diff --git a/frontend/src/components/ProgressView.tsx b/frontend/src/components/ProgressView.tsx
index 3921f16..f78072e 100644
--- a/frontend/src/components/ProgressView.tsx
+++ b/frontend/src/components/ProgressView.tsx
@@ -4,16 +4,41 @@ import Box from "@mui/material/Box";
 import Button from "@mui/material/Button";
 import LinearProgress from "@mui/material/LinearProgress";
 import Paper from "@mui/material/Paper";
+import Slider from "@mui/material/Slider";
 import Typography from "@mui/material/Typography";
 import { cancelRun } from "../api";
 
 type StreamEvent =
   | { type: "progress"; percent: number }
   | { type: "log"; message: string }
+  | {
+      type: "image";
+      data_url: string;
+      caption: string | null;
+      timestamp: string;
+    }
   | { type: "error"; message: string }
   | { type: "done"; message: string }
   | { type: "cancelled"; message: string };
 
+type StreamImage = {
+  dataUrl: string;
+  caption: string | null;
+  timestamp: string;
+};
+
+function formatStreamTime(iso: string): string {
+  return new Date(iso).toLocaleTimeString(undefined, {
+    hour: "2-digit",
+    minute: "2-digit",
+    second: "2-digit",
+  });
+}
+
+function isNearBottom(el: HTMLElement, threshold = 48) {
+  return el.scrollHeight - el.scrollTop - el.clientHeight <= threshold;
+}
+
 export function ProgressView({
   runId,
   onReset,
@@ -23,14 +48,32 @@ export function ProgressView({
 }) {
   const [percent, setPercent] = useState(0);
   const [logs, setLogs] = useState<string[]>([]);
+  const [images, setImages] = useState<StreamImage[]>([]);
+  const [imageIndex, setImageIndex] = useState(0);
   const [done, setDone] = useState<string | null>(null);
   const [cancelled, setCancelled] = useState<string | null>(null);
   const [error, setError] = useState<string | null>(null);
   const [cancelPending, setCancelPending] = useState(false);
   const [cancelError, setCancelError] = useState<string | null>(null);
-  const bottomRef = useRef<HTMLDivElement>(null);
+  const logContainerRef = useRef<HTMLDivElement>(null);
+  const stickToBottomRef = useRef(true);
+  const stickToLatestImageRef = useRef(true);
+
+  function handleLogScroll() {
+    const el = logContainerRef.current;
+    if (!el) return;
+    stickToBottomRef.current = isNearBottom(el);
+  }
+
+  function handleImageSliderChange(_: Event, value: number | number[]) {
+    const index = value as number;
+    setImageIndex(index);
+    stickToLatestImageRef.current = index === images.length - 1;
+  }
 
   useEffect(() => {
+    stickToBottomRef.current = true;
+    stickToLatestImageRef.current = true;
     const es = new EventSource(`/api/runs/${runId}/stream`);
     es.onmessage = (e) => {
       try {
@@ -38,7 +81,22 @@ export function ProgressView({
         if (ev.type === "progress")
           setPercent(Math.min(100, Math.max(0, Math.ceil(ev.percent))));
         else if (ev.type === "log") setLogs((x) => [...x, ev.message]);
-        else if (ev.type === "error") {
+        else if (ev.type === "image") {
+          setImages((prev) => {
+            const next = [
+              ...prev,
+              {
+                dataUrl: ev.data_url,
+                caption: ev.caption,
+                timestamp: ev.timestamp,
+              },
+            ];
+            if (stickToLatestImageRef.current) {
+              setImageIndex(next.length - 1);
+            }
+            return next;
+          });
+        } else if (ev.type === "error") {
           setError(ev.message);
           es.close();
         } else if (ev.type === "cancelled") {
@@ -57,10 +115,13 @@ export function ProgressView({
   }, [runId]);
 
   useEffect(() => {
-    bottomRef.current?.scrollIntoView({ behavior: "smooth" });
+    const el = logContainerRef.current;
+    if (!el || !stickToBottomRef.current) return;
+    el.scrollTop = el.scrollHeight;
   }, [logs]);
 
   const canCancel = !done && !error && !cancelled && !cancelPending;
+  const currentImage = images[imageIndex];
 
   return (
     <Box sx={{ maxWidth: 720 }}>
@@ -80,14 +141,6 @@ export function ProgressView({
           {error}
         </Alert>
       )}
-      {done && (
-        <Alert
-          severity="success"
-          sx={{ mb: 2, whiteSpace: "pre-wrap", fontFamily: "monospace" }}
-        >
-          {done}
-        </Alert>
-      )}
       {cancelled && (
         <Alert
           severity="warning"
@@ -101,7 +154,49 @@ export function ProgressView({
           {cancelError}
         </Alert>
       )}
+      <Paper variant="outlined" sx={{ p: 2, mb: 2 }}>
+        {currentImage ? (
+          <>
+            <Box
+              component="img"
+              src={currentImage.dataUrl}
+              sx={{ maxWidth: "100%", display: "block" }}
+            />
+            {currentImage.caption && (
+              <Typography variant="caption" display="block" sx={{ mt: 1 }}>
+                {currentImage.caption}
+              </Typography>
+            )}
+            {images.length > 1 && (
+              <Slider
+                sx={{ mt: 2 }}
+                min={0}
+                max={images.length - 1}
+                step={1}
+                value={imageIndex}
+                onChange={handleImageSliderChange}
+                valueLabelDisplay="auto"
+                valueLabelFormat={(i) => formatStreamTime(images[i].timestamp)}
+              />
+            )}
+          </>
+        ) : (
+          <Typography variant="body2" color="text.secondary">
+            No charts yet
+          </Typography>
+        )}
+      </Paper>
+      {done && (
+        <Alert
+          severity="success"
+          sx={{ mb: 2, whiteSpace: "pre-wrap", fontFamily: "monospace" }}
+        >
+          {done}
+        </Alert>
+      )}
       <Paper
+        ref={logContainerRef}
+        onScroll={handleLogScroll}
         variant="outlined"
         sx={{
           p: 2,
@@ -115,7 +210,6 @@ export function ProgressView({
         {logs.map((line, i) => (
           <div key={i}>{line}</div>
         ))}
-        <div ref={bottomRef} />
       </Paper>
       <Box sx={{ mt: 2, display: "flex", gap: 1 }}>
         <Button
diff --git a/pyproject.toml b/pyproject.toml
index 0fb8f0a..e1b1a90 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,6 +14,7 @@ dependencies = [
     "openapi-python-client>=0.27.1",
     "pandas>=2.3.3",
     "numpy>=2.3.4",
+    "matplotlib>=3.9",
     "pyvo>=1.8",
     "psycopg[binary]>=3.2.0",
     "click~=8.3.1",
diff --git a/uploader/app/lib/expression.py b/uploader/app/lib/expression.py
index e155a9d..3c55e4b 100644
--- a/uploader/app/lib/expression.py
+++ b/uploader/app/lib/expression.py
@@ -8,24 +8,25 @@
 import astropy.units as u
 import numpy as np
 
-CONST_PREFIX = "const_"
+COL_FUNCTION = "col"
 
 NAMED_CONSTANTS: dict[str, u.Quantity] = {
-    "const_pi": np.pi * u.dimensionless_unscaled,
-    "const_c": const.c,
-    "const_deg": 1 * u.deg,
-    "const_rad": 1 * u.rad,
-    "const_arcmin": 1 * u.arcmin,
-    "const_arcsec": 1 * u.arcsec,
-    "const_mag": 1 * u.mag,
+    "pi": np.pi * u.dimensionless_unscaled,
+    "c": const.c,
+    "deg": 1 * u.deg,
+    "rad": 1 * u.rad,
+    "arcmin": 1 * u.arcmin,
+    "arcsec": 1 * u.arcsec,
+    "mag": 1 * u.mag,
 }
 
 
 def expression_syntax_help() -> str:
     constants = ", ".join(sorted(NAMED_CONSTANTS))
     return (
-        "Bare identifiers refer to rawdata column names.\n"
-        "Identifiers starting with const_ refer to predefined constants.\n"
+        f'Use {COL_FUNCTION}("name") to refer to rawdata columns '
+        '(e.g. col("a"), col("SMASB22.5"), col("PA-LEDA")).\n'
+        "Bare identifiers refer to predefined constants.\n"
         "Operators: + - * /.\n"
         "Functions: sin(x), cos(x) (argument must be an angle).\n"
         "Numbers are dimensionless.\n"
@@ -55,6 +56,17 @@ def expression_syntax_help() -> str:
 }
 
 
+def _column_from_call(node: ast.Call) -> str | None:
+    if not isinstance(node.func, ast.Name) or node.func.id != COL_FUNCTION:
+        return None
+    if node.keywords or len(node.args) != 1:
+        raise ValueError(f"{COL_FUNCTION}() takes exactly one string argument")
+    arg = node.args[0]
+    if not isinstance(arg, ast.Constant) or not isinstance(arg.value, str):
+        raise ValueError(f"{COL_FUNCTION}() argument must be a string literal")
+    return arg.value
+
+
 @final
 @dataclass
 class Expression:
@@ -85,13 +97,13 @@ def collect(self, node: ast.AST) -> set[str]:
         return self.columns
 
     def visit_Call(self, node: ast.Call) -> None:
+        column = _column_from_call(node)
+        if column is not None:
+            self.columns.add(column)
+            return
         for arg in node.args:
             self.visit(arg)
 
-    def visit_Name(self, node: ast.Name) -> None:
-        if not node.id.startswith(CONST_PREFIX):
-            self.columns.add(node.id)
-
 
 @final
 class _Evaluator(ast.NodeVisitor):
@@ -105,10 +117,10 @@ def visit(self, node: ast.AST) -> u.Quantity:
                 return self._binop(left, op, right)
             case ast.UnaryOp(op=op, operand=operand):
                 return self._unaryop(op, operand)
-            case ast.Call(func=func, args=args, keywords=keywords):
-                return self._call(func, args, keywords)
+            case ast.Call() as call:
+                return self._call(call)
             case ast.Name(id=name):
-                return self._name(name)
+                return self._lookup_constant(name)
             case ast.Constant(value=value):
                 return self._constant(value)
             case _:
@@ -126,28 +138,32 @@ def _unaryop(self, op: ast.unaryop, operand: ast.AST) -> u.Quantity:
             raise ValueError(f"unsupported unary operator: {op_type.__name__}")
         return _UNARYOPS[op_type](self.visit(operand))
 
-    def _call(self, func: ast.AST, args: list[ast.AST], keywords: list[ast.keyword]) -> u.Quantity:
-        if keywords:
+    def _call(self, node: ast.Call) -> u.Quantity:
+        column = _column_from_call(node)
+        if column is not None:
+            return self._lookup_column(column)
+        if node.keywords:
             raise ValueError("keyword arguments are not allowed")
-        if not isinstance(func, ast.Name):
+        if not isinstance(node.func, ast.Name):
             raise ValueError("only simple function calls are allowed")
-        fn = _FUNCTIONS.get(func.id)
+        fn = _FUNCTIONS.get(node.func.id)
         if fn is None:
-            raise ValueError(f"unknown function: {func.id}")
-        if len(args) != 1:
-            raise ValueError(f"{func.id}() takes exactly one argument")
-        arg = self.visit(args[0]).to(u.rad)
+            raise ValueError(f"unknown function: {node.func.id}")
+        if len(node.args) != 1:
+            raise ValueError(f"{node.func.id}() takes exactly one argument")
+        arg = self.visit(node.args[0]).to(u.rad)
         result = fn(arg)
         if isinstance(result, u.Quantity):
             return result
         return float(result) * u.dimensionless_unscaled
 
-    def _name(self, name: str) -> u.Quantity:
-        if name.startswith(CONST_PREFIX):
-            constant = NAMED_CONSTANTS.get(name)
-            if constant is None:
-                raise ValueError(f"unknown constant {name!r}")
-            return constant
+    def _lookup_constant(self, name: str) -> u.Quantity:
+        constant = NAMED_CONSTANTS.get(name)
+        if constant is None:
+            raise ValueError(f"unknown constant {name!r}")
+        return constant
+
+    def _lookup_column(self, name: str) -> u.Quantity:
         if name not in self._values:
             raise ValueError(f"unknown column {name!r}")
         unit_str = self._units.get(name, "")
diff --git a/uploader/app/report.py b/uploader/app/report.py
index d96b1b1..8e8be4b 100644
--- a/uploader/app/report.py
+++ b/uploader/app/report.py
@@ -1,5 +1,13 @@
+import base64
+import io
 from dataclasses import dataclass
 
+import matplotlib
+import matplotlib.figure
+import matplotlib.pyplot as plt
+
+matplotlib.use("Agg")
+
 
 @dataclass(frozen=True)
 class LogEvent:
@@ -21,4 +29,24 @@ class ErrorEvent:
     message: str
 
 
-Event = LogEvent | ProgressEvent | DoneEvent | ErrorEvent
+@dataclass(frozen=True)
+class ImageEvent:
+    data_url: str
+    caption: str | None = None
+
+
+Event = LogEvent | ProgressEvent | DoneEvent | ErrorEvent | ImageEvent
+
+
+def image_event_from_figure(
+    fig: matplotlib.figure.Figure,
+    caption: str | None = None,
+    dpi: int = 120,
+) -> ImageEvent:
+    buf = io.BytesIO()
+    fig.savefig(buf, format="png", dpi=dpi, bbox_inches="tight")
+    plt.close(fig)
+    return ImageEvent(
+        data_url=f"data:image/png;base64,{base64.b64encode(buf.getvalue()).decode('ascii')}",
+        caption=caption,
+    )
diff --git a/uploader/app/structured/designations/upload.py b/uploader/app/structured/designations/upload.py
index 1fab55f..c65643e 100644
--- a/uploader/app/structured/designations/upload.py
+++ b/uploader/app/structured/designations/upload.py
@@ -1,5 +1,6 @@
 from collections.abc import Callable
 
+import matplotlib.pyplot as plt
 from psycopg import sql
 
 import uploader.app.report as report
@@ -15,6 +16,102 @@
     SaveStructuredDataRequest,
 )
 
+CHART_FIGSIZE = (8, 6)
+
+
+def _rule_distribution_bars(
+    rule_counts: dict[str, int],
+    unmatched: int,
+) -> list[tuple[str, int]]:
+    sorted_rules = sorted(rule_counts.items(), key=lambda kv: (-kv[1], kv[0]))
+    top = [(name, count) for name, count in sorted_rules[:10] if count > 0]
+    other_total = sum(count for _, count in sorted_rules[10:])
+    bars: list[tuple[str, int]] = list(top)
+    if other_total > 0:
+        bars.append(("(other rules)", other_total))
+    if unmatched > 0:
+        bars.append(("(unparsed)", unmatched))
+    return bars
+
+
+def _emit_rule_distribution_image(
+    report_func: Callable[[report.Event], None],
+    rule_counts: dict[str, int],
+    unmatched: int,
+    *,
+    caption: str,
+) -> None:
+    bars = _rule_distribution_bars(rule_counts, unmatched)
+    if not bars:
+        return
+    labels = [name for name, _ in bars]
+    counts = [count for _, count in bars]
+    fig, ax = plt.subplots(figsize=CHART_FIGSIZE)
+    ax.barh(labels, counts)
+    ax.invert_yaxis()
+    ax.set_xlabel("Count")
+    ax.set_title("Designation rule distribution")
+    report_func(report.image_event_from_figure(fig, caption=caption))
+
+
+def _report_batch_progress(
+    report_func: Callable[[report.Event], None],
+    *,
+    rows_read: int,
+    total_so_far: int,
+    matched: int,
+    unmatched: int,
+    progress_pct: int,
+    rule_counts: dict[str, int],
+) -> None:
+    report_func(report.ProgressEvent(percent=min(99, progress_pct)))
+    report_func(
+        report.LogEvent(
+            message=(
+                f"batch: rows_read={rows_read} cumulative_names={total_so_far} matched={matched} unmatched={unmatched}"
+            ),
+        ),
+    )
+    _emit_rule_distribution_image(
+        report_func,
+        rule_counts,
+        unmatched,
+        caption=f"{total_so_far} names processed",
+    )
+
+
+def _report_rule_distribution(
+    report_func: Callable[[report.Event], None],
+    rule_counts: dict[str, int],
+    unmatched: int,
+    total: int,
+) -> None:
+    def pct(n: int) -> float:
+        return (100.0 * n / total) if total else 0.0
+
+    table_rows = [
+        (name, rule_counts[name], pct(rule_counts[name]))
+        for name in sorted(rule_counts.keys(), key=lambda n: (-rule_counts[n], n))
+        if rule_counts[name] > 0
+    ]
+    table_rows.append(("(no rule matched)", unmatched, pct(unmatched)))
+
+    report_func(report.ProgressEvent(percent=100))
+
+    _emit_rule_distribution_image(
+        report_func,
+        rule_counts,
+        unmatched,
+        caption=f"Final: {total} names",
+    )
+
+    summary = format_table(
+        ("Rule", "Count", "%"),
+        table_rows,
+        title=f"Total names: {total}\n",
+    )
+    report_func(report.DoneEvent(message=summary))
+
 
 def upload_designations(
     storage: PgStorage,
@@ -89,34 +186,17 @@ def total_pct(n: int, t: int = total_so_far) -> float:
             unmatched_pct=round(total_pct(unmatched), 1),
         )
         progress_pct = int(100 * processed_rows / total_count) if total_count else 0
-        report_func(report.ProgressEvent(percent=min(99, progress_pct)))
-        report_func(
-            report.LogEvent(
-                message=(
-                    f"batch: rows_read={len(rows)} cumulative_names={total_so_far} "
-                    f"matched={sum(rule_counts.values())} unmatched={unmatched}"
-                ),
-            ),
+        _report_batch_progress(
+            report_func,
+            rows_read=len(rows),
+            total_so_far=total_so_far,
+            matched=sum(rule_counts.values()),
+            unmatched=unmatched,
+            progress_pct=progress_pct,
+            rule_counts=rule_counts,
         )
 
     total = sum(rule_counts.values()) + unmatched
-
-    def pct(n: int) -> float:
-        return (100.0 * n / total) if total else 0.0
-
-    table_rows = [
-        (name, rule_counts[name], pct(rule_counts[name]))
-        for name in sorted(rule_counts.keys(), key=lambda n: (-rule_counts[n], n))
-        if rule_counts[name] > 0
-    ]
-    table_rows.append(("(no rule matched)", unmatched, pct(unmatched)))
-
-    report_func(report.ProgressEvent(percent=100))
-    summary = format_table(
-        ("Rule", "Count", "%"),
-        table_rows,
-        title=f"Total names: {total}\n",
-    )
-    report_func(report.DoneEvent(message=summary))
+    _report_rule_distribution(report_func, rule_counts, unmatched, total)
 
     return total
diff --git a/uploader/app/structured/geometry/upload.py b/uploader/app/structured/geometry/upload.py
index 67738c2..b51c94a 100644
--- a/uploader/app/structured/geometry/upload.py
+++ b/uploader/app/structured/geometry/upload.py
@@ -1,6 +1,8 @@
 from collections.abc import Callable
 
 import astropy.units as u
+import matplotlib.pyplot as plt
+import numpy as np
 from psycopg import sql
 
 import uploader.app.report as report
@@ -31,6 +33,59 @@
     "isophote": "mag/arcmin2",
 }
 
+CHART_FIGSIZE = (8, 6)
+N_A_BINS = 80
+A_BIN_MIN = 0.05
+A_BIN_MAX = 500.0
+A_BIN_EDGES = np.logspace(np.log10(A_BIN_MIN), np.log10(A_BIN_MAX), N_A_BINS + 1)
+
+
+class _SemiMajorAxisAccumulator:
+    def __init__(self) -> None:
+        self._counts = np.zeros(N_A_BINS, dtype=np.int64)
+
+    def add(self, a_values: list[float]) -> None:
+        if not a_values:
+            return
+        positive = np.asarray([v for v in a_values if v > 0], dtype=np.float64)
+        if positive.size == 0:
+            return
+        batch_counts, _ = np.histogram(positive, bins=A_BIN_EDGES)
+        self._counts += batch_counts.astype(np.int64)
+
+    @property
+    def total(self) -> int:
+        return int(self._counts.sum())
+
+    def emit_image(
+        self,
+        report_func: Callable[[report.Event], None],
+        *,
+        caption: str,
+        a_mean: float | None = None,
+        a_min: float | None = None,
+        a_max: float | None = None,
+    ) -> None:
+        if self.total == 0:
+            return
+        centers = np.sqrt(A_BIN_EDGES[:-1] * A_BIN_EDGES[1:])
+        widths = np.diff(A_BIN_EDGES)
+        fig, ax = plt.subplots(figsize=CHART_FIGSIZE)
+        ax.bar(centers, self._counts, width=widths, align="center")
+        ax.set_xscale("log")
+        ax.set_yscale("log")
+        ax.set_xlabel("a (arcsec)")
+        ax.set_ylabel("Count")
+        ax.set_title("Semi-major axis distribution")
+        if a_min is not None and a_min > 0:
+            ax.axvline(a_min, color="gray", linestyle=":", linewidth=1)
+        if a_max is not None and a_max > 0:
+            ax.axvline(a_max, color="gray", linestyle=":", linewidth=1)
+        if a_mean is not None and a_mean > 0:
+            ax.axvline(a_mean, color="red", linestyle="--", linewidth=1, label=f"mean={a_mean:.2f}")
+            ax.legend(loc="upper right", fontsize="small")
+        report_func(report.image_event_from_figure(fig, caption=caption))
+
 
 def _fetch_column_units(
     client: adminapi.AuthenticatedClient,
@@ -101,10 +156,12 @@ def upload_geometry_isophotal(
     )
     total_count = int(cnt[0]["cnt"]) if cnt else 0
     processed_rows = 0
+    a_dist = _SemiMajorAxisAccumulator()
 
     for rows in rawdata_batches(storage, table_name, sorted(needed_cols), batch_size):
         batch_ids: list[str] = []
         batch_data: list[list[str | float]] = []
+        batch_a: list[float] = []
 
         for row in rows:
             if any(row[col] is None for col in needed_cols):
@@ -141,6 +198,9 @@ def upload_geometry_isophotal(
             a_min = min(a_min, a_val)
             a_max = max(a_max, a_val)
             a_sum += a_val
+            batch_a.append(a_val)
+
+        a_dist.add(batch_a)
 
         if write and batch_ids:
             handle_call(
@@ -164,6 +224,14 @@ def upload_geometry_isophotal(
                 message=f"batch: rows_read={len(rows)} uploaded={uploaded} skipped={skipped}",
             ),
         )
+        if uploaded > 0:
+            a_dist.emit_image(
+                report_func,
+                caption=f"a distribution: {uploaded} objects",
+                a_mean=a_sum / uploaded,
+                a_min=a_min,
+                a_max=a_max,
+            )
 
     total = uploaded + skipped
 
@@ -184,6 +252,14 @@ def row_pct_label(n: int) -> float:
             ],
         )
     report_func(report.ProgressEvent(percent=100))
+    if uploaded > 0:
+        a_dist.emit_image(
+            report_func,
+            caption=f"Final: {uploaded} objects",
+            a_mean=a_sum / uploaded,
+            a_min=a_min,
+            a_max=a_max,
+        )
     summary = format_table(
         ("Status", "Count", "%"),
         table_rows,
diff --git a/uploader/app/structured/icrs/upload.py b/uploader/app/structured/icrs/upload.py
index 4662ac7..9b011d4 100644
--- a/uploader/app/structured/icrs/upload.py
+++ b/uploader/app/structured/icrs/upload.py
@@ -1,5 +1,7 @@
 from collections.abc import Callable
 
+import matplotlib.pyplot as plt
+import numpy as np
 from psycopg import sql
 
 import uploader.app.report as report
@@ -18,6 +20,51 @@
 
 ICRS_COLUMNS = ["ra", "dec", "e_ra", "e_dec"]
 
+CHART_FIGSIZE = (10, 5)
+N_RA_BINS = 72
+N_DEC_BINS = 36
+RA_BIN_EDGES = np.linspace(-180.0, 180.0, N_RA_BINS + 1)
+DEC_BIN_EDGES = np.linspace(-90.0, 90.0, N_DEC_BINS + 1)
+
+
+def _ra_to_longitude_deg(ra: np.ndarray) -> np.ndarray:
+    return (np.mod(ra, 360.0) + 180.0) % 360.0 - 180.0
+
+
+class _SkyCoverageAccumulator:
+    def __init__(self) -> None:
+        self._counts = np.zeros((N_RA_BINS, N_DEC_BINS), dtype=np.int64)
+
+    def add(self, ra: list[float], dec: list[float]) -> None:
+        if not ra:
+            return
+        ra_arr = _ra_to_longitude_deg(np.asarray(ra, dtype=np.float64))
+        dec_arr = np.clip(np.asarray(dec, dtype=np.float64), -90.0, 90.0)
+        batch_counts, _, _ = np.histogram2d(ra_arr, dec_arr, bins=[RA_BIN_EDGES, DEC_BIN_EDGES])
+        self._counts += batch_counts.astype(np.int64)
+
+    @property
+    def total(self) -> int:
+        return int(self._counts.sum())
+
+    def emit_image(
+        self,
+        report_func: Callable[[report.Event], None],
+        *,
+        caption: str,
+    ) -> None:
+        if self.total == 0:
+            return
+        ra_centers = 0.5 * (RA_BIN_EDGES[:-1] + RA_BIN_EDGES[1:])
+        dec_centers = 0.5 * (DEC_BIN_EDGES[:-1] + DEC_BIN_EDGES[1:])
+        theta, phi = np.meshgrid(-np.deg2rad(ra_centers), np.deg2rad(dec_centers))
+        fig = plt.figure(figsize=CHART_FIGSIZE)
+        ax = fig.add_subplot(111, projection="aitoff")
+        ax.pcolormesh(theta, phi, self._counts.T, shading="auto", cmap="viridis")
+        ax.set_title("ICRS sky coverage")
+        ax.grid(True)
+        report_func(report.image_event_from_figure(fig, caption=caption))
+
 
 def _fetch_units(
     client: adminapi.AuthenticatedClient,
@@ -82,10 +129,13 @@ def upload_icrs(
     )
     total_count = int(cnt[0]["cnt"]) if cnt else 0
     processed_rows = 0
+    sky = _SkyCoverageAccumulator()
 
     for rows in rawdata_batches(storage, table_name, [ra_column, dec_column], batch_size):
         batch_ids: list[str] = []
         batch_data: list[list[float]] = []
+        batch_ra: list[float] = []
+        batch_dec: list[float] = []
 
         for row in rows:
             ra_val = row[ra_column]
@@ -104,6 +154,10 @@ def upload_icrs(
             dec_max = max(dec_max, dec_f)
             ra_sum += ra_f
             dec_sum += dec_f
+            batch_ra.append(ra_f)
+            batch_dec.append(dec_f)
+
+        sky.add(batch_ra, batch_dec)
 
         if write and batch_ids:
             handle_call(
@@ -127,6 +181,7 @@ def upload_icrs(
                 message=f"batch: rows_read={len(rows)} uploaded={uploaded} skipped={skipped}",
             ),
         )
+        sky.emit_image(report_func, caption=f"Sky coverage: {uploaded} objects")
 
     total = uploaded + skipped
 
@@ -151,6 +206,7 @@ def row_pct_label(n: int) -> float:
             ]
         )
     report_func(report.ProgressEvent(percent=100))
+    sky.emit_image(report_func, caption=f"Final: {uploaded} objects")
     summary = format_table(
         ("Status", "Count", "%"),
         table_rows,
diff --git a/uploader/app/structured/nature/upload.py b/uploader/app/structured/nature/upload.py
index 6b23335..abc7a8f 100644
--- a/uploader/app/structured/nature/upload.py
+++ b/uploader/app/structured/nature/upload.py
@@ -1,6 +1,7 @@
 from collections import Counter
 from collections.abc import Callable
 
+import matplotlib.pyplot as plt
 from psycopg import sql
 
 import uploader.app.report as report
@@ -16,6 +17,37 @@
 
 NATURE_COLUMNS = ["type_name"]
 
+CHART_FIGSIZE = (8, 6)
+
+
+def _type_distribution_bars(type_counts: Counter[str]) -> list[tuple[str, int]]:
+    sorted_types = sorted(type_counts.items(), key=lambda kv: (-kv[1], kv[0]))
+    top = [(name, count) for name, count in sorted_types[:10] if count > 0]
+    other_total = sum(count for _, count in sorted_types[10:])
+    bars: list[tuple[str, int]] = list(top)
+    if other_total > 0:
+        bars.append(("(other types)", other_total))
+    return bars
+
+
+def _emit_type_distribution_image(
+    report_func: Callable[[report.Event], None],
+    type_counts: Counter[str],
+    *,
+    caption: str,
+) -> None:
+    bars = _type_distribution_bars(type_counts)
+    if not bars:
+        return
+    labels = [name for name, _ in bars]
+    counts = [count for _, count in bars]
+    fig, ax = plt.subplots(figsize=CHART_FIGSIZE)
+    ax.barh(labels, counts)
+    ax.invert_yaxis()
+    ax.set_xlabel("Count")
+    ax.set_title("LEDA type distribution")
+    report_func(report.image_event_from_figure(fig, caption=caption))
+
 
 def upload_nature(
     storage: PgStorage,
@@ -80,6 +112,11 @@ def upload_nature(
                 message=f"batch: rows_read={len(rows)} total_uploaded_so_far={total_uploaded}",
             ),
         )
+        _emit_type_distribution_image(
+            report_func,
+            type_counts,
+            caption=f"{total_uploaded} rows classified",
+        )
 
     table_rows: list[tuple[str, int, float | str]] = [
         (
@@ -90,6 +127,11 @@ def upload_nature(
         for leda_type, count in sorted(type_counts.items())
     ]
     report_func(report.ProgressEvent(percent=100))
+    _emit_type_distribution_image(
+        report_func,
+        type_counts,
+        caption=f"Final: {total_uploaded} rows",
+    )
     summary = format_table(
         ("LEDA type", "Count", "%"),
         table_rows,
diff --git a/uploader/app/structured/redshift/upload.py b/uploader/app/structured/redshift/upload.py
index 4f406b4..a55c95d 100644
--- a/uploader/app/structured/redshift/upload.py
+++ b/uploader/app/structured/redshift/upload.py
@@ -1,5 +1,7 @@
 from collections.abc import Callable
 
+import matplotlib.pyplot as plt
+import numpy as np
 from psycopg import sql
 
 import uploader.app.report as report
@@ -22,6 +24,55 @@
 
 REDSHIFT_UNITS = SaveStructuredDataRequestUnits.from_dict({"cz": "km/s", "e_cz": "km/s"})
 
+CHART_FIGSIZE = (8, 6)
+N_CZ_BINS = 80
+CZ_BIN_MIN = -10_000.0
+CZ_BIN_MAX = 350_000.0
+CZ_BIN_EDGES = np.linspace(CZ_BIN_MIN, CZ_BIN_MAX, N_CZ_BINS + 1)
+
+
+class _CzDistributionAccumulator:
+    def __init__(self) -> None:
+        self._counts = np.zeros(N_CZ_BINS, dtype=np.int64)
+
+    def add(self, cz_values: list[float]) -> None:
+        if not cz_values:
+            return
+        batch_counts, _ = np.histogram(np.asarray(cz_values, dtype=np.float64), bins=CZ_BIN_EDGES)
+        self._counts += batch_counts.astype(np.int64)
+
+    @property
+    def total(self) -> int:
+        return int(self._counts.sum())
+
+    def emit_image(
+        self,
+        report_func: Callable[[report.Event], None],
+        *,
+        caption: str,
+        cz_mean: float | None = None,
+        cz_min: float | None = None,
+        cz_max: float | None = None,
+    ) -> None:
+        if self.total == 0:
+            return
+        centers = 0.5 * (CZ_BIN_EDGES[:-1] + CZ_BIN_EDGES[1:])
+        widths = np.diff(CZ_BIN_EDGES)
+        fig, ax = plt.subplots(figsize=CHART_FIGSIZE)
+        ax.bar(centers, self._counts, width=widths, align="center")
+        ax.set_yscale("log")
+        ax.set_xlabel("cz (km/s)")
+        ax.set_ylabel("Count")
+        ax.set_title("Redshift (cz) distribution")
+        if cz_min is not None:
+            ax.axvline(cz_min, color="gray", linestyle=":", linewidth=1)
+        if cz_max is not None:
+            ax.axvline(cz_max, color="gray", linestyle=":", linewidth=1)
+        if cz_mean is not None:
+            ax.axvline(cz_mean, color="red", linestyle="--", linewidth=1, label=f"mean={cz_mean:.0f}")
+            ax.legend(loc="upper right", fontsize="small")
+        report_func(report.image_event_from_figure(fig, caption=caption))
+
 
 def upload_redshift(
     storage: PgStorage,
@@ -46,10 +97,12 @@ def upload_redshift(
     )
     total_count = int(cnt[0]["cnt"]) if cnt else 0
     processed_rows = 0
+    cz_dist = _CzDistributionAccumulator()
 
     for rows in rawdata_batches(storage, table_name, [z_column], batch_size):
         batch_ids: list[str] = []
         batch_data: list[list[float]] = []
+        batch_cz: list[float] = []
 
         for row in rows:
             z_val = row[z_column]
@@ -64,6 +117,9 @@ def upload_redshift(
             cz_min = min(cz_min, cz_val)
             cz_max = max(cz_max, cz_val)
             cz_sum += cz_val
+            batch_cz.append(cz_val)
+
+        cz_dist.add(batch_cz)
 
         if write and batch_ids:
             handle_call(
@@ -80,13 +136,21 @@ def upload_redshift(
             )
 
         processed_rows += len(rows)
-        batch_pct = int(100 * processed_rows / total_count)
+        batch_pct = int(100 * processed_rows / total_count) if total_count else 0
         report_func(report.ProgressEvent(percent=min(99, batch_pct)))
         report_func(
             report.LogEvent(
                 message=f"batch: rows_read={len(rows)} uploaded={uploaded} skipped={skipped}",
             ),
         )
+        if uploaded > 0:
+            cz_dist.emit_image(
+                report_func,
+                caption=f"cz distribution: {uploaded} rows",
+                cz_mean=cz_sum / uploaded,
+                cz_min=cz_min,
+                cz_max=cz_max,
+            )
 
     total = uploaded + skipped
 
@@ -107,6 +171,14 @@ def row_pct_label(n: int) -> float:
             ]
         )
     report_func(report.ProgressEvent(percent=100))
+    if uploaded > 0:
+        cz_dist.emit_image(
+            report_func,
+            caption=f"Final: {uploaded} rows",
+            cz_mean=cz_sum / uploaded,
+            cz_min=cz_min,
+            cz_max=cz_max,
+        )
     summary = format_table(
         ("Status", "Count", "%"),
         table_rows,
diff --git a/uploader/forms/structured_designation.py b/uploader/forms/structured_designation.py
index c3f8afa..632ce13 100644
--- a/uploader/forms/structured_designation.py
+++ b/uploader/forms/structured_designation.py
@@ -64,7 +64,7 @@ def handle_structured_designation(
             f.column_name.strip(),
             advanced.batch_size,
             client,
-            write=advanced.write,
+            write=f.write,
             print_unmatched=advanced.print_unmatched,
             report_func=report_func,
         )
diff --git a/uploader/forms/structured_icrs.py b/uploader/forms/structured_icrs.py
index 011be77..9a72f0b 100644
--- a/uploader/forms/structured_icrs.py
+++ b/uploader/forms/structured_icrs.py
@@ -61,7 +61,7 @@ def handle_structured_icrs(
             f.dec_column.strip(),
             advanced.batch_size,
             client,
-            write=advanced.write,
+            write=f.write,
             ra_error=f.ra_error,
             ra_error_unit=f.ra_error_unit.strip(),
             dec_error=f.dec_error,
diff --git a/uploader/forms/structured_nature.py b/uploader/forms/structured_nature.py
index f750304..59b345f 100644
--- a/uploader/forms/structured_nature.py
+++ b/uploader/forms/structured_nature.py
@@ -93,6 +93,6 @@ def handle_structured_nature(
             default_t,
             advanced.batch_size,
             client,
-            write=advanced.write,
+            write=f.write,
             report_func=report_func,
         )
diff --git a/uploader/forms/structured_photometry_hyperleda.py b/uploader/forms/structured_photometry_hyperleda.py
index 6b80551..40f49b6 100644
--- a/uploader/forms/structured_photometry_hyperleda.py
+++ b/uploader/forms/structured_photometry_hyperleda.py
@@ -55,6 +55,6 @@ def handle_structured_photometry_hyperleda(
             f.table_name.strip(),
             advanced.batch_size,
             client,
-            write=advanced.write,
+            write=f.write,
             report_func=report_func,
         )
diff --git a/uploader/tasks.py b/uploader/tasks.py
index 2cc70b4..9ec5c6b 100644
--- a/uploader/tasks.py
+++ b/uploader/tasks.py
@@ -110,6 +110,15 @@ def append_report_event(event: report.Event) -> None:
                 final_status = "error"
                 final_message = msg
                 run.append({"type": "error", "message": msg})
+            case report.ImageEvent(data_url=url, caption=cap):
+                run.append(
+                    {
+                        "type": "image",
+                        "data_url": url,
+                        "caption": cap,
+                        "timestamp": datetime.now().astimezone().isoformat(),
+                    },
+                )
 
     def report_func(event: report.Event) -> None:
         if run.cancel_requested.is_set():
diff --git a/uv.lock b/uv.lock
index 55be838..479d823 100644
--- a/uv.lock
+++ b/uv.lock
@@ -237,6 +237,61 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
 ]
 
+[[package]]
+name = "contourpy"
+version = "1.3.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174, upload-time = "2025-07-26T12:03:12.549Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/68/35/0167aad910bbdb9599272bd96d01a9ec6852f36b9455cf2ca67bd4cc2d23/contourpy-1.3.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:177fb367556747a686509d6fef71d221a4b198a3905fe824430e5ea0fda54eb5", size = 293257, upload-time = "2025-07-26T12:01:39.367Z" },
+    { url = "https://files.pythonhosted.org/packages/96/e4/7adcd9c8362745b2210728f209bfbcf7d91ba868a2c5f40d8b58f54c509b/contourpy-1.3.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:d002b6f00d73d69333dac9d0b8d5e84d9724ff9ef044fd63c5986e62b7c9e1b1", size = 274034, upload-time = "2025-07-26T12:01:40.645Z" },
+    { url = "https://files.pythonhosted.org/packages/73/23/90e31ceeed1de63058a02cb04b12f2de4b40e3bef5e082a7c18d9c8ae281/contourpy-1.3.3-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:348ac1f5d4f1d66d3322420f01d42e43122f43616e0f194fc1c9f5d830c5b286", size = 334672, upload-time = "2025-07-26T12:01:41.942Z" },
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