def register_uc_4_1_callbacks(app, plot_service) -> None:
"""
Register UC-4.1 callbacks with Dash app.
Parameters
----------
app : Dash
Dash application instance.
plot_service : PlotService
Singleton PlotService instance (shared across all callbacks).
Notes
-----
- Registers 3 callbacks: panel toggle, sample dropdown initialization,
and horizontal bar chart rendering
- Refer to official documentation for processing logic details
"""
@app.callback(
Output("uc-4-1-collapse", "is_open"),
Input("uc-4-1-collapse-button", "n_clicks"),
State("uc-4-1-collapse", "is_open"),
prevent_initial_call=True,
)
def toggle_uc_4_1_info_panel(n_clicks, is_open):
"""
Toggle UC-4.1 informative panel collapse.
Parameters
----------
n_clicks : int
Number of clicks on collapse button.
is_open : bool
Current collapse state.
Returns
-------
bool
New collapse state (toggled).
"""
logger.info(
f"[UC-4.1] 🔘 Toggle clicked! n_clicks={n_clicks}, is_open={is_open}"
)
if n_clicks:
new_state = not is_open
logger.info(f"[UC-4.1] [OK] Panel toggled to: {new_state}")
return new_state
logger.info(f"[UC-4.1] ⊘ No clicks, keeping is_open={is_open}")
return is_open
@app.callback(
[
Output("uc-4-1-sample-dropdown", "options"),
Output("uc-4-1-sample-dropdown", "value"),
],
[
Input("merged-result-store", "data"),
Input("uc-4-1-accordion-group", "active_item"),
],
prevent_initial_call=True,
)
def initialize_sample_dropdown_uc_4_1(
merged_data: Optional[dict], active_item: Optional[str]
) -> Tuple[list, None]:
"""
Initialize sample dropdown with KEGG data.
This callback populates the dropdown menu with available samples
extracted from processed KEGG data, enabling users to select
specific samples for functional pathway profiling.
Data Processing (inline):
1. Extract KEGG data from store
2. Validate 'Sample' column exists
3. Extract unique samples
4. Sort alphabetically
5. Create dropdown options
Parameters
----------
merged_data : Optional[dict]
Pre-processed merged data stored in merged-result-store.
Expected structure: dict with 'kegg_df' key.
active_item : Optional[str]
Currently active accordion item (triggers re-initialization).
Returns
-------
Tuple[list, None]
- First element: List of dropdown option dictionaries with
label/value pairs for sample selection. Empty list
if no data available.
- Second element: Default selection value (None for no
initial selection).
Raises
------
PreventUpdate
If no data available or 'Sample' column not found.
"""
logger.info(
f"[UC-4.1] 🔄 Dropdown init triggered, data type: {type(merged_data)}"
)
if not merged_data:
logger.debug("[UC-4.1] No data in store, preventing dropdown init")
return [], None
# Check if this is initial call with empty/invalid data
if isinstance(merged_data, dict) and not merged_data:
logger.debug("[UC-4.1] Empty dict in store, preventing dropdown init")
return [], None
try:
# Extract KEGG DataFrame from store
if not isinstance(merged_data, dict) or "kegg_df" not in merged_data:
logger.error(
f"[UC-4.1] Invalid data format: expected dict with 'kegg_df', "
f"got {type(merged_data)}"
)
raise PreventUpdate
df = pd.DataFrame(merged_data["kegg_df"])
# Validate 'Sample' column exists
sample_col = None
for col_name in ["Sample", "sample", "sample_id"]:
if col_name in df.columns:
sample_col = col_name
break
if sample_col is None:
logger.error(
f"[UC-4.1] 'Sample' column not found in KEGG data. "
f"Available columns: {df.columns.tolist()}"
)
raise PreventUpdate
# DATA PROCESSING: Extract unique samples (inline)
samples = sorted(df[sample_col].dropna().unique())
# Create dropdown options
options = [{"label": sample, "value": sample} for sample in samples]
logger.info(f"[UC-4.1] Dropdown initialized with {len(options)} samples")
return options, None
except Exception as e:
logger.error(f"[UC-4.1] Dropdown error: {e}")
raise PreventUpdate
@app.callback(
Output("uc-4-1-chart-container", "children"),
Input("uc-4-1-sample-dropdown", "value"),
State("merged-result-store", "data"),
prevent_initial_call=True,
)
def render_uc_4_1(
selected_sample: Optional[str], merged_data: Optional[dict]
) -> Any:
"""
Render UC-4.1 horizontal bar chart based on selected sample.
Rendering Logic:
- Dropdown selection: Render chart for selected sample
- No auto-update (single trigger)
Parameters
----------
selected_sample : Optional[str]
Selected sample from dropdown.
merged_data : Optional[dict]
Merged data from store with 'kegg_df' key.
Returns
-------
dcc.Graph or html.Div
Horizontal bar chart component or error message.
Raises
------
PreventUpdate
If no sample selected or no data available.
"""
# Check dropdown selection
if not selected_sample:
logger.debug("[UC-4.1] No sample selected")
raise PreventUpdate
# Check data availability
if not merged_data:
logger.warning("[UC-4.1] No data available")
return _create_error_message("No data available for visualization")
try:
# Extract KEGG DataFrame from store
logger.debug(f"[UC-4.1] Received data type: {type(merged_data)}")
if not isinstance(merged_data, dict) or "kegg_df" not in merged_data:
logger.error(
f"[UC-4.1] Invalid data format: expected dict with 'kegg_df'"
)
return _create_error_message(
"KEGG database data not found. "
"Please ensure KEGG data is loaded."
)
df = pd.DataFrame(merged_data["kegg_df"])
# Validate required columns
required_cols = {
"Sample": ["Sample", "sample", "sample_id"],
"Pathway": ["Pathway", "pathname", "pathway_name"],
"KO": ["KO", "ko", "ko_id"],
}
col_mapping = {}
for required, candidates in required_cols.items():
found = False
for candidate in candidates:
if candidate in df.columns:
col_mapping[required] = candidate
found = True
break
if not found:
logger.error(
f"[UC-4.1] Required column '{required}' not found. "
f"Available: {df.columns.tolist()}"
)
return _create_error_message(f"Missing required column: {required}")
# Normalize column names if needed
if col_mapping["Sample"] != "Sample":
df = df.rename(columns={col_mapping["Sample"]: "Sample"})
if col_mapping["Pathway"] != "Pathway":
df = df.rename(columns={col_mapping["Pathway"]: "Pathway"})
if col_mapping["KO"] != "KO":
df = df.rename(columns={col_mapping["KO"]: "KO"})
# DATA PROCESSING: Filter by selected sample
sample_data = df[df["Sample"] == selected_sample].copy()
if sample_data.empty:
logger.warning(f"[UC-4.1] No data found for sample '{selected_sample}'")
return _create_error_message(
f"No pathway data found for sample: {selected_sample}"
)
logger.info(
f"[UC-4.1] Filtered data for sample '{selected_sample}': "
f"{len(sample_data)} rows"
)
# Generate plot using PlotService
# (Further processing defined in uc_4_1_config.yaml)
use_case_id = "UC-4.1"
logger.info(
f"[UC-4.1] Calling PlotService for {use_case_id} "
f"with {len(sample_data)} rows"
)
fig = plot_service.generate_plot(use_case_id=use_case_id, data=sample_data)
logger.info(f"[UC-4.1] [OK] Plot generated successfully")
# canonical filename
try:
suggested = sanitize_filename("UC-4.1", "pathway_richness", "png")
except Exception:
suggested = "pathway_richness.png"
base_filename = os.path.splitext(suggested)[0]
return dcc.Graph(
figure=fig,
config={
"displayModeBar": True,
"toImageButtonOptions": {
"format": "svg",
"filename": base_filename,
},
},
style={"height": "750px"},
)
except ValueError as ve:
logger.error(f"[UC-4.1] Value error: {ve}")
return _create_error_message(str(ve))
except Exception as e:
logger.error(f"[UC-4.1] Rendering error: {e}", exc_info=True)
return _create_error_message(f"Error generating chart: {str(e)}")