Update app.py

app.py

@@ -520,13 +520,10 @@ def track_plot(df, include_actual=True):
     )
     return fig
 
-# ---------- Preview modal (matplotlib) ----------
-# ---------- Preview modal (matplotlib) ----------
 def preview_tracks(df: pd.DataFrame, cols: list[str]):
     """
     Render quick-look tracks for the selected columns with DISTINCT colors per input,
-    instead of the previous single black line. Colors are stable across sessions
-    (ordered by column index) using matplotlib's 'tab20' palette.
+    and reserve the Y-axis across all plots so depth/index is aligned.
     """
     cols = [c for c in cols if c in df.columns]
     n = len(cols)
@@ -536,12 +533,7 @@ def preview_tracks(df: pd.DataFrame, cols: list[str]):
         ax.axis("off")
         return fig
 
-    # one row, n columns; share Y so depth/index aligns
-    fig, axes = plt.subplots(1, n, figsize=(2.2 * n, 7.0), sharey=True, dpi=100)
-    if n == 1:
-        axes = [axes]
-
-    # Determine vertical axis (Depth if present, else point index)
+    # Depth or fallback to point index
     depth_col = next((c for c in df.columns if 'depth' in str(c).lower()), None)
     if depth_col is not None:
         idx = pd.to_numeric(df[depth_col], errors="coerce")
@@ -550,23 +542,33 @@ def preview_tracks(df: pd.DataFrame, cols: list[str]):
         idx = pd.Series(np.arange(1, len(df) + 1))
         y_label = "Point Index"
 
-    # Build a stable color map for the selected columns
-    cmap = plt.get_cmap("tab20")  # 20 distinct hues; cycles if >20 inputs
+    # Y range is reserved across all subplots
+    y_min, y_max = float(idx.min()), float(idx.max())
+
+    fig, axes = plt.subplots(1, n, figsize=(2.2 * n, 7.0), sharey=True, dpi=100)
+    if n == 1:
+        axes = [axes]
+
+    # Stable color palette
+    cmap = plt.get_cmap("tab20")
     col_colors = {col: cmap(i % cmap.N) for i, col in enumerate(cols)}
 
-    # Plot each selected feature with its own color
-    for ax, col in zip(axes, cols):
+    for i, (ax, col) in enumerate(zip(axes, cols)):
         x = pd.to_numeric(df[col], errors="coerce")
         ax.plot(x, idx, '-', lw=1.6, color=col_colors[col])
         ax.set_xlabel(col)
         ax.xaxis.set_label_position('top')
         ax.xaxis.tick_top()
-        ax.invert_yaxis()
+        ax.set_ylim(y_max, y_min)  # reserve and invert depth axis
         ax.grid(True, linestyle=":", alpha=0.3)
-        for s in ax.spines.values():
-            s.set_visible(True)
 
-    axes[0].set_ylabel(y_label)
+        # Only show y-axis label + ticks on the first subplot
+        if i == 0:
+            ax.set_ylabel(y_label)
+        else:
+            ax.tick_params(labelleft=False)
+            ax.set_ylabel("")
+
     fig.tight_layout()
     return fig