def register_uc_6_2_callbacks(app, plot_service) -> None:
"""
Register all UC-6.2 callbacks with Dash app.
Parameters
----------
app : Dash
Dash application instance.
plot_service : PlotService
Singleton PlotService instance (shared across all callbacks).
Notes
-----
- Registers panel toggle and Sankey diagram rendering callbacks
- Refer to official documentation for processing logic details
"""
logger.info("[UC-6.2] Registering callbacks")
# ========================================
# Callback 1: Toggle Informative Panel
# ========================================
@app.callback(
Output("uc-6-2-collapse", "is_open"),
Input("uc-6-2-collapse-button", "n_clicks"),
State("uc-6-2-collapse", "is_open"),
prevent_initial_call=True,
)
def toggle_uc_6_2_info_panel(n_clicks: Optional[int], is_open: bool) -> bool:
"""Toggle UC-6.2 informative panel collapse state."""
if n_clicks:
logger.debug(f"[UC-6.2] Toggling info panel: {is_open} -> {not is_open}")
return not is_open
return is_open
# ========================================
# Callback 2: Render Sankey Diagram
# ========================================
@app.callback(
Output("uc-6-2-chart", "children"),
[
Input("merged-result-store", "data"),
Input("uc-6-2-accordion-group", "active_item"),
],
prevent_initial_call=True,
)
def render_uc_6_2(
merged_data: Optional[Dict[str, Any]], active_item: Optional[str]
) -> html.Div:
"""
Render UC-6.2 Sankey diagram on accordion activation.
Parameters
----------
merged_data : dict, optional
Dictionary containing merged result data with 'biorempp_df' key.
active_item : str, optional
Active accordion item ID.
Returns
-------
html.Div
Container with chart or error message.
Notes
-----
- Validates merged_data structure and extracts BioRemPP DataFrame
- Maps 3 column names flexibly (sample/compoundclass/enzymeactivity)
- Cleans data removing nulls and placeholder values
- Passes prepared data to SankeyStrategy via PlotService
- Generates multi-level biological interaction flow diagram
"""
logger.info(f"[UC-6.2] Render triggered, active_item: {active_item}")
# Check if accordion is active
if active_item != "uc-6-2-accordion":
logger.debug("[UC-6.2] Accordion not active. Skipping render.")
raise PreventUpdate
try:
# ========================================
# Step 1: Validate merged_data structure
# ========================================
if not merged_data:
logger.warning("[UC-6.2] merged_data is None or empty")
return _create_error_message(
"No data available. Please upload and process data first.",
"bi bi-exclamation-triangle",
)
if not isinstance(merged_data, dict):
logger.error("[UC-6.2] merged_data is not a dictionary")
return _create_error_message(
"Invalid data structure. Please reload the application.",
"bi bi-x-circle",
)
if "biorempp_df" not in merged_data:
logger.error("[UC-6.2] merged_data does not contain 'biorempp_df' key")
return _create_error_message(
"BioRemPP data not found. This use case requires "
"BioRemPP database.",
"bi bi-database-x",
)
# ========================================
# Step 2: Extract DataFrame
# ========================================
logger.debug("[UC-6.2] Extracting DataFrame from merged_data")
biorempp_data = merged_data["biorempp_df"]
if not biorempp_data:
logger.warning("[UC-6.2] biorempp_df is empty")
return _create_error_message(
"BioRemPP dataset is empty. Please check your input data.",
"bi bi-inbox",
)
df = pd.DataFrame(biorempp_data)
if df.empty:
logger.warning("[UC-6.2] DataFrame is empty after conversion")
return _create_error_message(
"No data available after processing.", "bi bi-inbox"
)
logger.info(
f"[UC-6.2] Processing DataFrame: {len(df)} rows, "
f"{len(df.columns)} columns"
)
logger.debug(f"[UC-6.2] Available columns: {df.columns.tolist()}")
# ========================================
# Step 3: Map column names flexibly
# ========================================
col_map = {}
# Sample column
sample_candidates = [
"sample",
"Sample",
"sample_name",
"SampleName",
"sample_id",
"SampleID",
]
for col_name in sample_candidates:
if col_name in df.columns:
col_map["sample"] = col_name
logger.debug(f"[UC-6.2] Mapped sample to '{col_name}'")
break
# Compound class column
class_candidates = [
"compoundclass",
"Compound_Class",
"compound_class",
"CompoundClass",
"class",
"Class",
"chemical_class",
]
for col_name in class_candidates:
if col_name in df.columns:
col_map["compoundclass"] = col_name
logger.debug(f"[UC-6.2] Mapped compoundclass to '{col_name}'")
break
# Enzyme activity column
enzyme_candidates = [
"enzymeactivity",
"Enzyme_Activity",
"enzyme_activity",
"EnzymeActivity",
"activity",
"Activity",
"enzyme",
]
for col_name in enzyme_candidates:
if col_name in df.columns:
col_map["enzymeactivity"] = col_name
logger.debug(f"[UC-6.2] Mapped enzymeactivity to '{col_name}'")
break
# ========================================
# Step 4: Validate required columns found
# ========================================
required = ["sample", "compoundclass", "enzymeactivity"]
missing_cols = [col for col in required if col not in col_map]
if missing_cols:
logger.error(
f"[UC-6.2] Missing columns: {missing_cols}. "
f"Available: {df.columns.tolist()}"
)
return _create_error_message(
f"Required columns not found: {', '.join(missing_cols)}. "
f"Available columns: {', '.join(df.columns[:5])}...",
"bi bi-exclamation-octagon",
)
# ========================================
# Step 5: Prepare data for strategy
# ========================================
df_for_plot = df[
[col_map["sample"], col_map["compoundclass"], col_map["enzymeactivity"]]
].rename(
columns={
col_map["sample"]: "sample",
col_map["compoundclass"]: "compoundclass",
col_map["enzymeactivity"]: "enzymeactivity",
}
)
# Clean data
initial_count = len(df_for_plot)
df_for_plot = df_for_plot.dropna()
# Strip whitespace and remove placeholders
for col in df_for_plot.columns:
df_for_plot[col] = df_for_plot[col].astype(str).str.strip()
# Remove placeholder values
placeholder_values = ["#N/D", "#N/A", "N/D", "", "nan", "None"]
for col in df_for_plot.columns:
df_for_plot = df_for_plot[~df_for_plot[col].isin(placeholder_values)]
cleaned_count = len(df_for_plot)
logger.info(
f"[UC-6.2] Data cleaned: {initial_count} -> {cleaned_count} rows"
)
if df_for_plot.empty:
return _create_error_message(
"No valid biological interaction flows found after cleaning.",
"bi bi-funnel",
)
# Log statistics
n_samples = df_for_plot["sample"].nunique()
n_classes = df_for_plot["compoundclass"].nunique()
n_enzymes = df_for_plot["enzymeactivity"].nunique()
logger.info(
f"[UC-6.2] Sankey statistics: "
f"{n_samples} samples, {n_classes} compound classes, "
f"{n_enzymes} enzyme activities"
)
# ========================================
# Step 6: Generate plot using PlotService
# ========================================
logger.debug("[UC-6.2] Calling PlotService to generate Sankey diagram")
fig = plot_service.generate_plot(
use_case_id="UC-6.2",
data=df_for_plot,
filters=None,
force_refresh=False,
)
logger.info("[UC-6.2] Sankey diagram generation successful")
# ========================================
# Step 7: Prepare filename and return chart component
# ========================================
try:
suggested = sanitize_filename(
"UC-6.2", "biological_interaction_flow", "png"
)
except Exception:
suggested = "biological_interaction_flow.png"
base_filename = os.path.splitext(suggested)[0]
return html.Div(
[
# Statistics summary
html.Div(
[
html.Small(
[
html.I(className="bi bi-info-circle me-2"),
f"Flow: {n_samples} samples -> {n_classes} compound classes -> "
f"{n_enzymes} enzyme activities "
f"({cleaned_count:,} interactions)",
],
className="text-muted",
)
],
className="mb-2",
),
# Graph container with overflow control
html.Div(
[
dcc.Graph(
id="uc-6-2-graph",
figure=fig,
config={
"displayModeBar": True,
"displaylogo": False,
"responsive": True,
"modeBarButtonsToRemove": [
"pan2d",
"lasso2d",
"select2d",
],
"toImageButtonOptions": {
"format": "svg",
"filename": base_filename,
"height": 1000,
"width": 1400,
"scale": 6,
},
},
style={
"height": "900px",
"width": "100%",
"minWidth": "100%",
},
className="mt-3",
)
],
style={
"width": "100%",
"overflowX": "auto",
"overflowY": "hidden",
},
),
]
)
except ValueError as ve:
logger.error(
f"[UC-6.2] ValueError during processing: {str(ve)}", exc_info=True
)
return _create_error_message(
f"Data validation error: {str(ve)}", "bi bi-exclamation-triangle"
)
except Exception as e:
logger.error(f"[UC-6.2] Unexpected error: {str(e)}", exc_info=True)
return _create_error_message(
f"An unexpected error occurred: {str(e)}", "bi bi-bug"
)
logger.info("[UC-6.2] All callbacks registered successfully")