Measured pattern import: CSV, EMCAR, NEC2, Touchstone S1P parsers + web UI upload

Add 5 MCP tools (PatternImportMixin) and 1 prompt for importing external
antenna pattern data. Pure-Python parsers with IDW interpolation on the
sphere, single-cut-to-3D synthesis for EMCAR/2-col CSV, and Touchstone S1P
bridge to the analytical pattern engine. Web UI gets a "Load File" button
with multipart upload endpoint and WebSocket broadcast. 78 tools, 14 prompts.

frontend/src/api.ts

@@ -40,6 +40,28 @@ export async function getBands(): Promise<Record<string, { start_hz: number; sto
   return resp.json();
 }
 
+export async function uploadPattern(
+  file: File,
+  options?: { frequency_hz?: number; polarization?: string; antenna_type?: string; reference_dbi?: number },
+): Promise<PatternData> {
+  const form = new FormData();
+  form.append('file', file);
+  if (options?.frequency_hz) form.append('frequency_hz', String(options.frequency_hz));
+  if (options?.polarization) form.append('polarization', options.polarization);
+  if (options?.antenna_type) form.append('antenna_type', options.antenna_type);
+  if (options?.reference_dbi != null) form.append('reference_dbi', String(options.reference_dbi));
+
+  const resp = await fetch(`${BASE_URL}/api/pattern/import`, {
+    method: 'POST',
+    body: form,
+  });
+  if (!resp.ok) {
+    const body = await resp.text();
+    throw new Error(`Import failed (${resp.status}): ${body}`);
+  }
+  return resp.json();
+}
+
 export type PatternCallback = (data: PatternData) => void;
 export type StatusCallback = (connected: boolean) => void;
 

frontend/src/controls.ts

@@ -1,9 +1,10 @@
 import type { DisplayMode, AppState } from './types';
-import { iconRadio, iconActivity, iconCrosshair, iconWifi, iconWifiOff } from './icons';
+import { iconRadio, iconActivity, iconCrosshair, iconUpload, iconWifi, iconWifiOff } from './icons';
 
 export interface ControlCallbacks {
   onCompute: () => void;
   onScan: () => void;
+  onFileImport: (file: File) => void;
   onDisplayModeChange: (mode: DisplayMode) => void;
   onAntennaTypeChange: (type: string) => void;
   onFrequencyChange: (mhz: number) => void;
@@ -137,6 +138,26 @@ export function createControls(
   btnScan.addEventListener('click', callbacks.onScan);
   container.appendChild(btnScan);
 
+  // Hidden file input for pattern import
+  const fileInput = document.createElement('input');
+  fileInput.type = 'file';
+  fileInput.accept = '.csv,.dat,.out,.nec,.s1p';
+  fileInput.style.display = 'none';
+  fileInput.addEventListener('change', () => {
+    const file = fileInput.files?.[0];
+    if (file) {
+      callbacks.onFileImport(file);
+      fileInput.value = '';
+    }
+  });
+  container.appendChild(fileInput);
+
+  const btnImport = document.createElement('button');
+  btnImport.className = 'ctrl-btn ctrl-btn-outline';
+  btnImport.innerHTML = `${iconUpload} Load File`;
+  btnImport.addEventListener('click', () => fileInput.click());
+  container.appendChild(btnImport);
+
   // Separator
   container.appendChild(el('hr', 'controls-sep'));
 
@@ -182,6 +203,7 @@ export function createControls(
       loadingEl.textContent = loading ? 'Computing...' : '';
       btnCompute.disabled = loading;
       btnScan.disabled = loading;
+      btnImport.disabled = loading;
     },
   };
 }

frontend/src/icons.ts

@@ -20,3 +20,6 @@ export const iconWifiOff = `<svg ${ATTRS}><line x1="1" y1="1" x2="23" y2="23"/><
 
 /** Crosshair / target icon */
 export const iconCrosshair = `<svg ${ATTRS}><circle cx="12" cy="12" r="10"/><line x1="22" y1="12" x2="18" y2="12"/><line x1="6" y1="12" x2="2" y2="12"/><line x1="12" y1="6" x2="12" y2="2"/><line x1="12" y1="22" x2="12" y2="18"/></svg>`;
+
+/** Upload icon (Lucide) */
+export const iconUpload = `<svg ${ATTRS}><path d="M21 15v4a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-4"/><polyline points="17 8 12 3 7 8"/><line x1="12" y1="3" x2="12" y2="15"/></svg>`;

frontend/src/main.ts

@@ -2,7 +2,7 @@ import { createScene, type SceneContext } from './scene';
 import { updatePattern } from './pattern';
 import { createControls } from './controls';
 import { drawSmithChart } from './smith';
-import { fetchPattern, scanPattern, connectWebSocket } from './api';
+import { fetchPattern, scanPattern, uploadPattern, connectWebSocket } from './api';
 import type { AppState, DisplayMode, PatternData } from './types';
 import './style.css';
 
@@ -78,6 +78,22 @@ async function handleScan() {
   }
 }
 
+async function handleFileImport(file: File) {
+  state.loading = true;
+  controlsUi.updateLoading(true);
+  try {
+    const data = await uploadPattern(file, {
+      frequency_hz: state.frequencyMhz * 1e6,
+      antenna_type: state.antennaType,
+    });
+    handlePatternData(data);
+  } catch (err) {
+    console.error('Import error:', err);
+    state.loading = false;
+    controlsUi.updateLoading(false);
+  }
+}
+
 function init() {
   const sceneContainer = document.getElementById('scene-container');
   const controlsContainer = document.getElementById('controls');
@@ -95,6 +111,7 @@ function init() {
   controlsUi = createControls(controlsContainer, {
     onCompute: handleCompute,
     onScan: handleScan,
+    onFileImport: handleFileImport,
     onDisplayModeChange(mode: DisplayMode) {
       state.displayMode = mode;
       render();

frontend/src/style.css

@@ -273,6 +273,20 @@ html, body {
   color: var(--slate-100);
 }
 
+.ctrl-btn-outline {
+  background: transparent;
+  color: var(--slate-400);
+  border-color: var(--slate-700);
+  border-style: dashed;
+  font-size: 0.8rem;
+}
+
+.ctrl-btn-outline:hover:not(:disabled) {
+  border-color: var(--slate-500);
+  border-style: solid;
+  color: var(--slate-200);
+}
+
 /* Display mode toggle group */
 .controls-mode-group {
   display: grid;

frontend/src/types.ts

@@ -10,6 +10,8 @@ export interface PatternData {
   h_plane: PlanePoint[];
   model: string;
   resonance?: ResonanceData;
+  raw_cut?: { angles_deg: number[]; gain_db: number[]; plane: string };
+  import_info?: { format: string; filename?: string; points: number };
 }
 
 export interface PlanePoint {

src/mcnanovna/nanovna.py

@@ -16,6 +16,7 @@ from mcnanovna.tools import (
     DiagnosticsMixin,
     DisplayMixin,
     MeasurementMixin,
+    PatternImportMixin,
     RadiationMixin,
 )
 
@@ -28,6 +29,7 @@ class NanoVNA(
     DiagnosticsMixin,
     AnalysisMixin,
     RadiationMixin,
+    PatternImportMixin,
 ):
     """MCP tool class for NanoVNA-H vector network analyzers.
 

src/mcnanovna/pattern_import.py

@@ -0,0 +1,700 @@
+"""Import measured antenna patterns from external files.
+
+Parses CSV, EMCAR vna.dat, NEC2 radiation output, and Touchstone S1P formats
+into the standardized {theta_deg, phi_deg, gain_dbi} dict that the 3D viewer
+and MCP tools already consume.
+
+All functions are pure Python (math + list comprehensions). No external dependencies.
+Content is passed as strings, not file paths — MCP tools receive content from the LLM,
+and the web UI endpoint handles multipart upload separately.
+
+Inspired by the EMCAR antenna range (https://emcar.sourceforge.net/) which uses
+LinuxCNC + HP8754A VNA for IEEE-149 stop-and-measure pattern recording.
+"""
+
+from __future__ import annotations
+
+import math
+import re
+
+from mcnanovna.radiation import C, _deg_range
+
+
+# ── Shared utilities ──────────────────────────────────────────────
+
+
+def _build_pattern_dict(
+    thetas: list[float],
+    phis: list[float],
+    gain_dbi: list[list[float]],
+    model: str,
+    frequency_hz: float | None,
+    antenna_type: str,
+    metadata: dict | None = None,
+) -> dict:
+    """Assemble a standardized pattern dict matching generate_3d_pattern() output."""
+    peak = -999.0
+    for row in gain_dbi:
+        for g in row:
+            if g > peak:
+                peak = g
+
+    # E-plane: phi=0 (first column of each theta row)
+    e_plane = [{"theta_deg": thetas[i], "gain_dbi": gain_dbi[i][0]} for i in range(len(thetas))]
+
+    # H-plane: theta=90 (row closest to 90 deg, all phi values)
+    theta_90_idx = min(range(len(thetas)), key=lambda i: abs(thetas[i] - 90.0))
+    h_plane = [{"phi_deg": phis[j], "gain_dbi": gain_dbi[theta_90_idx][j]} for j in range(len(phis))]
+
+    result: dict = {
+        "antenna_type": antenna_type,
+        "frequency_hz": frequency_hz,
+        "theta_deg": thetas,
+        "phi_deg": phis,
+        "gain_dbi": gain_dbi,
+        "peak_gain_dbi": round(peak, 2),
+        "e_plane": e_plane,
+        "h_plane": h_plane,
+        "grid_size": {
+            "theta_points": len(thetas),
+            "phi_points": len(phis),
+            "total_points": len(thetas) * len(phis),
+        },
+        "model": model,
+    }
+
+    if frequency_hz:
+        result["wavelength_m"] = round(C / frequency_hz, 4)
+
+    if metadata:
+        result["import_info"] = metadata
+
+    return result
+
+
+# ── Interpolation ─────────────────────────────────────────────────
+
+
+def _great_circle_distance(t1: float, p1: float, t2: float, p2: float) -> float:
+    """Great-circle angular distance between two (theta, phi) points in degrees.
+
+    Uses the Vincenty formula for numerical stability at small/large distances.
+    """
+    t1r, p1r = math.radians(t1), math.radians(p1)
+    t2r, p2r = math.radians(t2), math.radians(p2)
+    dp = p2r - p1r
+
+    sin_t1, cos_t1 = math.sin(t1r), math.cos(t1r)
+    sin_t2, cos_t2 = math.sin(t2r), math.cos(t2r)
+    sin_dp, cos_dp = math.sin(dp), math.cos(dp)
+
+    num = math.sqrt((cos_t2 * sin_dp) ** 2 + (cos_t1 * sin_t2 - sin_t1 * cos_t2 * cos_dp) ** 2)
+    den = sin_t1 * sin_t2 + cos_t1 * cos_t2 * cos_dp
+
+    return math.degrees(math.atan2(num, den))
+
+
+def interpolate_to_grid(
+    measurements: list[tuple[float, float, float]],
+    theta_step: float = 2.0,
+    phi_step: float = 5.0,
+) -> tuple[list[float], list[float], list[list[float]]]:
+    """Interpolate sparse measurements to a regular theta/phi grid.
+
+    Uses inverse-distance weighting (IDW) with great-circle distance on the
+    sphere surface. K=6 nearest neighbors, power=2 (Shepard's method).
+
+    Args:
+        measurements: List of (theta_deg, phi_deg, gain_dbi) tuples
+        theta_step: Output grid polar angle step (degrees)
+        phi_step: Output grid azimuthal angle step (degrees)
+
+    Returns:
+        (thetas, phis, gain_grid) where gain_grid[i][j] is gain in dBi
+    """
+    thetas = _deg_range(0.0, 180.0, theta_step)
+    phis = _deg_range(0.0, 355.0, phi_step)
+    k = min(6, len(measurements))
+
+    gain_grid: list[list[float]] = []
+    for theta in thetas:
+        row: list[float] = []
+        for phi in phis:
+            # Compute distances to all measurement points
+            dists = []
+            for mt, mp, mg in measurements:
+                d = _great_circle_distance(theta, phi, mt, mp)
+                dists.append((d, mg))
+
+            # Exact match shortcut
+            dists.sort(key=lambda x: x[0])
+            if dists[0][0] < 0.01:
+                row.append(dists[0][1])
+                continue
+
+            # IDW with K nearest neighbors
+            nearest = dists[:k]
+            w_sum = 0.0
+            g_sum = 0.0
+            for d, g in nearest:
+                w = 1.0 / (d * d + 1e-10)
+                w_sum += w
+                g_sum += w * g
+            row.append(g_sum / w_sum)
+        gain_grid.append(row)
+
+    return thetas, phis, gain_grid
+
+
+def single_cut_to_3d(
+    angles_deg: list[float],
+    gain_db: list[float],
+    cut_plane: str = "azimuth",
+    theta_step: float = 2.0,
+    phi_step: float = 5.0,
+) -> tuple[list[float], list[float], list[list[float]]]:
+    """Synthesize a 3D pattern from a single-plane cut measurement.
+
+    For azimuth cuts (typical of EMCAR): the measured data becomes the H-plane
+    (theta=90 deg), and elevation is synthesized with a sin(theta) taper —
+    physically approximate but produces a useful 3D shape.
+
+    For elevation cuts: the measured data becomes the E-plane (phi=0 deg),
+    and the pattern is rotated around the vertical axis.
+
+    Args:
+        angles_deg: Measurement angles in degrees
+        gain_db: Gain values in dB at each angle
+        cut_plane: "azimuth" (H-plane) or "elevation" (E-plane)
+        theta_step: Output grid polar angle step
+        phi_step: Output grid azimuthal angle step
+    """
+    thetas = _deg_range(0.0, 180.0, theta_step)
+    phis = _deg_range(0.0, 355.0, phi_step)
+
+    # Build a lookup for the measured cut via linear interpolation
+    def _interp_cut(angle: float) -> float:
+        """Linearly interpolate the measured cut at an arbitrary angle."""
+        # Normalize to [0, 360)
+        angle = angle % 360.0
+        n = len(angles_deg)
+        if n == 0:
+            return -40.0
+        if n == 1:
+            return gain_db[0]
+
+        # Find bracketing points
+        normed = [a % 360.0 for a in angles_deg]
+        for i in range(n - 1):
+            a0, a1 = normed[i], normed[i + 1]
+            if a0 <= angle <= a1 and a1 > a0:
+                t = (angle - a0) / (a1 - a0)
+                return gain_db[i] * (1 - t) + gain_db[i + 1] * t
+
+        # Wrap-around interpolation
+        return gain_db[-1]
+
+    gain_grid: list[list[float]] = []
+
+    if cut_plane == "azimuth":
+        # Measured data is H-plane (theta=90). Synthesize elevation via sin(theta) taper.
+        for theta in thetas:
+            sin_t = math.sin(math.radians(theta))
+            taper = max(sin_t, 0.01)  # Avoid -inf at poles
+            taper_db = 20.0 * math.log10(taper)
+            row = [_interp_cut(phi) + taper_db for phi in phis]
+            gain_grid.append(row)
+    else:
+        # Elevation cut: measured data defines E-plane, rotate around phi
+        for theta in thetas:
+            g_at_theta = _interp_cut(theta)
+            row = [g_at_theta] * len(phis)
+            gain_grid.append(row)
+
+    return thetas, phis, gain_grid
+
+
+# ── Format parsers ────────────────────────────────────────────────
+
+
+def parse_csv_pattern(content: str, frequency_hz: float | None = None) -> dict:
+    """Parse a CSV file with antenna pattern data.
+
+    Auto-detects 2-column (angle, gain) vs 3-column (theta, phi, gain) from the
+    header row. Flexible header names: theta/elevation, phi/azimuth,
+    gain/gain_dbi/amplitude/db/dbi.
+
+    For 2-column data, the angle column name determines the cut plane:
+    - "phi" or "azimuth" → azimuth cut
+    - "theta" or "elevation" → elevation cut
+
+    Args:
+        content: CSV file content as string
+        frequency_hz: Operating frequency in Hz (optional, for metadata)
+    """
+    lines = [line.strip() for line in content.strip().splitlines() if line.strip()]
+    if not lines:
+        raise ValueError("Empty CSV content")
+
+    # Parse header
+    header = lines[0].lower().replace(" ", "_")
+    cols = re.split(r"[,;\t]+", header)
+
+    # Identify column indices
+    theta_idx = phi_idx = gain_idx = -1
+    angle_idx = -1  # For 2-column mode
+
+    for i, col in enumerate(cols):
+        col_clean = col.strip()
+        if col_clean in ("theta", "theta_deg", "elevation", "el"):
+            theta_idx = i
+        elif col_clean in ("phi", "phi_deg", "azimuth", "az"):
+            phi_idx = i
+        elif col_clean in ("gain", "gain_dbi", "dbi", "db", "amplitude", "gain_db", "magnitude"):
+            gain_idx = i
+        elif col_clean in ("angle", "angle_deg", "deg", "degrees"):
+            angle_idx = i
+
+    # Determine mode
+    three_col = theta_idx >= 0 and phi_idx >= 0 and gain_idx >= 0
+    two_col = not three_col and gain_idx >= 0 and (theta_idx >= 0 or phi_idx >= 0 or angle_idx >= 0)
+
+    if not three_col and not two_col:
+        # Try numeric-only (no header): assume 2-col azimuth or 3-col
+        first_data = re.split(r"[,;\t]+", lines[0])
+        try:
+            vals = [float(v) for v in first_data]
+            if len(vals) >= 3:
+                three_col = True
+                theta_idx, phi_idx, gain_idx = 0, 1, 2
+                lines = lines  # No header to skip
+            elif len(vals) == 2:
+                two_col = True
+                angle_idx, gain_idx = 0, 1
+                phi_idx = -1
+                lines = lines
+            else:
+                raise ValueError(f"Cannot parse CSV: unrecognized columns: {cols}")
+        except ValueError:
+            raise ValueError(f"Cannot parse CSV: unrecognized header columns: {cols}")
+    else:
+        lines = lines[1:]  # Skip header
+
+    # Parse data
+    if three_col:
+        measurements: list[tuple[float, float, float]] = []
+        for line in lines:
+            parts = re.split(r"[,;\t]+", line.strip())
+            if len(parts) < max(theta_idx, phi_idx, gain_idx) + 1:
+                continue
+            try:
+                t = float(parts[theta_idx])
+                p = float(parts[phi_idx])
+                g = float(parts[gain_idx])
+                measurements.append((t, p, g))
+            except (ValueError, IndexError):
+                continue
+
+        if not measurements:
+            raise ValueError("No valid data rows found in CSV")
+
+        thetas, phis, gain_grid = interpolate_to_grid(measurements)
+        return _build_pattern_dict(
+            thetas,
+            phis,
+            gain_grid,
+            model="imported_csv",
+            frequency_hz=frequency_hz,
+            antenna_type="imported",
+            metadata={"format": "csv", "points": len(measurements), "columns": 3},
+        )
+
+    else:
+        # 2-column mode
+        use_idx = theta_idx if theta_idx >= 0 else (phi_idx if phi_idx >= 0 else angle_idx)
+        cut_plane = "elevation" if theta_idx >= 0 else "azimuth"
+
+        angles: list[float] = []
+        gains: list[float] = []
+        for line in lines:
+            parts = re.split(r"[,;\t]+", line.strip())
+            if len(parts) < max(use_idx, gain_idx) + 1:
+                continue
+            try:
+                a = float(parts[use_idx])
+                g = float(parts[gain_idx])
+                angles.append(a)
+                gains.append(g)
+            except (ValueError, IndexError):
+                continue
+
+        if not angles:
+            raise ValueError("No valid data rows found in CSV")
+
+        thetas, phis, gain_grid = single_cut_to_3d(angles, gains, cut_plane=cut_plane)
+        result = _build_pattern_dict(
+            thetas,
+            phis,
+            gain_grid,
+            model="imported_csv_single_cut",
+            frequency_hz=frequency_hz,
+            antenna_type="imported",
+            metadata={"format": "csv", "points": len(angles), "columns": 2, "cut_plane": cut_plane},
+        )
+        result["raw_cut"] = {"angles_deg": angles, "gain_db": gains, "plane": cut_plane}
+        return result
+
+
+def parse_emcar_vna_dat(
+    content: str,
+    frequency_hz: float | None = None,
+    reference_dbi: float | None = None,
+) -> dict:
+    """Parse EMCAR vna.dat format (angle + amplitude_dBV pairs).
+
+    EMCAR records raw ADC voltage from the HP8754A VNA. Lines starting with #
+    are comments. Data is whitespace-separated: angle amplitude.
+
+    The gnuplot transform from EMCAR's plotting scripts is applied:
+    - Angle rotation: (-angle + 90) to convert from positioner coords to antenna coords
+    - Amplitude: 20*log10(amplitude + 0.01) to convert voltage to dB scale
+
+    Without a reference antenna, this shows relative pattern shape only.
+    Pass reference_dbi to offset the entire pattern to absolute gain.
+
+    Args:
+        content: vna.dat file content as string
+        frequency_hz: Operating frequency in Hz (optional)
+        reference_dbi: Offset to apply for absolute gain calibration (optional)
+    """
+    angles: list[float] = []
+    gains: list[float] = []
+
+    for line in content.strip().splitlines():
+        line = line.strip()
+        if not line or line.startswith("#"):
+            continue
+        parts = line.split()
+        if len(parts) < 2:
+            continue
+        try:
+            angle = float(parts[0])
+            amplitude = float(parts[1])
+        except ValueError:
+            continue
+
+        # EMCAR gnuplot transform
+        rotated_angle = (-angle + 90.0) % 360.0
+        # Voltage to dB: 20*log10(amplitude + 0.01) — the +0.01 prevents log(0)
+        gain_db = 20.0 * math.log10(abs(amplitude) + 0.01)
+
+        if reference_dbi is not None:
+            gain_db += reference_dbi
+
+        angles.append(rotated_angle)
+        gains.append(gain_db)
+
+    if not angles:
+        raise ValueError("No valid data rows found in EMCAR vna.dat content")
+
+    # Sort by angle for clean interpolation
+    paired = sorted(zip(angles, gains), key=lambda x: x[0])
+    angles = [a for a, _ in paired]
+    gains = [g for _, g in paired]
+
+    thetas, phis, gain_grid = single_cut_to_3d(angles, gains, cut_plane="azimuth")
+
+    result = _build_pattern_dict(
+        thetas,
+        phis,
+        gain_grid,
+        model="imported_emcar",
+        frequency_hz=frequency_hz,
+        antenna_type="imported",
+        metadata={
+            "format": "emcar",
+            "points": len(angles),
+            "reference_dbi": reference_dbi,
+            "note": "Relative pattern shape" if reference_dbi is None else "Calibrated with reference",
+        },
+    )
+    result["raw_cut"] = {"angles_deg": angles, "gain_db": gains, "plane": "azimuth"}
+    return result
+
+
+def parse_nec2_radiation(content: str, polarization: str = "total") -> dict:
+    """Parse NEC2 radiation pattern output.
+
+    Finds the "RADIATION PATTERNS" section in NEC2 output files and extracts
+    the theta/phi/gain grid. Supports TOTAL, VERT, and HOR polarization selection.
+
+    NEC2 output columns (after the header):
+    THETA  PHI  VERT(dB)  HOR(dB)  TOTAL(dB)  AXIAL_RATIO  TILT  SENSE
+
+    Values of -999.99 are mapped to a -40 dBi floor.
+
+    Args:
+        content: NEC2 output file content as string
+        polarization: Which gain column to use: "total", "vert", or "hor"
+    """
+    lines = content.splitlines()
+    frequency_hz: float | None = None
+    measurements: list[tuple[float, float, float]] = []
+
+    # Column index for the selected polarization
+    pol_map = {"vert": 0, "hor": 1, "total": 2}
+    if polarization.lower() not in pol_map:
+        raise ValueError(f"polarization must be 'total', 'vert', or 'hor', got '{polarization}'")
+    pol_col = pol_map[polarization.lower()]
+
+    in_pattern = False
+    header_lines_remaining = 0
+
+    for line in lines:
+        stripped = line.strip()
+
+        # Look for frequency in the header area
+        freq_match = re.search(r"FREQUENCY\s*[=:]\s*([\d.]+)\s*(MHZ|MHz|GHZ|GHz|HZ|Hz)", stripped, re.IGNORECASE)
+        if freq_match:
+            freq_val = float(freq_match.group(1))
+            unit = freq_match.group(2).upper()
+            if unit == "MHZ":
+                frequency_hz = freq_val * 1e6
+            elif unit == "GHZ":
+                frequency_hz = freq_val * 1e9
+            else:
+                frequency_hz = freq_val
+
+        # Detect start of radiation pattern section
+        if "RADIATION PATTERNS" in stripped.upper():
+            in_pattern = True
+            header_lines_remaining = 3  # Skip title + column headers + separator
+            continue
+
+        if in_pattern:
+            if header_lines_remaining > 0:
+                header_lines_remaining -= 1
+                continue
+
+            # End of section: blank line or new section header
+            if not stripped or stripped.startswith("*") or stripped.startswith("-" * 10):
+                if measurements:
+                    break
+                continue
+
+            # Parse data line
+            parts = stripped.split()
+            if len(parts) < 5:
+                continue
+            try:
+                theta = float(parts[0])
+                phi = float(parts[1])
+                # Columns 2,3,4 are VERT(dB), HOR(dB), TOTAL(dB)
+                gain_val = float(parts[2 + pol_col])
+
+                # NEC2 uses -999.99 for undefined/zero
+                if gain_val < -900:
+                    gain_val = -40.0
+
+                measurements.append((theta, phi, gain_val))
+            except (ValueError, IndexError):
+                continue
+
+    if not measurements:
+        raise ValueError("No radiation pattern data found in NEC2 output")
+
+    thetas, phis, gain_grid = interpolate_to_grid(measurements)
+
+    return _build_pattern_dict(
+        thetas,
+        phis,
+        gain_grid,
+        model="imported_nec2",
+        frequency_hz=frequency_hz,
+        antenna_type="imported",
+        metadata={
+            "format": "nec2",
+            "points": len(measurements),
+            "polarization": polarization.lower(),
+        },
+    )
+
+
+def parse_touchstone_s1p(
+    content: str,
+    antenna_type: str = "auto",
+) -> dict:
+    """Parse Touchstone S1P file and generate an analytical pattern at resonance.
+
+    Reads S11 complex data, finds the resonant frequency (minimum |S11|),
+    computes impedance, estimates antenna type, and delegates to
+    radiation.generate_3d_pattern(). This bridges imported S1P data to the
+    Phase 1 analytical pattern engine.
+
+    Args:
+        content: Touchstone .s1p file content as string
+        antenna_type: Antenna model, or 'auto' to estimate from impedance
+    """
+    from mcnanovna.radiation import estimate_antenna_type, generate_3d_pattern
+
+    lines = content.strip().splitlines()
+    freq_mult = 1.0
+    data_format = "ri"  # Default: real/imaginary
+    z0 = 50.0
+
+    frequencies: list[float] = []
+    s11_complex: list[complex] = []
+
+    for line in lines:
+        line = line.strip()
+        if not line or line.startswith("!"):
+            continue
+
+        # Option line: # Hz S RI R 50
+        if line.startswith("#"):
+            parts = line[1:].strip().upper().split()
+            for i, p in enumerate(parts):
+                if p in ("HZ", "KHZ", "MHZ", "GHZ"):
+                    freq_mult = {"HZ": 1.0, "KHZ": 1e3, "MHZ": 1e6, "GHZ": 1e9}[p]
+                elif p in ("RI", "MA", "DB"):
+                    data_format = p.lower()
+                elif p == "R" and i + 1 < len(parts):
+                    try:
+                        z0 = float(parts[i + 1])
+                    except ValueError:
+                        pass
+            continue
+
+        # Data line
+        parts = line.split()
+        if len(parts) < 3:
+            continue
+        try:
+            freq = float(parts[0]) * freq_mult
+            v1, v2 = float(parts[1]), float(parts[2])
+
+            if data_format == "ri":
+                s11 = complex(v1, v2)
+            elif data_format == "ma":
+                s11 = v1 * complex(math.cos(math.radians(v2)), math.sin(math.radians(v2)))
+            elif data_format == "db":
+                mag = 10.0 ** (v1 / 20.0)
+                s11 = mag * complex(math.cos(math.radians(v2)), math.sin(math.radians(v2)))
+            else:
+                s11 = complex(v1, v2)
+
+            frequencies.append(freq)
+            s11_complex.append(s11)
+        except (ValueError, IndexError):
+            continue
+
+    if not frequencies:
+        raise ValueError("No valid S-parameter data found in Touchstone file")
+
+    # Find resonance: minimum |S11|
+    min_mag = 999.0
+    min_idx = 0
+    for i, s in enumerate(s11_complex):
+        mag = abs(s)
+        if mag < min_mag:
+            min_mag = mag
+            min_idx = i
+
+    res_freq = frequencies[min_idx]
+    s11_res = s11_complex[min_idx]
+
+    # Compute impedance at resonance: Z = Z0 * (1+S11) / (1-S11)
+    denom = 1.0 - s11_res
+    if abs(denom) < 1e-10:
+        z_real, z_imag = 9999.0, 0.0
+    else:
+        z = z0 * (1.0 + s11_res) / denom
+        z_real, z_imag = z.real, z.imag
+
+    # Estimate antenna type if needed
+    if antenna_type == "auto":
+        antenna_type = estimate_antenna_type(z_real, z_imag, res_freq)
+
+    pattern = generate_3d_pattern(
+        antenna_type=antenna_type,
+        frequency_hz=res_freq,
+        s11_analysis={
+            "resonance": {
+                "frequency_hz": res_freq,
+                "impedance_real": round(z_real, 2),
+                "impedance_imag": round(z_imag, 2),
+                "s11_magnitude": round(min_mag, 4),
+            }
+        },
+    )
+
+    pattern["model"] = "imported_s1p_analytical"
+    pattern["import_info"] = {
+        "format": "s1p",
+        "points": len(frequencies),
+        "frequency_range_hz": [frequencies[0], frequencies[-1]],
+        "resonant_frequency_hz": res_freq,
+        "impedance_at_resonance": {"real": round(z_real, 2), "imag": round(z_imag, 2)},
+        "z0": z0,
+    }
+
+    return pattern
+
+
+# ── Format auto-detection ─────────────────────────────────────────
+
+
+def detect_format(filename: str, content: str | None = None) -> str:
+    """Detect pattern file format from filename extension and optional content inspection.
+
+    Returns one of: "csv", "emcar", "nec2", "s1p"
+    """
+    ext = filename.rsplit(".", 1)[-1].lower() if "." in filename else ""
+
+    if ext == "csv":
+        return "csv"
+    if ext == "dat":
+        return "emcar"
+    if ext in ("out", "nec"):
+        return "nec2"
+    if ext == "s1p":
+        return "s1p"
+
+    # Content-based detection fallback
+    if content:
+        if "RADIATION PATTERNS" in content.upper():
+            return "nec2"
+        if content.lstrip().startswith("#") and re.search(r"#\s*\w+\s+S\s+", content[:200]):
+            return "s1p"
+        if re.match(r"[\w_]+[,;\t]", content.lstrip().split("\n")[0]):
+            return "csv"
+
+    raise ValueError(f"Cannot detect format from filename '{filename}'. Use .csv, .dat, .out, .nec, or .s1p")
+
+
+def parse_pattern(
+    content: str,
+    format: str,
+    frequency_hz: float | None = None,
+    **kwargs,
+) -> dict:
+    """Parse a pattern file with explicit format selection.
+
+    Convenience dispatcher that routes to the appropriate parser.
+
+    Args:
+        content: File content as string
+        format: One of "csv", "emcar", "nec2", "s1p"
+        frequency_hz: Operating frequency (used by CSV and EMCAR)
+        **kwargs: Additional format-specific arguments
+    """
+    if format == "csv":
+        return parse_csv_pattern(content, frequency_hz=frequency_hz)
+    elif format == "emcar":
+        return parse_emcar_vna_dat(content, frequency_hz=frequency_hz, reference_dbi=kwargs.get("reference_dbi"))
+    elif format == "nec2":
+        return parse_nec2_radiation(content, polarization=kwargs.get("polarization", "total"))
+    elif format == "s1p":
+        return parse_touchstone_s1p(content, antenna_type=kwargs.get("antenna_type", "auto"))
+    else:
+        raise ValueError(f"Unknown format '{format}'. Supported: csv, emcar, nec2, s1p")

src/mcnanovna/prompts.py

@@ -877,3 +877,110 @@ nothing. The tool returns up to 4 solutions.
 Let me compute the matching solutions now using `analyze_lc_match`.""",
             ),
         ]
+
+    @mcp.prompt
+    def import_pattern(
+        format: str = "csv",
+    ) -> list[Message]:
+        """Guide through importing a measured or simulated antenna pattern.
+
+        Walks through the process of importing pattern data from external files
+        (CSV, EMCAR vna.dat, NEC2 output, or Touchstone S1P) for 3D visualization.
+
+        Args:
+            format: File format \u2014 'csv', 'emcar', 'nec2', or 's1p'
+        """
+        format_details = {
+            "csv": {
+                "name": "CSV",
+                "ext": ".csv",
+                "tool": "import_pattern_csv",
+                "desc": (
+                    "Comma/semicolon/tab-separated with flexible headers.\n"
+                    "- **3-column**: theta, phi, gain_dbi \u2014 full 3D pattern\n"
+                    "- **2-column**: angle, gain \u2014 single cut, synthesized to 3D"
+                ),
+                "example": "theta,phi,gain_dbi\\n0,0,-40\\n90,0,2.15\\n90,90,2.15\\n180,0,-40",
+            },
+            "emcar": {
+                "name": "EMCAR vna.dat",
+                "ext": ".dat",
+                "tool": "import_pattern_emcar",
+                "desc": (
+                    "EMCAR antenna range format \u2014 angle + amplitude pairs.\n"
+                    "Single azimuth cut from a positioner-driven measurement.\n"
+                    "The gnuplot transform is applied automatically:\n"
+                    "(-angle+90) rotation and 20*log10(amplitude+0.01)."
+                ),
+                "example": "# EMCAR measurement\\n0 0.5\\n45 0.8\\n90 1.0\\n135 0.8\\n180 0.5",
+            },
+            "nec2": {
+                "name": "NEC2 Radiation Output",
+                "ext": ".out / .nec",
+                "tool": "import_pattern_nec2",
+                "desc": (
+                    "NEC2/NEC4 output containing a RADIATION PATTERNS section.\n"
+                    "Full theta\u00d7phi grid with VERT, HOR, and TOTAL gain columns.\n"
+                    "Frequency is auto-detected from the file header."
+                ),
+                "example": "(Standard NEC2 output file \u2014 run your .nec model first)",
+            },
+            "s1p": {
+                "name": "Touchstone S1P",
+                "ext": ".s1p",
+                "tool": "import_pattern_s1p",
+                "desc": (
+                    "Touchstone S-parameter file containing S11 data.\n"
+                    "Finds resonance, computes impedance, then generates an\n"
+                    "analytical pattern using the Phase 1 antenna models.\n"
+                    "Supports RI, MA, and DB formats."
+                ),
+                "example": "# Hz S RI R 50\\n! NanoVNA export\\n144000000 0.1 -0.05\\n145000000 0.02 -0.01",
+            },
+        }
+
+        fmt = format_details.get(format.lower(), format_details["csv"])
+
+        return [
+            Message(
+                role="user",
+                content=f"I want to import a {fmt['name']} ({fmt['ext']}) antenna pattern for 3D visualization.",
+            ),
+            Message(
+                role="assistant",
+                content=f"""I'll help you import a {fmt["name"]} pattern file.
+
+**Format**: {fmt["name"]} ({fmt["ext"]})
+**Tool**: `{fmt["tool"]}`
+
+**Data format:**
+{fmt["desc"]}
+
+**Example content:**
+```
+{fmt["example"]}
+```
+
+**Workflow:**
+1. Read the file content (or paste it directly)
+2. Call `{fmt["tool"]}` with the content string
+3. The tool returns a standard pattern dict with {{theta_deg, phi_deg, gain_dbi}}
+4. If the web UI is running, upload via the "Load File" button for instant 3D rendering
+
+**All supported formats** (use `list_pattern_formats` for full details):
+| Format | Extension | Data |
+|--------|-----------|------|
+| CSV | .csv | 2-col or 3-col angle/gain |
+| EMCAR | .dat | Positioner angle + amplitude |
+| NEC2 | .out, .nec | Full radiation pattern grid |
+| S1P | .s1p | S11 \u2192 analytical pattern |
+
+**Tips:**
+- CSV and EMCAR accept an optional `frequency_hz` parameter for metadata
+- NEC2 supports `polarization` selection: 'total', 'vert', or 'hor'
+- S1P supports `antenna_type` override or 'auto' detection from impedance
+- Single-cut data (2-col CSV, EMCAR) is synthesized to 3D with a sin(\u03b8) taper
+
+Please share the file content and I'll import it.""",
+            ),
+        ]

src/mcnanovna/server.py

@@ -94,6 +94,12 @@ _TOOL_METHODS = [
     "radiation_pattern",
     "radiation_pattern_from_data",
     "radiation_pattern_multi",
+    # tools/pattern_import.py — PatternImportMixin
+    "import_pattern_csv",
+    "import_pattern_emcar",
+    "import_pattern_nec2",
+    "import_pattern_s1p",
+    "list_pattern_formats",
 ]
 
 
@@ -120,10 +126,14 @@ def create_server() -> FastMCP:
             "'radiation_pattern_from_data' (compute pattern from known impedance, no hardware), "
             "'radiation_pattern_multi' (patterns at N frequencies for animation). "
             "Supported antenna types: dipole, monopole, efhw, loop, patch, or 'auto' to estimate.\n\n"
+            "Pattern import tools: 'import_pattern_csv', 'import_pattern_emcar', "
+            "'import_pattern_nec2', 'import_pattern_s1p' — import measured/simulated patterns "
+            "from external files (CSV, EMCAR vna.dat, NEC2 output, Touchstone S1P). "
+            "Use 'list_pattern_formats' for format details and examples.\n\n"
             "Prompts are available for guided workflows: calibrate, export_touchstone, "
             "analyze_antenna, measure_cable, compare_sweeps, analyze_crystal, "
             "analyze_filter_response, measure_tdr, measure_component, measure_lc_series, "
-            "measure_lc_shunt, impedance_match, and visualize_radiation_pattern."
+            "measure_lc_shunt, impedance_match, visualize_radiation_pattern, and import_pattern."
         ),
     )
     vna = NanoVNA()

src/mcnanovna/tools/__init__.py

@@ -15,6 +15,7 @@ from .device import DeviceMixin
 from .diagnostics import DiagnosticsMixin
 from .display import DisplayMixin
 from .measurement import MeasurementMixin
+from .pattern_import import PatternImportMixin
 from .radiation import RadiationMixin
 
 
@@ -31,5 +32,6 @@ __all__ = [
     "DiagnosticsMixin",
     "DisplayMixin",
     "MeasurementMixin",
+    "PatternImportMixin",
     "RadiationMixin",
 ]

src/mcnanovna/tools/pattern_import.py

@@ -0,0 +1,213 @@
+"""PatternImportMixin — MCP tools for importing measured antenna patterns."""
+
+from __future__ import annotations
+
+from fastmcp import Context
+
+
+class PatternImportMixin:
+    """Pattern import tools: import CSV, EMCAR, NEC2, S1P files and list formats."""
+
+    async def import_pattern_csv(
+        self,
+        content: str,
+        frequency_hz: float | None = None,
+        theta_step: float = 2.0,
+        phi_step: float = 5.0,
+        ctx: Context | None = None,
+    ) -> dict:
+        """Import an antenna pattern from CSV data.
+
+        Auto-detects 2-column (angle, gain) vs 3-column (theta, phi, gain) from
+        the header. Flexible header names: theta/elevation, phi/azimuth,
+        gain/gain_dbi/amplitude/db.
+
+        For 2-column data, the angle column name determines the cut plane —
+        the single cut is synthesized into a full 3D pattern with a sin(theta) taper.
+
+        Returns the standardized {theta_deg, phi_deg, gain_dbi} pattern dict
+        for 3D visualization.
+
+        Args:
+            content: CSV file content as string
+            frequency_hz: Operating frequency in Hz (optional, for metadata)
+            theta_step: Output grid polar angle step in degrees (default 2)
+            phi_step: Output grid azimuthal angle step in degrees (default 5)
+        """
+        from mcnanovna.pattern_import import parse_csv_pattern
+        from mcnanovna.tools import _progress
+
+        await _progress(ctx, 1, 2, "Parsing CSV pattern data...")
+        result = parse_csv_pattern(content, frequency_hz=frequency_hz)
+        await _progress(ctx, 2, 2, f"Imported CSV pattern: {result.get('import_info', {}).get('points', '?')} points")
+        return result
+
+    async def import_pattern_emcar(
+        self,
+        content: str,
+        frequency_hz: float | None = None,
+        reference_dbi: float | None = None,
+        theta_step: float = 2.0,
+        phi_step: float = 5.0,
+        ctx: Context | None = None,
+    ) -> dict:
+        """Import an antenna pattern from EMCAR vna.dat format.
+
+        EMCAR records angle + amplitude_dBV pairs from a positioner-driven
+        antenna range (typically HP8754A VNA). Lines starting with # are comments.
+
+        The gnuplot transform is applied: (-angle+90) rotation and
+        20*log10(amplitude+0.01) voltage-to-dB conversion. This produces a
+        single azimuth cut which is synthesized into a 3D pattern.
+
+        Without a reference antenna, the pattern shows relative shape only.
+        Pass reference_dbi to offset to absolute gain.
+
+        Args:
+            content: vna.dat file content as string
+            frequency_hz: Operating frequency in Hz (optional)
+            reference_dbi: Reference antenna gain offset in dBi (optional)
+            theta_step: Output grid polar angle step in degrees (default 2)
+            phi_step: Output grid azimuthal angle step in degrees (default 5)
+        """
+        from mcnanovna.pattern_import import parse_emcar_vna_dat
+        from mcnanovna.tools import _progress
+
+        await _progress(ctx, 1, 2, "Parsing EMCAR vna.dat data...")
+        result = parse_emcar_vna_dat(content, frequency_hz=frequency_hz, reference_dbi=reference_dbi)
+        await _progress(ctx, 2, 2, f"Imported EMCAR pattern: {result.get('import_info', {}).get('points', '?')} points")
+        return result
+
+    async def import_pattern_nec2(
+        self,
+        content: str,
+        polarization: str = "total",
+        theta_step: float = 2.0,
+        phi_step: float = 5.0,
+        ctx: Context | None = None,
+    ) -> dict:
+        """Import an antenna pattern from NEC2 radiation output.
+
+        Parses the RADIATION PATTERNS section from NEC2/NEC4 output files.
+        Extracts frequency from the header and provides full theta x phi gain data.
+
+        Supports polarization selection: 'total' (default), 'vert', or 'hor'.
+        Values of -999.99 in NEC2 output are mapped to a -40 dBi floor.
+
+        Args:
+            content: NEC2 output file content as string
+            polarization: Gain column to use — 'total', 'vert', or 'hor' (default 'total')
+            theta_step: Output grid polar angle step in degrees (default 2)
+            phi_step: Output grid azimuthal angle step in degrees (default 5)
+        """
+        from mcnanovna.pattern_import import parse_nec2_radiation
+        from mcnanovna.tools import _progress
+
+        await _progress(ctx, 1, 2, "Parsing NEC2 radiation pattern...")
+        result = parse_nec2_radiation(content, polarization=polarization)
+        await _progress(ctx, 2, 2, f"Imported NEC2 pattern: {result.get('import_info', {}).get('points', '?')} points")
+        return result
+
+    async def import_pattern_s1p(
+        self,
+        content: str,
+        antenna_type: str = "auto",
+        theta_step: float = 2.0,
+        phi_step: float = 5.0,
+        ctx: Context | None = None,
+    ) -> dict:
+        """Import S-parameters from a Touchstone S1P file and generate a pattern.
+
+        Parses S11 complex data, finds the resonant frequency (minimum |S11|),
+        computes impedance, and generates an analytical 3D pattern using the
+        Phase 1 antenna models. This bridges external VNA measurements to
+        pattern visualization.
+
+        Supports RI (real/imaginary), MA (magnitude/angle), and DB (dB/angle)
+        Touchstone formats. Reference impedance and frequency units are read
+        from the option line.
+
+        Args:
+            content: Touchstone .s1p file content as string
+            antenna_type: Antenna model — 'dipole', 'monopole', 'efhw', 'loop', 'patch', or 'auto'
+            theta_step: Output grid polar angle step in degrees (default 2)
+            phi_step: Output grid azimuthal angle step in degrees (default 5)
+        """
+        from mcnanovna.pattern_import import parse_touchstone_s1p
+        from mcnanovna.tools import _progress
+
+        await _progress(ctx, 1, 2, "Parsing Touchstone S1P data...")
+        result = parse_touchstone_s1p(content, antenna_type=antenna_type)
+        await _progress(
+            ctx,
+            2,
+            2,
+            f"Generated pattern from S1P: {result.get('antenna_type')} at "
+            f"{result.get('import_info', {}).get('resonant_frequency_hz', '?')} Hz",
+        )
+        return result
+
+    async def list_pattern_formats(
+        self,
+        ctx: Context | None = None,
+    ) -> dict:
+        """List supported pattern import formats with descriptions and examples.
+
+        Returns details about each format including file extensions, expected
+        data structure, and example content for testing.
+        """
+        return {
+            "formats": [
+                {
+                    "name": "CSV",
+                    "extensions": [".csv"],
+                    "tool": "import_pattern_csv",
+                    "description": (
+                        "Comma/semicolon/tab-separated pattern data. "
+                        "Auto-detects 2-column (angle, gain) or 3-column (theta, phi, gain) from headers."
+                    ),
+                    "example_3col": "theta,phi,gain_dbi\n0,0,-40\n90,0,2.15\n90,90,2.15\n180,0,-40",
+                    "example_2col": "azimuth,gain_dbi\n0,2.15\n90,1.5\n180,0.8\n270,1.5",
+                },
+                {
+                    "name": "EMCAR vna.dat",
+                    "extensions": [".dat"],
+                    "tool": "import_pattern_emcar",
+                    "description": (
+                        "EMCAR antenna range format: angle amplitude_dBV pairs. "
+                        "Single azimuth cut from positioner-driven measurement. "
+                        "Gnuplot transform applied: (-angle+90) rotation, 20*log10(amplitude+0.01)."
+                    ),
+                    "example": "# EMCAR measurement\n0 0.5\n45 0.8\n90 1.0\n135 0.8\n180 0.5\n225 0.3\n270 0.2\n315 0.3",
+                    "reference": "https://emcar.sourceforge.net/",
+                },
+                {
+                    "name": "NEC2 Radiation",
+                    "extensions": [".out", ".nec"],
+                    "tool": "import_pattern_nec2",
+                    "description": (
+                        "NEC2/NEC4 output file with RADIATION PATTERNS section. "
+                        "Full theta x phi grid with VERT, HOR, and TOTAL gain columns. "
+                        "Frequency auto-detected from header."
+                    ),
+                    "polarization_options": ["total", "vert", "hor"],
+                },
+                {
+                    "name": "Touchstone S1P",
+                    "extensions": [".s1p"],
+                    "tool": "import_pattern_s1p",
+                    "description": (
+                        "Touchstone S-parameter file (S11 only). "
+                        "Finds resonance, computes impedance, generates analytical pattern. "
+                        "Supports RI, MA, and DB data formats."
+                    ),
+                    "note": "Generates an analytical pattern from S11 data — not a measured pattern.",
+                },
+            ],
+            "common_workflow": (
+                "1. Read the file content\n"
+                "2. Call the appropriate import_pattern_* tool with the content string\n"
+                "3. The tool returns the standard {theta_deg, phi_deg, gain_dbi} pattern dict\n"
+                "4. If the web UI is running, the pattern is automatically available for 3D rendering"
+            ),
+        }

src/mcnanovna/webui/api.py

@@ -10,7 +10,7 @@ import asyncio
 import json
 from pathlib import Path
 
-from fastapi import FastAPI, WebSocket, WebSocketDisconnect
+from fastapi import FastAPI, File, Form, UploadFile, WebSocket, WebSocketDisconnect
 from fastapi.responses import FileResponse
 from fastapi.staticfiles import StaticFiles
 from pydantic import BaseModel
@@ -143,6 +143,44 @@ def create_app() -> FastAPI:
         await _broadcast_pattern(pattern)
         return pattern
 
+    @app.post("/api/pattern/import")
+    async def api_pattern_import(
+        file: UploadFile = File(...),
+        format: str | None = Form(None),
+        frequency_hz: float | None = Form(None),
+        polarization: str = Form("total"),
+        antenna_type: str = Form("auto"),
+        reference_dbi: float | None = Form(None),
+    ):
+        """Import a pattern file (CSV, EMCAR, NEC2, or S1P) via multipart upload."""
+        from mcnanovna.pattern_import import detect_format, parse_pattern
+
+        content = (await file.read()).decode("utf-8", errors="replace")
+        filename = file.filename or "unknown"
+
+        # Auto-detect format from extension if not specified
+        if not format:
+            format = detect_format(filename, content)
+
+        kwargs: dict = {}
+        if format == "nec2":
+            kwargs["polarization"] = polarization
+        elif format == "s1p":
+            kwargs["antenna_type"] = antenna_type
+        elif format == "emcar" and reference_dbi is not None:
+            kwargs["reference_dbi"] = reference_dbi
+
+        pattern = parse_pattern(content, format, frequency_hz=frequency_hz, **kwargs)
+
+        # Add filename to import_info
+        if "import_info" in pattern:
+            pattern["import_info"]["filename"] = filename
+        else:
+            pattern["import_info"] = {"format": format, "filename": filename}
+
+        await _broadcast_pattern(pattern)
+        return pattern
+
     # ── WebSocket ─────────────────────────────────────────────────
 
     @app.websocket("/ws/pattern")

src/mcnanovna/webui/static/index.html

@@ -5,8 +5,8 @@
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
   <title>mcnanovna -- Radiation Pattern</title>
   <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600&display=swap" rel="stylesheet">
-  <script type="module" crossorigin src="/assets/index-khVaWAJ2.js"></script>
-  <link rel="stylesheet" crossorigin href="/assets/index-DeJaSUhK.css">
+  <script type="module" crossorigin src="/assets/index-DVS5g-QC.js"></script>
+  <link rel="stylesheet" crossorigin href="/assets/index-C5SDH5i7.css">
 </head>
 <body>
   <div id="app">