duplicates()
# Make the pivot table based on _index, _cols, _vars
#print(_index)
#print(_cols)
#print(_vars)
#print(df)
dfp = df.pivot(index=_index, columns=_cols, values=_vars)
# Concatenate column levels to get one level with delimiter
dfp.columns = ['_'.join(reversed(t)) for t in dfp.columns]
# Clear the index so all columns are named
dfp = dfp.reset_index()
columns = utils.make_columns(dfp.columns)
records = dfp.to_dict('records')
else:
# Keep as to one row per assessor
# Determine columns to be included in the table
selected_cols = list(data.static_columns())
_vars = data.get_vars()
_vars = [x for x in df.columns if (
x in _vars and not pd.isnull(df[x]).all())]
selected_cols.extend(_vars)
# Get the table data as one row per assessor
columns = utils.make_columns(selected_cols)
records = df.reset_index().to_dict('records')
# Count how many rows are in the table
rowcount = '{} rows'.format(len(records))
# Return table, figure, dropdown options
logging.debug('update_all:returning data')
return [proc, proj, records, columns, tabs, rowcount]
