Add global heatmap preview and full-wave defaults

app.py

@@ -274,6 +274,69 @@ def prepare_results(
     return display_df, fig, status
 
 
+def make_global_heatmap(
+    df: pd.DataFrame,
+    alias: str,
+    variable_label: str,
+    valid_time_iso: str,
+) -> Optional[go.Figure]:
+    """Generate a Plotly heatmap from a gridded dataframe."""
+    if alias not in df.columns:
+        return None
+
+    subset = df.dropna(subset=[alias, "lat", "lon"]).copy()
+    if subset.empty:
+        return None
+
+    subset["lat"] = subset["lat"].astype(float)
+    subset["lon"] = subset["lon"].astype(float)
+
+    if "forecasted_time" in subset.columns:
+        subset["forecasted_time"] = pd.to_datetime(
+            subset["forecasted_time"], utc=True, errors="coerce"
+        )
+        subset = subset.dropna(subset=["forecasted_time"])
+        if not subset.empty:
+            target_time = subset["forecasted_time"].min()
+            subset = subset[subset["forecasted_time"] == target_time]
+
+    if "member" in subset.columns:
+        subset = subset.sort_values("member").groupby(["lat", "lon"], as_index=False).first()
+
+    subset = subset.sort_values(["lat", "lon"])
+    subset = subset.drop_duplicates(subset=["lat", "lon"], keep="first")
+
+    if subset.empty:
+        return None
+
+    pivot = subset.pivot(index="lat", columns="lon", values=alias)
+    if pivot.empty:
+        return None
+
+    pivot = pivot.sort_index(ascending=False)
+    lats = pivot.index.to_list()
+    lons = pivot.columns.to_list()
+    values = pivot.values
+
+    fig = go.Figure(
+        data=go.Heatmap(
+            x=lons,
+            y=lats,
+            z=values,
+            colorscale="Viridis",
+            colorbar=dict(title=variable_label),
+        )
+    )
+    fig.update_layout(
+        title=f"{variable_label} at {valid_time_iso}",
+        xaxis_title="Longitude",
+        yaxis_title="Latitude",
+        template="plotly_white",
+        margin=dict(l=60, r=20, t=60, b=40),
+    )
+    return fig
+
+
 def run_query(
     model: str,
     variable: str,
@@ -342,12 +405,13 @@ def run_global_download(
     max_horizon: int,
     grid_step: float,
     raw_members: str,
-) -> Tuple[str, Optional[pd.DataFrame], Optional[str]]:
+    preview_variable: str,
+) -> Tuple[str, Optional[pd.DataFrame], Optional[go.Figure], Optional[str]]:
     try:
         token = get_token()
 
         selected_from_dropdown = [name.lower() for name in (variables or [])]
-        custom_list = parse_variable_list(custom_variables)
+        custom_list = [name.lower() for name in parse_variable_list(custom_variables)]
         variable_names = list(dict.fromkeys([*selected_from_dropdown, *custom_list]))
         if not variable_names:
             raise ValueError("Select at least one variable or enter custom variable names.")
@@ -361,10 +425,12 @@ def run_global_download(
 
         members = build_members_list(raw_members, model)
 
-        variables_payload = [
-            {"name": name, "level": DEFAULT_LEVEL, "alias": make_alias(name)}
-            for name in variable_names
-        ]
+        alias_map = {}
+        variables_payload = []
+        for name in variable_names:
+            alias = make_alias(name)
+            alias_map[name] = alias
+            variables_payload.append({"name": name, "level": DEFAULT_LEVEL, "alias": alias})
 
         df = fetch_wave_history(
             token=token,
@@ -392,6 +458,10 @@ def run_global_download(
         for col in ("forecasted_time", "forecasted_at"):
             if col in df.columns:
                 df[col] = pd.to_datetime(df[col], utc=True, errors="coerce")
+        if {"forecasted_time", "forecasted_at"}.issubset(df.columns):
+            df["lead_time_hours"] = (
+                (df["forecasted_time"] - df["forecasted_at"]).dt.total_seconds() / 3600.0
+            )
 
         preview = df.head(500).copy()
         preview_rows = len(preview)
@@ -402,17 +472,37 @@ def run_global_download(
         finally:
             tmp.close()
 
+        preview_choice = (preview_variable or variable_names[0]).strip().lower()
+        if preview_choice not in alias_map:
+            preview_choice = variable_names[0]
+        alias_for_map = alias_map[preview_choice]
+        variable_label, unit = WAVE_VARIABLES.get(
+            preview_choice,
+            (preview_choice.upper(), ""),
+        )
+        display_label = f"{variable_label} ({unit})" if unit else variable_label
+
+        map_fig = make_global_heatmap(df, alias_for_map, display_label, valid_iso)
+
+        lead_info = ""
+        if "lead_time_hours" in df.columns:
+            lead_series = df["lead_time_hours"].dropna()
+            if not lead_series.empty:
+                lead_info = (
+                    f" Lead times {lead_series.min():.0f}–{lead_series.max():.0f} h."
+                )
+
         status = (
             f"Fetched {len(df)} rows across {len(variable_names)} variable(s) "
-            f"for {valid_iso}. Showing the first {preview_rows} rows."
+            f"for {valid_iso}.{lead_info} Showing the first {preview_rows} rows."
         )
 
-        return status, preview, tmp.name
+        return status, preview, map_fig, tmp.name
 
     except ConfigurationError as exc:
-        return f"⚠️ {exc}", None, None
+        return f"⚠️ {exc}", None, None, None
     except Exception as exc:  # noqa: BLE001
-        return f"❌ {exc}", None, None
+        return f"❌ {exc}", None, None, None
 
 
 def default_time_window(hours_back: int = 6, hours_forward: int = 24) -> Tuple[str, str]:
@@ -543,7 +633,7 @@ def build_interface() -> gr.Blocks:
                         global_variables_input = gr.CheckboxGroup(
                             label="Variables",
                             choices=[code.upper() for code in WAVE_VARIABLES.keys()],
-                            value=["SWH", "MWD", "MWP"],
+                            value=[code.upper() for code in WAVE_VARIABLES.keys()],
                             info="Select one or more parameters to include in the download.",
                         )
                         global_custom_variables_input = gr.Textbox(
@@ -582,6 +672,12 @@ def build_interface() -> gr.Blocks:
                             placeholder="e.g. 0,1,2,3",
                             info="Leave blank for default control member. Ignored for deterministic model.",
                         )
+                        global_preview_variable_input = gr.Dropdown(
+                            label="Preview variable for map",
+                            choices=[code.upper() for code in WAVE_VARIABLES.keys()],
+                            value="SWH",
+                            info="Used for the heatmap preview below.",
+                        )
                         global_submit = gr.Button("Download global snapshot", variant="primary")
 
                     with gr.Column(scale=2):
@@ -592,6 +688,7 @@ def build_interface() -> gr.Blocks:
                             interactive=False,
                             wrap=False,
                         )
+                        global_map_output = gr.Plot(label="Global map preview", show_label=True)
                         global_file_output = gr.File(label="Download CSV")
 
                 global_submit.click(
@@ -605,8 +702,9 @@ def build_interface() -> gr.Blocks:
                         global_max_horizon_input,
                         global_grid_step_input,
                         global_members_input,
+                        global_preview_variable_input,
                     ],
-                    outputs=[global_status_output, global_preview_output, global_file_output],
+                    outputs=[global_status_output, global_preview_output, global_map_output, global_file_output],
                 )
 
         demo.queue()