def register_uc_4_3_callbacks(app, plot_service) -> None:
"""
Register UC-4.3 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, pathway dropdown initialization,
and radar chart rendering
- Refer to official documentation for processing logic details
"""
@app.callback(
Output("uc-4-3-collapse", "is_open"),
Input("uc-4-3-collapse-button", "n_clicks"),
State("uc-4-3-collapse", "is_open"),
prevent_initial_call=True,
)
def toggle_uc_4_3_info_panel(n_clicks, is_open):
"""
Toggle UC-4.3 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-4.3] 🔘 Toggle clicked! n_clicks={n_clicks}, " f"is_open={is_open}"
)
if n_clicks:
new_state = not is_open
logger.info(f"[UC-4.3] [OK] Panel toggled to: {new_state}")
return new_state
logger.info(f"[UC-4.3] ⊘ No clicks, keeping is_open={is_open}")
return is_open
@app.callback(
[
Output("uc-4-3-pathway-dropdown", "options"),
Output("uc-4-3-pathway-dropdown", "value"),
],
[
Input("merged-result-store", "data"),
Input("uc-4-3-accordion-group", "active_item"),
],
prevent_initial_call=True,
)
def initialize_pathway_dropdown_uc_4_3(
merged_data: Optional[dict], active_item: Optional[str]
) -> Tuple[list, None]:
"""
Initialize pathway dropdown with KEGG data.
This callback populates the dropdown menu with available pathways
extracted from processed KEGG data, enabling users to select
specific pathways for comparative sample analysis.
Data Processing (inline):
1. Extract KEGG data from store
2. Validate 'Pathway' column exists
3. Extract unique pathways
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 pathway selection. Empty list
if no data available.
- Second element: Default selection value (None for no
initial selection).
Raises
------
PreventUpdate
If no data available or required column not found.
"""
logger.info(
f"[UC-4.3] 🔄 Dropdown init triggered, " f"data type: {type(merged_data)}"
)
if not merged_data:
logger.debug("[UC-4.3] 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-4.3] 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-4.3] Invalid data format: expected dict with "
f"'kegg_df', got {type(merged_data)}"
)
raise PreventUpdate
df = pd.DataFrame(merged_data["kegg_df"])
# Validate 'Pathway' column exists (check variants)
pathway_col_variants = [
"Pathway",
"pathway",
"pathname",
"Pathname",
"Path",
]
pathway_col = None
for variant in pathway_col_variants:
if variant in df.columns:
pathway_col = variant
logger.debug(f"[UC-4.3] Found pathway column: '{variant}'")
break
if not pathway_col:
logger.error(
f"[UC-4.3] Required column 'Pathway' not found. "
f"Available columns: {df.columns.tolist()}"
)
raise PreventUpdate
# Extract unique pathways
pathways = sorted(df[pathway_col].dropna().unique())
logger.debug(
f"[UC-4.3] Extracted {len(pathways)} unique pathways: "
f"{pathways[:5]}..." # Show first 5
)
# Create dropdown options
options = [{"label": pathway, "value": pathway} for pathway in pathways]
logger.info(
f"[UC-4.3] Dropdown initialized with {len(options)} " f"pathways"
)
return options, None
except Exception as e:
logger.error(f"[UC-4.3] Dropdown initialization error: {e}")
raise PreventUpdate
@app.callback(
Output("uc-4-3-chart-container", "children"),
Input("uc-4-3-pathway-dropdown", "value"),
State("merged-result-store", "data"),
prevent_initial_call=True,
)
def render_uc_4_3(
selected_pathway: Optional[str], merged_data: Optional[dict]
) -> Any:
"""
Render UC-4.3 radar chart for selected pathway.
This callback generates a radar chart visualization showing the
comparative functional richness of all samples for the selected
pathway based on unique KO counts.
Data Processing (inline):
1. Extract KEGG data from store
2. Validate required columns
3. Filter by selected pathway
4. Pass filtered data to PlotService
5. RadarChartStrategy processes:
- Groups by Sample
- Counts unique KOs
- Creates radar chart
Parameters
----------
selected_pathway : Optional[str]
Selected pathway from dropdown.
merged_data : Optional[dict]
Merged data from store with 'kegg_df' key.
Returns
-------
dcc.Graph or html.Div
Radar chart component or informative/error message.
Raises
------
PreventUpdate
If no pathway selected or no data available.
"""
if not selected_pathway:
logger.debug("[UC-4.3] No pathway selected, preventing update")
raise PreventUpdate
if not merged_data:
logger.warning("[UC-4.3] No data available")
return _create_error_message("No data available for visualization")
try:
# Extract KEGG DataFrame from store
logger.debug(f"[UC-4.3] Received data type: {type(merged_data)}")
if not isinstance(merged_data, dict) or "kegg_df" not in merged_data:
logger.error(
"[UC-4.3] 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 with variants
required_cols_variants = {
"sample": ["Sample", "sample", "sample_id"],
"pathway": ["Pathway", "pathway", "pathname", "Pathname"],
"ko": ["KO", "ko", "ko_id"],
}
col_mapping = {}
for required, variants in required_cols_variants.items():
found = False
for variant in variants:
if variant in df.columns:
col_mapping[required] = variant
found = True
logger.debug(f"[UC-4.3] Mapped '{required}' → '{variant}'")
break
if not found:
logger.error(
f"[UC-4.3] Required column '{required}' not found. "
f"Available: {df.columns.tolist()}"
)
return _create_error_message(f"Missing required column: {required}")
# Normalize column names
rename_mapping = {}
if col_mapping["sample"] != "Sample":
rename_mapping[col_mapping["sample"]] = "Sample"
if col_mapping["pathway"] != "Pathway":
rename_mapping[col_mapping["pathway"]] = "Pathway"
if col_mapping["ko"] != "KO":
rename_mapping[col_mapping["ko"]] = "KO"
if rename_mapping:
df = df.rename(columns=rename_mapping)
logger.debug(f"[UC-4.3] Renamed columns: {rename_mapping}")
# Filter by selected pathway
filtered_df = df[df["Pathway"] == selected_pathway].copy()
if filtered_df.empty:
logger.warning(
f"[UC-4.3] No data found for pathway: " f"'{selected_pathway}'"
)
return _create_error_message(
f"No data found for pathway '{selected_pathway}'. "
f"Try selecting a different pathway."
)
logger.info(
f"[UC-4.3] Filtered data: {len(filtered_df)} rows for "
f"pathway '{selected_pathway}'"
)
# Remove NaNs from required columns
filtered_df = filtered_df.dropna(subset=["Sample", "KO"])
if filtered_df.empty:
logger.warning("[UC-4.3] No valid data after removing NaNs")
return _create_error_message("No valid data found after data cleaning.")
# Generate plot using PlotService
# RadarChartStrategy handles aggregation
use_case_id = "UC-4.3"
logger.info(
f"[UC-4.3] Calling PlotService for {use_case_id} "
f"with {len(filtered_df)} rows"
)
fig = plot_service.generate_plot(use_case_id=use_case_id, data=filtered_df)
# Update title dynamically
fig.update_layout(
title=f"Sample Functional Richness for {selected_pathway}", title_x=0.5
)
logger.info("[UC-4.3] [OK] Radar chart generated successfully")
try:
suggested = sanitize_filename("UC-4.3", "compound_coverage", "png")
except Exception:
suggested = "compound_coverage.png"
base_filename = os.path.splitext(suggested)[0]
return dcc.Graph(
figure=fig,
config={
"displayModeBar": True,
"toImageButtonOptions": {
"format": "svg",
"filename": base_filename,
},
},
style={"height": "650px"},
)
except ValueError as ve:
logger.error(f"[UC-4.3] Value error: {ve}")
return _create_error_message(str(ve))
except Exception as e:
logger.error(f"[UC-4.3] Rendering error: {e}", exc_info=True)
return _create_error_message(f"Error generating radar chart: {str(e)}")