update timespoken w multi

utils.py

@@ -333,7 +333,7 @@ def build_fig_pie1(df3, catTypeColors):
     """Voice category pie chart."""
     fig = go.Figure()
     fig.update_layout(
-        title_text="Percentage of each Voice Category",
+        title_text="Percentage of each voice category",
         colorway=catTypeColors,
         **TRANSPARENT_BG,
     )
@@ -348,7 +348,7 @@ def build_fig_pie2(df4, speakerNames, speaker_color_map, catColors, get_display_
     colors = [speaker_color_map.get(n, _SPEAKER_PALETTE[i % len(_SPEAKER_PALETTE)])
               for i, n in enumerate(df4["names"])]
     fig = go.Figure()
-    fig.update_layout(title_text="Percentage of Speakers per Role", **TRANSPARENT_BG)
+    fig.update_layout(title_text="Percentage of speakers per role", **TRANSPARENT_BG)
     fig.add_trace(go.Pie(values=df4["values"], labels=df4["names"],
                          marker_colors=colors, sort=False))
     return fig
@@ -392,7 +392,7 @@ def build_fig_sunburst(df5, catTypeColors, speaker_color_map, get_display_name_f
         values="percentiles",
         custom_data=["labels", "valueStrings", "percentiles", "parentNames", "parentPercentiles"],
         color="labels",
-        title="Percentage of each Voice Category with Speakers",
+        title="Percentage of each voice category with speakers (Combination)",
         color_discrete_map=color_map,
     )
     fig.update_traces(hovertemplate="<br>".join([
@@ -406,6 +406,84 @@ def build_fig_sunburst(df5, catTypeColors, speaker_color_map, get_display_name_f
     return fig
 
 
+def build_fig_sunburst_single(df5, speaker_color_map, get_display_name_fn, currFile):
+    """Sunburst showing only Single Voice speakers."""
+    df5 = df5.copy()
+    df5["labels"]      = df5["labels"].apply(lambda s: get_display_name_fn(s, currFile))
+    df5["parentNames"] = df5["parentNames"].apply(lambda s: get_display_name_fn(s, currFile))
+
+    # Keep only the Single Voice parent row and its children
+    keep_ids = {"OV"} | {row["ids"] for _, row in df5.iterrows()
+                         if row["parents"] == "OV"}
+    df5 = df5[df5["ids"].isin(keep_ids)].copy()
+    # Re-root: Single Voice becomes the top-level (parent = "")
+    df5.loc[df5["ids"] == "OV", "parents"] = ""
+
+    color_map = {lbl: speaker_color_map.get(lbl, _SPEAKER_PALETTE[i % len(_SPEAKER_PALETTE)])
+                 for i, lbl in enumerate(df5["labels"])}
+    color_map["Single Voice"] = _PALETTE[0]
+
+    fig = px.sunburst(
+        df5,
+        branchvalues="total",
+        names="labels", ids="ids", parents="parents",
+        values="percentiles",
+        custom_data=["labels", "valueStrings", "percentiles", "parentNames", "parentPercentiles"],
+        color="labels",
+        title="Percentage of each voice category with speakers (Single Voice)",
+        color_discrete_map=color_map,
+    )
+    fig.update_traces(hovertemplate="<br>".join([
+        "<b>%{customdata[0]}</b>",
+        "Duration: %{customdata[1]}s",
+        "Percentage of Total: %{customdata[2]:.2f}%",
+        "Parent: %{customdata[3]}",
+        "Percentage of Parent: %{customdata[4]:.2f}%",
+    ]))
+    fig.update_layout(**TRANSPARENT_BG, font_color="#323236")
+    return fig
+
+
+def build_fig_sunburst_multi(df5, speaker_color_map, get_display_name_fn, currFile):
+    """Sunburst showing only Multi Voice speakers."""
+    df5 = df5.copy()
+    df5["labels"]      = df5["labels"].apply(lambda s: get_display_name_fn(s, currFile))
+    df5["parentNames"] = df5["parentNames"].apply(lambda s: get_display_name_fn(s, currFile))
+
+    # Keep only the Multi Voice parent row and its children
+    keep_ids = {"MV"} | {row["ids"] for _, row in df5.iterrows()
+                         if row["parents"] == "MV"}
+    df5 = df5[df5["ids"].isin(keep_ids)].copy()
+    if df5.empty:
+        return None
+    # Re-root: Multi Voice becomes the top-level (parent = "")
+    df5.loc[df5["ids"] == "MV", "parents"] = ""
+
+    color_map = {lbl: speaker_color_map.get(lbl, _SPEAKER_PALETTE[i % len(_SPEAKER_PALETTE)])
+                 for i, lbl in enumerate(df5["labels"])}
+    color_map["Multi Voice"] = _PALETTE[9]
+
+    fig = px.sunburst(
+        df5,
+        branchvalues="total",
+        names="labels", ids="ids", parents="parents",
+        values="percentiles",
+        custom_data=["labels", "valueStrings", "percentiles", "parentNames", "parentPercentiles"],
+        color="labels",
+        title="Percentage of each voice category with speakers (Multiple Voices)",
+        color_discrete_map=color_map,
+    )
+    fig.update_traces(hovertemplate="<br>".join([
+        "<b>%{customdata[0]}</b>",
+        "Duration: %{customdata[1]}s",
+        "Percentage of Total: %{customdata[2]:.2f}%",
+        "Parent: %{customdata[3]}",
+        "Percentage of Parent: %{customdata[4]:.2f}%",
+    ]))
+    fig.update_layout(**TRANSPARENT_BG, font_color="#323236")
+    return fig
+
+
 def build_fig_treemap(df5, catTypeColors, speaker_color_map, get_display_name_fn, currFile):
     """Treemap voice-category chart."""
     df5 = df5.copy()
@@ -421,7 +499,7 @@ def build_fig_treemap(df5, catTypeColors, speaker_color_map, get_display_name_fn
         values="percentiles",
         custom_data=["labels", "valueStrings", "percentiles", "parentNames", "parentPercentiles"],
         color="labels",
-        title="Division of Speakers in each Voice Category",
+        title="Division of speakers in each voice category",
         color_discrete_map=color_map,
     )
     fig.update_traces(hovertemplate="<br>".join([
@@ -455,7 +533,7 @@ def build_fig_timeline(speakers_dataFrame, currTotalTime, speaker_color_map, get
 
     fig = px.timeline(
         df, x_start="Start", x_end="Finish", y="Resource", color="Resource",
-        title="Timeline of Audio with Speakers",
+        title="Timeline of audio with speakers",
         color_discrete_map=speaker_color_map,
     )
     fig.update_yaxes(autorange="reversed")
@@ -506,63 +584,41 @@ def _darken_hex(hex_color, factor=0.55):
 
 def build_fig_bar(df2, speakerNames, catColors, speaker_color_map, get_display_name_fn, currFile, mv_per_speaker=None):
     """Horizontal bar chart — time spoken per speaker (hh:mm:ss.ss).
-    Only individual speakers are shown; role/category rows are excluded.
+    Each bar has a darker overlay showing the speaker's multi-voice portion.
     """
     mv_per_speaker = mv_per_speaker or {}
     df2 = df2.copy()
     df2 = df2[df2["names"].isin(speakerNames)]
 
-    # Map raw speaker names to display names
-    raw_to_display = {sp: get_display_name_fn(sp, currFile) for sp in df2["names"]}
-    df2["display"] = df2["names"].map(raw_to_display)
-    df2["mv_secs"] = df2["names"].map(lambda sp: mv_per_speaker.get(sp, 0.0))
-    df2["sv_secs"] = (df2["values"] - df2["mv_secs"]).clip(lower=0)
+    raw_to_display   = {sp: get_display_name_fn(sp, currFile) for sp in df2["names"]}
+    df2["display"]   = df2["names"].map(raw_to_display)
+    df2["mv_secs"]   = df2["names"].map(lambda sp: mv_per_speaker.get(sp, 0.0))
+    df2["sv_secs"]   = (df2["values"] - df2["mv_secs"]).clip(lower=0)
     df2["time_label"]    = df2["values"].apply(_seconds_to_hhmmss)
     df2["mv_time_label"] = df2["mv_secs"].apply(_seconds_to_hhmmss)
 
-    # Build display-keyed color maps
     disp_color_map = {raw_to_display[sp]: speaker_color_map.get(raw_to_display[sp], "#aaaaaa")
                       for sp in df2["names"]}
-    disp_dark_map  = {disp: _darken_hex(col) for disp, col in disp_color_map.items()}
+    disp_dark_map  = {d: _darken_hex(c) for d, c in disp_color_map.items()}
 
     fig = go.Figure()
-
-    # Trace 1: single-voice portion (base color)
     for _, row in df2.iterrows():
         col = disp_color_map.get(row["display"], "#aaaaaa")
         fig.add_trace(go.Bar(
-            x=[row["sv_secs"]],
-            y=[row["display"]],
-            orientation="h",
-            marker_color=col,
-            showlegend=False,
+            x=[row["sv_secs"]], y=[row["display"]], orientation="h",
+            marker_color=col, showlegend=False,
             customdata=[[row["display"], row["time_label"], row["mv_time_label"]]],
-            hovertemplate=(
-                "<b>%{customdata[0]}</b><br>"
-                "Total: %{customdata[1]}<br>"
-                "Multi Voice: %{customdata[2]}"
-                "<extra></extra>"
-            ),
+            hovertemplate="<b>%{customdata[0]}</b><br>Total: %{customdata[1]}<br>Multi Voice: %{customdata[2]}<extra></extra>",
         ))
-
-    # Trace 2: multi-voice portion (darker shade stacked on top)
     for _, row in df2.iterrows():
         if row["mv_secs"] <= 0:
             continue
         dark = disp_dark_map.get(row["display"], "#555555")
         fig.add_trace(go.Bar(
-            x=[row["mv_secs"]],
-            y=[row["display"]],
-            orientation="h",
-            marker_color=dark,
-            showlegend=False,
+            x=[row["mv_secs"]], y=[row["display"]], orientation="h",
+            marker_color=dark, showlegend=False,
             customdata=[[row["display"], row["time_label"], row["mv_time_label"]]],
-            hovertemplate=(
-                "<b>%{customdata[0]}</b><br>"
-                "Total: %{customdata[1]}<br>"
-                "Multi Voice: %{customdata[2]}"
-                "<extra></extra>"
-            ),
+            hovertemplate="<b>%{customdata[0]}</b><br>Total: %{customdata[1]}<br>Multi Voice: %{customdata[2]}<extra></extra>",
         ))
 
     fig.update_layout(