def __init__(self,
current_user: User,
analysis: Analysis):
super(AnalysisPageData, self).__init__(current_user, analysis, 'Analysis')
attached = [collection in analysis.collections for collection in get_collections(current_user)]
self.collection_table_data = ListTableData(current_user, get_collections(current_user),
'Collections',
attached, 'Attached')
self.attach_url = url_for('analyses_api.attach_collection', analysis_id=analysis.id)
self.merge_url = url_for('collections_api.merge_collections')
def render_collection_list():
try:
current_user = get_current_user()
return render_template('pages/list.html',
page_data=ListTableData(
current_user, get_collections(current_user),
'Collections'))
except Exception as e:
return handle_exception_browser(e)
def __init__(self,
current_user: User,
selected_collections: List[Collection] = None):
if selected_collections is None:
selected_collections = []
super(AnalysisCreateFormData, self).__init__(current_user,
'Create Analysis')
self.entries.append(
FormEntry('groupWriteCheckbox', 'group_can_write',
'User group members can edit or delete?', True,
'checkbox'))
collection_options = [
SelectOption(collection.id, f'{collection.id}: {collection.name}',
collection in selected_collections)
for collection in get_collections(current_user)
]
self.entries.append(
FormEntry('collectionSelect',
'collection_ids',
'Collections',
input_type='select',
select_options=collection_options,
select_multiple=True))
def get_post_form():
try:
collection_options = [
{'label': f'{collection.id}: {collection.name}', 'value': collection.id}
for collection in get_collections(current_user, {'kind': 'data'})
]
analysis_options = [
{'label': f'{analysis.id}: {analysis.name}', 'value': analysis.id}
for analysis in get_analyses(current_user)
]
except:
collection_options = []
analysis_options = []
try:
editor_data = CollectionProcessingModel(load_data=True)
loaded_badges = editor_data.get_collection_badges()
collection_load_info = editor_data.get_collection_load_info()
except:
loaded_badges = [html.Span([dbc.Badge('None', className='badge-pill')])]
collection_load_info = 'Loaded collections.'
return dbc.Card(
dbc.CardBody(
dbc.Form(
[
dbc.Row(
[
dbc.Col(
[
dbc.FormGroup(
[
dbc.Label('Collection IDs', html_for='collection-id'),
dcc.Dropdown(options=collection_options, id='collection-id', multi=True)
]
)
]
),
dbc.Col(
[
dbc.FormGroup(
[
dbc.Label(collection_load_info, html_for='loaded-display',
id='collections-label'),
dcc.Loading(
[
dbc.InputGroup(
[
dbc.Button('Get', id='get-collection',
className='col-sm-2 btn-success'),
html.H4(loaded_badges, id='loaded-collections',
className='col-sm-10')
], id='loaded-display'
)
]
)
]
)
]
)
]
),
html.H5('Post Collection'),
dbc.Row(
[
dbc.Col(
[
dbc.FormGroup(
[
dbc.Label('Name', html_for='name-input-wrapper-wrapper'),
html.Div(
html.Div(
dbc.Input(id='name-input'), id='name-input-wrapper'
), id='name-input-wrapper-wrapper'
)
]
)
]
),
dbc.Col(
[
dbc.FormGroup(
[
dbc.Label('Analyses', html_for='analysis-select'),
dcc.Dropdown(id='analysis-select', options=analysis_options, multi=True)
]
)
]
),
dbc.Col(
[
dbc.FormGroup(
[
dbc.Label('Post', html_for='post-button-group'),
dbc.FormGroup(
[
dbc.Button([html.I(className='fas fa-upload'), ' Post'],
id='post-button',
className='btn btn-success')
], id='post-button-group'
)
]
)
]
)
], className='form-row'
),
dcc.Loading(html.Small('', id='post-message', className='form-text'))
# will inject link when results posted
]
)
)
)
def get_pca_options_form():
try:
collection_options = [{
'label': f'{collection.id}: {collection.name}',
'value': collection.id
} for collection in get_collections(current_user, {'kind': 'data'})]
except:
collection_options = []
try:
pca_data = PCAModel(load_data=True)
label_options = [{
'label': label,
'value': label
} for label in pca_data.labels]
loaded_badges = pca_data.get_collection_badges()
collection_load_info = pca_data.get_collection_load_info()
except:
loaded_badges = [
html.Span([dbc.Badge('None', className='badge-pill')])
]
collection_load_info = 'Loaded collections.'
label_options = []
return dbc.Card(
dbc.CardBody(
dbc.Form([
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label('Collection ID',
html_for='collection-id'),
dcc.Dropdown(options=collection_options,
id='collection-id',
multi=True)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label(collection_load_info,
html_for='loaded-display',
id='collections-label'),
dcc.Loading([
dbc.InputGroup([
dbc.Button(
'Get',
id='get-collection',
className='col-sm-2 btn-success'),
html.H4(loaded_badges,
id='loaded-collections',
className='col-sm-10')
],
id='loaded-display')
])
])
])
]),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Scale by label(s)',
html.Abbr(
'\uFE56',
title=
'The mean of the records satisfying conditions on these '
'fields will be subtracted from each record, then each'
' record will be scaled by the standard deviation of the'
' records satisfying the conditions.')
],
html_for='scale-by'),
dcc.Dropdown(id='scale-by',
options=label_options,
multi=True),
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Scale by conditions',
html.Abbr(
'\uFE56',
title=
'The conditions for the records to use for scaling. If '
'left blank, then no scaling is performed.'
)
],
html_for='scale-by-value'),
dcc.Dropdown(id='scale-by-value',
options=[{
'label': 'All Records',
'value': 'index'
}],
multi=True),
])
])
]),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Model by label(s)',
html.Abbr(
'\uFE56',
title=
'Only consider records satisfying conditions on these'
' fields.')
],
html_for='model-by'),
dcc.Dropdown(id='model-by',
options=label_options,
multi=True)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Model by conditions',
html.Abbr(
'\uFE56',
title=
'The conditions which must be satisfied for the records'
'to be considered.')
],
html_for='model-by-value'),
dcc.Dropdown(id='model-by-value',
options=[],
multi=True)
])
])
]),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Ignore by label(s)',
html.Abbr(
'\uFE56',
title=
'Exclude records satisfying conditions on these fields'
)
],
html_for='ignore-by'),
dcc.Dropdown(id='ignore-by',
options=label_options,
multi=True)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Ignore by conditions',
html.Abbr(
'\uFE56',
title=
'Conditions which apply to records to be excluded.'
)
],
html_for='ignore-by-value'),
dcc.Dropdown(id='ignore-by-value',
options=[],
multi=True)
])
])
]),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Pair on label(s)',
html.Abbr(
'\uFE56',
title=
'The paired analysis works on the difference between '
'records in one class and other records, where the '
'records are "paired" by some identity condition. The '
'"pair on" label is used to pair all the records with '
'equal values for that field.')
],
html_for='pair-on'),
dcc.Dropdown(id='pair-on',
options=label_options,
multi=True)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Pair with label(s)',
html.Abbr(
'\uFE56',
title=
'The "pair with" condition applies to the records to be '
'subtracted from the others')
],
html_for='pair-with'),
dcc.Dropdown(id='pair-with',
options=label_options,
multi=True)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Pair with conditions',
html.Abbr(
'\uFE56',
title=
'The condition which must apply for the records which '
'will be subtracted.')
],
html_for='pair-with-value'),
dcc.Dropdown(id='pair-with-value',
options=[],
multi=True)
])
]),
]),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Project by label(s)',
html.Abbr(
'\uFE56',
title=
'Select additional data to be projected'
' on the PC axes created by the model.')
],
html_for='additional-projection'),
dcc.Dropdown(id='project-by',
options=label_options,
multi=True)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Project by conditions',
html.Abbr(
'\uFE56',
title=
'The conditions which must be satisfied for the records'
' to be projected.')
],
html_for='project-by-value'),
dcc.Dropdown(id='project-by-value',
options=[],
multi=True)
])
])
]),
dbc.FormGroup([
dcc.Loading([
dbc.Button('Run PCA',
id='pca-button',
color='primary',
className='btn btn-block form-control'),
html.Small('', id='message', className='form-text')
])
])
])))
def get_opls_options_form():
try:
collection_options = [{
'label': f'{collection.id}: {collection.name}',
'value': collection.id
} for collection in get_collections(current_user, {'kind': 'data'})]
except:
collection_options = []
try:
opls_data = OPLSModel(load_data=True)
label_options = [{
'label': label,
'value': label
} for label in opls_data.labels]
label_options_with_type = [{
'label': label,
'value': label
} for label in opls_data.get_label_data(True)]
loaded_badges = opls_data.get_collection_badges()
collection_load_info = opls_data.get_collection_load_info()
except:
loaded_badges = [
html.Span([dbc.Badge('None', className='badge-pill')])
]
collection_load_info = 'Loaded collections.'
label_options = []
label_options_with_type = []
return dbc.Form([
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label('Collection ID', html_for='collection-id'),
dcc.Dropdown(options=collection_options,
id='collection-id',
multi=False)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label(collection_load_info,
html_for='loaded-display',
id='collections-label'),
dcc.Loading([
dbc.InputGroup([
dbc.Button('Get',
id='get-collection',
className='col-sm-2 btn-success'),
html.H4(loaded_badges,
id='loaded-collections',
className='col-sm-10')
],
id='loaded-display')
])
])
])
]),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Target variable.',
html.Abbr('\uFE56', title='The target ("y") variable.')
],
html_for='target-variable'),
dcc.Dropdown(id='target-variable',
options=label_options_with_type,
multi=False)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Regression type.',
html.Abbr('\uFE56',
title='For categorical target variables,'
' choose Discrimination. When The'
' magnitudes of values are important'
' choose Regression.')
],
html_for='regression-type'),
dcc.Dropdown(id='regression-type',
options=[{
'label': 'Regression',
'value': 'regression'
}, {
'label': 'Discrimination',
'value': 'discrimination'
}],
multi=False)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Multiclass discrimination behavior',
html.Abbr(
'\uFE56',
title=
'How to handle multiclass targets. One regressor is'
' created for each class or pair of classes and the '
'cross-validation is performed on each one separately. '
'"One v. One" will try to discriminate between each pair'
' of classes. "One v. All" will discriminate each class '
'from all the other classes. You can select both.\n\n'
'If "Regression" is selected in the previous option, an '
'attempt will be made to coerce the values of the target '
'variable into floating-point numbers and treat the target '
'as a continuous variable and this option will be ignored.'
)
],
html_for='multiclass-behavior'),
dcc.Dropdown(id='multiclass-behavior',
options=[{
'label': 'One v. one',
'value': 'one_v_one',
}, {
'label': 'One v. all',
'value': 'one_v_all'
}],
multi=True)
])
]),
]),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Cross-validation folds',
html.Abbr(
'\uFE56',
title=
'The number of test/train splits for the test to determine '
'the significance of regression quality metrics.')
],
html_for='cross-val-k'),
dbc.Input(id='cross-val-k',
type='number',
value=-1,
min=-1)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Minimum orthogonal components.',
html.Abbr(
'\uFE56',
title=
'The minimum number of orthogonal components to remove.'
)
],
html_for='min-n-components'),
dbc.Input(id='min-n-components',
type='number',
value=1,
min=1)
])
]),
]),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Inner test \u03B1',
html.Abbr(
'\uFE56',
title=
'A two-sided p-value threshold which is used to determine '
'which features need further scrutiny.')
],
html_for='inner-test-alpha'),
dbc.Input(id='inner-test-alpha',
type='number',
value=0.2,
step=0.05)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Outer test \u03B1',
html.Abbr(
'\uFE56',
title=
'A two-sided p-value threshold which is used to determine '
'which features are significant.')
],
html_for='outer-test-alpha'),
dbc.Input(id='outer-test-alpha',
type='number',
value=0.01,
step=0.01)
])
])
]),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Metric test permutations',
html.Abbr(
'\uFE56',
title=
'How many permutations (of the target) to determine '
'p-values for regression quality metrics.')
],
html_for='permutations'),
dbc.Input(id='permutations', type='number', value=1000)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Inner test permutations',
html.Abbr(
'\uFE56',
title=
'How many permutations (of the data in one feature) to '
'perform for every feature to estimate its significance.'
)
],
html_for='inner-permutations'),
dbc.Input(id='inner-permutations',
type='number',
value=100)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Outer test permutations',
html.Abbr(
'\uFE56',
title=
'How many permutations (of the data in one feature) to '
'perform for every feature determined to be potentially '
'significant in the first round.')
],
html_for='outer-permutations'),
dbc.Input(id='outer-permutations',
type='number',
value=500)
])
])
]),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Scale by label(s)',
html.Abbr(
'\uFE56',
title=
'The mean of the records satisfying conditions on these '
'fields will be subtracted from each record, then each'
' record will be scaled by the standard deviation of the'
' records satisfying the conditions.')
],
html_for='scale-by'),
dcc.Dropdown(id='scale-by',
options=label_options,
multi=True),
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Scale by conditions',
html.Abbr(
'\uFE56',
title=
'The conditions for the records to use for scaling. If '
'left blank, then no scaling is performed.')
],
html_for='scale-by-value'),
dcc.Dropdown(id='scale-by-value',
options=[{
'label': 'All Records',
'value': 'index'
}],
multi=True),
])
])
]),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Model by label(s)',
html.Abbr(
'\uFE56',
title=
'Only consider records satisfying conditions on these'
' fields.')
],
html_for='model-by'),
dcc.Dropdown(id='model-by',
options=label_options,
multi=True)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Model by conditions',
html.Abbr(
'\uFE56',
title=
'The conditions which must be satisfied for the records'
'to be considered.')
],
html_for='model-by-value'),
dcc.Dropdown(id='model-by-value', options=[], multi=True)
])
])
]),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Ignore by label(s)',
html.Abbr(
'\uFE56',
title=
'Exclude records satisfying conditions on these fields'
)
],
html_for='ignore-by'),
dcc.Dropdown(id='ignore-by',
options=label_options,
multi=True)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Ignore by conditions',
html.Abbr(
'\uFE56',
title=
'Conditions which apply to records to be excluded.'
)
],
html_for='ignore-by-value'),
dcc.Dropdown(id='ignore-by-value', options=[], multi=True)
])
])
]),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Pair on label(s)',
html.Abbr(
'\uFE56',
title=
'The paired analysis works on the difference between '
'records in one class and other records, where the '
'records are "paired" by some identity condition. The '
'"pair on" label is used to pair all the records with '
'equal values for that field.')
],
html_for='pair-on'),
dcc.Dropdown(id='pair-on',
options=label_options,
multi=True)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Pair with label(s)',
html.Abbr(
'\uFE56',
title=
'The "pair with" condition applies to the records to be '
'subtracted from the others')
],
html_for='pair-with'),
dcc.Dropdown(id='pair-with',
options=label_options,
multi=True)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Pair with conditions',
html.Abbr(
'\uFE56',
title=
'The condition which must apply for the records which '
'will be subtracted.')
],
html_for='pair-with-value'),
dcc.Dropdown(id='pair-with-value', options=[], multi=True)
])
]),
]),
dbc.FormGroup([
dcc.Loading([
dbc.Button('Run OPLS',
id='opls-button',
color='primary',
className='btn btn-block form-control'),
html.Small('', id='message', className='form-text')
])
])
])
def get_load_results_form():
try:
analysis_options = [{
'label': f'{analysis.id}: {analysis.name}',
'value': analysis.id
} for analysis in get_analyses(current_user)]
except:
analysis_options = []
try:
collection_options = [{
'label': f'{collection.id}: {collection.name}',
'value': collection.id
} for collection in get_collections(current_user, {'kind': 'results'})
if collection.get_attr('analysis_type',
safe=True) == 'opls']
except:
collection_options = []
try:
opls_data = OPLSModel(load_data=True)
loaded_badges = opls_data.get_results_collection_badges()
except:
loaded_badges = [
html.Span([dbc.Badge('None', className='badge-pill')])
]
return dbc.Form([
html.H5('Load Results'),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label('Results Collection ID',
html_for='results-collection-id'),
dcc.Dropdown(options=collection_options,
id='results-collection-id',
multi=False)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label('Results collection'),
dcc.Loading([
dbc.InputGroup([
dbc.Button('Get',
id='get-results-collection',
className='col-sm-2 btn-success'),
html.H4(html.Div(loaded_badges,
id='loaded-results-collection'),
id='loaded-results-collection-wrapper',
className='col-sm-10')
],
id='loaded-results-display')
])
])
])
]),
html.H5('Save Results'),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label('Results', html_for='results-select'),
dcc.Dropdown(id='results-select',
multi=True,
options=[{
'label': 'Metrics',
'value': 'metrics'
}, {
'label': 'Loadings + p-Values',
'value': 'loadings'
}, {
'label': 'Scores',
'value': 'scores'
}, {
'label': 'Weights',
'value': 'weights'
}])
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label('File Format', html_for='file-format-select'),
dcc.Dropdown(id='file-format-select',
multi=False,
options=[{
'label': 'CSV',
'value': 'csv'
}])
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label('Assemble results',
html_for='download-button-group'),
dbc.FormGroup([
dbc.Button([html.I(className='fas fa-cogs')],
id='download-button',
className='btn btn-info')
],
id='download-button-group')
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label('Download', html_for='download-link-group'),
dbc.FormGroup([
html.A(
[html.I(className='fas fa-download'), ' Download'],
id='download-link',
className='btn btn-secondary disabled')
],
id='download-link-group')
])
])
],
className='form-row'),
html.Div(html.Small('', id='download-message', className='form-text')),
# dcc.Loading(html.Small('', id='download-message', className='form-text')),
# will inject link when results posted
html.H5('Save Plots'),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label('Width', html_for='width-input'),
dbc.Input(type='number',
min=0,
step=0.25,
value=6.5,
id='width-input')
])
],
className='col-2'),
dbc.Col([
dbc.FormGroup([
dbc.Label('Height', html_for='height-input'),
dbc.Input(type='number',
min=0,
step=0.25,
value=4,
id='height-input')
])
],
className='col-2'),
dbc.Col([
dbc.FormGroup([
dbc.Label('Units', html_for='units-select'),
dcc.Dropdown(options=[{
'label': 'in',
'value': 'in'
}, {
'label': 'cm',
'value': 'cm'
}, {
'label': 'px',
'value': 'px'
}],
value='in',
clearable=False,
id='units-select')
])
],
className='col-1'),
dbc.Col([
dbc.FormGroup([
dbc.Label('DPI', html_for='dpi-select'),
dbc.Input(type='number',
min=50,
step=25,
value=100,
id='dpi-input')
])
],
className='col-2'),
dbc.Col([
dbc.FormGroup([
dbc.Label('File Formats',
html_for='plot-file-format-select'),
dcc.Dropdown(options=[{
'label': 'SVG',
'value': 'svg'
}, {
'label': 'PNG',
'value': 'png'
}, {
'label': 'JPEG',
'value': 'jpg'
}, {
'label': 'PDF',
'value': 'pdf'
}, {
'label': 'TIFF',
'value': 'tif'
}, {
'label': 'EPS',
'value': 'eps'
}],
value=['png'],
clearable=False,
multi=True,
id='plot-file-format-select')
])
],
className='col-2'),
dbc.Col([
dbc.FormGroup([
dbc.Label('Prepare',
html_for='plot-download-button-group'),
dbc.FormGroup([
dbc.Button(
[html.I(className='fas fa-cogs'), ' Prepare'],
id='plot-download-button',
className='btn btn-info')
],
id='plot-download-button-group')
])
],
className='col-1'),
dbc.Col(
[
dbc.FormGroup([
dbc.Label('Download',
html_for='plot-download-link-group'),
dbc.FormGroup(
[
#dcc.Loading(
html.Div(
html.A([
html.I(className='fas fa-download'),
' Download'
],
id='plot-download-link',
className=
'btn btn-secondary disabled'))
],
id='plot-download-link-group')
])
],
className='col-1')
]),
dbc.Row([
dcc.Interval(
id='progress-interval', n_intervals=0, interval=3600000),
html.Div(dbc.FormText('Image export progress'),
id='progress-label'),
dbc.Progress(html.Div(
dbc.Badge('0/0', color='light', pill=True,
id='progress-badge')),
id='progress',
striped=True,
animated=True,
style={'height': '25px'},
color='info',
className='w-100')
],
id='progress-div'),
# will inject link when results posted
# dcc.Loading(html.Small('', id='plot-download-message', className='form-text')),
html.Div(
html.Small('', id='plot-download-message', className='form-text')),
html.H5('Post transformed collection'),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label('Name', html_for='name-input'),
dbc.Input(id='name-input')
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label('Analysis', html_for='analysis-select'),
dcc.Dropdown(id='analysis-select',
options=analysis_options)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label('Post', html_for='post-button-group'),
dbc.FormGroup([
dbc.Button(
[html.I(className='fas fa-upload'), ' Post'],
id='post-button',
className='btn btn-success')
],
id='post-button-group')
])
])
],
className='form-row'),
dcc.Loading(html.Small('', id='post-message', className='form-text'))
# will inject link when results posted
])
def get_plot_options_form():
try:
collection_options = [{
'label': f'{collection.id}: {collection.name}',
'value': collection.id
} for collection in get_collections(current_user, {'kind': 'data'})]
except:
collection_options = []
try:
viz_data = VisualizationModel(load_data=True)
label_options = [{
'label': label,
'value': label
} for label in viz_data.labels]
loaded_badges = viz_data.get_collection_badges()
collection_load_info = viz_data.get_collection_load_info()
except:
loaded_badges = [
html.Span([dbc.Badge('None', className='badge-pill')])
]
collection_load_info = 'Loaded collections.'
label_options = []
return dbc.Form([
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label('Collection ID', html_for='collection-id'),
dcc.Dropdown(options=collection_options,
id='collection-id',
multi=True)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label(collection_load_info,
html_for='loaded-display',
id='collections-label'),
dcc.Loading([
dbc.InputGroup([
dbc.Button('Get',
id='get-collection',
className='col-sm-2 btn-success'),
html.H4(loaded_badges,
id='loaded-collections',
className='col-sm-10')
],
id='loaded-display')
])
])
])
]),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label('Group By', html_for='group-by'),
dcc.Dropdown(options=label_options,
id='group-by',
multi=True)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label('Display Groups', html_for='group-by-value'),
dcc.Dropdown(options=[], id='group-by-value', multi=True)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label('Label By', html_for='label-by'),
dcc.Dropdown(options=label_options,
id='label-by',
multi=True)
])
])
]),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label('Show Bins From', html_for='bin-collection'),
dcc.Dropdown(options=collection_options,
id='bin-collection',
multi=False)
])
],
className='col-5'),
dbc.Col([
dbc.FormGroup([
dbc.Label('Legend Style', html_for='legend-style-select'),
dcc.Dropdown(options=[{
'label': 'Full',
'value': 'full'
}, {
'label': 'Groups',
'value': 'groups'
}, {
'label': 'None',
'value': 'none'
}],
value='full',
id='legend-style-select',
clearable=False)
])
],
className='col-3'),
dbc.Col([
dbc.FormGroup([
dbc.Label('Background Color',
html_for='background-color-select'),
dcc.Dropdown(options=[
{
'label': 'Transparent',
'value': 'rgba(0,0,0,0)'
},
{
'label': 'White',
'value': 'rgba(255,255,255,1)'
},
],
value='rgba(0,0,0,0)',
id='background-color-select',
clearable=False)
])
],
className='col-3'),
dbc.Col([
dbc.FormGroup([
dbc.Label('Plot', html_for='plot-button-group'),
dbc.InputGroup([
dbc.Button('Plot', color='primary', id='plot-button')
],
id='plot-button-group')
])
],
className='col-1'),
]),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label('Width', html_for='width-input'),
dbc.Input(type='number',
min=0,
step=0.25,
value=6.5,
id='width-input')
])
],
className='col-2'),
dbc.Col([
dbc.FormGroup([
dbc.Label('Height', html_for='height-input'),
dbc.Input(type='number',
min=0,
step=0.25,
value=4,
id='height-input')
])
],
className='col-2'),
dbc.Col([
dbc.FormGroup([
dbc.Label('Units', html_for='units-select'),
dcc.Dropdown(options=[{
'label': 'in',
'value': 'in'
}, {
'label': 'cm',
'value': 'cm'
}, {
'label': 'px',
'value': 'px'
}],
value='in',
clearable=False,
id='units-select')
])
],
className='col-1'),
dbc.Col([
dbc.FormGroup([
dbc.Label('DPI', html_for='dpi-select'),
dbc.Input(type='number',
min=50,
step=25,
value=100,
id='dpi-input')
])
],
className='col-2'),
dbc.Col([
dbc.FormGroup([
dbc.Label('File Format', html_for='file-format-select'),
dcc.Dropdown(options=[{
'label': 'SVG',
'value': 'svg'
}, {
'label': 'PNG',
'value': 'png'
}, {
'label': 'JPEG',
'value': 'jpg'
}, {
'label': 'PDF',
'value': 'pdf'
}, {
'label': 'TIFF',
'value': 'tif'
}, {
'label': 'EPS',
'value': 'eps'
}],
value='png',
clearable=False,
id='file-format-select')
])
],
className='col-2'),
dbc.Col([
dbc.FormGroup([
dbc.Label('Prepare', html_for='download-button-group'),
dbc.FormGroup([
dbc.Button([html.I(className='fas fa-cogs')],
id='download-button',
className='btn btn-info')
],
id='download-button-group')
])
],
className='col-1'),
dbc.Col([
dbc.FormGroup([
dbc.Label('Download', html_for='download-link-group'),
dbc.FormGroup([
dcc.Loading(
html.A([html.I(className='fas fa-download')],
id='download-link',
className='btn btn-secondary disabled'))
],
id='download-link-group')
])
],
className='col-1')
]),
dbc.Row(
dcc.Loading(
html.Small('', id='download-message', className='form-text'))),
])
def get_options_form():
try:
collection_options = [{
'label': f'{collection.id}: {collection.name}',
'value': collection.id
} for collection in get_collections(current_user, {'kind': 'data'})]
analysis_options = [{
'label': f'{analysis.id}: {analysis.name}',
'value': analysis.id
} for analysis in get_analyses(current_user)]
except:
collection_options = []
analysis_options = []
try:
editor_data = CollectionEditorModel(load_data=True)
label_options = [{
'label': label,
'value': label
} for label in editor_data.labels]
loaded_badges = editor_data.get_collection_badges()
collection_load_info = editor_data.get_collection_load_info()
if len(loaded_badges) == 2:
collection_ids = editor_data.unique_vals('original_collection_id')
collection_id_options = [{
'label': f'collection_id={i}',
'value': i
} for i in collection_ids]
else:
collection_id_options = []
except:
loaded_badges = [
html.Span([dbc.Badge('None', className='badge-pill')])
]
collection_load_info = 'Loaded collections.'
label_options = []
collection_id_options = []
return dbc.Form([
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label('Collection IDs', html_for='collection-id'),
dcc.Dropdown(options=collection_options,
id='collection-id',
multi=True)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label(collection_load_info,
html_for='loaded-display',
id='collections-label'),
dcc.Loading([
dbc.InputGroup([
dbc.Button('Get',
id='get-collection',
className='col-sm-2 btn-success'),
html.H4(loaded_badges,
id='loaded-collections',
className='col-sm-10')
],
id='loaded-display')
])
])
])
]),
html.H5('Filter/Join Collections'),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Filter by label(s)',
html.Abbr(
'\uFE56',
title=
'Only consider records satisfying conditions on these'
' fields.')
],
html_for='filter-by'),
dcc.Dropdown(id='filter-by',
options=label_options,
multi=True)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Filter by conditions',
html.Abbr(
'\uFE56',
title=
'The conditions which must be satisfied for the records'
'to be considered.')
],
html_for='filter-by-value'),
dcc.Dropdown(id='filter-by-value', options=[], multi=True)
])
])
]),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Ignore by label(s)',
html.Abbr(
'\uFE56',
title=
'Exclude records satisfying conditions on these fields'
)
],
html_for='ignore-by'),
dcc.Dropdown(id='ignore-by',
options=label_options,
multi=True)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Ignore by conditions',
html.Abbr(
'\uFE56',
title=
'Conditions which apply to records to be excluded.'
)
],
html_for='ignore-by-value'),
dcc.Dropdown(id='ignore-by-value', options=[], multi=True)
])
])
]),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label([
'Join on label(s)',
html.Abbr(
'\uFE56',
title=
'A combination of values forming a unique key on which '
'the two collections are joined.')
],
html_for='join-on'),
dcc.Dropdown(id='join-on',
options=label_options,
multi=True,
disabled=(len(loaded_badges) != 2))
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label([
'"Left" Collection ID',
html.Abbr(
'\uFE56',
title=
'The collection id for the collection which will have '
'positive values for "x" and appear on the left side of '
'the plots')
],
html_for='positive-collection'),
dcc.Dropdown(id='positive-collection',
options=collection_id_options,
multi=False,
disabled=(len(loaded_badges) != 2))
])
])
]),
html.H5('Post Collection'),
dbc.Row([
dbc.Col([
dbc.FormGroup([
dbc.Label('Name', html_for='name-input-wrapper-wrapper'),
html.Div(html.Div(dbc.Input(id='name-input'),
id='name-input-wrapper'),
id='name-input-wrapper-wrapper')
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label('Analyses', html_for='analysis-select'),
dcc.Dropdown(id='analysis-select',
options=analysis_options,
multi=True)
])
]),
dbc.Col([
dbc.FormGroup([
dbc.Label('Post', html_for='post-button-group'),
dbc.FormGroup([
dbc.Button(
[html.I(className='fas fa-upload'), ' Post'],
id='post-button',
className='btn btn-success')
],
id='post-button-group')
])
])
],
className='form-row'),
dcc.Loading(html.Small('', id='post-message', className='form-text'))
# will inject link when results posted
])